paulo037/starcoder · Datasets at Hugging Face

max_stars_repo_path stringlengths 4 237	max_stars_repo_name stringlengths 6 117	max_stars_count int64 0 95.2k	id stringlengths 1 7	content stringlengths 12 593k	input_ids sequencelengths 7 549k
test/test_children_tree.py	kisliakovsky/structures	0	30329	<filename>test/test_children_tree.py from unittest import TestCase from src.tree import ChildrenTree class TestChildrenTree(TestCase): def test_height(self): tree = ChildrenTree(1, [[], [3, 4], [], [], [0, 2]]) self.assertEqual(3, tree.height())	[ 1, 529, 9507, 29958, 1688, 29914, 1688, 29918, 11991, 29918, 8336, 29889, 2272, 13, 3166, 443, 27958, 1053, 4321, 8259, 13, 13, 3166, 4765, 29889, 8336, 1053, 20986, 9643, 13, 13, 13, 1990, 4321, 19334, 9643, 29898, 3057, 8259, 1125, 13, 13, 1678, 822, 1243, 29918, 3545, 29898, 1311, 1125, 13, 4706, 5447, 353, 20986, 9643, 29898, 29896, 29892, 5519, 1402, 518, 29941, 29892, 29871, 29946, 1402, 19997, 19997, 518, 29900, 29892, 29871, 29906, 24960, 13, 4706, 1583, 29889, 9294, 9843, 29898, 29941, 29892, 5447, 29889, 3545, 3101, 13, 2 ]
api/tests/integration/tests/layout/threads.py	f1nzer/Indigo	0	107132	<reponame>f1nzer/Indigo import sys from threading import Thread sys.path.append("../../common") from env_indigo import * class testThread(Thread): def __init__(self, id, smiles): Thread.__init__(self) self.id = id self.smiles = smiles def run(self): indigo_1 = Indigo() indigo_2 = Indigo() indigo_1.setOption("molfile-saving-skip-date", "1") indigo_2.setOption("molfile-saving-skip-date", "0") m1 = indigo_1.loadMolecule(self.smiles) m2 = indigo_2.loadMolecule(self.smiles) m1.layout() m2.layout() c1 = str(m1.molfile()).splitlines() c2 = str(m2.molfile()).splitlines() header_1 = "\n".join(c1[:3]) body_1 = "\n".join(c1[3:]) header_2 = "\n".join(c2[:3]) body_2 = "\n".join(c2[3:]) # Check that two instances of Indigo even in one thread have different options - headers should differ assert header_1 != header_2 # Bodies should be equal assert body_1 == body_2 threads = [] indigo = Indigo() for i, smi in enumerate( indigo.iterateSmilesFile( joinPathPy( "../../../../../data/molecules/basic/pubchem_slice_5000.smi", __file__, ) ) ): current = testThread(i, smi.rawData()) threads.append(current) current.start() for thread in threads: thread.join()	[ 1, 529, 276, 1112, 420, 29958, 29888, 29896, 29876, 3298, 29914, 2568, 5973, 13, 5215, 10876, 13, 3166, 3244, 292, 1053, 10480, 13, 13, 9675, 29889, 2084, 29889, 4397, 703, 21546, 9435, 1159, 13, 3166, 8829, 29918, 513, 5973, 1053, 334, 13, 13, 13, 1990, 1243, 4899, 29898, 4899, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1178, 29892, 1560, 5475, 1125, 13, 4706, 10480, 17255, 2344, 12035, 1311, 29897, 13, 4706, 1583, 29889, 333, 353, 1178, 13, 4706, 1583, 29889, 3844, 5475, 353, 1560, 5475, 13, 13, 1678, 822, 1065, 29898, 1311, 1125, 13, 4706, 1399, 5973, 29918, 29896, 353, 1894, 5973, 580, 13, 4706, 1399, 5973, 29918, 29906, 353, 1894, 5973, 580, 13, 4706, 1399, 5973, 29918, 29896, 29889, 842, 8375, 703, 29885, 324, 1445, 29899, 29879, 5555, 29899, 11014, 29899, 1256, 613, 376, 29896, 1159, 13, 4706, 1399, 5973, 29918, 29906, 29889, 842, 8375, 703, 29885, 324, 1445, 29899, 29879, 5555, 29899, 11014, 29899, 1256, 613, 376, 29900, 1159, 13, 13, 4706, 286, 29896, 353, 1399, 5973, 29918, 29896, 29889, 1359, 29924, 1772, 29883, 1297, 29898, 1311, 29889, 3844, 5475, 29897, 13, 4706, 286, 29906, 353, 1399, 5973, 29918, 29906, 29889, 1359, 29924, 1772, 29883, 1297, 29898, 1311, 29889, 3844, 5475, 29897, 13, 4706, 286, 29896, 29889, 2680, 580, 13, 4706, 286, 29906, 29889, 2680, 580, 13, 13, 4706, 274, 29896, 353, 851, 29898, 29885, 29896, 29889, 29885, 324, 1445, 16655, 5451, 9012, 580, 13, 4706, 274, 29906, 353, 851, 29898, 29885, 29906, 29889, 29885, 324, 1445, 16655, 5451, 9012, 580, 13, 13, 4706, 4839, 29918, 29896, 353, 6634, 29876, 1642, 7122, 29898, 29883, 29896, 7503, 29941, 2314, 13, 4706, 3573, 29918, 29896, 353, 6634, 29876, 1642, 7122, 29898, 29883, 29896, 29961, 29941, 29901, 2314, 13, 4706, 4839, 29918, 29906, 353, 6634, 29876, 1642, 7122, 29898, 29883, 29906, 7503, 29941, 2314, 13, 4706, 3573, 29918, 29906, 353, 6634, 29876, 1642, 7122, 29898, 29883, 29906, 29961, 29941, 29901, 2314, 13, 4706, 396, 5399, 393, 1023, 8871, 310, 1894, 5973, 1584, 297, 697, 3244, 505, 1422, 3987, 448, 9066, 881, 1163, 13, 4706, 4974, 4839, 29918, 29896, 2804, 4839, 29918, 29906, 13, 4706, 396, 19929, 583, 881, 367, 5186, 13, 4706, 4974, 3573, 29918, 29896, 1275, 3573, 29918, 29906, 13, 13, 13, 28993, 353, 5159, 13, 513, 5973, 353, 1894, 5973, 580, 13, 1454, 474, 29892, 1560, 29875, 297, 26985, 29898, 13, 1678, 1399, 5973, 29889, 1524, 403, 12636, 5475, 2283, 29898, 13, 4706, 5988, 2605, 19737, 29898, 13, 9651, 376, 21546, 21546, 6995, 1272, 29914, 29885, 1772, 21337, 29914, 16121, 29914, 5467, 14969, 29918, 18337, 29918, 29945, 29900, 29900, 29900, 29889, 29879, 2460, 613, 13, 9651, 4770, 1445, 1649, 29892, 13, 4706, 1723, 13, 1678, 1723, 13, 1125, 13, 1678, 1857, 353, 1243, 4899, 29898, 29875, 29892, 1560, 29875, 29889, 1610, 1469, 3101, 13, 1678, 9717, 29889, 4397, 29898, 3784, 29897, 13, 1678, 1857, 29889, 2962, 580, 13, 1454, 3244, 297, 9717, 29901, 13, 1678, 3244, 29889, 7122, 580, 13, 2 ]
sphinxcontrib/collections/drivers/copy_file.py	useblocks/sphinx-collections	4	30588	import os from shutil import copyfile from sphinxcontrib.collections.drivers import Driver class CopyFileDriver(Driver): def run(self): self.info('Copy file...') if not os.path.exists(self.config['source']): self.error('Source {} does not exist'.format(self.config['source'])) return try: copyfile(self.config['source'], self.config['target']) except IOError as e: self.error('Problems during copying file.', e) def clean(self): try: os.remove(self.config['target']) self.info('File deleted: {}'.format(self.config['target'])) except FileNotFoundError: pass # Already cleaned? I'm okay with it. except IOError as e: self.error('Problems during cleaning for collection {}'.format(self.config['name']), e)	[ 1, 1053, 2897, 13, 13, 3166, 528, 4422, 1053, 3509, 1445, 13, 13, 3166, 269, 561, 14668, 21570, 29889, 29027, 29889, 24477, 874, 1053, 26391, 13, 13, 13, 1990, 14187, 2283, 12376, 29898, 12376, 1125, 13, 13, 1678, 822, 1065, 29898, 1311, 1125, 13, 4706, 1583, 29889, 3888, 877, 11882, 934, 856, 1495, 13, 13, 4706, 565, 451, 2897, 29889, 2084, 29889, 9933, 29898, 1311, 29889, 2917, 1839, 4993, 2033, 1125, 13, 9651, 1583, 29889, 2704, 877, 4435, 6571, 947, 451, 1863, 4286, 4830, 29898, 1311, 29889, 2917, 1839, 4993, 25901, 13, 9651, 736, 13, 13, 4706, 1018, 29901, 13, 9651, 3509, 1445, 29898, 1311, 29889, 2917, 1839, 4993, 7464, 13, 462, 268, 1583, 29889, 2917, 1839, 5182, 11287, 13, 4706, 5174, 10663, 2392, 408, 321, 29901, 13, 9651, 1583, 29889, 2704, 877, 26604, 29879, 2645, 17596, 934, 29889, 742, 321, 29897, 13, 13, 1678, 822, 5941, 29898, 1311, 1125, 13, 4706, 1018, 29901, 13, 9651, 2897, 29889, 5992, 29898, 1311, 29889, 2917, 1839, 5182, 11287, 13, 9651, 1583, 29889, 3888, 877, 2283, 11132, 29901, 6571, 4286, 4830, 29898, 1311, 29889, 2917, 1839, 5182, 25901, 13, 4706, 5174, 3497, 17413, 2392, 29901, 13, 9651, 1209, 29871, 396, 838, 2040, 5941, 287, 29973, 306, 29915, 29885, 20759, 411, 372, 29889, 13, 4706, 5174, 10663, 2392, 408, 321, 29901, 13, 9651, 1583, 29889, 2704, 877, 26604, 29879, 2645, 5941, 292, 363, 4333, 6571, 4286, 4830, 29898, 1311, 29889, 2917, 1839, 978, 2033, 511, 321, 29897, 13, 13, 2 ]
notebooks/utils.py	wranda12/06-machine-learning	1	36750	<gh_stars>1-10 # # Some functions to be used in the tutorial # # Developed by <NAME> import datetime import pandas as pd import matplotlib.pyplot as plt # for 2D plotting import numpy as np import seaborn as sns # plot nicely =) from sklearn.base import clone from sklearn.decomposition import PCA from sklearn.model_selection import learning_curve def plot_decision_boundary(x_train, y_train, estimator): """Plot the decision boundary based on: http://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_versus_svm_iris.html Parameters ---------- x_train: training set y_train: labels of the training set estimator: classifier, probability must be set as True """ def make_meshgrid(x, y, h=.02): """Create a mesh of points to plot in Parameters ---------- x: data to base x-axis meshgrid on y: data to base y-axis meshgrid on h: stepsize for meshgrid, optional Returns ------- xx, yy : ndarray """ x_min, x_max = x.min() - 1, x.max() + 1 y_min, y_max = y.min() - 1, y.max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) return xx, yy # cloning the estimator clf = clone(estimator) pca = PCA(n_components=2) x_train_pca = pca.fit_transform(x_train) clf.fit(x_train_pca, y_train) xx, yy = make_meshgrid(x_train_pca[:, 0], x_train_pca[:, 1]) grid = np.c_[xx.ravel(), yy.ravel()] probs = clf.predict_proba(grid)[:, 1].reshape(xx.shape) # plotting the decision boundary f, ax = plt.subplots(figsize=(8, 6)) sns.set(context="notebook", style="whitegrid", rc={"axes.axisbelow": False}) contour = ax.contourf(xx, yy, probs, 25, cmap="RdBu", vmin=0, vmax=1) ax_c = f.colorbar(contour) ax_c.set_label("$P(y = 1)$") ax_c.set_ticks([0, .25, .5, .75, 1]) ax.scatter(x_train_pca[:,0], x_train_pca[:, 1], c=y_train, s=50, cmap="RdBu", vmin=-.2, vmax=1.2, edgecolor="white", linewidth=1) ax.set(aspect="equal", xlim=(-5, 5), ylim=(-5, 5), xlabel="$X_1$", ylabel="$X_2$") plt.show() def plot_roc_curve(fpr, tpr, label=None): plt.plot(fpr, tpr, linewidth=2, label=label) plt.plot([0, 1], [0, 1], 'k--') plt.axis([0, 1, 0, 1]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.show() def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)): """ Generate a simple plot of the test and training learning curve. based on: http://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html Parameters ---------- estimator : classifier title : title for the chart. X : training set, where n_samples is the number of samples and n_features is the number of features. y : labels of the training set ylim : defines minimum and maximum yvalues plotted. cv : determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - An object to be used as a cross-validation generator. - An iterable yielding train/test splits. n_jobs : number of jobs to run in parallel. train_sizes : relative or absolute numbers of training examples that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of the maximum size of the training set (that is determined by the selected validation method), i.e. it has to be within (0, 1]. Otherwise it is interpreted as absolute sizes of the training sets. Note that for classification the number of samples usually have to be big enough to contain at least one sample from each class. (default: np.linspace(0.1, 1.0, 5)) """ # cloning the estimator clf = clone(estimator) plt.figure() plt.title(title) if ylim is not None: plt.ylim(*ylim) plt.xlabel("Training examples") plt.ylabel("Score") train_sizes, train_scores, test_scores = learning_curve( clf, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes) train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) test_scores_mean = np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) plt.grid() plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="r") plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="g") plt.plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training score") plt.plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation score") plt.legend(loc="best") return plt	[ 1, 529, 12443, 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1353, 310, 5680, 29889, 13, 13, 1678, 343, 584, 11073, 310, 278, 6694, 731, 13, 13, 1678, 343, 2576, 584, 17645, 9212, 322, 7472, 343, 5975, 715, 15048, 29889, 13, 13, 1678, 13850, 584, 3683, 1475, 278, 4891, 29899, 18157, 24368, 13705, 29889, 13, 4706, 20049, 10970, 363, 13850, 526, 29901, 13, 3986, 448, 6213, 29892, 304, 671, 278, 2322, 29871, 29941, 29899, 8771, 4891, 29899, 18157, 29892, 13, 3986, 448, 6043, 29892, 304, 6084, 278, 1353, 310, 900, 6289, 29889, 13, 3986, 448, 530, 1203, 304, 367, 1304, 408, 263, 4891, 29899, 18157, 15299, 29889, 13, 3986, 448, 530, 4256, 519, 7709, 292, 7945, 29914, 1688, 8536, 1169, 29889, 13, 13, 1678, 302, 29918, 9057, 29879, 584, 1353, 310, 17643, 304, 1065, 297, 8943, 29889, 13, 13, 1678, 7945, 29918, 29879, 7093, 584, 6198, 470, 8380, 3694, 310, 6694, 6455, 393, 674, 367, 1304, 304, 13, 4706, 5706, 278, 6509, 11672, 29889, 960, 278, 26688, 338, 5785, 29892, 372, 338, 17878, 408, 263, 13, 4706, 15958, 310, 278, 7472, 2159, 310, 278, 6694, 731, 313, 5747, 338, 10087, 13, 4706, 491, 278, 4629, 8845, 1158, 511, 474, 29889, 29872, 29889, 372, 756, 304, 367, 2629, 313, 29900, 29892, 29871, 29896, 1822, 13, 4706, 13466, 372, 338, 21551, 408, 8380, 15786, 310, 278, 6694, 6166, 29889, 13, 4706, 3940, 393, 363, 12965, 278, 1353, 310, 11916, 5491, 505, 304, 13, 4706, 367, 4802, 3307, 304, 1712, 472, 3203, 697, 4559, 515, 1269, 770, 29889, 13, 4706, 313, 4381, 29901, 7442, 29889, 1915, 3493, 29898, 29900, 29889, 29896, 29892, 29871, 29896, 29889, 29900, 29892, 29871, 29945, 876, 13, 1678, 9995, 13, 1678, 396, 1067, 28259, 278, 4844, 1061, 13, 1678, 1067, 29888, 353, 17432, 29898, 342, 326, 1061, 29897, 13, 13, 1678, 14770, 29889, 4532, 580, 13, 1678, 14770, 29889, 3257, 29898, 3257, 29897, 13, 1678, 565, 343, 2576, 338, 451, 6213, 29901, 13, 4706, 14770, 29889, 29891, 2576, 10456, 29891, 2576, 29897, 13, 1678, 14770, 29889, 29916, 1643, 703, 5323, 2827, 6455, 1159, 13, 1678, 14770, 29889, 29891, 1643, 703, 20097, 1159, 13, 1678, 7945, 29918, 29879, 7093, 29892, 7945, 29918, 1557, 2361, 29892, 1243, 29918, 1557, 2361, 353, 6509, 29918, 2764, 345, 29898, 13, 4706, 1067, 29888, 29892, 1060, 29892, 343, 29892, 13850, 29922, 11023, 29892, 302, 29918, 9057, 29879, 29922, 29876, 29918, 9057, 29879, 29892, 7945, 29918, 29879, 7093, 29922, 14968, 29918, 29879, 7093, 29897, 13, 1678, 7945, 29918, 1557, 2361, 29918, 12676, 353, 7442, 29889, 12676, 29898, 14968, 29918, 1557, 2361, 29892, 9685, 29922, 29896, 29897, 13, 1678, 7945, 29918, 1557, 2361, 29918, 4172, 353, 7442, 29889, 4172, 29898, 14968, 29918, 1557, 2361, 29892, 9685, 29922, 29896, 29897, 13, 1678, 1243, 29918, 1557, 2361, 29918, 12676, 353, 7442, 29889, 12676, 29898, 1688, 29918, 1557, 2361, 29892, 9685, 29922, 29896, 29897, 13, 1678, 1243, 29918, 1557, 2361, 29918, 4172, 353, 7442, 29889, 4172, 29898, 1688, 29918, 1557, 2361, 29892, 9685, 29922, 29896, 29897, 13, 1678, 14770, 29889, 7720, 580, 13, 13, 1678, 14770, 29889, 5589, 29918, 14811, 29898, 14968, 29918, 29879, 7093, 29892, 7945, 29918, 1557, 2361, 29918, 12676, 448, 7945, 29918, 1557, 2361, 29918, 4172, 29892, 13, 462, 268, 7945, 29918, 1557, 2361, 29918, 12676, 718, 7945, 29918, 1557, 2361, 29918, 4172, 29892, 15595, 29922, 29900, 29889, 29896, 29892, 13, 462, 268, 2927, 543, 29878, 1159, 13, 1678, 14770, 29889, 5589, 29918, 14811, 29898, 14968, 29918, 29879, 7093, 29892, 1243, 29918, 1557, 2361, 29918, 12676, 448, 1243, 29918, 1557, 2361, 29918, 4172, 29892, 13, 462, 268, 1243, 29918, 1557, 2361, 29918, 12676, 718, 1243, 29918, 1557, 2361, 29918, 4172, 29892, 15595, 29922, 29900, 29889, 29896, 29892, 2927, 543, 29887, 1159, 13, 1678, 14770, 29889, 5317, 29898, 14968, 29918, 29879, 7093, 29892, 7945, 29918, 1557, 2361, 29918, 12676, 29892, 525, 29877, 29899, 742, 2927, 543, 29878, 613, 13, 632, 3858, 543, 5323, 2827, 8158, 1159, 13, 1678, 14770, 29889, 5317, 29898, 14968, 29918, 29879, 7093, 29892, 1243, 29918, 1557, 2361, 29918, 12676, 29892, 525, 29877, 29899, 742, 2927, 543, 29887, 613, 13, 632, 3858, 543, 29907, 2124, 29899, 18157, 8158, 1159, 13, 13, 1678, 14770, 29889, 26172, 29898, 2029, 543, 13318, 1159, 13, 1678, 736, 14770, 13, 2 ]
src/world/logosplashworld.py	alisonbento/steering-all	3	153725	import dotworld import ufrnsplashworld from src.define import * from src.dot.entities.dotpairg import DotPairg from src.dot.dottext import DotText import i18n _ = i18n.language.ugettext class LogoSplashWorld(dotworld.DotWorld): def __init__(self): dotworld.DotWorld.__init__(self) self.counter = 0 self.limit = 400 self.alpha = 0 self.animState = 1 self.logo = DotPairg() self.label = DotText("PAIRG - Physical Artifacts of Interaction Research Group", 16, (0, 0, 0), (255, 255, 255)) self.sublabel = DotText(_("Developed by") + " <NAME>", 16, (0, 0, 0), (255, 255, 255)) def onAttachScreen(self): self.logo.setMedium() self.logo.centerX(self.screen.width) self.logo.centerY(self.screen.height) self.logo.createSurface() self.label.centerX(self.screen.width) self.label.marginTop(dotget(1)) self.label.below(self.logo) self.sublabel.centerX(self.screen.width) self.sublabel.marginTop(dotget(1)) self.sublabel.below(self.label) def changeAlpha(self): self.logo.setDotAlpha(self.alpha) # self.logo.createSurface() self.label.surface.set_alpha(self.alpha) self.sublabel.surface.set_alpha(self.alpha) def listen(self, inputResult): if inputResult == GameDefine.COMMAND_EXIT: self.screen.turnOff() if inputResult == GameDefine.COMMAND_BOOST: self.pause() def step(self): if self.active: self.changeAlpha() self.logo.draw(self.screen.displaysurf) self.label.draw(self.screen.displaysurf) self.sublabel.draw(self.screen.displaysurf) self.counter += 1 if self.animState == 1: self.alpha += 2 if self.alpha > 255: self.animState = 2 self.counter = 0 if self.animState == 2: self.counter += 1 if self.counter > self.screen.fps * 3: self.animState = 3 if self.animState == 3: self.alpha -= 2 if self.alpha <= 0: self.pause() else: self.screen.setWorld(ufrnsplashworld.UfrnSplashWorld()) del self	[ 1, 1053, 8329, 11526, 13, 5215, 318, 1341, 1983, 572, 1161, 11526, 13, 3166, 4765, 29889, 7922, 1053, 334, 13, 3166, 4765, 29889, 6333, 29889, 296, 1907, 29889, 6333, 18784, 29887, 1053, 360, 327, 20547, 29887, 13, 3166, 4765, 29889, 6333, 29889, 6333, 726, 1053, 360, 327, 1626, 13, 13, 5215, 474, 29896, 29947, 29876, 13, 29918, 353, 474, 29896, 29947, 29876, 29889, 11675, 29889, 29884, 657, 726, 13, 13, 13, 1990, 4522, 29877, 29903, 572, 1161, 14058, 29898, 6333, 11526, 29889, 29928, 327, 14058, 1125, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 8329, 11526, 29889, 29928, 327, 14058, 17255, 2344, 12035, 1311, 29897, 13, 4706, 1583, 29889, 11808, 353, 29871, 29900, 13, 4706, 1583, 29889, 13400, 353, 29871, 29946, 29900, 29900, 13, 4706, 1583, 29889, 2312, 353, 29871, 29900, 13, 4706, 1583, 29889, 11576, 2792, 353, 29871, 29896, 13, 13, 4706, 1583, 29889, 14569, 353, 360, 327, 20547, 29887, 580, 13, 4706, 1583, 29889, 1643, 353, 360, 327, 1626, 703, 7228, 8193, 29954, 448, 11661, 936, 3012, 7060, 29879, 310, 4124, 2467, 10550, 6431, 613, 29871, 29896, 29953, 29892, 313, 29900, 29892, 29871, 29900, 29892, 29871, 29900, 511, 313, 29906, 29945, 29945, 29892, 29871, 29906, 29945, 29945, 29892, 29871, 29906, 29945, 29945, 876, 13, 4706, 1583, 29889, 1491, 1643, 353, 360, 327, 1626, 7373, 703, 21956, 287, 491, 1159, 718, 376, 529, 5813, 28341, 29871, 29896, 29953, 29892, 313, 29900, 29892, 29871, 29900, 29892, 29871, 29900, 511, 313, 29906, 29945, 29945, 29892, 29871, 29906, 29945, 29945, 29892, 29871, 29906, 29945, 29945, 876, 13, 13, 1678, 822, 373, 4165, 496, 11357, 29898, 1311, 1125, 13, 4706, 1583, 29889, 14569, 29889, 842, 19302, 1974, 580, 13, 4706, 1583, 29889, 14569, 29889, 5064, 29990, 29898, 1311, 29889, 10525, 29889, 2103, 29897, 13, 4706, 1583, 29889, 14569, 29889, 5064, 29979, 29898, 1311, 29889, 10525, 29889, 3545, 29897, 13, 4706, 1583, 29889, 14569, 29889, 3258, 18498, 2161, 580, 13, 13, 4706, 1583, 29889, 1643, 29889, 5064, 29990, 29898, 1311, 29889, 10525, 29889, 2103, 29897, 13, 4706, 1583, 29889, 1643, 29889, 9264, 7031, 29898, 6333, 657, 29898, 29896, 876, 13, 4706, 1583, 29889, 1643, 29889, 22503, 29898, 1311, 29889, 14569, 29897, 13, 13, 4706, 1583, 29889, 1491, 1643, 29889, 5064, 29990, 29898, 1311, 29889, 10525, 29889, 2103, 29897, 13, 4706, 1583, 29889, 1491, 1643, 29889, 9264, 7031, 29898, 6333, 657, 29898, 29896, 876, 13, 4706, 1583, 29889, 1491, 1643, 29889, 22503, 29898, 1311, 29889, 1643, 29897, 13, 13, 1678, 822, 1735, 28630, 29898, 1311, 1125, 13, 4706, 1583, 29889, 14569, 29889, 842, 29928, 327, 28630, 29898, 1311, 29889, 2312, 29897, 13, 4706, 396, 1583, 29889, 14569, 29889, 3258, 18498, 2161, 580, 13, 13, 4706, 1583, 29889, 1643, 29889, 7610, 2161, 29889, 842, 29918, 2312, 29898, 1311, 29889, 2312, 29897, 13, 4706, 1583, 29889, 1491, 1643, 29889, 7610, 2161, 29889, 842, 29918, 2312, 29898, 1311, 29889, 2312, 29897, 13, 13, 1678, 822, 11621, 29898, 1311, 29892, 1881, 3591, 1125, 13, 4706, 565, 1881, 3591, 1275, 8448, 3206, 457, 29889, 19795, 1529, 2797, 29918, 5746, 1806, 29901, 13, 9651, 1583, 29889, 10525, 29889, 685, 6880, 580, 13, 13, 4706, 565, 1881, 3591, 1275, 8448, 3206, 457, 29889, 19795, 1529, 2797, 29918, 8456, 3718, 29901, 13, 9651, 1583, 29889, 29886, 1071, 580, 13, 13, 1678, 822, 4331, 29898, 1311, 1125, 13, 4706, 565, 1583, 29889, 4925, 29901, 13, 9651, 1583, 29889, 3167, 28630, 580, 13, 13, 9651, 1583, 29889, 14569, 29889, 4012, 29898, 1311, 29889, 10525, 29889, 2218, 12922, 332, 29888, 29897, 13, 9651, 1583, 29889, 1643, 29889, 4012, 29898, 1311, 29889, 10525, 29889, 2218, 12922, 332, 29888, 29897, 13, 9651, 1583, 29889, 1491, 1643, 29889, 4012, 29898, 1311, 29889, 10525, 29889, 2218, 12922, 332, 29888, 29897, 13, 13, 9651, 1583, 29889, 11808, 4619, 29871, 29896, 13, 13, 9651, 565, 1583, 29889, 11576, 2792, 1275, 29871, 29896, 29901, 13, 18884, 1583, 29889, 2312, 4619, 29871, 29906, 13, 13, 18884, 565, 1583, 29889, 2312, 1405, 29871, 29906, 29945, 29945, 29901, 13, 462, 1678, 1583, 29889, 11576, 2792, 353, 29871, 29906, 13, 462, 1678, 1583, 29889, 11808, 353, 29871, 29900, 13, 13, 9651, 565, 1583, 29889, 11576, 2792, 1275, 29871, 29906, 29901, 13, 18884, 1583, 29889, 11808, 4619, 29871, 29896, 13, 18884, 565, 1583, 29889, 11808, 1405, 1583, 29889, 10525, 29889, 29888, 567, 334, 29871, 29941, 29901, 13, 462, 1678, 1583, 29889, 11576, 2792, 353, 29871, 29941, 13, 13, 9651, 565, 1583, 29889, 11576, 2792, 1275, 29871, 29941, 29901, 13, 18884, 1583, 29889, 2312, 22361, 29871, 29906, 13, 18884, 565, 1583, 29889, 2312, 5277, 29871, 29900, 29901, 13, 462, 1678, 1583, 29889, 29886, 1071, 580, 13, 13, 4706, 1683, 29901, 13, 9651, 1583, 29889, 10525, 29889, 842, 14058, 29898, 1137, 29878, 1983, 572, 1161, 11526, 29889, 29965, 1341, 29876, 29903, 572, 1161, 14058, 3101, 13, 9651, 628, 1583, 13, 2 ]
opps/contrib/multisite/models.py	jeanmask/opps	159	162537	<gh_stars>100-1000 # -- coding: utf-8 -- from django.utils.translation import ugettext_lazy as _ from opps.core.permissions.models import Permission class SitePermission(Permission): pass	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29900, 29899, 29896, 29900, 29900, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 3166, 9557, 29889, 13239, 29889, 3286, 18411, 1053, 318, 657, 726, 29918, 433, 1537, 408, 903, 13, 3166, 4575, 29879, 29889, 3221, 29889, 17858, 6847, 29889, 9794, 1053, 20894, 2333, 13, 13, 13, 1990, 10781, 27293, 29898, 27293, 1125, 13, 1678, 1209, 13, 2 ]
testing/performances.py	severin-lemaignan/underworlds	3	133871	#! /usr/bin/env python3 # -- coding: utf-8 -- import argparse import time import uuid from multiprocessing import Pipe, Pool import threading from concurrent.futures import ThreadPoolExecutor import logging; logger = logging.getLogger("underworlds.testing.performances") import underworlds import underworlds.server from underworlds.types import Node from underworlds.helpers.profile import profileonce import underworlds.underworlds_pb2 as gRPC def ms(duration): return "%.1fms" % (duration * 1000) def passthrough(world1, world2, signaling_pipe): """ Simple passthrough filter: wait for changes on a world world1 and propagate these changes to world world2. """ name = "passthrough_filter_%s_to_%s" % (world1, world2) with underworlds.Context(name) as ctx: world1 = ctx.worlds[world1] world2 = ctx.worlds[world2] try: print("Waiting for changes...") while not signaling_pipe.poll(): #print("%f -- %s waiting" % (time.time(), name)) change = world1.scene.waitforchanges(0.5) #print("%f -- %s Done waiting (last change: %s)" % (time.time(), name, str(change))) if change is not None: id, op = change #print("%f -- propagating from %s to %s" % (time.time(), name, world1, world2)) world2.scene.update_and_propagate(world1.scene.nodes[id]) change = None except Exception as e: import traceback traceback.print_exc() print("Stopping passthrough") print("Passthrough stopped") def wait_for_changes(world, nb_changes): print("Waiting for changes in world %s" % world) starttime = time.time() change = None for i in range(nb_changes): change = world.scene.waitforchanges(3) profileonce("waitforchanges triggered") return change, time.time()-starttime def test_propagation_time(nb_worlds, nb_changes): executor = ThreadPoolExecutor(max_workers=nb_worlds) pool = Pool(nb_worlds) pipes = [] res = [] for i in range(nb_worlds-1): print("Setting up passthrough between world %d and world %d" % (i, i+1)) #f = executor.submit(passthrough, "world%d" % i, "world%d" % (i+1)) conn1, conn2 = Pipe() pipes.append(conn1) res.append(pool.apply_async(passthrough, ["world%d" % i, "world%d" % (i+1), conn2])) time.sleep(0.5) ctx = underworlds.Context("test_client") entry_world = ctx.worlds["world0"] exit_world = ctx.worlds["world%d" % (nb_worlds-1)] future = executor.submit(wait_for_changes, exit_world, nb_changes) print("\n\n\nPropagating %d change(s) from world %s..." % (nb_changes, entry_world)) profileonce("start test with %d worlds" % nb_worlds) for i in range(nb_changes): n = Node() n.name = "node_%d" % i entry_world.scene.append_and_propagate(n) time.sleep(0.01) seen, duration = future.result() profileonce("end test") if seen is None: logger.error("The changes have not been seen!") duration = 0 else: print("It took %s to be notified of the %d change(s) in world %s" % (ms(duration), nb_changes, exit_world)) executor.shutdown(wait=True) for p in pipes: p.send(True) pool.close() pool.join() ctx.close() return duration if __name__ == '__main__': durations = {} parser = argparse.ArgumentParser() parser.add_argument("maxworlds", default=5, type=int, nargs="?", help="Maximum number of Underworlds worlds to spawn") parser.add_argument("-d", "--debug", help="debug mode", action="store_true") parser.add_argument("-dd", "--fulldebug", help="debug mode (verbose)", action="store_true") parser.add_argument("-i", "--incremental", action="store_true", help="test for every nb of worlds, from 2 to maxworlds") parser.add_argument("-r", "--repeat", default=1, type=int, nargs="?", help="how many times the test should be repeated (default: no repeat)") parser.add_argument("-c", "--changes", default=1, type=int, nargs="?", help="how many changes should be propagated (default: one)") args = parser.parse_args() if args.debug or args.fulldebug: if args.fulldebug: logging.basicConfig(level=logging.DEBUG) else: logging.basicConfig(level=logging.INFO) else: logger.setLevel(logging.WARN) minworlds = 2 if args.incremental else args.maxworlds for idx in range(0, args.repeat): print("\n\n\n-- Iteration #%d --\n" % idx) for nb in range(minworlds, args.maxworlds+1): print("\n\n\n-- %d worlds --\n" % nb) server = underworlds.server.start() durations.setdefault(nb,[]).append(test_propagation_time(nb, args.changes)) server.stop(0).wait() time.sleep(0.5) for nb,times in durations.items(): print("%d;%s" % (nb, ";".join([str("%.1f" % (d * 1000)) for d in times])))	[ 1, 396, 29991, 847, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 5215, 1852, 5510, 13, 5215, 931, 13, 5215, 318, 5416, 13, 13, 3166, 6674, 307, 985, 292, 1053, 7362, 412, 29892, 28625, 13, 5215, 3244, 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__init__.py	zznop/bn-kconfig-recover	18	35012	<filename>__init__.py """ Binary Ninja plugin for recovering kernel build configuration settings using BNIL """ import argparse import logging from binaryninja import (BinaryViewType, BinaryView, PluginCommand, SaveFileNameField, get_form_input, BackgroundTaskThread) class RecoverKConfigBackground(BackgroundTaskThread): """Class for running kernel configuration recovery in background """ def __init__(self, view: BinaryView, outpath: str) -> None: BackgroundTaskThread.__init__(self, 'Recovering Linux kernel configuration', False) self.outpath = outpath self.view = view def run(self): """Run analysis task """ self.view.reanalyze() self.view.update_analysis_and_wait() kconfigr = KConfigRecover(self.view) config = kconfigr.recover() save_kconfig(config, self.outpath) self.progress = "" def run_from_ui(view: BinaryView) -> None: """Run as a plugin under the UI Args: view: Binary view """ logger = logging.getLogger() logger.setLevel(logging.INFO) config_field = SaveFileNameField('Configuration Output Path') get_form_input([config_field], 'Kernel Configuration Recovery Options') outpath = 'generated.config' if config_field.result != '': outpath = config_field.result kconfig_task = RecoverKConfigBackground(view, outpath) kconfig_task.start() def parse_args() -> argparse.Namespace: """Parses command line arguments. Returns: Parsed command line arguments. """ parser = argparse.ArgumentParser() parser.add_argument( 'bndb', help='File path to kernel ELF or Binary Ninja database') parser.add_argument( 'kconfig', help='File path to save recovered kernel configuration') parser.add_argument('-d', '--debug', action='store_true', help='Enable debug logging') return parser.parse_args() def run_headless() -> None: """Parse command line arguments and run app. """ args = parse_args() logger = logging.getLogger() if args.debug: logger.setLevel(logging.DEBUG) else: logger.setLevel(logging.INFO) logging.info('Opening "%s" and getting view...', args.bndb) view = BinaryViewType.get_view_of_file(args.bndb) logging.info('Running BN analysis, this may take some time...') kconfig_task = RecoverKConfigBackground(view, args.kconfig) kconfig_task.start() if __name__ == '__main__': from kconfig import KConfigRecover, save_kconfig run_headless() else: from .kconfig import KConfigRecover, save_kconfig PluginCommand.register( "Recover Linux kernel config", 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Temperatura.py	russeleiser/ProjectTemperature	0	37205	print ("--------------------------------------------------------") print ("-------------Bienvenido a la Temperatura----------------") print ("--------------------------------------------------------\n") x = 0 while x < 10000: print ("Elige tus opciones:") print ("(1) Convertir de °C a °F") print ("(2) Convertir de °C a °K") print ("(3) Convertir de °F a °C") print ("(4) Convertir de °F a °K") print ("(5) Convertir de °K a °C") print ("(6) Convertir de °K a °F\n") op = int(input("Elige una opcion de acuerdo a su numero: ")) if op ==1: r11 = int(input("Escriba el numero que quiere convertir: ")) var11 = r11 var12 = var11 * 1.8 var13 = var12 + 32 print ("°F =", var11, "x 1.8 + 32\n°F =", var12,"+ 32\n°F =", var13,"\n") elif op ==2: r12 = int(input("Escriba el numero que quiere convertir: ")) var21 = r12 var22 = var21 + 273 print ("°K =", var21, "-273\n°K =", var22,"\n") elif op ==3: r13 = int(input("Escriba el numero que quiere convertir: ")) var31 = r13 var32 = var31 - 32 var33 = var32 / 1.8 print ("°C = (", var31,"- 32 ) / 1.8\n°C =", var32, "/ 1.8\n°C =", var33, "\n") elif op ==4: r14 = int(input("Escriba el numero que quiere convertir: ")) var41 = r14 var42 = var41 - 32 bla = 0.5 * var42 var43 = bla + 273 print ("°K = 5/9 (", var41,"- 32 ) + 273\n°K = 5/9", var42, "+ 273\n°K = 0.5 x", var42,"+ 273\n°K =",var43, "\n") elif op ==5: r15 = int(input("Escriba el numero que quiere convertir: ")) var51 = r15 var52 = var51 - 273 print ("El resultado es: ", var52) elif op ==6: r16 = int(input("Escriba el numero que quiere convertir: ")) var61 = r16 var62 = var61 - 273 num = 1.8 * var62 var63 = num + 32 print ("El resultado es: ", var63) else: print ("Esta opcion no existe, ingrese una nueva opcion...") x = x + 1	[ 1, 1596, 4852, 2683, 2683, 2683, 1378, 1159, 13, 2158, 4852, 9072, 29899, 29933, 819, 854, 1941, 263, 425, 6789, 546, 7969, 2683, 1159, 13, 2158, 4852, 2683, 2683, 2683, 1378, 29905, 29876, 1159, 13, 13, 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mura/eval.py	ZZ87229861/benchmarx	0	197623	<reponame>ZZ87229861/benchmarx<gh_stars>0 #!/usr/bin/env python3 import os import sys import simplejson as json import subprocess as sp if len(sys.argv) != 2: print("Usage: %s result_file" % sys.argv[0]) sys.exit(0) result_file = sys.argv[1] ROOT = os.path.abspath(os.path.dirname(__file__)) kapp = os.path.abspath(os.path.join(ROOT, 'kapp.pl')) official = os.path.abspath(os.path.join(ROOT, 'evaluate-v1.1.py')) gt = os.path.abspath(os.path.join(ROOT, '../data/mura/train_labeled_studies.csv')) if not os.path.isfile(gt): print("Create a symbolic link benchmarx/data/mura which should point to MURA-v1.1") print("You should see benchmarx/data/mura/train_labeled_studies.csv") sys.exit(0) lookup = {} with open(gt, 'r') as f: for l in f: k, v = l.strip().split(',') lookup[k] = v C = 0 with open(result_file, 'r') as f, \ open('temp_gt_xxxx', 'w') as of: for l in f: k, v = l.strip().split(',') assert k in lookup of.write('%s,%s\n' % (k, lookup[k])) C += 1 pass pass print('evaluating %s lines' % C) st = sp.check_output('python %s temp_gt_xxxx %s' % (official, result_file), shell=True) print(json.dumps(json.loads(st), indent=4, sort_keys=True)) sp.check_call('%s temp_gt_xxxx %s' % (kapp, result_file), shell=True) sp.check_call('rm temp_gt_xxxx', shell=True)	[ 1, 529, 276, 1112, 420, 29958, 29999, 29999, 29947, 29955, 29906, 29906, 29929, 29947, 29953, 29896, 29914, 1785, 305, 3034, 29916, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 5215, 2897, 13, 5215, 10876, 13, 5215, 2560, 3126, 408, 4390, 13, 5215, 1014, 5014, 408, 805, 13, 13, 361, 7431, 29898, 9675, 29889, 19218, 29897, 2804, 29871, 29906, 29901, 13, 1678, 1596, 703, 27573, 29901, 1273, 29879, 1121, 29918, 1445, 29908, 1273, 10876, 29889, 19218, 29961, 29900, 2314, 13, 1678, 10876, 29889, 13322, 29898, 29900, 29897, 13, 13, 2914, 29918, 1445, 353, 10876, 29889, 19218, 29961, 29896, 29962, 13, 13, 21289, 353, 2897, 29889, 2084, 29889, 370, 1028, 493, 29898, 359, 29889, 2084, 29889, 25721, 22168, 1445, 1649, 876, 13, 13, 29895, 932, 353, 2897, 29889, 2084, 29889, 370, 1028, 493, 29898, 359, 29889, 2084, 29889, 7122, 29898, 21289, 29892, 525, 29895, 932, 29889, 572, 8785, 13, 29877, 7880, 353, 2897, 29889, 2084, 29889, 370, 1028, 493, 29898, 359, 29889, 2084, 29889, 7122, 29898, 21289, 29892, 525, 24219, 403, 29899, 29894, 29896, 29889, 29896, 29889, 2272, 8785, 13, 13, 4141, 353, 2897, 29889, 2084, 29889, 370, 1028, 493, 29898, 359, 29889, 2084, 29889, 7122, 29898, 21289, 29892, 525, 6995, 1272, 29914, 29885, 2002, 29914, 14968, 29918, 29880, 24025, 29918, 18082, 583, 29889, 7638, 8785, 13, 13, 361, 451, 2897, 29889, 2084, 29889, 275, 1445, 29898, 4141, 1125, 13, 1678, 1596, 703, 4391, 263, 5829, 293, 1544, 3856, 305, 3034, 29916, 29914, 1272, 29914, 29885, 2002, 607, 881, 1298, 304, 341, 4574, 29909, 29899, 29894, 29896, 29889, 29896, 1159, 13, 1678, 1596, 703, 3492, 881, 1074, 3856, 305, 3034, 29916, 29914, 1272, 29914, 29885, 2002, 29914, 14968, 29918, 29880, 24025, 29918, 18082, 583, 29889, 7638, 1159, 13, 1678, 10876, 29889, 13322, 29898, 29900, 29897, 13, 13, 13, 20401, 353, 6571, 13, 2541, 1722, 29898, 4141, 29892, 525, 29878, 1495, 408, 285, 29901, 13, 1678, 363, 301, 297, 285, 29901, 13, 4706, 413, 29892, 325, 353, 301, 29889, 17010, 2141, 5451, 29317, 1495, 13, 4706, 16280, 29961, 29895, 29962, 353, 325, 13, 13, 29907, 353, 29871, 29900, 13, 2541, 1722, 29898, 2914, 29918, 1445, 29892, 525, 29878, 1495, 408, 285, 29892, 320, 13, 1678, 1722, 877, 7382, 29918, 4141, 29918, 14633, 742, 525, 29893, 1495, 408, 310, 29901, 13, 1678, 363, 301, 297, 285, 29901, 13, 4706, 413, 29892, 325, 353, 301, 29889, 17010, 2141, 5451, 29317, 1495, 13, 4706, 4974, 413, 297, 16280, 13, 4706, 310, 29889, 3539, 877, 29995, 29879, 24163, 29879, 29905, 29876, 29915, 1273, 313, 29895, 29892, 16280, 29961, 29895, 12622, 13, 4706, 315, 4619, 29871, 29896, 13, 4706, 1209, 13, 1678, 1209, 13, 13, 2158, 877, 24219, 1218, 1273, 29879, 3454, 29915, 1273, 315, 29897, 13, 303, 353, 805, 29889, 3198, 29918, 4905, 877, 4691, 1273, 29879, 5694, 29918, 4141, 29918, 14633, 1273, 29879, 29915, 1273, 313, 29877, 7880, 29892, 1121, 29918, 1445, 511, 6473, 29922, 5574, 29897, 13, 2158, 29898, 3126, 29889, 29881, 17204, 29898, 3126, 29889, 18132, 29898, 303, 511, 29536, 29922, 29946, 29892, 2656, 29918, 8149, 29922, 5574, 876, 13, 1028, 29889, 3198, 29918, 4804, 877, 29995, 29879, 5694, 29918, 4141, 29918, 14633, 1273, 29879, 29915, 1273, 313, 29895, 932, 29892, 1121, 29918, 1445, 511, 6473, 29922, 5574, 29897, 13, 1028, 29889, 3198, 29918, 4804, 877, 1758, 5694, 29918, 4141, 29918, 14633, 742, 6473, 29922, 5574, 29897, 13, 2 ]
python/language/lang.py	kenkeiras/codigoparallevar	2	64311	#!/usr/bin/env python # coding: utf-8 import re import os import sys # Define un color para mostrar los resultados color = True if color: colorfile = "\x1b[0;92m" # Verde colorend = "\x1b[0m" # Fin del color else: colorfile = colorend = "" # Diccionarios a comprobar dictionaries = ["american-english", "british-english", "galician", "spanish"] path = "/usr/share/dict" class LangReader: words = re.compile(r'\w+') def __init__(self, dicts): '''Carga los diccionarios con las palabras, una por línea, en minúsculas. ''' self.dicts = {} for d in dicts: self.dicts[d] = set(map(lambda x: x.strip().lower(), open(d, "rt").read().strip().split("\n"))) def get_file_props(self, f): return self.get_props(f.read()) def get_percents(self, data): '''Devuelve el porcentaje de palabras de cada idioma.''' props, total = self.get_props(data) percents = {} for lang in props: percents[lang] = round((float(props[lang]) / total) * 100, 2) return percents def get_props(self, data): '''Devuelve el número de palabras que se corresponde con el diccionario de cada idioma.''' counters = {} total = 0 words = map(lambda w: w.lower(), self.words.findall(data)) for lang in self.dicts: ldict = self.dicts[lang] counter = 0 for word in words: if word in ldict: counter += 1 counters[lang] = counter return counters, len(words) if __name__ == "__main__": if len(sys.argv) == 1: print >>sys.stderr, "%s <file>" % sys.argv[0] exit(0) files = map(lambda x: open(x, "rt"), sys.argv[1:]) os.chdir(path) ld = LangReader(dictionaries) first = True for f in files: if not first: print "\n" props, total = ld.get_file_props(f) if total > 0: print "%s%s%s [%i]\n" % (colorfile, f.name, colorend, total) for i in sorted(props, lambda x, y: props[x].__cmp__(props[y]), reverse=True): print "%5.2f%% %s" % ((props[i] / float(total)) * 100, i) else: print "%s%s%s nothing found" % (colorfile, f.name, colorend) first = False	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 14137, 29901, 23616, 29899, 29947, 13, 13, 5215, 337, 13, 5215, 2897, 13, 5215, 10876, 13, 13, 29937, 22402, 443, 2927, 1702, 1556, 13678, 1232, 1121, 2255, 13, 2780, 353, 5852, 13, 361, 2927, 29901, 13, 1678, 2927, 1445, 353, 6634, 29916, 29896, 29890, 29961, 29900, 29936, 29929, 29906, 29885, 29908, 29871, 396, 1798, 311, 13, 1678, 2927, 355, 353, 6634, 29916, 29896, 29890, 29961, 29900, 29885, 29908, 418, 396, 4231, 628, 2927, 13, 2870, 29901, 13, 1678, 2927, 1445, 353, 2927, 355, 353, 5124, 13, 13, 29937, 28550, 12401, 8596, 263, 752, 307, 1646, 13, 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tests/fixtures.py	vfxetc/sgcache	13	22341	<reponame>vfxetc/sgcache from . import uuid def task_crud(self, shotgun, trigger_poll=lambda: None): shot_name = uuid(8) shot = shotgun.create('Shot', {'code': shot_name}) name = uuid(8) task = shotgun.create('Task', {'content': name, 'entity': shot}) trigger_poll() x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['content']) self.assertSameEntity(task, x) # entity field x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['entity']) self.assertSameEntity(shot, x['entity']) # return through entity field x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['entity.Shot.code']) self.assertEqual(shot_name, x['entity.Shot.code']) # Updates name += '-2' shotgun.update('Task', task['id'], {'content': name}) trigger_poll() x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['content']) self.assertEqual(x['content'], name) # Delete shotgun.delete('Task', task['id']) trigger_poll() x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['content']) self.assertIs(x, None) x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['content'], retired_only=True) self.assertSameEntity(task, x) # Revive shotgun.revive('Task', task['id']) trigger_poll() x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['content']) self.assertSameEntity(task, x, 'Should get revived task.') x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['content'], retired_only=True) self.assertIs(x, None)	[ 1, 529, 276, 1112, 420, 29958, 29894, 11093, 7070, 29914, 5311, 8173, 13, 3166, 869, 1053, 318, 5416, 13, 13, 13, 1753, 3414, 29918, 7283, 566, 29898, 1311, 29892, 10322, 28798, 29892, 7135, 29918, 29886, 3028, 29922, 2892, 29901, 6213, 1125, 13, 13, 1678, 10322, 29918, 978, 353, 318, 5416, 29898, 29947, 29897, 13, 1678, 10322, 353, 10322, 28798, 29889, 3258, 877, 2713, 327, 742, 11117, 401, 2396, 10322, 29918, 978, 1800, 13, 13, 1678, 1024, 353, 318, 5416, 29898, 29947, 29897, 13, 1678, 3414, 353, 10322, 28798, 29889, 3258, 877, 5398, 742, 11117, 3051, 2396, 1024, 29892, 525, 10041, 2396, 10322, 1800, 13, 1678, 7135, 29918, 29886, 3028, 580, 13, 13, 1678, 921, 353, 1583, 29889, 29883, 3791, 29889, 2886, 29918, 650, 877, 5398, 742, 518, 877, 333, 742, 525, 275, 742, 3414, 1839, 333, 11287, 1402, 6024, 3051, 11287, 13, 1678, 1583, 29889, 9294, 29903, 420, 6691, 29898, 7662, 29892, 921, 29897, 13, 13, 1678, 396, 7855, 1746, 13, 1678, 921, 353, 1583, 29889, 29883, 3791, 29889, 2886, 29918, 650, 877, 5398, 742, 518, 877, 333, 742, 525, 275, 742, 3414, 1839, 333, 11287, 1402, 6024, 10041, 11287, 13, 1678, 1583, 29889, 9294, 29903, 420, 6691, 29898, 8962, 29892, 921, 1839, 10041, 11287, 13, 13, 1678, 396, 736, 1549, 7855, 1746, 13, 1678, 921, 353, 1583, 29889, 29883, 3791, 29889, 2886, 29918, 650, 877, 5398, 742, 518, 877, 333, 742, 525, 275, 742, 3414, 1839, 333, 11287, 1402, 6024, 10041, 29889, 2713, 327, 29889, 401, 11287, 13, 1678, 1583, 29889, 9294, 9843, 29898, 8962, 29918, 978, 29892, 921, 1839, 10041, 29889, 2713, 327, 29889, 401, 11287, 13, 13, 1678, 396, 5020, 15190, 13, 1678, 1024, 4619, 17411, 29906, 29915, 13, 1678, 10322, 28798, 29889, 5504, 877, 5398, 742, 3414, 1839, 333, 7464, 11117, 3051, 2396, 1024, 1800, 13, 1678, 7135, 29918, 29886, 3028, 580, 13, 1678, 921, 353, 1583, 29889, 29883, 3791, 29889, 2886, 29918, 650, 877, 5398, 742, 518, 877, 333, 742, 525, 275, 742, 3414, 1839, 333, 11287, 1402, 6024, 3051, 11287, 13, 1678, 1583, 29889, 9294, 9843, 29898, 29916, 1839, 3051, 7464, 1024, 29897, 13, 13, 1678, 396, 21267, 13, 1678, 10322, 28798, 29889, 8143, 877, 5398, 742, 3414, 1839, 333, 11287, 13, 1678, 7135, 29918, 29886, 3028, 580, 13, 1678, 921, 353, 1583, 29889, 29883, 3791, 29889, 2886, 29918, 650, 877, 5398, 742, 518, 877, 333, 742, 525, 275, 742, 3414, 1839, 333, 11287, 1402, 6024, 3051, 11287, 13, 1678, 1583, 29889, 9294, 3624, 29898, 29916, 29892, 6213, 29897, 13, 1678, 921, 353, 1583, 29889, 29883, 3791, 29889, 2886, 29918, 650, 877, 5398, 742, 518, 877, 333, 742, 525, 275, 742, 3414, 1839, 333, 11287, 1402, 6024, 3051, 7464, 16528, 29918, 6194, 29922, 5574, 29897, 13, 1678, 1583, 29889, 9294, 29903, 420, 6691, 29898, 7662, 29892, 921, 29897, 13, 13, 1678, 396, 11459, 573, 13, 1678, 10322, 28798, 29889, 13478, 573, 877, 5398, 742, 3414, 1839, 333, 11287, 13, 1678, 7135, 29918, 29886, 3028, 580, 13, 1678, 921, 353, 1583, 29889, 29883, 3791, 29889, 2886, 29918, 650, 877, 5398, 742, 518, 877, 333, 742, 525, 275, 742, 3414, 1839, 333, 11287, 1402, 6024, 3051, 11287, 13, 1678, 1583, 29889, 9294, 29903, 420, 6691, 29898, 7662, 29892, 921, 29892, 525, 26857, 679, 6664, 2347, 3414, 29889, 1495, 13, 1678, 921, 353, 1583, 29889, 29883, 3791, 29889, 2886, 29918, 650, 877, 5398, 742, 518, 877, 333, 742, 525, 275, 742, 3414, 1839, 333, 11287, 1402, 6024, 3051, 7464, 16528, 29918, 6194, 29922, 5574, 29897, 13, 1678, 1583, 29889, 9294, 3624, 29898, 29916, 29892, 6213, 29897, 13, 2 ]
commands/m_link.py	davidferguson/lektor-markdown-mactutor	2	67477	<filename>commands/m_link.py # -- coding: utf-8 -- import re import mistune name = 'm_link' type = 'inline' position = 0 regex = re.compile(r'\[m\s(?:=\s(?P<name>\w+)\s)?](?P<text>.?)\[\/m\]') def render(match): name = match.group('name') text = match.group('text') # special case if there is no explicit name specified, in which case we # have to derive it ourselves if name == None: name = text.split()[-1] # and remove all accents from it. name = purge(name) # format the appropriate link structure return '<a href="/Mathematicians/%s.html" data-name="%s" onclick="m_popup(this)">%s</a>' % (name,name,text) def purge(s): # The below code is modified from the original htmlformat function # to ensure compatibility rawch = ['á','à','â','ä','ã','Á','Â','Ä','é','è','ê','ë','É','î','í','ó','ô','ö','ò','õ','Ö','û','ú','ü','ù','Ü','ç','ï','ø','Ø','ñ'] transch = ['a','a','a','a','a','A','A','A','e','e','e','e','E','i','i','o','o','o','o','o','O','u','u','u','u','U','c','i','o','O','n'] for idx, raw in enumerate(rawch): s = s.replace(raw, transch[idx]) return s	[ 1, 529, 9507, 29958, 26381, 29914, 29885, 29918, 2324, 29889, 2272, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 5215, 337, 13, 5215, 5862, 1540, 13, 13, 13, 978, 353, 525, 29885, 29918, 2324, 29915, 13, 1853, 353, 525, 14764, 29915, 13, 3283, 353, 29871, 29900, 13, 13087, 353, 337, 29889, 12198, 29898, 29878, 12764, 29961, 29885, 29905, 29879, 29930, 10780, 29901, 2013, 29879, 29930, 10780, 29925, 29966, 978, 14247, 29893, 29974, 2144, 29879, 29930, 6877, 850, 29973, 29925, 29966, 726, 29958, 5575, 29973, 2144, 7110, 29914, 29885, 18899, 1495, 13, 13, 13, 1753, 4050, 29898, 4352, 1125, 13, 1678, 1024, 353, 1993, 29889, 2972, 877, 978, 1495, 13, 1678, 1426, 353, 1993, 29889, 2972, 877, 726, 1495, 13, 1678, 396, 4266, 1206, 565, 727, 338, 694, 6261, 1024, 6790, 29892, 297, 607, 1206, 591, 13, 1678, 396, 505, 304, 21340, 372, 20278, 13, 1678, 565, 1024, 1275, 6213, 29901, 13, 4706, 1024, 353, 1426, 29889, 5451, 580, 14352, 29896, 29962, 13, 4706, 396, 322, 3349, 599, 1035, 1237, 515, 372, 29889, 13, 4706, 1024, 353, 3708, 479, 29898, 978, 29897, 13, 1678, 396, 3402, 278, 8210, 1544, 3829, 13, 1678, 736, 12801, 29874, 2822, 13802, 11309, 4579, 14722, 22584, 29879, 29889, 1420, 29908, 848, 29899, 978, 543, 29995, 29879, 29908, 15630, 543, 29885, 29918, 7323, 786, 29898, 1366, 29897, 1013, 29995, 29879, 829, 29874, 16299, 1273, 313, 978, 29892, 978, 29892, 726, 29897, 13, 13, 13, 1753, 3708, 479, 29898, 29879, 1125, 13, 1678, 396, 450, 2400, 775, 338, 9120, 515, 278, 2441, 3472, 4830, 740, 13, 1678, 396, 304, 9801, 24521, 13, 1678, 10650, 305, 259, 353, 6024, 29976, 3788, 30001, 3788, 30057, 3788, 29986, 3788, 30033, 3788, 30102, 3788, 30212, 3788, 30125, 3788, 29948, 3788, 30000, 3788, 30037, 3788, 30083, 3788, 30062, 3788, 30058, 3788, 29983, 3788, 29980, 3788, 30069, 3788, 29997, 3788, 30059, 3788, 30084, 3788, 30094, 3788, 30095, 3788, 30030, 3788, 29993, 3788, 30071, 3788, 30104, 3788, 30019, 3788, 30085, 3788, 30077, 3788, 30222, 3788, 30046, 2033, 13, 1678, 1301, 305, 353, 6024, 29874, 3788, 29874, 3788, 29874, 3788, 29874, 3788, 29874, 3788, 29909, 3788, 29909, 3788, 29909, 3788, 29872, 3788, 29872, 3788, 29872, 3788, 29872, 3788, 29923, 3788, 29875, 3788, 29875, 3788, 29877, 3788, 29877, 3788, 29877, 3788, 29877, 3788, 29877, 3788, 29949, 3788, 29884, 3788, 29884, 3788, 29884, 3788, 29884, 3788, 29965, 3788, 29883, 3788, 29875, 3788, 29877, 3788, 29949, 3788, 29876, 2033, 13, 1678, 363, 22645, 29892, 10650, 297, 26985, 29898, 1610, 305, 1125, 13, 4706, 269, 353, 269, 29889, 6506, 29898, 1610, 29892, 1301, 305, 29961, 13140, 2314, 13, 1678, 736, 269, 13, 2 ]
read_data_objects.py	PawWitGit/alaska-methan-flux	0	180123	import pandas as pd import matplotlib as plt import matplotlib.pyplot as plt import numpy as np from read_data import ReadData # class DataObjects: # """Create ReadData objects""" # read_data = ReadData("CH4_Flux_BigTrail_Goldstream_AK.csv") # """Functions""" # read_data.return_data_file() # read_data.return_data_table()	[ 1, 1053, 11701, 408, 10518, 13, 5215, 22889, 408, 14770, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 5215, 12655, 408, 7442, 13, 13, 3166, 1303, 29918, 1272, 1053, 7523, 1469, 13, 13, 13, 29937, 770, 3630, 12724, 29901, 13, 13, 29937, 9995, 4391, 7523, 1469, 3618, 15945, 29908, 13, 13, 29937, 1303, 29918, 1272, 353, 7523, 1469, 703, 3210, 29946, 29918, 8754, 1314, 29918, 6970, 5323, 309, 29918, 29954, 1025, 5461, 29918, 22311, 29889, 7638, 1159, 13, 13, 29937, 9995, 6678, 29879, 15945, 29908, 13, 13, 29937, 1303, 29918, 1272, 29889, 2457, 29918, 1272, 29918, 1445, 580, 13, 29937, 1303, 29918, 1272, 29889, 2457, 29918, 1272, 29918, 2371, 580, 13, 2 ]
djnic/core/models.py	avdata99/nic	8	165687	<reponame>avdata99/nic from django.db import models class News(models.Model): """ novedades internas del sistema """ priority = models.IntegerField(default=50, help_text='0/100 priority level') title = models.CharField(max_length=90) description = models.TextField(null=True, blank=True) object_created = models.DateTimeField(auto_now_add=True) def __str__(self): return f'{self.title} {self.object_created}' class Meta: ordering = ['-id']	[ 1, 529, 276, 1112, 420, 29958, 485, 1272, 29929, 29929, 29914, 7823, 13, 3166, 9557, 29889, 2585, 1053, 4733, 13, 13, 13, 1990, 10130, 29898, 9794, 29889, 3195, 1125, 13, 1678, 9995, 2420, 287, 3076, 2836, 294, 628, 10502, 9995, 13, 1678, 20136, 353, 4733, 29889, 7798, 3073, 29898, 4381, 29922, 29945, 29900, 29892, 1371, 29918, 726, 2433, 29900, 29914, 29896, 29900, 29900, 20136, 3233, 1495, 13, 1678, 3611, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29929, 29900, 29897, 13, 1678, 6139, 353, 4733, 29889, 15778, 29898, 4304, 29922, 5574, 29892, 9654, 29922, 5574, 29897, 13, 1678, 1203, 29918, 11600, 353, 4733, 29889, 11384, 3073, 29898, 6921, 29918, 3707, 29918, 1202, 29922, 5574, 29897, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 285, 29915, 29912, 1311, 29889, 3257, 29913, 426, 1311, 29889, 3318, 29918, 11600, 10162, 13, 13, 1678, 770, 20553, 29901, 13, 4706, 20520, 353, 6024, 29899, 333, 2033, 2 ]
Backend/pose_analyser/models.py	saksham1991999/Yoga-Pose-Analyser	0	1601017	from django.db import models class Pose(models.Model): name = models.CharField(max_length=256) description = models.TextField(blank=True, null=True) file = models.FileField() def __str__(self): return self.name class PoseAnalysis(models.Model): pose = models.ForeignKey("pose_analyser.Pose", blank=True, null=True, on_delete=models.CASCADE) image = models.ImageField() analysis = models.TextField(blank=True, null=True)	[ 1, 515, 9557, 29889, 2585, 1053, 4733, 13, 13, 13, 1990, 349, 852, 29898, 9794, 29889, 3195, 1125, 13, 1678, 1024, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29945, 29953, 29897, 13, 1678, 6139, 353, 4733, 29889, 15778, 29898, 19465, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 13, 1678, 934, 353, 4733, 29889, 2283, 3073, 580, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 1583, 29889, 978, 13, 13, 13, 1990, 349, 852, 21067, 4848, 29898, 9794, 29889, 3195, 1125, 13, 1678, 18593, 353, 4733, 29889, 27755, 2558, 703, 4220, 29918, 24209, 643, 29889, 29925, 852, 613, 9654, 29922, 5574, 29892, 1870, 29922, 5574, 29892, 373, 29918, 8143, 29922, 9794, 29889, 29907, 3289, 5454, 2287, 29897, 13, 1678, 1967, 353, 4733, 29889, 2940, 3073, 580, 13, 1678, 7418, 353, 4733, 29889, 15778, 29898, 19465, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 13, 2 ]
python/data_helper.py	justinmzt/text_classification-system	4	106338	<reponame>justinmzt/text_classification-system<filename>python/data_helper.py # -- coding: UTF-8 -- import time class Timer(): def __init__(self): self.start = time.time() def print(self, op="训练"): print("%s用时 %f 秒。" % (op, time.time() - self.start)) def open_file(filename, mode='r'): """ 常用文件操作，可在python2和python3间切换. mode: 'r' or 'w' for read or write """ # if is_py3: return open(filename, mode, encoding='utf-8', errors='ignore') # else: # return open(filename, mode) def read_file(filename): """读取文件数据""" output = [] with open_file(filename) as f: output = [_.strip() for _ in f.readlines()] return output def write_list(filename, array): """读取文件数据""" with open_file(filename, 'w') as f: f.write('\n'.join(array) + '\n') f.close() def read_cnews(filename, cut_file=None): x, y = [], [] with open_file(filename) as tr: for line in tr: label, content = line.strip().split('\t') if content: x.append(content) y.append(label) if cut_file: x = read_file(cut_file) return x,y def write_file(filename, x, y): with open(filename,'a', encoding='utf8') as f: if isinstance(x, str) and isinstance(y, str): f.write('%s\t%s\n' % (y, x)) elif isinstance(x, list) and isinstance(y, list): if len(x) != len(y): print("Not same scale.") return False for i in range(len(x)): f.write('%s\t%s\n' % (y[i], x[i])) else: print("Write False")	[ 1, 529, 276, 1112, 420, 29958, 5143, 262, 29885, 2065, 29914, 726, 29918, 1990, 2450, 29899, 5205, 29966, 9507, 29958, 4691, 29914, 1272, 29918, 20907, 29889, 2272, 13, 29937, 448, 29930, 29899, 14137, 29901, 18351, 29899, 29947, 448, 29930, 29899, 13, 5215, 931, 13, 1990, 29168, 7295, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 1583, 29889, 2962, 353, 931, 29889, 2230, 580, 13, 268, 13, 1678, 822, 1596, 29898, 1311, 29892, 1015, 543, 235, 177, 176, 234, 190, 134, 29908, 1125, 13, 4706, 1596, 11702, 29879, 30406, 30594, 1273, 29888, 29871, 234, 170, 149, 30267, 29908, 1273, 313, 459, 29892, 931, 29889, 2230, 580, 448, 1583, 29889, 2962, 876, 13, 13, 1753, 1722, 29918, 1445, 29898, 9507, 29892, 4464, 2433, 29878, 29374, 13, 1678, 9995, 13, 268, 31190, 30406, 30333, 30631, 31904, 30732, 30214, 30682, 30505, 4691, 29906, 30503, 4691, 29941, 31016, 31757, 31640, 29889, 13, 1678, 4464, 29901, 525, 29878, 29915, 470, 525, 29893, 29915, 363, 1303, 470, 2436, 13, 1678, 9995, 13, 29937, 268, 565, 338, 29918, 2272, 29941, 29901, 13, 1678, 736, 1722, 29898, 9507, 29892, 4464, 29892, 8025, 2433, 9420, 29899, 29947, 742, 4436, 2433, 17281, 1495, 13, 29937, 268, 1683, 29901, 13, 29937, 308, 736, 1722, 29898, 9507, 29892, 4464, 29897, 13, 13, 1753, 1303, 29918, 1445, 29898, 9507, 1125, 13, 1678, 9995, 235, 178, 190, 30683, 30333, 30631, 30354, 30763, 15945, 29908, 13, 1678, 1962, 353, 5159, 13, 1678, 411, 1722, 29918, 1445, 29898, 9507, 29897, 408, 285, 29901, 13, 4706, 1962, 353, 518, 5396, 17010, 580, 363, 903, 297, 285, 29889, 949, 9012, 580, 29962, 13, 1678, 736, 1962, 13, 13, 1753, 2436, 29918, 1761, 29898, 9507, 29892, 1409, 1125, 13, 1678, 9995, 235, 178, 190, 30683, 30333, 30631, 30354, 30763, 15945, 29908, 13, 1678, 411, 1722, 29918, 1445, 29898, 9507, 29892, 525, 29893, 1495, 408, 285, 29901, 13, 4706, 285, 29889, 3539, 28909, 29876, 4286, 7122, 29898, 2378, 29897, 718, 11297, 29876, 1495, 13, 4706, 285, 29889, 5358, 580, 13, 13, 1753, 1303, 29918, 29883, 15753, 29898, 9507, 29892, 5700, 29918, 1445, 29922, 8516, 1125, 13, 1678, 921, 29892, 343, 353, 19997, 5159, 13, 1678, 411, 1722, 29918, 1445, 29898, 9507, 29897, 408, 534, 29901, 13, 4706, 363, 1196, 297, 534, 29901, 13, 9651, 3858, 29892, 2793, 353, 1196, 29889, 17010, 2141, 5451, 28909, 29873, 1495, 13, 9651, 565, 2793, 29901, 13, 18884, 921, 29889, 4397, 29898, 3051, 29897, 13, 18884, 343, 29889, 4397, 29898, 1643, 29897, 13, 4706, 565, 5700, 29918, 1445, 29901, 13, 9651, 921, 353, 1303, 29918, 1445, 29898, 7582, 29918, 1445, 29897, 13, 4706, 736, 921, 29892, 29891, 13, 13, 1753, 2436, 29918, 1445, 29898, 9507, 29892, 921, 29892, 343, 1125, 13, 1678, 411, 1722, 29898, 9507, 5501, 29874, 742, 8025, 2433, 9420, 29947, 1495, 408, 285, 29901, 13, 4706, 565, 338, 8758, 29898, 29916, 29892, 851, 29897, 322, 338, 8758, 29898, 29891, 29892, 851, 1125, 13, 9651, 285, 29889, 3539, 877, 29995, 29879, 29905, 29873, 29995, 29879, 29905, 29876, 29915, 1273, 313, 29891, 29892, 921, 876, 13, 4706, 25342, 338, 8758, 29898, 29916, 29892, 1051, 29897, 322, 338, 8758, 29898, 29891, 29892, 1051, 1125, 13, 9651, 565, 7431, 29898, 29916, 29897, 2804, 7431, 29898, 29891, 1125, 13, 18884, 1596, 703, 3664, 1021, 6287, 23157, 13, 18884, 736, 7700, 13, 9651, 363, 474, 297, 3464, 29898, 2435, 29898, 29916, 22164, 13, 18884, 285, 29889, 3539, 877, 29995, 29879, 29905, 29873, 29995, 29879, 29905, 29876, 29915, 1273, 313, 29891, 29961, 29875, 1402, 921, 29961, 29875, 12622, 13, 4706, 1683, 29901, 13, 9651, 1596, 703, 6113, 7700, 1159, 13, 13, 13, 2 ]
MMMaker/core/highlight_extractor.py	C4Ution/MMMaker	9	38707	<gh_stars>1-10 import numpy as np import scipy.io.wavfile as wave from moviepy.editor import VideoFileClip from scipy.fftpack import fft from misc import get_random_name HIGHLIGHT_LENGTH = 0.65 def extract_highlights(file_paths): """ :param file_paths: 동영상 경로 리스트 :return: 추출한 하이라이트 동영상 경로 리스트 """ max_highlights = [] min_highlights = [] for file_path in file_paths: video_clip = VideoFileClip(file_path) audio_clip = video_clip.audio src_audio_file_name = get_random_name('wav') audio_clip.write_audiofile(src_audio_file_name) source_wave = wave.read(src_audio_file_name) stereo_channel_wave = source_wave[1].T[1] normalize_wave = [(ele / 2 ** 8.) * 2 - 1 for ele in stereo_channel_wave] # this is 8-bit track, now normalized on [-1,1) # fourier_transform_wave = fft(normalize_wave) # calculate fourier transform (complex numbers list) fourier_transform_wave = normalize_wave normalize_time = len(fourier_transform_wave) / video_clip.duration argmax_frequency = np.argmax(fourier_transform_wave) / normalize_time argmin_frequency = np.argmin(fourier_transform_wave) / normalize_time max_highlight_path = max_highlights.append(get_random_name('mp4')) min_highlight_path = min_highlights.append(get_random_name('mp4')) start_max = argmax_frequency - (HIGHLIGHT_LENGTH/2) end_max = argmax_frequency + (HIGHLIGHT_LENGTH/2) if start_max < 0: end_max -= start_max start_max = 0 elif end_max > video_clip.duration: start_max -= end_max - video_clip.duration end_max = video_clip.duration video_clip.subclip(start_max, end_max).write_videofile(max_highlights[-1], codec='libx264', audio_codec='aac') start_min = argmin_frequency - (HIGHLIGHT_LENGTH/2) end_min = argmin_frequency + (HIGHLIGHT_LENGTH/2) if start_min < 0: end_min -= start_min start_min = 0 elif end_min > video_clip.duration: start_min -= end_min - video_clip.duration end_min = video_clip.duration video_clip.subclip(start_min, 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flask_security_bundle/commands/roles.py	briancappello/flask-security-bundle	0	158409	from flask_unchained import unchained from flask_unchained.cli import cli, click from flask_unchained.commands.utils import print_table from .utils import _query_to_role from ..services import RoleManager role_manager: RoleManager = unchained.services.role_manager @cli.group() def roles(): """ Role commands. """ @roles.command(name='list') def list_roles(): """ List roles. """ roles = role_manager.find_all() if roles: print_table(['ID', 'Name'], [(role.id, role.name) for role in roles]) else: click.echo('No roles found.') @roles.command(name='create') @click.option('--name', prompt='Role name', help='The name of the role to create, eg `ROLE_USER`.') def create_role(name): """ Create a new role. """ role = role_manager.create(name=name) if click.confirm(f'Are you sure you want to create {role!r}?'): role_manager.save(role, commit=True) click.echo(f'Successfully created {role!r}') else: click.echo('Cancelled.') @roles.command(name='delete') @click.argument('query', help='The query to search for a role by. For example, ' '`id=5` or `name=ROLE_USER`.') def delete_role(query): """ Delete a role. """ role = _query_to_role(query) if click.confirm(f'Are you sure you want to delete {role!r}?'): role_manager.delete(role, commit=True) click.echo(f'Successfully deleted {role!r}') else: click.echo('Cancelled.')	[ 1, 515, 29784, 29918, 3322, 7114, 1053, 443, 305, 7114, 13, 3166, 29784, 29918, 3322, 7114, 29889, 11303, 1053, 9335, 29892, 2828, 13, 3166, 29784, 29918, 3322, 7114, 29889, 26381, 29889, 13239, 1053, 1596, 29918, 2371, 13, 13, 3166, 869, 13239, 1053, 903, 1972, 29918, 517, 29918, 12154, 13, 3166, 6317, 9916, 1053, 1528, 280, 3260, 13, 13, 12154, 29918, 12847, 29901, 1528, 280, 3260, 353, 443, 305, 7114, 29889, 9916, 29889, 12154, 29918, 12847, 13, 13, 13, 29992, 11303, 29889, 2972, 580, 13, 1753, 16178, 7295, 13, 1678, 9995, 13, 1678, 1528, 280, 8260, 29889, 13, 1678, 9995, 13, 13, 13, 29992, 307, 793, 29889, 6519, 29898, 978, 2433, 1761, 1495, 13, 1753, 1051, 29918, 307, 793, 7295, 13, 1678, 9995, 13, 1678, 2391, 16178, 29889, 13, 1678, 9995, 13, 1678, 16178, 353, 6297, 29918, 12847, 29889, 2886, 29918, 497, 580, 13, 1678, 565, 16178, 29901, 13, 4706, 1596, 29918, 2371, 18959, 1367, 742, 525, 1170, 7464, 17288, 12154, 29889, 333, 29892, 6297, 29889, 978, 29897, 363, 6297, 297, 16178, 2314, 13, 1678, 1683, 29901, 13, 4706, 2828, 29889, 8057, 877, 3782, 16178, 1476, 29889, 1495, 13, 13, 13, 29992, 307, 793, 29889, 6519, 29898, 978, 2433, 3258, 1495, 13, 29992, 3808, 29889, 3385, 877, 489, 978, 742, 9508, 2433, 16727, 1024, 742, 13, 795, 1371, 2433, 1576, 1024, 310, 278, 6297, 304, 1653, 29892, 8087, 421, 1672, 1307, 29918, 11889, 1412, 1495, 13, 1753, 1653, 29918, 12154, 29898, 978, 1125, 13, 1678, 9995, 13, 1678, 6204, 263, 716, 6297, 29889, 13, 1678, 9995, 13, 1678, 6297, 353, 6297, 29918, 12847, 29889, 3258, 29898, 978, 29922, 978, 29897, 13, 1678, 565, 2828, 29889, 26897, 29898, 29888, 29915, 17506, 366, 1854, 366, 864, 304, 1653, 426, 12154, 29991, 29878, 29913, 17901, 1125, 13, 4706, 6297, 29918, 12847, 29889, 7620, 29898, 12154, 29892, 9063, 29922, 5574, 29897, 13, 4706, 2828, 29889, 8057, 29898, 29888, 29915, 14191, 3730, 2825, 426, 12154, 29991, 29878, 29913, 1495, 13, 1678, 1683, 29901, 13, 4706, 2828, 29889, 8057, 877, 19420, 839, 29889, 1495, 13, 13, 13, 29992, 307, 793, 29889, 6519, 29898, 978, 2433, 8143, 1495, 13, 29992, 3808, 29889, 23516, 877, 1972, 742, 1371, 2433, 1576, 2346, 304, 2740, 363, 263, 6297, 491, 29889, 1152, 1342, 29892, 525, 13, 462, 795, 525, 29952, 333, 29922, 29945, 29952, 470, 421, 978, 29922, 1672, 1307, 29918, 11889, 1412, 1495, 13, 1753, 5217, 29918, 12154, 29898, 1972, 1125, 13, 1678, 9995, 13, 1678, 21267, 263, 6297, 29889, 13, 1678, 9995, 13, 1678, 6297, 353, 903, 1972, 29918, 517, 29918, 12154, 29898, 1972, 29897, 13, 1678, 565, 2828, 29889, 26897, 29898, 29888, 29915, 17506, 366, 1854, 366, 864, 304, 5217, 426, 12154, 29991, 29878, 29913, 17901, 1125, 13, 4706, 6297, 29918, 12847, 29889, 8143, 29898, 12154, 29892, 9063, 29922, 5574, 29897, 13, 4706, 2828, 29889, 8057, 29898, 29888, 29915, 14191, 3730, 11132, 426, 12154, 29991, 29878, 29913, 1495, 13, 1678, 1683, 29901, 13, 4706, 2828, 29889, 8057, 877, 19420, 839, 29889, 1495, 13, 2 ]
nucypher/tests/config/test_firstula_circumstances.py	kanzeparov/NuCypher	0	22728	""" This file is part of nucypher. nucypher is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. nucypher 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 nucypher. If not, see <https://www.gnu.org/licenses/>. """ from functools import partial import maya import pytest import pytest_twisted from twisted.internet.threads import deferToThread from nucypher.network.middleware import RestMiddleware from nucypher.utilities.sandbox.ursula import make_federated_ursulas def test_proper_seed_node_instantiation(ursula_federated_test_config): lonely_ursula_maker = partial(make_federated_ursulas, ursula_config=ursula_federated_test_config, quantity=1, know_each_other=False) firstula = lonely_ursula_maker().pop() firstula_as_seed_node = firstula.seed_node_metadata() any_other_ursula = lonely_ursula_maker(seed_nodes=[firstula_as_seed_node]).pop() assert not any_other_ursula.known_nodes any_other_ursula.start_learning_loop(now=True) assert firstula in any_other_ursula.known_nodes.values() @pytest_twisted.inlineCallbacks def test_get_cert_from_running_seed_node(ursula_federated_test_config): lonely_ursula_maker = partial(make_federated_ursulas, ursula_config=ursula_federated_test_config, quantity=1, know_each_other=False) firstula = lonely_ursula_maker().pop() node_deployer = firstula.get_deployer() node_deployer.addServices() node_deployer.catalogServers(node_deployer.hendrix) node_deployer.start() certificate_as_deployed = node_deployer.cert.to_cryptography() firstula_as_seed_node = firstula.seed_node_metadata() any_other_ursula = lonely_ursula_maker(seed_nodes=[firstula_as_seed_node], network_middleware=RestMiddleware()).pop() assert not any_other_ursula.known_nodes def start_lonely_learning_loop(): any_other_ursula.start_learning_loop() start = maya.now() while not firstula in any_other_ursula.known_nodes.values(): passed = maya.now() - start if passed.seconds > 2: pytest.fail("Didn't find the seed node.") yield deferToThread(start_lonely_learning_loop) assert firstula in any_other_ursula.known_nodes.values() certificate_as_learned = list(any_other_ursula.known_nodes.values())[0].certificate assert certificate_as_learned == certificate_as_deployed any_other_ursula.stop_learning_loop()	[ 1, 9995, 13, 4013, 934, 338, 760, 310, 4948, 1270, 8096, 29889, 13, 13, 3433, 1270, 8096, 338, 3889, 7047, 29901, 366, 508, 2654, 391, 2666, 372, 322, 29914, 272, 6623, 13, 277, 1090, 278, 4958, 310, 278, 15143, 4593, 5236, 19245, 408, 6369, 491, 13, 1552, 12362, 18540, 10606, 29892, 2845, 1873, 29871, 29941, 310, 278, 19245, 29892, 470, 13, 29898, 271, 596, 2984, 29897, 738, 2678, 1873, 29889, 13, 13, 3433, 1270, 8096, 338, 13235, 297, 278, 4966, 393, 372, 674, 367, 5407, 29892, 13, 4187, 399, 1806, 8187, 2692, 13764, 29979, 399, 1718, 29934, 13566, 29979, 29936, 1728, 1584, 278, 2411, 2957, 1370, 21867, 29891, 310, 13, 29924, 1001, 3210, 13566, 2882, 6227, 11937, 470, 383, 1806, 8186, 1799, 15842, 319, 349, 8322, 2965, 13309, 1718, 349, 4574, 13152, 1660, 29889, 29871, 2823, 278, 13, 29954, 11601, 4593, 5236, 19245, 363, 901, 4902, 29889, 13, 13, 3492, 881, 505, 4520, 263, 3509, 310, 278, 15143, 4593, 5236, 19245, 13, 284, 549, 411, 4948, 1270, 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rok/tests/conftest.py	rak-n-rok/Krake	1	75308	import os import sys import logging.config # Prepend package directory for working imports package_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) sys.path.insert(0, package_dir) logging.config.dictConfig( { "version": 1, "handlers": {"console": {"class": "logging.StreamHandler", "level": "DEBUG"}}, "loggers": {"rok": {"handlers": ["console"]}}, } )	[ 1, 1053, 2897, 13, 5215, 10876, 13, 5215, 12183, 29889, 2917, 13, 13, 13, 29937, 349, 3445, 355, 3577, 3884, 363, 1985, 24802, 13, 5113, 29918, 3972, 353, 2897, 29889, 2084, 29889, 370, 1028, 493, 29898, 359, 29889, 2084, 29889, 7122, 29898, 359, 29889, 2084, 29889, 25721, 22168, 1445, 1649, 511, 376, 636, 5783, 13, 9675, 29889, 2084, 29889, 7851, 29898, 29900, 29892, 3577, 29918, 3972, 29897, 13, 13, 13, 21027, 29889, 2917, 29889, 8977, 3991, 29898, 13, 1678, 426, 13, 4706, 376, 3259, 1115, 29871, 29896, 29892, 13, 4706, 376, 3179, 9306, 1115, 8853, 11058, 1115, 8853, 1990, 1115, 376, 21027, 29889, 3835, 4598, 613, 376, 5563, 1115, 376, 18525, 29908, 11656, 13, 4706, 376, 1188, 5743, 1115, 8853, 16475, 1115, 8853, 3179, 9306, 1115, 6796, 11058, 3108, 11656, 13, 1678, 500, 13, 29897, 13, 2 ]
src/student.py	RushiRaut/SRMS-using-Flask	0	85214	<filename>src/student.py from flask import * from src.connection import cursor, db add_student = Blueprint ('add_student', __name__) manage_student = Blueprint('manage_student', __name__) delete_student = Blueprint('delete_student',__name__) edit = Blueprint('pre_edit',__name__) @add_student.route ('/add_student1', methods=['GET', 'POST']) def add_student1(): class_details = {} msg = 'Please Fill the Form' if request.method.lower() == 'post' and 'student_name' in request.form and request.form['roll_no']: id = request.form['id'] roll_no = request.form['roll_no'] cursor.execute ("select * from students where roll_no =%s", (roll_no,)) student_found = cursor.fetchone () if student_found: msg = 'Student roll number allready exixt' else: student_name = request.form['student_name'] student_gender = request.form['student_Gender'] student_mobile = request.form['student_mobile'] student_class_name = request.form['student_class_name'] cursor.execute('insert into students values (%s,%s,%s,%s,%s,%s)', (id, student_name, roll_no, student_gender, student_mobile, student_class_name)) db.commit () msg = 'Student Created Successfully' cursor.execute ('select * from students') students = cursor.fetchall () columns = ["id", "student_name", "roll_no", "student_gender", "student_mobile", "student_class_name"] student_details = [dict (zip (columns, row)) for row in students] return render_template ('add_student.html', rows=student_details) cursor.execute("select class_name from student_classes") class_found = cursor.fetchall() columns = ["class_name"] class_details = [dict(zip(columns, row)) for row in class_found] return render_template ('add_student.html', row=class_details,msg = msg) @manage_student.route('/manage_students', methods=['GET', 'POST']) def manage_students(): msg = '' cursor.execute('select * from students') students = cursor.fetchall() columns = ["id", "student_name", "roll_no", "student_gender", "student_mobile", "student_class_name"] student_details = [dict(zip(columns, row)) for row in students] return render_template('manage_student.html', rows=student_details) @delete_student.route('/delete_student/<id>',methods = ['GET','POST']) def delete1(id): cursor.execute('delete from students where id = %s', (id,)) db.commit() cursor.execute ('select * from students') students = cursor.fetchall () columns = ["id", "student_name", "roll_no", "student_gender", "student_mobile", "student_class_name"] student_details = [dict (zip (columns, row)) for row in students] return render_template ('manage_student.html', rows=student_details) @add_student.route('/pre_edit/<id>',methods=['GET','POST']) def pre_edit(id): cursor.execute ('select * from students where id = %s', (id,)) student = cursor.fetchone () columns = ["id", "student_name", "roll_no", "student_gender", "student_mobile", "student_class_name"] student_details = dict (zip (columns, student)) cursor.execute ("select class_name from student_classes") class_found = cursor.fetchall () columns = ["class_name"] class_details = [dict (zip (columns, row)) for row in class_found] return render_template ('edit_student.html', row=student_details, row1=class_details) @add_student.route('/edit_student', methods=['POST','GET']) def edit_student(): if request.method == 'POST' and 'student_name' in request.form and request.form['roll_no']: id = request.form['id'] roll_no = request.form['roll_no'] student_name = request.form['student_name'] student_gender = request.form['student_Gender'] student_mobile = request.form['student_mobile'] student_class_name = request.form['student_class_name'] cursor.execute ('update students set id =%s ,student_name = %s,roll_no=%s, student_gender=%s,' 'student_mobile=%s,student_class_name=%s where id = %s', (id, student_name, roll_no, student_gender, student_mobile, student_class_name, id)) db.commit () cursor.execute ('select * from students') students = cursor.fetchall () columns = ["id", 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modules/backend.py	Uncle-Yuanl/model_zoo	0	16881	import os, sys from distutils.util import strtobool import numpy as np import tensorflow as tf import tensorflow.keras.backend as K from tensorflow.python.util import nest, tf_inspect from tensorflow.python.eager import tape # from tensorflow.python.ops.custom_gradient import graph_mode_decorator # 是否使用重计算 do_recompute = strtobool(os.environ.get('RECOMPUTE', '0')) # 知乎：https://zhuanlan.zhihu.com/p/349492378 # 论文：https://arxiv.53yu.com/pdf/1606.08415.pdf def gelu_erf(x): """根据erf直接计算gelu """ # np的精度更高，默认64位，tf默认32位 return 0.5 * x * (1.0 + tf.math.erf(x / np.sqrt(2.0))) def gelu_tanh(x): cdf = 0.5 * ( 1 + K.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * K.pow(x,3))) ) return x * cdf def set_gelu(version): """设置gelu版本 """ version = version.lower() assert version in ['erf', 'tanh'], 'gelu version must in erf or tanh' if version == 'erf': tf.keras.utils.get_custom_objects()['gelu'] = gelu_erf elif version == 'tanh': tf.keras.utils.get_custom_objects()['gelu'] = gelu_tanh def align(tensor, axes, ndim=None): """重新对齐tensor（批量版expand_dims）感觉更像是transpose axes: 原来的第i维对齐新tensor的第axes[i]维； ndim: 新tensor的维度 Example: >>> tensor = tf.constant(np.arange(12).reshape(3,4), dtype=tf.float32) >>> print(tensor) tf.Tensor( [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]], shape=(3, 4), dtype=float32) >>> same_dim = align(tensor, [0, -1], 2) >>> print(same_dim) tf.Tensor( [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]], shape=(3, 4), dtype=float32) >>> more_dim = align(tensor, [0, -1], 3) >>> print(more_dim) tf.Tensor( [[[ 0. 1. 2. 3.]] <BLANKLINE> [[ 4. 5. 6. 7.]] <BLANKLINE> [[ 8. 9. 10. 11.]]], shape=(3, 1, 4), dtype=float32) """ assert len(axes) == K.ndim(tensor) indices = [None] * (ndim or max(axes)) for i in axes: indices[i] = slice(None) return tensor[indices] def sequence_masking(x, mask, value=0, axis=None): """为序列条件mask的函数 parameters: ----------- x: tensor 输入张量 mask: tensor 形如(batch_size, seq_len)的0-1矩阵 value: float or str mask部分要被替换成的值，允许'inf'与'-inf' axis: int 序列所在的轴，默认为1 """ if mask is None: return x # 确保x类型，可以执行运算 x_type = K.dtype(x) if x_type == 'bool': x = K.cast(x, 'int32') # 确保mask类型 = x类型 if K.dtype(mask) != K.dtype(x): mask = K.cast(mask, K.dtype(x)) if value == '-inf': # -----------是个函数吗？？--------------- value = -K.infinity if value == 'inf': value = K.infinity value = K.cast(value, K.dtype(x)) # 确定axis if axis is None: axis = 1 if axis < 0: axis = K.ndim(x) + axis assert axis > 0, 'axis must be greater than 0' # 统一shape for _ in range(axis - 1): # > 1时生效 mask = K.expand_dims(mask, 1) # 把第0维让给batch_size for _ in range(K.ndim(x) - K.ndim(mask)): mask = K.expand_dims(mask, K.ndim(mask)) x = x mask + value * (1 - mask) # 与输入x的类型统一 if x_type == 'bool': x = K.cast(x, x_type) return x def recompute_grad(call): # ----------------------完全没看懂？？？？------------------------ """重计算装饰器，用来装饰keras层的call函数目的是：通过一些额外的计算减少显存的占用论文：https://arxiv.org/abs/1604.06174 """ if not do_recompute: return call def inner(self, inputs, kwargs): # 2.x的tf.nest.flatten不会对numpy和tf.tensor进行展平 flat_inputs = nest.flatten(inputs) call_args = tf_inspect.getfullargspec(call).args for key in ['mask', 'training']: if key not in call_args and key in kwargs: del kwargs[key] def kernel_call(): """定义前向计算 """ return call(self, inputs, kwargs) def call_and_grad(inputs): """定义前向计算和反向计算 """ with tape.stop_recording(): outputs = kernel_call() outputs = tf.identity(outputs) def grad_fn(doutputs, variables=None): watches = list(inputs) if variables is not None: watches += list(variables) with tf.GradientTape() as t: t.watch(watches) with tf.control_dependencies([doutputs]): outputs = kernel_call() grads = t.gradient( outputs, watches, output_gradients=[doutputs] ) del t return grads[:len(inputs)], grads[len(inputs):] return outputs, grad_fn outputs, grad_fn = call_and_grad(flat_inputs) flat_outputs = nest.flatten(outputs) def actual_grad_fn(doutputs): grads = grad_fn(doutputs, variables=self.trainable_weights) return grads[0] + grads[1] watches = flat_inputs + self.trainable_weights watches = [tf.convert_to_tensor(x) for x in watches] tape.record_operation( call.__name__, flat_outputs, watches, actual_grad_fn ) return outputs return inner def infinity(): """返回默认的代表无穷大的数值 """ return tf.keras.utils.get_custom_objects().get('infinity', 1e12) def set_infinity(value): """设置新的代表无穷大的数值 """ tf.keras.utils.get_custom_objects()['infinity'] = value # 添加到 keras.backend 上，使其可以像 K.epsilon() 那样操作 K.infinity = infinity K.set_infinity = set_infinity sys.modules['tensorflow.keras.backend'] = K custom_objects = { 'gelu_erf': gelu_erf, 'gelu_tanh': gelu_tanh, 'gelu': gelu_erf, } tf.keras.utils.get_custom_objects().update(custom_objects) if __name__ == '__main__': import doctest doctest.testmod()	[ 1, 1053, 2897, 29892, 10876, 30004, 13, 3166, 1320, 13239, 29889, 4422, 1053, 851, 517, 11227, 30004, 13, 5215, 12655, 408, 7442, 30004, 13, 5215, 26110, 408, 15886, 30004, 13, 5215, 26110, 29889, 3946, 294, 29889, 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camd3/infrastructure/component/tests/test_component.py	mamrhein/CAmD3	0	30913	#!/usr/bin/env python # ---------------------------------------------------------------------------- # Name: test_component # Purpose: Test driver for module component # # Author: <NAME> (<EMAIL>) # # Copyright: (c) 2014 <NAME> # ---------------------------------------------------------------------------- # $Source$ # $Revision$ """Test driver for module component""" from abc import ABC from numbers import Number from typing import Tuple import unittest from camd3.infrastructure.component import ( Attribute, Component, ComponentLookupError, Immutable, implementer) from camd3.infrastructure.component.component import _ABCSet, ComponentMeta DFLT_NAMESPACE = ('__module__', '__qualname__', '__doc__') class TestComp1(Component): """TestComp1""" class TestComp2(Component): """TestComp2""" attr1 = Attribute() attr2 = Attribute() def meth(self): pass attr3 = Attribute() @property def prop(self): pass @implementer(TestComp1, TestComp2) class TestImpl(Component): def __init__(self): pass class TestComp1Factory: def __call__(self, i: Number) -> TestComp1: return TestImpl() def Number2TestComp1(i: Number) -> TestComp1: # noqa: D103 return TestImpl() def Str2TestComp2(s: str) -> TestComp2: # noqa: D103 return TestImpl() @implementer(TestComp1) class TestABC(ABC): pass class TestComp3(Component): """TestComp3""" def __init_subclass__(subcls, *kwds): # noqa: D105 try: param = kwds.pop('param') except KeyError: pass else: subcls.param = param class TestImpl3(TestComp3, param='P'): pass class TestComp4(Component, Immutable): """TestComp4""" class TestComp5(TestComp4): """TestComp5""" a = Attribute() b = Attribute() class TestComp6(TestComp5): """TestComp6""" c = Attribute() def __init__(self, a, b, c): self.a = a self.b = b self.c = c def Obj2TestComp6(obj: object) -> TestComp6: # noqa: D103 return TestComp6(obj, None, None) def Tuple2TestComp6(tpl: Tuple[int, int, str]) -> TestComp6: # noqa: D103 return TestComp6(tpl) class TestComp7(TestComp6): """TestComp7""" class TestComp8(TestComp1): """TestComp8""" def __init__(self, a): self.a = a self.i = self.initialized class ABCSetTest(unittest.TestCase): def testAdd(self): cls_list = (ABC, Immutable, TestComp4, Component) self.assertEqual(_ABCSet(cls_list), _ABCSet({ABC, TestComp4})) cls_list = (int, object, Number, float) self.assertEqual(_ABCSet(cls_list), _ABCSet({int, float})) class ComponentMetaTest(unittest.TestCase): def test_constructor(self): # name of descriptors for name in ('attr1', 'attr2', 'attr3'): self.assertEqual(getattr(getattr(TestComp2, name, None), 'name', None), name) # __slots__ forced? self.assertEqual(getattr(TestComp4, '__slots__', None), ()) self.assertEqual(getattr(TestComp5, '__slots__', None), ('_a', '_b')) self.assertEqual(getattr(TestComp6, '__slots__', None), ('_c',)) self.assertRaises(TypeError, ComponentMeta, 'Test', (TestImpl, TestComp4), {}) def test_init_subclass(self): # init_subclass turned into a class method? meth = TestComp3.__init_subclass__ self.assertTrue(getattr(meth, '__self__', None) is TestComp3) # __init_subclass called? self.assertEqual(getattr(TestImpl3, 'param', None), 'P') def test_attr_names(self): self.assertEqual(TestComp2.attr_names, ('attr1', 'attr2', 'attr3')) self.assertEqual(TestComp2.all_attr_names, ('attr1', 'attr2', 'attr3')) self.assertEqual(TestImpl.attr_names, ()) self.assertEqual(TestImpl.all_attr_names, ()) self.assertEqual(TestComp6.attr_names, ('c',)) self.assertEqual(TestComp6.all_attr_names, ('a', 'b', 'c')) def test_implementer(self): self.assertTrue(issubclass(TestImpl, TestComp1)) self.assertTrue(issubclass(TestImpl, TestComp2)) self.assertEqual(TestImpl.__virtual_bases__, {TestComp1, TestComp2}) self.assertTrue(issubclass(TestABC, TestComp1)) def test_adaptation(self): # wrong component self.assertRaises(AssertionError, TestComp2.add_adapter, TestComp1Factory()) self.assertRaises(AssertionError, TestComp2.add_adapter, Tuple2TestComp6) # wrong number of args func = lambda x, y: TestComp2() func.__annotations__ = {'return': TestComp2, 'x': int, 'y': int} self.assertRaises(AssertionError, TestComp2.add_adapter, func) # variable number of args func = lambda *args: TestComp2() func.__annotations__ = {'return': TestComp2, 'args': int} self.assertRaises(AssertionError, TestComp2.add_adapter, func) # register some adapters fct = TestComp1Factory() TestComp1.add_adapter(fct) self.assertIn(Number, TestComp1.__adapters__) self.assertIn(fct, TestComp1.__adapters__[Number]) TestComp1.add_adapter(Number2TestComp1) self.assertIn(Number2TestComp1, TestComp1.__adapters__[Number]) TestComp1.add_adapter(Number2TestComp1) self.assertEqual(len(TestComp1.__adapters__[Number]), 2) TestComp2.add_adapter(Str2TestComp2) self.assertIn(str, TestComp2.__adapters__) self.assertIn(Str2TestComp2, TestComp2.__adapters__[str]) TestComp6.add_adapter(Tuple2TestComp6) adapter = TestComp6.add_adapter(Obj2TestComp6) self.assertEqual(adapter, Obj2TestComp6) # retrieve adapters self.assertEqual(TestComp1.get_adapter(5), Number2TestComp1) self.assertEqual(TestComp1.get_adapter(5.0), Number2TestComp1) self.assertRaises(ComponentLookupError, TestComp1.get_adapter, 'x') self.assertEqual(TestComp2.get_adapter('abc'), Str2TestComp2) self.assertRaises(ComponentLookupError, TestComp2.get_adapter, 3) self.assertEqual(TestComp6.get_adapter((3, 1, 'x')), Tuple2TestComp6) self.assertEqual(TestComp6.get_adapter([3, 1, 'x']), Obj2TestComp6) self.assertEqual(TestComp6.get_adapter(TestComp6(3, 1, 'x')), Obj2TestComp6) self.assertEqual(TestComp4.get_adapter((3, 1, 'x')), Tuple2TestComp6) self.assertEqual(TestComp4.get_adapter([3, 1, 'x']), Obj2TestComp6) # adapt objects self.assertIsInstance(TestComp1.adapt(5), TestComp1) self.assertIsInstance(TestComp1[5.0], TestComp1) self.assertIsInstance(TestComp2.adapt('x'), TestComp2) t1 = TestComp6.adapt((5, 17, 'abc')) self.assertIsInstance(t1, TestComp6) self.assertEqual((t1.a, t1.b, t1.c), (5, 17, 'abc')) t2 = TestComp6.adapt(fct) self.assertIsInstance(t2, TestComp6) self.assertIs(t2.a, fct) self.assertRaises(TypeError, TestComp7.adapt, t2) t3 = TestComp6(4, 9, 'y') for ct in (TestComp6, TestComp5, TestComp4): self.assertIs(ct.adapt(t3), t3) def test_repr(self): self.assertEqual(repr(TestComp3), '.'.join((__name__, TestComp3.__qualname__))) class ComponentTest(unittest.TestCase): def test_constructor(self): comp = TestComp8(19) self.assertFalse(comp.i) self.assertTrue(comp.initialized) if __name__ == '__main__': unittest.main()	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 2683, 2683, 2683, 2683, 1378, 5634, 13, 29937, 4408, 29901, 4706, 1243, 29918, 9700, 13, 29937, 15247, 4220, 29901, 268, 4321, 7156, 363, 3883, 4163, 13, 29937, 13, 29937, 13361, 29901, 418, 529, 5813, 29958, 313, 29966, 26862, 6227, 12948, 13, 29937, 13, 29937, 14187, 1266, 29901, 259, 313, 29883, 29897, 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1583, 29889, 9294, 3624, 29898, 29873, 29906, 29889, 29874, 29892, 285, 312, 29897, 13, 4706, 1583, 29889, 9294, 29934, 1759, 267, 29898, 1542, 2392, 29892, 4321, 6843, 29955, 29889, 1114, 415, 29892, 260, 29906, 29897, 13, 4706, 260, 29941, 353, 4321, 6843, 29953, 29898, 29946, 29892, 29871, 29929, 29892, 525, 29891, 1495, 13, 4706, 363, 274, 29873, 297, 313, 3057, 6843, 29953, 29892, 4321, 6843, 29945, 29892, 4321, 6843, 29946, 1125, 13, 9651, 1583, 29889, 9294, 3624, 29898, 312, 29889, 1114, 415, 29898, 29873, 29941, 511, 260, 29941, 29897, 13, 13, 1678, 822, 1243, 29918, 276, 558, 29898, 1311, 1125, 13, 4706, 1583, 29889, 9294, 9843, 29898, 276, 558, 29898, 3057, 6843, 29941, 511, 13, 462, 308, 15300, 4286, 7122, 3552, 1649, 978, 1649, 29892, 4321, 6843, 29941, 17255, 15380, 978, 1649, 4961, 13, 13, 13, 1990, 15924, 3057, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 13, 1678, 822, 1243, 29918, 27821, 29898, 1311, 1125, 13, 4706, 752, 353, 4321, 6843, 29947, 29898, 29896, 29929, 29897, 13, 4706, 1583, 29889, 9294, 8824, 29898, 2388, 29889, 29875, 29897, 13, 4706, 1583, 29889, 9294, 5574, 29898, 2388, 29889, 11228, 1891, 29897, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 443, 27958, 29889, 3396, 580, 13, 2 ]
Algoritimos/uri1020.py	mathspin/Algoritimos-py	0	132671	<filename>Algoritimos/uri1020.py v = input("digite um valor em dias: ") a = int(int(v)/365) m = int((int(v)%365)/30) d = int((int(v)%365)%30) print(str(a)+" ano(s)") print(str(m)+" mes(es)") print(str(d)+" dias(s)")	[ 1, 529, 9507, 29958, 2499, 21208, 277, 18594, 29914, 5338, 29896, 29900, 29906, 29900, 29889, 2272, 13, 29894, 353, 1881, 703, 7501, 568, 1922, 16497, 953, 652, 294, 29901, 16521, 13, 29874, 353, 938, 29898, 524, 29898, 29894, 6802, 29941, 29953, 29945, 29897, 13, 29885, 353, 938, 3552, 524, 29898, 29894, 29897, 29995, 29941, 29953, 29945, 6802, 29941, 29900, 29897, 13, 29881, 353, 938, 3552, 524, 29898, 29894, 29897, 29995, 29941, 29953, 29945, 29897, 29995, 29941, 29900, 29897, 13, 2158, 29898, 710, 29898, 29874, 7240, 29908, 19410, 29898, 29879, 25760, 13, 2158, 29898, 710, 29898, 29885, 7240, 29908, 4883, 29898, 267, 25760, 13, 2158, 29898, 710, 29898, 29881, 7240, 29908, 652, 294, 29898, 29879, 25760, 13, 2 ]
orangecontrib/aps/shadow/widgets/extension/ow_bendable_ellipsoid_mirror.py	oasys-aps-kit/OASYS1-APS-ShadowOui	1	139140	import os, sys, numpy from scipy.interpolate import RectBivariateSpline, interp2d from scipy.optimize import curve_fit from matplotlib import cm from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg from matplotlib.figure import Figure try: from mpl_toolkits.mplot3d import Axes3D # necessario per caricare i plot 3D except: pass from PyQt5.QtWidgets import QApplication from PyQt5.QtCore import QSettings from orangewidget import gui from orangewidget.settings import Setting from oasys.widgets import gui as oasysgui from oasys.widgets import congruence from oasys.util.oasys_util import TriggerIn from orangecontrib.shadow.util.shadow_objects import ShadowOpticalElement, ShadowBeam, ShadowPreProcessorData from orangecontrib.shadow.util.shadow_util import ShadowPreProcessor from orangecontrib.shadow.widgets.gui import ow_ellipsoid_element, ow_optical_element from Shadow import ShadowTools as ST TRAPEZIUM = 0 RECTANGLE = 1 SINGLE_MOMENTUM = 0 DOUBLE_MOMENTUM = 1 class BendableEllipsoidMirror(ow_ellipsoid_element.EllipsoidElement): name = "Bendable Ellipsoid Mirror" description = "Shadow OE: Bendable Ellipsoid Mirror" icon = "icons/bendable_ellipsoid_mirror.png" maintainer = "<NAME>" maintainer_email = "<EMAIL>" priority = 6 category = "Optical Elements" keywords = ["data", "file", "load", "read"] send_footprint_beam = QSettings().value("output/send-footprint", 0, int) == 1 if send_footprint_beam: outputs = [{"name":"Beam", "type":ShadowBeam, "doc":"Shadow Beam", "id":"beam"}, {"name":"Footprint", "type":list, "doc":"Footprint", "id":"beam"}, {"name":"Trigger", "type": TriggerIn, "doc":"Feedback signal to start a new beam simulation", "id":"Trigger"}, {"name": "PreProcessor_Data", "type": ShadowPreProcessorData, "doc": "PreProcessor Data", "id": "PreProcessor_Data"} ] else: outputs = [{"name":"Beam", "type":ShadowBeam, "doc":"Shadow Beam", "id":"beam"}, {"name":"Trigger", "type": TriggerIn, "doc":"Feedback signal to start a new beam simulation", "id":"Trigger"}, {"name": "PreProcessor_Data", "type": ShadowPreProcessorData, "doc": "PreProcessor Data", "id": "PreProcessor_Data"} ] show_bender_plots = Setting(0) bender_bin_x = Setting(100) bender_bin_y = Setting(500) E = Setting(131000) h = Setting(10) kind_of_bender = Setting(1) shape = Setting(0) output_file_name = Setting("mirror_bender.dat") which_length = Setting(0) optimized_length = Setting(0.0) M1 = Setting(0.0) ratio = Setting(0.5) e = Setting(0.3) M1_out = 0.0 ratio_out = 0.0 e_out = 0.0 M1_fixed = Setting(False) ratio_fixed = Setting(False) e_fixed = Setting(False) M1_min = Setting(0.0) ratio_min = Setting(0.0) e_min = Setting(0.0) M1_max = Setting(1000.0) ratio_max = Setting(10.0) e_max = Setting(1.0) def __init__(self): graphical_Options=ow_optical_element.GraphicalOptions(is_mirror=True) super().__init__(graphical_Options) tabs = gui.tabWidget(oasysgui.createTabPage(self.tabs_basic_setting, "Bender")) tab_bender = oasysgui.createTabPage(tabs, "Bender Setting") surface_box = oasysgui.widgetBox(tab_bender, "Surface Setting", addSpace=False, orientation="vertical") oasysgui.lineEdit(surface_box, self, "bender_bin_x", "bins Sagittal", labelWidth=260, valueType=int, orientation="horizontal") oasysgui.lineEdit(surface_box, self, "bender_bin_y", "bins Transversal", labelWidth=260, valueType=int, orientation="horizontal") material_box = oasysgui.widgetBox(tab_bender, "Bender Setting", addSpace=False, orientation="vertical") self.le_E = oasysgui.lineEdit(material_box, self, "E", "Young's Modulus ", labelWidth=260, valueType=float, orientation="horizontal") self.le_h = oasysgui.lineEdit(material_box, self, "h", "Thickness ", labelWidth=260, valueType=float, orientation="horizontal") gui.comboBox(material_box, self, "kind_of_bender", label="Kind Of Bender ", items=["Single Momentum", "Double Momentum"], labelWidth=150, orientation="horizontal", callback=self.set_kind_of_bender) gui.comboBox(material_box, self, "shape", label="Shape ", items=["Trapezium", "Rectangle"], labelWidth=150, orientation="horizontal", callback=self.set_shape) tab_fit = oasysgui.createTabPage(tabs, "Fit Setting") fit_box = oasysgui.widgetBox(tab_fit, "", addSpace=False, orientation="vertical") file_box = oasysgui.widgetBox(fit_box, "", addSpace=False, orientation="horizontal", height=25) self.le_output_file_name = oasysgui.lineEdit(file_box, self, "output_file_name", "Out File Name", labelWidth=100, valueType=str, orientation="horizontal") gui.button(file_box, self, "...", callback=self.select_output_file, width=20) length_box = oasysgui.widgetBox(fit_box, "", addSpace=False, orientation="horizontal") self.cb_optimized_length = gui.comboBox(length_box, self, "which_length", label="Optimized Length ", items=["Total", "Partial"], labelWidth=150, orientation="horizontal", callback=self.set_which_length) self.le_optimized_length = oasysgui.lineEdit(length_box, self, "optimized_length", " ", labelWidth=10, valueType=float, orientation="horizontal") self.set_which_length() gui.separator(fit_box) def add_parameter_box(container_box, variable, label): box = oasysgui.widgetBox(container_box, "", addSpace=False, orientation="horizontal") oasysgui.lineEdit(box, self, variable, label, labelWidth=50, valueType=float, orientation="horizontal") gui.label(box, self, " ", labelWidth=58) box = oasysgui.widgetBox(container_box, "", addSpace=False, orientation="horizontal") setattr(self, "le_" + variable + "_min", oasysgui.lineEdit(box, self, variable + "_min", "Min", labelWidth=50, valueType=float, orientation="horizontal")) setattr(self, "le_" + variable + "_max", oasysgui.lineEdit(box, self, variable + "_max", "Max", labelWidth=35, valueType=float, orientation="horizontal")) gui.checkBox(box, self, variable + "_fixed", "Fixed", callback=getattr(self, "set_" + variable)) box = oasysgui.widgetBox(container_box, "", addSpace=False, orientation="horizontal") le = oasysgui.lineEdit(box, self, variable + "_out", "Fitted", labelWidth=50, valueType=float, orientation="horizontal") le.setEnabled(False) le.setStyleSheet("color: blue; background-color: rgb(254, 244, 205); font:bold") def set_variable_fit(): setattr(self, variable, getattr(self, variable + "_out")) gui.button(box, self, "<- Use", width=58, callback=set_variable_fit) getattr(self, "set_" + variable)() m1_box = oasysgui.widgetBox(fit_box, "", addSpace=False, orientation="vertical") gui.separator(fit_box, 10) self.ratio_box = oasysgui.widgetBox(fit_box, "", addSpace=False, orientation="vertical") gui.separator(fit_box, 10) self.e_box = oasysgui.widgetBox(fit_box, "", addSpace=False, orientation="vertical") gui.separator(fit_box, 10) add_parameter_box(m1_box, "M1", "M1") add_parameter_box(self.ratio_box, "ratio", "M1/M2") add_parameter_box(self.e_box, "e", "e") self.set_kind_of_bender() self.set_shape() ####################################################### plot_tab = oasysgui.createTabPage(self.main_tabs, "Bender Plots") view_box = oasysgui.widgetBox(plot_tab, "Plotting Style", addSpace=False, orientation="vertical", width=350) self.view_type_combo = gui.comboBox(view_box, self, "show_bender_plots", label="Show Plots", labelWidth=220, items=["No", "Yes"], sendSelectedValue=False, orientation="horizontal") bender_tabs = oasysgui.tabWidget(plot_tab) tabs = [oasysgui.createTabPage(bender_tabs, "Bender vs. Ideal (1D)"), oasysgui.createTabPage(bender_tabs, "Ideal - Bender (1D)"), oasysgui.createTabPage(bender_tabs, "Ideal - Bender (3D)"), oasysgui.createTabPage(bender_tabs, "Figure Error (3D)"), oasysgui.createTabPage(bender_tabs, "Ideal - Bender + Figure Error (3D)")] def create_figure_canvas(mode="3D"): figure = Figure(figsize=(100, 100)) figure.patch.set_facecolor('white') if mode == "3D": figure.add_subplot(111, projection='3d') else: figure.add_subplot(111) figure_canvas = FigureCanvasQTAgg(figure) figure_canvas.setFixedWidth(self.IMAGE_WIDTH) figure_canvas.setFixedHeight(self.IMAGE_HEIGHT-10) return figure_canvas self.figure_canvas = [create_figure_canvas("1D"), create_figure_canvas("1D"), create_figure_canvas(), create_figure_canvas(), create_figure_canvas()] for tab, figure_canvas in zip(tabs, self.figure_canvas): tab.layout().addWidget(figure_canvas) gui.rubber(self.controlArea) gui.rubber(self.mainArea) ################################################################ # # SHADOW MANAGEMENT # ################################################################ def select_output_file(self): self.le_output_file_name.setText(oasysgui.selectFileFromDialog(self, self.output_file_name, "Select Output File", file_extension_filter="Data Files (.dat)")) def set_kind_of_bender(self): self.ratio_box.setVisible(self.kind_of_bender==1) def set_shape(self): self.e_box.setVisible(self.shape==0) def set_which_length(self): self.le_optimized_length.setEnabled(self.which_length==1) def set_M1(self): self.le_M1_min.setEnabled(self.M1_fixed==False) self.le_M1_max.setEnabled(self.M1_fixed==False) def set_ratio(self): self.le_ratio_min.setEnabled(self.ratio_fixed==False) self.le_ratio_max.setEnabled(self.ratio_fixed==False) def set_e(self): self.le_e_min.setEnabled(self.e_fixed==False) self.le_e_max.setEnabled(self.e_fixed==False) def after_change_workspace_units(self): super().after_change_workspace_units() label = self.le_E.parent().layout().itemAt(0).widget() label.setText(label.text() + " [N/" + self.workspace_units_label + "^2]") label = self.le_h.parent().layout().itemAt(0).widget() label.setText(label.text() + " [" + self.workspace_units_label + "]") label = self.cb_optimized_length.parent().layout().itemAt(0).widget() label.setText(label.text() + " [" + self.workspace_units_label + "]") def checkFields(self): super().checkFields() if self.is_cylinder != 1: raise ValueError("Bender Ellipse must be a cylinder") if self.cylinder_orientation != 0: raise ValueError("Cylinder orientation must be 0") if self.is_infinite == 0: raise ValueError("This OE can't have infinite dimensions") if self.which_length==1: congruence.checkStrictlyPositiveNumber(self.optimized_length, "Optimized Length") congruence.checkLessOrEqualThan(self.optimized_length, self.dim_y_plus+self.dim_y_minus, "Optimized Length", "Total Length") if self.modified_surface > 0: if not (self.modified_surface == 1 and self.ms_type_of_defect == 2): raise ValueError("Only Preprocessor generated error profiles are admitted") congruence.checkStrictlyPositiveNumber(self.bender_bin_x, "Bins X") congruence.checkStrictlyPositiveNumber(self.bender_bin_y, "Bins Y") self.output_file_name_full = congruence.checkFileName(self.output_file_name) def completeOperations(self, shadow_oe): shadow_oe_temp = shadow_oe.duplicate() input_beam_temp = self.input_beam.duplicate(history=False) self.manage_acceptance_slits(shadow_oe_temp) ShadowBeam.traceFromOE(input_beam_temp, shadow_oe_temp, write_start_file=0, write_end_file=0, widget_class_name=type(self).__name__) x, y, z = self.calculate_ideal_surface(shadow_oe_temp) bender_parameter, z_bender_correction = self.calculate_bender_correction(y, z, self.kind_of_bender, self.shape) self.M1_out = round(bender_parameter[0], int(6self.workspace_units_to_mm)) if self.shape == TRAPEZIUM: self.e_out = round(bender_parameter[1], 5) if self.kind_of_bender == DOUBLE_MOMENTUM: self.ratio_out = round(bender_parameter[2], 5) elif self.shape == RECTANGLE: if self.kind_of_bender == DOUBLE_MOMENTUM: self.ratio_out = round(bender_parameter[1], 5) self.plot3D(x, y, z_bender_correction, 2, "Ideal - Bender Surfaces") if self.modified_surface > 0: x_e, y_e, z_e = ShadowPreProcessor.read_surface_error_file(self.ms_defect_file_name) if len(x) == len(x_e) and len(y) == len(y_e) and \ x[0] == x_e[0] and x[-1] == x_e[-1] and \ y[0] == y_e[0] and y[-1] == y_e[-1]: z_figure_error = z_e else: z_figure_error = interp2d(y_e, x_e, z_e, kind='cubic')(y, x) z_bender_correction += z_figure_error self.plot3D(x, y, z_figure_error, 3, "Figure Error Surface") self.plot3D(x, y, z_bender_correction, 4, "Ideal - Bender + Figure Error Surfaces") ST.write_shadow_surface(z_bender_correction.T, numpy.round(x, 6), numpy.round(y, 6), self.output_file_name_full) # Add new surface as figure error shadow_oe._oe.F_RIPPLE = 1 shadow_oe._oe.F_G_S = 2 shadow_oe._oe.FILE_RIP = bytes(self.output_file_name_full, 'utf-8') # Redo Raytracing with the new file super().completeOperations(shadow_oe) self.send("PreProcessor_Data", ShadowPreProcessorData(error_profile_data_file=self.output_file_name, error_profile_x_dim=self.dim_x_plus+self.dim_x_minus, error_profile_y_dim=self.dim_y_plus+self.dim_y_minus)) def instantiateShadowOE(self): return ShadowOpticalElement.create_ellipsoid_mirror() def calculate_ideal_surface(self, shadow_oe, sign=-1): x = numpy.linspace(-self.dim_x_minus, self.dim_x_plus, self.bender_bin_x + 1) y = numpy.linspace(-self.dim_y_minus, self.dim_y_plus, self.bender_bin_y + 1) c1 = round(shadow_oe._oe.CCC[0], 10) c2 = round(shadow_oe._oe.CCC[1], 10) c3 = round(shadow_oe._oe.CCC[2], 10) c4 = round(shadow_oe._oe.CCC[3], 10) c5 = round(shadow_oe._oe.CCC[4], 10) c6 = round(shadow_oe._oe.CCC[5], 10) c7 = round(shadow_oe._oe.CCC[6], 10) c8 = round(shadow_oe._oe.CCC[7], 10) c9 = round(shadow_oe._oe.CCC[8], 10) c10 = round(shadow_oe._oe.CCC[9], 10) xx, yy = numpy.meshgrid(x, y) c = c1(xx2) + c2(yy*2) + c4xxyy + c7xx + c8yy + c10 b = c5yy + c6xx + c9 a = c3 z = (-b + signnumpy.sqrt(b*2 - 4ac))/(2a) z[b*2 - 4ac < 0] = numpy.nan return x, y, z.T def calculate_bender_correction(self, y, z, kind_of_bender, shape): b0 = self.dim_x_plus + self.dim_x_minus L = self.dim_y_plus + self.dim_y_minus # add optimization length # flip the coordinate system to be consistent with Mike's formulas ideal_profile = z[0, :][::-1] # one row is the profile of the cylinder, enough for the minimizer ideal_profile += -ideal_profile[0] + ((L/2 + y)(ideal_profile[0]-ideal_profile[-1]))/L # Rotation if self.which_length == 0: y_fit = y ideal_profile_fit = ideal_profile else: cursor = numpy.where(numpy.logical_and(y >= -self.optimized_length/2, y <= self.optimized_length/2) ) y_fit = y[cursor] ideal_profile_fit = ideal_profile[cursor] epsilon_minus = 1 - 1e-8 epsilon_plus = 1 + 1e-8 Eh_3 = self.E * self.h ** 3 initial_guess = None constraints = None bender_function = None if shape == TRAPEZIUM: def general_bender_function(Y, M1, e, ratio): M2 = M1 * ratio A = (M1 + M2) / 2 B = (M1 - M2) / L C = Eh_3 * (2 * b0 + e * b0) / 24 D = Eh_3 * e * b0 / (12 * L) H = (A * D + B * C) / D ** 2 CDLP = C + D * L / 2 CDLM = C - D * L / 2 F = (H / L) * ((CDLM * numpy.log(CDLM) - CDLP * numpy.log(CDLP)) / D + L) G = (-H * ((CDLM * numpy.log(CDLM) + CDLP * numpy.log(CDLP))) + (B * L ** 2) / 4) / (2 * D) CDY = C + D * Y return H * ((CDY / D) * numpy.log(CDY) - Y) - (B * Y ** 2) / (2 * D) + F * Y + G def bender_function_2m(Y, M1, e, ratio): return general_bender_function(Y, M1, e, ratio) def bender_function_1m(Y, M1, e): return general_bender_function(Y, M1, e, 1.0) if kind_of_bender == SINGLE_MOMENTUM: bender_function = bender_function_1m initial_guess = [self.M1, self.e] constraints = [[self.M1_min if self.M1_fixed == False else (self.M1 * epsilon_minus), self.e_min if self.e_fixed == False else (self.e * epsilon_minus)], [self.M1_max if self.M1_fixed == False else (self.M1 * epsilon_plus), self.e_max if self.e_fixed == False else (self.e * epsilon_plus)]] elif kind_of_bender == DOUBLE_MOMENTUM: bender_function = bender_function_2m initial_guess = [self.M1, self.e, self.ratio] constraints = [[self.M1_min if self.M1_fixed == False else (self.M1epsilon_minus), self.e_min if self.e_fixed == False else (self.eepsilon_minus), self.ratio_min if self.ratio_fixed == False else (self.ratioepsilon_minus)], [self.M1_max if self.M1_fixed == False else (self.M1epsilon_plus), self.e_max if self.e_fixed == False else (self.eepsilon_plus), self.ratio_max if self.ratio_fixed == False else (self.ratioepsilon_plus)]] elif shape == RECTANGLE: def general_bender_function(Y, M1, ratio): M2 = M1 * ratio A = (M1 + M2) / 2 B = (M1 - M2) / L C = Eh_3 * b0 / 12 F = (B * L*2) / (24 C) G = -(A * L*2) / (8 C) return -(B * Y*3) / (6 C) + (A * Y*2) / (2 C) + F * Y + G def bender_function_2m(Y, M1, ratio): return general_bender_function(Y, M1, ratio) def bender_function_1m(Y, M1): return general_bender_function(Y, M1, 1.0) if kind_of_bender == SINGLE_MOMENTUM: bender_function = bender_function_1m initial_guess = [self.M1] constraints = [[self.M1_min if self.M1_fixed == False else (self.M1 * epsilon_minus)], [self.M1_max if self.M1_fixed == False else (self.M1 * epsilon_plus)]] elif kind_of_bender == DOUBLE_MOMENTUM: bender_function = bender_function_2m initial_guess = [self.M1, self.ratio] constraints = [[self.M1_min if self.M1_fixed == False else (self.M1epsilon_minus), self.ratio_min if self.ratio_fixed == False else (self.ratioepsilon_minus)], [self.M1_max if self.M1_fixed == False else (self.M1epsilon_plus), self.ratio_max if self.ratio_fixed == False else (self.ratioepsilon_plus)]] parameters, _ = curve_fit(f=bender_function, xdata=y_fit, ydata=ideal_profile_fit, p0=initial_guess, bounds=constraints, method='trf') if len(parameters) == 1: bender_profile = bender_function(y, parameters[0]) elif len(parameters) == 2: bender_profile = bender_function(y, parameters[0], parameters[1]) else: bender_profile = bender_function(y, parameters[0], parameters[1], parameters[2]) # rotate back to Shadow system bender_profile = bender_profile[::-1] ideal_profile = ideal_profile[::-1] # from here it's Shadow Axis system correction_profile = ideal_profile - bender_profile if self.which_length == 1: correction_profile_fit = correction_profile[cursor] # r-squared = 1 - residual sum of squares / total sum of squares r_squared = 1 - (numpy.sum(correction_profile2) / numpy.sum((ideal_profile - numpy.mean(ideal_profile))2)) rms = round(correction_profile.std()1e9self.workspace_units_to_m, 6) if self.which_length == 1: rms_opt = round(correction_profile_fit.std()1e9self.workspace_units_to_m, 6) self.plot1D(y, bender_profile, y_values_2=ideal_profile, index=0, title = "Bender vs. Ideal Profiles" + "\n" + r'$R^2$ = ' + str(r_squared), um=1) self.plot1D(y, correction_profile, index=1, title="Correction Profile 1D, r.m.s. = " + str(rms) + " nm" + ("" if self.which_length == 0 else (", " + str(rms_opt) + " nm (optimized)"))) z_bender_correction = numpy.zeros(z.shape) for i in range(z_bender_correction.shape[0]): z_bender_correction[i, :] = numpy.copy(correction_profile) return parameters, z_bender_correction def plot1D(self, x_coords, y_values, y_values_2=None, index=0, title="", um=0): if self.show_bender_plots == 1: figure = self.figure_canvas[index].figure axis = figure.gca() axis.clear() axis.set_xlabel("Y [" + self.workspace_units_label + "]") axis.set_ylabel("Z [" + ("nm" if um==0 else "\u03bcm") + "]") axis.set_title(title) axis.plot(x_coords, (y_values * self.workspace_units_to_m * (1e9 if um==0 else 1e6)), color="blue", label="bender", linewidth=2) if not y_values_2 is None: axis.plot(x_coords, (y_values_2 * self.workspace_units_to_m * (1e9 if um==0 else 1e6)), "-.r", label="ideal") axis.legend(loc=0, fontsize='small') figure.canvas.draw() def plot3D(self, x_coords, y_coords, z_values, index, title=""): if self.show_bender_plots == 1: figure = self.figure_canvas[index].figure x_to_plot, y_to_plot = numpy.meshgrid(x_coords, y_coords) z_to_plot = z_values.T axis = figure.gca() axis.clear() axis.set_xlabel("X [" + self.workspace_units_label + "]") axis.set_ylabel("Y [" + self.workspace_units_label + "]") axis.set_zlabel("Z [nm]") axis.set_title(title) axis.plot_surface(x_to_plot, y_to_plot, (z_to_plot * self.workspace_units_to_m * 1e9), rstride=1, cstride=1, cmap=cm.autumn, linewidth=0.5, antialiased=True) 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azext/batch/models/_models.py	bgklein/azure-batch-cli-extensions	0	138319	<gh_stars>0 # -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- # pylint: disable=too-many-lines import os import azure.batch.models as models from msrest.serialization import Model from .constants import ATTRS_RESERVED_FOR_TEMPLATES class TaskFactoryBase(Model): """A Task Factory for automatically adding a collection of tasks to a job on submission. :param merge_task: An optional additional task to be run after all the other generated tasks have completed successfully. :type merge_task: :class:`MergeTask <azext.batch.models.MergeTask>` """ _validation = { 'type': {'required': True}, } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'merge_task': {'key': 'mergeTask', 'type': 'MergeTask'} } _subtype_map = { 'type': {'parametricSweep': 'ParametricSweepTaskFactory', 'taskPerFile': 'FileCollectionTaskFactory', 'taskCollection': 'TaskCollectionTaskFactory'} } def __init__(self, kwargs): super(TaskFactoryBase, self).__init__(kwargs) self.merge_task = kwargs.get('merge_task', None) self.type = None class PackageReferenceBase(Model): """A reference to a package to be installed on the compute nodes using a package manager. :param str id: The name of the package. :param str version: The version of the package to be installed. If omitted, the latest version (according to the package repository) will be installed. """ _validation = { 'type': {'required': True}, 'id': {'required': True}, } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'id': {'key': 'id', 'type': 'str'}, 'version': {'key': 'version', 'type': 'str'}, } _subtype_map = { 'type': {'aptPackage': 'AptPackageReference', 'chocolateyPackage': 'ChocolateyPackageReference', 'yumPackage': 'YumPackageReference'} } def __init__(self, kwargs): super(PackageReferenceBase, self).__init__(kwargs) self.type = None self.id = kwargs.get('id') self.version = kwargs.get('version', None) class ApplicationTemplate(Model): """An Azure Batch Application Template. :param job_manager_task: Details of a Job Manager task to be launched when the job is started. If the job does not specify a Job Manager task, the user must explicitly add tasks to the job. If the job does specify a Job Manager task, the Batch service creates the Job Manager task when the job is created, and will try to schedule the Job Manager task before scheduling other tasks in the job. The Job Manager task's typical purpose is to control and/or monitor job execution, for example by deciding what additional tasks to run, determining when the work is complete, etc. (However, a Job Manager task is not restricted to these activities - it is a fully-fledged task in the system and perform whatever actions are required for the job.) For example, a Job Manager task might download a file specified as a parameter, analyze the contents of that file and submit additional tasks based on those contents. :type job_manager_task: :class:`JobManagerTask <azure.batch.models.JobManagerTask>` :param job_preparation_task: The Job Preparation task. If a job has a Job Preparation task, the Batch service will run the Job Preparation task on a compute node before starting any tasks of that job on that compute node. :type job_preparation_task: :class:`JobPreparationTask <azure.batch.models.JobPreparationTask>` :param job_release_task: The Job Release task. A Job Release task cannot be specified without also specifying a Job Preparation task for the job. The Batch service runs the Job Release task on the compute nodes that have run the Job Preparation task. The primary purpose of the Job Release task is to undo changes to compute nodes made by the Job Preparation task. Example activities include deleting local files, or shutting down services that were started as part of job preparation. :type job_release_task: :class:`JobReleaseTask <azure.batch.models.JobReleaseTask>` :param common_environment_settings: The list of common environment variable settings. These environment variables are set for all tasks in the job (including the Job Manager, Job Preparation and Job Release tasks). Individual tasks can override an environment setting specified here by specifying the same setting name with a different value. :type common_environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param on_all_tasks_complete: The action the Batch service should take when all tasks in the job are in the completed state. Note that if a job contains no tasks, then all tasks are considered complete. This option is therefore most commonly used with a Job Manager task; if you want to use automatic job termination without a Job Manager, you should initially set onAllTasksComplete to noAction and update the job properties to set onAllTasksComplete to terminateJob once you have finished adding tasks. Permitted values are: noAction - do nothing. The job remains active unless terminated or disabled by some other means. terminateJob - terminate the job. The job's terminateReason is set to 'AllTasksComplete'. The default is noAction. Possible values include: 'noAction', 'terminateJob' :type on_all_tasks_complete: str or :class:`OnAllTasksComplete <azure.batch.models.OnAllTasksComplete>` :param on_task_failure: The action the Batch service should take when any task in the job fails. A task is considered to have failed if has a failureInfo. A failureInfo is set if the task completes with a non-zero exit code after exhausting its retry count, or if there was an error starting the task, for example due to a resource file download error. noAction - do nothing. performExitOptionsJobAction - take the action associated with the task exit condition in the task's exitConditions collection. (This may still result in no action being taken, if that is what the task specifies.) The default is noAction. Possible values include: 'noAction', 'performExitOptionsJobAction' :type on_task_failure: str or :class:`OnTaskFailure <azure.batch.models.OnTaskFailure>` :param metadata: A list of name-value pairs associated with the job as metadata. The Batch service does not assign any meaning to metadata; it is solely for the use of user code. :type metadata: list of :class:`MetadataItem <azure.batch.models.MetadataItem>` :param uses_task_dependencies: Whether tasks in the job can define dependencies on each other. The default is false. :type uses_task_dependencies: bool :param task_factory: A task factory reference to automatically generate a set of tasks to be added to the job. :type task_factory: :class:`TaskFactoryBase <azext.batch.models.TaskFactoryBase>` """ _attribute_map = { 'job_manager_task': {'key': 'jobManagerTask', 'type': 'JobManagerTask'}, 'job_preparation_task': {'key': 'jobPreparationTask', 'type': 'JobPreparationTask'}, 'job_release_task': {'key': 'jobReleaseTask', 'type': 'JobReleaseTask'}, 'common_environment_settings': {'key': 'commonEnvironmentSettings', 'type': '[EnvironmentSetting]'}, 'on_all_tasks_complete': {'key': 'onAllTasksComplete', 'type': 'OnAllTasksComplete'}, 'on_task_failure': {'key': 'onTaskFailure', 'type': 'OnTaskFailure'}, 'metadata': {'key': 'metadata', 'type': '[MetadataItem]'}, 'uses_task_dependencies': {'key': 'usesTaskDependencies', 'type': 'bool'}, 'task_factory': {'key': 'taskFactory', 'type': 'TaskFactoryBase'}, } def __init__(self, kwargs): super(ApplicationTemplate, self).__init__(kwargs) self.job_manager_task = kwargs.get('job_manager_task', None) self.job_preparation_task = kwargs.get('job_preparation_task', None) self.job_release_task = kwargs.get('job_release_task', None) self.common_environment_settings = kwargs.get('common_environment_settings', None) self.on_all_tasks_complete = kwargs.get('on_all_tasks_complete', None) self.on_task_failure = kwargs.get('on_task_failure', None) self.metadata = kwargs.get('metadata', None) self.uses_task_dependencies = kwargs.get('uses_task_dependencies', None) self.task_factory = kwargs.get('task_factory', None) class ApplicationTemplateInfo(Model): """A reference to an Azure Batch Application Template. :param str file_path: The path to an application template file. This can be a full path, or relative to the current working directory. Alternatively a relative directory can be supplied with the 'current_directory' argument. A ValueError will be raised if the supplied file path cannot be found. :param dict parameters: A dictory of parameter names and values to be subtituted into the application template. """ _validation = { 'file_path': {'required': True}, } _attribute_map = { 'file_path': {'key': 'filePath', 'type': 'str'}, 'parameters': {'key': 'parameters', 'type': 'object'}, } def __init__(self, kwargs): super(ApplicationTemplateInfo, self).__init__(kwargs) self.file_path = kwargs.get('file_path', None) if not os.path.isfile(self.file_path): current_directory = kwargs.get('current_directory', ".") self.file_path = os.path.abspath(os.path.join(current_directory, str(self.file_path))) self.parameters = kwargs.get('parameters', None) # Rule: Template file must exist # (We do this in order to give a good diagnostic in the most common case, knowing that this is # technically a race condition because someone could delete the file between our check here and # reading the file later on. We expect such cases to be rare.) try: with open(self.file_path, 'r'): pass except EnvironmentError as error: raise ValueError("Unable to read the template '{}': {}".format(self.file_path, error)) class AptPackageReference(PackageReferenceBase): """A reference to a package to be installed using the APT package manager on a Linux node (apt-get). :param str id: The name of the package. :param str version: The version of the package to be installed. If omitted, the latest version (according to the package repository) will be installed. """ _validation = { 'type': {'required': True}, 'id': {'required': True}, } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'id': {'key': 'id', 'type': 'str'}, 'version': {'key': 'version', 'type': 'str'}, } def __init__(self, kwargs): super(AptPackageReference, self).__init__(kwargs) self.type = 'aptPackage' class AutoPoolSpecification(Model): """Specifies characteristics for a temporary 'auto pool'. The Batch service will create this auto pool when the job is submitted. :param auto_pool_id_prefix: A prefix to be added to the unique identifier when a pool is automatically created. The Batch service assigns each auto pool a unique identifier on creation. To distinguish between pools created for different purposes, you can specify this element to add a prefix to the ID that is assigned. The prefix can be up to 20 characters long. :type auto_pool_id_prefix: str :param pool_lifetime_option: The minimum lifetime of created auto pools, and how multiple jobs on a schedule are assigned to pools. When the pool lifetime is jobSchedule the pool exists for the lifetime of the job schedule. The Batch Service creates the pool when it creates the first job on the schedule. You may apply this option only to job schedules, not to jobs. When the pool lifetime is job the pool exists for the lifetime of the job to which it is dedicated. The Batch service creates the pool when it creates the job. If the 'job' option is applied to a job schedule, the Batch service creates a new auto pool for every job created on the schedule. Possible values include: 'jobSchedule', 'job' :type pool_lifetime_option: str or :class:`PoolLifetimeOption <azure.batch.models.PoolLifetimeOption>` :param keep_alive: Whether to keep an auto pool alive after its lifetime expires. If false, the Batch service deletes the pool once its lifetime (as determined by the poolLifetimeOption setting) expires; that is, when the job or job schedule completes. If true, the Batch service does not delete the pool automatically. It is up to the user to delete auto pools created with this option. :type keep_alive: bool :param pool: The pool specification for the auto pool. :type pool: :class:`PoolSpecification <azure.batch.models.PoolSpecification>` """ _validation = { 'pool_lifetime_option': {'required': True}, } _attribute_map = { 'auto_pool_id_prefix': {'key': 'autoPoolIdPrefix', 'type': 'str'}, 'pool_lifetime_option': {'key': 'poolLifetimeOption', 'type': 'PoolLifetimeOption'}, 'keep_alive': {'key': 'keepAlive', 'type': 'bool'}, 'pool': {'key': 'pool', 'type': 'ExtendedPoolSpecification'}, } def __init__(self, kwargs): super(AutoPoolSpecification, self).__init__(kwargs) self.auto_pool_id_prefix = kwargs.get('auto_pool_id_prefix', None) self.pool_lifetime_option = kwargs.get('pool_lifetime_option', None) self.keep_alive = kwargs.get('keep_alive', None) self.pool = kwargs.get('pool', None) class ChocolateyPackageReference(PackageReferenceBase): """A reference to a package to be installed using the Chocolatey package manager on a Windows node. :param str id: The name of the package. :param str version: The version of the package to be installed. If omitted, the latest version (according to the package repository) will be installed. :param bool allow_empty_checksums: Whether Chocolatey will install packages without a checksum for validation. Default is false. """ _validation = { 'type': {'required': True}, 'id': {'required': True}, } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'id': {'key': 'id', 'type': 'str'}, 'version': {'key': 'version', 'type': 'str'}, 'allow_empty_checksums': {'key': 'allowEmptyChecksums', 'type': 'bool'} } def __init__(self, kwargs): super(ChocolateyPackageReference, self).__init__(kwargs) self.allow_empty_checksums = kwargs.get('allow_empty_checksums', None) self.type = 'chocolateyPackage' class ExtendedJobParameter(models.JobAddParameter): """An Azure Batch job to add. :param id: A string that uniquely identifies the job within the account. The ID can contain any combination of alphanumeric characters including hyphens and underscores, and cannot contain more than 64 characters. The ID is case-preserving and case-insensitive (that is, you may not have two IDs within an account that differ only by case). :type id: str :param display_name: The display name for the job. The display name need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param priority: The priority of the job. Priority values can range from -1000 to 1000, with -1000 being the lowest priority and 1000 being the highest priority. The default value is 0. :type priority: int :param constraints: The execution constraints for the job. :type constraints: :class:`JobConstraints <azure.batch.models.JobConstraints>` :param job_manager_task: Details of a Job Manager task to be launched when the job is started. If the job does not specify a Job Manager task, the user must explicitly add tasks to the job. If the job does specify a Job Manager task, the Batch service creates the Job Manager task when the job is created, and will try to schedule the Job Manager task before scheduling other tasks in the job. The Job Manager task's typical purpose is to control and/or monitor job execution, for example by deciding what additional tasks to run, determining when the work is complete, etc. (However, a Job Manager task is not restricted to these activities - it is a fully-fledged task in the system and perform whatever actions are required for the job.) For example, a Job Manager task might download a file specified as a parameter, analyze the contents of that file and submit additional tasks based on those contents. :type job_manager_task: :class:`JobManagerTask <azure.batch.models.JobManagerTask>` :param job_preparation_task: The Job Preparation task. If a job has a Job Preparation task, the Batch service will run the Job Preparation task on a compute node before starting any tasks of that job on that compute node. :type job_preparation_task: :class:`JobPreparationTask <azure.batch.models.JobPreparationTask>` :param job_release_task: The Job Release task. A Job Release task cannot be specified without also specifying a Job Preparation task for the job. The Batch service runs the Job Release task on the compute nodes that have run the Job Preparation task. The primary purpose of the Job Release task is to undo changes to compute nodes made by the Job Preparation task. Example activities include deleting local files, or shutting down services that were started as part of job preparation. :type job_release_task: :class:`JobReleaseTask <azure.batch.models.JobReleaseTask>` :param common_environment_settings: The list of common environment variable settings. These environment variables are set for all tasks in the job (including the Job Manager, Job Preparation and Job Release tasks). Individual tasks can override an environment setting specified here by specifying the same setting name with a different value. :type common_environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param pool_info: The pool on which the Batch service runs the job's tasks. :type pool_info: :class:`PoolInformation <azure.batch.models.PoolInformation>` :param on_all_tasks_complete: The action the Batch service should take when all tasks in the job are in the completed state. Note that if a job contains no tasks, then all tasks are considered complete. This option is therefore most commonly used with a Job Manager task; if you want to use automatic job termination without a Job Manager, you should initially set onAllTasksComplete to noAction and update the job properties to set onAllTasksComplete to terminateJob once you have finished adding tasks. Permitted values are: noAction - do nothing. The job remains active unless terminated or disabled by some other means. terminateJob - terminate the job. The job's terminateReason is set to 'AllTasksComplete'. The default is noAction. Possible values include: 'noAction', 'terminateJob' :type on_all_tasks_complete: str or :class:`OnAllTasksComplete <azure.batch.models.OnAllTasksComplete>` :param on_task_failure: The action the Batch service should take when any task in the job fails. A task is considered to have failed if has a failureInfo. A failureInfo is set if the task completes with a non-zero exit code after exhausting its retry count, or if there was an error starting the task, for example due to a resource file download error. noAction - do nothing. performExitOptionsJobAction - take the action associated with the task exit condition in the task's exitConditions collection. (This may still result in no action being taken, if that is what the task specifies.) The default is noAction. Possible values include: 'noAction', 'performExitOptionsJobAction' :type on_task_failure: str or :class:`OnTaskFailure <azure.batch.models.OnTaskFailure>` :param metadata: A list of name-value pairs associated with the job as metadata. The Batch service does not assign any meaning to metadata; it is solely for the use of user code. :type metadata: list of :class:`MetadataItem <azure.batch.models.MetadataItem>` :param uses_task_dependencies: Whether tasks in the job can define dependencies on each other. The default is false. :type uses_task_dependencies: bool :param task_factory: A task factory reference to automatically generate a set of tasks to be added to the job. :type task_factory: :class:`TaskFactoryBase <azext.batch.models.TaskFactoryBase>` :param application_template_info: A reference to an application template file to be expanded to complete the job specification. If supplied, the following arugments cannot also be supplied or they will be overwritten: 'job_manager_task', 'common_environment_settings', 'uses_task_dependencies', 'on_all_tasks_complete', 'on_task_failure', 'task_factory', 'job_preparation_task', 'job_release_task'. :type application_template_info: :class:`ApplicationTemplateInfo <azext.batch.models.ApplicationTemplateInfo>` """ _validation = { 'id': {'required': True}, 'pool_info': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'priority': {'key': 'priority', 'type': 'int'}, 'constraints': {'key': 'constraints', 'type': 'JobConstraints'}, 'job_manager_task': {'key': 'jobManagerTask', 'type': 'JobManagerTask'}, 'job_preparation_task': {'key': 'jobPreparationTask', 'type': 'JobPreparationTask'}, 'job_release_task': {'key': 'jobReleaseTask', 'type': 'JobReleaseTask'}, 'common_environment_settings': {'key': 'commonEnvironmentSettings', 'type': '[EnvironmentSetting]'}, 'pool_info': {'key': 'poolInfo', 'type': 'PoolInformation'}, 'on_all_tasks_complete': {'key': 'onAllTasksComplete', 'type': 'OnAllTasksComplete'}, 'on_task_failure': {'key': 'onTaskFailure', 'type': 'OnTaskFailure'}, 'metadata': {'key': 'metadata', 'type': '[MetadataItem]'}, 'uses_task_dependencies': {'key': 'usesTaskDependencies', 'type': 'bool'}, 'task_factory': {'key': 'taskFactory', 'type': 'TaskFactoryBase'}, 'application_template_info': {'key': 'applicationTemplateInfo', 'type': 'ApplicationTemplateInfo'} } def __init__(self, kwargs): super(ExtendedJobParameter, self).