Datasets:

ytzi

/

the-stack-dedup-python-filtered-docstrings

like 0

626e79d1097e3ee7dcfeaedcec4d5a17f8a45607

py

Python

networkmonitor/monitor.py

luther38/NetworkMonitor

bd5e9336342e7af71c19ca484f8d386b6b79ad69

networkmonitor/monitor.py

luther38/NetworkMonitor

bd5e9336342e7af71c19ca484f8d386b6b79ad69

networkmonitor/monitor.py

luther38/NetworkMonitor

bd5e9336342e7af71c19ca484f8d386b6b79ad69

import datetime import typing from networkmonitor import OldConfig from networkmonitor.src.configuration import IConfig, ContextConfig from networkmonitor.src.protocols import IProtocols, ContextProtocols from networkmonitor.src import Configuration from networkmonitor.src import Nodes from networkmonitor.src import InvalidProtocol, InvalidNodeConfiguration from networkmonitor.src import RefreshTimer class Monitor(): """ Monitor is the working class that checks all the requested nodes. """

import datetime import typing from networkmonitor import OldConfig from networkmonitor.src.configuration import IConfig, ContextConfig from networkmonitor.src.protocols import IProtocols, ContextProtocols from networkmonitor.src import Configuration from networkmonitor.src import Nodes from networkmonitor.src import InvalidProtocol, InvalidNodeConfiguration from networkmonitor.src import RefreshTimer class Monitor(): """ Monitor is the working class that checks all the requested nodes. """ def __init__(self, iconfig:IConfig): self.__iconfig__:IConfig = iconfig self.__config__:ContextConfig = ContextConfig(self.__iconfig__) self.__config__.GetWorkingConfigClass(True) self.__config__.ReadConfig() self.__configuration__:Configuration = Configuration() self.__configuration__ = self.__config__.configuration self.refresh = RefreshTimer(self.__iconfig__) self.report = [] self.LastRefresh = datetime.datetime.now() self.NextRefresh = datetime.datetime.now() pass def Start(self, force:bool=False) -> None: if force == False: res = self.refresh.CheckRefreshTimer() #res = self.__CheckRefreshTimer() if res == True: self.__Worker() else: self.__Worker() def __ReadConfig__(self) -> None: self.__config__.ReadConfig() self.__configuration__ = Configuration() self.__configuration__ = self.__config__.configuration def __Worker(self)->None: self.__ReadConfig__() report = self.__configuration__.nodes requirement:bool = True for node in report: if requirement == True: np = node.protocol.lower() if np == "icmp": cp = ContextProtocols(IProtocols(node.address, 'ICMP')) cp.GetWorkingClass(True) cp.configuration = self.__configuration__ cp.Start() elif np == "http:get": cp = ContextProtocols(IProtocols(node.address, "HTTP:GET")) cp.GetWorkingClass(True) cp.Start() elif np == "http:post": cp = ContextProtocols(IProtocols(node.address, "Http:Post")) cp.GetWorkingClass(True) cp.Start() else: raise InvalidProtocol(f"{node.protocol} is invalid. Use ICMP, HTTP:Get, HTTP:POST.") node.ms = cp.MS node.status = cp.Status if node.required == True and node.status == "Offline": requirement = False else: node.status = "Offline" # Work has been finished #Copy our local version to the global scope self.LastRefresh = datetime.datetime.now() self.report = report

a43b1675c8e19994c0be2bd6717197a732815f09

py

Python

welltory/admin.py

vasmedvedev/welltory_test

9dd1ea35850916a2203241798d0acd9415d762b7

welltory/admin.py

vasmedvedev/welltory_test

9dd1ea35850916a2203241798d0acd9415d762b7

welltory/admin.py

vasmedvedev/welltory_test

9dd1ea35850916a2203241798d0acd9415d762b7

from django.contrib import admin from welltory import models admin.site.register(models.Sleep, SleepAdmin) admin.site.register(models.Steps, StepsAdmin) admin.site.register(models.Geo, GeoAdmin)

from django.contrib import admin from welltory import models class SleepAdmin(admin.ModelAdmin): pass class StepsAdmin(admin.ModelAdmin): pass class GeoAdmin(admin.ModelAdmin): pass admin.site.register(models.Sleep, SleepAdmin) admin.site.register(models.Steps, StepsAdmin) admin.site.register(models.Geo, GeoAdmin)

b95fcac1f93d1bd02d1dcd14dd2453199e1bd50c

py

Python

pymmcore_plus/client/callbacks/basic.py

ianhi/pymmcore-remote

54f93985ab73898b0cfc484e5ca43bf16d35c522

2021-07-15T10:35:08.000Z

2022-03-02T23:36:34.000Z

pymmcore_plus/client/callbacks/basic.py

fdrgsp/pymmcore-remote

929f62cca703a8a6437240d0c46446c0a524ed9a

2021-07-03T02:16:14.000Z

2022-03-29T16:11:59.000Z

pymmcore_plus/client/callbacks/basic.py

fdrgsp/pymmcore-remote

929f62cca703a8a6437240d0c46446c0a524ed9a

2021-05-19T19:59:27.000Z

2021-07-01T19:08:35.000Z

from Pyro5 import api from ...core._signals import _CMMCoreSignaler

from Pyro5 import api from ...core._signals import _CMMCoreSignaler class SynchronousCallback(_CMMCoreSignaler): def __init__(self) -> None: super().__init__() @api.expose def receive_core_callback(self, signal_name: str, args: tuple) -> None: """Will be called by server with name of signal, and tuple of args.""" getattr(self, signal_name).emit(*args)

14fe2fcea35b1656d97913ad5e54df7ffd928511

py

Python

app/recipies/admin.py

sourabhsinha396/Rest-api-recipie

a9937d5119c706d1193654ece280ed46b599a344

app/recipies/admin.py

sourabhsinha396/Rest-api-recipie

a9937d5119c706d1193654ece280ed46b599a344

2021-03-30T14:10:47.000Z

2021-09-22T19:29:50.000Z

app/recipies/admin.py

sourabhsinha396/Rest-api-recipie

a9937d5119c706d1193654ece280ed46b599a344

from django.contrib import admin from .models import Tag,Ingredient admin.site.register(Tag) admin.site.register(Ingredient)

from django.contrib import admin from .models import Tag,Ingredient admin.site.register(Tag) admin.site.register(Ingredient)

1471e53db5b729dc1c33d31786115ca692ce7cf8

py

Python

RunProject.py

badeaadi/Colorization

b18d4267aa611b7e87ddfe8a32fef834259e8a45

RunProject.py

badeaadi/Colorization

b18d4267aa611b7e87ddfe8a32fef834259e8a45

RunProject.py

badeaadi/Colorization

b18d4267aa611b7e87ddfe8a32fef834259e8a45

""" PROIECT Colorarea imaginilor folosind autoencoder si invatarea automata Badea Adrian Catalin, grupa 334, anul III, FMI """ import pdb from DataSet import * from AeModel import * data_set: DataSet = DataSet() data_set.scene_name = 'forest' ae_model: AeModel = AeModel(data_set) ae_model.define_the_model() ae_model.compile_the_model() ae_model.train_the_model() ae_model.evaluate_the_model() data_set: DataSet = DataSet() data_set.scene_name = 'coast' ae_model: AeModel = AeModel(data_set) ae_model.define_the_model() ae_model.compile_the_model() ae_model.train_the_model() ae_model.evaluate_the_model()

""" PROIECT Colorarea imaginilor folosind autoencoder si invatarea automata Badea Adrian Catalin, grupa 334, anul III, FMI """ import pdb from DataSet import * from AeModel import * data_set: DataSet = DataSet() data_set.scene_name = 'forest' ae_model: AeModel = AeModel(data_set) ae_model.define_the_model() ae_model.compile_the_model() ae_model.train_the_model() ae_model.evaluate_the_model() data_set: DataSet = DataSet() data_set.scene_name = 'coast' ae_model: AeModel = AeModel(data_set) ae_model.define_the_model() ae_model.compile_the_model() ae_model.train_the_model() ae_model.evaluate_the_model()

379ff9e7c9ab8025f20aee92eea805b694ff479d

py

Python

lib/nodes/net.py

velexi-corporation/rsi-2020-bemish

3454ecdff66802494d5c3c1678767fddc42d72b6

2021-07-11T00:50:22.000Z

2021-07-11T00:50:22.000Z

lib/nodes/net.py

velexi-corporation/rsi-2020-bemish

3454ecdff66802494d5c3c1678767fddc42d72b6

lib/nodes/net.py

velexi-corporation/rsi-2020-bemish

3454ecdff66802494d5c3c1678767fddc42d72b6

from nodes.core import * from nodes.connect import * from nodes.util import * import numpy as np import math

from nodes.core import * from nodes.connect import * from nodes.util import * import numpy as np import math class FoldiakShapedNet(net): def connect_foldiak(self, layerin, layerout): cg1 = ShapedCGroup(layerin, layerout) cg1.mkconnects(HebbianConnect) cg1.normbiases() self.cgroups.append(cg1) cg2 = ShapedCGroup(layerout, layerout) cg2.mkconnects(AntiHebbianConnect) self.cgroups.append(cg2) self.inihblayers.append([layerout,cg1,cg2]) def __init__(self): self.layers = [] self.cgroups = [] self.connects = [] self.meta_timing = [] self.valdict = dict() self.inihblayers = [] self.diffeqs = [] self.isinit = False def setup(self): for i in self.layers: i.setup() self.meta_timing = [] self.diffeqs = [] for i in self.inihblayers: odesolver = FoldiakShapedDiffEq(i[0], i[1], i[2]) self.diffeqs.append(odesolver) self.isinit = True def update(self): for i in self.diffeqs: i.update() for i in self.cgroups: i.update() for i in self.connects: i.update() for i in self.layers: i.update(self.connects) def updatethresonly(self): for i in self.diffeqs: i.update() for i in self.layers: i.update(self.connects)

8f2f930b5a2dacd7576259cb4a0ce498b4941c73

py

Python

ch02/similarity.py

YaGiNA/DLfS2

3dbaba7a62c198b50849de2e3b74d92897a4cae7

2019-05-15T09:17:23.000Z

2019-05-15T09:17:23.000Z

ch02/similarity.py

YaGiNA/DLfS2

3dbaba7a62c198b50849de2e3b74d92897a4cae7

ch02/similarity.py

YaGiNA/DLfS2

3dbaba7a62c198b50849de2e3b74d92897a4cae7

import sys sys.path.append("..") from common.util import preprocess, create_co_matrix, cos_similarity text = "You say goobye and I say hello." corpus, word_to_id, id_to_word = preprocess(text) vocab_size = len(word_to_id) C = create_co_matrix(corpus, vocab_size) c0 = C[word_to_id["you"]] c1 = C[word_to_id["i"]] print(cos_similarity(c0, c1))

import sys sys.path.append("..") from common.util import preprocess, create_co_matrix, cos_similarity text = "You say goobye and I say hello." corpus, word_to_id, id_to_word = preprocess(text) vocab_size = len(word_to_id) C = create_co_matrix(corpus, vocab_size) c0 = C[word_to_id["you"]] c1 = C[word_to_id["i"]] print(cos_similarity(c0, c1))

69b5c654c37a2e2d72fe9be3cc0958d62171dbd6

py

Python

pydht/pydht.py

scottcunningham/pydht

9a2ecfc8da3794b2dc6587d17b8d51337a8e7df4

2015-01-04T07:02:54.000Z

2015-01-04T07:02:54.000Z

pydht/pydht.py

scottcunningham/pydht

9a2ecfc8da3794b2dc6587d17b8d51337a8e7df4

pydht/pydht.py

scottcunningham/pydht

9a2ecfc8da3794b2dc6587d17b8d51337a8e7df4

import math import json import random import uuid import SocketServer import threading import time import key_derivation from .bucketset import BucketSet from .hashing import hash_function, random_id from .peer import Peer from .shortlist import Shortlist k = 20 alpha = 3 id_bits = 128 iteration_sleep = 1 keysize = 2048 DEFAULT_TTL = 604800 # = 7 days, in seconds.

import math import json import random import uuid import SocketServer import threading import time import key_derivation from .bucketset import BucketSet from .hashing import hash_function, random_id from .peer import Peer from .shortlist import Shortlist k = 20 alpha = 3 id_bits = 128 iteration_sleep = 1 keysize = 2048 DEFAULT_TTL = 604800 # = 7 days, in seconds. class DHTRequestHandler(SocketServer.BaseRequestHandler): def handle(self): try: message = json.loads(self.request[0].strip()) message_type = message["message_type"] print "Received message of type", message_type, "from", message["peer_id"] if message_type == "ping": self.handle_ping(message) elif message_type == "pong": self.handle_pong(message) elif message_type == "find_node": self.handle_find(message) elif message_type == "find_value": self.handle_find(message, find_value=True) elif message_type == "found_nodes": self.handle_found_nodes(message) elif message_type == "found_value": self.handle_found_value(message) elif message_type == "store": print "Request to store" self.handle_store(message) elif message_type == "downvote": print "Asked to downvote an item" self.handle_downvote(message) except KeyError, ValueError: pass client_host, client_port = self.client_address peer_id = message["peer_id"] new_peer = Peer(client_host, client_port, peer_id) self.server.dht.buckets.insert(new_peer) def handle_ping(self, message): client_host, client_port = self.client_address id = message["peer_id"] peer = Peer(client_host, client_port, id) peer.pong(socket=self.server.socket, peer_id=self.server.dht.peer.id, lock=self.server.send_lock) def handle_pong(self, message): pass def handle_find(self, message, find_value=False): key = message["id"] id = message["peer_id"] client_host, client_port = self.client_address peer = Peer(client_host, client_port, id) response_socket = self.request[1] if find_value and (key in self.server.dht.data): value = self.server.dht.data[key] peer.found_value(id, value, message["rpc_id"], socket=response_socket, peer_id=self.server.dht.peer.id, lock=self.server.send_lock) else: nearest_nodes = self.server.dht.buckets.nearest_nodes(id) if not nearest_nodes: nearest_nodes.append(self.server.dht.peer) nearest_nodes = [nearest_peer.astriple() for nearest_peer in nearest_nodes] peer.found_nodes(id, nearest_nodes, message["rpc_id"], socket=response_socket, peer_id=self.server.dht.peer.id, lock=self.server.send_lock) def handle_found_nodes(self, message): rpc_id = message["rpc_id"] shortlist = self.server.dht.rpc_ids[rpc_id] del self.server.dht.rpc_ids[rpc_id] nearest_nodes = [Peer(*peer) for peer in message["nearest_nodes"]] shortlist.update(nearest_nodes) def handle_found_value(self, message): rpc_id = message["rpc_id"] shortlist = self.server.dht.rpc_ids[rpc_id] del self.server.dht.rpc_ids[rpc_id] shortlist.set_complete(message["value"]) def handle_store(self, message): key = message["id"] print "Asked to store data for id", key print "Ciphertext is", message["value"] self.server.dht.data[key] = message["value"] self.server.dht.ttls[key] = DEFAULT_TTL def handle_downvote(self, message): key = message["id"] print "Downvote for key", key, " -- uuid is ", message["uid"] self.server.dht.handle_downvote(key, uuid) class DHTServer(SocketServer.ThreadingMixIn, SocketServer.UDPServer): def __init__(self, host_address, handler_cls): SocketServer.UDPServer.__init__(self, host_address, handler_cls) self.send_lock = threading.Lock() class DHT(object): def __init__(self, host, port, id=None, boot_host=None, boot_port=None): if not id: id = random_id() self.id = id self.peer = Peer(unicode(host), port, id) # Data and data decay data structures self.data = {} self.recent_downvotes = [] self.downvotes = {} self.ttls = {} self.pending_replies = {} self.buckets = BucketSet(k, id_bits, self.peer.id) self.rpc_ids = {} # should probably have a lock for this self.server = DHTServer(self.peer.address(), DHTRequestHandler) self.server.dht = self self.server_thread = threading.Thread(target=self.server.serve_forever) self.server_thread.daemon = True self.server_thread.start() self.bootstrap(unicode(boot_host), boot_port) def iterative_find_nodes(self, key, boot_peer=None): shortlist = Shortlist(k, key) shortlist.update(self.buckets.nearest_nodes(key, limit=alpha)) if boot_peer: rpc_id = random.getrandbits(id_bits) self.rpc_ids[rpc_id] = shortlist boot_peer.find_node(key, rpc_id, socket=self.server.socket, peer_id=self.peer.id) while (not shortlist.complete()) or boot_peer: nearest_nodes = shortlist.get_next_iteration(alpha) for peer in nearest_nodes: shortlist.mark(peer) rpc_id = random.getrandbits(id_bits) self.rpc_ids[rpc_id] = shortlist peer.find_node(key, rpc_id, socket=self.server.socket, peer_id=self.peer.id) ###### time.sleep(iteration_sleep) boot_peer = None return shortlist.results() def iterative_find_value(self, key): shortlist = Shortlist(k, key) shortlist.update(self.buckets.nearest_nodes(key, limit=alpha)) while not shortlist.complete(): nearest_nodes = shortlist.get_next_iteration(alpha) for peer in nearest_nodes: shortlist.mark(peer) rpc_id = random.getrandbits(id_bits) self.rpc_ids[rpc_id] = shortlist peer.find_value(key, rpc_id, socket=self.server.socket, peer_id=self.peer.id) ##### time.sleep(iteration_sleep) return shortlist.completion_result() def bootstrap(self, boot_host, boot_port): if boot_host and boot_port: boot_peer = Peer(boot_host, boot_port, 0) self.iterative_find_nodes(self.peer.id, boot_peer=boot_peer) def __getitem__(self, key): hashed_key = hash_function(key) if hashed_key in self.data: return self.data[hashed_key] result = self.iterative_find_value(hashed_key) if result: return result raise KeyError def __setitem__(self, key, value): hashed_key = hash_function(key) nearest_nodes = self.iterative_find_nodes(hashed_key) if not nearest_nodes: self.data[hashed_key] = value for node in nearest_nodes: node.store(hashed_key, value, socket=self.server.socket, peer_id=self.peer.id) def publish(self, value): key = str(uuid.uuid4()) print "Publishing content under new key:", key hashed_key = hash_function(key) print "Hashed key is:", hashed_key # need to encrypt value ciphertext = key_derivation.do_encrypt(key, value) print "Cyphertext is:", ciphertext nearest_nodes = self.iterative_find_nodes(hashed_key) if not nearest_nodes: print "Storing data for key {} locally".format(key) self.data[hashed_key] = ciphertext for node in nearest_nodes: print "Sending data for key {} to closer nodes.".format(key) node.store(hashed_key, ciphertext, socket=self.server.socket, peer_id=self.peer.id) return key def retrieve(self, key): # Retrieve result print "Looking up key:", key hashed_key = hash_function(key) print "Hashed key is", hashed_key result = None if hashed_key in self.data: print "Data for key", "stored locally" result = self.data[hashed_key] else: print "Data stored somewhere else: forwarding request" result = self.iterative_find_value(hashed_key) if not result: print "Key", key, "not found" raise KeyError # result is encrypted + hmac'd # Can throw ValueError if HMAC fails print "Ciphertext is", result plaintext = key_derivation.do_decrypt(key, result) return plaintext def downvote(self, key): uid = str(uuid.uuid4()) hashed_key = hash_function(key) nearest_nodes = self.iterative_find_nodes(hashed_key) print "Downvoting", key if not nearest_nodes: print "Asked myself to downvote a key: {}".format(key) for node in nearest_nodes: print "Asking another node to downvote", key node.downvote(hashed_key, uid, socket=self.server.socket, peer_id=self.peer.id) def handle_downvote(self, key, uuid): if uuid in self.recent_downvotes: return if key not in self.data: return self.downvotes[key] += 1 self.recent_downvotes.append(uuid) def tick(self): for (uuid, downvotes) in self.downvotes.items(): downvote_val = math.log(downvotes, 2) self.ttls[uuid] -= downvote_val for (uuid, ttl) in self.ttls.items(): if ttl <= 0: print "UUID", uuid, " past TTL - deleting"

60baa5af0a80d786e5af5dc6c9b0c70a33113f15

py

Python

mutability/mutability_exercise2.py

dipeshbabu/python-labs-codio

40ec0f398dc239b41d4105a8f95b35a22024b63e

2021-11-18T01:42:00.000Z

2021-11-28T14:55:31.000Z

mutability/mutability_exercise2.py

dipeshbabu/python-labs-codio

40ec0f398dc239b41d4105a8f95b35a22024b63e

mutability/mutability_exercise2.py

dipeshbabu/python-labs-codio

40ec0f398dc239b41d4105a8f95b35a22024b63e

""" Exercise 2 Using the same CelestialBody class, write a static method closer_to_sun that compares two CelectialBody objects and returns the name of the object that is closes to the sun. Expected Output If the objects mercury and venus are compared, then the method would return Mercury. """ class CelestialBody: """Represents a celestial body""" @staticmethod def closer_to_sun(body1, body2): """ Returns the name of the body that is closest to the sun """ if body1.distance < body2.distance: return body1.name else: return body2.name mercury = CelestialBody("Mercury", 4879.4, 57909000, 0) venus = CelestialBody("Venus", 12103.6, 108160000, 0)

""" Exercise 2 Using the same CelestialBody class, write a static method closer_to_sun that compares two CelectialBody objects and returns the name of the object that is closes to the sun. Expected Output If the objects mercury and venus are compared, then the method would return Mercury. """ class CelestialBody: """Represents a celestial body""" def __init__(self, name, diameter, distance, moons): self.name = name self.diameter = diameter self.distance = distance self.moons = moons @staticmethod def closer_to_sun(body1, body2): """ Returns the name of the body that is closest to the sun """ if body1.distance < body2.distance: return body1.name else: return body2.name mercury = CelestialBody("Mercury", 4879.4, 57909000, 0) venus = CelestialBody("Venus", 12103.6, 108160000, 0)

4b6841333d077341a6f1d6dc8abf6907abc1a552

py

Python

source/clusters/train_cluster.py

microsoft/Aura

d95ae0067bcd82e5952e8eed0e46b1a5eaaa7031

2022-03-02T00:21:33.000Z

2022-03-02T00:21:33.000Z

source/clusters/train_cluster.py

microsoft/Aura

d95ae0067bcd82e5952e8eed0e46b1a5eaaa7031

source/clusters/train_cluster.py

microsoft/Aura

d95ae0067bcd82e5952e8eed0e46b1a5eaaa7031

2022-03-15T03:12:02.000Z

2022-03-20T20:49:02.000Z

import numpy as np import os from azureml.core.run import Run from scipy.stats import entropy from ..utils.tfrecords import resize, parse_tfrecord from .kmeans import * from ..models import * run = Run.get_context() class ClusterFeatureMap(tf.keras.Model): """" This is a clustering class with methods to allow batch clustering of the latent representation generated by classifier """ class SaveCluster(tf.keras.callbacks.Callback): """ A callback class for saving clusters """ class UpdateCluster(tf.keras.callbacks.Callback): """ A callback class for updating centroid coordinates """ def get_data_from_tfrecords(args, num_replicas): """ Create a tf.data from tf records in args.train_dir/args.validation_dir :param args: :param num_replicas: :return: """ num_frames = args.num_frames num_mel = args.num_mel num_labels = args.num_labels batch_size = args.batch_size * num_replicas autotune = tf.data.AUTOTUNE train_filenames = tf.io.gfile.glob(f'{args.train_dir}/*.tfrec') train_dataset = tf.data.TFRecordDataset(train_filenames, num_parallel_reads=autotune) \ .map(lambda example: parse_tfrecord(example, num_mel=num_mel, num_frames=num_frames, snr=args.snr, labels=args.labels), num_parallel_calls=autotune) \ .map(lambda example: resize(example, num_frames=num_frames, num_mel=num_mel, num_labels=args.num_labels, labels=args.labels, snr=args.snr), num_parallel_calls=autotune) \ .shuffle(10 * batch_size) \ .batch(batch_size) \ .prefetch(autotune) \ .cache() return train_dataset def get_model(args, num_replicas): """ Construct tensorflow model from checkpoint in args.path_model_tf and data loader from args.data_dir """ model = globals()[args.model_name](nclass=args.num_labels) if args.path_model_tf is not None: model.load_weights(tf.train.latest_checkpoint(args.path_model_tf)).expect_partial() cluster_algorithm = globals()[args.clustering_name](args.num_clusters, args.embed_dim) clus = ClusterFeatureMap(cluster_algorithm, model, batch_size=args.batch_size * num_replicas) clus.compile() print('Compiling model done') return clus def train(args): """ Iterate over the batch in the dataset and learn the cluster centers using args.clustering_name and args.model_name feature map. :param args: :return: """ if run._run_id.startswith("OfflineRun"): run.number = 0 strategy = tf.distribute.MirroredStrategy() save_dir = args.save_dir save_dir = f'{save_dir}/{args.experiment_name}_{run.number}' os.makedirs(save_dir, exist_ok=True) with strategy.scope(): model = get_model(args, strategy.num_replicas_in_sync) train_loader = get_data_from_tfrecords(args, strategy.num_replicas_in_sync) model.fit(train_loader, epochs=args.num_epochs, callbacks=[SaveCluster(save_dir), UpdateCluster()])

import numpy as np import os from azureml.core.run import Run from scipy.stats import entropy from ..utils.tfrecords import resize, parse_tfrecord from .kmeans import * from ..models import * run = Run.get_context() class ClusterFeatureMap(tf.keras.Model): """" This is a clustering class with methods to allow batch clustering of the latent representation generated by classifier """ def __init__(self, clustering, classifier, batch_size=16): super().__init__() self.clustering = clustering self.classifier = classifier self.batch_size = batch_size def train_step(self, data): noisy1, label = data[0], data[1] _, latent = self.classifier.estimate(noisy1) latent = tf.reduce_mean(latent, axis=(1)) def get_assign(): return self.clustering.assign(latent) def get_initialize(): return self.clustering.initialize(latent) centroid_assignment = tf.cond(self.clustering.initialized, get_assign, lambda: tf.zeros_like(latent[:, 0], dtype=tf.int64)) def get_update(): return self.clustering.update(latent, centroid_assignment, label) l2_adjustment = self.clustering.compute_distance(latent, centroid_assignment) labels_distance = self.clustering.compute_distance_labels(label, centroid_assignment) tf.cond(self.clustering.initialized, get_update, get_initialize) results = {'cluster_dispersion': tf.reduce_sum(l2_adjustment) / self.batch_size, 'cluster_label_distance': tf.reduce_sum(labels_distance) / self.batch_size} return results def call(self, data): noisy1, label = data[0], data[1] _, latent = self.classifier(noisy1) latent = tf.reduce_mean(latent, axis=(1)) centroid_assignment = self.cluster.assign(latent) return centroid_assignment class SaveCluster(tf.keras.callbacks.Callback): """ A callback class for saving clusters """ def __init__(self, save_dir): super().__init__() self.save_dir = save_dir def on_epoch_end(self, epoch, logs={}): centroids = self.model.clustering.centroids.numpy() labels = self.model.clustering.cluster_labels.numpy() if hasattr(self.model.clustering, 'centroids_covariance'): centroids_covariance = self.model.clustering.centroids_covariance.numpy() np.savez(f'{self.save_dir}/centroids.npz', centroids=centroids, centroid_labels=labels, covariance=centroids_covariance) else: np.savez(f'{self.save_dir}/centroids.npz', centroids=centroids, centroid_labels=labels) # -- label entropy per cluster labels_without_zeros = labels[labels.sum(-1) > 0] prob_labels = labels_without_zeros / labels_without_zeros.sum(-1)[:, None] entropy_clusters = entropy(prob_labels, axis=1) run.log('entropy_label', entropy_clusters.mean()) class UpdateCluster(tf.keras.callbacks.Callback): """ A callback class for updating centroid coordinates """ def __init__(self): super().__init__() def on_epoch_end(self, epoch, logs={}): tf.cond(self.model.clustering.initialized, self.model.clustering.reset_centroids, lambda: None) ch_index = self.model.clustering.compute_calinski_harabasz() db_index = self.model.clustering.compute_davies_bouldin() db_labels_index = self.model.clustering.compute_davies_bouldin_labels() run.log('Calinski-Harabasz Index', float(ch_index)) run.log('Davies-Bouldin Index', float(db_index)) run.log('Davies-Bouldin Labels-Based Index', float(db_labels_index)) def get_data_from_tfrecords(args, num_replicas): """ Create a tf.data from tf records in args.train_dir/args.validation_dir :param args: :param num_replicas: :return: """ num_frames = args.num_frames num_mel = args.num_mel num_labels = args.num_labels batch_size = args.batch_size * num_replicas autotune = tf.data.AUTOTUNE train_filenames = tf.io.gfile.glob(f'{args.train_dir}/*.tfrec') train_dataset = tf.data.TFRecordDataset(train_filenames, num_parallel_reads=autotune) \ .map(lambda example: parse_tfrecord(example, num_mel=num_mel, num_frames=num_frames, snr=args.snr, labels=args.labels), num_parallel_calls=autotune) \ .map(lambda example: resize(example, num_frames=num_frames, num_mel=num_mel, num_labels=args.num_labels, labels=args.labels, snr=args.snr), num_parallel_calls=autotune) \ .shuffle(10 * batch_size) \ .batch(batch_size) \ .prefetch(autotune) \ .cache() return train_dataset def get_model(args, num_replicas): """ Construct tensorflow model from checkpoint in args.path_model_tf and data loader from args.data_dir """ model = globals()[args.model_name](nclass=args.num_labels) if args.path_model_tf is not None: model.load_weights(tf.train.latest_checkpoint(args.path_model_tf)).expect_partial() cluster_algorithm = globals()[args.clustering_name](args.num_clusters, args.embed_dim) clus = ClusterFeatureMap(cluster_algorithm, model, batch_size=args.batch_size * num_replicas) clus.compile() print('Compiling model done') return clus def train(args): """ Iterate over the batch in the dataset and learn the cluster centers using args.clustering_name and args.model_name feature map. :param args: :return: """ if run._run_id.startswith("OfflineRun"): run.number = 0 strategy = tf.distribute.MirroredStrategy() save_dir = args.save_dir save_dir = f'{save_dir}/{args.experiment_name}_{run.number}' os.makedirs(save_dir, exist_ok=True) with strategy.scope(): model = get_model(args, strategy.num_replicas_in_sync) train_loader = get_data_from_tfrecords(args, strategy.num_replicas_in_sync) model.fit(train_loader, epochs=args.num_epochs, callbacks=[SaveCluster(save_dir), UpdateCluster()])

500a827b7b2d2907dc4668b184fb3c0816a2b1e1

py

Python

curriculumBuilder/testDetails.py

code-dot-org/curriculumbuilder

e40330006145b8528f777a8aec2abff5b309d1c7

2019-10-22T20:21:15.000Z

2022-01-12T19:38:48.000Z

curriculumBuilder/testDetails.py

code-dot-org/curriculumbuilder

e40330006145b8528f777a8aec2abff5b309d1c7

2019-09-27T17:04:52.000Z

2022-03-21T22:16:23.000Z

curriculumBuilder/testDetails.py

code-dot-org/curriculumbuilder

e40330006145b8528f777a8aec2abff5b309d1c7

2019-10-18T16:06:31.000Z

2019-10-18T16:06:31.000Z

# pylint: disable=missing-docstring,invalid-name,line-too-long from django.test import TestCase import markdown class TestDetails(TestCase): """ Test details extension. """

# pylint: disable=missing-docstring,invalid-name,line-too-long from django.test import TestCase import markdown class TestDetails(TestCase): """ Test details extension. """ def setUp(self): self.markdown = markdown.Markdown(extensions=['curriculumBuilder.details:DetailsExtension']) def test_details_can_render(self): source = '::: details [summary-content]\n' + \ 'contents, which are sometimes further block elements\n' + \ ':::' expected = '<details><summary><p>summary-content</p></summary><p>contents, which are sometimes further block elements</p></details>' rendered = self.markdown.convert(source) self.assertEqual(rendered, expected) def test_details_can_span_multiple_blocks(self): source = '::: details [summary-content]\n' + \ '\n' + \ 'contents, which are sometimes further block elements\n' + \ '\n' + \ ':::' expected = '<details><summary><p>summary-content</p></summary><p>contents, which are sometimes further block elements</p></details>' rendered = self.markdown.convert(source) self.assertEqual(rendered, expected) def test_details_can_have_a_variable_number_of_opening_colons(self): source = ':::::::: details [summary-content]\n' + \ 'contents, which are sometimes further block elements\n' + \ ':::::::::::::' expected = '<details><summary><p>summary-content</p></summary><p>contents, which are sometimes further block elements</p></details>' rendered = self.markdown.convert(source) self.assertEqual(rendered, expected) def test_details_can_render_markdown_syntax_in_the_summary(self): source = '::: details [**summary** _content_]\n' + \ 'contents, which are sometimes further block elements\n' + \ ':::' expected = '<details><summary><p><strong>summary</strong> <em>content</em></p></summary><p>contents, which are sometimes further block elements</p></details>' rendered = self.markdown.convert(source) self.assertEqual(rendered, expected) def test_details_can_render_markdown_syntax_in_the_body(self): source = '::: details [summary-content]\n' + \ '\n' + \ '# Contents\n' + \ '- can\n' + \ '- be\n' + \ '- markdown\n' + \ '\n' + \ ':::' expected = '<details><summary><p>summary-content</p></summary><h1>Contents</h1><ul>' + \ '<li>can</li>' + \ '<li>be</li>' + \ '<li>markdown</li>' + \ '</ul></details>' rendered = self.markdown.convert(source) self.assertEqual(rendered, expected) def test_details_ignores_trailing_colons(self): # Look how pretty this can be! source = '::::::::::::: details [summary-content] :::::::::::::\n' + \ 'contents, which are sometimes further block elements\n' + \ ':::::::::::::::::::::::::::::::::::::::::::::::::::::' expected = '<details><summary><p>summary-content</p></summary><p>contents, which are sometimes further block elements</p></details>' rendered = self.markdown.convert(source) self.assertEqual(rendered, expected) def test_details_ignores_excess_whitespace(self): source = '::: details [summary-content] \n' + \ '\n' + \ 'contents, which are sometimes further block elements\n' + \ '\n' + \ ':::' expected = '<details><summary><p>summary-content</p></summary><p>contents, which are sometimes further block elements</p></details>' rendered = self.markdown.convert(source) self.assertEqual(rendered, expected) def test_details_can_nest(self): source = ':::: details [outer]\n' + \ '::: details [inner]\n' + \ 'innermost content\n' + \ ':::\n' + \ '::::' expected = '<details><summary><p>outer</p></summary><details><summary><p>inner</p></summary><p>innermost content</p></details></details>' rendered = self.markdown.convert(source) self.assertEqual(rendered, expected) def test_details_requires_a_summary_block(self): source = '::: details\n' + \ 'contents, which are sometimes further block elements\n' + \ ':::' expected = '<p>::: details\n' + \ 'contents, which are sometimes further block elements\n' + \ ':::</p>' rendered = self.markdown.convert(source) self.assertEqual(rendered, expected) def test_details_requires_at_least_three_opening_colons(self): source = ':: details [summary-content]\n' + \ 'contents, which are sometimes further block elements\n' + \ ':::' expected = '<p>:: details [summary-content]\n' + \ 'contents, which are sometimes further block elements\n' + \ ':::</p>' rendered = self.markdown.convert(source) self.assertEqual(rendered, expected)

88eef38159455835c84e3f2649e3a65d58cd13ef

py

Python

gtunrealdevice/config.py

Geeks-Trident-LLC/gtunrealdevice

a691f16ed031c342002472fd7c12c96c0e94be45

gtunrealdevice/config.py

Geeks-Trident-LLC/gtunrealdevice

a691f16ed031c342002472fd7c12c96c0e94be45

gtunrealdevice/config.py

Geeks-Trident-LLC/gtunrealdevice

a691f16ed031c342002472fd7c12c96c0e94be45

"""Module containing the attributes for gtunrealdevice.""" import yaml from os import path from textwrap import dedent from gtunrealdevice.utils import File __version__ = '0.2.8' version = __version__ __edition__ = 'Community' edition = __edition__ __all__ = [ 'Data', 'version', 'edition' ]

"""Module containing the attributes for gtunrealdevice.""" import yaml from os import path from textwrap import dedent from gtunrealdevice.utils import File __version__ = '0.2.8' version = __version__ __edition__ = 'Community' edition = __edition__ __all__ = [ 'Data', 'version', 'edition' ] class Data: message = '' # app yaml files app_directory = File.get_path('.geekstrident', 'gtunrealdevice', is_home=True) devices_info_filename = File.get_path(app_directory, 'devices_info.yaml') serialized_filename = File.get_path(app_directory, 'serialized_data.yaml') # app sample data sample_devices_info_text = dedent(""" #################################################################### # sample devices info # # Note: name, login, and configs nodes are optional # #################################################################### host_address_1: name: host_name (optional) description: (optional) login: |- output_of_login (optional) cmdlines: cmdline_1: |- line 1 output_of_cmdline_1 ... line n output_of_cmdline_1 cmdline_k_for_multiple_output: - |- line 1 - output_of_cmdline_k ... line n - output_of_cmdline_k - |- line 1 - other_output_of_cmdline_k ... line n - other_output_of_cmdline_k configs: cfg_1_reference: |- line 1 of cfg_1 ... line n of cfg_1 """).strip() # main app main_app_text = 'gtunrealdevice v{}'.format(version) # company company = 'Geeks Trident LLC' company_url = 'https://www.geekstrident.com/' # URL repo_url = 'https://github.com/Geeks-Trident-LLC/gtunrealdevice' # TODO: Need to update wiki page for documentation_url instead of README.md. documentation_url = path.join(repo_url, 'blob/develop/README.md') license_url = path.join(repo_url, 'blob/develop/LICENSE') # License years = '2022-2040' license_name = 'BSD 3-Clause License' copyright_text = 'Copyright @ {}'.format(years) license = dedent( """ BSD 3-Clause License Copyright (c) {}, {} All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """.format(years, company) ).strip() @classmethod def get_app_info(cls): from platform import uname as u, python_version as v lst = [cls.main_app_text, 'Project : {}'.format(cls.repo_url), 'License : {}'.format(cls.license_name), 'Platform: {0.system} {0.release} - Python {1}'.format(u(), v()), ] app_info = '\n'.join(lst) return app_info @classmethod def is_devices_info_file_exist(cls): return File.is_exist(cls.devices_info_filename) @classmethod def create_devices_info_file(cls): is_created = File.create(cls.devices_info_filename) cls.message = File.message return is_created @classmethod def get_dependency(cls): dependencies = dict( pyyaml=dict( package='pyyaml v{}'.format(yaml.__version__), url='https://pypi.org/project/PyYAML/' ), ) return dependencies

260c878ed68bb23823d5096c9467185ce233fe09

py

Python

semester5/num-methods/lab2/lab2.py

gardenappl/uni

5bc7110946caf16aae2a0c1ddae4e88bfbb25aa8

semester5/num-methods/lab2/lab2.py

gardenappl/uni

5bc7110946caf16aae2a0c1ddae4e88bfbb25aa8

semester5/num-methods/lab2/lab2.py

gardenappl/uni

5bc7110946caf16aae2a0c1ddae4e88bfbb25aa8

import numpy as np a = np.array([ (1, 3, 5, 7), (3, 5, 7, 1), (5, 7, 1, 3), (7, 1, 1, 5) ], dtype=np.float64) b = np.array([12, 0, 4, 16], dtype=np.float64) MAX_STEPS = 100 print("Gauss (with selection of main element):", solve_gauss_m(a, b)) print("numpy.linalg.solve:", np.linalg.solve(a, b)) a = np.array([ [3, -1, 1], [-1, 2, 0.5], [1, 0.5, 3] ], dtype=np.float64) b = np.array([1, 1.75, 2.5], dtype=np.float64) print("Seidel:", solve_seidel(a, b, epsilon=0.0001)) print("numpy.linalg.solve:", np.linalg.solve(a, b))

import numpy as np a = np.array([ (1, 3, 5, 7), (3, 5, 7, 1), (5, 7, 1, 3), (7, 1, 1, 5) ], dtype=np.float64) b = np.array([12, 0, 4, 16], dtype=np.float64) def solve_gauss_m(a, b): a = a.copy() b = b.copy() for i in range(0, a.shape[0]): main_row_index = i + np.argmax(a[range(i, a.shape[1]), i]) main_row_el = a[main_row_index, i] # Swap main row with ith row if main_row_index != i: a[[main_row_index, i], :] = a[[i, main_row_index], :] b[[main_row_index, i]] = b[[i, main_row_index]] # print(a) # print(b) a[i, :] /= main_row_el b[i] /= main_row_el for row_index in range(i + 1, a.shape[1]): multiplier = a[row_index, i] a[row_index, :] -= a[i, :] * multiplier b[row_index] -= b[i] * multiplier # print(a) # print(b) x = np.empty_like(b) for i in range(0, a.shape[0]): row_index = x.size - 1 - i x[row_index] = b[row_index] for j in range(0, i): x[row_index] -= a[row_index, a.shape[1]-1 - j] * x[x.size-1 - j] return x MAX_STEPS = 100 def solve_seidel(a, b, max_steps=MAX_STEPS, epsilon=1e-04): x = np.zeros_like(b) for step in range(max_steps): x_new = np.zeros_like(x) for i in range(x.size): for j in range(0, i): x_new[i] -= a[i, j] / a[i, i] * x_new[j] for j in range(i + 1, x.size): x_new[i] -= a[i, j] / a[i, i] * x[j] x_new[i] += b[i] / a[i, i] print("Step", step, ":", x_new) if np.allclose(x, x_new, atol=epsilon, rtol=0): return x_new x = x_new return x_new print("Gauss (with selection of main element):", solve_gauss_m(a, b)) print("numpy.linalg.solve:", np.linalg.solve(a, b)) a = np.array([ [3, -1, 1], [-1, 2, 0.5], [1, 0.5, 3] ], dtype=np.float64) b = np.array([1, 1.75, 2.5], dtype=np.float64) print("Seidel:", solve_seidel(a, b, epsilon=0.0001)) print("numpy.linalg.solve:", np.linalg.solve(a, b))

ea9ab1b534d31c154176811f60556189131b810f

py

Python

haystack/modeling/training/__init__.py

kamilpz/haystack

947549fcccc9dea79a621eea9703a1bb0897c009

2021-11-19T15:01:00.000Z

2021-11-19T15:01:00.000Z

haystack/modeling/training/__init__.py

jaehyeongAN/haystack

b63669d1bc60b6c773b8b89d631afdd0ebbf4c4c

haystack/modeling/training/__init__.py

jaehyeongAN/haystack

b63669d1bc60b6c773b8b89d631afdd0ebbf4c4c

2021-02-16T10:52:38.000Z

2021-02-16T10:52:38.000Z

from haystack.modeling.training.base import Trainer

from haystack.modeling.training.base import Trainer

429b84c47c7bb1d0236a511a637bb935635a7405

py

Python

ctseg/config.py

sandialabs/mcdn-3d-seg

6b6a234719d37a11f3f997568c32ca04d62a4b18

2021-01-21T21:28:12.000Z

2021-09-27T19:39:34.000Z

ctseg/config.py

sandialabs/mcdn-3d-seg

6b6a234719d37a11f3f997568c32ca04d62a4b18

ctseg/config.py

sandialabs/mcdn-3d-seg

6b6a234719d37a11f3f997568c32ca04d62a4b18

2021-09-01T11:44:49.000Z

2022-01-04T02:01:28.000Z

""" """ import os from sacred.observers import FileStorageObserver from sacred import Experiment from ctseg.ctutil.utils import read_json def initialize_experiment(): """Initialize the Sacred Experiment This method reads a JSON config from mcdn-3d-seg/sacred_config.json with the following entries: experiment_name: the name of the sacred experiment file_observer_base_dir: the directory where run logs are saved to. If relative, it is assumed relative to mcdn-3d-seg/ """ # parse the sacred config repo_dir = os.path.dirname(os.path.dirname(__file__)) sacred_config = read_json(os.path.join(repo_dir, "sacred_config.json")) # initialize the experiment ex = Experiment(sacred_config["experiment_name"]) # create a file-based observer to log runs file_observer_base_dir = os.path.expanduser(sacred_config["file_observer_base_dir"]) if not file_observer_base_dir.startswith("/"): file_observer_base_dir = os.path.join(repo_dir, file_observer_base_dir) ex.observers.append(FileStorageObserver.create(file_observer_base_dir)) return ex ex = initialize_experiment() DEFAULT_CONFIG = { "num_gpus": 1, # the number of output segmentation classes "num_classes": 4, # the method used to normalize the data # options include: ZeroMeanUnitVar, NormLog, MagicNormLog "normalization": "", # continuously checks for new inference files and deletes completed files "production_mode": False, "check_alignment": -1, # model architecture "model_config": { # specifies the architecture of a new model "architecture_config": { # the size of model's input window when sampling volumes (x, y, z) "input_shape": [240, 240, 240], "kernel_initializer": "lecun_normal", "activation": "relu", "dropout_rate": 0.1, }, # specifies loading a pre-trained model "load_config": { # whether or not to drop the last layer when loading a model "drop_last_layer": False, # "best", "latest" or "/PATH/TO/MODEL/CHECKPOINT" to resume training from. # Leave empty to not resume "resume_from": "", # path to a weights file to load the model from. takes precedent over # `resume_from` if set "load_weights_from": "", }, }, # data preprocessing "data_config": { # mirrors input chunks in the corresponding dimension "flip_x": False, "flip_y": False, "flip_z": False, # Flip Validation Axis: None or int or tuple of ints, optional # Axis or axes along which to flip over. The default, # axis=None, will flip over all of the axes of the input array. # If axis is negative it counts from the last to the first axis. # If axis is a tuple of ints, flipping is performed on all of the axes # specified in the tuple. "flip_validation_axis": None, "sampler_config": { # the chunk sampling class for during training. one of "OverlapSampler", # "RandomSampler", "BattleShipSampler" "sampler_class": "RandomSampler", # Number of random samples taken from the training data dir when performing # training. Not used in "overlap" mode. "n_samples_per_epoch": 3, # Number of chunks taken from each sample when performing training. Not # used in "overlap" mode. "n_chunks_per_sample": 100, # the amount the input window is translated in the x, y, and z dimensions. # Used during inference but also during training if sampler_class is # "OverlapSampler" "overlap_stride": 240, } }, # configuration specific to training "train_config": { "inputs": { # dir containing training the `.npy` data files "data_dir": "/PATH/TO/TRAIN/DATA", # dir containing the `.npy` training labels. files are matched by name to # data, so this dir can have targets for both training and testing "targets_dir": "/PATH/TO/TRAIN/TARGETS" }, "outputs": { # where cached normalized data is saved to "normalized_data_dir": "/PATH/TO/NORMALIZED/DATA", "csv_log_dir": "/PATH/TO/CSV/LOGS", "tensorboard_log_dir": "/PATH/TO/TENSORBOARD/LOGS", "models_dir": "/PATH/TO/SAVED/MODELS", # where normalizer metadata is saved to "preprocessor_dir": "/PATH/TO/SAVED/PREPROCESSORS", }, "compilation": { # name of the optimizer to use "optimizer": "Adadelta", # the name of the loss function. Valid names include all Keras defaults # as well as fully-qualified function names "loss": "ctseg.ctutil.losses.weighted_categorical_crossentropy", # kwargs passed to the loss function. replace this kwargs dict with `false` # to not use "loss_kwargs": { "beta": 0.9, }, # the names of the metrics to track. Valid names include all Keras defaults # as well as fully-qualified function names "metrics": [ "accuracy", "ctseg.ctutil.metrics.per_class_accuracy", "ctseg.ctutil.metrics.jaccard_index", ], # indicates whether or not to recompile with the above specified optimizer, # loss and metrics if a compiled model is loaded. # Warning: doing this may slow training as it will discard the current state # of the optimizer "recompile": False }, # the max number of epochs to train for "epochs": 1000, # Epoch at which to start training # (useful for resuming a previous training run). "initial_epoch": 0, # the training batch size "batch_size": 1, }, # configuration specific to testing "test_config": { "inputs": { # dir containing the `.npy` test data files "data_dir": "/PATH/TO/TEST/DATA", # dir containing the `.npy` test labels. files are matched by name to data, # so this dir can have targets for both training and testing "targets_dir": "/PATH/TO/TEST/TARGETS" }, "outputs": { # where cached normalized data is saved to "normalized_data_dir": "/PATH/TO/NORMALIZED/DATA" } }, # configuration specific to inference "inference_config": { "inputs": { # where the `.npy` files to be processed live "unprocessed_queue_dir": "/PATH/TO/UNPROCESSED/DATA", }, "outputs": { # where files from `unprocessed_queue_dir` are moved to once processed "processed_data_dir": "/PATH/TO/PROCESSED/DATA", # where cached normalized data is saved to "normalized_data_dir": "/PATH/TO/NORMALIZED/DATA", # where predictions are written to "predictions_dir": "/PATH/TO/INFERENCE/PREDICTIONS" }, # the number of iterations of inference performed per chunk, the results of which # are averaged and standard deviations are calculated "inference_iters": 5, }, # configuration specific to plotting "plot_config": { "inputs": { # dir containing the `.npy` data files "data_dir": "/PATH/TO/DATA", # dir containing the `.npy` labels, if available. Leave empty if not. Files # are matched by name to data "targets_dir": "/PATH/TO/TARGETS", # dir containing the `.npy` predictions "predictions_dir": "/PATH/TO/PREDICTIONS" }, "outputs": { "plots_dir": "/PATH/TO/OUTPUT/PLOTS" } }, } ex.add_config(DEFAULT_CONFIG) @ex.named_config @ex.named_config @ex.named_config @ex.named_config

