fumiyau/python_comments_free_500000 · Datasets at Hugging Face

1c2e83de294b08d097eda13bb958e629ecbb541e

7,059

py

Python

ParamGenerator/Phoenics/BayesianNeuralNetwork/pymc3_interface.py

Tabor-Research-Group/ChemOS

50117f572e95e68dc4dccb624cedb28dbfc6e419

[ "Apache-2.0" ]

37

2018-03-20T21:23:11.000Z

2022-03-26T08:19:20.000Z

ParamGenerator/Phoenics/BayesianNeuralNetwork/pymc3_interface.py

Tabor-Research-Group/ChemOS

50117f572e95e68dc4dccb624cedb28dbfc6e419

[ "Apache-2.0" ]

1

2021-06-29T10:03:22.000Z

ParamGenerator/Phoenics/BayesianNeuralNetwork/pymc3_interface.py

Tabor-Research-Group/ChemOS

50117f572e95e68dc4dccb624cedb28dbfc6e419

[ "Apache-2.0" ]

10

2018-05-16T21:04:05.000Z

2021-10-15T18:14:06.000Z

#!/usr/bin/env python __author__ = 'Florian Hase' #======================================================================== import numpy as np import theano import theano.tensor as T import pymc3 as pm from Utils.utils import VarDictParser #from BayesianNeuralNetwork.distributions import DiscreteLaplace #======================================================================== class Pymc3Network(VarDictParser): def __init__(self, var_dicts, observed_params, observed_losses, batch_size, model_details): VarDictParser.__init__(self, var_dicts) self.observed_params = observed_params self.observed_losses = observed_losses self.num_obs = len(self.observed_losses) self.batch_size = batch_size self.model_details = model_details for key, value in self.model_details.items(): setattr(self, str(key), value) self._process_network_inputs() self._get_weights_and_bias_shapes() def __get_weights(self, index, shape, scale = None): return pm.Normal('w%d' % index, self.weight_loc, self.weight_scale, shape = shape) def __get_biases(self, index, shape, scale = None): return pm.Normal('b%d' % index, self.weight_loc, self.weight_scale, shape = shape) def weight(self, index): return getattr(self, 'w%d' % index) def bias(self, index): return getattr(self, 'b%d' % index) def _get_weights_and_bias_shapes(self): self.weight_shapes = [[self.network_input.shape[1], self.hidden_shape]] self.bias_shapes = [[self.hidden_shape]] for index in range(1, self.num_layers - 1): self.weight_shapes.append([self.hidden_shape, self.hidden_shape]) self.bias_shapes.append([self.hidden_shape]) self.weight_shapes.append([self.hidden_shape, self.network_input.shape[1]]) self.bias_shapes.append([self.network_input.shape[1]]) def _process_network_inputs(self): self.network_input = np.zeros((self.num_obs, self.complete_size)) #+ 10.**-4 self.network_output = np.zeros((self.num_obs, self.total_size)) for obs_index, obs in enumerate(self.observed_params): current_index = 0 for var_index, value in enumerate(obs): if self.var_p_types[var_index] == 'categorical': self.network_input[obs_index, int(current_index + value)] += 1. #- 2 * 10.**-4 self.network_output[obs_index, var_index] = value current_index += len(self.var_p_options[var_index]) else: self.network_input[obs_index, current_index] = value self.network_output[obs_index, var_index] = value current_index += 1 for att in ['floats', 'ints', 'cats']: setattr(self, att, np.array([False for i in range(self.complete_size)])) self.upper_rescalings = np.empty(self.complete_size) self.lower_rescalings = np.empty(self.complete_size) for var_e_index, var_e_name in enumerate(self.var_e_names): high = self.var_e_highs[var_e_index] low = self.var_e_lows[var_e_index] if self.var_e_types[var_e_index] == 'float': self.upper_rescalings[var_e_index] = high + 0.1 * (high - low) self.lower_rescalings[var_e_index] = low - 0.1 * (high - low) self.floats[var_e_index] = True elif self.var_e_types[var_e_index] == 'integer': self.upper_rescalings[var_e_index] = high# + np.ceil(0.1 * (high - low)) self.lower_rescalings[var_e_index] = low# - np.ceil(0.1 * (high - low)) self.ints[var_e_index] = True elif self.var_e_types[var_e_index] == 'categorical': self.upper_rescalings[var_e_index] = 1. self.lower_rescalings[var_e_index] = 0. self.cats[var_e_index] = True self.network_input = 2. * (self.network_input - self.lower_rescalings) / (self.upper_rescalings - self.lower_rescalings) - 1. def _create_model(self): with pm.Model() as self.model: # getting the location primers for layer_index in range(self.num_layers): setattr(self, 'w%d' % layer_index, self.__get_weights(layer_index, self.weight_shapes[layer_index])) setattr(self, 'b%d' % layer_index, self.__get_biases(layer_index, self.bias_shapes[layer_index])) if layer_index == 0: fc = pm.Deterministic('fc%d' % layer_index, pm.math.tanh(pm.math.dot(self.network_input, self.weight(layer_index)) + self.bias(layer_index))) setattr(self, 'fc%d' % layer_index, fc) elif 0 < layer_index < self.num_layers - 1: fc = pm.Deterministic('fc%d' % layer_index, pm.math.tanh(pm.math.dot(getattr(self, 'fc%d' % (layer_index - 1)), self.weight(layer_index)) + self.bias(layer_index))) setattr(self, 'fc%d' % layer_index, fc) else: self._loc = pm.Deterministic('bnn_out', pm.math.sigmoid(pm.math.dot(getattr(self, 'fc%d' % (layer_index - 1)), self.weight(layer_index)) + self.bias(layer_index)) ) # getting the precision / standard deviation / variance self.tau_rescaling = np.zeros((self.num_obs, self.network_input.shape[1])) for obs_index in range(self.num_obs): self.tau_rescaling[obs_index] += self.var_e_ranges self.tau_rescaling = self.tau_rescaling**2 tau = pm.Gamma('tau', self.num_obs**2, 1., shape = (self.num_obs, self.network_input.shape[1])) self.tau = tau / self.tau_rescaling self.scale = pm.Deterministic('scale', 1. / pm.math.sqrt(self.tau)) # learn the floats self.loc = pm.Deterministic('loc', (self.upper_rescalings - self.lower_rescalings) * self._loc + self.lower_rescalings) self.out_floats = pm.Normal('out_floats', self.loc[:, self.floats], tau = self.tau[:, self.floats], observed = self.network_output[:, self._floats]) # learn the integers self.int_scale = pm.Deterministic('int_scale', 1. * self.scale) self.out_ints = DiscreteLaplace('out_ints', loc = self.loc[:, self.ints], scale = self.int_scale[:, self.ints], observed = self.network_output[:, self._ints]) # learn the categories dist_counter, cat_var_index = 0, 0 self.alpha = pm.Deterministic('alpha', (self.loc + 1.) * self.scale) self.num_cats = 0 for var_e_index, var_e_type in enumerate(self.var_e_types): if var_e_type == 'categorical' and self.var_e_begin[var_e_index] == var_e_index: begin, end = self.var_e_begin[var_e_index], self.var_e_end[var_e_index] var_e_name = self.var_e_names[var_e_index] param_index = np.argwhere(self.var_p_names == var_e_name)[0, 0] self.param_index = param_index out_dirichlet = pm.Dirichlet('dirich_%d' % dist_counter, a = self.alpha[:, begin : end], shape = (self.num_obs, int(end - begin)) ) out_cats = pm.Categorical('out_cats_%d' % dist_counter, p = out_dirichlet, observed = self.network_output[:, param_index]) self.num_cats += 1 dist_counter += 1 def _sample(self, num_epochs = None, num_draws = None): if not num_epochs: num_epochs = self.num_epochs if not num_draws: num_draws = self.num_draws with self.model: # approx = pm.fit(method = 'svgd', n = 1000, obj_optimizer = pm.adam(learning_rate = self.learning_rate)) # self.trace = approx.sample(draws = num_draws) approx = pm.fit(n = num_epochs, obj_optimizer = pm.adam(learning_rate = self.learning_rate)) self.trace = approx.sample(draws = num_draws)

41.523529

170

0.696274

__author__ = 'Florian Hase' import numpy as np import theano import theano.tensor as T import pymc3 as pm from Utils.utils import VarDictParser class Pymc3Network(VarDictParser): def __init__(self, var_dicts, observed_params, observed_losses, batch_size, model_details): VarDictParser.__init__(self, var_dicts) self.observed_params = observed_params self.observed_losses = observed_losses self.num_obs = len(self.observed_losses) self.batch_size = batch_size self.model_details = model_details for key, value in self.model_details.items(): setattr(self, str(key), value) self._process_network_inputs() self._get_weights_and_bias_shapes() def __get_weights(self, index, shape, scale = None): return pm.Normal('w%d' % index, self.weight_loc, self.weight_scale, shape = shape) def __get_biases(self, index, shape, scale = None): return pm.Normal('b%d' % index, self.weight_loc, self.weight_scale, shape = shape) def weight(self, index): return getattr(self, 'w%d' % index) def bias(self, index): return getattr(self, 'b%d' % index) def _get_weights_and_bias_shapes(self): self.weight_shapes = [[self.network_input.shape[1], self.hidden_shape]] self.bias_shapes = [[self.hidden_shape]] for index in range(1, self.num_layers - 1): self.weight_shapes.append([self.hidden_shape, self.hidden_shape]) self.bias_shapes.append([self.hidden_shape]) self.weight_shapes.append([self.hidden_shape, self.network_input.shape[1]]) self.bias_shapes.append([self.network_input.shape[1]]) def _process_network_inputs(self): self.network_input = np.zeros((self.num_obs, self.complete_size)) self.network_output = np.zeros((self.num_obs, self.total_size)) for obs_index, obs in enumerate(self.observed_params): current_index = 0 for var_index, value in enumerate(obs): if self.var_p_types[var_index] == 'categorical': self.network_input[obs_index, int(current_index + value)] += 1. self.network_output[obs_index, var_index] = value current_index += len(self.var_p_options[var_index]) else: self.network_input[obs_index, current_index] = value self.network_output[obs_index, var_index] = value current_index += 1 for att in ['floats', 'ints', 'cats']: setattr(self, att, np.array([False for i in range(self.complete_size)])) self.upper_rescalings = np.empty(self.complete_size) self.lower_rescalings = np.empty(self.complete_size) for var_e_index, var_e_name in enumerate(self.var_e_names): high = self.var_e_highs[var_e_index] low = self.var_e_lows[var_e_index] if self.var_e_types[var_e_index] == 'float': self.upper_rescalings[var_e_index] = high + 0.1 * (high - low) self.lower_rescalings[var_e_index] = low - 0.1 * (high - low) self.floats[var_e_index] = True elif self.var_e_types[var_e_index] == 'integer': self.upper_rescalings[var_e_index] = high self.lower_rescalings[var_e_index] = low self.ints[var_e_index] = True elif self.var_e_types[var_e_index] == 'categorical': self.upper_rescalings[var_e_index] = 1. self.lower_rescalings[var_e_index] = 0. self.cats[var_e_index] = True self.network_input = 2. * (self.network_input - self.lower_rescalings) / (self.upper_rescalings - self.lower_rescalings) - 1. def _create_model(self): with pm.Model() as self.model: for layer_index in range(self.num_layers): setattr(self, 'w%d' % layer_index, self.__get_weights(layer_index, self.weight_shapes[layer_index])) setattr(self, 'b%d' % layer_index, self.__get_biases(layer_index, self.bias_shapes[layer_index])) if layer_index == 0: fc = pm.Deterministic('fc%d' % layer_index, pm.math.tanh(pm.math.dot(self.network_input, self.weight(layer_index)) + self.bias(layer_index))) setattr(self, 'fc%d' % layer_index, fc) elif 0 < layer_index < self.num_layers - 1: fc = pm.Deterministic('fc%d' % layer_index, pm.math.tanh(pm.math.dot(getattr(self, 'fc%d' % (layer_index - 1)), self.weight(layer_index)) + self.bias(layer_index))) setattr(self, 'fc%d' % layer_index, fc) else: self._loc = pm.Deterministic('bnn_out', pm.math.sigmoid(pm.math.dot(getattr(self, 'fc%d' % (layer_index - 1)), self.weight(layer_index)) + self.bias(layer_index)) ) self.tau_rescaling = np.zeros((self.num_obs, self.network_input.shape[1])) for obs_index in range(self.num_obs): self.tau_rescaling[obs_index] += self.var_e_ranges self.tau_rescaling = self.tau_rescaling**2 tau = pm.Gamma('tau', self.num_obs**2, 1., shape = (self.num_obs, self.network_input.shape[1])) self.tau = tau / self.tau_rescaling self.scale = pm.Deterministic('scale', 1. / pm.math.sqrt(self.tau)) self.loc = pm.Deterministic('loc', (self.upper_rescalings - self.lower_rescalings) * self._loc + self.lower_rescalings) self.out_floats = pm.Normal('out_floats', self.loc[:, self.floats], tau = self.tau[:, self.floats], observed = self.network_output[:, self._floats]) self.int_scale = pm.Deterministic('int_scale', 1. * self.scale) self.out_ints = DiscreteLaplace('out_ints', loc = self.loc[:, self.ints], scale = self.int_scale[:, self.ints], observed = self.network_output[:, self._ints]) dist_counter, cat_var_index = 0, 0 self.alpha = pm.Deterministic('alpha', (self.loc + 1.) * self.scale) self.num_cats = 0 for var_e_index, var_e_type in enumerate(self.var_e_types): if var_e_type == 'categorical' and self.var_e_begin[var_e_index] == var_e_index: begin, end = self.var_e_begin[var_e_index], self.var_e_end[var_e_index] var_e_name = self.var_e_names[var_e_index] param_index = np.argwhere(self.var_p_names == var_e_name)[0, 0] self.param_index = param_index out_dirichlet = pm.Dirichlet('dirich_%d' % dist_counter, a = self.alpha[:, begin : end], shape = (self.num_obs, int(end - begin)) ) out_cats = pm.Categorical('out_cats_%d' % dist_counter, p = out_dirichlet, observed = self.network_output[:, param_index]) self.num_cats += 1 dist_counter += 1 def _sample(self, num_epochs = None, num_draws = None): if not num_epochs: num_epochs = self.num_epochs if not num_draws: num_draws = self.num_draws with self.model: approx = pm.fit(n = num_epochs, obj_optimizer = pm.adam(learning_rate = self.learning_rate)) self.trace = approx.sample(draws = num_draws)

true

1c2e84f9242aa5d5ab7410159ed00ce8f79e48be

460

py

Python

Matplotlib/Part 4/Part4.py

aritraroy24/Learning_NumPy_SciPy

cd33fbcb7dee11547b7c6e30692866373fb0ea5b

[ "MIT" ]

null

Matplotlib/Part 4/Part4.py

aritraroy24/Learning_NumPy_SciPy

cd33fbcb7dee11547b7c6e30692866373fb0ea5b

[ "MIT" ]

null

Matplotlib/Part 4/Part4.py

aritraroy24/Learning_NumPy_SciPy

cd33fbcb7dee11547b7c6e30692866373fb0ea5b

[ "MIT" ]

null

from matplotlib import pyplot as plt plt.style.use("fivethirtyeight") hours = [1, 2, 3, 4, 5, 6, 7, 8, 9] dev1 = [8, 6, 5, 5, 4, 2, 1, 1, 0] dev2 = [0, 1, 2, 2, 2, 4, 4, 4, 4] dev3 = [0, 1, 1, 1, 2, 2, 3, 3, 4] labels = ['Dev1', 'Dev2', 'Dev3'] colors = ['#6d904f', '#fc4f30', '#008fd5'] plt.stackplot(hours, dev1, dev2, dev3, labels=labels, colors=colors) plt.legend(loc=(0.075, 0.05)) plt.title("My Awesome Stack Plot") plt.tight_layout() plt.show()

20.909091

68

0.591304

from matplotlib import pyplot as plt plt.style.use("fivethirtyeight") hours = [1, 2, 3, 4, 5, 6, 7, 8, 9] dev1 = [8, 6, 5, 5, 4, 2, 1, 1, 0] dev2 = [0, 1, 2, 2, 2, 4, 4, 4, 4] dev3 = [0, 1, 1, 1, 2, 2, 3, 3, 4] labels = ['Dev1', 'Dev2', 'Dev3'] colors = ['#6d904f', '#fc4f30', '#008fd5'] plt.stackplot(hours, dev1, dev2, dev3, labels=labels, colors=colors) plt.legend(loc=(0.075, 0.05)) plt.title("My Awesome Stack Plot") plt.tight_layout() plt.show()

true

1c2e85128466df3e710be0f1ad094ceda7965972

10,591

py

Python

PythonExercises.py

jaxalbert/PythonExperiments

e5c658be1969b59be9a85a969db0e298f7e23bd6

[ "MIT" ]

2

2018-03-05T06:14:35.000Z

2019-01-08T15:32:30.000Z

PythonExercises.py

jaxalbert/PythonExperiments

e5c658be1969b59be9a85a969db0e298f7e23bd6

[ "MIT" ]

null

PythonExercises.py

jaxalbert/PythonExperiments

e5c658be1969b59be9a85a969db0e298f7e23bd6

[ "MIT" ]

null

#!/usr/bin/env python def stemWord(w): """Renders a word in to its generic form. This function is used preprocess words for NLP. It removes all trailing punctuation marks "';-?.,!:". It also removes the possessive "'s" from words. And it converts it to all lower case Args: w (str): A string containing the input word Returns: str a simpler version of the word """ w=w.split("'")[0]#Separates and eliminates all after the "'" w=[letter for letter in w if not (letter in ';-?.,!:')] #Eliminates the simbols on w return ''.join(w).lower() #Joins and puts in lowercase class LanguageModel: """Class that contains the data needed for the analysis of text """ @staticmethod def getWordFreq(txt): """Returns a dictionary where the keys are stemmed words and values are the number they occured in txt. As na example if txt == "The dog likes the cat!" the resulting frequency dictionary should be {"the":2,"dog":1,"likes":1,"cat":1} Hints: -Use split to break the text in to a list of words stem all words in the list -Make sure a dictionary element exists before operating on it. Args: txt (str): a string containing free text Returns: dict: A dictionary whith stemmed words (str) for keys and int values containing the occurence count of these words. """ list1 = txt.split() newlist = [] dic = {} for word in list1: newlist.append(stemWord(word)) for word in newlist: if dic.has_key(word) == True: dic[word] = dic[word] +1 else: dic[word] = 1 return dic @staticmethod def mergeWordFreqDict(frDict1,frDict2): """Takes two dictionaries containing word frequencies and returns a single dictionary containing their sum. In essence this fuction takes the frequencies produced from two different strings with text and returns a dictionary with the word frequencies of the concatenation of these two strings. Hints: -Dictionary .keys() returns a list so you might need to cast it to a set, if you want to get the union. -It is better if you create a dictionary with 0 for all words in both dictionaries and then update the values -If frDict1=={"hello":1,"world":1} and frDict2=={"goodbye":1,"world":1}, the result must be {"hello":1,"world":2,"goodbye":1} -The simplest solution involves 3 non nested for loops. Args: frDict1 (dict): a dictionary with stemmed words as keys and positive integers as values. frDict2 (dict): a dictionary with stemmed words as keys and positive integers as values. Returns: dict: a dictionary with stemmed words as keys and positive integers as values. """ new_dict = frDict1 for i in frDict2: if i in new_dict: new_dict[i] += frDict2[i] else: new_dict[i] = frDict2[i] return new_dict def __init__(self,txtList=[]): """LangueModel constructor Initialises the class members to valid values. __texts is a list with one or more strings with texts. __wordFreq is a dictionary with stemmed words for keys and the count of the occurences of each word (int) as values. Args: txtList (list): A list of strings where each string will contains some text. """ self.__wordFreq={}#Dictionary with the word's frequencies. self.__texts=[]#A list with a text string if txtList.__class__ != [].__class__: raise Exception('txtList must be a list of strings') for txt in txtList: self.addText(txt)#add text to the list def addText(self,txt): """Adds a string containing text to the model This method just uses getWordFreq and mergeWordFreqDict static methods on a specific instance of the class Args: txt (str): the string containing text to be added """ self.__texts.append(txt) newFreq=LanguageModel.getWordFreq(txt) #newFreq is a dictionary with the word's frequencies of the added text self.__wordFreq=LanguageModel.mergeWordFreqDict(self.__wordFreq,newFreq) #Sum of the two dictionaries def addTextFile(self,fileName): """Ads text contained in a text-file Args: fileName (str): the absolute or relative path to a file containing text. """ self.addText(open(fileName).read())#Adds text in a file def wordCount(self): """Returns the total number of words found in self.__texts Hints: -The answer can be writen in a single line -The method values() of dict is the key to solving this question -The distionary __wordFreq contains how many times each word was found in the texts Returns: int: the count of all the words """ return sum(self.__wordFreq.values()) def uniqueWordCount(self): """Returns the number of unique words found in self.__texts Unique word means that a word occuring twice or more times, counts as one. Hints: -The answer can be writen in a single line -The method keys() of dict is the key to solving this question Returns: int: the count of unique words """ return len(self.__wordFreq.keys()) def getWordProbabillity(self,word): """Returns the probabillity of a word occuring according to the model The probabillity of a word occuring is the number of times it has occured divided by the count of all word occurences in __texts Args: word (str): an string with a word which is not necessarilly stemmed. Returns: float: a float between 0 and 1 that contains the probabillity """ stemmedWord=stemWord(word)#Converts 'word' in a simple string in lowercase if stemmedWord in self.__wordFreq.keys():#if the word is in the list return self.__wordFreq[stemmedWord]/float(self.wordCount())#Divide between all the words to know the probabillity else: return 0#If not found return 0 def __str__(self): """Generate a string description of the Language Model Hints: -The result must be constructed with string concatenation -Cast an integer to a string before concatening it. -Use the already availabe methods to obtain information -lm=LanguageModel(['hello world','Goodbye World!']) lm.__str__() will return "LanguageModel\n\t#texts:2\n\t#words:4\n\t#unique words:3\n" -self.__texts, is a list containing all texts the LanguageModel has seen. Returns: string: A description of the language model spanning 4 lines. """ uwords = self.uniqueWordCount(self) nwords = self.wordCount(self) texts = len(self.__texts(self)) lm = "The lenguage model has:\n\t" + "texts:" + str(texts) + "\n\t"+ "words:" + str(nwords) + "\n\t" + "unique words:" + str(uwords) + "\n" return lm def __repr__(self): """Generate a string description of the Language Model that allows to reconstruct it Returns: string: A python expression that invockes the constructor of the class so that if executed a deep copy of the LangueageModel is obtained. """ res=str(self.__class__)+'('+self.__texts.__repr__()+')' return res def getWordsByProbabillity(self): """Produces a list containing all stemmed words from the language model sorted from the most probable to the least probable Hints: -function reversed returns a list with reverse order of the input list -function sorted returns a list with the elements of the input sorted in ascending order. -A list of tuples is sorted by the first element of each tuple Returns: list: a list of strings (not tuples!) """ finalist = {} newdict = self.__wordFreq.keys() for i in newdict: finalist[i] = self.getWordProbabillity(i) sorted_keys = sorted(finalist, key=finalist.__getitem__) l = list(reversed(sorted_keys)) return l def isPalindrome(sentence): """Tells us whether a string is a palindrome. Pallindromes are sentences whos characters read in both directions are the same. Testing for pallindromes ignores spaces and puntuation marks as if they did not exist. Hits: -A list can be indexed form the end with negative values. -The first character in a string is at position 0 If a=[1,"b",3,4] Then a[-1] is 4, a[-2] is 3, etc. -The expression a[len(a)-1]==a[-1] is always True if a is not empty -You will need to use .split() and .join methods of the str type Args: sentence (str): A string with one or more words assumed to have no possessive (stemWord can help). Returns: bool: The return value. True if the sentence was a palindrome, False otherwise. """ answer = True newstring = stemWord(sentence) s_no_space = newstring.replace(" ","")#remplaza el espacio if s_no_space == s_no_space[::-1]:#compara las dos listas answer = True#si son iguales es que son cap i cua else: answer = False return answer if __name__ == '__main__': #Everything here is ignored by joc-de-proves #You can debug your program by testing your functions and classes here pass

35.901695

147

0.589274

def stemWord(w): w=w.split("'")[0]#Separates and eliminates all after the "'" w=[letter for letter in w if not (letter in ';-?.,!:')] return ''.join(w).lower() class LanguageModel: @staticmethod def getWordFreq(txt): list1 = txt.split() newlist = [] dic = {} for word in list1: newlist.append(stemWord(word)) for word in newlist: if dic.has_key(word) == True: dic[word] = dic[word] +1 else: dic[word] = 1 return dic @staticmethod def mergeWordFreqDict(frDict1,frDict2): new_dict = frDict1 for i in frDict2: if i in new_dict: new_dict[i] += frDict2[i] else: new_dict[i] = frDict2[i] return new_dict def __init__(self,txtList=[]): self.__wordFreq={} self.__texts=[]#A list with a text string if txtList.__class__ != [].__class__: raise Exception('txtList must be a list of strings') for txt in txtList: self.addText(txt)#add text to the list def addText(self,txt): self.__texts.append(txt) newFreq=LanguageModel.getWordFreq(txt) #newFreq is a dictionary with the word's frequencies of the added text self.__wordFreq=LanguageModel.mergeWordFreqDict(self.__wordFreq,newFreq) def addTextFile(self,fileName): self.addText(open(fileName).read()) def wordCount(self): return sum(self.__wordFreq.values()) def uniqueWordCount(self): return len(self.__wordFreq.keys()) def getWordProbabillity(self,word): stemmedWord=stemWord(word) if stemmedWord in self.__wordFreq.keys(): return self.__wordFreq[stemmedWord]/float(self.wordCount()) else: return 0 def __str__(self): uwords = self.uniqueWordCount(self) nwords = self.wordCount(self) texts = len(self.__texts(self)) lm = "The lenguage model has:\n\t" + "texts:" + str(texts) + "\n\t"+ "words:" + str(nwords) + "\n\t" + "unique words:" + str(uwords) + "\n" return lm def __repr__(self): res=str(self.__class__)+'('+self.__texts.__repr__()+')' return res def getWordsByProbabillity(self): finalist = {} newdict = self.__wordFreq.keys() for i in newdict: finalist[i] = self.getWordProbabillity(i) sorted_keys = sorted(finalist, key=finalist.__getitem__) l = list(reversed(sorted_keys)) return l def isPalindrome(sentence): answer = True newstring = stemWord(sentence) s_no_space = newstring.replace(" ","") if s_no_space == s_no_space[::-1]: answer = True else: answer = False return answer if __name__ == '__main__': pass

true

1c2e8556fed0cb0df722a9c46df711a1dc400983

14,733

py

Python

yolov3_tf2/models.py

makra89/Quake_AI

8df69c75b117079f5e40929341c4638e741de11d

[ "BSD-3-Clause" ]

4

2022-02-10T22:13:37.000Z

2022-03-27T18:44:38.000Z

yolov3_tf2/models.py

makra89/Quake_AI

8df69c75b117079f5e40929341c4638e741de11d

[ "BSD-3-Clause" ]

14

2021-08-29T13:36:12.000Z

2021-09-22T05:22:57.000Z

yolov3_tf2/models.py

makra89/Quake_AI

8df69c75b117079f5e40929341c4638e741de11d

[ "BSD-3-Clause" ]

1

2021-11-21T21:01:31.000Z

import numpy as np import tensorflow as tf from tensorflow.keras import Model from tensorflow.keras.layers import ( Add, Concatenate, Conv2D, Input, Lambda, LeakyReLU, MaxPool2D, UpSampling2D, ZeroPadding2D, BatchNormalization, ) from tensorflow.keras.regularizers import l2 from tensorflow.keras.losses import ( binary_crossentropy, sparse_categorical_crossentropy ) from .utils import broadcast_iou # TODO: Add these values to config? YOLO_MAX_BOXES = 100 YOLO_IOU_THRESHOLD = 0.5 YOLO_SCORE_THRESHOLD = 0.5 yolo_anchors = np.array([(10, 13), (16, 30), (33, 23), (30, 61), (62, 45), (59, 119), (116, 90), (156, 198), (373, 326)], np.float32) / 416 yolo_anchor_masks = yolo_tiny_3l_anchor_masks = np.array([[6, 7, 8], [3, 4, 5], [0, 1, 2]]) yolo_tiny_3l_anchors = np.array([(11, 20), (15, 31), (16, 56), (24, 44), (27, 70), (35, 101), (51, 120), (71, 179), (132, 245)], np.float32) / 416 # Determined for custom dataset yolo_tiny_anchors = np.array([(14, 28), (22, 54), (30, 87), (45, 95), (52, 145), (106, 216)], np.float32) / 416 yolo_tiny_anchor_masks = np.array([[3, 4, 5], [0, 1, 2]]) def DarknetConv(x, filters, size, strides=1, batch_norm=True): if strides == 1: padding = 'same' else: x = ZeroPadding2D(((1, 0), (1, 0)))(x) # top left half-padding padding = 'valid' x = Conv2D(filters=filters, kernel_size=size, strides=strides, padding=padding, use_bias=not batch_norm, kernel_regularizer=l2(0.0005))(x) if batch_norm: x = BatchNormalization()(x) x = LeakyReLU(alpha=0.1)(x) return x def DarknetResidual(x, filters): prev = x x = DarknetConv(x, filters // 2, 1) x = DarknetConv(x, filters, 3) x = Add()([prev, x]) return x def DarknetBlock(x, filters, blocks): x = DarknetConv(x, filters, 3, strides=2) for _ in range(blocks): x = DarknetResidual(x, filters) return x def Darknet(name=None): x = inputs = Input([None, None, 3]) x = DarknetConv(x, 32, 3) x = DarknetBlock(x, 64, 1) x = DarknetBlock(x, 128, 2) # skip connection x = x_36 = DarknetBlock(x, 256, 8) # skip connection x = x_61 = DarknetBlock(x, 512, 8) x = DarknetBlock(x, 1024, 4) return tf.keras.Model(inputs, (x_36, x_61, x), name=name) def DarknetTiny(name=None): x = inputs = Input([None, None, 3]) x = DarknetConv(x, 16, 3) x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 32, 3) x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 64, 3) x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 128, 3) x = MaxPool2D(2, 2, 'same')(x) x = x_8 = DarknetConv(x, 256, 3) # skip connection x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 512, 3) x = MaxPool2D(2, 1, 'same')(x) x = DarknetConv(x, 1024, 3) return tf.keras.Model(inputs, (x_8, x), name=name) def DarknetTiny3L(name=None): x = inputs = Input([None, None, 3]) x = DarknetConv(x, 16, 3) x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 32, 3) x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 64, 3) x = MaxPool2D(2, 2, 'same')(x) x = x_6 = DarknetConv(x, 128, 3) x = MaxPool2D(2, 2, 'same')(x) x = x_8 = DarknetConv(x, 256, 3) # skip connection x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 512, 3) x = MaxPool2D(2, 1, 'same')(x) x = DarknetConv(x, 1024, 3) return tf.keras.Model(inputs, (x_6, x_8, x), name=name) def YoloConv(filters, name=None): def yolo_conv(x_in): if isinstance(x_in, tuple): inputs = Input(x_in[0].shape[1:]), Input(x_in[1].shape[1:]) x, x_skip = inputs # concat with skip connection x = DarknetConv(x, filters, 1) x = UpSampling2D(2)(x) x = Concatenate()([x, x_skip]) else: x = inputs = Input(x_in.shape[1:]) x = DarknetConv(x, filters, 1) x = DarknetConv(x, filters * 2, 3) x = DarknetConv(x, filters, 1) x = DarknetConv(x, filters * 2, 3) x = DarknetConv(x, filters, 1) return Model(inputs, x, name=name)(x_in) return yolo_conv def YoloConvTiny(filters, name=None): def yolo_conv(x_in): if isinstance(x_in, tuple): inputs = Input(x_in[0].shape[1:]), Input(x_in[1].shape[1:]) x, x_skip = inputs # concat with skip connection x = DarknetConv(x, filters, 1) x = UpSampling2D(2)(x) x = Concatenate()([x, x_skip]) else: x = inputs = Input(x_in.shape[1:]) x = DarknetConv(x, filters, 1) return Model(inputs, x, name=name)(x_in) return yolo_conv def YoloOutput(filters, anchors, classes, name=None): def yolo_output(x_in): x = inputs = Input(x_in.shape[1:]) x = DarknetConv(x, filters * 2, 3) x = DarknetConv(x, anchors * (classes + 5), 1, batch_norm=False) x = Lambda(lambda x: tf.reshape(x, (-1, tf.shape(x)[1], tf.shape(x)[2], anchors, classes + 5)))(x) return tf.keras.Model(inputs, x, name=name)(x_in) return yolo_output # As tensorflow lite doesn't support tf.size used in tf.meshgrid, # we reimplemented a simple meshgrid function that use basic tf function. def _meshgrid(n_a, n_b): return [ tf.reshape(tf.tile(tf.range(n_a), [n_b]), (n_b, n_a)), tf.reshape(tf.repeat(tf.range(n_b), n_a), (n_b, n_a)) ] def yolo_boxes(pred, anchors, classes): # pred: (batch_size, grid, grid, anchors, (x, y, w, h, obj, ...classes)) grid_size = tf.shape(pred)[1:3] box_xy, box_wh, objectness, class_probs = tf.split( pred, (2, 2, 1, classes), axis=-1) box_xy = tf.sigmoid(box_xy) objectness = tf.sigmoid(objectness) class_probs = tf.sigmoid(class_probs) pred_box = tf.concat((box_xy, box_wh), axis=-1) # original xywh for loss # !!! grid[x][y] == (y, x) grid = _meshgrid(grid_size[1],grid_size[0]) grid = tf.expand_dims(tf.stack(grid, axis=-1), axis=2) # [gx, gy, 1, 2] box_xy = (box_xy + tf.cast(grid, tf.float32)) / \ tf.cast(grid_size, tf.float32) box_wh = tf.exp(box_wh) * anchors box_x1y1 = box_xy - box_wh / 2 box_x2y2 = box_xy + box_wh / 2 bbox = tf.concat([box_x1y1, box_x2y2], axis=-1) return bbox, objectness, class_probs, pred_box def yolo_nms(outputs, anchors, masks, classes): # boxes, conf, type b, c, t = [], [], [] for o in outputs: b.append(tf.reshape(o[0], (tf.shape(o[0])[0], -1, tf.shape(o[0])[-1]))) c.append(tf.reshape(o[1], (tf.shape(o[1])[0], -1, tf.shape(o[1])[-1]))) t.append(tf.reshape(o[2], (tf.shape(o[2])[0], -1, tf.shape(o[2])[-1]))) bbox = tf.concat(b, axis=1) confidence = tf.concat(c, axis=1) class_probs = tf.concat(t, axis=1) # If we only have one class, do not multiply by class_prob (always 0.5) if classes == 1: scores = confidence else: scores = confidence * class_probs dscores = tf.squeeze(scores, axis=0) scores = tf.reduce_max(dscores,[1]) bbox = tf.reshape(bbox,(-1,4)) classes = tf.argmax(dscores,1) selected_indices, selected_scores = tf.image.non_max_suppression_with_scores( boxes=bbox, scores=scores, max_output_size=YOLO_MAX_BOXES, iou_threshold=YOLO_IOU_THRESHOLD, score_threshold=YOLO_SCORE_THRESHOLD, soft_nms_sigma=0.5 ) num_valid_nms_boxes = tf.shape(selected_indices)[0] selected_indices = tf.concat([selected_indices,tf.zeros(YOLO_MAX_BOXES-num_valid_nms_boxes, tf.int32)], 0) selected_scores = tf.concat([selected_scores,tf.zeros(YOLO_MAX_BOXES-num_valid_nms_boxes,tf.float32)], -1) boxes=tf.gather(bbox, selected_indices) boxes = tf.expand_dims(boxes, axis=0) scores=selected_scores scores = tf.expand_dims(scores, axis=0) classes = tf.gather(classes,selected_indices) classes = tf.expand_dims(classes, axis=0) valid_detections=num_valid_nms_boxes valid_detections = tf.expand_dims(valid_detections, axis=0) return boxes, scores, classes, valid_detections def YoloV3(size=None, channels=3, anchors=yolo_anchors, masks=yolo_anchor_masks, classes=80, training=False): x = inputs = Input([size, size, channels], name='input') x_36, x_61, x = Darknet(name='yolo_darknet')(x) x = YoloConv(512, name='yolo_conv_0')(x) output_0 = YoloOutput(512, len(masks[0]), classes, name='yolo_output_0')(x) x = YoloConv(256, name='yolo_conv_1')((x, x_61)) output_1 = YoloOutput(256, len(masks[1]), classes, name='yolo_output_1')(x) x = YoloConv(128, name='yolo_conv_2')((x, x_36)) output_2 = YoloOutput(128, len(masks[2]), classes, name='yolo_output_2')(x) if training: return Model(inputs, (output_0, output_1, output_2), name='yolov3') boxes_0 = Lambda(lambda x: yolo_boxes(x, anchors[masks[0]], classes), name='yolo_boxes_0')(output_0) boxes_1 = Lambda(lambda x: yolo_boxes(x, anchors[masks[1]], classes), name='yolo_boxes_1')(output_1) boxes_2 = Lambda(lambda x: yolo_boxes(x, anchors[masks[2]], classes), name='yolo_boxes_2')(output_2) outputs = Lambda(lambda x: yolo_nms(x, anchors, masks, classes), name='yolo_nms')((boxes_0[:3], boxes_1[:3], boxes_2[:3])) return Model(inputs, outputs, name='yolov3') def YoloV3Tiny(size=None, channels=3, anchors=yolo_tiny_anchors, masks=yolo_tiny_anchor_masks, classes=80, training=False): x = inputs = Input([size, size, channels], name='input') x_8, x = DarknetTiny(name='yolo_darknet')(x) x = YoloConvTiny(256, name='yolo_conv_0')(x) output_0 = YoloOutput(256, len(masks[0]), classes, name='yolo_output_0')(x) x = YoloConvTiny(128, name='yolo_conv_1')((x, x_8)) output_1 = YoloOutput(128, len(masks[1]), classes, name='yolo_output_1')(x) if training: return Model(inputs, (output_0, output_1), name='yolov3') boxes_0 = Lambda(lambda x: yolo_boxes(x, anchors[masks[0]], classes), name='yolo_boxes_0')(output_0) boxes_1 = Lambda(lambda x: yolo_boxes(x, anchors[masks[1]], classes), name='yolo_boxes_1')(output_1) outputs = Lambda(lambda x: yolo_nms(x, anchors, masks, classes), name='yolo_nms')((boxes_0[:3], boxes_1[:3])) return Model(inputs, outputs, name='yolov3_tiny') def YoloV3Tiny3L(size=None, channels=3, anchors=yolo_tiny_3l_anchors, masks=yolo_tiny_3l_anchor_masks, classes=80, training=False): x = inputs = Input([size, size, channels], name='input') x_6, x_8, x = DarknetTiny3L(name='yolo_darknet')(x) x = YoloConvTiny(256, name='yolo_conv_0')(x) output_0 = YoloOutput(256, len(masks[0]), classes, name='yolo_output_0')(x) x = YoloConvTiny(128, name='yolo_conv_1')((x, x_8)) output_1 = YoloOutput(128, len(masks[1]), classes, name='yolo_output_1')(x) x = YoloConvTiny(128, name='yolo_conv_2')((x, x_6)) output_2 = YoloOutput(64, len(masks[2]), classes, name='yolo_output_2')(x) if training: return Model(inputs, (output_0, output_1, output_2), name='yolov3') boxes_0 = Lambda(lambda x: yolo_boxes(x, anchors[masks[0]], classes), name='yolo_boxes_0')(output_0) boxes_1 = Lambda(lambda x: yolo_boxes(x, anchors[masks[1]], classes), name='yolo_boxes_1')(output_1) boxes_2 = Lambda(lambda x: yolo_boxes(x, anchors[masks[2]], classes), name='yolo_boxes_2')(output_2) outputs = Lambda(lambda x: yolo_nms(x, anchors, masks, classes), name='yolo_nms')((boxes_0[:3], boxes_1[:3], boxes_2[:3])) return Model(inputs, outputs, name='yolov3_tiny_3l') def YoloLoss(anchors, classes=80, ignore_thresh=0.5): def yolo_loss(y_true, y_pred): # 1. transform all pred outputs # y_pred: (batch_size, grid, grid, anchors, (x, y, w, h, obj, ...cls)) pred_box, pred_obj, pred_class, pred_xywh = yolo_boxes( y_pred, anchors, classes) pred_xy = pred_xywh[..., 0:2] pred_wh = pred_xywh[..., 2:4] # 2. transform all true outputs # y_true: (batch_size, grid, grid, anchors, (x1, y1, x2, y2, obj, cls)) true_box, true_obj, true_class_idx = tf.split( y_true, (4, 1, 1), axis=-1) true_xy = (true_box[..., 0:2] + true_box[..., 2:4]) / 2 true_wh = true_box[..., 2:4] - true_box[..., 0:2] # give higher weights to small boxes box_loss_scale = 2 - true_wh[..., 0] * true_wh[..., 1] # 3. inverting the pred box equations grid_size = tf.shape(y_true)[1] grid = tf.meshgrid(tf.range(grid_size), tf.range(grid_size)) grid = tf.expand_dims(tf.stack(grid, axis=-1), axis=2) true_xy = true_xy * tf.cast(grid_size, tf.float32) - \ tf.cast(grid, tf.float32) true_wh = tf.math.log(true_wh / anchors) true_wh = tf.where(tf.math.is_inf(true_wh), tf.zeros_like(true_wh), true_wh) # 4. calculate all masks obj_mask = tf.squeeze(true_obj, -1) # ignore false positive when iou is over threshold best_iou = tf.map_fn( lambda x: tf.reduce_max(broadcast_iou(x[0], tf.boolean_mask( x[1], tf.cast(x[2], tf.bool))), axis=-1), (pred_box, true_box, obj_mask), tf.float32) ignore_mask = tf.cast(best_iou < ignore_thresh, tf.float32) # 5. calculate all losses xy_loss = obj_mask * box_loss_scale * \ tf.reduce_sum(tf.square(true_xy - pred_xy), axis=-1) wh_loss = obj_mask * box_loss_scale * \ tf.reduce_sum(tf.square(true_wh - pred_wh), axis=-1) obj_loss = binary_crossentropy(true_obj, pred_obj) obj_loss = obj_mask * obj_loss + \ (1 - obj_mask) * ignore_mask * obj_loss class_loss = obj_mask * sparse_categorical_crossentropy( true_class_idx, pred_class) # 6. sum over (batch, gridx, gridy, anchors) => (batch, 1) xy_loss = tf.reduce_sum(xy_loss, axis=(1, 2, 3)) wh_loss = tf.reduce_sum(wh_loss, axis=(1, 2, 3)) obj_loss = tf.reduce_sum(obj_loss, axis=(1, 2, 3)) class_loss = tf.reduce_sum(class_loss, axis=(1, 2, 3)) return xy_loss + wh_loss + obj_loss + class_loss return yolo_loss

36.924812

110

0.602525

import numpy as np import tensorflow as tf from tensorflow.keras import Model from tensorflow.keras.layers import ( Add, Concatenate, Conv2D, Input, Lambda, LeakyReLU, MaxPool2D, UpSampling2D, ZeroPadding2D, BatchNormalization, ) from tensorflow.keras.regularizers import l2 from tensorflow.keras.losses import ( binary_crossentropy, sparse_categorical_crossentropy ) from .utils import broadcast_iou YOLO_MAX_BOXES = 100 YOLO_IOU_THRESHOLD = 0.5 YOLO_SCORE_THRESHOLD = 0.5 yolo_anchors = np.array([(10, 13), (16, 30), (33, 23), (30, 61), (62, 45), (59, 119), (116, 90), (156, 198), (373, 326)], np.float32) / 416 yolo_anchor_masks = yolo_tiny_3l_anchor_masks = np.array([[6, 7, 8], [3, 4, 5], [0, 1, 2]]) yolo_tiny_3l_anchors = np.array([(11, 20), (15, 31), (16, 56), (24, 44), (27, 70), (35, 101), (51, 120), (71, 179), (132, 245)], np.float32) / 416 yolo_tiny_anchors = np.array([(14, 28), (22, 54), (30, 87), (45, 95), (52, 145), (106, 216)], np.float32) / 416 yolo_tiny_anchor_masks = np.array([[3, 4, 5], [0, 1, 2]]) def DarknetConv(x, filters, size, strides=1, batch_norm=True): if strides == 1: padding = 'same' else: x = ZeroPadding2D(((1, 0), (1, 0)))(x) padding = 'valid' x = Conv2D(filters=filters, kernel_size=size, strides=strides, padding=padding, use_bias=not batch_norm, kernel_regularizer=l2(0.0005))(x) if batch_norm: x = BatchNormalization()(x) x = LeakyReLU(alpha=0.1)(x) return x def DarknetResidual(x, filters): prev = x x = DarknetConv(x, filters // 2, 1) x = DarknetConv(x, filters, 3) x = Add()([prev, x]) return x def DarknetBlock(x, filters, blocks): x = DarknetConv(x, filters, 3, strides=2) for _ in range(blocks): x = DarknetResidual(x, filters) return x def Darknet(name=None): x = inputs = Input([None, None, 3]) x = DarknetConv(x, 32, 3) x = DarknetBlock(x, 64, 1) x = DarknetBlock(x, 128, 2) x = x_36 = DarknetBlock(x, 256, 8) x = x_61 = DarknetBlock(x, 512, 8) x = DarknetBlock(x, 1024, 4) return tf.keras.Model(inputs, (x_36, x_61, x), name=name) def DarknetTiny(name=None): x = inputs = Input([None, None, 3]) x = DarknetConv(x, 16, 3) x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 32, 3) x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 64, 3) x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 128, 3) x = MaxPool2D(2, 2, 'same')(x) x = x_8 = DarknetConv(x, 256, 3) x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 512, 3) x = MaxPool2D(2, 1, 'same')(x) x = DarknetConv(x, 1024, 3) return tf.keras.Model(inputs, (x_8, x), name=name) def DarknetTiny3L(name=None): x = inputs = Input([None, None, 3]) x = DarknetConv(x, 16, 3) x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 32, 3) x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 64, 3) x = MaxPool2D(2, 2, 'same')(x) x = x_6 = DarknetConv(x, 128, 3) x = MaxPool2D(2, 2, 'same')(x) x = x_8 = DarknetConv(x, 256, 3) x = MaxPool2D(2, 2, 'same')(x) x = DarknetConv(x, 512, 3) x = MaxPool2D(2, 1, 'same')(x) x = DarknetConv(x, 1024, 3) return tf.keras.Model(inputs, (x_6, x_8, x), name=name) def YoloConv(filters, name=None): def yolo_conv(x_in): if isinstance(x_in, tuple): inputs = Input(x_in[0].shape[1:]), Input(x_in[1].shape[1:]) x, x_skip = inputs x = DarknetConv(x, filters, 1) x = UpSampling2D(2)(x) x = Concatenate()([x, x_skip]) else: x = inputs = Input(x_in.shape[1:]) x = DarknetConv(x, filters, 1) x = DarknetConv(x, filters * 2, 3) x = DarknetConv(x, filters, 1) x = DarknetConv(x, filters * 2, 3) x = DarknetConv(x, filters, 1) return Model(inputs, x, name=name)(x_in) return yolo_conv def YoloConvTiny(filters, name=None): def yolo_conv(x_in): if isinstance(x_in, tuple): inputs = Input(x_in[0].shape[1:]), Input(x_in[1].shape[1:]) x, x_skip = inputs x = DarknetConv(x, filters, 1) x = UpSampling2D(2)(x) x = Concatenate()([x, x_skip]) else: x = inputs = Input(x_in.shape[1:]) x = DarknetConv(x, filters, 1) return Model(inputs, x, name=name)(x_in) return yolo_conv def YoloOutput(filters, anchors, classes, name=None): def yolo_output(x_in): x = inputs = Input(x_in.shape[1:]) x = DarknetConv(x, filters * 2, 3) x = DarknetConv(x, anchors * (classes + 5), 1, batch_norm=False) x = Lambda(lambda x: tf.reshape(x, (-1, tf.shape(x)[1], tf.shape(x)[2], anchors, classes + 5)))(x) return tf.keras.Model(inputs, x, name=name)(x_in) return yolo_output # we reimplemented a simple meshgrid function that use basic tf function. def _meshgrid(n_a, n_b): return [ tf.reshape(tf.tile(tf.range(n_a), [n_b]), (n_b, n_a)), tf.reshape(tf.repeat(tf.range(n_b), n_a), (n_b, n_a)) ] def yolo_boxes(pred, anchors, classes): # pred: (batch_size, grid, grid, anchors, (x, y, w, h, obj, ...classes)) grid_size = tf.shape(pred)[1:3] box_xy, box_wh, objectness, class_probs = tf.split( pred, (2, 2, 1, classes), axis=-1) box_xy = tf.sigmoid(box_xy) objectness = tf.sigmoid(objectness) class_probs = tf.sigmoid(class_probs) pred_box = tf.concat((box_xy, box_wh), axis=-1) # original xywh for loss # !!! grid[x][y] == (y, x) grid = _meshgrid(grid_size[1],grid_size[0]) grid = tf.expand_dims(tf.stack(grid, axis=-1), axis=2) # [gx, gy, 1, 2] box_xy = (box_xy + tf.cast(grid, tf.float32)) / \ tf.cast(grid_size, tf.float32) box_wh = tf.exp(box_wh) * anchors box_x1y1 = box_xy - box_wh / 2 box_x2y2 = box_xy + box_wh / 2 bbox = tf.concat([box_x1y1, box_x2y2], axis=-1) return bbox, objectness, class_probs, pred_box def yolo_nms(outputs, anchors, masks, classes): # boxes, conf, type b, c, t = [], [], [] for o in outputs: b.append(tf.reshape(o[0], (tf.shape(o[0])[0], -1, tf.shape(o[0])[-1]))) c.append(tf.reshape(o[1], (tf.shape(o[1])[0], -1, tf.shape(o[1])[-1]))) t.append(tf.reshape(o[2], (tf.shape(o[2])[0], -1, tf.shape(o[2])[-1]))) bbox = tf.concat(b, axis=1) confidence = tf.concat(c, axis=1) class_probs = tf.concat(t, axis=1) # If we only have one class, do not multiply by class_prob (always 0.5) if classes == 1: scores = confidence else: scores = confidence * class_probs dscores = tf.squeeze(scores, axis=0) scores = tf.reduce_max(dscores,[1]) bbox = tf.reshape(bbox,(-1,4)) classes = tf.argmax(dscores,1) selected_indices, selected_scores = tf.image.non_max_suppression_with_scores( boxes=bbox, scores=scores, max_output_size=YOLO_MAX_BOXES, iou_threshold=YOLO_IOU_THRESHOLD, score_threshold=YOLO_SCORE_THRESHOLD, soft_nms_sigma=0.5 ) num_valid_nms_boxes = tf.shape(selected_indices)[0] selected_indices = tf.concat([selected_indices,tf.zeros(YOLO_MAX_BOXES-num_valid_nms_boxes, tf.int32)], 0) selected_scores = tf.concat([selected_scores,tf.zeros(YOLO_MAX_BOXES-num_valid_nms_boxes,tf.float32)], -1) boxes=tf.gather(bbox, selected_indices) boxes = tf.expand_dims(boxes, axis=0) scores=selected_scores scores = tf.expand_dims(scores, axis=0) classes = tf.gather(classes,selected_indices) classes = tf.expand_dims(classes, axis=0) valid_detections=num_valid_nms_boxes valid_detections = tf.expand_dims(valid_detections, axis=0) return boxes, scores, classes, valid_detections def YoloV3(size=None, channels=3, anchors=yolo_anchors, masks=yolo_anchor_masks, classes=80, training=False): x = inputs = Input([size, size, channels], name='input') x_36, x_61, x = Darknet(name='yolo_darknet')(x) x = YoloConv(512, name='yolo_conv_0')(x) output_0 = YoloOutput(512, len(masks[0]), classes, name='yolo_output_0')(x) x = YoloConv(256, name='yolo_conv_1')((x, x_61)) output_1 = YoloOutput(256, len(masks[1]), classes, name='yolo_output_1')(x) x = YoloConv(128, name='yolo_conv_2')((x, x_36)) output_2 = YoloOutput(128, len(masks[2]), classes, name='yolo_output_2')(x) if training: return Model(inputs, (output_0, output_1, output_2), name='yolov3') boxes_0 = Lambda(lambda x: yolo_boxes(x, anchors[masks[0]], classes), name='yolo_boxes_0')(output_0) boxes_1 = Lambda(lambda x: yolo_boxes(x, anchors[masks[1]], classes), name='yolo_boxes_1')(output_1) boxes_2 = Lambda(lambda x: yolo_boxes(x, anchors[masks[2]], classes), name='yolo_boxes_2')(output_2) outputs = Lambda(lambda x: yolo_nms(x, anchors, masks, classes), name='yolo_nms')((boxes_0[:3], boxes_1[:3], boxes_2[:3])) return Model(inputs, outputs, name='yolov3') def YoloV3Tiny(size=None, channels=3, anchors=yolo_tiny_anchors, masks=yolo_tiny_anchor_masks, classes=80, training=False): x = inputs = Input([size, size, channels], name='input') x_8, x = DarknetTiny(name='yolo_darknet')(x) x = YoloConvTiny(256, name='yolo_conv_0')(x) output_0 = YoloOutput(256, len(masks[0]), classes, name='yolo_output_0')(x) x = YoloConvTiny(128, name='yolo_conv_1')((x, x_8)) output_1 = YoloOutput(128, len(masks[1]), classes, name='yolo_output_1')(x) if training: return Model(inputs, (output_0, output_1), name='yolov3') boxes_0 = Lambda(lambda x: yolo_boxes(x, anchors[masks[0]], classes), name='yolo_boxes_0')(output_0) boxes_1 = Lambda(lambda x: yolo_boxes(x, anchors[masks[1]], classes), name='yolo_boxes_1')(output_1) outputs = Lambda(lambda x: yolo_nms(x, anchors, masks, classes), name='yolo_nms')((boxes_0[:3], boxes_1[:3])) return Model(inputs, outputs, name='yolov3_tiny') def YoloV3Tiny3L(size=None, channels=3, anchors=yolo_tiny_3l_anchors, masks=yolo_tiny_3l_anchor_masks, classes=80, training=False): x = inputs = Input([size, size, channels], name='input') x_6, x_8, x = DarknetTiny3L(name='yolo_darknet')(x) x = YoloConvTiny(256, name='yolo_conv_0')(x) output_0 = YoloOutput(256, len(masks[0]), classes, name='yolo_output_0')(x) x = YoloConvTiny(128, name='yolo_conv_1')((x, x_8)) output_1 = YoloOutput(128, len(masks[1]), classes, name='yolo_output_1')(x) x = YoloConvTiny(128, name='yolo_conv_2')((x, x_6)) output_2 = YoloOutput(64, len(masks[2]), classes, name='yolo_output_2')(x) if training: return Model(inputs, (output_0, output_1, output_2), name='yolov3') boxes_0 = Lambda(lambda x: yolo_boxes(x, anchors[masks[0]], classes), name='yolo_boxes_0')(output_0) boxes_1 = Lambda(lambda x: yolo_boxes(x, anchors[masks[1]], classes), name='yolo_boxes_1')(output_1) boxes_2 = Lambda(lambda x: yolo_boxes(x, anchors[masks[2]], classes), name='yolo_boxes_2')(output_2) outputs = Lambda(lambda x: yolo_nms(x, anchors, masks, classes), name='yolo_nms')((boxes_0[:3], boxes_1[:3], boxes_2[:3])) return Model(inputs, outputs, name='yolov3_tiny_3l') def YoloLoss(anchors, classes=80, ignore_thresh=0.5): def yolo_loss(y_true, y_pred): # 1. transform all pred outputs # y_pred: (batch_size, grid, grid, anchors, (x, y, w, h, obj, ...cls)) pred_box, pred_obj, pred_class, pred_xywh = yolo_boxes( y_pred, anchors, classes) pred_xy = pred_xywh[..., 0:2] pred_wh = pred_xywh[..., 2:4] # 2. transform all true outputs # y_true: (batch_size, grid, grid, anchors, (x1, y1, x2, y2, obj, cls)) true_box, true_obj, true_class_idx = tf.split( y_true, (4, 1, 1), axis=-1) true_xy = (true_box[..., 0:2] + true_box[..., 2:4]) / 2 true_wh = true_box[..., 2:4] - true_box[..., 0:2] # give higher weights to small boxes box_loss_scale = 2 - true_wh[..., 0] * true_wh[..., 1] # 3. inverting the pred box equations grid_size = tf.shape(y_true)[1] grid = tf.meshgrid(tf.range(grid_size), tf.range(grid_size)) grid = tf.expand_dims(tf.stack(grid, axis=-1), axis=2) true_xy = true_xy * tf.cast(grid_size, tf.float32) - \ tf.cast(grid, tf.float32) true_wh = tf.math.log(true_wh / anchors) true_wh = tf.where(tf.math.is_inf(true_wh), tf.zeros_like(true_wh), true_wh) # 4. calculate all masks obj_mask = tf.squeeze(true_obj, -1) # ignore false positive when iou is over threshold best_iou = tf.map_fn( lambda x: tf.reduce_max(broadcast_iou(x[0], tf.boolean_mask( x[1], tf.cast(x[2], tf.bool))), axis=-1), (pred_box, true_box, obj_mask), tf.float32) ignore_mask = tf.cast(best_iou < ignore_thresh, tf.float32) # 5. calculate all losses xy_loss = obj_mask * box_loss_scale * \ tf.reduce_sum(tf.square(true_xy - pred_xy), axis=-1) wh_loss = obj_mask * box_loss_scale * \ tf.reduce_sum(tf.square(true_wh - pred_wh), axis=-1) obj_loss = binary_crossentropy(true_obj, pred_obj) obj_loss = obj_mask * obj_loss + \ (1 - obj_mask) * ignore_mask * obj_loss class_loss = obj_mask * sparse_categorical_crossentropy( true_class_idx, pred_class) # 6. sum over (batch, gridx, gridy, anchors) => (batch, 1) xy_loss = tf.reduce_sum(xy_loss, axis=(1, 2, 3)) wh_loss = tf.reduce_sum(wh_loss, axis=(1, 2, 3)) obj_loss = tf.reduce_sum(obj_loss, axis=(1, 2, 3)) class_loss = tf.reduce_sum(class_loss, axis=(1, 2, 3)) return xy_loss + wh_loss + obj_loss + class_loss return yolo_loss

true

1c2e86674db23f39db6bd3763d114c2f1fca0050

2,508

py

Python

test_jsondb.py

andreixk/SimpleRecipeManagerAPI

240d536fc6783005c8fb48f54a75dfb7654b7680

[ "MIT" ]

null

test_jsondb.py

andreixk/SimpleRecipeManagerAPI

240d536fc6783005c8fb48f54a75dfb7654b7680

[ "MIT" ]

null

test_jsondb.py

andreixk/SimpleRecipeManagerAPI

240d536fc6783005c8fb48f54a75dfb7654b7680

[ "MIT" ]

null

import pytest from fastapi.testclient import TestClient from unittest import mock from unittest.mock import mock_open import json from jsondb import PrimitiveStorage, ObjectStorage from conftest import Stub class TestStorage(): @pytest.mark.parametrize('Storage,fake_data', [ (PrimitiveStorage, ['a','b']), (ObjectStorage, [{'a':'b'}]) ]) def test_gets_list_of_items_from_file(self, Storage, fake_data): collection = 'hello' mocked_open = mock_open(read_data=json.dumps(fake_data)) with mock.patch('builtins.open', mocked_open) as open_stub: store = Storage(collection) rs = store.read() assert rs == fake_data assert Stub(open_stub).called_with(f'{collection}.json', 'r') @pytest.mark.parametrize('Storage,fake_data', [ (PrimitiveStorage, ['a','b','c','d']), (ObjectStorage, [{'a':'b'},{'c':'d'},{'e':'f'},{'g':'h'}]) ]) def test_can_limit_number_of_items_returned(self, Storage, fake_data): collection = 'hello' mocked_open = mock_open(read_data=json.dumps(fake_data)) start=1 end=3 with mock.patch('builtins.open', mocked_open) as open_stub: store = Storage(collection) rs = store.read(start=start,end=end) assert rs == fake_data[start:end] assert Stub(open_stub).called_with(f'{collection}.json', 'r') @pytest.mark.parametrize('Storage,input_data,expected_data', [ (PrimitiveStorage, ['a','b', 'c'], ['a', 'b', 'c']), (ObjectStorage, {'a':'b'}, [{'a':'b'}]) ]) @mock.patch('jsondb.json.dump') def test_adds_items_to_file(self, jdump_stub, Storage, input_data, expected_data): collection = 'byebye' mocked_open = mock_open() with mock.patch('builtins.open', mocked_open) as open_stub: Storage(collection).create(input_data) assert Stub(open_stub).called_with(f'{collection}.json', 'w') assert Stub(jdump_stub).called_with(expected_data, mocked_open()) @mock.patch('jsondb.json.dump') def test_primitive_adds_unique_sorted_items_to_file(self, jdump_stub): collection = 'byebye' input_data = ['b', 'b', 'c', 'a', 'a'] expected_data = ['a','b','c'] mocked_open = mock_open() with mock.patch('builtins.open', mocked_open) as open_stub: PrimitiveStorage(collection).create(input_data) assert Stub(jdump_stub).called_with(expected_data, mocked_open())

41.8

86

0.639155

import pytest from fastapi.testclient import TestClient from unittest import mock from unittest.mock import mock_open import json from jsondb import PrimitiveStorage, ObjectStorage from conftest import Stub class TestStorage(): @pytest.mark.parametrize('Storage,fake_data', [ (PrimitiveStorage, ['a','b']), (ObjectStorage, [{'a':'b'}]) ]) def test_gets_list_of_items_from_file(self, Storage, fake_data): collection = 'hello' mocked_open = mock_open(read_data=json.dumps(fake_data)) with mock.patch('builtins.open', mocked_open) as open_stub: store = Storage(collection) rs = store.read() assert rs == fake_data assert Stub(open_stub).called_with(f'{collection}.json', 'r') @pytest.mark.parametrize('Storage,fake_data', [ (PrimitiveStorage, ['a','b','c','d']), (ObjectStorage, [{'a':'b'},{'c':'d'},{'e':'f'},{'g':'h'}]) ]) def test_can_limit_number_of_items_returned(self, Storage, fake_data): collection = 'hello' mocked_open = mock_open(read_data=json.dumps(fake_data)) start=1 end=3 with mock.patch('builtins.open', mocked_open) as open_stub: store = Storage(collection) rs = store.read(start=start,end=end) assert rs == fake_data[start:end] assert Stub(open_stub).called_with(f'{collection}.json', 'r') @pytest.mark.parametrize('Storage,input_data,expected_data', [ (PrimitiveStorage, ['a','b', 'c'], ['a', 'b', 'c']), (ObjectStorage, {'a':'b'}, [{'a':'b'}]) ]) @mock.patch('jsondb.json.dump') def test_adds_items_to_file(self, jdump_stub, Storage, input_data, expected_data): collection = 'byebye' mocked_open = mock_open() with mock.patch('builtins.open', mocked_open) as open_stub: Storage(collection).create(input_data) assert Stub(open_stub).called_with(f'{collection}.json', 'w') assert Stub(jdump_stub).called_with(expected_data, mocked_open()) @mock.patch('jsondb.json.dump') def test_primitive_adds_unique_sorted_items_to_file(self, jdump_stub): collection = 'byebye' input_data = ['b', 'b', 'c', 'a', 'a'] expected_data = ['a','b','c'] mocked_open = mock_open() with mock.patch('builtins.open', mocked_open) as open_stub: PrimitiveStorage(collection).create(input_data) assert Stub(jdump_stub).called_with(expected_data, mocked_open())

true

1c2e8693a0bd5d8977f78731eb94fec0b7bbd19d

22,241

py

Python

test/functional/test_framework/util.py

minblock/watacoin

39b64687af9237feed324ee01e330e153aa5bc36

[ "MIT" ]

null

test/functional/test_framework/util.py

minblock/watacoin

39b64687af9237feed324ee01e330e153aa5bc36

[ "MIT" ]

null

test/functional/test_framework/util.py

minblock/watacoin

39b64687af9237feed324ee01e330e153aa5bc36

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2014-2018 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Helpful routines for regression testing.""" from base64 import b64encode from binascii import hexlify, unhexlify from decimal import Decimal, ROUND_DOWN import hashlib import inspect import json import logging import os import random import re from subprocess import CalledProcessError import time from . import coverage from .authproxy import AuthServiceProxy, JSONRPCException logger = logging.getLogger("TestFramework.utils") # Assert functions ################## def assert_fee_amount(fee, tx_size, fee_per_kB): """Assert the fee was in range""" target_fee = round(tx_size * fee_per_kB / 1000, 8) if fee < target_fee: raise AssertionError("Fee of %s WTC too low! (Should be %s WTC)" % (str(fee), str(target_fee))) # allow the wallet's estimation to be at most 2 bytes off if fee > (tx_size + 2) * fee_per_kB / 1000: raise AssertionError("Fee of %s WTC too high! (Should be %s WTC)" % (str(fee), str(target_fee))) def assert_equal(thing1, thing2, *args): if thing1 != thing2 or any(thing1 != arg for arg in args): raise AssertionError("not(%s)" % " == ".join(str(arg) for arg in (thing1, thing2) + args)) def assert_greater_than(thing1, thing2): if thing1 <= thing2: raise AssertionError("%s <= %s" % (str(thing1), str(thing2))) def assert_greater_than_or_equal(thing1, thing2): if thing1 < thing2: raise AssertionError("%s < %s" % (str(thing1), str(thing2))) def assert_raises(exc, fun, *args, **kwds): assert_raises_message(exc, None, fun, *args, **kwds) def assert_raises_message(exc, message, fun, *args, **kwds): try: fun(*args, **kwds) except JSONRPCException: raise AssertionError("Use assert_raises_rpc_error() to test RPC failures") except exc as e: if message is not None and message not in e.error['message']: raise AssertionError("Expected substring not found:" + e.error['message']) except Exception as e: raise AssertionError("Unexpected exception raised: " + type(e).__name__) else: raise AssertionError("No exception raised") def assert_raises_process_error(returncode, output, fun, *args, **kwds): """Execute a process and asserts the process return code and output. Calls function `fun` with arguments `args` and `kwds`. Catches a CalledProcessError and verifies that the return code and output are as expected. Throws AssertionError if no CalledProcessError was raised or if the return code and output are not as expected. Args: returncode (int): the process return code. output (string): [a substring of] the process output. fun (function): the function to call. This should execute a process. args*: positional arguments for the function. kwds**: named arguments for the function. """ try: fun(*args, **kwds) except CalledProcessError as e: if returncode != e.returncode: raise AssertionError("Unexpected returncode %i" % e.returncode) if output not in e.output: raise AssertionError("Expected substring not found:" + e.output) else: raise AssertionError("No exception raised") def assert_raises_rpc_error(code, message, fun, *args, **kwds): """Run an RPC and verify that a specific JSONRPC exception code and message is raised. Calls function `fun` with arguments `args` and `kwds`. Catches a JSONRPCException and verifies that the error code and message are as expected. Throws AssertionError if no JSONRPCException was raised or if the error code/message are not as expected. Args: code (int), optional: the error code returned by the RPC call (defined in src/rpc/protocol.h). Set to None if checking the error code is not required. message (string), optional: [a substring of] the error string returned by the RPC call. Set to None if checking the error string is not required. fun (function): the function to call. This should be the name of an RPC. args*: positional arguments for the function. kwds**: named arguments for the function. """ assert try_rpc(code, message, fun, *args, **kwds), "No exception raised" def try_rpc(code, message, fun, *args, **kwds): """Tries to run an rpc command. Test against error code and message if the rpc fails. Returns whether a JSONRPCException was raised.""" try: fun(*args, **kwds) except JSONRPCException as e: # JSONRPCException was thrown as expected. Check the code and message values are correct. if (code is not None) and (code != e.error["code"]): raise AssertionError("Unexpected JSONRPC error code %i" % e.error["code"]) if (message is not None) and (message not in e.error['message']): raise AssertionError("Expected substring not found:" + e.error['message']) return True except Exception as e: raise AssertionError("Unexpected exception raised: " + type(e).__name__) else: return False def assert_is_hex_string(string): try: int(string, 16) except Exception as e: raise AssertionError( "Couldn't interpret %r as hexadecimal; raised: %s" % (string, e)) def assert_is_hash_string(string, length=64): if not isinstance(string, str): raise AssertionError("Expected a string, got type %r" % type(string)) elif length and len(string) != length: raise AssertionError( "String of length %d expected; got %d" % (length, len(string))) elif not re.match('[abcdef0-9]+$', string): raise AssertionError( "String %r contains invalid characters for a hash." % string) def assert_array_result(object_array, to_match, expected, should_not_find=False): """ Pass in array of JSON objects, a dictionary with key/value pairs to match against, and another dictionary with expected key/value pairs. If the should_not_find flag is true, to_match should not be found in object_array """ if should_not_find: assert_equal(expected, {}) num_matched = 0 for item in object_array: all_match = True for key, value in to_match.items(): if item[key] != value: all_match = False if not all_match: continue elif should_not_find: num_matched = num_matched + 1 for key, value in expected.items(): if item[key] != value: raise AssertionError("%s : expected %s=%s" % (str(item), str(key), str(value))) num_matched = num_matched + 1 if num_matched == 0 and not should_not_find: raise AssertionError("No objects matched %s" % (str(to_match))) if num_matched > 0 and should_not_find: raise AssertionError("Objects were found %s" % (str(to_match))) # Utility functions ################### def check_json_precision(): """Make sure json library being used does not lose precision converting BTC values""" n = Decimal("20000000.00000003") satoshis = int(json.loads(json.dumps(float(n))) * 1.0e8) if satoshis != 2000000000000003: raise RuntimeError("JSON encode/decode loses precision") def count_bytes(hex_string): return len(bytearray.fromhex(hex_string)) def bytes_to_hex_str(byte_str): return hexlify(byte_str).decode('ascii') def hash256(byte_str): sha256 = hashlib.sha256() sha256.update(byte_str) sha256d = hashlib.sha256() sha256d.update(sha256.digest()) return sha256d.digest()[::-1] def hex_str_to_bytes(hex_str): return unhexlify(hex_str.encode('ascii')) def str_to_b64str(string): return b64encode(string.encode('utf-8')).decode('ascii') def satoshi_round(amount): return Decimal(amount).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) def wait_until(predicate, *, attempts=float('inf'), timeout=float('inf'), lock=None): if attempts == float('inf') and timeout == float('inf'): timeout = 60 attempt = 0 time_end = time.time() + timeout while attempt < attempts and time.time() < time_end: if lock: with lock: if predicate(): return else: if predicate(): return attempt += 1 time.sleep(0.05) # Print the cause of the timeout predicate_source = "''''\n" + inspect.getsource(predicate) + "'''" logger.error("wait_until() failed. Predicate: {}".format(predicate_source)) if attempt >= attempts: raise AssertionError("Predicate {} not true after {} attempts".format(predicate_source, attempts)) elif time.time() >= time_end: raise AssertionError("Predicate {} not true after {} seconds".format(predicate_source, timeout)) raise RuntimeError('Unreachable') # RPC/P2P connection constants and functions ############################################ # The maximum number of nodes a single test can spawn MAX_NODES = 8 # Don't assign rpc or p2p ports lower than this PORT_MIN = 11000 # The number of ports to "reserve" for p2p and rpc, each PORT_RANGE = 5000 class PortSeed: # Must be initialized with a unique integer for each process n = None def get_rpc_proxy(url, node_number, timeout=None, coveragedir=None): """ Args: url (str): URL of the RPC server to call node_number (int): the node number (or id) that this calls to Kwargs: timeout (int): HTTP timeout in seconds Returns: AuthServiceProxy. convenience object for making RPC calls. """ proxy_kwargs = {} if timeout is not None: proxy_kwargs['timeout'] = timeout proxy = AuthServiceProxy(url, **proxy_kwargs) proxy.url = url # store URL on proxy for info coverage_logfile = coverage.get_filename( coveragedir, node_number) if coveragedir else None return coverage.AuthServiceProxyWrapper(proxy, coverage_logfile) def p2p_port(n): assert(n <= MAX_NODES) return PORT_MIN + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def rpc_port(n): return PORT_MIN + PORT_RANGE + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def rpc_url(datadir, i, rpchost=None): rpc_u, rpc_p = get_auth_cookie(datadir) host = '127.0.0.1' port = rpc_port(i) if rpchost: parts = rpchost.split(':') if len(parts) == 2: host, port = parts else: host = rpchost return "http://%s:%s@%s:%d" % (rpc_u, rpc_p, host, int(port)) # Node functions ################ def initialize_datadir(dirname, n): datadir = get_datadir_path(dirname, n) if not os.path.isdir(datadir): os.makedirs(datadir) with open(os.path.join(datadir, "watacoin.conf"), 'w', encoding='utf8') as f: f.write("regtest=1\n") f.write("[regtest]\n") f.write("port=" + str(p2p_port(n)) + "\n") f.write("rpcport=" + str(rpc_port(n)) + "\n") f.write("server=1\n") f.write("keypool=1\n") f.write("discover=0\n") f.write("listenonion=0\n") f.write("printtoconsole=0\n") os.makedirs(os.path.join(datadir, 'stderr'), exist_ok=True) os.makedirs(os.path.join(datadir, 'stdout'), exist_ok=True) return datadir def get_datadir_path(dirname, n): return os.path.join(dirname, "node" + str(n)) def append_config(datadir, options): with open(os.path.join(datadir, "watacoin.conf"), 'a', encoding='utf8') as f: for option in options: f.write(option + "\n") def get_auth_cookie(datadir): user = None password = None if os.path.isfile(os.path.join(datadir, "watacoin.conf")): with open(os.path.join(datadir, "watacoin.conf"), 'r', encoding='utf8') as f: for line in f: if line.startswith("rpcuser="): assert user is None # Ensure that there is only one rpcuser line user = line.split("=")[1].strip("\n") if line.startswith("rpcpassword="): assert password is None # Ensure that there is only one rpcpassword line password = line.split("=")[1].strip("\n") if os.path.isfile(os.path.join(datadir, "regtest", ".cookie")) and os.access(os.path.join(datadir, "regtest", ".cookie"), os.R_OK): with open(os.path.join(datadir, "regtest", ".cookie"), 'r', encoding="ascii") as f: userpass = f.read() split_userpass = userpass.split(':') user = split_userpass[0] password = split_userpass[1] if user is None or password is None: raise ValueError("No RPC credentials") return user, password # If a cookie file exists in the given datadir, delete it. def delete_cookie_file(datadir): if os.path.isfile(os.path.join(datadir, "regtest", ".cookie")): logger.debug("Deleting leftover cookie file") os.remove(os.path.join(datadir, "regtest", ".cookie")) def get_bip9_status(node, key): info = node.getblockchaininfo() return info['bip9_softforks'][key] def set_node_times(nodes, t): for node in nodes: node.setmocktime(t) def disconnect_nodes(from_connection, node_num): for peer_id in [peer['id'] for peer in from_connection.getpeerinfo() if "testnode%d" % node_num in peer['subver']]: try: from_connection.disconnectnode(nodeid=peer_id) except JSONRPCException as e: # If this node is disconnected between calculating the peer id # and issuing the disconnect, don't worry about it. # This avoids a race condition if we're mass-disconnecting peers. if e.error['code'] != -29: # RPC_CLIENT_NODE_NOT_CONNECTED raise # wait to disconnect wait_until(lambda: [peer['id'] for peer in from_connection.getpeerinfo() if "testnode%d" % node_num in peer['subver']] == [], timeout=5) def connect_nodes(from_connection, node_num): ip_port = "127.0.0.1:" + str(p2p_port(node_num)) from_connection.addnode(ip_port, "onetry") # poll until version handshake complete to avoid race conditions # with transaction relaying wait_until(lambda: all(peer['version'] != 0 for peer in from_connection.getpeerinfo())) def connect_nodes_bi(nodes, a, b): connect_nodes(nodes[a], b) connect_nodes(nodes[b], a) def sync_blocks(rpc_connections, *, wait=1, timeout=60): """ Wait until everybody has the same tip. sync_blocks needs to be called with an rpc_connections set that has least one node already synced to the latest, stable tip, otherwise there's a chance it might return before all nodes are stably synced. """ stop_time = time.time() + timeout while time.time() <= stop_time: best_hash = [x.getbestblockhash() for x in rpc_connections] if best_hash.count(best_hash[0]) == len(rpc_connections): return time.sleep(wait) raise AssertionError("Block sync timed out:{}".format("".join("\n {!r}".format(b) for b in best_hash))) def sync_mempools(rpc_connections, *, wait=1, timeout=60, flush_scheduler=True): """ Wait until everybody has the same transactions in their memory pools """ stop_time = time.time() + timeout while time.time() <= stop_time: pool = [set(r.getrawmempool()) for r in rpc_connections] if pool.count(pool[0]) == len(rpc_connections): if flush_scheduler: for r in rpc_connections: r.syncwithvalidationinterfacequeue() return time.sleep(wait) raise AssertionError("Mempool sync timed out:{}".format("".join("\n {!r}".format(m) for m in pool))) # Transaction/Block functions ############################# def find_output(node, txid, amount, *, blockhash=None): """ Return index to output of txid with value amount Raises exception if there is none. """ txdata = node.getrawtransaction(txid, 1, blockhash) for i in range(len(txdata["vout"])): if txdata["vout"][i]["value"] == amount: return i raise RuntimeError("find_output txid %s : %s not found" % (txid, str(amount))) def gather_inputs(from_node, amount_needed, confirmations_required=1): """ Return a random set of unspent txouts that are enough to pay amount_needed """ assert(confirmations_required >= 0) utxo = from_node.listunspent(confirmations_required) random.shuffle(utxo) inputs = [] total_in = Decimal("0.00000000") while total_in < amount_needed and len(utxo) > 0: t = utxo.pop() total_in += t["amount"] inputs.append({"txid": t["txid"], "vout": t["vout"], "address": t["address"]}) if total_in < amount_needed: raise RuntimeError("Insufficient funds: need %d, have %d" % (amount_needed, total_in)) return (total_in, inputs) def make_change(from_node, amount_in, amount_out, fee): """ Create change output(s), return them """ outputs = {} amount = amount_out + fee change = amount_in - amount if change > amount * 2: # Create an extra change output to break up big inputs change_address = from_node.getnewaddress() # Split change in two, being careful of rounding: outputs[change_address] = Decimal(change / 2).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) change = amount_in - amount - outputs[change_address] if change > 0: outputs[from_node.getnewaddress()] = change return outputs def random_transaction(nodes, amount, min_fee, fee_increment, fee_variants): """ Create a random transaction. Returns (txid, hex-encoded-transaction-data, fee) """ from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment * random.randint(0, fee_variants) (total_in, inputs) = gather_inputs(from_node, amount + fee) outputs = make_change(from_node, total_in, amount, fee) outputs[to_node.getnewaddress()] = float(amount) rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransactionwithwallet(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"], fee) # Helper to create at least "count" utxos # Pass in a fee that is sufficient for relay and mining new transactions. def create_confirmed_utxos(fee, node, count): to_generate = int(0.5 * count) + 101 while to_generate > 0: node.generate(min(25, to_generate)) to_generate -= 25 utxos = node.listunspent() iterations = count - len(utxos) addr1 = node.getnewaddress() addr2 = node.getnewaddress() if iterations <= 0: return utxos for i in range(iterations): t = utxos.pop() inputs = [] inputs.append({"txid": t["txid"], "vout": t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr1] = satoshi_round(send_value / 2) outputs[addr2] = satoshi_round(send_value / 2) raw_tx = node.createrawtransaction(inputs, outputs) signed_tx = node.signrawtransactionwithwallet(raw_tx)["hex"] node.sendrawtransaction(signed_tx) while (node.getmempoolinfo()['size'] > 0): node.generate(1) utxos = node.listunspent() assert(len(utxos) >= count) return utxos # Create large OP_RETURN txouts that can be appended to a transaction # to make it large (helper for constructing large transactions). def gen_return_txouts(): # Some pre-processing to create a bunch of OP_RETURN txouts to insert into transactions we create # So we have big transactions (and therefore can't fit very many into each block) # create one script_pubkey script_pubkey = "6a4d0200" # OP_RETURN OP_PUSH2 512 bytes for i in range(512): script_pubkey = script_pubkey + "01" # concatenate 128 txouts of above script_pubkey which we'll insert before the txout for change txouts = "81" for k in range(128): # add txout value txouts = txouts + "0000000000000000" # add length of script_pubkey txouts = txouts + "fd0402" # add script_pubkey txouts = txouts + script_pubkey return txouts # Create a spend of each passed-in utxo, splicing in "txouts" to each raw # transaction to make it large. See gen_return_txouts() above. def create_lots_of_big_transactions(node, txouts, utxos, num, fee): addr = node.getnewaddress() txids = [] for _ in range(num): t = utxos.pop() inputs = [{"txid": t["txid"], "vout": t["vout"]}] outputs = {} change = t['amount'] - fee outputs[addr] = satoshi_round(change) rawtx = node.createrawtransaction(inputs, outputs) newtx = rawtx[0:92] newtx = newtx + txouts newtx = newtx + rawtx[94:] signresult = node.signrawtransactionwithwallet(newtx, None, "NONE") txid = node.sendrawtransaction(signresult["hex"], True) txids.append(txid) return txids def mine_large_block(node, utxos=None): # generate a 66k transaction, # and 14 of them is close to the 1MB block limit num = 14 txouts = gen_return_txouts() utxos = utxos if utxos is not None else [] if len(utxos) < num: utxos.clear() utxos.extend(node.listunspent()) fee = 100 * node.getnetworkinfo()["relayfee"] create_lots_of_big_transactions(node, txouts, utxos, num, fee=fee) node.generate(1) def find_vout_for_address(node, txid, addr): """ Locate the vout index of the given transaction sending to the given address. Raises runtime error exception if not found. """ tx = node.getrawtransaction(txid, True) for i in range(len(tx["vout"])): if any([addr == a for a in tx["vout"][i]["scriptPubKey"]["addresses"]]): return i raise RuntimeError("Vout not found for address: txid=%s, addr=%s" % (txid, addr))

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from base64 import b64encode from binascii import hexlify, unhexlify from decimal import Decimal, ROUND_DOWN import hashlib import inspect import json import logging import os import random import re from subprocess import CalledProcessError import time from . import coverage from .authproxy import AuthServiceProxy, JSONRPCException logger = logging.getLogger("TestFramework.utils") nError("Fee of %s WTC too low! (Should be %s WTC)" % (str(fee), str(target_fee))) if fee > (tx_size + 2) * fee_per_kB / 1000: raise AssertionError("Fee of %s WTC too high! (Should be %s WTC)" % (str(fee), str(target_fee))) def assert_equal(thing1, thing2, *args): if thing1 != thing2 or any(thing1 != arg for arg in args): raise AssertionError("not(%s)" % " == ".join(str(arg) for arg in (thing1, thing2) + args)) def assert_greater_than(thing1, thing2): if thing1 <= thing2: raise AssertionError("%s <= %s" % (str(thing1), str(thing2))) def assert_greater_than_or_equal(thing1, thing2): if thing1 < thing2: raise AssertionError("%s < %s" % (str(thing1), str(thing2))) def assert_raises(exc, fun, *args, **kwds): assert_raises_message(exc, None, fun, *args, **kwds) def assert_raises_message(exc, message, fun, *args, **kwds): try: fun(*args, **kwds) except JSONRPCException: raise AssertionError("Use assert_raises_rpc_error() to test RPC failures") except exc as e: if message is not None and message not in e.error['message']: raise AssertionError("Expected substring not found:" + e.error['message']) except Exception as e: raise AssertionError("Unexpected exception raised: " + type(e).__name__) else: raise AssertionError("No exception raised") def assert_raises_process_error(returncode, output, fun, *args, **kwds): try: fun(*args, **kwds) except CalledProcessError as e: if returncode != e.returncode: raise AssertionError("Unexpected returncode %i" % e.returncode) if output not in e.output: raise AssertionError("Expected substring not found:" + e.output) else: raise AssertionError("No exception raised") def assert_raises_rpc_error(code, message, fun, *args, **kwds): assert try_rpc(code, message, fun, *args, **kwds), "No exception raised" def try_rpc(code, message, fun, *args, **kwds): try: fun(*args, **kwds) except JSONRPCException as e: # JSONRPCException was thrown as expected. Check the code and message values are correct. if (code is not None) and (code != e.error["code"]): raise AssertionError("Unexpected JSONRPC error code %i" % e.error["code"]) if (message is not None) and (message not in e.error['message']): raise AssertionError("Expected substring not found:" + e.error['message']) return True except Exception as e: raise AssertionError("Unexpected exception raised: " + type(e).__name__) else: return False def assert_is_hex_string(string): try: int(string, 16) except Exception as e: raise AssertionError( "Couldn't interpret %r as hexadecimal; raised: %s" % (string, e)) def assert_is_hash_string(string, length=64): if not isinstance(string, str): raise AssertionError("Expected a string, got type %r" % type(string)) elif length and len(string) != length: raise AssertionError( "String of length %d expected; got %d" % (length, len(string))) elif not re.match('[abcdef0-9]+$', string): raise AssertionError( "String %r contains invalid characters for a hash." % string) def assert_array_result(object_array, to_match, expected, should_not_find=False): if should_not_find: assert_equal(expected, {}) num_matched = 0 for item in object_array: all_match = True for key, value in to_match.items(): if item[key] != value: all_match = False if not all_match: continue elif should_not_find: num_matched = num_matched + 1 for key, value in expected.items(): if item[key] != value: raise AssertionError("%s : expected %s=%s" % (str(item), str(key), str(value))) num_matched = num_matched + 1 if num_matched == 0 and not should_not_find: raise AssertionError("No objects matched %s" % (str(to_match))) if num_matched > 0 and should_not_find: raise AssertionError("Objects were found %s" % (str(to_match))) raise RuntimeError("JSON encode/decode loses precision") def count_bytes(hex_string): return len(bytearray.fromhex(hex_string)) def bytes_to_hex_str(byte_str): return hexlify(byte_str).decode('ascii') def hash256(byte_str): sha256 = hashlib.sha256() sha256.update(byte_str) sha256d = hashlib.sha256() sha256d.update(sha256.digest()) return sha256d.digest()[::-1] def hex_str_to_bytes(hex_str): return unhexlify(hex_str.encode('ascii')) def str_to_b64str(string): return b64encode(string.encode('utf-8')).decode('ascii') def satoshi_round(amount): return Decimal(amount).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) def wait_until(predicate, *, attempts=float('inf'), timeout=float('inf'), lock=None): if attempts == float('inf') and timeout == float('inf'): timeout = 60 attempt = 0 time_end = time.time() + timeout while attempt < attempts and time.time() < time_end: if lock: with lock: if predicate(): return else: if predicate(): return attempt += 1 time.sleep(0.05) predicate_source = "''''\n" + inspect.getsource(predicate) + "'''" logger.error("wait_until() failed. Predicate: {}".format(predicate_source)) if attempt >= attempts: raise AssertionError("Predicate {} not true after {} attempts".format(predicate_source, attempts)) elif time.time() >= time_end: raise AssertionError("Predicate {} not true after {} seconds".format(predicate_source, timeout)) raise RuntimeError('Unreachable') # RPC/P2P connection constants and functions ############################################ # The maximum number of nodes a single test can spawn MAX_NODES = 8 # Don't assign rpc or p2p ports lower than this PORT_MIN = 11000 PORT_RANGE = 5000 class PortSeed: n = None def get_rpc_proxy(url, node_number, timeout=None, coveragedir=None): proxy_kwargs = {} if timeout is not None: proxy_kwargs['timeout'] = timeout proxy = AuthServiceProxy(url, **proxy_kwargs) proxy.url = url coverage_logfile = coverage.get_filename( coveragedir, node_number) if coveragedir else None return coverage.AuthServiceProxyWrapper(proxy, coverage_logfile) def p2p_port(n): assert(n <= MAX_NODES) return PORT_MIN + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def rpc_port(n): return PORT_MIN + PORT_RANGE + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def rpc_url(datadir, i, rpchost=None): rpc_u, rpc_p = get_auth_cookie(datadir) host = '127.0.0.1' port = rpc_port(i) if rpchost: parts = rpchost.split(':') if len(parts) == 2: host, port = parts else: host = rpchost return "http://%s:%s@%s:%d" % (rpc_u, rpc_p, host, int(port)) os.makedirs(datadir) with open(os.path.join(datadir, "watacoin.conf"), 'w', encoding='utf8') as f: f.write("regtest=1\n") f.write("[regtest]\n") f.write("port=" + str(p2p_port(n)) + "\n") f.write("rpcport=" + str(rpc_port(n)) + "\n") f.write("server=1\n") f.write("keypool=1\n") f.write("discover=0\n") f.write("listenonion=0\n") f.write("printtoconsole=0\n") os.makedirs(os.path.join(datadir, 'stderr'), exist_ok=True) os.makedirs(os.path.join(datadir, 'stdout'), exist_ok=True) return datadir def get_datadir_path(dirname, n): return os.path.join(dirname, "node" + str(n)) def append_config(datadir, options): with open(os.path.join(datadir, "watacoin.conf"), 'a', encoding='utf8') as f: for option in options: f.write(option + "\n") def get_auth_cookie(datadir): user = None password = None if os.path.isfile(os.path.join(datadir, "watacoin.conf")): with open(os.path.join(datadir, "watacoin.conf"), 'r', encoding='utf8') as f: for line in f: if line.startswith("rpcuser="): assert user is None user = line.split("=")[1].strip("\n") if line.startswith("rpcpassword="): assert password is None password = line.split("=")[1].strip("\n") if os.path.isfile(os.path.join(datadir, "regtest", ".cookie")) and os.access(os.path.join(datadir, "regtest", ".cookie"), os.R_OK): with open(os.path.join(datadir, "regtest", ".cookie"), 'r', encoding="ascii") as f: userpass = f.read() split_userpass = userpass.split(':') user = split_userpass[0] password = split_userpass[1] if user is None or password is None: raise ValueError("No RPC credentials") return user, password def delete_cookie_file(datadir): if os.path.isfile(os.path.join(datadir, "regtest", ".cookie")): logger.debug("Deleting leftover cookie file") os.remove(os.path.join(datadir, "regtest", ".cookie")) def get_bip9_status(node, key): info = node.getblockchaininfo() return info['bip9_softforks'][key] def set_node_times(nodes, t): for node in nodes: node.setmocktime(t) def disconnect_nodes(from_connection, node_num): for peer_id in [peer['id'] for peer in from_connection.getpeerinfo() if "testnode%d" % node_num in peer['subver']]: try: from_connection.disconnectnode(nodeid=peer_id) except JSONRPCException as e: # This avoids a race condition if we're mass-disconnecting peers. if e.error['code'] != -29: raise wait_until(lambda: [peer['id'] for peer in from_connection.getpeerinfo() if "testnode%d" % node_num in peer['subver']] == [], timeout=5) def connect_nodes(from_connection, node_num): ip_port = "127.0.0.1:" + str(p2p_port(node_num)) from_connection.addnode(ip_port, "onetry") wait_until(lambda: all(peer['version'] != 0 for peer in from_connection.getpeerinfo())) def connect_nodes_bi(nodes, a, b): connect_nodes(nodes[a], b) connect_nodes(nodes[b], a) def sync_blocks(rpc_connections, *, wait=1, timeout=60): stop_time = time.time() + timeout while time.time() <= stop_time: best_hash = [x.getbestblockhash() for x in rpc_connections] if best_hash.count(best_hash[0]) == len(rpc_connections): return time.sleep(wait) raise AssertionError("Block sync timed out:{}".format("".join("\n {!r}".format(b) for b in best_hash))) def sync_mempools(rpc_connections, *, wait=1, timeout=60, flush_scheduler=True): stop_time = time.time() + timeout while time.time() <= stop_time: pool = [set(r.getrawmempool()) for r in rpc_connections] if pool.count(pool[0]) == len(rpc_connections): if flush_scheduler: for r in rpc_connections: r.syncwithvalidationinterfacequeue() return time.sleep(wait) raise AssertionError("Mempool sync timed out:{}".format("".join("\n {!r}".format(m) for m in pool))) equired >= 0) utxo = from_node.listunspent(confirmations_required) random.shuffle(utxo) inputs = [] total_in = Decimal("0.00000000") while total_in < amount_needed and len(utxo) > 0: t = utxo.pop() total_in += t["amount"] inputs.append({"txid": t["txid"], "vout": t["vout"], "address": t["address"]}) if total_in < amount_needed: raise RuntimeError("Insufficient funds: need %d, have %d" % (amount_needed, total_in)) return (total_in, inputs) def make_change(from_node, amount_in, amount_out, fee): outputs = {} amount = amount_out + fee change = amount_in - amount if change > amount * 2: change_address = from_node.getnewaddress() outputs[change_address] = Decimal(change / 2).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) change = amount_in - amount - outputs[change_address] if change > 0: outputs[from_node.getnewaddress()] = change return outputs def random_transaction(nodes, amount, min_fee, fee_increment, fee_variants): from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment * random.randint(0, fee_variants) (total_in, inputs) = gather_inputs(from_node, amount + fee) outputs = make_change(from_node, total_in, amount, fee) outputs[to_node.getnewaddress()] = float(amount) rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransactionwithwallet(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"], fee) def create_confirmed_utxos(fee, node, count): to_generate = int(0.5 * count) + 101 while to_generate > 0: node.generate(min(25, to_generate)) to_generate -= 25 utxos = node.listunspent() iterations = count - len(utxos) addr1 = node.getnewaddress() addr2 = node.getnewaddress() if iterations <= 0: return utxos for i in range(iterations): t = utxos.pop() inputs = [] inputs.append({"txid": t["txid"], "vout": t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr1] = satoshi_round(send_value / 2) outputs[addr2] = satoshi_round(send_value / 2) raw_tx = node.createrawtransaction(inputs, outputs) signed_tx = node.signrawtransactionwithwallet(raw_tx)["hex"] node.sendrawtransaction(signed_tx) while (node.getmempoolinfo()['size'] > 0): node.generate(1) utxos = node.listunspent() assert(len(utxos) >= count) return utxos def gen_return_txouts(): # create one script_pubkey script_pubkey = "6a4d0200" # OP_RETURN OP_PUSH2 512 bytes for i in range(512): script_pubkey = script_pubkey + "01" # concatenate 128 txouts of above script_pubkey which we'll insert before the txout for change txouts = "81" for k in range(128): txouts = txouts + "0000000000000000" txouts = txouts + "fd0402" txouts = txouts + script_pubkey return txouts def create_lots_of_big_transactions(node, txouts, utxos, num, fee): addr = node.getnewaddress() txids = [] for _ in range(num): t = utxos.pop() inputs = [{"txid": t["txid"], "vout": t["vout"]}] outputs = {} change = t['amount'] - fee outputs[addr] = satoshi_round(change) rawtx = node.createrawtransaction(inputs, outputs) newtx = rawtx[0:92] newtx = newtx + txouts newtx = newtx + rawtx[94:] signresult = node.signrawtransactionwithwallet(newtx, None, "NONE") txid = node.sendrawtransaction(signresult["hex"], True) txids.append(txid) return txids def mine_large_block(node, utxos=None): num = 14 txouts = gen_return_txouts() utxos = utxos if utxos is not None else [] if len(utxos) < num: utxos.clear() utxos.extend(node.listunspent()) fee = 100 * node.getnetworkinfo()["relayfee"] create_lots_of_big_transactions(node, txouts, utxos, num, fee=fee) node.generate(1) def find_vout_for_address(node, txid, addr): tx = node.getrawtransaction(txid, True) for i in range(len(tx["vout"])): if any([addr == a for a in tx["vout"][i]["scriptPubKey"]["addresses"]]): return i raise RuntimeError("Vout not found for address: txid=%s, addr=%s" % (txid, addr))

true

1c2e869b0603b84c2ae7af7ba218f65b9f92ec56

17,754

py

Python

wagl/data.py

ASVincent/wagl

cf3a72e53e53f3a7b2f2b5308068069b1b714f2a

[ "Apache-2.0" ]

null

wagl/data.py

ASVincent/wagl

cf3a72e53e53f3a7b2f2b5308068069b1b714f2a

[ "Apache-2.0" ]

null

wagl/data.py

ASVincent/wagl

cf3a72e53e53f3a7b2f2b5308068069b1b714f2a

[ "Apache-2.0" ]

