xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

a9166c08a6ba96040e8b6b603afa425bfd5c7ee9

170

py

Python

espresso/__init__.py

labbcb/espresso-caller

a38dc8e93629c86d5973a69c55002db4923d3c02

[ "MIT" ]

null

espresso/__init__.py

labbcb/espresso-caller

a38dc8e93629c86d5973a69c55002db4923d3c02

[ "MIT" ]

null

espresso/__init__.py

labbcb/espresso-caller

a38dc8e93629c86d5973a69c55002db4923d3c02

[ "MIT" ]

1

2020-12-12T00:59:52.000Z

"""Espresso-Caller: automated and reproducible tool for identifying genomic variations at scale""" # TODO: is it really necessary for packaging? name = 'espresso-caller'

42.5

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1

0

null

0

1

0

5

a92d33d2bce797f8846a2c0d6595bbdaec6507ef

118

py

Python

input.py

ralymuhif/Python_Course

b35dd412e1c185f49c349334914306953dadb583

[ "MIT" ]

null

input.py

ralymuhif/Python_Course

b35dd412e1c185f49c349334914306953dadb583

[ "MIT" ]

null

input.py

ralymuhif/Python_Course

b35dd412e1c185f49c349334914306953dadb583

[ "MIT" ]

null

name = input ("What is your name? ") color = input ("What is your favorite colour? ") print (name + " Likes " + color)

39.333333

48

0.644068

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118

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null

0

5

a931e975f2dfb0de45d5db6da6089a118c24c99c

15,692

py

Python

ml-playground/theanets/audio_autoencoder.py

JBloodless/ml

c12bf6680c233a3580c69209922c57748a3fe0c2

[ "MIT" ]

88

2016-09-27T19:47:16.000Z

2021-11-08T12:32:12.000Z

ml-playground/theanets/audio_autoencoder.py

JBloodless/ml

c12bf6680c233a3580c69209922c57748a3fe0c2

[ "MIT" ]

38

2016-09-25T08:48:11.000Z

2019-10-10T02:27:41.000Z

ml-playground/theanets/audio_autoencoder.py

JBloodless/ml

c12bf6680c233a3580c69209922c57748a3fe0c2

[ "MIT" ]

30

2016-10-01T04:05:56.000Z

2022-03-16T11:48:12.000Z

import numpy as np from sklearn.preprocessing import MinMaxScaler, StandardScaler from sklearn.cross_validation import train_test_split import theanets import climate climate.enable_default_logging() X_orig = np.load('/Users/bzamecnik/Documents/music-processing/music-processing-experiments/c-scale-piano_spectrogram_2048_hamming.npy') sample_count, feature_count = X_orig.shape X = MinMaxScaler().fit_transform(X_orig) X = X.astype(np.float32) X_train, X_test = train_test_split(X, test_size=0.4, random_state=42) X_val, X_test = train_test_split(X_test, test_size=0.5, random_state=42) # (np.maximum(0, 44100/512*np.arange(13)-2)).astype('int') #blocks = [0, 84, 170, 256, 342, 428, 514, 600, 687, 773, 859, 945, 1031, 1205] blocks = [0, 48, 98, 148, 198, 248, 298, 348, 398, 448, 498, 548, 598, 700] def make_labels(blocks): label_count = len(blocks) - 1 labels = np.zeros(blocks[-1]) for i in range(label_count): labels[blocks[i]:blocks[i+1]] = i return labels y = make_labels(blocks) def score(exp, Xs): X_train, X_val, X_test = Xs def sc(exp, X): return r2_score(X, exp.network.predict(X)) print("training: ", sc(exp, X_train)) # NOTE: only optimize to validation dataset's score! print("validation:", sc(exp, X_val)) print("test: ", sc(exp, X_test)) exp1 = theanets.Experiment( theanets.Autoencoder, layers=(feature_count, 500, feature_count), hidden_l1=0.1) exp1.train(X_train, X_val, optimize='nag', learning_rate=1e-3, momentum=0.9) exp2 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 500, feature_count), hidden_l1=0.1) exp2.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9) # gives quite nice prediction, trains slow exp3 = theanets.Experiment( theanets.Autoencoder, layers=(feature_count, 500, feature_count), hidden_l1=0.1, hidden_activation='relu') exp3.train(X_train, X_val, optimize='nag', learning_rate=1e-3, momentum=0.9) exp4 = theanets.Experiment( theanets.Autoencoder, layers=(feature_count, 500, feature_count), hidden_l1=0.1, input_dropout=0.3) exp4.train(X_train, X_val, optimize='nag', learning_rate=1e-3, momentum=0.9) # rmsprop - converges faster in this case than nag exp5 = theanets.Experiment( theanets.Autoencoder, layers=(feature_count, 500, feature_count), hidden_l1=0.1) exp5.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9) # tied weighs - work good, much lower loss function values # r2: 0.75037549551862703 exp6 = theanets.Experiment( theanets.Autoencoder, layers=(feature_count, 500, feature_count), hidden_l1=0.1, tied_weights=True) exp6.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9) # higher hidden L1 penalty - worse exp7 = theanets.Experiment( theanets.Autoencoder, layers=(feature_count, 500, feature_count), hidden_l1=0.7, tied_weights=True) exp7.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9) # hidden L2 penalty - a bit worse exp8 = theanets.Experiment( theanets.Autoencoder, layers=(feature_count, 500, feature_count), hidden_l1=0.1, hidden_l2=0.1, tied_weights=True) exp8.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9) # no regularization - in this case better # r2: 0.82211329411744094 exp10 = theanets.Experiment( theanets.Autoencoder, layers=(feature_count, 500, feature_count), tied_weights=True) exp10.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9) # layerwise autoencoder training exp11 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 500, feature_count), tied_weights=True) exp11.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9) # wow - this actually is able to to a 2D visualization exp12 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 100, 10, 2, 10, 100, feature_count), tied_weights=True) exp12.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9) def compute_middle_layer(X, model): X_pred_ff = model.feed_forward(X) middle = int(len(X_pred_ff)/2) X_middle = X_pred_ff[middle] return X_middle def visualize_2d(X, y=None): colors = y/max(y) if y is not None else np.linspace(0,1,len(X)) scatter(X[:,0], X[:,1], c=colors, alpha=0.2, edgecolors='none', cmap='rainbow') # same visualization, a little bit better r2 exp13 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 256, 64, 16, 2, 16, 64, 256, feature_count), tied_weights=True) exp13.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9) # contractive - better than without # r2: 0.82820148664941162 exp14 = theanets.Experiment( theanets.Autoencoder, layers=(feature_count, 500, feature_count), tied_weights=True, contractive=0.8) exp14.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9) # tanh - bad exp15 = theanets.Experiment( theanets.Autoencoder, layers=(feature_count, 500, feature_count), tied_weights=True, hidden_activation='tanh') exp15.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9) # relu, contractive exp16 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 128, 16, 2, 16, 128, feature_count), tied_weights=True, hidden_activation='relu', contractive=0.5) exp16.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9) exp17 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 128, 16, 2, 16, 128, feature_count), tied_weights=True, contractive=0.8) exp17.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9) exp18 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, input_dropout=0.8) exp18.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9) # r2: 0.83371355062803953 exp19 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, input_dropout=0.8, hidden_dropout=0.8) exp19.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9) exp20 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, input_dropout=0.9, hidden_dropout=0.9) exp20.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9) # ----------------- # animate the 2D point movement import matplotlib.animation as animation def export_animation(X_2d, y, filename): fig = plt.figure() # 854x480 px (480p) in inches, note that 8.54 gives 853px width :/ fig.set_size_inches(8.545, 4.80) plt.axis('equal') # plt.tight_layout() # plt.xlim(-0.1, 1.1) # plt.ylim(-0.1, 1.1) images = [] im1 = scatter(X_2d[:, 0], X_2d[:, 1], c=y/max(y), cmap='rainbow', alpha=0.2) for i in range(len(X_2d)): im2 = scatter(X_2d[i, 0], X_2d[i, 1], c=y[i]/max(y), cmap='rainbow') images.append([im1, im2]) ani = animation.ArtistAnimation(fig, images, interval=20, blit=False, repeat=False) writer = animation.writers['ffmpeg'](fps=50, bitrate=5000) ani.save(filename, writer=writer, dpi=100) export_animation(X_tsne, y, 'piano-tsne.mp4') #---------------------- exp21 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, input_dropout=0.3, hidden_dropout=0.5, batch_size=len(X_train)) exp21.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9) exp22 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, input_dropout=0.3, hidden_dropout=0.5) exp22.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9) exp23 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, 256, 128, 64, 32, 16, 8, 4, 2, 4, 8, 16, 32, 64, 128, 256, 512, feature_count), tied_weights=True, input_dropout=0.3, hidden_dropout=0.5) exp23.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9) exp24 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, input_dropout=0.3, hidden_dropout=0.5, hidden_activation='linear') exp24.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9) # r2: 0.833454635805 exp25 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True) exp25.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9) # r2: 0.731835366439 exp26 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True) exp26.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.1) # r2: 0.854741515141 (*) exp27 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True) exp27.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) # r2: 0.84260338122 exp28 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True) exp28.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.7) exp29 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True) exp29.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp30 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, input_dropout=0.9) exp30.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp31 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 100, feature_count), tied_weights=True) exp31.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp32 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 200, 20, 2, 20, 200, feature_count), tied_weights=True, input_dropout=0.5, hidden_dropout=0.5) exp32.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) # bad - makes a single curve exp33 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 200, 20, 2, 20, 200, feature_count), tied_weights=True, hidden_l1=0.1) exp33.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) # bad - makes a non-discriminative curve exp34 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 200, 20, 2, 20, 200, feature_count), tied_weights=True, input_dropout=0.5) exp34.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp35 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 200, 20, 2, 20, 200, feature_count), tied_weights=True, hidden_dropout=0.5) exp35.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp36 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 200, 20, 2, 20, 200, feature_count), tied_weights=True) exp36.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp33 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, 256, 128, 64, 32, 16, 8, 4, 2, 4, 8, 16, 32, 64, 128, 256, 512, feature_count), tied_weights=True) exp33.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) X_zca_train, X_zca_test = train_test_split(X_zca, test_size=0.4, random_state=42) X_zca_val, X_zca_test = train_test_split(X_zca_test, test_size=0.5, random_state=42) exp34 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True) exp34.train(X_zca_train, X_zca_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp35 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, 256, 128, 64, 32, 16, 8, 4, 2, 4, 8, 16, 32, 64, 128, 256, 512, feature_count), tied_weights=True, hidden_activation='relu') exp35.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) # - try tanh and relu for deeper networks # - try other normalization (mean-std instead od min-max) X_ms = StandardScaler().fit_transform(X_orig).astype(np.float32) X_ms_train, X_ms_test = train_test_split(X_ms, test_size=0.4, random_state=42) X_ms_val, X_ms_test = train_test_split(X_ms_test, test_size=0.5, random_state=42) exp36 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True) exp36.train(X_ms_train, X_ms_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp37 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, hidden_activation='tanh') exp37.train(X_ms_train, X_ms_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp38 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True) exp38.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) X_orig_train, X_orig_test = train_test_split(X_orig.astype('float32'), test_size=0.4, random_state=42) X_orig_val, X_orig_test = train_test_split(X_orig_test, test_size=0.5, random_state=42) exp39 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True) exp39.train(X_orig_train, X_orig_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp40 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, hidden_activation='linear', hidden_l1=0.5) exp40.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp41 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, hidden_activation='relu', hidden_l1=0.5) exp41.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp42 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, hidden_activation='relu', weight_l1=0.5) exp42.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) # bad exp43 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, hidden_activation='relu', contractive=0.9) exp43.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) # not bad exp44 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, hidden_activation='relu') exp45.train(X_ms_train, X_ms_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp45 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, hidden_activation='relu', contractive=0.5) exp45.train(X_ms_train, X_ms_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) # r2: 0.849283267068 exp46 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, hidden_activation='linear', contractive=0.5) exp46.train(X_ms_train, X_ms_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5) exp47 = theanets.Experiment(theanets.Autoencoder, layers=(feature_count, 512, feature_count), tied_weights=True, hidden_activation='linear', contractive=0.5) exp47.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)

40.971279

135

0.74248

2,399

15,692

4.65694

0.137974

0.056391

0.116362

0.165593

0.745614

0.735589

0.730487

0.721984

0.681704

0.669262

0

0.083213

0.119296

15,692

382

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41.078534

0.725181

0.075835

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0.362595

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0.003817

0.047082

0.007951

0

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0.022901

false

0

0.022901

0.003817

0.057252

0.01145

0

null

0

1

0

1

0

1

0

null

0

5

a945984e6747e68fdcd06965311d7b5db551f5b0

290

py

Python

src/python/Structures/edge.py

rmallermartins/graphun

9ac252ccd3de5ce5c86c0ec1ffa06576bf02dca9

[ "MIT" ]

1

2015-09-20T20:53:38.000Z

src/python/Structures/edge.py

rmallermartins/graphun

9ac252ccd3de5ce5c86c0ec1ffa06576bf02dca9

[ "MIT" ]

null

src/python/Structures/edge.py

rmallermartins/graphun

9ac252ccd3de5ce5c86c0ec1ffa06576bf02dca9

[ "MIT" ]

null

class Edge(object): def __init__(self, u, v, w): self.__orig = u self.__dest = v self.__w = w def getOrig(self): return self.__orig def getDest(self): return self.__dest def getW(self): return self.__w

18.125

32

0.506897

36

290

3.638889

0.416667

0.229008

0.320611

0

0.4

290

15

33

19.333333

0.752874

0

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0.363636

false

0

0.272727

0.727273

0

null

1

0

null

0

1

0

1

0

5

a96a46af017878f3df006d302c89a9c814b4a9f0

87

py

Python

test_code/boj/bronze5/10998.py

yjinheon/solve

f47cd19d3c81d0b16586159c754deb2ffcb31ca0

[ "Apache-2.0" ]

null

test_code/boj/bronze5/10998.py

yjinheon/solve

f47cd19d3c81d0b16586159c754deb2ffcb31ca0

[ "Apache-2.0" ]

null

test_code/boj/bronze5/10998.py

yjinheon/solve

f47cd19d3c81d0b16586159c754deb2ffcb31ca0

[ "Apache-2.0" ]

null

a,b = map(int,input().split()) def multiply(a,b): return a*b print(multiply(a,b))

14.5

30

0.62069

17

87

3.176471

0.588235

0.148148

0.37037

0

0.149425

87

6

31

14.5

0.72973

0

1

0.25

false

0

0.25

0.5

0.25

1

0

null

0

1

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1

0

null

0

1

0

1

0

5

a98a8e9d64388dff1f8ea111b47d2a0c0afd0a1c

184

py

Python

vote/templatetags/index.py

RohanDukare/OnlineVoting

e1c355fab0fdd21cc63c4be9e16fc55731479f17

[ "MIT" ]

7

2019-05-17T06:12:57.000Z

2021-02-07T03:48:57.000Z

vote/templatetags/index.py

RohanDukare/OnlineVoting

e1c355fab0fdd21cc63c4be9e16fc55731479f17

[ "MIT" ]

null

vote/templatetags/index.py

RohanDukare/OnlineVoting

e1c355fab0fdd21cc63c4be9e16fc55731479f17

[ "MIT" ]

5

2019-05-17T06:13:10.000Z

2021-02-07T03:49:21.000Z

from django import template import calendar register = template.Library() @register.filter def index(List, i): return List[int(i)] def monthName(List,i): return List[int(i)]

16.727273

29

0.722826

27

184

4.925926

0.555556

0.075188

0.165414

0.225564

0.285714

0

0.157609

184

10

30

18.4

0.858065

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0.25

0

1

0.25

false

0

0.25

0.75

0

1

0

null

0

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Python

news/admin.py

studentisgss/booking

e0e28f42cf2a466688b4ea3787eb28dbc0980cac

[ "MIT" ]

7

2015-12-11T19:18:39.000Z

2020-10-30T12:50:19.000Z

news/admin.py

studentisgss/booking

e0e28f42cf2a466688b4ea3787eb28dbc0980cac

[ "MIT" ]

119

2015-11-03T22:21:09.000Z

2021-03-17T21:36:49.000Z

news/admin.py

studentisgss/booking

e0e28f42cf2a466688b4ea3787eb28dbc0980cac

[ "MIT" ]

null

from django.contrib import admin from news.models import * # Register your models here. admin.site.register(News) admin.site.register(Message)

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Python

predict/tests.py

darshpatel2000/Plant-leaves-disease-analyzer

2a354bd8838bc2db61399f16c499a6aae0eb0d44

[ "MIT" ]

2

2020-02-20T17:23:32.000Z

2020-08-10T02:26:30.000Z

predict/tests.py

darshpatel2000/Plant-leaves-disease-analyzer

2a354bd8838bc2db61399f16c499a6aae0eb0d44

[ "MIT" ]

1

2021-02-04T11:18:43.000Z

predict/tests.py

darshpatel2000/Plant-leaves-disease-analyzer

2a354bd8838bc2db61399f16c499a6aae0eb0d44

[ "MIT" ]

1

2020-10-01T04:56:26.000Z

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Python

dataset/__init__.py

ZikeYan/RoutedFusion

866699ff1eba48cdad20bbde9cc498c17848ac50

[ "BSD-3-Clause" ]

null

dataset/__init__.py

ZikeYan/RoutedFusion

866699ff1eba48cdad20bbde9cc498c17848ac50

[ "BSD-3-Clause" ]

null

dataset/__init__.py

ZikeYan/RoutedFusion

866699ff1eba48cdad20bbde9cc498c17848ac50

[ "BSD-3-Clause" ]

null

#from .shapenet import ShapeNet #from .modelnet import ModelNet from .ICL import ICL

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Python

ros_ws/src/baxter_interface/scripts/head_action_server.py

mesneym/Baxter-Arm-PP

fdbf86309bc64c31af105daa026b2f8519710129

[ "MIT" ]

null

ros_ws/src/baxter_interface/scripts/head_action_server.py

mesneym/Baxter-Arm-PP

fdbf86309bc64c31af105daa026b2f8519710129

[ "MIT" ]

null

ros_ws/src/baxter_interface/scripts/head_action_server.py

mesneym/Baxter-Arm-PP

fdbf86309bc64c31af105daa026b2f8519710129

[ "MIT" ]

null

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py

Python

vcboost/__init__.py

sepauly/vcboost

7005bf158c1a4673a50ee8c3f04b90b3dcee28b2

[ "MIT" ]

null

vcboost/__init__.py

sepauly/vcboost

7005bf158c1a4673a50ee8c3f04b90b3dcee28b2

[ "MIT" ]

null

vcboost/__init__.py

sepauly/vcboost

7005bf158c1a4673a50ee8c3f04b90b3dcee28b2

[ "MIT" ]

null

from .boost import VCBooster

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Python

Desafio/ex047.py

NathanMuniz/Exercises-Python

21dcf5fafdc3bd20baec997986b3ae97f5e08784

[ "MIT" ]

null

Desafio/ex047.py

NathanMuniz/Exercises-Python

21dcf5fafdc3bd20baec997986b3ae97f5e08784

[ "MIT" ]

null

Desafio/ex047.py

NathanMuniz/Exercises-Python

21dcf5fafdc3bd20baec997986b3ae97f5e08784

[ "MIT" ]

null

#for c in range(2, 52, 2): #print(c, end=' ') #print('Acabo') for c in range(1, 11): print(c +c)