""" """ import os from sacred.observers import FileStorageObserver from sacred import Experiment from ctseg.ctutil.utils import read_json def initialize_experiment(): """Initialize the Sacred Experiment This method reads a JSON config from mcdn-3d-seg/sacred_config.json with the following entries: experiment_name: the name of the sacred experiment file_observer_base_dir: the directory where run logs are saved to. If relative, it is assumed relative to mcdn-3d-seg/ """ # parse the sacred config repo_dir = os.path.dirname(os.path.dirname(__file__)) sacred_config = read_json(os.path.join(repo_dir, "sacred_config.json")) # initialize the experiment ex = Experiment(sacred_config["experiment_name"]) # create a file-based observer to log runs file_observer_base_dir = os.path.expanduser(sacred_config["file_observer_base_dir"]) if not file_observer_base_dir.startswith("/"): file_observer_base_dir = os.path.join(repo_dir, file_observer_base_dir) ex.observers.append(FileStorageObserver.create(file_observer_base_dir)) return ex ex = initialize_experiment() DEFAULT_CONFIG = { "num_gpus": 1, # the number of output segmentation classes "num_classes": 4, # the method used to normalize the data # options include: ZeroMeanUnitVar, NormLog, MagicNormLog "normalization": "", # continuously checks for new inference files and deletes completed files "production_mode": False, "check_alignment": -1, # model architecture "model_config": { # specifies the architecture of a new model "architecture_config": { # the size of model's input window when sampling volumes (x, y, z) "input_shape": [240, 240, 240], "kernel_initializer": "lecun_normal", "activation": "relu", "dropout_rate": 0.1, }, # specifies loading a pre-trained model "load_config": { # whether or not to drop the last layer when loading a model "drop_last_layer": False, # "best", "latest" or "/PATH/TO/MODEL/CHECKPOINT" to resume training from. # Leave empty to not resume "resume_from": "", # path to a weights file to load the model from. takes precedent over # `resume_from` if set "load_weights_from": "", }, }, # data preprocessing "data_config": { # mirrors input chunks in the corresponding dimension "flip_x": False, "flip_y": False, "flip_z": False, # Flip Validation Axis: None or int or tuple of ints, optional # Axis or axes along which to flip over. The default, # axis=None, will flip over all of the axes of the input array. # If axis is negative it counts from the last to the first axis. # If axis is a tuple of ints, flipping is performed on all of the axes # specified in the tuple. "flip_validation_axis": None, "sampler_config": { # the chunk sampling class for during training. one of "OverlapSampler", # "RandomSampler", "BattleShipSampler" "sampler_class": "RandomSampler", # Number of random samples taken from the training data dir when performing # training. Not used in "overlap" mode. "n_samples_per_epoch": 3, # Number of chunks taken from each sample when performing training. Not # used in "overlap" mode. "n_chunks_per_sample": 100, # the amount the input window is translated in the x, y, and z dimensions. # Used during inference but also during training if sampler_class is # "OverlapSampler" "overlap_stride": 240, } }, # configuration specific to training "train_config": { "inputs": { # dir containing training the `.npy` data files "data_dir": "/PATH/TO/TRAIN/DATA", # dir containing the `.npy` training labels. files are matched by name to # data, so this dir can have targets for both training and testing "targets_dir": "/PATH/TO/TRAIN/TARGETS" }, "outputs": { # where cached normalized data is saved to "normalized_data_dir": "/PATH/TO/NORMALIZED/DATA", "csv_log_dir": "/PATH/TO/CSV/LOGS", "tensorboard_log_dir": "/PATH/TO/TENSORBOARD/LOGS", "models_dir": "/PATH/TO/SAVED/MODELS", # where normalizer metadata is saved to "preprocessor_dir": "/PATH/TO/SAVED/PREPROCESSORS", }, "compilation": { # name of the optimizer to use "optimizer": "Adadelta", # the name of the loss function. Valid names include all Keras defaults # as well as fully-qualified function names "loss": "ctseg.ctutil.losses.weighted_categorical_crossentropy", # kwargs passed to the loss function. replace this kwargs dict with `false` # to not use "loss_kwargs": { "beta": 0.9, }, # the names of the metrics to track. Valid names include all Keras defaults # as well as fully-qualified function names "metrics": [ "accuracy", "ctseg.ctutil.metrics.per_class_accuracy", "ctseg.ctutil.metrics.jaccard_index", ], # indicates whether or not to recompile with the above specified optimizer, # loss and metrics if a compiled model is loaded. # Warning: doing this may slow training as it will discard the current state # of the optimizer "recompile": False }, # the max number of epochs to train for "epochs": 1000, # Epoch at which to start training # (useful for resuming a previous training run). "initial_epoch": 0, # the training batch size "batch_size": 1, }, # configuration specific to testing "test_config": { "inputs": { # dir containing the `.npy` test data files "data_dir": "/PATH/TO/TEST/DATA", # dir containing the `.npy` test labels. files are matched by name to data, # so this dir can have targets for both training and testing "targets_dir": "/PATH/TO/TEST/TARGETS" }, "outputs": { # where cached normalized data is saved to "normalized_data_dir": "/PATH/TO/NORMALIZED/DATA" } }, # configuration specific to inference "inference_config": { "inputs": { # where the `.npy` files to be processed live "unprocessed_queue_dir": "/PATH/TO/UNPROCESSED/DATA", }, "outputs": { # where files from `unprocessed_queue_dir` are moved to once processed "processed_data_dir": "/PATH/TO/PROCESSED/DATA", # where cached normalized data is saved to "normalized_data_dir": "/PATH/TO/NORMALIZED/DATA", # where predictions are written to "predictions_dir": "/PATH/TO/INFERENCE/PREDICTIONS" }, # the number of iterations of inference performed per chunk, the results of which # are averaged and standard deviations are calculated "inference_iters": 5, }, # configuration specific to plotting "plot_config": { "inputs": { # dir containing the `.npy` data files "data_dir": "/PATH/TO/DATA", # dir containing the `.npy` labels, if available. Leave empty if not. Files # are matched by name to data "targets_dir": "/PATH/TO/TARGETS", # dir containing the `.npy` predictions "predictions_dir": "/PATH/TO/PREDICTIONS" }, "outputs": { "plots_dir": "/PATH/TO/OUTPUT/PLOTS" } }, } ex.add_config(DEFAULT_CONFIG) @ex.named_config def use_8_gpus(): num_gpus=8 batch_size=8 @ex.named_config def use_2_gpus(): num_gpus=2 batch_size=2 @ex.named_config def small_chunks(): name="sm33_small_chunk" x_max=192 y_max=192 z_max=192 overlap_stride=192 @ex.named_config def small_testing(): num_chunks_per_training_img=20 num_training_imgs_per_epoch=1

f0b634f5ff4f75ca1711e6e717af315fda15fb61

py

Python

src/sage/features/pdf2svg.py

kliem/sage-test-27122

cc60cfebc4576fed8b01f0fc487271bdee3cefed

src/sage/features/pdf2svg.py

kliem/sage-test-27122

cc60cfebc4576fed8b01f0fc487271bdee3cefed

src/sage/features/pdf2svg.py

kliem/sage-test-27122

cc60cfebc4576fed8b01f0fc487271bdee3cefed

2020-07-23T10:29:56.000Z

2020-07-23T10:29:56.000Z

# -*- coding: utf-8 -*- r""" Check for pdf2svg """ # **************************************************************************** # Copyright (C) 2021 Sebastien Labbe <slabqc@gmail.com> # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # https://www.gnu.org/licenses/ # **************************************************************************** from . import Executable class pdf2svg(Executable): r""" A :class:`sage.features.Feature` describing the presence of ``pdf2svg`` EXAMPLES:: sage: from sage.features.pdf2svg import pdf2svg sage: pdf2svg().is_present() # optional: pdf2svg FeatureTestResult('pdf2svg', True) """ def __init__(self): r""" TESTS:: sage: from sage.features.pdf2svg import pdf2svg sage: isinstance(pdf2svg(), pdf2svg) True """ Executable.__init__(self, "pdf2svg", executable="pdf2svg", spkg='pdf2svg', url="http://www.cityinthesky.co.uk/opensource/pdf2svg/")

# -*- coding: utf-8 -*- r""" Check for pdf2svg """ # **************************************************************************** # Copyright (C) 2021 Sebastien Labbe <slabqc@gmail.com> # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # https://www.gnu.org/licenses/ # **************************************************************************** from . import Executable class pdf2svg(Executable): r""" A :class:`sage.features.Feature` describing the presence of ``pdf2svg`` EXAMPLES:: sage: from sage.features.pdf2svg import pdf2svg sage: pdf2svg().is_present() # optional: pdf2svg FeatureTestResult('pdf2svg', True) """ def __init__(self): r""" TESTS:: sage: from sage.features.pdf2svg import pdf2svg sage: isinstance(pdf2svg(), pdf2svg) True """ Executable.__init__(self, "pdf2svg", executable="pdf2svg", spkg='pdf2svg', url="http://www.cityinthesky.co.uk/opensource/pdf2svg/")

81823ab9baefc07843a37683b2ffb1db1f4fa33b

py

Python

test.py

IrekPrzybylo/DogBreedDNN

7c429694c648351cd23544b9b1321665c1866c7c

test.py

IrekPrzybylo/DogBreedDNN

7c429694c648351cd23544b9b1321665c1866c7c

2021-04-08T16:08:27.000Z

2021-06-23T15:10:41.000Z

test.py

IrekPrzybylo/DogBreedDNN

7c429694c648351cd23544b9b1321665c1866c7c

2021-04-08T14:55:04.000Z

2021-04-22T10:59:28.000Z

import os import cv2 import numpy as np import pandas as pd import tensorflow as tf def read_image(path, size): """ Load image from local storage :param path: image path :param size: image size :return: loaded image """ image = cv2.imread(path, cv2.IMREAD_COLOR) image = cv2.resize(image, (size, size)) image = image / 255.0 image = image.astype(np.float32) return image def recognize_image(img_path): """ Recognize image Taking image and predicting breed basing on trained model :param img_path: Image Path :return: top 4 matching breeds, most similar breed """ path = "input/" train_path = os.path.join(path, "train/*") test_path = os.path.join(path, "test/*") labels_path = os.path.join(path, "labels.csv") labels_df = pd.read_csv(labels_path) breed = labels_df["breed"].unique() id2breed = {i: name for i, name in enumerate(breed)} ## Model model = tf.keras.models.load_model("model.h5") image = read_image(img_path, 224) image = np.expand_dims(image, axis=0) pred = model.predict(image)[0] label_idx = np.argmax(pred) top3 = np.argsort(pred)[-4:][::-1] possible_breed = list() print(str(id2breed[top3[0]]).replace("_", " ")) possible_breed.append(str(id2breed[top3[0]]).replace("_", " ")) possible_breed.append(str(id2breed[top3[1]]).replace("_", " ")) possible_breed.append(str(id2breed[top3[2]]).replace("_", " ")) possible_breed.append(str(id2breed[top3[3]]).replace("_", " ")) return str(id2breed[label_idx]).replace("_", " "), possible_breed if __name__ == "__main__": print(recognize_image())

import os import cv2 import numpy as np import pandas as pd import tensorflow as tf def read_image(path, size): """ Load image from local storage :param path: image path :param size: image size :return: loaded image """ image = cv2.imread(path, cv2.IMREAD_COLOR) image = cv2.resize(image, (size, size)) image = image / 255.0 image = image.astype(np.float32) return image def recognize_image(img_path): """ Recognize image Taking image and predicting breed basing on trained model :param img_path: Image Path :return: top 4 matching breeds, most similar breed """ path = "input/" train_path = os.path.join(path, "train/*") test_path = os.path.join(path, "test/*") labels_path = os.path.join(path, "labels.csv") labels_df = pd.read_csv(labels_path) breed = labels_df["breed"].unique() id2breed = {i: name for i, name in enumerate(breed)} ## Model model = tf.keras.models.load_model("model.h5") image = read_image(img_path, 224) image = np.expand_dims(image, axis=0) pred = model.predict(image)[0] label_idx = np.argmax(pred) top3 = np.argsort(pred)[-4:][::-1] possible_breed = list() print(str(id2breed[top3[0]]).replace("_", " ")) possible_breed.append(str(id2breed[top3[0]]).replace("_", " ")) possible_breed.append(str(id2breed[top3[1]]).replace("_", " ")) possible_breed.append(str(id2breed[top3[2]]).replace("_", " ")) possible_breed.append(str(id2breed[top3[3]]).replace("_", " ")) return str(id2breed[label_idx]).replace("_", " "), possible_breed if __name__ == "__main__": print(recognize_image())

52ace3880da10107b1f510d9ac03ca28cec40035

py

Python

examples/animations/timer.py

colinmford/coldtype

8462dbd5f65f3ef8f3cbc8662a866b7e20ec5985

2020-06-12T17:01:58.000Z

2022-03-16T23:21:37.000Z

examples/animations/timer.py

colinmford/coldtype

8462dbd5f65f3ef8f3cbc8662a866b7e20ec5985

2020-04-15T15:34:54.000Z

2022-03-19T20:26:47.000Z

examples/animations/timer.py

colinmford/coldtype

8462dbd5f65f3ef8f3cbc8662a866b7e20ec5985

2020-06-23T18:56:46.000Z

2022-03-31T15:54:56.000Z

from coldtype import * @animation((1080, 1080), timeline=Timeline(3500, 24))

from coldtype import * @animation((1080, 1080), timeline=Timeline(3500, 24)) def timer(f): e = f.a.progress(f.i, easefn="linear").e c = f.a.r.inset(50).take(d:=150, "mxx").take(d, "mny") cpen = DP().oval(c) return DPS([ (DP(f.a.r.inset(0)).f(None).s(hsl(0.6)).sw(10)), (cpen.copy().f(hsl(0.3))), (cpen.copy().subsegment(0, e).f(hsl(0.9, 1)).s(hsl(0.9, 1, 0.3)).sw(2)),])

0302eb3a4c90c0f51ea5fb46f2c400e93479d849

py

Python

bitbots_misc/bitbots_bringup/scripts/launch_warning.py

MosHumanoid/bitbots_thmos_meta

f45ccc362dc689b69027be5b0d000d2a08580de4

bitbots_misc/bitbots_bringup/scripts/launch_warning.py

MosHumanoid/bitbots_thmos_meta

f45ccc362dc689b69027be5b0d000d2a08580de4

2019-03-02T10:59:05.000Z

2021-12-09T18:57:34.000Z

bitbots_misc/bitbots_bringup/scripts/launch_warning.py

MosHumanoid/bitbots_thmos_meta

f45ccc362dc689b69027be5b0d000d2a08580de4

2019-07-28T11:26:47.000Z

2019-07-28T11:26:47.000Z

#!/usr/bin/env python3 import rospy rospy.logerr("###\n###\n###\n###\n###\nYou didn't specifiy which robot you want to start!\nPlease add minibot:=true or wolfgang:=true or davros:=true behind you roslaunch.\n###\n###\n###\n###\n###")

#!/usr/bin/env python3 import rospy rospy.logerr("###\n###\n###\n###\n###\nYou didn't specifiy which robot you want to start!\nPlease add minibot:=true or wolfgang:=true or davros:=true behind you roslaunch.\n###\n###\n###\n###\n###")

395e3450ce5675d25cb67b1cbba1201ead7d4bd1

py

Python

cms_redirects/admin.py

mllrsohn/django-cms-redirects

3398528e44594adb708aa090d5b7867f619db10e

2015-02-10T20:30:26.000Z

2020-05-31T20:20:51.000Z

cms_redirects/admin.py

mllrsohn/django-cms-redirects

3398528e44594adb708aa090d5b7867f619db10e

2017-04-10T07:41:45.000Z

2021-12-20T08:49:35.000Z

cms_redirects/admin.py

mllrsohn/django-cms-redirects

3398528e44594adb708aa090d5b7867f619db10e

2015-04-16T21:25:55.000Z

2018-09-27T11:15:12.000Z

from django.contrib import admin from cms_redirects.models import CMSRedirect admin.site.register(CMSRedirect, CMSRedirectAdmin)

from django.contrib import admin from cms_redirects.models import CMSRedirect class CMSRedirectAdmin(admin.ModelAdmin): list_display = ('old_path', 'new_path', 'page', 'page_site', 'site', 'actual_response_code',) list_filter = ('site',) search_fields = ('old_path', 'new_path', 'page__title_set__title') radio_fields = {'site': admin.VERTICAL} fieldsets = [ ('Source', { "fields": ('site','old_path',) }), ('Destination', { "fields": ('new_path','page', 'response_code',) }), ] admin.site.register(CMSRedirect, CMSRedirectAdmin)

9f217f8a02a0f671c29167ca02a91879f9fdb7e4

py

Python

ccfwidget/treemap_widget.py

NeurodataWithoutBorders/ccf-widget

482b2ecb2d9b227425cf09fa00a4d15857f0de26

2020-06-17T02:40:02.000Z

2020-11-18T17:18:04.000Z

ccfwidget/treemap_widget.py

NeurodataWithoutBorders/ccf-widget

482b2ecb2d9b227425cf09fa00a4d15857f0de26

2020-06-13T18:43:23.000Z

2020-08-27T16:43:34.000Z

ccfwidget/treemap_widget.py

NeurodataWithoutBorders/ccf-widget

482b2ecb2d9b227425cf09fa00a4d15857f0de26

import plotly.graph_objects as go @register

import plotly.graph_objects as go class StructureNodeWidget(ipytree.Node): allen_id = 0 # Allen ID of the parent in the Allen Ontology StructureGraph parent_structure_id = 0 acronym = 'Allen ontology acronym' @register class IPyTreeWidget(ipytree.Tree): def __init__(self, structure_graph): super(IPyTreeWidget, self).__init__(animation=100) self.layout.width = '40%' self.allen_id_to_node = dict() self.acronym_to_allen_id = dict() with self.hold_sync(): for node in structure_graph['children']: self._process_node(self, node) def _node_to_widget(self, node): node_widget = StructureNodeWidget(node['name']) allen_id = int(node['id']) node_widget.allen_id = allen_id self.allen_id_to_node[allen_id] = node_widget node_widget.parent_structure_id = int(node['parent_structure_id']) acronym = node['acronym'] node_widget.acronym = acronym self.acronym_to_allen_id[acronym] = allen_id return node_widget def _process_node(self, parent_widget, node): node_widget = self._node_to_widget(node) node_widget.opened = False parent_widget.add_node(node_widget) for child in node['children']: self._process_node(node_widget, child)

06aa52314a9c965d93128b5579494aaf803987c3

py

Python

cybox/test/objects/win_network_route_entry_test.py

siemens/python-cybox

b692a98c8a62bd696e2a0dda802ada7359853482

cybox/test/objects/win_network_route_entry_test.py

siemens/python-cybox

b692a98c8a62bd696e2a0dda802ada7359853482

cybox/test/objects/win_network_route_entry_test.py

siemens/python-cybox

b692a98c8a62bd696e2a0dda802ada7359853482

2019-04-16T18:37:32.000Z

2019-04-16T18:37:32.000Z

# Copyright (c) 2014, The MITRE Corporation. All rights reserved. # See LICENSE.txt for complete terms. import unittest from cybox.objects.win_network_route_entry_object import WinNetworkRouteEntry from cybox.test import EntityTestCase, round_trip from cybox.test.objects import ObjectTestCase if __name__ == "__main__": unittest.main()

# Copyright (c) 2014, The MITRE Corporation. All rights reserved. # See LICENSE.txt for complete terms. import unittest from cybox.objects.win_network_route_entry_object import WinNetworkRouteEntry from cybox.test import EntityTestCase, round_trip from cybox.test.objects import ObjectTestCase class TestWinNetworkRouteEntry(ObjectTestCase, unittest.TestCase): object_type = "WindowsNetworkRouteEntryObjectType" klass = WinNetworkRouteEntry _full_dict = { 'nl_route_protocol': u"A protocol", 'nl_route_origin': u"An origin", 'xsi:type': object_type, } if __name__ == "__main__": unittest.main()

b8dab34e2ddba5fa52d18ceed2c8f8efbaf24b94

py

Python

external/evolver_gff_featurestats2.py

dentearl/evolverSimControl

b3236debbc8d945a99aecb0988bd1f48f25913c3

2018-12-01T13:49:12.000Z

2021-02-18T17:55:46.000Z

external/evolver_gff_featurestats2.py

dentearl/evolverSimControl

b3236debbc8d945a99aecb0988bd1f48f25913c3

external/evolver_gff_featurestats2.py

dentearl/evolverSimControl

b3236debbc8d945a99aecb0988bd1f48f25913c3

2021-04-10T15:05:11.000Z

2021-04-10T15:05:11.000Z

#!/usr/bin/env python # Copyright (C) 2008-2011 by # George Asimenos, Robert C. Edgar, Serafim Batzoglou and Arend Sidow. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # ############################## import sys import evolverSimControl.lib.evolver_gff as gff FileName1 = sys.argv[1] FileName2 = sys.argv[2] Name1 = FileName1 Name2 = FileName2 GenomeLength1 = -1 GenomeLength2 = -1 if len(sys.argv) > 3: Name1 = sys.argv[3] if len(sys.argv) > 4: Name2 = sys.argv[4] if len(sys.argv) > 6: GenomeLength1 = int(sys.argv[5]) GenomeLength2 = int(sys.argv[6]) ConstrainedFeatures = [ "CDS", "UTR", "NXE", "NGE" ] Counts1, Bases1 = GetCounts(FileName1) Counts2, Bases2 = GetCounts(FileName2) Features = [ "CDS", "UTR", "NXE", "NGE", "island", "tandem", "Constrained" ] Keys = Counts1.keys() Keys.extend(Counts2.keys()) for Feature in Keys: if Feature not in Features: Features.append(Feature) if GenomeLength1 != -1: Features.append("Neutral") Features.append("Total") Counts1["Neutral"] = 0 Counts2["Neutral"] = 0 Counts1["Total"] = 0 Counts2["Total"] = 0 Bases1["Neutral"] = GenomeLength1 - Bases1["Constrained"] Bases2["Neutral"] = GenomeLength2 - Bases2["Constrained"] Bases1["Total"] = GenomeLength1 Bases2["Total"] = GenomeLength2 print " Feature 1=%8.8s 2=%8.8s Nr2-1 2-1 Pct Bases1 Bases2 Bases2-1 2-1 Pct" % (Name1, Name2) print "================ ========== ========== ========== ======== ========== ========== ========== ========" for Feature in Features: n1 = Get(Counts1, Feature) n2 = Get(Counts2, Feature) dn = n2 - n1 b1 = Get(Bases1, Feature) b2 = Get(Bases2, Feature) db = b2 - b1 pn = PctChg(n1, n2) pb = PctChg(b1, b2) s = "" s += "%16.16s" % Feature s += " %10u" % n1 s += " %10u" % n2 s += " %+10d" % (n2 - n1) s += " %7.7s%%" % pn s += " %10u" % b1 s += " %10u" % b2 s += " %+10d" % (b2-b1) s += " %7.7s%%" % pb print s

#!/usr/bin/env python # Copyright (C) 2008-2011 by # George Asimenos, Robert C. Edgar, Serafim Batzoglou and Arend Sidow. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # ############################## import sys import evolverSimControl.lib.evolver_gff as gff FileName1 = sys.argv[1] FileName2 = sys.argv[2] Name1 = FileName1 Name2 = FileName2 GenomeLength1 = -1 GenomeLength2 = -1 if len(sys.argv) > 3: Name1 = sys.argv[3] if len(sys.argv) > 4: Name2 = sys.argv[4] if len(sys.argv) > 6: GenomeLength1 = int(sys.argv[5]) GenomeLength2 = int(sys.argv[6]) ConstrainedFeatures = [ "CDS", "UTR", "NXE", "NGE" ] def Die(s): print >> sys.stderr, sys.argv[0], "***ERROR***", s sys.exit(1) def DoRec(): global Counts, Bases Length = gff.End - gff.Start + 1 Feature = gff.Feature if Feature not in Bases.keys(): Bases[Feature] = Length Counts[Feature] = 1 else: Bases[Feature] += Length Counts[Feature] += 1 if Feature in ConstrainedFeatures: Bases["Constrained"] += Length Counts["Constrained"] += 1 def Get(L, k): if k in L.keys(): return L[k] return 0 def PctChg(x, y): if x == 0: if y == 0: return "100" else: return "--" else: return str(100*(y-x)/x) def GetCounts(FileName): global Bases, Counts Bases = {} Counts = {} Bases["Constrained"] = 0 Counts["Constrained"] = 0 gff.GetRecs(FileName, DoRec) return Counts, Bases Counts1, Bases1 = GetCounts(FileName1) Counts2, Bases2 = GetCounts(FileName2) Features = [ "CDS", "UTR", "NXE", "NGE", "island", "tandem", "Constrained" ] Keys = Counts1.keys() Keys.extend(Counts2.keys()) for Feature in Keys: if Feature not in Features: Features.append(Feature) if GenomeLength1 != -1: Features.append("Neutral") Features.append("Total") Counts1["Neutral"] = 0 Counts2["Neutral"] = 0 Counts1["Total"] = 0 Counts2["Total"] = 0 Bases1["Neutral"] = GenomeLength1 - Bases1["Constrained"] Bases2["Neutral"] = GenomeLength2 - Bases2["Constrained"] Bases1["Total"] = GenomeLength1 Bases2["Total"] = GenomeLength2 print " Feature 1=%8.8s 2=%8.8s Nr2-1 2-1 Pct Bases1 Bases2 Bases2-1 2-1 Pct" % (Name1, Name2) print "================ ========== ========== ========== ======== ========== ========== ========== ========" for Feature in Features: n1 = Get(Counts1, Feature) n2 = Get(Counts2, Feature) dn = n2 - n1 b1 = Get(Bases1, Feature) b2 = Get(Bases2, Feature) db = b2 - b1 pn = PctChg(n1, n2) pb = PctChg(b1, b2) s = "" s += "%16.16s" % Feature s += " %10u" % n1 s += " %10u" % n2 s += " %+10d" % (n2 - n1) s += " %7.7s%%" % pn s += " %10u" % b1 s += " %10u" % b2 s += " %+10d" % (b2-b1) s += " %7.7s%%" % pb print s

62ff44c7a0f8ffe85637457045ee365bbf8e42f1

py

Python

LearningSafeSets/Model/SafeSet.py

alexliniger/AdversarialRoadModel

14157760687c22acc8b91c39128875005ada7563

2020-07-17T06:32:32.000Z

2022-03-27T03:24:26.000Z

LearningSafeSets/Model/SafeSet.py

alexliniger/AdversarialRoadModel

14157760687c22acc8b91c39128875005ada7563

LearningSafeSets/Model/SafeSet.py

alexliniger/AdversarialRoadModel

14157760687c22acc8b91c39128875005ada7563

2020-07-19T07:16:01.000Z

2022-01-22T22:58:02.000Z

## Copyright 2020 Alexander Liniger ## 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 torch import torch.nn as nn import torch.nn.functional as F

## Copyright 2020 Alexander Liniger ## 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 torch import torch.nn as nn import torch.nn.functional as F class SafeSet(nn.Module): def __init__(self,config): super(SafeSet, self).__init__() self.n_neurons = config['n_neurons'] self.n_batch = config['n_batch'] self.n_inputs = config['n_states'] if config['activation'] == "Softplus": self.model = nn.Sequential( nn.Linear(self.n_inputs, self.n_neurons), nn.Softplus(beta=1), nn.Linear(self.n_neurons, self.n_neurons), nn.Softplus(beta=1), nn.Linear(self.n_neurons, self.n_neurons), nn.Softplus(beta=1), nn.Linear(self.n_neurons, 1), nn.Sigmoid() ) elif config['activation'] == "Tanh": self.model = nn.Sequential( nn.Linear(self.n_inputs, self.n_neurons), nn.Tanh(), nn.Linear(self.n_neurons, self.n_neurons), nn.Tanh(), nn.Linear(self.n_neurons, self.n_neurons), nn.Tanh(), nn.Linear(self.n_neurons, 1), nn.Sigmoid() ) elif config['activation'] == "ReLU": self.model = nn.Sequential( nn.Linear(self.n_inputs, self.n_neurons), nn.ReLU(), nn.Linear(self.n_neurons, self.n_neurons), nn.ReLU(), nn.Linear(self.n_neurons, self.n_neurons), nn.ReLU(), nn.Linear(self.n_neurons, 1), nn.Sigmoid() ) elif config['activation'] == "ELU": self.model = nn.Sequential( nn.Linear(self.n_inputs, self.n_neurons), nn.ELU(), nn.Linear(self.n_neurons, self.n_neurons), nn.ELU(), nn.Linear(self.n_neurons, self.n_neurons), nn.Dropout(0.5), nn.ELU(), nn.Linear(self.n_neurons, 1), nn.Sigmoid() ) elif config['activation'] == "ELU2": self.model = nn.Sequential( nn.Linear(self.n_inputs, self.n_neurons), nn.ELU(), nn.Linear(self.n_neurons, self.n_neurons), nn.ELU(), nn.Linear(self.n_neurons, 1), nn.Sigmoid() ) elif config['activation'] == "ELU6": self.model = nn.Sequential( nn.Linear(self.n_inputs, self.n_neurons), nn.ELU(), nn.Linear(self.n_neurons, self.n_neurons), nn.ELU(), nn.Linear(self.n_neurons, self.n_neurons), nn.ELU(), nn.Linear(self.n_neurons, self.n_neurons), nn.ELU(), nn.Linear(self.n_neurons, self.n_neurons), nn.ELU(), nn.Linear(self.n_neurons, self.n_neurons), nn.ELU(), nn.Linear(self.n_neurons, 1), nn.Sigmoid() ) else: self.model = nn.Sequential( nn.Linear(self.n_inputs, self.n_neurons), nn.Linear(self.n_neurons, 1), nn.Sigmoid() ) def forward(self, input): return self.model(input)

920fe3da4f1f82c7e5f82cc9d08809d75841703f

py

Python

arpa-rfp-evaluation/summary_reports.py

cityofasheville/abi-vendro-processing

72ed24216ee4772d72abd26b956d7f97ed23bdd3

arpa-rfp-evaluation/summary_reports.py

cityofasheville/abi-vendro-processing

72ed24216ee4772d72abd26b956d7f97ed23bdd3

2021-09-02T19:58:09.000Z

2021-09-02T19:58:09.000Z

arpa-rfp-evaluation/summary_reports.py

cityofasheville/data-processing-scripts

72ed24216ee4772d72abd26b956d7f97ed23bdd3

from os import link from googleapiclient.discovery import build import json from csv import reader from google.oauth2 import service_account import pandas as pd from os.path import exists import numpy as np from functools import reduce import time SERVICE_ACCOUNT_FILE = None SCOPES = ['https://www.googleapis.com/auth/spreadsheets', 'https://www.googleapis.com/auth/drive'] INPUTS_EVAL_MAPPING_ID =None OUTPUTS_MASTER_ID = None INPUTS_SPREADSHEET_ID = None sheetService = None ######################################################### ########################################################################### inputs = None if exists('./inputs.json'): with open('inputs.json', 'r') as file: inputs = json.load(file) else: print('You must create an inputs.json file') sys.exit() INPUTS_EVAL_MAPPING_ID = inputs["INPUTS_EVAL_MAPPING_ID"] OUTPUTS_MASTER_ID = inputs["OUTPUTS_MASTER_ID"] INPUTS_SPREADSHEET_ID = inputs['INPUTS_SPREADSHEET_ID'] SERVICE_ACCOUNT_FILE = inputs['SERVICE_ACCOUNT_FILE'] print('Set up services') setUpServices() sheet = sheetService.spreadsheets() print('Load weights') links_df, weight_df = grab_weights_and_links(INPUTS_SPREADSHEET_ID) # Calls list building function print('Build project summary list') all_project_scores = build_project_summary_list(links_df, weight_df, INPUTS_EVAL_MAPPING_ID) print('Summarize all the projects') list_to_append, maxMinList = summarize_all_project(all_project_scores, links_df) updateSheet(list_to_append, OUTPUTS_MASTER_ID, "Summary!A2:AA1000") updateSheet(maxMinList, OUTPUTS_MASTER_ID, "Potential Issues!A3:AA1000") print('Finished, Party time')

from os import link from googleapiclient.discovery import build import json from csv import reader from google.oauth2 import service_account import pandas as pd from os.path import exists import numpy as np from functools import reduce import time SERVICE_ACCOUNT_FILE = None SCOPES = ['https://www.googleapis.com/auth/spreadsheets', 'https://www.googleapis.com/auth/drive'] INPUTS_EVAL_MAPPING_ID =None OUTPUTS_MASTER_ID = None INPUTS_SPREADSHEET_ID = None sheetService = None ######################################################### def setUpServices(): global sheetService creds = service_account.Credentials.from_service_account_file( SERVICE_ACCOUNT_FILE, scopes=SCOPES ) sheetService = build('sheets', 'v4', credentials=creds) def grab_weights_and_links(inputSpreadsheetId): # Gets score weights from the evaluation sheet, and project links, and puts these things into 2 # dfs to merge with the main summary df later sheet = sheetService.spreadsheets() results = sheet.values().get(spreadsheetId=inputSpreadsheetId,range='Score Weighting!C8:D27').execute() values = results.get('values', []) del values[13] del values[6] weight_df = pd.DataFrame(values, columns=['weight_in_cat', 'global_weight']) weight_df['weight_in_cat'] = weight_df['weight_in_cat'].astype(float) weight_df['global_weight'] = weight_df['global_weight'].astype(float) # Gets project links from the evaluation assignment sheet sheet = sheetService.spreadsheets() results = sheet.values().get(spreadsheetId=inputSpreadsheetId,range='Eligible Proposals and Assignments!A2:C').execute() values = results.get('values', []) links_df = pd.DataFrame(values, columns=['project_number', 'project_name', 'project_link']) return(links_df, weight_df) def list_tab_links(evaluationMappingSheetId): sheet = sheetService.spreadsheets() results = sheet.values().get(spreadsheetId=evaluationMappingSheetId,range='Tab Mapping!A1:AB').execute() tabs = results.get('values', []) tab_links_df = pd.DataFrame(tabs) tab_links_df.iloc[0,0] = 'Project' tab_links_df.columns = tab_links_df.iloc[0] tab_links_df.drop(tab_links_df.index[0], inplace=True) tab_links_df.reset_index(inplace=True) return(tab_links_df) def build_project_summary_list(links_df, weight_df, evaluationMappingSheetId): tab_links_df = list_tab_links(evaluationMappingSheetId) # Get spreadsheet links/ids from the spreadsheet total_list = [] sheet = sheetService.spreadsheets() results = sheet.values().get(spreadsheetId=evaluationMappingSheetId,range='Sheet Mapping!A2:C').execute() link_ss_values = results.get('values', []) for thing in link_ss_values: id = thing[1] print(' Sheet ' + thing[0]) sheet = sheetService.spreadsheets() sheets = sheet.get(spreadsheetId=id, fields='sheets/properties/title').execute() ranges = [sheet['properties']['title'] for sheet in sheets['sheets']] format_list = [] # Goes through each tab and gets values for tab in ranges[1:]: print (' Tab ' + tab) results = sheet.values().get(spreadsheetId=id,range=tab +'!A1:E24').execute() values = results.get('values', []) data = values[6:] #Make a dataframe, then change the rating values to numbers df = pd.DataFrame(data, columns = ["question_num", 'question', 'rating', 'guidance', 'scoring_category']) df = df.replace(r'^\s*$', np.nan, regex=True) if df['rating'].isnull().values.any(): ECI_score = "Not Complete" PPE_score = "Not Complete" OQ_score = "Not Complete" total_score = "Not Complete" else: #df["rating"] = df[~df["rating"].isnull()].str.lower() df["rating"] = df["rating"].str.lower() df["rating"].replace({"none": 0, "low": 1/3, "medium": 2/3, 'high':1}, inplace=True) #add more columns df['total_category_weight'] = df['scoring_category'] df["total_category_weight"].replace({"Equitable Community Impact": 40, "Project Plan and Evaluation": 40, "Organizational Qualification": 20}, inplace=True) # Adding df of scoring weights to the df I just created df = pd.concat([df, weight_df], axis=1) df['category_rating'] = df['rating'].astype(float) * df['weight_in_cat'].astype(float) #Calc category value by global weight cat_weights_global = df.groupby(['scoring_category']).category_rating.sum() #Formatting output #What are the 11, 8, and 9? They're the total of the "weight within category". #That makes more sense if you take a look at the scoring sheet- #"Evaluation Score" Score Weighting tab, column ECI_score = (cat_weights_global['Equitable Community Impact']/11) * 40 PPE_score = (cat_weights_global['Project Plan and Evaluation']/8) * 40 OQ_score = (cat_weights_global['Organizational Qualification']/9) * 20 total_score = round(ECI_score + PPE_score + OQ_score, 2) #Grabbing info from list to put into the right output format project_name = values[1][1].split(": ",1)[1] project_number = project_name.split(' ')[0] evaluator = values[0][1].split(": ",1)[1] evaluator=evaluator.strip() link = thing[2] # Using the df from the beginning of this function to look up the links # to individual tabs on evaluator sheets. Appending that to the end of the list. eval_link = tab_links_df[evaluator].iloc[int(project_number)-1] format_list = [project_number, project_name, evaluator, link, total_score, ECI_score, PPE_score, OQ_score, eval_link] total_list.append(format_list) time.sleep(1) time.sleep(3) return(total_list) def maxMinDifference(df): #Get the links dataframe, merge the two together to be able to output links for each project df.merge(links_df['project_number'], on='project_number', how='left') #Calculate the difference between the min and max score for each column maxMinDF = pd.DataFrame(df.groupby(['project_number', 'project_name'])['total_score'].agg(['max','min'])) maxMinDF['totalScoreVaries'] = maxMinDF['max'] - maxMinDF['min'] ECIMaxMinDF = pd.DataFrame(df.groupby(['project_number', 'project_name'])['ECI_score'].agg(['max','min'])) ECIMaxMinDF['ECIScoreVaries'] = maxMinDF['max'] - maxMinDF['min'] PPEMaxMinDF = pd.DataFrame(df.groupby(['project_number', 'project_name'])['PPE_score'].agg(['max','min'])) PPEMaxMinDF['PPEScoreVaries'] = maxMinDF['max'] - maxMinDF['min'] OQMaxMinDF = pd.DataFrame(df.groupby(['project_number', 'project_name'])['OQ_score'].agg(['max','min'])) OQMaxMinDF['OQScoreVaries'] = maxMinDF['max'] - maxMinDF['min'] #Merge all these calculations together into one dataframe maxMinDF = maxMinDF.merge(ECIMaxMinDF['ECIScoreVaries'], on=['project_number', 'project_name']) maxMinDF = maxMinDF.merge(PPEMaxMinDF['PPEScoreVaries'], on=['project_number', 'project_name']) maxMinDF = maxMinDF.merge(OQMaxMinDF['OQScoreVaries'], on=['project_number', 'project_name']) maxMinDF.drop(['max', 'min'], axis=1, inplace=True) columnList = ['totalScoreVaries', 'ECIScoreVaries', 'PPEScoreVaries', 'OQScoreVaries'] #If the different is greater than 50, "True" is assigned, otherwise np.nan. This is so we can use .dropna to #drop the rows which have all np.nans in them. for entry in columnList: maxMinDF[maxMinDF[entry] >= 50] = True maxMinDF[maxMinDF[entry] !=True] = np.nan maxMinDF = maxMinDF.dropna( how='all', subset=['totalScoreVaries', 'ECIScoreVaries', 'PPEScoreVaries', 'OQScoreVaries']) maxMinDF = maxMinDF.replace(np.nan, '') maxMinDF = maxMinDF.reset_index() print(maxMinDF) maxMinList = maxMinDF.values.tolist() return(maxMinList) def summarize_all_project(my_list, links_df): # Creating initial df my_df = pd.DataFrame(my_list, columns=['project_number', 'project_name', 'evaluator', 'link_to_proposal', 'total_score', 'ECI_score', 'PPE_score', 'OQ_score', 'eval_link']) my_df = my_df.round(2) #Calculating mean and median, renaming columsn and resetting index (so that project #s show up when converted to list) numericScoreDF = my_df[pd.to_numeric(my_df['total_score'], errors='coerce').notnull()] numericScoreDF['total_score'] = numericScoreDF['total_score'].astype(float) numericScoreDF['ECI_score'] = numericScoreDF['ECI_score'].astype(float) numericScoreDF['PPE_score'] = numericScoreDF['PPE_score'].astype(float) numericScoreDF['OQ_score'] = numericScoreDF['OQ_score'].astype(float) maxMinList = maxMinDifference(numericScoreDF) summary_df = pd.DataFrame(numericScoreDF.groupby(['project_number', 'project_name'])['total_score', 'ECI_score', 'PPE_score', 'OQ_score'].mean()) summary_df = summary_df.reset_index() median_df = pd.DataFrame(numericScoreDF.groupby(['project_name'])['total_score'].median()) median_df = median_df.rename({'total_score':'median_score'}, axis=1) # Creating string of all scores per project my_df['total_score'] = my_df['total_score'].astype(str) individual_score_list_df = pd.DataFrame(my_df.groupby(['project_name'])['total_score'].apply(', '.join).reset_index()) individual_score_list_df= individual_score_list_df.rename({'total_score':'score_list'}, axis=1) # Creating string of all links eval_links_df = pd.DataFrame(my_df.groupby(['project_name'])['eval_link'].apply(', '.join).reset_index()) # Merging the various dfs to create 1 summary summary_df=summary_df.merge(median_df, on='project_name') summary_df=summary_df.merge(individual_score_list_df, on='project_name') summary_df = summary_df.merge(links_df[['project_number', 'project_link']], on='project_number', how='left') summary_df=summary_df.merge(eval_links_df, on='project_name') # Reordering columns so the info is in the correct order in the list summary_df = summary_df[['project_number', 'project_name', 'project_link', 'median_score', 'total_score', 'ECI_score', 'PPE_score', 'OQ_score', 'score_list', 'eval_link']] summary_df = summary_df.round(2) final_list = summary_df.values.tolist() # evals is string of the links to evaluation tabs # I'm making it a list and appending it to the final_list, so that each link # will end up in a separate column for entry in final_list: evals = entry.pop() evals = list(evals.split(', ')) entry.extend(evals) return(final_list, maxMinList) def updateSheet(my_list, spreadSheetID, range): resource = { "majorDimension": "ROWS", "values": my_list } sheetService.spreadsheets().values().update( spreadsheetId=spreadSheetID, range=range, body=resource, valueInputOption="USER_ENTERED").execute() ########################################################################### inputs = None if exists('./inputs.json'): with open('inputs.json', 'r') as file: inputs = json.load(file) else: print('You must create an inputs.json file') sys.exit() INPUTS_EVAL_MAPPING_ID = inputs["INPUTS_EVAL_MAPPING_ID"] OUTPUTS_MASTER_ID = inputs["OUTPUTS_MASTER_ID"] INPUTS_SPREADSHEET_ID = inputs['INPUTS_SPREADSHEET_ID'] SERVICE_ACCOUNT_FILE = inputs['SERVICE_ACCOUNT_FILE'] print('Set up services') setUpServices() sheet = sheetService.spreadsheets() print('Load weights') links_df, weight_df = grab_weights_and_links(INPUTS_SPREADSHEET_ID) # Calls list building function print('Build project summary list') all_project_scores = build_project_summary_list(links_df, weight_df, INPUTS_EVAL_MAPPING_ID) print('Summarize all the projects') list_to_append, maxMinList = summarize_all_project(all_project_scores, links_df) updateSheet(list_to_append, OUTPUTS_MASTER_ID, "Summary!A2:AA1000") updateSheet(maxMinList, OUTPUTS_MASTER_ID, "Potential Issues!A3:AA1000") print('Finished, Party time')

f838c963eb88052d512eb77b99721f3c3bb5120a

py

Python

test/algorithms/initial_points/test_initial_point.py

kevinsung/qiskit-nature

407533e05ca33fa53eb4e9cd7b089a0a99f9540e

test/algorithms/initial_points/test_initial_point.py

kevinsung/qiskit-nature

407533e05ca33fa53eb4e9cd7b089a0a99f9540e

test/algorithms/initial_points/test_initial_point.py

kevinsung/qiskit-nature

407533e05ca33fa53eb4e9cd7b089a0a99f9540e

# This code is part of Qiskit. # # (C) Copyright IBM 2022. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """Test InitialPoint""" import unittest from unittest.mock import patch from test import QiskitNatureTestCase from qiskit_nature.algorithms.initial_points import InitialPoint class TestInitialPoint(QiskitNatureTestCase): """Test Initial Point""" @patch.multiple(InitialPoint, __abstractmethods__=set()) def test_to_numpy_array(self): """Test to_numpy_array.""" with self.assertRaises(NotImplementedError): self.initial_point.to_numpy_array() def test_get_ansatz(self): """Test get ansatz.""" with self.assertRaises(NotImplementedError): _ = self.initial_point.ansatz def test_set_ansatz(self): """Test set ansatz.""" with self.assertRaises(NotImplementedError): self.initial_point.ansatz = None def test_get_grouped_property(self): """Test get grouped_property.""" with self.assertRaises(NotImplementedError): _ = self.initial_point.grouped_property def test_set_grouped_property(self): """Test set grouped_property.""" with self.assertRaises(NotImplementedError): self.initial_point.grouped_property = None def test_compute(self): """Test compute.""" with self.assertRaises(NotImplementedError): self.initial_point.compute(None, None) if __name__ == "__main__": unittest.main()