__init__(kwargs) self.task_factory = kwargs.get('task_factory', None) self.application_template_info = kwargs.get('application_template_info', None) if self.application_template_info: # Rule: Jobs may not use properties reserved for template use reserved = [k for k, v in self.__dict__.items() \ if k in ATTRS_RESERVED_FOR_TEMPLATES and v is not None] if reserved: raise ValueError("Jobs using application templates may not use these " "properties: {}".format(', '.join(reserved))) class ExtendedOutputFileDestination(Model): """The specification for where output files should be uploaded to on task completion. :param container: A location in Azure blob storage to which files are uploaded. This cannot be combined with auto_storage. :type container: :class:`OutputFileBlobContainerDestination <azure.batch.models.OutputFileBlobContainerDestination>` :param auto_storage: An auto-storage file group reference. This cannot be combined with container. :type auto_storage: :class:`OutputFileAutoStorageDestination <azext.batch.models.OutputFileAutoStorageDestination>` """ _attribute_map = { 'container': {'key': 'container', 'type': 'OutputFileBlobContainerDestination'}, 'auto_storage': {'key': 'autoStorage', 'type': 'OutputFileAutoStorageDestination'}, } def __init__(self, kwargs): super(ExtendedOutputFileDestination, self).__init__(kwargs) self.container = kwargs.get('container', None) self.auto_storage = kwargs.get('auto_storage', None) if self.container and self.auto_storage: raise ValueError("Cannot specify both container and auto_storage.") class ExtendedPoolParameter(models.PoolAddParameter): """A pool in the Azure Batch service to add. :param id: A string that uniquely identifies the pool within the account. The ID can contain any combination of alphanumeric characters including hyphens and underscores, and cannot contain more than 64 characters. The ID is case-preserving and case-insensitive (that is, you may not have two pool IDs within an account that differ only by case). :type id: str :param display_name: The display name for the pool. The display name need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param vm_size: The size of virtual machines in the pool. All virtual machines in a pool are the same size. For information about available sizes of virtual machines for Cloud Services pools (pools created with cloudServiceConfiguration), see Sizes for Cloud Services (http://azure.microsoft.com/documentation/articles/cloud-services-sizes-specs/). Batch supports all Cloud Services VM sizes except ExtraSmall, A1V2 and A2V2. For information about available VM sizes for pools using images from the Virtual Machines Marketplace (pools created with virtualMachineConfiguration) see Sizes for Virtual Machines (Linux) (https://azure.microsoft.com/documentation/articles/virtual-machines-linux-sizes/) or Sizes for Virtual Machines (Windows) (https://azure.microsoft.com/documentation/articles/virtual-machines-windows-sizes/). Batch supports all Azure VM sizes except STANDARD_A0 and those with premium storage (STANDARD_GS, STANDARD_DS, and STANDARD_DSV2 series). :type vm_size: str :param cloud_service_configuration: The cloud service configuration for the pool. This property and virtualMachineConfiguration are mutually exclusive and one of the properties must be specified. This property cannot be specified if the Batch account was created with its poolAllocationMode property set to 'UserSubscription'. :type cloud_service_configuration: :class:`CloudServiceConfiguration <azure.batch.models.CloudServiceConfiguration>` :param virtual_machine_configuration: The virtual machine configuration for the pool. This property and cloudServiceConfiguration are mutually exclusive and one of the properties must be specified. :type virtual_machine_configuration: :class:`VirtualMachineConfiguration <azure.batch.models.VirtualMachineConfiguration>` :param resize_timeout: The timeout for allocation of compute nodes to the pool. This timeout applies only to manual scaling; it has no effect when enableAutoScale is set to true. The default value is 15 minutes. The minimum value is 5 minutes. If you specify a value less than 5 minutes, the Batch service returns an error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). :type resize_timeout: timedelta :param target_dedicated_nodes: The desired number of dedicated compute nodes in the pool. This property must not be specified if enableAutoScale is set to true. If enableAutoScale is set to false, then you must set either targetDedicatedNodes, targetLowPriorityNodes, or both. :type target_dedicated_nodes: int :param target_low_priority_nodes: The desired number of low-priority compute nodes in the pool. This property must not be specified if enableAutoScale is set to true. If enableAutoScale is set to false, then you must set either targetDedicatedNodes, targetLowPriorityNodes, or both. :type target_low_priority_nodes: int :param enable_auto_scale: Whether the pool size should automatically adjust over time. If false, at least one of targetDedicateNodes and targetLowPriorityNodes must be specified. If true, the autoScaleFormula property is required and the pool automatically resizes according to the formula. The default value is false. :type enable_auto_scale: bool :param auto_scale_formula: A formula for the desired number of compute nodes in the pool. This property must not be specified if enableAutoScale is set to false. It is required if enableAutoScale is set to true. The formula is checked for validity before the pool is created. If the formula is not valid, the Batch service rejects the request with detailed error information. For more information about specifying this formula, see 'Automatically scale compute nodes in an Azure Batch pool' (https://azure.microsoft.com/documentation/articles/batch-automatic-scaling/). :type auto_scale_formula: str :param auto_scale_evaluation_interval: The time interval at which to automatically adjust the pool size according to the autoscale formula. The default value is 15 minutes. The minimum and maximum value are 5 minutes and 168 hours respectively. If you specify a value less than 5 minutes or greater than 168 hours, the Batch service returns an error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). :type auto_scale_evaluation_interval: timedelta :param enable_inter_node_communication: Whether the pool permits direct communication between nodes. Enabling inter-node communication limits the maximum size of the pool due to deployment restrictions on the nodes of the pool. This may result in the pool not reaching its desired size. The default value is false. :type enable_inter_node_communication: bool :param network_configuration: The network configuration for the pool. :type network_configuration: :class:`NetworkConfiguration <azure.batch.models.NetworkConfiguration>` :param start_task: A task specified to run on each compute node as it joins the pool. The task runs when the node is added to the pool or when the node is restarted. :type start_task: :class:`StartTask <azure.batch.models.StartTask>` :param certificate_references: The list of certificates to be installed on each compute node in the pool. For Windows compute nodes, the Batch service installs the certificates to the specified certificate store and location. For Linux compute nodes, the certificates are stored in a directory inside the task working directory and an environment variable AZ_BATCH_CERTIFICATES_DIR is supplied to the task to query for this location. For certificates with visibility of 'remoteUser', a 'certs' directory is created in the user's home directory (e.g., /home/{user-name}/certs) and certificates are placed in that directory. :type certificate_references: list of :class:`CertificateReference <azure.batch.models.CertificateReference>` :param application_package_references: The list of application packages to be installed on each compute node in the pool. :type application_package_references: list of :class:`ApplicationPackageReference <azure.batch.models.ApplicationPackageReference>` :param application_licenses: The list of application licenses the Batch service will make available on each compute node in the pool. The list of application licenses must be a subset of available Batch service application licenses. If a license is requested which is not supported, pool creation will fail. :type application_licenses: list of str :param max_tasks_per_node: The maximum number of tasks that can run concurrently on a single compute node in the pool. The default value is 1. The maximum value of this setting depends on the size of the compute nodes in the pool (the vmSize setting). :type max_tasks_per_node: int :param task_scheduling_policy: How tasks are distributed across compute nodes in a pool. :type task_scheduling_policy: :class:`TaskSchedulingPolicy <azure.batch.models.TaskSchedulingPolicy>` :param user_accounts: The list of user accounts to be created on each node in the pool. :type user_accounts: list of :class:`UserAccount <azure.batch.models.UserAccount>` :param metadata: A list of name-value pairs associated with the pool as metadata. The Batch service does not assign any meaning to metadata; it is solely for the use of user code. :type metadata: list of :class:`MetadataItem <azure.batch.models.MetadataItem>` :param package_references: A list of packages to be installed on the compute nodes. Must be of a Package Manager type in accordance with the selected operating system. :type package_references: list of :class:`PackageReferenceBase <azext.batch.models.PackageReferenceBase>` """ _validation = { 'id': {'required': True}, 'vm_size': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'vm_size': {'key': 'vmSize', 'type': 'str'}, 'cloud_service_configuration': {'key': 'cloudServiceConfiguration', 'type': 'CloudServiceConfiguration'}, 'virtual_machine_configuration': {'key': 'virtualMachineConfiguration', 'type': 'VirtualMachineConfiguration'}, 'resize_timeout': {'key': 'resizeTimeout', 'type': 'duration'}, 'target_dedicated_nodes': {'key': 'targetDedicatedNodes', 'type': 'int'}, 'target_low_priority_nodes': {'key': 'targetLowPriorityNodes', 'type': 'int'}, 'enable_auto_scale': {'key': 'enableAutoScale', 'type': 'bool'}, 'auto_scale_formula': {'key': 'autoScaleFormula', 'type': 'str'}, 'auto_scale_evaluation_interval': {'key': 'autoScaleEvaluationInterval', 'type': 'duration'}, 'enable_inter_node_communication': {'key': 'enableInterNodeCommunication', 'type': 'bool'}, 'network_configuration': {'key': 'networkConfiguration', 'type': 'NetworkConfiguration'}, 'start_task': {'key': 'startTask', 'type': 'StartTask'}, 'certificate_references': {'key': 'certificateReferences', 'type': '[CertificateReference]'}, 'application_package_references': {'key': 'applicationPackageReferences', 'type': '[ApplicationPackageReference]'}, 'application_licenses': {'key': 'applicationLicenses', 'type': '[str]'}, 'max_tasks_per_node': {'key': 'maxTasksPerNode', 'type': 'int'}, 'task_scheduling_policy': {'key': 'taskSchedulingPolicy', 'type': 'TaskSchedulingPolicy'}, 'user_accounts': {'key': 'userAccounts', 'type': '[UserAccount]'}, 'metadata': {'key': 'metadata', 'type': '[MetadataItem]'}, 'package_references': {'key': 'packageReferences', 'type': '[PackageReferenceBase]'} } def __init__(self, kwargs): super(ExtendedPoolParameter, self).__init__(kwargs) self.package_references = kwargs.get('package_references', None) class ExtendedPoolSpecification(models.PoolSpecification): """Specification for creating a new pool. :param display_name: The display name for the pool. The display name need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param vm_size: The size of the virtual machines in the pool. All virtual machines in a pool are the same size. For information about available sizes of virtual machines for Cloud Services pools (pools created with cloudServiceConfiguration), see Sizes for Cloud Services (http://azure.microsoft.com/documentation/articles/cloud-services-sizes-specs/). Batch supports all Cloud Services VM sizes except ExtraSmall, A1V2 and A2V2. For information about available VM sizes for pools using images from the Virtual Machines Marketplace (pools created with virtualMachineConfiguration) see Sizes for Virtual Machines (Linux) (https://azure.microsoft.com/documentation/articles/virtual-machines-linux-sizes/) or Sizes for Virtual Machines (Windows) (https://azure.microsoft.com/documentation/articles/virtual-machines-windows-sizes/). Batch supports all Azure VM sizes except STANDARD_A0 and those with premium storage (STANDARD_GS, STANDARD_DS, and STANDARD_DSV2 series). :type vm_size: str :param cloud_service_configuration: The cloud service configuration for the pool. This property must be specified if the pool needs to be created with Azure PaaS VMs. This property and virtualMachineConfiguration are mutually exclusive and one of the properties must be specified. If neither is specified then the Batch service returns an error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). This property cannot be specified if the Batch account was created with its poolAllocationMode property set to 'UserSubscription'. :type cloud_service_configuration: :class:`CloudServiceConfiguration <azure.batch.models.CloudServiceConfiguration>` :param virtual_machine_configuration: The virtual machine configuration for the pool. This property must be specified if the pool needs to be created with Azure IaaS VMs. This property and cloudServiceConfiguration are mutually exclusive and one of the properties must be specified. If neither is specified then the Batch service returns an error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). :type virtual_machine_configuration: :class:`VirtualMachineConfiguration <azure.batch.models.VirtualMachineConfiguration>` :param max_tasks_per_node: The maximum number of tasks that can run concurrently on a single compute node in the pool. The default value is 1. The maximum value of this setting depends on the size of the compute nodes in the pool (the vmSize setting). :type max_tasks_per_node: int :param task_scheduling_policy: How tasks are distributed across compute nodes in a pool. :type task_scheduling_policy: :class:`TaskSchedulingPolicy <azure.batch.models.TaskSchedulingPolicy>` :param resize_timeout: The timeout for allocation of compute nodes to the pool. This timeout applies only to manual scaling; it has no effect when enableAutoScale is set to true. The default value is 15 minutes. The minimum value is 5 minutes. If you specify a value less than 5 minutes, the Batch service rejects the request with an error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). :type resize_timeout: timedelta :param target_dedicated_nodes: The desired number of dedicated compute nodes in the pool. This property must not be specified if enableAutoScale is set to true. If enableAutoScale is set to false, then you must set either targetDedicatedNodes, targetLowPriorityNodes, or both. :type target_dedicated_nodes: int :param target_low_priority_nodes: The desired number of low-priority compute nodes in the pool. This property must not be specified if enableAutoScale is set to true. If enableAutoScale is set to false, then you must set either targetDedicatedNodes, targetLowPriorityNodes, or both. :type target_low_priority_nodes: int :param enable_auto_scale: Whether the pool size should automatically adjust over time. If false, the targetDedicated element is required. If true, the autoScaleFormula element is required. The pool automatically resizes according to the formula. The default value is false. :type enable_auto_scale: bool :param auto_scale_formula: The formula for the desired number of compute nodes in the pool. This property must not be specified if enableAutoScale is set to false. It is required if enableAutoScale is set to true. The formula is checked for validity before the pool is created. If the formula is not valid, the Batch service rejects the request with detailed error information. :type auto_scale_formula: str :param auto_scale_evaluation_interval: The time interval at which to automatically adjust the pool size according to the autoscale formula. The default value is 15 minutes. The minimum and maximum value are 5 minutes and 168 hours respectively. If you specify a value less than 5 minutes or greater than 168 hours, the Batch service rejects the request with an invalid property value error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). :type auto_scale_evaluation_interval: timedelta :param enable_inter_node_communication: Whether the pool permits direct communication between nodes. Enabling inter-node communication limits the maximum size of the pool due to deployment restrictions on the nodes of the pool. This may result in the pool not reaching its desired size. The default value is false. :type enable_inter_node_communication: bool :param network_configuration: The network configuration for the pool. :type network_configuration: :class:`NetworkConfiguration <azure.batch.models.NetworkConfiguration>` :param start_task: A task to run on each compute node as it joins the pool. The task runs when the node is added to the pool or when the node is restarted. :type start_task: :class:`StartTask <azure.batch.models.StartTask>` :param certificate_references: A list of certificates to be installed on each compute node in the pool. For Windows compute nodes, the Batch service installs the certificates to the specified certificate store and location. For Linux compute nodes, the certificates are stored in a directory inside the task working directory and an environment variable AZ_BATCH_CERTIFICATES_DIR is supplied to the task to query for this location. For certificates with visibility of 'remoteUser', a 'certs' directory is created in the user's home directory (e.g., /home/{user-name}/certs) and certificates are placed in that directory. :type certificate_references: list of :class:`CertificateReference <azure.batch.models.CertificateReference>` :param application_package_references: The list of application packages to be installed on each compute node in the pool. :type application_package_references: list of :class:`ApplicationPackageReference <azure.batch.models.ApplicationPackageReference>` :param application_licenses: The list of application licenses the Batch service will make available on each compute node in the pool. The list of application licenses must be a subset of available Batch service application licenses. If a license is requested which is not supported, pool creation will fail. :type application_licenses: list of str :param user_accounts: The list of user accounts to be created on each node in the pool. :type user_accounts: list of :class:`UserAccount <azure.batch.models.UserAccount>` :param metadata: A list of name-value pairs associated with the pool as metadata. The Batch service does not assign any meaning to metadata; it is solely for the use of user code. :type metadata: list of :class:`MetadataItem <azure.batch.models.MetadataItem>` :param package_references: A list of packages to be installed on the compute nodes. Must be of a Package Manager type in accordance with the selected operating system. :type package_references: list of :class:`PackageReferenceBase <azext.batch.models.PackageReferenceBase>` """ _validation = { 'vm_size': {'required': True}, } _attribute_map = { 'display_name': {'key': 'displayName', 'type': 'str'}, 'vm_size': {'key': 'vmSize', 'type': 'str'}, 'cloud_service_configuration': {'key': 'cloudServiceConfiguration', 'type': 'CloudServiceConfiguration'}, 'virtual_machine_configuration': {'key': 'virtualMachineConfiguration', 'type': 'VirtualMachineConfiguration'}, 'max_tasks_per_node': {'key': 'maxTasksPerNode', 'type': 'int'}, 'task_scheduling_policy': {'key': 'taskSchedulingPolicy', 'type': 'TaskSchedulingPolicy'}, 'resize_timeout': {'key': 'resizeTimeout', 'type': 'duration'}, 'target_dedicated_nodes': {'key': 'targetDedicatedNodes', 'type': 'int'}, 'target_low_priority_nodes': {'key': 'targetLowPriorityNodes', 'type': 'int'}, 'enable_auto_scale': {'key': 'enableAutoScale', 'type': 'bool'}, 'auto_scale_formula': {'key': 'autoScaleFormula', 'type': 'str'}, 'auto_scale_evaluation_interval': {'key': 'autoScaleEvaluationInterval', 'type': 'duration'}, 'enable_inter_node_communication': {'key': 'enableInterNodeCommunication', 'type': 'bool'}, 'network_configuration': {'key': 'networkConfiguration', 'type': 'NetworkConfiguration'}, 'start_task': {'key': 'startTask', 'type': 'StartTask'}, 'certificate_references': {'key': 'certificateReferences', 'type': '[CertificateReference]'}, 'application_package_references': {'key': 'applicationPackageReferences', 'type': '[ApplicationPackageReference]'}, 'application_licenses': {'key': 'applicationLicenses', 'type': '[str]'}, 'user_accounts': {'key': 'userAccounts', 'type': '[UserAccount]'}, 'metadata': {'key': 'metadata', 'type': '[MetadataItem]'}, 'package_references': {'key': 'packageReferences', 'type': '[PackageReferenceBase]'} } def __init__(self, kwargs): super(ExtendedPoolSpecification, self).__init__(kwargs) self.package_references = kwargs.get('package_references', None) class ExtendedResourceFile(models.ResourceFile): """A file to be downloaded from Azure blob storage to a compute node. :param http_url: The URL of the file within Azure Blob Storage. This URL must be readable using anonymous access; that is, the Batch service does not present any credentials when downloading the blob. There are two ways to get such a URL for a blob in Azure storage: include a Shared Access Signature (SAS) granting read permissions on the blob, or set the ACL for the blob or its container to allow public access. :type http_url: str :param auto_storage_container_name: The storage container name in the auto storage account. The autoStorageContainerName, storageContainerUrl and httpUrl properties are mutually exclusive and one of them must be specified. :type auto_storage_container_name: str :param storage_container_url: The URL of the blob container within Azure Blob Storage. The autoStorageContainerName, storageContainerUrl and httpUrl properties are mutually exclusive and one of them must be specified. This URL must be readable and listable using anonymous access; that is, the Batch service does not present any credentials when downloading blobs from the container. There are two ways to get such a URL for a container in Azure storage: include a Shared Access Signature (SAS) granting read and list permissions on the container, or set the ACL for the container to allow public access. :type storage_container_url: str :param blob_prefix: The blob prefix to use when downloading blobs from an Azure Storage container. Only the blobs whose names begin with the specified prefix will be downloaded. The property is valid only when autoStorageContainerName or storageContainerUrl is used. This prefix can be a partial filename or a subdirectory. If a prefix is not specified, all the files in the container will be downloaded. :type blob_prefix: str :param file_path: The location on the compute node to which to download the file, relative to the task's working directory. If using a file group source that references more than one file, this will be considered the name of a directory, otherwise it will be treated as the destination file name. :type file_path: str :param file_mode: The file permission mode attribute in octal format. This property applies only to files being downloaded to Linux compute nodes. It will be ignored if it is specified for a resourceFile which will be downloaded to a Windows node. If this property is not specified for a Linux node, then a default value of 0770 is applied to the file. If using a file group source that references more than one file, this will be applied to all files in the group. :type file_mode: str :param source: A file source reference which could include a collection of files from a Azure Storage container or an auto-storage file group. :type source: :class:`FileSource <azext.batch.models.FileSource>` """ _attribute_map = { 'http_url': {'key': 'httpUrl', 'type': 'str'}, 'auto_storage_container_name': {'key': 'autoStorageContainerName', 'type': 'str'}, 'blob_prefix': {'key': 'blobPrefix', 'type': 'str'}, 'storage_container_url': {'key': 'storageContainerUrl', 'type': 'str'}, 'file_path': {'key': 'filePath', 'type': 'str'}, 'file_mode': {'key': 'fileMode', 'type': 'str'}, 'source': {'key': 'source', 'type': 'FileSource'} } def __init__(self, kwargs): super(ExtendedResourceFile, self).__init__(kwargs) self.source = kwargs.get('source', None) class ExtendedTaskParameter(models.TaskAddParameter): """An Azure Batch task to add. :param id: A string that uniquely identifies the task within the job. The ID can contain any combination of alphanumeric characters including hyphens and underscores, and cannot contain more than 64 characters. The ID is case-preserving and case-insensitive (that is, you may not have two IDs within a job that differ only by case). :type id: str :param display_name: A display name for the task. The display name need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param command_line: The command line of the task. For multi-instance tasks, the command line is executed as the primary task, after the primary task and all subtasks have finished executing the coordination command line. The command line does not run under a shell, and therefore cannot take advantage of shell features such as environment variable expansion. If you want to take advantage of such features, you should invoke the shell in the command line, for example using "cmd /c MyCommand" in Windows or "/bin/sh -c MyCommand" in Linux. :type command_line: str :param container_settings: The settings for the container under which the task runs. If the pool that will run this task has containerConfiguration set, this must be set as well. If the pool that will run this task doesn't have containerConfiguration set, this must not be set. When this is specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR (the root of Azure Batch directories on the node) are mapped into the container, all task environment variables are mapped into the container, and the task command line is executed in the container. :type container_settings: :class:`TaskContainerSettings <azure.batch.models.TaskContainerSettings>` :param exit_conditions: How the Batch service should respond when the task completes. :type exit_conditions: :class:`ExitConditions <azure.batch.models.ExitConditions>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. For multi-instance tasks, the resource files will only be downloaded to the compute node on which the primary task is executed. :type resource_files: list of :class:`ResourceFile <azure.batch.models.ResourceFile>` :param environment_settings: A list of environment variable settings for the task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param affinity_info: A locality hint that can be used by the Batch service to select a compute node on which to start the new task. :type affinity_info: :class:`AffinityInformation <azure.batch.models.AffinityInformation>` :param constraints: The execution constraints that apply to this task. If you do not specify constraints, the maxTaskRetryCount is the maxTaskRetryCount specified for the job, and the maxWallClockTime and retentionTime are infinite. :type constraints: :class:`TaskConstraints <azure.batch.models.TaskConstraints>` :param user_identity: The user identity under which the task runs. If omitted, the task runs as a non-administrative user unique to the task. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` :param multi_instance_settings: An object that indicates that the task is a multi-instance task, and contains information about how to run the multi-instance task. :type multi_instance_settings: :class:`MultiInstanceSettings <azure.batch.models.MultiInstanceSettings>` :param depends_on: The tasks that this task depends on. This task will not be scheduled until all tasks that it depends on have completed successfully. If any of those tasks fail and exhaust their retry counts, this task will never be scheduled. If the job does not have usesTaskDependencies set to true, and this element is present, the request fails with error code TaskDependenciesNotSpecifiedOnJob. :type depends_on: :class:`TaskDependencies <azure.batch.models.TaskDependencies>` :param application_package_references: A list of application packages that the Batch service will deploy to the compute node before running the command line. Application packages are downloaded and deployed to a shared directory, not the task working directory. Therefore, if a referenced package is already on the compute node, and is up to date, then it is not re-downloaded; the existing copy on the compute node is used. If a referenced application package cannot be installed, for example because the package has been deleted or because download failed, the task fails. :type application_package_references: list of :class:`ApplicationPackageReference <azure.batch.models.ApplicationPackageReference>` :param authentication_token_settings: The settings for an authentication token that the task can use to perform Batch service operations. If this property is set, the Batch service provides the task with an authentication token which can be used to authenticate Batch service operations without requiring an account access key. The token is provided via the AZ_BATCH_AUTHENTICATION_TOKEN environment variable. The operations that the task can carry out using the token depend on the settings. For example, a task can request job permissions in order to add other tasks to the job, or check the status of the job or of other tasks under the job. :type authentication_token_settings: :class:`AuthenticationTokenSettings <azure.batch.models.AuthenticationTokenSettings>` :param output_files: A list of files that the Batch service will upload from the compute node after running the command line. For multi-instance tasks, the files will only be uploaded from the compute node on which the primary task is executed. :type output_files: list of :class:`OutputFile <azext.batch.models.OutputFile>` :param package_references: A list of packages to be installed on the compute nodes. Must be of a Package Manager type in accordance with the selected operating system. :type package_references: list of :class:`PackageReferenceBase <azext.batch.models.PackageReferenceBase>` """ _validation = { 'id': {'required': True}, 'command_line': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'command_line': {'key': 'commandLine', 'type': 'str'}, 'container_settings': {'key': 'containerSettings', 'type': 'TaskContainerSettings'}, 'exit_conditions': {'key': 'exitConditions', 'type': 'ExitConditions'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'output_files': {'key': 'outputFiles', 'type': '[OutputFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'affinity_info': {'key': 'affinityInfo', 'type': 'AffinityInformation'}, 'constraints': {'key': 'constraints', 'type': 'TaskConstraints'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, 'multi_instance_settings': {'key': 'multiInstanceSettings', 'type': 'MultiInstanceSettings'}, 'depends_on': {'key': 'dependsOn', 'type': 'TaskDependencies'}, 'application_package_references': {'key': 'applicationPackageReferences', 'type': '[ApplicationPackageReference]'}, 'authentication_token_settings': {'key': 'authenticationTokenSettings', 'type': 'AuthenticationTokenSettings'}, 'package_references': {'key': 'packageReferences', 'type': '[PackageReferenceBase]'} } def __init__(self, kwargs): super(ExtendedTaskParameter, self).__init__(kwargs) self.package_references = kwargs.get('package_references', None) class FileCollectionTaskFactory(TaskFactoryBase): """A Task Factory for generating a set of tasks based on the contents of an Azure Storage container or auto-storage file group. One task will be generated per input file, and automatically added to the job. :param source: The input file source from which the tasks will be generated. :type source: :class:`FileSource <azext.batch.models.FileSource>` :param repeat_task: The task template the will be used to generate each task. :type repeat_task: :class:`RepeatTask <azext.batch.models.RepeatTask>` :param merge_task: An optional additional task to be run after all the other generated tasks have completed successfully. :type merge_task: :class:`MergeTask <azext.batch.models.MergeTask>` """ _validation = { 'type': {'required': True}, 'source': {'required': True}, 'repeat_task': {'required': True} } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'source': {'key': 'source', 'type': 'FileSource'}, 'repeat_task': {'key': 'repeatTask', 'type': 'RepeatTask'}, 'merge_task': {'key': 'mergeTask', 'type': 'MergeTask'} } def __init__(self, kwargs): super(FileCollectionTaskFactory, self).__init__(kwargs) self.source = kwargs.get('source', None) self.repeat_task = kwargs.get('repeat_task', None) self.type = 'taskPerFile' class FileSource(Model): """A source of input files to be downloaded onto a compute node. :param str file_group: The name of an auto-storage file group. :param str url: The URL of a file to be downloaded. :param str container_url: The SAS URL of an Azure Storage container. :param str prefix: The filename prefix or subdirectory of input files in either an auto-storage file group or container. Will be ignored if conbined with url. """ _attribute_map = { 'file_group': {'key': 'fileGroup', 'type': 'str'}, 'url': {'key': 'url', 'type': 'str'}, 'container_url': {'key': 'containerUrl', 'type': 'str'}, 'prefix': {'key': 'prefix', 'type': 'str'}, } def __init__(self, kwargs): super(FileSource, self).__init__(kwargs) self.file_group = kwargs.get('file_group', None) self.url = kwargs.get('url', None) self.container_url = kwargs.get('container_url', None) self.prefix = kwargs.get('prefix', None) class JobManagerTask(Model): """Specifies details of a Job Manager task. The Job Manager task is automatically started when the job is created. The Batch service tries to schedule the Job Manager task before any other tasks in the job. When shrinking a pool, the Batch service tries to preserve compute nodes where Job Manager tasks are running for as long as possible (that is, nodes running 'normal' tasks are removed before nodes running Job Manager tasks). When a Job Manager task fails and needs to be restarted, the system tries to schedule it at the highest priority. If there are no idle nodes available, the system may terminate one of the running tasks in the pool and return it to the queue in order to make room for the Job Manager task to restart. Note that a Job Manager task in one job does not have priority over tasks in other jobs. Across jobs, only job level priorities are observed. For example, if a Job Manager in a priority 0 job needs to be restarted, it will not displace tasks of a priority 1 job. :param id: A string that uniquely identifies the Job Manager task within the job. The ID can contain any combination of alphanumeric characters including hyphens and underscores and cannot contain more than 64 characters. :type id: str :param display_name: The display name of the Job Manager task. It need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param command_line: The command line of the Job Manager task. The command line does not run under a shell, and therefore cannot take advantage of shell features such as environment variable expansion. If you want to take advantage of such features, you should invoke the shell in the command line, for example using "cmd /c MyCommand" in Windows or "/bin/sh -c MyCommand" in Linux. :type command_line: str :param container_settings: The settings for the container under which the Job Manager task runs. If the pool that will run this task has containerConfiguration set, this must be set as well. If the pool that will run this task doesn't have containerConfiguration set, this must not be set. When this is specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR (the root of Azure Batch directories on the node) are mapped into the container, all task environment variables are mapped into the container, and the task command line is executed in the container. :type container_settings: :class:`TaskContainerSettings <azure.batch.models.TaskContainerSettings>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. Files listed under this element are located in the task's working directory. :type resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` :param output_files: A list of files that the Batch service will upload from the compute node after running the command line. For multi-instance tasks, the files will only be uploaded from the compute node on which the primary task is executed. :type output_files: list of :class:`OutputFile <azure.batch.models.OutputFile>` :param environment_settings: A list of environment variable settings for the Job Manager task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param constraints: Constraints that apply to the Job Manager task. :type constraints: :class:`TaskConstraints <azure.batch.models.TaskConstraints>` :param kill_job_on_completion: Whether completion of the Job Manager task signifies completion of the entire job. If true, when the Job Manager task completes, the Batch service marks the job as complete. If any tasks are still running at this time (other than Job Release), those tasks are terminated. If false, the completion of the Job Manager task does not affect the job status. In this case, you should either use the onAllTasksComplete attribute to terminate the job, or have a client or user terminate the job explicitly. An example of this is if the Job Manager creates a set of tasks but then takes no further role in their execution. The default value is true. If you are using the onAllTasksComplete and onTaskFailure attributes to control job lifetime, and using the Job Manager task only to create the tasks for the job (not to monitor progress), then it is important to set killJobOnCompletion to false. :type kill_job_on_completion: bool :param user_identity: The user identity under which the Job Manager task runs. If omitted, the task runs as a non-administrative user unique to the task. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` :param run_exclusive: Whether the Job Manager task requires exclusive use of the compute node where it runs. If true, no other tasks will run on the same compute node for as long as the Job Manager is running. If false, other tasks can run simultaneously with the Job Manager on a compute node. The Job Manager task counts normally against the node's concurrent task limit, so this is only relevant if the node allows multiple concurrent tasks. The default value is true. :type run_exclusive: bool :param application_package_references: A list of application packages that the Batch service will deploy to the compute node before running the command line. Application packages are downloaded and deployed to a shared directory, not the task working directory. Therefore, if a referenced package is already on the compute node, and is up to date, then it is not re-downloaded; the existing copy on the compute node is used. If a referenced application package cannot be installed, for example because the package has been deleted or because download failed, the task fails. :type application_package_references: list of :class:`ApplicationPackageReference <azure.batch.models.ApplicationPackageReference>` :param authentication_token_settings: The settings for an authentication token that the task can use to perform Batch service operations. If this property is set, the Batch service provides the task with an authentication token which can be used to authenticate Batch service operations without requiring an account access key. The token is provided via the AZ_BATCH_AUTHENTICATION_TOKEN environment variable. The operations that the task can carry out using the token depend on the settings. For example, a task can request job permissions in order to add other tasks to the job, or check the status of the job or of other tasks under the job. :type authentication_token_settings: :class:`AuthenticationTokenSettings <azure.batch.models.AuthenticationTokenSettings>` :param allow_low_priority_node: Whether the Job Manager task may run on a low-priority compute node. The default value is false. :type allow_low_priority_node: bool """ _validation = { 'id': {'required': True}, 'command_line': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'command_line': {'key': 'commandLine', 'type': 'str'}, 'container_settings': {'key': 'containerSettings', 'type': 'TaskContainerSettings'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'output_files': {'key': 'outputFiles', 'type': '[OutputFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'constraints': {'key': 'constraints', 'type': 'TaskConstraints'}, 'kill_job_on_completion': {'key': 'killJobOnCompletion', 'type': 'bool'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, 'run_exclusive': {'key': 'runExclusive', 'type': 'bool'}, 'application_package_references': {'key': 'applicationPackageReferences', 'type': '[ApplicationPackageReference]'}, 'authentication_token_settings': {'key': 'authenticationTokenSettings', 'type': 'AuthenticationTokenSettings'}, 'allow_low_priority_node': {'key': 'allowLowPriorityNode', 'type': 'bool'}, } def __init__(self, kwargs): super(JobManagerTask, self).__init__(kwargs) self.id = kwargs.get('id', None) self.display_name = kwargs.get('display_name', None) self.command_line = kwargs.get('command_line', None) self.container_settings = kwargs.get('container_settings', None) self.resource_files = kwargs.get('resource_files', None) self.output_files = kwargs.get('output_files', None) self.environment_settings = kwargs.get('environment_settings', None) self.constraints = kwargs.get('constraints', None) self.kill_job_on_completion = kwargs.get('kill_job_on_completion', None) self.user_identity = kwargs.get('user_identity', None) self.run_exclusive = kwargs.get('run_exclusive', None) self.application_package_references = kwargs.get('application_package_references', None) self.authentication_token_settings = kwargs.get('authentication_token_settings', None) self.allow_low_priority_node = kwargs.get('allow_low_priority_node', None) class JobPreparationTask(Model): """A Job Preparation task to run before any tasks of the job on any given compute node. You can use Job Preparation to prepare a compute node to run tasks for the job. Activities commonly performed in Job Preparation include: Downloading common resource files used by all the tasks in the job. The Job Preparation task can download these common resource files to the shared location on the compute node. (AZ_BATCH_NODE_ROOT_DIR\\shared), or starting a local service on the compute node so that all tasks of that job can communicate with it. If the Job Preparation task fails (that is, exhausts its retry count before exiting with exit code 0), Batch will not run tasks of this job on the compute node. The node remains ineligible to run tasks of this job until it is reimaged. The node remains active and can be used for other jobs. The Job Preparation task can run multiple times on the same compute node. Therefore, you should write the Job Preparation task to handle re-execution. If the compute node is rebooted, the Job Preparation task is run again on the node before scheduling any other task of the job, if rerunOnNodeRebootAfterSuccess is true or if the Job Preparation task did not previously complete. If the compute node is reimaged, the Job Preparation task is run again before scheduling any task of the job. :param id: A string that uniquely identifies the Job Preparation task within the job. The ID can contain any combination of alphanumeric characters including hyphens and underscores and cannot contain more than 64 characters. If you do not specify this property, the Batch service assigns a default value of 'jobpreparation'. No other task in the job can have the same ID as the Job Preparation task. If you try to submit a task with the same ID, the Batch service rejects the request with error code TaskIdSameAsJobPreparationTask; if you are calling the REST API directly, the HTTP status code is 409 (Conflict). :type id: str :param command_line: The command line of the Job Preparation task. The command line does not run under a shell, and therefore cannot take advantage of shell features such as environment variable expansion. If you want to take advantage of such features, you should invoke the shell in the command line, for example using "cmd /c MyCommand" in Windows or "/bin/sh -c MyCommand" in Linux. :type command_line: str :param container_settings: The settings for the container under which the Job Preparation task runs. When this is specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR (the root of Azure Batch directories on the node) are mapped into the container, all task environment variables are mapped into the container, and the task command line is executed in the container. :type container_settings: :class:`TaskContainerSettings <azure.batch.models.TaskContainerSettings>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. Files listed under this element are located in the task's working directory. :type resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` :param environment_settings: A list of environment variable settings for the Job Preparation task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param constraints: Constraints that apply to the Job Preparation task. :type constraints: :class:`TaskConstraints <azure.batch.models.TaskConstraints>` :param wait_for_success: Whether the Batch service should wait for the Job Preparation task to complete successfully before scheduling any other tasks of the job on the compute node. A Job Preparation task has completed successfully if it exits with exit code 0. If true and the Job Preparation task fails on a compute node, the Batch service retries the Job Preparation task up to its maximum retry count (as specified in the constraints element). If the task has still not completed successfully after all retries, then the Batch service will not schedule tasks of the job to the compute node. The compute node remains active and eligible to run tasks of other jobs. If false, the Batch service will not wait for the Job Preparation task to complete. In this case, other tasks of the job can start executing on the compute node while the Job Preparation task is still running; and even if the Job Preparation task fails, new tasks will continue to be scheduled on the node. The default value is true. :type wait_for_success: bool :param user_identity: The user identity under which the Job Preparation task runs. If omitted, the task runs as a non-administrative user unique to the task on Windows nodes, or a a non-administrative user unique to the pool on Linux nodes. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` :param rerun_on_node_reboot_after_success: Whether the Batch service should rerun the Job Preparation task after a compute node reboots. The Job Preparation task is always rerun if a compute node is reimaged, or if the Job Preparation task did not complete (e.g. because the reboot occurred while the task was running). Therefore, you should always write a Job Preparation task to be idempotent and to behave correctly if run multiple times. The default value is true. :type rerun_on_node_reboot_after_success: bool """ _validation = { 'command_line': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'command_line': {'key': 'commandLine', 'type': 'str'}, 'container_settings': {'key': 'containerSettings', 'type': 'TaskContainerSettings'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'constraints': {'key': 'constraints', 'type': 'TaskConstraints'}, 'wait_for_success': {'key': 'waitForSuccess', 'type': 'bool'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, 'rerun_on_node_reboot_after_success': {'key': 'rerunOnNodeRebootAfterSuccess', 'type': 'bool'}, } def __init__(self, kwargs): super(JobPreparationTask, self).__init__(kwargs) self.id = kwargs.get('id', None) self.command_line = kwargs.get('command_line', None) self.container_settings = kwargs.get('container_settings', None) self.resource_files = kwargs.get('resource_files', None) self.environment_settings = kwargs.get('environment_settings', None) self.constraints = kwargs.get('constraints', None) self.wait_for_success = kwargs.get('wait_for_success', None) self.user_identity = kwargs.get('user_identity', None) self.rerun_on_node_reboot_after_success = kwargs.get('rerun_on_node_reboot_after_success', None) class JobReleaseTask(Model): """A Job Release task to run on job completion on any compute node where the job has run. The Job Release task runs when the job ends, because of one of the following: The user calls the Terminate Job API, or the Delete Job API while the job is still active, the job's maximum wall clock time constraint is reached, and the job is still active, or the job's Job Manager task completed, and the job is configured to terminate when the Job Manager completes. The Job Release task runs on each compute node where tasks of the job have run and the Job Preparation task ran and completed. If you reimage a compute node after it has run the Job Preparation task, and the job ends without any further tasks of the job running on that compute node (and hence the Job Preparation task does not re-run), then the Job Release task does not run on that node. If a compute node reboots while the Job Release task is still running, the Job Release task runs again when the compute node starts up. The job is not marked as complete until all Job Release tasks have completed. The Job Release task runs in the background. It does not occupy a scheduling slot; that is, it does not count towards the maxTasksPerNode limit specified on the pool. :param id: A string that uniquely identifies the Job Release task within the job. The ID can contain any combination of alphanumeric characters including hyphens and underscores and cannot contain more than 64 characters. If you do not specify this property, the Batch service assigns a default value of 'jobrelease'. No other task in the job can have the same ID as the Job Release task. If you try to submit a task with the same ID, the Batch service rejects the request with error code TaskIdSameAsJobReleaseTask; if you are calling the REST API directly, the HTTP status code is 409 (Conflict). :type id: str :param command_line: The command line of the Job Release task. The command line does not run under a shell, and therefore cannot take advantage of shell features such as environment variable expansion. If you want to take advantage of such features, you should invoke the shell in the command line, for example using "cmd /c MyCommand" in Windows or "/bin/sh -c MyCommand" in Linux. :type command_line: str :param container_settings: The settings for the container under which the Job Release task runs. When this is specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR (the root of Azure Batch directories on the node) are mapped into the container, all task environment variables are mapped into the container, and the task command line is executed in the container. :type container_settings: :class:`TaskContainerSettings <azure.batch.models.TaskContainerSettings>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. Files listed under this element are located in the task's working directory. :type resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` :param environment_settings: A list of environment variable settings for the Job Release task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param max_wall_clock_time: The maximum elapsed time that the Job Release task may run on a given compute node, measured from the time the task starts. If the task does not complete within the time limit, the Batch service terminates it. The default value is 15 minutes. You may not specify a timeout longer than 15 minutes. If you do, the Batch service rejects it with an error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). :type max_wall_clock_time: timedelta :param retention_time: The minimum time to retain the task directory for the Job Release task on the compute node. After this time, the Batch service may delete the task directory and all its contents. The default is infinite, i.e. the task directory will be retained until the compute node is removed or reimaged. :type retention_time: timedelta :param user_identity: The user identity under which the Job Release task runs. If omitted, the task runs as a non-administrative user unique to the task. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` """ _validation = { 'command_line': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'command_line': {'key': 'commandLine', 'type': 'str'}, 'container_settings': {'key': 'containerSettings', 'type': 'TaskContainerSettings'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'max_wall_clock_time': {'key': 'maxWallClockTime', 'type': 'duration'}, 'retention_time': {'key': 'retentionTime', 'type': 'duration'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, } def __init__(self, kwargs): super(JobReleaseTask, self).__init__(kwargs) self.id = kwargs.get('id', None) self.command_line = kwargs.get('command_line', None) self.container_settings = kwargs.get('container_settings', None) self.resource_files = kwargs.get('resource_files', None) self.environment_settings = kwargs.get('environment_settings', None) self.max_wall_clock_time = kwargs.get('max_wall_clock_time', None) self.retention_time = kwargs.get('retention_time', None) self.user_identity = kwargs.get('user_identity', None) class JobTemplate(Model): """A Job Template. :ivar type: The type of object described by the template. Must be: "Microsoft.Batch/batchAccounts/jobs" :type type: str :param api_version: The API version that the template conforms to. :type api_version: str :param properties: The specificaton of the job. :type properties: :class:`ExtendedJobParameter<azext.batch.models.ExtendedJobParameter>` """ _validation = { 'type': {'required': True, 'constant': True}, 'properties': {'required': True}, } _attribute_map = { 'type': {'key': 'id', 'type': 'str'}, 'api_version': {'key': 'apiVersion', 'type': 'str'}, 'properties': {'key': 'properties', 'type': 'ExtendedJobParameter'}, } type = "Microsoft.Batch/batchAccounts/jobs" def __init__(self, kwargs): super(JobTemplate, self).__init__(kwargs) self.properties = kwargs.get('properties') self.api_version = kwargs.get('api_version', None) class MergeTask(Model): """An Azure Batch task template to repeat. :param str id: The ID of the merge task. :param display_name: A display name for the task. The display name need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param command_line: The command line of the task. For multi-instance tasks, the command line is executed as the primary task, after the primary task and all subtasks have finished executing the coordination command line. The command line does not run under a shell, and therefore cannot take advantage of shell features such as environment variable expansion. If you want to take advantage of such features, you should invoke the shell in the command line, for example using "cmd /c MyCommand" in Windows or "/bin/sh -c MyCommand" in Linux. :type command_line: str :param exit_conditions: How the Batch service should respond when the task completes. :type exit_conditions: :class:`ExitConditions <azure.batch.models.ExitConditions>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. For multi-instance tasks, the resource files will only be downloaded to the compute node on which the primary task is executed. :type resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` :param environment_settings: A list of environment variable settings for the task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param affinity_info: A locality hint that can be used by the Batch service to select a compute node on which to start the new task. :type affinity_info: :class:`AffinityInformation <azure.batch.models.AffinityInformation>` :param constraints: The execution constraints that apply to this task. If you do not specify constraints, the maxTaskRetryCount is the maxTaskRetryCount specified for the job, and the maxWallClockTime and retentionTime are infinite. :type constraints: :class:`TaskConstraints <azure.batch.models.TaskConstraints>` :param user_identity: The user identity under which the task runs. If omitted, the task runs as a non-administrative user unique to the task. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` :param depends_on: The tasks that this task depends on. This task will not be scheduled until all tasks that it depends on have completed successfully. If any of those tasks fail and exhaust their retry counts, this task will never be scheduled. If the job does not have usesTaskDependencies set to true, and this element is present, the request fails with error code TaskDependenciesNotSpecifiedOnJob. :type depends_on: :class:`TaskDependencies <azure.batch.models.TaskDependencies>` :param application_package_references: A list of application packages that the Batch service will deploy to the compute node before running the command line. :type application_package_references: list of :class:`ApplicationPackageReference <azure.batch.models.ApplicationPackageReference>` :param authentication_token_settings: The settings for an authentication token that the task can use to perform Batch service operations. If this property is set, the Batch service provides the task with an authentication token which can be used to authenticate Batch service operations without requiring an account access key. The token is provided via the AZ_BATCH_AUTHENTICATION_TOKEN environment variable. The operations that the task can carry out using the token depend on the settings. For example, a task can request job permissions in order to add other tasks to the job, or check the status of the job or of other tasks under the job. :type authentication_token_settings: :class:`AuthenticationTokenSettings <azure.batch.models.AuthenticationTokenSettings>` :param output_files: A list of output file references to up persisted once the task has completed. :type output_files: list of :class:`OutputFile <azext.batch.models.OutputFile>` :param package_references: A list of packages to be installed on the compute nodes. Must be of a Package Manager type in accordance with the selected operating system. :type package_references: list of :class:`PackageReferenceBase <azext.batch.models.PackageReferenceBase>` """ _validation = { 'command_line': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'command_line': {'key': 'commandLine', 'type': 'str'}, 'exit_conditions': {'key': 'exitConditions', 'type': 'ExitConditions'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'affinity_info': {'key': 'affinityInfo', 'type': 'AffinityInformation'}, 'constraints': {'key': 'constraints', 'type': 'TaskConstraints'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, 'depends_on': {'key': 'dependsOn', 'type': 'TaskDependencies'}, 'application_package_references': {'key': 'applicationPackageReferences', 'type': '[ApplicationPackageReference]'}, 'authentication_token_settings': {'key': 'authenticationTokenSettings', 'type': 'AuthenticationTokenSettings'}, 'output_files': {'key': 'outputFiles', 'type': '[OutputFile]'}, 'package_references': {'key': 'packageReferences', 'type': '[PackageReferenceBase]'}, } def __init__(self, kwargs): super(MergeTask, self).__init__(kwargs) self.id = kwargs.get('id', None) self.display_name = kwargs.get('display_name', None) self.command_line = kwargs.get('command_line') self.exit_conditions = kwargs.get('exit_conditions', None) self.resource_files = kwargs.get('resource_files', None) self.environment_settings = kwargs.get('environment_settings', None) self.affinity_info = kwargs.get('affinity_info', None) self.constraints = kwargs.get('constraints', None) self.user_identity = kwargs.get('user_identity', None) self.depends_on = kwargs.get('depends_on', None) self.application_package_references = kwargs.get('application_package_references', None) self.authentication_token_settings = kwargs.get('authentication_token_settings', None) self.output_files = kwargs.get('output_files', None) self.package_references = kwargs.get('package_references', None) class MultiInstanceSettings(Model): """Settings which specify how to run a multi-instance task. Multi-instance tasks are commonly used to support MPI tasks. :param number_of_instances: The number of compute nodes required by the task. If omitted, the default is 1. :type number_of_instances: int :param coordination_command_line: The command line to run on all the compute nodes to enable them to coordinate when the primary runs the main task command. A typical coordination command line launches a background service and verifies that the service is ready to process inter-node messages. :type coordination_command_line: str :param common_resource_files: A list of files that the Batch service will download before running the coordination command line. The difference between common resource files and task resource files is that common resource files are downloaded for all subtasks including the primary, whereas task resource files are downloaded only for the primary. Also note that these resource files are not downloaded to the task working directory, but instead are downloaded to the task root directory (one directory above the working directory). :type common_resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` """ _validation = { 'coordination_command_line': {'required': True}, } _attribute_map = { 'number_of_instances': {'key': 'numberOfInstances', 'type': 'int'}, 'coordination_command_line': {'key': 'coordinationCommandLine', 'type': 'str'}, 'common_resource_files': {'key': 'commonResourceFiles', 'type': '[ExtendedResourceFile]'}, } def __init__(self, kwargs): super(MultiInstanceSettings, self).__init__(kwargs) self.number_of_instances = kwargs.get('number_of_instances') self.coordination_command_line = kwargs.get('coordination_command_line', None) self.common_resource_files = kwargs.get('common_resource_files', None) class OutputFile(Model): """A specification for uploading files from an Azure Batch node to another location after the Batch service has finished executing the task process. :param file_pattern: A pattern indicating which file(s) to upload. Both relative and absolute paths are supported. Relative paths are relative to the task working directory. The following wildcards are supported: * matches 0 or more characters (for example pattern abc* would match abc or abcdef), ** matches any directory, ? matches any single character, [abc] matches one character in the brackets, and [a-c] matches one character in the range. Brackets can include a negation to match any character not specified (for example [!abc] matches any character but a, b, or c). If a file name starts with "." it is ignored by default but may be matched by specifying it explicitly (for example .gif will not match .a.gif, but ..gif will). A simple example: *\\.txt matches any file that does not start in '.' and ends with .txt in the task working directory or any subdirectory. If the filename contains a wildcard character it can be escaped using brackets (for example abc[] would match a file named abc). Note that both \\ and / are treated as directory separators on Windows, but only / is on Linux. Environment variables (%var% on Windows or $var on Linux) are expanded prior to the pattern being applied. :type file_pattern: str :param destination: The destination for the output file(s). :type destination: :class:`ExtendedOutputFileDestination <azext.batch.models.ExtendedOutputFileDestination>` :param upload_options: Additional options for the upload operation, including under what conditions to perform the upload. :type upload_options: :class:`OutputFileUploadOptions <azure.batch.models.OutputFileUploadOptions>` """ _validation = { 'file_pattern': {'required': True}, 'destination': {'required': True}, 'upload_options': {'required': True}, } _attribute_map = { 'file_pattern': {'key': 'filePattern', 'type': 'str'}, 'destination': {'key': 'destination', 'type': 'ExtendedOutputFileDestination'}, 'upload_options': {'key': 'uploadOptions', 'type': 'OutputFileUploadOptions'}, } def __init__(self, kwargs): super(OutputFile, self).__init__(kwargs) self.file_pattern = kwargs.get('file_pattern') self.destination = kwargs.get('destination') self.upload_options = kwargs.get('upload_options') class OutputFileAutoStorageDestination(Model): """An speficition of output files upload destination that uses an auto-storage file group. :param str file_group: The name of the file group that the output files will be uploaded to. :param str path: The destination path within the file group that the files will be uploaded to. Is the output file specification refers to a single file, this will be treated as a file name. If the output file specification refers to potentially multiple files, this will be treated as a subfolder. """ _validation = { 'file_group': {'required': True} } _attribute_map = { 'file_group': {'key': 'fileGroup', 'type': 'str'}, 'path': {'key': 'path', 'type': 'str'}, } def __init__(self, kwargs): super(OutputFileAutoStorageDestination, self).__init__(kwargs) self.file_group = kwargs.get('file_group') self.path = kwargs.get('path', None) class ParameterSet(Model): """A set of parametric sweep range range parameters. :param int start: The starting value of the sweep. :param int end: The ending value of the sweep (inclusive). :param int step: The incremental step value, default is 1. The step value can be negative (i.e. a decending sweep), but only id the start value is a higher value than the end. """ _validation = { 'start': {'required': True}, 'end': {'required': True}, } _attribute_map = { 'start': {'key': 'start', 'type': 'int'}, 'end': {'key': 'end', 'type': 'int'}, 'step': {'key': 'step', 'type': 'int'}, } def __init__(self, kwargs): super(ParameterSet, self).__init__(kwargs) try: self.start = int(kwargs.get('start')) self.end = int(kwargs.get('end')) self.step = int(kwargs.get('step', 1)) except (TypeError, ValueError): raise ValueError("'start', 'end' and 'step' parameters must be integers.") if self.step == 0: raise ValueError("'step' parameter cannot be 0.") elif self.start > self.end and self.step > 0: raise ValueError( "'step' must be a negative number when 'start' is greater than 'end'") elif self.start < self.end and self.step < 0: raise ValueError( "'step' must be a positive number when 'end' is greater than 'start'") class ParametricSweepTaskFactory(TaskFactoryBase): """A Task Factory for generating a set of tasks based on one or more parameter sets to define a numeric input range. Each parameter set will have a start, end and step value. A task will be generated for each integer in this range. Multiple parameter sets can be combined for a multi-dimensional sweep. :param parameter_sets: A list if parameter sets from which tasks will be generated. :type parameter_sets: A list of :class:`ParameterSet<azext.batch.models.ParameterSet>` :param repeat_task: The task template the will be used to generate each task. :type repeat_task: :class:`RepeatTask <azext.batch.models.RepeatTask>` :param merge_task: An optional additional task to be run after all the other generated tasks have completed successfully. :type merge_task: :class:`MergeTask <azext.batch.models.MergeTask>` """ _validation = { 'type': {'required': True}, 'parameter_sets': {'required': True, 'min_items': 1}, 'repeat_task': {'required': True} } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'parameter_sets': {'key': 'parameterSets', 'type': '[ParameterSet]'}, 'repeat_task': {'key': 'repeatTask', 'type': 'RepeatTask'}, 'merge_task': {'key': 'mergeTask', 'type': 'MergeTask'} } def __init__(self, kwargs): super(ParametricSweepTaskFactory, self).__init__(kwargs) self.parameter_sets = kwargs.get('parameter_sets', None) self.repeat_task = kwargs.get('repeat_task', None) self.type = 'parametricSweep' if not self.parameter_sets: raise ValueError("Parametric Sweep task factory requires at least one parameter set.") class PoolTemplate(Model): """A Pool Template. :ivar type: The type of object described by the template. Must be: "Microsoft.Batch/batchAccounts/pools" :type type: str :param api_version: The API version that the template conforms to. :type api_version: str :param properties: The specificaton of the pool. :type properties: :class:`ExtendedPoolParameter<azext.batch.models.ExtendedPoolParameter>` """ _validation = { 'type': {'required': True, 'constant': True}, 'properties': {'required': True}, } _attribute_map = { 'type': {'key': 'id', 'type': 'str'}, 'api_version': {'key': 'apiVersion', 'type': 'str'}, 'properties': {'key': 'properties', 'type': 'ExtendedPoolParameter'}, } type = "Microsoft.Batch/batchAccounts/pools" def __init__(self, kwargs): super(PoolTemplate, self).__init__(kwargs) self.properties = kwargs.get('properties') self.api_version = kwargs.get('api_version', None) class RepeatTask(Model): """An Azure Batch task template to repeat. :param display_name: A display name for the task. The display name need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param command_line: The command line of the task. :type command_line: str :param container_settings: The settings for the container under which the task runs. If the pool that will run this task has containerConfiguration set, this must be set as well. If the pool that will run this task doesn't have containerConfiguration set, this must not be set. When this is specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR (the root of Azure Batch directories on the node) are mapped into the container, all task environment variables are mapped into the container, and the task command line is executed in the container. :type container_settings: :class:`TaskContainerSettings <azure.batch.models.TaskContainerSettings>` :param exit_conditions: How the Batch service should respond when the task completes. :type exit_conditions: :class:`ExitConditions <azure.batch.models.ExitConditions>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. :type resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` :param environment_settings: A list of environment variable settings for the task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param affinity_info: A locality hint that can be used by the Batch service to select a compute node on which to start the new task. :type affinity_info: :class:`AffinityInformation <azure.batch.models.AffinityInformation>` :param constraints: The execution constraints that apply to this task. If you do not specify constraints, the maxTaskRetryCount is the maxTaskRetryCount specified for the job, and the maxWallClockTime and retentionTime are infinite. :type constraints: :class:`TaskConstraints <azure.batch.models.TaskConstraints>` :param user_identity: The user identity under which the task runs. If omitted, the task runs as a non-administrative user unique to the task. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` :param application_package_references: A list of application packages that the Batch service will deploy to the compute node before running the command line. :type application_package_references: list of :class:`ApplicationPackageReference <azure.batch.models.ApplicationPackageReference>` :param authentication_token_settings: The settings for an authentication token that the task can use to perform Batch service operations. If this property is set, the Batch service provides the task with an authentication token which can be used to authenticate Batch service operations without requiring an account access key. The token is provided via the AZ_BATCH_AUTHENTICATION_TOKEN environment variable. The operations that the task can carry out using the token depend on the settings. For example, a task can request job permissions in order to add other tasks to the job, or check the status of the job or of other tasks under the job. :type authentication_token_settings: :class:`AuthenticationTokenSettings <azure.batch.models.AuthenticationTokenSettings>` :param output_files: A list of output file references to up persisted once the task has completed. :type output_files: list of :class:`OutputFile <azext.batch.models.OutputFile>` :param package_references: A list of packages to be installed on the compute nodes. Must be of a Package Manager type in accordance with the selected operating system. :type package_references: list of :class:`PackageReferenceBase <azext.batch.models.PackageReferenceBase>` """ _validation = { 'command_line': {'required': True}, } _attribute_map = { 'display_name': {'key': 'displayName', 'type': 'str'}, 'command_line': {'key': 'commandLine', 'type': 'str'}, 'container_settings': {'key': 'containerSettings', 'type': 'TaskContainerSettings'}, 'exit_conditions': {'key': 'exitConditions', 'type': 'ExitConditions'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'affinity_info': {'key': 'affinityInfo', 'type': 'AffinityInformation'}, 'constraints': {'key': 'constraints', 'type': 'TaskConstraints'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, 'application_package_references': {'key': 'applicationPackageReferences', 'type': '[ApplicationPackageReference]'}, 'authentication_token_settings': {'key': 'authenticationTokenSettings', 'type': 'AuthenticationTokenSettings'}, 'output_files': {'key': 'outputFiles', 'type': '[OutputFile]'}, 'package_references': {'key': 'packageReferences', 'type': '[PackageReferenceBase]'} } def __init__(self, kwargs): super(RepeatTask, self).__init__(kwargs) self.display_name = kwargs.get('display_name') self.command_line = kwargs.get('command_line', None) self.exit_conditions = kwargs.get('exit_conditions', None) self.resource_files = kwargs.get('resource_files', None) self.environment_settings = kwargs.get('environment_settings', None) self.affinity_info = kwargs.get('affinity_info', None) self.constraints = kwargs.get('constraints', None) self.user_identity = kwargs.get('user_identity', None) self.application_package_references = kwargs.get('application_package_references', None) self.authentication_token_settings = kwargs.get('authentication_token_settings', None) self.output_files = kwargs.get('output_files', None) self.package_references = kwargs.get('package_references', None) class StartTask(Model): """A task which is run when a compute node joins a pool in the Azure Batch service, or when the compute node is rebooted or reimaged. :param command_line: The command line of the start task. The command line does not run under a shell, and therefore cannot take advantage of shell features such as environment variable expansion. If you want to take advantage of such features, you should invoke the shell in the command line, for example using "cmd /c MyCommand" in Windows or "/bin/sh -c MyCommand" in Linux. :type command_line: str :param container_settings: The settings for the container under which the start task runs. When this is specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR (the root of Azure Batch directories on the node) are mapped into the container, all task environment variables are mapped into the container, and the task command line is executed in the container. :type container_settings: :class:`TaskContainerSettings <azure.batch.models.TaskContainerSettings>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. Files listed under this element are located in the task's working directory. :type resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` :param environment_settings: A list of environment variable settings for the start task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param user_identity: The user identity under which the start task runs. If omitted, the task runs as a non-administrative user unique to the task. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` :param max_task_retry_count: The maximum number of times the task may be retried. The Batch service retries a task if its exit code is nonzero. Note that this value specifically controls the number of retries. The Batch service will try the task once, and may then retry up to this limit. For example, if the maximum retry count is 3, Batch tries the task up to 4 times (one initial try and 3 retries). If the maximum retry count is 0, the Batch service does not retry the task. If the maximum retry count is -1, the Batch service retries the task without limit. :type max_task_retry_count: int :param wait_for_success: Whether the Batch service should wait for the start task to complete successfully (that is, to exit with exit code 0) before scheduling any tasks on the compute node. If true and the start task fails on a compute node, the Batch service retries the start task up to its maximum retry count (maxTaskRetryCount). If the task has still not completed successfully after all retries, then the Batch service marks the compute node unusable, and will not schedule tasks to it. This condition can be detected via the node state and failure info details. If false, the Batch service will not wait for the start task to complete. In this case, other tasks can start executing on the compute node while the start task is still running; and even if the start task fails, new tasks will continue to be scheduled on the node. The default is false. :type wait_for_success: bool """ _validation = { 'command_line': {'required': True}, } _attribute_map = { 'command_line': {'key': 'commandLine', 'type': 'str'}, 'container_settings': {'key': 'containerSettings', 'type': 'TaskContainerSettings'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, 'max_task_retry_count': {'key': 'maxTaskRetryCount', 'type': 'int'}, 'wait_for_success': {'key': 'waitForSuccess', 'type': 'bool'}, } def __init__(self, kwargs): super(StartTask, self).__init__(kwargs) self.command_line = kwargs.get('command_line') self.container_settings = kwargs.get('container_settings', None) self.resource_files = kwargs.get('resource_files', None) self.environment_settings = kwargs.get('environment_settings', None) self.user_identity = kwargs.get('user_identity', None) self.max_task_retry_count = kwargs.get('max_task_retry_count', None) self.wait_for_success = kwargs.get('wait_for_success', None) class TaskCollectionTaskFactory(TaskFactoryBase): """A Task Factory for adding a predefined collection of tasks automatically to a job on submission. :param tasks: A list if task parameters, each of which will be added straight to the job. :type tasks: A list of :class:`ExtendedTaskParameter <azext.batch.models.ExtendedTaskParameter>` """ _validation = { 'type': {'required': True}, 'tasks': {'required': True}, } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'tasks': {'key': 'tasks', 'type': '[ExtendedTaskParameter]'}, } def __init__(self, kwargs): super(TaskCollectionTaskFactory, self).__init__(kwargs) self.tasks = kwargs.get('tasks') self.type = 'taskCollection' class YumPackageReference(PackageReferenceBase): """A reference to a package to be installed using the YUM package manager on a Linux node. :param str id: The name of the package. :param str version: The version of the package to be installed. If omitted, the latest version (according to the package repository) will be installed. :param bool disable_excludes: Whether to allow packages that might otherwise be excluded by VM configuration (e.g. kernel packages). Default is False. 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tests/conftest.py	DD-DeCaF/model_warehouse	0	69435	<reponame>DD-DeCaF/model_warehouse # Copyright (c) 2018, Novo Nordisk Foundation Center for Biosustainability, # Technical University of Denmark. # # 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. """Provide session level fixtures.""" import json import pytest from jose import jwt from model_storage.app import app as app_ from model_storage.app import init_app from model_storage.models import Model from model_storage.models import db as db_ @pytest.fixture(scope="session") def app(): """Provide an initialized Flask for use in certain test cases.""" init_app(app_, db_) with app_.app_context(): yield app_ @pytest.fixture(scope="session") def client(app): """Provide a Flask test client for integration tests.""" with app.test_client() as client: yield client @pytest.fixture(scope="session") def reset_tables(app): """Ensure a clean database.""" # Clean up anything that might reside in the testing database. db_.session.remove() db_.drop_all() # Re-create tables. db_.create_all() @pytest.fixture(scope="session") def connection(): """ Use a connection such that transactions can be used. Notes ----- Follows a transaction pattern described in the following: http://docs.sqlalchemy.org/en/latest/orm/session_transaction.html#session-begin-nested """ with db_.engine.connect() as connection: yield connection @pytest.fixture(scope="session") def tokens(app): """Provide read, write and admin JWT claims to project 4.""" return { "read": jwt.encode( {"prj": {4: "read"}}, app.config["JWT_PRIVATE_KEY"], "RS512" ), "write": jwt.encode( {"prj": {4: "write"}}, app.config["JWT_PRIVATE_KEY"], "RS512" ), "admin": jwt.encode( {"prj": {4: "admin"}}, app.config["JWT_PRIVATE_KEY"], "RS512" ), } @pytest.fixture(scope="function") def session(reset_tables, connection): """ Create a transaction and session per test unit. Rolling back a transaction removes even committed rows (``session.commit``) from the database. https://docs.sqlalchemy.org/en/latest/orm/session_transaction.html#joining-a-session-into-an-external-transaction-such-as-for-test-suites """ flask_sqlalchemy_session = db_.session transaction = connection.begin() db_.session = db_.create_scoped_session( options={"bind": connection, "binds": {}} ) yield db_.session db_.session.close() transaction.rollback() db_.session = flask_sqlalchemy_session @pytest.fixture(scope="session") def e_coli_core(): """Provide the e. coli core model as a dict.""" with open("tests/data/e_coli_core.json") as file_: return json.load(file_) @pytest.fixture(scope="session") def model(reset_tables): """Return a fixture with test data for the Model data model.""" fixture = Model( name="iJO1366", organism_id=4, project_id=4, default_biomass_reaction="BIOMASS", model_serialized={"Reactions": [{"GAPDH": "x->y"}]}, ec_model=False, ) db_.session.add(fixture) 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Chapter08/rs_cem_comparison.py	verdimrc/Mastering-Reinforcement-Learning-with-Python	58	93365	from math import sin, pi import random import numpy as np from scipy.stats import norm def black_box_projectile(theta, v0=10, g=9.81): assert theta >= 0 assert theta <= 90 return (v0 ** 2) * sin(2 * pi * theta / 180) / g def random_shooting(n=1, min_a=0, max_a=90): assert min_a <= max_a return [random.uniform(min_a, max_a) for i in range(n)] def pick_elites(actions, M_elites): actions = np.array(actions) assert M_elites <= len(actions) assert M_elites > 0 results = np.array([black_box_projectile(a) for a in actions]) sorted_ix = np.argsort(results)[-M_elites:][::-1] return actions[sorted_ix], results[sorted_ix] def try_random_shooting(trials=10000, n=20): print("Trying random shooting.") best_results = [] best_actions = [] for i in range(trials): actions_to_try = random_shooting(n) best_action, best_result = pick_elites(actions_to_try, 1) best_results.append(best_result[0]) best_actions.append(best_action[0]) print(f"Out of {trials} trials:") print(f"- Average score is {round(np.mean(best_results), 2)}") print(f"- Average action is {round(np.mean(best_actions), 2)}") print(f"- Action SD is {round(np.std(best_actions), 2)}") def try_cem_normal(trials=10000, N=5, M_elites=2, iterations=3): print("Trying CEM.") best_results = [] best_actions = [] for i in range(trials): print(f"================ trial {i}") print("--iteration: 1") actions_to_try = random_shooting(N) elite_acts, _ = pick_elites(actions_to_try, M_elites) print(f"actions_to_try: {np.round(actions_to_try, 2)}") print(f"elites: {np.round(elite_acts, 2)}") for r in range(iterations - 1): print(f"--iteration: {r + 2}") mu, std = norm.fit(elite_acts) print(f"fitted normal mu: {np.round(mu, 2)}, std: {np.round(std, 2)}") actions_to_try = np.clip(norm.rvs(mu, std, N), 0, 90) elite_acts, elite_results = pick_elites(actions_to_try, M_elites) print(f"actions_to_try: {np.round(actions_to_try, 2)}") print(f"elites: {np.round(elite_acts, 2)}") mu, std = norm.fit(elite_acts) print(f"final action: {np.round(mu, 2)}") best_results.append(black_box_projectile(mu)) best_actions.append(np.clip(mu, 0, 90)) print(f"Out of {trials} trials:") print(f"- Average score is {round(np.mean(best_results), 2)}") print(f"- Average action is {round(np.mean(best_actions), 2)}") print(f"- Action SD is {round(np.std(best_actions), 2)}")	[ 1, 515, 5844, 1053, 4457, 29892, 2930, 13, 5215, 4036, 13, 5215, 12655, 408, 7442, 13, 3166, 4560, 2272, 29889, 16202, 1053, 6056, 13, 13, 1753, 4628, 29918, 1884, 29918, 4836, 488, 29898, 3416, 29892, 325, 29900, 29922, 29896, 29900, 29892, 330, 29922, 29929, 29889, 29947, 29896, 1125, 13, 1678, 4974, 278, 941, 6736, 29871, 29900, 13, 1678, 4974, 278, 941, 5277, 29871, 29929, 29900, 13, 1678, 736, 313, 29894, 29900, 3579, 29871, 29906, 29897, 334, 4457, 29898, 29906, 334, 2930, 334, 278, 941, 847, 29871, 29896, 29947, 29900, 29897, 847, 330, 13, 13, 13, 1753, 4036, 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epi/variable.py	Epikem/deep-learning-from-scratch	0	1602054	import numpy as np import matplotlib.pylab as plt import sys def run(): visualizeTarget = sys.argv[1] print(visualizeTarget) if(visualizeTarget=='step'): x=np.arange(-5.0,5.0,0.1) y=step(x) plt.plot(x,y) plt.ylim(-0.1,1.1) plt.show() elif(visualizeTarget=='sigmoid'): x=np.arange(-5.0,5.0,0.1) y=sigmoid(x) plt.plot(x,y) plt.ylim(-0.1,1.1) plt.show() elif(visualizeTarget=='relu'): x=np.arange(-5.0,5.0,0.1) y=relu(x) plt.plot(x,y) # plt.ylim(-0.1,1.1) plt.show() elif(visualizeTarget=='all'): x=np.arange(-5.0,5.0,0.1) y=step(x) plt.plot(x,y) # plt.ylim(-0.1,1.1) x=np.arange(-5.0,5.0,0.1) y=sigmoid(x) plt.plot(x,y) x=np.arange(-5.0,5.0,0.1) y=relu(x) plt.plot(x,y) # plt.ylim(-0.1,3.0) plt.show() # for x in sys.argv: # print(x) class variable(): def __init__(self, value): self.data = value pass def read(self): return self.data def test(): v = variable(424) print(v.read() == 424) a = np.array([2,3,1,4,2]) print(a) print(sigmoid(a)) def TestSimpleANDGate(): print('simple AND gate test') print(SimpleANDGate(0,0)) print(SimpleANDGate(0,1)) print(SimpleANDGate(1,0)) print(SimpleANDGate(1,1)) def SimpleANDGate(x1,x2): w1,w2,theta = 0.5,0.5,0.7 tmp = x1w1+x2w2 if(tmp<=theta): return 0 elif(tmp>theta): return 1 def TestANDGate(): print('and gate test') print(ANDGate(0,0)) print(ANDGate(0,1)) print(ANDGate(1,0)) print(ANDGate(1,1)) def ANDGate(x1,x2): x = np.array([x1,x2]) w=np.array([0.5,0.5]) b=-0.7 tmp=np.sum(wx)+b if(tmp<=0): return 0 else: return 1 def TestNANDGate(): print('nand gate test') print(NANDGate(0,0)) print(NANDGate(0,1)) print(NANDGate(1,0)) print(NANDGate(1,1)) def NANDGate(x1,x2): x = np.array([x1,x2]) w=np.array([-0.5,-0.5]) b=0.7 tmp=np.sum(wx)+b if(tmp<=0): return 0 else: return 1 def TestORGate(): print('OR gate test') print(ORGate(0,0)) print(ORGate(0,1)) print(ORGate(1,0)) print(ORGate(1,1)) def ORGate(x1,x2): x = np.array([x1,x2]) w=np.array([0.5,0.5]) b=-0.2 tmp=np.sum(wx)+b if(tmp<=0): return 0 else: return 1 def XORGate(x1,x2): a = ORGate(x1,x2) b = NANDGate(x1,x2) return ANDGate(a,b) def step(x): y=x>0 return y.astype(np.int) def simple_step(value): if(value <= 0): return 0 else: return 1 def sigmoid(value): return 1/(1+np.exp(-value)) def relu(x): return np.maximum(0,x) class MultiplyLayer: def __init__(self): self.x = None self.y = None def forward(self, x, y): self.x=x self.y=y out=xy return out def backward(self, dout): dx=doutself.y dy=doutself.x return dx,dy def matrixTest1(): print('mat') b = np.array([[1,2],[3,4],[5,6]]) print(b) print(np.ndim(b)) # 배열의 차원 수 print(b.shape) # 배열의 형상 (모든 차원의 각 길이) def matrixMultiplyTest(): print('multiply') a = np.array([[1,2],[3,4]]) print(a.shape) b=np.array([[5,6],[7,8]]) print(b.shape) print(np.dot(a,b)) # 행렬 곱셈 a = np.array([[1,2],[3,4],[5,6]]) print(a.shape) b=np.array([7,8]) print(b.shape) print(np.dot(a,b)) x = np.array([1,2]) W = np.array([[1,3,5],[2,4,6]]) y = np.dot(x,W) print(y) if(__name__=='main'): # test() # 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integrations-and-supported-tools/fastai/scripts/Neptune_fastai.py	neptune-ai/examples	15	33954	import fastai from neptune.new.integrations.fastai import NeptuneCallback from fastai.vision.all import * import neptune.new as neptune run = neptune.init( project="common/fastai-integration", api_token="<PASSWORD>", tags="basic" ) path = untar_data(URLs.MNIST_TINY) dls = ImageDataLoaders.from_csv(path) # Log all training phases of the learner learn = cnn_learner(dls, resnet18, cbs=[NeptuneCallback(run=run, base_namespace="experiment")]) learn.fit_one_cycle(2) learn.fit_one_cycle(1) run.stop()	[ 1, 1053, 5172, 1794, 13, 3166, 452, 415, 1540, 29889, 1482, 29889, 14146, 800, 29889, 11255, 1794, 1053, 2448, 415, 1540, 10717, 13, 3166, 5172, 1794, 29889, 4924, 29889, 497, 1053, 334, 13, 5215, 452, 415, 1540, 29889, 1482, 408, 452, 415, 1540, 13, 13, 3389, 353, 452, 415, 1540, 29889, 2344, 29898, 13, 1678, 2060, 543, 9435, 29914, 11255, 1794, 29899, 27925, 613, 7882, 29918, 6979, 543, 29966, 25711, 17013, 28341, 8282, 543, 16121, 29908, 13, 29897, 13, 13, 2084, 353, 443, 12637, 29918, 1272, 29898, 4219, 29879, 29889, 29924, 29940, 9047, 29918, 29911, 1177, 29979, 29897, 13, 29881, 3137, 353, 7084, 1469, 5896, 414, 29889, 3166, 29918, 7638, 29898, 2084, 29897, 13, 13, 29937, 4522, 599, 6694, 29540, 310, 278, 24298, 1089, 13, 19668, 353, 274, 15755, 29918, 1945, 1089, 29898, 29881, 3137, 29892, 620, 1212, 29896, 29947, 29892, 274, 5824, 11759, 8139, 415, 1540, 10717, 29898, 3389, 29922, 3389, 29892, 2967, 29918, 22377, 543, 735, 15362, 1159, 2314, 13, 19668, 29889, 9202, 29918, 650, 29918, 23090, 29898, 29906, 29897, 13, 19668, 29889, 9202, 29918, 650, 29918, 23090, 29898, 29896, 29897, 13, 13, 3389, 29889, 9847, 580, 13, 2 ]
first_steps_in_coding_and_simple_operations_and_calculations/exercise/charity_campaign.py	PetkoAndreev/Python-basics	0	16561	days = int(input()) sladkar = int(input()) cake = int(input()) gofreta = int(input()) pancake = int(input()) cake_price = cake45 gofreta_price = gofreta5.8 pancake_price = pancake3.2 day_price = (cake_price + gofreta_price + pancake_price)sladkar total_price = days*day_price campaign = total_price - (total_price/8) print(campaign)	[ 1, 3841, 353, 938, 29898, 2080, 3101, 13, 2536, 328, 5689, 353, 938, 29898, 2080, 3101, 13, 1113, 446, 353, 938, 29898, 2080, 3101, 13, 29887, 974, 276, 941, 353, 938, 29898, 2080, 3101, 13, 8357, 1113, 446, 353, 938, 29898, 2080, 3101, 13, 1113, 446, 29918, 9175, 353, 274, 1296, 29930, 29946, 29945, 13, 29887, 974, 276, 941, 29918, 9175, 353, 748, 10745, 941, 29930, 29945, 29889, 29947, 13, 8357, 1113, 446, 29918, 9175, 353, 7243, 1113, 446, 29930, 29941, 29889, 29906, 13, 3250, 29918, 9175, 353, 313, 1113, 446, 29918, 9175, 718, 748, 10745, 941, 29918, 9175, 718, 7243, 1113, 446, 29918, 9175, 11877, 2536, 328, 5689, 13, 7827, 29918, 9175, 353, 3841, 29930, 3250, 29918, 9175, 13, 24821, 8729, 353, 3001, 29918, 9175, 448, 313, 7827, 29918, 9175, 29914, 29947, 29897, 13, 2158, 29898, 24821, 8729, 29897, 2 ]
src/learning/train.py	mari-linhares/deep-audio	0	166616	<reponame>mari-linhares/deep-audio import tensorflow as tf tf.enable_eager_execution() import tensorflowjs as tfjs import numpy as np from data_sources.data_source import DataSource from optimizers.optimizer import Optimizer import models.model_factory as mf import utilities.utils as utils from tensorflow.python.client import device_lib print(device_lib.list_local_devices()) def train(): ds_train = DataSource('audio_files/speech/train_data.npy', 'audio_files/speech/train_labels.npy', classes_dict='audio_files/speech/classes') ds_eval = DataSource('audio_files/speech/eval_data.npy', 'audio_files/speech/eval_labels.npy', classes_dict='audio_files/speech/classes') ds_test = DataSource('audio_files/speech/test_data.npy', 'audio_files/speech/test_labels.npy', classes_dict='audio_files/speech/classes') model = mf.build_model('CNN', input_shape=ds_train.input_shape, num_classes = ds_train.num_classes) optimizer = Optimizer('Adam') EPOCHS = 1 for epoch in range(EPOCHS): train_losses = [] train_accuracy = 0 train_instances = 0 for (batch, (img_tensor, label)) in enumerate(ds_train.dataset): train_loss, predicted = optimizer.compute_and_apply_gradients(model, img_tensor, label) train_losses.append(train_loss) train_accuracy += sum(np.argmax(predicted, axis=1) == label.numpy()) train_instances += label.numpy().shape[0] # print ('Epoch {} Batch {} Train Loss {:.6f}'.format(epoch + 1, batch + 1, sum(train_losses)/len(train_losses))) eval_losses = [] accuracy = 0 instances = 0 for (batch, (img_tensor, label)) in enumerate(ds_eval.dataset): eval_loss, predicted = model.compute_loss(img_tensor, label, training=False) accuracy += sum(np.argmax(predicted, axis=1) == label.numpy()) instances += label.numpy().shape[0] eval_losses.append(eval_loss) print ('Epoch {} Train Accuracy: {:.6f} \| Test Accuracy: {:.6f}'.format(epoch +1, train_accuracy/train_instances, accuracy/instances)) print ('Epoch {} Train Loss {:.6f} \| Eval Loss {:.6f}'.format(epoch + 1, sum(train_losses)/len(train_losses), sum(eval_losses)/len(eval_losses))) eval_losses = [] accuracy = 0 instances = 0 for (batch, (img_tensor, label)) in enumerate(ds_test.dataset): eval_loss, predicted = model.compute_loss(img_tensor, label, training=False) accuracy += sum(np.argmax(predicted, axis=1) == label.numpy()) instances += label.numpy().shape[0] eval_losses.append(eval_loss) print ('Epoch {} Train Accuracy: {:.6f} \| Test Accuracy: {:.6f}'.format(epoch +1, train_accuracy/train_instances, accuracy/instances)) print ('Epoch {} Train Loss {:.6f} \| Eval Loss {:.6f}'.format(epoch + 1, sum(train_losses)/len(train_losses), sum(eval_losses)/len(eval_losses))) import ipdb; ipdb.set_trace() saver = tf.train.Checkpoint(model=model, optimizer=optimizer.optimizer) saver.save('/home/marianne/deep-audio/saver/save') tfjs.converters.convert_tf_saved_model('/home/marianne/deep-audio/saver/save.ckpt', output_node_names=['test'], 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3858, 876, 297, 26985, 29898, 6289, 29918, 1688, 29889, 24713, 1125, 13, 4706, 19745, 29918, 6758, 29892, 25383, 353, 1904, 29889, 26017, 29918, 6758, 29898, 2492, 29918, 20158, 29892, 3858, 29892, 6694, 29922, 8824, 29897, 13, 4706, 13600, 4619, 2533, 29898, 9302, 29889, 1191, 3317, 29898, 11965, 18186, 29892, 9685, 29922, 29896, 29897, 1275, 3858, 29889, 23749, 3101, 13, 4706, 8871, 4619, 3858, 29889, 23749, 2141, 12181, 29961, 29900, 29962, 13, 4706, 19745, 29918, 6758, 267, 29889, 4397, 29898, 14513, 29918, 6758, 29897, 13, 268, 13, 1678, 1596, 6702, 29923, 1129, 305, 6571, 28186, 4831, 332, 4135, 29901, 12365, 29889, 29953, 29888, 29913, 891, 4321, 4831, 332, 4135, 29901, 12365, 29889, 29953, 29888, 29913, 4286, 4830, 29898, 1022, 2878, 718, 29896, 29892, 7945, 29918, 562, 2764, 4135, 29914, 14968, 29918, 2611, 2925, 29892, 13600, 29914, 2611, 2925, 876, 13, 1678, 1596, 6702, 29923, 1129, 305, 6571, 28186, 365, 2209, 12365, 29889, 29953, 29888, 29913, 891, 382, 791, 365, 2209, 12365, 29889, 29953, 29888, 29913, 4286, 4830, 29898, 1022, 2878, 718, 29871, 29896, 29892, 2533, 29898, 14968, 29918, 6758, 267, 6802, 2435, 29898, 14968, 29918, 6758, 267, 511, 2533, 29898, 14513, 29918, 6758, 267, 6802, 2435, 29898, 14513, 29918, 6758, 267, 4961, 13, 13, 13, 1678, 1053, 10377, 2585, 29936, 10377, 2585, 29889, 842, 29918, 15003, 580, 13, 13, 1678, 872, 369, 353, 15886, 29889, 14968, 29889, 5596, 3149, 29898, 4299, 29922, 4299, 29892, 5994, 3950, 29922, 20640, 3950, 29889, 20640, 3950, 29897, 13, 1678, 872, 369, 29889, 7620, 11219, 5184, 29914, 3034, 713, 484, 29914, 24535, 29899, 18494, 29914, 4977, 369, 29914, 7620, 1495, 13, 13, 1678, 15886, 1315, 29889, 535, 369, 2153, 29889, 13441, 29918, 13264, 29918, 17314, 29918, 4299, 11219, 5184, 29914, 3034, 713, 484, 29914, 24535, 29899, 18494, 29914, 4977, 369, 29914, 7620, 29889, 384, 415, 742, 1962, 29918, 3177, 29918, 7039, 29922, 1839, 1688, 7464, 1962, 29918, 3972, 2433, 29914, 5184, 29914, 3034, 713, 484, 29914, 24535, 29899, 18494, 29914, 1495, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 7945, 580, 2 ]
ABC/ABC127/abc127_a.py	yatabis/AtCoder-in-Python3	0	158108	<filename>ABC/ABC127/abc127_a.py # 問題URL: https://atcoder.jp/contests/abc127/tasks/abc127_a # 解答URL: https://atcoder.jp/contests/abc127/submissions/14655259 a, b = map(int, input().split()) if a <= 5: print(0) elif a <= 12: print(b // 2) else: print(b)	[ 1, 529, 9507, 29958, 19658, 29914, 19658, 29896, 29906, 29955, 29914, 10736, 29896, 29906, 29955, 29918, 29874, 29889, 2272, 13, 29937, 29871, 232, 152, 146, 236, 164, 143, 4219, 29901, 2045, 597, 271, 29883, 6119, 29889, 16865, 29914, 1285, 9197, 29914, 10736, 29896, 29906, 29955, 29914, 20673, 29914, 10736, 29896, 29906, 29955, 29918, 29874, 13, 29937, 29871, 31201, 234, 176, 151, 4219, 29901, 2045, 597, 271, 29883, 6119, 29889, 16865, 29914, 1285, 9197, 29914, 10736, 29896, 29906, 29955, 29914, 1491, 29885, 6847, 29914, 29896, 29946, 29953, 29945, 29945, 29906, 29945, 29929, 13, 13, 29874, 29892, 289, 353, 2910, 29898, 524, 29892, 1881, 2141, 5451, 3101, 13, 361, 263, 5277, 29871, 29945, 29901, 13, 1678, 1596, 29898, 29900, 29897, 13, 23681, 263, 5277, 29871, 29896, 29906, 29901, 13, 1678, 1596, 29898, 29890, 849, 29871, 29906, 29897, 13, 2870, 29901, 13, 1678, 1596, 29898, 29890, 29897, 13, 2 ]
components/workstation.py	cqzhao/FooProxy	0	21908	#coding:utf-8 """ @author : linkin @email : <EMAIL> @date : 2018-10-04 """ import logging from APIserver.apiserver import app from components.collector import Collector from components.validator import Validator from components.detector import Detector from components.scanner import Scaner from components.tentacle import Tentacle from multiprocessing import Pool from multiprocessing import Manager from config.config import MODE from const.settings import RUN_FUNC logger = logging.getLogger() class Workstation(object): """ 整个项目的启动工作面板 """ def __init__(self): self.collector = Collector() self.validator = Validator() self.detector = Detector() self.scanner = Scaner() self.tentacle = Tentacle() self.proxyList = Manager().list() def run_validator(self,proxyList): self.validator.run(proxyList) def run_collector(self,proxyList): self.collector.run(proxyList) def run_detector(self,params): self.detector.run() def run_scanner(self,params): self.scanner.run() def run_tentacle(self,*params): self.tentacle.run() def work(self): """ 项目启动，根据config中的MODE配置执行对应的部件这样可以隔离部件功能，耦合性较低。异步多进程执行需要共享变量，使用了multiprocessing的Manager来生成共享List. """ pool = Pool(5) func = [] for i in MODE: if MODE[i]: func.append(eval('self.'+RUN_FUNC[i])) [pool.apply_async(fun,args=(self.proxyList,)) for fun in func] pool.close() app.run(host='0.0.0.0',port=2020)	[ 1, 396, 29883, 3689, 29901, 9420, 29899, 29947, 13, 13, 15945, 29908, 13, 1678, 732, 8921, 29871, 584, 1544, 262, 13, 1678, 732, 5269, 259, 584, 529, 26862, 6227, 29958, 13, 1678, 732, 1256, 1678, 584, 29871, 29906, 29900, 29896, 29947, 29899, 29896, 29900, 29899, 29900, 29946, 13, 15945, 29908, 13, 13, 5215, 12183, 13, 3166, 3450, 2974, 29889, 481, 7608, 369, 1678, 1053, 623, 13, 3166, 7117, 29889, 15914, 272, 259, 1053, 24930, 272, 13, 3166, 7117, 29889, 3084, 1061, 259, 1053, 15758, 1061, 13, 3166, 7117, 29889, 4801, 3019, 1678, 1053, 5953, 3019, 13, 3166, 7117, 29889, 1557, 7310, 268, 1053, 2522, 273, 261, 13, 3166, 7117, 29889, 29873, 296, 6436, 1678, 1053, 323, 296, 6436, 13, 3166, 6674, 307, 985, 292, 4706, 1053, 28625, 13, 3166, 6674, 307, 985, 292, 4706, 1053, 15629, 13, 3166, 2295, 29889, 2917, 3986, 1053, 16999, 2287, 13, 3166, 1040, 29889, 11027, 308, 1053, 27694, 29918, 29943, 3904, 29907, 13, 13, 21707, 353, 12183, 29889, 657, 16363, 580, 13, 13, 1990, 5244, 19569, 29898, 3318, 1125, 13, 1678, 9995, 13, 268, 233, 152, 183, 30502, 31888, 30895, 30210, 232, 147, 178, 30846, 31041, 30732, 30806, 233, 160, 194, 13, 1678, 9995, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 1583, 29889, 15914, 272, 353, 24930, 272, 580, 13, 4706, 1583, 29889, 3084, 1061, 353, 15758, 1061, 580, 13, 4706, 1583, 29889, 4801, 3019, 29871, 353, 5953, 3019, 580, 13, 4706, 1583, 29889, 1557, 7310, 259, 353, 2522, 273, 261, 580, 13, 4706, 1583, 29889, 29873, 296, 6436, 29871, 353, 323, 296, 6436, 580, 13, 4706, 1583, 29889, 14701, 1293, 353, 15629, 2141, 1761, 580, 13, 13, 1678, 822, 1065, 29918, 3084, 1061, 29898, 1311, 29892, 14701, 1293, 1125, 13, 4706, 1583, 29889, 3084, 1061, 29889, 3389, 29898, 14701, 1293, 29897, 13, 13, 1678, 822, 1065, 29918, 15914, 272, 29898, 1311, 29892, 14701, 1293, 1125, 13, 4706, 1583, 29889, 15914, 272, 29889, 3389, 29898, 14701, 1293, 29897, 13, 13, 1678, 822, 1065, 29918, 4801, 3019, 29898, 1311, 29892, 29930, 7529, 1125, 13, 4706, 1583, 29889, 4801, 3019, 29889, 3389, 580, 13, 13, 1678, 822, 1065, 29918, 1557, 7310, 29898, 1311, 29892, 29930, 7529, 1125, 13, 4706, 1583, 29889, 1557, 7310, 29889, 3389, 580, 13, 13, 1678, 822, 1065, 29918, 29873, 296, 6436, 29898, 1311, 29892, 29930, 7529, 1125, 13, 4706, 1583, 29889, 29873, 296, 6436, 29889, 3389, 580, 13, 13, 1678, 822, 664, 29898, 1311, 1125, 13, 4706, 9995, 13, 308, 31888, 30895, 232, 147, 178, 30846, 30214, 31393, 30763, 2917, 30275, 30210, 20387, 31361, 30669, 233, 140, 170, 30448, 30783, 31370, 30210, 30636, 30631, 13, 308, 30810, 31819, 30682, 30651, 236, 157, 151, 234, 169, 190, 30636, 30631, 31134, 30815, 30214, 235, 131, 169, 30733, 30952, 235, 193, 134, 231, 192, 145, 30267, 232, 191, 133, 233, 176, 168, 30923, 31174, 31101, 233, 140, 170, 30448, 31383, 30698, 13, 308, 31611, 231, 189, 174, 31462, 31180, 30214, 30785, 30406, 30743, 18056, 307, 985, 292, 30210, 3260, 30805, 30486, 30494, 13, 308, 31611, 231, 189, 174, 1293, 29889, 13, 4706, 9995, 13, 4706, 11565, 353, 28625, 29898, 29945, 29897, 13, 4706, 3653, 353, 5159, 13, 4706, 363, 474, 297, 16999, 2287, 29901, 13, 9651, 565, 16999, 2287, 29961, 29875, 5387, 13, 18884, 3653, 29889, 4397, 29898, 14513, 877, 1311, 6169, 29974, 29934, 3904, 29918, 29943, 3904, 29907, 29961, 29875, 12622, 13, 4706, 518, 10109, 29889, 7302, 29918, 12674, 29898, 7692, 29892, 5085, 7607, 1311, 29889, 14701, 1293, 29892, 876, 363, 2090, 297, 3653, 29962, 13, 4706, 11565, 29889, 5358, 580, 13, 4706, 623, 29889, 3389, 29898, 3069, 2433, 29900, 29889, 29900, 29889, 29900, 29889, 29900, 742, 637, 29922, 29906, 29900, 29906, 29900, 29897, 13, 13, 13, 13, 2 ]
src/djanban/apps/api/views/cards.py	diegojromerolopez/djanban	33	109250	<gh_stars>10-100 # -- coding: utf-8 -- from __future__ import unicode_literals import json import re import tempfile import dateutil from django.core.files.base import ContentFile from django.core.serializers.json import DjangoJSONEncoder from django.db import transaction from django.db.models import Q from django.http import JsonResponse, Http404 from django.utils import timezone from djanban.apps.api.http import JsonResponseBadRequest, JsonResponseMethodNotAllowed, JsonResponseNotFound from djanban.apps.api.serializers import Serializer from djanban.apps.api.util import get_list_or_404, get_card_or_404, get_board_or_404 from djanban.apps.base.auth import get_user_boards from djanban.apps.base.decorators import member_required from djanban.apps.boards.models import Board, Card, CardComment, List, CardAttachment # Point of access to several actions from djanban.apps.forecasters.models import Forecaster from djanban.utils.custom_uuid import custom_uuid @member_required def modify_cards(request, board_id): # Create a new card if request.method == "PUT": return _add_card(request, board_id) # Move all cards in a list if request.method == "POST": return _move_all_list_cards(request, board_id) # Otherwise, return HTTP ERROR 405 return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) # Adds a new card in the board # Used by modify_cards def _add_card(request, board_id): if request.method != "PUT": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member put_params = json.loads(request.body) if not put_params.get("name") or not put_params.get("list") or not put_params.get("position"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) if put_params.get("position") != "top" and put_params.get("position") != "bottom": return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) try: list_ = get_list_or_404(request, board_id, put_params.get("list")) except Http404: return JsonResponseNotFound({"message": "List not found"}) new_card = list_.add_card(member=member, name=put_params.get("name"), position=put_params.get("position")) serializer = Serializer(board=new_card.board) return JsonResponse(serializer.serialize_card(new_card)) # Move all cards from a list to another # Used by modify_cards def _move_all_list_cards(request, board_id): if request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: board = get_board_or_404(request, board_id) except Http404: return JsonResponseNotFound({"message": "Board not found"}) post_params = json.loads(request.body) if not post_params.get("source_list") or not post_params.get("destination_list"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) # Check if the lists exists and if they are different try: source_list = board.active_lists.get(id=post_params.get("source_list")) destination_list = board.active_lists.get(id=post_params.get("destination_list")) if source_list.id == destination_list.id: raise AssertionError() except (List.DoesNotExist, AssertionError): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) # Move the cards source_list.move_cards(member=member, destination_list=destination_list) serializer = Serializer(board=board) return JsonResponse(serializer.serialize_board()) # Return the JSON representation of a card @member_required def get_card(request, board_id, card_id): if request.method != "GET": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) serializer = Serializer(board=card.board) card_json = serializer.serialize_card(card) return JsonResponse(card_json) # Change the name or description of the card @member_required @transaction.atomic def change(request, board_id, card_id): if request.method != "PUT": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) put_params = json.loads(request.body) if put_params.get("name"): card.change_attribute(member, attribute="name", value=put_params.get("name")) elif put_params.get("description"): card.change_attribute(member, attribute="description", value=put_params.get("name")) elif put_params.get("is_closed") is not None: card.change_attribute(member, attribute="is_closed", value=put_params.get("name")) elif put_params.get("due_datetime"): due_datetime_str = put_params.get("due_datetime") due_datetime = dateutil.parser.parse(due_datetime_str) card.change_attribute(member, attribute="due_datetime", value=due_datetime) elif "due_datetime" in put_params and put_params.get("due_datetime") is None: card.change_attribute(member, attribute="due_datetime", value=None) elif "value" in put_params: card_value = put_params.get("value") if card_value == "": card_value = None card.change_value(member=member, value=card_value) else: return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Change the labels of the card @member_required @transaction.atomic def change_labels(request, board_id, card_id): if request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) board = card.board post_params = json.loads(request.body) if not post_params.get("labels"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) label_ids = post_params.get("labels") card.labels.clear() for label_id in label_ids: if board.labels.filter(id=label_id).exists(): label = board.labels.get(id=label_id) card.labels.add(label) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) @member_required @transaction.atomic def change_members(request, board_id, card_id): if request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) board = card.board post_params = json.loads(request.body) if not post_params.get("members"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) member_ids = post_params.get("members") card.update_members(member, board.members.filter(id__in=member_ids)) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Change list @member_required @transaction.atomic def move_to_list(request, board_id, card_id): if request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) post_params = json.loads(request.body) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) # The new position of the card new_position = post_params.get("position", "top") # If there is no new_list param, the card is going to be moved in the same list currently is if post_params.get("new_list"): list_ = card.board.lists.get(id=post_params.get("new_list")) card.move(member, destination_list=list_, destination_position=new_position) else: card.change_order(member, destination_position=new_position) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_board()) # Creates a new attachment @member_required @transaction.atomic def add_attachment(request, board_id, card_id): if request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) except (Board.DoesNotExist, Card.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) uploaded_file_content = request.body if uploaded_file_content is None: return JsonResponseNotFound({"message": "No file sent."}) with tempfile.TemporaryFile() as uploaded_file: uploaded_file.write(uploaded_file_content) uploaded_file_name = request.GET.get("uploaded_file_name", custom_uuid()) attachment = card.add_new_attachment(member, uploaded_file, uploaded_file_name) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card_attachment(attachment)) # Deletes an attachment @member_required @transaction.atomic def delete_attachment(request, board_id, card_id, attachment_id): if request.method != "DELETE": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) attachment = card.attachments.get(id=attachment_id) except (Board.DoesNotExist, Card.DoesNotExist, CardAttachment.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) card.delete_attachment(member, attachment) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card_attachment(attachment)) # Creates a new comment @member_required @transaction.atomic def add_new_comment(request, board_id, card_id): if request.method != "PUT": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) put_params = json.loads(request.body) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) # Getting the comment content comment_content = put_params.get("content") # If the comment is empty, fail if not comment_content: return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) # Otherwise, add the comment new_comment = card.add_comment(member, comment_content) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card_comment(new_comment)) # Add S/E time to card @member_required @transaction.atomic def add_se_time(request, board_id, card_id): if request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) post_params = json.loads(request.body) spent_time = post_params.get("spent_time") estimated_time = post_params.get("estimated_time") date = post_params.get("date") description = post_params.get("description", card.name) if spent_time != "": try: spent_time = float(str(spent_time).replace(",", ".")) except ValueError: return JsonResponseNotFound({"message": "Not found."}) else: spent_time = None if estimated_time != "": try: estimated_time = float(str(estimated_time).replace(",", ".")) except ValueError: return JsonResponseNotFound({"message": "Not found."}) else: estimated_time = None if spent_time is None and estimated_time is None: return JsonResponseNotFound({"message": "Not found."}) # Optional days ago parameter days_ago = None matches = re.match(r"^\-(?P<days_ago>\d+)$", date) if matches: days_ago = int(matches.group("days_ago")) card.add_spent_estimated_time(member, spent_time, estimated_time, days_ago, description) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Create a new estimation for a card @member_required def update_forecasts(request, board_id, card_id): try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) except (Board.DoesNotExist, Card.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) available_card_forecasters = Forecaster.objects.filter( (Q(board=None) & Q(member=None)) \| (Q(board=card.board) & Q(member=None)) \| (Q(board=None) & Q(member__in=card.members.all())) ) serializer = Serializer(board=card.board) estimations = [] for forecaster in available_card_forecasters: forecast = forecaster.make_forecast(card=card) estimations.append(serializer.serialize_forecast(forecast)) return JsonResponse(estimations, safe=False, encoder=DjangoJSONEncoder) # Add a new blocking card to this card @member_required def add_blocking_card(request, board_id, card_id): if request.method != "PUT": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member put_body = json.loads(request.body) if not put_body.get("blocking_card"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) blocking_card = board.cards.exclude(id=card_id).get(id=put_body.get("blocking_card")) except (Board.DoesNotExist, Card.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) card.add_blocking_card(member, blocking_card) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Remove a blocking card to this card @member_required def remove_blocking_card(request, board_id, card_id, blocking_card_id): if request.method != "DELETE": return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) member = request.user.member try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) blocking_card = card.blocking_cards.exclude(id=card_id).get(id=blocking_card_id) except (Board.DoesNotExist, Card.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) card.remove_blocking_card(member, blocking_card) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Add a new review @member_required @transaction.atomic def add_new_review(request, board_id, card_id): if request.method != "PUT": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) board = card.board put_body = json.loads(request.body) if not put_body.get("members"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) reviewers = board.members.filter(id__in=put_body.get("members")) description = put_body.get("description", "") card.add_review(member, reviewers=reviewers, description=description) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Delete a review @member_required @transaction.atomic def delete_review(request, board_id, card_id, review_id): if request.method != "DELETE": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) review = card.reviews.get(id=review_id) except (Board.DoesNotExist, Card.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) card.delete_review(member, review) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Add a requirement to the card @member_required @transaction.atomic def add_requirement(request, board_id, card_id): if request.method != "PUT": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) board = card.board put_body = json.loads(request.body) if not put_body.get("requirement"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) requirement = board.requirements.get(id=put_body.get("requirement")) # If the requirement is already in the card, we can't continue if card.requirements.filter(id=requirement.id).exists(): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) card.add_requirement(member, requirement) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Remove a requirement of the card @member_required @transaction.atomic def remove_requirement(request, board_id, card_id, requirement_id): if request.method != "DELETE": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) requirement = card.requirements.get(id=requirement_id) except (Board.DoesNotExist, Card.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) card.remove_requirement(member, requirement) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Delete or update a comment @member_required @transaction.atomic def modify_comment(request, board_id, card_id, comment_id): if request.method != "DELETE" and request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) try: comment = card.comments.get(id=comment_id) except CardComment.DoesNotExist as e: return JsonResponseNotFound({"message": "Not found."}) if request.method == "DELETE": comment = _delete_comment(member, card, comment) elif request.method == "POST": post_params = json.loads(request.body) new_comment_content = post_params.get("content") if not new_comment_content: return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) comment = _edit_comment(member, card, comment, new_comment_content) else: return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card_comment(comment)) # Edit comment @member_required def _edit_comment(member, card, comment_to_edit, new_comment_content): edited_comment = card.edit_comment(member, comment_to_edit, new_comment_content) return edited_comment # Delete a comment @member_required def _delete_comment(member, card, comment_to_delete): # Delete the comment card.delete_comment(member, comment_to_delete) return comment_to_delete	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 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contests/atcoder/intro-heuristics/intro_heuristics_b/generate.py	conao3/coder	0	193688	<reponame>conao3/coder<filename>contests/atcoder/intro-heuristics/intro_heuristics_b/generate.py #!/usr/bin/env python3 # usage: $ oj generate-input 'python3 generate.py' import random # generated by online-judge-template-generator v4.1.0 (https://github.com/kmyk/online-judge-template-generator) def main(): c = [None for _ in range(26)] D = random.randint(1, 10 9) # TODO: edit here s = [[None for _ in range(26)] for _ in range(D + 28)] t = [None for _ in range(D)] for i in range(26): c[i] = random.randint(1, 10 9) # TODO: edit here for j in range(D + 2): for i in range(26): s[i + j][i] = random.randint(1, 10 9) # TODO: edit here for i in range(D): t[i] = random.randint(1, 10 9) # TODO: edit here print(D) print([c[i] for i in range(26)]) for j in range(D + 2): print([s[i + j][i] for i in range(26)]) for i in range(D): print(t[i]) if __name__ == "__main__": main()	[ 1, 529, 276, 1112, 420, 29958, 535, 6241, 29941, 29914, 29883, 6119, 29966, 9507, 29958, 1285, 9197, 29914, 271, 29883, 6119, 29914, 23333, 29899, 354, 332, 6765, 29914, 23333, 29918, 354, 332, 6765, 29918, 29890, 29914, 17158, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 29937, 8744, 29901, 395, 288, 29926, 5706, 29899, 2080, 525, 4691, 29941, 5706, 29889, 2272, 29915, 13, 5215, 4036, 13, 13, 29937, 5759, 491, 7395, 29899, 17675, 479, 29899, 6886, 29899, 27959, 325, 29946, 29889, 29896, 29889, 29900, 313, 991, 597, 3292, 29889, 510, 29914, 29895, 1357, 29895, 29914, 14627, 29899, 17675, 479, 29899, 6886, 29899, 27959, 29897, 13, 1753, 1667, 7295, 13, 1678, 274, 353, 518, 8516, 363, 903, 297, 3464, 29898, 29906, 29953, 4638, 13, 1678, 360, 353, 4036, 29889, 9502, 524, 29898, 29896, 29892, 29871, 29896, 29900, 3579, 29871, 29929, 29897, 29871, 396, 14402, 29901, 3863, 1244, 13, 1678, 269, 353, 5519, 8516, 363, 903, 297, 3464, 29898, 29906, 29953, 4638, 363, 903, 297, 3464, 29898, 29928, 718, 29871, 29906, 29947, 4638, 13, 1678, 260, 353, 518, 8516, 363, 903, 297, 3464, 29898, 29928, 4638, 13, 1678, 363, 474, 297, 3464, 29898, 29906, 29953, 1125, 13, 4706, 274, 29961, 29875, 29962, 353, 4036, 29889, 9502, 524, 29898, 29896, 29892, 29871, 29896, 29900, 3579, 29871, 29929, 29897, 29871, 396, 14402, 29901, 3863, 1244, 13, 1678, 363, 432, 297, 3464, 29898, 29928, 718, 29871, 29906, 1125, 13, 4706, 363, 474, 297, 3464, 29898, 29906, 29953, 1125, 13, 9651, 269, 29961, 29875, 718, 432, 3816, 29875, 29962, 353, 4036, 29889, 9502, 524, 29898, 29896, 29892, 29871, 29896, 29900, 3579, 29871, 29929, 29897, 29871, 396, 14402, 29901, 3863, 1244, 13, 1678, 363, 474, 297, 3464, 29898, 29928, 1125, 13, 4706, 260, 29961, 29875, 29962, 353, 4036, 29889, 9502, 524, 29898, 29896, 29892, 29871, 29896, 29900, 3579, 29871, 29929, 29897, 29871, 396, 14402, 29901, 3863, 1244, 13, 1678, 1596, 29898, 29928, 29897, 13, 1678, 1596, 10456, 29961, 29883, 29961, 29875, 29962, 363, 474, 297, 3464, 29898, 29906, 29953, 29897, 2314, 13, 1678, 363, 432, 297, 3464, 29898, 29928, 718, 29871, 29906, 1125, 13, 4706, 1596, 10456, 29961, 29879, 29961, 29875, 718, 432, 3816, 29875, 29962, 363, 474, 297, 3464, 29898, 29906, 29953, 29897, 2314, 13, 1678, 363, 474, 297, 3464, 29898, 29928, 1125, 13, 4706, 1596, 29898, 29873, 29961, 29875, 2314, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 1667, 580, 13, 2 ]
SharedData/list_all_files.py	kylemcdonald/SoundParts	22	33453	<reponame>kylemcdonald/SoundParts<filename>SharedData/list_all_files.py<gh_stars>10-100 import os import fnmatch # extensions can be a single tring like '.png' or '.jpg' # or a list of extensions. they should all be lowercase # but the . is important. def list_all_files(directory, extensions=None): for root, dirnames, filenames in os.walk(directory): for filename in filenames: base, ext = os.path.splitext(filename) joined = os.path.join(root, filename) if extensions is None or ( len(ext) and ext.lower() in extensions ): yield joined	[ 1, 529, 276, 1112, 420, 29958, 29895, 1508, 29885, 2252, 265, 2741, 29914, 29456, 29925, 5708, 29966, 9507, 29958, 21741, 1469, 29914, 1761, 29918, 497, 29918, 5325, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 5215, 2897, 13, 5215, 7876, 4352, 13, 13, 29937, 17752, 508, 367, 263, 2323, 534, 292, 763, 15300, 2732, 29915, 470, 15300, 6173, 29915, 13, 29937, 470, 263, 1051, 310, 17752, 29889, 896, 881, 599, 367, 5224, 4878, 13, 29937, 541, 278, 869, 338, 4100, 29889, 13, 1753, 1051, 29918, 497, 29918, 5325, 29898, 12322, 29892, 17752, 29922, 8516, 1125, 13, 1678, 363, 3876, 29892, 4516, 7039, 29892, 977, 264, 1280, 297, 2897, 29889, 20919, 29898, 12322, 1125, 13, 4706, 363, 10422, 297, 977, 264, 1280, 29901, 13, 9651, 2967, 29892, 1294, 353, 2897, 29889, 2084, 29889, 23579, 568, 486, 29898, 9507, 29897, 13, 9651, 8772, 353, 2897, 29889, 2084, 29889, 7122, 29898, 4632, 29892, 10422, 29897, 13, 9651, 565, 17752, 338, 6213, 470, 313, 7431, 29898, 1062, 29897, 322, 1294, 29889, 13609, 580, 297, 17752, 29871, 1125, 13, 18884, 7709, 8772, 2 ]
