xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

98b2322f409cabff5a6f9e3bdd26864e30bcd813

170

py

Python

pacote/modulo.py

benamoreira/PythonBirds

b0a8809288dcc32892760e6080c9b0e73a8273c9

[ "MIT" ]

null

pacote/modulo.py

benamoreira/PythonBirds

b0a8809288dcc32892760e6080c9b0e73a8273c9

[ "MIT" ]

null

pacote/modulo.py

benamoreira/PythonBirds

b0a8809288dcc32892760e6080c9b0e73a8273c9

[ "MIT" ]

null

def soma(*args): total = 0 for n in args: total += n return total print(__name__) print(soma()) print(soma(1)) print(soma(1, 2)) print(soma(1, 2, 5))

15.454545

20

0.582353

29

170

3.275862

0.482759

0.378947

0.315789

0.231579

0

0.054688

0.247059

170

11

20

15.454545

0.6875

0

1

0.1

false

0

0.2

0.5

1

0

null

1

0

null

0

1

0

5

98b31c5b00e8df145236b72d1e7474e7b6782951

33

py

Python

exercises/isogram/isogram.py

kishankj/python

82042de746128127502e109111e6c4e8ab002af6

[ "MIT" ]

1,177

2017-06-21T20:24:06.000Z

2022-03-29T02:30:55.000Z

exercises/isogram/isogram.py

kishankj/python

82042de746128127502e109111e6c4e8ab002af6

[ "MIT" ]

1,890

2017-06-18T20:06:10.000Z

2022-03-31T18:35:51.000Z

exercises/isogram/isogram.py

kishankj/python

82042de746128127502e109111e6c4e8ab002af6

[ "MIT" ]

1,095

2017-06-26T23:06:19.000Z

2022-03-29T03:25:38.000Z

def is_isogram(string): pass

11

23

0.69697

5

33

4.4

1

0

0.212121

33

2

24

16.5

0.846154

0

1

0.5

false

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

7f394efd9df34f4c8ef16a5411df483068515ada

273

py

Python

cardea/primitives/processing/__init__.py

sarahmish/Cardea

85c4246c12178e6d1b9cc12eb39c264f3c20f3e9

[ "MIT" ]

69

2021-01-28T22:25:10.000Z

2022-03-15T00:23:33.000Z

cardea/primitives/processing/__init__.py

sarahmish/Cardea

85c4246c12178e6d1b9cc12eb39c264f3c20f3e9

[ "MIT" ]

30

2018-08-29T12:45:23.000Z

2019-12-24T11:08:12.000Z

cardea/primitives/processing/__init__.py

sarahmish/Cardea

85c4246c12178e6d1b9cc12eb39c264f3c20f3e9

[ "MIT" ]

14

2021-03-24T01:21:25.000Z

2022-03-12T11:53:40.000Z

# -*- coding: utf-8 -*- from cardea.primitives.processing.categorizer import Categorizer from cardea.primitives.processing.imputer import Imputer from cardea.primitives.processing.one_hot_encoder import OneHotEncoder from cardea.primitives.processing.pruner import Pruner

39

70

0.842491

33

273

6.909091

0.454545

0.175439

0.350877

0.526316

0

0.003984

0.080586

273

6

71

45.5

0.904382

0.076923

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

7f51e0bf6593f295c02ac2f78601e7a30902d309

65

py

Python

pegasusio/commands/__init__.py

hoondy/pegasusio

a98d252607df83ef471486005ee688cb281ca1d1

[ "BSD-3-Clause" ]

8

2020-05-01T17:27:46.000Z

2022-01-25T04:54:35.000Z

pegasusio/commands/__init__.py

hoondy/pegasusio

a98d252607df83ef471486005ee688cb281ca1d1

[ "BSD-3-Clause" ]

5

2020-07-14T02:35:46.000Z

2022-02-11T04:01:56.000Z

pegasusio/commands/__init__.py

hoondy/pegasusio

a98d252607df83ef471486005ee688cb281ca1d1

[ "BSD-3-Clause" ]

3

2020-07-12T15:10:48.000Z

2022-01-25T16:58:22.000Z

from .AggregateMatrix import AggregateMatrix as aggregate_matrix

32.5

64

0.892308

7

65

8.142857

0.857143

0

0.092308

65

1

65

0.966102

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

7f7d90adb2efbe639efd77c3895641a21b188d4c

1,588

py

Python

zabby/items/net/interface.py

blin/zabby

0f9358ca435ea7368202a6d0fc20cea749f896e1

[ "MIT" ]

4

2016-04-23T13:26:43.000Z

2017-11-19T14:07:36.000Z

zabby/items/net/interface.py

blin/zabby

0f9358ca435ea7368202a6d0fc20cea749f896e1

[ "MIT" ]

null

zabby/items/net/interface.py

blin/zabby

0f9358ca435ea7368202a6d0fc20cea749f896e1

[ "MIT" ]

4

2015-09-07T05:55:57.000Z

2022-02-14T13:54:14.000Z

from zabby.core.utils import validate_mode from zabby.hostos import detect_host_os __all__ = ['incoming', 'outgoing', ] NET_MODES = ['bytes', 'packets', 'errors', 'dropped', ] def incoming(interface_name, mode="bytes", host_os=detect_host_os()): """ Returns amount of received bytes or packets, dropped incoming packets or receive errors :depends on: [host_os.net_interface_names, host_os.net_interface_info] :raises: WrongArgument if unsupported mode is supplied :raises: WrongArgument if interface is not present on this host :type interface_name: str """ validate_mode(mode, NET_MODES) interface_names = host_os.net_interface_names() validate_mode(interface_name, interface_names) info = host_os.net_interface_info(interface_name) return info._asdict()[ "{direction}_{mode}".format(direction='in', mode=mode) ] def outgoing(interface_name, mode="bytes", host_os=detect_host_os()): """ Returns amount of sent bytes or packets, dropped outgoing packets or send errors :depends on: [host_os.net_interface_names, host_os.net_interface_info] :raises: WrongArgument if unsupported mode is supplied :raises: WrongArgument if interface is not present on this host :type interface_name: str """ validate_mode(mode, NET_MODES) interface_names = host_os.net_interface_names() validate_mode(interface_name, interface_names) info = host_os.net_interface_info(interface_name) return info._asdict()[ "{direction}_{mode}".format(direction='out', mode=mode) ]

31.137255

76

0.72733

209

1,588

5.239234

0.248804

0.071233

0.065753

0.131507

0.759817

0

0.175063

1,588

50

77

31.76

0.835878

0.379093

0

0.5

0

0.100546

0

1

0.1

false

0

0.1

0

0.3

0

null

0

1

0

null

0

5

f69c549b02c4217f3bc790f5daea908b61b8fef0

29

py

Python

templates/base/models/my_models.py

timojl/tralo

90b928c0cb38dbc2a324d8761bce1b2a422f5e31

[ "MIT" ]

null

templates/base/models/my_models.py

timojl/tralo

90b928c0cb38dbc2a324d8761bce1b2a422f5e31

[ "MIT" ]

null

templates/base/models/my_models.py

timojl/tralo

90b928c0cb38dbc2a324d8761bce1b2a422f5e31

[ "MIT" ]

null

# put your custom models here

29

0.793103

5

29

4.6

1

0

0.172414

29

1

29

0.958333

0.931034

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

f6b3cd32b6274da1d052f8e608dac24857ddb939

230

py

Python

tests/test_libs_json_utils.py

fyntex/lib-cl-sii-python

b6ffb72be1f173a1d2e44b17ae5c08caf96ebf34

[ "MIT" ]

8

2020-03-07T19:58:40.000Z

2021-12-15T13:47:40.000Z

tests/test_libs_json_utils.py

fyntex/lib-cl-sii-python

b6ffb72be1f173a1d2e44b17ae5c08caf96ebf34

[ "MIT" ]

141

2020-01-17T22:47:35.000Z

2022-03-31T18:29:47.000Z

tests/test_libs_json_utils.py

fyntex/lib-cl-sii-python

b6ffb72be1f173a1d2e44b17ae5c08caf96ebf34

[ "MIT" ]

3

2020-03-07T20:30:02.000Z

2021-03-22T03:14:26.000Z

import unittest from cl_sii.libs.json_utils import read_json_schema # noqa: F401 from .utils import read_test_file_json_dict # noqa: F401 class FunctionReadJsonSchemaTest(unittest.TestCase): # TODO: implement pass

17.692308

65

0.778261

31

230

5.516129

0.677419

0.128655

0.175439

0

0.03125

0.165217

230

12

66

19.166667

0.859375

0.16087

0

0.083333

0

1

0

true

0.2

0.6

0

0.8

0

null

0

null

0

1

0

1

0

1

0

5

f6e1638c8daa68a27bf2f02768cf84b05bd7e455

361

py

Python

graficar.py

TmsRC/Final_Punto15

3009162bf84680bf73df1f8ef3df5b9d7c75ddf0

[ "MIT" ]

null

graficar.py

TmsRC/Final_Punto15

3009162bf84680bf73df1f8ef3df5b9d7c75ddf0

[ "MIT" ]

null

graficar.py

TmsRC/Final_Punto15

3009162bf84680bf73df1f8ef3df5b9d7c75ddf0

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt datos = np.loadtxt("datos.dat").T plt.figure() plt.plot(datos[1],datos[2]) plt.title("Trayectoria") plt.ylabel("Y") plt.xlabel("X") plt.savefig("RubioTomas_final_15.pdf") plt.figure() plt.plot(datos[1],datos[2]) plt.title("Trayectoria") plt.ylabel("Y") plt.xlabel("X") plt.savefig("RubioTomas_final_15.png")

20.055556

38

0.728532

61

361

4.245902

0.442623

0.069498

0.092664

0.123552

0.725869

0

0.02381

0.069252

361

18

39

20.055556

0.747024

0

0.666667

0

0.223757

0.127072

0

1

0

false

0

0.133333

0

0.133333

0

null

0

1

0

null

0

5

100660cf47aa30faf164dc251974237d0ad26da4

79

py

Python

snarl/__init__.py

JoshKarpel/snarl

73b56a5a4f5c1126bc424e82cded01e2aadf8666

[ "MIT" ]

5

2019-08-29T13:42:55.000Z

2021-11-11T00:45:38.000Z

snarl/__init__.py

JoshKarpel/snarl

73b56a5a4f5c1126bc424e82cded01e2aadf8666

[ "MIT" ]

null

snarl/__init__.py

JoshKarpel/snarl

73b56a5a4f5c1126bc424e82cded01e2aadf8666

[ "MIT" ]

null

from .snarl import Snarl from .snarled import snarled from . import exceptions

19.75

28

0.810127

11

79

5.818182

0.454545

0

0.151899

79

3

29

26.333333

0.955224

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

101175b56760fd79303547b6e7a1575ac33fd7ab

34

py

Python

app/services/__init__.py

hatamiarash7/Prometheus-Telegram

2ef9d158d2b53b9ffad0c80abfe7daa18075156d

[ "MIT" ]

null

app/services/__init__.py

hatamiarash7/Prometheus-Telegram

2ef9d158d2b53b9ffad0c80abfe7daa18075156d

[ "MIT" ]

2

2022-03-09T17:33:26.000Z

2022-03-11T09:43:14.000Z

app/services/__init__.py

hatamiarash7/Prometheus-Telegram

2ef9d158d2b53b9ffad0c80abfe7daa18075156d

[ "MIT" ]

null

from .message import send_message

17

33

0.852941

5

34

5.6

0.8

0

0.117647

34

1

34

0.933333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

1012a5bee8c15af1f7a1e372958c0e48ac6e537b

33,161

py

Python

algorithms/neural_networks_historic.py

sugarbrain/bank-marketing

1fd7687a44e5b834a8c10203970caf4bc21845a8

[ "MIT" ]

null

algorithms/neural_networks_historic.py

sugarbrain/bank-marketing

1fd7687a44e5b834a8c10203970caf4bc21845a8

[ "MIT" ]

null

algorithms/neural_networks_historic.py

sugarbrain/bank-marketing

1fd7687a44e5b834a8c10203970caf4bc21845a8

[ "MIT" ]

null

# -*- coding: utf-8 -*- import pandas import numpy as np from sklearn import preprocessing from sklearn import neighbors from sklearn.model_selection import StratifiedKFold, cross_val_score import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier # Tira limite de vizualição do dataframe quando printado pandas.set_option('display.max_columns', None) pandas.set_option('display.max_rows', None) SEED = 42 np.random.seed(SEED) # Full train set train_file = "../datasets/train.csv" def get_train_set(filepath, size=0.20): dataset = pandas.read_csv(train_file) test_size = 1.0 - size # use 20% of the train to search best params train, _ = train_test_split(dataset, test_size=test_size, random_state=SEED) return train # Neural Networks Params def generate_nn_params(): solvers = ["lbfgs", "sgd", "adam"] # hidden_layer_sizes = (neurons_per_layer, number_of_layers) neurons_per_layer = list(range(1, 51)) number_of_layers = list(range(1, 3)) params = [] for solver in solvers: for num_layers in number_of_layers: for num_neurons in neurons_per_layer: params.append({ "id": f"{solver[0:3].upper()}_{num_neurons}_{num_layers}", "solver": solver, "hidden_layer_sizes": (num_neurons, num_layers) }) return params def setup_kfold(X, Y, n_splits): kf = StratifiedKFold(n_splits=n_splits, random_state=SEED) kf.get_n_splits(X) return kf def run_nn_score(X, Y, params, kfold): print("Busca de Parametros Neural Networks") all_scores = [] for param in params: clf = MLPClassifier(solver=param["solver"], hidden_layer_sizes=param["hidden_layer_sizes"], random_state=SEED) scores = cross_val_score(clf, X, Y, cv=kfold) mean = scores.mean() all_scores.append({ "id": param["id"], "solver": param["solver"], "hidden_layer_sizes": param["hidden_layer_sizes"], "result": mean }) print("%s | %0.4f" % (param["id"], mean)) best = max(all_scores, key=lambda s: s["result"]) print(f"Best param: {best}") print(all_scores) return all_scores def plot(scores): # options plt.figure(figsize=(70, 8)) plt.margins(x=0.005) plt.rc('font', size=14) plt.xticks(rotation=90) plt.grid(linestyle='--') x = list(map(lambda x: x["id"], scores)) # names y = list(map(lambda x: x["result"], scores)) # scores plt.plot(x, y, 'o--') plt.suptitle('Busca de Parametros Neural Networks') plt.show() def print_markdown_table(scores): print("Variação | *solver* | *hidden_layer_sizes* | Acurácia média") print("------ | ------- | -------- | ----------") for s in scores: name = s["id"] solver = s["solver"] sizes = s["hidden_layer_sizes"] result = '{:0.4f}'.format(s["result"]) print(f"{name} | {solver} | {sizes} | {result}") K_SPLITS = 10 # split train set by 20% train = get_train_set(train_file, 0.20) # separate class from other columns X = train.values[:, :-1] Y = train['y'] # KFold kfold = setup_kfold(X, Y, K_SPLITS) # Generate params params = generate_nn_params() # Run scoring for best params #scores = run_nn_score(X, Y, params, kfold) scores = [{'id': 'LBF_1_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (1, 1), 'result': 0.8837815297949678}, {'id': 'LBF_2_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (2, 1), 'result': 0.9028840353941454}, {'id': 'LBF_3_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (3, 1), 'result': 0.8920389398540804}, {'id': 'LBF_4_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (4, 1), 'result': 0.892684090992536}, {'id': 'LBF_5_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (5, 1), 'result': 0.8928456410337106}, {'id': 'LBF_6_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (6, 1), 'result': 0.9035307600719632}, {'id': 'LBF_7_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (7, 1), 'result': 0.8894457469853609}, {'id': 'LBF_8_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (8, 1), 'result': 0.8837815297949678}, {'id': 'LBF_9_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (9, 1), 'result': 0.898837416667978}, {'id': 'LBF_10_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (10, 1), 'result': 0.894461665958574}, {'id': 'LBF_11_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (11, 1), 'result': 0.8900950942287821}, {'id': 'LBF_12_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (12, 1), 'result': 0.90385254887151}, {'id': 'LBF_13_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (13, 1), 'result': 0.8879917966147923}, {'id': 'LBF_14_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (14, 1), 'result': 0.8837825788212091}, {'id': 'LBF_15_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (15, 1), 'result': 0.8837815297949678}, {'id': 'LBF_16_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (16, 1), 'result': 0.8837815297949678}, {'id': 'LBF_17_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (17, 1), 'result': 0.8847524035813755}, {'id': 'LBF_18_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (18, 1), 'result': 0.8824854578737288}, {'id': 'LBF_19_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (19, 1), 'result': 0.9025611975683571}, {'id': 'LBF_20_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (20, 1), 'result': 0.8797343865556797}, {'id': 'LBF_21_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (21, 1), 'result': 0.8837815297949678}, {'id': 'LBF_22_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (22, 1), 'result': 0.8863726246112046}, {'id': 'LBF_23_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (23, 1), 'result': 0.8844303525252684}, {'id': 'LBF_24_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (24, 1), 'result': 0.8839457024017456}, {'id': 'LBF_25_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (25, 1), 'result': 0.8875032126428642}, {'id': 'LBF_26_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (26, 1), 'result': 0.8829753531284584}, {'id': 'LBF_27_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (27, 1), 'result': 0.8873437606541728}, {'id': 'LBF_28_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (28, 1), 'result': 0.8852407252967431}, {'id': 'LBF_29_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (29, 1), 'result': 0.8858877122311215}, {'id': 'LBF_30_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (30, 1), 'result': 0.8837815297949678}, {'id': 'LBF_31_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (31, 1), 'result': 0.8837815297949678}, {'id': 'LBF_32_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (32, 1), 'result': 0.8823239078325544}, {'id': 'LBF_33_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (33, 1), 'result': 0.883943866605823}, {'id': 'LBF_34_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (34, 1), 'result': 0.8842693269972148}, {'id': 'LBF_35_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (35, 1), 'result': 0.8881541334256475}, {'id': 'LBF_36_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (36, 1), 'result': 0.8860461151935717}, {'id': 'LBF_37_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (37, 1), 'result': 0.8837815297949678}, {'id': 'LBF_38_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (38, 1), 'result': 0.8837815297949678}, {'id': 'LBF_39_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (39, 1), 'result': 0.8866954624369928}, {'id': 'LBF_40_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (40, 1), 'result': 0.879897510136216}, {'id': 'LBF_41_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (41, 1), 'result': 0.8868562257084862}, {'id': 'LBF_42_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (42, 1), 'result': 0.8837815297949678}, {'id': 'LBF_43_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (43, 1), 'result': 0.8837815297949678}, {'id': 'LBF_44_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (44, 1), 'result': 0.8883135854143391}, {'id': 'LBF_45_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (45, 1), 'result': 0.8837815297949678}, {'id': 'LBF_46_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (46, 1), 'result': 0.8899343309572888}, {'id': 'LBF_47_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (47, 1), 'result': 0.8871832596392398}, {'id': 'LBF_48_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (48, 1), 'result': 0.8884772335079963}, {'id': 'LBF_49_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (49, 1), 'result': 0.8837815297949678}, {'id': 'LBF_50_1', 'solver': 'lbfgs', 'hidden_layer_sizes': (50, 1), 'result': 0.8837815297949678}, {'id': 'LBF_1_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (1, 2), 'result': 0.8837815297949678}, {'id': 'LBF_2_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (2, 2), 'result': 0.9002934650910293}, {'id': 'LBF_3_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (3, 2), 'result': 0.8837815297949678}, {'id': 'LBF_4_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (4, 2), 'result': 0.8936586363707889}, {'id': 'LBF_5_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (5, 2), 'result': 0.8925207051554395}, {'id': 'LBF_6_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (6, 2), 'result': 0.8978639203159666}, {'id': 'LBF_7_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (7, 2), 'result': 0.9001321773064154}, {'id': 'LBF_8_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (8, 2), 'result': 0.8975402957204975}, {'id': 'LBF_9_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (9, 2), 'result': 0.8931721504513435}, {'id': 'LBF_10_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (10, 2), 'result': 0.8936583741142284}, {'id': 'LBF_11_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (11, 2), 'result': 0.8892826234048246}, {'id': 'LBF_12_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (12, 2), 'result': 0.893005879792083}, {'id': 'LBF_13_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (13, 2), 'result': 0.8850789129990086}, {'id': 'LBF_14_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (14, 2), 'result': 0.886048737759175}, {'id': 'LBF_15_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (15, 2), 'result': 0.8858861386917594}, {'id': 'LBF_16_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (16, 2), 'result': 0.8813548698420691}, {'id': 'LBF_17_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (17, 2), 'result': 0.8960863453499289}, {'id': 'LBF_18_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (18, 2), 'result': 0.8826512040198686}, {'id': 'LBF_19_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (19, 2), 'result': 0.8907441792156432}, {'id': 'LBF_20_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (20, 2), 'result': 0.8834581674560589}, {'id': 'LBF_21_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (21, 2), 'result': 0.884915527161912}, {'id': 'LBF_22_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (22, 2), 'result': 0.8808723177710289}, {'id': 'LBF_23_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (23, 2), 'result': 0.886698609515717}, {'id': 'LBF_24_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (24, 2), 'result': 0.8803803244638164}, {'id': 'LBF_25_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (25, 2), 'result': 0.8810286226809962}, {'id': 'LBF_26_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (26, 2), 'result': 0.8852394140139417}, {'id': 'LBF_27_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (27, 2), 'result': 0.8847521413248153}, {'id': 'LBF_28_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (28, 2), 'result': 0.8862118613397115}, {'id': 'LBF_29_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (29, 2), 'result': 0.8933337004925178}, {'id': 'LBF_30_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (30, 2), 'result': 0.8826498927370668}, {'id': 'LBF_31_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (31, 2), 'result': 0.8834584297126193}, {'id': 'LBF_32_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (32, 2), 'result': 0.885079175255569}, {'id': 'LBF_33_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (33, 2), 'result': 0.8881536089125269}, {'id': 'LBF_34_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (34, 2), 'result': 0.8891239581858141}, {'id': 'LBF_35_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (35, 2), 'result': 0.8873448096804142}, {'id': 'LBF_36_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (36, 2), 'result': 0.8807081451642512}, {'id': 'LBF_37_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (37, 2), 'result': 0.8837820543080885}, {'id': 'LBF_38_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (38, 2), 'result': 0.8866967737197946}, {'id': 'LBF_39_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (39, 2), 'result': 0.8879881250229473}, {'id': 'LBF_40_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (40, 2), 'result': 0.8847534526076171}, {'id': 'LBF_41_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (41, 2), 'result': 0.8826491059673858}, {'id': 'LBF_42_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (42, 2), 'result': 0.8828127540610428}, {'id': 'LBF_43_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (43, 2), 'result': 0.8865352236786203}, {'id': 'LBF_44_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (44, 2), 'result': 0.8844332373474322}, {'id': 'LBF_45_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (45, 2), 'result': 0.889124744955495}, {'id': 'LBF_46_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (46, 2), 'result': 0.8892847214573072}, {'id': 'LBF_47_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (47, 2), 'result': 0.883460527765102}, {'id': 'LBF_48_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (48, 2), 'result': 0.8815187801922866}, {'id': 'LBF_49_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (49, 2), 'result': 0.8857264244465075}, {'id': 'LBF_50_2', 'solver': 'lbfgs', 'hidden_layer_sizes': (50, 2), 'result': 0.8836205042669143}, {'id': 'SGD_1_1', 'solver': 'sgd', 'hidden_layer_sizes': (1, 1), 'result': 0.8837815297949678}, {'id': 'SGD_2_1', 'solver': 'sgd', 'hidden_layer_sizes': (2, 1), 'result': 0.9051491453058699}, {'id': 'SGD_3_1', 'solver': 'sgd', 'hidden_layer_sizes': (3, 1), 'result': 0.8912293538522865}, {'id': 'SGD_4_1', 'solver': 'sgd', 'hidden_layer_sizes': (4, 1), 'result': 0.8896067725134144}, {'id': 'SGD_5_1', 'solver': 'sgd', 'hidden_layer_sizes': (5, 1), 'result': 0.9064420701483847}, {'id': 'SGD_6_1', 'solver': 'sgd', 'hidden_layer_sizes': (6, 1), 'result': 0.9077373552999429}, {'id': 'SGD_7_1', 'solver': 'sgd', 'hidden_layer_sizes': (7, 1), 'result': 0.9066044069592399}, {'id': 'SGD_8_1', 'solver': 'sgd', 'hidden_layer_sizes': (8, 1), 'result': 0.9061197568357173}, {'id': 'SGD_9_1', 'solver': 'sgd', 'hidden_layer_sizes': (9, 1), 'result': 0.9038535978977513}, {'id': 'SGD_10_1', 'solver': 'sgd', 'hidden_layer_sizes': (10, 1), 'result': 0.9041753866972982}, {'id': 'SGD_11_1', 'solver': 'sgd', 'hidden_layer_sizes': (11, 1), 'result': 0.9012653879036783}, {'id': 'SGD_12_1', 'solver': 'sgd', 'hidden_layer_sizes': (12, 1), 'result': 0.8837815297949678}, {'id': 'SGD_13_1', 'solver': 'sgd', 'hidden_layer_sizes': (13, 1), 'result': 0.9040172459914085}, {'id': 'SGD_14_1', 'solver': 'sgd', 'hidden_layer_sizes': (14, 1), 'result': 0.9041780092629018}, {'id': 'SGD_15_1', 'solver': 'sgd', 'hidden_layer_sizes': (15, 1), 'result': 0.8837815297949678}, {'id': 'SGD_16_1', 'solver': 'sgd', 'hidden_layer_sizes': (16, 1), 'result': 0.8837815297949678}, {'id': 'SGD_17_1', 'solver': 'sgd', 'hidden_layer_sizes': (17, 1), 'result': 0.9070893193393234}, {'id': 'SGD_18_1', 'solver': 'sgd', 'hidden_layer_sizes': (18, 1), 'result': 0.9043411328434381}, {'id': 'SGD_19_1', 'solver': 'sgd', 'hidden_layer_sizes': (19, 1), 'result': 0.9035299733022821}, {'id': 'SGD_20_1', 'solver': 'sgd', 'hidden_layer_sizes': (20, 1), 'result': 0.9067667437700953}, {'id': 'SGD_21_1', 'solver': 'sgd', 'hidden_layer_sizes': (21, 1), 'result': 0.9067667437700953}, {'id': 'SGD_22_1', 'solver': 'sgd', 'hidden_layer_sizes': (22, 1), 'result': 0.9061192323225965}, {'id': 'SGD_23_1', 'solver': 'sgd', 'hidden_layer_sizes': (23, 1), 'result': 0.9045008470886898}, {'id': 'SGD_24_1', 'solver': 'sgd', 'hidden_layer_sizes': (24, 1), 'result': 0.898674555344002}, {'id': 'SGD_25_1', 'solver': 'sgd', 'hidden_layer_sizes': (25, 1), 'result': 0.903692572369698}, {'id': 'SGD_26_1', 'solver': 'sgd', 'hidden_layer_sizes': (26, 1), 'result': 0.8837815297949678}, {'id': 'SGD_27_1', 'solver': 'sgd', 'hidden_layer_sizes': (27, 1), 'result': 0.9062805201072106}, {'id': 'SGD_28_1', 'solver': 'sgd', 'hidden_layer_sizes': (28, 1), 'result': 0.8837815297949678}, {'id': 'SGD_29_1', 'solver': 'sgd', 'hidden_layer_sizes': (29, 1), 'result': 0.9056335331728324}, {'id': 'SGD_30_1', 'solver': 'sgd', 'hidden_layer_sizes': (30, 1), 'result': 0.8837815297949678}, {'id': 'SGD_31_1', 'solver': 'sgd', 'hidden_layer_sizes': (31, 1), 'result': 0.9045000603190088}, {'id': 'SGD_32_1', 'solver': 'sgd', 'hidden_layer_sizes': (32, 1), 'result': 0.9036920478565771}, {'id': 'SGD_33_1', 'solver': 'sgd', 'hidden_layer_sizes': (33, 1), 'result': 0.9001300792539325}, {'id': 'SGD_34_1', 'solver': 'sgd', 'hidden_layer_sizes': (34, 1), 'result': 0.9057963944968084}, {'id': 'SGD_35_1', 'solver': 'sgd', 'hidden_layer_sizes': (35, 1), 'result': 0.9054738189275806}, {'id': 'SGD_36_1', 'solver': 'sgd', 'hidden_layer_sizes': (36, 1), 'result': 0.9074142552175942}, {'id': 'SGD_37_1', 'solver': 'sgd', 'hidden_layer_sizes': (37, 1), 'result': 0.9056340576859532}, {'id': 'SGD_38_1', 'solver': 'sgd', 'hidden_layer_sizes': (38, 1), 'result': 0.9036915233434565}, {'id': 'SGD_39_1', 'solver': 'sgd', 'hidden_layer_sizes': (39, 1), 'result': 0.8837815297949678}, {'id': 'SGD_40_1', 'solver': 'sgd', 'hidden_layer_sizes': (40, 1), 'result': 0.8837815297949678}, {'id': 'SGD_41_1', 'solver': 'sgd', 'hidden_layer_sizes': (41, 1), 'result': 0.8837815297949678}, {'id': 'SGD_42_1', 'solver': 'sgd', 'hidden_layer_sizes': (42, 1), 'result': 0.8913867077884954}, {'id': 'SGD_43_1', 'solver': 'sgd', 'hidden_layer_sizes': (43, 1), 'result': 0.9062813068768915}, {'id': 'SGD_44_1', 'solver': 'sgd', 'hidden_layer_sizes': (44, 1), 'result': 0.8837815297949678}, {'id': 'SGD_45_1', 'solver': 'sgd', 'hidden_layer_sizes': (45, 1), 'result': 0.9062810446203311}, {'id': 'SGD_46_1', 'solver': 'sgd', 'hidden_layer_sizes': (46, 1), 'result': 0.9040169837348483}, {'id': 'SGD_47_1', 'solver': 'sgd', 'hidden_layer_sizes': (47, 1), 'result': 0.905794558700886}, {'id': 'SGD_48_1', 'solver': 'sgd', 'hidden_layer_sizes': (48, 1), 'result': 0.9017500380272011}, {'id': 'SGD_49_1', 'solver': 'sgd', 'hidden_layer_sizes': (49, 1), 'result': 0.9056337954293927}, {'id': 'SGD_50_1', 'solver': 'sgd', 'hidden_layer_sizes': (50, 1), 'result': 0.8837815297949678}, {'id': 'SGD_1_2', 'solver': 'sgd', 'hidden_layer_sizes': (1, 2), 'result': 0.8837815297949678}, {'id': 'SGD_2_2', 'solver': 'sgd', 'hidden_layer_sizes': (2, 2), 'result': 0.9046637084126659}, {'id': 'SGD_3_2', 'solver': 'sgd', 'hidden_layer_sizes': (3, 2), 'result': 0.9054735566710201}, {'id': 'SGD_4_2', 'solver': 'sgd', 'hidden_layer_sizes': (4, 2), 'result': 0.9056337954293927}, {'id': 'SGD_5_2', 'solver': 'sgd', 'hidden_layer_sizes': (5, 2), 'result': 0.8837815297949678}, {'id': 'SGD_6_2', 'solver': 'sgd', 'hidden_layer_sizes': (6, 2), 'result': 0.899969053725879}, {'id': 'SGD_7_2', 'solver': 'sgd', 'hidden_layer_sizes': (7, 2), 'result': 0.9025590995158744}, {'id': 'SGD_8_2', 'solver': 'sgd', 'hidden_layer_sizes': (8, 2), 'result': 0.9009401897688469}, {'id': 'SGD_9_2', 'solver': 'sgd', 'hidden_layer_sizes': (9, 2), 'result': 0.9048260452235214}, {'id': 'SGD_10_2', 'solver': 'sgd', 'hidden_layer_sizes': (10, 2), 'result': 0.8837815297949678}, {'id': 'SGD_11_2', 'solver': 'sgd', 'hidden_layer_sizes': (11, 2), 'result': 0.8837815297949678}, {'id': 'SGD_12_2', 'solver': 'sgd', 'hidden_layer_sizes': (12, 2), 'result': 0.8983490949526104}, {'id': 'SGD_13_2', 'solver': 'sgd', 'hidden_layer_sizes': (13, 2), 'result': 0.8991615657765679}, {'id': 'SGD_14_2', 'solver': 'sgd', 'hidden_layer_sizes': (14, 2), 'result': 0.9054746056972615}, {'id': 'SGD_15_2', 'solver': 'sgd', 'hidden_layer_sizes': (15, 2), 'result': 0.9067680550528969}, {'id': 'SGD_16_2', 'solver': 'sgd', 'hidden_layer_sizes': (16, 2), 'result': 0.9007799510104746}, {'id': 'SGD_17_2', 'solver': 'sgd', 'hidden_layer_sizes': (17, 2), 'result': 0.9041761734669793}, {'id': 'SGD_18_2', 'solver': 'sgd', 'hidden_layer_sizes': (18, 2), 'result': 0.9075758052587686}, {'id': 'SGD_19_2', 'solver': 'sgd', 'hidden_layer_sizes': (19, 2), 'result': 0.9033668497217459}, {'id': 'SGD_20_2', 'solver': 'sgd', 'hidden_layer_sizes': (20, 2), 'result': 0.9032076599896147}, {'id': 'SGD_21_2', 'solver': 'sgd', 'hidden_layer_sizes': (21, 2), 'result': 0.8837815297949678}, {'id': 'SGD_22_2', 'solver': 'sgd', 'hidden_layer_sizes': (22, 2), 'result': 0.9061210681185191}, {'id': 'SGD_23_2', 'solver': 'sgd', 'hidden_layer_sizes': (23, 2), 'result': 0.8842669666881717}, {'id': 'SGD_24_2', 'solver': 'sgd', 'hidden_layer_sizes': (24, 2), 'result': 0.9051475717665077}, {'id': 'SGD_25_2', 'solver': 'sgd', 'hidden_layer_sizes': (25, 2), 'result': 0.9020739248792309}, {'id': 'SGD_26_2', 'solver': 'sgd', 'hidden_layer_sizes': (26, 2), 'result': 0.9033697345439096}, {'id': 'SGD_27_2', 'solver': 'sgd', 'hidden_layer_sizes': (27, 2), 'result': 0.9049870707515749}, {'id': 'SGD_28_2', 'solver': 'sgd', 'hidden_layer_sizes': (28, 2), 'result': 0.9048244716841591}, {'id': 'SGD_29_2', 'solver': 'sgd', 'hidden_layer_sizes': (29, 2), 'result': 0.9041774847497811}, {'id': 'SGD_30_2', 'solver': 'sgd', 'hidden_layer_sizes': (30, 2), 'result': 0.9038546469239929}, {'id': 'SGD_31_2', 'solver': 'sgd', 'hidden_layer_sizes': (31, 2), 'result': 0.9033692100307888}, {'id': 'SGD_32_2', 'solver': 'sgd', 'hidden_layer_sizes': (32, 2), 'result': 0.9025598862855553}, {'id': 'SGD_33_2', 'solver': 'sgd', 'hidden_layer_sizes': (33, 2), 'result': 0.8837815297949678}, {'id': 'SGD_34_2', 'solver': 'sgd', 'hidden_layer_sizes': (34, 2), 'result': 0.9066065050117228}, {'id': 'SGD_35_2', 'solver': 'sgd', 'hidden_layer_sizes': (35, 2), 'result': 0.9048247339407194}, {'id': 'SGD_36_2', 'solver': 'sgd', 'hidden_layer_sizes': (36, 2), 'result': 0.9046637084126659}, {'id': 'SGD_37_2', 'solver': 'sgd', 'hidden_layer_sizes': (37, 2), 'result': 0.882001594519887}, {'id': 'SGD_38_2', 'solver': 'sgd', 'hidden_layer_sizes': (38, 2), 'result': 0.9069298673506317}, {'id': 'SGD_39_2', 'solver': 'sgd', 'hidden_layer_sizes': (39, 2), 'result': 0.9032084467592958}, {'id': 'SGD_40_2', 'solver': 'sgd', 'hidden_layer_sizes': (40, 2), 'result': 0.904339559304076}, {'id': 'SGD_41_2', 'solver': 'sgd', 'hidden_layer_sizes': (41, 2), 'result': 0.9066038824461196}, {'id': 'SGD_42_2', 'solver': 'sgd', 'hidden_layer_sizes': (42, 2), 'result': 0.9041780092629018}, {'id': 'SGD_43_2', 'solver': 'sgd', 'hidden_layer_sizes': (43, 2), 'result': 0.9083867025433641}, {'id': 'SGD_44_2', 'solver': 'sgd', 'hidden_layer_sizes': (44, 2), 'result': 0.9053099085773629}, {'id': 'SGD_45_2', 'solver': 'sgd', 'hidden_layer_sizes': (45, 2), 'result': 0.9066078162945246}, {'id': 'SGD_46_2', 'solver': 'sgd', 'hidden_layer_sizes': (46, 2), 'result': 0.902720125043928}, {'id': 'SGD_47_2', 'solver': 'sgd', 'hidden_layer_sizes': (47, 2), 'result': 0.9072521806632994}, {'id': 'SGD_48_2', 'solver': 'sgd', 'hidden_layer_sizes': (48, 2), 'result': 0.9030453231787592}, {'id': 'SGD_49_2', 'solver': 'sgd', 'hidden_layer_sizes': (49, 2), 'result': 0.9062818313900124}, {'id': 'SGD_50_2', 'solver': 'sgd', 'hidden_layer_sizes': (50, 2), 'result': 0.9046639706692264}, {'id': 'ADA_1_1', 'solver': 'adam', 'hidden_layer_sizes': (1, 1), 'result': 0.8837815297949678}, {'id': 'ADA_2_1', 'solver': 'adam', 'hidden_layer_sizes': (2, 1), 'result': 0.9074166155266374}, {'id': 'ADA_3_1', 'solver': 'adam', 'hidden_layer_sizes': (3, 1), 'result': 0.9049886442909371}, {'id': 'ADA_4_1', 'solver': 'adam', 'hidden_layer_sizes': (4, 1), 'result': 0.9009388784860454}, {'id': 'ADA_5_1', 'solver': 'adam', 'hidden_layer_sizes': (5, 1), 'result': 0.8994838790892354}, {'id': 'ADA_6_1', 'solver': 'adam', 'hidden_layer_sizes': (6, 1), 'result': 0.9027237966357727}, {'id': 'ADA_7_1', 'solver': 'adam', 'hidden_layer_sizes': (7, 1), 'result': 0.9014298227670168}, {'id': 'ADA_8_1', 'solver': 'adam', 'hidden_layer_sizes': (8, 1), 'result': 0.900132701819536}, {'id': 'ADA_9_1', 'solver': 'adam', 'hidden_layer_sizes': (9, 1), 'result': 0.9007770661883108}, {'id': 'ADA_10_1', 'solver': 'adam', 'hidden_layer_sizes': (10, 1), 'result': 0.8965686351644087}, {'id': 'ADA_11_1', 'solver': 'adam', 'hidden_layer_sizes': (11, 1), 'result': 0.8999701027521203}, {'id': 'ADA_12_1', 'solver': 'adam', 'hidden_layer_sizes': (12, 1), 'result': 0.8853996527723142}, {'id': 'ADA_13_1', 'solver': 'adam', 'hidden_layer_sizes': (13, 1), 'result': 0.9027214363267296}, {'id': 'ADA_14_1', 'solver': 'adam', 'hidden_layer_sizes': (14, 1), 'result': 0.9015895370122685}, {'id': 'ADA_15_1', 'solver': 'adam', 'hidden_layer_sizes': (15, 1), 'result': 0.8842695892537753}, {'id': 'ADA_16_1', 'solver': 'adam', 'hidden_layer_sizes': (16, 1), 'result': 0.8868588482740897}, {'id': 'ADA_17_1', 'solver': 'adam', 'hidden_layer_sizes': (17, 1), 'result': 0.8985124807897069}, {'id': 'ADA_18_1', 'solver': 'adam', 'hidden_layer_sizes': (18, 1), 'result': 0.9015887502425874}, {'id': 'ADA_19_1', 'solver': 'adam', 'hidden_layer_sizes': (19, 1), 'result': 0.8944653375504188}, {'id': 'ADA_20_1', 'solver': 'adam', 'hidden_layer_sizes': (20, 1), 'result': 0.8952754480653333}, {'id': 'ADA_21_1', 'solver': 'adam', 'hidden_layer_sizes': (21, 1), 'result': 0.8990013270181955}, {'id': 'ADA_22_1', 'solver': 'adam', 'hidden_layer_sizes': (22, 1), 'result': 0.8907378850581947}, {'id': 'ADA_23_1', 'solver': 'adam', 'hidden_layer_sizes': (23, 1), 'result': 0.896733332284307}, {'id': 'ADA_24_1', 'solver': 'adam', 'hidden_layer_sizes': (24, 1), 'result': 0.8962473708779826}, {'id': 'ADA_25_1', 'solver': 'adam', 'hidden_layer_sizes': (25, 1), 'result': 0.8998075036847049}, {'id': 'ADA_26_1', 'solver': 'adam', 'hidden_layer_sizes': (26, 1), 'result': 0.8913901171237798}, {'id': 'ADA_27_1', 'solver': 'adam', 'hidden_layer_sizes': (27, 1), 'result': 0.8930087646142468}, {'id': 'ADA_28_1', 'solver': 'adam', 'hidden_layer_sizes': (28, 1), 'result': 0.8875058352084677}, {'id': 'ADA_29_1', 'solver': 'adam', 'hidden_layer_sizes': (29, 1), 'result': 0.8943061478182877}, {'id': 'ADA_30_1', 'solver': 'adam', 'hidden_layer_sizes': (30, 1), 'result': 0.8837815297949678}, {'id': 'ADA_31_1', 'solver': 'adam', 'hidden_layer_sizes': (31, 1), 'result': 0.8983514552616534}, {'id': 'ADA_32_1', 'solver': 'adam', 'hidden_layer_sizes': (32, 1), 'result': 0.8933337004925178}, {'id': 'ADA_33_1', 'solver': 'adam', 'hidden_layer_sizes': (33, 1), 'result': 0.8931697901423004}, {'id': 'ADA_34_1', 'solver': 'adam', 'hidden_layer_sizes': (34, 1), 'result': 0.8904202923636134}, {'id': 'ADA_35_1', 'solver': 'adam', 'hidden_layer_sizes': (35, 1), 'result': 0.8960852963236874}, {'id': 'ADA_36_1', 'solver': 'adam', 'hidden_layer_sizes': (36, 1), 'result': 0.8985148410987499}, {'id': 'ADA_37_1', 'solver': 'adam', 'hidden_layer_sizes': (37, 1), 'result': 0.89608713211961}, {'id': 'ADA_38_1', 'solver': 'adam', 'hidden_layer_sizes': (38, 1), 'result': 0.898351193005093}, {'id': 'ADA_39_1', 'solver': 'adam', 'hidden_layer_sizes': (39, 1), 'result': 0.8954372603630679}, {'id': 'ADA_40_1', 'solver': 'adam', 'hidden_layer_sizes': (40, 1), 'result': 0.8928474768296327}, {'id': 'ADA_41_1', 'solver': 'adam', 'hidden_layer_sizes': (41, 1), 'result': 0.8876660739668404}, {'id': 'ADA_42_1', 'solver': 'adam', 'hidden_layer_sizes': (42, 1), 'result': 0.8931690033726195}, {'id': 'ADA_43_1', 'solver': 'adam', 'hidden_layer_sizes': (43, 1), 'result': 0.896894620068921}, {'id': 'ADA_44_1', 'solver': 'adam', 'hidden_layer_sizes': (44, 1), 'result': 0.8934973485861748}, {'id': 'ADA_45_1', 'solver': 'adam', 'hidden_layer_sizes': (45, 1), 'result': 0.8999701027521205}, {'id': 'ADA_46_1', 'solver': 'adam', 'hidden_layer_sizes': (46, 1), 'result': 0.8989981799394713}, {'id': 'ADA_47_1', 'solver': 'adam', 'hidden_layer_sizes': (47, 1), 'result': 0.8970580059060177}, {'id': 'ADA_48_1', 'solver': 'adam', 'hidden_layer_sizes': (48, 1), 'result': 0.8949526102395451}, {'id': 'ADA_49_1', 'solver': 'adam', 'hidden_layer_sizes': (49, 1), 'result': 0.897704992840396}, {'id': 'ADA_50_1', 'solver': 'adam', 'hidden_layer_sizes': (50, 1), 'result': 0.8964091831757172}, {'id': 'ADA_1_2', 'solver': 'adam', 'hidden_layer_sizes': (1, 2), 'result': 0.8837815297949678}, {'id': 'ADA_2_2', 'solver': 'adam', 'hidden_layer_sizes': (2, 2), 'result': 0.9002937273475897}, {'id': 'ADA_3_2', 'solver': 'adam', 'hidden_layer_sizes': (3, 2), 'result': 0.9028840353941453}, {'id': 'ADA_4_2', 'solver': 'adam', 'hidden_layer_sizes': (4, 2), 'result': 0.904828143276004}, {'id': 'ADA_5_2', 'solver': 'adam', 'hidden_layer_sizes': (5, 2), 'result': 0.9030419138434749}, {'id': 'ADA_6_2', 'solver': 'adam', 'hidden_layer_sizes': (6, 2), 'result': 0.8994849281154768}, {'id': 'ADA_7_2', 'solver': 'adam', 'hidden_layer_sizes': (7, 2), 'result': 0.8977026325313527}, {'id': 'ADA_8_2', 'solver': 'adam', 'hidden_layer_sizes': (8, 2), 'result': 0.9030461099484404}, {'id': 'ADA_9_2', 'solver': 'adam', 'hidden_layer_sizes': (9, 2), 'result': 0.9028824618547834}, {'id': 'ADA_10_2', 'solver': 'adam', 'hidden_layer_sizes': (10, 2), 'result': 0.8955967123517595}, {'id': 'ADA_11_2', 'solver': 'adam', 'hidden_layer_sizes': (11, 2), 'result': 0.9014272002014131}, {'id': 'ADA_12_2', 'solver': 'adam', 'hidden_layer_sizes': (12, 2), 'result': 0.8988379411810987}, {'id': 'ADA_13_2', 'solver': 'adam', 'hidden_layer_sizes': (13, 2), 'result': 0.8926861890450191}, {'id': 'ADA_14_2', 'solver': 'adam', 'hidden_layer_sizes': (14, 2), 'result': 0.8951128489979177}, {'id': 'ADA_15_2', 'solver': 'adam', 'hidden_layer_sizes': (15, 2), 'result': 0.8967320210015053}, {'id': 'ADA_16_2', 'solver': 'adam', 'hidden_layer_sizes': (16, 2), 'result': 0.8944655998069793}, {'id': 'ADA_17_2', 'solver': 'adam', 'hidden_layer_sizes': (17, 2), 'result': 0.8981901674770395}, {'id': 'ADA_18_2', 'solver': 'adam', 'hidden_layer_sizes': (18, 2), 'result': 0.9023957136787777}, {'id': 'ADA_19_2', 'solver': 'adam', 'hidden_layer_sizes': (19, 2), 'result': 0.8993215422783802}, {'id': 'ADA_20_2', 'solver': 'adam', 'hidden_layer_sizes': (20, 2), 'result': 0.8926848777622174}, {'id': 'ADA_21_2', 'solver': 'adam', 'hidden_layer_sizes': (21, 2), 'result': 0.8972166711250281}, {'id': 'ADA_22_2', 'solver': 'adam', 'hidden_layer_sizes': (22, 2), 'result': 0.8986735063177607}, {'id': 'ADA_23_2', 'solver': 'adam', 'hidden_layer_sizes': (23, 2), 'result': 0.902237048459767}, {'id': 'ADA_24_2', 'solver': 'adam', 'hidden_layer_sizes': (24, 2), 'result': 0.9002934650910293}, {'id': 'ADA_25_2', 'solver': 'adam', 'hidden_layer_sizes': (25, 2), 'result': 0.8930087646142468}, {'id': 'ADA_26_2', 'solver': 'adam', 'hidden_layer_sizes': (26, 2), 'result': 0.8930077155880056}, {'id': 'ADA_27_2', 'solver': 'adam', 'hidden_layer_sizes': (27, 2), 'result': 0.8954343755409042}, {'id': 'ADA_28_2', 'solver': 'adam', 'hidden_layer_sizes': (28, 2), 'result': 0.8946279366178347}, {'id': 'ADA_29_2', 'solver': 'adam', 'hidden_layer_sizes': (29, 2), 'result': 0.8978644448290873}, {'id': 'ADA_30_2', 'solver': 'adam', 'hidden_layer_sizes': (30, 2), 'result': 0.8970564323666557}, {'id': 'ADA_31_2', 'solver': 'adam', 'hidden_layer_sizes': (31, 2), 'result': 0.8968917352467572}, {'id': 'ADA_32_2', 'solver': 'adam', 'hidden_layer_sizes': (32, 2), 'result': 0.8967330700277467}, {'id': 'ADA_33_2', 'solver': 'adam', 'hidden_layer_sizes': (33, 2), 'result': 0.8936552270355044}, {'id': 'ADA_34_2', 'solver': 'adam', 'hidden_layer_sizes': (34, 2), 'result': 0.8977031570444733}, {'id': 'ADA_35_2', 'solver': 'adam', 'hidden_layer_sizes': (35, 2), 'result': 0.8938165148201183}, {'id': 'ADA_36_2', 'solver': 'adam', 'hidden_layer_sizes': (36, 2), 'result': 0.8951154715635212}, {'id': 'ADA_37_2', 'solver': 'adam', 'hidden_layer_sizes': (37, 2), 'result': 0.8957611472150976}, {'id': 'ADA_38_2', 'solver': 'adam', 'hidden_layer_sizes': (38, 2), 'result': 0.8865346991654997}, {'id': 'ADA_39_2', 'solver': 'adam', 'hidden_layer_sizes': (39, 2), 'result': 0.8970548588272935}, {'id': 'ADA_40_2', 'solver': 'adam', 'hidden_layer_sizes': (40, 2), 'result': 0.8926830419662949}, {'id': 'ADA_41_2', 'solver': 'adam', 'hidden_layer_sizes': (41, 2), 'result': 0.8980288796924254}, {'id': 'ADA_42_2', 'solver': 'adam', 'hidden_layer_sizes': (42, 2), 'result': 0.8938180883594804}, {'id': 'ADA_43_2', 'solver': 'adam', 'hidden_layer_sizes': (43, 2), 'result': 0.8967312342318243}, {'id': 'ADA_44_2', 'solver': 'adam', 'hidden_layer_sizes': (44, 2), 'result': 0.8920389398540804}, {'id': 'ADA_45_2', 'solver': 'adam', 'hidden_layer_sizes': (45, 2), 'result': 0.8946281988743949}, {'id': 'ADA_46_2', 'solver': 'adam', 'hidden_layer_sizes': (46, 2), 'result': 0.8886377345229292}, {'id': 'ADA_47_2', 'solver': 'adam', 'hidden_layer_sizes': (47, 2), 'result': 0.8894491563206455}, {'id': 'ADA_48_2', 'solver': 'adam', 'hidden_layer_sizes': (48, 2), 'result': 0.8917142662323698}, {'id': 'ADA_49_2', 'solver': 'adam', 'hidden_layer_sizes': (49, 2), 'result': 0.8973813682449266}, {'id': 'ADA_50_2', 'solver': 'adam', 'hidden_layer_sizes': (50, 2), 'result': 0.8918742427341819}] # plot plot(scores) #print_markdown_table(scores)