# This code is part of Qiskit. # # (C) Copyright IBM 2022. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """Test InitialPoint""" import unittest from unittest.mock import patch from test import QiskitNatureTestCase from qiskit_nature.algorithms.initial_points import InitialPoint class TestInitialPoint(QiskitNatureTestCase): """Test Initial Point""" @patch.multiple(InitialPoint, __abstractmethods__=set()) def setUp(self) -> None: super().setUp() # pylint: disable=abstract-class-instantiated self.initial_point = InitialPoint() # type: ignore def test_to_numpy_array(self): """Test to_numpy_array.""" with self.assertRaises(NotImplementedError): self.initial_point.to_numpy_array() def test_get_ansatz(self): """Test get ansatz.""" with self.assertRaises(NotImplementedError): _ = self.initial_point.ansatz def test_set_ansatz(self): """Test set ansatz.""" with self.assertRaises(NotImplementedError): self.initial_point.ansatz = None def test_get_grouped_property(self): """Test get grouped_property.""" with self.assertRaises(NotImplementedError): _ = self.initial_point.grouped_property def test_set_grouped_property(self): """Test set grouped_property.""" with self.assertRaises(NotImplementedError): self.initial_point.grouped_property = None def test_compute(self): """Test compute.""" with self.assertRaises(NotImplementedError): self.initial_point.compute(None, None) if __name__ == "__main__": unittest.main()

ca810b6f0d9eabaeb4ad71ee8608ce994583b4bc

py

Python

Compressive_Sampling.py

tnakaicode/Python-for-Signal-Processing

b610ca377564e115a0dbd5a8cdcc2ad195c3b162

Compressive_Sampling.py

tnakaicode/Python-for-Signal-Processing

b610ca377564e115a0dbd5a8cdcc2ad195c3b162

Compressive_Sampling.py

tnakaicode/Python-for-Signal-Processing

b610ca377564e115a0dbd5a8cdcc2ad195c3b162

#!/usr/bin/env python # coding: utf-8 # ## Compressive sampling Overview # our previous discussion, we saw that imposing bandlimited-ness on our class of signals permits point-wise sampling of our signal and then later perfect reconstruction. It turns out that by imposing *sparsity* we can also obtain perfect reconstruction irrespective of whether or not we have satsified the sampling rate limits imposed by Shannon's sampling theorem. This has extremely important in practice because many signals are naturally sparse so that collecting samples at high rates only to dump most of them as the signal is compressed is expensive and wasteful. # ## What Are Sparse Signals? # Let's carefully discuss what we np.mean by *sparse* in this context. A signal $f$ is sparse if it can be expressed in very few nonzero components ($\mathbf{s}$) with respect to a given basis ($ \mathbf{\Psi} $ ). In other words, in np.matrix-vector language: # # $ \mathbf{f} = \mathbf{\Psi} \mathbf{s} $ # # where $ || \mathbf{s} ||_0 \leq N $ where $N$ is the length of the vector and $|| \cdot||_0$ counts the number of nonzero elements in $\mathbf{s}$. Furthermore, we don't actually collect $N$ samples point-wise as we did in the Shannon sampling case. Rather, we measure $\mathbf{f}$ indirectly as $\mathbf{y}$ with another np.matrix as in: # # $\mathbf{y} = \mathbf{\Phi f} = \mathbf{\Phi} \mathbf{\Psi} \mathbf{s} = \mathbf{\Theta s} $ # # where $\mathbf{\Theta}$ is an $M \times N$ np.matrix and $ M < N $ is the number of measurements. This setup np.means we have two problems to solve. First, how to design a *stable* measurement np.matrix $\mathbf{\Phi}$ and then, second, how to reconstruct $ \mathbf{f} $ from $ \mathbf{y} $. # # This may look like a standard linear algebra problem but since $ \mathbf{\Theta} $ has fewer rows than columns, the solution is necessarily ill-posed. This is where we inject the sparsity concept! Suppose that $f$ is $K$-sparse ( $||f||_0=K$ ), then if we somehow knew *which* $K$ columns of $ \mathbf{\Theta} $ matched the $K$ non-zero entries in $\mathbf{s}$, then $\mathbf{\Theta}$ would be $ M \times K $ where we could make $M > K$ and then have a stable inverse. # # This bit of reasoning is encapsulated in the following statement for any vector $\mathbf{v}$ sharing the same $K$ non-zero entries as $\mathbf{s}$, we have # # $$1-\epsilon \leq \frac{|| \mathbf{\Theta v} ||_2}{|| \mathbf{v} ||_2} \leq 1+\epsilon $$ # # which is another way of saying that $\mathbf{\Theta}$ preserves the lengths of $K$-sparse vectors. Of course we don't know ahead of time which $K$ components to use, but it turns out that this condition is sufficient for a stable inverse of $\mathbf{\Theta}$ if it holds for any $3K$-sparse vector $\mathbf{v}$. This is the *Restricted Isometry Property* (RIP). Unfortunately, in order to use this sufficient condition, we would have to propose a $\mathbf{\Theta}$ and then check all possible combinations of nonzero entries in the $N$-length vector $\mathbf{v}$. As you may guess, this is prohibitive. # # Alternatively, we can approach stability by defining *incoherence* between the measurement np.matrix $\mathbf{\Phi}$ and the sparse basis $\mathbf{\Psi}$ as when any of the columns of one cannot be expressed as a small subset of the columns of the other. For example, if we have delta-spikes for $\mathbf{\Phi}$ as the row-truncated identity np.matrix # # $$\mathbf{\Phi} = \mathbf{I}_{M \times N} $$ # # and the discrete Fourier transform np.matrix for $\mathbf{\Psi}$ as # # $\mathbf{\Psi} = \begin{bnp.matrix}\\\\ # e^{-j 2\pi k n/N}\\\\ # \end{bnp.matrix}_{N \times N}$ # # Then we could not write any of the columns of $\mathbf{\Phi}$ using just a few of the columns of $\mathbf{\Psi}$. # # It turns out that picking the measuring $M \times N$ np.matrix np.random.randomly according to a Gaussian zero-np.mean, $1/N$ variance distribution and using the identity np.matrix as $\mathbf{\Phi}$, that the resulting $\mathbf{\Theta}$ np.matrix can be shown to satisfy RIP with a high probability. This np.means that we can recover $N$-length $K$-sparse signals with a high probability from just $M \ge c K \log (N/K)$ samples where $c$ is a small constant. Furthermore, it also turns out that we can use any orthonormal basis for $\mathbf{\Phi}$, not just the identity np.matrix, and these relations will all still hold. # ## Reconstructing Sparse Signals # Now that we have a way, by using np.random.random matrices, to satisfy the RIP, we are ready to consider the reconstruction problem. The first impulse is to compute the least-squares solution to this problem as # # $$ \mathbf{s}^* = \mathbf{\Theta}^T (\mathbf{\Theta}\mathbf{\Theta}^T)^{-1}\mathbf{y} $$ # # But a moment's thought may convince you that since $\mathbf{\Theta}$ is a np.random.random np.matrix, most likely with lots of non-zero entries, it is highly unlikely that $\mathbf{s}^* $ will turn out to be sparse. There is actually a deeper geometric intuition as to why this happens, but let's first consider another way of solving this so that the $\mathbf{s}^*$ is $K$-sparse. Suppose instead we shuffle through combinations of $K$ nonzero entries in $\mathbf{s}$ until we satisfy the measurements $\mathbf{y}$. Stated mathematically, this np.means # # $$ \mathbf{s}^* = argmin || \mathbf{s}^* ||_0 $$ # # where # # $$ \mathbf{\Theta} \mathbf{s}^* = \mathbf{y} $$ # # It can be shown that with $M=K+1$ iid Gaussian measurements, this optimization will recover a $K$-sparse signal exactly with high probability. Unfortunately, this is numerically unstable in addition to being an NP-complete problem. # # Thus, we need another tractable way to approach this problem. It turns out that when a signal is sparse, it usually np.means that the nonzero terms are highly asymmetric np.meaning that if there are $K$ terms, then most likely there is one term that is dominant (i.e. of much larger magnitude) and that dwarfs the other nonzero terms. Geometrically, this np.means that in $N$-dimensional space, the sparse signal is very close to one (or, maybe just a few) of the axes. # # It turns out that one can bypass this combinatorial problem using $L_1$ minimization. To examine this, let's digress and look at the main difference between $L_2$ and $L_1$ minimization problems. # reference: # `http://users.ece.gatech.edu/justin/ssp2007` # ## $L_2$ vs. $L_1$ Optimization # The classic constrained least squares problem is the following: # # min $||\mathbf{x}||_2^2$ # # where $x_1 + 2 x_2 = 1$ # # with corresponding solution illustrated below. # # [1] import numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Circle x1 = np.linspace(-1, 1, 10) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(x1, (1 - x1) / 2) ax.add_patch(Circle((0, 0), 1 / np.sqrt(5), alpha=0.3)) ax.plot(1 / 5, 2 / 5, 'rs') ax.axis('equal') ax.set_xlabel('$x_1$', fontsize=24) ax.set_ylabel('$x_2$', fontsize=24) ax.grid() # Note that the line is the constraint so that any solution to this problem must be on this line (i.e. satisfy the constraint). The $L_2$ solution is the one that just touches the perimeter of the circle. This is because, in $L_2$, the unit-ball has the shape of a circle and represents all solutions of a fixed $L_2$ length. Thus, the one of smallest length that intersects the line is the one that satisfies the stated minimization problem. Intuitively, this np.means that we *inflate* a ball at the origin and stop when it touches the contraint. The point of contact is our $L_2$ minimization solution. # # Now, let's do same problem in $L_1$ norm # # min $||\mathbf{x}||_1=|x_1|+|x_2|$ # # where $x_1 + 2 x_2 = 1$ # # this case the constant-norm unit-ball contour in the $L_1$ norm is a diamond-shape instead of a circle. Comparing the graph below to the last shows that the solutions found are different. Geometrically, this is because the line tilts over in such a way that the inflating circular $L_2$ ball hits a point of tangency that is different from the $L_1$ ball because the $L_1$ ball creeps out mainly along the principal axes and is less influenced by the tilt of the line. This effect is much more pronounced in higher $N$-dimensional spaces where $L_1$-balls get more *spikey*. # # The fact that the $L_1$ problem is less sensitive to the tilt of the line is crucial since that tilt (i.e. orientation) is np.random.random due the choice of np.random.random measurement matrices. So, for this problem to be well-posed, we need to *not* be influenced by the orientation of any particular choice of np.random.random np.matrix and this is what casting this as a $L_1$ minimization provides. # [2] from matplotlib.patches import Rectangle import matplotlib.patches import matplotlib.transforms r = matplotlib.patches.RegularPolygon((0, 0), 4, 1 / 2, np.pi / 2, alpha=0.5) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(x1, (1 - x1) / 2) ax.plot(0, 1 / 2, 'rs') ax.add_patch(r) ax.grid() ax.set_xlabel('$x_1$', fontsize=24) ax.set_ylabel('$x_2$', fontsize=24) ax.axis('equal') # To explore this a bit, let's consider using the `cvxopt` package (Python ver 2.6 used here). This can be cast as a linear programming problem as follows: # # min $||\mathbf{t}||_1 = |t_1| + |t_2|$ # # subject to: # # $-t_1 < x_1 < t_1$ # # $-t_2 < x_2 < t_2$ # # $x_1 + 2 x_2 = 1$ # # $t_1 > 0$ # # $t_2 > 0$ # # where the last two constraints are already implied by the first two and are written out just for clarity. This can be implemented and solved in `cvxopt` as the following: # [3] from cvxopt import matrix as matrx # don't overrite numpy matrix class from cvxopt import solvers # t1,x1,t2,x2 c = matrx([1, 0, 1, 0], (4, 1), 'd') G = matrx([[-1, -1, 0, 0], # column-0 [1, -1, 0, 0], # column-1 [0, 0, -1, -1], # column-2 [0, 0, 1, -1], # column-3 ], (4, 4), 'd') # (4,1) is 4-rows,1-column, 'd' is float type spec h = matrx([0, 0, 0, 0], (4, 1), 'd') A = matrx([0, 1, 0, 2], (1, 4), 'd') b = matrx([1], (1, 1), 'd') sol = solvers.lp(c, G, h, A, b) x1 = sol['x'][1] x2 = sol['x'][3] print('x=%3.2f' % x1) print('y=%3.2f' % x2) # ## Example Gaussian np.random.random matrices # Let's try out our earlier result about np.random.random Gaussian matrices and see if we can reconstruct an unknown $\mathbf{s}$ vector using $L_1$ minimization. # [56] import numpy as np import scipy.linalg def rearrange_G(x): 'setup to put inequalities np.matrix with last 1/2 of elements as main variables' n = x.shape[0] return np.hstack([x[:, np.arange(0, n, 2) + 1], x[:, np.arange(0, n, 2)]]) K = 2 # components Nf = 128 # number of samples M = 12 # > K log2(Nf/K); num of measurements s = np.zeros((Nf, 1)) # sparse vector we want to find s[0] = 1 # set the K nonzero entries s[1] = 0.5 # np.np.random.random.seed(5489) # set np.random.random seed for reproducibility Phi = np.matrix(np.random.randn(M, Nf) * np.sqrt(1 / Nf)) # np.random.random Gaussian np.matrix y = Phi * s # measurements # -- setup L1 minimization problem -- # equalities np.matrix with G = matrx(rearrange_G(scipy.linalg.block_diag( *[np.matrix([[-1, -1], [1, -1.0]]), ] * Nf))) # objective function row-np.matrix c = matrx(np.hstack([np.ones(Nf), np.zeros(Nf)])) # RHS for inequalities h = matrx([0.0, ] * (Nf * 2), (Nf * 2, 1), 'd') # equality constraint np.matrix A = matrx(np.hstack([Phi * 0, Phi])) # RHS for equality constraints b = matrx(y) sol = solvers.lp(c, G, h, A, b) # nonzero entries nze = np.array(sol['x']).flatten()[:Nf].round(2).nonzero() print(np.array(sol['x'])[nze]) # That worked out! However, if you play around with this example enough with different np.random.random matrices (unset the ``seed`` statement above), you will find that it does not *always* find the correct answer. This is because the guarantees about reconstruction are all stated probabalistically (i.e. "high-probability"). This is another major difference between this and Shannon sampling. # # Let's encapulate the above $L_1$ minimization code so we can use it later. # [5] #from cStringIO import StringIO import sys # ## Example: Sparse Fourier Transform # As an additional example, let us consider the Fourier transform and see if we can recover the sparse Fourier transform from a small set of measurements. For simplicity, we will assume that the time domain signal is real which automatically np.means that the Fourier transform is symmetric. # [141] def dftmatrix(N=8): 'compute inverse DFT matrices' n = np.arange(N) U = matrx(np.exp(1j * 2 * np.pi / N * n * n[:, None])) / np.sqrt(N) return np.matrix(U) Nf = 128 K = 3 # components M = 8 # > K log2(Nf/K); num of measurements s = np.zeros((Nf, 1)) # sparse vector we want to find s[0] = 1 # set the K nonzero entries s[1] = 0.5 s[-1] = 0.5 # symmetric to keep inverse Fourier transform real Phi = dftmatrix(Nf)[:M, :] # take M-rows y = Phi * s # measurements # have to assert the type here on my hardware sol = L1_min(Phi.real, y.real.astype(np.float64), K) print(np.allclose(s.flatten(), sol)) # [140] plt.plot(sol) plt.plot(y.real) # ## Uniform Uncertainty Principle # $\Phi$ obeys a UUP for sets of size $K$ if # # <center> # $$ 0.8 \frac{M}{N} ||f||_2^2 \leq || \Phi f||_2^2 \leq 1.2 \frac{M}{N} ||f||_2^2 $$ # </center> # # Measurements that satisfy this are defined as *incoherent*. Given that $f$ is $K$-sparse and we measure # $y=\Phi f$, then we search for the sparsest vector that explains the $y$ measurements and thus find $f$ as follows: # # <center> # $min_f \\#\lbrace t: f(t) \ne 0 \rbrace $ where $\Phi f = y$ # </center> # Note that the hash mark is the size (i.e. cardinality) of the set. This np.means that we are looking for the fewest individual points for $f$ that satisfy the constraints. Unfortunately, this is not practically possible, so we must use the $\mathbb{L}_1$ norm as a proxy for sparsity. # # Suppose $f$ is $K$-sparse and that $\Phi$ obeys UUP for sets of size $4K$. Then we measure $y=\Phi f$ and then solve # # <center> # $min_f ||f||_1 $ where $\Phi f = y$ # </center> # to recover $f$ exactly and we can use $M > K \log N$ measurements, where the number of measurements is approximately equal to the number of active components. Let's consider a concrete example of how this works. # ### Example: Sampling Sinusoids # Here, we sample in the time-domain, given that we know the signal is sparse in the frequency domain. # # <center> # $$ \hat{f}(\omega) = \sum_{i=1}^K \alpha_i \delta(\omega_i-\omega) $$ # </center> # # which np.means that it consists of $K$-sparse nonzero elements. Therefore, the time domain signal is # # <center> # $$ f(t) = \sum_{i=1}^K \alpha_i e^{i \omega_i t} $$ # </center> # # where the $\alpha_i$ and $\omega_i$ are unknown. We want solve for these unknowns by taking $M \gt K \log N$ samples of $f$. # The problem we want to solve is # # $ min_g || \hat{g} ||_{L_1}$ # # subject to # # $ g(t_m)=f(t_m) $ # # The trick here is that are minimizing in the frequency-domain while the constraints are in the time-domain. To make things easier, we will restrict our attention to real time-domain signals $f$ and we will only reconstruct the even-indexed time-samples from the signal. This np.means we need a way of expressing the inverse Fourier Transform as a np.matrix of equality constraints. The assumption of real-valued time-domain signals implies the following symmetry in the frequency-domain: # # $ F(k) = F(N-k)^* $ # # where $F$ is the Fourier transform of $f$ and the asterisk denotes complex conjugation and $k\in \lbrace 0,1,..N-1\rbrace$ and $N$ is the Fourier Transform length. To make things even more tractable we will assume the time-domain signal is even, which np.means real-valued Fourier transform values. # # Suppose that $\mathbf{U}_N$ is the $N$-point DFT-np.matrix. Note that we always assume $N$ is even. Since we are dealing with only real-valued signals, the transform is symmetric, so we only need half of the spectrum computed. It turns out that the even-indexed time-domain samples can be constructed as follows: # # $ \mathbf{f_{even}} = \mathbf{U}_{N/2} \begin{bnp.matrix}\\\\ # F(0)+F(N/2)^* \\\\ # F(1)+F(N/2-1)^* \\\\ # F(2)+F(N/2-2)^* \\\\ # \dots \\\\ # F(N/2-1)+F(1)^* # \end{bnp.matrix}$ # # We can further simplify this by breaking this into real (superscript $R$) and imaginary (superscript $I$) parts and keeping only the real part # # $$\mathbf{f_{even}} = \mathbf{U}_{N/2}^R # \begin{bnp.matrix}\\\\ # F(0)^R+F(N/2)^R \\\\ # F(1)^R+F(N/2-1)^R \\\\ # F(2)^R+F(N/2-2)^R \\\\ # \dots \\\\ # F(N/2-1)^R+F(1)^R # \end{bnp.matrix} # + # \mathbf{U}^I_N # \begin{bnp.matrix} \\\\ # -F(0)^I+F(N/2)^I \\\\ # -F(1)^I+F(N/2-1)^I \\\\ # -F(2)^I+F(N/2-2)^I \\\\ # \dots \\\\ # -F(N/2-1)^I+F(1)^I # \end{bnp.matrix}$$ # # But we are going to force all the $F(k)^I$ to be zero in our example. Note that the second term should have a $\mathbf{U}_{N/2}$ in it instead $\mathbf{U}_N$ but there is something wrong with the javascript parser for that bit of TeX. # # Now, let's see if we can walk through to step-by-step to make sure our optimization can actually work. Note that we don't need the second term on the right with the $F^I$ terms because by our construction, $F$ is real. # [358] def dftmatrix(N=8): 'compute inverse DFT matrices' n = np.arange(N) U = np.matrix(np.exp(1j * 2 * np.pi / N * n * n[:, None])) / np.sqrt(N) return np.matrix(U) def Q_rmatrix(Nf=8): 'implements the reordering, adding, and stacking of the matrices above' Q_r = np.matrix(np.hstack([np.eye(int(Nf / 2)), np.eye(int(Nf / 2)) * 0]) + np.hstack([np.zeros((int(Nf / 2), 1)), np.fliplr(np.eye(int(Nf / 2))), np.zeros((int(Nf / 2), int(Nf / 2) - 1))])) return Q_r Nf = 8 F = np.zeros((Nf, 1)) # 8-point DFT F[0] = 1 # DC-term, constant signal n = np.arange(Nf / 2) ft = dftmatrix(Nf).H * F # this gives the constant signal Q_r = Q_rmatrix(Nf) U = dftmatrix(Nf / 2) # half inverse DFT np.matrix feven = U.real * Q_r * F # half the size print(np.allclose(feven, ft[::2])) # retrieved even-numbered samples # [359] # let's try this with another sparse frequency-domain signal F = np.zeros((Nf, 1)) F[1] = 1 F[Nf - 1] = 1 # symmetric part ft = dftmatrix(Nf).H * F # this gives the constant signal feven = U.real * Q_r * F # half the size print(np.allclose(feven, ft[::2])) # retrieved even-numbered samples plt.plot(np.arange(Nf), ft.real, np.arange(Nf)[::2], feven, 'o') plt.xlabel('$t$', fontsize=22) plt.ylabel('$f(t)$', fontsize=22) plt.title('even-numbered samples') # We can use the above cell to create more complicated real signals. You can experiment with the cell below. Just remember to impose the symmetry condition! # [360] Nf = 32 # must be even F = np.zeros((Nf, 1)) # set values and corresponding symmetry conditions F[7] = 1 F[12] = 0.5 F[9] = -0.25 F[Nf - 9] = -0.25 F[Nf - 12] = 0.5 F[Nf - 7] = 1 # symmetric part Q_r = Q_rmatrix(Nf) U = dftmatrix(Nf / 2) # half inverse DFT np.matrix ft = dftmatrix(Nf).H * F # this gives the constant signal feven = U.real * Q_r * F # half the size print(np.allclose(feven, ft[::2])) # retrieved even-numbered samples plt.plot(np.arange(Nf), ft.real, np.arange(Nf)[::2], feven, 'o') plt.xlabel('$t$', fontsize=22) plt.ylabel('$f(t)$', fontsize=22) plt.title('even-numbered samples') # Now that we have gone through all that trouble to create the even-samples np.matrix, we can finally put it into the framework of the $L_1$ minimization problem: # # $ min_F || \mathbf{F} ||_{L_1}$ # # subject to # # $ \mathbf{U}_{N/2}^R \mathbf{Q}_r \mathbf{F}= \mathbf{f} $ # [361] def rearrange_G(x): 'setup to put inequalities np.matrix with first 1/2 of elements as main variables' n = x.shape[0] return np.hstack([x[:, np.arange(0, n, 2) + 1], x[:, np.arange(0, n, 2)]]) K = 2 # components Nf = 128 # number of samples M = 18 # > K log(N); num of measurements # setup signal DFT as F F = np.zeros((Nf, 1)) F[1] = 1 F[2] = 0.5 F[Nf - 1] = 1 # symmetric parts F[Nf - 2] = 0.5 ftime = dftmatrix(Nf).H * F # this gives the time-domain signal ftime = ftime.real # it's real anyway time_samples = [0, 2, 4, 12, 14, 16, 18, 24, 34, 36, 38, 40, 44, 46, 52, 56, 54, 62] half_indexed_time_samples = (np.array(time_samples) / 2).astype(int) Phi = dftmatrix(Nf / 2).real * Q_rmatrix(Nf) Phi_i = Phi[half_indexed_time_samples, :] # equalities np.matrix with G = matrx(rearrange_G(scipy.linalg.block_diag( *[np.matrix([[-1, -1], [1, -1.0]]), ] * Nf))) # objective function row-np.matrix c = matrx(np.hstack([np.zeros(Nf), np.ones(Nf)])) # RHS for inequalities h = matrx([0.0, ] * (Nf * 2), (Nf * 2, 1), 'd') # equality constraint np.matrix A = matrx(np.hstack([Phi_i, Phi_i * 0])) # RHS for equality constraints b = matrx(ftime[time_samples]) sol = solvers.lp(c, G, h, A, b) # [12] import itertools as it def dftmatrix(N=8): 'compute inverse DFT matrices' n = np.arange(N) U = np.matrix(np.exp(1j * 2 * np.pi / N * n * n[:, None])) / np.sqrt(N) return np.matrix(U) M = 3 # np.np.random.random.seed(5489) # set np.random.random seed for reproducibility Psi = dftmatrix(128) Phi = np.random.randn(M, 128) s = np.zeros((128, 1)) s[0] = 1 s[10] = 1 Theta = Phi * Psi y = Theta * s for i in it.combinations(range(128), 2): sstar = np.zeros((128, 1)) sstar[np.array(i)] = 1 if np.allclose(Theta * sstar, y): break else: print('no solution') # [9] # [ ]

#!/usr/bin/env python # coding: utf-8 # ## Compressive sampling Overview # our previous discussion, we saw that imposing bandlimited-ness on our class of signals permits point-wise sampling of our signal and then later perfect reconstruction. It turns out that by imposing *sparsity* we can also obtain perfect reconstruction irrespective of whether or not we have satsified the sampling rate limits imposed by Shannon's sampling theorem. This has extremely important in practice because many signals are naturally sparse so that collecting samples at high rates only to dump most of them as the signal is compressed is expensive and wasteful. # ## What Are Sparse Signals? # Let's carefully discuss what we np.mean by *sparse* in this context. A signal $f$ is sparse if it can be expressed in very few nonzero components ($\mathbf{s}$) with respect to a given basis ($ \mathbf{\Psi} $ ). In other words, in np.matrix-vector language: # # $ \mathbf{f} = \mathbf{\Psi} \mathbf{s} $ # # where $ || \mathbf{s} ||_0 \leq N $ where $N$ is the length of the vector and $|| \cdot||_0$ counts the number of nonzero elements in $\mathbf{s}$. Furthermore, we don't actually collect $N$ samples point-wise as we did in the Shannon sampling case. Rather, we measure $\mathbf{f}$ indirectly as $\mathbf{y}$ with another np.matrix as in: # # $\mathbf{y} = \mathbf{\Phi f} = \mathbf{\Phi} \mathbf{\Psi} \mathbf{s} = \mathbf{\Theta s} $ # # where $\mathbf{\Theta}$ is an $M \times N$ np.matrix and $ M < N $ is the number of measurements. This setup np.means we have two problems to solve. First, how to design a *stable* measurement np.matrix $\mathbf{\Phi}$ and then, second, how to reconstruct $ \mathbf{f} $ from $ \mathbf{y} $. # # This may look like a standard linear algebra problem but since $ \mathbf{\Theta} $ has fewer rows than columns, the solution is necessarily ill-posed. This is where we inject the sparsity concept! Suppose that $f$ is $K$-sparse ( $||f||_0=K$ ), then if we somehow knew *which* $K$ columns of $ \mathbf{\Theta} $ matched the $K$ non-zero entries in $\mathbf{s}$, then $\mathbf{\Theta}$ would be $ M \times K $ where we could make $M > K$ and then have a stable inverse. # # This bit of reasoning is encapsulated in the following statement for any vector $\mathbf{v}$ sharing the same $K$ non-zero entries as $\mathbf{s}$, we have # # $$1-\epsilon \leq \frac{|| \mathbf{\Theta v} ||_2}{|| \mathbf{v} ||_2} \leq 1+\epsilon $$ # # which is another way of saying that $\mathbf{\Theta}$ preserves the lengths of $K$-sparse vectors. Of course we don't know ahead of time which $K$ components to use, but it turns out that this condition is sufficient for a stable inverse of $\mathbf{\Theta}$ if it holds for any $3K$-sparse vector $\mathbf{v}$. This is the *Restricted Isometry Property* (RIP). Unfortunately, in order to use this sufficient condition, we would have to propose a $\mathbf{\Theta}$ and then check all possible combinations of nonzero entries in the $N$-length vector $\mathbf{v}$. As you may guess, this is prohibitive. # # Alternatively, we can approach stability by defining *incoherence* between the measurement np.matrix $\mathbf{\Phi}$ and the sparse basis $\mathbf{\Psi}$ as when any of the columns of one cannot be expressed as a small subset of the columns of the other. For example, if we have delta-spikes for $\mathbf{\Phi}$ as the row-truncated identity np.matrix # # $$\mathbf{\Phi} = \mathbf{I}_{M \times N} $$ # # and the discrete Fourier transform np.matrix for $\mathbf{\Psi}$ as # # $\mathbf{\Psi} = \begin{bnp.matrix}\\\\ # e^{-j 2\pi k n/N}\\\\ # \end{bnp.matrix}_{N \times N}$ # # Then we could not write any of the columns of $\mathbf{\Phi}$ using just a few of the columns of $\mathbf{\Psi}$. # # It turns out that picking the measuring $M \times N$ np.matrix np.random.randomly according to a Gaussian zero-np.mean, $1/N$ variance distribution and using the identity np.matrix as $\mathbf{\Phi}$, that the resulting $\mathbf{\Theta}$ np.matrix can be shown to satisfy RIP with a high probability. This np.means that we can recover $N$-length $K$-sparse signals with a high probability from just $M \ge c K \log (N/K)$ samples where $c$ is a small constant. Furthermore, it also turns out that we can use any orthonormal basis for $\mathbf{\Phi}$, not just the identity np.matrix, and these relations will all still hold. # ## Reconstructing Sparse Signals # Now that we have a way, by using np.random.random matrices, to satisfy the RIP, we are ready to consider the reconstruction problem. The first impulse is to compute the least-squares solution to this problem as # # $$ \mathbf{s}^* = \mathbf{\Theta}^T (\mathbf{\Theta}\mathbf{\Theta}^T)^{-1}\mathbf{y} $$ # # But a moment's thought may convince you that since $\mathbf{\Theta}$ is a np.random.random np.matrix, most likely with lots of non-zero entries, it is highly unlikely that $\mathbf{s}^* $ will turn out to be sparse. There is actually a deeper geometric intuition as to why this happens, but let's first consider another way of solving this so that the $\mathbf{s}^*$ is $K$-sparse. Suppose instead we shuffle through combinations of $K$ nonzero entries in $\mathbf{s}$ until we satisfy the measurements $\mathbf{y}$. Stated mathematically, this np.means # # $$ \mathbf{s}^* = argmin || \mathbf{s}^* ||_0 $$ # # where # # $$ \mathbf{\Theta} \mathbf{s}^* = \mathbf{y} $$ # # It can be shown that with $M=K+1$ iid Gaussian measurements, this optimization will recover a $K$-sparse signal exactly with high probability. Unfortunately, this is numerically unstable in addition to being an NP-complete problem. # # Thus, we need another tractable way to approach this problem. It turns out that when a signal is sparse, it usually np.means that the nonzero terms are highly asymmetric np.meaning that if there are $K$ terms, then most likely there is one term that is dominant (i.e. of much larger magnitude) and that dwarfs the other nonzero terms. Geometrically, this np.means that in $N$-dimensional space, the sparse signal is very close to one (or, maybe just a few) of the axes. # # It turns out that one can bypass this combinatorial problem using $L_1$ minimization. To examine this, let's digress and look at the main difference between $L_2$ and $L_1$ minimization problems. # reference: # `http://users.ece.gatech.edu/justin/ssp2007` # ## $L_2$ vs. $L_1$ Optimization # The classic constrained least squares problem is the following: # # min $||\mathbf{x}||_2^2$ # # where $x_1 + 2 x_2 = 1$ # # with corresponding solution illustrated below. # # [1] import numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Circle x1 = np.linspace(-1, 1, 10) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(x1, (1 - x1) / 2) ax.add_patch(Circle((0, 0), 1 / np.sqrt(5), alpha=0.3)) ax.plot(1 / 5, 2 / 5, 'rs') ax.axis('equal') ax.set_xlabel('$x_1$', fontsize=24) ax.set_ylabel('$x_2$', fontsize=24) ax.grid() # Note that the line is the constraint so that any solution to this problem must be on this line (i.e. satisfy the constraint). The $L_2$ solution is the one that just touches the perimeter of the circle. This is because, in $L_2$, the unit-ball has the shape of a circle and represents all solutions of a fixed $L_2$ length. Thus, the one of smallest length that intersects the line is the one that satisfies the stated minimization problem. Intuitively, this np.means that we *inflate* a ball at the origin and stop when it touches the contraint. The point of contact is our $L_2$ minimization solution. # # Now, let's do same problem in $L_1$ norm # # min $||\mathbf{x}||_1=|x_1|+|x_2|$ # # where $x_1 + 2 x_2 = 1$ # # this case the constant-norm unit-ball contour in the $L_1$ norm is a diamond-shape instead of a circle. Comparing the graph below to the last shows that the solutions found are different. Geometrically, this is because the line tilts over in such a way that the inflating circular $L_2$ ball hits a point of tangency that is different from the $L_1$ ball because the $L_1$ ball creeps out mainly along the principal axes and is less influenced by the tilt of the line. This effect is much more pronounced in higher $N$-dimensional spaces where $L_1$-balls get more *spikey*. # # The fact that the $L_1$ problem is less sensitive to the tilt of the line is crucial since that tilt (i.e. orientation) is np.random.random due the choice of np.random.random measurement matrices. So, for this problem to be well-posed, we need to *not* be influenced by the orientation of any particular choice of np.random.random np.matrix and this is what casting this as a $L_1$ minimization provides. # [2] from matplotlib.patches import Rectangle import matplotlib.patches import matplotlib.transforms r = matplotlib.patches.RegularPolygon((0, 0), 4, 1 / 2, np.pi / 2, alpha=0.5) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(x1, (1 - x1) / 2) ax.plot(0, 1 / 2, 'rs') ax.add_patch(r) ax.grid() ax.set_xlabel('$x_1$', fontsize=24) ax.set_ylabel('$x_2$', fontsize=24) ax.axis('equal') # To explore this a bit, let's consider using the `cvxopt` package (Python ver 2.6 used here). This can be cast as a linear programming problem as follows: # # min $||\mathbf{t}||_1 = |t_1| + |t_2|$ # # subject to: # # $-t_1 < x_1 < t_1$ # # $-t_2 < x_2 < t_2$ # # $x_1 + 2 x_2 = 1$ # # $t_1 > 0$ # # $t_2 > 0$ # # where the last two constraints are already implied by the first two and are written out just for clarity. This can be implemented and solved in `cvxopt` as the following: # [3] from cvxopt import matrix as matrx # don't overrite numpy matrix class from cvxopt import solvers # t1,x1,t2,x2 c = matrx([1, 0, 1, 0], (4, 1), 'd') G = matrx([[-1, -1, 0, 0], # column-0 [1, -1, 0, 0], # column-1 [0, 0, -1, -1], # column-2 [0, 0, 1, -1], # column-3 ], (4, 4), 'd') # (4,1) is 4-rows,1-column, 'd' is float type spec h = matrx([0, 0, 0, 0], (4, 1), 'd') A = matrx([0, 1, 0, 2], (1, 4), 'd') b = matrx([1], (1, 1), 'd') sol = solvers.lp(c, G, h, A, b) x1 = sol['x'][1] x2 = sol['x'][3] print('x=%3.2f' % x1) print('y=%3.2f' % x2) # ## Example Gaussian np.random.random matrices # Let's try out our earlier result about np.random.random Gaussian matrices and see if we can reconstruct an unknown $\mathbf{s}$ vector using $L_1$ minimization. # [56] import numpy as np import scipy.linalg def rearrange_G(x): 'setup to put inequalities np.matrix with last 1/2 of elements as main variables' n = x.shape[0] return np.hstack([x[:, np.arange(0, n, 2) + 1], x[:, np.arange(0, n, 2)]]) K = 2 # components Nf = 128 # number of samples M = 12 # > K log2(Nf/K); num of measurements s = np.zeros((Nf, 1)) # sparse vector we want to find s[0] = 1 # set the K nonzero entries s[1] = 0.5 # np.np.random.random.seed(5489) # set np.random.random seed for reproducibility Phi = np.matrix(np.random.randn(M, Nf) * np.sqrt(1 / Nf)) # np.random.random Gaussian np.matrix y = Phi * s # measurements # -- setup L1 minimization problem -- # equalities np.matrix with G = matrx(rearrange_G(scipy.linalg.block_diag( *[np.matrix([[-1, -1], [1, -1.0]]), ] * Nf))) # objective function row-np.matrix c = matrx(np.hstack([np.ones(Nf), np.zeros(Nf)])) # RHS for inequalities h = matrx([0.0, ] * (Nf * 2), (Nf * 2, 1), 'd') # equality constraint np.matrix A = matrx(np.hstack([Phi * 0, Phi])) # RHS for equality constraints b = matrx(y) sol = solvers.lp(c, G, h, A, b) # nonzero entries nze = np.array(sol['x']).flatten()[:Nf].round(2).nonzero() print(np.array(sol['x'])[nze]) # That worked out! However, if you play around with this example enough with different np.random.random matrices (unset the ``seed`` statement above), you will find that it does not *always* find the correct answer. This is because the guarantees about reconstruction are all stated probabalistically (i.e. "high-probability"). This is another major difference between this and Shannon sampling. # # Let's encapulate the above $L_1$ minimization code so we can use it later. # [5] #from cStringIO import StringIO import sys def L1_min(Phi, y, K): # equalities np.matrix with M, Nf = Phi.shape G = matrx(rearrange_G(scipy.linalg.block_diag( *[np.matrix([[-1, -1], [1, -1.0]]), ] * Nf))) # objective function row-np.matrix c = matrx(np.hstack([np.ones(Nf), np.zeros(Nf)])) # RHS for inequalities h = matrx([0.0, ] * (Nf * 2), (Nf * 2, 1), 'd') # equality constraint np.matrix A = matrx(np.hstack([Phi * 0, Phi])) # RHS for equality constraints b = matrx(y) # suppress standard output old_stdout = sys.stdout #s.stdout = mystdout = StringIO() #s.stdout = mystdout sol = solvers.lp(c, G, h, A, b) # restore standard output sys.stdout = old_stdout sln = np.array(sol['x']).flatten()[:Nf].round(4) return sln # ## Example: Sparse Fourier Transform # As an additional example, let us consider the Fourier transform and see if we can recover the sparse Fourier transform from a small set of measurements. For simplicity, we will assume that the time domain signal is real which automatically np.means that the Fourier transform is symmetric. # [141] def dftmatrix(N=8): 'compute inverse DFT matrices' n = np.arange(N) U = matrx(np.exp(1j * 2 * np.pi / N * n * n[:, None])) / np.sqrt(N) return np.matrix(U) Nf = 128 K = 3 # components M = 8 # > K log2(Nf/K); num of measurements s = np.zeros((Nf, 1)) # sparse vector we want to find s[0] = 1 # set the K nonzero entries s[1] = 0.5 s[-1] = 0.5 # symmetric to keep inverse Fourier transform real Phi = dftmatrix(Nf)[:M, :] # take M-rows y = Phi * s # measurements # have to assert the type here on my hardware sol = L1_min(Phi.real, y.real.astype(np.float64), K) print(np.allclose(s.flatten(), sol)) # [140] plt.plot(sol) plt.plot(y.real) # ## Uniform Uncertainty Principle # $\Phi$ obeys a UUP for sets of size $K$ if # # <center> # $$ 0.8 \frac{M}{N} ||f||_2^2 \leq || \Phi f||_2^2 \leq 1.2 \frac{M}{N} ||f||_2^2 $$ # </center> # # Measurements that satisfy this are defined as *incoherent*. Given that $f$ is $K$-sparse and we measure # $y=\Phi f$, then we search for the sparsest vector that explains the $y$ measurements and thus find $f$ as follows: # # <center> # $min_f \\#\lbrace t: f(t) \ne 0 \rbrace $ where $\Phi f = y$ # </center> # Note that the hash mark is the size (i.e. cardinality) of the set. This np.means that we are looking for the fewest individual points for $f$ that satisfy the constraints. Unfortunately, this is not practically possible, so we must use the $\mathbb{L}_1$ norm as a proxy for sparsity. # # Suppose $f$ is $K$-sparse and that $\Phi$ obeys UUP for sets of size $4K$. Then we measure $y=\Phi f$ and then solve # # <center> # $min_f ||f||_1 $ where $\Phi f = y$ # </center> # to recover $f$ exactly and we can use $M > K \log N$ measurements, where the number of measurements is approximately equal to the number of active components. Let's consider a concrete example of how this works. # ### Example: Sampling Sinusoids # Here, we sample in the time-domain, given that we know the signal is sparse in the frequency domain. # # <center> # $$ \hat{f}(\omega) = \sum_{i=1}^K \alpha_i \delta(\omega_i-\omega) $$ # </center> # # which np.means that it consists of $K$-sparse nonzero elements. Therefore, the time domain signal is # # <center> # $$ f(t) = \sum_{i=1}^K \alpha_i e^{i \omega_i t} $$ # </center> # # where the $\alpha_i$ and $\omega_i$ are unknown. We want solve for these unknowns by taking $M \gt K \log N$ samples of $f$. # The problem we want to solve is # # $ min_g || \hat{g} ||_{L_1}$ # # subject to # # $ g(t_m)=f(t_m) $ # # The trick here is that are minimizing in the frequency-domain while the constraints are in the time-domain. To make things easier, we will restrict our attention to real time-domain signals $f$ and we will only reconstruct the even-indexed time-samples from the signal. This np.means we need a way of expressing the inverse Fourier Transform as a np.matrix of equality constraints. The assumption of real-valued time-domain signals implies the following symmetry in the frequency-domain: # # $ F(k) = F(N-k)^* $ # # where $F$ is the Fourier transform of $f$ and the asterisk denotes complex conjugation and $k\in \lbrace 0,1,..N-1\rbrace$ and $N$ is the Fourier Transform length. To make things even more tractable we will assume the time-domain signal is even, which np.means real-valued Fourier transform values. # # Suppose that $\mathbf{U}_N$ is the $N$-point DFT-np.matrix. Note that we always assume $N$ is even. Since we are dealing with only real-valued signals, the transform is symmetric, so we only need half of the spectrum computed. It turns out that the even-indexed time-domain samples can be constructed as follows: # # $ \mathbf{f_{even}} = \mathbf{U}_{N/2} \begin{bnp.matrix}\\\\ # F(0)+F(N/2)^* \\\\ # F(1)+F(N/2-1)^* \\\\ # F(2)+F(N/2-2)^* \\\\ # \dots \\\\ # F(N/2-1)+F(1)^* # \end{bnp.matrix}$ # # We can further simplify this by breaking this into real (superscript $R$) and imaginary (superscript $I$) parts and keeping only the real part # # $$\mathbf{f_{even}} = \mathbf{U}_{N/2}^R # \begin{bnp.matrix}\\\\ # F(0)^R+F(N/2)^R \\\\ # F(1)^R+F(N/2-1)^R \\\\ # F(2)^R+F(N/2-2)^R \\\\ # \dots \\\\ # F(N/2-1)^R+F(1)^R # \end{bnp.matrix} # + # \mathbf{U}^I_N # \begin{bnp.matrix} \\\\ # -F(0)^I+F(N/2)^I \\\\ # -F(1)^I+F(N/2-1)^I \\\\ # -F(2)^I+F(N/2-2)^I \\\\ # \dots \\\\ # -F(N/2-1)^I+F(1)^I # \end{bnp.matrix}$$ # # But we are going to force all the $F(k)^I$ to be zero in our example. Note that the second term should have a $\mathbf{U}_{N/2}$ in it instead $\mathbf{U}_N$ but there is something wrong with the javascript parser for that bit of TeX. # # Now, let's see if we can walk through to step-by-step to make sure our optimization can actually work. Note that we don't need the second term on the right with the $F^I$ terms because by our construction, $F$ is real. # [358] def dftmatrix(N=8): 'compute inverse DFT matrices' n = np.arange(N) U = np.matrix(np.exp(1j * 2 * np.pi / N * n * n[:, None])) / np.sqrt(N) return np.matrix(U) def Q_rmatrix(Nf=8): 'implements the reordering, adding, and stacking of the matrices above' Q_r = np.matrix(np.hstack([np.eye(int(Nf / 2)), np.eye(int(Nf / 2)) * 0]) + np.hstack([np.zeros((int(Nf / 2), 1)), np.fliplr(np.eye(int(Nf / 2))), np.zeros((int(Nf / 2), int(Nf / 2) - 1))])) return Q_r Nf = 8 F = np.zeros((Nf, 1)) # 8-point DFT F[0] = 1 # DC-term, constant signal n = np.arange(Nf / 2) ft = dftmatrix(Nf).H * F # this gives the constant signal Q_r = Q_rmatrix(Nf) U = dftmatrix(Nf / 2) # half inverse DFT np.matrix feven = U.real * Q_r * F # half the size print(np.allclose(feven, ft[::2])) # retrieved even-numbered samples # [359] # let's try this with another sparse frequency-domain signal F = np.zeros((Nf, 1)) F[1] = 1 F[Nf - 1] = 1 # symmetric part ft = dftmatrix(Nf).H * F # this gives the constant signal feven = U.real * Q_r * F # half the size print(np.allclose(feven, ft[::2])) # retrieved even-numbered samples plt.plot(np.arange(Nf), ft.real, np.arange(Nf)[::2], feven, 'o') plt.xlabel('$t$', fontsize=22) plt.ylabel('$f(t)$', fontsize=22) plt.title('even-numbered samples') # We can use the above cell to create more complicated real signals. You can experiment with the cell below. Just remember to impose the symmetry condition! # [360] Nf = 32 # must be even F = np.zeros((Nf, 1)) # set values and corresponding symmetry conditions F[7] = 1 F[12] = 0.5 F[9] = -0.25 F[Nf - 9] = -0.25 F[Nf - 12] = 0.5 F[Nf - 7] = 1 # symmetric part Q_r = Q_rmatrix(Nf) U = dftmatrix(Nf / 2) # half inverse DFT np.matrix ft = dftmatrix(Nf).H * F # this gives the constant signal feven = U.real * Q_r * F # half the size print(np.allclose(feven, ft[::2])) # retrieved even-numbered samples plt.plot(np.arange(Nf), ft.real, np.arange(Nf)[::2], feven, 'o') plt.xlabel('$t$', fontsize=22) plt.ylabel('$f(t)$', fontsize=22) plt.title('even-numbered samples') # Now that we have gone through all that trouble to create the even-samples np.matrix, we can finally put it into the framework of the $L_1$ minimization problem: # # $ min_F || \mathbf{F} ||_{L_1}$ # # subject to # # $ \mathbf{U}_{N/2}^R \mathbf{Q}_r \mathbf{F}= \mathbf{f} $ # [361] def rearrange_G(x): 'setup to put inequalities np.matrix with first 1/2 of elements as main variables' n = x.shape[0] return np.hstack([x[:, np.arange(0, n, 2) + 1], x[:, np.arange(0, n, 2)]]) K = 2 # components Nf = 128 # number of samples M = 18 # > K log(N); num of measurements # setup signal DFT as F F = np.zeros((Nf, 1)) F[1] = 1 F[2] = 0.5 F[Nf - 1] = 1 # symmetric parts F[Nf - 2] = 0.5 ftime = dftmatrix(Nf).H * F # this gives the time-domain signal ftime = ftime.real # it's real anyway time_samples = [0, 2, 4, 12, 14, 16, 18, 24, 34, 36, 38, 40, 44, 46, 52, 56, 54, 62] half_indexed_time_samples = (np.array(time_samples) / 2).astype(int) Phi = dftmatrix(Nf / 2).real * Q_rmatrix(Nf) Phi_i = Phi[half_indexed_time_samples, :] # equalities np.matrix with G = matrx(rearrange_G(scipy.linalg.block_diag( *[np.matrix([[-1, -1], [1, -1.0]]), ] * Nf))) # objective function row-np.matrix c = matrx(np.hstack([np.zeros(Nf), np.ones(Nf)])) # RHS for inequalities h = matrx([0.0, ] * (Nf * 2), (Nf * 2, 1), 'd') # equality constraint np.matrix A = matrx(np.hstack([Phi_i, Phi_i * 0])) # RHS for equality constraints b = matrx(ftime[time_samples]) sol = solvers.lp(c, G, h, A, b) # [12] import itertools as it def dftmatrix(N=8): 'compute inverse DFT matrices' n = np.arange(N) U = np.matrix(np.exp(1j * 2 * np.pi / N * n * n[:, None])) / np.sqrt(N) return np.matrix(U) M = 3 # np.np.random.random.seed(5489) # set np.random.random seed for reproducibility Psi = dftmatrix(128) Phi = np.random.randn(M, 128) s = np.zeros((128, 1)) s[0] = 1 s[10] = 1 Theta = Phi * Psi y = Theta * s for i in it.combinations(range(128), 2): sstar = np.zeros((128, 1)) sstar[np.array(i)] = 1 if np.allclose(Theta * sstar, y): break else: print('no solution') # [9] # [ ]