src/arima_model.py	SaharCarmel/ARIMA	0	16990	""" The ARIMA model. """ import torch import numpy as np class ARIMA(torch.nn.Module): """ARIMA [summary] """ def __init__(self, p: int = 0, d: int = 0, q: int = 0) -> None: """__init__ General ARIMA model constructor. Args: p (int): The number of lag observations included in the model, also called the lag order. d (int): The number of times that the raw observations are differenced, also called the degree of differencing. q (int): The size of the moving average window, also called the order of moving average. """ super(ARIMA, self).__init__() self.p = p self.pWeights = torch.rand(p) self.pWeights.requires_grad = True self.q = q self.qWeights = torch.rand(q) self.qWeights.requires_grad = True self.d = d self.dWeights = torch.rand(d) self.dWeights.requires_grad = True self.drift = torch.rand(1) pass def forward(self, x: torch.Tensor, err: torch.Tensor) -> torch.Tensor: """forward the function that defines the ARIMA(0,1,1) model. It was written specifically for the case of ARIMA(0,1,1). Args: x (torch.Tensor): The input data. All the past observations err (torch.Tensor): The error term. A normal distribution vector. Returns: torch.Tensor: The output of the model. The current prediction. """ zData = torch.diff(x) zPred = self.dWeightszData[-1] + \ self.qWeightserr[-2] + err[-1] + self.drift aPred = zPred + x[-1] return aPred def generateSample(self, length: int) -> torch.Tensor: """generateSample An helper function to generate a sample of data. Args: length (int): The length of the sample. Returns: torch.Tensor: The generated sample. """ sample = torch.zeros(length) noise = torch.tensor(np.random.normal( loc=0, scale=1, size=length), dtype=torch.float32) sample[0] = noise[0] with torch.no_grad(): for i in range(length-2): sample[i+2] = self.forward(sample[:i+2], noise[:i+2]) pass return sample def fit(self, trainData: torch.Tensor, epochs: int, learningRate: float) -> None: """fit A function to fit the model. It is a wrapper of the Args: trainData (torch.Tensor): The training data. epochs (int): The number of epochs. learningRate (float): The learning rate. """ dataLength = len(trainData) errors = torch.tensor(np.random.normal( loc=0, scale=1, size=dataLength), dtype=torch.float32) for epoch in range(epochs): prediction = torch.zeros(dataLength) for i in range(dataLength-2): prediction[i + 2] = self.forward(trainData[0:i+2], errors[0:i+2]) pass loss = torch.mean(torch.pow(trainData - prediction, 2)) print(f'Epoch {epoch} Loss {loss}') loss.backward() self.dWeights.data = self.dWeights.data - \ learningRate * self.dWeights.grad.data self.dWeights.grad.data.zero_() self.qWeights.data = self.qWeights.data - \ learningRate * self.qWeights.grad.data self.qWeights.grad.data.zero_() pass	[ 1, 9995, 450, 9033, 2260, 1904, 29889, 9995, 13, 13, 5215, 4842, 305, 13, 5215, 12655, 408, 7442, 13, 13, 13, 1990, 9033, 2260, 29898, 7345, 305, 29889, 15755, 29889, 7355, 1125, 13, 1678, 9995, 1718, 2260, 518, 7727, 29962, 13, 1678, 9995, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 13, 462, 282, 29901, 938, 353, 29871, 29900, 29892, 13, 462, 270, 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13, 9651, 21502, 12168, 29901, 938, 29892, 13, 9651, 6509, 19907, 29901, 5785, 29897, 1599, 6213, 29901, 13, 4706, 9995, 9202, 319, 740, 304, 6216, 278, 1904, 29889, 739, 338, 263, 14476, 310, 278, 13, 13, 4706, 826, 3174, 29901, 13, 9651, 7945, 1469, 313, 7345, 305, 29889, 29911, 6073, 1125, 450, 6694, 848, 29889, 13, 9651, 21502, 12168, 313, 524, 1125, 450, 1353, 310, 21502, 12168, 29889, 13, 9651, 6509, 19907, 313, 7411, 1125, 450, 6509, 6554, 29889, 13, 4706, 9995, 13, 4706, 848, 6513, 353, 7431, 29898, 14968, 1469, 29897, 13, 4706, 4436, 353, 4842, 305, 29889, 20158, 29898, 9302, 29889, 8172, 29889, 8945, 29898, 13, 9651, 1180, 29922, 29900, 29892, 6287, 29922, 29896, 29892, 2159, 29922, 1272, 6513, 511, 26688, 29922, 7345, 305, 29889, 7411, 29941, 29906, 29897, 13, 4706, 363, 21502, 305, 297, 3464, 29898, 1022, 2878, 29879, 1125, 13, 9651, 18988, 353, 4842, 305, 29889, 3298, 359, 29898, 1272, 6513, 29897, 13, 9651, 363, 474, 297, 3464, 29898, 1272, 6513, 29899, 29906, 1125, 13, 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29889, 5105, 29889, 1272, 13, 9651, 1583, 29889, 29939, 4806, 5861, 29889, 5105, 29889, 1272, 29889, 9171, 29918, 580, 13, 1678, 1209, 13, 2 ]
model-optimizer/mo/front/caffe/extractors/batchnorm.py	tdp2110/dldt	0	139454	<gh_stars>0 """ Copyright (c) 2018-2019 Intel Corporation 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 numpy as np from mo.front.caffe.extractors.utils import embed_input from mo.front.common.partial_infer.elemental import copy_shape_infer def batch_norm_ext(pb_layer, pb_model): """ Extracts properties of the BatchNorm layer. In case of scale, scale is merged into mean and variance Args: pb_layer: proto layer, contains own properties of the layer, i.e epsilon pb_model: caffemodel layer, contains blobs with 0: mean, 1: variance, (opt)2: scale Returns: attrs object with type, partial inference function and mean/variance properties. """ assert pb_layer, 'Protobuf layer can not be empty' param = pb_layer.batch_norm_param attrs = { 'op': 'BatchNormalization', 'type': 'BatchNormalization', 'eps': param.eps, 'infer': copy_shape_infer } if not pb_model: return attrs blobs = pb_model.blobs assert len(blobs) >= 2, 'BatchNorm accepts not less then two input blobs' mean = np.array(blobs[0].data) variance = np.array(blobs[1].data) if len(blobs) == 3: scale = blobs[2].data[0] if scale != 0: scale = 1.0 / scale mean = scale variance = scale embed_input(attrs, 1, 'gamma', np.ones(mean.shape), 'gamma') embed_input(attrs, 2, 'beta', np.zeros(variance.shape), 'beta') embed_input(attrs, 3, 'mean', mean, 'biases') embed_input(attrs, 4, 'variance', variance, 'weights') return attrs	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 15945, 29908, 13, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29947, 29899, 29906, 29900, 29896, 29929, 18555, 15025, 13, 13, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 13, 418, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 29900, 13, 13, 25870, 3734, 491, 22903, 4307, 470, 15502, 304, 297, 5007, 29892, 7047, 13, 13235, 1090, 278, 19245, 338, 13235, 373, 385, 376, 3289, 8519, 29908, 350, 3289, 3235, 29892, 13, 399, 1806, 8187, 2692, 399, 1718, 29934, 13566, 29059, 6323, 8707, 29928, 22122, 29903, 8079, 13764, 29979, 476, 22255, 29892, 2845, 4653, 470, 2411, 2957, 29889, 13, 2823, 278, 19245, 363, 278, 2702, 4086, 14765, 1076, 11239, 322, 13, 27028, 1090, 278, 19245, 29889, 13, 15945, 29908, 13, 13, 5215, 12655, 408, 7442, 13, 13, 3166, 2730, 29889, 8862, 29889, 1113, 17615, 29889, 21111, 943, 29889, 13239, 1053, 8297, 29918, 2080, 13, 3166, 2730, 29889, 8862, 29889, 9435, 29889, 3846, 29918, 262, 571, 29889, 5029, 284, 1053, 3509, 29918, 12181, 29918, 262, 571, 13, 13, 13, 1753, 9853, 29918, 12324, 29918, 1062, 29898, 24381, 29918, 13148, 29892, 282, 29890, 29918, 4299, 1125, 13, 1678, 9995, 13, 1678, 7338, 1461, 29879, 4426, 310, 278, 350, 905, 29940, 555, 7546, 29889, 13, 1678, 512, 1206, 310, 6287, 29892, 6287, 338, 19412, 964, 2099, 322, 20162, 13, 1678, 826, 3174, 29901, 13, 4706, 282, 29890, 29918, 13148, 29901, 17814, 7546, 29892, 3743, 1914, 4426, 310, 278, 7546, 29892, 474, 29889, 29872, 321, 3232, 13, 4706, 282, 29890, 29918, 4299, 29901, 274, 3470, 331, 27224, 7546, 29892, 3743, 23755, 29879, 411, 29871, 29900, 29901, 2099, 29892, 29871, 29896, 29901, 20162, 29892, 313, 3670, 29897, 29906, 29901, 6287, 13, 13, 1678, 16969, 29901, 13, 4706, 12421, 29879, 1203, 411, 1134, 29892, 7687, 27262, 740, 322, 2099, 29914, 1707, 8837, 4426, 29889, 13, 1678, 9995, 13, 1678, 4974, 282, 29890, 29918, 13148, 29892, 525, 1184, 517, 9721, 7546, 508, 451, 367, 4069, 29915, 13, 1678, 1828, 353, 282, 29890, 29918, 13148, 29889, 16175, 29918, 12324, 29918, 3207, 13, 1678, 12421, 29879, 353, 426, 13, 4706, 525, 459, 2396, 525, 23145, 19077, 2133, 742, 13, 4706, 525, 1853, 2396, 525, 23145, 19077, 2133, 742, 13, 4706, 525, 8961, 2396, 1828, 29889, 8961, 29892, 13, 4706, 525, 262, 571, 2396, 3509, 29918, 12181, 29918, 262, 571, 13, 1678, 500, 13, 13, 1678, 565, 451, 282, 29890, 29918, 4299, 29901, 13, 4706, 736, 12421, 29879, 13, 13, 1678, 23755, 29879, 353, 282, 29890, 29918, 4299, 29889, 10054, 29879, 13, 1678, 4974, 7431, 29898, 10054, 29879, 29897, 6736, 29871, 29906, 29892, 525, 23145, 29940, 555, 21486, 451, 3109, 769, 1023, 1881, 23755, 29879, 29915, 13, 1678, 2099, 353, 7442, 29889, 2378, 29898, 10054, 29879, 29961, 29900, 1822, 1272, 29897, 13, 1678, 20162, 353, 7442, 29889, 2378, 29898, 10054, 29879, 29961, 29896, 1822, 1272, 29897, 13, 13, 1678, 565, 7431, 29898, 10054, 29879, 29897, 1275, 29871, 29941, 29901, 13, 4706, 6287, 353, 23755, 29879, 29961, 29906, 1822, 1272, 29961, 29900, 29962, 13, 4706, 565, 6287, 2804, 29871, 29900, 29901, 13, 9651, 6287, 353, 29871, 29896, 29889, 29900, 847, 6287, 13, 4706, 2099, 334, 29922, 6287, 13, 4706, 20162, 334, 29922, 6287, 13, 13, 1678, 8297, 29918, 2080, 29898, 5552, 29879, 29892, 29871, 29896, 29892, 525, 4283, 742, 7442, 29889, 2873, 29898, 12676, 29889, 12181, 511, 525, 4283, 1495, 13, 1678, 8297, 29918, 2080, 29898, 5552, 29879, 29892, 29871, 29906, 29892, 525, 3571, 742, 7442, 29889, 3298, 359, 29898, 1707, 8837, 29889, 12181, 511, 525, 3571, 1495, 13, 1678, 8297, 29918, 2080, 29898, 5552, 29879, 29892, 29871, 29941, 29892, 525, 12676, 742, 2099, 29892, 525, 5365, 2129, 1495, 13, 1678, 8297, 29918, 2080, 29898, 5552, 29879, 29892, 29871, 29946, 29892, 525, 1707, 8837, 742, 20162, 29892, 525, 705, 5861, 1495, 13, 13, 1678, 736, 12421, 29879, 13, 2 ]
flare_removal/python/models.py	shaun95/google-research	1	158586	# coding=utf-8 # Copyright 2022 The Google Research Authors. # # 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. """Models for flare removal.""" from flare_removal.python import u_net from flare_removal.python import vgg def build_model(model_type, batch_size): """Returns a Keras model specified by name.""" if model_type == 'unet': return u_net.get_model( input_shape=(512, 512, 3), scales=4, bottleneck_depth=1024, bottleneck_layers=2) elif model_type == 'can': return vgg.build_can( input_shape=(512, 512, 3), conv_channels=64, out_channels=3) else: raise ValueError(model_type)	[ 1, 396, 14137, 29922, 9420, 29899, 29947, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29906, 29906, 450, 5087, 10550, 13189, 943, 29889, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 268, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 29900, 13, 29937, 13, 29937, 25870, 3734, 491, 22903, 4307, 470, 15502, 304, 297, 5007, 29892, 7047, 13, 29937, 13235, 1090, 278, 19245, 338, 13235, 373, 385, 376, 3289, 8519, 29908, 350, 3289, 3235, 29892, 13, 29937, 399, 1806, 8187, 2692, 399, 1718, 29934, 13566, 29059, 6323, 8707, 29928, 22122, 29903, 8079, 13764, 29979, 476, 22255, 29892, 2845, 4653, 470, 2411, 2957, 29889, 13, 29937, 2823, 278, 19245, 363, 278, 2702, 4086, 14765, 1076, 11239, 322, 13, 29937, 27028, 1090, 278, 19245, 29889, 13, 13, 15945, 29908, 23785, 363, 285, 8663, 28744, 1213, 15945, 13, 13, 3166, 285, 8663, 29918, 1745, 10611, 29889, 4691, 1053, 318, 29918, 1212, 13, 3166, 285, 8663, 29918, 1745, 10611, 29889, 4691, 1053, 325, 1505, 13, 13, 13, 1753, 2048, 29918, 4299, 29898, 4299, 29918, 1853, 29892, 9853, 29918, 2311, 1125, 13, 29871, 9995, 11609, 29879, 263, 12693, 294, 1904, 6790, 491, 1024, 1213, 15945, 13, 29871, 565, 1904, 29918, 1853, 1275, 525, 348, 300, 2396, 13, 1678, 736, 318, 29918, 1212, 29889, 657, 29918, 4299, 29898, 13, 4706, 1881, 29918, 12181, 7607, 29945, 29896, 29906, 29892, 29871, 29945, 29896, 29906, 29892, 29871, 29941, 511, 13, 4706, 23431, 29922, 29946, 29892, 13, 4706, 18046, 29880, 1600, 384, 29918, 19488, 29922, 29896, 29900, 29906, 29946, 29892, 13, 4706, 18046, 29880, 1600, 384, 29918, 29277, 29922, 29906, 29897, 13, 29871, 25342, 1904, 29918, 1853, 1275, 525, 3068, 2396, 13, 1678, 736, 325, 1505, 29889, 4282, 29918, 3068, 29898, 13, 4706, 1881, 29918, 12181, 7607, 29945, 29896, 29906, 29892, 29871, 29945, 29896, 29906, 29892, 29871, 29941, 511, 7602, 29918, 305, 12629, 29922, 29953, 29946, 29892, 714, 29918, 305, 12629, 29922, 29941, 29897, 13, 29871, 1683, 29901, 13, 1678, 12020, 7865, 2392, 29898, 4299, 29918, 1853, 29897, 13, 2 ]
tests/components/image_processing/common.py	pcaston/core	1	186189	<gh_stars>1-10 """Collection of helper methods. All containing methods are legacy helpers that should not be used by new components. Instead call the service directly. """ from openpeerpower.components.image_processing import DOMAIN, SERVICE_SCAN from openpeerpower.const import ATTR_ENTITY_ID, ENTITY_MATCH_ALL from openpeerpower.core import callback from openpeerpower.loader import bind_opp @bind_opp def scan(opp, entity_id=ENTITY_MATCH_ALL): """Force process of all cameras or given entity.""" opp.add_job(async_scan, opp, entity_id) @callback @bind_opp def async_scan(opp, entity_id=ENTITY_MATCH_ALL): """Force process of all cameras or given entity.""" data = {ATTR_ENTITY_ID: entity_id} if entity_id else None opp.async_add_job(opp.services.async_call(DOMAIN, SERVICE_SCAN, data))	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 15945, 29908, 7196, 310, 16876, 3519, 29889, 13, 13, 3596, 6943, 3519, 526, 25000, 1371, 414, 393, 881, 451, 367, 1304, 491, 716, 13, 14036, 29889, 8669, 1246, 278, 2669, 4153, 29889, 13, 15945, 29908, 13, 3166, 1722, 412, 261, 13519, 29889, 14036, 29889, 3027, 29918, 19170, 1053, 11662, 29032, 29892, 26996, 19059, 29918, 7187, 2190, 13, 3166, 1722, 412, 261, 13519, 29889, 3075, 1053, 15531, 5659, 29918, 3919, 11937, 29918, 1367, 29892, 12524, 29911, 11937, 29918, 29924, 14789, 29918, 9818, 13, 3166, 1722, 412, 261, 13519, 29889, 3221, 1053, 6939, 13, 3166, 1722, 412, 261, 13519, 29889, 12657, 1053, 7868, 29918, 9354, 13, 13, 13, 29992, 5355, 29918, 9354, 13, 1753, 12812, 29898, 9354, 29892, 7855, 29918, 333, 29922, 3919, 11937, 29918, 29924, 14789, 29918, 9818, 1125, 13, 1678, 9995, 2831, 346, 1889, 310, 599, 3949, 18464, 470, 2183, 7855, 1213, 15945, 13, 1678, 4575, 29889, 1202, 29918, 9057, 29898, 12674, 29918, 16192, 29892, 4575, 29892, 7855, 29918, 333, 29897, 13, 13, 13, 29992, 14035, 13, 29992, 5355, 29918, 9354, 13, 1753, 7465, 29918, 16192, 29898, 9354, 29892, 7855, 29918, 333, 29922, 3919, 11937, 29918, 29924, 14789, 29918, 9818, 1125, 13, 1678, 9995, 2831, 346, 1889, 310, 599, 3949, 18464, 470, 2183, 7855, 1213, 15945, 13, 1678, 848, 353, 426, 1299, 5659, 29918, 3919, 11937, 29918, 1367, 29901, 7855, 29918, 333, 29913, 565, 7855, 29918, 333, 1683, 6213, 13, 1678, 4575, 29889, 12674, 29918, 1202, 29918, 9057, 29898, 9354, 29889, 9916, 29889, 12674, 29918, 4804, 29898, 3970, 29032, 29892, 26996, 19059, 29918, 7187, 2190, 29892, 848, 876, 13, 2 ]
plugins/classbrowser/parser_diff.py	thioshp/MyGeditV2Full	4	1607572	<gh_stars>1-10 # -- coding: utf-8 -- # Copyright (C) 2007 <NAME> (<EMAIL>) # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, # Boston, MA 02111-1307, USA. import gtk import gobject import pango import os import options import imagelibrary from parserinterface import ClassParserInterface class Token: def __init__(self): self.start = 0 self.end = 0 self.name = None self.parent = None self.children = [] self.type = 'changeset' class DiffParser(ClassParserInterface): def parse(self, geditdoc): text = geditdoc.get_text(geditdoc.get_bounds()) linecount = -1 current_file = None changeset = None files = [] uri = geditdoc.get_uri() for line in text.splitlines(): linecount += 1 lstrip = line.lstrip() ln = lstrip.split() if len(ln) == 0: continue if ln[0] == '---': if current_file is not None: current_file.end = linecount - 1 current_file = Token() current_file.name = ln[1] current_file.start = linecount current_file.type = 'file' current_file.uri = uri files.append(current_file) elif current_file == None: continue elif ln[0] == '@@' and ln[-1] == '@@': if changeset is not None: changeset.end = linecount changeset = Token() changeset.name = ' '.join(ln[1:-1]) changeset.start = linecount changeset.uri = uri current_file.children.append(changeset) changeset.parent = current_file # Ending line of last tokens if len(files) > 0: f = files[-1] f.end = linecount + 2 if len(f.children) > 0: f.children[-1].end = linecount + 2 model = gtk.TreeStore(gobject.TYPE_PYOBJECT) pp = None # "Fake" common top folder, if any # TODO: Create hierarchy if patch applies in multiple directories if len(files) > 0: paths = map(lambda f:f.name, files) prefix = os.path.dirname(os.path.commonprefix(paths)) + '/' if len(prefix) > 1: parent_path = Token() parent_path.type = 'path' parent_path.name = prefix for f in files: f.name = f.name.replace(prefix,'',1) pp = model.append(None,(parent_path,)) # Build tree for f in files: tree_iter = model.append(pp,(f,)) for c in f.children: model.append(tree_iter,(c,)) return model def cellrenderer(self, treeviewcolumn, cellrenderertext, treemodel, it): token = treemodel.get_value(it,0) colour = options.singleton().colours["member"] if token.type == 'path': colour = options.singleton().colours["namespace"] elif token.type == 'file': colour = options.singleton().colours["class"] cellrenderertext.set_property("text", token.name) cellrenderertext.set_property("style", pango.STYLE_NORMAL) cellrenderertext.set_property("foreground-gdk", colour) def get_tag_position(self, model, path): tok = model.get_value(model.get_iter(path),0) try: return tok.uri, tok.start + 1 except: return None def get_tag_at_line(self, model, doc, linenumber): def find_path(model, path, iter, data): line = data[0] token = model.get_value(iter, 0) if token.start <= line and token.end > line: # print path data[1].append(path) #return True return False path_found = [] model.foreach(find_path, (linenumber, path_found)) if len(path_found) > 0: return path_found[-1] return None def pixbufrenderer(self, treeviewcolumn, cellrendererpixbuf, treemodel, it): token = treemodel.get_value(it,0) if token.type == 'path': cellrendererpixbuf.set_property("stock-id", gtk.STOCK_DIRECTORY) elif token.type == 'file': cellrendererpixbuf.set_property("stock-id", gtk.STOCK_FILE) else: cellrendererpixbuf.set_property("pixbuf",imagelibrary.pixbufs['patch']) # -- coding: utf-8 -- # Copyright (C) 2007 <NAME> (<EMAIL>) # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, # Boston, MA 02111-1307, USA. import gtk import gobject import pango import os import options import imagelibrary from parserinterface import ClassParserInterface class Token: def __init__(self): self.start = 0 self.end = 0 self.name = None self.parent = None self.children = [] self.type = 'changeset' class DiffParser(ClassParserInterface): def parse(self, geditdoc): text = geditdoc.get_text(geditdoc.get_bounds()) linecount = -1 current_file = None changeset = None files = [] uri = geditdoc.get_uri() for line in text.splitlines(): linecount += 1 lstrip = line.lstrip() ln = lstrip.split() if len(ln) == 0: continue if ln[0] == '---': if current_file is not None: current_file.end = linecount - 1 current_file = Token() current_file.name = ln[1] current_file.start = linecount current_file.type = 'file' current_file.uri = uri files.append(current_file) elif current_file == None: continue elif ln[0] == '@@' and ln[-1] == '@@': if changeset is not None: changeset.end = linecount changeset = Token() changeset.name = ' '.join(ln[1:-1]) changeset.start = linecount changeset.uri = uri current_file.children.append(changeset) changeset.parent = current_file # Ending line of last tokens if len(files) > 0: f = files[-1] f.end = linecount + 2 if len(f.children) > 0: f.children[-1].end = linecount + 2 model = gtk.TreeStore(gobject.TYPE_PYOBJECT) pp = None # "Fake" common top folder, if any # TODO: Create hierarchy if patch applies in multiple directories if len(files) > 0: paths = map(lambda f:f.name, files) prefix = os.path.dirname(os.path.commonprefix(paths)) + '/' if len(prefix) > 1: parent_path = Token() parent_path.type = 'path' parent_path.name = prefix for f in files: f.name = f.name.replace(prefix,'',1) pp = model.append(None,(parent_path,)) # Build tree for f in files: tree_iter = model.append(pp,(f,)) for c in f.children: model.append(tree_iter,(c,)) return model def cellrenderer(self, treeviewcolumn, cellrenderertext, treemodel, it): token = treemodel.get_value(it,0) colour = options.singleton().colours["member"] if token.type == 'path': colour = options.singleton().colours["namespace"] elif token.type == 'file': colour = options.singleton().colours["class"] cellrenderertext.set_property("text", token.name) cellrenderertext.set_property("style", pango.STYLE_NORMAL) cellrenderertext.set_property("foreground-gdk", colour) def get_tag_position(self, model, path): tok = model.get_value(model.get_iter(path),0) try: return tok.uri, tok.start + 1 except: return None def get_tag_at_line(self, model, doc, linenumber): def find_path(model, path, iter, data): line = data[0] token = model.get_value(iter, 0) if token.start <= line and token.end > line: # print path data[1].append(path) #return True return False path_found = [] model.foreach(find_path, (linenumber, path_found)) if len(path_found) > 0: return path_found[-1] return None def pixbufrenderer(self, treeviewcolumn, cellrendererpixbuf, treemodel, it): token = treemodel.get_value(it,0) if token.type == 'path': cellrendererpixbuf.set_property("stock-id", gtk.STOCK_DIRECTORY) elif token.type == 'file': cellrendererpixbuf.set_property("stock-id", gtk.STOCK_FILE) else: cellrendererpixbuf.set_property("pixbuf",imagelibrary.pixbufs['patch'])	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 14187, 1266, 313, 29907, 29897, 29871, 29906, 29900, 29900, 29955, 529, 5813, 29958, 313, 29966, 26862, 6227, 12948, 13, 29937, 13, 29937, 910, 1824, 338, 3889, 7047, 29936, 366, 508, 2654, 391, 2666, 372, 322, 29914, 272, 6623, 13, 29937, 372, 1090, 278, 4958, 310, 278, 15143, 4593, 5236, 19245, 408, 6369, 491, 13, 29937, 278, 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d05.py	nayunhwan/SMaSH_Python	0	1615677	<reponame>nayunhwan/SMaSH_Python<filename>d05.py #coding: utf-8 count1 = 0 count2 = 0 count3 = 0 count4 = 0 count5 = 0 while(True): s = input("직사각형의 가로 세로 입력") width, height = s.split(" ") width = int(width) height = int(height) if(width <= 0 or height <= 0): break else: if(width == height): count1 += 1 elif(width >= 2height): count2 += 1 elif(width2 <= height): count3 += 1 elif(width >= height): count4 += 1 elif(width <= height): count5 += 1 print("정사각형의 개수는",count1,"개 입니다.") print("좌우로 길쭉한 직사각형은",count2,"개 입니다.") print("위아래로 길쭉한 직사각혀의 개수는",count3,"개 입니다.") print("일반적인 가로형 직사각형의 개수는",count4,"개 입니다.") print("일반적인 세로형 직사각형의 개수는",count5,"개 입니다.")	[ 1, 529, 276, 1112, 420, 29958, 29876, 388, 348, 29882, 11440, 29914, 17061, 29874, 7068, 29918, 11980, 29966, 9507, 29958, 29881, 29900, 29945, 29889, 2272, 13, 29937, 29883, 3689, 29901, 23616, 29899, 29947, 13, 13, 2798, 29896, 353, 29871, 29900, 13, 2798, 29906, 353, 29871, 29900, 13, 2798, 29941, 353, 29871, 29900, 13, 2798, 29946, 353, 29871, 29900, 13, 2798, 29945, 353, 29871, 29900, 13, 13, 8000, 29898, 5574, 1125, 13, 12, 29879, 353, 1881, 703, 239, 170, 132, 30791, 237, 179, 132, 240, 155, 152, 30708, 29871, 30903, 30906, 29871, 31578, 30906, 29871, 239, 161, 136, 238, 163, 168, 1159, 13, 12, 2103, 29892, 3171, 353, 269, 29889, 5451, 703, 16521, 13, 12, 2103, 353, 938, 29898, 2103, 29897, 13, 12, 3545, 353, 938, 29898, 3545, 29897, 13, 12, 361, 29898, 2103, 5277, 29871, 29900, 470, 3171, 5277, 29871, 29900, 1125, 13, 12, 12, 8690, 13, 12, 2870, 29901, 13, 12, 12, 361, 29898, 2103, 1275, 3171, 1125, 13, 12, 12, 12, 2798, 29896, 4619, 29871, 29896, 13, 12, 12, 23681, 29898, 2103, 6736, 29871, 29906, 29930, 3545, 1125, 13, 12, 12, 12, 2798, 29906, 4619, 29871, 29896, 13, 12, 12, 23681, 29898, 2103, 29930, 29906, 5277, 3171, 1125, 13, 12, 12, 12, 2798, 29941, 4619, 29871, 29896, 13, 12, 12, 23681, 29898, 2103, 6736, 3171, 1125, 13, 12, 12, 12, 2798, 29946, 4619, 29871, 29896, 13, 12, 12, 23681, 29898, 2103, 5277, 3171, 1125, 13, 12, 12, 12, 2798, 29945, 4619, 29871, 29896, 13, 13, 13, 2158, 703, 30852, 30791, 237, 179, 132, 240, 155, 152, 30708, 29871, 31789, 30970, 31081, 613, 2798, 29896, 1699, 31789, 29871, 239, 161, 136, 31063, 30709, 23157, 13, 2158, 703, 239, 165, 143, 31327, 30906, 29871, 237, 187, 187, 239, 176, 140, 30877, 29871, 239, 170, 132, 30791, 237, 179, 132, 240, 155, 152, 31354, 613, 2798, 29906, 1699, 31789, 29871, 239, 161, 136, 31063, 30709, 23157, 13, 2158, 703, 31724, 30860, 238, 161, 155, 30906, 29871, 237, 187, 187, 239, 176, 140, 30877, 29871, 239, 170, 132, 30791, 237, 179, 132, 240, 155, 131, 30708, 29871, 31789, 30970, 31081, 613, 2798, 29941, 1699, 31789, 29871, 239, 161, 136, 31063, 30709, 23157, 13, 2158, 703, 31153, 238, 179, 155, 239, 163, 132, 30918, 29871, 30903, 30906, 240, 155, 152, 29871, 239, 170, 132, 30791, 237, 179, 132, 240, 155, 152, 30708, 29871, 31789, 30970, 31081, 613, 2798, 29946, 1699, 31789, 29871, 239, 161, 136, 31063, 30709, 23157, 13, 2158, 703, 31153, 238, 179, 155, 239, 163, 132, 30918, 29871, 31578, 30906, 240, 155, 152, 29871, 239, 170, 132, 30791, 237, 179, 132, 240, 155, 152, 30708, 29871, 31789, 30970, 31081, 613, 2798, 29945, 1699, 31789, 29871, 239, 161, 136, 31063, 30709, 23157, 2 ]
tests/graph/utils.py	kvh/basis	11	46611	<filename>tests/graph/utils.py<gh_stars>10-100 import textwrap from pathlib import Path from dataclasses import dataclass, asdict from typing import Any, Union, List, Dict, Collection, Set, Tuple from basis.graph.builder import graph_manifest_from_yaml, GraphManifest from basis.configuration.path import GraphEdge, NodeId, PortId from basis.graph.configured_node import ( ParameterDefinition, ParameterType, OutputDefinition, PortType, InputDefinition, ConfiguredNode, NodeType, NodeInterface, GraphManifest, GraphError, StateDefinition, ResolvedParameterValue, ) class _IgnoreType: def __eq__(self, other): return isinstance(other, _IgnoreType) def __repr__(self): return "IGNORE" IGNORE = _IgnoreType() @dataclass class NodeAssertion: name: Union[str, _IgnoreType] node_type: Union[NodeType, _IgnoreType] id: Union[NodeId, _IgnoreType] parent_node_id: Union[str, None] interface: Union[NodeInterface, _IgnoreType] description: Union[str, _IgnoreType] file_path_to_node_script_relative_to_root: Union[str, _IgnoreType] parameter_values: Union[Dict[str, Any], _IgnoreType] schedule: Union[str, _IgnoreType] local_edges: Union[List[str], _IgnoreType] resolved_edges: Union[List[str], _IgnoreType] resolved_parameter_values: Union[Dict[str, Tuple[Any, str]], _IgnoreType, None] errors: List[str] def _calculate_graph(nodes: List[ConfiguredNode]): nodes_by_id = {n.id: n for n in nodes} ids_by_path = {None: None} for node in nodes: parts = [node.name] parent = node.parent_node_id while parent: n = nodes_by_id[parent] parts.append(n.name) parent = n.parent_node_id ids_by_path[".".join(reversed(parts))] = node.id return ids_by_path def _assert_node( node: ConfiguredNode, assertion: NodeAssertion, ids_by_path: dict, paths_by_id: dict ): for k, v in asdict(assertion).items(): if k == "errors": continue actual = getattr(node, k) if k == "id": assert actual, "all ids must be defined" if v == IGNORE: continue if k in ("local_edges", "resolved_edges"): v = _unordered([_ge(s, ids_by_path) for s in v]) actual = _unordered(actual) elif k == "parent_node_id" and v is not None: actual = paths_by_id[actual] elif k == "resolved_parameter_values": if v is None: if assertion.parameter_values == IGNORE: continue v = { kk: ResolvedParameterValue(value=vv, source=node.id) for kk, vv in assertion.parameter_values.items() } else: v = { kk: ResolvedParameterValue(value=vv[0], source=ids_by_path[vv[1]]) for kk, vv in v.items() } def check(k, ex, ac): assert ac == ex, ( f"{assertion.name}: {k}:\n expected: {_tostr(ex, paths_by_id)}" + f"\n but was: {_tostr(ac, paths_by_id)}" ) if k == "interface": check("interface inputs", v.inputs, actual.inputs) check("interface outputs", v.outputs, actual.outputs) check("interface parameters", v.parameters, actual.parameters) else: check(k, v, actual) def _tostr(it, paths_by_id: dict) -> str: if isinstance(it, (list, set)): return str(sorted(_tostr(i, paths_by_id) for i in it)) if isinstance(it, PortId): return f"{paths_by_id[it.node_id]}:{it.port}" if isinstance(it, GraphEdge): return f"{_tostr(it.input, paths_by_id)} -> {_tostr(it.output, paths_by_id)}" if isinstance(it, GraphError): return f"{paths_by_id[it.node_id] if it.node_id else '<root>'}: {it.message}" return repr(it) def assert_nodes( manifest: GraphManifest, expected: NodeAssertion, assert_length: bool = True ): nodes = manifest.nodes assert len(nodes) == len({node.id for node in nodes}), "all ids must be unique" if assert_length: assert len(nodes) == len( expected ), f"expected nodes {[n.name for n in expected]} , got {[n.name for n in nodes]}" nodes_by_name_and_parent = { (node.name, node.parent_node_id): node for node in nodes } ids_by_path = _calculate_graph(nodes) paths_by_id = {v: k for (k, v) in ids_by_path.items()} errors = [] for assertion in expected: parent_id = ids_by_path[assertion.parent_node_id] k = (assertion.name, parent_id) assert ( k in nodes_by_name_and_parent ), f"no node {assertion.name} in {list(nodes_by_name_and_parent.keys())}" node = nodes_by_name_and_parent[k] errors.extend(GraphError(node_id=node.id, message=e) for e in assertion.errors) ac = _unordered(manifest.errors) ex = _unordered(errors) assert ( ex == ac ), f"errors:\nexpected:\n{_tostr(ex, paths_by_id)}\nbut was:\n{_tostr(ac, paths_by_id)}" for assertion in expected: parent_id = ids_by_path[assertion.parent_node_id] node = nodes_by_name_and_parent[assertion.name, parent_id] _assert_node(node, assertion, ids_by_path, paths_by_id) # noinspection PyDefaultArgument def n( name: str, node_type: Union[NodeType, _IgnoreType] = NodeType.Node, id: Union[str, _IgnoreType] = IGNORE, parent: Union[str, None] = None, interface: Union[ List[ Union[ InputDefinition, OutputDefinition, ParameterDefinition, StateDefinition ] ], _IgnoreType, ] = IGNORE, description: Union[str, None, _IgnoreType] = None, file_path: Union[str, _IgnoreType] = IGNORE, parameter_values: Union[Dict[str, Any], _IgnoreType] = {}, schedule: Union[str, None, _IgnoreType] = None, local_edges: Union[List[str], _IgnoreType] = IGNORE, resolved_edges: Union[List[str], None, _IgnoreType] = None, resolved_params: Union[Dict[str, Tuple[Any, str]], _IgnoreType] = None, errors: List[str] = [], ) -> NodeAssertion: return NodeAssertion( name=name, node_type=node_type, id=IGNORE if id == IGNORE else NodeId(id.lower()), parent_node_id=parent, interface=interface if interface == IGNORE else NodeInterface( inputs=[i for i in interface if isinstance(i, InputDefinition)], outputs=[i for i in interface if isinstance(i, OutputDefinition)], parameters=[i for i in interface if isinstance(i, ParameterDefinition)], state=next((i for i in interface if isinstance(i, StateDefinition)), None), ), description=description, file_path_to_node_script_relative_to_root=file_path, parameter_values=parameter_values, resolved_parameter_values=resolved_params, schedule=schedule, local_edges=local_edges, resolved_edges=local_edges if resolved_edges is None else resolved_edges, errors=errors, ) def p( name: str, parameter_type: str = None, description: str = None, default: Any = None, ) -> ParameterDefinition: return ParameterDefinition( name=name, parameter_type=ParameterType(parameter_type) if parameter_type else None, description=description, default=default, ) def ostream( name: str, description: str = None, schema: str = None, ) -> OutputDefinition: return OutputDefinition( port_type=PortType.Stream, name=name, description=description, schema_name=schema, ) def istream( name: str, description: str = None, schema: str = None, required: bool = True, ) -> InputDefinition: return InputDefinition( port_type=PortType.Stream, name=name, description=description, schema_name=schema, required=required, ) def itable( name: str, description: str = None, schema: str = None, required: bool = True, ) -> InputDefinition: return InputDefinition( port_type=PortType.Table, name=name, description=description, schema_name=schema, required=required, ) def otable( name: str, description: str = None, schema: str = None, ) -> OutputDefinition: return OutputDefinition( port_type=PortType.Table, name=name, description=description, schema_name=schema, ) def s(name: str): return StateDefinition(name=name) def _ge(s: str, ids_by_path: dict) -> GraphEdge: l, r = s.split(" -> ") ln, lp = l.split(":") rn, rp = r.split(":") return GraphEdge( input=PortId(node_id=ids_by_path[ln], port=lp), output=PortId(node_id=ids_by_path[rn], port=rp), ) def _unordered(c: Collection) -> Set: s = set(c) assert len(c) == len(s) return s def read_manifest(name: str) -> GraphManifest: pth = Path(__file__).parent / name / "graph.yml" return graph_manifest_from_yaml(pth, allow_errors=True) def setup_manifest(root: Path, files: Dict[str, str]) -> GraphManifest: for path, content in files.items(): content = textwrap.dedent(content).strip() if path.endswith(".py"): if not content.startswith("@node"): content = f"@node\ndef generated_node(\n{content}\n):\n pass" content = "from basis import \n\n" + content abspath = root / path if len(Path(path).parts) > 1: abspath.parent.mkdir(parents=True, exist_ok=True) abspath.write_text(content) return graph_manifest_from_yaml(root / "graph.yml", allow_errors=True)	[ 1, 529, 9507, 29958, 21150, 29914, 4262, 29914, 13239, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 5215, 1426, 6312, 13, 3166, 2224, 1982, 1053, 10802, 13, 3166, 848, 13203, 1053, 848, 1990, 29892, 408, 8977, 13, 3166, 19229, 1053, 3139, 29892, 7761, 29892, 2391, 29892, 360, 919, 29892, 14348, 29892, 3789, 29892, 12603, 552, 13, 3166, 8405, 29889, 4262, 29889, 16409, 1053, 3983, 29918, 29135, 29918, 3166, 29918, 25162, 29892, 12367, 2517, 7004, 13, 13, 3166, 8405, 29889, 13305, 29889, 2084, 1053, 12367, 23894, 29892, 9071, 1204, 29892, 3371, 1204, 13, 3166, 8405, 29889, 4262, 29889, 2917, 2955, 29918, 3177, 1053, 313, 13, 1678, 24953, 14683, 29892, 13, 1678, 24953, 1542, 29892, 13, 1678, 10604, 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angeline_yu/SARS.py	homologus/2020-intermediate-class	3	50431	<filename>angeline_yu/SARS.py from Bio import SeqIO from Bio.Seq import Seq #Accessing SARS Genome and storing it in reads reads = list(SeqIO.parse("/share/SARS/SARS-2020.fasta", "fasta")) #translating the 3 reading frames translation1 = reads[0].seq.translate() translation2 = reads[0].seq.translate() translation3 = reads[0].seq.translate() for i in reads: #reading frame 1 translation1 = i.seq[0:].translate() #reading frame 2 translation2 = i.seq[1:].translate() #reading frame 3 translation3 = i.seq[2:].translate() #total genes total = 0 #count variable acts as place holder to check if sequence is 100 long count = 0 #toReturn variable keeps track of how many 100 amino acid sequences there are toReturn = 0 #saves previous i location prev_i = 0 #For loops checks for end codens ("") #reading frame 1 for i in range(len(translation1)): if(str(translation1[i]) != ""): count += 1 continue else: if(count >= 100): toReturn += 1 print("\n>Amino Acid Sequence " +str(toReturn)) print("Start Location = " +str(prev_i)) print("End Location = " +str(i+1)) print("Length = " +str((i+1)-prev_i)) print(translation1[prev_i+1:i]+"\n") prev_i = i count = 0 print("\nThere are " + str(toReturn) + " 100 amino acid sequences in the SARS-CoV2 Genome in reading frame 1. \n") total += toReturn count = 0 toReturn = 0 prev_i = 0 for i in range(len(translation2)): if(str(translation2[i]) != ""): count += 1 continue else: if(count >= 100): toReturn += 1 print("\n>Amino Acid Sequence " +str(toReturn)) print("Start Location = " +str(prev_i)) print("End Location = " +str(i+1)) print("Length = " +str((i+1)-prev_i)) print(translation2[prev_i+1:i]+"\n") prev_i = i count = 0 print("There are " + str(toReturn) + " 100 amino acid sequences in the SARS-CoV2 Genome in reading frame 2. \n") total += toReturn count = 0 toReturn = 0 prev_i = 0 for i in range(len(translation3)): if(str(translation3[i]) != ""): count += 1 continue else: if(count >= 100): toReturn += 1 print("\n>Amino Acid Sequence " +str(toReturn)) print("Start Location = " +str(prev_i)) print("End Location = " +str(i+1)) print("Length = " +str((i+1)-prev_i)) print(translation3[prev_i+1:i]+"\n") prev_i = i count = 0 print("There are " + str(toReturn) + " 100 amino acid sequences in the SARS-CoV2 Genome in reading frame 3. \n") total += toReturn print("There are " +str(total) + " total 100 amino acid sequences in the SARS-CoV2 Genome. \n")	[ 1, 529, 9507, 29958, 574, 5570, 29918, 29891, 29884, 29914, 29903, 1718, 29903, 29889, 2272, 13, 3166, 21184, 1053, 25981, 5971, 13, 3166, 21184, 29889, 23718, 1053, 25981, 13, 13, 29937, 6638, 292, 317, 1718, 29903, 5739, 608, 322, 15446, 372, 297, 13623, 13, 949, 29879, 353, 1051, 29898, 23718, 5971, 29889, 5510, 11974, 13653, 29914, 29903, 1718, 29903, 29914, 29903, 1718, 29903, 29899, 29906, 29900, 29906, 29900, 29889, 29888, 5427, 613, 376, 29888, 5427, 5783, 13, 13, 29937, 3286, 29880, 1218, 278, 29871, 29941, 5183, 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2020/day_16/day_16.py	viddrobnic/adventofcode	0	36732	<gh_stars>0 def read_data(): rules = dict() your_ticket = None nearby_tickets = [] state = 0 with open('in') as f: for line in map(lambda x: x.strip(), f.readlines()): if line == '': state += 1 continue if line == 'your ticket:': continue if line == 'nearby tickets:': continue if state == 0: parts = line.split(':') ranges = parts[1].split('or') rules[parts[0]] = [] for r in ranges: nums = r.split('-') rules[parts[0]].append((int(nums[0]), int(nums[1]))) if state == 1: your_ticket = list(map(int, line.split(','))) if state == 2: nearby_tickets.append(list(map(int, line.split(',')))) return rules, your_ticket, nearby_tickets def part_one(rules, tickets): error_rate = 0 invalid_tickets = [] for i, ticket in enumerate(tickets): for val in ticket: valid_val = False for rule in rules.keys(): valid = False for m, M in rules[rule]: if val >= m and val <= M: valid = True break if valid: valid_val = True break if not valid_val: error_rate += val invalid_tickets.append(i) return invalid_tickets, error_rate def part_two(rules, your_ticket, tickets): possible_values = [set(rules.keys()) for i in range(len(rules.keys()))] for ticket in tickets: for i, val in enumerate(ticket): for rule in rules.keys(): valid = False for m, M in rules[rule]: if val >= m and val <= M: valid = True break if not valid and rule in possible_values[i]: possible_values[i].remove(rule) while True: to_filter = False for v in map(len, possible_values): if v > 1: to_filter = True if not to_filter: break for i in range(len(possible_values)): if len(possible_values[i]) == 1: for j in range(len(possible_values)): if i != j: possible_values[j] -= possible_values[i] res = 1 for i in range(len(possible_values)): if 'departure' in list(possible_values[i])[0]: res *= your_ticket[i] return res def main(): rules, your_ticket, nearby_tickets = read_data() invalid_tickets, error_rate = part_one(rules, nearby_tickets) p_two = part_two(rules, your_ticket, [t for i, t 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vae-dpc/vae_main.py	lovish1234/TPC	0	1609206	<filename>vae-dpc/vae_main.py<gh_stars>0 # Self-supervised pretraining using DPC with VAE import os import random import sys import time import re import argparse import ntpath import numpy as np import pickle from tqdm import tqdm import copy # visualization from tensorboardX import SummaryWriter import matplotlib.pyplot as plt plt.switch_backend('agg') sys.path.append('../tpc') # use same dataset file as TPC sys.path.append('../utils') sys.path.append('../utils/diversity_meas') from div_vae import * from cvae_model import * from vrnn_model import * from dataset_epic import * from dataset_synthepic import * from dataset_other import * from dataset_toy import * # backbone from resnet_2d3d import neq_load_customized # data augmentation methods from augmentation import * # collect results from utils import AverageMeter, save_checkpoint, denorm, calc_topk_accuracy, single_action, get_action_from_frame import torch import torch.optim as optim from torch.utils import data from torchvision import datasets, models, transforms from torch import autograd # anomaly detection from present_matching import * import copy # saving log, calculating accuracy etc. import torchvision.utils as vutils from train_util import * torch.backends.cudnn.benchmark = True parser = argparse.ArgumentParser() parser.add_argument('--net', default='resnet18', type=str) parser.add_argument('--dataset', default='k400', type=str, help='k400 / ucf / epic_unlabeled / epic_before / epic_end') # frame preprocessing parser.add_argument('--seq_len', default=5, type=int, help='number of frames in each video block') parser.add_argument('--num_seq', default=8, type=int, help='number of video blocks') parser.add_argument('--pred_step', default=3, type=int) parser.add_argument('--ds', default=6, type=int, help='frame downsampling rate') # optimizer learning rate and decay parser.add_argument('--batch_size', default=64, type=int) parser.add_argument('--lr', default=1e-3, type=float, help='learning rate') parser.add_argument('--wd', default=1e-5, type=float, help='weight decay') # resume training parser.add_argument('--resume', default='', type=str, help='path of model to resume') parser.add_argument('--pretrain', default='', type=str, help='path of pretrained model') parser.add_argument('--epochs', default=300, type=int, help='number of total epochs to run') # help in re-setting the lr to appropriate value parser.add_argument('--start-epoch', default=0, type=int, help='manual epoch number (useful on restarts)') # gpu setting parser.add_argument('--gpu', default='0,1', type=str) parser.add_argument('--print_freq', default=1, type=int, help='frequency of printing output during training') parser.add_argument('--reset_lr', action='store_true', help='Reset learning rate when resume training?') parser.add_argument('--prefix', default='conv', type=str, help='prefix of checkpoint filename') # which layers to train [ use for fine-tuning ] parser.add_argument('--train_what', default='all', type=str) parser.add_argument('--img_dim', default=128, type=int) # number of sequences sampled from one video in epic and block_toy dataset parser.add_argument('--num_clips', default=10000, type=int, help='Number of sequences to sample in total from train_split') parser.add_argument('--drive', default='ssd', type=str, help='Type of disk drive training data is located at') parser.add_argument('--new_augment', action='store_true', help='Use data augmentation with consistent train-test distortion') # CVAE / diversity specific parameters parser.add_argument('--model', default='vrnn', type=str, help='cvae / vrnn / vrnn-i') parser.add_argument('--cvae_arch', default='conv_e', type=str, help='Architecture of the CVAE mapping present to future (fc / conv_a / conv_b / conv_c / conv_d / conv_e) (default: conv_e)') parser.add_argument('--vrnn_latent_size', default=16, type=int, help='Dimensionality of the VRNN probabilistic latent space') parser.add_argument('--vrnn_kernel_size', default=1, type=int, help='Kernel size of all VRNN convolutional layers (prior, enc, dec, rnn)') parser.add_argument('--vrnn_dropout', default=0.1, type=float, help='Dropout in RNN for aggregation (GRU cell) (default: 0.1)') parser.add_argument('--vae_kl_weight', default=1.0, type=float, help='Base weight factor of KL divergence term in VAE-DPC loss (default: 1.0)') parser.add_argument('--vae_no_kl_norm', default=False, action='store_true', help='If True, disable KL loss weight normalization') parser.add_argument('--vae_encoderless_epochs', default=25, type=int, help='Number of epochs to train the prediction network deterministically (DPC-like) before enabling the encoder and beta warmup (default: 100)') parser.add_argument('--vae_inter_kl_epochs', default=25, type=int, help='Number of epochs to wait after starting the encoder (and training with zero KL loss) before starting beta warmup (default: 50)') parser.add_argument('--vae_kl_beta_warmup', default=150, type=int, help='Number of epochs during which to warmup the KL loss term weight from 0% to 100% according to a cosine curve (default: 100)') parser.add_argument('--pred_divers_wt', default=0.001, type=float, help='Importance of variance (diversity) in predictions as measured by cosine distance in loss (default: 0.1)') parser.add_argument('--pred_divers_formula', default='inv', type=str, help='Use -var (neg) or 1/var (inv) (default: inv)') parser.add_argument('--test_diversity', default=False, action='store_true', help='If True, evaluate model by measuring diversity of multiple generated samples. Note: specify pretrained model path as well.') parser.add_argument('--paths', default=20, type=int, help='Future paths generated by VAE (default: 20)') # parser.add_argument('--vrnn_context_noise', default=0.0, type=float, # help='Add Gaussian noise to input of VRNN decoder for regularization (default: 0)') # parser.add_argument('--vrnn_time_indep', default=False, action='store_true', # help='If True, z does not see or influence context (i.e. VRNN becomes CVAE)') parser.add_argument('--select_best_among', default=1, type=int, help='[do not use for now] During validation and when the encoder is disabled, generate the specified number of futures in a holistic way, and judge the best performing path only (default = 1).') parser.add_argument('--force_context_dropout', default=False, action='store_true', help='If True, use different dropout mask (p = 0.1) on c_t for every path during uncertainty measurements.') # Present matching parameters parser.add_argument('--pm_start', default=1000, type=int, help='Number of epochs present matching kicks in') parser.add_argument('--pm_freq', default=10, type=int, help='Frequency of present matching (every 10 epochs by default)') parser.add_argument('--pm_num_samples', default=100000, type=int, help='Number of videos sequences to sample during present matching') parser.add_argument('--pm_num_ngbrs', default=4, type=int, help='Number of nearest neighbors to put in one batch') parser.add_argument('--pm_gap_thres', default=30, type=int, help='Minimum pair-wise frame gap between two sequences in the same batch') # Action classification custom arguments # parser.add_argument('--diverse_actions', default=False, action='store_true', help='If True, evaluate model by measuring diversity of multiple generated future samples (labels in action space). Note: specify TEST model path as well.') # parser.add_argument('--test_path', default='', type=str, help='Path of finetuned model for multimodal action classification') # parser.add_argument('--class_type', default='verb', type=str) # TODO: support 'both' as well def main(): # Set constant random state for consistent results torch.manual_seed(704) np.random.seed(704) random.seed(704) global args args = parser.parse_args() os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) global cuda cuda = torch.device('cuda') args.cuda = cuda ### dpc model ### if args.model == 'cvae': model = DPC_CVAE(img_dim=args.img_dim, num_seq=args.num_seq, seq_len=args.seq_len, pred_step=args.pred_step, network=args.net, cvae_arch=args.cvae_arch) elif 'vrnn' in args.model: model = DPC_VRNN(img_dim=args.img_dim, num_seq=args.num_seq, seq_len=args.seq_len, pred_step=args.pred_step, network=args.net, latent_size=args.vrnn_latent_size, kernel_size=args.vrnn_kernel_size, rnn_dropout=args.vrnn_dropout, time_indep='-i' in args.model) else: raise ValueError('Unknown / wrong model: ' + args.model) # parallelize the model model = nn.DataParallel(model) model = model.to(cuda) # loss function (change this) # why cross-entropy, we can take a direct cosine distance instead criterion = nn.CrossEntropyLoss() ### optimizer ### # dont' think we need to use 'last' keyword during pre-training anywhere if args.train_what == 'last': for name, param in model.module.resnet.named_parameters(): param.requires_grad = False else: pass # train all layers # check if gradient flowing to appropriate layers print('\n===========Check Grad============') for name, param in model.named_parameters(): print(name, param.requires_grad) print('=================================\n') params = model.parameters() optimizer = optim.Adam(params, lr=args.lr, weight_decay=args.wd) args.old_lr = None best_acc = 0 global iteration iteration = 0 ### restart training ### if args.resume: if os.path.isfile(args.resume): # load the old model and set the learning rate accordingly # get the old learning rate args.old_lr = float(re.search('_lr(.+?)_', args.resume).group(1)) print("=> loading resumed checkpoint '{}'".format(args.resume)) # checkpoint = torch.load( args.resume, map_location=torch.device('cpu')) args.start_epoch = checkpoint['epoch'] iteration = checkpoint['iteration'] best_acc = checkpoint['best_acc'] model.load_state_dict(checkpoint['state_dict']) # load old optimizer, start with the corresponding learning rate if not args.reset_lr: # if didn't reset lr, load old optimizer optimizer.load_state_dict(checkpoint['optimizer']) else: # reset to new learning rate print('==== Change lr from %f to %f ====' % (args.old_lr, args.lr)) print("=> loaded resumed checkpoint '{}' (epoch {})".format( args.resume, checkpoint['epoch'])) else: print("[Warning] no checkpoint found at '{}'".format(args.resume)) if args.pretrain: if os.path.isfile(args.pretrain): print("=> loading pretrained checkpoint '{}'".format(args.pretrain)) checkpoint = torch.load( args.pretrain, map_location=torch.device('cpu')) # neq_load_customized model = neq_load_customized(model, checkpoint['state_dict']) print("=> loaded pretrained checkpoint '{}' (epoch {})" .format(args.pretrain, checkpoint['epoch'])) else: print("=> no checkpoint found at '{}'".format(args.pretrain)) ### load transform & dataset ### transform_train = get_transform(args, mode = 'train') transform_val = get_transform(args, mode = 'val') train_loader = get_data(args, transform_train, 'train') val_loader = get_data(args, transform_val, 'val') # setup tools # Initialize denormalize transform to display input images via tensorboard de_normalize = denorm() # Get paths global img_path img_path, model_path, divers_path, pm_cache = set_path(args) global writer_train if not(args.test_diversity): # Train & validate for multiple epochs # book-keeping writer_val_enc = SummaryWriter(os.path.join(img_path, 'val_enc')) writer_val_noenc = SummaryWriter(os.path.join(img_path, 'val_noenc')) writer_val_pm_enc = SummaryWriter(os.path.join(img_path, 'val_pm_enc')) writer_val_pm_noenc = SummaryWriter(os.path.join(img_path, 'val_pm_noenc')) writer_train = SummaryWriter(os.path.join(img_path, 'train')) global cur_vae_kl_weight, cur_pred_divers_wt ### main loop ### for epoch in range(args.start_epoch, args.epochs): # Initially train without latent space if specified if epoch < args.vae_encoderless_epochs: train_with_latent = False cur_pred_divers_wt = 0.0 # encouraging diversity doesn't apply right now else: train_with_latent = True cur_pred_divers_wt = args.pred_divers_wt # hard transition print('Encoder enabled for training this epoch:', train_with_latent) # Then train for a while with beta = 0 (not explicitly coded) # Beta warmup = VAE KL loss weight adjustment over time if train_with_latent: epoch_post = epoch - (args.vae_encoderless_epochs + args.vae_inter_kl_epochs) if epoch_post < 0: cur_vae_kl_weight = 0.0 elif 0 <= epoch_post and epoch_post < args.vae_kl_beta_warmup: cur_vae_kl_weight = args.vae_kl_weight * \ (1.0 - np.cos(epoch_post / args.vae_kl_beta_warmup * np.pi)) / 2 else: cur_vae_kl_weight = args.vae_kl_weight else: cur_vae_kl_weight = 0.0 print('Current VAE KL loss term weight:', cur_vae_kl_weight) if epoch < args.pm_start: print('\n\nTraining with randomly sampled sequences from %s' % args.dataset) train_loss_dpc, train_loss_kl, train_loss_divers, train_loss, train_acc, train_accuracy_list = train( train_loader, model, optimizer, epoch, criterion, writer_train, args, de_normalize, cur_vae_kl_weight, cur_pred_divers_wt, do_encode=train_with_latent) else: # change torch multithreading setting to circumvent 'open files' limit torch.multiprocessing.set_sharing_strategy('file_system') print('\n\nTraining with matched sequences (nearest neighbours) from %s\n\n' % args.dataset) if epoch % args.pm_freq == 0: print(' ######################################## \n Matching sequences in embedding space... \n ########################################\n\n') # present matching and save results for future training present_matching(args, model, pm_cache, epoch, 'train') present_matching(args, model, pm_cache, epoch, 'val') args_pm = copy.copy(args) args_pm.dataset = 'epic_present_matching' pm_path = os.path.join(pm_cache, 'epoch_train_%d' % ( epoch - epoch % args_pm.pm_freq)) # retrieve last present matching results train_loader_pm = get_data( args_pm, transform_train, mode='train', pm_path=pm_path, epoch=epoch) train_loss_dpc, train_loss_kl, train_loss_divers, train_loss, train_acc, train_accuracy_list = train( train_loader_pm, model, optimizer, epoch, criterion, writer_train, args, de_normalize, cur_vae_kl_weight, cur_pred_divers_wt, do_encode=train_with_latent) del train_loader_pm pm_path = os.path.join(pm_cache, 'epoch_val_%d' % ( epoch - epoch % args_pm.pm_freq)) # retrieve last present matching results val_loader_pm = get_data( args_pm, transform_val, mode='val', pm_path=pm_path, epoch=epoch) val_loss_dpc_pm_enc, val_loss_kl_pm_enc, val_loss_divers_pm_enc, \ val_loss_pm_enc, val_acc_pm_enc, val_acc_pm_enc_list = validate( val_loader_pm, model, epoch, args, criterion, cur_vae_kl_weight, cur_pred_divers_wt, do_encode=train_with_latent, select_best_among=1 if train_with_latent else args.select_best_among) # only one path when encoding val_loss_dpc_pm_noenc, val_loss_kl_pm_noenc, val_loss_divers_pm_noenc, \ val_loss_pm_noenc, val_acc_pm_noenc, val_acc_pm_noenc_list = validate( val_loader_pm, model, epoch, args, criterion, cur_vae_kl_weight, cur_pred_divers_wt, do_encode=False, select_best_among=args.select_best_among) del val_loader_pm val_loss_dpc_enc, val_loss_kl_enc, val_loss_divers_enc, \ val_loss_enc, val_acc_enc, val_acc_enc_list = validate( val_loader, model, epoch, args, criterion, cur_vae_kl_weight, cur_pred_divers_wt, do_encode=train_with_latent, select_best_among=1 if train_with_latent else args.select_best_among) # only one path when encoding val_loss_dpc_noenc, val_loss_kl_noenc, val_loss_divers_noenc, \ val_loss_noenc, val_acc_noenc, val_acc_noenc_list = validate( val_loader, model, epoch, args, criterion, cur_vae_kl_weight, cur_pred_divers_wt, do_encode=False, select_best_among=args.select_best_among) # Train curves writer_train.add_scalar('global/loss_dpc', train_loss_dpc, epoch) writer_train.add_scalar('global/loss_vae_kl', train_loss_kl, epoch) writer_train.add_scalar('global/loss_vae_divers', train_loss_divers, epoch) writer_train.add_scalar('global/loss', train_loss, epoch) writer_train.add_scalar('global/vae_kl_weight', cur_vae_kl_weight, epoch) writer_train.add_scalar('global/accuracy', train_acc, epoch) # Val curves writer_val_enc.add_scalar('global/loss_dpc', val_loss_dpc_enc, epoch) writer_val_enc.add_scalar('global/loss_vae_kl', val_loss_kl_enc, epoch) writer_val_enc.add_scalar('global/loss_vae_divers', val_loss_divers_enc, epoch) writer_val_enc.add_scalar('global/loss', val_loss_enc, epoch) writer_val_enc.add_scalar('global/accuracy', val_acc_enc, epoch) writer_val_noenc.add_scalar('global/loss_dpc', val_loss_dpc_noenc, epoch) writer_val_noenc.add_scalar('global/loss_vae_kl', val_loss_kl_noenc, epoch) writer_val_noenc.add_scalar('global/loss_vae_divers', val_loss_divers_noenc, epoch) writer_val_noenc.add_scalar('global/loss', val_loss_noenc, epoch) writer_val_noenc.add_scalar('global/accuracy', val_acc_noenc, epoch) if epoch >= args.pm_start: # Add present matching curves writer_val_pm_enc.add_scalar('global/loss_dpc', val_loss_dpc_pm_enc, epoch) writer_val_pm_enc.add_scalar('global/loss_vae_kl', val_loss_kl_pm_enc, epoch) writer_val_pm_enc.add_scalar('global/loss_vae_divers', val_loss_divers_pm_enc, epoch) writer_val_pm_enc.add_scalar('global/loss', val_loss_pm_enc, epoch) writer_val_pm_enc.add_scalar('global/accuracy', val_acc_pm_enc, epoch) writer_val_pm_noenc.add_scalar('global/loss_dpc', val_loss_dpc_pm_noenc, epoch) writer_val_pm_noenc.add_scalar('global/loss_vae_kl', val_loss_kl_pm_noenc, epoch) writer_val_pm_noenc.add_scalar('global/loss_vae_divers', val_loss_divers_pm_noenc, epoch) writer_val_pm_noenc.add_scalar('global/loss', val_loss_pm_noenc, epoch) writer_val_pm_noenc.add_scalar('global/accuracy', val_acc_pm_noenc, epoch) # Train accuracies writer_train.add_scalar('accuracy/top1', train_accuracy_list[0], epoch) writer_train.add_scalar('accuracy/top3', train_accuracy_list[1], epoch) writer_train.add_scalar('accuracy/top5', train_accuracy_list[2], epoch) # Val accuracies writer_val_noenc.add_scalar('accuracy/top1', val_acc_noenc_list[0], epoch) writer_val_noenc.add_scalar('accuracy/top3', val_acc_noenc_list[1], epoch) writer_val_noenc.add_scalar('accuracy/top5', val_acc_noenc_list[2], epoch) writer_val_enc.add_scalar('accuracy/top1', val_acc_enc_list[0], epoch) writer_val_enc.add_scalar('accuracy/top3', val_acc_enc_list[1], epoch) writer_val_enc.add_scalar('accuracy/top5', val_acc_enc_list[2], epoch) if epoch >= args.pm_start: # Add present matching curves writer_val_pm_noenc.add_scalar('accuracy/top1', val_acc_pm_noenc_list[0], epoch) writer_val_pm_noenc.add_scalar('accuracy/top3', val_acc_pm_noenc_list[1], epoch) writer_val_pm_noenc.add_scalar('accuracy/top5', val_acc_pm_noenc_list[2], epoch) writer_val_pm_enc.add_scalar('accuracy/top1', val_acc_pm_enc_list[0], epoch) writer_val_pm_enc.add_scalar('accuracy/top3', val_acc_pm_enc_list[1], epoch) writer_val_pm_enc.add_scalar('accuracy/top5', val_acc_pm_enc_list[2], epoch) # save check_point (best accuracy measured without encoder) is_best = val_acc_noenc > best_acc best_acc = max(val_acc_noenc, best_acc) save_checkpoint({'epoch': epoch + 1, 'net': args.net, 'state_dict': model.state_dict(), 'best_acc': best_acc, 'optimizer': optimizer.state_dict(), 'iteration': iteration}, is_best, filename=os.path.join(model_path, 'epoch%s.pth.tar' % str(epoch + 1)), save_every=10) print('Training from ep %d to ep %d finished' % (args.start_epoch, args.epochs)) else: # Uncertainty evaluation: full video & no color adjustments # NOTE: be careful with training augmentation to prevent train-test resolution / scaling discrepancy tf_diversity = transforms.Compose([ RandomHorizontalFlip(consistent=True), CenterCrop(224), Scale(size=(args.img_dim, args.img_dim)), ToTensor(), Normalize() ]) print('Measuring diversity of generated samples...') val_divers_loader = get_data(args, tf_diversity, 'val') results = measure_vae_uncertainty_loader(val_divers_loader, model, args.paths, print_freq=args.print_freq, collect_actions=False, force_context_dropout=args.force_context_dropout) cur_path = os.path.join(divers_path, 'epoch' + str(checkpoint['epoch']) + \ '_paths' + str(args.paths) + \ ('_ctdrop' if args.force_context_dropout else '') + '.p') with open(cur_path, 'wb') as f: pickle.dump(results, f) print('For future use, uncertainty evaluation results stored to ' + cur_path) def process_output(mask): '''task mask as input, compute the target for contrastive loss''' # dot product is computed in parallel gpus, so get less easy neg, bounded by batch size in each gpu''' # mask meaning: -2: omit, -1: temporal neg (hard), 0: easy neg, 1: pos, -3: spatial neg # [4, 3, 16, 4, 3, 16] # B and B2 must be same always ?? (B, NP, SQ, B2, NS, _) = mask.size() # [B, P, SQ, B, N, SQ] #print (B,B2,NS,NP,SQ) # mark out the positives # not using temporal negatives and spatial negatives differently target = mask == 1 target.requires_grad = False return target, (B, B2, NS, NP, SQ) def loss_kl_divergence(mu, logvar, normalization=True): ''' Calculates KL divergence loss term for CVAE. mu, logvar: from one item within minibatch, same size as latent space. NOTE: normalization enabled by default as of 05/27, divides by batch_size * pred_step. ''' # mu and logvar are probably many-dimensional, but this code is agnostic of that loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp()) if normalization: loss /= (mu.shape[0] * mu.shape[1]) return loss def loss_kl_divergence_prior(mu_prio, mu_post, logvar_prio, logvar_post, normalization=True): ''' Calculates KL divergence loss term for VRNN. all arguments: from one item within minibatch, same size as latent space. NOTE: normalization enabled by default as of 05/27, divides by batch_size * pred_step. ''' std_prio = logvar_prio.mul(0.5).exp() std_post = logvar_post.mul(0.5).exp() loss = -0.5 * torch.sum(1 + logvar_post - logvar_prio - (std_post.pow(2) + (mu_post - mu_prio).pow(2)) / std_prio.pow(2)) if normalization: loss /= (mu_prio.shape[0] * mu_prio.shape[1]) return loss def loss_pred_variance(pred_cos_sim, latent_dist, pred_divers_formula): ''' Calculates diversity loss term for CVAE and VRNN. pred_cos_sim: cosine similarity of one pair, from one item within minibatch and one time step. latent_dist: L2 distance between latent vectors that generated the pair. As of 05/20, multiply by latent_dist. ''' # pred_cos_sim is tensor with a cosine distance from a random pair # lower is better; typical variance range was [0.999990, 0.999998] if not encouraged (old model) # mean_cos_sim = pred_cos_sim.mean() # across time steps # TODO: normalize by latent_dist? what about mode collapse in prior? delta = 1.0 - pred_cos_sim if pred_divers_formula == 'neg': loss = -delta elif pred_divers_formula in ['div', 'inv']: # causes diminishing returns for larger variance loss = 1.0 / (delta + 1e-6) else: raise Exception('Unknown diversity loss formula: ', pred_divers_formula) loss *= latent_dist.detach() / 5.0 # typical L2 between 8/16-dim randn x, y return loss if __name__ == '__main__': main()	[ 1, 529, 9507, 29958, 1564, 29872, 29899, 6099, 29883, 29914, 1564, 29872, 29918, 3396, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 21782, 29899, 9136, 11292, 758, 26495, 773, 360, 9026, 411, 478, 16036, 13, 13, 5215, 2897, 13, 5215, 4036, 13, 5215, 10876, 13, 5215, 931, 13, 5215, 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tests/py_tests/test_faint/test_linear_gradient.py	lukas-ke/faint-graphics-editor	10	156861	<reponame>lukas-ke/faint-graphics-editor<gh_stars>1-10 #!/usr/bin/env python3 # -- coding: utf-8 -- import unittest from faint import LinearGradient class TestLinearGradient(unittest.TestCase): def test_init_types(self): # Arguments required ar = self.assertRaises(ValueError) with ar: LinearGradient(); with ar: LinearGradient(42.0); with ar: LinearGradient(0.0, (255,0,255)); # Integer and float offset (was broken once!) LinearGradient((0, (255,0,255))) LinearGradient((0.0, (255,0,255))) LinearGradient((1, (255,0,255))) # Angle LinearGradient(42, (0.0, (255,0,255))) LinearGradient(42.0, (0.0, (255,0,255))) # Multiple color stops LinearGradient(42.0, (0.0, (255,0,255)), (1.0, (255,255,255))) def test_angle(self): lg1 = LinearGradient((42, (255,0,255))) # Fixme: Replace set, get_angle with property self.assertEqual(lg1.get_angle(), 0, "Default angle is 0") lg2 = LinearGradient(42, (0.0, (255, 0, 255))) self.assertAlmostEqual(lg2.get_angle(), 42.0) lg1.set_angle(20.0) self.assertEqual(lg1.get_angle(), 20.0) def test_color_stops(self): lg = LinearGradient((0.0, (255,254,253)), (0.5, (0, 1, 2, 3))) self.assertEqual(lg.get_num_stops(), 2) s0, s1 = lg.get_stops() self.assertEqual(s0, (0.0, (255, 254, 253, 255))) self.assertEqual(s1, (0.5, (0, 1, 2, 3))) lg.add_stop(0.6, (50, 60, 70)) stops = lg.get_stops() self.assertEqual(len(stops), 3) s2 = stops[2] self.assertEqual(s2, (0.6, (50, 60, 70, 255))) 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nitmis_admin/controllers/register.py	kalesh13/nitmis	0	15832	from rest_framework.views import APIView from rest_framework.response import Response from django.shortcuts import render from django.http.response import JsonResponse from nitmis_admin.serializers.UserSerializer import UserSerializer def create_user(role="Guest"): """ """ def fun_wrapper(func): def wrapper(args, *kwargs): serializer = UserSerializer(data=args[1].data) # # If the data is valid, create a new user # and return the access token details. if serializer.is_valid(): serializer.save(role=role) return JsonResponse(serializer.data) return JsonResponse({"errors": serializer.errors}, status=422) return wrapper return fun_wrapper class Register(APIView): ''' Parent register controller. Post requests create a general Guest account ''' def get(self, request): ''' Renders the base layout on GET request. Frontend handles the rendering of forms ''' return render(request, 'base.html') @create_user() def post(self, request): ''' Registers a new user and assigns the user a Guest role. ''' class AdminRegister(Register): ''' Register controller for administrators. ''' @create_user(role="Administrator") def post(self, request): ''' Overriden post function. Registers the user as an administrator '''	[ 1, 515, 1791, 29918, 4468, 29889, 7406, 1053, 3450, 1043, 13, 3166, 1791, 29918, 4468, 29889, 5327, 1053, 13291, 13, 3166, 9557, 29889, 12759, 7582, 29879, 1053, 4050, 13, 3166, 9557, 29889, 1124, 29889, 5327, 1053, 14355, 5103, 13, 3166, 21767, 26737, 29918, 6406, 29889, 15550, 19427, 29889, 2659, 17679, 1053, 4911, 17679, 13, 13, 13, 1753, 1653, 29918, 1792, 29898, 12154, 543, 9485, 342, 29908, 1125, 13, 1678, 9995, 13, 1678, 9995, 13, 1678, 822, 2090, 29918, 17699, 29898, 9891, 1125, 13, 4706, 822, 14476, 10456, 5085, 29892, 3579, 19290, 1125, 13, 9651, 7797, 3950, 353, 4911, 17679, 29898, 1272, 29922, 5085, 29961, 29896, 1822, 1272, 29897, 13, 9651, 396, 13, 9651, 396, 960, 278, 848, 338, 2854, 29892, 1653, 263, 716, 1404, 13, 9651, 396, 322, 736, 278, 2130, 5993, 4902, 29889, 13, 9651, 565, 7797, 3950, 29889, 275, 29918, 3084, 7295, 13, 18884, 7797, 3950, 29889, 7620, 29898, 12154, 29922, 12154, 29897, 13, 18884, 736, 14355, 5103, 29898, 15550, 3950, 29889, 1272, 29897, 13, 9651, 736, 14355, 5103, 3319, 29908, 12523, 1115, 7797, 3950, 29889, 12523, 1118, 4660, 29922, 29946, 29906, 29906, 29897, 13, 4706, 736, 14476, 13, 1678, 736, 2090, 29918, 17699, 13, 13, 13, 1990, 12577, 29898, 8787, 1043, 1125, 13, 1678, 14550, 13, 1678, 22280, 6036, 4701, 29889, 4918, 7274, 1653, 13, 1678, 263, 2498, 2088, 342, 3633, 13, 1678, 14550, 13, 13, 1678, 822, 679, 29898, 1311, 29892, 2009, 1125, 13, 4706, 14550, 13, 4706, 390, 21043, 278, 2967, 5912, 373, 12354, 2009, 29889, 13960, 355, 13, 4706, 17766, 278, 15061, 310, 7190, 13, 4706, 14550, 13, 4706, 736, 4050, 29898, 3827, 29892, 525, 3188, 29889, 1420, 1495, 13, 13, 1678, 732, 3258, 29918, 1792, 580, 13, 1678, 822, 1400, 29898, 1311, 29892, 2009, 1125, 13, 4706, 14550, 13, 4706, 12577, 29879, 263, 716, 1404, 322, 3566, 29879, 278, 1404, 13, 4706, 263, 2088, 342, 6297, 29889, 13, 4706, 14550, 13, 13, 13, 1990, 10229, 15213, 29898, 15213, 1125, 13, 1678, 14550, 13, 1678, 12577, 4701, 363, 6343, 4097, 29889, 13, 1678, 14550, 13, 13, 1678, 732, 3258, 29918, 1792, 29898, 12154, 543, 12754, 2132, 1061, 1159, 13, 1678, 822, 1400, 29898, 1311, 29892, 2009, 1125, 13, 4706, 14550, 13, 4706, 6811, 2429, 264, 1400, 740, 29889, 12577, 29879, 278, 1404, 408, 13, 4706, 385, 27443, 13, 4706, 14550, 13, 2 ]
src/rreil/variable.py	tizmd/python-gdsl	0	127256	class Variable(object): def __init__(self, _id , offset): self._id = _id self.offset = offset def __str__(self): s = str(self._id) if self.offset: s = s + "/%d" % self.offset return s class VariableLimited(object): def __init__(self, _id , offset, size): self._id = _id self.offset = offset self.size = size def __str__(self): s = str(self._id) if self.offset: s = s + "/%d" % self.offset return s + ":%d" % self.size def VariableLimitedListInit(): return list() def VariableLimitedListNext(a, l): l.append(a) return l	[ 1, 29871, 13, 1990, 28736, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 903, 333, 1919, 9210, 1125, 13, 4706, 1583, 3032, 333, 353, 903, 333, 13, 4706, 1583, 29889, 10289, 353, 9210, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 269, 353, 851, 29898, 1311, 3032, 333, 29897, 13, 4706, 565, 1583, 29889, 10289, 29901, 13, 9651, 269, 353, 269, 718, 5591, 29995, 29881, 29908, 1273, 1583, 29889, 10289, 13, 4706, 736, 269, 13, 13, 1990, 28736, 29931, 326, 1573, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 903, 333, 1919, 9210, 29892, 2159, 1125, 13, 4706, 1583, 3032, 333, 353, 903, 333, 13, 4706, 1583, 29889, 10289, 353, 9210, 13, 4706, 1583, 29889, 2311, 353, 2159, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 269, 353, 851, 29898, 1311, 3032, 333, 29897, 13, 4706, 565, 1583, 29889, 10289, 29901, 13, 9651, 269, 353, 269, 718, 5591, 29995, 29881, 29908, 1273, 1583, 29889, 10289, 13, 4706, 736, 269, 718, 376, 16664, 29881, 29908, 1273, 1583, 29889, 2311, 13, 13, 1753, 28736, 29931, 326, 1573, 1293, 6644, 7295, 13, 1678, 736, 1051, 580, 13, 13, 1753, 28736, 29931, 326, 1573, 1293, 9190, 29898, 29874, 29892, 301, 1125, 13, 1678, 301, 29889, 4397, 29898, 29874, 29897, 13, 1678, 736, 301, 13, 308, 13, 308, 13, 2 ]
ShapeFlow/deform_3_obj.py	vikasTmz/SP-GAN	46	1600756	<reponame>vikasTmz/SP-GAN<filename>ShapeFlow/deform_3_obj.py import torch.optim as optim from shapeflow.layers.chamfer_layer import ChamferDistKDTree from shapeflow.layers.deformation_layer import NeuralFlowDeformer from shapeflow.layers.pointnet_layer import PointNetEncoder import torch import numpy as np from time import time import trimesh from glob import glob files = sorted(glob("data/shapenet_watertight/val/03001627//.ply")) m1 = trimesh.load(files[1]) m2 = trimesh.load(files[6]) m3 = trimesh.load(files[7]) device = torch.device("cuda:0") chamfer_dist = ChamferDistKDTree(reduction="mean", njobs=1).to(device) criterion = torch.nn.MSELoss() latent_size = 3 deformer = NeuralFlowDeformer( latent_size=latent_size, f_nlayers=6, f_width=100, s_nlayers=2, s_width=5, method="dopri5", nonlinearity="elu", arch="imnet", adjoint=True, atol=1e-4, rtol=1e-4, ).to(device) encoder = PointNetEncoder( nf=16, out_features=latent_size, dropout_prob=0.0 ).to(device) # this is an awkward workaround to get gradients for encoder via adjoint solver deformer.add_encoder(encoder) deformer.to(device) encoder = deformer.net.encoder optimizer = optim.Adam(list(deformer.parameters()), lr=1e-3) niter = 1000 npts = 5000 V1 = torch.tensor(m1.vertices.astype(np.float32)).to(device) # .unsqueeze(0) V2 = torch.tensor(m2.vertices.astype(np.float32)).to(device) # .unsqueeze(0) V3 = torch.tensor(m3.vertices.astype(np.float32)).to(device) # .unsqueeze(0) loss_min = 1e30 tic = time() encoder.train() for it in range(0, niter): optimizer.zero_grad() seq1 = torch.randperm(V1.shape[0], device=device)[:npts] seq2 = torch.randperm(V2.shape[0], device=device)[:npts] seq3 = torch.randperm(V3.shape[0], device=device)[:npts] V1_samp = V1[seq1] V2_samp = V2[seq2] V3_samp = V3[seq3] V_src = torch.stack( [V1_samp, V1_samp, V2_samp], dim=0 ) # [batch, npoints, 3] V_tar = torch.stack( [V2_samp, V3_samp, V3_samp], dim=0 ) # [batch, npoints, 3] V_src_tar = torch.cat([V_src, V_tar], dim=0) V_tar_src = torch.cat([V_tar, V_src], dim=0) batch_latent_src_tar = encoder(V_src_tar) batch_latent_tar_src = torch.cat( [batch_latent_src_tar[3:], batch_latent_src_tar[:3]] ) V_deform = deformer(V_src_tar, batch_latent_src_tar, batch_latent_tar_src) _, _, dist = chamfer_dist(V_deform, V_tar_src) loss = criterion(dist, torch.zeros_like(dist)) loss.backward() optimizer.step() if it % 100 == 0 or True: print(f"iter={it}, loss={np.sqrt(loss.item())}") toc = time() print("Time for {} iters: {:.4f} s".format(niter, toc - tic)) # save deformed mesh encoder.eval() with torch.no_grad(): V1_latent = encoder(V1.unsqueeze(0)) V2_latent = encoder(V2.unsqueeze(0)) V3_latent = encoder(V3.unsqueeze(0)) V1_2 = ( deformer(V1.unsqueeze(0), V1_latent, V2_latent) .detach() .cpu() .numpy()[0] ) V2_1 = ( deformer(V2.unsqueeze(0), V2_latent, V1_latent) .detach() .cpu() .numpy()[0] ) V1_3 = ( deformer(V1.unsqueeze(0), V1_latent, V3_latent) .detach() .cpu() .numpy()[0] ) V3_1 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shadow4/syned/absorbers/absorber.py	srio/minishadow	1	189031	from syned.beamline.optical_element import OpticalElement from syned.beamline.shape import BoundaryShape class Absorber(OpticalElement): def __init__(self, name="Undefined", boundary_shape=None): if boundary_shape is None: boundary_shape = BoundaryShape() OpticalElement.__init__(self, name=name, boundary_shape=boundary_shape)	[ 1, 29871, 13, 3166, 5222, 287, 29889, 915, 314, 1220, 29889, 3670, 936, 29918, 5029, 1053, 20693, 936, 2642, 13, 3166, 5222, 287, 29889, 915, 314, 1220, 29889, 12181, 1053, 350, 618, 653, 24111, 13, 13, 1990, 24650, 272, 495, 29898, 20624, 936, 2642, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1024, 543, 25263, 5598, 613, 10452, 29918, 12181, 29922, 8516, 1125, 13, 4706, 565, 10452, 29918, 12181, 338, 6213, 29901, 13, 9651, 10452, 29918, 12181, 353, 350, 618, 653, 24111, 580, 13, 4706, 20693, 936, 2642, 17255, 2344, 12035, 1311, 29892, 1024, 29922, 978, 29892, 10452, 29918, 12181, 29922, 9917, 653, 29918, 12181, 29897, 2 ]
climpred/tests/__init__.py	aaronspring/climpred	0	151058	import importlib from distutils import version import pytest def _importorskip(modname, minversion=None): try: mod = importlib.import_module(modname) has = True if minversion is not None: if LooseVersion(mod.__version__) < LooseVersion(minversion): raise ImportError("Minimum version not satisfied") except ImportError: has = False func = pytest.mark.skipif(not has, reason=f"requires {modname}") return has, func def LooseVersion(vstring): # Our development version is something like '0.10.9+aac7bfc' # This function just ignored the git commit id. vstring = vstring.split("+")[0] return version.LooseVersion(vstring) has_matplotlib, requires_matplotlib = _importorskip("matplotlib") has_nc_time_axis, requires_nc_time_axis = _importorskip("nc_time_axis", "1.4.0") has_xclim, requires_xclim = _importorskip("xclim", "0.31") has_bias_correction, requires_bias_correction = _importorskip("bias_correction") has_xesmf, requires_xesmf = _importorskip("xesmf") has_xrft, requires_xrft = _importorskip("xrft") has_eofs, requires_eofs = _importorskip("eofs")	[ 1, 1053, 1053, 1982, 13, 3166, 1320, 13239, 1053, 1873, 13, 13, 5215, 11451, 1688, 13, 13, 13, 1753, 903, 5215, 272, 11014, 29898, 1545, 978, 29892, 1375, 3259, 29922, 8516, 1125, 13, 1678, 1018, 29901, 13, 4706, 878, 353, 1053, 1982, 29889, 5215, 29918, 5453, 29898, 1545, 978, 29897, 13, 4706, 756, 353, 5852, 13, 4706, 565, 1375, 3259, 338, 451, 6213, 29901, 13, 9651, 565, 4309, 852, 6594, 29898, 1545, 17255, 3259, 1649, 29897, 529, 4309, 852, 6594, 29898, 1195, 3259, 1125, 13, 18884, 12020, 16032, 2392, 703, 8140, 12539, 1873, 451, 15787, 1159, 13, 1678, 5174, 16032, 2392, 29901, 13, 4706, 756, 353, 7700, 13, 1678, 3653, 353, 11451, 1688, 29889, 3502, 29889, 11014, 361, 29898, 1333, 756, 29892, 2769, 29922, 29888, 29908, 276, 339, 2658, 426, 1545, 978, 27195, 13, 1678, 736, 756, 29892, 3653, 13, 13, 13, 1753, 4309, 852, 6594, 29898, 29894, 1807, 1125, 13, 1678, 396, 8680, 5849, 1873, 338, 1554, 763, 525, 29900, 29889, 29896, 29900, 29889, 29929, 29974, 29874, 562, 29955, 1635, 29883, 29915, 13, 1678, 396, 910, 740, 925, 17262, 278, 6315, 9063, 1178, 29889, 13, 1678, 325, 1807, 353, 325, 1807, 29889, 5451, 703, 29974, 1159, 29961, 29900, 29962, 13, 1678, 736, 1873, 29889, 3410, 852, 6594, 29898, 29894, 1807, 29897, 13, 13, 13, 5349, 29918, 2922, 17357, 29892, 6858, 29918, 2922, 17357, 353, 903, 5215, 272, 11014, 703, 2922, 17357, 1159, 13, 5349, 29918, 17608, 29918, 2230, 29918, 8990, 29892, 6858, 29918, 17608, 29918, 2230, 29918, 8990, 353, 903, 5215, 272, 11014, 703, 17608, 29918, 2230, 29918, 8990, 613, 376, 29896, 29889, 29946, 29889, 29900, 1159, 13, 5349, 29918, 29916, 695, 326, 29892, 6858, 29918, 29916, 695, 326, 353, 903, 5215, 272, 11014, 703, 29916, 695, 326, 613, 376, 29900, 29889, 29941, 29896, 1159, 13, 5349, 29918, 29890, 3173, 29918, 2616, 276, 428, 29892, 6858, 29918, 29890, 3173, 29918, 2616, 276, 428, 353, 903, 5215, 272, 11014, 703, 29890, 3173, 29918, 2616, 276, 428, 1159, 13, 5349, 29918, 9100, 29885, 29888, 29892, 6858, 29918, 9100, 29885, 29888, 353, 903, 5215, 272, 11014, 703, 9100, 29885, 29888, 1159, 13, 5349, 29918, 29916, 29878, 615, 29892, 6858, 29918, 29916, 29878, 615, 353, 903, 5215, 272, 11014, 703, 29916, 29878, 615, 1159, 13, 5349, 29918, 29872, 974, 29879, 29892, 6858, 29918, 29872, 974, 29879, 353, 903, 5215, 272, 11014, 703, 29872, 974, 29879, 1159, 13, 2 ]
Intermediate/main.py	fredtheninja/100-Days-of-Code	0	65389	<reponame>fredtheninja/100-Days-of-Code from turtle import Screen from snake import Snake import time screen = Screen() screen.setup(width=600, height=600) screen.bgcolor("black") screen.title("Snake") screen.tracer(0) # -----------------------------Angela-----------------------------------# snake = Snake() screen.listen() screen.onkey(snake.up, "Up") screen.onkey(snake.down, "Down") screen.onkey(snake.left, "Left") screen.onkey(snake.right, "Right") game_is_on = True while game_is_on: screen.update() time.sleep(0.1) snake.move() # -----------------------------FredTheNinja-----------------------------------# # # my solution to challenge 1 # bodies = 3 # x = 0 # y = 0 # for i in range(0, bodies): # snake = Turtle(shape="square") # snake.color("white") # snake.shapesize(1) # snake.pu() # snake.goto(x, y) # x -= 20 screen.exitonclick()	[ 1, 529, 276, 1112, 420, 29958, 18447, 6098, 262, 1764, 29914, 29896, 29900, 29900, 29899, 25991, 29899, 974, 29899, 3399, 13, 3166, 260, 4227, 280, 1053, 22666, 13, 3166, 269, 21040, 1053, 317, 21040, 13, 5215, 931, 13, 13, 10525, 353, 22666, 580, 13, 10525, 29889, 14669, 29898, 2103, 29922, 29953, 29900, 29900, 29892, 3171, 29922, 29953, 29900, 29900, 29897, 13, 10525, 29889, 16264, 2780, 703, 8517, 1159, 13, 10525, 29889, 3257, 703, 29903, 21040, 1159, 13, 10525, 29889, 29873, 945, 261, 29898, 29900, 29897, 13, 13, 29937, 448, 2683, 9072, 9928, 3100, 2683, 2683, 5634, 29937, 13, 13, 29879, 21040, 353, 317, 21040, 580, 13, 10525, 29889, 20631, 580, 13, 10525, 29889, 265, 1989, 29898, 29879, 21040, 29889, 786, 29892, 376, 3373, 1159, 13, 10525, 29889, 265, 1989, 29898, 29879, 21040, 29889, 3204, 29892, 376, 6767, 1159, 13, 10525, 29889, 265, 1989, 29898, 29879, 21040, 29889, 1563, 29892, 376, 8091, 1159, 13, 10525, 29889, 265, 1989, 29898, 29879, 21040, 29889, 1266, 29892, 376, 7341, 1159, 13, 13, 11802, 29918, 275, 29918, 265, 353, 5852, 13, 13, 8000, 3748, 29918, 275, 29918, 265, 29901, 13, 1678, 4315, 29889, 5504, 580, 13, 1678, 931, 29889, 17059, 29898, 29900, 29889, 29896, 29897, 13, 1678, 269, 21040, 29889, 11631, 580, 13, 13, 13, 29937, 448, 2683, 9072, 29943, 1127, 1576, 29940, 262, 1764, 2683, 2683, 5634, 29937, 13, 13, 29937, 396, 590, 1650, 304, 18766, 29871, 29896, 13, 29937, 17873, 353, 29871, 29941, 13, 29937, 921, 353, 29871, 29900, 13, 29937, 343, 353, 29871, 29900, 13, 13, 29937, 363, 474, 297, 3464, 29898, 29900, 29892, 17873, 1125, 13, 29937, 268, 269, 21040, 353, 323, 4227, 280, 29898, 12181, 543, 17619, 1159, 13, 29937, 268, 269, 21040, 29889, 2780, 703, 10921, 1159, 13, 29937, 268, 269, 21040, 29889, 845, 11603, 675, 29898, 29896, 29897, 13, 29937, 268, 269, 21040, 29889, 3746, 580, 13, 29937, 268, 269, 21040, 29889, 27102, 29898, 29916, 29892, 343, 29897, 13, 29937, 268, 921, 22361, 29871, 29906, 29900, 13, 13, 13, 10525, 29889, 13322, 265, 3808, 580, 13, 2 ]
sample.py	leeamen/threadpool	3	166982	#!/usr/bin/python #coding:utf-8 import mylog import mythreadpool as tp import logging if __name__ == '__main__': logger = logging.getLogger(__name__) def process(args): logger.info('task %d is finished!', args['taskid']) threadpool = tp.MyThreadPool(2) for i in range(1,10): threadpool.DispatchTask(process, {'taskid':i}) threadpool.Destroy()	[ 1, 18787, 4855, 29914, 2109, 29914, 4691, 13, 29937, 29883, 3689, 29901, 9420, 29899, 29947, 13, 13, 5215, 590, 1188, 13, 5215, 590, 7097, 10109, 408, 260, 29886, 13, 5215, 12183, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 29871, 17927, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 259, 13, 29871, 822, 1889, 29898, 5085, 1125, 13, 1678, 17927, 29889, 3888, 877, 7662, 1273, 29881, 338, 7743, 29991, 742, 6389, 1839, 7662, 333, 11287, 13, 259, 13, 29871, 3244, 10109, 353, 260, 29886, 29889, 3421, 23574, 29898, 29906, 29897, 13, 29871, 363, 474, 297, 3464, 29898, 29896, 29892, 29896, 29900, 1125, 13, 1678, 3244, 10109, 29889, 14777, 5398, 29898, 5014, 29892, 11117, 7662, 333, 2396, 29875, 1800, 13, 29871, 3244, 10109, 29889, 14994, 4727, 580, 13, 13, 2 ]
hatch/structures.py	kapb14/hatch	1	92496	<gh_stars>1-10 import os from urllib.parse import urlencode class File: def __init__(self, file_name, contents, binary=False): self.file_name = file_name self.contents = contents self.write_mode = 'wb' if binary else 'w' def write(self, d): if not os.path.exists(d): os.makedirs(d) with open(os.path.join(d, self.file_name), self.write_mode) as f: f.write(self.contents) class Badge: def __init__(self, image, target, alt, params=None): self.image = image self.target = target self.alt = alt if params: self.image += '?' + urlencode(params)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 2897, 13, 3166, 3142, 1982, 29889, 5510, 1053, 3142, 12508, 13, 13, 13, 1990, 3497, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 934, 29918, 978, 29892, 8118, 29892, 7581, 29922, 8824, 1125, 13, 4706, 1583, 29889, 1445, 29918, 978, 353, 934, 29918, 978, 13, 4706, 1583, 29889, 10853, 353, 8118, 13, 4706, 1583, 29889, 3539, 29918, 8513, 353, 525, 29893, 29890, 29915, 565, 7581, 1683, 525, 29893, 29915, 13, 13, 1678, 822, 2436, 29898, 1311, 29892, 270, 1125, 13, 4706, 565, 451, 2897, 29889, 2084, 29889, 9933, 29898, 29881, 1125, 13, 9651, 2897, 29889, 29885, 12535, 12935, 29898, 29881, 29897, 13, 13, 4706, 411, 1722, 29898, 359, 29889, 2084, 29889, 7122, 29898, 29881, 29892, 1583, 29889, 1445, 29918, 978, 511, 1583, 29889, 3539, 29918, 8513, 29897, 408, 285, 29901, 13, 9651, 285, 29889, 3539, 29898, 1311, 29889, 10853, 29897, 13, 13, 13, 1990, 9178, 479, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1967, 29892, 3646, 29892, 5272, 29892, 8636, 29922, 8516, 1125, 13, 4706, 1583, 29889, 3027, 353, 1967, 13, 4706, 1583, 29889, 5182, 353, 3646, 13, 4706, 1583, 29889, 1997, 353, 5272, 13, 13, 4706, 565, 8636, 29901, 13, 9651, 1583, 29889, 3027, 4619, 525, 17901, 718, 3142, 12508, 29898, 7529, 29897, 13, 2 ]
ansible/venv/lib/python2.7/site-packages/ansible/modules/network/f5/bigip_asm_policy_import.py	gvashchenkolineate/gvashchenkolineate_infra_trytravis	17	11250	#!/usr/bin/python # -- coding: utf-8 -- # # Copyright: (c) 2018, F5 Networks Inc. # GNU General Public License v3.0 (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'certified'} DOCUMENTATION = r''' --- module: bigip_asm_policy_import short_description: Manage BIG-IP ASM policy imports description: - Manage BIG-IP ASM policies policy imports. version_added: 2.8 options: name: description: - The ASM policy to create or override. type: str required: True inline: description: - When specified the ASM policy is created from a provided string. - Content needs to be provided in a valid XML format otherwise the operation will fail. type: str source: description: - Full path to a policy file to be imported into the BIG-IP ASM. - Policy files exported from newer versions of BIG-IP cannot be imported into older versions of BIG-IP. The opposite, however, is true; you can import older into newer. - The file format can be binary of XML. type: path force: description: - When set to C(yes) any existing policy with the same name will be overwritten by the new import. - Works for both inline and file imports, if the policy does not exist this setting is ignored. default: no type: bool partition: description: - Device partition to create policy on. type: str default: Common extends_documentation_fragment: f5 author: - <NAME> (@wojtek0806) ''' EXAMPLES = r''' - name: Import ASM policy bigip_asm_policy_import: name: new_asm_policy file: /root/asm_policy.xml provider: server: lb.mydomain.com user: admin password: <PASSWORD> delegate_to: localhost - name: Import ASM policy inline bigip_asm_policy_import: name: foo-policy4 inline: <xml>content</xml> provider: server: lb.mydomain.com user: admin password: <PASSWORD> delegate_to: localhost - name: Override existing ASM policy bigip_asm_policy: name: new_asm_policy file: /root/asm_policy_new.xml force: yes provider: server: lb.mydomain.com user: admin password: <PASSWORD> delegate_to: localhost ''' RETURN = r''' source: description: Local path to an ASM policy file. returned: changed type: str sample: /root/some_policy.xml inline: description: Contents of policy as an inline string returned: changed type: str sample: <xml>foobar contents</xml> name: description: Name of the ASM policy to be created/overwritten returned: changed type: str sample: Asm_APP1_Transparent force: description: Set when overwriting an existing policy returned: changed type: bool sample: yes ''' import os import time from ansible.module_utils.basic import AnsibleModule from ansible.module_utils.basic import env_fallback try: from library.module_utils.network.f5.bigip import F5RestClient from library.module_utils.network.f5.common import F5ModuleError from library.module_utils.network.f5.common import AnsibleF5Parameters from library.module_utils.network.f5.common import fq_name from library.module_utils.network.f5.common import transform_name from library.module_utils.network.f5.common import f5_argument_spec from library.module_utils.network.f5.icontrol import upload_file from library.module_utils.network.f5.icontrol import module_provisioned except ImportError: from ansible.module_utils.network.f5.bigip import F5RestClient from ansible.module_utils.network.f5.common import F5ModuleError from ansible.module_utils.network.f5.common import AnsibleF5Parameters from ansible.module_utils.network.f5.common import fq_name from ansible.module_utils.network.f5.common import transform_name from ansible.module_utils.network.f5.common import f5_argument_spec from ansible.module_utils.network.f5.icontrol import upload_file from ansible.module_utils.network.f5.icontrol import module_provisioned class Parameters(AnsibleF5Parameters): updatables = [] returnables = [ 'name', 'inline', 'source', 'force' ] api_attributes = [ 'file', 'name', ] api_map = { 'file': 'inline', 'filename': 'source', } class ApiParameters(Parameters): pass class ModuleParameters(Parameters): pass class Changes(Parameters): def to_return(self): result = {} try: for returnable in self.returnables: result[returnable] = getattr(self, returnable) result = self._filter_params(result) except Exception: pass return result class UsableChanges(Changes): pass class ReportableChanges(Changes): pass class Difference(object): def __init__(self, want, have=None): self.want = want self.have = have def compare(self, param): try: result = getattr(self, param) return result except AttributeError: return self.__default(param) def __default(self, param): attr1 = getattr(self.want, param) try: attr2 = getattr(self.have, param) if attr1 != attr2: return attr1 except AttributeError: return attr1 class ModuleManager(object): def __init__(self, args, kwargs): self.module = kwargs.get('module', None) self.client = F5RestClient(self.module.params) self.want = ModuleParameters(params=self.module.params) self.changes = UsableChanges() def _set_changed_options(self): changed = {} for key in Parameters.returnables: if getattr(self.want, key) is not None: changed[key] = getattr(self.want, key) if changed: self.changes = UsableChanges(params=changed) def _announce_deprecations(self, result): warnings = result.pop('__warnings', []) for warning in warnings: self.client.module.deprecate( msg=warning['msg'], version=warning['version'] ) def exec_module(self): if not module_provisioned(self.client, 'asm'): raise F5ModuleError( "ASM must be provisioned to use this module." ) result = dict() changed = self.policy_import() reportable = ReportableChanges(params=self.changes.to_return()) changes = reportable.to_return() result.update(changes) result.update(dict(changed=changed)) self._announce_deprecations(result) return result def policy_import(self): self._set_changed_options() if self.module.check_mode: return True if self.exists(): if self.want.force is False: return False if self.want.inline: task = self.inline_import() self.wait_for_task(task) return True self.import_file_to_device() self.remove_temp_policy_from_device() return True def exists(self): uri = 'https://{0}:{1}/mgmt/tm/asm/policies/'.format( self.client.provider['server'], self.client.provider['server_port'], ) query = "?$filter=contains(name,'{0}')+and+contains(partition,'{1}')&$select=name,partition".format( self.want.name, self.want.partition ) resp = self.client.api.get(uri + query) try: response = resp.json() except ValueError as ex: raise F5ModuleError(str(ex)) if 'items' in response and response['items'] != []: return True return False def upload_file_to_device(self, content, name): url = 'https://{0}:{1}/mgmt/shared/file-transfer/uploads'.format( self.client.provider['server'], self.client.provider['server_port'] ) try: upload_file(self.client, url, content, name) except F5ModuleError: raise F5ModuleError( "Failed to upload the file." ) def _get_policy_link(self): uri = 'https://{0}:{1}/mgmt/tm/asm/policies/'.format( self.client.provider['server'], self.client.provider['server_port'], ) query = "?$filter=contains(name,'{0}')+and+contains(partition,'{1}')&$select=name,partition".format( self.want.name, self.want.partition ) resp = self.client.api.get(uri + query) try: response = resp.json() except ValueError as ex: raise F5ModuleError(str(ex)) policy_link = response['items'][0]['selfLink'] return policy_link def inline_import(self): params = self.changes.api_params() uri = "https://{0}:{1}/mgmt/tm/asm/tasks/import-policy/".format( self.client.provider['server'], self.client.provider['server_port'], ) if self.want.force: params.update(dict(policyReference={'link': self._get_policy_link()})) params.pop('name') resp = self.client.api.post(uri, json=params) try: response = resp.json() except ValueError as ex: raise F5ModuleError(str(ex)) if 'code' in response and response['code'] in [400, 403]: if 'message' in response: raise F5ModuleError(response['message']) else: raise F5ModuleError(resp.content) return response['id'] def wait_for_task(self, task_id): uri = "https://{0}:{1}/mgmt/tm/asm/tasks/import-policy/{2}".format( self.client.provider['server'], self.client.provider['server_port'], task_id ) while True: resp = self.client.api.get(uri) try: response = resp.json() except ValueError as ex: raise F5ModuleError(str(ex)) if 'code' in response and response['code'] == 400: if 'message' in response: raise F5ModuleError(response['message']) else: raise F5ModuleError(resp.content) if response['status'] in ['COMPLETED', 'FAILURE']: break time.sleep(1) if response['status'] == 'FAILURE': raise F5ModuleError( 'Failed to import ASM policy.' ) if response['status'] == 'COMPLETED': return True def import_file_to_device(self): name = os.path.split(self.want.source)[1] self.upload_file_to_device(self.want.source, name) time.sleep(2) full_name = fq_name(self.want.partition, self.want.name) if self.want.force: cmd = 'tmsh load asm policy {0} file /var/config/rest/downloads/{1} overwrite'.format(full_name, name) else: cmd = 'tmsh load asm policy {0} file /var/config/rest/downloads/{1}'.format(full_name, name) uri = "https://{0}:{1}/mgmt/tm/util/bash/".format( self.client.provider['server'], self.client.provider['server_port'], ) args = dict( command='run', utilCmdArgs='-c "{0}"'.format(cmd) ) resp = self.client.api.post(uri, json=args) try: response = resp.json() if 'commandResult' in response: if 'Unexpected Error' in response['commandResult']: raise F5ModuleError(response['commandResult']) except ValueError as ex: raise F5ModuleError(str(ex)) if 'code' in response and response['code'] == 400: if 'message' in response: raise F5ModuleError(response['message']) else: raise F5ModuleError(resp.content) return True def remove_temp_policy_from_device(self): name = os.path.split(self.want.source)[1] tpath_name = '/var/config/rest/downloads/{0}'.format(name) uri = "https://{0}:{1}/mgmt/tm/util/unix-rm/".format( self.client.provider['server'], self.client.provider['server_port'], ) args = dict( command='run', utilCmdArgs=tpath_name ) resp = self.client.api.post(uri, json=args) try: response = resp.json() except ValueError as ex: raise F5ModuleError(str(ex)) if 'code' in response and response['code'] == 400: if 'message' in response: raise F5ModuleError(response['message']) else: raise F5ModuleError(resp.content) class ArgumentSpec(object): def __init__(self): self.supports_check_mode = True argument_spec = dict( name=dict( required=True, ), source=dict(type='path'), inline=dict(), force=dict( type='bool', default='no' ), partition=dict( default='Common', fallback=(env_fallback, ['F5_PARTITION']) ) ) self.argument_spec = {} self.argument_spec.update(f5_argument_spec) self.argument_spec.update(argument_spec) self.mutually_exclusive = [ ['source', 'inline'] ] def main(): spec = ArgumentSpec() module = AnsibleModule( argument_spec=spec.argument_spec, 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12_module_release/message/__init__.py	DeveloperLY/Python-practice	0	1334	<filename>12_module_release/message/__init__.py from . import send_message from . import receive_message	[ 1, 529, 9507, 29958, 29896, 29906, 29918, 5453, 29918, 14096, 29914, 4906, 29914, 1649, 2344, 26914, 2272, 13, 3166, 869, 1053, 3638, 29918, 4906, 13, 3166, 869, 1053, 7150, 29918, 4906, 2 ]
file_util.py	d4yvie/advent_of_code_2021	0	184525	from functools import reduce import numpy as np def read_all(file_name="measurements") -> str: with open(file_name) as f: return reduce(lambda a, b: a + b, f, "") def read_lines(file_name="measurements") -> list[str]: with open(file_name) as f: return [line.strip() for line in f] def read_first_line(file_name="measurements") -> str: return read_lines(file_name)[0] def line_to_integers(line: str) -> list[int]: return list(map(int, line.split(","))) def read_first_line_as_integers(file_name="measurements") -> list[int]: return line_to_integers(read_first_line(file_name)) def read_line_seperated_data_sets(file_name="measurements") -> list[str]: with open(file_name) as f: return f.read().rstrip().split('\n\n') def read_lines_as_int(file_name="measurements") -> list[list[int]]: lines = read_lines(file_name) return [[int(char) for char in x] for x in lines] def read_lines_as_np_matrix(file_name="measurements") -> np.ndarray: lines = read_lines(file_name) return np.array([[float(char) for char in line] for line in lines])	[ 1, 515, 2090, 312, 8789, 1053, 10032, 13, 5215, 12655, 408, 7442, 13, 13, 13, 1753, 1303, 29918, 497, 29898, 1445, 29918, 978, 543, 26658, 1860, 1159, 1599, 851, 29901, 13, 1678, 411, 1722, 29898, 1445, 29918, 978, 29897, 408, 285, 29901, 13, 4706, 736, 10032, 29898, 2892, 263, 29892, 289, 29901, 263, 718, 289, 29892, 285, 29892, 20569, 13, 13, 13, 1753, 1303, 29918, 9012, 29898, 1445, 29918, 978, 543, 26658, 1860, 1159, 1599, 1051, 29961, 710, 5387, 13, 1678, 411, 1722, 29898, 1445, 29918, 978, 29897, 408, 285, 29901, 13, 4706, 736, 518, 1220, 29889, 17010, 580, 363, 1196, 297, 285, 29962, 13, 13, 13, 1753, 1303, 29918, 4102, 29918, 1220, 29898, 1445, 29918, 978, 543, 26658, 1860, 1159, 1599, 851, 29901, 13, 1678, 736, 1303, 29918, 9012, 29898, 1445, 29918, 978, 9601, 29900, 29962, 13, 13, 13, 1753, 1196, 29918, 517, 29918, 6693, 5743, 29898, 1220, 29901, 851, 29897, 1599, 1051, 29961, 524, 5387, 13, 1678, 736, 1051, 29898, 1958, 29898, 524, 29892, 1196, 29889, 5451, 29898, 3284, 4961, 13, 13, 13, 1753, 1303, 29918, 4102, 29918, 1220, 29918, 294, 29918, 6693, 5743, 29898, 1445, 29918, 978, 543, 26658, 1860, 1159, 1599, 1051, 29961, 524, 5387, 13, 1678, 736, 1196, 29918, 517, 29918, 6693, 5743, 29898, 949, 29918, 4102, 29918, 1220, 29898, 1445, 29918, 978, 876, 13, 13, 13, 1753, 1303, 29918, 1220, 29918, 344, 546, 630, 29918, 1272, 29918, 7224, 29898, 1445, 29918, 978, 543, 26658, 1860, 1159, 1599, 1051, 29961, 710, 5387, 13, 1678, 411, 1722, 29898, 1445, 29918, 978, 29897, 408, 285, 29901, 13, 4706, 736, 285, 29889, 949, 2141, 29878, 17010, 2141, 5451, 28909, 29876, 29905, 29876, 1495, 13, 13, 13, 1753, 1303, 29918, 9012, 29918, 294, 29918, 524, 29898, 1445, 29918, 978, 543, 26658, 1860, 1159, 1599, 1051, 29961, 1761, 29961, 524, 5262, 29901, 13, 1678, 3454, 353, 1303, 29918, 9012, 29898, 1445, 29918, 978, 29897, 13, 1678, 736, 5519, 524, 29898, 3090, 29897, 363, 1373, 297, 921, 29962, 363, 921, 297, 3454, 29962, 13, 13, 13, 1753, 1303, 29918, 9012, 29918, 294, 29918, 9302, 29918, 5344, 29898, 1445, 29918, 978, 543, 26658, 1860, 1159, 1599, 7442, 29889, 299, 2378, 29901, 13, 1678, 3454, 353, 1303, 29918, 9012, 29898, 1445, 29918, 978, 29897, 13, 1678, 736, 7442, 29889, 2378, 4197, 29961, 7411, 29898, 3090, 29897, 363, 1373, 297, 1196, 29962, 363, 1196, 297, 3454, 2314, 13, 2 ]
ffttest.py	teslaworksumn/Reactor	0	14333	# From http://julip.co/2012/05/arduino-python-soundlight-spectrum/ # Python 2.7 code to analyze sound and interface with Arduino import pyaudio # from http://people.csail.mit.edu/hubert/pyaudio/ import serial # from http://pyserial.sourceforge.net/ import numpy # from http://numpy.scipy.org/ import audioop import sys import math import struct ''' Sources http://www.swharden.com/blog/2010-03-05-realtime-fft-graph-of-audio-wav-file-or-microphone-input-with-python-scipy-and-wckgraph/ http://macdevcenter.com/pub/a/python/2001/01/31/numerically.html?page=2 ''' MAX = 0 NUM = 20 def list_devices(): # List all audio input devices p = pyaudio.PyAudio() i = 0 n = p.get_device_count() while i < n: dev = p.get_device_info_by_index(i) if dev['maxInputChannels'] > 0: print str(i)+'. '+dev['name'] i += 1 def fft(): chunk = 211 # Change if too fast/slow, never less than 211 scale = 25 # Change if too dim/bright exponent = 3 # Change if too little/too much difference between loud and quiet sounds samplerate = 44100 # CHANGE THIS TO CORRECT INPUT DEVICE # Enable stereo mixing in your sound card # to make you sound output an input # Use list_devices() to list all your input devices device = 1 # Mic #device = 4 # SF2 p = pyaudio.PyAudio() stream = p.open(format = pyaudio.paInt16, channels = 1, rate = 44100, input = True, frames_per_buffer = chunk, input_device_index = device) print "Starting, use Ctrl+C to stop" try: ser = serial.Serial( port='/dev/ttyS0', timeout=1 ) while True: data = stream.read(chunk) # Do FFT levels = calculate_levels(data, chunk, samplerate) # Make it look better and send to serial for level in levels: level = max(min(level / scale, 1.0), 0.0) level = level*exponent level = int(level 255) #ser.write(chr(level)) #sys.stdout.write(str(level)+' ') #sys.stdout.write('\n') #s = ser.read(6) except KeyboardInterrupt: pass finally: print "\nStopping" stream.close() p.terminate() #ser.close() def calculate_levels(data, chunk, samplerate): # Use FFT to calculate volume for each frequency global MAX # Convert raw sound data to Numpy array fmt = "%dH"%(len(data)/2) data2 = struct.unpack(fmt, data) data2 = numpy.array(data2, dtype='h') # Apply FFT fourier = numpy.fft.fft(data2) ffty = numpy.abs(fourier[0:len(fourier)/2])/1000 ffty1=ffty[:len(ffty)/2] ffty2=ffty[len(ffty)/2::]+2 ffty2=ffty2[::-1] ffty=ffty1+ffty2 ffty=numpy.log(ffty)-2 fourier = list(ffty)[4:-4] fourier = fourier[:len(fourier)/2] size = len(fourier) # Add up for 6 lights levels = [sum(fourier[i:(i+size/NUM)]) for i in xrange(0, size, size/NUM)][:NUM] return levels if __name__ == '__main__': #list_devices() fft()	[ 1, 396, 3645, 1732, 597, 29926, 352, 666, 29889, 1111, 29914, 29906, 29900, 29896, 29906, 29914, 29900, 29945, 29914, 538, 29884, 1789, 29899, 4691, 29899, 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src/knn.py	zhangxyz/MLFSdel	0	133802	#!usr/bin/env python #-- coding: utf-8 -- import logging import numpy as np import sklearn from sklearn import metrics from sklearn import neighbors from sklearn import cross_validation from sklearn.datasets import load_svmlight_file from sklearn.cross_validation import cross_val_score import math import sys import os from subprocess import * from sklearn.feature_selection import SelectPercentile, f_classif, SelectKBest from sklearn.pipeline import Pipeline from sklearn import metrics from sklearn.neighbors import DistanceMetric from sklearn.neighbors import KNeighborsClassifier def calculate_result(actual,pred): m_precision = metrics.precision_score(actual,pred); m_recall = metrics.recall_score(actual,pred); print 'predict info:' print 'accuracy:{0:.3f}'.format(metrics.accuracy_score(actual,pred)) print 'precision:{0:.3f}'.format(m_precision) print 'recall:{0:0.3f}'.format(m_recall); print 'f1-score:{0:.3f}'.format(metrics.f1_score(actual,pred)); def calculate_k(x,y): clf = KNeighborsClassifier(n_neighbors=3) max_metric=cross_val_score(clf,x,y,cv=10,scoring='f1').mean() k=3 for i in range(4,100): clf = KNeighborsClassifier(n_neighbors=i)#default with k=5 metric = cross_val_score(clf,x,y,cv=10,scoring='f1').mean() if max_metric < metric : max_metric=metric k=i return k def knn_result(train_x,train_y,test_x,test_y,out_file): print '***********************\nKNN\n***********************' #c= calculate_k(train_x,train_y) clf = KNeighborsClassifier(n_neighbors=10) #clf = KNeighborsClassifier(n_neighbors=c) clf.fit(train_x,train_y) pred = clf.predict(test_x); calculate_result(test_y,pred); np.savetxt(out_file,pred,fmt='%d') #print(c) def knn_select(train_x,train_y,test_x,test_y): #c= calculate_k(train_x,train_y) clf = KNeighborsClassifier(n_neighbors=10) #clf = KNeighborsClassifier(n_neighbors=c) clf.fit(train_x,train_y) pred = clf.predict(test_x); return metrics.precision_score(test_y,pred) #print(c)	[ 1, 396, 29991, 4855, 29914, 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ext_modules/_maix_nn/example/yolo2_camera.py	sipeed/python3-maix	93	6851	<gh_stars>10-100 from maix import nn from PIL import Image, ImageDraw, ImageFont from maix import display, camera import time from maix.nn import decoder def draw_rectangle_with_title(draw, box, disp_str, bg_color=(255, 0, 0, 255), font_color=(255, 255, 255, 255)): # draw = ImageDraw.Draw(img) font = ImageFont.load_default() font_w, font_h = font.getsize(disp_str) draw.rectangle((box[0], box[1], box[0] + box[2], box[1] + box[3]), fill=None, outline=bg_color, width=2) draw.rectangle((box[0], box[1] - font_h, box[0] + font_w, box[1]), fill=bg_color) draw.text((box[0], box[1] - font_h), disp_str, fill=font_color, font=font) camera.config(size=(224, 224)) model = { "param": "/root/models/yolo2_face_awnn.param", "bin": "/root/models/yolo2_face_awnn.bin" } options = { "model_type": "awnn", "inputs": { "input0": (224, 224, 3) }, "outputs": { "output0": (7, 7, (1+4+1)5) }, "mean": [127.5, 127.5, 127.5], "norm": [0.0078125, 0.0078125, 0.0078125], } print("-- load model:", model) m = nn.load(model, opt=options) print("-- load ok") print("-- read image") w = options["inputs"]["input0"][1] h = options["inputs"]["input0"][0] # # img.show() print("-- read image ok") labels = ["person"] anchors = [1.19, 1.98, 2.79, 4.59, 4.53, 8.92, 8.06, 5.29, 10.32, 10.65] yolo2_decoder = decoder.Yolo2(len(labels), anchors, net_in_size=(w, h), net_out_size=(7, 7)) while 1: img = camera.capture() if not img: time.sleep(0.01) continue t = time.time() out = m.forward(img, quantize=True, layout="hwc") print("-- forward: ", time.time() - t ) t = time.time() boxes, probs = yolo2_decoder.run(out, nms=0.3, threshold=0.5, img_size=(240, 240)) print("-- decode: ", time.time() - t ) t = time.time() for i, box in enumerate(boxes): class_id = probs[i][0] prob = probs[i][1][class_id] disp_str = "{}:{:.2f}%".format(labels[class_id], prob100) draw_rectangle_with_title(display.get_draw(), box, disp_str) print("-- draw: ", 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python/6kyu/playing_with_digits.py	momchilantonov/codewars	0	128589	<reponame>momchilantonov/codewars def dig_pow(n, p): """ Some numbers have funny properties. For example: 89 --> 8¹ + 9² = 89 * 1 695 --> 6² + 9³ + 5⁴= 1390 = 695 * 2 46288 --> 4³ + 6⁴+ 2⁵ + 8⁶ + 8⁷ = 2360688 = 46288 * 51 Given a positive integer n written as abcd... (a, b, c, d... being digits) and a positive integer p we want to find a positive integer k, if it exists, such that the sum of the digits of n taken to the successive powers of p is equal to k * n. In other words: Is there an integer k such as : (a ^ p + b ^ (p+1) + c ^(p+2) + d ^ (p+3) + ...) = n * k If it is the case we will return k, if not return -1. Note: n and p will always be given as strictly positive integers. """ num = 0 n = str(n) for i in range(len(n)): num += int(n[i]) ** p p += 1 k = num / int(n) if k.is_integer(): return k else: return -1 # TESTS dig_pow(89, 1, 1) dig_pow(92, 1, -1) dig_pow(46288, 3, 51) dig_pow(114, 3, 9)	[ 1, 529, 276, 1112, 420, 29958, 29885, 290, 305, 309, 11960, 586, 29914, 401, 29893, 1503, 13, 1753, 4697, 29918, 12248, 29898, 29876, 29892, 282, 1125, 13, 1678, 9995, 13, 1678, 3834, 3694, 505, 2090, 1460, 4426, 29889, 1152, 1342, 29901, 13, 268, 29947, 29929, 6660, 29871, 29947, 30193, 718, 29871, 29929, 30088, 353, 29871, 29947, 29929, 334, 29871, 29896, 13, 268, 29953, 29929, 29945, 6660, 29871, 29953, 30088, 718, 29871, 29929, 30168, 718, 29871, 29945, 30379, 29922, 29871, 29896, 29941, 29929, 29900, 353, 29871, 29953, 29929, 29945, 334, 29871, 29906, 13, 268, 29946, 29953, 29906, 29947, 29947, 6660, 29871, 29946, 30168, 718, 29871, 29953, 30379, 29974, 29871, 29906, 30509, 718, 29871, 29947, 30522, 718, 29871, 29947, 30749, 353, 29871, 29906, 29941, 29953, 29900, 29953, 29947, 29947, 353, 29871, 29946, 29953, 29906, 29947, 29947, 334, 29871, 29945, 29896, 13, 1678, 11221, 263, 6374, 6043, 302, 3971, 408, 633, 2252, 856, 313, 29874, 29892, 289, 29892, 274, 29892, 270, 856, 1641, 13340, 29897, 322, 263, 6374, 6043, 282, 13, 1678, 591, 864, 304, 1284, 263, 6374, 6043, 413, 29892, 565, 372, 4864, 29892, 1316, 393, 278, 2533, 310, 278, 13340, 310, 302, 4586, 304, 278, 2551, 573, 10801, 310, 282, 338, 5186, 304, 413, 334, 302, 29889, 13, 1678, 512, 916, 3838, 29901, 13, 1678, 1317, 727, 385, 6043, 413, 1316, 408, 584, 313, 29874, 6228, 282, 718, 289, 6228, 313, 29886, 29974, 29896, 29897, 718, 274, 6228, 29898, 29886, 29974, 29906, 29897, 718, 270, 6228, 313, 29886, 29974, 29941, 29897, 718, 29757, 353, 302, 334, 413, 13, 1678, 960, 372, 338, 278, 1206, 591, 674, 736, 413, 29892, 565, 451, 736, 448, 29896, 29889, 13, 1678, 3940, 29901, 302, 322, 282, 674, 2337, 367, 2183, 408, 18719, 6374, 11920, 29889, 13, 1678, 9995, 13, 13, 1678, 954, 353, 29871, 29900, 13, 1678, 302, 353, 851, 29898, 29876, 29897, 13, 13, 1678, 363, 474, 297, 3464, 29898, 2435, 29898, 29876, 22164, 13, 4706, 954, 4619, 938, 29898, 29876, 29961, 29875, 2314, 3579, 282, 13, 4706, 282, 4619, 29871, 29896, 13, 13, 1678, 413, 353, 954, 847, 938, 29898, 29876, 29897, 13, 13, 1678, 565, 413, 29889, 275, 29918, 16031, 7295, 13, 4706, 736, 413, 13, 1678, 1683, 29901, 13, 4706, 736, 448, 29896, 13, 13, 13, 29937, 17067, 1254, 29903, 13, 7501, 29918, 12248, 29898, 29947, 29929, 29892, 29871, 29896, 29892, 29871, 29896, 29897, 13, 7501, 29918, 12248, 29898, 29929, 29906, 29892, 29871, 29896, 29892, 448, 29896, 29897, 13, 7501, 29918, 12248, 29898, 29946, 29953, 29906, 29947, 29947, 29892, 29871, 29941, 29892, 29871, 29945, 29896, 29897, 13, 7501, 29918, 12248, 29898, 29896, 29896, 29946, 29892, 29871, 29941, 29892, 29871, 29929, 29897, 13, 2 ]
ensemble/management/commands/seeddata.py	evanlouie/activelearning	0	49525	""" Test data scaffolding. Read: https://docs.djangoproject.com/en/dev/howto/custom-management-commands/ """ import json from random import randint from django.core.management.base import BaseCommand, CommandError from ensemble.models import ( Classification, Model, ModelVersion, MediaFile, VideoPrediction, AudioPrediction, ) class Command(BaseCommand): help = "Scaffold some test data for ensemble" def handle(self, args, *options): gunshot = Classification(name="GUNSHOT") gunshot.save() audioset_model = Model(name="audioset") audioset_model.save() audioset_model.classifications.add(gunshot) audioset_model_trained = ModelVersion( model=audioset_model, version="0.01alpha2" ) audioset_model_trained.save() for movie in json.loads(self.__movie_json, strict=False): media_file = MediaFile( name=movie["title"], url=movie["sources"][0], description=movie["description"], ) media_file.save() video_predictions = [ self.__generate_random_video_prediction( media_file, gunshot, audioset_model_trained ) for _ in range(1000) ] audio_predictions = [ self.__generate_random_audio_prediction( media_file, gunshot, audioset_model_trained ) for _ in range(1000) ] # VideoPrediction.objects.bulk_create(video_predictions) # AudioPrediction.objects.bulk_create(audio_predictions) for prediction in video_predictions + audio_predictions: prediction.save() def __generate_random_video_prediction(self, media_file, classification, model): """ Generate a random gunshot video prediction for the video """ x = randint(0, 1280) y = randint(0, 720) width = randint(1, 1280 - x + 1) height = randint(1, 720 - y + 1) return VideoPrediction( media_file=media_file, classification=classification, confidence=randint(0, 100), model_version=model, time=randint(0, 600000), x=x, y=y, width=width, height=height, ) def __generate_random_audio_prediction(self, media_file, classification, model): """ Generate a random 10 second audio prediction for a gunshot """ return AudioPrediction( media_file=media_file, classification=classification, confidence=randint(0, 100), model_version=model, time=randint(0, 600000), duration="10000", ) __movie_json = """ [ { "description": "Big Buck Bunny tells the story of a giant rabbit with a heart bigger than himself. When one sunny day three rodents rudely harass him, something snaps... and the rabbit ain't no bunny anymore! In the typical cartoon tradition he prepares the nasty rodents a comical revenge.\n\nLicensed under the Creative Commons Attribution license\nhttp://www.bigbuckbunny.org", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/BigBuckBunny.mp4" ], "subtitle": "By Blender Foundation", "thumb": "images/BigBuckBunny.jpg", "title": "Big Buck Bunny" }, { "description": "The first Blender Open Movie from 2006", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ElephantsDream.mp4" ], "subtitle": "By Blender Foundation", "thumb": "images/ElephantsDream.jpg", "title": "Elephant Dream" }, { "description": "HBO GO now works with Chromecast -- the easiest way to enjoy online video on your TV. For when you want to settle into your Iron Throne to watch the latest episodes. For $35.\nLearn how to use Chromecast with HBO GO and more at google.com/chromecast.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerBlazes.mp4" ], "subtitle": "By Google", "thumb": "images/ForBiggerBlazes.jpg", "title": "For Bigger Blazes" }, { "description": "Introducing Chromecast. The easiest way to enjoy online video and music on your TV—for when Batman's escapes aren't quite big enough. For $35. Learn how to use Chromecast with Google Play Movies and more at google.com/chromecast.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerEscapes.mp4" ], "subtitle": "By Google", "thumb": "images/ForBiggerEscapes.jpg", "title": "For Bigger Escape" }, { "description": "Introducing Chromecast. The easiest way to enjoy online video and music on your TV. For $35. Find out more at google.com/chromecast.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerFun.mp4" ], "subtitle": "By Google", "thumb": "images/ForBiggerFun.jpg", "title": "For Bigger Fun" }, { "description": "Introducing Chromecast. The easiest way to enjoy online video and music on your TV—for the times that call for bigger joyrides. For $35. Learn how to use Chromecast with YouTube and more at google.com/chromecast.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerJoyrides.mp4" ], "subtitle": "By Google", "thumb": "images/ForBiggerJoyrides.jpg", "title": "For Bigger Joyrides" }, { "description": "Introducing Chromecast. The easiest way to enjoy online video and music on your TV—for when you want to make Buster's big meltdowns even bigger. For $35. Learn how to use Chromecast with Netflix and more at google.com/chromecast.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerMeltdowns.mp4" ], "subtitle": "By Google", "thumb": "images/ForBiggerMeltdowns.jpg", "title": "For Bigger Meltdowns" }, { "description": "Sintel is an independently produced short film, initiated by the Blender Foundation as a means to further improve and validate the free/open source 3D creation suite Blender. With initial funding provided by 1000s of donations via the internet community, it has again proven to be a viable development model for both open 3D technology as for independent animation film.\nThis 15 minute film has been realized in the studio of the Amsterdam Blender Institute, by an international team of artists and developers. In addition to that, several crucial technical and creative targets have been realized online, by developers and artists and teams all over the world.\nwww.sintel.org", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/Sintel.mp4" ], "subtitle": "By Blender Foundation", "thumb": "images/Sintel.jpg", "title": "Sintel" }, { "description": "Smoking Tire takes the all-new Subaru Outback to the highest point we can find in hopes our customer-appreciation Balloon Launch will get some free T-shirts into the hands of our viewers.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/SubaruOutbackOnStreetAndDirt.mp4" ], "subtitle": "By Garage419", "thumb": "images/SubaruOutbackOnStreetAndDirt.jpg", "title": "Subaru Outback On Street And Dirt" }, { "description": "Tears of Steel was realized with crowd-funding by users of the open source 3D creation tool Blender. Target was to improve and test a complete open and free pipeline for visual effects in film - and to make a compelling sci-fi film in Amsterdam, the Netherlands. The film itself, and all raw material used for making it, have been released under the Creatieve Commons 3.0 Attribution license. Visit the tearsofsteel.org website to find out more about this, or to purchase the 4-DVD box with a lot of extras. (CC) Blender Foundation - http://www.tearsofsteel.org", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/TearsOfSteel.mp4" ], "subtitle": "By Blender Foundation", "thumb": "images/TearsOfSteel.jpg", "title": "Tears of Steel" }, { "description": "The Smoking Tire heads out to Adams Motorsports Park in Riverside, CA to test the most requested car of 2010, the Volkswagen GTI. Will it beat the Mazdaspeed3's standard-setting lap time? Watch and see...", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/VolkswagenGTIReview.mp4" ], "subtitle": "By Garage419", "thumb": "images/VolkswagenGTIReview.jpg", "title": "Volkswagen GTI Review" }, { "description": "The Smoking Tire is going on the 2010 Bullrun Live Rally in a 2011 Shelby GT500, and posting a video from the road every single day! The only place to watch them is by subscribing to The Smoking Tire or watching at BlackMagicShine.com", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/WeAreGoingOnBullrun.mp4" ], "subtitle": "By Garage419", "thumb": "images/WeAreGoingOnBullrun.jpg", "title": "We Are Going On Bullrun" }, { "description": "The Smoking Tire meets up with Chris and Jorge from CarsForAGrand.com to see just how far $1,000 can go when looking for a car.The Smoking Tire meets up with Chris and Jorge from CarsForAGrand.com to see just how far $1,000 can go when looking for a car.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/WhatCarCanYouGetForAGrand.mp4" ], "subtitle": "By Garage419", "thumb": "images/WhatCarCanYouGetForAGrand.jpg", "title": "What care can you get for a grand?" } ] """	[ 1, 9995, 13, 3057, 848, 885, 3470, 1025, 292, 29889, 13, 6359, 29901, 2045, 597, 2640, 29889, 19776, 574, 26555, 622, 29889, 510, 29914, 264, 29914, 3359, 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7392, 417, 265, 997, 3322, 674, 679, 777, 3889, 323, 29899, 845, 381, 1372, 964, 278, 6567, 310, 1749, 1776, 414, 19602, 13, 4706, 376, 29879, 2863, 1115, 518, 13, 4706, 376, 1124, 597, 2055, 898, 271, 579, 6022, 29889, 15947, 29889, 510, 29914, 4141, 29894, 29899, 29894, 7958, 29899, 21454, 29914, 11249, 29914, 4035, 18071, 3744, 1627, 2951, 855, 4521, 2855, 29928, 2728, 29889, 1526, 29946, 29908, 13, 4706, 21251, 13, 4706, 376, 1491, 3257, 1115, 376, 2059, 7455, 482, 29946, 29896, 29929, 613, 13, 4706, 376, 386, 3774, 1115, 376, 8346, 29914, 4035, 18071, 3744, 1627, 2951, 855, 4521, 2855, 29928, 2728, 29889, 6173, 613, 13, 4706, 376, 3257, 1115, 376, 4035, 18071, 4451, 1627, 1551, 7103, 1126, 360, 2728, 29908, 13, 1678, 2981, 13, 1678, 426, 13, 4706, 376, 8216, 1115, 376, 29911, 15451, 310, 2443, 295, 471, 16387, 411, 19174, 29899, 27159, 292, 491, 4160, 310, 278, 1722, 2752, 29871, 29941, 29928, 11265, 5780, 3164, 1581, 29889, 17157, 471, 304, 11157, 322, 1243, 263, 4866, 1722, 322, 3889, 16439, 363, 7604, 9545, 297, 2706, 448, 322, 304, 1207, 263, 752, 7807, 4560, 29899, 7241, 2706, 297, 17399, 29892, 278, 24553, 29889, 29871, 450, 2706, 3528, 29892, 322, 599, 10650, 5518, 1304, 363, 3907, 372, 29892, 505, 1063, 5492, 1090, 278, 6760, 271, 2418, 3468, 29871, 29941, 29889, 29900, 6212, 3224, 19405, 29889, 5741, 277, 278, 734, 279, 578, 29888, 1655, 295, 29889, 990, 4700, 304, 1284, 714, 901, 1048, 445, 29892, 470, 304, 20590, 278, 29871, 29946, 29899, 29928, 10699, 3800, 411, 263, 3287, 310, 429, 10678, 29889, 29871, 313, 4174, 29897, 3164, 1581, 10606, 448, 1732, 597, 1636, 29889, 371, 279, 578, 29888, 1655, 295, 29889, 990, 613, 13, 4706, 376, 29879, 2863, 1115, 518, 13, 4706, 376, 1124, 597, 2055, 898, 271, 579, 6022, 29889, 15947, 29889, 510, 29914, 4141, 29894, 29899, 29894, 7958, 29899, 21454, 29914, 11249, 29914, 29911, 15451, 2776, 7789, 295, 29889, 1526, 29946, 29908, 13, 4706, 21251, 13, 4706, 376, 1491, 3257, 1115, 376, 2059, 3164, 1581, 10606, 613, 13, 4706, 376, 386, 3774, 1115, 376, 8346, 29914, 29911, 15451, 2776, 7789, 295, 29889, 6173, 613, 13, 4706, 376, 3257, 1115, 376, 29911, 15451, 310, 2443, 295, 29908, 13, 1678, 2981, 13, 1678, 426, 13, 4706, 376, 8216, 1115, 376, 1576, 4116, 17223, 323, 533, 15883, 714, 304, 20359, 7142, 943, 4011, 4815, 297, 390, 1536, 680, 29892, 12766, 304, 1243, 278, 1556, 13877, 1559, 310, 29871, 29906, 29900, 29896, 29900, 29892, 278, 14639, 23702, 21342, 29902, 29889, 2811, 372, 16646, 278, 17326, 17370, 412, 287, 29941, 29915, 29879, 3918, 29899, 26740, 23012, 931, 29973, 24274, 322, 1074, 856, 613, 13, 4706, 376, 29879, 2863, 1115, 518, 13, 4706, 376, 1124, 597, 2055, 898, 271, 579, 6022, 29889, 15947, 29889, 510, 29914, 4141, 29894, 29899, 29894, 7958, 29899, 21454, 29914, 11249, 29914, 13072, 2039, 23702, 23799, 29902, 1123, 1493, 29889, 1526, 29946, 29908, 13, 4706, 21251, 13, 4706, 376, 1491, 3257, 1115, 376, 2059, 7455, 482, 29946, 29896, 29929, 613, 13, 4706, 376, 386, 3774, 1115, 376, 8346, 29914, 13072, 2039, 23702, 23799, 29902, 1123, 1493, 29889, 6173, 613, 13, 4706, 376, 3257, 1115, 376, 13072, 2039, 23702, 21342, 29902, 13957, 29908, 13, 1678, 2981, 13, 1678, 426, 13, 4706, 376, 8216, 1115, 376, 1576, 4116, 17223, 323, 533, 338, 2675, 373, 278, 29871, 29906, 29900, 29896, 29900, 20293, 3389, 10782, 390, 635, 297, 263, 29871, 29906, 29900, 29896, 29896, 1383, 295, 1609, 21342, 29945, 29900, 29900, 29892, 322, 16742, 263, 4863, 515, 278, 6520, 1432, 2323, 2462, 29991, 450, 871, 2058, 304, 6505, 963, 338, 491, 21696, 10549, 304, 450, 4116, 17223, 323, 533, 470, 21217, 472, 6054, 19095, 293, 2713, 457, 29889, 510, 613, 13, 4706, 376, 29879, 2863, 1115, 518, 13, 4706, 376, 1124, 597, 2055, 898, 271, 579, 6022, 29889, 15947, 29889, 510, 29914, 4141, 29894, 29899, 29894, 7958, 29899, 21454, 29914, 11249, 29914, 4806, 17506, 8120, 292, 2951, 29933, 913, 3389, 29889, 1526, 29946, 29908, 13, 4706, 21251, 13, 4706, 376, 1491, 3257, 1115, 376, 2059, 7455, 482, 29946, 29896, 29929, 613, 13, 4706, 376, 386, 3774, 1115, 376, 8346, 29914, 4806, 17506, 8120, 292, 2951, 29933, 913, 3389, 29889, 6173, 613, 13, 4706, 376, 3257, 1115, 376, 4806, 4683, 2921, 292, 1551, 20293, 3389, 29908, 13, 1678, 2981, 13, 1678, 426, 13, 4706, 376, 8216, 1115, 376, 1576, 4116, 17223, 323, 533, 28103, 701, 411, 12821, 322, 21801, 515, 315, 1503, 2831, 29909, 3338, 392, 29889, 510, 304, 1074, 925, 920, 2215, 395, 29896, 29892, 29900, 29900, 29900, 508, 748, 746, 3063, 363, 263, 1559, 29889, 1576, 4116, 17223, 323, 533, 28103, 701, 411, 12821, 322, 21801, 515, 315, 1503, 2831, 29909, 3338, 392, 29889, 510, 304, 1074, 925, 920, 2215, 395, 29896, 29892, 29900, 29900, 29900, 508, 748, 746, 3063, 363, 263, 1559, 19602, 13, 4706, 376, 29879, 2863, 1115, 518, 13, 4706, 376, 1124, 597, 2055, 898, 271, 579, 6022, 29889, 15947, 29889, 510, 29914, 4141, 29894, 29899, 29894, 7958, 29899, 21454, 29914, 11249, 29914, 5618, 8179, 6028, 3492, 2577, 2831, 29909, 3338, 392, 29889, 1526, 29946, 29908, 13, 4706, 21251, 13, 4706, 376, 1491, 3257, 1115, 376, 2059, 7455, 482, 29946, 29896, 29929, 613, 13, 4706, 376, 386, 3774, 1115, 376, 8346, 29914, 5618, 8179, 6028, 3492, 2577, 2831, 29909, 3338, 392, 29889, 6173, 613, 13, 4706, 376, 3257, 1115, 376, 5618, 2562, 508, 366, 679, 363, 263, 4595, 3026, 13, 1678, 500, 13, 1678, 4514, 13, 1678, 9995, 13, 2 ]
src/terra/contracts/levana.py	fentas/staketaxcsv	0	22832	# known contracts from protocol CONTRACTS = [ # NFT - Meteor Dust "<KEY>", # NFT - Eggs "<KEY>", # NFT - Dragons "<KEY>", # NFT - Loot "<KEY>", ] def handle(exporter, elem, txinfo, contract): print(f"Levana! {contract}") #print(elem)	[ 1, 396, 2998, 8078, 29879, 515, 9608, 13, 22412, 4717, 1783, 29903, 353, 518, 13, 1678, 396, 405, 7818, 448, 29573, 360, 504, 13, 1678, 9872, 10818, 28341, 13, 1678, 396, 405, 7818, 448, 382, 1505, 29879, 13, 1678, 9872, 10818, 28341, 13, 1678, 396, 405, 7818, 448, 12225, 787, 13, 1678, 9872, 10818, 28341, 13, 1678, 396, 405, 7818, 448, 365, 3155, 13, 1678, 9872, 10818, 28341, 13, 29962, 13, 13, 1753, 4386, 29898, 735, 18505, 29892, 21268, 29892, 25568, 3888, 29892, 8078, 1125, 13, 1678, 1596, 29898, 29888, 29908, 3226, 29894, 1648, 29991, 426, 1285, 1461, 27195, 13, 1678, 396, 2158, 29898, 20461, 29897, 13, 2 ]
webdriver/applicationstate.py	jiteshmohite/Python-Page-Object-Model-Framework	6	1601865	class ApplicationState(object): NOT_INSTALLED = 0 NOT_RUNNING = 1 RUNNING_IN_BACKGROUND_SUSPENDED = 2 RUNNING_IN_BACKGROUND = 3 RUNNING_IN_FOREGROUND = 4	[ 1, 770, 8427, 2792, 29898, 3318, 1125, 13, 1678, 6058, 29918, 25580, 1964, 20566, 353, 29871, 29900, 13, 1678, 6058, 29918, 29934, 3904, 29940, 4214, 353, 29871, 29896, 13, 1678, 27694, 29940, 4214, 29918, 1177, 29918, 29933, 11375, 29954, 1672, 18783, 29918, 29903, 3308, 29925, 1430, 2287, 29928, 353, 29871, 29906, 13, 1678, 27694, 29940, 4214, 29918, 1177, 29918, 29933, 11375, 29954, 1672, 18783, 353, 29871, 29941, 13, 1678, 27694, 29940, 4214, 29918, 1177, 29918, 5800, 18166, 1672, 18783, 353, 29871, 29946, 13, 2 ]