231.895105

29,571

0.641718

4,676

33,161

4.274594

0.09645

0.169502

0.246548

0.101061

0.622023

0.603712

0.408745

0.038123

0.005403

0

0.230108

0.108591

33,161

143

29,572

231.895105

0.446076

0.012635

0

0.046512

0

0.417723

0.002108

0

1

0.069767

false

0

0.093023

0

0.209302

0.093023

0

null

0

1

0

1

0

1

0

null

0

5

63d8fe51705207425ae49fc5d200f3bbc54e07e0

2,977

py

Python

python/test/test_pcb_api.py

thracesystems/powermeter-api

7bdab034ff916ee49e986de88f157bd044e981c1

[ "Apache-2.0" ]

null

python/test/test_pcb_api.py

thracesystems/powermeter-api

7bdab034ff916ee49e986de88f157bd044e981c1

[ "Apache-2.0" ]

null

python/test/test_pcb_api.py

thracesystems/powermeter-api

7bdab034ff916ee49e986de88f157bd044e981c1

[ "Apache-2.0" ]

null

# coding: utf-8 """ PowerMeter API API # noqa: E501 The version of the OpenAPI document: 2021.4.1 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import unittest import powermeter_api from powermeter_api.api.pcb_api import PcbApi # noqa: E501 from powermeter_api.rest import ApiException class TestPcbApi(unittest.TestCase): """PcbApi unit test stubs""" def setUp(self): self.api = powermeter_api.api.pcb_api.PcbApi() # noqa: E501 def tearDown(self): pass def test_pcb_commit_create(self): """Test case for pcb_commit_create """ pass def test_pcb_library_clone(self): """Test case for pcb_library_clone """ pass def test_pcb_library_create(self): """Test case for pcb_library_create """ pass def test_pcb_library_delete(self): """Test case for pcb_library_delete """ pass def test_pcb_library_list(self): """Test case for pcb_library_list """ pass def test_pcb_library_read(self): """Test case for pcb_library_read """ pass def test_pcb_library_update(self): """Test case for pcb_library_update """ pass def test_pcb_library_update_list(self): """Test case for pcb_library_update_list """ pass def test_pcb_library_version_list(self): """Test case for pcb_library_version_list """ pass def test_pcb_permissions_list(self): """Test case for pcb_permissions_list """ pass def test_pcb_permissions_update(self): """Test case for pcb_permissions_update """ pass def test_pcb_read(self): """Test case for pcb_read """ pass def test_pcb_restore_create(self): """Test case for pcb_restore_create """ pass def test_pcb_supply_clone(self): """Test case for pcb_supply_clone """ pass def test_pcb_supply_create(self): """Test case for pcb_supply_create """ pass def test_pcb_supply_delete(self): """Test case for pcb_supply_delete """ pass def test_pcb_supply_list(self): """Test case for pcb_supply_list """ pass def test_pcb_supply_read(self): """Test case for pcb_supply_read """ pass def test_pcb_supply_update(self): """Test case for pcb_supply_update """ pass def test_pcb_update(self): """Test case for pcb_update """ pass def test_pcb_update_list(self): """Test case for pcb_update_list """ pass def test_pcb_version_list(self): """Test case for pcb_version_list """ pass if __name__ == '__main__': unittest.main()

17.933735

68

0.590863

365

2,977

4.465753

0.153425

0.094479

0.148466

0.188957

0.746012

0.6

0.119018

0

0.00794

0.323144

2,977

165

69

18.042424

0.800993

0.372187

0

0.410714

1

0

0.00463

0

1

0.428571

false

0.410714

0.089286

0

0.535714

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

1244b95452209a7f8a6d0f009884cb2359ecd6f5

119

py

Python

python_projects/pc_shutdown.py

vijay0707/PYTHON-PROJECTS

3f5bf995b431e5c83c601200b08a7eae536e3571

[ "MIT" ]

null

python_projects/pc_shutdown.py

vijay0707/PYTHON-PROJECTS

3f5bf995b431e5c83c601200b08a7eae536e3571

[ "MIT" ]

null

python_projects/pc_shutdown.py

vijay0707/PYTHON-PROJECTS

3f5bf995b431e5c83c601200b08a7eae536e3571

[ "MIT" ]

null

# Shutdown Computer using Python import os def pc_shutdown(): os.system("shutdown /s /t 1") pc_shutdown()

14.875

34

0.663866

17

119

4.529412

0.705882

0.25974

0

0.01087

0.226891

119

8

35

14.875

0.826087

0.252101

0

0.197531

0

1

0.25

true

0

0.25

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

12497934137e96806cb206fd1a36b24806a3546b

19

py

Python

lib/python3.7/site-packages/braintree/version.py

edbolivar/perfectpair-django

798a88d16c6689bad2248add0e4e4958bda64545

[ "MIT" ]

null

lib/python3.7/site-packages/braintree/version.py

edbolivar/perfectpair-django

798a88d16c6689bad2248add0e4e4958bda64545

[ "MIT" ]

9

2019-12-04T23:15:54.000Z

2022-02-10T11:05:43.000Z

lib/python3.7/site-packages/braintree/version.py

edbolivar/perfectpair

c165cff40353c602fe0dc418375b90e9b25de674

[ "MIT" ]

null

Version = "3.53.0"

9.5

18

0.578947

4

19

2.75

1

0

0.25

0.157895

19

1

19

0.4375

0

0.315789

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

125f68a4685966ea0cb2f8a96ae574c06c5c5236

72

py

Python

TorrentBOX/test.py

herokukuki/pp

8ea412b85bbe55b33afc09461b7d194ce5a733ab

[ "BSD-3-Clause" ]

62

2015-07-30T17:51:53.000Z

2021-05-10T03:43:18.000Z

TorrentBOX/test.py

herokukuki/pp

8ea412b85bbe55b33afc09461b7d194ce5a733ab

[ "BSD-3-Clause" ]

11

2015-08-05T04:57:51.000Z

2018-10-05T10:40:34.000Z

TorrentBOX/test.py

herokukuki/pp

8ea412b85bbe55b33afc09461b7d194ce5a733ab

[ "BSD-3-Clause" ]

28

2015-07-30T18:03:01.000Z

2020-12-11T00:38:23.000Z

from pathlib import Path print(Path(__file__).resolve().parent.parent)

18

45

0.791667

10

72

5.3

0.8

0

0.083333

72

3

46

24

0.80303

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

89e1372201764f23c20c90cc2bc66538086e2020

47

py

Python

code/abc069_a_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

3

2019-08-16T16:55:48.000Z

2021-04-11T10:21:40.000Z

code/abc069_a_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

code/abc069_a_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

n,m=map(int,input().split()) print((n-1)*(m-1))

23.5

28

0.574468

11

47

2.454545

0.727273

0

0.043478

0.021277

47

2

29

23.5

0.543478

0

1

0

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

d6048798efe8366547773de6adaf83fafd33a305

32

py

Python

xueshanlinghu/__init__.py

xueshanlinghu/xueshanlinghu-package

fc1f75a30fc1dfd83eeb0bcd6876021239c0af69

[ "MIT" ]

1

2017-03-20T12:35:21.000Z

xueshanlinghu/__init__.py

xueshanlinghu/xueshanlinghu-package

fc1f75a30fc1dfd83eeb0bcd6876021239c0af69

[ "MIT" ]

null

xueshanlinghu/__init__.py

xueshanlinghu/xueshanlinghu-package

fc1f75a30fc1dfd83eeb0bcd6876021239c0af69

[ "MIT" ]

null

from xueshanlinghu.math import *

32

0.84375

4

32

6.75

1

0

0.09375

32

1

32

0.931034

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

c3fc5e4997f7f0a56044f8e6473ce8d6cd80f367

15,031

py

Python

simple_tensor/tensor_operations.py

fatchur/simple_tensor

ebc66d46d54fcfb65ef104978a6feca0a156b9b3

[ "MIT" ]

21

2019-03-08T16:02:46.000Z

2022-02-09T03:31:26.000Z

simple_tensor/tensor_operations.py

fatchur/simple_tensor

ebc66d46d54fcfb65ef104978a6feca0a156b9b3

[ "MIT" ]

3

2020-02-04T08:43:20.000Z

2020-10-20T13:52:16.000Z

simple_tensor/tensor_operations.py

fatchur/simple_tensor

ebc66d46d54fcfb65ef104978a6feca0a156b9b3

[ "MIT" ]

5

2019-11-30T03:40:04.000Z

2021-12-26T07:01:53.000Z

''' File name: test.py Author: [Mochammad F Rahman] Date created: / /2019 Date last modified: 17/07/2019 Python Version: >= 3.5 Simple-tensor version: v0.6.2 License: MIT License Maintainer: [Mochammad F Rahman] ''' import tensorflow as tf from tensorflow.python import control_flow_ops def new_weights(shape, name, data_type=tf.float32): """ Creating new trainable tensor (filter) as weight Args: shape: a list of integer as the shape of this weight. - example (convolution case), [filter height, filter width, input channels, output channels] - example (fully connected case), [num input, num output] name: a string, basic name of this filter/weight Return: a trainable weight/filter tensor with float 32 data type """ return tf.Variable(tf.truncated_normal(shape, stddev=0.05, dtype=data_type), dtype=data_type, name='weight_'+str(name)) def new_biases(length, name, data_type=tf.float32): """ Creating new trainable tensor as bias Args: length: an integer, the num of output features - Note, number output neurons = number of bias values name: a string, basic name of this bias Return: a trainable bias tensor with float 32 data type """ return tf.Variable(tf.constant(0.05, shape=[length], dtype=data_type), dtype=data_type, name='bias_'+str(name)) def new_fc_layer(input, num_inputs, num_outputs, name=None, dropout_val=0.85, activation="LRELU", lrelu_alpha=0.2, data_type=tf.float32, is_training=True, use_bias=True): """[summary] Arguments: input {[type]} -- [description] num_outputs {[type]} -- [description] Keyword Arguments: name {[type]} -- [description] (default: {None}) dropout_val {float} -- [description] (default: {0.85}) activation {str} -- [description] (default: {"LRELU"}) lrelu_alpha {float} -- [description] (default: {0.2}) data_type {[type]} -- [description] (default: {tf.float32}) is_training {bool} -- [description] (default: {True}) use_bias {bool} -- [description] (default: {True}) Returns: [type] -- [description] """ weights = new_weights(shape=[num_inputs, num_outputs], name=name, data_type=data_type) layer = tf.matmul(input, weights) if use_bias: biases = new_biases(length=num_outputs, name=name, data_type=data_type) layer += biases if activation in ["RELU", "relu", "Relu"]: layer = tf.nn.relu(layer) elif activation in ["LRELU", "lrelu", "Lrelu"]: layer = tf.nn.leaky_relu(layer, alpha=lrelu_alpha) elif activation in ["SELU", "selu", "Selu"]: layer = tf.nn.selu(layer) elif activation in ["ELU", "elu", "Elu"]: layer = tf.nn.elu(layer) elif activation in ["SIGMOID", "sigmoid", "Sigmoid"]: layer = tf.nn.sigmoid(layer) elif activation in ["SOFTMAX", "softmax", "Softmax"]: layer == tf.nn.softmax(layer) layer = tf.nn.dropout(layer, dropout_val) return layer def new_conv1d_layer(input, filter_shape, name=None, dropout_val=0.85, activation='LRELU', lrelu_alpha=0.2, padding='SAME', strides=1, data_type=tf.float32, is_training=True, use_bias=True, use_batchnorm=False): """[summary] Arguments: input {[type]} -- [description] filter_shape {[type]} -- [description] Keyword Arguments: name {[type]} -- [description] (default: {None}) dropout_val {float} -- [description] (default: {0.85}) activation {str} -- [description] (default: {'LRELU'}) lrelu_alpha {float} -- [description] (default: {0.2}) padding {str} -- [description] (default: {'SAME'}) strides {int} -- [description] (default: {1}) data_type {[type]} -- [description] (default: {tf.float32}) is_training {bool} -- [description] (default: {True}) use_bias {bool} -- [description] (default: {True}) use_batchnorm {bool} -- [description] (default: {False}) Returns: [type] -- [description] """ shape = filter_shape weights = new_weights(shape=shape, name=name, data_type=data_type) layer = tf.nn.conv1d(input, filters=weights, stride=strides, padding=padding, name='convolution1d_' + str(name)) if use_bias: biases = new_biases(length=filter_shape[2], name=name, data_type=data_type) layer += biases if use_batchnorm: layer = batch_norm(inputs=layer, training=is_training) if activation in ["RELU", "relu", "Relu"]: layer = tf.nn.relu(layer) elif activation in ["LRELU", "lrelu", "Lrelu"]: layer = tf.nn.leaky_relu(layer, alpha=lrelu_alpha) elif activation in ["SELU", "selu", "Selu"]: layer = tf.nn.selu(layer) elif activation in ["ELU", "elu", "Elu"]: layer = tf.nn.elu(layer) elif activation in ["SIGMOID", "sigmoid", "Sigmoid"]: layer = tf.nn.sigmoid(layer) elif activation in ["SOFTMAX", "softmax", "Softmax"]: layer == tf.nn.softmax(layer) layer = tf.nn.dropout(layer, dropout_val) return layer def new_conv2d_layer(input, filter_shape, name=None, dropout_val=0.85, activation = 'LRELU', lrelu_alpha=0.2, padding='SAME', strides=[1, 1, 1, 1], data_type=tf.float32, is_training=True, use_bias=True, use_batchnorm=False): """[summary] Arguments: input {[type]} -- [description] filter_shape {[type]} -- [description] Keyword Arguments: name {[type]} -- [description] (default: {None}) dropout_val {float} -- [description] (default: {0.85}) activation {str} -- [description] (default: {'LRELU'}) lrelu_alpha {float} -- [description] (default: {0.2}) padding {str} -- [description] (default: {'SAME'}) strides {list} -- [description] (default: {[1, 1, 1, 1]}) data_type {[type]} -- [description] (default: {tf.float32}) is_training {bool} -- [description] (default: {True}) use_bias {bool} -- [description] (default: {True}) use_batchnorm {bool} -- [description] (default: {False}) Returns: [type] -- [description] """ shape = filter_shape weights = new_weights(shape=shape, name=name, data_type=data_type) layer = tf.nn.conv2d(input=input, filter=weights, strides=strides, padding=padding, name='convolution_'+str(name)) if use_bias: biases = new_biases(length=filter_shape[3], name=name, data_type=data_type) layer += biases if use_batchnorm: layer = batch_norm(inputs=layer, training=is_training) if activation in ["RELU", "relu", "Relu"]: layer = tf.nn.relu(layer) elif activation in ["LRELU", "lrelu", "Lrelu"]: layer = tf.nn.leaky_relu(layer, alpha=lrelu_alpha) elif activation in ["SELU", "selu", "Selu"]: layer = tf.nn.selu(layer) elif activation in ["ELU", "elu", "Elu"]: layer = tf.nn.elu(layer) elif activation in ["SIGMOID", "sigmoid", "Sigmoid"]: layer = tf.nn.sigmoid(layer) elif activation in ["SOFTMAX", "softmax", "Softmax"]: layer == tf.nn.softmax(layer) layer = tf.nn.dropout(layer, dropout_val) return layer def new_conv2d_depthwise_layer(input, filter_shape, name=None, dropout_val=0.85, activation = 'LRELU', lrelu_alpha=0.2, padding='SAME', strides=[1, 1, 1, 1], data_type=tf.float32, is_training=True, use_bias=True, use_batchnorm=False): """[summary] Arguments: input {[type]} -- [description] filter_shape {[type]} -- [description] Keyword Arguments: name {[type]} -- [description] (default: {None}) dropout_val {float} -- [description] (default: {0.85}) activation {str} -- [description] (default: {'LRELU'}) lrelu_alpha {float} -- [description] (default: {0.2}) padding {str} -- [description] (default: {'SAME'}) strides {list} -- [description] (default: {[1, 1, 1, 1]}) data_type {[type]} -- [description] (default: {tf.float32}) is_training {bool} -- [description] (default: {True}) use_bias {bool} -- [description] (default: {True}) use_batchnorm {bool} -- [description] (default: {False}) Returns: [type] -- [description] """ shape = filter_shape weights = new_weights(shape=shape, name=name, data_type=data_type) layer = tf.nn.depthwise_conv2d(input=input, filter=weights, strides=strides, padding=padding, name='convolution_depthwise_'+str(name)) if use_bias: biases = new_biases(length=filter_shape[3], name=name, data_type=data_type) layer += biases if use_batchnorm: layer = batch_norm(inputs=layer, training=is_training) if activation in ["RELU", "relu", "Relu"]: layer = tf.nn.relu(layer) elif activation in ["LRELU", "lrelu", "Lrelu"]: layer = tf.nn.leaky_relu(layer, alpha=lrelu_alpha) elif activation in ["SELU", "selu", "Selu"]: layer = tf.nn.selu(layer) elif activation in ["ELU", "elu", "Elu"]: layer = tf.nn.elu(layer) elif activation in ["SIGMOID", "sigmoid", "Sigmoid"]: layer = tf.nn.sigmoid(layer) elif activation in ["SOFTMAX", "softmax", "Softmax"]: layer == tf.nn.softmax(layer) layer = tf.nn.dropout(layer, dropout_val) return layer def new_deconv_layer(input, filter_shape, output_shape, name=None, activation = 'LRELU', lrelu_alpha=0.2, padding = 'SAME', strides = [1,1,1,1], data_type=tf.float32, use_bias=True): """[summary] Arguments: input {[type]} -- [description] filter_shape {[type]} -- [description] output_shape {[type]} -- [description] Keyword Arguments: name {[type]} -- [description] (default: {None}) activation {str} -- [description] (default: {'LRELU'}) lrelu_alpha {float} -- [description] (default: {0.2}) padding {str} -- [description] (default: {'SAME'}) strides {list} -- [description] (default: {[1,1,1,1]}) data_type {[type]} -- [description] (default: {tf.float32}) use_bias {bool} -- [description] (default: {True}) Returns: [type] -- [description] """ weights = tf.Variable(tf.truncated_normal(shape=[filter_shape[0], filter_shape[1], filter_shape[3], filter_shape[2]], stddev=0.05), name="weight_" + name, dtype=data_type) deconv_shape = tf.stack(output_shape) layer = tf.nn.conv2d_transpose(value=input, filter = weights, output_shape = deconv_shape, strides = strides, padding = padding, name="deconv_"+str(name)) if use_bias: biases = new_biases(length=filter_shape[3], name=name, data_type=data_type) layer += biases if activation in ["RELU", "relu", "Relu"]: layer = tf.nn.relu(layer) elif activation in ["LRELU", "lrelu", "Lrelu"]: layer = tf.nn.leaky_relu(layer, alpha=lrelu_alpha) elif activation in ["SELU", "selu", "Selu"]: layer = tf.nn.selu(layer) elif activation in ["ELU", "elu", "Elu"]: layer = tf.nn.elu(layer) elif activation in ["SIGMOID", "sigmoid", "Sigmoid"]: layer = tf.nn.sigmoid(layer) elif activation in ["SOFTMAX", "softmax", "Softmax"]: layer == tf.nn.softmax(layer) return layer def new_batch_norm(x, axis, phase_train, name='bn'): """ Batch normalization on convolutional maps. Args: x: Tensor, 4D BHWD input maps phase_train: boolean tf.Varialbe, true indicates training phase name: string, variable scope Return: normed: batch-normalized maps """ beta = tf.Variable(tf.constant(0.0, shape=[x.get_shape().as_list()[-1]]), name='beta_' + name) gamma = tf.Variable(tf.constant(1.0, shape=[x.get_shape().as_list()[-1]]), name='gamma_' + name) mean, var = tf.nn.moments(x, axis, name='moments_' + name) ''' ema = tf.train.ExponentialMovingAverage(decay=0.5) def mean_var_with_update(): ema_apply_op = ema.apply([batch_mean, batch_var]) with tf.control_dependencies([ema_apply_op]): return tf.identity(batch_mean), tf.identity(batch_var) mean, var = tf.cond(tf.cast(phase_train, tf.bool), mean_var_with_update, lambda: (ema.average(batch_mean), ema.average(batch_var))) ''' normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3) return normed, beta, gamma def batch_norm(inputs, training, momentum=0.9, epsilon=1e-5, scale=True, data_format='channel_last'): """Performs a batch normalization using a standard set of parameters. Arguments: inputs {[type]} -- [description] training {[type]} -- [description] Keyword Arguments: data_format {str} -- [description] (default: {'channel_last'}) Returns: [type] -- [description] """ return tf.layers.batch_normalization( inputs=inputs, axis=1 if data_format == 'channels_first' else 3, momentum=momentum, epsilon=epsilon, scale=scale, training=training)

36.840686

136

0.546138

1,626

15,031

4.919434

0.116851

0.101263

0.042755

0.052507

0.738967

0.727716

0.721215

0.703463

0.697337

0.665208

0

0.015358

0.319872

15,031

407

137

36.931204

0.767094

0.321535

0

0.748768

0

0.066922

0.002406

0

1

0.044335

false

0

0.009852

0

0.098522

0

null

0

1

0

1

0

1

0

null

0

5

7f1b7c585e3c56a28986d9dd8cfa1ca575c77962

151

py

Python

manim_express/backend/manimgl/__init__.py

beidongjiedeguang/manim-express

e9c89b74da3692db3ea9b568727e78d5cbcef503

[ "MIT" ]

12

2021-06-14T07:28:29.000Z

2022-02-25T02:49:49.000Z

manim_express/backend/manimgl/__init__.py

beidongjiedeguang/manim-kunyuan

e9c89b74da3692db3ea9b568727e78d5cbcef503

[ "MIT" ]

1

2022-02-01T12:30:14.000Z

manim_express/backend/manimgl/__init__.py

beidongjiedeguang/manim-express

e9c89b74da3692db3ea9b568727e78d5cbcef503

[ "MIT" ]

2

2021-05-13T13:24:15.000Z

2021-05-18T02:56:22.000Z

from .utils import * from .mobject.geometry import * from .scene import SceneGL from manim_express.backend.manimgl.express.eager import EagerModeScene

30.2

70

0.827815

20

151

6.2

0.65

0.16129

0

0.10596

151

4

71

37.75

0.918519

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

61542aff6a4815b5538c9d4168bb2b6ee2f0e84b

225

py

Python

ifpd2/scripts/db/__init__.py

ggirelli/ifpd2

b480240433898dd678fb4c9650dd6aee23348bf8

[ "MIT" ]

1

2021-03-03T09:19:21.000Z

ifpd2/scripts/db/__init__.py

ggirelli/ifpd2

b480240433898dd678fb4c9650dd6aee23348bf8

[ "MIT" ]

85

2021-02-26T08:51:02.000Z

2022-03-09T09:26:04.000Z

ifpd2/scripts/db/__init__.py

ggirelli/ifpd2

b480240433898dd678fb4c9650dd6aee23348bf8

[ "MIT" ]

1

2022-03-08T09:35:41.000Z

""" @author: Gabriele Girelli @contact: gigi.ga90@gmail.com """ from ifpd2.scripts.db import check, dump, info, make from ifpd2.scripts.db import run, settings __all__ = ["check", "dump", "info", "make", "run", "settings"]

22.5

62

0.688889

31

225

4.870968

0.645161

0.119205

0.211921

0.238411

0.317881

0

0.020408

0.128889

225

9

63

25

0.75

0.244444

0

0.17284

0

1

0

false

0

0.666667

0

0.666667

0

null

0

1

0

1

0

null

0

1

0

1

0

5

61c564395a6f15cc4928dc69b57c2fd5a90af1f8

73

py

Python

teachnlp/__init__.py

abhinavdayal/TeachNLP

57bc2b0d003aa3291c67de244c32d262fadacf21

[ "MIT" ]

null

teachnlp/__init__.py

abhinavdayal/TeachNLP

57bc2b0d003aa3291c67de244c32d262fadacf21

[ "MIT" ]

null

teachnlp/__init__.py

abhinavdayal/TeachNLP

57bc2b0d003aa3291c67de244c32d262fadacf21

[ "MIT" ]

null

from .languagemodels import ngram from .classification import naive_bayes

36.5

39

0.876712

9

73

7

0.777778

0

0.09589

73

2

39

36.5

0.954545

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

f63fd49bf51a5606d86ce5a700901200bb46e47b

3,537

py

Python

tests/test_statevalue.py

kentokura/GobbletGobblers_py

90ad6722370f82780ee62510dfef4d7a1056178c

[ "MIT" ]

1

2020-10-14T20:31:03.000Z

tests/test_statevalue.py

kentokura/GobbletGobblers_py

90ad6722370f82780ee62510dfef4d7a1056178c

[ "MIT" ]

null

tests/test_statevalue.py

kentokura/GobbletGobblers_py

90ad6722370f82780ee62510dfef4d7a1056178c

[ "MIT" ]

null

# coding=utf-8 import unittest import gobbletgobblers as game import statevalue as value class TestMiniMax(unittest.TestCase): # 価値計算のテスト # 以下の条件で正しく価値を計算する(先手目線、後手には-1をかけて適用する) # - 勝ち盤面 # - 負け盤面 # - 引き分け盤面 # - 手数がかかる盤面 # - 自分のコマを置いて勝ち # - 相手がどこに置いても、次に自分がコマを置いて勝ち # - 自分がどこに置いても、次に相手がコマを置いて負け def test_mini_max(self): patterns = [ # 勝ち盤面 # small # --- # --- # --- # large # o-- # oo- # o-- # ↓ # # visible # o-- # oo- # o-- (([0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 1, 1, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]), 1), # 負け盤面 # small # --- # --- # --- # large # x-- # xx- # x-- # ↓ # # visible # x-- # xx- # x-- (([0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 1, 1, 0, 1, 0, 0]), -1), # 引き分け盤面 # small # oxo # xxo # oox # large # oxo # xxo # oox # ↓ # # visible # oxo # xxo # oox (([1, 0, 1, 0, 0, 1, 1, 1, 0], [0, 1, 0, 1, 1, 0, 0, 0, 1], [1, 0, 1, 0, 0, 1, 1, 1, 0], [0, 1, 0, 1, 1, 0, 0, 0, 1]), 0), # 自分のコマを置いて勝ち # small # x-- # xx- # --- # large # o-- # oo- # --- # ↓ # # visible # o-- # oo- # --- (([0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 1, 1, 0, 0, 0, 0], [1, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]), 1), # 相手がどこに置いても、次に自分がコマを置いて勝ち # small # x-- # x-- # --- # large # o-- # oo- # --- # ↓ # # visible # o-- # oo- # --- (([0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 1, 0, 0, 0, 0, 0], [1, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]), 1), # 自分がどこに置いても、次に相手がコマを置いて負け # small # o-- # oo- # --- # large # x-- # xx- # --- # ↓ # # visible # x-- # xx- # --- (([1, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 1, 1, 0, 0, 0, 0]), -1), ] for input_param, expect_param in patterns: my_small_pieces, enemy_small_pieces, my_large_pieces, enemy_large_pieces = input_param state = game.State(my_small_pieces, enemy_small_pieces, my_large_pieces, enemy_large_pieces) expect = expect_param # 負けなら-1, 引き分けなら0 NegaMaxなので、-1をかける actual = value.mini_max(state) self.assertEqual(expect, actual)

26.795455

104

0.284705

383

3,537

2.584856

0.159269

0.286869

0.351515

0.416162

0.379798

0.378788

0.375758

0

0.145887

0.560079

3,537

132

105

26.795455

0.486504

0.216568

0

0.12

0

0.04

1

0.04

false

0

0.12

0

0.2

0

null

1

0

1

0

1

0

null

0

5

f674d1953c19ce52a1e918299cfbee353d110061

20,336

py

Python

python-2-apps/fn_fortinet_nac/fn_fortinet_nac/util/customize.py

JayDi11a/Geralds-IBM-SOAR-Integrations

0e0eb18adbaf3a266e1dc5a316df7cd5a93f88d0

[ "MIT" ]

null

python-2-apps/fn_fortinet_nac/fn_fortinet_nac/util/customize.py

JayDi11a/Geralds-IBM-SOAR-Integrations

0e0eb18adbaf3a266e1dc5a316df7cd5a93f88d0

[ "MIT" ]