1

2019-01-23T00:51:56.000Z

""" Data access functions --------------------- """ from __future__ import absolute_import from os.path import join as pjoin, basename, dirname import subprocess import tempfile import logging import numpy as np import h5py import rasterio from rasterio.crs import CRS from rasterio.warp import reproject from rasterio.enums import Resampling from wagl.geobox import GriddedGeoBox from wagl.tiling import generate_tiles def get_pixel(filename, lonlat, band=1): """Return a pixel from `filename` at the longitude and latitude given by the tuple `lonlat`. Optionally, the `band` can be specified.""" with rasterio.open(filename) as src: x, y = [int(v) for v in ~src.transform * lonlat] if isinstance(band, list): data = src.read(band, window=((y, y + 1), (x, x + 1))).ravel() else: data = src.read(band, window=((y, y + 1), (x, x + 1))).flat[0] return data def select_acquisitions(acqs_list, fn=(lambda acq: True)): """ Given a list of acquisitions, apply the supplied fn to select the desired acquisitions. """ acqs = [acq for acq in acqs_list if fn(acq)] return acqs def stack_data(acqs_list, fn=(lambda acq: True), window=None, masked=False): """ Given a list of acquisitions, return the data from each acquisition collected in a 3D numpy array (first index is the acquisition number). If window is defined, then the subset contained within the window is returned along with a GriddedGeoBox instance detailing the spatial information associated with that subset. :param acqs_list: The list of acquisitions from which to generate a stack of data. :param window: Defines a subset ((ystart, yend), (xstart, xend)) in array co-ordinates. Default is None. :param masked: Indicates whether or not to return a masked array. Default is False. :return: A 2-tuple containing: * 1. A 3D numpy array (or None) containing the corresponding acquisition data. (None if no data). * 2. A GriddedGeoBox instance specifying the spatial context of the 3D numpy array. Note: All Acquisitions share the same GriddedGeoBox. """ # determine data type and dimensions by reading the first band acqs = acqs_list a, geo_box = acqs[0].data_and_box(window=window, masked=masked) # create the result array, setting datatype based on source type stack_shape = (len(acqs), a.shape[0], a.shape[1]) stack = np.empty(stack_shape, a.dtype) stack[0] = a del a # read remaining aquisitions into it for i in range(1, stack_shape[0]): # can't use this statement because it will cause data to be # resampled. But we want an exception thrown if the user # tries to stack irreqular aquisitions stack[i] = acqs[i].data(window=window, masked=masked) return stack, geo_box def write_img(array, filename, driver='GTiff', geobox=None, nodata=None, tags=None, options=None, cogtif=False, levels=None, resampling=Resampling.nearest): """ Writes a 2D/3D image to disk using rasterio. :param array: A 2D/3D NumPy array. :param filename: A string containing the output file name. :param driver: A string containing a GDAL compliant image driver. Default is 'GTiff'. :param geobox: An instance of a GriddedGeoBox object. :param nodata: A value representing the no data value for the array. :param tags: A dictionary of dataset-level metadata. :param options: A dictionary containing other dataset creation options. See creation options for the respective GDAL formats. :param cogtif: If set to True, override the `driver` keyword with `GTiff` and create a Cloud Optimised GeoTiff. Default is False. See: https://trac.osgeo.org/gdal/wiki/CloudOptimizedGeoTIFF :param levels: If cogtif is set to True, build overviews/pyramids according to levels. Default levels are [2, 4, 8, 16, 32]. :param resampling: If cogtif is set to True, build overviews/pyramids using a resampling method from `rasterio.enums.Resampling`. Default is `Resampling.nearest`. :notes: If array is an instance of a `h5py.Dataset`, then the output file will include blocksizes based on the `h5py.Dataset's` chunks. To override the blocksizes, specify them using the `options` keyword. Eg {'blockxsize': 512, 'blockysize': 512}. If `cogtif` is set to True, the default blocksizes will be 256x256. To override this behaviour, specify them using the `options` keyword. Eg {'blockxsize': 512, 'blockysize': 512}. """ # Get the datatype of the array dtype = array.dtype.name # Check for excluded datatypes excluded_dtypes = ['int64', 'int8', 'uint64'] if dtype in excluded_dtypes: msg = "Datatype not supported: {dt}".format(dt=dtype) raise TypeError(msg) # convert any bools to uin8 if dtype == 'bool': array = np.uint8(array) dtype = 'uint8' ndims = array.ndim dims = array.shape # Get the (z, y, x) dimensions (assuming BSQ interleave) if ndims == 2: samples = dims[1] lines = dims[0] bands = 1 elif ndims == 3: samples = dims[2] lines = dims[1] bands = dims[0] else: logging.error('Input array is not of 2 or 3 dimensions!!!') err = 'Array dimensions: {dims}'.format(dims=ndims) raise IndexError(err) # If we have a geobox, then retrieve the geotransform and projection if geobox is not None: transform = geobox.transform projection = geobox.crs.ExportToWkt() else: transform = None projection = None # override the driver if we are creating a cogtif if cogtif: driver = 'GTiff' # compression predictor choices predictor = {'int8': 2, 'uint8': 2, 'int16': 2, 'uint16': 2, 'int32': 2, 'uint32': 2, 'int64': 2, 'uint64': 2, 'float32': 3, 'float64': 3} kwargs = {'count': bands, 'width': samples, 'height': lines, 'crs': projection, 'transform': transform, 'dtype': dtype, 'driver': driver, 'nodata': nodata, 'predictor': predictor[dtype]} if isinstance(array, h5py.Dataset): # TODO: if array is 3D get x & y chunks if array.chunks[1] == array.shape[1]: # GDAL doesn't like tiled or blocksize options to be set # the same length as the columns (probably true for rows as well) array = array[:] else: y_tile, x_tile = array.chunks tiles = generate_tiles(samples, lines, x_tile, y_tile) # add blocksizes to the creation keywords kwargs['tiled'] = 'yes' kwargs['blockxsize'] = x_tile kwargs['blockysize'] = y_tile # the user can override any derived blocksizes by supplying `options` if options is not None: for key in options: kwargs[key] = options[key] with tempfile.TemporaryDirectory() as tmpdir: out_fname = pjoin(tmpdir, basename(filename)) if cogtif else filename with rasterio.open(out_fname, 'w', **kwargs) as outds: if bands == 1: if isinstance(array, h5py.Dataset): for tile in tiles: idx = (slice(tile[0][0], tile[0][1]), slice(tile[1][0], tile[1][1])) outds.write(array[idx], 1, window=tile) else: outds.write(array, 1) else: if isinstance(array, h5py.Dataset): for tile in tiles: idx = (slice(tile[0][0], tile[0][1]), slice(tile[1][0], tile[1][1])) subs = array[:, idx[0], idx[1]] for i in range(bands): outds.write(subs[i], i + 1, window=tile) else: for i in range(bands): outds.write(array[i], i + 1) if tags is not None: outds.update_tags(**tags) # overviews/pyramids if cogtif: if levels is None: levels = [2, 4, 8, 16, 32] outds.build_overviews(levels, resampling) if cogtif: cmd = ['gdal_translate', '-co', 'TILED=YES', '-co', 'COPY_SRC_OVERVIEWS=YES', '-co', '{}={}'.format('PREDICTOR', predictor[dtype])] for key, value in options.items(): cmd.extend(['-co', '{}={}'.format(key, value)]) cmd.extend([out_fname, filename]) subprocess.check_call(cmd, cwd=dirname(filename)) def read_subset(fname, ul_xy, ur_xy, lr_xy, ll_xy, bands=1): """ Return a 2D or 3D NumPy array subsetted to the given bounding extents. :param fname: A string containing the full file pathname to an image on disk. :param ul_xy: A tuple containing the Upper Left (x,y) co-ordinate pair in real world (map) co-ordinates. Co-ordinate pairs can be (longitude, latitude) or (eastings, northings), but they must be of the same reference as the image of interest. :param ur_xy: A tuple containing the Upper Right (x,y) co-ordinate pair in real world (map) co-ordinates. Co-ordinate pairs can be (longitude, latitude) or (eastings, northings), but they must be of the same reference as the image of interest. :param lr_xy: A tuple containing the Lower Right (x,y) co-ordinate pair in real world (map) co-ordinates. Co-ordinate pairs can be (longitude, latitude) or (eastings, northings), but they must be of the same reference as the image of interest. :param ll_xy: A tuple containing the Lower Left (x,y) co-ordinate pair in real world (map) co-ordinates. Co-ordinate pairs can be (longitude, latitude) or (eastings, northings), but they must be of the same reference as the image of interest. :param bands: Can be an integer of list of integers representing the band(s) to be read from disk. If bands is a list, then the returned subset will be 3D, otherwise the subset will be strictly 2D. :return: A tuple of 3 elements: * 1. 2D or 3D NumPy array containing the image subset. * 2. A list of length 6 containing the GDAL geotransform. * 3. A WKT formatted string representing the co-ordinate reference system (projection). :additional notes: The ending array co-ordinates are increased by +1, i.e. xend = 270 + 1 to account for Python's [inclusive, exclusive) index notation. """ if isinstance(fname, h5py.Dataset): geobox = GriddedGeoBox.from_dataset(fname) prj = fname.attrs['crs_wkt'] else: # Open the file with rasterio.open(fname) as src: # Get the inverse transform of the affine co-ordinate reference geobox = GriddedGeoBox.from_dataset(src) prj = src.crs.wkt # rasterio returns a unicode inv = ~geobox.transform rows, cols = geobox.shape # Convert each map co-ordinate to image/array co-ordinates img_ul_x, img_ul_y = [int(v) for v in inv * ul_xy] img_ur_x, img_ur_y = [int(v) for v in inv * ur_xy] img_lr_x, img_lr_y = [int(v) for v in inv * lr_xy] img_ll_x, img_ll_y = [int(v) for v in inv * ll_xy] # Calculate the min and max array extents # The ending array extents have +1 to account for Python's # [inclusive, exclusive) index notation. xstart = min(img_ul_x, img_ll_x) ystart = min(img_ul_y, img_ur_y) xend = max(img_ur_x, img_lr_x) + 1 yend = max(img_ll_y, img_lr_y) + 1 # Check for out of bounds if (((xstart < 0) or (ystart < 0)) or ((xend -1 > cols) or (yend -1 > rows))): msg = ("Error! Attempt to read a subset that is outside of the" "image domain. Index: ({ys}, {ye}), ({xs}, {xe}))") msg = msg.format(ys=ystart, ye=yend, xs=xstart, xe=xend) raise IndexError(msg) if isinstance(fname, h5py.Dataset): subs = fname[ystart:yend, xstart:xend] else: with rasterio.open(fname) as src: subs = src.read(bands, window=((ystart, yend), (xstart, xend))) # Get the new UL co-ordinates of the array ul_x, ul_y = geobox.transform * (xstart, ystart) geobox_subs = GriddedGeoBox(shape=subs.shape, origin=(ul_x, ul_y), pixelsize=geobox.pixelsize, crs=prj) return (subs, geobox_subs) def reproject_file_to_array(src_filename, src_band=1, dst_geobox=None, resampling=Resampling.nearest): """ Given an image on file, reproject to the desired coordinate reference system. :param src_filename: A string containing the full file path name to the source image on disk. :param src_band: An integer representing the band number to be reprojected. Default is 1, the 1st band. :param dst_geobox: An instance of a GriddedGeoBox object containing the destination parameters such as origin, affine, projection, and array dimensions. :param resampling: An integer representing the resampling method to be used. check rasterio.warp.RESMPLING for more details. Default is 0, nearest neighbour resampling. :return: A NumPy array containing the reprojected result. """ if not isinstance(dst_geobox, GriddedGeoBox): msg = 'dst_geobox must be an instance of a GriddedGeoBox! Type: {}' msg = msg.format(type(dst_geobox)) raise TypeError(msg) with rasterio.open(src_filename) as src: # Define a rasterio band rio_band = rasterio.band(src, src_band) # Define the output NumPy array dst_arr = np.zeros(dst_geobox.shape, dtype=src.dtypes[0]) # Get the rasterio proj4 styled dict prj = CRS.from_string(dst_geobox.crs.ExportToProj4()) reproject(rio_band, dst_arr, dst_transform=dst_geobox.transform, dst_crs=prj, resampling=resampling) return dst_arr def reproject_img_to_img(src_img, src_geobox, dst_geobox, resampling=Resampling.nearest): """ Reprojects an image/array to the desired co-ordinate reference system. :param src_img: A NumPy array containing the source image. :param src_geobox: An instance of a GriddedGeoBox object containing the source parameters such as origin, affine, projection. :param dst_geobox: An instance of a GriddedGeoBox object containing the destination parameters such as origin, affine, projection, and array dimensions. :param resampling: An integer representing the resampling method to be used. check rasterio.warp.RESMPLING for more details. Default is 0, nearest neighbour resampling. :return: A NumPy array containing the reprojected result. """ if not isinstance(dst_geobox, GriddedGeoBox): msg = 'dst_geobox must be an instance of a GriddedGeoBox! Type: {}' msg = msg.format(type(dst_geobox)) raise TypeError(msg) if not isinstance(src_geobox, GriddedGeoBox): msg = 'src_geobox must be an instance of a GriddedGeoBox! Type: {}' msg = msg.format(type(src_geobox)) raise TypeError(msg) # Get the source and destination projections in Proj4 styled dicts src_prj = CRS.from_string(src_geobox.crs.ExportToProj4()) dst_prj = CRS.from_string(dst_geobox.crs.ExportToProj4()) # Get the source and destination transforms src_trans = src_geobox.transform dst_trans = dst_geobox.transform # Define the output NumPy array dst_arr = np.zeros(dst_geobox.shape, dtype=src_img.dtype) reproject(src_img, dst_arr, src_transform=src_trans, src_crs=src_prj, dst_transform=dst_trans, dst_crs=dst_prj, resampling=resampling) return dst_arr def as_array(array, dtype, transpose=False): """ Given an array and dtype, array will be converted to dtype if and only if array.dtype != dtype. If transpose is set to True then array will be transposed before returning. :param array: A NumPy array. :param dtype: The type to return the array as. :type dtype: A NumPy data type (e.g. ``numpy.float32``). :param transpose: If set then array will be transposed before returning. Useful for passing arrays into Fortran routiines. Default is False. :type transpose: Bool. :return: A :py:class:`numpy.ndarry` of type ``dtype`` with the same dimensions as array. """ if array.dtype != dtype: if transpose: return array.astype(dtype).transpose() return array.astype(dtype) if transpose: return array.transpose() return array

34.340426

77

0.611299

from __future__ import absolute_import from os.path import join as pjoin, basename, dirname import subprocess import tempfile import logging import numpy as np import h5py import rasterio from rasterio.crs import CRS from rasterio.warp import reproject from rasterio.enums import Resampling from wagl.geobox import GriddedGeoBox from wagl.tiling import generate_tiles def get_pixel(filename, lonlat, band=1): with rasterio.open(filename) as src: x, y = [int(v) for v in ~src.transform * lonlat] if isinstance(band, list): data = src.read(band, window=((y, y + 1), (x, x + 1))).ravel() else: data = src.read(band, window=((y, y + 1), (x, x + 1))).flat[0] return data def select_acquisitions(acqs_list, fn=(lambda acq: True)): acqs = [acq for acq in acqs_list if fn(acq)] return acqs def stack_data(acqs_list, fn=(lambda acq: True), window=None, masked=False): acqs = acqs_list a, geo_box = acqs[0].data_and_box(window=window, masked=masked) stack_shape = (len(acqs), a.shape[0], a.shape[1]) stack = np.empty(stack_shape, a.dtype) stack[0] = a del a for i in range(1, stack_shape[0]): # resampled. But we want an exception thrown if the user # tries to stack irreqular aquisitions stack[i] = acqs[i].data(window=window, masked=masked) return stack, geo_box def write_img(array, filename, driver='GTiff', geobox=None, nodata=None, tags=None, options=None, cogtif=False, levels=None, resampling=Resampling.nearest): # Get the datatype of the array dtype = array.dtype.name # Check for excluded datatypes excluded_dtypes = ['int64', 'int8', 'uint64'] if dtype in excluded_dtypes: msg = "Datatype not supported: {dt}".format(dt=dtype) raise TypeError(msg) # convert any bools to uin8 if dtype == 'bool': array = np.uint8(array) dtype = 'uint8' ndims = array.ndim dims = array.shape # Get the (z, y, x) dimensions (assuming BSQ interleave) if ndims == 2: samples = dims[1] lines = dims[0] bands = 1 elif ndims == 3: samples = dims[2] lines = dims[1] bands = dims[0] else: logging.error('Input array is not of 2 or 3 dimensions!!!') err = 'Array dimensions: {dims}'.format(dims=ndims) raise IndexError(err) # If we have a geobox, then retrieve the geotransform and projection if geobox is not None: transform = geobox.transform projection = geobox.crs.ExportToWkt() else: transform = None projection = None # override the driver if we are creating a cogtif if cogtif: driver = 'GTiff' # compression predictor choices predictor = {'int8': 2, 'uint8': 2, 'int16': 2, 'uint16': 2, 'int32': 2, 'uint32': 2, 'int64': 2, 'uint64': 2, 'float32': 3, 'float64': 3} kwargs = {'count': bands, 'width': samples, 'height': lines, 'crs': projection, 'transform': transform, 'dtype': dtype, 'driver': driver, 'nodata': nodata, 'predictor': predictor[dtype]} if isinstance(array, h5py.Dataset): # TODO: if array is 3D get x & y chunks if array.chunks[1] == array.shape[1]: # GDAL doesn't like tiled or blocksize options to be set array = array[:] else: y_tile, x_tile = array.chunks tiles = generate_tiles(samples, lines, x_tile, y_tile) kwargs['tiled'] = 'yes' kwargs['blockxsize'] = x_tile kwargs['blockysize'] = y_tile if options is not None: for key in options: kwargs[key] = options[key] with tempfile.TemporaryDirectory() as tmpdir: out_fname = pjoin(tmpdir, basename(filename)) if cogtif else filename with rasterio.open(out_fname, 'w', **kwargs) as outds: if bands == 1: if isinstance(array, h5py.Dataset): for tile in tiles: idx = (slice(tile[0][0], tile[0][1]), slice(tile[1][0], tile[1][1])) outds.write(array[idx], 1, window=tile) else: outds.write(array, 1) else: if isinstance(array, h5py.Dataset): for tile in tiles: idx = (slice(tile[0][0], tile[0][1]), slice(tile[1][0], tile[1][1])) subs = array[:, idx[0], idx[1]] for i in range(bands): outds.write(subs[i], i + 1, window=tile) else: for i in range(bands): outds.write(array[i], i + 1) if tags is not None: outds.update_tags(**tags) if cogtif: if levels is None: levels = [2, 4, 8, 16, 32] outds.build_overviews(levels, resampling) if cogtif: cmd = ['gdal_translate', '-co', 'TILED=YES', '-co', 'COPY_SRC_OVERVIEWS=YES', '-co', '{}={}'.format('PREDICTOR', predictor[dtype])] for key, value in options.items(): cmd.extend(['-co', '{}={}'.format(key, value)]) cmd.extend([out_fname, filename]) subprocess.check_call(cmd, cwd=dirname(filename)) def read_subset(fname, ul_xy, ur_xy, lr_xy, ll_xy, bands=1): if isinstance(fname, h5py.Dataset): geobox = GriddedGeoBox.from_dataset(fname) prj = fname.attrs['crs_wkt'] else: with rasterio.open(fname) as src: geobox = GriddedGeoBox.from_dataset(src) prj = src.crs.wkt inv = ~geobox.transform rows, cols = geobox.shape img_ul_x, img_ul_y = [int(v) for v in inv * ul_xy] img_ur_x, img_ur_y = [int(v) for v in inv * ur_xy] img_lr_x, img_lr_y = [int(v) for v in inv * lr_xy] img_ll_x, img_ll_y = [int(v) for v in inv * ll_xy] # [inclusive, exclusive) index notation. xstart = min(img_ul_x, img_ll_x) ystart = min(img_ul_y, img_ur_y) xend = max(img_ur_x, img_lr_x) + 1 yend = max(img_ll_y, img_lr_y) + 1 # Check for out of bounds if (((xstart < 0) or (ystart < 0)) or ((xend -1 > cols) or (yend -1 > rows))): msg = ("Error! Attempt to read a subset that is outside of the" "image domain. Index: ({ys}, {ye}), ({xs}, {xe}))") msg = msg.format(ys=ystart, ye=yend, xs=xstart, xe=xend) raise IndexError(msg) if isinstance(fname, h5py.Dataset): subs = fname[ystart:yend, xstart:xend] else: with rasterio.open(fname) as src: subs = src.read(bands, window=((ystart, yend), (xstart, xend))) # Get the new UL co-ordinates of the array ul_x, ul_y = geobox.transform * (xstart, ystart) geobox_subs = GriddedGeoBox(shape=subs.shape, origin=(ul_x, ul_y), pixelsize=geobox.pixelsize, crs=prj) return (subs, geobox_subs) def reproject_file_to_array(src_filename, src_band=1, dst_geobox=None, resampling=Resampling.nearest): if not isinstance(dst_geobox, GriddedGeoBox): msg = 'dst_geobox must be an instance of a GriddedGeoBox! Type: {}' msg = msg.format(type(dst_geobox)) raise TypeError(msg) with rasterio.open(src_filename) as src: # Define a rasterio band rio_band = rasterio.band(src, src_band) # Define the output NumPy array dst_arr = np.zeros(dst_geobox.shape, dtype=src.dtypes[0]) # Get the rasterio proj4 styled dict prj = CRS.from_string(dst_geobox.crs.ExportToProj4()) reproject(rio_band, dst_arr, dst_transform=dst_geobox.transform, dst_crs=prj, resampling=resampling) return dst_arr def reproject_img_to_img(src_img, src_geobox, dst_geobox, resampling=Resampling.nearest): if not isinstance(dst_geobox, GriddedGeoBox): msg = 'dst_geobox must be an instance of a GriddedGeoBox! Type: {}' msg = msg.format(type(dst_geobox)) raise TypeError(msg) if not isinstance(src_geobox, GriddedGeoBox): msg = 'src_geobox must be an instance of a GriddedGeoBox! Type: {}' msg = msg.format(type(src_geobox)) raise TypeError(msg) # Get the source and destination projections in Proj4 styled dicts src_prj = CRS.from_string(src_geobox.crs.ExportToProj4()) dst_prj = CRS.from_string(dst_geobox.crs.ExportToProj4()) # Get the source and destination transforms src_trans = src_geobox.transform dst_trans = dst_geobox.transform # Define the output NumPy array dst_arr = np.zeros(dst_geobox.shape, dtype=src_img.dtype) reproject(src_img, dst_arr, src_transform=src_trans, src_crs=src_prj, dst_transform=dst_trans, dst_crs=dst_prj, resampling=resampling) return dst_arr def as_array(array, dtype, transpose=False): if array.dtype != dtype: if transpose: return array.astype(dtype).transpose() return array.astype(dtype) if transpose: return array.transpose() return array

true

1c2e87352e339ee35eb0dfb1569501950c26402b

388

py

Python

nudni/wsgi.py

misli/nudni

a0c03cd3104a57d5104dd1f593a777a3571fca4d

[ "BSD-3-Clause" ]

2

2017-11-18T11:12:10.000Z

2017-11-18T11:12:12.000Z

nudni/wsgi.py

misli/nudni

a0c03cd3104a57d5104dd1f593a777a3571fca4d

[ "BSD-3-Clause" ]

null

nudni/wsgi.py

misli/nudni

a0c03cd3104a57d5104dd1f593a777a3571fca4d

[ "BSD-3-Clause" ]

null

""" WSGI config for nudni project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/1.11/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "nudni.settings") application = get_wsgi_application()

22.823529

78

0.783505

import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "nudni.settings") application = get_wsgi_application()

true

1c2e875a17af7e0890ffebcddd9c7877a9fa8af2

1,168

py

Python

setup.py

yucelkilic/ASTRiDE

cca4e3ee0d907c3675d8f2dcf9246acf69027dae

[ "MIT" ]

null

setup.py

yucelkilic/ASTRiDE

cca4e3ee0d907c3675d8f2dcf9246acf69027dae

[ "MIT" ]

null

setup.py

yucelkilic/ASTRiDE

cca4e3ee0d907c3675d8f2dcf9246acf69027dae

[ "MIT" ]

null

from setuptools import find_packages, setup def readme(): with open('README.rst') as f: return f.read() setup( name='astride', version='0.3.5', description='Automated Streak Detection for High Velocity Objects', long_description=readme(), platforms=['any'], packages=find_packages(), include_package_data=True, url='https://github.com/dwkim78/astride', license='MIT', author='Dae-Won Kim', author_email='dwkim78@gmail.com', install_requires=['numpy>=1.14', 'photutils>=0.4', 'astropy>=3.0', 'matplotlib>=2.1.1', 'scipy>=1.0.0', 'scikit-image>=0.13.1'], keywords=['astronomy', 'image', 'streak', 'satellite', 'meteor', 'NEO', 'fast-moving objects', 'boundary-tracing', 'contour-tracing'], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3.5', 'Topic :: Scientific/Engineering :: Astronomy' ] )

34.352941

76

0.598459

from setuptools import find_packages, setup def readme(): with open('README.rst') as f: return f.read() setup( name='astride', version='0.3.5', description='Automated Streak Detection for High Velocity Objects', long_description=readme(), platforms=['any'], packages=find_packages(), include_package_data=True, url='https://github.com/dwkim78/astride', license='MIT', author='Dae-Won Kim', author_email='dwkim78@gmail.com', install_requires=['numpy>=1.14', 'photutils>=0.4', 'astropy>=3.0', 'matplotlib>=2.1.1', 'scipy>=1.0.0', 'scikit-image>=0.13.1'], keywords=['astronomy', 'image', 'streak', 'satellite', 'meteor', 'NEO', 'fast-moving objects', 'boundary-tracing', 'contour-tracing'], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3.5', 'Topic :: Scientific/Engineering :: Astronomy' ] )

true

1c2e878fc87fe5d8f0093738d896b1c599ff30cc

3,195

py

Python

cobrinha trabalhadeira/lab06/lab6MT.py

ansattz/solving_sto

4f66a0a78fdbfe101f07e0f7cb8f7c8b595f16b9

[ "MIT" ]

null

cobrinha trabalhadeira/lab06/lab6MT.py

ansattz/solving_sto

4f66a0a78fdbfe101f07e0f7cb8f7c8b595f16b9

[ "MIT" ]

null

cobrinha trabalhadeira/lab06/lab6MT.py

ansattz/solving_sto

4f66a0a78fdbfe101f07e0f7cb8f7c8b595f16b9

[ "MIT" ]

null

# Douglas Vieira # @ansattz #1 - Não delete nem modifique esta linha def lettercount(s): phrase = str.split(s) letters = len(phrase) return letters #Casos de teste da questão 1 - Não delete nem modifique esta linha print(lettercount("Vamos terminar este lab hoje mesmo")) # 2 - Não delete nem modifique esta linha def phrasecount(s): fpoint = str.replace(s,'...','.') phtype1 = str.count(fpoint,'.') phtype2 = str.count(s,'!') phtype3 = str.count(s,'?') phrases = phtype1 + phtype2 + phtype3 return phrases #Casos de teste da questão 2 - Não delete nem modifique esta linha print(phrasecount("O produto! Sim... o produto de ponto euclidiano. Fornece uma maneira de definir o tamanho ou a norma de um vetor em Rn.")) #3 - Não delete nem modifique esta linha def modifyph(s): fpoint = str.replace(s,'...','.') tpoint = str.replace(fpoint,'..','.') esppoint = str.replace(tpoint,'.',' ') esptravess = str.replace(esppoint,'-',' ') espvirg = str.replace(esptravess,',',' ') esppointvirg = str.replace(espvirg,';',' ') espexcl = str.replace(esppointvirg,'!',' ') espint = str.replace(espexcl,'?',' ') return espint #Casos de teste da questão 3 - Não delete nem modifique esta linha print(modifyph("Toda base de um espaço vetorial - que pode ter o mesmo número de vetore - pode ser referenciada como a dimensão do espaço vetorial dado o seu número de vetores!")) #4 - Não delete nem modifique esta linha def revphr(s): modfpoint = str.replace(s,'...','.') fpoint = str.replace(modfpoint,'.','') reexcl = str.replace(fpoint,'!','') reint = str.replace(reexcl,'?','') reevirg = str.replace(reint,',','') repointvirg = str.replace(reevirg,';','') redoisp = str.replace(repointvirg,':','') retr = str.replace(redoisp,'-',' ') reasp = str.replace(retr,'"','') repar1 = str.replace(reasp,'(','') repar2 = str.replace(repar1,')','') withoutupp = str.lower(repar2) splitng = withoutupp.split() revletters = list(reversed(splitng)) return " ".join(revletters) #Casos de teste da questão 4 - Não delete nem modifique esta linha print(revphr("A SSI RU.")) #5 - Não delete nem modifique esta linha def insere(lista_numero,n): addint = lista_numero + [n,] list.sort(addint) return addint #Casos de teste da questão 5 - Não delete nem modifique esta linha print(insere([1,2,15,5,0,8,9], 4)) #6 - Não delete nem modifique esta linha def greatlist(lista,n): list.append(lista,n) list.sort(lista) ind = list.index(lista,n) return lista[ind+1:] #Casos de teste da questão 6 - Não delete nem modifique esta linha print(greatlist([1,2,5,9,15,4],8)) #7 - Não delete nem modifique esta linha def acima_da_media(lista): nullist = [] orlist = lista[:] media = sum(lista)/len(lista) list.append(lista,media) list.sort(lista) ind = list.index(lista,media) if media in orlist: return lista[ind+2:] elif len(lista) > 2: return lista[ind+1:] else: return nullist #Casos de teste da questão 7 - Não delete nem modifique esta linha print(acima_da_media([1, 6, 9, 4, 0, 8, 5, 7]))

28.783784

179

0.650704

def lettercount(s): phrase = str.split(s) letters = len(phrase) return letters print(lettercount("Vamos terminar este lab hoje mesmo")) def phrasecount(s): fpoint = str.replace(s,'...','.') phtype1 = str.count(fpoint,'.') phtype2 = str.count(s,'!') phtype3 = str.count(s,'?') phrases = phtype1 + phtype2 + phtype3 return phrases print(phrasecount("O produto! Sim... o produto de ponto euclidiano. Fornece uma maneira de definir o tamanho ou a norma de um vetor em Rn.")) def modifyph(s): fpoint = str.replace(s,'...','.') tpoint = str.replace(fpoint,'..','.') esppoint = str.replace(tpoint,'.',' ') esptravess = str.replace(esppoint,'-',' ') espvirg = str.replace(esptravess,',',' ') esppointvirg = str.replace(espvirg,';',' ') espexcl = str.replace(esppointvirg,'!',' ') espint = str.replace(espexcl,'?',' ') return espint print(modifyph("Toda base de um espaço vetorial - que pode ter o mesmo número de vetore - pode ser referenciada como a dimensão do espaço vetorial dado o seu número de vetores!")) def revphr(s): modfpoint = str.replace(s,'...','.') fpoint = str.replace(modfpoint,'.','') reexcl = str.replace(fpoint,'!','') reint = str.replace(reexcl,'?','') reevirg = str.replace(reint,',','') repointvirg = str.replace(reevirg,';','') redoisp = str.replace(repointvirg,':','') retr = str.replace(redoisp,'-',' ') reasp = str.replace(retr,'"','') repar1 = str.replace(reasp,'(','') repar2 = str.replace(repar1,')','') withoutupp = str.lower(repar2) splitng = withoutupp.split() revletters = list(reversed(splitng)) return " ".join(revletters) #Casos de teste da questão 4 - Não delete nem modifique esta linha print(revphr("A SSI RU.")) #5 - Não delete nem modifique esta linha def insere(lista_numero,n): addint = lista_numero + [n,] list.sort(addint) return addint #Casos de teste da questão 5 - Não delete nem modifique esta linha print(insere([1,2,15,5,0,8,9], 4)) #6 - Não delete nem modifique esta linha def greatlist(lista,n): list.append(lista,n) list.sort(lista) ind = list.index(lista,n) return lista[ind+1:] #Casos de teste da questão 6 - Não delete nem modifique esta linha print(greatlist([1,2,5,9,15,4],8)) #7 - Não delete nem modifique esta linha def acima_da_media(lista): nullist = [] orlist = lista[:] media = sum(lista)/len(lista) list.append(lista,media) list.sort(lista) ind = list.index(lista,media) if media in orlist: return lista[ind+2:] elif len(lista) > 2: return lista[ind+1:] else: return nullist #Casos de teste da questão 7 - Não delete nem modifique esta linha print(acima_da_media([1, 6, 9, 4, 0, 8, 5, 7]))

true

1c2e88c6ed8136dab2b63811698e0b327fcdc54f

3,758

py

Python

dl/scan/part_tflite.py

showkeyjar/beauty

7c944cf896c899d9e23b2e50e293103bb03fe6cd

[ "MulanPSL-1.0" ]

1

2022-01-29T12:32:38.000Z

dl/scan/part_tflite.py

showkeyjar/beauty

7c944cf896c899d9e23b2e50e293103bb03fe6cd

[ "MulanPSL-1.0" ]

null

dl/scan/part_tflite.py

showkeyjar/beauty

7c944cf896c899d9e23b2e50e293103bb03fe6cd

[ "MulanPSL-1.0" ]

null

#!/usr/bin/python # -*- encoding: utf-8 -*- import numpy as np import cv2 from os.path import dirname, join import tensorflow as tf """ 判断人脸部位 """ # Load TFLite model and allocate tensors. model_file = join(dirname(__file__), "face_part.tflite") interpreter = tf.compat.v1.lite.Interpreter(model_path=model_file) # Get input and output tensors. input_details = interpreter.get_input_details() print("input:", input_details) output_details = interpreter.get_output_details() print("output:", output_details) def vis_parsing_maps(im, parsing_anno, stride, save_im=False, save_path='parsing_face.jpg'): # Colors for all 19 parts part_colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 0, 85], [255, 0, 170], [0, 255, 0], [85, 255, 0], [170, 255, 0], [0, 255, 85], [0, 255, 170], [0, 0, 255], [85, 0, 255], [170, 0, 255], [0, 85, 255], [0, 170, 255], [255, 255, 0], [255, 255, 85], [255, 255, 170], [255, 0, 255], [255, 85, 255], [255, 170, 255], [0, 255, 255], [85, 255, 255], [170, 255, 255]] # part_names = ['hair', 'l_brow', 'r_brow', 'l_eye', 'r_eye', 'eye_g', 'l_ear', 'r_ear', 'ear_r', 'nose', 'mouth', 'skin', 'u_lip', 'l_lip', 'neck', 'neck_l', 'cloth', 'bg', 'hat'] part_names = ['skin', 'l_brow', 'r_brow', 'l_eye', 'r_eye', 'eye_g', 'l_ear', 'r_ear', 'ear_r', 'nose', 'mouth', 'u_lip', 'l_lip', 'neck', 'neck_l', 'cloth', 'hair', 'hat', 'bg'] im = np.array(im) vis_im = im.copy().astype(np.uint8) vis_parsing_anno = parsing_anno.copy().astype(np.uint8) vis_parsing_anno = cv2.resize(vis_parsing_anno, None, fx=stride, fy=stride, interpolation=cv2.INTER_NEAREST) vis_parsing_anno_color = np.zeros((vis_parsing_anno.shape[0], vis_parsing_anno.shape[1], 3)) + 255 num_of_class = np.max(vis_parsing_anno) for pi in range(0, num_of_class + 1): index = np.where(vis_parsing_anno == pi) print("cls ", pi," find shape:", index[0].shape, index[1].shape) vis_parsing_anno_color[index[0], index[1], :] = part_colors[pi] # 确保文字在最上层 for pi in range(0, num_of_class + 1): index = np.where(vis_parsing_anno == pi) try: cv2.putText(vis_parsing_anno_color, str(pi) + ":" + part_names[pi], (index[0][0] + 1,index[1][0] + 1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2, cv2.LINE_AA) except Exception as e: print(e) vis_parsing_anno_color = vis_parsing_anno_color.astype(np.uint8) # print(vis_parsing_anno_color.shape, vis_im.shape) vis_im = cv2.addWeighted(cv2.cvtColor(vis_im, cv2.COLOR_RGB2BGR), 0.4, vis_parsing_anno_color, 0.6, 0) # Save result or not if save_im: # cv2.imwrite(save_path[:-4] +'.png', vis_parsing_anno) cv2.imwrite(save_path, vis_im, [int(cv2.IMWRITE_JPEG_QUALITY), 100]) # return vis_im def evaluate(img_path='./data'): global interpreter image = cv2.imread(img_path) image = cv2.resize(image, (512, 512), interpolation=cv2.INTER_NEAREST) img = image / 255 mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] img = (img - mean) / std img = img.astype(np.float32) # change to channel first img = np.moveaxis(img, 2, 0) print(img.shape, img.dtype) interpreter.allocate_tensors() interpreter.set_tensor(interpreter.get_input_details()[0]['index'], [img]) interpreter.invoke() preds = interpreter.get_tensor(interpreter.get_output_details()[0]['index']) parsing = preds[0].argmax(0) print(np.unique(parsing)) vis_parsing_maps(image, parsing, stride=1, save_im=True) if __name__ == "__main__": evaluate(img_path='1.png')

40.847826

184

0.61868

import numpy as np import cv2 from os.path import dirname, join import tensorflow as tf model_file = join(dirname(__file__), "face_part.tflite") interpreter = tf.compat.v1.lite.Interpreter(model_path=model_file) input_details = interpreter.get_input_details() print("input:", input_details) output_details = interpreter.get_output_details() print("output:", output_details) def vis_parsing_maps(im, parsing_anno, stride, save_im=False, save_path='parsing_face.jpg'): part_colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 0, 85], [255, 0, 170], [0, 255, 0], [85, 255, 0], [170, 255, 0], [0, 255, 85], [0, 255, 170], [0, 0, 255], [85, 0, 255], [170, 0, 255], [0, 85, 255], [0, 170, 255], [255, 255, 0], [255, 255, 85], [255, 255, 170], [255, 0, 255], [255, 85, 255], [255, 170, 255], [0, 255, 255], [85, 255, 255], [170, 255, 255]] part_names = ['skin', 'l_brow', 'r_brow', 'l_eye', 'r_eye', 'eye_g', 'l_ear', 'r_ear', 'ear_r', 'nose', 'mouth', 'u_lip', 'l_lip', 'neck', 'neck_l', 'cloth', 'hair', 'hat', 'bg'] im = np.array(im) vis_im = im.copy().astype(np.uint8) vis_parsing_anno = parsing_anno.copy().astype(np.uint8) vis_parsing_anno = cv2.resize(vis_parsing_anno, None, fx=stride, fy=stride, interpolation=cv2.INTER_NEAREST) vis_parsing_anno_color = np.zeros((vis_parsing_anno.shape[0], vis_parsing_anno.shape[1], 3)) + 255 num_of_class = np.max(vis_parsing_anno) for pi in range(0, num_of_class + 1): index = np.where(vis_parsing_anno == pi) print("cls ", pi," find shape:", index[0].shape, index[1].shape) vis_parsing_anno_color[index[0], index[1], :] = part_colors[pi] for pi in range(0, num_of_class + 1): index = np.where(vis_parsing_anno == pi) try: cv2.putText(vis_parsing_anno_color, str(pi) + ":" + part_names[pi], (index[0][0] + 1,index[1][0] + 1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2, cv2.LINE_AA) except Exception as e: print(e) vis_parsing_anno_color = vis_parsing_anno_color.astype(np.uint8) vis_im = cv2.addWeighted(cv2.cvtColor(vis_im, cv2.COLOR_RGB2BGR), 0.4, vis_parsing_anno_color, 0.6, 0) if save_im: cv2.imwrite(save_path, vis_im, [int(cv2.IMWRITE_JPEG_QUALITY), 100]) def evaluate(img_path='./data'): global interpreter image = cv2.imread(img_path) image = cv2.resize(image, (512, 512), interpolation=cv2.INTER_NEAREST) img = image / 255 mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] img = (img - mean) / std img = img.astype(np.float32) img = np.moveaxis(img, 2, 0) print(img.shape, img.dtype) interpreter.allocate_tensors() interpreter.set_tensor(interpreter.get_input_details()[0]['index'], [img]) interpreter.invoke() preds = interpreter.get_tensor(interpreter.get_output_details()[0]['index']) parsing = preds[0].argmax(0) print(np.unique(parsing)) vis_parsing_maps(image, parsing, stride=1, save_im=True) if __name__ == "__main__": evaluate(img_path='1.png')

true

1c2e88e126f2fb3f6cce316611db58129c930a48

391

py

Python

django_projects/chat_app_channels/chatapp/chatapp/asgi.py

phiratio/lpthw

a32240d4355fb331805d515f96e1d009914e5c47

[ "MIT" ]

1

2021-04-21T09:38:38.000Z

django_projects/chat_app_channels/chatapp/chatapp/asgi.py

phiratio/lpthw

a32240d4355fb331805d515f96e1d009914e5c47

[ "MIT" ]

34

2019-12-16T16:53:24.000Z

2022-01-13T02:29:30.000Z

django_projects/chat_app_channels/chatapp/chatapp/asgi.py

phiratio/lpthw

a32240d4355fb331805d515f96e1d009914e5c47

[ "MIT" ]

null

""" ASGI config for chatapp project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.0/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'chatapp.settings') application = get_asgi_application()

23

78

0.785166

import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'chatapp.settings') application = get_asgi_application()

true

1c2e88eb86dd7141662fd801b624b54caf1cf2ef

1,370

py

Python

backend/database/migration.py

GCETTB-HYLAND-Hackathon2012-22/Miracurol-Rebuild

a6748e93b45872efdbaca013388dd6277824a9a5

[ "Apache-2.0" ]

null

backend/database/migration.py

GCETTB-HYLAND-Hackathon2012-22/Miracurol-Rebuild

a6748e93b45872efdbaca013388dd6277824a9a5

[ "Apache-2.0" ]

null

backend/database/migration.py

GCETTB-HYLAND-Hackathon2012-22/Miracurol-Rebuild

a6748e93b45872efdbaca013388dd6277824a9a5

[ "Apache-2.0" ]

null

import pathlib from alembic import command from alembic.config import Config as AlembicConfig from sqlalchemy.engine import make_url from backend.commons.enums import Config from backend.utils.config_utils import read_config def run_migrations(downgrade_first: bool = False) -> None: script_location: str = f'{read_config(Config.APP.HOME)}/db_migrations' db_url: str = read_config(Config.DATABASE.URL) alembic_cfg = AlembicConfig() alembic_cfg.set_main_option('script_location', script_location) alembic_cfg.set_main_option('sqlalchemy.url', db_url) alembic_cfg.print_stdout('INFO: Running DB Migration on %r', make_url(db_url)) try: # Check and create path/to/script_location/versions # Git ignores all empty directories, but it is a necessary for this path to exist pathlib.Path(f'{script_location}/versions').mkdir(parents=True, exist_ok=True) # Run alembic downgrade command if downgrade_first: command.downgrade(alembic_cfg, 'base') # Run alembic upgrade command command.upgrade(alembic_cfg, 'head') # Display current status command.history(alembic_cfg, indicate_current=True) alembic_cfg.print_stdout('INFO: DB Migrations Successful') except Exception: alembic_cfg.print_stdout('INFO: DB Migrations Failed') raise

35.128205

89

0.729197

import pathlib from alembic import command from alembic.config import Config as AlembicConfig from sqlalchemy.engine import make_url from backend.commons.enums import Config from backend.utils.config_utils import read_config def run_migrations(downgrade_first: bool = False) -> None: script_location: str = f'{read_config(Config.APP.HOME)}/db_migrations' db_url: str = read_config(Config.DATABASE.URL) alembic_cfg = AlembicConfig() alembic_cfg.set_main_option('script_location', script_location) alembic_cfg.set_main_option('sqlalchemy.url', db_url) alembic_cfg.print_stdout('INFO: Running DB Migration on %r', make_url(db_url)) try: pathlib.Path(f'{script_location}/versions').mkdir(parents=True, exist_ok=True) if downgrade_first: command.downgrade(alembic_cfg, 'base') command.upgrade(alembic_cfg, 'head') command.history(alembic_cfg, indicate_current=True) alembic_cfg.print_stdout('INFO: DB Migrations Successful') except Exception: alembic_cfg.print_stdout('INFO: DB Migrations Failed') raise

true

1c2e897ad5c9e8578ce18e6f53c03911e852907f

700

py

Python

brain/src/sm_main.py

FabianFalck/de_niro

1f9d05b7232cb9e76eff975e5ef1c8bf3fb5cde6

[ "MIT" ]

7

2019-06-12T03:36:15.000Z

2021-06-27T18:39:40.000Z

brain/src/sm_main.py

Hankfirst/de_niro

1f9d05b7232cb9e76eff975e5ef1c8bf3fb5cde6

[ "MIT" ]

null

brain/src/sm_main.py

Hankfirst/de_niro

1f9d05b7232cb9e76eff975e5ef1c8bf3fb5cde6

[ "MIT" ]

3

2019-06-12T03:35:36.000Z

2021-06-27T04:37:31.000Z

#!/usr/bin/env python """ Main thread for running the state machine. Author: Sagar Doshi Date: 05/2018 """ import rospy import smach_ros import unittest import threading import time # Custom modules import import sm_mach if __name__ == "__main__": rospy.init_node('fezzik_state_machine') # Create the state machine sm = sm_mach.init_sm() # Create and start introspection server - automatically traverses sm's child # containers, so only need to add this to the top-level state machine sis = smach_ros.IntrospectionServer("Fezzik_Introspection_Server", sm, "/SM_TOP") sis.start() # Execute SMACH plan outcome = sm.execute() rospy.spin() sis.stop()

20

85

0.715714

import rospy import smach_ros import unittest import threading import time import sm_mach if __name__ == "__main__": rospy.init_node('fezzik_state_machine') sm = sm_mach.init_sm() # containers, so only need to add this to the top-level state machine sis = smach_ros.IntrospectionServer("Fezzik_Introspection_Server", sm, "/SM_TOP") sis.start() # Execute SMACH plan outcome = sm.execute() rospy.spin() sis.stop()

true

1c2e8983ec719dce811b9524bf40526bfd2eedb5

193

py

Python

py_tdlib/constructors/set_scope_notification_settings.py

Mr-TelegramBot/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

24

2018-10-05T13:04:30.000Z

2020-05-12T08:45:34.000Z

py_tdlib/constructors/set_scope_notification_settings.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

3

2019-06-26T07:20:20.000Z

2021-05-24T13:06:56.000Z

py_tdlib/constructors/set_scope_notification_settings.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

5

2018-10-05T14:29:28.000Z

2020-08-11T15:04:10.000Z

from ..factory import Method class setScopeNotificationSettings(Method): scope = None # type: "NotificationSettingsScope" notification_settings = None # type: "scopeNotificationSettings"

27.571429

66

0.797927

from ..factory import Method class setScopeNotificationSettings(Method): scope = None notification_settings = None

true

1c2e8a2ecb1994da7171f9cf7c06b1e07ef5eb21

717

py

Python

testutils/decorators.py

getmetamapper/metamapper

0b2f67eec03fbf7ece35ff9f58ea9bb2dde4d85f

[ "BSD-2-Clause" ]

53

2020-07-01T23:11:59.000Z

2022-03-31T19:10:28.000Z

testutils/decorators.py

getmetamapper/metamapper

0b2f67eec03fbf7ece35ff9f58ea9bb2dde4d85f

[ "BSD-2-Clause" ]

5

2020-11-25T19:48:57.000Z

2022-02-27T23:50:18.000Z

testutils/decorators.py

getmetamapper/metamapper

0b2f67eec03fbf7ece35ff9f58ea9bb2dde4d85f

[ "BSD-2-Clause" ]

5

2020-08-29T16:43:59.000Z

2022-01-17T19:05:30.000Z

# -*- coding: utf-8 -*- import functools import testutils.helpers as helpers def as_someone(user_types): """Decorator to mimic different user roles. """ def the_decorator(func): @functools.wraps(func) def func_wrapper(view, *args, **kwargs): cached_user = view.user for user_type in user_types: view.user_type = user_type view.user = view.users[user_type] view._client = helpers.api_client(view.user, uuid=view.workspace.id) func(view, *args, **kwargs) view.user = cached_user view._client = helpers.api_client(view.user) return func_wrapper return the_decorator

32.590909

84

0.608089

import functools import testutils.helpers as helpers def as_someone(user_types): def the_decorator(func): @functools.wraps(func) def func_wrapper(view, *args, **kwargs): cached_user = view.user for user_type in user_types: view.user_type = user_type view.user = view.users[user_type] view._client = helpers.api_client(view.user, uuid=view.workspace.id) func(view, *args, **kwargs) view.user = cached_user view._client = helpers.api_client(view.user) return func_wrapper return the_decorator

true

1c2e8be0c07c4c79c57782db9882b7b968cd8d52

7,198

py

Python

models.py

mrdbarros/csgo_analyze

a4d7487bd56a14a99dc762920965060a5a43fa15

[ "Apache-2.0" ]

null

models.py

mrdbarros/csgo_analyze

a4d7487bd56a14a99dc762920965060a5a43fa15

[ "Apache-2.0" ]

4

2020-06-17T20:24:53.000Z

2021-09-28T03:55:45.000Z

models.py

mrdbarros/csgo_analyze

a4d7487bd56a14a99dc762920965060a5a43fa15

[ "Apache-2.0" ]

null

import data_loading import torch def emb_sz_rule(n_cat): "Rule of thumb to pick embedding size corresponding to `n_cat`" return min(600, round(1.6 * n_cat ** 0.56)) class LinBnDrop(torch.nn.Sequential): "Module grouping `BatchNorm1d`, `Dropout` and `Linear` layers" def __init__(self, n_in, n_out, bn=True, p=0., act=None, lin_first=False): layers = [torch.nn.BatchNorm1d(n_in)] if bn else [] if p != 0: layers.append(torch.nn.Dropout(p)) lin = [torch.nn.Linear(n_in, n_out, bias=not bn)] if act is not None: lin.append(act) layers = lin + layers if lin_first else layers + lin super().__init__(*layers) class TabularModelCustom(torch.nn.Module): "Basic model for tabular data." def __init__(self, category_list, class_groups_sizes, n_cont, layers, ps=None, embed_p=0., use_bn=True, bn_final=True, bn_cont=True): super().__init__() ps = ps class_group_map = {} for i, cat in enumerate(category_list): class_group = cat[cat.rfind("_") + 1:] class_group_index, _ = class_groups_sizes[class_group] if class_group_index in class_group_map: class_group_map[class_group_index].append(i) else: class_group_map[class_group_index] = [i] self.class_group_map = class_group_map self.embeds = torch.nn.ModuleList( [torch.nn.Embedding(index_ni[1], emb_sz_rule(index_ni[1])) for _, index_ni in class_groups_sizes.items() if index_ni[1] > 2]) self.emb_drop = torch.nn.Dropout(embed_p) self.bn_cont = torch.nn.BatchNorm1d(n_cont) if bn_cont else None binary_size = sum(len(class_group_map[i]) for i in range(len(self.embeds), len(class_group_map))) n_emb = sum(e.embedding_dim * len(class_group_map[i]) for i, e in enumerate(self.embeds)) + binary_size self.n_emb, self.n_cont = n_emb, n_cont sizes = [n_emb + n_cont] + layers actns = [torch.nn.ReLU(inplace=True) for _ in range(len(sizes) - 1)] _layers = [LinBnDrop(sizes[i], sizes[i + 1], bn=use_bn and (i != len(actns) - 1 or bn_final), p=p, act=a) for i, (p, a) in enumerate(zip(ps, actns))] self.layers = torch.nn.Sequential(*_layers) def forward(self, x_cat, x_cont=None): if self.n_emb != 0: x_cat_binary = [] for i in range(len(self.embeds), len(self.class_group_map)): x_cat_binary += self.class_group_map[i] with torch.no_grad(): x_cat_binary = x_cat[:, x_cat_binary].float() x_cat_nonbinary = [torch.flatten(e(x_cat[:, self.class_group_map[i]]), start_dim=1) for i, e in enumerate(self.embeds)] x = torch.cat(x_cat_nonbinary + [x_cat_binary], 1) x = self.emb_drop(x) if self.n_cont != 0: if self.bn_cont is not None: x_cont = self.bn_cont(x_cont) x = torch.cat([x, x_cont], 1) if self.n_emb != 0 else x_cont return self.layers(x) class CustomMixedModel(torch.nn.Module): def __init__(self, image_model, tab_model, seq_model, image_output_size, embeds_size,prepare_and_pad,max_image_batch): super(CustomMixedModel, self).__init__() self.image_model = image_model # embedding types are primaries, secondaries, flashbangs and binaries # self.classifier = TabularModel_NoCat(emb_sizes,1536, 30,[400],ps=[0.1],use_bn=False) self.tab_model = tab_model # n_emb = sum(e.embedding_dim for e in self.embeds) self.seq_model = seq_model self.classifier = torch.nn.Sequential(LinBnDrop(200 + image_output_size + embeds_size , 1, act=None, p=0.)) self.prepare_and_pad = prepare_and_pad self.max_image_batch = max_image_batch def forward(self, input_cat, input_cont, input_image, attention_mask, train_embeds=True, train_seq_model=True): valid_sizes = torch.sum((attention_mask == 1), dim=1) if train_embeds: input_embed = self.forward_embeds(input_cat, input_cont, input_image,valid_sizes) else: with torch.no_grad(): input_embed = self.forward_embeds(input_cat, input_cont, input_image,valid_sizes) input_embed=self.prepare_and_pad(input_embed,valid_sizes) if train_seq_model: bert_out = self.forward_seq_model(input_embed, attention_mask) else: with torch.no_grad(): bert_out = self.forward_seq_model(input_embed, attention_mask) output = self.classifier( torch.cat((input_embed[range(input_embed.shape[0]), valid_sizes-1], bert_out), dim=1)) # output = self.classifier(input_embed[range(input_embed.shape[0]), (input_embed.shape[1] - mask_size - 1)]) # output = self.classifier(bert_out) return output def forward_embeds(self, input_cat, input_cont, input_image,valid_size): n_batches = (input_image.shape[0]//self.max_image_batch)+1*(input_image.shape[0]%self.max_image_batch>0) tab_out = self.tab_model(input_cat,input_cont) image_out = torch.cat([self.image_model(input_image[i*self.max_image_batch:min((i+1)*self.max_image_batch,input_image.shape[0])]) for i in range(n_batches)],dim=0) # comprehension to break inputs in 'n_batches' of 'self.max_image_batch' size input_embed = torch.cat((tab_out,image_out),dim=1) input_embed = torch.nn.ReLU()(input_embed) return input_embed def forward_seq_model(self, input_embed, attention_mask): # bert_out = self.seq_model(input_embed.permute((1, 0, 2)), # src_key_padding_mask=attention_mask).permute((1, 0, 2))[:, 0] bert_out = torch.mean(self.seq_model(inputs_embeds=input_embed, attention_mask=attention_mask)[0],dim=1) bert_out = torch.nn.ReLU()(bert_out) return bert_out class CustomMixedModelSingleImage(torch.nn.Module): def __init__(self, image_model, tab_model, image_output_size,class_p): super(CustomMixedModelSingleImage, self).__init__() self.image_model = image_model # embedding types are primaries, secondaries, flashbangs and binaries # self.classifier = TabularModel_NoCat(emb_sizes,1536, 30,[400],ps=[0.1],use_bn=False) self.tab_model = tab_model # n_emb = sum(e.embedding_dim for e in self.embeds) self.classifier = torch.nn.Sequential(LinBnDrop(50 + image_output_size, 40, act=torch.nn.ReLU(), p=class_p), LinBnDrop(40, 1, act=None, p=class_p)) def forward(self, input_cat, input_cont, input_image): output_tabular = self.tab_model(input_cat, input_cont) output_image = self.image_model(input_image) logits = self.classifier(torch.nn.ReLU()(torch.cat((output_tabular, output_image), dim=1))) return logits

45.556962

137

0.634065

import data_loading import torch def emb_sz_rule(n_cat): return min(600, round(1.6 * n_cat ** 0.56)) class LinBnDrop(torch.nn.Sequential): def __init__(self, n_in, n_out, bn=True, p=0., act=None, lin_first=False): layers = [torch.nn.BatchNorm1d(n_in)] if bn else [] if p != 0: layers.append(torch.nn.Dropout(p)) lin = [torch.nn.Linear(n_in, n_out, bias=not bn)] if act is not None: lin.append(act) layers = lin + layers if lin_first else layers + lin super().__init__(*layers) class TabularModelCustom(torch.nn.Module): def __init__(self, category_list, class_groups_sizes, n_cont, layers, ps=None, embed_p=0., use_bn=True, bn_final=True, bn_cont=True): super().__init__() ps = ps class_group_map = {} for i, cat in enumerate(category_list): class_group = cat[cat.rfind("_") + 1:] class_group_index, _ = class_groups_sizes[class_group] if class_group_index in class_group_map: class_group_map[class_group_index].append(i) else: class_group_map[class_group_index] = [i] self.class_group_map = class_group_map self.embeds = torch.nn.ModuleList( [torch.nn.Embedding(index_ni[1], emb_sz_rule(index_ni[1])) for _, index_ni in class_groups_sizes.items() if index_ni[1] > 2]) self.emb_drop = torch.nn.Dropout(embed_p) self.bn_cont = torch.nn.BatchNorm1d(n_cont) if bn_cont else None binary_size = sum(len(class_group_map[i]) for i in range(len(self.embeds), len(class_group_map))) n_emb = sum(e.embedding_dim * len(class_group_map[i]) for i, e in enumerate(self.embeds)) + binary_size self.n_emb, self.n_cont = n_emb, n_cont sizes = [n_emb + n_cont] + layers actns = [torch.nn.ReLU(inplace=True) for _ in range(len(sizes) - 1)] _layers = [LinBnDrop(sizes[i], sizes[i + 1], bn=use_bn and (i != len(actns) - 1 or bn_final), p=p, act=a) for i, (p, a) in enumerate(zip(ps, actns))] self.layers = torch.nn.Sequential(*_layers) def forward(self, x_cat, x_cont=None): if self.n_emb != 0: x_cat_binary = [] for i in range(len(self.embeds), len(self.class_group_map)): x_cat_binary += self.class_group_map[i] with torch.no_grad(): x_cat_binary = x_cat[:, x_cat_binary].float() x_cat_nonbinary = [torch.flatten(e(x_cat[:, self.class_group_map[i]]), start_dim=1) for i, e in enumerate(self.embeds)] x = torch.cat(x_cat_nonbinary + [x_cat_binary], 1) x = self.emb_drop(x) if self.n_cont != 0: if self.bn_cont is not None: x_cont = self.bn_cont(x_cont) x = torch.cat([x, x_cont], 1) if self.n_emb != 0 else x_cont return self.layers(x) class CustomMixedModel(torch.nn.Module): def __init__(self, image_model, tab_model, seq_model, image_output_size, embeds_size,prepare_and_pad,max_image_batch): super(CustomMixedModel, self).__init__() self.image_model = image_model self.tab_model = tab_model self.seq_model = seq_model self.classifier = torch.nn.Sequential(LinBnDrop(200 + image_output_size + embeds_size , 1, act=None, p=0.)) self.prepare_and_pad = prepare_and_pad self.max_image_batch = max_image_batch def forward(self, input_cat, input_cont, input_image, attention_mask, train_embeds=True, train_seq_model=True): valid_sizes = torch.sum((attention_mask == 1), dim=1) if train_embeds: input_embed = self.forward_embeds(input_cat, input_cont, input_image,valid_sizes) else: with torch.no_grad(): input_embed = self.forward_embeds(input_cat, input_cont, input_image,valid_sizes) input_embed=self.prepare_and_pad(input_embed,valid_sizes) if train_seq_model: bert_out = self.forward_seq_model(input_embed, attention_mask) else: with torch.no_grad(): bert_out = self.forward_seq_model(input_embed, attention_mask) output = self.classifier( torch.cat((input_embed[range(input_embed.shape[0]), valid_sizes-1], bert_out), dim=1)) return output def forward_embeds(self, input_cat, input_cont, input_image,valid_size): n_batches = (input_image.shape[0]//self.max_image_batch)+1*(input_image.shape[0]%self.max_image_batch>0) tab_out = self.tab_model(input_cat,input_cont) image_out = torch.cat([self.image_model(input_image[i*self.max_image_batch:min((i+1)*self.max_image_batch,input_image.shape[0])]) for i in range(n_batches)],dim=0) input_embed = torch.cat((tab_out,image_out),dim=1) input_embed = torch.nn.ReLU()(input_embed) return input_embed def forward_seq_model(self, input_embed, attention_mask): bert_out = torch.mean(self.seq_model(inputs_embeds=input_embed, attention_mask=attention_mask)[0],dim=1) bert_out = torch.nn.ReLU()(bert_out) return bert_out class CustomMixedModelSingleImage(torch.nn.Module): def __init__(self, image_model, tab_model, image_output_size,class_p): super(CustomMixedModelSingleImage, self).__init__() self.image_model = image_model self.tab_model = tab_model self.classifier = torch.nn.Sequential(LinBnDrop(50 + image_output_size, 40, act=torch.nn.ReLU(), p=class_p), LinBnDrop(40, 1, act=None, p=class_p)) def forward(self, input_cat, input_cont, input_image): output_tabular = self.tab_model(input_cat, input_cont) output_image = self.image_model(input_image) logits = self.classifier(torch.nn.ReLU()(torch.cat((output_tabular, output_image), dim=1))) return logits

true

1c2e8bf63583af76879137c28d1e1bfd7558d730

5,464

py

Python

quantumGAN/deprecated_files/discriminator_minimax.py

tomiock/qGAN

fb98a2b5286eb479665ade353efa40bd6e55dc36

[ "Apache-2.0" ]

1

2021-06-14T09:33:32.000Z

quantumGAN/deprecated_files/discriminator_minimax.py

tomiock/gGAN

fb98a2b5286eb479665ade353efa40bd6e55dc36

[ "Apache-2.0" ]

7

2021-06-14T12:21:40.000Z

2021-10-01T07:29:01.000Z

quantumGAN/deprecated_files/discriminator_minimax.py

tomiock/qGAN

fb98a2b5286eb479665ade353efa40bd6e55dc36

[ "Apache-2.0" ]

null

"""DISCRIMINATOR BCE""" import json import random from typing import Dict, List import numpy as np from quantumGAN.functions import minimax_derivative_fake, minimax_derivative_real, sigmoid, \ sigmoid_prime def load(filename): f = open(filename, "r") data = json.load(f) f.close() # cost = getattr(sys.modules[__name__], data["cost"]) net = ClassicalDiscriminatorMINIMAX(None, None, data["sizes"], data["loss"]) net.weights = [np.array(w) for w in data["weights"]] net.biases = [np.array(b) for b in data["biases"]] return net class ClassicalDiscriminatorMINIMAX: def __init__(self, training_data: List or None, mini_batch_size: int or None, sizes: List[int], loss_BCE: bool) -> None: self.training_data = training_data self.mini_batch_size: int = mini_batch_size self.num_layers = len(sizes) self.sizes = sizes self.loss_BCE = loss_BCE self.data_loss = {"real": [], "fake": []} self.ret: Dict[str, any] = {"loss": [], "label real": [], "label fake": [], "label fake time": [], "label real time": []} self.biases = [np.random.randn(y, ) for y in sizes[1:]] self.weights = [np.random.randn(y, x) for x, y in zip(sizes[:-1], sizes[1:])] def feedforward(self, a): """Return the output of the network if ``a`` is input.""" for b, w in zip(self.biases, self.weights): a = sigmoid(np.dot(w, a) + b) return a def predict(self, x): # feedforward activation = x zs = [] # list to store all the z vectors, layer by layer for b, w in zip(self.biases, self.weights): z = np.dot(w, activation) + b zs.append(z) activation = sigmoid(z) return activation def evaluate(self, test_data): test_results = [(np.argmax(self.feedforward(x)), y) for (x, y) in test_data] return sum(int(x == y) for (x, y) in test_results) def save(self, filename): """Save the neural network to the file ``filename``.""" data = {"sizes": self.sizes, "weights": [w.tolist() for w in self.weights], "biases": [b.tolist() for b in self.biases], "loss": self.loss_BCE # , # "cost": str(self..__name__) } f = open(filename, "w") json.dump(data, f) f.close() def forwardprop(self, x: np.ndarray): activation = x activations = [x] # list to store all the activations, layer by layer zs = [] # list to store all the z vectors, layer by layer for b, w in zip(self.biases, self.weights): z = np.dot(w, activation) + b zs.append(z) activation = sigmoid(z) activations.append(activation) return activation, activations, zs def backprop(self, real_image, fake_image, is_real): """Return a tuple ``(nabla_b, nabla_w)`` representing the gradient for the cost function C_x. ``nabla_b`` and ``nabla_w`` are layer-by-layer lists of numpy arrays, similar to ``self.biases`` and ``self.weights``.""" nabla_b = [np.zeros(b.shape) for b in self.biases] nabla_w = [np.zeros(w.shape) for w in self.weights] # feedforward and back error calculation depending on type of image activation_real, activations_real, zs_real = self.forwardprop(real_image) activation_fake, activations_fake, zs_fake = self.forwardprop(fake_image) if is_real: delta = minimax_derivative_real(activations_real[-1]) * sigmoid_prime(zs_real[-1]) activations, zs = activations_real, zs_real else: delta = minimax_derivative_fake(activations_fake[-1]) * sigmoid_prime(zs_fake[-1]) activations, zs = activations_fake, zs_fake # backward pass nabla_b[-1] = delta nabla_w[-1] = np.dot(delta, activations[-2].reshape(1, activations[-2].shape[0])) for l in range(2, self.num_layers): z = zs[-l] delta = np.dot(self.weights[-l + 1].transpose(), delta) * sigmoid_prime(z) nabla_b[-l] = delta nabla_w[-l] = np.dot(delta.reshape(delta.shape[0], 1), activations[-l - 1].reshape(1, activations[-l - 1].shape[0])) return nabla_b, nabla_w, activations[-1] def train_mini_batch(self, mini_batch, learning_rate): global label_real, label_fake nabla_b = [np.zeros(b.shape) for b in self.biases] nabla_w = [np.zeros(w.shape) for w in self.weights] for real_image, fake_image in mini_batch: delta_nabla_b, delta_nabla_w, label_real = self.backprop(real_image, fake_image, True) nabla_b = [nb + dnb for nb, dnb in zip(nabla_b, delta_nabla_b)] nabla_w = [nw + dnw for nw, dnw in zip(nabla_w, delta_nabla_w)] delta_nabla_b, delta_nabla_w, label_fake = self.backprop(real_image, fake_image, False) nabla_b = [nb + dnb for nb, dnb in zip(nabla_b, delta_nabla_b)] nabla_w = [nw + dnw for nw, dnw in zip(nabla_w, delta_nabla_w)] # gradient descent # nabla_w and nabla_b are multiplied by the learning rate # and taken the mean of (dividing by the mini batch size) self.weights = [w - (learning_rate / len(mini_batch)) * nw for w, nw in zip(self.weights, nabla_w)] self.biases = [b - (learning_rate / len(mini_batch)) * nb for b, nb in zip(self.biases, nabla_b)] def create_mini_batches(self): n = len(self.training_data) random.shuffle(self.training_data) mini_batches = [ self.training_data[k:k + self.mini_batch_size] for k in range(0, n, self.mini_batch_size)] return [mini_batches[0]]

35.947368

93

0.646962

import json import random from typing import Dict, List import numpy as np from quantumGAN.functions import minimax_derivative_fake, minimax_derivative_real, sigmoid, \ sigmoid_prime def load(filename): f = open(filename, "r") data = json.load(f) f.close() net = ClassicalDiscriminatorMINIMAX(None, None, data["sizes"], data["loss"]) net.weights = [np.array(w) for w in data["weights"]] net.biases = [np.array(b) for b in data["biases"]] return net class ClassicalDiscriminatorMINIMAX: def __init__(self, training_data: List or None, mini_batch_size: int or None, sizes: List[int], loss_BCE: bool) -> None: self.training_data = training_data self.mini_batch_size: int = mini_batch_size self.num_layers = len(sizes) self.sizes = sizes self.loss_BCE = loss_BCE self.data_loss = {"real": [], "fake": []} self.ret: Dict[str, any] = {"loss": [], "label real": [], "label fake": [], "label fake time": [], "label real time": []} self.biases = [np.random.randn(y, ) for y in sizes[1:]] self.weights = [np.random.randn(y, x) for x, y in zip(sizes[:-1], sizes[1:])] def feedforward(self, a): for b, w in zip(self.biases, self.weights): a = sigmoid(np.dot(w, a) + b) return a def predict(self, x): activation = x zs = [] for b, w in zip(self.biases, self.weights): z = np.dot(w, activation) + b zs.append(z) activation = sigmoid(z) return activation def evaluate(self, test_data): test_results = [(np.argmax(self.feedforward(x)), y) for (x, y) in test_data] return sum(int(x == y) for (x, y) in test_results) def save(self, filename): data = {"sizes": self.sizes, "weights": [w.tolist() for w in self.weights], "biases": [b.tolist() for b in self.biases], "loss": self.loss_BCE } f = open(filename, "w") json.dump(data, f) f.close() def forwardprop(self, x: np.ndarray): activation = x activations = [x] zs = [] for b, w in zip(self.biases, self.weights): z = np.dot(w, activation) + b zs.append(z) activation = sigmoid(z) activations.append(activation) return activation, activations, zs def backprop(self, real_image, fake_image, is_real): nabla_b = [np.zeros(b.shape) for b in self.biases] nabla_w = [np.zeros(w.shape) for w in self.weights] activation_real, activations_real, zs_real = self.forwardprop(real_image) activation_fake, activations_fake, zs_fake = self.forwardprop(fake_image) if is_real: delta = minimax_derivative_real(activations_real[-1]) * sigmoid_prime(zs_real[-1]) activations, zs = activations_real, zs_real else: delta = minimax_derivative_fake(activations_fake[-1]) * sigmoid_prime(zs_fake[-1]) activations, zs = activations_fake, zs_fake nabla_b[-1] = delta nabla_w[-1] = np.dot(delta, activations[-2].reshape(1, activations[-2].shape[0])) for l in range(2, self.num_layers): z = zs[-l] delta = np.dot(self.weights[-l + 1].transpose(), delta) * sigmoid_prime(z) nabla_b[-l] = delta nabla_w[-l] = np.dot(delta.reshape(delta.shape[0], 1), activations[-l - 1].reshape(1, activations[-l - 1].shape[0])) return nabla_b, nabla_w, activations[-1] def train_mini_batch(self, mini_batch, learning_rate): global label_real, label_fake nabla_b = [np.zeros(b.shape) for b in self.biases] nabla_w = [np.zeros(w.shape) for w in self.weights] for real_image, fake_image in mini_batch: delta_nabla_b, delta_nabla_w, label_real = self.backprop(real_image, fake_image, True) nabla_b = [nb + dnb for nb, dnb in zip(nabla_b, delta_nabla_b)] nabla_w = [nw + dnw for nw, dnw in zip(nabla_w, delta_nabla_w)] delta_nabla_b, delta_nabla_w, label_fake = self.backprop(real_image, fake_image, False) nabla_b = [nb + dnb for nb, dnb in zip(nabla_b, delta_nabla_b)] nabla_w = [nw + dnw for nw, dnw in zip(nabla_w, delta_nabla_w)] self.weights = [w - (learning_rate / len(mini_batch)) * nw for w, nw in zip(self.weights, nabla_w)] self.biases = [b - (learning_rate / len(mini_batch)) * nb for b, nb in zip(self.biases, nabla_b)] def create_mini_batches(self): n = len(self.training_data) random.shuffle(self.training_data) mini_batches = [ self.training_data[k:k + self.mini_batch_size] for k in range(0, n, self.mini_batch_size)] return [mini_batches[0]]

true

1c2e8da7f1a8c7cb5c1ba3839e249911f1c92d46

271

py

Python

18_Math3/Step01/wowo0709.py

StudyForCoding/BEAKJOON

84e1c5e463255e919ccf6b6a782978c205420dbf

[ "MIT" ]

null

18_Math3/Step01/wowo0709.py

StudyForCoding/BEAKJOON

84e1c5e463255e919ccf6b6a782978c205420dbf

[ "MIT" ]

3

2020-11-04T05:38:53.000Z

2021-03-02T02:15:19.000Z

18_Math3/Step01/wowo0709.py

StudyForCoding/BEAKJOON

84e1c5e463255e919ccf6b6a782978c205420dbf

[ "MIT" ]

null

while(1): a,b = map(int,input().split()) if a==0 and b==0: exit(0) elif (a!=0 and b==0) or (a==0 and b!=0): print('neither') elif a%b==0: print('multiple') elif b%a==0: print('factor') else: print('neither')

22.583333

44

0.457565

while(1): a,b = map(int,input().split()) if a==0 and b==0: exit(0) elif (a!=0 and b==0) or (a==0 and b!=0): print('neither') elif a%b==0: print('multiple') elif b%a==0: print('factor') else: print('neither')

true

1c2e8db06813e97495b935f3a62d070441bf6d9a

1,756

py

Python

code/venv/lib/python3.8/site-packages/datadog_api_client/v1/model/synthetics_basic_auth_ntlm_type.py

Valisback/hiring-engineers

7196915dd5a429ae27c21fa43d527f0332e662ed

[ "Apache-2.0" ]

null

code/venv/lib/python3.8/site-packages/datadog_api_client/v1/model/synthetics_basic_auth_ntlm_type.py

Valisback/hiring-engineers

7196915dd5a429ae27c21fa43d527f0332e662ed

[ "Apache-2.0" ]

null

code/venv/lib/python3.8/site-packages/datadog_api_client/v1/model/synthetics_basic_auth_ntlm_type.py

Valisback/hiring-engineers

7196915dd5a429ae27c21fa43d527f0332e662ed

[ "Apache-2.0" ]

null

# Unless explicitly stated otherwise all files in this repository are licensed under the Apache-2.0 License. # This product includes software developed at Datadog (https://www.datadoghq.com/). # Copyright 2019-Present Datadog, Inc. from datadog_api_client.v1.model_utils import ( ModelSimple, cached_property, ) class SyntheticsBasicAuthNTLMType(ModelSimple): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ allowed_values = { "value": { "NTLM": "ntlm", }, } validations = {} @cached_property def openapi_types(): return { "value": (str,), } def __init__(self, *args, **kwargs): """SyntheticsBasicAuthNTLMType - a model defined in OpenAPI Note that value can be passed either in args or in kwargs, but not in both. Args: args[0] (str): The type of authentication to use when performing the test. If omitted defaults to "ntlm". Must be one of ["ntlm"]. Keyword Args: value (str): The type of authentication to use when performing the test. If omitted defaults to "ntlm". Must be one of ["ntlm"]. """ super().__init__(kwargs) if "value" in kwargs: value = kwargs.pop("value") elif args: args = list(args) value = args.pop(0) else: value = "ntlm" self._check_pos_args(args) self.value = value self._check_kw_args(kwargs) @classmethod def _from_openapi_data(cls, *args, **kwargs): """Helper creating a new instance from a response.""" return cls(*args, **kwargs)

27.4375

142

0.612756

from datadog_api_client.v1.model_utils import ( ModelSimple, cached_property, ) class SyntheticsBasicAuthNTLMType(ModelSimple): allowed_values = { "value": { "NTLM": "ntlm", }, } validations = {} @cached_property def openapi_types(): return { "value": (str,), } def __init__(self, *args, **kwargs): super().__init__(kwargs) if "value" in kwargs: value = kwargs.pop("value") elif args: args = list(args) value = args.pop(0) else: value = "ntlm" self._check_pos_args(args) self.value = value self._check_kw_args(kwargs) @classmethod def _from_openapi_data(cls, *args, **kwargs): return cls(*args, **kwargs)

true

1c2e8e6321d95ddbf511a3d098115f8a1538a5ba

549

py

Python

yamtbx/dataproc/xds/command_line/xds_integrate_lp_to_xparm.py

7l2icj/kamo_clone

5f4a5eed3cd9d91a021d805e46125c19cc2ed1b6

[ "BSD-3-Clause" ]

16

2016-05-20T11:19:40.000Z

2021-01-01T19:44:23.000Z

yamtbx/dataproc/xds/command_line/xds_integrate_lp_to_xparm.py

7l2icj/kamo_clone

5f4a5eed3cd9d91a021d805e46125c19cc2ed1b6

[ "BSD-3-Clause" ]

4

2017-03-10T00:51:11.000Z

2021-02-07T17:18:46.000Z

yamtbx/dataproc/xds/command_line/xds_integrate_lp_to_xparm.py

7l2icj/kamo_clone

5f4a5eed3cd9d91a021d805e46125c19cc2ed1b6

[ "BSD-3-Clause" ]

9

2016-12-15T16:00:06.000Z

2021-09-10T08:34:14.000Z

#!/usr/bin/env yamtbx.python """ (c) RIKEN 2015. All rights reserved. Author: Keitaro Yamashita This software is released under the new BSD License; see LICENSE. """ import os from yamtbx.dataproc.xds.xparm import get_xparm_from_integrate_lp def run(integrate_lp, frame_num): xparm_str = get_xparm_from_integrate_lp(integrate_lp, frame_num) open("XPARM.XDS.%.4d"%frame_num, "w").write(xparm_str) # run() if __name__ == "__main__": import sys integrate_lp = sys.argv[1] frame = int(sys.argv[2]) run(integrate_lp, frame)

23.869565

68

0.724954

import os from yamtbx.dataproc.xds.xparm import get_xparm_from_integrate_lp def run(integrate_lp, frame_num): xparm_str = get_xparm_from_integrate_lp(integrate_lp, frame_num) open("XPARM.XDS.%.4d"%frame_num, "w").write(xparm_str) if __name__ == "__main__": import sys integrate_lp = sys.argv[1] frame = int(sys.argv[2]) run(integrate_lp, frame)

true

1c2e8ee4df75228959da73fbbd7d2db0011e6712

430

py

Python

Week2/problem1.py

AustinKladke/6.00.1x-MIT-CS-Python

0f9e2840fdd5f005bae2a53a3b8a251e0635654e

[ "MIT" ]

null

Week2/problem1.py

AustinKladke/6.00.1x-MIT-CS-Python

0f9e2840fdd5f005bae2a53a3b8a251e0635654e

[ "MIT" ]

null

Week2/problem1.py

AustinKladke/6.00.1x-MIT-CS-Python

0f9e2840fdd5f005bae2a53a3b8a251e0635654e

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sun Feb 7 11:40:18 2021 @author: akladke """ #balance = 42 balance = 484 annualInterestRate = 0.2 monthlyPaymentRate = 0.04 for i in range(0, 12): min_payment = balance * monthlyPaymentRate unpaid_balance = balance - min_payment interest = annualInterestRate/12 * unpaid_balance balance = round(unpaid_balance + interest, 2) print("Remaining balance: {}".format(balance))

23.888889

53

0.702326

balance = 484 annualInterestRate = 0.2 monthlyPaymentRate = 0.04 for i in range(0, 12): min_payment = balance * monthlyPaymentRate unpaid_balance = balance - min_payment interest = annualInterestRate/12 * unpaid_balance balance = round(unpaid_balance + interest, 2) print("Remaining balance: {}".format(balance))

true

1c2e8f5126310bf5dc00013e478e20cf77af87a8

1,858

py

Python

Hash.py

V3noM-Cyber/Hashjokar

9d29bbdee22e29488074bc2b16178d91cee2778e

[ "MIT" ]

null

Hash.py

V3noM-Cyber/Hashjokar

9d29bbdee22e29488074bc2b16178d91cee2778e

[ "MIT" ]

null

Hash.py

V3noM-Cyber/Hashjokar

9d29bbdee22e29488074bc2b16178d91cee2778e

[ "MIT" ]

null

import hashlib from colorama import init from time import * from termcolor import colored import sys import os import hashlib, binascii from colorama import Fore, Back, Style hash = "" init() print( Fore.RED+''' ●🔥════════════════════════◄►═══════════════════🔥● ██╗░░░██╗██████╗░███╗░░██╗░█████╗░███╗░░░███╗ ██║░░░██║╚════██╗████╗░██║██╔══██╗████╗░████║ ╚██╗░██╔╝░█████╔╝██╔██╗██║██║░░██║██╔████╔██║ ░╚████╔╝░░╚═══██╗██║╚████║██║░░██║██║╚██╔╝██║ ░░╚██╔╝░░██████╔╝██║░╚███║╚█████╔╝██║░╚═╝░██║ ░░░╚═╝░░░╚═════╝░╚═╝░░╚══╝░╚════╝░╚═╝░░░░░╚═╝ Telegram:https://t.me/V3n0M_Cyber YouTube:V3n0m Cyber ✪ vercon 1.0 ●🔥════════════════════════◄►═══════════════════🔥● ''') os.system("xdg-open https://t.me/V3n0M_Cyber") hash =input("Creating Hash: ") result = hashlib.md5(hash.encode()) print(Fore.RED +"[+]Hash MD5: ",end="") print(result.hexdigest()) print("●════════════════════════════●") sleep(1.0) result = hashlib.sha1(hash.encode()) print(Fore.RED +"[+]Hash SHA1: ",end="") print(result.hexdigest()) print("●════════════════════════════●") sleep(1.0) result = hashlib.sha224(hash.encode()) print(Fore.RED +"[+]Hash SHA224: ",end="") print(result.hexdigest()) print("●════════════════════════════●") sleep(1.0) result = hashlib.sha256(hash.encode()) print(Fore.RED+"[+]Hash SHA256: ",end="") print(result.hexdigest()) print("●════════════════════════════●") sleep(1.0) result = hashlib.sha384(hash.encode()) print(Fore.RED+"[+]Hash SHA384: ",end="") print(result.hexdigest()) print("●════════════════════════════●") sleep(1.1) result = hashlib.sha512(hash.encode()) print(Fore.RED+"[+]Hash SHA512: ",end="") print(result.hexdigest()) print("●════════════════════════════●") print("Hash Crack website ") print("Bo Ragrtn CTRL+Z Dabgra ") sleep(3) sleep(200) v= os.system("clear")

29.967742

70

0.453714

import hashlib from colorama import init from time import * from termcolor import colored import sys import os import hashlib, binascii from colorama import Fore, Back, Style hash = "" init() print( Fore.RED+''' ●🔥════════════════════════◄►═══════════════════🔥● ██╗░░░██╗██████╗░███╗░░██╗░█████╗░███╗░░░███╗ ██║░░░██║╚════██╗████╗░██║██╔══██╗████╗░████║ ╚██╗░██╔╝░█████╔╝██╔██╗██║██║░░██║██╔████╔██║ ░╚████╔╝░░╚═══██╗██║╚████║██║░░██║██║╚██╔╝██║ ░░╚██╔╝░░██████╔╝██║░╚███║╚█████╔╝██║░╚═╝░██║ ░░░╚═╝░░░╚═════╝░╚═╝░░╚══╝░╚════╝░╚═╝░░░░░╚═╝ Telegram:https://t.me/V3n0M_Cyber YouTube:V3n0m Cyber ✪ vercon 1.0 ●🔥════════════════════════◄►═══════════════════🔥● ''') os.system("xdg-open https://t.me/V3n0M_Cyber") hash =input("Creating Hash: ") result = hashlib.md5(hash.encode()) print(Fore.RED +"[+]Hash MD5: ",end="") print(result.hexdigest()) print("●════════════════════════════●") sleep(1.0) result = hashlib.sha1(hash.encode()) print(Fore.RED +"[+]Hash SHA1: ",end="") print(result.hexdigest()) print("●════════════════════════════●") sleep(1.0) result = hashlib.sha224(hash.encode()) print(Fore.RED +"[+]Hash SHA224: ",end="") print(result.hexdigest()) print("●════════════════════════════●") sleep(1.0) result = hashlib.sha256(hash.encode()) print(Fore.RED+"[+]Hash SHA256: ",end="") print(result.hexdigest()) print("●════════════════════════════●") sleep(1.0) result = hashlib.sha384(hash.encode()) print(Fore.RED+"[+]Hash SHA384: ",end="") print(result.hexdigest()) print("●════════════════════════════●") sleep(1.1) result = hashlib.sha512(hash.encode()) print(Fore.RED+"[+]Hash SHA512: ",end="") print(result.hexdigest()) print("●════════════════════════════●") print("Hash Crack website ") print("Bo Ragrtn CTRL+Z Dabgra ") sleep(3) sleep(200) v= os.system("clear")

true

1c2e8f59e079c1df103e60b7754c0feb424f213c

363

py

Python

doc/tutorials/data/03_halide_basic.py

Hconk/AutoKernel

edfb409efe37e2aea5350961e3a8160c11856853

[ "Apache-2.0" ]

754

2020-11-11T06:34:26.000Z

2022-03-29T03:45:36.000Z

doc/tutorials/data/03_halide_basic.py

Hconk/AutoKernel

edfb409efe37e2aea5350961e3a8160c11856853

[ "Apache-2.0" ]

16

2020-12-01T03:27:01.000Z

2021-08-13T06:20:58.000Z

doc/tutorials/data/03_halide_basic.py

Hconk/AutoKernel

edfb409efe37e2aea5350961e3a8160c11856853

[ "Apache-2.0" ]

82

2020-11-12T00:14:45.000Z

2022-03-27T09:22:08.000Z

#!/usr/bin/python3 import halide as hl x, y = hl.Var("x"), hl.Var("y") func = hl.Func("func") func[x,y] = x + 10*y #func.trace_stores() out = func.realize(3, 4) # width, height = 3,4 print("=============================") for j in range(out.height()): for i in range(out.width()): print("out[x=%i,y=%i]=%i"%(i,j,out[i,j])) print("Success!")

16.5

49

0.517906

import halide as hl x, y = hl.Var("x"), hl.Var("y") func = hl.Func("func") func[x,y] = x + 10*y out = func.realize(3, 4) print("=============================") for j in range(out.height()): for i in range(out.width()): print("out[x=%i,y=%i]=%i"%(i,j,out[i,j])) print("Success!")

true

1c2e90a0f9ba63d07086acc94891fb7c5086ab1e

4,403

py

Python

usaspending_api/etl/transaction_loaders/data_load_helpers.py

gaybro8777/usaspending-api

fe9d730acd632401bbbefa168e3d86d59560314b

[ "CC0-1.0" ]

null

usaspending_api/etl/transaction_loaders/data_load_helpers.py

gaybro8777/usaspending-api

fe9d730acd632401bbbefa168e3d86d59560314b

[ "CC0-1.0" ]

null

usaspending_api/etl/transaction_loaders/data_load_helpers.py

gaybro8777/usaspending-api

fe9d730acd632401bbbefa168e3d86d59560314b

[ "CC0-1.0" ]

null

from datetime import datetime import os import re import boto3 import csv import logging from django.conf import settings logger = logging.getLogger("console") def capitalize_if_string(val): try: return val.upper() except AttributeError: return val # 10/31/2019: According to PO direction, this functionality is NOT desired, and should be phased out as soon as it's safe def false_if_null(val): if val is None: return False return val def truncate_timestamp(val): if isinstance(val, datetime): return val.date() elif isinstance(val, str): return datetime.strptime(val, "%Y-%m-%d %H:%M:%S").date() elif val is None: return None else: raise ValueError("{} is not parsable as a date!".format(val.type)) def format_value_for_sql(val, cur): return str(cur.mogrify("%s", (val,)), "utf-8") def format_bulk_insert_list_column_sql(cursor, load_objects, type): """creates formatted sql text to put into a bulk insert statement""" keys = load_objects[0][type].keys() columns = ['"{}"'.format(key) for key in load_objects[0][type].keys()] values = [[format_value_for_sql(load_object[type][key], cursor) for key in keys] for load_object in load_objects] col_string = "({})".format(",".join(columns)) val_string = ",".join(["({})".format(",".join(map(str, value))) for value in values]) return col_string, val_string def format_insert_or_update_column_sql(cursor, load_object, type): """creates formatted sql text to put into a single row insert or update statement""" columns = [] values = [] update_pairs = [] for key in load_object[type].keys(): columns.append('"{}"'.format(key)) val = format_value_for_sql(load_object[type][key], cursor) values.append(val) if key not in ["create_date", "created_at"]: update_pairs.append(" {}={}".format(key, val)) col_string = "({})".format(",".join(map(str, columns))) val_string = "({})".format(",".join(map(str, values))) pairs_string = ",".join(update_pairs) return col_string, val_string, pairs_string def get_deleted_fpds_data_from_s3(date): ids_to_delete = [] regex_str = ".*_delete_records_(IDV|award).*" if settings.IS_LOCAL: for file in os.listdir(settings.CSV_LOCAL_PATH): if re.search(regex_str, file) and datetime.strptime(file[: file.find("_")], "%m-%d-%Y").date() >= date: with open(settings.CSV_LOCAL_PATH + file, "r") as current_file: # open file, split string to array, skip the header reader = csv.reader(current_file.read().splitlines()) next(reader) unique_key_list = [rows[0] for rows in reader] ids_to_delete += unique_key_list else: # Connect to AWS aws_region = settings.USASPENDING_AWS_REGION fpds_bucket_name = settings.FPDS_BUCKET_NAME if not (aws_region and fpds_bucket_name): raise Exception("Missing required environment variables: USASPENDING_AWS_REGION, FPDS_BUCKET_NAME") s3client = boto3.client("s3", region_name=aws_region) s3resource = boto3.resource("s3", region_name=aws_region) s3_bucket = s3resource.Bucket(fpds_bucket_name) # make an array of all the keys in the bucket file_list = [item.key for item in s3_bucket.objects.all()] # Only use files that match the date we're currently checking for item in file_list: # if the date on the file is the same day as we're checking if ( re.search(regex_str, item) and "/" not in item and datetime.strptime(item[: item.find("_")], "%m-%d-%Y").date() >= date ): s3_item = s3client.get_object(Bucket=fpds_bucket_name, Key=item) reader = csv.reader(s3_item["Body"].read().decode("utf-8").splitlines()) # skip the header, the reader doesn't ignore it for some reason next(reader) # make an array of all the detached_award_procurement_ids unique_key_list = [rows[0] for rows in reader] ids_to_delete += unique_key_list logger.info("Number of records to delete: %s" % str(len(ids_to_delete))) return ids_to_delete

35.796748

121

0.633205

from datetime import datetime import os import re import boto3 import csv import logging from django.conf import settings logger = logging.getLogger("console") def capitalize_if_string(val): try: return val.upper() except AttributeError: return val def false_if_null(val): if val is None: return False return val def truncate_timestamp(val): if isinstance(val, datetime): return val.date() elif isinstance(val, str): return datetime.strptime(val, "%Y-%m-%d %H:%M:%S").date() elif val is None: return None else: raise ValueError("{} is not parsable as a date!".format(val.type)) def format_value_for_sql(val, cur): return str(cur.mogrify("%s", (val,)), "utf-8") def format_bulk_insert_list_column_sql(cursor, load_objects, type): keys = load_objects[0][type].keys() columns = ['"{}"'.format(key) for key in load_objects[0][type].keys()] values = [[format_value_for_sql(load_object[type][key], cursor) for key in keys] for load_object in load_objects] col_string = "({})".format(",".join(columns)) val_string = ",".join(["({})".format(",".join(map(str, value))) for value in values]) return col_string, val_string def format_insert_or_update_column_sql(cursor, load_object, type): columns = [] values = [] update_pairs = [] for key in load_object[type].keys(): columns.append('"{}"'.format(key)) val = format_value_for_sql(load_object[type][key], cursor) values.append(val) if key not in ["create_date", "created_at"]: update_pairs.append(" {}={}".format(key, val)) col_string = "({})".format(",".join(map(str, columns))) val_string = "({})".format(",".join(map(str, values))) pairs_string = ",".join(update_pairs) return col_string, val_string, pairs_string def get_deleted_fpds_data_from_s3(date): ids_to_delete = [] regex_str = ".*_delete_records_(IDV|award).*" if settings.IS_LOCAL: for file in os.listdir(settings.CSV_LOCAL_PATH): if re.search(regex_str, file) and datetime.strptime(file[: file.find("_")], "%m-%d-%Y").date() >= date: with open(settings.CSV_LOCAL_PATH + file, "r") as current_file: # open file, split string to array, skip the header reader = csv.reader(current_file.read().splitlines()) next(reader) unique_key_list = [rows[0] for rows in reader] ids_to_delete += unique_key_list else: # Connect to AWS aws_region = settings.USASPENDING_AWS_REGION fpds_bucket_name = settings.FPDS_BUCKET_NAME if not (aws_region and fpds_bucket_name): raise Exception("Missing required environment variables: USASPENDING_AWS_REGION, FPDS_BUCKET_NAME") s3client = boto3.client("s3", region_name=aws_region) s3resource = boto3.resource("s3", region_name=aws_region) s3_bucket = s3resource.Bucket(fpds_bucket_name) # make an array of all the keys in the bucket file_list = [item.key for item in s3_bucket.objects.all()] # Only use files that match the date we're currently checking for item in file_list: if ( re.search(regex_str, item) and "/" not in item and datetime.strptime(item[: item.find("_")], "%m-%d-%Y").date() >= date ): s3_item = s3client.get_object(Bucket=fpds_bucket_name, Key=item) reader = csv.reader(s3_item["Body"].read().decode("utf-8").splitlines()) # skip the header, the reader doesn't ignore it for some reason next(reader) unique_key_list = [rows[0] for rows in reader] ids_to_delete += unique_key_list logger.info("Number of records to delete: %s" % str(len(ids_to_delete))) return ids_to_delete

true

1c2e90c332fe1e210457721c098a7ba2d97cb487

7,344

py

Python

src/external_model.py

alex-calderwood/twine_generation

8a3ba3779bffeb7a0b18bfd0960685b26bd4e5e8

[ "BSD-4-Clause-UC" ]

2

2021-11-18T07:28:09.000Z

2022-03-23T13:56:18.000Z

src/external_model.py

alex-calderwood/twine_generation

8a3ba3779bffeb7a0b18bfd0960685b26bd4e5e8

[ "BSD-4-Clause-UC" ]

null

src/external_model.py

alex-calderwood/twine_generation

8a3ba3779bffeb7a0b18bfd0960685b26bd4e5e8

[ "BSD-4-Clause-UC" ]

null

import openai import os import time import twee_utils as utils # Load your API key from an environment variable or secret management service openai.api_key = os.getenv("OPENAI_API_KEY") ZERO_SHOT = open('src/zero_shot.txt', 'r').read() class TwineGenerator: def __init__(self, model='context', verbose=True): """ :param model: must be one of: 'context', 'mock', or 'naive' context: Call GPT-3 with a context description and passage title naive: Call GPT-3 with only a passage title mock: Mock the language model call Each will return a passage body that can be appended to the title to make a complete Twee passage. """ # Decide which generator to use (GPT-3 or mock) self.model = model.lower() self.verbose = bool(verbose) if self.model == 'events': self._call_model = TwineGenerator._call_contextual_event_model elif self.model == 'context': self._call_model = TwineGenerator._call_contextual_model elif self.model == 'mock': self._call_model = TwineGenerator._mock_generate elif self.model == 'naive': self._call_model = TwineGenerator._call_naive_model else: raise ValueError(f"TwineGenerator({self.model}) is invalid") def get_completion(self, prompt): """ call the correct language model """ if self.verbose: print("prompt", prompt) while True: try: return self._call_model(prompt) except openai.error.RateLimitError as e: print(e) print('retrying...') def _call_model(self, prompt): raise RuntimeError("This should have been defined in the constructor") @staticmethod def _call_naive_model(prompt): """ Return a generated twee passage from a given title. The code is elsewhere modular to allow this to be re-implemented with any language model. Here, we call a fine-tuned GPT-3 instance trained to generate scaffolded Twee. """ model = 'curie:ft-user-wmco7qacght9seweh8jgp4ib-2021-10-28-04-55-18' response = openai.Completion.create( model=model, prompt=prompt, temperature=0.7, max_tokens=1000, top_p=1, frequency_penalty=0, presence_penalty=0, stop=utils.END, ) return response['choices'][0]['text'] @staticmethod def _call_contextual_model(prompt): """ Return a generated twee passage from a given title. The code is elsewhere modular to allow this to be re-implemented with any language model. Here, we call a fine-tuned GPT-3 instance trained to generate scaffolded Twee. """ model = 'curie:ft-user-wmco7qacght9seweh8jgp4ib-2021-11-29-05-45-10' response = openai.Completion.create( model=model, prompt=prompt, temperature=0.7, max_tokens=1000, top_p=1, frequency_penalty=0, presence_penalty=0, stop=utils.END, ) return response['choices'][0]['text'] @staticmethod def _call_contextual_event_model(prompt): """ Return a generated twee passage from a given title. The code is elsewhere modular to allow this to be re-implemented with any language model. Here, we call a fine-tuned GPT-3 instance trained to generate scaffolded Twee. """ model = 'curie:ft-user-wmco7qacght9seweh8jgp4ib-2021-12-07-08-07-09' response = openai.Completion.create( model=model, prompt=prompt, temperature=0.7, max_tokens=1000, top_p=1, frequency_penalty=0, presence_penalty=0, stop=utils.END, ) return response['choices'][0]['text'] @staticmethod def summarize(passage, clean_passage=True): """ Use GPT-3 as a zero-shot summarization for the given passage. :param clean_passage: (bool) whether to clean the passage of extraneous twee formatting """ if clean_passage: passage = utils.passage_to_text(passage) prompt = ZERO_SHOT.format(passage.strip()) model = 'davinci' # It doesn't really work with curie response = openai.Completion.create( model=model, prompt=prompt, temperature=0.7, max_tokens=280, top_p=1, frequency_penalty=0, presence_penalty=0, stop='.', ) response = response['choices'][0]['text'] return response.strip() + '.' @staticmethod def _mock_generate(prompt): time.sleep(1) result = {} result['completion'] = \ """\"You don't have to answer, I've seen it a million times." She pulls out a wallet and hands you a business card.<newline>DR. CHAE YEON-SEOK<newline>SPECIALIST<newline>[["Specialist?"|specialist]]<newline>[["..."|dotdotdot]]<|end|>""" return result # Usage: python src/external_model.py if __name__ == '__main__': mem = "This happened sometime ago now, but I still treasure this memory close to my heart. I've always wanted to execute the perfect date for a girl, and that day would come when I met my Loretta. One night, I really wanted to do something romantic for her. I'm twenty two but my ideas are rather old fashioned. I decided a surprise picnic would be a perfect date for Loretta. I thought she'd love it. I ended up secretly buying all her favorite foods I could think of, creating a playlist of all our favorite love songs, and packing her favorite blankets. Everything was going to plan. Well, until the day I decided to actually put my plan into motion. On the day of the picnic, I finally realized the one thing I forgot to do while planning this picnic, check the weather. It ended up pouring down hard that day. However, that didn't stop my plans. Instead of feeling defeated and calling it a night I decided to execute a quick plan B. I had brought Loretta to my room, and I began to set the stage. With the lights dimmed and a fake fireplace roaring on my tv, I turned on my blue tooth speaker and prepared the playlist I made. I had set up the blankets and cups, along with everything else I had ready for the picnic, and had everything laid out nicely on the clear floor. Loretta was overjoyed that I had gone through all this trouble for her. After we ate she snuggled up with me as we kissed and let the music play until it's end. We're still happily together and of course now I always remember to check the weather, but Loretta still says that night was very special to her, and it is to me as well! " # ex = "She was gorgeous and I was in love with her. When we made out for the first time, the world came to a halt. It literally blew up. No one survived, except for us." ex = "Long ago there lived a man in a room with an ipad. The man was obsessed with a game called townscaper. The game was played by desiging towns. There was no winning or losing in the game, it was purely for fun." sum = TwineGenerator.summarize(ex, False) print('summary: ', sum)

45.614907

1,616

0.651144

import openai import os import time import twee_utils as utils openai.api_key = os.getenv("OPENAI_API_KEY") ZERO_SHOT = open('src/zero_shot.txt', 'r').read() class TwineGenerator: def __init__(self, model='context', verbose=True): self.model = model.lower() self.verbose = bool(verbose) if self.model == 'events': self._call_model = TwineGenerator._call_contextual_event_model elif self.model == 'context': self._call_model = TwineGenerator._call_contextual_model elif self.model == 'mock': self._call_model = TwineGenerator._mock_generate elif self.model == 'naive': self._call_model = TwineGenerator._call_naive_model else: raise ValueError(f"TwineGenerator({self.model}) is invalid") def get_completion(self, prompt): if self.verbose: print("prompt", prompt) while True: try: return self._call_model(prompt) except openai.error.RateLimitError as e: print(e) print('retrying...') def _call_model(self, prompt): raise RuntimeError("This should have been defined in the constructor") @staticmethod def _call_naive_model(prompt): model = 'curie:ft-user-wmco7qacght9seweh8jgp4ib-2021-10-28-04-55-18' response = openai.Completion.create( model=model, prompt=prompt, temperature=0.7, max_tokens=1000, top_p=1, frequency_penalty=0, presence_penalty=0, stop=utils.END, ) return response['choices'][0]['text'] @staticmethod def _call_contextual_model(prompt): model = 'curie:ft-user-wmco7qacght9seweh8jgp4ib-2021-11-29-05-45-10' response = openai.Completion.create( model=model, prompt=prompt, temperature=0.7, max_tokens=1000, top_p=1, frequency_penalty=0, presence_penalty=0, stop=utils.END, ) return response['choices'][0]['text'] @staticmethod def _call_contextual_event_model(prompt): model = 'curie:ft-user-wmco7qacght9seweh8jgp4ib-2021-12-07-08-07-09' response = openai.Completion.create( model=model, prompt=prompt, temperature=0.7, max_tokens=1000, top_p=1, frequency_penalty=0, presence_penalty=0, stop=utils.END, ) return response['choices'][0]['text'] @staticmethod def summarize(passage, clean_passage=True): if clean_passage: passage = utils.passage_to_text(passage) prompt = ZERO_SHOT.format(passage.strip()) model = 'davinci' response = openai.Completion.create( model=model, prompt=prompt, temperature=0.7, max_tokens=280, top_p=1, frequency_penalty=0, presence_penalty=0, stop='.', ) response = response['choices'][0]['text'] return response.strip() + '.' @staticmethod def _mock_generate(prompt): time.sleep(1) result = {} result['completion'] = \ """\"You don't have to answer, I've seen it a million times." She pulls out a wallet and hands you a business card.<newline>DR. CHAE YEON-SEOK<newline>SPECIALIST<newline>[["Specialist?"|specialist]]<newline>[["..."|dotdotdot]]<|end|>""" return result # Usage: python src/external_model.py if __name__ == '__main__': mem = "This happened sometime ago now, but I still treasure this memory close to my heart. I've always wanted to execute the perfect date for a girl, and that day would come when I met my Loretta. One night, I really wanted to do something romantic for her. I'm twenty two but my ideas are rather old fashioned. I decided a surprise picnic would be a perfect date for Loretta. I thought she'd love it. I ended up secretly buying all her favorite foods I could think of, creating a playlist of all our favorite love songs, and packing her favorite blankets. Everything was going to plan. Well, until the day I decided to actually put my plan into motion. On the day of the picnic, I finally realized the one thing I forgot to do while planning this picnic, check the weather. It ended up pouring down hard that day. However, that didn't stop my plans. Instead of feeling defeated and calling it a night I decided to execute a quick plan B. I had brought Loretta to my room, and I began to set the stage. With the lights dimmed and a fake fireplace roaring on my tv, I turned on my blue tooth speaker and prepared the playlist I made. I had set up the blankets and cups, along with everything else I had ready for the picnic, and had everything laid out nicely on the clear floor. Loretta was overjoyed that I had gone through all this trouble for her. After we ate she snuggled up with me as we kissed and let the music play until it's end. We're still happily together and of course now I always remember to check the weather, but Loretta still says that night was very special to her, and it is to me as well! " # ex = "She was gorgeous and I was in love with her. When we made out for the first time, the world came to a halt. It literally blew up. No one survived, except for us." ex = "Long ago there lived a man in a room with an ipad. The man was obsessed with a game called townscaper. The game was played by desiging towns. There was no winning or losing in the game, it was purely for fun." sum = TwineGenerator.summarize(ex, False) print('summary: ', sum)

true

1c2e9132a54de644c28ae19522b0d1693090fb3f

10,968

py

Python

models/llc_score.py

litosly/RankingOptimizationApprochtoLLC

b6b79fb1364fcc481aa84093a8e8399966b19d02

[ "MIT" ]