8afc13e213c403a3dacc0931aa5029c3a13cf2e0

py

Python

lineartransporteqn.py

killacamron/CFDcourse21

5ae59303d042819e0246e793271f420de8e1bbdb

lineartransporteqn.py

killacamron/CFDcourse21

5ae59303d042819e0246e793271f420de8e1bbdb

lineartransporteqn.py

killacamron/CFDcourse21

5ae59303d042819e0246e793271f420de8e1bbdb

# ============================================================================= # # Explicit Finite Difference Method Code to Solve the 1D Linear Transport Equation # Adapted by: Cameron Armstrong (2019) # Source: Lorena Barba, 12 Steps to NS in Python # Institution: Virginia Commonwealth University # # ============================================================================= # Required Modules import numpy as np from matplotlib import pyplot as plt import time xl = 2 # x length nx = 600 # number of grid points x = np.linspace(0,xl,nx) # x grid dx = xl/(nx-1) # x stepsize nt = 350 # number of timesteps dt = 0.0025 # time stepsize c = 1 # wave speed g = .01 # gaussian variance parameter (peak width) theta = x/(0.5*xl) # gaussian mean parameter (peak position) cfl = round(c*dt/dx,2) # cfl condition 2 decimal places # Fun little CFL condition check and print report if cfl >= 1: print('Hold your horses! The CFL is %s, which is over 1' %(cfl)) else: print('CFL = %s' %(cfl)) # Array Initialization u = np.ones(nx) # initializing solution array un = np.ones(nx) # initializing temporary solution array u = (1/(2*np.sqrt(np.pi*(g))))*np.exp(-(1-theta)**2/(4*g)) # initial condition (IC) as a gaussian ui = u.copy() plt.plot(x,u); # plots IC # BDS/Upwind with inner for-loop with example on process timing start = time.process_time() for n in range(nt): un = u.copy() for i in range(1,nx-1): u[i] = un[i] - c*dt/(dx)*(un[i]-un[i-1]) # periodic BC's u[0] = u[nx-2] u[nx-1] = u[1] end = time.process_time() print(end-start) # # BDS/Upwind with vectorization # for n in range(nt): # un = u.copy() # u[1:-1] = un[1:-1] - c*dt/(dx)*(un[1:-1]-un[:-2]) # # periodic BC's # u[0] = u[nx-2] # u[nx-1] = u[1] # # CDS with inner for-loop #for n in range(nt): # un = u.copy() # for i in range(1,nx-1): # u[i] = un[i] - c*dt/(2*dx)*(un[i+1]-un[i-1]) # # periodic BC's # u[0] = u[nx-2] # u[nx-1] = u[1] # # CDS with vectorization #for n in range(nt): # un = u.copy() # u[1:-1] = un[1:-1] - c*dt/(2*dx)*(un[2:]-un[:-2]) # # periodic BC's # u[0] = u[nx-2] # u[nx-1] = u[1] plt.plot(x,u);

# ============================================================================= # # Explicit Finite Difference Method Code to Solve the 1D Linear Transport Equation # Adapted by: Cameron Armstrong (2019) # Source: Lorena Barba, 12 Steps to NS in Python # Institution: Virginia Commonwealth University # # ============================================================================= # Required Modules import numpy as np from matplotlib import pyplot as plt import time xl = 2 # x length nx = 600 # number of grid points x = np.linspace(0,xl,nx) # x grid dx = xl/(nx-1) # x stepsize nt = 350 # number of timesteps dt = 0.0025 # time stepsize c = 1 # wave speed g = .01 # gaussian variance parameter (peak width) theta = x/(0.5*xl) # gaussian mean parameter (peak position) cfl = round(c*dt/dx,2) # cfl condition 2 decimal places # Fun little CFL condition check and print report if cfl >= 1: print('Hold your horses! The CFL is %s, which is over 1' %(cfl)) else: print('CFL = %s' %(cfl)) # Array Initialization u = np.ones(nx) # initializing solution array un = np.ones(nx) # initializing temporary solution array u = (1/(2*np.sqrt(np.pi*(g))))*np.exp(-(1-theta)**2/(4*g)) # initial condition (IC) as a gaussian ui = u.copy() plt.plot(x,u); # plots IC # BDS/Upwind with inner for-loop with example on process timing start = time.process_time() for n in range(nt): un = u.copy() for i in range(1,nx-1): u[i] = un[i] - c*dt/(dx)*(un[i]-un[i-1]) # periodic BC's u[0] = u[nx-2] u[nx-1] = u[1] end = time.process_time() print(end-start) # # BDS/Upwind with vectorization # for n in range(nt): # un = u.copy() # u[1:-1] = un[1:-1] - c*dt/(dx)*(un[1:-1]-un[:-2]) # # periodic BC's # u[0] = u[nx-2] # u[nx-1] = u[1] # # CDS with inner for-loop #for n in range(nt): # un = u.copy() # for i in range(1,nx-1): # u[i] = un[i] - c*dt/(2*dx)*(un[i+1]-un[i-1]) # # periodic BC's # u[0] = u[nx-2] # u[nx-1] = u[1] # # CDS with vectorization #for n in range(nt): # un = u.copy() # u[1:-1] = un[1:-1] - c*dt/(2*dx)*(un[2:]-un[:-2]) # # periodic BC's # u[0] = u[nx-2] # u[nx-1] = u[1] plt.plot(x,u);

452c277e6f370c2445473182b1051da62995480e

py

Python

monty/exts/info/docs/_html.py

onerandomusername/monty-python

fcd8b2827eb9bbb2a05d28f80ac9e215589f03f7

2021-12-31T10:17:20.000Z

2022-03-31T04:16:17.000Z

monty/exts/info/docs/_html.py

onerandomusername/monty-bot

b1c769e44b56bc45f37fc809064571d59c80db27

2022-03-13T22:34:33.000Z

2022-03-13T22:34:52.000Z

monty/exts/info/docs/_html.py

onerandomusername/monty-bot

b1c769e44b56bc45f37fc809064571d59c80db27

2022-01-02T15:21:46.000Z

2022-03-05T09:37:54.000Z

import re from functools import partial from typing import Callable, Container, Iterable, List, Union from bs4 import BeautifulSoup from bs4.element import NavigableString, PageElement, SoupStrainer, Tag from monty.log import get_logger from . import MAX_SIGNATURE_AMOUNT log = get_logger(__name__) _UNWANTED_SIGNATURE_SYMBOLS_RE = re.compile(r"\[source]|\\\\|¶") _SEARCH_END_TAG_ATTRS = ( "data", "function", "class", "exception", "seealso", "section", "rubric", "sphinxsidebar", ) class Strainer(SoupStrainer): """Subclass of SoupStrainer to allow matching of both `Tag`s and `NavigableString`s.""" Markup = Union[PageElement, List["Markup"]] def search(self, markup: Markup) -> Union[PageElement, str]: """Extend default SoupStrainer behaviour to allow matching both `Tag`s` and `NavigableString`s.""" if isinstance(markup, str): # Let everything through the text filter if we're including strings and tags. if not self.name and not self.attrs and self.include_strings: return markup else: return super().search(markup) def _find_elements_until_tag( start_element: PageElement, end_tag_filter: Union[Container[str], Callable[[Tag], bool]], *, func: Callable, include_strings: bool = False, limit: int = None, ) -> List[Union[Tag, NavigableString]]: """ Get all elements up to `limit` or until a tag matching `end_tag_filter` is found. `end_tag_filter` can be either a container of string names to check against, or a filtering callable that's applied to tags. When `include_strings` is True, `NavigableString`s from the document will be included in the result along `Tag`s. `func` takes in a BeautifulSoup unbound method for finding multiple elements, such as `BeautifulSoup.find_all`. The method is then iterated over and all elements until the matching tag or the limit are added to the return list. """ use_container_filter = not callable(end_tag_filter) elements = [] for element in func(start_element, name=Strainer(include_strings=include_strings), limit=limit): if isinstance(element, Tag): if use_container_filter: if element.name in end_tag_filter: break elif end_tag_filter(element): break elements.append(element) return elements _find_next_children_until_tag = partial(_find_elements_until_tag, func=partial(BeautifulSoup.find_all, recursive=False)) _find_recursive_children_until_tag = partial(_find_elements_until_tag, func=BeautifulSoup.find_all) _find_next_siblings_until_tag = partial(_find_elements_until_tag, func=BeautifulSoup.find_next_siblings) _find_previous_siblings_until_tag = partial(_find_elements_until_tag, func=BeautifulSoup.find_previous_siblings) def _class_filter_factory(class_names: Iterable[str]) -> Callable[[Tag], bool]: """Create callable that returns True when the passed in tag's class is in `class_names` or when it's a table.""" return match_tag def get_general_description(start_element: PageElement) -> List[Union[Tag, NavigableString]]: """ Get page content to a table or a tag with its class in `SEARCH_END_TAG_ATTRS`. A headerlink tag is attempted to be found to skip repeating the symbol information in the description. If it's found it's used as the tag to start the search from instead of the `start_element`. """ child_tags = _find_recursive_children_until_tag(start_element, _class_filter_factory(["section"]), limit=100) header = next(filter(_class_filter_factory(["headerlink"]), child_tags), None) start_tag = header.parent if header is not None else start_element return _find_next_siblings_until_tag(start_tag, _class_filter_factory(_SEARCH_END_TAG_ATTRS), include_strings=True) def get_dd_description(symbol: PageElement) -> List[Union[Tag, NavigableString]]: """Get the contents of the next dd tag, up to a dt or a dl tag.""" description_tag = symbol.find_next("dd") return _find_next_children_until_tag(description_tag, ("dt", "dl"), include_strings=True) def get_signatures(start_signature: PageElement) -> List[str]: """ Collect up to `_MAX_SIGNATURE_AMOUNT` signatures from dt tags around the `start_signature` dt tag. First the signatures under the `start_signature` are included; if less than 2 are found, tags above the start signature are added to the result if any are present. """ signatures = [] for element in ( *reversed(_find_previous_siblings_until_tag(start_signature, ("dd",), limit=2)), start_signature, *_find_next_siblings_until_tag(start_signature, ("dd",), limit=2), )[-MAX_SIGNATURE_AMOUNT:]: signature = _UNWANTED_SIGNATURE_SYMBOLS_RE.sub("", element.text) if signature: signatures.append(signature) return signatures

import re from functools import partial from typing import Callable, Container, Iterable, List, Union from bs4 import BeautifulSoup from bs4.element import NavigableString, PageElement, SoupStrainer, Tag from monty.log import get_logger from . import MAX_SIGNATURE_AMOUNT log = get_logger(__name__) _UNWANTED_SIGNATURE_SYMBOLS_RE = re.compile(r"\[source]|\\\\|¶") _SEARCH_END_TAG_ATTRS = ( "data", "function", "class", "exception", "seealso", "section", "rubric", "sphinxsidebar", ) class Strainer(SoupStrainer): """Subclass of SoupStrainer to allow matching of both `Tag`s and `NavigableString`s.""" def __init__(self, *, include_strings: bool, **kwargs): self.include_strings = include_strings passed_text = kwargs.pop("text", None) if passed_text is not None: log.warning("`text` is not a supported kwarg in the custom strainer.") super().__init__(**kwargs) Markup = Union[PageElement, List["Markup"]] def search(self, markup: Markup) -> Union[PageElement, str]: """Extend default SoupStrainer behaviour to allow matching both `Tag`s` and `NavigableString`s.""" if isinstance(markup, str): # Let everything through the text filter if we're including strings and tags. if not self.name and not self.attrs and self.include_strings: return markup else: return super().search(markup) def _find_elements_until_tag( start_element: PageElement, end_tag_filter: Union[Container[str], Callable[[Tag], bool]], *, func: Callable, include_strings: bool = False, limit: int = None, ) -> List[Union[Tag, NavigableString]]: """ Get all elements up to `limit` or until a tag matching `end_tag_filter` is found. `end_tag_filter` can be either a container of string names to check against, or a filtering callable that's applied to tags. When `include_strings` is True, `NavigableString`s from the document will be included in the result along `Tag`s. `func` takes in a BeautifulSoup unbound method for finding multiple elements, such as `BeautifulSoup.find_all`. The method is then iterated over and all elements until the matching tag or the limit are added to the return list. """ use_container_filter = not callable(end_tag_filter) elements = [] for element in func(start_element, name=Strainer(include_strings=include_strings), limit=limit): if isinstance(element, Tag): if use_container_filter: if element.name in end_tag_filter: break elif end_tag_filter(element): break elements.append(element) return elements _find_next_children_until_tag = partial(_find_elements_until_tag, func=partial(BeautifulSoup.find_all, recursive=False)) _find_recursive_children_until_tag = partial(_find_elements_until_tag, func=BeautifulSoup.find_all) _find_next_siblings_until_tag = partial(_find_elements_until_tag, func=BeautifulSoup.find_next_siblings) _find_previous_siblings_until_tag = partial(_find_elements_until_tag, func=BeautifulSoup.find_previous_siblings) def _class_filter_factory(class_names: Iterable[str]) -> Callable[[Tag], bool]: """Create callable that returns True when the passed in tag's class is in `class_names` or when it's a table.""" def match_tag(tag: Tag) -> bool: for attr in class_names: if attr in tag.get("class", ()): return True return tag.name == "table" return match_tag def get_general_description(start_element: PageElement) -> List[Union[Tag, NavigableString]]: """ Get page content to a table or a tag with its class in `SEARCH_END_TAG_ATTRS`. A headerlink tag is attempted to be found to skip repeating the symbol information in the description. If it's found it's used as the tag to start the search from instead of the `start_element`. """ child_tags = _find_recursive_children_until_tag(start_element, _class_filter_factory(["section"]), limit=100) header = next(filter(_class_filter_factory(["headerlink"]), child_tags), None) start_tag = header.parent if header is not None else start_element return _find_next_siblings_until_tag(start_tag, _class_filter_factory(_SEARCH_END_TAG_ATTRS), include_strings=True) def get_dd_description(symbol: PageElement) -> List[Union[Tag, NavigableString]]: """Get the contents of the next dd tag, up to a dt or a dl tag.""" description_tag = symbol.find_next("dd") return _find_next_children_until_tag(description_tag, ("dt", "dl"), include_strings=True) def get_signatures(start_signature: PageElement) -> List[str]: """ Collect up to `_MAX_SIGNATURE_AMOUNT` signatures from dt tags around the `start_signature` dt tag. First the signatures under the `start_signature` are included; if less than 2 are found, tags above the start signature are added to the result if any are present. """ signatures = [] for element in ( *reversed(_find_previous_siblings_until_tag(start_signature, ("dd",), limit=2)), start_signature, *_find_next_siblings_until_tag(start_signature, ("dd",), limit=2), )[-MAX_SIGNATURE_AMOUNT:]: signature = _UNWANTED_SIGNATURE_SYMBOLS_RE.sub("", element.text) if signature: signatures.append(signature) return signatures

23d3fbbdeeaccf228df906ea84306f0500785058

py

Python

preflibtools/instances/sampling.py

nmattei/PrefLib-Tools

d1a1137efdc6a5722bbb0e15a0c1174a0236aefb

2015-06-01T15:00:09.000Z

2019-09-18T18:05:38.000Z

preflibtools/instances/sampling.py

nmattei/PrefLib-Tools

d1a1137efdc6a5722bbb0e15a0c1174a0236aefb

2016-06-06T07:40:41.000Z

2018-01-04T22:09:21.000Z

preflibtools/instances/sampling.py

nmattei/PrefLib-Tools

d1a1137efdc6a5722bbb0e15a0c1174a0236aefb

2015-06-02T04:58:13.000Z

2019-12-13T13:26:58.000Z

""" This module describes procedures to sample preferences for different probability distributions. """ import numpy as np def generateICStrictProfile(nbVoters, alternatives): """ Generates a profile following the impartial culture. :param nbVoters: Number of orders to sample. :type nbVoters: int :param alternatives: List of alternatives. :type alternatives: list of int :return: A vote map, i.e., a dictionary whose keys are orders, mapping to the number of voters with the given order as their preferences. :rtype: dict """ return urnModel(nbVoters, 0, alternatives) def generateICAnonymousStrictProfile(nbVoters, alternatives): """ Generates a profile following the anonymous impartial culture. :param nbVoters: Number of orders to sample. :type nbVoters: int :param alternatives: List of alternatives. :type alternatives: list of int :return: A vote map, i.e., a dictionary whose keys are orders, mapping to the number of voters with the given order as their preferences. :rtype: dict """ return urnModel(nbVoters, 1, alternatives) def mallowsModel(nbVoters, nbAlternatives, mixture, dispersions, references): """ Generates a profile following a mixture of Mallow's models. :param nbVoters: Number of orders to sample. :type nbVoters: int :param nbAlternatives: Number of alternatives for the sampled orders. :type nbAlternatives: int :param mixture: A list of the weights of each element of the mixture. :type replace: list of int :param dispersions: A list of the dispersion coefficient of each element of the mixture. :type dispersions: list of float :param references: A list of the reference orders for each element of the mixture. :type references: list of tuples of tuples of int :return: A vote map, i.e., a dictionary whose keys are orders, mapping to the number of voters with the given order as their preferences. :rtype: dict """ if len(mixture) != len(dispersions) or len(mixture) != len(references): raise ValueError("Parameters of Mallows' mixture do not have the same length.") # We normalize the mixture so that it sums up to 1 if sum(mixture) != 1: mixture = [m / sum(mixture) for m in mixture] #Precompute the distros for each Phi. insertDistributions = [] for i in range(len(mixture)): insertDistributions.append(mallowsInsertDistributions(nbAlternatives, dispersions[i])) #Now, generate votes... votemap = {} for cvoter in range(nbVoters): cmodel = np.random.choice(range(len(mixture)), 1, p = mixture)[0] #Generate a vote for the selected model insertVector = [0] * nbAlternatives for i in range(1, len(insertVector) + 1): #options are 1...max insertVector[i - 1] = np.random.choice(range(1, i + 1), 1, p = insertDistributions[cmodel][i])[0] vote = [] for i in range(len(references[cmodel])): vote.insert(insertVector[i] - 1, references[cmodel][i]) tvote = tuple((alt,) for alt in vote) votemap[tvote] = votemap.get(tvote, 0) + 1 return votemap def mallowsMixture(nbVoters, nbReferences, alternatives): """ Generates a profile following a mixture of Mallow's models for which reference points and dispersion coefficients are independently and identically distributed. :param nbVoters: Number of orders to sample. :type nbVoters: int :param nbAlternatives: Number of alternatives for the sampled orders. :type nbAlternatives: int :param nbReferences: Number of element :type nbReferences: int :return: A vote map, i.e., a dictionary whose keys are orders, mapping to the number of voters with the given order as their preferences. :rtype: dict """ mixture = [] dispersions = [] references = [] for i in range(nbReferences): references.append(tuple(generateICStrictProfile(1, alternatives))[0]) dispersions.append(round(np.random.rand(), 5)) mixture.append(np.random.randint(1, 101)) sumMixture = sum(mixture) mixture = [float(i) / float(sumMixture) for i in mixture] return mallowsModel(nbVoters, len(alternatives), mixture, dispersions, references) def urnModel(nbVoters, replace, alternatives): """ Generates a profile following the urn model. :param nbVoters: Number of orders to sample. :type nbVoters: int :param replace: The number of replacements for the urn model. :type replace: int :param alternatives: List of alternatives. :type alternatives: list of int :return: A vote map, i.e., a dictionary whose keys are orders, mapping to the number of voters with the given order as their preferences. :rtype: dict """ voteMap = {} ReplaceVotes = {} ICsize = np.math.factorial(len(alternatives)) ReplaceSize = 0 for x in range(nbVoters): flip = np.random.randint(1, ICsize + ReplaceSize + 1) if flip <= ICsize: #generate an IC vote and make a suitable number of replacements... tvote = generateICVote(alternatives) voteMap[tvote] = (voteMap.get(tvote, 0) + 1) ReplaceVotes[tvote] = (ReplaceVotes.get(tvote, 0) + replace) ReplaceSize += replace #print("made " + str(tvote)) else: #iterate over replacement hash and select proper vote. flip = flip - ICsize for vote in ReplaceVotes.keys(): flip = flip - ReplaceVotes[vote] if flip <= 0: vote = tuple((alt,) for alt in vote) voteMap[vote] = (voteMap.get(vote, 0) + 1) ReplaceVotes[vote] = (ReplaceVotes.get(vote, 0) + replace) ReplaceSize += replace break else: print("We Have a problem... replace fell through....") exit() return voteMap def generateICVote(alternatives): """ Generates a strict order over the set of alternatives following the impartial culture. :param alternatives: List of alternatives. :type alternatives: list of int :return: A strict order over the alternatives, i.e., a tuple of tuples of size 1. :rtype: tuple """ options = list(alternatives) vote = [] while(len(options) > 0): #randomly select an option vote.append(options.pop(np.random.randint(0, len(options)))) return tuple((alt,) for alt in vote)

""" This module describes procedures to sample preferences for different probability distributions. """ import numpy as np def generateICStrictProfile(nbVoters, alternatives): """ Generates a profile following the impartial culture. :param nbVoters: Number of orders to sample. :type nbVoters: int :param alternatives: List of alternatives. :type alternatives: list of int :return: A vote map, i.e., a dictionary whose keys are orders, mapping to the number of voters with the given order as their preferences. :rtype: dict """ return urnModel(nbVoters, 0, alternatives) def generateICAnonymousStrictProfile(nbVoters, alternatives): """ Generates a profile following the anonymous impartial culture. :param nbVoters: Number of orders to sample. :type nbVoters: int :param alternatives: List of alternatives. :type alternatives: list of int :return: A vote map, i.e., a dictionary whose keys are orders, mapping to the number of voters with the given order as their preferences. :rtype: dict """ return urnModel(nbVoters, 1, alternatives) def mallowsModel(nbVoters, nbAlternatives, mixture, dispersions, references): """ Generates a profile following a mixture of Mallow's models. :param nbVoters: Number of orders to sample. :type nbVoters: int :param nbAlternatives: Number of alternatives for the sampled orders. :type nbAlternatives: int :param mixture: A list of the weights of each element of the mixture. :type replace: list of int :param dispersions: A list of the dispersion coefficient of each element of the mixture. :type dispersions: list of float :param references: A list of the reference orders for each element of the mixture. :type references: list of tuples of tuples of int :return: A vote map, i.e., a dictionary whose keys are orders, mapping to the number of voters with the given order as their preferences. :rtype: dict """ def mallowsInsertDistributions(nbAlternatives, phi): distributions = {} for i in range(1, nbAlternatives + 1): #Start with an empty distro of length i distribution = [0] * i #compute the denom = phi^0 + phi^1 + ... phi^(i-1) denominator = sum([pow(phi, k) for k in range(i)]) #Fill each element of the distro with phi^(i-j) / denominator for j in range(1, i+1): distribution[j-1] = pow(phi, i - j) / denominator distributions[i] = distribution return distributions if len(mixture) != len(dispersions) or len(mixture) != len(references): raise ValueError("Parameters of Mallows' mixture do not have the same length.") # We normalize the mixture so that it sums up to 1 if sum(mixture) != 1: mixture = [m / sum(mixture) for m in mixture] #Precompute the distros for each Phi. insertDistributions = [] for i in range(len(mixture)): insertDistributions.append(mallowsInsertDistributions(nbAlternatives, dispersions[i])) #Now, generate votes... votemap = {} for cvoter in range(nbVoters): cmodel = np.random.choice(range(len(mixture)), 1, p = mixture)[0] #Generate a vote for the selected model insertVector = [0] * nbAlternatives for i in range(1, len(insertVector) + 1): #options are 1...max insertVector[i - 1] = np.random.choice(range(1, i + 1), 1, p = insertDistributions[cmodel][i])[0] vote = [] for i in range(len(references[cmodel])): vote.insert(insertVector[i] - 1, references[cmodel][i]) tvote = tuple((alt,) for alt in vote) votemap[tvote] = votemap.get(tvote, 0) + 1 return votemap def mallowsMixture(nbVoters, nbReferences, alternatives): """ Generates a profile following a mixture of Mallow's models for which reference points and dispersion coefficients are independently and identically distributed. :param nbVoters: Number of orders to sample. :type nbVoters: int :param nbAlternatives: Number of alternatives for the sampled orders. :type nbAlternatives: int :param nbReferences: Number of element :type nbReferences: int :return: A vote map, i.e., a dictionary whose keys are orders, mapping to the number of voters with the given order as their preferences. :rtype: dict """ mixture = [] dispersions = [] references = [] for i in range(nbReferences): references.append(tuple(generateICStrictProfile(1, alternatives))[0]) dispersions.append(round(np.random.rand(), 5)) mixture.append(np.random.randint(1, 101)) sumMixture = sum(mixture) mixture = [float(i) / float(sumMixture) for i in mixture] return mallowsModel(nbVoters, len(alternatives), mixture, dispersions, references) def urnModel(nbVoters, replace, alternatives): """ Generates a profile following the urn model. :param nbVoters: Number of orders to sample. :type nbVoters: int :param replace: The number of replacements for the urn model. :type replace: int :param alternatives: List of alternatives. :type alternatives: list of int :return: A vote map, i.e., a dictionary whose keys are orders, mapping to the number of voters with the given order as their preferences. :rtype: dict """ voteMap = {} ReplaceVotes = {} ICsize = np.math.factorial(len(alternatives)) ReplaceSize = 0 for x in range(nbVoters): flip = np.random.randint(1, ICsize + ReplaceSize + 1) if flip <= ICsize: #generate an IC vote and make a suitable number of replacements... tvote = generateICVote(alternatives) voteMap[tvote] = (voteMap.get(tvote, 0) + 1) ReplaceVotes[tvote] = (ReplaceVotes.get(tvote, 0) + replace) ReplaceSize += replace #print("made " + str(tvote)) else: #iterate over replacement hash and select proper vote. flip = flip - ICsize for vote in ReplaceVotes.keys(): flip = flip - ReplaceVotes[vote] if flip <= 0: vote = tuple((alt,) for alt in vote) voteMap[vote] = (voteMap.get(vote, 0) + 1) ReplaceVotes[vote] = (ReplaceVotes.get(vote, 0) + replace) ReplaceSize += replace break else: print("We Have a problem... replace fell through....") exit() return voteMap def generateICVote(alternatives): """ Generates a strict order over the set of alternatives following the impartial culture. :param alternatives: List of alternatives. :type alternatives: list of int :return: A strict order over the alternatives, i.e., a tuple of tuples of size 1. :rtype: tuple """ options = list(alternatives) vote = [] while(len(options) > 0): #randomly select an option vote.append(options.pop(np.random.randint(0, len(options)))) return tuple((alt,) for alt in vote)

1a8e4c575c1b0238e1aaf8f4d51c772141dd949b

py

Python

module1-introduction-to-sql/assignment/buddymove_holidayiq.py

jonathanmendoza-tx/DS-Unit-3-Sprint-2-SQL-and-Databases

7976332b75b8c81b2dd06682c3be1acd390dbd8c

module1-introduction-to-sql/assignment/buddymove_holidayiq.py

jonathanmendoza-tx/DS-Unit-3-Sprint-2-SQL-and-Databases

7976332b75b8c81b2dd06682c3be1acd390dbd8c

module1-introduction-to-sql/assignment/buddymove_holidayiq.py

jonathanmendoza-tx/DS-Unit-3-Sprint-2-SQL-and-Databases

7976332b75b8c81b2dd06682c3be1acd390dbd8c

import sqlite3 import pandas as pd !wget https://raw.githubusercontent.com/jonathanmendoza-tx/DS-Unit-3-Sprint-2-SQL-and-Databases/master/module1-introduction-to-sql/buddymove_holidayiq.csv conn = sqlite3.connect('buddymove_holidayiq.sqlite3') cur = conn.cursor() df = pd.read_csv('/content/buddymove_holidayiq.csv', index_col= 'User Id') df.to_sql(name = 'review', con = conn) query_rows = """ SELECT COUNT(*) FROM review """ cur.execute(query_rows) total_people = cur.fetchall() print(f'There are a total of {total_people[0][0]} rows') query_nature_shopping = """ SELECT COUNT(*) FROM review WHERE Nature >= 100 AND Shopping >= 100 """ cur.execute(query_nature_shopping) nature_shop = cur.fetchall() print(f'There are {nature_shop[0][0]} people who reviewed nature and shopping at least 100 times') columns = ['Sports', 'Religious', 'Nature', 'Theatre', 'Shopping', 'Picnic'] for ii in range(len(columns)): query = """ SELECT AVG(%s) FROM review """ cur.execute(query %columns[ii]) avg = cur.fetchall() print(f'Average number of reviews for {columns[ii]} is {avg[0][0]}')

import sqlite3 import pandas as pd !wget https://raw.githubusercontent.com/jonathanmendoza-tx/DS-Unit-3-Sprint-2-SQL-and-Databases/master/module1-introduction-to-sql/buddymove_holidayiq.csv conn = sqlite3.connect('buddymove_holidayiq.sqlite3') cur = conn.cursor() df = pd.read_csv('/content/buddymove_holidayiq.csv', index_col= 'User Id') df.to_sql(name = 'review', con = conn) query_rows = """ SELECT COUNT(*) FROM review """ cur.execute(query_rows) total_people = cur.fetchall() print(f'There are a total of {total_people[0][0]} rows') query_nature_shopping = """ SELECT COUNT(*) FROM review WHERE Nature >= 100 AND Shopping >= 100 """ cur.execute(query_nature_shopping) nature_shop = cur.fetchall() print(f'There are {nature_shop[0][0]} people who reviewed nature and shopping at least 100 times') columns = ['Sports', 'Religious', 'Nature', 'Theatre', 'Shopping', 'Picnic'] for ii in range(len(columns)): query = """ SELECT AVG(%s) FROM review """ cur.execute(query %columns[ii]) avg = cur.fetchall() print(f'Average number of reviews for {columns[ii]} is {avg[0][0]}')

95e7082386e5418aa14fda8e81fb4e55fd79a141

py

Python

quart_rapidoc/version.py

marirs/quart-rapidoc

fd86604ee5ffea7bd33b08af537472f0df21e8c8

2020-07-06T17:11:02.000Z

2020-07-06T17:11:02.000Z

quart_rapidoc/version.py

marirs/quart-rapidoc

fd86604ee5ffea7bd33b08af537472f0df21e8c8

quart_rapidoc/version.py

marirs/quart-rapidoc

fd86604ee5ffea7bd33b08af537472f0df21e8c8

"""quart_redoc version file.""" __version__ = "0.5.1"

"""quart_redoc version file.""" __version__ = "0.5.1"

e2e6c98c7ef19d0a3e295150199aeb3a5229e053

py

Python

cli/cyberPanel.py

uzairAK/serverom-panel

3dcde05ad618e6bef280db7d3180f926fe2ab1db

cli/cyberPanel.py

uzairAK/serverom-panel

3dcde05ad618e6bef280db7d3180f926fe2ab1db

cli/cyberPanel.py

uzairAK/serverom-panel

3dcde05ad618e6bef280db7d3180f926fe2ab1db

#!/usr/local/CyberCP/bin/python import os,sys sys.path.append('/usr/local/CyberCP') import django os.environ.setdefault("DJANGO_SETTINGS_MODULE", "CyberCP.settings") django.setup() from inspect import stack from cli.cliLogger import cliLogger as logger import json from plogical.virtualHostUtilities import virtualHostUtilities import re from websiteFunctions.models import Websites, ChildDomains from plogical.dnsUtilities import DNS import time import plogical.backupUtilities as backupUtilities import requests from loginSystem.models import Administrator from packages.models import Package from plogical.mysqlUtilities import mysqlUtilities from cli.cliParser import cliParser from plogical.vhost import vhost from plogical.mailUtilities import mailUtilities from plogical.ftpUtilities import FTPUtilities from plogical.sslUtilities import sslUtilities from plogical.processUtilities import ProcessUtilities from plogical.backupSchedule import backupSchedule # All that we see or seem is but a dream within a dream. ## Website Functions ## DNS Functions ## Backup Functions ## Packages ## Database functions ## Email functions ## FTP Functions ## FTP Functions # FTP Functions ## FTP Functions ## SSL Functions if __name__ == "__main__": main()