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Python

dcase_util/data/encoders.py

ankitshah009/dcase_util

738571ce78faf60b0fdfa1d59fd42f42c8944f3d

[ "MIT" ]

null

dcase_util/data/encoders.py

ankitshah009/dcase_util

738571ce78faf60b0fdfa1d59fd42f42c8944f3d

[ "MIT" ]

null

dcase_util/data/encoders.py

ankitshah009/dcase_util

738571ce78faf60b0fdfa1d59fd42f42c8944f3d

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import print_function, absolute_import import numpy from dcase_util.containers import BinaryMatrix2DContainer from dcase_util.ui import FancyStringifier class BinaryMatrixEncoder(BinaryMatrix2DContainer): """Binary matrix encoder base class""" def __init__(self, label_list=None, time_resolution=None, **kwargs): """Constructor Parameters ---------- label_list : list or str Label list Default value None time_resolution : float Time resolution Default value None """ kwargs.update({ 'label_list': label_list, 'time_resolution': time_resolution }) super(BinaryMatrixEncoder, self).__init__(**kwargs) if not self.time_resolution: message = '{name}: No time resolution set.'.format(name=self.__class__.__name__) self.logger.exception(message) raise ValueError(message) class OneHotEncoder(BinaryMatrixEncoder): """One hot encoder class""" def __init__(self, label_list=None, time_resolution=1.0, length_frames=1, length_seconds=None, **kwargs): """Constructor Parameters ---------- label_list : list or str Label list Default value None time_resolution : float Time resolution Default value 1.0 length_frames : int length of binary matrix in frames Default value 1 length_seconds : float length of binary matrix in seconds Default value None """ kwargs.update({ 'label_list': label_list, 'time_resolution': time_resolution }) super(OneHotEncoder, self).__init__(**kwargs) self.length_frames = length_frames if self.length_frames is None and length_seconds is not None: self.length_frames = self._length_to_frames(length_seconds) if not self.label_list: message = '{name}: No label_list set.'.format(name=self.__class__.__name__) self.logger.exception(message) raise ValueError(message) def __str__(self): ui = FancyStringifier() output = super(OneHotEncoder, self).__str__() output += ui.line(field='Data') + '\n' output += ui.data(indent=4, field='data', value=self.data) + '\n' output += ui.line(indent=4, field='Dimensions') + '\n' output += ui.data(indent=6, field='time_axis', value=self.time_axis) + '\n' output += ui.data(indent=6, field='data_axis', value=self.data_axis) + '\n' output += ui.line(indent=4, field='Timing information') + '\n' output += ui.data(indent=6, field='time_resolution', value=self.time_resolution, unit="sec") + '\n' output += ui.line(field='Duration') + '\n' output += ui.data(indent=6, field='Frames', value=self.length) + '\n' if self.time_resolution: output += ui.data(indent=6, field='Seconds', value=self._frame_to_time(frame_id=self.length), unit='sec') + '\n' output += ui.line(indent=4, field='Labels') + '\n' output += ui.data(indent=6, field='label_list', value=self.label_list) + '\n' return output def encode(self, label, length_frames=None, length_seconds=None): """Generate one hot binary matrix Parameters ---------- label : str Class label to be hot length_frames : int length of binary matrix in frames, use either this or length_seconds, if none set, one set in constructor is used. Default value None length_seconds : float length of binary matrix in seconds, use either this or length_frames, if none set, one set in constructor is used. Default value None Returns ------- self """ if length_frames is None and length_seconds is None: length_frames = self.length_frames elif length_seconds is not None: length_frames = self._length_to_frames(length_seconds) # Initialize binary matrix binary_matrix = numpy.zeros((len(self.label_list), length_frames)) # Find correct row if label in self.label_list: pos = self.label_list.index(label) # Mark row to be hot binary_matrix[pos, :] = 1 else: # Unknown channel label given message = '{name}: Unknown label [{label}]'.format(name=self.__class__.__name__, label=label) self.logger.exception(message) raise ValueError(message) self.data = binary_matrix return self class ManyHotEncoder(BinaryMatrixEncoder): """Many hot encoder class""" def __init__(self, label_list=None, time_resolution=None, length_frames=None, length_seconds=None, **kwargs): """Constructor Parameters ---------- label_list : list or str Label list Default value None time_resolution : float Time resolution Default value None length_frames : int length of binary matrix Default value None length_seconds : float length of binary matrix in seconds Default value None """ kwargs.update({ 'label_list': label_list, 'time_resolution': time_resolution }) super(ManyHotEncoder, self).__init__(**kwargs) self.length_frames = length_frames if self.length_frames is None and length_seconds is not None: self.length_frames = self._length_to_frames(length_seconds) if not self.label_list: message = '{name}: No label_list set.'.format(name=self.__class__.__name__) self.logger.exception(message) raise ValueError(message) def __str__(self): ui = FancyStringifier() output = super(ManyHotEncoder, self).__str__() output += ui.line(field='Data') + '\n' output += ui.data(indent=4, field='data', value=self.data) + '\n' output += ui.line(indent=4, field='Dimensions') + '\n' output += ui.data(indent=6, field='time_axis', value=self.time_axis) + '\n' output += ui.data(indent=6, field='data_axis', value=self.data_axis) + '\n' output += ui.line(indent=4, field='Timing information') + '\n' output += ui.data(indent=6, field='time_resolution', value=self.time_resolution, unit="sec") + '\n' output += ui.line(field='Duration') + '\n' output += ui.data(indent=6, field='Frames', value=self.length) + '\n' if self.time_resolution: output += ui.data(indent=6, field='Seconds', value=self._frame_to_time(frame_id=self.length), unit='sec') + '\n' output += ui.line(indent=4, field='Labels') + '\n' output += ui.data(indent=6, field='label_list', value=self.label_list) + '\n' return output def encode(self, label_list, length_frames=None, length_seconds=None): """Generate one hot binary matrix Parameters ---------- label_list : list of str Class labels to be hot length_frames : int length of binary matrix Default value None length_seconds : float length of binary matrix in seconds Default value None Returns ------- self """ if length_frames is None and length_seconds is None: length_frames = self.length_frames elif length_seconds is not None: length_frames = self._length_to_frames(length_seconds) # Initialize binary matrix binary_matrix = numpy.zeros((len(self.label_list), length_frames)) for label in label_list: if label in self.label_list: # Find correct row pos = self.label_list.index(label) # Mark row to be hot binary_matrix[pos, :] = 1 else: # Unknown channel label given message = '{name}: Unknown label [{label}]'.format(name=self.__class__.__name__, label=label) self.logger.exception(message) raise ValueError(message) self.data = binary_matrix return self class EventRollEncoder(BinaryMatrixEncoder): """Event list encoder class""" def __init__(self, label_list=None, time_resolution=None, label='event_label', **kwargs): """Event roll Event roll is binary matrix indicating event activity withing time segment defined by time_resolution. Parameters ---------- label_list : list List of labels in correct order Default value None time_resolution : float > 0.0 Time resolution used when converting event into event roll. Default value None label : str Meta data field used to create event roll Default value 'event_label' """ kwargs.update({ 'label_list': label_list, 'time_resolution': time_resolution, 'label': label }) super(EventRollEncoder, self).__init__(**kwargs) self.label = label if not self.label_list: message = '{name}: No label_list set.'.format(name=self.__class__.__name__) self.logger.exception(message) raise ValueError(message) def __str__(self): ui = FancyStringifier() output = super(EventRollEncoder, self).__str__() output += ui.line(field='Data') + '\n' output += ui.data(indent=4, field='data', value=self.data) + '\n' output += ui.line(indent=4, field='Dimensions') + '\n' output += ui.data(indent=6, field='time_axis', value=self.time_axis) + '\n' output += ui.data(indent=6, field='data_axis', value=self.data_axis) + '\n' output += ui.line(indent=4, field='Timing information') + '\n' output += ui.data(indent=6, field='time_resolution', value=self.time_resolution, unit="sec") + '\n' output += ui.line(field='Duration') + '\n' output += ui.data(indent=6, field='Frames', value=self.length) + '\n' if self.time_resolution: output += ui.data(indent=6, field='Seconds', value=self._frame_to_time(frame_id=self.length), unit='sec') + '\n' output += ui.line(indent=4, field='Labels') + '\n' output += ui.data(indent=6, field='Label list', value=self.label_list) + '\n' output += ui.data(indent=6, field='label_field', value=self.label) + '\n' return output def encode(self, metadata_container, label=None, length_frames=None, length_seconds=None): """Generate event roll from MetaDataContainer Parameters ---------- metadata_container : MetaDataContainer Meta data label : str Meta data field used to create event roll Default value None length_frames : int length of event roll Default value None length_seconds : int, optional length of event roll in seconds, if none given max offset of the meta data is used. Default value None Returns ------- self """ if label is None: label = self.label if length_frames is None: if length_seconds is None: max_offset_seconds = metadata_container.max_offset else: max_offset_seconds = length_seconds max_offset_frames = self._length_to_frames(max_offset_seconds) else: max_offset_frames = length_frames # Initialize event roll event_roll = numpy.zeros((len(self.label_list), max_offset_frames)) # Fill-in event_roll for item in metadata_container: if item.onset is not None and item.offset is not None: if item[label]: pos = self.label_list.index(item[label]) onset = self._onset_to_frames(item.onset) offset = self._offset_to_frames(item.offset) if offset > event_roll.shape[self.time_axis]: # we have event which continues beyond max_offset_value offset = event_roll.shape[self.time_axis] if onset <= event_roll.shape[self.time_axis]: # We have event inside the event roll event_roll[pos, onset:offset] = 1 self.data = event_roll return self

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pip_services3_mysql/connect/__init__.py

pip-services3-python/pip-services3-mysql-python

94145b789a61fad2e566d66ce62c351dc8b020b3

[ "MIT" ]

null

pip_services3_mysql/connect/__init__.py

pip-services3-python/pip-services3-mysql-python

94145b789a61fad2e566d66ce62c351dc8b020b3

[ "MIT" ]

null

pip_services3_mysql/connect/__init__.py

pip-services3-python/pip-services3-mysql-python

94145b789a61fad2e566d66ce62c351dc8b020b3

[ "MIT" ]

null

# -*- coding: utf-8 -*- __all__ = ['MySqlConnectionResolver', 'MySqlConnection'] from .MySqlConnection import MySqlConnection from .MySqlConnectionResolver import MySqlConnectionResolver

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Python

bin/doxygen_catkin/__init__.py

tradr-project/doxygen_catkin

12e5592b62830ddc6c4a4ca8612310f55ac97e4e

[ "BSD-3-Clause" ]

5

2018-01-15T08:25:39.000Z

2022-03-07T01:03:50.000Z

bin/doxygen_catkin/__init__.py

jackiecx/doxygen_catkin

12e5592b62830ddc6c4a4ca8612310f55ac97e4e

[ "BSD-3-Clause" ]

1

2021-08-31T04:00:09.000Z

bin/doxygen_catkin/__init__.py

jackiecx/doxygen_catkin

12e5592b62830ddc6c4a4ca8612310f55ac97e4e

[ "BSD-3-Clause" ]

14

2015-08-11T07:29:20.000Z

2022-03-24T08:30:05.000Z

from doxyfile import * from mainpage import *

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py

Python

auth_app/views/__init__.py

ThePokerFaCcCe/messenger

2db3d5c2ccd05ac40d2442a13d664ca9ad3cb14c

[ "MIT" ]

null

auth_app/views/__init__.py

ThePokerFaCcCe/messenger

2db3d5c2ccd05ac40d2442a13d664ca9ad3cb14c

[ "MIT" ]

null

auth_app/views/__init__.py

ThePokerFaCcCe/messenger

2db3d5c2ccd05ac40d2442a13d664ca9ad3cb14c

[ "MIT" ]

null

from .verifycode_views import VerifyCodeViewSet from .access_views import AccessViewSet

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py

Python

python/testData/codeInsight/smartEnter/spaceInsertedAfterHashSignInComment_after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/codeInsight/smartEnter/spaceInsertedAfterHashSignInComment_after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/codeInsight/smartEnter/spaceInsertedAfterHashSignInComment_after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

# foo # <caret> pass

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wsgi

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learn/adapter.wsgi

jphacks/KB_1810

08de8654e6d782882de24c6f484a6dca242a4c07

[ "MIT" ]

null

learn/adapter.wsgi

jphacks/KB_1810

08de8654e6d782882de24c6f484a6dca242a4c07

[ "MIT" ]

null

learn/adapter.wsgi

jphacks/KB_1810

08de8654e6d782882de24c6f484a6dca242a4c07

[ "MIT" ]

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2018-10-20T00:54:05.000Z

2018-10-23T08:07:29.000Z

import sys sys.path.insert(0, '/var/www/KB_1810/learn') from appv2 import app as application

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Python

pkwscraper/lib/scraper/base_scraper.py

msmiglo/pkwscraper

ff50498ffa8c5f2fcbb013b07d8d8c6792f35d98

[ "MIT" ]

null

pkwscraper/lib/scraper/base_scraper.py

msmiglo/pkwscraper

ff50498ffa8c5f2fcbb013b07d8d8c6792f35d98

[ "MIT" ]

22

2021-12-19T14:21:46.000Z

2022-02-18T21:54:44.000Z

pkwscraper/lib/scraper/base_scraper.py

msmiglo/pkwscraper

ff50498ffa8c5f2fcbb013b07d8d8c6792f35d98

[ "MIT" ]

null

class BaseScraper: pass

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18

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null

0

1

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f56d5a2c4d7df863ecd3af8a278c122f3f6aca59

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py

Python

app/administrator/__init__.py

EandrewJones/srdp-database

22b9f5bcbffcd14b17cd62c6b268e5be079bf4fe

[ "MIT" ]

null

app/administrator/__init__.py

EandrewJones/srdp-database

22b9f5bcbffcd14b17cd62c6b268e5be079bf4fe

[ "MIT" ]

null

app/administrator/__init__.py

EandrewJones/srdp-database

22b9f5bcbffcd14b17cd62c6b268e5be079bf4fe

[ "MIT" ]

null

from flask import Blueprint bp = Blueprint("admin_bp", __name__) from app.administrator import views

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0

1

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5

f56e65bf40bed6ec4cdecee5ee6f000c7ca4d7a0

68

py

Python

cosima_cookbook/summary/__init__.py

navidcy/cosima-cookbook

b8d9ac7c4482e1029e07ee74d125fef70fab3ef0

[ "Apache-2.0" ]

null

cosima_cookbook/summary/__init__.py

navidcy/cosima-cookbook

b8d9ac7c4482e1029e07ee74d125fef70fab3ef0

[ "Apache-2.0" ]

null

cosima_cookbook/summary/__init__.py

navidcy/cosima-cookbook

b8d9ac7c4482e1029e07ee74d125fef70fab3ef0

[ "Apache-2.0" ]

1

2020-01-30T05:36:08.000Z

from . nml_diff import * from . nml_summary import * # __all__ = []

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0

1

0

1

0

5

f573a8138c6b009840aac56c9f2a95ead8bd5e47

869

py

Python

algorithm/graph/GraphImplementation.py

BinRay/Learning

36a2380a9686e6922632e6b85ddb3d1f0903b37a

[ "MIT" ]

null

algorithm/graph/GraphImplementation.py

BinRay/Learning

36a2380a9686e6922632e6b85ddb3d1f0903b37a

[ "MIT" ]

null

algorithm/graph/GraphImplementation.py

BinRay/Learning

36a2380a9686e6922632e6b85ddb3d1f0903b37a

[ "MIT" ]

null

a, b, c, d, e, f, g, h = range(8) # 邻接集 N1 = [ {b, c, d, e, f}, {c, e}, {d}, {e}, {f}, {c, g, h}, {f, h}, {f, g} ] # 邻接列表 N2 = [ [b, c, d, e, f], [c, e], [d], [e], [f], [c, g, h], [f, h], [f, g] ] # 加权邻接字典 N3 = [ {b: 2, c: 1, d: 3, e: 9, f: 4}, {c: 4, e: 3}, {d: 8}, {e: 7}, {f: 5}, {c: 2, g: 2, h: 2}, {f: 1, h: 6}, {f: 9, g: 8} ] # 邻接集的字典表示法 N4 = { 'a': set('bcdef'), 'b': set('ce'), 'c': set('d'), 'd': set('e'), 'e': set('f'), 'f': set('cfg'), 'g': set('fh'), 'h': set('fg') } # 邻接矩阵 N5 = [ [0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 1, 1, 0] ]

14.016129

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869

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0.329004

0.428571

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null

1

0

1

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null

0

5

f5778b7afe7cf29bcf57a8fb4957417a2681c47f

1,888

py

Python

ctf/ctf/views.py

bobbyluig/CCTF-Platform

4020a757f733f828b745f13bc7e66570e0f706c6

[ "MIT" ]

5

2015-04-27T01:47:31.000Z

2016-01-28T23:20:19.000Z

ctf/ctf/views.py

bobbyluig/cctf-platform

4020a757f733f828b745f13bc7e66570e0f706c6

[ "MIT" ]

null

ctf/ctf/views.py

bobbyluig/cctf-platform

4020a757f733f828b745f13bc7e66570e0f706c6

[ "MIT" ]

null

from django.http import HttpResponse, HttpResponseRedirect from django.shortcuts import render from django.db import models from api.interact import get_ip from api.latest import latest from api.account import pre_load from api.config import config def home(request): if 'teamid' not in request.session: return render(request, 'home_out.html', latest()) return render(request, 'home.html', latest()) def challenge(request): if 'teamid' not in request.session or not config.comp_started(): return HttpResponseRedirect('/') return render(request, 'challenge.html') def scoreboard(request): return render(request, 'scoreboard.html') def interact(request): if 'teamid' not in request.session or not config.comp_started(): return HttpResponseRedirect('/') return render(request, 'interact.html', {'ip': get_ip(request)}) def stats(request): return render(request, 'stats.html') def account(request): if 'teamid' not in request.session: return HttpResponseRedirect('/') return render(request, 'account.html', pre_load(request)) def login(request): if 'teamid' in request.session: return HttpResponseRedirect('/') return render(request, 'login.html') def register(request): if 'teamid' in request.session: return HttpResponseRedirect('/') return render(request, 'register.html') def forgot(request): if 'teamid' in request.session: return HttpResponseRedirect('/') return render(request, 'forgot.html') def license(request): return render(request, 'license.html') def irc(request): return render(request, 'irc.html') def readme(request): return render(request, 'readme.html') def handler500(request): return render(request, '500.html') def handler404(request): return render(request, '404.html') def handler403(request): return render(request, '403.html') def handler400(request): return render(request, '400.html')