mummi_ras/online/aa/aa_get_tiltrot_z_state.py	mummi-framework/mummi-ras	4	896	############################################################################### # @todo add Pilot2-splash-app disclaimer ############################################################################### """ Get's KRAS states """ import MDAnalysis as mda from MDAnalysis.analysis import align from MDAnalysis.lib.mdamath import make_whole import os import numpy as np import math ############## Below section needs to be uncommented ############ import mummi_core import mummi_ras from mummi_core.utils import Naming # # Logger has to be initialized the first thing in the script from logging import getLogger LOGGER = getLogger(__name__) # # Innitilize MuMMI if it has not been done before # MUMMI_ROOT = mummi.init(True) # This is needed so the Naming works below #@TODO fix this so we don't have these on import make them as an init mummi_core.init() dirKRASStates = Naming.dir_res('states') dirKRASStructures = Naming.dir_res('structures') # #RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-ONLY.microstates.txt")) RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-states.txt"),comments='#') # #RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-RAF.microstates.txt")) RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-raf-states.txt"),comments='#') # Note diffrent number of columns so index change below # TODO: CS, my edits to test # RAS_ONLY_macrostate = np.loadtxt('ras-states.txt') # RAS_RAF_macrostate = np.loadtxt('ras-raf-states.txt') ############## above section needs to be uncommented ############ # TODO: CS, my edits to test # TODO: TSC, The reference structure has to currently be set as the 'RAS-ONLY-reference-structure.gro' # TODO: TSC, path to the reference structure is: mummi_resources/structures/ kras_ref_universe = mda.Universe(os.path.join(dirKRASStructures, "RAS-ONLY-reference-structure.gro")) # kras_ref_universe = mda.Universe("RAS-ONLY-reference-structure.gro") # kras_ref_universe = mda.Universe('AA_pfpatch_000000004641_RAS_RAF2_411.gro') # TODO: CS, not using these for x4 proteins; instead using protein_systems below to set num_res ######### Below hard codes the number of residues within RAS-only and RAS-RAF ########## RAS_only_num_res = 184 RAS_RAF_num_res = 320 ######### Above hard codes the number of residues within RAS-only and RAS-RAF ########## ####### This can be removed # def get_kras(syst, kras_start): # """Gets all atoms for a KRAS protein starting at 'kras_start'.""" # return syst.atoms[kras_start:kras_start+428] ####### This can be removed def get_segids(u): """Identifies the list of segments within the system. Only needs to be called x1 time""" segs = u.segments segs = segs.segids ras_segids = [] rasraf_segids = [] for i in range(len(segs)): # print(segs[i]) if segs[i][-3:] == 'RAS': ras_segids.append(segs[i]) if segs[i][-3:] == 'RAF': rasraf_segids.append(segs[i]) return ras_segids, rasraf_segids def get_protein_info(u,tag): """Uses the segments identified in get_segids to make a list of all proteins in the systems.\ Outputs a list of the first residue number of the protein, and whether it is 'RAS-ONLY', or 'RAS-RAF'.\ The 'tag' input defines what is used to identify the first residue of the protein. i.e. 'resname ACE1 and name BB'.\ Only needs to be called x1 time""" ras_segids, rasraf_segids = get_segids(u) if len(ras_segids) > 0: RAS = u.select_atoms('segid '+ras_segids[0]+' and '+str(tag)) else: RAS = [] if len(rasraf_segids) > 0: RAF = u.select_atoms('segid '+rasraf_segids[0]+' and '+str(tag)) else: RAF = [] protein_info = []#np.empty([len(RAS)+len(RAF),2]) for i in range(len(RAS)): protein_info.append((RAS[i].resid,'RAS-ONLY')) for i in range(len(RAF)): protein_info.append((RAF[i].resid,'RAS-RAF')) ######## sort protein info protein_info = sorted(protein_info) ######## sort protein info return protein_info def get_ref_kras(): """Gets the reference KRAS struct. Only called x1 time when class is loaded""" start_of_g_ref = kras_ref_universe.residues[0].resid ref_selection = 'resid '+str(start_of_g_ref)+':'+str(start_of_g_ref+24)+' ' +\ str(start_of_g_ref+38)+':'+str(start_of_g_ref+54)+' ' +\ str(start_of_g_ref+67)+':'+str(start_of_g_ref+164)+' ' +\ 'and (name CA or name BB)' r2_26r40_56r69_166_ref = kras_ref_universe.select_atoms(str(ref_selection)) return kras_ref_universe.select_atoms(str(ref_selection)).positions - kras_ref_universe.select_atoms(str(ref_selection)).center_of_mass() # Load inital ref frames (only need to do this once) ref0 = get_ref_kras() def getKRASstates(u,kras_indices): """Gets states for all KRAS proteins in path.""" # res_shift = 8 # all_glycine = u.select_atoms("resname GLY") # kras_indices = [] # for i in range(0, len(all_glycine), 26): # kras_indices.append(all_glycine[i].index) ########## Below is taken out of the function so it is only done once ######### # kras_indices = get_protein_info(u,'resname ACE1 and name BB') ########## Above is taken out of the function so it is only done once ######### # CS, for x4 cases: # [{protein_x4: (protein_type, num_res)}] protein_systems = [{'ras4a': ('RAS-ONLY', 185), 'ras4araf': ('RAS-RAF', 321), 'ras': ('RAS-ONLY', 184), 'rasraf': ('RAS-RAF', 320)}] ALLOUT = [] for k in range(len(kras_indices)): start_of_g = kras_indices[k][0] protein_x4 = str(kras_indices[k][1]) try: protein_type = [item[protein_x4] for item in protein_systems][0][0] # 'RAS-ONLY' OR 'RAS-RAF' num_res = [item[protein_x4] for item in protein_systems][0][1] except: LOGGER.error('Check KRas naming between modules') raise Exception('Error: unknown KRas name') # TODO: CS, replacing this comment section with the above, to handle x4 protein types # --------------------------------------- # ALLOUT = [] # for k in range(len(kras_indices)): # start_of_g = kras_indices[k][0] # protein_type = str(kras_indices[k][1]) # ########## BELOW SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ############## # ########## POTENTIALLY REDO WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) ####### # ########## HAS BEEN REDONE WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) ######## # # if len(kras_indices) == 1: # # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB') ####### HAS TO BE FIXED FOR BACKBONE ATOMS FOR SPECIFIC PROTEIN # # elif len(kras_indices) > 1: # # if k == len(kras_indices)-1: # # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB') # # else: # # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(kras_indices[k+1][0])+' and name BB') # ########## ABOVE SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ############## # # ########## Below hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations ######################### # if protein_type == 'RAS-ONLY': # num_res = RAS_only_num_res # elif protein_type == 'RAS-RAF': # num_res = RAS_RAF_num_res # ########## Above hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations ######################### # --------------------------------------- # TODO: TSC, I changed the selection below, which can be used for the make_whole... # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res)+' and (name CA or name BB)') krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res)) krases0_BB.guess_bonds() r2_26r40_56r69_166 = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+24)+' ' +\ str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\ str(start_of_g+67)+':'+str(start_of_g+164)+\ ' and (name CA or name BB)') u_selection = \ 'resid '+str(start_of_g)+':'+str(start_of_g+24)+' '+str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\ str(start_of_g+67)+':'+str(start_of_g+164)+' and (name CA or name BB)' mobile0 = u.select_atoms(str(u_selection)).positions - u.select_atoms(str(u_selection)).center_of_mass() # TODO: CS, something wrong with ref0 from get_kras_ref() # just making ref0 = mobile0 to test for now # ref0 = mobile0 # TSC removed this R, RMSD_junk = align.rotation_matrix(mobile0, ref0) ######## TODO: TSC, Adjusted for AA lipid names ######## # lipids = u.select_atoms('resname POPX POPC PAPC POPE DIPE DPSM PAPS PAP6 CHOL') lipids = u.select_atoms('resname POPC PAPC POPE DIPE SSM PAPS SAPI CHL1') coords = ref0 RotMat = [] OS = [] r152_165 = krases0_BB.select_atoms('resid '+str(start_of_g+150)+':'+str(start_of_g+163)+' and (name CA or name BB)') r65_74 = krases0_BB.select_atoms('resid '+str(start_of_g+63)+':'+str(start_of_g+72)+' and (name CA or name BB)') timeframes = [] # TODO: CS, for AA need bonds to run make_whole() # krases0_BB.guess_bonds() # TODO: CS, turn off for now to test beyond this point ''' * for AA, need to bring that back on once all else runs * ''' # @Tim and <NAME>. this was commented out - please check. #make_whole(krases0_BB) j, rmsd_junk = mda.analysis.align.rotation_matrix((r2_26r40_56r69_166.positions-r2_26r40_56r69_166.center_of_mass()), coords) RotMat.append(j) OS.append(r65_74.center_of_mass()-r152_165.center_of_mass()) timeframes.append(u.trajectory.time) if protein_type == 'RAS-RAF': z_pos = [] ############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES BELOW #################### ############### TODO: TSC, zshifting is set to -1 (instead of -2), as there are ACE caps that are separate residues in AA #zshifting=-1 if protein_x4 == 'rasraf': zshifting = -1 elif protein_x4 == 'ras4araf': zshifting = 0 else: zshifting = 0 LOGGER.error('Found unsupported protein_x4 type') raf_loops_selection = u.select_atoms('resid '+str(start_of_g+zshifting+291)+':'+str(start_of_g+zshifting+294)+' ' +\ str(start_of_g+zshifting+278)+':'+str(start_of_g+zshifting+281)+' ' +\ ' and (name CA or name BB)') ############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES ABOVE #################### diff = (lipids.center_of_mass()[2]-raf_loops_selection.center_of_mass(unwrap=True)[2])/10 if diff < 0: diff = diff+(u.dimensions[2]/10) z_pos.append(diff) z_pos = np.array(z_pos) RotMatNP = np.array(RotMat) OS = np.array(OS) OA = RotMatNP[:, 2, :]/(((RotMatNP[:, 2, 0]2)+(RotMatNP[:, 2, 1]2)+(RotMatNP[:, 2, 2]2))0.5)[:, None] OWAS = np.arccos(RotMatNP[:, 2, 2])180/math.pi OC_temp = np.concatenate((OA, OS), axis=1) t = ((OC_temp[:, 0]OC_temp[:, 3])+(OC_temp[:, 1]OC_temp[:, 4]) + (OC_temp[:, 2]OC_temp[:, 5]))/((OC_temp[:, 0]2)+(OC_temp[:, 1]2)+(OC_temp[:, 2]*2)) OC = OAt[:, None] ORS_tp = np.concatenate((OC, OS), axis=1) ORS_norm = (((ORS_tp[:, 3]-ORS_tp[:, 0])2)+((ORS_tp[:, 4]-ORS_tp[:, 1])2)+((ORS_tp[:, 5]-ORS_tp[:, 2])2))0.5 ORS = (OS - OC)/ORS_norm[:, None] OACRS = np.cross(OA, ORS) OZCA = OA * OA[:, 2][:, None] Z_unit = np.full([len(OZCA), 3], 1) Z_adjust = np.array([0, 0, 1]) Z_unit = Z_unitZ_adjust Z_OZCA = Z_unit-OZCA OZPACB = Z_OZCA/((Z_OZCA[:, 0]2+Z_OZCA[:, 1]2+Z_OZCA[:, 2]2)0.5)[:, None] OROTNOTSIGNED = np.zeros([len(ORS)]) for i in range(len(ORS)): OROTNOTSIGNED[i] = np.arccos(np.dot(OZPACB[i, :], ORS[i, :]) / (np.sqrt(np.dot(OZPACB[i, :], OZPACB[i, :]))) (np.sqrt(np.dot(ORS[i, :], ORS[i, :]))))180/math.pi OZPACBCRS_cross = np.cross(OZPACB, ORS) OZPACBCRS = OZPACBCRS_cross/((OZPACBCRS_cross[:, 0]2+OZPACBCRS_cross[:, 1]2+OZPACBCRS_cross[:, 2]2)0.5)[:, None] OFORSIGN_temp = (OA - OZPACBCRS)2 OFORSIGN = OFORSIGN_temp[:, 0]+OFORSIGN_temp[:, 1]+OFORSIGN_temp[:, 2] OROT = OROTNOTSIGNED for i in range(len(OROT)): if OROT[i] < 0: OROT[i] = -(OROT[i]) for i in range(len(OROT)): if OFORSIGN[i] < 0.25: OROT[i] = -(OROT[i]) ###### Below introduces new shift to account for upper vs. lower leaflet ##### for i in range(len(OWAS)): OWAS[i] = abs(-(OWAS[i])+180) # made this an absolute value so that the tilt remains positive for i in range(len(OROT)): if OROT[i] < 0: OROT[i] = OROT[i]+180 elif OROT[i] > 0: OROT[i] = OROT[i]-180 ###### Above introduces new shift to account for upper vs. lower leaflet ##### ###### Below might have to be updated to take into account the periodic nature of the rotation ###### if protein_type == 'RAS-ONLY': states = np.zeros(len(OROT)) for j in range(len(OROT)): diff0 = [] for i in range(len(RAS_ONLY_macrostate)): #diff0.append([((RAS_ONLY_macrostate[i,0]-OWAS[j])2+(RAS_ONLY_macrostate[i,1]-OROT[j])2)0.5, RAS_ONLY_macrostate[i,6]]) diff0.append([((RAS_ONLY_macrostate[i,1]-OWAS[j])2+(RAS_ONLY_macrostate[i,0]-OROT[j])2)0.5, RAS_ONLY_macrostate[i,5]]) diff0.sort() states[j] = diff0[0][1] elif protein_type == 'RAS-RAF': states = np.zeros(len(OROT)) for j in range(len(OROT)): ### below: adding in the requirements for the 'high-z' state ### if (OROT[j] < -45 or OROT[j] > 140) and z_pos[j] > 4.8: states[j] = 3 else: ### above: adding in the requirements for the 'high-z' state ### diff0 = [] for i in range(len(RAS_RAF_macrostate)): #diff0.append([((RAS_RAF_macrostate[i,0]-OWAS[j])2+(RAS_RAF_macrostate[i,1]-OROT[j])2)0.5, RAS_RAF_macrostate[i,6]]) diff0.append([((RAS_RAF_macrostate[i,1]-OWAS[j])2+(RAS_RAF_macrostate[i,0]-OROT[j])2)0.5, RAS_RAF_macrostate[i,4]]) diff0.sort() states[j] = diff0[0][1] ###### Above might have to be updated to take into account the periodic nature of the rotation ###### ###### Assume we want to remove this? Where is the code that reads this information? i.e. will there be knock-on effects? ###### ###### If feedback code needs index 5 (two_states) from the output, deleting this four_states will shift that to index 4 ####### # four_states = np.zeros(len(OROT)) # for j in range(len(OROT)): # diff0 = [] # for i in range(len(macrostate4)): # diff0.append([((macrostate4[i,0]-OWAS[j])2+(macrostate4[i,1]-OROT[j])2)*0.5, macrostate4[i,6]]) # diff0.sort() # four_states[j] = diff0[0][1]+1 ###### below: old output details.... ###################################### ###### Updated - RAS-only to NOT HAVE the Z-distance ###################### ###### Updated - Added in the protein 'tag', i.e. RAS-ONLY or RAS-RAF ##### # OUTPUT = np.zeros([len(OROT), 6]) # for i in range(len(OROT)): # OUTPUT[i] = timeframes[i], OWAS[i], OROT[i], z_pos[i], four_states[i], two_states[i] ###### above: old output details.... ###################################### ###### below: NEW output details.... ###################################### if protein_type == 'RAS-ONLY': OUTPUT = np.zeros([len(OROT), 6]).astype(object) for i in range(len(OROT)): OUTPUT[i] = str(protein_type), timeframes[i], OWAS[i], OROT[i], 'n/a', int(states[i]) elif protein_type == 'RAS-RAF': OUTPUT = np.zeros([len(OROT), 6]).astype(object) for i in range(len(OROT)): OUTPUT[i] = str(protein_type), timeframes[i], OWAS[i], OROT[i], z_pos[i], int(states[i]) ALLOUT.append(OUTPUT) return np.asarray(ALLOUT) #np.savetxt(str(tpr)+"_tilt_rot_z_state.KRAS_"+str(k+1)+".txt", OUTPUT, fmt=['%i','%10.3f','%10.3f','%10.3f','%i','%i'], delimiter=' ')	[ 1, 835, 13383, 13383, 13383, 13383, 7346, 4136, 13, 29937, 732, 29873, 8144, 788, 14970, 327, 29906, 29899, 23579, 1161, 29899, 932, 2313, 433, 4193, 13, 13383, 13383, 13383, 13383, 7346, 4136, 2277, 29937, 13, 13, 15945, 29908, 3617, 29915, 29879, 476, 29934, 3289, 5922, 9995, 13, 13, 5215, 20672, 21067, 4848, 408, 286, 1388, 13, 3166, 20672, 21067, 4848, 29889, 15916, 1053, 7595, 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test/test_icap.py	antmicro/netv2	0	64553	#!/usr/bin/env python3 from litex import RemoteClient wb = RemoteClient() wb.open() # # # def icap_send(addr, data): wb.regs.icap_addr.write(addr) wb.regs.icap_data.write(data) wb.regs.icap_send.write(1) while (wb.regs.icap_done.read() == 0): pass # iprog icap_send(0x04, 0x0000000f) # # # wb.close()	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 13, 3166, 301, 568, 29916, 1053, 5240, 866, 4032, 13, 13, 29893, 29890, 353, 5240, 866, 4032, 580, 13, 29893, 29890, 29889, 3150, 580, 13, 13, 29937, 396, 396, 13, 13, 1753, 16077, 481, 29918, 6717, 29898, 10030, 29892, 848, 1125, 13, 12, 29893, 29890, 29889, 1727, 29879, 29889, 293, 481, 29918, 10030, 29889, 3539, 29898, 10030, 29897, 13, 12, 29893, 29890, 29889, 1727, 29879, 29889, 293, 481, 29918, 1272, 29889, 3539, 29898, 1272, 29897, 13, 12, 29893, 29890, 29889, 1727, 29879, 29889, 293, 481, 29918, 6717, 29889, 3539, 29898, 29896, 29897, 13, 12, 8000, 313, 29893, 29890, 29889, 1727, 29879, 29889, 293, 481, 29918, 15091, 29889, 949, 580, 1275, 29871, 29900, 1125, 13, 12, 12, 3364, 13, 13, 29937, 474, 29097, 13, 293, 481, 29918, 6717, 29898, 29900, 29916, 29900, 29946, 29892, 29871, 29900, 29916, 29900, 29900, 29900, 29900, 29900, 29900, 29900, 29888, 29897, 13, 13, 29937, 396, 396, 13, 13, 29893, 29890, 29889, 5358, 580, 13, 2 ]
tests/test_linearalgebra.py	JHay0112/jmath	7	91094	<filename>tests/test_linearalgebra.py """ jmath/tests/test_linearalgebra.py Runs tests on linear algebra component using pytest Author: <NAME> Date: 2021-06-25 """ # - Imports from ..jmath.linearalgebra import Vector, Point, Line from .tools import random_integer, random_integers, repeat from typing import Tuple, List from math import sqrt # - Functions def vector_component_pair(len: int = random_integer()) -> Tuple[Vector, List[int]]: """ Generates a vector and component pair randomly. Parameters ---------- len The length vector/components to generate """ components = random_integers(len) return (Vector(components), components) @repeat def test_vector_equality(): """Tests that vectors are equal as expected.""" v, c = vector_component_pair() assert v == v assert v == Vector(c) assert v.components == c @repeat def test_vector_addition(): """Tests vector addition.""" len = random_integer() v1, c1 = vector_component_pair(len) v2, c2 = vector_component_pair(len) expected = Vector([i + j for i, j in zip(c1, c2)]) assert (v1 + v2) == expected @repeat def test_vector_subtraction(): """Tests vector subtraction.""" len = random_integer() v1, c1 = vector_component_pair(len) v2, c2 = vector_component_pair(len) expected = Vector([i - j for i, j in zip(c1, c2)]) assert (v1 - v2) == expected @repeat def test_vector_scaling(): """Tests vector multiplication and division""" # Produce initial conditions length = random_integer(min = 3, max = 10) scalor = random_integer(min = 1, max = 10) v, c = vector_component_pair(length) # Mult/div vectors mult = v * scalor div = v / scalor # Compute expected mult_expected = Vector([scalor * i for i in c]) div_expected = Vector([round(i / scalor, 5) for i in c]) # Round division vector to factor out floating point error div.components = [round(i, 5) for i in div.components] # Test multiply assert mult_expected == mult # Test divide assert div_expected == div @repeat def test_dot_product(): """Tests the dot product""" # Generate vectors and components len = random_integer() v1, c1 = vector_component_pair(len) v2, c2 = vector_component_pair(len) # Compute dot product dot = v1 @ v2 # Predict dot product predicted_dot = sum([i * j for i, j in zip(c1, c2)]) assert dot == predicted_dot def test_projection(): """Tests projecting vectors""" # Test with vector vec1 = Vector(3, 4) vec2 = Vector(1, 1) expected = Vector(7/2, 7/2) assert vec1.projection(vec2) == expected # Test with line line = Line(Point(0, 0), vec2) assert vec1.projection(line) == expected @repeat def test_magnitude(): """Tests vector magnitude""" v, c = vector_component_pair() # Square components, sum, and sqrt predicted_magnitude = sqrt(sum([i ** 2 for i in c])) assert round(predicted_magnitude, 5) == round(v.magnitude(), 5) @repeat def test_vector_size(): """Tests that a vector will return the correct size""" v, c = vector_component_pair() assert len(v) == len(c) def test_point_in_line(): """Tests whether a point is in a line""" # Test point that should be on line point = Point(3, 4) line = Line(Point(0, 0), 2 * point) assert point.on_line(line) # Test point that shouldn't be point = Point(8, 1) assert not point.on_line(line) # Testing in 3-space for certainty point = Point(1, 2, 3) d_vector = Vector(4, 5, 6) known_point_on_line = Point(9, 12, 15) line = Line(point, d_vector) assert known_point_on_line.on_line(line) def test_angle_between(): """Tests that angle between vectors is correct""" vec1 = Vector(1, 1) vec2 = Vector(2, 2) assert vec1.angle_between(vec2) == 0 @repeat def test_negative(): """Test that a negative vector does indeed give one with all the components reversed""" # Generate vector component pair v, c = vector_component_pair() # Make negative vector v = v.negative() # Make components negative c = Vector([-i for i in c]) assert v == c def test_unit_vector(): """Tests that a unit vector is produced correctly""" vec = Vector(1, 2) unit_vec = vec.unit() assert round(unit_vec.magnitude(), 10) == 1 assert vec.magnitude() * unit_vec == vec @repeat def test_vector_subscripts(): """Tests that a vector can be subscripted.""" # Generate vector component pair v, c = vector_component_pair() # Iterate through components and check they match for i in range(len(c)): assert v[i] == c[i]	[ 1, 529, 9507, 29958, 21150, 29914, 1688, 29918, 10660, 15742, 29889, 2272, 13, 15945, 29908, 13, 1678, 432, 755, 29914, 21150, 29914, 1688, 29918, 10660, 15742, 29889, 2272, 13, 13, 1678, 390, 6948, 6987, 373, 5608, 9623, 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towers/monkey_village.py	56kyle/bloons_auto	0	48319	<filename>towers/monkey_village.py from tower import Tower from config import keybinds class MonkeyVillage(Tower): name = 'monkey_village' range = 215 width = 119 height = 103 size = 'xl' keybind = keybinds[name] aquatic = False def __init__(self, kwargs): super().__init__(kwargs)	[ 1, 529, 9507, 29958, 29873, 340, 414, 29914, 3712, 1989, 29918, 29894, 453, 482, 29889, 2272, 13, 3166, 19372, 1053, 23615, 13, 3166, 2295, 1053, 1820, 5355, 29879, 13, 13, 13, 1990, 2598, 1989, 29963, 453, 482, 29898, 29911, 1680, 1125, 13, 1678, 1024, 353, 525, 3712, 1989, 29918, 29894, 453, 482, 29915, 13, 1678, 3464, 353, 29871, 29906, 29896, 29945, 13, 1678, 2920, 353, 29871, 29896, 29896, 29929, 13, 1678, 3171, 353, 29871, 29896, 29900, 29941, 13, 1678, 2159, 353, 525, 15524, 29915, 13, 1678, 1820, 5355, 353, 1820, 5355, 29879, 29961, 978, 29962, 13, 1678, 10592, 2454, 353, 7700, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 3579, 19290, 1125, 13, 4706, 2428, 2141, 1649, 2344, 12035, 1068, 19290, 29897, 13, 2 ]
model.py	Jeffrey0Liao/2020-2021-Final-Year-Project-Joint-Sentimental-Analysis-Based-on-Tree-topology	2	114274	import torch as th import torch.nn as nn from torch.utils.data import DataLoader from torch.utils.data import Dataset import torch.nn.functional as F import numpy as np import dgl import dgl.function as fn class TreeLSTMCell(nn.Module): def __init__(self, x_size, h_size): super(TreeLSTMCell, self).__init__() self.W_iou = nn.Linear(x_size, 3 * h_size, bias=False) self.U_iou = nn.Linear(2 * h_size, 3 * h_size, bias=False) self.b_iou = nn.Parameter(th.zeros(1, 3 * h_size)) self.U_f = nn.Linear(2 * h_size, 2 * h_size) def message_func(self, edges): return {'h': edges.src['h'], 'c': edges.src['c']} def reduce_func(self, nodes): # concatenate h_jl for equation (1), (2), (3), (4) h_cat = nodes.mailbox['h'].view(nodes.mailbox['h'].size(0), -1) # equation (2) f = th.sigmoid(self.U_f(h_cat)).view(nodes.mailbox['h'].size()) # second term of equation (5) c = th.sum(f nodes.mailbox['c'], 1) return {'iou': self.U_iou(h_cat), 'c': c} def apply_node_func(self, nodes): # equation (1), (3), (4) iou = nodes.data['iou'] + self.b_iou i, o, u = th.chunk(iou, 3, 1) i, o, u = th.sigmoid(i), th.sigmoid(o), th.tanh(u) # equation (5) c = i * u + nodes.data['c'] # equation (6) h = o * th.tanh(c) return {'h' : h, 'c' : c} class Attention(nn.Module): def __init__(self, embed_size, feature_size): super(Attention, self).__init__() self.embed_size = embed_size self.feature_size = feature_size self.linear_in = nn.Linear(feature_size, embed_size, bias=False) self.linear_out = nn.Linear(embed_size+feature_size, embed_size) def forward(self, sent, img, mask): # sent: snetence_len * embed_size # img: num_region * feature_size snetence_len = sent.size(0) num_region = img.size(0) # img_in: num_region * embed_size img_in = self.linear_in(img) atten = th.mm(sent, img_in.transpose(0, 1)) #atten.data.masked_fill(mask, -1e6) atten = F.softmax(atten, dim=1) # atten: snetence_len * num_region context = th.mm(atten, img) # context: snetence_len * feature_size output = th.cat((context, sent), dim=1) # output: snetence_len * (feature_size+embed_size) output = th.tanh(self.linear_out(output)) # output: snetence_len * embed_size return output class TreeLSTM(nn.Module): def __init__(self, num_vocabs, x_size, h_size, feature_size, num_classes, dropout, pretrained_emb=None): super(TreeLSTM, self).__init__() self.x_size = x_size self.embedding = nn.Embedding(num_vocabs, x_size) self.attention = Attention(x_size, feature_size) if pretrained_emb is not None: print('Using glove') self.embedding.from_pretrained(pretrained_emb) self.embedding.weight.requires_grad = True self.dropout = nn.Dropout(dropout) self.linear = nn.Linear(h_size, num_classes) self.cell = TreeLSTMCell(x_size, h_size) def forward(self, batch, h, c): """Compute tree-lstm prediction given a batch. 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""" g = batch.graph # to heterogenous graph g = dgl.graph(g.edges()) # feed embedding embeds = self.embedding(batch.wordid * batch.mask) attn_mask = batch.mask.expand(batch.image.shape[0], batch.wordid.shape[0]).T attn_embeds = self.attention(embeds, batch.image, attn_mask) g.ndata['iou'] = self.cell.W_iou(self.dropout(attn_embeds)) * batch.mask.float().unsqueeze(-1) g.ndata['h'] = h g.ndata['c'] = c # propagate dgl.prop_nodes_topo(g, message_func=self.cell.message_func, reduce_func=self.cell.reduce_func, apply_node_func=self.cell.apply_node_func) # compute logits h = self.dropout(g.ndata.pop('h')) logits = self.linear(h) return logits	[ 1, 1053, 4842, 305, 408, 266, 13, 5215, 4842, 305, 29889, 15755, 408, 302, 29876, 13, 3166, 4842, 305, 29889, 13239, 29889, 1272, 1053, 3630, 10036, 13, 3166, 4842, 305, 29889, 13239, 29889, 1272, 1053, 13373, 24541, 13, 5215, 4842, 305, 29889, 15755, 29889, 2220, 284, 408, 383, 13, 5215, 12655, 408, 7442, 13, 13, 5215, 270, 3820, 13, 5215, 270, 3820, 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RepositoryStatusMonitor/repository_status_monitor_stack.py	awslabs/aws-repository-status-monitor	8	156628	<gh_stars>1-10 from sys import stderr from aws_cdk import ( aws_apigateway as apigw, aws_events as events, aws_events_targets as targets, aws_iam as iam, aws_lambda as _lambda, aws_lambda_event_sources as lambda_event_source, aws_sqs as sqs, core ) from lambda_dir import http_handler as hh class RepositoryStatusMonitorStack(core.Stack): """ A class used to represent an AWS CloudFormation stack. ... Methods ------- create_role_and_policy() Creates an AWS IAM role, attaches a custom policy to it, and returns the role create_event_with_permissions(lambda_function: _lambda.Function) Creates an AWS EventBridge rule and attaches the AWS Lambda function parameter as a target handle_parameters() Retrieves all context variables, checks for valid input, performs all necessary processing, and returns a dictionary of the processed variables validate_repo_names(repo_names: str) Retrieves the list of GitHub repositories to which the user has access and validates that all repositories specified for the dashboard exist in that list """ def __init__(self, scope: core.Construct, id: str, kwargs) -> None: """Initializes the stack :param scope: parent of this stack, scope within which resources defined here are accessible :type file_loc: Optional[aws_cdk.core.Construct] :param id: the id of the stack :type id: Optional[str] :param kwargs: additional optional arguments ``description``: a description of the stack (`Optional[str]`). ``env`` AWS environment (account/region) where this stack will be deployed (`Optional[aws_cdk.core.Environment]`). ``stack_name`` name with which to deploy the stack (`Optional[str]`). ``synthesizer`` synthesis method to use while deploying this stack (`Optional[aws_cdk.core.IStackSynthesizer]`). ``tags`` stack tags that will be applied to all taggable resources as well as the stack (`Optional[Mapping[str, str]]`). ``termination_protection`` whether to enable termination protection for this stack (`Optional[bool]`). """ super().__init__(scope, id, *kwargs) metric_handler_dict, webhook_creator_dict = self.handle_parameters() # timeout used for lambda and sqs, in seconds lambda_timeout = 300 if self.node.try_get_context('lambda_timeout'): lambda_timeout = int(self.node.try_get_context('lambda_timeout')) dead_letter_queue = sqs.Queue( self, 'DeadLetterQueue', queue_name='DeadLetterQueue' ) webhook_queue = sqs.Queue( self, 'WebhookQueue', queue_name='WebhookQueue', visibility_timeout=core.Duration.seconds(lambda_timeout), dead_letter_queue=sqs.DeadLetterQueue( max_receive_count=3, queue=dead_letter_queue) ) metric_handler_dict['queue_url'] = webhook_queue.queue_url metric_handler_management_role = self.create_lambda_role_and_policy( 'MetricHandlerManagementRole', [ 'cloudwatch:GetDashboard', 'cloudwatch:GetMetricData', 'cloudwatch:ListDashboards', 'cloudwatch:PutDashboard', 'cloudwatch:PutMetricData', 'logs:CreateLogGroup', 'logs:CreateLogStream', 'logs:PutLogEvents', 'secretsmanager:GetSecretValue' ] ) metric_handler_timeout = lambda_timeout metric_handler_function = _lambda.Function( self, 'MetricsHandler', function_name='MetricsHandler', runtime=_lambda.Runtime.PYTHON_3_7, code=_lambda.Code.asset('lambda_dir'), handler='cloudwatch_dashboard_handler.handler', role=metric_handler_management_role, environment=metric_handler_dict, timeout=core.Duration.seconds(metric_handler_timeout) ) self.create_event_with_permissions(metric_handler_function) # Connect SQS to Lambda sqs_event_source = lambda_event_source.SqsEventSource(webhook_queue) metric_handler_function.add_event_source(sqs_event_source) apigw_webhook_url = self.create_and_integrate_apigw(webhook_queue, metric_handler_dict['dashboard_name_prefix']) webhook_creator_dict['apigw_endpoint'] = apigw_webhook_url webhook_role = self.create_lambda_role_and_policy('WebhookCreatorRole', ['secretsmanager:GetSecretValue']) _lambda.Function( self, 'WebhookCreator', function_name='WebhookCreator', runtime=_lambda.Runtime.PYTHON_3_7, code=_lambda.Code.asset('lambda_dir'), handler='webhook_creator.handler', role=webhook_role, environment=webhook_creator_dict, timeout=core.Duration.seconds(5) ) def create_lambda_role_and_policy(self, name: str, actions: list) -> iam.Role: """Creates an AWS IAM role, attaches a managed and a custom policy to it, and returns the role :param name: the name and ID of the role :type name: str :param actions: the actions that the Lambda role can execute :type actions: list :returns: a role with a custom and a managed policy attached :rtype: aws_cdk.aws_iam.Role """ role = iam.Role(self, name, role_name=name, assumed_by=iam.ServicePrincipal( 'lambda.amazonaws.com'), managed_policies=[iam.ManagedPolicy.from_aws_managed_policy_name( 'service-role/AWSLambdaBasicExecutionRole')] ) role.add_to_policy(iam.PolicyStatement( resources=[''], actions=actions )) return role def create_event_with_permissions(self, lambda_function: _lambda.Function) -> None: """Creates an AWS EventBridge rule and attaches the AWS Lambda function parameter as a target :param lambda_function: the AWS Lambda function for which to create the rule :type lambda_function: aws_cdk.aws_lambda.Function """ hourly_metric_retrieval_rule = events.Rule( self, 'Rule', rule_name='HourlyMetricRetrieval', enabled=True, schedule=events.Schedule.expression('rate(1 hour)') ) hourly_metric_retrieval_rule.add_target(targets.LambdaFunction(lambda_function)) def handle_parameters(self) -> tuple: """Retrieves all context variables, checks for valid input, performs all necessary processing, and returns a dictionary of the processed variables :returns: a tuple containing two dictionaries, each containing all the processed context variables for each Lambda function :rtype: tuple """ repo_names_unvalidated = self.node.try_get_context('repo_names') docker_bool = self.node.try_get_context('get_docker') dashboard_name_prefix = self.node.try_get_context('dashboard_name_prefix') owner = self.node.try_get_context('owner') github_fields_paginated = self.node.try_get_context('github_fields_paginated') if self.node.try_get_context( 'github_fields_paginated') is not None else "" github_fields_unpaginated = self.node.try_get_context('github_fields_unpaginated') if self.node.try_get_context( 'github_fields_unpaginated') is not None else "" docker_fields = self.node.try_get_context('docker_fields') if self.node.try_get_context( 'docker_fields') is not None else "" namespace = self.node.try_get_context('namespace') user_agent_header = self.node.try_get_context('user_agent_header') widgets = self.node.try_get_context('widgets') if self.node.try_get_context('widgets') is not None else "" default_metric_widget_name = self.node.try_get_context('default_metric_widget_name') default_text_widget_name = self.node.try_get_context('default_text_widget_name') if not self.node.try_get_context('github_token'): raise ValueError('Need to specify GitHub token.') if not repo_names_unvalidated: raise ValueError('Need to specify repository names.') if not dashboard_name_prefix: raise ValueError('Need to specify prefix for dashboard names.') if not docker_bool: raise ValueError('Need to specify whether to include docker metrics.') if owner is None: raise ValueError('Need to specify GitHub owner.') if not namespace: raise ValueError('Need to specify namespace for CloudWatch metrics.') if not user_agent_header: raise ValueError('Need to specify User-Agent header for GitHub requests.') if not default_metric_widget_name: raise ValueError('Need to specify default metric widget name.') if not default_text_widget_name: raise ValueError('Need to specify default text widget name.') repo_names = self.validate_repo_names(repo_names_unvalidated, owner, user_agent_header) metric_handler_dict = { 'repo_names': repo_names, 'docker_bool': docker_bool, 'dashboard_name_prefix': dashboard_name_prefix, 'github_fields_unpaginated': github_fields_unpaginated, 'github_fields_paginated': github_fields_paginated, 'docker_fields': docker_fields, 'owner': owner, 'namespace': namespace, 'user_agent_header': user_agent_header, 'widgets': widgets, 'default_metric_widget_name': default_metric_widget_name, 'default_text_widget_name': default_text_widget_name } webhook_creator_dict = { 'repo_names': repo_names, 'owner': owner, 'user_agent_header': user_agent_header } return metric_handler_dict, webhook_creator_dict def validate_repo_names(self, repo_names: str, owner: str, user_agent_header: str) -> str: """Retrieves the list of GitHub repositories to which the user has access and validates that all repositories specified for the dashboard exist in that list :param repo_names: the repository names passed in as context variables :type repo_names: str :param owner: the GitHub owner of the repositories :type owner: str :param user_agent_header: the User-Agent header for GitHub requests :type user_agent_header: str :returns: the repository names that exist for the user in GitHub :rtype: str """ headers = {'Authorization': 'token ' + self.node.try_get_context('github_token'), 'Accept': 'application/vnd.github.nebula-preview+json', 'User-Agent': user_agent_header} url = 'https://api.github.com/user/repos' success, data, response_headers = hh.request_handler(url, headers=headers) if not success: raise RuntimeError(data['message']) existing_repos = set() for repo in data: existing_repos.add(repo['full_name']) valid_names = [] invalid_names = [] for repo_name in repo_names.split(','): if '/' not in repo_name: repo_name = owner + '/' + repo_name if repo_name not in existing_repos: invalid_names.append(repo_name) else: valid_names.append(repo_name) if len(invalid_names) > 0: invalid_repos = ', '.join(invalid_names) valid_repos = "none" if not valid_names else ', '.join(valid_names) print('Invalid repositories: ' + invalid_repos, file=stderr) print('Valid repositories: ' + valid_repos, file=stderr) raise RuntimeError("All repository names must be valid.") return ','.join(valid_names) def create_and_integrate_apigw(self, queue: sqs.Queue, dashboard_name_prefix: str) -> str: """Creates API Gateway and integrates with SQS queue :param queue: the SQS queue to integrate with :type queue: aws_cdk.aws_sqs.Queue :param dashboard_name_prefix: the dashboard name to use as the API Gateway resource name :type dashboard_name_prefix: str :returns: the url that the webhooks will post to :rtype: str """ webhook_apigw_role = iam.Role( self, 'WebhookAPIRole', role_name='WebhookAPIRole', assumed_by=iam.ServicePrincipal('apigateway.amazonaws.com') ) webhook_apigw_role.add_to_policy(iam.PolicyStatement( resources=['*'], actions=[ 'sqs:SendMessage' ] )) webhook_apigw = apigw.RestApi(self, 'RepositoryStatusMonitorAPI', rest_api_name='RepositoryStatusMonitorAPI') webhook_apigw_resource = webhook_apigw.root.add_resource(dashboard_name_prefix) apigw_integration_response = apigw.IntegrationResponse( status_code='200', response_templates={'application/json': ""} ) apigw_integration_options = apigw.IntegrationOptions( credentials_role=webhook_apigw_role, integration_responses=[apigw_integration_response], request_templates={'application/json': 'Action=SendMessage&MessageBody=$input.body'}, passthrough_behavior=apigw.PassthroughBehavior.NEVER, request_parameters={'integration.request.header.Content-Type': "'application/x-www-form-urlencoded'"} ) webhook_apigw_resource_sqs_integration = apigw.AwsIntegration( service='sqs', integration_http_method='POST', path='{}/{}'.format(core.Aws.ACCOUNT_ID, queue.queue_name), options=apigw_integration_options ) webhook_apigw_resource.add_method( 'POST', webhook_apigw_resource_sqs_integration, method_responses=[apigw.MethodResponse(status_code='200')] ) path = '/' + dashboard_name_prefix return webhook_apigw.url_for_path(path)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 10876, 1053, 380, 20405, 13, 13, 3166, 25879, 29918, 2252, 29895, 1053, 313, 13, 1678, 25879, 29918, 481, 335, 403, 1582, 408, 3095, 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corefacility/authorizations/ihna/test/account_set_object.py	serik1987/corefacility	0	122176	from core.test.entity_set.entity_set_objects.external_account_set_object import ExternalAccountSetObject from authorizations.ihna.entity import Account class AccountSetObject(ExternalAccountSetObject): _entity_class = Account _alias_field = "email" def data_provider(self): return [ dict(email="<EMAIL>", user=self._user_set_object[0]), dict(email="<EMAIL>", user=self._user_set_object[2]), dict(email="<EMAIL>", user=self._user_set_object[4]), dict(email="<EMAIL>", user=self._user_set_object[6]), dict(email="<EMAIL>", user=self._user_set_object[8]), ]	[ 1, 515, 7136, 29889, 1688, 29889, 10041, 29918, 842, 29889, 10041, 29918, 842, 29918, 12650, 29889, 23176, 29918, 10149, 29918, 842, 29918, 3318, 1053, 3985, 10601, 2697, 2061, 13, 3166, 4148, 17063, 29889, 4861, 1056, 29889, 10041, 1053, 16535, 13, 13, 13, 1990, 16535, 2697, 2061, 29898, 25865, 10601, 2697, 2061, 1125, 13, 268, 13, 1678, 903, 10041, 29918, 1990, 353, 16535, 13, 13, 1678, 903, 19973, 29918, 2671, 353, 376, 5269, 29908, 13, 13, 1678, 822, 848, 29918, 18121, 29898, 1311, 1125, 13, 4706, 736, 518, 13, 9651, 9657, 29898, 5269, 543, 29966, 26862, 6227, 28341, 1404, 29922, 1311, 3032, 1792, 29918, 842, 29918, 3318, 29961, 29900, 11724, 13, 9651, 9657, 29898, 5269, 543, 29966, 26862, 6227, 28341, 1404, 29922, 1311, 3032, 1792, 29918, 842, 29918, 3318, 29961, 29906, 11724, 13, 9651, 9657, 29898, 5269, 543, 29966, 26862, 6227, 28341, 1404, 29922, 1311, 3032, 1792, 29918, 842, 29918, 3318, 29961, 29946, 11724, 13, 9651, 9657, 29898, 5269, 543, 29966, 26862, 6227, 28341, 1404, 29922, 1311, 3032, 1792, 29918, 842, 29918, 3318, 29961, 29953, 11724, 13, 9651, 9657, 29898, 5269, 543, 29966, 26862, 6227, 28341, 1404, 29922, 1311, 3032, 1792, 29918, 842, 29918, 3318, 29961, 29947, 11724, 13, 4706, 4514, 13, 2 ]
download/Cost-Function-Of-ML/costFunctionExam.py	chenjian158978/chenjian.github.io	3	52700	<reponame>chenjian158978/chenjian.github.io # -- coding:utf8 -- """ @author: <EMAIL> @date: Tue, May 23 2017 @time: 19:05:20 GMT+8 """ import matplotlib.pyplot as plt import numpy as np # 都转换成列向量 X = np.array([[0, 1, 2, 4]]).T Y = np.array([[0, 1, 2, 4]]).T # 三个不同的theta_1值 theta1 = np.array([[0, 0]]).T theta2 = np.array([[0, 0.5]]).T theta3 = np.array([[0, 1]]).T # 矩阵X的行列(m,n) X_size = X.shape # 创建一个(4,1)的单位矩阵 X_0 = np.ones((X_size[0], 1)) # 形成点的坐标 X_with_x0 = np.concatenate((X_0, X), axis=1) # 两个数组点积 h1 = np.dot(X_with_x0, theta1) h2 = np.dot(X_with_x0, theta2) h3 = np.dot(X_with_x0, theta3) # r:red x: x marker plt.plot(X, Y, 'rx', label='y') plt.title("Cost Function Example") plt.grid(True) plt.plot(X, h1, color='b', label='h1, theta_1=0') plt.plot(X, h2, color='m', label='h2, theta_1=0.5') plt.plot(X, h3, color='g', label='h3, theta_1=1') # 坐标轴名称 plt.xlabel('X') plt.ylabel('y/h') # 坐标轴范围 plt.axis([-0.1, 4.5, -0.1, 4.5]) # plt.legend(loc='upper left') plt.legend(loc='best') plt.savefig('liner_gression_error.png', dpi=200) plt.show()	[ 1, 529, 276, 1112, 420, 29958, 2724, 29926, 713, 29896, 29945, 29947, 29929, 29955, 29947, 29914, 2724, 29926, 713, 29889, 3292, 29889, 601, 13, 29937, 448, 29930, 29899, 14137, 29901, 9420, 29947, 448, 29930, 29899, 13, 13, 15945, 29908, 13, 29992, 8921, 29901, 529, 26862, 6227, 29958, 13, 13, 29992, 1256, 29901, 323, 434, 29892, 2610, 29871, 29906, 29941, 29871, 29906, 29900, 29896, 29955, 13, 13, 29992, 2230, 29901, 29871, 29896, 29929, 29901, 29900, 29945, 29901, 29906, 29900, 402, 11490, 29974, 29947, 13, 15945, 29908, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 5215, 12655, 408, 7442, 13, 13, 29937, 29871, 30769, 31415, 31640, 30494, 31025, 31331, 31180, 13, 29990, 353, 7442, 29889, 2378, 4197, 29961, 29900, 29892, 29871, 29896, 29892, 29871, 29906, 29892, 29871, 29946, 5262, 467, 29911, 13, 29979, 353, 7442, 29889, 2378, 4197, 29961, 29900, 29892, 29871, 29896, 29892, 29871, 29906, 29892, 29871, 29946, 5262, 467, 29911, 13, 13, 29937, 29871, 30457, 30502, 30413, 30980, 30210, 3416, 29918, 29896, 30959, 13, 3416, 29896, 353, 7442, 29889, 2378, 4197, 29961, 29900, 29892, 29871, 29900, 5262, 467, 29911, 13, 3416, 29906, 353, 7442, 29889, 2378, 4197, 29961, 29900, 29892, 29871, 29900, 29889, 29945, 5262, 467, 29911, 13, 3416, 29941, 353, 7442, 29889, 2378, 4197, 29961, 29900, 29892, 29871, 29896, 5262, 467, 29911, 13, 13, 29937, 29871, 234, 162, 172, 236, 155, 184, 29990, 30210, 30448, 31025, 29898, 29885, 29892, 29876, 29897, 13, 29990, 29918, 2311, 353, 1060, 29889, 12181, 13, 13, 29937, 29871, 31441, 30886, 30287, 30502, 29898, 29946, 29892, 29896, 29897, 30210, 31166, 30956, 234, 162, 172, 236, 155, 184, 13, 29990, 29918, 29900, 353, 7442, 29889, 2873, 3552, 29990, 29918, 2311, 29961, 29900, 1402, 29871, 29896, 876, 13, 13, 29937, 29871, 31305, 30494, 30940, 30210, 232, 160, 147, 31062, 13, 29990, 29918, 2541, 29918, 29916, 29900, 353, 7442, 29889, 535, 29883, 2579, 403, 3552, 29990, 29918, 29900, 29892, 1060, 511, 9685, 29922, 29896, 29897, 13, 13, 29937, 29871, 31977, 30502, 30354, 31263, 30940, 234, 170, 178, 13, 29882, 29896, 353, 7442, 29889, 6333, 29898, 29990, 29918, 2541, 29918, 29916, 29900, 29892, 278, 941, 29896, 29897, 13, 29882, 29906, 353, 7442, 29889, 6333, 29898, 29990, 29918, 2541, 29918, 29916, 29900, 29892, 278, 941, 29906, 29897, 13, 29882, 29941, 353, 7442, 29889, 6333, 29898, 29990, 29918, 2541, 29918, 29916, 29900, 29892, 278, 941, 29941, 29897, 13, 13, 29937, 364, 29901, 1127, 921, 29901, 921, 17456, 13, 572, 29873, 29889, 5317, 29898, 29990, 29892, 612, 29892, 525, 17697, 742, 3858, 2433, 29891, 1495, 13, 572, 29873, 29889, 3257, 703, 25733, 6680, 8741, 1159, 13, 572, 29873, 29889, 7720, 29898, 5574, 29897, 13, 572, 29873, 29889, 5317, 29898, 29990, 29892, 298, 29896, 29892, 2927, 2433, 29890, 742, 3858, 2433, 29882, 29896, 29892, 278, 941, 29918, 29896, 29922, 29900, 1495, 13, 572, 29873, 29889, 5317, 29898, 29990, 29892, 298, 29906, 29892, 2927, 2433, 29885, 742, 3858, 2433, 29882, 29906, 29892, 278, 941, 29918, 29896, 29922, 29900, 29889, 29945, 1495, 13, 572, 29873, 29889, 5317, 29898, 29990, 29892, 298, 29941, 29892, 2927, 2433, 29887, 742, 3858, 2433, 29882, 29941, 29892, 278, 941, 29918, 29896, 29922, 29896, 1495, 13, 13, 29937, 29871, 232, 160, 147, 31062, 235, 192, 183, 30548, 31685, 13, 572, 29873, 29889, 29916, 1643, 877, 29990, 1495, 13, 572, 29873, 29889, 29891, 1643, 877, 29891, 29914, 29882, 1495, 13, 13, 29937, 29871, 232, 160, 147, 31062, 235, 192, 183, 235, 143, 134, 232, 158, 183, 13, 572, 29873, 29889, 8990, 4197, 29899, 29900, 29889, 29896, 29892, 29871, 29946, 29889, 29945, 29892, 448, 29900, 29889, 29896, 29892, 29871, 29946, 29889, 29945, 2314, 13, 29937, 14770, 29889, 26172, 29898, 2029, 2433, 21064, 2175, 1495, 13, 572, 29873, 29889, 26172, 29898, 2029, 2433, 13318, 1495, 13, 572, 29873, 29889, 7620, 1003, 877, 1915, 261, 29918, 11476, 29918, 2704, 29889, 2732, 742, 270, 1631, 29922, 29906, 29900, 29900, 29897, 13, 572, 29873, 29889, 4294, 580, 13, 2 ]
Aprendizaje-Guia/Talleres-Python/ejemplo.py	JuanCamilo9915/Learning-Python	0	1611766	print=("nombre completo") nombrecompleto=input() print("cedula de ciudadania") cedula=input() cedulah=int(cedula) print("fecha de nacimiento") fecha=input() fecha6=int(fecha) print("edad:") edad=input() edadnum=int(edad) if(edadnum>=18): print("Mayor de edad") else: print("menor de edad")	[ 1, 1596, 29922, 703, 11522, 1030, 1614, 517, 1159, 13, 11522, 1030, 5729, 517, 29922, 2080, 580, 13, 2158, 703, 1133, 2497, 316, 9866, 4807, 1159, 13, 1133, 2497, 29922, 2080, 580, 13, 1133, 352, 801, 29922, 524, 29898, 1133, 2497, 29897, 13, 2158, 703, 1725, 5815, 316, 22227, 9043, 1159, 13, 1725, 5815, 29922, 2080, 580, 13, 1725, 5815, 29953, 29922, 524, 29898, 1725, 5815, 29897, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 2158, 703, 20483, 29901, 1159, 13, 20483, 29922, 2080, 580, 13, 20483, 1949, 29922, 524, 29898, 20483, 29897, 13, 13, 361, 29898, 20483, 1949, 18572, 29896, 29947, 1125, 13, 1678, 1596, 703, 12703, 272, 316, 1226, 328, 1159, 13, 2870, 29901, 13, 1678, 1596, 703, 1527, 272, 316, 1226, 328, 1159, 13, 2 ]
{{cookiecutter.project_slug}}/backend/app/app/api/api_v1/endpoints/utils.py	scil/full-stack-fastapi-postgresql-for-uper	0	68632	<gh_stars>0 from fastapi import APIRouter, Depends from pydantic import EmailStr from app.api.utils.security import get_current_active_superuser from app.core.celery_app import celery_app from app.models.msg import Msg from app.models.user import UserInDB from app.utils import send_test_email router = APIRouter() @router.post("/test-celery/", response_model=Msg, status_code=201) def test_celery( msg: Msg, current_user: UserInDB = Depends(get_current_active_superuser) ): """ Test Celery worker. """ celery_app.send_task("app.worker.test_celery", args=[msg.msg]) return {"msg": "Word received"} @router.post("/test-email/", response_model=Msg, status_code=201) def test_email( email_to: EmailStr, current_user: UserInDB = Depends(get_current_active_superuser) ): """ Test emails. """ send_test_email(email_to=email_to) return {"msg": "Test email sent"}	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 5172, 2754, 1053, 3450, 23971, 29892, 10034, 1975, 13, 3166, 282, 2941, 7716, 1053, 22608, 5015, 13, 13, 3166, 623, 29889, 2754, 29889, 13239, 29889, 8926, 1053, 679, 29918, 3784, 29918, 4925, 29918, 9136, 1792, 13, 3166, 623, 29889, 3221, 29889, 2242, 708, 29918, 932, 1053, 6432, 708, 29918, 932, 13, 3166, 623, 29889, 9794, 29889, 7645, 1053, 28264, 13, 3166, 623, 29889, 9794, 29889, 1792, 1053, 4911, 797, 4051, 13, 3166, 623, 29889, 13239, 1053, 3638, 29918, 1688, 29918, 5269, 13, 13, 15140, 353, 3450, 23971, 580, 13, 13, 13, 29992, 15140, 29889, 2490, 11974, 1688, 29899, 2242, 708, 29914, 613, 2933, 29918, 4299, 29922, 16190, 29892, 4660, 29918, 401, 29922, 29906, 29900, 29896, 29897, 13, 1753, 1243, 29918, 2242, 708, 29898, 13, 1678, 10191, 29901, 28264, 29892, 1857, 29918, 1792, 29901, 4911, 797, 4051, 353, 10034, 1975, 29898, 657, 29918, 3784, 29918, 4925, 29918, 9136, 1792, 29897, 13, 1125, 13, 1678, 9995, 13, 1678, 4321, 14227, 708, 15645, 29889, 13, 1678, 9995, 13, 1678, 6432, 708, 29918, 932, 29889, 6717, 29918, 7662, 703, 932, 29889, 24602, 29889, 1688, 29918, 2242, 708, 613, 6389, 11759, 7645, 29889, 7645, 2314, 13, 1678, 736, 8853, 7645, 1115, 376, 14463, 4520, 9092, 13, 13, 13, 29992, 15140, 29889, 2490, 11974, 1688, 29899, 5269, 29914, 613, 2933, 29918, 4299, 29922, 16190, 29892, 4660, 29918, 401, 29922, 29906, 29900, 29896, 29897, 13, 1753, 1243, 29918, 5269, 29898, 13, 1678, 4876, 29918, 517, 29901, 22608, 5015, 29892, 1857, 29918, 1792, 29901, 4911, 797, 4051, 353, 10034, 1975, 29898, 657, 29918, 3784, 29918, 4925, 29918, 9136, 1792, 29897, 13, 1125, 13, 1678, 9995, 13, 1678, 4321, 24609, 29889, 13, 1678, 9995, 13, 1678, 3638, 29918, 1688, 29918, 5269, 29898, 5269, 29918, 517, 29922, 5269, 29918, 517, 29897, 13, 1678, 736, 8853, 7645, 1115, 376, 3057, 4876, 2665, 9092, 13, 2 ]
src/tests/test.py	ThomazDiniz/Projeto-TC	1	179722	<reponame>ThomazDiniz/Projeto-TC execfile("../main.py") readInputFile() simulate()	[ 1, 529, 276, 1112, 420, 29958, 1349, 290, 834, 29928, 262, 466, 29914, 1184, 4026, 29877, 29899, 9472, 13, 4258, 1445, 703, 6995, 3396, 29889, 2272, 1159, 13, 13, 949, 4290, 2283, 580, 13, 3601, 5987, 580, 2 ]
src/pdf/plugins/_pdf/external.py	ChakshuGautam/PDF-Package	0	114106	import json import os import traceback from celery import shared_task from interface import implements import pdfkit from pydrive.auth import GoogleAuth from pydrive.drive import GoogleDrive from ...base.interfaces.plugin import Plugin from ...models import GenericConfig from ...shorteners.yaus import YausShortner from ...uploaders.minio import MinioUploader from ...utils import publish_to_url, build_pdf import requests from dotenv import load_dotenv load_dotenv("../../.env") class PDFPlugin(implements(Plugin)): def __init__(self, data, token): """ To do authentication """ # generate path try: self.config = GenericConfig.objects.get(pk=data['config_id']) except KeyError: self.config = GenericConfig.objects.get(pk=1) self.user_config = json.loads(self.config.data) self._data = data['data'] self.template_id = data['template_id'] self.token = token self.settings_file_loc = f"pdf/creds/{self.user_config['APPLICATION_SETTINGS_FILE']}" self.creds_file_location = f"pdf/creds/{self.user_config['CREDENTIAL_SETTINGS_FILE']}" self.uploader = self.config.uploader_ref self.shortener = self.config.shortener_ref def get_client(self): gauth = GoogleAuth(settings_file=self.settings_file_loc) gauth.LoadCredentialsFile(self.creds_file_location) if gauth.credentials is None: # Authenticate if they're not there gauth.CommandLineAuth() elif gauth.access_token_expired: # Refresh them if expired gauth.Refresh() else: # Initialize the saved creds gauth.Authorize() # Save the current credentials to a file gauth.SaveCredentialsFile(self.creds_file_location) drive = GoogleDrive(gauth) self.drive = drive return self.drive def fetch_data(self): """ Fetches "new" data from the database/server/websocket whatever and provides it in the form of dictionary, one PDF at a time """ pass def fetch_template(self): """ Fetches template and returns it in the form of string """ try: template_string = error_code = error_msg = None data = json.dumps({ "id": self.template_id, "data": self._data }) headers = { 'Content-Type': 'application/json' } response = requests.post(f"{os.getenv('TEMPLATOR_URL')}/process", data=data, headers=headers) if response.status_code == 201: template_string = response.json()['processed'] if "processed" in response.json() else None else: error = response.text except Exception as e: traceback.print_exc() error_msg = f"Failed to fetch template: {e}" error_code = 801 return error_code, error_msg, template_string def build_file(self, template): """ Function to build PDF and return a file (fetch template and build pdf) """ is_successful = error_code = error_msg = None drive_file_loc = f'pdf/drivefiles/{self.token}.pdf' try: # path_wkhtmltopdf = r'C:\Program Files\wkhtmltopdf\bin\wkhtmltopdf.exe' # config = pdfkit.configuration(wkhtmltopdf=path_wkhtmltopdf) # pdfkit.from_string(template, drive_file_loc, configuration=config) pdfkit.from_string(template, drive_file_loc) is_successful = True except Exception as e: traceback.print_exc() error_msg = f"Failed to generate doc: {e}" error_code = 803 return error_code, error_msg, is_successful def upload_file(self): """ Function to save PDF """ error_code = error_msg = final_data = None try: drive_file_loc = f'pdf/drivefiles/{self.token}.pdf' if self.uploader == "minio": host = self.user_config["MINIO_HOST"] access_key = self.user_config["MINIO_ACCESS_KEY"] secret_key = self.user_config["MINIO_SECRET_KEY"] bucket_name = self.user_config["MINIO_BUCKET_NAME"] uploader = MinioUploader(host, access_key, secret_key, bucket_name) error_code, error_msg, final_data = uploader.put(f'{self.token}.pdf', f'{self.token}.pdf', None) if error_code is None: if os.path.exists(drive_file_loc): os.remove(drive_file_loc) else: raise Exception("Failed to build the pdf") return error_code, error_msg, final_data else: raise Exception("Uploader plugin not supported") except Exception as e: traceback.print_exc() error_code = 805 error_msg = f"Something went wrong: {e}" finally: return error_code, error_msg, final_data def retrieve_file(self, object_name): """ Function to get the previously saved PDF from the key """ def shorten_url(self, url): """ Function to generate short url for the uploaded doc """ error_code = error_msg = final_data = None try: if self.shortener == "yaus": host = self.user_config["SHORTENER_URL"] shortener = YausShortner(host) error_code, error_msg, final_data = shortener.apply(url, self.token) if error_code is None: final_data = final_data['shortUrl'] else: raise Exception("Shortener plugin not available") except Exception as e: traceback.print_exc() error_code = 806 error_msg = f"Something went wrong: {e}" finally: return error_code, error_msg, final_data def publish(self, id, url, headers): """ Function to publish response on external url/webhook """ publish_to_url(id, url, headers=headers)	[ 1, 1053, 4390, 13, 5215, 2897, 13, 5215, 9637, 1627, 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suspects.py	ht21992/Online-Board-Game	0	161709	import random import pickle with open("run_number", 'rb') as f: run_number = pickle.load(f) def sus_info(): global run_number suspects = {0: {"Name": "<NAME>", "Blood Type": "AB+", "Occupation": "actor", "Hair Color": "brown", "Age": 64, "Sex": "man"}, 1: {"Name": "<NAME>", "Blood Type": "A+", "Occupation": "painter", "Hair Color": "black", "Age": 32, "Sex": "man"}, 2: {"Name": "<NAME>", "Blood Type": "B+", "Occupation": "surgeon", "Hair Color": "black", "Age": 28, "Sex": "woman"}, 3: {"Name": "<NAME>", "Blood Type": "A+", "Occupation": "tailor", "Hair Color": "blonde", "Age": 41, "Sex": "woman"}, 4: {"Name": "<NAME>", "Blood Type": "O+", "Occupation": "surgeon", "Hair Color": "blonde", "Age": 29, "Sex": "man"}, 5: {"Name": "<NAME>", "Blood Type": "AB+", "Occupation": "nurse", "Hair Color": "brown", "Age": 52, "Sex": "woman"}, 6: {"Name": "<NAME>", "Blood Type": "O+", "Occupation": "painter", "Hair Color": "black", "Age": 27, "Sex": "woman"}, 7: {"Name": "<NAME>", "Blood Type": "O-", "Occupation": "actor", "Hair Color": "blonde", "Age": 31, "Sex": "woman"}, 8: {"Name": "<NAME>", "Blood Type": "B+", "Occupation": "tailor", "Hair Color": "black", "Age": 35, "Sex": "man"}, 9: {"Name": "<NAME>", "Blood Type": "O-", "Occupation": "nurse", "Hair Color": "blonde", "Age": 47, "Sex": "man"}, } with open("used_positions", 'rb') as f: used_positions = pickle.load(f) if run_number == 0: main_sus = random.choice(suspects) with open("main_sus", 'wb') as f: pickle.dump(main_sus, f) # question_numbers = pickle.load(f) with open("run_number", 'wb') as f: pickle.dump(run_number, f) else: with open("main_sus", 'rb') as f: main_sus = pickle.load(f) run_number+=1 with open("run_number", 'wb') as f: pickle.dump(run_number, f) # print(main_sus) hints = [f"It was {random.choice(['Sunday', 'Monday', 'Tuesday', 'Thursday', 'Wednesday', 'Friday', 'Saturday'])}", f"The hair color was {main_sus['Hair Color']}", f"The guy had a mask in the elevator", f"The guy looked to be around {random.randint(main_sus['Age'] - 3, main_sus['Age'] + 5)}", f"I might put you off the scent :)", f"Not sure but {random.choice([main_sus['Hair Color'], 'black', 'blonde'])} hair color", f"maybe a scissor", f"When I was coming into the building I saw a {main_sus['Sex']}", f"He had strange cloths but I guess he was a man", f"Maybe she was a woman", f"They were fighting and suddenly they became silent", f"I know this neighbourhood very well", f"I'm not sure about hair color", f"it was around {random.randrange(1, 13)} {random.choice(['a.m', 'p.m'])}", f"the assassin had a knife or sth like that", f"I also heard {main_sus['Name'].split(' ')[0]} multiple times", f"I also heard {random.choice(['John', 'Elizabeth', 'Eric', 'Stephanie', 'Randall', 'Norma', 'Kathleen', 'Christina', 'Nicholas', 'David'])} multiple times", f"I heard the name {random.choice(['John', 'Elizabeth', 'Eric', 'Stephanie', 'Randall', 'Norma', 'Kathleen', 'Christina', 'Nicholas', 'David'])}", f"The test shows the blood type is {main_sus['Blood Type']}", f"I took a look at the corpse", f"You must believe me", f"The assassin is {main_sus['Occupation']}", f"The assassin is {random.choice(['tailor', 'butcher', 'nurse', 'actor', 'painter', 'surgeon', 'goldsmith', 'cook'])}", f"I think I have no more information", ] if main_sus['Occupation'] == 'surgeon': hints.append('The assassin knew how to use a knife ') elif main_sus['Occupation'] == 'tailor': hints.append('The assassin knew how to use a scissor ') elif main_sus['Occupation'] == "actor": hints.append('The assassin must like the art ') else: hints.append('The assassin was a noob') run_number return suspects, main_sus, hints, used_positions	[ 1, 1053, 4036, 30004, 13, 5215, 5839, 280, 30004, 13, 30004, 13, 2541, 1722, 703, 3389, 29918, 4537, 613, 525, 6050, 1495, 408, 285, 29901, 30004, 13, 1678, 1065, 29918, 4537, 353, 5839, 280, 29889, 1359, 29898, 29888, 8443, 13, 30004, 13, 30004, 13, 30004, 13, 30004, 13, 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utils/config_import.py	pyoor/distiller	30	73435	<reponame>pyoor/distiller import yaml import os import shutil import sqlite3 import sys class DistillerConfig: def __init__(self, config_file, section): self.config = read_config(config_file, section) try: self.project_name = self.config['name'] except KeyError: raise Exception(" Project name not defined.") self.trace_queue = "%s-trace-queue" % self.project_name self.trace_results = "%s-trace-results" % self.project_name self.min_queue = "%s-min-queue" % self.project_name self.min_results = "%s-min-results" % self.project_name try: self.operations = self.config['operations'] if len(self.operations) == 0: raise Exception("You must select atleast one mode of operation.") except: raise Exception("You must select atleast one mode of operation.") try: self.mode = self.config['filter']['mode'] self.modules = self.config['modules'] except KeyError: # Optional arguments self.mode = None self.modules = None if section == "server": try: self.seed_dir = self.config['seed_dir'] except KeyError: raise Exception("No working path defined.") try: self.working_dir = self.config['working_dir'] except KeyError: raise Exception("No working path defined.") self.project_dir = os.path.join(self.working_dir, self.project_name) self.db_path = os.path.join(self.project_dir, "backup.db") self.min_dir = os.path.join(self.project_dir, "minimized") self.trace_dir = os.path.join(self.project_dir, "traces") self.results_dir = os.path.join(self.project_dir, "results") if os.path.isdir(self.project_dir): action = None while action != "R" and action != "A": action = raw_input("Project Exists! Replace or Append? [R/A]: ").upper() if action == "R": confirm = None while confirm != "Y" and confirm != "N": confirm = raw_input("Are you sure? All data will be deleted! [Y/N]: ").upper() if confirm == "Y": shutil.rmtree(self.project_dir) os.makedirs(self.project_dir) os.makedirs(self.min_dir) os.makedirs(self.trace_dir) os.makedirs(self.results_dir) sql = sqlite3.connect(self.db_path) c = sql.cursor() c.execute('BEGIN TRANSACTION') c.execute('''CREATE TABLE IF NOT EXISTS modules (num INTEGER PRIMARY KEY, name TEXT, UNIQUE (name))''') c.execute('''CREATE TABLE IF NOT EXISTS seeds (num INTEGER PRIMARY KEY, seed_name TEXT, trace_name TEXT, ublock_cnt INT, UNIQUE (seed_name))''') c.execute('''CREATE TABLE IF NOT EXISTS master_lookup (bblock TEXT PRIMARY KEY)''') # Results are calculated using the full data set - Wipe if they exist! c.execute('''DROP TABLE IF EXISTS results''') c.execute('''CREATE TABLE results (name TEXT PRIMARY KEY, ublock_cnt INT)''') sql.commit() else: sys.exit() elif section == "client": try: self.host = self.config['host'] except KeyError: raise Exception("No host defined.") try: self.drio_path = self.config['drio_path'] if not os.path.isfile(self.drio_path): raise Exception("Can not find DynamoRio - %s" % self.drio_path) except KeyError: raise Exception("No DynamoRio path defined.") try: self.target_path = self.config['target_path'] if not os.path.isfile(self.target_path): raise Exception("Can not find target - %s" % self.target_path) except KeyError: raise Exception("No target path defined.") try: self.w_time = self.config['wait_time'] except KeyError: raise Exception("No wait time defined.") try: self.m_time = self.config['max_timeout'] except KeyError: raise Exception("No max timeout defined.") # Optional args try: self.target_args = self.config['target_args'] if self.target_args is None: self.target_args = '' except KeyError: self.target_args = None try: self.pre_cmd = self.config['pre_cmd'] except KeyError: self.pre_cmd = None try: self.post_cmd = self.config['post_cmd'] except KeyError: self.post_cmd = None def read_config(config_file, section): sections = ['project', section] with open(config_file, 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constants.py	jlduhaime/combatTest	0	1600898	SCREEN_WIDTH = 640 SCREEN_HEIGHT = 480 FPS = 60 BLACK = (0, 0, 0)	[ 1, 12314, 1525, 1430, 29918, 22574, 353, 29871, 29953, 29946, 29900, 13, 7187, 1525, 1430, 29918, 9606, 22530, 353, 29871, 29946, 29947, 29900, 13, 29943, 7024, 353, 29871, 29953, 29900, 13, 13, 13367, 11375, 353, 313, 29900, 29892, 29871, 29900, 29892, 29871, 29900, 29897, 2 ]
problems/algoexpert/list/shift_linked_list.py	JayMonari/py-personal	0	79779	<filename>problems/algoexpert/list/shift_linked_list.py<gh_stars>0 from typing import Optional class ListNode: def __init__(self, value: int, _next=None): self.value: int = value self.next: Optional[ListNode] = _next def shift_linked_list( head: Optional[ListNode], k: int) -> Optional[ListNode]: list_length: int = 1 list_tail: Optional[ListNode] = head while list_tail.next is not None: list_tail = list_tail.next list_length += 1 offset: int = abs(k) % list_length if offset == 0: return head new_tail_position: int = list_length - offset if k > 0 else offset new_tail: Optional[ListNode] = head for _ in range(1, new_tail_position): new_tail = new_tail.next new_head: Optional[ListNode] = new_tail.next new_tail.next = None list_tail.next = head return new_head	[ 1, 529, 9507, 29958, 17199, 29879, 29914, 284, 1484, 735, 10700, 29914, 1761, 29914, 10889, 29918, 2324, 287, 29918, 1761, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 19229, 1053, 28379, 13, 13, 13, 1990, 2391, 4247, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 995, 29901, 938, 29892, 903, 4622, 29922, 8516, 1125, 13, 4706, 1583, 29889, 1767, 29901, 938, 353, 995, 13, 4706, 1583, 29889, 4622, 29901, 28379, 29961, 1293, 4247, 29962, 353, 903, 4622, 13, 13, 13, 1753, 9500, 29918, 2324, 287, 29918, 1761, 29898, 13, 4706, 2343, 29901, 28379, 29961, 1293, 4247, 1402, 13, 4706, 413, 29901, 938, 29897, 1599, 28379, 29961, 1293, 4247, 5387, 13, 1678, 1051, 29918, 2848, 29901, 938, 353, 29871, 29896, 13, 1678, 1051, 29918, 18237, 29901, 28379, 29961, 1293, 4247, 29962, 353, 2343, 13, 1678, 1550, 1051, 29918, 18237, 29889, 4622, 338, 451, 6213, 29901, 13, 4706, 1051, 29918, 18237, 353, 1051, 29918, 18237, 29889, 4622, 13, 4706, 1051, 29918, 2848, 4619, 29871, 29896, 13, 13, 1678, 9210, 29901, 938, 353, 6425, 29898, 29895, 29897, 1273, 1051, 29918, 2848, 13, 1678, 565, 9210, 1275, 29871, 29900, 29901, 13, 4706, 736, 2343, 13, 1678, 716, 29918, 18237, 29918, 3283, 29901, 938, 353, 1051, 29918, 2848, 448, 9210, 565, 413, 1405, 29871, 29900, 1683, 9210, 13, 1678, 716, 29918, 18237, 29901, 28379, 29961, 1293, 4247, 29962, 353, 2343, 13, 1678, 363, 903, 297, 3464, 29898, 29896, 29892, 716, 29918, 18237, 29918, 3283, 1125, 13, 4706, 716, 29918, 18237, 353, 716, 29918, 18237, 29889, 4622, 13, 1678, 716, 29918, 2813, 29901, 28379, 29961, 1293, 4247, 29962, 353, 716, 29918, 18237, 29889, 4622, 13, 1678, 716, 29918, 18237, 29889, 4622, 353, 6213, 13, 1678, 1051, 29918, 18237, 29889, 4622, 353, 2343, 13, 1678, 736, 716, 29918, 2813, 13, 2 ]
scrapytest/spiders/ScrapyDemo.py	liang1024/Scrapy	0	100429	import scrapy class QuotesSpider(scrapy.Spider): name = "demo1" def start_requests(self): urls = [ 'http://quotes.toscrape.com/page/1/', 'http://quotes.toscrape.com/page/2/', ] for url in urls: yield scrapy.Request(url=url, callback=self.parse) def parse(self, response): page = response.url.split("/")[-2] filename = 'demo1-%s.html' % page with open(filename, 'wb') as f: f.write(response.body) self.log('Saved file %s' % filename) ''' name：识别蜘蛛。它在项目中必须是唯一的，也就是说，您不能为不同的Spiders设置相同的名称。 start_requests()：必须返回一个可迭代的请求（您可以返回一个请求列表或写一个生成器函数），Spider将开始爬行。随后的请求将从这些初始请求连续生成。 parse()：一种将被调用来处理为每个请求下载的响应的方法。response参数是一个TextResponse保存页面内容的实例，并且还有其他有用的方法来处理它。该parse()方法通常解析响应，将刮取的数据提取为示例，并且还可以查找新的URL以从中创建新的请求（Request）。 ''' ''' 要使我们的蜘蛛工作，请转到项目的顶级目录并运行： scrapy crawl demo1 现在，检查当前目录下的文件。您应该注意到，已经创建了两个新文件：quotes-1.html和quotes-2.html，其中包含各个URL的内容，正如我们的parse方法指示。 '''	[ 1, 1053, 24559, 2272, 13, 13, 13, 1990, 751, 4769, 5592, 1241, 29898, 1557, 336, 2272, 29889, 5592, 1241, 1125, 13, 1678, 1024, 353, 376, 17482, 29896, 29908, 13, 13, 1678, 822, 1369, 29918, 24830, 29898, 1311, 1125, 13, 4706, 23942, 353, 518, 13, 9651, 525, 1124, 597, 339, 4769, 29889, 29873, 14174, 336, 412, 29889, 510, 29914, 3488, 29914, 29896, 29914, 742, 13, 9651, 525, 1124, 597, 339, 4769, 29889, 29873, 14174, 336, 412, 29889, 510, 29914, 3488, 29914, 29906, 29914, 742, 13, 4706, 4514, 13, 4706, 363, 3142, 297, 23942, 29901, 13, 9651, 7709, 24559, 2272, 29889, 3089, 29898, 2271, 29922, 2271, 29892, 6939, 29922, 1311, 29889, 5510, 29897, 13, 13, 1678, 822, 6088, 29898, 1311, 29892, 2933, 1125, 13, 4706, 1813, 353, 2933, 29889, 2271, 29889, 5451, 11974, 1159, 14352, 29906, 29962, 13, 4706, 10422, 353, 525, 17482, 29896, 19222, 29879, 29889, 1420, 29915, 1273, 1813, 13, 4706, 411, 1722, 29898, 9507, 29892, 525, 29893, 29890, 1495, 408, 285, 29901, 13, 9651, 285, 29889, 3539, 29898, 5327, 29889, 2587, 29897, 13, 4706, 1583, 29889, 1188, 877, 29903, 10511, 934, 1273, 29879, 29915, 1273, 10422, 29897, 13, 13, 12008, 13, 978, 30383, 235, 178, 137, 232, 139, 174, 235, 159, 155, 235, 158, 158, 30267, 232, 177, 134, 30505, 31888, 30895, 30275, 31641, 236, 164, 190, 30392, 232, 151, 178, 30287, 30210, 30214, 30953, 31238, 30392, 31639, 30214, 233, 133, 171, 30413, 30815, 30573, 30413, 30980, 30210, 5592, 11376, 30872, 30669, 30990, 30980, 30210, 30548, 31685, 30267, 13, 2962, 29918, 24830, 580, 30383, 31641, 236, 164, 190, 31086, 30742, 30287, 30502, 30682, 235, 194, 176, 30690, 30210, 31088, 31376, 30419, 233, 133, 171, 30682, 30651, 31086, 30742, 30287, 30502, 31088, 31376, 31025, 30746, 31391, 31479, 30287, 30502, 30486, 30494, 30943, 31629, 30354, 30409, 30214, 5592, 1241, 30998, 31026, 31020, 234, 139, 175, 30448, 30267, 236, 157, 146, 30822, 30210, 31088, 31376, 30998, 31594, 30810, 31959, 31120, 31020, 31088, 31376, 31903, 234, 190, 176, 30486, 30494, 30267, 13, 5510, 580, 30383, 30287, 31893, 30998, 31407, 31268, 30406, 30805, 31548, 30687, 30573, 31951, 30502, 31088, 31376, 30557, 31526, 30210, 232, 150, 144, 31370, 30210, 30525, 30545, 30267, 5327, 31125, 30354, 30392, 30287, 30502, 1626, 5103, 30982, 30946, 31610, 30806, 30728, 31294, 30210, 31195, 31507, 30214, 31666, 231, 187, 151, 31994, 30417, 31149, 31221, 30417, 30406, 30210, 30525, 30545, 30805, 31548, 30687, 232, 177, 134, 30267, 13, 31751, 5510, 580, 30525, 30545, 30768, 31190, 31201, 233, 161, 147, 232, 150, 144, 31370, 30214, 30998, 232, 139, 177, 30683, 30210, 30354, 30763, 31302, 30683, 30573, 30858, 31507, 30214, 31666, 231, 187, 151, 31994, 30682, 30651, 31213, 233, 140, 193, 30374, 30210, 4219, 30651, 31594, 30275, 31441, 30886, 30374, 30210, 31088, 31376, 30419, 3089, 30409, 30267, 13, 13, 12008, 13, 13, 13, 13, 12008, 13, 30698, 30785, 30672, 31381, 30210, 235, 159, 155, 235, 158, 158, 31041, 30732, 30214, 31088, 31415, 30780, 31888, 30895, 30210, 236, 164, 185, 234, 189, 170, 30895, 31283, 31666, 31894, 30448, 30383, 13, 1557, 336, 2272, 29349, 29880, 13455, 29896, 13, 13, 31424, 30505, 30214, 233, 166, 131, 31213, 30948, 30658, 30895, 31283, 30557, 30210, 30333, 30631, 30267, 13, 233, 133, 171, 31370, 31751, 31368, 31474, 30780, 30214, 31290, 31412, 31441, 30886, 30743, 31977, 30502, 30374, 30333, 30631, 30383, 339, 4769, 29899, 29896, 29889, 1420, 30503, 339, 4769, 29899, 29906, 29889, 1420, 30214, 13, 31149, 30275, 31473, 232, 147, 174, 232, 147, 135, 30502, 4219, 30210, 30728, 31294, 30214, 30724, 30847, 30672, 31381, 30210, 5510, 30525, 30545, 31084, 30858, 30267, 13, 12008, 2 ]
old/core/migrations/0005_client.py	rngallen/django_ninja	0	76665	<filename>old/core/migrations/0005_client.py # Generated by Django 3.1.7 on 2021-03-21 15:06 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ("core", "0004_department_employee"), ] operations = [ migrations.CreateModel( name="Client", fields=[ ( "id", models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name="ID", ), ), ("api_key", models.CharField(max_length=50, verbose_name="api_key")), ( "client", models.OneToOneField( on_delete=django.db.models.deletion.CASCADE, related_name="client", to=settings.AUTH_USER_MODEL, verbose_name="user", ), ), ], ), ]	[ 1, 529, 9507, 29958, 1025, 29914, 3221, 29914, 26983, 800, 29914, 29900, 29900, 29900, 29945, 29918, 4645, 29889, 2272, 13, 29937, 3251, 630, 491, 15337, 29871, 29941, 29889, 29896, 29889, 29955, 373, 29871, 29906, 29900, 29906, 29896, 29899, 29900, 29941, 29899, 29906, 29896, 29871, 29896, 29945, 29901, 29900, 29953, 13, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 5215, 9557, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 9725, 800, 29889, 2774, 932, 519, 29918, 10836, 29898, 11027, 29889, 20656, 29950, 29918, 11889, 29918, 20387, 29931, 511, 13, 4706, 4852, 3221, 613, 376, 29900, 29900, 29900, 29946, 29918, 311, 8076, 29918, 26143, 4968, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 4391, 3195, 29898, 13, 9651, 1024, 543, 4032, 613, 13, 9651, 4235, 11759, 13, 18884, 313, 13, 462, 1678, 376, 333, 613, 13, 462, 1678, 4733, 29889, 12300, 3073, 29898, 13, 462, 4706, 4469, 29918, 11600, 29922, 5574, 29892, 13, 462, 4706, 7601, 29918, 1989, 29922, 5574, 29892, 13, 462, 4706, 28755, 29922, 8824, 29892, 13, 462, 4706, 26952, 29918, 978, 543, 1367, 613, 13, 462, 1678, 10353, 13, 18884, 10353, 13, 18884, 4852, 2754, 29918, 1989, 613, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29945, 29900, 29892, 26952, 29918, 978, 543, 2754, 29918, 1989, 1159, 511, 13, 18884, 313, 13, 462, 1678, 376, 4645, 613, 13, 462, 1678, 4733, 29889, 6716, 1762, 6716, 3073, 29898, 13, 462, 4706, 373, 29918, 8143, 29922, 14095, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 29889, 29907, 3289, 5454, 2287, 29892, 13, 462, 4706, 4475, 29918, 978, 543, 4645, 613, 13, 462, 4706, 304, 29922, 11027, 29889, 20656, 29950, 29918, 11889, 29918, 20387, 29931, 29892, 13, 462, 4706, 26952, 29918, 978, 543, 1792, 613, 13, 462, 1678, 10353, 13, 18884, 10353, 13, 9651, 21251, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
ptrack/templatetags/ptrack.py	unformatt/django-ptrack	0	139338	"""Ptrack Template Tag""" import logging from django import template from django.utils.html import mark_safe logger = logging.getLogger(__name__) register = template.Library() @register.simple_tag def ptrack(args, kwargs): """Generate a tracking pixel html img element.""" from ptrack import create_img img = create_img(args, **kwargs) return mark_safe(img)	[ 1, 9995, 29925, 11294, 25663, 10522, 15945, 29908, 13, 5215, 12183, 13, 13, 3166, 9557, 1053, 4472, 13, 3166, 9557, 29889, 13239, 29889, 1420, 1053, 2791, 29918, 11177, 13, 13, 21707, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 9573, 353, 4472, 29889, 12284, 580, 13, 13, 13, 29992, 9573, 29889, 12857, 29918, 4039, 13, 1753, 282, 11294, 10456, 5085, 29892, 3579, 19290, 1125, 13, 1678, 9995, 5631, 403, 263, 23110, 15526, 3472, 10153, 1543, 1213, 15945, 13, 1678, 515, 282, 11294, 1053, 1653, 29918, 2492, 13, 1678, 10153, 353, 1653, 29918, 2492, 10456, 5085, 29892, 3579, 19290, 29897, 13, 1678, 736, 2791, 29918, 11177, 29898, 2492, 29897, 13, 2 ]
hwm3/venv/Lib/site-packages/pymorphy2/opencorpora_dict/parse.py	liubov-volkova/pythondev	0	145514	# -- coding: utf-8 -- """ :mod:`pymorphy2.opencorpora_dict.parse` is a module for OpenCorpora XML dictionaries parsing. """ from __future__ import absolute_import, unicode_literals, division import logging import collections try: from lxml.etree import iterparse def xml_clear_elem(elem): elem.clear() while elem.getprevious() is not None: del elem.getparent()[0] except ImportError: try: from xml.etree.cElementTree import iterparse except ImportError: from xml.etree.ElementTree import iterparse def xml_clear_elem(elem): elem.clear() logger = logging.getLogger(__name__) ParsedDictionary = collections.namedtuple('ParsedDictionary', 'lexemes links grammemes version revision') def parse_opencorpora_xml(filename): """ Parse OpenCorpora dict XML and return a ``ParsedDictionary`` namedtuple. """ links = [] lexemes = {} grammemes = [] version, revision = None, None _lexemes_len = 0 for ev, elem in iterparse(filename, events=(str('start'), str('end'))): if ev == 'start': if elem.tag == 'dictionary': version = elem.get('version') revision = elem.get('revision') logger.info("dictionary v%s, rev%s", version, revision) xml_clear_elem(elem) continue if elem.tag == 'grammeme': name = elem.find('name').text parent = elem.get('parent') alias = elem.find('alias').text description = elem.find('description').text grammeme = (name, parent, alias, description) grammemes.append(grammeme) xml_clear_elem(elem) if elem.tag == 'lemma': if not lexemes: logger.info('parsing xml:lemmas...') lex_id, word_forms = _word_forms_from_xml_elem(elem) lexemes[lex_id] = word_forms xml_clear_elem(elem) elif elem.tag == 'link': if not links: logger.info('parsing xml:links...') link_tuple = ( elem.get('from'), elem.get('to'), elem.get('type'), ) links.append(link_tuple) xml_clear_elem(elem) if len(lexemes) != _lexemes_len and not (len(lexemes) % 50000): logger.debug("%d lexemes parsed" % len(lexemes)) _lexemes_len = len(lexemes) return ParsedDictionary(lexemes, links, grammemes, version, revision) def _grammemes_from_elem(elem): return ",".join(g.get('v') for g in elem.findall('g')) def _word_forms_from_xml_elem(elem): """ Return a list of (word, tag) pairs given "lemma" XML element. 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uppergeodesic.py	drewitz/VIPer	0	65703	#!/usr/bin/env python3 # class file uppergeodesic.py # started as a script to visualize what happens to hyperbolic plane # if different isometries act on it import geodesic as gd import numpy as np import numpy.linalg as lina import matplotlib.pyplot as plt # upper half space as the basic model class UpperGeodesic(gd.Geodesic): """UpperGeodesic line in upper half space takes endpoints on boundary as arguments stores x and y data as points in x and y string "inf" is point at infinity, i.e. y=inf """ xmin = 0 xmax = 0 ymin = 0 # just for consistency, shouldn't change ymax = 0 inf = "inf" def __init__(self, a, b, color="b", label=''): """initialize UpperGeodesic by endpoints a, b - x values of the endpoints or "inf" if infinity res is resolution """ super().__init__(a, b, color, label) # adjust the boundaries of hyperbolic space if self.start != UpperGeodesic.inf: if self.start < UpperGeodesic.xmin: UpperGeodesic.xmin = self.start if self.end > UpperGeodesic.xmax: UpperGeodesic.xmax = self.end UpperGeodesic.ymax = (UpperGeodesic.xmax - UpperGeodesic.xmin)/2 @classmethod def vertical(cls, real): return cls(cls.inf, real) @classmethod def from_midpoint_and_radius(cls, m, r): """ m is only the real part of the circle thing """ return cls(m-r, m+r) def sort_se(self): """sort start and end""" if self.end == self.inf: # just want to assume that the first value is inf if any self.end = self.start self.start = self.inf if self.start != self.inf and self.end < self.start: # swap a and self.end such that a < self.end c = self.start self.start = self.end self.end = c def get_data(self): if self.start == UpperGeodesic.inf: # vertical line xs = [self.end, self.end] ys = [self.ymin, self.ymax] else: # calculate semicircle t = np.linspace(0, np.pi, self.res) r = (self.end - self.start)/2 xs = r(1 + np.cos(t)) + self.start ys = rnp.sin(t) return(xs, ys) ## the next two functions create new geodesics from existing ones def new_geod(self, a, b, c, d): """return new geodesic by received by moebius trafo apply the matrix \| a b \| \| c d \| on the geodesic self and return the resulting geodesic """ start = self.apply_moebius(a, b, c, d, self.start) end = self.apply_moebius(a, b, c, d, self.end) return(UpperGeodesic(start, end)) def new_from_matrix(self, M): return self.new_geod(M[0,0], M[0,1], M[1,0], M[1,1]) ## apply transformations to ONE geodesic def apply_matrix(self, M): self.start = self.apply_moebius(M[0,0], M[0,1], M[1, 0], M[1,1], self.start) self.end = self.apply_moebius(M[0,0], M[0,1], M[1, 0], M[1,1], self.end) self.sort_se() def translate_one_geod(self, dx): if self.start != UpperGeodesic.inf: self.start += dx if self.end != UpperGeodesic.inf: self.end += dx def translate_one_at_zero(self, dx): """inverts at unit sphere, translates and inverts again""" a = self.inversion_on_unit_circle(self.start) b = self.inversion_on_unit_circle(self.end) if a != UpperGeodesic.inf: a += dx if b != UpperGeodesic.inf: b += dx self.start = self.inversion_on_unit_circle(a) self.end = self.inversion_on_unit_circle(b) self.sort_se() def rotate_one_geod(self, phi): """rotates the geodesic on upper half space conjugate to a rotation around origin in the disc model """ if self.start == UpperGeodesic.inf: alpha = -np.pi/2 else: alpha = self.from_upper_to_disc(self.start) beta = self.from_upper_to_disc(self.end) alpha += phi beta += phi self.start = self.from_disc_to_upper(alpha) self.end = self.from_disc_to_upper(beta) self.sort_se() def hyperbolic_translate_one(self, dmult=1.001): """translates one geodesic along UpperGeodesic(-1,1)""" diag = (dmult + 1.0/dmult)/2.0 off = (1.0/dmult - dmult)/2.0 matrix = np.matrix([[diag, off], [off, diag]]) self.apply_matrix(matrix) # tesselate hyperbolic space @classmethod def tesselate(self, depth=10): """Tesselates according to SL(2,Z)""" g0 = UpperGeodesic(-1,1, "r") g1 = UpperGeodesic(-0.5,self.inf, "r") g2 = UpperGeodesic(0.5,self.inf, "r") first = [g0,g1,g2] for k in range(1, depth): for g in first: g.new_geod(1, k, 0, 1) g.new_geod(1, -k, 0, 1) kmax = len(UpperGeodesic.all_geods) for geod in UpperGeodesic.all_geods[:kmax]: temp = [geod.new_geod(0, -1, 1, 0)] for k in range(1, 2*depth): temp.append(geod.new_geod(1, 0, k, 1)) temp.append(geod.new_geod(1, 0, -k, 1)) for k in range(1, depth//2): for t in temp: t.new_geod(1, k, 0, 1) t.new_geod(1, -k, 0, 1) UpperGeodesic.xmin= -3 UpperGeodesic.xmax= 3 UpperGeodesic.ymax= 3 ## plot commands @classmethod def set_plot_limits(cls): highest = max(abs(i) for i in [cls.ymin, cls.ymax, cls.xmax, cls.xmin]) cls.ax.axis([-highest, highest, 0, highest]) @classmethod def plot_all(cls): if UpperGeodesic.ymax <= UpperGeodesic.ymin: UpperGeodesic.ymax = UpperGeodesic.ymin + 1 # else nothing to plot super().plot_all()	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 13, 29937, 770, 934, 7568, 479, 2631, 293, 29889, 2272, 13, 13, 29937, 4687, 408, 263, 2471, 304, 7604, 675, 825, 5930, 304, 11266, 2095, 293, 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grodddroid/BranchExplorer/branchexp/explo/tags.py	demirdagemir/thesis	0	1614795	<gh_stars>0 """ Functions to work with tags and tag files. """ import logging log = logging.getLogger("branchexp") def is_method_tag(tag): return tag.startswith("BEG") def is_branch_tag(tag): return tag.startswith("BRA") and not "/" in tag def is_cond_tag(tag): return tag.startswith("BRA") and "/" in tag class TagSet(object): """ Container for tags, as used by ForceCFI. A TagSet can contain different types of tags, depending on who computed it: * method tags, which look like "BEGINN1" * branch tags, which look like "BRANCHN2" * cond tags, which look like "BRANCHN3/BRANCHN4" Method tags can be written by ForceCFI or during the logcat monitoring. Branches alone are usually only written by logcat or when we compute a set of branches to force. Cond tags are written exclusively by ForceCFI, to keep paired together two branches of a same conditional jump. I'm not sure if it's useful right now but it may be in the future. Beware of not mixing them with branch tags though! Attributes: tags: set of tags (strings) """ def __init__(self, tag_filepath = None): self.reset() if tag_filepath is not None: self.load_file(tag_filepath) def reset(self): self.tags = set() def load_file(self, tag_filepath): self.reset() with open(tag_filepath, "r") as tag_file: for line in tag_file.readlines(): self.tags.add(line.strip("\n")) def write_file(self, tag_filepath): with open(tag_filepath, "w") as tag_file: for tag in self.tags: tag_file.write(tag + "\n") def get_method_tags(self): return set([tag for tag in self.tags if is_method_tag(tag)]) def get_branch_tags(self): return set([tag for tag in self.tags if is_branch_tag(tag)]) def get_cond_tags(self): return set([tag for tag in self.tags if is_cond_tag(tag)]) def split_cond_in_branches(self, cond): """ Removes a cond BRANCH1/BRANCH2 from the tags and adds BRANCH1 and BRANCH2. 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facility_management/facility_management/web_form/new_maintenance/new_maintenance.py	pazari/facility_management	13	86853	<filename>facility_management/facility_management/web_form/new_maintenance/new_maintenance.py from __future__ import unicode_literals from toolz import first import frappe def get_context(context): tenant = _get_tenant(_get_customer(frappe.session.user)) context.properties = _get_properties(tenant) def _get_properties(tenant): properties = frappe.get_all( "Rental Contract", fields=["property"], filters={"status": "Active", "tenant": tenant} ) return list(map(lambda x: x.get("property"), properties)) def _get_customer(user): customer = frappe.db.sql( """ SELECT dl.link_name FROM `tabContact` c INNER JOIN `tabDynamic Link` dl ON dl.parent = c.name WHERE dl.link_doctype = "Customer" AND c.user = %s """, user, as_dict=1, ) return first(customer).get("link_name") if customer else None def _get_tenant(customer): tenant = frappe.get_all("Tenant Master", filters={"customer": customer}) return first(tenant).get("name") if tenant else None	[ 1, 529, 9507, 29958, 17470, 1793, 29918, 21895, 29914, 17470, 1793, 29918, 21895, 29914, 2676, 29918, 689, 29914, 1482, 29918, 3396, 841, 749, 29914, 1482, 29918, 3396, 841, 749, 29889, 2272, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 3166, 5780, 29920, 1053, 937, 13, 5215, 5227, 4798, 13, 13, 13, 1753, 679, 29918, 4703, 29898, 4703, 1125, 13, 1678, 3006, 424, 353, 903, 657, 29918, 841, 424, 7373, 657, 29918, 15539, 29898, 20910, 4798, 29889, 7924, 29889, 1792, 876, 13, 1678, 3030, 29889, 11330, 353, 903, 657, 29918, 11330, 29898, 841, 424, 29897, 13, 13, 13, 1753, 903, 657, 29918, 11330, 29898, 841, 424, 1125, 13, 1678, 4426, 353, 5227, 4798, 29889, 657, 29918, 497, 29898, 13, 4706, 376, 29934, 13703, 2866, 1461, 613, 13, 4706, 4235, 29922, 3366, 6799, 12436, 13, 4706, 18094, 3790, 29908, 4882, 1115, 376, 9966, 613, 376, 841, 424, 1115, 3006, 424, 29913, 13, 1678, 1723, 13, 1678, 736, 1051, 29898, 1958, 29898, 2892, 921, 29901, 921, 29889, 657, 703, 6799, 4968, 4426, 876, 13, 13, 13, 1753, 903, 657, 29918, 15539, 29898, 1792, 1125, 13, 1678, 11962, 353, 5227, 4798, 29889, 2585, 29889, 2850, 29898, 13, 4706, 9995, 13, 9651, 5097, 270, 29880, 29889, 2324, 29918, 978, 13, 9651, 3895, 421, 3891, 13443, 29952, 274, 13, 9651, 20735, 8780, 421, 3891, 24001, 6645, 29952, 270, 29880, 6732, 270, 29880, 29889, 3560, 353, 274, 29889, 978, 13, 9651, 5754, 270, 29880, 29889, 2324, 29918, 1867, 312, 668, 353, 376, 15122, 29908, 5300, 274, 29889, 1792, 353, 1273, 29879, 13, 4706, 5124, 613, 13, 4706, 1404, 29892, 13, 4706, 408, 29918, 8977, 29922, 29896, 29892, 13, 1678, 1723, 13, 1678, 736, 937, 29898, 15539, 467, 657, 703, 2324, 29918, 978, 1159, 565, 11962, 1683, 6213, 13, 13, 13, 1753, 903, 657, 29918, 841, 424, 29898, 15539, 1125, 13, 1678, 3006, 424, 353, 5227, 4798, 29889, 657, 29918, 497, 703, 29911, 27153, 9082, 613, 18094, 3790, 29908, 15539, 1115, 11962, 1800, 13, 1678, 736, 937, 29898, 841, 424, 467, 657, 703, 978, 1159, 565, 3006, 424, 1683, 6213, 13, 2 ]
license_plate_images_generator.py	aboerzel/ANPR-keras	6	36046	import argparse import json import os.path import random import re import time import cv2 import numpy as np import requests from PIL import Image from config import config class GermanLicensePlateImagesGenerator: def __init__(self, output): self.output = output self.COUNTRY_MARKS = np.asarray([d['CM'] for d in json.loads(open(config.GERMAN_COUNTY_MARKS, encoding='utf-8').read())]) self.LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZÄÖÜ" self.DIGITS = "0123456789" self.COUNTRIES = ['BW', 'BY', 'BE', 'BB', 'HB', 'HH', 'HE', 'MV', 'NI', 'NW', 'RP', 'SL', 'SN', 'ST', 'SH', 'TH'] self.MONTHS = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'] self.YEARS = ['06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17'] random.seed() @staticmethod def get_image_url(license_number, country, month, year): license_number = license_number.replace("-", "%3A").replace("Ä", "%C4").replace("Ö", "%D6").replace("Ü", "%DC") return "http://nummernschild.heisnbrg.net/fe/task?action=startTask&kennzeichen={0}&kennzeichenZeile2=&engschrift=false&pixelHoehe=32&breiteInMM=520&breiteInMMFest=true&sonder=FE&dd=01&mm=01&yy=00&kreis={1}&kreisName=&humm={2}&huyy={3}&sonderKreis=LEER&mm1=01&mm2=01&farbe=SCHWARZ&effekt=KEIN&tgaDownload=false".format( license_number, country, month, year) def __generate_license_number(self): country = random.choice(self.COUNTRY_MARKS) letter_count = random.randint(1, 2) letters = "{}".format(random.choice(self.LETTERS)) if letter_count == 1 else "{}{}".format( random.choice(self.LETTERS), random.choice(self.LETTERS)) min = 1 if letter_count == 2 else 1 digit_count = random.randint(min, max((8 - len(country) - letter_count), 4)) digits = "" for i in range(digit_count): digits += random.choice(self.DIGITS) return "{}-{}{}".format(country, letters, digits) def __create_license_plate_picture(self, n, license_number, country, front): file_path = self.output + '/{0}#{1}.png'.format("F" if front else "R", license_number) if os.path.exists(file_path): return False month = random.choice(self.MONTHS) if front else '' year = random.choice(self.YEARS) if front else '' create_image_url = GermanLicensePlateImagesGenerator.get_image_url(license_number, country, month, year) r = requests.get(create_image_url) if r.status_code != 200: return False id = re.compile('<id>(.*?)</id>', re.DOTALL \| re.IGNORECASE).findall( r.content.decode("utf-8"))[0] status_url = 'http://nummernschild.heisnbrg.net/fe/task?action=status&id=%s' % id time.sleep(.200) r = requests.get(status_url) if r.status_code != 200: return False show_image_url = 'http://nummernschild.heisnbrg.net/fe/task?action=showInPage&id=%s' show_image_url = show_image_url % id time.sleep(.200) r = requests.get(show_image_url) if r.status_code != 200: return False # sometimes the web service returns a corrupted image, check the image by getting the size and skip if corrupted try: numpyarray = np.fromstring(r.content, np.uint8) image = cv2.imdecode(numpyarray, cv2.IMREAD_COLOR) image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) im = Image.fromarray(image) # don't use cv2.imwrite() because there is a bug with utf-8 encoded filepaths im.save(file_path) print("{0:06d} : {1}".format(n, file_path)) return True except: return False def generate(self, items): for n in range(items): while True: license_number = self.__generate_license_number() country = random.choice(self.COUNTRIES) if not self.__create_license_plate_picture(n, license_number, country, True): break time.sleep(.200) self.__create_license_plate_picture(n, license_number, country, False) time.sleep(.200) ap = argparse.ArgumentParser() ap.add_argument("-i", "--items", default="60000", help="Number of items to generate") ap.add_argument("-o", "--output", default=config.PLATE_IMAGES, help="Output path") args = vars(ap.parse_args()) lpdg = GermanLicensePlateImagesGenerator(os.path.abspath(args["output"])) lpdg.generate(int(args["items"]))	[ 1, 1053, 1852, 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lib/googlecloudsdk/api_lib/compute/daisy_utils.py	bopopescu/Google-Cloud-SDK-1	0	132131	<reponame>bopopescu/Google-Cloud-SDK-1 # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Utilities for running Daisy builds on Google Container Builder.""" from apitools.base.py import encoding from googlecloudsdk.api_lib.cloudbuild import cloudbuild_util from googlecloudsdk.api_lib.cloudbuild import logs as cb_logs from googlecloudsdk.api_lib.cloudresourcemanager import projects_api from googlecloudsdk.api_lib.storage import storage_api from googlecloudsdk.calliope import arg_parsers from googlecloudsdk.calliope import base from googlecloudsdk.command_lib.cloudbuild import execution from googlecloudsdk.command_lib.projects import util as projects_util from googlecloudsdk.core import exceptions as core_exceptions from googlecloudsdk.core import execution_utils from googlecloudsdk.core import log from googlecloudsdk.core import properties from googlecloudsdk.core import resources from googlecloudsdk.core.console import console_io _BUILDER = 'gcr.io/compute-image-tools/daisy:release' class FailedBuildException(core_exceptions.Error): """Exception for builds that did not succeed.""" def __init__(self, build): super(FailedBuildException, self).__init__( 'build {id} completed with status "{status}"'.format( id=build.id, status=build.status)) def AddCommonDaisyArgs(parser): """Common arguments for Daisy builds.""" parser.add_argument( '--log-location', help='Directory in Google Cloud Storage to hold build logs. If not ' 'set, ```gs://<project num>.cloudbuild-logs.googleusercontent.com/``` ' 'will be created and used.', ) parser.add_argument( '--timeout', type=arg_parsers.Duration(), default='2h', help="""\ Maximum time a build can last before it is failed as "TIMEOUT". For example, specifying ``2h'' will fail the process after 2 hours. See $ gcloud topic datetimes for information on duration formats. """ ) base.ASYNC_FLAG.AddToParser(parser) def CheckIamPermissions(project_id): """Check for needed IAM permissions and prompt to add if missing. Args: project_id: A string with the name of the project. """ project = projects_api.Get(project_id) service_account = 'serviceAccount:{0}@cloudbuild.gserviceaccount.com'.format( project.projectNumber) expected_permissions = {'roles/compute.admin': service_account, 'roles/iam.serviceAccountActor': service_account} permissions = projects_api.GetIamPolicy(project_id) for binding in permissions.bindings: if expected_permissions.get(binding.role) in binding.members: del expected_permissions[binding.role] if expected_permissions: ep_table = ['{0} {1}'.format(role, account) for role, account in expected_permissions.items()] prompt_message = ( 'The following IAM permissions are needed for this operation:\n' '[{0}]\n'.format('\n'.join(ep_table))) console_io.PromptContinue( message=prompt_message, prompt_string='Would you like to add the permissions', throw_if_unattended=True, cancel_on_no=True) for role, account in expected_permissions.items(): log.info('Adding [{0}] to [{1}]'.format(account, role)) projects_api.AddIamPolicyBinding(project_id, account, role) def _CreateCloudBuild(build_config, client, messages): """Create a build in cloud build. Args: build_config: A cloud build Build message. client: The cloud build api client. messages: The cloud build api messages module. Returns: Tuple containing a cloud build build object and the resource reference for that build. """ log.debug('submitting build: {0}'.format(repr(build_config))) op = client.projects_builds.Create( messages.CloudbuildProjectsBuildsCreateRequest( build=build_config, projectId=properties.VALUES.core.project.Get())) json = encoding.MessageToJson(op.metadata) build = encoding.JsonToMessage(messages.BuildOperationMetadata, json).build build_ref = resources.REGISTRY.Create( collection='cloudbuild.projects.builds', projectId=build.projectId, id=build.id) log.CreatedResource(build_ref) if build.logUrl: log.status.Print('Logs are available at [{0}].'.format(build.logUrl)) else: log.status.Print('Logs are available in the Cloud Console.') return build, build_ref def GetAndCreateDaisyBucket(bucket_name=None, storage_client=None): """Determine the name of the GCS bucket to use and create if necessary. Args: bucket_name: A string containing a bucket name to use, otherwise the bucket will be named based on the project id. storage_client: The storage_api client object. Returns: A string containing the name of the GCS bucket to use. """ project = properties.VALUES.core.project.GetOrFail() safe_project = project.replace(':', '-') safe_project = safe_project.replace('.', '-') bucket_name = bucket_name or '{0}-daisy-bkt'.format(safe_project) safe_bucket_name = bucket_name.replace('google', 'elgoog') if not storage_client: storage_client = storage_api.StorageClient() storage_client.CreateBucketIfNotExists(safe_bucket_name) return safe_bucket_name def RunDaisyBuild(args, workflow, variables, daisy_bucket=None, tags=None): """Run a build with Daisy on Google Cloud Builder. Args: args: an argparse namespace. All the arguments that were provided to this command invocation. workflow: The path to the Daisy workflow to run. variables: A string of key-value pairs to pass to Daisy. daisy_bucket: A string containing the name of the GCS bucket that daisy should use. tags: A list of strings for adding tags to the Argo build. Returns: A build object that either streams the output or is displayed as a link to the build. Raises: FailedBuildException: If the build is completed and not 'SUCCESS'. """ client = cloudbuild_util.GetClientInstance() messages = cloudbuild_util.GetMessagesModule() project_id = projects_util.ParseProject( properties.VALUES.core.project.GetOrFail()) CheckIamPermissions(project_id) timeout_str = '{0}s'.format(args.timeout) daisy_bucket = daisy_bucket or GetAndCreateDaisyBucket() daisy_args = ['-gcs_path=gs://{0}/'.format(daisy_bucket), '-variables={0}'.format(variables), workflow, ] build_tags = ['gce-daisy'] if tags: build_tags.extend(tags) # First, create the build request. build_config = messages.Build( steps=[ messages.BuildStep( name=_BUILDER, args=daisy_args, ), ], tags=build_tags, timeout=timeout_str, ) if args.log_location: gcs_log_dir = resources.REGISTRY.Parse( args.log_location, collection='storage.objects') build_config.logsBucket = ( 'gs://{0}/{1}'.format(gcs_log_dir.bucket, gcs_log_dir.object)) # Start the build. build, build_ref = _CreateCloudBuild(build_config, client, messages) # If the command is run --async, we just print out a reference to the build. if args.async: return build mash_handler = execution.MashHandler( execution.GetCancelBuildHandler(client, messages, build_ref)) # Otherwise, logs are streamed from GCS. with execution_utils.CtrlCSection(mash_handler): build = cb_logs.CloudBuildClient(client, messages).Stream(build_ref) if build.status == messages.Build.StatusValueValuesEnum.TIMEOUT: log.status.Print( 'Your build timed out. 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source/blog/migrations/0004_postcomments.py	JakubGutowski/PersonalBlog	0	9743	<filename>source/blog/migrations/0004_postcomments.py # Generated by Django 2.0.5 on 2018-07-02 19:46 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('blog', '0003_blogpost_author'), ] operations = [ migrations.CreateModel( name='PostComments', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('nick', models.CharField(max_length=20)), ('comment', models.CharField(max_length=140)), ('post', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='blog.BlogPost')), ], ), ]	[ 1, 529, 9507, 29958, 4993, 29914, 7312, 29914, 26983, 800, 29914, 29900, 29900, 29900, 29946, 29918, 2490, 21032, 29889, 2272, 13, 29937, 3251, 630, 491, 15337, 29871, 29906, 29889, 29900, 29889, 29945, 373, 29871, 29906, 29900, 29896, 29947, 29899, 29900, 29955, 29899, 29900, 29906, 29871, 29896, 29929, 29901, 29946, 29953, 30004, 13, 30004, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 30004, 13, 5215, 9557, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 30004, 13, 30004, 13, 30004, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 30004, 13, 30004, 13, 1678, 9962, 353, 518, 30004, 13, 4706, 6702, 7312, 742, 525, 29900, 29900, 29900, 29941, 29918, 7312, 2490, 29918, 8921, 5477, 30004, 13, 1678, 4514, 30004, 13, 30004, 13, 1678, 6931, 353, 518, 30004, 13, 4706, 9725, 800, 29889, 4391, 3195, 29898, 30004, 13, 9651, 1024, 2433, 6747, 1523, 1860, 23592, 13, 9651, 4235, 11759, 30004, 13, 18884, 6702, 333, 742, 4733, 29889, 12300, 3073, 29898, 6921, 29918, 11600, 29922, 5574, 29892, 7601, 29918, 1989, 29922, 5574, 29892, 28755, 29922, 8824, 29892, 26952, 29918, 978, 2433, 1367, 1495, 511, 30004, 13, 18884, 6702, 19254, 742, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29900, 8243, 30004, 13, 18884, 6702, 9342, 742, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29896, 29946, 29900, 8243, 30004, 13, 18884, 6702, 2490, 742, 4733, 29889, 27755, 2558, 29898, 265, 29918, 8143, 29922, 14095, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 29889, 29907, 3289, 5454, 2287, 29892, 304, 2433, 7312, 29889, 29933, 1188, 6747, 1495, 511, 30004, 13, 9651, 21251, 30004, 13, 4706, 10353, 30004, 13, 1678, 4514, 30004, 13, 2 ]
01_strings_interpolacao/04_strings_metodos.py	smartao/estudos_python	0	187075	#!/usr/bin/python3 import pyperclip ''' String puras Podemos inserir um r antes das pastas de inicio em uma string para transofrmá-la em uma string pura (raw string), com isso a string ignorará todos os caracteres de scape ''' print('Exemplo de um string pura:') print(r'\n\t That is Carlos \'s cat.') ''' Strings em multiplas linhas ''' print('''\nTeste de 'String' de Multiplas linhas ''') ''' Indexacao e Slices As String usam indices e slices (Fatias) do mesmo modo que as listas ''' print('\nTeste de slice string') spam = 'Hello World!' print(spam[0]) # Primeiro caractere print(spam[4]) # Quinta caractere print(spam[-1]) # Ultimo caractere print(spam[0:5]) # do 0 ao 5 print(spam[:5]) # do 0 ao 5 print(spam[6:]) # do 6 até o final ''' Operadores in e not in com strings Os operadora in e not in podem ser usados com strings assim como em valores de listas ''' print('\nTeste de strings:') print('Hello' in 'Hello World') # True print('Hello' in 'Hello') # True print('HELLO' in 'Hello World') # False print('' in 'spam') # True print('cats' not in 'cats and dogs') # False ''' upper(), lower(), title() Retornam um nova string em que todas as letras da string original foram convertidas para maiusculas ou minusculas. Os métodos não alteram a string em si, mas retornam novos valores de strings Os métodos upper() e lower() serão úteis caso seja necessário fazer uma comparação sen kevar en cibta a diferença de maisculas e minusculas ''' print('\nTeste dos métodos upper e lower') spam = 'Hello World' spam = spam.upper() print(spam) spam = spam.lower() print(spam) spam = spam.title() # Primeira letra maisuculas print(spam) print('\nComparando strings:') print('How are you?') feeling = 'GREATE' if feeling.lower() == 'greate': print('I feel greate too') # Como os métodos retornam string, # podemos chamar os métodos de string nesses valores print('\nTeste de aninhamento de métodos') print('Hello'.upper()) print('Hello'.upper().lower()) print('Hello'.upper().lower().upper()) print('Hello'.upper().lower().upper().lower()) ''' isupper(), islower() Retornarão um valor booleano True se a string tiver pelo menos uma letra e todas as letras forem maiusculas ou minusculas, respectivalmente. ''' print('\nTeste dos métodos isupper() e islower()') spam = 'Hello World' print(spam.islower()) # False print(spam.isupper()) # False print('HELLO'.isupper()) # True print('abc12345'.islower()) # True print('12345'.islower()) # False print('12345'.isupper()) # False ''' Métodos de string isXa isalpha() retornará True se a string for constituida somente de letras e não estiver vazia isalnum() retornará True se a string for constituida somente de letas e numeros e não estiver vazia isdeciamal() retornará True se a string for constituida somente de caracteres numéricos e não estiver vazia isspace() retornará True se a string for constituida somente de espaços, tabulações e quebras de linhas e não estiver vazia istitle() retornará True se a string for constituida somente de palavras que comecer com uma letras maiuscula seguida de letas minusculas Esse métodos são úteis para validar dados de entrada de usuúario, exemplo ''' while True: print('\nEnter you age:') age = input() if age.isdecimal(): break print('Please enter a number for your age.') print('tks') ''' startswith() endswith() Retornarão True se o valor de string com o qual forem chamados comeceçar ou terminar (respectivamente) com a string passada para o método ''' print('\nTeste dos metodos startswith e endswith') print('Hello world!'.endswith('world!')) # True print('abc123'.startswith('abcdef')) # False print('abc123'.endswith('12')) # False print('Hello world!'.startswith('Hello world!')) # True print('Hello world!'.endswith('Hello world!')) # True ''' join() O método join() é útil quando temos uma lista de strings que devem ser unidas em um único valor de string. Esse método é interessante pois podemos usar uma separador dos itens ''' listanimals = ['cats', 'dogs', 'bears'] stringanimals = ', '.join(listanimals) print('\nTeste1 do metodo join:') print(stringanimals) stringanimals = ' '.join(listanimals) print('Teste2 do metodo join, usando um separador') print(stringanimals) ''' split() O método split faz o inverso do join. É chamado em um valor de string e retorna uma lista de strings. Um uso comum esta em dividir uma string de multiplas linhas nos caracteres de quebra de linha ''' frase = 'My name is Simon' print('\nTeste1 do método split:') print(frase.split()) print('\nTeste2 do método split, usando um separador') print(frase.split('is')) spam = '''Dear Alice, How have you been? I am fine. There is a container in the fridge that is labeled "Milk Experiment". Please do not drink it. Sincerely Bob''' print('\nTeste2 do método split, usando um separador\n') print(spam.split('\n')) ''' Justificando texto rjust(), ljust(), center() Os métodos de string retornam uma versão preenchida da string em que são chamadas com espeços inseridos para justificar o texto 1 - Argumemto é um inteiro referente ao tamanho da string justificada Perceba que se o inteiro for menor que o tamanho da string, nada acontecera! 2 - Argumento opcional, especifica o caractere de preenchimento ''' print('\nTeste dos métodos rsjust e ljust:') hello = 'Hello world' print(hello.rjust(20)) print(hello.ljust(30)) print(hello.rjust(15, '*')) print(hello.ljust(25, '-')) print(hello.center(15)) print(hello.center(25, '=')) ''' Esse métodos serão especialmente úteis quando for necessário exibir dados tabulares que tiverem o espacámento correto, Exemplo: ''' print('\nExemplo mais complexo do ljust, rjust e center:\n') def printPicnic(ItemDict, leftWidth, rightWidth): print('PICNIC ITEMS'.center(leftWidth + rightWidth, '-')) for k, v in ItemDict.items(): print(k.ljust(leftWidth, '.') + str(v).rjust(rightWidth)) picnicItems = {'sandwiches': 4, 'apples': 12, 'cups': 4, 'cookies': 8000} printPicnic(picnicItems, 12, 5) # Executando uma vez printPicnic(picnicItems, 20, 7) # Executando segunda vez ''' strip(), rstrip(), lstrip() removendo espaços em brancos strip() retornará uma nova string sem caracteres de espacos em branco no inicio ou fim lstrip() rstrip()s Removerao caracteres de espacoes em branco das extremidades da esquerda e direita respectivamente Opcionalmente, um argumento do tipo string especifica quais caracteres deverao ser removidos, nao importando a ordem ''' print('\nTestando metodos para remover os espacos:') spam = ' Hello World ' print(spam.strip()) print(spam.lstrip()) print(spam.rstrip()) spam = 'SpamSpamBaconSpamEggsSpamSpamSpam' print(spam.strip('ampS')) ''' Copiando e colando strings com módulo pyperclip O modulo pyperclip tem as funcoes de copy() e paste() capazes de enviar e receber textos do clipboard (area de tranferencia) do seu computador Enviar a saída de seu programa para o clipboard facilitará colá em um email, um processador de texto uo em outro software Necessario usar o import pyperclip para funcionar pip3 install pyperclip ''' print('\nExemplo de pyperclip:') pyperclip.copy('The text to be copied to the clipboard.') print(pyperclip.paste()) # Fontes # Livro Automatiando tarefas maçantes com python # Capitulo 6, Manipulação de strings	[ 1, 18787, 4855, 29914, 2109, 29914, 4691, 29941, 13, 5215, 11451, 546, 24049, 13, 12008, 13, 1231, 3708, 294, 13, 29925, 356, 7681, 13534, 381, 1922, 364, 12971, 1697, 4940, 294, 316, 29871, 297, 11088, 953, 3672, 1347, 1702, 13, 3286, 974, 1758, 29976, 29899, 433, 953, 3672, 1347, 282, 2002, 313, 1610, 1347, 511, 419, 338, 578, 263, 1347, 13, 647, 272, 20484, 10843, 2897, 15215, 267, 316, 885, 4085, 13, 12008, 13, 2158, 877, 1252, 13141, 316, 1922, 1347, 282, 2002, 29901, 1495, 13, 2158, 29898, 29878, 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conanfile.py	bincrafters/conan-boost_timer	0	114497	#!/usr/bin/env python # -- coding: utf-8 -- from conans import python_requires base = python_requires("boost_base/2.0.0@bincrafters/testing") class BoostTimerConan(base.BoostBaseConan): name = "boost_timer" version = "1.70.0" @property def boost_build_requires(self): return ["io"]	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 3166, 378, 550, 1053, 3017, 29918, 276, 339, 2658, 13, 13, 13, 3188, 353, 3017, 29918, 276, 339, 2658, 703, 17079, 29918, 3188, 29914, 29906, 29889, 29900, 29889, 29900, 29992, 2109, 26844, 906, 29879, 29914, 13424, 1159, 13, 13, 13, 1990, 1952, 520, 14745, 1168, 273, 29898, 3188, 29889, 8431, 520, 5160, 1168, 273, 1125, 13, 1678, 1024, 353, 376, 17079, 29918, 20404, 29908, 13, 1678, 1873, 353, 376, 29896, 29889, 29955, 29900, 29889, 29900, 29908, 13, 13, 1678, 732, 6799, 13, 1678, 822, 14505, 29918, 4282, 29918, 276, 339, 2658, 29898, 1311, 1125, 13, 4706, 736, 6796, 601, 3108, 13, 2 ]
tests/sentinel2/test_metadata.py	jamesvrt/stactools	0	172612	import unittest from stactools.sentinel2.safe_manifest import SafeManifest from stactools.sentinel2.product_metadata import ProductMetadata from stactools.sentinel2.granule_metadata import GranuleMetadata from tests.utils import TestData class Sentinel2MetadataTest(unittest.TestCase): def test_parses_product_metadata_properties(self): manifest_path = TestData.get_path( 'data-files/sentinel2/S2A_MSIL2A_20190212T192651_N0212_R013_T07HFE_20201007T160857.SAFE' ) manifest = SafeManifest(manifest_path) product_metadata = ProductMetadata(manifest.product_metadata_href) granulemetadata = GranuleMetadata(manifest.granule_metadata_href) s2_props = product_metadata.metadata_dict s2_props.update(granulemetadata.metadata_dict) expected = { # From product metadata 's2:product_uri': 'S2A_MSIL2A_20190212T192651_N0212_R013_T07HFE_20201007T160857.SAFE', 's2:generation_time': '2020-10-07T16:08:57.135Z', 's2:processing_baseline': '02.12', 's2:product_type': 'S2MSI2A', 's2:datatake_id': 'GS2A_20190212T192651_019029_N02.12', 's2:datatake_type': 'INS-NOBS', 's2:datastrip_id': 'S2A_OPER_MSI_L2A_DS_ESRI_20201007T160858_S20190212T192646_N02.12', 's2:granule_id': 'S2A_OPER_MSI_L2A_TL_ESRI_20201007T160858_A019029_T07HFE_N02.12', 's2:mgrs_tile': '07HFE', 's2:reflectance_conversion_factor': 1.02763689829235, # From granule metadata 's2:degraded_msi_data_percentage': 0.0, 's2:nodata_pixel_percentage': 96.769553, 's2:saturated_defective_pixel_percentage': 0.0, 's2:dark_features_percentage': 0.0, 's2:cloud_shadow_percentage': 0.0, 's2:vegetation_percentage': 0.000308, 's2:not_vegetated_percentage': 0.069531, 's2:water_percentage': 48.349833, 's2:unclassified_percentage': 0.0, 's2:medium_proba_clouds_percentage': 14.61311, 's2:high_proba_clouds_percentage': 24.183494, 's2:thin_sirrus_percentage': 12.783723, 's2:snow_ice_percentage': 0.0, 's2:mean_solar_zenith': 32.707073851362, 's2:mean_solar_azimuth': 62.3286549448294 } for k, v in 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examples/pull_entry_values.py	c0gnac/dealcloud-python	1	48350	""" This script pulls the values of the fields for the "Project Genome" entry on the Deal list """ import getpass as gp import sys import requests import dealcloud as dc # Create an instance of a client for the DealCloud Data Service and a service # proxy try: client = dc.create_client( email=input('Email: '), password=<PASSWORD>(), hostname=input('Hostname: ') ) except requests.exceptions.ConnectionError: print('Failed to connect to the DealCloud Web Service.') sys.exit() service = dc.bind_service(client) # Find all of the lists in the site and try to find the Deal list by name lists = service.GetEntryLists() try: deal_list = list(filter(lambda l: l.Name == 'Deal', lists))[0] except IndexError: print('Deal list could not be found.') sys.exit() # Find all of the entries on the Deal list and try to find the 'Project Genome' # entry by name entries = service.GetListEntries(deal_list.Id) try: deal_entry = list(filter(lambda e: e.Name == 'Project Genome', entries))[0] except IndexError: print('Project Genome could not be found.') sys.exit() # Find all of the fields on all of the lists in a site, get the ones on the # Deal list fields = service.GetFields() deal_fields = list(filter(lambda f: f.EntryListId == deal_list.Id, fields)) # Create a type factory to access the types provided by the service factory = client.type_factory('ns0') # Build the payload for your request with the types provided by the factory # and send it requests = factory.ArrayOfDCPull() for f in deal_fields: p = factory.DCPull(EntryId=deal_entry.Id, FieldId=f.Id) requests.DCPull.append(p) response = service.ProcessDCPull( requests=requests, resolveReferenceUrls=True, fillExtendedData=True ) # Iterate over your responses and print them to the console for r in response: try: field = list(filter(lambda f: f.Id == r.FieldId, deal_fields))[0] except IndexError: print(f'Field {r.FieldId} cound not be named.') continue print(f'{field.Name}: 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stubs/workspaces.py	claytonbrown/troposphere	0	13640	<reponame>claytonbrown/troposphere from . import AWSObject, AWSProperty from .validators import * from .constants import * # ------------------------------------------- class WorkSpacesWorkspace(AWSObject): """# AWS::WorkSpaces::Workspace - CloudFormationResourceSpecification version: 1.4.0 { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html", "Properties": { "BundleId": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-bundleid", "PrimitiveType": "String", "Required": true, "UpdateType": "Conditional" }, "DirectoryId": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-directoryid", "PrimitiveType": "String", "Required": true, "UpdateType": "Conditional" }, "RootVolumeEncryptionEnabled": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-rootvolumeencryptionenabled", "PrimitiveType": "Boolean", "Required": false, "UpdateType": "Conditional" }, "UserName": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-username", "PrimitiveType": "String", "Required": true, "UpdateType": "Immutable" }, "UserVolumeEncryptionEnabled": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-uservolumeencryptionenabled", "PrimitiveType": "Boolean", "Required": false, "UpdateType": "Conditional" }, "VolumeEncryptionKey": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-volumeencryptionkey", "PrimitiveType": "String", "Required": false, "UpdateType": "Conditional" } } } """ resource_type = "AWS::WorkSpaces::Workspace" props = { 'BundleId': (basestring, True, 'Conditional', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-bundleid'), 'DirectoryId': (basestring, True, 'Conditional', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-directoryid'), 'RootVolumeEncryptionEnabled': (boolean, False, 'Conditional', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-rootvolumeencryptionenabled'), 'UserName': (basestring, True, 'Immutable', 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env/Lib/site-packages/countdowntimer_model/apps.py	gtkacz/fantasytrashtalk	4	29798	<reponame>gtkacz/fantasytrashtalk from django.apps import AppConfig class CountdowntimerModelConfig(AppConfig): name = 'countdowntimer_model'	[ 1, 529, 276, 1112, 420, 29958, 4141, 29895, 10086, 29914, 29888, 424, 8995, 10678, 400, 2235, 13, 3166, 9557, 29889, 13371, 1053, 2401, 3991, 13, 13, 13, 1990, 3917, 29881, 340, 593, 4193, 3195, 3991, 29898, 2052, 3991, 1125, 13, 1678, 1024, 353, 525, 2798, 29881, 340, 593, 4193, 29918, 4299, 29915, 13, 2 ]
websocket/_abnf.py	benhoyt/websocket-client	147	1611486	""" """ """ _abnf.py websocket - WebSocket client library for Python Copyright 2021 engn33r 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 array import os import struct import sys from ._exceptions import * from ._utils import validate_utf8 from threading import Lock try: # If wsaccel is available, use compiled routines to mask data. # wsaccel only provides around a 10% speed boost compared # to the websocket-client _mask() implementation. # Note that wsaccel is unmaintained. from wsaccel.xormask import XorMaskerSimple def _mask(_m, _d): return XorMaskerSimple(_m).process(_d) except ImportError: # wsaccel is not available, use websocket-client _mask() native_byteorder = sys.byteorder def _mask(mask_value, data_value): datalen = len(data_value) data_value = int.from_bytes(data_value, native_byteorder) mask_value = int.from_bytes(mask_value * (datalen // 4) + mask_value[: datalen % 4], native_byteorder) return (data_value ^ mask_value).to_bytes(datalen, native_byteorder) __all__ = [ 'ABNF', 'continuous_frame', 'frame_buffer', 'STATUS_NORMAL', 'STATUS_GOING_AWAY', 'STATUS_PROTOCOL_ERROR', 'STATUS_UNSUPPORTED_DATA_TYPE', 'STATUS_STATUS_NOT_AVAILABLE', 'STATUS_ABNORMAL_CLOSED', 'STATUS_INVALID_PAYLOAD', 'STATUS_POLICY_VIOLATION', 'STATUS_MESSAGE_TOO_BIG', 'STATUS_INVALID_EXTENSION', 'STATUS_UNEXPECTED_CONDITION', 'STATUS_BAD_GATEWAY', 'STATUS_TLS_HANDSHAKE_ERROR', ] # closing frame status codes. STATUS_NORMAL = 1000 STATUS_GOING_AWAY = 1001 STATUS_PROTOCOL_ERROR = 1002 STATUS_UNSUPPORTED_DATA_TYPE = 1003 STATUS_STATUS_NOT_AVAILABLE = 1005 STATUS_ABNORMAL_CLOSED = 1006 STATUS_INVALID_PAYLOAD = 1007 STATUS_POLICY_VIOLATION = 1008 STATUS_MESSAGE_TOO_BIG = 1009 STATUS_INVALID_EXTENSION = 1010 STATUS_UNEXPECTED_CONDITION = 1011 STATUS_BAD_GATEWAY = 1014 STATUS_TLS_HANDSHAKE_ERROR = 1015 VALID_CLOSE_STATUS = ( STATUS_NORMAL, STATUS_GOING_AWAY, STATUS_PROTOCOL_ERROR, STATUS_UNSUPPORTED_DATA_TYPE, STATUS_INVALID_PAYLOAD, STATUS_POLICY_VIOLATION, STATUS_MESSAGE_TOO_BIG, STATUS_INVALID_EXTENSION, STATUS_UNEXPECTED_CONDITION, STATUS_BAD_GATEWAY, ) class ABNF(object): """ ABNF frame class. See http://tools.ietf.org/html/rfc5234 and http://tools.ietf.org/html/rfc6455#section-5.2 """ # operation code values. OPCODE_CONT = 0x0 OPCODE_TEXT = 0x1 OPCODE_BINARY = 0x2 OPCODE_CLOSE = 0x8 OPCODE_PING = 0x9 OPCODE_PONG = 0xa # available operation code value tuple OPCODES = (OPCODE_CONT, OPCODE_TEXT, OPCODE_BINARY, OPCODE_CLOSE, OPCODE_PING, OPCODE_PONG) # opcode human readable string OPCODE_MAP = { OPCODE_CONT: "cont", OPCODE_TEXT: "text", OPCODE_BINARY: "binary", OPCODE_CLOSE: "close", OPCODE_PING: "ping", OPCODE_PONG: "pong" } # data length threshold. LENGTH_7 = 0x7e LENGTH_16 = 1 << 16 LENGTH_63 = 1 << 63 def __init__(self, fin=0, rsv1=0, rsv2=0, rsv3=0, opcode=OPCODE_TEXT, mask=1, data=""): """ Constructor for ABNF. Please check RFC for arguments. """ self.fin = fin self.rsv1 = rsv1 self.rsv2 = rsv2 self.rsv3 = rsv3 self.opcode = opcode self.mask = mask if data is None: data = "" self.data = data self.get_mask_key = os.urandom def validate(self, skip_utf8_validation=False): """ Validate the ABNF frame. Parameters ---------- skip_utf8_validation: skip utf8 validation. """ if self.rsv1 or self.rsv2 or self.rsv3: raise WebSocketProtocolException("rsv is not implemented, yet") if self.opcode not in ABNF.OPCODES: raise WebSocketProtocolException("Invalid opcode %r", self.opcode) if self.opcode == ABNF.OPCODE_PING and not self.fin: raise WebSocketProtocolException("Invalid ping frame.") if self.opcode == ABNF.OPCODE_CLOSE: l = len(self.data) if not l: return if l == 1 or l >= 126: raise WebSocketProtocolException("Invalid close frame.") if l > 2 and not skip_utf8_validation and not validate_utf8(self.data[2:]): raise WebSocketProtocolException("Invalid close frame.") code = 256 * self.data[0] + self.data[1] if not self._is_valid_close_status(code): raise WebSocketProtocolException("Invalid close opcode.") @staticmethod def _is_valid_close_status(code): return code in VALID_CLOSE_STATUS or (3000 <= code < 5000) def __str__(self): return "fin=" + str(self.fin) \ + " opcode=" + str(self.opcode) \ + " data=" + str(self.data) @staticmethod def create_frame(data, opcode, fin=1): """ Create frame to send text, binary and other data. Parameters ---------- data: <type> data to send. This is string value(byte array). If opcode is OPCODE_TEXT and this value is unicode, data value is converted into unicode string, automatically. opcode: <type> operation code. please see OPCODE_XXX. fin: <type> fin flag. if set to 0, create continue fragmentation. """ if opcode == ABNF.OPCODE_TEXT and isinstance(data, str): data = data.encode("utf-8") # mask must be set if send data from client return ABNF(fin, 0, 0, 0, opcode, 1, data) def format(self): """ Format this object to string(byte array) to send data to server. """ if any(x not in (0, 1) for x in [self.fin, self.rsv1, self.rsv2, self.rsv3]): raise ValueError("not 0 or 1") if self.opcode not in ABNF.OPCODES: raise ValueError("Invalid OPCODE") length = len(self.data) if length >= ABNF.LENGTH_63: raise ValueError("data is too long") frame_header = chr(self.fin << 7 \| self.rsv1 << 6 \| self.rsv2 << 5 \| self.rsv3 << 4 \| self.opcode).encode('latin-1') if length < ABNF.LENGTH_7: frame_header += chr(self.mask << 7 \| length).encode('latin-1') elif length < ABNF.LENGTH_16: frame_header += chr(self.mask << 7 \| 0x7e).encode('latin-1') frame_header += struct.pack("!H", length) else: frame_header += chr(self.mask << 7 \| 0x7f).encode('latin-1') frame_header += struct.pack("!Q", length) if not self.mask: return frame_header + self.data else: mask_key = self.get_mask_key(4) return frame_header + self._get_masked(mask_key) def _get_masked(self, mask_key): s = ABNF.mask(mask_key, self.data) if isinstance(mask_key, str): mask_key = mask_key.encode('utf-8') return mask_key + s @staticmethod def mask(mask_key, data): """ Mask or unmask data. Just do xor for each byte Parameters ---------- mask_key: <type> 4 byte string. data: <type> data to mask/unmask. """ if data is None: data = "" if isinstance(mask_key, str): mask_key = mask_key.encode('latin-1') if isinstance(data, str): data = data.encode('latin-1') return _mask(array.array("B", mask_key), array.array("B", data)) class frame_buffer(object): _HEADER_MASK_INDEX = 5 _HEADER_LENGTH_INDEX = 6 def __init__(self, recv_fn, skip_utf8_validation): self.recv = recv_fn self.skip_utf8_validation = skip_utf8_validation # Buffers over the packets from the layer beneath until desired amount # bytes of bytes are received. self.recv_buffer = [] self.clear() self.lock = Lock() def clear(self): self.header = None self.length = None self.mask = None def has_received_header(self): return self.header is None def recv_header(self): header = self.recv_strict(2) b1 = header[0] fin = b1 >> 7 & 1 rsv1 = b1 >> 6 & 1 rsv2 = b1 >> 5 & 1 rsv3 = b1 >> 4 & 1 opcode = b1 & 0xf b2 = header[1] has_mask = b2 >> 7 & 1 length_bits = b2 & 0x7f self.header = (fin, rsv1, rsv2, rsv3, opcode, has_mask, length_bits) def has_mask(self): if not self.header: return False return self.header[frame_buffer._HEADER_MASK_INDEX] def has_received_length(self): return self.length is None def recv_length(self): bits = self.header[frame_buffer._HEADER_LENGTH_INDEX] length_bits = bits & 0x7f if length_bits == 0x7e: v = self.recv_strict(2) self.length = struct.unpack("!H", v)[0] elif length_bits == 0x7f: v = self.recv_strict(8) self.length = struct.unpack("!Q", v)[0] else: self.length = length_bits def has_received_mask(self): return self.mask is None def recv_mask(self): self.mask = self.recv_strict(4) if self.has_mask() else "" def recv_frame(self): with self.lock: # Header if self.has_received_header(): self.recv_header() (fin, rsv1, rsv2, rsv3, opcode, has_mask, _) = self.header # Frame length if self.has_received_length(): self.recv_length() length = self.length # Mask if self.has_received_mask(): self.recv_mask() mask = self.mask # Payload payload = self.recv_strict(length) if has_mask: payload = ABNF.mask(mask, payload) # Reset for next frame self.clear() frame = ABNF(fin, rsv1, rsv2, rsv3, opcode, has_mask, payload) frame.validate(self.skip_utf8_validation) return frame def recv_strict(self, bufsize): shortage = bufsize - sum(map(len, self.recv_buffer)) while shortage > 0: # Limit buffer size that we pass to socket.recv() to avoid # fragmenting the heap -- the number of bytes recv() actually # reads is limited by socket buffer and is relatively small, # yet passing large numbers repeatedly causes lots of large # buffers allocated and then shrunk, which results in # fragmentation. bytes_ = self.recv(min(16384, shortage)) self.recv_buffer.append(bytes_) shortage -= len(bytes_) unified = bytes("", 'utf-8').join(self.recv_buffer) if shortage == 0: self.recv_buffer = [] return unified else: self.recv_buffer = [unified[bufsize:]] return unified[:bufsize] class continuous_frame(object): def __init__(self, fire_cont_frame, skip_utf8_validation): self.fire_cont_frame = fire_cont_frame self.skip_utf8_validation = skip_utf8_validation self.cont_data = None self.recving_frames = None def validate(self, frame): if not self.recving_frames and frame.opcode == ABNF.OPCODE_CONT: raise WebSocketProtocolException("Illegal frame") if self.recving_frames and \ frame.opcode in (ABNF.OPCODE_TEXT, ABNF.OPCODE_BINARY): raise WebSocketProtocolException("Illegal frame") def add(self, frame): if self.cont_data: self.cont_data[1] += frame.data else: if frame.opcode in (ABNF.OPCODE_TEXT, ABNF.OPCODE_BINARY): self.recving_frames = frame.opcode self.cont_data = [frame.opcode, frame.data] if frame.fin: self.recving_frames = None def is_fire(self, frame): return frame.fin or self.fire_cont_frame def extract(self, frame): data = self.cont_data self.cont_data = None frame.data = data[1] if not self.fire_cont_frame and data[0] == ABNF.OPCODE_TEXT and not self.skip_utf8_validation and not validate_utf8(frame.data): raise WebSocketPayloadException( "cannot decode: " + repr(frame.data)) return 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test/test_children_tree.py