#!/usr/local/CyberCP/bin/python import os,sys sys.path.append('/usr/local/CyberCP') import django os.environ.setdefault("DJANGO_SETTINGS_MODULE", "CyberCP.settings") django.setup() from inspect import stack from cli.cliLogger import cliLogger as logger import json from plogical.virtualHostUtilities import virtualHostUtilities import re from websiteFunctions.models import Websites, ChildDomains from plogical.dnsUtilities import DNS import time import plogical.backupUtilities as backupUtilities import requests from loginSystem.models import Administrator from packages.models import Package from plogical.mysqlUtilities import mysqlUtilities from cli.cliParser import cliParser from plogical.vhost import vhost from plogical.mailUtilities import mailUtilities from plogical.ftpUtilities import FTPUtilities from plogical.sslUtilities import sslUtilities from plogical.processUtilities import ProcessUtilities from plogical.backupSchedule import backupSchedule # All that we see or seem is but a dream within a dream. class cyberPanel: def printStatus(self, operationStatus, errorMessage): data = json.dumps({'success': operationStatus, 'errorMessage': errorMessage }) print(data) ## Website Functions def createWebsite(self, package, owner, domainName, email, php, ssl, dkim, openBasedir): try: externalApp = "".join(re.findall("[a-zA-Z]+", domainName))[:7] phpSelection = 'PHP ' + php result = virtualHostUtilities.createVirtualHost(domainName, email, phpSelection, externalApp, ssl, dkim, openBasedir, owner, package, 0) if result[0] == 1: self.printStatus(1,'None') else: self.printStatus(0, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def createDomain(self, masterDomain, domainName, owner, php, ssl, dkim, openBasedir): try: path = '/home/' + masterDomain + '/public_html/' + domainName phpSelection = 'PHP ' + php result = virtualHostUtilities.createDomain(masterDomain, domainName, phpSelection, path, ssl, dkim, openBasedir, owner, 0) if result[0] == 1: self.printStatus(1,'None') else: self.printStatus(0, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def deleteWebsite(self, domainName): try: vhost.deleteVirtualHostConfigurations(domainName) self.printStatus(1, 'None') except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) def deleteChild(self, childDomain): try: result = virtualHostUtilities.deleteDomain(childDomain) if result[0] == 1: self.printStatus(1,'None') else: self.printStatus(0, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) def listWebsitesJson(self): try: websites = Websites.objects.all() ipFile = "/etc/cyberpanel/machineIP" f = open(ipFile) ipData = f.read() ipAddress = ipData.split('\n', 1)[0] json_data = "[" checker = 0 for items in websites: if items.state == 0: state = "Suspended" else: state = "Active" dic = {'domain': items.domain, 'adminEmail': items.adminEmail,'ipAddress':ipAddress,'admin': items.admin.userName,'package': items.package.packageName,'state':state} if checker == 0: json_data = json_data + json.dumps(dic) checker = 1 else: json_data = json_data +',' + json.dumps(dic) json_data = json_data + ']' final_json = json.dumps(json_data) print(final_json) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) def listWebsitesPretty(self): try: from prettytable import PrettyTable websites = Websites.objects.all() ipFile = "/etc/cyberpanel/machineIP" f = open(ipFile) ipData = f.read() ipAddress = ipData.split('\n', 1)[0] table = PrettyTable(['ID','Domain', 'IP Address', 'Package', 'Owner', 'State', 'Email']) for items in websites: if items.state == 0: state = "Suspended" else: state = "Active" table.add_row([items.id, items.domain, ipAddress, items.package.packageName, items.admin.userName, state, items.adminEmail]) print(table) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) def changePHP(self, virtualHostName, phpVersion): try: phpVersion = 'PHP ' + phpVersion confPath = virtualHostUtilities.Server_root + "/conf/vhosts/" + virtualHostName completePathToConfigFile = confPath + "/vhost.conf" result = vhost.changePHP(completePathToConfigFile, phpVersion) if result[0] == 1: self.printStatus(1,'None') else: self.printStatus(0, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def changePackage(self, virtualHostName, packageName): try: if Websites.objects.filter(domain=virtualHostName).count() == 0: self.printStatus(0, 'This website does not exists.') if Package.objects.filter(packageName=packageName).count() == 0: self.printStatus(0, 'This package does not exists.') website = Websites.objects.get(domain=virtualHostName) package = Package.objects.get(packageName=packageName) website.package = package website.save() self.printStatus(1, 'None') except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) ## DNS Functions def listDNSJson(self, virtualHostName): try: records = DNS.getDNSRecords(virtualHostName) json_data = "[" checker = 0 for items in records: dic = {'id': items.id, 'type': items.type, 'name': items.name, 'content': items.content, 'priority': items.prio, 'ttl': items.ttl } if checker == 0: json_data = json_data + json.dumps(dic) checker = 1 else: json_data = json_data + ',' + json.dumps(dic) json_data = json_data + ']' final_json = json.dumps(json_data) print(final_json) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) def listDNSPretty(self, virtualHostName): try: from prettytable import PrettyTable records = DNS.getDNSRecords(virtualHostName) table = PrettyTable(['ID', 'TYPE', 'Name', 'Value', 'Priority', 'TTL']) for items in records: if len(items.content) >= 30: content = items.content[0:30] + " ..." else: content = items.content table.add_row([items.id, items.type, items.name, content, items.prio, items.ttl]) print(table) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) def listDNSZonesJson(self): try: records = DNS.getDNSZones() json_data = "[" checker = 0 for items in records: dic = {'id': items.id, 'name': items.name, } if checker == 0: json_data = json_data + json.dumps(dic) checker = 1 else: json_data = json_data + ',' + json.dumps(dic) json_data = json_data + ']' final_json = json.dumps(json_data) print(final_json) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) def listDNSZonesPretty(self): try: from prettytable import PrettyTable records = records = DNS.getDNSZones() table = PrettyTable(['ID', 'Name']) for items in records: table.add_row([items.id, items.name]) print(table) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) def createDNSZone(self, virtualHostName, owner): try: admin = Administrator.objects.get(userName=owner) DNS.dnsTemplate(virtualHostName, admin) self.printStatus(1, 'None') except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def createDNSRecord(self, virtualHostName, name, recordType, value, priority, ttl): try: zone = DNS.getZoneObject(virtualHostName) DNS.createDNSRecord(zone, name, recordType, value, int(priority), int(ttl)) self.printStatus(1, 'None') except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def deleteDNSZone(self, virtualHostName): try: DNS.deleteDNSZone(virtualHostName) self.printStatus(1, 'None') except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def deleteDNSRecord(self, recordID): try: DNS.deleteDNSRecord(recordID) self.printStatus(1, 'None') except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) ## Backup Functions def createBackup(self, virtualHostName): try: backupLogPath = "/usr/local/lscp/logs/backup_log."+time.strftime("%I-%M-%S-%a-%b-%Y") print('Backup logs to be generated in %s' % (backupLogPath)) backupSchedule.createLocalBackup(virtualHostName, backupLogPath) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) def restoreBackup(self, fileName): try: if os.path.exists('/home/backup/' + fileName): dir = "CyberPanelRestore" else: dir = 'CLI' backupUtilities.submitRestore(fileName, dir) while (1): time.sleep(1) finalData = json.dumps({'backupFile': fileName, "dir": dir}) r = requests.post("http://localhost:5003/backup/restoreStatus", data=finalData, verify=False) data = json.loads(r.text) if data['abort'] == 1 and data['running'] == "Error": print('Failed to restore backup, Error message : ' + data['status'] + '\n') break elif data['abort'] == 1 and data['running'] == "Completed": print('\n\n') print('Backup restore completed.\n') break else: print('Waiting for restore to complete. Current status: ' + data['status']) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) ## Packages def createPackage(self, owner, packageName, diskSpace, bandwidth, emailAccounts, dataBases, ftpAccounts, allowedDomains): try: admin = Administrator.objects.get(userName=owner) newPack = Package(admin=admin, packageName=packageName, diskSpace=diskSpace, bandwidth=bandwidth, emailAccounts=emailAccounts, dataBases=dataBases, ftpAccounts=ftpAccounts, allowedDomains=allowedDomains) newPack.save() self.printStatus(1, 'None') except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def deletePackage(self, packageName): try: delPack = Package.objects.get(packageName=packageName) delPack.delete() self.printStatus(1, 'None') except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def listPackagesJson(self): try: records = Package.objects.all() json_data = "[" checker = 0 for items in records: dic = {'id': items.id, 'packageName': items.packageName, 'domains': items.allowedDomains, 'diskSpace': items.diskSpace, 'bandwidth': items.bandwidth, 'ftpAccounts ': items.ftpAccounts, 'dataBases': items.dataBases, 'emailAccounts':items.emailAccounts } if checker == 0: json_data = json_data + json.dumps(dic) checker = 1 else: json_data = json_data + ',' + json.dumps(dic) json_data = json_data + ']' final_json = json.dumps(json_data) print(final_json) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) def listPackagesPretty(self): try: from prettytable import PrettyTable records = Package.objects.all() table = PrettyTable(['Name', 'Domains', 'Disk Space', 'Bandwidth', 'FTP Accounts', 'Databases', 'Email Accounts']) for items in records: table.add_row([items.packageName, items.allowedDomains, items.diskSpace, items.bandwidth, items.ftpAccounts, items.dataBases, items.emailAccounts]) print(table) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) ## Database functions def createDatabase(self, dbName, dbUsername, dbPassword, databaseWebsite): try: result = mysqlUtilities.submitDBCreation(dbName, dbUsername, dbPassword, databaseWebsite) if result[0] == 1: self.printStatus(1, 'None') else: self.printStatus(1, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def deleteDatabase(self, dbName): try: result = mysqlUtilities.submitDBDeletion(dbName) if result[0] == 1: self.printStatus(1, 'None') else: self.printStatus(1, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def listDatabasesJson(self, virtualHostName): try: records = mysqlUtilities.getDatabases(virtualHostName) json_data = "[" checker = 0 for items in records: dic = {'id': items.id, 'dbName': items.dbName, 'dbUser': items.dbUser, } if checker == 0: json_data = json_data + json.dumps(dic) checker = 1 else: json_data = json_data + ',' + json.dumps(dic) json_data = json_data + ']' final_json = json.dumps(json_data) print(final_json) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) def listDatabasesPretty(self, virtualHostName): try: from prettytable import PrettyTable records = mysqlUtilities.getDatabases(virtualHostName) table = PrettyTable(['ID', 'Database Name', 'Database User']) for items in records: table.add_row([items.id, items.dbName, items.dbUser]) print(table) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) ## Email functions def createEmail(self, domain, userName, password): try: result = mailUtilities.createEmailAccount(domain, userName, password) if result[0] == 1: self.printStatus(1, 'None') else: self.printStatus(1, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def deleteEmail(self, email): try: result = mailUtilities.deleteEmailAccount(email) if result[0] == 1: self.printStatus(1, 'None') else: self.printStatus(1, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def changeEmailPassword(self, email, password): try: result = mailUtilities.changeEmailPassword(email, password) if result[0] == 1: self.printStatus(1, 'None') else: self.printStatus(1, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def listEmailsJson(self, virtualHostName): try: records = mailUtilities.getEmailAccounts(virtualHostName) json_data = "[" checker = 0 for items in records: dic = { 'email': items.email, } if checker == 0: json_data = json_data + json.dumps(dic) checker = 1 else: json_data = json_data + ',' + json.dumps(dic) json_data = json_data + ']' final_json = json.dumps(json_data) print(final_json) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) def listEmailsPretty(self, virtualHostName): try: from prettytable import PrettyTable records = mailUtilities.getEmailAccounts(virtualHostName) table = PrettyTable(['Email']) for items in records: table.add_row([items.email]) print(table) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) ## FTP Functions ## FTP Functions # FTP Functions def createFTPAccount(self, domain, userName, password, owner): try: result = FTPUtilities.submitFTPCreation(domain, userName, password, 'None', owner) if result[0] == 1: self.printStatus(1, 'None') else: self.printStatus(1, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def deleteFTPAccount(self, userName): try: result = FTPUtilities.submitFTPDeletion(userName) if result[0] == 1: self.printStatus(1, 'None') else: self.printStatus(1, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def changeFTPPassword(self, userName, password): try: result = FTPUtilities.changeFTPPassword(userName, password) if result[0] == 1: self.printStatus(1, 'None') else: self.printStatus(1, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def listFTPJson(self, virtualHostName): try: records = FTPUtilities.getFTPRecords(virtualHostName) json_data = "[" checker = 0 for items in records: dic = {'id': items.id, 'username': items.user, 'path': items.dir } if checker == 0: json_data = json_data + json.dumps(dic) checker = 1 else: json_data = json_data + ',' + json.dumps(dic) json_data = json_data + ']' final_json = json.dumps(json_data) print(final_json) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) def listFTPPretty(self, virtualHostName): try: from prettytable import PrettyTable records = FTPUtilities.getFTPRecords(virtualHostName) table = PrettyTable(['ID', 'User', 'Path']) for items in records: table.add_row([items.id, items.user, items.dir]) print(table) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) print(0) ## FTP Functions ## SSL Functions def issueSSL(self, virtualHost): try: path = '' adminEmail = '' try: website = ChildDomains.objects.get(domain=virtualHost) adminEmail = website.master.adminEmail path = website.path except: website = Websites.objects.get(domain=virtualHost) adminEmail = website.adminEmail path = "/home/" + virtualHost + "/public_html" result = virtualHostUtilities.issueSSL(virtualHost, path, adminEmail) if result[0] == 1: self.printStatus(1, 'None') else: self.printStatus(1, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def issueSSLForHostName(self, virtualHost): try: path = '' adminEmail = '' try: website = ChildDomains.objects.get(domain=virtualHost) adminEmail = website.master.adminEmail path = website.path except: website = Websites.objects.get(domain=virtualHost) adminEmail = website.adminEmail path = "/home/" + virtualHost + "/public_html" result = virtualHostUtilities.issueSSLForHostName(virtualHost, path) if result[0] == 1: self.printStatus(1, 'None') else: self.printStatus(1, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def issueSSLForMailServer(self, virtualHost): try: path = '' adminEmail = '' try: website = ChildDomains.objects.get(domain=virtualHost) adminEmail = website.master.adminEmail path = website.path except: website = Websites.objects.get(domain=virtualHost) adminEmail = website.adminEmail path = "/home/" + virtualHost + "/public_html" result = virtualHostUtilities.issueSSLForMailServer(virtualHost, path) if result[0] == 1: self.printStatus(1, 'None') else: self.printStatus(1, result[1]) except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def issueSelfSignedSSL(self, virtualHost): try: try: website = ChildDomains.objects.get(domain=virtualHost) adminEmail = website.master.adminEmail except: website = Websites.objects.get(domain=virtualHost) adminEmail = website.adminEmail pathToStoreSSL = "/etc/letsencrypt/live/" + virtualHost command = 'mkdir -p ' + pathToStoreSSL ProcessUtilities.executioner(command) pathToStoreSSLPrivKey = "/etc/letsencrypt/live/" + virtualHost + "/privkey.pem" pathToStoreSSLFullChain = "/etc/letsencrypt/live/" + virtualHost + "/fullchain.pem" command = 'openssl req -newkey rsa:2048 -new -nodes -x509 -days 3650 -subj "/C=US/ST=Denial/L=Springfield/O=Dis/CN=www.example.com" -keyout ' + pathToStoreSSLPrivKey + ' -out ' + pathToStoreSSLFullChain ProcessUtilities.executioner(command) sslUtilities.installSSLForDomain(virtualHost, adminEmail) ProcessUtilities.restartLitespeed() self.printStatus(1, 'None') except BaseException as msg: logger.writeforCLI(str(msg), "Error", stack()[0][3]) self.printStatus(0, str(msg)) def main(): parser = cliParser() args = parser.prepareArguments() cyberpanel = cyberPanel() ## Website functions if args.function == "createWebsite": completeCommandExample = 'cyberpanel createWebsite --package Detault --owner admin --domainName cyberpanel.net --email support@cyberpanel.net --php 5.6' if not args.package: print("\n\nPlease enter the package name. For example:\n\n" + completeCommandExample + "\n\n") return if not args.owner: print("\n\nPlease enter the owner name. For example:\n\n" + completeCommandExample + "\n\n") return if not args.domainName: print("\n\nPlease enter the domain name. For example:\n\n" + completeCommandExample + "\n\n") return if not args.email: print("\n\nPlease enter the email. For example:\n\n" + completeCommandExample + "\n\n") return if not args.php: print("\n\nPlease enter the PHP version such as 5.6 for PHP version 5.6. For example:\n\n" + completeCommandExample + "\n\n") return if args.ssl: ssl = int(args.ssl) else: ssl = 0 if args.dkim: dkim = int(args.dkim) else: dkim = 0 if args.openBasedir: openBasedir = int(args.openBasedir) else: openBasedir = 0 cyberpanel.createWebsite(args.package, args.owner, args.domainName, args.email, args.php, ssl, dkim, openBasedir) elif args.function == "deleteWebsite": completeCommandExample = 'cyberpanel deleteWebsite --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter the domain to delete. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.deleteWebsite(args.domainName) elif args.function == "createChild": completeCommandExample = 'cyberpanel createChild --masterDomain cyberpanel.net --childDomain child.cyberpanel.net' \ ' --owner admin --php 5.6' if not args.masterDomain: print("\n\nPlease enter Master domain. For example:\n\n" + completeCommandExample + "\n\n") return if not args.childDomain: print("\n\nPlease enter the Child Domain. For example:\n\n" + completeCommandExample + "\n\n") return if not args.owner: print("\n\nPlease enter owner for this domain DNS records. For example:\n\n" + completeCommandExample + "\n\n") return if not args.php: print("\n\nPlease enter required PHP version. For example:\n\n" + completeCommandExample + "\n\n") return if args.ssl: ssl = int(args.ssl) else: ssl = 0 if args.dkim: dkim = int(args.dkim) else: dkim = 0 if args.openBasedir: openBasedir = int(args.openBasedir) else: openBasedir = 0 cyberpanel.createDomain(args.masterDomain, args.childDomain, args.owner, args.php, ssl, dkim, openBasedir) elif args.function == "deleteChild": completeCommandExample = 'cyberpanel deleteChild --childDomain cyberpanel.net' if not args.childDomain: print("\n\nPlease enter the child domain to delete. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.deleteChild(args.childDomain) elif args.function == "listWebsitesJson": cyberpanel.listWebsitesJson() elif args.function == "listWebsitesPretty": cyberpanel.listWebsitesPretty() elif args.function == "changePHP": completeCommandExample = 'cyberpanel changePHP --domainName cyberpanel.net --php 5.6' if not args.domainName: print("\n\nPlease enter Domain. For example:\n\n" + completeCommandExample + "\n\n") return if not args.php: print("\n\nPlease enter required PHP version. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.changePHP(args.domainName, args.php) elif args.function == "changePackage": completeCommandExample = 'cyberpanel changePackage --domainName cyberpanel.net --packageName CLI' if not args.domainName: print("\n\nPlease enter the Domain. For example:\n\n" + completeCommandExample + "\n\n") return if not args.packageName: print("\n\nPlease enter the package name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.changePackage(args.domainName, args.packageName) ## DNS Functions elif args.function == "listDNSJson": completeCommandExample = 'cyberpanel listDNSJson --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter the domain. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.listDNSJson(args.domainName) elif args.function == "listDNSPretty": completeCommandExample = 'cyberpanel listDNSPretty --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter the domain. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.listDNSPretty(args.domainName) elif args.function == "listDNSZonesJson": cyberpanel.listDNSZonesJson() elif args.function == "listDNSZonesPretty": cyberpanel.listDNSZonesPretty() elif args.function == "createDNSZone": completeCommandExample = 'cyberpanel createDNSZone --owner admin --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter the domain. For example:\n\n" + completeCommandExample + "\n\n") return if not args.owner: print("\n\nPlease enter the owner name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.createDNSZone(args.domainName, args.owner) elif args.function == "deleteDNSZone": completeCommandExample = 'cyberpanel deleteDNSZone --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter the domain. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.deleteDNSZone(args.domainName) elif args.function == "createDNSRecord": completeCommandExample = 'cyberpanel createDNSRecord --domainName cyberpanel.net --name cyberpanel.net' \ ' --recordType A --value 192.168.100.1 --priority 0 --ttl 3600' if not args.domainName: print("\n\nPlease enter the domain. For example:\n\n" + completeCommandExample + "\n\n") return if not args.name: print("\n\nPlease enter the record name. For example:\n\n" + completeCommandExample + "\n\n") return if not args.recordType: print("\n\nPlease enter the record type. For example:\n\n" + completeCommandExample + "\n\n") return if not args.value: print("\n\nPlease enter the record value. For example:\n\n" + completeCommandExample + "\n\n") return if not args.priority: print("\n\nPlease enter the priority. For example:\n\n" + completeCommandExample + "\n\n") return if not args.ttl: print("\n\nPlease enter the ttl. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.createDNSRecord(args.domainName, args.name, args.recordType, args.value, args.priority, args.ttl) elif args.function == "deleteDNSRecord": completeCommandExample = 'cyberpanel deleteDNSRecord --recordID 200' if not args.recordID: print("\n\nPlease enter the record ID to be deleted, you can find record ID by listing the current DNS records. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.deleteDNSRecord(args.recordID) ## Backup Functions. elif args.function == "createBackup": completeCommandExample = 'cyberpanel createBackup --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter the domain. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.createBackup(args.domainName) elif args.function == "restoreBackup": completeCommandExample = 'cyberpanel restoreBackup --fileName /home/talkshosting.com/backup/backup-talksho-01-30-53-Fri-Jun-2018.tar.gz' if not args.fileName: print("\n\nPlease enter the file name or complete path to file. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.restoreBackup(args.fileName) ## Package functions. elif args.function == "createPackage": completeCommandExample = 'cyberpanel createPackage --owner admin --packageName CLI --diskSpace 1000 --bandwidth 10000 --emailAccounts 100' \ ' --dataBases 100 --ftpAccounts 100 --allowedDomains 100' if not args.owner: print("\n\nPlease enter the owner name. For example:\n\n" + completeCommandExample + "\n\n") return if not args.packageName: print("\n\nPlease enter the package name. For example:\n\n" + completeCommandExample + "\n\n") return if not args.diskSpace: print("\n\nPlease enter value for Disk Space. For example:\n\n" + completeCommandExample + "\n\n") return if not args.bandwidth: print("\n\nPlease enter value for Bandwidth. For example:\n\n" + completeCommandExample + "\n\n") return if not args.emailAccounts: print("\n\nPlease enter value for Email accounts. For example:\n\n" + completeCommandExample + "\n\n") return if not args.dataBases: print("\n\nPlease enter value for Databases. For example:\n\n" + completeCommandExample + "\n\n") return if not args.ftpAccounts: print("\n\nPlease enter value for Ftp accounts. For example:\n\n" + completeCommandExample + "\n\n") return if not args.allowedDomains: print("\n\nPlease enter value for Allowed Child Domains. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.createPackage(args.owner, args.packageName, args.diskSpace, args.bandwidth, args.emailAccounts, args.dataBases, args.ftpAccounts, args.allowedDomains) elif args.function == "deletePackage": completeCommandExample = 'cyberpanel deletePackage --packageName CLI' if not args.packageName: print("\n\nPlease enter the package name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.deletePackage(args.packageName) elif args.function == "listPackagesJson": cyberpanel.listPackagesJson() elif args.function == "listPackagesPretty": cyberpanel.listPackagesPretty() ## Database functions. elif args.function == "createDatabase": completeCommandExample = 'cyberpanel createDatabase --databaseWebsite cyberpanel.net --dbName cyberpanel ' \ '--dbUsername cyberpanel --dbPassword cyberpanel' if not args.databaseWebsite: print("\n\nPlease enter database website. For example:\n\n" + completeCommandExample + "\n\n") return if not args.dbName: print("\n\nPlease enter the database name. For example:\n\n" + completeCommandExample + "\n\n") return if not args.dbUsername: print("\n\nPlease enter the database username. For example:\n\n" + completeCommandExample + "\n\n") return if not args.dbPassword: print("\n\nPlease enter the password for database. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.createDatabase(args.dbName, args.dbUsername, args.dbPassword, args.databaseWebsite) elif args.function == "deleteDatabase": completeCommandExample = 'cyberpanel deleteDatabase --dbName cyberpanel' if not args.dbName: print("\n\nPlease enter the database name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.deleteDatabase(args.dbName) elif args.function == "listDatabasesJson": completeCommandExample = 'cyberpanel listDatabasesJson --databaseWebsite cyberpanel.net' if not args.databaseWebsite: print("\n\nPlease enter database website. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.listDatabasesJson(args.databaseWebsite) elif args.function == "listDatabasesPretty": completeCommandExample = 'cyberpanel listDatabasesPretty --databaseWebsite cyberpanel.net' if not args.databaseWebsite: print("\n\nPlease enter database website. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.listDatabasesPretty(args.databaseWebsite) ## Email Functions elif args.function == "createEmail": completeCommandExample = 'cyberpanel createEmail --domainName cyberpanel.net --userName cyberpanel ' \ '--password cyberpanel' if not args.domainName: print("\n\nPlease enter Domain name. For example:\n\n" + completeCommandExample + "\n\n") return if not args.userName: print("\n\nPlease enter the user name. For example:\n\n" + completeCommandExample + "\n\n") return if not args.password: print("\n\nPlease enter the password for database. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.createEmail(args.domainName, args.userName, args.password) elif args.function == "deleteEmail": completeCommandExample = 'cyberpanel deleteEmail --email cyberpanel@cyberpanel.net' if not args.email: print("\n\nPlease enter the email. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.deleteEmail(args.email) elif args.function == "changeEmailPassword": completeCommandExample = 'cyberpanel changeEmailPassword --email cyberpanel@cyberpanel.net --password cyberpanel' if not args.email: print("\n\nPlease enter email. For example:\n\n" + completeCommandExample + "\n\n") return if not args.password: print("\n\nPlease enter the password. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.changeEmailPassword(args.email, args.password) elif args.function == "listEmailsJson": completeCommandExample = 'cyberpanel listEmailsJson --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter domain name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.listEmailsJson(args.domainName) elif args.function == "listEmailsPretty": completeCommandExample = 'cyberpanel listEmailsPretty --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter domain name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.listEmailsPretty(args.domainName) ## FTP Functions elif args.function == "createFTPAccount": completeCommandExample = 'cyberpanel createFTPAccount --domainName cyberpanel.net --userName cyberpanel ' \ '--password cyberpanel --owner admin' if not args.domainName: print("\n\nPlease enter Domain name. For example:\n\n" + completeCommandExample + "\n\n") return if not args.userName: print("\n\nPlease enter the user name. For example:\n\n" + completeCommandExample + "\n\n") return if not args.password: print("\n\nPlease enter the password for database. For example:\n\n" + completeCommandExample + "\n\n") return if not args.owner: print("\n\nPlease enter the owner name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.createFTPAccount(args.domainName, args.userName, args.password, args.owner) elif args.function == "deleteFTPAccount": completeCommandExample = 'cyberpanel deleteFTPAccount --userName cyberpanel' if not args.userName: print("\n\nPlease enter the user name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.deleteFTPAccount(args.userName) elif args.function == "changeFTPPassword": completeCommandExample = 'cyberpanel changeFTPPassword --userName cyberpanel --password cyberpanel' if not args.userName: print("\n\nPlease enter the user name. For example:\n\n" + completeCommandExample + "\n\n") return if not args.password: print("\n\nPlease enter the password for database. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.changeFTPPassword(args.userName, args.password) elif args.function == "listFTPJson": completeCommandExample = 'cyberpanel listFTPJson --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter domain name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.listFTPJson(args.domainName) elif args.function == "listFTPPretty": completeCommandExample = 'cyberpanel listFTPPretty --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter domain name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.listFTPPretty(args.domainName) ## SSL Functions elif args.function == "issueSSL": completeCommandExample = 'cyberpanel issueSSL --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter Domain name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.issueSSL(args.domainName) elif args.function == "hostNameSSL": completeCommandExample = 'cyberpanel hostNameSSL --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter Domain name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.issueSSLForHostName(args.domainName) elif args.function == "mailServerSSL": completeCommandExample = 'cyberpanel mailServerSSL --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter Domain name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.issueSSLForMailServer(args.domainName) elif args.function == "issueSelfSignedSSL": completeCommandExample = 'cyberpanel issueSelfSignedSSL --domainName cyberpanel.net' if not args.domainName: print("\n\nPlease enter Domain name. For example:\n\n" + completeCommandExample + "\n\n") return cyberpanel.issueSelfSignedSSL(args.domainName) elif args.function == 'utility': if not os.path.exists('/usr/bin/cyberpanel_utility'): command = 'wget -q -O /usr/bin/cyberpanel_utility https://cyberpanel.sh/misc/cyberpanel_utility.sh' ProcessUtilities.executioner(command) command = 'chmod 700 /usr/bin/cyberpanel_utility' ProcessUtilities.executioner(command) command = '/usr/bin/cyberpanel_utility' ProcessUtilities.executioner(command) elif args.function == 'upgrade' or args.function == 'update': if not os.path.exists('/usr/bin/cyberpanel_utility'): command = 'wget -q -O /usr/bin/cyberpanel_utility https://cyberpanel.sh/misc/cyberpanel_utility.sh' ProcessUtilities.executioner(command) command = 'chmod 700 /usr/bin/cyberpanel_utility' ProcessUtilities.executioner(command) command = '/usr/bin/cyberpanel_utility --upgrade' ProcessUtilities.executioner(command) elif args.function == 'help': if not os.path.exists('/usr/bin/cyberpanel_utility'): command = 'wget -q -O /usr/bin/cyberpanel_utility https://cyberpanel.sh/misc/cyberpanel_utility.sh' ProcessUtilities.executioner(command) command = 'chmod 700 /usr/bin/cyberpanel_utility' ProcessUtilities.executioner(command) command = '/usr/bin/cyberpanel_utility --help' ProcessUtilities.executioner(command) elif args.function == 'version' or args.function == 'v' or args.function == 'V': ## Get CurrentVersion with open('/usr/local/CyberCP/version.txt') as file: file_contents = file.read() version = re.search('\d.\d', file_contents) version = version.group() build = file_contents[-2:] build = build[0:1] currentversion = version + '.' + build print (currentversion) if __name__ == "__main__": main()

ca330354912944efbfce5a46ab08e1b882f586b6

py

Python

SkipgramSampler/fixedarray.py

InzamamRahaman/SkipgramSampling

16a4bbab0e85ceb46191f074b61e639d9b408dc8

2017-10-13T00:37:25.000Z

2017-10-13T00:37:25.000Z

SkipgramSampler/fixedarray.py

InzamamRahaman/SkipgramSampling

16a4bbab0e85ceb46191f074b61e639d9b408dc8

2017-10-13T04:43:02.000Z

2017-10-14T20:33:39.000Z

SkipgramSampler/fixedarray.py

InzamamRahaman/SkipgramSampling

16a4bbab0e85ceb46191f074b61e639d9b408dc8

from collections import deque class FixedArray(object): """ Object acts a queue of fixed length """

from collections import deque class FixedArray(object): """ Object acts a queue of fixed length """ def __init__(self, limit): self.count = 0 self.limit = limit self.contents = deque() def append(self, x, callback=None): if self.count < self.limit: self.contents.append(x) self.count += 1 else: elem = self.contents.popleft() if callback: callback(elem, x, self) self.contents.append(x) self.count += 1 def __contains__(self, item): item in self.contents def __len__(self): return self.count def get_distinct_elements(self): return set(self.contents) def empty_contents(self): for i in range(self.count): print('Pop') self.contents.popleft() print(self.contents)

3fb46c294a5360bb8f1d5b3c59d0fde197c8961d

py

Python

setup.py

Tobi-Alonso/ResNet50-PYNQ

7c203c2b249479c5384afe152dde2bb06576339b

setup.py

Tobi-Alonso/ResNet50-PYNQ

7c203c2b249479c5384afe152dde2bb06576339b

setup.py

Tobi-Alonso/ResNet50-PYNQ

7c203c2b249479c5384afe152dde2bb06576339b

2020-03-27T18:20:47.000Z

2020-03-27T18:20:47.000Z

# Copyright (c) 2019, Xilinx # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. from setuptools import setup, find_packages from distutils.dir_util import copy_tree import os from pynq.utils import build_py as _build_py __author__ = "Lucian Petrica" __copyright__ = "Copyright 2019, Xilinx" # global variables module_name = "resnet50_pynq" data_files = [] class build_py(_build_py): """Overload the pynq.utils 'build_py' command (that performs overlay download) to also call the function 'copy_notebooks'. """ with open("README.md", encoding="utf-8") as fh: readme_lines = fh.readlines() readme_lines = readme_lines[ readme_lines.index("## PYNQ quick start\n") + 2: readme_lines.index("## Author\n"): ] long_description = ("".join(readme_lines)) extend_package(os.path.join(module_name, "notebooks")) setup(name=module_name, version="1.0", description="Quantized dataflow implementation of ResNet50 on Alveo", long_description=long_description, long_description_content_type="text/markdown", author="Lucian Petrica", url="https://github.com/Xilinx/ResNet50-PYNQ", packages=find_packages(), download_url="https://github.com/Xilinx/ResNet50-PYNQ", package_data={ "": data_files, }, python_requires=">=3.5.2", # keeping 'setup_requires' only for readability - relying on # pyproject.toml and PEP 517/518 setup_requires=[ "pynq>=2.5.1" ], install_requires=[ "pynq>=2.5.1", "jupyter", "jupyterlab", "plotly", "opencv-python", "wget" ], extras_require={ ':python_version<"3.6"': [ 'matplotlib<3.1', 'ipython==7.9' ], ':python_version>="3.6"': [ 'matplotlib' ] }, entry_points={ "pynq.notebooks": [ "ResNet50 = {}.notebooks".format(module_name) ] }, cmdclass={"build_py": build_py}, license="BSD 3-Clause" )

# Copyright (c) 2019, Xilinx # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. from setuptools import setup, find_packages from distutils.dir_util import copy_tree import os from pynq.utils import build_py as _build_py __author__ = "Lucian Petrica" __copyright__ = "Copyright 2019, Xilinx" # global variables module_name = "resnet50_pynq" data_files = [] def extend_package(path): if os.path.isdir(path): data_files.extend( [os.path.join("..", root, f) for root, _, files in os.walk(path) for f in files] ) elif os.path.isfile(path): data_files.append(os.path.join("..", path)) class build_py(_build_py): """Overload the pynq.utils 'build_py' command (that performs overlay download) to also call the function 'copy_notebooks'. """ def copy_notebooks(self): cmd = self.get_finalized_command("build_py") for package, src_dir, build_dir, _ in cmd.data_files: if "." not in package: # sub-packages are skipped src_folder = os.path.join(os.path.dirname(src_dir), "host") dst_folder = os.path.join(build_dir, "notebooks") if os.path.isdir(src_folder): copy_tree(src_folder, dst_folder) def run(self): super().run() self.copy_notebooks() with open("README.md", encoding="utf-8") as fh: readme_lines = fh.readlines() readme_lines = readme_lines[ readme_lines.index("## PYNQ quick start\n") + 2: readme_lines.index("## Author\n"): ] long_description = ("".join(readme_lines)) extend_package(os.path.join(module_name, "notebooks")) setup(name=module_name, version="1.0", description="Quantized dataflow implementation of ResNet50 on Alveo", long_description=long_description, long_description_content_type="text/markdown", author="Lucian Petrica", url="https://github.com/Xilinx/ResNet50-PYNQ", packages=find_packages(), download_url="https://github.com/Xilinx/ResNet50-PYNQ", package_data={ "": data_files, }, python_requires=">=3.5.2", # keeping 'setup_requires' only for readability - relying on # pyproject.toml and PEP 517/518 setup_requires=[ "pynq>=2.5.1" ], install_requires=[ "pynq>=2.5.1", "jupyter", "jupyterlab", "plotly", "opencv-python", "wget" ], extras_require={ ':python_version<"3.6"': [ 'matplotlib<3.1', 'ipython==7.9' ], ':python_version>="3.6"': [ 'matplotlib' ] }, entry_points={ "pynq.notebooks": [ "ResNet50 = {}.notebooks".format(module_name) ] }, cmdclass={"build_py": build_py}, license="BSD 3-Clause" )

c04249abf3a5ebc265209326af85d9f62c50c23b

py

Python

testing/python_lib/test_faucet_state_collector.py

pbatta/forch

df033bc5b7cbac06e1c406257193cb0cb62f2742

2019-12-12T23:13:24.000Z

2019-12-12T23:13:24.000Z

testing/python_lib/test_faucet_state_collector.py

pbatta/forch

df033bc5b7cbac06e1c406257193cb0cb62f2742

2019-12-13T03:30:35.000Z

2021-11-11T16:16:13.000Z

testing/python_lib/test_faucet_state_collector.py

pbatta/forch

df033bc5b7cbac06e1c406257193cb0cb62f2742

2020-01-11T14:12:46.000Z

2021-01-25T17:30:55.000Z

"""Unit tests for Faucet State Collector""" import unittest from unit_base import FaucetStateCollectorTestBase from forch.proto.faucet_event_pb2 import StackTopoChange from forch.utils import dict_proto class DataplaneStateTestCase(FaucetStateCollectorTestBase): """Test cases for dataplane state""" def test_topology_loop(self): """test faucet_state_collector behavior when faucet sends loop in path to egress topology""" self._faucet_state_collector.topo_state = self._build_loop_topo_obj() egress_path = self._faucet_state_collector.get_switch_egress_path('sw1') self.assertEqual(egress_path['path_state'], 1) self.assertEqual(egress_path['path_state_detail'], 'No path to root found. Loop in topology.') def test_egress_path(self): """test faucet_state_collector behavior when faucet sends loop in path to egress topology""" self._faucet_state_collector.topo_state = self._build_topo_obj() # pylint: disable=protected-access self._faucet_state_collector._get_egress_port = lambda port: 28 egress_path = self._faucet_state_collector.get_switch_egress_path('sw3') self.assertEqual(egress_path['path_state'], 5) self.assertEqual(egress_path['path'], [{'switch': 'sw3', 'out': 1}, {'switch': 'sw1', 'in': 2, 'out': 28}]) if __name__ == '__main__': unittest.main()

"""Unit tests for Faucet State Collector""" import unittest from unit_base import FaucetStateCollectorTestBase from forch.proto.faucet_event_pb2 import StackTopoChange from forch.utils import dict_proto class DataplaneStateTestCase(FaucetStateCollectorTestBase): """Test cases for dataplane state""" def _build_link(self, dp1, port1, dp2, port2): return { 'key': dp1 + ':' + port1 + '-' + dp2 + ':' + port2, 'source': dp1, 'target': dp2, 'port_map': { 'dp_a': dp1, 'port_a': 'Port ' + port1, 'dp_z': dp2, 'port_z': 'Port ' + port2 } } def _build_loop_topo_obj(self): dps = { 'sw1': StackTopoChange.StackDp(root_hop_port=1), 'sw2': StackTopoChange.StackDp(root_hop_port=1), 'sw3': StackTopoChange.StackDp(root_hop_port=1), } links = [ self._build_link('sw1', '1', 'sw2', '2'), self._build_link('sw2', '1', 'sw3', '2'), self._build_link('sw3', '1', 'sw1', '2'), ] links_graph = [dict_proto(link, StackTopoChange.StackLink) for link in links] return { 'dps': dps, 'links_graph': links_graph } def _build_topo_obj(self): dps = { 'sw1': StackTopoChange.StackDp(), 'sw2': StackTopoChange.StackDp(root_hop_port=1), 'sw3': StackTopoChange.StackDp(root_hop_port=1), } links = [ self._build_link('sw1', '1', 'sw2', '1'), self._build_link('sw2', '2', 'sw3', '2'), self._build_link('sw3', '1', 'sw1', '2'), ] links_graph = [dict_proto(link, StackTopoChange.StackLink) for link in links] return { 'active_root': 'sw1', 'dps': dps, 'links_graph': links_graph } def test_topology_loop(self): """test faucet_state_collector behavior when faucet sends loop in path to egress topology""" self._faucet_state_collector.topo_state = self._build_loop_topo_obj() egress_path = self._faucet_state_collector.get_switch_egress_path('sw1') self.assertEqual(egress_path['path_state'], 1) self.assertEqual(egress_path['path_state_detail'], 'No path to root found. Loop in topology.') def test_egress_path(self): """test faucet_state_collector behavior when faucet sends loop in path to egress topology""" self._faucet_state_collector.topo_state = self._build_topo_obj() # pylint: disable=protected-access self._faucet_state_collector._get_egress_port = lambda port: 28 egress_path = self._faucet_state_collector.get_switch_egress_path('sw3') self.assertEqual(egress_path['path_state'], 5) self.assertEqual(egress_path['path'], [{'switch': 'sw3', 'out': 1}, {'switch': 'sw1', 'in': 2, 'out': 28}]) if __name__ == '__main__': unittest.main()

3c98d96e351e9f0cf0c5d2fb68fa0eae5f624451

py

Python

tests/python/gaia-ui-tests/gaiatest/apps/persona/app.py

pdehaan/gaia

0ea959d81cefa0128157ec3ff0e2b7bdd29afacf

2015-03-02T04:03:00.000Z

2015-03-02T04:03:00.000Z

tests/python/gaia-ui-tests/gaiatest/apps/persona/app.py

caseyyee/gaia

fa82433dda06e9ae7d35a1f74cc16f4dd72cc514

tests/python/gaia-ui-tests/gaiatest/apps/persona/app.py

caseyyee/gaia

fa82433dda06e9ae7d35a1f74cc16f4dd72cc514

# This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/. from marionette.by import By from gaiatest.apps.base import Base

# This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/. from marionette.by import By from gaiatest.apps.base import Base class Persona(Base): # iframes _persona_frame_locator = (By.CSS_SELECTOR, "iframe.screen[data-url*='persona.org/sign_in#NATIVE']") # persona login _body_loading_locator = (By.CSS_SELECTOR, 'body.loading') _email_input_locator = (By.ID, 'authentication_email') _password_input_locator = (By.ID, 'authentication_password') _continue_button_locator = (By.CSS_SELECTOR, '.continue.right') _returning_button_locator = (By.CSS_SELECTOR, 'button.isReturning') def __init__(self, marionette): Base.__init__(self, marionette) def login(self, email, password): # This only supports logging in with a known user and no existing session self.type_email(email) self.tap_continue() self.type_password(password) self.tap_returning() self.marionette.switch_to_frame() self.wait_for_element_not_present(*self._persona_frame_locator) self.apps.switch_to_displayed_app() def wait_for_persona_to_load(self): self.wait_for_element_not_displayed(*self._body_loading_locator) def switch_to_persona_frame(self): self.marionette.switch_to_frame() self.frame = self.wait_for_element_present(*self._persona_frame_locator) self.marionette.switch_to_frame(self.frame) self.wait_for_persona_to_load() def type_email(self, value): self.marionette.find_element(*self._email_input_locator).send_keys(value) self.keyboard.dismiss() self.switch_to_persona_frame() def type_password(self, value): self.marionette.find_element(*self._password_input_locator).send_keys(value) self.keyboard.dismiss() self.switch_to_persona_frame() def tap_continue(self): self.marionette.find_element(*self._continue_button_locator).tap() self.wait_for_element_not_displayed(*self._continue_button_locator) self.wait_for_element_displayed(*self._password_input_locator) def tap_returning(self): self.marionette.find_element(*self._returning_button_locator).tap()

46007c370fa322eb1ca7e4346385565ed9dfbbd8

py

Python

ethevents/client/connection.py

ezdac/ethevents

9f4b0ff1ba0d303180abe3b5336805335bc0765b

2018-08-21T01:06:30.000Z

2019-03-05T08:15:55.000Z

ethevents/client/connection.py

ezdac/ethevents

9f4b0ff1ba0d303180abe3b5336805335bc0765b

2018-04-23T14:01:51.000Z

2018-04-23T14:09:51.000Z

ethevents/client/connection.py

ezdac/ethevents

9f4b0ff1ba0d303180abe3b5336805335bc0765b

2022-03-22T04:57:16.000Z

2022-03-22T04:57:16.000Z

import time import click import requests from elasticsearch.connection import Connection from elasticsearch.connection_pool import DummyConnectionPool from elasticsearch.transport import Transport from elasticsearch.exceptions import ( ConnectionError, ConnectionTimeout, SSLError ) from elasticsearch.compat import urlencode from requests import Session from ethevents.client.app import App import logging log = logging.getLogger(__name__) @click.option( '--limits/--no-limits', default=True ) @click.command() if __name__ == '__main__': main()

import time import click import requests from elasticsearch.connection import Connection from elasticsearch.connection_pool import DummyConnectionPool from elasticsearch.transport import Transport from elasticsearch.exceptions import ( ConnectionError, ConnectionTimeout, SSLError ) from elasticsearch.compat import urlencode from requests import Session from ethevents.client.app import App import logging log = logging.getLogger(__name__) class MicroRaidenConnection(Connection): def __init__( self, host, port, session: Session, use_ssl=False, headers=None, **kwargs ): super(MicroRaidenConnection, self).__init__( host=host, port=port, use_ssl=use_ssl, **kwargs ) self.base_url = 'http%s://%s:%d%s' % ( 's' if self.use_ssl else '', host, port, self.url_prefix ) self.session = session self.session.headers = headers or {} self.session.headers.setdefault('content-type', 'application/json') def perform_request( self, method, url, params=None, body=None, timeout=None, ignore=(), headers=None ): url = self.base_url + url if params: url = '%s?%s' % (url, urlencode(params or {})) start = time.time() request = requests.Request(method=method, headers=headers, url=url, data=body) prepared_request = self.session.prepare_request(request) settings = self.session.merge_environment_settings( prepared_request.url, {}, None, None, None ) send_kwargs = {'timeout': timeout or self.timeout} send_kwargs.update(settings) try: response = self.session.request( prepared_request.method, prepared_request.url, data=prepared_request.body, headers=prepared_request.headers, **send_kwargs ) duration = time.time() - start raw_data = response.text except Exception as e: self.log_request_fail( method, url, prepared_request.path_url, body, time.time() - start, exception=e ) if isinstance(e, requests.exceptions.SSLError): raise SSLError('N/A', str(e), e) if isinstance(e, requests.Timeout): raise ConnectionTimeout('TIMEOUT', str(e), e) raise ConnectionError('N/A', str(e), e) # raise errors based on http status codes, let the client handle those if needed if not (200 <= response.status_code < 300) and response.status_code not in ignore: self.log_request_fail( method, url, response.request.path_url, body, duration, response.status_code, raw_data ) self._raise_error(response.status_code, raw_data) self.log_request_success( method, url, response.request.path_url, body, response.status_code, raw_data, duration ) return response.status_code, response.headers, raw_data class MicroRaidenTransport(Transport): def __init__( self, hosts, *args, session: Session, connection_class=MicroRaidenConnection, connection_pool_class=DummyConnectionPool, **kwargs ): self.hosts = hosts log.debug('initializing transport') super(MicroRaidenTransport, self).__init__( hosts, *args, connection_class=connection_class, connection_pool_class=connection_pool_class, session=session, **kwargs ) @click.option( '--limits/--no-limits', default=True ) @click.command() def main(limits: bool): logging.basicConfig(level=logging.DEBUG) log.debug('in main') app = App() app.start(ignore_security_limits=not limits, endpoint_url='https://api.eth.events') log.debug('session started') if app.account.unlocked: import elasticsearch es = elasticsearch.Elasticsearch( transport_class=MicroRaidenTransport, hosts=['api.eth.events:443'], use_ssl=True, session=app.session ) response = es.search('ethereum', 'block', body=dict(query=dict(match_all=dict()))) print(response) if __name__ == '__main__': main()

df6d818cc916d501a3e8707af88d5a3730dc6550

py

Python

source/client/pages/SpeciesElement.py

RobinSatterthwaite/BirdTable

ef35c0135ee54910e535281e7f690643c3b4f6c4

source/client/pages/SpeciesElement.py

RobinSatterthwaite/BirdTable

ef35c0135ee54910e535281e7f690643c3b4f6c4

source/client/pages/SpeciesElement.py

RobinSatterthwaite/BirdTable

ef35c0135ee54910e535281e7f690643c3b4f6c4

from pystache import TemplateSpec

from pystache import TemplateSpec class SpeciesElement(TemplateSpec): template_name = "SpeciesElement" def __init__(self, name, binomial_name, count, times_seen, include_times_seen, seen, heard): self.name = name self.binomialName = binomial_name self.count = count if include_times_seen: self.timesSeen = "&nbsp;/&nbsp;{0}".format(times_seen) else: self.timesSeen = "" if seen: self.seen = "\u26ab" else: self.seen = "" if heard: self.heard = "\u26ab" else: self.heard = ""

2abdd1a8347e55f710b0bd9bf098d6715d1155a9

py

Python

Number Guessing.py

GamePlayer-7/Gaming

4466f2e693f0c10d3bc041b388526484713dc2e1

Number Guessing.py

GamePlayer-7/Gaming

4466f2e693f0c10d3bc041b388526484713dc2e1

Number Guessing.py

GamePlayer-7/Gaming

4466f2e693f0c10d3bc041b388526484713dc2e1

import random # imports the random module, which contains a variety of things to do with random number generation. number = random.randint(1,10) #If we wanted a random integer, we can use the randint function Randint accepts two parameters: a lowest and a highest number. for i in range(0,3): user = int(input("guess the number")) if user == number: print("Hurray!!") print(f"you guessed the number right it's {number}") break if user != number: print(f"Your guess is incorrect the number is {number}")

import random # imports the random module, which contains a variety of things to do with random number generation. number = random.randint(1,10) #If we wanted a random integer, we can use the randint function Randint accepts two parameters: a lowest and a highest number. for i in range(0,3): user = int(input("guess the number")) if user == number: print("Hurray!!") print(f"you guessed the number right it's {number}") break if user != number: print(f"Your guess is incorrect the number is {number}")

1afcc354de4e4e1ba67d59086c2b25d41157da44

py

Python

src/waldur_auth_saml2/utils.py

geant-multicloud/MCMS-mastermind

81333180f5e56a0bc88d7dad448505448e01f24e

2017-10-18T13:49:58.000Z

2021-09-19T04:44:09.000Z

src/waldur_auth_saml2/utils.py

geant-multicloud/MCMS-mastermind

81333180f5e56a0bc88d7dad448505448e01f24e

2018-12-10T14:14:51.000Z

2021-06-07T10:33:39.000Z

src/waldur_auth_saml2/utils.py

geant-multicloud/MCMS-mastermind

81333180f5e56a0bc88d7dad448505448e01f24e

2017-09-24T03:10:45.000Z

2021-10-16T16:41:09.000Z

from django.conf import settings from django.core.exceptions import ObjectDoesNotExist from djangosaml2.conf import get_config from djangosaml2.utils import available_idps from saml2.attribute_converter import ac_factory from saml2.mdstore import InMemoryMetaData, MetaDataFile from saml2.mdstore import name as get_idp_name from saml2.s_utils import UnknownSystemEntity from . import models

from django.conf import settings from django.core.exceptions import ObjectDoesNotExist from djangosaml2.conf import get_config from djangosaml2.utils import available_idps from saml2.attribute_converter import ac_factory from saml2.mdstore import InMemoryMetaData, MetaDataFile from saml2.mdstore import name as get_idp_name from saml2.s_utils import UnknownSystemEntity from . import models def load_providers(): metadata = {} for filename in settings.WALDUR_AUTH_SAML2['IDP_METADATA_LOCAL']: mdf = MetaDataFile(ac_factory(), filename) mdf.load() metadata.update(mdf.items()) return metadata def sync_providers(): providers = load_providers() current_idps = list(models.IdentityProvider.objects.all().only('url', 'pk')) backend_urls = set(providers.keys()) stale_idps = set(idp.pk for idp in current_idps if idp.url not in backend_urls) models.IdentityProvider.objects.filter(pk__in=stale_idps).delete() existing_urls = set(idp.url for idp in current_idps) for url, metadata in providers.items(): name = get_idp_name(metadata) if not name: # It is expected that every provider has name. For corner cases check entity_id name = metadata.get('entity_id') if not name: # Skip invalid identity provider continue if url in existing_urls: # Skip identity provider if its url is already in the database continue models.IdentityProvider.objects.create(url=url, name=name, metadata=metadata) for provider in models.IdentityProvider.objects.all().iterator(): backend_metadata = providers.get(provider.url) if backend_metadata and provider.metadata != backend_metadata: provider.metadata = backend_metadata provider.save() def is_valid_idp(value): remote_providers = available_idps(get_config()).keys() return ( value in remote_providers or models.IdentityProvider.objects.filter(url=value).exists() ) def get_idp_sso_supported_bindings(idp_entity_id, config): try: return config.metadata.service( idp_entity_id, 'idpsso_descriptor', 'single_sign_on_service' ).keys() except (UnknownSystemEntity, AttributeError): return [] class DatabaseMetadataLoader(InMemoryMetaData): def load(self, *args, **kwargs): # Skip default parsing because data is not stored in file pass def __getitem__(self, item): try: return models.IdentityProvider.objects.get(url=item).metadata except ObjectDoesNotExist: raise KeyError

a8f717c691e08e576daf5d6b539ccd45bbb8b08f

py

Python

src/blockdiag/imagedraw/utils/__init__.py

Dridi/blockdiag

bbb16f8a731cdf79a675a63c1ff847e70fdc4a5b

src/blockdiag/imagedraw/utils/__init__.py

Dridi/blockdiag

bbb16f8a731cdf79a675a63c1ff847e70fdc4a5b

src/blockdiag/imagedraw/utils/__init__.py

Dridi/blockdiag

bbb16f8a731cdf79a675a63c1ff847e70fdc4a5b

# -*- coding: utf-8 -*- # Copyright 2011 Takeshi KOMIYA # # 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 math import unicodedata from functools import wraps from blockdiag.utils import Size from blockdiag.utils.compat import u def is_zenkaku(char): """Detect given character is Japanese ZENKAKU character""" char_width = unicodedata.east_asian_width(char) return char_width in u("WFA") def zenkaku_len(string): """Count Japanese ZENKAKU characters from string""" return len([x for x in string if is_zenkaku(x)]) def hankaku_len(string): """Count non Japanese ZENKAKU characters from string""" return len([x for x in string if not is_zenkaku(x)]) def string_width(string): """Measure rendering width of string. Count ZENKAKU-character as 2-point and non ZENKAKU-character as 1-point """ widthmap = {'Na': 1, 'N': 1, 'H': 1, 'W': 2, 'F': 2, 'A': 2} return sum(widthmap[unicodedata.east_asian_width(c)] for c in string) def textsize(string, font): """Measure rendering size (width and height) of line. Returned size will not be exactly as rendered text size, Because this method does not use fonts to measure size. """ width = (zenkaku_len(string) * font.size + hankaku_len(string) * font.size * 0.55) return Size(int(math.ceil(width)), font.size)

# -*- coding: utf-8 -*- # Copyright 2011 Takeshi KOMIYA # # 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 math import unicodedata from functools import wraps from blockdiag.utils import Size from blockdiag.utils.compat import u def is_zenkaku(char): """Detect given character is Japanese ZENKAKU character""" char_width = unicodedata.east_asian_width(char) return char_width in u("WFA") def zenkaku_len(string): """Count Japanese ZENKAKU characters from string""" return len([x for x in string if is_zenkaku(x)]) def hankaku_len(string): """Count non Japanese ZENKAKU characters from string""" return len([x for x in string if not is_zenkaku(x)]) def string_width(string): """Measure rendering width of string. Count ZENKAKU-character as 2-point and non ZENKAKU-character as 1-point """ widthmap = {'Na': 1, 'N': 1, 'H': 1, 'W': 2, 'F': 2, 'A': 2} return sum(widthmap[unicodedata.east_asian_width(c)] for c in string) def textsize(string, font): """Measure rendering size (width and height) of line. Returned size will not be exactly as rendered text size, Because this method does not use fonts to measure size. """ width = (zenkaku_len(string) * font.size + hankaku_len(string) * font.size * 0.55) return Size(int(math.ceil(width)), font.size) def memoize(fn): fn.cache = {} @wraps(fn) def func(*args, **kwargs): key = str(args) + str(kwargs) if key not in fn.cache: fn.cache[key] = fn(*args, **kwargs) return fn.cache[key] return func

4b72d9fce27cf73da17b49970b55c087c4464e31

py

Python

telefones/models.py

projeto-agro-tcc/osvaldo-backend

7e8b6b2ed849cd54f0bb5b855c4016fa062d3c33

telefones/models.py

projeto-agro-tcc/osvaldo-backend

7e8b6b2ed849cd54f0bb5b855c4016fa062d3c33

telefones/models.py

projeto-agro-tcc/osvaldo-backend

7e8b6b2ed849cd54f0bb5b855c4016fa062d3c33

from django.db import models

from django.db import models class Telefone(models.Model): residencial = models.CharField(max_length=14, null=True, blank=True) celular = models.CharField(max_length=12, null=False) outro = models.CharField(max_length=14, null=True, blank=True) class Meta: db_table = "en_telefones" def __str__(self): return self.celular