24.842105

65

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1,888

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1,888

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24.842105

0.835566

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false

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0.132075

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null

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1

0

null

0

1

0

1

0

5

f59a77a65e2559fbb18b734f60f5858a8c4edd3b

130

py

Python

python-sdk/nuscenes/prediction/models/mtp.py

tanjiangyuan/Classification_nuScence

b94c4b0b6257fc1c048a676e3fd9e71183108d53

[ "Apache-2.0" ]

null

python-sdk/nuscenes/prediction/models/mtp.py

tanjiangyuan/Classification_nuScence

b94c4b0b6257fc1c048a676e3fd9e71183108d53

[ "Apache-2.0" ]

null

python-sdk/nuscenes/prediction/models/mtp.py

tanjiangyuan/Classification_nuScence

b94c4b0b6257fc1c048a676e3fd9e71183108d53

[ "Apache-2.0" ]

null

version https://git-lfs.github.com/spec/v1 oid sha256:765631c329befe3434590379a1d56d890cfb25744a408de428b1bbd98c13368d size 11960

32.5

75

0.884615

13

130

8.846154

1

0

0.427419

0.046154

130

3

76

43.333333

0.5

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

null

1

0

1

0

5

f59aa4c5a9252197922bb4db48fdf153a2d27b28

71

py

Python

tests/test_developer_tools.py

confluentinc/developer-tools

b5842544d7c7f3938a5e24e5e5fe5f2bdfc316c3

[ "MIT" ]

null

tests/test_developer_tools.py

confluentinc/developer-tools

b5842544d7c7f3938a5e24e5e5fe5f2bdfc316c3

[ "MIT" ]

null

tests/test_developer_tools.py

confluentinc/developer-tools

b5842544d7c7f3938a5e24e5e5fe5f2bdfc316c3

[ "MIT" ]

1

2021-01-14T11:36:31.000Z

def test_developer_tools(): """ Test is code's best friend. ^_^ """

35.5

43

0.633803

10

71

4.2

0.9

0

0.183099

71

2

43

35.5

0.724138

0.43662

0

1

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

f5a19eaeed5773bb0cd8c2d2f2b270408d6e5cd4

128

py

Python

discodo/client/__init__.py

AkiaCode/discodo

0a76afb196a7945f525896f56f431e82aaf83f44

[ "MIT" ]

null

discodo/client/__init__.py

AkiaCode/discodo

0a76afb196a7945f525896f56f431e82aaf83f44

[ "MIT" ]

null

discodo/client/__init__.py

AkiaCode/discodo

0a76afb196a7945f525896f56f431e82aaf83f44

[ "MIT" ]

null

from .node import Node try: import discord except ModuleNotFoundError: pass else: from .DPYClient import DPYClient

14.222222

36

0.742188

15

128

6.333333

0.666667

0

0.21875

128

8

37

16

0.95

0

1

0

true

0.142857

0.428571

0

0.428571

0

1

0

null

0

1

0

null

0

1

0

5

193ade8029f1e602bb4bbe4b644035a963d6446c

224

py

Python

stlearn/em.py

duypham2108/dev_st

47adcfa5803eba7549b1185ec69d2317b386d9ff

[ "BSD-3-Clause" ]

67

2020-06-01T05:19:23.000Z

2022-03-31T20:47:50.000Z

stlearn/em.py

duypham2108/dev_st

47adcfa5803eba7549b1185ec69d2317b386d9ff

[ "BSD-3-Clause" ]

34

2020-11-02T18:01:43.000Z

2022-03-16T21:58:54.000Z

stlearn/em.py

duypham2108/dev_st

47adcfa5803eba7549b1185ec69d2317b386d9ff

[ "BSD-3-Clause" ]

13

2020-05-14T05:10:22.000Z

2022-03-09T14:05:38.000Z

from .embedding.pca import run_pca from .embedding.umap import run_umap from .embedding.ica import run_ica # from .embedding.scvi import run_ldvae from .embedding.fa import run_fa from .embedding.diffmap import run_diffmap

28

42

0.825893

36

224

4.972222

0.305556

0.435754

0

0.116071

224

7

43

32

0.90404

0.165179

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

5

198a9e3646d093392f76cdd5020b95889bd06e33

61

py

Python

test/__init__.py

tchar/ulauncher-calculate-anything

ee0903174c8b87cd1f7c3b6c1acef10702547507

[ "MIT" ]

41

2021-07-12T08:40:28.000Z

2022-03-11T03:03:05.000Z

test/__init__.py

tchar/ulauncher-calculate-anything

ee0903174c8b87cd1f7c3b6c1acef10702547507

[ "MIT" ]

28

2021-07-09T22:36:09.000Z

2022-03-28T08:54:15.000Z

test/__init__.py

tchar/ulauncher-calculate-anything

ee0903174c8b87cd1f7c3b6c1acef10702547507

[ "MIT" ]

3

2021-07-12T04:52:20.000Z

2022-03-03T20:08:11.000Z

import pytest pytest.register_assert_rewrite('test.tutils')

15.25

45

0.836066

8

61

6.125

0.875

0

0.065574

61

3

46

20.333333

0.859649

0

0.180328

0

0.5

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

270cfa65f40e750b1448f8b2bc73742aa159b8e3

461

py

Python

tests/expectations/cat-hs-x-mr-tbl-stddev.py

Crunch-io/crunch-cube

80986d5b2106c774f05176fb6c6a5ea0d840f09d

[ "MIT" ]

3

2021-01-22T20:42:31.000Z

2021-06-02T17:53:19.000Z

tests/expectations/cat-hs-x-mr-tbl-stddev.py

Crunch-io/crunch-cube

80986d5b2106c774f05176fb6c6a5ea0d840f09d

[ "MIT" ]

331

2017-11-13T22:41:56.000Z

2021-12-02T21:59:43.000Z

tests/expectations/cat-hs-x-mr-tbl-stddev.py

Crunch-io/crunch-cube

80986d5b2106c774f05176fb6c6a5ea0d840f09d

[ "MIT" ]

1

2021-02-19T02:49:00.000Z

[ [0.26982777, 0.20175242, 0.10614473, 0.17290444, 0.22056401], [0.18268971, 0.2363915, 0.26958793, 0.29282782, 0.36225248], [0.31735855, 0.30254544, 0.28661105, 0.33136132, 0.40489719], [0.0, 0.0, 0.0, 0.0, 0.0], [0.15203597, 0.32214688, 0.40445594, 0.45318467, 0.44650059], [0.20070068, 0.31904263, 0.40822282, 0.48738756, 0.46520183], [0.0, 0.0, 0.0, 0.0, 0.0], [0.24779961, 0.42250711, 0.49311729, 0.46756128, 0.49151833], ]

41.909091

65

0.626898

80

461

3.6125

0.3875

0.138408

0.186851

0.221453

0.076125

0

0.74677

0.160521

461

10

66

46.1

0

0.2

0

1

0

true

0

null

0

1

0

1

0

1

0

null

0

1

0

5

271de9d901a1ce0f5dbe78a373f15a13445c7720

67

py

Python

src/revisao/03_conversao.py

SamuelPossamai/material_auxilio_conceitos_python

44c15e72f7409441fe0db38288dac782f0cbc94d

[ "MIT" ]

1

2022-02-08T23:39:11.000Z

src/revisao/03_conversao.py

SamuelPossamai/material_auxilio_conceitos_python

44c15e72f7409441fe0db38288dac782f0cbc94d

[ "MIT" ]

null

src/revisao/03_conversao.py

SamuelPossamai/material_auxilio_conceitos_python

44c15e72f7409441fe0db38288dac782f0cbc94d

[ "MIT" ]

null

print(int('3') + 3) print(float('3.2') + 3.5) print(str(3) + '2')

13.4

25

0.507463

14

67

2.428571

0.5

0.117647

0

0.140351

0.149254

67

4

26

16.75

0.45614

0

0.075758

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

2770171350124d7615ac8cce30bb6d8be1c47134

22

py

Python

a10sdk/version.py

deepfield/a10sdk-python

bfaa58099f51f085d5e91652d1d1a3fd5c529d5d

[ "Apache-2.0" ]

16

2015-05-20T07:26:30.000Z

2021-01-23T11:56:57.000Z

a10sdk/version.py

deepfield/a10sdk-python

bfaa58099f51f085d5e91652d1d1a3fd5c529d5d

[ "Apache-2.0" ]

6

2015-03-24T22:07:11.000Z

2017-03-28T21:31:18.000Z

a10sdk/version.py

deepfield/a10sdk-python

bfaa58099f51f085d5e91652d1d1a3fd5c529d5d

[ "Apache-2.0" ]

23

2015-03-29T15:43:01.000Z

2021-06-02T17:12:01.000Z

VERSION = "4.0.1.214"

11

21

0.590909

5

22

2.6

1

0

0.315789

0.136364

22

1

22

0.368421

0

0.409091

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

278d33a8e17757a631b3171ea629a84d1c07e7d1

21

py

Python

plugins/cockroachdb/dbt/adapters/cockroachdb/__version__.py

IS-Josh/dbt

e56c5224685f242822b1bd70d90357334215ce62

[ "Apache-2.0" ]

1

2021-09-09T20:22:43.000Z

plugins/cockroachdb/dbt/adapters/cockroachdb/__version__.py

IS-Josh/dbt

e56c5224685f242822b1bd70d90357334215ce62

[ "Apache-2.0" ]

1

2021-08-14T03:52:23.000Z

plugins/cockroachdb/dbt/adapters/cockroachdb/__version__.py

IS-Josh/dbt

e56c5224685f242822b1bd70d90357334215ce62

[ "Apache-2.0" ]

1

2021-08-14T03:50:50.000Z

version = '0.21.0b1'

10.5

20

0.619048

4

21

3.25

1

0

0.277778

0.142857

21

1

21

0.444444

0

0.380952

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

27905e20c5ce59af9d6042caa00f1bace86d91c8

3,382

py

Python

conexa_sexta.py

afmaster/Open_Conexa_Saude

9fce6df404459e1ed705a0b33e7eff50d1e299bf

[ "MIT" ]

null

conexa_sexta.py

afmaster/Open_Conexa_Saude

9fce6df404459e1ed705a0b33e7eff50d1e299bf

[ "MIT" ]

null

conexa_sexta.py

afmaster/Open_Conexa_Saude

9fce6df404459e1ed705a0b33e7eff50d1e299bf

[ "MIT" ]

null

from selenium import webdriver #from webdriver_manager.chrome import ChromeDriverManager from selenium.webdriver.chrome.options import Options import time, send_mail chrome_options = webdriver.ChromeOptions() chrome_options.add_argument('--headless') chrome_options.add_argument('--no-sandbox') chrome_options.add_argument('--disable-dev-shm-usage') wd = webdriver.Chrome('chromedriver', chrome_options=chrome_options) #wd = webdriver.Chrome(ChromeDriverManager().install()) def navigate_friday(): try: wd.get('https://app.conexasaude.com.br/') time.sleep(8) email = wd.find_element_by_xpath('//*[@id="email"]') email.send_keys('seu@email.com') senha = wd.find_element_by_xpath('//*[@id="senha"]') senha.send_keys('1234SuaSenha') botao_entrar = wd.find_element_by_xpath('//*[@id="submit"]') botao_entrar.click() time.sleep(8) wd.get('https://app.conexasaude.com.br/horarios') time.sleep(8) botao_adicionar_horario = wd.find_element_by_xpath('/html/body/div[8]/div[5]/div/div[4]/div/div/div[2]/button') botao_adicionar_horario.click() time.sleep(3) campo_horario_inicio = wd.find_element_by_xpath('/html/body/div[8]/div[5]/div/div[4]/div/div/div[1]/div/div[1]/div/input') campo_horario_inicio.send_keys('0900') time.sleep(1) campo_horario_termino = wd.find_element_by_xpath('/html/body/div[8]/div[5]/div/div[4]/div/div/div[1]/div/div[2]/div/input') campo_horario_termino.send_keys('1100') time.sleep(1) botao_adicionar_horario = wd.find_element_by_xpath('/html/body/div[8]/div[5]/div/div[4]/div/div/div[2]/button') botao_adicionar_horario.click() time.sleep(3) except: time.sleep(180) try: wd.get('https://app.conexasaude.com.br/') time.sleep(8) email = wd.find_element_by_xpath('//*[@id="email"]') email.send_keys('andre@franciscatto.com') senha = wd.find_element_by_xpath('//*[@id="senha"]') senha.send_keys('1234@Unimed') botao_entrar = wd.find_element_by_xpath('//*[@id="submit"]') botao_entrar.click() time.sleep(8) wd.get('https://app.conexasaude.com.br/horarios') time.sleep(8) botao_adicionar_horario = wd.find_element_by_xpath( '/html/body/div[8]/div[5]/div/div[4]/div/div/div[2]/button') botao_adicionar_horario.click() time.sleep(3) campo_horario_inicio = wd.find_element_by_xpath( '/html/body/div[8]/div[5]/div/div[4]/div/div/div[1]/div/div[1]/div/input') campo_horario_inicio.send_keys('0900') time.sleep(1) campo_horario_termino = wd.find_element_by_xpath( '/html/body/div[8]/div[5]/div/div[4]/div/div/div[1]/div/div[2]/div/input') campo_horario_termino.send_keys('1100') time.sleep(1) botao_adicionar_horario = wd.find_element_by_xpath( '/html/body/div[8]/div[5]/div/div[4]/div/div/div[2]/button') botao_adicionar_horario.click() time.sleep(3) except Exception as err: send_mail.sendmail(err, 'afmaster@gmail.com')

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null

0

5

27c37cfb35c5302fe59b8290a3f371602f82175e

31

py

Python

object_detector/src/object_detector/__init__.py

Ajapaik/ml-2021-ajapaik

31e318f10329405237a5773a1d963b3ab867fa02

[ "Apache-2.0" ]

null

object_detector/src/object_detector/__init__.py

Ajapaik/ml-2021-ajapaik

31e318f10329405237a5773a1d963b3ab867fa02

[ "Apache-2.0" ]

9

2021-11-12T16:54:24.000Z

2021-12-12T14:13:49.000Z

object_detector/src/object_detector/__init__.py

iharsuvorau/ml-2021-ajapaik

31e318f10329405237a5773a1d963b3ab867fa02

[ "Apache-2.0" ]

1

2022-02-24T21:23:06.000Z

from .object_detector import *

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null

0

1

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1

0

5

27e50986a34cacadb6fcafbc81a3c2600345db0c

175

py

Python

nodes/__init__.py

brianGit78/EightSleepPoly

47d6941788dfb5e1c1fa02ada0ab1cf6b80df852

[ "MIT" ]

6

2017-12-12T00:52:38.000Z

2022-02-20T23:32:02.000Z

nodes/__init__.py

brianGit78/EightSleepPoly

47d6941788dfb5e1c1fa02ada0ab1cf6b80df852

[ "MIT" ]

6

2018-08-25T04:05:48.000Z

2020-09-08T05:03:51.000Z

nodes/__init__.py

brianGit78/EightSleepPoly

47d6941788dfb5e1c1fa02ada0ab1cf6b80df852

[ "MIT" ]

10

2017-12-18T19:09:24.000Z

2020-09-03T23:04:18.000Z

""" Node classes used by the Wireless Sensor Tags Node Server. """ from .TemplateNode import TemplateNode from .TemplateController import TemplateController

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true

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null

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1

0

1

0

5

7e114e8332651b47e93ba239b7c48b1bce8f9e0e

105

py

Python

gui/communication/__init__.py

a-bombarda/mvm-gui

e00c3fe39cf25c6fb2d2725891610da8885d1d76

[ "MIT" ]

2

2020-04-13T19:22:45.000Z

2020-04-14T17:17:12.000Z

gui/communication/__init__.py

a-bombarda/mvm-gui

e00c3fe39cf25c6fb2d2725891610da8885d1d76

[ "MIT" ]

null

gui/communication/__init__.py

a-bombarda/mvm-gui

e00c3fe39cf25c6fb2d2725891610da8885d1d76

[ "MIT" ]

null

""" Init file for ESP32Serial and ESP32Alarms """ from .esp32alarm import * from .esp32serial import *

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105

6.416667

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7

42

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1

0

true

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1

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1

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1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

fd676da64f575686496b6c02588be79a1bc6054e

16

py

Python

pybasler/__init__.py

bolirev/PyPylon

9610343e9ffdfb3001a88b995ae5339bfe95e8b8

[ "BSD-3-Clause" ]

null

pybasler/__init__.py

bolirev/PyPylon

9610343e9ffdfb3001a88b995ae5339bfe95e8b8

[ "BSD-3-Clause" ]

null

pybasler/__init__.py

bolirev/PyPylon

9610343e9ffdfb3001a88b995ae5339bfe95e8b8

[ "BSD-3-Clause" ]

null

# init pybasler

8

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1

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null

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1

0

1

0

null

0

1

0

5

fd72a353f5a9cfa5c949e123861ea1fdd360c784

148

py

Python

run.py

zultron/catkin_lint

7076a3626f5673e58c519346fa52cc78e759d100

[ "BSD-3-Clause" ]

null

run.py

zultron/catkin_lint

7076a3626f5673e58c519346fa52cc78e759d100

[ "BSD-3-Clause" ]

null

run.py

zultron/catkin_lint

7076a3626f5673e58c519346fa52cc78e759d100

[ "BSD-3-Clause" ]

null

#!/usr/bin/python import sys import os sys.path.insert(0, os.path.join(os.path.dirname(__file__), "src")) from catkin_lint.main import main main()

18.5

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148

4.038462

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0

0.007463

0.094595

148

7

67

21.142857

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null

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1

0

1

0

1

0

5

fd9ccfec2663e322d11680a1a40304e9a3f9a878

40

py

Python

ozpricechecker/ozpricechecker/__init__.py

ericziethen/oz-price-checker

812ae4a468cd5209af348648e4b475cc6965643b

[ "MIT" ]

2

2019-09-30T00:12:59.000Z

2020-12-21T22:23:11.000Z

ozpricechecker/ozpricechecker/__init__.py

ericziethen/oz-price-checker

812ae4a468cd5209af348648e4b475cc6965643b

[ "MIT" ]

79

2019-09-30T00:04:20.000Z

2021-12-13T20:38:07.000Z

ozpricechecker/ozpricechecker/__init__.py

ericziethen/oz-price-checker

812ae4a468cd5209af348648e4b475cc6965643b

[ "MIT" ]

null

"""Pricechecker main config package."""