1

2022-03-06T00:10:13.000Z

python-2-apps/fn_fortinet_nac/fn_fortinet_nac/util/customize.py

JayDi11a/Geralds-IBM-SOAR-Integrations

0e0eb18adbaf3a266e1dc5a316df7cd5a93f88d0

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Generate the Resilient customizations required for fn_fortinet_nac""" from __future__ import print_function from resilient_circuits.util import * def customization_data(client=None): """Produce any customization definitions (types, fields, message destinations, etc) that should be installed by `resilient-circuits customize` """ # This import data contains: # Function inputs: # artifact_id # artifact_type # artifact_value # hostname # incident_id # login_id # DataTables: # host_results # Message Destinations: # fortinet_nac # Functions: # nac_query # Workflows: # fortinet_nac_search yield ImportDefinition(u""" eyJ0YXNrX29yZGVyIjogW10sICJ3b3JrZmxvd3MiOiBbeyJ1dWlkIjogIjg1N2Q4MzQzLTA0MDYt NDRkYi1hNGYzLTg1Y2YyOTJhOGJlZiIsICJkZXNjcmlwdGlvbiI6ICJUaGlzIHRha2VzIHRoZSBo b3N0bmFtZSBvciBsb2dpbiB1c2VyIG5hbWUsIHBhc3NlcyBpdCB0byB0aGUgcXVlcnkgbG9naWMg aW4gdGhlIGZ1bmN0aW9uIGFuZCByZXR1cm5zIHRoZSBxdWVyeSByZXN1bHRzIGFzIGFuIG9iamVj dCIsICJvYmplY3RfdHlwZSI6ICJhcnRpZmFjdCIsICJleHBvcnRfa2V5IjogImZvcnRpbmV0X25h Y19zZWFyY2giLCAid29ya2Zsb3dfaWQiOiAxNDAsICJsYXN0X21vZGlmaWVkX2J5IjogImdlcmFs ZC50cm90bWFuQGlibS5jb20iLCAiY29udGVudCI6IHsieG1sIjogIjw/eG1sIHZlcnNpb249XCIx LjBcIiBlbmNvZGluZz1cIlVURi04XCI/PjxkZWZpbml0aW9ucyB4bWxucz1cImh0dHA6Ly93d3cu b21nLm9yZy9zcGVjL0JQTU4vMjAxMDA1MjQvTU9ERUxcIiB4bWxuczpicG1uZGk9XCJodHRwOi8v d3d3Lm9tZy5vcmcvc3BlYy9CUE1OLzIwMTAwNTI0L0RJXCIgeG1sbnM6b21nZGM9XCJodHRwOi8v d3d3Lm9tZy5vcmcvc3BlYy9ERC8yMDEwMDUyNC9EQ1wiIHhtbG5zOm9tZ2RpPVwiaHR0cDovL3d3 dy5vbWcub3JnL3NwZWMvREQvMjAxMDA1MjQvRElcIiB4bWxuczpyZXNpbGllbnQ9XCJodHRwOi8v cmVzaWxpZW50LmlibS5jb20vYnBtblwiIHhtbG5zOnhzZD1cImh0dHA6Ly93d3cudzMub3JnLzIw MDEvWE1MU2NoZW1hXCIgeG1sbnM6eHNpPVwiaHR0cDovL3d3dy53My5vcmcvMjAwMS9YTUxTY2hl bWEtaW5zdGFuY2VcIiB0YXJnZXROYW1lc3BhY2U9XCJodHRwOi8vd3d3LmNhbXVuZGEub3JnL3Rl c3RcIj48cHJvY2VzcyBpZD1cImZvcnRpbmV0X25hY19zZWFyY2hcIiBpc0V4ZWN1dGFibGU9XCJ0 cnVlXCIgbmFtZT1cIkZvcnRpbmV0IE5BQyBTZWFyY2hcIj48ZG9jdW1lbnRhdGlvbj5UaGlzIHRh a2VzIHRoZSBob3N0bmFtZSBvciBsb2dpbiB1c2VyIG5hbWUsIHBhc3NlcyBpdCB0byB0aGUgcXVl cnkgbG9naWMgaW4gdGhlIGZ1bmN0aW9uIGFuZCByZXR1cm5zIHRoZSBxdWVyeSByZXN1bHRzIGFz IGFuIG9iamVjdDwvZG9jdW1lbnRhdGlvbj48c3RhcnRFdmVudCBpZD1cIlN0YXJ0RXZlbnRfMTU1 YXN4bVwiPjxvdXRnb2luZz5TZXF1ZW5jZUZsb3dfMXA2azNxNzwvb3V0Z29pbmc+PC9zdGFydEV2 ZW50PjxzZXJ2aWNlVGFzayBpZD1cIlNlcnZpY2VUYXNrXzF3Z3RoN2ZcIiBuYW1lPVwiTkFDIFF1 ZXJ5XCIgcmVzaWxpZW50OnR5cGU9XCJmdW5jdGlvblwiPjxleHRlbnNpb25FbGVtZW50cz48cmVz aWxpZW50OmZ1bmN0aW9uIHV1aWQ9XCJmNDM5MTIwYi0zZGY0LTQ3MjItYmNmYi0yZGVmZjQyNzc3 NmFcIj57XCJpbnB1dHNcIjp7fSxcInByZV9wcm9jZXNzaW5nX3NjcmlwdFwiOlwiaW5wdXRzLmlu Y2lkZW50X2lkID0gaW5jaWRlbnQuaWRcXG5pbnB1dHMubG9naW5faWQgPSBhcnRpZmFjdC52YWx1 ZVwifTwvcmVzaWxpZW50OmZ1bmN0aW9uPjwvZXh0ZW5zaW9uRWxlbWVudHM+PGluY29taW5nPlNl cXVlbmNlRmxvd18xcDZrM3E3PC9pbmNvbWluZz48b3V0Z29pbmc+U2VxdWVuY2VGbG93XzA1aXoz NmM8L291dGdvaW5nPjwvc2VydmljZVRhc2s+PHNlcXVlbmNlRmxvdyBpZD1cIlNlcXVlbmNlRmxv d18xcDZrM3E3XCIgc291cmNlUmVmPVwiU3RhcnRFdmVudF8xNTVhc3htXCIgdGFyZ2V0UmVmPVwi U2VydmljZVRhc2tfMXdndGg3ZlwiLz48ZW5kRXZlbnQgaWQ9XCJFbmRFdmVudF8wMmlhM2ZiXCI+ PGluY29taW5nPlNlcXVlbmNlRmxvd18wNWl6MzZjPC9pbmNvbWluZz48L2VuZEV2ZW50PjxzZXF1 ZW5jZUZsb3cgaWQ9XCJTZXF1ZW5jZUZsb3dfMDVpejM2Y1wiIHNvdXJjZVJlZj1cIlNlcnZpY2VU YXNrXzF3Z3RoN2ZcIiB0YXJnZXRSZWY9XCJFbmRFdmVudF8wMmlhM2ZiXCIvPjx0ZXh0QW5ub3Rh dGlvbiBpZD1cIlRleHRBbm5vdGF0aW9uXzFreHhpeXRcIj48dGV4dD5TdGFydCB5b3VyIHdvcmtm bG93IGhlcmU8L3RleHQ+PC90ZXh0QW5ub3RhdGlvbj48YXNzb2NpYXRpb24gaWQ9XCJBc3NvY2lh dGlvbl8xc2V1ajQ4XCIgc291cmNlUmVmPVwiU3RhcnRFdmVudF8xNTVhc3htXCIgdGFyZ2V0UmVm PVwiVGV4dEFubm90YXRpb25fMWt4eGl5dFwiLz48L3Byb2Nlc3M+PGJwbW5kaTpCUE1ORGlhZ3Jh bSBpZD1cIkJQTU5EaWFncmFtXzFcIj48YnBtbmRpOkJQTU5QbGFuZSBicG1uRWxlbWVudD1cInVu ZGVmaW5lZFwiIGlkPVwiQlBNTlBsYW5lXzFcIj48YnBtbmRpOkJQTU5TaGFwZSBicG1uRWxlbWVu dD1cIlN0YXJ0RXZlbnRfMTU1YXN4bVwiIGlkPVwiU3RhcnRFdmVudF8xNTVhc3htX2RpXCI+PG9t Z2RjOkJvdW5kcyBoZWlnaHQ9XCIzNlwiIHdpZHRoPVwiMzZcIiB4PVwiMTYyXCIgeT1cIjE4OFwi Lz48YnBtbmRpOkJQTU5MYWJlbD48b21nZGM6Qm91bmRzIGhlaWdodD1cIjBcIiB3aWR0aD1cIjkw XCIgeD1cIjE1N1wiIHk9XCIyMjNcIi8+PC9icG1uZGk6QlBNTkxhYmVsPjwvYnBtbmRpOkJQTU5T aGFwZT48YnBtbmRpOkJQTU5TaGFwZSBicG1uRWxlbWVudD1cIlRleHRBbm5vdGF0aW9uXzFreHhp eXRcIiBpZD1cIlRleHRBbm5vdGF0aW9uXzFreHhpeXRfZGlcIj48b21nZGM6Qm91bmRzIGhlaWdo dD1cIjMwXCIgd2lkdGg9XCIxMDBcIiB4PVwiOTlcIiB5PVwiMjU0XCIvPjwvYnBtbmRpOkJQTU5T aGFwZT48YnBtbmRpOkJQTU5FZGdlIGJwbW5FbGVtZW50PVwiQXNzb2NpYXRpb25fMXNldWo0OFwi IGlkPVwiQXNzb2NpYXRpb25fMXNldWo0OF9kaVwiPjxvbWdkaTp3YXlwb2ludCB4PVwiMTY5XCIg eHNpOnR5cGU9XCJvbWdkYzpQb2ludFwiIHk9XCIyMjBcIi8+PG9tZ2RpOndheXBvaW50IHg9XCIx NTNcIiB4c2k6dHlwZT1cIm9tZ2RjOlBvaW50XCIgeT1cIjI1NFwiLz48L2JwbW5kaTpCUE1ORWRn ZT48YnBtbmRpOkJQTU5TaGFwZSBicG1uRWxlbWVudD1cIlNlcnZpY2VUYXNrXzF3Z3RoN2ZcIiBp ZD1cIlNlcnZpY2VUYXNrXzF3Z3RoN2ZfZGlcIj48b21nZGM6Qm91bmRzIGhlaWdodD1cIjgwXCIg d2lkdGg9XCIxMDBcIiB4PVwiMzU0XCIgeT1cIjE2NlwiLz48L2JwbW5kaTpCUE1OU2hhcGU+PGJw bW5kaTpCUE1ORWRnZSBicG1uRWxlbWVudD1cIlNlcXVlbmNlRmxvd18xcDZrM3E3XCIgaWQ9XCJT ZXF1ZW5jZUZsb3dfMXA2azNxN19kaVwiPjxvbWdkaTp3YXlwb2ludCB4PVwiMTk4XCIgeHNpOnR5 cGU9XCJvbWdkYzpQb2ludFwiIHk9XCIyMDZcIi8+PG9tZ2RpOndheXBvaW50IHg9XCIzNTRcIiB4 c2k6dHlwZT1cIm9tZ2RjOlBvaW50XCIgeT1cIjIwNlwiLz48YnBtbmRpOkJQTU5MYWJlbD48b21n ZGM6Qm91bmRzIGhlaWdodD1cIjEzXCIgd2lkdGg9XCIwXCIgeD1cIjI3NlwiIHk9XCIxODRcIi8+ PC9icG1uZGk6QlBNTkxhYmVsPjwvYnBtbmRpOkJQTU5FZGdlPjxicG1uZGk6QlBNTlNoYXBlIGJw bW5FbGVtZW50PVwiRW5kRXZlbnRfMDJpYTNmYlwiIGlkPVwiRW5kRXZlbnRfMDJpYTNmYl9kaVwi PjxvbWdkYzpCb3VuZHMgaGVpZ2h0PVwiMzZcIiB3aWR0aD1cIjM2XCIgeD1cIjY0MFwiIHk9XCIx ODhcIi8+PGJwbW5kaTpCUE1OTGFiZWw+PG9tZ2RjOkJvdW5kcyBoZWlnaHQ9XCIxM1wiIHdpZHRo PVwiMFwiIHg9XCI2NThcIiB5PVwiMjI3XCIvPjwvYnBtbmRpOkJQTU5MYWJlbD48L2JwbW5kaTpC UE1OU2hhcGU+PGJwbW5kaTpCUE1ORWRnZSBicG1uRWxlbWVudD1cIlNlcXVlbmNlRmxvd18wNWl6 MzZjXCIgaWQ9XCJTZXF1ZW5jZUZsb3dfMDVpejM2Y19kaVwiPjxvbWdkaTp3YXlwb2ludCB4PVwi NDU0XCIgeHNpOnR5cGU9XCJvbWdkYzpQb2ludFwiIHk9XCIyMDZcIi8+PG9tZ2RpOndheXBvaW50 IHg9XCI2NDBcIiB4c2k6dHlwZT1cIm9tZ2RjOlBvaW50XCIgeT1cIjIwNlwiLz48YnBtbmRpOkJQ TU5MYWJlbD48b21nZGM6Qm91bmRzIGhlaWdodD1cIjEzXCIgd2lkdGg9XCIwXCIgeD1cIjU0N1wi IHk9XCIxODRcIi8+PC9icG1uZGk6QlBNTkxhYmVsPjwvYnBtbmRpOkJQTU5FZGdlPjwvYnBtbmRp OkJQTU5QbGFuZT48L2JwbW5kaTpCUE1ORGlhZ3JhbT48L2RlZmluaXRpb25zPiIsICJ3b3JrZmxv d19pZCI6ICJmb3J0aW5ldF9uYWNfc2VhcmNoIiwgInZlcnNpb24iOiA0fSwgImxhc3RfbW9kaWZp ZWRfdGltZSI6IDE1NDMzNTYxOTY2MTQsICJjcmVhdG9yX2lkIjogImdlcmFsZC50cm90bWFuQGli bS5jb20iLCAiYWN0aW9ucyI6IFtdLCAicHJvZ3JhbW1hdGljX25hbWUiOiAiZm9ydGluZXRfbmFj X3NlYXJjaCIsICJuYW1lIjogIkZvcnRpbmV0IE5BQyBTZWFyY2gifV0sICJhY3Rpb25zIjogW10s ICJsYXlvdXRzIjogW10sICJleHBvcnRfZm9ybWF0X3ZlcnNpb24iOiAyLCAiaWQiOiAzOCwgImlu ZHVzdHJpZXMiOiBudWxsLCAicGhhc2VzIjogW10sICJhY3Rpb25fb3JkZXIiOiBbXSwgImdlb3Mi OiBudWxsLCAibG9jYWxlIjogbnVsbCwgInNlcnZlcl92ZXJzaW9uIjogeyJtYWpvciI6IDMxLCAi dmVyc2lvbiI6ICIzMS4wLjQyNTQiLCAiYnVpbGRfbnVtYmVyIjogNDI1NCwgIm1pbm9yIjogMH0s ICJ0aW1lZnJhbWVzIjogbnVsbCwgIndvcmtzcGFjZXMiOiBbXSwgImF1dG9tYXRpY190YXNrcyI6 IFtdLCAiZnVuY3Rpb25zIjogW3siZGlzcGxheV9uYW1lIjogIk5BQyBRdWVyeSIsICJkZXNjcmlw dGlvbiI6IHsiY29udGVudCI6ICJUaGlzIGZ1bmN0aW9uIHRha2VzIGEgaG9zdG5hbWUgKE5UIElE KSBvciB1c2VyIElEIGFuZCByZXR1cm5zIHRoZSBjb3JyZXNwb25kaW5nIGVuZHBvaW50IGluZm9y bWF0aW9uIChsb2NhdGlvbiAtIHN3aXRjaCBhbmQgcG9ydCBuYW1lLCBzeXN0ZW0gaXAgYWRkcmVz cywgZXRjKSIsICJmb3JtYXQiOiAidGV4dCJ9LCAiY3JlYXRvciI6IHsiZGlzcGxheV9uYW1lIjog IkdlcmFsZCBUcm90bWFuIiwgInR5cGUiOiAidXNlciIsICJpZCI6IDQsICJuYW1lIjogImdlcmFs ZC50cm90bWFuQGlibS5jb20ifSwgInZpZXdfaXRlbXMiOiBbeyJzaG93X2lmIjogbnVsbCwgImZp ZWxkX3R5cGUiOiAiX19mdW5jdGlvbiIsICJzaG93X2xpbmtfaGVhZGVyIjogZmFsc2UsICJlbGVt ZW50IjogImZpZWxkX3V1aWQiLCAiY29udGVudCI6ICJlYWQyMTRjMi0xM2ZlLTQzZjYtYTNjNy02 NzZhODgzMzhkYmIiLCAic3RlcF9sYWJlbCI6IG51bGx9LCB7InNob3dfaWYiOiBudWxsLCAiZmll bGRfdHlwZSI6ICJfX2Z1bmN0aW9uIiwgInNob3dfbGlua19oZWFkZXIiOiBmYWxzZSwgImVsZW1l bnQiOiAiZmllbGRfdXVpZCIsICJjb250ZW50IjogIjliYTQ5ODg3LTBkY2YtNDBjZS1hNWVhLTlj MGM0M2Y4MzFiZiIsICJzdGVwX2xhYmVsIjogbnVsbH0sIHsic2hvd19pZiI6IG51bGwsICJmaWVs ZF90eXBlIjogIl9fZnVuY3Rpb24iLCAic2hvd19saW5rX2hlYWRlciI6IGZhbHNlLCAiZWxlbWVu dCI6ICJmaWVsZF91dWlkIiwgImNvbnRlbnQiOiAiNWU5NGIxZDMtNDhhMS00MThhLTljZTMtNDQz M2M2ODg5ZTE0IiwgInN0ZXBfbGFiZWwiOiBudWxsfSwgeyJzaG93X2lmIjogbnVsbCwgImZpZWxk X3R5cGUiOiAiX19mdW5jdGlvbiIsICJzaG93X2xpbmtfaGVhZGVyIjogZmFsc2UsICJlbGVtZW50 IjogImZpZWxkX3V1aWQiLCAiY29udGVudCI6ICIzYTJlMzQ3Yi02NzJlLTQyNjMtODc4Ny1hM2U5 ZWJhNGFjOTEiLCAic3RlcF9sYWJlbCI6IG51bGx9LCB7InNob3dfaWYiOiBudWxsLCAiZmllbGRf dHlwZSI6ICJfX2Z1bmN0aW9uIiwgInNob3dfbGlua19oZWFkZXIiOiBmYWxzZSwgImVsZW1lbnQi OiAiZmllbGRfdXVpZCIsICJjb250ZW50IjogImEyNTkxM2I2LTc2M2MtNDY5Ni1hN2Y3LTg0MWFk Y2I0MTA3YyIsICJzdGVwX2xhYmVsIjogbnVsbH0sIHsic2hvd19pZiI6IG51bGwsICJmaWVsZF90 eXBlIjogIl9fZnVuY3Rpb24iLCAic2hvd19saW5rX2hlYWRlciI6IGZhbHNlLCAiZWxlbWVudCI6 ICJmaWVsZF91dWlkIiwgImNvbnRlbnQiOiAiMjU5Nzc2NmItNGFjNS00OWQ0LThjNzAtMTA3OTk2 MzA1MTAwIiwgInN0ZXBfbGFiZWwiOiBudWxsfV0sICJleHBvcnRfa2V5IjogIm5hY19xdWVyeSIs ICJ1dWlkIjogImY0MzkxMjBiLTNkZjQtNDcyMi1iY2ZiLTJkZWZmNDI3Nzc2YSIsICJsYXN0X21v ZGlmaWVkX2J5IjogeyJkaXNwbGF5X25hbWUiOiAiR2VyYWxkIFRyb3RtYW4iLCAidHlwZSI6ICJ1 c2VyIiwgImlkIjogNCwgIm5hbWUiOiAiZ2VyYWxkLnRyb3RtYW5AaWJtLmNvbSJ9LCAidmVyc2lv biI6IDE0LCAid29ya2Zsb3dzIjogW3siZGVzY3JpcHRpb24iOiBudWxsLCAib2JqZWN0X3R5cGUi OiAiYXJ0aWZhY3QiLCAiYWN0aW9ucyI6IFtdLCAibmFtZSI6ICJGb3J0aW5ldCBOQUMgU2VhcmNo IiwgIndvcmtmbG93X2lkIjogMTQwLCAicHJvZ3JhbW1hdGljX25hbWUiOiAiZm9ydGluZXRfbmFj X3NlYXJjaCIsICJ1dWlkIjogbnVsbH1dLCAibGFzdF9tb2RpZmllZF90aW1lIjogMTU0MzM0OTc3 ODkyMSwgImRlc3RpbmF0aW9uX2hhbmRsZSI6ICJmb3J0aW5ldF9uYWMiLCAiaWQiOiAxNjAsICJu YW1lIjogIm5hY19xdWVyeSJ9XSwgIm5vdGlmaWNhdGlvbnMiOiBudWxsLCAicmVndWxhdG9ycyI6 IG51bGwsICJpbmNpZGVudF90eXBlcyI6IFt7ImNyZWF0ZV9kYXRlIjogMTU0MzM2NDg0ODE2Miwg ImRlc2NyaXB0aW9uIjogIkN1c3RvbWl6YXRpb24gUGFja2FnZXMgKGludGVybmFsKSIsICJleHBv cnRfa2V5IjogIkN1c3RvbWl6YXRpb24gUGFja2FnZXMgKGludGVybmFsKSIsICJpZCI6IDAsICJu YW1lIjogIkN1c3RvbWl6YXRpb24gUGFja2FnZXMgKGludGVybmFsKSIsICJ1cGRhdGVfZGF0ZSI6 IDE1NDMzNjQ4NDgxNjIsICJ1dWlkIjogImJmZWVjMmQ0LTM3NzAtMTFlOC1hZDM5LTRhMDAwNDA0 NGFhMCIsICJlbmFibGVkIjogZmFsc2UsICJzeXN0ZW0iOiBmYWxzZSwgInBhcmVudF9pZCI6IG51 bGwsICJoaWRkZW4iOiBmYWxzZX1dLCAic2NyaXB0cyI6IFtdLCAidHlwZXMiOiBbeyJkaXNwbGF5 X25hbWUiOiAiSG9zdCBSZXN1bHRzIiwgInV1aWQiOiAiYjkyOWZkN2MtNzY3Mi00ODY3LWE3YzMt NzI4Mzc4ZTQ0ZjM5IiwgInR5cGVfaWQiOiA4LCAiZmllbGRzIjogeyJvcyI6IHsib3BlcmF0aW9u cyI6IFtdLCAidHlwZV9pZCI6IDEwMDUsICJvcGVyYXRpb25fcGVybXMiOiB7fSwgInRleHQiOiAi T1MiLCAiYmxhbmtfb3B0aW9uIjogZmFsc2UsICJwcmVmaXgiOiBudWxsLCAiY2hhbmdlYWJsZSI6 IHRydWUsICJpZCI6IDI2OCwgInJlYWRfb25seSI6IGZhbHNlLCAidXVpZCI6ICI0YzYzOTgzMS1l Mjc4LTQ2MGYtYmEwNS1kZTRhZmM5NjIwOWIiLCAiY2hvc2VuIjogZmFsc2UsICJpbnB1dF90eXBl IjogInRleHQiLCAidG9vbHRpcCI6ICIiLCAid2lkdGgiOiAxMDMsICJpbnRlcm5hbCI6IGZhbHNl LCAicmljaF90ZXh0IjogZmFsc2UsICJ0ZW1wbGF0ZXMiOiBbXSwgImV4cG9ydF9rZXkiOiAiaG9z dF9yZXN1bHRzL29zIiwgImhpZGVfbm90aWZpY2F0aW9uIjogZmFsc2UsICJwbGFjZWhvbGRlciI6 ICIiLCAibmFtZSI6ICJvcyIsICJkZXByZWNhdGVkIjogZmFsc2UsICJkZWZhdWx0X2Nob3Nlbl9i eV9zZXJ2ZXIiOiBmYWxzZSwgInZhbHVlcyI6IFtdLCAib3JkZXIiOiA1fSwgImhvc3RuYW1lIjog eyJvcGVyYXRpb25zIjogW10sICJ0eXBlX2lkIjogMTAwNSwgIm9wZXJhdGlvbl9wZXJtcyI6IHt9 LCAidGV4dCI6ICJIb3N0bmFtZSIsICJibGFua19vcHRpb24iOiBmYWxzZSwgInByZWZpeCI6IG51 bGwsICJjaGFuZ2VhYmxlIjogdHJ1ZSwgImlkIjogMjY2LCAicmVhZF9vbmx5IjogZmFsc2UsICJ1 dWlkIjogImNhNDZmNmYxLTA1MmEtNGQ1Ny04MjdkLWE2NTNhNDVkYTM1MSIsICJjaG9zZW4iOiBm YWxzZSwgImlucHV0X3R5cGUiOiAidGV4dCIsICJ0b29sdGlwIjogIiIsICJ3aWR0aCI6IDEwMiwg ImludGVybmFsIjogZmFsc2UsICJyaWNoX3RleHQiOiBmYWxzZSwgInRlbXBsYXRlcyI6IFtdLCAi ZXhwb3J0X2tleSI6ICJob3N0X3Jlc3VsdHMvaG9zdG5hbWUiLCAiaGlkZV9ub3RpZmljYXRpb24i OiBmYWxzZSwgInBsYWNlaG9sZGVyIjogIiIsICJuYW1lIjogImhvc3RuYW1lIiwgImRlcHJlY2F0 ZWQiOiBmYWxzZSwgImRlZmF1bHRfY2hvc2VuX2J5X3NlcnZlciI6IGZhbHNlLCAidmFsdWVzIjog W10sICJvcmRlciI6IDN9LCAidXNlciI6IHsib3BlcmF0aW9ucyI6IFtdLCAidHlwZV9pZCI6IDEw MDUsICJvcGVyYXRpb25fcGVybXMiOiB7fSwgInRleHQiOiAiVXNlciIsICJibGFua19vcHRpb24i OiBmYWxzZSwgInByZWZpeCI6IG51bGwsICJjaGFuZ2VhYmxlIjogdHJ1ZSwgImlkIjogMjYzLCAi cmVhZF9vbmx5IjogZmFsc2UsICJ1dWlkIjogImUyMjZiNDFjLTA1ZTAtNGYxOS1hMDI5LTVmNTc3 NTA5MThhYSIsICJjaG9zZW4iOiBmYWxzZSwgImlucHV0X3R5cGUiOiAidGV4dCIsICJ0b29sdGlw IjogIiIsICJ3aWR0aCI6IDEwMSwgImludGVybmFsIjogZmFsc2UsICJyaWNoX3RleHQiOiBmYWxz ZSwgInRlbXBsYXRlcyI6IFtdLCAiZXhwb3J0X2tleSI6ICJob3N0X3Jlc3VsdHMvdXNlciIsICJo aWRlX25vdGlmaWNhdGlvbiI6IGZhbHNlLCAicGxhY2Vob2xkZXIiOiAiIiwgIm5hbWUiOiAidXNl ciIsICJkZXByZWNhdGVkIjogZmFsc2UsICJkZWZhdWx0X2Nob3Nlbl9ieV9zZXJ2ZXIiOiBmYWxz ZSwgInZhbHVlcyI6IFtdLCAib3JkZXIiOiAwfSwgIm1hY19hZGRyZXNzIjogeyJvcGVyYXRpb25z IjogW10sICJ0eXBlX2lkIjogMTAwNSwgIm9wZXJhdGlvbl9wZXJtcyI6IHt9LCAidGV4dCI6ICJN YWMgQWRkcmVzcyIsICJibGFua19vcHRpb24iOiBmYWxzZSwgInByZWZpeCI6IG51bGwsICJjaGFu Z2VhYmxlIjogdHJ1ZSwgImlkIjogMjY3LCAicmVhZF9vbmx5IjogZmFsc2UsICJ1dWlkIjogImU4 YTEzODEzLWMxY2YtNGFhZi04YmQ2LWU5N2IxMDM4N2I3NSIsICJjaG9zZW4iOiBmYWxzZSwgImlu cHV0X3R5cGUiOiAidGV4dCIsICJ0b29sdGlwIjogIiIsICJ3aWR0aCI6IDExMCwgImludGVybmFs IjogZmFsc2UsICJyaWNoX3RleHQiOiBmYWxzZSwgInRlbXBsYXRlcyI6IFtdLCAiZXhwb3J0X2tl eSI6ICJob3N0X3Jlc3VsdHMvbWFjX2FkZHJlc3MiLCAiaGlkZV9ub3RpZmljYXRpb24iOiBmYWxz ZSwgInBsYWNlaG9sZGVyIjogIiIsICJuYW1lIjogIm1hY19hZGRyZXNzIiwgImRlcHJlY2F0ZWQi OiBmYWxzZSwgImRlZmF1bHRfY2hvc2VuX2J5X3NlcnZlciI6IGZhbHNlLCAidmFsdWVzIjogW10s ICJvcmRlciI6IDR9LCAiaXBfYWRkcmVzcyI6IHsib3BlcmF0aW9ucyI6IFtdLCAidHlwZV9pZCI6 IDEwMDUsICJvcGVyYXRpb25fcGVybXMiOiB7fSwgInRleHQiOiAiSVAgQWRkcmVzcyIsICJibGFu a19vcHRpb24iOiBmYWxzZSwgInByZWZpeCI6IG51bGwsICJjaGFuZ2VhYmxlIjogdHJ1ZSwgImlk IjogMjY1LCAicmVhZF9vbmx5IjogZmFsc2UsICJ1dWlkIjogIjNlMGViYjcwLTI1MzYtNDI2Ny05 NmJiLTY0Yzk2ZGJhMWMwMCIsICJjaG9zZW4iOiBmYWxzZSwgImlucHV0X3R5cGUiOiAidGV4dCIs ICJ0b29sdGlwIjogIiIsICJ3aWR0aCI6IDEwMiwgImludGVybmFsIjogZmFsc2UsICJyaWNoX3Rl eHQiOiBmYWxzZSwgInRlbXBsYXRlcyI6IFtdLCAiZXhwb3J0X2tleSI6ICJob3N0X3Jlc3VsdHMv aXBfYWRkcmVzcyIsICJoaWRlX25vdGlmaWNhdGlvbiI6IGZhbHNlLCAicGxhY2Vob2xkZXIiOiAi IiwgIm5hbWUiOiAiaXBfYWRkcmVzcyIsICJkZXByZWNhdGVkIjogZmFsc2UsICJkZWZhdWx0X2No b3Nlbl9ieV9zZXJ2ZXIiOiBmYWxzZSwgInZhbHVlcyI6IFtdLCAib3JkZXIiOiAyfSwgImxvY2F0 aW9uIjogeyJvcGVyYXRpb25zIjogW10sICJ0eXBlX2lkIjogMTAwNSwgIm9wZXJhdGlvbl9wZXJt cyI6IHt9LCAidGV4dCI6ICJMb2NhdGlvbiIsICJibGFua19vcHRpb24iOiBmYWxzZSwgInByZWZp eCI6IG51bGwsICJjaGFuZ2VhYmxlIjogdHJ1ZSwgImlkIjogMjY0LCAicmVhZF9vbmx5IjogZmFs c2UsICJ1dWlkIjogImRmY2UwNWFjLTFjYTktNGQ1YS1hOWU0LWM4ZDIwMGE2ZmM1MCIsICJjaG9z ZW4iOiBmYWxzZSwgImlucHV0X3R5cGUiOiAidGV4dCIsICJ0b29sdGlwIjogIiIsICJ3aWR0aCI6 IDE4NCwgImludGVybmFsIjogZmFsc2UsICJyaWNoX3RleHQiOiBmYWxzZSwgInRlbXBsYXRlcyI6 IFtdLCAiZXhwb3J0X2tleSI6ICJob3N0X3Jlc3VsdHMvbG9jYXRpb24iLCAiaGlkZV9ub3RpZmlj YXRpb24iOiBmYWxzZSwgInBsYWNlaG9sZGVyIjogIiIsICJuYW1lIjogImxvY2F0aW9uIiwgImRl cHJlY2F0ZWQiOiBmYWxzZSwgImRlZmF1bHRfY2hvc2VuX2J5X3NlcnZlciI6IGZhbHNlLCAidmFs dWVzIjogW10sICJvcmRlciI6IDF9fSwgInBhcmVudF90eXBlcyI6IFsiaW5jaWRlbnQiXSwgInR5 cGVfbmFtZSI6ICJob3N0X3Jlc3VsdHMiLCAiZXhwb3J0X2tleSI6ICJob3N0X3Jlc3VsdHMiLCAi Zm9yX2N1c3RvbV9maWVsZHMiOiBmYWxzZSwgImFjdGlvbnMiOiBbXSwgImlkIjogbnVsbCwgImZv cl9hY3Rpb25zIjogZmFsc2UsICJmb3Jfbm90aWZpY2F0aW9ucyI6IGZhbHNlLCAic2NyaXB0cyI6 IFtdLCAicHJvcGVydGllcyI6IHsiZm9yX3dobyI6IFtdLCAiY2FuX2Rlc3Ryb3kiOiBmYWxzZSwg ImNhbl9jcmVhdGUiOiBmYWxzZX19XSwgIm1lc3NhZ2VfZGVzdGluYXRpb25zIjogW3sidXVpZCI6 ICJhZGZmODQ2YS0yY2VhLTRjZGItOGU4Yi03OWY5NDdlZDIyYmQiLCAiZXhwb3J0X2tleSI6ICJm b3J0aW5ldF9uYWMiLCAibmFtZSI6ICJGb3J0aW5ldCBOQUMiLCAiZGVzdGluYXRpb25fdHlwZSI6 IDAsICJwcm9ncmFtbWF0aWNfbmFtZSI6ICJmb3J0aW5ldF9uYWMiLCAiZXhwZWN0X2FjayI6IHRy dWUsICJ1c2VycyI6IFtdfV0sICJpbmNpZGVudF9hcnRpZmFjdF90eXBlcyI6IFtdLCAicm9sZXMi OiBbXSwgImZpZWxkcyI6IFt7Im9wZXJhdGlvbnMiOiBbXSwgInR5cGVfaWQiOiAwLCAib3BlcmF0 aW9uX3Blcm1zIjoge30sICJ0ZXh0IjogIlNpbXVsYXRpb24iLCAiYmxhbmtfb3B0aW9uIjogZmFs c2UsICJwcmVmaXgiOiBudWxsLCAiY2hhbmdlYWJsZSI6IHRydWUsICJpZCI6IDM3LCAicmVhZF9v bmx5IjogdHJ1ZSwgInV1aWQiOiAiYzNmMGUzZWQtMjFlMS00ZDUzLWFmZmItZmU1Y2EzMzA4Y2Nh IiwgImNob3NlbiI6IGZhbHNlLCAiaW5wdXRfdHlwZSI6ICJib29sZWFuIiwgInRvb2x0aXAiOiAi V2hldGhlciB0aGUgaW5jaWRlbnQgaXMgYSBzaW11bGF0aW9uIG9yIGEgcmVndWxhciBpbmNpZGVu dC4gIFRoaXMgZmllbGQgaXMgcmVhZC1vbmx5LiIsICJpbnRlcm5hbCI6IGZhbHNlLCAicmljaF90 ZXh0IjogZmFsc2UsICJ0ZW1wbGF0ZXMiOiBbXSwgImV4cG9ydF9rZXkiOiAiaW5jaWRlbnQvaW5j X3RyYWluaW5nIiwgImhpZGVfbm90aWZpY2F0aW9uIjogZmFsc2UsICJuYW1lIjogImluY190cmFp bmluZyIsICJkZXByZWNhdGVkIjogZmFsc2UsICJkZWZhdWx0X2Nob3Nlbl9ieV9zZXJ2ZXIiOiBm YWxzZSwgInZhbHVlcyI6IFtdfSwgeyJvcGVyYXRpb25zIjogW10sICJ0eXBlX2lkIjogMTEsICJv cGVyYXRpb25fcGVybXMiOiB7fSwgInRleHQiOiAiaW5jaWRlbnRfaWQiLCAiYmxhbmtfb3B0aW9u IjogZmFsc2UsICJwcmVmaXgiOiBudWxsLCAiY2hhbmdlYWJsZSI6IHRydWUsICJpZCI6IDExNSwg InJlYWRfb25seSI6IGZhbHNlLCAidXVpZCI6ICJlYWQyMTRjMi0xM2ZlLTQzZjYtYTNjNy02NzZh ODgzMzhkYmIiLCAiY2hvc2VuIjogZmFsc2UsICJpbnB1dF90eXBlIjogIm51bWJlciIsICJ0b29s dGlwIjogIiIsICJpbnRlcm5hbCI6IGZhbHNlLCAicmljaF90ZXh0IjogZmFsc2UsICJ0ZW1wbGF0 ZXMiOiBbXSwgImV4cG9ydF9rZXkiOiAiX19mdW5jdGlvbi9pbmNpZGVudF9pZCIsICJoaWRlX25v dGlmaWNhdGlvbiI6IGZhbHNlLCAicGxhY2Vob2xkZXIiOiAiIiwgIm5hbWUiOiAiaW5jaWRlbnRf aWQiLCAiZGVwcmVjYXRlZCI6IGZhbHNlLCAiZGVmYXVsdF9jaG9zZW5fYnlfc2VydmVyIjogZmFs c2UsICJ2YWx1ZXMiOiBbXX0sIHsib3BlcmF0aW9ucyI6IFtdLCAidHlwZV9pZCI6IDExLCAib3Bl cmF0aW9uX3Blcm1zIjoge30sICJ0ZXh0IjogImxvZ2luX2lkIiwgImJsYW5rX29wdGlvbiI6IGZh bHNlLCAicHJlZml4IjogbnVsbCwgImNoYW5nZWFibGUiOiB0cnVlLCAiaWQiOiAyMTUsICJyZWFk X29ubHkiOiBmYWxzZSwgInV1aWQiOiAiMjU5Nzc2NmItNGFjNS00OWQ0LThjNzAtMTA3OTk2MzA1 MTAwIiwgImNob3NlbiI6IGZhbHNlLCAiaW5wdXRfdHlwZSI6ICJ0ZXh0IiwgInRvb2x0aXAiOiAi IiwgImludGVybmFsIjogZmFsc2UsICJyaWNoX3RleHQiOiBmYWxzZSwgInRlbXBsYXRlcyI6IFtd LCAiZXhwb3J0X2tleSI6ICJfX2Z1bmN0aW9uL2xvZ2luX2lkIiwgImhpZGVfbm90aWZpY2F0aW9u IjogZmFsc2UsICJwbGFjZWhvbGRlciI6ICIiLCAibmFtZSI6ICJsb2dpbl9pZCIsICJkZXByZWNh dGVkIjogZmFsc2UsICJkZWZhdWx0X2Nob3Nlbl9ieV9zZXJ2ZXIiOiBmYWxzZSwgInZhbHVlcyI6 IFtdfSwgeyJvcGVyYXRpb25zIjogW10sICJ0eXBlX2lkIjogMTEsICJvcGVyYXRpb25fcGVybXMi OiB7fSwgInRleHQiOiAiaG9zdG5hbWUiLCAiYmxhbmtfb3B0aW9uIjogZmFsc2UsICJwcmVmaXgi OiBudWxsLCAiY2hhbmdlYWJsZSI6IHRydWUsICJpZCI6IDIxNCwgInJlYWRfb25seSI6IGZhbHNl LCAidXVpZCI6ICJhMjU5MTNiNi03NjNjLTQ2OTYtYTdmNy04NDFhZGNiNDEwN2MiLCAiY2hvc2Vu IjogZmFsc2UsICJpbnB1dF90eXBlIjogInRleHQiLCAidG9vbHRpcCI6ICIiLCAiaW50ZXJuYWwi OiBmYWxzZSwgInJpY2hfdGV4dCI6IGZhbHNlLCAidGVtcGxhdGVzIjogW10sICJleHBvcnRfa2V5 IjogIl9fZnVuY3Rpb24vaG9zdG5hbWUiLCAiaGlkZV9ub3RpZmljYXRpb24iOiBmYWxzZSwgInBs YWNlaG9sZGVyIjogIiIsICJuYW1lIjogImhvc3RuYW1lIiwgImRlcHJlY2F0ZWQiOiBmYWxzZSwg ImRlZmF1bHRfY2hvc2VuX2J5X3NlcnZlciI6IGZhbHNlLCAidmFsdWVzIjogW119LCB7Im9wZXJh dGlvbnMiOiBbXSwgInR5cGVfaWQiOiAxMSwgIm9wZXJhdGlvbl9wZXJtcyI6IHt9LCAidGV4dCI6 ICJhcnRpZmFjdF92YWx1ZSIsICJibGFua19vcHRpb24iOiBmYWxzZSwgInByZWZpeCI6IG51bGws ICJjaGFuZ2VhYmxlIjogdHJ1ZSwgImlkIjogMTQ5LCAicmVhZF9vbmx5IjogZmFsc2UsICJ1dWlk IjogIjliYTQ5ODg3LTBkY2YtNDBjZS1hNWVhLTljMGM0M2Y4MzFiZiIsICJjaG9zZW4iOiBmYWxz ZSwgImlucHV0X3R5cGUiOiAidGV4dCIsICJ0b29sdGlwIjogIiIsICJpbnRlcm5hbCI6IGZhbHNl LCAicmljaF90ZXh0IjogZmFsc2UsICJ0ZW1wbGF0ZXMiOiBbXSwgImV4cG9ydF9rZXkiOiAiX19m dW5jdGlvbi9hcnRpZmFjdF92YWx1ZSIsICJoaWRlX25vdGlmaWNhdGlvbiI6IGZhbHNlLCAicGxh Y2Vob2xkZXIiOiAiIiwgIm5hbWUiOiAiYXJ0aWZhY3RfdmFsdWUiLCAiZGVwcmVjYXRlZCI6IGZh bHNlLCAiZGVmYXVsdF9jaG9zZW5fYnlfc2VydmVyIjogZmFsc2UsICJ2YWx1ZXMiOiBbXX0sIHsi b3BlcmF0aW9ucyI6IFtdLCAidHlwZV9pZCI6IDExLCAib3BlcmF0aW9uX3Blcm1zIjoge30sICJ0 ZXh0IjogImFydGlmYWN0X2lkIiwgImJsYW5rX29wdGlvbiI6IGZhbHNlLCAicHJlZml4IjogbnVs bCwgImNoYW5nZWFibGUiOiB0cnVlLCAiaWQiOiAyNjIsICJyZWFkX29ubHkiOiBmYWxzZSwgInV1 aWQiOiAiNWU5NGIxZDMtNDhhMS00MThhLTljZTMtNDQzM2M2ODg5ZTE0IiwgImNob3NlbiI6IGZh bHNlLCAiaW5wdXRfdHlwZSI6ICJudW1iZXIiLCAidG9vbHRpcCI6ICIiLCAiaW50ZXJuYWwiOiBm YWxzZSwgInJpY2hfdGV4dCI6IGZhbHNlLCAidGVtcGxhdGVzIjogW10sICJleHBvcnRfa2V5Ijog Il9fZnVuY3Rpb24vYXJ0aWZhY3RfaWQiLCAiaGlkZV9ub3RpZmljYXRpb24iOiBmYWxzZSwgInBs YWNlaG9sZGVyIjogIiIsICJuYW1lIjogImFydGlmYWN0X2lkIiwgImRlcHJlY2F0ZWQiOiBmYWxz ZSwgImRlZmF1bHRfY2hvc2VuX2J5X3NlcnZlciI6IGZhbHNlLCAidmFsdWVzIjogW119LCB7Im9w ZXJhdGlvbnMiOiBbXSwgInR5cGVfaWQiOiAxMSwgIm9wZXJhdGlvbl9wZXJtcyI6IHt9LCAidGV4 dCI6ICJhcnRpZmFjdF90eXBlIiwgImJsYW5rX29wdGlvbiI6IGZhbHNlLCAicHJlZml4IjogbnVs bCwgImNoYW5nZWFibGUiOiB0cnVlLCAiaWQiOiAxNTAsICJyZWFkX29ubHkiOiBmYWxzZSwgInV1 aWQiOiAiM2EyZTM0N2ItNjcyZS00MjYzLTg3ODctYTNlOWViYTRhYzkxIiwgImNob3NlbiI6IGZh bHNlLCAiaW5wdXRfdHlwZSI6ICJ0ZXh0IiwgInRvb2x0aXAiOiAiIiwgImludGVybmFsIjogZmFs c2UsICJyaWNoX3RleHQiOiBmYWxzZSwgInRlbXBsYXRlcyI6IFtdLCAiZXhwb3J0X2tleSI6ICJf X2Z1bmN0aW9uL2FydGlmYWN0X3R5cGUiLCAiaGlkZV9ub3RpZmljYXRpb24iOiBmYWxzZSwgInBs YWNlaG9sZGVyIjogIiIsICJuYW1lIjogImFydGlmYWN0X3R5cGUiLCAiZGVwcmVjYXRlZCI6IGZh bHNlLCAiZGVmYXVsdF9jaG9zZW5fYnlfc2VydmVyIjogZmFsc2UsICJ2YWx1ZXMiOiBbXX1dLCAi b3ZlcnJpZGVzIjogW10sICJleHBvcnRfZGF0ZSI6IDE1NDMzNTYyMTY3OTl9 """ )

70.366782

87

0.972315

350

20,336

56.44

0.942857

0.001671

0

0.117534

0.023505

20,336

288

88

70.611111

0.877228

0.024882

0

1

0

0.987469

0.974584

0

1

0

1

0.003846

false

0

0.011538

0

0.015385

0.003846

0

1

null

0

1

0

1

null

1

0

5

f677f7d9f598122efd60b483f44ed1ed16c2ecae

117

py

Python

ezusbfifo/__init__.py

jix/ezusbfifo

a6deff0bf76c3418de915e09e0d70fd5f3a5a214

[ "BSD-2-Clause" ]

1

2016-07-26T04:46:59.000Z

ezusbfifo/__init__.py

jix/ezusbfifo

a6deff0bf76c3418de915e09e0d70fd5f3a5a214

[ "BSD-2-Clause" ]

null

ezusbfifo/__init__.py

jix/ezusbfifo

a6deff0bf76c3418de915e09e0d70fd5f3a5a214

[ "BSD-2-Clause" ]

null

from ezusbfifo.actor import USBActor from ezusbfifo.async import AsyncUSBActor from ezusbfifo.sim import SimUSBActor

29.25

41

0.871795

15

117

6.8

0.6

0.382353

0

0.102564

117

3

42

39

0.971429

0

null

0

1

null

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

9c8eab0bb09347eee6dc1b748164f40377af4f0f

194

py

Python

server/src/factory/i_controller_factory.py

konrad2508/picgal

7f7822a02145fd2efa697e1c7750af9af680a3da

[ "MIT" ]

4

2021-12-31T10:06:34.000Z

2022-01-16T16:34:50.000Z

server/src/factory/i_controller_factory.py

konrad2508/picgal

7f7822a02145fd2efa697e1c7750af9af680a3da

[ "MIT" ]

null

server/src/factory/i_controller_factory.py

konrad2508/picgal

7f7822a02145fd2efa697e1c7750af9af680a3da

[ "MIT" ]

null

from abc import ABC, abstractmethod from controller.i_controller import IController class IControllerFactory(ABC): @abstractmethod def get_controllers(self) -> list[IController]: ...

21.555556

55

0.773196

21

194

7.047619

0.666667

0.22973

0

0.14433

194

8

56

24.25

0.891566

0

1

0.2

false

0

0.4

0

0.8

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

9c9b5671046cc8a038b40030402f9a2a5fcb0b35

126

py

Python

elpa/elpy-20161229.1103/elpy/tests/__init__.py

fengxia41103/emacs.d

5f776ff18de0ea6dedf3671843bd69c571e76c39

[ "BSD-2-Clause-FreeBSD" ]

11

2018-01-12T02:13:04.000Z

2021-05-14T20:47:47.000Z

elpa/elpy-20180916.839/elpy/tests/__init__.py

jueqingsizhe66/ranEmacs.d

d1d2684857feeaf505a3cddcf044f7a60d10d1a4

[ "Unlicense" ]

1

2020-03-24T12:50:29.000Z

2020-03-24T12:51:11.000Z

elpa/elpy-20180916.839/elpy/tests/__init__.py

jueqingsizhe66/ranEmacs.d

d1d2684857feeaf505a3cddcf044f7a60d10d1a4

[ "Unlicense" ]

1

2020-11-04T05:05:12.000Z

"""Unit tests for elpy.""" try: import unittest2 import sys sys.modules['unittest'] = unittest2 except: pass

14

39

0.634921

15

126

5.333333

0.8

0

0.021053

0.246032

126

8

40

15.75

0.821053

0.15873

0

0.08

0

1

0

true

0.166667

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

5

9ccb9881e77ea01a70b2e7ac954801d289e8a993

132

py

Python

exercicios/ex003.py

oGuilhermeViana/python

69c03ea77d367769d68664368c51e9203606e42b

[ "MIT" ]

null

exercicios/ex003.py

oGuilhermeViana/python

69c03ea77d367769d68664368c51e9203606e42b

[ "MIT" ]

null

exercicios/ex003.py

oGuilhermeViana/python

69c03ea77d367769d68664368c51e9203606e42b

[ "MIT" ]

null

n1 = int(input('Digite o 1° número: ')) n2 = int(input('Digite o 2° número: ')) s = n1 + n2 print('{} + {} = {}'.format(n1, n2, s))

26.4

39

0.522727

24

132

2.958333

0.541667

0.225352

0.394366

0.422535

0

0.075472

0.19697

132

4

40

33

0.575472

0

0.393939

0

1

0

false

0

0.25

1

0

null

1

0

1

0

null

0

5

9ccc4455d5e4a81dc41a34017c39a02d0489fd3c

161

py

Python

capture/lib/exceptions.py

SirLez/capture.py

9f97f78d90ccba520908a2af1acbab26c366bb84

[ "Apache-2.0" ]

1

2022-03-27T09:48:19.000Z

capture/lib/exceptions.py

SirLez/capture.py

9f97f78d90ccba520908a2af1acbab26c366bb84

[ "Apache-2.0" ]

null

capture/lib/exceptions.py

SirLez/capture.py

9f97f78d90ccba520908a2af1acbab26c366bb84

[ "Apache-2.0" ]

null

class Except(Exception): def __init__(*args, **kwargs): Exception.__init__(*args, **kwargs) def CheckExceptions(data): raise Except(data)

23

44

0.652174

17

161

5.705882

0.588235

0.164948

0.28866

0

0.21118

161

6

45

26.833333

0.76378

0

1

0.4

false

0

0.6

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

140134243b138e2c3a5c8effb9c54a70769dd3c6

2,988

py

Python

tests/gamification/test_rule_models.py

mrgambal/ner_trainer

4ea617bb9a1c4778ce6dfa084c53e2667d037f67

[ "BSD-3-Clause" ]

33

2015-01-20T12:12:40.000Z

2020-02-23T14:21:24.000Z

tests/gamification/test_rule_models.py

mrgambal/vulyk

4ea617bb9a1c4778ce6dfa084c53e2667d037f67

[ "BSD-3-Clause" ]

48

2015-01-13T16:29:44.000Z

2020-10-21T13:09:23.000Z

tests/gamification/test_rule_models.py

mrgambal/ner_trainer

4ea617bb9a1c4778ce6dfa084c53e2667d037f67

[ "BSD-3-Clause" ]

9

2015-04-01T15:19:13.000Z

2021-06-21T15:44:28.000Z

# -*- coding: utf-8 -*- """ test_rule_models """ from vulyk.blueprints.gamification.core.rules import Rule, ProjectRule from ..base import BaseTest class TestRuleModels(BaseTest): BADGE_IMAGE = 'data:image/png;base64,' RULE_ID = 100 RULE_NAME = 'name' RULE_DESCRIPTION = 'description' TASKS_NUMBER = 20 DAYS_NUMBER = 5 IS_WEEKEND = True IS_ADJACENT = True TASK_TYPE_NAME = 'declarations' def test_task_rule_to_dict(self): rule = Rule( badge=self.BADGE_IMAGE, name=self.RULE_NAME, description=self.RULE_DESCRIPTION, bonus=0, tasks_number=self.TASKS_NUMBER, days_number=self.DAYS_NUMBER, is_weekend=self.IS_WEEKEND, is_adjacent=False, rule_id=self.RULE_ID) expected = { 'id': self.RULE_ID, 'badge': self.BADGE_IMAGE, 'name': self.RULE_NAME, 'description': self.RULE_DESCRIPTION, 'bonus': 0, 'tasks_number': self.TASKS_NUMBER, 'days_number': self.DAYS_NUMBER, 'is_weekend': self.IS_WEEKEND, 'is_adjacent': False } self.assertDictEqual(expected, rule.to_dict()) def test_days_rule_to_dict(self): rule = Rule( badge=self.BADGE_IMAGE, name=self.RULE_NAME, description=self.RULE_DESCRIPTION, bonus=0, tasks_number=0, days_number=self.DAYS_NUMBER, is_weekend=self.IS_WEEKEND, is_adjacent=self.IS_ADJACENT, rule_id=self.RULE_ID) expected = { 'id': self.RULE_ID, 'badge': self.BADGE_IMAGE, 'name': self.RULE_NAME, 'description': self.RULE_DESCRIPTION, 'bonus': 0, 'tasks_number': 0, 'days_number': self.DAYS_NUMBER, 'is_weekend': self.IS_WEEKEND, 'is_adjacent': self.IS_ADJACENT } self.assertDictEqual(expected, rule.to_dict()) def test_project_rule_to_dict(self): rule = ProjectRule( task_type_name=self.TASK_TYPE_NAME, badge=self.BADGE_IMAGE, name=self.RULE_NAME, description=self.RULE_DESCRIPTION, bonus=0, tasks_number=0, days_number=self.DAYS_NUMBER, is_weekend=self.IS_WEEKEND, is_adjacent=self.IS_ADJACENT, rule_id=self.RULE_ID) expected = { 'id': self.RULE_ID, 'badge': self.BADGE_IMAGE, 'name': self.RULE_NAME, 'description': self.RULE_DESCRIPTION, 'bonus': 0, 'tasks_number': 0, 'days_number': self.DAYS_NUMBER, 'is_weekend': self.IS_WEEKEND, 'is_adjacent': self.IS_ADJACENT, 'task_type_name': self.TASK_TYPE_NAME } self.assertDictEqual(expected, rule.to_dict())

30.804124

70

0.568942

334

2,988

4.778443

0.146707

0.105263

0.037594

0.071429

0.801378

0.7901

0.766917

0.73183

0.676692

0

0.0095

0.330656

2,988

96

71

31.125

0.7885

0.013052

0

0.674699

0

0.093846

0.00748

0

0.036145

1

0.036145

false

0

0.024096

0

0.180723

0.012048

0

null

0

1

0

1

0

null

0

5

141aae554698825a14bb6b15c459828b7f427d8f

266

py

Python

inat_creds.py

Afrisnake/iNaturalist-Scraper-and-Data-Pipeline

e209f0470103ec2a374663ace1260a27b826b65c

[ "MIT" ]

1

2021-11-11T01:34:54.000Z

inat_creds.py

Afrisnake/iNaturalist-Scraper-and-Data-Pipeline

e209f0470103ec2a374663ace1260a27b826b65c

[ "MIT" ]

null

inat_creds.py

Afrisnake/iNaturalist-Scraper-and-Data-Pipeline

e209f0470103ec2a374663ace1260a27b826b65c

[ "MIT" ]

null

''' Specifies the username and password required to log into the iNaturalist website. These variables are imported into the 'inaturalist_scraper.py' script ''' username = 'your_iNaturalist_username_here' password = 'your_iNaturalist_password_here'

29.555556

86

0.766917

32

266

6.15625

0.625

0.071066

0.182741

0

0.172932

266

9

87

29.555556

0.895455

0.571429

0

0.666667

0

1

0

false

0.5

0

null

0

1

0

1

0

1

null

0

1

0

5

14395ab290a11d20d7720998151df0772ee04295

5,132

py

Python

tikz2graphml/grammar/TikzListener.py

ysahil97/tikz-to-yed-graphml

de3cc51675b9d09c68445ac7f40cccd64a28e448

[ "Apache-2.0" ]

null

tikz2graphml/grammar/TikzListener.py

ysahil97/tikz-to-yed-graphml

de3cc51675b9d09c68445ac7f40cccd64a28e448

[ "Apache-2.0" ]

3

2019-05-03T04:46:19.000Z

2019-05-03T04:47:01.000Z

tikz2graphml/grammar/TikzListener.py

ysahil97/tikz-to-yed-graphml

de3cc51675b9d09c68445ac7f40cccd64a28e448

[ "Apache-2.0" ]

null

# Generated from tikz2graphml/grammar/Tikz.g4 by ANTLR 4.7.2 from antlr4 import * if __name__ is not None and "." in __name__: from .TikzParser import TikzParser else: from TikzParser import TikzParser # This class defines a complete listener for a parse tree produced by TikzParser. class TikzListener(ParseTreeListener): # Enter a parse tree produced by TikzParser#begin. def enterBegin(self, ctx:TikzParser.BeginContext): pass # Exit a parse tree produced by TikzParser#begin. def exitBegin(self, ctx:TikzParser.BeginContext): pass # Enter a parse tree produced by TikzParser#instructions. def enterInstructions(self, ctx:TikzParser.InstructionsContext): pass # Exit a parse tree produced by TikzParser#instructions. def exitInstructions(self, ctx:TikzParser.InstructionsContext): pass # Enter a parse tree produced by TikzParser#draw. def enterDraw(self, ctx:TikzParser.DrawContext): pass # Exit a parse tree produced by TikzParser#draw. def exitDraw(self, ctx:TikzParser.DrawContext): pass # Enter a parse tree produced by TikzParser#radius. def enterRadius(self, ctx:TikzParser.RadiusContext): pass # Exit a parse tree produced by TikzParser#radius. def exitRadius(self, ctx:TikzParser.RadiusContext): pass # Enter a parse tree produced by TikzParser#nodeList. def enterNodeList(self, ctx:TikzParser.NodeListContext): pass # Exit a parse tree produced by TikzParser#nodeList. def exitNodeList(self, ctx:TikzParser.NodeListContext): pass # Enter a parse tree produced by TikzParser#edgeNode. def enterEdgeNode(self, ctx:TikzParser.EdgeNodeContext): pass # Exit a parse tree produced by TikzParser#edgeNode. def exitEdgeNode(self, ctx:TikzParser.EdgeNodeContext): pass # Enter a parse tree produced by TikzParser#edgeProperties. def enterEdgeProperties(self, ctx:TikzParser.EdgePropertiesContext): pass # Exit a parse tree produced by TikzParser#edgeProperties. def exitEdgeProperties(self, ctx:TikzParser.EdgePropertiesContext): pass # Enter a parse tree produced by TikzParser#node. def enterNode(self, ctx:TikzParser.NodeContext): pass # Exit a parse tree produced by TikzParser#node. def exitNode(self, ctx:TikzParser.NodeContext): pass # Enter a parse tree produced by TikzParser#nodeId. def enterNodeId(self, ctx:TikzParser.NodeIdContext): pass # Exit a parse tree produced by TikzParser#nodeId. def exitNodeId(self, ctx:TikzParser.NodeIdContext): pass # Enter a parse tree produced by TikzParser#allGlobalProperties. def enterAllGlobalProperties(self, ctx:TikzParser.AllGlobalPropertiesContext): pass # Exit a parse tree produced by TikzParser#allGlobalProperties. def exitAllGlobalProperties(self, ctx:TikzParser.AllGlobalPropertiesContext): pass # Enter a parse tree produced by TikzParser#globalProperties. def enterGlobalProperties(self, ctx:TikzParser.GlobalPropertiesContext): pass # Exit a parse tree produced by TikzParser#globalProperties. def exitGlobalProperties(self, ctx:TikzParser.GlobalPropertiesContext): pass # Enter a parse tree produced by TikzParser#nodeProperties. def enterNodeProperties(self, ctx:TikzParser.NodePropertiesContext): pass # Exit a parse tree produced by TikzParser#nodeProperties. def exitNodeProperties(self, ctx:TikzParser.NodePropertiesContext): pass # Enter a parse tree produced by TikzParser#properties. def enterProperties(self, ctx:TikzParser.PropertiesContext): pass # Exit a parse tree produced by TikzParser#properties. def exitProperties(self, ctx:TikzParser.PropertiesContext): pass # Enter a parse tree produced by TikzParser#individualProperty. def enterIndividualProperty(self, ctx:TikzParser.IndividualPropertyContext): pass # Exit a parse tree produced by TikzParser#individualProperty. def exitIndividualProperty(self, ctx:TikzParser.IndividualPropertyContext): pass # Enter a parse tree produced by TikzParser#cartesianCoordinates. def enterCartesianCoordinates(self, ctx:TikzParser.CartesianCoordinatesContext): pass # Exit a parse tree produced by TikzParser#cartesianCoordinates. def exitCartesianCoordinates(self, ctx:TikzParser.CartesianCoordinatesContext): pass # Enter a parse tree produced by TikzParser#polarCoordinates. def enterPolarCoordinates(self, ctx:TikzParser.PolarCoordinatesContext): pass # Exit a parse tree produced by TikzParser#polarCoordinates. def exitPolarCoordinates(self, ctx:TikzParser.PolarCoordinatesContext): pass # Enter a parse tree produced by TikzParser#label. def enterLabel(self, ctx:TikzParser.LabelContext): pass # Exit a parse tree produced by TikzParser#label. def exitLabel(self, ctx:TikzParser.LabelContext): pass

31.292683

84

0.730709

557

5,132

6.718133

0.193896

0.05612

0.093533

0.168359

0.800909

0.482897

0.47488

0.473544

0

0.001473

0.206157

5,132

163

85

31.484663

0.917035

0.380553

0

0.459459

1

0

0.000322

0

1

0.459459

false

0.459459

0.040541

0

0.513514

0

null

0

1

0

1

0

null

0

1

0

1

0

5

14762532b0b2c9e0dc177730a543ef1db73e4310

77

py

Python

pyinformix/requirements.py

homedepot/pyinformix

06a037510f79d12557a0dc0b795e2a92eb79ecb3

[ "Apache-2.0" ]

null

pyinformix/requirements.py

homedepot/pyinformix

06a037510f79d12557a0dc0b795e2a92eb79ecb3

[ "Apache-2.0" ]

null

pyinformix/requirements.py

homedepot/pyinformix

06a037510f79d12557a0dc0b795e2a92eb79ecb3

[ "Apache-2.0" ]

null

from ibm_db_sa import requirements Requirements = requirements.Requirements

19.25

40

0.87013

9

77

7.222222

0.666667

1.107692

0

0.103896

77

3

41

25.666667

0.942029

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

14816114c6f7af25067aa2b008e30452096dc622

63

py

Python

imapfw/conf/__init__.py

paralax/imapfw

740a4fed1a1de28e4134a115a1dd9c6e90e29ec1

[ "MIT" ]

492

2015-10-12T18:18:48.000Z

2022-02-14T11:46:46.000Z

imapfw/conf/__init__.py

paralax/imapfw

740a4fed1a1de28e4134a115a1dd9c6e90e29ec1

[ "MIT" ]

21

2015-11-10T00:49:07.000Z

2021-12-30T07:51:25.000Z

imapfw/conf/__init__.py

paralax/imapfw

740a4fed1a1de28e4134a115a1dd9c6e90e29ec1

[ "MIT" ]

40

2015-10-15T13:27:31.000Z

2021-12-30T07:52:24.000Z

from .conf import ImapfwConfig from .clioptions import Parser

15.75

30

0.825397

8

63

6.5

0.75

0

0.142857

63

3

31

21

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

148ecc0ecfb7d7517fa392b5820b6f90d8523146

65

py

Python

nst/datamodules/__init__.py

Gradient-PG/live-nst

02244172646375ff4a4a417bc8220064fadae5a9

[ "MIT" ]

5

2022-02-09T19:31:06.000Z

2022-02-25T18:38:18.000Z

nst/datamodules/__init__.py

Gradient-PG/live-nst

02244172646375ff4a4a417bc8220064fadae5a9

[ "MIT" ]