5a4d29d31fc8b9261b5b5f65d7bb0b5cb3b90e4d

py

Python

xt/framework/comm/comm_conf.py

ZZHsunsky/xingtian

0484e2c968d9e6b2e5f43a3b86c0213a095ba309

xt/framework/comm/comm_conf.py

ZZHsunsky/xingtian

0484e2c968d9e6b2e5f43a3b86c0213a095ba309

xt/framework/comm/comm_conf.py

ZZHsunsky/xingtian

0484e2c968d9e6b2e5f43a3b86c0213a095ba309

# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. import socket import time from subprocess import Popen import redis MAX_ACTOR_NUM = 40 MAX_LEARNER_NUM = 10 START_PORT = 20000 PORTNUM_PERLEARNER = MAX_ACTOR_NUM + 1 # 初始化，查看redis，连接redis, 生成端口池，即检测端口号哪些可用 def get_port(start_port): ''' get port used by module ''' predict_port = start_port + 1 if (predict_port + MAX_ACTOR_NUM - start_port) > PORTNUM_PERLEARNER: raise Exception("port num is not enough") return start_port, predict_port def test(): ''' test interface''' test_comm_conf = CommConf() redis_key = 'port_pool' print("{} len: {}".format(redis_key, test_comm_conf.redis.llen(redis_key))) for _ in range(test_comm_conf.redis.llen(redis_key)): pop_val = test_comm_conf.redis.lpop(redis_key) print("pop val: {} from '{}'".format(pop_val, redis_key)) start = time.time() test_comm_conf.init_portpool() print("use time", time.time() - start) train_port = get_port(20000) print(train_port) if __name__ == "__main__": test()

# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. import socket import time from subprocess import Popen import redis MAX_ACTOR_NUM = 40 MAX_LEARNER_NUM = 10 START_PORT = 20000 PORTNUM_PERLEARNER = MAX_ACTOR_NUM + 1 # 初始化，查看redis，连接redis, 生成端口池，即检测端口号哪些可用 class CommConf(object): def __init__(self): try: redis.Redis(host="127.0.0.1", port=6379, db=0).ping() except redis.ConnectionError: Popen("echo save '' | setsid redis-server -", shell=True) time.sleep(0.3) self.redis = redis.Redis(host="127.0.0.1", port=6379, db=0) self.pool_name = "port_pool" if not self.redis.exists(self.pool_name): self.init_portpool() def init_portpool(self): ''' init port pool ''' start_port = START_PORT try_num = 10 for _ in range(MAX_LEARNER_NUM): for _ in range(try_num): check_flag, next_port = self.check_learner_port(start_port) if not check_flag: break else: start_port = next_port self.redis.lpush(self.pool_name, start_port) self.redis.incr('port_num', amount=1) self.redis.incr('max_port_num', amount=1) start_port = next_port def get_start_port(self): ''' get start port ''' if int(self.redis.get('port_num')) == 0: raise Exception("Dont have available port") start_port = self.redis.lpop(self.pool_name) self.redis.decr('port_num', amount=1) return int(start_port) def release_start_port(self, start_port): ''' release start port ''' self.redis.lpush(self.pool_name, start_port) self.redis.incr('port_num', amount=1) if self.redis.get('port_num') == self.redis.get('max_port_num'): self.redis.delete('port_num') self.redis.delete('max_port_num') self.redis.delete('port_pool') print("shutdown redis") self.redis.shutdown(nosave=True) return def check_learner_port(self, start_port): ''' check if multi-port is in use ''' ip = "localhost" for i in range(PORTNUM_PERLEARNER): if self.check_port(ip, start_port + i): return True, start_port + i + 1 return False, start_port + PORTNUM_PERLEARNER def check_port(self, ip, port): ''' check if port is in use ''' s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) try: s.connect((ip, int(port))) s.shutdown(2) print("port is used", int(port)) return True except BaseException: return False def get_port(start_port): ''' get port used by module ''' predict_port = start_port + 1 if (predict_port + MAX_ACTOR_NUM - start_port) > PORTNUM_PERLEARNER: raise Exception("port num is not enough") return start_port, predict_port def test(): ''' test interface''' test_comm_conf = CommConf() redis_key = 'port_pool' print("{} len: {}".format(redis_key, test_comm_conf.redis.llen(redis_key))) for _ in range(test_comm_conf.redis.llen(redis_key)): pop_val = test_comm_conf.redis.lpop(redis_key) print("pop val: {} from '{}'".format(pop_val, redis_key)) start = time.time() test_comm_conf.init_portpool() print("use time", time.time() - start) train_port = get_port(20000) print(train_port) if __name__ == "__main__": test()

3e25d2bb70c9499de1f4cb505fe2880342dc5c50

py

Python

python/nbdb/anomaly/static.py

rubrikinc/nbdb2

359db63a39e016e3eb197b8ea511d6e8cffa1853

2022-03-21T15:48:33.000Z

2022-03-27T00:43:12.000Z

python/nbdb/anomaly/static.py

rubrikinc/nbdb2

359db63a39e016e3eb197b8ea511d6e8cffa1853

python/nbdb/anomaly/static.py

rubrikinc/nbdb2

359db63a39e016e3eb197b8ea511d6e8cffa1853

2022-03-27T00:43:31.000Z

2022-03-27T00:43:31.000Z

""" Static threshold based anomaly detection """ from typing import List, Tuple import logging import numpy as np import pandas as pd from nbdb.anomaly.anomaly_interface import AnomalyInterface from nbdb.readapi.graphite_response import Anomaly from nbdb.readapi.time_series_response import TimeRange logger = logging.getLogger(__name__) class Static(AnomalyInterface): # pylint: disable=too-few-public-methods """ Simple algorithm to do threshold based anomaly detection. Currently supports two functions (lt, gt). """ def find_anomalies(self, baseline: np.ndarray, raw_data: pd.Series) -> List[Tuple]: """ Use static threshold to determine anomalies in the raw data. Supports the lt, gt functions to compare against the threshold :param baseline: :param raw_data: :return: """ comparator_fn = self.config.get('comparator_fn', 'gt') threshold = self.config.get('threshold') raw_data.dropna(inplace=True) if comparator_fn == 'gt': anomalous_points = raw_data[raw_data > threshold] elif comparator_fn == 'lt': anomalous_points = raw_data[raw_data < threshold] else: raise NotImplementedError('Unknown comparator fn: {}'.format( comparator_fn)) anomalies = [] # No anomalous points found. Return early if len(anomalous_points) == 0: return anomalies previous_epoch = anomalous_points.index[0] anomaly_start = anomalous_points.index[0] sampling_interval = np.diff(raw_data.index).min() anomaly_score = 1.0 epoch = None for epoch, _ in anomalous_points.iteritems(): if (epoch - previous_epoch) / sampling_interval > 1: # Mark the current anomaly as ended and start a new one anomaly_window = TimeRange(anomaly_start, previous_epoch, sampling_interval) anomalies.append(Anomaly(anomaly_window, anomaly_score)) anomaly_score = 1.0 anomaly_start = epoch else: previous_epoch = epoch anomaly_score += 1 # append the final anomaly if epoch is not None: anomaly_window = TimeRange(anomaly_start, epoch, sampling_interval) anomalies.append(Anomaly(anomaly_window, anomaly_score)) return anomalies

""" Static threshold based anomaly detection """ from typing import List, Tuple import logging import numpy as np import pandas as pd from nbdb.anomaly.anomaly_interface import AnomalyInterface from nbdb.readapi.graphite_response import Anomaly from nbdb.readapi.time_series_response import TimeRange logger = logging.getLogger(__name__) class Static(AnomalyInterface): # pylint: disable=too-few-public-methods """ Simple algorithm to do threshold based anomaly detection. Currently supports two functions (lt, gt). """ def find_anomalies(self, baseline: np.ndarray, raw_data: pd.Series) -> List[Tuple]: """ Use static threshold to determine anomalies in the raw data. Supports the lt, gt functions to compare against the threshold :param baseline: :param raw_data: :return: """ comparator_fn = self.config.get('comparator_fn', 'gt') threshold = self.config.get('threshold') raw_data.dropna(inplace=True) if comparator_fn == 'gt': anomalous_points = raw_data[raw_data > threshold] elif comparator_fn == 'lt': anomalous_points = raw_data[raw_data < threshold] else: raise NotImplementedError('Unknown comparator fn: {}'.format( comparator_fn)) anomalies = [] # No anomalous points found. Return early if len(anomalous_points) == 0: return anomalies previous_epoch = anomalous_points.index[0] anomaly_start = anomalous_points.index[0] sampling_interval = np.diff(raw_data.index).min() anomaly_score = 1.0 epoch = None for epoch, _ in anomalous_points.iteritems(): if (epoch - previous_epoch) / sampling_interval > 1: # Mark the current anomaly as ended and start a new one anomaly_window = TimeRange(anomaly_start, previous_epoch, sampling_interval) anomalies.append(Anomaly(anomaly_window, anomaly_score)) anomaly_score = 1.0 anomaly_start = epoch else: previous_epoch = epoch anomaly_score += 1 # append the final anomaly if epoch is not None: anomaly_window = TimeRange(anomaly_start, epoch, sampling_interval) anomalies.append(Anomaly(anomaly_window, anomaly_score)) return anomalies

9288bc7c0b122d032f93019718b7a23eb2c872b0

py

Python

tests/test_help.py

thomasvolk/R_ev3dev

53b8c83af49e88eb4766deea0a690c55d1304d6a

tests/test_help.py

thomasvolk/R_ev3dev

53b8c83af49e88eb4766deea0a690c55d1304d6a

tests/test_help.py

thomasvolk/R_ev3dev

53b8c83af49e88eb4766deea0a690c55d1304d6a

#!/usr/bin/env python3 import unittest from R_ev3dev.interpreter import Interpreter, Command from R_ev3dev.help import Help, Version class TestCommand01(Command): """ this is the test command 01 usage: c01 """ class TestCommand02(Command): """ this is the test command 02 """

#!/usr/bin/env python3 import unittest from R_ev3dev.interpreter import Interpreter, Command from R_ev3dev.help import Help, Version class TestCommand01(Command): """ this is the test command 01 usage: c01 """ def invoke(self, interpreter_context, args): return 1 class TestCommand02(Command): """ this is the test command 02 """ def invoke(self, interpreter_context, args): return 2 class TestCommand03(Command): def invoke(self, interpreter_context, args): return 3 class TestHelp(unittest.TestCase): def setUp(self): self.interpreter = Interpreter([ TestCommand01('c01'), TestCommand02('c02'), TestCommand03('c03'), Help('help'), Version('version') ]) def test_overview(self): self.assertEqual("""--- R_ev3 protocol language version 0.0.1 author: Thomas Volk license: Apache License Version 2.0 source: https://github.com/thomasvolk/R_ev3dev possible commands: c01 - this is the test command 01 c02 - this is the test command 02 c03 - help - show help version - show version use help <command> for details ---""", self.interpreter.evaluate_internal("help").value) def test_help(self): self.assertEqual("""--- c01 this is the test command 01 usage: c01 ---""", self.interpreter.evaluate_internal("help c01").value) def test_version(self): self.assertEqual('0.0.1', self.interpreter.evaluate_internal("version").value)

0cb3d0dd6f38e1ffc07fd4e85e3458786f9cf6d8

py

Python

news/urls.py

vigen-b/FakeNews

fc19f623529d1661c9f3d475adc9db98ee95a38a

news/urls.py

vigen-b/FakeNews

fc19f623529d1661c9f3d475adc9db98ee95a38a

news/urls.py

vigen-b/FakeNews

fc19f623529d1661c9f3d475adc9db98ee95a38a

from django.urls import path from rest_framework.urlpatterns import format_suffix_patterns from news import views app_name = "news" urlpatterns = [ path("news/", views.NewsList.as_view()), path("news/<int:pk>/", views.NewsDetail.as_view()), path("category/", views.CategoryList.as_view()), path("category/<str:pk>/", views.CategoryDetail.as_view()), ] urlpatterns = format_suffix_patterns(urlpatterns)

from django.urls import path from rest_framework.urlpatterns import format_suffix_patterns from news import views app_name = "news" urlpatterns = [ path("news/", views.NewsList.as_view()), path("news/<int:pk>/", views.NewsDetail.as_view()), path("category/", views.CategoryList.as_view()), path("category/<str:pk>/", views.CategoryDetail.as_view()), ] urlpatterns = format_suffix_patterns(urlpatterns)

8a0003c5108f33e3f1329656eaa782586e2568a7

py

Python

realtime_hand_3d/segmentation/models/csm.py

NeelayS/realtime_hand

219c772b9b7df60c390edac7da23f9cdddebca4d

realtime_hand_3d/segmentation/models/csm.py

NeelayS/realtime_hand

219c772b9b7df60c390edac7da23f9cdddebca4d

realtime_hand_3d/segmentation/models/csm.py

NeelayS/realtime_hand

219c772b9b7df60c390edac7da23f9cdddebca4d

import torch from torch import nn from .retrieve import SEG_MODELS_REGISTRY @SEG_MODELS_REGISTRY.register()

import torch from torch import nn from .retrieve import SEG_MODELS_REGISTRY class Bottleneck(nn.Module): expansion = 4 def __init__(self, in_channels, out_channels, stride=1, downsample=None): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.conv2 = nn.Conv2d( out_channels, out_channels, kernel_size=3, stride=stride, bias=False, padding=1, ) self.bn2 = nn.BatchNorm2d(out_channels) self.conv3 = nn.Conv2d( out_channels, out_channels * self.expansion, kernel_size=1, bias=False ) self.bn3 = nn.BatchNorm2d(out_channels * self.expansion) self.relu = nn.ReLU() self.downsample = downsample def forward(self, x): shortcut = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) out = self.relu(out) if self.downsample is not None: shortcut = self.downsample(x) out += shortcut out = self.relu(out) return out class DeconvBottleneck(nn.Module): def __init__(self, in_channels, out_channels, expansion=2, stride=1, upsample=None): super(DeconvBottleneck, self).__init__() self.expansion = expansion self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) if stride == 1: self.conv2 = nn.Conv2d( out_channels, out_channels, kernel_size=3, stride=stride, bias=False, padding=1, ) else: self.conv2 = nn.ConvTranspose2d( out_channels, out_channels, kernel_size=3, stride=stride, bias=False, padding=1, output_padding=1, ) self.bn2 = nn.BatchNorm2d(out_channels) self.conv3 = nn.Conv2d( out_channels, out_channels * self.expansion, kernel_size=1, bias=False ) self.bn3 = nn.BatchNorm2d(out_channels * self.expansion) self.relu = nn.ReLU() self.upsample = upsample def forward(self, x): shortcut = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) out = self.relu(out) if self.upsample is not None: shortcut = self.upsample(x) out += shortcut out = self.relu(out) return out class CSM_model(nn.Module): def __init__( self, downblock, upblock, in_channels, n_classes, with_energy=True, n_stages=2 ): super(CSM_model, self).__init__() self.start_channels = 32 self.n_classes = n_classes self.n_stages = n_stages self.with_energy = with_energy down_layer_size = 3 up_layer_size = 3 self.relu = nn.ReLU() self.sigmoid = nn.Sigmoid() self.conv1 = nn.Conv2d( in_channels, 32, kernel_size=7, stride=2, padding=3, bias=False ) self.bn1 = nn.BatchNorm2d(32) self.avgpool = nn.AvgPool2d(kernel_size=3, stride=2, padding=1) self.dlayer1 = self._make_downlayer(downblock, 32, down_layer_size) self.dlayer2 = self._make_downlayer(downblock, 64, down_layer_size, stride=2) self.dlayer3 = self._make_downlayer(downblock, 128, down_layer_size, stride=2) self.dlayer4 = self._make_downlayer(downblock, 256, down_layer_size, stride=2) # stage1 if self.n_stages >= 1 or self.n_stages == -1: self.uplayer1_1 = self._make_up_block(upblock, 256, up_layer_size, stride=2) self.uplayer2_1 = self._make_up_block(upblock, 128, up_layer_size, stride=2) upsample_1 = nn.Sequential( nn.ConvTranspose2d( self.start_channels, 32, kernel_size=1, stride=2, bias=False, output_padding=1, ), nn.BatchNorm2d(32), ) self.uplayer_stage_1 = DeconvBottleneck( self.start_channels, 32, 1, 2, upsample_1 ) self.conv_seg_out_1 = nn.Conv2d( 32, n_classes, kernel_size=1, stride=1, bias=False ) if self.with_energy: self.conv_e_out_1 = nn.Conv2d( 32, n_classes, kernel_size=1, stride=1, bias=False ) # stage2 if self.n_stages >= 2 or self.n_stages == -1: self.uplayer1_2 = self._make_up_block(upblock, 64, up_layer_size, stride=2) if self.with_energy: self.post_cat_2 = nn.Conv2d( 134, 128, kernel_size=1, stride=1, bias=False ) else: self.post_cat_2 = nn.Conv2d( 131, 128, kernel_size=1, stride=1, bias=False ) self.bn_2 = nn.BatchNorm2d(128) self.uplayer2_2 = self._make_up_block(upblock, 32, up_layer_size) upsample_2 = nn.Sequential( nn.ConvTranspose2d( self.start_channels, 32, kernel_size=1, stride=2, bias=False, output_padding=1, ), nn.BatchNorm2d(32), ) self.uplayer_stage_2 = DeconvBottleneck(64, 32, 1, 2, upsample_2) self.conv_seg_out_2 = nn.Conv2d( 32, n_classes, kernel_size=1, stride=1, bias=False ) if self.with_energy: self.conv_e_out_2 = nn.Conv2d( 32, n_classes, kernel_size=1, stride=1, bias=False ) def _make_downlayer(self, block, init_channels, num_layer, stride=1): downsample = None if stride != 1 or self.start_channels != init_channels * block.expansion: downsample = nn.Sequential( nn.Conv2d( self.start_channels, init_channels * block.expansion, kernel_size=1, stride=stride, bias=False, ), nn.BatchNorm2d(init_channels * block.expansion), ) layers = [] layers.append(block(self.start_channels, init_channels, stride, downsample)) self.start_channels = init_channels * block.expansion for i in range(1, num_layer): layers.append(block(self.start_channels, init_channels)) return nn.Sequential(*layers) def _make_up_block(self, block, init_channels, num_layer, stride=1): upsample = None if stride != 1 or self.start_channels != init_channels * 2: if stride == 1: output_padding = 0 else: output_padding = 1 upsample = nn.Sequential( nn.ConvTranspose2d( self.start_channels, init_channels * 2, kernel_size=1, stride=stride, bias=False, output_padding=output_padding, ), # 1), nn.BatchNorm2d(init_channels * 2), ) layers = [] for i in range(1, num_layer): layers.append(block(self.start_channels, init_channels, 4)) layers.append(block(self.start_channels, init_channels, 2, stride, upsample)) self.start_channels = init_channels * 2 return nn.Sequential(*layers) def forward(self, x): img = x x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.avgpool(x) x = self.dlayer1(x) x = self.dlayer2(x) x = self.dlayer3(x) x = self.dlayer4(x) # Mid x = self.uplayer1_1(x) x_mid = self.uplayer2_1(x) # Stage 1 x_stage1 = self.uplayer_stage_1(x_mid) x_seg_out1 = self.conv_seg_out_1(x_stage1) x_hands1 = x_seg_out1 if self.with_energy: x_e_out1 = self.sigmoid(self.conv_e_out_1(x_stage1)) if self.n_stages == 1: if self.with_energy: return x_hands1, x_e_out1 else: return x_hands1 # stage2 x_mid2 = self.uplayer1_2(x_mid) if self.with_energy: x = torch.cat([x_mid2, x_seg_out1, x_e_out1], dim=1) else: x = torch.cat([x_mid2, x_seg_out1], dim=1) x = self.post_cat_2(x) x = self.bn_2(x) x = self.relu(x) x = self.uplayer2_2(x) x = self.uplayer_stage_2(x) x_seg_out2 = self.conv_seg_out_2(x) x_hands2 = x_seg_out2 if self.with_energy: x_e_out2 = self.sigmoid(self.conv_e_out_2(x)) if self.n_stages == 2: if self.with_energy: return x_e_out2, x_hands2 else: return x_hands2 else: if self.with_energy: return x_hands1, x_e_out1, x_hands2, x_e_out2 else: return x_hands1, x_hands2 @SEG_MODELS_REGISTRY.register() def CSM(in_channels=3, n_classes=3, **kwargs): return CSM_model( Bottleneck, DeconvBottleneck, in_channels=in_channels, n_classes=n_classes, **kwargs )

9967baa443818e97fb20549f70a4bd20685b2cd4

py

Python

bobstack/sipmessaging/sipMessage.py

bobjects/BobStack

c177b286075044832f44baf9ace201780c8b4320

bobstack/sipmessaging/sipMessage.py

bobjects/BobStack

c177b286075044832f44baf9ace201780c8b4320

bobstack/sipmessaging/sipMessage.py

bobjects/BobStack

c177b286075044832f44baf9ace201780c8b4320

try: from cStringIO import StringIO except ImportError: from StringIO import StringIO from sipHeader import SIPHeader from sipStartLineFactory import SIPStartLineFactory

try: from cStringIO import StringIO except ImportError: from StringIO import StringIO from sipHeader import SIPHeader from sipStartLineFactory import SIPStartLineFactory class SIPMessage(object): @classmethod def new_parsed_from(cls, a_string): answer = cls() answer.raw_string = a_string return answer @classmethod def _new_for_attributes(cls, start_line=None, header=None, content=""): answer = cls() answer.start_line = start_line if header: answer.header = header else: answer.header = SIPHeader.new_for_attributes(header_fields=None) answer.content = content return answer def __init__(self): self._content = None self._startLine = None self._header = None self._rawString = None @property def deep_copy(self): return self.__class__.new_parsed_from(self.raw_string) @property def raw_string(self): if self._rawString is None: self.render_raw_string_from_attributes() return self._rawString @raw_string.setter def raw_string(self, a_string): self._rawString = a_string self.clear_attributes() @property def body(self): return self.content def clear_raw_string(self): self._rawString = None def clear_attributes(self): self._content = None self._startLine = None self._header = None def parse_attributes_from_raw_string(self): self._content = "" string_io = StringIO(self._rawString) self._startLine = SIPStartLineFactory().next_for_stringio(string_io) self._header = SIPHeader.new_parsed_from(string_io) self._content = string_io.read() string_io.close() def render_raw_string_from_attributes(self): stringio = StringIO() stringio.write(self._startLine.raw_string) stringio.write("\r\n") self._header.render_raw_string_from_attributes(stringio) stringio.write(self._content) self._rawString = stringio.getvalue() stringio.close() @property def start_line(self): if self._startLine is None: self.parse_attributes_from_raw_string() return self._startLine @start_line.setter def start_line(self, a_sip_start_line): self._startLine = a_sip_start_line self.clear_raw_string() @property def header(self): if self._header is None: self.parse_attributes_from_raw_string() return self._header @header.setter def header(self, a_sip_header): self._header = a_sip_header self.clear_raw_string() @property def content(self): if self._content is None: self.parse_attributes_from_raw_string() return self._content @content.setter def content(self, a_string): self._content = a_string self.clear_raw_string() @property def vias(self): return self.header.vias @property def via_header_fields(self): return self.header.via_header_fields @property def route_uris(self): return self.header.route_uris @property def record_route_uris(self): return self.header.record_route_uris @property def transaction_hash(self): return self.header.transaction_hash @property def dialog_hash(self): return self.header.dialog_hash # TODO: This is a hot method. Should we cache? @property def is_valid(self): if self.is_malformed: return False if not self.header.is_valid: return False if self.header.content_length is not None: if self.header.content_length != self.content.__len__(): return False return True @property def is_invalid(self): return not self.is_valid @property def is_unknown(self): return not self.is_known @property def is_known(self): return False @property def is_malformed(self): return False @property def is_request(self): return False @property def is_response(self): return False @property def is_ack_request(self): return False @property def is_bye_request(self): return False @property def is_cancel_request(self): return False @property def is_info_request(self): return False @property def is_invite_request(self): return False @property def is_message_request(self): return False @property def is_notify_request(self): return False @property def is_options_request(self): return False @property def is_publish_request(self): return False @property def is_prack_request(self): return False @property def is_refer_request(self): return False @property def is_register_request(self): return False @property def is_subscribe_request(self): return False @property def is_update_request(self): return False

943c8ed1cd17178b2e7dd6ef67854da8a007f148

py

Python

codes_auto/1635.number-of-good-pairs.py

smartmark-pro/leetcode_record

6504b733d892a705571eb4eac836fb10e94e56db

codes_auto/1635.number-of-good-pairs.py

smartmark-pro/leetcode_record

6504b733d892a705571eb4eac836fb10e94e56db

codes_auto/1635.number-of-good-pairs.py

smartmark-pro/leetcode_record

6504b733d892a705571eb4eac836fb10e94e56db

# # @lc app=leetcode.cn id=1635 lang=python3 # # [1635] number-of-good-pairs # None # @lc code=end

# # @lc app=leetcode.cn id=1635 lang=python3 # # [1635] number-of-good-pairs # None # @lc code=end

3d707e5f1c279e06637838e9d88dd40ec499c8ba

py

Python

python-the-hard-way/12-prompting-people.py

Valka7a/python-playground

f08d4374f2cec2e8b1afec3753854b1ec10ff480

python-the-hard-way/12-prompting-people.py

Valka7a/python-playground

f08d4374f2cec2e8b1afec3753854b1ec10ff480

python-the-hard-way/12-prompting-people.py

Valka7a/python-playground

f08d4374f2cec2e8b1afec3753854b1ec10ff480

# Exercise 12: Prompting People # Variables age = raw_input("How old are you? ") height = raw_input("How tall are you? ") weight = raw_input("How much do you weigh? ") # Print print "So, you're %r old, %r tall and %r heavy." % (age, height, weight) # Study Drills # 1. In Terminal where you normally run python to run your scripts, # type pydoc raw_input. Read what it says. If you're on Windows # try python -m pydoc raw_input instead. # 2. Get out of pydoc by typing q to quit. # 3. Look onine for what the pydoc command does. # 4. Use pydoc to also read about open, file, os and sys. It's # alright if you do not understand thosel just read through # and take notes about interesting things. # Drill 1 # Help on built-in function raw_input in module __builtin__: # raw_input(...) # raw_input([prompt]) -> string # # Read a string from standard input. The trailing newline is stripped. # If the user hits EOF (Unix: Ctl-D, Windows: Ctl-Z+Return), raise EOFError. # On Unix, GNU readline is used if enabled. The prompt string, if given, # is printed without a trailing newline before reading.

# Exercise 12: Prompting People # Variables age = raw_input("How old are you? ") height = raw_input("How tall are you? ") weight = raw_input("How much do you weigh? ") # Print print "So, you're %r old, %r tall and %r heavy." % (age, height, weight) # Study Drills # 1. In Terminal where you normally run python to run your scripts, # type pydoc raw_input. Read what it says. If you're on Windows # try python -m pydoc raw_input instead. # 2. Get out of pydoc by typing q to quit. # 3. Look onine for what the pydoc command does. # 4. Use pydoc to also read about open, file, os and sys. It's # alright if you do not understand thosel just read through # and take notes about interesting things. # Drill 1 # Help on built-in function raw_input in module __builtin__: # raw_input(...) # raw_input([prompt]) -> string # # Read a string from standard input. The trailing newline is stripped. # If the user hits EOF (Unix: Ctl-D, Windows: Ctl-Z+Return), raise EOFError. # On Unix, GNU readline is used if enabled. The prompt string, if given, # is printed without a trailing newline before reading.

17884ad7e858a3c341d64db09625d9ca52b143f6

py

Python

alipay/aop/api/domain/InvestorMaterialInfo.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

alipay/aop/api/domain/InvestorMaterialInfo.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

alipay/aop/api/domain/InvestorMaterialInfo.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.constant.ParamConstants import *

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.constant.ParamConstants import * class InvestorMaterialInfo(object): def __init__(self): self._file_id = None self._file_url = None self._type = None @property def file_id(self): return self._file_id @file_id.setter def file_id(self, value): self._file_id = value @property def file_url(self): return self._file_url @file_url.setter def file_url(self, value): self._file_url = value @property def type(self): return self._type @type.setter def type(self, value): self._type = value def to_alipay_dict(self): params = dict() if self.file_id: if hasattr(self.file_id, 'to_alipay_dict'): params['file_id'] = self.file_id.to_alipay_dict() else: params['file_id'] = self.file_id if self.file_url: if hasattr(self.file_url, 'to_alipay_dict'): params['file_url'] = self.file_url.to_alipay_dict() else: params['file_url'] = self.file_url if self.type: if hasattr(self.type, 'to_alipay_dict'): params['type'] = self.type.to_alipay_dict() else: params['type'] = self.type return params @staticmethod def from_alipay_dict(d): if not d: return None o = InvestorMaterialInfo() if 'file_id' in d: o.file_id = d['file_id'] if 'file_url' in d: o.file_url = d['file_url'] if 'type' in d: o.type = d['type'] return o

715eab9e05e4e3e6f81c12646f271a7236441291

py

Python

msticpy/nbtools/azure_ml_tools.py

ekmixon/msticpy

8676a648ba9bfb4d848a8dda964820d4942a32ca

msticpy/nbtools/azure_ml_tools.py

ekmixon/msticpy

8676a648ba9bfb4d848a8dda964820d4942a32ca

2021-05-15T02:16:39.000Z

2022-01-19T13:13:25.000Z

msticpy/nbtools/azure_ml_tools.py

ekmixon/msticpy

8676a648ba9bfb4d848a8dda964820d4942a32ca

# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- """Checker functions for Azure ML notebooks.""" import json import os import socket import sys import urllib from pathlib import Path from typing import Any, List, Mapping, Optional, Tuple, Union from IPython import get_ipython from IPython.display import HTML, display from pkg_resources import parse_version from .._version import VERSION from ..common.pkg_config import refresh_config __version__ = VERSION AZ_GET_STARTED = ( "https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/A%20Getting" "%20Started%20Guide%20For%20Azure%20Sentinel%20ML%20Notebooks.ipynb" ) TROUBLE_SHOOTING = ( "https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/" "TroubleShootingNotebooks.ipynb" ) MISSING_PKG_ERR = """ <h4><font color='orange'>The package '<b>{package}</b>' is not installed or has an unsupported version (installed version = '{inst_ver}')</font></h4> Please install or upgrade before continuing: required version is {package}>={req_ver} """ MP_INSTALL_FAILED = """ <h4><font color='red'>The notebook may not run correctly without the correct version of '<b>{pkg}</b>' ({ver} or later).</font></h4> Please see the <a href="{nbk_uri}"> Getting Started Guide For Azure Sentinel ML Notebooks</a></b> for more information<br><hr> """ RELOAD_MP = """ <h4><font color='orange'>Kernel restart needed</h4> An error was detected trying to load the updated version of MSTICPy.<br> Please restart the notebook kernel and re-run this cell - it should run without error. """ MIN_PYTHON_VER_DEF = "3.6" MSTICPY_REQ_VERSION = __version__ VER_RGX = r"(?P<maj>\d+)\.(?P<min>\d+).(?P<pnt>\d+)(?P<suff>.*)" MP_ENV_VAR = "MSTICPYCONFIG" MP_FILE = "msticpyconfig.yaml" NB_CHECK_URI = ( "https://raw.githubusercontent.com/Azure/Azure-Sentinel-" "Notebooks/master/utils/nb_check.py" ) def is_in_aml(): """Return True if running in Azure Machine Learning.""" return os.environ.get("APPSETTING_WEBSITE_SITE_NAME") == "AMLComputeInstance" def check_versions( min_py_ver: Union[str, Tuple] = MIN_PYTHON_VER_DEF, min_mp_ver: Union[str, Tuple] = MSTICPY_REQ_VERSION, extras: Optional[List[str]] = None, mp_release: Optional[str] = None, **kwargs, ): """ Check the current versions of the Python kernel and MSTICPy. Parameters ---------- min_py_ver : Union[Tuple[int, int], str] Minimum Python version min_mp_ver : Union[Tuple[int, int], str] Minimum MSTICPy version extras : Optional[List[str]], optional A list of extras required for MSTICPy mp_release : Optional[str], optional Override the MSTICPy release version. This can also be specified in the environment variable 'MP_TEST_VER' Raises ------ RuntimeError If the Python version does not support the notebook. If the MSTICPy version does not support the notebook and the user chose not to upgrade """ del kwargs _disp_html("<h4>Starting notebook pre-checks...</h4>") if isinstance(min_py_ver, str): min_py_ver = _get_pkg_version(min_py_ver).release check_python_ver(min_py_ver=min_py_ver) _check_mp_install(min_mp_ver, mp_release, extras) _check_kql_prereqs() _set_kql_env_vars(extras) _run_user_settings() _set_mpconfig_var() _disp_html("<h4>Notebook pre-checks complete.</h4>") def check_python_ver(min_py_ver: Union[str, Tuple] = MIN_PYTHON_VER_DEF): """ Check the current version of the Python kernel. Parameters ---------- min_py_ver : Tuple[int, int] Minimum Python version Raises ------ RuntimeError If the Python version does not support the notebook. """ min_py_ver = _get_pkg_version(min_py_ver) sys_ver = _get_pkg_version(sys.version_info[:3]) _disp_html("Checking Python kernel version...") if sys_ver < min_py_ver: # Bandit SQL inject error found here _disp_html( f""" <h4><font color='red'>This notebook requires a later (Python) kernel version.</h4></font> Select a kernel from the notebook toolbar (above), that is Python {min_py_ver} or later (Python 3.8 recommended)<br> """ # nosec ) _disp_html( f""" Please see the <a href="{TROUBLE_SHOOTING}">TroubleShootingNotebooks</a> for more information<br><br><hr> """ ) # Bandit SQL inject error found here raise RuntimeError(f"Python {min_py_ver} or later kernel is required.") # nosec if sys_ver < _get_pkg_version("3.8"): _disp_html( "Recommended: switch to using the 'Python 3.8 - AzureML' notebook kernel" " if this is available." ) _disp_html(f"Info: Python kernel version {sys_ver} - OK<br>") def _check_mp_install( min_mp_ver: Union[str, Tuple], mp_release: Optional[str], extras: Optional[List[str]], ): """Check for and try to install required MSTICPy version.""" # Use the release ver specified in params, in the environment or # the notebook default. pkg_version = _get_pkg_version(min_mp_ver) mp_install_version = mp_release or os.environ.get("MP_TEST_VER") or str(pkg_version) check_mp_ver(min_msticpy_ver=mp_install_version, extras=extras) def check_mp_ver(min_msticpy_ver: Union[str, Tuple], extras: Optional[List[str]]): """ Check and optionally update the current version of msticpy. Parameters ---------- min_msticpy_ver : Tuple[int, int] Minimum MSTICPy version extras : Optional[List[str]], optional A list of extras required for MSTICPy Raises ------ ImportError If MSTICPy version is insufficient and we need to upgrade """ mp_min_pkg_ver = _get_pkg_version(min_msticpy_ver) _disp_html("Checking msticpy version...<br>") inst_version = _get_pkg_version(__version__) if inst_version < mp_min_pkg_ver: _disp_html( MISSING_PKG_ERR.format( package="msticpy", inst_ver=inst_version, req_ver=mp_min_pkg_ver, ) ) mp_pkg_spec = f"msticpy[{','.join(extras)}]" if extras else "msticpy" mp_pkg_spec = f"{mp_pkg_spec}>={min_msticpy_ver}" _disp_html( f"Please run the following command to upgrade MSTICPy<br>" f"<pre>!{mp_pkg_spec}</pre><br>" ) raise ImportError( "Unsupported version of MSTICPy installed", f"Installed version: {inst_version}", f"Required version: {mp_min_pkg_ver}", ) _disp_html(f"Info: msticpy version {inst_version} (>= {mp_min_pkg_ver}) - OK<br>") def _set_kql_env_vars(extras: Optional[List[str]]): """Set environment variables for Kqlmagic based on MP extras.""" jp_extended = ("azsentinel", "azuresentinel", "kql") if extras and any(extra for extra in extras if extra in jp_extended): os.environ["KQLMAGIC_EXTRAS_REQUIRE"] = "jupyter-extended" else: os.environ["KQLMAGIC_EXTRAS_REQUIRE"] = "jupyter-basic" if is_in_aml(): os.environ["KQLMAGIC_AZUREML_COMPUTE"] = _get_vm_fqdn() def _get_pkg_version(version: Union[str, Tuple]) -> Any: """Return pkg_resources parsed version from string or tuple.""" if isinstance(version, str): return parse_version(version) if isinstance(version, tuple): return parse_version(".".join(str(ver) for ver in version)) raise TypeError(f"Unparseable type version {version}") def _disp_html(text: str): """Display the HTML text.""" display(HTML(text)) def get_aml_user_folder() -> Optional[Path]: """Return the root of the user folder.""" path_parts = Path(".").absolute().parts if "Users" not in path_parts: return None # find the index of the last occurrence of "users" users_idx = len(path_parts) - path_parts[::-1].index("Users") # the user folder is one item below this if len(path_parts) < users_idx + 1: return None return Path("/".join(path_parts[: users_idx + 1])) # pylint: disable=import-outside-toplevel, unused-import, import-error def _run_user_settings(): """Import nbuser_settings.py, if it exists.""" user_folder = get_aml_user_folder() if user_folder.joinpath("nbuser_settings.py").is_file(): sys.path.append(str(user_folder)) import nbuser_settings # noqa: F401 # pylint: enable=import-outside-toplevel, unused-import, import-error def _set_mpconfig_var(): """Set MSTICPYCONFIG to file in user directory if no other found.""" mp_path_val = os.environ.get(MP_ENV_VAR) if ( # If a valid MSTICPYCONFIG value is found - return (mp_path_val and Path(mp_path_val).is_file()) # Or if there is a msticpconfig in the current folder. or Path(".").joinpath(MP_FILE).is_file() ): return # Otherwise check the user's root folder user_dir = get_aml_user_folder() mp_path = Path(user_dir).joinpath(MP_FILE) if mp_path.is_file(): # If there's a file there, set the env variable to that. os.environ[MP_ENV_VAR] = str(mp_path) # Since we have already imported msticpy to check the version # it will have already configured settings so we need to refresh. refresh_config() _disp_html( f"<br>No {MP_FILE} found. Will use {MP_FILE} in user folder {user_dir}<br>" ) def _get_vm_metadata() -> Mapping[str, Any]: """Use local request to get VM metadata.""" vm_uri = "http://169.254.169.254/metadata/instance?api-version=2017-08-01" req = urllib.request.Request(vm_uri) # type: ignore req.add_header("Metadata", "true") # Bandit warning on urlopen - Fixed private URL with urllib.request.urlopen(req) as resp: # type: ignore # nosec metadata = json.loads(resp.read()) return metadata if isinstance(metadata, dict) else {} def _get_vm_fqdn() -> str: """Get the FQDN of the host.""" az_region = _get_vm_metadata().get("compute", {}).get("location") return ".".join( [ socket.gethostname(), az_region, "instances.azureml.ms", ] if az_region else "" ) def _check_kql_prereqs(): """ Check and install packages for Kqlmagic/msal_extensions. Notes ----- Kqlmagic may trigger warnings about a missing PyGObject package and some system library dependencies. To fix this do the following:<br> From a notebook run: %pip uninstall enum34 !sudo apt-get --yes install libgirepository1.0-dev !sudo apt-get --yes install gir1.2-secret-1 %pip install pygobject You can also do this from a terminal - but ensure that you've activated the environment corresponding to the kernel you are using prior to running the pip commands. # Install the libgi dependency sudo apt install libgirepository1.0-dev sudo apt install gir1.2-secret-1 # activate the environment # conda activate azureml_py38 # source ./env_path/scripts/activate # Uninstall enum34 python -m pip uninstall enum34 # Install pygobject python -m install pygobject """ if not is_in_aml(): return try: # If this successfully imports, we are ok # pylint: disable=import-outside-toplevel import gi # pylint: enable=import-outside-toplevel del gi except ImportError: # Check for system packages ip_shell = get_ipython() if not ip_shell: return apt_list = ip_shell.run_line_magic("sx", "apt list") apt_list = [apt.split("/", maxsplit=1)[0] for apt in apt_list] missing_lx_pkg = [ apt_pkg for apt_pkg in ("libgirepository1.0-dev", "gir1.2-secret-1") if apt_pkg not in apt_list ] if missing_lx_pkg: _disp_html( "Kqlmagic/msal-extensions pre-requisite PyGObject not installed." ) _disp_html( "To prevent warnings when loading the Kqlmagic data provider," " Please run the following command:<br>" "!conda install --yes -c conda-forge pygobject<br>" )

# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- """Checker functions for Azure ML notebooks.""" import json import os import socket import sys import urllib from pathlib import Path from typing import Any, List, Mapping, Optional, Tuple, Union from IPython import get_ipython from IPython.display import HTML, display from pkg_resources import parse_version from .._version import VERSION from ..common.pkg_config import refresh_config __version__ = VERSION AZ_GET_STARTED = ( "https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/A%20Getting" "%20Started%20Guide%20For%20Azure%20Sentinel%20ML%20Notebooks.ipynb" ) TROUBLE_SHOOTING = ( "https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/" "TroubleShootingNotebooks.ipynb" ) MISSING_PKG_ERR = """ <h4><font color='orange'>The package '<b>{package}</b>' is not installed or has an unsupported version (installed version = '{inst_ver}')</font></h4> Please install or upgrade before continuing: required version is {package}>={req_ver} """ MP_INSTALL_FAILED = """ <h4><font color='red'>The notebook may not run correctly without the correct version of '<b>{pkg}</b>' ({ver} or later).</font></h4> Please see the <a href="{nbk_uri}"> Getting Started Guide For Azure Sentinel ML Notebooks</a></b> for more information<br><hr> """ RELOAD_MP = """ <h4><font color='orange'>Kernel restart needed</h4> An error was detected trying to load the updated version of MSTICPy.<br> Please restart the notebook kernel and re-run this cell - it should run without error. """ MIN_PYTHON_VER_DEF = "3.6" MSTICPY_REQ_VERSION = __version__ VER_RGX = r"(?P<maj>\d+)\.(?P<min>\d+).(?P<pnt>\d+)(?P<suff>.*)" MP_ENV_VAR = "MSTICPYCONFIG" MP_FILE = "msticpyconfig.yaml" NB_CHECK_URI = ( "https://raw.githubusercontent.com/Azure/Azure-Sentinel-" "Notebooks/master/utils/nb_check.py" ) def is_in_aml(): """Return True if running in Azure Machine Learning.""" return os.environ.get("APPSETTING_WEBSITE_SITE_NAME") == "AMLComputeInstance" def check_versions( min_py_ver: Union[str, Tuple] = MIN_PYTHON_VER_DEF, min_mp_ver: Union[str, Tuple] = MSTICPY_REQ_VERSION, extras: Optional[List[str]] = None, mp_release: Optional[str] = None, **kwargs, ): """ Check the current versions of the Python kernel and MSTICPy. Parameters ---------- min_py_ver : Union[Tuple[int, int], str] Minimum Python version min_mp_ver : Union[Tuple[int, int], str] Minimum MSTICPy version extras : Optional[List[str]], optional A list of extras required for MSTICPy mp_release : Optional[str], optional Override the MSTICPy release version. This can also be specified in the environment variable 'MP_TEST_VER' Raises ------ RuntimeError If the Python version does not support the notebook. If the MSTICPy version does not support the notebook and the user chose not to upgrade """ del kwargs _disp_html("<h4>Starting notebook pre-checks...</h4>") if isinstance(min_py_ver, str): min_py_ver = _get_pkg_version(min_py_ver).release check_python_ver(min_py_ver=min_py_ver) _check_mp_install(min_mp_ver, mp_release, extras) _check_kql_prereqs() _set_kql_env_vars(extras) _run_user_settings() _set_mpconfig_var() _disp_html("<h4>Notebook pre-checks complete.</h4>") def check_python_ver(min_py_ver: Union[str, Tuple] = MIN_PYTHON_VER_DEF): """ Check the current version of the Python kernel. Parameters ---------- min_py_ver : Tuple[int, int] Minimum Python version Raises ------ RuntimeError If the Python version does not support the notebook. """ min_py_ver = _get_pkg_version(min_py_ver) sys_ver = _get_pkg_version(sys.version_info[:3]) _disp_html("Checking Python kernel version...") if sys_ver < min_py_ver: # Bandit SQL inject error found here _disp_html( f""" <h4><font color='red'>This notebook requires a later (Python) kernel version.</h4></font> Select a kernel from the notebook toolbar (above), that is Python {min_py_ver} or later (Python 3.8 recommended)<br> """ # nosec ) _disp_html( f""" Please see the <a href="{TROUBLE_SHOOTING}">TroubleShootingNotebooks</a> for more information<br><br><hr> """ ) # Bandit SQL inject error found here raise RuntimeError(f"Python {min_py_ver} or later kernel is required.") # nosec if sys_ver < _get_pkg_version("3.8"): _disp_html( "Recommended: switch to using the 'Python 3.8 - AzureML' notebook kernel" " if this is available." ) _disp_html(f"Info: Python kernel version {sys_ver} - OK<br>") def _check_mp_install( min_mp_ver: Union[str, Tuple], mp_release: Optional[str], extras: Optional[List[str]], ): """Check for and try to install required MSTICPy version.""" # Use the release ver specified in params, in the environment or # the notebook default. pkg_version = _get_pkg_version(min_mp_ver) mp_install_version = mp_release or os.environ.get("MP_TEST_VER") or str(pkg_version) check_mp_ver(min_msticpy_ver=mp_install_version, extras=extras) def check_mp_ver(min_msticpy_ver: Union[str, Tuple], extras: Optional[List[str]]): """ Check and optionally update the current version of msticpy. Parameters ---------- min_msticpy_ver : Tuple[int, int] Minimum MSTICPy version extras : Optional[List[str]], optional A list of extras required for MSTICPy Raises ------ ImportError If MSTICPy version is insufficient and we need to upgrade """ mp_min_pkg_ver = _get_pkg_version(min_msticpy_ver) _disp_html("Checking msticpy version...<br>") inst_version = _get_pkg_version(__version__) if inst_version < mp_min_pkg_ver: _disp_html( MISSING_PKG_ERR.format( package="msticpy", inst_ver=inst_version, req_ver=mp_min_pkg_ver, ) ) mp_pkg_spec = f"msticpy[{','.join(extras)}]" if extras else "msticpy" mp_pkg_spec = f"{mp_pkg_spec}>={min_msticpy_ver}" _disp_html( f"Please run the following command to upgrade MSTICPy<br>" f"<pre>!{mp_pkg_spec}</pre><br>" ) raise ImportError( "Unsupported version of MSTICPy installed", f"Installed version: {inst_version}", f"Required version: {mp_min_pkg_ver}", ) _disp_html(f"Info: msticpy version {inst_version} (>= {mp_min_pkg_ver}) - OK<br>") def _set_kql_env_vars(extras: Optional[List[str]]): """Set environment variables for Kqlmagic based on MP extras.""" jp_extended = ("azsentinel", "azuresentinel", "kql") if extras and any(extra for extra in extras if extra in jp_extended): os.environ["KQLMAGIC_EXTRAS_REQUIRE"] = "jupyter-extended" else: os.environ["KQLMAGIC_EXTRAS_REQUIRE"] = "jupyter-basic" if is_in_aml(): os.environ["KQLMAGIC_AZUREML_COMPUTE"] = _get_vm_fqdn() def _get_pkg_version(version: Union[str, Tuple]) -> Any: """Return pkg_resources parsed version from string or tuple.""" if isinstance(version, str): return parse_version(version) if isinstance(version, tuple): return parse_version(".".join(str(ver) for ver in version)) raise TypeError(f"Unparseable type version {version}") def _disp_html(text: str): """Display the HTML text.""" display(HTML(text)) def get_aml_user_folder() -> Optional[Path]: """Return the root of the user folder.""" path_parts = Path(".").absolute().parts if "Users" not in path_parts: return None # find the index of the last occurrence of "users" users_idx = len(path_parts) - path_parts[::-1].index("Users") # the user folder is one item below this if len(path_parts) < users_idx + 1: return None return Path("/".join(path_parts[: users_idx + 1])) # pylint: disable=import-outside-toplevel, unused-import, import-error def _run_user_settings(): """Import nbuser_settings.py, if it exists.""" user_folder = get_aml_user_folder() if user_folder.joinpath("nbuser_settings.py").is_file(): sys.path.append(str(user_folder)) import nbuser_settings # noqa: F401 # pylint: enable=import-outside-toplevel, unused-import, import-error def _set_mpconfig_var(): """Set MSTICPYCONFIG to file in user directory if no other found.""" mp_path_val = os.environ.get(MP_ENV_VAR) if ( # If a valid MSTICPYCONFIG value is found - return (mp_path_val and Path(mp_path_val).is_file()) # Or if there is a msticpconfig in the current folder. or Path(".").joinpath(MP_FILE).is_file() ): return # Otherwise check the user's root folder user_dir = get_aml_user_folder() mp_path = Path(user_dir).joinpath(MP_FILE) if mp_path.is_file(): # If there's a file there, set the env variable to that. os.environ[MP_ENV_VAR] = str(mp_path) # Since we have already imported msticpy to check the version # it will have already configured settings so we need to refresh. refresh_config() _disp_html( f"<br>No {MP_FILE} found. Will use {MP_FILE} in user folder {user_dir}<br>" ) def _get_vm_metadata() -> Mapping[str, Any]: """Use local request to get VM metadata.""" vm_uri = "http://169.254.169.254/metadata/instance?api-version=2017-08-01" req = urllib.request.Request(vm_uri) # type: ignore req.add_header("Metadata", "true") # Bandit warning on urlopen - Fixed private URL with urllib.request.urlopen(req) as resp: # type: ignore # nosec metadata = json.loads(resp.read()) return metadata if isinstance(metadata, dict) else {} def _get_vm_fqdn() -> str: """Get the FQDN of the host.""" az_region = _get_vm_metadata().get("compute", {}).get("location") return ".".join( [ socket.gethostname(), az_region, "instances.azureml.ms", ] if az_region else "" ) def _check_kql_prereqs(): """ Check and install packages for Kqlmagic/msal_extensions. Notes ----- Kqlmagic may trigger warnings about a missing PyGObject package and some system library dependencies. To fix this do the following:<br> From a notebook run: %pip uninstall enum34 !sudo apt-get --yes install libgirepository1.0-dev !sudo apt-get --yes install gir1.2-secret-1 %pip install pygobject You can also do this from a terminal - but ensure that you've activated the environment corresponding to the kernel you are using prior to running the pip commands. # Install the libgi dependency sudo apt install libgirepository1.0-dev sudo apt install gir1.2-secret-1 # activate the environment # conda activate azureml_py38 # source ./env_path/scripts/activate # Uninstall enum34 python -m pip uninstall enum34 # Install pygobject python -m install pygobject """ if not is_in_aml(): return try: # If this successfully imports, we are ok # pylint: disable=import-outside-toplevel import gi # pylint: enable=import-outside-toplevel del gi except ImportError: # Check for system packages ip_shell = get_ipython() if not ip_shell: return apt_list = ip_shell.run_line_magic("sx", "apt list") apt_list = [apt.split("/", maxsplit=1)[0] for apt in apt_list] missing_lx_pkg = [ apt_pkg for apt_pkg in ("libgirepository1.0-dev", "gir1.2-secret-1") if apt_pkg not in apt_list ] if missing_lx_pkg: _disp_html( "Kqlmagic/msal-extensions pre-requisite PyGObject not installed." ) _disp_html( "To prevent warnings when loading the Kqlmagic data provider," " Please run the following command:<br>" "!conda install --yes -c conda-forge pygobject<br>" )

b65206728e5f3f6cbab0f87066d7ed1dc8784f63

py

Python

konfuzio_sdk/urls.py

atraining/document-ai-python-sdk

ea2df68af0254053da7e6f4c6e2c2df6d7911233

konfuzio_sdk/urls.py

atraining/document-ai-python-sdk

ea2df68af0254053da7e6f4c6e2c2df6d7911233

konfuzio_sdk/urls.py

atraining/document-ai-python-sdk

ea2df68af0254053da7e6f4c6e2c2df6d7911233

"""Endpoints of the Konfuzio Host.""" import logging from konfuzio_sdk import KONFUZIO_HOST, KONFUZIO_PROJECT_ID logger = logging.getLogger(__name__) def get_auth_token_url() -> str: """ Generate URL that creates an authentication token for the user. :return: URL to generate the token. """ return f"{KONFUZIO_HOST}/api/token-auth/" def get_project_list_url() -> str: """ Generate URL to load all the projects available for the user. :return: URL to get all the projects for the user. """ return f"{KONFUZIO_HOST}/api/projects/" def create_new_project_url() -> str: """ Generate URL to create a new project. :return: URL to create a new project. """ return f"{KONFUZIO_HOST}/api/projects/" def get_documents_meta_url() -> str: """ Generate URL to load meta information about documents. :return: URL to get all the documents details. """ return f"{KONFUZIO_HOST}/api/projects/{KONFUZIO_PROJECT_ID}/docs/" def get_upload_document_url() -> str: """ Generate URL to upload a document. :return: URL to upload a document """ return f"{KONFUZIO_HOST}/api/v2/docs/" def get_create_label_url() -> str: """ Generate URL to create a label. :return: URL to create a label. """ return f"{KONFUZIO_HOST}/api/v2/labels/" def get_document_ocr_file_url(document_id: int) -> str: """ Generate URL to access OCR version of document. :param document_id: ID of the document as integer :return: URL to get OCR document file. """ return f'{KONFUZIO_HOST}/doc/show/{document_id}/' def get_document_original_file_url(document_id: int) -> str: """ Generate URL to access original version of the document. :param document_id: ID of the document as integer :return: URL to get the original document """ return f'{KONFUZIO_HOST}/doc/show-original/{document_id}/' def get_document_api_details_url(document_id: int, include_extractions: bool = False, extra_fields='bbox') -> str: """ Generate URL to access document details of one document in a project. :param document_id: ID of the document as integer :param include_extractions: Bool to include extractions :param extra_fields: Extra information to include in the response :return: URL to get document details """ return ( f'{KONFUZIO_HOST}/api/projects/{KONFUZIO_PROJECT_ID}/docs/{document_id}/' f'?include_extractions={include_extractions}&extra_fields={extra_fields}' ) def get_project_url(project_id=None) -> str: """ Generate URL to get project details. :param project_id: ID of the project :return: URL to get project details. """ project_id = project_id if project_id else KONFUZIO_PROJECT_ID return f'{KONFUZIO_HOST}/api/projects/{project_id}/' def post_project_api_document_annotations_url(document_id: int) -> str: """ Add new annotations to a document. :param document_id: ID of the document as integer :return: URL for adding annotations to a document """ return f'{KONFUZIO_HOST}/api/projects/{KONFUZIO_PROJECT_ID}/docs/{document_id}/annotations/' def delete_project_api_document_annotations_url(document_id: int, annotation_id: int) -> str: """ Delete the annotation of a document. :param document_id: ID of the document as integer :param annotation_id: ID of the annotation as integer :return: URL to delete annotation of a document """ return f'{KONFUZIO_HOST}/api/projects/{KONFUZIO_PROJECT_ID}/docs/{document_id}/' f'annotations/{annotation_id}/' def get_document_result_v1(document_id: int) -> str: """ Generate URL to access web interface for labeling of this project. :param document_id: ID of the document as integer :return: URL for labeling of the project. """ return f'{KONFUZIO_HOST}/api/v1/docs/{document_id}/' def get_document_segmentation_details_url(document_id: int, project_id, action='segmentation') -> str: """ Generate URL to get the segmentation results of a document. :param document_id: ID of the document as integer :param project_id: ID of the project :param action: Action from where to get the results :return: URL to access the segmentation results of a document """ return f'https://app.konfuzio.com/api/projects/{project_id}/docs/{document_id}/{action}/'

"""Endpoints of the Konfuzio Host.""" import logging from konfuzio_sdk import KONFUZIO_HOST, KONFUZIO_PROJECT_ID logger = logging.getLogger(__name__) def get_auth_token_url() -> str: """ Generate URL that creates an authentication token for the user. :return: URL to generate the token. """ return f"{KONFUZIO_HOST}/api/token-auth/" def get_project_list_url() -> str: """ Generate URL to load all the projects available for the user. :return: URL to get all the projects for the user. """ return f"{KONFUZIO_HOST}/api/projects/" def create_new_project_url() -> str: """ Generate URL to create a new project. :return: URL to create a new project. """ return f"{KONFUZIO_HOST}/api/projects/" def get_documents_meta_url() -> str: """ Generate URL to load meta information about documents. :return: URL to get all the documents details. """ return f"{KONFUZIO_HOST}/api/projects/{KONFUZIO_PROJECT_ID}/docs/" def get_upload_document_url() -> str: """ Generate URL to upload a document. :return: URL to upload a document """ return f"{KONFUZIO_HOST}/api/v2/docs/" def get_create_label_url() -> str: """ Generate URL to create a label. :return: URL to create a label. """ return f"{KONFUZIO_HOST}/api/v2/labels/" def get_document_ocr_file_url(document_id: int) -> str: """ Generate URL to access OCR version of document. :param document_id: ID of the document as integer :return: URL to get OCR document file. """ return f'{KONFUZIO_HOST}/doc/show/{document_id}/' def get_document_original_file_url(document_id: int) -> str: """ Generate URL to access original version of the document. :param document_id: ID of the document as integer :return: URL to get the original document """ return f'{KONFUZIO_HOST}/doc/show-original/{document_id}/' def get_document_api_details_url(document_id: int, include_extractions: bool = False, extra_fields='bbox') -> str: """ Generate URL to access document details of one document in a project. :param document_id: ID of the document as integer :param include_extractions: Bool to include extractions :param extra_fields: Extra information to include in the response :return: URL to get document details """ return ( f'{KONFUZIO_HOST}/api/projects/{KONFUZIO_PROJECT_ID}/docs/{document_id}/' f'?include_extractions={include_extractions}&extra_fields={extra_fields}' ) def get_project_url(project_id=None) -> str: """ Generate URL to get project details. :param project_id: ID of the project :return: URL to get project details. """ project_id = project_id if project_id else KONFUZIO_PROJECT_ID return f'{KONFUZIO_HOST}/api/projects/{project_id}/' def post_project_api_document_annotations_url(document_id: int) -> str: """ Add new annotations to a document. :param document_id: ID of the document as integer :return: URL for adding annotations to a document """ return f'{KONFUZIO_HOST}/api/projects/{KONFUZIO_PROJECT_ID}/docs/{document_id}/annotations/' def delete_project_api_document_annotations_url(document_id: int, annotation_id: int) -> str: """ Delete the annotation of a document. :param document_id: ID of the document as integer :param annotation_id: ID of the annotation as integer :return: URL to delete annotation of a document """ return f'{KONFUZIO_HOST}/api/projects/{KONFUZIO_PROJECT_ID}/docs/{document_id}/' f'annotations/{annotation_id}/' def get_document_result_v1(document_id: int) -> str: """ Generate URL to access web interface for labeling of this project. :param document_id: ID of the document as integer :return: URL for labeling of the project. """ return f'{KONFUZIO_HOST}/api/v1/docs/{document_id}/' def get_document_segmentation_details_url(document_id: int, project_id, action='segmentation') -> str: """ Generate URL to get the segmentation results of a document. :param document_id: ID of the document as integer :param project_id: ID of the project :param action: Action from where to get the results :return: URL to access the segmentation results of a document """ return f'https://app.konfuzio.com/api/projects/{project_id}/docs/{document_id}/{action}/'

5a5179184e11bd69c115e048377711a912dc3761

py

Python

Web/user/models.py

Pancras-Zheng/Graduation-Project

5d1ae78d5e890fa7ecc2456d0d3d22bdea7c29f0

2018-01-25T03:14:24.000Z

2021-12-15T10:02:37.000Z

Web/user/models.py

Pancras-Zheng/Graduation-Project

5d1ae78d5e890fa7ecc2456d0d3d22bdea7c29f0

Web/user/models.py

Pancras-Zheng/Graduation-Project

5d1ae78d5e890fa7ecc2456d0d3d22bdea7c29f0

2019-04-11T07:27:10.000Z

2021-11-24T11:16:14.000Z

from django.db import models from django.contrib.auth.models import AbstractUser from django.utils.translation import ugettext_lazy as _ from django.contrib.auth.base_user import BaseUserManager # Create your models here.

from django.db import models from django.contrib.auth.models import AbstractUser from django.utils.translation import ugettext_lazy as _ from django.contrib.auth.base_user import BaseUserManager # Create your models here. class User(AbstractUser): nickname = models.CharField(_('昵称'),max_length=50,blank=True) info = models.CharField(_('备注'),max_length=200,blank=True) class Meta(AbstractUser.Meta): pass

db4b3216850356cdd188fbda35706bb2acbe536c

py

Python

src/huggingface_hub/commands/user.py

FrancescoSaverioZuppichini/huggingface_hub

9e7ffda07ddcd668302a61156bcae0d9ec97a26e

2022-03-28T14:15:24.000Z

2022-03-28T14:15:24.000Z

src/huggingface_hub/commands/user.py

osanseviero/huggingface_hub

b1cf2d8f47088d3fce2244058d222a4d8234b3ab

src/huggingface_hub/commands/user.py

osanseviero/huggingface_hub

b1cf2d8f47088d3fce2244058d222a4d8234b3ab

# Copyright 2020 The HuggingFace Team. All rights reserved. # # 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 subprocess from argparse import ArgumentParser from getpass import getpass from typing import List, Union from huggingface_hub.commands import BaseHuggingfaceCLICommand from huggingface_hub.constants import ( REPO_TYPES, REPO_TYPES_URL_PREFIXES, SPACES_SDK_TYPES, ) from huggingface_hub.hf_api import HfApi, HfFolder from requests.exceptions import HTTPError class ANSI: """ Helper for en.wikipedia.org/wiki/ANSI_escape_code """ _bold = "\u001b[1m" _red = "\u001b[31m" _gray = "\u001b[90m" _reset = "\u001b[0m" @classmethod @classmethod @classmethod def tabulate(rows: List[List[Union[str, int]]], headers: List[str]) -> str: """ Inspired by: - stackoverflow.com/a/8356620/593036 - stackoverflow.com/questions/9535954/printing-lists-as-tabular-data """ col_widths = [max(len(str(x)) for x in col) for col in zip(*rows, headers)] row_format = ("{{:{}}} " * len(headers)).format(*col_widths) lines = [] lines.append(row_format.format(*headers)) lines.append(row_format.format(*["-" * w for w in col_widths])) for row in rows: lines.append(row_format.format(*row)) return "\n".join(lines) NOTEBOOK_LOGIN_PASSWORD_HTML = """<center> <img src=https://huggingface.co/front/assets/huggingface_logo-noborder.svg alt='Hugging Face'> <br> Immediately click login after typing your password or it might be stored in plain text in this notebook file. </center>""" NOTEBOOK_LOGIN_TOKEN_HTML_START = """<center> <img src=https://huggingface.co/front/assets/huggingface_logo-noborder.svg alt='Hugging Face'> <br> Copy a token from <a href="https://huggingface.co/settings/tokens" target="_blank">your Hugging Face tokens page</a> and paste it below. <br> Immediately click login after copying your token or it might be stored in plain text in this notebook file. </center>""" NOTEBOOK_LOGIN_TOKEN_HTML_END = """ <b>Pro Tip:</b> If you don't already have one, you can create a dedicated 'notebooks' token with 'write' access, that you can then easily reuse for all notebooks. <br> <i>Logging in with your username and password is deprecated and won't be possible anymore in the near future. You can still use them for now by clicking below.</i> </center>""" def notebook_login(): """ Displays a widget to login to the HF website and store the token. """ try: import ipywidgets.widgets as widgets from IPython.display import clear_output, display except ImportError: raise ImportError( "The `notebook_login` function can only be used in a notebook (Jupyter or Colab) and you need the " "`ipywdidgets` module: `pip install ipywidgets`." ) box_layout = widgets.Layout( display="flex", flex_flow="column", align_items="center", width="50%" ) token_widget = widgets.Password(description="Token:") token_finish_button = widgets.Button(description="Login") switch_button = widgets.Button(description="Use password") login_token_widget = widgets.VBox( [ widgets.HTML(NOTEBOOK_LOGIN_TOKEN_HTML_START), token_widget, token_finish_button, widgets.HTML(NOTEBOOK_LOGIN_TOKEN_HTML_END), switch_button, ], layout=box_layout, ) display(login_token_widget) # Deprecated page for login input_widget = widgets.Text(description="Username:") password_widget = widgets.Password(description="Password:") password_finish_button = widgets.Button(description="Login") login_password_widget = widgets.VBox( [ widgets.HTML(value=NOTEBOOK_LOGIN_PASSWORD_HTML), widgets.HBox([input_widget, password_widget]), password_finish_button, ], layout=box_layout, ) # On click events token_finish_button.on_click(login_token_event) password_finish_button.on_click(login_password_event) switch_button.on_click(switch_event)

# Copyright 2020 The HuggingFace Team. All rights reserved. # # 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 subprocess from argparse import ArgumentParser from getpass import getpass from typing import List, Union from huggingface_hub.commands import BaseHuggingfaceCLICommand from huggingface_hub.constants import ( REPO_TYPES, REPO_TYPES_URL_PREFIXES, SPACES_SDK_TYPES, ) from huggingface_hub.hf_api import HfApi, HfFolder from requests.exceptions import HTTPError class UserCommands(BaseHuggingfaceCLICommand): @staticmethod def register_subcommand(parser: ArgumentParser): login_parser = parser.add_parser( "login", help="Log in using the same credentials as on huggingface.co" ) login_parser.set_defaults(func=lambda args: LoginCommand(args)) whoami_parser = parser.add_parser( "whoami", help="Find out which huggingface.co account you are logged in as." ) whoami_parser.set_defaults(func=lambda args: WhoamiCommand(args)) logout_parser = parser.add_parser("logout", help="Log out") logout_parser.set_defaults(func=lambda args: LogoutCommand(args)) # new system: git-based repo system repo_parser = parser.add_parser( "repo", help="{create, ls-files} Commands to interact with your huggingface.co repos.", ) repo_subparsers = repo_parser.add_subparsers( help="huggingface.co repos related commands" ) repo_create_parser = repo_subparsers.add_parser( "create", help="Create a new repo on huggingface.co" ) repo_create_parser.add_argument( "name", type=str, help="Name for your repo. Will be namespaced under your username to build the repo id.", ) repo_create_parser.add_argument( "--type", type=str, help='Optional: repo_type: set to "dataset" or "space" if creating a dataset or space, default is model.', ) repo_create_parser.add_argument( "--organization", type=str, help="Optional: organization namespace." ) repo_create_parser.add_argument( "--space_sdk", type=str, help='Optional: Hugging Face Spaces SDK type. Required when --type is set to "space".', choices=SPACES_SDK_TYPES, ) repo_create_parser.add_argument( "-y", "--yes", action="store_true", help="Optional: answer Yes to the prompt", ) repo_create_parser.set_defaults(func=lambda args: RepoCreateCommand(args)) class ANSI: """ Helper for en.wikipedia.org/wiki/ANSI_escape_code """ _bold = "\u001b[1m" _red = "\u001b[31m" _gray = "\u001b[90m" _reset = "\u001b[0m" @classmethod def bold(cls, s): return f"{cls._bold}{s}{cls._reset}" @classmethod def red(cls, s): return f"{cls._bold + cls._red}{s}{cls._reset}" @classmethod def gray(cls, s): return f"{cls._gray}{s}{cls._reset}" def tabulate(rows: List[List[Union[str, int]]], headers: List[str]) -> str: """ Inspired by: - stackoverflow.com/a/8356620/593036 - stackoverflow.com/questions/9535954/printing-lists-as-tabular-data """ col_widths = [max(len(str(x)) for x in col) for col in zip(*rows, headers)] row_format = ("{{:{}}} " * len(headers)).format(*col_widths) lines = [] lines.append(row_format.format(*headers)) lines.append(row_format.format(*["-" * w for w in col_widths])) for row in rows: lines.append(row_format.format(*row)) return "\n".join(lines) def currently_setup_credential_helpers(directory=None) -> List[str]: try: output = subprocess.run( "git config --list".split(), stderr=subprocess.PIPE, stdout=subprocess.PIPE, encoding="utf-8", check=True, cwd=directory, ).stdout.split("\n") current_credential_helpers = [] for line in output: if "credential.helper" in line: current_credential_helpers.append(line.split("=")[-1]) except subprocess.CalledProcessError as exc: raise EnvironmentError(exc.stderr) return current_credential_helpers class BaseUserCommand: def __init__(self, args): self.args = args self._api = HfApi() class LoginCommand(BaseUserCommand): def run(self): print( # docstyle-ignore """ _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _| _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_| _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _| _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_| To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/tokens. (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C. """ ) try: token = getpass("Token: ") _login(self._api, token=token) except KeyboardInterrupt: username = input("\rUsername: ") password = getpass() _login(self._api, username, password) class WhoamiCommand(BaseUserCommand): def run(self): token = HfFolder.get_token() if token is None: print("Not logged in") exit() try: info = self._api.whoami(token) print(info["name"]) orgs = [org["name"] for org in info["orgs"]] if orgs: print(ANSI.bold("orgs: "), ",".join(orgs)) except HTTPError as e: print(e) print(ANSI.red(e.response.text)) exit(1) class LogoutCommand(BaseUserCommand): def run(self): token = HfFolder.get_token() if token is None: print("Not logged in") exit() HfFolder.delete_token() HfApi.unset_access_token() try: self._api.logout(token) except HTTPError as e: # Logging out with an access token will return a client error. if not e.response.status_code == 400: raise e print("Successfully logged out.") class RepoCreateCommand(BaseUserCommand): def run(self): token = HfFolder.get_token() if token is None: print("Not logged in") exit(1) try: stdout = subprocess.check_output(["git", "--version"]).decode("utf-8") print(ANSI.gray(stdout.strip())) except FileNotFoundError: print("Looks like you do not have git installed, please install.") try: stdout = subprocess.check_output(["git-lfs", "--version"]).decode("utf-8") print(ANSI.gray(stdout.strip())) except FileNotFoundError: print( ANSI.red( "Looks like you do not have git-lfs installed, please install." " You can install from https://git-lfs.github.com/." " Then run `git lfs install` (you only have to do this once)." ) ) print("") user = self._api.whoami(token)["name"] namespace = ( self.args.organization if self.args.organization is not None else user ) repo_id = f"{namespace}/{self.args.name}" if self.args.type not in REPO_TYPES: print("Invalid repo --type") exit(1) if self.args.type in REPO_TYPES_URL_PREFIXES: prefixed_repo_id = REPO_TYPES_URL_PREFIXES[self.args.type] + repo_id else: prefixed_repo_id = repo_id print(f"You are about to create {ANSI.bold(prefixed_repo_id)}") if not self.args.yes: choice = input("Proceed? [Y/n] ").lower() if not (choice == "" or choice == "y" or choice == "yes"): print("Abort") exit() try: url = self._api.create_repo( repo_id=repo_id, token=token, repo_type=self.args.type, space_sdk=self.args.space_sdk, ) except HTTPError as e: print(e) print(ANSI.red(e.response.text)) exit(1) print("\nYour repo now lives at:") print(f" {ANSI.bold(url)}") print( "\nYou can clone it locally with the command below," " and commit/push as usual." ) print(f"\n git clone {url}") print("") NOTEBOOK_LOGIN_PASSWORD_HTML = """<center> <img src=https://huggingface.co/front/assets/huggingface_logo-noborder.svg alt='Hugging Face'> <br> Immediately click login after typing your password or it might be stored in plain text in this notebook file. </center>""" NOTEBOOK_LOGIN_TOKEN_HTML_START = """<center> <img src=https://huggingface.co/front/assets/huggingface_logo-noborder.svg alt='Hugging Face'> <br> Copy a token from <a href="https://huggingface.co/settings/tokens" target="_blank">your Hugging Face tokens page</a> and paste it below. <br> Immediately click login after copying your token or it might be stored in plain text in this notebook file. </center>""" NOTEBOOK_LOGIN_TOKEN_HTML_END = """ <b>Pro Tip:</b> If you don't already have one, you can create a dedicated 'notebooks' token with 'write' access, that you can then easily reuse for all notebooks. <br> <i>Logging in with your username and password is deprecated and won't be possible anymore in the near future. You can still use them for now by clicking below.</i> </center>""" def notebook_login(): """ Displays a widget to login to the HF website and store the token. """ try: import ipywidgets.widgets as widgets from IPython.display import clear_output, display except ImportError: raise ImportError( "The `notebook_login` function can only be used in a notebook (Jupyter or Colab) and you need the " "`ipywdidgets` module: `pip install ipywidgets`." ) box_layout = widgets.Layout( display="flex", flex_flow="column", align_items="center", width="50%" ) token_widget = widgets.Password(description="Token:") token_finish_button = widgets.Button(description="Login") switch_button = widgets.Button(description="Use password") login_token_widget = widgets.VBox( [ widgets.HTML(NOTEBOOK_LOGIN_TOKEN_HTML_START), token_widget, token_finish_button, widgets.HTML(NOTEBOOK_LOGIN_TOKEN_HTML_END), switch_button, ], layout=box_layout, ) display(login_token_widget) # Deprecated page for login input_widget = widgets.Text(description="Username:") password_widget = widgets.Password(description="Password:") password_finish_button = widgets.Button(description="Login") login_password_widget = widgets.VBox( [ widgets.HTML(value=NOTEBOOK_LOGIN_PASSWORD_HTML), widgets.HBox([input_widget, password_widget]), password_finish_button, ], layout=box_layout, ) # On click events def login_token_event(t): token = token_widget.value # Erase token and clear value to make sure it's not saved in the notebook. token_widget.value = "" clear_output() _login(HfApi(), token=token) token_finish_button.on_click(login_token_event) def login_password_event(t): username = input_widget.value password = password_widget.value # Erase password and clear value to make sure it's not saved in the notebook. password_widget.value = "" clear_output() _login(HfApi(), username=username, password=password) password_finish_button.on_click(login_password_event) def switch_event(t): clear_output() display(login_password_widget) switch_button.on_click(switch_event) def _login(hf_api, username=None, password=None, token=None): if token is None: try: token = hf_api.login(username, password) except HTTPError as e: # probably invalid credentials, display error message. print(e) print(ANSI.red(e.response.text)) exit(1) else: token, name = hf_api._validate_or_retrieve_token(token) hf_api.set_access_token(token) HfFolder.save_token(token) print("Login successful") print("Your token has been saved to", HfFolder.path_token) helpers = currently_setup_credential_helpers() if "store" not in helpers: print( ANSI.red( "Authenticated through git-credential store but this isn't the helper defined on your machine.\nYou " "might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your " "terminal in case you want to set this credential helper as the default\n\ngit config --global credential.helper store" ) )

293ccee023fc91f0aa073e2ba442d3ed89f6b0d4

py

Python

src/ramstk/logger.py

TahaEntezari/ramstk

f82e5b31ef5c4e33cc02252263247b99a9abe129

2019-05-15T02:03:47.000Z

2022-02-21T07:28:11.000Z

src/ramstk/logger.py

TahaEntezari/ramstk

f82e5b31ef5c4e33cc02252263247b99a9abe129

2019-05-10T12:31:52.000Z

2022-03-31T12:56:26.000Z

src/ramstk/logger.py

TahaEntezari/ramstk

f82e5b31ef5c4e33cc02252263247b99a9abe129

2019-04-20T23:06:29.000Z

2022-01-24T21:21:04.000Z

# -*- coding: utf-8 -*- # # ramstk.logger.py is part of The RAMSTK Project # # All rights reserved. # Copyright 2019 Doyle Rowland doyle.rowland <AT> reliaqual <DOT> com """RAMSTK Logger Module.""" # Standard Library Imports import logging import sys from typing import Dict # Third Party Imports from pubsub import pub LOGFORMAT = logging.Formatter("%(asctime)s - %(name)s - %(lineno)s : %(message)s") class RAMSTKLogManager: """Class to manage logging of RAMSTK messages.""" loggers: Dict[str, logging.Logger] = {} def __init__(self, log_file: str) -> None: """Initialize an instance of the LogManager. :param log_file: the absolute path to the log file to use with this log manager. """ # Initialize private dictionary attributes. # Initialize private list attributes. # Initialize private scalar attributes. # Initialize public dictionary attributes. # Initialize public list attributes. # Initialize public scalar attributes. self.log_file = log_file # Subscribe to PyPubSub messages. pub.subscribe(self._do_log_fail_message, "fail_connect_program_database") pub.subscribe(self._do_log_fail_message, "fail_delete_environment") pub.subscribe(self._do_log_fail_message, "fail_delete_failure_definition") pub.subscribe(self._do_log_fail_message, "fail_delete_fmea") pub.subscribe(self._do_log_fail_message, "fail_delete_function") pub.subscribe(self._do_log_fail_message, "fail_delete_hazard") pub.subscribe(self._do_log_fail_message, "fail_delete_mission") pub.subscribe(self._do_log_fail_message, "fail_delete_mission_phase") pub.subscribe(self._do_log_fail_message, "fail_delete_revision") pub.subscribe(self._do_log_fail_message, "fail_import_module") pub.subscribe(self._do_log_fail_message, "fail_insert_action") pub.subscribe(self._do_log_fail_message, "fail_insert_cause") pub.subscribe(self._do_log_fail_message, "fail_insert_control") pub.subscribe(self._do_log_fail_message, "fail_insert_environment") pub.subscribe(self._do_log_fail_message, "fail_insert_failure_definition") pub.subscribe(self._do_log_fail_message, "fail_insert_mechanism") pub.subscribe(self._do_log_fail_message, "fail_insert_mission") pub.subscribe(self._do_log_fail_message, "fail_insert_mission_phase") pub.subscribe(self._do_log_fail_message, "fail_insert_mode") pub.subscribe(self._do_log_fail_message, "fail_insert_function") pub.subscribe(self._do_log_fail_message, "fail_insert_hazard") pub.subscribe(self._do_log_fail_message, "fail_insert_hardware") pub.subscribe(self._do_log_fail_message, "fail_insert_validation") pub.subscribe(self._do_log_fail_message, "fail_insert_stakeholder") pub.subscribe(self._do_log_fail_message, "fail_insert_revision") pub.subscribe(self._do_log_fail_message, "fail_insert_requirement") pub.subscribe(self._do_log_fail_message, "fail_insert_opload") pub.subscribe(self._do_log_fail_message, "fail_insert_opstress") pub.subscribe(self._do_log_fail_message, "fail_insert_record") pub.subscribe(self._do_log_fail_message, "fail_insert_test_method") pub.subscribe(self._do_log_fail_message, "fail_update_fmea") pub.subscribe(self._do_log_fail_message, "fail_update_function") pub.subscribe(self._do_log_fail_message, "fail_update_hardware") pub.subscribe(self._do_log_fail_message, "fail_update_record") pub.subscribe(self._do_log_fail_message, "fail_update_requirement") pub.subscribe(self._do_log_fail_message, "fail_update_revision") pub.subscribe(self.do_log_debug, "do_log_debug_msg") pub.subscribe(self.do_log_info, "do_log_info_msg") pub.subscribe(self.do_log_warning, "do_log_warning_msg") pub.subscribe(self.do_log_error, "do_log_error_msg") pub.subscribe(self.do_log_critical, "do_log_critical_msg") # Create a logger for the pypubsub fail_* messages. self.do_create_logger(__name__, "WARN") def _do_log_fail_message(self, error_message: str) -> None: """Log PyPubSub broadcast fail messages. :param error_message: the error message that was part of the broadcast package. :return: None :rtype: None """ self.loggers[__name__].warning(error_message) @staticmethod def _get_console_handler(log_level: str) -> logging.Handler: """Create the log handler for console output. :return: _c_handler :rtype: :class:`logging.Handler` """ _c_handler = logging.StreamHandler(sys.stdout) _c_handler.setLevel(log_level) _c_handler.setFormatter(LOGFORMAT) return _c_handler def _get_file_handler(self, log_level: str) -> logging.Handler: """Create the log handler for file output. :return: _f_handler :rtype: :class:`logging.Handler` """ _f_handler = logging.FileHandler(self.log_file) _f_handler.setLevel(log_level) _f_handler.setFormatter(LOGFORMAT) return _f_handler def do_create_logger( self, logger_name: str, log_level: str, to_tty: bool = False ) -> None: """Create a logger instance. :param logger_name: the name of the logger used in the application. :param log_level: the level of messages to log. :param to_tty: boolean indicating whether this logger will also dump messages to the terminal. :return: None :rtype: None """ _logger = logging.getLogger(logger_name) _logger.setLevel(log_level) _logger.addHandler(self._get_file_handler(log_level)) if to_tty: _logger.addHandler(self._get_console_handler(log_level)) self.loggers[logger_name] = _logger def do_log_debug(self, logger_name: str, message: str) -> None: """Log DEBUG level messages. :param logger_name: the name of the logger used in the application. :param message: the message to log. :return: None :rtype: None """ if self.loggers[logger_name].isEnabledFor(logging.DEBUG): self.loggers[logger_name].debug(message) def do_log_exception(self, logger_name: str, exception: object) -> None: """Log EXCEPTIONS. :param logger_name: the name of the logger used in the application. :param exception: the exception to log. :return: None :rtype: None """ if self.loggers[logger_name].isEnabledFor(logging.WARNING): self.loggers[logger_name].exception(exception) def do_log_info(self, logger_name: str, message: str) -> None: """Log INFO level messages. :param logger_name: the name of the logger used in the application. :param message: the message to log. :return: None :rtype: None """ if self.loggers[logger_name].isEnabledFor(logging.INFO): self.loggers[logger_name].info(message) def do_log_warning(self, logger_name: str, message: str) -> None: """Log WARN level messages. :param logger_name: the name of the logger used in the application. :param message: the message to log. :return: None :rtype: None """ if self.loggers[logger_name].isEnabledFor(logging.WARNING): self.loggers[logger_name].warning(message) def do_log_error(self, logger_name: str, message: str) -> None: """Log ERROR level messages. :param logger_name: the name of the logger used in the application. :param message: the message to log. :return: None :rtype: None """ if self.loggers[logger_name].isEnabledFor(logging.ERROR): self.loggers[logger_name].error(message) def do_log_critical(self, logger_name: str, message: str) -> None: """Log CRITICAL level messages. :param logger_name: the name of the logger used in the application. :param message: the message to log. :return: None :rtype: None """ self.loggers[logger_name].critical(message)

# -*- coding: utf-8 -*- # # ramstk.logger.py is part of The RAMSTK Project # # All rights reserved. # Copyright 2019 Doyle Rowland doyle.rowland <AT> reliaqual <DOT> com """RAMSTK Logger Module.""" # Standard Library Imports import logging import sys from typing import Dict # Third Party Imports from pubsub import pub LOGFORMAT = logging.Formatter("%(asctime)s - %(name)s - %(lineno)s : %(message)s") class RAMSTKLogManager: """Class to manage logging of RAMSTK messages.""" loggers: Dict[str, logging.Logger] = {} def __init__(self, log_file: str) -> None: """Initialize an instance of the LogManager. :param log_file: the absolute path to the log file to use with this log manager. """ # Initialize private dictionary attributes. # Initialize private list attributes. # Initialize private scalar attributes. # Initialize public dictionary attributes. # Initialize public list attributes. # Initialize public scalar attributes. self.log_file = log_file # Subscribe to PyPubSub messages. pub.subscribe(self._do_log_fail_message, "fail_connect_program_database") pub.subscribe(self._do_log_fail_message, "fail_delete_environment") pub.subscribe(self._do_log_fail_message, "fail_delete_failure_definition") pub.subscribe(self._do_log_fail_message, "fail_delete_fmea") pub.subscribe(self._do_log_fail_message, "fail_delete_function") pub.subscribe(self._do_log_fail_message, "fail_delete_hazard") pub.subscribe(self._do_log_fail_message, "fail_delete_mission") pub.subscribe(self._do_log_fail_message, "fail_delete_mission_phase") pub.subscribe(self._do_log_fail_message, "fail_delete_revision") pub.subscribe(self._do_log_fail_message, "fail_import_module") pub.subscribe(self._do_log_fail_message, "fail_insert_action") pub.subscribe(self._do_log_fail_message, "fail_insert_cause") pub.subscribe(self._do_log_fail_message, "fail_insert_control") pub.subscribe(self._do_log_fail_message, "fail_insert_environment") pub.subscribe(self._do_log_fail_message, "fail_insert_failure_definition") pub.subscribe(self._do_log_fail_message, "fail_insert_mechanism") pub.subscribe(self._do_log_fail_message, "fail_insert_mission") pub.subscribe(self._do_log_fail_message, "fail_insert_mission_phase") pub.subscribe(self._do_log_fail_message, "fail_insert_mode") pub.subscribe(self._do_log_fail_message, "fail_insert_function") pub.subscribe(self._do_log_fail_message, "fail_insert_hazard") pub.subscribe(self._do_log_fail_message, "fail_insert_hardware") pub.subscribe(self._do_log_fail_message, "fail_insert_validation") pub.subscribe(self._do_log_fail_message, "fail_insert_stakeholder") pub.subscribe(self._do_log_fail_message, "fail_insert_revision") pub.subscribe(self._do_log_fail_message, "fail_insert_requirement") pub.subscribe(self._do_log_fail_message, "fail_insert_opload") pub.subscribe(self._do_log_fail_message, "fail_insert_opstress") pub.subscribe(self._do_log_fail_message, "fail_insert_record") pub.subscribe(self._do_log_fail_message, "fail_insert_test_method") pub.subscribe(self._do_log_fail_message, "fail_update_fmea") pub.subscribe(self._do_log_fail_message, "fail_update_function") pub.subscribe(self._do_log_fail_message, "fail_update_hardware") pub.subscribe(self._do_log_fail_message, "fail_update_record") pub.subscribe(self._do_log_fail_message, "fail_update_requirement") pub.subscribe(self._do_log_fail_message, "fail_update_revision") pub.subscribe(self.do_log_debug, "do_log_debug_msg") pub.subscribe(self.do_log_info, "do_log_info_msg") pub.subscribe(self.do_log_warning, "do_log_warning_msg") pub.subscribe(self.do_log_error, "do_log_error_msg") pub.subscribe(self.do_log_critical, "do_log_critical_msg") # Create a logger for the pypubsub fail_* messages. self.do_create_logger(__name__, "WARN") def _do_log_fail_message(self, error_message: str) -> None: """Log PyPubSub broadcast fail messages. :param error_message: the error message that was part of the broadcast package. :return: None :rtype: None """ self.loggers[__name__].warning(error_message) @staticmethod def _get_console_handler(log_level: str) -> logging.Handler: """Create the log handler for console output. :return: _c_handler :rtype: :class:`logging.Handler` """ _c_handler = logging.StreamHandler(sys.stdout) _c_handler.setLevel(log_level) _c_handler.setFormatter(LOGFORMAT) return _c_handler def _get_file_handler(self, log_level: str) -> logging.Handler: """Create the log handler for file output. :return: _f_handler :rtype: :class:`logging.Handler` """ _f_handler = logging.FileHandler(self.log_file) _f_handler.setLevel(log_level) _f_handler.setFormatter(LOGFORMAT) return _f_handler def do_create_logger( self, logger_name: str, log_level: str, to_tty: bool = False ) -> None: """Create a logger instance. :param logger_name: the name of the logger used in the application. :param log_level: the level of messages to log. :param to_tty: boolean indicating whether this logger will also dump messages to the terminal. :return: None :rtype: None """ _logger = logging.getLogger(logger_name) _logger.setLevel(log_level) _logger.addHandler(self._get_file_handler(log_level)) if to_tty: _logger.addHandler(self._get_console_handler(log_level)) self.loggers[logger_name] = _logger def do_log_debug(self, logger_name: str, message: str) -> None: """Log DEBUG level messages. :param logger_name: the name of the logger used in the application. :param message: the message to log. :return: None :rtype: None """ if self.loggers[logger_name].isEnabledFor(logging.DEBUG): self.loggers[logger_name].debug(message) def do_log_exception(self, logger_name: str, exception: object) -> None: """Log EXCEPTIONS. :param logger_name: the name of the logger used in the application. :param exception: the exception to log. :return: None :rtype: None """ if self.loggers[logger_name].isEnabledFor(logging.WARNING): self.loggers[logger_name].exception(exception) def do_log_info(self, logger_name: str, message: str) -> None: """Log INFO level messages. :param logger_name: the name of the logger used in the application. :param message: the message to log. :return: None :rtype: None """ if self.loggers[logger_name].isEnabledFor(logging.INFO): self.loggers[logger_name].info(message) def do_log_warning(self, logger_name: str, message: str) -> None: """Log WARN level messages. :param logger_name: the name of the logger used in the application. :param message: the message to log. :return: None :rtype: None """ if self.loggers[logger_name].isEnabledFor(logging.WARNING): self.loggers[logger_name].warning(message) def do_log_error(self, logger_name: str, message: str) -> None: """Log ERROR level messages. :param logger_name: the name of the logger used in the application. :param message: the message to log. :return: None :rtype: None """ if self.loggers[logger_name].isEnabledFor(logging.ERROR): self.loggers[logger_name].error(message) def do_log_critical(self, logger_name: str, message: str) -> None: """Log CRITICAL level messages. :param logger_name: the name of the logger used in the application. :param message: the message to log. :return: None :rtype: None """ self.loggers[logger_name].critical(message)

f4bbd3c26bf1e8d647337c4dd66784c1c9d86a9f

py

Python

examples/demo/status_overlay.py

martinRenou/chaco

1888da3ecee89f9b2d11900cda9333b32fc5e89a

2017-09-17T17:32:06.000Z

2022-03-15T13:04:43.000Z

examples/demo/status_overlay.py

martinRenou/chaco

1888da3ecee89f9b2d11900cda9333b32fc5e89a

examples/demo/status_overlay.py

martinRenou/chaco

1888da3ecee89f9b2d11900cda9333b32fc5e89a

2015-05-17T16:08:11.000Z

2021-02-23T09:23:42.000Z

import numpy from chaco.api import Plot, ArrayPlotData from chaco.layers.api import ErrorLayer, WarningLayer, StatusLayer from enable.component_editor import ComponentEditor from traits.api import HasTraits, Instance, Button from traitsui.api import UItem, View, HGroup class MyPlot(HasTraits): """ Displays a plot with a few buttons to control which overlay to display """ plot = Instance(Plot) status_overlay = Instance(StatusLayer) error_button = Button('Error') warn_button = Button('Warning') no_problem_button = Button('No problem') traits_view = View( HGroup(UItem('error_button'), UItem('warn_button'), UItem('no_problem_button')), UItem('plot', editor=ComponentEditor()), width=700, height=600, resizable=True, ) def _error_button_fired(self, event): """ removes the old overlay and replaces it with an error overlay """ self.clear_status() self.status_overlay = ErrorLayer(component=self.plot, align='ul', scale_factor=0.25) self.plot.overlays.append(self.status_overlay) self.plot.request_redraw() def _warn_button_fired(self, event): """ removes the old overlay and replaces it with an warning overlay """ self.clear_status() self.status_overlay = WarningLayer(component=self.plot, align='ur', scale_factor=0.25) self.plot.overlays.append(self.status_overlay) self.plot.request_redraw() def _no_problem_button_fired(self, event): """ removes the old overlay """ self.clear_status() self.plot.request_redraw() index = numpy.array([1,2,3,4,5]) data_series = index**2 my_plot = MyPlot(index, data_series) my_plot.configure_traits()

import numpy from chaco.api import Plot, ArrayPlotData from chaco.layers.api import ErrorLayer, WarningLayer, StatusLayer from enable.component_editor import ComponentEditor from traits.api import HasTraits, Instance, Button from traitsui.api import UItem, View, HGroup class MyPlot(HasTraits): """ Displays a plot with a few buttons to control which overlay to display """ plot = Instance(Plot) status_overlay = Instance(StatusLayer) error_button = Button('Error') warn_button = Button('Warning') no_problem_button = Button('No problem') traits_view = View( HGroup(UItem('error_button'), UItem('warn_button'), UItem('no_problem_button')), UItem('plot', editor=ComponentEditor()), width=700, height=600, resizable=True, ) def __init__(self, index, data_series, **kw): super(MyPlot, self).__init__(**kw) plot_data = ArrayPlotData(index=index) plot_data.set_data('data_series', data_series) self.plot = Plot(plot_data) self.plot.plot(('index', 'data_series')) def _error_button_fired(self, event): """ removes the old overlay and replaces it with an error overlay """ self.clear_status() self.status_overlay = ErrorLayer(component=self.plot, align='ul', scale_factor=0.25) self.plot.overlays.append(self.status_overlay) self.plot.request_redraw() def _warn_button_fired(self, event): """ removes the old overlay and replaces it with an warning overlay """ self.clear_status() self.status_overlay = WarningLayer(component=self.plot, align='ur', scale_factor=0.25) self.plot.overlays.append(self.status_overlay) self.plot.request_redraw() def _no_problem_button_fired(self, event): """ removes the old overlay """ self.clear_status() self.plot.request_redraw() def clear_status(self): if self.status_overlay in self.plot.overlays: # fade_out will remove the overlay when its done self.status_overlay.fade_out() index = numpy.array([1,2,3,4,5]) data_series = index**2 my_plot = MyPlot(index, data_series) my_plot.configure_traits()