20

39

0.725

4

40

7.25

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0

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40

1

40

0.805556

0.825

0

null

0

null

0

null

0

null

1

null

true

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null

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null

0

1

0

1

0

null

0

1

0

5

fd9ee5321652a3118c8a8d3aae46719fb15ecbae

152

py

Python

problems/excel-sheet-column-number/solution-1.py

MleMoe/LeetCode-1

14f275ba3c8079b820808da17c4952fcf9c8253c

[ "MIT" ]

2

2021-03-25T01:58:55.000Z

2021-08-06T12:47:13.000Z

problems/excel-sheet-column-number/solution-1.py

MleMoe/LeetCode-1

14f275ba3c8079b820808da17c4952fcf9c8253c

[ "MIT" ]

3

2019-08-27T13:25:42.000Z

2021-08-28T17:49:34.000Z

problems/excel-sheet-column-number/solution-1.py

MleMoe/LeetCode-1

14f275ba3c8079b820808da17c4952fcf9c8253c

[ "MIT" ]

1

2021-08-14T08:49:39.000Z

class Solution: def titleToNumber(self, s: str) -> int: return sum((ord(s[i]) - ord('A') + 1)*(26**(len(s) - i - 1)) for i in range(len(s)))

50.666667

92

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0.04878

0

0.033613

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152

3

92

50.666667

0.655462

0

0.006536

0

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0.333333

false

0

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1

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null

0

1

0

null

0

1

0

1

0

5

fdbec67000e28f4ed07fd8e5fc93a10a41c91223

187

py

Python

python-is-easy/assignments/functions/main.py

eDyablo/pirple

08910c7574203f685a0971cba61a54166d805a1c

[ "MIT" ]

null

python-is-easy/assignments/functions/main.py

eDyablo/pirple

08910c7574203f685a0971cba61a54166d805a1c

[ "MIT" ]

null

python-is-easy/assignments/functions/main.py

eDyablo/pirple

08910c7574203f685a0971cba61a54166d805a1c

[ "MIT" ]

null

def artist(): return "The Hardkiss" def genre(): return "Pop" def year(): return 2014 def restrictedContent(): return False def forGeneralAudiences(): return True

12.466667

26

0.657754

21

187

5.857143

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0.028169

0.240642

187

14

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0

1

0.5

true

0

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1

0

1

0

null

0

null

0

1

0

1

0

5

fdbf0955c59d7c44f6818a91b193ad1d8b279e86

45

py

Python

tests/integration/test_errors.py

MarSoft/aiohttp_json_api

1d4864a0f73e4df33278e16d499642a60fa89aaa

[ "MIT" ]

19

2017-08-10T07:58:33.000Z

2022-02-13T01:30:10.000Z

tests/integration/test_errors.py

MarSoft/aiohttp_json_api

1d4864a0f73e4df33278e16d499642a60fa89aaa

[ "MIT" ]

218

2017-06-14T22:41:25.000Z

2021-07-19T02:57:58.000Z

tests/integration/test_errors.py

MarSoft/aiohttp_json_api

1d4864a0f73e4df33278e16d499642a60fa89aaa

[ "MIT" ]

5

2017-08-23T08:17:05.000Z

2022-03-27T12:27:19.000Z

class TestErrors: """Errors""" pass

9

17

0.555556

4

45

6.25

1

0

0.288889

45

4

18

11.25

0.78125

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0

1

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true

0.5

0

0.5

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1

0

null

0

1

0

null

0

1

0

5

fdeb738ad2593ddee90c7cb77fc9224a38654923

373

bzl

Python

bazel/pkg_path_name.bzl

leonard951/nsync_aarch64

6ce032fd5f1910b7bdfcd521062d55898411558c

[ "Apache-2.0" ]

null

bazel/pkg_path_name.bzl

leonard951/nsync_aarch64

6ce032fd5f1910b7bdfcd521062d55898411558c

[ "Apache-2.0" ]

2

2017-11-10T15:56:47.000Z

2017-11-11T14:28:51.000Z

bazel/pkg_path_name.bzl

leonard951/nsync_aarch64

6ce032fd5f1910b7bdfcd521062d55898411558c

[ "Apache-2.0" ]

2

2020-10-01T04:12:08.000Z

2021-07-01T07:46:13.000Z

# -*- mode: python; -*- # Return the pathname of the calling package. # (This is used to recover the directory name to pass to cc -I<dir>, when # choosing from among alternative header files for different platforms.) def pkg_path_name(): return "./" + Label(REPOSITORY_NAME + "//" + PACKAGE_NAME + ":nsync").workspace_root + "/" + PACKAGE_NAME

41.444444

73

0.654155

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373

4.958333

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0.092437

0

0.22252

373

8

74

46.625

0.82069

0.557641

0

0.06875

0

1

0.333333

true

0

0.333333

0.666667

0

null

0

1

0

null

0

1

0

1

0

5

8bf5c8654dff2e3080dff226169635166d989739

217

py

Python

neo3/wallet/__init__.py

CityOfZion/neo-mamba

36973e8f9318ec096e2bc5ffbc21683407ab2032

[ "MIT" ]

12

2020-08-27T19:56:02.000Z

2022-03-08T03:23:43.000Z

neo3/wallet/__init__.py

CityOfZion/neo-mamba

36973e8f9318ec096e2bc5ffbc21683407ab2032

[ "MIT" ]

101

2020-07-24T08:23:00.000Z

2021-11-17T13:37:45.000Z

neo3/wallet/__init__.py

CityOfZion/neo-mamba

36973e8f9318ec096e2bc5ffbc21683407ab2032

[ "MIT" ]

11

2021-02-11T22:24:13.000Z

2021-11-18T06:45:03.000Z

from .account import Account from .scrypt_parameters import ScryptParameters from .utils import calculate_system_fee, calculate_network_fee, add_network_fee, add_system_fee from .wallet import Wallet, MultiSigContext

43.4

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0.870968

29

217

6.206897

0.482759

0.1

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0

0.092166

217

4

96

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0

1

0

true

0

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0

1

0

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0

null

0

1

0

1

0

1

0

5

e347377eea53d91dfd6758d9abeae42b26e3b5da

49

py

Python

build/lib/annotation_utils/coco/dataset_specific/__init__.py

HienDT27/annotation_utils

1f4e95f4cfa08de5bbab20f90a6a75fba66a69b9

[ "MIT" ]

13

2020-01-28T04:45:22.000Z

2022-03-10T03:35:49.000Z

build/lib/annotation_utils/coco/dataset_specific/__init__.py

HienDT27/annotation_utils

1f4e95f4cfa08de5bbab20f90a6a75fba66a69b9

[ "MIT" ]

4

2020-02-14T08:56:03.000Z

2021-05-21T10:38:30.000Z

build/lib/annotation_utils/coco/dataset_specific/__init__.py

HienDT27/annotation_utils

1f4e95f4cfa08de5bbab20f90a6a75fba66a69b9

[ "MIT" ]

7

2020-04-10T07:56:25.000Z

2021-12-17T11:19:23.000Z

from .measure_dataset import Measure_COCO_Dataset

49

0.918367

7

49

6

0.714286

0

0.061224

49

1

49

0.913043

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

e36eb3fbf500fbd31d0d993b72b76a08b9ea0b33

86

py

Python

copper/ui/panels/scene_view_panel/scene_manager/__init__.py

cinepost/Copperfield_FX

1900b506d0a407a3fb5774ab129b984a547ee0b5

[ "Unlicense" ]

6

2016-07-28T13:59:34.000Z

2021-12-28T05:44:15.000Z

copper/ui/panels/scene_view_panel/scene_manager/__init__.py

cinepost/Copperfield_FX

1900b506d0a407a3fb5774ab129b984a547ee0b5

[ "Unlicense" ]

5

2016-06-30T10:19:25.000Z

2022-03-11T23:19:01.000Z

copper/ui/panels/scene_view_panel/scene_manager/__init__.py

cinepost/Copperfield_FX

1900b506d0a407a3fb5774ab129b984a547ee0b5

[ "Unlicense" ]

3

2019-03-18T05:17:10.000Z

2020-02-14T06:56:40.000Z

from .ogl_scene_manager import OGL_Scene_Manager scene_manager = OGL_Scene_Manager()

21.5

48

0.860465

13

86

5.153846

0.384615

0.716418

0.671642

0

0.093023

86

3

49

28.666667

0.858974

0

1

0

false

0

0.5

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0

null

1

0

1

0

null

0

1

0

5

8b6ed4624e209718ae39c3a8a06892bae3487201

206

py

Python

webui/db_insert.py

jphacks/NG_1703

0c0bec03d71f467f90d8fbe01b31ed4341f3e1a5

[ "MIT" ]

1

2017-10-31T10:18:08.000Z

webui/db_insert.py

jphacks/NG_1703

0c0bec03d71f467f90d8fbe01b31ed4341f3e1a5

[ "MIT" ]

null

webui/db_insert.py

jphacks/NG_1703

0c0bec03d71f467f90d8fbe01b31ed4341f3e1a5

[ "MIT" ]

null

from flaski.database import init_db from flaski.database import db_session from flaski.models import WikiContent c1 = WikiContent("VisitorsBell", "VisitorsBell.gif") db_session.add(c1) db_session.commit()

25.75

52

0.820388

29

206

5.689655

0.482759

0.181818

0.218182

0.290909

0

0.010695

0.092233

206

7

53

29.428571

0.871658

0

0.135922

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

5

8ba61e9a7a3a8baf23680692e720866976e6e918

39

py

Python

spacetimeformer/lstm_model/__init__.py

bernhein/spacetimeformer

de252b68085943d979606fe69e177ac2a14586e7

[ "MIT" ]

null

spacetimeformer/lstm_model/__init__.py

bernhein/spacetimeformer

de252b68085943d979606fe69e177ac2a14586e7

[ "MIT" ]

null

spacetimeformer/lstm_model/__init__.py

bernhein/spacetimeformer

de252b68085943d979606fe69e177ac2a14586e7

[ "MIT" ]

null

from .lstm_model import LSTM_Predictor

19.5

38

0.871795

6

39

5.333333

0.833333

0

0.102564

39

1

39

0.914286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

8bc892d58583a7a4d06b47d67321f2a037c755fb

33

py

Python

tree_walk_with_alternates/__init__.py

mwisslead/tree_walk_with_alternates

44a05a3d66d1cbb0767c2cc9c9de9589065547fc

[ "MIT" ]

null

tree_walk_with_alternates/__init__.py

mwisslead/tree_walk_with_alternates

44a05a3d66d1cbb0767c2cc9c9de9589065547fc

[ "MIT" ]

null

tree_walk_with_alternates/__init__.py

mwisslead/tree_walk_with_alternates

44a05a3d66d1cbb0767c2cc9c9de9589065547fc

[ "MIT" ]

null

from .treewalk import TreeWalker

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Python

tests/test_t.py

mike-dobbles/community-tranformers

7f1139e30c5ac435c0d972c55c39aed2c6a58a79

[ "MIT" ]

null

tests/test_t.py

mike-dobbles/community-tranformers

7f1139e30c5ac435c0d972c55c39aed2c6a58a79

[ "MIT" ]

null

tests/test_t.py

mike-dobbles/community-tranformers

7f1139e30c5ac435c0d972c55c39aed2c6a58a79

[ "MIT" ]

null

import pytest import transformers.t as ct from pyspark.sql import SparkSession from mlflow.spark import save_model, load_model from pyspark.ml import Pipeline import os import pandas as pd from nltk.util import ngrams # This runs before the tests and creates objects that can be used by the tests @pytest.fixture def simple_test_dataframe(): """This is a simple dataframe for test use""" # get a reference to spark spark = SparkSession.builder.getOrCreate() # create a test data frame pdf = pd.DataFrame(columns=['text'], data=["This sentence ends with br and will prevent nltk sentence tokenization<br>This sentence ends normally. As does this one", "Some sentences run together.The previous was an example", "This is a normal first sentence. This is a normal second sentence." ]) return spark.createDataFrame(pdf) @pytest.fixture def numbers_dataframe(): """This is a dataframe filled with text of numbers for test use""" # get a reference to spark spark = SparkSession.builder.getOrCreate() # create a test data frame pdf = pd.DataFrame(columns=['text'], data=["onethousand two three four five", "six seven eight nine eight-hundred-ninetyfive" ]) return spark.createDataFrame(pdf) def test__NLTKWordPunctTokenizer(simple_test_dataframe): # Create the transformer transformer = ct.NLTKWordPunctTokenizer(inputCol="text", outputCol="words", stopwords=['are', 'I']) # Create a pipeline from the transformer pipeline = Pipeline(stages=[transformer]) # fit the test data (which also builds the pipeline) model = pipeline.fit(simple_test_dataframe) # Test the pipeline df_original_transformed = model.transform(simple_test_dataframe) # Delete any previously save model (if it exists) # (There may be a more elegant way to do this) if os.path.exists("unit_test_model"): os.system("rm -rf unit_test_model") # Log the model and performance save_model(model, "unit_test_model") retrieved_model = load_model("unit_test_model") df_retreived_transformed = retrieved_model.transform(simple_test_dataframe) # Assert the retrieved model give the same results as the saved model rows_in_common = df_original_transformed.intersect(df_retreived_transformed).count() assert (df_original_transformed.count() == rows_in_common) # Print results for visual inspection print("\n") print("test__NLTKWordPunctTokenizer: The following should show sentences broken into words") df_retreived_transformed.show(truncate=False) # If we make it this far without crashing we pass (plus I'm visually reviewing results) assert True def test__RegexSubstituter(simple_test_dataframe): # Create the transformer regexMatchers= ['(?<=[a-zA-Z])\.(?=[A-Z])', '<BR>', '<br>'] substitutions= ['. ', '. ', '. '] transformer = ct.RegexSubstituter(inputCol="text", outputCol="regexcorrected", regexMatchers=regexMatchers, substitutions=substitutions) # Create a pipeline from the transformer pipeline = Pipeline(stages=[transformer]) # fit the test data (which also builds the pipeline) model = pipeline.fit(simple_test_dataframe) # Test the pipeline df_original_transformed = model.transform(simple_test_dataframe) # Delete any previously save model (if it exists) # (There may be a more elegant way to do this) if os.path.exists("unit_test_model"): os.system("rm -rf unit_test_model") # Log the model and performance save_model(model, "unit_test_model") retrieved_model = load_model("unit_test_model") df_retreived_transformed = retrieved_model.transform(simple_test_dataframe) # Assert the retrieved model give the same results as the saved model rows_in_common = df_original_transformed.intersect(df_retreived_transformed).count() assert (df_original_transformed.count() == rows_in_common) # Print results for visual inspection print("\n") print("test__RegexSubstituter: The following should show sentences broken into words") df_retreived_transformed.show(truncate=False) # If we make it this far without crashing we pass (plus I'm visually reviewing results) assert True def test__TokenSubstituter(numbers_dataframe): # Create the transformer tokenizer = ct.NLTKWordPunctTokenizer(inputCol="text", outputCol="tokens") # Create the transformer tokenMatchers= ['two', 'four', 'nine'] substitutions= ['two-sub', 'four-sub', 'nine-sub'] toksub = ct.TokenSubstituter(inputCol="tokens", outputCol="swapped_tokens", tokenMatchers=tokenMatchers, substitutions=substitutions) # Create a pipeline from the transformer pipeline = Pipeline(stages=[tokenizer, toksub]) # fit the test data (which also builds the pipeline) model = pipeline.fit(numbers_dataframe) # Test the pipeline df_original_transformed = model.transform(numbers_dataframe) # Delete any previously save model (if it exists) # (There may be a more elegant way to do this) if os.path.exists("unit_test_model"): os.system("rm -rf unit_test_model") # Log the model and performance save_model(model, "unit_test_model") retrieved_model = load_model("unit_test_model") df_retreived_transformed = retrieved_model.transform(numbers_dataframe) # Assert the retrieved model give the same results as the saved model rows_in_common = df_original_transformed.intersect(df_retreived_transformed).count() assert (df_original_transformed.count() == rows_in_common) # Print results for visual inspection print("\n") print("test__TokenSubstituter: two, four, and nine should be substituted") df_retreived_transformed.show(truncate=False) # If we make it this far without crashing we pass (plus I'm visually reviewing results) assert True def test__SentenceSplitter(simple_test_dataframe): # Create the transformer transformer = ct.SentenceSplitter(inputCol="text", outputCol="sentences") # Create a pipeline from the transformer pipeline = Pipeline(stages=[transformer]) # fit the test data (which also builds the pipeline) model = pipeline.fit(simple_test_dataframe) # Test the pipeline df_original_transformed = model.transform(simple_test_dataframe) # Delete any previously save model (if it exists) # (There may be a more elegant way to do this) if os.path.exists("unit_test_model"): os.system("rm -rf unit_test_model") # Log the model and performance save_model(model, "unit_test_model") retrieved_model = load_model("unit_test_model") df_retreived_transformed = retrieved_model.transform(simple_test_dataframe) # Assert the retrieved model give the same results as the saved model rows_in_common = df_original_transformed.intersect(df_retreived_transformed).count() assert (df_original_transformed.count() == rows_in_common) # Print results for visual inspection print("\n") print("test__SentenceSplitter: The following should show text broken into sentences") df_retreived_transformed.show(truncate=False) # If we make it this far without crashing we pass (plus I'm visually reviewing results) assert True def test__LevenshteinSubstituter(numbers_dataframe): # Create the transformer tokenizer = ct.NLTKWordPunctTokenizer(inputCol="text", outputCol="tokens") # Create the transformer tokenMatchers= ['two1', 'four2', 'nineee'] toksub = ct.LevenshteinSubstituter(inputCol="tokens", outputCol="swapped_tokens", tokenMatchers=tokenMatchers, levenshteinThresh=1) # Create a pipeline from the transformer pipeline = Pipeline(stages=[tokenizer, toksub]) # fit the test data (which also builds the pipeline) model = pipeline.fit(numbers_dataframe) # Test the pipeline df_original_transformed = model.transform(numbers_dataframe) # Delete any previously save model (if it exists) # (There may be a more elegant way to do this) if os.path.exists("unit_test_model"): os.system("rm -rf unit_test_model") # Log the model and performance save_model(model, "unit_test_model") retrieved_model = load_model("unit_test_model") df_retreived_transformed = retrieved_model.transform(numbers_dataframe) # Assert the retrieved model give the same results as the saved model rows_in_common = df_original_transformed.intersect(df_retreived_transformed).count() assert (df_original_transformed.count() == rows_in_common) # Print results for visual inspection print("\n") print("test__LevenshteinSubstituter: two and four shold be substituted and nine should not") df_retreived_transformed.show(truncate=False) # If we make it this far without crashing we pass (plus I'm visually reviewing results) assert True def test__GoWordFilter(numbers_dataframe): # Create the transformer tokenizer = ct.NLTKWordPunctTokenizer(inputCol="text", outputCol="tokens") # Create the transformer goWords= ['two','four','eight','nine'] toksub = ct.GoWordFilter(inputCol="tokens", outputCol="go_word_filtered_tokens", goWords=goWords) # Create a pipeline from the transformer pipeline = Pipeline(stages=[tokenizer, toksub]) # fit the test data (which also builds the pipeline) model = pipeline.fit(numbers_dataframe) # Test the pipeline df_original_transformed = model.transform(numbers_dataframe) # Delete any previously save model (if it exists) # (There may be a more elegant way to do this) if os.path.exists("unit_test_model"): os.system("rm -rf unit_test_model") # Log the model and performance save_model(model, "unit_test_model") retrieved_model = load_model("unit_test_model") df_retreived_transformed = retrieved_model.transform(numbers_dataframe) # Assert the retrieved model give the same results as the saved model rows_in_common = df_original_transformed.intersect(df_retreived_transformed).count() assert (df_original_transformed.count() == rows_in_common) # Print results for visual inspection print("\n") print("test__GoWordFilter: two, four, eight, nine should be the only tokesn left") df_retreived_transformed.show(truncate=False) # If we make it this far without crashing we pass (plus I'm visually reviewing results) assert True def test__NgramSet(numbers_dataframe): # Create the transformer tokenizer = ct.NLTKWordPunctTokenizer(inputCol="text", outputCol="tokens") # Filter to go words goWords= ['two','three','four','five'] gofilt = ct.GoWordFilter(inputCol="tokens", outputCol="go_word_filtered_tokens", goWords=goWords) # Create the transformer ngrams = ct.NgramSet(inputCol="go_word_filtered_tokens", outputCol="ngram_set", maxN=5) # Create a pipeline from the transformer pipeline = Pipeline(stages=[tokenizer, gofilt, ngrams]) # fit the test data (which also builds the pipeline) model = pipeline.fit(numbers_dataframe) # Test the pipeline df_original_transformed = model.transform(numbers_dataframe) # Delete any previously save model (if it exists) # (There may be a more elegant way to do this) if os.path.exists("unit_test_model"): os.system("rm -rf unit_test_model") # Log the model and performance save_model(model, "unit_test_model") retrieved_model = load_model("unit_test_model") df_retreived_transformed = retrieved_model.transform(numbers_dataframe) # Assert the retrieved model give the same results as the saved model rows_in_common = df_original_transformed.intersect(df_retreived_transformed).count() assert (df_original_transformed.count() == rows_in_common) # Print results for visual inspection print("\n") print("test__NgramSet: should see a set of 1-5 ngram set") df_retreived_transformed.show(truncate=False) # If we make it this far without crashing we pass (plus I'm visually reviewing results) assert True def test__ngram_udf(): maxN = 5 original_token_array =['two', 'three', 'four', 'five'] def f(original_token_array): returned_ngram_array = [] # Use the nltk utility to create a range of ngrams adjusted_max = min(len(original_token_array),maxN) for n in range(1,min(len(original_token_array),maxN)): n_grams = ngrams(original_token_array, n) returned_ngram_array.extend([' '.join(grams) for grams in n_grams]) return returned_ngram_array ngram_array = f(original_token_array) print(ngram_array) assert True