3

2022-02-25T22:26:42.000Z

2022-03-19T11:21:46.000Z

nst/datamodules/__init__.py

Gradient-PG/live-nst

02244172646375ff4a4a417bc8220064fadae5a9

[ "MIT" ]

null

from nst.datamodules.coco128_datamodule import COCO128DataModule

32.5

64

0.907692

7

65

8.285714

1

0

0.098361

0.061538

65

1

65

0.852459

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1490c04b5d587c6d0515353e99919cb56a6bf15f

96

py

Python

funk_svd/__init__.py

sicotfre/funk-svd

2f2297130242cf7efd07872e880d4060f7b3ca68

[ "MIT" ]

null

funk_svd/__init__.py

sicotfre/funk-svd

2f2297130242cf7efd07872e880d4060f7b3ca68

[ "MIT" ]

null

funk_svd/__init__.py

sicotfre/funk-svd

2f2297130242cf7efd07872e880d4060f7b3ca68

[ "MIT" ]

null

from . import dataset from .svd import SVD from . import utils __version__ = '0.0.1.dev2'

16

27

0.6875

15

96

4.133333

0.6

0.322581

0

0.053333

0.21875

96

5

28

19.2

0.773333

0

0.10989

0

1

0

false

0

0.75

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

213c6c52eb636ee7df71ebabf3bf6ffa2c9f2483

52,994

py

Python

python_pb2/go/chromium/org/luci/swarming/proto/api/plugin_prpc_pb2.py

xswz8015/infra

f956b78ce4c39cc76acdda47601b86794ae0c1ba

[ "BSD-3-Clause" ]

null

python_pb2/go/chromium/org/luci/swarming/proto/api/plugin_prpc_pb2.py

xswz8015/infra

f956b78ce4c39cc76acdda47601b86794ae0c1ba

[ "BSD-3-Clause" ]

7

2022-02-15T01:11:37.000Z

2022-03-02T12:46:13.000Z

python_pb2/go/chromium/org/luci/swarming/proto/api/plugin_prpc_pb2.py

NDevTK/chromium-infra

d38e088e158d81f7f2065a38aa1ea1894f735ec4

[ "BSD-3-Clause" ]