5

2020-10-10T02:11:16.000Z

2021-11-08T22:55:34.000Z

models/llc_score.py

litosly/RankingOptimizationApprochtoLLC

b6b79fb1364fcc481aa84093a8e8399966b19d02

[ "MIT" ]

null

models/llc_score.py

litosly/RankingOptimizationApprochtoLLC

b6b79fb1364fcc481aa84093a8e8399966b19d02

[ "MIT" ]

2

2020-10-22T14:50:43.000Z

2021-08-15T07:00:43.000Z

from prediction.predictor import predict_scores, predict_vector from sklearn.linear_model import LinearRegression from utils.critique import LP1SimplifiedOptimize from utils.keyphrase_selection import * import numpy as np from tqdm import tqdm class LP1Simplified(object): def __init__(self, keyphrase_freq, item_keyphrase_freq, row, matrix_Train, matrix_Test, test_users, target_ranks, num_items_sampled, num_keyphrases, df, max_iteration_threshold, keyphrase_popularity, dataset_name, model, parameters_row, keyphrase_selection_method, topk=None, lamb = None,**unused): self.keyphrase_freq = keyphrase_freq self.item_keyphrase_freq = item_keyphrase_freq.T self.row = row self.matrix_Train = matrix_Train self.num_users, self.num_items = matrix_Train.shape self.matrix_Test = matrix_Test self.test_users = test_users self.target_ranks = target_ranks self.num_items_sampled = num_items_sampled self.num_keyphrases = num_keyphrases self.df = df self.max_iteration_threshold = max_iteration_threshold self.keyphrase_popularity = keyphrase_popularity self.dataset_name = dataset_name self.model = model self.parameters_row = parameters_row self.keyphrase_selection_method = keyphrase_selection_method self.max_wanted_keyphrase = max_iteration_threshold # Set diff length to be equal to max_iteration_threshold self.topk = topk def start_critiquing(self): self.get_initial_predictions() for user in tqdm(self.test_users): # User id starts from 0 self.row['user_id'] = user # The iteration will stop if the wanted item is in top n for target_rank in self.target_ranks: self.row['target_rank'] = target_rank # Pick wanted items in test items candidate_items = self.matrix_Test[user].nonzero()[1] train_items = self.matrix_Train[user].nonzero()[1] wanted_items = np.setdiff1d(candidate_items, train_items) for item in wanted_items: # Item id starts from 0 self.row['item_id'] = item # Set the wanted item's initial rank as None self.row['item_rank'] = None # Set the wanted item's initial prediction score as None self.row['item_score'] = None # Get the item's existing keyphrases item_keyphrases = self.item_keyphrase_freq[item].nonzero()[0] # Get keyphrases that don't belong to the item (we can critique) remaining_keyphrases = np.setdiff1d(range(self.num_keyphrases), item_keyphrases) # print("The number of remaining_keyphrases is {}. remaining_keyphrases are: {}".format(len(remaining_keyphrases), remaining_keyphrases)) self.row['num_existing_keyphrases'] = len(remaining_keyphrases) if self.keyphrase_selection_method == "diff": # For keyphrase selection method 'diff' target_keyphrase_freq = get_item_keyphrase_freq(self.item_keyphrase_freq,item = item) # diff_keyphrase_freq = top_recommended_keyphrase_freq - target_keyphrase_freq # remaining_keyphrases = np.argsort(np.ravel(diff_keyphrase_freq))[::-1][:self.max_wanted_keyphrase] self.row['num_existing_keyphrases'] = self.max_iteration_threshold if len(remaining_keyphrases) == 0: break self.row['iteration'] = 0 self.row['critiqued_keyphrase'] = None self.row['result'] = None self.df = self.df.append(self.row, ignore_index=True) query = [] affected_items = np.array([]) for iteration in range(self.max_iteration_threshold): self.row['iteration'] = iteration + 1 if self.keyphrase_selection_method == "pop": # Always critique the most popular keyphrase critiqued_keyphrase = remaining_keyphrases[np.argmax(self.keyphrase_popularity[remaining_keyphrases])] elif self.keyphrase_selection_method == "random": # Randomly critique a remaining keyphrase critiqued_keyphrase = np.random.choice(remaining_keyphrases, size=1, replace=False)[0] elif self.keyphrase_selection_method == "diff": if iteration == 0: initial_prediction_items = predict_vector(rating_vector=self.prediction_scores[user], train_vector=self.matrix_Train[user], remove_train=True) top_recommended_keyphrase_freq = get_item_keyphrase_freq(self.item_keyphrase_freq,item = initial_prediction_items[0]) self.row["Recommended Item"] = initial_prediction_items[0] # self.row['Recommended Item Name'] = get_restaurant_name(self.df_train, self.business_df,initial_prediction_items[0]) else: top_recommended_keyphrase_freq = get_item_keyphrase_freq(self.item_keyphrase_freq,item = prediction_items[0]) self.row["Recommended Item"] = prediction_items[0] # self.row['Recommended Item Name'] = get_restaurant_name(self.df_train, self.business_df,prediction_items[0]) diff_keyphrase_freq = top_recommended_keyphrase_freq - target_keyphrase_freq remaining_keyphrases = np.argsort(np.ravel(diff_keyphrase_freq))[::-1][:self.max_wanted_keyphrase] critiqued_keyphrase = remaining_keyphrases[0] self.row['critiqued_keyphrase'] = critiqued_keyphrase query.append(critiqued_keyphrase) # Get affected items (items have critiqued keyphrase) current_affected_items = self.item_keyphrase_freq[:, critiqued_keyphrase].nonzero()[0] affected_items = np.unique(np.concatenate((affected_items, current_affected_items))).astype(int) unaffected_items = np.setdiff1d(range(self.num_items), affected_items) if iteration == 0: prediction_items = predict_vector(rating_vector=self.prediction_scores[user], train_vector=self.matrix_Train[user], remove_train=True) affected_items_mask = np.in1d(prediction_items, affected_items) affected_items_index_rank = np.where(affected_items_mask == True) unaffected_items_index_rank = np.where(affected_items_mask == False) import copy top_k = int(self.topk) prediction_scores_u, lambdas = LP1SimplifiedOptimize(initial_prediction_u=self.prediction_scores[user], keyphrase_freq=copy.deepcopy(self.keyphrase_freq), affected_items=np.intersect1d(affected_items, prediction_items[affected_items_index_rank[0][:top_k]]), unaffected_items=np.intersect1d(unaffected_items, prediction_items[unaffected_items_index_rank[0][:top_k]]), num_keyphrases=self.num_keyphrases, query=query, test_user=user, item_latent=self.Y, reg=self.reg) self.row['lambda'] = lambdas prediction_items = predict_vector(rating_vector=prediction_scores_u, train_vector=self.matrix_Train[user], remove_train=True) recommended_items = prediction_items # Current item rank item_rank = np.where(recommended_items == item)[0][0] self.row['item_rank'] = item_rank self.row['item_score'] = prediction_scores_u[item] if item_rank + 1 <= target_rank: # Items is ranked within target rank self.row['result'] = 'successful' self.df = self.df.append(self.row, ignore_index=True) break else: remaining_keyphrases = np.setdiff1d(remaining_keyphrases, critiqued_keyphrase) # Continue if more keyphrases and iterations remained if len(remaining_keyphrases) > 0 and self.row['iteration'] < self.max_iteration_threshold: self.row['result'] = None self.df = self.df.append(self.row, ignore_index=True) else: # Otherwise, mark fail self.row['result'] = 'fail' self.df = self.df.append(self.row, ignore_index=True) break return self.df def get_initial_predictions(self): self.RQ, Yt, Bias = self.model(self.matrix_Train, iteration=self.parameters_row['iter'].values[0], lamb=self.parameters_row['lambda'].values[0], rank=self.parameters_row['rank'].values[0]) self.Y = Yt.T self.reg = LinearRegression().fit(self.keyphrase_freq, self.RQ) self.prediction_scores = predict_scores(matrix_U=self.RQ, matrix_V=self.Y, bias=Bias)

60.596685

185

0.535284

from prediction.predictor import predict_scores, predict_vector from sklearn.linear_model import LinearRegression from utils.critique import LP1SimplifiedOptimize from utils.keyphrase_selection import * import numpy as np from tqdm import tqdm class LP1Simplified(object): def __init__(self, keyphrase_freq, item_keyphrase_freq, row, matrix_Train, matrix_Test, test_users, target_ranks, num_items_sampled, num_keyphrases, df, max_iteration_threshold, keyphrase_popularity, dataset_name, model, parameters_row, keyphrase_selection_method, topk=None, lamb = None,**unused): self.keyphrase_freq = keyphrase_freq self.item_keyphrase_freq = item_keyphrase_freq.T self.row = row self.matrix_Train = matrix_Train self.num_users, self.num_items = matrix_Train.shape self.matrix_Test = matrix_Test self.test_users = test_users self.target_ranks = target_ranks self.num_items_sampled = num_items_sampled self.num_keyphrases = num_keyphrases self.df = df self.max_iteration_threshold = max_iteration_threshold self.keyphrase_popularity = keyphrase_popularity self.dataset_name = dataset_name self.model = model self.parameters_row = parameters_row self.keyphrase_selection_method = keyphrase_selection_method self.max_wanted_keyphrase = max_iteration_threshold self.topk = topk def start_critiquing(self): self.get_initial_predictions() for user in tqdm(self.test_users): self.row['user_id'] = user for target_rank in self.target_ranks: self.row['target_rank'] = target_rank candidate_items = self.matrix_Test[user].nonzero()[1] train_items = self.matrix_Train[user].nonzero()[1] wanted_items = np.setdiff1d(candidate_items, train_items) for item in wanted_items: self.row['item_id'] = item self.row['item_rank'] = None # Set the wanted item's initial prediction score as None self.row['item_score'] = None item_keyphrases = self.item_keyphrase_freq[item].nonzero()[0] # Get keyphrases that don't belong to the item (we can critique) remaining_keyphrases = np.setdiff1d(range(self.num_keyphrases), item_keyphrases) self.row['num_existing_keyphrases'] = len(remaining_keyphrases) if self.keyphrase_selection_method == "diff": target_keyphrase_freq = get_item_keyphrase_freq(self.item_keyphrase_freq,item = item) self.row['num_existing_keyphrases'] = self.max_iteration_threshold if len(remaining_keyphrases) == 0: break self.row['iteration'] = 0 self.row['critiqued_keyphrase'] = None self.row['result'] = None self.df = self.df.append(self.row, ignore_index=True) query = [] affected_items = np.array([]) for iteration in range(self.max_iteration_threshold): self.row['iteration'] = iteration + 1 if self.keyphrase_selection_method == "pop": critiqued_keyphrase = remaining_keyphrases[np.argmax(self.keyphrase_popularity[remaining_keyphrases])] elif self.keyphrase_selection_method == "random": critiqued_keyphrase = np.random.choice(remaining_keyphrases, size=1, replace=False)[0] elif self.keyphrase_selection_method == "diff": if iteration == 0: initial_prediction_items = predict_vector(rating_vector=self.prediction_scores[user], train_vector=self.matrix_Train[user], remove_train=True) top_recommended_keyphrase_freq = get_item_keyphrase_freq(self.item_keyphrase_freq,item = initial_prediction_items[0]) self.row["Recommended Item"] = initial_prediction_items[0] else: top_recommended_keyphrase_freq = get_item_keyphrase_freq(self.item_keyphrase_freq,item = prediction_items[0]) self.row["Recommended Item"] = prediction_items[0] diff_keyphrase_freq = top_recommended_keyphrase_freq - target_keyphrase_freq remaining_keyphrases = np.argsort(np.ravel(diff_keyphrase_freq))[::-1][:self.max_wanted_keyphrase] critiqued_keyphrase = remaining_keyphrases[0] self.row['critiqued_keyphrase'] = critiqued_keyphrase query.append(critiqued_keyphrase) current_affected_items = self.item_keyphrase_freq[:, critiqued_keyphrase].nonzero()[0] affected_items = np.unique(np.concatenate((affected_items, current_affected_items))).astype(int) unaffected_items = np.setdiff1d(range(self.num_items), affected_items) if iteration == 0: prediction_items = predict_vector(rating_vector=self.prediction_scores[user], train_vector=self.matrix_Train[user], remove_train=True) affected_items_mask = np.in1d(prediction_items, affected_items) affected_items_index_rank = np.where(affected_items_mask == True) unaffected_items_index_rank = np.where(affected_items_mask == False) import copy top_k = int(self.topk) prediction_scores_u, lambdas = LP1SimplifiedOptimize(initial_prediction_u=self.prediction_scores[user], keyphrase_freq=copy.deepcopy(self.keyphrase_freq), affected_items=np.intersect1d(affected_items, prediction_items[affected_items_index_rank[0][:top_k]]), unaffected_items=np.intersect1d(unaffected_items, prediction_items[unaffected_items_index_rank[0][:top_k]]), num_keyphrases=self.num_keyphrases, query=query, test_user=user, item_latent=self.Y, reg=self.reg) self.row['lambda'] = lambdas prediction_items = predict_vector(rating_vector=prediction_scores_u, train_vector=self.matrix_Train[user], remove_train=True) recommended_items = prediction_items item_rank = np.where(recommended_items == item)[0][0] self.row['item_rank'] = item_rank self.row['item_score'] = prediction_scores_u[item] if item_rank + 1 <= target_rank: self.row['result'] = 'successful' self.df = self.df.append(self.row, ignore_index=True) break else: remaining_keyphrases = np.setdiff1d(remaining_keyphrases, critiqued_keyphrase) if len(remaining_keyphrases) > 0 and self.row['iteration'] < self.max_iteration_threshold: self.row['result'] = None self.df = self.df.append(self.row, ignore_index=True) else: self.row['result'] = 'fail' self.df = self.df.append(self.row, ignore_index=True) break return self.df def get_initial_predictions(self): self.RQ, Yt, Bias = self.model(self.matrix_Train, iteration=self.parameters_row['iter'].values[0], lamb=self.parameters_row['lambda'].values[0], rank=self.parameters_row['rank'].values[0]) self.Y = Yt.T self.reg = LinearRegression().fit(self.keyphrase_freq, self.RQ) self.prediction_scores = predict_scores(matrix_U=self.RQ, matrix_V=self.Y, bias=Bias)

true

1c2e91ad8c46cbfb50cd64e65f5ea767cbea1915

4,982

py

Python

sld-api-backend/helpers/get_data.py

guorenxi/Stack-Lifecycle-Deployment

2780441cb692392993116cf94c14a94ae8edbc6c

[ "MIT" ]

null

sld-api-backend/helpers/get_data.py

guorenxi/Stack-Lifecycle-Deployment

2780441cb692392993116cf94c14a94ae8edbc6c

[ "MIT" ]

null

sld-api-backend/helpers/get_data.py

guorenxi/Stack-Lifecycle-Deployment

2780441cb692392993116cf94c14a94ae8edbc6c

[ "MIT" ]

null

from fastapi import HTTPException from celery.result import AsyncResult from datetime import datetime from functools import wraps from croniter import croniter import redis from crud import stacks as crud_stacks from crud import deploys as crud_deploys from crud import user as crud_users from crud import activityLogs as crud_activity from config.api import settings r = redis.Redis(host=settings.BACKEND_SERVER, port=6379, db=2, charset="utf-8", decode_responses=True) def user_squad_scope(db, user, squad): try: if user.isdigit(): user_info = crud_users.get_user_by_id(db=db, id=user) else: user_info = crud_users.get_user_by_username(db=db, username=user) if user_info == None: raise ValueError(f"User {user} no exists") if user_info.squad == squad: return True except Exception as err: raise HTTPException( status_code=400, detail=str(err)) def stack(db, stack_name: str): try: stack_data = crud_stacks.get_stack_by_name( db=db, stack_name=stack_name) if stack_data is None: raise Exception("Stack Name Not Found") return stack_data except Exception as err: raise HTTPException( status_code=404, detail=f"{err}") def deploy(db, deploy_id: int): try: deploy_data = crud_deploys.get_deploy_by_id( db=db, deploy_id=deploy_id) if deploy_data is None: raise Exception("Deploy id Not Found") return deploy_data except Exception as err: raise HTTPException( status_code=404, detail=f"{err}") def deploy_squad(db, deploy_id: int, squad: str): try: deploy_data = crud_deploys.get_deploy_by_id_squad( db=db, deploy_id=deploy_id, squad=squad) if deploy_data is None: raise Exception("Deploy id Not Found") return deploy_data except Exception as err: raise HTTPException( status_code=404, detail=f"{err}") def get_deploy(db, deploy_id: int): try: deploy_data = crud_deploys.get_deploy_by_id( db=db, deploy_id=deploy_id) if deploy_data is None: raise Exception("Deploy id Not Found") return deploy_data except Exception as err: raise HTTPException( status_code=404, detail=f"{err}") def check_deploy_exist(db, deploy_name: str, squad: str, env: str, stack: str): data_source_check = f'{deploy_name}-{squad}-{env}-{stack}' try: db_data = crud_deploys.get_deploy_by_name_squad( db=db, deploy_name=deploy_name, squad=squad, environment=env) if db_data is not None: data_db_check = f'{db_data.name}-{db_data.squad}-{db_data.environment}-{db_data.stack_name}' if data_source_check == data_db_check: raise Exception( "The name of the deployment already exists in the current squad and with specified environment") except Exception as err: raise HTTPException( status_code=409, detail=f"{err}") def check_deploy_state(task_id: str): result = AsyncResult(task_id) list_state = ["SUCCESS", "FAILURE", "REVOKED", "PENDING"] return any(result.state == i for i in list_state) def check_deploy_task_pending_state(deploy_name, squad, environment, task_id=None): if task_id: result = AsyncResult(str(task_id)) list_state = ["REVOKED"] if any(result.state == i for i in list_state): return True try: if r.exists(f"{deploy_name}-{squad}-{environment}"): raise Exception("Task already exists in pending state waiting to be executed") except Exception as err: raise HTTPException( status_code=409, detail=f"{err}") r.set(f"{deploy_name}-{squad}-{environment}", "Locked") r.expire(f"{deploy_name}-{squad}-{environment}", settings.TASK_LOCKED_EXPIRED) def check_providers(stack_name): providers_support = settings.PROVIDERS_SUPPORT if any(i in stack_name.lower() for i in providers_support): return True else: raise HTTPException( status_code=404, detail=f"stack name {stack_name.lower()} no content providers support name preffix: {providers_support}") def activity_log(func): @wraps(func) async def wrapper(*args, **kwargs): crud_activity.create_activity_log( db=kwargs['db'], username=kwargs['current_user'].username, squad=kwargs['current_user'].squad, action=f'Delete User {kwargs["user"]}' ) return await func(*args, **kwargs) return wrapper def check_cron_schedule(cron_time: str): if cron_time: if not croniter.is_valid(cron_time): raise ValueError("Cron time its no valid") return True

32.776316

117

0.642914

from fastapi import HTTPException from celery.result import AsyncResult from datetime import datetime from functools import wraps from croniter import croniter import redis from crud import stacks as crud_stacks from crud import deploys as crud_deploys from crud import user as crud_users from crud import activityLogs as crud_activity from config.api import settings r = redis.Redis(host=settings.BACKEND_SERVER, port=6379, db=2, charset="utf-8", decode_responses=True) def user_squad_scope(db, user, squad): try: if user.isdigit(): user_info = crud_users.get_user_by_id(db=db, id=user) else: user_info = crud_users.get_user_by_username(db=db, username=user) if user_info == None: raise ValueError(f"User {user} no exists") if user_info.squad == squad: return True except Exception as err: raise HTTPException( status_code=400, detail=str(err)) def stack(db, stack_name: str): try: stack_data = crud_stacks.get_stack_by_name( db=db, stack_name=stack_name) if stack_data is None: raise Exception("Stack Name Not Found") return stack_data except Exception as err: raise HTTPException( status_code=404, detail=f"{err}") def deploy(db, deploy_id: int): try: deploy_data = crud_deploys.get_deploy_by_id( db=db, deploy_id=deploy_id) if deploy_data is None: raise Exception("Deploy id Not Found") return deploy_data except Exception as err: raise HTTPException( status_code=404, detail=f"{err}") def deploy_squad(db, deploy_id: int, squad: str): try: deploy_data = crud_deploys.get_deploy_by_id_squad( db=db, deploy_id=deploy_id, squad=squad) if deploy_data is None: raise Exception("Deploy id Not Found") return deploy_data except Exception as err: raise HTTPException( status_code=404, detail=f"{err}") def get_deploy(db, deploy_id: int): try: deploy_data = crud_deploys.get_deploy_by_id( db=db, deploy_id=deploy_id) if deploy_data is None: raise Exception("Deploy id Not Found") return deploy_data except Exception as err: raise HTTPException( status_code=404, detail=f"{err}") def check_deploy_exist(db, deploy_name: str, squad: str, env: str, stack: str): data_source_check = f'{deploy_name}-{squad}-{env}-{stack}' try: db_data = crud_deploys.get_deploy_by_name_squad( db=db, deploy_name=deploy_name, squad=squad, environment=env) if db_data is not None: data_db_check = f'{db_data.name}-{db_data.squad}-{db_data.environment}-{db_data.stack_name}' if data_source_check == data_db_check: raise Exception( "The name of the deployment already exists in the current squad and with specified environment") except Exception as err: raise HTTPException( status_code=409, detail=f"{err}") def check_deploy_state(task_id: str): result = AsyncResult(task_id) list_state = ["SUCCESS", "FAILURE", "REVOKED", "PENDING"] return any(result.state == i for i in list_state) def check_deploy_task_pending_state(deploy_name, squad, environment, task_id=None): if task_id: result = AsyncResult(str(task_id)) list_state = ["REVOKED"] if any(result.state == i for i in list_state): return True try: if r.exists(f"{deploy_name}-{squad}-{environment}"): raise Exception("Task already exists in pending state waiting to be executed") except Exception as err: raise HTTPException( status_code=409, detail=f"{err}") r.set(f"{deploy_name}-{squad}-{environment}", "Locked") r.expire(f"{deploy_name}-{squad}-{environment}", settings.TASK_LOCKED_EXPIRED) def check_providers(stack_name): providers_support = settings.PROVIDERS_SUPPORT if any(i in stack_name.lower() for i in providers_support): return True else: raise HTTPException( status_code=404, detail=f"stack name {stack_name.lower()} no content providers support name preffix: {providers_support}") def activity_log(func): @wraps(func) async def wrapper(*args, **kwargs): crud_activity.create_activity_log( db=kwargs['db'], username=kwargs['current_user'].username, squad=kwargs['current_user'].squad, action=f'Delete User {kwargs["user"]}' ) return await func(*args, **kwargs) return wrapper def check_cron_schedule(cron_time: str): if cron_time: if not croniter.is_valid(cron_time): raise ValueError("Cron time its no valid") return True

true

1c2e91e14d21753133705a1872b6a3cc03cf96d4

1,675

py

Python

Autoencoding/save32by32ForAll.py

MiningMyBusiness/SketchyExperiments

11f3baee63a984501b9707aa58420540b19db59f

[ "MIT" ]

2

2019-01-31T19:56:50.000Z

2019-03-21T12:26:57.000Z

Autoencoding/save32by32ForAll.py

MiningMyBusiness/SketchyExperiments

11f3baee63a984501b9707aa58420540b19db59f

[ "MIT" ]

null

Autoencoding/save32by32ForAll.py

MiningMyBusiness/SketchyExperiments

11f3baee63a984501b9707aa58420540b19db59f

[ "MIT" ]

null

## Name: Save 32 by 32 image vector ## Description: Reads in the matrix representation of the images ## for each class and resizes each image from 256 by 256 ## to 32 by 32 and then converts it to a vector with ## 1024 elements (32*32). A stack of these vectors (matrix) ## is then saved in the class directory. ## NOTE: This code uses the output from PNGtoNumpy.py ## Author: Kiran Bhattacharyya import glob import numpy as np import matplotlib.pyplot as plt import cv2 # location of the data dataLoc = '256X256/sketch/tx_000100000000' allFolders = glob.glob(dataLoc + '/*') # get all folders with data newSize = 32 def readImgAndResize(folderName, multi, newSize): imgStack_mat = np.load(folder + '/matStack.npy') num_of_images = imgStack_mat.shape[2] smallerImgs = np.zeros((num_of_images, newSize**2)) for i in range(0, int(multi*num_of_images)): smallerImgs[i, :] = resizeAndVectorize(imgStack_mat[:, :, i], newSize) return smallerImgs def resizeAndVectorize(thisImg, newSize): resized = cv2.resize(thisImg, (newSize, newSize), interpolation=cv2.INTER_AREA) resized = np.reshape(resized, newSize**2) resized = resized - np.min(resized) resized = resized/np.max(resized) return resized folderCount = 0 iter = 0 multi = 1.0 allData = [] for folder in allFolders: print('Folder ' + str(folderCount+1) + ' of ' + str(len(allFolders)) + '.') smallerImgs = readImgAndResize(folder, multi, newSize) np.save(folder + '/32by32vecStack.npy', smallerImgs) folderCount += 1

34.895833

80

0.654328

smallerImgs[i, :] = resizeAndVectorize(imgStack_mat[:, :, i], newSize) return smallerImgs def resizeAndVectorize(thisImg, newSize): resized = cv2.resize(thisImg, (newSize, newSize), interpolation=cv2.INTER_AREA) resized = np.reshape(resized, newSize**2) resized = resized - np.min(resized) resized = resized/np.max(resized) return resized folderCount = 0 iter = 0 multi = 1.0 allData = [] for folder in allFolders: print('Folder ' + str(folderCount+1) + ' of ' + str(len(allFolders)) + '.') smallerImgs = readImgAndResize(folder, multi, newSize) np.save(folder + '/32by32vecStack.npy', smallerImgs) folderCount += 1

true

1c2e9290459d49bd1262122f8dd1051edf6b6a31

1,516

py

Python

crypto_data_app/__init__.py

gldeng/crypto-data-app

4460ffd2417a02605862b66073e172519ea5fbd7

[ "MIT" ]

null

crypto_data_app/__init__.py

gldeng/crypto-data-app

4460ffd2417a02605862b66073e172519ea5fbd7

[ "MIT" ]

null

crypto_data_app/__init__.py

gldeng/crypto-data-app

4460ffd2417a02605862b66073e172519ea5fbd7

[ "MIT" ]

null

from flask_redis import FlaskRedis from flask import Flask from flask_restful import Resource, Api from datetime import datetime def create_app(config_pyfile): app = Flask(__name__, instance_relative_config=False) app.config.from_pyfile(config_pyfile) redis_store = FlaskRedis(app, strict=False) api = Api(app) class Pairs(Resource): def get(self): return sorted(list(set(map(lambda x: x.split(':')[0], redis_store.keys())))) class OnePair(Resource): def get(self, pair): keys = redis_store.keys('%s*' % pair) exchanges = [] min_ = None max_ = None for k in keys: d = redis_store.hgetall(k) if 'trades_date_time' not in d: continue ts = datetime.strptime(d['trades_date_time'], '%Y%m%d %H:%M:%S.%f') if (datetime.utcnow() - ts).total_seconds() >= 600: # exclude if older than 10 minutes continue price = float(d['trade_px']) if min_ is None or price < min_: min_ = price if max_ is None or price > max_: max_ = price exchanges.append(d) return { 'min': min_, 'max': max_, 'exchanges': exchanges } api.add_resource(Pairs, '/pairs') api.add_resource(OnePair, '/pairs/<pair>') return app

31.583333

88

0.525066

from flask_redis import FlaskRedis from flask import Flask from flask_restful import Resource, Api from datetime import datetime def create_app(config_pyfile): app = Flask(__name__, instance_relative_config=False) app.config.from_pyfile(config_pyfile) redis_store = FlaskRedis(app, strict=False) api = Api(app) class Pairs(Resource): def get(self): return sorted(list(set(map(lambda x: x.split(':')[0], redis_store.keys())))) class OnePair(Resource): def get(self, pair): keys = redis_store.keys('%s*' % pair) exchanges = [] min_ = None max_ = None for k in keys: d = redis_store.hgetall(k) if 'trades_date_time' not in d: continue ts = datetime.strptime(d['trades_date_time'], '%Y%m%d %H:%M:%S.%f') if (datetime.utcnow() - ts).total_seconds() >= 600: continue price = float(d['trade_px']) if min_ is None or price < min_: min_ = price if max_ is None or price > max_: max_ = price exchanges.append(d) return { 'min': min_, 'max': max_, 'exchanges': exchanges } api.add_resource(Pairs, '/pairs') api.add_resource(OnePair, '/pairs/<pair>') return app

true

1c2e930bbcde5c690c5b9f48f7f7e63d0c74e976

2,587

py

Python

Space Invaders/utilities/deus_ex.py

msutic/python_project_alpha

5fed153babf294ce5f5714de8f711b1a5943d2d5

[ "MIT" ]

1

2021-01-08T00:38:02.000Z

Space Invaders/utilities/deus_ex.py

msutic/python_project_alpha

5fed153babf294ce5f5714de8f711b1a5943d2d5

[ "MIT" ]

4

2021-01-12T20:07:19.000Z

2021-03-19T22:21:31.000Z

Space Invaders/utilities/deus_ex.py

msutic/python_project_alpha

5fed153babf294ce5f5714de8f711b1a5943d2d5

[ "MIT" ]

null

from time import time, sleep from PyQt5.QtCore import pyqtSignal, QThread, pyqtSlot from PyQt5.QtWidgets import QLabel from config import cfg class DeusEx(QThread): empower = pyqtSignal(QLabel) collision_occured = pyqtSignal(QLabel, QLabel, int) def __init__(self): super().__init__() self.is_not_done = True self.powers = [] self.index = 0 self.players = [] def add_power(self, power: QLabel, index: int): self.powers.append(power) self.index = index self.time_added = time() def rem_power(self, power: QLabel): self.powers.remove(power) def add_player(self, player: QLabel): self.players.append(player) def rem_player(self, player: QLabel): self.players.remove(player) @pyqtSlot() def run(self): while self.is_not_done: collided = False time_now = time() if len(self.powers) > 0: if time_now - self.time_added > 2: for power in self.powers: self.powers.remove(power) self.empower.emit(power) for player in self.players: player_xy_begin = [player.geometry().x(), player.geometry().y()] player_xy_end = [ player.geometry().x() + cfg.SPACESHIP_WIDTH, player.geometry().y() + cfg.SPACESHIP_HEIGHT ] player_x_coordinates = range(player_xy_begin[0], player_xy_end[0]) player_y_coordinates = range(player_xy_begin[1], player_xy_end[1]) for power in self.powers: power_xy_begin = [power.geometry().x(), power.geometry().y()] power_xy_end = [power.geometry().x() + 30, power.geometry().y() + 30] power_x_coords = range(power_xy_begin[0], power_xy_end[0]) power_y_coords = range(power_xy_begin[1], power_xy_end[1]) for player_y in player_y_coordinates: if collided: break if player_y in power_y_coords: for player_x in player_x_coordinates: if player_x in power_x_coords: self.rem_power(power) self.collision_occured.emit(player, power, self.index) collided = True break sleep(0.05)

33.166667

90

0.526479

from time import time, sleep from PyQt5.QtCore import pyqtSignal, QThread, pyqtSlot from PyQt5.QtWidgets import QLabel from config import cfg class DeusEx(QThread): empower = pyqtSignal(QLabel) collision_occured = pyqtSignal(QLabel, QLabel, int) def __init__(self): super().__init__() self.is_not_done = True self.powers = [] self.index = 0 self.players = [] def add_power(self, power: QLabel, index: int): self.powers.append(power) self.index = index self.time_added = time() def rem_power(self, power: QLabel): self.powers.remove(power) def add_player(self, player: QLabel): self.players.append(player) def rem_player(self, player: QLabel): self.players.remove(player) @pyqtSlot() def run(self): while self.is_not_done: collided = False time_now = time() if len(self.powers) > 0: if time_now - self.time_added > 2: for power in self.powers: self.powers.remove(power) self.empower.emit(power) for player in self.players: player_xy_begin = [player.geometry().x(), player.geometry().y()] player_xy_end = [ player.geometry().x() + cfg.SPACESHIP_WIDTH, player.geometry().y() + cfg.SPACESHIP_HEIGHT ] player_x_coordinates = range(player_xy_begin[0], player_xy_end[0]) player_y_coordinates = range(player_xy_begin[1], player_xy_end[1]) for power in self.powers: power_xy_begin = [power.geometry().x(), power.geometry().y()] power_xy_end = [power.geometry().x() + 30, power.geometry().y() + 30] power_x_coords = range(power_xy_begin[0], power_xy_end[0]) power_y_coords = range(power_xy_begin[1], power_xy_end[1]) for player_y in player_y_coordinates: if collided: break if player_y in power_y_coords: for player_x in player_x_coordinates: if player_x in power_x_coords: self.rem_power(power) self.collision_occured.emit(player, power, self.index) collided = True break sleep(0.05)

true

1c2e93b8c4720408303fc37f202fa46b4a4ef25f

5,329

py

Python

confidnet/loaders/loader.py

luoyan407/predict_trustworthiness_smallscale

b7e1e2a68b0aee9b484228d1b5686f7252919e97

[ "Apache-2.0" ]

149

2019-10-01T14:04:05.000Z

2022-03-24T12:25:15.000Z

confidnet/loaders/loader.py

luoyan407/predict_trustworthiness_smallscale

b7e1e2a68b0aee9b484228d1b5686f7252919e97

[ "Apache-2.0" ]

10

2019-12-12T09:45:50.000Z

2021-12-27T04:45:22.000Z

confidnet/loaders/loader.py

luoyan407/predict_trustworthiness_smallscale

b7e1e2a68b0aee9b484228d1b5686f7252919e97

[ "Apache-2.0" ]

30

2019-12-02T16:25:22.000Z

2022-02-16T10:48:47.000Z

from pathlib import Path import numpy as np import torch from torch.utils.data.sampler import SubsetRandomSampler from confidnet.augmentations import get_composed_augmentations from confidnet.utils.logger import get_logger LOGGER = get_logger(__name__, level="DEBUG") class AbstractDataLoader: def __init__(self, config_args): self.output_folder = config_args['training']['output_folder'] self.data_dir = config_args['data']['data_dir'] self.batch_size = config_args['training']['batch_size'] self.img_size = (config_args['data']['input_size'][0], config_args['data']['input_size'][1], config_args['data']['input_channels']) self.augmentations = config_args['training'].get('augmentations', None) self.ft_on_val = config_args['training'].get('ft_on_val', None) self.resume_folder = config_args['model']['resume'].parent if isinstance(config_args['model']['resume'], Path) else None self.valid_size = config_args['data']['valid_size'] self.perturbed_folder = config_args['data'].get('perturbed_images', None) self.pin_memory = config_args['training']['pin_memory'] self.num_workers = config_args['training']['num_workers'] self.train_loader, self.val_loader, self.test_loader = None, None, None # Set up augmentations self.augmentations_train, self.augmentations_train_lbl = None, None self.augmentations_test, self.augmentations_test_lbl = None, None if self.augmentations: LOGGER.info("--- Augmentations ---") self.add_augmentations() # Load dataset self.train_dataset, self.val_dataset, self.test_dataset = None, None, None self.load_dataset() def add_augmentations(self): self.augmentations_train = get_composed_augmentations( self.augmentations, training="classif" ) self.augmentations_train_lbl = get_composed_augmentations( { key: self.augmentations[key] for key in self.augmentations if key not in ["normalize", "color_jitter"] }, verbose=False, training="classif", ) self.augmentations_test = get_composed_augmentations( {key: self.augmentations[key] for key in self.augmentations if key == "normalize"}, verbose=False, training="classif", ) self.augmentations_test_lbl = get_composed_augmentations( None, verbose=False, training="classif" ) def load_dataset(self): pass def make_loaders(self): self.test_loader = torch.utils.data.DataLoader( self.test_dataset, batch_size=self.batch_size, shuffle=False, pin_memory=self.pin_memory, num_workers=self.num_workers, ) if self.valid_size == 0: LOGGER.warning("Valid size=0, no validation loader") self.train_loader = torch.utils.data.DataLoader( self.train_dataset, batch_size=self.batch_size, shuffle=True, pin_memory=self.pin_memory, num_workers=self.num_workers, ) else: num_train = len(self.train_dataset) indices = list(range(num_train)) if (self.output_folder / "train_idx.npy").exists(): LOGGER.warning("Loading existing train-val split indices") train_idx = np.load(self.output_folder / "train_idx.npy") val_idx = np.load(self.output_folder / "val_idx.npy") # Splitting indices elif self.resume_folder: LOGGER.warning("Loading existing train-val split indices from ORIGINAL training") train_idx = np.load(self.resume_folder / "train_idx.npy") val_idx = np.load(self.resume_folder / "val_idx.npy") else: split = int(np.floor(self.valid_size * num_train)) np.random.seed(42) np.random.shuffle(indices) train_idx, val_idx = indices[split:], indices[:split] np.save(self.output_folder / "train_idx.npy", train_idx) np.save(self.output_folder / "val_idx.npy", val_idx) # Make samplers train_sampler = SubsetRandomSampler(train_idx) val_sampler = SubsetRandomSampler(val_idx) # Special case where val set is used for training if self.ft_on_val: LOGGER.warning("Using val set as training") train_sampler = val_sampler # Make loaders self.train_loader = torch.utils.data.DataLoader( dataset=self.train_dataset, batch_size=self.batch_size, sampler=train_sampler, pin_memory=self.pin_memory, num_workers=self.num_workers, ) self.val_loader = torch.utils.data.DataLoader( dataset=self.train_dataset, batch_size=self.batch_size, sampler=val_sampler, pin_memory=self.pin_memory, num_workers=self.num_workers, )

42.632

128

0.605555

from pathlib import Path import numpy as np import torch from torch.utils.data.sampler import SubsetRandomSampler from confidnet.augmentations import get_composed_augmentations from confidnet.utils.logger import get_logger LOGGER = get_logger(__name__, level="DEBUG") class AbstractDataLoader: def __init__(self, config_args): self.output_folder = config_args['training']['output_folder'] self.data_dir = config_args['data']['data_dir'] self.batch_size = config_args['training']['batch_size'] self.img_size = (config_args['data']['input_size'][0], config_args['data']['input_size'][1], config_args['data']['input_channels']) self.augmentations = config_args['training'].get('augmentations', None) self.ft_on_val = config_args['training'].get('ft_on_val', None) self.resume_folder = config_args['model']['resume'].parent if isinstance(config_args['model']['resume'], Path) else None self.valid_size = config_args['data']['valid_size'] self.perturbed_folder = config_args['data'].get('perturbed_images', None) self.pin_memory = config_args['training']['pin_memory'] self.num_workers = config_args['training']['num_workers'] self.train_loader, self.val_loader, self.test_loader = None, None, None self.augmentations_train, self.augmentations_train_lbl = None, None self.augmentations_test, self.augmentations_test_lbl = None, None if self.augmentations: LOGGER.info("--- Augmentations ---") self.add_augmentations() self.train_dataset, self.val_dataset, self.test_dataset = None, None, None self.load_dataset() def add_augmentations(self): self.augmentations_train = get_composed_augmentations( self.augmentations, training="classif" ) self.augmentations_train_lbl = get_composed_augmentations( { key: self.augmentations[key] for key in self.augmentations if key not in ["normalize", "color_jitter"] }, verbose=False, training="classif", ) self.augmentations_test = get_composed_augmentations( {key: self.augmentations[key] for key in self.augmentations if key == "normalize"}, verbose=False, training="classif", ) self.augmentations_test_lbl = get_composed_augmentations( None, verbose=False, training="classif" ) def load_dataset(self): pass def make_loaders(self): self.test_loader = torch.utils.data.DataLoader( self.test_dataset, batch_size=self.batch_size, shuffle=False, pin_memory=self.pin_memory, num_workers=self.num_workers, ) if self.valid_size == 0: LOGGER.warning("Valid size=0, no validation loader") self.train_loader = torch.utils.data.DataLoader( self.train_dataset, batch_size=self.batch_size, shuffle=True, pin_memory=self.pin_memory, num_workers=self.num_workers, ) else: num_train = len(self.train_dataset) indices = list(range(num_train)) if (self.output_folder / "train_idx.npy").exists(): LOGGER.warning("Loading existing train-val split indices") train_idx = np.load(self.output_folder / "train_idx.npy") val_idx = np.load(self.output_folder / "val_idx.npy") elif self.resume_folder: LOGGER.warning("Loading existing train-val split indices from ORIGINAL training") train_idx = np.load(self.resume_folder / "train_idx.npy") val_idx = np.load(self.resume_folder / "val_idx.npy") else: split = int(np.floor(self.valid_size * num_train)) np.random.seed(42) np.random.shuffle(indices) train_idx, val_idx = indices[split:], indices[:split] np.save(self.output_folder / "train_idx.npy", train_idx) np.save(self.output_folder / "val_idx.npy", val_idx) train_sampler = SubsetRandomSampler(train_idx) val_sampler = SubsetRandomSampler(val_idx) if self.ft_on_val: LOGGER.warning("Using val set as training") train_sampler = val_sampler self.train_loader = torch.utils.data.DataLoader( dataset=self.train_dataset, batch_size=self.batch_size, sampler=train_sampler, pin_memory=self.pin_memory, num_workers=self.num_workers, ) self.val_loader = torch.utils.data.DataLoader( dataset=self.train_dataset, batch_size=self.batch_size, sampler=val_sampler, pin_memory=self.pin_memory, num_workers=self.num_workers, )

true

1c2e9443cf5eb3a35aeb948b351e7d6cdc5b1396

13,537

py

Python

plugins/modules/oci_analytics_private_access_channel.py

slmjy/oci-ansible-collection

349c91e2868bf4706a6e3d6fb3b47fc622bfe11b

[ "Apache-2.0" ]

108

2020-05-19T20:46:10.000Z

2022-03-25T14:10:01.000Z

plugins/modules/oci_analytics_private_access_channel.py

slmjy/oci-ansible-collection

349c91e2868bf4706a6e3d6fb3b47fc622bfe11b

[ "Apache-2.0" ]

90

2020-06-14T22:07:11.000Z

2022-03-07T05:40:29.000Z

plugins/modules/oci_analytics_private_access_channel.py

slmjy/oci-ansible-collection

349c91e2868bf4706a6e3d6fb3b47fc622bfe11b

[ "Apache-2.0" ]

42

2020-08-30T23:09:12.000Z

2022-03-25T16:58:01.000Z

#!/usr/bin/python # Copyright (c) 2020, 2021 Oracle and/or its affiliates. # This software is made available to you under the terms of the GPL 3.0 license or the Apache 2.0 license. # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) # Apache License v2.0 # See LICENSE.TXT for details. # GENERATED FILE - DO NOT EDIT - MANUAL CHANGES WILL BE OVERWRITTEN from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_analytics_private_access_channel short_description: Manage a PrivateAccessChannel resource in Oracle Cloud Infrastructure description: - This module allows the user to create, update and delete a PrivateAccessChannel resource in Oracle Cloud Infrastructure - For I(state=present), create an Private access Channel for the Analytics instance. The operation is long-running and creates a new WorkRequest. version_added: "2.9.0" author: Oracle (@oracle) options: analytics_instance_id: description: - The OCID of the AnalyticsInstance. type: str aliases: ["id"] required: true display_name: description: - Display Name of the Private Access Channel. - Required for create using I(state=present). - This parameter is updatable. type: str aliases: ["name"] vcn_id: description: - OCID of the customer VCN peered with private access channel. - Required for create using I(state=present). - This parameter is updatable. type: str subnet_id: description: - OCID of the customer subnet connected to private access channel. - Required for create using I(state=present). - This parameter is updatable. type: str private_source_dns_zones: description: - List of Private Source DNS zones registered with Private Access Channel, where datasource hostnames from these dns zones / domains will be resolved in the peered VCN for access from Analytics Instance. Min of 1 is required and Max of 30 Private Source DNS zones can be registered. - Required for create using I(state=present). - This parameter is updatable. type: list elements: dict suboptions: dns_zone: description: - "Private Source DNS Zone. Ex: example-vcn.oraclevcn.com, corp.example.com." type: str required: true description: description: - Description of private source dns zone. type: str private_access_channel_key: description: - The unique identifier key of the Private Access Channel. - Required for update using I(state=present) with analytics_instance_id present. - Required for delete using I(state=absent). type: str state: description: - The state of the PrivateAccessChannel. - Use I(state=present) to create or update a PrivateAccessChannel. - Use I(state=absent) to delete a PrivateAccessChannel. type: str required: false default: 'present' choices: ["present", "absent"] extends_documentation_fragment: [ oracle.oci.oracle, oracle.oci.oracle_creatable_resource, oracle.oci.oracle_wait_options ] """ EXAMPLES = """ - name: Create private_access_channel oci_analytics_private_access_channel: # required analytics_instance_id: "ocid1.analyticsinstance.oc1..xxxxxxEXAMPLExxxxxx" display_name: display_name_example vcn_id: "ocid1.vcn.oc1..xxxxxxEXAMPLExxxxxx" subnet_id: "ocid1.subnet.oc1..xxxxxxEXAMPLExxxxxx" private_source_dns_zones: - # required dns_zone: dns_zone_example # optional description: description_example - name: Update private_access_channel oci_analytics_private_access_channel: # required analytics_instance_id: "ocid1.analyticsinstance.oc1..xxxxxxEXAMPLExxxxxx" private_access_channel_key: private_access_channel_key_example # optional display_name: display_name_example vcn_id: "ocid1.vcn.oc1..xxxxxxEXAMPLExxxxxx" subnet_id: "ocid1.subnet.oc1..xxxxxxEXAMPLExxxxxx" private_source_dns_zones: - # required dns_zone: dns_zone_example # optional description: description_example - name: Delete private_access_channel oci_analytics_private_access_channel: # required analytics_instance_id: "ocid1.analyticsinstance.oc1..xxxxxxEXAMPLExxxxxx" private_access_channel_key: private_access_channel_key_example state: absent """ RETURN = """ private_access_channel: description: - Details of the PrivateAccessChannel resource acted upon by the current operation returned: on success type: complex contains: key: description: - Private Access Channel unique identifier key. returned: on success type: str sample: key_example display_name: description: - Display Name of the Private Access Channel. returned: on success type: str sample: display_name_example vcn_id: description: - OCID of the customer VCN peered with private access channel. returned: on success type: str sample: "ocid1.vcn.oc1..xxxxxxEXAMPLExxxxxx" subnet_id: description: - OCID of the customer subnet connected to private access channel. returned: on success type: str sample: "ocid1.subnet.oc1..xxxxxxEXAMPLExxxxxx" ip_address: description: - IP Address of the Private Access channel. returned: on success type: str sample: ip_address_example egress_source_ip_addresses: description: - The list of IP addresses from the customer subnet connected to private access channel, used as a source Ip by Private Access Channel for network traffic from the AnalyticsInstance to Private Sources. returned: on success type: list sample: [] private_source_dns_zones: description: - List of Private Source DNS zones registered with Private Access Channel, where datasource hostnames from these dns zones / domains will be resolved in the peered VCN for access from Analytics Instance. Min of 1 is required and Max of 30 Private Source DNS zones can be registered. returned: on success type: complex contains: dns_zone: description: - "Private Source DNS Zone. Ex: example-vcn.oraclevcn.com, corp.example.com." returned: on success type: str sample: dns_zone_example description: description: - Description of private source dns zone. returned: on success type: str sample: description_example sample: { "key": "key_example", "display_name": "display_name_example", "vcn_id": "ocid1.vcn.oc1..xxxxxxEXAMPLExxxxxx", "subnet_id": "ocid1.subnet.oc1..xxxxxxEXAMPLExxxxxx", "ip_address": "ip_address_example", "egress_source_ip_addresses": [], "private_source_dns_zones": [{ "dns_zone": "dns_zone_example", "description": "description_example" }] } """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import ( oci_common_utils, oci_wait_utils, ) from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceHelperBase, get_custom_class, ) try: from oci.analytics import AnalyticsClient from oci.analytics.models import CreatePrivateAccessChannelDetails from oci.analytics.models import UpdatePrivateAccessChannelDetails HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class PrivateAccessChannelHelperGen(OCIResourceHelperBase): """Supported operations: create, update, get and delete""" def get_module_resource_id_param(self): return "analytics_instance_id" def get_module_resource_id(self): return self.module.params.get("analytics_instance_id") def get_get_fn(self): return self.client.get_private_access_channel def get_resource(self): return oci_common_utils.call_with_backoff( self.client.get_private_access_channel, private_access_channel_key=self.module.params.get( "private_access_channel_key" ), analytics_instance_id=self.module.params.get("analytics_instance_id"), ) def get_create_model_class(self): return CreatePrivateAccessChannelDetails def create_resource(self): create_details = self.get_create_model() return oci_wait_utils.call_and_wait( call_fn=self.client.create_private_access_channel, call_fn_args=(), call_fn_kwargs=dict( analytics_instance_id=self.module.params.get("analytics_instance_id"), create_private_access_channel_details=create_details, ), waiter_type=oci_wait_utils.WORK_REQUEST_WAITER_KEY, operation=oci_common_utils.CREATE_OPERATION_KEY, waiter_client=self.get_waiter_client(), resource_helper=self, wait_for_states=oci_common_utils.get_work_request_completed_states(), ) def get_update_model_class(self): return UpdatePrivateAccessChannelDetails def update_resource(self): update_details = self.get_update_model() return oci_wait_utils.call_and_wait( call_fn=self.client.update_private_access_channel, call_fn_args=(), call_fn_kwargs=dict( private_access_channel_key=self.module.params.get( "private_access_channel_key" ), analytics_instance_id=self.module.params.get("analytics_instance_id"), update_private_access_channel_details=update_details, ), waiter_type=oci_wait_utils.WORK_REQUEST_WAITER_KEY, operation=oci_common_utils.UPDATE_OPERATION_KEY, waiter_client=self.get_waiter_client(), resource_helper=self, wait_for_states=oci_common_utils.get_work_request_completed_states(), ) def delete_resource(self): return oci_wait_utils.call_and_wait( call_fn=self.client.delete_private_access_channel, call_fn_args=(), call_fn_kwargs=dict( private_access_channel_key=self.module.params.get( "private_access_channel_key" ), analytics_instance_id=self.module.params.get("analytics_instance_id"), ), waiter_type=oci_wait_utils.WORK_REQUEST_WAITER_KEY, operation=oci_common_utils.DELETE_OPERATION_KEY, waiter_client=self.get_waiter_client(), resource_helper=self, wait_for_states=oci_common_utils.get_work_request_completed_states(), ) PrivateAccessChannelHelperCustom = get_custom_class("PrivateAccessChannelHelperCustom") class ResourceHelper(PrivateAccessChannelHelperCustom, PrivateAccessChannelHelperGen): pass def main(): module_args = oci_common_utils.get_common_arg_spec( supports_create=True, supports_wait=True ) module_args.update( dict( analytics_instance_id=dict(aliases=["id"], type="str", required=True), display_name=dict(aliases=["name"], type="str"), vcn_id=dict(type="str"), subnet_id=dict(type="str"), private_source_dns_zones=dict( type="list", elements="dict", options=dict( dns_zone=dict(type="str", required=True), description=dict(type="str"), ), ), private_access_channel_key=dict(type="str", no_log=True), state=dict(type="str", default="present", choices=["present", "absent"]), ) ) module = AnsibleModule(argument_spec=module_args, supports_check_mode=True) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_helper = ResourceHelper( module=module, resource_type="private_access_channel", service_client_class=AnalyticsClient, namespace="analytics", ) result = dict(changed=False) if resource_helper.is_delete(): result = resource_helper.delete() elif resource_helper.is_update(): result = resource_helper.update() elif resource_helper.is_create(): result = resource_helper.create() module.exit_json(**result) if __name__ == "__main__": main()

36.785326

150

0.650218

from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_analytics_private_access_channel short_description: Manage a PrivateAccessChannel resource in Oracle Cloud Infrastructure description: - This module allows the user to create, update and delete a PrivateAccessChannel resource in Oracle Cloud Infrastructure - For I(state=present), create an Private access Channel for the Analytics instance. The operation is long-running and creates a new WorkRequest. version_added: "2.9.0" author: Oracle (@oracle) options: analytics_instance_id: description: - The OCID of the AnalyticsInstance. type: str aliases: ["id"] required: true display_name: description: - Display Name of the Private Access Channel. - Required for create using I(state=present). - This parameter is updatable. type: str aliases: ["name"] vcn_id: description: - OCID of the customer VCN peered with private access channel. - Required for create using I(state=present). - This parameter is updatable. type: str subnet_id: description: - OCID of the customer subnet connected to private access channel. - Required for create using I(state=present). - This parameter is updatable. type: str private_source_dns_zones: description: - List of Private Source DNS zones registered with Private Access Channel, where datasource hostnames from these dns zones / domains will be resolved in the peered VCN for access from Analytics Instance. Min of 1 is required and Max of 30 Private Source DNS zones can be registered. - Required for create using I(state=present). - This parameter is updatable. type: list elements: dict suboptions: dns_zone: description: - "Private Source DNS Zone. Ex: example-vcn.oraclevcn.com, corp.example.com." type: str required: true description: description: - Description of private source dns zone. type: str private_access_channel_key: description: - The unique identifier key of the Private Access Channel. - Required for update using I(state=present) with analytics_instance_id present. - Required for delete using I(state=absent). type: str state: description: - The state of the PrivateAccessChannel. - Use I(state=present) to create or update a PrivateAccessChannel. - Use I(state=absent) to delete a PrivateAccessChannel. type: str required: false default: 'present' choices: ["present", "absent"] extends_documentation_fragment: [ oracle.oci.oracle, oracle.oci.oracle_creatable_resource, oracle.oci.oracle_wait_options ] """ EXAMPLES = """ - name: Create private_access_channel oci_analytics_private_access_channel: # required analytics_instance_id: "ocid1.analyticsinstance.oc1..xxxxxxEXAMPLExxxxxx" display_name: display_name_example vcn_id: "ocid1.vcn.oc1..xxxxxxEXAMPLExxxxxx" subnet_id: "ocid1.subnet.oc1..xxxxxxEXAMPLExxxxxx" private_source_dns_zones: - # required dns_zone: dns_zone_example # optional description: description_example - name: Update private_access_channel oci_analytics_private_access_channel: # required analytics_instance_id: "ocid1.analyticsinstance.oc1..xxxxxxEXAMPLExxxxxx" private_access_channel_key: private_access_channel_key_example # optional display_name: display_name_example vcn_id: "ocid1.vcn.oc1..xxxxxxEXAMPLExxxxxx" subnet_id: "ocid1.subnet.oc1..xxxxxxEXAMPLExxxxxx" private_source_dns_zones: - # required dns_zone: dns_zone_example # optional description: description_example - name: Delete private_access_channel oci_analytics_private_access_channel: # required analytics_instance_id: "ocid1.analyticsinstance.oc1..xxxxxxEXAMPLExxxxxx" private_access_channel_key: private_access_channel_key_example state: absent """ RETURN = """ private_access_channel: description: - Details of the PrivateAccessChannel resource acted upon by the current operation returned: on success type: complex contains: key: description: - Private Access Channel unique identifier key. returned: on success type: str sample: key_example display_name: description: - Display Name of the Private Access Channel. returned: on success type: str sample: display_name_example vcn_id: description: - OCID of the customer VCN peered with private access channel. returned: on success type: str sample: "ocid1.vcn.oc1..xxxxxxEXAMPLExxxxxx" subnet_id: description: - OCID of the customer subnet connected to private access channel. returned: on success type: str sample: "ocid1.subnet.oc1..xxxxxxEXAMPLExxxxxx" ip_address: description: - IP Address of the Private Access channel. returned: on success type: str sample: ip_address_example egress_source_ip_addresses: description: - The list of IP addresses from the customer subnet connected to private access channel, used as a source Ip by Private Access Channel for network traffic from the AnalyticsInstance to Private Sources. returned: on success type: list sample: [] private_source_dns_zones: description: - List of Private Source DNS zones registered with Private Access Channel, where datasource hostnames from these dns zones / domains will be resolved in the peered VCN for access from Analytics Instance. Min of 1 is required and Max of 30 Private Source DNS zones can be registered. returned: on success type: complex contains: dns_zone: description: - "Private Source DNS Zone. Ex: example-vcn.oraclevcn.com, corp.example.com." returned: on success type: str sample: dns_zone_example description: description: - Description of private source dns zone. returned: on success type: str sample: description_example sample: { "key": "key_example", "display_name": "display_name_example", "vcn_id": "ocid1.vcn.oc1..xxxxxxEXAMPLExxxxxx", "subnet_id": "ocid1.subnet.oc1..xxxxxxEXAMPLExxxxxx", "ip_address": "ip_address_example", "egress_source_ip_addresses": [], "private_source_dns_zones": [{ "dns_zone": "dns_zone_example", "description": "description_example" }] } """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import ( oci_common_utils, oci_wait_utils, ) from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceHelperBase, get_custom_class, ) try: from oci.analytics import AnalyticsClient from oci.analytics.models import CreatePrivateAccessChannelDetails from oci.analytics.models import UpdatePrivateAccessChannelDetails HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class PrivateAccessChannelHelperGen(OCIResourceHelperBase): def get_module_resource_id_param(self): return "analytics_instance_id" def get_module_resource_id(self): return self.module.params.get("analytics_instance_id") def get_get_fn(self): return self.client.get_private_access_channel def get_resource(self): return oci_common_utils.call_with_backoff( self.client.get_private_access_channel, private_access_channel_key=self.module.params.get( "private_access_channel_key" ), analytics_instance_id=self.module.params.get("analytics_instance_id"), ) def get_create_model_class(self): return CreatePrivateAccessChannelDetails def create_resource(self): create_details = self.get_create_model() return oci_wait_utils.call_and_wait( call_fn=self.client.create_private_access_channel, call_fn_args=(), call_fn_kwargs=dict( analytics_instance_id=self.module.params.get("analytics_instance_id"), create_private_access_channel_details=create_details, ), waiter_type=oci_wait_utils.WORK_REQUEST_WAITER_KEY, operation=oci_common_utils.CREATE_OPERATION_KEY, waiter_client=self.get_waiter_client(), resource_helper=self, wait_for_states=oci_common_utils.get_work_request_completed_states(), ) def get_update_model_class(self): return UpdatePrivateAccessChannelDetails def update_resource(self): update_details = self.get_update_model() return oci_wait_utils.call_and_wait( call_fn=self.client.update_private_access_channel, call_fn_args=(), call_fn_kwargs=dict( private_access_channel_key=self.module.params.get( "private_access_channel_key" ), analytics_instance_id=self.module.params.get("analytics_instance_id"), update_private_access_channel_details=update_details, ), waiter_type=oci_wait_utils.WORK_REQUEST_WAITER_KEY, operation=oci_common_utils.UPDATE_OPERATION_KEY, waiter_client=self.get_waiter_client(), resource_helper=self, wait_for_states=oci_common_utils.get_work_request_completed_states(), ) def delete_resource(self): return oci_wait_utils.call_and_wait( call_fn=self.client.delete_private_access_channel, call_fn_args=(), call_fn_kwargs=dict( private_access_channel_key=self.module.params.get( "private_access_channel_key" ), analytics_instance_id=self.module.params.get("analytics_instance_id"), ), waiter_type=oci_wait_utils.WORK_REQUEST_WAITER_KEY, operation=oci_common_utils.DELETE_OPERATION_KEY, waiter_client=self.get_waiter_client(), resource_helper=self, wait_for_states=oci_common_utils.get_work_request_completed_states(), ) PrivateAccessChannelHelperCustom = get_custom_class("PrivateAccessChannelHelperCustom") class ResourceHelper(PrivateAccessChannelHelperCustom, PrivateAccessChannelHelperGen): pass def main(): module_args = oci_common_utils.get_common_arg_spec( supports_create=True, supports_wait=True ) module_args.update( dict( analytics_instance_id=dict(aliases=["id"], type="str", required=True), display_name=dict(aliases=["name"], type="str"), vcn_id=dict(type="str"), subnet_id=dict(type="str"), private_source_dns_zones=dict( type="list", elements="dict", options=dict( dns_zone=dict(type="str", required=True), description=dict(type="str"), ), ), private_access_channel_key=dict(type="str", no_log=True), state=dict(type="str", default="present", choices=["present", "absent"]), ) ) module = AnsibleModule(argument_spec=module_args, supports_check_mode=True) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_helper = ResourceHelper( module=module, resource_type="private_access_channel", service_client_class=AnalyticsClient, namespace="analytics", ) result = dict(changed=False) if resource_helper.is_delete(): result = resource_helper.delete() elif resource_helper.is_update(): result = resource_helper.update() elif resource_helper.is_create(): result = resource_helper.create() module.exit_json(**result) if __name__ == "__main__": main()

true

1c2e95f5e12bce802fd887828e3ab4159233eaa3

414

py

Python

reapit/reapit/doctype/item_sync_settings/item_sync_settings.py

rtdany10/reapit

aba62ac13fb1a81da9e2e182506712416b63aa92

[ "MIT" ]

null

reapit/reapit/doctype/item_sync_settings/item_sync_settings.py

rtdany10/reapit

aba62ac13fb1a81da9e2e182506712416b63aa92

[ "MIT" ]

null

reapit/reapit/doctype/item_sync_settings/item_sync_settings.py

rtdany10/reapit

aba62ac13fb1a81da9e2e182506712416b63aa92

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2021, Wahni Green Technologies and contributors # For license information, please see license.txt from __future__ import unicode_literals import frappe from frappe.model.document import Document class ItemSyncSettings(Document): def validate(self): if self.enabled: if not (self.api_endpoint and self.price_list): frappe.throw("Invalid endpoint or price list.")

29.571429

63

0.768116

from __future__ import unicode_literals import frappe from frappe.model.document import Document class ItemSyncSettings(Document): def validate(self): if self.enabled: if not (self.api_endpoint and self.price_list): frappe.throw("Invalid endpoint or price list.")

true

1c2e972200daba906a25b9d9ebe3ed09289c0ab6

10,914

py

Python

tests/test_scrape_ew_and_errew.py

mwanakijiji/rrlfe2

0637b348b8d3e54ff34c56caa8b4c6fdac1c699e

[ "MIT" ]

null

tests/test_scrape_ew_and_errew.py

mwanakijiji/rrlfe2

0637b348b8d3e54ff34c56caa8b4c6fdac1c699e

[ "MIT" ]

18

2022-01-13T14:43:57.000Z

2022-03-24T12:52:41.000Z

tests/test_scrape_ew_and_errew.py

mwanakijiji/rrlfe

4a822bb499bd0af4543f8b34d9322e812a5a3d2c

[ "MIT" ]

null

import matplotlib matplotlib.use('Agg') import sys, os from configparser import ConfigParser, ExtendedInterpolation import pandas as pd import astropy current_dir = os.path.dirname(__file__) target_dir = os.path.abspath(os.path.join(current_dir, "../")) sys.path.insert(0, target_dir) # import more things with changed system path from modules import * from modules import scrape_ew_and_errew from conf import * import numpy as np import glob # configuration data for reduction config_red = ConfigParser(interpolation=ExtendedInterpolation()) # for parsing values in .init file # config for reduction to find a, b, c, d config_red.read(os.path.join(os.path.dirname(__file__), '../conf', 'config_red.ini')) def test_line_order_check(): dummy_lines = [3933.660,3970.075,4101.7100,4340.472,4861.290] test_num_glitches_0 = scrape_ew_and_errew.line_order_check(dummy_lines) dummy_lines[1] = 3990. dummy_lines[3] = 4320. test_num_glitches_1 = scrape_ew_and_errew.line_order_check(dummy_lines) dummy_lines[0] = 3913. dummy_lines[2] = 4121. dummy_lines[4] = 4881. test_num_glitches_2 = scrape_ew_and_errew.line_order_check(dummy_lines) # assert glitches is boolean assert test_num_glitches_0 == 0 assert test_num_glitches_1 == 1 assert test_num_glitches_2 == 1 def test_Scraper(): ''' write_dir_test = config_red["data_dirs"]["TEST_DIR_BIN"] robo_dir = config_red["sys_dirs"]["DIR_ROBO"] file_names_test = glob.glob(config_red["data_dirs"]["TEST_DIR_SRC"] + "spec_norm_final/*") ''' # instantiate scraper_instance = scrape_ew_and_errew.Scraper(subdir = config_red["data_dirs"]["TEST_DIR_ROBO_OUTPUT"], file_scraped_info = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/"+config_red["file_names"]["SCRAPED_EW_ALL_DATA"]) # try a single instance; does it work? # note the writing of files is not directly tested here function_state = True try: test = scraper_instance() except Exception as e: # e contains printable attributes of exception object function_state = False # assert that instantiation worked assert function_state # make sure lines are really being identified correctly assert np.allclose(test.where(test["line_name"]=="CaIIK").dropna()["wavel_found_center"],3933.660, atol=2.) assert np.allclose(test.where(test["line_name"]=="Heps").dropna()["wavel_found_center"],3970.075, atol=2.) assert np.allclose(test.where(test["line_name"]=="Hdel").dropna()["wavel_found_center"],4101.710, atol=2.) assert np.allclose(test.where(test["line_name"]=="Hgam").dropna()["wavel_found_center"],4340.472, atol=2.) assert np.allclose(test.where(test["line_name"]=="Hbet").dropna()["wavel_found_center"],4861.290, atol=2.) # only 2 of the 3 spectra should have been scraped, because one should have # triggered a parsing errors assert len(test.where(test["line_name"]=="CaIIK").dropna()) == 2 def test_quality_check(): data_out_test = scrape_ew_and_errew.quality_check( read_in_filename = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/"+config_red["file_names"]["TEST_SCRAPED_EW_ALL_DATA"], write_out_filename = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/"+config_red["file_names"]["TEST_SCRAPED_EW_DATA_GOOD_ONLY"]) # lots of checks of data types # note this uses .iloc[0] instead of [0], because bad rows with index 0 may # have been removed assert isinstance(data_out_test["wavel_stated_center"].iloc[0],np.float64) assert isinstance(data_out_test["wavel_found_center"].iloc[0],np.float64) assert isinstance(data_out_test["gaussianSigma"].iloc[0],np.float64) assert isinstance(data_out_test["gaussianAmp"].iloc[0],np.float64) assert isinstance(data_out_test["uncertaintyMu"].iloc[0],np.float64) assert isinstance(data_out_test["uncertaintySigma"].iloc[0],np.float64) assert isinstance(data_out_test["uncertaintyAmp"].iloc[0],np.float64) assert isinstance(data_out_test["priorMu"].iloc[0],np.float64) assert isinstance(data_out_test["priorSigma"].iloc[0],np.float64) assert isinstance(data_out_test["priorAmp"].iloc[0],np.float64) assert isinstance(data_out_test["EQW"].iloc[0],np.float64) assert isinstance(data_out_test["uncertaintyEQW"].iloc[0],np.float64) assert isinstance(data_out_test["chiSqr"].iloc[0],np.float64) assert isinstance(data_out_test["flags"].iloc[0],str) assert isinstance(data_out_test["blendGroup"].iloc[0],np.int64) assert isinstance(data_out_test["line_name"].iloc[0],str) assert isinstance(data_out_test["robolines_file_name"].iloc[0],str) assert isinstance(data_out_test["realization_spec_file_name"].iloc[0],str) assert isinstance(data_out_test["quality"].iloc[0],str) def test_stack_spectra(): print("input list") print(config_red["data_dirs"]["TEST_DIR_SRC"] + "test_input_file_list.list") print("read in file name") data_stacked_test = scrape_ew_and_errew.stack_spectra( read_in_filename = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/"+config_red["file_names"]["TEST_SCRAPED_EW_DATA_GOOD_ONLY"], write_out_filename = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/"+config_red["file_names"]["TEST_RESTACKED_EW_DATA_GOOD_ONLY"], input_list = config_red["data_dirs"]["TEST_DIR_SRC"] + "test_input_file_list.list") print("data_stacked") print(data_stacked_test.keys()) # lots of checks of data types # note this uses .iloc[0] instead of [0], because bad rows with index 0 may # have been removed assert isinstance(data_stacked_test["realization_spec_file_name"].iloc[0],str) assert isinstance(data_stacked_test["orig_spec_file_name"].iloc[0],str) assert isinstance(data_stacked_test["EW_Hbeta"].iloc[0],np.float64) assert isinstance(data_stacked_test["err_EW_Hbeta_from_robo"].iloc[0],np.float64) assert isinstance(data_stacked_test["EW_Hdelta"].iloc[0],np.float64) assert isinstance(data_stacked_test["err_EW_Hdelta_from_robo"].iloc[0],np.float64) assert isinstance(data_stacked_test["EW_Hgamma"].iloc[0],np.float64) assert isinstance(data_stacked_test["err_EW_Hgamma_from_robo"].iloc[0],np.float64) assert isinstance(data_stacked_test["EW_Heps"].iloc[0],np.float64) assert isinstance(data_stacked_test["err_EW_Heps_from_robo"].iloc[0],np.float64) assert isinstance(data_stacked_test["EW_CaIIK"].iloc[0],np.float64) assert isinstance(data_stacked_test["err_EW_CaIIK_from_robo"].iloc[0],np.float64) def test_generate_net_balmer(): ## ## CONTINUE HERE; FINISH THIS TEST # generate the fake data: H_del = params_data, data_net_balmer_test = scrape_ew_and_errew.generate_net_balmer( read_in_filename = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/test_stacked_data_pre_net_balmer_calc.csv", write_out_filename = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/test_stacked_data_post_net_balmer_calc.csv") # is the Balmer line a true element wise average? #assert np.array(data_net_balmer_test["EW_Balmer"]) == np.mean([data_net_balmer_test["EW_Hgamma"],data_net_balmer_test["EW_Hdelta"]], axis=0) ''' # check data type of newly-added data assert isinstance(data_net_balmer_test["EW_Balmer"].iloc[0],np.float64) assert isinstance(data_net_balmer_test["err_EW_Balmer_based_Robo"].iloc[0],np.float64) ''' assert 1<2 def test_generate_addl_ew_errors(): # placeholder for now, until more decisions about how to calculate EW errors test_df_postbalmer_errors = scrape_ew_and_errew.generate_addl_ew_errors(read_in_filename=config_red["data_dirs"]["TEST_DIR_SRC"]+config_red["file_names"]["TEST_RESTACKED_EW_DATA_W_NET_BALMER"], write_out_filename=config_red["data_dirs"]["TEST_DIR_BIN"]+config_red["file_names"]["TEST_RESTACKED_EW_DATA_W_NET_BALMER_ERRORS"]) # loop through batches of rows corresponding to an individual spectrum, and # make sure the errors are consistent and the value expected array_1 = test_df_postbalmer_errors["err_EW_Balmer_based_noise_churning"].where(test_df_postbalmer_errors["orig_spec_file_name"]=="575020m10.smo").dropna().values array_2 = test_df_postbalmer_errors["err_EW_Balmer_based_noise_churning"].where(test_df_postbalmer_errors["orig_spec_file_name"]=="575020m15.smo").dropna().values array_3 = test_df_postbalmer_errors["err_EW_Balmer_based_noise_churning"].where(test_df_postbalmer_errors["orig_spec_file_name"]=="575020m20.smo").dropna().values # check that error values are same assert np.all(array_1) assert np.all(array_2) assert np.all(array_3) # check that one value has expected value assert round(array_1[0], 3) == 0.023 assert round(array_2[0], 3) == 0.020 assert round(array_3[0], 3) == 0.048 # in unusual case where collapsing the noise-churned spectra is not desired test_df_postbalmer_errors_nogrouping = scrape_ew_and_errew.generate_addl_ew_errors(read_in_filename=config_red["data_dirs"]["TEST_DIR_SRC"]+config_red["file_names"]["TEST_RESTACKED_EW_DATA_W_NET_BALMER"], write_out_filename=config_red["data_dirs"]["TEST_DIR_BIN"]+config_red["file_names"]["TEST_RESTACKED_EW_DATA_W_NET_BALMER_ERRORS"], groupby_parent = False) array_1_children = test_df_postbalmer_errors_nogrouping.where(test_df_postbalmer_errors["orig_spec_file_name"]=="575020m10.smo").dropna() # there are 40 rows for each parent spectrum, and the Balmer EW values are different in each assert len(array_1_children) == 20 assert np.std(array_1_children["EW_Balmer"]) > 0 def test_add_synthetic_meta_data(): combined_data = scrape_ew_and_errew.add_synthetic_meta_data(input_list = config_red["data_dirs"]["TEST_DIR_SRC"] + config_red["file_names"]["TEST_LIST_SPEC_PHASE"], read_in_filename = config_red["data_dirs"]["TEST_DIR_EW_PRODS"]+config_red["file_names"]["TEST_RESTACKED_EW_DATA_W_NET_BALMER_ERRORS"], write_out_filename = config_red["data_dirs"]["TEST_DIR_EW_PRODS"]+config_red["file_names"]["TEST_RESTACKED_EW_DATA_W_METADATA_WRITEOUT"]) # columns from Robospect output and meta-data are all there assert "wavel_stated_center" in combined_data.columns assert "feh" in combined_data.columns assert "teff" in combined_data.columns # there are no nans in the table assert np.sum(combined_data.isnull().sum()) == 0

52.724638

208

0.723566

import matplotlib matplotlib.use('Agg') import sys, os from configparser import ConfigParser, ExtendedInterpolation import pandas as pd import astropy current_dir = os.path.dirname(__file__) target_dir = os.path.abspath(os.path.join(current_dir, "../")) sys.path.insert(0, target_dir) from modules import * from modules import scrape_ew_and_errew from conf import * import numpy as np import glob config_red = ConfigParser(interpolation=ExtendedInterpolation()) config_red.read(os.path.join(os.path.dirname(__file__), '../conf', 'config_red.ini')) def test_line_order_check(): dummy_lines = [3933.660,3970.075,4101.7100,4340.472,4861.290] test_num_glitches_0 = scrape_ew_and_errew.line_order_check(dummy_lines) dummy_lines[1] = 3990. dummy_lines[3] = 4320. test_num_glitches_1 = scrape_ew_and_errew.line_order_check(dummy_lines) dummy_lines[0] = 3913. dummy_lines[2] = 4121. dummy_lines[4] = 4881. test_num_glitches_2 = scrape_ew_and_errew.line_order_check(dummy_lines) assert test_num_glitches_0 == 0 assert test_num_glitches_1 == 1 assert test_num_glitches_2 == 1 def test_Scraper(): scraper_instance = scrape_ew_and_errew.Scraper(subdir = config_red["data_dirs"]["TEST_DIR_ROBO_OUTPUT"], file_scraped_info = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/"+config_red["file_names"]["SCRAPED_EW_ALL_DATA"]) function_state = True try: test = scraper_instance() except Exception as e: function_state = False assert function_state assert np.allclose(test.where(test["line_name"]=="CaIIK").dropna()["wavel_found_center"],3933.660, atol=2.) assert np.allclose(test.where(test["line_name"]=="Heps").dropna()["wavel_found_center"],3970.075, atol=2.) assert np.allclose(test.where(test["line_name"]=="Hdel").dropna()["wavel_found_center"],4101.710, atol=2.) assert np.allclose(test.where(test["line_name"]=="Hgam").dropna()["wavel_found_center"],4340.472, atol=2.) assert np.allclose(test.where(test["line_name"]=="Hbet").dropna()["wavel_found_center"],4861.290, atol=2.) assert len(test.where(test["line_name"]=="CaIIK").dropna()) == 2 def test_quality_check(): data_out_test = scrape_ew_and_errew.quality_check( read_in_filename = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/"+config_red["file_names"]["TEST_SCRAPED_EW_ALL_DATA"], write_out_filename = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/"+config_red["file_names"]["TEST_SCRAPED_EW_DATA_GOOD_ONLY"]) assert isinstance(data_out_test["wavel_stated_center"].iloc[0],np.float64) assert isinstance(data_out_test["wavel_found_center"].iloc[0],np.float64) assert isinstance(data_out_test["gaussianSigma"].iloc[0],np.float64) assert isinstance(data_out_test["gaussianAmp"].iloc[0],np.float64) assert isinstance(data_out_test["uncertaintyMu"].iloc[0],np.float64) assert isinstance(data_out_test["uncertaintySigma"].iloc[0],np.float64) assert isinstance(data_out_test["uncertaintyAmp"].iloc[0],np.float64) assert isinstance(data_out_test["priorMu"].iloc[0],np.float64) assert isinstance(data_out_test["priorSigma"].iloc[0],np.float64) assert isinstance(data_out_test["priorAmp"].iloc[0],np.float64) assert isinstance(data_out_test["EQW"].iloc[0],np.float64) assert isinstance(data_out_test["uncertaintyEQW"].iloc[0],np.float64) assert isinstance(data_out_test["chiSqr"].iloc[0],np.float64) assert isinstance(data_out_test["flags"].iloc[0],str) assert isinstance(data_out_test["blendGroup"].iloc[0],np.int64) assert isinstance(data_out_test["line_name"].iloc[0],str) assert isinstance(data_out_test["robolines_file_name"].iloc[0],str) assert isinstance(data_out_test["realization_spec_file_name"].iloc[0],str) assert isinstance(data_out_test["quality"].iloc[0],str) def test_stack_spectra(): print("input list") print(config_red["data_dirs"]["TEST_DIR_SRC"] + "test_input_file_list.list") print("read in file name") data_stacked_test = scrape_ew_and_errew.stack_spectra( read_in_filename = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/"+config_red["file_names"]["TEST_SCRAPED_EW_DATA_GOOD_ONLY"], write_out_filename = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/"+config_red["file_names"]["TEST_RESTACKED_EW_DATA_GOOD_ONLY"], input_list = config_red["data_dirs"]["TEST_DIR_SRC"] + "test_input_file_list.list") print("data_stacked") print(data_stacked_test.keys()) assert isinstance(data_stacked_test["realization_spec_file_name"].iloc[0],str) assert isinstance(data_stacked_test["orig_spec_file_name"].iloc[0],str) assert isinstance(data_stacked_test["EW_Hbeta"].iloc[0],np.float64) assert isinstance(data_stacked_test["err_EW_Hbeta_from_robo"].iloc[0],np.float64) assert isinstance(data_stacked_test["EW_Hdelta"].iloc[0],np.float64) assert isinstance(data_stacked_test["err_EW_Hdelta_from_robo"].iloc[0],np.float64) assert isinstance(data_stacked_test["EW_Hgamma"].iloc[0],np.float64) assert isinstance(data_stacked_test["err_EW_Hgamma_from_robo"].iloc[0],np.float64) assert isinstance(data_stacked_test["EW_Heps"].iloc[0],np.float64) assert isinstance(data_stacked_test["err_EW_Heps_from_robo"].iloc[0],np.float64) assert isinstance(data_stacked_test["EW_CaIIK"].iloc[0],np.float64) assert isinstance(data_stacked_test["err_EW_CaIIK_from_robo"].iloc[0],np.float64) def test_generate_net_balmer(): read_in_filename = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/test_stacked_data_pre_net_balmer_calc.csv", write_out_filename = config_red["data_dirs"]["TEST_DIR_BIN"]+"scraper_output/test_stacked_data_post_net_balmer_calc.csv") assert 1<2 def test_generate_addl_ew_errors(): test_df_postbalmer_errors = scrape_ew_and_errew.generate_addl_ew_errors(read_in_filename=config_red["data_dirs"]["TEST_DIR_SRC"]+config_red["file_names"]["TEST_RESTACKED_EW_DATA_W_NET_BALMER"], write_out_filename=config_red["data_dirs"]["TEST_DIR_BIN"]+config_red["file_names"]["TEST_RESTACKED_EW_DATA_W_NET_BALMER_ERRORS"]) array_1 = test_df_postbalmer_errors["err_EW_Balmer_based_noise_churning"].where(test_df_postbalmer_errors["orig_spec_file_name"]=="575020m10.smo").dropna().values array_2 = test_df_postbalmer_errors["err_EW_Balmer_based_noise_churning"].where(test_df_postbalmer_errors["orig_spec_file_name"]=="575020m15.smo").dropna().values array_3 = test_df_postbalmer_errors["err_EW_Balmer_based_noise_churning"].where(test_df_postbalmer_errors["orig_spec_file_name"]=="575020m20.smo").dropna().values assert np.all(array_1) assert np.all(array_2) assert np.all(array_3) assert round(array_1[0], 3) == 0.023 assert round(array_2[0], 3) == 0.020 assert round(array_3[0], 3) == 0.048 test_df_postbalmer_errors_nogrouping = scrape_ew_and_errew.generate_addl_ew_errors(read_in_filename=config_red["data_dirs"]["TEST_DIR_SRC"]+config_red["file_names"]["TEST_RESTACKED_EW_DATA_W_NET_BALMER"], write_out_filename=config_red["data_dirs"]["TEST_DIR_BIN"]+config_red["file_names"]["TEST_RESTACKED_EW_DATA_W_NET_BALMER_ERRORS"], groupby_parent = False) array_1_children = test_df_postbalmer_errors_nogrouping.where(test_df_postbalmer_errors["orig_spec_file_name"]=="575020m10.smo").dropna() assert len(array_1_children) == 20 assert np.std(array_1_children["EW_Balmer"]) > 0 def test_add_synthetic_meta_data(): combined_data = scrape_ew_and_errew.add_synthetic_meta_data(input_list = config_red["data_dirs"]["TEST_DIR_SRC"] + config_red["file_names"]["TEST_LIST_SPEC_PHASE"], read_in_filename = config_red["data_dirs"]["TEST_DIR_EW_PRODS"]+config_red["file_names"]["TEST_RESTACKED_EW_DATA_W_NET_BALMER_ERRORS"], write_out_filename = config_red["data_dirs"]["TEST_DIR_EW_PRODS"]+config_red["file_names"]["TEST_RESTACKED_EW_DATA_W_METADATA_WRITEOUT"]) assert "wavel_stated_center" in combined_data.columns assert "feh" in combined_data.columns assert "teff" in combined_data.columns assert np.sum(combined_data.isnull().sum()) == 0

true

1c2e97e86be84acb21ba351d83b002528e614107

23,642

py

Python

src/translate.py

CloudRenderVR/human-motion-prediction-pytorch-uncertainty

cb96f3b2d8a2b54916b2f3f76c1c9b982af5b46b

[ "MIT" ]

null

src/translate.py

CloudRenderVR/human-motion-prediction-pytorch-uncertainty

cb96f3b2d8a2b54916b2f3f76c1c9b982af5b46b

[ "MIT" ]

null

src/translate.py

CloudRenderVR/human-motion-prediction-pytorch-uncertainty

cb96f3b2d8a2b54916b2f3f76c1c9b982af5b46b

[ "MIT" ]

null

"""Simple code for training an RNN for motion prediction.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import os import random import sys import time import h5py import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin import data_utils import seq2seq_model import torch import torch.optim as optim from torch.autograd import Variable import argparse # Learning parser = argparse.ArgumentParser(description='Train RNN for human pose estimation') parser.add_argument('--learning_rate', dest='learning_rate', help='Learning rate', default=0.005, type=float) parser.add_argument('--learning_rate_decay_factor', dest='learning_rate_decay_factor', help='Learning rate is multiplied by this much. 1 means no decay.', default=0.95, type=float) parser.add_argument('--learning_rate_step', dest='learning_rate_step', help='Every this many steps, do decay.', default=10000, type=int) parser.add_argument('--batch_size', dest='batch_size', help='Batch size to use during training.', default=16, type=int) parser.add_argument('--max_gradient_norm', dest='max_gradient_norm', help='Clip gradients to this norm.', default=5, type=float) parser.add_argument('--iterations', dest='iterations', help='Iterations to train for.', default=1e5, type=int) parser.add_argument('--test_every', dest='test_every', help='', default=1000, type=int) # Architecture parser.add_argument('--architecture', dest='architecture', help='Seq2seq architecture to use: [basic, tied].', default='tied', type=str) parser.add_argument('--loss_to_use', dest='loss_to_use', help='The type of loss to use, supervised or sampling_based', default='sampling_based', type=str) parser.add_argument('--residual_velocities', dest='residual_velocities', help='Add a residual connection that effectively models velocities',action='store_true', default=False) parser.add_argument('--size', dest='size', help='Size of each model layer.', default=1024, type=int) parser.add_argument('--num_layers', dest='num_layers', help='Number of layers in the model.', default=1, type=int) parser.add_argument('--seq_length_in', dest='seq_length_in', help='Number of frames to feed into the encoder. 25 fp', default=50, type=int) parser.add_argument('--seq_length_out', dest='seq_length_out', help='Number of frames that the decoder has to predict. 25fps', default=10, type=int) parser.add_argument('--omit_one_hot', dest='omit_one_hot', help='', action='store_true', default=False) parser.add_argument('--taylor', dest='finite_taylor_extrapolate', help='Whether to augment the network with a taylor series extrapolation from a finite difference scheme of the previous frames', action='store_true', default=False) # Directories parser.add_argument('--data_dir', dest='data_dir', help='Data directory', default=os.path.normpath("./data/h3.6m/dataset"), type=str) parser.add_argument('--train_dir', dest='train_dir', help='Training directory', default=os.path.normpath("./experiments/"), type=str) parser.add_argument('--action', dest='action', help='The action to train on. all means all the actions, all_periodic means walking, eating and smoking', default='all', type=str) parser.add_argument('--use_cpu', dest='use_cpu', help='', action='store_true', default=False) parser.add_argument('--load', dest='load', help='Try to load a previous checkpoint.', default=0, type=int) parser.add_argument('--sample', dest='sample', help='Set to True for sampling.', action='store_true', default=False) parser.add_argument('--distribution_output_direct', dest='distribution_output_direct', default=False) args = parser.parse_args() train_dir = os.path.normpath(os.path.join( args.train_dir, args.action, 'out_{0}'.format(args.seq_length_out), 'iterations_{0}'.format(args.iterations), args.architecture, args.loss_to_use, 'omit_one_hot' if args.omit_one_hot else 'one_hot', 'depth_{0}'.format(args.num_layers), 'size_{0}'.format(args.size), 'lr_{0}'.format(args.learning_rate), 'residual_vel' if args.residual_velocities else 'not_residual_vel')) print(train_dir) os.makedirs(train_dir, exist_ok=True) def create_model(actions, sampling=False): """Create translation model and initialize or load parameters in session.""" model = seq2seq_model.Seq2SeqModel( args.architecture, args.seq_length_in if not sampling else 50, args.seq_length_out if not sampling else 100, args.size, # hidden layer size args.num_layers, args.max_gradient_norm, args.batch_size, args.learning_rate, args.learning_rate_decay_factor, args.loss_to_use if not sampling else "sampling_based", len( actions ), not args.omit_one_hot, args.residual_velocities, args.finite_taylor_extrapolate, output_as_normal_distribution = args.distribution_output_direct, dtype=torch.float32) if args.load <= 0: return model print("Loading model") model = torch.load(train_dir + '/model_' + str(args.load)) if sampling: model.source_seq_len = 50 model.target_seq_len = 100 return model def clean_batch(batch): encoder_inputs, decoder_inputs, decoder_outputs = batch encoder_inputs = torch.from_numpy(encoder_inputs).float() decoder_inputs = torch.from_numpy(decoder_inputs).float() decoder_outputs = torch.from_numpy(decoder_outputs).float() if not args.use_cpu: encoder_inputs = encoder_inputs.cuda() decoder_inputs = decoder_inputs.cuda() decoder_outputs = decoder_outputs.cuda() encoder_inputs = Variable(encoder_inputs) decoder_inputs = Variable(decoder_inputs) decoder_outputs = Variable(decoder_outputs) return (encoder_inputs, decoder_inputs, decoder_outputs) import flags def get_loss(output, truth): if flags.translate_loss_func == "mse": return ( (output-truth)**2 ).mean() if flags.translate_loss_func == "me": return ( np.abs(output-truth) ).mean() if flags.translate_loss_func == "mle": assert(output.shape[-1] == truth.shape[-1] * 2) means = output[..., :int(truth.shape[-1])] sigmas = output[..., int(truth.shape[-1]):] #print("################################") neg_log_likelihood = torch.sum(torch.log(torch.pow(sigmas, 2))) / 2.0 p1 = (means - truth) p2 = p1 / sigmas p3 = torch.pow(p2, 2) #print("Sigma likelihood cont:", neg_log_likelihood) neg_log_likelihood += torch.numel(means) / 2.0 * np.log(2.0*3.1415926) neg_log_likelihood += torch.sum(p3) / 2.0 #print("Max Means:", torch.max(means)) #print("Min Sigmas:", torch.min(sigmas)) #print("p1 max:", torch.max(torch.abs(p1))) #print("p1 avg:", torch.mean(torch.abs(p1))) #print("p2 max:", torch.max(torch.abs(p2))) #print("p3 max:", torch.max(p3)) #print("p3 min:", torch.min(p3)) #print("p3 avg:", torch.mean(p3)) #print("likelihood:", neg_log_likelihood) return neg_log_likelihood def train(): """Train a seq2seq model on human motion""" actions = define_actions( args.action ) number_of_actions = len( actions ) #these will all be expangles train_set, test_set, data_mean, data_std, dim_to_ignore, dim_to_use = read_all_data( actions, args.seq_length_in, args.seq_length_out, args.data_dir, not args.omit_one_hot ) # Limit TF to take a fraction of the GPU memory if True: model = create_model(actions, args.sample) if not args.use_cpu: model = model.cuda() # === Read and denormalize the gt with srnn's seeds, as we'll need them # many times for evaluation in Euler Angles === srnn_gts_euler = get_srnn_gts( actions, model, test_set, data_mean, data_std, dim_to_ignore, not args.omit_one_hot ) #=== This is the training loop === step_time, loss, val_loss = 0.0, 0.0, 0.0 current_step = 0 if args.load <= 0 else args.load + 1 previous_losses = [] step_time, loss = 0, 0 optimiser = optim.SGD(model.parameters(), lr=args.learning_rate) #optimiser = optim.Adam(model.parameters(), lr=learning_rate, betas = (0.9, 0.999)) for _ in range( args.iterations ): optimiser.zero_grad() model.train() start_time = time.time() # Actual training # === Training step === encoder_inputs, decoder_inputs, decoder_outputs = clean_batch(model.get_batch( train_set, not args.omit_one_hot )) preds = model(encoder_inputs, decoder_inputs) step_loss = get_loss(preds, decoder_outputs) # Actual backpropagation step_loss.backward() optimiser.step() step_loss = step_loss.cpu().data.numpy() # TODO: preds = preds[..., :54] #if current_step % 100 == 0: # print("step {0:04d}; step_loss: {1:.4f}".format(current_step, step_loss )) step_time += (time.time() - start_time) / args.test_every loss += step_loss / args.test_every current_step += 1 # === step decay === if current_step % args.learning_rate_step == 0: args.learning_rate = args.learning_rate*args.learning_rate_decay_factor optimiser = optim.Adam(model.parameters(), lr=args.learning_rate, betas = (0.9, 0.999)) print("Decay learning rate. New value at " + str(args.learning_rate)) #cuda.empty_cache() # Once in a while, we save checkpoint, print statistics, and run evals. if current_step % args.test_every == 0: model.eval() # === Validation with randomly chosen seeds === encoder_inputs, decoder_inputs, decoder_outputs = clean_batch(model.get_batch( test_set, not args.omit_one_hot )) preds = model(encoder_inputs, decoder_inputs) mse_loss = torch.mean( (preds[..., :54] - decoder_outputs)**2) step_loss = get_loss(preds, decoder_outputs) val_loss = step_loss # Loss book-keeping # TODO: preds = preds[..., :54] print() print("{0: <16} |".format("milliseconds"), end="") for ms in [80, 160, 320, 400, 560, 1000]: print(" {0:5d} |".format(ms), end="") print() # === Validation with srnn's seeds === for action in actions: # Evaluate the model on the test batches #### Evaluate model on action encoder_inputs, decoder_inputs, decoder_outputs = clean_batch(model.get_batch(test_set, action)) srnn_poses = model(encoder_inputs, decoder_inputs) srnn_loss = get_loss(srnn_poses, decoder_outputs) #TODO: srnn_poses = srnn_poses[...,:54] srnn_poses = srnn_poses.cpu().data.numpy() srnn_poses = srnn_poses.transpose([1,0,2]) srnn_loss = srnn_loss.cpu().data.numpy() # Denormalize the output srnn_pred_expmap = data_utils.revert_output_format( srnn_poses, data_mean, data_std, dim_to_ignore, actions, not args.omit_one_hot ) # Save the errors here mean_errors = np.zeros( (len(srnn_pred_expmap), srnn_pred_expmap[0].shape[0]) ) # Training is done in exponential map, but the error is reported in # Euler angles, as in previous work. # See https://github.com/asheshjain399/RNNexp/issues/6#issuecomment-247769197 N_SEQUENCE_TEST = 8 for i in np.arange(N_SEQUENCE_TEST): eulerchannels_pred = srnn_pred_expmap[i] # Convert from exponential map to Euler angles for j in np.arange( eulerchannels_pred.shape[0] ): for k in np.arange(3,97,3): eulerchannels_pred[j,k:k+3] = data_utils.rotmat2euler( data_utils.expmap2rotmat( eulerchannels_pred[j,k:k+3] )) # The global translation (first 3 entries) and global rotation # (next 3 entries) are also not considered in the error, so the_key # are set to zero. # See https://github.com/asheshjain399/RNNexp/issues/6#issuecomment-249404882 gt_i=np.copy(srnn_gts_euler[action][i]) gt_i[:,0:6] = 0 # Now compute the l2 error. The following is numpy port of the error # function provided by Ashesh Jain (in matlab), available at # https://github.com/asheshjain399/RNNexp/blob/srnn/structural_rnn/CRFProblems/H3.6m/dataParser/Utils/motionGenerationError.m#L40-L54 idx_to_use = np.where( np.std( gt_i, 0 ) > 1e-4 )[0] euc_error = np.power( gt_i[:,idx_to_use] - eulerchannels_pred[:,idx_to_use], 2) euc_error = np.sum(euc_error, 1) euc_error = np.sqrt( euc_error ) mean_errors[i,:] = euc_error # This is simply the mean error over the N_SEQUENCE_TEST examples mean_mean_errors = np.mean( mean_errors, 0 ) # Pretty print of the results for 80, 160, 320, 400, 560 and 1000 ms print("{0: <16} |".format(action), end="") for ms in [1,3,7,9,13,24]: if args.seq_length_out >= ms+1: print(" {0:.3f} |".format( mean_mean_errors[ms] ), end="") else: print(" n/a |", end="") print() print() print("============================\n" "Global step: %d\n" "Learning rate: %.4f\n" "Step-time (ms): %.4f\n" "Train loss avg: %.4f\n" "--------------------------\n" "Val loss: %.4f\n" "srnn loss: %.4f\n" "MSE loss: %.4f\n" "============================" % (current_step, args.learning_rate, step_time*1000, loss, val_loss, srnn_loss, mse_loss)) with open("training_out.txt", 'a+') as f: f.write(action + " " + str(current_step)+": "+str(val_loss)+"\n") torch.save(model, train_dir + '/model_' + str(current_step)) print() previous_losses.append(loss) # Reset global time and loss step_time, loss = 0, 0 sys.stdout.flush() def get_srnn_gts( actions, model, test_set, data_mean, data_std, dim_to_ignore, one_hot, to_euler=True ): """ Get the ground truths for srnn's sequences, and convert to Euler angles (by default). (the error is always computed in Euler angles). Args actions: a list of actions to get ground truths for. model: training model we are using (we only use the "get_batch" method). test_set: dictionary with normalized training data. data_mean: d-long vector with the mean of the training data. data_std: d-long vector with the standard deviation of the training data. dim_to_ignore: dimensions that we are not using to train/predict. one_hot: whether the data comes with one-hot encoding indicating action. to_euler: whether to convert the angles to Euler format or keep thm in exponential map Returns srnn_gts_euler: a dictionary where the keys are actions, and the values are the ground_truth, denormalized expected outputs of srnns's seeds. """ srnn_gts_euler = {} for action in actions: srnn_gt_euler = [] _, _, srnn_expmap = model.get_batch_srnn( test_set, action ) # expmap -> rotmat -> euler for i in np.arange( srnn_expmap.shape[0] ): denormed = data_utils.unNormalizeData(srnn_expmap[i,:,:], data_mean, data_std, dim_to_ignore, actions, one_hot ) if to_euler: for j in np.arange( denormed.shape[0] ): for k in np.arange(3,97,3): denormed[j,k:k+3] = data_utils.rotmat2euler( data_utils.expmap2rotmat( denormed[j,k:k+3] )) srnn_gt_euler.append( denormed ); # Put back in the dictionary srnn_gts_euler[action] = srnn_gt_euler return srnn_gts_euler def sample(): """Sample predictions for srnn's seeds""" actions = define_actions( args.action ) if True: # === Create the model === print("Creating %d layers of %d units." % (args.num_layers, args.size)) sampling = True model = create_model(actions, sampling) if not args.use_cpu: model = model.cuda() print("Model created") # Load all the data train_set, test_set, data_mean, data_std, dim_to_ignore, dim_to_use = read_all_data( actions, args.seq_length_in, args.seq_length_out, args.data_dir, not args.omit_one_hot ) # === Read and denormalize the gt with srnn's seeds, as we'll need them # many times for evaluation in Euler Angles === srnn_gts_expmap = get_srnn_gts( actions, model, test_set, data_mean, data_std, dim_to_ignore, not args.omit_one_hot, to_euler=False ) srnn_gts_euler = get_srnn_gts( actions, model, test_set, data_mean, data_std, dim_to_ignore, not args.omit_one_hot ) # Clean and create a new h5 file of samples SAMPLES_FNAME = 'samples.h5' try: os.remove( SAMPLES_FNAME ) except OSError: pass # Predict and save for each action for action in actions: # Make prediction with srnn' seeds. These will just be in expangles. encoder_inputs, decoder_inputs, decoder_outputs = model.get_batch_srnn( test_set, action ) encoder_inputs = torch.from_numpy(encoder_inputs).float() decoder_inputs = torch.from_numpy(decoder_inputs).float() decoder_outputs = torch.from_numpy(decoder_outputs).float() if not args.use_cpu: encoder_inputs = encoder_inputs.cuda() decoder_inputs = decoder_inputs.cuda() decoder_outputs = decoder_outputs.cuda() encoder_inputs = Variable(encoder_inputs) decoder_inputs = Variable(decoder_inputs) decoder_outputs = Variable(decoder_outputs) srnn_poses = model(encoder_inputs, decoder_inputs) srnn_loss = (srnn_poses[..., :54] - decoder_outputs)**2 srnn_loss.cpu().data.numpy() srnn_loss = srnn_loss.mean() srnn_poses = srnn_poses.cpu().data.numpy() srnn_poses = srnn_poses.transpose([1,0,2]) srnn_loss = srnn_loss.cpu().data.numpy() # denormalizes too srnn_pred_expmap = data_utils.revert_output_format(srnn_poses[..., :54], data_mean, data_std, dim_to_ignore, actions, not args.omit_one_hot ) # Save the samples with h5py.File( SAMPLES_FNAME, 'a' ) as hf: for i in np.arange(8): # Save conditioning ground truth node_name = 'expmap/gt/{1}_{0}'.format(i, action) hf.create_dataset( node_name, data=srnn_gts_expmap[action][i] ) # Save prediction node_name = 'expmap/preds/{1}_{0}'.format(i, action) hf.create_dataset( node_name, data=srnn_pred_expmap[i] ) # Compute and save the errors here mean_errors = np.zeros( (len(srnn_pred_expmap), srnn_pred_expmap[0].shape[0]) ) for i in np.arange(8): eulerchannels_pred = srnn_pred_expmap[i] for j in np.arange( eulerchannels_pred.shape[0] ): for k in np.arange(3,97,3): eulerchannels_pred[j,k:k+3] = data_utils.rotmat2euler( data_utils.expmap2rotmat( eulerchannels_pred[j,k:k+3] )) eulerchannels_pred[:,0:6] = 0 # Pick only the dimensions with sufficient standard deviation. Others are ignored. idx_to_use = np.where( np.std( eulerchannels_pred, 0 ) > 1e-4 )[0] euc_error = np.power( srnn_gts_euler[action][i][:,idx_to_use] - eulerchannels_pred[:,idx_to_use], 2) euc_error = np.sum(euc_error, 1) euc_error = np.sqrt( euc_error ) mean_errors[i,:] = euc_error mean_mean_errors = np.mean( mean_errors, 0 ) print( action ) print( ','.join(map(str, mean_mean_errors.tolist() )) ) with h5py.File( SAMPLES_FNAME, 'a' ) as hf: node_name = 'mean_{0}_error'.format( action ) hf.create_dataset( node_name, data=mean_mean_errors ) return def define_actions( action ): """ Define the list of actions we are using. Args action: String with the passed action. Could be "all" Returns actions: List of strings of actions Raises ValueError if the action is not included in H3.6M """ actions = ["walking", "eating", "smoking", "discussion", "directions", "greeting", "phoning", "posing", "purchases", "sitting", "sittingdown", "takingphoto", "waiting", "walkingdog", "walkingtogether"] if action in actions: return [action] if action == "all": return actions if action == "all_srnn": return ["walking", "eating", "smoking", "discussion"] raise( ValueError, "Unrecognized action: %d" % action ) def read_all_data( actions, seq_length_in, seq_length_out, data_dir, one_hot ): """ Loads data for training/testing and normalizes it ALSO REMOVING UNUSED DIMENSIONS AS DEFINED IN normalization_stats()! Does nothing to rotation format. Args actions: list of strings (actions) to load seq_length_in: number of frames to use in the burn-in sequence seq_length_out: number of frames to use in the output sequence data_dir: directory to load the data from one_hot: whether to use one-hot encoding per action Returns train_set: dictionary with normalized training data test_set: dictionary with test data data_mean: d-long vector with the mean of the training data data_std: d-long vector with the standard dev of the training data dim_to_ignore: dimensions that are not used becaused stdev is too small dim_to_use: dimensions that we are actually using in the model """ # === Read training data === print ("Reading training data (seq_len_in: {0}, seq_len_out {1}).".format( seq_length_in, seq_length_out)) train_subject_ids = [1,6,7,8,9,11] test_subject_ids = [5] train_set, complete_train = data_utils.load_data( data_dir, train_subject_ids, actions, one_hot ) test_set, complete_test = data_utils.load_data( data_dir, test_subject_ids, actions, one_hot ) #Convert to euler angles here I guess? # Compute normalization stats data_mean, data_std, dim_to_ignore, dim_to_use = data_utils.normalization_stats(complete_train) # Normalize -- subtract mean, divide by stdev train_set = data_utils.normalize_data( train_set, data_mean, data_std, dim_to_use, actions, one_hot ) test_set = data_utils.normalize_data( test_set, data_mean, data_std, dim_to_use, actions, one_hot ) print("done reading data.") return train_set, test_set, data_mean, data_std, dim_to_ignore, dim_to_use def main(): if args.sample: sample() else: import sys with open("training_out.txt", 'a+') as f: f.write("============================================================\n"+str(sys.argv)+"\n") train() if __name__ == "__main__": main()