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py

Python

__init__.py

nickm324/sensor.rpi_power

3e0d6f502f8387e8cbcc0f8e7c0ee22241df84fe

[ "MIT" ]

297

2018-02-12T09:36:12.000Z

2022-03-25T22:14:06.000Z

__init__.py

nickm324/sensor.rpi_power

3e0d6f502f8387e8cbcc0f8e7c0ee22241df84fe

[ "MIT" ]

44

2018-02-22T06:21:09.000Z

2021-08-07T14:56:21.000Z

__init__.py

nickm324/sensor.rpi_power

3e0d6f502f8387e8cbcc0f8e7c0ee22241df84fe

[ "MIT" ]

64

2018-10-31T13:39:20.000Z

2022-03-29T10:55:30.000Z

"""Raspberry Pi Power Supply Checker"""

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py

Python

flexget/ui/plugins/schema/__init__.py

tvcsantos/Flexget

e08ce2957dd4f0668911d1e56347369939e4d0a5

[ "MIT" ]

null

flexget/ui/plugins/schema/__init__.py

tvcsantos/Flexget

e08ce2957dd4f0668911d1e56347369939e4d0a5

[ "MIT" ]

1

2018-06-09T18:03:35.000Z

flexget/ui/plugins/schema/__init__.py

tvcsantos/Flexget

e08ce2957dd4f0668911d1e56347369939e4d0a5

[ "MIT" ]

null

from __future__ import unicode_literals, division, absolute_import from .schema import *

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5

47f55369129093e0df6ab9a10d8bb4018500eee3

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py

Python

setup.py

caesar0301/FlexVersion

c9414d5ed4185a96a8c69e39fdcd391f8ae59f5f

[ "Apache-2.0" ]

5

2018-05-19T07:48:05.000Z

2020-05-23T19:41:42.000Z

setup.py

caesar0301/FlexVersion

c9414d5ed4185a96a8c69e39fdcd391f8ae59f5f

[ "Apache-2.0" ]

null

setup.py

caesar0301/FlexVersion

c9414d5ed4185a96a8c69e39fdcd391f8ae59f5f

[ "Apache-2.0" ]

2

2018-09-04T03:40:24.000Z

2019-10-07T13:32:33.000Z

from setuptools import setup setup( name="flex_version", version='1.2.3', url='https://github.com/caesar0301/FlexVersion', author='Xiaming Chen', author_email='chenxm35@gmail.com', description='A cute Python library to manipulate version stuff.', license="Apache License, Version 2.0", packages=['flex_version'], keywords=['utility', 'versioning'], classifiers=[ 'Development Status :: 4 - Beta', 'Environment :: Console', 'Intended Audience :: Developers', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: Software Development :: Libraries :: Python Modules', ], )

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9a0eb4f3502f58cf7d579a6ab07c5192e8446a23

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py

Python

tests/unit/test_doubly_linked_list.py

ali92hm/data-structure-implementation

c0b0bd7d711db3085312565dc509c6d8efad03fa

[ "MIT" ]

null

tests/unit/test_doubly_linked_list.py

ali92hm/data-structure-implementation

c0b0bd7d711db3085312565dc509c6d8efad03fa

[ "MIT" ]

null

tests/unit/test_doubly_linked_list.py

ali92hm/data-structure-implementation

c0b0bd7d711db3085312565dc509c6d8efad03fa

[ "MIT" ]

null

class TestDoublyLinkedList: pass

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py

Python

ekorpkit/utils/batch/__init__.py

entelecheia/eKorpKit

9521ae4c4749419fa2b088d1b9e518e5927b7cb8

[ "CC-BY-4.0" ]

4

2022-02-26T10:54:16.000Z

2022-02-26T11:01:56.000Z

ekorpkit/utils/batch/__init__.py

entelecheia/eKorpKit

9521ae4c4749419fa2b088d1b9e518e5927b7cb8

[ "CC-BY-4.0" ]

1

2022-03-25T06:37:12.000Z

2022-03-25T06:45:53.000Z

ekorpkit/utils/batch/__init__.py

entelecheia/eKorpKit

9521ae4c4749419fa2b088d1b9e518e5927b7cb8

[ "CC-BY-4.0" ]

null

from .batcher import Batcher from .apply import decorator_apply from .apply_batch import decorator_apply_batch

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py

Python

zntrack/dvc/__init__.py

zincware/ZnTrack

7767e133720a75ccb289a5b19d7960584e9dc74f

[ "Apache-2.0" ]

16

2021-12-08T15:35:22.000Z

2022-03-29T09:43:31.000Z

zntrack/dvc/__init__.py

zincware/ZnTrack

7767e133720a75ccb289a5b19d7960584e9dc74f

[ "Apache-2.0" ]

108

2021-10-20T08:00:57.000Z

2022-03-30T14:52:30.000Z

zntrack/dvc/__init__.py

zincware/ZnTrack

7767e133720a75ccb289a5b19d7960584e9dc74f

[ "Apache-2.0" ]

2

2021-11-18T07:41:52.000Z

2022-03-17T15:39:56.000Z

"""Collection of DVC options Based on ZnTrackOption python descriptors this gives access to them being used to define e.g. dependencies Examples -------- >>> from zntrack import Node, dvc >>> class HelloWorld(Node) >>> vars = dvc.params() """ import logging from zntrack import utils from zntrack.core.zntrackoption import ZnTrackOption from zntrack.dvc.custom_base import PlotsModifyOption log = logging.getLogger(__name__) # All available DVC cmd options + results # detailed explanations on https://dvc.org/doc/command-reference/run#options class params(ZnTrackOption): """Identify DVC option See https://dvc.org/doc/command-reference/run#options for more information on the available options """ zn_type = utils.ZnTypes.DVC file = utils.Files.zntrack class deps(ZnTrackOption): """Identify DVC option See https://dvc.org/doc/command-reference/run#options for more information on the available options """ zn_type = utils.ZnTypes.DEPS file = utils.Files.zntrack def __get__(self, instance, owner=None): """Use load_node_dependency before returning the value""" if instance is None: return self value = super().__get__(instance, owner) value = utils.utils.load_node_dependency(value, log_warning=True) setattr(instance, self.name, value) return value class outs(ZnTrackOption): """Identify DVC option See https://dvc.org/doc/command-reference/run#options for more information on the available options """ zn_type = utils.ZnTypes.DVC file = utils.Files.zntrack class checkpoints(ZnTrackOption): """Identify DVC option See https://dvc.org/doc/command-reference/run#options for more information on the available options """ zn_type = utils.ZnTypes.DVC file = utils.Files.zntrack class outs_no_cache(ZnTrackOption): """Identify DVC option See https://dvc.org/doc/command-reference/run#options for more information on the available options """ zn_type = utils.ZnTypes.DVC file = utils.Files.zntrack class outs_persistent(ZnTrackOption): """Identify DVC option See https://dvc.org/doc/command-reference/run#options for more information on the available options """ zn_type = utils.ZnTypes.DVC file = utils.Files.zntrack class metrics(ZnTrackOption): """Identify DVC option See https://dvc.org/doc/command-reference/run#options for more information on the available options """ zn_type = utils.ZnTypes.DVC file = utils.Files.zntrack class metrics_no_cache(ZnTrackOption): """Identify DVC option See https://dvc.org/doc/command-reference/run#options for more information on the available options """ zn_type = utils.ZnTypes.DVC file = utils.Files.zntrack class plots(PlotsModifyOption): """Identify DVC option See https://dvc.org/doc/command-reference/run#options for more information on the available options """ zn_type = utils.ZnTypes.DVC file = utils.Files.zntrack class plots_no_cache(ZnTrackOption): """Identify DVC option See https://dvc.org/doc/command-reference/run#options for more information on the available options """ zn_type = utils.ZnTypes.DVC file = utils.Files.zntrack

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null

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5

9a55e4024caeebf1cc2dd4f353455e5df8571087

214

py

Python

comment.py

teuneboon/PoEFilter

e0c5a2805e6822acc64eabdad024ca1d948bf998

[ "MIT" ]

null

comment.py

teuneboon/PoEFilter

e0c5a2805e6822acc64eabdad024ca1d948bf998

[ "MIT" ]

null

comment.py

teuneboon/PoEFilter

e0c5a2805e6822acc64eabdad024ca1d948bf998

[ "MIT" ]

null

from filter_part import FilterPart class Comment(FilterPart): comment = '' def __init__(self, comment): self.comment = comment def __str__(self): return '# {0}'.format(self.comment)

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9a55fafc23788286e2d0f753bf84993c95944238

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py

Python

cogs/config/__init__.py

AbhishekACST/Obsidion

1474eaf077dae1673ad6d3cf4e3ad133fba145ba

[ "MIT" ]

null

cogs/config/__init__.py

AbhishekACST/Obsidion

1474eaf077dae1673ad6d3cf4e3ad133fba145ba

[ "MIT" ]

null

cogs/config/__init__.py

AbhishekACST/Obsidion

1474eaf077dae1673ad6d3cf4e3ad133fba145ba

[ "MIT" ]

null

from cogs.config.Config import Configurable def setup(bot): bot.add_cog(Configurable(bot))

23.75

43

0.778947

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95

5.214286

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0.115789

95

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44

23.75

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null

0

1

0

1

0

1

0

5

9a589b77b3210fecc3a35f0ab35f1b404364a104

35

py

Python

tests/test_element.py

cgarjun/Pyno

fa2a5d57fb926564f5acc0e6c7310de255bab531

[ "MIT" ]

163

2015-12-29T02:28:04.000Z

2022-02-02T02:18:28.000Z

tests/test_element.py

cgarjun/Pyno

fa2a5d57fb926564f5acc0e6c7310de255bab531

[ "MIT" ]

42

2017-12-19T15:31:54.000Z

2019-09-21T20:14:06.000Z

tests/test_element.py

cgarjun/Pyno

fa2a5d57fb926564f5acc0e6c7310de255bab531

[ "MIT" ]

34

2017-09-23T09:08:56.000Z

2021-09-16T23:49:24.000Z

"""Tests for pyno.element""" pass

8.75

28

0.657143

5

35

4.6

1

0

0.142857

35

3

29

11.666667

0.766667

0.628571

0

1

0

true

1

0

1

0

null

0

1

0

null

0

1

0

5

9a6790b4eef2bcdc1db181918b74aa5df38bb98c

122

py

Python

Hearthstone/test.py

dragonkoko/Free_Time_Projects

2bf3ef8d1f4634fdd0de1b4deb2392ff1c40a734

[ "MIT" ]

null

Hearthstone/test.py

dragonkoko/Free_Time_Projects

2bf3ef8d1f4634fdd0de1b4deb2392ff1c40a734

[ "MIT" ]

null

Hearthstone/test.py

dragonkoko/Free_Time_Projects

2bf3ef8d1f4634fdd0de1b4deb2392ff1c40a734

[ "MIT" ]

null

from CardGenerator import CardGenerator test = CardGenerator() test.generate(2) #4 is how much mana should the card cost

24.4

57

0.795082

18

122

5.388889

0.833333

0.350515

0

0.019231

0.147541

122

4

58

30.5

0.913462

0.319672

0

1

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

1

0

null

0

1

0

5

d001565a065494ddcd8f6a58deaf4acc5b12bd25

56

py

Python

enthought/traits/ui/wx/boolean_editor.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/traits/ui/wx/boolean_editor.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/traits/ui/wx/boolean_editor.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from traitsui.wx.boolean_editor import *

18.666667

40

0.803571

8

56

5.5

1

0

0.125

56

2

41

28

0.897959

0.214286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d016e229d4b39f32719fe96a4b446c2c866cc331

144

py

Python

blogs/admin.py

nuke504/travelblog-project

b40ea96065f491d9323ed917cd239470137b6362

[ "MIT" ]

null

blogs/admin.py

nuke504/travelblog-project

b40ea96065f491d9323ed917cd239470137b6362

[ "MIT" ]

11

2020-06-05T20:24:36.000Z

2022-03-12T00:10:28.000Z

blogs/admin.py

nuke504/travelblog-project

b40ea96065f491d9323ed917cd239470137b6362

[ "MIT" ]

null

from django.contrib import admin from .models import Vote, Blog # Register your models here. admin.site.register(Vote) admin.site.register(Blog)

28.8

32

0.805556

22

144

5.272727

0.545455

0.155172

0.293103

0

0.104167

144

5

33

28.8

0.899225

0.180556

0

1

0

true

0

0.5

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1

0

1

0

null

0

1

0

1

0

5

d02bf35304e5250e7e3672eecedb133533725e46

126

py

Python

src/softframe/misc/__init__.py

pcastanha/frame

f3392e3660742db6beb3b6e1702d7aee6acedf62

[ "BSD-2-Clause" ]

null

src/softframe/misc/__init__.py

pcastanha/frame

f3392e3660742db6beb3b6e1702d7aee6acedf62

[ "BSD-2-Clause" ]

null

src/softframe/misc/__init__.py

pcastanha/frame

f3392e3660742db6beb3b6e1702d7aee6acedf62

[ "BSD-2-Clause" ]

null

from .routines import read_and_convert, classify_paragraphs __all__ = ['read_and_convert', 'classify_paragraphs']