null

# Generated by the pRPC protocol buffer compiler plugin. DO NOT EDIT! # source: go.chromium.org/luci/swarming/proto/api/plugin.proto import base64 import zlib from google.protobuf import descriptor_pb2 # Includes description of the go.chromium.org/luci/swarming/proto/api/plugin.proto and all of its transitive # dependencies. Includes source code info. FILE_DESCRIPTOR_SET = descriptor_pb2.FileDescriptorSet() FILE_DESCRIPTOR_SET.ParseFromString(zlib.decompress(base64.b64decode( 'eJzUvQ18ZNdRJ5rbrc8rzcydnhl7LH9dyx5LslutGc14bM/ExhqpPSNHIyndUiZOcOSr7iup41' 'Z307d7NHII7IZAAiEfEGDXrHEg2c3jhSULm102yVt4JGTzcBIe+XgvH5AfmyUbIHnL4+sFeBuy' 'u/WvU+fcc1vSzNgBfqx/Tqyuez7q1KlTp6pOnTru7z/mnlqv50obzfpmpb2ZqzfXJ6rtUmUi2g' 'qam5Xa+kSjWW/VJ4JGZaJRba9XajkGZAZ0gdzlE0O3r9fr69VQlV1tr020Kpth1Ao2G6r00Onr' '7cO0yqDh/+y4fUtB9FSxEZYy+91UpXzU8Z3R/gL9lcm4Xa1gPTqa8tME4b8zObcnqlZKYXQ0Td' 'CByRtyFp65Ij6hrYKUymTdbsKzFR7tomb3dxTnnvG1oApljrg9q/XWCmHRzVh006/Zcua73H1h' '7XvaYTssr2DkR3vo68DkUE6RJafJklvSZCkM6goADd/r9hvUMre5bpmAtahSr0U0XAzOggw/4v' 'bOlqvhuXrLwsex8Um2kNrRwk87bmYqiirrNYwwKoSEStTK3OEORqWNsNyuhs24zQEDo5ZPuP0V' '6nuF+lEND0weTpBMMCv0VdQfmJEupkn6mjThcsNL7qEEblGDUA4zD7kDAYNpFC1FloHJm3dM15' 'QpU7DLD5fc/cnPe9HuRre3RQUBTzG8Bz/pA6iDOVqptTdXwyaPp5uoA9g8g4Zf6t54PmxNB7VS' 'WK0GLdD6+mk7/MWUe3RndRn+K919JfuDEGAyQYC9audsaCHZ0NB/ctxB+/sLJszL3Z5mGET1Gp' 'Nk/+SDLxynXIEbKEhDmVtdN7zSagYrldpanZdmf6GfIbMEGJ5we1SFzACthflXTM3Nzngvyexz' '+xcL+fzFxaX8jOdk+t3ufKGwUPBSw1X3wHy9VVnbZp6abYWbhi2d62PLzBiETfQUj31g8shOOQ' 'GpwkWGn6XlZXX3ApbXOXdfDRUrJZlltcRuSfTWMZJCskrmdnegEq2Ugmp1NSg9xXPSV3Ar0bRA' 'ho+4hxLYqfkYfsA9xHOlSr0Qxj3hHk7WFJ69ye0TjtFirFexTDT5xyn3YP5KK2zWgmpRN5VZdA' 'espZ+5PTHsnQJryN+7gKCw4nqdDJi56xr8qdo+dl1cDJQtYnagvJMJOlDeZR4yRXfQpmbG34lI' 'coqG7rhKCdXouYlXjV/nDnyW/tdYfeyjZ9x+r8d7ifdrjue433T6BvlXZvJ9jj9db2w3K+sbLX' '/y+IkH/KWN0J9bnp71p9qtjXozyvlT1arPBSK/GUZh83JYzrn+chT69TW/tVGJ/KjebpZCv1Qv' 'hz79XK9fJlYIy367Vg6bVCT0pxpBCQ2TdKUBZP1XhE1sXv5k7rhLBYKWT2LMXw39tTpV8is1rj' 'U3O52fL+b9tQqtZXfyYUKOmufBMcwvh1GpWVkNIy4eCgv6hp19peT4U4uzOdft60t5fTTsw/RX' 'n9dPf80A2Ddg/k73vcQboL/H+G/HG6S/H3E/ku7rog9H6ccJzxl6S9rXIsJCIPCxGohCPIt+gF' 'EQxVjTyNKwFB0a7WajHlFxJl3o7oazQtafbREZWrS3EZ2IA7b9sFopo6OIOm5xCx19Mh2DJs1B' 'sxlWw8tBrUXEreumiTEI31KFtYYsPjwVhg01g4qm0SZ1xLg2wib9ZxOLxFdSPAJOTH+SpFGwjq' 'miAi1MOFEYYw2DJqFJI14PqX1QYDPYxqRG7dXXEs7oco2RBWLUS3g5pEmn9p8CbqWNoLbOA9xr' 'Noky6K5Zr1aJu+rtFjHFMf9cEFVKfmu7QXXbEX1Y3Va0tud+EFNIM3qUOMBzv+3wzxTN6m1eyv' 'OG/ovjz5bRNirqZZSk7uwMKLBABTCVzK0oExCXV6gEjbvJdeo+bWylp7ilamUtLG2XqvFSkday' 'LlEM/dUbASpTrZ3jzfnntmlR1S4rJsjGiLUjw3EjkUv03dwMmtuEoj9KnVd4qkeOj4z5hCxxH2' '0pjE6zXVPD8PTw+7qZAH0WxCFIvzdgQdIE2e8dcC8IxPHuoDo3D5GwICVdU61aISJhmIAFjUa1' 'QlMBamDYNiWt/mkauC0b0k2QAe+gBUF/Ge8GC5ImyE3ekFsVSMo7Rq34Q9/ts+JtcIpCQYl6Vy' 'aCrEQILVpGJNzitbzZpgEQuVtBpeb6xNlVYvyWf0LVtLBOEdbHElin+noIMkCSJYY4BDni3WxB' '0gS5zbvdPSOQtDdKrdwwdI/PZolGutSm5VtrKdFhGAdjsHBIU4+oPWBBHIIMWpRLcw+HvSNuTS' 'BdXpbqHB56jU/KPHGL9FgpizzyV+siRuJZA9O2azWwFKl2LJaIbWvVbde/HJBI8itrCtWHCsvz' '87Pz5y0su2g2swn+6iIss8RfByxImiAZ75CZzW7vONXJ0WzmLbNKIwv9DbOztVEpbWgRuEVCQ9' 'tgNo4KRTfGcTE/P5PEsZsoif5utiAOQW6hXSCGpAmS9cbdSzyCU7QV3E9bwaxvDL29twJmn923' 'AM17IqAcbrqPpjDPvyCfThNqdwzd588Ygw9jA2MbE1BEj83bFsdyMzTzpw3HOrLyT5t15sjKP0' '3zcIsFSRPkds9n5FLegzTq7yL9gX9R6QcJ1QPMzSlG9Szz1j3MW+Wr8BZ2jHJV45cSbM4aPkkJ' 'NmcNn6QEm7PMJ+cE4ngPM3EmO4iDziziiAxgXKqMhtUzJNDDhjIpkUAPG8qkRAI9bCiTEgn0MF' 'PmXubfaaLMRc+ZvJW3KNHkJrS+pvYmmWMs02ki3JD73fwLhMsTAkNDc77RnXdbmvE2GC9Qw2Ib' '9Xa1jK0WIp6WqRpOWkibN6RNC2nzRNojFiRNkKPeTe7jAnG8C0zaWV9cAJ0iVZMyirWOVnNbNs' 'B1KoMlgL0cWjwURtMZKH7BUFxBeggy4GUsCBA4JBRPC8UvMMV/yBFQypujZo4NXfZt8aClJ4uJ' '0aBUqjfLgpbePsdi8UE1IjUOl8cQtMgGa7SkwkZwOewYiNrRwk1rQBD+c0aApIU150iA+BYkTZ' 'A7vbuYB7q8BWKYoiwlCJQF4oib3Sb/AkeQmevdM7Tqxx6OzgkYJaSzjNyYb7lGNHNY/OKSaCLV' 'gkZO6MecshmU9RLsEhFRMNOiID0EGSCuiCEOQYa8YxYkTZBREpWDLOyXaVwrMi6I0WUa1w28YL' 't5XJdYREwmtx8jGGLkDLkDe+PrFn6+ZPi5W7C6ZERFt2B1iUXFBYE43quozhFWWchmNV2zoN6r' 'bzXVlsDoFvHwqkT/DrfeT2plDEkT5BCpAz/gCCjlvYYq3TQUqR1DuZjMlALkb1VoTddeGFr+LF' 'kBqxHNfFZaOU7tkBKvKrQ3zdJjJAh3oNFrQRyC9Ini0i2c+hrvRu8oz2GPF9CMlmVGe6h0QKVv' 'Z9nVwzNa+juSXT0y1yVD6x6Z65KRXT0y1yWWXfsI0uutEb5vdAThXiq+RgirhdjL9t06Nfj9Xj' 'qGUJl1mrsb3McMBAOrkIl8dOj0Hqwqe3yMvLjhYJdgCIfttmgYaG2wA+oQdB8xaRKaJugN3o3u' 'RQvqeFWqf9PQg9fBvjYiFv/anYCHqzsQcribfcQLSWiaoOCHGApruEb1t4iTElBqocZb530JKM' 'jZAJcN3erP1pTWqKzKrF+us3lCFg0hebSzGjXYYC1j55cUfcES/76OL44XQegMKe2KNcNYf6V5' '26o3nwqbWX+13eKPMLKbzXajpSzHgLBhE4/FjmpDzNhmSHqWKmVcNDtxdgSDg7t8SdEX6OM/7H' 'R8SnmXqUpm6HtUhxsBfCCKULCTm816Lay32U4n07xVCUXQbwZPhbAkQUKySWlTDlarocsoxmbk' 'ksWqKBmPZRf8QUEgs2+XL0DTo5Gdsxgk5T3NKyXnKy+u5k01wTGbgtaGNzsYElvo0zsYEpg8vW' 'OFQEA9zSukYUHT3vdR/VtgM2jHsjKvlfsk62+0N8lmJ6TKoJDP7mcfDmk4MaAnBqt1cMTGtnLM' 'XBPnNC2i79uBM9S77yOcb+yAAr8h2uI3GYoF8QMO7/Hf7SdckVfZ5RtBpSk6PqskyXXvJhc+VB' 'pC+KDujnZ37tAG9QA0QKjGIAego7TBx6A0QNjh9xOoz/shhwTsj0LAQt72UQ2C9FEjRf6Jkb3Z' 'Yb3snL+32QbIegXuH0Nn2foUlI01wb9PkOVmb7ZADkDQsmJQGiCoWecF5HhvcVjnOK1WwTUddg' 'nr28IASupbYgr2yUp/Cyh4wAKlAYJcArn6vbeBXD+tydVPNd7msPb/BP8Eud7h/B1soQd188Sl' '3EGfBXIAwiYag9IAYRd9tYAc752oNj70mD9vn0js5lpJHFmwph3WjJ4Sy6EYK9gA74yJ2S/0fS' 'eIOWSBGImbvVELlAboXi/r/qojsJT3DJq6cegXHH/WHIywf4ewopne2gjFawdlP4ksu0shF7Sp' 'Vnf9dqNMcy8LbCtshprQskOQFA4vVyCObe+8j0Ma4yStqJVargTrtXrUqpQsu79W3bZoAaWMB9' 'BjgRyAeskiikFpgI6QmjJAINd7Fox1G3OVS8WfddiCANMNeM/h289pphugz8+pz0/wTzDdzzpe' 'F4nLv32mGxCm4w5skAOQljYDwnQEGhK0B733AO3/RaM9SDXeA7Rvcdv8E2i/F4S6fais9KDyLs' '5HIxn38CFvMdbVylMsMYVXOnhilV3LjOigCM/3xtw6KCN8r8OqTgxyAMoIAw/KCAl0K83Tfyb+' 'Jvx/0ZFDmKGfcPwdR2Z6NHAiEKMpUWmcvjghKYfVcB1uQRRTFgErNmsucXG5crlSbgfVpLuVF2' 'T8UYz2Ogk8izxh8zJ7jIbv9ovS3/iOUxRa1I06qUu0sUwe8YuqDqG0VqlV+NzXpYnrYUL8Iibu' 'JneDf2Li/jXoNzN0ybfO9WK7NNLbAoM7OUs+ap5sK9+yG/sgeK56jMZIffXwxGhQCqAM7QoxKA' '3QhHeOlkSPqI3/FigWaUl0Hg9eG8/E1quKdODlqA56aP3GoBRAN3o5C5QG6EHv5e73CyjlfUiR' 'ruZb54uiuUQdq1TpMAwzzmMuPhpWWATWwi2l2kRZKJZcij2R4RVaQfrMIxqzMAdpPpSkqGBlUx' 'Ty6UOKos86Akt7vwrUzw/9iJOUlLJIbcwhLAO9qWgRUynHa3nXo7KtoKaYuRmWQlIc4hVt0wpA' 'e1X3GE3tVzGujAVKAXTIu9sC8ShOePnVHj4kO+n++Qn3WhFSmQMdAQjDZ91+E4OQOer2RmGpXu' 'OTdGc0XdA/M4fd7hpZIBEHJnQX1I9zP+i4h0r1zc6ohnP7TZOLAC06r5qUIuv1akDWCU6GYxzh' 'hZx4qlbfqsX4Nlb/2nHenUqfXzz3vtRt51XtRR02cYmY+mWosIS6j/3phNvv3UYS7MchpD81SD' 'L7Nj5G/sigz3VK9ap/rr22FpKiOu6r1kZo/QStQNlZ6oxPjg7dxNnz8QekApmIpZy/x7HzRqvV' 'iM5MTJTDy2EVtlCkqUIEUmMlJMZXFRITtB0XwnIFDnKy91jNJ82EjE1s+nJsDchqpYYDNF5TWf' 'bEYHngv8Q0rr9ZL5v9Ics6QwMCoQWNgPokyQq/jN511urVan2LRTZNasUoGtRO2DpDKOGfezoQ' '41VoH6TzcRSZaYEoxsS/YHBNMZc3LRws8K6sN0C7R+hgCXSov1I1oKknXWwPJKgzixYaCRpjuV' '0KYzzcGJHvCA9Xq/3leqkNB2agJ2mC6K8sceKUsFnBtmZIzRPEQsHG3gxqXqQdGq4FmywHbd6q' '1eNvTPcK/L84+uOm6sS9YvLzeTIOZ2tlgoZgCkJis06yVdGkhd2hSaKn7K/RB1cHRKy1tsAmwk' 'FaGSUtsFkBYzXBOzXFRVGk9k5/6cJs0S8uPLp0aaqQ9+nvxcLCK2Zn8jP+ucfpY96fXlh8vDB7' '/sKSf2FhbiZfKPpT8zMEnV8qzJ5bXlooFF1/eKpIVYf5y9T8437+lYuFfLHoLxT82YuLc7PUGj' 'VfmJpfms0Xs/7s/PTcMk7Esj614M8vLLn+3OzF2SUqt7SQ5W531vMXHvUv5gvTF+jn1LnZudml' 'x7nDR2eX5tHZowsF15/yF6cKS7PTy3NTBX9xubC4UMz7GNnMbHF6bmr2Yn4mR/1Tn37+Ffn5Jb' '94YWpuLjlQ11+4NJ8vAHt7mP65PGE5dW4uj654nDOzhfz0EgYU/zVNxCME57KuX1zMT8/SX0SP' 'PA1nqvB4Vhot5l++TKXooz8zdXHqPI1u9FpUoYmZXi7kLwJrIkVx+VxxaXZpeSnvn19YmGFiF/' 'OFV8xO54tn/bmFIhNsuZgnRGamlqa4a2qDyEXf6e9zy8VZJtzs/FK+UFheXJpdmB+jWb5ElCEs' 'p6juDFN4YR6jBa/kFwqPo1nQgWcg61+6kCd4AURlak2BDEWi2vSSXYw6JCLSkOJx+vP583Oz5/' 'Pz03l8XkAzl2aL+TGasNkiCsxyx8QD1OkyjxoTRXi56m+LdbM8n/7so/7UzCtmgbmUJg4ozgq7' 'MNmmLwjNJTrHp93kKEfnDNNfZzk655j8Deid9NftDL1d/gb0LvprViJ51N+AHqO/sgx15G9A76' 'a/Jhiq/8ZfI/TXMENd+RvQUfrrDobeJX9/5Vb2fb7JkT1w6LdvRQSB3kxZUoYRn8OQlQhdmeUb' 'u0Fq5bBBYgS+BYTu1LYV/Ol6jcVKtU5qCpskVChoZknkYBsoIxwmINnUVvVEUWChutYMSvHWoT' '9gZ4DWwL+xddarSjpyaJdqiM04aLeXQ20KNOo4TW/5y0vT/malXGPRXq+5/mNBrY394ETWP/Hg' '/cezWmKT/KuGDVi255vhep0kdM1gLzowNLYa63AkqXcpBU2NxGSZVbjtkCBEDEhC7P2kb7ehnE' 'KMnj5uxletk17jz4VBIx4ylRiONql+WB4m2at24lodARwNV4qRYgu/H428FoagKzRO1kka2GTV' 'zt6OVEjPqydPjZPcplkhpT+gXYpbf2L06toH5nOCS44pPwAZMKzuwBlAm4N//PjxE+P879Lx42' 'f431dh6A/SP+MnJsdPnliaPHnmvgfp39yD+p9XIQzIxUTS7lTSR5M8RG6d1BUydGtRG0ewymPB' 'piIHDjVban7V7uS/uvDotOufPHnywXgsW1tbuUrYWmNlsblWwv9QIte60hqD6qZDmzgC7E6yWo' 'nTq2FEP+RP/8QZ0uQ2GzRd1lrgDmnFz77SfxKUGR17Mie6T1zI6KFn1ZdYjyZTYEUmeJSrzy/P' 'zY2N7VqO+X30OH2McZq8Fk7rYQut1NfKwbaFG421XVLHx5fJ1mhdlh4Txe9uXc76jNDZFzuky7' 'nWZfy62ohUIdJBSqTUnCDuSYzw5J4jvFSpnZz0nySbq7gdtcJNfJ6KHq1Uw6XkRDw6O5dfoo3Y' 'X2sJGnvVuXutpTFdpk3q9ClCGKbcQ/7o6KiCjK21cuWtCyQ4ZohpUGvMf+lL/ZOTY/73+vxtrr' '6lP2m6TUyQACV8y/WtiJvEYqGhWjIsypkCSkqdOL1zGZnWUP3E6VOnTt1/8vTxWGyshrTeQ3+5' 'VrmiWyFh1tlK7sVN5qgaP5FCEWWCJwv/jJEZZKFzDQ5GOyCXbueY1Q4zwFiCAU7tyQCPBZcD/0' 'k1kTnxaKPIxUqVFHSLASBNSdICSlO5d4WrsDnVM9BcLdw6165USSUeHcPAikIh6UIRZky1hX9Q' 'Zl6NnWQxRi4l1dBl2EyBsdwqWmZcYhrcdw0azNYIVGvlyIi1hi1Q2iO2CP1EmauONEb82kOm1n' 'IkNPJgNgUbHbNGnhy9FMaP0T1GenrPkSZCXmh3Xtwmo6Omx7rrRI2OdXIhHDTxvNN3yPrHiqRv' 'XgwaDRL/CEmuKYgy37OsBlh02m7wVpdQXCQqVukMLm9AL2j/UV1Bd+HAsaxqJg49Hn4d9IbXj7' '9uk6y3DfoviefXL70Om/frz7yOdAj6f1qmr3917nVQl7BkX//Eq4ZdHAqQPFC10VBQ3Qq2Cfkr' '0OBg9SldYA1aQLmyjqMhUmpoHqSnrM9dkUavOqPf6E3FPXOXrJc8HTbr442gXJbAja26bi0MSh' 'tKJ9N6HPQ/ESlZ0aCwka/X/XaD1QRddbSSC3MCPLG7tjeWVQFN+gBU9TT8KtKP2mtrJAThES7J' 'MYc6nWNNdHSYFMDhsbMJqOuL37/SRHjllAoXP6mYIWLjvEID1Q5RISW8LNAmR/lwT/WGAxQXaI' 'xhAmowh2tKpdnJSiBkkOiqEZAN3rTPPFmng4ZTKpEuCqfrBveJunKOL2OIduABtbe+tkZrkNW1' 'R3HQrtZa1h+ePH7ifuwOJ+5bOn7izMnjZ07clzt+gsinuJs2Gfw220sjiEjv5pLcf70W6833ZX' 'Gz4v6cLCASS8VSs9Kg9QOC26pa4M+w19ZEy7MzQZhd8SOzP8QV6c9lWk+t+mxxociLbHRsFwU1' 't1l/miRqwKsrrI0vFyfK9VI0cSlcnYhRmSiEa7QcaqVw4ny1vhpUVxYYh2gCCE1YnYyxE2ujji' 'hbLWmyvM4VSv6T0BhB9Jz+40k9ILniIaOFB3+3IdKgniSpscZVrRER1rmGkmwYy+REtbLaJAKz' '2p3baG1W7+S/dN0xdr648eGzdAJXjD9y7PHxY5vjx8pLxy6cOXbxzLFi7tjaq0bIsKg8FW5VcE' 'WlouYqniXiZ9XaY/VywMw6EhGuRBqt1DyqhFVZftKG88SoclmKnHst1WTs8cc42wtBo8IToqHK' 'ilC4Tuxsm8epOzg2OUP/uv4YCFlfZVdhIOMkw4awbvACIfNwPayFzUAtNb3M5Mzd0J841OWjNx' 'yivMnhGJafjW9KyEH62xy/EJu5egFQF+B7JjTNYslWtdzddS3/IpyLuPNzFdvI3c04ehUhXqoS' 't1zWJ/Q6zIpx7LVAfELfJyf0+uhHTuj/SI/N8X4M9TJDn3f8+XptvIYzNtjGCQs70JYkjMvdLe' 'x5qWiMTjIh2jg1gr8yboy9qlELcXocYlqz++SmpaK+G8VGO00SjGXtUeiknxiSWfmfuyuNEPn1' 'Y0kaOWr4CPiJQWmAPO+gOfT47V/ocl/kte/k5fLbOo9Oyu2mujWq7pbf0vldGWTy9VoHL8NfcF' 'zvXL2Vx50Zc+txjzuwN7v9jWA9XIlo85IDlz4AivQbl1b5Y6v+VKjuwvYXuPgSAJkHXZe6bbbU' 'DfGua1477efS+J25z+0La3K1vPuaFXupLN8qf6170BqYXC8cd3vCy9YV6uRNVl2+IIUyd7sHau' 'GV1oo1MnX7dx/Ai3p0w6dcV4n7xaDSzHhu+qlwW2iHP3FMxQwqtdWP4TPuflVrrhK19qh5g9uj' 'WFsussuv4Q+k3PRV7sDTbEQhO8rjG8v9AqHPhE+jXq+qNAFUiX9kJt0eHHC2I0kHMNRJnCJ/xd' 'lWQUpyR/zXyma0LnkB+hXkYrQOAoq2vaKvT/coAmqjSd2iPpu4s9+7y/X2JKXsC/2ZUbeLb0r3' 'MXMc7sQasW0FLjH8Z11ur0Coy0E+Gg/5GKUqV6Fv3MFaRV5PhURhnEheVlcyhbj6Jy4g64PXlU' 'pDFoKrQbONzJjrBW3aFkmPY81qJYjkjveBBHwKE9JfCqIVkDMS1k8y7PRUEXMSFfqoHP+VeczN' '4JimvFLCDVJduWe3m9QoNo1Sqg2vZgDS1hPuTaVKo0zsX3oKlxRVm9KkmqThJD6zizOLqrDV8A' '1oRMBRDM9MuF0b9agl85ac7sWN7YjoUM0TOVrbBS5IkqCXFDXOb9G/C4t01NFlMy91B6ukb5It' 'E9ZWCHP3mkLERfkiFV+KMg+7+zjpA+/SqD5wzeoDqFBE+aVo+Hvc/UnEkLkDpJYVy3/v4MbUC+' 'FGpAbRrEZ/DX+f26c5AzfYVcqGlUor3NQH6AMKhhv0EfAxIj1d4L8Jn4E62eiRCOxrp69wVXEW' 'vdvugQ7m2nXMu/VL1Oa5IuXxenseQIXliDW84Wcc98iuXLgrBnsvY41b+mq4XXsbS+D27xy3T2' '8x2BB5k1m5zjwM/VyaN8RhN01SXljE6xR3BXyktdXNFSQrxU277nMsyVU5bO4KH8hxJY/6GEBi' 'fPiS20sMtdQMQ2xIHFzQFELKL8DJ2iZkdX4M9Stzi9vPx8YkQ0KtFhjA8Fm3Z0aVI4JvBNGGnh' '78zZsLEX5ldbsVRsIm/YCcA2D4Ne4gYWVMMbA6xGWF3VMlPdMDBJsVUObeBJYDk4cSdFGoaNSH' '190Bi5XQvAjCFYuRBgQ2f3V+IvLyamoErQ0hQx8Ai/R7+Jy9XkhINHeXEYk2Uh1t/GqKsFWXcT' 'nByzF3f7W+RcueT9BJ7HBTfYV9DF0UYKboeqW41gocCdz0/snRpGSPC9l/MwcdKCUBmRH3QLWy' 'WSFMm/USvEORrKP9DF7U0Mx3ubeogq06ybGVUn1TBYmsbIab9eY282G6cBOXWUKRaV3iIhcYvu' 'Ae6MAmc9DdN7+wtMIH2I/O5pEapc/tml+Yz3sO/ppaXlrwUiQz3ccWzq0snHssP73kpYff16ty' '5CyaGwOZk66r+KnRbkWyRA937r9YFIV+5jEUoyHtN5VWmnVapxmueFNnRcO3hUFdu0DFSTAM8K' 'Yr3aocKEf32mYLLgqbvgftvV+SQO216zOj0U4Q7/rgX8xBUCsT6TllifwE8+sT0JXSls4ANaBh' '01vlOG1N0FxXGodOWzNFAE6lEpaaYUuWcy+1MFgYUDBe0KTIeRvQd+xifcy1+wletEom9cXdlI' 'Gr6ItjbjqsXSY1IM1b7M5aXANlMg+QVKxd5hWHjf+a3ZClchnLMcqcoWmMOfPoIDPB0b3WVMEu' 'nHnUPRheCUtsJPP+UG+3ju4TNurcImbEHix4ps6SqkLYu5W6aWD/tRror9R1TVKZ1sn8DldoLV' 'Tq5aMHrlV3gIsvcmlk4KqUw81GHek4jnqSg8dAwGbUCbP3QcVm8pOkVhftrs2jh1gEHUyQi7bR' 'ZoE/D3/Rcfv5Ljiu8IEZ4ns+sk535saKF3bBKs778JVGRQ1DdoWrjNMqnLnHPbgVkPBaqzdX4D' 'oqQcqqjEMH8OHRenNawBCJcacrvNOpTXZ/DL5A0OH3dbsDQFfb4ve7A2w5SV43Z5e8boYSBbel' '/4wyQ26fEf3aWNdSn7CREOYV3Phti55A2Ah4SkGhBtKiI3PkehUeVxVnPUXvYd1JnY+z1vVYWe' 'syMum9qhz+ztzl7ktYQ8z+ZDYmgLBjqbvqJvM22bH8I3PK7WuGUbvaKq8K4ybXXYE/zpybXlsv' 'mJJ2hq++RIavu9z9jSBhwvbz90EFFQt22N0npZptNroHtHoAYKENs9snIUoi3rTjKeMQMGnlNn' 'eAS0gbB5W+BJBqYcY91GivRu3VFTsEXyyapDKz2F4ttlcLGVXevpOSOe9m4DTAKRXtvNWwyerS' '4DUFDFVapDpLusrw426P6gYz0ao3KiXthuAf2BIwYQnXST8gyilELIrJRrir1on07+F/1+e6ah' '1gfsgU7pUoZ1ngR3csAVkxBV2wk3lTL4h5kw6ra1shlsPqIXcwWKVNU4T39ZgJUv478HehmnYN' 'SgbGq0ylKRqngOy9nhSQeXc//7GC9bcOFa2Pq922e7VpKVXYF9k/wReke+hcgv0snfoJoq55E0' '6ZhNeIZR8zeXfBsxxHSvyLOTRwNXNIBB6pw6KQR8TuaS3wwqbk14og00sblWq5CVeBTt+2j4se' '0B/kLg00mnJYbjdI4YJLWYTQgMAeJZAtVA4khMoRt0fWuCeii9c3PD7scSEjhlf/Do8PVD/6WO' 'hjpwr9RWbpgJV+StTNW3buf3GZgl2BLb8rtI3hnI433gypMQSYpt+ZXLxVH76KAmw28Cn3ANRf' '9VPpv0eupf/uoxoLXIEVYDKztVBWGRBv2KUBLcPZuTeoy3N+xGfIzNZk2tNchZperYDDxH4T8b' 'C3or1PldfWIG0y2i0mevbeVU3J4Q+l3QMdk4FEgaU6uxWUEzcFrRteg3LmEXe/soyQIW4jDMoi' 'EK6ysvdxhQUpjxb4XDdu4Zoqzj6uYFo4RQIibLUbIgN3rnRdUjn7VGFSf/taYdAs17dqIv+uVd' 'GUZyGDRmKce/ZooYhiuhkSMvbPzLx7UDcZt9TLLd2xo6UlKWka81odkOH3ptyDOzpNiF3n+sXu' 'Q7At4Uwl+2FT5iQ5PjbJluirQam/pEGZcbcL61/m5KYdnGfqcDHiggHLMJQJuX0Pu9DUdWPTkO' 'RimlapsN/RzrVsqqDQ8F847uHdaPpiaSV977pAO/ruHGn6hY/0NNnA1TCoEcd37SJKp9U3U1UX' 'Hn7MPbhjyl7kiIfz7qA9iy+2mZe5mZ2DfbGNvddxByxyv9jJPE6cW69qMXRL52zCKVGR84cCl6' 'QtKL1Rb8lUXr0CCrJPKDlJL3bI/9JRKz4hqP7hDrxBA0/CsbeTkpVw/fcRQPn9SddRuwt7WqSQ' '8rIe4A/sa1FlSb9X33vYbaN+DB9zByxjCpttWEMEhfga5RctjUF7v4bej/Mcy8FpfrPvoHa5Xo' 'rtcbKQYsg9/9Fx9yWOIqm1G87B4bc0tbRcXFmetz1/A27vxdkiLmR4Dm20R16+PMVXcubzMyvn' 'Hl/BpZN8wUsR5pmOT9SklyYV79YFKnEhPzWzcnEKt03mp+an8yv5Vy7lC/NTc14XWjVFgAdfSc' 'GnbjJs919YKC6tFPLnFhaWgEQPPJDnlouPe72ZQbevkGcMZrw+wGdn5vJe/z1vT7uDtoeeSDKE' 'hvnSz8rS44v5jkEecg/g+3z+Eg2IBrswT4M96h62sVl5dGp2brmQp7ESVvhyYWHhZSsqRXQ641' 'GXGja3cJ6GRRQBxKC+gpHQmKRk8cLy0szCpXka0QF3AJCZ/Fweqad7NUATvg8u2dn54lJheZoQ' 'wiBdUowPGRDNXIEGNlV8mTcAvM0HIg9/wlwMZm5xj5ovy4szU0t5pvf0wkze2wcuMF9pyBdn56' 'WAtx8jRutU9OKiQvIwpo1hOwh0BPjzp5fNzs1R2RvumXa74HyiReAt04R1kJ9Gx9DipSl0C06j' 'Dhm0lL+4uFCYKjzupe75tAiThGGUudO9nfsC9+ZXpun/zi8UHt85wVahiyDUASBjikuWRs9LQC' 'W/pOeTGLjRQJeIy4uzYKUZ+MSPJz7mX5mfXsZlKvXxkcTHeVyuWqHq6uPiPZ9JixuObUPiGAvL' 'ODE5rcEEfvKDGGZmeRHQgygSI0szKT9WzMIq5peWF707yHC8ace3pfxUgVlxmGdO5h5t3Qkvv0' 'w6fk+Au4D/jkk/jqozy0sGcEKvqpnZ4tTiInVBMzGZTLJ+Ej9jnnoEP3E7YGZlYXnJmyIb76D5' 'uVKcncNtMe9cxnV7hLOmwSiyTAinxeWlojcDYjBpqMAifuRfuTiL3l8OiTEN8YO6BWA8v4A1sr' 'BcoIaL6H5uAcS6QN+XMB/6I83rhanlIrBcxjoqvmyWxjSzkw6vmCy6PSR8phZnM7Nuj4qeydy6' '6+mhycB9216fJbf6S154Au43/ydiLU4t/Zeu57hfRrqLgf/5MnD/87Sdgpv3ZYSdqYjO9VAiLn' 'XI5jr9aq/y1SilTxAtIvvP1Wp9dWIziFphU8AIXG0heGeiGeKWrUrWFU1cPkE7d2tywkVU+WYb' 'V9UiyaAc4RhEwoBHkd0Kn8c4CexGgM+EWlClASHgu4a0qYwrWaoRt6bzXXDpQDW+VQHZEMBMOr' 'iKRFZfy5W1NdXMGlEpUPEKuB4eVjkqnRPEqdRByYZVkpLNilxwpnkDqoh6qDdbKvu1csYgWyOH' 'eZyRAHr6qNJccJS6/m3ORpB+upWl/0dKE79wTl+l3GcSne+3Ep3rv5EIz6O/RyXR+UH6+27+O+' 'VlOAH6Ec5XciP9fYvnTPYiJ4lKYaoSfNzoUcPuS01+j5u8lPfQUNZXSwX31tvNWuSH+mc10DfX' 'EE6rUyrGOTtu4kxanpWy4yYawq0WJE2QUe9B9x6+gnm7ZOC9RWdbZeqrlZ5TSFhZv28nbI8Stj' 'qU9Q6OZM1yXRXTTjWRPptTSYCvLyPpD1Q8dXWvI2f2HTtyZt+RyGn8Es5QjRjTf5EyMaYq2fNP' 'pPwFE5OPJawCcCthlS8IqIviKoH5ZnClstne9JVbUSXGUChRQUS/KyrHGc+VL0jfPVS/+Cb7Wr' 'vJwfC0UCMkCK9do33TMGfC1jyHqMEcMk7y9U8dOQm2PJ61+lcJKJFpvqwyBpj23eQATDe5ZP7v' 'UZOiUsenjnIssp3/W+W1/j0dwZvyclTp6NBvOVelrkn2ZV3IRyp7SS3DyQsExaxrJSywUj7pLE' '+XcH1B55gqZ2N6ZP3Yz86SQzu/VdjvZtDim7FBqVWVVHOJtkdwn3WdcyEkUo538wj7LIhDkH5i' 'MTvBeI4T471dkyXtTVKle4e+36JKPmhWK/xcAHBSDE+zwBKGlysi++PIYcw3i6rEUCs1JceIuG' 'Us6HatVan6eD1AVnzQ3HV6kbZ8MpFsG9JnkmTM3Ym05ZPemHcPp3ZUacuRvHpsqGYN42Kd56pk' 'Lj7tOpL8lauOpByuBZofO5MHJ9KY9zAGN1sQJMu+xbsrkcb8tDdCEvUe/nqGBNSUCCjJLUZctp' 'uAcrg00iU1+nTW74c4zdWTWpyaLGXw9hOapHjU6tX6OoLw/HqzjHyWmzFBwPFNyWm3Xuc7FfBb' 'sfTFRTWZpI4E4Q91JAjvIciA8JdOEP4QLbuhRILwhzi91b9wBOR4j1AztyK91VK89Kx78SI7NA' 'PxSVpC7LLECK+09Mo0QsOIHg4Z5stfw1koAZthQFtNrW7EnJHckucj+fCAI2LmEbOeHBEzj9B6' 'OmpB0gS52bvF/aeYXqSbfom3QFP6zcQNCFIZ1AU5voVvHoHQV9+MNEYekkCKv/SpcDurxvFwlo' 'QBX16Ks5bhihHfmotTTyE7U0mYGGXiwA5Op96KwirEdyPky00VUonKocllKi92ZI2Qc5UswtMc' '9abchiKUTKoW+jsyRdT7FqT9ZXABcZMmTdrTDKzSXfd5Gc7jpnLBXyTixhnUsWde3JHf/SKRez' 'CR3/2id8DzTCuON091DiUys88nWnG4TD9pInZm9nlSZzLus2pNvpIm7TU0aW9NXdek6fw11pX3' 'PWbPEFtFsF99Ht1rTKQOuPl7m0yTi/6VnKF7f5/ORf+4mTidPf7xHdnjHzcTp7PHP84T94xOze' '54T7AUeLPDtotZtNYTBYRVTgOT+Of8C4lcrSanosyTXP/j1LdQ/tUFQGjrTW22IPgIIoQmcoQ6' 'GulIP/9ER/r5boIMWKN2eAAZkQU6/fwTLAv+L4yxy1sntnoOGUB+yWHd0c4rahR/peb658JSAF' 'si+eUq6rylzbOAK9fDqDbS4stCLmcDqTGn0fBIrNOMK5PBF77brLP2IK/1kPonWbeqSKTE6zjG' 'uCGJ+3Qi+nVihwH3/+dBMj9ssqr8R45oyGxXJZnJD0rNeqQkuxngWjUMCYXzfO0MrxetBpFJSU' '0morhH5RquTq3dbONlotW2WkQjEe/Xgb56qkYlt55YbeLcg7xEVL690tq64RazSwTVqK4uQ5VV' '0Th0zuwQWC8jlbJmlC5h/U3D+jr9/abR83X6+03W87+kKeZ4TdZEn1MUkzsqTLmpmn7uh3ET2d' 'IM1WMligJxeSSHYpAEnQIk+ZRBvZzSQIVyuHBWXRtvN9abQRmvLxHXqOAPMFkt3OpoPOcOjyJH' 'nkpwtY2p0lZ7qbnaXmej/f4HTt9/8r6YIlgozQRFHB6uVkK7ZKE0WQl9PiUgJIhOebcMfTDlL8' 'K0t3KJrnIyaiQhgAqWI4yam8wtJtn95UoQTy3IRfPOjWgetFiAP2geiJtSQ98IOCs+JzxQ27MY' 'NVRJsZz6KDvBJikQlUZVulNshEjjcs7kd103vL0RVhtrbfVyFplXxFtYVbLOo++AI9G3zZNQhZ' '9OPNIAw+BpI7z0vvk08eSNFgQZs5FntSEQZKPGC05P+nLn3/gt2nA3Wb9ESqn3iWpW9maRYJy3' '0fVLYVPdPt3UiYosFbpLFH/0ecCCIFO2J+86dfXpLNl41+lZvZq6vDc4vJx+xPGL1hWRROZumi' '4tfg1qeiw7t5JiGPqJAxdDcuiRohK0rP0Gn3FZX7epblMydkT8N8TJlbvEMHiDw0siBqUBwpp4' 'Ro+rG1mzsUO+yTFTwElb1aNiKpW7ses5CXiF9QUjLeQ6uRa4WfXckWFrbkw8evbLEWo14AGD2r' 'bOwK1mSqPbrZHrs0Cc5RvKcQxKA4Qd8YPdAuvx/onDdtp7u1n2kQajFJKYvXM2qzNrvY4Y/Yz/' 'aqUJPPF6+q5yITS31Upz9Sh0Kr5yWKoGsmdY6fDFhIdoRDotPaczy0v+6Axvj66/zN7QJTIFxs' 'Qzyjn/6tV4UXMvOsc6hGpYkh0S1OYNuSVFYa1TYYnD3VYZFrSDsCSJz4nSVIl2Ml2MR+0Kb53R' 'LEZbszXyxKbPcqdT2wQ+RmBrN7RyoSpXMAtwWiTNQHmm8X/jje3ceqU1ca/2uZIKHELihbHjul' 'wvTcwg6SQNbXwmBOPkNst3EtOMxzPnuvY0XlwuLu3QPuUBQF43ouWxat2h6rl+HiXi15og9nbo' 'qkxZaQVTHxKNSwjTr3ISh4VCVmdzh7ajPCzg77plL6KZHcJAc3MPCZp/Emd4ViBmZzx/E4McgI' 'Y47a0GpQGC2f9pvbZ7vZ9BU4eGfs1JLgNLZiGLrfISM+PtYHFLmoGjs24sc5V1oJV7i7/3Zm+2' 'XJA1uZTQp+nf+YWlq7C8HmUv0YLH1G+BHIBcMr1iUBog2F7vTpOAeIn388iT9w1oyT+W7pC5ov' 'LonM4tk4kEYxRyVHjZJXejTeR+jkRM6vQF8lHlEN59K+L311yd4IOKbdTrT5mHQVBzPs69wmRD' '7g6aC/vkWra/iiQTdjlx3mWW3VjotpqOT/GRg77iju2HlHOblzeRloqGRSwSqIZqKnMJyUorJQ' 'nEf6A33Vp9y/XlnI1VF804RDTr2D+b9JaohANSjXN1d7NKS7PUTXM2yT+h8r9PbU13mLda4gno' 'eK/loK5DzXCtoxYoBRA2iN9wBOaoNOD7h97j2B5qmMallp1B14yTtN+K6GRxeVraoFqOGF7lcV' 'eGscIwjERCx+MOSYiuIR9G4MvdEbbKyDiLr3qLgiivo1AVspMG/XNBWXgzHqujh9FvgTjD+KC3' 'z/0ZPdaU9ysOq72rLATwwsX3tANSDluVXT32u6IjOqI9eJMDtymCJLw+Xf6++x88eSIeBSaDEb' 'zRAjHO0BPfp0eR9v69w+9W/VNtzpCVYQQajalNPB5UWxvbyieukjKJG5Wks+yRVHDEGv2IttPr' 'ZMfLc7fXHL+YQjsGb/CHQsnYHrZAKYDwetFzaYF1eZ9AobuG/mNqryHprEJIP6/yp5eTb7TSyC' 'o1ZEQuE/NJMFUuztUVacTPuP44czpelUP+FSRpFJm3l+SG2lCv+xuV9Y2sEfNs4vn4hos4WmSV' 'YJrAs4tuWPNhfzw7STAqDuXDLxyj4r1c6ylGUkn07jDaIM06bLGbytcycSzrh63SdzQzk/f6Ot' 'JMVlHWF25RUqsmOZSRMcXMGTRonqHbLVAKoGHvTvfXUwLr9j6n1tcbX/g0KhJx8k/sxNecwYJs' 'E9gzIgCmNWHbDSibVL+yxo8fIjsXZ3L1OV4TZc8pWcaHQnriAl/C9n2x2Pcic7kSBZurlfW2fv' 'OBM2PRLPKMImiNz1+uSwQ8cP/x+ydPxoSGVv+5pAjoJkJ/TomAr2sR0ON9yeG3837R0WPhwwQ+' 'gI8M2zXD1XqdBwie5dt/JSX2qnV2vWuHJ7hF75S6mHqwpV7HGxP1NhifFGD9jh0OLXEeTyOLN6' '9ri+8XQREoeDxYzwKlAMJbfpcE1Ot9BWUGhk4ZxjCvFF7HruL5F2QNdO4s0Km+ol+C0aAUQP2k' 'mH5WT0if9zUUOjD0U2ZCRnRYyAizV51ljcpb1G6x95gDDuARsT4mlE8cs6g3w7mJIqSTygdpS6' 'e/Lco/eOLkg/c/EA8dSZq/phVwDUoBtI80zFcLqN/7uqL8Y3rgWpyA52zJyMv9upA1/eFtpq8n' 'qd9PKHxdUf+3+SFH70+h0f5sijTa/+mVeyT6ggj4U5Uf6wfZncH631+ql8n+wvHtrBz6aWylpq' '81Q5VRTWXm4xce1OO+Sm7Ku3ohPyri7vLQmh5yQnNeq3DCbMt24/PDiH1zW2wN1LbxSua6r18S' 'bkcs5le3jcuO1I+oRVp207zQwjp6W7mhoXFf5plwxTOqjih1Dwc1GUjw/aVWaeOHRv8SoSrHLF' 'AaIDyP9qOagI73N0pg/ldmEdpoRdjv4QG2dVXjP9dJH7Q0XaM9QC1b3+QRV1Er/LCEerIKXNOE' '/NTOEfYba4Yrc27wvzOvAVbaZvlOhUlMRziN/yZ2J+kHU//G4RdTY1AaIIjZH42fTH1DinXP15' 'mXkvzZRT8ol5GhE2x/uaIiv3YJf8G6My+n+vNTSyOgKhEHdkAYN4fS02qByqMRs4uXT2FTp/+e' 'tvgBGgfj02eBHID6WeGMX08lEBTOSwJKe29EtduGHuWAPrnsy8ETmMRKa7vD7mEo4hNoiLuxjO' '4LDxC+MYkRVOA3AqObLBAjcIt3q1sSUJf3ZkXYgjw8L2uNT81I/guFCQFWYux3RHf6yVRaB5cj' 'BCMLO8RMcDc2yAFowKIXfKNvVvT6oJ73bu+tqDc59F4niR9falE4Rf4oY7AKRbzU1CJKPV60G8' 'JuhyRkkq+GtXCtIiFedkM0KVl+I8znTLy8kxTNa0+xowsYWWPuJtK+NTlmvC7/1pR51q5b9C4C' '3eyNW6A0QMe9E+4/12To8X4cTX3X0I93kAE3aXx9A+/qUyQzRKKkWoWYpK1GZBEerpEm9hqcfO' 'dBsiuhZgRZPD54zn48OeQehfqAPMzYLYoVge7wzligNEAPeQ+7OQH1ej+Z4qcQ8UrsbvvFhtIk' 'dRNwS3ENzwI5AB2Utw27xS1FILxt+CZN2z7vGdS7e+jyHj1JDCaWRE18E3UbhcTOfrWt2rxig8' 'QTjQ0cGNmrpI/I90ySfH00qmdSxvHYLdoRgYa8OyxQGqC7aC9aE1C/9yxayg0t26E3eq3Stmpd' 'YtBuq7qP96BMjlSVfVdHwwUIIYJ1RsvBwrmfEOSebrZADkC3yEuNCpQG6F7i8vsE5HrvQrWJob' 'uUTVxR57+GdYmWJZwJI9uY1Z9L/b0r2R9ePHwX+huzQGmAsl7O/SUlff7XFOlq/wG62rtSfjJl' 'ma2ldE4/zak+RFBcsNt0c4ndzq7ZuJPj+KSapqjq6qAAyxpCTRPlgZeQnpJqpG0hsksdOFiIag' 'ekatAqiaeFSLAhTCzhTK2v8t4ocq4FhYPqrIfqjRJRBrFIiWYIB/m806ff935/ijOjfszxcfVO' 'yc1EOBfCwYW4vtrPdDblQKm3sOX08ashHktOKaXIJeW0HIpXYRwtwIdgpBxSxaBS1VpglmQWch' 'IbrRCRIVEcGoKWEByi151+Wfz98d6pnxZ/P/bOfRYoDRBePH5zSmCO929SrB7/5d+5eixs9A9R' 'Q2ZK0BpjWhy1QEwerSH3iGZHIGjI/13TMOX9GovtoT9OXV2js8Mf8BTyyiZReEzMJDmTY80liE' 'L7BDDhP7DOGeLO4H23jkUrtAj2aqxjnVfi98RlzaoVa/fpdvSW6Ku1Ee5Y5Dt7TywLeRfUalDw' 'M2XUurgeZbZHlNlfS7I/OPvXwP4DFigN0H6yDh8hUK/3m5Con4JEPc6mh3i69lAbLaYWCYPd+T' 'chYTz3QJ9+B/t51kWtB6e7Fag38Sz186h2OPEs9fNKb9QtOd4nUhy7psvA8vhEsiVHlerzBi1Q' 'GqADFk4p75OoljVloIx9Mt6BeoVcn8QONGKB0gDd493rnurDU9m/DXJ9DuS6yyKXpcXuQiJs9b' '+d4ge1D/TpB7U/zTLYeua6W4H6Eo9hfzoWXvox7E8r4aVbcrzPxCTqExJ9JiaRftT6MzGJ9KPW' 'n4lJ1Mck+izrGqYMSPTZmER9QqLPxpt0n5Dos9ikx92zfXge+/Mg0e+CRPdaJLI13D0pBZ3j80' 'D0VsZKvaX9hZhS+vXrL8SU0q9ffyGmlH79+gsxpdTr119EtfiFbFDqi8mWHFVKm7H6eWoCwYzV' 'LaW8L8U01+89fymmuX7v+UsxzfV7z1+Kad7PgN+Jac4AovnvxDTvFwPwd2Ka94sB+DuK5i9j2+' 'P3QPPfSBPNzyZPN1UoVSQh00HEb9lumzLszFVBZWU5PnRUc900vNP8E3PwFaB0x9CxxPGh6JvU' 'zS5HiDq6nGveYoFSAN1OhsS7HIE53h+m+B7Ny5lfYv0cfhL2seNv3rUk2k28gDyEnEkJuptn9q' 'w6SNA3Q+QmA2+j/JSGuFLZ1VCqN0RMi+dXI+1oFA9aoBRAuLDyX/Q4Ut4f88wN/Y7yI24EZXVk' 'LJ6JNVJw+EWvurgYzXPq6hjQ8usqFFwd9CoGoBofP8QedDQRlFUccjNo1Zt6J7EdTq5+ENoP+A' 'riVUkoe5iKeG426yZC2SIK5o3He4MFYhIg2uuDmihp78951cHyt4jCR/Wq6T3IcdUxu9ce9Hc8' 'QKy7P9emqAalAIIs+PG0wLq8/5ri0JB/lFYDxMiq9fX1pANZhRKtSgFleFApJR2r23rsSljSB+' 'V90W/GiBNZrBUaccg7jhz/ITGXUryZ0YNq8vFzN+b3LSLfhvq6rU/gE1QUy0Qt7Q4vdzOoyHMZ' 'tMr9mbbRxEt1FWCvzuCoUmFx2n9tWy7woUFpNjYF0CaxZCNArFR1+6x/OYrHvi53LK5r+kDEEG' 'lCramDV4pnZb8FSgGESJZPad7s9v67cpp9WE5d8OZ5oKTNhrp6gwMwdtiZmI1IYjZUsIm6hSIB' 'pqq4G2uZJkQB7ilZ/tc9MBHXIGF8EGdpw1o91CPEPsDjOWyBUgBBpXqdgHq8H0gzu77WnGJutF' 'vcNo4AVSy4eigXpox9wFkTl/QLHIGFIsxR7n6/BUoBhHn5Mz0vvd6b02yh/lvHx1PV1bAcX+hj' 'p4LtHOY9rBxWQ7WHKaWbTftIblSoo3GCEYOzTaser9bhM08h7l5Q1s56VxqnejPn5HwX70rpSP' 'ZEPAux9Rbbb2xVXN9h2fH7jj9wf0wZqNE86H0WKAUQVJgf1JTp896iKNPSs8dvDddUHDUtcI5T' 'Wg3DGu5OITFuu6Zd0+wo0Jiv8fOktXU818zxSeUwKONlTHMwoSL7hcTYl6x5hEb7liS2ONp7i8' 'L2Z1IC6/d+Is2y/yOJ2CBI6CY/s2wOjOrY57eVl8g4O7UfScXH0njUnoiTEnMmKBHneE6QZpLj' 'ZVlA1oG/1QBv9iakCB4Wvj8RXb8mcYEp3qhX2Ukh19HOKmnIzUsaYvZSmL1MRleu+/JQjEVFaL' 'tMoAMWKAVQhtbwz+k5d71n0qxWvP26qMhvTllH19ccWlwIhOx4CkMVdxPLmqUZ7hsYtOG7YySP' 'WKAUQEdJF/g3eiQD3nNpju9497VHIsInfm1b3YUo853zmEegCmixvfNWxB4k0NtoaKbJRLjsIl' 'cHaHCM9w0WKAXQTaSl/+96cIPee1DIR0zFNafJDObqwjSJpI4vadVFEON+hZucPmgcOV0w4b5S' 'QtDcS7bd/jIAGicPYcgCpQC61bsdt0EVbJ/382k++3rn9bBjvcG+SXH4/D2y5D5EQaZN+KICpQ' 'C6mazLv9Kj2e/9Mk/t0JbYHtjklQbw97OGJjd9pKfxq5W1sLRdwr0xuR9sqJa4NJATp1IUWvYX' 'brbjrHbPzpOhETxwog8P3bNAKYAO0Sr+Ky3CD3gfTnPk6NdSV7VpRndLWTO2l27vyruLMkAr3O' 'YFGzruDktnF0NnKdZpE1MHFZuPUWIPXTLVzYs1Iq6DH67Bvwdofpj0N1qgFEBDZE6/U8+P531U' 'qXP/OKU1AtoXS5VV2gifqlSrWmfUzGwfeki4Iz+baG/0JqXCqMoBlPVNPiE+/EJ5V4lZdpWSyc' 'HiZtRkEsr6O5IKjf09rn2PaPfRpHriEe0+CvUk434IByGu93zae4n3BbhMft6J/SH2a+86ENw6' 'iNUUlIk2J3GreKRS8JlanFXvXMJSOFdZf3k7RNg5O+ytC5oqeF0NAJ9G4AtXa59jaaGjQviP6I' 'iMEXGSYbMl5LXH1WUHzScw3HvZQcQA0vMZdLMFcgBCVoYYlAYIaRl+0xGY432aFfShX3H8Yi1o' 'kG5rQkzP6dgeSIHYTuQYy/iwYSscQeCW0oFYI4t0O63kbRqjkepdyVUNaiUbOmB8L5jV85fP+c' 'SbnKSIs/pwqD1LbZVuXRzirpxl8Fi6LRAPr0cuPbji3iMQ4rwfE1DK+2yazcIH/SUJzVSX5eTO' '7SgjOSYhHs0KmfqiidpOBGkLPlRt8WiQA9ABcX272oeaZjvtZ/REpL3P8/Ifekvimpw6LLzGTT' 'nJlLHnRbnYP/ii7skxemQ9M4J9FsgBqF+cta44Kgl0hHSnP8GNkgHvG1h2/w3L7hNd/ox4JwN/' 'Wg4cpsyBQ5HkO5bYaGDcxcQn0KJL4ZicvjVDjnEUfspKOCTfmTNQdUMuV4nqVTbtVdorl/omrm' 'OeOePPRvWg2op1GX0ZZbOy3gwkUqBwLj8+PVVUKWietJNDP+kaMqpXeSAxdPEOmWt8KajM4efq' 'UEdtfyORxN6oM2cBSd5qfWkqORTr3WR9bfZvOyRM+pN7ZPr6GDofV527cBqwY0Q9JQ20cDOM93' 'XJFaDGxfY5l70MoRin3lIX6ohg5iFq5fhq86kesWfzqbDKEw4W1TlMTYjSZEZpURExSrldVf4j' 'CEso799IczTkbzj8G9LyT8C4h4d+2bFmXB0pmuh+Q2QpQVO1FuIS2ihNTaMdbYyZQzJOJqXusM' 'u+hJj7YT0Fw5jMYXkxdhiybq1yJaf7Oc/z4k9X622+d8iv6vI1erh59PZN5PEVNx3Ug6D19yfx' '+hsQCf8nab7MHoPSAOE2+7/Sw3e8v1DD/xmlwG+EV8z73nj0g/WgWkyAoFnaQLYdmbjwSsNEz3' 'AgXvHC1PgJ1x89dRwCEiuUTAtWFPBl8r7T/ujpU/anbCJxgG9e2lLRcG7iUHpAzmn+IjlUR43C' 'HioE+V+oof77tMBS3reVDH1fmkMdVDfSrwww6W6Or7nYJycgCzuFguo6XifZ2JQYzk1+WZyP+M' '0X7F2Jlqr1ehSaFsvqApxiA3+YFBoi0fiw6S8wZJNecxCTJqURUzuHFuTR7+FxqomBKD4bX3+6' '0rBbk68xgjmd6YplUJwmRXjM6NLZjtkx1zxD5YHXqJ+fXhxW3m36y7Qi+R0iMxoQTEc8SI48zv' 'egiYg7xSTUrXmHg/bbyXkH93473mIGZO/8ttpikPFk0PtHXbTD/FCX57AAgFVLEGS24xiiQRYA' 'b+xiL9pzKeNICPxXq6fVnng1xwfmVoOnw2pOpSzMmVweucuT8gTbE8peQL5D4ig33h+wx7JJcK' 'VRDWoSsy/BoDQDSJNUVfNTUUefuyVX5N4ZEcmaOB5nV7z//tLa6qlTwQMP3Hf//cH9QTm8r1ye' 'PHX8+KnJ+9ZOl+87fur0g2HpdHlC1eempJUJM5CJy5MCW4kHx3L1zrn77zs5Pnf/gyfUVAyKtG' 'Ga9VkgByB96Doo0oZA+tB1kKfrB7vEiBEAtcSgXgvEpfpkUgdlMRNIT+o+782Y1LfpSYVx/+Yu' 'jh7g6/f7eFLf0sWRhm9y/EfbVXUojzmSiYkPhvwnG0rORhOvk7/ItHj9hC5AYP3n65+k6QlztA' 'ZNFb2jjrNeblWSxGPmbT9FvX1CvbfE1Nsn1HsLqHfEAqUBQlDjwwJyvLd2sage5zWlGE9dnola' 'HDDCApuNXqTt4yTZubhjqMBvTXbsqEa15NwnxCYQS05Qc7/3E6D2e4jaQ7/gGCUNG2TiFB8xNi' 'oO1edXm+ReVq3T5kwGt8JVGgesAvWp6bkRK3Y1Pq2CUipnSCbJiMtf2GWvvPIq+4fKdwDegGPj' 'J8Abh9xP8miYN55RvPEhx1zdY6Go9bbEuGomKG7LPOetAuyaFSjSbKbQVoXdMau4Y1hpUyrNKf' 'wOtJ6qE3e9Dr1AoXn9MGtvCzMLo5eDcmWT1Icz/lyl9pRyDpfasW5vLrirziTUSyZ1v3DTM/Gk' '7hdueibmpv3CTc8obpoWkOP9sy52x0/6r+BDfD1g7S/syK0Wz4nVO9bvP0v27qiWddDEfmEpAu' 'Gg9G0pgaW8d7MoGPobx8f7czx02iJKpOhv+/qVPu3nEd0Xr7OgVNaK2NJoi/t3NdRsKOc/az5N' 'L6K97cI6cswU1SKZexCiI4EYziUNWpyfjvVZ3ge1ia8bDeKsulqbKFVpRZq4MWS6UGmi4tM/TS' 'Nsb+9OUhJz9O4us73tl+3t3UoSfgEW1AHvl7E4P4zF+XFYUHGOKjsKidk3Pm5thm1L4xJPxpTf' '8dAiRyHKrSJJqGgy+4ifBXa9SpqrJ07yljbAQ8wzcScmi5S+ubW0oWN/4voVE4CCGXkpXhyijw' '9PfC8eNPxekyBQVcNRX62uFIco6ZvRMijmHbOvZcU2s7pkDuEDIjceHJ848P0Mk1+LM2HKFMcN' 'iDuWKyFW1hp7fPFLHf6actq1HAmP6txv/JHDhWUsnJeOG6iV9VKM4i/WLGfZ0HdxZKE4VylysV' 'UFEsYxjOuhmkh9sUjsF5GbcDj+chdHqb2cf0JsfkDpSY/4yyotmN5TzYzkrHy7c2QvtjiVAmdF' 'VuicOv6gjlI8IMLrAzHTHxDh9YFYkTggwusDSpH4liMwx/uQEh9f/w7FRyJYD8ER9R0iZIopB5' 'tdOybJegGDbmKziUNu1bUV4jreP/SlDDnno1bUkgHtsfxVvgdIkwlCiU+2ktNKOjqcNdvEudjf' 'LbJB6n4oSTZHUUTLigMidT+kZMW/gqzwvOchK74KWfFc7G1h+ohjy3rSk8+VVHh1o20teVpXm0' 'icIc56V0JkzYZGH9thlfazXS7jHn/ggZOnreuLuAFPltBC0YRkscE9h/d+xU3QCtfx/qNfJd7G' 'ia5/qVIr17ei3a+cnr7vgdNo4THSwhdW2Xi+epX7T04+cOIBVJlR8bpUnDbi9hVM47W6OoV6pf' 'VmHXfEpd6unZw+derBk3yrHKJNzjfwtiinIOG0hjGavNA3g9JCcc+O72MqIV38VshZ5TeQciRm' 'f1x3b3LsmGk1K8OKG4dXYpc5OnHi1IkTSgbAcf680p0y/BNpuT8Bvvtcl5dmPlO5cR1Ae4nPXm' '1AEBef6vK6vBuGHvUvxLEWbNRnk1l6c771riyHm9RVUmtZbtisiL+O2I1Tn9z8wQ5wCmDE4Z21' 'wI73f6LsvqG7/fm6zeSyFShVnRWeGvsa7DYdXb2vA5wCeMAbdM9b4JT3adXVpCSnDhOLiuRys0' '5LiC97ifYgSmFHt+x739mt9IBuL1ngtPdZlD00dI673YP/L9myR6sAcnlJsIg60IAXl5ve3wFO' 'AYw4nc86DMeEf1Gp1b/uqCUsqYDlCVe9UYgUUSH/HJMhQZ05WoPKISDeAtcmnXhkkNAsPlQOeG' 'NWXt2o3bwMkW/uG1mbOLG9SuNBH6t8+zbOiK7T8pvzVS1pPdmgeFQ9FsgBqFe0a082qC8q7foB' 'ATne76LayNDINQWtsLduDFYa1z1ogbi5jDdsgdIAHfPudn9IT0DK+0oXB1ScpMW+WRE1wRppva' 'bTX/Olr9Ae8uQjdg4udrrpecA2hYsuOzcIXEPA4UMpMQKosl+JjXpPuPkrSp2IQWmAkIliTkBp' '7/dRbWzorD0AftjJSvakXlrnrGnssOebjLvMHc4kfj+JBrj594HGXRaIe0V+r3s5Qu4PsEn+v9' 'gkb2aKsHjWOUZiO3af3ASn4t1evzsjl4Nf4v2R0ktOkpJkJNxZnTFfCRrsqWQbjqtNTacolyNL' 'nTuZ28lYoBRA2M2fNHeT/x/lD1jYIWHPSjqU2O0I9ANLcVTDgjZvbqQ3sUoCGw9H93HAAqUAgn' 'tAjznl/bGyJGnMkTb3a+FWlvO/VC6TioPw1z0IqRvmCOIuE4KQipuG3fgeMPlB75uYnG900+S8' 'yUksrBHcihuR5pXSRt1VyLB92jKkk2e18JaI04E/sfeEI830tSp1n9hVooRfSeWzBH8+/0q8C3' 'XGnzzuDvf7s/o0A7vmQRrHN7v4kt2HU/wbXPHfuvgM8edTOF2qStZnPttBKtKgoa0KZH3h6Etj' 'c+48nLL81Nc4nHoSL5tyP/yw6ZOuMXwTJ0/6YMyczIgZbJ1bJXCwLFw5duJNWzL344hX3rgIdB' 'icmRh/AdvNVgXBmCPycSR2JhuVQC3jg3J4zdTrt0AOQK6cmR4UEUwgnJm+1RGY472hm+rdOfS9' '2ldmgsJ3H2OSWjk5vtPZD8umjevwCcvsPWENBIKdEdpvgRjHA96tFigNkE+y/j9o9kl5P9LNm8' 'kHUgmmh++d0El61K7XD7fEUUMJR5zysCm5HtXXWlsqC6daDbUwxJnQaGDSU5S2x0j2Whaz4kTM' 'syRuS3KJ3MI3VI6yxtvmxojI3RF+td4KCohTP/Dzfp0Z0RvhHq47awIQnc+EtEE9AA1wNJ8GOQ' 'DdKJvtQdmqCITN9luaudLeO9BUFgZo0bhHEgkMzMqWpbVzbcskqPIujnarFXkgAQTVx544LW5B' 'rYl9N/A5sITnlN9+7M9REbAS5K9Dd3ZzF1yTlLZl3EHJNFHyHUlK4qrSO7pNGoSDstsS6Ga5QX' 'dQdlsC4Qbdc5qSXd47uzmE8Ecdfn7AEhdK59vg4DetRXJ+QCNXjMTo8LyV9WGwv4CYE7khGksc' 'Whcj4ZVWM1gJmuvRiAkC3UUAdWkMbVA3QAOioymQA1BGLjoqUBogJOZ4tx5st/dT3awkvwNPTI' 'iXYgdjxMG7vLcZrCu1nS+aNPmSgXh8LBuwafYxk7mtg0Ryp8QKcRUcNZZ9FsgBSLuUFSgNEJTe' 'r+nR9XjPdvPVsM85yOhCCkWI6FL93A3jzJEE5prsdc5jjm+96oBu5nbW8+2Tad6IYk+l4ZGYBd' 'wXzAPWtmcRCCkxnk1yRA/R7NkkR+B6w7PgiFssUBog3Hd7QkC93nPdrBrM+cWw1Axb6tiGdUQ1' 'b7YTi2w4WgUhUyJreaT02yplTrRmodpLeHEHvRbIAajP2j2RQeO5bt49Lwioz/vZbjYm7tfHw3' 'YPWSzKlYgRVo+x6jyc/Gpc2xYWfYQCt9VjgRyAesUcUKA0QDAH/kCzU7/3vm62Bz5Gyl6cjpiv' 'Ltk6Ggc9qcc3dAZXnRehQ9m8Rp7hqfmZrFzosZ5yUmn6TPfi4laZtsLyLirtrnKVTKz9fr52ud' 'Ks1+TtJz32fuKn9yX5CSk43tdtcoQokAOQTk6sQGmAYLy8RkCu9360dPvQRbuz+LRKBZy31PLZ' 'TS/YHXfTpUu4vj+JK9J3vB+4HrFADkA3WOgjfcf7IQ1vc/+qV2AD3vNqgr/WqyJ2WCcz3lu4XW' 'UlhFpoBJLTPNxlcCa8ijNqqzNKDDdYw/xu1svQSWNKsIayOLV0IesvPr50YWFe/a0Turj8bZxL' '7dZbpMIc+HEmfqgjzslXic64/j3+Et6qinAEpNJBkGbDAxvX3hb9EYUv6qhGdjYHqyTW2riaco' '+/KKOvmLBnbcarrGKi1RKOhBqtCD2j9Whc9SePyPg08Y1AhUqPPnn2yYR78Mkz/HtxoTj7yjFX' 'snYjVzdWER96Gkd357QwHbaNaSHQUL0t1zIkinAPSV2Jcu3Ex7wDEfIrql2mne+/2h+l3h8axh' 'wMy0tK0UOvHl6r1+nn8GrQHH5iLIuSvpScJj194WJneZUIA2X9J2T9Kz88H4YRTtwbaj00AQQm' 'WvU46xmtD9pRJ6jPM3t+JEzO3IX6aCfGYe/WFEaEyyKPl1daUN0KtqPE7sOkUscCxCrURnlcuE' 'UljeV6fPoVtOBMZuYIo1LQiBVIfeYxWsnRFqprmHjpJ3O5J8dylkOeswxS11OPLuUL+lgb7PQk' 'zc+Te2qMKiG8itNVTyRpQ7CmrueZlPZaGAyQFHk+KUUGSIo8n5R4CNd7HhLvTguUBuhuknhv1t' 'vEoPcpLfJm5OkA3h5Ipyp3Oq3M/QdjAFui2z7UuL70onKiobEbpDEwLvsskAPQfmtYgzSGTylJ' '+FN6DPu8z6Le5NDTJAg6H22UtOXW6aHsbKvhGicXj+/U2CH/tPh4Twxaxssh9fXzacQDJjSf9q' 'd+f6myqaIlNa77ELHcLdeANMgB6GZJn6FAaYAmvBMqST5g+73Po969yKN5HUOSW6YRKXE0TaO1' 'ugS6KlW4jEtfNbhi6O+w2RyLh85HxnEC2Y22JKittOK7DxYdwqDJe33ZvTo59E0Fixb7iRafT9' 'IC4SWfBy2OWaA0QKPePe7bNC0OeF9CvfGh1/vzHSTA+Na10ce+aDOV+r5wcfY83ojWQ47kxmPg' '4gsuZagos1xugv61EjVKKvRxfmsEAYsGxwPI+ZscCQ58v5Q01g4gQwWMtaz7W9oV4Xlf6Wb/5v' '+W8pea7VAeLREPWT2UB51KagC1bXP9xs5bWQtbnN2YpyniEE+lWirXg87vr1Rg3EqJDVhhCnnX' 'REPZnYC7UK6cRev7O1GbMcHTPcqgNjfe9JzrXUvsahpOox5xiiLaXKphUJaISHng2mYYEyGjLH' 'H1xDJvK9yYdY8hDt0mqWHrxh5c5UndGKduX4FufNACpQE67N3gvk5AB70/UDLvtbKN7OU/ZJ1J' 'xeyqJMwqjQYYKSDMoHUVeDQ5K7D8Gjog/Vd1b4O6AbLtHzhC/yBpER+kgfyBknz3CijjfR0tHR' 'wawsnVDgex9kdLYeLbryfJlUFSX5BrwAKlAdrvecjG0UVd/Fm39xLvR3o8Z+h3HeuagZ5p1VP8' 'QtuaibePPSjqOdt2zTxwZiVyXWsG6rqjXOi/lLiqzJdS+FY1vzxfxWpImLtqDrgf/couJINrla' '/XoABE8YWkGPc12tAwsdr/DIL8WTc/p/vlFP+G//mvu/k26idSrAE19FM19pkcW27blgt91nqP' 'iOSxyeWL3Udlm2vJU+HyRE/OspLkGK4SyT3tiMeBWJsajbSNWDkluyubm2EZB6IcE12rmAe42O' 'sl92kqJhQ/gY4OmohC20CLDz2Q+rDmj1qPzo/lJGN7cr22LVyVYSgXMUg4yJ162AXWCwfClRnx' 'UDN9PQvkAHRQblxlxENNoNu82/mRrgw7Gr+FavcMrShaawQMrfiYXF1iwBbO+4FOgs3FEe4okd' 'n8GiJ8HLEeLvdFLVThg/5WLPgz4oP+VryFZcQH/a1uzuj2h5qFUt4/7uGrn/93SvF3YAKeLAyV' 'kiE37ON5UNNFS+5yUKlaSfvjPVgNImZ4TL7MAQ1lMT8/Mzt/nm0zfhGuxksn5pycLDr1xaJI3G' 'KC9XjnEfYeUS/L6qegrWWXTZwjt0LLx2GxlLmlyqxkLh0pHwEXfjSoyhuWl+uVst9GRiMcQ9Fq' 'k9m0pgg+QiZ1jwVyAOqVPHwZcUkTCI+v/JUjsLT3w2qKvuZYBx6BOQhTp1l8joshLsbr1jIBTB' 'JdveGBVEL/FYYhuSivbtK4Nht1vvQCzmWvD0I5WBXzrSdagkhnOFYXyeQpMwkvIu6hbYnkH2fl' 'je8EgXiIaMNEJbtaAz0tkuGkl4feZ4EcgPotkqUVgUCyHwBXH/Le0UPbwjexLfxLZ/ebnECPwx' 'Elm4CSD7jsEdR2+t30A7Gu7CBrVGML/rpxHq/K46gOJy2ZL0KwzRc/1buOxq+ng/PwHGicmWt4' 'xi+aq01nlLGjFlRW5K78UPlJlUfM3IMulXCSLrvFIfjoezjG50+ZKLxb/DSIeWzo91L+nLyWa6' '2JgGOsteitNK1rVj7cC5twxcU8Ze6emGdim6yAvaDN103uvntuvsoTAeET35anHVYe0FZmgctP' 'OypJHuo3iZBUlPMWmVSSUbirJPWnWlALpeiOg0vVCR4R7rR4HogvWqtnIYWBmeKkUDHNbVAPQD' 'qV9iHZVAh0RNIuH5JNhUB3ene5oYAc77kePo1fUi41fXIizwmb2Lrks1QYmQoDt7yjq6Snw2lz' 'ggc1ed99FsqIQeSOei0Q990n++Ah2UkIhBP8n+sRWMp7vxJTb+8xuhiz8AKyp0+CzTlBelC1zE' 'O++ce8jQRZvB3z5iBczey2qDkqHgI7d8b9kRpN1sgZKy+diWxOrgp+N7FajZ8AtOLDctIWUmec' '4TZUEg3O9qOqZ1n/jpR4I7GlNr04J7zy5yinlS/jRbPj/kuR4dYPN2l/fFg1ri7e6lR1nXhCJr' 'asR0tF/9dN85Omo3hadUQKrEgLK9LCSPyYKiQtNqGxxFMEOHpR/+ib9fyQu0mE1NGe9SxrK4zD' 'xDvx5sDWeGAqa5nOc6d9MLItZV1RKPAsnDrJMjKjrnySZEeK9cXyKI517ugXbfFNvLKaiar9sC' 'THfYxGdV7Rskg52CMsj+WMZsYZ5mtRu9KSixgh3hPW29JDvMfzmZJ+OCnYY/A11+5cXLKsw5hl' 'Ax3g/fGGdkh0gPfHG9oh0QHerza0dQGlvQ/08Kn0LfHjwRKpb2V2nHxAiQb74jhJ7QA37s7Ez1' 'Ly2Yg5aVFGge4bx73c1c0WyAFIJ0w9JFsugXDc+7OOwLq8D/ZwGPnbHIUFUwVSv1yJGtVg22+0' 'mzTRoWamM7YqIUpxhTa2K7AUaHa2622TYABXUIU/vocTLZinYtz4milf3siqRRasx4mVNdY43f' '1gkvo43f1gj4lKPySnuwRCVPrH9M7Z7X2UaTL0r8nOQtOQonif/AxjP6IfnVfrI35RfCtxq8Ik' '/x6tkHyBFrkJo6Okr+WPxToSpKcK42lHlpeEn0SPHfac+F3f2AX5dDUWhC67+XWYB79czLpPoG' '52iFmVM85zGZZMQVatT/tCO2gehXA6qFUZXsELUHyWFl7LvcBUJB75aHI37Fak1e6FQ3IkTaCM' 'hG4ckiNpAiHj5KyAeryPKW57QLkXTJowTWnWk0ia6reRcuz05bSmQRItHPF+LMkWOOL9WJItcM' 'T7McUWUwLq9T7ew0epx+33QRBbUa2YpyXF0g3LMXpW3zjG/Xiybxzjfhx932CB0gAhEdXP6eXW' '5z3fw0GCb5flRkKguplQcCV9/dZGnaeRs/nw0176SoLS0HCzbT3EY0YlxNxW8XqcUnfkzehd9t' 'MkY8SJAKub1uBwQPx8cnA4IH4eg9tvgdIAHSRN9TEB9XufVIR90CiEZ5Qza+ac3B1Qwi35vE6s' 'YVpI4MCVW9tngRyA9lsUxoHrJxWFn9MUdr3PKYWrEp8uac2VE9EwWENIEEO4DhPfDft4JNzk/u' 'aVOztjojTbm3zRYlTcvUpzGDk+Qst/cljN5UaFZorWmXoeK5nm0+Ds4iW9JHlxPPu5HnNH75Ac' 'z35OaWpvTwtswPuyou+3U/5iwPuqUbzVbAuOeLh+TaK9le/aToVp8bMyCfhNAhPAYrkgdV4fe8' 'xqyJZagShVyx5MvIPdbtWxn6kkqso44+Op+BFtxas4bVBOXJe5ktSIGI2OuzgaKegPsYKBm2mV' 'shmSeYoDV3aUucdX3Mn0hnXIYWwNJuGK1IQ4NoaeaylEgR4TXxap27vuAE3ll5NTiTOyL2Mqb7' 'RAaYAQSvE3mkkHva+qqfyGs/tUwrh6gdNoxsxxiLX4BAPR9KKVKU9Rsiulh+Ddb1048T26Np8o' 'EnawyYmRrD8yySoYbJsxi26DRLevJumGc7mvJsUnzuW+qhb3RQHt876mNvSX+gUcpKpdA77Lmn' 'mkK87U32hxuowmmbLNiM90ZDi6h32Ex9eSeOA07Ws9JrLqkJymEQjJEl8uoP3eH/Zw2NgjFh6Y' 'shePy37C5Q+TuOA06w97zHNXh+Q0i0A3eje5RQEd8L6BasND5/xi2CF21Fxh6tqrUXuVfjZIb4' 'HAVZ6MyMRhd4hfnEh9I4kNTqS+AWxusUBpgG737nC/pIWU5/1/qHdy6P9Im3PGcljl40Rlvq7K' 'I8ORPkHT10eSJ4fIeapT0TV3PPvY4fOUAiajLJ/KRXEmRitmFU6s5rbL3iA59uS0fWzx4agqkp' 'eozM2VSL3KiDQ0VLVaD8gasd4QlKPSRjPEsb+4XXnIao/VB4Q4Fhay4/6huYCqqJMMNMjaCLPf' 'o1QNKnGGWHHBgEZqy+erRPp8ldTVpqXA0g6Gl4hmZvMzydycJY6nkuFtweqrRFFbh+IE/rniwo' 'zFFB4xBU/ukAVyANJPax2S0zECHfcm3XfBx3DY+9Fe7yXeT/V6ztDbevzF9mqROJGTekhenfbq' 'RNEwJ8dDwcxkN4NwKVJpqiAVxax1NuyzCd8T2cBTjUaesyLtMPVYuYWJ6vqkW4XRhFoQOfpPtR' 'LBcwGwlpq74KX7D3Z5MXD40YWFc1OFXNBoRA2kxatvDscTaKOBw2X4uVzf6pivDNKopJlHHtHt' 'rEt17SJEu7Zv2H5nMI6GAO6SUIqDItRIcRAdTaiD4HFRMbktwWiXYe0cVSfWai8Aedw4BcdiYe' 'GxCQZGE0sLi2zYnXHhvFhn3CAHgoRAivCSyXgl2Bxv1KuV0vb4akWtmO/WLg9qJ/4xPi59+ejK' 'hm+G7N0Tsk0psp0Rsl6FqnYbzAe784ikaLs+SidfHlyTXTDx8pD4fA/TGqIlgiQ4Zf4Jl+/bez' '0kjiyqbClqaajUF7isFLXsY5B4laiEF64/bGbjpZXyw3o28PewrGfuhbYc7qfPAjkAaUX/sPg0' 'CYS7he9wBOZ470S9o0Pfr6NkJSWdXE/m4Lph0GCF3VTDCIoiVm2rR/VEBoh3P1YwjE9hy4qqTT' 'jgdw2sPSz+z3cmx+IoNPvJQolBaYBuIO1sXkAp7ydR7Yahh/ziLmPALSn4Ya45At0FvA4/mUQE' '9PpJIHLQAqUBws3YH+4n2BHvlyAkn+sjIfnXfbbyW9HPg/AvPkCyDysC44uz09HqxaytHas9K4' 'cm/Gs6WG1UvPkcbcuexwmJpljjlEhaOx8VG4NMgKRHq8ZvoJEo8UfX4mBCP//KxdlCfmYsaw73' '0Ks0opyAtfiSWO0fjCVxlqvKE3M4xhWU1XERENhWD3PXQq1VM764zivez0DhyQ4wlYE2QRllQO' '6amTcnW10oLokRlcWioWKjVX/Vep0jSwJ5lM7Yp2Q1dJpr1sHpls6Jpw8R+YLa8vw8/pTz4pqr' 'QlPoixQyGpfkbsfbhjp3Kqgc6IOjFUX7Fb7eKLu4wKIVQZFUp3ptfL1uDAzxXzGSKyvzy3NzKy' 'uwzlqcgsuPKtiNaEwr/IAhXCU1lS5ZBSCMQJVoVmhweOWiLclGSYZvRfzAIbxqMbaR/fSaJGsK' 'sSMztZrhOgd2IIQ90RJiulrwbTZxsaGlImMq6zWtv2q8XQtxBICq67gQjzilM/2VlD0qDSTJwe' 'Fj7t7bzGplnZ2pE+qlEao3Dgk1DoE1rj/eCZDVnWu2oOQOZN2dPKm2oyMkrX6plxN+HeCf2I5+' 'udeofUfkNIxB+yyQA9B+MZyOyM5BIFxJeVRAjvcrvewQvw2xjaHFl4GxbtklfkRiHzv2S900Ai' 'e4pZstEDeu/d1HRNwTCP7uZx2BpbwP93Ic5I84HSjox4zMtTmVe5nkYVZbKrJQVhZekS9cyE/N' 'rBTzS8v8pPEMz98wt6H4dFwMa/41bN2UV8cqskYlN5bGGB7SDyfHBaJ9uNekBj4iuweBkBr4Ey' 'mBpb2P9HJM4wdTalxbOw4cAjxFw4m9oOPBdIgfqZcEOMazzPe8cDUo8hNCyxJ1aFLnoFZqubqY' 'Yj1ViQdpgEugfatx8io7zokEPNJhw3stL+oY21Ave+LUoj6YMEGhSv/Zu/edL566nBC7LMl4Em' 'aSNQs4TflIchZwmvKRXvPg6hE5TSHQvV5WJV04wqcpH+/lGxS/3sldVvJ6yMWQU9bgUZesBBLt' '4K2l/FRhZuHSvMVeRgiO+zGnDSvrDxoSAiS1tavc/CoQVFytJHmVMFUEQ0NrTXUIJo9kwsDdoE' 'Yiixp42vXjSWrg/OXjoMZdFigNEO6+TAmo2/tNVBsdOu7PaGevFeEmtmh45Sp94zXn30zKHhw0' 'EOhmiUI/IgcNBLqblv4VAfV4v6XUuov+tNzOiOw3nZkuo+xEN9FMMgNZf3rh4uJcfgmMLfpLFt' '51eHqP+nkTGrG7aOrRfQ9YIAegQVEAj8i5xG8pBfATmnl6vc/08r3iDzqSjKsVrvPlQcnpJAOJ' 'Vf/YnFd5gCoqjIfja+uYbKTeXtuOtz9Jz22F9Fmx9/Glrcqa5Vspq6t6iPqTN3ksL40SDvqudU' 'wFPBXNozlkgRyADstN3iNyQkIg3OT9phZkfd6Xezmk+aup2IEPdUvFbsSHbcfl5EDOhiVuTTE4' '0ndJDoWcKW7HRlfkfSHXVnH0iZyEjJ2wg19UzjbZIow/qlZW2cECLUfX9KOwOtSEhAypbCf4sN' 'DO12YLZqXN7nI+WIJzHX6etmTKNvHSrabWc9UmaQIoVGCcftpUMZDrT08t5c8vFB7/H+19e3Bc' '53WfdpcgF5evS5AggeUDlxAtkuICBPgSKYp2QWBJrkUCNBaQoqQOucAugI2AXXR3IQtxOknGrj' 'MZS9N6NGk8mapuYycaNXLSdlLHjWZaP2bUOJPW9Xgq144d2VVqy43GnaRiJ7JbT8/vnPM97i5A' 'we54kj+kGWruHtz7Pc53vvOd7zxvTZHAWcjnxqdujU2M57wFg9WHEb/FAyUAMqk/u9XqQyCkvf' 'iqWbDO8NUt7M36H5JRHlZI0713TxFTAjtSNqJjnhegKhtMvvaFSsOK746XeyXW1QFLz6E13Qlt' 'tHLeFEkL7Hd04a8sruFGWLHujXbTWZTlfio3Oj2VnxhnlPFOsH8bzxG3vgUhXtFpr1vqN2mKvB' 'RbfPzMSVfyPW5iZn1GLRTl8UWBFexVLErGA6UAQiGYDxteEoTfxnc7Mz/fVijAOg6zTBCrk2td' 'btVbhGOeTR1tj2fb82LGFaVdo2qoGSAiEnk4HR4oAZBJ9t+tJq9vg0vuCN4w09gavi4s8b8lSC' 'pmbajmwzR7qbYCd7e4OWTEu86Z9Jk6eVWp4ppoMu4ZUyA280LRD+yT2uF347HMM2ebwjClBqY/' 'SCgRJGSihNLOMyvCMsBWiDLpLsROeta/wmAC4Vg8bx/UAdBW7wiBqYlAXVqkvltNTQQ6Qvz0Rb' 'M9t4V3RCT5vwnrPhgzC0X5sYa3/ZTfqvnbr3QckwrFpypeQrleRq2/oh5RCIxrMTVJjlitl6h9' 'qEd+7MVBiXe0nn/CKW2+16NyRz8qHr1ioK3Uo6M+w1Hrnt7xj9LRfb962ZSkaLqst7XYrk2522' 'gl7sRXAsarO/GVgPHqDlbiXg+UAgiBce9X0PbwTZaiMj8nA6GLuvAAG/vCdyDns8HMSf1+fb0v' 'E+2PYHLrVlPXm04t1a2mrje3WFNXt5q63tzCpq5xBe0If2D1Y6piUKJpNwurR6DLqmd8dL2BwM' '71g/hAYOf6wRZr/u5WOxeBUO3o9wwh7ww/kGbD/rPJaGQWN3iVPPTsp5VtJWPvCGnTMUrxk7Lx' '3SOyzRqvm1Z9iQmWct6ogTto7Vmg4qMwIKNbag/X1ZUjxlubrZgUQRUJNVn1iJx7ma2RyNdYru' 'llsb56S8UK4sB1rjlS1iq8qgsa9lC9k1DNKEt7oARAJsF0txrxCISqdp82qA7Dp9Js3/yO8Axb' 'fASmDMb8cqWKUwP5XW7GUkP7LqIll9gGeHgCxGMzyEjWH5tnpuLlArIHDKyZ1QGzYwPH+0Vy50' 'IiYqzqR9KJRrP/eFy0dREO7LKtPjHNleVArI16JyPucEk2pfG74x2ogzkGMgIXr8xU4BMvjpl0' 'kDPHkGq2BpuwkTHufFACIOM63K02MgLBjPu0Oep2hf8wremRCsg2TVifNZ5n5mpkSn3aJBv+mQ' 'wD/fKKloVouDM7mq/x4ggN8+yznoO3uY5ygVE36l00ER7QLg+UAKhL0yMJKAUQ0iP9goK6wl9L' 's23gPaiYwW4L5SehxK+VynHPrhtiOLVmSw3PGBhmccwFLgdiluXbFxbpaMNziGAHBDfsLqjf4z' 'SPCKxfS9tcrAJKAdQd7gseU9Du8KNpvnZccynBNATPnYGxtFuczUtekXResWxe3ph2Eyq58U4P' 'lAAo8LC7m8ZEIEQV3q+gPeFv4LMjmV6bJ1/6a8kSw+/CQT1t01kJKAHQzvCQB0oBdJhOqksK6g' '6fTbP/AF2AjbMWp3wol3zH2JinntdvN/X7bLxfaBCfRb8HPFAKIHgKwG9hb/jP0+E94Sdh7hi1' 'XuceT2nJN/8+jjOzycFLK/WWNBlQXO6lbqlZ+Lt/M8G/obl8Ls3udv9JrtTctjGr26TD8R65My' '4no55CreVj3qp4TMwfB2yEeEhjZUbiTaTYqVI2ltFkIeRBqJrMxmmUAiNj6aDFdU7xv1eNeM85' 'gt+rqtjnHJPfq6rY59KcJvCnFZQIn09zcqC8xCAK8k2urhOtgrM/PHd7mFVFukfxe1U9y61v90' 'Dc4Q4lir2qnn0+zel3VhWUDF8Qil+wsupbk4Nfak4SNKyIlYj28RNAoZ+ATBL6moPEGzOUdy84' 'zr1XtbEvpG06sr2qjX0BnLvPA6UA6qc9hSy3+8J/Cdr+Y9D2CboQO59u4eVmqGLU8Y4Q0PA+KN' '/TnJjz/fyT83yL2ENiI6feNAW9XbYY6z9bjY5MF8ZMFUorl/vB0qItDVo1pZrPs0GSh1nIfUpd' '3P0WD5QAyISV7FPqIhCcFf8iqbBE+Gl8N5j5alLHbQ8xNsnEymY62ZaukrN6htHfadc0uaqY7n' 'aTnsooNKFVaazUNbcJS1bKuweXPVFDUpbajypVtUsh8q/O3mva8LHjXsEXU47LFOmq1edtfREt' '0CX/N1W5HuR6Xa40l800Yh5u0VxPopIgzsKT/hiWVy8uXjo/PHyxVJ+9VJorDp+ZPXf+3OnS8J' 'ni2QvnLszMnJkrF4fOnz9z7sL5s6XzF4rl0gPn3DJhvzG6Mx6IV2B/eNwDpQDKhgNBTUHJ8EVZ' 'pZ+NJszyGNfztmWy1bZMCkJJLQDC0SuLvBtoMFCrQnefbqkX4yMFrbwYHym21Isy0qcSCkuF/x' '7fHcq8P3YKw7rHMs0tS19q3RiU0NFYsiB+0w5sMHalchqhYH1DjxkhTAQ8nt0eKAHQHg2w36cm' 'AgIh5dw/NvPYFH5e+O4HE20TMXLYhuZiXv5xpuPZFsxYoeb/fHxGUPN/HjM64IFSAIFnzyqoI3' 'wpzTqayZ8gvmEIeCk+OhgCXkpbffI+NQQQCPrkioI2h19Is13sp37iGNxsOtvrgRIA7VOz0T7V' '/BMIZqPTBOoJ/yOOi/+K4+JetwkrbecGo1KPiR4YFMCDe4MB/olj4otpNrYcaDG2VJxlRcfKr9' 'NYv+j2YY8y9i+mrWGlRxk7gWBYOaGgRPgluaZkolEpZlKvLLHiZo1ewJe+5FatR/nSl7BqhzxQ' 'CqDDtJBnFZQMvyyXiCPu/J+3Z77Orr0/LPeXnTDao9zlyxBGd3ugFEDwAhpSUCr8L2mOse2Tsi' 'tWnSmZW9foCgyAv9njgRIAmYDQHmUABEJA6HEFbQq/wjSS6eGyUaA/uJSu1Qe25FecGNWjW/Ir' 'EKN2eaAUQDAlnaMfveGfgqD+HAR1390Iyrv0gaZ6kdUcNHWAaaqXaeobG6WpXqWpbzia6lWa+o' 'ajqV6lqW8ITR1VUCJ8RVCyl1GysrwWQnqVnl5xCOlVenrFIaRX6ekVQcgpBSXDb8n6Ho6t70p1' 'nRXuVWL6llvhXiWmb7kV7lVi+pas8KCCUuGrclIdjGzmYRKdy8WqzwtdRyClV91Vu1dJ6dW0Td' 'bSq6T0qpwlDxEoE34Hy/xdLHP2bss8G9ulutgZuJYLA9nJPzlVOi82d5jR5XzNLWdGl/M1t5wZ' 'Xc7XZDnh5LE/fB3D+p8Y1rm7DWu9ba0D3A/ldpqzYg7wTwzwexulxv06/O+54e/X4X/PDX+/Dv' '97MvwJAh0I/wrDv4Phv+tuwxcqGpXMwpaI6o6idB4HqMu/SrO5ZoB/Yh5vbHQeB3Qeb7h5HNB5' 'vOHmcUDn8YbMY4xAB8O/xjx+uZPmceauyxBjQXXdezr4g0ibkuZsBAP8E4P//kYHf1AH/303+I' 'M6+O+7wR/UwX9fBr+qoET4wzQryxd4iLArVGLXvyoXlZLkO3oBFEMUTUJygCvli1mnZFzmG1Jv' 'QUpfueRX3pjBZH7otuNBZTI/TNvgxoPKZAiE4Mb3KigZ/lIna02vrzNmay4x4+JYzVKlhBA0Uf' 'qyMwxsKA2U/yz7uAQ/4g52eaAEQF2qwj+o/IhAcLp6kECHwg92EiH8Cgjh/ruyCeVPJG4aKeMQ' 'tU5f4zI6wD+x/B/q3ODyH9Ll5w8yHigBkFn+Q7r8BMLyfxBScl/4YYz5NzDmZkxGRjEVNq5CFj' 'KTgbaU6zzhL1N6qMX/6KTo9vu4iqj11vOwjwb64U4rY/Xx7J/e6Oz7dPZPu9n36eyfdrPv09k/' '3WmJv48X9SOdfwPE36fE/xFHZH1K/B/ptMTfp8RPIEP8fUz8z/ykiL9Pif+Z+LjYDd0Rf58S/z' 'NC/Kh9FIXPgpB+C4T0sK9Vl/B2NRFXCG/+kM1AUYDtSda05E9OxKNUQR8RtJyyO3byT9DHP+20' '4n+k6hMGbfFACYDSOpFIKYBAkFdMS4nwY/jskH0H/u0fi7eUkLfSKiNEui4EgozAOXUiXphP4L' 'uuzF9qUp2ZFUzc2D+0um+xYWoRH+XgpWZxwJSdRoaYmBMGX/EXVT1nS4gjhojrnKqNAkizOXj9' '1PkNIlRLDsNDQ48HML7NlwejgvogqPFmsVifLx/mmAu4wUL1eBKlOxsn+S+s4jF+0jVOzB2wAX' '+xLGZjJR9GAqHvE3H0AcmfAPq2e6AUQAgW/wO5TL4A+vk86Oe3k8J5cB0cNT4iLUq0s57fgfqR' 'VDTGzrlsmUAi8eUWM6L38jFz07wxUniY90ncZYfhxzVbWxBLwTFfi47Ccfuo57ktzRinlou+M2' 'fW+AwhoT9H8SLSHFpp9lbP2hylqvaBdZ89bLzbsewKkzqT3eNRiQfiK6Gug7bCYf6JvfG7wP99' 'mZB48BMutc2glJxJ6dbglw57oCRAR8J3BKMKSoT/Cu/0Zk5FlyuIvtNhGI+jghpmOYtJuchEJ7' 'P3ukqYZnZ5oCRAe8KeIKegZPj7wtPOOE8039FIzIBim+X6V2JtrGjiLNMyxswNdXkgbrub9u5R' 'BaXCT0lne2OdqZbXaw7I/VS8OdT/+pQ091pSYZvCf8e4zLyM1KDEU1l1zwELTHRiiVipmvSeAr' 'zIcZDWeY5jI1z8ScBa/0oVpasWVxDHqhbe1Vh6QD8sz1GVBE0slVEjrNJYUi01b/eGBDwGUb/x' '9uuPWcLZW49dfWf84ocm5kHtbkjm6dz50DwrwJ0LbksYUSm7VuI0zmaWFXdiUZVLyw0uD4mJmH' '1ssI87OeO61wMlATpAF8UhBXWEn5H16PPyEsDpQF2O1NPSaxcKrs/E20VFrM9Iu6cVtDn8nLTb' '79qlU5VlBM6mwfGhNTplvaahl/pcvOnN1PTnpOmnEpz044/A/L4ZEPP7BW9ntfA8r56pYUVKXs' 'IbptQnix16fH8750wRzVSaS2q/aOOxylGA4z8CR9kR3Mc/wVH+WBhBt+UoMTa7y7xHn/KbezxQ' 'EiAUWPizTQpLhF9lhpB5YVNsA7q0ibrVrYHPc+nUBJj1cnHZ923nPNV5rfBK41KPKUPOgfojmu' 'yWtg3HYTnHorhzL1dU5Cwv0htlvq5x9uxa0xJpdCNfKLAPs5crt9iaHrElBsFrGuTupT5oOFfG' 'QfcWR0RJXZqiSY/p8tFo8uj5mnU5cJnqtUPnHyl+CqumvBBmhCDJxuMVqZ8ifq/e8KNjjXJZjK' 'MSZMPZgo47ZtKoNFc09t06QAjZie8yVoHpz8Q/yR+LkrxTB6RBRVrNkAk50LhrXh/P9HzqkOiV' 'lehdw3zyPujIMGEorNMDJQHaFobBd5MKS4bflGPgk8m7k6HwQlOo2DgGw43C5v80DsaBOr3xoo' 'qUbZymi/NwT206xrp+QwucKiXwIhlWJQqrjY/HqIBDyTiY0mC8MhfryWUBC1wMHZgzTOdeNlpu' 'w5gyxWmL80mXOXlfS3r47JrFWs+ePnPKrQhXQXQH6SY9l78pB+lXEwpLha8JY3gusc6KuDOaRT' 'vj9dZweevY2cxzn5K4tzq2/1JZwv1qiyX/q8A52bYbR6ZGJqeYr7f9iVC8Fk36QqVHkxAlXovT' 'JESJ14QmP2EwsCn8i0728K7EEOD7DheNq6xN5cAeFCIAwNFLKo5xtmtJbwenbOPKyKEoZb8Yyw' 'aW84EL5x44+4CbDI4IHmePB0oCtD/sC34rpbCO8H91sgHjuyLRt0TmaKCt9QOreGlyB6MJ8Qji' 'N7UqiefIt1jTZFzCaiSQTDRqXsp/3wJV0XqBgR8RJB6Evq8Fkw8qvjX0LDD+VNhjsws1ydzaaN' 'aWbT5iFYkDPUWKntDOp1GZs0Y0F/xgl5XqYrnhM0DsxJV6WR1uAuMQpXMyl7r3aZJ1iVemm19L' 'POuGVvP88Okzw97mhPDDC7XfAyUBOiS1+AS2OXyzk70jfjXOLgX3a1QciPsH6gFjD+DHOSSkbr' 'J/a04P1vPUJWCKUDAoiQC8EE3jIiBRmsZxoGbcBNgvAP8bWF4dnK80rcfAWs4CXsWA+XpxFo7m' 'A2b4NJBbxceLAzTaAdp7AA8QWeEZIx8o4bzaSKUM1K0+N+SwDXmQEbndAyUBCkls+qTB9pbwlw' 'J2p/rLRMveybZtHBP+a4kGG8PbShq7G5hzGdKLNXvr8tEhzzVBJR9Js1kUjmJcCDX8YL5h/fVN' 'BMCUjZ2W4hzlJmr/SKqN4vto0yipx/PVSvoTOhsJTfPMumh41Y0fMK00jIgnRtg2D5QECObIPz' 'DsNR1+KGB70S/Gbgb2SMYsFzjRi7tM6c7EnjXOel5dMuNj78XAGFIvcaB03RZ9gJpGsr7QufGO' '1nNjDd2Dd3wgQIiHvtcDJQHqDQ8Gzxiq6QyfCniPfiURn6DkNzXc18RdiSwiS8Zs0+N3xvMsaK' 'uiFw+vAb/D4VRclDAtwl+9xPUzDUvTq8fY9FQQHRsrc5qWac7PPEVbn3M6jsBtb3ahRaSplq0Q' 'pdfIjQkfQ6fOD3mnFeJ4GC3bPVASIOy4PzW0EYT/KOBLzsc83NlIZY9ARGjCwB4vl5eNBrOKlA' 'EmD5TR5NCZhbqWa3JCbY64/wLczGZpdyxIinBHMqbdJ9gn2xDPBrnO2dMOBwgC4unt8kBJgKCF' 'ecqc2FvDX8dLuzP/Odm+QaCQkOBh46mk+160kOUndcMYYbYuwXLEA7QoJ8RIyRVvTm2bX4L1Yi' 'JfG72EvfyJYz/Yd4S8vnIst/Ulgxi5mQdKrOjNnp3MAaH6FFfQsiRl4rnQlQrlvtW/D8+23qac' '5ji4a5y1olFfWTbucYGZOktZiP1c5Vwxy7VYVTkWDbhO6cYX7YJbNIQd8Xps9UBJgHaQIP3rZt' 'NvCz8ui/b8hja9fy1t8xrXDFtDESoOmRUx2sOWsA6XMDUeAM8utbE7TyycUu5yp2nTqz5beaBU' 'XpcEFaqy9ZRA8GxrZ5rxgMFWpolgoY/H8beN8Pdxwd9/Nxt/e/i84O/LLfjj1aOrqjjiyQxN1m' 'lJkFENWgsKWG//W1qEqE2HoUwEySrEe1b3VLN1VYp+3imT5doIKpEnqEQsjBj2TFIN1xx1AVY6' 'TbgFx7GxnbDxvGDj04aadoS/F7BF7P942MBc2EnfMK+qJn4TsdskRTL1qjikCtWqePuYYniEok' 'qt1F4BxsNepfa3BW0b3a7nHIIRXMW42+2BkgDtDTPBnxhy2xn+Pl4KM3+YWJvHLhWr4uqtMzRM' '76hknz0KJvc3jR47Q4Q58XzSHigJ0NZwZ/BRQ1Vh+IcBe5l93Zs0JC2fGkSzKKFAzgY2V1upCu' '15/rGcrPAuzozxMovqoa3Ft0Ges5XlkvnJleClaLUWvIGFDYnTVSHoUmn4BfWcjhuycvmJNaST' 'jZzMiFli3IQeKAnQ7nBf8HVDNbvCz/Lxnfl4IqZ89k26rIM2eXhaWC4jW8MfNU4/iOZWqrO2Yi' 'FNskIrUJbked5dVeKD5AhvryJWhhbRiZCNgcqa4m08fLuVUyPeiefX6YGSAEE78ppBQlf4kiDh' '5bh+iI30pjhcdRURmRchbbhMWcq2FqEhQ1Rucbk4qzVXWhXMcuYt0AWGlX2eNNDgpkxGF1EyFK' 'WwZLkK/WsgdQ/YCN9a8ccLHphTD4pWba+3rRBJ9VIcIV2EkJcEIUsK2h1+IWCD89916LBzVnOE' 'z1CEgazHUzQRDG40GiALFbs3KIRScYc+KAnQdpIr/22HwvaEL8sl5LkOr+ooNShD09gzo6I0g/' 'T1j7QjsYWyNrhO7aI2bYYNP41XxTLL2oZy4SaBDak2m0S/oyWoNdjs1FhB+q+yjeD3sujIhjO1' 'tmxOLBv/6OnDJd6p6MUEtuYB8/XslbnAjfwYKOi45FNeqkmICCfEcgQDvdMtWqlb/nS5JGGzJi' 'eAb0v2rilGR8/2AGuS4EJDnl55MLparpbrTDDEnYuP22yvVqUV+Jm9GnTGIwl6zWVw8VT7pnya' 'LEiFFZdI71GslDRRXSB6PIO8hmJP7HRSx0xuD5o4lrbbQLNWGyCJHPVbiImxQqGIkZUCrY5Goi' 'WWuqghFCZccM396FE44vhejl8V9xCFvyxXxT8zfKg7/JpIjL+ZiFO4nGDmINdqXTalCPL/1eow' 'o9qVa6zUZxfYxUI0KUiCaC5JsOGyAG3rYXL+Wr4g0ndzRV3oVqbG3hlNbWzDZ9I5T5uCsMKvxU' 'XFbkLD10RUfFJBe8Ov450DmUsxZnw3HBDvXJmbq8zy3envrdSaxY2fmd6NHvGH3PceD5QEaF+4' 'P/iCWaZ94SsBx1s+nViTP3q7ZMNKH91D6yp9iqZFZKBYR/nzY0gJiFbjyWQ8UBKgg+HhYIUAh8' 'M/D8J7wu/AiFz+sasCuILI9NIy50MyTU0uzzZMa6Nzxr35MA2MOobj6t/hn7AWf1tOpZPRlcXi' 'vKx7iRP8iaTqD4WdPLAtjQvaYXXm4jY2e6AEQFvUh+iwOnN9O2AfIviD94f/Awj430DAIRdgO9' 'kWYKsj70eW7YD9nt/FPzHy1+XkGnTfQzmDaEZJ+Br1GxmquFwZnK0PlspP9MvA+3Xgrxtp2IAS' 'AJmUpP068NcDDuJ7JqmwRHhHtE+/nGSPZz97FrFRbxmfqM0aaUGH5GANDnUbMFryAVZ1+3mFB5' 'DHeqZcnhvuR40ZSUB1d2Lhk5tFHF6mtvBkrZbpmCBf/iWhY0ntclzjruENXjLs6hxJwpqvVI1R' 'ctChE654d+LoTAimAg0V6FdXvDvY+j0zm2mVm7XTwQfuCw6JheAkQ2ZW5k6aoMRBhnTt1DyP5u' '/9DwZp42ra1RNs0YTlPYkocSw1aX527Qk6qsVqrdGTJHjHpPy4/IFEsJuwO9jS5uXtpsWbgNxM' '/PSwvjFfWyxW5znQ0Q6wubpcbpzkE88OdnnmrxOJ30ymrt68/C+Sh67Kxzf1i8FHSZh7GO9P4d' 'N3/4MjQTo8RHugESaCP9mW3sY/uk59dlvEn8zWFqPL7JLYiAYiaYxoS4xv4Hywws+zFX4J2WNG' 'iQOIIv/U0NB5/SDKV2cHI1beaGZq+KjUWVKxjIx2RXkR+60Ry9qsgxgQv8jGSSKeyTLOcU73a1' 'IkQDDjwx62HYbMVKpFKWm01BCVHFirquaCaKlWsnn4s1IDD2JiU7e9KK9tygxb7xJssFQRmYN9' 'IZbKTUmfHUX3twyMXQt1RKIoQ5w6XXuKej+W5GOzBmMBFweYNRkTjQun36PaKN1wqD9OPY9De5' '1BUGceLswg/FrRPI7ADeT/axzBmrd/k7JYLkpLUG9yyTqLapP7N4j80dtJjasNEA0bNuDTVrXm' '/sZ4r8DrhjOpo6ka8hiJmtKk0yNRlqDs1EKDYKdswUmzEStEEqjjSm2uyRYKo9y1xeb4usc5sZ' 'skFTMVNWwNqGv5QlSYuDL16MhkLqLnm5MTj+THcmPR5cfojzm6Mt98bDJ/9dpUdG3i+lhushCN' 'jI8RdHxqMn95empishBE/SMF+rSf/zIy/hhEZroHFKKJyShPF+48tUbN0zV5Kp8rZKP8+Oj1aU' 'mZSC3QrWEqiK7nb+TpXh5NTWS52/bvookr0Y3c5Og1+jlyOX89P/UYd3glPzWOzq5MTMLsfXNk' 'cio/On19ZDK6OT15c6KQizCzsXxh9PoIEn+SeDNOfUZ0dR+figrXRq5fj080iCYeHc9NYvT+NK' 'PLORrlyOXrOXTF8xyji8HoFCbknkYJeTTA61m64NzMjebpCQpdms7I5GNZbbSQe880vUV/jMZG' 'boxcpdkdeyus0MKMTk/mbmDUhIrC9OXCVH5qeioXXZ2YGGNkF3KTj+RHc4WL0fWJAiNsupCjgY' 'yNTI1w19QGoYv+Ts+Xpwt5Rhyb2yenb0LjfJxW+VEoNaLREfp2jDE8MY7ZglZyE5OPoVnggVcg' 'Gz16LUfwSSCVsTUCNBQIa6NT/mvUISERtjI7z2g8d/V6/mpunG6L9OcJNPNovpA7TguWhxMa2g' 'SaHx2hTqd51lgoGlcgzx7pZnk9o/yVaGTskTxGrm8TBRTySi6MttFrinMif1gbIzpNeugJGRju' 'CS8CmH6HPgN6Lz31MbRPnwE9Qk/XGLpVnwF9Bz1lGZrQZ0DvoyfuzD7j6Sg99TM00GdAj9HTYY' 'Ye0ed/FpJEcE84Jydg5iMhEbmNHvGcKYuReNNko7nKk+XSwGK5Ok8cq7FclCATqPbt65xPTuTh' 'FSkzo6KA5JfjnAnmfDB/QMFDEgu0mDlnxxAWqHkvW+rWFKu4EdIoSsW6qUQJlZtWB+kvFVf7WR' 'nZv0RMcKHfNOPdTJABl67DS8vCsfWoMx6js7aiaSB52u3bWjCUBX6LKnWTNYqNUkk8IE2KCGCE' '9RlF1xAJzCw6oKFl4r1PVpZElXNiYHgoOzQ0RBedotTGvTfKiV62AV9VzXY8/CBJG0vLyPlvhy' 'FJJfzh8gG43CivlGp8Cg/qae3mI3kUL0WDg4MXW/8GPxz/L7Yjm65C/yp/tvKiWdZLaMH+0rzA' '5vfFlo+YAPQTeTYf8C/TCUnfx9o6eigaiu67r7Wtd0ZDx6P3m/IZbR+duBQNX2z7q3Z9CdEf+p' '++9PejMmpzrzmAd645gIfuPoCBuwzgxFoD8Jb/lFt+t168/u7nCbdgPzoVrLvW69OI/Mlf8kvx' 'JacRtSLhovvIEIC36P4HbVTgvonjOUZzPordB2ti1y2ve/Gd/ovr9HFi7T7WJCFvBU+vt4FhG2' 'TXS7YhItyJPXdXSfKqmpVrAuntLx4jxte4dDob0eWUGm5cGh46Ht9m9Jnp7VjLnwavUO9Ttqlm' '6TjznncX6Iy+QTwKXnbsUMsQufKISsiOHxcyUVBIeBYfA1p+hV5ZMHZhRPTUZlAFJuulutIXuT' 'qWySwF5deTUX+jPzqmKhG8oqjnZHeBpD8tz8IVyli8XC1tfVUvQe6UgQ3fBZgF9lyC+dxU3oqZ' 'xE7b04pbGoq1pQpIpPM1c6/EEAVU9J9u9Ge1atjp2KE47DWmbXFMpBviWq0NGuoaRrtop6VVuq' 'BVEFDpt7vRZqlNIrYg2AYJgUSOOc6q9EqCf0Lx83Oh5i4riKOt6dkks/VkA01Shnx6IPCB08Nn' 's2cfOIdTDv8CnMcnWoDiYsxhPLa0b5kLaa9UNVPZrGwfuak8GETnhjCIk0T7dGeiH/RwcqFOz6' 'fORAv1k7Q36Pn0ubODp85G2CgnccISiLepHLdBEJoJpjt4ils8SIIg6XCnB0kRBInLPpRSUCKs' 'sybvTtLgJSbw2Hi0mMTjCTw++gKHP7PDSMARL9iiBKmY1uox+cvUeCHM3tZlua0p36Tk/HKtUW' 'F/Eda3zhf5+TYPSF8U4jesocFD8TrULK5ZTi5eHfj5cr2mbi4m3C3WmingG9jp4RYLmdI4/7aM' 's5ViLly4kNV/Qi0ewKMUt3xQhNVjy5fgtUFIpYOkCBKGu6wW7LePBAdatWDigbmeDuyjiWBzgd' '/ouhhsljR6PYkodWzrqXtblVuD8uLgFX4rR7fz1Un9JPOeYKsH7gqD1OPlVValdU7isSsbdDCC' 'WY229dTetsYfwV8n5aUHk+cT/f86GXQwkEYWVFcWF29JA2h0x6lMWwPj9Aq/f+2eyc6q+dF1b7' 'BNuOkt13+CXtkqUPuScG99KYWB4yWBykt005mp1cwwNtEraXQFmLzwELdCKNJXOniq+9bBozZP' 'T3aW0Nbot5v52/ZZInWfneWi+XF5c7DpcTpg+i8GnfaNrsFgs4j7uqLrIV3fun9/0GmR2LUjCL' 'jsziMj16dz4T2Xf3FthedWmYxRd57coLpTJv4jKTs/e0iVnT/7trLzbWXn28rOt5Wdbys731Z2' '/m1Qdl7xlJ1X3kLZmfWUndkfSdn5b1hGD0/Rj7NImpGKbsvZe7tF1WlCbkpy9vHhnjX18TTdvI' 'htga3suMzxK6vElLQOJA5rTSPh1XDGob6CZMFZ2zkuaeDESIGzsrxcq2t+WZKsRS63g5NbevxW' 'Cldh+KaKo71tXvSg7y7Y6Yj2090QNK2GXMLFwb2E42pRL3DFZku/fHA6P9hmbb4sMYa2biWf32' 'YO9h5ghmQin/i62dI03rVrQQPld3Ro7gZ6isvQPmQvoGc47DMbTdOJIAWfsAyos7jeMvi3u838' '/Z7Y7e5M2B3uj93uzpCM1IeIPgRwXiLKuUGU86lkdJtlvBbCWadfL70M7t4VQRsEbL4+sRSdtX' 'qSLIer1pDxKstFBExRdl1HpUYJ+zYHtiGzET3YRPVh4w7ovl+eVb03u+qZ9AYQ45BCp1hFJvni' 'TIM136pg1z94riiIWUH6lbdcS8WOWUoTDoGVTDAicQ27j3+laSXfRStxPUxl9nCrkMPt8HXN+D' '368l305TbOQZ9gGhgJN4UHMxmShLx1AGpbvpYVx9vbPUiCIDuIZzlIiiD7wwNBViGJcJS+OZA5' 'EO+hVFuBX1xrH7h04n0fgha2hvs8SIogGaIz00cyzK3Vh2rDWvtAkHAu1gdGmYv1gbjwHPcxoJ' 'BUeJW+6ckcjPehhNbWSYo6wQdpD5IgCOoVOwgaNYlKExyKneeJRG0TMfy0tR8kEc3HJoN9lo9N' 'BilE8zwZ009H+DAve0s/RIhltu4o+Xn9IEPvw7HFRwjvw7HFR0qlh3nxfwfucPeEBdruj9B2/y' 'eJ6La91t0Wyw+c3hfLTSS8oA1cNurPmtsNqoY0lOhins3mYF3pSlXFx7vvp3j3/NpttH2bN1UH' 'k3KBE5Ac5F/YGtPs9709Go/vhg7L7aY5+NNBkgTZGYaS5y4Z/ozcFDNPRLftjVhn/746XEWRD4' 'MjDTzUa12BOcdx7z6xeMv8WrFeL64qswBp/wznI7uUlhQI94TvZQ/KAax9S59vwff5cyLS97I/' 'toNsJshWmreDJAiyS0kwqXzhvSBBoyj6f8gpKeQ='))) _INDEX = { f.name: { 'descriptor': f, 'services': {s.name: s for s in f.service}, } for f in FILE_DESCRIPTOR_SET.file } ExternalSchedulerServiceDescription = { 'file_descriptor_set': FILE_DESCRIPTOR_SET, 'file_descriptor': _INDEX[u'go.chromium.org/luci/swarming/proto/api/plugin.proto']['descriptor'], 'service_descriptor': _INDEX[u'go.chromium.org/luci/swarming/proto/api/plugin.proto']['services'][u'ExternalScheduler'], }