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import os import random import sys import time import h5py import numpy as np from six.moves import xrange import data_utils import seq2seq_model import torch import torch.optim as optim from torch.autograd import Variable import argparse parser = argparse.ArgumentParser(description='Train RNN for human pose estimation') parser.add_argument('--learning_rate', dest='learning_rate', help='Learning rate', default=0.005, type=float) parser.add_argument('--learning_rate_decay_factor', dest='learning_rate_decay_factor', help='Learning rate is multiplied by this much. 1 means no decay.', default=0.95, type=float) parser.add_argument('--learning_rate_step', dest='learning_rate_step', help='Every this many steps, do decay.', default=10000, type=int) parser.add_argument('--batch_size', dest='batch_size', help='Batch size to use during training.', default=16, type=int) parser.add_argument('--max_gradient_norm', dest='max_gradient_norm', help='Clip gradients to this norm.', default=5, type=float) parser.add_argument('--iterations', dest='iterations', help='Iterations to train for.', default=1e5, type=int) parser.add_argument('--test_every', dest='test_every', help='', default=1000, type=int) parser.add_argument('--architecture', dest='architecture', help='Seq2seq architecture to use: [basic, tied].', default='tied', type=str) parser.add_argument('--loss_to_use', dest='loss_to_use', help='The type of loss to use, supervised or sampling_based', default='sampling_based', type=str) parser.add_argument('--residual_velocities', dest='residual_velocities', help='Add a residual connection that effectively models velocities',action='store_true', default=False) parser.add_argument('--size', dest='size', help='Size of each model layer.', default=1024, type=int) parser.add_argument('--num_layers', dest='num_layers', help='Number of layers in the model.', default=1, type=int) parser.add_argument('--seq_length_in', dest='seq_length_in', help='Number of frames to feed into the encoder. 25 fp', default=50, type=int) parser.add_argument('--seq_length_out', dest='seq_length_out', help='Number of frames that the decoder has to predict. 25fps', default=10, type=int) parser.add_argument('--omit_one_hot', dest='omit_one_hot', help='', action='store_true', default=False) parser.add_argument('--taylor', dest='finite_taylor_extrapolate', help='Whether to augment the network with a taylor series extrapolation from a finite difference scheme of the previous frames', action='store_true', default=False) parser.add_argument('--data_dir', dest='data_dir', help='Data directory', default=os.path.normpath("./data/h3.6m/dataset"), type=str) parser.add_argument('--train_dir', dest='train_dir', help='Training directory', default=os.path.normpath("./experiments/"), type=str) parser.add_argument('--action', dest='action', help='The action to train on. all means all the actions, all_periodic means walking, eating and smoking', default='all', type=str) parser.add_argument('--use_cpu', dest='use_cpu', help='', action='store_true', default=False) parser.add_argument('--load', dest='load', help='Try to load a previous checkpoint.', default=0, type=int) parser.add_argument('--sample', dest='sample', help='Set to True for sampling.', action='store_true', default=False) parser.add_argument('--distribution_output_direct', dest='distribution_output_direct', default=False) args = parser.parse_args() train_dir = os.path.normpath(os.path.join( args.train_dir, args.action, 'out_{0}'.format(args.seq_length_out), 'iterations_{0}'.format(args.iterations), args.architecture, args.loss_to_use, 'omit_one_hot' if args.omit_one_hot else 'one_hot', 'depth_{0}'.format(args.num_layers), 'size_{0}'.format(args.size), 'lr_{0}'.format(args.learning_rate), 'residual_vel' if args.residual_velocities else 'not_residual_vel')) print(train_dir) os.makedirs(train_dir, exist_ok=True) def create_model(actions, sampling=False): model = seq2seq_model.Seq2SeqModel( args.architecture, args.seq_length_in if not sampling else 50, args.seq_length_out if not sampling else 100, args.size, args.num_layers, args.max_gradient_norm, args.batch_size, args.learning_rate, args.learning_rate_decay_factor, args.loss_to_use if not sampling else "sampling_based", len( actions ), not args.omit_one_hot, args.residual_velocities, args.finite_taylor_extrapolate, output_as_normal_distribution = args.distribution_output_direct, dtype=torch.float32) if args.load <= 0: return model print("Loading model") model = torch.load(train_dir + '/model_' + str(args.load)) if sampling: model.source_seq_len = 50 model.target_seq_len = 100 return model def clean_batch(batch): encoder_inputs, decoder_inputs, decoder_outputs = batch encoder_inputs = torch.from_numpy(encoder_inputs).float() decoder_inputs = torch.from_numpy(decoder_inputs).float() decoder_outputs = torch.from_numpy(decoder_outputs).float() if not args.use_cpu: encoder_inputs = encoder_inputs.cuda() decoder_inputs = decoder_inputs.cuda() decoder_outputs = decoder_outputs.cuda() encoder_inputs = Variable(encoder_inputs) decoder_inputs = Variable(decoder_inputs) decoder_outputs = Variable(decoder_outputs) return (encoder_inputs, decoder_inputs, decoder_outputs) import flags def get_loss(output, truth): if flags.translate_loss_func == "mse": return ( (output-truth)**2 ).mean() if flags.translate_loss_func == "me": return ( np.abs(output-truth) ).mean() if flags.translate_loss_func == "mle": assert(output.shape[-1] == truth.shape[-1] * 2) means = output[..., :int(truth.shape[-1])] sigmas = output[..., int(truth.shape[-1]):] neg_log_likelihood = torch.sum(torch.log(torch.pow(sigmas, 2))) / 2.0 p1 = (means - truth) p2 = p1 / sigmas p3 = torch.pow(p2, 2) neg_log_likelihood += torch.numel(means) / 2.0 * np.log(2.0*3.1415926) neg_log_likelihood += torch.sum(p3) / 2.0 return neg_log_likelihood def train(): actions = define_actions( args.action ) number_of_actions = len( actions ) train_set, test_set, data_mean, data_std, dim_to_ignore, dim_to_use = read_all_data( actions, args.seq_length_in, args.seq_length_out, args.data_dir, not args.omit_one_hot ) if True: model = create_model(actions, args.sample) if not args.use_cpu: model = model.cuda() srnn_gts_euler = get_srnn_gts( actions, model, test_set, data_mean, data_std, dim_to_ignore, not args.omit_one_hot ) step_time, loss, val_loss = 0.0, 0.0, 0.0 current_step = 0 if args.load <= 0 else args.load + 1 previous_losses = [] step_time, loss = 0, 0 optimiser = optim.SGD(model.parameters(), lr=args.learning_rate) for _ in range( args.iterations ): optimiser.zero_grad() model.train() start_time = time.time() encoder_inputs, decoder_inputs, decoder_outputs = clean_batch(model.get_batch( train_set, not args.omit_one_hot )) preds = model(encoder_inputs, decoder_inputs) step_loss = get_loss(preds, decoder_outputs) step_loss.backward() optimiser.step() step_loss = step_loss.cpu().data.numpy() preds = preds[..., :54] step_time += (time.time() - start_time) / args.test_every loss += step_loss / args.test_every current_step += 1 if current_step % args.learning_rate_step == 0: args.learning_rate = args.learning_rate*args.learning_rate_decay_factor optimiser = optim.Adam(model.parameters(), lr=args.learning_rate, betas = (0.9, 0.999)) print("Decay learning rate. New value at " + str(args.learning_rate)) if current_step % args.test_every == 0: model.eval() encoder_inputs, decoder_inputs, decoder_outputs = clean_batch(model.get_batch( test_set, not args.omit_one_hot )) preds = model(encoder_inputs, decoder_inputs) mse_loss = torch.mean( (preds[..., :54] - decoder_outputs)**2) step_loss = get_loss(preds, decoder_outputs) val_loss = step_loss preds = preds[..., :54] print() print("{0: <16} |".format("milliseconds"), end="") for ms in [80, 160, 320, 400, 560, 1000]: print(" {0:5d} |".format(ms), end="") print() for action in actions: # Evaluate the model on the test batches #### Evaluate model on action encoder_inputs, decoder_inputs, decoder_outputs = clean_batch(model.get_batch(test_set, action)) srnn_poses = model(encoder_inputs, decoder_inputs) srnn_loss = get_loss(srnn_poses, decoder_outputs) #TODO: srnn_poses = srnn_poses[...,:54] srnn_poses = srnn_poses.cpu().data.numpy() srnn_poses = srnn_poses.transpose([1,0,2]) srnn_loss = srnn_loss.cpu().data.numpy() # Denormalize the output srnn_pred_expmap = data_utils.revert_output_format( srnn_poses, data_mean, data_std, dim_to_ignore, actions, not args.omit_one_hot ) # Save the errors here mean_errors = np.zeros( (len(srnn_pred_expmap), srnn_pred_expmap[0].shape[0]) ) # Training is done in exponential map, but the error is reported in # Euler angles, as in previous work. # See https://github.com/asheshjain399/RNNexp/issues/6#issuecomment-247769197 N_SEQUENCE_TEST = 8 for i in np.arange(N_SEQUENCE_TEST): eulerchannels_pred = srnn_pred_expmap[i] # Convert from exponential map to Euler angles for j in np.arange( eulerchannels_pred.shape[0] ): for k in np.arange(3,97,3): eulerchannels_pred[j,k:k+3] = data_utils.rotmat2euler( data_utils.expmap2rotmat( eulerchannels_pred[j,k:k+3] )) # The global translation (first 3 entries) and global rotation # (next 3 entries) are also not considered in the error, so the_key # are set to zero. # See https://github.com/asheshjain399/RNNexp/issues/6#issuecomment-249404882 gt_i=np.copy(srnn_gts_euler[action][i]) gt_i[:,0:6] = 0 # Now compute the l2 error. The following is numpy port of the error # function provided by Ashesh Jain (in matlab), available at # https://github.com/asheshjain399/RNNexp/blob/srnn/structural_rnn/CRFProblems/H3.6m/dataParser/Utils/motionGenerationError.m#L40-L54 idx_to_use = np.where( np.std( gt_i, 0 ) > 1e-4 )[0] euc_error = np.power( gt_i[:,idx_to_use] - eulerchannels_pred[:,idx_to_use], 2) euc_error = np.sum(euc_error, 1) euc_error = np.sqrt( euc_error ) mean_errors[i,:] = euc_error # This is simply the mean error over the N_SEQUENCE_TEST examples mean_mean_errors = np.mean( mean_errors, 0 ) # Pretty print of the results for 80, 160, 320, 400, 560 and 1000 ms print("{0: <16} |".format(action), end="") for ms in [1,3,7,9,13,24]: if args.seq_length_out >= ms+1: print(" {0:.3f} |".format( mean_mean_errors[ms] ), end="") else: print(" n/a |", end="") print() print() print("============================\n" "Global step: %d\n" "Learning rate: %.4f\n" "Step-time (ms): %.4f\n" "Train loss avg: %.4f\n" "--------------------------\n" "Val loss: %.4f\n" "srnn loss: %.4f\n" "MSE loss: %.4f\n" "============================" % (current_step, args.learning_rate, step_time*1000, loss, val_loss, srnn_loss, mse_loss)) with open("training_out.txt", 'a+') as f: f.write(action + " " + str(current_step)+": "+str(val_loss)+"\n") torch.save(model, train_dir + '/model_' + str(current_step)) print() previous_losses.append(loss) # Reset global time and loss step_time, loss = 0, 0 sys.stdout.flush() def get_srnn_gts( actions, model, test_set, data_mean, data_std, dim_to_ignore, one_hot, to_euler=True ): srnn_gts_euler = {} for action in actions: srnn_gt_euler = [] _, _, srnn_expmap = model.get_batch_srnn( test_set, action ) # expmap -> rotmat -> euler for i in np.arange( srnn_expmap.shape[0] ): denormed = data_utils.unNormalizeData(srnn_expmap[i,:,:], data_mean, data_std, dim_to_ignore, actions, one_hot ) if to_euler: for j in np.arange( denormed.shape[0] ): for k in np.arange(3,97,3): denormed[j,k:k+3] = data_utils.rotmat2euler( data_utils.expmap2rotmat( denormed[j,k:k+3] )) srnn_gt_euler.append( denormed ); # Put back in the dictionary srnn_gts_euler[action] = srnn_gt_euler return srnn_gts_euler def sample(): actions = define_actions( args.action ) if True: # === Create the model === print("Creating %d layers of %d units." % (args.num_layers, args.size)) sampling = True model = create_model(actions, sampling) if not args.use_cpu: model = model.cuda() print("Model created") # Load all the data train_set, test_set, data_mean, data_std, dim_to_ignore, dim_to_use = read_all_data( actions, args.seq_length_in, args.seq_length_out, args.data_dir, not args.omit_one_hot ) # === Read and denormalize the gt with srnn's seeds, as we'll need them # many times for evaluation in Euler Angles === srnn_gts_expmap = get_srnn_gts( actions, model, test_set, data_mean, data_std, dim_to_ignore, not args.omit_one_hot, to_euler=False ) srnn_gts_euler = get_srnn_gts( actions, model, test_set, data_mean, data_std, dim_to_ignore, not args.omit_one_hot ) # Clean and create a new h5 file of samples SAMPLES_FNAME = 'samples.h5' try: os.remove( SAMPLES_FNAME ) except OSError: pass # Predict and save for each action for action in actions: # Make prediction with srnn' seeds. These will just be in expangles. encoder_inputs, decoder_inputs, decoder_outputs = model.get_batch_srnn( test_set, action ) encoder_inputs = torch.from_numpy(encoder_inputs).float() decoder_inputs = torch.from_numpy(decoder_inputs).float() decoder_outputs = torch.from_numpy(decoder_outputs).float() if not args.use_cpu: encoder_inputs = encoder_inputs.cuda() decoder_inputs = decoder_inputs.cuda() decoder_outputs = decoder_outputs.cuda() encoder_inputs = Variable(encoder_inputs) decoder_inputs = Variable(decoder_inputs) decoder_outputs = Variable(decoder_outputs) srnn_poses = model(encoder_inputs, decoder_inputs) srnn_loss = (srnn_poses[..., :54] - decoder_outputs)**2 srnn_loss.cpu().data.numpy() srnn_loss = srnn_loss.mean() srnn_poses = srnn_poses.cpu().data.numpy() srnn_poses = srnn_poses.transpose([1,0,2]) srnn_loss = srnn_loss.cpu().data.numpy() srnn_pred_expmap = data_utils.revert_output_format(srnn_poses[..., :54], data_mean, data_std, dim_to_ignore, actions, not args.omit_one_hot ) with h5py.File( SAMPLES_FNAME, 'a' ) as hf: for i in np.arange(8): node_name = 'expmap/gt/{1}_{0}'.format(i, action) hf.create_dataset( node_name, data=srnn_gts_expmap[action][i] ) node_name = 'expmap/preds/{1}_{0}'.format(i, action) hf.create_dataset( node_name, data=srnn_pred_expmap[i] ) mean_errors = np.zeros( (len(srnn_pred_expmap), srnn_pred_expmap[0].shape[0]) ) for i in np.arange(8): eulerchannels_pred = srnn_pred_expmap[i] for j in np.arange( eulerchannels_pred.shape[0] ): for k in np.arange(3,97,3): eulerchannels_pred[j,k:k+3] = data_utils.rotmat2euler( data_utils.expmap2rotmat( eulerchannels_pred[j,k:k+3] )) eulerchannels_pred[:,0:6] = 0 idx_to_use = np.where( np.std( eulerchannels_pred, 0 ) > 1e-4 )[0] euc_error = np.power( srnn_gts_euler[action][i][:,idx_to_use] - eulerchannels_pred[:,idx_to_use], 2) euc_error = np.sum(euc_error, 1) euc_error = np.sqrt( euc_error ) mean_errors[i,:] = euc_error mean_mean_errors = np.mean( mean_errors, 0 ) print( action ) print( ','.join(map(str, mean_mean_errors.tolist() )) ) with h5py.File( SAMPLES_FNAME, 'a' ) as hf: node_name = 'mean_{0}_error'.format( action ) hf.create_dataset( node_name, data=mean_mean_errors ) return def define_actions( action ): actions = ["walking", "eating", "smoking", "discussion", "directions", "greeting", "phoning", "posing", "purchases", "sitting", "sittingdown", "takingphoto", "waiting", "walkingdog", "walkingtogether"] if action in actions: return [action] if action == "all": return actions if action == "all_srnn": return ["walking", "eating", "smoking", "discussion"] raise( ValueError, "Unrecognized action: %d" % action ) def read_all_data( actions, seq_length_in, seq_length_out, data_dir, one_hot ): print ("Reading training data (seq_len_in: {0}, seq_len_out {1}).".format( seq_length_in, seq_length_out)) train_subject_ids = [1,6,7,8,9,11] test_subject_ids = [5] train_set, complete_train = data_utils.load_data( data_dir, train_subject_ids, actions, one_hot ) test_set, complete_test = data_utils.load_data( data_dir, test_subject_ids, actions, one_hot ) data_mean, data_std, dim_to_ignore, dim_to_use = data_utils.normalization_stats(complete_train) train_set = data_utils.normalize_data( train_set, data_mean, data_std, dim_to_use, actions, one_hot ) test_set = data_utils.normalize_data( test_set, data_mean, data_std, dim_to_use, actions, one_hot ) print("done reading data.") return train_set, test_set, data_mean, data_std, dim_to_ignore, dim_to_use def main(): if args.sample: sample() else: import sys with open("training_out.txt", 'a+') as f: f.write("============================================================\n"+str(sys.argv)+"\n") train() if __name__ == "__main__": main()

true

1c2e97f80754cf03aef7cce4dbf0e63f4c25eeb5

141

py

Python

solutions/Video_analysis/webservice/service/__init__.py

xiaogaozi/bootcamp

d1a4b69edc3f7e31ce3809043128348aa3597df6

[ "Apache-2.0" ]

1

2021-04-06T06:13:20.000Z

solutions/Video_analysis/webservice/service/__init__.py

xiaogaozi/bootcamp

d1a4b69edc3f7e31ce3809043128348aa3597df6

[ "Apache-2.0" ]

null

solutions/Video_analysis/webservice/service/__init__.py

xiaogaozi/bootcamp

d1a4b69edc3f7e31ce3809043128348aa3597df6

[ "Apache-2.0" ]

null

import logging logging.basicConfig(filename='app.log', filemode='w', format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)

47

125

0.723404

import logging logging.basicConfig(filename='app.log', filemode='w', format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)

true

1c2e98c5d3a6ae7e454bd0a11c1fd19a668d87f1

727

py

Python

0-python-tutorial/15-whileLoops01.py

luis2ra/py3-00-w3schools

6bb851837f8ef9520491d13fa2c909047c9b18cf

[ "MIT" ]

null

0-python-tutorial/15-whileLoops01.py

luis2ra/py3-00-w3schools

6bb851837f8ef9520491d13fa2c909047c9b18cf

[ "MIT" ]

null

0-python-tutorial/15-whileLoops01.py

luis2ra/py3-00-w3schools

6bb851837f8ef9520491d13fa2c909047c9b18cf

[ "MIT" ]

null

# Demo Python While Loops - The while Loop ''' Python Loops Python has two primitive loop commands: * while loops * for loops The while Loop With the while loop we can execute a set of statements as long as a condition is true. The break Statement With the break statement we can stop the loop even if the while condition is true. The continue Statement With the continue statement we can stop the current iteration, and continue with the next. The else Statement With the else statement we can run a block of code once when the condition no longer is true. ''' # Print i as long as i is less than 6: i = 1 while i < 6: print(i) i += 1 # Note: remember to increment i, or else the loop will continue forever.

24.233333

93

0.742779

i = 1 while i < 6: print(i) i += 1

true

1c2e9b907048749d4a615844f89a74dedb32a1ae

2,412

py

Python

autoimpute/analysis/metrics.py

gjdv/autoimpute

aa418102d3b64fc7e0c0dafa6839746f0b9a6545

[ "MIT" ]

191

2019-03-16T17:00:33.000Z

2022-03-11T12:14:17.000Z

autoimpute/analysis/metrics.py

MlataIbrahim/autoimpute

d22cd86db6facd4a68746c8c0fcb3fae70071dac

[ "MIT" ]

57

2019-03-09T23:59:38.000Z

2022-03-01T08:17:33.000Z

autoimpute/analysis/metrics.py

MlataIbrahim/autoimpute

d22cd86db6facd4a68746c8c0fcb3fae70071dac

[ "MIT" ]

19

2019-04-13T19:01:23.000Z

2021-05-14T08:59:27.000Z

"""This module devises metrics to compare estimates from analysis models.""" import numpy as np import pandas as pd def raw_bias(Q_bar, Q): """Calculate raw bias between coefficients Q and actual Q. Q_bar can be one estimate (scalar) or a vector of estimates. This equation subtracts the expected Q_bar from Q, element-wise. The result is the bias of each coefficient from its true value. Args: Q_bar (number, array): single estimate or array of estimates. Q (number, array): single truth or array of truths. Returns: scalar, array: element-wise difference between estimates and truths. Raises: ValueError: Shape mismatch ValueError: Q_bar and Q not the same length """ # handle errors first shape_err = "Q_bar & Q must be scalars or vectors of same length." if isinstance(Q_bar, pd.DataFrame): s = len(Q_bar.shape) if s != 1: raise ValueError(shape_err) if isinstance(Q, pd.DataFrame): s = len(Q.shape) if s != 1: raise ValueError(shape_err) if len(Q_bar) != len(Q): raise ValueError(shape_err) # convert any lists to ensure element-wise performed if isinstance(Q_bar, (tuple, list)): Q_bar = np.array(Q_bar) if isinstance(Q, (tuple, list)): Q = np.array(Q) # perform element-wise subtraction rb = Q_bar - Q return rb def percent_bias(Q_bar, Q): """Calculate precent bias between coefficients Q and actual Q. Q_bar can be one estimate (scalar) or a vector of estimates. This equation subtracts the expected Q_bar from Q, element-wise. The result is the bias of each coefficient from its true value. We then divide this number by Q itself, again in element-wise fashion, to produce % bias. Args: Q_bar (number, array): single estimate or array of estimates. Q (number, array): single truth or array of truths. Returns: scalar, array: element-wise difference between estimates and truths. Raises: ValueError: Shape mismatch ValueError: Q_bar and Q not the same length """ # calling this method will validate Q_bar and Q rb = raw_bias(Q_bar, Q) # convert Q if necessary. must re-perform operation if isinstance(Q, (tuple, list)): Q = np.array(Q) pct_bias = 100 * (abs(rb)/Q) return pct_bias

30.531646

78

0.65796

import numpy as np import pandas as pd def raw_bias(Q_bar, Q): shape_err = "Q_bar & Q must be scalars or vectors of same length." if isinstance(Q_bar, pd.DataFrame): s = len(Q_bar.shape) if s != 1: raise ValueError(shape_err) if isinstance(Q, pd.DataFrame): s = len(Q.shape) if s != 1: raise ValueError(shape_err) if len(Q_bar) != len(Q): raise ValueError(shape_err) if isinstance(Q_bar, (tuple, list)): Q_bar = np.array(Q_bar) if isinstance(Q, (tuple, list)): Q = np.array(Q) rb = Q_bar - Q return rb def percent_bias(Q_bar, Q): rb = raw_bias(Q_bar, Q) if isinstance(Q, (tuple, list)): Q = np.array(Q) pct_bias = 100 * (abs(rb)/Q) return pct_bias

true

1c2e9bcf8eeceddc793d357ac20131bc4c084d2d

2,020

py

Python

download_and_clean_data_scripts/pixabay/pixabay_main_custom.py

NMag-ze/Colorization

1c91fb69d3505de6cd746dab3ddc15a704aa028a

[ "MIT" ]

null

download_and_clean_data_scripts/pixabay/pixabay_main_custom.py

NMag-ze/Colorization

1c91fb69d3505de6cd746dab3ddc15a704aa028a

[ "MIT" ]

null

download_and_clean_data_scripts/pixabay/pixabay_main_custom.py

NMag-ze/Colorization

1c91fb69d3505de6cd746dab3ddc15a704aa028a

[ "MIT" ]

null

# -*- coding: utf-8 -*- # @Time : 2018/8/16 10:59 # @Author : 陈子昂 import os import requests from bs4 import BeautifulSoup from tqdm import tqdm import sys from utils import save_img, path_processor, img_name_processor def pexels(keyword): img_cnt = 0 if not keyword: sys.exit('程序退出：未输入关键字！') for page in tqdm(range(1, 50)): print(f'\n-----[{keyword}]正在爬取第{page}页-----') pexels_url = "https://www.pexels.com/search/%s/?page=%s" % (keyword, page) headers = {'User-Agent':'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36'} res = requests.get(pexels_url,headers=headers,verify=False) # print(res.text) if 'Sorry, no pictures found!' in res.text: print('-*--*--*-爬取完毕-*--*--*-') sys.exit(0) soup = BeautifulSoup(res.text, 'lxml') # print(soup) articles = soup.find_all('article') # print(len(articles)) for article in articles: src = article.img.attrs['src'] print(src) path = rf'D://人脸相关的图片//pexels//{keyword}' if not os.path.exists(path): os.makedirs(path) filename = img_name_processor(src) file = os.path.join(path, filename) save_img(file=file, src=src) if __name__ == "__main__": categories = ['male', 'old', 'vintage', 'dog', 'cat', 'building', 'nature', 'castle', 'water', 'ocean', 'cities', 'body', 'hands', 'people', 'culture', 'religion', 'color', 'patterns', 'houses', 'vintage', 'river', 'landscape', 'lights', 'animals', 'wallpaper', 'texture', 'current events', 'architecture', 'business', 'work', 'travel', 'fashion', 'food', 'drink', 'spirituality', 'experimental', 'health', 'arts', 'culture', 'children', 'people', 'events', 'trees', 'green', 'yellow', 'pink', 'blue', 'red', 'minimal', 'hands', 'head', 'eyes', 'mouth', 'eating', 'playing', 'sports'] for i in categories: pexels(i)

42.978723

588

0.587624

import os import requests from bs4 import BeautifulSoup from tqdm import tqdm import sys from utils import save_img, path_processor, img_name_processor def pexels(keyword): img_cnt = 0 if not keyword: sys.exit('程序退出：未输入关键字！') for page in tqdm(range(1, 50)): print(f'\n-----[{keyword}]正在爬取第{page}页-----') pexels_url = "https://www.pexels.com/search/%s/?page=%s" % (keyword, page) headers = {'User-Agent':'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36'} res = requests.get(pexels_url,headers=headers,verify=False) if 'Sorry, no pictures found!' in res.text: print('-*--*--*-爬取完毕-*--*--*-') sys.exit(0) soup = BeautifulSoup(res.text, 'lxml') articles = soup.find_all('article') for article in articles: src = article.img.attrs['src'] print(src) path = rf'D://人脸相关的图片//pexels//{keyword}' if not os.path.exists(path): os.makedirs(path) filename = img_name_processor(src) file = os.path.join(path, filename) save_img(file=file, src=src) if __name__ == "__main__": categories = ['male', 'old', 'vintage', 'dog', 'cat', 'building', 'nature', 'castle', 'water', 'ocean', 'cities', 'body', 'hands', 'people', 'culture', 'religion', 'color', 'patterns', 'houses', 'vintage', 'river', 'landscape', 'lights', 'animals', 'wallpaper', 'texture', 'current events', 'architecture', 'business', 'work', 'travel', 'fashion', 'food', 'drink', 'spirituality', 'experimental', 'health', 'arts', 'culture', 'children', 'people', 'events', 'trees', 'green', 'yellow', 'pink', 'blue', 'red', 'minimal', 'hands', 'head', 'eyes', 'mouth', 'eating', 'playing', 'sports'] for i in categories: pexels(i)

true

1c2e9bd62250529bf5b8c71a6f78cdc41dea29c9

1,568

py

Python

stats_scripts/data_management/validate_consistancy.py

LilithHafner/SymbulationEmp

fb53c7fbefa03f6d901cdd49bdb823a637449bba

[ "MIT" ]

null

stats_scripts/data_management/validate_consistancy.py

LilithHafner/SymbulationEmp

fb53c7fbefa03f6d901cdd49bdb823a637449bba

[ "MIT" ]

null

stats_scripts/data_management/validate_consistancy.py

LilithHafner/SymbulationEmp

fb53c7fbefa03f6d901cdd49bdb823a637449bba

[ "MIT" ]

null

lines = open('collated_data.data').readlines() header = [x.strip('"') for x in lines[0].split()] independent_variables = 'HRR', 'HRR', 'SLR', 'BS', 'BT', 'SL', 'SYN', 'POP', 'UPS', 'T' dependant_variables = 'host_count', 'sym_count', 'survival', 'moi' omit = ('file_name',) versions = '"Standard-1.1"', '"Duplicate-1.1"' data = {} passes, fails = 0, 0 for line in lines: values = line.split() if values[0] in versions: independent_values = [] dependant_values = [] other_values = [] for name, value in zip(header, values): if name in independent_variables: independent_values.append(value) elif name in dependant_variables: dependant_values.append(value) elif name not in omit: other_values.append(value) independent_values = tuple(independent_values) dependant_values = tuple(dependant_values) other_values = tuple(other_values) if independent_values in data: if data[independent_values][0] != dependant_values: print(independent_values) print(data[independent_values][0]) print(dependant_values)#, other_values) print() fails += 1 else: passes += 1 else: data[independent_values] = [dependant_values] data[independent_values].append(other_values) print('Fail:' if fails else 'Pass:' if passes else 'No Data:', '{}/{}'.format(passes, passes+fails))

35.636364

100

0.58801

lines = open('collated_data.data').readlines() header = [x.strip('"') for x in lines[0].split()] independent_variables = 'HRR', 'HRR', 'SLR', 'BS', 'BT', 'SL', 'SYN', 'POP', 'UPS', 'T' dependant_variables = 'host_count', 'sym_count', 'survival', 'moi' omit = ('file_name',) versions = '"Standard-1.1"', '"Duplicate-1.1"' data = {} passes, fails = 0, 0 for line in lines: values = line.split() if values[0] in versions: independent_values = [] dependant_values = [] other_values = [] for name, value in zip(header, values): if name in independent_variables: independent_values.append(value) elif name in dependant_variables: dependant_values.append(value) elif name not in omit: other_values.append(value) independent_values = tuple(independent_values) dependant_values = tuple(dependant_values) other_values = tuple(other_values) if independent_values in data: if data[independent_values][0] != dependant_values: print(independent_values) print(data[independent_values][0]) print(dependant_values)#, other_values) print() fails += 1 else: passes += 1 else: data[independent_values] = [dependant_values] data[independent_values].append(other_values) print('Fail:' if fails else 'Pass:' if passes else 'No Data:', '{}/{}'.format(passes, passes+fails))

true

1c2e9ca1193e20f92c9e20e10de63accebbd6262

1,642

py

Python

laikaboss/modules/meta_iqy.py

sandialabs/laikaboss

3064ac1176911651d61c5176e9bd83eacec36b16

[ "Apache-2.0" ]

2

2019-11-02T23:40:23.000Z

2019-12-01T22:24:57.000Z

laikaboss/modules/meta_iqy.py

sandialabs/laikaboss

3064ac1176911651d61c5176e9bd83eacec36b16

[ "Apache-2.0" ]

null

laikaboss/modules/meta_iqy.py

sandialabs/laikaboss

3064ac1176911651d61c5176e9bd83eacec36b16

[ "Apache-2.0" ]

3

2017-08-09T23:58:40.000Z

2019-12-01T22:25:06.000Z

# Copyright 2020 National Technology & Engineering Solutions of Sandia, LLC # (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. # Government retains certain rights in this software. # # 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. # #A module to analyze.iqy files #import classes and helpers from the Laika framework from laikaboss.si_module import SI_MODULE class META_IQY(SI_MODULE): '''Laika module for collecting metadata from .iqy files''' def __init__(self): '''init, module name''' self.module_name = "META_IQY" def _run(self, scanObject, result, depth, args): '''add metadata and return result (empty)''' s = scanObject.buffer index_one = s.index(b"1") #urls start after WEB1 or WEB\n1\n newline = b'\n' #urls end with a newline url = s[index_one+1:s.index(newline,index_one+2)] if url[:1] == newline: url=url[1:] #shave off newline if necessary scanObject.addMetadata(self.module_name,"url",url) moduleResult = [] return moduleResult def _close(self): '''nothing to be done here''' pass

35.695652

76

0.696711

from laikaboss.si_module import SI_MODULE class META_IQY(SI_MODULE): def __init__(self): self.module_name = "META_IQY" def _run(self, scanObject, result, depth, args): s = scanObject.buffer index_one = s.index(b"1") newline = b'\n' url = s[index_one+1:s.index(newline,index_one+2)] if url[:1] == newline: url=url[1:] scanObject.addMetadata(self.module_name,"url",url) moduleResult = [] return moduleResult def _close(self): pass

true

1c2e9cc4b9a651b57cc3b5e361842704cab3a988

11,924

py

Python

docs/doxygen/swig_doc.py

hb9fxq/gr-aaronia_rtsa

ac66cc0630668b70fddc393735e6034ccd1cf40a

[ "MIT" ]

null

docs/doxygen/swig_doc.py

hb9fxq/gr-aaronia_rtsa

ac66cc0630668b70fddc393735e6034ccd1cf40a

[ "MIT" ]

null

docs/doxygen/swig_doc.py

hb9fxq/gr-aaronia_rtsa

ac66cc0630668b70fddc393735e6034ccd1cf40a

[ "MIT" ]

null

# # Copyright 2010-2012 Free Software Foundation, Inc. # # This file was generated by gr_modtool, a tool from the GNU Radio framework # This file is a part of gr-aaronia_rtsa # # GNU Radio 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, or (at your option) # any later version. # # GNU Radio 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 GNU Radio; see the file COPYING. If not, write to # the Free Software Foundation, Inc., 51 Franklin Street, # Boston, MA 02110-1301, USA. # """ Creates the swig_doc.i SWIG interface file. Execute using: python swig_doc.py xml_path outputfilename The file instructs SWIG to transfer the doxygen comments into the python docstrings. """ from __future__ import unicode_literals import sys, time from doxyxml import DoxyIndex, DoxyClass, DoxyFriend, DoxyFunction, DoxyFile from doxyxml import DoxyOther, base def py_name(name): bits = name.split('_') return '_'.join(bits[1:]) def make_name(name): bits = name.split('_') return bits[0] + '_make_' + '_'.join(bits[1:]) class Block(object): """ Checks if doxyxml produced objects correspond to a gnuradio block. """ @classmethod def includes(cls, item): if not isinstance(item, DoxyClass): return False # Check for a parsing error. if item.error(): return False friendname = make_name(item.name()) is_a_block = item.has_member(friendname, DoxyFriend) # But now sometimes the make function isn't a friend so check again. if not is_a_block: is_a_block = di.has_member(friendname, DoxyFunction) return is_a_block class Block2(object): """ Checks if doxyxml produced objects correspond to a new style gnuradio block. """ @classmethod def includes(cls, item): if not isinstance(item, DoxyClass): return False # Check for a parsing error. if item.error(): return False is_a_block2 = item.has_member('make', DoxyFunction) and item.has_member('sptr', DoxyOther) return is_a_block2 def utoascii(text): """ Convert unicode text into ascii and escape quotes and backslashes. """ if text is None: return '' out = text.encode('ascii', 'replace') # swig will require us to replace blackslash with 4 backslashes out = out.replace(b'\\', b'\\\\\\\\') out = out.replace(b'"', b'\\"').decode('ascii') return str(out) def combine_descriptions(obj): """ Combines the brief and detailed descriptions of an object together. """ description = [] bd = obj.brief_description.strip() dd = obj.detailed_description.strip() if bd: description.append(bd) if dd: description.append(dd) return utoascii('\n\n'.join(description)).strip() def format_params(parameteritems): output = ['Args:'] template = ' {0} : {1}' for pi in parameteritems: output.append(template.format(pi.name, pi.description)) return '\n'.join(output) entry_templ = '%feature("docstring") {name} "{docstring}"' def make_entry(obj, name=None, templ="{description}", description=None, params=[]): """ Create a docstring entry for a swig interface file. obj - a doxyxml object from which documentation will be extracted. name - the name of the C object (defaults to obj.name()) templ - an optional template for the docstring containing only one variable named 'description'. description - if this optional variable is set then it's value is used as the description instead of extracting it from obj. """ if name is None: name=obj.name() if "operator " in name: return '' if description is None: description = combine_descriptions(obj) if params: description += '\n\n' description += utoascii(format_params(params)) docstring = templ.format(description=description) if not docstring: return '' return entry_templ.format( name=name, docstring=docstring, ) def make_func_entry(func, name=None, description=None, params=None): """ Create a function docstring entry for a swig interface file. func - a doxyxml object from which documentation will be extracted. name - the name of the C object (defaults to func.name()) description - if this optional variable is set then it's value is used as the description instead of extracting it from func. params - a parameter list that overrides using func.params. """ #if params is None: # params = func.params #params = [prm.declname for prm in params] #if params: # sig = "Params: (%s)" % ", ".join(params) #else: # sig = "Params: (NONE)" #templ = "{description}\n\n" + sig #return make_entry(func, name=name, templ=utoascii(templ), # description=description) return make_entry(func, name=name, description=description, params=params) def make_class_entry(klass, description=None, ignored_methods=[], params=None): """ Create a class docstring for a swig interface file. """ if params is None: params = klass.params output = [] output.append(make_entry(klass, description=description, params=params)) for func in klass.in_category(DoxyFunction): if func.name() not in ignored_methods: name = klass.name() + '::' + func.name() output.append(make_func_entry(func, name=name)) return "\n\n".join(output) def make_block_entry(di, block): """ Create class and function docstrings of a gnuradio block for a swig interface file. """ descriptions = [] # Get the documentation associated with the class. class_desc = combine_descriptions(block) if class_desc: descriptions.append(class_desc) # Get the documentation associated with the make function make_func = di.get_member(make_name(block.name()), DoxyFunction) make_func_desc = combine_descriptions(make_func) if make_func_desc: descriptions.append(make_func_desc) # Get the documentation associated with the file try: block_file = di.get_member(block.name() + ".h", DoxyFile) file_desc = combine_descriptions(block_file) if file_desc: descriptions.append(file_desc) except base.Base.NoSuchMember: # Don't worry if we can't find a matching file. pass # And join them all together to make a super duper description. super_description = "\n\n".join(descriptions) # Associate the combined description with the class and # the make function. output = [] output.append(make_class_entry(block, description=super_description)) output.append(make_func_entry(make_func, description=super_description, params=block.params)) return "\n\n".join(output) def make_block2_entry(di, block): """ Create class and function docstrings of a new style gnuradio block for a swig interface file. """ descriptions = [] # For new style blocks all the relevant documentation should be # associated with the 'make' method. class_description = combine_descriptions(block) make_func = block.get_member('make', DoxyFunction) make_description = combine_descriptions(make_func) description = class_description + "\n\nConstructor Specific Documentation:\n\n" + make_description # Associate the combined description with the class and # the make function. output = [] output.append(make_class_entry( block, description=description, ignored_methods=['make'], params=make_func.params)) makename = block.name() + '::make' output.append(make_func_entry( make_func, name=makename, description=description, params=make_func.params)) return "\n\n".join(output) def make_swig_interface_file(di, swigdocfilename, custom_output=None): output = [""" /* * This file was automatically generated using swig_doc.py. * * Any changes to it will be lost next time it is regenerated. */ """] if custom_output is not None: output.append(custom_output) # Create docstrings for the blocks. blocks = di.in_category(Block) blocks2 = di.in_category(Block2) make_funcs = set([]) for block in blocks: try: make_func = di.get_member(make_name(block.name()), DoxyFunction) # Don't want to risk writing to output twice. if make_func.name() not in make_funcs: make_funcs.add(make_func.name()) output.append(make_block_entry(di, block)) except block.ParsingError: sys.stderr.write('Parsing error for block {0}\n'.format(block.name())) raise for block in blocks2: try: make_func = block.get_member('make', DoxyFunction) make_func_name = block.name() +'::make' # Don't want to risk writing to output twice. if make_func_name not in make_funcs: make_funcs.add(make_func_name) output.append(make_block2_entry(di, block)) except block.ParsingError: sys.stderr.write('Parsing error for block {0}\n'.format(block.name())) raise # Create docstrings for functions # Don't include the make functions since they have already been dealt with. funcs = [f for f in di.in_category(DoxyFunction) if f.name() not in make_funcs and not f.name().startswith('std::')] for f in funcs: try: output.append(make_func_entry(f)) except f.ParsingError: sys.stderr.write('Parsing error for function {0}\n'.format(f.name())) # Create docstrings for classes block_names = [block.name() for block in blocks] block_names += [block.name() for block in blocks2] klasses = [k for k in di.in_category(DoxyClass) if k.name() not in block_names and not k.name().startswith('std::')] for k in klasses: try: output.append(make_class_entry(k)) except k.ParsingError: sys.stderr.write('Parsing error for class {0}\n'.format(k.name())) # Docstrings are not created for anything that is not a function or a class. # If this excludes anything important please add it here. output = "\n\n".join(output) swig_doc = open(swigdocfilename, 'w') swig_doc.write(output) swig_doc.close() if __name__ == "__main__": # Parse command line options and set up doxyxml. err_msg = "Execute using: python swig_doc.py xml_path outputfilename" if len(sys.argv) != 3: raise Exception(err_msg) xml_path = sys.argv[1] swigdocfilename = sys.argv[2] di = DoxyIndex(xml_path) # gnuradio.gr.msq_queue.insert_tail and delete_head create errors unless docstrings are defined! # This is presumably a bug in SWIG. #msg_q = di.get_member(u'gr_msg_queue', DoxyClass) #insert_tail = msg_q.get_member(u'insert_tail', DoxyFunction) #delete_head = msg_q.get_member(u'delete_head', DoxyFunction) output = [] #output.append(make_func_entry(insert_tail, name='gr_py_msg_queue__insert_tail')) #output.append(make_func_entry(delete_head, name='gr_py_msg_queue__delete_head')) custom_output = "\n\n".join(output) # Generate the docstrings interface file. make_swig_interface_file(di, swigdocfilename, custom_output=custom_output)

35.807808

102

0.666555

from __future__ import unicode_literals import sys, time from doxyxml import DoxyIndex, DoxyClass, DoxyFriend, DoxyFunction, DoxyFile from doxyxml import DoxyOther, base def py_name(name): bits = name.split('_') return '_'.join(bits[1:]) def make_name(name): bits = name.split('_') return bits[0] + '_make_' + '_'.join(bits[1:]) class Block(object): @classmethod def includes(cls, item): if not isinstance(item, DoxyClass): return False if item.error(): return False friendname = make_name(item.name()) is_a_block = item.has_member(friendname, DoxyFriend) if not is_a_block: is_a_block = di.has_member(friendname, DoxyFunction) return is_a_block class Block2(object): @classmethod def includes(cls, item): if not isinstance(item, DoxyClass): return False # Check for a parsing error. if item.error(): return False is_a_block2 = item.has_member('make', DoxyFunction) and item.has_member('sptr', DoxyOther) return is_a_block2 def utoascii(text): if text is None: return '' out = text.encode('ascii', 'replace') # swig will require us to replace blackslash with 4 backslashes out = out.replace(b'\\', b'\\\\\\\\') out = out.replace(b'"', b'\\"').decode('ascii') return str(out) def combine_descriptions(obj): description = [] bd = obj.brief_description.strip() dd = obj.detailed_description.strip() if bd: description.append(bd) if dd: description.append(dd) return utoascii('\n\n'.join(description)).strip() def format_params(parameteritems): output = ['Args:'] template = ' {0} : {1}' for pi in parameteritems: output.append(template.format(pi.name, pi.description)) return '\n'.join(output) entry_templ = '%feature("docstring") {name} "{docstring}"' def make_entry(obj, name=None, templ="{description}", description=None, params=[]): if name is None: name=obj.name() if "operator " in name: return '' if description is None: description = combine_descriptions(obj) if params: description += '\n\n' description += utoascii(format_params(params)) docstring = templ.format(description=description) if not docstring: return '' return entry_templ.format( name=name, docstring=docstring, ) def make_func_entry(func, name=None, description=None, params=None): #if params is None: # params = func.params #params = [prm.declname for prm in params] #if params: # sig = "Params: (%s)" % ", ".join(params) #else: # sig = "Params: (NONE)" #templ = "{description}\n\n" + sig #return make_entry(func, name=name, templ=utoascii(templ), # description=description) return make_entry(func, name=name, description=description, params=params) def make_class_entry(klass, description=None, ignored_methods=[], params=None): if params is None: params = klass.params output = [] output.append(make_entry(klass, description=description, params=params)) for func in klass.in_category(DoxyFunction): if func.name() not in ignored_methods: name = klass.name() + '::' + func.name() output.append(make_func_entry(func, name=name)) return "\n\n".join(output) def make_block_entry(di, block): descriptions = [] # Get the documentation associated with the class. class_desc = combine_descriptions(block) if class_desc: descriptions.append(class_desc) # Get the documentation associated with the make function make_func = di.get_member(make_name(block.name()), DoxyFunction) make_func_desc = combine_descriptions(make_func) if make_func_desc: descriptions.append(make_func_desc) # Get the documentation associated with the file try: block_file = di.get_member(block.name() + ".h", DoxyFile) file_desc = combine_descriptions(block_file) if file_desc: descriptions.append(file_desc) except base.Base.NoSuchMember: # Don't worry if we can't find a matching file. pass # And join them all together to make a super duper description. super_description = "\n\n".join(descriptions) # Associate the combined description with the class and # the make function. output = [] output.append(make_class_entry(block, description=super_description)) output.append(make_func_entry(make_func, description=super_description, params=block.params)) return "\n\n".join(output) def make_block2_entry(di, block): descriptions = [] # For new style blocks all the relevant documentation should be # associated with the 'make' method. class_description = combine_descriptions(block) make_func = block.get_member('make', DoxyFunction) make_description = combine_descriptions(make_func) description = class_description + "\n\nConstructor Specific Documentation:\n\n" + make_description # Associate the combined description with the class and # the make function. output = [] output.append(make_class_entry( block, description=description, ignored_methods=['make'], params=make_func.params)) makename = block.name() + '::make' output.append(make_func_entry( make_func, name=makename, description=description, params=make_func.params)) return "\n\n".join(output) def make_swig_interface_file(di, swigdocfilename, custom_output=None): output = [""" /* * This file was automatically generated using swig_doc.py. * * Any changes to it will be lost next time it is regenerated. */ """] if custom_output is not None: output.append(custom_output) # Create docstrings for the blocks. blocks = di.in_category(Block) blocks2 = di.in_category(Block2) make_funcs = set([]) for block in blocks: try: make_func = di.get_member(make_name(block.name()), DoxyFunction) # Don't want to risk writing to output twice. if make_func.name() not in make_funcs: make_funcs.add(make_func.name()) output.append(make_block_entry(di, block)) except block.ParsingError: sys.stderr.write('Parsing error for block {0}\n'.format(block.name())) raise for block in blocks2: try: make_func = block.get_member('make', DoxyFunction) make_func_name = block.name() +'::make' if make_func_name not in make_funcs: make_funcs.add(make_func_name) output.append(make_block2_entry(di, block)) except block.ParsingError: sys.stderr.write('Parsing error for block {0}\n'.format(block.name())) raise # Create docstrings for functions # Don't include the make functions since they have already been dealt with. funcs = [f for f in di.in_category(DoxyFunction) if f.name() not in make_funcs and not f.name().startswith('std::')] for f in funcs: try: output.append(make_func_entry(f)) except f.ParsingError: sys.stderr.write('Parsing error for function {0}\n'.format(f.name())) block_names = [block.name() for block in blocks] block_names += [block.name() for block in blocks2] klasses = [k for k in di.in_category(DoxyClass) if k.name() not in block_names and not k.name().startswith('std::')] for k in klasses: try: output.append(make_class_entry(k)) except k.ParsingError: sys.stderr.write('Parsing error for class {0}\n'.format(k.name())) output = "\n\n".join(output) swig_doc = open(swigdocfilename, 'w') swig_doc.write(output) swig_doc.close() if __name__ == "__main__": err_msg = "Execute using: python swig_doc.py xml_path outputfilename" if len(sys.argv) != 3: raise Exception(err_msg) xml_path = sys.argv[1] swigdocfilename = sys.argv[2] di = DoxyIndex(xml_path) output = [] custom_output = "\n\n".join(output) make_swig_interface_file(di, swigdocfilename, custom_output=custom_output)

true

1c2e9da8b1410e0876c772e4e9d83e5a0a64b024

436

py

Python

src/dendrograms/dendrogram.py

Ellon-M/visualizations

5a42c213ea8fd0597e2035778d9ae6460eb9e821

[ "MIT" ]

null

src/dendrograms/dendrogram.py

Ellon-M/visualizations

5a42c213ea8fd0597e2035778d9ae6460eb9e821

[ "MIT" ]

null

src/dendrograms/dendrogram.py

Ellon-M/visualizations

5a42c213ea8fd0597e2035778d9ae6460eb9e821

[ "MIT" ]

null

# dendrogram import scipy.cluster.hierarchy as shc # Import Data df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/USArrests.csv') # Plot plt.figure(figsize=(16, 10), dpi= 80) plt.title("USArrests Dendograms", fontsize=22) dend = shc.dendrogram(shc.linkage(df[['Murder', 'Assault', 'UrbanPop', 'Rape']], method='ward'), labels=df.State.values, color_threshold=100) plt.xticks(fontsize=12) plt.show()

29.066667

143

0.729358

import scipy.cluster.hierarchy as shc df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/USArrests.csv') plt.figure(figsize=(16, 10), dpi= 80) plt.title("USArrests Dendograms", fontsize=22) dend = shc.dendrogram(shc.linkage(df[['Murder', 'Assault', 'UrbanPop', 'Rape']], method='ward'), labels=df.State.values, color_threshold=100) plt.xticks(fontsize=12) plt.show()

true

1c2e9f75bc7f7bb8f373ef0d4b1ceb3207725ff7

1,967

py

Python

.history/main_20171106224753.py

reecebenson/DADSA-Tennis-PartA

d0763f819b300fcd0ce27041f5bc4ef0519c00bf

[ "MIT" ]

null

.history/main_20171106224753.py

reecebenson/DADSA-Tennis-PartA

d0763f819b300fcd0ce27041f5bc4ef0519c00bf

[ "MIT" ]

null

.history/main_20171106224753.py

reecebenson/DADSA-Tennis-PartA

d0763f819b300fcd0ce27041f5bc4ef0519c00bf

[ "MIT" ]

null

# DADSA - Assignment 1 # Reece Benson from classes import Menu as Menu from classes import Handler as Handler class App(): # Define the variables we will be using debug = True handler = None # Define all of the properties we will need to use def __init__(self): # Load our handler self.handler = Handler.Handler(self) self.handler.load() # Generate rounds self.generate_rounds() # Hold the program self.exit() # Generate our rounds from our player list def generate_rounds(self): # Let's generate our random rounds from scratch round_data = { } # Write our new data to memory for seasonId in self.handler.get_seasons(): season = self.handler.get_season(seasonId) players = season.players() # Generate our rounds for gender in players: # Create our gendered rounds if(not gender in round_data): # Default Round Cap roundCap = 3 # Do we have a Round Cap overrider for this gender? if(gender + "_cap" in season.settings()): roundCap = season.settings()[gender + "_cap"] # Update our round data round_data.update({ gender: [ { "_roundCap": roundCap } ] }) # Create our round data from players for i in range(len(players[gender]) - 1): playerOne = players[gender][i] playerTwo = players[gender][i + 1] print("{0} vs {1} ".format(playerOne.name(), playerTwo.name())) i += 1 print(round_data) # A method which exits the program after the user has pressed the Return key def exit(self): input(">>> Press <Return> to terminate the program") exit() App()

31.725806

83

0.543467

from classes import Menu as Menu from classes import Handler as Handler class App(): debug = True handler = None def __init__(self): self.handler = Handler.Handler(self) self.handler.load() self.generate_rounds() self.exit() def generate_rounds(self): round_data = { } # Write our new data to memory for seasonId in self.handler.get_seasons(): season = self.handler.get_season(seasonId) players = season.players() # Generate our rounds for gender in players: # Create our gendered rounds if(not gender in round_data): # Default Round Cap roundCap = 3 # Do we have a Round Cap overrider for this gender? if(gender + "_cap" in season.settings()): roundCap = season.settings()[gender + "_cap"] # Update our round data round_data.update({ gender: [ { "_roundCap": roundCap } ] }) # Create our round data from players for i in range(len(players[gender]) - 1): playerOne = players[gender][i] playerTwo = players[gender][i + 1] print("{0} vs {1} ".format(playerOne.name(), playerTwo.name())) i += 1 print(round_data) # A method which exits the program after the user has pressed the Return key def exit(self): input(">>> Press <Return> to terminate the program") exit() App()

true

1c2e9fe71d358bad524ca7f4ba082ca2127b186a

2,471

py

Python

lib/reda/importers/geotom.py

j-gallistl/reda

13b1f9e1cda92bbbbafc5c28be2c691d3b722740

[ "MIT" ]

12

2017-12-11T08:32:46.000Z

2021-06-09T05:41:57.000Z

lib/reda/importers/geotom.py

j-gallistl/reda

13b1f9e1cda92bbbbafc5c28be2c691d3b722740

[ "MIT" ]

58

2017-11-12T11:10:42.000Z

2021-06-11T13:52:44.000Z

lib/reda/importers/geotom.py

geophysics-ubonn/REDA

8f0399031121f5a937171231a25f9ab03a3c8873

[ "MIT" ]

11

2017-11-12T12:02:35.000Z

2021-02-16T06:54:04.000Z

# -*- coding: utf-8 -*- from io import StringIO import pandas as pd import numpy as np from reda.containers.ERT import ERT from reda.importers.utils.decorators import enable_result_transforms @enable_result_transforms def _parse_wenner_file(filename, settings): """Parse a Geotom .wen (Wenner configuration) file Parsing problems ---------------- Due to column overflows it is necessary to make sure that spaces are present around the ; character. Example: 8.000 14.000 10835948.70; 0.001 -123.1853 -1.0 23.10.2014 """ # read data with open(filename, 'r') as fid2: geotom_data_orig = fid2.read() # replace all ';' by ' ; ' geotom_data = geotom_data_orig.replace(';', ' ; ') fid = StringIO() fid.write(geotom_data) fid.seek(0) header = [fid.readline() for i in range(0, 16)] header df = pd.read_csv( fid, delim_whitespace=True, header=None, names=( 'elec1_wenner', 'a_w', 'rho_a', 'c4', 'c5', 'c6', 'c6', 'c7', 'c8', 'c9', ), ) # compute geometric factor using the Wenner formula df['k'] = 2 * np.pi * df['a_w'] df['r'] = df['rho_a'] / df['k'] Am = df['elec1_wenner'] Bm = df['elec1_wenner'] + df['a_w'] Mm = df['elec1_wenner'] + 3 * df['a_w'] Nm = df['elec1_wenner'] + 2 * df['a_w'] df['a'] = Am / 2.0 + 1 df['b'] = Bm / 2.0 + 1 df['m'] = Mm / 2.0 + 1 df['n'] = Nm / 2.0 + 1 # remove any nan values df.dropna(axis=0, subset=['a', 'b', 'm', 'n', 'r'], inplace=True) return df def add_file(filename, settings, container=None): """ Parameters ---------- filename: string path settings: dict :: { timestep: [int|datetime], timestep relating to this measurement } container: :class:`reda.container.ERT.ERT` ERT container to add dataset to """ timestep = settings.get('timestep', 0) # Wenner if filename.endswith('.wen'): data = _parse_wenner_file(filename, settings) # add timestep column data['timestep'] = timestep else: raise Exception('Not a Wenner file') if container is None: container = ERT(data) else: container.df = pd.concat((container.df, data)) return container

22.463636

79

0.541076

from io import StringIO import pandas as pd import numpy as np from reda.containers.ERT import ERT from reda.importers.utils.decorators import enable_result_transforms @enable_result_transforms def _parse_wenner_file(filename, settings): with open(filename, 'r') as fid2: geotom_data_orig = fid2.read() geotom_data = geotom_data_orig.replace(';', ' ; ') fid = StringIO() fid.write(geotom_data) fid.seek(0) header = [fid.readline() for i in range(0, 16)] header df = pd.read_csv( fid, delim_whitespace=True, header=None, names=( 'elec1_wenner', 'a_w', 'rho_a', 'c4', 'c5', 'c6', 'c6', 'c7', 'c8', 'c9', ), ) df['k'] = 2 * np.pi * df['a_w'] df['r'] = df['rho_a'] / df['k'] Am = df['elec1_wenner'] Bm = df['elec1_wenner'] + df['a_w'] Mm = df['elec1_wenner'] + 3 * df['a_w'] Nm = df['elec1_wenner'] + 2 * df['a_w'] df['a'] = Am / 2.0 + 1 df['b'] = Bm / 2.0 + 1 df['m'] = Mm / 2.0 + 1 df['n'] = Nm / 2.0 + 1 df.dropna(axis=0, subset=['a', 'b', 'm', 'n', 'r'], inplace=True) return df def add_file(filename, settings, container=None): timestep = settings.get('timestep', 0) if filename.endswith('.wen'): data = _parse_wenner_file(filename, settings) data['timestep'] = timestep else: raise Exception('Not a Wenner file') if container is None: container = ERT(data) else: container.df = pd.concat((container.df, data)) return container

true

1c2ea03e03b381cfed1b2a2fb9de2e155e4a83d4

3,527

py

Python

tests/test_unicode_strings.py

alekseyl1992/pyrobuf

dc553f2c407d3ea1ca78c6b58e149d05ff811a23

[ "Apache-2.0" ]

578

2015-12-17T20:39:31.000Z

2022-02-15T05:14:03.000Z

tests/test_unicode_strings.py

alekseyl1992/pyrobuf

dc553f2c407d3ea1ca78c6b58e149d05ff811a23

[ "Apache-2.0" ]

121

2015-12-19T07:37:32.000Z

2022-02-22T05:22:55.000Z

tests/test_unicode_strings.py

alekseyl1992/pyrobuf

dc553f2c407d3ea1ca78c6b58e149d05ff811a23

[ "Apache-2.0" ]

82

2015-12-19T00:19:28.000Z

2022-02-21T09:00:21.000Z

import unittest Test = None TestSs1 = None # Alpha, Beta, Gamma, Delta GREEK_LETTERS = u"\u0391\u0392\u0393\u0394" class TestUnicodeStrings(unittest.TestCase): @classmethod def setUpClass(cls): global Test, TestSs1, TestFieldTypes from test_message_proto import Test, TestSs1 def test_unicode_string_parse_from_string(self): message = Test.FromString(b'\x1a\x08\xce\x91\xce\x92\xce\x93\xce\x94') self.assertEqual(message.string_field, GREEK_LETTERS) def test_unicode_string_parse_from_dict(self): message = Test() message.ParseFromDict({'string_field': u"\u0391\u0392\u0393\u0394", 'req_field': 1}) self.assertEqual(message.string_field, GREEK_LETTERS) def test_unicode_string_parse_from_json(self): message = Test() message.ParseFromJson('{"string_field": "\\u0391\\u0392\\u0393\\u0394", "req_field": 1}') self.assertEqual(message.string_field, GREEK_LETTERS) def test_unicode_string_serialize_to_string(self): message = Test() message.string_field = GREEK_LETTERS self.assertEqual(message.SerializePartialToString(), b'\x1a\x08\xce\x91\xce\x92\xce\x93\xce\x94') def test_unicode_string_serialize_to_dict(self): message = Test() message.string_field = GREEK_LETTERS message.req_field = 1 self.assertEqual( message.SerializeToDict(), {'string_field': u"\u0391\u0392\u0393\u0394", 'req_field': 1} ) def test_unicode_string_serialize_to_json(self): # Use TestSs1 here because Test has a required field message = TestSs1() message.field2 = GREEK_LETTERS self.assertEqual( message.SerializeToJson(), '{"field2": "\\u0391\\u0392\\u0393\\u0394"}' ) def test_repeated_unicode_parse_from_string(self): message = TestSs1.FromString(b'2\x04\xce\x91\xce\x922\x04\xce\x93\xce\x94') self.assertEqual(message.list_string, [u"\u0391\u0392", u"\u0393\u0394"]) def test_repeated_unicode_parse_from_dict(self): message = TestSs1() message.ParseFromDict({'list_string': [u'\u0391\u0392', u'\u0393\u0394']}) self.assertEqual(message.list_string, [u"\u0391\u0392", u"\u0393\u0394"]) def test_repeated_unicode_parse_from_json(self): message = TestSs1() message.ParseFromJson('{"list_string": ["\\u0391\\u0392", "\\u0393\\u0394"]}') self.assertEqual(message.list_string, [u"\u0391\u0392", u"\u0393\u0394"]) def test_repeated_unicode_string_serialize_to_string(self): message = TestSs1() message.list_string.append(GREEK_LETTERS[:2]) # Alpha, Beta message.list_string.append(GREEK_LETTERS[2:]) # Gamma, Delta self.assertEqual(message.SerializePartialToString(), b'2\x04\xce\x91\xce\x922\x04\xce\x93\xce\x94') def test_repeated_unicode_string_serialize_to_dict(self): message = TestSs1() message.list_string.append(GREEK_LETTERS[:2]) # Alpha, Beta message.list_string.append(GREEK_LETTERS[2:]) # Gamma, Delta self.assertEqual(message.SerializeToDict(), {'list_string': [u'\u0391\u0392', u'\u0393\u0394']}) def test_repeated_unicode_string_serialize_to_json(self): message = TestSs1() message.list_string.append(GREEK_LETTERS[:2]) # Alpha, Beta message.list_string.append(GREEK_LETTERS[2:]) # Gamma, Delta self.assertEqual(message.SerializeToJson(), '{"list_string": ["\\u0391\\u0392", "\\u0393\\u0394"]}')

43.012195

108

0.686703

import unittest Test = None TestSs1 = None GREEK_LETTERS = u"\u0391\u0392\u0393\u0394" class TestUnicodeStrings(unittest.TestCase): @classmethod def setUpClass(cls): global Test, TestSs1, TestFieldTypes from test_message_proto import Test, TestSs1 def test_unicode_string_parse_from_string(self): message = Test.FromString(b'\x1a\x08\xce\x91\xce\x92\xce\x93\xce\x94') self.assertEqual(message.string_field, GREEK_LETTERS) def test_unicode_string_parse_from_dict(self): message = Test() message.ParseFromDict({'string_field': u"\u0391\u0392\u0393\u0394", 'req_field': 1}) self.assertEqual(message.string_field, GREEK_LETTERS) def test_unicode_string_parse_from_json(self): message = Test() message.ParseFromJson('{"string_field": "\\u0391\\u0392\\u0393\\u0394", "req_field": 1}') self.assertEqual(message.string_field, GREEK_LETTERS) def test_unicode_string_serialize_to_string(self): message = Test() message.string_field = GREEK_LETTERS self.assertEqual(message.SerializePartialToString(), b'\x1a\x08\xce\x91\xce\x92\xce\x93\xce\x94') def test_unicode_string_serialize_to_dict(self): message = Test() message.string_field = GREEK_LETTERS message.req_field = 1 self.assertEqual( message.SerializeToDict(), {'string_field': u"\u0391\u0392\u0393\u0394", 'req_field': 1} ) def test_unicode_string_serialize_to_json(self): message = TestSs1() message.field2 = GREEK_LETTERS self.assertEqual( message.SerializeToJson(), '{"field2": "\\u0391\\u0392\\u0393\\u0394"}' ) def test_repeated_unicode_parse_from_string(self): message = TestSs1.FromString(b'2\x04\xce\x91\xce\x922\x04\xce\x93\xce\x94') self.assertEqual(message.list_string, [u"\u0391\u0392", u"\u0393\u0394"]) def test_repeated_unicode_parse_from_dict(self): message = TestSs1() message.ParseFromDict({'list_string': [u'\u0391\u0392', u'\u0393\u0394']}) self.assertEqual(message.list_string, [u"\u0391\u0392", u"\u0393\u0394"]) def test_repeated_unicode_parse_from_json(self): message = TestSs1() message.ParseFromJson('{"list_string": ["\\u0391\\u0392", "\\u0393\\u0394"]}') self.assertEqual(message.list_string, [u"\u0391\u0392", u"\u0393\u0394"]) def test_repeated_unicode_string_serialize_to_string(self): message = TestSs1() message.list_string.append(GREEK_LETTERS[:2]) message.list_string.append(GREEK_LETTERS[2:]) self.assertEqual(message.SerializePartialToString(), b'2\x04\xce\x91\xce\x922\x04\xce\x93\xce\x94') def test_repeated_unicode_string_serialize_to_dict(self): message = TestSs1() message.list_string.append(GREEK_LETTERS[:2]) message.list_string.append(GREEK_LETTERS[2:]) self.assertEqual(message.SerializeToDict(), {'list_string': [u'\u0391\u0392', u'\u0393\u0394']}) def test_repeated_unicode_string_serialize_to_json(self): message = TestSs1() message.list_string.append(GREEK_LETTERS[:2]) message.list_string.append(GREEK_LETTERS[2:]) self.assertEqual(message.SerializeToJson(), '{"list_string": ["\\u0391\\u0392", "\\u0393\\u0394"]}')

true

1c2ea062620b72b16560fe0603e880b6ea79c7f0

271

py

Python

buoi7/bai12.py

Viet7501/viet1

e0be1da1fb0f4736c8d0457733ccaaed9232c4e9

[ "Apache-2.0" ]

null

buoi7/bai12.py

Viet7501/viet1

e0be1da1fb0f4736c8d0457733ccaaed9232c4e9

[ "Apache-2.0" ]

null

buoi7/bai12.py

Viet7501/viet1

e0be1da1fb0f4736c8d0457733ccaaed9232c4e9

[ "Apache-2.0" ]

null

set1 = {0, 1, 2, 3, 4, 5, 6} set2 = {5, 6, 7, 8, 9, 0, 1} # set1.difference_update(set2) # set1.intersection_update(set2) print(set1.issubset(set2)) print(set1.isdisjoint(set2)) print(set1.issuperset(set2)) set1.symmetric_difference_update(set2) print(set1)

22.583333

39

0.682657

set1 = {0, 1, 2, 3, 4, 5, 6} set2 = {5, 6, 7, 8, 9, 0, 1} print(set1.issubset(set2)) print(set1.isdisjoint(set2)) print(set1.issuperset(set2)) set1.symmetric_difference_update(set2) print(set1)

true

1c2ea0d7071e64a32dcaa6aa1f4226fc529ca0a6

9,426

py

Python

saleor/payment/gateway.py

facundon/Cholitas-Backend

4d73e0697a6b6fc61b82b72ff1c99b2eabe8f4c6

[ "CC-BY-4.0" ]

null

saleor/payment/gateway.py

facundon/Cholitas-Backend

4d73e0697a6b6fc61b82b72ff1c99b2eabe8f4c6

[ "CC-BY-4.0" ]

null

saleor/payment/gateway.py

facundon/Cholitas-Backend

4d73e0697a6b6fc61b82b72ff1c99b2eabe8f4c6

[ "CC-BY-4.0" ]

null

import logging from decimal import Decimal from typing import TYPE_CHECKING, Callable, List, Optional from django.db import transaction from ..payment.interface import TokenConfig from ..plugins.manager import get_plugins_manager from . import GatewayError, PaymentError, TransactionKind from .models import Payment, Transaction from .utils import ( clean_authorize, clean_capture, create_payment_information, gateway_postprocess, get_already_processed_transaction_or_create_new_transaction, update_payment_method_details, validate_gateway_response, ) if TYPE_CHECKING: # flake8: noqa from ..payment.interface import CustomerSource, PaymentGateway logger = logging.getLogger(__name__) ERROR_MSG = "Oops! Something went wrong." GENERIC_TRANSACTION_ERROR = "La transacción no tuvo exito." def raise_payment_error(fn: Callable) -> Callable: def wrapped(*args, **kwargs): result = fn(*args, **kwargs) if not result.is_success: raise PaymentError(result.error or GENERIC_TRANSACTION_ERROR) return result return wrapped def payment_postprocess(fn: Callable) -> Callable: def wrapped(*args, **kwargs): txn = fn(*args, **kwargs) gateway_postprocess(txn, txn.payment) return txn return wrapped def require_active_payment(fn: Callable) -> Callable: def wrapped(payment: Payment, *args, **kwargs): if not payment.is_active: raise PaymentError("This payment is no longer active.") return fn(payment, *args, **kwargs) return wrapped def with_locked_payment(fn: Callable) -> Callable: """Lock payment to protect from asynchronous modification.""" def wrapped(payment: Payment, *args, **kwargs): with transaction.atomic(): payment = Payment.objects.select_for_update().get(id=payment.id) return fn(payment, *args, **kwargs) return wrapped @raise_payment_error @require_active_payment @with_locked_payment @payment_postprocess def process_payment( payment: Payment, token: str, store_source: bool = False, additional_data: Optional[dict] = None, ) -> Transaction: plugin_manager = get_plugins_manager() payment_data = create_payment_information( payment=payment, payment_token=token, store_source=store_source, additional_data=additional_data, ) response, error = _fetch_gateway_response( plugin_manager.process_payment, payment.gateway, payment_data ) action_required = response is not None and response.action_required if response and response.payment_method_info: update_payment_method_details(payment, response) return get_already_processed_transaction_or_create_new_transaction( payment=payment, kind=TransactionKind.CAPTURE, action_required=action_required, payment_information=payment_data, error_msg=error, gateway_response=response, ) @raise_payment_error @require_active_payment @with_locked_payment @payment_postprocess def authorize(payment: Payment, token: str, store_source: bool = False) -> Transaction: plugin_manager = get_plugins_manager() clean_authorize(payment) payment_data = create_payment_information( payment=payment, payment_token=token, store_source=store_source ) response, error = _fetch_gateway_response( plugin_manager.authorize_payment, payment.gateway, payment_data ) if response and response.payment_method_info: update_payment_method_details(payment, response) return get_already_processed_transaction_or_create_new_transaction( payment=payment, kind=TransactionKind.AUTH, payment_information=payment_data, error_msg=error, gateway_response=response, ) @payment_postprocess @raise_payment_error @require_active_payment @with_locked_payment @payment_postprocess def capture( payment: Payment, amount: Decimal = None, store_source: bool = False ) -> Transaction: plugin_manager = get_plugins_manager() if amount is None: amount = payment.get_charge_amount() clean_capture(payment, Decimal(amount)) token = _get_past_transaction_token(payment, TransactionKind.AUTH) payment_data = create_payment_information( payment=payment, payment_token=token, amount=amount, store_source=store_source ) response, error = _fetch_gateway_response( plugin_manager.capture_payment, payment.gateway, payment_data ) if response and response.payment_method_info: update_payment_method_details(payment, response) return get_already_processed_transaction_or_create_new_transaction( payment=payment, kind=TransactionKind.CAPTURE, payment_information=payment_data, error_msg=error, gateway_response=response, ) @raise_payment_error @require_active_payment @with_locked_payment @payment_postprocess def refund(payment: Payment, amount: Decimal = None) -> Transaction: plugin_manager = get_plugins_manager() if amount is None: amount = payment.captured_amount _validate_refund_amount(payment, amount) if not payment.can_refund(): raise PaymentError("This payment cannot be refunded.") token = _get_past_transaction_token(payment, TransactionKind.CAPTURE) payment_data = create_payment_information( payment=payment, payment_token=token, amount=amount ) response, error = _fetch_gateway_response( plugin_manager.refund_payment, payment.gateway, payment_data ) return get_already_processed_transaction_or_create_new_transaction( payment=payment, kind=TransactionKind.REFUND, payment_information=payment_data, error_msg=error, gateway_response=response, ) @raise_payment_error @require_active_payment @with_locked_payment @payment_postprocess def void(payment: Payment) -> Transaction: plugin_manager = get_plugins_manager() token = _get_past_transaction_token(payment, TransactionKind.AUTH) payment_data = create_payment_information(payment=payment, payment_token=token) response, error = _fetch_gateway_response( plugin_manager.void_payment, payment.gateway, payment_data ) return get_already_processed_transaction_or_create_new_transaction( payment=payment, kind=TransactionKind.VOID, payment_information=payment_data, error_msg=error, gateway_response=response, ) @raise_payment_error @require_active_payment @with_locked_payment @payment_postprocess def confirm(payment: Payment, additional_data: Optional[dict] = None) -> Transaction: plugin_manager = get_plugins_manager() txn = payment.transactions.filter( kind=TransactionKind.ACTION_TO_CONFIRM, is_success=True ).last() token = txn.token if txn else "" payment_data = create_payment_information( payment=payment, payment_token=token, additional_data=additional_data ) response, error = _fetch_gateway_response( plugin_manager.confirm_payment, payment.gateway, payment_data ) action_required = response is not None and response.action_required if response and response.payment_method_info: update_payment_method_details(payment, response) return get_already_processed_transaction_or_create_new_transaction( payment=payment, kind=TransactionKind.CONFIRM, payment_information=payment_data, action_required=action_required, error_msg=error, gateway_response=response, ) def list_payment_sources(gateway: str, customer_id: str) -> List["CustomerSource"]: plugin_manager = get_plugins_manager() return plugin_manager.list_payment_sources(gateway, customer_id) def get_client_token(gateway: str, customer_id: str = None) -> str: plugin_manager = get_plugins_manager() token_config = TokenConfig(customer_id=customer_id) return plugin_manager.get_client_token(gateway, token_config) def list_gateways() -> List["PaymentGateway"]: return get_plugins_manager().list_payment_gateways() def _fetch_gateway_response(fn, *args, **kwargs): response, error = None, None try: response = fn(*args, **kwargs) validate_gateway_response(response) except GatewayError: logger.exception("Gateway response validation failed!") response = None error = ERROR_MSG except PaymentError: logger.exception("Error encountered while executing payment gateway.") error = ERROR_MSG response = None return response, error def _get_past_transaction_token( payment: Payment, kind: str # for kind use "TransactionKind" ) -> Optional[str]: txn = payment.transactions.filter(kind=kind, is_success=True).last() if txn is None: raise PaymentError(f"Cannot find successful {kind} transaction.") return txn.token def _validate_refund_amount(payment: Payment, amount: Decimal): if amount <= 0: raise PaymentError("Amount should be a positive number.") if amount > payment.captured_amount: raise PaymentError("Cannot refund more than captured.") def payment_refund_or_void(payment: Optional[Payment]): if payment is None: return if payment.can_refund(): refund(payment) elif payment.can_void(): void(payment)

32.729167

87

0.736474

import logging from decimal import Decimal from typing import TYPE_CHECKING, Callable, List, Optional from django.db import transaction from ..payment.interface import TokenConfig from ..plugins.manager import get_plugins_manager from . import GatewayError, PaymentError, TransactionKind from .models import Payment, Transaction from .utils import ( clean_authorize, clean_capture, create_payment_information, gateway_postprocess, get_already_processed_transaction_or_create_new_transaction, update_payment_method_details, validate_gateway_response, ) if TYPE_CHECKING: from ..payment.interface import CustomerSource, PaymentGateway logger = logging.getLogger(__name__) ERROR_MSG = "Oops! Something went wrong." GENERIC_TRANSACTION_ERROR = "La transacción no tuvo exito." def raise_payment_error(fn: Callable) -> Callable: def wrapped(*args, **kwargs): result = fn(*args, **kwargs) if not result.is_success: raise PaymentError(result.error or GENERIC_TRANSACTION_ERROR) return result return wrapped def payment_postprocess(fn: Callable) -> Callable: def wrapped(*args, **kwargs): txn = fn(*args, **kwargs) gateway_postprocess(txn, txn.payment) return txn return wrapped def require_active_payment(fn: Callable) -> Callable: def wrapped(payment: Payment, *args, **kwargs): if not payment.is_active: raise PaymentError("This payment is no longer active.") return fn(payment, *args, **kwargs) return wrapped def with_locked_payment(fn: Callable) -> Callable: def wrapped(payment: Payment, *args, **kwargs): with transaction.atomic(): payment = Payment.objects.select_for_update().get(id=payment.id) return fn(payment, *args, **kwargs) return wrapped @raise_payment_error @require_active_payment @with_locked_payment @payment_postprocess def process_payment( payment: Payment, token: str, store_source: bool = False, additional_data: Optional[dict] = None, ) -> Transaction: plugin_manager = get_plugins_manager() payment_data = create_payment_information( payment=payment, payment_token=token, store_source=store_source, additional_data=additional_data, ) response, error = _fetch_gateway_response( plugin_manager.process_payment, payment.gateway, payment_data ) action_required = response is not None and response.action_required if response and response.payment_method_info: update_payment_method_details(payment, response) return get_already_processed_transaction_or_create_new_transaction( payment=payment, kind=TransactionKind.CAPTURE, action_required=action_required, payment_information=payment_data, error_msg=error, gateway_response=response, ) @raise_payment_error @require_active_payment @with_locked_payment @payment_postprocess def authorize(payment: Payment, token: str, store_source: bool = False) -> Transaction: plugin_manager = get_plugins_manager() clean_authorize(payment) payment_data = create_payment_information( payment=payment, payment_token=token, store_source=store_source ) response, error = _fetch_gateway_response( plugin_manager.authorize_payment, payment.gateway, payment_data ) if response and response.payment_method_info: update_payment_method_details(payment, response) return get_already_processed_transaction_or_create_new_transaction( payment=payment, kind=TransactionKind.AUTH, payment_information=payment_data, error_msg=error, gateway_response=response, ) @payment_postprocess @raise_payment_error @require_active_payment @with_locked_payment @payment_postprocess def capture( payment: Payment, amount: Decimal = None, store_source: bool = False ) -> Transaction: plugin_manager = get_plugins_manager() if amount is None: amount = payment.get_charge_amount() clean_capture(payment, Decimal(amount)) token = _get_past_transaction_token(payment, TransactionKind.AUTH) payment_data = create_payment_information( payment=payment, payment_token=token, amount=amount, store_source=store_source ) response, error = _fetch_gateway_response( plugin_manager.capture_payment, payment.gateway, payment_data ) if response and response.payment_method_info: update_payment_method_details(payment, response) return get_already_processed_transaction_or_create_new_transaction( payment=payment, kind=TransactionKind.CAPTURE, payment_information=payment_data, error_msg=error, gateway_response=response, ) @raise_payment_error @require_active_payment @with_locked_payment @payment_postprocess def refund(payment: Payment, amount: Decimal = None) -> Transaction: plugin_manager = get_plugins_manager() if amount is None: amount = payment.captured_amount _validate_refund_amount(payment, amount) if not payment.can_refund(): raise PaymentError("This payment cannot be refunded.") token = _get_past_transaction_token(payment, TransactionKind.CAPTURE) payment_data = create_payment_information( payment=payment, payment_token=token, amount=amount ) response, error = _fetch_gateway_response( plugin_manager.refund_payment, payment.gateway, payment_data ) return get_already_processed_transaction_or_create_new_transaction( payment=payment, kind=TransactionKind.REFUND, payment_information=payment_data, error_msg=error, gateway_response=response, ) @raise_payment_error @require_active_payment @with_locked_payment @payment_postprocess def void(payment: Payment) -> Transaction: plugin_manager = get_plugins_manager() token = _get_past_transaction_token(payment, TransactionKind.AUTH) payment_data = create_payment_information(payment=payment, payment_token=token) response, error = _fetch_gateway_response( plugin_manager.void_payment, payment.gateway, payment_data ) return get_already_processed_transaction_or_create_new_transaction( payment=payment, kind=TransactionKind.VOID, payment_information=payment_data, error_msg=error, gateway_response=response, ) @raise_payment_error @require_active_payment @with_locked_payment @payment_postprocess def confirm(payment: Payment, additional_data: Optional[dict] = None) -> Transaction: plugin_manager = get_plugins_manager() txn = payment.transactions.filter( kind=TransactionKind.ACTION_TO_CONFIRM, is_success=True ).last() token = txn.token if txn else "" payment_data = create_payment_information( payment=payment, payment_token=token, additional_data=additional_data ) response, error = _fetch_gateway_response( plugin_manager.confirm_payment, payment.gateway, payment_data ) action_required = response is not None and response.action_required if response and response.payment_method_info: update_payment_method_details(payment, response) return get_already_processed_transaction_or_create_new_transaction( payment=payment, kind=TransactionKind.CONFIRM, payment_information=payment_data, action_required=action_required, error_msg=error, gateway_response=response, ) def list_payment_sources(gateway: str, customer_id: str) -> List["CustomerSource"]: plugin_manager = get_plugins_manager() return plugin_manager.list_payment_sources(gateway, customer_id) def get_client_token(gateway: str, customer_id: str = None) -> str: plugin_manager = get_plugins_manager() token_config = TokenConfig(customer_id=customer_id) return plugin_manager.get_client_token(gateway, token_config) def list_gateways() -> List["PaymentGateway"]: return get_plugins_manager().list_payment_gateways() def _fetch_gateway_response(fn, *args, **kwargs): response, error = None, None try: response = fn(*args, **kwargs) validate_gateway_response(response) except GatewayError: logger.exception("Gateway response validation failed!") response = None error = ERROR_MSG except PaymentError: logger.exception("Error encountered while executing payment gateway.") error = ERROR_MSG response = None return response, error def _get_past_transaction_token( payment: Payment, kind: str ) -> Optional[str]: txn = payment.transactions.filter(kind=kind, is_success=True).last() if txn is None: raise PaymentError(f"Cannot find successful {kind} transaction.") return txn.token def _validate_refund_amount(payment: Payment, amount: Decimal): if amount <= 0: raise PaymentError("Amount should be a positive number.") if amount > payment.captured_amount: raise PaymentError("Cannot refund more than captured.") def payment_refund_or_void(payment: Optional[Payment]): if payment is None: return if payment.can_refund(): refund(payment) elif payment.can_void(): void(payment)

true

1c2ea26a0e074c408ad814c5bbffa80acfedad47

1,230

py

Python

tests/unit/objects/test_list_objects.py

dlite-tools/aws-s3-tools

f434ed36c1fc0530f2be6b52808cbc5e59ea8990

[ "MIT" ]

6

2021-02-18T10:03:17.000Z

2022-01-14T06:11:54.000Z

tests/unit/objects/test_list.py

FerrariDG/aws-s3-tools

0ddbb35cebc858bae1a0627aa8726e063a3ef5cd

[ "MIT" ]

4

2021-04-20T18:20:32.000Z

2022-02-04T08:32:39.000Z

tests/unit/objects/test_list_objects.py

dlite-tools/aws-s3-tools

f434ed36c1fc0530f2be6b52808cbc5e59ea8990

[ "MIT" ]

2

2021-03-04T00:25:51.000Z

2021-05-10T13:20:42.000Z

"""Unit tests for list.py""" from botocore.exceptions import ClientError from s3_tools import list_objects from tests.unit.conftest import ( create_bucket, BUCKET_NAME, FILENAME ) class TestList: def test_list_nonexisting_bucket(self, s3_client): try: list_objects(BUCKET_NAME) except ClientError as e: error = e.response["Error"]["Code"] assert error == "NoSuchBucket" def test_list_empty_bucket(self, s3_client): with create_bucket(s3_client, BUCKET_NAME): keys = list_objects(BUCKET_NAME, "prefix") assert len(keys) == 0 def test_list_bucket(self, s3_client): lst = [(f"prefix/mock_{i}.csv", FILENAME) for i in range(1)] with create_bucket(s3_client, BUCKET_NAME, keys_paths=lst): keys = list_objects(BUCKET_NAME, "prefix") assert len(keys) == 1 assert keys[0] == lst[0][0] def test_list_bucket_with_pagination(self, s3_client): lst = [(f"prefix/mock_{i}.csv", FILENAME) for i in range(10)] with create_bucket(s3_client, BUCKET_NAME, keys_paths=lst): keys = list_objects(BUCKET_NAME, "prefix", max_keys=3) assert len(keys) == 10

27.333333

69

0.647154

from botocore.exceptions import ClientError from s3_tools import list_objects from tests.unit.conftest import ( create_bucket, BUCKET_NAME, FILENAME ) class TestList: def test_list_nonexisting_bucket(self, s3_client): try: list_objects(BUCKET_NAME) except ClientError as e: error = e.response["Error"]["Code"] assert error == "NoSuchBucket" def test_list_empty_bucket(self, s3_client): with create_bucket(s3_client, BUCKET_NAME): keys = list_objects(BUCKET_NAME, "prefix") assert len(keys) == 0 def test_list_bucket(self, s3_client): lst = [(f"prefix/mock_{i}.csv", FILENAME) for i in range(1)] with create_bucket(s3_client, BUCKET_NAME, keys_paths=lst): keys = list_objects(BUCKET_NAME, "prefix") assert len(keys) == 1 assert keys[0] == lst[0][0] def test_list_bucket_with_pagination(self, s3_client): lst = [(f"prefix/mock_{i}.csv", FILENAME) for i in range(10)] with create_bucket(s3_client, BUCKET_NAME, keys_paths=lst): keys = list_objects(BUCKET_NAME, "prefix", max_keys=3) assert len(keys) == 10

true

1c2ea307dfc71029b6d922a7886d6cfe21a1e7e4

5,215

py

Python

tests/common/test_run/ascend/matmul_addn_transdata_run.py

tianjiashuo/akg

a9cbf642063fb1086a93e8bc6be6feb145689817

[ "Apache-2.0" ]

286

2020-06-23T06:40:44.000Z

2022-03-30T01:27:49.000Z

tests/common/test_run/ascend/matmul_addn_transdata_run.py

tianjiashuo/akg

a9cbf642063fb1086a93e8bc6be6feb145689817

[ "Apache-2.0" ]

10

2020-07-31T03:26:59.000Z

2021-12-27T15:00:54.000Z

tests/common/test_run/ascend/matmul_addn_transdata_run.py

tianjiashuo/akg

a9cbf642063fb1086a93e8bc6be6feb145689817

[ "Apache-2.0" ]

30

2020-07-17T01:04:14.000Z

2021-12-27T14:05:19.000Z

# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import akg.tvm import numpy as np from akg.utils import kernel_exec as utils from akg.ops.math.ascend import MatMul from tests.common.test_run.ascend.matmul_run import * from akg.ops.math import Addn from akg.ops.math import Add def matmul_addn_transdata(x, y, adds, b, out_dtype, left_format="zZ", right_format="nZ", out_format="zN", transpose_x=False, transpose_y=False, attrs={}, target='cce'): matmul_res, attrs_mat = MatMul(x, y, b, out_dtype, left_format, right_format, out_format, transpose_x, transpose_y, attrs=attrs) addn_res = Addn(adds, target=target) res = Add(matmul_res, addn_res, target=target) if out_format == 'zN': n1, m1, m0, n0 = matmul_res.shape[-4:] new_shape = matmul_res.shape[:-4] + [m1 * m0, n1 * n0] elif out_format == 'zZ': m1, n1, m0, n0 = matmul_res.shape[-4:] new_shape = matmul_res.shape[:-4] + [m1 * m0, n1 * n0] func = akg.tvm.get_global_func("TransData") res = func([res], {"src_format" : "FRACTAL_NZ", "dst_format" : "DefaultFormat", "output_shape": new_shape}) return res, attrs_mat def matmul_addn_transdata_compile(shape_x, shape_y, bias, add_n, left_format, right_format, output_format, adj_x, adj_y, dtype, bias_dtype, out_dtype, kernel_name, attrs, tuning=False): batch_tuple, m, k, n = extract_dim(shape_x, shape_y, adj_x, adj_y) m = (m + 15) // 16 * 16 n = (n + 15) // 16 * 16 k = (k + 15) // 16 * 16 shape_xx, shape_yy, bias_shape, out_shape, k = get_converted_shapes(m, n, k, batch_tuple, adj_x, adj_y, bias, left_format, right_format, output_format) addn_shapes = [] for i in range(add_n): addn_shapes.append(out_shape) input_shapes = [shape_xx, shape_yy, addn_shapes, bias_shape] input_types = [dtype, dtype, out_dtype, bias_dtype] has_bias = False if bias == 1: has_bias = True op_attrs = [out_dtype, left_format, right_format, output_format, adj_x, adj_y, attrs] if has_bias == False: input_shapes = [shape_xx, shape_yy, addn_shapes] input_types = [dtype, dtype, out_dtype] op_attrs = [None, out_dtype, left_format, right_format, output_format, adj_x, adj_y, attrs] return utils.op_build_test(matmul_addn_transdata, input_shapes, input_types, op_attrs, kernel_name, attrs=attrs, tuning=tuning) def matmul_addn_transdata_execute(shape_x, shape_y, bias, add_n, left_format, right_format, out_format, adj_x, adj_y, dtype, bias_dtype, out_dtype, kernel_name, attrs={}): batch_tuple, m, k, n = extract_dim(shape_x, shape_y, adj_x, adj_y) m = (m + 15) // 16 * 16 n = (n + 15) // 16 * 16 k = (k + 15) // 16 * 16 shape_xx, shape_yy, bias_shape, out_shape, k = get_converted_shapes(m, n, k, batch_tuple, adj_x, adj_y, bias, left_format, right_format, out_format) mod = matmul_addn_transdata_compile(shape_x, shape_y, bias, add_n, left_format, right_format, out_format, adj_x, adj_y, dtype, bias_dtype, out_dtype, kernel_name, attrs=attrs) # Generate data m_x, m_y, bench_mark, bias_data = matmul_data(batch_tuple, m, k, n, dtype, bias_dtype, out_dtype, bias, adj_x, adj_y, left_format, right_format, out_format) inputs = [] mod_data = [m_x, m_y] for i in range(add_n): input = random_gaussian(out_shape, miu=1, sigma=0.1).astype(out_dtype) inputs.append(input) mod_data.append(input) bench_mark = np.add(np.sum(inputs, axis=0), bench_mark) transpose_axis = [] new_shape = [] out_shape = list(out_shape) if out_format == 'zN': n1, m1, m0, n0 = out_shape[-4:] new_shape = out_shape[:-4] + [m1 * m0, n1 * n0] transpose_axis = [0, 1+1, 2+1, 0+1, 3+1] elif out_format == 'zZ': m1, n1, m0, n0 = out_shape[-4:] new_shape = out_shape[:-4] + [m1 * m0, n1 * n0] transpose_axis = [0, 0+1, 2+1, 1+1, 3+1] bench_mark = bench_mark.transpose(transpose_axis) bench_mark = np.reshape(bench_mark,new_shape) # mod launch output = np.full(bench_mark.shape, np.nan, out_dtype) if bias == 0: mod_data.append(output) output = utils.mod_launch(mod, mod_data, expect=bench_mark) elif bias == 1: mod_data.append(bias_data) mod_data.append(output) output = utils.mod_launch(mod, mod_data, expect=bench_mark) # compare result rtol, atol = get_rtol_atol("matmul", dtype) compare_result = compare_tensor(output, bench_mark, rtol=rtol, atol=atol, equal_nan=True) return (m_x, m_y), output, bench_mark, compare_result

48.287037

185

0.671333

import akg.tvm import numpy as np from akg.utils import kernel_exec as utils from akg.ops.math.ascend import MatMul from tests.common.test_run.ascend.matmul_run import * from akg.ops.math import Addn from akg.ops.math import Add def matmul_addn_transdata(x, y, adds, b, out_dtype, left_format="zZ", right_format="nZ", out_format="zN", transpose_x=False, transpose_y=False, attrs={}, target='cce'): matmul_res, attrs_mat = MatMul(x, y, b, out_dtype, left_format, right_format, out_format, transpose_x, transpose_y, attrs=attrs) addn_res = Addn(adds, target=target) res = Add(matmul_res, addn_res, target=target) if out_format == 'zN': n1, m1, m0, n0 = matmul_res.shape[-4:] new_shape = matmul_res.shape[:-4] + [m1 * m0, n1 * n0] elif out_format == 'zZ': m1, n1, m0, n0 = matmul_res.shape[-4:] new_shape = matmul_res.shape[:-4] + [m1 * m0, n1 * n0] func = akg.tvm.get_global_func("TransData") res = func([res], {"src_format" : "FRACTAL_NZ", "dst_format" : "DefaultFormat", "output_shape": new_shape}) return res, attrs_mat def matmul_addn_transdata_compile(shape_x, shape_y, bias, add_n, left_format, right_format, output_format, adj_x, adj_y, dtype, bias_dtype, out_dtype, kernel_name, attrs, tuning=False): batch_tuple, m, k, n = extract_dim(shape_x, shape_y, adj_x, adj_y) m = (m + 15) // 16 * 16 n = (n + 15) // 16 * 16 k = (k + 15) // 16 * 16 shape_xx, shape_yy, bias_shape, out_shape, k = get_converted_shapes(m, n, k, batch_tuple, adj_x, adj_y, bias, left_format, right_format, output_format) addn_shapes = [] for i in range(add_n): addn_shapes.append(out_shape) input_shapes = [shape_xx, shape_yy, addn_shapes, bias_shape] input_types = [dtype, dtype, out_dtype, bias_dtype] has_bias = False if bias == 1: has_bias = True op_attrs = [out_dtype, left_format, right_format, output_format, adj_x, adj_y, attrs] if has_bias == False: input_shapes = [shape_xx, shape_yy, addn_shapes] input_types = [dtype, dtype, out_dtype] op_attrs = [None, out_dtype, left_format, right_format, output_format, adj_x, adj_y, attrs] return utils.op_build_test(matmul_addn_transdata, input_shapes, input_types, op_attrs, kernel_name, attrs=attrs, tuning=tuning) def matmul_addn_transdata_execute(shape_x, shape_y, bias, add_n, left_format, right_format, out_format, adj_x, adj_y, dtype, bias_dtype, out_dtype, kernel_name, attrs={}): batch_tuple, m, k, n = extract_dim(shape_x, shape_y, adj_x, adj_y) m = (m + 15) // 16 * 16 n = (n + 15) // 16 * 16 k = (k + 15) // 16 * 16 shape_xx, shape_yy, bias_shape, out_shape, k = get_converted_shapes(m, n, k, batch_tuple, adj_x, adj_y, bias, left_format, right_format, out_format) mod = matmul_addn_transdata_compile(shape_x, shape_y, bias, add_n, left_format, right_format, out_format, adj_x, adj_y, dtype, bias_dtype, out_dtype, kernel_name, attrs=attrs) m_x, m_y, bench_mark, bias_data = matmul_data(batch_tuple, m, k, n, dtype, bias_dtype, out_dtype, bias, adj_x, adj_y, left_format, right_format, out_format) inputs = [] mod_data = [m_x, m_y] for i in range(add_n): input = random_gaussian(out_shape, miu=1, sigma=0.1).astype(out_dtype) inputs.append(input) mod_data.append(input) bench_mark = np.add(np.sum(inputs, axis=0), bench_mark) transpose_axis = [] new_shape = [] out_shape = list(out_shape) if out_format == 'zN': n1, m1, m0, n0 = out_shape[-4:] new_shape = out_shape[:-4] + [m1 * m0, n1 * n0] transpose_axis = [0, 1+1, 2+1, 0+1, 3+1] elif out_format == 'zZ': m1, n1, m0, n0 = out_shape[-4:] new_shape = out_shape[:-4] + [m1 * m0, n1 * n0] transpose_axis = [0, 0+1, 2+1, 1+1, 3+1] bench_mark = bench_mark.transpose(transpose_axis) bench_mark = np.reshape(bench_mark,new_shape) output = np.full(bench_mark.shape, np.nan, out_dtype) if bias == 0: mod_data.append(output) output = utils.mod_launch(mod, mod_data, expect=bench_mark) elif bias == 1: mod_data.append(bias_data) mod_data.append(output) output = utils.mod_launch(mod, mod_data, expect=bench_mark) rtol, atol = get_rtol_atol("matmul", dtype) compare_result = compare_tensor(output, bench_mark, rtol=rtol, atol=atol, equal_nan=True) return (m_x, m_y), output, bench_mark, compare_result

true

1c2ea46aad078a13c077e1ce5e6d9d9417d6e2d3

815

py

Python

gan/PyTorch-GAN-Mnist/test.py

hadleyhzy34/GANs-practice

37d1dcf4e4b492e6d070b0ba72f320913af9b17a

[ "MIT" ]

null

gan/PyTorch-GAN-Mnist/test.py

hadleyhzy34/GANs-practice

37d1dcf4e4b492e6d070b0ba72f320913af9b17a

[ "MIT" ]

null

gan/PyTorch-GAN-Mnist/test.py

hadleyhzy34/GANs-practice

37d1dcf4e4b492e6d070b0ba72f320913af9b17a

[ "MIT" ]

null

import torch from torchvision import transforms import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec plt.rcParams['figure.figsize'] = (10.0, 8.0) plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' def show_images(images): sqrtn = int(np.ceil(np.sqrt(images.shape[0]))) for index, image in enumerate(images): plt.subplot(sqrtn, sqrtn, index+1) plt.imshow(image.reshape(28, 28)) device = 'cuda:0' if torch.cuda.is_available() else 'cpu' print('GPU State:', device) # Model G = torch.load('Generator_epoch_200.pth') G.eval() # Generator noise = (torch.rand(16, 128)-0.5) / 0.5 noise = noise.to(device) fake_image = G(noise) imgs_numpy = (fake_image.data.cpu().numpy()+1.0)/2.0 show_images(imgs_numpy) plt.show()