kisliakovsky/structures

0

30329

<filename>test/test_children_tree.py from unittest import TestCase from src.tree import ChildrenTree class TestChildrenTree(TestCase): def test_height(self): tree = ChildrenTree(1, [[], [3, 4], [], [], [0, 2]]) self.assertEqual(3, tree.height())

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api/tests/integration/tests/layout/threads.py

f1nzer/Indigo

0

107132

<reponame>f1nzer/Indigo import sys from threading import Thread sys.path.append("../../common") from env_indigo import * class testThread(Thread): def __init__(self, id, smiles): Thread.__init__(self) self.id = id self.smiles = smiles def run(self): indigo_1 = Indigo() indigo_2 = Indigo() indigo_1.setOption("molfile-saving-skip-date", "1") indigo_2.setOption("molfile-saving-skip-date", "0") m1 = indigo_1.loadMolecule(self.smiles) m2 = indigo_2.loadMolecule(self.smiles) m1.layout() m2.layout() c1 = str(m1.molfile()).splitlines() c2 = str(m2.molfile()).splitlines() header_1 = "\n".join(c1[:3]) body_1 = "\n".join(c1[3:]) header_2 = "\n".join(c2[:3]) body_2 = "\n".join(c2[3:]) # Check that two instances of Indigo even in one thread have different options - headers should differ assert header_1 != header_2 # Bodies should be equal assert body_1 == body_2 threads = [] indigo = Indigo() for i, smi in enumerate( indigo.iterateSmilesFile( joinPathPy( "../../../../../data/molecules/basic/pubchem_slice_5000.smi", __file__, ) ) ): current = testThread(i, smi.rawData()) threads.append(current) current.start() for thread in threads: thread.join()

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sphinxcontrib/collections/drivers/copy_file.py

useblocks/sphinx-collections

4

30588

import os from shutil import copyfile from sphinxcontrib.collections.drivers import Driver class CopyFileDriver(Driver): def run(self): self.info('Copy file...') if not os.path.exists(self.config['source']): self.error('Source {} does not exist'.format(self.config['source'])) return try: copyfile(self.config['source'], self.config['target']) except IOError as e: self.error('Problems during copying file.', e) def clean(self): try: os.remove(self.config['target']) self.info('File deleted: {}'.format(self.config['target'])) except FileNotFoundError: pass # Already cleaned? I'm okay with it. except IOError as e: self.error('Problems during cleaning for collection {}'.format(self.config['name']), e)

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notebooks/utils.py

wranda12/06-machine-learning

1

36750

<gh_stars>1-10 # # Some functions to be used in the tutorial # # Developed by <NAME> import datetime import pandas as pd import matplotlib.pyplot as plt # for 2D plotting import numpy as np import seaborn as sns # plot nicely =) from sklearn.base import clone from sklearn.decomposition import PCA from sklearn.model_selection import learning_curve def plot_decision_boundary(x_train, y_train, estimator): """Plot the decision boundary based on: http://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_versus_svm_iris.html Parameters ---------- x_train: training set y_train: labels of the training set estimator: classifier, probability must be set as True """ def make_meshgrid(x, y, h=.02): """Create a mesh of points to plot in Parameters ---------- x: data to base x-axis meshgrid on y: data to base y-axis meshgrid on h: stepsize for meshgrid, optional Returns ------- xx, yy : ndarray """ x_min, x_max = x.min() - 1, x.max() + 1 y_min, y_max = y.min() - 1, y.max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) return xx, yy # cloning the estimator clf = clone(estimator) pca = PCA(n_components=2) x_train_pca = pca.fit_transform(x_train) clf.fit(x_train_pca, y_train) xx, yy = make_meshgrid(x_train_pca[:, 0], x_train_pca[:, 1]) grid = np.c_[xx.ravel(), yy.ravel()] probs = clf.predict_proba(grid)[:, 1].reshape(xx.shape) # plotting the decision boundary f, ax = plt.subplots(figsize=(8, 6)) sns.set(context="notebook", style="whitegrid", rc={"axes.axisbelow": False}) contour = ax.contourf(xx, yy, probs, 25, cmap="RdBu", vmin=0, vmax=1) ax_c = f.colorbar(contour) ax_c.set_label("$P(y = 1)$") ax_c.set_ticks([0, .25, .5, .75, 1]) ax.scatter(x_train_pca[:,0], x_train_pca[:, 1], c=y_train, s=50, cmap="RdBu", vmin=-.2, vmax=1.2, edgecolor="white", linewidth=1) ax.set(aspect="equal", xlim=(-5, 5), ylim=(-5, 5), xlabel="$X_1$", ylabel="$X_2$") plt.show() def plot_roc_curve(fpr, tpr, label=None): plt.plot(fpr, tpr, linewidth=2, label=label) plt.plot([0, 1], [0, 1], 'k--') plt.axis([0, 1, 0, 1]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.show() def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)): """ Generate a simple plot of the test and training learning curve. based on: http://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html Parameters ---------- estimator : classifier title : title for the chart. X : training set, where n_samples is the number of samples and n_features is the number of features. y : labels of the training set ylim : defines minimum and maximum yvalues plotted. cv : determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - An object to be used as a cross-validation generator. - An iterable yielding train/test splits. n_jobs : number of jobs to run in parallel. train_sizes : relative or absolute numbers of training examples that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of the maximum size of the training set (that is determined by the selected validation method), i.e. it has to be within (0, 1]. Otherwise it is interpreted as absolute sizes of the training sets. Note that for classification the number of samples usually have to be big enough to contain at least one sample from each class. (default: np.linspace(0.1, 1.0, 5)) """ # cloning the estimator clf = clone(estimator) plt.figure() plt.title(title) if ylim is not None: plt.ylim(*ylim) plt.xlabel("Training examples") plt.ylabel("Score") train_sizes, train_scores, test_scores = learning_curve( clf, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes) train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) test_scores_mean = np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) plt.grid() plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="r") plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="g") plt.plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training score") plt.plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation score") plt.legend(loc="best") return plt

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src/world/logosplashworld.py

alisonbento/steering-all

3

153725

import dotworld import ufrnsplashworld from src.define import * from src.dot.entities.dotpairg import DotPairg from src.dot.dottext import DotText import i18n _ = i18n.language.ugettext class LogoSplashWorld(dotworld.DotWorld): def __init__(self): dotworld.DotWorld.__init__(self) self.counter = 0 self.limit = 400 self.alpha = 0 self.animState = 1 self.logo = DotPairg() self.label = DotText("PAIRG - Physical Artifacts of Interaction Research Group", 16, (0, 0, 0), (255, 255, 255)) self.sublabel = DotText(_("Developed by") + " <NAME>", 16, (0, 0, 0), (255, 255, 255)) def onAttachScreen(self): self.logo.setMedium() self.logo.centerX(self.screen.width) self.logo.centerY(self.screen.height) self.logo.createSurface() self.label.centerX(self.screen.width) self.label.marginTop(dotget(1)) self.label.below(self.logo) self.sublabel.centerX(self.screen.width) self.sublabel.marginTop(dotget(1)) self.sublabel.below(self.label) def changeAlpha(self): self.logo.setDotAlpha(self.alpha) # self.logo.createSurface() self.label.surface.set_alpha(self.alpha) self.sublabel.surface.set_alpha(self.alpha) def listen(self, inputResult): if inputResult == GameDefine.COMMAND_EXIT: self.screen.turnOff() if inputResult == GameDefine.COMMAND_BOOST: self.pause() def step(self): if self.active: self.changeAlpha() self.logo.draw(self.screen.displaysurf) self.label.draw(self.screen.displaysurf) self.sublabel.draw(self.screen.displaysurf) self.counter += 1 if self.animState == 1: self.alpha += 2 if self.alpha > 255: self.animState = 2 self.counter = 0 if self.animState == 2: self.counter += 1 if self.counter > self.screen.fps * 3: self.animState = 3 if self.animState == 3: self.alpha -= 2 if self.alpha <= 0: self.pause() else: self.screen.setWorld(ufrnsplashworld.UfrnSplashWorld()) del self

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opps/contrib/multisite/models.py

jeanmask/opps

159

162537

<gh_stars>100-1000 # -*- coding: utf-8 -*- from django.utils.translation import ugettext_lazy as _ from opps.core.permissions.models import Permission class SitePermission(Permission): pass

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testing/performances.py

severin-lemaignan/underworlds

3

133871

#! /usr/bin/env python3 # -*- coding: utf-8 -*- import argparse import time import uuid from multiprocessing import Pipe, Pool import threading from concurrent.futures import ThreadPoolExecutor import logging; logger = logging.getLogger("underworlds.testing.performances") import underworlds import underworlds.server from underworlds.types import Node from underworlds.helpers.profile import profileonce import underworlds.underworlds_pb2 as gRPC def ms(duration): return "%.1fms" % (duration * 1000) def passthrough(world1, world2, signaling_pipe): """ Simple passthrough filter: wait for changes on a world world1 and propagate these changes to world world2. """ name = "passthrough_filter_%s_to_%s" % (world1, world2) with underworlds.Context(name) as ctx: world1 = ctx.worlds[world1] world2 = ctx.worlds[world2] try: print("Waiting for changes...") while not signaling_pipe.poll(): #print("%f -- %s waiting" % (time.time(), name)) change = world1.scene.waitforchanges(0.5) #print("%f -- %s Done waiting (last change: %s)" % (time.time(), name, str(change))) if change is not None: id, op = change #print("%f -- propagating from %s to %s" % (time.time(), name, world1, world2)) world2.scene.update_and_propagate(world1.scene.nodes[id]) change = None except Exception as e: import traceback traceback.print_exc() print("Stopping passthrough") print("Passthrough stopped") def wait_for_changes(world, nb_changes): print("Waiting for changes in world %s" % world) starttime = time.time() change = None for i in range(nb_changes): change = world.scene.waitforchanges(3) profileonce("waitforchanges triggered") return change, time.time()-starttime def test_propagation_time(nb_worlds, nb_changes): executor = ThreadPoolExecutor(max_workers=nb_worlds) pool = Pool(nb_worlds) pipes = [] res = [] for i in range(nb_worlds-1): print("Setting up passthrough between world %d and world %d" % (i, i+1)) #f = executor.submit(passthrough, "world%d" % i, "world%d" % (i+1)) conn1, conn2 = Pipe() pipes.append(conn1) res.append(pool.apply_async(passthrough, ["world%d" % i, "world%d" % (i+1), conn2])) time.sleep(0.5) ctx = underworlds.Context("test_client") entry_world = ctx.worlds["world0"] exit_world = ctx.worlds["world%d" % (nb_worlds-1)] future = executor.submit(wait_for_changes, exit_world, nb_changes) print("\n\n\nPropagating %d change(s) from world %s..." % (nb_changes, entry_world)) profileonce("start test with %d worlds" % nb_worlds) for i in range(nb_changes): n = Node() n.name = "node_%d" % i entry_world.scene.append_and_propagate(n) time.sleep(0.01) seen, duration = future.result() profileonce("end test") if seen is None: logger.error("The changes have not been seen!") duration = 0 else: print("It took %s to be notified of the %d change(s) in world %s" % (ms(duration), nb_changes, exit_world)) executor.shutdown(wait=True) for p in pipes: p.send(True) pool.close() pool.join() ctx.close() return duration if __name__ == '__main__': durations = {} parser = argparse.ArgumentParser() parser.add_argument("maxworlds", default=5, type=int, nargs="?", help="Maximum number of Underworlds worlds to spawn") parser.add_argument("-d", "--debug", help="debug mode", action="store_true") parser.add_argument("-dd", "--fulldebug", help="debug mode (verbose)", action="store_true") parser.add_argument("-i", "--incremental", action="store_true", help="test for every nb of worlds, from 2 to maxworlds") parser.add_argument("-r", "--repeat", default=1, type=int, nargs="?", help="how many times the test should be repeated (default: no repeat)") parser.add_argument("-c", "--changes", default=1, type=int, nargs="?", help="how many changes should be propagated (default: one)") args = parser.parse_args() if args.debug or args.fulldebug: if args.fulldebug: logging.basicConfig(level=logging.DEBUG) else: logging.basicConfig(level=logging.INFO) else: logger.setLevel(logging.WARN) minworlds = 2 if args.incremental else args.maxworlds for idx in range(0, args.repeat): print("\n\n\n-- Iteration #%d --\n" % idx) for nb in range(minworlds, args.maxworlds+1): print("\n\n\n-- %d worlds --\n" % nb) server = underworlds.server.start() durations.setdefault(nb,[]).append(test_propagation_time(nb, args.changes)) server.stop(0).wait() time.sleep(0.5) for nb,times in durations.items(): print("%d;%s" % (nb, ";".join([str("%.1f" % (d * 1000)) for d in times])))

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__init__.py

zznop/bn-kconfig-recover

18

35012

<filename>__init__.py """ Binary Ninja plugin for recovering kernel build configuration settings using BNIL """ import argparse import logging from binaryninja import (BinaryViewType, BinaryView, PluginCommand, SaveFileNameField, get_form_input, BackgroundTaskThread) class RecoverKConfigBackground(BackgroundTaskThread): """Class for running kernel configuration recovery in background """ def __init__(self, view: BinaryView, outpath: str) -> None: BackgroundTaskThread.__init__(self, 'Recovering Linux kernel configuration', False) self.outpath = outpath self.view = view def run(self): """Run analysis task """ self.view.reanalyze() self.view.update_analysis_and_wait() kconfigr = KConfigRecover(self.view) config = kconfigr.recover() save_kconfig(config, self.outpath) self.progress = "" def run_from_ui(view: BinaryView) -> None: """Run as a plugin under the UI Args: view: Binary view """ logger = logging.getLogger() logger.setLevel(logging.INFO) config_field = SaveFileNameField('Configuration Output Path') get_form_input([config_field], 'Kernel Configuration Recovery Options') outpath = 'generated.config' if config_field.result != '': outpath = config_field.result kconfig_task = RecoverKConfigBackground(view, outpath) kconfig_task.start() def parse_args() -> argparse.Namespace: """Parses command line arguments. Returns: Parsed command line arguments. """ parser = argparse.ArgumentParser() parser.add_argument( 'bndb', help='File path to kernel ELF or Binary Ninja database') parser.add_argument( 'kconfig', help='File path to save recovered kernel configuration') parser.add_argument('-d', '--debug', action='store_true', help='Enable debug logging') return parser.parse_args() def run_headless() -> None: """Parse command line arguments and run app. """ args = parse_args() logger = logging.getLogger() if args.debug: logger.setLevel(logging.DEBUG) else: logger.setLevel(logging.INFO) logging.info('Opening "%s" and getting view...', args.bndb) view = BinaryViewType.get_view_of_file(args.bndb) logging.info('Running BN analysis, this may take some time...') kconfig_task = RecoverKConfigBackground(view, args.kconfig) kconfig_task.start() if __name__ == '__main__': from kconfig import KConfigRecover, save_kconfig run_headless() else: from .kconfig import KConfigRecover, save_kconfig PluginCommand.register( "Recover Linux kernel config", "Analyze Linux kernel binary and recover kernel configuration options", run_from_ui, )

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Temperatura.py

russeleiser/ProjectTemperature

0

37205

print ("--------------------------------------------------------") print ("-------------Bienvenido a la Temperatura----------------") print ("--------------------------------------------------------\n") x = 0 while x < 10000: print ("Elige tus opciones:") print ("(1) Convertir de °C a °F") print ("(2) Convertir de °C a °K") print ("(3) Convertir de °F a °C") print ("(4) Convertir de °F a °K") print ("(5) Convertir de °K a °C") print ("(6) Convertir de °K a °F\n") op = int(input("Elige una opcion de acuerdo a su numero: ")) if op ==1: r11 = int(input("Escriba el numero que quiere convertir: ")) var11 = r11 var12 = var11 * 1.8 var13 = var12 + 32 print ("°F =", var11, "x 1.8 + 32\n°F =", var12,"+ 32\n°F =", var13,"\n") elif op ==2: r12 = int(input("Escriba el numero que quiere convertir: ")) var21 = r12 var22 = var21 + 273 print ("°K =", var21, "-273\n°K =", var22,"\n") elif op ==3: r13 = int(input("Escriba el numero que quiere convertir: ")) var31 = r13 var32 = var31 - 32 var33 = var32 / 1.8 print ("°C = (", var31,"- 32 ) / 1.8\n°C =", var32, "/ 1.8\n°C =", var33, "\n") elif op ==4: r14 = int(input("Escriba el numero que quiere convertir: ")) var41 = r14 var42 = var41 - 32 bla = 0.5 * var42 var43 = bla + 273 print ("°K = 5/9 (", var41,"- 32 ) + 273\n°K = 5/9", var42, "+ 273\n°K = 0.5 x", var42,"+ 273\n°K =",var43, "\n") elif op ==5: r15 = int(input("Escriba el numero que quiere convertir: ")) var51 = r15 var52 = var51 - 273 print ("El resultado es: ", var52) elif op ==6: r16 = int(input("Escriba el numero que quiere convertir: ")) var61 = r16 var62 = var61 - 273 num = 1.8 * var62 var63 = num + 32 print ("El resultado es: ", var63) else: print ("Esta opcion no existe, ingrese una nueva opcion...") x = x + 1

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mura/eval.py

ZZ87229861/benchmarx

0

197623

<reponame>ZZ87229861/benchmarx<gh_stars>0 #!/usr/bin/env python3 import os import sys import simplejson as json import subprocess as sp if len(sys.argv) != 2: print("Usage: %s result_file" % sys.argv[0]) sys.exit(0) result_file = sys.argv[1] ROOT = os.path.abspath(os.path.dirname(__file__)) kapp = os.path.abspath(os.path.join(ROOT, 'kapp.pl')) official = os.path.abspath(os.path.join(ROOT, 'evaluate-v1.1.py')) gt = os.path.abspath(os.path.join(ROOT, '../data/mura/train_labeled_studies.csv')) if not os.path.isfile(gt): print("Create a symbolic link benchmarx/data/mura which should point to MURA-v1.1") print("You should see benchmarx/data/mura/train_labeled_studies.csv") sys.exit(0) lookup = {} with open(gt, 'r') as f: for l in f: k, v = l.strip().split(',') lookup[k] = v C = 0 with open(result_file, 'r') as f, \ open('temp_gt_xxxx', 'w') as of: for l in f: k, v = l.strip().split(',') assert k in lookup of.write('%s,%s\n' % (k, lookup[k])) C += 1 pass pass print('evaluating %s lines' % C) st = sp.check_output('python %s temp_gt_xxxx %s' % (official, result_file), shell=True) print(json.dumps(json.loads(st), indent=4, sort_keys=True)) sp.check_call('%s temp_gt_xxxx %s' % (kapp, result_file), shell=True) sp.check_call('rm temp_gt_xxxx', shell=True)

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python/language/lang.py

kenkeiras/codigoparallevar

2

64311

#!/usr/bin/env python # coding: utf-8 import re import os import sys # Define un color para mostrar los resultados color = True if color: colorfile = "\x1b[0;92m" # Verde colorend = "\x1b[0m" # Fin del color else: colorfile = colorend = "" # Diccionarios a comprobar dictionaries = ["american-english", "british-english", "galician", "spanish"] path = "/usr/share/dict" class LangReader: words = re.compile(r'\w+') def __init__(self, dicts): '''Carga los diccionarios con las palabras, una por línea, en minúsculas. ''' self.dicts = {} for d in dicts: self.dicts[d] = set(map(lambda x: x.strip().lower(), open(d, "rt").read().strip().split("\n"))) def get_file_props(self, f): return self.get_props(f.read()) def get_percents(self, data): '''Devuelve el porcentaje de palabras de cada idioma.''' props, total = self.get_props(data) percents = {} for lang in props: percents[lang] = round((float(props[lang]) / total) * 100, 2) return percents def get_props(self, data): '''Devuelve el número de palabras que se corresponde con el diccionario de cada idioma.''' counters = {} total = 0 words = map(lambda w: w.lower(), self.words.findall(data)) for lang in self.dicts: ldict = self.dicts[lang] counter = 0 for word in words: if word in ldict: counter += 1 counters[lang] = counter return counters, len(words) if __name__ == "__main__": if len(sys.argv) == 1: print >>sys.stderr, "%s <file>" % sys.argv[0] exit(0) files = map(lambda x: open(x, "rt"), sys.argv[1:]) os.chdir(path) ld = LangReader(dictionaries) first = True for f in files: if not first: print "\n" props, total = ld.get_file_props(f) if total > 0: print "%s%s%s [%i]\n" % (colorfile, f.name, colorend, total) for i in sorted(props, lambda x, y: props[x].__cmp__(props[y]), reverse=True): print "%5.2f%% %s" % ((props[i] / float(total)) * 100, i) else: print "%s%s%s nothing found" % (colorfile, f.name, colorend) first = False

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tests/fixtures.py

vfxetc/sgcache

13

22341

<reponame>vfxetc/sgcache from . import uuid def task_crud(self, shotgun, trigger_poll=lambda: None): shot_name = uuid(8) shot = shotgun.create('Shot', {'code': shot_name}) name = uuid(8) task = shotgun.create('Task', {'content': name, 'entity': shot}) trigger_poll() x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['content']) self.assertSameEntity(task, x) # entity field x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['entity']) self.assertSameEntity(shot, x['entity']) # return through entity field x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['entity.Shot.code']) self.assertEqual(shot_name, x['entity.Shot.code']) # Updates name += '-2' shotgun.update('Task', task['id'], {'content': name}) trigger_poll() x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['content']) self.assertEqual(x['content'], name) # Delete shotgun.delete('Task', task['id']) trigger_poll() x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['content']) self.assertIs(x, None) x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['content'], retired_only=True) self.assertSameEntity(task, x) # Revive shotgun.revive('Task', task['id']) trigger_poll() x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['content']) self.assertSameEntity(task, x, 'Should get revived task.') x = self.cached.find_one('Task', [('id', 'is', task['id'])], ['content'], retired_only=True) self.assertIs(x, None)

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commands/m_link.py

davidferguson/lektor-markdown-mactutor

2

67477

<filename>commands/m_link.py # -*- coding: utf-8 -*- import re import mistune name = 'm_link' type = 'inline' position = 0 regex = re.compile(r'\[m\s*(?:=\s*(?P<name>\w+)\s*)?](?P<text>.*?)\[\/m\]') def render(match): name = match.group('name') text = match.group('text') # special case if there is no explicit name specified, in which case we # have to derive it ourselves if name == None: name = text.split()[-1] # and remove all accents from it. name = purge(name) # format the appropriate link structure return '<a href="/Mathematicians/%s.html" data-name="%s" onclick="m_popup(this)">%s</a>' % (name,name,text) def purge(s): # The below code is modified from the original htmlformat function # to ensure compatibility rawch = ['á','à','â','ä','ã','Á','Â','Ä','é','è','ê','ë','É','î','í','ó','ô','ö','ò','õ','Ö','û','ú','ü','ù','Ü','ç','ï','ø','Ø','ñ'] transch = ['a','a','a','a','a','A','A','A','e','e','e','e','E','i','i','o','o','o','o','o','O','u','u','u','u','U','c','i','o','O','n'] for idx, raw in enumerate(rawch): s = s.replace(raw, transch[idx]) return s

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read_data_objects.py

PawWitGit/alaska-methan-flux

0

180123

import pandas as pd import matplotlib as plt import matplotlib.pyplot as plt import numpy as np from read_data import ReadData # class DataObjects: # """Create ReadData objects""" # read_data = ReadData("CH4_Flux_BigTrail_Goldstream_AK.csv") # """Functions""" # read_data.return_data_file() # read_data.return_data_table()

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djnic/core/models.py

avdata99/nic

8

165687

<reponame>avdata99/nic from django.db import models class News(models.Model): """ novedades internas del sistema """ priority = models.IntegerField(default=50, help_text='0/100 priority level') title = models.CharField(max_length=90) description = models.TextField(null=True, blank=True) object_created = models.DateTimeField(auto_now_add=True) def __str__(self): return f'{self.title} {self.object_created}' class Meta: ordering = ['-id']

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Backend/pose_analyser/models.py

saksham1991999/Yoga-Pose-Analyser

0

1601017

from django.db import models class Pose(models.Model): name = models.CharField(max_length=256) description = models.TextField(blank=True, null=True) file = models.FileField() def __str__(self): return self.name class PoseAnalysis(models.Model): pose = models.ForeignKey("pose_analyser.Pose", blank=True, null=True, on_delete=models.CASCADE) image = models.ImageField() analysis = models.TextField(blank=True, null=True)

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python/data_helper.py

justinmzt/text_classification-system

4

106338

<reponame>justinmzt/text_classification-system<filename>python/data_helper.py # -*- coding: UTF-8 -*- import time class Timer(): def __init__(self): self.start = time.time() def print(self, op="训练"): print("%s用时 %f 秒。" % (op, time.time() - self.start)) def open_file(filename, mode='r'): """ 常用文件操作，可在python2和python3间切换. mode: 'r' or 'w' for read or write """ # if is_py3: return open(filename, mode, encoding='utf-8', errors='ignore') # else: # return open(filename, mode) def read_file(filename): """读取文件数据""" output = [] with open_file(filename) as f: output = [_.strip() for _ in f.readlines()] return output def write_list(filename, array): """读取文件数据""" with open_file(filename, 'w') as f: f.write('\n'.join(array) + '\n') f.close() def read_cnews(filename, cut_file=None): x, y = [], [] with open_file(filename) as tr: for line in tr: label, content = line.strip().split('\t') if content: x.append(content) y.append(label) if cut_file: x = read_file(cut_file) return x,y def write_file(filename, x, y): with open(filename,'a', encoding='utf8') as f: if isinstance(x, str) and isinstance(y, str): f.write('%s\t%s\n' % (y, x)) elif isinstance(x, list) and isinstance(y, list): if len(x) != len(y): print("Not same scale.") return False for i in range(len(x)): f.write('%s\t%s\n' % (y[i], x[i])) else: print("Write False")

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MMMaker/core/highlight_extractor.py

C4Ution/MMMaker

9

38707

<gh_stars>1-10 import numpy as np import scipy.io.wavfile as wave from moviepy.editor import VideoFileClip from scipy.fftpack import fft from misc import get_random_name HIGHLIGHT_LENGTH = 0.65 def extract_highlights(file_paths): """ :param file_paths: 동영상 경로 리스트 :return: 추출한 하이라이트 동영상 경로 리스트 """ max_highlights = [] min_highlights = [] for file_path in file_paths: video_clip = VideoFileClip(file_path) audio_clip = video_clip.audio src_audio_file_name = get_random_name('wav') audio_clip.write_audiofile(src_audio_file_name) source_wave = wave.read(src_audio_file_name) stereo_channel_wave = source_wave[1].T[1] normalize_wave = [(ele / 2 ** 8.) * 2 - 1 for ele in stereo_channel_wave] # this is 8-bit track, now normalized on [-1,1) # fourier_transform_wave = fft(normalize_wave) # calculate fourier transform (complex numbers list) fourier_transform_wave = normalize_wave normalize_time = len(fourier_transform_wave) / video_clip.duration argmax_frequency = np.argmax(fourier_transform_wave) / normalize_time argmin_frequency = np.argmin(fourier_transform_wave) / normalize_time max_highlight_path = max_highlights.append(get_random_name('mp4')) min_highlight_path = min_highlights.append(get_random_name('mp4')) start_max = argmax_frequency - (HIGHLIGHT_LENGTH/2) end_max = argmax_frequency + (HIGHLIGHT_LENGTH/2) if start_max < 0: end_max -= start_max start_max = 0 elif end_max > video_clip.duration: start_max -= end_max - video_clip.duration end_max = video_clip.duration video_clip.subclip(start_max, end_max).write_videofile(max_highlights[-1], codec='libx264', audio_codec='aac') start_min = argmin_frequency - (HIGHLIGHT_LENGTH/2) end_min = argmin_frequency + (HIGHLIGHT_LENGTH/2) if start_min < 0: end_min -= start_min start_min = 0 elif end_min > video_clip.duration: start_min -= end_min - video_clip.duration end_min = video_clip.duration video_clip.subclip(start_min, end_min).write_videofile(min_highlights[-1], codec='libx264', audio_codec='aac') video_clip.close() return max_highlights, min_highlights

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flask_security_bundle/commands/roles.py

briancappello/flask-security-bundle

0

158409

from flask_unchained import unchained from flask_unchained.cli import cli, click from flask_unchained.commands.utils import print_table from .utils import _query_to_role from ..services import RoleManager role_manager: RoleManager = unchained.services.role_manager @cli.group() def roles(): """ Role commands. """ @roles.command(name='list') def list_roles(): """ List roles. """ roles = role_manager.find_all() if roles: print_table(['ID', 'Name'], [(role.id, role.name) for role in roles]) else: click.echo('No roles found.') @roles.command(name='create') @click.option('--name', prompt='Role name', help='The name of the role to create, eg `ROLE_USER`.') def create_role(name): """ Create a new role. """ role = role_manager.create(name=name) if click.confirm(f'Are you sure you want to create {role!r}?'): role_manager.save(role, commit=True) click.echo(f'Successfully created {role!r}') else: click.echo('Cancelled.') @roles.command(name='delete') @click.argument('query', help='The query to search for a role by. For example, ' '`id=5` or `name=ROLE_USER`.') def delete_role(query): """ Delete a role. """ role = _query_to_role(query) if click.confirm(f'Are you sure you want to delete {role!r}?'): role_manager.delete(role, commit=True) click.echo(f'Successfully deleted {role!r}') else: click.echo('Cancelled.')