25.2

59

0.753968

14

126

6.071429

0.642857

0.164706

0.329412

0.517647

0.752941

0

0.166667

126

4

60

31.5

0.809524

0

0.277778

0

1

0

false

0

0.333333

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0.333333

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0

1

0

1

0

null

0

1

0

5

d05869b84e9f5fc3d17c9b20fd82895847f208f2

5,820

py

Python

Course_I_Hidden_messages/problem_sets/week1/001_counting_kmer.py

Betaglutamate/Bioinformatics

0935c1306a452a0a3d4f53d8d964489b52a6c18d

[ "MIT" ]

null

Course_I_Hidden_messages/problem_sets/week1/001_counting_kmer.py

Betaglutamate/Bioinformatics

0935c1306a452a0a3d4f53d8d964489b52a6c18d

[ "MIT" ]

null

Course_I_Hidden_messages/problem_sets/week1/001_counting_kmer.py

Betaglutamate/Bioinformatics

0935c1306a452a0a3d4f53d8d964489b52a6c18d

[ "MIT" ]

null

# Here I will attempt to count the occurences of a kmer in a patter def count_kmer(kmer, pattern): num_matches = 0 for num, _ in enumerate(kmer): window = kmer[num: (num+len(pattern))] if window == pattern: num_matches = num_matches + 1 return num_matches count_kmer("ACAACTATGCATACTATCGGGAACTATCCT", "ACTAT") #3 #problem set kmer_to_match = "GGAGGATTCTCCTGAAAAGGATTCAAGCGAGGATTCAAGATATCGCCGTACAGTAGGATTCTAACAGGATTCAGGATTCCTAGACCAAAAGGATTCGACTAGGATTCAGGATTCAGCAAGGATTCAGGATTCAGGATTCTTAGGATTCTGCAGGATTCAGGATTCGAGGATTCTGAGGATTCGCAAGCTCTAGGATTCAGGATTCTTAGGATTCAGGATTCAGAGGATTCAGGATTCAGGATTCGTATGAAAGGATTCCGGAGGATTCCGGGTAGGATTCAGGATTCAAGGATTCAAGGATTCAGGATTCAGGATTCCGAGGATTCAGGATTCGGAGGATTCTTAGGATTCCCAGGATTCACGGGCAGACCTAGGATTCAGGATTCGAAAGGATTCTTGAGGATTCAGGATTCAAAGGATTCCGAGGATTCTAGGATTCGAAGTACCGAGGATTCCCCAGGATTCATGTAGGATTCAGGATTCTAGGATTCGTACGAGGATTCAGGATTCCGTTCTAGGATTCCTTAGGATTCCAGGATTCAGGATTCGGAGGATTCAGAAGGATTCCAGGATTCCTCACAAAATAGGATTCGAGGATTCTAGAGGATTCGCAGGATTCTAAGGATTCATTGTCCAGGATTCTTAAGGATTCAGGATTCAGGATTCAGCCTAGGATTCAGGATTCGGAGGATTCATTCAGGATTCGATCGTGACAGAGGATTCACCAGGATTCTCAGGATTCTAGGATTCAGGATTCGAGGATTCTAGGATTCAAGGATTCAGGATTCGTTATTCACTGGGCAGGATTCAAGGATTCATAGGATTCAGACGCAGGATTCAGGATTCAGGATTCCAGGATTCTGTGAGGATTCATCGAAGGATTCATCCAATAGGATTCCTTTGAGGATTCTAGGATTCGGGCGACTTTAGCAGGATTCGGCCGAAGGATTCAGGATTCATGTTGGTCGCAGGATTCCGCATTTAGTATAGGATTCAGGATTCAGGATTCCGCAAGTTCTGAGGATTCGAGGATTCAGGATTC" pattern = "AGGATTCAG" count_kmer(kmer_to_match, pattern) #OK now I want to find the most frequent Kmer in the dataset def list_of_all_kmer_in_string(input_string, kmer_length): all_kmer = [] for num in range(0, len(input_string) - kmer_length): window = input_string[num: (num+kmer_length)] all_kmer.append(window) kmers_unique = list(set(all_kmer)) #this gives you unique kmers return kmers_unique test_new = list_of_all_kmer_in_string(kmer_to_match, 5) high_match = 0 for kmer in test_new: num_matches = count_kmer(kmer_to_match, kmer) if num_matches > high_match: freq_kmer = kmer high_match = num_matches # note that this is not efficient O^2 # instead it is better to use a frequency table. # so each KMER gets its own dictionary entry text = "GGAGGATTCTCCTGAAAAGGATTCAAGCGAGGATTCAAGATATCGCCGTACAGTAGGATTCTAACAGGATTCAGGATTCCTAGACCAAAAGGATTCGACTAGGATTCAGGATTCAGCAAGGATTCAGGATTCAGGATTCTTAGGATTCTGCAGGATTCAGGATTCGAGGATTCTGAGGATTCGCAAGCTCTAGGATTCAGGATTCTTAGGATTCAGGATTCAGAGGATTCAGGATTCAGGATTCGTATGAAAGGATTCCGGAGGATTCCGGGTAGGATTCAGGATTCAAGGATTCAAGGATTCAGGATTCAGGATTCCGAGGATTCAGGATTCGGAGGATTCTTAGGATTCCCAGGATTCACGGGCAGACCTAGGATTCAGGATTCGAAAGGATTCTTGAGGATTCAGGATTCAAAGGATTCCGAGGATTCTAGGATTCGAAGTACCGAGGATTCCCCAGGATTCATGTAGGATTCAGGATTCTAGGATTCGTACGAGGATTCAGGATTCCGTTCTAGGATTCCTTAGGATTCCAGGATTCAGGATTCGGAGGATTCAGAAGGATTCCAGGATTCCTCACAAAATAGGATTCGAGGATTCTAGAGGATTCGCAGGATTCTAAGGATTCATTGTCCAGGATTCTTAAGGATTCAGGATTCAGGATTCAGCCTAGGATTCAGGATTCGGAGGATTCATTCAGGATTCGATCGTGACAGAGGATTCACCAGGATTCTCAGGATTCTAGGATTCAGGATTCGAGGATTCTAGGATTCAAGGATTCAGGATTCGTTATTCACTGGGCAGGATTCAAGGATTCATAGGATTCAGACGCAGGATTCAGGATTCAGGATTCCAGGATTCTGTGAGGATTCATCGAAGGATTCATCCAATAGGATTCCTTTGAGGATTCTAGGATTCGGGCGACTTTAGCAGGATTCGGCCGAAGGATTCAGGATTCATGTTGGTCGCAGGATTCCGCATTTAGTATAGGATTCAGGATTCAGGATTCCGCAAGTTCTGAGGATTCGAGGATTCAGGATTC" def frequency_table(text, kmer_len): freq_map = {} nt = len(text) nk = kmer_len for i in range(0, nt-nk): pattern = text[i : i+nk] if not freq_map.get(pattern): freq_map[pattern] = 1 else: freq_map[pattern] = freq_map[pattern] + 1 return freq_map freq_map = frequency_table(text, 5) max(freq_map, key=freq_map.get) # this is the easy way to get the highest freq one test_pattern = "TATGCTAGGTCCAAGTCCAATATATGCTAGCTCTACGTCCAATATATGCTAGTCCAATAGTCTTCTTCCAATAGTCCAAGGTCTTCTCTCTACGGTCTTCTTATGCTAGCTCTACGCTCTACGTATGCTAGTCCAATACTCTACGTATGCTAGGTCCAAGGTCTTCTTATGCTAGGTCTTCTCTCTACGCTCTACGTATGCTAGTATGCTAGCTCTACGGTCCAAGCTCTACGTCCAATACTCTACGTATGCTAGGTCCAAGGTCTTCTGTCTTCTTCCAATATCCAATAGTCCAAGTATGCTAGGTCCAAGGTCTTCTGTCTTCTGTCCAAGGTCCAAGGTCCAAGCTCTACGGTCTTCTTATGCTAGCTCTACGTATGCTAGGTCCAAGTCCAATATCCAATATATGCTAGTCCAATATCCAATAGTCTTCTGTCTTCTCTCTACGCTCTACGGTCCAAGGTCCAAGTCCAATATATGCTAGGTCCAAGGTCTTCTTATGCTAGGTCTTCTTCCAATAGTCTTCTGTCCAAGTCCAATAGTCCAAGGTCTTCTGTCTTCTTATGCTAGTATGCTAGGTCTTCTTCCAATATCCAATATCCAATATCCAATAGTCCAAGCTCTACGTCCAATATATGCTAGTATGCTAGCTCTACGGTCCAAGTATGCTAGCTCTACGTCCAATAGTCTTCTTCCAATATATGCTAGCTCTACGGTCCAAGCTCTACGTCCAATAGTCTTCTTATGCTAGCTCTACGGTCCAAGGTCCAAGTATGCTAGGTCTTCTGTCCAAGTCCAATACTCTACGTATGCTAGGTCTTCTTATGCTAGGTCTTCTGTCTTCTCTCTACGGTCTTCTCTCTACGGTCTTCTCTCTACG" test_pattern_1 = "CTTCCCAAAGACTTCTCTGATGTAGCAAAGACTTCTCTCGCTTTGCCGGTCTCGAGGATGTAGCAAAGACTTCTCTAGACTTCTCTAGACTTCTCTCTTCCCAACTTCCCAAGTCTCGAGGATGTAGCAAGATGTAGCAACGCTTTGCCGCTTCCCAAGTCTCGAGAGACTTCTCTGTCTCGAGCTTCCCAACGCTTTGCCGGTCTCGAGGTCTCGAGCTTCCCAAGTCTCGAGCTTCCCAACGCTTTGCCGCTTCCCAAGTCTCGAGCTTCCCAACTTCCCAAGTCTCGAGGTCTCGAGCTTCCCAAAGACTTCTCTGTCTCGAGCTTCCCAAAGACTTCTCTCTTCCCAACGCTTTGCCGCTTCCCAACGCTTTGCCGGATGTAGCAAAGACTTCTCTCGCTTTGCCGCGCTTTGCCGGATGTAGCAAAGACTTCTCTAGACTTCTCTCTTCCCAACGCTTTGCCGAGACTTCTCTCTTCCCAAGATGTAGCAACGCTTTGCCGGTCTCGAGCGCTTTGCCGGATGTAGCAAGTCTCGAGCTTCCCAAGATGTAGCAAGTCTCGAGCGCTTTGCCGGATGTAGCAAAGACTTCTCTCTTCCCAAGTCTCGAGCTTCCCAAAGACTTCTCTAGACTTCTCTGTCTCGAGGATGTAGCAAGTCTCGAGCTTCCCAAAGACTTCTCTAGACTTCTCTAGACTTCTCTCTTCCCAACTTCCCAAGTCTCGAGGTCTCGAGGTCTCGAGGTCTCGAGGTCTCGAGGATGTAGCAACTTCCCAACTTCCCAAAGACTTCTCTAGACTTCTCTGATGTAGCAACTTCCCAACGCTTTGCCGGTCTCGAGAGACTTCTCTGATGTAGCAACTTCCCAAGATGTAGCAAGTCTCGAGGTCTCGAGCGCTTTGCCGGATGTAGCAAAGACTTCTCTAGACTTCTCTGATGTAGCAAGTCTCGAGGATGTAGCAAAGACTTCTCT" test_length = 11 freq_map = frequency_table(test_pattern_1, test_length) max_value = max(freq_map.values()) [k for k,v in freq_map.items() if v == max_value]

74.615385

1,060

0.894158

299

5,820

17.160535

0.324415

0.016371

0.008575

0.007406

0.024557

0.008186

0

0.002794

0.077491

5,820

77

1,061

75.584416

0.952878

0.058076

0

0.72807

0.725512

0

1

0

1

0.066667

false

0

0.133333

0

1

null

0

1

0

1

null

1

0

5

d089c3501a6194bb756e8c03bb3b010be1cbe7ab

52

py

Python

cources/20181116/demo/import/lower/test.py

YDSS/python_lessons

ef2fa442c18d408bb46bb33dfda47e718f37d251

[ "MIT" ]

null

cources/20181116/demo/import/lower/test.py

YDSS/python_lessons

ef2fa442c18d408bb46bb33dfda47e718f37d251

[ "MIT" ]

null

cources/20181116/demo/import/lower/test.py

YDSS/python_lessons

ef2fa442c18d408bb46bb33dfda47e718f37d251

[ "MIT" ]

1

2019-10-21T02:33:26.000Z

#coding=utf-8 from test.test2 import * print count

10.4

24

0.75

9

52

4.333333

1

0

0.045455

0.153846

52

5

25

10.4

0.840909

0.230769

0

null

0

0.5

null

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d0d71fecbf1e921a76fa9c155a44260e93bdbc97

47

py

Python

blinkpy/helpers/__init__.py

magicalyak/blinkpy

21f29ad302072d16efdc8205aaba826013e69176

[ "MIT" ]

272

2017-01-29T18:43:25.000Z

2022-03-27T20:43:50.000Z

blinkpy/helpers/__init__.py

magicalyak/blinkpy

21f29ad302072d16efdc8205aaba826013e69176

[ "MIT" ]

434

2017-01-23T20:22:51.000Z

2022-03-31T18:10:36.000Z

blinkpy/helpers/__init__.py

magicalyak/blinkpy

21f29ad302072d16efdc8205aaba826013e69176

[ "MIT" ]

77

2017-04-15T17:04:04.000Z

2022-03-04T10:03:39.000Z

"""Init file for blinkpy helper functions."""

23.5

46

0.702128

6

47

5.5

1

0

0.148936

47

1

47

0.825

0.829787

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

d0ddd453e6a33a68d512f4a876548c4ac8ee859e

30

py

Python

depfetch/pm/__init__.py

ChristopherPtak/DepFetch

3122b8749970b254e5fcf3bf366c8bc21e80f71e

[ "MIT" ]

null

depfetch/pm/__init__.py

ChristopherPtak/DepFetch

3122b8749970b254e5fcf3bf366c8bc21e80f71e

[ "MIT" ]

null

depfetch/pm/__init__.py

ChristopherPtak/DepFetch

3122b8749970b254e5fcf3bf366c8bc21e80f71e

[ "MIT" ]

null

from depfetch.pm import apt

7.5

27

0.766667

5

30

4.6

1

0

0.2

30

3

28

10

0.958333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

d0e80088cdcec2e32051279ad95f5d38c773f6f3

70

py

Python

tests/e2e/test_main.py

ralphribeiro/facilita-DOU

e695ac0f58369d61fad2723bd5e52ecd80d0b33f

[ "MIT" ]

null

tests/e2e/test_main.py

ralphribeiro/facilita-DOU

e695ac0f58369d61fad2723bd5e52ecd80d0b33f

[ "MIT" ]

null

tests/e2e/test_main.py

ralphribeiro/facilita-DOU

e695ac0f58369d61fad2723bd5e52ecd80d0b33f

[ "MIT" ]

null

# from src import main_app # def test_main_(): # main_app.main()

14

26

0.657143

11

70

3.818182

0.636364

0.333333

0

0.214286

70

5

27

14

0.763636

0.828571

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

1

0

1

0

1

0

null

0

1

0

5

ef9d71fb6c35d6657929488be8611f70708b71d7

73

py

Python

LearnAF/ops/__init__.py

itsnarsi/LearnAF

5c7d76a89ab824e8e3ea1bd83ac4f07c94ce0819

[ "MIT" ]

null

LearnAF/ops/__init__.py

itsnarsi/LearnAF

5c7d76a89ab824e8e3ea1bd83ac4f07c94ce0819

[ "MIT" ]

null

LearnAF/ops/__init__.py

itsnarsi/LearnAF

5c7d76a89ab824e8e3ea1bd83ac4f07c94ce0819

[ "MIT" ]

null

from .autograd import * from .trigonometry import * from .matops import *

24.333333

27

0.767123

9

73

6.222222

0.555556

0.357143

0

0.150685

73

3

28

24.333333

0.903226

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

4bf32b984c9196549393a40e39ed10e8607f1dc3

69

py

Python

Wedding/admin.py

wgordon17/django-wedding

be4995b997dea285a94bab5ad310788a36c25c99

[ "MIT" ]

null

Wedding/admin.py

wgordon17/django-wedding

be4995b997dea285a94bab5ad310788a36c25c99

[ "MIT" ]

null

Wedding/admin.py

wgordon17/django-wedding

be4995b997dea285a94bab5ad310788a36c25c99

[ "MIT" ]

null

from django.contrib import admin from Wedding.models import Article

17.25

34

0.84058

10

69

5.8

0.8

0

0.130435

69

3

35

23

0.966667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

4bf39cf8a781fd6a3d4d29fcefa02406b3f6805d

474

py

Python

gchatautorespond/apps/autorespond/urls.py

merrlyne/gchatautorespond

a7f8d7b715ca9851a65588a268ce39addb906b6d

[ "BSD-2-Clause" ]

null

gchatautorespond/apps/autorespond/urls.py

merrlyne/gchatautorespond

a7f8d7b715ca9851a65588a268ce39addb906b6d

[ "BSD-2-Clause" ]

null

gchatautorespond/apps/autorespond/urls.py

merrlyne/gchatautorespond

a7f8d7b715ca9851a65588a268ce39addb906b6d

[ "BSD-2-Clause" ]

1

2018-12-03T19:12:24.000Z

from django.conf.urls import url urlpatterns = [ url(r'auth/$', 'gchatautorespond.apps.autorespond.views.auth_view'), url(r'oauth2callback/$', 'gchatautorespond.apps.autorespond.views.auth_return_view'), url(r'worker_status/$', 'gchatautorespond.apps.autorespond.views.worker_status_view'), url(r'test/$', 'gchatautorespond.apps.autorespond.views.test_view'), url(r'$', 'gchatautorespond.apps.autorespond.views.autorespond_view', name='autorespond'), ]

47.4

94

0.751055

56

474

6.214286

0.357143

0.057471

0.445402

0.517241

0.229885

0

0.002304

0.084388

474

9

95

52.666667

0.799539

0

0.681435

0.565401

0

1

0

false

0

0.125

0

0.125

0

null

0

1

0

1

0

1

null

0

5

ef3aa40d79524c8bc25d59bcb6246eaece61087e

206

py

Python

utils/tools/__init__.py

thearyadev/Athena-Discord-Bot

2cec42649257ade87829e382fc826cdcdfd94109

[ "MIT" ]

4

2022-02-14T17:27:03.000Z

2022-02-17T04:29:32.000Z

utils/tools/__init__.py

thearyadev/Athena

2cec42649257ade87829e382fc826cdcdfd94109

[ "MIT" ]

null

utils/tools/__init__.py

thearyadev/Athena

2cec42649257ade87829e382fc826cdcdfd94109

[ "MIT" ]

null

from .Athena import Athena from .Configuration import configuration from .console import Console from .Embeds import embeds from .PugTools import PugSession from .Database import GuildDatabase, Guild

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null

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1

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5

ef43b8d8cd00eb045a55548b5bddae03b08be32e

113

py

Python

omicron/dal/__init__.py

evimacs/omicron

abe77fd25a93cf3d0d17661ae957373474724535

[ "MIT" ]

4

2020-11-09T02:23:51.000Z

2021-01-24T00:45:21.000Z

omicron/dal/__init__.py

evimacs/omicron

abe77fd25a93cf3d0d17661ae957373474724535

[ "MIT" ]

14

2020-11-09T02:31:34.000Z

2021-12-22T10:15:47.000Z

omicron/dal/__init__.py

evimacs/omicron

abe77fd25a93cf3d0d17661ae957373474724535

[ "MIT" ]

2

2021-01-24T00:45:25.000Z

2021-12-24T06:18:37.000Z

from omicron.dal.cache import cache from omicron.dal.postgres import db, init __all__ = ["init", "db", "cache"]

22.6

41

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4.588235

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42

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5

ef61f0c934335071816553535535f72554f14786

39

py

Python

autograde/cli/__init__.py

wanLo/autograde

165160a040868a1d3319066520f8a5623b623506

[ "MIT" ]

8

2020-02-28T14:28:02.000Z

2022-01-17T14:05:14.000Z

autograde/cli/__init__.py

wanLo/autograde

165160a040868a1d3319066520f8a5623b623506

[ "MIT" ]

24

2020-03-02T15:57:41.000Z

2022-03-12T01:05:47.000Z

autograde/cli/__init__.py

wanLo/autograde

165160a040868a1d3319066520f8a5623b623506

[ "MIT" ]

4

2020-04-30T07:56:34.000Z

2021-07-19T12:04:04.000Z

from autograde.cli.__main__ import cli

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0

1

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1

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5

322cb52cf86048b533f8e6d8be22a49f9a0fe693

192

py

Python

section4/timestable.py

jgyy/python-masterclass

20b3dd49b2b2d3a9dccba0b0c52a261828c6cfd5

[ "Unlicense" ]

null

section4/timestable.py

jgyy/python-masterclass

20b3dd49b2b2d3a9dccba0b0c52a261828c6cfd5

[ "Unlicense" ]

null

section4/timestable.py

jgyy/python-masterclass

20b3dd49b2b2d3a9dccba0b0c52a261828c6cfd5

[ "Unlicense" ]

null

""" Shows the mathematical timestable """ for i in range(1, 13): for j in range(1, 13): print("{0} times {1} is {2}".format(j, i, i * j)) print("--------------------------------")

24

57

0.458333

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192

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7

58

27.428571

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1

0

1

0

null

0

1

0

5

323a08bc1abfff316b14c49d9682abad84b776bd

39

py

Python

tests/__init__.py

alexmarco/pyspinner

f8f861ce160fa9a141cbf7990b4e2c999f229429

[ "MIT" ]

null

tests/__init__.py

alexmarco/pyspinner

f8f861ce160fa9a141cbf7990b4e2c999f229429

[ "MIT" ]

null

tests/__init__.py

alexmarco/pyspinner

f8f861ce160fa9a141cbf7990b4e2c999f229429

[ "MIT" ]

null

"""Unit test package for pyspinner."""