21.447368

57

0.70184

import torch from torchvision import transforms import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec plt.rcParams['figure.figsize'] = (10.0, 8.0) plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' def show_images(images): sqrtn = int(np.ceil(np.sqrt(images.shape[0]))) for index, image in enumerate(images): plt.subplot(sqrtn, sqrtn, index+1) plt.imshow(image.reshape(28, 28)) device = 'cuda:0' if torch.cuda.is_available() else 'cpu' print('GPU State:', device) G = torch.load('Generator_epoch_200.pth') G.eval() noise = (torch.rand(16, 128)-0.5) / 0.5 noise = noise.to(device) fake_image = G(noise) imgs_numpy = (fake_image.data.cpu().numpy()+1.0)/2.0 show_images(imgs_numpy) plt.show()

true

1c2ea46c2dbae491088472dec33c9b4319f71f78

8,389

py

Python

src/m2_functions.py

ElliotBoutell/ObjectsFunctionsAndMethods

2f022fc7dbca5a5ad3b90cdb280e59b1ef28dd93

[ "MIT" ]

null

src/m2_functions.py

ElliotBoutell/ObjectsFunctionsAndMethods

2f022fc7dbca5a5ad3b90cdb280e59b1ef28dd93

[ "MIT" ]

null

src/m2_functions.py

ElliotBoutell/ObjectsFunctionsAndMethods

2f022fc7dbca5a5ad3b90cdb280e59b1ef28dd93

[ "MIT" ]

null

""" Practice DEFINING and CALLING FUNCTIONS Authors: David Mutchler, Dave Fisher, Valerie Galluzzi, Amanda Stouder, their colleagues and Elliot Boutell. """ ######################################################################## # # Done: 1. PUT YOUR NAME IN THE ABOVE LINE and... # # Allow this file to use the rosegraphics.py file by marking the src # directory as a "Sources Root". Do that by right clicking on the src folder, # then selector Mark Directory As --> Sources Root # # Expand the import lines below and watch the red underlines disappear as you do that step. # You will do that once for every project that uses rosegraphics so get used to it. :) # # Then run this module and look for more TO DO's later in the file. ######################################################################## import rosegraphics as rg import random def main(): """ Makes a TurtleWindow, calls the other functions in this module to demo them, and waits for the user to click anywhere in the window to close it. """ # A TurtleWindow works "behind the scenes" to enable Turtle movement window = rg.TurtleWindow() turtle1() turtle4() turtle5() turtle3() turtle2() turtle2() window.close_on_mouse_click() def turtle1(): """ Constructs a square SimpleTurtle. Makes that SimpleTurtle draw a yellow-filled circle. """ ada = rg.SimpleTurtle('square') ada.pen = rg.Pen('aquamarine', 30) ada.paint_bucket = rg.PaintBucket('yellow') ada.begin_fill() ada.draw_circle(150) ada.end_fill() def turtle2(): """ Constructs a triangle SimpleTurtle. Makes that SimpleTurle go to a RANDOM point, draws a cool shape, and return to where it started from. """ grace = rg.SimpleTurtle('triangle') grace.pen = rg.Pen('blue', 15) grace.paint_bucket = rg.PaintBucket('magenta') # Keep track of where I am, to go back to it at the end. # Then choose a RANDOM starting point for the motion in here. i_began_here = rg.Point(grace.x_cor(), grace.y_cor()) i_am_going_here = rg.Point(random.randrange(-500, 500), random.randrange(-300, 0)) grace.pen_up() grace.go_to(i_am_going_here) grace.pen_down() # Do the motion. grace.left(90) grace.forward(200) grace.begin_fill() grace.draw_circle(25) grace.end_fill() # Go back to where I was when this function began its run. grace.go_to(i_began_here) def turtle3(): """ Constructs a default SimpleTurtle. Makes that SimpleTurtle go forward 300 units and then draw a black-filled circle. """ maja = rg.SimpleTurtle() maja.pen = rg.Pen('black', 10) maja.forward(300) maja.begin_fill() maja.draw_circle(50) maja.end_fill() def turtle4(): caleb = rg.SimpleTurtle() caleb.pen = rg.Pen('purple', 3) caleb.backward(100) caleb.draw_circle(40) def turtle5(): connor = rg.SimpleTurtle() connor.pen = rg.Pen('orange', 5) connor.right(180) connor.forward(100) connor.draw_square(20) gabe = rg.SimpleTurtle() gabe.pen = rg.Pen('blue', 8) gabe.left(45) gabe.draw_circle(6) gabe.forward(6) ######################################################################## # # Done: 2. # READ the code above. Be sure you understand: # -- How many functions are defined above? # (Answer: 4) # -- For each function definition: # -- Where does that function definition begin? # Where does it end? # -- How many times does main call the turtle1 function? # (Answer: 1) # -- How many times does main call the turtle2 function? # (Hint: the answer is NOT 1.) # -- What line of code calls the main function? # (Answer: look at the LAST line of this module, far below.) # # ** ASK QUESTIONS if you are uncertain. ** # # RELATE what is DRAWN to the CODE above. Be sure you understand: # -- WHEN does the code in main run? # -- WHEN does the code in turtle1 run? # the code in turtle2 run? # the code in turtle3 run? # -- For each of the above, WHY does that code run when it does? # # ** ASK QUESTIONS if you are uncertain. ** # # When you believe you understand the answers # to all of the above questions, change the above TO DO to DONE. # ######################################################################## ######################################################################## # # Done: 3. # Define another function, # immediately below the end of the definition of turtle3 above. # Name your new function turtle4. # # The Python "pep8" coding standard says to leave exactly 2 blank # lines between function definitions, so be sure to do so. # # Your new function should: # 1. Define a SimpleTurtle (as turtle3 as other functions did). # 2. Set your SimpleTurtle's # pen # to a new rg.Pen with a color and thickness of your own choosing. # See the COLORS.txt file in this project for a list of legal color-names. # 3. Make your SimpleTurtle move around a bit. # # ---------------------------------------------------------------------- # ** IMPORTANT: ** # ** Nothing fancy is required. ** # ** Save fancy stuff for exercises later today. ** # ---------------------------------------------------------------------- # # BTW, if you see a RED underline, that means that there is # a SYNTAX (notation) error at that line or elsewhere. # Get help as needed to fix any such errors. # ######################################################################## ######################################################################## # # Done: 4. # Add a line to main that CALLS your new function immediately # AFTER main calls turtle1. So: # -- the SimpleTurtle from turtle1 should move, # -- then YOUR SimpleTurtle should move, # -- then the other 3 SimpleTurtles should move. # # Run this module. Check that there is another SimpleTurtle (yours) # that uses the pen you chose and moves around a bit. # If your code has errors (shows RED in the Console window) # or does not do what it should, get help as needed to fix it. # ######################################################################## ######################################################################## # # Done: 5. # The previous two TODOs IMPLEMENTED a function (TO DO 3) # and TESTED that function (TO DO 4). # # Now implement AND test one more function, defining it immediately # below the definition of your turtle4 function. # Name your new function turtle5. # # The Python "pep8" coding standard says to leave exactly 2 blank # lines between function definitions, so be sure to do so. # # Your new function should define TWO new SimpleTurtles, # set their characteristics (i.e., instance variables) as you choose, # and make each move a bit. # # ---------------------------------------------------------------------- # ** IMPORTANT: ** # ** Nothing fancy is required. ** # ** Save fancy stuff for exercises later today. ** # ---------------------------------------------------------------------- # # Get help as needed on this (and every!) exercise! # # As always COMMIT and Push your work as often as you want, but for sure after # you have tested it and believe that it is correct. # # Reminder of those steps... # COMMIT your work by selecting VCS from the menu bar, then select Commit Changes # Make sure only the files you want to commit are checked and optionally # add a quick Commit message to describe your work. Then hover over the # Commit button and select Commit and Push. Commit saves the work to # your computer. "and Push" saves a copy of your work up into your Github # repository (saving to the cloud is a better way to permanently safe work). # ######################################################################## # ---------------------------------------------------------------------- # Calls main to start the ball rolling. # ---------------------------------------------------------------------- main()

34.101626

93

0.561092

module, far below.) # # ** ASK QUESTIONS if you are uncertain. ** # # RELATE what is DRAWN to the CODE above. Be sure you understand: # -- WHEN does the code in main run? # -- WHEN does the code in turtle1 run? # the code in turtle2 run? # the code in turtle3 run? # -- For each of the above, WHY does that code run when it does? # # ** ASK QUESTIONS if you are uncertain. ** # # When you believe you understand the answers # to all of the above questions, change the above TO DO to DONE. # ######################################################################## ######################################################################## # # Done: 3. # Define another function, # immediately below the end of the definition of turtle3 above. # Name your new function turtle4. # # The Python "pep8" coding standard says to leave exactly 2 blank # lines between function definitions, so be sure to do so. # # Your new function should: # 1. Define a SimpleTurtle (as turtle3 as other functions did). # 2. Set your SimpleTurtle's

true

1c2ea51069614377d2b53c72c7b82fa4c473af18

3,559

py

Python

anuvaad-etl/anuvaad-extractor/aligner/etl-aligner/kafkawrapper/alignmentwflowconsumer.py

ManavTriesStuff/anuvaad

6993e3ac78818c171c173ccf8acf962ff57856a4

[ "MIT" ]

15

2021-01-08T08:42:30.000Z

2022-03-12T17:52:15.000Z

anuvaad-etl/anuvaad-extractor/aligner/etl-aligner/kafkawrapper/alignmentwflowconsumer.py

ManavTriesStuff/anuvaad

6993e3ac78818c171c173ccf8acf962ff57856a4

[ "MIT" ]

16

2021-01-21T01:38:51.000Z

2022-01-20T08:59:52.000Z

anuvaad-etl/anuvaad-extractor/aligner/etl-aligner/kafkawrapper/alignmentwflowconsumer.py

ManavTriesStuff/anuvaad

6993e3ac78818c171c173ccf8acf962ff57856a4

[ "MIT" ]

25

2020-08-26T11:25:38.000Z

2022-03-29T04:40:21.000Z

import json import logging import random import string import threading from kafka import KafkaConsumer, TopicPartition from service.alignmentservice import AlignmentService from utilities.alignmentutils import AlignmentUtils from logging.config import dictConfig from configs.alignerconfig import kafka_bootstrap_server_host from configs.alignerconfig import align_job_consumer_grp from configs.alignerconfig import anu_dp_wf_aligner_in_topic from anuvaad_auditor.loghandler import log_info from anuvaad_auditor.loghandler import log_exception log = logging.getLogger('file') class WflowConsumer: def __init__(self): pass # Method to instantiate the kafka consumer def instantiate(self, topics): consumer = KafkaConsumer(*topics, bootstrap_servers=list(str(kafka_bootstrap_server_host).split(",")), api_version=(1, 0, 0), group_id=align_job_consumer_grp, auto_offset_reset='latest', enable_auto_commit=True, value_deserializer=lambda x: self.handle_json(x)) return consumer # Method to read and process the requests from the kafka queue def consume(self): topics = [anu_dp_wf_aligner_in_topic] consumer = self.instantiate(topics) service = AlignmentService() util = AlignmentUtils() rand_str = ''.join(random.choice(string.ascii_letters) for i in range(4)) prefix = "Align-WFM-Consumer(" + rand_str + ")" log_info(prefix + " running.......", None) while True: #thread_count = 0 for msg in consumer: data = {} try: data = msg.value if data: log_info(prefix + " | Received on Topic: " + msg.topic + " | Partition: " + str(msg.partition), data) service.wf_process(data) break except Exception as e: log_exception("Exception while consuming: " + str(e), data, e) util.error_handler("ALIGNER_CONSUMER_ERROR", "Exception while consuming", data, True) break # Method that provides a deserialiser for the kafka record. def handle_json(self, x): try: return json.loads(x.decode('utf-8')) except Exception as e: log_exception("handle_json", "Exception while deserialising: ", None, e) return {} # Log config dictConfig({ 'version': 1, 'formatters': {'default': { 'format': '[%(asctime)s] {%(filename)s:%(lineno)d} %(threadName)s %(levelname)s in %(module)s: %(message)s', }}, 'handlers': { 'info': { 'class': 'logging.FileHandler', 'level': 'DEBUG', 'formatter': 'default', 'filename': 'info.log' }, 'console': { 'class': 'logging.StreamHandler', 'level': 'DEBUG', 'formatter': 'default', 'stream': 'ext://sys.stdout', } }, 'loggers': { 'file': { 'level': 'DEBUG', 'handlers': ['info', 'console'], 'propagate': '' } }, 'root': { 'level': 'DEBUG', 'handlers': ['info', 'console'] } })

35.237624

125

0.540882

import json import logging import random import string import threading from kafka import KafkaConsumer, TopicPartition from service.alignmentservice import AlignmentService from utilities.alignmentutils import AlignmentUtils from logging.config import dictConfig from configs.alignerconfig import kafka_bootstrap_server_host from configs.alignerconfig import align_job_consumer_grp from configs.alignerconfig import anu_dp_wf_aligner_in_topic from anuvaad_auditor.loghandler import log_info from anuvaad_auditor.loghandler import log_exception log = logging.getLogger('file') class WflowConsumer: def __init__(self): pass def instantiate(self, topics): consumer = KafkaConsumer(*topics, bootstrap_servers=list(str(kafka_bootstrap_server_host).split(",")), api_version=(1, 0, 0), group_id=align_job_consumer_grp, auto_offset_reset='latest', enable_auto_commit=True, value_deserializer=lambda x: self.handle_json(x)) return consumer def consume(self): topics = [anu_dp_wf_aligner_in_topic] consumer = self.instantiate(topics) service = AlignmentService() util = AlignmentUtils() rand_str = ''.join(random.choice(string.ascii_letters) for i in range(4)) prefix = "Align-WFM-Consumer(" + rand_str + ")" log_info(prefix + " running.......", None) while True: for msg in consumer: data = {} try: data = msg.value if data: log_info(prefix + " | Received on Topic: " + msg.topic + " | Partition: " + str(msg.partition), data) service.wf_process(data) break except Exception as e: log_exception("Exception while consuming: " + str(e), data, e) util.error_handler("ALIGNER_CONSUMER_ERROR", "Exception while consuming", data, True) break def handle_json(self, x): try: return json.loads(x.decode('utf-8')) except Exception as e: log_exception("handle_json", "Exception while deserialising: ", None, e) return {} dictConfig({ 'version': 1, 'formatters': {'default': { 'format': '[%(asctime)s] {%(filename)s:%(lineno)d} %(threadName)s %(levelname)s in %(module)s: %(message)s', }}, 'handlers': { 'info': { 'class': 'logging.FileHandler', 'level': 'DEBUG', 'formatter': 'default', 'filename': 'info.log' }, 'console': { 'class': 'logging.StreamHandler', 'level': 'DEBUG', 'formatter': 'default', 'stream': 'ext://sys.stdout', } }, 'loggers': { 'file': { 'level': 'DEBUG', 'handlers': ['info', 'console'], 'propagate': '' } }, 'root': { 'level': 'DEBUG', 'handlers': ['info', 'console'] } })

true

1c2ea52c32488d1ead6f2d2517e097d076dc1c24

13,804

py

Python

circuit.py

Diimu/circuitsimulator

3a4aeaaa89b5e9bba1598736140e32b3ca1e0da4

[ "MIT" ]

null

circuit.py

Diimu/circuitsimulator

3a4aeaaa89b5e9bba1598736140e32b3ca1e0da4

[ "MIT" ]

3

2019-09-08T18:34:27.000Z

2019-09-08T21:51:18.000Z

circuit.py

Diimu/circuitsimulator

3a4aeaaa89b5e9bba1598736140e32b3ca1e0da4

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Class "Circuit" defines simple one port object/circuit having frequency (single point or array), impedance, and possibly a list of components that constitute the circuit. """ import numpy as np import scipy import matplotlib.pyplot as plt import skrf class Circuit: def __init__(self, f, Z, Z0=50, draw_smith=True): self.Z = Z self.f = f self.Z0 = Z0 self.draw_smith=draw_smith self.components = [('start', Z)] if np.size(self.f)==1 and self.draw_smith: #single frequency -> plot transition on smith chart skrf.plotting.plot_smith(self.refl(self.Z), marker='o', color='k', linestyle=None, x_label='',y_label='', title='Smith chart, matching network', label='start') #self.components is an array of tuples containing the history of the circuit, #i.e., all components attached to the circuit def __str__(self): if not isinstance(self.f, np.ndarray): freq, multi = find_si_prefix(self.f) ret = "{:.2f}+j{:2f} ohm at {:.2f} {:s}Hz:\n".format(self.Z.real, self.Z.imag, freq, multi) else: return "not implemented for freq range" for component in self.components: prefix = find_si_prefix(component[1], False) ct = '' #component type cc = '' #component connection unit = '' n = component[0] if n.find('cap') >= 0: ct = 'capacitor' unit = 'F' elif n.find('ind') >= 0: ct = 'inductor' unit = 'H' elif n.find('line') >= 0: ct = 'line' unit = '\pi' if n.find('ser') >= 0: cc = 'Series' elif n.find('par') >= 0: cc = 'Parallel' elif n.find('start') >= 0: cc = 'Start' ret += '{:s} {:s}: {:.2f} {:s}{:s}\n'.format(cc, ct, prefix[0], prefix[1], unit) return ret def cap(self, C): ''' impedance of a capacitor at frequency of this circuit ''' return cap(self.f, C) def ind(self, L): return ind(self.f, L) def refl(self, Z=None): ''' returns the reflection coefficient of the present circuit ''' if Z is None: return refl(self.Z,self.Z0) else: return refl(Z, self.Z0) def ser(self, Z): self.components.append(('ser',Z)) if np.size(self.f)==1 and self.draw_smith: #single frequency -> plot transition on smith chart tmp = np.logspace(np.log10(Z/1000), np.log10(Z), 100) #impedance of a capacitance array, starting from very large cap #(large series capacitance == small change in impedance) tmp = ser(self.Z, tmp) #the array transformed into impedance scaled_value = find_si_prefix(Z) label = 'ser {:2g} {:s}$\Omega$'.format(scaled_value[0], scaled_value[1]) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z += Z #after plotting, add series capacitance to the current impedance def par(self, Z): self.components.append(('par',Z)) if np.size(self.f)==1 and self.draw_smith: #single frequency -> plot transition on smith chart tmp = np.logspace(np.log10(Z*1000), np.log10(Z), 100) #impedance of a capacitance array, starting from very large cap #(large series capacitance == small change in impedance) tmp = par(self.Z, tmp) #the array transformed into impedance scaled_value = find_si_prefix(Z) label = 'par {:2g} {:s}$\Omega$'.format(scaled_value[0], scaled_value[1]) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = par(Z, self.Z) #after plotting, add series capacitance to the current impedance def sercap(self, C): ''' Set a capacitor in series with the present circuit ''' self.components.append(('sercap',C)) if np.size(self.f)==1 and self.draw_smith: #single frequency -> plot transition on smith chart tmp = self.cap(np.logspace(np.log10(C*1000), np.log10(C), 100)) #impedance of a capacitance array, starting from very large cap #(large series capacitance == small change in impedance) tmp = ser(self.Z, tmp) #the array transformed into impedance scaled_value = find_si_prefix(C) label = 'ser {:2g} {:s}F'.format(scaled_value[0], scaled_value[1]) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = ser(self.Z, self.cap(C)) #after plotting, add series capacitance to the current impedance def serind(self, L): ''' Set an inductor in series with the present circuit ''' self.components.append(('serind',L)) if np.size(self.f)==1 and self.draw_smith: #single frequency -> plot transition on smith chart tmp = self.ind(np.logspace(np.log10(L/1000), np.log10(L), 100)) #impedance of a capacitance array, starting from very small inductance tmp = ser(self.Z, tmp) #the array transformed into impedance scaled_value = find_si_prefix(L) label = 'ser {:2g} {:s}H'.format(scaled_value[0], scaled_value[1]) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = ser(self.Z, self.ind(L)) #after plotting, add series inductance to the current impedance def parcap(self, C): ''' Set a capacitor parallel to the present circuit ''' self.components.append(('parcap',C)) if np.size(self.f)==1 and self.draw_smith: #single frequency -> plot transition on smith chart tmp = self.cap(np.logspace(np.log10(C/1000), np.log10(C), 100)) #impedance of a capacitance array, starting from very small cap tmp = par(self.Z, tmp) #the array transformed into impedance scaled_value = find_si_prefix(C) label = 'ser {:2g} {:s}F'.format(scaled_value[0], scaled_value[1]) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = par(self.Z, self.cap(C)) #after plotting, add series capacitance to the current impedance def parind(self, L): ''' Set an inductor parallel to the present circuit ''' self.components.append(('parind',L)) if np.size(self.f)==1 and self.draw_smith: #single frequency -> plot transition on smith chart tmp = self.ind(np.logspace(np.log10(L*1000), np.log10(L), 100)) #impedance of a capacitance array, starting from very large inductance #(large series capacitance == small change in impedance) tmp = par(self.Z, tmp) #the array transformed into impedance scaled_value = find_si_prefix(L) label = 'par {:2g} {:s}H'.format(scaled_value[0], scaled_value[1]) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = par(self.Z, self.ind(L)) #after plotting, add series inductance to the current impedance def serline(self, kl, Z0=None): ''' perform impedance as seen through a transmission line ''' if Z0==None: Z0=self.Z0 self.components.append(('serline',kl, Z0)) if np.size(self.f)==1 and self.draw_smith: #single frequency -> plot transition on smith chart tmp = line(self.Z, np.linspace(0, kl, 100), Z0) label = 'line {:2g}'.format(kl) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = line(self.Z, kl, Z0) def parline(self, kl, Zl=np.inf, Z0=None): ''' add a transmission line parallel to the current impedance (defaults to open self.Z0 ohm line) ''' if Z0==None: Z0=self.Z0 self.components.append(('parline',kl, Zl, Z0)) if np.size(self.f)==1 and self.draw_smith: #single frequency -> plot transition on smith chart tmp = par(self.Z, line(Zl, np.linspace(0, kl, 100), Z0)) label = 'par line {:2g}'.format(kl) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = par(self.Z0, line(Zl, kl, Z0)) def db(self, noextrastuff=False): ''' For a point frequency return reflection coefficient, for a frequency range plot the response curve. noextrastuff means the labels and grids ''' if np.size(self.f)==1: return 20*np.log10(abs(self.refl())) elif noextrastuff: plt.plot(self.f, 20*np.log10(abs(self.refl()))) else: plt.figure() plt.plot(self.f, 20*np.log10(np.abs(self.refl()))) plt.ylabel('Reflection coefficient, dB') plt.xlabel('Frequency, Hz') plt.grid() def smith(self, annotations=True,smith_r=1, chart_type='z', x_label='', y_label='', title='Smith chart, frequency', show_legend=True, axis='equal', ax=None, force_chart = False, *args, **kwargs): ''' plots the current mathcing network on the smith chart as the function of frequency ''' if np.size(self.f)>1: plt.figure() skrf.plotting.plot_smith(self.refl(), smith_r=smith_r, chart_type=chart_type, x_label=x_label, y_label=y_label, title=title, show_legend=show_legend, axis=axis, ax=ax, force_chart = force_chart, *args, **kwargs) if annotations: xy=(np.real(self.refl()[0]),np.imag(self.refl()[0])) plt.annotate('{:.2e}'.format(self.f[0]) , xy=xy,xycoords='data', xytext=(xy[0]/ np.abs(xy[0]), xy[1]/ np.abs(xy[1])), textcoords='data', arrowprops=dict(arrowstyle="->")).draggable() xy=(np.real(self.refl()[-1]),np.imag(self.refl()[-1])) plt.annotate('{:.2e}'.format(self.f[-1]) , xy=xy,xycoords='data', xytext=(xy[0]/ np.abs(xy[0]), xy[1]/ np.abs(xy[1])), textcoords='data', arrowprops=dict(arrowstyle="->")).draggable() ind = np.argmin(np.abs(self.refl())) xy=(np.real(self.refl()[ind]),np.imag(self.refl()[ind])) plt.annotate('{:.2e}\n{:.1f} dB'.format(self.f[ind], 20*np.log10(np.abs(self.refl()[ind]))) , xy=xy,xycoords='data', xytext=(xy[0]/ np.abs(xy[0])+0.2, xy[1]/ np.abs(xy[1])-0.2), textcoords='data', arrowprops=dict(arrowstyle="->")).draggable() ''' After this point the file defines basic equations used in Circuit class ''' def cap(f, C): ''' impedance of a capacitor ''' if isinstance(C, np.ndarray) or isinstance(f, np.ndarray): return 1/(1j*2*np.pi*f*C) elif np.iscomplex(C) or f<0 or C<0: raise ValueError #bullshit numbers given else: return 1/(1j*2*np.pi*f*C) def ind(f, L): ''' impedance of a inductor ''' #TODO: check that input is valid. return 1j*2*np.pi*f*L def ser(Z1, Z2, Z3=0, Z4=0, Z5=0, Z6=0, Z7=0, Z8=0, Z9=0, Z10=0): ''' impedance of a series of compoents ''' return Z1+Z2+Z3+Z4+Z5+Z6+Z7+Z8+Z9+Z10 def par(Z1, Z2, Z3=np.inf, Z4=np.inf, Z5=np.inf, Z6=np.inf, Z7=np.inf, Z8=np.inf, Z9=np.inf, Z10=np.inf): ''' impedance of parallel compoents ''' return 1/(1/Z1+1/Z2+1/Z3+1/Z4+1/Z5+1/Z6+1/Z7+1/Z8+1/Z9+1/Z10) def refl(Z, Z0=50): if isinstance(Z, np.ndarray): return (Z-Z0)/(Z+Z0) elif Z==np.inf and Z0<np.inf: return 1 else: return (Z-Z0)/(Z+Z0) def find_si_prefix(x, use_latex = True): ''' return (numeric value , si-prefix) >>> find_si_prefix(5000) (5.0, 'k') ''' if use_latex: prefixes = ['f', 'p', 'n', '$\mu$', 'm', '', 'k','M','G'] else: prefixes = ['f', 'p', 'n', 'u', 'm', '', 'k','M','G'] multipliers = np.asarray([1e-15,1e-12, 1e-9, 1e-6,1e-3,1,1e3,1e6,1e9]) for n, k in enumerate(x/multipliers): if 0.1<k<=(100 + 1e-10): break #loop the list until we find a prefix that sets the k to be in nice range return (k, prefixes[n]) def line(Z, kl, Z0=50): #from scipy.special import cotdg if Z != np.inf: Z = Z0 * (Z+1j*Z0*np.tan(kl))/(Z0 + 1j* Z*np.tan(kl)) else: np.seterr(divide='ignore') #lazy solution to situation kl=0 == divide by error Z = -1j*Z0*np.true_divide(1,np.tan(kl))# if np.tan(kl)==0 else 1j*np.inf #Z = -1j*Z0*np.cos(kl)/np.sin(kl) np.seterr(divide='warn') return Z def db(Z, Z0=50): if Z==Z0: #avoid log(0) return -np.inf else: return 20*np.log10(abs(refl(Z, Z0))) if __name__ == '__main__': plt.ion() c = Circuit(64e6, 30) c.sercap(30e-12) c.parind(100e-9)

41.083333

262

0.562808

import numpy as np import scipy import matplotlib.pyplot as plt import skrf class Circuit: def __init__(self, f, Z, Z0=50, draw_smith=True): self.Z = Z self.f = f self.Z0 = Z0 self.draw_smith=draw_smith self.components = [('start', Z)] if np.size(self.f)==1 and self.draw_smith: skrf.plotting.plot_smith(self.refl(self.Z), marker='o', color='k', linestyle=None, x_label='',y_label='', title='Smith chart, matching network', label='start') def __str__(self): if not isinstance(self.f, np.ndarray): freq, multi = find_si_prefix(self.f) ret = "{:.2f}+j{:2f} ohm at {:.2f} {:s}Hz:\n".format(self.Z.real, self.Z.imag, freq, multi) else: return "not implemented for freq range" for component in self.components: prefix = find_si_prefix(component[1], False) ct = '' cc = '' unit = '' n = component[0] if n.find('cap') >= 0: ct = 'capacitor' unit = 'F' elif n.find('ind') >= 0: ct = 'inductor' unit = 'H' elif n.find('line') >= 0: ct = 'line' unit = '\pi' if n.find('ser') >= 0: cc = 'Series' elif n.find('par') >= 0: cc = 'Parallel' elif n.find('start') >= 0: cc = 'Start' ret += '{:s} {:s}: {:.2f} {:s}{:s}\n'.format(cc, ct, prefix[0], prefix[1], unit) return ret def cap(self, C): return cap(self.f, C) def ind(self, L): return ind(self.f, L) def refl(self, Z=None): if Z is None: return refl(self.Z,self.Z0) else: return refl(Z, self.Z0) def ser(self, Z): self.components.append(('ser',Z)) if np.size(self.f)==1 and self.draw_smith: tmp = np.logspace(np.log10(Z/1000), np.log10(Z), 100) tmp = ser(self.Z, tmp) scaled_value = find_si_prefix(Z) label = 'ser {:2g} {:s}$\Omega$'.format(scaled_value[0], scaled_value[1]) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z += Z def par(self, Z): self.components.append(('par',Z)) if np.size(self.f)==1 and self.draw_smith: tmp = np.logspace(np.log10(Z*1000), np.log10(Z), 100) tmp = par(self.Z, tmp) scaled_value = find_si_prefix(Z) label = 'par {:2g} {:s}$\Omega$'.format(scaled_value[0], scaled_value[1]) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = par(Z, self.Z) def sercap(self, C): self.components.append(('sercap',C)) if np.size(self.f)==1 and self.draw_smith: tmp = self.cap(np.logspace(np.log10(C*1000), np.log10(C), 100)) tmp = ser(self.Z, tmp) scaled_value = find_si_prefix(C) label = 'ser {:2g} {:s}F'.format(scaled_value[0], scaled_value[1]) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = ser(self.Z, self.cap(C)) def serind(self, L): self.components.append(('serind',L)) if np.size(self.f)==1 and self.draw_smith: tmp = self.ind(np.logspace(np.log10(L/1000), np.log10(L), 100)) tmp = ser(self.Z, tmp) scaled_value = find_si_prefix(L) label = 'ser {:2g} {:s}H'.format(scaled_value[0], scaled_value[1]) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = ser(self.Z, self.ind(L)) def parcap(self, C): self.components.append(('parcap',C)) if np.size(self.f)==1 and self.draw_smith: tmp = self.cap(np.logspace(np.log10(C/1000), np.log10(C), 100)) tmp = par(self.Z, tmp) scaled_value = find_si_prefix(C) label = 'ser {:2g} {:s}F'.format(scaled_value[0], scaled_value[1]) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = par(self.Z, self.cap(C)) def parind(self, L): self.components.append(('parind',L)) if np.size(self.f)==1 and self.draw_smith: tmp = self.ind(np.logspace(np.log10(L*1000), np.log10(L), 100)) tmp = par(self.Z, tmp) scaled_value = find_si_prefix(L) label = 'par {:2g} {:s}H'.format(scaled_value[0], scaled_value[1]) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = par(self.Z, self.ind(L)) def serline(self, kl, Z0=None): if Z0==None: Z0=self.Z0 self.components.append(('serline',kl, Z0)) if np.size(self.f)==1 and self.draw_smith: tmp = line(self.Z, np.linspace(0, kl, 100), Z0) label = 'line {:2g}'.format(kl) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = line(self.Z, kl, Z0) def parline(self, kl, Zl=np.inf, Z0=None): if Z0==None: Z0=self.Z0 self.components.append(('parline',kl, Zl, Z0)) if np.size(self.f)==1 and self.draw_smith: tmp = par(self.Z, line(Zl, np.linspace(0, kl, 100), Z0)) label = 'par line {:2g}'.format(kl) skrf.plotting.plot_smith(self.refl(tmp), x_label='',y_label='', title='Smith chart, matching network', label=label) self.Z = par(self.Z0, line(Zl, kl, Z0)) def db(self, noextrastuff=False): if np.size(self.f)==1: return 20*np.log10(abs(self.refl())) elif noextrastuff: plt.plot(self.f, 20*np.log10(abs(self.refl()))) else: plt.figure() plt.plot(self.f, 20*np.log10(np.abs(self.refl()))) plt.ylabel('Reflection coefficient, dB') plt.xlabel('Frequency, Hz') plt.grid() def smith(self, annotations=True,smith_r=1, chart_type='z', x_label='', y_label='', title='Smith chart, frequency', show_legend=True, axis='equal', ax=None, force_chart = False, *args, **kwargs): if np.size(self.f)>1: plt.figure() skrf.plotting.plot_smith(self.refl(), smith_r=smith_r, chart_type=chart_type, x_label=x_label, y_label=y_label, title=title, show_legend=show_legend, axis=axis, ax=ax, force_chart = force_chart, *args, **kwargs) if annotations: xy=(np.real(self.refl()[0]),np.imag(self.refl()[0])) plt.annotate('{:.2e}'.format(self.f[0]) , xy=xy,xycoords='data', xytext=(xy[0]/ np.abs(xy[0]), xy[1]/ np.abs(xy[1])), textcoords='data', arrowprops=dict(arrowstyle="->")).draggable() xy=(np.real(self.refl()[-1]),np.imag(self.refl()[-1])) plt.annotate('{:.2e}'.format(self.f[-1]) , xy=xy,xycoords='data', xytext=(xy[0]/ np.abs(xy[0]), xy[1]/ np.abs(xy[1])), textcoords='data', arrowprops=dict(arrowstyle="->")).draggable() ind = np.argmin(np.abs(self.refl())) xy=(np.real(self.refl()[ind]),np.imag(self.refl()[ind])) plt.annotate('{:.2e}\n{:.1f} dB'.format(self.f[ind], 20*np.log10(np.abs(self.refl()[ind]))) , xy=xy,xycoords='data', xytext=(xy[0]/ np.abs(xy[0])+0.2, xy[1]/ np.abs(xy[1])-0.2), textcoords='data', arrowprops=dict(arrowstyle="->")).draggable() def cap(f, C): if isinstance(C, np.ndarray) or isinstance(f, np.ndarray): return 1/(1j*2*np.pi*f*C) elif np.iscomplex(C) or f<0 or C<0: raise ValueError else: return 1/(1j*2*np.pi*f*C) def ind(f, L): return 1j*2*np.pi*f*L def ser(Z1, Z2, Z3=0, Z4=0, Z5=0, Z6=0, Z7=0, Z8=0, Z9=0, Z10=0): return Z1+Z2+Z3+Z4+Z5+Z6+Z7+Z8+Z9+Z10 def par(Z1, Z2, Z3=np.inf, Z4=np.inf, Z5=np.inf, Z6=np.inf, Z7=np.inf, Z8=np.inf, Z9=np.inf, Z10=np.inf): return 1/(1/Z1+1/Z2+1/Z3+1/Z4+1/Z5+1/Z6+1/Z7+1/Z8+1/Z9+1/Z10) def refl(Z, Z0=50): if isinstance(Z, np.ndarray): return (Z-Z0)/(Z+Z0) elif Z==np.inf and Z0<np.inf: return 1 else: return (Z-Z0)/(Z+Z0) def find_si_prefix(x, use_latex = True): if use_latex: prefixes = ['f', 'p', 'n', '$\mu$', 'm', '', 'k','M','G'] else: prefixes = ['f', 'p', 'n', 'u', 'm', '', 'k','M','G'] multipliers = np.asarray([1e-15,1e-12, 1e-9, 1e-6,1e-3,1,1e3,1e6,1e9]) for n, k in enumerate(x/multipliers): if 0.1<k<=(100 + 1e-10): break return (k, prefixes[n]) def line(Z, kl, Z0=50): if Z != np.inf: Z = Z0 * (Z+1j*Z0*np.tan(kl))/(Z0 + 1j* Z*np.tan(kl)) else: np.seterr(divide='ignore') Z = -1j*Z0*np.true_divide(1,np.tan(kl)) np.seterr(divide='warn') return Z def db(Z, Z0=50): if Z==Z0: return -np.inf else: return 20*np.log10(abs(refl(Z, Z0))) if __name__ == '__main__': plt.ion() c = Circuit(64e6, 30) c.sercap(30e-12) c.parind(100e-9)

true

1c2ea563c1498911db332ccc6f834146e3a96988

105,906

py

Python

sdk/network/azure-mgmt-network/azure/mgmt/network/v2019_04_01/aio/operations/_network_watchers_operations.py

praveenkuttappan/azure-sdk-for-python

4b79413667b7539750a6c7dde15737013a3d4bd5

[ "MIT" ]

2,728

2015-01-09T10:19:32.000Z

2022-03-31T14:50:33.000Z

sdk/network/azure-mgmt-network/azure/mgmt/network/v2019_04_01/aio/operations/_network_watchers_operations.py

v-xuto/azure-sdk-for-python

9c6296d22094c5ede410bc83749e8df8694ccacc

[ "MIT" ]

17,773

2015-01-05T15:57:17.000Z

2022-03-31T23:50:25.000Z

sdk/network/azure-mgmt-network/azure/mgmt/network/v2019_04_01/aio/operations/_network_watchers_operations.py

v-xuto/azure-sdk-for-python

9c6296d22094c5ede410bc83749e8df8694ccacc

[ "MIT" ]

1,916

2015-01-19T05:05:41.000Z

2022-03-31T19:36:44.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union import warnings from azure.core.async_paging import AsyncItemPaged, AsyncList from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling from ... import models as _models T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class NetworkWatchersOperations: """NetworkWatchersOperations async operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.network.v2019_04_01.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config async def create_or_update( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.NetworkWatcher", **kwargs: Any ) -> "_models.NetworkWatcher": """Creates or updates a network watcher in the specified resource group. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher. :type network_watcher_name: str :param parameters: Parameters that define the network watcher resource. :type parameters: ~azure.mgmt.network.v2019_04_01.models.NetworkWatcher :keyword callable cls: A custom type or function that will be passed the direct response :return: NetworkWatcher, or the result of cls(response) :rtype: ~azure.mgmt.network.v2019_04_01.models.NetworkWatcher :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.NetworkWatcher"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.create_or_update.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'NetworkWatcher') body_content_kwargs['content'] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 201]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('NetworkWatcher', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('NetworkWatcher', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}'} # type: ignore async def get( self, resource_group_name: str, network_watcher_name: str, **kwargs: Any ) -> "_models.NetworkWatcher": """Gets the specified network watcher by resource group. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher. :type network_watcher_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: NetworkWatcher, or the result of cls(response) :rtype: ~azure.mgmt.network.v2019_04_01.models.NetworkWatcher :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.NetworkWatcher"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" accept = "application/json" # Construct URL url = self.get.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('NetworkWatcher', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}'} # type: ignore async def _delete_initial( self, resource_group_name: str, network_watcher_name: str, **kwargs: Any ) -> None: cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" accept = "application/json" # Construct URL url = self._delete_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [202, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}'} # type: ignore async def begin_delete( self, resource_group_name: str, network_watcher_name: str, **kwargs: Any ) -> AsyncLROPoller[None]: """Deletes the specified network watcher resource. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher. :type network_watcher_name: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either None or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[None] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType[None] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._delete_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}'} # type: ignore async def update_tags( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.TagsObject", **kwargs: Any ) -> "_models.NetworkWatcher": """Updates a network watcher tags. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher. :type network_watcher_name: str :param parameters: Parameters supplied to update network watcher tags. :type parameters: ~azure.mgmt.network.v2019_04_01.models.TagsObject :keyword callable cls: A custom type or function that will be passed the direct response :return: NetworkWatcher, or the result of cls(response) :rtype: ~azure.mgmt.network.v2019_04_01.models.NetworkWatcher :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.NetworkWatcher"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.update_tags.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'TagsObject') body_content_kwargs['content'] = body_content request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('NetworkWatcher', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized update_tags.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}'} # type: ignore def list( self, resource_group_name: str, **kwargs: Any ) -> AsyncIterable["_models.NetworkWatcherListResult"]: """Gets all network watchers by resource group. :param resource_group_name: The name of the resource group. :type resource_group_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either NetworkWatcherListResult or the result of cls(response) :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.network.v2019_04_01.models.NetworkWatcherListResult] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.NetworkWatcherListResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" accept = "application/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request async def extract_data(pipeline_response): deserialized = self._deserialize('NetworkWatcherListResult', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return None, AsyncList(list_of_elem) async def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return AsyncItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers'} # type: ignore def list_all( self, **kwargs: Any ) -> AsyncIterable["_models.NetworkWatcherListResult"]: """Gets all network watchers by subscription. :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either NetworkWatcherListResult or the result of cls(response) :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.network.v2019_04_01.models.NetworkWatcherListResult] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.NetworkWatcherListResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" accept = "application/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list_all.metadata['url'] # type: ignore path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request async def extract_data(pipeline_response): deserialized = self._deserialize('NetworkWatcherListResult', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return None, AsyncList(list_of_elem) async def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return AsyncItemPaged( get_next, extract_data ) list_all.metadata = {'url': '/subscriptions/{subscriptionId}/providers/Microsoft.Network/networkWatchers'} # type: ignore async def get_topology( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.TopologyParameters", **kwargs: Any ) -> "_models.Topology": """Gets the current network topology by resource group. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher. :type network_watcher_name: str :param parameters: Parameters that define the representation of topology. :type parameters: ~azure.mgmt.network.v2019_04_01.models.TopologyParameters :keyword callable cls: A custom type or function that will be passed the direct response :return: Topology, or the result of cls(response) :rtype: ~azure.mgmt.network.v2019_04_01.models.Topology :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.Topology"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.get_topology.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'TopologyParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('Topology', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_topology.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/topology'} # type: ignore async def _verify_ip_flow_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.VerificationIPFlowParameters", **kwargs: Any ) -> "_models.VerificationIPFlowResult": cls = kwargs.pop('cls', None) # type: ClsType["_models.VerificationIPFlowResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._verify_ip_flow_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'VerificationIPFlowParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('VerificationIPFlowResult', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('VerificationIPFlowResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _verify_ip_flow_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/ipFlowVerify'} # type: ignore async def begin_verify_ip_flow( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.VerificationIPFlowParameters", **kwargs: Any ) -> AsyncLROPoller["_models.VerificationIPFlowResult"]: """Verify IP flow from the specified VM to a location given the currently configured NSG rules. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher. :type network_watcher_name: str :param parameters: Parameters that define the IP flow to be verified. :type parameters: ~azure.mgmt.network.v2019_04_01.models.VerificationIPFlowParameters :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either VerificationIPFlowResult or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2019_04_01.models.VerificationIPFlowResult] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.VerificationIPFlowResult"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._verify_ip_flow_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('VerificationIPFlowResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_verify_ip_flow.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/ipFlowVerify'} # type: ignore async def _get_next_hop_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.NextHopParameters", **kwargs: Any ) -> "_models.NextHopResult": cls = kwargs.pop('cls', None) # type: ClsType["_models.NextHopResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._get_next_hop_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'NextHopParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('NextHopResult', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('NextHopResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_next_hop_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/nextHop'} # type: ignore async def begin_get_next_hop( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.NextHopParameters", **kwargs: Any ) -> AsyncLROPoller["_models.NextHopResult"]: """Gets the next hop from the specified VM. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher. :type network_watcher_name: str :param parameters: Parameters that define the source and destination endpoint. :type parameters: ~azure.mgmt.network.v2019_04_01.models.NextHopParameters :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either NextHopResult or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2019_04_01.models.NextHopResult] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.NextHopResult"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._get_next_hop_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('NextHopResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_next_hop.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/nextHop'} # type: ignore async def _get_vm_security_rules_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.SecurityGroupViewParameters", **kwargs: Any ) -> "_models.SecurityGroupViewResult": cls = kwargs.pop('cls', None) # type: ClsType["_models.SecurityGroupViewResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._get_vm_security_rules_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'SecurityGroupViewParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('SecurityGroupViewResult', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('SecurityGroupViewResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_vm_security_rules_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/securityGroupView'} # type: ignore async def begin_get_vm_security_rules( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.SecurityGroupViewParameters", **kwargs: Any ) -> AsyncLROPoller["_models.SecurityGroupViewResult"]: """Gets the configured and effective security group rules on the specified VM. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher. :type network_watcher_name: str :param parameters: Parameters that define the VM to check security groups for. :type parameters: ~azure.mgmt.network.v2019_04_01.models.SecurityGroupViewParameters :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either SecurityGroupViewResult or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2019_04_01.models.SecurityGroupViewResult] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.SecurityGroupViewResult"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._get_vm_security_rules_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('SecurityGroupViewResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_vm_security_rules.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/securityGroupView'} # type: ignore async def _get_troubleshooting_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.TroubleshootingParameters", **kwargs: Any ) -> "_models.TroubleshootingResult": cls = kwargs.pop('cls', None) # type: ClsType["_models.TroubleshootingResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._get_troubleshooting_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'TroubleshootingParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('TroubleshootingResult', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('TroubleshootingResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_troubleshooting_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/troubleshoot'} # type: ignore async def begin_get_troubleshooting( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.TroubleshootingParameters", **kwargs: Any ) -> AsyncLROPoller["_models.TroubleshootingResult"]: """Initiate troubleshooting on a specified resource. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher resource. :type network_watcher_name: str :param parameters: Parameters that define the resource to troubleshoot. :type parameters: ~azure.mgmt.network.v2019_04_01.models.TroubleshootingParameters :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either TroubleshootingResult or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2019_04_01.models.TroubleshootingResult] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.TroubleshootingResult"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._get_troubleshooting_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('TroubleshootingResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_troubleshooting.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/troubleshoot'} # type: ignore async def _get_troubleshooting_result_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.QueryTroubleshootingParameters", **kwargs: Any ) -> "_models.TroubleshootingResult": cls = kwargs.pop('cls', None) # type: ClsType["_models.TroubleshootingResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._get_troubleshooting_result_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'QueryTroubleshootingParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('TroubleshootingResult', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('TroubleshootingResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_troubleshooting_result_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/queryTroubleshootResult'} # type: ignore async def begin_get_troubleshooting_result( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.QueryTroubleshootingParameters", **kwargs: Any ) -> AsyncLROPoller["_models.TroubleshootingResult"]: """Get the last completed troubleshooting result on a specified resource. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher resource. :type network_watcher_name: str :param parameters: Parameters that define the resource to query the troubleshooting result. :type parameters: ~azure.mgmt.network.v2019_04_01.models.QueryTroubleshootingParameters :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either TroubleshootingResult or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2019_04_01.models.TroubleshootingResult] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.TroubleshootingResult"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._get_troubleshooting_result_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('TroubleshootingResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_troubleshooting_result.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/queryTroubleshootResult'} # type: ignore async def _set_flow_log_configuration_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.FlowLogInformation", **kwargs: Any ) -> "_models.FlowLogInformation": cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowLogInformation"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._set_flow_log_configuration_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'FlowLogInformation') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('FlowLogInformation', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('FlowLogInformation', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _set_flow_log_configuration_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/configureFlowLog'} # type: ignore async def begin_set_flow_log_configuration( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.FlowLogInformation", **kwargs: Any ) -> AsyncLROPoller["_models.FlowLogInformation"]: """Configures flow log and traffic analytics (optional) on a specified resource. :param resource_group_name: The name of the network watcher resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher resource. :type network_watcher_name: str :param parameters: Parameters that define the configuration of flow log. :type parameters: ~azure.mgmt.network.v2019_04_01.models.FlowLogInformation :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either FlowLogInformation or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2019_04_01.models.FlowLogInformation] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowLogInformation"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._set_flow_log_configuration_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('FlowLogInformation', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_set_flow_log_configuration.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/configureFlowLog'} # type: ignore async def _get_flow_log_status_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.FlowLogStatusParameters", **kwargs: Any ) -> "_models.FlowLogInformation": cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowLogInformation"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._get_flow_log_status_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'FlowLogStatusParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('FlowLogInformation', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('FlowLogInformation', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_flow_log_status_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/queryFlowLogStatus'} # type: ignore async def begin_get_flow_log_status( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.FlowLogStatusParameters", **kwargs: Any ) -> AsyncLROPoller["_models.FlowLogInformation"]: """Queries status of flow log and traffic analytics (optional) on a specified resource. :param resource_group_name: The name of the network watcher resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher resource. :type network_watcher_name: str :param parameters: Parameters that define a resource to query flow log and traffic analytics (optional) status. :type parameters: ~azure.mgmt.network.v2019_04_01.models.FlowLogStatusParameters :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either FlowLogInformation or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2019_04_01.models.FlowLogInformation] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowLogInformation"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._get_flow_log_status_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('FlowLogInformation', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_flow_log_status.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/queryFlowLogStatus'} # type: ignore async def _check_connectivity_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.ConnectivityParameters", **kwargs: Any ) -> "_models.ConnectivityInformation": cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectivityInformation"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._check_connectivity_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'ConnectivityParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('ConnectivityInformation', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('ConnectivityInformation', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _check_connectivity_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/connectivityCheck'} # type: ignore async def begin_check_connectivity( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.ConnectivityParameters", **kwargs: Any ) -> AsyncLROPoller["_models.ConnectivityInformation"]: """Verifies the possibility of establishing a direct TCP connection from a virtual machine to a given endpoint including another VM or an arbitrary remote server. :param resource_group_name: The name of the network watcher resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher resource. :type network_watcher_name: str :param parameters: Parameters that determine how the connectivity check will be performed. :type parameters: ~azure.mgmt.network.v2019_04_01.models.ConnectivityParameters :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either ConnectivityInformation or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2019_04_01.models.ConnectivityInformation] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectivityInformation"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._check_connectivity_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('ConnectivityInformation', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_check_connectivity.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/connectivityCheck'} # type: ignore async def _get_azure_reachability_report_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.AzureReachabilityReportParameters", **kwargs: Any ) -> "_models.AzureReachabilityReport": cls = kwargs.pop('cls', None) # type: ClsType["_models.AzureReachabilityReport"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._get_azure_reachability_report_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'AzureReachabilityReportParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('AzureReachabilityReport', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('AzureReachabilityReport', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_azure_reachability_report_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/azureReachabilityReport'} # type: ignore async def begin_get_azure_reachability_report( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.AzureReachabilityReportParameters", **kwargs: Any ) -> AsyncLROPoller["_models.AzureReachabilityReport"]: """Gets the relative latency score for internet service providers from a specified location to Azure regions. :param resource_group_name: The name of the network watcher resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher resource. :type network_watcher_name: str :param parameters: Parameters that determine Azure reachability report configuration. :type parameters: ~azure.mgmt.network.v2019_04_01.models.AzureReachabilityReportParameters :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either AzureReachabilityReport or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2019_04_01.models.AzureReachabilityReport] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.AzureReachabilityReport"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._get_azure_reachability_report_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('AzureReachabilityReport', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_azure_reachability_report.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/azureReachabilityReport'} # type: ignore async def _list_available_providers_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.AvailableProvidersListParameters", **kwargs: Any ) -> "_models.AvailableProvidersList": cls = kwargs.pop('cls', None) # type: ClsType["_models.AvailableProvidersList"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._list_available_providers_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'AvailableProvidersListParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('AvailableProvidersList', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('AvailableProvidersList', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _list_available_providers_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/availableProvidersList'} # type: ignore async def begin_list_available_providers( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.AvailableProvidersListParameters", **kwargs: Any ) -> AsyncLROPoller["_models.AvailableProvidersList"]: """Lists all available internet service providers for a specified Azure region. :param resource_group_name: The name of the network watcher resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher resource. :type network_watcher_name: str :param parameters: Parameters that scope the list of available providers. :type parameters: ~azure.mgmt.network.v2019_04_01.models.AvailableProvidersListParameters :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either AvailableProvidersList or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2019_04_01.models.AvailableProvidersList] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.AvailableProvidersList"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._list_available_providers_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('AvailableProvidersList', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_list_available_providers.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/availableProvidersList'} # type: ignore async def _get_network_configuration_diagnostic_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.NetworkConfigurationDiagnosticParameters", **kwargs: Any ) -> "_models.NetworkConfigurationDiagnosticResponse": cls = kwargs.pop('cls', None) # type: ClsType["_models.NetworkConfigurationDiagnosticResponse"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._get_network_configuration_diagnostic_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'NetworkConfigurationDiagnosticParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('NetworkConfigurationDiagnosticResponse', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('NetworkConfigurationDiagnosticResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_network_configuration_diagnostic_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/networkConfigurationDiagnostic'} # type: ignore async def begin_get_network_configuration_diagnostic( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.NetworkConfigurationDiagnosticParameters", **kwargs: Any ) -> AsyncLROPoller["_models.NetworkConfigurationDiagnosticResponse"]: """Get network configuration diagnostic. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param network_watcher_name: The name of the network watcher. :type network_watcher_name: str :param parameters: Parameters to get network configuration diagnostic. :type parameters: ~azure.mgmt.network.v2019_04_01.models.NetworkConfigurationDiagnosticParameters :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either NetworkConfigurationDiagnosticResponse or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2019_04_01.models.NetworkConfigurationDiagnosticResponse] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.NetworkConfigurationDiagnosticResponse"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._get_network_configuration_diagnostic_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('NetworkConfigurationDiagnosticResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_network_configuration_diagnostic.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/networkConfigurationDiagnostic'} # type: ignore

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from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union import warnings from azure.core.async_paging import AsyncItemPaged, AsyncList from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling from ... import models as _models T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class NetworkWatchersOperations: models = _models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config async def create_or_update( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.NetworkWatcher", **kwargs: Any ) -> "_models.NetworkWatcher": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self.create_or_update.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'NetworkWatcher') body_content_kwargs['content'] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 201]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('NetworkWatcher', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('NetworkWatcher', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}'} async def get( self, resource_group_name: str, network_watcher_name: str, **kwargs: Any ) -> "_models.NetworkWatcher": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" accept = "application/json" url = self.get.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('NetworkWatcher', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}'} async def _delete_initial( self, resource_group_name: str, network_watcher_name: str, **kwargs: Any ) -> None: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" accept = "application/json" url = self._delete_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [202, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}'} async def begin_delete( self, resource_group_name: str, network_watcher_name: str, **kwargs: Any ) -> AsyncLROPoller[None]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._delete_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}'} async def update_tags( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.TagsObject", **kwargs: Any ) -> "_models.NetworkWatcher": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self.update_tags.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'TagsObject') body_content_kwargs['content'] = body_content request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('NetworkWatcher', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized update_tags.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}'} def list( self, resource_group_name: str, **kwargs: Any ) -> AsyncIterable["_models.NetworkWatcherListResult"]: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" accept = "application/json" def prepare_request(next_link=None): header_parameters = {} header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: url = self.list.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} request = self._client.get(url, query_parameters, header_parameters) return request async def extract_data(pipeline_response): deserialized = self._deserialize('NetworkWatcherListResult', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return None, AsyncList(list_of_elem) async def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return AsyncItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers'} def list_all( self, **kwargs: Any ) -> AsyncIterable["_models.NetworkWatcherListResult"]: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" accept = "application/json" def prepare_request(next_link=None): header_parameters = {} header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: url = self.list_all.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} request = self._client.get(url, query_parameters, header_parameters) return request async def extract_data(pipeline_response): deserialized = self._deserialize('NetworkWatcherListResult', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return None, AsyncList(list_of_elem) async def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return AsyncItemPaged( get_next, extract_data ) list_all.metadata = {'url': '/subscriptions/{subscriptionId}/providers/Microsoft.Network/networkWatchers'} async def get_topology( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.TopologyParameters", **kwargs: Any ) -> "_models.Topology": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self.get_topology.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'TopologyParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('Topology', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_topology.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/topology'} async def _verify_ip_flow_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.VerificationIPFlowParameters", **kwargs: Any ) -> "_models.VerificationIPFlowResult": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self._verify_ip_flow_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'VerificationIPFlowParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('VerificationIPFlowResult', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('VerificationIPFlowResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _verify_ip_flow_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/ipFlowVerify'} async def begin_verify_ip_flow( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.VerificationIPFlowParameters", **kwargs: Any ) -> AsyncLROPoller["_models.VerificationIPFlowResult"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._verify_ip_flow_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('VerificationIPFlowResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_verify_ip_flow.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/ipFlowVerify'} async def _get_next_hop_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.NextHopParameters", **kwargs: Any ) -> "_models.NextHopResult": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self._get_next_hop_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'NextHopParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('NextHopResult', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('NextHopResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_next_hop_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/nextHop'} async def begin_get_next_hop( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.NextHopParameters", **kwargs: Any ) -> AsyncLROPoller["_models.NextHopResult"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._get_next_hop_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('NextHopResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_next_hop.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/nextHop'} async def _get_vm_security_rules_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.SecurityGroupViewParameters", **kwargs: Any ) -> "_models.SecurityGroupViewResult": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self._get_vm_security_rules_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'SecurityGroupViewParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('SecurityGroupViewResult', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('SecurityGroupViewResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_vm_security_rules_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/securityGroupView'} async def begin_get_vm_security_rules( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.SecurityGroupViewParameters", **kwargs: Any ) -> AsyncLROPoller["_models.SecurityGroupViewResult"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._get_vm_security_rules_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('SecurityGroupViewResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_vm_security_rules.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/securityGroupView'} async def _get_troubleshooting_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.TroubleshootingParameters", **kwargs: Any ) -> "_models.TroubleshootingResult": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self._get_troubleshooting_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'TroubleshootingParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('TroubleshootingResult', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('TroubleshootingResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_troubleshooting_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/troubleshoot'} async def begin_get_troubleshooting( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.TroubleshootingParameters", **kwargs: Any ) -> AsyncLROPoller["_models.TroubleshootingResult"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._get_troubleshooting_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('TroubleshootingResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_troubleshooting.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/troubleshoot'} async def _get_troubleshooting_result_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.QueryTroubleshootingParameters", **kwargs: Any ) -> "_models.TroubleshootingResult": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self._get_troubleshooting_result_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'QueryTroubleshootingParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('TroubleshootingResult', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('TroubleshootingResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_troubleshooting_result_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/queryTroubleshootResult'} async def begin_get_troubleshooting_result( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.QueryTroubleshootingParameters", **kwargs: Any ) -> AsyncLROPoller["_models.TroubleshootingResult"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._get_troubleshooting_result_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('TroubleshootingResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_troubleshooting_result.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/queryTroubleshootResult'} async def _set_flow_log_configuration_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.FlowLogInformation", **kwargs: Any ) -> "_models.FlowLogInformation": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self._set_flow_log_configuration_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'FlowLogInformation') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('FlowLogInformation', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('FlowLogInformation', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _set_flow_log_configuration_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/configureFlowLog'} async def begin_set_flow_log_configuration( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.FlowLogInformation", **kwargs: Any ) -> AsyncLROPoller["_models.FlowLogInformation"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._set_flow_log_configuration_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('FlowLogInformation', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_set_flow_log_configuration.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/configureFlowLog'} async def _get_flow_log_status_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.FlowLogStatusParameters", **kwargs: Any ) -> "_models.FlowLogInformation": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self._get_flow_log_status_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'FlowLogStatusParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('FlowLogInformation', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('FlowLogInformation', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_flow_log_status_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/queryFlowLogStatus'} async def begin_get_flow_log_status( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.FlowLogStatusParameters", **kwargs: Any ) -> AsyncLROPoller["_models.FlowLogInformation"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._get_flow_log_status_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('FlowLogInformation', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_flow_log_status.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/queryFlowLogStatus'} async def _check_connectivity_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.ConnectivityParameters", **kwargs: Any ) -> "_models.ConnectivityInformation": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self._check_connectivity_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'ConnectivityParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('ConnectivityInformation', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('ConnectivityInformation', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _check_connectivity_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/connectivityCheck'} async def begin_check_connectivity( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.ConnectivityParameters", **kwargs: Any ) -> AsyncLROPoller["_models.ConnectivityInformation"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._check_connectivity_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('ConnectivityInformation', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_check_connectivity.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/connectivityCheck'} async def _get_azure_reachability_report_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.AzureReachabilityReportParameters", **kwargs: Any ) -> "_models.AzureReachabilityReport": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self._get_azure_reachability_report_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'AzureReachabilityReportParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('AzureReachabilityReport', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('AzureReachabilityReport', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_azure_reachability_report_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/azureReachabilityReport'} async def begin_get_azure_reachability_report( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.AzureReachabilityReportParameters", **kwargs: Any ) -> AsyncLROPoller["_models.AzureReachabilityReport"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._get_azure_reachability_report_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('AzureReachabilityReport', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_azure_reachability_report.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/azureReachabilityReport'} async def _list_available_providers_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.AvailableProvidersListParameters", **kwargs: Any ) -> "_models.AvailableProvidersList": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self._list_available_providers_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'AvailableProvidersListParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('AvailableProvidersList', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('AvailableProvidersList', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _list_available_providers_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/availableProvidersList'} async def begin_list_available_providers( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.AvailableProvidersListParameters", **kwargs: Any ) -> AsyncLROPoller["_models.AvailableProvidersList"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._list_available_providers_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('AvailableProvidersList', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_list_available_providers.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/availableProvidersList'} async def _get_network_configuration_diagnostic_initial( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.NetworkConfigurationDiagnosticParameters", **kwargs: Any ) -> "_models.NetworkConfigurationDiagnosticResponse": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self._get_network_configuration_diagnostic_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'NetworkConfigurationDiagnosticParameters') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('NetworkConfigurationDiagnosticResponse', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('NetworkConfigurationDiagnosticResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _get_network_configuration_diagnostic_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/networkConfigurationDiagnostic'} async def begin_get_network_configuration_diagnostic( self, resource_group_name: str, network_watcher_name: str, parameters: "_models.NetworkConfigurationDiagnosticParameters", **kwargs: Any ) -> AsyncLROPoller["_models.NetworkConfigurationDiagnosticResponse"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._get_network_configuration_diagnostic_initial( resource_group_name=resource_group_name, network_watcher_name=network_watcher_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('NetworkConfigurationDiagnosticResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_get_network_configuration_diagnostic.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/networkConfigurationDiagnostic'}

true

1c2ea56f126d1754020d9b2394105da6f3ce0700

7,580

py

Python

sleepy/http.py

HitSyr/Sleepy

70ec9479fb947a624e3c658ea39c886c30bf794d

[ "MIT" ]

4

2021-12-08T21:38:14.000Z

2022-01-30T04:16:38.000Z

sleepy/http.py

HitSyr/Sleepy

70ec9479fb947a624e3c658ea39c886c30bf794d

[ "MIT" ]

2

2021-09-29T13:33:26.000Z

2021-12-12T12:52:16.000Z

sleepy/http.py

HitSyr/Sleepy

70ec9479fb947a624e3c658ea39c886c30bf794d

[ "MIT" ]

null

""" Copyright (c) 2018-present HitchedSyringe This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at https://mozilla.org/MPL/2.0/. """ from __future__ import annotations __all__ = ( "HTTPRequester", "HTTPRequestFailed", ) import asyncio import logging from collections.abc import MutableMapping from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union import aiohttp from discord.ext import commands _LOG = logging.getLogger(__name__) if TYPE_CHECKING: from multidict import CIMultiDictProxy from yarl import URL HTTPResponseData = Union[str, bytes, Dict[str, Any]] RequestUrl = Union[str, URL] class HTTPRequestFailed(commands.CommandError): """Exception raised when an HTTP request fails. This inherits from :exc:`commands.CommandError`. .. versionadded:: 1.10 .. versionchanged:: 2.0 * Renamed to ``HTTPError``. * This now subclasses :exc:`commands.CommandError` for ease of use with command error handlers. .. versionchanged:: 3.0 Renamed to ``HTTPRequestFailed``. Attributes ---------- response: :class:`aiohttp.ClientResponse` The response of the failed request. status: :class:`int` The HTTP status code. reason: :class:`str` The HTTP status reason. .. versionadded:: 3.0 headers: multidict.CIMultiDictProxy[:class:`str`] The response headers. .. versionadded:: 3.0 data: Union[:class:`dict`, :class:`str`, :class:`bytes`] The data returned from the failed request. """ def __init__(self, response: aiohttp.ClientResponse, data: HTTPResponseData) -> None: self.response: aiohttp.ClientResponse = response self.status = status = response.status # type: int self.reason: str = response.reason self.headers: CIMultiDictProxy[str] = response.headers self.data: HTTPResponseData = data super().__init__( f"{response.method} {response.url} failed with HTTP status code {status}." ) class HTTPRequester: """An HTTP requests handler that optionally implements caching. .. versionadded:: 1.10 .. versionchanged:: 2.0 * Renamed to ``CachedHTTPRequester``. * Classes can now be manually constructed. .. versionchanged:: 3.0 Renamed to ``HTTPRequester``. .. versionchanged:: 3.2 Removed the `loop` kwarg and property. Parameters ---------- cache: Optional[:class:`MutableMapping`] The mapping to use for caching the received data. ``None`` (the default) denotes disabling caching HTTP requests entirely. .. versionadded:: 3.0 """ __slots__: Tuple[str, ...] = ("_cache", "_request_lock", "__session") def __init__( self, *, cache: Optional[MutableMapping[str, Any]] = None, **kwargs: Any ) -> None: if cache is not None and not isinstance(cache, MutableMapping): raise TypeError(f"cache must be MutableMapping or NoneType, not {type(cache)!r}.") self._cache: Optional[MutableMapping[str, Any]] = cache self._request_lock: asyncio.Lock = asyncio.Lock() self.__session: aiohttp.ClientSession = aiohttp.ClientSession(**kwargs) _LOG.info("Started a new session.") @property def cache(self) -> Optional[MutableMapping[str, Any]]: """Optional[:class:`MutableMapping`]: The mapping used for caching received data. .. versionadded:: 3.0 """ return self._cache @cache.setter def cache(self, value: Optional[MutableMapping[str, Any]]) -> None: if value is not None and not isinstance(value, MutableMapping): raise TypeError(f"cache must be MutableMapping or NoneType, not {type(value)!r}.") self._cache = value @property def session(self) -> aiohttp.ClientSession: """:class:`aiohttp.ClientSession`: The client session used for handling requests.""" return self.__session async def close(self) -> None: """|coro| Closes the session. """ await self.__session.close() _LOG.info("Session closed.") async def __request( self, method: str, url: RequestUrl, /, **kwargs: Any ) -> HTTPResponseData: # Allows this to work with params__ in case an API requires # a parameter that is the same name as a reserved keyword. params = kwargs.pop("params__", {}) params.update(kwargs.copy()) kwargs = {k[:-2]: params.pop(k) for k in kwargs if k.endswith("__")} async with self.__session.request(method, url, params=params, **kwargs) as resp: if "application/json" in resp.content_type: data = await resp.json() elif "text/" in resp.content_type: data = await resp.text("utf-8") else: data = await resp.read() # aiohttp takes care of HTTP 1xx and 3xx internally, so # it's probably safe to exclude these from the range of # successful status codes. if not 200 <= resp.status < 300: _LOG.warning("%s %s failed with HTTP status %s.", method, url, resp.status) raise HTTPRequestFailed(resp, data) _LOG.info("%s %s succeeded with HTTP status %s.", method, url, resp.status) return data async def request( self, method: str, url: RequestUrl, /, *, cache__: bool = False, **kwargs: Any ) -> HTTPResponseData: """|coro| Performs an HTTP request and optionally caches the response. .. note:: Any kwargs that :meth:`aiohttp.ClientSession.request` takes must be suffixed with a dunder. .. versionchanged:: 3.0 Renamed ``cache`` argument to ``cache__``. Parameters ---------- method: :class:`str` The HTTP method. .. versionchanged:: 3.0 This is now a positional-only argument. url: Union[:class:`str`, :class:`yarl.URL`] The URL to make a request to. .. versionchanged:: 3.0 This is now a positional-only argument. cache__: :class:`bool` Whether or not to cache the response data. If :attr:`cache` is ``None``, then caching the data will be disabled regardless of this setting. Defaults to ``False``. Returns ------- Union[:class:`dict`, :class:`str`, :class:`bytes`] The raw response data. Raises ------ :exc:`.HTTPRequestFailed` The request returned a status code of either 4xx or 5xx. """ if not cache__ or self._cache is None: return await self.__request(method, url, **kwargs) async with self._request_lock: key = f"{method}:{url}:<{' '.join(f'{k}={v}' for k, v in kwargs.items())}>" if (cached := self._cache.get(key)) is not None: _LOG.debug("%s %s got %s from the cache.", method, url, cached) return cached data = await self.__request(method, url, **kwargs) self._cache[key] = data _LOG.debug("Inserted %s into the cache.", data) return data

29.84252

94

0.596042

from __future__ import annotations __all__ = ( "HTTPRequester", "HTTPRequestFailed", ) import asyncio import logging from collections.abc import MutableMapping from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union import aiohttp from discord.ext import commands _LOG = logging.getLogger(__name__) if TYPE_CHECKING: from multidict import CIMultiDictProxy from yarl import URL HTTPResponseData = Union[str, bytes, Dict[str, Any]] RequestUrl = Union[str, URL] class HTTPRequestFailed(commands.CommandError): def __init__(self, response: aiohttp.ClientResponse, data: HTTPResponseData) -> None: self.response: aiohttp.ClientResponse = response self.status = status = response.status self.reason: str = response.reason self.headers: CIMultiDictProxy[str] = response.headers self.data: HTTPResponseData = data super().__init__( f"{response.method} {response.url} failed with HTTP status code {status}." ) class HTTPRequester: __slots__: Tuple[str, ...] = ("_cache", "_request_lock", "__session") def __init__( self, *, cache: Optional[MutableMapping[str, Any]] = None, **kwargs: Any ) -> None: if cache is not None and not isinstance(cache, MutableMapping): raise TypeError(f"cache must be MutableMapping or NoneType, not {type(cache)!r}.") self._cache: Optional[MutableMapping[str, Any]] = cache self._request_lock: asyncio.Lock = asyncio.Lock() self.__session: aiohttp.ClientSession = aiohttp.ClientSession(**kwargs) _LOG.info("Started a new session.") @property def cache(self) -> Optional[MutableMapping[str, Any]]: return self._cache @cache.setter def cache(self, value: Optional[MutableMapping[str, Any]]) -> None: if value is not None and not isinstance(value, MutableMapping): raise TypeError(f"cache must be MutableMapping or NoneType, not {type(value)!r}.") self._cache = value @property def session(self) -> aiohttp.ClientSession: return self.__session async def close(self) -> None: await self.__session.close() _LOG.info("Session closed.") async def __request( self, method: str, url: RequestUrl, /, **kwargs: Any ) -> HTTPResponseData: params = kwargs.pop("params__", {}) params.update(kwargs.copy()) kwargs = {k[:-2]: params.pop(k) for k in kwargs if k.endswith("__")} async with self.__session.request(method, url, params=params, **kwargs) as resp: if "application/json" in resp.content_type: data = await resp.json() elif "text/" in resp.content_type: data = await resp.text("utf-8") else: data = await resp.read() # successful status codes. if not 200 <= resp.status < 300: _LOG.warning("%s %s failed with HTTP status %s.", method, url, resp.status) raise HTTPRequestFailed(resp, data) _LOG.info("%s %s succeeded with HTTP status %s.", method, url, resp.status) return data async def request( self, method: str, url: RequestUrl, /, *, cache__: bool = False, **kwargs: Any ) -> HTTPResponseData: if not cache__ or self._cache is None: return await self.__request(method, url, **kwargs) async with self._request_lock: key = f"{method}:{url}:<{' '.join(f'{k}={v}' for k, v in kwargs.items())}>" if (cached := self._cache.get(key)) is not None: _LOG.debug("%s %s got %s from the cache.", method, url, cached) return cached data = await self.__request(method, url, **kwargs) self._cache[key] = data _LOG.debug("Inserted %s into the cache.", data) return data

true

1c2ea76b9cf021143d713f6a07b53b3b5319c5e6

3,425

py

Python

server.py

umran23/2_Threaded_server

eed32916ae645419c9634f7f6f4994300fe3feef

[ "Apache-2.0" ]

null

server.py

umran23/2_Threaded_server

eed32916ae645419c9634f7f6f4994300fe3feef

[ "Apache-2.0" ]

null

server.py

umran23/2_Threaded_server

eed32916ae645419c9634f7f6f4994300fe3feef

[ "Apache-2.0" ]

null

import socket from threading import Thread class Server: addr = '' port = 3030 connections = 4 def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.logging('Socket has created') self.port, self.addr = self.ask_for_port_addr() self.bind() self.logging(f'Socket binded to port {self.port}') self.sock.listen(self.connections) self.logging(f'Socket is listening {self.connections} connections') self.main() @staticmethod def logging(data): with open("log_server.txt", "a") as f: f.write(f'{data}\n') def cleaning(self): with open("log_server.txt", "a") as f: f.write(f'\n{self.addr}, {self.port}') def main(self): try: while 1: conn, addr = self.sock.accept() self.logging(f'Client {addr} was connected') Thread(target=self.for_client, args=(conn,)).start() finally: self.logging('Server is closing') self.sock.close() def send(self, conn, message): assert len(message) <= 1024 conn.send(f'{message}'.encode()) self.logging(f'Sending this message {message}') def recv(self, conn): data = conn.recv(1024).decode() self.logging(f'Receiving a message => {data}') return data @staticmethod def for_port(port): try: if 1023 < int(port) < 65536: return True else: return False except ValueError: return False @staticmethod def for_ip(ip): try: sum = 0 if ip == 'localhost': return True parts = ip.split(".", 4) if len(parts) == 4: for part in parts: part = int(part) if -1 < part < 256: sum += 1 else: return False if sum != 4: return False except ValueError: return False def ask_for_port_addr(self): user_port = input("Enter Port:") if self.for_port(user_port) is False: print(f'Wrong input, Port by Default is : - {self.port}') user_port = str(self.port) user_ip = input("Enter IP Address:") if self.for_ip(user_ip) is False: print(f'Wrong Input, IP Address by Default is : - {self.addr}') user_ip = self.addr return int(user_port), user_ip def bind(self): try: self.sock.bind((self.addr, self.port)) except OSError: self.sock.bind((self.addr, 0)) self.port = self.sock.getsockname()[1] print(f'New PORT is {self.port}') def for_client(self, conn): self.logging(f'Client {self.addr} was connected') while 1: data = self.recv(conn) if not data: self.logging(f'Client {self.addr} was disconnected\n') break if data == 'exit': self.logging('Сonnection is closing\n') break self.logging(f'Received message:{data}') self.logging(f'Sending message: {data}') self.send(conn, data) Server()

29.525862

76

0.52

import socket from threading import Thread class Server: addr = '' port = 3030 connections = 4 def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.logging('Socket has created') self.port, self.addr = self.ask_for_port_addr() self.bind() self.logging(f'Socket binded to port {self.port}') self.sock.listen(self.connections) self.logging(f'Socket is listening {self.connections} connections') self.main() @staticmethod def logging(data): with open("log_server.txt", "a") as f: f.write(f'{data}\n') def cleaning(self): with open("log_server.txt", "a") as f: f.write(f'\n{self.addr}, {self.port}') def main(self): try: while 1: conn, addr = self.sock.accept() self.logging(f'Client {addr} was connected') Thread(target=self.for_client, args=(conn,)).start() finally: self.logging('Server is closing') self.sock.close() def send(self, conn, message): assert len(message) <= 1024 conn.send(f'{message}'.encode()) self.logging(f'Sending this message {message}') def recv(self, conn): data = conn.recv(1024).decode() self.logging(f'Receiving a message => {data}') return data @staticmethod def for_port(port): try: if 1023 < int(port) < 65536: return True else: return False except ValueError: return False @staticmethod def for_ip(ip): try: sum = 0 if ip == 'localhost': return True parts = ip.split(".", 4) if len(parts) == 4: for part in parts: part = int(part) if -1 < part < 256: sum += 1 else: return False if sum != 4: return False except ValueError: return False def ask_for_port_addr(self): user_port = input("Enter Port:") if self.for_port(user_port) is False: print(f'Wrong input, Port by Default is : - {self.port}') user_port = str(self.port) user_ip = input("Enter IP Address:") if self.for_ip(user_ip) is False: print(f'Wrong Input, IP Address by Default is : - {self.addr}') user_ip = self.addr return int(user_port), user_ip def bind(self): try: self.sock.bind((self.addr, self.port)) except OSError: self.sock.bind((self.addr, 0)) self.port = self.sock.getsockname()[1] print(f'New PORT is {self.port}') def for_client(self, conn): self.logging(f'Client {self.addr} was connected') while 1: data = self.recv(conn) if not data: self.logging(f'Client {self.addr} was disconnected\n') break if data == 'exit': self.logging('Сonnection is closing\n') break self.logging(f'Received message:{data}') self.logging(f'Sending message: {data}') self.send(conn, data) Server()

true

1c2ea7d11faf357ec52b98e2b099ef65b110baa3

4,548

py

Python

temboo/core/Library/Twitter/Trends/Available.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

7

2016-03-07T02:07:21.000Z

2022-01-21T02:22:41.000Z

temboo/core/Library/Twitter/Trends/Available.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

null

temboo/core/Library/Twitter/Trends/Available.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

8

2016-06-14T06:01:11.000Z

2020-04-22T09:21:44.000Z

# -*- coding: utf-8 -*- ############################################################################### # # Available # Retrieves the locations that Twitter has trending topic information for. # # Python versions 2.6, 2.7, 3.x # # Copyright 2014, Temboo Inc. # # 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 temboo.core.choreography import Choreography from temboo.core.choreography import InputSet from temboo.core.choreography import ResultSet from temboo.core.choreography import ChoreographyExecution import json class Available(Choreography): def __init__(self, temboo_session): """ Create a new instance of the Available Choreo. A TembooSession object, containing a valid set of Temboo credentials, must be supplied. """ super(Available, self).__init__(temboo_session, '/Library/Twitter/Trends/Available') def new_input_set(self): return AvailableInputSet() def _make_result_set(self, result, path): return AvailableResultSet(result, path) def _make_execution(self, session, exec_id, path): return AvailableChoreographyExecution(session, exec_id, path) class AvailableInputSet(InputSet): """ An InputSet with methods appropriate for specifying the inputs to the Available Choreo. The InputSet object is used to specify input parameters when executing this Choreo. """ def set_AccessTokenSecret(self, value): """ Set the value of the AccessTokenSecret input for this Choreo. ((required, string) The Access Token Secret provided by Twitter or retrieved during the OAuth process.) """ super(AvailableInputSet, self)._set_input('AccessTokenSecret', value) def set_AccessToken(self, value): """ Set the value of the AccessToken input for this Choreo. ((required, string) The Access Token provided by Twitter or retrieved during the OAuth process.) """ super(AvailableInputSet, self)._set_input('AccessToken', value) def set_ConsumerKey(self, value): """ Set the value of the ConsumerKey input for this Choreo. ((required, string) The API Key (or Consumer Key) provided by Twitter.) """ super(AvailableInputSet, self)._set_input('ConsumerKey', value) def set_ConsumerSecret(self, value): """ Set the value of the ConsumerSecret input for this Choreo. ((required, string) The API Secret (or Consumer Secret) provided by Twitter.) """ super(AvailableInputSet, self)._set_input('ConsumerSecret', value) class AvailableResultSet(ResultSet): """ A ResultSet with methods tailored to the values returned by the Available Choreo. The ResultSet object is used to retrieve the results of a Choreo execution. """ def getJSONFromString(self, str): return json.loads(str) def get_Response(self): """ Retrieve the value for the "Response" output from this Choreo execution. ((json) The response from Twitter.) """ return self._output.get('Response', None) def get_Limit(self): """ Retrieve the value for the "Limit" output from this Choreo execution. ((integer) The rate limit ceiling for this particular request.) """ return self._output.get('Limit', None) def get_Remaining(self): """ Retrieve the value for the "Remaining" output from this Choreo execution. ((integer) The number of requests left for the 15 minute window.) """ return self._output.get('Remaining', None) def get_Reset(self): """ Retrieve the value for the "Reset" output from this Choreo execution. ((date) The remaining window before the rate limit resets in UTC epoch seconds.) """ return self._output.get('Reset', None) class AvailableChoreographyExecution(ChoreographyExecution): def _make_result_set(self, response, path): return AvailableResultSet(response, path)

40.247788

173

0.677661

true

1c2ea7fb4024eb02de28a8b3543f21c6f2b00b6c

67

py

Python

tuiuiu/tuiuiutenant/__init__.py

caputomarcos/tuiuiu.io

d8fb57cf95487e7fe1454b2130ef18acc916da46

[ "BSD-3-Clause" ]

3

2019-08-08T09:09:35.000Z

2020-12-15T18:04:17.000Z

tuiuiu/tuiuiutenant/__init__.py

caputomarcos/tuiuiu.io

d8fb57cf95487e7fe1454b2130ef18acc916da46

[ "BSD-3-Clause" ]

null

tuiuiu/tuiuiutenant/__init__.py

caputomarcos/tuiuiu.io

d8fb57cf95487e7fe1454b2130ef18acc916da46

[ "BSD-3-Clause" ]

1

2017-09-09T20:10:40.000Z

default_app_config = 'tuiuiu.tuiuiutenant.apps.TuiuiuTenantConfig'

33.5

66

0.865672

default_app_config = 'tuiuiu.tuiuiutenant.apps.TuiuiuTenantConfig'

true

1c2ea8d8d7ef40216683485021e310b67b3e922f

1,963

py

Python

genanki/deck.py

jahzielv/genanki

6d173fcd8a50b29164d733fce750276bc9a9f39d

[ "MIT" ]

null

genanki/deck.py

jahzielv/genanki

6d173fcd8a50b29164d733fce750276bc9a9f39d

[ "MIT" ]

null

genanki/deck.py

jahzielv/genanki

6d173fcd8a50b29164d733fce750276bc9a9f39d

[ "MIT" ]

null

import json from .apkg_col import APKG_COL class Deck: def __init__(self, deck_id=None, name=None): self.deck_id = deck_id self.name = name self.notes = [] self.models = {} # map of model id to model def add_note(self, note): self.notes.append(note) def add_model(self, model): self.models[model.model_id] = model def write_to_db(self, cursor, now_ts): if not isinstance(self.deck_id, int): raise TypeError('Deck .deck_id must be an integer, not {}.'.format(self.deck_id)) if not isinstance(self.name, str): raise TypeError('Deck .name must be a string, not {}.'.format(self.name)) for note in self.notes: self.add_model(note.model) models = {model.model_id: model.to_json(now_ts, self.deck_id) for model in self.models.values()} cursor.execute(APKG_COL, [self.name, self.deck_id, json.dumps(models)]) for note in self.notes: note.write_to_db(cursor, now_ts, self.deck_id) def write_to_file(self, file): """ Write this deck to a .apkg file. """ from .package import Package Package(self).write_to_file(file) def write_to_collection_from_addon(self): """ Write to local collection. *Only usable when running inside an Anki addon!* Only tested on Anki 2.1. This writes to a temporary file and then calls the code that Anki uses to import packages. Note: the caller may want to use mw.checkpoint and mw.reset as follows: # creates a menu item called "Undo Add Notes From MyAddon" after this runs mw.checkpoint('Add Notes From MyAddon') # run import my_package.write_to_collection_from_addon() # refreshes main view so new deck is visible mw.reset() Tip: if your deck has the same name and ID as an existing deck, then the notes will get placed in that deck rather than a new deck being created. """ from .package import Package Package(self).write_to_collection_from_addon()

32.716667

118

0.691798

import json from .apkg_col import APKG_COL class Deck: def __init__(self, deck_id=None, name=None): self.deck_id = deck_id self.name = name self.notes = [] self.models = {} def add_note(self, note): self.notes.append(note) def add_model(self, model): self.models[model.model_id] = model def write_to_db(self, cursor, now_ts): if not isinstance(self.deck_id, int): raise TypeError('Deck .deck_id must be an integer, not {}.'.format(self.deck_id)) if not isinstance(self.name, str): raise TypeError('Deck .name must be a string, not {}.'.format(self.name)) for note in self.notes: self.add_model(note.model) models = {model.model_id: model.to_json(now_ts, self.deck_id) for model in self.models.values()} cursor.execute(APKG_COL, [self.name, self.deck_id, json.dumps(models)]) for note in self.notes: note.write_to_db(cursor, now_ts, self.deck_id) def write_to_file(self, file): from .package import Package Package(self).write_to_file(file) def write_to_collection_from_addon(self): from .package import Package Package(self).write_to_collection_from_addon()

true

1c2ea92a4a85689ac010f07469d5822e50872e06

9,408

py

Python

nssrc/com/citrix/netscaler/nitro/resource/config/aaa/aaauser_authorizationpolicy_binding.py

guardicore/nitro-python

5346a5086134aead80968f15a41ff527adaa0ec1

[ "Apache-2.0" ]

null

nssrc/com/citrix/netscaler/nitro/resource/config/aaa/aaauser_authorizationpolicy_binding.py

guardicore/nitro-python

5346a5086134aead80968f15a41ff527adaa0ec1

[ "Apache-2.0" ]

null

nssrc/com/citrix/netscaler/nitro/resource/config/aaa/aaauser_authorizationpolicy_binding.py

guardicore/nitro-python

5346a5086134aead80968f15a41ff527adaa0ec1

[ "Apache-2.0" ]

null

# # Copyright (c) 2021 Citrix Systems, Inc. # # 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 nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_resource from nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_response from nssrc.com.citrix.netscaler.nitro.service.options import options from nssrc.com.citrix.netscaler.nitro.exception.nitro_exception import nitro_exception from nssrc.com.citrix.netscaler.nitro.util.nitro_util import nitro_util class aaauser_authorizationpolicy_binding(base_resource) : """ Binding class showing the authorizationpolicy that can be bound to aaauser. """ def __init__(self) : self._policy = None self._priority = None self._acttype = None self._type = None self._gotopriorityexpression = None self._username = None self.___count = None @property def priority(self) : r"""Integer specifying the priority of the policy. A lower number indicates a higher priority. Policies are evaluated in the order of their priority numbers. Maximum value for default syntax policies is 2147483647 and for classic policies max priority is 64000. . Minimum value = 0 Maximum value = 2147483647. """ try : return self._priority except Exception as e: raise e @priority.setter def priority(self, priority) : r"""Integer specifying the priority of the policy. A lower number indicates a higher priority. Policies are evaluated in the order of their priority numbers. Maximum value for default syntax policies is 2147483647 and for classic policies max priority is 64000. . Minimum value = 0 Maximum value = 2147483647 """ try : self._priority = priority except Exception as e: raise e @property def username(self) : r"""User account to which to bind the policy. Minimum length = 1. """ try : return self._username except Exception as e: raise e @username.setter def username(self, username) : r"""User account to which to bind the policy. Minimum length = 1 """ try : self._username = username except Exception as e: raise e @property def gotopriorityexpression(self) : r"""Expression specifying the priority of the next policy which will get evaluated if the current policy rule evaluates to TRUE. """ try : return self._gotopriorityexpression except Exception as e: raise e @gotopriorityexpression.setter def gotopriorityexpression(self, gotopriorityexpression) : r"""Expression specifying the priority of the next policy which will get evaluated if the current policy rule evaluates to TRUE. """ try : self._gotopriorityexpression = gotopriorityexpression except Exception as e: raise e @property def policy(self) : r"""The policy Name. """ try : return self._policy except Exception as e: raise e @policy.setter def policy(self, policy) : r"""The policy Name. """ try : self._policy = policy except Exception as e: raise e @property def type(self) : r"""Bindpoint to which the policy is bound. Default value: REQUEST Possible values = REQUEST, UDP_REQUEST, DNS_REQUEST, ICMP_REQUEST. """ try : return self._type except Exception as e: raise e @type.setter def type(self, type) : r"""Bindpoint to which the policy is bound. Default value: REQUEST Possible values = REQUEST, UDP_REQUEST, DNS_REQUEST, ICMP_REQUEST """ try : self._type = type except Exception as e: raise e @property def acttype(self) : try : return self._acttype except Exception as e: raise e def _get_nitro_response(self, service, response) : r""" converts nitro response into object and returns the object array in case of get request. """ try : result = service.payload_formatter.string_to_resource(aaauser_authorizationpolicy_binding_response, response, self.__class__.__name__) if(result.errorcode != 0) : if (result.errorcode == 444) : service.clear_session(self) if result.severity : if (result.severity == "ERROR") : raise nitro_exception(result.errorcode, str(result.message), str(result.severity)) else : raise nitro_exception(result.errorcode, str(result.message), str(result.severity)) return result.aaauser_authorizationpolicy_binding except Exception as e : raise e def _get_object_name(self) : r""" Returns the value of object identifier argument """ try : if self.username is not None : return str(self.username) return None except Exception as e : raise e @classmethod def filter_add_parameters(cls, resource) : r""" Use this function to create a resource with only add operation specific parameters. """ addresource = aaauser_authorizationpolicy_binding() addresource.username = resource.username addresource.policy = resource.policy addresource.priority = resource.priority addresource.type = resource.type addresource.gotopriorityexpression = resource.gotopriorityexpression return addresource @classmethod def add(cls, client, resource) : try : if resource and type(resource) is not list : updateresource = cls.filter_add_parameters(resource) return updateresource.update_resource(client) else : if resource and len(resource) > 0 : updateresources = [aaauser_authorizationpolicy_binding() for _ in range(len(resource))] for i in range(len(resource)) : updateresources[i] = cls.filter_add_parameters(resource[i]) return cls.update_bulk_request(client, updateresources) except Exception as e : raise e @classmethod def filter_delete_parameters(cls, resource) : r""" Use this function to create a resource with only delete operation specific parameters. """ deleteresource = aaauser_authorizationpolicy_binding() deleteresource.username = resource.username deleteresource.policy = resource.policy deleteresource.type = resource.type return deleteresource @classmethod def delete(cls, client, resource) : try : if resource and type(resource) is not list : deleteresource = cls.filter_delete_parameters(resource) return deleteresource.delete_resource(client) else : if resource and len(resource) > 0 : deleteresources = [aaauser_authorizationpolicy_binding() for _ in range(len(resource))] for i in range(len(resource)) : deleteresources[i] = cls.filter_delete_parameters(resource[i]) return cls.delete_bulk_request(client, deleteresources) except Exception as e : raise e @classmethod def get(cls, service, username="", option_="") : r""" Use this API to fetch aaauser_authorizationpolicy_binding resources. """ try : if not username : obj = aaauser_authorizationpolicy_binding() response = obj.get_resources(service, option_) else : obj = aaauser_authorizationpolicy_binding() obj.username = username response = obj.get_resources(service) return response except Exception as e: raise e @classmethod def get_filtered(cls, service, username, filter_) : r""" Use this API to fetch filtered set of aaauser_authorizationpolicy_binding resources. Filter string should be in JSON format.eg: "port:80,servicetype:HTTP". """ try : obj = aaauser_authorizationpolicy_binding() obj.username = username option_ = options() option_.filter = filter_ response = obj.getfiltered(service, option_) return response except Exception as e: raise e @classmethod def count(cls, service, username) : r""" Use this API to count aaauser_authorizationpolicy_binding resources configued on NetScaler. """ try : obj = aaauser_authorizationpolicy_binding() obj.username = username option_ = options() option_.count = True response = obj.get_resources(service, option_) if response : return response[0].__dict__['___count'] return 0 except Exception as e: raise e @classmethod def count_filtered(cls, service, username, filter_) : r""" Use this API to count the filtered set of aaauser_authorizationpolicy_binding resources. Filter string should be in JSON format.eg: "port:80,servicetype:HTTP". """ try : obj = aaauser_authorizationpolicy_binding() obj.username = username option_ = options() option_.count = True option_.filter = filter_ response = obj.getfiltered(service, option_) if response : return response[0].__dict__['___count'] return 0 except Exception as e: raise e class Type: REQUEST = "REQUEST" UDP_REQUEST = "UDP_REQUEST" DNS_REQUEST = "DNS_REQUEST" ICMP_REQUEST = "ICMP_REQUEST" class aaauser_authorizationpolicy_binding_response(base_response) : def __init__(self, length=1) : self.aaauser_authorizationpolicy_binding = [] self.errorcode = 0 self.message = "" self.severity = "" self.sessionid = "" self.aaauser_authorizationpolicy_binding = [aaauser_authorizationpolicy_binding() for _ in range(length)]

32

322

0.737883

from nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_resource from nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_response from nssrc.com.citrix.netscaler.nitro.service.options import options from nssrc.com.citrix.netscaler.nitro.exception.nitro_exception import nitro_exception from nssrc.com.citrix.netscaler.nitro.util.nitro_util import nitro_util class aaauser_authorizationpolicy_binding(base_resource) : def __init__(self) : self._policy = None self._priority = None self._acttype = None self._type = None self._gotopriorityexpression = None self._username = None self.___count = None @property def priority(self) : try : return self._priority except Exception as e: raise e @priority.setter def priority(self, priority) : try : self._priority = priority except Exception as e: raise e @property def username(self) : try : return self._username except Exception as e: raise e @username.setter def username(self, username) : try : self._username = username except Exception as e: raise e @property def gotopriorityexpression(self) : try : return self._gotopriorityexpression except Exception as e: raise e @gotopriorityexpression.setter def gotopriorityexpression(self, gotopriorityexpression) : try : self._gotopriorityexpression = gotopriorityexpression except Exception as e: raise e @property def policy(self) : try : return self._policy except Exception as e: raise e @policy.setter def policy(self, policy) : try : self._policy = policy except Exception as e: raise e @property def type(self) : try : return self._type except Exception as e: raise e @type.setter def type(self, type) : try : self._type = type except Exception as e: raise e @property def acttype(self) : try : return self._acttype except Exception as e: raise e def _get_nitro_response(self, service, response) : try : result = service.payload_formatter.string_to_resource(aaauser_authorizationpolicy_binding_response, response, self.__class__.__name__) if(result.errorcode != 0) : if (result.errorcode == 444) : service.clear_session(self) if result.severity : if (result.severity == "ERROR") : raise nitro_exception(result.errorcode, str(result.message), str(result.severity)) else : raise nitro_exception(result.errorcode, str(result.message), str(result.severity)) return result.aaauser_authorizationpolicy_binding except Exception as e : raise e def _get_object_name(self) : try : if self.username is not None : return str(self.username) return None except Exception as e : raise e @classmethod def filter_add_parameters(cls, resource) : addresource = aaauser_authorizationpolicy_binding() addresource.username = resource.username addresource.policy = resource.policy addresource.priority = resource.priority addresource.type = resource.type addresource.gotopriorityexpression = resource.gotopriorityexpression return addresource @classmethod def add(cls, client, resource) : try : if resource and type(resource) is not list : updateresource = cls.filter_add_parameters(resource) return updateresource.update_resource(client) else : if resource and len(resource) > 0 : updateresources = [aaauser_authorizationpolicy_binding() for _ in range(len(resource))] for i in range(len(resource)) : updateresources[i] = cls.filter_add_parameters(resource[i]) return cls.update_bulk_request(client, updateresources) except Exception as e : raise e @classmethod def filter_delete_parameters(cls, resource) : deleteresource = aaauser_authorizationpolicy_binding() deleteresource.username = resource.username deleteresource.policy = resource.policy deleteresource.type = resource.type return deleteresource @classmethod def delete(cls, client, resource) : try : if resource and type(resource) is not list : deleteresource = cls.filter_delete_parameters(resource) return deleteresource.delete_resource(client) else : if resource and len(resource) > 0 : deleteresources = [aaauser_authorizationpolicy_binding() for _ in range(len(resource))] for i in range(len(resource)) : deleteresources[i] = cls.filter_delete_parameters(resource[i]) return cls.delete_bulk_request(client, deleteresources) except Exception as e : raise e @classmethod def get(cls, service, username="", option_="") : try : if not username : obj = aaauser_authorizationpolicy_binding() response = obj.get_resources(service, option_) else : obj = aaauser_authorizationpolicy_binding() obj.username = username response = obj.get_resources(service) return response except Exception as e: raise e @classmethod def get_filtered(cls, service, username, filter_) : try : obj = aaauser_authorizationpolicy_binding() obj.username = username option_ = options() option_.filter = filter_ response = obj.getfiltered(service, option_) return response except Exception as e: raise e @classmethod def count(cls, service, username) : try : obj = aaauser_authorizationpolicy_binding() obj.username = username option_ = options() option_.count = True response = obj.get_resources(service, option_) if response : return response[0].__dict__['___count'] return 0 except Exception as e: raise e @classmethod def count_filtered(cls, service, username, filter_) : try : obj = aaauser_authorizationpolicy_binding() obj.username = username option_ = options() option_.count = True option_.filter = filter_ response = obj.getfiltered(service, option_) if response : return response[0].__dict__['___count'] return 0 except Exception as e: raise e class Type: REQUEST = "REQUEST" UDP_REQUEST = "UDP_REQUEST" DNS_REQUEST = "DNS_REQUEST" ICMP_REQUEST = "ICMP_REQUEST" class aaauser_authorizationpolicy_binding_response(base_response) : def __init__(self, length=1) : self.aaauser_authorizationpolicy_binding = [] self.errorcode = 0 self.message = "" self.severity = "" self.sessionid = "" self.aaauser_authorizationpolicy_binding = [aaauser_authorizationpolicy_binding() for _ in range(length)]

true

1c2ea92c956ad3a9d2609c38a046d8ef976c4efa

4,139

py

Python

benchmark/startQiskit_noisy954.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

benchmark/startQiskit_noisy954.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

benchmark/startQiskit_noisy954.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