79.095522

122

0.881987

2,209

52,994

21.1512

0.937076

0.001798

0.001819

0.001455

0.005265

0.004452

0.002568

0

0.152349

0.062743

52,994

669

123

79.213752

0.788339

0.005227

0

1

0

0.905999

0.904007

0

1

0

1

0

false

0

0.004545

0

0.004545

0

1

null

0

1

0

1

null

1

0

5

b4bb195eec2b20471c77fe032401472ffb653406

348

py

Python

adder_python/adder.py

ToolPilotNeural/new_repo

6ced9dc22e3751c08662ff9c3ef54e97e1bf92df

[ "MIT" ]

null

adder_python/adder.py

ToolPilotNeural/new_repo

6ced9dc22e3751c08662ff9c3ef54e97e1bf92df

[ "MIT" ]

3

2021-12-13T18:03:14.000Z

2022-01-19T19:07:42.000Z

adder_python/adder.py

ToolPilotNeural/test_python

6ced9dc22e3751c08662ff9c3ef54e97e1bf92df

[ "MIT" ]

null

import math def add_two_numbers(a, b) -> int: return a + b def subtract_two_numbers(a, b) -> int: return a - b def multiply_two_numbers(a, b) -> int: return a * b def divide_two_numbers(a, b) -> int: try: return a/b except ZeroDivisionError: print('You cannot divide a number by zero') return None

17.4

51

0.62069

55

348

3.781818

0.418182

0.076923

0.211538

0.230769

0.447115

0.375

0

0.281609

348

19

52

18.315789

0.832

0

0.097701

0

1

0.307692

false

0

0.076923

0.230769

0.769231

0.076923

0

null

0

1

0

null

0

1

0

1

0

5

b4c3ffd1d3bd630c2530efd64dbb9472b608ec40

121

py

Python

Python-Web-Basics-Softuni/recipes/app/admin.py

borisboychev/SoftUni

22062312f08e29a1d85377a6d41ef74966d37e99

[ "MIT" ]

1

2020-12-14T23:25:19.000Z

Python-Web-Basics-Softuni/recipes/app/admin.py

borisboychev/SoftUni

22062312f08e29a1d85377a6d41ef74966d37e99

[ "MIT" ]

null

Python-Web-Basics-Softuni/recipes/app/admin.py

borisboychev/SoftUni

22062312f08e29a1d85377a6d41ef74966d37e99

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from app.models import Recipe admin.site.register(Recipe)

20.166667

32

0.809917

18

121

5.444444

0.666667

0

0.123967

121

6

33

20.166667

0.924528

0.214876

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

b4f147e40e2d8510ec00177ad9ec5fe15dd62d06

88

py

Python

Python/100Excersises/1 to 25/22/22.py

magusikrak/NAMI-TERM-I-GroupWork

f0a9a5f219ccbec024eb5316361db3fca46e171c

[ "MIT" ]

null

Python/100Excersises/1 to 25/22/22.py

magusikrak/NAMI-TERM-I-GroupWork

f0a9a5f219ccbec024eb5316361db3fca46e171c

[ "MIT" ]

1

2021-07-24T03:18:30.000Z

2021-07-24T12:45:07.000Z

Python/100Excersises/1 to 25/22/22.py

magusikrak/NAMI-TERM-I-GroupWork

f0a9a5f219ccbec024eb5316361db3fca46e171c

[ "MIT" ]

null

d = {"a":list(range(1, 11)), "b":list(range(11, 21)), "c":list(range(21, 31))} print(d)

29.333333

78

0.545455

18

88

2.666667

0.611111

0.5625

0

0.139241

0.102273

88

3

79

29.333333

0.468354

0

0.034091

0

1

0

false

0

0.5

1

0

null

1

0

1

0

1

0

null

0

1

0

5

372fa41edbba2761d124aa1014161e6a16bae0f4

39

py

Python

constants.py

navyad/chatty

4f7a09aa4138bc6e192b03864409304c182d955f

[ "Apache-2.0" ]

2

2022-02-01T07:32:08.000Z

2022-02-01T07:32:19.000Z

constants.py

navyad/chatty

4f7a09aa4138bc6e192b03864409304c182d955f

[ "Apache-2.0" ]

null

constants.py

navyad/chatty

4f7a09aa4138bc6e192b03864409304c182d955f

[ "Apache-2.0" ]

null

SERVER_HOST_PORT = ("127.0.0.1", 1234)

19.5

38

0.666667

8

39

3

0.875

0

0.285714

0.102564

39

1

39

0.4

0

0.230769

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

2ede6cc4d387af5578fadfc946cb91916cd256e0

351

py

Python

Ekeopara_Praise/Phase 2/LIST/Day40 Tasks/Task9.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

6

2020-05-23T19:53:25.000Z

2021-05-08T20:21:30.000Z

Ekeopara_Praise/Phase 2/LIST/Day40 Tasks/Task9.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

8

2020-05-14T18:53:12.000Z

2020-07-03T00:06:20.000Z

Ekeopara_Praise/Phase 2/LIST/Day40 Tasks/Task9.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

39

2020-05-10T20:55:02.000Z

2020-09-12T17:40:59.000Z

'''9. Write a Python program to get the difference between the two lists. ''' def difference_twoLists(lst1, lst2): lst1 = set(lst1) lst2 = set(lst2) return list(lst1 - lst2) print(difference_twoLists([1, 2, 3, 4], [4, 5, 6, 7])) print(difference_twoLists([1, 2, 3, 4], [0, 5, 6, 7])) print(difference_twoLists([1, 2, 3, 4], [3, 5, 6, 7]))

39

77

0.632479

61

351

3.57377

0.459016

0.330275

0.316514

0.330275

0.399083

0.275229

0

0.114583

0.179487

351

9

78

39

0.642361

0.19943

0

1

0.142857

false

0

0.285714

0.428571

0

null

1

0

1

0

null

0

1

0

5

258a7c3baf93ec0002ce14952944a78a429cccac

93

py

Python

texion/utils/preprocess.py

AnjanaRita/texion

edb2eb581600af7507f7315dfc0ee92e567c6b47

[ "MIT" ]

1

2022-02-08T23:06:28.000Z

texion/utils/preprocess.py

AnjanaRita/texion

edb2eb581600af7507f7315dfc0ee92e567c6b47

[ "MIT" ]

null

texion/utils/preprocess.py

AnjanaRita/texion

edb2eb581600af7507f7315dfc0ee92e567c6b47

[ "MIT" ]

null

"""text preprocessing utility functions""" from textacy import preprocess, preprocess_text

18.6

47

0.806452

10

93

7.4

0.8

0

0.11828

93

4

48

23.25

0.902439

0.387097

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

25c249aab71e7dd04bf396079882afff615eeadf

105

py

Python

libs/uix/baseclass/home_screen.py

RanulfoSoares/adminfuneraria

c4b602d984b465d819dc050813f8994d585dafdf

[ "MIT" ]

51

2020-12-15T21:29:25.000Z

2022-03-31T11:41:38.000Z

libs/uix/baseclass/home_screen.py

RanulfoSoares/adminfuneraria

c4b602d984b465d819dc050813f8994d585dafdf

[ "MIT" ]

8

2020-12-23T21:40:12.000Z

2021-10-04T11:57:16.000Z

libs/applibs/templates/base/libs/uix/baseclass/home_screen.py

Kulothungan16/KivyMD_Project_Creator

f96f1128800b19de4ad5941270fc2cfdbbbcf331

[ "MIT" ]

14

2021-01-02T04:08:53.000Z

2022-02-15T19:36:59.000Z

from kivymd.uix.screen import MDScreen class HomeScreen(MDScreen): """ Example Screen. """

13.125

38

0.657143

11

105

6.272727

0.818182

0

0.228571

105

7

39

15

0.851852

0.142857

0

1

0

true

0

0.5

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

25d8997b6f0f1e9af0a329c18c99e3387778ca10

106

py

Python

CodeWars/7 Kyu/Filter unused digits.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

CodeWars/7 Kyu/Filter unused digits.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

CodeWars/7 Kyu/Filter unused digits.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

def unused_digits(*args): return "".join(number for number in "0123456789" if number not in str(args))

53

80

0.726415

17

106

4.470588

0.764706

0

0.111111

0.150943

106

2

80

53

0.733333

0

0.093458

0

1

0.5

true

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

25e2eaa429d5ea5a32f08ee7c6f1090c648e5767

84

py

Python

forty/torch_utils.py

philippeller/IceNet

56294ad9ce1bce59212bea14c53f53c6ccdf3ea2

[ "Apache-2.0" ]

null

forty/torch_utils.py

philippeller/IceNet

56294ad9ce1bce59212bea14c53f53c6ccdf3ea2

[ "Apache-2.0" ]

null

forty/torch_utils.py

philippeller/IceNet

56294ad9ce1bce59212bea14c53f53c6ccdf3ea2

[ "Apache-2.0" ]

null

import numpy as np def torch_to_numpy(x): return np.asarray(x.cpu().detach())

14

39

0.690476

15

84

3.733333

0.8

0

0.166667

84

5

40

16.8

0.8

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

25f106996bf0f40a07910c15ee0ebdb383f2a837

33

py

Python

mathTools/normals.py

brenttyler/piTrace

802d3ddb8df7a55795b5eb6cbd763572cc735b9c

[ "Apache-2.0" ]

null

mathTools/normals.py

brenttyler/piTrace

802d3ddb8df7a55795b5eb6cbd763572cc735b9c

[ "Apache-2.0" ]

null

mathTools/normals.py

brenttyler/piTrace

802d3ddb8df7a55795b5eb6cbd763572cc735b9c

[ "Apache-2.0" ]

null

class Normal3(object): pass

8.25

22

0.666667

4

33

5.5

1

0

0.04

0.242424

33

4

23

8.25

0.84

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

d32d4780c46e5de092f552c25b87a8477337f7dd

505

py

Python

tests/better_sqrt_test.py

GigaSer/beatiful-py-sqrt

a216ecb16ad31ab30533843dcc7d5d35a816a945

[ "Apache-2.0" ]

null

tests/better_sqrt_test.py

GigaSer/beatiful-py-sqrt

a216ecb16ad31ab30533843dcc7d5d35a816a945

[ "Apache-2.0" ]

null

tests/better_sqrt_test.py

GigaSer/beatiful-py-sqrt

a216ecb16ad31ab30533843dcc7d5d35a816a945

[ "Apache-2.0" ]

null

from src.bsqrt import * def test_better_sqrt(): assert better_sqrt(-180) == (-6, 5) assert better_sqrt(-100) == (-10, 1) assert better_sqrt(-70) == (-1, 70) assert better_sqrt(-16) == (-4, 1) assert better_sqrt(-1) == (-1, 1) assert better_sqrt(0) == (0, 0) assert better_sqrt(1) == (1, 1) assert better_sqrt(16) == (4, 1) assert better_sqrt(18) == (3, 2) assert better_sqrt(20) == (2, 5) assert better_sqrt(72) == (6, 2) assert better_sqrt(98) == (7, 2)

29.705882

40

0.574257

80

505

3.45

0.325

0.471014

0.695652

0.307971

0.398551

0

0.125964

0.229703

505

16

41

31.5625

0.583548

0

0.857143

1

0.071429

true

0

0.071429

0

0.142857

0

null

1

0

null

0

1

0

1

0

5

d36abe6f988882c72521db054be1f0bc5d216b69

142

pyde

Python

Talk 1/c_three_three_three/c_three_three_three.pyde

purrcat259/introduction-to-artistic-programming

6694c9c8c8c4ff321e6ec8b6917cc0d6c3c95e20

[ "MIT" ]

1

2018-12-05T20:04:48.000Z

Talk 1/c_three_three_three/c_three_three_three.pyde

purrcat259/introduction-to-artistic-programming

6694c9c8c8c4ff321e6ec8b6917cc0d6c3c95e20

[ "MIT" ]

null

Talk 1/c_three_three_three/c_three_three_three.pyde

purrcat259/introduction-to-artistic-programming

6694c9c8c8c4ff321e6ec8b6917cc0d6c3c95e20

[ "MIT" ]

null

size(800, 600) background(255) triangle(20, 20, 20, 50, 50, 20) triangle(200, 100, 200, 150, 300, 320) triangle(700, 500, 800, 550, 600, 600)

23.666667

38

0.669014

26

142

3.653846

0.615385

0.084211

0

0.471074

0.147887

142

6

39

23.666667

0.31405

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

5

d370fc7ca9fbadb0547a9aa5f039080bf93158a5

163

py

Python

app/production/__init__.py

PadamSethia/noice

2915407b53420a36ec4a4fe84ff7c39780cf2805

[ "MIT" ]

2

2019-12-04T07:21:28.000Z

2020-04-09T03:03:15.000Z

app/production/__init__.py

PadamSethia/noice

2915407b53420a36ec4a4fe84ff7c39780cf2805

[ "MIT" ]

2

2019-12-04T07:11:36.000Z

2019-12-16T09:56:33.000Z

app/production/__init__.py

highoncarbs/noice

2915407b53420a36ec4a4fe84ff7c39780cf2805

[ "MIT" ]

null

from flask import Blueprint bp = Blueprint('production' , __name__ ) from app.production import routes , prod_activity_add , prod_basic_add , prod_materials_add

32.6

91

0.803681

22

163

5.5

0.636364

0.115702

0

0.134969

163

5

91

32.6

0.858156

0

0.060976

0

1

0

false

0

0.666667

0

0.666667

1

0

null

0

1

0

null

0

1

0

1

0

5

d39e07c91739bf8fbdcac88055c6ffc3d370201b

25

py

Python

catalyst/__version__.py

stalkermustang/catalyst

687bc6c31dfdc44ae3ff62938e11e69ce1999cd4

[ "MIT" ]

null

catalyst/__version__.py

stalkermustang/catalyst

687bc6c31dfdc44ae3ff62938e11e69ce1999cd4

[ "MIT" ]

null

catalyst/__version__.py

stalkermustang/catalyst

687bc6c31dfdc44ae3ff62938e11e69ce1999cd4

[ "MIT" ]

null

__version__ = '19.06rc1'

12.5

24

0.72

3

25

4.666667

1

0

0.227273

0.12

25

1

25

0.409091

0

0.32

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

d3a734900ab1751df30a4b99993c76fe17a2cff1

5,351

py

Python

smda/intel/definitions.py

mewbak/smda

a8f7e706b53d2492ad3934ba1c2ed82f9ae84ff1

[ "BSD-2-Clause" ]

null

smda/intel/definitions.py

mewbak/smda

a8f7e706b53d2492ad3934ba1c2ed82f9ae84ff1

[ "BSD-2-Clause" ]

null

smda/intel/definitions.py

mewbak/smda

a8f7e706b53d2492ad3934ba1c2ed82f9ae84ff1

[ "BSD-2-Clause" ]

null

# some mnemonics as specific to capstone CJMP_INS = ["je", "jne", "js", "jns", "jp", "jnp", "jo", "jno", "jl", "jle", "jg", "jge", "jb", "jbe", "ja", "jae", "jcxz", "jecxz", "jrcxz"] LOOP_INS = ["loop", "loopne", "loope"] JMP_INS = ["jmp", "ljmp"] CALL_INS = ["call", "lcall"] RET_INS = ["ret", "retn", "retf", "iret"] END_INS = ["ret", "retn", "retf", "iret", "int3"] REGS_32BIT = ["eax", "ebx", "ecx", "edx", "esi", "edi", "ebp", "esp"] DOUBLE_ZERO = bytearray(b"\x00\x00") DEFAULT_PROLOGUES = [ b"\x8B\xFF\x55\x8B\xEC", b"\x89\xFF\x55\x8B\xEC", b"\x55\x8B\xEC" ] # these cover 80%+ of manually confirmed function starts in the reference data set COMMON_PROLOGUES = { "5": { 32: { b"\x8B\xFF\x55\x8B\xEC": 5, # mov edi, edi, push ebp, mov ebp, esp b"\x89\xFF\x55\x8B\xEC": 3, # mov edi, edi, push ebp, mov ebp, esp }, 64: {} }, "3": { 32: { b"\x55\x8B\xEC": 3, # push ebp, mov ebp, esp }, 64: {} }, "2": { 32: { b"\x8B\xFF": 3, # mov edi, edi b"\xFF\x25": 3, # jmp dword ptr <addr> b"\x33\xC0": 2, # xor eax, eax b"\x83\xEC": 2, # sub esp, <byte> b"\x8B\x44": 2, # mov eax, dword ptr <esp + byte> b"\x81\xEC": 2, # sub esp, <byte> b"\x8D\x4D": 2, # lea ecx, dword ptr <ebp/esp +- byte> b"\x8D\x8D": 2, # lea ecx, dword ptr <ebp/esp +- byte> b"\xFF\x74": 2, # push dword ptr <addr> }, 64: {} }, "1": { 32: { b"\x6a": 3, # push <const byte> b"\x56": 3, # push esi b"\x53": 2, # push ebx b"\x51": 2, # push ecx b"\x57": 2, # push edi b"\xE8": 1, # call <offset> b"\xc3": 1 # ret }, 64: { b"\x40": 1, # x64 - push rxx b"\x44": 1, # x64 - mov rxx, ptr b"\x48": 1, # x64 - mov *, * b"\x33": 1, # xor, eax, * b"\x4c": 1, # x64 - mov reg, reg b"\xb8": 1, # mov reg, const b"\x8b": 1, # mov dword ptr, reg b"\x89": 1, # mov dword ptr, reg b"\x45": 1, # x64 - xor, reg, reg b"\xc3": 1 # retn } } } #TODO: 2018-06-27 expand the coverage in this list # https://stackoverflow.com/questions/25545470/long-multi-byte-nops-commonly-understood-macros-or-other-notation GAP_SEQUENCES = { 1: [ "\x90", # NOP1_OVERRIDE_NOP - AMD / nop - INTEL "\xCC" # int3 ], 2: [ b"\x66\x90", # NOP2_OVERRIDE_NOP - AMD / nop - INTEL b"\x8b\xc0", b"\x8b\xff", # mov edi, edi b"\x8d\x00", # lea eax, dword ptr [eax] b"\x86\xc0", # xchg al, al ], 3: [ b"\x0f\x1f\x00", # NOP3_OVERRIDE_NOP - AMD / nop - INTEL b"\x8d\x40\x00", # lea eax, dword ptr [eax] b"\x8d\x00\x00", # lea eax, dword ptr [eax] b"\x8d\x49\x00", # lea ecx, dword ptr [ecx] b"\x8d\x64\x24", # lea esp, dword ptr [esp] b"\x8d\x76\x00", b"\x66\x66\x90" ], 4: [ b"\x0f\x1f\x40\x00", # NOP4_OVERRIDE_NOP - AMD / nop - INTEL b"\x8d\x74\x26\x00", b"\x66\x66\x66\x90" ], 5: [ b"\x0f\x1f\x44\x00\x00", # NOP5_OVERRIDE_NOP - AMD / nop - INTEL b"\x90\x8d\x74\x26\x00" ], 6: [ b"\x66\x0f\x1f\x44\x00\x00", # NOP6_OVERRIDE_NOP - AMD / nop - INTEL b"\x8d\xb6\x00\x00\x00\x00" ], 7: [ b"\x0f\x1f\x80\x00\x00\x00\x00", # NOP7_OVERRIDE_NOP - AMD / nop - INTEL, b"\x8d\xb4\x26\x00\x00\x00\x00", b"\x8D\xBC\x27\x00\x00\x00\x00" ], 8: [ b"\x0f\x1f\x84\x00\x00\x00\x00\x00", # NOP8_OVERRIDE_NOP - AMD / nop - INTEL b"\x90\x8d\xb4\x26\x00\x00\x00\x00" ], 9: [ b"\x66\x0f\x1f\x84\x00\x00\x00\x00\x00", # NOP9_OVERRIDE_NOP - AMD / nop - INTEL b"\x89\xf6\x8d\xbc\x27\x00\x00\x00\x00" ], 10: [ b"\x66\x66\x0f\x1f\x84\x00\x00\x00\x00\x00", # NOP10_OVERRIDE_NOP - AMD b"\x8d\x76\x00\x8d\xbc\x27\x00\x00\x00\x00", b"\x66\x2e\x0f\x1f\x84\x00\x00\x00\x00\x00" ], 11: [ b"\x66\x66\x66\x0f\x1f\x84\x00\x00\x00\x00\x00", # NOP11_OVERRIDE_NOP - AMD b"\x8d\x74\x26\x00\x8d\xbc\x27\x00\x00\x00\x00", b"\x66\x66\x2e\x0f\x1f\x84\x00\x00\x00\x00\x00" ], 12: [ b"\x8d\xb6\x00\x00\x00\x00\x8d\xbf\x00\x00\x00\x00", b"\x66\x66\x66\x2e\x0f\x1f\x84\x00\x00\x00\x00\x00" ], 13: [ b"\x8d\xb6\x00\x00\x00\x00\x8d\xbc\x27\x00\x00\x00\x00", b"\x66\x66\x66\x66\x2e\x0f\x1f\x84\x00\x00\x00\x00\x00" ], 14: [ b"\x8d\xb4\x26\x00\x00\x00\x00\x8d\xbc\x27\x00\x00\x00\x00", b"\x66\x66\x66\x66\x66\x2e\x0f\x1f\x84\x00\x00\x00\x00\x00" ], 15: [ b"\x66\x66\x66\x66\x66\x66\x2e\x0f\x1f\x84\x00\x00\x00\x00\x00" ] } COMMON_START_BYTES = { "32": { "55": 8334, "6a": 758, "56": 756, "51": 312, "8d": 566, "83": 558, "53": 548 }, "64": { "48": 1341, "40": 349, "4c": 59, "33": 56, "44": 18, "45": 17, "e9": 16 } }

31.662722

141

0.468884

812

5,351

3.046798

0.267241

0.208569

0.210994

0.164915

0.515764

0.454729

0.375505

0.296281

0.193614

0.161681

0

0.201335

0.327976

5,351

168

142

31.85119

0.486652

0.259951

0

0.14557

0

0.063291

0.395033

0.228367

0

0.005952

0

1

0

false

0

null

1

0

1

0

1

0

null

0

5

6c9b774543e0497ae8d1de5c53929564e64220f3

169

py

Python

source/specs/admin.py

rubiorubio/online-shop

75b15cc9834a3548fe4beb67a34aca33d2df9b3d

[ "Apache-2.0" ]

null

source/specs/admin.py

rubiorubio/online-shop

75b15cc9834a3548fe4beb67a34aca33d2df9b3d

[ "Apache-2.0" ]

null

source/specs/admin.py

rubiorubio/online-shop

75b15cc9834a3548fe4beb67a34aca33d2df9b3d

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import * admin.site.register(ProductFeatures) admin.site.register(FeatureValidator) admin.site.register(CategoryFeature)

21.125

37

0.83432

20

169

7.05

0.55

0.191489

0.361702

0

0.076923

169

7

38

24.142857

0.903846

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

6cb2243c8fbf990487c3caa4e999a053cc62ff2e

245

py

Python

mrpeace/commands/checkauth.py

claudiolau/mrpeace

bb5d729ed8c4060b25ff42c612240503eda93d2c

[ "Apache-2.0" ]

null

mrpeace/commands/checkauth.py

claudiolau/mrpeace

bb5d729ed8c4060b25ff42c612240503eda93d2c

[ "Apache-2.0" ]

1

2021-05-15T19:14:40.000Z

mrpeace/commands/checkauth.py

claudiolau/mrpeace

bb5d729ed8c4060b25ff42c612240503eda93d2c

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import requests class CheckAuth: def __init__(self, http_link) -> None: self.http_link = http_link self.request = requests.get(self.http_link) def json(self): return self.request.json()

18.846154

51

0.657143

33

245

4.636364

0.545455

0.20915

0.235294

0

0.005319

0.232653

245

12

52

20.416667

0.808511

0.085714

0

1

0.285714

false

0

0.142857

0.714286

0

null

1

0

null

0

1

0

1

0

5

6cbf18ca0122d21e019a4325dff7e8a667cd5697

188

py

Python

Tech_Problems/technical_problems/apps.py

PEI-I1/Nos_Tech_Problems

cf8b0b51285a912988a96cc96438f81c75fa45b7

[ "MIT" ]

null

Tech_Problems/technical_problems/apps.py

PEI-I1/Nos_Tech_Problems

cf8b0b51285a912988a96cc96438f81c75fa45b7

[ "MIT" ]

14

2020-06-05T20:19:18.000Z

2021-09-22T18:18:23.000Z

Tech_Problems/technical_problems/apps.py

PEI-I1/Nos_Tech_Problems

cf8b0b51285a912988a96cc96438f81c75fa45b7

[ "MIT" ]

null

from django.apps import AppConfig class TechnicalProblemsConfig(AppConfig): name = 'technical_problems' def ready(self): # TODO: load problem solving model pass

18.8

42

0.696809

20

188

6.5

0.95

0

0.239362

188

9

43

20.888889

0.909091

0.170213

0

0.116883

0

0.111111

0

1

0.2

false

0.2

0

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

9f2bf6686fbeb52982f8615e3de6ae4b130398b6

168

py

Python

view_page/forms.py

ParkJonghyeon/tor_hs_archive_web

b9afab04e98740ffd0049cd92f1e29dd85415000

[ "Apache-2.0" ]

null

view_page/forms.py

ParkJonghyeon/tor_hs_archive_web

b9afab04e98740ffd0049cd92f1e29dd85415000

[ "Apache-2.0" ]

null

view_page/forms.py

ParkJonghyeon/tor_hs_archive_web

b9afab04e98740ffd0049cd92f1e29dd85415000

[ "Apache-2.0" ]

null

from django.forms import ModelForm from .models import Date_info class Date_info_form(ModelForm): class Meta: model = Date_info fields = ['date']

21

34

0.690476

22

168

5.090909

0.590909

0.214286

0

0.238095

168

7

35

24

0.875

0

0.02381

0

1

0

false

0

0.333333

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

9f3bdfa21bdb2e5d30cdca2028fd998284850f16

43

py

Python

docx2python/__init__.py

usr3/docx2python

3d0cdd6b94388e7dd2318cb61c9bb99da8853b77

[ "MIT" ]

null

docx2python/__init__.py

usr3/docx2python

3d0cdd6b94388e7dd2318cb61c9bb99da8853b77

[ "MIT" ]

null

docx2python/__init__.py

usr3/docx2python

3d0cdd6b94388e7dd2318cb61c9bb99da8853b77

[ "MIT" ]

null

from .main import docx2python # noqa: 401

21.5

42

0.744186

6

43

5.333333

1

0

0.114286

0.186047

43

1

43

0.8

0.209302

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

9f4c946319e9ddd030adf7853eba98c92ff7aac6

110

py

Python

apps/python/RaceDash/lib/__init__.py

newman-simracing/RaceDash

57282d3968d46c048275861ce5bd7feb4f7904c9

[ "MIT" ]

1

2019-05-21T09:56:03.000Z

apps/python/RaceDash/lib/__init__.py

newman-simracing/RaceDash

57282d3968d46c048275861ce5bd7feb4f7904c9

[ "MIT" ]

null

apps/python/RaceDash/lib/__init__.py

newman-simracing/RaceDash

57282d3968d46c048275861ce5bd7feb4f7904c9

[ "MIT" ]

null

from .RaceDash import RaceDash from .Data import Data from .Display import Display from .Module import Module