19.5

38

0.692308

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39

5.4

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1

39

0.794118

0.820513

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0

1

0

1

0

null

0

1

0

5

3286217bf25c7bb0336af3da1ae8c11d7dbc59d3

102

py

Python

adjacency matrix.py

hanqi-qi/snippets

62e59e2e7e880a434093e09d12cff0c45996ca68

[ "MIT" ]

null

adjacency matrix.py

hanqi-qi/snippets

62e59e2e7e880a434093e09d12cff0c45996ca68

[ "MIT" ]

null

adjacency matrix.py

hanqi-qi/snippets

62e59e2e7e880a434093e09d12cff0c45996ca68

[ "MIT" ]

null

adj_mat = sp.dok_matrix((self.n_users + self.m_items, self.n_users + self.m_items), dtype=np.float32)

51

101

0.754902

20

102

3.55

0.65

0.140845

0.28169

0.394366

0.56338

0

0.021505

0.088235

102

1

102

0.741935

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

32872cc844bc57c44bad5ba85e861882e3bc90d0

76

py

Python

bsm/cmd/output/python.py

bsmsoft/bsm

e45ec5442de39e5f948023cd5b4c6181073cf9a2

[ "MIT" ]

3

2019-06-12T17:19:12.000Z

2022-01-07T02:10:06.000Z

bsm/cmd/output/python.py

bsmsoft/bsm

e45ec5442de39e5f948023cd5b4c6181073cf9a2

[ "MIT" ]

null

bsm/cmd/output/python.py

bsmsoft/bsm

e45ec5442de39e5f948023cd5b4c6181073cf9a2

[ "MIT" ]

null

class Python(object): def dump(self, value): return repr(value)

19

26

0.631579

10

76

4.8

0.9

0

0.25

76

3

27

25.333333

0.842105

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

32975ae69a1054b70d4129df274cbb3681ab02d8

244

py

Python

users/models.py

illimites/social-status

55f4a0f24f10722d85260f2c6c37a57e81bd1b54

[ "MIT" ]

null

users/models.py

illimites/social-status

55f4a0f24f10722d85260f2c6c37a57e81bd1b54

[ "MIT" ]

null

users/models.py

illimites/social-status

55f4a0f24f10722d85260f2c6c37a57e81bd1b54

[ "MIT" ]

null

from django.contrib.auth.models import AbstractUser class CustomUser(AbstractUser): # We want a custom user model from the beginning so that it's easy to customize later # and we are definitely going to want to customize it. pass

30.5

89

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244

4.868421

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0

0.20082

244

7

90

34.857143

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0

1

0

true

0.333333

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null

0

1

0

null

0

1

0

1

0

5

32a7c35908e688e80fef3e105e73387feaccc8d1

133

py

Python

Python/data_exchange.py

PushpneetSingh/Hello-world

def0f44737e02fb40063cd347e93e456658e2532

[ "MIT" ]

1,428

2018-10-03T15:15:17.000Z

2019-03-31T18:38:36.000Z

Python/data_exchange.py

PushpneetSingh/Hello-world

def0f44737e02fb40063cd347e93e456658e2532

[ "MIT" ]

1,162

2018-10-03T15:05:49.000Z

2018-10-18T14:17:52.000Z

Python/data_exchange.py

PushpneetSingh/Hello-world

def0f44737e02fb40063cd347e93e456658e2532

[ "MIT" ]

3,909

2018-10-03T15:07:19.000Z

2019-03-31T18:39:08.000Z

a = 1 b = 2 print('a = ' + str(a) + ',' + 'b = ' + str(b)) temp = a a = b b = temp print('a = ' + str(a) + ',' + 'b = ' + str(b))

12.090909

46

0.345865

24

133

1.916667

0.291667

0.130435

0.391304

0.434783

0.652174

0

0.022472

0.330827

133

10

47

13.3

0.494382

0

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0

0.136364

0

0.285714

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1

0

false

0

0.285714

1

0

1

null

0

1

0

1

0

null

0

5

32ab8b2debb7cc45f401da35a931ca3157693d6e

170

py

Python

bananas/dataset/__init__.py

owahltinez/bananas

4d37af1713b7f166ead3459a7004748f954d336e

[ "MIT" ]

null

bananas/dataset/__init__.py

owahltinez/bananas

4d37af1713b7f166ead3459a7004748f954d336e

[ "MIT" ]

null

bananas/dataset/__init__.py

owahltinez/bananas

4d37af1713b7f166ead3459a7004748f954d336e

[ "MIT" ]

null

""" Classes that wrap representations of data types, datasets and features. """ from .datatype import DataType from .feature import Feature from .dataset import DataSet

21.25

71

0.788235

22

170

6.090909

0.681818

0

0.147059

170

7

72

24.285714

0.924138

0.417647

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0892835f307e99018d5ea549c4c05012c0b719d2

117

py

Python

my_utils/functions.py

Damego/DiscordBOT

a7f6115a064043c0f8c6834756096086636d3f0f

[ "MIT" ]

3

2021-09-22T21:12:29.000Z

2021-12-23T16:22:25.000Z

my_utils/functions.py

Damego/DiscordBOT

a7f6115a064043c0f8c6834756096086636d3f0f

[ "MIT" ]

null

my_utils/functions.py

Damego/DiscordBOT

a7f6115a064043c0f8c6834756096086636d3f0f

[ "MIT" ]

1

2021-09-19T08:24:23.000Z

def transform_permission(permission: str): return permission.replace('_', ' ').replace('guild', 'server').title()

58.5

74

0.709402

12

117

6.75

0.75

0

0.094017

117

2

74

58.5

0.764151

0

0.110169

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

08a582fea5954148de82d7d3d0d1dc2cbdcfc2c7

127

py

Python

Python3/Exercises/Intersection/intersection.py

norbertosanchezdichi/TIL

2e9719ddd288022f53b094a42679e849bdbcc625

[ "MIT" ]

null

Python3/Exercises/Intersection/intersection.py

norbertosanchezdichi/TIL

2e9719ddd288022f53b094a42679e849bdbcc625

[ "MIT" ]

null

Python3/Exercises/Intersection/intersection.py

norbertosanchezdichi/TIL

2e9719ddd288022f53b094a42679e849bdbcc625

[ "MIT" ]

null

def intersection(list1, list2): return list(set(list1) & set(list2)) print(intersection(['a','b','z'], ['x','y','z']))

31.75

49

0.582677

18

127

4.111111

0.722222

0

0.036364

0.133858

127

4

49

31.75

0.636364

0

0.046875

0

1

0.333333

false

0

0.333333

0.666667

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

5

3eba16978c75aa5659413345b770ac32e03613fb

90

py

Python

batchs/tools/swallow/test/__init__.py

GalakFayyar/TabordNG

fdf0d32836a3ad273eaa94cf8c54e7c7b932ee18

[ "MIT" ]

1

2016-05-27T09:16:05.000Z

batchs/tools/swallow/test/__init__.py

GalakFayyar/TabordNG

fdf0d32836a3ad273eaa94cf8c54e7c7b932ee18

[ "MIT" ]

null

batchs/tools/swallow/test/__init__.py

GalakFayyar/TabordNG

fdf0d32836a3ad273eaa94cf8c54e7c7b932ee18

[ "MIT" ]

null

import logging import sys logging.basicConfig(level=logging.DEBUG, stream=sys.stdout)

22.5

59

0.8

12

90

6

0.666667

0

0.111111

90

4

59

22.5

0.9

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

3ee0a70dae82376f15d08fa2431c2c12262d797c

202

py

Python

paikkala/models/__init__.py

tracon/paikkala

dc859d924e4acfba95f3446a169bf5f88eecc6a2

[ "MIT" ]

null

paikkala/models/__init__.py

tracon/paikkala

dc859d924e4acfba95f3446a169bf5f88eecc6a2

[ "MIT" ]

null

paikkala/models/__init__.py

tracon/paikkala

dc859d924e4acfba95f3446a169bf5f88eecc6a2

[ "MIT" ]

null

from .blocks import PerProgramBlock from .programs import Program from .rooms import Room from .rows import Row from .tickets import Ticket from .zones import Zone from .qualifiers import SeatQualifier

25.25

37

0.826733

28

202

5.964286

0.571429

0

0.138614

202

7

38

28.857143

0.95977

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

4126883cdb14dfc0dca43900667a5b06e97f12c9

411

py

Python

utilities/readProperties.py

bunnycodec/pySelenium_framework_2

4f4ea0e2c0769baa833b33bc7ba64de3f071ce41

[ "MIT" ]

null

utilities/readProperties.py

bunnycodec/pySelenium_framework_2

4f4ea0e2c0769baa833b33bc7ba64de3f071ce41

[ "MIT" ]

null

utilities/readProperties.py

bunnycodec/pySelenium_framework_2

4f4ea0e2c0769baa833b33bc7ba64de3f071ce41

[ "MIT" ]

null

import configparser config = configparser.RawConfigParser() config.read("Configurations/config.ini") class ReadConfig: @staticmethod def getApplicationUrl(): return config.get('common info', 'baseUrl') @staticmethod def getUsername(): return config.get('common info', 'username') @staticmethod def getPassword(): return config.get('common info', 'password')

21.631579

52

0.683698

39

411

7.205128

0.538462

0.160142

0.224199

0.266904

0

0.199513

411

18

53

22.833333

0.854103

0

0.230769

0

0.19708

0.060827

0

1

0.230769

false

0.153846

0.076923

0.230769

0.615385

0

null

0

1

0

null

0

1

0

1

0

1

0

5

eb0b53d5dc266571d468b5c8fc7ba71f5b74588b

38

py

Python

os_v4_hek/defs/sppg.py

holy-crust/reclaimer

0aa693da3866ce7999c68d5f71f31a9c932cdb2c

[ "MIT" ]

null

os_v4_hek/defs/sppg.py

holy-crust/reclaimer

0aa693da3866ce7999c68d5f71f31a9c932cdb2c

[ "MIT" ]

null

os_v4_hek/defs/sppg.py

holy-crust/reclaimer

0aa693da3866ce7999c68d5f71f31a9c932cdb2c

[ "MIT" ]

null

from ...os_v3_hek.defs.sppg import *

19

37

0.710526

7

38

3.571429

1

0

0.030303

0.131579

38

1

38

0.727273

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

eb2674fdc27343b048262ac67ae7c80ff94f5cf8

225

py

Python

pyogrio/errors.py

srenoes/pyogrio

9398a7f1dae001cc04c7c52c5e4c67882fea20f5

[ "MIT" ]

52

2021-07-09T03:33:53.000Z

2022-03-25T10:52:53.000Z

pyogrio/errors.py

srenoes/pyogrio

9398a7f1dae001cc04c7c52c5e4c67882fea20f5

[ "MIT" ]

49

2021-05-28T00:54:10.000Z

2022-03-31T16:42:09.000Z

pyogrio/errors.py

srenoes/pyogrio

9398a7f1dae001cc04c7c52c5e4c67882fea20f5

[ "MIT" ]

4

2021-07-09T08:54:59.000Z

2022-03-17T14:50:14.000Z

class CRSError(Exception): pass class DriverError(Exception): pass class TransactionError(RuntimeError): pass class UnsupportedGeometryTypeError(Exception): pass class DriverIOError(IOError): pass

11.842105

46

0.742222

20

225

8.35

0.5

0.215569

0.323353

0

0.191111

225

18

47

12.5

0.917582

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

null

0

1

0

5

eb2b3556e4395cd97dda340c59288069a86c650c

304

py

Python

back/core/admin.py

CaesiumY/good-idea-cards

b8ccd71dbdf9f9420624b1b2c3f961cef094f5b9

[ "MIT" ]

7

2020-02-11T08:41:14.000Z

2022-03-26T09:50:48.000Z

back/core/admin.py

CaesiumY/good-idea-cards

b8ccd71dbdf9f9420624b1b2c3f961cef094f5b9

[ "MIT" ]

8

2021-03-30T12:32:07.000Z

2022-02-18T17:47:38.000Z

back/core/admin.py

CaesiumY/good-idea-cards

b8ccd71dbdf9f9420624b1b2c3f961cef094f5b9

[ "MIT" ]

1

2020-07-15T02:06:52.000Z

from django.contrib import admin from .models import Post, Draft from import_export.admin import ImportExportModelAdmin # Register your models here. # admin.site.register(Post) # admin.site.register(Draft) @admin.register(Draft) @admin.register(Post) class ViewAdmin(ImportExportModelAdmin): pass

21.714286

54

0.799342

38

304

6.368421

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0.07438

0.140496

0.214876

0

0.111842

304

13

55

23.384615

0.896296

0.259868

0

1

0

true

0.142857

0.571429

0

0.714286

0

null

0

1

0

null

0

1

0

1

0

5

de1a2940c1c115ec5c7e942bb02593bfe9ec172c

93

py

Python

seed/utils/regex_patterns.py

h4wldev/seed

2febcb39edb6086128022e40d8734b0e3f93ebb1

[ "MIT" ]

3

2020-12-24T12:01:13.000Z

2021-06-01T06:23:41.000Z

seed/utils/regex_patterns.py

h4wldev/seed

2febcb39edb6086128022e40d8734b0e3f93ebb1

[ "MIT" ]

null

seed/utils/regex_patterns.py

h4wldev/seed

2febcb39edb6086128022e40d8734b0e3f93ebb1

[ "MIT" ]

null

import re email_pattern: 'Pattern' = re.compile(r'^[a-z0-9]+[\._]?[a-z0-9]+[@]\w+[.]\w+$')

18.6

80

0.526882

16

93

2.9375

0.625

0.12766

0.170213

0

0.047059

0.086022

93

4

81

23.25

0.505882

0

0.483871

0.408602

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

null

0

1

0

1

0

5

de62fa0afa423e9df8b53e52eef84647dab553aa

157

py

Python

admin.py

panchbhai1969/gerbil-client-django-webapp

5ada300af6ce1bc7926376e2836ee2a0014d13fe

[ "MIT" ]

null

admin.py

panchbhai1969/gerbil-client-django-webapp

5ada300af6ce1bc7926376e2836ee2a0014d13fe

[ "MIT" ]

null

admin.py

panchbhai1969/gerbil-client-django-webapp

5ada300af6ce1bc7926376e2836ee2a0014d13fe

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from .models import Question,Choice admin.site.register(Question) admin.site.register(Choice)

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py

Python

shop/__init__.py

goobes/shop

532726a8ef9d37cd818ad5bea4d8fe2b068e47c8

[ "MIT" ]

null

shop/__init__.py

goobes/shop

532726a8ef9d37cd818ad5bea4d8fe2b068e47c8

[ "MIT" ]

7

2020-06-05T16:46:45.000Z

2022-01-13T00:39:03.000Z

shop/__init__.py

goobes/shop

532726a8ef9d37cd818ad5bea4d8fe2b068e47c8

[ "MIT" ]

null

""" shop """

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de94fcb971848c9d82f5dc0d65aec692932cef74

255

py

Python

tomo_encoders/__init__.py

aniketkt/TomoEncoders

88fa8ba5ef4a950362fbce69f9963250a02f5f4d

[ "BSD-3-Clause" ]

1

2021-06-23T18:09:57.000Z

tomo_encoders/__init__.py

aniketkt/TomoEncoders

88fa8ba5ef4a950362fbce69f9963250a02f5f4d

[ "BSD-3-Clause" ]

3

2021-08-24T17:53:48.000Z

2021-11-26T07:50:43.000Z

tomo_encoders/__init__.py

aniketkt/TomoEncoders

88fa8ba5ef4a950362fbce69f9963250a02f5f4d

[ "BSD-3-Clause" ]

5

2021-07-01T20:56:24.000Z

2022-03-22T18:25:47.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ """ from tomo_encoders.structures.patches import Patches from tomo_encoders.structures.datafile import DataFile from tomo_encoders.misc import viewer from tomo_encoders.misc import voxel_processing

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py

Python

webapp/Application/app/migrations/0001_initial.py

GroupProjectSem3/Haley

f9ec2a7114624fab23db59f5afa9696c2bc3a1da

[ "MIT" ]

null

webapp/Application/app/migrations/0001_initial.py

GroupProjectSem3/Haley

f9ec2a7114624fab23db59f5afa9696c2bc3a1da

[ "MIT" ]

311

2020-09-27T21:56:58.000Z

2020-12-16T17:37:31.000Z

webapp/Application/app/migrations/0001_initial.py

GroupProjectSem3/Haley

f9ec2a7114624fab23db59f5afa9696c2bc3a1da

[ "MIT" ]

5

2020-09-27T21:38:11.000Z

2021-01-05T11:36:51.000Z

# Generated by Django 3.0.5 on 2020-10-22 23:00 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='User_profile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('first_name', models.CharField(max_length=100)), ('last_name', models.CharField(max_length=100)), ('email', models.CharField(max_length=100)), ('password', models.CharField(max_length=100)), ('confirm_password', models.CharField(max_length=100)), ('address', models.CharField(max_length=100)), ('city', models.CharField(max_length=100)), ('country', models.CharField(max_length=30)), ('zipcode', models.CharField(max_length=10)), ('dob', models.DateField()), ('gender', models.CharField(max_length=10)), ('height', models.DecimalField(decimal_places=3, max_digits=10)), ('weight', models.DecimalField(decimal_places=3, max_digits=10)), ], ), ]

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py

Python

matti/__init__.py

sh1ma/matti

d48ef8d73f705ec42b056577072e09bda2477f6e

[ "MIT" ]