# qubit number=5 # total number=45 import cirq import qiskit from qiskit.providers.aer import QasmSimulator from qiskit.test.mock import FakeVigo from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister from qiskit import BasicAer, execute, transpile from pprint import pprint from qiskit.test.mock import FakeVigo from math import log2,floor, sqrt, pi import numpy as np import networkx as nx def build_oracle(n: int, f) -> QuantumCircuit: # implement the oracle O_f^\pm # NOTE: use U1 gate (P gate) with \lambda = 180 ==> CZ gate # or multi_control_Z_gate (issue #127) controls = QuantumRegister(n, "ofc") oracle = QuantumCircuit(controls, name="Zf") for i in range(2 ** n): rep = np.binary_repr(i, n) if f(rep) == "1": for j in range(n): if rep[j] == "0": oracle.x(controls[j]) # oracle.h(controls[n]) if n >= 2: oracle.mcu1(pi, controls[1:], controls[0]) for j in range(n): if rep[j] == "0": oracle.x(controls[j]) # oracle.barrier() return oracle def make_circuit(n:int,f) -> QuantumCircuit: # circuit begin input_qubit = QuantumRegister(n,"qc") classical = ClassicalRegister(n, "qm") prog = QuantumCircuit(input_qubit, classical) prog.h(input_qubit[0]) # number=3 prog.h(input_qubit[1]) # number=4 prog.h(input_qubit[2]) # number=5 prog.h(input_qubit[3]) # number=6 prog.h(input_qubit[4]) # number=21 Zf = build_oracle(n, f) repeat = floor(sqrt(2 ** n) * pi / 4) for i in range(1): prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)]) prog.h(input_qubit[0]) # number=1 prog.h(input_qubit[1]) # number=2 prog.h(input_qubit[2]) # number=7 prog.cx(input_qubit[1],input_qubit[2]) # number=38 prog.h(input_qubit[3]) # number=8 prog.h(input_qubit[0]) # number=39 prog.cz(input_qubit[1],input_qubit[0]) # number=40 prog.h(input_qubit[0]) # number=41 prog.h(input_qubit[0]) # number=31 prog.cz(input_qubit[1],input_qubit[0]) # number=32 prog.h(input_qubit[0]) # number=33 prog.x(input_qubit[0]) # number=29 prog.cx(input_qubit[1],input_qubit[0]) # number=30 prog.h(input_qubit[0]) # number=34 prog.cz(input_qubit[1],input_qubit[0]) # number=35 prog.h(input_qubit[0]) # number=36 prog.x(input_qubit[1]) # number=10 prog.cx(input_qubit[0],input_qubit[2]) # number=25 prog.x(input_qubit[2]) # number=26 prog.cx(input_qubit[0],input_qubit[2]) # number=27 prog.x(input_qubit[3]) # number=12 if n>=2: prog.mcu1(pi,input_qubit[1:],input_qubit[0]) prog.x(input_qubit[0]) # number=13 prog.x(input_qubit[1]) # number=14 prog.x(input_qubit[2]) # number=15 prog.x(input_qubit[3]) # number=16 prog.h(input_qubit[0]) # number=17 prog.h(input_qubit[1]) # number=18 prog.h(input_qubit[2]) # number=19 prog.h(input_qubit[4]) # number=42 prog.cz(input_qubit[2],input_qubit[4]) # number=43 prog.h(input_qubit[4]) # number=44 prog.h(input_qubit[3]) # number=20 prog.h(input_qubit[0]) prog.h(input_qubit[1]) prog.h(input_qubit[2]) prog.h(input_qubit[3]) # circuit end for i in range(n): prog.measure(input_qubit[i], classical[i]) return prog if __name__ == '__main__': key = "00000" f = lambda rep: str(int(rep == key)) prog = make_circuit(5,f) backend = FakeVigo() sample_shot =7924 info = execute(prog, backend=backend, shots=sample_shot).result().get_counts() backend = FakeVigo() circuit1 = transpile(prog,backend,optimization_level=2) writefile = open("../data/startQiskit_noisy954.csv","w") print(info,file=writefile) print("results end", file=writefile) print(circuit1.depth(),file=writefile) print(circuit1,file=writefile) writefile.close()

30.88806

82

0.60691

import cirq import qiskit from qiskit.providers.aer import QasmSimulator from qiskit.test.mock import FakeVigo from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister from qiskit import BasicAer, execute, transpile from pprint import pprint from qiskit.test.mock import FakeVigo from math import log2,floor, sqrt, pi import numpy as np import networkx as nx def build_oracle(n: int, f) -> QuantumCircuit: controls = QuantumRegister(n, "ofc") oracle = QuantumCircuit(controls, name="Zf") for i in range(2 ** n): rep = np.binary_repr(i, n) if f(rep) == "1": for j in range(n): if rep[j] == "0": oracle.x(controls[j]) if n >= 2: oracle.mcu1(pi, controls[1:], controls[0]) for j in range(n): if rep[j] == "0": oracle.x(controls[j]) return oracle def make_circuit(n:int,f) -> QuantumCircuit: input_qubit = QuantumRegister(n,"qc") classical = ClassicalRegister(n, "qm") prog = QuantumCircuit(input_qubit, classical) prog.h(input_qubit[0]) prog.h(input_qubit[1]) prog.h(input_qubit[2]) prog.h(input_qubit[3]) prog.h(input_qubit[4]) Zf = build_oracle(n, f) repeat = floor(sqrt(2 ** n) * pi / 4) for i in range(1): prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)]) prog.h(input_qubit[0]) prog.h(input_qubit[1]) prog.h(input_qubit[2]) prog.cx(input_qubit[1],input_qubit[2]) prog.h(input_qubit[3]) prog.h(input_qubit[0]) prog.cz(input_qubit[1],input_qubit[0]) prog.h(input_qubit[0]) prog.h(input_qubit[0]) prog.cz(input_qubit[1],input_qubit[0]) prog.h(input_qubit[0]) prog.x(input_qubit[0]) prog.cx(input_qubit[1],input_qubit[0]) prog.h(input_qubit[0]) prog.cz(input_qubit[1],input_qubit[0]) prog.h(input_qubit[0]) prog.x(input_qubit[1]) prog.cx(input_qubit[0],input_qubit[2]) prog.x(input_qubit[2]) prog.cx(input_qubit[0],input_qubit[2]) prog.x(input_qubit[3]) if n>=2: prog.mcu1(pi,input_qubit[1:],input_qubit[0]) prog.x(input_qubit[0]) prog.x(input_qubit[1]) prog.x(input_qubit[2]) prog.x(input_qubit[3]) prog.h(input_qubit[0]) prog.h(input_qubit[1]) prog.h(input_qubit[2]) prog.h(input_qubit[4]) prog.cz(input_qubit[2],input_qubit[4]) prog.h(input_qubit[4]) prog.h(input_qubit[3]) prog.h(input_qubit[0]) prog.h(input_qubit[1]) prog.h(input_qubit[2]) prog.h(input_qubit[3]) for i in range(n): prog.measure(input_qubit[i], classical[i]) return prog if __name__ == '__main__': key = "00000" f = lambda rep: str(int(rep == key)) prog = make_circuit(5,f) backend = FakeVigo() sample_shot =7924 info = execute(prog, backend=backend, shots=sample_shot).result().get_counts() backend = FakeVigo() circuit1 = transpile(prog,backend,optimization_level=2) writefile = open("../data/startQiskit_noisy954.csv","w") print(info,file=writefile) print("results end", file=writefile) print(circuit1.depth(),file=writefile) print(circuit1,file=writefile) writefile.close()

true

1c2ea9c9fc370bfa92466b9a39561738eb589c91

1,175

py

Python

calories_screen.py

RossinesP/pygamer_bike_game

2b852126f1d04d6fc5e1053a3217862d86c7856d

[ "MIT" ]

null

calories_screen.py

RossinesP/pygamer_bike_game

2b852126f1d04d6fc5e1053a3217862d86c7856d

[ "MIT" ]

null

calories_screen.py

RossinesP/pygamer_bike_game

2b852126f1d04d6fc5e1053a3217862d86c7856d

[ "MIT" ]

null

from screen import Screen from tracker import Tracker from adafruit_display_text import label import neopixel import terminalio class CaloriesScreen(Screen): def __init__(self, screen_manager, pixels: neopixel.NeoPixel, tracker: Tracker): super().__init__(screen_manager, pixels, tracker) font = terminalio.FONT color = 0xFFFFFF self.cal_label = label.Label(font, text="0.00", color=color, scale= 5) self.cal_label.x = 25 self.cal_label.y = 50 self.cal_label.anchor_point = (1,0) self.cal_label.text = self.get_calories_text(tracker.calories) self.cal_unit_label = label.Label(font, text="km", color=color, scale=2) self.cal_unit_label.x = 70 self.cal_unit_label.y = 100 self.cal_unit_label.text = "cal" self.group.append(self.cal_label) self.group.append(self.cal_unit_label) def get_calories_text(self, calories: float): if calories >= 10000: return "LOTS" else: return f"{calories:.0f}" def on_tracker_update(self, now: int): self.cal_label.text = self.get_calories_text(self.tracker.calories)

33.571429

84

0.670638

from screen import Screen from tracker import Tracker from adafruit_display_text import label import neopixel import terminalio class CaloriesScreen(Screen): def __init__(self, screen_manager, pixels: neopixel.NeoPixel, tracker: Tracker): super().__init__(screen_manager, pixels, tracker) font = terminalio.FONT color = 0xFFFFFF self.cal_label = label.Label(font, text="0.00", color=color, scale= 5) self.cal_label.x = 25 self.cal_label.y = 50 self.cal_label.anchor_point = (1,0) self.cal_label.text = self.get_calories_text(tracker.calories) self.cal_unit_label = label.Label(font, text="km", color=color, scale=2) self.cal_unit_label.x = 70 self.cal_unit_label.y = 100 self.cal_unit_label.text = "cal" self.group.append(self.cal_label) self.group.append(self.cal_unit_label) def get_calories_text(self, calories: float): if calories >= 10000: return "LOTS" else: return f"{calories:.0f}" def on_tracker_update(self, now: int): self.cal_label.text = self.get_calories_text(self.tracker.calories)

true

1c2eaa0ab91564a8c0e4fbb3f53ce5a5e680221a

10,973

py

Python

deeppavlov/tasks/insults/build.py

deepmipt/kpi2017

0f6b13c6ea76e544804ce66ba372c66d5ef9ee30

[ "Apache-2.0" ]

3

2018-02-19T15:34:44.000Z

2018-06-05T10:02:00.000Z

deeppavlov/tasks/insults/build.py

deepmipt/kpi2017

0f6b13c6ea76e544804ce66ba372c66d5ef9ee30

[ "Apache-2.0" ]

null

deeppavlov/tasks/insults/build.py

deepmipt/kpi2017

0f6b13c6ea76e544804ce66ba372c66d5ef9ee30

[ "Apache-2.0" ]

1

2021-03-22T09:06:52.000Z

# Copyright 2017 Neural Networks and Deep Learning lab, MIPT # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import parlai.core.build_data as build_data import os import re import string import numpy as np import pandas as pd import urllib def data_preprocessing(f): """Preprocess the data. Args: f: list of text samples Returns: preprocessed list of text samples """ f = [x.lower() for x in f] f = [re.sub(r'^"|"$', '', x) for x in f] f = [x.replace("\\n", " ") for x in f] f = [x.replace("\\t", " ") for x in f] f = [x.replace("\\xa0", " ") for x in f] f = [x.replace("\\xc2", " ") for x in f] f = [re.sub('!!+', ' !! ', x) for x in f] f = [re.sub('!', ' ! ', x) for x in f] f = [re.sub('! !', '!!', x) for x in f] f = [re.sub('\?\?+', ' ?? ', x) for x in f] f = [re.sub('\?', ' ? ', x) for x in f] f = [re.sub('\? \?', '??', x) for x in f] f = [re.sub('\?!+', ' ?! ', x) for x in f] f = [re.sub('\.\.+', '..', x) for x in f] f = [re.sub('\.', ' . ', x) for x in f] f = [re.sub('\. \.', '..', x) for x in f] f = [re.sub(',', ' , ', x) for x in f] f = [re.sub(':', ' : ', x) for x in f] f = [re.sub(';', ' ; ', x) for x in f] f = [re.sub('\%', ' % ', x) for x in f] f = [x.replace("$", "s") for x in f] f = [x.replace(" u ", " you ") for x in f] f = [x.replace(" em ", " them ") for x in f] f = [x.replace(" da ", " the ") for x in f] f = [x.replace(" yo ", " you ") for x in f] f = [x.replace(" ur ", " your ") for x in f] f = [x.replace("you\'re", "you are") for x in f] f = [x.replace(" u r ", " you are ") for x in f] f = [x.replace("yo\'re", " you are ") for x in f] f = [x.replace("yu\'re", " you are ") for x in f] f = [x.replace("u\'re", " you are ") for x in f] f = [x.replace(" urs ", " yours ") for x in f] f = [x.replace("y'all", "you all") for x in f] f = [x.replace(" r u ", " are you ") for x in f] f = [x.replace(" r you", " are you") for x in f] f = [x.replace(" are u ", " are you ") for x in f] f = [x.replace(" mom ", " mother ") for x in f] f = [x.replace(" momm ", " mother ") for x in f] f = [x.replace(" mommy ", " mother ") for x in f] f = [x.replace(" momma ", " mother ") for x in f] f = [x.replace(" mama ", " mother ") for x in f] f = [x.replace(" mamma ", " mother ") for x in f] f = [x.replace(" mum ", " mother ") for x in f] f = [x.replace(" mummy ", " mother ") for x in f] f = [x.replace("won't", "will not") for x in f] f = [x.replace("can't", "cannot") for x in f] f = [x.replace("i'm", "i am") for x in f] f = [x.replace(" im ", " i am ") for x in f] f = [x.replace("ain't", "is not") for x in f] f = [x.replace("'ll", " will") for x in f] f = [x.replace("'t", " not") for x in f] f = [x.replace("'ve", " have") for x in f] f = [x.replace("'s", " is") for x in f] f = [x.replace("'re", " are") for x in f] f = [x.replace("'d", " would") for x in f] # replace multiple letters (3 and more) by 2 letters for letter in string.ascii_lowercase: f = [re.sub(letter * 3 + '+', letter, x).strip() for x in f] bad_words_file = open(os.path.join(os.path.dirname(os.path.realpath(__file__)), "badwords.txt"), "r") bwMap = dict() for line in bad_words_file: sp = line.strip().lower().split(",") if len(sp) == 2: bwMap[sp[0].strip()] = sp[1].strip() for key, value in bwMap.items(): kpad = " " + key + " " vpad = " " + value + " " f = [x.replace(kpad, vpad) for x in f] # stemming f = [re.sub("ies( |$)", "y ", x) for x in f] f = [re.sub("s( |$)", " ", x) for x in f] f = [re.sub("ing( |$)", " ", x) for x in f] f = [x.replace("tard ", " ") for x in f] f = [re.sub(" [*$%&#@][*$%&#@]+", " xexp ", x) for x in f] f = [re.sub(" [0-9]+ ", " DD ", x) for x in f] f = [re.sub("<\S*>", "", x) for x in f] f = [re.sub('\s+', ' ', x) for x in f] return f def write_input_fasttext_cls(data, path, data_name): """Write down input files for fasttext classificator. Args: data: array of text samples path: path to folder to put the files data_name: mode of writing files "train" or "test" Returns: nothing """ f = open(path + '_fasttext_cls.txt', 'w') for i in range(data.shape[0]): if data_name == 'train': f.write('__label__' + str(data.iloc[i,0]) + ' ' + data.iloc[i,1] + '\n') elif data_name == 'test': f.write(data.iloc[i,1] + '\n') else: print('Incorrect data name') f.close() def write_input_fasttext_emb(data, path, data_name): """Write down input files for fasttext embedding. Args: data: array of text samples path: path to folder to put the files data_name: mode of writing files "train" or "test" Returns: nothing """ f = open(path + '_fasttext_emb.txt', 'w') for i in range(data.shape[0]): if data_name == 'train' or data_name == 'test': f.write(data.iloc[i,1] + '\n') else: print('Incorrect data name') f.close() def balance_dataset(dataset_0, labels_0, dataset_1, labels_1, ratio=1): """Balance the dataset_0 with samples from dataset_1 up to given ratio. Args: dataset_0: array of text samples labels_0: array of labels for dataset_0 dataset_1: array of text samples labels_1: array of labels for dataset_1 ratio: ratio of samples of class 1 to samples of class 0 (default 1.0) Returns: balanced array of text samples, corresponding array of labels """ initial_train_size = dataset_0.shape[0] insult_inds = np.nonzero(labels_1)[0] num_insults_0 = len(np.nonzero(labels_0)[0]) num_insults_1 = len(np.nonzero(labels_1)[0]) insult_inds_to_add = insult_inds[np.random.randint(low=0, high=num_insults_1, size=(ratio * (initial_train_size - num_insults_0) - num_insults_0))] result = dataset_0.append(dataset_1.iloc[insult_inds_to_add]) result_labels = labels_0.append(labels_1.iloc[insult_inds_to_add]) return result, result_labels def build(opt): """Read and preprocess data, save preprocessed data, balance data, create input files for fasttext classifier and embeddings. Args: opt: given parameters Returns: nothing """ # get path to data directory dpath = os.path.join(opt['datapath'], 'insults') # define version if any version = '1.0' # check if data had been previously built if not build_data.built(dpath, version_string=version): print('[building data: ' + dpath + ']') # make a clean directory if needed if build_data.built(dpath): # an older version exists, so remove these outdated files. build_data.remove_dir(dpath) build_data.make_dir(dpath) raw_path = os.path.abspath(opt['raw_dataset_path'] or ".") train_file = os.path.join(raw_path, 'train.csv') valid_file = os.path.join(raw_path, 'test_with_solutions.csv') test_file = os.path.join(raw_path, 'impermium_verification_labels.csv') if not os.path.isfile(train_file) or not os.path.isfile(valid_file) or not os.path.isfile(test_file): ds_path = os.environ.get('DATASETS_URL') file_name = 'insults.tar.gz' if not ds_path: raise RuntimeError('Please download dataset files from' ' https://www.kaggle.com/c/detecting-insults-in-social-commentary/data' ' and set path to their directory in raw-dataset-path parameter') print('Trying to download a insults dataset from the repository') url = urllib.parse.urljoin(ds_path, file_name) print(repr(url)) build_data.download(url, dpath, file_name) build_data.untar(dpath, file_name) opt['raw_dataset_path'] = dpath print('Downloaded a insults dataset') raw_path = os.path.abspath(opt['raw_dataset_path']) train_file = os.path.join(raw_path, 'train.csv') valid_file = os.path.join(raw_path, 'test_with_solutions.csv') test_file = os.path.join(raw_path, 'impermium_verification_labels.csv') train_data = pd.read_csv(train_file) train_data = train_data.drop('Date', axis=1) test_data = pd.read_csv(test_file) test_data = test_data.drop('id', axis=1) test_data = test_data.drop('Usage', axis=1) test_data = test_data.drop('Date', axis=1) valid_data = pd.read_csv(valid_file) valid_data = valid_data.drop('Date', axis=1) valid_data = valid_data.drop('Usage', axis=1) # merge train and valid due to use of cross validation train_data = train_data.append(valid_data) if opt.get('balance_train_dataset'): if opt['balance_train_dataset']: train_data['Comment'],train_data['Insult'] = balance_dataset(train_data['Comment'], train_data['Insult'], train_data['Comment'], train_data['Insult'], ratio=1) print('Preprocessing train') train_data['Comment'] = data_preprocessing(train_data['Comment']) print('Preprocessing test') test_data['Comment'] = data_preprocessing(test_data['Comment']) print('Writing input files for fasttext') write_input_fasttext_cls(train_data, os.path.join(dpath, 'train'), 'train') write_input_fasttext_cls(test_data, os.path.join(dpath, 'test'), 'test') write_input_fasttext_emb(train_data, os.path.join(dpath, 'train'), 'train') write_input_fasttext_emb(test_data, os.path.join(dpath, 'test'), 'test') print('Writing input normalized input files') train_data.to_csv(os.path.join(dpath, 'train.csv'), index=False) test_data.to_csv(os.path.join(dpath, 'test.csv'), index=False) # mark the data as built build_data.mark_done(dpath, version_string=version)

38.773852

124

0.564385

import parlai.core.build_data as build_data import os import re import string import numpy as np import pandas as pd import urllib def data_preprocessing(f): f = [x.lower() for x in f] f = [re.sub(r'^"|"$', '', x) for x in f] f = [x.replace("\\n", " ") for x in f] f = [x.replace("\\t", " ") for x in f] f = [x.replace("\\xa0", " ") for x in f] f = [x.replace("\\xc2", " ") for x in f] f = [re.sub('!!+', ' !! ', x) for x in f] f = [re.sub('!', ' ! ', x) for x in f] f = [re.sub('! !', '!!', x) for x in f] f = [re.sub('\?\?+', ' ?? ', x) for x in f] f = [re.sub('\?', ' ? ', x) for x in f] f = [re.sub('\? \?', '??', x) for x in f] f = [re.sub('\?!+', ' ?! ', x) for x in f] f = [re.sub('\.\.+', '..', x) for x in f] f = [re.sub('\.', ' . ', x) for x in f] f = [re.sub('\. \.', '..', x) for x in f] f = [re.sub(',', ' , ', x) for x in f] f = [re.sub(':', ' : ', x) for x in f] f = [re.sub(';', ' ; ', x) for x in f] f = [re.sub('\%', ' % ', x) for x in f] f = [x.replace("$", "s") for x in f] f = [x.replace(" u ", " you ") for x in f] f = [x.replace(" em ", " them ") for x in f] f = [x.replace(" da ", " the ") for x in f] f = [x.replace(" yo ", " you ") for x in f] f = [x.replace(" ur ", " your ") for x in f] f = [x.replace("you\'re", "you are") for x in f] f = [x.replace(" u r ", " you are ") for x in f] f = [x.replace("yo\'re", " you are ") for x in f] f = [x.replace("yu\'re", " you are ") for x in f] f = [x.replace("u\'re", " you are ") for x in f] f = [x.replace(" urs ", " yours ") for x in f] f = [x.replace("y'all", "you all") for x in f] f = [x.replace(" r u ", " are you ") for x in f] f = [x.replace(" r you", " are you") for x in f] f = [x.replace(" are u ", " are you ") for x in f] f = [x.replace(" mom ", " mother ") for x in f] f = [x.replace(" momm ", " mother ") for x in f] f = [x.replace(" mommy ", " mother ") for x in f] f = [x.replace(" momma ", " mother ") for x in f] f = [x.replace(" mama ", " mother ") for x in f] f = [x.replace(" mamma ", " mother ") for x in f] f = [x.replace(" mum ", " mother ") for x in f] f = [x.replace(" mummy ", " mother ") for x in f] f = [x.replace("won't", "will not") for x in f] f = [x.replace("can't", "cannot") for x in f] f = [x.replace("i'm", "i am") for x in f] f = [x.replace(" im ", " i am ") for x in f] f = [x.replace("ain't", "is not") for x in f] f = [x.replace("'ll", " will") for x in f] f = [x.replace("'t", " not") for x in f] f = [x.replace("'ve", " have") for x in f] f = [x.replace("'s", " is") for x in f] f = [x.replace("'re", " are") for x in f] f = [x.replace("'d", " would") for x in f] # replace multiple letters (3 and more) by 2 letters for letter in string.ascii_lowercase: f = [re.sub(letter * 3 + '+', letter, x).strip() for x in f] bad_words_file = open(os.path.join(os.path.dirname(os.path.realpath(__file__)), "badwords.txt"), "r") bwMap = dict() for line in bad_words_file: sp = line.strip().lower().split(",") if len(sp) == 2: bwMap[sp[0].strip()] = sp[1].strip() for key, value in bwMap.items(): kpad = " " + key + " " vpad = " " + value + " " f = [x.replace(kpad, vpad) for x in f] # stemming f = [re.sub("ies( |$)", "y ", x) for x in f] f = [re.sub("s( |$)", " ", x) for x in f] f = [re.sub("ing( |$)", " ", x) for x in f] f = [x.replace("tard ", " ") for x in f] f = [re.sub(" [*$%&#@][*$%&#@]+", " xexp ", x) for x in f] f = [re.sub(" [0-9]+ ", " DD ", x) for x in f] f = [re.sub("<\S*>", "", x) for x in f] f = [re.sub('\s+', ' ', x) for x in f] return f def write_input_fasttext_cls(data, path, data_name): f = open(path + '_fasttext_cls.txt', 'w') for i in range(data.shape[0]): if data_name == 'train': f.write('__label__' + str(data.iloc[i,0]) + ' ' + data.iloc[i,1] + '\n') elif data_name == 'test': f.write(data.iloc[i,1] + '\n') else: print('Incorrect data name') f.close() def write_input_fasttext_emb(data, path, data_name): f = open(path + '_fasttext_emb.txt', 'w') for i in range(data.shape[0]): if data_name == 'train' or data_name == 'test': f.write(data.iloc[i,1] + '\n') else: print('Incorrect data name') f.close() def balance_dataset(dataset_0, labels_0, dataset_1, labels_1, ratio=1): initial_train_size = dataset_0.shape[0] insult_inds = np.nonzero(labels_1)[0] num_insults_0 = len(np.nonzero(labels_0)[0]) num_insults_1 = len(np.nonzero(labels_1)[0]) insult_inds_to_add = insult_inds[np.random.randint(low=0, high=num_insults_1, size=(ratio * (initial_train_size - num_insults_0) - num_insults_0))] result = dataset_0.append(dataset_1.iloc[insult_inds_to_add]) result_labels = labels_0.append(labels_1.iloc[insult_inds_to_add]) return result, result_labels def build(opt): # get path to data directory dpath = os.path.join(opt['datapath'], 'insults') # define version if any version = '1.0' # check if data had been previously built if not build_data.built(dpath, version_string=version): print('[building data: ' + dpath + ']') # make a clean directory if needed if build_data.built(dpath): # an older version exists, so remove these outdated files. build_data.remove_dir(dpath) build_data.make_dir(dpath) raw_path = os.path.abspath(opt['raw_dataset_path'] or ".") train_file = os.path.join(raw_path, 'train.csv') valid_file = os.path.join(raw_path, 'test_with_solutions.csv') test_file = os.path.join(raw_path, 'impermium_verification_labels.csv') if not os.path.isfile(train_file) or not os.path.isfile(valid_file) or not os.path.isfile(test_file): ds_path = os.environ.get('DATASETS_URL') file_name = 'insults.tar.gz' if not ds_path: raise RuntimeError('Please download dataset files from' ' https://www.kaggle.com/c/detecting-insults-in-social-commentary/data' ' and set path to their directory in raw-dataset-path parameter') print('Trying to download a insults dataset from the repository') url = urllib.parse.urljoin(ds_path, file_name) print(repr(url)) build_data.download(url, dpath, file_name) build_data.untar(dpath, file_name) opt['raw_dataset_path'] = dpath print('Downloaded a insults dataset') raw_path = os.path.abspath(opt['raw_dataset_path']) train_file = os.path.join(raw_path, 'train.csv') valid_file = os.path.join(raw_path, 'test_with_solutions.csv') test_file = os.path.join(raw_path, 'impermium_verification_labels.csv') train_data = pd.read_csv(train_file) train_data = train_data.drop('Date', axis=1) test_data = pd.read_csv(test_file) test_data = test_data.drop('id', axis=1) test_data = test_data.drop('Usage', axis=1) test_data = test_data.drop('Date', axis=1) valid_data = pd.read_csv(valid_file) valid_data = valid_data.drop('Date', axis=1) valid_data = valid_data.drop('Usage', axis=1) # merge train and valid due to use of cross validation train_data = train_data.append(valid_data) if opt.get('balance_train_dataset'): if opt['balance_train_dataset']: train_data['Comment'],train_data['Insult'] = balance_dataset(train_data['Comment'], train_data['Insult'], train_data['Comment'], train_data['Insult'], ratio=1) print('Preprocessing train') train_data['Comment'] = data_preprocessing(train_data['Comment']) print('Preprocessing test') test_data['Comment'] = data_preprocessing(test_data['Comment']) print('Writing input files for fasttext') write_input_fasttext_cls(train_data, os.path.join(dpath, 'train'), 'train') write_input_fasttext_cls(test_data, os.path.join(dpath, 'test'), 'test') write_input_fasttext_emb(train_data, os.path.join(dpath, 'train'), 'train') write_input_fasttext_emb(test_data, os.path.join(dpath, 'test'), 'test') print('Writing input normalized input files') train_data.to_csv(os.path.join(dpath, 'train.csv'), index=False) test_data.to_csv(os.path.join(dpath, 'test.csv'), index=False) # mark the data as built build_data.mark_done(dpath, version_string=version)

true

1c2eab47e8c5b4f21800af9cd5ed1e8c95cb0209

4,176

py

Python

src/compas_assembly/datastructures/block.py

BlockResearchGroup/compas_assembly

6a257e1afaf304f9ddad02baed2396e5bacf91f8

[ "MIT" ]

8

2019-01-30T18:08:07.000Z

2021-06-25T09:35:01.000Z

src/compas_assembly/datastructures/block.py

BlockResearchGroup/compas_assembly

6a257e1afaf304f9ddad02baed2396e5bacf91f8

[ "MIT" ]

6

2019-07-17T11:29:45.000Z

2020-03-20T13:32:38.000Z

src/compas_assembly/datastructures/block.py

BlockResearchGroup/compas_assembly

6a257e1afaf304f9ddad02baed2396e5bacf91f8

[ "MIT" ]

18

2019-01-29T09:02:40.000Z

2021-12-09T09:52:25.000Z

from __future__ import print_function from __future__ import absolute_import from __future__ import division from compas.geometry import centroid_points from compas.geometry import cross_vectors from compas.geometry import dot_vectors from compas.geometry import normalize_vector from compas.geometry import centroid_polyhedron from compas.geometry import volume_polyhedron from compas.datastructures import Mesh class Block(Mesh): """A data structure for the individual blocks of a discrete element assembly. Examples -------- >>> """ def __init__(self): super(Block, self).__init__() @classmethod def from_polysurface(cls, guid): """Class method for constructing a block from a Rhino poly-surface. Parameters ---------- guid : str The GUID of the poly-surface. Returns ------- Block The block corresponding to the poly-surface. Notes ----- In Rhino, poly-surfaces are organised such that the cycle directions of the individual sub-surfaces produce normal vectors that point out of the enclosed volume. The normal vectors of the faces of the mesh, therefore also point "out" of the enclosed volume. """ from compas_rhino.geometry import RhinoSurface surface = RhinoSurface.from_guid(guid) return surface.to_compas(cls) @classmethod def from_rhinomesh(cls, guid): """Class method for constructing a block from a Rhino mesh. Parameters ---------- guid : str The GUID of the mesh. Returns ------- Block The block corresponding to the Rhino mesh. """ from compas_rhino.geometry import RhinoMesh mesh = RhinoMesh.from_guid(guid) return mesh.to_compas(cls) def centroid(self): """Compute the centroid of the block. Returns ------- point The XYZ location of the centroid. """ return centroid_points( [self.vertex_coordinates(key) for key in self.vertices()]) def frames(self): """Compute the local frame of each face of the block. Returns ------- dict A dictionary mapping face identifiers to face frames. """ return {fkey: self.frame(fkey) for fkey in self.faces()} def frame(self, fkey): """Compute the frame of a specific face. Parameters ---------- fkey : hashable The identifier of the frame. Returns ------- frame The frame of the specified face. """ xyz = self.face_coordinates(fkey) o = self.face_center(fkey) w = self.face_normal(fkey) u = [xyz[1][i] - xyz[0][i] for i in range(3)] # align with longest edge instead? v = cross_vectors(w, u) uvw = normalize_vector(u), normalize_vector(v), normalize_vector(w) return o, uvw def top(self): """Identify the *top* face of the block. Returns ------- int The identifier of the face. """ z = [0, 0, 1] faces = list(self.faces()) normals = [self.face_norma(face) for face in faces] return sorted(zip(faces, normals), key=lambda x: dot_vectors(x[1], z))[-1][0] def center(self): """Compute the center of mass of the block. Returns ------- point The center of mass of the block. """ vertices = [self.vertex_coordinates(key) for key in self.vertices()] faces = [self.face_vertices(fkey) for fkey in self.faces()] return centroid_polyhedron((vertices, faces)) def volume(self): """Compute the volume of the block. Returns ------- float The volume of the block. """ vertices = [self.vertex_coordinates(key) for key in self.vertices()] faces = [self.face_vertices(fkey) for fkey in self.faces()] v = volume_polyhedron((vertices, faces)) return v

28.216216

89

0.588362

from __future__ import print_function from __future__ import absolute_import from __future__ import division from compas.geometry import centroid_points from compas.geometry import cross_vectors from compas.geometry import dot_vectors from compas.geometry import normalize_vector from compas.geometry import centroid_polyhedron from compas.geometry import volume_polyhedron from compas.datastructures import Mesh class Block(Mesh): def __init__(self): super(Block, self).__init__() @classmethod def from_polysurface(cls, guid): from compas_rhino.geometry import RhinoSurface surface = RhinoSurface.from_guid(guid) return surface.to_compas(cls) @classmethod def from_rhinomesh(cls, guid): from compas_rhino.geometry import RhinoMesh mesh = RhinoMesh.from_guid(guid) return mesh.to_compas(cls) def centroid(self): return centroid_points( [self.vertex_coordinates(key) for key in self.vertices()]) def frames(self): return {fkey: self.frame(fkey) for fkey in self.faces()} def frame(self, fkey): xyz = self.face_coordinates(fkey) o = self.face_center(fkey) w = self.face_normal(fkey) u = [xyz[1][i] - xyz[0][i] for i in range(3)] v = cross_vectors(w, u) uvw = normalize_vector(u), normalize_vector(v), normalize_vector(w) return o, uvw def top(self): z = [0, 0, 1] faces = list(self.faces()) normals = [self.face_norma(face) for face in faces] return sorted(zip(faces, normals), key=lambda x: dot_vectors(x[1], z))[-1][0] def center(self): vertices = [self.vertex_coordinates(key) for key in self.vertices()] faces = [self.face_vertices(fkey) for fkey in self.faces()] return centroid_polyhedron((vertices, faces)) def volume(self): vertices = [self.vertex_coordinates(key) for key in self.vertices()] faces = [self.face_vertices(fkey) for fkey in self.faces()] v = volume_polyhedron((vertices, faces)) return v

true

1c2eabd376142cf807c1e8d6a65fe520113a747a

2,247

py

Python

rbapi/apps/api/models.py

Yariquezz/check_gov_ua

89d9a4d66ad5a675f64c8172de9d343b1024b102

[ "Apache-2.0" ]

null

rbapi/apps/api/models.py

Yariquezz/check_gov_ua

89d9a4d66ad5a675f64c8172de9d343b1024b102

[ "Apache-2.0" ]

9

2020-04-04T12:38:15.000Z

2021-10-02T09:56:42.000Z

rbapi/apps/api/models.py

Yariquezz/check_gov_ua

89d9a4d66ad5a675f64c8172de9d343b1024b102

[ "Apache-2.0" ]

null

from django.db import models import uuid def get_default_uuid(): return uuid.uuid4().hex class BankInfo(models.Model): tax_code = models.IntegerField(primary_key=True) bank_name = models.CharField(max_length=200, default=None, null=True) support_number_1 = models.CharField(max_length=13, default=None, null=True) support_number_2 = models.CharField(max_length=13, default=None, null=True) support_number_3 = models.CharField(max_length=13, default=None, null=True) email = models.CharField(max_length=20, default=None, null=True) website = models.CharField(max_length=20, default=None, null=True) info = models.TextField(max_length=500, default=None, null=True) signature_info = models.TextField(max_length=100, default=None, null=True) signature_person = models.TextField(max_length=100, default=None, null=True) sign = models.ImageField(null=True, blank=True) logo = models.ImageField(null=True, blank=True) class Meta: ordering = [ 'tax_code', 'bank_name' ] def __str__(self): return '{} {}'.format(self.bank_name, self.tax_code) class RBAResponse(models.Model): receipt_id = models.IntegerField(primary_key=True, unique=True) sender = models.CharField(max_length=200, default=None, null=True) recipient = models.CharField(max_length=200, default=None, null=True) amount = models.IntegerField(null=True, blank=False) date = models.DateTimeField() description = models.CharField(max_length=500, default=None, null=True) CURRENCY_CODES = [ (980, 'UAH'), (840, 'USD'), (978, 'EUR'), ] currencyCode = models.IntegerField(default=None, null=True, choices=CURRENCY_CODES) commissionRate = models.IntegerField(null=True, blank=False) link_code = models.UUIDField(default=get_default_uuid(), editable=False) sender_bank_tax_code = models.ForeignKey( BankInfo, default=None, null=True, related_name='sender_bank_tax_code', on_delete=models.CASCADE ) class Meta: ordering = [ 'receipt_id', 'date' ] def __str__(self): return 'receipt {}'.format(self.receipt_id)

34.569231

87

0.683133

from django.db import models import uuid def get_default_uuid(): return uuid.uuid4().hex class BankInfo(models.Model): tax_code = models.IntegerField(primary_key=True) bank_name = models.CharField(max_length=200, default=None, null=True) support_number_1 = models.CharField(max_length=13, default=None, null=True) support_number_2 = models.CharField(max_length=13, default=None, null=True) support_number_3 = models.CharField(max_length=13, default=None, null=True) email = models.CharField(max_length=20, default=None, null=True) website = models.CharField(max_length=20, default=None, null=True) info = models.TextField(max_length=500, default=None, null=True) signature_info = models.TextField(max_length=100, default=None, null=True) signature_person = models.TextField(max_length=100, default=None, null=True) sign = models.ImageField(null=True, blank=True) logo = models.ImageField(null=True, blank=True) class Meta: ordering = [ 'tax_code', 'bank_name' ] def __str__(self): return '{} {}'.format(self.bank_name, self.tax_code) class RBAResponse(models.Model): receipt_id = models.IntegerField(primary_key=True, unique=True) sender = models.CharField(max_length=200, default=None, null=True) recipient = models.CharField(max_length=200, default=None, null=True) amount = models.IntegerField(null=True, blank=False) date = models.DateTimeField() description = models.CharField(max_length=500, default=None, null=True) CURRENCY_CODES = [ (980, 'UAH'), (840, 'USD'), (978, 'EUR'), ] currencyCode = models.IntegerField(default=None, null=True, choices=CURRENCY_CODES) commissionRate = models.IntegerField(null=True, blank=False) link_code = models.UUIDField(default=get_default_uuid(), editable=False) sender_bank_tax_code = models.ForeignKey( BankInfo, default=None, null=True, related_name='sender_bank_tax_code', on_delete=models.CASCADE ) class Meta: ordering = [ 'receipt_id', 'date' ] def __str__(self): return 'receipt {}'.format(self.receipt_id)

true

1c2eabda09f4342028ede80cfd64ae89c9792eb2

2,844

py

Python

apps/backend/users/views.py

12roshan12/Hotel-website

81c9ca74ea1f080c7fffb7cc350a39ccb2f2596f

[ "MIT" ]

null

apps/backend/users/views.py

12roshan12/Hotel-website

81c9ca74ea1f080c7fffb7cc350a39ccb2f2596f

[ "MIT" ]

null

apps/backend/users/views.py

12roshan12/Hotel-website

81c9ca74ea1f080c7fffb7cc350a39ccb2f2596f

[ "MIT" ]

null

from django.urls import reverse from django.shortcuts import render, redirect, HttpResponseRedirect from django.contrib.messages.views import SuccessMessageMixin from django.utils.translation import templatize from django.views.generic import CreateView, ListView, DeleteView, UpdateView from .forms import EnquiryForm, UserRegistrationForm, UserUpdateForm from django.contrib.auth.views import LoginView, LogoutView from .models import Events, User from django.contrib import messages from django.urls import reverse_lazy # Create your views here. class UserRegistrationField(SuccessMessageMixin, CreateView): template_name = 'backend/user/register.html' form_class = UserRegistrationForm success_message = 'User Successfully Created' success_url = reverse_lazy('backend.users:index') def form_valid(self, form): user = form.save(commit=False) user_type = form.cleaned_data['user_types'] if user_type == 'is_buyer': user.is_buyer = True elif user_type == 'is_seller': user.is_seller = True user.save() messages.success(self.request, self.success_message) return redirect(self.success_url) class UserLoginField(LoginView): template_name = 'backend/user/login.html' class UserLogoutField(LogoutView): template_name = 'backend/user/login.html' class IndexUserView(ListView): template_name = 'backend/user/index.html' model = User context_object_name = 'users' paginate_by = 20 def get_context_data(self, *, object_list=None, **kwargs): context = super(IndexUserView, self).get_context_data(**kwargs) context['users'] = User.object.all() return context class UpdateUserView(SuccessMessageMixin, UpdateView): model = User template_name = 'backend/user/register.html' success_message = 'User Successfully Updated' success_url = reverse_lazy() form_class = UserUpdateForm def get(self, request, *args, **kwargs): self.object = self.get_object() return super(UpdateUserView, self).get(self, request, *args, **kwargs) def get_success_url(self): return reverse('backend.users:index') class DeleteUserView(SuccessMessageMixin, DeleteView): model = User template_name = 'backend/layouts/partial/delete.html' success_message = 'User Successfully deleted' success_url = reverse_lazy('backend.users:index') def delete(self, request, *args, **kwargs): self.object = self.get_object() self.object.delete() messages.success(self.request, self.success_message) return HttpResponseRedirect(self.success_url) def get(self, request, *args, **kwargs): self.object = self.get_object() return super(DeleteUserView, self).get(self, request, *args, **kwargs)

32.689655

78

0.716245

from django.urls import reverse from django.shortcuts import render, redirect, HttpResponseRedirect from django.contrib.messages.views import SuccessMessageMixin from django.utils.translation import templatize from django.views.generic import CreateView, ListView, DeleteView, UpdateView from .forms import EnquiryForm, UserRegistrationForm, UserUpdateForm from django.contrib.auth.views import LoginView, LogoutView from .models import Events, User from django.contrib import messages from django.urls import reverse_lazy class UserRegistrationField(SuccessMessageMixin, CreateView): template_name = 'backend/user/register.html' form_class = UserRegistrationForm success_message = 'User Successfully Created' success_url = reverse_lazy('backend.users:index') def form_valid(self, form): user = form.save(commit=False) user_type = form.cleaned_data['user_types'] if user_type == 'is_buyer': user.is_buyer = True elif user_type == 'is_seller': user.is_seller = True user.save() messages.success(self.request, self.success_message) return redirect(self.success_url) class UserLoginField(LoginView): template_name = 'backend/user/login.html' class UserLogoutField(LogoutView): template_name = 'backend/user/login.html' class IndexUserView(ListView): template_name = 'backend/user/index.html' model = User context_object_name = 'users' paginate_by = 20 def get_context_data(self, *, object_list=None, **kwargs): context = super(IndexUserView, self).get_context_data(**kwargs) context['users'] = User.object.all() return context class UpdateUserView(SuccessMessageMixin, UpdateView): model = User template_name = 'backend/user/register.html' success_message = 'User Successfully Updated' success_url = reverse_lazy() form_class = UserUpdateForm def get(self, request, *args, **kwargs): self.object = self.get_object() return super(UpdateUserView, self).get(self, request, *args, **kwargs) def get_success_url(self): return reverse('backend.users:index') class DeleteUserView(SuccessMessageMixin, DeleteView): model = User template_name = 'backend/layouts/partial/delete.html' success_message = 'User Successfully deleted' success_url = reverse_lazy('backend.users:index') def delete(self, request, *args, **kwargs): self.object = self.get_object() self.object.delete() messages.success(self.request, self.success_message) return HttpResponseRedirect(self.success_url) def get(self, request, *args, **kwargs): self.object = self.get_object() return super(DeleteUserView, self).get(self, request, *args, **kwargs)

true

1c2eabe68bf4698868e90a9f2d54a2c3d20e6bd8

12,464

py

Python

nonebot/matcher.py

Lancercmd/nonebot2

59d49becf49d108442c7ca05e4f3e2fb98c3a972

[ "MIT" ]

null

nonebot/matcher.py

Lancercmd/nonebot2

59d49becf49d108442c7ca05e4f3e2fb98c3a972

[ "MIT" ]

null

nonebot/matcher.py

Lancercmd/nonebot2

59d49becf49d108442c7ca05e4f3e2fb98c3a972

[ "MIT" ]

null

""" 事件响应器 ========== 该模块实现事件响应器的创建与运行，并提供一些快捷方法来帮助用户更好的与机器人进行对话。 """ from nonebot.log import logger import typing import inspect from functools import wraps from datetime import datetime from contextvars import ContextVar from collections import defaultdict from nonebot.rule import Rule from nonebot.permission import Permission, USER from nonebot.typing import Type, List, Dict, Union, Callable, Optional, NoReturn from nonebot.typing import Bot, Event, Handler, Message, ArgsParser, MessageSegment from nonebot.exception import PausedException, RejectedException, FinishedException matchers: Dict[int, List[Type["Matcher"]]] = defaultdict(list) """ :类型: ``Dict[int, List[Type[Matcher]]]`` :说明: 用于存储当前所有的事件响应器 """ current_bot: ContextVar = ContextVar("current_bot") current_event: ContextVar = ContextVar("current_event") class MatcherMeta(type): def __repr__(self) -> str: return (f"<Matcher from {self.module or 'unknow'}, " # type: ignore f"type={self.type}, priority={self.priority}, " # type: ignore f"temp={self.temp}>") # type: ignore def __str__(self) -> str: return repr(self) class Matcher(metaclass=MatcherMeta): """事件响应器类""" module: Optional[str] = None """ :类型: ``Optional[str]`` :说明: 事件响应器所在模块名称 """ type: str = "" """ :类型: ``str`` :说明: 事件响应器类型 """ rule: Rule = Rule() """ :类型: ``Rule`` :说明: 事件响应器匹配规则 """ permission: Permission = Permission() """ :类型: ``Permission`` :说明: 事件响应器触发权限 """ handlers: List[Handler] = [] """ :类型: ``List[Handler]`` :说明: 事件响应器拥有的事件处理函数列表 """ priority: int = 1 """ :类型: ``int`` :说明: 事件响应器优先级 """ block: bool = False """ :类型: ``bool`` :说明: 事件响应器是否阻止事件传播 """ temp: bool = False """ :类型: ``bool`` :说明: 事件响应器是否为临时 """ expire_time: Optional[datetime] = None """ :类型: ``Optional[datetime]`` :说明: 事件响应器过期时间点 """ _default_state: dict = {} """ :类型: ``dict`` :说明: 事件响应器默认状态 """ _default_parser: Optional[ArgsParser] = None """ :类型: ``Optional[ArgsParser]`` :说明: 事件响应器默认参数解析函数 """ def __init__(self): """实例化 Matcher 以便运行""" self.handlers = self.handlers.copy() self.state = self._default_state.copy() def __repr__(self) -> str: return (f"<Matcher from {self.module or 'unknow'}, type={self.type}, " f"priority={self.priority}, temp={self.temp}>") def __str__(self) -> str: return self.__repr__() @classmethod def new(cls, type_: str = "", rule: Optional[Rule] = None, permission: Optional[Permission] = None, handlers: Optional[List[Handler]] = None, temp: bool = False, priority: int = 1, block: bool = False, *, module: Optional[str] = None, default_state: Optional[dict] = None, expire_time: Optional[datetime] = None) -> Type["Matcher"]: """ :说明: 创建一个新的事件响应器，并存储至 `matchers <#matchers>`_ :参数: * ``type_: str``: 事件响应器类型，与 ``event.type`` 一致时触发，空字符串表示任意 * ``rule: Optional[Rule]``: 匹配规则 * ``permission: Optional[Permission]``: 权限 * ``handlers: Optional[List[Handler]]``: 事件处理函数列表 * ``temp: bool``: 是否为临时事件响应器，即触发一次后删除 * ``priority: int``: 响应优先级 * ``block: bool``: 是否阻止事件向更低优先级的响应器传播 * ``module: Optional[str]``: 事件响应器所在模块名称 * ``default_state: Optional[dict]``: 默认状态 ``state`` * ``expire_time: Optional[datetime]``: 事件响应器最终有效时间点，过时即被删除 :返回: - ``Type[Matcher]``: 新的事件响应器类 """ NewMatcher = type( "Matcher", (Matcher,), { "module": module, "type": type_, "rule": rule or Rule(), "permission": permission or Permission(), "handlers": handlers or [], "temp": temp, "expire_time": expire_time, "priority": priority, "block": block, "_default_state": default_state or {} }) matchers[priority].append(NewMatcher) return NewMatcher @classmethod async def check_perm(cls, bot: Bot, event: Event) -> bool: """ :说明: 检查是否满足触发权限 :参数: * ``bot: Bot``: Bot 对象 * ``event: Event``: 上报事件 :返回: - ``bool``: 是否满足权限 """ return await cls.permission(bot, event) @classmethod async def check_rule(cls, bot: Bot, event: Event, state: dict) -> bool: """ :说明: 检查是否满足匹配规则 :参数: * ``bot: Bot``: Bot 对象 * ``event: Event``: 上报事件 * ``state: dict``: 当前状态 :返回: - ``bool``: 是否满足匹配规则 """ return (event.type == (cls.type or event.type) and await cls.rule(bot, event, state)) @classmethod def args_parser(cls, func: ArgsParser) -> ArgsParser: """ :说明: 装饰一个函数来更改当前事件响应器的默认参数解析函数 :参数: * ``func: ArgsParser``: 参数解析函数 """ cls._default_parser = func return func @classmethod def handle(cls) -> Callable[[Handler], Handler]: """ :说明: 装饰一个函数来向事件响应器直接添加一个处理函数 :参数: * 无 """ def _decorator(func: Handler) -> Handler: cls.handlers.append(func) return func return _decorator @classmethod def receive(cls) -> Callable[[Handler], Handler]: """ :说明: 装饰一个函数来指示 NoneBot 在接收用户新的一条消息后继续运行该函数 :参数: * 无 """ async def _receive(bot: Bot, event: Event, state: dict) -> NoReturn: raise PausedException if cls.handlers: # 已有前置handlers则接受一条新的消息，否则视为接收初始消息 cls.handlers.append(_receive) def _decorator(func: Handler) -> Handler: if not cls.handlers or cls.handlers[-1] is not func: cls.handlers.append(func) return func return _decorator @classmethod def got( cls, key: str, prompt: Optional[Union[str, Message, MessageSegment]] = None, args_parser: Optional[ArgsParser] = None ) -> Callable[[Handler], Handler]: """ :说明: 装饰一个函数来指示 NoneBot 当要获取的 ``key`` 不存在时接收用户新的一条消息并经过 ``ArgsParser`` 处理后再运行该函数，如果 ``key`` 已存在则直接继续运行 :参数: * ``key: str``: 参数名 * ``prompt: Optional[Union[str, Message, MessageSegment]]``: 在参数不存在时向用户发送的消息 * ``args_parser: Optional[ArgsParser]``: 可选参数解析函数，空则使用默认解析函数 """ async def _key_getter(bot: Bot, event: Event, state: dict): state["_current_key"] = key if key not in state: if prompt: await bot.send(event=event, message=str(prompt).format(**state)) raise PausedException else: state["_skip_key"] = True async def _key_parser(bot: Bot, event: Event, state: dict): if key in state and state.get("_skip_key"): del state["_skip_key"] return parser = args_parser or cls._default_parser if parser: await parser(bot, event, state) else: state[state["_current_key"]] = str(event.message) cls.handlers.append(_key_getter) cls.handlers.append(_key_parser) def _decorator(func: Handler) -> Handler: if not hasattr(cls.handlers[-1], "__wrapped__"): parser = cls.handlers.pop() @wraps(func) async def wrapper(bot: Bot, event: Event, state: dict): await parser(bot, event, state) await func(bot, event, state) if "_current_key" in state: del state["_current_key"] cls.handlers.append(wrapper) return func return _decorator @classmethod async def send(cls, message: Union[str, Message, MessageSegment], **kwargs): """ :说明: 发送一条消息给当前交互用户 :参数: * ``message: Union[str, Message, MessageSegment]``: 消息内容 * ``**kwargs``: 其他传递给 ``bot.send`` 的参数，请参考对应 adapter 的 bot 对象 api """ bot = current_bot.get() event = current_event.get() await bot.send(event=event, message=message, **kwargs) @classmethod async def finish(cls, message: Optional[Union[str, Message, MessageSegment]] = None, **kwargs) -> NoReturn: """ :说明: 发送一条消息给当前交互用户并结束当前事件响应器 :参数: * ``message: Union[str, Message, MessageSegment]``: 消息内容 * ``**kwargs``: 其他传递给 ``bot.send`` 的参数，请参考对应 adapter 的 bot 对象 api """ bot = current_bot.get() event = current_event.get() if message: await bot.send(event=event, message=message, **kwargs) raise FinishedException @classmethod async def pause(cls, prompt: Optional[Union[str, Message, MessageSegment]] = None, **kwargs) -> NoReturn: """ :说明: 发送一条消息给当前交互用户并暂停事件响应器，在接收用户新的一条消息后继续下一个处理函数 :参数: * ``prompt: Union[str, Message, MessageSegment]``: 消息内容 * ``**kwargs``: 其他传递给 ``bot.send`` 的参数，请参考对应 adapter 的 bot 对象 api """ bot = current_bot.get() event = current_event.get() if prompt: await bot.send(event=event, message=prompt, **kwargs) raise PausedException @classmethod async def reject(cls, prompt: Optional[Union[str, Message, MessageSegment]] = None, **kwargs) -> NoReturn: """ :说明: 发送一条消息给当前交互用户并暂停事件响应器，在接收用户新的一条消息后重新运行当前处理函数 :参数: * ``prompt: Union[str, Message, MessageSegment]``: 消息内容 * ``**kwargs``: 其他传递给 ``bot.send`` 的参数，请参考对应 adapter 的 bot 对象 api """ bot = current_bot.get() event = current_event.get() if prompt: await bot.send(event=event, message=prompt, **kwargs) raise RejectedException # 运行handlers async def run(self, bot: Bot, event: Event, state: dict): b_t = current_bot.set(bot) e_t = current_event.set(event) try: # Refresh preprocess state self.state.update(state) for _ in range(len(self.handlers)): handler = self.handlers.pop(0) annotation = typing.get_type_hints(handler) BotType = annotation.get("bot") if BotType and inspect.isclass(BotType) and not isinstance( bot, BotType): continue await handler(bot, event, self.state) except RejectedException: self.handlers.insert(0, handler) # type: ignore Matcher.new( self.type, Rule(), USER(event.user_id, perm=self.permission), # type:ignore self.handlers, temp=True, priority=0, block=True, module=self.module, default_state=self.state, expire_time=datetime.now() + bot.config.session_expire_timeout) except PausedException: Matcher.new( self.type, Rule(), USER(event.user_id, perm=self.permission), # type:ignore self.handlers, temp=True, priority=0, block=True, module=self.module, default_state=self.state, expire_time=datetime.now() + bot.config.session_expire_timeout) except FinishedException: pass finally: logger.info(f"Matcher {self} running complete") current_bot.reset(b_t) current_event.reset(e_t)

27.575221

106

0.522866

from nonebot.log import logger import typing import inspect from functools import wraps from datetime import datetime from contextvars import ContextVar from collections import defaultdict from nonebot.rule import Rule from nonebot.permission import Permission, USER from nonebot.typing import Type, List, Dict, Union, Callable, Optional, NoReturn from nonebot.typing import Bot, Event, Handler, Message, ArgsParser, MessageSegment from nonebot.exception import PausedException, RejectedException, FinishedException matchers: Dict[int, List[Type["Matcher"]]] = defaultdict(list) current_bot: ContextVar = ContextVar("current_bot") current_event: ContextVar = ContextVar("current_event") class MatcherMeta(type): def __repr__(self) -> str: return (f"<Matcher from {self.module or 'unknow'}, " f"type={self.type}, priority={self.priority}, " f"temp={self.temp}>") def __str__(self) -> str: return repr(self) class Matcher(metaclass=MatcherMeta): module: Optional[str] = None type: str = "" rule: Rule = Rule() permission: Permission = Permission() handlers: List[Handler] = [] priority: int = 1 block: bool = False temp: bool = False expire_time: Optional[datetime] = None _default_state: dict = {} _default_parser: Optional[ArgsParser] = None def __init__(self): self.handlers = self.handlers.copy() self.state = self._default_state.copy() def __repr__(self) -> str: return (f"<Matcher from {self.module or 'unknow'}, type={self.type}, " f"priority={self.priority}, temp={self.temp}>") def __str__(self) -> str: return self.__repr__() @classmethod def new(cls, type_: str = "", rule: Optional[Rule] = None, permission: Optional[Permission] = None, handlers: Optional[List[Handler]] = None, temp: bool = False, priority: int = 1, block: bool = False, *, module: Optional[str] = None, default_state: Optional[dict] = None, expire_time: Optional[datetime] = None) -> Type["Matcher"]: NewMatcher = type( "Matcher", (Matcher,), { "module": module, "type": type_, "rule": rule or Rule(), "permission": permission or Permission(), "handlers": handlers or [], "temp": temp, "expire_time": expire_time, "priority": priority, "block": block, "_default_state": default_state or {} }) matchers[priority].append(NewMatcher) return NewMatcher @classmethod async def check_perm(cls, bot: Bot, event: Event) -> bool: return await cls.permission(bot, event) @classmethod async def check_rule(cls, bot: Bot, event: Event, state: dict) -> bool: return (event.type == (cls.type or event.type) and await cls.rule(bot, event, state)) @classmethod def args_parser(cls, func: ArgsParser) -> ArgsParser: cls._default_parser = func return func @classmethod def handle(cls) -> Callable[[Handler], Handler]: def _decorator(func: Handler) -> Handler: cls.handlers.append(func) return func return _decorator @classmethod def receive(cls) -> Callable[[Handler], Handler]: async def _receive(bot: Bot, event: Event, state: dict) -> NoReturn: raise PausedException if cls.handlers: cls.handlers.append(_receive) def _decorator(func: Handler) -> Handler: if not cls.handlers or cls.handlers[-1] is not func: cls.handlers.append(func) return func return _decorator @classmethod def got( cls, key: str, prompt: Optional[Union[str, Message, MessageSegment]] = None, args_parser: Optional[ArgsParser] = None ) -> Callable[[Handler], Handler]: async def _key_getter(bot: Bot, event: Event, state: dict): state["_current_key"] = key if key not in state: if prompt: await bot.send(event=event, message=str(prompt).format(**state)) raise PausedException else: state["_skip_key"] = True async def _key_parser(bot: Bot, event: Event, state: dict): if key in state and state.get("_skip_key"): del state["_skip_key"] return parser = args_parser or cls._default_parser if parser: await parser(bot, event, state) else: state[state["_current_key"]] = str(event.message) cls.handlers.append(_key_getter) cls.handlers.append(_key_parser) def _decorator(func: Handler) -> Handler: if not hasattr(cls.handlers[-1], "__wrapped__"): parser = cls.handlers.pop() @wraps(func) async def wrapper(bot: Bot, event: Event, state: dict): await parser(bot, event, state) await func(bot, event, state) if "_current_key" in state: del state["_current_key"] cls.handlers.append(wrapper) return func return _decorator @classmethod async def send(cls, message: Union[str, Message, MessageSegment], **kwargs): bot = current_bot.get() event = current_event.get() await bot.send(event=event, message=message, **kwargs) @classmethod async def finish(cls, message: Optional[Union[str, Message, MessageSegment]] = None, **kwargs) -> NoReturn: bot = current_bot.get() event = current_event.get() if message: await bot.send(event=event, message=message, **kwargs) raise FinishedException @classmethod async def pause(cls, prompt: Optional[Union[str, Message, MessageSegment]] = None, **kwargs) -> NoReturn: bot = current_bot.get() event = current_event.get() if prompt: await bot.send(event=event, message=prompt, **kwargs) raise PausedException @classmethod async def reject(cls, prompt: Optional[Union[str, Message, MessageSegment]] = None, **kwargs) -> NoReturn: bot = current_bot.get() event = current_event.get() if prompt: await bot.send(event=event, message=prompt, **kwargs) raise RejectedException async def run(self, bot: Bot, event: Event, state: dict): b_t = current_bot.set(bot) e_t = current_event.set(event) try: self.state.update(state) for _ in range(len(self.handlers)): handler = self.handlers.pop(0) annotation = typing.get_type_hints(handler) BotType = annotation.get("bot") if BotType and inspect.isclass(BotType) and not isinstance( bot, BotType): continue await handler(bot, event, self.state) except RejectedException: self.handlers.insert(0, handler) Matcher.new( self.type, Rule(), USER(event.user_id, perm=self.permission), self.handlers, temp=True, priority=0, block=True, module=self.module, default_state=self.state, expire_time=datetime.now() + bot.config.session_expire_timeout) except PausedException: Matcher.new( self.type, Rule(), USER(event.user_id, perm=self.permission), self.handlers, temp=True, priority=0, block=True, module=self.module, default_state=self.state, expire_time=datetime.now() + bot.config.session_expire_timeout) except FinishedException: pass finally: logger.info(f"Matcher {self} running complete") current_bot.reset(b_t) current_event.reset(e_t)

true

1c2eacfafb6429adc60a79646324136bfdb1e1d0

786

py

Python

var/spack/repos/builtin/packages/py-lit/package.py

kkauder/spack

6ae8d5c380c1f42094b05d38be26b03650aafb39

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2,360

2017-11-06T08:47:01.000Z

2022-03-31T14:45:33.000Z

var/spack/repos/builtin/packages/py-lit/package.py

kkauder/spack

6ae8d5c380c1f42094b05d38be26b03650aafb39

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

13,838

2017-11-04T07:49:45.000Z

2022-03-31T23:38:39.000Z

var/spack/repos/builtin/packages/py-lit/package.py

kkauder/spack

6ae8d5c380c1f42094b05d38be26b03650aafb39

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

1,793

2017-11-04T07:45:50.000Z

2022-03-30T14:31:53.000Z

# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class PyLit(PythonPackage): """lit is a portable tool for executing LLVM and Clang style test suites, summarizing their results, and providing indication of failures. lit is designed to be a lightweight testing tool with as simple a user interface as possible.""" pypi = "lit/lit-0.5.0.tar.gz" version('0.7.1', sha256='ecef2833aef7f411cb923dac109c7c9dcc7dbe7cafce0650c1e8d19c243d955f') version('0.5.0', sha256='3ea4251e78ebeb2e07be2feb33243d1f8931d956efc96ccc2b0846ced212b58c') depends_on('py-setuptools', type='build')

37.428571

96

0.749364

from spack import * class PyLit(PythonPackage): pypi = "lit/lit-0.5.0.tar.gz" version('0.7.1', sha256='ecef2833aef7f411cb923dac109c7c9dcc7dbe7cafce0650c1e8d19c243d955f') version('0.5.0', sha256='3ea4251e78ebeb2e07be2feb33243d1f8931d956efc96ccc2b0846ced212b58c') depends_on('py-setuptools', type='build')

true

1c2ead19859c5bebfcacdc7ae5358b7e31a8fe09

2,097

py

Python

is_ros_pkg/examples/client.py

viniciusbaltoe/is-ros-pkg

e1ec6d5aa40d82b9259e406a1d6d196402e7a246

[ "MIT" ]

null

is_ros_pkg/examples/client.py

viniciusbaltoe/is-ros-pkg

e1ec6d5aa40d82b9259e406a1d6d196402e7a246

[ "MIT" ]

null

is_ros_pkg/examples/client.py

viniciusbaltoe/is-ros-pkg

e1ec6d5aa40d82b9259e406a1d6d196402e7a246

[ "MIT" ]

null

from is_wire.core import Channel, Subscription, Message from google.protobuf.empty_pb2 import Empty from is_msgs.common_pb2 import Position import json import socket import time if __name__ == "__main__": # -------------------------- Options ------------------------- cont = True config_file = '../etc/conf/config.json' config = json.load(open(config_file, 'r')) channel = Channel(config["broker_uri"]) robot_config = config["robot"] subscription = Subscription(channel) # ---------------------- Get Position ------------------------ topic = "ROSRobot.{}.GetPosition".format(robot_config["robot_id"]) print("Publishing to topic: {}".format(topic)) request = Empty() channel.publish( Message(content=request, reply_to=subscription), topic=topic) try: reply = channel.consume(timeout=3.0) unpacked_msg = reply.unpack(Position) print('Position:\n', unpacked_msg) except socket.timeout: print('No reply to Get 1 :(') # ---------------------- Set Goal Position ------------------- topic = "ROSRobot.{}.GoalPosition".format(robot_config["robot_id"]) print("Publishing to topic: {}".format(topic)) request = Position() request.x = 1.5 request.y = -0.5 request.z = 0.0 channel.publish( Message(content=request, reply_to=subscription), topic=topic) try: reply = channel.consume() unpacked_msg = reply.unpack(Empty) print('RPC Status:', reply.status) except socket.timeout: print('No reply to Goal_Position :(') # ---------------------- Get Position ------------------------ topic = "ROSRobot.{}.GetPosition".format(robot_config["robot_id"]) print("Publishing to topic: {}".format(topic)) request = Empty() channel.publish( Message(content=request, reply_to=subscription), topic=topic) try: reply = channel.consume(timeout=3.0) unpacked_msg = reply.unpack(Position) print('Position:\n', unpacked_msg) except socket.timeout: print('No reply :(')

33.822581

71

0.592752

from is_wire.core import Channel, Subscription, Message from google.protobuf.empty_pb2 import Empty from is_msgs.common_pb2 import Position import json import socket import time if __name__ == "__main__": cont = True config_file = '../etc/conf/config.json' config = json.load(open(config_file, 'r')) channel = Channel(config["broker_uri"]) robot_config = config["robot"] subscription = Subscription(channel) topic = "ROSRobot.{}.GetPosition".format(robot_config["robot_id"]) print("Publishing to topic: {}".format(topic)) request = Empty() channel.publish( Message(content=request, reply_to=subscription), topic=topic) try: reply = channel.consume(timeout=3.0) unpacked_msg = reply.unpack(Position) print('Position:\n', unpacked_msg) except socket.timeout: print('No reply to Get 1 :(') topic = "ROSRobot.{}.GoalPosition".format(robot_config["robot_id"]) print("Publishing to topic: {}".format(topic)) request = Position() request.x = 1.5 request.y = -0.5 request.z = 0.0 channel.publish( Message(content=request, reply_to=subscription), topic=topic) try: reply = channel.consume() unpacked_msg = reply.unpack(Empty) print('RPC Status:', reply.status) except socket.timeout: print('No reply to Goal_Position :(') topic = "ROSRobot.{}.GetPosition".format(robot_config["robot_id"]) print("Publishing to topic: {}".format(topic)) request = Empty() channel.publish( Message(content=request, reply_to=subscription), topic=topic) try: reply = channel.consume(timeout=3.0) unpacked_msg = reply.unpack(Position) print('Position:\n', unpacked_msg) except socket.timeout: print('No reply :(')

true

1c2eadfcae187ab3eede7b128082e792f9694294

95

py

Python

tests/__init__.py

rebornix/jedi-language-server

13297ba1cc109c5f8c2e3b72a82ea79cc9711473

[ "MIT" ]

300

2019-08-20T14:00:37.000Z

2022-03-31T00:10:25.000Z

tests/__init__.py

rebornix/jedi-language-server

13297ba1cc109c5f8c2e3b72a82ea79cc9711473

[ "MIT" ]

136

2019-08-19T16:04:16.000Z

2022-03-17T21:31:18.000Z

tests/__init__.py

rebornix/jedi-language-server

13297ba1cc109c5f8c2e3b72a82ea79cc9711473

[ "MIT" ]

37

2020-05-03T14:20:51.000Z

2022-03-23T06:12:22.000Z

"""Testing entrypoint.""" import py TEST_DATA = py.path.local(__file__) / ".." / "test_data"

15.833333

56

0.652632

import py TEST_DATA = py.path.local(__file__) / ".." / "test_data"

true

1c2eaf20ba4549dae9aed8b51f3a802ab0cf1569

2,506

py

Python

bigml/tests/inspect_model_steps.py

deven96/python

46be8622fe58f004bdbd636a08a8904ef4134bcd

[ "Apache-2.0" ]

1

2021-08-30T20:18:38.000Z

bigml/tests/inspect_model_steps.py

deven96/python

46be8622fe58f004bdbd636a08a8904ef4134bcd

[ "Apache-2.0" ]

null

bigml/tests/inspect_model_steps.py

deven96/python

46be8622fe58f004bdbd636a08a8904ef4134bcd

[ "Apache-2.0" ]

1

2021-08-30T20:18:40.000Z

# -*- coding: utf-8 -*- #!/usr/bin/env python # # Copyright 2012, 2015-2019 BigML # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import io import os from bigml.tests.world import res_filename from world import world from nose.tools import eq_ #@step(r'I translate the tree into IF-THEN rules$') def i_translate_the_tree_into_IF_THEN_rules(step): output = io.BytesIO() world.local_model.rules(out=output) world.output = output.getvalue() #@step(r'I check data distribution with "(.*)" file$') def i_check_the_data_distribution(step, file): distribution = world.local_model.get_data_distribution() distribution_str = '' for bin_value, bin_instances in distribution: distribution_str += "[%s,%s]\n" % (bin_value, bin_instances) world.output = distribution_str.encode('utf-8') i_check_if_the_output_is_like_expected_file(step, file) #@step(r'I check the predictions distribution with "(.*)" file$') def i_check_the_predictions_distribution(step, file): predictions = world.local_model.get_prediction_distribution() distribution_str = '' for group, instances in predictions: distribution_str += "[%s,%s]\n" % (group, instances) world.output = distribution_str.encode('utf-8') i_check_if_the_output_is_like_expected_file(step, file) #@step(r'I check the model summary with "(.*)" file$') def i_check_the_model_summary_with(step, file): output = io.BytesIO() world.local_model.summarize(out=output) world.output = output.getvalue() i_check_if_the_output_is_like_expected_file(step, file) #@step(r'I check the output is like "(.*)" expected file') def i_check_if_the_output_is_like_expected_file(step, expected_file): file = open(res_filename(expected_file), "rb") expected_content = file.read() file.close() eq_(world.output.strip(), expected_content.strip()) def update_content(filename, content): with open(res_filename(filename), "w") as file_handler: file_handler.write(content)

32.545455

75

0.735834

import io import os from bigml.tests.world import res_filename from world import world from nose.tools import eq_ def i_translate_the_tree_into_IF_THEN_rules(step): output = io.BytesIO() world.local_model.rules(out=output) world.output = output.getvalue() def i_check_the_data_distribution(step, file): distribution = world.local_model.get_data_distribution() distribution_str = '' for bin_value, bin_instances in distribution: distribution_str += "[%s,%s]\n" % (bin_value, bin_instances) world.output = distribution_str.encode('utf-8') i_check_if_the_output_is_like_expected_file(step, file) def i_check_the_predictions_distribution(step, file): predictions = world.local_model.get_prediction_distribution() distribution_str = '' for group, instances in predictions: distribution_str += "[%s,%s]\n" % (group, instances) world.output = distribution_str.encode('utf-8') i_check_if_the_output_is_like_expected_file(step, file) def i_check_the_model_summary_with(step, file): output = io.BytesIO() world.local_model.summarize(out=output) world.output = output.getvalue() i_check_if_the_output_is_like_expected_file(step, file) def i_check_if_the_output_is_like_expected_file(step, expected_file): file = open(res_filename(expected_file), "rb") expected_content = file.read() file.close() eq_(world.output.strip(), expected_content.strip()) def update_content(filename, content): with open(res_filename(filename), "w") as file_handler: file_handler.write(content)

true

1c2eaf3facbffed299455ef3e1efd26f63e89301

591

py

Python

runners/fastwsgi_server.py

timb-machine-mirrors/pcf

d697a531da8c4206a6d874e689312a359446f8da

[ "MIT" ]

null

runners/fastwsgi_server.py

timb-machine-mirrors/pcf

d697a531da8c4206a6d874e689312a359446f8da

[ "MIT" ]

null

runners/fastwsgi_server.py

timb-machine-mirrors/pcf

d697a531da8c4206a6d874e689312a359446f8da

[ "MIT" ]

null

import fastwsgi import app def start_server(): host, port, debug, ssl_context = app.config_prepare() def requires_authorization(f): @wraps(f) def decorated(*args, **kwargs): if config['security']['basic_auth'] == '0': return f(*args, **kwargs) auth = request.authorization if not auth or not ok_user_and_password(auth.username, auth.password): return authenticate() return f(*args, **kwargs) return decorated fastwsgi.run(wsgi_app=app.application, host=host, port=port)

28.142857

82

0.605753

import fastwsgi import app def start_server(): host, port, debug, ssl_context = app.config_prepare() def requires_authorization(f): @wraps(f) def decorated(*args, **kwargs): if config['security']['basic_auth'] == '0': return f(*args, **kwargs) auth = request.authorization if not auth or not ok_user_and_password(auth.username, auth.password): return authenticate() return f(*args, **kwargs) return decorated fastwsgi.run(wsgi_app=app.application, host=host, port=port)

true

1c2eaf46a4ed2fe9166ba288f3f111543133d5b3

10,128

py

Python

workbench/deals/models.py

yoshson/workbench

701558cac3357cd82e4dc99f0fefed12ee81ddc5

[ "MIT" ]

15

2020-09-02T22:17:34.000Z

2022-02-01T20:09:10.000Z

workbench/deals/models.py

yoshson/workbench

701558cac3357cd82e4dc99f0fefed12ee81ddc5

[ "MIT" ]

18

2020-01-08T15:28:26.000Z

2022-02-28T02:46:41.000Z

workbench/deals/models.py

yoshson/workbench

701558cac3357cd82e4dc99f0fefed12ee81ddc5

[ "MIT" ]

8

2020-09-29T08:00:24.000Z

2022-01-16T11:58:19.000Z

import datetime as dt from functools import total_ordering from django.db import models from django.db.models import Q from django.utils import timezone from django.utils.html import format_html from django.utils.translation import gettext_lazy as _ from workbench.accounts.models import User from workbench.contacts.models import Organization, Person from workbench.tools.formats import Z2, currency, local_date_format from workbench.tools.models import Model, MoneyField, SearchQuerySet from workbench.tools.urls import model_urls class NotArchivedQuerySet(models.QuerySet): def active(self, include=None): return self.filter(Q(is_archived=False) | Q(id=include)) class AttributeGroup(models.Model): title = models.CharField(_("title"), max_length=200) position = models.PositiveIntegerField(_("position"), default=0) is_archived = models.BooleanField(_("is archived"), default=False) is_required = models.BooleanField(_("is required"), default=True) class Meta: ordering = ("position", "id") verbose_name = _("attribute group") verbose_name_plural = _("attribute groups") def __str__(self): return self.title class Attribute(models.Model): group = models.ForeignKey( AttributeGroup, on_delete=models.CASCADE, related_name="attributes", verbose_name=_("attribute group"), ) title = models.CharField(_("title"), max_length=200) position = models.PositiveIntegerField(_("position"), default=0) is_archived = models.BooleanField(_("is archived"), default=False) objects = NotArchivedQuerySet.as_manager() class Meta: ordering = ("position", "id") verbose_name = _("attribute") verbose_name_plural = _("attributes") def __str__(self): return self.title class ClosingType(models.Model): title = models.CharField(_("title"), max_length=200) represents_a_win = models.BooleanField(_("represents a win"), default=False) position = models.PositiveIntegerField(_("position"), default=0) class Meta: ordering = ("position", "id") verbose_name = _("closing type") verbose_name_plural = _("closing types") def __str__(self): return self.title class DealQuerySet(SearchQuerySet): def maybe_actionable(self, *, user): return self.filter( Q(status=Deal.OPEN), Q(owned_by=user) | Q(owned_by__is_active=False) ).select_related("owned_by") @model_urls class Deal(Model): OPEN = 10 ACCEPTED = 20 DECLINED = 30 STATUS_CHOICES = ( (OPEN, _("Open")), (ACCEPTED, _("Accepted")), (DECLINED, _("Declined")), ) UNKNOWN = 10 NORMAL = 20 HIGH = 30 PROBABILITY_CHOICES = [ (UNKNOWN, _("unknown")), (NORMAL, _("normal")), (HIGH, _("high")), ] customer = models.ForeignKey( Organization, on_delete=models.PROTECT, verbose_name=_("customer") ) contact = models.ForeignKey( Person, on_delete=models.SET_NULL, blank=True, null=True, verbose_name=_("contact"), ) title = models.CharField(_("title"), max_length=200) description = models.TextField(_("description"), blank=True) owned_by = models.ForeignKey( User, on_delete=models.PROTECT, verbose_name=_("contact person") ) value = MoneyField(_("value")) status = models.PositiveIntegerField( _("status"), choices=STATUS_CHOICES, default=OPEN ) probability = models.IntegerField( _("probability"), choices=PROBABILITY_CHOICES, default=UNKNOWN ) decision_expected_on = models.DateField( _("decision expected on"), blank=True, null=True ) created_at = models.DateTimeField(_("created at"), default=timezone.now) attributes = models.ManyToManyField( Attribute, verbose_name=_("attributes"), through="DealAttribute", ) closed_on = models.DateField(_("closed on"), blank=True, null=True) closing_type = models.ForeignKey( ClosingType, on_delete=models.PROTECT, blank=True, null=True, verbose_name=_("closing type"), ) closing_notice = models.TextField(_("closing notice"), blank=True) _fts = models.TextField(editable=False, blank=True) related_offers = models.ManyToManyField( "offers.Offer", blank=True, related_name="deals", verbose_name=_("related offers"), ) contributors = models.ManyToManyField( User, verbose_name=_("contributors"), related_name="+", through="Contribution", help_text=_( "The value of the deal will be distributed among all" " contributors in the accepted deals report." ), ) objects = DealQuerySet.as_manager() class Meta: ordering = ["-pk"] verbose_name = _("deal") verbose_name_plural = _("deals") def __str__(self): return f"{self.code} {self.title} - {self.owned_by.get_short_name()}" def __html__(self): return format_html( "{} {} - {}", self.code, self.title, self.owned_by.get_short_name(), ) def get_related_offers(self): return self.related_offers.select_related("owned_by", "project") def save(self, *args, **kwargs): skip_value_calculation = kwargs.pop("skip_value_calculation", False) if not skip_value_calculation: self.value = sum((v.value for v in self.values.all()), Z2) self._fts = " ".join( str(part) for part in [ self.code, self.customer.name, self.contact.full_name if self.contact else "", ] ) super().save(*args, **kwargs) save.alters_data = True @property def pretty_status(self): d = { "created_at": local_date_format(self.created_at.date()), "closed_on": self.closed_on and local_date_format(self.closed_on), "decision_expected_on": self.decision_expected_on and local_date_format(self.decision_expected_on), "status": self.get_status_display(), } if self.status != self.OPEN: return _("%(status)s on %(closed_on)s") % d if self.decision_expected_on: return _("Decision expected on %(decision_expected_on)s") % d return _("Open since %(created_at)s") % d @property def status_badge(self): if self.status != self.OPEN: css = {self.ACCEPTED: "success", self.DECLINED: "danger"}[self.status] elif self.decision_expected_on: css = "warning" if self.decision_expected_on < dt.date.today() else "info" else: open_since = (dt.date.today() - self.created_at.date()).days if ( open_since > {self.UNKNOWN: 90, self.NORMAL: 45, self.HIGH: 20}[self.probability] ): css = "caveat" else: css = "info" return format_html( '{}', css, self.pretty_status ) @property def pretty_closing_type(self): return self.closing_type or _("<closing type missing>") @property def all_contributions(self): contributors = {} for contribution in self.contributions.all(): contributors[contribution.user] = contribution.weight total = sum(contributors.values(), 0) return sorted( ( {"user": user, "value": self.value * weight / total} for user, weight in contributors.items() ), key=lambda row: row["value"], reverse=True, ) class DealAttribute(models.Model): deal = models.ForeignKey(Deal, on_delete=models.CASCADE, verbose_name=_("deal")) attribute = models.ForeignKey( Attribute, on_delete=models.PROTECT, verbose_name=_("attribute") ) class Meta: verbose_name = _("deal attribute") verbose_name_plural = _("deal attributes") def __str__(self): return f"{self.deal} - {self.attribute}" @total_ordering class ValueType(models.Model): title = models.CharField(_("title"), max_length=200) position = models.PositiveIntegerField(_("position"), default=0) is_archived = models.BooleanField(_("is archived"), default=False) weekly_target = MoneyField(_("weekly target"), blank=True, null=True) class Meta: ordering = ("position", "id") verbose_name = _("value type") verbose_name_plural = _("value types") def __str__(self): return self.title def __lt__(self, other): return ( (self.position, -self.pk) < (other.position, -other.pk) if isinstance(other, self.__class__) else 1 ) class Value(models.Model): deal = models.ForeignKey( Deal, on_delete=models.CASCADE, related_name="values", verbose_name=_("deal") ) type = models.ForeignKey( ValueType, on_delete=models.PROTECT, verbose_name=_("type") ) value = MoneyField(_("value")) class Meta: ordering = ["type"] unique_together = [("deal", "type")] verbose_name = _("value") verbose_name_plural = _("values") def __str__(self): return currency(self.value) class Contribution(models.Model): deal = models.ForeignKey( Deal, on_delete=models.CASCADE, verbose_name=_("deal"), related_name="contributions", ) user = models.ForeignKey(User, on_delete=models.PROTECT, verbose_name=_("user")) weight = models.SmallIntegerField(_("weight"), default=100) class Meta: ordering = ["-weight"] unique_together = [("deal", "user")] verbose_name = _("contribution") verbose_name_plural = _("contributions") def __str__(self): return f"{self.user}: {self.weight}"

30.053412

86

0.61957

import datetime as dt from functools import total_ordering from django.db import models from django.db.models import Q from django.utils import timezone from django.utils.html import format_html from django.utils.translation import gettext_lazy as _ from workbench.accounts.models import User from workbench.contacts.models import Organization, Person from workbench.tools.formats import Z2, currency, local_date_format from workbench.tools.models import Model, MoneyField, SearchQuerySet from workbench.tools.urls import model_urls class NotArchivedQuerySet(models.QuerySet): def active(self, include=None): return self.filter(Q(is_archived=False) | Q(id=include)) class AttributeGroup(models.Model): title = models.CharField(_("title"), max_length=200) position = models.PositiveIntegerField(_("position"), default=0) is_archived = models.BooleanField(_("is archived"), default=False) is_required = models.BooleanField(_("is required"), default=True) class Meta: ordering = ("position", "id") verbose_name = _("attribute group") verbose_name_plural = _("attribute groups") def __str__(self): return self.title class Attribute(models.Model): group = models.ForeignKey( AttributeGroup, on_delete=models.CASCADE, related_name="attributes", verbose_name=_("attribute group"), ) title = models.CharField(_("title"), max_length=200) position = models.PositiveIntegerField(_("position"), default=0) is_archived = models.BooleanField(_("is archived"), default=False) objects = NotArchivedQuerySet.as_manager() class Meta: ordering = ("position", "id") verbose_name = _("attribute") verbose_name_plural = _("attributes") def __str__(self): return self.title class ClosingType(models.Model): title = models.CharField(_("title"), max_length=200) represents_a_win = models.BooleanField(_("represents a win"), default=False) position = models.PositiveIntegerField(_("position"), default=0) class Meta: ordering = ("position", "id") verbose_name = _("closing type") verbose_name_plural = _("closing types") def __str__(self): return self.title class DealQuerySet(SearchQuerySet): def maybe_actionable(self, *, user): return self.filter( Q(status=Deal.OPEN), Q(owned_by=user) | Q(owned_by__is_active=False) ).select_related("owned_by") @model_urls class Deal(Model): OPEN = 10 ACCEPTED = 20 DECLINED = 30 STATUS_CHOICES = ( (OPEN, _("Open")), (ACCEPTED, _("Accepted")), (DECLINED, _("Declined")), ) UNKNOWN = 10 NORMAL = 20 HIGH = 30 PROBABILITY_CHOICES = [ (UNKNOWN, _("unknown")), (NORMAL, _("normal")), (HIGH, _("high")), ] customer = models.ForeignKey( Organization, on_delete=models.PROTECT, verbose_name=_("customer") ) contact = models.ForeignKey( Person, on_delete=models.SET_NULL, blank=True, null=True, verbose_name=_("contact"), ) title = models.CharField(_("title"), max_length=200) description = models.TextField(_("description"), blank=True) owned_by = models.ForeignKey( User, on_delete=models.PROTECT, verbose_name=_("contact person") ) value = MoneyField(_("value")) status = models.PositiveIntegerField( _("status"), choices=STATUS_CHOICES, default=OPEN ) probability = models.IntegerField( _("probability"), choices=PROBABILITY_CHOICES, default=UNKNOWN ) decision_expected_on = models.DateField( _("decision expected on"), blank=True, null=True ) created_at = models.DateTimeField(_("created at"), default=timezone.now) attributes = models.ManyToManyField( Attribute, verbose_name=_("attributes"), through="DealAttribute", ) closed_on = models.DateField(_("closed on"), blank=True, null=True) closing_type = models.ForeignKey( ClosingType, on_delete=models.PROTECT, blank=True, null=True, verbose_name=_("closing type"), ) closing_notice = models.TextField(_("closing notice"), blank=True) _fts = models.TextField(editable=False, blank=True) related_offers = models.ManyToManyField( "offers.Offer", blank=True, related_name="deals", verbose_name=_("related offers"), ) contributors = models.ManyToManyField( User, verbose_name=_("contributors"), related_name="+", through="Contribution", help_text=_( "The value of the deal will be distributed among all" " contributors in the accepted deals report." ), ) objects = DealQuerySet.as_manager() class Meta: ordering = ["-pk"] verbose_name = _("deal") verbose_name_plural = _("deals") def __str__(self): return f"{self.code} {self.title} - {self.owned_by.get_short_name()}" def __html__(self): return format_html( "{} {} - {}", self.code, self.title, self.owned_by.get_short_name(), ) def get_related_offers(self): return self.related_offers.select_related("owned_by", "project") def save(self, *args, **kwargs): skip_value_calculation = kwargs.pop("skip_value_calculation", False) if not skip_value_calculation: self.value = sum((v.value for v in self.values.all()), Z2) self._fts = " ".join( str(part) for part in [ self.code, self.customer.name, self.contact.full_name if self.contact else "", ] ) super().save(*args, **kwargs) save.alters_data = True @property def pretty_status(self): d = { "created_at": local_date_format(self.created_at.date()), "closed_on": self.closed_on and local_date_format(self.closed_on), "decision_expected_on": self.decision_expected_on and local_date_format(self.decision_expected_on), "status": self.get_status_display(), } if self.status != self.OPEN: return _("%(status)s on %(closed_on)s") % d if self.decision_expected_on: return _("Decision expected on %(decision_expected_on)s") % d return _("Open since %(created_at)s") % d @property def status_badge(self): if self.status != self.OPEN: css = {self.ACCEPTED: "success", self.DECLINED: "danger"}[self.status] elif self.decision_expected_on: css = "warning" if self.decision_expected_on < dt.date.today() else "info" else: open_since = (dt.date.today() - self.created_at.date()).days if ( open_since > {self.UNKNOWN: 90, self.NORMAL: 45, self.HIGH: 20}[self.probability] ): css = "caveat" else: css = "info" return format_html( '{}', css, self.pretty_status ) @property def pretty_closing_type(self): return self.closing_type or _("<closing type missing>") @property def all_contributions(self): contributors = {} for contribution in self.contributions.all(): contributors[contribution.user] = contribution.weight total = sum(contributors.values(), 0) return sorted( ( {"user": user, "value": self.value * weight / total} for user, weight in contributors.items() ), key=lambda row: row["value"], reverse=True, ) class DealAttribute(models.Model): deal = models.ForeignKey(Deal, on_delete=models.CASCADE, verbose_name=_("deal")) attribute = models.ForeignKey( Attribute, on_delete=models.PROTECT, verbose_name=_("attribute") ) class Meta: verbose_name = _("deal attribute") verbose_name_plural = _("deal attributes") def __str__(self): return f"{self.deal} - {self.attribute}" @total_ordering class ValueType(models.Model): title = models.CharField(_("title"), max_length=200) position = models.PositiveIntegerField(_("position"), default=0) is_archived = models.BooleanField(_("is archived"), default=False) weekly_target = MoneyField(_("weekly target"), blank=True, null=True) class Meta: ordering = ("position", "id") verbose_name = _("value type") verbose_name_plural = _("value types") def __str__(self): return self.title def __lt__(self, other): return ( (self.position, -self.pk) < (other.position, -other.pk) if isinstance(other, self.__class__) else 1 ) class Value(models.Model): deal = models.ForeignKey( Deal, on_delete=models.CASCADE, related_name="values", verbose_name=_("deal") ) type = models.ForeignKey( ValueType, on_delete=models.PROTECT, verbose_name=_("type") ) value = MoneyField(_("value")) class Meta: ordering = ["type"] unique_together = [("deal", "type")] verbose_name = _("value") verbose_name_plural = _("values") def __str__(self): return currency(self.value) class Contribution(models.Model): deal = models.ForeignKey( Deal, on_delete=models.CASCADE, verbose_name=_("deal"), related_name="contributions", ) user = models.ForeignKey(User, on_delete=models.PROTECT, verbose_name=_("user")) weight = models.SmallIntegerField(_("weight"), default=100) class Meta: ordering = ["-weight"] unique_together = [("deal", "user")] verbose_name = _("contribution") verbose_name_plural = _("contributions") def __str__(self): return f"{self.user}: {self.weight}"

true

1c2eb04a1a9b3067e99eeca4aaa9170032411906

6,912

py

Python

pyastar/astar.py

julesy89/pyastar

8f063d55c9f88e1d1f3c6a08b652038abf33b90c

[ "Apache-2.0" ]

3

2020-02-10T16:33:31.000Z

2022-01-30T06:09:46.000Z

pyastar/astar.py

julesy89/pyastar

8f063d55c9f88e1d1f3c6a08b652038abf33b90c

[ "Apache-2.0" ]

null

pyastar/astar.py

julesy89/pyastar

8f063d55c9f88e1d1f3c6a08b652038abf33b90c

[ "Apache-2.0" ]

null

from pyastar.problem import Problem from pyastar.util.node import NodeFactory from pyastar.util.pq import PriorityQueueSet class AStar: def __init__(self, problem, heuristic_is_inconsistent=False, open_set_max_size=None, open_set_truncate_size=None, verbose=False, ): # either the user provides the problem or directly a node factory (allows more customization) if isinstance(problem, Problem): self.factory = NodeFactory(problem) else: self.factory = problem # whether printout in each iteration is desired self.verbose = verbose # the optimal path to be returned self.opt = None # the current node that is processed self.node = None # initialize the open set as custom priority queue (implemented as a set) self.open_set = PriorityQueueSet() # the maximum size of the open set - if it is not None what is the size it should be truncated to self.open_set_max_size = open_set_max_size self.open_set_truncate_size = open_set_truncate_size # keep track of all nodes that are already closed self.closed_set = {} # assuming the heuristic is inconsistent we change the behavior slightly self.heuristic_is_inconsistent = heuristic_is_inconsistent if self.heuristic_is_inconsistent: self.pop = self.pop_if_inconsistent self.skip = self.skip_if_inconsistent def find(self, **kwargs): self.initialize() while self.has_next(): self.next() return self.result(**kwargs) def initialize(self): self.add(self.factory.create()) return self def next(self): # retrieve the first node and remove it self.pop() # if pop was successful if self.node is not None: # if the verbose flag is set to true, print some information about the current status if self.verbose: self.info() # actually process the node self.process() # does the truncation of the open queue if it is enabled - otherwise nothing happens self.truncate_if_necessary() def process(self): # get access to the current node directly node = self.node # if the verbose flag is set to true, print some information about the current status if self.verbose: self.info() # mark this key to be visited by adding it to the closed set self.closed_set[node.key] = node # if the goal has been found - we know it is optimal if the heuristic is admissible if node.is_goal(): self.goal_found() else: for neighbor in node.get_neighbors(): neighbor = self.factory.create(neighbor) neighbor.set_previous(node) # if the node is not supposed to be skipped if not self.skip(neighbor): self.add(neighbor) def add(self, node): # if we have been there - remove it from the priority queue because we have found a better one self.open_set.remove(node.key) # create a new entry containing the necessary information node.calc_g_h_f() # finally add to the open set self.open_set.push(node.key, node.f, node) return True def has_next(self): # if there are no nodes to process the algorithm always terminates if self.open_set.empty(): return False else: return self.opt is None def goal_found(self): self.opt = {"path": reconstruct_path(self.node), "node": self.node, "costs": self.node.g} def pop(self): self.node = self.open_set.pop() def truncate_if_necessary(self): if self.open_set_max_size is not None and self.open_set.size() > self.open_set_max_size: if self.open_set_truncate_size is None: raise Exception("Please set open_set_truncate_size if you have enabled a maximum size!") else: return self.truncate() def truncate(self): if self.open_set_max_size is not None and self.open_set.size() > self.open_set_max_size: _node = self.node pq = PriorityQueueSet() while self.open_set.size() > 0 and pq.size() < self.open_set_truncate_size: self.pop() n = self.node pq.push(n.key, n.f, n) self.open_set = pq self.node = _node def skip(self, node): if node.key in self.closed_set: return True else: if not self.open_set.contains(node.key): return False else: node.calc_g() node_in_open_set = self.open_set.get(node.key) return node_in_open_set.g <= node.g def skip_if_inconsistent(self, node): if node.key in self.closed_set: node.calc_g() node_in_closed_set = self.closed_set[node.key] return node_in_closed_set.g <= node.g else: if not self.open_set.contains(node.key): return False else: node.calc_g() node_in_open_set = self.open_set.get(node.key) return node_in_open_set.g <= node.g def pop_if_inconsistent(self): self.node = None while not self.open_set.empty(): node = self.open_set.pop() if not self.skip_if_inconsistent(node): self.node = node break def result(self, **kwargs): return extract(self.opt, **kwargs) def info(self): from copy import deepcopy _node, _open = self.node, deepcopy(self.open_set) print("CURRENT") print(_node.key, "->", _node.__dict__) print() print("OPEN SET") for k in range(min(10, _open.size())): entry = _open.pop() print(entry.key, "->", entry.__dict__) print() print("-----------------------------------------------------------------") def extract(opt, return_path=True, return_costs=True, return_node=False): ret = [] if return_path: ret.append(opt["path"]) if return_costs: ret.append(opt["costs"]) if return_node: ret.append(opt["node"]) return tuple(ret) def reconstruct_path(node): path = [] while node.prev is not None: path.append(node.key) node = node.prev path.append(node.key) return tuple(path[::-1]) def evaluate_path(problem, path): costs = 0 for k in range(len(path) - 1): costs += problem.get_costs(path[k], path[k + 1]) return costs

31.135135

105

0.584346

from pyastar.problem import Problem from pyastar.util.node import NodeFactory from pyastar.util.pq import PriorityQueueSet class AStar: def __init__(self, problem, heuristic_is_inconsistent=False, open_set_max_size=None, open_set_truncate_size=None, verbose=False, ): if isinstance(problem, Problem): self.factory = NodeFactory(problem) else: self.factory = problem self.verbose = verbose self.opt = None self.node = None self.open_set = PriorityQueueSet() self.open_set_max_size = open_set_max_size self.open_set_truncate_size = open_set_truncate_size self.closed_set = {} self.heuristic_is_inconsistent = heuristic_is_inconsistent if self.heuristic_is_inconsistent: self.pop = self.pop_if_inconsistent self.skip = self.skip_if_inconsistent def find(self, **kwargs): self.initialize() while self.has_next(): self.next() return self.result(**kwargs) def initialize(self): self.add(self.factory.create()) return self def next(self): self.pop() if self.node is not None: if self.verbose: self.info() self.process() self.truncate_if_necessary() def process(self): node = self.node if self.verbose: self.info() self.closed_set[node.key] = node if node.is_goal(): self.goal_found() else: for neighbor in node.get_neighbors(): neighbor = self.factory.create(neighbor) neighbor.set_previous(node) if not self.skip(neighbor): self.add(neighbor) def add(self, node): self.open_set.remove(node.key) node.calc_g_h_f() self.open_set.push(node.key, node.f, node) return True def has_next(self): if self.open_set.empty(): return False else: return self.opt is None def goal_found(self): self.opt = {"path": reconstruct_path(self.node), "node": self.node, "costs": self.node.g} def pop(self): self.node = self.open_set.pop() def truncate_if_necessary(self): if self.open_set_max_size is not None and self.open_set.size() > self.open_set_max_size: if self.open_set_truncate_size is None: raise Exception("Please set open_set_truncate_size if you have enabled a maximum size!") else: return self.truncate() def truncate(self): if self.open_set_max_size is not None and self.open_set.size() > self.open_set_max_size: _node = self.node pq = PriorityQueueSet() while self.open_set.size() > 0 and pq.size() < self.open_set_truncate_size: self.pop() n = self.node pq.push(n.key, n.f, n) self.open_set = pq self.node = _node def skip(self, node): if node.key in self.closed_set: return True else: if not self.open_set.contains(node.key): return False else: node.calc_g() node_in_open_set = self.open_set.get(node.key) return node_in_open_set.g <= node.g def skip_if_inconsistent(self, node): if node.key in self.closed_set: node.calc_g() node_in_closed_set = self.closed_set[node.key] return node_in_closed_set.g <= node.g else: if not self.open_set.contains(node.key): return False else: node.calc_g() node_in_open_set = self.open_set.get(node.key) return node_in_open_set.g <= node.g def pop_if_inconsistent(self): self.node = None while not self.open_set.empty(): node = self.open_set.pop() if not self.skip_if_inconsistent(node): self.node = node break def result(self, **kwargs): return extract(self.opt, **kwargs) def info(self): from copy import deepcopy _node, _open = self.node, deepcopy(self.open_set) print("CURRENT") print(_node.key, "->", _node.__dict__) print() print("OPEN SET") for k in range(min(10, _open.size())): entry = _open.pop() print(entry.key, "->", entry.__dict__) print() print("-----------------------------------------------------------------") def extract(opt, return_path=True, return_costs=True, return_node=False): ret = [] if return_path: ret.append(opt["path"]) if return_costs: ret.append(opt["costs"]) if return_node: ret.append(opt["node"]) return tuple(ret) def reconstruct_path(node): path = [] while node.prev is not None: path.append(node.key) node = node.prev path.append(node.key) return tuple(path[::-1]) def evaluate_path(problem, path): costs = 0 for k in range(len(path) - 1): costs += problem.get_costs(path[k], path[k + 1]) return costs

true

1c2eb1241d77c82f23ac9ba30d8c63a85cb425c1

4,431

py

Python

source/runbooks/ssm-py-scripts/start_secondary_ssm_automation_aurora_cluster.py

elduds/aws-instance-scheduler

4b4c8a628fd0ab34e7d5c17af215f0bf10e48bd8

[ "Apache-2.0" ]

338

2018-02-09T17:24:26.000Z

2021-10-06T01:33:52.000Z

source/runbooks/ssm-py-scripts/start_secondary_ssm_automation_aurora_cluster.py

elduds/aws-instance-scheduler

4b4c8a628fd0ab34e7d5c17af215f0bf10e48bd8

[ "Apache-2.0" ]

267

2018-02-09T10:45:25.000Z

2021-10-15T14:47:34.000Z

source/runbooks/ssm-py-scripts/start_secondary_ssm_automation_aurora_cluster.py

elduds/aws-instance-scheduler

4b4c8a628fd0ab34e7d5c17af215f0bf10e48bd8

[ "Apache-2.0" ]

193

2018-02-14T08:25:30.000Z

2021-10-06T14:59:14.000Z

###################################################################################################################### # Copyright 2021 Amazon.com, Inc. or its affiliates. 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. A copy of the License is located at # # # # http://www.apache.org/licenses/ # # # # or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES # # OR CONDITIONS OF ANY KIND, express or implied. See the License for the specific language governing permissions # # and limitations under the License. # ###################################################################################################################### import boto3 import logging from botocore.config import Config def start_rds_secondary_ssm_automation(event, context) -> list: """ start new SSM automation document to start or stop RDS clusters. Parameters: event (dict): event from SSM automation example: { "RdsResourceArns": [ "arn:aws:rds:us-east-1:account:cluster:cluster-3d3r", "arn:aws:rds:us-east-1:account:cluster:cluster-1", "arn:aws:rds:us-east-1:account:cluster:cluster-2", "arn:aws:rds:us-east-1:account:cluster:cluster-a1df" ], "Action": "start"|"stop" "Region": "us-east-1", "SecondaryAutomationName": "secondary-automation-name", "SolutionId": "SO0030", "SolutionVersion": "v2.0.0", "ExecutionRoleArn": "arn:aws:iam::account:role/role-name" } context (dict): context object Returns: list : list of secondary execution ids """ logging.debug(context) execution_ids = [] # handle empty arn list if not event.get('RdsResourceArns'): return execution_ids ssm_client = get_client(event) resource_list = get_resource_list(event.get('RdsResourceArns')) for rds_cluster_name in resource_list: execution_id = ssm_client.start_automation_execution( # Launch SSM Doc via Automation DocumentName=event.get('SecondaryAutomationName'), Parameters={ "ClusterName": [ rds_cluster_name ], "Action": [ event.get('Action') ], "AutomationAssumeRole": [ event.get('ExecutionRoleArn') ] } )['AutomationExecutionId'] execution_ids.append(execution_id) return execution_ids def get_client(event): """Create Boto3 client for SSM service""" boto_config = Config( user_agent_extra=f'AwsSolution/{event.get("SolutionId")}/' f'{event.get("SolutionVersion")}', retries={ 'mode': 'standard' } ) return boto3.client('ssm', region_name=event.get('Region'), config=boto_config) def get_resource_list(arn_list: list) -> list: """ Parse the arn list and return rds db or cluster ids""" cluster_separator = ":cluster:" cluster_prefix = "cluster-" logging.info("Found RDS Cluster list.") cluster_ids = split_arn_list(arn_list, cluster_separator) # filtering cluster ids for duplicate cluster ids return [x for x in cluster_ids if not x.startswith(cluster_prefix)] def split_arn_list(arn_list, separator): resource_ids = [] for arn in arn_list: resource_id = arn.split(separator)[1] resource_ids.append(resource_id) return resource_ids