22

30

0.818182

16

110

5.625

0.375

0

0.145455

110

4

31

27.5

0.957447

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

9fc0afa5c360cf7d2488fa10fb3cc453524a1d3e

153

py

Python

Problems/lcof-020/solve.py

luanshiyinyang/LCNotes

093b109aefd65c2263f59472f7118059d7eeb256

[ "MIT" ]

3

2021-09-04T05:24:20.000Z

2022-02-19T08:31:28.000Z

Problems/lcof-020/solve.py

luanshiyinyang/LCNotes

093b109aefd65c2263f59472f7118059d7eeb256

[ "MIT" ]

null

Problems/lcof-020/solve.py

luanshiyinyang/LCNotes

093b109aefd65c2263f59472f7118059d7eeb256

[ "MIT" ]

null

class Solution: def isNumber(self, s: str) -> bool: return re.compile('^[+-]?(\.\d+|\d+\.?\d*)([eE][+-]?\d+)?$').match(s.strip()) is not None

51

97

0.503268

22

153

3.5

0.818182

0.051948

0

0.156863

153

3

97

51

0.596899

0

0.253247

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

4ccbc5aaacb0ba2fa2ecf0a040d1f5cce6ee7c76

179

py

Python

smartdc/__init__.py

yetu/py-smartdc

8cfbb0dd542fdce8f24863082a28b196af173d3f

[ "MIT" ]

null

smartdc/__init__.py

yetu/py-smartdc

8cfbb0dd542fdce8f24863082a28b196af173d3f

[ "MIT" ]

null

smartdc/__init__.py

yetu/py-smartdc

8cfbb0dd542fdce8f24863082a28b196af173d3f

[ "MIT" ]

null

from .datacenter import * from .machine import * from .legacy import LegacyDataCenter from ._version import get_versions __version__ = get_versions()['version'] del get_versions

22.375

39

0.804469

22

179

6.181818

0.454545

0.242647

0.264706

0

0.122905

179

7

40

25.571429

0.866242

0

0.039106

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

5

4ce9d1cafedf43309903919ebefbab3c45c20af8

58

py

Python

openslides/projector/exceptions.py

DebVortex/OpenSlides

f17f1a723a034dd7ebe80cd4ff4385d97d020c5f

[ "MIT" ]

null

openslides/projector/exceptions.py

DebVortex/OpenSlides

f17f1a723a034dd7ebe80cd4ff4385d97d020c5f

[ "MIT" ]

null

openslides/projector/exceptions.py

DebVortex/OpenSlides

f17f1a723a034dd7ebe80cd4ff4385d97d020c5f

[ "MIT" ]

null

class ProjectorExceptionWarning(RuntimeWarning): pass

19.333333

48

0.827586

4

58

12

1

0

0.12069

58

2

49

29

0.941176

0

1

0

true

0.5

0

0.5

0

1

0

1

null

0

1

0

null

0

1

0

5

4cfe8bbbe9eb31f6952077c794db51e74832b5b7

13,506

py

Python

promapi/prometheus.py

manojrege/python-prometheus-api

8c6f3402ce0fc8efc39f30fa3f7733b88bcae900

[ "BSD-3-Clause" ]

null

promapi/prometheus.py

manojrege/python-prometheus-api

8c6f3402ce0fc8efc39f30fa3f7733b88bcae900

[ "BSD-3-Clause" ]

1

2021-06-01T23:54:19.000Z

venv/lib/python3.6/site-packages/python_prometheus_api-0.1.3-py3.6.egg/promapi/prometheus.py

ahiresantosh/prom_python_flask_api

b48d48f0e86d80da96ef7cb7156ca97f9f7ec15b

[ "MIT" ]

1

2022-02-14T05:43:34.000Z

import yaml import requests from requests.exceptions import HTTPError import traceback from promapi.__init__ import get_data config = yaml.safe_load(open(get_data("config.yml"))) def set_endpoint(url, port): """ Sets the prometheus endpoint base url :param hostname: Prometheus endpoint :param port: Prometheus port number :return: """ config['prometheus']['url'] = url config['prometheus']['port'] = port def query_instant(query, time=None, timeout=None): """ Evaluates an instant query at a single point in time :param query: Prometheus expression query string :param time: Evaluation timestamp :param timeout: Evaluation timeout :return: Query result """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['instant_query']), params={ 'query': query, 'time': time, 'timeout': timeout}) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: results = response.json()['data']['result'] return response.status_code, results def query_range(query, start, end, step, timeout=None): """ Evaluates an expression query over a range of time :param query: Prometheus expression query string :param start: Start timestamp :param end: End timestamp :param step: Query resolution step width in duration format or float number of seconds :param timeout: Evaluation timeout :return: Query result """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['range_query']), params={ 'query': query, 'start': start, 'end': end, 'step': step, 'timeout': timeout}) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: results = response.json()['data']['result'] return response.status_code, results def query_metadata_series(match, start=None, end=None): """ Finds series by label matchers :param match: Repeated series selector argument that selects the series to return :param start: Start timestamp :param end: End timestamp :return: Finds series by label matchers """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['label_matcher_series_query']), params={ 'match[]': match, 'start': start, 'end': end}) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: results = response.json()['data'] return response.status_code, results def query_label_names(): """ Returns a list of label names :return: Prometheus Label names """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['label_names_query'])) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: results = response.json()['data'] return response.status_code, results def query_label_values(label_name): """ Returns a list of string label values for a given label :param label_name: Label name :return: Prometheus label values for the label name """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['label_values_query'].replace( '<label_name>', label_name))) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: results = response.json()['data'] return response.status_code, results def query_targets(): """ returns an overview of the current state of the Prometheus target discovery :return: State of Prometheus target discovery """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['targets_query'])) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: results = response.json()['data'] return response.status_code, results def query_rules(): """ returns a list of currently loaded alerting and recording rules :return: Alerting and recording rules """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['rules_query'])) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: results = response.json()['data'] return response.status_code, results def query_alerts(): """ returns a list of all active alerts :return: Active alerts """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['alerts_query'])) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: results = response.json()['data'] return response.status_code, results def query_target_metadata(match_target=None, metric=None, limit=None): """ Returns metadata about metrics scraped by targets :param match_target: Label selectors that match targets by their label sets. :param metric: A metric name to retrieve metadata :param limit: Maximum number of targets to match :return: Returns metadata about metrics scraped by targets """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['targets_metatdata_query']), params={ 'match_target': match_target, 'metric': metric, 'limit': limit}) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: results = response.json()['data'] return response.status_code, results def query_alertmanagers(): """ returns info on current state of the Prometheus alertmanager discovery :return: current state of the Prometheus alertmanager discovery """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['alermanagers_query'])) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: results = response.json()['data'] return response.status_code, results def query_status_config(): """ returns currently loaded configuration file content :return: Configurattion file content """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['status_config_query'])) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: results = response.json()['data'] return response.status_code, results def query_status_flags(): """ Returns flag values that Prometheus was configured with :return: Status flags """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['status_flags_query'])) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: results = response.json()['data'] return response.status_code, results def create_snapshot(skip_head=False): """ Creates a snapshot of all current data into snapshots/<datetime>-<rand> under the TSDB's data directory and returns the directory as response :param skip_head: Boolean flag If True skips head :return: snapshot of all current data into snapshots/<datetime>-<rand> under the TSDB's data directory and returns the directory as response """ try: response = requests.post( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['tsdb_snapshot_query']), params={ 'skip_head': skip_head}) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: results = response.json()['data'] return response.status_code, results def delete_series(match, start, end): """ Deletes data for a selection of series in a time range :return: 204 if deletion is successful """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['tsdb_delete_series_query']), params={ 'match[]': match, 'start': start, 'end': end}) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: print('Error {}'.format(err)) traceback.print_exc() else: return response.status_code def clean_tombstones(): """ Removes the deleted data from disk and cleans up the existing tombstones :return: 204 if deletion is successful """ try: response = requests.get( '{}:{}{}'.format( config['prometheus']['url'], config['prometheus']['port'], config['prometheus']['endpoints']['clean_tombstones_query'])) response.raise_for_status() except HTTPError as http_err: print('HTTP Error {}'.format(http_err)) return response.status_code, response.content except Exception as err: traceback.print_exc() print('Error {}'.format(err)) else: return response.status_code

33.348148

145

0.591293

1,411

13,506

5.537916

0.121899

0.096238

0.076785

0.092142

0.742769

0.726005

0.703865

0.680445

0

0.000621

0.28454

13,506

404

146

33.430693

0.808031

0.179994

0

0.829861

0

0.148532

0.00888

0

1

0.055556

false

0

0.017361

0

0.177083

0.15625

0

null

0

1

0

1

0

1

0

null

0

5

e225124a028db7513df11d88c0b02be5b1224b73

546

py

Python

tools/pylint.py

SamBajanik/sb_daipe_test

4da6b3c5233e12131b0d3fc8bbfa975ccf841693

[ "MIT" ]

null

tools/pylint.py

SamBajanik/sb_daipe_test

4da6b3c5233e12131b0d3fc8bbfa975ccf841693

[ "MIT" ]

null

tools/pylint.py

SamBajanik/sb_daipe_test

4da6b3c5233e12131b0d3fc8bbfa975ccf841693

[ "MIT" ]

null

# Databricks notebook source # MAGIC %run ../bootstrap/install_benvy # COMMAND ---------- from benvy.databricks.repos import bootstrap from benvy.databricks.detector import is_databricks_repo if is_databricks_repo(): bootstrap.install_dev() # COMMAND ---------- from benvy.databricks.repos import bootstrap from benvy.databricks.detector import is_databricks_repo if is_databricks_repo(): bootstrap.setup_env() # COMMAND ---------- # MAGIC %load_ext benvy.databricks.repos.pylint.magic # COMMAND ---------- # MAGIC %pylint src/

20.222222

56

0.728938

65

546

5.938462

0.353846

0.194301

0.196891

0.134715

0.632124

0

0.124542

546

26

57

21

0.807531

0.386447

0

0.75

0

1

0

true

0

0.5

0

0.5

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

5

e242c2592f3cd024258ff312cfc9117cfa04f3c6

473

py

Python

tests/test10.py

pombreda/pygir-ctypes

8ea2ad6f6cc792a3edb6ced9c0027a3ac2c52ecb

[ "BSD-3-Clause" ]

null

tests/test10.py

pombreda/pygir-ctypes

8ea2ad6f6cc792a3edb6ced9c0027a3ac2c52ecb

[ "BSD-3-Clause" ]

null

tests/test10.py

pombreda/pygir-ctypes

8ea2ad6f6cc792a3edb6ced9c0027a3ac2c52ecb

[ "BSD-3-Clause" ]

null

class instancemethod(object): def __init__(self, func): self._func = func def __get__(self, obj, type_=None): return lambda *args, **kwargs: self._func(obj, *args, **kwargs) class Func(object): def __init__(self): pass def __call__(self, *args, **kwargs): return self, args, kwargs class A(object): def __init__(self): pass f1 = classmethod(Func()) f2 = instancemethod(Func()) a = A() print(a.f1(10, 20)) print(a.f2(10, 20)) print(A.f1(10, 20))

17.518519

65

0.659619

71

473

4.070423

0.338028

0.138408

0.134948

0.176471

0.228374

0

0.043257

0.169133

473

26

66

18.192308

0.692112

0

0.210526

0

1

0.263158

false

0.105263

0

0.105263

0.631579

0.157895

0

null

0

1

0

null

0

1

0

1

0

1

0

5

e24f9bbf52d945113f5f998eea1d14d15ee6b667

62

py

Python

tests/test_lib.py

LegitStack/knot

4661eb53c3ca7aacbad33b17b4a92a156db5b6dc

[ "MIT" ]

null

tests/test_lib.py

LegitStack/knot

4661eb53c3ca7aacbad33b17b4a92a156db5b6dc

[ "MIT" ]

null

tests/test_lib.py

LegitStack/knot

4661eb53c3ca7aacbad33b17b4a92a156db5b6dc

[ "MIT" ]

null

'''knot> pytest tests ''' def test_noting(): assert True

12.4

25

0.629032

8

62

4.75

1

0

0.209677

62

4

26

15.5

0.77551

0.290323

0

0.5

1

0.5

true

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

e26fa4524a8d12d068cf421ab2eb7016cde26bc7

1,020

py

Python

sdi_pipeline/subtract.py

andrewhstewart/SDI

19d52bd5e13c2128c083776712672becf8b6ab45

[ "MIT" ]

null

sdi_pipeline/subtract.py

andrewhstewart/SDI

19d52bd5e13c2128c083776712672becf8b6ab45

[ "MIT" ]

null

sdi_pipeline/subtract.py

andrewhstewart/SDI

19d52bd5e13c2128c083776712672becf8b6ab45

[ "MIT" ]

null

from initialize import loc from master_residual import MR import subtract_hotpants import subtract_ais def SUBTRACT(): path = input("\n-> Enter path to exposure time directory: ") method = input("\n-> Choose subtraction method: hotpants or AIS: ") if method == 'hotpants': # align_skimage.skimage_template(location) subtract_hotpants.hotpants(path) # MR(path) elif method == 'AIS': # align_chi2.chi2(location) subtract_ais.isis_sub(path) # MR(path) else: print("\n-> Error: Unknown method") if __name__ == '__main__': path = input("\n-> Enter path to exposure time directory: ") method = input("\n-> Choose subtraction method: hotpants or ais: ") if method == 'hotpants': # align_chi2.chi2(location) subtract_hotpants.hotpants(path) # MR(path) elif method == 'ais': # align_chi2.chi2(location) subtract_ais.isis_sub(path) # MR(path) else: print("\n-> Error: Unknown method")

31.875

71

0.634314

123

1,020

5.081301

0.308943

0.0384

0.064

0.1008

0.7904

0.7776

0

0.007782

0.244118

1,020

32

72

31.875

0.802853

0.19902

0

0.545455

0

0.334165

0

1

0.045455

false

0

0.181818

0

0.227273

0.090909

0

null

0

1

0

null

0

5

e28840215788c0f41b1225e67322851b834d1188

2,498

py

Python

tests/uuid_str/test_uuid_str_substitutor.py

nikitanovosibirsk/district42-exp-types

e36e43da62f32d58d4b14c65afa16856dc8849e1

[ "Apache-2.0" ]

null

tests/uuid_str/test_uuid_str_substitutor.py

nikitanovosibirsk/district42-exp-types

e36e43da62f32d58d4b14c65afa16856dc8849e1

[ "Apache-2.0" ]

2

2021-08-01T05:02:21.000Z

2021-08-01T10:06:28.000Z

tests/uuid_str/test_uuid_str_substitutor.py

nikitanovosibirsk/district42-exp-types

e36e43da62f32d58d4b14c65afa16856dc8849e1

[ "Apache-2.0" ]

null

from uuid import uuid4 from baby_steps import given, then, when from pytest import raises from revolt import substitute from revolt.errors import SubstitutionError from district42_exp_types.uuid_str import schema_uuid_str def test_uuid_str_substitution(): with given: value = str(uuid4()) sch = schema_uuid_str with when: res = substitute(sch, value) with then: assert res == schema_uuid_str(value) assert res != sch def test_uuid_str_value_substitution(): with given: value = str(uuid4()) sch = schema_uuid_str(value) with when: res = substitute(sch, value) with then: assert res == schema_uuid_str(value) assert id(res) != id(sch) def test_uuid_str_substitution_invalid_value_error(): with given: value = str(uuid4()) sch = schema_uuid_str(value) with when, raises(Exception) as exception: substitute(sch, uuid4()) with then: assert exception.type is SubstitutionError def test_uuid_str_substitution_incorrect_value_error(): with given: value = str(uuid4()) sch = schema_uuid_str(value) with when, raises(Exception) as exception: substitute(sch, str(uuid4())) with then: assert exception.type is SubstitutionError def test_uuid_str_lowercase_substitution(): with given: value = str(uuid4()).lower() sch = schema_uuid_str.lowercase() with when: res = substitute(sch, value) with then: assert res == schema_uuid_str(value).lowercase() assert res != sch def test_uuid_str_lowercase_substitution_error(): with given: value = str(uuid4()).upper() sch = schema_uuid_str.lowercase() with when, raises(Exception) as exception: substitute(sch, value) with then: assert exception.type is SubstitutionError def test_uuid_str_uppercase_substitution(): with given: value = str(uuid4()).upper() sch = schema_uuid_str.uppercase() with when: res = substitute(sch, value) with then: assert res == schema_uuid_str(value).uppercase() assert res != sch def test_uuid_str_uppercase_substitution_error(): with given: value = str(uuid4()).lower() sch = schema_uuid_str.uppercase() with when, raises(Exception) as exception: substitute(sch, value) with then: assert exception.type is SubstitutionError

22.917431

57

0.658127

307

2,498

5.136808

0.130293

0.097654

0.107166

0.071021

0.850983

0.816107

0.77806

0.688015

0

0.006997

0.256205

2,498

108

58

23.12963

0.841765

0

0.72973

0

0.162162

1

0.108108

false

0

0.081081

0

0.189189

0

null

0

1

0

1

0

1

0

null

0

5

e292c477f8ff0b3b52de47ccd885f8471d0aeda4

43

py

Python

CodeUp/6008.py

chae-heechan/Algorithm_Study

183a77e2cfe352cd82fb5e988b493082529a73dd

[ "MIT" ]

null

CodeUp/6008.py

chae-heechan/Algorithm_Study

183a77e2cfe352cd82fb5e988b493082529a73dd

[ "MIT" ]

null

CodeUp/6008.py

chae-heechan/Algorithm_Study

183a77e2cfe352cd82fb5e988b493082529a73dd

[ "MIT" ]

null

# 출력하기 08 print("print(\"Hello\\nWorld\")")

21.5

33

0.627907

6

43

4.5

0.833333

0

0.05

0.069767

43

2

33

21.5

0.625

0.162791

0

0.228571

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

e2b73e3d615e2cf061e4f9fcf6321a8a7ae8d472

179

py

Python

Lotayou_unit_test.py

Lotayou/BasicSR

6cf9a706dd680d54f7dc26e87318ff79f76c0dbf

[ "Apache-2.0", "MIT" ]

null

Lotayou_unit_test.py

Lotayou/BasicSR

6cf9a706dd680d54f7dc26e87318ff79f76c0dbf

[ "Apache-2.0", "MIT" ]

null

Lotayou_unit_test.py

Lotayou/BasicSR

6cf9a706dd680d54f7dc26e87318ff79f76c0dbf

[ "Apache-2.0", "MIT" ]

null

from basicsr.models.archs.hifacegan_arch import HiFaceGAN from basicsr.models.archs.hifacegan_options import test_options opt = test_options() model = HiFaceGAN(opt) print(model)

29.833333

63

0.837989

25

179

5.84

0.48

0.150685

0.232877

0.30137

0.424658

0

0.083799

179

5

64

35.8

0.890244

0

1

0

false

0

0.4

0

0.4

0.2

1

0

null

0

1

0

1

0

null

0

1

0

5

2c4c892e78b842b467b7643360fde751aeda3fba

399

py

Python

small_text/integrations/transformers/__init__.py

chschroeder/small-text

ef28e91ba0c94fe938dde4f16253aa8695ea13b7

[ "MIT" ]

218

2021-05-26T16:38:53.000Z

2022-03-30T09:48:54.000Z

small_text/integrations/transformers/__init__.py

chschroeder/small-text

ef28e91ba0c94fe938dde4f16253aa8695ea13b7

[ "MIT" ]

9

2021-10-16T23:23:02.000Z

2022-02-22T15:23:11.000Z

small_text/integrations/transformers/__init__.py

chschroeder/small-text

ef28e91ba0c94fe938dde4f16253aa8695ea13b7

[ "MIT" ]

21

2021-06-24T11:19:44.000Z

2022-03-12T16:29:53.000Z

from small_text.integrations.pytorch.exceptions import PytorchNotFoundError try: from small_text.integrations.transformers.datasets import TransformersDataset from small_text.integrations.transformers.classifiers.classification import ( TransformerModelArguments, TransformerBasedClassification, TransformerBasedEmbeddingMixin) except PytorchNotFoundError: pass

36.272727

81

0.819549

31

399

10.451613

0.612903

0.083333

0.12037

0.231481

0.228395

0

0.140351

399

10

82

39.9

0.944606

0

1

0

true

0.111111

0.333333

0

0.333333

0

1

null

0

1

0

null

0

1

0

5

2c6546a9855eb903db2de6b9f51172ce636ee60b

287

py

Python

mmdet/datasets/samplers/__init__.py

ljpadam/CGPS

cec60a06d66738283bc3bd466190696275762d77

[ "Apache-2.0" ]

9

2021-06-23T05:07:43.000Z

2022-01-04T14:25:15.000Z

mmdet/datasets/samplers/__init__.py

ljpadam/CGPS

cec60a06d66738283bc3bd466190696275762d77

[ "Apache-2.0" ]

4

2021-07-29T10:52:12.000Z

2022-02-17T06:05:00.000Z

mmdet/datasets/samplers/__init__.py

ljpadam/CGPS

cec60a06d66738283bc3bd466190696275762d77

[ "Apache-2.0" ]

3

2021-07-07T08:39:12.000Z

2021-09-08T18:18:29.000Z

from .distributed_sampler import DistributedSampler from .group_sampler import DistributedGroupSampler, GroupSampler from .constrastive_batch_sampler import ConstrastiveBatchSampler __all__ = ['DistributedSampler', 'DistributedGroupSampler', 'GroupSampler', 'ConstrastiveBatchSampler']

47.833333

103

0.867596

22

287

10.954545

0.545455

0.161826

0

0.069686

287

5

104

57.4

0.902622

0

0.268293

0.163763

0

1

0

false

0

0.75

0

0.75

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

2c67de645f682090bf7e08f11cac9744fa38f321

1,301

py

Python

riberry/policy/helpers.py

srafehi/riberry

2ffa48945264177c6cef88512c1bc80ca4bf1d5e

[ "MIT" ]

2

2019-12-09T10:24:36.000Z

2019-12-09T10:26:56.000Z

riberry/policy/helpers.py

srafehi/riberry

2ffa48945264177c6cef88512c1bc80ca4bf1d5e

[ "MIT" ]

2

2018-06-11T11:34:28.000Z

2018-08-22T12:00:19.000Z

riberry/policy/helpers.py

srafehi/riberry

2ffa48945264177c6cef88512c1bc80ca4bf1d5e

[ "MIT" ]

null

from .engine import Rule, AttributeContext, Policy class ShorthandRule(Rule): def __init__(self, func): self.func = func def target_clause(self, context: AttributeContext) -> bool: return True def condition(self, context: AttributeContext) -> bool: return self.func(context) class ShorthandPolicy(Policy): def __init__(self, func, *collection): super(ShorthandPolicy, self).__init__(*collection) self.func = func def target_clause(self, context: AttributeContext) -> bool: return True def condition(self, context: AttributeContext) -> bool: return self.func(context) class ShorthandPolicySet(Policy): def __init__(self, func, *collection): super(ShorthandPolicySet, self).__init__(*collection) self.func = func def target_clause(self, context: AttributeContext) -> bool: return True def condition(self, context: AttributeContext) -> bool: return self.func(context) def rule(func): return ShorthandRule(func) def policy(func): def builder(*collection): return ShorthandPolicy(func, *collection) return builder def policy_set(func): def builder(*collection): return ShorthandPolicySet(func, *collection) return builder

22.050847

63

0.680246

136

1,301

6.330882

0.183824

0.083624

0.188153

0.216028

0.666667

0.59698

0.513357

0

0.222905

1,301

59

64

22.050847

0.851632

0

0.617647

0

1

0.411765

false

0

0.029412

0.264706

0.852941

0

null

0

1

0

null

0

1

0

1

0

5

2c7611a596e0b889df119a6ddcb1a76d6422ef28

123

py

Python

server/api/v1/user/__init__.py

TeamDDH/bbk-server

3fdd201e8b7854759b6f5113790d90adb9879b88

[ "MIT" ]

3

2018-08-20T04:57:57.000Z

2021-11-01T01:27:34.000Z

server/api/v1/user/__init__.py

TeamDDH/bbk-server

3fdd201e8b7854759b6f5113790d90adb9879b88

[ "MIT" ]

null

server/api/v1/user/__init__.py

TeamDDH/bbk-server

3fdd201e8b7854759b6f5113790d90adb9879b88

[ "MIT" ]

2

2019-06-18T09:00:46.000Z

2020-04-09T20:32:45.000Z

""" bbk-server api v1: user ~~~~~~~~~~ :author: Wendell Hu <wendzhue@gmail.com> """ from main import UserApi

13.666667

44

0.569106

15

123

4.666667

1

0

0.010638

0.235772

123

8

45

15.375

0.734043

0.617886

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

2c91395c0be9a22dd852941f54b1ae21bf80dc14

181

py

Python

patrols/forms.py

sinkva/pktroop

72a8f22f0b0f7c994d6ba2239b2ea17a46b6e133

[ "MIT" ]

null

patrols/forms.py

sinkva/pktroop

72a8f22f0b0f7c994d6ba2239b2ea17a46b6e133

[ "MIT" ]

null

patrols/forms.py

sinkva/pktroop

72a8f22f0b0f7c994d6ba2239b2ea17a46b6e133

[ "MIT" ]

null

from django.forms import ModelForm from django.forms.models import modelform_factory class PatrolForm(ModelForm): class Meta: model = Patrol fields = '__all__'

22.625

49

0.723757

21

181

6

0.666667

0.15873

0.238095

0

0.21547

181

7

50

25.857143

0.887324

0

0.038674

0

1

0

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

2cbbd5aff2f06a0fbc0b71489b8ec9b443421e1d

89

py

Python

l3ns/swarm/__init__.py

OlegJakushkin/l3ns

320184cb03837b9d6d13cb6ff006263ad1a99544

[ "MIT" ]

3

2021-04-02T11:05:54.000Z

2021-12-17T17:46:02.000Z

l3ns/swarm/__init__.py

OlegJakushkin/l3ns

320184cb03837b9d6d13cb6ff006263ad1a99544

[ "MIT" ]

1

2020-10-31T08:36:11.000Z

l3ns/swarm/__init__.py

OlegJakushkin/l3ns

320184cb03837b9d6d13cb6ff006263ad1a99544

[ "MIT" ]

1

2020-06-08T03:48:58.000Z

from .node import SwarmNode from .subnet import SwarmSubnet from .utils import SwarmHost

22.25

31

0.831461

12

89

6.166667

0.666667

0

0.134831

89

3

32

29.666667

0.961039

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

2cccf3ccc062fbd79d885fe9b3f6469acfead951

85

py

Python

jsonrpc/proxy.py

SureshMatsui/SaveCoin

706cd53e2bcec1fac16a3f1347a9143bc48101df

[ "MIT" ]

null

jsonrpc/proxy.py

SureshMatsui/SaveCoin

706cd53e2bcec1fac16a3f1347a9143bc48101df

[ "MIT" ]

1

2016-08-17T02:12:08.000Z

jsonrpc/proxy.py

SureshMatsui/SaveCoin

706cd53e2bcec1fac16a3f1347a9143bc48101df

[ "MIT" ]

1

2021-09-26T01:58:02.000Z

from SaveCoinrpc.authproxy import AuthServiceProxy as ServiceProxy, JSONRPCException

42.5

84

0.894118

8

85

9.5

1

0

0.082353

85

1

85

0.974359

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

e2eded38d24c25e331fbffb0cebc5294830b6c6b

56

py

Python

eating_timer_runner.py

szaster/100daysOfPython

10c15d0f815fe40a9cf56cf50f05813d256fadf4

[ "MIT" ]

null

eating_timer_runner.py

szaster/100daysOfPython

10c15d0f815fe40a9cf56cf50f05813d256fadf4

[ "MIT" ]

2

2019-11-20T22:44:28.000Z

2019-11-21T03:41:23.000Z

eating_timer_runner.py

szaster/100daysOfPython

10c15d0f815fe40a9cf56cf50f05813d256fadf4

[ "MIT" ]

null

from time_exercise import eating_timer eating_timer()

11.2

38

0.839286

8

56

5.5

0.75

0.5

0

0.125

56

4

39

14

0.897959

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

39014ed3ce60eb679f3ffb93c6cc66b695c9ded1

89

py

Python

25/02/getattr.py

pylangstudy/201707

c1cc72667f1e0b6e8eef4ee85067d7fa4ca500b6

[ "CC0-1.0" ]

null

25/02/getattr.py

pylangstudy/201707

c1cc72667f1e0b6e8eef4ee85067d7fa4ca500b6

[ "CC0-1.0" ]

46

2017-06-30T22:19:07.000Z

2017-07-31T22:51:31.000Z

25/02/getattr.py

pylangstudy/201707

c1cc72667f1e0b6e8eef4ee85067d7fa4ca500b6

[ "CC0-1.0" ]

null

# getattr(object, name[, default]) class C: def A(self): pass print(getattr(C, 'A'))

17.8

34

0.629213

14

89

4

0.785714

0

0.168539

89

4

35

22.25

0.756757

0.359551

0

0.018182

0

1

0.333333

false

0.333333

0

0.666667

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

3912313ac7efde96701cf33f3abf886e363a2085

234

py

Python

libra/cli/dev_commands.py

devos50/libra-client

7d1558848ff45ca8f42d756ef11e04846154e3cf

[ "MIT" ]

null

libra/cli/dev_commands.py

devos50/libra-client

7d1558848ff45ca8f42d756ef11e04846154e3cf

[ "MIT" ]

null

libra/cli/dev_commands.py

devos50/libra-client

7d1558848ff45ca8f42d756ef11e04846154e3cf

[ "MIT" ]

null

from command import * class DevCommand(Command): def get_aliases(self): return ["dev"] def get_description(self): return "Local move development" def execute(self, client, params): pass

21.272727

40

0.615385

26

234

5.461538

0.730769

0.084507

0

0.294872

234

11

41

21.272727

0.860606

0

0.111111

0

1

0.375

false

0.125

0.25

0.875

0

1

0

null

0

null

0

1

0

1

0

1

0

5

39352297d3fbabd8674f167f1bbd01929520d368

181

py

Python

test_project/test_app/tests/__init__.py

tickettext/django-test-utils

4bf1d7f53df0f901ddb58eb9063f479dd6a49020

[ "MIT" ]

54

2015-02-25T01:12:00.000Z

2022-02-06T16:27:42.000Z

test_project/test_app/tests/__init__.py

justquick/django-test-utils

01ee9a47a80e389dd946085fafa5fb192e87c467

[ "MIT" ]

10

2015-04-25T13:50:23.000Z

2021-02-08T09:42:29.000Z

test_project/test_app/tests/__init__.py

justquick/django-test-utils

01ee9a47a80e389dd946085fafa5fb192e87c467

[ "MIT" ]

23

2015-07-21T09:13:04.000Z

2022-01-13T13:18:11.000Z

from assertions_tests import * from templatetags_tests import * from testmaker_tests import * from crawler_tests import * import twill_tests __test__ = { 'TWILL': twill_tests, }

16.454545

32

0.790055

23

181

5.782609

0.391304

0.330827

0.338346

0

0.149171

181

10

33

18.1

0.863636

0

0.027624

0

0.125

1

0

false

0

0.625

0

0.625

0

1

0

null

1

0

null

0

1

0

1

0

5

3945ff48a79af25e809d6265d6ca7023276fe286

74

py

Python

src/Parser/LR1/__init__.py

ChalkCode/cool-compiler-2021

9bab662676c3281b496aad63228583db4a7244db

[ "MIT" ]

null

src/Parser/LR1/__init__.py

ChalkCode/cool-compiler-2021

9bab662676c3281b496aad63228583db4a7244db

[ "MIT" ]

null

src/Parser/LR1/__init__.py

ChalkCode/cool-compiler-2021

9bab662676c3281b496aad63228583db4a7244db

[ "MIT" ]

1

2022-02-24T17:16:42.000Z

from ..shift_reduce import ShiftReduceParser from .parser import LR1Parser

37

44

0.864865

9

74

7

0.777778

0

0.014925

0.094595

74

2

45

37

0.925373

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1a41a3e6af15333f1423e2c636fdb69a410d3e77

234

py

Python

Python/BACKUP/YOUTUBE/Operadores/UDMY2 - Operadores.py

ccpn1988/Python

94c84aa6f3ef9d05d64c3d87212dfd67694f4544

[ "MIT" ]

null

Python/BACKUP/YOUTUBE/Operadores/UDMY2 - Operadores.py

ccpn1988/Python

94c84aa6f3ef9d05d64c3d87212dfd67694f4544

[ "MIT" ]

null

Python/BACKUP/YOUTUBE/Operadores/UDMY2 - Operadores.py

ccpn1988/Python

94c84aa6f3ef9d05d64c3d87212dfd67694f4544

[ "MIT" ]

null

print('Adição + ',10 + 10 ) print('Subtração - ', 10 - 10 ) print('Multiplicação * ', 10 * 10 ) print('Divisão / ', 10 / 10 ) print('Potencia ** ', 10 ** 10 ) print('Divisão Inteiro // ', 10 // 3 ) print('Resto Divisão % ', 10 % 3 )

29.25

38

0.555556

30

234

4.333333

0.333333

0.153846

0.346154

0.246154

0

0.142077

0.217949

234

7

39

33.428571

0.568306

0

0.405983

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

1ac9596d88ac26d2915b62ed8d578cef1375d09b

202

py

Python

tests/test_helpers_python.py

nikolasj/crosspm

cddf2dc0ec6150acf0fdaa3640834e5c7c29b351

[ "MIT" ]

32

2015-07-22T13:46:40.000Z

2020-11-23T12:42:02.000Z

tests/test_helpers_python.py

nikolasj/crosspm

cddf2dc0ec6150acf0fdaa3640834e5c7c29b351

[ "MIT" ]

46

2016-04-01T13:04:33.000Z

2020-06-03T04:16:59.000Z

tests/test_helpers_python.py

nikolasj/crosspm

cddf2dc0ec6150acf0fdaa3640834e5c7c29b351

[ "MIT" ]

15

2016-04-20T08:26:48.000Z

2020-05-24T12:30:22.000Z

# -*- coding: utf-8 -*- import os from crosspm.helpers.python import get_object_from_string def test_get_object_from_string(): obj_ = get_object_from_string('os.path') assert os.path is obj_

20.2

57

0.742574

32

202

4.3125

0.5625

0.195652

0.282609

0.413043

0

0.005848

0.153465

202

9

58

22.444444

0.80117

0.10396

0

0.039106

0

0.2

1

0.2

false

0

0.4

0

0.6

0

null

0

1

0

1

0

null

0

1

0

1

0

5

1acfc677b1f71665d6d13b1cbe32f8aefb16e291

114

py

Python

run.py

zl1704/gemFI

13bd47b147592dbba6e3b4520cd33515488cf205

[ "BSD-3-Clause" ]

null

run.py

zl1704/gemFI

13bd47b147592dbba6e3b4520cd33515488cf205

[ "BSD-3-Clause" ]

null

run.py

zl1704/gemFI

13bd47b147592dbba6e3b4520cd33515488cf205

[ "BSD-3-Clause" ]

null

import os res = os.popen("build/ARM/gem5.opt configs/example/se.py -c configs/fi/qs-arm").read() #print(res)

16.285714

88

0.684211

21

114

3.714286

0.809524

0

0.01

0.122807

114

6

89

19

0.77

0.087719

0

0.5

0.61165

0.203884

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

5

20166dff6b10b36f23bf6275623fcc30a520ef4e

244

py

Python

Python-programming-3/ascii.py

sanxy/hacktoberfest-1

913582b310688d496602e8b1bc9166cb64866e38

[ "MIT" ]

null

Python-programming-3/ascii.py

sanxy/hacktoberfest-1

913582b310688d496602e8b1bc9166cb64866e38

[ "MIT" ]

1

2020-10-24T18:08:27.000Z

2020-10-24T18:10:52.000Z

Python-programming-3/ascii.py

sanxy/hacktoberfest-1

913582b310688d496602e8b1bc9166cb64866e38

[ "MIT" ]

4

2020-10-24T14:01:29.000Z

2020-10-25T09:21:07.000Z

# Python program to print # ASCII Value of Character # In c we can assign different # characters of which we want ASCII value c = 'g' # print the ASCII value of assigned character in c print("The ASCII value of '" + c + "' is", ord(c))

24.4

51

0.680328

41

244

4.04878

0.536585

0.240964

0.216867

0.240964

0

0.233607

244

9

52

27.111111

0.887701

0.696721

0

0.373134

0

1

0

false

0

0.5

0

null

1

0

1

0

null

0

1

0

5

20323e24a6f18986f213c4cb1c06251c7ee55723

140

py

Python

tests/endtoend/queue_functions/put_queue_return_multiple/main.py

yojagad/azure-functions-python-worker

d5a1587a4ccf56af64f211a64f0b7a3d6cf976c9

[ "MIT" ]

null

tests/endtoend/queue_functions/put_queue_return_multiple/main.py

yojagad/azure-functions-python-worker

d5a1587a4ccf56af64f211a64f0b7a3d6cf976c9

[ "MIT" ]

null

tests/endtoend/queue_functions/put_queue_return_multiple/main.py

yojagad/azure-functions-python-worker

d5a1587a4ccf56af64f211a64f0b7a3d6cf976c9

[ "MIT" ]

null

import typing import azure.functions as azf def main(req: azf.HttpRequest, msgs: azf.Out[typing.List[str]]): msgs.set(['one', 'two'])

20

64

0.692857

22

140

4.409091

0.772727

0

0.135714

140

6

65

23.333333

0.801653

0

0.042857

0

1

0.25

false

0

0.5

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

203845014d8f976cd98ce07bf36e44879ce9e95e

164

py

Python

Exercicios/Todos/ex112/text.py

Edson921/exerciciosResolvidos

72a3089f4848650c62ac0dd876abf5695a64525a

[ "MIT" ]

null

Exercicios/Todos/ex112/text.py

Edson921/exerciciosResolvidos

72a3089f4848650c62ac0dd876abf5695a64525a

[ "MIT" ]

null

Exercicios/Todos/ex112/text.py

Edson921/exerciciosResolvidos

72a3089f4848650c62ac0dd876abf5695a64525a

[ "MIT" ]

null

from exerci.ex112.utilitarioscev import moeda from exerci.ex112.utilitarioscev import dados preço = dados.leidinheiro('Qual é o preço: ') moeda.resumo(preço, 15, 8)

41

45

0.79878

24

164

5.458333

0.625

0.152672

0.229008

0.442748

0.534351

0

0.061224

0.103659

164

4

46

41

0.829932

0

0.09697

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

5

6472b843129753c41a2659fd57be1824591f94ae

40

py

Python

vvvvid_downloader/__init__.py

Barbait/vvvvid-downloader-1

3437c60e8f6c78a09d72b0d376c50e795e369744

[ "MIT" ]

1

2020-12-14T14:43:19.000Z

vvvvid_downloader/__init__.py

Barbait/vvvvid-downloader-1

3437c60e8f6c78a09d72b0d376c50e795e369744

[ "MIT" ]

null

vvvvid_downloader/__init__.py

Barbait/vvvvid-downloader-1

3437c60e8f6c78a09d72b0d376c50e795e369744

[ "MIT" ]

null

from vvvvid_downloader.vvvvid import ds

20

39

0.875

6

40

5.666667

0.833333

0

0.1

40

1

40

0.944444

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

648b61667af6116f5330e078987cb5660787aac0

1,030

py

Python

QUIC/rsa/rsaGen.py

prchander/boringssl

0993d04161159acb7f95012b794746491f5cd53c

[ "MIT" ]

1

2021-07-19T23:46:39.000Z

QUIC/rsa/rsaGen.py

prchander/boringssl

0993d04161159acb7f95012b794746491f5cd53c

[ "MIT" ]

null

QUIC/rsa/rsaGen.py

prchander/boringssl

0993d04161159acb7f95012b794746491f5cd53c

[ "MIT" ]

1

2021-10-12T05:30:12.000Z

import os openssl_dir = os.path.expanduser('~/openssl') myCmd = f'{openssl_dir}/apps/openssl req -x509 -new -newkey rsa:3072 -keyout /home/pi/openssl/lsquic/rsa/key_CA.key -out /home/pi/openssl/lsquic/rsa/key_CA.pem -pkeyopt rsa_keygen_bits:3072 -nodes -subj "/CN=oqstest CA" -days 365 -config {openssl_dir}/apps/openssl.cnf' os.system(myCmd) myCmd = f'{openssl_dir}/apps/openssl genpkey -algorithm rsa -out /home/pi/openssl/lsquic/rsa/key_srv.pem -pkeyopt rsa_keygen_bits:3072' os.system(myCmd) myCmd = f'{openssl_dir}/apps/openssl req -new -key /home/pi/openssl/lsquic/rsa/key_srv.pem -out /home/pi/openssl/lsquic/rsa/key_srv.csr -nodes -pkeyopt rsa_keygen_bits:3072 -subj \'/CN=oqstest server\' -config {openssl_dir}/apps/openssl.cnf' os.system(myCmd) myCmd = f'{openssl_dir}/apps/openssl x509 -req -in /home/pi/openssl/lsquic/rsa/key_srv.csr -out /home/pi/openssl/lsquic/rsa/key_crt.pem -CA /home/pi/openssl/lsquic/rsa/key_CA.pem -CAkey /home/pi/openssl/lsquic/rsa/key_CA.key -CAcreateserial -days 365' os.system(myCmd)

60.588235

269

0.764078

180

1,030

4.25

0.244444

0.070588

0.152941

0.223529

0.739869

0.708497

0.645752

0.562092

0.235294

0.183007

0

0.029474

0.07767

1,030

16

270

64.375

0.775789

0

0.4

0

0.4

0.819512

0.498537

0

1

0

false

0

0.1

0

0.1

0

null

0

1

0

1

0

1

null

0

5

64a7f4b3100a6222be3d504457bb37f164dac826

155

py

Python

banknet/libraries/Matrix.py

epolish/BankNet

815c566ba6e3514c51634e50b492a3748f81c03a

[ "MIT" ]

null

banknet/libraries/Matrix.py

epolish/BankNet

815c566ba6e3514c51634e50b492a3748f81c03a

[ "MIT" ]

4

2020-06-05T19:45:16.000Z

2021-06-10T21:07:53.000Z

banknet/libraries/Matrix.py

epolish/BankNet

815c566ba6e3514c51634e50b492a3748f81c03a

[ "MIT" ]

null

class Matrix: def transpose(self, matrix): return list(zip(*matrix)) def column(self, matrix, i): return [row[i] for row in matrix]

31

41

0.619355

22

155

4.363636

0.590909

0.1875

0

0.258065

155

5

41

31

0.834783

0

1

0.4

false

0

0.4

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

64ab7c2bca7d993697f450aab6dd71fc231de39b

20,175

py

Python

gan.py

statsu1990/gan_simple_2d_problem

5b62e79fcab5a66d49536f43863169a001e3089b

[ "MIT" ]

2

2019-09-09T08:02:36.000Z

2020-07-30T13:20:55.000Z

gan.py

statsu1990/gan_simple_2d_problem

5b62e79fcab5a66d49536f43863169a001e3089b

[ "MIT" ]