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nucypher/tests/config/test_firstula_circumstances.py

kanzeparov/NuCypher

0

22728

""" This file is part of nucypher. nucypher is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. nucypher 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 nucypher. If not, see <https://www.gnu.org/licenses/>. """ from functools import partial import maya import pytest import pytest_twisted from twisted.internet.threads import deferToThread from nucypher.network.middleware import RestMiddleware from nucypher.utilities.sandbox.ursula import make_federated_ursulas def test_proper_seed_node_instantiation(ursula_federated_test_config): lonely_ursula_maker = partial(make_federated_ursulas, ursula_config=ursula_federated_test_config, quantity=1, know_each_other=False) firstula = lonely_ursula_maker().pop() firstula_as_seed_node = firstula.seed_node_metadata() any_other_ursula = lonely_ursula_maker(seed_nodes=[firstula_as_seed_node]).pop() assert not any_other_ursula.known_nodes any_other_ursula.start_learning_loop(now=True) assert firstula in any_other_ursula.known_nodes.values() @pytest_twisted.inlineCallbacks def test_get_cert_from_running_seed_node(ursula_federated_test_config): lonely_ursula_maker = partial(make_federated_ursulas, ursula_config=ursula_federated_test_config, quantity=1, know_each_other=False) firstula = lonely_ursula_maker().pop() node_deployer = firstula.get_deployer() node_deployer.addServices() node_deployer.catalogServers(node_deployer.hendrix) node_deployer.start() certificate_as_deployed = node_deployer.cert.to_cryptography() firstula_as_seed_node = firstula.seed_node_metadata() any_other_ursula = lonely_ursula_maker(seed_nodes=[firstula_as_seed_node], network_middleware=RestMiddleware()).pop() assert not any_other_ursula.known_nodes def start_lonely_learning_loop(): any_other_ursula.start_learning_loop() start = maya.now() while not firstula in any_other_ursula.known_nodes.values(): passed = maya.now() - start if passed.seconds > 2: pytest.fail("Didn't find the seed node.") yield deferToThread(start_lonely_learning_loop) assert firstula in any_other_ursula.known_nodes.values() certificate_as_learned = list(any_other_ursula.known_nodes.values())[0].certificate assert certificate_as_learned == certificate_as_deployed any_other_ursula.stop_learning_loop()

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rok/tests/conftest.py

rak-n-rok/Krake

1

75308

import os import sys import logging.config # Prepend package directory for working imports package_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) sys.path.insert(0, package_dir) logging.config.dictConfig( { "version": 1, "handlers": {"console": {"class": "logging.StreamHandler", "level": "DEBUG"}}, "loggers": {"rok": {"handlers": ["console"]}}, } )

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src/student.py

RushiRaut/SRMS-using-Flask

0

85214

<filename>src/student.py from flask import * from src.connection import cursor, db add_student = Blueprint ('add_student', __name__) manage_student = Blueprint('manage_student', __name__) delete_student = Blueprint('delete_student',__name__) edit = Blueprint('pre_edit',__name__) @add_student.route ('/add_student1', methods=['GET', 'POST']) def add_student1(): class_details = {} msg = 'Please Fill the Form' if request.method.lower() == 'post' and 'student_name' in request.form and request.form['roll_no']: id = request.form['id'] roll_no = request.form['roll_no'] cursor.execute ("select * from students where roll_no =%s", (roll_no,)) student_found = cursor.fetchone () if student_found: msg = 'Student roll number allready exixt' else: student_name = request.form['student_name'] student_gender = request.form['student_Gender'] student_mobile = request.form['student_mobile'] student_class_name = request.form['student_class_name'] cursor.execute('insert into students values (%s,%s,%s,%s,%s,%s)', (id, student_name, roll_no, student_gender, student_mobile, student_class_name)) db.commit () msg = 'Student Created Successfully' cursor.execute ('select * from students') students = cursor.fetchall () columns = ["id", "student_name", "roll_no", "student_gender", "student_mobile", "student_class_name"] student_details = [dict (zip (columns, row)) for row in students] return render_template ('add_student.html', rows=student_details) cursor.execute("select class_name from student_classes") class_found = cursor.fetchall() columns = ["class_name"] class_details = [dict(zip(columns, row)) for row in class_found] return render_template ('add_student.html', row=class_details,msg = msg) @manage_student.route('/manage_students', methods=['GET', 'POST']) def manage_students(): msg = '' cursor.execute('select * from students') students = cursor.fetchall() columns = ["id", "student_name", "roll_no", "student_gender", "student_mobile", "student_class_name"] student_details = [dict(zip(columns, row)) for row in students] return render_template('manage_student.html', rows=student_details) @delete_student.route('/delete_student/<id>',methods = ['GET','POST']) def delete1(id): cursor.execute('delete from students where id = %s', (id,)) db.commit() cursor.execute ('select * from students') students = cursor.fetchall () columns = ["id", "student_name", "roll_no", "student_gender", "student_mobile", "student_class_name"] student_details = [dict (zip (columns, row)) for row in students] return render_template ('manage_student.html', rows=student_details) @add_student.route('/pre_edit/<id>',methods=['GET','POST']) def pre_edit(id): cursor.execute ('select * from students where id = %s', (id,)) student = cursor.fetchone () columns = ["id", "student_name", "roll_no", "student_gender", "student_mobile", "student_class_name"] student_details = dict (zip (columns, student)) cursor.execute ("select class_name from student_classes") class_found = cursor.fetchall () columns = ["class_name"] class_details = [dict (zip (columns, row)) for row in class_found] return render_template ('edit_student.html', row=student_details, row1=class_details) @add_student.route('/edit_student', methods=['POST','GET']) def edit_student(): if request.method == 'POST' and 'student_name' in request.form and request.form['roll_no']: id = request.form['id'] roll_no = request.form['roll_no'] student_name = request.form['student_name'] student_gender = request.form['student_Gender'] student_mobile = request.form['student_mobile'] student_class_name = request.form['student_class_name'] cursor.execute ('update students set id =%s ,student_name = %s,roll_no=%s, student_gender=%s,' 'student_mobile=%s,student_class_name=%s where id = %s', (id, student_name, roll_no, student_gender, student_mobile, student_class_name, id)) db.commit () cursor.execute ('select * from students') students = cursor.fetchall () columns = ["id", "student_name", "roll_no", "student_gender", "student_mobile", "student_class_name"] student_details = [dict (zip (columns, row)) for row in students] return render_template ('manage_student.html', rows=student_details)

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modules/backend.py

Uncle-Yuanl/model_zoo

0

16881

import os, sys from distutils.util import strtobool import numpy as np import tensorflow as tf import tensorflow.keras.backend as K from tensorflow.python.util import nest, tf_inspect from tensorflow.python.eager import tape # from tensorflow.python.ops.custom_gradient import graph_mode_decorator # 是否使用重计算 do_recompute = strtobool(os.environ.get('RECOMPUTE', '0')) # 知乎：https://zhuanlan.zhihu.com/p/349492378 # 论文：https://arxiv.53yu.com/pdf/1606.08415.pdf def gelu_erf(x): """根据erf直接计算gelu """ # np的精度更高，默认64位，tf默认32位 return 0.5 * x * (1.0 + tf.math.erf(x / np.sqrt(2.0))) def gelu_tanh(x): cdf = 0.5 * ( 1 + K.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * K.pow(x,3))) ) return x * cdf def set_gelu(version): """设置gelu版本 """ version = version.lower() assert version in ['erf', 'tanh'], 'gelu version must in erf or tanh' if version == 'erf': tf.keras.utils.get_custom_objects()['gelu'] = gelu_erf elif version == 'tanh': tf.keras.utils.get_custom_objects()['gelu'] = gelu_tanh def align(tensor, axes, ndim=None): """重新对齐tensor（批量版expand_dims）感觉更像是transpose axes: 原来的第i维对齐新tensor的第axes[i]维； ndim: 新tensor的维度 Example: >>> tensor = tf.constant(np.arange(12).reshape(3,4), dtype=tf.float32) >>> print(tensor) tf.Tensor( [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]], shape=(3, 4), dtype=float32) >>> same_dim = align(tensor, [0, -1], 2) >>> print(same_dim) tf.Tensor( [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]], shape=(3, 4), dtype=float32) >>> more_dim = align(tensor, [0, -1], 3) >>> print(more_dim) tf.Tensor( [[[ 0. 1. 2. 3.]] <BLANKLINE> [[ 4. 5. 6. 7.]] <BLANKLINE> [[ 8. 9. 10. 11.]]], shape=(3, 1, 4), dtype=float32) """ assert len(axes) == K.ndim(tensor) indices = [None] * (ndim or max(axes)) for i in axes: indices[i] = slice(None) return tensor[indices] def sequence_masking(x, mask, value=0, axis=None): """为序列条件mask的函数 parameters: ----------- x: tensor 输入张量 mask: tensor 形如(batch_size, seq_len)的0-1矩阵 value: float or str mask部分要被替换成的值，允许'inf'与'-inf' axis: int 序列所在的轴，默认为1 """ if mask is None: return x # 确保x类型，可以执行*运算 x_type = K.dtype(x) if x_type == 'bool': x = K.cast(x, 'int32') # 确保mask类型 = x类型 if K.dtype(mask) != K.dtype(x): mask = K.cast(mask, K.dtype(x)) if value == '-inf': # -----------是个函数吗？？--------------- value = -K.infinity if value == 'inf': value = K.infinity value = K.cast(value, K.dtype(x)) # 确定axis if axis is None: axis = 1 if axis < 0: axis = K.ndim(x) + axis assert axis > 0, 'axis must be greater than 0' # 统一shape for _ in range(axis - 1): # > 1时生效 mask = K.expand_dims(mask, 1) # 把第0维让给batch_size for _ in range(K.ndim(x) - K.ndim(mask)): mask = K.expand_dims(mask, K.ndim(mask)) x = x * mask + value * (1 - mask) # 与输入x的类型统一 if x_type == 'bool': x = K.cast(x, x_type) return x def recompute_grad(call): # ----------------------完全没看懂？？？？------------------------ """重计算装饰器，用来装饰keras层的call函数目的是：通过一些额外的计算减少显存的占用论文：https://arxiv.org/abs/1604.06174 """ if not do_recompute: return call def inner(self, inputs, **kwargs): # 2.x的tf.nest.flatten不会对numpy和tf.tensor进行展平 flat_inputs = nest.flatten(inputs) call_args = tf_inspect.getfullargspec(call).args for key in ['mask', 'training']: if key not in call_args and key in kwargs: del kwargs[key] def kernel_call(): """定义前向计算 """ return call(self, inputs, **kwargs) def call_and_grad(*inputs): """定义前向计算和反向计算 """ with tape.stop_recording(): outputs = kernel_call() outputs = tf.identity(outputs) def grad_fn(doutputs, variables=None): watches = list(inputs) if variables is not None: watches += list(variables) with tf.GradientTape() as t: t.watch(watches) with tf.control_dependencies([doutputs]): outputs = kernel_call() grads = t.gradient( outputs, watches, output_gradients=[doutputs] ) del t return grads[:len(inputs)], grads[len(inputs):] return outputs, grad_fn outputs, grad_fn = call_and_grad(*flat_inputs) flat_outputs = nest.flatten(outputs) def actual_grad_fn(*doutputs): grads = grad_fn(*doutputs, variables=self.trainable_weights) return grads[0] + grads[1] watches = flat_inputs + self.trainable_weights watches = [tf.convert_to_tensor(x) for x in watches] tape.record_operation( call.__name__, flat_outputs, watches, actual_grad_fn ) return outputs return inner def infinity(): """返回默认的代表无穷大的数值 """ return tf.keras.utils.get_custom_objects().get('infinity', 1e12) def set_infinity(value): """设置新的代表无穷大的数值 """ tf.keras.utils.get_custom_objects()['infinity'] = value # 添加到 keras.backend 上，使其可以像 K.epsilon() 那样操作 K.infinity = infinity K.set_infinity = set_infinity sys.modules['tensorflow.keras.backend'] = K custom_objects = { 'gelu_erf': gelu_erf, 'gelu_tanh': gelu_tanh, 'gelu': gelu_erf, } tf.keras.utils.get_custom_objects().update(custom_objects) if __name__ == '__main__': import doctest doctest.testmod()

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camd3/infrastructure/component/tests/test_component.py

mamrhein/CAmD3

0

30913

#!/usr/bin/env python # ---------------------------------------------------------------------------- # Name: test_component # Purpose: Test driver for module component # # Author: <NAME> (<EMAIL>) # # Copyright: (c) 2014 <NAME> # ---------------------------------------------------------------------------- # $Source$ # $Revision$ """Test driver for module component""" from abc import ABC from numbers import Number from typing import Tuple import unittest from camd3.infrastructure.component import ( Attribute, Component, ComponentLookupError, Immutable, implementer) from camd3.infrastructure.component.component import _ABCSet, ComponentMeta DFLT_NAMESPACE = ('__module__', '__qualname__', '__doc__') class TestComp1(Component): """TestComp1""" class TestComp2(Component): """TestComp2""" attr1 = Attribute() attr2 = Attribute() def meth(self): pass attr3 = Attribute() @property def prop(self): pass @implementer(TestComp1, TestComp2) class TestImpl(Component): def __init__(self): pass class TestComp1Factory: def __call__(self, i: Number) -> TestComp1: return TestImpl() def Number2TestComp1(i: Number) -> TestComp1: # noqa: D103 return TestImpl() def Str2TestComp2(s: str) -> TestComp2: # noqa: D103 return TestImpl() @implementer(TestComp1) class TestABC(ABC): pass class TestComp3(Component): """TestComp3""" def __init_subclass__(subcls, **kwds): # noqa: D105 try: param = kwds.pop('param') except KeyError: pass else: subcls.param = param class TestImpl3(TestComp3, param='P'): pass class TestComp4(Component, Immutable): """TestComp4""" class TestComp5(TestComp4): """TestComp5""" a = Attribute() b = Attribute() class TestComp6(TestComp5): """TestComp6""" c = Attribute() def __init__(self, a, b, c): self.a = a self.b = b self.c = c def Obj2TestComp6(obj: object) -> TestComp6: # noqa: D103 return TestComp6(obj, None, None) def Tuple2TestComp6(tpl: Tuple[int, int, str]) -> TestComp6: # noqa: D103 return TestComp6(*tpl) class TestComp7(TestComp6): """TestComp7""" class TestComp8(TestComp1): """TestComp8""" def __init__(self, a): self.a = a self.i = self.initialized class ABCSetTest(unittest.TestCase): def testAdd(self): cls_list = (ABC, Immutable, TestComp4, Component) self.assertEqual(_ABCSet(cls_list), _ABCSet({ABC, TestComp4})) cls_list = (int, object, Number, float) self.assertEqual(_ABCSet(cls_list), _ABCSet({int, float})) class ComponentMetaTest(unittest.TestCase): def test_constructor(self): # name of descriptors for name in ('attr1', 'attr2', 'attr3'): self.assertEqual(getattr(getattr(TestComp2, name, None), 'name', None), name) # __slots__ forced? self.assertEqual(getattr(TestComp4, '__slots__', None), ()) self.assertEqual(getattr(TestComp5, '__slots__', None), ('_a', '_b')) self.assertEqual(getattr(TestComp6, '__slots__', None), ('_c',)) self.assertRaises(TypeError, ComponentMeta, 'Test', (TestImpl, TestComp4), {}) def test_init_subclass(self): # init_subclass turned into a class method? meth = TestComp3.__init_subclass__ self.assertTrue(getattr(meth, '__self__', None) is TestComp3) # __init_subclass called? self.assertEqual(getattr(TestImpl3, 'param', None), 'P') def test_attr_names(self): self.assertEqual(TestComp2.attr_names, ('attr1', 'attr2', 'attr3')) self.assertEqual(TestComp2.all_attr_names, ('attr1', 'attr2', 'attr3')) self.assertEqual(TestImpl.attr_names, ()) self.assertEqual(TestImpl.all_attr_names, ()) self.assertEqual(TestComp6.attr_names, ('c',)) self.assertEqual(TestComp6.all_attr_names, ('a', 'b', 'c')) def test_implementer(self): self.assertTrue(issubclass(TestImpl, TestComp1)) self.assertTrue(issubclass(TestImpl, TestComp2)) self.assertEqual(TestImpl.__virtual_bases__, {TestComp1, TestComp2}) self.assertTrue(issubclass(TestABC, TestComp1)) def test_adaptation(self): # wrong component self.assertRaises(AssertionError, TestComp2.add_adapter, TestComp1Factory()) self.assertRaises(AssertionError, TestComp2.add_adapter, Tuple2TestComp6) # wrong number of args func = lambda x, y: TestComp2() func.__annotations__ = {'return': TestComp2, 'x': int, 'y': int} self.assertRaises(AssertionError, TestComp2.add_adapter, func) # variable number of args func = lambda *args: TestComp2() func.__annotations__ = {'return': TestComp2, 'args': int} self.assertRaises(AssertionError, TestComp2.add_adapter, func) # register some adapters fct = TestComp1Factory() TestComp1.add_adapter(fct) self.assertIn(Number, TestComp1.__adapters__) self.assertIn(fct, TestComp1.__adapters__[Number]) TestComp1.add_adapter(Number2TestComp1) self.assertIn(Number2TestComp1, TestComp1.__adapters__[Number]) TestComp1.add_adapter(Number2TestComp1) self.assertEqual(len(TestComp1.__adapters__[Number]), 2) TestComp2.add_adapter(Str2TestComp2) self.assertIn(str, TestComp2.__adapters__) self.assertIn(Str2TestComp2, TestComp2.__adapters__[str]) TestComp6.add_adapter(Tuple2TestComp6) adapter = TestComp6.add_adapter(Obj2TestComp6) self.assertEqual(adapter, Obj2TestComp6) # retrieve adapters self.assertEqual(TestComp1.get_adapter(5), Number2TestComp1) self.assertEqual(TestComp1.get_adapter(5.0), Number2TestComp1) self.assertRaises(ComponentLookupError, TestComp1.get_adapter, 'x') self.assertEqual(TestComp2.get_adapter('abc'), Str2TestComp2) self.assertRaises(ComponentLookupError, TestComp2.get_adapter, 3) self.assertEqual(TestComp6.get_adapter((3, 1, 'x')), Tuple2TestComp6) self.assertEqual(TestComp6.get_adapter([3, 1, 'x']), Obj2TestComp6) self.assertEqual(TestComp6.get_adapter(TestComp6(3, 1, 'x')), Obj2TestComp6) self.assertEqual(TestComp4.get_adapter((3, 1, 'x')), Tuple2TestComp6) self.assertEqual(TestComp4.get_adapter([3, 1, 'x']), Obj2TestComp6) # adapt objects self.assertIsInstance(TestComp1.adapt(5), TestComp1) self.assertIsInstance(TestComp1[5.0], TestComp1) self.assertIsInstance(TestComp2.adapt('x'), TestComp2) t1 = TestComp6.adapt((5, 17, 'abc')) self.assertIsInstance(t1, TestComp6) self.assertEqual((t1.a, t1.b, t1.c), (5, 17, 'abc')) t2 = TestComp6.adapt(fct) self.assertIsInstance(t2, TestComp6) self.assertIs(t2.a, fct) self.assertRaises(TypeError, TestComp7.adapt, t2) t3 = TestComp6(4, 9, 'y') for ct in (TestComp6, TestComp5, TestComp4): self.assertIs(ct.adapt(t3), t3) def test_repr(self): self.assertEqual(repr(TestComp3), '.'.join((__name__, TestComp3.__qualname__))) class ComponentTest(unittest.TestCase): def test_constructor(self): comp = TestComp8(19) self.assertFalse(comp.i) self.assertTrue(comp.initialized) if __name__ == '__main__': unittest.main()

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Algoritimos/uri1020.py

mathspin/Algoritimos-py

0

132671

<filename>Algoritimos/uri1020.py v = input("digite um valor em dias: ") a = int(int(v)/365) m = int((int(v)%365)/30) d = int((int(v)%365)%30) print(str(a)+" ano(s)") print(str(m)+" mes(es)") print(str(d)+" dias(s)")

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orangecontrib/aps/shadow/widgets/extension/ow_bendable_ellipsoid_mirror.py

oasys-aps-kit/OASYS1-APS-ShadowOui

1

139140

import os, sys, numpy from scipy.interpolate import RectBivariateSpline, interp2d from scipy.optimize import curve_fit from matplotlib import cm from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg from matplotlib.figure import Figure try: from mpl_toolkits.mplot3d import Axes3D # necessario per caricare i plot 3D except: pass from PyQt5.QtWidgets import QApplication from PyQt5.QtCore import QSettings from orangewidget import gui from orangewidget.settings import Setting from oasys.widgets import gui as oasysgui from oasys.widgets import congruence from oasys.util.oasys_util import TriggerIn from orangecontrib.shadow.util.shadow_objects import ShadowOpticalElement, ShadowBeam, ShadowPreProcessorData from orangecontrib.shadow.util.shadow_util import ShadowPreProcessor from orangecontrib.shadow.widgets.gui import ow_ellipsoid_element, ow_optical_element from Shadow import ShadowTools as ST TRAPEZIUM = 0 RECTANGLE = 1 SINGLE_MOMENTUM = 0 DOUBLE_MOMENTUM = 1 class BendableEllipsoidMirror(ow_ellipsoid_element.EllipsoidElement): name = "Bendable Ellipsoid Mirror" description = "Shadow OE: Bendable Ellipsoid Mirror" icon = "icons/bendable_ellipsoid_mirror.png" maintainer = "<NAME>" maintainer_email = "<EMAIL>" priority = 6 category = "Optical Elements" keywords = ["data", "file", "load", "read"] send_footprint_beam = QSettings().value("output/send-footprint", 0, int) == 1 if send_footprint_beam: outputs = [{"name":"Beam", "type":ShadowBeam, "doc":"Shadow Beam", "id":"beam"}, {"name":"Footprint", "type":list, "doc":"Footprint", "id":"beam"}, {"name":"Trigger", "type": TriggerIn, "doc":"Feedback signal to start a new beam simulation", "id":"Trigger"}, {"name": "PreProcessor_Data", "type": ShadowPreProcessorData, "doc": "PreProcessor Data", "id": "PreProcessor_Data"} ] else: outputs = [{"name":"Beam", "type":ShadowBeam, "doc":"Shadow Beam", "id":"beam"}, {"name":"Trigger", "type": TriggerIn, "doc":"Feedback signal to start a new beam simulation", "id":"Trigger"}, {"name": "PreProcessor_Data", "type": ShadowPreProcessorData, "doc": "PreProcessor Data", "id": "PreProcessor_Data"} ] show_bender_plots = Setting(0) bender_bin_x = Setting(100) bender_bin_y = Setting(500) E = Setting(131000) h = Setting(10) kind_of_bender = Setting(1) shape = Setting(0) output_file_name = Setting("mirror_bender.dat") which_length = Setting(0) optimized_length = Setting(0.0) M1 = Setting(0.0) ratio = Setting(0.5) e = Setting(0.3) M1_out = 0.0 ratio_out = 0.0 e_out = 0.0 M1_fixed = Setting(False) ratio_fixed = Setting(False) e_fixed = Setting(False) M1_min = Setting(0.0) ratio_min = Setting(0.0) e_min = Setting(0.0) M1_max = Setting(1000.0) ratio_max = Setting(10.0) e_max = Setting(1.0) def __init__(self): graphical_Options=ow_optical_element.GraphicalOptions(is_mirror=True) super().__init__(graphical_Options) tabs = gui.tabWidget(oasysgui.createTabPage(self.tabs_basic_setting, "Bender")) tab_bender = oasysgui.createTabPage(tabs, "Bender Setting") surface_box = oasysgui.widgetBox(tab_bender, "Surface Setting", addSpace=False, orientation="vertical") oasysgui.lineEdit(surface_box, self, "bender_bin_x", "bins Sagittal", labelWidth=260, valueType=int, orientation="horizontal") oasysgui.lineEdit(surface_box, self, "bender_bin_y", "bins Transversal", labelWidth=260, valueType=int, orientation="horizontal") material_box = oasysgui.widgetBox(tab_bender, "Bender Setting", addSpace=False, orientation="vertical") self.le_E = oasysgui.lineEdit(material_box, self, "E", "Young's Modulus ", labelWidth=260, valueType=float, orientation="horizontal") self.le_h = oasysgui.lineEdit(material_box, self, "h", "Thickness ", labelWidth=260, valueType=float, orientation="horizontal") gui.comboBox(material_box, self, "kind_of_bender", label="Kind Of Bender ", items=["Single Momentum", "Double Momentum"], labelWidth=150, orientation="horizontal", callback=self.set_kind_of_bender) gui.comboBox(material_box, self, "shape", label="Shape ", items=["Trapezium", "Rectangle"], labelWidth=150, orientation="horizontal", callback=self.set_shape) tab_fit = oasysgui.createTabPage(tabs, "Fit Setting") fit_box = oasysgui.widgetBox(tab_fit, "", addSpace=False, orientation="vertical") file_box = oasysgui.widgetBox(fit_box, "", addSpace=False, orientation="horizontal", height=25) self.le_output_file_name = oasysgui.lineEdit(file_box, self, "output_file_name", "Out File Name", labelWidth=100, valueType=str, orientation="horizontal") gui.button(file_box, self, "...", callback=self.select_output_file, width=20) length_box = oasysgui.widgetBox(fit_box, "", addSpace=False, orientation="horizontal") self.cb_optimized_length = gui.comboBox(length_box, self, "which_length", label="Optimized Length ", items=["Total", "Partial"], labelWidth=150, orientation="horizontal", callback=self.set_which_length) self.le_optimized_length = oasysgui.lineEdit(length_box, self, "optimized_length", " ", labelWidth=10, valueType=float, orientation="horizontal") self.set_which_length() gui.separator(fit_box) def add_parameter_box(container_box, variable, label): box = oasysgui.widgetBox(container_box, "", addSpace=False, orientation="horizontal") oasysgui.lineEdit(box, self, variable, label, labelWidth=50, valueType=float, orientation="horizontal") gui.label(box, self, " ", labelWidth=58) box = oasysgui.widgetBox(container_box, "", addSpace=False, orientation="horizontal") setattr(self, "le_" + variable + "_min", oasysgui.lineEdit(box, self, variable + "_min", "Min", labelWidth=50, valueType=float, orientation="horizontal")) setattr(self, "le_" + variable + "_max", oasysgui.lineEdit(box, self, variable + "_max", "Max", labelWidth=35, valueType=float, orientation="horizontal")) gui.checkBox(box, self, variable + "_fixed", "Fixed", callback=getattr(self, "set_" + variable)) box = oasysgui.widgetBox(container_box, "", addSpace=False, orientation="horizontal") le = oasysgui.lineEdit(box, self, variable + "_out", "Fitted", labelWidth=50, valueType=float, orientation="horizontal") le.setEnabled(False) le.setStyleSheet("color: blue; background-color: rgb(254, 244, 205); font:bold") def set_variable_fit(): setattr(self, variable, getattr(self, variable + "_out")) gui.button(box, self, "<- Use", width=58, callback=set_variable_fit) getattr(self, "set_" + variable)() m1_box = oasysgui.widgetBox(fit_box, "", addSpace=False, orientation="vertical") gui.separator(fit_box, 10) self.ratio_box = oasysgui.widgetBox(fit_box, "", addSpace=False, orientation="vertical") gui.separator(fit_box, 10) self.e_box = oasysgui.widgetBox(fit_box, "", addSpace=False, orientation="vertical") gui.separator(fit_box, 10) add_parameter_box(m1_box, "M1", "M1") add_parameter_box(self.ratio_box, "ratio", "M1/M2") add_parameter_box(self.e_box, "e", "e") self.set_kind_of_bender() self.set_shape() ####################################################### plot_tab = oasysgui.createTabPage(self.main_tabs, "Bender Plots") view_box = oasysgui.widgetBox(plot_tab, "Plotting Style", addSpace=False, orientation="vertical", width=350) self.view_type_combo = gui.comboBox(view_box, self, "show_bender_plots", label="Show Plots", labelWidth=220, items=["No", "Yes"], sendSelectedValue=False, orientation="horizontal") bender_tabs = oasysgui.tabWidget(plot_tab) tabs = [oasysgui.createTabPage(bender_tabs, "Bender vs. Ideal (1D)"), oasysgui.createTabPage(bender_tabs, "Ideal - Bender (1D)"), oasysgui.createTabPage(bender_tabs, "Ideal - Bender (3D)"), oasysgui.createTabPage(bender_tabs, "Figure Error (3D)"), oasysgui.createTabPage(bender_tabs, "Ideal - Bender + Figure Error (3D)")] def create_figure_canvas(mode="3D"): figure = Figure(figsize=(100, 100)) figure.patch.set_facecolor('white') if mode == "3D": figure.add_subplot(111, projection='3d') else: figure.add_subplot(111) figure_canvas = FigureCanvasQTAgg(figure) figure_canvas.setFixedWidth(self.IMAGE_WIDTH) figure_canvas.setFixedHeight(self.IMAGE_HEIGHT-10) return figure_canvas self.figure_canvas = [create_figure_canvas("1D"), create_figure_canvas("1D"), create_figure_canvas(), create_figure_canvas(), create_figure_canvas()] for tab, figure_canvas in zip(tabs, self.figure_canvas): tab.layout().addWidget(figure_canvas) gui.rubber(self.controlArea) gui.rubber(self.mainArea) ################################################################ # # SHADOW MANAGEMENT # ################################################################ def select_output_file(self): self.le_output_file_name.setText(oasysgui.selectFileFromDialog(self, self.output_file_name, "Select Output File", file_extension_filter="Data Files (*.dat)")) def set_kind_of_bender(self): self.ratio_box.setVisible(self.kind_of_bender==1) def set_shape(self): self.e_box.setVisible(self.shape==0) def set_which_length(self): self.le_optimized_length.setEnabled(self.which_length==1) def set_M1(self): self.le_M1_min.setEnabled(self.M1_fixed==False) self.le_M1_max.setEnabled(self.M1_fixed==False) def set_ratio(self): self.le_ratio_min.setEnabled(self.ratio_fixed==False) self.le_ratio_max.setEnabled(self.ratio_fixed==False) def set_e(self): self.le_e_min.setEnabled(self.e_fixed==False) self.le_e_max.setEnabled(self.e_fixed==False) def after_change_workspace_units(self): super().after_change_workspace_units() label = self.le_E.parent().layout().itemAt(0).widget() label.setText(label.text() + " [N/" + self.workspace_units_label + "^2]") label = self.le_h.parent().layout().itemAt(0).widget() label.setText(label.text() + " [" + self.workspace_units_label + "]") label = self.cb_optimized_length.parent().layout().itemAt(0).widget() label.setText(label.text() + " [" + self.workspace_units_label + "]") def checkFields(self): super().checkFields() if self.is_cylinder != 1: raise ValueError("Bender Ellipse must be a cylinder") if self.cylinder_orientation != 0: raise ValueError("Cylinder orientation must be 0") if self.is_infinite == 0: raise ValueError("This OE can't have infinite dimensions") if self.which_length==1: congruence.checkStrictlyPositiveNumber(self.optimized_length, "Optimized Length") congruence.checkLessOrEqualThan(self.optimized_length, self.dim_y_plus+self.dim_y_minus, "Optimized Length", "Total Length") if self.modified_surface > 0: if not (self.modified_surface == 1 and self.ms_type_of_defect == 2): raise ValueError("Only Preprocessor generated error profiles are admitted") congruence.checkStrictlyPositiveNumber(self.bender_bin_x, "Bins X") congruence.checkStrictlyPositiveNumber(self.bender_bin_y, "Bins Y") self.output_file_name_full = congruence.checkFileName(self.output_file_name) def completeOperations(self, shadow_oe): shadow_oe_temp = shadow_oe.duplicate() input_beam_temp = self.input_beam.duplicate(history=False) self.manage_acceptance_slits(shadow_oe_temp) ShadowBeam.traceFromOE(input_beam_temp, shadow_oe_temp, write_start_file=0, write_end_file=0, widget_class_name=type(self).__name__) x, y, z = self.calculate_ideal_surface(shadow_oe_temp) bender_parameter, z_bender_correction = self.calculate_bender_correction(y, z, self.kind_of_bender, self.shape) self.M1_out = round(bender_parameter[0], int(6*self.workspace_units_to_mm)) if self.shape == TRAPEZIUM: self.e_out = round(bender_parameter[1], 5) if self.kind_of_bender == DOUBLE_MOMENTUM: self.ratio_out = round(bender_parameter[2], 5) elif self.shape == RECTANGLE: if self.kind_of_bender == DOUBLE_MOMENTUM: self.ratio_out = round(bender_parameter[1], 5) self.plot3D(x, y, z_bender_correction, 2, "Ideal - Bender Surfaces") if self.modified_surface > 0: x_e, y_e, z_e = ShadowPreProcessor.read_surface_error_file(self.ms_defect_file_name) if len(x) == len(x_e) and len(y) == len(y_e) and \ x[0] == x_e[0] and x[-1] == x_e[-1] and \ y[0] == y_e[0] and y[-1] == y_e[-1]: z_figure_error = z_e else: z_figure_error = interp2d(y_e, x_e, z_e, kind='cubic')(y, x) z_bender_correction += z_figure_error self.plot3D(x, y, z_figure_error, 3, "Figure Error Surface") self.plot3D(x, y, z_bender_correction, 4, "Ideal - Bender + Figure Error Surfaces") ST.write_shadow_surface(z_bender_correction.T, numpy.round(x, 6), numpy.round(y, 6), self.output_file_name_full) # Add new surface as figure error shadow_oe._oe.F_RIPPLE = 1 shadow_oe._oe.F_G_S = 2 shadow_oe._oe.FILE_RIP = bytes(self.output_file_name_full, 'utf-8') # Redo Raytracing with the new file super().completeOperations(shadow_oe) self.send("PreProcessor_Data", ShadowPreProcessorData(error_profile_data_file=self.output_file_name, error_profile_x_dim=self.dim_x_plus+self.dim_x_minus, error_profile_y_dim=self.dim_y_plus+self.dim_y_minus)) def instantiateShadowOE(self): return ShadowOpticalElement.create_ellipsoid_mirror() def calculate_ideal_surface(self, shadow_oe, sign=-1): x = numpy.linspace(-self.dim_x_minus, self.dim_x_plus, self.bender_bin_x + 1) y = numpy.linspace(-self.dim_y_minus, self.dim_y_plus, self.bender_bin_y + 1) c1 = round(shadow_oe._oe.CCC[0], 10) c2 = round(shadow_oe._oe.CCC[1], 10) c3 = round(shadow_oe._oe.CCC[2], 10) c4 = round(shadow_oe._oe.CCC[3], 10) c5 = round(shadow_oe._oe.CCC[4], 10) c6 = round(shadow_oe._oe.CCC[5], 10) c7 = round(shadow_oe._oe.CCC[6], 10) c8 = round(shadow_oe._oe.CCC[7], 10) c9 = round(shadow_oe._oe.CCC[8], 10) c10 = round(shadow_oe._oe.CCC[9], 10) xx, yy = numpy.meshgrid(x, y) c = c1*(xx**2) + c2*(yy**2) + c4*xx*yy + c7*xx + c8*yy + c10 b = c5*yy + c6*xx + c9 a = c3 z = (-b + sign*numpy.sqrt(b**2 - 4*a*c))/(2*a) z[b**2 - 4*a*c < 0] = numpy.nan return x, y, z.T def calculate_bender_correction(self, y, z, kind_of_bender, shape): b0 = self.dim_x_plus + self.dim_x_minus L = self.dim_y_plus + self.dim_y_minus # add optimization length # flip the coordinate system to be consistent with Mike's formulas ideal_profile = z[0, :][::-1] # one row is the profile of the cylinder, enough for the minimizer ideal_profile += -ideal_profile[0] + ((L/2 + y)*(ideal_profile[0]-ideal_profile[-1]))/L # Rotation if self.which_length == 0: y_fit = y ideal_profile_fit = ideal_profile else: cursor = numpy.where(numpy.logical_and(y >= -self.optimized_length/2, y <= self.optimized_length/2) ) y_fit = y[cursor] ideal_profile_fit = ideal_profile[cursor] epsilon_minus = 1 - 1e-8 epsilon_plus = 1 + 1e-8 Eh_3 = self.E * self.h ** 3 initial_guess = None constraints = None bender_function = None if shape == TRAPEZIUM: def general_bender_function(Y, M1, e, ratio): M2 = M1 * ratio A = (M1 + M2) / 2 B = (M1 - M2) / L C = Eh_3 * (2 * b0 + e * b0) / 24 D = Eh_3 * e * b0 / (12 * L) H = (A * D + B * C) / D ** 2 CDLP = C + D * L / 2 CDLM = C - D * L / 2 F = (H / L) * ((CDLM * numpy.log(CDLM) - CDLP * numpy.log(CDLP)) / D + L) G = (-H * ((CDLM * numpy.log(CDLM) + CDLP * numpy.log(CDLP))) + (B * L ** 2) / 4) / (2 * D) CDY = C + D * Y return H * ((CDY / D) * numpy.log(CDY) - Y) - (B * Y ** 2) / (2 * D) + F * Y + G def bender_function_2m(Y, M1, e, ratio): return general_bender_function(Y, M1, e, ratio) def bender_function_1m(Y, M1, e): return general_bender_function(Y, M1, e, 1.0) if kind_of_bender == SINGLE_MOMENTUM: bender_function = bender_function_1m initial_guess = [self.M1, self.e] constraints = [[self.M1_min if self.M1_fixed == False else (self.M1 * epsilon_minus), self.e_min if self.e_fixed == False else (self.e * epsilon_minus)], [self.M1_max if self.M1_fixed == False else (self.M1 * epsilon_plus), self.e_max if self.e_fixed == False else (self.e * epsilon_plus)]] elif kind_of_bender == DOUBLE_MOMENTUM: bender_function = bender_function_2m initial_guess = [self.M1, self.e, self.ratio] constraints = [[self.M1_min if self.M1_fixed == False else (self.M1*epsilon_minus), self.e_min if self.e_fixed == False else (self.e*epsilon_minus), self.ratio_min if self.ratio_fixed == False else (self.ratio*epsilon_minus)], [self.M1_max if self.M1_fixed == False else (self.M1*epsilon_plus), self.e_max if self.e_fixed == False else (self.e*epsilon_plus), self.ratio_max if self.ratio_fixed == False else (self.ratio*epsilon_plus)]] elif shape == RECTANGLE: def general_bender_function(Y, M1, ratio): M2 = M1 * ratio A = (M1 + M2) / 2 B = (M1 - M2) / L C = Eh_3 * b0 / 12 F = (B * L**2) / (24 * C) G = -(A * L**2) / (8 * C) return -(B * Y**3) / (6 * C) + (A * Y**2) / (2 * C) + F * Y + G def bender_function_2m(Y, M1, ratio): return general_bender_function(Y, M1, ratio) def bender_function_1m(Y, M1): return general_bender_function(Y, M1, 1.0) if kind_of_bender == SINGLE_MOMENTUM: bender_function = bender_function_1m initial_guess = [self.M1] constraints = [[self.M1_min if self.M1_fixed == False else (self.M1 * epsilon_minus)], [self.M1_max if self.M1_fixed == False else (self.M1 * epsilon_plus)]] elif kind_of_bender == DOUBLE_MOMENTUM: bender_function = bender_function_2m initial_guess = [self.M1, self.ratio] constraints = [[self.M1_min if self.M1_fixed == False else (self.M1*epsilon_minus), self.ratio_min if self.ratio_fixed == False else (self.ratio*epsilon_minus)], [self.M1_max if self.M1_fixed == False else (self.M1*epsilon_plus), self.ratio_max if self.ratio_fixed == False else (self.ratio*epsilon_plus)]] parameters, _ = curve_fit(f=bender_function, xdata=y_fit, ydata=ideal_profile_fit, p0=initial_guess, bounds=constraints, method='trf') if len(parameters) == 1: bender_profile = bender_function(y, parameters[0]) elif len(parameters) == 2: bender_profile = bender_function(y, parameters[0], parameters[1]) else: bender_profile = bender_function(y, parameters[0], parameters[1], parameters[2]) # rotate back to Shadow system bender_profile = bender_profile[::-1] ideal_profile = ideal_profile[::-1] # from here it's Shadow Axis system correction_profile = ideal_profile - bender_profile if self.which_length == 1: correction_profile_fit = correction_profile[cursor] # r-squared = 1 - residual sum of squares / total sum of squares r_squared = 1 - (numpy.sum(correction_profile**2) / numpy.sum((ideal_profile - numpy.mean(ideal_profile))**2)) rms = round(correction_profile.std()*1e9*self.workspace_units_to_m, 6) if self.which_length == 1: rms_opt = round(correction_profile_fit.std()*1e9*self.workspace_units_to_m, 6) self.plot1D(y, bender_profile, y_values_2=ideal_profile, index=0, title = "Bender vs. Ideal Profiles" + "\n" + r'$R^2$ = ' + str(r_squared), um=1) self.plot1D(y, correction_profile, index=1, title="Correction Profile 1D, r.m.s. = " + str(rms) + " nm" + ("" if self.which_length == 0 else (", " + str(rms_opt) + " nm (optimized)"))) z_bender_correction = numpy.zeros(z.shape) for i in range(z_bender_correction.shape[0]): z_bender_correction[i, :] = numpy.copy(correction_profile) return parameters, z_bender_correction def plot1D(self, x_coords, y_values, y_values_2=None, index=0, title="", um=0): if self.show_bender_plots == 1: figure = self.figure_canvas[index].figure axis = figure.gca() axis.clear() axis.set_xlabel("Y [" + self.workspace_units_label + "]") axis.set_ylabel("Z [" + ("nm" if um==0 else "\u03bcm") + "]") axis.set_title(title) axis.plot(x_coords, (y_values * self.workspace_units_to_m * (1e9 if um==0 else 1e6)), color="blue", label="bender", linewidth=2) if not y_values_2 is None: axis.plot(x_coords, (y_values_2 * self.workspace_units_to_m * (1e9 if um==0 else 1e6)), "-.r", label="ideal") axis.legend(loc=0, fontsize='small') figure.canvas.draw() def plot3D(self, x_coords, y_coords, z_values, index, title=""): if self.show_bender_plots == 1: figure = self.figure_canvas[index].figure x_to_plot, y_to_plot = numpy.meshgrid(x_coords, y_coords) z_to_plot = z_values.T axis = figure.gca() axis.clear() axis.set_xlabel("X [" + self.workspace_units_label + "]") axis.set_ylabel("Y [" + self.workspace_units_label + "]") axis.set_zlabel("Z [nm]") axis.set_title(title) axis.plot_surface(x_to_plot, y_to_plot, (z_to_plot * self.workspace_units_to_m * 1e9), rstride=1, cstride=1, cmap=cm.autumn, linewidth=0.5, antialiased=True) figure.canvas.draw() axis.mouse_init() if __name__ == "__main__": a = QApplication(sys.argv) ow = BendableEllipsoidMirror() ow.show() a.exec_() ow.saveSettings()

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azext/batch/models/_models.py

bgklein/azure-batch-cli-extensions

0

138319

<gh_stars>0 # -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- # pylint: disable=too-many-lines import os import azure.batch.models as models from msrest.serialization import Model from .constants import ATTRS_RESERVED_FOR_TEMPLATES class TaskFactoryBase(Model): """A Task Factory for automatically adding a collection of tasks to a job on submission. :param merge_task: An optional additional task to be run after all the other generated tasks have completed successfully. :type merge_task: :class:`MergeTask <azext.batch.models.MergeTask>` """ _validation = { 'type': {'required': True}, } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'merge_task': {'key': 'mergeTask', 'type': 'MergeTask'} } _subtype_map = { 'type': {'parametricSweep': 'ParametricSweepTaskFactory', 'taskPerFile': 'FileCollectionTaskFactory', 'taskCollection': 'TaskCollectionTaskFactory'} } def __init__(self, **kwargs): super(TaskFactoryBase, self).__init__(**kwargs) self.merge_task = kwargs.get('merge_task', None) self.type = None class PackageReferenceBase(Model): """A reference to a package to be installed on the compute nodes using a package manager. :param str id: The name of the package. :param str version: The version of the package to be installed. If omitted, the latest version (according to the package repository) will be installed. """ _validation = { 'type': {'required': True}, 'id': {'required': True}, } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'id': {'key': 'id', 'type': 'str'}, 'version': {'key': 'version', 'type': 'str'}, } _subtype_map = { 'type': {'aptPackage': 'AptPackageReference', 'chocolateyPackage': 'ChocolateyPackageReference', 'yumPackage': 'YumPackageReference'} } def __init__(self, **kwargs): super(PackageReferenceBase, self).__init__(**kwargs) self.type = None self.id = kwargs.get('id') self.version = kwargs.get('version', None) class ApplicationTemplate(Model): """An Azure Batch Application Template. :param job_manager_task: Details of a Job Manager task to be launched when the job is started. If the job does not specify a Job Manager task, the user must explicitly add tasks to the job. If the job does specify a Job Manager task, the Batch service creates the Job Manager task when the job is created, and will try to schedule the Job Manager task before scheduling other tasks in the job. The Job Manager task's typical purpose is to control and/or monitor job execution, for example by deciding what additional tasks to run, determining when the work is complete, etc. (However, a Job Manager task is not restricted to these activities - it is a fully-fledged task in the system and perform whatever actions are required for the job.) For example, a Job Manager task might download a file specified as a parameter, analyze the contents of that file and submit additional tasks based on those contents. :type job_manager_task: :class:`JobManagerTask <azure.batch.models.JobManagerTask>` :param job_preparation_task: The Job Preparation task. If a job has a Job Preparation task, the Batch service will run the Job Preparation task on a compute node before starting any tasks of that job on that compute node. :type job_preparation_task: :class:`JobPreparationTask <azure.batch.models.JobPreparationTask>` :param job_release_task: The Job Release task. A Job Release task cannot be specified without also specifying a Job Preparation task for the job. The Batch service runs the Job Release task on the compute nodes that have run the Job Preparation task. The primary purpose of the Job Release task is to undo changes to compute nodes made by the Job Preparation task. Example activities include deleting local files, or shutting down services that were started as part of job preparation. :type job_release_task: :class:`JobReleaseTask <azure.batch.models.JobReleaseTask>` :param common_environment_settings: The list of common environment variable settings. These environment variables are set for all tasks in the job (including the Job Manager, Job Preparation and Job Release tasks). Individual tasks can override an environment setting specified here by specifying the same setting name with a different value. :type common_environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param on_all_tasks_complete: The action the Batch service should take when all tasks in the job are in the completed state. Note that if a job contains no tasks, then all tasks are considered complete. This option is therefore most commonly used with a Job Manager task; if you want to use automatic job termination without a Job Manager, you should initially set onAllTasksComplete to noAction and update the job properties to set onAllTasksComplete to terminateJob once you have finished adding tasks. Permitted values are: noAction - do nothing. The job remains active unless terminated or disabled by some other means. terminateJob - terminate the job. The job's terminateReason is set to 'AllTasksComplete'. The default is noAction. Possible values include: 'noAction', 'terminateJob' :type on_all_tasks_complete: str or :class:`OnAllTasksComplete <azure.batch.models.OnAllTasksComplete>` :param on_task_failure: The action the Batch service should take when any task in the job fails. A task is considered to have failed if has a failureInfo. A failureInfo is set if the task completes with a non-zero exit code after exhausting its retry count, or if there was an error starting the task, for example due to a resource file download error. noAction - do nothing. performExitOptionsJobAction - take the action associated with the task exit condition in the task's exitConditions collection. (This may still result in no action being taken, if that is what the task specifies.) The default is noAction. Possible values include: 'noAction', 'performExitOptionsJobAction' :type on_task_failure: str or :class:`OnTaskFailure <azure.batch.models.OnTaskFailure>` :param metadata: A list of name-value pairs associated with the job as metadata. The Batch service does not assign any meaning to metadata; it is solely for the use of user code. :type metadata: list of :class:`MetadataItem <azure.batch.models.MetadataItem>` :param uses_task_dependencies: Whether tasks in the job can define dependencies on each other. The default is false. :type uses_task_dependencies: bool :param task_factory: A task factory reference to automatically generate a set of tasks to be added to the job. :type task_factory: :class:`TaskFactoryBase <azext.batch.models.TaskFactoryBase>` """ _attribute_map = { 'job_manager_task': {'key': 'jobManagerTask', 'type': 'JobManagerTask'}, 'job_preparation_task': {'key': 'jobPreparationTask', 'type': 'JobPreparationTask'}, 'job_release_task': {'key': 'jobReleaseTask', 'type': 'JobReleaseTask'}, 'common_environment_settings': {'key': 'commonEnvironmentSettings', 'type': '[EnvironmentSetting]'}, 'on_all_tasks_complete': {'key': 'onAllTasksComplete', 'type': 'OnAllTasksComplete'}, 'on_task_failure': {'key': 'onTaskFailure', 'type': 'OnTaskFailure'}, 'metadata': {'key': 'metadata', 'type': '[MetadataItem]'}, 'uses_task_dependencies': {'key': 'usesTaskDependencies', 'type': 'bool'}, 'task_factory': {'key': 'taskFactory', 'type': 'TaskFactoryBase'}, } def __init__(self, **kwargs): super(ApplicationTemplate, self).__init__(**kwargs) self.job_manager_task = kwargs.get('job_manager_task', None) self.job_preparation_task = kwargs.get('job_preparation_task', None) self.job_release_task = kwargs.get('job_release_task', None) self.common_environment_settings = kwargs.get('common_environment_settings', None) self.on_all_tasks_complete = kwargs.get('on_all_tasks_complete', None) self.on_task_failure = kwargs.get('on_task_failure', None) self.metadata = kwargs.get('metadata', None) self.uses_task_dependencies = kwargs.get('uses_task_dependencies', None) self.task_factory = kwargs.get('task_factory', None) class ApplicationTemplateInfo(Model): """A reference to an Azure Batch Application Template. :param str file_path: The path to an application template file. This can be a full path, or relative to the current working directory. Alternatively a relative directory can be supplied with the 'current_directory' argument. A ValueError will be raised if the supplied file path cannot be found. :param dict parameters: A dictory of parameter names and values to be subtituted into the application template. """ _validation = { 'file_path': {'required': True}, } _attribute_map = { 'file_path': {'key': 'filePath', 'type': 'str'}, 'parameters': {'key': 'parameters', 'type': 'object'}, } def __init__(self, **kwargs): super(ApplicationTemplateInfo, self).__init__(**kwargs) self.file_path = kwargs.get('file_path', None) if not os.path.isfile(self.file_path): current_directory = kwargs.get('current_directory', ".") self.file_path = os.path.abspath(os.path.join(current_directory, str(self.file_path))) self.parameters = kwargs.get('parameters', None) # Rule: Template file must exist # (We do this in order to give a good diagnostic in the most common case, knowing that this is # technically a race condition because someone could delete the file between our check here and # reading the file later on. We expect such cases to be rare.) try: with open(self.file_path, 'r'): pass except EnvironmentError as error: raise ValueError("Unable to read the template '{}': {}".format(self.file_path, error)) class AptPackageReference(PackageReferenceBase): """A reference to a package to be installed using the APT package manager on a Linux node (apt-get). :param str id: The name of the package. :param str version: The version of the package to be installed. If omitted, the latest version (according to the package repository) will be installed. """ _validation = { 'type': {'required': True}, 'id': {'required': True}, } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'id': {'key': 'id', 'type': 'str'}, 'version': {'key': 'version', 'type': 'str'}, } def __init__(self, **kwargs): super(AptPackageReference, self).__init__(**kwargs) self.type = 'aptPackage' class AutoPoolSpecification(Model): """Specifies characteristics for a temporary 'auto pool'. The Batch service will create this auto pool when the job is submitted. :param auto_pool_id_prefix: A prefix to be added to the unique identifier when a pool is automatically created. The Batch service assigns each auto pool a unique identifier on creation. To distinguish between pools created for different purposes, you can specify this element to add a prefix to the ID that is assigned. The prefix can be up to 20 characters long. :type auto_pool_id_prefix: str :param pool_lifetime_option: The minimum lifetime of created auto pools, and how multiple jobs on a schedule are assigned to pools. When the pool lifetime is jobSchedule the pool exists for the lifetime of the job schedule. The Batch Service creates the pool when it creates the first job on the schedule. You may apply this option only to job schedules, not to jobs. When the pool lifetime is job the pool exists for the lifetime of the job to which it is dedicated. The Batch service creates the pool when it creates the job. If the 'job' option is applied to a job schedule, the Batch service creates a new auto pool for every job created on the schedule. Possible values include: 'jobSchedule', 'job' :type pool_lifetime_option: str or :class:`PoolLifetimeOption <azure.batch.models.PoolLifetimeOption>` :param keep_alive: Whether to keep an auto pool alive after its lifetime expires. If false, the Batch service deletes the pool once its lifetime (as determined by the poolLifetimeOption setting) expires; that is, when the job or job schedule completes. If true, the Batch service does not delete the pool automatically. It is up to the user to delete auto pools created with this option. :type keep_alive: bool :param pool: The pool specification for the auto pool. :type pool: :class:`PoolSpecification <azure.batch.models.PoolSpecification>` """ _validation = { 'pool_lifetime_option': {'required': True}, } _attribute_map = { 'auto_pool_id_prefix': {'key': 'autoPoolIdPrefix', 'type': 'str'}, 'pool_lifetime_option': {'key': 'poolLifetimeOption', 'type': 'PoolLifetimeOption'}, 'keep_alive': {'key': 'keepAlive', 'type': 'bool'}, 'pool': {'key': 'pool', 'type': 'ExtendedPoolSpecification'}, } def __init__(self, **kwargs): super(AutoPoolSpecification, self).__init__(**kwargs) self.auto_pool_id_prefix = kwargs.get('auto_pool_id_prefix', None) self.pool_lifetime_option = kwargs.get('pool_lifetime_option', None) self.keep_alive = kwargs.get('keep_alive', None) self.pool = kwargs.get('pool', None) class ChocolateyPackageReference(PackageReferenceBase): """A reference to a package to be installed using the Chocolatey package manager on a Windows node. :param str id: The name of the package. :param str version: The version of the package to be installed. If omitted, the latest version (according to the package repository) will be installed. :param bool allow_empty_checksums: Whether Chocolatey will install packages without a checksum for validation. Default is false. """ _validation = { 'type': {'required': True}, 'id': {'required': True}, } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'id': {'key': 'id', 'type': 'str'}, 'version': {'key': 'version', 'type': 'str'}, 'allow_empty_checksums': {'key': 'allowEmptyChecksums', 'type': 'bool'} } def __init__(self, **kwargs): super(ChocolateyPackageReference, self).__init__(**kwargs) self.allow_empty_checksums = kwargs.get('allow_empty_checksums', None) self.type = 'chocolateyPackage' class ExtendedJobParameter(models.JobAddParameter): """An Azure Batch job to add. :param id: A string that uniquely identifies the job within the account. The ID can contain any combination of alphanumeric characters including hyphens and underscores, and cannot contain more than 64 characters. The ID is case-preserving and case-insensitive (that is, you may not have two IDs within an account that differ only by case). :type id: str :param display_name: The display name for the job. The display name need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param priority: The priority of the job. Priority values can range from -1000 to 1000, with -1000 being the lowest priority and 1000 being the highest priority. The default value is 0. :type priority: int :param constraints: The execution constraints for the job. :type constraints: :class:`JobConstraints <azure.batch.models.JobConstraints>` :param job_manager_task: Details of a Job Manager task to be launched when the job is started. If the job does not specify a Job Manager task, the user must explicitly add tasks to the job. If the job does specify a Job Manager task, the Batch service creates the Job Manager task when the job is created, and will try to schedule the Job Manager task before scheduling other tasks in the job. The Job Manager task's typical purpose is to control and/or monitor job execution, for example by deciding what additional tasks to run, determining when the work is complete, etc. (However, a Job Manager task is not restricted to these activities - it is a fully-fledged task in the system and perform whatever actions are required for the job.) For example, a Job Manager task might download a file specified as a parameter, analyze the contents of that file and submit additional tasks based on those contents. :type job_manager_task: :class:`JobManagerTask <azure.batch.models.JobManagerTask>` :param job_preparation_task: The Job Preparation task. If a job has a Job Preparation task, the Batch service will run the Job Preparation task on a compute node before starting any tasks of that job on that compute node. :type job_preparation_task: :class:`JobPreparationTask <azure.batch.models.JobPreparationTask>` :param job_release_task: The Job Release task. A Job Release task cannot be specified without also specifying a Job Preparation task for the job. The Batch service runs the Job Release task on the compute nodes that have run the Job Preparation task. The primary purpose of the Job Release task is to undo changes to compute nodes made by the Job Preparation task. Example activities include deleting local files, or shutting down services that were started as part of job preparation. :type job_release_task: :class:`JobReleaseTask <azure.batch.models.JobReleaseTask>` :param common_environment_settings: The list of common environment variable settings. These environment variables are set for all tasks in the job (including the Job Manager, Job Preparation and Job Release tasks). Individual tasks can override an environment setting specified here by specifying the same setting name with a different value. :type common_environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param pool_info: The pool on which the Batch service runs the job's tasks. :type pool_info: :class:`PoolInformation <azure.batch.models.PoolInformation>` :param on_all_tasks_complete: The action the Batch service should take when all tasks in the job are in the completed state. Note that if a job contains no tasks, then all tasks are considered complete. This option is therefore most commonly used with a Job Manager task; if you want to use automatic job termination without a Job Manager, you should initially set onAllTasksComplete to noAction and update the job properties to set onAllTasksComplete to terminateJob once you have finished adding tasks. Permitted values are: noAction - do nothing. The job remains active unless terminated or disabled by some other means. terminateJob - terminate the job. The job's terminateReason is set to 'AllTasksComplete'. The default is noAction. Possible values include: 'noAction', 'terminateJob' :type on_all_tasks_complete: str or :class:`OnAllTasksComplete <azure.batch.models.OnAllTasksComplete>` :param on_task_failure: The action the Batch service should take when any task in the job fails. A task is considered to have failed if has a failureInfo. A failureInfo is set if the task completes with a non-zero exit code after exhausting its retry count, or if there was an error starting the task, for example due to a resource file download error. noAction - do nothing. performExitOptionsJobAction - take the action associated with the task exit condition in the task's exitConditions collection. (This may still result in no action being taken, if that is what the task specifies.) The default is noAction. Possible values include: 'noAction', 'performExitOptionsJobAction' :type on_task_failure: str or :class:`OnTaskFailure <azure.batch.models.OnTaskFailure>` :param metadata: A list of name-value pairs associated with the job as metadata. The Batch service does not assign any meaning to metadata; it is solely for the use of user code. :type metadata: list of :class:`MetadataItem <azure.batch.models.MetadataItem>` :param uses_task_dependencies: Whether tasks in the job can define dependencies on each other. The default is false. :type uses_task_dependencies: bool :param task_factory: A task factory reference to automatically generate a set of tasks to be added to the job. :type task_factory: :class:`TaskFactoryBase <azext.batch.models.TaskFactoryBase>` :param application_template_info: A reference to an application template file to be expanded to complete the job specification. If supplied, the following arugments cannot also be supplied or they will be overwritten: 'job_manager_task', 'common_environment_settings', 'uses_task_dependencies', 'on_all_tasks_complete', 'on_task_failure', 'task_factory', 'job_preparation_task', 'job_release_task'. :type application_template_info: :class:`ApplicationTemplateInfo <azext.batch.models.ApplicationTemplateInfo>` """ _validation = { 'id': {'required': True}, 'pool_info': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'priority': {'key': 'priority', 'type': 'int'}, 'constraints': {'key': 'constraints', 'type': 'JobConstraints'}, 'job_manager_task': {'key': 'jobManagerTask', 'type': 'JobManagerTask'}, 'job_preparation_task': {'key': 'jobPreparationTask', 'type': 'JobPreparationTask'}, 'job_release_task': {'key': 'jobReleaseTask', 'type': 'JobReleaseTask'}, 'common_environment_settings': {'key': 'commonEnvironmentSettings', 'type': '[EnvironmentSetting]'}, 'pool_info': {'key': 'poolInfo', 'type': 'PoolInformation'}, 'on_all_tasks_complete': {'key': 'onAllTasksComplete', 'type': 'OnAllTasksComplete'}, 'on_task_failure': {'key': 'onTaskFailure', 'type': 'OnTaskFailure'}, 'metadata': {'key': 'metadata', 'type': '[MetadataItem]'}, 'uses_task_dependencies': {'key': 'usesTaskDependencies', 'type': 'bool'}, 'task_factory': {'key': 'taskFactory', 'type': 'TaskFactoryBase'}, 'application_template_info': {'key': 'applicationTemplateInfo', 'type': 'ApplicationTemplateInfo'} } def __init__(self, **kwargs): super(ExtendedJobParameter, self).__init__(**kwargs) self.task_factory = kwargs.get('task_factory', None) self.application_template_info = kwargs.get('application_template_info', None) if self.application_template_info: # Rule: Jobs may not use properties reserved for template use reserved = [k for k, v in self.__dict__.items() \ if k in ATTRS_RESERVED_FOR_TEMPLATES and v is not None] if reserved: raise ValueError("Jobs using application templates may not use these " "properties: {}".format(', '.join(reserved))) class ExtendedOutputFileDestination(Model): """The specification for where output files should be uploaded to on task completion. :param container: A location in Azure blob storage to which files are uploaded. This cannot be combined with auto_storage. :type container: :class:`OutputFileBlobContainerDestination <azure.batch.models.OutputFileBlobContainerDestination>` :param auto_storage: An auto-storage file group reference. This cannot be combined with container. :type auto_storage: :class:`OutputFileAutoStorageDestination <azext.batch.models.OutputFileAutoStorageDestination>` """ _attribute_map = { 'container': {'key': 'container', 'type': 'OutputFileBlobContainerDestination'}, 'auto_storage': {'key': 'autoStorage', 'type': 'OutputFileAutoStorageDestination'}, } def __init__(self, **kwargs): super(ExtendedOutputFileDestination, self).__init__(**kwargs) self.container = kwargs.get('container', None) self.auto_storage = kwargs.get('auto_storage', None) if self.container and self.auto_storage: raise ValueError("Cannot specify both container and auto_storage.") class ExtendedPoolParameter(models.PoolAddParameter): """A pool in the Azure Batch service to add. :param id: A string that uniquely identifies the pool within the account. The ID can contain any combination of alphanumeric characters including hyphens and underscores, and cannot contain more than 64 characters. The ID is case-preserving and case-insensitive (that is, you may not have two pool IDs within an account that differ only by case). :type id: str :param display_name: The display name for the pool. The display name need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param vm_size: The size of virtual machines in the pool. All virtual machines in a pool are the same size. For information about available sizes of virtual machines for Cloud Services pools (pools created with cloudServiceConfiguration), see Sizes for Cloud Services (http://azure.microsoft.com/documentation/articles/cloud-services-sizes-specs/). Batch supports all Cloud Services VM sizes except ExtraSmall, A1V2 and A2V2. For information about available VM sizes for pools using images from the Virtual Machines Marketplace (pools created with virtualMachineConfiguration) see Sizes for Virtual Machines (Linux) (https://azure.microsoft.com/documentation/articles/virtual-machines-linux-sizes/) or Sizes for Virtual Machines (Windows) (https://azure.microsoft.com/documentation/articles/virtual-machines-windows-sizes/). Batch supports all Azure VM sizes except STANDARD_A0 and those with premium storage (STANDARD_GS, STANDARD_DS, and STANDARD_DSV2 series). :type vm_size: str :param cloud_service_configuration: The cloud service configuration for the pool. This property and virtualMachineConfiguration are mutually exclusive and one of the properties must be specified. This property cannot be specified if the Batch account was created with its poolAllocationMode property set to 'UserSubscription'. :type cloud_service_configuration: :class:`CloudServiceConfiguration <azure.batch.models.CloudServiceConfiguration>` :param virtual_machine_configuration: The virtual machine configuration for the pool. This property and cloudServiceConfiguration are mutually exclusive and one of the properties must be specified. :type virtual_machine_configuration: :class:`VirtualMachineConfiguration <azure.batch.models.VirtualMachineConfiguration>` :param resize_timeout: The timeout for allocation of compute nodes to the pool. This timeout applies only to manual scaling; it has no effect when enableAutoScale is set to true. The default value is 15 minutes. The minimum value is 5 minutes. If you specify a value less than 5 minutes, the Batch service returns an error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). :type resize_timeout: timedelta :param target_dedicated_nodes: The desired number of dedicated compute nodes in the pool. This property must not be specified if enableAutoScale is set to true. If enableAutoScale is set to false, then you must set either targetDedicatedNodes, targetLowPriorityNodes, or both. :type target_dedicated_nodes: int :param target_low_priority_nodes: The desired number of low-priority compute nodes in the pool. This property must not be specified if enableAutoScale is set to true. If enableAutoScale is set to false, then you must set either targetDedicatedNodes, targetLowPriorityNodes, or both. :type target_low_priority_nodes: int :param enable_auto_scale: Whether the pool size should automatically adjust over time. If false, at least one of targetDedicateNodes and targetLowPriorityNodes must be specified. If true, the autoScaleFormula property is required and the pool automatically resizes according to the formula. The default value is false. :type enable_auto_scale: bool :param auto_scale_formula: A formula for the desired number of compute nodes in the pool. This property must not be specified if enableAutoScale is set to false. It is required if enableAutoScale is set to true. The formula is checked for validity before the pool is created. If the formula is not valid, the Batch service rejects the request with detailed error information. For more information about specifying this formula, see 'Automatically scale compute nodes in an Azure Batch pool' (https://azure.microsoft.com/documentation/articles/batch-automatic-scaling/). :type auto_scale_formula: str :param auto_scale_evaluation_interval: The time interval at which to automatically adjust the pool size according to the autoscale formula. The default value is 15 minutes. The minimum and maximum value are 5 minutes and 168 hours respectively. If you specify a value less than 5 minutes or greater than 168 hours, the Batch service returns an error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). :type auto_scale_evaluation_interval: timedelta :param enable_inter_node_communication: Whether the pool permits direct communication between nodes. Enabling inter-node communication limits the maximum size of the pool due to deployment restrictions on the nodes of the pool. This may result in the pool not reaching its desired size. The default value is false. :type enable_inter_node_communication: bool :param network_configuration: The network configuration for the pool. :type network_configuration: :class:`NetworkConfiguration <azure.batch.models.NetworkConfiguration>` :param start_task: A task specified to run on each compute node as it joins the pool. The task runs when the node is added to the pool or when the node is restarted. :type start_task: :class:`StartTask <azure.batch.models.StartTask>` :param certificate_references: The list of certificates to be installed on each compute node in the pool. For Windows compute nodes, the Batch service installs the certificates to the specified certificate store and location. For Linux compute nodes, the certificates are stored in a directory inside the task working directory and an environment variable AZ_BATCH_CERTIFICATES_DIR is supplied to the task to query for this location. For certificates with visibility of 'remoteUser', a 'certs' directory is created in the user's home directory (e.g., /home/{user-name}/certs) and certificates are placed in that directory. :type certificate_references: list of :class:`CertificateReference <azure.batch.models.CertificateReference>` :param application_package_references: The list of application packages to be installed on each compute node in the pool. :type application_package_references: list of :class:`ApplicationPackageReference <azure.batch.models.ApplicationPackageReference>` :param application_licenses: The list of application licenses the Batch service will make available on each compute node in the pool. The list of application licenses must be a subset of available Batch service application licenses. If a license is requested which is not supported, pool creation will fail. :type application_licenses: list of str :param max_tasks_per_node: The maximum number of tasks that can run concurrently on a single compute node in the pool. The default value is 1. The maximum value of this setting depends on the size of the compute nodes in the pool (the vmSize setting). :type max_tasks_per_node: int :param task_scheduling_policy: How tasks are distributed across compute nodes in a pool. :type task_scheduling_policy: :class:`TaskSchedulingPolicy <azure.batch.models.TaskSchedulingPolicy>` :param user_accounts: The list of user accounts to be created on each node in the pool. :type user_accounts: list of :class:`UserAccount <azure.batch.models.UserAccount>` :param metadata: A list of name-value pairs associated with the pool as metadata. The Batch service does not assign any meaning to metadata; it is solely for the use of user code. :type metadata: list of :class:`MetadataItem <azure.batch.models.MetadataItem>` :param package_references: A list of packages to be installed on the compute nodes. Must be of a Package Manager type in accordance with the selected operating system. :type package_references: list of :class:`PackageReferenceBase <azext.batch.models.PackageReferenceBase>` """ _validation = { 'id': {'required': True}, 'vm_size': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'vm_size': {'key': 'vmSize', 'type': 'str'}, 'cloud_service_configuration': {'key': 'cloudServiceConfiguration', 'type': 'CloudServiceConfiguration'}, 'virtual_machine_configuration': {'key': 'virtualMachineConfiguration', 'type': 'VirtualMachineConfiguration'}, 'resize_timeout': {'key': 'resizeTimeout', 'type': 'duration'}, 'target_dedicated_nodes': {'key': 'targetDedicatedNodes', 'type': 'int'}, 'target_low_priority_nodes': {'key': 'targetLowPriorityNodes', 'type': 'int'}, 'enable_auto_scale': {'key': 'enableAutoScale', 'type': 'bool'}, 'auto_scale_formula': {'key': 'autoScaleFormula', 'type': 'str'}, 'auto_scale_evaluation_interval': {'key': 'autoScaleEvaluationInterval', 'type': 'duration'}, 'enable_inter_node_communication': {'key': 'enableInterNodeCommunication', 'type': 'bool'}, 'network_configuration': {'key': 'networkConfiguration', 'type': 'NetworkConfiguration'}, 'start_task': {'key': 'startTask', 'type': 'StartTask'}, 'certificate_references': {'key': 'certificateReferences', 'type': '[CertificateReference]'}, 'application_package_references': {'key': 'applicationPackageReferences', 'type': '[ApplicationPackageReference]'}, 'application_licenses': {'key': 'applicationLicenses', 'type': '[str]'}, 'max_tasks_per_node': {'key': 'maxTasksPerNode', 'type': 'int'}, 'task_scheduling_policy': {'key': 'taskSchedulingPolicy', 'type': 'TaskSchedulingPolicy'}, 'user_accounts': {'key': 'userAccounts', 'type': '[UserAccount]'}, 'metadata': {'key': 'metadata', 'type': '[MetadataItem]'}, 'package_references': {'key': 'packageReferences', 'type': '[PackageReferenceBase]'} } def __init__(self, **kwargs): super(ExtendedPoolParameter, self).__init__(**kwargs) self.package_references = kwargs.get('package_references', None) class ExtendedPoolSpecification(models.PoolSpecification): """Specification for creating a new pool. :param display_name: The display name for the pool. The display name need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param vm_size: The size of the virtual machines in the pool. All virtual machines in a pool are the same size. For information about available sizes of virtual machines for Cloud Services pools (pools created with cloudServiceConfiguration), see Sizes for Cloud Services (http://azure.microsoft.com/documentation/articles/cloud-services-sizes-specs/). Batch supports all Cloud Services VM sizes except ExtraSmall, A1V2 and A2V2. For information about available VM sizes for pools using images from the Virtual Machines Marketplace (pools created with virtualMachineConfiguration) see Sizes for Virtual Machines (Linux) (https://azure.microsoft.com/documentation/articles/virtual-machines-linux-sizes/) or Sizes for Virtual Machines (Windows) (https://azure.microsoft.com/documentation/articles/virtual-machines-windows-sizes/). Batch supports all Azure VM sizes except STANDARD_A0 and those with premium storage (STANDARD_GS, STANDARD_DS, and STANDARD_DSV2 series). :type vm_size: str :param cloud_service_configuration: The cloud service configuration for the pool. This property must be specified if the pool needs to be created with Azure PaaS VMs. This property and virtualMachineConfiguration are mutually exclusive and one of the properties must be specified. If neither is specified then the Batch service returns an error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). This property cannot be specified if the Batch account was created with its poolAllocationMode property set to 'UserSubscription'. :type cloud_service_configuration: :class:`CloudServiceConfiguration <azure.batch.models.CloudServiceConfiguration>` :param virtual_machine_configuration: The virtual machine configuration for the pool. This property must be specified if the pool needs to be created with Azure IaaS VMs. This property and cloudServiceConfiguration are mutually exclusive and one of the properties must be specified. If neither is specified then the Batch service returns an error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). :type virtual_machine_configuration: :class:`VirtualMachineConfiguration <azure.batch.models.VirtualMachineConfiguration>` :param max_tasks_per_node: The maximum number of tasks that can run concurrently on a single compute node in the pool. The default value is 1. The maximum value of this setting depends on the size of the compute nodes in the pool (the vmSize setting). :type max_tasks_per_node: int :param task_scheduling_policy: How tasks are distributed across compute nodes in a pool. :type task_scheduling_policy: :class:`TaskSchedulingPolicy <azure.batch.models.TaskSchedulingPolicy>` :param resize_timeout: The timeout for allocation of compute nodes to the pool. This timeout applies only to manual scaling; it has no effect when enableAutoScale is set to true. The default value is 15 minutes. The minimum value is 5 minutes. If you specify a value less than 5 minutes, the Batch service rejects the request with an error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). :type resize_timeout: timedelta :param target_dedicated_nodes: The desired number of dedicated compute nodes in the pool. This property must not be specified if enableAutoScale is set to true. If enableAutoScale is set to false, then you must set either targetDedicatedNodes, targetLowPriorityNodes, or both. :type target_dedicated_nodes: int :param target_low_priority_nodes: The desired number of low-priority compute nodes in the pool. This property must not be specified if enableAutoScale is set to true. If enableAutoScale is set to false, then you must set either targetDedicatedNodes, targetLowPriorityNodes, or both. :type target_low_priority_nodes: int :param enable_auto_scale: Whether the pool size should automatically adjust over time. If false, the targetDedicated element is required. If true, the autoScaleFormula element is required. The pool automatically resizes according to the formula. The default value is false. :type enable_auto_scale: bool :param auto_scale_formula: The formula for the desired number of compute nodes in the pool. This property must not be specified if enableAutoScale is set to false. It is required if enableAutoScale is set to true. The formula is checked for validity before the pool is created. If the formula is not valid, the Batch service rejects the request with detailed error information. :type auto_scale_formula: str :param auto_scale_evaluation_interval: The time interval at which to automatically adjust the pool size according to the autoscale formula. The default value is 15 minutes. The minimum and maximum value are 5 minutes and 168 hours respectively. If you specify a value less than 5 minutes or greater than 168 hours, the Batch service rejects the request with an invalid property value error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). :type auto_scale_evaluation_interval: timedelta :param enable_inter_node_communication: Whether the pool permits direct communication between nodes. Enabling inter-node communication limits the maximum size of the pool due to deployment restrictions on the nodes of the pool. This may result in the pool not reaching its desired size. The default value is false. :type enable_inter_node_communication: bool :param network_configuration: The network configuration for the pool. :type network_configuration: :class:`NetworkConfiguration <azure.batch.models.NetworkConfiguration>` :param start_task: A task to run on each compute node as it joins the pool. The task runs when the node is added to the pool or when the node is restarted. :type start_task: :class:`StartTask <azure.batch.models.StartTask>` :param certificate_references: A list of certificates to be installed on each compute node in the pool. For Windows compute nodes, the Batch service installs the certificates to the specified certificate store and location. For Linux compute nodes, the certificates are stored in a directory inside the task working directory and an environment variable AZ_BATCH_CERTIFICATES_DIR is supplied to the task to query for this location. For certificates with visibility of 'remoteUser', a 'certs' directory is created in the user's home directory (e.g., /home/{user-name}/certs) and certificates are placed in that directory. :type certificate_references: list of :class:`CertificateReference <azure.batch.models.CertificateReference>` :param application_package_references: The list of application packages to be installed on each compute node in the pool. :type application_package_references: list of :class:`ApplicationPackageReference <azure.batch.models.ApplicationPackageReference>` :param application_licenses: The list of application licenses the Batch service will make available on each compute node in the pool. The list of application licenses must be a subset of available Batch service application licenses. If a license is requested which is not supported, pool creation will fail. :type application_licenses: list of str :param user_accounts: The list of user accounts to be created on each node in the pool. :type user_accounts: list of :class:`UserAccount <azure.batch.models.UserAccount>` :param metadata: A list of name-value pairs associated with the pool as metadata. The Batch service does not assign any meaning to metadata; it is solely for the use of user code. :type metadata: list of :class:`MetadataItem <azure.batch.models.MetadataItem>` :param package_references: A list of packages to be installed on the compute nodes. Must be of a Package Manager type in accordance with the selected operating system. :type package_references: list of :class:`PackageReferenceBase <azext.batch.models.PackageReferenceBase>` """ _validation = { 'vm_size': {'required': True}, } _attribute_map = { 'display_name': {'key': 'displayName', 'type': 'str'}, 'vm_size': {'key': 'vmSize', 'type': 'str'}, 'cloud_service_configuration': {'key': 'cloudServiceConfiguration', 'type': 'CloudServiceConfiguration'}, 'virtual_machine_configuration': {'key': 'virtualMachineConfiguration', 'type': 'VirtualMachineConfiguration'}, 'max_tasks_per_node': {'key': 'maxTasksPerNode', 'type': 'int'}, 'task_scheduling_policy': {'key': 'taskSchedulingPolicy', 'type': 'TaskSchedulingPolicy'}, 'resize_timeout': {'key': 'resizeTimeout', 'type': 'duration'}, 'target_dedicated_nodes': {'key': 'targetDedicatedNodes', 'type': 'int'}, 'target_low_priority_nodes': {'key': 'targetLowPriorityNodes', 'type': 'int'}, 'enable_auto_scale': {'key': 'enableAutoScale', 'type': 'bool'}, 'auto_scale_formula': {'key': 'autoScaleFormula', 'type': 'str'}, 'auto_scale_evaluation_interval': {'key': 'autoScaleEvaluationInterval', 'type': 'duration'}, 'enable_inter_node_communication': {'key': 'enableInterNodeCommunication', 'type': 'bool'}, 'network_configuration': {'key': 'networkConfiguration', 'type': 'NetworkConfiguration'}, 'start_task': {'key': 'startTask', 'type': 'StartTask'}, 'certificate_references': {'key': 'certificateReferences', 'type': '[CertificateReference]'}, 'application_package_references': {'key': 'applicationPackageReferences', 'type': '[ApplicationPackageReference]'}, 'application_licenses': {'key': 'applicationLicenses', 'type': '[str]'}, 'user_accounts': {'key': 'userAccounts', 'type': '[UserAccount]'}, 'metadata': {'key': 'metadata', 'type': '[MetadataItem]'}, 'package_references': {'key': 'packageReferences', 'type': '[PackageReferenceBase]'} } def __init__(self, **kwargs): super(ExtendedPoolSpecification, self).__init__(**kwargs) self.package_references = kwargs.get('package_references', None) class ExtendedResourceFile(models.ResourceFile): """A file to be downloaded from Azure blob storage to a compute node. :param http_url: The URL of the file within Azure Blob Storage. This URL must be readable using anonymous access; that is, the Batch service does not present any credentials when downloading the blob. There are two ways to get such a URL for a blob in Azure storage: include a Shared Access Signature (SAS) granting read permissions on the blob, or set the ACL for the blob or its container to allow public access. :type http_url: str :param auto_storage_container_name: The storage container name in the auto storage account. The autoStorageContainerName, storageContainerUrl and httpUrl properties are mutually exclusive and one of them must be specified. :type auto_storage_container_name: str :param storage_container_url: The URL of the blob container within Azure Blob Storage. The autoStorageContainerName, storageContainerUrl and httpUrl properties are mutually exclusive and one of them must be specified. This URL must be readable and listable using anonymous access; that is, the Batch service does not present any credentials when downloading blobs from the container. There are two ways to get such a URL for a container in Azure storage: include a Shared Access Signature (SAS) granting read and list permissions on the container, or set the ACL for the container to allow public access. :type storage_container_url: str :param blob_prefix: The blob prefix to use when downloading blobs from an Azure Storage container. Only the blobs whose names begin with the specified prefix will be downloaded. The property is valid only when autoStorageContainerName or storageContainerUrl is used. This prefix can be a partial filename or a subdirectory. If a prefix is not specified, all the files in the container will be downloaded. :type blob_prefix: str :param file_path: The location on the compute node to which to download the file, relative to the task's working directory. If using a file group source that references more than one file, this will be considered the name of a directory, otherwise it will be treated as the destination file name. :type file_path: str :param file_mode: The file permission mode attribute in octal format. This property applies only to files being downloaded to Linux compute nodes. It will be ignored if it is specified for a resourceFile which will be downloaded to a Windows node. If this property is not specified for a Linux node, then a default value of 0770 is applied to the file. If using a file group source that references more than one file, this will be applied to all files in the group. :type file_mode: str :param source: A file source reference which could include a collection of files from a Azure Storage container or an auto-storage file group. :type source: :class:`FileSource <azext.batch.models.FileSource>` """ _attribute_map = { 'http_url': {'key': 'httpUrl', 'type': 'str'}, 'auto_storage_container_name': {'key': 'autoStorageContainerName', 'type': 'str'}, 'blob_prefix': {'key': 'blobPrefix', 'type': 'str'}, 'storage_container_url': {'key': 'storageContainerUrl', 'type': 'str'}, 'file_path': {'key': 'filePath', 'type': 'str'}, 'file_mode': {'key': 'fileMode', 'type': 'str'}, 'source': {'key': 'source', 'type': 'FileSource'} } def __init__(self, **kwargs): super(ExtendedResourceFile, self).__init__(**kwargs) self.source = kwargs.get('source', None) class ExtendedTaskParameter(models.TaskAddParameter): """An Azure Batch task to add. :param id: A string that uniquely identifies the task within the job. The ID can contain any combination of alphanumeric characters including hyphens and underscores, and cannot contain more than 64 characters. The ID is case-preserving and case-insensitive (that is, you may not have two IDs within a job that differ only by case). :type id: str :param display_name: A display name for the task. The display name need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param command_line: The command line of the task. For multi-instance tasks, the command line is executed as the primary task, after the primary task and all subtasks have finished executing the coordination command line. The command line does not run under a shell, and therefore cannot take advantage of shell features such as environment variable expansion. If you want to take advantage of such features, you should invoke the shell in the command line, for example using "cmd /c MyCommand" in Windows or "/bin/sh -c MyCommand" in Linux. :type command_line: str :param container_settings: The settings for the container under which the task runs. If the pool that will run this task has containerConfiguration set, this must be set as well. If the pool that will run this task doesn't have containerConfiguration set, this must not be set. When this is specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR (the root of Azure Batch directories on the node) are mapped into the container, all task environment variables are mapped into the container, and the task command line is executed in the container. :type container_settings: :class:`TaskContainerSettings <azure.batch.models.TaskContainerSettings>` :param exit_conditions: How the Batch service should respond when the task completes. :type exit_conditions: :class:`ExitConditions <azure.batch.models.ExitConditions>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. For multi-instance tasks, the resource files will only be downloaded to the compute node on which the primary task is executed. :type resource_files: list of :class:`ResourceFile <azure.batch.models.ResourceFile>` :param environment_settings: A list of environment variable settings for the task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param affinity_info: A locality hint that can be used by the Batch service to select a compute node on which to start the new task. :type affinity_info: :class:`AffinityInformation <azure.batch.models.AffinityInformation>` :param constraints: The execution constraints that apply to this task. If you do not specify constraints, the maxTaskRetryCount is the maxTaskRetryCount specified for the job, and the maxWallClockTime and retentionTime are infinite. :type constraints: :class:`TaskConstraints <azure.batch.models.TaskConstraints>` :param user_identity: The user identity under which the task runs. If omitted, the task runs as a non-administrative user unique to the task. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` :param multi_instance_settings: An object that indicates that the task is a multi-instance task, and contains information about how to run the multi-instance task. :type multi_instance_settings: :class:`MultiInstanceSettings <azure.batch.models.MultiInstanceSettings>` :param depends_on: The tasks that this task depends on. This task will not be scheduled until all tasks that it depends on have completed successfully. If any of those tasks fail and exhaust their retry counts, this task will never be scheduled. If the job does not have usesTaskDependencies set to true, and this element is present, the request fails with error code TaskDependenciesNotSpecifiedOnJob. :type depends_on: :class:`TaskDependencies <azure.batch.models.TaskDependencies>` :param application_package_references: A list of application packages that the Batch service will deploy to the compute node before running the command line. Application packages are downloaded and deployed to a shared directory, not the task working directory. Therefore, if a referenced package is already on the compute node, and is up to date, then it is not re-downloaded; the existing copy on the compute node is used. If a referenced application package cannot be installed, for example because the package has been deleted or because download failed, the task fails. :type application_package_references: list of :class:`ApplicationPackageReference <azure.batch.models.ApplicationPackageReference>` :param authentication_token_settings: The settings for an authentication token that the task can use to perform Batch service operations. If this property is set, the Batch service provides the task with an authentication token which can be used to authenticate Batch service operations without requiring an account access key. The token is provided via the AZ_BATCH_AUTHENTICATION_TOKEN environment variable. The operations that the task can carry out using the token depend on the settings. For example, a task can request job permissions in order to add other tasks to the job, or check the status of the job or of other tasks under the job. :type authentication_token_settings: :class:`AuthenticationTokenSettings <azure.batch.models.AuthenticationTokenSettings>` :param output_files: A list of files that the Batch service will upload from the compute node after running the command line. For multi-instance tasks, the files will only be uploaded from the compute node on which the primary task is executed. :type output_files: list of :class:`OutputFile <azext.batch.models.OutputFile>` :param package_references: A list of packages to be installed on the compute nodes. Must be of a Package Manager type in accordance with the selected operating system. :type package_references: list of :class:`PackageReferenceBase <azext.batch.models.PackageReferenceBase>` """ _validation = { 'id': {'required': True}, 'command_line': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'command_line': {'key': 'commandLine', 'type': 'str'}, 'container_settings': {'key': 'containerSettings', 'type': 'TaskContainerSettings'}, 'exit_conditions': {'key': 'exitConditions', 'type': 'ExitConditions'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'output_files': {'key': 'outputFiles', 'type': '[OutputFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'affinity_info': {'key': 'affinityInfo', 'type': 'AffinityInformation'}, 'constraints': {'key': 'constraints', 'type': 'TaskConstraints'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, 'multi_instance_settings': {'key': 'multiInstanceSettings', 'type': 'MultiInstanceSettings'}, 'depends_on': {'key': 'dependsOn', 'type': 'TaskDependencies'}, 'application_package_references': {'key': 'applicationPackageReferences', 'type': '[ApplicationPackageReference]'}, 'authentication_token_settings': {'key': 'authenticationTokenSettings', 'type': 'AuthenticationTokenSettings'}, 'package_references': {'key': 'packageReferences', 'type': '[PackageReferenceBase]'} } def __init__(self, **kwargs): super(ExtendedTaskParameter, self).__init__(**kwargs) self.package_references = kwargs.get('package_references', None) class FileCollectionTaskFactory(TaskFactoryBase): """A Task Factory for generating a set of tasks based on the contents of an Azure Storage container or auto-storage file group. One task will be generated per input file, and automatically added to the job. :param source: The input file source from which the tasks will be generated. :type source: :class:`FileSource <azext.batch.models.FileSource>` :param repeat_task: The task template the will be used to generate each task. :type repeat_task: :class:`RepeatTask <azext.batch.models.RepeatTask>` :param merge_task: An optional additional task to be run after all the other generated tasks have completed successfully. :type merge_task: :class:`MergeTask <azext.batch.models.MergeTask>` """ _validation = { 'type': {'required': True}, 'source': {'required': True}, 'repeat_task': {'required': True} } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'source': {'key': 'source', 'type': 'FileSource'}, 'repeat_task': {'key': 'repeatTask', 'type': 'RepeatTask'}, 'merge_task': {'key': 'mergeTask', 'type': 'MergeTask'} } def __init__(self, **kwargs): super(FileCollectionTaskFactory, self).__init__(**kwargs) self.source = kwargs.get('source', None) self.repeat_task = kwargs.get('repeat_task', None) self.type = 'taskPerFile' class FileSource(Model): """A source of input files to be downloaded onto a compute node. :param str file_group: The name of an auto-storage file group. :param str url: The URL of a file to be downloaded. :param str container_url: The SAS URL of an Azure Storage container. :param str prefix: The filename prefix or subdirectory of input files in either an auto-storage file group or container. Will be ignored if conbined with url. """ _attribute_map = { 'file_group': {'key': 'fileGroup', 'type': 'str'}, 'url': {'key': 'url', 'type': 'str'}, 'container_url': {'key': 'containerUrl', 'type': 'str'}, 'prefix': {'key': 'prefix', 'type': 'str'}, } def __init__(self, **kwargs): super(FileSource, self).__init__(**kwargs) self.file_group = kwargs.get('file_group', None) self.url = kwargs.get('url', None) self.container_url = kwargs.get('container_url', None) self.prefix = kwargs.get('prefix', None) class JobManagerTask(Model): """Specifies details of a Job Manager task. The Job Manager task is automatically started when the job is created. The Batch service tries to schedule the Job Manager task before any other tasks in the job. When shrinking a pool, the Batch service tries to preserve compute nodes where Job Manager tasks are running for as long as possible (that is, nodes running 'normal' tasks are removed before nodes running Job Manager tasks). When a Job Manager task fails and needs to be restarted, the system tries to schedule it at the highest priority. If there are no idle nodes available, the system may terminate one of the running tasks in the pool and return it to the queue in order to make room for the Job Manager task to restart. Note that a Job Manager task in one job does not have priority over tasks in other jobs. Across jobs, only job level priorities are observed. For example, if a Job Manager in a priority 0 job needs to be restarted, it will not displace tasks of a priority 1 job. :param id: A string that uniquely identifies the Job Manager task within the job. The ID can contain any combination of alphanumeric characters including hyphens and underscores and cannot contain more than 64 characters. :type id: str :param display_name: The display name of the Job Manager task. It need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param command_line: The command line of the Job Manager task. The command line does not run under a shell, and therefore cannot take advantage of shell features such as environment variable expansion. If you want to take advantage of such features, you should invoke the shell in the command line, for example using "cmd /c MyCommand" in Windows or "/bin/sh -c MyCommand" in Linux. :type command_line: str :param container_settings: The settings for the container under which the Job Manager task runs. If the pool that will run this task has containerConfiguration set, this must be set as well. If the pool that will run this task doesn't have containerConfiguration set, this must not be set. When this is specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR (the root of Azure Batch directories on the node) are mapped into the container, all task environment variables are mapped into the container, and the task command line is executed in the container. :type container_settings: :class:`TaskContainerSettings <azure.batch.models.TaskContainerSettings>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. Files listed under this element are located in the task's working directory. :type resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` :param output_files: A list of files that the Batch service will upload from the compute node after running the command line. For multi-instance tasks, the files will only be uploaded from the compute node on which the primary task is executed. :type output_files: list of :class:`OutputFile <azure.batch.models.OutputFile>` :param environment_settings: A list of environment variable settings for the Job Manager task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param constraints: Constraints that apply to the Job Manager task. :type constraints: :class:`TaskConstraints <azure.batch.models.TaskConstraints>` :param kill_job_on_completion: Whether completion of the Job Manager task signifies completion of the entire job. If true, when the Job Manager task completes, the Batch service marks the job as complete. If any tasks are still running at this time (other than Job Release), those tasks are terminated. If false, the completion of the Job Manager task does not affect the job status. In this case, you should either use the onAllTasksComplete attribute to terminate the job, or have a client or user terminate the job explicitly. An example of this is if the Job Manager creates a set of tasks but then takes no further role in their execution. The default value is true. If you are using the onAllTasksComplete and onTaskFailure attributes to control job lifetime, and using the Job Manager task only to create the tasks for the job (not to monitor progress), then it is important to set killJobOnCompletion to false. :type kill_job_on_completion: bool :param user_identity: The user identity under which the Job Manager task runs. If omitted, the task runs as a non-administrative user unique to the task. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` :param run_exclusive: Whether the Job Manager task requires exclusive use of the compute node where it runs. If true, no other tasks will run on the same compute node for as long as the Job Manager is running. If false, other tasks can run simultaneously with the Job Manager on a compute node. The Job Manager task counts normally against the node's concurrent task limit, so this is only relevant if the node allows multiple concurrent tasks. The default value is true. :type run_exclusive: bool :param application_package_references: A list of application packages that the Batch service will deploy to the compute node before running the command line. Application packages are downloaded and deployed to a shared directory, not the task working directory. Therefore, if a referenced package is already on the compute node, and is up to date, then it is not re-downloaded; the existing copy on the compute node is used. If a referenced application package cannot be installed, for example because the package has been deleted or because download failed, the task fails. :type application_package_references: list of :class:`ApplicationPackageReference <azure.batch.models.ApplicationPackageReference>` :param authentication_token_settings: The settings for an authentication token that the task can use to perform Batch service operations. If this property is set, the Batch service provides the task with an authentication token which can be used to authenticate Batch service operations without requiring an account access key. The token is provided via the AZ_BATCH_AUTHENTICATION_TOKEN environment variable. The operations that the task can carry out using the token depend on the settings. For example, a task can request job permissions in order to add other tasks to the job, or check the status of the job or of other tasks under the job. :type authentication_token_settings: :class:`AuthenticationTokenSettings <azure.batch.models.AuthenticationTokenSettings>` :param allow_low_priority_node: Whether the Job Manager task may run on a low-priority compute node. The default value is false. :type allow_low_priority_node: bool """ _validation = { 'id': {'required': True}, 'command_line': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'command_line': {'key': 'commandLine', 'type': 'str'}, 'container_settings': {'key': 'containerSettings', 'type': 'TaskContainerSettings'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'output_files': {'key': 'outputFiles', 'type': '[OutputFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'constraints': {'key': 'constraints', 'type': 'TaskConstraints'}, 'kill_job_on_completion': {'key': 'killJobOnCompletion', 'type': 'bool'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, 'run_exclusive': {'key': 'runExclusive', 'type': 'bool'}, 'application_package_references': {'key': 'applicationPackageReferences', 'type': '[ApplicationPackageReference]'}, 'authentication_token_settings': {'key': 'authenticationTokenSettings', 'type': 'AuthenticationTokenSettings'}, 'allow_low_priority_node': {'key': 'allowLowPriorityNode', 'type': 'bool'}, } def __init__(self, **kwargs): super(JobManagerTask, self).__init__(**kwargs) self.id = kwargs.get('id', None) self.display_name = kwargs.get('display_name', None) self.command_line = kwargs.get('command_line', None) self.container_settings = kwargs.get('container_settings', None) self.resource_files = kwargs.get('resource_files', None) self.output_files = kwargs.get('output_files', None) self.environment_settings = kwargs.get('environment_settings', None) self.constraints = kwargs.get('constraints', None) self.kill_job_on_completion = kwargs.get('kill_job_on_completion', None) self.user_identity = kwargs.get('user_identity', None) self.run_exclusive = kwargs.get('run_exclusive', None) self.application_package_references = kwargs.get('application_package_references', None) self.authentication_token_settings = kwargs.get('authentication_token_settings', None) self.allow_low_priority_node = kwargs.get('allow_low_priority_node', None) class JobPreparationTask(Model): """A Job Preparation task to run before any tasks of the job on any given compute node. You can use Job Preparation to prepare a compute node to run tasks for the job. Activities commonly performed in Job Preparation include: Downloading common resource files used by all the tasks in the job. The Job Preparation task can download these common resource files to the shared location on the compute node. (AZ_BATCH_NODE_ROOT_DIR\\shared), or starting a local service on the compute node so that all tasks of that job can communicate with it. If the Job Preparation task fails (that is, exhausts its retry count before exiting with exit code 0), Batch will not run tasks of this job on the compute node. The node remains ineligible to run tasks of this job until it is reimaged. The node remains active and can be used for other jobs. The Job Preparation task can run multiple times on the same compute node. Therefore, you should write the Job Preparation task to handle re-execution. If the compute node is rebooted, the Job Preparation task is run again on the node before scheduling any other task of the job, if rerunOnNodeRebootAfterSuccess is true or if the Job Preparation task did not previously complete. If the compute node is reimaged, the Job Preparation task is run again before scheduling any task of the job. :param id: A string that uniquely identifies the Job Preparation task within the job. The ID can contain any combination of alphanumeric characters including hyphens and underscores and cannot contain more than 64 characters. If you do not specify this property, the Batch service assigns a default value of 'jobpreparation'. No other task in the job can have the same ID as the Job Preparation task. If you try to submit a task with the same ID, the Batch service rejects the request with error code TaskIdSameAsJobPreparationTask; if you are calling the REST API directly, the HTTP status code is 409 (Conflict). :type id: str :param command_line: The command line of the Job Preparation task. The command line does not run under a shell, and therefore cannot take advantage of shell features such as environment variable expansion. If you want to take advantage of such features, you should invoke the shell in the command line, for example using "cmd /c MyCommand" in Windows or "/bin/sh -c MyCommand" in Linux. :type command_line: str :param container_settings: The settings for the container under which the Job Preparation task runs. When this is specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR (the root of Azure Batch directories on the node) are mapped into the container, all task environment variables are mapped into the container, and the task command line is executed in the container. :type container_settings: :class:`TaskContainerSettings <azure.batch.models.TaskContainerSettings>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. Files listed under this element are located in the task's working directory. :type resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` :param environment_settings: A list of environment variable settings for the Job Preparation task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param constraints: Constraints that apply to the Job Preparation task. :type constraints: :class:`TaskConstraints <azure.batch.models.TaskConstraints>` :param wait_for_success: Whether the Batch service should wait for the Job Preparation task to complete successfully before scheduling any other tasks of the job on the compute node. A Job Preparation task has completed successfully if it exits with exit code 0. If true and the Job Preparation task fails on a compute node, the Batch service retries the Job Preparation task up to its maximum retry count (as specified in the constraints element). If the task has still not completed successfully after all retries, then the Batch service will not schedule tasks of the job to the compute node. The compute node remains active and eligible to run tasks of other jobs. If false, the Batch service will not wait for the Job Preparation task to complete. In this case, other tasks of the job can start executing on the compute node while the Job Preparation task is still running; and even if the Job Preparation task fails, new tasks will continue to be scheduled on the node. The default value is true. :type wait_for_success: bool :param user_identity: The user identity under which the Job Preparation task runs. If omitted, the task runs as a non-administrative user unique to the task on Windows nodes, or a a non-administrative user unique to the pool on Linux nodes. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` :param rerun_on_node_reboot_after_success: Whether the Batch service should rerun the Job Preparation task after a compute node reboots. The Job Preparation task is always rerun if a compute node is reimaged, or if the Job Preparation task did not complete (e.g. because the reboot occurred while the task was running). Therefore, you should always write a Job Preparation task to be idempotent and to behave correctly if run multiple times. The default value is true. :type rerun_on_node_reboot_after_success: bool """ _validation = { 'command_line': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'command_line': {'key': 'commandLine', 'type': 'str'}, 'container_settings': {'key': 'containerSettings', 'type': 'TaskContainerSettings'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'constraints': {'key': 'constraints', 'type': 'TaskConstraints'}, 'wait_for_success': {'key': 'waitForSuccess', 'type': 'bool'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, 'rerun_on_node_reboot_after_success': {'key': 'rerunOnNodeRebootAfterSuccess', 'type': 'bool'}, } def __init__(self, **kwargs): super(JobPreparationTask, self).__init__(**kwargs) self.id = kwargs.get('id', None) self.command_line = kwargs.get('command_line', None) self.container_settings = kwargs.get('container_settings', None) self.resource_files = kwargs.get('resource_files', None) self.environment_settings = kwargs.get('environment_settings', None) self.constraints = kwargs.get('constraints', None) self.wait_for_success = kwargs.get('wait_for_success', None) self.user_identity = kwargs.get('user_identity', None) self.rerun_on_node_reboot_after_success = kwargs.get('rerun_on_node_reboot_after_success', None) class JobReleaseTask(Model): """A Job Release task to run on job completion on any compute node where the job has run. The Job Release task runs when the job ends, because of one of the following: The user calls the Terminate Job API, or the Delete Job API while the job is still active, the job's maximum wall clock time constraint is reached, and the job is still active, or the job's Job Manager task completed, and the job is configured to terminate when the Job Manager completes. The Job Release task runs on each compute node where tasks of the job have run and the Job Preparation task ran and completed. If you reimage a compute node after it has run the Job Preparation task, and the job ends without any further tasks of the job running on that compute node (and hence the Job Preparation task does not re-run), then the Job Release task does not run on that node. If a compute node reboots while the Job Release task is still running, the Job Release task runs again when the compute node starts up. The job is not marked as complete until all Job Release tasks have completed. The Job Release task runs in the background. It does not occupy a scheduling slot; that is, it does not count towards the maxTasksPerNode limit specified on the pool. :param id: A string that uniquely identifies the Job Release task within the job. The ID can contain any combination of alphanumeric characters including hyphens and underscores and cannot contain more than 64 characters. If you do not specify this property, the Batch service assigns a default value of 'jobrelease'. No other task in the job can have the same ID as the Job Release task. If you try to submit a task with the same ID, the Batch service rejects the request with error code TaskIdSameAsJobReleaseTask; if you are calling the REST API directly, the HTTP status code is 409 (Conflict). :type id: str :param command_line: The command line of the Job Release task. The command line does not run under a shell, and therefore cannot take advantage of shell features such as environment variable expansion. If you want to take advantage of such features, you should invoke the shell in the command line, for example using "cmd /c MyCommand" in Windows or "/bin/sh -c MyCommand" in Linux. :type command_line: str :param container_settings: The settings for the container under which the Job Release task runs. When this is specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR (the root of Azure Batch directories on the node) are mapped into the container, all task environment variables are mapped into the container, and the task command line is executed in the container. :type container_settings: :class:`TaskContainerSettings <azure.batch.models.TaskContainerSettings>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. Files listed under this element are located in the task's working directory. :type resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` :param environment_settings: A list of environment variable settings for the Job Release task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param max_wall_clock_time: The maximum elapsed time that the Job Release task may run on a given compute node, measured from the time the task starts. If the task does not complete within the time limit, the Batch service terminates it. The default value is 15 minutes. You may not specify a timeout longer than 15 minutes. If you do, the Batch service rejects it with an error; if you are calling the REST API directly, the HTTP status code is 400 (Bad Request). :type max_wall_clock_time: timedelta :param retention_time: The minimum time to retain the task directory for the Job Release task on the compute node. After this time, the Batch service may delete the task directory and all its contents. The default is infinite, i.e. the task directory will be retained until the compute node is removed or reimaged. :type retention_time: timedelta :param user_identity: The user identity under which the Job Release task runs. If omitted, the task runs as a non-administrative user unique to the task. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` """ _validation = { 'command_line': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'command_line': {'key': 'commandLine', 'type': 'str'}, 'container_settings': {'key': 'containerSettings', 'type': 'TaskContainerSettings'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'max_wall_clock_time': {'key': 'maxWallClockTime', 'type': 'duration'}, 'retention_time': {'key': 'retentionTime', 'type': 'duration'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, } def __init__(self, **kwargs): super(JobReleaseTask, self).__init__(**kwargs) self.id = kwargs.get('id', None) self.command_line = kwargs.get('command_line', None) self.container_settings = kwargs.get('container_settings', None) self.resource_files = kwargs.get('resource_files', None) self.environment_settings = kwargs.get('environment_settings', None) self.max_wall_clock_time = kwargs.get('max_wall_clock_time', None) self.retention_time = kwargs.get('retention_time', None) self.user_identity = kwargs.get('user_identity', None) class JobTemplate(Model): """A Job Template. :ivar type: The type of object described by the template. Must be: "Microsoft.Batch/batchAccounts/jobs" :type type: str :param api_version: The API version that the template conforms to. :type api_version: str :param properties: The specificaton of the job. :type properties: :class:`ExtendedJobParameter<azext.batch.models.ExtendedJobParameter>` """ _validation = { 'type': {'required': True, 'constant': True}, 'properties': {'required': True}, } _attribute_map = { 'type': {'key': 'id', 'type': 'str'}, 'api_version': {'key': 'apiVersion', 'type': 'str'}, 'properties': {'key': 'properties', 'type': 'ExtendedJobParameter'}, } type = "Microsoft.Batch/batchAccounts/jobs" def __init__(self, **kwargs): super(JobTemplate, self).__init__(**kwargs) self.properties = kwargs.get('properties') self.api_version = kwargs.get('api_version', None) class MergeTask(Model): """An Azure Batch task template to repeat. :param str id: The ID of the merge task. :param display_name: A display name for the task. The display name need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param command_line: The command line of the task. For multi-instance tasks, the command line is executed as the primary task, after the primary task and all subtasks have finished executing the coordination command line. The command line does not run under a shell, and therefore cannot take advantage of shell features such as environment variable expansion. If you want to take advantage of such features, you should invoke the shell in the command line, for example using "cmd /c MyCommand" in Windows or "/bin/sh -c MyCommand" in Linux. :type command_line: str :param exit_conditions: How the Batch service should respond when the task completes. :type exit_conditions: :class:`ExitConditions <azure.batch.models.ExitConditions>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. For multi-instance tasks, the resource files will only be downloaded to the compute node on which the primary task is executed. :type resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` :param environment_settings: A list of environment variable settings for the task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param affinity_info: A locality hint that can be used by the Batch service to select a compute node on which to start the new task. :type affinity_info: :class:`AffinityInformation <azure.batch.models.AffinityInformation>` :param constraints: The execution constraints that apply to this task. If you do not specify constraints, the maxTaskRetryCount is the maxTaskRetryCount specified for the job, and the maxWallClockTime and retentionTime are infinite. :type constraints: :class:`TaskConstraints <azure.batch.models.TaskConstraints>` :param user_identity: The user identity under which the task runs. If omitted, the task runs as a non-administrative user unique to the task. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` :param depends_on: The tasks that this task depends on. This task will not be scheduled until all tasks that it depends on have completed successfully. If any of those tasks fail and exhaust their retry counts, this task will never be scheduled. If the job does not have usesTaskDependencies set to true, and this element is present, the request fails with error code TaskDependenciesNotSpecifiedOnJob. :type depends_on: :class:`TaskDependencies <azure.batch.models.TaskDependencies>` :param application_package_references: A list of application packages that the Batch service will deploy to the compute node before running the command line. :type application_package_references: list of :class:`ApplicationPackageReference <azure.batch.models.ApplicationPackageReference>` :param authentication_token_settings: The settings for an authentication token that the task can use to perform Batch service operations. If this property is set, the Batch service provides the task with an authentication token which can be used to authenticate Batch service operations without requiring an account access key. The token is provided via the AZ_BATCH_AUTHENTICATION_TOKEN environment variable. The operations that the task can carry out using the token depend on the settings. For example, a task can request job permissions in order to add other tasks to the job, or check the status of the job or of other tasks under the job. :type authentication_token_settings: :class:`AuthenticationTokenSettings <azure.batch.models.AuthenticationTokenSettings>` :param output_files: A list of output file references to up persisted once the task has completed. :type output_files: list of :class:`OutputFile <azext.batch.models.OutputFile>` :param package_references: A list of packages to be installed on the compute nodes. Must be of a Package Manager type in accordance with the selected operating system. :type package_references: list of :class:`PackageReferenceBase <azext.batch.models.PackageReferenceBase>` """ _validation = { 'command_line': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'command_line': {'key': 'commandLine', 'type': 'str'}, 'exit_conditions': {'key': 'exitConditions', 'type': 'ExitConditions'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'affinity_info': {'key': 'affinityInfo', 'type': 'AffinityInformation'}, 'constraints': {'key': 'constraints', 'type': 'TaskConstraints'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, 'depends_on': {'key': 'dependsOn', 'type': 'TaskDependencies'}, 'application_package_references': {'key': 'applicationPackageReferences', 'type': '[ApplicationPackageReference]'}, 'authentication_token_settings': {'key': 'authenticationTokenSettings', 'type': 'AuthenticationTokenSettings'}, 'output_files': {'key': 'outputFiles', 'type': '[OutputFile]'}, 'package_references': {'key': 'packageReferences', 'type': '[PackageReferenceBase]'}, } def __init__(self, **kwargs): super(MergeTask, self).__init__(**kwargs) self.id = kwargs.get('id', None) self.display_name = kwargs.get('display_name', None) self.command_line = kwargs.get('command_line') self.exit_conditions = kwargs.get('exit_conditions', None) self.resource_files = kwargs.get('resource_files', None) self.environment_settings = kwargs.get('environment_settings', None) self.affinity_info = kwargs.get('affinity_info', None) self.constraints = kwargs.get('constraints', None) self.user_identity = kwargs.get('user_identity', None) self.depends_on = kwargs.get('depends_on', None) self.application_package_references = kwargs.get('application_package_references', None) self.authentication_token_settings = kwargs.get('authentication_token_settings', None) self.output_files = kwargs.get('output_files', None) self.package_references = kwargs.get('package_references', None) class MultiInstanceSettings(Model): """Settings which specify how to run a multi-instance task. Multi-instance tasks are commonly used to support MPI tasks. :param number_of_instances: The number of compute nodes required by the task. If omitted, the default is 1. :type number_of_instances: int :param coordination_command_line: The command line to run on all the compute nodes to enable them to coordinate when the primary runs the main task command. A typical coordination command line launches a background service and verifies that the service is ready to process inter-node messages. :type coordination_command_line: str :param common_resource_files: A list of files that the Batch service will download before running the coordination command line. The difference between common resource files and task resource files is that common resource files are downloaded for all subtasks including the primary, whereas task resource files are downloaded only for the primary. Also note that these resource files are not downloaded to the task working directory, but instead are downloaded to the task root directory (one directory above the working directory). :type common_resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` """ _validation = { 'coordination_command_line': {'required': True}, } _attribute_map = { 'number_of_instances': {'key': 'numberOfInstances', 'type': 'int'}, 'coordination_command_line': {'key': 'coordinationCommandLine', 'type': 'str'}, 'common_resource_files': {'key': 'commonResourceFiles', 'type': '[ExtendedResourceFile]'}, } def __init__(self, **kwargs): super(MultiInstanceSettings, self).__init__(**kwargs) self.number_of_instances = kwargs.get('number_of_instances') self.coordination_command_line = kwargs.get('coordination_command_line', None) self.common_resource_files = kwargs.get('common_resource_files', None) class OutputFile(Model): """A specification for uploading files from an Azure Batch node to another location after the Batch service has finished executing the task process. :param file_pattern: A pattern indicating which file(s) to upload. Both relative and absolute paths are supported. Relative paths are relative to the task working directory. The following wildcards are supported: * matches 0 or more characters (for example pattern abc* would match abc or abcdef), ** matches any directory, ? matches any single character, [abc] matches one character in the brackets, and [a-c] matches one character in the range. Brackets can include a negation to match any character not specified (for example [!abc] matches any character but a, b, or c). If a file name starts with "." it is ignored by default but may be matched by specifying it explicitly (for example *.gif will not match .a.gif, but .*.gif will). A simple example: **\\*.txt matches any file that does not start in '.' and ends with .txt in the task working directory or any subdirectory. If the filename contains a wildcard character it can be escaped using brackets (for example abc[*] would match a file named abc*). Note that both \\ and / are treated as directory separators on Windows, but only / is on Linux. Environment variables (%var% on Windows or $var on Linux) are expanded prior to the pattern being applied. :type file_pattern: str :param destination: The destination for the output file(s). :type destination: :class:`ExtendedOutputFileDestination <azext.batch.models.ExtendedOutputFileDestination>` :param upload_options: Additional options for the upload operation, including under what conditions to perform the upload. :type upload_options: :class:`OutputFileUploadOptions <azure.batch.models.OutputFileUploadOptions>` """ _validation = { 'file_pattern': {'required': True}, 'destination': {'required': True}, 'upload_options': {'required': True}, } _attribute_map = { 'file_pattern': {'key': 'filePattern', 'type': 'str'}, 'destination': {'key': 'destination', 'type': 'ExtendedOutputFileDestination'}, 'upload_options': {'key': 'uploadOptions', 'type': 'OutputFileUploadOptions'}, } def __init__(self, **kwargs): super(OutputFile, self).__init__(**kwargs) self.file_pattern = kwargs.get('file_pattern') self.destination = kwargs.get('destination') self.upload_options = kwargs.get('upload_options') class OutputFileAutoStorageDestination(Model): """An speficition of output files upload destination that uses an auto-storage file group. :param str file_group: The name of the file group that the output files will be uploaded to. :param str path: The destination path within the file group that the files will be uploaded to. Is the output file specification refers to a single file, this will be treated as a file name. If the output file specification refers to potentially multiple files, this will be treated as a subfolder. """ _validation = { 'file_group': {'required': True} } _attribute_map = { 'file_group': {'key': 'fileGroup', 'type': 'str'}, 'path': {'key': 'path', 'type': 'str'}, } def __init__(self, **kwargs): super(OutputFileAutoStorageDestination, self).__init__(**kwargs) self.file_group = kwargs.get('file_group') self.path = kwargs.get('path', None) class ParameterSet(Model): """A set of parametric sweep range range parameters. :param int start: The starting value of the sweep. :param int end: The ending value of the sweep (inclusive). :param int step: The incremental step value, default is 1. The step value can be negative (i.e. a decending sweep), but only id the start value is a higher value than the end. """ _validation = { 'start': {'required': True}, 'end': {'required': True}, } _attribute_map = { 'start': {'key': 'start', 'type': 'int'}, 'end': {'key': 'end', 'type': 'int'}, 'step': {'key': 'step', 'type': 'int'}, } def __init__(self, **kwargs): super(ParameterSet, self).__init__(**kwargs) try: self.start = int(kwargs.get('start')) self.end = int(kwargs.get('end')) self.step = int(kwargs.get('step', 1)) except (TypeError, ValueError): raise ValueError("'start', 'end' and 'step' parameters must be integers.") if self.step == 0: raise ValueError("'step' parameter cannot be 0.") elif self.start > self.end and self.step > 0: raise ValueError( "'step' must be a negative number when 'start' is greater than 'end'") elif self.start < self.end and self.step < 0: raise ValueError( "'step' must be a positive number when 'end' is greater than 'start'") class ParametricSweepTaskFactory(TaskFactoryBase): """A Task Factory for generating a set of tasks based on one or more parameter sets to define a numeric input range. Each parameter set will have a start, end and step value. A task will be generated for each integer in this range. Multiple parameter sets can be combined for a multi-dimensional sweep. :param parameter_sets: A list if parameter sets from which tasks will be generated. :type parameter_sets: A list of :class:`ParameterSet<azext.batch.models.ParameterSet>` :param repeat_task: The task template the will be used to generate each task. :type repeat_task: :class:`RepeatTask <azext.batch.models.RepeatTask>` :param merge_task: An optional additional task to be run after all the other generated tasks have completed successfully. :type merge_task: :class:`MergeTask <azext.batch.models.MergeTask>` """ _validation = { 'type': {'required': True}, 'parameter_sets': {'required': True, 'min_items': 1}, 'repeat_task': {'required': True} } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'parameter_sets': {'key': 'parameterSets', 'type': '[ParameterSet]'}, 'repeat_task': {'key': 'repeatTask', 'type': 'RepeatTask'}, 'merge_task': {'key': 'mergeTask', 'type': 'MergeTask'} } def __init__(self, **kwargs): super(ParametricSweepTaskFactory, self).__init__(**kwargs) self.parameter_sets = kwargs.get('parameter_sets', None) self.repeat_task = kwargs.get('repeat_task', None) self.type = 'parametricSweep' if not self.parameter_sets: raise ValueError("Parametric Sweep task factory requires at least one parameter set.") class PoolTemplate(Model): """A Pool Template. :ivar type: The type of object described by the template. Must be: "Microsoft.Batch/batchAccounts/pools" :type type: str :param api_version: The API version that the template conforms to. :type api_version: str :param properties: The specificaton of the pool. :type properties: :class:`ExtendedPoolParameter<azext.batch.models.ExtendedPoolParameter>` """ _validation = { 'type': {'required': True, 'constant': True}, 'properties': {'required': True}, } _attribute_map = { 'type': {'key': 'id', 'type': 'str'}, 'api_version': {'key': 'apiVersion', 'type': 'str'}, 'properties': {'key': 'properties', 'type': 'ExtendedPoolParameter'}, } type = "Microsoft.Batch/batchAccounts/pools" def __init__(self, **kwargs): super(PoolTemplate, self).__init__(**kwargs) self.properties = kwargs.get('properties') self.api_version = kwargs.get('api_version', None) class RepeatTask(Model): """An Azure Batch task template to repeat. :param display_name: A display name for the task. The display name need not be unique and can contain any Unicode characters up to a maximum length of 1024. :type display_name: str :param command_line: The command line of the task. :type command_line: str :param container_settings: The settings for the container under which the task runs. If the pool that will run this task has containerConfiguration set, this must be set as well. If the pool that will run this task doesn't have containerConfiguration set, this must not be set. When this is specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR (the root of Azure Batch directories on the node) are mapped into the container, all task environment variables are mapped into the container, and the task command line is executed in the container. :type container_settings: :class:`TaskContainerSettings <azure.batch.models.TaskContainerSettings>` :param exit_conditions: How the Batch service should respond when the task completes. :type exit_conditions: :class:`ExitConditions <azure.batch.models.ExitConditions>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. :type resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` :param environment_settings: A list of environment variable settings for the task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param affinity_info: A locality hint that can be used by the Batch service to select a compute node on which to start the new task. :type affinity_info: :class:`AffinityInformation <azure.batch.models.AffinityInformation>` :param constraints: The execution constraints that apply to this task. If you do not specify constraints, the maxTaskRetryCount is the maxTaskRetryCount specified for the job, and the maxWallClockTime and retentionTime are infinite. :type constraints: :class:`TaskConstraints <azure.batch.models.TaskConstraints>` :param user_identity: The user identity under which the task runs. If omitted, the task runs as a non-administrative user unique to the task. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` :param application_package_references: A list of application packages that the Batch service will deploy to the compute node before running the command line. :type application_package_references: list of :class:`ApplicationPackageReference <azure.batch.models.ApplicationPackageReference>` :param authentication_token_settings: The settings for an authentication token that the task can use to perform Batch service operations. If this property is set, the Batch service provides the task with an authentication token which can be used to authenticate Batch service operations without requiring an account access key. The token is provided via the AZ_BATCH_AUTHENTICATION_TOKEN environment variable. The operations that the task can carry out using the token depend on the settings. For example, a task can request job permissions in order to add other tasks to the job, or check the status of the job or of other tasks under the job. :type authentication_token_settings: :class:`AuthenticationTokenSettings <azure.batch.models.AuthenticationTokenSettings>` :param output_files: A list of output file references to up persisted once the task has completed. :type output_files: list of :class:`OutputFile <azext.batch.models.OutputFile>` :param package_references: A list of packages to be installed on the compute nodes. Must be of a Package Manager type in accordance with the selected operating system. :type package_references: list of :class:`PackageReferenceBase <azext.batch.models.PackageReferenceBase>` """ _validation = { 'command_line': {'required': True}, } _attribute_map = { 'display_name': {'key': 'displayName', 'type': 'str'}, 'command_line': {'key': 'commandLine', 'type': 'str'}, 'container_settings': {'key': 'containerSettings', 'type': 'TaskContainerSettings'}, 'exit_conditions': {'key': 'exitConditions', 'type': 'ExitConditions'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'affinity_info': {'key': 'affinityInfo', 'type': 'AffinityInformation'}, 'constraints': {'key': 'constraints', 'type': 'TaskConstraints'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, 'application_package_references': {'key': 'applicationPackageReferences', 'type': '[ApplicationPackageReference]'}, 'authentication_token_settings': {'key': 'authenticationTokenSettings', 'type': 'AuthenticationTokenSettings'}, 'output_files': {'key': 'outputFiles', 'type': '[OutputFile]'}, 'package_references': {'key': 'packageReferences', 'type': '[PackageReferenceBase]'} } def __init__(self, **kwargs): super(RepeatTask, self).__init__(**kwargs) self.display_name = kwargs.get('display_name') self.command_line = kwargs.get('command_line', None) self.exit_conditions = kwargs.get('exit_conditions', None) self.resource_files = kwargs.get('resource_files', None) self.environment_settings = kwargs.get('environment_settings', None) self.affinity_info = kwargs.get('affinity_info', None) self.constraints = kwargs.get('constraints', None) self.user_identity = kwargs.get('user_identity', None) self.application_package_references = kwargs.get('application_package_references', None) self.authentication_token_settings = kwargs.get('authentication_token_settings', None) self.output_files = kwargs.get('output_files', None) self.package_references = kwargs.get('package_references', None) class StartTask(Model): """A task which is run when a compute node joins a pool in the Azure Batch service, or when the compute node is rebooted or reimaged. :param command_line: The command line of the start task. The command line does not run under a shell, and therefore cannot take advantage of shell features such as environment variable expansion. If you want to take advantage of such features, you should invoke the shell in the command line, for example using "cmd /c MyCommand" in Windows or "/bin/sh -c MyCommand" in Linux. :type command_line: str :param container_settings: The settings for the container under which the start task runs. When this is specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR (the root of Azure Batch directories on the node) are mapped into the container, all task environment variables are mapped into the container, and the task command line is executed in the container. :type container_settings: :class:`TaskContainerSettings <azure.batch.models.TaskContainerSettings>` :param resource_files: A list of files that the Batch service will download to the compute node before running the command line. Files listed under this element are located in the task's working directory. :type resource_files: list of :class:`ExtendedResourceFile <azext.batch.models.ExtendedResourceFile>` :param environment_settings: A list of environment variable settings for the start task. :type environment_settings: list of :class:`EnvironmentSetting <azure.batch.models.EnvironmentSetting>` :param user_identity: The user identity under which the start task runs. If omitted, the task runs as a non-administrative user unique to the task. :type user_identity: :class:`UserIdentity <azure.batch.models.UserIdentity>` :param max_task_retry_count: The maximum number of times the task may be retried. The Batch service retries a task if its exit code is nonzero. Note that this value specifically controls the number of retries. The Batch service will try the task once, and may then retry up to this limit. For example, if the maximum retry count is 3, Batch tries the task up to 4 times (one initial try and 3 retries). If the maximum retry count is 0, the Batch service does not retry the task. If the maximum retry count is -1, the Batch service retries the task without limit. :type max_task_retry_count: int :param wait_for_success: Whether the Batch service should wait for the start task to complete successfully (that is, to exit with exit code 0) before scheduling any tasks on the compute node. If true and the start task fails on a compute node, the Batch service retries the start task up to its maximum retry count (maxTaskRetryCount). If the task has still not completed successfully after all retries, then the Batch service marks the compute node unusable, and will not schedule tasks to it. This condition can be detected via the node state and failure info details. If false, the Batch service will not wait for the start task to complete. In this case, other tasks can start executing on the compute node while the start task is still running; and even if the start task fails, new tasks will continue to be scheduled on the node. The default is false. :type wait_for_success: bool """ _validation = { 'command_line': {'required': True}, } _attribute_map = { 'command_line': {'key': 'commandLine', 'type': 'str'}, 'container_settings': {'key': 'containerSettings', 'type': 'TaskContainerSettings'}, 'resource_files': {'key': 'resourceFiles', 'type': '[ExtendedResourceFile]'}, 'environment_settings': {'key': 'environmentSettings', 'type': '[EnvironmentSetting]'}, 'user_identity': {'key': 'userIdentity', 'type': 'UserIdentity'}, 'max_task_retry_count': {'key': 'maxTaskRetryCount', 'type': 'int'}, 'wait_for_success': {'key': 'waitForSuccess', 'type': 'bool'}, } def __init__(self, **kwargs): super(StartTask, self).__init__(**kwargs) self.command_line = kwargs.get('command_line') self.container_settings = kwargs.get('container_settings', None) self.resource_files = kwargs.get('resource_files', None) self.environment_settings = kwargs.get('environment_settings', None) self.user_identity = kwargs.get('user_identity', None) self.max_task_retry_count = kwargs.get('max_task_retry_count', None) self.wait_for_success = kwargs.get('wait_for_success', None) class TaskCollectionTaskFactory(TaskFactoryBase): """A Task Factory for adding a predefined collection of tasks automatically to a job on submission. :param tasks: A list if task parameters, each of which will be added straight to the job. :type tasks: A list of :class:`ExtendedTaskParameter <azext.batch.models.ExtendedTaskParameter>` """ _validation = { 'type': {'required': True}, 'tasks': {'required': True}, } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'tasks': {'key': 'tasks', 'type': '[ExtendedTaskParameter]'}, } def __init__(self, **kwargs): super(TaskCollectionTaskFactory, self).__init__(**kwargs) self.tasks = kwargs.get('tasks') self.type = 'taskCollection' class YumPackageReference(PackageReferenceBase): """A reference to a package to be installed using the YUM package manager on a Linux node. :param str id: The name of the package. :param str version: The version of the package to be installed. If omitted, the latest version (according to the package repository) will be installed. :param bool disable_excludes: Whether to allow packages that might otherwise be excluded by VM configuration (e.g. kernel packages). Default is False. """ _validation = { 'type': {'required': True}, 'id': {'required': True}, } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'id': {'key': 'id', 'type': 'str'}, 'version': {'key': 'version', 'type': 'str'}, 'disable_excludes': {'key': 'disableExcludes', 'type': 'bool'} } def __init__(self, **kwargs): super(YumPackageReference, self).__init__(**kwargs) self.id = kwargs.get('id') self.version = kwargs.get('version', None) self.disable = kwargs.get('disable', None)