41.027778

118

0.488152

true

1c2eb12c6402438db405004fbe1816c60fc14302

969

py

Python

MSKHackMAG/api.py

kelbin/MCHProject2021Mosru

3acf7bef228da7a42a1f2ae42562d4ac98a53acb

[ "MIT" ]

null

MSKHackMAG/api.py

kelbin/MCHProject2021Mosru

3acf7bef228da7a42a1f2ae42562d4ac98a53acb

[ "MIT" ]

null

MSKHackMAG/api.py

kelbin/MCHProject2021Mosru

3acf7bef228da7a42a1f2ae42562d4ac98a53acb

[ "MIT" ]

1

2021-06-13T23:33:51.000Z

from afisha import get_afisha from analytic import write_like, calculate_recomendation from result import Result import json def get_feed(): r = get_afisha() return r def send_like(data): userId = data["userId"] afishaId = data["afishaId"] result = write_like(userId, afishaId, 1) return result def send_likes(data): userId = data["userId"] stats = data["stats"] for stat in stats: afishaId = stat["id"] like = stat["value"] write_like(userId, afishaId, like) return Result.void_success() def get_recomendation_feed(userId): afishas = json.loads(get_feed().resut) values = [] for afisha in afishas: value = calculate_recomendation(userId, afisha["id"]) values.append((value, afisha)) values.sort(key=lambda x: x[0], reverse=True) resulted_feed = list(map(lambda x: x[1], values)) print(resulted_feed) return Result(result=json.dumps(resulted_feed))

22.534884

61

0.668731

from afisha import get_afisha from analytic import write_like, calculate_recomendation from result import Result import json def get_feed(): r = get_afisha() return r def send_like(data): userId = data["userId"] afishaId = data["afishaId"] result = write_like(userId, afishaId, 1) return result def send_likes(data): userId = data["userId"] stats = data["stats"] for stat in stats: afishaId = stat["id"] like = stat["value"] write_like(userId, afishaId, like) return Result.void_success() def get_recomendation_feed(userId): afishas = json.loads(get_feed().resut) values = [] for afisha in afishas: value = calculate_recomendation(userId, afisha["id"]) values.append((value, afisha)) values.sort(key=lambda x: x[0], reverse=True) resulted_feed = list(map(lambda x: x[1], values)) print(resulted_feed) return Result(result=json.dumps(resulted_feed))

true

1c2eb33b28bbe6a77f7479bf5efcdd305388856e

539

py

Python

flame/core/model/model_wrapper.py

v-tronglh/flamev2

7f376e8fbcfb592e0ad6f72fdaad0af5f2cf9231

[ "MIT" ]

3

2021-08-05T15:42:05.000Z

2022-03-17T08:56:58.000Z

flame/core/model/model_wrapper.py

v-tronglh/flamev2

7f376e8fbcfb592e0ad6f72fdaad0af5f2cf9231

[ "MIT" ]

null

flame/core/model/model_wrapper.py

v-tronglh/flamev2

7f376e8fbcfb592e0ad6f72fdaad0af5f2cf9231

[ "MIT" ]

null

from typing import Any, Callable from torch.nn import Module class ModelWrapper: def __init__( self, model: Module, input_transform: Callable = lambda x: x, ): super(ModelWrapper, self).__init__() self.model = model self.input_transform = input_transform def __call__(self, *args) -> Any: args = self.input_transform(args) output = self.model(*args) return output def __getattr__(self, name: str) -> Any: return getattr(self.model, name)

23.434783

48

0.621521

from typing import Any, Callable from torch.nn import Module class ModelWrapper: def __init__( self, model: Module, input_transform: Callable = lambda x: x, ): super(ModelWrapper, self).__init__() self.model = model self.input_transform = input_transform def __call__(self, *args) -> Any: args = self.input_transform(args) output = self.model(*args) return output def __getattr__(self, name: str) -> Any: return getattr(self.model, name)

true

1c2eb363382ba5f5dcee9f8740c9c3dcf0ae5183

362

py

Python

idact/detail/log/set_paramiko_log_level.py

intdata-bsc/idact

54cb65a711c145351e205970c27c83e6393cccf5

[ "MIT" ]

5

2018-12-06T15:40:34.000Z

2019-06-19T11:22:58.000Z

idact/detail/log/set_paramiko_log_level.py

garstka/idact

b9c8405c94db362c4a51d6bfdf418b14f06f0da1

[ "MIT" ]

9

2018-12-06T16:35:26.000Z

2019-04-28T19:01:40.000Z

idact/detail/log/set_paramiko_log_level.py

intdata-bsc/idact

54cb65a711c145351e205970c27c83e6393cccf5

[ "MIT" ]

2

2019-04-28T19:18:58.000Z

2019-06-17T06:56:28.000Z

"""This module contains a function for setting the log level for paramiko.""" import logging from idact.detail.log.get_logger import get_debug_logger PARAMIKO_LOG_LEVEL = logging.WARNING def set_paramiko_log_level(): """Sets the log level for paramiko.""" transport = get_debug_logger('paramiko.transport') transport.setLevel(PARAMIKO_LOG_LEVEL)

27.846154

77

0.779006

import logging from idact.detail.log.get_logger import get_debug_logger PARAMIKO_LOG_LEVEL = logging.WARNING def set_paramiko_log_level(): transport = get_debug_logger('paramiko.transport') transport.setLevel(PARAMIKO_LOG_LEVEL)

true

1c2eb66c68f8848733b17a3cf4239ec78a433d0d

7,298

py

Python

configs/trainval/daotad_eccv2022/2.a.iii.1.py

klauscc/vedatad

c59f5ddc8fb227ef08baccbb513948bb1bb23857

[ "Apache-2.0" ]

null

configs/trainval/daotad_eccv2022/2.a.iii.1.py

klauscc/vedatad

c59f5ddc8fb227ef08baccbb513948bb1bb23857

[ "Apache-2.0" ]

null

configs/trainval/daotad_eccv2022/2.a.iii.1.py

klauscc/vedatad

c59f5ddc8fb227ef08baccbb513948bb1bb23857

[ "Apache-2.0" ]

null

# 1. data dataset_type = "Thumos14Dataset" data_root = "data/thumos14/" img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True ) num_frames = 480 img_shape = (224, 224) overlap_ratio = 0.25 img_dir = "frames_15fps_256x256" data = dict( samples_per_gpu=4, workers_per_gpu=4, train=dict( typename=dataset_type, ann_file=data_root + "annotations/val.json", video_prefix=data_root + f"{img_dir}/val", pipeline=[ dict(typename="LoadMetaInfo"), dict(typename="LoadAnnotations"), dict(typename="Time2Frame"), dict(typename="TemporalRandomCrop", num_frames=num_frames, iof_th=0.75), dict(typename="LoadFrames", to_float32=True), dict(typename="SpatialRandomCrop", crop_size=img_shape), dict( typename="PhotoMetricDistortion", brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18, p=0.5, ), dict(typename="Rotate", limit=(-45, 45), border_mode="reflect101", p=0.5), dict(typename="SpatialRandomFlip", flip_ratio=0.5), dict(typename="Normalize", **img_norm_cfg), dict(typename="Pad", size=(num_frames, *img_shape)), dict(typename="DefaultFormatBundle"), dict( typename="Collect", keys=["imgs", "gt_segments", "gt_labels", "gt_segments_ignore"], ), ], ), val=dict( typename=dataset_type, ann_file=data_root + "annotations/test.json", video_prefix=data_root + f"{img_dir}/test", pipeline=[ dict(typename="LoadMetaInfo"), dict(typename="Time2Frame"), dict( typename="OverlapCropAug", num_frames=num_frames, overlap_ratio=overlap_ratio, transforms=[ dict(typename="TemporalCrop"), dict(typename="LoadFrames", to_float32=True), dict(typename="SpatialCenterCrop", crop_size=img_shape), dict(typename="Normalize", **img_norm_cfg), dict(typename="Pad", size=(num_frames, *img_shape)), dict(typename="DefaultFormatBundle"), dict(typename="Collect", keys=["imgs"]), ], ), ], ), ) # 2. model num_classes = 20 strides = [8, 16, 32, 64, 128] use_sigmoid = True scales_per_octave = 5 octave_base_scale = 2 num_anchors = scales_per_octave model = dict( typename="SingleStageDetector", backbone=dict( typename="GradDropChunkVideoSwinV2", keep_ratio=0.4, chunk_size=32, bp_idx_mode="uniform_jitter", forward_mode="split", shift_inp=False, frozen_stages=2, use_checkpoint=True, patch_size=(2, 4, 4), in_chans=3, embed_dim=128, drop_path_rate=0.2, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=(8, 7, 7), patch_norm=True, ), neck=[ dict( typename="SRMSwin", srm_cfg=dict( in_channels=1024, out_channels=512, ), ), dict( typename="TDM", in_channels=512, stage_layers=(1, 1, 1, 1), out_channels=512, conv_cfg=dict(typename="Conv1d"), norm_cfg=dict(typename="SyncBN"), act_cfg=dict(typename="ReLU"), out_indices=(0, 1, 2, 3, 4), ), dict( typename="FPN", in_channels=[512, 512, 512, 512, 512], out_channels=256, num_outs=5, start_level=0, conv_cfg=dict(typename="Conv1d"), norm_cfg=dict(typename="SyncBN"), ), ], head=dict( typename="RetinaHead", num_classes=num_classes, num_anchors=num_anchors, in_channels=256, stacked_convs=4, feat_channels=256, use_sigmoid=use_sigmoid, conv_cfg=dict(typename="Conv1d"), norm_cfg=dict(typename="SyncBN"), ), ) # 3. engines meshgrid = dict( typename="SegmentAnchorMeshGrid", strides=strides, base_anchor=dict( typename="SegmentBaseAnchor", base_sizes=strides, octave_base_scale=octave_base_scale, scales_per_octave=scales_per_octave, ), ) segment_coder = dict( typename="DeltaSegmentCoder", target_means=[0.0, 0.0], target_stds=[1.0, 1.0] ) train_engine = dict( typename="TrainEngine", model=model, criterion=dict( typename="SegmentAnchorCriterion", num_classes=num_classes, meshgrid=meshgrid, segment_coder=segment_coder, reg_decoded_segment=True, loss_cls=dict( typename="FocalLoss", use_sigmoid=use_sigmoid, gamma=2.0, alpha=0.25, loss_weight=1.0, ), loss_segment=dict(typename="DIoULoss", loss_weight=1.0), train_cfg=dict( assigner=dict( typename="MaxIoUAssigner", pos_iou_thr=0.6, neg_iou_thr=0.4, min_pos_iou=0, ignore_iof_thr=-1, ignore_wrt_candidates=True, iou_calculator=dict(typename="SegmentOverlaps"), ), allowed_border=-1, pos_weight=-1, debug=False, ), ), optimizer=dict( typename="SGD", lr=0.01, momentum=0.9, weight_decay=0.0001, paramwise_cfg=dict(custom_keys=dict(backbone={"lr_mult": 0.4})), ), ) # 3.2 val engine val_engine = dict( typename="ValEngine", model=model, meshgrid=meshgrid, converter=dict( typename="SegmentAnchorConverter", num_classes=num_classes, segment_coder=segment_coder, nms_pre=1000, use_sigmoid=use_sigmoid, ), num_classes=num_classes, test_cfg=dict( score_thr=0.005, nms=dict(typename="nmw", iou_thr=0.5), max_per_video=1200 ), use_sigmoid=use_sigmoid, ) # 4. hooks hooks = [ dict(typename="OptimizerHook"), dict( typename="CosineRestartLrSchedulerHook", periods=[100] * 12, restart_weights=[1] * 12, warmup="linear", warmup_iters=500, warmup_ratio=1e-1, min_lr_ratio=1e-2, ), dict(typename="EvalHook", eval_cfg=dict(mode="anet")), dict(typename="SnapshotHook", interval=100), dict(typename="LoggerHook", interval=10), ] # 5. work modes modes = ["train"] max_epochs = 1200 # 6. checkpoint # weights = dict(filepath='open-mmlab://i3d_r50_256p_32x2x1_100e_kinetics400_rgb') weights = dict( filepath="data/pretrained_models/vswin/swin_base_patch244_window877_kinetics400_22k_keysfrom_backbone.pth" ) # optimizer = dict(filepath='epoch_900_optim.pth') # meta = dict(filepath='epoch_900_meta.pth') # 7. misc seed = 10 dist_params = dict(backend="nccl") log_level = "INFO" find_unused_parameters = False deterministic = True # gpu_mem_fraction = 0.65

28.960317

110

0.571252

dataset_type = "Thumos14Dataset" data_root = "data/thumos14/" img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True ) num_frames = 480 img_shape = (224, 224) overlap_ratio = 0.25 img_dir = "frames_15fps_256x256" data = dict( samples_per_gpu=4, workers_per_gpu=4, train=dict( typename=dataset_type, ann_file=data_root + "annotations/val.json", video_prefix=data_root + f"{img_dir}/val", pipeline=[ dict(typename="LoadMetaInfo"), dict(typename="LoadAnnotations"), dict(typename="Time2Frame"), dict(typename="TemporalRandomCrop", num_frames=num_frames, iof_th=0.75), dict(typename="LoadFrames", to_float32=True), dict(typename="SpatialRandomCrop", crop_size=img_shape), dict( typename="PhotoMetricDistortion", brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18, p=0.5, ), dict(typename="Rotate", limit=(-45, 45), border_mode="reflect101", p=0.5), dict(typename="SpatialRandomFlip", flip_ratio=0.5), dict(typename="Normalize", **img_norm_cfg), dict(typename="Pad", size=(num_frames, *img_shape)), dict(typename="DefaultFormatBundle"), dict( typename="Collect", keys=["imgs", "gt_segments", "gt_labels", "gt_segments_ignore"], ), ], ), val=dict( typename=dataset_type, ann_file=data_root + "annotations/test.json", video_prefix=data_root + f"{img_dir}/test", pipeline=[ dict(typename="LoadMetaInfo"), dict(typename="Time2Frame"), dict( typename="OverlapCropAug", num_frames=num_frames, overlap_ratio=overlap_ratio, transforms=[ dict(typename="TemporalCrop"), dict(typename="LoadFrames", to_float32=True), dict(typename="SpatialCenterCrop", crop_size=img_shape), dict(typename="Normalize", **img_norm_cfg), dict(typename="Pad", size=(num_frames, *img_shape)), dict(typename="DefaultFormatBundle"), dict(typename="Collect", keys=["imgs"]), ], ), ], ), ) num_classes = 20 strides = [8, 16, 32, 64, 128] use_sigmoid = True scales_per_octave = 5 octave_base_scale = 2 num_anchors = scales_per_octave model = dict( typename="SingleStageDetector", backbone=dict( typename="GradDropChunkVideoSwinV2", keep_ratio=0.4, chunk_size=32, bp_idx_mode="uniform_jitter", forward_mode="split", shift_inp=False, frozen_stages=2, use_checkpoint=True, patch_size=(2, 4, 4), in_chans=3, embed_dim=128, drop_path_rate=0.2, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=(8, 7, 7), patch_norm=True, ), neck=[ dict( typename="SRMSwin", srm_cfg=dict( in_channels=1024, out_channels=512, ), ), dict( typename="TDM", in_channels=512, stage_layers=(1, 1, 1, 1), out_channels=512, conv_cfg=dict(typename="Conv1d"), norm_cfg=dict(typename="SyncBN"), act_cfg=dict(typename="ReLU"), out_indices=(0, 1, 2, 3, 4), ), dict( typename="FPN", in_channels=[512, 512, 512, 512, 512], out_channels=256, num_outs=5, start_level=0, conv_cfg=dict(typename="Conv1d"), norm_cfg=dict(typename="SyncBN"), ), ], head=dict( typename="RetinaHead", num_classes=num_classes, num_anchors=num_anchors, in_channels=256, stacked_convs=4, feat_channels=256, use_sigmoid=use_sigmoid, conv_cfg=dict(typename="Conv1d"), norm_cfg=dict(typename="SyncBN"), ), ) meshgrid = dict( typename="SegmentAnchorMeshGrid", strides=strides, base_anchor=dict( typename="SegmentBaseAnchor", base_sizes=strides, octave_base_scale=octave_base_scale, scales_per_octave=scales_per_octave, ), ) segment_coder = dict( typename="DeltaSegmentCoder", target_means=[0.0, 0.0], target_stds=[1.0, 1.0] ) train_engine = dict( typename="TrainEngine", model=model, criterion=dict( typename="SegmentAnchorCriterion", num_classes=num_classes, meshgrid=meshgrid, segment_coder=segment_coder, reg_decoded_segment=True, loss_cls=dict( typename="FocalLoss", use_sigmoid=use_sigmoid, gamma=2.0, alpha=0.25, loss_weight=1.0, ), loss_segment=dict(typename="DIoULoss", loss_weight=1.0), train_cfg=dict( assigner=dict( typename="MaxIoUAssigner", pos_iou_thr=0.6, neg_iou_thr=0.4, min_pos_iou=0, ignore_iof_thr=-1, ignore_wrt_candidates=True, iou_calculator=dict(typename="SegmentOverlaps"), ), allowed_border=-1, pos_weight=-1, debug=False, ), ), optimizer=dict( typename="SGD", lr=0.01, momentum=0.9, weight_decay=0.0001, paramwise_cfg=dict(custom_keys=dict(backbone={"lr_mult": 0.4})), ), ) val_engine = dict( typename="ValEngine", model=model, meshgrid=meshgrid, converter=dict( typename="SegmentAnchorConverter", num_classes=num_classes, segment_coder=segment_coder, nms_pre=1000, use_sigmoid=use_sigmoid, ), num_classes=num_classes, test_cfg=dict( score_thr=0.005, nms=dict(typename="nmw", iou_thr=0.5), max_per_video=1200 ), use_sigmoid=use_sigmoid, ) hooks = [ dict(typename="OptimizerHook"), dict( typename="CosineRestartLrSchedulerHook", periods=[100] * 12, restart_weights=[1] * 12, warmup="linear", warmup_iters=500, warmup_ratio=1e-1, min_lr_ratio=1e-2, ), dict(typename="EvalHook", eval_cfg=dict(mode="anet")), dict(typename="SnapshotHook", interval=100), dict(typename="LoggerHook", interval=10), ] modes = ["train"] max_epochs = 1200 weights = dict( filepath="data/pretrained_models/vswin/swin_base_patch244_window877_kinetics400_22k_keysfrom_backbone.pth" ) seed = 10 dist_params = dict(backend="nccl") log_level = "INFO" find_unused_parameters = False deterministic = True

true

1c2eb69e1e3be5add0d649df44930718da7344ef

797

py

Python

miniurl_generic/url/migrations/0001_initial.py

yannisVentura/Django_MiniUrl_Bootstrap

89a7cdb8c12ba75cf0cd2cb6206961506aad9287

[ "BSD-3-Clause" ]

null

miniurl_generic/url/migrations/0001_initial.py

yannisVentura/Django_MiniUrl_Bootstrap

89a7cdb8c12ba75cf0cd2cb6206961506aad9287

[ "BSD-3-Clause" ]

8

2017-04-05T13:15:39.000Z

2017-10-17T09:39:04.000Z

miniurl_generic/url/migrations/0001_initial.py

yannisVentura/Django_MiniUrl_Bootstrap

89a7cdb8c12ba75cf0cd2cb6206961506aad9287

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.10.1 on 2016-10-04 08:06 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='MiniUrl', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('url_complete', models.CharField(max_length=255)), ('date_creation', models.DateTimeField(auto_now_add=True, verbose_name='Date de parution')), ('pseudo', models.IntegerField(max_length=100)), ('nb_acces', models.CharField(max_length=10)), ], ), ]

29.518519

114

0.603513

from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='MiniUrl', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('url_complete', models.CharField(max_length=255)), ('date_creation', models.DateTimeField(auto_now_add=True, verbose_name='Date de parution')), ('pseudo', models.IntegerField(max_length=100)), ('nb_acces', models.CharField(max_length=10)), ], ), ]

true

1c2eb6b0bb8c9e52dc65101ee61fd393746c6d9b

1,205

py

Python

airflow/ti_deps/deps/dag_unpaused_dep.py

wileeam/airflow

f46be8152a4d89c57db4ca46f5b3339e4876b723

[ "Apache-2.0" ]

8

2017-04-20T16:15:44.000Z

2020-10-11T13:44:10.000Z

airflow/ti_deps/deps/dag_unpaused_dep.py

devlocalca/airflow

58c3542ed25061320ce61dbe0adf451a44c738dd

[ "Apache-2.0" ]

219

2017-03-15T18:40:16.000Z

2022-02-28T22:52:43.000Z

airflow/ti_deps/deps/dag_unpaused_dep.py

devlocalca/airflow

58c3542ed25061320ce61dbe0adf451a44c738dd

[ "Apache-2.0" ]

3

2016-07-14T21:51:10.000Z

2020-10-12T13:26:36.000Z

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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 airflow.ti_deps.deps.base_ti_dep import BaseTIDep from airflow.utils.session import provide_session class DagUnpausedDep(BaseTIDep): NAME = "Dag Not Paused" IGNOREABLE = True @provide_session def _get_dep_statuses(self, ti, session, dep_context): if ti.task.dag.is_paused: yield self._failing_status( reason="Task's DAG '{0}' is paused.".format(ti.dag_id))

37.65625

71

0.744398

from airflow.ti_deps.deps.base_ti_dep import BaseTIDep from airflow.utils.session import provide_session class DagUnpausedDep(BaseTIDep): NAME = "Dag Not Paused" IGNOREABLE = True @provide_session def _get_dep_statuses(self, ti, session, dep_context): if ti.task.dag.is_paused: yield self._failing_status( reason="Task's DAG '{0}' is paused.".format(ti.dag_id))

true

1c2eb6fa73a233d255344a1bb49e88539d1b2bb5

616

py

Python

leetcode/easy/intersection_of_two_arrays_ii/py/solution.py

lilsweetcaligula/Online-Judges

48454a8e6b5b86f80e89eca1b396480df8960cfd

[ "MIT" ]

null

leetcode/easy/intersection_of_two_arrays_ii/py/solution.py

lilsweetcaligula/Online-Judges

48454a8e6b5b86f80e89eca1b396480df8960cfd

[ "MIT" ]

null

leetcode/easy/intersection_of_two_arrays_ii/py/solution.py

lilsweetcaligula/Online-Judges

48454a8e6b5b86f80e89eca1b396480df8960cfd

[ "MIT" ]

null

class Solution(object): def intersect(self, nums1, nums2): """ :type nums1: List[int] :type nums2: List[int] :rtype: List[int] """ result = [] nums1 = sorted(nums1) nums2 = sorted(nums2) i = 0 j = 0 while i < len(nums1) and j < len(nums2): if nums1[i] == nums2[j]: result.append(nums1[i]) i += 1 j += 1 elif nums1[i] > nums2[j]: j += 1 else: i += 1 return result

23.692308

48

0.371753

class Solution(object): def intersect(self, nums1, nums2): result = [] nums1 = sorted(nums1) nums2 = sorted(nums2) i = 0 j = 0 while i < len(nums1) and j < len(nums2): if nums1[i] == nums2[j]: result.append(nums1[i]) i += 1 j += 1 elif nums1[i] > nums2[j]: j += 1 else: i += 1 return result

true

1c2eb71ead2363009a56bf89e169532913118a38

160

py

Python

cards/apps.py

neosergio/WisdomBox

f41bd828f5e264c7ad05262b29c8f02cf904b54a

[ "MIT" ]

null

cards/apps.py

neosergio/WisdomBox

f41bd828f5e264c7ad05262b29c8f02cf904b54a

[ "MIT" ]

2

2017-02-09T14:52:43.000Z

2017-02-10T19:31:38.000Z

cards/apps.py

neosergio/WisdomBox

f41bd828f5e264c7ad05262b29c8f02cf904b54a

[ "MIT" ]

null

from __future__ import unicode_literals from django.apps import AppConfig class CardsConfig(AppConfig): name = 'cards' verbose_name = 'Wisdom Cards'

17.777778

39

0.7625

from __future__ import unicode_literals from django.apps import AppConfig class CardsConfig(AppConfig): name = 'cards' verbose_name = 'Wisdom Cards'

true

1c2eb726a627a42117538c0779f66d6db0987c03

218

py

Python

stupidArtnet/__init__.py

verycollective/stupidArtnet

80e7722598d06e1c533ffd28a92e26c784e23389

[ "MIT" ]

13

2018-11-07T20:19:57.000Z

2020-07-31T23:15:00.000Z

stupidArtnet/__init__.py

digitalanimalscollective/stupidArtnet

80e7722598d06e1c533ffd28a92e26c784e23389

[ "MIT" ]

2

2019-04-10T17:22:23.000Z

2020-02-11T14:55:44.000Z

stupidArtnet/__init__.py

digitalanimalscollective/stupidArtnet

80e7722598d06e1c533ffd28a92e26c784e23389

[ "MIT" ]

9

2019-03-01T12:00:27.000Z

2020-07-30T19:34:15.000Z

"""Facilitates library imports.""" from stupidArtnet.StupidArtnetServer import StupidArtnetServer from stupidArtnet.ArtnetUtils import shift_this, put_in_range, make_address_mask from .StupidArtnet import StupidArtnet

43.6

80

0.866972

from stupidArtnet.StupidArtnetServer import StupidArtnetServer from stupidArtnet.ArtnetUtils import shift_this, put_in_range, make_address_mask from .StupidArtnet import StupidArtnet

true

1c2eb746dbf03c9401fcbe7364388d36d331e74a

72

py

Python

straph/paths/__init__.py

GiulioRossetti/Straph

edc021d25243bcca619c62dca1f28cf05b73a92c

[ "Apache-2.0" ]

3

2021-05-24T16:23:51.000Z

2021-08-07T20:14:53.000Z

straph/paths/__init__.py

GiulioRossetti/Straph

edc021d25243bcca619c62dca1f28cf05b73a92c

[ "Apache-2.0" ]

1

2021-05-25T12:30:36.000Z

straph/paths/__init__.py

GiulioRossetti/Straph

edc021d25243bcca619c62dca1f28cf05b73a92c

[ "Apache-2.0" ]

3

2021-05-25T09:04:43.000Z

2021-11-02T16:27:23.000Z

from straph.paths.path_object import * from straph.paths.paths import *

24

38

0.805556

from straph.paths.path_object import * from straph.paths.paths import *

true

1c2eb7cfed9158485e706c5f38abfc820fbb10d4

3,169

py

Python

src/ef/field/__init__.py

fizmat/ef_python

1fccdc90b1dd628c9adb5f8713bee4e4df633daf

[ "MIT" ]

1

2018-12-13T08:32:46.000Z

src/ef/field/__init__.py

dumbman/ef_python

d26e48d3afd81c3b24a5ec207fce674cdc9609d3

[ "MIT" ]

3

2018-05-21T11:26:57.000Z

2020-03-12T12:28:44.000Z

src/ef/field/__init__.py

dumbman/ef_python

d26e48d3afd81c3b24a5ec207fce674cdc9609d3

[ "MIT" ]

null

import inject import numpy as np from ef.util.serializable_h5 import SerializableH5 class Field(SerializableH5): def __init__(self, name, electric_or_magnetic): self.name = name self.electric_or_magnetic = electric_or_magnetic def __add__(self, b): return FieldSum.factory((self, b)) def get_at_points(self, positions, time): raise NotImplementedError() @staticmethod def import_h5(g): from ef.field.expression import FieldExpression from ef.field.from_csv import FieldFromCSVFile from ef.field.uniform import FieldUniform ga = g.attrs ft = ga['field_type'] name = g.name.split('/')[-1] if ft == b'electric_uniform': return FieldUniform(name, 'electric', np.array([ga['electric_uniform_field_{}'.format(c)] for c in 'xyz']).reshape(3)) elif ft == b'electric_tinyexpr': return FieldExpression(name, 'electric', *[ga['electric_tinyexpr_field_{}'.format(c)].decode('utf8') for c in 'xyz']) elif ft == b'electric_on_regular_grid': return FieldFromCSVFile(name, 'electric', ga['electric_h5filename'].decode('utf8')) elif ft == b'magnetic_uniform': return FieldUniform(name, 'magnetic', np.array([ga['magnetic_uniform_field_{}'.format(c)] for c in 'xyz']).reshape(3)) elif ft == b'magnetic_tinyexpr': return FieldExpression(name, 'magnetic', *[ga['magnetic_tinyexpr_field_{}'.format(c)].decode('utf8') for c in 'xyz']) elif ft == b'magnetic_on_regular_grid': return FieldFromCSVFile(name, 'magnetic', ga['magnetic_h5filename'].decode('utf8')) class FieldZero(Field): xp = inject.attr(np) def get_at_points(self, positions, time): positions = self.xp.array(positions) return self.xp.zeros_like(positions) class FieldSum(Field): def __init__(self, electric_or_magnetic, fields): super().__init__('FieldSum', electric_or_magnetic) self.fields = fields @classmethod def factory(cls, fields, electric_or_magnetic=None): try: fields = [f for f in fields if f is not None] except TypeError: fields = [] if fields is None else [fields] em = (set(f.electric_or_magnetic for f in fields) | {electric_or_magnetic}) - {None} if len(em) > 1: raise ValueError('Trying to combine inconsistent fields') elif em: em = em.pop() else: raise ValueError('FieldSum type unknown') sums = [f for f in fields if type(f) is FieldSum] fields = [f for f in fields if type(f) not in (FieldZero, FieldSum)] for f in sums: fields += f.fields if len(fields) > 1: return cls(em, fields) elif len(fields) == 1: return fields[0] else: return FieldZero('ZeroSum', em) def get_at_points(self, positions, time): return sum(f.get_at_points(positions, time) for f in self.fields)

38.180723

112

0.603976

import inject import numpy as np from ef.util.serializable_h5 import SerializableH5 class Field(SerializableH5): def __init__(self, name, electric_or_magnetic): self.name = name self.electric_or_magnetic = electric_or_magnetic def __add__(self, b): return FieldSum.factory((self, b)) def get_at_points(self, positions, time): raise NotImplementedError() @staticmethod def import_h5(g): from ef.field.expression import FieldExpression from ef.field.from_csv import FieldFromCSVFile from ef.field.uniform import FieldUniform ga = g.attrs ft = ga['field_type'] name = g.name.split('/')[-1] if ft == b'electric_uniform': return FieldUniform(name, 'electric', np.array([ga['electric_uniform_field_{}'.format(c)] for c in 'xyz']).reshape(3)) elif ft == b'electric_tinyexpr': return FieldExpression(name, 'electric', *[ga['electric_tinyexpr_field_{}'.format(c)].decode('utf8') for c in 'xyz']) elif ft == b'electric_on_regular_grid': return FieldFromCSVFile(name, 'electric', ga['electric_h5filename'].decode('utf8')) elif ft == b'magnetic_uniform': return FieldUniform(name, 'magnetic', np.array([ga['magnetic_uniform_field_{}'.format(c)] for c in 'xyz']).reshape(3)) elif ft == b'magnetic_tinyexpr': return FieldExpression(name, 'magnetic', *[ga['magnetic_tinyexpr_field_{}'.format(c)].decode('utf8') for c in 'xyz']) elif ft == b'magnetic_on_regular_grid': return FieldFromCSVFile(name, 'magnetic', ga['magnetic_h5filename'].decode('utf8')) class FieldZero(Field): xp = inject.attr(np) def get_at_points(self, positions, time): positions = self.xp.array(positions) return self.xp.zeros_like(positions) class FieldSum(Field): def __init__(self, electric_or_magnetic, fields): super().__init__('FieldSum', electric_or_magnetic) self.fields = fields @classmethod def factory(cls, fields, electric_or_magnetic=None): try: fields = [f for f in fields if f is not None] except TypeError: fields = [] if fields is None else [fields] em = (set(f.electric_or_magnetic for f in fields) | {electric_or_magnetic}) - {None} if len(em) > 1: raise ValueError('Trying to combine inconsistent fields') elif em: em = em.pop() else: raise ValueError('FieldSum type unknown') sums = [f for f in fields if type(f) is FieldSum] fields = [f for f in fields if type(f) not in (FieldZero, FieldSum)] for f in sums: fields += f.fields if len(fields) > 1: return cls(em, fields) elif len(fields) == 1: return fields[0] else: return FieldZero('ZeroSum', em) def get_at_points(self, positions, time): return sum(f.get_at_points(positions, time) for f in self.fields)

true

1c2eb7eb9834caf7eb2dfffa95fda486d9a9f4e3

35,676

py

Python

pyrex/generation.py

abigailbishop/pyrex

10ba2e9f4c8820f4fcf5f00bd866927dacb0b2b5

[ "MIT" ]

6

2018-06-19T16:01:35.000Z

2020-05-21T20:02:53.000Z

pyrex/generation.py

bhokansonfasig/pyrex

8b2abc954f2cf4945424042f33847b783c72dcfa

[ "MIT" ]

4

2018-01-19T14:52:33.000Z

2021-09-02T17:20:07.000Z

pyrex/generation.py

abigailbishop/pyrex

10ba2e9f4c8820f4fcf5f00bd866927dacb0b2b5

[ "MIT" ]

5

2018-01-19T14:49:16.000Z

2021-01-21T12:34:59.000Z

""" Module for particle (neutrino) generators. Generators are responsible for the input of events into the simulation. """ from collections.abc import Iterable from enum import Enum import logging import numpy as np from pyrex.internal_functions import get_from_enum from pyrex.earth_model import earth from pyrex.particle import Event, Particle, NeutrinoInteraction from pyrex.io import File logger = logging.getLogger(__name__) class Generator: """ Base class for neutrino generators. Provides methods for generating neutrino attributes except for neutrino vertex, which should be provided by child classes to generate neutrinos in specific volumes. Parameters ---------- energy : float or function Energy (GeV) of the neutrinos. If ``float``, all neutrinos have the same constant energy. If ``function``, neutrinos are generated with the energy returned by successive function calls. shadow : bool, optional Whether Earth shadowing effects should be used to reject events. If ``True`` then neutrinos which don't survive transit through the Earth will be skipped when creating events. If ``False`` then all events are allowed and assigned a weight to scale their probability of occurrence. flavor_ratio : array_like, optional Flavor ratio of neutrinos to be generated. Of the form [electron, muon, tau] neutrino fractions. source : optional Source type of neutrinos to be generated. Used in the determination of per-flavor neutrino/antineutrino fractions. interaction_model : optional Class to use to describe interactions of the generated particles. Should inherit from (or behave like) the base ``Interaction`` class. Attributes ---------- count : int Number of neutrinos produced by the generator, including those not returned due to Earth shadowing or other effects. get_energy : function Function returning energy (GeV) of the neutrinos by successive function calls. shadow : bool Whether Earth shadowing effects will be used to reject events. ratio : ndarray (Normalized) flavor ratio of neutrinos to be generated. Of the form [electron, muon, tau] neutrino fractions. source : Generator.SourceType Source type of neutrinos to be generated. Used in the determination of per-flavor neutrino/antineutrino fractions. interaction_model : Interaction Class to use to describe interactions of the generated particles. volume solid_angle See Also -------- pyrex.particle.Interaction : Base class for describing neutrino interaction attributes. """ class SourceType(Enum): """ Enum containing possible sources for neutrinos. Attributes ---------- pgamma, cosmogenic pp, astrophysical unknown, undefined """ undefined = 0 unknown = 0 cosmogenic = 1 pgamma = 1 astrophysical = 2 pp = 2 def __init__(self, energy, shadow=False, flavor_ratio=(1,1,1), source="cosmogenic", interaction_model=NeutrinoInteraction, earth_model=earth): if not callable(energy): try: e = float(energy) except TypeError: raise ValueError("energy argument must be a function "+ "or a number") else: energy = lambda: e self.get_energy = energy self.shadow = shadow self.ratio = np.array(flavor_ratio)/np.sum(flavor_ratio) self.source = source self.interaction_model = interaction_model self.earth_model = earth_model self.count = 0 @property def source(self): """ Value of the source type. Should always be a value from the ``Interaction.Type`` enum. Setting with integer or string values may work if carefully chosen. """ return self._source @source.setter def source(self, src_type): if src_type is None: self._source = self.SourceType.undefined else: self._source = get_from_enum(src_type, self.SourceType) @property def volume(self): """ Generation volume (m^3) in which event vertices are produced. """ raise NotImplementedError("volume property must be implemented by "+ "inheriting class") @property def solid_angle(self): """ Generation solid angle (sr) in which event directions are produced. """ logger.debug("Using default solid_angle from "+ "pyrex.generation.Generator") return 4 * np.pi def get_vertex(self): """ Get the vertex of the next particle to be generated. For the `Generator` class, this method is not implemented. Subclasses should override this method with their own procedure for generating neutrino vertices in some volume. Raises ------ NotImplementedError Always, unless a subclass overrides the function. """ logger.debug("Using default get_vertex from "+ "pyrex.generation.Generator") raise NotImplementedError("get_vertex method must be implemented by " +"inheriting class") def get_direction(self): """ Get the direction of the next particle to be generated. Randomly generates a cartesian unit vector uniformly distributed over the unit sphere. Returns ------- ndarray (Unit) vector direction. Notes ----- Generates random vector direction by pulling from uniform distributions for -1<cos(theta)<1 and 0<phi<2*pi. """ cos_theta = np.random.random_sample()*2-1 sin_theta = np.sqrt(1 - cos_theta**2) phi = np.random.random_sample() * 2*np.pi return np.array([sin_theta * np.cos(phi), sin_theta * np.sin(phi), cos_theta]) def get_particle_type(self): """ Get the particle type of the next particle to be generated. Randomly generates a neutrino flavor according to the flavor ratio of the generator, and chooses neutrino or antineutrino based on ratios derived from the source type. Returns ------- Particle.Type Enum value for the type of the particle. See Also -------- pyrex.Particle : Class for storing particle attributes. Notes ----- The neutrino/antineutrino choice is based on Section 3 of [1]_. References ---------- .. [1] A. Bhattacharya et al, "The Glashow resonance at IceCube." JCAP **1110**, 017 (2011). :arxiv:`1108.3163` :doi:`10.1088/1475-7516/2011/10/017` """ rand_flavor = np.random.rand() rand_nunubar = np.random.rand() if self.source==self.SourceType.cosmogenic: nunubar_ratios = [0.78, 0.61, 0.61] elif self.source==self.SourceType.astrophysical: nunubar_ratios = [0.5, 0.5, 0.5] else: raise ValueError("Source type not supported") # Electron neutrinos if rand_flavor<self.ratio[0]: if rand_nunubar<nunubar_ratios[0]: return Particle.Type.electron_neutrino else: return Particle.Type.electron_antineutrino # Muon neutrinos elif rand_flavor<self.ratio[0]+self.ratio[1]: if rand_nunubar<nunubar_ratios[1]: return Particle.Type.muon_neutrino else: return Particle.Type.muon_antineutrino # Tau neutrinos else: if rand_nunubar<nunubar_ratios[2]: return Particle.Type.tau_neutrino else: return Particle.Type.tau_antineutrino def get_exit_points(self, particle): """ Get the intersections of the particle path with the ice volume edges. For the `Generator` class, this method is not implemented. Subclasses should override this method with their own procedure for calculating exit points given the generation volume. Parameters ---------- particle : Particle Particle traveling through the ice. Raises ------ NotImplementedError Always, unless a subclass overrides the function. See Also -------- pyrex.Particle : Class for storing particle attributes. """ logger.debug("Using default get_exit_points from "+ "pyrex.generation.Generator") raise NotImplementedError("get_exit_points method must be implemented " +"by inheriting class") def get_weights(self, particle): """ Get the weighting factors to be applied to the particle. Calculates both the survival and interaction weights of `particle`. The survival weight is based on the probability of interaction along the path through the Earth. The interaction weight of `particle` based on the probability of interaction at its given vertex in the ice volume. Parameters ---------- particle : Particle Particle to be weighted. Returns ------- survival_weight : float Survival weight of the given `particle`. interaction_weight : float Interaction weight of the given `particle`. See Also -------- pyrex.Particle : Class for storing particle attributes. """ t = self.earth_model.slant_depth(particle.vertex, -particle.direction) x = t / particle.interaction.total_interaction_length survival_weight = np.exp(-x) entry_point, exit_point = self.get_exit_points(particle) in_ice_vector = np.array(exit_point) - np.array(entry_point) in_ice_length = np.sqrt(np.sum(in_ice_vector**2)) vertex_vector = particle.vertex - np.array(entry_point) travel_length = np.sqrt(np.sum(vertex_vector**2)) # Convert cm water equivalent interaction length to meters in ice interaction_length = (particle.interaction.total_interaction_length / 0.92 / 100) interaction_weight = (in_ice_length/interaction_length * np.exp(-travel_length/interaction_length)) return survival_weight, interaction_weight def create_event(self): """ Generate a neutrino event in the ice volume. Creates a neutrino with a random vertex in the volume, a random direction, and an energy based on ``get_energy``. Particle type is randomly chosen, and its interaction type is also randomly chosen based on the branching ratio. Weights the particles according to their survival probability through the Earth and their probability of interacting in the ice at their vertex. If Earth shadowing has been turned on then particles which don't survive transit through the Earth are skipped, and surviving particles are given a survival weight of 1. Currently each `Event` returned consists of only a single `Particle`. Returns ------- Event Random neutrino event not shadowed by the Earth. See Also -------- pyrex.Event : Class for storing a tree of `Particle` objects representing an event. pyrex.Particle : Class for storing particle attributes. """ self.count += 1 vtx = self.get_vertex() u = self.get_direction() E = self.get_energy() particle_id = self.get_particle_type() particle = Particle(particle_id=particle_id, vertex=vtx, direction=u, energy=E, interaction_model=self.interaction_model) weights = self.get_weights(particle) if not self.shadow: particle.survival_weight = weights[0] particle.interaction_weight = weights[1] logger.debug("Successfully created %s with survival weight %d and " +"interaction weight %d", particle, weights[0], weights[1]) return Event(particle) elif np.random.rand() < weights[0]: particle.survival_weight = 1 particle.interaction_weight = weights[1] logger.debug("Successfully created %s with survival weight %d and " +"interaction weight %d", particle, weights[0], weights[1]) return Event(particle) else: # Particle was shadowed by the earth. Try again logger.debug("Particle creation shadowed by the Earth") return self.create_event() class CylindricalGenerator(Generator): """ Class to generate neutrino vertices in a cylindrical ice volume. Generates neutrinos in a cylinder with given radius and height. Parameters ---------- dr : float Radius of the ice volume. Neutrinos generated within (0, `dr`). dz : float Height of the ice volume in the z-direction. Neutrinos generated within (-`dz`, 0). energy : float or function Energy (GeV) of the neutrinos. If ``float``, all neutrinos have the same constant energy. If ``function``, neutrinos are generated with the energy returned by successive function calls. shadow : bool, optional Whether Earth shadowing effects should be used to reject events. If ``True`` then neutrinos which don't survive transit through the Earth will be skipped when creating events. If ``False`` then all events are allowed and assigned a weight to scale their probability of occurrence. flavor_ratio : array_like, optional Flavor ratio of neutrinos to be generated. Of the form [electron, muon, tau] neutrino fractions. source : optional Source type of neutrinos to be generated. Used in the determination of per-flavor neutrino/antineutrino fractions. interaction_model : optional Class to use to describe interactions of the generated particles. Should inherit from (or behave like) the base ``Interaction`` class. Attributes ---------- count : int Number of neutrinos produced by the generator, including those not returned due to Earth shadowing or other effects. dr : float Radius of the ice volume. Neutrinos generated within (0, `dr`). dz : float Height of the ice volume in the z-direction. Neutrinos generated within (-`dz`, 0). get_energy : function Function returning energy (GeV) of the neutrinos by successive function calls. shadow : bool Whether Earth shadowing effects will be used to reject events. ratio : ndarray (Normalized) flavor ratio of neutrinos to be generated. Of the form [electron, muon, tau] neutrino fractions. source : Generator.SourceType Source type of neutrinos to be generated. Used in the determination of per-flavor neutrino/antineutrino fractions. interaction_model : Interaction Class to use to describe interactions of the generated particles. volume solid_angle See Also -------- pyrex.particle.Interaction : Base class for describing neutrino interaction attributes. """ def __init__(self, dr, dz, energy, shadow=False, flavor_ratio=(1,1,1), source="cosmogenic", interaction_model=NeutrinoInteraction, earth_model=earth): self.dr = dr self.dz = dz super().__init__(energy=energy, shadow=shadow, flavor_ratio=flavor_ratio, source=source, interaction_model=interaction_model, earth_model=earth_model) @property def volume(self): """ Generation volume (m^3) in which event vertices are produced. """ return np.pi * self.dr**2 * self.dz def get_vertex(self): """ Get the vertex of the next particle to be generated. Randomly generates a vertex uniformly distributed within the specified ice volume. Returns ------- ndarray Vector vertex in the ice volume. """ r = self.dr * np.sqrt(np.random.random_sample()) theta = 2*np.pi * np.random.random_sample() z = -self.dz * np.random.random_sample() return np.array([r*np.cos(theta), r*np.sin(theta), z]) def get_exit_points(self, particle): """ Get the intersections of the particle path with the ice volume edges. For the given `particle`, calculates where its travel path intersects with the edges of the ice volume. Parameters ---------- particle : Particle Particle traveling through the ice. Returns ------- enter_point, exit_point : ndarray Vector points where the particle's path intersects with the edges of the ice volume. See Also -------- pyrex.Particle : Class for storing particle attributes. """ enter_point = None exit_point = None # Find the intersection points of the circle, assuming infinite z if particle.direction[0]==0: x0 = particle.vertex[0] y0 = -np.sqrt(self.dr**2 - x0**2) z0 = (particle.vertex[2] + (y0-particle.vertex[1]) * particle.direction[2]/particle.direction[1]) x1 = particle.vertex[0] y1 = np.sqrt(self.dr**2 - x1**2) z1 = (particle.vertex[2] + (y1-particle.vertex[1]) * particle.direction[2]/particle.direction[1]) else: slope = particle.direction[1]/particle.direction[0] a = 1 + slope**2 b = particle.vertex[1] - slope*particle.vertex[0] x0 = - (slope*b + np.sqrt(-b**2 + a*self.dr**2)) / a y0 = (particle.vertex[1] - slope * (particle.vertex[0] + np.sqrt(-b**2 + a*self.dr**2))) / a z0 = (particle.vertex[2] + (x0-particle.vertex[0]) * particle.direction[2]/particle.direction[0]) x1 = (-slope*b + np.sqrt(-b**2 + a*self.dr**2)) / a y1 = (particle.vertex[1] + slope * (-particle.vertex[0] + np.sqrt(-b**2 + a*self.dr**2))) / a z1 = (particle.vertex[2] + (x1-particle.vertex[0]) * particle.direction[2]/particle.direction[0]) for pt in ([x0, y0, z0], [x1, y1, z1]): # Check for intersections at the top & bottom that supersede the # intersections at the sides z = None if pt[2]>0: z = 0 elif pt[2]<-self.dz: z = -self.dz if z is not None: pt[0] = (particle.vertex[0] + (z-particle.vertex[2]) * particle.direction[0]/particle.direction[2]) pt[1] = (particle.vertex[1] + (z-particle.vertex[2]) * particle.direction[1]/particle.direction[2]) pt[2] = z pt = np.array(pt) # Sort into enter and exit points based on particle direction nonzero = particle.direction!=0 direction = ((pt[nonzero]-particle.vertex[nonzero]) /particle.direction[nonzero]) if np.all(direction<0): enter_point = pt elif np.all(direction>0): exit_point = pt elif np.all(direction==0): if enter_point is None: enter_point = pt if exit_point is None: exit_point = pt if enter_point is not None and exit_point is not None: return enter_point, exit_point else: raise ValueError("Could not determine exit points") class RectangularGenerator(Generator): """ Class to generate neutrino vertices in a rectangular ice volume. Generates neutrinos in a box with given width, length, and height. Parameters ---------- dx : float Width of the ice volume in the x-direction. Neutrinos generated within (-`dx` / 2, `dx` / 2). dy : float Length of the ice volume in the y-direction. Neutrinos generated within (-`dy` / 2, `dy` / 2). dz : float Height of the ice volume in the z-direction. Neutrinos generated within (-`dz`, 0). energy : float or function Energy (GeV) of the neutrinos. If ``float``, all neutrinos have the same constant energy. If ``function``, neutrinos are generated with the energy returned by successive function calls. shadow : bool, optional Whether Earth shadowing effects should be used to reject events. If ``True`` then neutrinos which don't survive transit through the Earth will be skipped when creating events. If ``False`` then all events are allowed and assigned a weight to scale their probability of occurrence. flavor_ratio : array_like, optional Flavor ratio of neutrinos to be generated. Of the form [electron, muon, tau] neutrino fractions. source : optional Source type of neutrinos to be generated. Used in the determination of per-flavor neutrino/antineutrino fractions. interaction_model : optional Class to use to describe interactions of the generated particles. Should inherit from (or behave like) the base ``Interaction`` class. Attributes ---------- count : int Number of neutrinos produced by the generator, including those not returned due to Earth shadowing or other effects. dx : float Width of the ice volume in the x-direction. Neutrinos generated within (-`dx` / 2, `dx` / 2). dy : float Length of the ice volume in the y-direction. Neutrinos generated within (-`dy` / 2, `dy` / 2). dz : float Height of the ice volume in the z-direction. Neutrinos generated within (-`dz`, 0). get_energy : function Function returning energy (GeV) of the neutrinos by successive function calls. shadow : bool Whether Earth shadowing effects will be used to reject events. ratio : ndarray (Normalized) flavor ratio of neutrinos to be generated. Of the form [electron, muon, tau] neutrino fractions. source : Generator.SourceType Source type of neutrinos to be generated. Used in the determination of per-flavor neutrino/antineutrino fractions. interaction_model : Interaction Class to use to describe interactions of the generated particles. volume solid_angle See Also -------- pyrex.particle.Interaction : Base class for describing neutrino interaction attributes. """ def __init__(self, dx, dy, dz, energy, shadow=False, flavor_ratio=(1,1,1), source="cosmogenic", interaction_model=NeutrinoInteraction, earth_model=earth): self.dx = dx self.dy = dy self.dz = dz super().__init__(energy=energy, shadow=shadow, flavor_ratio=flavor_ratio, source=source, interaction_model=interaction_model, earth_model=earth_model) @property def volume(self): """ Generation volume (m^3) in which event vertices are produced. """ return self.dx * self.dy * self.dz def get_vertex(self): """ Get the vertex of the next particle to be generated. Randomly generates a vertex uniformly distributed within the specified ice volume. Returns ------- ndarray Vector vertex in the ice volume. """ return np.random.uniform(low=(-self.dx/2, -self.dy/2, -self.dz), high=(self.dx/2, self.dy/2, 0)) def get_exit_points(self, particle): """ Get the intersections of the particle path with the ice volume edges. For the given `particle`, calculates where its travel path intersects with the edges of the ice volume. Parameters ---------- particle : Particle Particle traveling through the ice. Returns ------- enter_point, exit_point : ndarray Vector points where the particle's path intersects with the edges of the ice volume. See Also -------- pyrex.Particle : Class for storing particle attributes. """ enter_point = None exit_point = None sides = ((-self.dx/2, self.dx/2), (-self.dy/2, self.dy/2), (-self.dz, 0)) for count in range(6): coord = int(count/2) min_max = count%2 if particle.direction[coord]==0: continue scale = ((sides[coord][min_max] - particle.vertex[coord]) / particle.direction[coord]) intersection = particle.vertex + particle.direction * scale valid = True for i, pair in enumerate(sides): if i==coord: continue if intersection[i]<pair[0] or intersection[i]>pair[1]: valid = False if valid: sign = 1 if min_max==1 else -1 if sign*particle.direction[coord]<0: enter_point = intersection else: exit_point = intersection if enter_point is not None and exit_point is not None: return enter_point, exit_point raise ValueError("Could not determine exit points") class ListGenerator: """ Class to generate neutrino events from a list. Generates events by simply pulling them from a list of `Event` objects. By default returns to the start of the list once the end is reached, but can optionally fail after reaching the list's end. Parameters ---------- events : Event, or list of Event List of `Event` objects to draw from. If only a single `Event` object is given, creates a list of that event alone. loop : boolean, optional Whether or not to return to the start of the list after throwing the last `Event`. If ``False``, raises an error if trying to throw after the last `Event`. Attributes ---------- count : int Number of neutrinos produced by the generator, including those not returned due to Earth shadowing or other effects. events : list of Event List to draw `Event` objects from, sequentially. loop : boolean Whether or not to loop through the list more than once. See Also -------- pyrex.Event : Class for storing a tree of `Particle` objects representing an event. pyrex.Particle : Class for storing particle attributes. """ def __init__(self, events, loop=True): if (isinstance(events, Iterable) and not isinstance(events, Event)): self.events = events else: self.events = [events] for i, event in enumerate(self.events): if isinstance(event, Particle): self.events[i] = Event(event) self.loop = loop self._index = 0 self._additional_counts = 0 @property def count(self): """ Number of neutrinos produced by the generator. Count includes events which were not returned due to Earth shadowing or other effects. """ return self._index + self._additional_counts @count.setter def count(self, custom_count): self._additional_counts = custom_count - self._index def create_event(self): """ Generate a neutrino event. Pulls the next `Event` object from the class's list of events. Returns ------- Event Next `Event` object in the list of events. See Also -------- pyrex.Event : Class for storing a tree of `Particle` objects representing an event. pyrex.Particle : Class for storing particle attributes. Raises ------ StopIteration If ``loop`` is ``False`` and the end of the list has been exceeded. """ if not self.loop and self._index>=len(self.events): raise StopIteration("No more events to be generated") self._index += 1 return self.events[(self._index-1)%len(self.events)] class FileGenerator: """ Class to generate neutrino events from simulation file(s). Generates neutrinos by pulling their attributes from a (list of) simulation output file(s). Designed to make reproducing simulations easier. Parameters ---------- files : str or list of str List of file names containing neutrino event information. If only a single file name is provided, creates a list with that file alone. slice_range : int, optional Number of events to load into memory at a time from the files. Increasing this value should result in an improvement in speed, while decreasing this value should result in an improvement in memory consumption. interaction_model : optional Class used to describe the interactions of the stored particles. Attributes ---------- count : int Number of neutrinos produced by the generator, including those not returned due to Earth shadowing or other effects. files : list of str List of file names containing neutrino information. Warnings -------- This generator only supports `Event` objects containing a single level of `Particle` objects. Any dependencies among `Particle` objects will be ignored and they will all appear in the root level. See Also -------- pyrex.particle.Interaction : Base class for describing neutrino interaction attributes. pyrex.Event : Class for storing a tree of `Particle` objects representing an event. pyrex.Particle : Class for storing particle attributes. """ def __init__(self, files, slice_range=100, interaction_model=NeutrinoInteraction): if isinstance(files, str): self.files = [files] else: self.files = files self.slice_range = slice_range self.interaction_model = interaction_model self._file_index = -1 self._file_counts = [0] * (len(self.files)+1) self._load_events() @property def count(self): """ Number of neutrinos produced by the generator. Count includes events which were not returned due to Earth shadowing or other effects. """ return sum(self._file_counts) @count.setter def count(self, custom_count): self._file_counts[0] = custom_count - sum(self._file_counts[1:]) def _load_events(self): """ Pulls the next chunk of events into memory. Reads events up to the ``slice_range`` into memory from the current file. If the current file is exhausted, loads the next file. Returns ------- list List of `Event` objects read from the current file. Raises ------ StopIteration If the end of the last file in the file list has been reached. """ if self._file_index<0 or self._event_index>=len(self._file): self._next_file() start = self._event_index stop = self._event_index + self.slice_range self._event_index += self.slice_range if stop>len(self._file): stop = len(self._file) self._events = [] self._event_counts = [] for file_event in self._file[start:stop]: info = file_event.get_particle_info() particles = [] for p in info: part = Particle( particle_id=p['particle_id'], vertex=(p['vertex_x'], p['vertex_y'], p['vertex_z']), direction=(p['direction_x'], p['direction_y'], p['direction_z']), energy=p['energy'], interaction_model=self.interaction_model, interaction_type=p['interaction_kind'] ) part.interaction.inelasticity = p['interaction_inelasticity'] part.interaction.em_frac = p['interaction_em_frac'] part.interaction.had_frac = p['interaction_had_frac'] part.survival_weight = p['survival_weight'] part.interaction_weight = p['interaction_weight'] particles.append(part) self._events.append(Event(particles)) self._event_counts.append(file_event.total_events_thrown) def _next_file(self): """ Pulls the next file into memory. Reads in the next file from the ``files`` list and stores its `Event` objects in memory. Raises ------ StopIteration If the end of the last file in the file list has been reached. """ self._file_index += 1 self._event_index = 0 if self._file_index>0: self._file.close() if self._file_index>=len(self.files): raise StopIteration("No more events to be generated") # Try to open the next file with the appropriate slice range, # otherwise just settle for opening it at all try: self._file = File(self.files[self._file_index], 'r', slice_range=self.slice_range) except TypeError: self._file = File(self.files[self._file_index], 'r') self._file.open() def create_event(self): """ Generate a neutrino. Pulls the next `Event` object from the file(s). Returns ------- Event Next neutrino `Event` object from the file(s). Raises ------ StopIteration If the end of the last file in the file list has been reached. See Also -------- pyrex.Event : Class for storing a tree of `Particle` objects representing an event. pyrex.Particle : Class for storing particle attributes. """ if len(self._events)==0: self._load_events() self._file_counts[self._file_index+1] = self._event_counts.pop(0) return self._events.pop(0)

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from collections.abc import Iterable from enum import Enum import logging import numpy as np from pyrex.internal_functions import get_from_enum from pyrex.earth_model import earth from pyrex.particle import Event, Particle, NeutrinoInteraction from pyrex.io import File logger = logging.getLogger(__name__) class Generator: class SourceType(Enum): undefined = 0 unknown = 0 cosmogenic = 1 pgamma = 1 astrophysical = 2 pp = 2 def __init__(self, energy, shadow=False, flavor_ratio=(1,1,1), source="cosmogenic", interaction_model=NeutrinoInteraction, earth_model=earth): if not callable(energy): try: e = float(energy) except TypeError: raise ValueError("energy argument must be a function "+ "or a number") else: energy = lambda: e self.get_energy = energy self.shadow = shadow self.ratio = np.array(flavor_ratio)/np.sum(flavor_ratio) self.source = source self.interaction_model = interaction_model self.earth_model = earth_model self.count = 0 @property def source(self): return self._source @source.setter def source(self, src_type): if src_type is None: self._source = self.SourceType.undefined else: self._source = get_from_enum(src_type, self.SourceType) @property def volume(self): raise NotImplementedError("volume property must be implemented by "+ "inheriting class") @property def solid_angle(self): logger.debug("Using default solid_angle from "+ "pyrex.generation.Generator") return 4 * np.pi def get_vertex(self): logger.debug("Using default get_vertex from "+ "pyrex.generation.Generator") raise NotImplementedError("get_vertex method must be implemented by " +"inheriting class") def get_direction(self): cos_theta = np.random.random_sample()*2-1 sin_theta = np.sqrt(1 - cos_theta**2) phi = np.random.random_sample() * 2*np.pi return np.array([sin_theta * np.cos(phi), sin_theta * np.sin(phi), cos_theta]) def get_particle_type(self): rand_flavor = np.random.rand() rand_nunubar = np.random.rand() if self.source==self.SourceType.cosmogenic: nunubar_ratios = [0.78, 0.61, 0.61] elif self.source==self.SourceType.astrophysical: nunubar_ratios = [0.5, 0.5, 0.5] else: raise ValueError("Source type not supported") if rand_flavor<self.ratio[0]: if rand_nunubar<nunubar_ratios[0]: return Particle.Type.electron_neutrino else: return Particle.Type.electron_antineutrino elif rand_flavor<self.ratio[0]+self.ratio[1]: if rand_nunubar<nunubar_ratios[1]: return Particle.Type.muon_neutrino else: return Particle.Type.muon_antineutrino else: if rand_nunubar<nunubar_ratios[2]: return Particle.Type.tau_neutrino else: return Particle.Type.tau_antineutrino def get_exit_points(self, particle): logger.debug("Using default get_exit_points from "+ "pyrex.generation.Generator") raise NotImplementedError("get_exit_points method must be implemented " +"by inheriting class") def get_weights(self, particle): t = self.earth_model.slant_depth(particle.vertex, -particle.direction) x = t / particle.interaction.total_interaction_length survival_weight = np.exp(-x) entry_point, exit_point = self.get_exit_points(particle) in_ice_vector = np.array(exit_point) - np.array(entry_point) in_ice_length = np.sqrt(np.sum(in_ice_vector**2)) vertex_vector = particle.vertex - np.array(entry_point) travel_length = np.sqrt(np.sum(vertex_vector**2)) interaction_length = (particle.interaction.total_interaction_length / 0.92 / 100) interaction_weight = (in_ice_length/interaction_length * np.exp(-travel_length/interaction_length)) return survival_weight, interaction_weight def create_event(self): self.count += 1 vtx = self.get_vertex() u = self.get_direction() E = self.get_energy() particle_id = self.get_particle_type() particle = Particle(particle_id=particle_id, vertex=vtx, direction=u, energy=E, interaction_model=self.interaction_model) weights = self.get_weights(particle) if not self.shadow: particle.survival_weight = weights[0] particle.interaction_weight = weights[1] logger.debug("Successfully created %s with survival weight %d and " +"interaction weight %d", particle, weights[0], weights[1]) return Event(particle) elif np.random.rand() < weights[0]: particle.survival_weight = 1 particle.interaction_weight = weights[1] logger.debug("Successfully created %s with survival weight %d and " +"interaction weight %d", particle, weights[0], weights[1]) return Event(particle) else: logger.debug("Particle creation shadowed by the Earth") return self.create_event() class CylindricalGenerator(Generator): def __init__(self, dr, dz, energy, shadow=False, flavor_ratio=(1,1,1), source="cosmogenic", interaction_model=NeutrinoInteraction, earth_model=earth): self.dr = dr self.dz = dz super().__init__(energy=energy, shadow=shadow, flavor_ratio=flavor_ratio, source=source, interaction_model=interaction_model, earth_model=earth_model) @property def volume(self): return np.pi * self.dr**2 * self.dz def get_vertex(self): r = self.dr * np.sqrt(np.random.random_sample()) theta = 2*np.pi * np.random.random_sample() z = -self.dz * np.random.random_sample() return np.array([r*np.cos(theta), r*np.sin(theta), z]) def get_exit_points(self, particle): enter_point = None exit_point = None if particle.direction[0]==0: x0 = particle.vertex[0] y0 = -np.sqrt(self.dr**2 - x0**2) z0 = (particle.vertex[2] + (y0-particle.vertex[1]) * particle.direction[2]/particle.direction[1]) x1 = particle.vertex[0] y1 = np.sqrt(self.dr**2 - x1**2) z1 = (particle.vertex[2] + (y1-particle.vertex[1]) * particle.direction[2]/particle.direction[1]) else: slope = particle.direction[1]/particle.direction[0] a = 1 + slope**2 b = particle.vertex[1] - slope*particle.vertex[0] x0 = - (slope*b + np.sqrt(-b**2 + a*self.dr**2)) / a y0 = (particle.vertex[1] - slope * (particle.vertex[0] + np.sqrt(-b**2 + a*self.dr**2))) / a z0 = (particle.vertex[2] + (x0-particle.vertex[0]) * particle.direction[2]/particle.direction[0]) x1 = (-slope*b + np.sqrt(-b**2 + a*self.dr**2)) / a y1 = (particle.vertex[1] + slope * (-particle.vertex[0] + np.sqrt(-b**2 + a*self.dr**2))) / a z1 = (particle.vertex[2] + (x1-particle.vertex[0]) * particle.direction[2]/particle.direction[0]) for pt in ([x0, y0, z0], [x1, y1, z1]): z = None if pt[2]>0: z = 0 elif pt[2]<-self.dz: z = -self.dz if z is not None: pt[0] = (particle.vertex[0] + (z-particle.vertex[2]) * particle.direction[0]/particle.direction[2]) pt[1] = (particle.vertex[1] + (z-particle.vertex[2]) * particle.direction[1]/particle.direction[2]) pt[2] = z pt = np.array(pt) nonzero = particle.direction!=0 direction = ((pt[nonzero]-particle.vertex[nonzero]) /particle.direction[nonzero]) if np.all(direction<0): enter_point = pt elif np.all(direction>0): exit_point = pt elif np.all(direction==0): if enter_point is None: enter_point = pt if exit_point is None: exit_point = pt if enter_point is not None and exit_point is not None: return enter_point, exit_point else: raise ValueError("Could not determine exit points") class RectangularGenerator(Generator): def __init__(self, dx, dy, dz, energy, shadow=False, flavor_ratio=(1,1,1), source="cosmogenic", interaction_model=NeutrinoInteraction, earth_model=earth): self.dx = dx self.dy = dy self.dz = dz super().__init__(energy=energy, shadow=shadow, flavor_ratio=flavor_ratio, source=source, interaction_model=interaction_model, earth_model=earth_model) @property def volume(self): return self.dx * self.dy * self.dz def get_vertex(self): return np.random.uniform(low=(-self.dx/2, -self.dy/2, -self.dz), high=(self.dx/2, self.dy/2, 0)) def get_exit_points(self, particle): enter_point = None exit_point = None sides = ((-self.dx/2, self.dx/2), (-self.dy/2, self.dy/2), (-self.dz, 0)) for count in range(6): coord = int(count/2) min_max = count%2 if particle.direction[coord]==0: continue scale = ((sides[coord][min_max] - particle.vertex[coord]) / particle.direction[coord]) intersection = particle.vertex + particle.direction * scale valid = True for i, pair in enumerate(sides): if i==coord: continue if intersection[i]<pair[0] or intersection[i]>pair[1]: valid = False if valid: sign = 1 if min_max==1 else -1 if sign*particle.direction[coord]<0: enter_point = intersection else: exit_point = intersection if enter_point is not None and exit_point is not None: return enter_point, exit_point raise ValueError("Could not determine exit points") class ListGenerator: def __init__(self, events, loop=True): if (isinstance(events, Iterable) and not isinstance(events, Event)): self.events = events else: self.events = [events] for i, event in enumerate(self.events): if isinstance(event, Particle): self.events[i] = Event(event) self.loop = loop self._index = 0 self._additional_counts = 0 @property def count(self): return self._index + self._additional_counts @count.setter def count(self, custom_count): self._additional_counts = custom_count - self._index def create_event(self): if not self.loop and self._index>=len(self.events): raise StopIteration("No more events to be generated") self._index += 1 return self.events[(self._index-1)%len(self.events)] class FileGenerator: def __init__(self, files, slice_range=100, interaction_model=NeutrinoInteraction): if isinstance(files, str): self.files = [files] else: self.files = files self.slice_range = slice_range self.interaction_model = interaction_model self._file_index = -1 self._file_counts = [0] * (len(self.files)+1) self._load_events() @property def count(self): return sum(self._file_counts) @count.setter def count(self, custom_count): self._file_counts[0] = custom_count - sum(self._file_counts[1:]) def _load_events(self): if self._file_index<0 or self._event_index>=len(self._file): self._next_file() start = self._event_index stop = self._event_index + self.slice_range self._event_index += self.slice_range if stop>len(self._file): stop = len(self._file) self._events = [] self._event_counts = [] for file_event in self._file[start:stop]: info = file_event.get_particle_info() particles = [] for p in info: part = Particle( particle_id=p['particle_id'], vertex=(p['vertex_x'], p['vertex_y'], p['vertex_z']), direction=(p['direction_x'], p['direction_y'], p['direction_z']), energy=p['energy'], interaction_model=self.interaction_model, interaction_type=p['interaction_kind'] ) part.interaction.inelasticity = p['interaction_inelasticity'] part.interaction.em_frac = p['interaction_em_frac'] part.interaction.had_frac = p['interaction_had_frac'] part.survival_weight = p['survival_weight'] part.interaction_weight = p['interaction_weight'] particles.append(part) self._events.append(Event(particles)) self._event_counts.append(file_event.total_events_thrown) def _next_file(self): self._file_index += 1 self._event_index = 0 if self._file_index>0: self._file.close() if self._file_index>=len(self.files): raise StopIteration("No more events to be generated") try: self._file = File(self.files[self._file_index], 'r', slice_range=self.slice_range) except TypeError: self._file = File(self.files[self._file_index], 'r') self._file.open() def create_event(self): if len(self._events)==0: self._load_events() self._file_counts[self._file_index+1] = self._event_counts.pop(0) return self._events.pop(0)

true

1c2eb94162bd8c245bbcdc3f3cc42fa660d07460

6,069

py

Python

routes.py

kuldeep24680/RESTAPI-for-analyzing-data-to-excel-uploaded-file-and-downloading-resultant-csv-files-on-flask

5e3f35de2599f71078974c4e5a46bb94da426200

[ "MIT" ]

null

routes.py

kuldeep24680/RESTAPI-for-analyzing-data-to-excel-uploaded-file-and-downloading-resultant-csv-files-on-flask

5e3f35de2599f71078974c4e5a46bb94da426200

[ "MIT" ]

null

routes.py

kuldeep24680/RESTAPI-for-analyzing-data-to-excel-uploaded-file-and-downloading-resultant-csv-files-on-flask

5e3f35de2599f71078974c4e5a46bb94da426200

[ "MIT" ]

null

from app import app from flask import render_template, request,send_file import pandas as pd import csv import xlrd # to route the user the upload the .xlsx file @app.route('/') def upload_file(): return render_template('upload.html') # function that converts the .xlsx file into .csv file and saves it into input folder as test.csv which is used for further analytics @app.route('/Fileupload', methods = ['POST']) def upload(): if request.method == 'POST': f = request.files['file'] f.save("input/sample.xlsx") wb = xlrd.open_workbook('input/sample.xlsx') sh = wb.sheet_by_name('Raw Data') your_csv_file = open('input/test.csv', 'w') wr = csv.writer(your_csv_file, quoting=csv.QUOTE_ALL) for rownum in range(sh.nrows): wr.writerow(sh.row_values(rownum)) your_csv_file.close() return 'file uploaded successfully' # function that reads the test.csv and creates dataframe from which a filtered dataframe is generated which contains all the metabolics with suffix as PC and later write it a file @app.route('/metabolic_suffix_PC') def metabolic_suffix_PC(): column_names = pd.read_csv('input/test.csv', engine='python') columns=list(column_names.columns.values) dict={} for i in range(len(columns)): dict[i]=columns[i] csv_df = pd.read_csv('input/test.csv', header=None, skiprows=1, engine='python') child_dataset1 = csv_df[csv_df[2].str.endswith('PC', na=False)] child_dataset1.rename(columns=dict,inplace=True) tfile = open('outputs/output_PC.csv', 'w') tfile.write(child_dataset1.to_string()) tfile.close() return send_file('outputs/output_PC.csv', mimetype='text/csv', attachment_filename='output_PC.csv', as_attachment=True) # function that reads the test.csv and creates dataframe from which a filtered dataframe is generated which contains all the metabolics with suffix as LPC and later write it a file @app.route('/metabolic_suffix_LPC') def metabolic_suffix_LPC(): column_names = pd.read_csv('input/test.csv', engine='python') columns=list(column_names.columns.values) dict={} for i in range(len(columns)): dict[i]=columns[i] csv_df = pd.read_csv('input/test.csv', header=None, skiprows=1, engine='python') child_dataset2 = csv_df[csv_df[2].str.endswith('LPC', na=False)] child_dataset2.rename(columns=dict,inplace=True) tfile = open('outputs/output_LPC.csv', 'w') tfile.write(child_dataset2.to_string()) tfile.close() return send_file('outputs/output_LPC.csv', mimetype='text/csv', attachment_filename='output_LPC.csv', as_attachment=True) # function that reads the test.csv and creates dataframe from which a filtered dataframe is generated which contains all the metabolics with suffix as plasmalogen and later write it a file @app.route('/metabolic_suffix_plasmalogen') def metabolic_suffix_plasmalogen(): column_names = pd.read_csv('input/test.csv', engine='python') columns=list(column_names.columns.values) dict={} for i in range(len(columns)): dict[i]=columns[i] csv_df = pd.read_csv('input/test.csv', header=None, skiprows=1, engine='python') child_dataset3 = csv_df[csv_df[2].str.endswith('plasmalogen', na=False)] child_dataset3.rename(columns=dict,inplace=True) tfile = open('outputs/output_plasmalogen.csv', 'w') tfile.write(child_dataset3.to_string()) tfile.close() return send_file('outputs/output_plasmalogen.csv', mimetype='text/csv', attachment_filename='output_plasmalogen.csv', as_attachment=True) # function that appends the Retention Time Roundoff column in the main dataframe and later write it a file @app.route('/parentfile_with_RTR') def parentfile_with_RTR(): column_names = pd.read_csv('input/test.csv', engine='python') columns=list(column_names.columns.values) dict={} for i in range(len(columns)): dict[i]=columns[i] csv_df = pd.read_csv('input/test.csv', header=None, skiprows=1, engine='python') csv_df[1050] = round(csv_df[1]) csv_df.rename(columns={1050:'Retention Time Roundoff(in mins)'}, inplace=True) csv_df.rename(columns=dict,inplace=True) tfile = open('outputs/outputfile_with_RTR.csv', 'w') tfile.write(csv_df.to_string()) tfile.close() return send_file('outputs/outputfile_with_RTR.csv', mimetype='text/csv', attachment_filename='outputfile_with_RTR.csv', as_attachment=True) # function that creates a dataframe that has mean of all metabolics readings grouped by Retention Time Roundoff(in mins) and these dataframe doesnot include unnecessary columns and finally it is written to the file @app.route('/metabolicmean_with_similarRTR') def metabolicmean_with_similarRTR(): column_names = pd.read_csv('input/test.csv', engine='python') columns=list(column_names.columns.values) dict={} for i in range(3,len(columns)): dict[i]=columns[i] csv_df = pd.read_csv('input/test.csv', header=None, skiprows=1, engine='python') csv_df[1050] = round(csv_df[1]) csv_df.drop([0,1,2], axis = 1, inplace = True) csv_df.rename(columns=dict,inplace=True) csv_df.rename(columns={1050:'Retention Time Roundoff(in mins)'}, inplace=True) final =csv_df.groupby('Retention Time Roundoff(in mins)').agg('mean') tfile = open('outputs/outputfile_metabolicmean_with_similarRTR.csv', 'w') tfile.write(final.to_string()) tfile.close() return send_file('outputs/outputfile_metabolicmean_with_similarRTR.csv', mimetype='text/csv', attachment_filename='outputfile_metabolicmean_with_similarRTR.csv', as_attachment=True)

42.145833

215

0.672928

from app import app from flask import render_template, request,send_file import pandas as pd import csv import xlrd @app.route('/') def upload_file(): return render_template('upload.html') @app.route('/Fileupload', methods = ['POST']) def upload(): if request.method == 'POST': f = request.files['file'] f.save("input/sample.xlsx") wb = xlrd.open_workbook('input/sample.xlsx') sh = wb.sheet_by_name('Raw Data') your_csv_file = open('input/test.csv', 'w') wr = csv.writer(your_csv_file, quoting=csv.QUOTE_ALL) for rownum in range(sh.nrows): wr.writerow(sh.row_values(rownum)) your_csv_file.close() return 'file uploaded successfully' @app.route('/metabolic_suffix_PC') def metabolic_suffix_PC(): column_names = pd.read_csv('input/test.csv', engine='python') columns=list(column_names.columns.values) dict={} for i in range(len(columns)): dict[i]=columns[i] csv_df = pd.read_csv('input/test.csv', header=None, skiprows=1, engine='python') child_dataset1 = csv_df[csv_df[2].str.endswith('PC', na=False)] child_dataset1.rename(columns=dict,inplace=True) tfile = open('outputs/output_PC.csv', 'w') tfile.write(child_dataset1.to_string()) tfile.close() return send_file('outputs/output_PC.csv', mimetype='text/csv', attachment_filename='output_PC.csv', as_attachment=True) @app.route('/metabolic_suffix_LPC') def metabolic_suffix_LPC(): column_names = pd.read_csv('input/test.csv', engine='python') columns=list(column_names.columns.values) dict={} for i in range(len(columns)): dict[i]=columns[i] csv_df = pd.read_csv('input/test.csv', header=None, skiprows=1, engine='python') child_dataset2 = csv_df[csv_df[2].str.endswith('LPC', na=False)] child_dataset2.rename(columns=dict,inplace=True) tfile = open('outputs/output_LPC.csv', 'w') tfile.write(child_dataset2.to_string()) tfile.close() return send_file('outputs/output_LPC.csv', mimetype='text/csv', attachment_filename='output_LPC.csv', as_attachment=True) @app.route('/metabolic_suffix_plasmalogen') def metabolic_suffix_plasmalogen(): column_names = pd.read_csv('input/test.csv', engine='python') columns=list(column_names.columns.values) dict={} for i in range(len(columns)): dict[i]=columns[i] csv_df = pd.read_csv('input/test.csv', header=None, skiprows=1, engine='python') child_dataset3 = csv_df[csv_df[2].str.endswith('plasmalogen', na=False)] child_dataset3.rename(columns=dict,inplace=True) tfile = open('outputs/output_plasmalogen.csv', 'w') tfile.write(child_dataset3.to_string()) tfile.close() return send_file('outputs/output_plasmalogen.csv', mimetype='text/csv', attachment_filename='output_plasmalogen.csv', as_attachment=True) @app.route('/parentfile_with_RTR') def parentfile_with_RTR(): column_names = pd.read_csv('input/test.csv', engine='python') columns=list(column_names.columns.values) dict={} for i in range(len(columns)): dict[i]=columns[i] csv_df = pd.read_csv('input/test.csv', header=None, skiprows=1, engine='python') csv_df[1050] = round(csv_df[1]) csv_df.rename(columns={1050:'Retention Time Roundoff(in mins)'}, inplace=True) csv_df.rename(columns=dict,inplace=True) tfile = open('outputs/outputfile_with_RTR.csv', 'w') tfile.write(csv_df.to_string()) tfile.close() return send_file('outputs/outputfile_with_RTR.csv', mimetype='text/csv', attachment_filename='outputfile_with_RTR.csv', as_attachment=True) @app.route('/metabolicmean_with_similarRTR') def metabolicmean_with_similarRTR(): column_names = pd.read_csv('input/test.csv', engine='python') columns=list(column_names.columns.values) dict={} for i in range(3,len(columns)): dict[i]=columns[i] csv_df = pd.read_csv('input/test.csv', header=None, skiprows=1, engine='python') csv_df[1050] = round(csv_df[1]) csv_df.drop([0,1,2], axis = 1, inplace = True) csv_df.rename(columns=dict,inplace=True) csv_df.rename(columns={1050:'Retention Time Roundoff(in mins)'}, inplace=True) final =csv_df.groupby('Retention Time Roundoff(in mins)').agg('mean') tfile = open('outputs/outputfile_metabolicmean_with_similarRTR.csv', 'w') tfile.write(final.to_string()) tfile.close() return send_file('outputs/outputfile_metabolicmean_with_similarRTR.csv', mimetype='text/csv', attachment_filename='outputfile_metabolicmean_with_similarRTR.csv', as_attachment=True)

true

1c2eb95134eb8fd3953659373a21b5dc998d5027

1,365

py

Python

run_results.py

phylatechnologies/ibd_classification_benchmark

667e0b42e70bd56c6675062b10dae38407e785b0

[ "MIT" ]

null

run_results.py

phylatechnologies/ibd_classification_benchmark

667e0b42e70bd56c6675062b10dae38407e785b0

[ "MIT" ]

null

run_results.py

phylatechnologies/ibd_classification_benchmark

667e0b42e70bd56c6675062b10dae38407e785b0

[ "MIT" ]

null

import os import pandas as pd import numpy as np from metadata.getters import get_pwd from analysis.evaluation_methods import get_performance import random np.random.seed(26) random.seed(26) def get_sample_info(dataset): ''' :param exp_name: experiment string :return: unpickled data set ''' data_file = '{}.pkl'.format(dataset) data_path = '{}/datasets/{}'.format(get_pwd(), data_file) df = pd.read_pickle(data_path) info = df[['diagnosis','studyID']] return info if (__name__=="__main__"): res_path = get_pwd() + '/results' res = os.listdir(res_path) for e in res: output = '{}/metrics'.format(get_pwd()) if not os.path.isdir(output): os.mkdir(output) exp_name = e.split('.')[0] dataset = exp_name.split('-')[0] if not os.path.isfile('{}/{}.csv'.format(output, exp_name)): info = get_sample_info(dataset) file = '{}/{}'.format(res_path, e) y_pred = pd.read_csv(file, index_col=0) y_pred = np.round(y_pred) y = pd.concat([info, y_pred], axis = 1, sort=False) y.columns = ['true', 'studyID', 'pred'] metrics = get_performance(y_df=y, index_name=exp_name) print(metrics) metrics.to_csv('{}/{}.csv'.format(output, exp_name))

26.25

68

0.589744

import os import pandas as pd import numpy as np from metadata.getters import get_pwd from analysis.evaluation_methods import get_performance import random np.random.seed(26) random.seed(26) def get_sample_info(dataset): data_file = '{}.pkl'.format(dataset) data_path = '{}/datasets/{}'.format(get_pwd(), data_file) df = pd.read_pickle(data_path) info = df[['diagnosis','studyID']] return info if (__name__=="__main__"): res_path = get_pwd() + '/results' res = os.listdir(res_path) for e in res: output = '{}/metrics'.format(get_pwd()) if not os.path.isdir(output): os.mkdir(output) exp_name = e.split('.')[0] dataset = exp_name.split('-')[0] if not os.path.isfile('{}/{}.csv'.format(output, exp_name)): info = get_sample_info(dataset) file = '{}/{}'.format(res_path, e) y_pred = pd.read_csv(file, index_col=0) y_pred = np.round(y_pred) y = pd.concat([info, y_pred], axis = 1, sort=False) y.columns = ['true', 'studyID', 'pred'] metrics = get_performance(y_df=y, index_name=exp_name) print(metrics) metrics.to_csv('{}/{}.csv'.format(output, exp_name))

true

1c2eb97d2acd29ed9d533618df715e94812ce8e5

27,136

py

Python

macOS/Xcode/Maestral/Maestral/app_packages/survey/tools.py

productinfo/maestral-cocoa

d1626c68ace1939bd53cda53c53cbb43c9fb7a8e

[ "MIT" ]

8

2020-11-13T08:48:01.000Z

2021-12-16T06:30:27.000Z

macOS/Xcode/Maestral/Maestral/app_packages/survey/tools.py

productinfo/maestral-cocoa

d1626c68ace1939bd53cda53c53cbb43c9fb7a8e

[ "MIT" ]

4

2022-01-05T09:16:30.000Z

2022-03-29T09:32:44.000Z

macOS/Xcode/Maestral/Maestral/app_packages/survey/tools.py

productinfo/maestral-cocoa

d1626c68ace1939bd53cda53c53cbb43c9fb7a8e

[ "MIT" ]

1

2022-01-05T08:56:59.000Z

import enum import types import wrapio import os import string import itertools from . import helpers __all__ = ('Source', 'Translator', 'LineEditor', 'MultiLineEditor', 'Select', 'MultiSelect') _blocks = string.whitespace + string.punctuation class Source(helpers.Handle): """ Turns stdin reads into events. """ Event = enum.Enum( 'Event', 'move_left move_right jump_left jump_right move_up move_down ' 'delete_left delete_right escape indent enter insert' ) _events = types.SimpleNamespace( arrows = { 'D': Event.move_left, 'C': Event.move_right, 'A': Event.move_up, 'B': Event.move_down }, normal = { '\x0d': Event.enter, '\x0a': Event.enter, '\x7f': Event.delete_left, '\x08': Event.delete_right, '\x09': Event.indent }, special = { '': Event.escape, 'b': Event.jump_left, 'f': Event.jump_right } ) __slots__ = ('_io', '_done') def __init__(self, io, *args, **kwargs): super().__init__(*args, **kwargs) self._io = io self._done = False def _escape(self): key = self._io.recv() if key == '[': key = self._io.recv() events = self._events.arrows else: events = self._events.special return (events, key) def _advance(self): key = self._io.recv() if key == '\x1b': (events, key) = self._escape() else: events = self._events.normal event = events.get(key, self.Event.insert) self._dispatch(event, key) def done(self): self._done = True def stream(self): with self._io.atomic: while not self._done: self._advance() self._done = False class Abort(Exception): """ Raise when something's wrong. """ __slots__ = () class Translator(helpers.Handle): """ Combines related io events into single events with relevant info. .. code-block: python translator = Translator(callback = ...) source = Source(io, callback = translator.invoke) """ Event = enum.Enum( 'Event', 'move_x jump_x move_y delete insert enter' ) __slots__ = ('_io',) def __init__(self, io, *args, **kwargs): super().__init__(*args, **kwargs) self._io = io def _move_x(self, left): self._dispatch(self.Event.move_x, left) @wrapio.event(Source.Event.move_left) def _nnc(self, key): self._move_x(True) @wrapio.event(Source.Event.move_right) def _nnc(self, key): self._move_x(False) def _jump_x(self, left): self._dispatch(self.Event.jump_x, left) @wrapio.event(Source.Event.jump_left) def _nnc(self, key): self._jump_x(True) @wrapio.event(Source.Event.jump_right) def _nnc(self, key): self._jump_x(False) def _move_y(self, up): self._dispatch(self.Event.move_y, up) @wrapio.event(Source.Event.move_up) def _nnc(self, key): self._move_y(True) @wrapio.event(Source.Event.move_down) def _nnc(self, key): self._move_y(False) def _delete(self, left): self._dispatch(self.Event.delete, left) @wrapio.event(Source.Event.delete_left) def _nnc(self, key): self._delete(True) @wrapio.event(Source.Event.delete_right) def _nnc(self, key): self._delete(False) def _insert(self, key): self._dispatch(self.Event.insert, key) @wrapio.event(Source.Event.insert) def _nnc(self, key): self._insert(key) @wrapio.event(Source.Event.indent) def _nnc(self, key): self._insert('\t') def _enter(self, key): self._dispatch(self.Event.enter, key) @wrapio.event(Source.Event.enter) def _nnc(self, key): self._enter(key) def invoke(self, *args, **kwargs): try: fail = super().invoke(*args, **kwargs) except Abort: fail = True else: if fail: return fail = False if fail: self._io.ring() return fail class WindowView: """ ABC for classes implementing something that can be partially viewed. """ __slots__ = () # ('_index', '_lower', '_bound') on each subclass def __init__(self, bound): self._index = 0 self._lower = 0 self._bound = bound @property def _upper(self): return self._lower + self._bound @property def _among(self): return self._index - self._lower @property def among(self): return self._among @property def index(self): return self._index def _calibrate(self): if self._index < self._lower: # |[abc] <- [|ab]c self._lower = self._index elif self._index > self._upper: # [abc]| -> a[bc|] self._lower = self._index - self._bound else: return False return True def _resize(self, size): bound = self._bound + size if bound < 0: raise ValueError('bound would be negative') self._bound += size if size > 0: self._lower = max(0, self._lower - size) self._calibrate() def _reset(self): self._index = 0 self._lower = 0 class Tool(WindowView, helpers.Handle): """ ABC for partially-viewable handlers. """ __slots__ = ('_index', '_lower', '_bound', '_io', '_cursor') def __init__(self, io, cursor, bound, *args, **kwargs): WindowView.__init__(self, bound) helpers.Handle.__init__(self, *args, **kwargs) self._io = io self._cursor = cursor def _clear(self): raise NotImplementedError() def clear(self): self._clear() def _draw(self, lower): raise NotImplementedError() def draw(self): self._draw(self._lower) def _focus(self): raise NotImplementedError() def focus(self): self._focus() def _redraw(self, skip = False): if not skip: self._clear() self._draw(self._lower) self._focus() def resize(self, size, full = True): if full: self._clear() self._resize(size) if full: self._redraw(skip = True) def _move_y(self, up, size): pass def _e_move_y(self, up, size): self._move_y(up, size) self._dispatch('move_y', up, size) @wrapio.event(Translator.Event.move_y) def _nnc(self, up): self._e_move_y(up, 1) def _move_x(self, left, size): pass def _e_move_x(self, left, size): self._move_x(left, size) self._dispatch('move_x', left, size) @wrapio.event(Translator.Event.move_x) def _nnc(self, left): self._e_move_x(left, 1) def _jump_x(self, left): pass def _e_jump_x(self, left): self._jump_x(left) self._dispatch('jump_x', left) @wrapio.event(Translator.Event.jump_x) def _nnc(self, left): self._e_jump_x(left) def _tab(self): pass def _e_tab(self): self._tab() self._dispatch('tab') def _insert(self, runes): pass def _e_insert(self, runes): if '\t' in runes: self._e_tab() return runes = self._insert(runes) self._dispatch('insert', runes) return runes def insert(self, runes): runes = self._e_insert(runes) return runes @wrapio.event(Translator.Event.insert) def _nnc(self, rune): runes = (rune,) self._e_insert(runes) def _delete(self, left, size): pass def _e_delete(self, left, size): self._delete(left, size) self._dispatch('delete', left, size) def delete(self, left, size): self._e_delete(left, size) @wrapio.event(Translator.Event.delete) def _nnc(self, left): self._e_delete(left, 1) def _submit(self): self._dispatch('submit') def _enter(self): raise NotImplementedError() @wrapio.event(Translator.Event.enter) def _nnc(self, rune): self._enter() def _clean(value): value = helpers.seq.clean(value) value = helpers.clean(value) return value class LineEditor(Tool): """ Use for editing a single line of text. Does not support line breaks or moving vertically. """ __slots__ = ('_limit', '_funnel', '_buffer') def __init__(self, io, cursor, width, limit, funnel, *args, **kwargs): super().__init__(io, cursor, width, **kwargs) self._limit = limit self._funnel = funnel self._buffer = [] @property def buffer(self): return self._buffer def _place(self): self._cursor.left(self._among) def _clear(self): self._place() self._cursor.erase() def _transform(self, rune): rune = self._funnel(rune) if not len(rune) == 1: raise RuntimeError('rune must be of size 1') if not rune.isprintable(): raise RuntimeError('rune must be printable') return rune def _show(self, runes): if self._funnel: runes = map(self._transform, runes) runes = tuple(runes) value = ''.join(runes) self._io.send(value) def _chunk(self, lower): runes = self._buffer[lower:self._upper] return runes def _draw(self, lower): runes = self._chunk(lower) self._show(runes) @property def _shown(self): return len(self._chunk(self._lower)) def _focus(self): size = self._shown - self._among self._cursor.left(size) def _move_x(self, left, size): if left: limit = self._index else: limit = len(self._buffer) - self._index excess = size - limit if excess > 0: raise Abort(excess) if left: index = self._index - size limit = self._among self._cursor.left(min(limit, size)) else: index = self._index + size limit = self._shown - self._among self._cursor.right(min(limit, size)) self._index = index change = self._calibrate() if change: self._redraw() return change def move(self, left, size): self._move_x(left, size) def _jump_x_left(self): limit = 0 stop = self._index - 1 if stop < limit: raise Abort() indexes = [] for block in _blocks: try: index = helpers.rindex(self._buffer, block, 0, stop) except ValueError: continue indexes.append(index + 1) else: indexes.append(limit) size = min(self._index - index for index in indexes) self._move_x(True, size) def _jump_x_right(self): limit = len(self._buffer) start = self._index + 1 if start > limit: raise Abort() indexes = [] for block in _blocks: try: index = self._buffer.index(block, start) except ValueError: continue indexes.append(index) else: indexes.append(limit) size = min(index - self._index for index in indexes) self._move_x(False, size) def _jump_x(self, left): if left: self._jump_x_left() else: self._jump_x_right() def jump(self, left): self._jump_x(left) def _ensure(self, runes): value = ''.join(runes) value = _clean(value) return value def _insert(self, runes): runes = self._ensure(runes) runes = tuple(runes) esize = len(runes) osize = len(self._buffer) nsize = osize + esize if not self._limit is None and nsize > self._limit: raise Abort() start = self._index for (index, rune) in enumerate(runes): self._buffer.insert(start + index, rune) among = not start == osize self._index = start + esize change = self._calibrate() if change: self._redraw() elif among: self._draw(start) self._focus() else: self._show(runes) return runes def _delete(self, left, size): if left: self._move_x(True, size) limit = len(self._buffer) - self._index excess = size - limit if excess > 0: raise Abort(excess) for _ in range(size): del self._buffer[self._index] self._cursor.erase() self._draw(self._index) self._focus() def _enter(self): self._submit() class Originful: __slots__ = () # ('_origin',) on each subclass def _originate(self): (cy, cx) = self._cursor.locate() self._origin = cx - 1 class MultiLineEditor(Tool, Originful): """ Use for editing multiple lines of text. Supports line breaks or moving vertically. """ __slots__ = ('_origin', '_finchk', '_subs', '_make', '_limit', '_indent') def __init__(self, io, cursor, finchk, height, width, limit, funnel, indent, *args, **kwargs): Tool.__init__(self, io, cursor, height - 1, *args, **kwargs) self._finchk = finchk make = lambda: LineEditor(io, cursor, width, None, funnel) self._subs = [make()] self._make = make self._limit = limit self._indent = indent self._originate() @property def _sub(self): return self._subs[self._index] @property def subs(self): return self._subs def _place(self): self._cursor.last(self._among) self._cursor.right(self._origin) def _clear(self): self._place() self._cursor.clear() def _chunk(self, lower): upper = self._upper + 1 runes = self._subs[lower:upper] return runes def _draw(self, lower): self._originate() subs = self._chunk(lower) last = len(subs) - 1 for (index, sub) in enumerate(subs): sub.draw() if index == last: break self._io.send(os.linesep) @property def _shown(self): return len(self._chunk(self._lower)) def _focus(self): # if 1 shown and among 0, then move 0 ysize = self._shown - self._among - 1 self._cursor.last(ysize) xsize = self._sub.among if not self._among: xsize += self._origin self._cursor.right(xsize) _SpotType = enum.Enum('SpotType', 'match left right') def _spot(self, old, new, type): to_left = - new.index to_right = len(new.buffer) + to_left if type is self._SpotType.match: difference = old.index - new.index size = max(to_left, min(to_right, difference)) elif type is self._SpotType.left: size = to_left elif type is self._SpotType.right: size = to_right else: raise ValueError('unknown move type') new.move(size < 0, abs(size)) def _move_y(self, up, size, type = _SpotType.match): if up: limit = self._index else: # if 1 sub and index 0, then limit is 0 limit = len(self._subs) - self._index - 1 excess = size - limit if excess > 0: raise Abort(excess) if up: index = self._index - size limit = self._among self._cursor.last(min(limit, size)) else: index = self._index + size limit = self._shown - self._among - 1 self._cursor.next(min(limit, size)) old = self._sub self._index = index new = self._sub xsize = new.among if not self._among: xsize += self._origin self._cursor.right(xsize) change = self._calibrate() if change: self._redraw() if not type is None: self._spot(old, new, type) def _rcut(self, left): if left: (*subs, sub) = self._subs[:self._index + 1] buffer = sub.buffer[:sub.index] subs = reversed(subs) else: (sub, *subs) = self._subs[self._index:] buffer = sub.buffer[sub.index:] buffers = (buffer, *(sub.buffer for sub in subs)) return buffers def _rmsr(self, buffers, xsize): ysize = 0 nsize = xsize for buffer in buffers: nsize -= len(buffer) + 1 if nsize < 0: break xsize = nsize ysize += 1 return (ysize, xsize) def _rclc(self, left, xsize): buffers = self._rcut(left) # remove one to account for current line limit = sum(map(len, buffers)) + len(buffers) - 1 excess = xsize - limit if excess > 0: raise Abort(excess) (ysize, xsize) = self._rmsr(buffers, xsize) return (ysize, xsize) def _move_x(self, left, xsize): (ysize, xsize) = self._rclc(left, xsize) if ysize: type = self._SpotType.right if left else self._SpotType.left self._move_y(left, ysize, type) self._sub.move(left, xsize) return (ysize, xsize) def move(self, left, size): self._move_x(left, size) def _jump_x(self, left): try: self._sub.jump(left) except Abort: self._move_x(left, 1) def _ensure(self, runes): esize = len(runes) buffers = tuple(sub.buffer for sub in self._subs) osize = sum(map(len, buffers)) + len(buffers) - 1 nsize = osize + esize if not self._limit is None and nsize > self._limit: raise Abort() def _tab(self): self._e_insert((' ',) * self._indent) def _insert(self, runes): values = helpers.split(runes, os.linesep) values = tuple(values) runes = tuple(itertools.chain.from_iterable(values)) self._ensure(runes) last = len(values) - 1 buffer = [] for (index, runes) in enumerate(values): runes = self._sub.insert(runes) buffer.extend(runes) if index == last: break self._newsub() buffer.append(os.linesep) return buffer def _delete(self, left, size): if left: self._move_x(True, size) (ysize, xsize) = self._rclc(False, size) kli = self._index + 1 sub = self._sub for index in range(ysize): nsub = self._subs.pop(kli) sub.buffer.extend(nsub.buffer) if ysize: self._redraw() sub.delete(False, size - ysize) def _newsub(self): old = self._sub new = self._make() while True: try: rune = old.buffer.pop(old.index) except IndexError: break new.buffer.append(rune) last = self._index == len(self._subs) - 1 and self._among < self._bound full = not last if full: self._clear() else: self._cursor.erase() index = self._index + 1 self._subs.insert(index, new) self._index = index runes = (os.linesep,) if full: self._calibrate() self._redraw(skip = True) else: self._io.send(*runes) self._draw(self._index) self._focus() self._dispatch('insert', runes) def newsub(self): self._newsub() def _enter(self): done = self._finchk() (self._submit if done else self._newsub)() class Select(Tool, Originful): """ Use for cycling through and selecting options. """ __slots__ = ('_origin', '_options', '_visible', '_changed', '_buffer', '_width', '_prefix', '_indent', '_funnel', '_filter') def __init__(self, io, cursor, height, width, options, prefix, indent, funnel, filter, *args, **kwargs): Tool.__init__(self, io, cursor, height - 1, *args, **kwargs) self._options = options self._visible = tuple(range(len(options))) self._changed = {} self._buffer = [] self._width = width self._prefix = prefix self._indent = indent self._funnel = funnel self._filter = filter self._originate() @property def buffer(self): return self._buffer def _place(self): self._cursor.last(self._among) self._cursor.right(self._origin) def _clear(self): self._place() self._cursor.clear() def _tran(self, index, current, option): return option def _chunk(self, lower): return self._visible[lower:self._upper + 1] def _fetch(self, index, current): option = self._options[index][:self._width] if current: try: option = self._changed[index] except KeyError: if self._funnel: option = self._funnel(index, option) self._changed[index] = option prefix = self._prefix if current else ' ' * self._indent option = prefix + self._tran(index, current, option) return option def _show(self, index, current): self._cursor.erase() option = self._fetch(index, current) self._io.send(option) self._cursor.goto(0) def _draw(self, lower): indexes = self._chunk(lower) options = [] for (cindex, oindex) in enumerate(indexes, start = lower): current = cindex == self._index option = self._fetch(oindex, current) options.append(option) result = os.linesep.join(options) self._io.send(result) @property def _shown(self): return len(self._chunk(self._lower)) def _focus(self): # if 1 shown and among 0, then move 0 ysize = self._shown - self._among - 1 self._cursor.last(ysize) xsize = 0 # doesn't matter if not self._among: xsize += self._origin self._cursor.right(xsize) def _slide(self, up, size): limit = len(self._visible) size = size % limit index = self._index + (- size if up else size) if index < 0: index = limit - 1 else: extra = index - limit if not extra < 0: index = extra size = index - self._index up = size < 0 size = abs(size) return (up, size, index) def _move_y(self, up, size): (up, size, index) = self._slide(up, size) if up: limit = self._index else: # if 1 sub and index 0, then limit is 0 limit = len(self._visible) - self._index - 1 # no need to check excess, ``_slide`` ensures self._show(self._visible[self._index], False) if up: limit = self._among self._cursor.last(min(limit, size)) else: limit = self._shown - self._among - 1 self._cursor.next(min(limit, size)) self._index = index change = self._calibrate() if change: self._redraw() else: self._show(self._visible[index], True) def move(self, up, size): self._move_y(up, size) def _specify(self, new): argument = ''.join(self._buffer) if new: indexes = self._visible options = (self._options[index] for index in indexes) pairs = zip(indexes, options) pairs = self._filter(pairs, argument) (indexes, options) = zip(*pairs) else: indexes = range(len(self._options)) self._clear() self._visible = indexes self._index = 0 self._calibrate() self._redraw(skip = True) self._dispatch('filter', argument) def _insert(self, runes): save = self._buffer.copy() value = ''.join(runes) value = _clean(value) self._buffer.extend(value) try: self._specify(True) except ValueError: self._buffer.clear() self._buffer.extend(save) raise Abort() def _delete(self, left, size): if not self._buffer: raise Abort() self._buffer.clear() self._specify(False) def _enter(self): self._submit() class MultiSelect(Select): __slots__ = ('_unpin', '_pin', '_chosen') def __init__(self, unpin, pin, indexes, *args, **kwargs): super().__init__(*args, **kwargs) self._unpin = unpin self._pin = pin self._chosen = set(indexes) @property def indexes(self): return self._chosen def _tran(self, index, current, option): signal = self._pin if index in self._chosen else self._unpin return signal + super()._tran(index, current, option) def _add(self, index, full): if full: limit = len(self._options) if len(self._chosen) == limit: raise Abort() self._chosen.update(range(limit)) else: self._chosen.add(index) def _pop(self, index, full): if full: if not self._chosen: raise Abort() self._chosen.clear() else: self._chosen.remove(index) def _inform(self, new): index = self._visible[self._index] exists = index in self._chosen full = exists if new else not exists (self._add if new else self._pop)(index, full) self._redraw() self._dispatch('inform', new, full) def _move_x(self, left, size): new = not left self._inform(new)