2

2020-09-05T17:37:41.000Z

2020-09-05T20:05:31.000Z

matti/__init__.py

sh1ma/matti

d48ef8d73f705ec42b056577072e09bda2477f6e

[ "MIT" ]

null

matti/__init__.py

sh1ma/matti

d48ef8d73f705ec42b056577072e09bda2477f6e

[ "MIT" ]

null

from .matti import match, case, default

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py

Python

test.py

odemeo-usgs/learning_python

f19b5a26c2fb434142db7fa47a57d6f33805a3a3

[ "CC0-1.0" ]

null

test.py

odemeo-usgs/learning_python

f19b5a26c2fb434142db7fa47a57d6f33805a3a3

[ "CC0-1.0" ]

null

test.py

odemeo-usgs/learning_python

f19b5a26c2fb434142db7fa47a57d6f33805a3a3

[ "CC0-1.0" ]

null

{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "6ff1526a", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "id": "3cea144b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'C:\\\\Users\\\\odemeo\\\\Documents\\\\Field_Data_Processing\\\\CACO_MET\\\\HoM_2021-06-29.csv'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_nm='C:\\\\Users\\\\odemeo\\\\Documents\\\\Field_Data_Processing\\\\CACO_MET\\\\'\n", "file_name='HoM_2021-06-29.csv'\n", "path_nm + file_name" ] }, { "cell_type": "code", "execution_count": 3, "id": "bf4fe8b9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Date and Time in UTC</th>\n", " <th>SampNum</th>\n", " <th>Battery</th>\n", " <th>BoardTemp</th>\n", " <th>signalPercent</th>\n", " <th>WXTDn</th>\n", " <th>WXTDm</th>\n", " <th>WXTDx</th>\n", " <th>WXTSn</th>\n", " <th>WXTSm</th>\n", " <th>WXTSx</th>\n", " <th>WXTTa</th>\n", " <th>WXTUa</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2021-06-29 14:56:00</td>\n", " <td>2</td>\n", " <td>3.684</td>\n", " <td>32.25</td>\n", " <td>-9999</td>\n", " <td>170</td>\n", " <td>202</td>\n", " <td>238</td>\n", " <td>0.0</td>\n", " <td>5.1</td>\n", " <td>7.0</td>\n", " <td>26.3</td>\n", " <td>77.4</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2021-06-29 14:57:00</td>\n", " <td>3</td>\n", " <td>3.654</td>\n", " <td>32.00</td>\n", " <td>-9999</td>\n", " <td>173</td>\n", " <td>205</td>\n", " <td>232</td>\n", " <td>3.1</td>\n", " <td>4.8</td>\n", " <td>7.1</td>\n", " <td>26.4</td>\n", " <td>77.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2021-06-29 14:58:00</td>\n", " <td>4</td>\n", " <td>3.654</td>\n", " <td>31.75</td>\n", " <td>-9999</td>\n", " <td>180</td>\n", " <td>210</td>\n", " <td>247</td>\n", " <td>2.8</td>\n", " <td>4.7</td>\n", " <td>7.1</td>\n", " <td>26.4</td>\n", " <td>78.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2021-06-29 14:59:00</td>\n", " <td>2</td>\n", " <td>4.745</td>\n", " <td>32.00</td>\n", " <td>-9999</td>\n", " <td>190</td>\n", " <td>215</td>\n", " <td>247</td>\n", " <td>2.6</td>\n", " <td>4.5</td>\n", " <td>7.0</td>\n", " <td>26.4</td>\n", " <td>77.7</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2021-06-29 15:00:00</td>\n", " <td>3</td>\n", " <td>4.730</td>\n", " <td>32.00</td>\n", " <td>-9999</td>\n", " <td>169</td>\n", " <td>207</td>\n", " <td>243</td>\n", " <td>2.6</td>\n", " <td>4.8</td>\n", " <td>7.0</td>\n", " <td>26.4</td>\n", " <td>76.7</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>122589</th>\n", " <td>2021-09-22 18:06:00</td>\n", " <td>-8486</td>\n", " <td>3.639</td>\n", " <td>31.25</td>\n", " <td>-9999</td>\n", " <td>145</td>\n", " <td>182</td>\n", " <td>229</td>\n", " <td>2.4</td>\n", " <td>5.4</td>\n", " <td>9.1</td>\n", " <td>24.8</td>\n", " <td>74.5</td>\n", " </tr>\n", " <tr>\n", " <th>122590</th>\n", " <td>2021-09-22 18:07:00</td>\n", " <td>-8485</td>\n", " <td>3.639</td>\n", " <td>31.25</td>\n", " <td>-9999</td>\n", " <td>135</td>\n", " <td>189</td>\n", " <td>243</td>\n", " <td>2.4</td>\n", " <td>4.9</td>\n", " <td>8.3</td>\n", " <td>24.9</td>\n", " <td>74.2</td>\n", " </tr>\n", " <tr>\n", " <th>122591</th>\n", " <td>2021-09-22 18:08:00</td>\n", " <td>-8484</td>\n", " <td>3.639</td>\n", " <td>31.50</td>\n", " <td>-9999</td>\n", " <td>135</td>\n", " <td>194</td>\n", " <td>246</td>\n", " <td>2.5</td>\n", " <td>4.9</td>\n", " <td>8.3</td>\n", " <td>25.1</td>\n", " <td>73.2</td>\n", " </tr>\n", " <tr>\n", " <th>122592</th>\n", " <td>2021-09-22 18:09:00</td>\n", " <td>-8483</td>\n", " <td>3.639</td>\n", " <td>31.50</td>\n", " <td>-9999</td>\n", " <td>153</td>\n", " <td>201</td>\n", " <td>246</td>\n", " <td>2.5</td>\n", " <td>5.3</td>\n", " <td>8.5</td>\n", " <td>25.2</td>\n", " <td>71.0</td>\n", " </tr>\n", " <tr>\n", " <th>122593</th>\n", " <td>2021-09-22 18:10:00</td>\n", " <td>-8482</td>\n", " <td>3.654</td>\n", " <td>31.25</td>\n", " <td>-9999</td>\n", " <td>171</td>\n", " <td>203</td>\n", " <td>232</td>\n", " <td>3.5</td>\n", " <td>6.3</td>\n", " <td>9.4</td>\n", " <td>25.0</td>\n", " <td>71.4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>122594 rows × 13 columns</p>\n", "</div>" ], "text/plain": [ " Date and Time in UTC SampNum Battery BoardTemp signalPercent \\\n", "0 2021-06-29 14:56:00 2 3.684 32.25 -9999 \n", "1 2021-06-29 14:57:00 3 3.654 32.00 -9999 \n", "2 2021-06-29 14:58:00 4 3.654 31.75 -9999 \n", "3 2021-06-29 14:59:00 2 4.745 32.00 -9999 \n", "4 2021-06-29 15:00:00 3 4.730 32.00 -9999 \n", "... ... ... ... ... ... \n", "122589 2021-09-22 18:06:00 -8486 3.639 31.25 -9999 \n", "122590 2021-09-22 18:07:00 -8485 3.639 31.25 -9999 \n", "122591 2021-09-22 18:08:00 -8484 3.639 31.50 -9999 \n", "122592 2021-09-22 18:09:00 -8483 3.639 31.50 -9999 \n", "122593 2021-09-22 18:10:00 -8482 3.654 31.25 -9999 \n", "\n", " WXTDn WXTDm WXTDx WXTSn WXTSm WXTSx WXTTa WXTUa \n", "0 170 202 238 0.0 5.1 7.0 26.3 77.4 \n", "1 173 205 232 3.1 4.8 7.1 26.4 77.0 \n", "2 180 210 247 2.8 4.7 7.1 26.4 78.0 \n", "3 190 215 247 2.6 4.5 7.0 26.4 77.7 \n", "4 169 207 243 2.6 4.8 7.0 26.4 76.7 \n", "... ... ... ... ... ... ... ... ... \n", "122589 145 182 229 2.4 5.4 9.1 24.8 74.5 \n", "122590 135 189 243 2.4 4.9 8.3 24.9 74.2 \n", "122591 135 194 246 2.5 4.9 8.3 25.1 73.2 \n", "122592 153 201 246 2.5 5.3 8.5 25.2 71.0 \n", "122593 171 203 232 3.5 6.3 9.4 25.0 71.4 \n", "\n", "[122594 rows x 13 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df=pd.read_csv(path_nm+file_name,skiprows=7)\n", "df" ] }, { "cell_type": "code", "execution_count": 4, "id": "9b2247aa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>SampNum</th>\n", " <th>Battery</th>\n", " <th>BoardTemp</th>\n", " <th>signalPercent</th>\n", " <th>WXTDn</th>\n", " <th>WXTDm</th>\n", " <th>WXTDx</th>\n", " <th>WXTSn</th>\n", " <th>WXTSm</th>\n", " <th>WXTSx</th>\n", " <th>WXTTa</th>\n", " <th>WXTUa</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>122594.000000</td>\n", " <td>122594.000000</td>\n", " <td>122594.000000</td>\n", " <td>122594.0</td>\n", " <td>122594.000000</td>\n", " <td>122594.000000</td>\n", " <td>122594.000000</td>\n", " <td>122594.000000</td>\n", " <td>122594.000000</td>\n", " <td>122594.000000</td>\n", " <td>122594.000000</td>\n", " <td>122594.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>292.856078</td>\n", " <td>3.623003</td>\n", " <td>23.456788</td>\n", " <td>-9999.0</td>\n", " <td>130.246709</td>\n", " <td>183.433667</td>\n", " <td>219.829209</td>\n", " <td>2.371266</td>\n", " <td>4.099445</td>\n", " <td>5.886937</td>\n", " <td>20.863037</td>\n", " <td>86.168375</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>19517.272594</td>\n", " <td>0.025389</td>\n", " <td>5.225634</td>\n", " <td>0.0</td>\n", " <td>78.654022</td>\n", " <td>91.405097</td>\n", " <td>88.711210</td>\n", " <td>1.723976</td>\n", " <td>2.263441</td>\n", " <td>3.041321</td>\n", " <td>2.998218</td>\n", " <td>11.434929</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>-32768.000000</td>\n", " <td>3.563000</td>\n", " <td>11.500000</td>\n", " <td>-9999.0</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>12.300000</td>\n", " <td>40.300000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>-17444.000000</td>\n", " <td>3.608000</td>\n", " <td>19.500000</td>\n", " <td>-9999.0</td>\n", " <td>46.000000</td>\n", " <td>106.000000</td>\n", " <td>178.000000</td>\n", " <td>1.200000</td>\n", " <td>2.500000</td>\n", " <td>3.600000</td>\n", " <td>18.800000</td>\n", " <td>78.100000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>2119.000000</td>\n", " <td>3.624000</td>\n", " <td>22.500000</td>\n", " <td>-9999.0</td>\n", " <td>162.000000</td>\n", " <td>207.000000</td>\n", " <td>241.000000</td>\n", " <td>2.100000</td>\n", " <td>3.800000</td>\n", " <td>5.600000</td>\n", " <td>20.700000</td>\n", " <td>88.300000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>17443.000000</td>\n", " <td>3.639000</td>\n", " <td>27.250000</td>\n", " <td>-9999.0</td>\n", " <td>189.000000</td>\n", " <td>231.000000</td>\n", " <td>270.000000</td>\n", " <td>3.100000</td>\n", " <td>5.200000</td>\n", " <td>7.600000</td>\n", " <td>22.700000</td>\n", " <td>96.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>32767.000000</td>\n", " <td>4.745000</td>\n", " <td>39.750000</td>\n", " <td>-9999.0</td>\n", " <td>351.000000</td>\n", " <td>358.000000</td>\n", " <td>358.000000</td>\n", " <td>16.200000</td>\n", " <td>21.600000</td>\n", " <td>27.900000</td>\n", " <td>31.400000</td>\n", " <td>100.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " SampNum Battery BoardTemp signalPercent \\\n", "count 122594.000000 122594.000000 122594.000000 122594.0 \n", "mean 292.856078 3.623003 23.456788 -9999.0 \n", "std 19517.272594 0.025389 5.225634 0.0 \n", "min -32768.000000 3.563000 11.500000 -9999.0 \n", "25% -17444.000000 3.608000 19.500000 -9999.0 \n", "50% 2119.000000 3.624000 22.500000 -9999.0 \n", "75% 17443.000000 3.639000 27.250000 -9999.0 \n", "max 32767.000000 4.745000 39.750000 -9999.0 \n", "\n", " WXTDn WXTDm WXTDx WXTSn \\\n", "count 122594.000000 122594.000000 122594.000000 122594.000000 \n", "mean 130.246709 183.433667 219.829209 2.371266 \n", "std 78.654022 91.405097 88.711210 1.723976 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 46.000000 106.000000 178.000000 1.200000 \n", "50% 162.000000 207.000000 241.000000 2.100000 \n", "75% 189.000000 231.000000 270.000000 3.100000 \n", "max 351.000000 358.000000 358.000000 16.200000 \n", "\n", " WXTSm WXTSx WXTTa WXTUa \n", "count 122594.000000 122594.000000 122594.000000 122594.000000 \n", "mean 4.099445 5.886937 20.863037 86.168375 \n", "std 2.263441 3.041321 2.998218 11.434929 \n", "min 0.000000 0.000000 12.300000 40.300000 \n", "25% 2.500000 3.600000 18.800000 78.100000 \n", "50% 3.800000 5.600000 20.700000 88.300000 \n", "75% 5.200000 7.600000 22.700000 96.000000 \n", "max 21.600000 27.900000 31.400000 100.000000 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 5, "id": "c5dd8235", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1b77898dac0>]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(df[\"WXTSn\"].values)\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "a472a001", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.any(np.isnan(df[\"WXTSn\"].values))" ] }, { "cell_type": "code", "execution_count": 7, "id": "aba1f75b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0. , 3.1, 2.8, ..., 2.5, 2.5, 3.5])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wind_sp=df[\"WXTSn\"].values\n", "wind_sp" ] }, { "cell_type": "code", "execution_count": 8, "id": "1f938b5f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "122594\n" ] } ], "source": [ "def windspeed_mean(ws,cf=1.):\n", " mn=np.sum(cf*ws)/len(ws)\n", " n=len(ws)\n", " return mn,n\n", "print(windspeed_mean(wind_sp,2)[1])" ] }, { "cell_type": "code", "execution_count": 9, "id": "f8c3cb8c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.742532260958938\n", "122594\n" ] } ], "source": [ "x,y=windspeed_mean(wind_sp,2)\n", "print(x)\n", "print(y)" ] }, { "cell_type": "code", "execution_count": 10, "id": "8917271c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.742532260958938\n" ] } ], "source": [ "x,_=windspeed_mean(wind_sp,2)\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 11, "id": "bf9d6e59", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "122594\n" ] } ], "source": [ "_,y=windspeed_mean(wind_sp,2)\n", "print(y)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }

56.438316

18,052

0.635859

3,227

38,886

7.637124

0.289123

0.029093

0.044634

0.014729

0.1662

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0.120146

0.108379

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0.075796

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0.19002

0.218176

38,886

688

18,053

56.520349

0.620584

0

0.481105

0

0.03343

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0

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0

1

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true

0

0.00436

0

0.00436

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0

null

0

1

0

1

null

1

0

1

0

5

a0ec66adb91f6c4db721c8cd0f8e7feba6a012b3

370

py

Python

models/utils/compression_utils.py

Euler21/leaf

32b9f7b43774e2a24ddeb158fafc7904a02d1c7c

[ "BSD-2-Clause" ]

1

2021-10-08T02:59:27.000Z

models/utils/compression_utils.py

Euler21/leaf

32b9f7b43774e2a24ddeb158fafc7904a02d1c7c

[ "BSD-2-Clause" ]

null

models/utils/compression_utils.py

Euler21/leaf

32b9f7b43774e2a24ddeb158fafc7904a02d1c7c

[ "BSD-2-Clause" ]

2

2020-10-17T22:37:08.000Z

2021-10-03T23:14:06.000Z

from abc import ABC, abstractmethod class Sketcher(ABC): @abstractmethod def compress(self, updates): pass @abstractmethod def uncompress(self, compressed_updates): pass class VoidSketcher(Sketcher): def compress(self, updates): return updates def uncompress(self, compressed_updates): return compressed_updates

23.125

45

0.697297

38

370

6.710526

0.394737

0.2

0.117647

0.172549

0.266667

0

0.235135

370

15

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24.666667

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0.307692

false

0.153846

0.076923

0.153846

0.692308

0

null

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1

0

null

0

1

0

1

0

1

0

5

19c05d7dfc7a4104510821b3be1ddf1fa1027243

51

py

Python

common/__init__.py

omerk2511/dropbox

8d7ff4b74b412d2fc10a3450b7648f973d5f961b

[ "MIT" ]

4

2020-05-14T12:03:07.000Z

2020-12-22T14:25:54.000Z

common/__init__.py

omerk2511/dropbox

8d7ff4b74b412d2fc10a3450b7648f973d5f961b

[ "MIT" ]

null

common/__init__.py

omerk2511/dropbox

8d7ff4b74b412d2fc10a3450b7648f973d5f961b

[ "MIT" ]

null

from codes import Codes from message import Message

25.5

27

0.862745

8

51

5.5

0.5

0

0.137255

51

2

27

25.5

1

0

1

0

true

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1

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1

0

1

0

null

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0

null

0

1

0

1

0

1

0

5

19c071812e98e80d4dac9d1ca9efac3c079cabb9

62

py

Python

tests/file/test_lookup.py

noahfx/mopidy

09d609b52f5ee4dad867deb55cac26697614c7a2

[ "Apache-2.0" ]

2

2019-02-13T15:16:55.000Z

2019-02-18T08:47:29.000Z

tests/file/test_lookup.py

Ma5onic/mopidy

33856830c946182a623079853a8590575c5d23d2

[ "Apache-2.0" ]

40

2019-02-13T09:33:00.000Z

2019-02-19T13:21:12.000Z

tests/file/test_lookup.py

Ma5onic/mopidy

33856830c946182a623079853a8590575c5d23d2

[ "Apache-2.0" ]

1

2021-10-01T17:26:30.000Z

from __future__ import unicode_literals # TODO Test lookup()

15.5

39

0.806452

8

62

5.625

1

0

0.145161

62

3

40

20.666667

0.849057

0.290323

0

0.333333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

19cf0c6a5f5387dca14359c03cc2e6777703039a

123

py

Python

twitchstreams/admin.py

naelstrof/PugBot-Discord-Django

e187353ea48eabde08b7efd331386728b93672d4

[ "MIT" ]

3

2019-04-26T03:50:36.000Z

2020-12-21T11:39:48.000Z

twitchstreams/admin.py

naelstrof/PugBot-Discord-Django

e187353ea48eabde08b7efd331386728b93672d4

[ "MIT" ]

null

twitchstreams/admin.py

naelstrof/PugBot-Discord-Django

e187353ea48eabde08b7efd331386728b93672d4

[ "MIT" ]

3

2020-05-15T19:28:11.000Z

2021-11-11T19:37:50.000Z

from twitchstreams.models import * from django.contrib import admin admin.site.register(Channel) admin.site.register(Tag)

20.5

34

0.821138

17

123

5.941176

0.647059

0.178218

0.336634

0

0.089431

123

5

35

24.6

0.901786

0

1

0

true

0

0.5

0

0.5

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null

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0

1

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null

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1

0

1

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5