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tests/conftest.py

DD-DeCaF/model_warehouse

0

69435

<reponame>DD-DeCaF/model_warehouse # Copyright (c) 2018, Novo Nordisk Foundation Center for Biosustainability, # Technical University of Denmark. # # 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. """Provide session level fixtures.""" import json import pytest from jose import jwt from model_storage.app import app as app_ from model_storage.app import init_app from model_storage.models import Model from model_storage.models import db as db_ @pytest.fixture(scope="session") def app(): """Provide an initialized Flask for use in certain test cases.""" init_app(app_, db_) with app_.app_context(): yield app_ @pytest.fixture(scope="session") def client(app): """Provide a Flask test client for integration tests.""" with app.test_client() as client: yield client @pytest.fixture(scope="session") def reset_tables(app): """Ensure a clean database.""" # Clean up anything that might reside in the testing database. db_.session.remove() db_.drop_all() # Re-create tables. db_.create_all() @pytest.fixture(scope="session") def connection(): """ Use a connection such that transactions can be used. Notes ----- Follows a transaction pattern described in the following: http://docs.sqlalchemy.org/en/latest/orm/session_transaction.html#session-begin-nested """ with db_.engine.connect() as connection: yield connection @pytest.fixture(scope="session") def tokens(app): """Provide read, write and admin JWT claims to project 4.""" return { "read": jwt.encode( {"prj": {4: "read"}}, app.config["JWT_PRIVATE_KEY"], "RS512" ), "write": jwt.encode( {"prj": {4: "write"}}, app.config["JWT_PRIVATE_KEY"], "RS512" ), "admin": jwt.encode( {"prj": {4: "admin"}}, app.config["JWT_PRIVATE_KEY"], "RS512" ), } @pytest.fixture(scope="function") def session(reset_tables, connection): """ Create a transaction and session per test unit. Rolling back a transaction removes even committed rows (``session.commit``) from the database. https://docs.sqlalchemy.org/en/latest/orm/session_transaction.html#joining-a-session-into-an-external-transaction-such-as-for-test-suites """ flask_sqlalchemy_session = db_.session transaction = connection.begin() db_.session = db_.create_scoped_session( options={"bind": connection, "binds": {}} ) yield db_.session db_.session.close() transaction.rollback() db_.session = flask_sqlalchemy_session @pytest.fixture(scope="session") def e_coli_core(): """Provide the e. coli core model as a dict.""" with open("tests/data/e_coli_core.json") as file_: return json.load(file_) @pytest.fixture(scope="session") def model(reset_tables): """Return a fixture with test data for the Model data model.""" fixture = Model( name="iJO1366", organism_id=4, project_id=4, default_biomass_reaction="BIOMASS", model_serialized={"Reactions": [{"GAPDH": "x->y"}]}, ec_model=False, ) db_.session.add(fixture) db_.session.commit() return fixture

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Chapter08/rs_cem_comparison.py

verdimrc/Mastering-Reinforcement-Learning-with-Python

58

93365

from math import sin, pi import random import numpy as np from scipy.stats import norm def black_box_projectile(theta, v0=10, g=9.81): assert theta >= 0 assert theta <= 90 return (v0 ** 2) * sin(2 * pi * theta / 180) / g def random_shooting(n=1, min_a=0, max_a=90): assert min_a <= max_a return [random.uniform(min_a, max_a) for i in range(n)] def pick_elites(actions, M_elites): actions = np.array(actions) assert M_elites <= len(actions) assert M_elites > 0 results = np.array([black_box_projectile(a) for a in actions]) sorted_ix = np.argsort(results)[-M_elites:][::-1] return actions[sorted_ix], results[sorted_ix] def try_random_shooting(trials=10000, n=20): print("Trying random shooting.") best_results = [] best_actions = [] for i in range(trials): actions_to_try = random_shooting(n) best_action, best_result = pick_elites(actions_to_try, 1) best_results.append(best_result[0]) best_actions.append(best_action[0]) print(f"Out of {trials} trials:") print(f"- Average score is {round(np.mean(best_results), 2)}") print(f"- Average action is {round(np.mean(best_actions), 2)}") print(f"- Action SD is {round(np.std(best_actions), 2)}") def try_cem_normal(trials=10000, N=5, M_elites=2, iterations=3): print("Trying CEM.") best_results = [] best_actions = [] for i in range(trials): print(f"================ trial {i}") print("--iteration: 1") actions_to_try = random_shooting(N) elite_acts, _ = pick_elites(actions_to_try, M_elites) print(f"actions_to_try: {np.round(actions_to_try, 2)}") print(f"elites: {np.round(elite_acts, 2)}") for r in range(iterations - 1): print(f"--iteration: {r + 2}") mu, std = norm.fit(elite_acts) print(f"fitted normal mu: {np.round(mu, 2)}, std: {np.round(std, 2)}") actions_to_try = np.clip(norm.rvs(mu, std, N), 0, 90) elite_acts, elite_results = pick_elites(actions_to_try, M_elites) print(f"actions_to_try: {np.round(actions_to_try, 2)}") print(f"elites: {np.round(elite_acts, 2)}") mu, std = norm.fit(elite_acts) print(f"final action: {np.round(mu, 2)}") best_results.append(black_box_projectile(mu)) best_actions.append(np.clip(mu, 0, 90)) print(f"Out of {trials} trials:") print(f"- Average score is {round(np.mean(best_results), 2)}") print(f"- Average action is {round(np.mean(best_actions), 2)}") print(f"- Action SD is {round(np.std(best_actions), 2)}")

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epi/variable.py

Epikem/deep-learning-from-scratch

0

1602054

import numpy as np import matplotlib.pylab as plt import sys def run(): visualizeTarget = sys.argv[1] print(visualizeTarget) if(visualizeTarget=='step'): x=np.arange(-5.0,5.0,0.1) y=step(x) plt.plot(x,y) plt.ylim(-0.1,1.1) plt.show() elif(visualizeTarget=='sigmoid'): x=np.arange(-5.0,5.0,0.1) y=sigmoid(x) plt.plot(x,y) plt.ylim(-0.1,1.1) plt.show() elif(visualizeTarget=='relu'): x=np.arange(-5.0,5.0,0.1) y=relu(x) plt.plot(x,y) # plt.ylim(-0.1,1.1) plt.show() elif(visualizeTarget=='all'): x=np.arange(-5.0,5.0,0.1) y=step(x) plt.plot(x,y) # plt.ylim(-0.1,1.1) x=np.arange(-5.0,5.0,0.1) y=sigmoid(x) plt.plot(x,y) x=np.arange(-5.0,5.0,0.1) y=relu(x) plt.plot(x,y) # plt.ylim(-0.1,3.0) plt.show() # for x in sys.argv: # print(x) class variable(): def __init__(self, value): self.data = value pass def read(self): return self.data def test(): v = variable(424) print(v.read() == 424) a = np.array([2,3,1,4,2]) print(a) print(sigmoid(a)) def TestSimpleANDGate(): print('simple AND gate test') print(SimpleANDGate(0,0)) print(SimpleANDGate(0,1)) print(SimpleANDGate(1,0)) print(SimpleANDGate(1,1)) def SimpleANDGate(x1,x2): w1,w2,theta = 0.5,0.5,0.7 tmp = x1*w1+x2*w2 if(tmp<=theta): return 0 elif(tmp>theta): return 1 def TestANDGate(): print('and gate test') print(ANDGate(0,0)) print(ANDGate(0,1)) print(ANDGate(1,0)) print(ANDGate(1,1)) def ANDGate(x1,x2): x = np.array([x1,x2]) w=np.array([0.5,0.5]) b=-0.7 tmp=np.sum(w*x)+b if(tmp<=0): return 0 else: return 1 def TestNANDGate(): print('nand gate test') print(NANDGate(0,0)) print(NANDGate(0,1)) print(NANDGate(1,0)) print(NANDGate(1,1)) def NANDGate(x1,x2): x = np.array([x1,x2]) w=np.array([-0.5,-0.5]) b=0.7 tmp=np.sum(w*x)+b if(tmp<=0): return 0 else: return 1 def TestORGate(): print('OR gate test') print(ORGate(0,0)) print(ORGate(0,1)) print(ORGate(1,0)) print(ORGate(1,1)) def ORGate(x1,x2): x = np.array([x1,x2]) w=np.array([0.5,0.5]) b=-0.2 tmp=np.sum(w*x)+b if(tmp<=0): return 0 else: return 1 def XORGate(x1,x2): a = ORGate(x1,x2) b = NANDGate(x1,x2) return ANDGate(a,b) def step(x): y=x>0 return y.astype(np.int) def simple_step(value): if(value <= 0): return 0 else: return 1 def sigmoid(value): return 1/(1+np.exp(-value)) def relu(x): return np.maximum(0,x) class MultiplyLayer: def __init__(self): self.x = None self.y = None def forward(self, x, y): self.x=x self.y=y out=x*y return out def backward(self, dout): dx=dout*self.y dy=dout*self.x return dx,dy def matrixTest1(): print('mat') b = np.array([[1,2],[3,4],[5,6]]) print(b) print(np.ndim(b)) # 배열의 차원 수 print(b.shape) # 배열의 형상 (모든 차원의 각 길이) def matrixMultiplyTest(): print('multiply') a = np.array([[1,2],[3,4]]) print(a.shape) b=np.array([[5,6],[7,8]]) print(b.shape) print(np.dot(a,b)) # 행렬 곱셈 a = np.array([[1,2],[3,4],[5,6]]) print(a.shape) b=np.array([7,8]) print(b.shape) print(np.dot(a,b)) x = np.array([1,2]) W = np.array([[1,3,5],[2,4,6]]) y = np.dot(x,W) print(y) if(__name__=='main'): # test() # TestSimpleANDGate() # matrixTest1() # matrixMultiplyTest() run()

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integrations-and-supported-tools/fastai/scripts/Neptune_fastai.py

neptune-ai/examples

15

33954

import fastai from neptune.new.integrations.fastai import NeptuneCallback from fastai.vision.all import * import neptune.new as neptune run = neptune.init( project="common/fastai-integration", api_token="<PASSWORD>", tags="basic" ) path = untar_data(URLs.MNIST_TINY) dls = ImageDataLoaders.from_csv(path) # Log all training phases of the learner learn = cnn_learner(dls, resnet18, cbs=[NeptuneCallback(run=run, base_namespace="experiment")]) learn.fit_one_cycle(2) learn.fit_one_cycle(1) run.stop()

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first_steps_in_coding_and_simple_operations_and_calculations/exercise/charity_campaign.py

PetkoAndreev/Python-basics

0

16561

days = int(input()) sladkar = int(input()) cake = int(input()) gofreta = int(input()) pancake = int(input()) cake_price = cake*45 gofreta_price = gofreta*5.8 pancake_price = pancake*3.2 day_price = (cake_price + gofreta_price + pancake_price)*sladkar total_price = days*day_price campaign = total_price - (total_price/8) print(campaign)

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src/learning/train.py

mari-linhares/deep-audio

0

166616

<reponame>mari-linhares/deep-audio import tensorflow as tf tf.enable_eager_execution() import tensorflowjs as tfjs import numpy as np from data_sources.data_source import DataSource from optimizers.optimizer import Optimizer import models.model_factory as mf import utilities.utils as utils from tensorflow.python.client import device_lib print(device_lib.list_local_devices()) def train(): ds_train = DataSource('audio_files/speech/train_data.npy', 'audio_files/speech/train_labels.npy', classes_dict='audio_files/speech/classes') ds_eval = DataSource('audio_files/speech/eval_data.npy', 'audio_files/speech/eval_labels.npy', classes_dict='audio_files/speech/classes') ds_test = DataSource('audio_files/speech/test_data.npy', 'audio_files/speech/test_labels.npy', classes_dict='audio_files/speech/classes') model = mf.build_model('CNN', input_shape=ds_train.input_shape, num_classes = ds_train.num_classes) optimizer = Optimizer('Adam') EPOCHS = 1 for epoch in range(EPOCHS): train_losses = [] train_accuracy = 0 train_instances = 0 for (batch, (img_tensor, label)) in enumerate(ds_train.dataset): train_loss, predicted = optimizer.compute_and_apply_gradients(model, img_tensor, label) train_losses.append(train_loss) train_accuracy += sum(np.argmax(predicted, axis=1) == label.numpy()) train_instances += label.numpy().shape[0] # print ('Epoch {} Batch {} Train Loss {:.6f}'.format(epoch + 1, batch + 1, sum(train_losses)/len(train_losses))) eval_losses = [] accuracy = 0 instances = 0 for (batch, (img_tensor, label)) in enumerate(ds_eval.dataset): eval_loss, predicted = model.compute_loss(img_tensor, label, training=False) accuracy += sum(np.argmax(predicted, axis=1) == label.numpy()) instances += label.numpy().shape[0] eval_losses.append(eval_loss) print ('Epoch {} Train Accuracy: {:.6f} | Test Accuracy: {:.6f}'.format(epoch +1, train_accuracy/train_instances, accuracy/instances)) print ('Epoch {} Train Loss {:.6f} | Eval Loss {:.6f}'.format(epoch + 1, sum(train_losses)/len(train_losses), sum(eval_losses)/len(eval_losses))) eval_losses = [] accuracy = 0 instances = 0 for (batch, (img_tensor, label)) in enumerate(ds_test.dataset): eval_loss, predicted = model.compute_loss(img_tensor, label, training=False) accuracy += sum(np.argmax(predicted, axis=1) == label.numpy()) instances += label.numpy().shape[0] eval_losses.append(eval_loss) print ('Epoch {} Train Accuracy: {:.6f} | Test Accuracy: {:.6f}'.format(epoch +1, train_accuracy/train_instances, accuracy/instances)) print ('Epoch {} Train Loss {:.6f} | Eval Loss {:.6f}'.format(epoch + 1, sum(train_losses)/len(train_losses), sum(eval_losses)/len(eval_losses))) import ipdb; ipdb.set_trace() saver = tf.train.Checkpoint(model=model, optimizer=optimizer.optimizer) saver.save('/home/marianne/deep-audio/saver/save') tfjs.converters.convert_tf_saved_model('/home/marianne/deep-audio/saver/save.ckpt', output_node_names=['test'], output_dir='/home/marianne/deep-audio/') if __name__ == '__main__': train()

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ABC/ABC127/abc127_a.py

yatabis/AtCoder-in-Python3

0

158108

<filename>ABC/ABC127/abc127_a.py # 問題URL: https://atcoder.jp/contests/abc127/tasks/abc127_a # 解答URL: https://atcoder.jp/contests/abc127/submissions/14655259 a, b = map(int, input().split()) if a <= 5: print(0) elif a <= 12: print(b // 2) else: print(b)

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components/workstation.py

cqzhao/FooProxy

0

21908

#coding:utf-8 """ @author : linkin @email : <EMAIL> @date : 2018-10-04 """ import logging from APIserver.apiserver import app from components.collector import Collector from components.validator import Validator from components.detector import Detector from components.scanner import Scaner from components.tentacle import Tentacle from multiprocessing import Pool from multiprocessing import Manager from config.config import MODE from const.settings import RUN_FUNC logger = logging.getLogger() class Workstation(object): """ 整个项目的启动工作面板 """ def __init__(self): self.collector = Collector() self.validator = Validator() self.detector = Detector() self.scanner = Scaner() self.tentacle = Tentacle() self.proxyList = Manager().list() def run_validator(self,proxyList): self.validator.run(proxyList) def run_collector(self,proxyList): self.collector.run(proxyList) def run_detector(self,*params): self.detector.run() def run_scanner(self,*params): self.scanner.run() def run_tentacle(self,*params): self.tentacle.run() def work(self): """ 项目启动，根据config中的MODE配置执行对应的部件这样可以隔离部件功能，耦合性较低。异步多进程执行需要共享变量，使用了multiprocessing的Manager来生成共享List. """ pool = Pool(5) func = [] for i in MODE: if MODE[i]: func.append(eval('self.'+RUN_FUNC[i])) [pool.apply_async(fun,args=(self.proxyList,)) for fun in func] pool.close() app.run(host='0.0.0.0',port=2020)

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src/djanban/apps/api/views/cards.py

diegojromerolopez/djanban

33

109250

<gh_stars>10-100 # -*- coding: utf-8 -*- from __future__ import unicode_literals import json import re import tempfile import dateutil from django.core.files.base import ContentFile from django.core.serializers.json import DjangoJSONEncoder from django.db import transaction from django.db.models import Q from django.http import JsonResponse, Http404 from django.utils import timezone from djanban.apps.api.http import JsonResponseBadRequest, JsonResponseMethodNotAllowed, JsonResponseNotFound from djanban.apps.api.serializers import Serializer from djanban.apps.api.util import get_list_or_404, get_card_or_404, get_board_or_404 from djanban.apps.base.auth import get_user_boards from djanban.apps.base.decorators import member_required from djanban.apps.boards.models import Board, Card, CardComment, List, CardAttachment # Point of access to several actions from djanban.apps.forecasters.models import Forecaster from djanban.utils.custom_uuid import custom_uuid @member_required def modify_cards(request, board_id): # Create a new card if request.method == "PUT": return _add_card(request, board_id) # Move all cards in a list if request.method == "POST": return _move_all_list_cards(request, board_id) # Otherwise, return HTTP ERROR 405 return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) # Adds a new card in the board # Used by modify_cards def _add_card(request, board_id): if request.method != "PUT": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member put_params = json.loads(request.body) if not put_params.get("name") or not put_params.get("list") or not put_params.get("position"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) if put_params.get("position") != "top" and put_params.get("position") != "bottom": return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) try: list_ = get_list_or_404(request, board_id, put_params.get("list")) except Http404: return JsonResponseNotFound({"message": "List not found"}) new_card = list_.add_card(member=member, name=put_params.get("name"), position=put_params.get("position")) serializer = Serializer(board=new_card.board) return JsonResponse(serializer.serialize_card(new_card)) # Move all cards from a list to another # Used by modify_cards def _move_all_list_cards(request, board_id): if request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: board = get_board_or_404(request, board_id) except Http404: return JsonResponseNotFound({"message": "Board not found"}) post_params = json.loads(request.body) if not post_params.get("source_list") or not post_params.get("destination_list"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) # Check if the lists exists and if they are different try: source_list = board.active_lists.get(id=post_params.get("source_list")) destination_list = board.active_lists.get(id=post_params.get("destination_list")) if source_list.id == destination_list.id: raise AssertionError() except (List.DoesNotExist, AssertionError): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) # Move the cards source_list.move_cards(member=member, destination_list=destination_list) serializer = Serializer(board=board) return JsonResponse(serializer.serialize_board()) # Return the JSON representation of a card @member_required def get_card(request, board_id, card_id): if request.method != "GET": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) serializer = Serializer(board=card.board) card_json = serializer.serialize_card(card) return JsonResponse(card_json) # Change the name or description of the card @member_required @transaction.atomic def change(request, board_id, card_id): if request.method != "PUT": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) put_params = json.loads(request.body) if put_params.get("name"): card.change_attribute(member, attribute="name", value=put_params.get("name")) elif put_params.get("description"): card.change_attribute(member, attribute="description", value=put_params.get("name")) elif put_params.get("is_closed") is not None: card.change_attribute(member, attribute="is_closed", value=put_params.get("name")) elif put_params.get("due_datetime"): due_datetime_str = put_params.get("due_datetime") due_datetime = dateutil.parser.parse(due_datetime_str) card.change_attribute(member, attribute="due_datetime", value=due_datetime) elif "due_datetime" in put_params and put_params.get("due_datetime") is None: card.change_attribute(member, attribute="due_datetime", value=None) elif "value" in put_params: card_value = put_params.get("value") if card_value == "": card_value = None card.change_value(member=member, value=card_value) else: return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Change the labels of the card @member_required @transaction.atomic def change_labels(request, board_id, card_id): if request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) board = card.board post_params = json.loads(request.body) if not post_params.get("labels"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) label_ids = post_params.get("labels") card.labels.clear() for label_id in label_ids: if board.labels.filter(id=label_id).exists(): label = board.labels.get(id=label_id) card.labels.add(label) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) @member_required @transaction.atomic def change_members(request, board_id, card_id): if request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) board = card.board post_params = json.loads(request.body) if not post_params.get("members"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) member_ids = post_params.get("members") card.update_members(member, board.members.filter(id__in=member_ids)) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Change list @member_required @transaction.atomic def move_to_list(request, board_id, card_id): if request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) post_params = json.loads(request.body) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) # The new position of the card new_position = post_params.get("position", "top") # If there is no new_list param, the card is going to be moved in the same list currently is if post_params.get("new_list"): list_ = card.board.lists.get(id=post_params.get("new_list")) card.move(member, destination_list=list_, destination_position=new_position) else: card.change_order(member, destination_position=new_position) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_board()) # Creates a new attachment @member_required @transaction.atomic def add_attachment(request, board_id, card_id): if request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) except (Board.DoesNotExist, Card.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) uploaded_file_content = request.body if uploaded_file_content is None: return JsonResponseNotFound({"message": "No file sent."}) with tempfile.TemporaryFile() as uploaded_file: uploaded_file.write(uploaded_file_content) uploaded_file_name = request.GET.get("uploaded_file_name", custom_uuid()) attachment = card.add_new_attachment(member, uploaded_file, uploaded_file_name) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card_attachment(attachment)) # Deletes an attachment @member_required @transaction.atomic def delete_attachment(request, board_id, card_id, attachment_id): if request.method != "DELETE": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) attachment = card.attachments.get(id=attachment_id) except (Board.DoesNotExist, Card.DoesNotExist, CardAttachment.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) card.delete_attachment(member, attachment) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card_attachment(attachment)) # Creates a new comment @member_required @transaction.atomic def add_new_comment(request, board_id, card_id): if request.method != "PUT": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) put_params = json.loads(request.body) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) # Getting the comment content comment_content = put_params.get("content") # If the comment is empty, fail if not comment_content: return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) # Otherwise, add the comment new_comment = card.add_comment(member, comment_content) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card_comment(new_comment)) # Add S/E time to card @member_required @transaction.atomic def add_se_time(request, board_id, card_id): if request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) post_params = json.loads(request.body) spent_time = post_params.get("spent_time") estimated_time = post_params.get("estimated_time") date = post_params.get("date") description = post_params.get("description", card.name) if spent_time != "": try: spent_time = float(str(spent_time).replace(",", ".")) except ValueError: return JsonResponseNotFound({"message": "Not found."}) else: spent_time = None if estimated_time != "": try: estimated_time = float(str(estimated_time).replace(",", ".")) except ValueError: return JsonResponseNotFound({"message": "Not found."}) else: estimated_time = None if spent_time is None and estimated_time is None: return JsonResponseNotFound({"message": "Not found."}) # Optional days ago parameter days_ago = None matches = re.match(r"^\-(?P<days_ago>\d+)$", date) if matches: days_ago = int(matches.group("days_ago")) card.add_spent_estimated_time(member, spent_time, estimated_time, days_ago, description) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Create a new estimation for a card @member_required def update_forecasts(request, board_id, card_id): try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) except (Board.DoesNotExist, Card.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) available_card_forecasters = Forecaster.objects.filter( (Q(board=None) & Q(member=None)) | (Q(board=card.board) & Q(member=None)) | (Q(board=None) & Q(member__in=card.members.all())) ) serializer = Serializer(board=card.board) estimations = [] for forecaster in available_card_forecasters: forecast = forecaster.make_forecast(card=card) estimations.append(serializer.serialize_forecast(forecast)) return JsonResponse(estimations, safe=False, encoder=DjangoJSONEncoder) # Add a new blocking card to this card @member_required def add_blocking_card(request, board_id, card_id): if request.method != "PUT": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member put_body = json.loads(request.body) if not put_body.get("blocking_card"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) blocking_card = board.cards.exclude(id=card_id).get(id=put_body.get("blocking_card")) except (Board.DoesNotExist, Card.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) card.add_blocking_card(member, blocking_card) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Remove a blocking card to this card @member_required def remove_blocking_card(request, board_id, card_id, blocking_card_id): if request.method != "DELETE": return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) member = request.user.member try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) blocking_card = card.blocking_cards.exclude(id=card_id).get(id=blocking_card_id) except (Board.DoesNotExist, Card.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) card.remove_blocking_card(member, blocking_card) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Add a new review @member_required @transaction.atomic def add_new_review(request, board_id, card_id): if request.method != "PUT": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) board = card.board put_body = json.loads(request.body) if not put_body.get("members"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) reviewers = board.members.filter(id__in=put_body.get("members")) description = put_body.get("description", "") card.add_review(member, reviewers=reviewers, description=description) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Delete a review @member_required @transaction.atomic def delete_review(request, board_id, card_id, review_id): if request.method != "DELETE": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) review = card.reviews.get(id=review_id) except (Board.DoesNotExist, Card.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) card.delete_review(member, review) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Add a requirement to the card @member_required @transaction.atomic def add_requirement(request, board_id, card_id): if request.method != "PUT": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) board = card.board put_body = json.loads(request.body) if not put_body.get("requirement"): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) requirement = board.requirements.get(id=put_body.get("requirement")) # If the requirement is already in the card, we can't continue if card.requirements.filter(id=requirement.id).exists(): return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) card.add_requirement(member, requirement) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Remove a requirement of the card @member_required @transaction.atomic def remove_requirement(request, board_id, card_id, requirement_id): if request.method != "DELETE": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: board = get_user_boards(request.user).get(id=board_id) card = board.cards.get(id=card_id) requirement = card.requirements.get(id=requirement_id) except (Board.DoesNotExist, Card.DoesNotExist) as e: return JsonResponseNotFound({"message": "Not found."}) card.remove_requirement(member, requirement) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card(card)) # Delete or update a comment @member_required @transaction.atomic def modify_comment(request, board_id, card_id, comment_id): if request.method != "DELETE" and request.method != "POST": return JsonResponseMethodNotAllowed({"message": "HTTP method not allowed."}) member = request.user.member try: card = get_card_or_404(request, board_id, card_id) except Http404: return JsonResponseNotFound({"message": "Card not found."}) try: comment = card.comments.get(id=comment_id) except CardComment.DoesNotExist as e: return JsonResponseNotFound({"message": "Not found."}) if request.method == "DELETE": comment = _delete_comment(member, card, comment) elif request.method == "POST": post_params = json.loads(request.body) new_comment_content = post_params.get("content") if not new_comment_content: return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) comment = _edit_comment(member, card, comment, new_comment_content) else: return JsonResponseBadRequest({"message": "Bad request: some parameters are missing."}) serializer = Serializer(board=card.board) return JsonResponse(serializer.serialize_card_comment(comment)) # Edit comment @member_required def _edit_comment(member, card, comment_to_edit, new_comment_content): edited_comment = card.edit_comment(member, comment_to_edit, new_comment_content) return edited_comment # Delete a comment @member_required def _delete_comment(member, card, comment_to_delete): # Delete the comment card.delete_comment(member, comment_to_delete) return comment_to_delete

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contests/atcoder/intro-heuristics/intro_heuristics_b/generate.py

conao3/coder

0

193688

<reponame>conao3/coder<filename>contests/atcoder/intro-heuristics/intro_heuristics_b/generate.py #!/usr/bin/env python3 # usage: $ oj generate-input 'python3 generate.py' import random # generated by online-judge-template-generator v4.1.0 (https://github.com/kmyk/online-judge-template-generator) def main(): c = [None for _ in range(26)] D = random.randint(1, 10 ** 9) # TODO: edit here s = [[None for _ in range(26)] for _ in range(D + 28)] t = [None for _ in range(D)] for i in range(26): c[i] = random.randint(1, 10 ** 9) # TODO: edit here for j in range(D + 2): for i in range(26): s[i + j][i] = random.randint(1, 10 ** 9) # TODO: edit here for i in range(D): t[i] = random.randint(1, 10 ** 9) # TODO: edit here print(D) print(*[c[i] for i in range(26)]) for j in range(D + 2): print(*[s[i + j][i] for i in range(26)]) for i in range(D): print(t[i]) if __name__ == "__main__": main()

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SharedData/list_all_files.py

kylemcdonald/SoundParts

22

33453

<reponame>kylemcdonald/SoundParts<filename>SharedData/list_all_files.py<gh_stars>10-100 import os import fnmatch # extensions can be a single tring like '.png' or '.jpg' # or a list of extensions. they should all be lowercase # but the . is important. def list_all_files(directory, extensions=None): for root, dirnames, filenames in os.walk(directory): for filename in filenames: base, ext = os.path.splitext(filename) joined = os.path.join(root, filename) if extensions is None or ( len(ext) and ext.lower() in extensions ): yield joined

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src/arima_model.py

SaharCarmel/ARIMA

0

16990

""" The ARIMA model. """ import torch import numpy as np class ARIMA(torch.nn.Module): """ARIMA [summary] """ def __init__(self, p: int = 0, d: int = 0, q: int = 0) -> None: """__init__ General ARIMA model constructor. Args: p (int): The number of lag observations included in the model, also called the lag order. d (int): The number of times that the raw observations are differenced, also called the degree of differencing. q (int): The size of the moving average window, also called the order of moving average. """ super(ARIMA, self).__init__() self.p = p self.pWeights = torch.rand(p) self.pWeights.requires_grad = True self.q = q self.qWeights = torch.rand(q) self.qWeights.requires_grad = True self.d = d self.dWeights = torch.rand(d) self.dWeights.requires_grad = True self.drift = torch.rand(1) pass def forward(self, x: torch.Tensor, err: torch.Tensor) -> torch.Tensor: """forward the function that defines the ARIMA(0,1,1) model. It was written specifically for the case of ARIMA(0,1,1). Args: x (torch.Tensor): The input data. All the past observations err (torch.Tensor): The error term. A normal distribution vector. Returns: torch.Tensor: The output of the model. The current prediction. """ zData = torch.diff(x) zPred = self.dWeights*zData[-1] + \ self.qWeights*err[-2] + err[-1] + self.drift aPred = zPred + x[-1] return aPred def generateSample(self, length: int) -> torch.Tensor: """generateSample An helper function to generate a sample of data. Args: length (int): The length of the sample. Returns: torch.Tensor: The generated sample. """ sample = torch.zeros(length) noise = torch.tensor(np.random.normal( loc=0, scale=1, size=length), dtype=torch.float32) sample[0] = noise[0] with torch.no_grad(): for i in range(length-2): sample[i+2] = self.forward(sample[:i+2], noise[:i+2]) pass return sample def fit(self, trainData: torch.Tensor, epochs: int, learningRate: float) -> None: """fit A function to fit the model. It is a wrapper of the Args: trainData (torch.Tensor): The training data. epochs (int): The number of epochs. learningRate (float): The learning rate. """ dataLength = len(trainData) errors = torch.tensor(np.random.normal( loc=0, scale=1, size=dataLength), dtype=torch.float32) for epoch in range(epochs): prediction = torch.zeros(dataLength) for i in range(dataLength-2): prediction[i + 2] = self.forward(trainData[0:i+2], errors[0:i+2]) pass loss = torch.mean(torch.pow(trainData - prediction, 2)) print(f'Epoch {epoch} Loss {loss}') loss.backward() self.dWeights.data = self.dWeights.data - \ learningRate * self.dWeights.grad.data self.dWeights.grad.data.zero_() self.qWeights.data = self.qWeights.data - \ learningRate * self.qWeights.grad.data self.qWeights.grad.data.zero_() pass

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model-optimizer/mo/front/caffe/extractors/batchnorm.py

tdp2110/dldt

0

139454

<gh_stars>0 """ Copyright (c) 2018-2019 Intel Corporation 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 numpy as np from mo.front.caffe.extractors.utils import embed_input from mo.front.common.partial_infer.elemental import copy_shape_infer def batch_norm_ext(pb_layer, pb_model): """ Extracts properties of the BatchNorm layer. In case of scale, scale is merged into mean and variance Args: pb_layer: proto layer, contains own properties of the layer, i.e epsilon pb_model: caffemodel layer, contains blobs with 0: mean, 1: variance, (opt)2: scale Returns: attrs object with type, partial inference function and mean/variance properties. """ assert pb_layer, 'Protobuf layer can not be empty' param = pb_layer.batch_norm_param attrs = { 'op': 'BatchNormalization', 'type': 'BatchNormalization', 'eps': param.eps, 'infer': copy_shape_infer } if not pb_model: return attrs blobs = pb_model.blobs assert len(blobs) >= 2, 'BatchNorm accepts not less then two input blobs' mean = np.array(blobs[0].data) variance = np.array(blobs[1].data) if len(blobs) == 3: scale = blobs[2].data[0] if scale != 0: scale = 1.0 / scale mean *= scale variance *= scale embed_input(attrs, 1, 'gamma', np.ones(mean.shape), 'gamma') embed_input(attrs, 2, 'beta', np.zeros(variance.shape), 'beta') embed_input(attrs, 3, 'mean', mean, 'biases') embed_input(attrs, 4, 'variance', variance, 'weights') return attrs

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flare_removal/python/models.py

shaun95/google-research

1

158586

# coding=utf-8 # Copyright 2022 The Google Research Authors. # # 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. """Models for flare removal.""" from flare_removal.python import u_net from flare_removal.python import vgg def build_model(model_type, batch_size): """Returns a Keras model specified by name.""" if model_type == 'unet': return u_net.get_model( input_shape=(512, 512, 3), scales=4, bottleneck_depth=1024, bottleneck_layers=2) elif model_type == 'can': return vgg.build_can( input_shape=(512, 512, 3), conv_channels=64, out_channels=3) else: raise ValueError(model_type)

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tests/components/image_processing/common.py

pcaston/core

1

186189

<gh_stars>1-10 """Collection of helper methods. All containing methods are legacy helpers that should not be used by new components. Instead call the service directly. """ from openpeerpower.components.image_processing import DOMAIN, SERVICE_SCAN from openpeerpower.const import ATTR_ENTITY_ID, ENTITY_MATCH_ALL from openpeerpower.core import callback from openpeerpower.loader import bind_opp @bind_opp def scan(opp, entity_id=ENTITY_MATCH_ALL): """Force process of all cameras or given entity.""" opp.add_job(async_scan, opp, entity_id) @callback @bind_opp def async_scan(opp, entity_id=ENTITY_MATCH_ALL): """Force process of all cameras or given entity.""" data = {ATTR_ENTITY_ID: entity_id} if entity_id else None opp.async_add_job(opp.services.async_call(DOMAIN, SERVICE_SCAN, data))

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plugins/classbrowser/parser_diff.py

thioshp/MyGeditV2Full

4

1607572

<gh_stars>1-10 # -*- coding: utf-8 -*- # Copyright (C) 2007 <NAME> (<EMAIL>) # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, # Boston, MA 02111-1307, USA. import gtk import gobject import pango import os import options import imagelibrary from parserinterface import ClassParserInterface class Token: def __init__(self): self.start = 0 self.end = 0 self.name = None self.parent = None self.children = [] self.type = 'changeset' class DiffParser(ClassParserInterface): def parse(self, geditdoc): text = geditdoc.get_text(*geditdoc.get_bounds()) linecount = -1 current_file = None changeset = None files = [] uri = geditdoc.get_uri() for line in text.splitlines(): linecount += 1 lstrip = line.lstrip() ln = lstrip.split() if len(ln) == 0: continue if ln[0] == '---': if current_file is not None: current_file.end = linecount - 1 current_file = Token() current_file.name = ln[1] current_file.start = linecount current_file.type = 'file' current_file.uri = uri files.append(current_file) elif current_file == None: continue elif ln[0] == '@@' and ln[-1] == '@@': if changeset is not None: changeset.end = linecount changeset = Token() changeset.name = ' '.join(ln[1:-1]) changeset.start = linecount changeset.uri = uri current_file.children.append(changeset) changeset.parent = current_file # Ending line of last tokens if len(files) > 0: f = files[-1] f.end = linecount + 2 if len(f.children) > 0: f.children[-1].end = linecount + 2 model = gtk.TreeStore(gobject.TYPE_PYOBJECT) pp = None # "Fake" common top folder, if any # TODO: Create hierarchy if patch applies in multiple directories if len(files) > 0: paths = map(lambda f:f.name, files) prefix = os.path.dirname(os.path.commonprefix(paths)) + '/' if len(prefix) > 1: parent_path = Token() parent_path.type = 'path' parent_path.name = prefix for f in files: f.name = f.name.replace(prefix,'',1) pp = model.append(None,(parent_path,)) # Build tree for f in files: tree_iter = model.append(pp,(f,)) for c in f.children: model.append(tree_iter,(c,)) return model def cellrenderer(self, treeviewcolumn, cellrenderertext, treemodel, it): token = treemodel.get_value(it,0) colour = options.singleton().colours["member"] if token.type == 'path': colour = options.singleton().colours["namespace"] elif token.type == 'file': colour = options.singleton().colours["class"] cellrenderertext.set_property("text", token.name) cellrenderertext.set_property("style", pango.STYLE_NORMAL) cellrenderertext.set_property("foreground-gdk", colour) def get_tag_position(self, model, path): tok = model.get_value(model.get_iter(path),0) try: return tok.uri, tok.start + 1 except: return None def get_tag_at_line(self, model, doc, linenumber): def find_path(model, path, iter, data): line = data[0] token = model.get_value(iter, 0) if token.start <= line and token.end > line: # print path data[1].append(path) #return True return False path_found = [] model.foreach(find_path, (linenumber, path_found)) if len(path_found) > 0: return path_found[-1] return None def pixbufrenderer(self, treeviewcolumn, cellrendererpixbuf, treemodel, it): token = treemodel.get_value(it,0) if token.type == 'path': cellrendererpixbuf.set_property("stock-id", gtk.STOCK_DIRECTORY) elif token.type == 'file': cellrendererpixbuf.set_property("stock-id", gtk.STOCK_FILE) else: cellrendererpixbuf.set_property("pixbuf",imagelibrary.pixbufs['patch']) # -*- coding: utf-8 -*- # Copyright (C) 2007 <NAME> (<EMAIL>) # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, # Boston, MA 02111-1307, USA. import gtk import gobject import pango import os import options import imagelibrary from parserinterface import ClassParserInterface class Token: def __init__(self): self.start = 0 self.end = 0 self.name = None self.parent = None self.children = [] self.type = 'changeset' class DiffParser(ClassParserInterface): def parse(self, geditdoc): text = geditdoc.get_text(*geditdoc.get_bounds()) linecount = -1 current_file = None changeset = None files = [] uri = geditdoc.get_uri() for line in text.splitlines(): linecount += 1 lstrip = line.lstrip() ln = lstrip.split() if len(ln) == 0: continue if ln[0] == '---': if current_file is not None: current_file.end = linecount - 1 current_file = Token() current_file.name = ln[1] current_file.start = linecount current_file.type = 'file' current_file.uri = uri files.append(current_file) elif current_file == None: continue elif ln[0] == '@@' and ln[-1] == '@@': if changeset is not None: changeset.end = linecount changeset = Token() changeset.name = ' '.join(ln[1:-1]) changeset.start = linecount changeset.uri = uri current_file.children.append(changeset) changeset.parent = current_file # Ending line of last tokens if len(files) > 0: f = files[-1] f.end = linecount + 2 if len(f.children) > 0: f.children[-1].end = linecount + 2 model = gtk.TreeStore(gobject.TYPE_PYOBJECT) pp = None # "Fake" common top folder, if any # TODO: Create hierarchy if patch applies in multiple directories if len(files) > 0: paths = map(lambda f:f.name, files) prefix = os.path.dirname(os.path.commonprefix(paths)) + '/' if len(prefix) > 1: parent_path = Token() parent_path.type = 'path' parent_path.name = prefix for f in files: f.name = f.name.replace(prefix,'',1) pp = model.append(None,(parent_path,)) # Build tree for f in files: tree_iter = model.append(pp,(f,)) for c in f.children: model.append(tree_iter,(c,)) return model def cellrenderer(self, treeviewcolumn, cellrenderertext, treemodel, it): token = treemodel.get_value(it,0) colour = options.singleton().colours["member"] if token.type == 'path': colour = options.singleton().colours["namespace"] elif token.type == 'file': colour = options.singleton().colours["class"] cellrenderertext.set_property("text", token.name) cellrenderertext.set_property("style", pango.STYLE_NORMAL) cellrenderertext.set_property("foreground-gdk", colour) def get_tag_position(self, model, path): tok = model.get_value(model.get_iter(path),0) try: return tok.uri, tok.start + 1 except: return None def get_tag_at_line(self, model, doc, linenumber): def find_path(model, path, iter, data): line = data[0] token = model.get_value(iter, 0) if token.start <= line and token.end > line: # print path data[1].append(path) #return True return False path_found = [] model.foreach(find_path, (linenumber, path_found)) if len(path_found) > 0: return path_found[-1] return None def pixbufrenderer(self, treeviewcolumn, cellrendererpixbuf, treemodel, it): token = treemodel.get_value(it,0) if token.type == 'path': cellrendererpixbuf.set_property("stock-id", gtk.STOCK_DIRECTORY) elif token.type == 'file': cellrendererpixbuf.set_property("stock-id", gtk.STOCK_FILE) else: cellrendererpixbuf.set_property("pixbuf",imagelibrary.pixbufs['patch'])

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11287, 13, 2 ]

d05.py

nayunhwan/SMaSH_Python

0

1615677

<reponame>nayunhwan/SMaSH_Python<filename>d05.py #coding: utf-8 count1 = 0 count2 = 0 count3 = 0 count4 = 0 count5 = 0 while(True): s = input("직사각형의 가로 세로 입력") width, height = s.split(" ") width = int(width) height = int(height) if(width <= 0 or height <= 0): break else: if(width == height): count1 += 1 elif(width >= 2*height): count2 += 1 elif(width*2 <= height): count3 += 1 elif(width >= height): count4 += 1 elif(width <= height): count5 += 1 print("정사각형의 개수는",count1,"개 입니다.") print("좌우로 길쭉한 직사각형은",count2,"개 입니다.") print("위아래로 길쭉한 직사각혀의 개수는",count3,"개 입니다.") print("일반적인 가로형 직사각형의 개수는",count4,"개 입니다.") print("일반적인 세로형 직사각형의 개수는",count5,"개 입니다.")

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tests/graph/utils.py

kvh/basis

11

46611

<filename>tests/graph/utils.py<gh_stars>10-100 import textwrap from pathlib import Path from dataclasses import dataclass, asdict from typing import Any, Union, List, Dict, Collection, Set, Tuple from basis.graph.builder import graph_manifest_from_yaml, GraphManifest from basis.configuration.path import GraphEdge, NodeId, PortId from basis.graph.configured_node import ( ParameterDefinition, ParameterType, OutputDefinition, PortType, InputDefinition, ConfiguredNode, NodeType, NodeInterface, GraphManifest, GraphError, StateDefinition, ResolvedParameterValue, ) class _IgnoreType: def __eq__(self, other): return isinstance(other, _IgnoreType) def __repr__(self): return "IGNORE" IGNORE = _IgnoreType() @dataclass class NodeAssertion: name: Union[str, _IgnoreType] node_type: Union[NodeType, _IgnoreType] id: Union[NodeId, _IgnoreType] parent_node_id: Union[str, None] interface: Union[NodeInterface, _IgnoreType] description: Union[str, _IgnoreType] file_path_to_node_script_relative_to_root: Union[str, _IgnoreType] parameter_values: Union[Dict[str, Any], _IgnoreType] schedule: Union[str, _IgnoreType] local_edges: Union[List[str], _IgnoreType] resolved_edges: Union[List[str], _IgnoreType] resolved_parameter_values: Union[Dict[str, Tuple[Any, str]], _IgnoreType, None] errors: List[str] def _calculate_graph(nodes: List[ConfiguredNode]): nodes_by_id = {n.id: n for n in nodes} ids_by_path = {None: None} for node in nodes: parts = [node.name] parent = node.parent_node_id while parent: n = nodes_by_id[parent] parts.append(n.name) parent = n.parent_node_id ids_by_path[".".join(reversed(parts))] = node.id return ids_by_path def _assert_node( node: ConfiguredNode, assertion: NodeAssertion, ids_by_path: dict, paths_by_id: dict ): for k, v in asdict(assertion).items(): if k == "errors": continue actual = getattr(node, k) if k == "id": assert actual, "all ids must be defined" if v == IGNORE: continue if k in ("local_edges", "resolved_edges"): v = _unordered([_ge(s, ids_by_path) for s in v]) actual = _unordered(actual) elif k == "parent_node_id" and v is not None: actual = paths_by_id[actual] elif k == "resolved_parameter_values": if v is None: if assertion.parameter_values == IGNORE: continue v = { kk: ResolvedParameterValue(value=vv, source=node.id) for kk, vv in assertion.parameter_values.items() } else: v = { kk: ResolvedParameterValue(value=vv[0], source=ids_by_path[vv[1]]) for kk, vv in v.items() } def check(k, ex, ac): assert ac == ex, ( f"{assertion.name}: {k}:\n expected: {_tostr(ex, paths_by_id)}" + f"\n but was: {_tostr(ac, paths_by_id)}" ) if k == "interface": check("interface inputs", v.inputs, actual.inputs) check("interface outputs", v.outputs, actual.outputs) check("interface parameters", v.parameters, actual.parameters) else: check(k, v, actual) def _tostr(it, paths_by_id: dict) -> str: if isinstance(it, (list, set)): return str(sorted(_tostr(i, paths_by_id) for i in it)) if isinstance(it, PortId): return f"{paths_by_id[it.node_id]}:{it.port}" if isinstance(it, GraphEdge): return f"{_tostr(it.input, paths_by_id)} -> {_tostr(it.output, paths_by_id)}" if isinstance(it, GraphError): return f"{paths_by_id[it.node_id] if it.node_id else '<root>'}: {it.message}" return repr(it) def assert_nodes( manifest: GraphManifest, *expected: NodeAssertion, assert_length: bool = True ): nodes = manifest.nodes assert len(nodes) == len({node.id for node in nodes}), "all ids must be unique" if assert_length: assert len(nodes) == len( expected ), f"expected nodes {[n.name for n in expected]} , got {[n.name for n in nodes]}" nodes_by_name_and_parent = { (node.name, node.parent_node_id): node for node in nodes } ids_by_path = _calculate_graph(nodes) paths_by_id = {v: k for (k, v) in ids_by_path.items()} errors = [] for assertion in expected: parent_id = ids_by_path[assertion.parent_node_id] k = (assertion.name, parent_id) assert ( k in nodes_by_name_and_parent ), f"no node {assertion.name} in {list(nodes_by_name_and_parent.keys())}" node = nodes_by_name_and_parent[k] errors.extend(GraphError(node_id=node.id, message=e) for e in assertion.errors) ac = _unordered(manifest.errors) ex = _unordered(errors) assert ( ex == ac ), f"errors:\nexpected:\n{_tostr(ex, paths_by_id)}\nbut was:\n{_tostr(ac, paths_by_id)}" for assertion in expected: parent_id = ids_by_path[assertion.parent_node_id] node = nodes_by_name_and_parent[assertion.name, parent_id] _assert_node(node, assertion, ids_by_path, paths_by_id) # noinspection PyDefaultArgument def n( name: str, node_type: Union[NodeType, _IgnoreType] = NodeType.Node, id: Union[str, _IgnoreType] = IGNORE, parent: Union[str, None] = None, interface: Union[ List[ Union[ InputDefinition, OutputDefinition, ParameterDefinition, StateDefinition ] ], _IgnoreType, ] = IGNORE, description: Union[str, None, _IgnoreType] = None, file_path: Union[str, _IgnoreType] = IGNORE, parameter_values: Union[Dict[str, Any], _IgnoreType] = {}, schedule: Union[str, None, _IgnoreType] = None, local_edges: Union[List[str], _IgnoreType] = IGNORE, resolved_edges: Union[List[str], None, _IgnoreType] = None, resolved_params: Union[Dict[str, Tuple[Any, str]], _IgnoreType] = None, errors: List[str] = [], ) -> NodeAssertion: return NodeAssertion( name=name, node_type=node_type, id=IGNORE if id == IGNORE else NodeId(id.lower()), parent_node_id=parent, interface=interface if interface == IGNORE else NodeInterface( inputs=[i for i in interface if isinstance(i, InputDefinition)], outputs=[i for i in interface if isinstance(i, OutputDefinition)], parameters=[i for i in interface if isinstance(i, ParameterDefinition)], state=next((i for i in interface if isinstance(i, StateDefinition)), None), ), description=description, file_path_to_node_script_relative_to_root=file_path, parameter_values=parameter_values, resolved_parameter_values=resolved_params, schedule=schedule, local_edges=local_edges, resolved_edges=local_edges if resolved_edges is None else resolved_edges, errors=errors, ) def p( name: str, parameter_type: str = None, description: str = None, default: Any = None, ) -> ParameterDefinition: return ParameterDefinition( name=name, parameter_type=ParameterType(parameter_type) if parameter_type else None, description=description, default=default, ) def ostream( name: str, description: str = None, schema: str = None, ) -> OutputDefinition: return OutputDefinition( port_type=PortType.Stream, name=name, description=description, schema_name=schema, ) def istream( name: str, description: str = None, schema: str = None, required: bool = True, ) -> InputDefinition: return InputDefinition( port_type=PortType.Stream, name=name, description=description, schema_name=schema, required=required, ) def itable( name: str, description: str = None, schema: str = None, required: bool = True, ) -> InputDefinition: return InputDefinition( port_type=PortType.Table, name=name, description=description, schema_name=schema, required=required, ) def otable( name: str, description: str = None, schema: str = None, ) -> OutputDefinition: return OutputDefinition( port_type=PortType.Table, name=name, description=description, schema_name=schema, ) def s(name: str): return StateDefinition(name=name) def _ge(s: str, ids_by_path: dict) -> GraphEdge: l, r = s.split(" -> ") ln, lp = l.split(":") rn, rp = r.split(":") return GraphEdge( input=PortId(node_id=ids_by_path[ln], port=lp), output=PortId(node_id=ids_by_path[rn], port=rp), ) def _unordered(c: Collection) -> Set: s = set(c) assert len(c) == len(s) return s def read_manifest(name: str) -> GraphManifest: pth = Path(__file__).parent / name / "graph.yml" return graph_manifest_from_yaml(pth, allow_errors=True) def setup_manifest(root: Path, files: Dict[str, str]) -> GraphManifest: for path, content in files.items(): content = textwrap.dedent(content).strip() if path.endswith(".py"): if not content.startswith("@node"): content = f"@node\ndef generated_node(\n{content}\n):\n pass" content = "from basis import *\n\n" + content abspath = root / path if len(Path(path).parts) > 1: abspath.parent.mkdir(parents=True, exist_ok=True) abspath.write_text(content) return graph_manifest_from_yaml(root / "graph.yml", allow_errors=True)

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angeline_yu/SARS.py

homologus/2020-intermediate-class

3

50431

<filename>angeline_yu/SARS.py from Bio import SeqIO from Bio.Seq import Seq #Accessing SARS Genome and storing it in reads reads = list(SeqIO.parse("/share/SARS/SARS-2020.fasta", "fasta")) #translating the 3 reading frames translation1 = reads[0].seq.translate() translation2 = reads[0].seq.translate() translation3 = reads[0].seq.translate() for i in reads: #reading frame 1 translation1 = i.seq[0:].translate() #reading frame 2 translation2 = i.seq[1:].translate() #reading frame 3 translation3 = i.seq[2:].translate() #total genes total = 0 #count variable acts as place holder to check if sequence is 100 long count = 0 #toReturn variable keeps track of how many 100 amino acid sequences there are toReturn = 0 #saves previous i location prev_i = 0 #For loops checks for end codens ("*") #reading frame 1 for i in range(len(translation1)): if(str(translation1[i]) != "*"): count += 1 continue else: if(count >= 100): toReturn += 1 print("\n>Amino Acid Sequence " +str(toReturn)) print("Start Location = " +str(prev_i)) print("End Location = " +str(i+1)) print("Length = " +str((i+1)-prev_i)) print(translation1[prev_i+1:i]+"\n") prev_i = i count = 0 print("\nThere are " + str(toReturn) + " 100 amino acid sequences in the SARS-CoV2 Genome in reading frame 1. \n") total += toReturn count = 0 toReturn = 0 prev_i = 0 for i in range(len(translation2)): if(str(translation2[i]) != "*"): count += 1 continue else: if(count >= 100): toReturn += 1 print("\n>Amino Acid Sequence " +str(toReturn)) print("Start Location = " +str(prev_i)) print("End Location = " +str(i+1)) print("Length = " +str((i+1)-prev_i)) print(translation2[prev_i+1:i]+"\n") prev_i = i count = 0 print("There are " + str(toReturn) + " 100 amino acid sequences in the SARS-CoV2 Genome in reading frame 2. \n") total += toReturn count = 0 toReturn = 0 prev_i = 0 for i in range(len(translation3)): if(str(translation3[i]) != "*"): count += 1 continue else: if(count >= 100): toReturn += 1 print("\n>Amino Acid Sequence " +str(toReturn)) print("Start Location = " +str(prev_i)) print("End Location = " +str(i+1)) print("Length = " +str((i+1)-prev_i)) print(translation3[prev_i+1:i]+"\n") prev_i = i count = 0 print("There are " + str(toReturn) + " 100 amino acid sequences in the SARS-CoV2 Genome in reading frame 3. \n") total += toReturn print("There are " +str(total) + " total 100 amino acid sequences in the SARS-CoV2 Genome. \n")

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2020/day_16/day_16.py

viddrobnic/adventofcode

0

36732

<gh_stars>0 def read_data(): rules = dict() your_ticket = None nearby_tickets = [] state = 0 with open('in') as f: for line in map(lambda x: x.strip(), f.readlines()): if line == '': state += 1 continue if line == 'your ticket:': continue if line == 'nearby tickets:': continue if state == 0: parts = line.split(':') ranges = parts[1].split('or') rules[parts[0]] = [] for r in ranges: nums = r.split('-') rules[parts[0]].append((int(nums[0]), int(nums[1]))) if state == 1: your_ticket = list(map(int, line.split(','))) if state == 2: nearby_tickets.append(list(map(int, line.split(',')))) return rules, your_ticket, nearby_tickets def part_one(rules, tickets): error_rate = 0 invalid_tickets = [] for i, ticket in enumerate(tickets): for val in ticket: valid_val = False for rule in rules.keys(): valid = False for m, M in rules[rule]: if val >= m and val <= M: valid = True break if valid: valid_val = True break if not valid_val: error_rate += val invalid_tickets.append(i) return invalid_tickets, error_rate def part_two(rules, your_ticket, tickets): possible_values = [set(rules.keys()) for i in range(len(rules.keys()))] for ticket in tickets: for i, val in enumerate(ticket): for rule in rules.keys(): valid = False for m, M in rules[rule]: if val >= m and val <= M: valid = True break if not valid and rule in possible_values[i]: possible_values[i].remove(rule) while True: to_filter = False for v in map(len, possible_values): if v > 1: to_filter = True if not to_filter: break for i in range(len(possible_values)): if len(possible_values[i]) == 1: for j in range(len(possible_values)): if i != j: possible_values[j] -= possible_values[i] res = 1 for i in range(len(possible_values)): if 'departure' in list(possible_values[i])[0]: res *= your_ticket[i] return res def main(): rules, your_ticket, nearby_tickets = read_data() invalid_tickets, error_rate = part_one(rules, nearby_tickets) p_two = part_two(rules, your_ticket, [t for i, t in enumerate(nearby_tickets) if i not in invalid_tickets]) print(f'Part One: {error_rate}') print(f'Part Two: {p_two}') if __name__ == '__main__': main()

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vae-dpc/vae_main.py

lovish1234/TPC

0

1609206

<filename>vae-dpc/vae_main.py<gh_stars>0 # Self-supervised pretraining using DPC with VAE import os import random import sys import time import re import argparse import ntpath import numpy as np import pickle from tqdm import tqdm import copy # visualization from tensorboardX import SummaryWriter import matplotlib.pyplot as plt plt.switch_backend('agg') sys.path.append('../tpc') # use same dataset file as TPC sys.path.append('../utils') sys.path.append('../utils/diversity_meas') from div_vae import * from cvae_model import * from vrnn_model import * from dataset_epic import * from dataset_synthepic import * from dataset_other import * from dataset_toy import * # backbone from resnet_2d3d import neq_load_customized # data augmentation methods from augmentation import * # collect results from utils import AverageMeter, save_checkpoint, denorm, calc_topk_accuracy, single_action, get_action_from_frame import torch import torch.optim as optim from torch.utils import data from torchvision import datasets, models, transforms from torch import autograd # anomaly detection from present_matching import * import copy # saving log, calculating accuracy etc. import torchvision.utils as vutils from train_util import * torch.backends.cudnn.benchmark = True parser = argparse.ArgumentParser() parser.add_argument('--net', default='resnet18', type=str) parser.add_argument('--dataset', default='k400', type=str, help='k400 / ucf / epic_unlabeled / epic_before / epic_end') # frame preprocessing parser.add_argument('--seq_len', default=5, type=int, help='number of frames in each video block') parser.add_argument('--num_seq', default=8, type=int, help='number of video blocks') parser.add_argument('--pred_step', default=3, type=int) parser.add_argument('--ds', default=6, type=int, help='frame downsampling rate') # optimizer learning rate and decay parser.add_argument('--batch_size', default=64, type=int) parser.add_argument('--lr', default=1e-3, type=float, help='learning rate') parser.add_argument('--wd', default=1e-5, type=float, help='weight decay') # resume training parser.add_argument('--resume', default='', type=str, help='path of model to resume') parser.add_argument('--pretrain', default='', type=str, help='path of pretrained model') parser.add_argument('--epochs', default=300, type=int, help='number of total epochs to run') # help in re-setting the lr to appropriate value parser.add_argument('--start-epoch', default=0, type=int, help='manual epoch number (useful on restarts)') # gpu setting parser.add_argument('--gpu', default='0,1', type=str) parser.add_argument('--print_freq', default=1, type=int, help='frequency of printing output during training') parser.add_argument('--reset_lr', action='store_true', help='Reset learning rate when resume training?') parser.add_argument('--prefix', default='conv', type=str, help='prefix of checkpoint filename') # which layers to train [ use for fine-tuning ] parser.add_argument('--train_what', default='all', type=str) parser.add_argument('--img_dim', default=128, type=int) # number of sequences sampled from one video in epic and block_toy dataset parser.add_argument('--num_clips', default=10000, type=int, help='Number of sequences to sample in total from train_split') parser.add_argument('--drive', default='ssd', type=str, help='Type of disk drive training data is located at') parser.add_argument('--new_augment', action='store_true', help='Use data augmentation with consistent train-test distortion') # CVAE / diversity specific parameters parser.add_argument('--model', default='vrnn', type=str, help='cvae / vrnn / vrnn-i') parser.add_argument('--cvae_arch', default='conv_e', type=str, help='Architecture of the CVAE mapping present to future (fc / conv_a / conv_b / conv_c / conv_d / conv_e) (default: conv_e)') parser.add_argument('--vrnn_latent_size', default=16, type=int, help='Dimensionality of the VRNN probabilistic latent space') parser.add_argument('--vrnn_kernel_size', default=1, type=int, help='Kernel size of all VRNN convolutional layers (prior, enc, dec, rnn)') parser.add_argument('--vrnn_dropout', default=0.1, type=float, help='Dropout in RNN for aggregation (GRU cell) (default: 0.1)') parser.add_argument('--vae_kl_weight', default=1.0, type=float, help='Base weight factor of KL divergence term in VAE-DPC loss (default: 1.0)') parser.add_argument('--vae_no_kl_norm', default=False, action='store_true', help='If True, disable KL loss weight normalization') parser.add_argument('--vae_encoderless_epochs', default=25, type=int, help='Number of epochs to train the prediction network deterministically (DPC-like) before enabling the encoder and beta warmup (default: 100)') parser.add_argument('--vae_inter_kl_epochs', default=25, type=int, help='Number of epochs to wait after starting the encoder (and training with zero KL loss) before starting beta warmup (default: 50)') parser.add_argument('--vae_kl_beta_warmup', default=150, type=int, help='Number of epochs during which to warmup the KL loss term weight from 0% to 100% according to a cosine curve (default: 100)') parser.add_argument('--pred_divers_wt', default=0.001, type=float, help='Importance of variance (diversity) in predictions as measured by cosine distance in loss (default: 0.1)') parser.add_argument('--pred_divers_formula', default='inv', type=str, help='Use -var (neg) or 1/var (inv) (default: inv)') parser.add_argument('--test_diversity', default=False, action='store_true', help='If True, evaluate model by measuring diversity of multiple generated samples. Note: specify pretrained model path as well.') parser.add_argument('--paths', default=20, type=int, help='Future paths generated by VAE (default: 20)') # parser.add_argument('--vrnn_context_noise', default=0.0, type=float, # help='Add Gaussian noise to input of VRNN decoder for regularization (default: 0)') # parser.add_argument('--vrnn_time_indep', default=False, action='store_true', # help='If True, z does not see or influence context (i.e. VRNN becomes CVAE)') parser.add_argument('--select_best_among', default=1, type=int, help='[do not use for now] During validation and when the encoder is disabled, generate the specified number of futures in a holistic way, and judge the best performing path only (default = 1).') parser.add_argument('--force_context_dropout', default=False, action='store_true', help='If True, use different dropout mask (p = 0.1) on c_t for every path during uncertainty measurements.') # Present matching parameters parser.add_argument('--pm_start', default=1000, type=int, help='Number of epochs present matching kicks in') parser.add_argument('--pm_freq', default=10, type=int, help='Frequency of present matching (every 10 epochs by default)') parser.add_argument('--pm_num_samples', default=100000, type=int, help='Number of videos sequences to sample during present matching') parser.add_argument('--pm_num_ngbrs', default=4, type=int, help='Number of nearest neighbors to put in one batch') parser.add_argument('--pm_gap_thres', default=30, type=int, help='Minimum pair-wise frame gap between two sequences in the same batch') # Action classification custom arguments # parser.add_argument('--diverse_actions', default=False, action='store_true', help='If True, evaluate model by measuring diversity of multiple generated future samples (labels in action space). Note: specify TEST model path as well.') # parser.add_argument('--test_path', default='', type=str, help='Path of finetuned model for multimodal action classification') # parser.add_argument('--class_type', default='verb', type=str) # TODO: support 'both' as well def main(): # Set constant random state for consistent results torch.manual_seed(704) np.random.seed(704) random.seed(704) global args args = parser.parse_args() os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) global cuda cuda = torch.device('cuda') args.cuda = cuda ### dpc model ### if args.model == 'cvae': model = DPC_CVAE(img_dim=args.img_dim, num_seq=args.num_seq, seq_len=args.seq_len, pred_step=args.pred_step, network=args.net, cvae_arch=args.cvae_arch) elif 'vrnn' in args.model: model = DPC_VRNN(img_dim=args.img_dim, num_seq=args.num_seq, seq_len=args.seq_len, pred_step=args.pred_step, network=args.net, latent_size=args.vrnn_latent_size, kernel_size=args.vrnn_kernel_size, rnn_dropout=args.vrnn_dropout, time_indep='-i' in args.model) else: raise ValueError('Unknown / wrong model: ' + args.model) # parallelize the model model = nn.DataParallel(model) model = model.to(cuda) # loss function (change this) # why cross-entropy, we can take a direct cosine distance instead criterion = nn.CrossEntropyLoss() ### optimizer ### # dont' think we need to use 'last' keyword during pre-training anywhere if args.train_what == 'last': for name, param in model.module.resnet.named_parameters(): param.requires_grad = False else: pass # train all layers # check if gradient flowing to appropriate layers print('\n===========Check Grad============') for name, param in model.named_parameters(): print(name, param.requires_grad) print('=================================\n') params = model.parameters() optimizer = optim.Adam(params, lr=args.lr, weight_decay=args.wd) args.old_lr = None best_acc = 0 global iteration iteration = 0 ### restart training ### if args.resume: if os.path.isfile(args.resume): # load the old model and set the learning rate accordingly # get the old learning rate args.old_lr = float(re.search('_lr(.+?)_', args.resume).group(1)) print("=> loading resumed checkpoint '{}'".format(args.resume)) # checkpoint = torch.load( args.resume, map_location=torch.device('cpu')) args.start_epoch = checkpoint['epoch'] iteration = checkpoint['iteration'] best_acc = checkpoint['best_acc'] model.load_state_dict(checkpoint['state_dict']) # load old optimizer, start with the corresponding learning rate if not args.reset_lr: # if didn't reset lr, load old optimizer optimizer.load_state_dict(checkpoint['optimizer']) else: # reset to new learning rate print('==== Change lr from %f to %f ====' % (args.old_lr, args.lr)) print("=> loaded resumed checkpoint '{}' (epoch {})".format( args.resume, checkpoint['epoch'])) else: print("[Warning] no checkpoint found at '{}'".format(args.resume)) if args.pretrain: if os.path.isfile(args.pretrain): print("=> loading pretrained checkpoint '{}'".format(args.pretrain)) checkpoint = torch.load( args.pretrain, map_location=torch.device('cpu')) # neq_load_customized model = neq_load_customized(model, checkpoint['state_dict']) print("=> loaded pretrained checkpoint '{}' (epoch {})" .format(args.pretrain, checkpoint['epoch'])) else: print("=> no checkpoint found at '{}'".format(args.pretrain)) ### load transform & dataset ### transform_train = get_transform(args, mode = 'train') transform_val = get_transform(args, mode = 'val') train_loader = get_data(args, transform_train, 'train') val_loader = get_data(args, transform_val, 'val') # setup tools # Initialize denormalize transform to display input images via tensorboard de_normalize = denorm() # Get paths global img_path img_path, model_path, divers_path, pm_cache = set_path(args) global writer_train if not(args.test_diversity): # Train & validate for multiple epochs # book-keeping writer_val_enc = SummaryWriter(os.path.join(img_path, 'val_enc')) writer_val_noenc = SummaryWriter(os.path.join(img_path, 'val_noenc')) writer_val_pm_enc = SummaryWriter(os.path.join(img_path, 'val_pm_enc')) writer_val_pm_noenc = SummaryWriter(os.path.join(img_path, 'val_pm_noenc')) writer_train = SummaryWriter(os.path.join(img_path, 'train')) global cur_vae_kl_weight, cur_pred_divers_wt ### main loop ### for epoch in range(args.start_epoch, args.epochs): # Initially train without latent space if specified if epoch < args.vae_encoderless_epochs: train_with_latent = False cur_pred_divers_wt = 0.0 # encouraging diversity doesn't apply right now else: train_with_latent = True cur_pred_divers_wt = args.pred_divers_wt # hard transition print('Encoder enabled for training this epoch:', train_with_latent) # Then train for a while with beta = 0 (not explicitly coded) # Beta warmup = VAE KL loss weight adjustment over time if train_with_latent: epoch_post = epoch - (args.vae_encoderless_epochs + args.vae_inter_kl_epochs) if epoch_post < 0: cur_vae_kl_weight = 0.0 elif 0 <= epoch_post and epoch_post < args.vae_kl_beta_warmup: cur_vae_kl_weight = args.vae_kl_weight * \ (1.0 - np.cos(epoch_post / args.vae_kl_beta_warmup * np.pi)) / 2 else: cur_vae_kl_weight = args.vae_kl_weight else: cur_vae_kl_weight = 0.0 print('Current VAE KL loss term weight:', cur_vae_kl_weight) if epoch < args.pm_start: print('\n\nTraining with randomly sampled sequences from %s' % args.dataset) train_loss_dpc, train_loss_kl, train_loss_divers, train_loss, train_acc, train_accuracy_list = train( train_loader, model, optimizer, epoch, criterion, writer_train, args, de_normalize, cur_vae_kl_weight, cur_pred_divers_wt, do_encode=train_with_latent) else: # change torch multithreading setting to circumvent 'open files' limit torch.multiprocessing.set_sharing_strategy('file_system') print('\n\nTraining with matched sequences (nearest neighbours) from %s\n\n' % args.dataset) if epoch % args.pm_freq == 0: print(' ######################################## \n Matching sequences in embedding space... \n ########################################\n\n') # present matching and save results for future training present_matching(args, model, pm_cache, epoch, 'train') present_matching(args, model, pm_cache, epoch, 'val') args_pm = copy.copy(args) args_pm.dataset = 'epic_present_matching' pm_path = os.path.join(pm_cache, 'epoch_train_%d' % ( epoch - epoch % args_pm.pm_freq)) # retrieve last present matching results train_loader_pm = get_data( args_pm, transform_train, mode='train', pm_path=pm_path, epoch=epoch) train_loss_dpc, train_loss_kl, train_loss_divers, train_loss, train_acc, train_accuracy_list = train( train_loader_pm, model, optimizer, epoch, criterion, writer_train, args, de_normalize, cur_vae_kl_weight, cur_pred_divers_wt, do_encode=train_with_latent) del train_loader_pm pm_path = os.path.join(pm_cache, 'epoch_val_%d' % ( epoch - epoch % args_pm.pm_freq)) # retrieve last present matching results val_loader_pm = get_data( args_pm, transform_val, mode='val', pm_path=pm_path, epoch=epoch) val_loss_dpc_pm_enc, val_loss_kl_pm_enc, val_loss_divers_pm_enc, \ val_loss_pm_enc, val_acc_pm_enc, val_acc_pm_enc_list = validate( val_loader_pm, model, epoch, args, criterion, cur_vae_kl_weight, cur_pred_divers_wt, do_encode=train_with_latent, select_best_among=1 if train_with_latent else args.select_best_among) # only one path when encoding val_loss_dpc_pm_noenc, val_loss_kl_pm_noenc, val_loss_divers_pm_noenc, \ val_loss_pm_noenc, val_acc_pm_noenc, val_acc_pm_noenc_list = validate( val_loader_pm, model, epoch, args, criterion, cur_vae_kl_weight, cur_pred_divers_wt, do_encode=False, select_best_among=args.select_best_among) del val_loader_pm val_loss_dpc_enc, val_loss_kl_enc, val_loss_divers_enc, \ val_loss_enc, val_acc_enc, val_acc_enc_list = validate( val_loader, model, epoch, args, criterion, cur_vae_kl_weight, cur_pred_divers_wt, do_encode=train_with_latent, select_best_among=1 if train_with_latent else args.select_best_among) # only one path when encoding val_loss_dpc_noenc, val_loss_kl_noenc, val_loss_divers_noenc, \ val_loss_noenc, val_acc_noenc, val_acc_noenc_list = validate( val_loader, model, epoch, args, criterion, cur_vae_kl_weight, cur_pred_divers_wt, do_encode=False, select_best_among=args.select_best_among) # Train curves writer_train.add_scalar('global/loss_dpc', train_loss_dpc, epoch) writer_train.add_scalar('global/loss_vae_kl', train_loss_kl, epoch) writer_train.add_scalar('global/loss_vae_divers', train_loss_divers, epoch) writer_train.add_scalar('global/loss', train_loss, epoch) writer_train.add_scalar('global/vae_kl_weight', cur_vae_kl_weight, epoch) writer_train.add_scalar('global/accuracy', train_acc, epoch) # Val curves writer_val_enc.add_scalar('global/loss_dpc', val_loss_dpc_enc, epoch) writer_val_enc.add_scalar('global/loss_vae_kl', val_loss_kl_enc, epoch) writer_val_enc.add_scalar('global/loss_vae_divers', val_loss_divers_enc, epoch) writer_val_enc.add_scalar('global/loss', val_loss_enc, epoch) writer_val_enc.add_scalar('global/accuracy', val_acc_enc, epoch) writer_val_noenc.add_scalar('global/loss_dpc', val_loss_dpc_noenc, epoch) writer_val_noenc.add_scalar('global/loss_vae_kl', val_loss_kl_noenc, epoch) writer_val_noenc.add_scalar('global/loss_vae_divers', val_loss_divers_noenc, epoch) writer_val_noenc.add_scalar('global/loss', val_loss_noenc, epoch) writer_val_noenc.add_scalar('global/accuracy', val_acc_noenc, epoch) if epoch >= args.pm_start: # Add present matching curves writer_val_pm_enc.add_scalar('global/loss_dpc', val_loss_dpc_pm_enc, epoch) writer_val_pm_enc.add_scalar('global/loss_vae_kl', val_loss_kl_pm_enc, epoch) writer_val_pm_enc.add_scalar('global/loss_vae_divers', val_loss_divers_pm_enc, epoch) writer_val_pm_enc.add_scalar('global/loss', val_loss_pm_enc, epoch) writer_val_pm_enc.add_scalar('global/accuracy', val_acc_pm_enc, epoch) writer_val_pm_noenc.add_scalar('global/loss_dpc', val_loss_dpc_pm_noenc, epoch) writer_val_pm_noenc.add_scalar('global/loss_vae_kl', val_loss_kl_pm_noenc, epoch) writer_val_pm_noenc.add_scalar('global/loss_vae_divers', val_loss_divers_pm_noenc, epoch) writer_val_pm_noenc.add_scalar('global/loss', val_loss_pm_noenc, epoch) writer_val_pm_noenc.add_scalar('global/accuracy', val_acc_pm_noenc, epoch) # Train accuracies writer_train.add_scalar('accuracy/top1', train_accuracy_list[0], epoch) writer_train.add_scalar('accuracy/top3', train_accuracy_list[1], epoch) writer_train.add_scalar('accuracy/top5', train_accuracy_list[2], epoch) # Val accuracies writer_val_noenc.add_scalar('accuracy/top1', val_acc_noenc_list[0], epoch) writer_val_noenc.add_scalar('accuracy/top3', val_acc_noenc_list[1], epoch) writer_val_noenc.add_scalar('accuracy/top5', val_acc_noenc_list[2], epoch) writer_val_enc.add_scalar('accuracy/top1', val_acc_enc_list[0], epoch) writer_val_enc.add_scalar('accuracy/top3', val_acc_enc_list[1], epoch) writer_val_enc.add_scalar('accuracy/top5', val_acc_enc_list[2], epoch) if epoch >= args.pm_start: # Add present matching curves writer_val_pm_noenc.add_scalar('accuracy/top1', val_acc_pm_noenc_list[0], epoch) writer_val_pm_noenc.add_scalar('accuracy/top3', val_acc_pm_noenc_list[1], epoch) writer_val_pm_noenc.add_scalar('accuracy/top5', val_acc_pm_noenc_list[2], epoch) writer_val_pm_enc.add_scalar('accuracy/top1', val_acc_pm_enc_list[0], epoch) writer_val_pm_enc.add_scalar('accuracy/top3', val_acc_pm_enc_list[1], epoch) writer_val_pm_enc.add_scalar('accuracy/top5', val_acc_pm_enc_list[2], epoch) # save check_point (best accuracy measured without encoder) is_best = val_acc_noenc > best_acc best_acc = max(val_acc_noenc, best_acc) save_checkpoint({'epoch': epoch + 1, 'net': args.net, 'state_dict': model.state_dict(), 'best_acc': best_acc, 'optimizer': optimizer.state_dict(), 'iteration': iteration}, is_best, filename=os.path.join(model_path, 'epoch%s.pth.tar' % str(epoch + 1)), save_every=10) print('Training from ep %d to ep %d finished' % (args.start_epoch, args.epochs)) else: # Uncertainty evaluation: full video & no color adjustments # NOTE: be careful with training augmentation to prevent train-test resolution / scaling discrepancy tf_diversity = transforms.Compose([ RandomHorizontalFlip(consistent=True), CenterCrop(224), Scale(size=(args.img_dim, args.img_dim)), ToTensor(), Normalize() ]) print('Measuring diversity of generated samples...') val_divers_loader = get_data(args, tf_diversity, 'val') results = measure_vae_uncertainty_loader(val_divers_loader, model, args.paths, print_freq=args.print_freq, collect_actions=False, force_context_dropout=args.force_context_dropout) cur_path = os.path.join(divers_path, 'epoch' + str(checkpoint['epoch']) + \ '_paths' + str(args.paths) + \ ('_ctdrop' if args.force_context_dropout else '') + '.p') with open(cur_path, 'wb') as f: pickle.dump(results, f) print('For future use, uncertainty evaluation results stored to ' + cur_path) def process_output(mask): '''task mask as input, compute the target for contrastive loss''' # dot product is computed in parallel gpus, so get less easy neg, bounded by batch size in each gpu''' # mask meaning: -2: omit, -1: temporal neg (hard), 0: easy neg, 1: pos, -3: spatial neg # [4, 3, 16, 4, 3, 16] # B and B2 must be same always ?? (B, NP, SQ, B2, NS, _) = mask.size() # [B, P, SQ, B, N, SQ] #print (B,B2,NS,NP,SQ) # mark out the positives # not using temporal negatives and spatial negatives differently target = mask == 1 target.requires_grad = False return target, (B, B2, NS, NP, SQ) def loss_kl_divergence(mu, logvar, normalization=True): ''' Calculates KL divergence loss term for CVAE. mu, logvar: from one item within minibatch, same size as latent space. NOTE: normalization enabled by default as of 05/27, divides by batch_size * pred_step. ''' # mu and logvar are probably many-dimensional, but this code is agnostic of that loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp()) if normalization: loss /= (mu.shape[0] * mu.shape[1]) return loss def loss_kl_divergence_prior(mu_prio, mu_post, logvar_prio, logvar_post, normalization=True): ''' Calculates KL divergence loss term for VRNN. all arguments: from one item within minibatch, same size as latent space. NOTE: normalization enabled by default as of 05/27, divides by batch_size * pred_step. ''' std_prio = logvar_prio.mul(0.5).exp() std_post = logvar_post.mul(0.5).exp() loss = -0.5 * torch.sum(1 + logvar_post - logvar_prio - (std_post.pow(2) + (mu_post - mu_prio).pow(2)) / std_prio.pow(2)) if normalization: loss /= (mu_prio.shape[0] * mu_prio.shape[1]) return loss def loss_pred_variance(pred_cos_sim, latent_dist, pred_divers_formula): ''' Calculates diversity loss term for CVAE and VRNN. pred_cos_sim: cosine similarity of one pair, from one item within minibatch and one time step. latent_dist: L2 distance between latent vectors that generated the pair. As of 05/20, multiply by latent_dist. ''' # pred_cos_sim is tensor with a cosine distance from a random pair # lower is better; typical variance range was [0.999990, 0.999998] if not encouraged (old model) # mean_cos_sim = pred_cos_sim.mean() # across time steps # TODO: normalize by latent_dist? what about mode collapse in prior? delta = 1.0 - pred_cos_sim if pred_divers_formula == 'neg': loss = -delta elif pred_divers_formula in ['div', 'inv']: # causes diminishing returns for larger variance loss = 1.0 / (delta + 1e-6) else: raise Exception('Unknown diversity loss formula: ', pred_divers_formula) loss *= latent_dist.detach() / 5.0 # typical L2 between 8/16-dim randn x, y return loss if __name__ == '__main__': main()

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tests/py_tests/test_faint/test_linear_gradient.py

lukas-ke/faint-graphics-editor

10

156861

<reponame>lukas-ke/faint-graphics-editor<gh_stars>1-10 #!/usr/bin/env python3 # -*- coding: utf-8 -*- import unittest from faint import LinearGradient class TestLinearGradient(unittest.TestCase): def test_init_types(self): # Arguments required ar = self.assertRaises(ValueError) with ar: LinearGradient(); with ar: LinearGradient(42.0); with ar: LinearGradient(0.0, (255,0,255)); # Integer and float offset (was broken once!) LinearGradient((0, (255,0,255))) LinearGradient((0.0, (255,0,255))) LinearGradient((1, (255,0,255))) # Angle LinearGradient(42, (0.0, (255,0,255))) LinearGradient(42.0, (0.0, (255,0,255))) # Multiple color stops LinearGradient(42.0, (0.0, (255,0,255)), (1.0, (255,255,255))) def test_angle(self): lg1 = LinearGradient((42, (255,0,255))) # Fixme: Replace set, get_angle with property self.assertEqual(lg1.get_angle(), 0, "Default angle is 0") lg2 = LinearGradient(42, (0.0, (255, 0, 255))) self.assertAlmostEqual(lg2.get_angle(), 42.0) lg1.set_angle(20.0) self.assertEqual(lg1.get_angle(), 20.0) def test_color_stops(self): lg = LinearGradient((0.0, (255,254,253)), (0.5, (0, 1, 2, 3))) self.assertEqual(lg.get_num_stops(), 2) s0, s1 = lg.get_stops() self.assertEqual(s0, (0.0, (255, 254, 253, 255))) self.assertEqual(s1, (0.5, (0, 1, 2, 3))) lg.add_stop(0.6, (50, 60, 70)) stops = lg.get_stops() self.assertEqual(len(stops), 3) s2 = stops[2] self.assertEqual(s2, (0.6, (50, 60, 70, 255))) self.assertEqual(s2, lg.get_stop(2)) with self.assertRaises(IndexError): lg.get_stop(3) with self.assertRaises(ValueError): lg.get_stop(-1)

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nitmis_admin/controllers/register.py

kalesh13/nitmis

0

15832

from rest_framework.views import APIView from rest_framework.response import Response from django.shortcuts import render from django.http.response import JsonResponse from nitmis_admin.serializers.UserSerializer import UserSerializer def create_user(role="Guest"): """ """ def fun_wrapper(func): def wrapper(*args, **kwargs): serializer = UserSerializer(data=args[1].data) # # If the data is valid, create a new user # and return the access token details. if serializer.is_valid(): serializer.save(role=role) return JsonResponse(serializer.data) return JsonResponse({"errors": serializer.errors}, status=422) return wrapper return fun_wrapper class Register(APIView): ''' Parent register controller. Post requests create a general Guest account ''' def get(self, request): ''' Renders the base layout on GET request. Frontend handles the rendering of forms ''' return render(request, 'base.html') @create_user() def post(self, request): ''' Registers a new user and assigns the user a Guest role. ''' class AdminRegister(Register): ''' Register controller for administrators. ''' @create_user(role="Administrator") def post(self, request): ''' Overriden post function. Registers the user as an administrator '''

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src/rreil/variable.py

tizmd/python-gdsl

0

127256

class Variable(object): def __init__(self, _id , offset): self._id = _id self.offset = offset def __str__(self): s = str(self._id) if self.offset: s = s + "/%d" % self.offset return s class VariableLimited(object): def __init__(self, _id , offset, size): self._id = _id self.offset = offset self.size = size def __str__(self): s = str(self._id) if self.offset: s = s + "/%d" % self.offset return s + ":%d" % self.size def VariableLimitedListInit(): return list() def VariableLimitedListNext(a, l): l.append(a) return l

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ShapeFlow/deform_3_obj.py

vikasTmz/SP-GAN

46

1600756

<reponame>vikasTmz/SP-GAN<filename>ShapeFlow/deform_3_obj.py import torch.optim as optim from shapeflow.layers.chamfer_layer import ChamferDistKDTree from shapeflow.layers.deformation_layer import NeuralFlowDeformer from shapeflow.layers.pointnet_layer import PointNetEncoder import torch import numpy as np from time import time import trimesh from glob import glob files = sorted(glob("data/shapenet_watertight/val/03001627/*/*.ply")) m1 = trimesh.load(files[1]) m2 = trimesh.load(files[6]) m3 = trimesh.load(files[7]) device = torch.device("cuda:0") chamfer_dist = ChamferDistKDTree(reduction="mean", njobs=1).to(device) criterion = torch.nn.MSELoss() latent_size = 3 deformer = NeuralFlowDeformer( latent_size=latent_size, f_nlayers=6, f_width=100, s_nlayers=2, s_width=5, method="dopri5", nonlinearity="elu", arch="imnet", adjoint=True, atol=1e-4, rtol=1e-4, ).to(device) encoder = PointNetEncoder( nf=16, out_features=latent_size, dropout_prob=0.0 ).to(device) # this is an awkward workaround to get gradients for encoder via adjoint solver deformer.add_encoder(encoder) deformer.to(device) encoder = deformer.net.encoder optimizer = optim.Adam(list(deformer.parameters()), lr=1e-3) niter = 1000 npts = 5000 V1 = torch.tensor(m1.vertices.astype(np.float32)).to(device) # .unsqueeze(0) V2 = torch.tensor(m2.vertices.astype(np.float32)).to(device) # .unsqueeze(0) V3 = torch.tensor(m3.vertices.astype(np.float32)).to(device) # .unsqueeze(0) loss_min = 1e30 tic = time() encoder.train() for it in range(0, niter): optimizer.zero_grad() seq1 = torch.randperm(V1.shape[0], device=device)[:npts] seq2 = torch.randperm(V2.shape[0], device=device)[:npts] seq3 = torch.randperm(V3.shape[0], device=device)[:npts] V1_samp = V1[seq1] V2_samp = V2[seq2] V3_samp = V3[seq3] V_src = torch.stack( [V1_samp, V1_samp, V2_samp], dim=0 ) # [batch, npoints, 3] V_tar = torch.stack( [V2_samp, V3_samp, V3_samp], dim=0 ) # [batch, npoints, 3] V_src_tar = torch.cat([V_src, V_tar], dim=0) V_tar_src = torch.cat([V_tar, V_src], dim=0) batch_latent_src_tar = encoder(V_src_tar) batch_latent_tar_src = torch.cat( [batch_latent_src_tar[3:], batch_latent_src_tar[:3]] ) V_deform = deformer(V_src_tar, batch_latent_src_tar, batch_latent_tar_src) _, _, dist = chamfer_dist(V_deform, V_tar_src) loss = criterion(dist, torch.zeros_like(dist)) loss.backward() optimizer.step() if it % 100 == 0 or True: print(f"iter={it}, loss={np.sqrt(loss.item())}") toc = time() print("Time for {} iters: {:.4f} s".format(niter, toc - tic)) # save deformed mesh encoder.eval() with torch.no_grad(): V1_latent = encoder(V1.unsqueeze(0)) V2_latent = encoder(V2.unsqueeze(0)) V3_latent = encoder(V3.unsqueeze(0)) V1_2 = ( deformer(V1.unsqueeze(0), V1_latent, V2_latent) .detach() .cpu() .numpy()[0] ) V2_1 = ( deformer(V2.unsqueeze(0), V2_latent, V1_latent) .detach() .cpu() .numpy()[0] ) V1_3 = ( deformer(V1.unsqueeze(0), V1_latent, V3_latent) .detach() .cpu() .numpy()[0] ) V3_1 = ( deformer(V3.unsqueeze(0), V3_latent, V1_latent) .detach() .cpu() .numpy()[0] ) V2_3 = ( deformer(V2.unsqueeze(0), V2_latent, V3_latent) .detach() .cpu() .numpy()[0] ) V3_2 = ( deformer(V3.unsqueeze(0), V3_latent, V2_latent) .detach() .cpu() .numpy()[0] ) trimesh.Trimesh(V1_2, m1.faces).export("demo/output_1_2.obj") trimesh.Trimesh(V2_1, m2.faces).export("demo/output_2_1.obj") trimesh.Trimesh(V1_3, m1.faces).export("demo/output_1_3.obj") trimesh.Trimesh(V3_1, m3.faces).export("demo/output_3_1.obj") trimesh.Trimesh(V2_3, m2.faces).export("demo/output_2_3.obj") trimesh.Trimesh(V3_2, m3.faces).export("demo/output_3_2.obj") m1.export("demo/output_1.obj") m2.export("demo/output_2.obj") m3.export("demo/output_3.obj")

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shadow4/syned/absorbers/absorber.py

srio/minishadow

1

189031

from syned.beamline.optical_element import OpticalElement from syned.beamline.shape import BoundaryShape class Absorber(OpticalElement): def __init__(self, name="Undefined", boundary_shape=None): if boundary_shape is None: boundary_shape = BoundaryShape() OpticalElement.__init__(self, name=name, boundary_shape=boundary_shape)

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climpred/tests/__init__.py

aaronspring/climpred

0

151058

import importlib from distutils import version import pytest def _importorskip(modname, minversion=None): try: mod = importlib.import_module(modname) has = True if minversion is not None: if LooseVersion(mod.__version__) < LooseVersion(minversion): raise ImportError("Minimum version not satisfied") except ImportError: has = False func = pytest.mark.skipif(not has, reason=f"requires {modname}") return has, func def LooseVersion(vstring): # Our development version is something like '0.10.9+aac7bfc' # This function just ignored the git commit id. vstring = vstring.split("+")[0] return version.LooseVersion(vstring) has_matplotlib, requires_matplotlib = _importorskip("matplotlib") has_nc_time_axis, requires_nc_time_axis = _importorskip("nc_time_axis", "1.4.0") has_xclim, requires_xclim = _importorskip("xclim", "0.31") has_bias_correction, requires_bias_correction = _importorskip("bias_correction") has_xesmf, requires_xesmf = _importorskip("xesmf") has_xrft, requires_xrft = _importorskip("xrft") has_eofs, requires_eofs = _importorskip("eofs")

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Intermediate/main.py

fredtheninja/100-Days-of-Code

0

65389

<reponame>fredtheninja/100-Days-of-Code from turtle import Screen from snake import Snake import time screen = Screen() screen.setup(width=600, height=600) screen.bgcolor("black") screen.title("Snake") screen.tracer(0) # -----------------------------Angela-----------------------------------# snake = Snake() screen.listen() screen.onkey(snake.up, "Up") screen.onkey(snake.down, "Down") screen.onkey(snake.left, "Left") screen.onkey(snake.right, "Right") game_is_on = True while game_is_on: screen.update() time.sleep(0.1) snake.move() # -----------------------------FredTheNinja-----------------------------------# # # my solution to challenge 1 # bodies = 3 # x = 0 # y = 0 # for i in range(0, bodies): # snake = Turtle(shape="square") # snake.color("white") # snake.shapesize(1) # snake.pu() # snake.goto(x, y) # x -= 20 screen.exitonclick()

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sample.py

leeamen/threadpool

3

166982

#!/usr/bin/python #coding:utf-8 import mylog import mythreadpool as tp import logging if __name__ == '__main__': logger = logging.getLogger(__name__) def process(args): logger.info('task %d is finished!', args['taskid']) threadpool = tp.MyThreadPool(2) for i in range(1,10): threadpool.DispatchTask(process, {'taskid':i}) threadpool.Destroy()

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hatch/structures.py

kapb14/hatch

1

92496

<gh_stars>1-10 import os from urllib.parse import urlencode class File: def __init__(self, file_name, contents, binary=False): self.file_name = file_name self.contents = contents self.write_mode = 'wb' if binary else 'w' def write(self, d): if not os.path.exists(d): os.makedirs(d) with open(os.path.join(d, self.file_name), self.write_mode) as f: f.write(self.contents) class Badge: def __init__(self, image, target, alt, params=None): self.image = image self.target = target self.alt = alt if params: self.image += '?' + urlencode(params)

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ansible/venv/lib/python2.7/site-packages/ansible/modules/network/f5/bigip_asm_policy_import.py

gvashchenkolineate/gvashchenkolineate_infra_trytravis

17

11250

#!/usr/bin/python # -*- coding: utf-8 -*- # # Copyright: (c) 2018, F5 Networks Inc. # GNU General Public License v3.0 (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'certified'} DOCUMENTATION = r''' --- module: bigip_asm_policy_import short_description: Manage BIG-IP ASM policy imports description: - Manage BIG-IP ASM policies policy imports. version_added: 2.8 options: name: description: - The ASM policy to create or override. type: str required: True inline: description: - When specified the ASM policy is created from a provided string. - Content needs to be provided in a valid XML format otherwise the operation will fail. type: str source: description: - Full path to a policy file to be imported into the BIG-IP ASM. - Policy files exported from newer versions of BIG-IP cannot be imported into older versions of BIG-IP. The opposite, however, is true; you can import older into newer. - The file format can be binary of XML. type: path force: description: - When set to C(yes) any existing policy with the same name will be overwritten by the new import. - Works for both inline and file imports, if the policy does not exist this setting is ignored. default: no type: bool partition: description: - Device partition to create policy on. type: str default: Common extends_documentation_fragment: f5 author: - <NAME> (@wojtek0806) ''' EXAMPLES = r''' - name: Import ASM policy bigip_asm_policy_import: name: new_asm_policy file: /root/asm_policy.xml provider: server: lb.mydomain.com user: admin password: <PASSWORD> delegate_to: localhost - name: Import ASM policy inline bigip_asm_policy_import: name: foo-policy4 inline: <xml>content</xml> provider: server: lb.mydomain.com user: admin password: <PASSWORD> delegate_to: localhost - name: Override existing ASM policy bigip_asm_policy: name: new_asm_policy file: /root/asm_policy_new.xml force: yes provider: server: lb.mydomain.com user: admin password: <PASSWORD> delegate_to: localhost ''' RETURN = r''' source: description: Local path to an ASM policy file. returned: changed type: str sample: /root/some_policy.xml inline: description: Contents of policy as an inline string returned: changed type: str sample: <xml>foobar contents</xml> name: description: Name of the ASM policy to be created/overwritten returned: changed type: str sample: Asm_APP1_Transparent force: description: Set when overwriting an existing policy returned: changed type: bool sample: yes ''' import os import time from ansible.module_utils.basic import AnsibleModule from ansible.module_utils.basic import env_fallback try: from library.module_utils.network.f5.bigip import F5RestClient from library.module_utils.network.f5.common import F5ModuleError from library.module_utils.network.f5.common import AnsibleF5Parameters from library.module_utils.network.f5.common import fq_name from library.module_utils.network.f5.common import transform_name from library.module_utils.network.f5.common import f5_argument_spec from library.module_utils.network.f5.icontrol import upload_file from library.module_utils.network.f5.icontrol import module_provisioned except ImportError: from ansible.module_utils.network.f5.bigip import F5RestClient from ansible.module_utils.network.f5.common import F5ModuleError from ansible.module_utils.network.f5.common import AnsibleF5Parameters from ansible.module_utils.network.f5.common import fq_name from ansible.module_utils.network.f5.common import transform_name from ansible.module_utils.network.f5.common import f5_argument_spec from ansible.module_utils.network.f5.icontrol import upload_file from ansible.module_utils.network.f5.icontrol import module_provisioned class Parameters(AnsibleF5Parameters): updatables = [] returnables = [ 'name', 'inline', 'source', 'force' ] api_attributes = [ 'file', 'name', ] api_map = { 'file': 'inline', 'filename': 'source', } class ApiParameters(Parameters): pass class ModuleParameters(Parameters): pass class Changes(Parameters): def to_return(self): result = {} try: for returnable in self.returnables: result[returnable] = getattr(self, returnable) result = self._filter_params(result) except Exception: pass return result class UsableChanges(Changes): pass class ReportableChanges(Changes): pass class Difference(object): def __init__(self, want, have=None): self.want = want self.have = have def compare(self, param): try: result = getattr(self, param) return result except AttributeError: return self.__default(param) def __default(self, param): attr1 = getattr(self.want, param) try: attr2 = getattr(self.have, param) if attr1 != attr2: return attr1 except AttributeError: return attr1 class ModuleManager(object): def __init__(self, *args, **kwargs): self.module = kwargs.get('module', None) self.client = F5RestClient(**self.module.params) self.want = ModuleParameters(params=self.module.params) self.changes = UsableChanges() def _set_changed_options(self): changed = {} for key in Parameters.returnables: if getattr(self.want, key) is not None: changed[key] = getattr(self.want, key) if changed: self.changes = UsableChanges(params=changed) def _announce_deprecations(self, result): warnings = result.pop('__warnings', []) for warning in warnings: self.client.module.deprecate( msg=warning['msg'], version=warning['version'] ) def exec_module(self): if not module_provisioned(self.client, 'asm'): raise F5ModuleError( "ASM must be provisioned to use this module." ) result = dict() changed = self.policy_import() reportable = ReportableChanges(params=self.changes.to_return()) changes = reportable.to_return() result.update(**changes) result.update(dict(changed=changed)) self._announce_deprecations(result) return result def policy_import(self): self._set_changed_options() if self.module.check_mode: return True if self.exists(): if self.want.force is False: return False if self.want.inline: task = self.inline_import() self.wait_for_task(task) return True self.import_file_to_device() self.remove_temp_policy_from_device() return True def exists(self): uri = 'https://{0}:{1}/mgmt/tm/asm/policies/'.format( self.client.provider['server'], self.client.provider['server_port'], ) query = "?$filter=contains(name,'{0}')+and+contains(partition,'{1}')&$select=name,partition".format( self.want.name, self.want.partition ) resp = self.client.api.get(uri + query) try: response = resp.json() except ValueError as ex: raise F5ModuleError(str(ex)) if 'items' in response and response['items'] != []: return True return False def upload_file_to_device(self, content, name): url = 'https://{0}:{1}/mgmt/shared/file-transfer/uploads'.format( self.client.provider['server'], self.client.provider['server_port'] ) try: upload_file(self.client, url, content, name) except F5ModuleError: raise F5ModuleError( "Failed to upload the file." ) def _get_policy_link(self): uri = 'https://{0}:{1}/mgmt/tm/asm/policies/'.format( self.client.provider['server'], self.client.provider['server_port'], ) query = "?$filter=contains(name,'{0}')+and+contains(partition,'{1}')&$select=name,partition".format( self.want.name, self.want.partition ) resp = self.client.api.get(uri + query) try: response = resp.json() except ValueError as ex: raise F5ModuleError(str(ex)) policy_link = response['items'][0]['selfLink'] return policy_link def inline_import(self): params = self.changes.api_params() uri = "https://{0}:{1}/mgmt/tm/asm/tasks/import-policy/".format( self.client.provider['server'], self.client.provider['server_port'], ) if self.want.force: params.update(dict(policyReference={'link': self._get_policy_link()})) params.pop('name') resp = self.client.api.post(uri, json=params) try: response = resp.json() except ValueError as ex: raise F5ModuleError(str(ex)) if 'code' in response and response['code'] in [400, 403]: if 'message' in response: raise F5ModuleError(response['message']) else: raise F5ModuleError(resp.content) return response['id'] def wait_for_task(self, task_id): uri = "https://{0}:{1}/mgmt/tm/asm/tasks/import-policy/{2}".format( self.client.provider['server'], self.client.provider['server_port'], task_id ) while True: resp = self.client.api.get(uri) try: response = resp.json() except ValueError as ex: raise F5ModuleError(str(ex)) if 'code' in response and response['code'] == 400: if 'message' in response: raise F5ModuleError(response['message']) else: raise F5ModuleError(resp.content) if response['status'] in ['COMPLETED', 'FAILURE']: break time.sleep(1) if response['status'] == 'FAILURE': raise F5ModuleError( 'Failed to import ASM policy.' ) if response['status'] == 'COMPLETED': return True def import_file_to_device(self): name = os.path.split(self.want.source)[1] self.upload_file_to_device(self.want.source, name) time.sleep(2) full_name = fq_name(self.want.partition, self.want.name) if self.want.force: cmd = 'tmsh load asm policy {0} file /var/config/rest/downloads/{1} overwrite'.format(full_name, name) else: cmd = 'tmsh load asm policy {0} file /var/config/rest/downloads/{1}'.format(full_name, name) uri = "https://{0}:{1}/mgmt/tm/util/bash/".format( self.client.provider['server'], self.client.provider['server_port'], ) args = dict( command='run', utilCmdArgs='-c "{0}"'.format(cmd) ) resp = self.client.api.post(uri, json=args) try: response = resp.json() if 'commandResult' in response: if 'Unexpected Error' in response['commandResult']: raise F5ModuleError(response['commandResult']) except ValueError as ex: raise F5ModuleError(str(ex)) if 'code' in response and response['code'] == 400: if 'message' in response: raise F5ModuleError(response['message']) else: raise F5ModuleError(resp.content) return True def remove_temp_policy_from_device(self): name = os.path.split(self.want.source)[1] tpath_name = '/var/config/rest/downloads/{0}'.format(name) uri = "https://{0}:{1}/mgmt/tm/util/unix-rm/".format( self.client.provider['server'], self.client.provider['server_port'], ) args = dict( command='run', utilCmdArgs=tpath_name ) resp = self.client.api.post(uri, json=args) try: response = resp.json() except ValueError as ex: raise F5ModuleError(str(ex)) if 'code' in response and response['code'] == 400: if 'message' in response: raise F5ModuleError(response['message']) else: raise F5ModuleError(resp.content) class ArgumentSpec(object): def __init__(self): self.supports_check_mode = True argument_spec = dict( name=dict( required=True, ), source=dict(type='path'), inline=dict(), force=dict( type='bool', default='no' ), partition=dict( default='Common', fallback=(env_fallback, ['F5_PARTITION']) ) ) self.argument_spec = {} self.argument_spec.update(f5_argument_spec) self.argument_spec.update(argument_spec) self.mutually_exclusive = [ ['source', 'inline'] ] def main(): spec = ArgumentSpec() module = AnsibleModule( argument_spec=spec.argument_spec, 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12_module_release/message/__init__.py

DeveloperLY/Python-practice

0

1334

<filename>12_module_release/message/__init__.py from . import send_message from . import receive_message

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file_util.py

d4yvie/advent_of_code_2021

0

184525

from functools import reduce import numpy as np def read_all(file_name="measurements") -> str: with open(file_name) as f: return reduce(lambda a, b: a + b, f, "") def read_lines(file_name="measurements") -> list[str]: with open(file_name) as f: return [line.strip() for line in f] def read_first_line(file_name="measurements") -> str: return read_lines(file_name)[0] def line_to_integers(line: str) -> list[int]: return list(map(int, line.split(","))) def read_first_line_as_integers(file_name="measurements") -> list[int]: return line_to_integers(read_first_line(file_name)) def read_line_seperated_data_sets(file_name="measurements") -> list[str]: with open(file_name) as f: return f.read().rstrip().split('\n\n') def read_lines_as_int(file_name="measurements") -> list[list[int]]: lines = read_lines(file_name) return [[int(char) for char in x] for x in lines] def read_lines_as_np_matrix(file_name="measurements") -> np.ndarray: lines = read_lines(file_name) return np.array([[float(char) for char in line] for line in lines])

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ffttest.py

teslaworksumn/Reactor

0

14333

# From http://julip.co/2012/05/arduino-python-soundlight-spectrum/ # Python 2.7 code to analyze sound and interface with Arduino import pyaudio # from http://people.csail.mit.edu/hubert/pyaudio/ import serial # from http://pyserial.sourceforge.net/ import numpy # from http://numpy.scipy.org/ import audioop import sys import math import struct ''' Sources http://www.swharden.com/blog/2010-03-05-realtime-fft-graph-of-audio-wav-file-or-microphone-input-with-python-scipy-and-wckgraph/ http://macdevcenter.com/pub/a/python/2001/01/31/numerically.html?page=2 ''' MAX = 0 NUM = 20 def list_devices(): # List all audio input devices p = pyaudio.PyAudio() i = 0 n = p.get_device_count() while i < n: dev = p.get_device_info_by_index(i) if dev['maxInputChannels'] > 0: print str(i)+'. '+dev['name'] i += 1 def fft(): chunk = 2**11 # Change if too fast/slow, never less than 2**11 scale = 25 # Change if too dim/bright exponent = 3 # Change if too little/too much difference between loud and quiet sounds samplerate = 44100 # CHANGE THIS TO CORRECT INPUT DEVICE # Enable stereo mixing in your sound card # to make you sound output an input # Use list_devices() to list all your input devices device = 1 # Mic #device = 4 # SF2 p = pyaudio.PyAudio() stream = p.open(format = pyaudio.paInt16, channels = 1, rate = 44100, input = True, frames_per_buffer = chunk, input_device_index = device) print "Starting, use Ctrl+C to stop" try: ser = serial.Serial( port='/dev/ttyS0', timeout=1 ) while True: data = stream.read(chunk) # Do FFT levels = calculate_levels(data, chunk, samplerate) # Make it look better and send to serial for level in levels: level = max(min(level / scale, 1.0), 0.0) level = level**exponent level = int(level * 255) #ser.write(chr(level)) #sys.stdout.write(str(level)+' ') #sys.stdout.write('\n') #s = ser.read(6) except KeyboardInterrupt: pass finally: print "\nStopping" stream.close() p.terminate() #ser.close() def calculate_levels(data, chunk, samplerate): # Use FFT to calculate volume for each frequency global MAX # Convert raw sound data to Numpy array fmt = "%dH"%(len(data)/2) data2 = struct.unpack(fmt, data) data2 = numpy.array(data2, dtype='h') # Apply FFT fourier = numpy.fft.fft(data2) ffty = numpy.abs(fourier[0:len(fourier)/2])/1000 ffty1=ffty[:len(ffty)/2] ffty2=ffty[len(ffty)/2::]+2 ffty2=ffty2[::-1] ffty=ffty1+ffty2 ffty=numpy.log(ffty)-2 fourier = list(ffty)[4:-4] fourier = fourier[:len(fourier)/2] size = len(fourier) # Add up for 6 lights levels = [sum(fourier[i:(i+size/NUM)]) for i in xrange(0, size, size/NUM)][:NUM] return levels if __name__ == '__main__': #list_devices() fft()

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src/knn.py

zhangxyz/MLFSdel

0

133802

#!usr/bin/env python #-*- coding: utf-8 -*- import logging import numpy as np import sklearn from sklearn import metrics from sklearn import neighbors from sklearn import cross_validation from sklearn.datasets import load_svmlight_file from sklearn.cross_validation import cross_val_score import math import sys import os from subprocess import * from sklearn.feature_selection import SelectPercentile, f_classif, SelectKBest from sklearn.pipeline import Pipeline from sklearn import metrics from sklearn.neighbors import DistanceMetric from sklearn.neighbors import KNeighborsClassifier def calculate_result(actual,pred): m_precision = metrics.precision_score(actual,pred); m_recall = metrics.recall_score(actual,pred); print 'predict info:' print 'accuracy:{0:.3f}'.format(metrics.accuracy_score(actual,pred)) print 'precision:{0:.3f}'.format(m_precision) print 'recall:{0:0.3f}'.format(m_recall); print 'f1-score:{0:.3f}'.format(metrics.f1_score(actual,pred)); def calculate_k(x,y): clf = KNeighborsClassifier(n_neighbors=3) max_metric=cross_val_score(clf,x,y,cv=10,scoring='f1').mean() k=3 for i in range(4,100): clf = KNeighborsClassifier(n_neighbors=i)#default with k=5 metric = cross_val_score(clf,x,y,cv=10,scoring='f1').mean() if max_metric < metric : max_metric=metric k=i return k def knn_result(train_x,train_y,test_x,test_y,out_file): print '*************************\nKNN\n*************************' #c= calculate_k(train_x,train_y) clf = KNeighborsClassifier(n_neighbors=10) #clf = KNeighborsClassifier(n_neighbors=c) clf.fit(train_x,train_y) pred = clf.predict(test_x); calculate_result(test_y,pred); np.savetxt(out_file,pred,fmt='%d') #print(c) def knn_select(train_x,train_y,test_x,test_y): #c= calculate_k(train_x,train_y) clf = KNeighborsClassifier(n_neighbors=10) #clf = KNeighborsClassifier(n_neighbors=c) clf.fit(train_x,train_y) pred = clf.predict(test_x); return metrics.precision_score(test_y,pred) #print(c)

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ext_modules/_maix_nn/example/yolo2_camera.py

sipeed/python3-maix

93

6851

<gh_stars>10-100 from maix import nn from PIL import Image, ImageDraw, ImageFont from maix import display, camera import time from maix.nn import decoder def draw_rectangle_with_title(draw, box, disp_str, bg_color=(255, 0, 0, 255), font_color=(255, 255, 255, 255)): # draw = ImageDraw.Draw(img) font = ImageFont.load_default() font_w, font_h = font.getsize(disp_str) draw.rectangle((box[0], box[1], box[0] + box[2], box[1] + box[3]), fill=None, outline=bg_color, width=2) draw.rectangle((box[0], box[1] - font_h, box[0] + font_w, box[1]), fill=bg_color) draw.text((box[0], box[1] - font_h), disp_str, fill=font_color, font=font) camera.config(size=(224, 224)) model = { "param": "/root/models/yolo2_face_awnn.param", "bin": "/root/models/yolo2_face_awnn.bin" } options = { "model_type": "awnn", "inputs": { "input0": (224, 224, 3) }, "outputs": { "output0": (7, 7, (1+4+1)*5) }, "mean": [127.5, 127.5, 127.5], "norm": [0.0078125, 0.0078125, 0.0078125], } print("-- load model:", model) m = nn.load(model, opt=options) print("-- load ok") print("-- read image") w = options["inputs"]["input0"][1] h = options["inputs"]["input0"][0] # # img.show() print("-- read image ok") labels = ["person"] anchors = [1.19, 1.98, 2.79, 4.59, 4.53, 8.92, 8.06, 5.29, 10.32, 10.65] yolo2_decoder = decoder.Yolo2(len(labels), anchors, net_in_size=(w, h), net_out_size=(7, 7)) while 1: img = camera.capture() if not img: time.sleep(0.01) continue t = time.time() out = m.forward(img, quantize=True, layout="hwc") print("-- forward: ", time.time() - t ) t = time.time() boxes, probs = yolo2_decoder.run(out, nms=0.3, threshold=0.5, img_size=(240, 240)) print("-- decode: ", time.time() - t ) t = time.time() for i, box in enumerate(boxes): class_id = probs[i][0] prob = probs[i][1][class_id] disp_str = "{}:{:.2f}%".format(labels[class_id], prob*100) draw_rectangle_with_title(display.get_draw(), box, disp_str) print("-- draw: ", time.time() - t ) t = time.time() display.show() print("-- show: ", time.time() - t )