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0.532098

import enum import types import wrapio import os import string import itertools from . import helpers __all__ = ('Source', 'Translator', 'LineEditor', 'MultiLineEditor', 'Select', 'MultiSelect') _blocks = string.whitespace + string.punctuation class Source(helpers.Handle): Event = enum.Enum( 'Event', 'move_left move_right jump_left jump_right move_up move_down ' 'delete_left delete_right escape indent enter insert' ) _events = types.SimpleNamespace( arrows = { 'D': Event.move_left, 'C': Event.move_right, 'A': Event.move_up, 'B': Event.move_down }, normal = { '\x0d': Event.enter, '\x0a': Event.enter, '\x7f': Event.delete_left, '\x08': Event.delete_right, '\x09': Event.indent }, special = { '': Event.escape, 'b': Event.jump_left, 'f': Event.jump_right } ) __slots__ = ('_io', '_done') def __init__(self, io, *args, **kwargs): super().__init__(*args, **kwargs) self._io = io self._done = False def _escape(self): key = self._io.recv() if key == '[': key = self._io.recv() events = self._events.arrows else: events = self._events.special return (events, key) def _advance(self): key = self._io.recv() if key == '\x1b': (events, key) = self._escape() else: events = self._events.normal event = events.get(key, self.Event.insert) self._dispatch(event, key) def done(self): self._done = True def stream(self): with self._io.atomic: while not self._done: self._advance() self._done = False class Abort(Exception): __slots__ = () class Translator(helpers.Handle): Event = enum.Enum( 'Event', 'move_x jump_x move_y delete insert enter' ) __slots__ = ('_io',) def __init__(self, io, *args, **kwargs): super().__init__(*args, **kwargs) self._io = io def _move_x(self, left): self._dispatch(self.Event.move_x, left) @wrapio.event(Source.Event.move_left) def _nnc(self, key): self._move_x(True) @wrapio.event(Source.Event.move_right) def _nnc(self, key): self._move_x(False) def _jump_x(self, left): self._dispatch(self.Event.jump_x, left) @wrapio.event(Source.Event.jump_left) def _nnc(self, key): self._jump_x(True) @wrapio.event(Source.Event.jump_right) def _nnc(self, key): self._jump_x(False) def _move_y(self, up): self._dispatch(self.Event.move_y, up) @wrapio.event(Source.Event.move_up) def _nnc(self, key): self._move_y(True) @wrapio.event(Source.Event.move_down) def _nnc(self, key): self._move_y(False) def _delete(self, left): self._dispatch(self.Event.delete, left) @wrapio.event(Source.Event.delete_left) def _nnc(self, key): self._delete(True) @wrapio.event(Source.Event.delete_right) def _nnc(self, key): self._delete(False) def _insert(self, key): self._dispatch(self.Event.insert, key) @wrapio.event(Source.Event.insert) def _nnc(self, key): self._insert(key) @wrapio.event(Source.Event.indent) def _nnc(self, key): self._insert('\t') def _enter(self, key): self._dispatch(self.Event.enter, key) @wrapio.event(Source.Event.enter) def _nnc(self, key): self._enter(key) def invoke(self, *args, **kwargs): try: fail = super().invoke(*args, **kwargs) except Abort: fail = True else: if fail: return fail = False if fail: self._io.ring() return fail class WindowView: __slots__ = () def __init__(self, bound): self._index = 0 self._lower = 0 self._bound = bound @property def _upper(self): return self._lower + self._bound @property def _among(self): return self._index - self._lower @property def among(self): return self._among @property def index(self): return self._index def _calibrate(self): if self._index < self._lower: self._lower = self._index elif self._index > self._upper: self._lower = self._index - self._bound else: return False return True def _resize(self, size): bound = self._bound + size if bound < 0: raise ValueError('bound would be negative') self._bound += size if size > 0: self._lower = max(0, self._lower - size) self._calibrate() def _reset(self): self._index = 0 self._lower = 0 class Tool(WindowView, helpers.Handle): __slots__ = ('_index', '_lower', '_bound', '_io', '_cursor') def __init__(self, io, cursor, bound, *args, **kwargs): WindowView.__init__(self, bound) helpers.Handle.__init__(self, *args, **kwargs) self._io = io self._cursor = cursor def _clear(self): raise NotImplementedError() def clear(self): self._clear() def _draw(self, lower): raise NotImplementedError() def draw(self): self._draw(self._lower) def _focus(self): raise NotImplementedError() def focus(self): self._focus() def _redraw(self, skip = False): if not skip: self._clear() self._draw(self._lower) self._focus() def resize(self, size, full = True): if full: self._clear() self._resize(size) if full: self._redraw(skip = True) def _move_y(self, up, size): pass def _e_move_y(self, up, size): self._move_y(up, size) self._dispatch('move_y', up, size) @wrapio.event(Translator.Event.move_y) def _nnc(self, up): self._e_move_y(up, 1) def _move_x(self, left, size): pass def _e_move_x(self, left, size): self._move_x(left, size) self._dispatch('move_x', left, size) @wrapio.event(Translator.Event.move_x) def _nnc(self, left): self._e_move_x(left, 1) def _jump_x(self, left): pass def _e_jump_x(self, left): self._jump_x(left) self._dispatch('jump_x', left) @wrapio.event(Translator.Event.jump_x) def _nnc(self, left): self._e_jump_x(left) def _tab(self): pass def _e_tab(self): self._tab() self._dispatch('tab') def _insert(self, runes): pass def _e_insert(self, runes): if '\t' in runes: self._e_tab() return runes = self._insert(runes) self._dispatch('insert', runes) return runes def insert(self, runes): runes = self._e_insert(runes) return runes @wrapio.event(Translator.Event.insert) def _nnc(self, rune): runes = (rune,) self._e_insert(runes) def _delete(self, left, size): pass def _e_delete(self, left, size): self._delete(left, size) self._dispatch('delete', left, size) def delete(self, left, size): self._e_delete(left, size) @wrapio.event(Translator.Event.delete) def _nnc(self, left): self._e_delete(left, 1) def _submit(self): self._dispatch('submit') def _enter(self): raise NotImplementedError() @wrapio.event(Translator.Event.enter) def _nnc(self, rune): self._enter() def _clean(value): value = helpers.seq.clean(value) value = helpers.clean(value) return value class LineEditor(Tool): __slots__ = ('_limit', '_funnel', '_buffer') def __init__(self, io, cursor, width, limit, funnel, *args, **kwargs): super().__init__(io, cursor, width, **kwargs) self._limit = limit self._funnel = funnel self._buffer = [] @property def buffer(self): return self._buffer def _place(self): self._cursor.left(self._among) def _clear(self): self._place() self._cursor.erase() def _transform(self, rune): rune = self._funnel(rune) if not len(rune) == 1: raise RuntimeError('rune must be of size 1') if not rune.isprintable(): raise RuntimeError('rune must be printable') return rune def _show(self, runes): if self._funnel: runes = map(self._transform, runes) runes = tuple(runes) value = ''.join(runes) self._io.send(value) def _chunk(self, lower): runes = self._buffer[lower:self._upper] return runes def _draw(self, lower): runes = self._chunk(lower) self._show(runes) @property def _shown(self): return len(self._chunk(self._lower)) def _focus(self): size = self._shown - self._among self._cursor.left(size) def _move_x(self, left, size): if left: limit = self._index else: limit = len(self._buffer) - self._index excess = size - limit if excess > 0: raise Abort(excess) if left: index = self._index - size limit = self._among self._cursor.left(min(limit, size)) else: index = self._index + size limit = self._shown - self._among self._cursor.right(min(limit, size)) self._index = index change = self._calibrate() if change: self._redraw() return change def move(self, left, size): self._move_x(left, size) def _jump_x_left(self): limit = 0 stop = self._index - 1 if stop < limit: raise Abort() indexes = [] for block in _blocks: try: index = helpers.rindex(self._buffer, block, 0, stop) except ValueError: continue indexes.append(index + 1) else: indexes.append(limit) size = min(self._index - index for index in indexes) self._move_x(True, size) def _jump_x_right(self): limit = len(self._buffer) start = self._index + 1 if start > limit: raise Abort() indexes = [] for block in _blocks: try: index = self._buffer.index(block, start) except ValueError: continue indexes.append(index) else: indexes.append(limit) size = min(index - self._index for index in indexes) self._move_x(False, size) def _jump_x(self, left): if left: self._jump_x_left() else: self._jump_x_right() def jump(self, left): self._jump_x(left) def _ensure(self, runes): value = ''.join(runes) value = _clean(value) return value def _insert(self, runes): runes = self._ensure(runes) runes = tuple(runes) esize = len(runes) osize = len(self._buffer) nsize = osize + esize if not self._limit is None and nsize > self._limit: raise Abort() start = self._index for (index, rune) in enumerate(runes): self._buffer.insert(start + index, rune) among = not start == osize self._index = start + esize change = self._calibrate() if change: self._redraw() elif among: self._draw(start) self._focus() else: self._show(runes) return runes def _delete(self, left, size): if left: self._move_x(True, size) limit = len(self._buffer) - self._index excess = size - limit if excess > 0: raise Abort(excess) for _ in range(size): del self._buffer[self._index] self._cursor.erase() self._draw(self._index) self._focus() def _enter(self): self._submit() class Originful: __slots__ = () def _originate(self): (cy, cx) = self._cursor.locate() self._origin = cx - 1 class MultiLineEditor(Tool, Originful): __slots__ = ('_origin', '_finchk', '_subs', '_make', '_limit', '_indent') def __init__(self, io, cursor, finchk, height, width, limit, funnel, indent, *args, **kwargs): Tool.__init__(self, io, cursor, height - 1, *args, **kwargs) self._finchk = finchk make = lambda: LineEditor(io, cursor, width, None, funnel) self._subs = [make()] self._make = make self._limit = limit self._indent = indent self._originate() @property def _sub(self): return self._subs[self._index] @property def subs(self): return self._subs def _place(self): self._cursor.last(self._among) self._cursor.right(self._origin) def _clear(self): self._place() self._cursor.clear() def _chunk(self, lower): upper = self._upper + 1 runes = self._subs[lower:upper] return runes def _draw(self, lower): self._originate() subs = self._chunk(lower) last = len(subs) - 1 for (index, sub) in enumerate(subs): sub.draw() if index == last: break self._io.send(os.linesep) @property def _shown(self): return len(self._chunk(self._lower)) def _focus(self): ysize = self._shown - self._among - 1 self._cursor.last(ysize) xsize = self._sub.among if not self._among: xsize += self._origin self._cursor.right(xsize) _SpotType = enum.Enum('SpotType', 'match left right') def _spot(self, old, new, type): to_left = - new.index to_right = len(new.buffer) + to_left if type is self._SpotType.match: difference = old.index - new.index size = max(to_left, min(to_right, difference)) elif type is self._SpotType.left: size = to_left elif type is self._SpotType.right: size = to_right else: raise ValueError('unknown move type') new.move(size < 0, abs(size)) def _move_y(self, up, size, type = _SpotType.match): if up: limit = self._index else: limit = len(self._subs) - self._index - 1 excess = size - limit if excess > 0: raise Abort(excess) if up: index = self._index - size limit = self._among self._cursor.last(min(limit, size)) else: index = self._index + size limit = self._shown - self._among - 1 self._cursor.next(min(limit, size)) old = self._sub self._index = index new = self._sub xsize = new.among if not self._among: xsize += self._origin self._cursor.right(xsize) change = self._calibrate() if change: self._redraw() if not type is None: self._spot(old, new, type) def _rcut(self, left): if left: (*subs, sub) = self._subs[:self._index + 1] buffer = sub.buffer[:sub.index] subs = reversed(subs) else: (sub, *subs) = self._subs[self._index:] buffer = sub.buffer[sub.index:] buffers = (buffer, *(sub.buffer for sub in subs)) return buffers def _rmsr(self, buffers, xsize): ysize = 0 nsize = xsize for buffer in buffers: nsize -= len(buffer) + 1 if nsize < 0: break xsize = nsize ysize += 1 return (ysize, xsize) def _rclc(self, left, xsize): buffers = self._rcut(left) limit = sum(map(len, buffers)) + len(buffers) - 1 excess = xsize - limit if excess > 0: raise Abort(excess) (ysize, xsize) = self._rmsr(buffers, xsize) return (ysize, xsize) def _move_x(self, left, xsize): (ysize, xsize) = self._rclc(left, xsize) if ysize: type = self._SpotType.right if left else self._SpotType.left self._move_y(left, ysize, type) self._sub.move(left, xsize) return (ysize, xsize) def move(self, left, size): self._move_x(left, size) def _jump_x(self, left): try: self._sub.jump(left) except Abort: self._move_x(left, 1) def _ensure(self, runes): esize = len(runes) buffers = tuple(sub.buffer for sub in self._subs) osize = sum(map(len, buffers)) + len(buffers) - 1 nsize = osize + esize if not self._limit is None and nsize > self._limit: raise Abort() def _tab(self): self._e_insert((' ',) * self._indent) def _insert(self, runes): values = helpers.split(runes, os.linesep) values = tuple(values) runes = tuple(itertools.chain.from_iterable(values)) self._ensure(runes) last = len(values) - 1 buffer = [] for (index, runes) in enumerate(values): runes = self._sub.insert(runes) buffer.extend(runes) if index == last: break self._newsub() buffer.append(os.linesep) return buffer def _delete(self, left, size): if left: self._move_x(True, size) (ysize, xsize) = self._rclc(False, size) kli = self._index + 1 sub = self._sub for index in range(ysize): nsub = self._subs.pop(kli) sub.buffer.extend(nsub.buffer) if ysize: self._redraw() sub.delete(False, size - ysize) def _newsub(self): old = self._sub new = self._make() while True: try: rune = old.buffer.pop(old.index) except IndexError: break new.buffer.append(rune) last = self._index == len(self._subs) - 1 and self._among < self._bound full = not last if full: self._clear() else: self._cursor.erase() index = self._index + 1 self._subs.insert(index, new) self._index = index runes = (os.linesep,) if full: self._calibrate() self._redraw(skip = True) else: self._io.send(*runes) self._draw(self._index) self._focus() self._dispatch('insert', runes) def newsub(self): self._newsub() def _enter(self): done = self._finchk() (self._submit if done else self._newsub)() class Select(Tool, Originful): __slots__ = ('_origin', '_options', '_visible', '_changed', '_buffer', '_width', '_prefix', '_indent', '_funnel', '_filter') def __init__(self, io, cursor, height, width, options, prefix, indent, funnel, filter, *args, **kwargs): Tool.__init__(self, io, cursor, height - 1, *args, **kwargs) self._options = options self._visible = tuple(range(len(options))) self._changed = {} self._buffer = [] self._width = width self._prefix = prefix self._indent = indent self._funnel = funnel self._filter = filter self._originate() @property def buffer(self): return self._buffer def _place(self): self._cursor.last(self._among) self._cursor.right(self._origin) def _clear(self): self._place() self._cursor.clear() def _tran(self, index, current, option): return option def _chunk(self, lower): return self._visible[lower:self._upper + 1] def _fetch(self, index, current): option = self._options[index][:self._width] if current: try: option = self._changed[index] except KeyError: if self._funnel: option = self._funnel(index, option) self._changed[index] = option prefix = self._prefix if current else ' ' * self._indent option = prefix + self._tran(index, current, option) return option def _show(self, index, current): self._cursor.erase() option = self._fetch(index, current) self._io.send(option) self._cursor.goto(0) def _draw(self, lower): indexes = self._chunk(lower) options = [] for (cindex, oindex) in enumerate(indexes, start = lower): current = cindex == self._index option = self._fetch(oindex, current) options.append(option) result = os.linesep.join(options) self._io.send(result) @property def _shown(self): return len(self._chunk(self._lower)) def _focus(self): ysize = self._shown - self._among - 1 self._cursor.last(ysize) xsize = 0 if not self._among: xsize += self._origin self._cursor.right(xsize) def _slide(self, up, size): limit = len(self._visible) size = size % limit index = self._index + (- size if up else size) if index < 0: index = limit - 1 else: extra = index - limit if not extra < 0: index = extra size = index - self._index up = size < 0 size = abs(size) return (up, size, index) def _move_y(self, up, size): (up, size, index) = self._slide(up, size) if up: limit = self._index else: # if 1 sub and index 0, then limit is 0 limit = len(self._visible) - self._index - 1 # no need to check excess, ``_slide`` ensures self._show(self._visible[self._index], False) if up: limit = self._among self._cursor.last(min(limit, size)) else: limit = self._shown - self._among - 1 self._cursor.next(min(limit, size)) self._index = index change = self._calibrate() if change: self._redraw() else: self._show(self._visible[index], True) def move(self, up, size): self._move_y(up, size) def _specify(self, new): argument = ''.join(self._buffer) if new: indexes = self._visible options = (self._options[index] for index in indexes) pairs = zip(indexes, options) pairs = self._filter(pairs, argument) (indexes, options) = zip(*pairs) else: indexes = range(len(self._options)) self._clear() self._visible = indexes self._index = 0 self._calibrate() self._redraw(skip = True) self._dispatch('filter', argument) def _insert(self, runes): save = self._buffer.copy() value = ''.join(runes) value = _clean(value) self._buffer.extend(value) try: self._specify(True) except ValueError: self._buffer.clear() self._buffer.extend(save) raise Abort() def _delete(self, left, size): if not self._buffer: raise Abort() self._buffer.clear() self._specify(False) def _enter(self): self._submit() class MultiSelect(Select): __slots__ = ('_unpin', '_pin', '_chosen') def __init__(self, unpin, pin, indexes, *args, **kwargs): super().__init__(*args, **kwargs) self._unpin = unpin self._pin = pin self._chosen = set(indexes) @property def indexes(self): return self._chosen def _tran(self, index, current, option): signal = self._pin if index in self._chosen else self._unpin return signal + super()._tran(index, current, option) def _add(self, index, full): if full: limit = len(self._options) if len(self._chosen) == limit: raise Abort() self._chosen.update(range(limit)) else: self._chosen.add(index) def _pop(self, index, full): if full: if not self._chosen: raise Abort() self._chosen.clear() else: self._chosen.remove(index) def _inform(self, new): index = self._visible[self._index] exists = index in self._chosen full = exists if new else not exists (self._add if new else self._pop)(index, full) self._redraw() self._dispatch('inform', new, full) def _move_x(self, left, size): new = not left self._inform(new)

true

1c2eba00650c8b64ca478183c3d227a97692f36e

1,439

py

Python

nonebot/adapters/feishu/utils.py

nonebot/adapter-feishu

392d734bbf09b88c2e7557102f2562111b84fce8

[ "MIT" ]

2

2021-11-29T16:05:17.000Z

2022-03-21T07:45:50.000Z

nonebot/adapters/feishu/utils.py

nonebot/adapter-feishu

392d734bbf09b88c2e7557102f2562111b84fce8

[ "MIT" ]

1

2022-03-16T07:26:10.000Z

nonebot/adapters/feishu/utils.py

nonebot/adapter-feishu

392d734bbf09b88c2e7557102f2562111b84fce8

[ "MIT" ]

null

import base64 import hashlib from typing import Any, Dict, Optional from cashews import cache from Crypto.Cipher import AES from nonebot.utils import logger_wrapper from .exception import ActionFailed log = logger_wrapper("FEISHU") cache.setup("mem://") def _handle_api_result(result: Optional[Dict[str, Any]]) -> Any: """ :说明: 处理 API 请求返回值。 :参数: * ``result: Optional[Dict[str, Any]]``: API 返回数据 :返回: - ``Any``: API 调用返回数据 :异常: - ``ActionFailed``: API 调用失败 """ if isinstance(result, dict): if result.get("code") != 0: raise ActionFailed(**result) return result.get("data") else: return result class AESCipher(object): def __init__(self, key): self.block_size = AES.block_size self.key = hashlib.sha256(AESCipher.str_to_bytes(key)).digest() @staticmethod def str_to_bytes(data): u_type = type(b"".decode("utf8")) if isinstance(data, u_type): return data.encode("utf8") return data @staticmethod def _unpad(s): return s[: -ord(s[len(s) - 1 :])] def decrypt(self, enc): iv = enc[: AES.block_size] cipher = AES.new(self.key, AES.MODE_CBC, iv) return self._unpad(cipher.decrypt(enc[AES.block_size :])) def decrypt_string(self, enc): enc = base64.b64decode(enc) return self.decrypt(enc).decode("utf8")

21.80303

71

0.607366

import base64 import hashlib from typing import Any, Dict, Optional from cashews import cache from Crypto.Cipher import AES from nonebot.utils import logger_wrapper from .exception import ActionFailed log = logger_wrapper("FEISHU") cache.setup("mem://") def _handle_api_result(result: Optional[Dict[str, Any]]) -> Any: if isinstance(result, dict): if result.get("code") != 0: raise ActionFailed(**result) return result.get("data") else: return result class AESCipher(object): def __init__(self, key): self.block_size = AES.block_size self.key = hashlib.sha256(AESCipher.str_to_bytes(key)).digest() @staticmethod def str_to_bytes(data): u_type = type(b"".decode("utf8")) if isinstance(data, u_type): return data.encode("utf8") return data @staticmethod def _unpad(s): return s[: -ord(s[len(s) - 1 :])] def decrypt(self, enc): iv = enc[: AES.block_size] cipher = AES.new(self.key, AES.MODE_CBC, iv) return self._unpad(cipher.decrypt(enc[AES.block_size :])) def decrypt_string(self, enc): enc = base64.b64decode(enc) return self.decrypt(enc).decode("utf8")

true

1c2ebcb9f1c7e30c527922e1b531e8cc615065de

1,998

py

Python

src/oidcop/session/info.py

MdreW/oidc-op

684355981ea12516e1b5ef3ed72a4ecf572109bf

[ "Apache-2.0" ]

null

src/oidcop/session/info.py

MdreW/oidc-op

684355981ea12516e1b5ef3ed72a4ecf572109bf

[ "Apache-2.0" ]

null

src/oidcop/session/info.py

MdreW/oidc-op

684355981ea12516e1b5ef3ed72a4ecf572109bf

[ "Apache-2.0" ]

null

from typing import List from typing import Optional from oidcmsg.impexp import ImpExp class SessionInfo(ImpExp): parameter = {"subordinate": [], "revoked": bool, "type": "", "extra_args": {}} def __init__( self, subordinate: Optional[List[str]] = None, revoked: Optional[bool] = False, type: Optional[str] = "", **kwargs ): ImpExp.__init__(self) self.subordinate = subordinate or [] self.revoked = revoked self.type = type self.extra_args = {} def add_subordinate(self, value: str) -> "SessionInfo": if value not in self.subordinate: self.subordinate.append(value) return self def remove_subordinate(self, value: str) -> "SessionInfo": self.subordinate.remove(value) return self def revoke(self) -> "SessionInfo": self.revoked = True return self def is_revoked(self) -> bool: return self.revoked def keys(self): return self.parameter.keys() class UserSessionInfo(SessionInfo): parameter = SessionInfo.parameter.copy() parameter.update( {"user_id": "",} ) def __init__(self, **kwargs): SessionInfo.__init__(self, **kwargs) self.type = "UserSessionInfo" self.user_id = kwargs.get("user_id", "") self.extra_args = {k: v for k, v in kwargs.items() if k not in self.parameter} class ClientSessionInfo(SessionInfo): parameter = SessionInfo.parameter.copy() parameter.update({"client_id": ""}) def __init__(self, **kwargs): SessionInfo.__init__(self, **kwargs) self.type = "ClientSessionInfo" self.client_id = kwargs.get("client_id", "") self.extra_args = {k: v for k, v in kwargs.items() if k not in self.parameter} def find_grant_and_token(self, val: str): for grant in self.subordinate: token = grant.get_token(val) if token: return grant, token

28.140845

86

0.610611

from typing import List from typing import Optional from oidcmsg.impexp import ImpExp class SessionInfo(ImpExp): parameter = {"subordinate": [], "revoked": bool, "type": "", "extra_args": {}} def __init__( self, subordinate: Optional[List[str]] = None, revoked: Optional[bool] = False, type: Optional[str] = "", **kwargs ): ImpExp.__init__(self) self.subordinate = subordinate or [] self.revoked = revoked self.type = type self.extra_args = {} def add_subordinate(self, value: str) -> "SessionInfo": if value not in self.subordinate: self.subordinate.append(value) return self def remove_subordinate(self, value: str) -> "SessionInfo": self.subordinate.remove(value) return self def revoke(self) -> "SessionInfo": self.revoked = True return self def is_revoked(self) -> bool: return self.revoked def keys(self): return self.parameter.keys() class UserSessionInfo(SessionInfo): parameter = SessionInfo.parameter.copy() parameter.update( {"user_id": "",} ) def __init__(self, **kwargs): SessionInfo.__init__(self, **kwargs) self.type = "UserSessionInfo" self.user_id = kwargs.get("user_id", "") self.extra_args = {k: v for k, v in kwargs.items() if k not in self.parameter} class ClientSessionInfo(SessionInfo): parameter = SessionInfo.parameter.copy() parameter.update({"client_id": ""}) def __init__(self, **kwargs): SessionInfo.__init__(self, **kwargs) self.type = "ClientSessionInfo" self.client_id = kwargs.get("client_id", "") self.extra_args = {k: v for k, v in kwargs.items() if k not in self.parameter} def find_grant_and_token(self, val: str): for grant in self.subordinate: token = grant.get_token(val) if token: return grant, token

true

1c2ebce233d748bc3dbd55e3e7f871b51601686e

21,795

py

Python

ModernWarfare/modernwarfare.py

EthanC/Hyde

0325eb12c5849dbe4e4b317bd8d3f026c989e7f9

[ "MIT" ]

14

2020-05-01T13:54:48.000Z

2022-02-14T21:58:35.000Z

ModernWarfare/modernwarfare.py

EthanC/Hyde

0325eb12c5849dbe4e4b317bd8d3f026c989e7f9

[ "MIT" ]

3

2020-06-09T19:24:48.000Z

2021-03-06T11:34:04.000Z

ModernWarfare/modernwarfare.py

EthanC/Hyde

0325eb12c5849dbe4e4b317bd8d3f026c989e7f9

[ "MIT" ]

4

2020-05-24T19:15:08.000Z

2022-02-04T21:20:29.000Z

import logging from typing import Any, Dict, List, Optional, TypedDict from utility import Utility from .database import Database from .XAssets import ( Accessories, BattlePasses, BattlePassItems, Bundles, CallingCards, Camos, Charms, Consumables, Emblems, Equipment, Executions, Features, GameTypes, Gestures, ItemSources, Killstreaks, KioskBR, KioskBRTruck, Maps, MasteryChallenges, MiscellaneousChallenges, MissionItems, Missions, OfficerChallenges, Operators, PlaylistEvents, ProgressionRewards, Quips, Reticles, SeasonalChallenges, SeasonalEvents, Skins, SpecialItems, Splashes, Sprays, Stickers, TurboChallenges, UnlockItemsT9, VehicleCamos, VehicleHorns, Vehicles, VehicleTracks, Weapons, WeaponUnlockChallenges, WeeklyChallengesBR, WeeklyChallengesMP, ) log: logging.Logger = logging.getLogger(__name__) class LootMaster(TypedDict): """Structure of loot/loot_master.csv""" rangeStart: int rangeEnd: int typeName: str typeValue: str hidden: int typeNameLoc: str typeDesc: str typeImg: str breadcrumb: str baseWeaponRef: str class ItemSourceTable(TypedDict): """Structure of mp/itemsourcetable.csv""" marketPlaceID: int refType: str refName: str gameSourceID: str equippableIW8MP: int # bool equippableWZ: int # bool equippableT9: int # bool equippableS4: int # bool lookupType: str class OperatorIDs(TypedDict): """Structure of loot/operator_ids.csv""" id: int ref: str rarity: int price: int salvage: int license: int premium: int # bool class WeaponClassTable(TypedDict): """Structure of mp/weaponClassTable.csv""" index: int ref: str slot: int name: str pluralName: str showInMenus: int # bool unlockTablePrefix: str showInCP: int # bool image: str showInArmory: int # bool previewScene: str attachScenePrefix: str unknown1: str # Not defined in luashared/csvutils.csv unknown2: str # Not defined in luashared/csvutils.csv classImage: str canBeGunsmithed: int # bool attachCategoryWhitelist: str # Array of strings hasVariants: int # bool isWZOnly: int # bool extraAttachCategoryWhitelist: str # Array of strings class AttachmentCategoryTable(TypedDict): """Structure of mp/attachmentcategorytable.csv""" index: int ref: str name: str buttonIndex: int displayOrder: int categoryScene: str smallCategoryScene: str largeCategoryScene: str bone: str defaultLineOffsetX: int defaultLineOffsetY: int defaultLineOffsetZ: int enableBigGunPreviewCamera: int # bool enableSmallGunPreviewCamera: int # bool enableBigShotgunPreviewCamera: int # bool class CamoCategoryTable(TypedDict): """Structure of mp/camocategorytable.csv""" index: int ref: str name: str class ModernWarfare: """Call of Duty: Modern Warfare (IW8)""" def __init__(self: Any, config: dict) -> None: self.ModernWarfare = self self.config: Dict[str, Any] = config.get("ModernWarfare") self.iXAssets: str = self.config["import"]["xassets"] self.iImages: str = self.config["import"]["images"] self.eXAssets: str = self.config["export"]["xassets"] self.eImages: str = self.config["export"]["images"] self.eVideos: str = self.config["export"]["videos"] self.eDatabase: str = self.config["export"]["database"] def Compile(self: Any) -> None: """Compile and export all supported XAsset types for Modern Warfare.""" log.info("Compiling XAssets for Call of Duty: Modern Warfare...") # Global and reused XAssets self.localize: Dict[str, Optional[str]] = ModernWarfare.LoadLocalize(self) self.lootTypes: List[Dict[str, Any]] = Utility.ReadCSV( self, f"{self.iXAssets}/loot/loot_master.csv", LootMaster, 1 ) self.itemSources: List[Dict[str, Any]] = Utility.ReadCSV( self, f"{self.iXAssets}/mp/itemsourcetable.csv", ItemSourceTable ) self.operatorIds: List[Dict[str, Any]] = Utility.ReadCSV( self, f"{self.iXAssets}/loot/operator_ids.csv", OperatorIDs ) self.weaponClasses: List[Dict[str, Any]] = Utility.ReadCSV( self, f"{self.iXAssets}/mp/weaponClassTable.csv", WeaponClassTable ) self.attachCategories: List[Dict[str, Any]] = Utility.ReadCSV( self, f"{self.iXAssets}/mp/attachmentcategorytable.csv", AttachmentCategoryTable, ) self.camoCategories: List[Dict[str, Any]] = Utility.ReadCSV( self, f"{self.iXAssets}/mp/camocategorytable.csv", CamoCategoryTable ) Accessories.Compile(self) BattlePasses.Compile(self) BattlePassItems.Compile(self) Bundles.Compile(self) CallingCards.Compile(self) Camos.Compile(self) Charms.Compile(self) Consumables.Compile(self) Emblems.Compile(self) Equipment.Compile(self) Executions.Compile(self) Features.Compile(self) GameTypes.Compile(self) Gestures.Compile(self) ItemSources.Compile(self) Killstreaks.Compile(self) KioskBR.Compile(self) KioskBRTruck.Compile(self) Maps.Compile(self) MasteryChallenges.Compile(self) MiscellaneousChallenges.Compile(self) MissionItems.Compile(self) Missions.Compile(self) OfficerChallenges.Compile(self) Operators.Compile(self) PlaylistEvents.Compile(self) ProgressionRewards.Compile(self) Quips.Compile(self) Reticles.Compile(self) SeasonalChallenges.Compile(self) SeasonalEvents.Compile(self) Skins.Compile(self) SpecialItems.Compile(self) Splashes.Compile(self) Sprays.Compile(self) Stickers.Compile(self) TurboChallenges.Compile(self) UnlockItemsT9.Compile(self) VehicleCamos.Compile(self) VehicleHorns.Compile(self) Vehicles.Compile(self) VehicleTracks.Compile(self) Weapons.Compile(self) WeaponUnlockChallenges.Compile(self) WeeklyChallengesBR.Compile(self) WeeklyChallengesMP.Compile(self) if self.config.get("compileDatabase") is True: Database.Compile(self) def LoadLocalize(self: Any) -> Dict[str, Optional[str]]: """Load and filter the localized string entries for Modern Warfare.""" localize: Dict[str, Optional[str]] = Utility.ReadFile( self, f"{self.iXAssets}/localize.json" ) placeholders: dict = Utility.ReadFile(self, "ModernWarfare/placeholders.json") for key in localize: value: Optional[str] = localize.get(key) if value is None: continue for placeholder in placeholders.get("whole"): if value.lower() == placeholder.lower(): localize[key] = None break for placeholder in placeholders.get("begins"): if value.lower().startswith(placeholder.lower()) is True: localize[key] = None break for placeholder in placeholders.get("ends"): if value.lower().endswith(placeholder.lower()) is True: localize[key] = None break if (value := localize.get(key)) is not None: value = Utility.StripColorCodes(self, value) value = Utility.StripButtonCodes(self, value) localize[key] = value return localize def GetLootRarity(self: Any, value: int) -> Optional[str]: """Get the loot rarity for the provided value.""" return self.localize.get(f"LOOT_MP/QUALITY_{value}") def GetLootType(self: Any, id: int) -> Optional[str]: """Get the loot type for the provided ID.""" if id is None: return for loot in self.lootTypes: start: int = loot.get("rangeStart") end: int = loot.get("rangeEnd") if (id >= start) and (id <= end): typeNameLoc: Optional[str] = loot.get("typeNameLoc") if typeNameLoc == "LOOT_MP/PLACEHOLDER": continue return self.localize.get(typeNameLoc) for source in self.itemSources: if source.get("marketPlaceID") == id: refType: Optional[str] = source.get("refType") # Partially defined in ui/utils/lootutils.lua if refType == "weapon": return self.localize.get("LOOT_MP/ITEM_TYPE_WEAPON") elif refType == "operator": return self.localize.get("LOOT_MP/OPERATOR") elif refType == "operator_skin": return self.localize.get("LOOT_MP/OPERATOR_SKIN") elif refType == "executions": return self.localize.get("LOOT_MP/OPERATOR_EXECUTION") elif refType == "equipment": return self.localize.get("LOOT_MP/EQUIPMENT") elif refType == "accessory": return self.localize.get("LOOT_MP/WATCH") elif refType == "playercards": return self.localize.get("LOOT_MP/CALLING_CARD") elif refType == "weapon_charm": return self.localize.get("LOOT_MP/CHARM") elif refType == "quip": return self.localize.get("LOOT_MP/OPERATOR_QUIP") elif refType == "camo": return self.localize.get("LOOT_MP/CAMO") elif refType == "emblems": return self.localize.get("LOOT_MP/EMBLEM") elif refType == "attachment": return self.localize.get("LOOT_MP/ATTACHMENT") elif refType == "sticker": return self.localize.get("LOOT_MP/STICKER") elif refType == "xp_token": return self.localize.get("LOOT_MP/CONSUMABLE") elif refType == "markeritem": return self.localize.get("LOOT_MP/CONSUMABLE") elif refType == "reticle": return self.localize.get("LOOT_MP/RETICLE") elif refType == "blueprint": return self.localize.get("LOOT_MP/ITEM_TYPE_WEAPON") elif refType == "battlepass": return self.localize.get("LOOT_MP/BATTLE_PASS") elif refType == "vehicle_track": return self.localize.get("LOOT_MP/VEHICLE_TRACK") elif refType == "vehicle_horn": return self.localize.get("LOOT_MP/VEHICLE_HORN") elif refType == "feature": return self.localize.get("LOOT_MP/FEATURE") elif refType == "gestures": return self.localize.get("LOOT_MP/GESTURES") elif refType == "mission": return self.localize.get("LOOT_MP/MISSION") elif refType == "weapon_attachment": return self.localize.get("LOOT_MP/ATTACHMENT") elif refType == "perk": return self.localize.get("LOOT_MP/PERK") elif refType == "t9_equipment": return self.localize.get("LOOT_MP/EQUIPMENT") elif refType == "killstreak": return self.localize.get("LOOT_MP/STREAK") elif refType == "class": return self.localize.get("LOOT_MP/FEATURE") elif refType == "zm_unlockable": return self.localize.get("LOOT_MP/FEATURE") elif refType == "weapon_skill": return self.localize.get("LOOT_MP/FEATURE") elif refType == "bonuscard": return self.localize.get("LOOT_MP/FEATURE") elif refType == "vehicleskin": return self.localize.get("LOOT_MP/VEHICLE_SKIN") elif refType == "bundle": return self.localize.get("MENU/BUNDLE_TYPE_VARIETY") elif refType == "placeholder": return elif refType == "arcadegame": return self.localize.get("LOOT_MP/ARCADE_GAME") elif refType == "sprays": return self.localize.get("LOOT_MP/SPRAYS") else: log.warning(f"Unknown loot refType {refType} for ID {id}") def GetLootSeason(self: Any, license: int) -> Optional[str]: """Get the loot season for the provided value.""" if license == 0: return elif license == 99: # Defined in ui/utils/lootutils.lua return "Unreleased" elif ((license - 1) % 1000) == 0: # For instances such as the Season 4: Reloaded update. license -= 1 elif ((license - 2) % 1000) == 0: # For instances such as the Season 6 extension. license -= 2 elif (license % 1000) != 0: # Seasonal licenses are multiples of 1,000. return return self.localize.get(f"SEASONS/SEASON_{round(license / 1000)}") def GetOperatorID(self: Any, reference: str) -> Optional[int]: """Get the ID for the specified Operator.""" if reference == "universal_ref": # Universal Operator items do not have an ID, so we'll just # set one ourselves. return 29999 elif reference == "universal_base_ref": # Same reason as universal_ref, however, this is only intended # for use with Operators where isLaunchOperator is True. return 29998 elif reference == "t9_exclusive_ref": # Same reason as universal_ref, however, this is only intended # for use with Black Ops Cold War Operators. return 29997 for operator in self.operatorIds: if reference == operator.get("ref"): return operator.get("id") def GetWeaponClass(self: Any, reference: str) -> Optional[str]: """Get the name of the specified Weapon Class.""" for weaponClass in self.weaponClasses: if reference == weaponClass.get("ref"): return self.localize.get(weaponClass.get("name")) def GetAttachmentCategory(self: Any, reference: str) -> Optional[str]: """Get the name of the specified attachment category.""" for category in self.attachCategories: if category.get("ref") == reference: return self.localize.get(category.get("name")) def GetCamoCategory(self: Any, reference: str) -> Optional[str]: """Get the name of the specified camo category.""" for category in self.camoCategories: if category.get("ref") == reference: return self.localize.get(category.get("name")) def GetAttribute(self: Any, reference: str) -> Optional[str]: """ Get the name of the specified attribute. Defined in ui/utils/weaponutils.lua and ui/utils/vehicleutils.lua """ if reference is None: return None attributes: Dict[str, str] = { "red": "WEAPON/TRACER_RED", "blue": "WEAPON/TRACER_BLUE", "pink": "WEAPON/TRACER_PINK", "green": "WEAPON/TRACER_GREEN", "purple": "WEAPON/TRACER_PURPLE", "freedom": "WEAPON/TRACER_FREEDOM", "shadow": "WEAPON/TRACER_SHADOW", "gold": "WEAPON/TRACER_GOLD", "morte": "WEAPON/TRACER_MORTE", "tesla": "WEAPON/TRACER_TESLA", "sixteenBit": "WEAPON/TRACER_16BIT", "dark": "WEAPON/TRACER_DARK", "light": "WEAPON/TRACER_LIGHT", "orange": "WEAPON/TRACER_ORANGE", "yellow": "WEAPON/TRACER_YELLOW", "soul": "WEAPON/TRACER_SOUL", "purpleGreen": "WEAPON/TRACER_PURPLE_GREEN", "goldPurple": "WEAPON/TRACER_GOLD_PURPLE", "bluePurple": "WEAPON/TRACER_BLUE_PURPLE", "yellowGreen": "WEAPON/TRACER_YELLOW_GREEN", "orangeGreen": "WEAPON/TRACER_ORANGE_GREEN", "whitepink": "WEAPON/TRACER_WHITE_PINK", "paintball": "WEAPON/TRACER_PAINTBALL", "cyan": "WEAPON/TRACER_CYAN", "purplecyan": "WEAPON/TRACER_PURPLE_CYAN", "bluered": "WEAPON/TRACER_BLUE_RED", "greengold": "WEAPON/TRACER_GREEN_GOLD", "numbers": "WEAPON/TRACER_NUMBERS", "paintballalt01": "WEAPON/TRACER_PAINTBALL_ALT_01", "paintballalt02": "WEAPON/TRACER_PAINTBALL_ALT_02", "electricgold": "WEAPON/TRACER_ELECTRIC_GOLD", "lazer": "WEAPON/TRACER_LAZER", "redwhite": "WEAPON/TRACER_RED_WHITE", "orangepurple": "WEAPON/TRACER_ORANGE_PURPLE", "standardDis": "WEAPON/DISMEMBERMENT", "cryoDis": "WEAPON/CRYO_DISMEMBERMENT", "goldDis": "WEAPON/DISMEMBERMENT_GOLD", "electricDis": "WEAPON/DISMEMBERMENT_ELECTRIC", "acidDis": "WEAPON/DISMEMBERMENT_ACID", "runicDis": "WEAPON/DISMEMBERMENT_RUNIC", "shatterBlast": "WEAPON/DISMEMBERMENT_SHATTERBLAST", "radioactive": "WEAPON/DISMEMBERMENT_RADIOACTIVE", "fire": "WEAPON/DISMEMBERMENT_FIRE", "fireworkDis": "WEAPON/DISMEMBERMENT_FIREWORK", "sunburstDis": "WEAPON/DISMEMBERMENT_SUNBURST", "beerDis": "WEAPON/DISMEMBERMENT_BEER", "electricgoldDis": "WEAPON/DISMEMBERMENT_ELECTRIC_GOLD", "numbersdDis": "WEAPON/DISMEMBERMENT_NUMBERS", "purpleelectric": "WEAPON/DISMEMBERMENT_PURPLE_ELECTRIC", "purpleelectricDis": "WEAPON/DISMEMBERMENT_PURPLE_ELECTRIC", "radioactiveblue": "WEAPON/DISMEMBERMENT_RADIOACTIVE_BLUE", "tailLightTracerRed": "VEHICLES/ATTRIBUTE_TAIL_LIGHT_TRACER_RED", "flightTrailStandard": "VEHICLES/ATTRIBUTE_FLIGHT_TRAIL_STANDARD", "flightTrailShadow": "VEHICLES/ATTRIBUTE_FLIGHT_TRAIL_SHADOW", "tireTrailFlame": "VEHICLES/ATTRIBUTE_TIRE_TRAIL_FLAME", "smoke": "VEHICLES/ATTRIBUTE_SMOKE", "tireTrailTesla": "VEHICLES/ATTRIBUTE_TIRE_TRAIL_TESLA", "crimsonGold": "WEAPON/TRACER_CRIMSON_GOLD", "emerald": "WEAPON/TRACER_EMERALD", "amethyst": "WEAPON/TRACER_AMETHYST", "cherryBlossom": "WEAPON/TRACER_CHERRY_BLOSSOM", "ice": "WEAPON/TRACER_ICE", "rainbow": "WEAPON/TRACER_RAINBOW", "black": "WEAPON/TRACER_BLACK", "crimsonRonin": "WEAPON/TRACER_CRIMSON_RONIN", "acid": "WEAPON/TRACER_ACID", "tailLightTracerAkira": "VEHICLES/ATTRIBUTE_TAIL_LIGHT_TRACER_AKIRA", "flightTrailRainbow": "VEHICLES/ATTRIBUTE_FLIGHT_TRAIL_RAINBOW", } if attributes.get(reference) is None: log.warning(f"Unknown attribute for ref {reference}") return self.localize.get(attributes.get(reference)) def GetGameTypeCategory(self: Any, reference: str) -> Optional[str]: """ Get the name of the specified game type category. Defined in ui/utils/mplobbyutils.lua and ui/frontend/mp/gamemodes.lua """ if reference is None: return None categories: Dict[str, str] = { "PrivateTournament": "LUA_MENU/TOURNAMENT", "Plunder": "LUA_MENU/GAMEMODE_PLUNDER", "BattleRoyale": "LUA_MENU/GAMEMODE_BATTLE_ROYALE", "WarzoneAlternate": "LUA_MENU/GAMEMODE_WARZONE_ALTERNATE", "MyModes": "LUA_MENU/MY_MODES", "Cwl": "LUA_MENU/CWL_MODES", "Standard": "LUA_MENU/STANDARD_MODES", "Alternate": "LUA_MENU/ALTERNATE_MODES", "Rebirth": "LUA_MENU_CANTEEN/GAMEMODE_REBIRTH", } if categories.get(reference) is None: log.warning(f"Unknown game type category for ref {reference}") return self.localize.get(categories.get(reference)) def GetPlatformExclusivity(self: Any, reference: str) -> str: """ Get the name of the specified platform. Defined in ui/utils/lui.lua """ if reference is None: return elif reference == "pc": return "battlenet" elif reference == "sy": return "playstation" elif reference == "ms": return "xbox" else: log.warning(f"Unknown exclusivity platform for ref {reference}") def GetTitleAvailability(self: Any, id: int) -> Dict[str, bool]: """Get the title availability for the specified item.""" for item in self.itemSources: if id == item.get("marketPlaceID"): return { "vanguard": bool(item.get("equippableS4")), "coldWar": bool(item.get("equippableT9")), "warzone": bool(item.get("equippableWZ")), "modernWarfare": bool(item.get("equippableIW8MP")), } return { "vanguard": False, "coldWar": False, "warzone": True, "modernWarfare": True, }

36.446488

86

0.590411

import logging from typing import Any, Dict, List, Optional, TypedDict from utility import Utility from .database import Database from .XAssets import ( Accessories, BattlePasses, BattlePassItems, Bundles, CallingCards, Camos, Charms, Consumables, Emblems, Equipment, Executions, Features, GameTypes, Gestures, ItemSources, Killstreaks, KioskBR, KioskBRTruck, Maps, MasteryChallenges, MiscellaneousChallenges, MissionItems, Missions, OfficerChallenges, Operators, PlaylistEvents, ProgressionRewards, Quips, Reticles, SeasonalChallenges, SeasonalEvents, Skins, SpecialItems, Splashes, Sprays, Stickers, TurboChallenges, UnlockItemsT9, VehicleCamos, VehicleHorns, Vehicles, VehicleTracks, Weapons, WeaponUnlockChallenges, WeeklyChallengesBR, WeeklyChallengesMP, ) log: logging.Logger = logging.getLogger(__name__) class LootMaster(TypedDict): rangeStart: int rangeEnd: int typeName: str typeValue: str hidden: int typeNameLoc: str typeDesc: str typeImg: str breadcrumb: str baseWeaponRef: str class ItemSourceTable(TypedDict): marketPlaceID: int refType: str refName: str gameSourceID: str equippableIW8MP: int equippableWZ: int equippableT9: int equippableS4: int lookupType: str class OperatorIDs(TypedDict): id: int ref: str rarity: int price: int salvage: int license: int premium: int class WeaponClassTable(TypedDict): index: int ref: str slot: int name: str pluralName: str showInMenus: int unlockTablePrefix: str showInCP: int image: str showInArmory: int previewScene: str attachScenePrefix: str unknown1: str unknown2: str classImage: str canBeGunsmithed: int attachCategoryWhitelist: str hasVariants: int isWZOnly: int extraAttachCategoryWhitelist: str class AttachmentCategoryTable(TypedDict): index: int ref: str name: str buttonIndex: int displayOrder: int categoryScene: str smallCategoryScene: str largeCategoryScene: str bone: str defaultLineOffsetX: int defaultLineOffsetY: int defaultLineOffsetZ: int enableBigGunPreviewCamera: int enableSmallGunPreviewCamera: int enableBigShotgunPreviewCamera: int class CamoCategoryTable(TypedDict): index: int ref: str name: str class ModernWarfare: def __init__(self: Any, config: dict) -> None: self.ModernWarfare = self self.config: Dict[str, Any] = config.get("ModernWarfare") self.iXAssets: str = self.config["import"]["xassets"] self.iImages: str = self.config["import"]["images"] self.eXAssets: str = self.config["export"]["xassets"] self.eImages: str = self.config["export"]["images"] self.eVideos: str = self.config["export"]["videos"] self.eDatabase: str = self.config["export"]["database"] def Compile(self: Any) -> None: log.info("Compiling XAssets for Call of Duty: Modern Warfare...") self.localize: Dict[str, Optional[str]] = ModernWarfare.LoadLocalize(self) self.lootTypes: List[Dict[str, Any]] = Utility.ReadCSV( self, f"{self.iXAssets}/loot/loot_master.csv", LootMaster, 1 ) self.itemSources: List[Dict[str, Any]] = Utility.ReadCSV( self, f"{self.iXAssets}/mp/itemsourcetable.csv", ItemSourceTable ) self.operatorIds: List[Dict[str, Any]] = Utility.ReadCSV( self, f"{self.iXAssets}/loot/operator_ids.csv", OperatorIDs ) self.weaponClasses: List[Dict[str, Any]] = Utility.ReadCSV( self, f"{self.iXAssets}/mp/weaponClassTable.csv", WeaponClassTable ) self.attachCategories: List[Dict[str, Any]] = Utility.ReadCSV( self, f"{self.iXAssets}/mp/attachmentcategorytable.csv", AttachmentCategoryTable, ) self.camoCategories: List[Dict[str, Any]] = Utility.ReadCSV( self, f"{self.iXAssets}/mp/camocategorytable.csv", CamoCategoryTable ) Accessories.Compile(self) BattlePasses.Compile(self) BattlePassItems.Compile(self) Bundles.Compile(self) CallingCards.Compile(self) Camos.Compile(self) Charms.Compile(self) Consumables.Compile(self) Emblems.Compile(self) Equipment.Compile(self) Executions.Compile(self) Features.Compile(self) GameTypes.Compile(self) Gestures.Compile(self) ItemSources.Compile(self) Killstreaks.Compile(self) KioskBR.Compile(self) KioskBRTruck.Compile(self) Maps.Compile(self) MasteryChallenges.Compile(self) MiscellaneousChallenges.Compile(self) MissionItems.Compile(self) Missions.Compile(self) OfficerChallenges.Compile(self) Operators.Compile(self) PlaylistEvents.Compile(self) ProgressionRewards.Compile(self) Quips.Compile(self) Reticles.Compile(self) SeasonalChallenges.Compile(self) SeasonalEvents.Compile(self) Skins.Compile(self) SpecialItems.Compile(self) Splashes.Compile(self) Sprays.Compile(self) Stickers.Compile(self) TurboChallenges.Compile(self) UnlockItemsT9.Compile(self) VehicleCamos.Compile(self) VehicleHorns.Compile(self) Vehicles.Compile(self) VehicleTracks.Compile(self) Weapons.Compile(self) WeaponUnlockChallenges.Compile(self) WeeklyChallengesBR.Compile(self) WeeklyChallengesMP.Compile(self) if self.config.get("compileDatabase") is True: Database.Compile(self) def LoadLocalize(self: Any) -> Dict[str, Optional[str]]: localize: Dict[str, Optional[str]] = Utility.ReadFile( self, f"{self.iXAssets}/localize.json" ) placeholders: dict = Utility.ReadFile(self, "ModernWarfare/placeholders.json") for key in localize: value: Optional[str] = localize.get(key) if value is None: continue for placeholder in placeholders.get("whole"): if value.lower() == placeholder.lower(): localize[key] = None break for placeholder in placeholders.get("begins"): if value.lower().startswith(placeholder.lower()) is True: localize[key] = None break for placeholder in placeholders.get("ends"): if value.lower().endswith(placeholder.lower()) is True: localize[key] = None break if (value := localize.get(key)) is not None: value = Utility.StripColorCodes(self, value) value = Utility.StripButtonCodes(self, value) localize[key] = value return localize def GetLootRarity(self: Any, value: int) -> Optional[str]: return self.localize.get(f"LOOT_MP/QUALITY_{value}") def GetLootType(self: Any, id: int) -> Optional[str]: if id is None: return for loot in self.lootTypes: start: int = loot.get("rangeStart") end: int = loot.get("rangeEnd") if (id >= start) and (id <= end): typeNameLoc: Optional[str] = loot.get("typeNameLoc") if typeNameLoc == "LOOT_MP/PLACEHOLDER": continue return self.localize.get(typeNameLoc) for source in self.itemSources: if source.get("marketPlaceID") == id: refType: Optional[str] = source.get("refType") if refType == "weapon": return self.localize.get("LOOT_MP/ITEM_TYPE_WEAPON") elif refType == "operator": return self.localize.get("LOOT_MP/OPERATOR") elif refType == "operator_skin": return self.localize.get("LOOT_MP/OPERATOR_SKIN") elif refType == "executions": return self.localize.get("LOOT_MP/OPERATOR_EXECUTION") elif refType == "equipment": return self.localize.get("LOOT_MP/EQUIPMENT") elif refType == "accessory": return self.localize.get("LOOT_MP/WATCH") elif refType == "playercards": return self.localize.get("LOOT_MP/CALLING_CARD") elif refType == "weapon_charm": return self.localize.get("LOOT_MP/CHARM") elif refType == "quip": return self.localize.get("LOOT_MP/OPERATOR_QUIP") elif refType == "camo": return self.localize.get("LOOT_MP/CAMO") elif refType == "emblems": return self.localize.get("LOOT_MP/EMBLEM") elif refType == "attachment": return self.localize.get("LOOT_MP/ATTACHMENT") elif refType == "sticker": return self.localize.get("LOOT_MP/STICKER") elif refType == "xp_token": return self.localize.get("LOOT_MP/CONSUMABLE") elif refType == "markeritem": return self.localize.get("LOOT_MP/CONSUMABLE") elif refType == "reticle": return self.localize.get("LOOT_MP/RETICLE") elif refType == "blueprint": return self.localize.get("LOOT_MP/ITEM_TYPE_WEAPON") elif refType == "battlepass": return self.localize.get("LOOT_MP/BATTLE_PASS") elif refType == "vehicle_track": return self.localize.get("LOOT_MP/VEHICLE_TRACK") elif refType == "vehicle_horn": return self.localize.get("LOOT_MP/VEHICLE_HORN") elif refType == "feature": return self.localize.get("LOOT_MP/FEATURE") elif refType == "gestures": return self.localize.get("LOOT_MP/GESTURES") elif refType == "mission": return self.localize.get("LOOT_MP/MISSION") elif refType == "weapon_attachment": return self.localize.get("LOOT_MP/ATTACHMENT") elif refType == "perk": return self.localize.get("LOOT_MP/PERK") elif refType == "t9_equipment": return self.localize.get("LOOT_MP/EQUIPMENT") elif refType == "killstreak": return self.localize.get("LOOT_MP/STREAK") elif refType == "class": return self.localize.get("LOOT_MP/FEATURE") elif refType == "zm_unlockable": return self.localize.get("LOOT_MP/FEATURE") elif refType == "weapon_skill": return self.localize.get("LOOT_MP/FEATURE") elif refType == "bonuscard": return self.localize.get("LOOT_MP/FEATURE") elif refType == "vehicleskin": return self.localize.get("LOOT_MP/VEHICLE_SKIN") elif refType == "bundle": return self.localize.get("MENU/BUNDLE_TYPE_VARIETY") elif refType == "placeholder": return elif refType == "arcadegame": return self.localize.get("LOOT_MP/ARCADE_GAME") elif refType == "sprays": return self.localize.get("LOOT_MP/SPRAYS") else: log.warning(f"Unknown loot refType {refType} for ID {id}") def GetLootSeason(self: Any, license: int) -> Optional[str]: if license == 0: return elif license == 99: return "Unreleased" elif ((license - 1) % 1000) == 0: license -= 1 elif ((license - 2) % 1000) == 0: license -= 2 elif (license % 1000) != 0: return return self.localize.get(f"SEASONS/SEASON_{round(license / 1000)}") def GetOperatorID(self: Any, reference: str) -> Optional[int]: if reference == "universal_ref": # set one ourselves. return 29999 elif reference == "universal_base_ref": # Same reason as universal_ref, however, this is only intended # for use with Operators where isLaunchOperator is True. return 29998 elif reference == "t9_exclusive_ref": # Same reason as universal_ref, however, this is only intended # for use with Black Ops Cold War Operators. return 29997 for operator in self.operatorIds: if reference == operator.get("ref"): return operator.get("id") def GetWeaponClass(self: Any, reference: str) -> Optional[str]: for weaponClass in self.weaponClasses: if reference == weaponClass.get("ref"): return self.localize.get(weaponClass.get("name")) def GetAttachmentCategory(self: Any, reference: str) -> Optional[str]: for category in self.attachCategories: if category.get("ref") == reference: return self.localize.get(category.get("name")) def GetCamoCategory(self: Any, reference: str) -> Optional[str]: for category in self.camoCategories: if category.get("ref") == reference: return self.localize.get(category.get("name")) def GetAttribute(self: Any, reference: str) -> Optional[str]: if reference is None: return None attributes: Dict[str, str] = { "red": "WEAPON/TRACER_RED", "blue": "WEAPON/TRACER_BLUE", "pink": "WEAPON/TRACER_PINK", "green": "WEAPON/TRACER_GREEN", "purple": "WEAPON/TRACER_PURPLE", "freedom": "WEAPON/TRACER_FREEDOM", "shadow": "WEAPON/TRACER_SHADOW", "gold": "WEAPON/TRACER_GOLD", "morte": "WEAPON/TRACER_MORTE", "tesla": "WEAPON/TRACER_TESLA", "sixteenBit": "WEAPON/TRACER_16BIT", "dark": "WEAPON/TRACER_DARK", "light": "WEAPON/TRACER_LIGHT", "orange": "WEAPON/TRACER_ORANGE", "yellow": "WEAPON/TRACER_YELLOW", "soul": "WEAPON/TRACER_SOUL", "purpleGreen": "WEAPON/TRACER_PURPLE_GREEN", "goldPurple": "WEAPON/TRACER_GOLD_PURPLE", "bluePurple": "WEAPON/TRACER_BLUE_PURPLE", "yellowGreen": "WEAPON/TRACER_YELLOW_GREEN", "orangeGreen": "WEAPON/TRACER_ORANGE_GREEN", "whitepink": "WEAPON/TRACER_WHITE_PINK", "paintball": "WEAPON/TRACER_PAINTBALL", "cyan": "WEAPON/TRACER_CYAN", "purplecyan": "WEAPON/TRACER_PURPLE_CYAN", "bluered": "WEAPON/TRACER_BLUE_RED", "greengold": "WEAPON/TRACER_GREEN_GOLD", "numbers": "WEAPON/TRACER_NUMBERS", "paintballalt01": "WEAPON/TRACER_PAINTBALL_ALT_01", "paintballalt02": "WEAPON/TRACER_PAINTBALL_ALT_02", "electricgold": "WEAPON/TRACER_ELECTRIC_GOLD", "lazer": "WEAPON/TRACER_LAZER", "redwhite": "WEAPON/TRACER_RED_WHITE", "orangepurple": "WEAPON/TRACER_ORANGE_PURPLE", "standardDis": "WEAPON/DISMEMBERMENT", "cryoDis": "WEAPON/CRYO_DISMEMBERMENT", "goldDis": "WEAPON/DISMEMBERMENT_GOLD", "electricDis": "WEAPON/DISMEMBERMENT_ELECTRIC", "acidDis": "WEAPON/DISMEMBERMENT_ACID", "runicDis": "WEAPON/DISMEMBERMENT_RUNIC", "shatterBlast": "WEAPON/DISMEMBERMENT_SHATTERBLAST", "radioactive": "WEAPON/DISMEMBERMENT_RADIOACTIVE", "fire": "WEAPON/DISMEMBERMENT_FIRE", "fireworkDis": "WEAPON/DISMEMBERMENT_FIREWORK", "sunburstDis": "WEAPON/DISMEMBERMENT_SUNBURST", "beerDis": "WEAPON/DISMEMBERMENT_BEER", "electricgoldDis": "WEAPON/DISMEMBERMENT_ELECTRIC_GOLD", "numbersdDis": "WEAPON/DISMEMBERMENT_NUMBERS", "purpleelectric": "WEAPON/DISMEMBERMENT_PURPLE_ELECTRIC", "purpleelectricDis": "WEAPON/DISMEMBERMENT_PURPLE_ELECTRIC", "radioactiveblue": "WEAPON/DISMEMBERMENT_RADIOACTIVE_BLUE", "tailLightTracerRed": "VEHICLES/ATTRIBUTE_TAIL_LIGHT_TRACER_RED", "flightTrailStandard": "VEHICLES/ATTRIBUTE_FLIGHT_TRAIL_STANDARD", "flightTrailShadow": "VEHICLES/ATTRIBUTE_FLIGHT_TRAIL_SHADOW", "tireTrailFlame": "VEHICLES/ATTRIBUTE_TIRE_TRAIL_FLAME", "smoke": "VEHICLES/ATTRIBUTE_SMOKE", "tireTrailTesla": "VEHICLES/ATTRIBUTE_TIRE_TRAIL_TESLA", "crimsonGold": "WEAPON/TRACER_CRIMSON_GOLD", "emerald": "WEAPON/TRACER_EMERALD", "amethyst": "WEAPON/TRACER_AMETHYST", "cherryBlossom": "WEAPON/TRACER_CHERRY_BLOSSOM", "ice": "WEAPON/TRACER_ICE", "rainbow": "WEAPON/TRACER_RAINBOW", "black": "WEAPON/TRACER_BLACK", "crimsonRonin": "WEAPON/TRACER_CRIMSON_RONIN", "acid": "WEAPON/TRACER_ACID", "tailLightTracerAkira": "VEHICLES/ATTRIBUTE_TAIL_LIGHT_TRACER_AKIRA", "flightTrailRainbow": "VEHICLES/ATTRIBUTE_FLIGHT_TRAIL_RAINBOW", } if attributes.get(reference) is None: log.warning(f"Unknown attribute for ref {reference}") return self.localize.get(attributes.get(reference)) def GetGameTypeCategory(self: Any, reference: str) -> Optional[str]: if reference is None: return None categories: Dict[str, str] = { "PrivateTournament": "LUA_MENU/TOURNAMENT", "Plunder": "LUA_MENU/GAMEMODE_PLUNDER", "BattleRoyale": "LUA_MENU/GAMEMODE_BATTLE_ROYALE", "WarzoneAlternate": "LUA_MENU/GAMEMODE_WARZONE_ALTERNATE", "MyModes": "LUA_MENU/MY_MODES", "Cwl": "LUA_MENU/CWL_MODES", "Standard": "LUA_MENU/STANDARD_MODES", "Alternate": "LUA_MENU/ALTERNATE_MODES", "Rebirth": "LUA_MENU_CANTEEN/GAMEMODE_REBIRTH", } if categories.get(reference) is None: log.warning(f"Unknown game type category for ref {reference}") return self.localize.get(categories.get(reference)) def GetPlatformExclusivity(self: Any, reference: str) -> str: if reference is None: return elif reference == "pc": return "battlenet" elif reference == "sy": return "playstation" elif reference == "ms": return "xbox" else: log.warning(f"Unknown exclusivity platform for ref {reference}") def GetTitleAvailability(self: Any, id: int) -> Dict[str, bool]: for item in self.itemSources: if id == item.get("marketPlaceID"): return { "vanguard": bool(item.get("equippableS4")), "coldWar": bool(item.get("equippableT9")), "warzone": bool(item.get("equippableWZ")), "modernWarfare": bool(item.get("equippableIW8MP")), } return { "vanguard": False, "coldWar": False, "warzone": True, "modernWarfare": True, }

true

1c2ebfbba2889019f3daeaf8c2c41e1faae71ede

896

py

Python

examples/core/produce_c_file.py

fengjixuchui/src

0c5a6cd8057717f73b1373f8d85eb9b19e1934e1

[ "BSD-3-Clause" ]

1,160

2015-05-02T15:13:20.000Z

2022-03-31T20:04:28.000Z

examples/core/produce_c_file.py

fengjixuchui/src

0c5a6cd8057717f73b1373f8d85eb9b19e1934e1

[ "BSD-3-Clause" ]

19

2015-04-20T13:47:00.000Z

2021-07-07T13:00:42.000Z

examples/core/produce_c_file.py

fengjixuchui/src

0c5a6cd8057717f73b1373f8d85eb9b19e1934e1

[ "BSD-3-Clause" ]

257

2015-04-01T21:42:33.000Z

2022-03-10T11:57:51.000Z

""" summary: decompile entire file description: automate IDA to perform auto-analysis on a file and, once that is done, produce a .c file containing the decompilation of all the functions in that file. Run like so: ida -A "-S...path/to/produce_c_file.py" <binary-file> where: * -A instructs IDA to run in non-interactive mode * -S holds a path to the script to run (note this is a single token; there is no space between '-S' and its path.) """ import ida_pro import ida_auto import ida_loader import ida_hexrays # derive output file name idb_path = ida_loader.get_path(ida_loader.PATH_TYPE_IDB) c_path = "%s.c" % idb_path ida_auto.auto_wait() # wait for end of auto-analysis ida_hexrays.decompile_many( # generate .c file c_path, None, ida_hexrays.VDRUN_NEWFILE |ida_hexrays.VDRUN_SILENT |ida_hexrays.VDRUN_MAYSTOP) ida_pro.qexit(0)

23.578947

72

0.722098

import ida_pro import ida_auto import ida_loader import ida_hexrays idb_path = ida_loader.get_path(ida_loader.PATH_TYPE_IDB) c_path = "%s.c" % idb_path ida_auto.auto_wait() ida_hexrays.decompile_many( c_path, None, ida_hexrays.VDRUN_NEWFILE |ida_hexrays.VDRUN_SILENT |ida_hexrays.VDRUN_MAYSTOP) ida_pro.qexit(0)

true

1c2ebfd75fe3944d8065afbbfce8e4cb94d66e50

13,075

py

Python

src/saml2/httpbase.py

skanct/pysaml2

0c1e26a6dd8759962857a30ebd67f63fe9e881ee

[ "Apache-2.0" ]

5,079

2015-01-01T03:39:46.000Z

2022-03-31T07:38:22.000Z

desktop/core/ext-py/pysaml2-4.4.0/src/saml2/httpbase.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

1,623

2015-01-01T08:06:24.000Z

2022-03-30T19:48:52.000Z

desktop/core/ext-py/pysaml2-4.4.0/src/saml2/httpbase.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

2,033

2015-01-04T07:18:02.000Z

2022-03-28T19:55:47.000Z

import calendar import six from six.moves import http_cookiejar import copy import re from six.moves.urllib.parse import urlparse from six.moves.urllib.parse import urlencode import requests import time from six.moves.http_cookies import SimpleCookie from saml2.time_util import utc_now from saml2 import class_name, SAMLError from saml2.pack import http_form_post_message from saml2.pack import http_post_message from saml2.pack import make_soap_enveloped_saml_thingy from saml2.pack import http_redirect_message import logging logger = logging.getLogger(__name__) if requests.__version__ < "2.0.0": DICT_HEADERS = False else: DICT_HEADERS = True __author__ = 'rolandh' ATTRS = {"version": None, "name": "", "value": None, "port": None, "port_specified": False, "domain": "", "domain_specified": False, "domain_initial_dot": False, "path": "", "path_specified": False, "secure": False, "expires": None, "discard": True, "comment": None, "comment_url": None, "rest": "", "rfc2109": True} PAIRS = { "port": "port_specified", "domain": "domain_specified", "path": "path_specified" } class ConnectionError(SAMLError): pass class HTTPError(SAMLError): pass TIME_FORMAT = ["%d-%b-%Y %H:%M:%S %Z", "%d-%b-%y %H:%M:%S %Z", "%d %b %Y %H:%M:%S %Z"] def _since_epoch(cdate): """ :param cdate: date format 'Wed, 06-Jun-2012 01:34:34 GMT' :return: UTC time """ if len(cdate) < 29: # somethings broken if len(cdate) < 5: return utc_now() cdate = cdate[5:] # assume short weekday, i.e. do not support obsolete RFC 1036 date format t = -1 for time_format in TIME_FORMAT : try: t = time.strptime(cdate, time_format) # e.g. 18-Apr-2014 12:30:51 GMT except ValueError: pass else: break if t == -1: raise (Exception, 'ValueError: Date "{0}" does not match any of: {1}'.format( cdate,TIME_FORMAT)) return calendar.timegm(t) def set_list2dict(sl): return dict(sl) def dict2set_list(dic): return [(k, v) for k, v in dic.items()] class HTTPBase(object): def __init__(self, verify=True, ca_bundle=None, key_file=None, cert_file=None): self.request_args = {"allow_redirects": False} #self.cookies = {} self.cookiejar = http_cookiejar.CookieJar() self.request_args["verify"] = verify if verify: if ca_bundle: self.request_args["verify"] = ca_bundle if key_file: self.request_args["cert"] = (cert_file, key_file) self.sec = None self.user = None self.passwd = None def cookies(self, url): """ Return cookies that are matching the path and are still valid :param url: :return: """ part = urlparse(url) #if part.port: # _domain = "%s:%s" % (part.hostname, part.port) #else: _domain = part.hostname cookie_dict = {} now = utc_now() for _, a in list(self.cookiejar._cookies.items()): for _, b in a.items(): for cookie in list(b.values()): # print(cookie) if cookie.expires and cookie.expires <= now: continue if not re.search("%s$" % cookie.domain, _domain): continue if not re.match(cookie.path, part.path): continue cookie_dict[cookie.name] = cookie.value return cookie_dict def set_cookie(self, kaka, request): """Returns a http_cookiejar.Cookie based on a set-cookie header line""" if not kaka: return part = urlparse(request.url) _domain = part.hostname logger.debug("%s: '%s'", _domain, kaka) for cookie_name, morsel in kaka.items(): std_attr = ATTRS.copy() std_attr["name"] = cookie_name _tmp = morsel.coded_value if _tmp.startswith('"') and _tmp.endswith('"'): std_attr["value"] = _tmp[1:-1] else: std_attr["value"] = _tmp std_attr["version"] = 0 # copy attributes that have values for attr in morsel.keys(): if attr in ATTRS: if morsel[attr]: if attr == "expires": std_attr[attr] = _since_epoch(morsel[attr]) elif attr == "path": if morsel[attr].endswith(","): std_attr[attr] = morsel[attr][:-1] else: std_attr[attr] = morsel[attr] else: std_attr[attr] = morsel[attr] elif attr == "max-age": if morsel["max-age"]: std_attr["expires"] = time.time() + int(morsel["max-age"]) for att, item in PAIRS.items(): if std_attr[att]: std_attr[item] = True if std_attr["domain"]: if std_attr["domain"].startswith("."): std_attr["domain_initial_dot"] = True else: std_attr["domain"] = _domain std_attr["domain_specified"] = True if morsel["max-age"] is 0: try: self.cookiejar.clear(domain=std_attr["domain"], path=std_attr["path"], name=std_attr["name"]) except ValueError: pass elif std_attr["expires"] and std_attr["expires"] < utc_now(): try: self.cookiejar.clear(domain=std_attr["domain"], path=std_attr["path"], name=std_attr["name"]) except ValueError: pass else: new_cookie = http_cookiejar.Cookie(**std_attr) self.cookiejar.set_cookie(new_cookie) def send(self, url, method="GET", **kwargs): _kwargs = copy.copy(self.request_args) if kwargs: _kwargs.update(kwargs) if self.cookiejar: _cd = self.cookies(url) if _cd: _kwargs["cookies"] = _cd if self.user and self.passwd: _kwargs["auth"] = (self.user, self.passwd) if "headers" in _kwargs and isinstance(_kwargs["headers"], list): if DICT_HEADERS: # requests.request wants a dict of headers, not a list of tuples _kwargs["headers"] = dict(_kwargs["headers"]) try: logger.debug("%s to %s", method, url) for arg in ["cookies", "data", "auth"]: try: logger.debug("%s: %s", arg.upper(), _kwargs[arg]) except KeyError: pass r = requests.request(method, url, **_kwargs) logger.debug("Response status: %s", r.status_code) except requests.ConnectionError as exc: raise ConnectionError("%s" % exc) try: self.set_cookie(SimpleCookie(r.headers["set-cookie"]), r) except AttributeError: pass except KeyError: pass return r @staticmethod def use_http_post(message, destination, relay_state, typ="SAMLRequest"): """ Return a urlencoded message that should be POSTed to the recipient. :param message: The response :param destination: Where the response should be sent :param relay_state: The relay_state received in the request :param typ: Whether a Request, Response or Artifact :return: dictionary """ if not isinstance(message, six.string_types): message = "%s" % (message,) return http_post_message(message, relay_state, typ) @staticmethod def use_http_form_post(message, destination, relay_state, typ="SAMLRequest"): """ Return a form that will automagically execute and POST the message to the recipient. :param message: :param destination: :param relay_state: :param typ: Whether a Request, Response or Artifact :return: dictionary """ if not isinstance(message, six.string_types): message = "%s" % (message,) return http_form_post_message(message, destination, relay_state, typ) @staticmethod def use_http_artifact(message, destination="", relay_state=""): if relay_state: query = urlencode({"SAMLart": message, "RelayState": relay_state}) else: query = urlencode({"SAMLart": message}) info = { "data": "", "url": "%s?%s" % (destination, query) } return info @staticmethod def use_http_uri(message, typ, destination="", relay_state=""): if "\n" in message: data = message.split("\n")[1] else: data = message.strip() if typ == "SAMLResponse": info = { "data": data, "headers": [ ("Content-Type", "application/samlassertion+xml"), ("Cache-Control", "no-cache, no-store"), ("Pragma", "no-cache") ] } elif typ == "SAMLRequest": # msg should be an identifier if relay_state: query = urlencode({"ID": message, "RelayState": relay_state}) else: query = urlencode({"ID": message}) info = { "data": "", "url": "%s?%s" % (destination, query) } else: raise NotImplemented return info def use_soap(self, request, destination="", soap_headers=None, sign=False, **kwargs): """ Construct the necessary information for using SOAP+POST :param request: :param destination: :param soap_headers: :param sign: :return: dictionary """ headers = [("content-type", "application/soap+xml")] soap_message = make_soap_enveloped_saml_thingy(request, soap_headers) logger.debug("SOAP message: %s", soap_message) if sign and self.sec: _signed = self.sec.sign_statement(soap_message, class_name=class_name(request), node_id=request.id) soap_message = _signed return {"url": destination, "method": "POST", "data": soap_message, "headers": headers} def send_using_soap(self, request, destination, headers=None, sign=False): """ Send a message using SOAP+POST :param request: :param destination: :param headers: :param sign: :return: """ # _response = self.server.post(soap_message, headers, path=path) try: args = self.use_soap(request, destination, headers, sign) args["headers"] = dict(args["headers"]) response = self.send(**args) except Exception as exc: logger.info("HTTPClient exception: %s", exc) raise if response.status_code == 200: logger.info("SOAP response: %s", response.text) return response else: raise HTTPError("%d:%s" % (response.status_code, response.content)) def add_credentials(self, user, passwd): self.user = user self.passwd = passwd @staticmethod def use_http_get(message, destination, relay_state, typ="SAMLRequest", sigalg="", signer=None, **kwargs): """ Send a message using GET, this is the HTTP-Redirect case so no direct response is expected to this request. :param message: :param destination: :param relay_state: :param typ: Whether a Request, Response or Artifact :param sigalg: Which algorithm the signature function will use to sign the message :param signer: A signing function that can be used to sign the message :return: dictionary """ if not isinstance(message, six.string_types): message = "%s" % (message,) return http_redirect_message(message, destination, relay_state, typ, sigalg, signer)

31.735437

95

0.524512

import calendar import six from six.moves import http_cookiejar import copy import re from six.moves.urllib.parse import urlparse from six.moves.urllib.parse import urlencode import requests import time from six.moves.http_cookies import SimpleCookie from saml2.time_util import utc_now from saml2 import class_name, SAMLError from saml2.pack import http_form_post_message from saml2.pack import http_post_message from saml2.pack import make_soap_enveloped_saml_thingy from saml2.pack import http_redirect_message import logging logger = logging.getLogger(__name__) if requests.__version__ < "2.0.0": DICT_HEADERS = False else: DICT_HEADERS = True __author__ = 'rolandh' ATTRS = {"version": None, "name": "", "value": None, "port": None, "port_specified": False, "domain": "", "domain_specified": False, "domain_initial_dot": False, "path": "", "path_specified": False, "secure": False, "expires": None, "discard": True, "comment": None, "comment_url": None, "rest": "", "rfc2109": True} PAIRS = { "port": "port_specified", "domain": "domain_specified", "path": "path_specified" } class ConnectionError(SAMLError): pass class HTTPError(SAMLError): pass TIME_FORMAT = ["%d-%b-%Y %H:%M:%S %Z", "%d-%b-%y %H:%M:%S %Z", "%d %b %Y %H:%M:%S %Z"] def _since_epoch(cdate): if len(cdate) < 29: if len(cdate) < 5: return utc_now() cdate = cdate[5:] t = -1 for time_format in TIME_FORMAT : try: t = time.strptime(cdate, time_format) except ValueError: pass else: break if t == -1: raise (Exception, 'ValueError: Date "{0}" does not match any of: {1}'.format( cdate,TIME_FORMAT)) return calendar.timegm(t) def set_list2dict(sl): return dict(sl) def dict2set_list(dic): return [(k, v) for k, v in dic.items()] class HTTPBase(object): def __init__(self, verify=True, ca_bundle=None, key_file=None, cert_file=None): self.request_args = {"allow_redirects": False} self.cookiejar = http_cookiejar.CookieJar() self.request_args["verify"] = verify if verify: if ca_bundle: self.request_args["verify"] = ca_bundle if key_file: self.request_args["cert"] = (cert_file, key_file) self.sec = None self.user = None self.passwd = None def cookies(self, url): part = urlparse(url) _domain = part.hostname cookie_dict = {} now = utc_now() for _, a in list(self.cookiejar._cookies.items()): for _, b in a.items(): for cookie in list(b.values()): if cookie.expires and cookie.expires <= now: continue if not re.search("%s$" % cookie.domain, _domain): continue if not re.match(cookie.path, part.path): continue cookie_dict[cookie.name] = cookie.value return cookie_dict def set_cookie(self, kaka, request): if not kaka: return part = urlparse(request.url) _domain = part.hostname logger.debug("%s: '%s'", _domain, kaka) for cookie_name, morsel in kaka.items(): std_attr = ATTRS.copy() std_attr["name"] = cookie_name _tmp = morsel.coded_value if _tmp.startswith('"') and _tmp.endswith('"'): std_attr["value"] = _tmp[1:-1] else: std_attr["value"] = _tmp std_attr["version"] = 0 for attr in morsel.keys(): if attr in ATTRS: if morsel[attr]: if attr == "expires": std_attr[attr] = _since_epoch(morsel[attr]) elif attr == "path": if morsel[attr].endswith(","): std_attr[attr] = morsel[attr][:-1] else: std_attr[attr] = morsel[attr] else: std_attr[attr] = morsel[attr] elif attr == "max-age": if morsel["max-age"]: std_attr["expires"] = time.time() + int(morsel["max-age"]) for att, item in PAIRS.items(): if std_attr[att]: std_attr[item] = True if std_attr["domain"]: if std_attr["domain"].startswith("."): std_attr["domain_initial_dot"] = True else: std_attr["domain"] = _domain std_attr["domain_specified"] = True if morsel["max-age"] is 0: try: self.cookiejar.clear(domain=std_attr["domain"], path=std_attr["path"], name=std_attr["name"]) except ValueError: pass elif std_attr["expires"] and std_attr["expires"] < utc_now(): try: self.cookiejar.clear(domain=std_attr["domain"], path=std_attr["path"], name=std_attr["name"]) except ValueError: pass else: new_cookie = http_cookiejar.Cookie(**std_attr) self.cookiejar.set_cookie(new_cookie) def send(self, url, method="GET", **kwargs): _kwargs = copy.copy(self.request_args) if kwargs: _kwargs.update(kwargs) if self.cookiejar: _cd = self.cookies(url) if _cd: _kwargs["cookies"] = _cd if self.user and self.passwd: _kwargs["auth"] = (self.user, self.passwd) if "headers" in _kwargs and isinstance(_kwargs["headers"], list): if DICT_HEADERS: _kwargs["headers"] = dict(_kwargs["headers"]) try: logger.debug("%s to %s", method, url) for arg in ["cookies", "data", "auth"]: try: logger.debug("%s: %s", arg.upper(), _kwargs[arg]) except KeyError: pass r = requests.request(method, url, **_kwargs) logger.debug("Response status: %s", r.status_code) except requests.ConnectionError as exc: raise ConnectionError("%s" % exc) try: self.set_cookie(SimpleCookie(r.headers["set-cookie"]), r) except AttributeError: pass except KeyError: pass return r @staticmethod def use_http_post(message, destination, relay_state, typ="SAMLRequest"): if not isinstance(message, six.string_types): message = "%s" % (message,) return http_post_message(message, relay_state, typ) @staticmethod def use_http_form_post(message, destination, relay_state, typ="SAMLRequest"): if not isinstance(message, six.string_types): message = "%s" % (message,) return http_form_post_message(message, destination, relay_state, typ) @staticmethod def use_http_artifact(message, destination="", relay_state=""): if relay_state: query = urlencode({"SAMLart": message, "RelayState": relay_state}) else: query = urlencode({"SAMLart": message}) info = { "data": "", "url": "%s?%s" % (destination, query) } return info @staticmethod def use_http_uri(message, typ, destination="", relay_state=""): if "\n" in message: data = message.split("\n")[1] else: data = message.strip() if typ == "SAMLResponse": info = { "data": data, "headers": [ ("Content-Type", "application/samlassertion+xml"), ("Cache-Control", "no-cache, no-store"), ("Pragma", "no-cache") ] } elif typ == "SAMLRequest": if relay_state: query = urlencode({"ID": message, "RelayState": relay_state}) else: query = urlencode({"ID": message}) info = { "data": "", "url": "%s?%s" % (destination, query) } else: raise NotImplemented return info def use_soap(self, request, destination="", soap_headers=None, sign=False, **kwargs): headers = [("content-type", "application/soap+xml")] soap_message = make_soap_enveloped_saml_thingy(request, soap_headers) logger.debug("SOAP message: %s", soap_message) if sign and self.sec: _signed = self.sec.sign_statement(soap_message, class_name=class_name(request), node_id=request.id) soap_message = _signed return {"url": destination, "method": "POST", "data": soap_message, "headers": headers} def send_using_soap(self, request, destination, headers=None, sign=False): try: args = self.use_soap(request, destination, headers, sign) args["headers"] = dict(args["headers"]) response = self.send(**args) except Exception as exc: logger.info("HTTPClient exception: %s", exc) raise if response.status_code == 200: logger.info("SOAP response: %s", response.text) return response else: raise HTTPError("%d:%s" % (response.status_code, response.content)) def add_credentials(self, user, passwd): self.user = user self.passwd = passwd @staticmethod def use_http_get(message, destination, relay_state, typ="SAMLRequest", sigalg="", signer=None, **kwargs): if not isinstance(message, six.string_types): message = "%s" % (message,) return http_redirect_message(message, destination, relay_state, typ, sigalg, signer)

true

1c2ec000659f19143f2f3a883502db0eb1d71d33

6,398

py

Python

Chapter_Clustering_GMM/ProcessMonitoring_GMM.py

ML-PSE/Machine_Learning_for_PSE

b53578d7cc0e0eca4907527b188a60de06d6710e

[ "Apache-2.0" ]

2

2022-02-20T18:57:46.000Z

2022-03-03T07:07:12.000Z

Chapter_Clustering_GMM/ProcessMonitoring_GMM.py

ML-PSE/Machine_Learning_for_PSE

b53578d7cc0e0eca4907527b188a60de06d6710e

[ "Apache-2.0" ]

null

Chapter_Clustering_GMM/ProcessMonitoring_GMM.py

ML-PSE/Machine_Learning_for_PSE

b53578d7cc0e0eca4907527b188a60de06d6710e

[ "Apache-2.0" ]

null

##%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## Process Monitoring of Etch data ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #%% import required packages import numpy as np import matplotlib.pyplot as plt from sklearn.mixture import GaussianMixture #%% fetch data import scipy.io matlab_data = scipy.io.loadmat('MACHINE_Data.mat', struct_as_record = False) Etch_data = matlab_data['LAMDATA'] calibration_dataAll = Etch_data[0,0].calibration # calibration_dataAll[i,0] corresponds to a 2D data from ith batch where columns correspond to different variables variable_names = Etch_data[0,0].variables ##%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## perform Multiway PCA ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #%% generate unfolded data matrix n_vars = variable_names.size - 2 # first 2 columns are not process variables n_samples = 85 # following the work of He et al. unfolded_dataMatrix = np.empty((1,n_vars*n_samples)) for expt in range(calibration_dataAll.size): calibration_expt = calibration_dataAll[expt,0][5:90,2:] # removing first 5 measurements as done in He et al. if calibration_expt.shape[0] < 85: continue unfolded_row = np.ravel(calibration_expt, order='F')[np.newaxis,:] unfolded_dataMatrix = np.vstack((unfolded_dataMatrix, unfolded_row)) unfolded_dataMatrix = unfolded_dataMatrix[1:,:] #%% scale data from sklearn.preprocessing import StandardScaler scaler = StandardScaler() data_train_normal = scaler.fit_transform(unfolded_dataMatrix) #%% fit PCA model to calibration data from sklearn.decomposition import PCA pca = PCA(n_components = 3) # following the work of He et al. score_train = pca.fit_transform(data_train_normal) #%% visualize in 2D plt.figure() plt.scatter(score_train[:,0],score_train[:,1]) plt.xlabel('PC1 scores') plt.ylabel('PC2 scores') ##%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## GMM on PCA scores ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #%% finding # of components via BIC BICs = [] lowestBIC = np.inf for n_cluster in range(1, 10): gmm = GaussianMixture(n_components = n_cluster, random_state = 100) gmm.fit(score_train) BIC = gmm.bic(score_train) BICs.append(BIC) if BIC < lowestBIC: optimal_n_cluster = n_cluster lowestBIC = BIC plt.figure() plt.plot(range(1,10), BICs, marker='o') plt.xlabel('Number of components') plt.ylabel('BIC') plt.show() #%% fit GMM model to metal-etch data gmm = GaussianMixture(n_components = optimal_n_cluster, random_state = 100) cluster_label = gmm.fit_predict(score_train) plt.figure() plt.scatter(score_train[:, 0], score_train[:, 1], c = cluster_label, s=20, cmap='viridis') plt.xlabel('PC1 scores') plt.ylabel('PC2 scores') cluster_centers = gmm.means_ cluster_plot_labels = ['Cluster ' + str(i+1) for i in range(optimal_n_cluster)] for i in range(optimal_n_cluster): plt.scatter(cluster_centers[i, 0], cluster_centers[i, 1], c='red', s=20, marker = '*', alpha=0.5) plt.annotate(cluster_plot_labels[i], (cluster_centers[i,0], cluster_centers[i,1])) ##%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## Fault detection metric for training data ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #%% global mahalonobis distance metric Dglobal_train = np.zeros((score_train.shape[0],)) for i in range(score_train.shape[0]): x = score_train[i,:,np.newaxis] probs = gmm.predict_proba(x.T) for component in range(3): Dlocal = np.dot(np.dot((x-gmm.means_[component,:,np.newaxis]).T,np.linalg.inv(gmm.covariances_[component,:])),(x-gmm.means_[component,:,np.newaxis])) Dglobal_train[i] = Dglobal_train[i] + probs[0,component]*Dlocal #%% Dglobal control limit N = score_train.shape[0] r = 3 alpha = 0.05 # 95% control limit Dglobal_CL = r*(N**2-1)*scipy.stats.f.ppf(1-alpha,r,N-r)/(N*(N-r)) #%% Dglobal plot with CL plt.figure() plt.plot(Dglobal_train) plt.plot([1,len(Dglobal_train)],[Dglobal_CL, Dglobal_CL], color='red') plt.xlabel('Sample #') plt.ylabel('D_global for training data') plt.show() ##%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## test data ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #%% fetch test data and unfold test_dataAll = Etch_data[0,0].test unfolded_TestdataMatrix = np.empty((1,n_vars*n_samples)) for expt in range(test_dataAll.size): test_expt = test_dataAll[expt,0][5:90,2:] if test_expt.shape[0] < 85: continue unfolded_row = np.ravel(test_expt, order='F')[np.newaxis,:] unfolded_TestdataMatrix = np.vstack((unfolded_TestdataMatrix, unfolded_row)) unfolded_TestdataMatrix = unfolded_TestdataMatrix[1:,:] #%% PCA on fault data data_test_normal = scaler.transform(unfolded_TestdataMatrix) score_test = pca.transform(data_test_normal) #%% visualize in 2D (both test and calibration data) plt.figure() plt.scatter(score_train[:,0],score_train[:,1], c='blue', alpha=0.1) plt.scatter(score_test[:,0],score_test[:,1], c='red', marker = '*') plt.xlabel('PC1 scores') plt.ylabel('PC2 scores') #%% compute Dglobal_test Dglobal_test = np.zeros((score_test.shape[0],)) for i in range(score_test.shape[0]): x = score_test[i,:,np.newaxis] probs = gmm.predict_proba(x.T) for component in range(3): Dlocal = np.dot(np.dot((x-gmm.means_[component,:,np.newaxis]).T,np.linalg.inv(gmm.covariances_[component,:])),(x-gmm.means_[component,:,np.newaxis])) Dglobal_test[i] = Dglobal_test[i] + probs[0,component]*Dlocal #%% Dglobal plot with CL plt.figure() plt.plot(Dglobal_test, marker = '*') plt.plot([1,len(Dglobal_test)],[Dglobal_CL,Dglobal_CL], color='red') plt.xlabel('Sample #') plt.ylabel('D_global for test data') plt.show() print('Number of faults identified: ', np.sum(Dglobal_test > Dglobal_CL), ' out of ', len(Dglobal_test))

36.770115

165

0.595186

atlab_data['LAMDATA'] calibration_dataAll = Etch_data[0,0].calibration variable_names = Etch_data[0,0].variables 0][5:90,2:] if calibration_expt.shape[0] < 85: continue unfolded_row = np.ravel(calibration_expt, order='F')[np.newaxis,:] unfolded_dataMatrix = np.vstack((unfolded_dataMatrix, unfolded_row)) unfolded_dataMatrix = unfolded_dataMatrix[1:,:] from sklearn.preprocessing import StandardScaler scaler = StandardScaler() data_train_normal = scaler.fit_transform(unfolded_dataMatrix) from sklearn.decomposition import PCA pca = PCA(n_components = 3) score_train = pca.fit_transform(data_train_normal) plt.figure() plt.scatter(score_train[:,0],score_train[:,1]) plt.xlabel('PC1 scores') plt.ylabel('PC2 scores') if BIC < lowestBIC: optimal_n_cluster = n_cluster lowestBIC = BIC plt.figure() plt.plot(range(1,10), BICs, marker='o') plt.xlabel('Number of components') plt.ylabel('BIC') plt.show() gmm = GaussianMixture(n_components = optimal_n_cluster, random_state = 100) cluster_label = gmm.fit_predict(score_train) plt.figure() plt.scatter(score_train[:, 0], score_train[:, 1], c = cluster_label, s=20, cmap='viridis') plt.xlabel('PC1 scores') plt.ylabel('PC2 scores') cluster_centers = gmm.means_ cluster_plot_labels = ['Cluster ' + str(i+1) for i in range(optimal_n_cluster)] for i in range(optimal_n_cluster): plt.scatter(cluster_centers[i, 0], cluster_centers[i, 1], c='red', s=20, marker = '*', alpha=0.5) plt.annotate(cluster_plot_labels[i], (cluster_centers[i,0], cluster_centers[i,1])) np.dot(np.dot((x-gmm.means_[component,:,np.newaxis]).T,np.linalg.inv(gmm.covariances_[component,:])),(x-gmm.means_[component,:,np.newaxis])) Dglobal_train[i] = Dglobal_train[i] + probs[0,component]*Dlocal N = score_train.shape[0] r = 3 alpha = 0.05 Dglobal_CL = r*(N**2-1)*scipy.stats.f.ppf(1-alpha,r,N-r)/(N*(N-r)) plt.figure() plt.plot(Dglobal_train) plt.plot([1,len(Dglobal_train)],[Dglobal_CL, Dglobal_CL], color='red') plt.xlabel('Sample #') plt.ylabel('D_global for training data') plt.show() if test_expt.shape[0] < 85: continue unfolded_row = np.ravel(test_expt, order='F')[np.newaxis,:] unfolded_TestdataMatrix = np.vstack((unfolded_TestdataMatrix, unfolded_row)) unfolded_TestdataMatrix = unfolded_TestdataMatrix[1:,:] data_test_normal = scaler.transform(unfolded_TestdataMatrix) score_test = pca.transform(data_test_normal) plt.figure() plt.scatter(score_train[:,0],score_train[:,1], c='blue', alpha=0.1) plt.scatter(score_test[:,0],score_test[:,1], c='red', marker = '*') plt.xlabel('PC1 scores') plt.ylabel('PC2 scores') Dglobal_test = np.zeros((score_test.shape[0],)) for i in range(score_test.shape[0]): x = score_test[i,:,np.newaxis] probs = gmm.predict_proba(x.T) for component in range(3): Dlocal = np.dot(np.dot((x-gmm.means_[component,:,np.newaxis]).T,np.linalg.inv(gmm.covariances_[component,:])),(x-gmm.means_[component,:,np.newaxis])) Dglobal_test[i] = Dglobal_test[i] + probs[0,component]*Dlocal plt.figure() plt.plot(Dglobal_test, marker = '*') plt.plot([1,len(Dglobal_test)],[Dglobal_CL,Dglobal_CL], color='red') plt.xlabel('Sample #') plt.ylabel('D_global for test data') plt.show() print('Number of faults identified: ', np.sum(Dglobal_test > Dglobal_CL), ' out of ', len(Dglobal_test))

true

1c2ec0204f13deadf34e31a40a561e0198ba50a8

665

py

Python

src/genie/libs/parser/bigip/get_net_sfcchain.py

nujo/genieparser

083b01efc46afc32abe1a1858729578beab50cd3

[ "Apache-2.0" ]

204

2018-06-27T00:55:27.000Z

2022-03-06T21:12:18.000Z

src/genie/libs/parser/bigip/get_net_sfcchain.py

nujo/genieparser

083b01efc46afc32abe1a1858729578beab50cd3

[ "Apache-2.0" ]

468

2018-06-19T00:33:18.000Z

2022-03-31T23:23:35.000Z

src/genie/libs/parser/bigip/get_net_sfcchain.py

nujo/genieparser

083b01efc46afc32abe1a1858729578beab50cd3

[ "Apache-2.0" ]

309

2019-01-16T20:21:07.000Z

2022-03-30T12:56:41.000Z

# Global Imports import json from collections import defaultdict # Metaparser from genie.metaparser import MetaParser # ============================================= # Collection for '/mgmt/tm/net/sfc/chain' resources # ============================================= class NetSfcChainSchema(MetaParser): schema = {} class NetSfcChain(NetSfcChainSchema): """ To F5 resource for /mgmt/tm/net/sfc/chain """ cli_command = "/mgmt/tm/net/sfc/chain" def rest(self): response = self.device.get(self.cli_command) response_json = response.json() if not response_json: return {} return response_json

19.558824

52

0.578947

import json from collections import defaultdict from genie.metaparser import MetaParser class NetSfcChainSchema(MetaParser): schema = {} class NetSfcChain(NetSfcChainSchema): cli_command = "/mgmt/tm/net/sfc/chain" def rest(self): response = self.device.get(self.cli_command) response_json = response.json() if not response_json: return {} return response_json

true

1c2ec05a5b77d971e797ce93d09ecd74e83f2274

585

py

Python

tests/common/server_sent_events/sse4python/test_web_request_factory.py

yellowdog/yellowdog-sdk-python-public

da69a7d6e45c92933e34fefcaef8b5d98dcd6036

[ "Apache-2.0" ]

null

tests/common/server_sent_events/sse4python/test_web_request_factory.py

yellowdog/yellowdog-sdk-python-public

da69a7d6e45c92933e34fefcaef8b5d98dcd6036

[ "Apache-2.0" ]

null

tests/common/server_sent_events/sse4python/test_web_request_factory.py

yellowdog/yellowdog-sdk-python-public

da69a7d6e45c92933e34fefcaef8b5d98dcd6036

[ "Apache-2.0" ]

null

from yellowdog_client.common.server_sent_events.sse4python import WebRequestFactory class TestCreate(object): def test__expect_requester_returned_with_headers_passed(self, mocker): mock_requester = mocker.patch( "yellowdog_client.common.server_sent_events.sse4python.web_request_factory.WebRequester" ) auth_base = mocker.MagicMock() factory = WebRequestFactory(auth_base=auth_base) res = factory.create() assert res == mock_requester.return_value mock_requester.assert_called_once_with(auth_base=auth_base)

34.411765

100

0.752137

from yellowdog_client.common.server_sent_events.sse4python import WebRequestFactory class TestCreate(object): def test__expect_requester_returned_with_headers_passed(self, mocker): mock_requester = mocker.patch( "yellowdog_client.common.server_sent_events.sse4python.web_request_factory.WebRequester" ) auth_base = mocker.MagicMock() factory = WebRequestFactory(auth_base=auth_base) res = factory.create() assert res == mock_requester.return_value mock_requester.assert_called_once_with(auth_base=auth_base)

true