08cdc43106ee16eac03626a91a328ff78df10a22

py

Python

multi_threadpool_executor.py

Dev-Bobbie/multi_spider

8fd19ab70de04b6cac021d354850b07ffcf360f2

multi_threadpool_executor.py

Dev-Bobbie/multi_spider

8fd19ab70de04b6cac021d354850b07ffcf360f2

multi_threadpool_executor.py

Dev-Bobbie/multi_spider

8fd19ab70de04b6cac021d354850b07ffcf360f2

from concurrent.futures import ThreadPoolExecutor import time if __name__ == '__main__': main()

from concurrent.futures import ThreadPoolExecutor import time def sayhello(a): print("hello: "+a) time.sleep(2) def main(): seed=["a","b","c"] start1=time.time() for each in seed: sayhello(each) end1=time.time() print("time1: "+str(end1-start1)) start2=time.time() with ThreadPoolExecutor(3) as executor: for each in seed: executor.submit(sayhello,each) end2=time.time() print("time2: "+str(end2-start2)) start3=time.time() with ThreadPoolExecutor(3) as executor1: executor1.map(sayhello,seed) end3=time.time() print("time3: "+str(end3-start3)) if __name__ == '__main__': main()

61b5793ee25a599b5e5738633cc2cd220b7bf9e9

py

Python

boss2.py

Jamhacks2018/TheJamExpansion

1acec353e666fef6608e06b57e82683053e7f060

boss2.py

Jamhacks2018/TheJamExpansion

1acec353e666fef6608e06b57e82683053e7f060

boss2.py

Jamhacks2018/TheJamExpansion

1acec353e666fef6608e06b57e82683053e7f060

2018-05-05T19:59:56.000Z

2020-11-15T21:06:27.000Z

from pygame import * from enemies import * import random init() fontGeneral = font.Font('resources/fonts/Calibri.ttf', 30) fontHealth = font.Font('resources/fonts/Calibri Bold.ttf', 15) #draws itself and its health

from pygame import * from enemies import * import random init() fontGeneral = font.Font('resources/fonts/Calibri.ttf', 30) fontHealth = font.Font('resources/fonts/Calibri Bold.ttf', 15) class Cap(): def __init__(self): #initialize the image and pos of cap: self.img = image.load('resources/jam/boss/cap.png') self.x = 0 self.y = -150 self.rect = Rect(self.x, self.y, 722, 149) def draw(self, screen): screen.blit(self.image[self.phase], self.Rect()) self.rect = Rect(self.x, self.y, self.image[1].get_width(), self.image[1].get_height()) def check(self): for b in bullets: if b.rect.colliderrect(self.rect): self.health -= b.dmg #check if it is supposed to die, if dead start boss phase 2: class Boss(): def __init__(self): #initialize the image and pos: self.image = image.load('resources/jam/boss/uncapped.png').convert_alpha() self.w = self.image.get_width() // 5 self.h = self.image.get_width() // 5 self.x = 300 self.y = 25 self.rect = Rect(self.x, self.y, self.w, self.h) self.image = transform.scale(self.image, (self.w, self.h)) self.gun3 = (self.rect.bottomleft[0]+10, self.rect.bottomleft[1]-10) self.gun2 = (self.rect.bottomright[0]+10, self.rect.bottomright[1]-10) self.gun1 = (self.rect.bottomright[0] + self.w // 2, self.rect.bottomright[1]-10) self.guns = [self.gun1, self.gun2] self.firing_speed = [25, 20, 15] self.firing_time = 0 #grace time is reset if grace time is reached self.grace_timers = [120, 90, 65] self.grace_time = 180 #initialize boss properties self.phase = 0 self.max_health = 12000 self.health = self.max_health self.vulnerable = True self.attacks = [False, False] self.directions = 0 #counter of how much boss moved self.frames_spent_moving = 0 #draws itself and its health def draw(self, screen): screen.blit(self.image, self.rect) draw.rect(screen, (255, 0, 255), (15, 700 - 85, int(985 * self.health / self.max_health), 75)) screen.blit(fontGeneral.render("Boss health: %i/%i" %(self.health, self.max_health), 1, (0, 255, 0)), (467 - fontHealth.size("Boss health: %i/%i" %(self.health, self.max_health))[0] // 2, 700 - 55 - fontHealth.size("Boss health: %i/%i" %(self.health, self.max_health))[1] // 2)) def update(self, pl, eb): if self.grace_time == 0: #handles attack timings with some randomness self.attacks[random.randint(0,1)] = True self.directions = random.randint(0,3) #resets movement during attacks self.frames_spent_moving = 0 #handles in between attack grace timers self.grace_time = self.grace_timers[self.phase] else: #handles movement between attacks if self.frames_spent_moving <= 30: self.move() self.frames_spent_moving += 1 self.grace_time -= 1 self.rect = Rect(self.x, self.y, self.w, self.h) self.gun3 = (self.rect.bottomleft[0]+10, self.rect.bottomleft[1]-10) self.gun2 = (self.rect.bottomright[0]+10, self.rect.bottomright[1]-10) self.gun1 = (self.rect.bottomright[0] - self.w // 2, self.rect.bottomright[1]-10) self.guns = [self.gun1, self.gun2] #tries to fire each attack self.sweeper(eb) self.ring(eb) def check(self, bullets, pickups, pl): for b in bullets: if b.rect.colliderect(self.rect): self.health -= b.dmg + pl.dmg_upG #if health permits, spawns a randomly placed heart if 0 <= self.health%500 <= 10 and self.health != self.max_health: pickups.append(Heart(random.randint(300, 700), random.randint(200, 500), random.randint(250, 500))) if 0 <= self.health%250 <= 10 and self.health != self.max_health: self.weakpoint = random.randint(0, 4) self.health -= 11 # checks if it is supposed to die if self.health <= 0: self.health = self.max_health return False #check for phase change elif self.health < 8000: self.phase = 2 elif self.health < 4000: self.phase = 3 return True def move(self): #very similar to pl.directions, moves if it can if self.directions == 0: if self.y < 100: self.y += 3 print("move 1") elif self.directions == 1: if 0 < self.y: self.y -= 3 print("move 2") elif self.directions == 2: if 0 < self.x: self.x -= 10 print("move 3") elif self.directions == 3: if self.x + 800 < 1000: self.x += 10 print("move 4") def sweeper(self, enemyBullets): #shoots stream of bullets from left to right from random guns if self.attacks[1]: for angle in range(10, 170, 5): #checks if timer conditions are just right if self.firing_time + 10 == angle: self.target_angle = (self.gun2[0] + 50 * cos(radians(angle)), self.gun2[1] + 50 * sin(radians(angle))) enemyBullets.append(JamBullet(self.gun2[0], self.gun2[1], self.target_angle[0], self.target_angle[1], 15 * (self.phase + 1))) self.target_angle = (self.gun3[0] + 50 * cos(radians(180 - angle)), self.gun3[1] + 50 * sin(radians(180 -angle))) enemyBullets.append(JamBullet(self.gun3[0], self.gun3[1], self.target_angle[0], self.target_angle[1], 15 * (self.phase + 1))) #ends attack if self.firing_time + 10 >= 170: self.attacks[1] = False self.firing_time = 0 break else: self.firing_time += 2 def ring(self, enemyBullets): if self.attacks[0]: for angle in range(0, 360, 10): if self.firing_time == angle: self.target_angle = (self.rect.centerx + 50 * cos(radians(angle)), self.rect.centery + 50 * sin(radians(angle))) enemyBullets.append(JamBullet(self.rect.centerx, self.rect.centery, self.target_angle[0], self.target_angle[1], 15 * self.phase)) if self.firing_time >= 360: self.attacks[0] = False self.firing_time = 0 break else: self.firing_time += 2.5

9084b7ccd8e3dba852fd6469469662507b5a8c2b

py

Python

src/simulator/network_wrong_mi.py

ChenGeng-ZJU/PCC-RL

6627a186643175ea68269d78e206e6bc45ac634f

src/simulator/network_wrong_mi.py

ChenGeng-ZJU/PCC-RL

6627a186643175ea68269d78e206e6bc45ac634f

src/simulator/network_wrong_mi.py

ChenGeng-ZJU/PCC-RL

6627a186643175ea68269d78e206e6bc45ac634f

# Copyright 2019 Nathan Jay and Noga Rotman # # 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 csv import heapq import os import random import sys import time import math import warnings warnings.simplefilter(action='ignore', category=UserWarning) import gym from gym import spaces from gym.envs.registration import register from gym.utils import seeding import numpy as np from common import sender_obs from common.utils import pcc_aurora_reward, read_json_file from simulator.trace import Trace import pandas as pd MAX_CWND = 5000 MIN_CWND = 4 MAX_RATE = 20000 MIN_RATE = 5 REWARD_SCALE = 0.001 EVENT_TYPE_SEND = 'S' EVENT_TYPE_ACK = 'A' BYTES_PER_PACKET = 1500 LATENCY_PENALTY = 1.0 LOSS_PENALTY = 1.0 USE_LATENCY_NOISE = True MAX_LATENCY_NOISE = 1.1 # DEBUG = True DEBUG = False MI_RTT_PROPORTION = 1.0 # PACKET_LOG_FLAG = False PACKET_LOG_FLAG = True register(id='PccNs-v0', entry_point='simulator.network:SimulatedNetworkEnv')

# Copyright 2019 Nathan Jay and Noga Rotman # # 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 csv import heapq import os import random import sys import time import math import warnings warnings.simplefilter(action='ignore', category=UserWarning) import gym from gym import spaces from gym.envs.registration import register from gym.utils import seeding import numpy as np from common import sender_obs from common.utils import pcc_aurora_reward, read_json_file from simulator.trace import Trace import pandas as pd MAX_CWND = 5000 MIN_CWND = 4 MAX_RATE = 20000 MIN_RATE = 5 REWARD_SCALE = 0.001 EVENT_TYPE_SEND = 'S' EVENT_TYPE_ACK = 'A' BYTES_PER_PACKET = 1500 LATENCY_PENALTY = 1.0 LOSS_PENALTY = 1.0 USE_LATENCY_NOISE = True MAX_LATENCY_NOISE = 1.1 # DEBUG = True DEBUG = False MI_RTT_PROPORTION = 1.0 # PACKET_LOG_FLAG = False PACKET_LOG_FLAG = True def debug_print(msg): if DEBUG: print(msg, file=sys.stderr, flush=True) class EmuReplay: def __init__(self, ): df = pd.read_csv('aurora_emulation_log.csv') self.ts = df['timestamp'].tolist() self.send_rate = df['send_rate'].tolist() self.idx = 0 def get_ts(self): if self.idx > len(self.ts): self.idx = len(self.ts) -1 ts = self.ts[self.idx] self.idx += 1 return ts def get_rate(self): return self.send_rate[self.idx] / 8 / BYTES_PER_PACKET def reset(self): self.idx = 0 class Link(): def __init__(self, trace: Trace): self.trace = trace self.queue_delay = 0.0 self.queue_delay_update_time = 0.0 self.queue_size = self.trace.get_queue_size() self.pkt_in_queue = 0 def get_cur_queue_delay(self, event_time): self.pkt_in_queue = max(0, self.pkt_in_queue - (event_time - self.queue_delay_update_time) * self.get_bandwidth(event_time)) self.queue_delay_update_time = event_time cur_queue_delay = math.ceil( self.pkt_in_queue) / self.get_bandwidth(event_time) return cur_queue_delay def get_cur_latency(self, event_time): q_delay = self.get_cur_queue_delay(event_time) # print('queue delay: ', q_delay) return self.trace.get_delay(event_time) / 1000.0 + q_delay def packet_enters_link(self, event_time): if (random.random() < self.trace.get_loss_rate()): return False self.queue_delay = self.get_cur_queue_delay(event_time) extra_delay = 1.0 / self.get_bandwidth(event_time) if 1 + math.ceil(self.pkt_in_queue) > self.queue_size: # print("{}\tDrop!".format(event_time)) return False self.queue_delay += extra_delay self.pkt_in_queue += 1 return True def print_debug(self): print("Link:") # TODO: Do not use timestamp 0. print("Bandwidth: %.3fMbps" % (self.trace.get_bandwidth(0))) # TODO: Do not use timestamp 0. print("Delay: %.3fms" % (self.trace.get_delay(0))) print("Queue Delay: %.3fms" % (self.queue_delay * 1000)) print("One Packet Queue Delay: %.3fms" % ( 1000.0 * 1 / (self.trace.get_bandwidth(0) * 1e6 / 8 / BYTES_PER_PACKET))) print("Queue size: %dpackets" % self.queue_size) print("Loss: %.4f" % self.trace.get_loss_rate()) def reset(self): self.queue_delay = 0.0 self.queue_delay_update_time = 0.0 self.pkt_in_queue = 0 def get_bandwidth(self, ts): return self.trace.get_bandwidth(ts) * 1e6 / 8 / BYTES_PER_PACKET class Network(): def __init__(self, senders, links, env): self.event_count = 0 self.q = [] self.cur_time = 0.0 self.senders = senders self.links = links self.queue_initial_packets() self.env = env self.pkt_log = [] def queue_initial_packets(self): for sender in self.senders: sender.register_network(self) sender.reset_obs() heapq.heappush(self.q, (0, sender, EVENT_TYPE_SEND, 0, 0.0, False, self.event_count, sender.rto, 0)) self.event_count += 1 def reset(self): self.pkt_log = [] self.cur_time = 0.0 self.q = [] [link.reset() for link in self.links] [sender.reset() for sender in self.senders] self.queue_initial_packets() def get_cur_time(self): return self.cur_time def run_for_dur(self, dur, action=None): # if self.cur_time > 1.75: # pass # else: # self.senders[0].rate = self.env.replay.get_rate() # dur = self.env.replay.get_ts() - self.cur_time end_time = min(self.cur_time + dur, self.env.current_trace.timestamps[-1]) debug_print('MI from {} to {}, dur {}'.format( self.cur_time, end_time, dur)) for sender in self.senders: sender.reset_obs() while True: event_time, sender, event_type, next_hop, cur_latency, dropped, \ event_id, rto, event_queue_delay = self.q[0] if event_time >= end_time: self.cur_time = end_time break event_time, sender, event_type, next_hop, cur_latency, dropped, \ event_id, rto, event_queue_delay = heapq.heappop(self.q) self.cur_time = event_time new_event_time = event_time new_event_type = event_type new_next_hop = next_hop new_latency = cur_latency new_dropped = dropped new_event_queue_delay = event_queue_delay push_new_event = False debug_print("Got %d event %s, to link %d, latency %f at time %f, " "next_hop %d, dropped %s, event_q length %f, " "sender rate %f, duration: %f, queue_size: %f, " "rto: %f, cwnd: %f, ssthresh: %f, sender rto %f, " "pkt in flight %d, wait time %d" % ( event_id, event_type, next_hop, cur_latency, event_time, next_hop, dropped, len(self.q), sender.rate, dur, self.links[0].queue_size, rto, sender.cwnd, sender.ssthresh, sender.rto, int(sender.bytes_in_flight/BYTES_PER_PACKET), sender.pkt_loss_wait_time)) if event_type == EVENT_TYPE_ACK: if next_hop == len(sender.path): # if cur_latency > 1.0: # sender.timeout(cur_latency) # sender.on_packet_lost(cur_latency) if rto >= 0 and cur_latency > rto and sender.pkt_loss_wait_time <= 0: sender.timeout() dropped = True new_dropped = True elif dropped: sender.on_packet_lost(cur_latency) if PACKET_LOG_FLAG: self.pkt_log.append([self.cur_time, event_id, 'lost', BYTES_PER_PACKET]) else: sender.on_packet_acked(cur_latency) debug_print('Ack packet at {}'.format(self.cur_time)) # log packet acked if PACKET_LOG_FLAG: self.pkt_log.append([self.cur_time, event_id, 'acked', BYTES_PER_PACKET, cur_latency, event_queue_delay]) else: new_next_hop = next_hop + 1 new_event_queue_delay += sender.path[next_hop].get_cur_queue_delay( self.cur_time) link_latency = sender.path[next_hop].get_cur_latency( self.cur_time) # link_latency *= self.env.current_trace.get_delay_noise_replay(self.cur_time) # if USE_LATENCY_NOISE: # link_latency *= random.uniform(1.0, MAX_LATENCY_NOISE) new_latency += link_latency new_event_time += link_latency push_new_event = True elif event_type == EVENT_TYPE_SEND: if next_hop == 0: if sender.can_send_packet(): sender.on_packet_sent() # print('Send packet at {}'.format(self.cur_time)) if PACKET_LOG_FLAG: self.pkt_log.append([self.cur_time, event_id, 'sent', BYTES_PER_PACKET]) push_new_event = True heapq.heappush(self.q, (self.cur_time + (1.0 / sender.rate), sender, EVENT_TYPE_SEND, 0, 0.0, False, self.event_count, sender.rto, 0)) self.event_count += 1 else: push_new_event = True if next_hop == sender.dest: new_event_type = EVENT_TYPE_ACK new_next_hop = next_hop + 1 new_event_queue_delay += sender.path[next_hop].get_cur_queue_delay( self.cur_time) link_latency = sender.path[next_hop].get_cur_latency( self.cur_time) # if USE_LATENCY_NOISE: # link_latency *= random.uniform(1.0, MAX_LATENCY_NOISE) # link_latency += self.env.current_trace.get_delay_noise(self.cur_time) / 1000 # link_latency *= self.env.current_trace.get_delay_noise_replay(self.cur_time) new_latency += link_latency new_event_time += link_latency new_dropped = not sender.path[next_hop].packet_enters_link( self.cur_time) if not new_dropped: sender.queue_delay_samples.append(new_event_queue_delay) if push_new_event: heapq.heappush(self.q, (new_event_time, sender, new_event_type, new_next_hop, new_latency, new_dropped, event_id, rto, new_event_queue_delay)) for sender in self.senders: sender.record_run() sender_mi = self.senders[0].get_run_data() throughput = sender_mi.get("recv rate") # bits/sec latency = sender_mi.get("avg latency") # second loss = sender_mi.get("loss ratio") debug_print("thpt %f, delay %f, loss %f, bytes sent %f, bytes acked %f" % ( throughput/1e6, latency, loss, sender_mi.bytes_sent, sender_mi.bytes_acked)) reward = pcc_aurora_reward( throughput / 8 / BYTES_PER_PACKET, latency, loss, np.mean(self.env.current_trace.bandwidths) * 1e6 / 8 / BYTES_PER_PACKET) if latency > 0.0: self.env.run_dur = MI_RTT_PROPORTION * sender_mi.get("avg latency") + (1 / self.links[0].get_bandwidth(self.cur_time)) # self.env.run_dur = max(MI_RTT_PROPORTION * sender_mi.get("avg latency"), 5 * (1 / self.senders[0].rate)) # print(self.env.run_dur) return reward * REWARD_SCALE class Sender(): def __init__(self, rate, path, dest, features, cwnd=25, history_len=10, delta_scale=1): self.id = Sender._get_next_id() self.delta_scale = delta_scale self.starting_rate = rate self.rate = rate self.sent = 0 self.acked = 0 self.lost = 0 self.bytes_in_flight = 0 self.min_latency = None self.rtt_samples = [] self.queue_delay_samples = [] self.prev_rtt_samples = self.rtt_samples self.sample_time = [] self.net = None self.path = path self.dest = dest self.history_len = history_len self.features = features self.history = sender_obs.SenderHistory(self.history_len, self.features, self.id) self.cwnd = cwnd self.use_cwnd = False self.rto = -1 self.ssthresh = 0 self.pkt_loss_wait_time = -1 self.estRTT = 1000000 / 1e6 # SynInterval in emulation self.RTTVar = self.estRTT / 2 # RTT variance # self.got_data = False _next_id = 1 def _get_next_id(): result = Sender._next_id Sender._next_id += 1 return result def apply_rate_delta(self, delta): # if self.got_data: delta *= self.delta_scale #print("Applying delta %f" % delta) if delta >= 0.0: self.set_rate(self.rate * (1.0 + delta)) else: self.set_rate(self.rate / (1.0 - delta)) def apply_cwnd_delta(self, delta): delta *= self.delta_scale #print("Applying delta %f" % delta) if delta >= 0.0: self.set_cwnd(self.cwnd * (1.0 + delta)) else: self.set_cwnd(self.cwnd / (1.0 - delta)) def can_send_packet(self): if self.use_cwnd: return int(self.bytes_in_flight) / BYTES_PER_PACKET < self.cwnd else: return True def register_network(self, net): self.net = net def on_packet_sent(self): self.sent += 1 self.bytes_in_flight += BYTES_PER_PACKET def on_packet_acked(self, rtt): self.estRTT = (7.0 * self.estRTT + rtt) / 8.0 # RTT of emulation way self.RTTVar = (self.RTTVar * 7.0 + abs(rtt - self.estRTT) * 1.0) / 8.0 self.acked += 1 self.rtt_samples.append(rtt) # self.rtt_samples.append(self.estRTT) if (self.min_latency is None) or (rtt < self.min_latency): self.min_latency = rtt self.bytes_in_flight -= BYTES_PER_PACKET def on_packet_lost(self, rtt): self.lost += 1 self.bytes_in_flight -= BYTES_PER_PACKET def set_rate(self, new_rate): self.rate = new_rate # print("Attempt to set new rate to %f (min %f, max %f)" % (new_rate, MIN_RATE, MAX_RATE)) if self.rate > MAX_RATE: self.rate = MAX_RATE if self.rate < MIN_RATE: self.rate = MIN_RATE def set_cwnd(self, new_cwnd): self.cwnd = int(new_cwnd) #print("Attempt to set new rate to %f (min %f, max %f)" % (new_rate, MIN_RATE, MAX_RATE)) # if self.cwnd > MAX_CWND: # self.cwnd = MAX_CWND # if self.cwnd < MIN_CWND: # self.cwnd = MIN_CWND def record_run(self): smi = self.get_run_data() # if not self.got_data and smi.rtt_samples: # self.got_data = True # self.history.step(smi) # else: self.history.step(smi) def get_obs(self): return self.history.as_array() def get_run_data(self): obs_end_time = self.net.get_cur_time() #obs_dur = obs_end_time - self.obs_start_time #print("Got %d acks in %f seconds" % (self.acked, obs_dur)) #print("Sent %d packets in %f seconds" % (self.sent, obs_dur)) #print("self.rate = %f" % self.rate) # print(self.acked, self.sent) rtt_samples = self.rtt_samples if self.rtt_samples else self.prev_rtt_samples # if not self.rtt_samples: # print(self.obs_start_time, obs_end_time, self.rate) # rtt_samples is empty when there is no packet acked in MI # Solution: inherit from previous rtt_samples. return sender_obs.SenderMonitorInterval( self.id, bytes_sent=self.sent * BYTES_PER_PACKET, bytes_acked=self.acked * BYTES_PER_PACKET, bytes_lost=self.lost * BYTES_PER_PACKET, send_start=self.obs_start_time, send_end=obs_end_time, recv_start=self.obs_start_time, recv_end=obs_end_time, rtt_samples=self.rtt_samples, queue_delay_samples=self.queue_delay_samples, packet_size=BYTES_PER_PACKET ) def reset_obs(self): self.sent = 0 self.acked = 0 self.lost = 0 if self.rtt_samples: self.prev_rtt_samples = self.rtt_samples self.rtt_samples = [] self.queue_delay_samples = [] self.obs_start_time = self.net.get_cur_time() def print_debug(self): print("Sender:") print("Obs: %s" % str(self.get_obs())) print("Rate: %f" % self.rate) print("Sent: %d" % self.sent) print("Acked: %d" % self.acked) print("Lost: %d" % self.lost) print("Min Latency: %s" % str(self.min_latency)) def reset(self): #print("Resetting sender!") self.rate = self.starting_rate self.bytes_in_flight = 0 self.min_latency = None self.reset_obs() self.history = sender_obs.SenderHistory(self.history_len, self.features, self.id) self.estRTT = 1000000 / 1e6 # SynInterval in emulation self.RTTVar = self.estRTT / 2 # RTT variance # self.got_data = False def timeout(self): # placeholder pass class SimulatedNetworkEnv(gym.Env): def __init__(self, traces, history_len=10, features="sent latency inflation,latency ratio,send ratio", congestion_control_type="aurora", train_flag=False, delta_scale=1.0): """Network environment used in simulation. congestion_control_type: aurora is pcc-rl. cubic is TCPCubic. """ assert congestion_control_type in {"aurora", "cubic"}, \ "Unrecognized congestion_control_type {}.".format( congestion_control_type) # self.replay = EmuReplay() self.delta_scale = delta_scale self.traces = traces self.current_trace = np.random.choice(self.traces) self.train_flag = train_flag self.congestion_control_type = congestion_control_type if self.congestion_control_type == 'aurora': self.use_cwnd = False elif self.congestion_control_type == 'cubic': self.use_cwnd = True self.history_len = history_len # print("History length: %d" % history_len) self.features = features.split(",") # print("Features: %s" % str(self.features)) self.links = None self.senders = None self.create_new_links_and_senders() self.net = Network(self.senders, self.links, self) self.run_dur = None self.run_period = 0.1 self.steps_taken = 0 self.debug_thpt_changes = False self.last_thpt = None self.last_rate = None if self.use_cwnd: self.action_space = spaces.Box( np.array([-1e12, -1e12]), np.array([1e12, 1e12]), dtype=np.float32) else: self.action_space = spaces.Box( np.array([-1e12]), np.array([1e12]), dtype=np.float32) self.observation_space = None # use_only_scale_free = True single_obs_min_vec = sender_obs.get_min_obs_vector(self.features) single_obs_max_vec = sender_obs.get_max_obs_vector(self.features) self.observation_space = spaces.Box(np.tile(single_obs_min_vec, self.history_len), np.tile(single_obs_max_vec, self.history_len), dtype=np.float32) self.reward_sum = 0.0 self.reward_ewma = 0.0 self.episodes_run = -1 def seed(self, seed=None): self.rand, seed = seeding.np_random(seed) return [seed] def _get_all_sender_obs(self): sender_obs = self.senders[0].get_obs() sender_obs = np.array(sender_obs).reshape(-1,) return sender_obs def step(self, actions): #print("Actions: %s" % str(actions)) # print(actions) for i in range(0, 1): # len(actions)): #print("Updating rate for sender %d" % i) action = actions self.senders[i].apply_rate_delta(action[0]) if self.use_cwnd: self.senders[i].apply_cwnd_delta(action[1]) # print("Running for %fs" % self.run_dur) reward = self.net.run_for_dur(self.run_dur, action=actions[0]) self.steps_taken += 1 sender_obs = self._get_all_sender_obs() should_stop = self.current_trace.is_finished(self.net.get_cur_time()) self.reward_sum += reward # print('env step: {}s'.format(time.time() - t_start)) return sender_obs, reward, should_stop, {} def print_debug(self): print("---Link Debug---") for link in self.links: link.print_debug() print("---Sender Debug---") for sender in self.senders: sender.print_debug() def create_new_links_and_senders(self): # self.replay.reset() self.links = [Link(self.current_trace), Link(self.current_trace)] if self.congestion_control_type == "aurora": if not self.train_flag: self.senders = [Sender( #self.replay.get_rate(), # 2500000 / 8 /BYTES_PER_PACKET / 0.048, # 12000000 / 8 /BYTES_PER_PACKET / 0.048, # 10 / (self.current_trace.get_delay(0) *2/1000), 100, [self.links[0], self.links[1]], 0, self.features, history_len=self.history_len, delta_scale=self.delta_scale)] else: # self.senders = [Sender(random.uniform(0.3, 1.5) * bw, # [self.links[0], self.links[1]], 0, # self.features, # history_len=self.history_len)] # self.senders = [Sender(random.uniform(10/bw, 1.5) * bw, # [self.links[0], self.links[1]], 0, # self.features, # history_len=self.history_len, # delta_scale=self.delta_scale)] self.senders = [Sender(100, [self.links[0], self.links[1]], 0, self.features, history_len=self.history_len, delta_scale=self.delta_scale)] elif self.congestion_control_type == "cubic": raise NotImplementedError else: raise RuntimeError("Unrecognized congestion_control_type {}".format( self.congestion_control_type)) # self.run_dur = 3 * lat # self.run_dur = 1 * lat if not self.senders[0].rtt_samples: # self.run_dur = 0.473 # self.run_dur = 5 / self.senders[0].rate self.run_dur = 0.01 # self.run_dur = self.current_trace.get_delay(0) * 2 / 1000 # self.run_dur = self.replay.get_ts() - 0 def reset(self): self.steps_taken = 0 self.net.reset() self.current_trace = np.random.choice(self.traces) self.current_trace.reset() self.create_new_links_and_senders() self.net = Network(self.senders, self.links, self) self.episodes_run += 1 # self.replay.reset() self.net.run_for_dur(self.run_dur) self.reward_ewma *= 0.99 self.reward_ewma += 0.01 * self.reward_sum # print("Reward: %0.2f, Ewma Reward: %0.2f" % (self.reward_sum, self.reward_ewma)) self.reward_sum = 0.0 return self._get_all_sender_obs() register(id='PccNs-v0', entry_point='simulator.network:SimulatedNetworkEnv')

e2b17755e0aaa5b3a5cbb71d2ff79a60e5f99eea

py

Python

cheddar_oauth_example/settings.py

brianbrunner/cheddar-oauth-demo

7768023a355d9cdc2e861aded2c05ebe3246c930

2015-05-26T18:21:32.000Z

2015-05-26T18:21:32.000Z

cheddar_oauth_example/settings.py

brianbrunner/cheddar-oauth-demo

7768023a355d9cdc2e861aded2c05ebe3246c930

cheddar_oauth_example/settings.py

brianbrunner/cheddar-oauth-demo

7768023a355d9cdc2e861aded2c05ebe3246c930

""" Django settings for cheddar_oauth_example project. For more information on this file, see https://docs.djangoproject.com/en/1.7/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.7/ref/settings/ """ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) import os BASE_DIR = os.path.dirname(os.path.dirname(__file__)) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.7/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '43cy=fmsak_xqkme&yi9@c^+-*0pvr%s+-of!yzx6rdiw*!bxt' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True TEMPLATE_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', 'social.apps.django_app.default', 'django.contrib.humanize', 'app', ) MIDDLEWARE_CLASSES = ( '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 = 'cheddar_oauth_example.urls' WSGI_APPLICATION = 'cheddar_oauth_example.wsgi.application' AUTHENTICATION_BACKENDS = ( 'oauth.cheddar.CheddarOAuth2', ) TEMPLATE_CONTEXT_PROCESSORS = ( 'social.apps.django_app.context_processors.backends', 'social.apps.django_app.context_processors.login_redirect', ) # Database # https://docs.djangoproject.com/en/1.7/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Internationalization # https://docs.djangoproject.com/en/1.7/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.7/howto/static-files/ STATIC_URL = '/static/' # SOCIAL_AUTH_CHEDDAR_SCOPE = [] SOCIAL_AUTH_LOGIN_REDIRECT_URL = '/' SOCIAL_AUTH_LOGIN_ERROR_URL = '/login_error' # Logging LOGGING = { 'version': 1, 'formatters': { 'verbose': { 'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s' }, 'simple': { 'format': '%(levelname)s %(message)s' }, }, 'handlers': { 'console': { 'level': 'DEBUG', 'class': 'logging.StreamHandler', 'formatter': 'simple' }, }, 'loggers': { 'django': { 'handlers': ['console'], 'level': 'DEBUG', 'propagate': True, }, } } # Import Local Settings try: from local_settings import * except ImportError as e: print "FAILED TO IMPORT LOCAL SETTINGS: %s" % e

""" Django settings for cheddar_oauth_example project. For more information on this file, see https://docs.djangoproject.com/en/1.7/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.7/ref/settings/ """ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) import os BASE_DIR = os.path.dirname(os.path.dirname(__file__)) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.7/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '43cy=fmsak_xqkme&yi9@c^+-*0pvr%s+-of!yzx6rdiw*!bxt' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True TEMPLATE_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', 'social.apps.django_app.default', 'django.contrib.humanize', 'app', ) MIDDLEWARE_CLASSES = ( '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 = 'cheddar_oauth_example.urls' WSGI_APPLICATION = 'cheddar_oauth_example.wsgi.application' AUTHENTICATION_BACKENDS = ( 'oauth.cheddar.CheddarOAuth2', ) TEMPLATE_CONTEXT_PROCESSORS = ( 'social.apps.django_app.context_processors.backends', 'social.apps.django_app.context_processors.login_redirect', ) # Database # https://docs.djangoproject.com/en/1.7/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Internationalization # https://docs.djangoproject.com/en/1.7/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.7/howto/static-files/ STATIC_URL = '/static/' # SOCIAL_AUTH_CHEDDAR_SCOPE = [] SOCIAL_AUTH_LOGIN_REDIRECT_URL = '/' SOCIAL_AUTH_LOGIN_ERROR_URL = '/login_error' # Logging LOGGING = { 'version': 1, 'formatters': { 'verbose': { 'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s' }, 'simple': { 'format': '%(levelname)s %(message)s' }, }, 'handlers': { 'console': { 'level': 'DEBUG', 'class': 'logging.StreamHandler', 'formatter': 'simple' }, }, 'loggers': { 'django': { 'handlers': ['console'], 'level': 'DEBUG', 'propagate': True, }, } } # Import Local Settings try: from local_settings import * except ImportError as e: print "FAILED TO IMPORT LOCAL SETTINGS: %s" % e

1097c97b6b77b2f181e0c5a9531a0851278011cb

py

Python

django101/django102/urls.py

Minkov/python-web-2020-09

a43baf4dd4dd811caf25aad971a0f1a4d3d486a4

2020-10-30T23:13:50.000Z

2020-12-26T21:35:00.000Z

django101/django102/urls.py

Minkov/python-web-2020-09

a43baf4dd4dd811caf25aad971a0f1a4d3d486a4

django101/django102/urls.py

Minkov/python-web-2020-09

a43baf4dd4dd811caf25aad971a0f1a4d3d486a4

2020-09-17T13:08:35.000Z

2020-10-31T15:01:46.000Z

from django.urls import path from django102.views import index as index_view, UsersListView, GamesListView, something, methods_demo, \ raises_exception, create_game urlpatterns = [ path('', index_view, name='index'), path('2/', UsersListView.as_view()), path('games/', GamesListView.as_view()), path('smth/', something), path('methods/', methods_demo), path('raises/', raises_exception), path('creategame/', create_game), ]

from django.urls import path from django102.views import index as index_view, UsersListView, GamesListView, something, methods_demo, \ raises_exception, create_game urlpatterns = [ path('', index_view, name='index'), path('2/', UsersListView.as_view()), path('games/', GamesListView.as_view()), path('smth/', something), path('methods/', methods_demo), path('raises/', raises_exception), path('creategame/', create_game), ]

699f409bdd5d561bb93770f28b38f939f53fc421

py

Python

3_dataset_create.py

shivanirmishra/musicgenre

954214b6f7756c05de1253702811fd69dd99b0e2

3_dataset_create.py

shivanirmishra/musicgenre

954214b6f7756c05de1253702811fd69dd99b0e2

3_dataset_create.py

shivanirmishra/musicgenre

954214b6f7756c05de1253702811fd69dd99b0e2

from google.colab import drive drive.mount('/content/drive') import librosa import os import pandas as pd from numpy import mean import warnings; warnings.filterwarnings('ignore'); folders_5s = { 'pop_5s':'/content/drive/My Drive/ML_Project/New_Data/pop_test_5s', 'rnb_5s':'/content/drive/My Drive/ML_Project/New_Data/rnb_test_5s', 'blues_5s':'/content/drive/My Drive/ML_Project/New_Data/blues_test_5s', 'hiphop_5s':'/content/drive/My Drive/ML_Project/New_Data/hiphop_test_5s', 'rock_5s':'/content/drive/My Drive/ML_Project/New_Data/rock_test_5s' } folders_10s = { 'pop_10s':'/content/drive/My Drive/ML_Project/New_Data/pop_test_10s', 'rnb_10s':'/content/drive/My Drive/ML_Project/New_Data/rnb_test_10s', 'blues_10s':'/content/drive/My Drive/ML_Project/New_Data/blues_test_10s', 'hiphop_10s':'/content/drive/My Drive/ML_Project/New_Data/hiphop_test_10s', 'rock_10s':'/content/drive/My Drive/ML_Project/New_Data/rock_test_10s' } folders_20s = { 'pop_20s':'/content/drive/My Drive/ML_Project/New_Data/pop_test_20s', 'rnb_20s':'/content/drive/My Drive/ML_Project/New_Data/rnb_test_20s', 'blues_20s':'/content/drive/My Drive/ML_Project/New_Data/blues_test_20s', 'hiphop_20s':'/content/drive/My Drive/ML_Project/New_Data/hiphop_test_20s', 'rock_20s':'/content/drive/My Drive/ML_Project/New_Data/rock_test_20s' } label = { 'pop_5s': 0, 'rnb_5s': 1, 'blues_5s': 2, 'hiphop_5s': 3, 'rock_5s': 4, 'pop_10s': 0, 'rnb_10s': 1, 'blues_10s': 2, 'hiphop_10s': 3, 'rock_10s': 4, 'pop_20s': 0, 'rnb_20s': 1, 'blues_20s': 2, 'hiphop_20s': 3, 'rock_20s': 4 } data_5s = [] data_10s = [] data_20s = [] for name, path in folders_5s.items(): #count_5s = 3000 for filename in os.listdir(path): # if(count_5s == 0): # break songData = [] songname = f'{path}/{filename}' y, sr = librosa.load(songname, mono=True) tempo, beats = librosa.beat.beat_track(y=y, sr=sr) songData.append(tempo) songData.append(mean(beats)) chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr) songData.append(mean(chroma_stft)) rmse = librosa.feature.rmse(y=y) songData.append(mean(rmse)) spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr) songData.append(mean(spec_cent)) spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr) songData.append(mean(spec_bw)) rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr) songData.append(mean(rolloff)) zcr = librosa.feature.zero_crossing_rate(y) songData.append(mean(zcr)) mfcc = librosa.feature.mfcc(y=y, sr=sr) for i in mfcc: songData.append(mean(i)) songData.append(label[name]) data_5s.append(songData) #count_5s -= 1 for name, path in folders_10s.items(): #count_10s = 1500 for filename in os.listdir(path): # if(count_10s == 0): # break songData = [] songname = f'{path}/{filename}' y, sr = librosa.load(songname, mono=True) tempo, beats = librosa.beat.beat_track(y=y, sr=sr) songData.append(tempo) songData.append(mean(beats)) chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr) songData.append(mean(chroma_stft)) rmse = librosa.feature.rmse(y=y) songData.append(mean(rmse)) spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr) songData.append(mean(spec_cent)) spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr) songData.append(mean(spec_bw)) rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr) songData.append(mean(rolloff)) zcr = librosa.feature.zero_crossing_rate(y) songData.append(mean(zcr)) mfcc = librosa.feature.mfcc(y=y, sr=sr) for i in mfcc: songData.append(mean(i)) songData.append(label[name]) data_10s.append(songData) #count_10s -= 1 for name, path in folders_20s.items(): #count_20s = 900 for filename in os.listdir(path): # if(count_20s == 0): # break songData = [] songname = f'{path}/{filename}' y, sr = librosa.load(songname, mono=True) tempo, beats = librosa.beat.beat_track(y=y, sr=sr) songData.append(tempo) songData.append(mean(beats)) chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr) songData.append(mean(chroma_stft)) rmse = librosa.feature.rmse(y=y) songData.append(mean(rmse)) spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr) songData.append(mean(spec_cent)) spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr) songData.append(mean(spec_bw)) rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr) songData.append(mean(rolloff)) zcr = librosa.feature.zero_crossing_rate(y) songData.append(mean(zcr)) mfcc = librosa.feature.mfcc(y=y, sr=sr) for i in mfcc: songData.append(mean(i)) songData.append(label[name]) data_20s.append(songData) #count_20s -= 1 data_5s = pd.DataFrame(data_5s) data_5s.to_csv('/content/drive/My Drive/ML_Project/data_5s_test_all_genres.csv') data_10s = pd.DataFrame(data_10s) data_10s.to_csv('/content/drive/My Drive/ML_Project/data_10s_test_all_genres.csv') data_20s = pd.DataFrame(data_20s) data_20s.to_csv('/content/drive/My Drive/ML_Project/data_20s_test_all_genres.csv') data_10s

from google.colab import drive drive.mount('/content/drive') import librosa import os import pandas as pd from numpy import mean import warnings; warnings.filterwarnings('ignore'); folders_5s = { 'pop_5s':'/content/drive/My Drive/ML_Project/New_Data/pop_test_5s', 'rnb_5s':'/content/drive/My Drive/ML_Project/New_Data/rnb_test_5s', 'blues_5s':'/content/drive/My Drive/ML_Project/New_Data/blues_test_5s', 'hiphop_5s':'/content/drive/My Drive/ML_Project/New_Data/hiphop_test_5s', 'rock_5s':'/content/drive/My Drive/ML_Project/New_Data/rock_test_5s' } folders_10s = { 'pop_10s':'/content/drive/My Drive/ML_Project/New_Data/pop_test_10s', 'rnb_10s':'/content/drive/My Drive/ML_Project/New_Data/rnb_test_10s', 'blues_10s':'/content/drive/My Drive/ML_Project/New_Data/blues_test_10s', 'hiphop_10s':'/content/drive/My Drive/ML_Project/New_Data/hiphop_test_10s', 'rock_10s':'/content/drive/My Drive/ML_Project/New_Data/rock_test_10s' } folders_20s = { 'pop_20s':'/content/drive/My Drive/ML_Project/New_Data/pop_test_20s', 'rnb_20s':'/content/drive/My Drive/ML_Project/New_Data/rnb_test_20s', 'blues_20s':'/content/drive/My Drive/ML_Project/New_Data/blues_test_20s', 'hiphop_20s':'/content/drive/My Drive/ML_Project/New_Data/hiphop_test_20s', 'rock_20s':'/content/drive/My Drive/ML_Project/New_Data/rock_test_20s' } label = { 'pop_5s': 0, 'rnb_5s': 1, 'blues_5s': 2, 'hiphop_5s': 3, 'rock_5s': 4, 'pop_10s': 0, 'rnb_10s': 1, 'blues_10s': 2, 'hiphop_10s': 3, 'rock_10s': 4, 'pop_20s': 0, 'rnb_20s': 1, 'blues_20s': 2, 'hiphop_20s': 3, 'rock_20s': 4 } data_5s = [] data_10s = [] data_20s = [] for name, path in folders_5s.items(): #count_5s = 3000 for filename in os.listdir(path): # if(count_5s == 0): # break songData = [] songname = f'{path}/{filename}' y, sr = librosa.load(songname, mono=True) tempo, beats = librosa.beat.beat_track(y=y, sr=sr) songData.append(tempo) songData.append(mean(beats)) chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr) songData.append(mean(chroma_stft)) rmse = librosa.feature.rmse(y=y) songData.append(mean(rmse)) spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr) songData.append(mean(spec_cent)) spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr) songData.append(mean(spec_bw)) rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr) songData.append(mean(rolloff)) zcr = librosa.feature.zero_crossing_rate(y) songData.append(mean(zcr)) mfcc = librosa.feature.mfcc(y=y, sr=sr) for i in mfcc: songData.append(mean(i)) songData.append(label[name]) data_5s.append(songData) #count_5s -= 1 for name, path in folders_10s.items(): #count_10s = 1500 for filename in os.listdir(path): # if(count_10s == 0): # break songData = [] songname = f'{path}/{filename}' y, sr = librosa.load(songname, mono=True) tempo, beats = librosa.beat.beat_track(y=y, sr=sr) songData.append(tempo) songData.append(mean(beats)) chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr) songData.append(mean(chroma_stft)) rmse = librosa.feature.rmse(y=y) songData.append(mean(rmse)) spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr) songData.append(mean(spec_cent)) spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr) songData.append(mean(spec_bw)) rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr) songData.append(mean(rolloff)) zcr = librosa.feature.zero_crossing_rate(y) songData.append(mean(zcr)) mfcc = librosa.feature.mfcc(y=y, sr=sr) for i in mfcc: songData.append(mean(i)) songData.append(label[name]) data_10s.append(songData) #count_10s -= 1 for name, path in folders_20s.items(): #count_20s = 900 for filename in os.listdir(path): # if(count_20s == 0): # break songData = [] songname = f'{path}/{filename}' y, sr = librosa.load(songname, mono=True) tempo, beats = librosa.beat.beat_track(y=y, sr=sr) songData.append(tempo) songData.append(mean(beats)) chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr) songData.append(mean(chroma_stft)) rmse = librosa.feature.rmse(y=y) songData.append(mean(rmse)) spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr) songData.append(mean(spec_cent)) spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr) songData.append(mean(spec_bw)) rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr) songData.append(mean(rolloff)) zcr = librosa.feature.zero_crossing_rate(y) songData.append(mean(zcr)) mfcc = librosa.feature.mfcc(y=y, sr=sr) for i in mfcc: songData.append(mean(i)) songData.append(label[name]) data_20s.append(songData) #count_20s -= 1 data_5s = pd.DataFrame(data_5s) data_5s.to_csv('/content/drive/My Drive/ML_Project/data_5s_test_all_genres.csv') data_10s = pd.DataFrame(data_10s) data_10s.to_csv('/content/drive/My Drive/ML_Project/data_10s_test_all_genres.csv') data_20s = pd.DataFrame(data_20s) data_20s.to_csv('/content/drive/My Drive/ML_Project/data_20s_test_all_genres.csv') data_10s

412a8b42be8c6054311e076c95465833bdd45355

py

Python

data/train_test_split.py

ttaoREtw/A-Pytorch-Implementation-of-Tacotron-End-to-end-Text-to-speech-Deep-Learning-Model

6b0f615cafb0530370631a880aac5736fe9a2c64

2018-09-13T02:45:10.000Z

2021-06-24T03:31:15.000Z

data/train_test_split.py

henryhenrychen/Tacotron-pytorch

4a4d1ea0d83fd88a50464999f5d55fe012c86687

2018-12-11T02:37:58.000Z

2021-03-18T02:42:40.000Z

data/train_test_split.py

henryhenrychen/Tacotron-pytorch

4a4d1ea0d83fd88a50464999f5d55fe012c86687

2018-09-15T14:51:31.000Z

2021-01-19T07:37:14.000Z

import os import argparse import random if __name__ == '__main__': parser = argparse.ArgumentParser(description='Split the data') parser.add_argument('--meta-all', type=str, help='The meta file generated by preprocess.py', required=True) parser.add_argument('--ratio-test', default=0.1, type=float, help='ratio of testing examples', required=False) args = parser.parse_args() split_and_save(args)