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python/6kyu/playing_with_digits.py

momchilantonov/codewars

0

128589

<reponame>momchilantonov/codewars def dig_pow(n, p): """ Some numbers have funny properties. For example: 89 --> 8¹ + 9² = 89 * 1 695 --> 6² + 9³ + 5⁴= 1390 = 695 * 2 46288 --> 4³ + 6⁴+ 2⁵ + 8⁶ + 8⁷ = 2360688 = 46288 * 51 Given a positive integer n written as abcd... (a, b, c, d... being digits) and a positive integer p we want to find a positive integer k, if it exists, such that the sum of the digits of n taken to the successive powers of p is equal to k * n. In other words: Is there an integer k such as : (a ^ p + b ^ (p+1) + c ^(p+2) + d ^ (p+3) + ...) = n * k If it is the case we will return k, if not return -1. Note: n and p will always be given as strictly positive integers. """ num = 0 n = str(n) for i in range(len(n)): num += int(n[i]) ** p p += 1 k = num / int(n) if k.is_integer(): return k else: return -1 # TESTS dig_pow(89, 1, 1) dig_pow(92, 1, -1) dig_pow(46288, 3, 51) dig_pow(114, 3, 9)

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ensemble/management/commands/seeddata.py

evanlouie/activelearning

0

49525

""" Test data scaffolding. Read: https://docs.djangoproject.com/en/dev/howto/custom-management-commands/ """ import json from random import randint from django.core.management.base import BaseCommand, CommandError from ensemble.models import ( Classification, Model, ModelVersion, MediaFile, VideoPrediction, AudioPrediction, ) class Command(BaseCommand): help = "Scaffold some test data for ensemble" def handle(self, *args, **options): gunshot = Classification(name="GUNSHOT") gunshot.save() audioset_model = Model(name="audioset") audioset_model.save() audioset_model.classifications.add(gunshot) audioset_model_trained = ModelVersion( model=audioset_model, version="0.01alpha2" ) audioset_model_trained.save() for movie in json.loads(self.__movie_json, strict=False): media_file = MediaFile( name=movie["title"], url=movie["sources"][0], description=movie["description"], ) media_file.save() video_predictions = [ self.__generate_random_video_prediction( media_file, gunshot, audioset_model_trained ) for _ in range(1000) ] audio_predictions = [ self.__generate_random_audio_prediction( media_file, gunshot, audioset_model_trained ) for _ in range(1000) ] # VideoPrediction.objects.bulk_create(video_predictions) # AudioPrediction.objects.bulk_create(audio_predictions) for prediction in video_predictions + audio_predictions: prediction.save() def __generate_random_video_prediction(self, media_file, classification, model): """ Generate a random gunshot video prediction for the video """ x = randint(0, 1280) y = randint(0, 720) width = randint(1, 1280 - x + 1) height = randint(1, 720 - y + 1) return VideoPrediction( media_file=media_file, classification=classification, confidence=randint(0, 100), model_version=model, time=randint(0, 600000), x=x, y=y, width=width, height=height, ) def __generate_random_audio_prediction(self, media_file, classification, model): """ Generate a random 10 second audio prediction for a gunshot """ return AudioPrediction( media_file=media_file, classification=classification, confidence=randint(0, 100), model_version=model, time=randint(0, 600000), duration="10000", ) __movie_json = """ [ { "description": "Big Buck Bunny tells the story of a giant rabbit with a heart bigger than himself. When one sunny day three rodents rudely harass him, something snaps... and the rabbit ain't no bunny anymore! In the typical cartoon tradition he prepares the nasty rodents a comical revenge.\n\nLicensed under the Creative Commons Attribution license\nhttp://www.bigbuckbunny.org", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/BigBuckBunny.mp4" ], "subtitle": "By Blender Foundation", "thumb": "images/BigBuckBunny.jpg", "title": "Big Buck Bunny" }, { "description": "The first Blender Open Movie from 2006", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ElephantsDream.mp4" ], "subtitle": "By Blender Foundation", "thumb": "images/ElephantsDream.jpg", "title": "Elephant Dream" }, { "description": "HBO GO now works with Chromecast -- the easiest way to enjoy online video on your TV. For when you want to settle into your Iron Throne to watch the latest episodes. For $35.\nLearn how to use Chromecast with HBO GO and more at google.com/chromecast.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerBlazes.mp4" ], "subtitle": "By Google", "thumb": "images/ForBiggerBlazes.jpg", "title": "For Bigger Blazes" }, { "description": "Introducing Chromecast. The easiest way to enjoy online video and music on your TV—for when Batman's escapes aren't quite big enough. For $35. Learn how to use Chromecast with Google Play Movies and more at google.com/chromecast.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerEscapes.mp4" ], "subtitle": "By Google", "thumb": "images/ForBiggerEscapes.jpg", "title": "For Bigger Escape" }, { "description": "Introducing Chromecast. The easiest way to enjoy online video and music on your TV. For $35. Find out more at google.com/chromecast.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerFun.mp4" ], "subtitle": "By Google", "thumb": "images/ForBiggerFun.jpg", "title": "For Bigger Fun" }, { "description": "Introducing Chromecast. The easiest way to enjoy online video and music on your TV—for the times that call for bigger joyrides. For $35. Learn how to use Chromecast with YouTube and more at google.com/chromecast.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerJoyrides.mp4" ], "subtitle": "By Google", "thumb": "images/ForBiggerJoyrides.jpg", "title": "For Bigger Joyrides" }, { "description": "Introducing Chromecast. The easiest way to enjoy online video and music on your TV—for when you want to make Buster's big meltdowns even bigger. For $35. Learn how to use Chromecast with Netflix and more at google.com/chromecast.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerMeltdowns.mp4" ], "subtitle": "By Google", "thumb": "images/ForBiggerMeltdowns.jpg", "title": "For Bigger Meltdowns" }, { "description": "Sintel is an independently produced short film, initiated by the Blender Foundation as a means to further improve and validate the free/open source 3D creation suite Blender. With initial funding provided by 1000s of donations via the internet community, it has again proven to be a viable development model for both open 3D technology as for independent animation film.\nThis 15 minute film has been realized in the studio of the Amsterdam Blender Institute, by an international team of artists and developers. In addition to that, several crucial technical and creative targets have been realized online, by developers and artists and teams all over the world.\nwww.sintel.org", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/Sintel.mp4" ], "subtitle": "By Blender Foundation", "thumb": "images/Sintel.jpg", "title": "Sintel" }, { "description": "Smoking Tire takes the all-new Subaru Outback to the highest point we can find in hopes our customer-appreciation Balloon Launch will get some free T-shirts into the hands of our viewers.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/SubaruOutbackOnStreetAndDirt.mp4" ], "subtitle": "By Garage419", "thumb": "images/SubaruOutbackOnStreetAndDirt.jpg", "title": "Subaru Outback On Street And Dirt" }, { "description": "Tears of Steel was realized with crowd-funding by users of the open source 3D creation tool Blender. Target was to improve and test a complete open and free pipeline for visual effects in film - and to make a compelling sci-fi film in Amsterdam, the Netherlands. The film itself, and all raw material used for making it, have been released under the Creatieve Commons 3.0 Attribution license. Visit the tearsofsteel.org website to find out more about this, or to purchase the 4-DVD box with a lot of extras. (CC) Blender Foundation - http://www.tearsofsteel.org", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/TearsOfSteel.mp4" ], "subtitle": "By Blender Foundation", "thumb": "images/TearsOfSteel.jpg", "title": "Tears of Steel" }, { "description": "The Smoking Tire heads out to Adams Motorsports Park in Riverside, CA to test the most requested car of 2010, the Volkswagen GTI. Will it beat the Mazdaspeed3's standard-setting lap time? Watch and see...", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/VolkswagenGTIReview.mp4" ], "subtitle": "By Garage419", "thumb": "images/VolkswagenGTIReview.jpg", "title": "Volkswagen GTI Review" }, { "description": "The Smoking Tire is going on the 2010 Bullrun Live Rally in a 2011 Shelby GT500, and posting a video from the road every single day! The only place to watch them is by subscribing to The Smoking Tire or watching at BlackMagicShine.com", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/WeAreGoingOnBullrun.mp4" ], "subtitle": "By Garage419", "thumb": "images/WeAreGoingOnBullrun.jpg", "title": "We Are Going On Bullrun" }, { "description": "The Smoking Tire meets up with Chris and Jorge from CarsForAGrand.com to see just how far $1,000 can go when looking for a car.The Smoking Tire meets up with Chris and Jorge from CarsForAGrand.com to see just how far $1,000 can go when looking for a car.", "sources": [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/WhatCarCanYouGetForAGrand.mp4" ], "subtitle": "By Garage419", "thumb": "images/WhatCarCanYouGetForAGrand.jpg", "title": "What care can you get for a grand?" } ] """

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src/terra/contracts/levana.py

fentas/staketaxcsv

0

22832

# known contracts from protocol CONTRACTS = [ # NFT - Meteor Dust "<KEY>", # NFT - Eggs "<KEY>", # NFT - Dragons "<KEY>", # NFT - Loot "<KEY>", ] def handle(exporter, elem, txinfo, contract): print(f"Levana! {contract}") #print(elem)

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webdriver/applicationstate.py

jiteshmohite/Python-Page-Object-Model-Framework

6

1601865

class ApplicationState(object): NOT_INSTALLED = 0 NOT_RUNNING = 1 RUNNING_IN_BACKGROUND_SUSPENDED = 2 RUNNING_IN_BACKGROUND = 3 RUNNING_IN_FOREGROUND = 4

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mummi_ras/online/aa/aa_get_tiltrot_z_state.py

mummi-framework/mummi-ras

4

896

############################################################################### # @todo add Pilot2-splash-app disclaimer ############################################################################### """ Get's KRAS states """ import MDAnalysis as mda from MDAnalysis.analysis import align from MDAnalysis.lib.mdamath import make_whole import os import numpy as np import math ############## Below section needs to be uncommented ############ import mummi_core import mummi_ras from mummi_core.utils import Naming # # Logger has to be initialized the first thing in the script from logging import getLogger LOGGER = getLogger(__name__) # # Innitilize MuMMI if it has not been done before # MUMMI_ROOT = mummi.init(True) # This is needed so the Naming works below #@TODO fix this so we don't have these on import make them as an init mummi_core.init() dirKRASStates = Naming.dir_res('states') dirKRASStructures = Naming.dir_res('structures') # #RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-ONLY.microstates.txt")) RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-states.txt"),comments='#') # #RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-RAF.microstates.txt")) RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-raf-states.txt"),comments='#') # Note diffrent number of columns so index change below # TODO: CS, my edits to test # RAS_ONLY_macrostate = np.loadtxt('ras-states.txt') # RAS_RAF_macrostate = np.loadtxt('ras-raf-states.txt') ############## above section needs to be uncommented ############ # TODO: CS, my edits to test # TODO: TSC, The reference structure has to currently be set as the 'RAS-ONLY-reference-structure.gro' # TODO: TSC, path to the reference structure is: mummi_resources/structures/ kras_ref_universe = mda.Universe(os.path.join(dirKRASStructures, "RAS-ONLY-reference-structure.gro")) # kras_ref_universe = mda.Universe("RAS-ONLY-reference-structure.gro") # kras_ref_universe = mda.Universe('AA_pfpatch_000000004641_RAS_RAF2_411.gro') # TODO: CS, not using these for x4 proteins; instead using protein_systems below to set num_res ######### Below hard codes the number of residues within RAS-only and RAS-RAF ########## RAS_only_num_res = 184 RAS_RAF_num_res = 320 ######### Above hard codes the number of residues within RAS-only and RAS-RAF ########## ####### This can be removed # def get_kras(syst, kras_start): # """Gets all atoms for a KRAS protein starting at 'kras_start'.""" # return syst.atoms[kras_start:kras_start+428] ####### This can be removed def get_segids(u): """Identifies the list of segments within the system. Only needs to be called x1 time""" segs = u.segments segs = segs.segids ras_segids = [] rasraf_segids = [] for i in range(len(segs)): # print(segs[i]) if segs[i][-3:] == 'RAS': ras_segids.append(segs[i]) if segs[i][-3:] == 'RAF': rasraf_segids.append(segs[i]) return ras_segids, rasraf_segids def get_protein_info(u,tag): """Uses the segments identified in get_segids to make a list of all proteins in the systems.\ Outputs a list of the first residue number of the protein, and whether it is 'RAS-ONLY', or 'RAS-RAF'.\ The 'tag' input defines what is used to identify the first residue of the protein. i.e. 'resname ACE1 and name BB'.\ Only needs to be called x1 time""" ras_segids, rasraf_segids = get_segids(u) if len(ras_segids) > 0: RAS = u.select_atoms('segid '+ras_segids[0]+' and '+str(tag)) else: RAS = [] if len(rasraf_segids) > 0: RAF = u.select_atoms('segid '+rasraf_segids[0]+' and '+str(tag)) else: RAF = [] protein_info = []#np.empty([len(RAS)+len(RAF),2]) for i in range(len(RAS)): protein_info.append((RAS[i].resid,'RAS-ONLY')) for i in range(len(RAF)): protein_info.append((RAF[i].resid,'RAS-RAF')) ######## sort protein info protein_info = sorted(protein_info) ######## sort protein info return protein_info def get_ref_kras(): """Gets the reference KRAS struct. Only called x1 time when class is loaded""" start_of_g_ref = kras_ref_universe.residues[0].resid ref_selection = 'resid '+str(start_of_g_ref)+':'+str(start_of_g_ref+24)+' ' +\ str(start_of_g_ref+38)+':'+str(start_of_g_ref+54)+' ' +\ str(start_of_g_ref+67)+':'+str(start_of_g_ref+164)+' ' +\ 'and (name CA or name BB)' r2_26r40_56r69_166_ref = kras_ref_universe.select_atoms(str(ref_selection)) return kras_ref_universe.select_atoms(str(ref_selection)).positions - kras_ref_universe.select_atoms(str(ref_selection)).center_of_mass() # Load inital ref frames (only need to do this once) ref0 = get_ref_kras() def getKRASstates(u,kras_indices): """Gets states for all KRAS proteins in path.""" # res_shift = 8 # all_glycine = u.select_atoms("resname GLY") # kras_indices = [] # for i in range(0, len(all_glycine), 26): # kras_indices.append(all_glycine[i].index) ########## Below is taken out of the function so it is only done once ######### # kras_indices = get_protein_info(u,'resname ACE1 and name BB') ########## Above is taken out of the function so it is only done once ######### # CS, for x4 cases: # [{protein_x4: (protein_type, num_res)}] protein_systems = [{'ras4a': ('RAS-ONLY', 185), 'ras4araf': ('RAS-RAF', 321), 'ras': ('RAS-ONLY', 184), 'rasraf': ('RAS-RAF', 320)}] ALLOUT = [] for k in range(len(kras_indices)): start_of_g = kras_indices[k][0] protein_x4 = str(kras_indices[k][1]) try: protein_type = [item[protein_x4] for item in protein_systems][0][0] # 'RAS-ONLY' OR 'RAS-RAF' num_res = [item[protein_x4] for item in protein_systems][0][1] except: LOGGER.error('Check KRas naming between modules') raise Exception('Error: unknown KRas name') # TODO: CS, replacing this comment section with the above, to handle x4 protein types # --------------------------------------- # ALLOUT = [] # for k in range(len(kras_indices)): # start_of_g = kras_indices[k][0] # protein_type = str(kras_indices[k][1]) # ########## BELOW SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ############## # ########## POTENTIALLY REDO WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) ####### # ########## HAS BEEN REDONE WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) ######## # # if len(kras_indices) == 1: # # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB') ####### HAS TO BE FIXED FOR BACKBONE ATOMS FOR SPECIFIC PROTEIN # # elif len(kras_indices) > 1: # # if k == len(kras_indices)-1: # # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB') # # else: # # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(kras_indices[k+1][0])+' and name BB') # ########## ABOVE SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ############## # # ########## Below hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations ######################### # if protein_type == 'RAS-ONLY': # num_res = RAS_only_num_res # elif protein_type == 'RAS-RAF': # num_res = RAS_RAF_num_res # ########## Above hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations ######################### # --------------------------------------- # TODO: TSC, I changed the selection below, which can be used for the make_whole... # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res)+' and (name CA or name BB)') krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res)) krases0_BB.guess_bonds() r2_26r40_56r69_166 = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+24)+' ' +\ str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\ str(start_of_g+67)+':'+str(start_of_g+164)+\ ' and (name CA or name BB)') u_selection = \ 'resid '+str(start_of_g)+':'+str(start_of_g+24)+' '+str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\ str(start_of_g+67)+':'+str(start_of_g+164)+' and (name CA or name BB)' mobile0 = u.select_atoms(str(u_selection)).positions - u.select_atoms(str(u_selection)).center_of_mass() # TODO: CS, something wrong with ref0 from get_kras_ref() # just making ref0 = mobile0 to test for now # ref0 = mobile0 # TSC removed this R, RMSD_junk = align.rotation_matrix(mobile0, ref0) ######## TODO: TSC, Adjusted for AA lipid names ######## # lipids = u.select_atoms('resname POPX POPC PAPC POPE DIPE DPSM PAPS PAP6 CHOL') lipids = u.select_atoms('resname POPC PAPC POPE DIPE SSM PAPS SAPI CHL1') coords = ref0 RotMat = [] OS = [] r152_165 = krases0_BB.select_atoms('resid '+str(start_of_g+150)+':'+str(start_of_g+163)+' and (name CA or name BB)') r65_74 = krases0_BB.select_atoms('resid '+str(start_of_g+63)+':'+str(start_of_g+72)+' and (name CA or name BB)') timeframes = [] # TODO: CS, for AA need bonds to run make_whole() # krases0_BB.guess_bonds() # TODO: CS, turn off for now to test beyond this point ''' *** for AA, need to bring that back on once all else runs *** ''' # @Tim and <NAME>. this was commented out - please check. #make_whole(krases0_BB) j, rmsd_junk = mda.analysis.align.rotation_matrix((r2_26r40_56r69_166.positions-r2_26r40_56r69_166.center_of_mass()), coords) RotMat.append(j) OS.append(r65_74.center_of_mass()-r152_165.center_of_mass()) timeframes.append(u.trajectory.time) if protein_type == 'RAS-RAF': z_pos = [] ############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES BELOW #################### ############### TODO: TSC, zshifting is set to -1 (instead of -2), as there are ACE caps that are separate residues in AA #zshifting=-1 if protein_x4 == 'rasraf': zshifting = -1 elif protein_x4 == 'ras4araf': zshifting = 0 else: zshifting = 0 LOGGER.error('Found unsupported protein_x4 type') raf_loops_selection = u.select_atoms('resid '+str(start_of_g+zshifting+291)+':'+str(start_of_g+zshifting+294)+' ' +\ str(start_of_g+zshifting+278)+':'+str(start_of_g+zshifting+281)+' ' +\ ' and (name CA or name BB)') ############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES ABOVE #################### diff = (lipids.center_of_mass()[2]-raf_loops_selection.center_of_mass(unwrap=True)[2])/10 if diff < 0: diff = diff+(u.dimensions[2]/10) z_pos.append(diff) z_pos = np.array(z_pos) RotMatNP = np.array(RotMat) OS = np.array(OS) OA = RotMatNP[:, 2, :]/(((RotMatNP[:, 2, 0]**2)+(RotMatNP[:, 2, 1]**2)+(RotMatNP[:, 2, 2]**2))**0.5)[:, None] OWAS = np.arccos(RotMatNP[:, 2, 2])*180/math.pi OC_temp = np.concatenate((OA, OS), axis=1) t = ((OC_temp[:, 0]*OC_temp[:, 3])+(OC_temp[:, 1]*OC_temp[:, 4]) + (OC_temp[:, 2]*OC_temp[:, 5]))/((OC_temp[:, 0]**2)+(OC_temp[:, 1]**2)+(OC_temp[:, 2]**2)) OC = OA*t[:, None] ORS_tp = np.concatenate((OC, OS), axis=1) ORS_norm = (((ORS_tp[:, 3]-ORS_tp[:, 0])**2)+((ORS_tp[:, 4]-ORS_tp[:, 1])**2)+((ORS_tp[:, 5]-ORS_tp[:, 2])**2))**0.5 ORS = (OS - OC)/ORS_norm[:, None] OACRS = np.cross(OA, ORS) OZCA = OA * OA[:, 2][:, None] Z_unit = np.full([len(OZCA), 3], 1) Z_adjust = np.array([0, 0, 1]) Z_unit = Z_unit*Z_adjust Z_OZCA = Z_unit-OZCA OZPACB = Z_OZCA/((Z_OZCA[:, 0]**2+Z_OZCA[:, 1]**2+Z_OZCA[:, 2]**2)**0.5)[:, None] OROTNOTSIGNED = np.zeros([len(ORS)]) for i in range(len(ORS)): OROTNOTSIGNED[i] = np.arccos(np.dot(OZPACB[i, :], ORS[i, :]) / (np.sqrt(np.dot(OZPACB[i, :], OZPACB[i, :]))) * (np.sqrt(np.dot(ORS[i, :], ORS[i, :]))))*180/math.pi OZPACBCRS_cross = np.cross(OZPACB, ORS) OZPACBCRS = OZPACBCRS_cross/((OZPACBCRS_cross[:, 0]**2+OZPACBCRS_cross[:, 1]**2+OZPACBCRS_cross[:, 2]**2)**0.5)[:, None] OFORSIGN_temp = (OA - OZPACBCRS)**2 OFORSIGN = OFORSIGN_temp[:, 0]+OFORSIGN_temp[:, 1]+OFORSIGN_temp[:, 2] OROT = OROTNOTSIGNED for i in range(len(OROT)): if OROT[i] < 0: OROT[i] = -(OROT[i]) for i in range(len(OROT)): if OFORSIGN[i] < 0.25: OROT[i] = -(OROT[i]) ###### Below introduces new shift to account for upper vs. lower leaflet ##### for i in range(len(OWAS)): OWAS[i] = abs(-(OWAS[i])+180) # made this an absolute value so that the tilt remains positive for i in range(len(OROT)): if OROT[i] < 0: OROT[i] = OROT[i]+180 elif OROT[i] > 0: OROT[i] = OROT[i]-180 ###### Above introduces new shift to account for upper vs. lower leaflet ##### ###### Below might have to be updated to take into account the periodic nature of the rotation ###### if protein_type == 'RAS-ONLY': states = np.zeros(len(OROT)) for j in range(len(OROT)): diff0 = [] for i in range(len(RAS_ONLY_macrostate)): #diff0.append([((RAS_ONLY_macrostate[i,0]-OWAS[j])**2+(RAS_ONLY_macrostate[i,1]-OROT[j])**2)**0.5, RAS_ONLY_macrostate[i,6]]) diff0.append([((RAS_ONLY_macrostate[i,1]-OWAS[j])**2+(RAS_ONLY_macrostate[i,0]-OROT[j])**2)**0.5, RAS_ONLY_macrostate[i,5]]) diff0.sort() states[j] = diff0[0][1] elif protein_type == 'RAS-RAF': states = np.zeros(len(OROT)) for j in range(len(OROT)): ### below: adding in the requirements for the 'high-z' state ### if (OROT[j] < -45 or OROT[j] > 140) and z_pos[j] > 4.8: states[j] = 3 else: ### above: adding in the requirements for the 'high-z' state ### diff0 = [] for i in range(len(RAS_RAF_macrostate)): #diff0.append([((RAS_RAF_macrostate[i,0]-OWAS[j])**2+(RAS_RAF_macrostate[i,1]-OROT[j])**2)**0.5, RAS_RAF_macrostate[i,6]]) diff0.append([((RAS_RAF_macrostate[i,1]-OWAS[j])**2+(RAS_RAF_macrostate[i,0]-OROT[j])**2)**0.5, RAS_RAF_macrostate[i,4]]) diff0.sort() states[j] = diff0[0][1] ###### Above might have to be updated to take into account the periodic nature of the rotation ###### ###### Assume we want to remove this? Where is the code that reads this information? i.e. will there be knock-on effects? ###### ###### If feedback code needs index 5 (two_states) from the output, deleting this four_states will shift that to index 4 ####### # four_states = np.zeros(len(OROT)) # for j in range(len(OROT)): # diff0 = [] # for i in range(len(macrostate4)): # diff0.append([((macrostate4[i,0]-OWAS[j])**2+(macrostate4[i,1]-OROT[j])**2)**0.5, macrostate4[i,6]]) # diff0.sort() # four_states[j] = diff0[0][1]+1 ###### below: old output details.... ###################################### ###### Updated - RAS-only to NOT HAVE the Z-distance ###################### ###### Updated - Added in the protein 'tag', i.e. RAS-ONLY or RAS-RAF ##### # OUTPUT = np.zeros([len(OROT), 6]) # for i in range(len(OROT)): # OUTPUT[i] = timeframes[i], OWAS[i], OROT[i], z_pos[i], four_states[i], two_states[i] ###### above: old output details.... ###################################### ###### below: NEW output details.... ###################################### if protein_type == 'RAS-ONLY': OUTPUT = np.zeros([len(OROT), 6]).astype(object) for i in range(len(OROT)): OUTPUT[i] = str(protein_type), timeframes[i], OWAS[i], OROT[i], 'n/a', int(states[i]) elif protein_type == 'RAS-RAF': OUTPUT = np.zeros([len(OROT), 6]).astype(object) for i in range(len(OROT)): OUTPUT[i] = str(protein_type), timeframes[i], OWAS[i], OROT[i], z_pos[i], int(states[i]) ALLOUT.append(OUTPUT) return np.asarray(ALLOUT) #np.savetxt(str(tpr)+"_tilt_rot_z_state.KRAS_"+str(k+1)+".txt", OUTPUT, fmt=['%i','%10.3f','%10.3f','%10.3f','%i','%i'], delimiter=' ')

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test/test_icap.py

antmicro/netv2

0

64553

#!/usr/bin/env python3 from litex import RemoteClient wb = RemoteClient() wb.open() # # # def icap_send(addr, data): wb.regs.icap_addr.write(addr) wb.regs.icap_data.write(data) wb.regs.icap_send.write(1) while (wb.regs.icap_done.read() == 0): pass # iprog icap_send(0x04, 0x0000000f) # # # wb.close()

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tests/test_linearalgebra.py

JHay0112/jmath

7

91094

<filename>tests/test_linearalgebra.py """ jmath/tests/test_linearalgebra.py Runs tests on linear algebra component using pytest Author: <NAME> Date: 2021-06-25 """ # - Imports from ..jmath.linearalgebra import Vector, Point, Line from .tools import random_integer, random_integers, repeat from typing import Tuple, List from math import sqrt # - Functions def vector_component_pair(len: int = random_integer()) -> Tuple[Vector, List[int]]: """ Generates a vector and component pair randomly. Parameters ---------- len The length vector/components to generate """ components = random_integers(len) return (Vector(components), components) @repeat def test_vector_equality(): """Tests that vectors are equal as expected.""" v, c = vector_component_pair() assert v == v assert v == Vector(c) assert v.components == c @repeat def test_vector_addition(): """Tests vector addition.""" len = random_integer() v1, c1 = vector_component_pair(len) v2, c2 = vector_component_pair(len) expected = Vector([i + j for i, j in zip(c1, c2)]) assert (v1 + v2) == expected @repeat def test_vector_subtraction(): """Tests vector subtraction.""" len = random_integer() v1, c1 = vector_component_pair(len) v2, c2 = vector_component_pair(len) expected = Vector([i - j for i, j in zip(c1, c2)]) assert (v1 - v2) == expected @repeat def test_vector_scaling(): """Tests vector multiplication and division""" # Produce initial conditions length = random_integer(min = 3, max = 10) scalor = random_integer(min = 1, max = 10) v, c = vector_component_pair(length) # Mult/div vectors mult = v * scalor div = v / scalor # Compute expected mult_expected = Vector([scalor * i for i in c]) div_expected = Vector([round(i / scalor, 5) for i in c]) # Round division vector to factor out floating point error div.components = [round(i, 5) for i in div.components] # Test multiply assert mult_expected == mult # Test divide assert div_expected == div @repeat def test_dot_product(): """Tests the dot product""" # Generate vectors and components len = random_integer() v1, c1 = vector_component_pair(len) v2, c2 = vector_component_pair(len) # Compute dot product dot = v1 @ v2 # Predict dot product predicted_dot = sum([i * j for i, j in zip(c1, c2)]) assert dot == predicted_dot def test_projection(): """Tests projecting vectors""" # Test with vector vec1 = Vector(3, 4) vec2 = Vector(1, 1) expected = Vector(7/2, 7/2) assert vec1.projection(vec2) == expected # Test with line line = Line(Point(0, 0), vec2) assert vec1.projection(line) == expected @repeat def test_magnitude(): """Tests vector magnitude""" v, c = vector_component_pair() # Square components, sum, and sqrt predicted_magnitude = sqrt(sum([i ** 2 for i in c])) assert round(predicted_magnitude, 5) == round(v.magnitude(), 5) @repeat def test_vector_size(): """Tests that a vector will return the correct size""" v, c = vector_component_pair() assert len(v) == len(c) def test_point_in_line(): """Tests whether a point is in a line""" # Test point that should be on line point = Point(3, 4) line = Line(Point(0, 0), 2 * point) assert point.on_line(line) # Test point that shouldn't be point = Point(8, 1) assert not point.on_line(line) # Testing in 3-space for certainty point = Point(1, 2, 3) d_vector = Vector(4, 5, 6) known_point_on_line = Point(9, 12, 15) line = Line(point, d_vector) assert known_point_on_line.on_line(line) def test_angle_between(): """Tests that angle between vectors is correct""" vec1 = Vector(1, 1) vec2 = Vector(2, 2) assert vec1.angle_between(vec2) == 0 @repeat def test_negative(): """Test that a negative vector does indeed give one with all the components reversed""" # Generate vector component pair v, c = vector_component_pair() # Make negative vector v = v.negative() # Make components negative c = Vector([-i for i in c]) assert v == c def test_unit_vector(): """Tests that a unit vector is produced correctly""" vec = Vector(1, 2) unit_vec = vec.unit() assert round(unit_vec.magnitude(), 10) == 1 assert vec.magnitude() * unit_vec == vec @repeat def test_vector_subscripts(): """Tests that a vector can be subscripted.""" # Generate vector component pair v, c = vector_component_pair() # Iterate through components and check they match for i in range(len(c)): assert v[i] == c[i]

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towers/monkey_village.py

56kyle/bloons_auto

0

48319

<filename>towers/monkey_village.py from tower import Tower from config import keybinds class MonkeyVillage(Tower): name = 'monkey_village' range = 215 width = 119 height = 103 size = 'xl' keybind = keybinds[name] aquatic = False def __init__(self, **kwargs): super().__init__(**kwargs)

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model.py

Jeffrey0Liao/2020-2021-Final-Year-Project-Joint-Sentimental-Analysis-Based-on-Tree-topology

2

114274

import torch as th import torch.nn as nn from torch.utils.data import DataLoader from torch.utils.data import Dataset import torch.nn.functional as F import numpy as np import dgl import dgl.function as fn class TreeLSTMCell(nn.Module): def __init__(self, x_size, h_size): super(TreeLSTMCell, self).__init__() self.W_iou = nn.Linear(x_size, 3 * h_size, bias=False) self.U_iou = nn.Linear(2 * h_size, 3 * h_size, bias=False) self.b_iou = nn.Parameter(th.zeros(1, 3 * h_size)) self.U_f = nn.Linear(2 * h_size, 2 * h_size) def message_func(self, edges): return {'h': edges.src['h'], 'c': edges.src['c']} def reduce_func(self, nodes): # concatenate h_jl for equation (1), (2), (3), (4) h_cat = nodes.mailbox['h'].view(nodes.mailbox['h'].size(0), -1) # equation (2) f = th.sigmoid(self.U_f(h_cat)).view(*nodes.mailbox['h'].size()) # second term of equation (5) c = th.sum(f * nodes.mailbox['c'], 1) return {'iou': self.U_iou(h_cat), 'c': c} def apply_node_func(self, nodes): # equation (1), (3), (4) iou = nodes.data['iou'] + self.b_iou i, o, u = th.chunk(iou, 3, 1) i, o, u = th.sigmoid(i), th.sigmoid(o), th.tanh(u) # equation (5) c = i * u + nodes.data['c'] # equation (6) h = o * th.tanh(c) return {'h' : h, 'c' : c} class Attention(nn.Module): def __init__(self, embed_size, feature_size): super(Attention, self).__init__() self.embed_size = embed_size self.feature_size = feature_size self.linear_in = nn.Linear(feature_size, embed_size, bias=False) self.linear_out = nn.Linear(embed_size+feature_size, embed_size) def forward(self, sent, img, mask): # sent: snetence_len * embed_size # img: num_region * feature_size snetence_len = sent.size(0) num_region = img.size(0) # img_in: num_region * embed_size img_in = self.linear_in(img) atten = th.mm(sent, img_in.transpose(0, 1)) #atten.data.masked_fill(mask, -1e6) atten = F.softmax(atten, dim=1) # atten: snetence_len * num_region context = th.mm(atten, img) # context: snetence_len * feature_size output = th.cat((context, sent), dim=1) # output: snetence_len * (feature_size+embed_size) output = th.tanh(self.linear_out(output)) # output: snetence_len * embed_size return output class TreeLSTM(nn.Module): def __init__(self, num_vocabs, x_size, h_size, feature_size, num_classes, dropout, pretrained_emb=None): super(TreeLSTM, self).__init__() self.x_size = x_size self.embedding = nn.Embedding(num_vocabs, x_size) self.attention = Attention(x_size, feature_size) if pretrained_emb is not None: print('Using glove') self.embedding.from_pretrained(pretrained_emb) self.embedding.weight.requires_grad = True self.dropout = nn.Dropout(dropout) self.linear = nn.Linear(h_size, num_classes) self.cell = TreeLSTMCell(x_size, h_size) def forward(self, batch, h, c): """Compute tree-lstm prediction given a batch. Parameters ---------- batch : dgl.data.SSTBatch The data batch. h : Tensor Initial hidden state. c : Tensor Initial cell state. Returns ------- logits : Tensor The prediction of each node. """ g = batch.graph # to heterogenous graph g = dgl.graph(g.edges()) # feed embedding embeds = self.embedding(batch.wordid * batch.mask) attn_mask = batch.mask.expand(batch.image.shape[0], batch.wordid.shape[0]).T attn_embeds = self.attention(embeds, batch.image, attn_mask) g.ndata['iou'] = self.cell.W_iou(self.dropout(attn_embeds)) * batch.mask.float().unsqueeze(-1) g.ndata['h'] = h g.ndata['c'] = c # propagate dgl.prop_nodes_topo(g, message_func=self.cell.message_func, reduce_func=self.cell.reduce_func, apply_node_func=self.cell.apply_node_func) # compute logits h = self.dropout(g.ndata.pop('h')) logits = self.linear(h) return logits

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RepositoryStatusMonitor/repository_status_monitor_stack.py

awslabs/aws-repository-status-monitor

8

156628

<gh_stars>1-10 from sys import stderr from aws_cdk import ( aws_apigateway as apigw, aws_events as events, aws_events_targets as targets, aws_iam as iam, aws_lambda as _lambda, aws_lambda_event_sources as lambda_event_source, aws_sqs as sqs, core ) from lambda_dir import http_handler as hh class RepositoryStatusMonitorStack(core.Stack): """ A class used to represent an AWS CloudFormation stack. ... Methods ------- create_role_and_policy() Creates an AWS IAM role, attaches a custom policy to it, and returns the role create_event_with_permissions(lambda_function: _lambda.Function) Creates an AWS EventBridge rule and attaches the AWS Lambda function parameter as a target handle_parameters() Retrieves all context variables, checks for valid input, performs all necessary processing, and returns a dictionary of the processed variables validate_repo_names(repo_names: str) Retrieves the list of GitHub repositories to which the user has access and validates that all repositories specified for the dashboard exist in that list """ def __init__(self, scope: core.Construct, id: str, **kwargs) -> None: """Initializes the stack :param scope: parent of this stack, scope within which resources defined here are accessible :type file_loc: Optional[aws_cdk.core.Construct] :param id: the id of the stack :type id: Optional[str] :param **kwargs: additional optional arguments ``description``: a description of the stack (`Optional[str]`). ``env`` AWS environment (account/region) where this stack will be deployed (`Optional[aws_cdk.core.Environment]`). ``stack_name`` name with which to deploy the stack (`Optional[str]`). ``synthesizer`` synthesis method to use while deploying this stack (`Optional[aws_cdk.core.IStackSynthesizer]`). ``tags`` stack tags that will be applied to all taggable resources as well as the stack (`Optional[Mapping[str, str]]`). ``termination_protection`` whether to enable termination protection for this stack (`Optional[bool]`). """ super().__init__(scope, id, **kwargs) metric_handler_dict, webhook_creator_dict = self.handle_parameters() # timeout used for lambda and sqs, in seconds lambda_timeout = 300 if self.node.try_get_context('lambda_timeout'): lambda_timeout = int(self.node.try_get_context('lambda_timeout')) dead_letter_queue = sqs.Queue( self, 'DeadLetterQueue', queue_name='DeadLetterQueue' ) webhook_queue = sqs.Queue( self, 'WebhookQueue', queue_name='WebhookQueue', visibility_timeout=core.Duration.seconds(lambda_timeout), dead_letter_queue=sqs.DeadLetterQueue( max_receive_count=3, queue=dead_letter_queue) ) metric_handler_dict['queue_url'] = webhook_queue.queue_url metric_handler_management_role = self.create_lambda_role_and_policy( 'MetricHandlerManagementRole', [ 'cloudwatch:GetDashboard', 'cloudwatch:GetMetricData', 'cloudwatch:ListDashboards', 'cloudwatch:PutDashboard', 'cloudwatch:PutMetricData', 'logs:CreateLogGroup', 'logs:CreateLogStream', 'logs:PutLogEvents', 'secretsmanager:GetSecretValue' ] ) metric_handler_timeout = lambda_timeout metric_handler_function = _lambda.Function( self, 'MetricsHandler', function_name='MetricsHandler', runtime=_lambda.Runtime.PYTHON_3_7, code=_lambda.Code.asset('lambda_dir'), handler='cloudwatch_dashboard_handler.handler', role=metric_handler_management_role, environment=metric_handler_dict, timeout=core.Duration.seconds(metric_handler_timeout) ) self.create_event_with_permissions(metric_handler_function) # Connect SQS to Lambda sqs_event_source = lambda_event_source.SqsEventSource(webhook_queue) metric_handler_function.add_event_source(sqs_event_source) apigw_webhook_url = self.create_and_integrate_apigw(webhook_queue, metric_handler_dict['dashboard_name_prefix']) webhook_creator_dict['apigw_endpoint'] = apigw_webhook_url webhook_role = self.create_lambda_role_and_policy('WebhookCreatorRole', ['secretsmanager:GetSecretValue']) _lambda.Function( self, 'WebhookCreator', function_name='WebhookCreator', runtime=_lambda.Runtime.PYTHON_3_7, code=_lambda.Code.asset('lambda_dir'), handler='webhook_creator.handler', role=webhook_role, environment=webhook_creator_dict, timeout=core.Duration.seconds(5) ) def create_lambda_role_and_policy(self, name: str, actions: list) -> iam.Role: """Creates an AWS IAM role, attaches a managed and a custom policy to it, and returns the role :param name: the name and ID of the role :type name: str :param actions: the actions that the Lambda role can execute :type actions: list :returns: a role with a custom and a managed policy attached :rtype: aws_cdk.aws_iam.Role """ role = iam.Role(self, name, role_name=name, assumed_by=iam.ServicePrincipal( 'lambda.amazonaws.com'), managed_policies=[iam.ManagedPolicy.from_aws_managed_policy_name( 'service-role/AWSLambdaBasicExecutionRole')] ) role.add_to_policy(iam.PolicyStatement( resources=['*'], actions=actions )) return role def create_event_with_permissions(self, lambda_function: _lambda.Function) -> None: """Creates an AWS EventBridge rule and attaches the AWS Lambda function parameter as a target :param lambda_function: the AWS Lambda function for which to create the rule :type lambda_function: aws_cdk.aws_lambda.Function """ hourly_metric_retrieval_rule = events.Rule( self, 'Rule', rule_name='HourlyMetricRetrieval', enabled=True, schedule=events.Schedule.expression('rate(1 hour)') ) hourly_metric_retrieval_rule.add_target(targets.LambdaFunction(lambda_function)) def handle_parameters(self) -> tuple: """Retrieves all context variables, checks for valid input, performs all necessary processing, and returns a dictionary of the processed variables :returns: a tuple containing two dictionaries, each containing all the processed context variables for each Lambda function :rtype: tuple """ repo_names_unvalidated = self.node.try_get_context('repo_names') docker_bool = self.node.try_get_context('get_docker') dashboard_name_prefix = self.node.try_get_context('dashboard_name_prefix') owner = self.node.try_get_context('owner') github_fields_paginated = self.node.try_get_context('github_fields_paginated') if self.node.try_get_context( 'github_fields_paginated') is not None else "" github_fields_unpaginated = self.node.try_get_context('github_fields_unpaginated') if self.node.try_get_context( 'github_fields_unpaginated') is not None else "" docker_fields = self.node.try_get_context('docker_fields') if self.node.try_get_context( 'docker_fields') is not None else "" namespace = self.node.try_get_context('namespace') user_agent_header = self.node.try_get_context('user_agent_header') widgets = self.node.try_get_context('widgets') if self.node.try_get_context('widgets') is not None else "" default_metric_widget_name = self.node.try_get_context('default_metric_widget_name') default_text_widget_name = self.node.try_get_context('default_text_widget_name') if not self.node.try_get_context('github_token'): raise ValueError('Need to specify GitHub token.') if not repo_names_unvalidated: raise ValueError('Need to specify repository names.') if not dashboard_name_prefix: raise ValueError('Need to specify prefix for dashboard names.') if not docker_bool: raise ValueError('Need to specify whether to include docker metrics.') if owner is None: raise ValueError('Need to specify GitHub owner.') if not namespace: raise ValueError('Need to specify namespace for CloudWatch metrics.') if not user_agent_header: raise ValueError('Need to specify User-Agent header for GitHub requests.') if not default_metric_widget_name: raise ValueError('Need to specify default metric widget name.') if not default_text_widget_name: raise ValueError('Need to specify default text widget name.') repo_names = self.validate_repo_names(repo_names_unvalidated, owner, user_agent_header) metric_handler_dict = { 'repo_names': repo_names, 'docker_bool': docker_bool, 'dashboard_name_prefix': dashboard_name_prefix, 'github_fields_unpaginated': github_fields_unpaginated, 'github_fields_paginated': github_fields_paginated, 'docker_fields': docker_fields, 'owner': owner, 'namespace': namespace, 'user_agent_header': user_agent_header, 'widgets': widgets, 'default_metric_widget_name': default_metric_widget_name, 'default_text_widget_name': default_text_widget_name } webhook_creator_dict = { 'repo_names': repo_names, 'owner': owner, 'user_agent_header': user_agent_header } return metric_handler_dict, webhook_creator_dict def validate_repo_names(self, repo_names: str, owner: str, user_agent_header: str) -> str: """Retrieves the list of GitHub repositories to which the user has access and validates that all repositories specified for the dashboard exist in that list :param repo_names: the repository names passed in as context variables :type repo_names: str :param owner: the GitHub owner of the repositories :type owner: str :param user_agent_header: the User-Agent header for GitHub requests :type user_agent_header: str :returns: the repository names that exist for the user in GitHub :rtype: str """ headers = {'Authorization': 'token ' + self.node.try_get_context('github_token'), 'Accept': 'application/vnd.github.nebula-preview+json', 'User-Agent': user_agent_header} url = 'https://api.github.com/user/repos' success, data, response_headers = hh.request_handler(url, headers=headers) if not success: raise RuntimeError(data['message']) existing_repos = set() for repo in data: existing_repos.add(repo['full_name']) valid_names = [] invalid_names = [] for repo_name in repo_names.split(','): if '/' not in repo_name: repo_name = owner + '/' + repo_name if repo_name not in existing_repos: invalid_names.append(repo_name) else: valid_names.append(repo_name) if len(invalid_names) > 0: invalid_repos = ', '.join(invalid_names) valid_repos = "none" if not valid_names else ', '.join(valid_names) print('Invalid repositories: ' + invalid_repos, file=stderr) print('Valid repositories: ' + valid_repos, file=stderr) raise RuntimeError("All repository names must be valid.") return ','.join(valid_names) def create_and_integrate_apigw(self, queue: sqs.Queue, dashboard_name_prefix: str) -> str: """Creates API Gateway and integrates with SQS queue :param queue: the SQS queue to integrate with :type queue: aws_cdk.aws_sqs.Queue :param dashboard_name_prefix: the dashboard name to use as the API Gateway resource name :type dashboard_name_prefix: str :returns: the url that the webhooks will post to :rtype: str """ webhook_apigw_role = iam.Role( self, 'WebhookAPIRole', role_name='WebhookAPIRole', assumed_by=iam.ServicePrincipal('apigateway.amazonaws.com') ) webhook_apigw_role.add_to_policy(iam.PolicyStatement( resources=['*'], actions=[ 'sqs:SendMessage' ] )) webhook_apigw = apigw.RestApi(self, 'RepositoryStatusMonitorAPI', rest_api_name='RepositoryStatusMonitorAPI') webhook_apigw_resource = webhook_apigw.root.add_resource(dashboard_name_prefix) apigw_integration_response = apigw.IntegrationResponse( status_code='200', response_templates={'application/json': ""} ) apigw_integration_options = apigw.IntegrationOptions( credentials_role=webhook_apigw_role, integration_responses=[apigw_integration_response], request_templates={'application/json': 'Action=SendMessage&MessageBody=$input.body'}, passthrough_behavior=apigw.PassthroughBehavior.NEVER, request_parameters={'integration.request.header.Content-Type': "'application/x-www-form-urlencoded'"} ) webhook_apigw_resource_sqs_integration = apigw.AwsIntegration( service='sqs', integration_http_method='POST', path='{}/{}'.format(core.Aws.ACCOUNT_ID, queue.queue_name), options=apigw_integration_options ) webhook_apigw_resource.add_method( 'POST', webhook_apigw_resource_sqs_integration, method_responses=[apigw.MethodResponse(status_code='200')] ) path = '/' + dashboard_name_prefix return webhook_apigw.url_for_path(path)

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corefacility/authorizations/ihna/test/account_set_object.py

serik1987/corefacility

0

122176

from core.test.entity_set.entity_set_objects.external_account_set_object import ExternalAccountSetObject from authorizations.ihna.entity import Account class AccountSetObject(ExternalAccountSetObject): _entity_class = Account _alias_field = "email" def data_provider(self): return [ dict(email="<EMAIL>", user=self._user_set_object[0]), dict(email="<EMAIL>", user=self._user_set_object[2]), dict(email="<EMAIL>", user=self._user_set_object[4]), dict(email="<EMAIL>", user=self._user_set_object[6]), dict(email="<EMAIL>", user=self._user_set_object[8]), ]

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download/Cost-Function-Of-ML/costFunctionExam.py

chenjian158978/chenjian.github.io

3

52700

<reponame>chenjian158978/chenjian.github.io # -*- coding:utf8 -*- """ @author: <EMAIL> @date: Tue, May 23 2017 @time: 19:05:20 GMT+8 """ import matplotlib.pyplot as plt import numpy as np # 都转换成列向量 X = np.array([[0, 1, 2, 4]]).T Y = np.array([[0, 1, 2, 4]]).T # 三个不同的theta_1值 theta1 = np.array([[0, 0]]).T theta2 = np.array([[0, 0.5]]).T theta3 = np.array([[0, 1]]).T # 矩阵X的行列(m,n) X_size = X.shape # 创建一个(4,1)的单位矩阵 X_0 = np.ones((X_size[0], 1)) # 形成点的坐标 X_with_x0 = np.concatenate((X_0, X), axis=1) # 两个数组点积 h1 = np.dot(X_with_x0, theta1) h2 = np.dot(X_with_x0, theta2) h3 = np.dot(X_with_x0, theta3) # r:red x: x marker plt.plot(X, Y, 'rx', label='y') plt.title("Cost Function Example") plt.grid(True) plt.plot(X, h1, color='b', label='h1, theta_1=0') plt.plot(X, h2, color='m', label='h2, theta_1=0.5') plt.plot(X, h3, color='g', label='h3, theta_1=1') # 坐标轴名称 plt.xlabel('X') plt.ylabel('y/h') # 坐标轴范围 plt.axis([-0.1, 4.5, -0.1, 4.5]) # plt.legend(loc='upper left') plt.legend(loc='best') plt.savefig('liner_gression_error.png', dpi=200) plt.show()

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Aprendizaje-Guia/Talleres-Python/ejemplo.py

JuanCamilo9915/Learning-Python

0

1611766

print=("nombre completo") nombrecompleto=input() print("cedula de ciudadania") cedula=input() cedulah=int(cedula) print("fecha de nacimiento") fecha=input() fecha6=int(fecha) print("edad:") edad=input() edadnum=int(edad) if(edadnum>=18): print("Mayor de edad") else: print("menor de edad")

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{{cookiecutter.project_slug}}/backend/app/app/api/api_v1/endpoints/utils.py

scil/full-stack-fastapi-postgresql-for-uper

0

68632

<gh_stars>0 from fastapi import APIRouter, Depends from pydantic import EmailStr from app.api.utils.security import get_current_active_superuser from app.core.celery_app import celery_app from app.models.msg import Msg from app.models.user import UserInDB from app.utils import send_test_email router = APIRouter() @router.post("/test-celery/", response_model=Msg, status_code=201) def test_celery( msg: Msg, current_user: UserInDB = Depends(get_current_active_superuser) ): """ Test Celery worker. """ celery_app.send_task("app.worker.test_celery", args=[msg.msg]) return {"msg": "Word received"} @router.post("/test-email/", response_model=Msg, status_code=201) def test_email( email_to: EmailStr, current_user: UserInDB = Depends(get_current_active_superuser) ): """ Test emails. """ send_test_email(email_to=email_to) return {"msg": "Test email sent"}

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src/tests/test.py

ThomazDiniz/Projeto-TC

1

179722

<reponame>ThomazDiniz/Projeto-TC execfile("../main.py") readInputFile() simulate()

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src/pdf/plugins/_pdf/external.py

ChakshuGautam/PDF-Package

0

114106

import json import os import traceback from celery import shared_task from interface import implements import pdfkit from pydrive.auth import GoogleAuth from pydrive.drive import GoogleDrive from ...base.interfaces.plugin import Plugin from ...models import GenericConfig from ...shorteners.yaus import YausShortner from ...uploaders.minio import MinioUploader from ...utils import publish_to_url, build_pdf import requests from dotenv import load_dotenv load_dotenv("../../.env") class PDFPlugin(implements(Plugin)): def __init__(self, data, token): """ To do authentication """ # generate path try: self.config = GenericConfig.objects.get(pk=data['config_id']) except KeyError: self.config = GenericConfig.objects.get(pk=1) self.user_config = json.loads(self.config.data) self._data = data['data'] self.template_id = data['template_id'] self.token = token self.settings_file_loc = f"pdf/creds/{self.user_config['APPLICATION_SETTINGS_FILE']}" self.creds_file_location = f"pdf/creds/{self.user_config['CREDENTIAL_SETTINGS_FILE']}" self.uploader = self.config.uploader_ref self.shortener = self.config.shortener_ref def get_client(self): gauth = GoogleAuth(settings_file=self.settings_file_loc) gauth.LoadCredentialsFile(self.creds_file_location) if gauth.credentials is None: # Authenticate if they're not there gauth.CommandLineAuth() elif gauth.access_token_expired: # Refresh them if expired gauth.Refresh() else: # Initialize the saved creds gauth.Authorize() # Save the current credentials to a file gauth.SaveCredentialsFile(self.creds_file_location) drive = GoogleDrive(gauth) self.drive = drive return self.drive def fetch_data(self): """ Fetches "new" data from the database/server/websocket whatever and provides it in the form of dictionary, one PDF at a time """ pass def fetch_template(self): """ Fetches template and returns it in the form of string """ try: template_string = error_code = error_msg = None data = json.dumps({ "id": self.template_id, "data": self._data }) headers = { 'Content-Type': 'application/json' } response = requests.post(f"{os.getenv('TEMPLATOR_URL')}/process", data=data, headers=headers) if response.status_code == 201: template_string = response.json()['processed'] if "processed" in response.json() else None else: error = response.text except Exception as e: traceback.print_exc() error_msg = f"Failed to fetch template: {e}" error_code = 801 return error_code, error_msg, template_string def build_file(self, template): """ Function to build PDF and return a file (fetch template and build pdf) """ is_successful = error_code = error_msg = None drive_file_loc = f'pdf/drivefiles/{self.token}.pdf' try: # path_wkhtmltopdf = r'C:\Program Files\wkhtmltopdf\bin\wkhtmltopdf.exe' # config = pdfkit.configuration(wkhtmltopdf=path_wkhtmltopdf) # pdfkit.from_string(template, drive_file_loc, configuration=config) pdfkit.from_string(template, drive_file_loc) is_successful = True except Exception as e: traceback.print_exc() error_msg = f"Failed to generate doc: {e}" error_code = 803 return error_code, error_msg, is_successful def upload_file(self): """ Function to save PDF """ error_code = error_msg = final_data = None try: drive_file_loc = f'pdf/drivefiles/{self.token}.pdf' if self.uploader == "minio": host = self.user_config["MINIO_HOST"] access_key = self.user_config["MINIO_ACCESS_KEY"] secret_key = self.user_config["MINIO_SECRET_KEY"] bucket_name = self.user_config["MINIO_BUCKET_NAME"] uploader = MinioUploader(host, access_key, secret_key, bucket_name) error_code, error_msg, final_data = uploader.put(f'{self.token}.pdf', f'{self.token}.pdf', None) if error_code is None: if os.path.exists(drive_file_loc): os.remove(drive_file_loc) else: raise Exception("Failed to build the pdf") return error_code, error_msg, final_data else: raise Exception("Uploader plugin not supported") except Exception as e: traceback.print_exc() error_code = 805 error_msg = f"Something went wrong: {e}" finally: return error_code, error_msg, final_data def retrieve_file(self, object_name): """ Function to get the previously saved PDF from the key """ def shorten_url(self, url): """ Function to generate short url for the uploaded doc """ error_code = error_msg = final_data = None try: if self.shortener == "yaus": host = self.user_config["SHORTENER_URL"] shortener = YausShortner(host) error_code, error_msg, final_data = shortener.apply(url, self.token) if error_code is None: final_data = final_data['shortUrl'] else: raise Exception("Shortener plugin not available") except Exception as e: traceback.print_exc() error_code = 806 error_msg = f"Something went wrong: {e}" finally: return error_code, error_msg, final_data def publish(self, id, url, headers): """ Function to publish response on external url/webhook """ publish_to_url(id, url, headers=headers)

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suspects.py

ht21992/Online-Board-Game

0

161709

import random import pickle with open("run_number", 'rb') as f: run_number = pickle.load(f) def sus_info(): global run_number suspects = {0: {"Name": "<NAME>", "Blood Type": "AB+", "Occupation": "actor", "Hair Color": "brown", "Age": 64, "Sex": "man"}, 1: {"Name": "<NAME>", "Blood Type": "A+", "Occupation": "painter", "Hair Color": "black", "Age": 32, "Sex": "man"}, 2: {"Name": "<NAME>", "Blood Type": "B+", "Occupation": "surgeon", "Hair Color": "black", "Age": 28, "Sex": "woman"}, 3: {"Name": "<NAME>", "Blood Type": "A+", "Occupation": "tailor", "Hair Color": "blonde", "Age": 41, "Sex": "woman"}, 4: {"Name": "<NAME>", "Blood Type": "O+", "Occupation": "surgeon", "Hair Color": "blonde", "Age": 29, "Sex": "man"}, 5: {"Name": "<NAME>", "Blood Type": "AB+", "Occupation": "nurse", "Hair Color": "brown", "Age": 52, "Sex": "woman"}, 6: {"Name": "<NAME>", "Blood Type": "O+", "Occupation": "painter", "Hair Color": "black", "Age": 27, "Sex": "woman"}, 7: {"Name": "<NAME>", "Blood Type": "O-", "Occupation": "actor", "Hair Color": "blonde", "Age": 31, "Sex": "woman"}, 8: {"Name": "<NAME>", "Blood Type": "B+", "Occupation": "tailor", "Hair Color": "black", "Age": 35, "Sex": "man"}, 9: {"Name": "<NAME>", "Blood Type": "O-", "Occupation": "nurse", "Hair Color": "blonde", "Age": 47, "Sex": "man"}, } with open("used_positions", 'rb') as f: used_positions = pickle.load(f) if run_number == 0: main_sus = random.choice(suspects) with open("main_sus", 'wb') as f: pickle.dump(main_sus, f) # question_numbers = pickle.load(f) with open("run_number", 'wb') as f: pickle.dump(run_number, f) else: with open("main_sus", 'rb') as f: main_sus = pickle.load(f) run_number+=1 with open("run_number", 'wb') as f: pickle.dump(run_number, f) # print(main_sus) hints = [f"It was {random.choice(['Sunday', 'Monday', 'Tuesday', 'Thursday', 'Wednesday', 'Friday', 'Saturday'])}", f"The hair color was {main_sus['Hair Color']}", f"The guy had a mask in the elevator", f"The guy looked to be around {random.randint(main_sus['Age'] - 3, main_sus['Age'] + 5)}", f"I might put you off the scent :)", f"Not sure but {random.choice([main_sus['Hair Color'], 'black', 'blonde'])} hair color", f"maybe a scissor", f"When I was coming into the building I saw a {main_sus['Sex']}", f"He had strange cloths but I guess he was a man", f"Maybe she was a woman", f"They were fighting and suddenly they became silent", f"I know this neighbourhood very well", f"I'm not sure about hair color", f"it was around {random.randrange(1, 13)} {random.choice(['a.m', 'p.m'])}", f"the assassin had a knife or sth like that", f"I also heard {main_sus['Name'].split(' ')[0]} multiple times", f"I also heard {random.choice(['John', 'Elizabeth', 'Eric', 'Stephanie', 'Randall', 'Norma', 'Kathleen', 'Christina', 'Nicholas', 'David'])} multiple times", f"I heard the name {random.choice(['John', 'Elizabeth', 'Eric', 'Stephanie', 'Randall', 'Norma', 'Kathleen', 'Christina', 'Nicholas', 'David'])}", f"The test shows the blood type is {main_sus['Blood Type']}", f"I took a look at the corpse", f"You must believe me", f"The assassin is {main_sus['Occupation']}", f"The assassin is {random.choice(['tailor', 'butcher', 'nurse', 'actor', 'painter', 'surgeon', 'goldsmith', 'cook'])}", f"I think I have no more information", ] if main_sus['Occupation'] == 'surgeon': hints.append('The assassin knew how to use a knife ') elif main_sus['Occupation'] == 'tailor': hints.append('The assassin knew how to use a scissor ') elif main_sus['Occupation'] == "actor": hints.append('The assassin must like the art ') else: hints.append('The assassin was a noob') run_number return suspects, main_sus, hints, used_positions

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utils/config_import.py

pyoor/distiller

30

73435

<reponame>pyoor/distiller import yaml import os import shutil import sqlite3 import sys class DistillerConfig: def __init__(self, config_file, section): self.config = read_config(config_file, section) try: self.project_name = self.config['name'] except KeyError: raise Exception(" Project name not defined.") self.trace_queue = "%s-trace-queue" % self.project_name self.trace_results = "%s-trace-results" % self.project_name self.min_queue = "%s-min-queue" % self.project_name self.min_results = "%s-min-results" % self.project_name try: self.operations = self.config['operations'] if len(self.operations) == 0: raise Exception("You must select atleast one mode of operation.") except: raise Exception("You must select atleast one mode of operation.") try: self.mode = self.config['filter']['mode'] self.modules = self.config['modules'] except KeyError: # Optional arguments self.mode = None self.modules = None if section == "server": try: self.seed_dir = self.config['seed_dir'] except KeyError: raise Exception("No working path defined.") try: self.working_dir = self.config['working_dir'] except KeyError: raise Exception("No working path defined.") self.project_dir = os.path.join(self.working_dir, self.project_name) self.db_path = os.path.join(self.project_dir, "backup.db") self.min_dir = os.path.join(self.project_dir, "minimized") self.trace_dir = os.path.join(self.project_dir, "traces") self.results_dir = os.path.join(self.project_dir, "results") if os.path.isdir(self.project_dir): action = None while action != "R" and action != "A": action = raw_input("Project Exists! Replace or Append? [R/A]: ").upper() if action == "R": confirm = None while confirm != "Y" and confirm != "N": confirm = raw_input("Are you sure? All data will be deleted! [Y/N]: ").upper() if confirm == "Y": shutil.rmtree(self.project_dir) os.makedirs(self.project_dir) os.makedirs(self.min_dir) os.makedirs(self.trace_dir) os.makedirs(self.results_dir) sql = sqlite3.connect(self.db_path) c = sql.cursor() c.execute('BEGIN TRANSACTION') c.execute('''CREATE TABLE IF NOT EXISTS modules (num INTEGER PRIMARY KEY, name TEXT, UNIQUE (name))''') c.execute('''CREATE TABLE IF NOT EXISTS seeds (num INTEGER PRIMARY KEY, seed_name TEXT, trace_name TEXT, ublock_cnt INT, UNIQUE (seed_name))''') c.execute('''CREATE TABLE IF NOT EXISTS master_lookup (bblock TEXT PRIMARY KEY)''') # Results are calculated using the full data set - Wipe if they exist! c.execute('''DROP TABLE IF EXISTS results''') c.execute('''CREATE TABLE results (name TEXT PRIMARY KEY, ublock_cnt INT)''') sql.commit() else: sys.exit() elif section == "client": try: self.host = self.config['host'] except KeyError: raise Exception("No host defined.") try: self.drio_path = self.config['drio_path'] if not os.path.isfile(self.drio_path): raise Exception("Can not find DynamoRio - %s" % self.drio_path) except KeyError: raise Exception("No DynamoRio path defined.") try: self.target_path = self.config['target_path'] if not os.path.isfile(self.target_path): raise Exception("Can not find target - %s" % self.target_path) except KeyError: raise Exception("No target path defined.") try: self.w_time = self.config['wait_time'] except KeyError: raise Exception("No wait time defined.") try: self.m_time = self.config['max_timeout'] except KeyError: raise Exception("No max timeout defined.") # Optional args try: self.target_args = self.config['target_args'] if self.target_args is None: self.target_args = '' except KeyError: self.target_args = None try: self.pre_cmd = self.config['pre_cmd'] except KeyError: self.pre_cmd = None try: self.post_cmd = self.config['post_cmd'] except KeyError: self.post_cmd = None def read_config(config_file, section): sections = ['project', section] with open(config_file, 'r') as stream: data = yaml.load(stream) config = {} try: for section in sections: for k, v in data[section].iteritems(): config[k] = v except KeyError: raise Exception(" Unable to find section %s" % section) return config

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constants.py

jlduhaime/combatTest

0

1600898

SCREEN_WIDTH = 640 SCREEN_HEIGHT = 480 FPS = 60 BLACK = (0, 0, 0)

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problems/algoexpert/list/shift_linked_list.py

JayMonari/py-personal

0

79779

<filename>problems/algoexpert/list/shift_linked_list.py<gh_stars>0 from typing import Optional class ListNode: def __init__(self, value: int, _next=None): self.value: int = value self.next: Optional[ListNode] = _next def shift_linked_list( head: Optional[ListNode], k: int) -> Optional[ListNode]: list_length: int = 1 list_tail: Optional[ListNode] = head while list_tail.next is not None: list_tail = list_tail.next list_length += 1 offset: int = abs(k) % list_length if offset == 0: return head new_tail_position: int = list_length - offset if k > 0 else offset new_tail: Optional[ListNode] = head for _ in range(1, new_tail_position): new_tail = new_tail.next new_head: Optional[ListNode] = new_tail.next new_tail.next = None list_tail.next = head return new_head

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scrapytest/spiders/ScrapyDemo.py

liang1024/Scrapy

0

100429

import scrapy class QuotesSpider(scrapy.Spider): name = "demo1" def start_requests(self): urls = [ 'http://quotes.toscrape.com/page/1/', 'http://quotes.toscrape.com/page/2/', ] for url in urls: yield scrapy.Request(url=url, callback=self.parse) def parse(self, response): page = response.url.split("/")[-2] filename = 'demo1-%s.html' % page with open(filename, 'wb') as f: f.write(response.body) self.log('Saved file %s' % filename) ''' name：识别蜘蛛。它在项目中必须是唯一的，也就是说，您不能为不同的Spiders设置相同的名称。 start_requests()：必须返回一个可迭代的请求（您可以返回一个请求列表或写一个生成器函数），Spider将开始爬行。随后的请求将从这些初始请求连续生成。 parse()：一种将被调用来处理为每个请求下载的响应的方法。response参数是一个TextResponse保存页面内容的实例，并且还有其他有用的方法来处理它。该parse()方法通常解析响应，将刮取的数据提取为示例，并且还可以查找新的URL以从中创建新的请求（Request）。 ''' ''' 要使我们的蜘蛛工作，请转到项目的顶级目录并运行： scrapy crawl demo1 现在，检查当前目录下的文件。您应该注意到，已经创建了两个新文件：quotes-1.html和quotes-2.html，其中包含各个URL的内容，正如我们的parse方法指示。 '''

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old/core/migrations/0005_client.py

rngallen/django_ninja

0

76665

<filename>old/core/migrations/0005_client.py # Generated by Django 3.1.7 on 2021-03-21 15:06 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ("core", "0004_department_employee"), ] operations = [ migrations.CreateModel( name="Client", fields=[ ( "id", models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name="ID", ), ), ("api_key", models.CharField(max_length=50, verbose_name="api_key")), ( "client", models.OneToOneField( on_delete=django.db.models.deletion.CASCADE, related_name="client", to=settings.AUTH_USER_MODEL, verbose_name="user", ), ), ], ), ]

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ptrack/templatetags/ptrack.py

unformatt/django-ptrack

0

139338

"""Ptrack Template Tag""" import logging from django import template from django.utils.html import mark_safe logger = logging.getLogger(__name__) register = template.Library() @register.simple_tag def ptrack(*args, **kwargs): """Generate a tracking pixel html img element.""" from ptrack import create_img img = create_img(*args, **kwargs) return mark_safe(img)

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hwm3/venv/Lib/site-packages/pymorphy2/opencorpora_dict/parse.py

liubov-volkova/pythondev

0

145514

# -*- coding: utf-8 -*- """ :mod:`pymorphy2.opencorpora_dict.parse` is a module for OpenCorpora XML dictionaries parsing. """ from __future__ import absolute_import, unicode_literals, division import logging import collections try: from lxml.etree import iterparse def xml_clear_elem(elem): elem.clear() while elem.getprevious() is not None: del elem.getparent()[0] except ImportError: try: from xml.etree.cElementTree import iterparse except ImportError: from xml.etree.ElementTree import iterparse def xml_clear_elem(elem): elem.clear() logger = logging.getLogger(__name__) ParsedDictionary = collections.namedtuple('ParsedDictionary', 'lexemes links grammemes version revision') def parse_opencorpora_xml(filename): """ Parse OpenCorpora dict XML and return a ``ParsedDictionary`` namedtuple. """ links = [] lexemes = {} grammemes = [] version, revision = None, None _lexemes_len = 0 for ev, elem in iterparse(filename, events=(str('start'), str('end'))): if ev == 'start': if elem.tag == 'dictionary': version = elem.get('version') revision = elem.get('revision') logger.info("dictionary v%s, rev%s", version, revision) xml_clear_elem(elem) continue if elem.tag == 'grammeme': name = elem.find('name').text parent = elem.get('parent') alias = elem.find('alias').text description = elem.find('description').text grammeme = (name, parent, alias, description) grammemes.append(grammeme) xml_clear_elem(elem) if elem.tag == 'lemma': if not lexemes: logger.info('parsing xml:lemmas...') lex_id, word_forms = _word_forms_from_xml_elem(elem) lexemes[lex_id] = word_forms xml_clear_elem(elem) elif elem.tag == 'link': if not links: logger.info('parsing xml:links...') link_tuple = ( elem.get('from'), elem.get('to'), elem.get('type'), ) links.append(link_tuple) xml_clear_elem(elem) if len(lexemes) != _lexemes_len and not (len(lexemes) % 50000): logger.debug("%d lexemes parsed" % len(lexemes)) _lexemes_len = len(lexemes) return ParsedDictionary(lexemes, links, grammemes, version, revision) def _grammemes_from_elem(elem): return ",".join(g.get('v') for g in elem.findall('g')) def _word_forms_from_xml_elem(elem): """ Return a list of (word, tag) pairs given "lemma" XML element. """ lexeme = [] lex_id = elem.get('id') if len(elem) == 0: # deleted lexeme? return lex_id, lexeme base_info = elem.findall('l') assert len(base_info) == 1 base_grammemes = _grammemes_from_elem(base_info[0]) for form_elem in elem.findall('f'): grammemes = _grammemes_from_elem(form_elem) form = form_elem.get('t').lower() lexeme.append( (form, " ".join([base_grammemes, grammemes]).strip()) ) return lex_id, lexeme

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uppergeodesic.py

drewitz/VIPer

0

65703

#!/usr/bin/env python3 # class file uppergeodesic.py # started as a script to visualize what happens to hyperbolic plane # if different isometries act on it import geodesic as gd import numpy as np import numpy.linalg as lina import matplotlib.pyplot as plt # upper half space as the basic model class UpperGeodesic(gd.Geodesic): """UpperGeodesic line in upper half space takes endpoints on boundary as arguments stores x and y data as points in x and y string "inf" is point at infinity, i.e. y=inf """ xmin = 0 xmax = 0 ymin = 0 # just for consistency, shouldn't change ymax = 0 inf = "inf" def __init__(self, a, b, color="b", label=''): """initialize UpperGeodesic by endpoints a, b - x values of the endpoints or "inf" if infinity res is resolution """ super().__init__(a, b, color, label) # adjust the boundaries of hyperbolic space if self.start != UpperGeodesic.inf: if self.start < UpperGeodesic.xmin: UpperGeodesic.xmin = self.start if self.end > UpperGeodesic.xmax: UpperGeodesic.xmax = self.end UpperGeodesic.ymax = (UpperGeodesic.xmax - UpperGeodesic.xmin)/2 @classmethod def vertical(cls, real): return cls(cls.inf, real) @classmethod def from_midpoint_and_radius(cls, m, r): """ m is only the real part of the circle thing """ return cls(m-r, m+r) def sort_se(self): """sort start and end""" if self.end == self.inf: # just want to assume that the first value is inf if any self.end = self.start self.start = self.inf if self.start != self.inf and self.end < self.start: # swap a and self.end such that a < self.end c = self.start self.start = self.end self.end = c def get_data(self): if self.start == UpperGeodesic.inf: # vertical line xs = [self.end, self.end] ys = [self.ymin, self.ymax] else: # calculate semicircle t = np.linspace(0, np.pi, self.res) r = (self.end - self.start)/2 xs = r*(1 + np.cos(t)) + self.start ys = r*np.sin(t) return(xs, ys) ## the next two functions create new geodesics from existing ones def new_geod(self, a, b, c, d): """return new geodesic by received by moebius trafo apply the matrix | a b | | c d | on the geodesic self and return the resulting geodesic """ start = self.apply_moebius(a, b, c, d, self.start) end = self.apply_moebius(a, b, c, d, self.end) return(UpperGeodesic(start, end)) def new_from_matrix(self, M): return self.new_geod(M[0,0], M[0,1], M[1,0], M[1,1]) ## apply transformations to ONE geodesic def apply_matrix(self, M): self.start = self.apply_moebius(M[0,0], M[0,1], M[1, 0], M[1,1], self.start) self.end = self.apply_moebius(M[0,0], M[0,1], M[1, 0], M[1,1], self.end) self.sort_se() def translate_one_geod(self, dx): if self.start != UpperGeodesic.inf: self.start += dx if self.end != UpperGeodesic.inf: self.end += dx def translate_one_at_zero(self, dx): """inverts at unit sphere, translates and inverts again""" a = self.inversion_on_unit_circle(self.start) b = self.inversion_on_unit_circle(self.end) if a != UpperGeodesic.inf: a += dx if b != UpperGeodesic.inf: b += dx self.start = self.inversion_on_unit_circle(a) self.end = self.inversion_on_unit_circle(b) self.sort_se() def rotate_one_geod(self, phi): """rotates the geodesic on upper half space conjugate to a rotation around origin in the disc model """ if self.start == UpperGeodesic.inf: alpha = -np.pi/2 else: alpha = self.from_upper_to_disc(self.start) beta = self.from_upper_to_disc(self.end) alpha += phi beta += phi self.start = self.from_disc_to_upper(alpha) self.end = self.from_disc_to_upper(beta) self.sort_se() def hyperbolic_translate_one(self, dmult=1.001): """translates one geodesic along UpperGeodesic(-1,1)""" diag = (dmult + 1.0/dmult)/2.0 off = (1.0/dmult - dmult)/2.0 matrix = np.matrix([[diag, off], [off, diag]]) self.apply_matrix(matrix) # tesselate hyperbolic space @classmethod def tesselate(self, depth=10): """Tesselates according to SL(2,Z)""" g0 = UpperGeodesic(-1,1, "r") g1 = UpperGeodesic(-0.5,self.inf, "r") g2 = UpperGeodesic(0.5,self.inf, "r") first = [g0,g1,g2] for k in range(1, depth): for g in first: g.new_geod(1, k, 0, 1) g.new_geod(1, -k, 0, 1) kmax = len(UpperGeodesic.all_geods) for geod in UpperGeodesic.all_geods[:kmax]: temp = [geod.new_geod(0, -1, 1, 0)] for k in range(1, 2*depth): temp.append(geod.new_geod(1, 0, k, 1)) temp.append(geod.new_geod(1, 0, -k, 1)) for k in range(1, depth//2): for t in temp: t.new_geod(1, k, 0, 1) t.new_geod(1, -k, 0, 1) UpperGeodesic.xmin= -3 UpperGeodesic.xmax= 3 UpperGeodesic.ymax= 3 ## plot commands @classmethod def set_plot_limits(cls): highest = max(abs(i) for i in [cls.ymin, cls.ymax, cls.xmax, cls.xmin]) cls.ax.axis([-highest, highest, 0, highest]) @classmethod def plot_all(cls): if UpperGeodesic.ymax <= UpperGeodesic.ymin: UpperGeodesic.ymax = UpperGeodesic.ymin + 1 # else nothing to plot super().plot_all()

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grodddroid/BranchExplorer/branchexp/explo/tags.py

demirdagemir/thesis

0

1614795

<gh_stars>0 """ Functions to work with tags and tag files. """ import logging log = logging.getLogger("branchexp") def is_method_tag(tag): return tag.startswith("BEG") def is_branch_tag(tag): return tag.startswith("BRA") and not "/" in tag def is_cond_tag(tag): return tag.startswith("BRA") and "/" in tag class TagSet(object): """ Container for tags, as used by ForceCFI. A TagSet can contain different types of tags, depending on who computed it: * method tags, which look like "BEGINN1" * branch tags, which look like "BRANCHN2" * cond tags, which look like "BRANCHN3/BRANCHN4" Method tags can be written by ForceCFI or during the logcat monitoring. Branches alone are usually only written by logcat or when we compute a set of branches to force. Cond tags are written exclusively by ForceCFI, to keep paired together two branches of a same conditional jump. I'm not sure if it's useful right now but it may be in the future. Beware of not mixing them with branch tags though! Attributes: tags: set of tags (strings) """ def __init__(self, tag_filepath = None): self.reset() if tag_filepath is not None: self.load_file(tag_filepath) def reset(self): self.tags = set() def load_file(self, tag_filepath): self.reset() with open(tag_filepath, "r") as tag_file: for line in tag_file.readlines(): self.tags.add(line.strip("\n")) def write_file(self, tag_filepath): with open(tag_filepath, "w") as tag_file: for tag in self.tags: tag_file.write(tag + "\n") def get_method_tags(self): return set([tag for tag in self.tags if is_method_tag(tag)]) def get_branch_tags(self): return set([tag for tag in self.tags if is_branch_tag(tag)]) def get_cond_tags(self): return set([tag for tag in self.tags if is_cond_tag(tag)]) def split_cond_in_branches(self, cond): """ Removes a cond BRANCH1/BRANCH2 from the tags and adds BRANCH1 and BRANCH2. """ if cond in self.tags: left_branch, right_branch = cond.split("/") self.tags.remove(cond) self.tags.add(left_branch) self.tags.add(right_branch)

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facility_management/facility_management/web_form/new_maintenance/new_maintenance.py

pazari/facility_management

13

86853

<filename>facility_management/facility_management/web_form/new_maintenance/new_maintenance.py from __future__ import unicode_literals from toolz import first import frappe def get_context(context): tenant = _get_tenant(_get_customer(frappe.session.user)) context.properties = _get_properties(tenant) def _get_properties(tenant): properties = frappe.get_all( "Rental Contract", fields=["property"], filters={"status": "Active", "tenant": tenant} ) return list(map(lambda x: x.get("property"), properties)) def _get_customer(user): customer = frappe.db.sql( """ SELECT dl.link_name FROM `tabContact` c INNER JOIN `tabDynamic Link` dl ON dl.parent = c.name WHERE dl.link_doctype = "Customer" AND c.user = %s """, user, as_dict=1, ) return first(customer).get("link_name") if customer else None def _get_tenant(customer): tenant = frappe.get_all("Tenant Master", filters={"customer": customer}) return first(tenant).get("name") if tenant else None

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license_plate_images_generator.py

aboerzel/ANPR-keras

6

36046

import argparse import json import os.path import random import re import time import cv2 import numpy as np import requests from PIL import Image from config import config class GermanLicensePlateImagesGenerator: def __init__(self, output): self.output = output self.COUNTRY_MARKS = np.asarray([d['CM'] for d in json.loads(open(config.GERMAN_COUNTY_MARKS, encoding='utf-8').read())]) self.LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZÄÖÜ" self.DIGITS = "0123456789" self.COUNTRIES = ['BW', 'BY', 'BE', 'BB', 'HB', 'HH', 'HE', 'MV', 'NI', 'NW', 'RP', 'SL', 'SN', 'ST', 'SH', 'TH'] self.MONTHS = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'] self.YEARS = ['06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17'] random.seed() @staticmethod def get_image_url(license_number, country, month, year): license_number = license_number.replace("-", "%3A").replace("Ä", "%C4").replace("Ö", "%D6").replace("Ü", "%DC") return "http://nummernschild.heisnbrg.net/fe/task?action=startTask&kennzeichen={0}&kennzeichenZeile2=&engschrift=false&pixelHoehe=32&breiteInMM=520&breiteInMMFest=true&sonder=FE&dd=01&mm=01&yy=00&kreis={1}&kreisName=&humm={2}&huyy={3}&sonderKreis=LEER&mm1=01&mm2=01&farbe=SCHWARZ&effekt=KEIN&tgaDownload=false".format( license_number, country, month, year) def __generate_license_number(self): country = random.choice(self.COUNTRY_MARKS) letter_count = random.randint(1, 2) letters = "{}".format(random.choice(self.LETTERS)) if letter_count == 1 else "{}{}".format( random.choice(self.LETTERS), random.choice(self.LETTERS)) min = 1 if letter_count == 2 else 1 digit_count = random.randint(min, max((8 - len(country) - letter_count), 4)) digits = "" for i in range(digit_count): digits += random.choice(self.DIGITS) return "{}-{}{}".format(country, letters, digits) def __create_license_plate_picture(self, n, license_number, country, front): file_path = self.output + '/{0}#{1}.png'.format("F" if front else "R", license_number) if os.path.exists(file_path): return False month = random.choice(self.MONTHS) if front else '' year = random.choice(self.YEARS) if front else '' create_image_url = GermanLicensePlateImagesGenerator.get_image_url(license_number, country, month, year) r = requests.get(create_image_url) if r.status_code != 200: return False id = re.compile('<id>(.*?)</id>', re.DOTALL | re.IGNORECASE).findall( r.content.decode("utf-8"))[0] status_url = 'http://nummernschild.heisnbrg.net/fe/task?action=status&id=%s' % id time.sleep(.200) r = requests.get(status_url) if r.status_code != 200: return False show_image_url = 'http://nummernschild.heisnbrg.net/fe/task?action=showInPage&id=%s' show_image_url = show_image_url % id time.sleep(.200) r = requests.get(show_image_url) if r.status_code != 200: return False # sometimes the web service returns a corrupted image, check the image by getting the size and skip if corrupted try: numpyarray = np.fromstring(r.content, np.uint8) image = cv2.imdecode(numpyarray, cv2.IMREAD_COLOR) image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) im = Image.fromarray(image) # don't use cv2.imwrite() because there is a bug with utf-8 encoded filepaths im.save(file_path) print("{0:06d} : {1}".format(n, file_path)) return True except: return False def generate(self, items): for n in range(items): while True: license_number = self.__generate_license_number() country = random.choice(self.COUNTRIES) if not self.__create_license_plate_picture(n, license_number, country, True): break time.sleep(.200) self.__create_license_plate_picture(n, license_number, country, False) time.sleep(.200) ap = argparse.ArgumentParser() ap.add_argument("-i", "--items", default="60000", help="Number of items to generate") ap.add_argument("-o", "--output", default=config.PLATE_IMAGES, help="Output path") args = vars(ap.parse_args()) lpdg = GermanLicensePlateImagesGenerator(os.path.abspath(args["output"])) lpdg.generate(int(args["items"]))

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lib/googlecloudsdk/api_lib/compute/daisy_utils.py

bopopescu/Google-Cloud-SDK-1

0

132131

<reponame>bopopescu/Google-Cloud-SDK-1 # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Utilities for running Daisy builds on Google Container Builder.""" from apitools.base.py import encoding from googlecloudsdk.api_lib.cloudbuild import cloudbuild_util from googlecloudsdk.api_lib.cloudbuild import logs as cb_logs from googlecloudsdk.api_lib.cloudresourcemanager import projects_api from googlecloudsdk.api_lib.storage import storage_api from googlecloudsdk.calliope import arg_parsers from googlecloudsdk.calliope import base from googlecloudsdk.command_lib.cloudbuild import execution from googlecloudsdk.command_lib.projects import util as projects_util from googlecloudsdk.core import exceptions as core_exceptions from googlecloudsdk.core import execution_utils from googlecloudsdk.core import log from googlecloudsdk.core import properties from googlecloudsdk.core import resources from googlecloudsdk.core.console import console_io _BUILDER = 'gcr.io/compute-image-tools/daisy:release' class FailedBuildException(core_exceptions.Error): """Exception for builds that did not succeed.""" def __init__(self, build): super(FailedBuildException, self).__init__( 'build {id} completed with status "{status}"'.format( id=build.id, status=build.status)) def AddCommonDaisyArgs(parser): """Common arguments for Daisy builds.""" parser.add_argument( '--log-location', help='Directory in Google Cloud Storage to hold build logs. If not ' 'set, ```gs://<project num>.cloudbuild-logs.googleusercontent.com/``` ' 'will be created and used.', ) parser.add_argument( '--timeout', type=arg_parsers.Duration(), default='2h', help="""\ Maximum time a build can last before it is failed as "TIMEOUT". For example, specifying ``2h'' will fail the process after 2 hours. See $ gcloud topic datetimes for information on duration formats. """ ) base.ASYNC_FLAG.AddToParser(parser) def CheckIamPermissions(project_id): """Check for needed IAM permissions and prompt to add if missing. Args: project_id: A string with the name of the project. """ project = projects_api.Get(project_id) service_account = 'serviceAccount:{0}@cloudbuild.gserviceaccount.com'.format( project.projectNumber) expected_permissions = {'roles/compute.admin': service_account, 'roles/iam.serviceAccountActor': service_account} permissions = projects_api.GetIamPolicy(project_id) for binding in permissions.bindings: if expected_permissions.get(binding.role) in binding.members: del expected_permissions[binding.role] if expected_permissions: ep_table = ['{0} {1}'.format(role, account) for role, account in expected_permissions.items()] prompt_message = ( 'The following IAM permissions are needed for this operation:\n' '[{0}]\n'.format('\n'.join(ep_table))) console_io.PromptContinue( message=prompt_message, prompt_string='Would you like to add the permissions', throw_if_unattended=True, cancel_on_no=True) for role, account in expected_permissions.items(): log.info('Adding [{0}] to [{1}]'.format(account, role)) projects_api.AddIamPolicyBinding(project_id, account, role) def _CreateCloudBuild(build_config, client, messages): """Create a build in cloud build. Args: build_config: A cloud build Build message. client: The cloud build api client. messages: The cloud build api messages module. Returns: Tuple containing a cloud build build object and the resource reference for that build. """ log.debug('submitting build: {0}'.format(repr(build_config))) op = client.projects_builds.Create( messages.CloudbuildProjectsBuildsCreateRequest( build=build_config, projectId=properties.VALUES.core.project.Get())) json = encoding.MessageToJson(op.metadata) build = encoding.JsonToMessage(messages.BuildOperationMetadata, json).build build_ref = resources.REGISTRY.Create( collection='cloudbuild.projects.builds', projectId=build.projectId, id=build.id) log.CreatedResource(build_ref) if build.logUrl: log.status.Print('Logs are available at [{0}].'.format(build.logUrl)) else: log.status.Print('Logs are available in the Cloud Console.') return build, build_ref def GetAndCreateDaisyBucket(bucket_name=None, storage_client=None): """Determine the name of the GCS bucket to use and create if necessary. Args: bucket_name: A string containing a bucket name to use, otherwise the bucket will be named based on the project id. storage_client: The storage_api client object. Returns: A string containing the name of the GCS bucket to use. """ project = properties.VALUES.core.project.GetOrFail() safe_project = project.replace(':', '-') safe_project = safe_project.replace('.', '-') bucket_name = bucket_name or '{0}-daisy-bkt'.format(safe_project) safe_bucket_name = bucket_name.replace('google', 'elgoog') if not storage_client: storage_client = storage_api.StorageClient() storage_client.CreateBucketIfNotExists(safe_bucket_name) return safe_bucket_name def RunDaisyBuild(args, workflow, variables, daisy_bucket=None, tags=None): """Run a build with Daisy on Google Cloud Builder. Args: args: an argparse namespace. All the arguments that were provided to this command invocation. workflow: The path to the Daisy workflow to run. variables: A string of key-value pairs to pass to Daisy. daisy_bucket: A string containing the name of the GCS bucket that daisy should use. tags: A list of strings for adding tags to the Argo build. Returns: A build object that either streams the output or is displayed as a link to the build. Raises: FailedBuildException: If the build is completed and not 'SUCCESS'. """ client = cloudbuild_util.GetClientInstance() messages = cloudbuild_util.GetMessagesModule() project_id = projects_util.ParseProject( properties.VALUES.core.project.GetOrFail()) CheckIamPermissions(project_id) timeout_str = '{0}s'.format(args.timeout) daisy_bucket = daisy_bucket or GetAndCreateDaisyBucket() daisy_args = ['-gcs_path=gs://{0}/'.format(daisy_bucket), '-variables={0}'.format(variables), workflow, ] build_tags = ['gce-daisy'] if tags: build_tags.extend(tags) # First, create the build request. build_config = messages.Build( steps=[ messages.BuildStep( name=_BUILDER, args=daisy_args, ), ], tags=build_tags, timeout=timeout_str, ) if args.log_location: gcs_log_dir = resources.REGISTRY.Parse( args.log_location, collection='storage.objects') build_config.logsBucket = ( 'gs://{0}/{1}'.format(gcs_log_dir.bucket, gcs_log_dir.object)) # Start the build. build, build_ref = _CreateCloudBuild(build_config, client, messages) # If the command is run --async, we just print out a reference to the build. if args.async: return build mash_handler = execution.MashHandler( execution.GetCancelBuildHandler(client, messages, build_ref)) # Otherwise, logs are streamed from GCS. with execution_utils.CtrlCSection(mash_handler): build = cb_logs.CloudBuildClient(client, messages).Stream(build_ref) if build.status == messages.Build.StatusValueValuesEnum.TIMEOUT: log.status.Print( 'Your build timed out. Use the [--timeout=DURATION] flag to change ' 'the timeout threshold.') if build.status != messages.Build.StatusValueValuesEnum.SUCCESS: raise FailedBuildException(build) return build

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source/blog/migrations/0004_postcomments.py

JakubGutowski/PersonalBlog

0

9743

<filename>source/blog/migrations/0004_postcomments.py # Generated by Django 2.0.5 on 2018-07-02 19:46 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('blog', '0003_blogpost_author'), ] operations = [ migrations.CreateModel( name='PostComments', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('nick', models.CharField(max_length=20)), ('comment', models.CharField(max_length=140)), ('post', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='blog.BlogPost')), ], ), ]

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01_strings_interpolacao/04_strings_metodos.py

smartao/estudos_python

0

187075

#!/usr/bin/python3 import pyperclip ''' String puras Podemos inserir um r antes das pastas de inicio em uma string para transofrmá-la em uma string pura (raw string), com isso a string ignorará todos os caracteres de scape ''' print('Exemplo de um string pura:') print(r'\n\t That is Carlos \'s cat.') ''' Strings em multiplas linhas ''' print('''\nTeste de 'String' de Multiplas linhas ''') ''' Indexacao e Slices As String usam indices e slices (Fatias) do mesmo modo que as listas ''' print('\nTeste de slice string') spam = 'Hello World!' print(spam[0]) # Primeiro caractere print(spam[4]) # Quinta caractere print(spam[-1]) # Ultimo caractere print(spam[0:5]) # do 0 ao 5 print(spam[:5]) # do 0 ao 5 print(spam[6:]) # do 6 até o final ''' Operadores in e not in com strings Os operadora in e not in podem ser usados com strings assim como em valores de listas ''' print('\nTeste de strings:') print('Hello' in 'Hello World') # True print('Hello' in 'Hello') # True print('HELLO' in 'Hello World') # False print('' in 'spam') # True print('cats' not in 'cats and dogs') # False ''' upper(), lower(), title() Retornam um nova string em que todas as letras da string original foram convertidas para maiusculas ou minusculas. Os métodos não alteram a string em si, mas retornam novos valores de strings Os métodos upper() e lower() serão úteis caso seja necessário fazer uma comparação sen kevar en cibta a diferença de maisculas e minusculas ''' print('\nTeste dos métodos upper e lower') spam = 'Hello World' spam = spam.upper() print(spam) spam = spam.lower() print(spam) spam = spam.title() # Primeira letra maisuculas print(spam) print('\nComparando strings:') print('How are you?') feeling = 'GREATE' if feeling.lower() == 'greate': print('I feel greate too') # Como os métodos retornam string, # podemos chamar os métodos de string nesses valores print('\nTeste de aninhamento de métodos') print('Hello'.upper()) print('Hello'.upper().lower()) print('Hello'.upper().lower().upper()) print('Hello'.upper().lower().upper().lower()) ''' isupper(), islower() Retornarão um valor booleano True se a string tiver pelo menos uma letra e todas as letras forem maiusculas ou minusculas, respectivalmente. ''' print('\nTeste dos métodos isupper() e islower()') spam = 'Hello World' print(spam.islower()) # False print(spam.isupper()) # False print('HELLO'.isupper()) # True print('abc12345'.islower()) # True print('12345'.islower()) # False print('12345'.isupper()) # False ''' Métodos de string isXa isalpha() retornará True se a string for constituida somente de letras e não estiver vazia isalnum() retornará True se a string for constituida somente de letas e numeros e não estiver vazia isdeciamal() retornará True se a string for constituida somente de caracteres numéricos e não estiver vazia isspace() retornará True se a string for constituida somente de espaços, tabulações e quebras de linhas e não estiver vazia istitle() retornará True se a string for constituida somente de palavras que comecer com uma letras maiuscula seguida de letas minusculas Esse métodos são úteis para validar dados de entrada de usuúario, exemplo ''' while True: print('\nEnter you age:') age = input() if age.isdecimal(): break print('Please enter a number for your age.') print('tks') ''' startswith() endswith() Retornarão True se o valor de string com o qual forem chamados comeceçar ou terminar (respectivamente) com a string passada para o método ''' print('\nTeste dos metodos startswith e endswith') print('Hello world!'.endswith('world!')) # True print('abc123'.startswith('abcdef')) # False print('abc123'.endswith('12')) # False print('Hello world!'.startswith('Hello world!')) # True print('Hello world!'.endswith('Hello world!')) # True ''' join() O método join() é útil quando temos uma lista de strings que devem ser unidas em um único valor de string. Esse método é interessante pois podemos usar uma separador dos itens ''' listanimals = ['cats', 'dogs', 'bears'] stringanimals = ', '.join(listanimals) print('\nTeste1 do metodo join:') print(stringanimals) stringanimals = ' '.join(listanimals) print('Teste2 do metodo join, usando um separador') print(stringanimals) ''' split() O método split faz o inverso do join. É chamado em um valor de string e retorna uma lista de strings. Um uso comum esta em dividir uma string de multiplas linhas nos caracteres de quebra de linha ''' frase = 'My name is Simon' print('\nTeste1 do método split:') print(frase.split()) print('\nTeste2 do método split, usando um separador') print(frase.split('is')) spam = '''Dear Alice, How have you been? I am fine. There is a container in the fridge that is labeled "Milk Experiment". Please do not drink it. Sincerely Bob''' print('\nTeste2 do método split, usando um separador\n') print(spam.split('\n')) ''' Justificando texto rjust(), ljust(), center() Os métodos de string retornam uma versão preenchida da string em que são chamadas com espeços inseridos para justificar o texto 1 - Argumemto é um inteiro referente ao tamanho da string justificada Perceba que se o inteiro for menor que o tamanho da string, nada acontecera! 2 - Argumento opcional, especifica o caractere de preenchimento ''' print('\nTeste dos métodos rsjust e ljust:') hello = 'Hello world' print(hello.rjust(20)) print(hello.ljust(30)) print(hello.rjust(15, '*')) print(hello.ljust(25, '-')) print(hello.center(15)) print(hello.center(25, '=')) ''' Esse métodos serão especialmente úteis quando for necessário exibir dados tabulares que tiverem o espacámento correto, Exemplo: ''' print('\nExemplo mais complexo do ljust, rjust e center:\n') def printPicnic(ItemDict, leftWidth, rightWidth): print('PICNIC ITEMS'.center(leftWidth + rightWidth, '-')) for k, v in ItemDict.items(): print(k.ljust(leftWidth, '.') + str(v).rjust(rightWidth)) picnicItems = {'sandwiches': 4, 'apples': 12, 'cups': 4, 'cookies': 8000} printPicnic(picnicItems, 12, 5) # Executando uma vez printPicnic(picnicItems, 20, 7) # Executando segunda vez ''' strip(), rstrip(), lstrip() removendo espaços em brancos strip() retornará uma nova string sem caracteres de espacos em branco no inicio ou fim lstrip() rstrip()s Removerao caracteres de espacoes em branco das extremidades da esquerda e direita respectivamente Opcionalmente, um argumento do tipo string especifica quais caracteres deverao ser removidos, nao importando a ordem ''' print('\nTestando metodos para remover os espacos:') spam = ' Hello World ' print(spam.strip()) print(spam.lstrip()) print(spam.rstrip()) spam = 'SpamSpamBaconSpamEggsSpamSpamSpam' print(spam.strip('ampS')) ''' Copiando e colando strings com módulo pyperclip O modulo pyperclip tem as funcoes de copy() e paste() capazes de enviar e receber textos do clipboard (area de tranferencia) do seu computador Enviar a saída de seu programa para o clipboard facilitará colá em um email, um processador de texto uo em outro software Necessario usar o import pyperclip para funcionar pip3 install pyperclip ''' print('\nExemplo de pyperclip:') pyperclip.copy('The text to be copied to the clipboard.') print(pyperclip.paste()) # Fontes # Livro Automatiando tarefas maçantes com python # Capitulo 6, Manipulação de strings

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conanfile.py

bincrafters/conan-boost_timer

0

114497

#!/usr/bin/env python # -*- coding: utf-8 -*- from conans import python_requires base = python_requires("boost_base/2.0.0@bincrafters/testing") class BoostTimerConan(base.BoostBaseConan): name = "boost_timer" version = "1.70.0" @property def boost_build_requires(self): return ["io"]

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tests/sentinel2/test_metadata.py

jamesvrt/stactools

0

172612

import unittest from stactools.sentinel2.safe_manifest import SafeManifest from stactools.sentinel2.product_metadata import ProductMetadata from stactools.sentinel2.granule_metadata import GranuleMetadata from tests.utils import TestData class Sentinel2MetadataTest(unittest.TestCase): def test_parses_product_metadata_properties(self): manifest_path = TestData.get_path( 'data-files/sentinel2/S2A_MSIL2A_20190212T192651_N0212_R013_T07HFE_20201007T160857.SAFE' ) manifest = SafeManifest(manifest_path) product_metadata = ProductMetadata(manifest.product_metadata_href) granulemetadata = GranuleMetadata(manifest.granule_metadata_href) s2_props = product_metadata.metadata_dict s2_props.update(granulemetadata.metadata_dict) expected = { # From product metadata 's2:product_uri': 'S2A_MSIL2A_20190212T192651_N0212_R013_T07HFE_20201007T160857.SAFE', 's2:generation_time': '2020-10-07T16:08:57.135Z', 's2:processing_baseline': '02.12', 's2:product_type': 'S2MSI2A', 's2:datatake_id': 'GS2A_20190212T192651_019029_N02.12', 's2:datatake_type': 'INS-NOBS', 's2:datastrip_id': 'S2A_OPER_MSI_L2A_DS_ESRI_20201007T160858_S20190212T192646_N02.12', 's2:granule_id': 'S2A_OPER_MSI_L2A_TL_ESRI_20201007T160858_A019029_T07HFE_N02.12', 's2:mgrs_tile': '07HFE', 's2:reflectance_conversion_factor': 1.02763689829235, # From granule metadata 's2:degraded_msi_data_percentage': 0.0, 's2:nodata_pixel_percentage': 96.769553, 's2:saturated_defective_pixel_percentage': 0.0, 's2:dark_features_percentage': 0.0, 's2:cloud_shadow_percentage': 0.0, 's2:vegetation_percentage': 0.000308, 's2:not_vegetated_percentage': 0.069531, 's2:water_percentage': 48.349833, 's2:unclassified_percentage': 0.0, 's2:medium_proba_clouds_percentage': 14.61311, 's2:high_proba_clouds_percentage': 24.183494, 's2:thin_sirrus_percentage': 12.783723, 's2:snow_ice_percentage': 0.0, 's2:mean_solar_zenith': 32.707073851362, 's2:mean_solar_azimuth': 62.3286549448294 } for k, v in expected.items(): self.assertIn(k, s2_props) self.assertEqual(s2_props[k], v) self.assertEqual(granulemetadata.cloudiness_percentage, 51.580326)

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examples/pull_entry_values.py

c0gnac/dealcloud-python

1

48350

""" This script pulls the values of the fields for the "Project Genome" entry on the Deal list """ import getpass as gp import sys import requests import dealcloud as dc # Create an instance of a client for the DealCloud Data Service and a service # proxy try: client = dc.create_client( email=input('Email: '), password=<PASSWORD>(), hostname=input('Hostname: ') ) except requests.exceptions.ConnectionError: print('Failed to connect to the DealCloud Web Service.') sys.exit() service = dc.bind_service(client) # Find all of the lists in the site and try to find the Deal list by name lists = service.GetEntryLists() try: deal_list = list(filter(lambda l: l.Name == 'Deal', lists))[0] except IndexError: print('Deal list could not be found.') sys.exit() # Find all of the entries on the Deal list and try to find the 'Project Genome' # entry by name entries = service.GetListEntries(deal_list.Id) try: deal_entry = list(filter(lambda e: e.Name == 'Project Genome', entries))[0] except IndexError: print('Project Genome could not be found.') sys.exit() # Find all of the fields on all of the lists in a site, get the ones on the # Deal list fields = service.GetFields() deal_fields = list(filter(lambda f: f.EntryListId == deal_list.Id, fields)) # Create a type factory to access the types provided by the service factory = client.type_factory('ns0') # Build the payload for your request with the types provided by the factory # and send it requests = factory.ArrayOfDCPull() for f in deal_fields: p = factory.DCPull(EntryId=deal_entry.Id, FieldId=f.Id) requests.DCPull.append(p) response = service.ProcessDCPull( requests=requests, resolveReferenceUrls=True, fillExtendedData=True ) # Iterate over your responses and print them to the console for r in response: try: field = list(filter(lambda f: f.Id == r.FieldId, deal_fields))[0] except IndexError: print(f'Field {r.FieldId} cound not be named.') continue print(f'{field.Name}: {r.Value}')

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stubs/workspaces.py

claytonbrown/troposphere

0

13640

<reponame>claytonbrown/troposphere from . import AWSObject, AWSProperty from .validators import * from .constants import * # ------------------------------------------- class WorkSpacesWorkspace(AWSObject): """# AWS::WorkSpaces::Workspace - CloudFormationResourceSpecification version: 1.4.0 { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html", "Properties": { "BundleId": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-bundleid", "PrimitiveType": "String", "Required": true, "UpdateType": "Conditional" }, "DirectoryId": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-directoryid", "PrimitiveType": "String", "Required": true, "UpdateType": "Conditional" }, "RootVolumeEncryptionEnabled": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-rootvolumeencryptionenabled", "PrimitiveType": "Boolean", "Required": false, "UpdateType": "Conditional" }, "UserName": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-username", "PrimitiveType": "String", "Required": true, "UpdateType": "Immutable" }, "UserVolumeEncryptionEnabled": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-uservolumeencryptionenabled", "PrimitiveType": "Boolean", "Required": false, "UpdateType": "Conditional" }, "VolumeEncryptionKey": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-volumeencryptionkey", "PrimitiveType": "String", "Required": false, "UpdateType": "Conditional" } } } """ resource_type = "AWS::WorkSpaces::Workspace" props = { 'BundleId': (basestring, True, 'Conditional', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-bundleid'), 'DirectoryId': (basestring, True, 'Conditional', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-directoryid'), 'RootVolumeEncryptionEnabled': (boolean, False, 'Conditional', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-rootvolumeencryptionenabled'), 'UserName': (basestring, True, 'Immutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-username'), 'UserVolumeEncryptionEnabled': (boolean, False, 'Conditional', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-uservolumeencryptionenabled'), 'VolumeEncryptionKey': (basestring, False, 'Conditional', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-workspaces-workspace.html#cfn-workspaces-workspace-volumeencryptionkey') }

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env/Lib/site-packages/countdowntimer_model/apps.py

gtkacz/fantasytrashtalk

4

29798

<reponame>gtkacz/fantasytrashtalk from django.apps import AppConfig class CountdowntimerModelConfig(AppConfig): name = 'countdowntimer_model'

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websocket/_abnf.py

benhoyt/websocket-client

147

1611486

""" """ """ _abnf.py websocket - WebSocket client library for Python Copyright 2021 engn33r 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 array import os import struct import sys from ._exceptions import * from ._utils import validate_utf8 from threading import Lock try: # If wsaccel is available, use compiled routines to mask data. # wsaccel only provides around a 10% speed boost compared # to the websocket-client _mask() implementation. # Note that wsaccel is unmaintained. from wsaccel.xormask import XorMaskerSimple def _mask(_m, _d): return XorMaskerSimple(_m).process(_d) except ImportError: # wsaccel is not available, use websocket-client _mask() native_byteorder = sys.byteorder def _mask(mask_value, data_value): datalen = len(data_value) data_value = int.from_bytes(data_value, native_byteorder) mask_value = int.from_bytes(mask_value * (datalen // 4) + mask_value[: datalen % 4], native_byteorder) return (data_value ^ mask_value).to_bytes(datalen, native_byteorder) __all__ = [ 'ABNF', 'continuous_frame', 'frame_buffer', 'STATUS_NORMAL', 'STATUS_GOING_AWAY', 'STATUS_PROTOCOL_ERROR', 'STATUS_UNSUPPORTED_DATA_TYPE', 'STATUS_STATUS_NOT_AVAILABLE', 'STATUS_ABNORMAL_CLOSED', 'STATUS_INVALID_PAYLOAD', 'STATUS_POLICY_VIOLATION', 'STATUS_MESSAGE_TOO_BIG', 'STATUS_INVALID_EXTENSION', 'STATUS_UNEXPECTED_CONDITION', 'STATUS_BAD_GATEWAY', 'STATUS_TLS_HANDSHAKE_ERROR', ] # closing frame status codes. STATUS_NORMAL = 1000 STATUS_GOING_AWAY = 1001 STATUS_PROTOCOL_ERROR = 1002 STATUS_UNSUPPORTED_DATA_TYPE = 1003 STATUS_STATUS_NOT_AVAILABLE = 1005 STATUS_ABNORMAL_CLOSED = 1006 STATUS_INVALID_PAYLOAD = 1007 STATUS_POLICY_VIOLATION = 1008 STATUS_MESSAGE_TOO_BIG = 1009 STATUS_INVALID_EXTENSION = 1010 STATUS_UNEXPECTED_CONDITION = 1011 STATUS_BAD_GATEWAY = 1014 STATUS_TLS_HANDSHAKE_ERROR = 1015 VALID_CLOSE_STATUS = ( STATUS_NORMAL, STATUS_GOING_AWAY, STATUS_PROTOCOL_ERROR, STATUS_UNSUPPORTED_DATA_TYPE, STATUS_INVALID_PAYLOAD, STATUS_POLICY_VIOLATION, STATUS_MESSAGE_TOO_BIG, STATUS_INVALID_EXTENSION, STATUS_UNEXPECTED_CONDITION, STATUS_BAD_GATEWAY, ) class ABNF(object): """ ABNF frame class. See http://tools.ietf.org/html/rfc5234 and http://tools.ietf.org/html/rfc6455#section-5.2 """ # operation code values. OPCODE_CONT = 0x0 OPCODE_TEXT = 0x1 OPCODE_BINARY = 0x2 OPCODE_CLOSE = 0x8 OPCODE_PING = 0x9 OPCODE_PONG = 0xa # available operation code value tuple OPCODES = (OPCODE_CONT, OPCODE_TEXT, OPCODE_BINARY, OPCODE_CLOSE, OPCODE_PING, OPCODE_PONG) # opcode human readable string OPCODE_MAP = { OPCODE_CONT: "cont", OPCODE_TEXT: "text", OPCODE_BINARY: "binary", OPCODE_CLOSE: "close", OPCODE_PING: "ping", OPCODE_PONG: "pong" } # data length threshold. LENGTH_7 = 0x7e LENGTH_16 = 1 << 16 LENGTH_63 = 1 << 63 def __init__(self, fin=0, rsv1=0, rsv2=0, rsv3=0, opcode=OPCODE_TEXT, mask=1, data=""): """ Constructor for ABNF. Please check RFC for arguments. """ self.fin = fin self.rsv1 = rsv1 self.rsv2 = rsv2 self.rsv3 = rsv3 self.opcode = opcode self.mask = mask if data is None: data = "" self.data = data self.get_mask_key = os.urandom def validate(self, skip_utf8_validation=False): """ Validate the ABNF frame. Parameters ---------- skip_utf8_validation: skip utf8 validation. """ if self.rsv1 or self.rsv2 or self.rsv3: raise WebSocketProtocolException("rsv is not implemented, yet") if self.opcode not in ABNF.OPCODES: raise WebSocketProtocolException("Invalid opcode %r", self.opcode) if self.opcode == ABNF.OPCODE_PING and not self.fin: raise WebSocketProtocolException("Invalid ping frame.") if self.opcode == ABNF.OPCODE_CLOSE: l = len(self.data) if not l: return if l == 1 or l >= 126: raise WebSocketProtocolException("Invalid close frame.") if l > 2 and not skip_utf8_validation and not validate_utf8(self.data[2:]): raise WebSocketProtocolException("Invalid close frame.") code = 256 * self.data[0] + self.data[1] if not self._is_valid_close_status(code): raise WebSocketProtocolException("Invalid close opcode.") @staticmethod def _is_valid_close_status(code): return code in VALID_CLOSE_STATUS or (3000 <= code < 5000) def __str__(self): return "fin=" + str(self.fin) \ + " opcode=" + str(self.opcode) \ + " data=" + str(self.data) @staticmethod def create_frame(data, opcode, fin=1): """ Create frame to send text, binary and other data. Parameters ---------- data: <type> data to send. This is string value(byte array). If opcode is OPCODE_TEXT and this value is unicode, data value is converted into unicode string, automatically. opcode: <type> operation code. please see OPCODE_XXX. fin: <type> fin flag. if set to 0, create continue fragmentation. """ if opcode == ABNF.OPCODE_TEXT and isinstance(data, str): data = data.encode("utf-8") # mask must be set if send data from client return ABNF(fin, 0, 0, 0, opcode, 1, data) def format(self): """ Format this object to string(byte array) to send data to server. """ if any(x not in (0, 1) for x in [self.fin, self.rsv1, self.rsv2, self.rsv3]): raise ValueError("not 0 or 1") if self.opcode not in ABNF.OPCODES: raise ValueError("Invalid OPCODE") length = len(self.data) if length >= ABNF.LENGTH_63: raise ValueError("data is too long") frame_header = chr(self.fin << 7 | self.rsv1 << 6 | self.rsv2 << 5 | self.rsv3 << 4 | self.opcode).encode('latin-1') if length < ABNF.LENGTH_7: frame_header += chr(self.mask << 7 | length).encode('latin-1') elif length < ABNF.LENGTH_16: frame_header += chr(self.mask << 7 | 0x7e).encode('latin-1') frame_header += struct.pack("!H", length) else: frame_header += chr(self.mask << 7 | 0x7f).encode('latin-1') frame_header += struct.pack("!Q", length) if not self.mask: return frame_header + self.data else: mask_key = self.get_mask_key(4) return frame_header + self._get_masked(mask_key) def _get_masked(self, mask_key): s = ABNF.mask(mask_key, self.data) if isinstance(mask_key, str): mask_key = mask_key.encode('utf-8') return mask_key + s @staticmethod def mask(mask_key, data): """ Mask or unmask data. Just do xor for each byte Parameters ---------- mask_key: <type> 4 byte string. data: <type> data to mask/unmask. """ if data is None: data = "" if isinstance(mask_key, str): mask_key = mask_key.encode('latin-1') if isinstance(data, str): data = data.encode('latin-1') return _mask(array.array("B", mask_key), array.array("B", data)) class frame_buffer(object): _HEADER_MASK_INDEX = 5 _HEADER_LENGTH_INDEX = 6 def __init__(self, recv_fn, skip_utf8_validation): self.recv = recv_fn self.skip_utf8_validation = skip_utf8_validation # Buffers over the packets from the layer beneath until desired amount # bytes of bytes are received. self.recv_buffer = [] self.clear() self.lock = Lock() def clear(self): self.header = None self.length = None self.mask = None def has_received_header(self): return self.header is None def recv_header(self): header = self.recv_strict(2) b1 = header[0] fin = b1 >> 7 & 1 rsv1 = b1 >> 6 & 1 rsv2 = b1 >> 5 & 1 rsv3 = b1 >> 4 & 1 opcode = b1 & 0xf b2 = header[1] has_mask = b2 >> 7 & 1 length_bits = b2 & 0x7f self.header = (fin, rsv1, rsv2, rsv3, opcode, has_mask, length_bits) def has_mask(self): if not self.header: return False return self.header[frame_buffer._HEADER_MASK_INDEX] def has_received_length(self): return self.length is None def recv_length(self): bits = self.header[frame_buffer._HEADER_LENGTH_INDEX] length_bits = bits & 0x7f if length_bits == 0x7e: v = self.recv_strict(2) self.length = struct.unpack("!H", v)[0] elif length_bits == 0x7f: v = self.recv_strict(8) self.length = struct.unpack("!Q", v)[0] else: self.length = length_bits def has_received_mask(self): return self.mask is None def recv_mask(self): self.mask = self.recv_strict(4) if self.has_mask() else "" def recv_frame(self): with self.lock: # Header if self.has_received_header(): self.recv_header() (fin, rsv1, rsv2, rsv3, opcode, has_mask, _) = self.header # Frame length if self.has_received_length(): self.recv_length() length = self.length # Mask if self.has_received_mask(): self.recv_mask() mask = self.mask # Payload payload = self.recv_strict(length) if has_mask: payload = ABNF.mask(mask, payload) # Reset for next frame self.clear() frame = ABNF(fin, rsv1, rsv2, rsv3, opcode, has_mask, payload) frame.validate(self.skip_utf8_validation) return frame def recv_strict(self, bufsize): shortage = bufsize - sum(map(len, self.recv_buffer)) while shortage > 0: # Limit buffer size that we pass to socket.recv() to avoid # fragmenting the heap -- the number of bytes recv() actually # reads is limited by socket buffer and is relatively small, # yet passing large numbers repeatedly causes lots of large # buffers allocated and then shrunk, which results in # fragmentation. bytes_ = self.recv(min(16384, shortage)) self.recv_buffer.append(bytes_) shortage -= len(bytes_) unified = bytes("", 'utf-8').join(self.recv_buffer) if shortage == 0: self.recv_buffer = [] return unified else: self.recv_buffer = [unified[bufsize:]] return unified[:bufsize] class continuous_frame(object): def __init__(self, fire_cont_frame, skip_utf8_validation): self.fire_cont_frame = fire_cont_frame self.skip_utf8_validation = skip_utf8_validation self.cont_data = None self.recving_frames = None def validate(self, frame): if not self.recving_frames and frame.opcode == ABNF.OPCODE_CONT: raise WebSocketProtocolException("Illegal frame") if self.recving_frames and \ frame.opcode in (ABNF.OPCODE_TEXT, ABNF.OPCODE_BINARY): raise WebSocketProtocolException("Illegal frame") def add(self, frame): if self.cont_data: self.cont_data[1] += frame.data else: if frame.opcode in (ABNF.OPCODE_TEXT, ABNF.OPCODE_BINARY): self.recving_frames = frame.opcode self.cont_data = [frame.opcode, frame.data] if frame.fin: self.recving_frames = None def is_fire(self, frame): return frame.fin or self.fire_cont_frame def extract(self, frame): data = self.cont_data self.cont_data = None frame.data = data[1] if not self.fire_cont_frame and data[0] == ABNF.OPCODE_TEXT and not self.skip_utf8_validation and not validate_utf8(frame.data): raise WebSocketPayloadException( "cannot decode: " + repr(frame.data)) return [data[0], frame]

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