1

2019-07-14T10:27:43.000Z

gan.py

statsu1990/gan_simple_2d_problem

5b62e79fcab5a66d49536f43863169a001e3089b

[ "MIT" ]

null

import numpy as np import keras from keras.models import Model from keras.layers import Input, Dense, Activation, Dropout, BatchNormalization, LeakyReLU from keras.optimizers import Adam from keras import backend as K # 今更きけないGAN # https://qiita.com/triwave33/items/1890ccc71fab6cbca87e # https://qiita.com/pacifinapacific/items/6811b711eee1a5ebbb03 class GAN(): def __init__(self, latent_dim=2, data_dim=2): # 潜在変数の次元数 self.latent_dim = latent_dim # データの次元 self.data_dim = data_dim return def make_model(self, gene_hidden_neurons, disc_hidden_neurons): # discriminator model self.disc_model = self.__build_discriminator(disc_hidden_neurons) #self.disc_model.compile(optimizer=Adam(lr=1e-5, beta_1=0.1), loss='binary_crossentropy', metrics=['accuracy']) self.disc_model.compile(optimizer=Adam(lr=5e-6, beta_1=0.1), loss='binary_crossentropy', metrics=['accuracy']) # generator model self.gene_model = self.__build_generator(gene_hidden_neurons) # combined model of generator and discriminator self.combined_model = self.__build_combined_gene_and_disc() #self.combined_model.compile(optimizer=Adam(lr=2e-4, beta_1=0.5), loss='binary_crossentropy') self.combined_model.compile(optimizer=Adam(lr=2e-4, beta_1=0.5), loss='binary_crossentropy') return def __build_generator(self, hidden_neurons): ''' build generator keras model the last activation is tanh. ''' # input latent_inputs = Input(shape=(self.latent_dim,)) # hidden layer x = latent_inputs for hidden_n in hidden_neurons: x = Dense(hidden_n)(x) #x = Activation('relu')(x) x = LeakyReLU()(x) x = BatchNormalization()(x) # output x = Dense(self.data_dim)(x) datas = Activation('tanh')(x) #datas = Activation('linear')(x) model = Model(input=latent_inputs, output=datas) model.summary() return model def __build_discriminator(self, hidden_neurons): ''' build discriminator keras model ''' # input datas = Input(shape=(self.data_dim,)) # hidden layer x = datas for hidden_n in hidden_neurons: x = Dense(hidden_n)(x) x = Activation('relu')(x) #x = LeakyReLU()(x) #x = BatchNormalization()(x) # output x = Dense(1)(x) real_or_fake = Activation('sigmoid')(x) # model = Model(input=datas, output=real_or_fake) model.summary() return model def __build_combined_gene_and_disc(self): ''' build combined keras model of generator and discriminator ''' # input latent_inputs = Input(shape=(self.latent_dim,)) # data data = self.gene_model(latent_inputs) # true or false self.disc_model.trainable = False real_or_fake = self.disc_model(data) # model = Model(input=latent_inputs, output=real_or_fake) model.summary() return model def train(self, real_datas, epoch, batch_size=32): ''' training gan model ''' print('start training gan model') for iep in range(epoch): #self.train_step(real_datas, batch_size, iep) self.train_step_test1(real_datas, batch_size, iep) print('end training') return def train_step(self, real_datas, batch_size=32, now_epoch=None, print_on_batch=False): ''' training gan model on one epoch discriminatorの学習時にrealとfakeを別々に学習 ''' # sample_num = real_datas.shape[0] half_batch_size = int(batch_size / 2) batch_num = int(sample_num / half_batch_size) + 1 # index for minibatch training shuffled_idx = np.random.permutation(sample_num) # roop of batch for i_batch in range(batch_num): if half_batch_size*i_batch < sample_num: # --------------------------- # training of discriminator # --------------------------- # real data real_x = real_datas[shuffled_idx[half_batch_size*i_batch : half_batch_size*(i_batch+1)]] x_num = real_x.shape[0] y = np.ones((x_num, 1)) # label = 1 # disc_loss_real = self.disc_model.train_on_batch(x=real_x, y=y) # fake data latents = np.random.normal(0, 1, (x_num, self.latent_dim)) fake_x = self.gene_model.predict(latents) y = np.zeros((x_num, 1)) # label = 0 # disc_loss_fake = self.disc_model.train_on_batch(x=fake_x, y=y) # loss disc_loss = 0.5 * np.add(disc_loss_real, disc_loss_fake) # --------------------------- # training of generator # --------------------------- # generated data # batch size = x_num * 2 (= real + fake in disc training) latents = np.random.normal(0, 1, (x_num * 2, self.latent_dim)) # x_num * 2 y = np.ones((x_num * 2, 1)) # label = 1 # gene_loss = self.combined_model.train_on_batch(x=latents, y=y) # training progress if print_on_batch: print_epoch = now_epoch if now_epoch is not None else 0 print ("epoch: %d, batch: %d, [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (print_epoch, i_batch+1, disc_loss[0], 100*disc_loss[1], gene_loss)) # training progress print_epoch = now_epoch if now_epoch is not None else 0 print ("epoch: %d, [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (print_epoch, disc_loss[0], 100*disc_loss[1], gene_loss)) #print ("epoch: %d, [D loss: %f, acc.: %.2f%%]" % (print_epoch, disc_loss[0], 100*disc_loss[1])) return def train_step_test1(self, real_datas, batch_size=32, now_epoch=None, print_on_batch=False): ''' training gan model on one epoch discriminatorの学習時にrealとfakeを一緒に学習 ''' # sample_num = real_datas.shape[0] half_batch_size = int(batch_size / 2) batch_num = int(sample_num / half_batch_size) + 1 # index for minibatch training shuffled_idx = np.random.permutation(sample_num) # roop of batch for i_batch in range(batch_num): if half_batch_size*i_batch < sample_num: # --------------------------- # training of discriminator # --------------------------- # real data real_x = real_datas[shuffled_idx[half_batch_size*i_batch : half_batch_size*(i_batch+1)]] x_num = real_x.shape[0] real_y = np.ones((x_num, 1)) # label = 1 # fake data latents = np.random.normal(0, 1, (x_num, self.latent_dim)) fake_x = self.gene_model.predict(latents) fake_y = np.zeros((x_num, 1)) # label = 0 # x = np.append(real_x, fake_x, axis=0) y = np.append(real_y, fake_y, axis=0) disc_loss = self.disc_model.train_on_batch(x=x, y=y) # --------------------------- # training of generator # --------------------------- # generated data # batch size = x_num * 2 (= real + fake in disc training) latents = np.random.normal(0, 1, (x_num * 2, self.latent_dim)) # x_num * 2 y = np.ones((x_num * 2, 1)) # label = 1 # gene_loss = self.combined_model.train_on_batch(x=latents, y=y) # training progress if print_on_batch: print_epoch = now_epoch if now_epoch is not None else 0 print ("epoch: %d, batch: %d, [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (print_epoch, i_batch+1, disc_loss[0], 100*disc_loss[1], gene_loss)) # training progress print_epoch = now_epoch if now_epoch is not None else 0 print ("epoch: %d, [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (print_epoch, disc_loss[0], 100*disc_loss[1], gene_loss)) #print ("epoch: %d, [D loss: %f, acc.: %.2f%%]" % (print_epoch, disc_loss[0], 100*disc_loss[1])) return def train_step_only_disc_with_random_noise(self, real_datas, batch_size=32, now_epoch=None, print_on_batch=False): ''' training gan model on one epoch discriminatorのみ学習 ''' # sample_num = real_datas.shape[0] half_batch_size = int(batch_size / 2) batch_num = int(sample_num / half_batch_size) + 1 # index for minibatch training shuffled_idx = np.random.permutation(sample_num) # roop of batch for i_batch in range(batch_num): if half_batch_size*i_batch < sample_num: # --------------------------- # training of discriminator # --------------------------- # real data real_x = real_datas[shuffled_idx[half_batch_size*i_batch : half_batch_size*(i_batch+1)]] x_num = real_x.shape[0] real_y = np.ones((x_num, 1)) # label = 1 # fake data fake_x = np.random.rand(x_num, 2) * 2 - 1 fake_y = np.zeros((x_num, 1)) # label = 0 # x = np.append(real_x, fake_x, axis=0) y = np.append(real_y, fake_y, axis=0) disc_loss = self.disc_model.train_on_batch(x=x, y=y) # training progress if print_on_batch: print_epoch = now_epoch if now_epoch is not None else 0 print ("epoch: %d, batch: %d, [D loss: %f, acc.: %.2f%%]" % (print_epoch, i_batch+1, disc_loss[0], 100*disc_loss[1])) # training progress print_epoch = now_epoch if now_epoch is not None else 0 print ("epoch: %d, [D loss: %f, acc.: %.2f%%]" % (print_epoch, disc_loss[0], 100*disc_loss[1])) return def train_step_only_gene(self, real_datas, batch_size=32, now_epoch=None, print_on_batch=False): ''' training gan model on one epoch generatorのみ学習 ''' # sample_num = real_datas.shape[0] half_batch_size = int(batch_size / 2) batch_num = int(sample_num / half_batch_size) + 1 # index for minibatch training shuffled_idx = np.random.permutation(sample_num) # roop of batch for i_batch in range(batch_num): if half_batch_size*i_batch < sample_num: # real data real_x = real_datas[shuffled_idx[half_batch_size*i_batch : half_batch_size*(i_batch+1)]] x_num = real_x.shape[0] # --------------------------- # training of generator # --------------------------- # generated data # batch size = x_num * 2 (= real + fake in disc training) latents = np.random.normal(0, 1, (x_num * 2, self.latent_dim)) # x_num * 2 y = np.ones((x_num * 2, 1)) # label = 1 # gene_loss = self.combined_model.train_on_batch(x=latents, y=y) # training progress if print_on_batch: print_epoch = now_epoch if now_epoch is not None else 0 #print ("epoch: %d, batch: %d, [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (print_epoch, i_batch+1, disc_loss[0], 100*disc_loss[1], gene_loss)) #print ("epoch: %d, batch: %d, [D loss: %f, acc.: %.2f%%]" % (print_epoch, i_batch+1, disc_loss[0], 100*disc_loss[1])) print ("epoch: %d, batch: %d, [G loss: %f]" % (print_epoch, i_batch+1, gene_loss)) # training progress print_epoch = now_epoch if now_epoch is not None else 0 #print ("epoch: %d, [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (print_epoch, disc_loss[0], 100*disc_loss[1], gene_loss)) #print ("epoch: %d, [D loss: %f, acc.: %.2f%%]" % (print_epoch, disc_loss[0], 100*disc_loss[1])) print ("epoch: %d, [G loss: %f]" % (print_epoch, gene_loss)) return class WGAN_GP(): def __init__(self, latent_dim=2, data_dim=2): # 潜在変数の次元数 self.latent_dim = latent_dim # データの次元 self.data_dim = data_dim return def make_model(self, gene_hidden_neurons, disc_hidden_neurons, batch_size, gradient_penalty_weight): # discriminator model self.disc_model = self.__build_discriminator(disc_hidden_neurons) # generator model self.gene_model = self.__build_generator(gene_hidden_neurons) # combinedモデルの学習時はdiscriminatorの学習をFalseにする for layer in self.disc_model.layers: layer.trainable = False self.disc_model.trainable = False self.netG_model, self.netG_train = self.__build_combined_gene_and_disc() # for layer in self.disc_model.layers: layer.trainable = True for layer in self.gene_model.layers: layer.trainable = False self.disc_model.trainable = True self.gene_model.trainable = False self.netD_train = self.__build_discriminator_with_own_loss(batch_size, gradient_penalty_weight) return def __build_generator(self, hidden_neurons): ''' build generator keras model the last activation is tanh. ''' # input latent_inputs = Input(shape=(self.latent_dim,)) # hidden layer x = latent_inputs for hidden_n in hidden_neurons: x = Dense(hidden_n)(x) #x = Activation('relu')(x) x = LeakyReLU()(x) x = BatchNormalization()(x) # output x = Dense(self.data_dim)(x) datas = Activation('tanh')(x) #datas = Activation('linear')(x) model = Model(input=latent_inputs, output=datas) model.summary() return model def __build_discriminator(self, hidden_neurons): ''' build discriminator keras model ''' # input datas = Input(shape=(self.data_dim,)) # hidden layer x = datas for hidden_n in hidden_neurons: x = Dense(hidden_n)(x) #x = Activation('relu')(x) x = LeakyReLU()(x) #x = BatchNormalization()(x) # output x = Dense(1)(x) #real_or_fake = Activation('sigmoid')(x) # sigmoid is not used in wgan # model = Model(input=datas, output=x) model.summary() return model def __build_combined_gene_and_disc(self): ''' build combined keras model of generator and discriminator ''' # input latent_inputs = Input(shape=(self.latent_dim,)) # generated data data = self.gene_model(latent_inputs) # valid = self.disc_model(data) # model = Model(input=latent_inputs, output=valid) model.summary() # loss = -1 * K.mean(valid) # training_updates = Adam(lr=1e-4, beta_1=0.5, beta_2=0.9).get_updates(self.gene_model.trainable_weights,[],loss) g_train = K.function([latent_inputs], [loss], training_updates) return model, g_train def __build_discriminator_with_own_loss(self, batch_size, gradient_penalty_weight): ##モデルの定義 # generatorの入力 latent_inputs = Input(shape=(self.latent_dim,)) # discriimnatorの入力 gene_data = self.gene_model(latent_inputs) real_data = Input(shape=(self.data_dim,)) # ave_rate = K.placeholder(shape=(None, 1)) ave_data = Input(shape=(self.data_dim,), tensor=ave_rate * real_data + (1-ave_rate) * gene_data) #ave_rate = Input(shape=(1,)) #ave_data = ave_rate * real_data + (1-ave_rate) * gene_data # discriminatorの出力 gene_out = self.disc_model(gene_data) real_out = self.disc_model(real_data) ave_out = self.disc_model(ave_data) ##モデルの定義終了 # 損失関数を定義する # original critic loss loss_real = K.mean(real_out) / batch_size loss_fake = K.mean(gene_out) / batch_size # gradient penalty grad_mixed = K.gradients(ave_out, [ave_data])[0] #norm_grad_mixed = K.sqrt(K.sum(K.square(grad_mixed), axis=[1,2,3])) norm_grad_mixed = K.sqrt(K.sum(K.square(grad_mixed), axis=1)) grad_penalty = K.mean(K.square(norm_grad_mixed -1)) # 最終的な損失関数 loss = loss_fake - loss_real + gradient_penalty_weight * grad_penalty # オプティマイザーと損失関数、学習する重みを指定する training_updates = Adam(lr=1e-4, beta_1=0.5, beta_2=0.9)\ .get_updates(self.disc_model.trainable_weights,[],loss) # 入出力とtraining_updatesをfunction化 d_train = K.function([real_data, latent_inputs, ave_rate], [loss_real, loss_fake], training_updates) return d_train def train(self, real_datas, epoch, batch_size=32, train_ratio=5): ''' train wgan-gp model ''' for epoch in range(epochs): self.train_on_epoch(real_datas, batch_size, train_ratio) return def train_step(self, real_datas, batch_size=32, train_ratio=5): ''' train wgan-gp model ''' sample_num = real_datas.shape[0] batch_num = int(sample_num / batch_size) + 1 # index for minibatch training shuffled_idx = np.array([np.random.permutation(sample_num) for i in range(train_ratio)]) # roop of batch for i_batch in range(batch_num): if batch_size*i_batch < sample_num: # --------------------- # Discriminatorの学習 # --------------------- for itr in range(train_ratio): # バッチサイズを教師データからピックアップ real_x = real_datas[shuffled_idx[itr, batch_size*i_batch : batch_size*(i_batch+1)]] real_x_num = real_x.shape[0] # ノイズ noise = np.random.normal(0, 1, (real_x_num, self.latent_dim)) # epsilon = np.random.uniform(size = (real_x_num, 1)) errD_real, errD_fake = self.netD_train([real_x, noise, epsilon]) d_loss = errD_real - errD_fake # --------------------- # Generatorの学習 # --------------------- noise = np.random.normal(0, 1, (batch_size, self.latent_dim)) # 生成データの正解ラベルは本物（1） valid_y = np.array([1] * batch_size) # Train the generator g_loss = self.netG_train([noise]) # 進捗の表示 print ("[D loss: %f] [G loss: %f]" % (d_loss, g_loss[0])) return

36.026786

163

0.530607

2,413

20,175

4.173228

0.084956

0.0429

0.025819

0.022344

0.796127

0.760973

0.729295

0.70715

0.701291

0.658888

0

0.019796

0.348996

20,175

559

164

36.091234

0.746916

0.198216

0

0.637131

0

0.021097

0.032427

0

1

0.075949

false

0

0.025316

0

0.185654

0.113924

0

null

0

1

0

null

0

5

64cd126116ea3adaccee2c56284dbb41ba028650

152

py

Python

pdfmajor/interpreter/commands/state/PDFItem/__init__.py

asosnovsky/pdfmajor

7e24c64b5b4fdc84c12b2f78dcaab0e1aa07f4ad

[ "MIT" ]

23

2019-01-13T23:32:24.000Z

2021-07-08T04:29:15.000Z

pdfmajor/interpreter/commands/state/PDFItem/__init__.py

asosnovsky/pdfmajor

7e24c64b5b4fdc84c12b2f78dcaab0e1aa07f4ad

[ "MIT" ]

3

2019-08-09T18:42:01.000Z

2019-12-13T15:43:24.000Z

pdfmajor/interpreter/commands/state/PDFItem/__init__.py

asosnovsky/pdfmajor

7e24c64b5b4fdc84c12b2f78dcaab0e1aa07f4ad

[ "MIT" ]

2

2020-01-09T11:18:20.000Z

2020-03-24T06:02:30.000Z

from ._base import PDFItem from .PDFShape import PDFShape from .PDFImage import PDFImage from .PDFText import PDFText from .PDFXObject import PDFXObject

30.4

34

0.842105

20

152

6.35

0.4

0

0.125

152

5

34

30.4

0.954887

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

b379b5df8f0f3b496f8f1bc1357527dc0e841f1d

23,928

py

Python

decisiorama/pda_gpu/aggregate.py

j-chacon/Hartmann_contaminants

316d543efcdc0bcc4442c56fda6748b405ca2e22

[ "MIT" ]

null

decisiorama/pda_gpu/aggregate.py

j-chacon/Hartmann_contaminants

316d543efcdc0bcc4442c56fda6748b405ca2e22

[ "MIT" ]

null

decisiorama/pda_gpu/aggregate.py

j-chacon/Hartmann_contaminants

316d543efcdc0bcc4442c56fda6748b405ca2e22

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Aggregate Module This module contains a collection of functions for utility aggregation. """ __author__ = "Juan Carlos Chacon-Hurtado" __credits__ = ["Juan Carlos Chacon-Hurtado", "Lisa Scholten"] __license__ = "MIT" __version__ = "0.1.0" __maintainer__ = "Juan Carlos Chacon-Hurtado" __email__ = "j.chaconhurtado@tudelft.nl" __status__ = "Development" __last_update__ = "01-07-2019" import numpy as np from numba import cuda, vectorize, guvectorize OFFSET = 1e-6 @np.vectorize def _rerange(utils, offset=OFFSET): '''Re-range utilities so they are in the open interval (0,1)''' return utils*(1.0 - 2.0*offset) + offset def _dimcheck(utils, w): '''Check the dimension consistency of inputs and weights''' if not utils.ndim == 2: msg = ('The dimensions of utils have to be (1, ) or (2, ) ' 'got {0}'.format(utils.ndim)) raise ValueError(msg) if w is None: w = np.ones(utils.shape[1]) / utils.shape[1] elif callable(w[0]): # check if its an iterable with a generator w = np.array([wi() for wi in w]) w = w / np.sum(w, axis=0) if w.ndim == 1: if utils.shape[1] != w.shape[0]: msg = ('Weights and solutions do not match. The shape of solutions' ' is {0} and of weights is {1}'.format(utils.shape[1], w.shape) ) raise ValueError(msg) elif w.ndim == 2: if utils.shape != w.shape: msg = ('Weights and solutions do not match. The shape of ' 'solutions is {0} and of weights is {1}'.format(utils.shape, w.shape) ) raise ValueError(msg) # @guvectorize def _w_normalize(w): '''Normalise the weights so the um is equal to 1''' if w.ndim == 1: w[:] = w / np.sum(w, axis=0) else: _w_sum = np.sum(w, axis=1) for i in range(w.shape[1]): w[:,i] = w[:,i] / _w_sum def additive(utils, w, w_norm=True, *args, **kwargs): '''Additive utility aggregation function Aggregate preferences using a weighted average Parameters ---------- utils : ndarray [n, u] Two-dimensional array with the provided utilities to aggregate. The dimensions corresponds to the number of random samples (n) and the number of utilities (u) w : ndarray [u], [n, u] Array with the provided weights to each of the utilities. If passed as a 1D-array, the same weights are used for of all the random samples. In case it is a 2D-array, w requires the same dimensions as `utils` w_norm : Bool, optional If True, the sum of the weights will be equal to 1 Returns ------- out : ndarray [n] Vector with the aggregated values Example ------- .. highlight:: python .. code-block:: python utils = np.array([0.0, 1.0]) w = np.array([0.8, 0.2]) print(additive(s,w)) >>> [0.2] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([0.8, 0.2]) print(additive(s,w)) >>> [0.2 0.8 0.5] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([[0.8, 0.2], [0.8, 0.2], [0.8, 0.2]]) print(additive(s,w)) >>> [0.2 0.8 0.5] ''' if utils.ndim == 1: utils = np.reshape(utils, [1, -1]) _dimcheck(utils, w) if w_norm: _w_normalize(w) if w.shape == utils.shape: out = np.sum(utils * w, axis=1) else: out = np.dot(utils, w) return out def cobb_douglas(utils, w, w_norm=True, *args, **kwargs): '''Cobb-Douglas utility aggregation function Aggregate preferences using the cobb-douglas aggregation function. This method is also known as the weighted geometric average Parameters ---------- utils : ndarray [n, u] Two-dimensional array with the provided utilities to aggregate. The dimensions corresponds to the number of random samples (n) and the number of utilities (u) w : ndarray [u], [n, u] Array with the provided weights to each of the utilities. If passed as a 1D-array, the same weights are used for of all the random samples. In case it is a 2D-array, w requires the same dimensions as `utils` w_norm : Bool, optional If True, the sum of the weights will be equal to 1 Returns ------- out : ndarray [n] Vector with the aggregated values Example ------- .. highlight:: python .. code-block:: python utils = np.array([0.0, 1.0]) w = np.array([0.8, 0.2]) print(cobb_douglas(utils, w)) >>> [0.] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([0.8, 0.2]) print(cobb_douglas(utils, w)) >>> [0. 0. 0.5] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([[0.8, 0.2], [0.8, 0.2], [0.8, 0.2]]) print(cobb_douglas(utils, w)) >>> [0. 0. 0.5] ''' if utils.ndim == 1: utils = np.reshape(utils, [1, -1]) _dimcheck(utils, w) if w_norm: _w_normalize(w) return np.prod(utils**w, axis=1) def mix_linear_cobb(utils, w, pars=[0.5, ], w_norm=True, *args, **kwargs): '''to be deprecated''' if callable(pars[0]): alpha = pars[0]() else: alpha = pars[0] add_model = additive(utils, w, w_norm) cd_model = cobb_douglas(utils, w, w_norm) return alpha*(add_model) + (1.0 - alpha)*cd_model def reverse_harmonic(utils, w, w_norm=True, *args, **kwargs): '''Reverse harmonic utility aggregation function Aggregate preferences using the cobb-douglas aggregation function. This method is also known as the weighted geometric average Parameters ---------- utils : ndarray [n, u] Two-dimensional array with the provided utilities to aggregate. The dimensions corresponds to the number of random samples (n) and the number of utilities (u) w : ndarray [u], [n, u] Array with the provided weights to each of the utilities. If passed as a 1D-array, the same weights are used for of all the random samples. In case it is a 2D-array, w requires the same dimensions as `utils` w_norm : Bool, optional If True, the sum of the weights will be equal to 1 Returns ------- out : ndarray [n] Vector with the aggregated values Example ------- .. highlight:: python .. code-block:: python utils = np.array([0.0, 1.0]) w = np.array([0.8, 0.2]) print(reverse_harmonic(utils, w)) >>> [1.] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([0.8, 0.2]) print(reverse_harmonic(utils, w)) >>> [1. 1. 0.5] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([[0.8, 0.2], [0.8, 0.2], [0.8, 0.2]]) print(reverse_harmonic(utils, w)) >>> [1. 1. 0.5] ''' if utils.ndim == 1: utils = np.reshape(utils, [1, -1]) _dimcheck(utils, w) if w_norm: _w_normalize(w) return 1.0 - 1.0 / (np.sum(w / (1.0 - utils), axis=1)) def reverse_power(utils, w, alpha, w_norm=True, *args, **kwargs): '''Reverse power utility aggregation function Aggregate preferences using the reverse power aggregation function. Parameters ---------- utils : ndarray [n, u] Two-dimensional array with the provided utilities to aggregate. The dimensions corresponds to the number of random samples (n) and the number of utilities (u) w : ndarray [u], [n, u] Array with the provided weights to each of the utilities. If passed as a 1D-array, the same weights are used for of all the random samples. In case it is a 2D-array, w requires the same dimensions as `utils` w_norm : Bool, optional, default True If True, the sum of the weights will be equal to 1 alpha : float, ndarray [n], default 1.0 power coefficient. If passed as a float, the values will remain the same over the whole computation. Otherwise, it is possible to pass a vector with a value for each random sample Returns ------- out : ndarray [n] Vector with the aggregated values Example ------- .. highlight:: python .. code-block:: python utils = np.array([0.0, 1.0]) w = np.array([0.8, 0.2]) alpha = 1.0 print(reverse_power(utils, w, alpha)) >>> [0.2] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([0.8, 0.2]) alpha = np.array([1.0, 1.0, 1.0]) print(reverse_power(utils, w, alpha)) >>> [0.2 0.8 0.5] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([[0.8, 0.2], [0.8, 0.2], [0.8, 0.2]]) alpha = np.array([1.0, 1.0,1.0]) print(reverse_power(utils, w, alpha)) >>> [0.2 0.8 0.5] ''' if utils.ndim == 1: utils = np.reshape(utils, [1, -1]) _dimcheck(utils, w) if w_norm: _w_normalize(w) if type(alpha) is np.ndarray: if alpha.ndim == 1: alpha = np.tile(alpha, (utils.shape[1], 1)).T # print('alpha: {0}'.format(alpha)) out = 1.0 - np.power(np.sum(np.power(w*(1.0 - utils), alpha), axis=1), 1.0/alpha[:, 0]) else: _msg = ('alpha has to be scalar or 1D array, ' 'got {0}'.format(alpha.ndim)) raise ValueError(_msg) else: out = 1.0 - np.power(np.sum(np.power(w*(1.0 - utils), alpha), axis=1), 1.0/alpha) return out def multiplicative(utils, w, w_norm=True, *args, **kwargs): raise NotImplementedError('This method has not been implemented yet') def split_power(utils, w, alpha, s, w_norm=True, *args, **kwargs): '''Split power utility aggregation function Aggregate preferences using the split power aggregation function. Parameters ---------- utils : ndarray [n, u] Two-dimensional array with the provided utilities to aggregate. The dimensions corresponds to the number of random samples (n) and the number of utilities (u) w : ndarray [u], [n, u] Array with the provided weights to each of the utilities. If passed as a 1D-array, the same weights are used for of all the random samples. In case it is a 2D-array, w requires the same dimensions as `utils` alpha : float, ndarray[n] Alpha parameter of the power function. In case a float value is used, it will be constant for all of the random samples s : float, ndarray[n] s parameter of the power function. In case a float value is used, it will be constant for all of the random samples w_norm : Bool, optional If True, the sum of the weights will be equal to 1 Returns ------- out : ndarray [n] Vector with the aggregated values Example ------- .. highlight:: python .. code-block:: python utils = np.array([0.0, 1.0]) w = np.array([0.8, 0.2]) alpha = 1.0 s = 1.0 print(split_power(utils, w, alpha, s)) >>> [0.2] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([0.8, 0.2]) alpha = np.array([1.0, 1.0, 1.0]) s = 1.0 print(split_power(utils, w, alpha, s)) >>> [0.2 0.8 0.5] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([[0.8, 0.2], [0.8, 0.2], [0.8, 0.2]]) alpha = np.array([1.0, 1.0, 1.0]) s = np.array([1.0, 1.0, 1.0]) print(split_power(utils, w, alpha, s)) >>> [0.2 0.8 0.5] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([[0.8, 0.2], [0.8, 0.2], [0.8, 0.2]]) alpha = 1.0 s = np.array([1.0, 1.0, 1.0]) print(split_power(utils, w, alpha, s)) >>> [0.2 0.8 0.5] ''' if utils.ndim == 1: utils = np.reshape(utils, [1, -1]) _dimcheck(utils, w) if w_norm: _w_normalize(w) @np.vectorize def _g(u, s, alpha): if u <= s: out = s*(u/s)**alpha else: out = 1.0 - (1.0 - s)*((1.0 - u)/(1.0 - s))**alpha return out @np.vectorize def _g_inv(u, s, alpha): if u <= s: out = s*(u/s)**(1.0/alpha) else: out = 1.0 - (1.0 - s)*((1.0 - u)/(1.0 - s))**(1.0/alpha) return out if type(alpha) is np.ndarray: if alpha.ndim == 1: _alpha = np.tile(alpha, (utils.shape[1], 1)).T else: _msg = ('alpha has to be scalar or 1D array, ' 'got {0}'.format(alpha.ndim)) raise ValueError(_msg) else: _alpha = alpha if type(s) is np.ndarray: if s.ndim == 1: _s = np.tile(s, (utils.shape[1], 1)).T else: _msg = ('s has to be scalar or 1D array, ' 'got {0}'.format(alpha.ndim)) raise ValueError(_msg) else: _s = s out = _g_inv(np.sum(w*_g(utils, _s, _alpha), axis=1), s, alpha) return out def harmonic(utils, w, w_norm=True, rerange=False, *args, **kwargs): '''Harmonic utility aggregation function Aggregate preferences using the reverse power aggregation function. Parameters ---------- utils : ndarray [n, u] Two-dimensional array with the provided utilities to aggregate. The dimensions corresponds to the number of random samples (n) and the number of utilities (u) w : ndarray [u], [n, u] Array with the provided weights to each of the utilities. If passed as a 1D-array, the same weights are used for of all the random samples. In case it is a 2D-array, w requires the same dimensions as `utils` w_norm : Bool, optional If True, the sum of the weights will be equal to 1 rerange : Bool, optional Changes the range of utils to be in the open interval (0,1), defined by the offset value (defined at a library level as OFFSET, 1e-6). By default is set to False. Returns ------- out : ndarray [n] Vector with the aggregated values Example ------- .. highlight:: python .. code-block:: python utils = np.array([0.0, 1.0]) w = np.array([0.8, 0.2]) print(harmonic(utils, w, rerange=True)) >>> [1.24999969e-06] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([0.8, 0.2]) print(harmonic(utils, w, rerange=True)) >>>[1.24999969e-06 4.99998000e-06 5.00000000e-01] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([[0.8, 0.2], [0.8, 0.2], [0.8, 0.2]]) print(harmonic(utils, w, rerange=True)) >>>[1.24999969e-06 4.99998000e-06 5.00000000e-01] utils = np.array([0.0, 1.0]) w = np.array([0.8, 0.2]) print(harmonic(utils, w, rerange=False)) >>> [0.] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([0.8, 0.2]) print(harmonic(utils, w, rerange=False)) >>> [0. 0. 0.5] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([[0.8, 0.2], [0.8, 0.2], [0.8, 0.2]]) print(harmonic(utils, w, rerange=False)) >>> [0. 0. 0.5] ''' if utils.ndim == 1: utils = np.reshape(utils, [1, -1]) _dimcheck(utils, w) if w_norm: _w_normalize(w) if rerange: utils = _rerange(utils, OFFSET) return 1.0 / np.sum(w/utils, axis=1) def maximum(utils, *args, **kwargs): '''Maximum utility aggregation function Aggregate preferences using the maximum aggregation function. Parameters ---------- utils : ndarray [n, u] Two-dimensional array with the provided utilities to aggregate. The dimensions corresponds to the number of random samples (n) and the number of utilities (u) Returns ------- out : ndarray [n] Vector with the aggregated values Example ------- .. highlight:: python .. code-block:: python utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) print(maximum(utils)) >>> [1. 1. 0.5] ''' if utils.ndim == 1: utils = np.reshape(utils, [1, -1]) return np.max(utils, axis=1) def minimum(utils, *args, **kwargs): '''Minimum utility aggregation function Aggregate preferences using the minimum aggregation function. Parameters ---------- utils : ndarray [n, u] Two-dimensional array with the provided utilities to aggregate. The dimensions corresponds to the number of random samples (n) and the number of utilities (u) Returns ------- out : ndarray [n] Vector with the aggregated values Example ------- .. highlight:: python .. code-block:: python utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) print(minimum(utils)) >>> [0. 0. 0.5] ''' if utils.ndim == 1: utils = np.reshape(utils, [1, -1]) return np.min(utils, axis=1) def mix(utils, w, methods, w_methods, mix_fun, w_norm=True, methods_args=None, mix_args=None, *args, **kwargs): '''mixed utility aggregation function Aggregate preferences using a mix of aggregation functions. Parameters ---------- utils : ndarray [n, u] Two-dimensional array with the provided utilities to aggregate. The dimensions corresponds to the number of random samples (n) and the number of utilities (u) w : ndarray [u], [n, u] Array with the provided weights to each of the utilities. If passed as a 1D-array, the same weights are used for of all the random samples. In case it is a 2D-array, w requires the same dimensions as `utils` methods : list [m] a list of functions that will create each individual member of the model mixture w_methods : ndarray [m], [n, m] An array for the weights that will be used to mix each of the methods mix_fun : function Function that will be used to aggregate each of the members of the methods w_norm : Bool, optional If True, the sum of the weights will be equal to 1 Returns ------- out : ndarray [n] Vector with the aggregated values Example ------- .. highlight:: python .. code-block:: python utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([[0.8, 0.2], [0.8, 0.2], [0.8, 0.2]]) methods = [cobb_douglas, additive,] w_methods = np.array([0.5, 0.5]) mix_fun = additive print(mix(utils, w, methods, w_methods, mix_fun)) >>> [0.1 0.4 0.5] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([[0.8, 0.2], [0.8, 0.2], [0.8, 0.2]]) methods = [cobb_douglas, split_power,] methods_args = [{}, dict(alpha = 1.0, s = 1.0)] w_methods = np.array([0.5, 0.5]) mix_fun = additive print(mix(utils, w, methods, w_methods, mix_fun, methods_args=methods_args)) >>> [0.1 0.4 0.5] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([[0.8, 0.2], [0.8, 0.2], [0.8, 0.2]]) methods = [cobb_douglas, additive,] mix_args = dict(alpha = 1.0, s = 1.0) w_methods = np.array([0.5, 0.5]) mix_fun = split_power print(mix(utils, w, methods, w_methods, mix_fun, mix_args=mix_args)) #>>> [0.1 0.4 0.5] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([[0.8, 0.2], [0.8, 0.2], [0.8, 0.2]]) methods = [cobb_douglas, additive,] mix_args = dict(alpha = 1.0, s = 1.0) w_methods = np.array([[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]) mix_fun = split_power print(mix(utils, w, methods, w_methods, mix_fun, mix_args=mix_args)) >>> [0.1 0.4 0.5] utils = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]]) w = np.array([[0.8, 0.2], [0.8, 0.2], [0.8, 0.2]]) methods = [cobb_douglas, additive,] mix_args = dict(alpha = np.array([1.0, 1.0, 1.0]), s = 1.0) w_methods = np.array([[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]) mix_fun = split_power print(mix(utils, w, methods, w_methods, mix_fun, mix_args=mix_args)) >>> [0.1 0.4 0.5] ''' if utils.ndim == 1: utils = np.reshape(utils, [1, -1]) if w_methods.ndim == 1: _dim_w_methods = w_methods.shape[0] elif w_methods.ndim == 2: _dim_w_methods = w_methods.shape[1] _dimcheck(utils, w) if len(methods) != _dim_w_methods: _msg = ('length of methods ({0}) and w_methods ({1}) are not ' 'the same'.format(len(methods), len(w_methods)) ) raise ValueError(_msg) if w_norm: _w_normalize(w) _w_normalize(w_methods) if methods_args is None: methods_args = [{}, ]*len(methods) if mix_args is None: mix_args = {} agg_util = [m(utils, w, **methods_args[i]) for i, m in enumerate(methods)] agg_util = np.array(agg_util).T return mix_fun(agg_util, w_methods, **mix_args) def bonferroni(utils, w=None, w_norm=False): raise NotImplementedError('Not implemented yet') def power(utils, w=None, w_norm=False): raise NotImplementedError('Not implemented yet') def choquet(utils, w=None, w_norm=False): raise NotImplementedError('Not implemented yet') def sugeno(utils, w=None, w_norm=False): raise NotImplementedError('Not implemented yet')

29.395577

79

0.508484

3,426

23,928

3.4892

0.07122

0.019408

0.018571

0.017735

0.789025

0.765351

0.746696

0.716748

0.707546

0

0.062548

0.35122

23,928

813

80

29.431734

0.707485

0.630809

0

0.457447

0

0.096871

0.003715

0

1

0.106383

false

0

0.010638

0

0.18617

0

null

0

1

0

null

0

5

b39291b802734719808e4b175e2d9eb68d3772e8

1,467

py

Python

module1/part1.py

Strugglingrookie/oldboy2

8ed6723cab1f54f2ff8ea0947c6f982aef7e1b47

[ "Apache-2.0" ]

1

2021-06-15T07:01:23.000Z

module1/part1.py

Strugglingrookie/oldboy2

8ed6723cab1f54f2ff8ea0947c6f982aef7e1b47

[ "Apache-2.0" ]

3

2020-02-13T14:35:36.000Z

2021-06-10T21:27:14.000Z

module1/part1.py

Strugglingrookie/oldboy2

8ed6723cab1f54f2ff8ea0947c6f982aef7e1b47

[ "Apache-2.0" ]

1

2020-04-09T02:13:12.000Z

# -*- coding: utf-8 -*- # @Time : 2018/11/21 16:31 # @Author : Xiao # 基础需求： # 让用户输入用户名密码 # 认证成功后显示欢迎信息 # 输错三次后退出程序 users = {"alex":123456,'Miller':654321,"Xiaogang":"123654"} count = 3 while count > 0: user = input("Your name :\n").strip() pwd = input("Password :\n").strip() if user in users and pwd == str(users.get(user)): print("欢迎%s登陆系统".center(50,"*")%user) break else: if count == 1: print("错误次数达到3次，已被锁定".center(50, "*")) else: print("用户名或密码有误，请重新输入".center(50, "*")) count -= 1 # 升级需求： # 可以支持多个用户登录 (提示，通过列表存多个账户信息) # 用户3次认证失败后，退出程序，再次启动程序尝试登录时，还是锁定状态（提示:需把用户锁定的状态存到文件里） users = {"alex":123456,'Miller':654321,"Xiaogang":"123654"} count = 3 with open("user.txt", "a+", encoding="utf-8") as f: f.seek(0) lock_users = f.read().splitlines() while count > 0: user = input("Your name :\n").strip() pwd = input("Password :\n").strip() if user not in lock_users: if user in users and pwd == str(users.get(user)): print("欢迎%s登陆系统".center(50,"*")%user) break else: if count == 1: print("错误次数达到3次，已被锁定".center(50, "*")) with open("user.txt","a+",encoding="utf-8") as f: f.write(user+"\n") else: print("用户名或密码有误，请重新输入".center(50, "*")) count -= 1 else: print("用户被锁定，请联系管理员解锁！".center(50, "*")) break

29.938776

65

0.532379

184

1,467

4.233696

0.407609

0.071887

0.038511

0.053915

0.703466

0.61104

0.490372

0

0.072573

0.276755

1,467

49

66

29.938776

0.66164

0.130198

0

0.864865

0

0.175355

0

1

0

false

0.054054

0

0.189189

0

null

0

1

0

1

0

null

0

1

0

5

b3c40859f1d14b30c32d50b8d95d8dfd64948dc8

291

py

Python

django_backend/administration/backends.py

holg/django_backend

6cef76a378664e6621619862e6db476788a58992

[ "BSD-3-Clause" ]

3

2015-09-10T07:10:49.000Z

2021-03-16T07:17:58.000Z

django_backend/administration/backends.py

holg/django_backend

6cef76a378664e6621619862e6db476788a58992

[ "BSD-3-Clause" ]

10

2015-09-09T13:40:24.000Z

2021-02-27T09:12:23.000Z

django_backend/administration/backends.py

holg/django_backend

6cef76a378664e6621619862e6db476788a58992

[ "BSD-3-Clause" ]

5

2016-06-12T08:20:38.000Z

2021-02-27T09:02:30.000Z

from django_viewset import URLView from ..backend import BaseBackend from .views import AdministrationView, PasswordChangeView class AdministrationBackend(BaseBackend): index = URLView(r'^$', AdministrationView) password_change = URLView(r'^password_change/$', PasswordChangeView)

32.333333

72

0.804124

28

291

8.25

0.571429

0.069264

0

0.113402

291

8

73

36.375

0.895349

0

0.068729

0

1

0

false

0.333333

0.5

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

b607669027db90c74e35c5ce4c13d0d3aabeecfb

140

py

Python

test/sync_tests/services/scan_package/service2.py

livioribeiro/dependency-injector

ab76415233d7e6f82ff13479d10c2aa0f100c173

[ "Unlicense" ]

1

2021-08-13T20:23:56.000Z

test/sync_tests/services/scan_package/service2.py

livioribeiro/dependency-injector

ab76415233d7e6f82ff13479d10c2aa0f100c173

[ "Unlicense" ]

null

test/sync_tests/services/scan_package/service2.py

livioribeiro/dependency-injector

ab76415233d7e6f82ff13479d10c2aa0f100c173

[ "Unlicense" ]

null

from dependency_injector import singleton class Service2: pass @singleton def service2_factory() -> Service2: return Service2()

12.727273

41

0.75

15

140

6.866667

0.733333

0

0.035088

0.185714

140

10

42

14

0.868421

0

1

0.166667

true

0.166667

0.666667

0

1

0

null

0

null

0

1

0

1

0

5

3758b3f237477aa9fc7f64de025b0dfed0a7e064

22,535

py

Python

schemas/validators_test.py

Bee-lee/insights-data-schemas

693e4040dbd4f25ea7a451ffc33b7e3f19869752

[ "Apache-2.0" ]

null

schemas/validators_test.py

Bee-lee/insights-data-schemas

693e4040dbd4f25ea7a451ffc33b7e3f19869752

[ "Apache-2.0" ]

null

schemas/validators_test.py

Bee-lee/insights-data-schemas

693e4040dbd4f25ea7a451ffc33b7e3f19869752

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # vim: set fileencoding=utf-8 # Copyright © 2020, 2011 Pavel Tisnovsky # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Unit tests for validators module.""" import pytest from voluptuous import Invalid from validators import * # proper positive integers positive_int_values = (1, 2, 3, 65535, 65536, 4294967295, 4294967296, 18446744073709551615, 18446744073709551616) # proper positive integers and a zero positive_int_values_and_zero = positive_int_values + (0, ) # improper positive integers not_positive_int_values = (0, -1, -65535) # proper negative integers negative_int_values = (-1, -2, -3, -65535, -65536, -4294967295, -4294967296, -18446744073709551615, -18446744073709551616) # proper negative integers and a zero negative_int_values_and_zero = negative_int_values + (0, ) # improper negative integers not_negative_int_values = (0, 1, 65535) # improper integers not_integer_type = ("", "0", "1", "-1", True, False, 3.14) # positive float values positive_float_values = (1.0, 3.14, 1e10, 1e100, 1e-10, 1e-100) # not positive float values not_positive_float_values = (0.0, -3.14, -1e10, -1e100, -1e-10, -1e-100) # positive float values and zero positive_float_values_and_zero = positive_float_values + (0.0, ) # negative float values negative_float_values = (-1.0, -3.14, -1e10, -1e100, -1e-10, -1e-100) # not negative float values not_negative_float_values = (0.0, 1.0, 3.14, 1e10, 1e100, 1e-10, 1e-100) # negative float values and zero negative_float_values_and_zero = negative_float_values + (0.0, ) # improper floats not_float_type = ("", "0", "1", "-1", True, False) # proper string values string_values = ("", " ", "non-empty", "ěščř") # proper string values non_empty_string_values = (" ", "non-empty", "ěščř") # improper string values not_string_type = (0, 3.14, True, False, None) # proper positive integers stored in string positive_int_values_in_string = ("1", "2", "3", "65535", "65536", "4294967295", "4294967296", "18446744073709551615", "18446744073709551616") # proper positive integers and a zero stored in string positive_int_values_and_zero_in_string = positive_int_values_in_string + ("0", ) # improper positive integers stored in string not_positive_int_values_in_string = ("0", "-1", "-65535") # proper negative integers stored in string negative_int_values_in_string = ("-1", "-2", "-3", "-65535", "-65536", "-4294967295", "-4294967296", "-18446744073709551615", "-18446744073709551616") # proper negative integers and a zero stored in string negative_int_values_and_zero_in_string = negative_int_values_in_string + ("0", ) # improper negative integers stored in string not_negative_int_values_in_string = ("0", "1", "65535") # positive float values stored in string positive_float_values_in_string = ("1.0", "3.14", "1e10", "1e100", "1e-10", "1e-100") # not positive float values stored in string not_positive_float_values_in_string = ("0.0", "-3.14", "-1e10", "-1e100", "-1e-10", "-1e-100") # positive float values and zero stored in string positive_float_values_and_zero_in_string = positive_float_values_in_string + ("0.0", ) # negative float values stored in string negative_float_values_in_string = ("-1.0", "-3.14", "-1e10", "-1e100", "-1e-10", "-1e-100") # not negative float values stored in string not_negative_float_values_in_string = ("0.0", "1.0", "3.14", "1e10", "1e100", "1e-10", "1e-100") # negative float values and zero stored in string negative_float_values_and_zero_in_string = negative_float_values_in_string + ("0.0", ) # strings with exactly 32 hexadecimal digits hexa32_strings = ( "00000000000000000000000000000000", "ffffffffffffffffffffffffffffffff", "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF", "0f0f0f0f0f0f0f0f0f0f0f0f0f0f0f0f", "0123456789abcdef0123456789abcdef", "0123456789ABCDEF0123456789ABCDEF", ) # strings that have not exactly 32 hexadecimal digits not_hexa32_strings = ( "", "0", # not hexa chars "zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz", "ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ", "0000000000000000000000000000000", # shorter by one character "fffffffffffffffffffffffffffffff", "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF", "0f0f0f0f0f0f0f0f0f0f0f0f0f0f0f0", "0123456789abcdef0123456789abcde", "0123456789ABCDEF0123456789ABCDE", # longer by one character "000000000000000000000000000000000", "fffffffffffffffffffffffffffffffff", "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF", "0f0f0f0f0f0f0f0f0f0f0f0f0f0f0f000", "0123456789abcdef0123456789abcdeee", "0123456789ABCDEF0123456789ABCDEEE", ) @pytest.mark.parametrize("value", positive_int_values+negative_int_values_and_zero) def test_intTypeValidator_correct_values(value): """Check if proper integer values are validated.""" # no exception is expected intTypeValidator(value) @pytest.mark.parametrize("value", positive_int_values+negative_int_values_and_zero) def test_intTypeValidator_incorrect_values(value): """Check if proper integer values are validated.""" # no exception is expected intTypeValidator(value) @pytest.mark.parametrize("value", not_integer_type) def test_posIntValidator_correct_values(value): """Check if inproper positive integer values are validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: intTypeValidator(value) @pytest.mark.parametrize("value", not_positive_int_values) def test_posIntValidator_wrong_values(value): """Check if improper positive integer values are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: posIntValidator(value) @pytest.mark.parametrize("value", not_integer_type) def test_posIntValidator_wrong_types(value): """Check if improper types are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: posIntValidator(value) @pytest.mark.parametrize("value", positive_int_values_and_zero) def test_posIntOrZeroValidator_correct_values(value): """Check if proper positive integer or zero values are validated.""" # no exception is expected posIntOrZeroValidator(value) @pytest.mark.parametrize("value", negative_int_values) def test_posIntOrZeroValidator_wrong_values(value): """Check if improper positive integer values are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: posIntOrZeroValidator(value) @pytest.mark.parametrize("value", not_integer_type) def test_posIntOrZeroValidator_wrong_types(value): """Check if improper types are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: posIntOrZeroValidator(value) @pytest.mark.parametrize("value", negative_int_values) def test_negIntValidator_correct_values(value): """Check if proper negative integer values are validated.""" # no exception is expected negIntValidator(value) @pytest.mark.parametrize("value", not_negative_int_values) def test_negIntValidator_wrong_values(value): """Check if improper negative integer values are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: negIntValidator(value) @pytest.mark.parametrize("value", not_integer_type) def test_negIntValidator_wrong_types(value): """Check if improper types are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: negIntValidator(value) @pytest.mark.parametrize("value", negative_int_values_and_zero) def test_negIntOrZeroValidator_correct_values(value): """Check if proper negative integer values or zero are validated.""" # no exception is expected negIntOrZeroValidator(value) @pytest.mark.parametrize("value", positive_int_values) def test_negIntOrZeroValidator_wrong_values(value): """Check if improper negative integer values are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: negIntOrZeroValidator(value) @pytest.mark.parametrize("value", not_integer_type) def test_negIntOrZeroValidator_wrong_types(value): """Check if improper types are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: negIntOrZeroValidator(value) @pytest.mark.parametrize("value", positive_float_values+negative_float_values_and_zero) def test_floatTypeValidator_correct_values(value): """Check if proper float values are validated.""" # no exception is expected floatTypeValidator(value) @pytest.mark.parametrize("value", not_float_type) def test_floatTypeValidator_incorrect_values(value): """Check if improper float values are validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: floatTypeValidator(value) @pytest.mark.parametrize("value", positive_float_values) def test_posFloatValidator_correct_values(value): """Check if proper positive float values are validated.""" # no exception is expected posFloatValidator(value) @pytest.mark.parametrize("value", not_positive_float_values) def test_posFloatValidator_wrong_values(value): """Check if improper positive float values are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: posFloatValidator(value) @pytest.mark.parametrize("value", not_float_type) def test_PosFloatValidator_wrong_types(value): """Check if improper types are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: posFloatValidator(value) def test_PosFloatValidator_nan(): """Check if NaN is not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: posFloatValidator(math.nan) @pytest.mark.parametrize("value", positive_float_values_and_zero) def test_posFloatOrZeroValidator_correct_values(value): """Check if proper positive float values are validated.""" # no exception is expected posFloatOrZeroValidator(value) @pytest.mark.parametrize("value", negative_float_values) def test_PosFloatOrZeroValidator_wrong_values(value): """Check if improper positive float values are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: posFloatOrZeroValidator(value) @pytest.mark.parametrize("value", not_float_type) def test_PosFloatOrZeroValidator_wrong_types(value): """Check if improper types are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: posFloatOrZeroValidator(value) def test_PosFloatOrZeroValidator_nan(): """Check if NaN is not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: posFloatOrZeroValidator(math.nan) def test_PosFloatOrZeroValidator_nan(): """Check if NaN is not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: posFloatOrZeroValidator(math.nan) @pytest.mark.parametrize("value", negative_float_values) def test_negFloatValidator_correct_values(value): """Check if proper negative float values are validated.""" # no exception is expected negFloatValidator(value) @pytest.mark.parametrize("value", not_negative_float_values) def test_negFloatValidator_wrong_values(value): """Check if improper negative float values are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: negFloatValidator(value) @pytest.mark.parametrize("value", not_float_type) def test_negFloatValidator_wrong_types(value): """Check if improper types are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: negFloatValidator(value) def test_NegFloatValidator_nan(): """Check if NaN is not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: negFloatValidator(math.nan) @pytest.mark.parametrize("value", negative_float_values_and_zero) def test_negFloatOrZeroValidator_correct_values(value): """Check if proper negative float values are validated.""" # no exception is expected negFloatOrZeroValidator(value) @pytest.mark.parametrize("value", positive_float_values) def test_negFloatOrZeroValidator_wrong_values(value): """Check if improper negative float values are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: negFloatOrZeroValidator(value) @pytest.mark.parametrize("value", not_float_type) def test_negFloatOrZeroValidator_wrong_types(value): """Check if improper types are not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: negFloatOrZeroValidator(value) def test_NegFloatOrZeroValidator_nan(): """Check if NaN is not validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: negFloatOrZeroValidator(math.nan) def test_isNaNValidator(): """Check if NaN value is validated properly.""" # exception is not expected isNaNValidator(math.nan) @pytest.mark.parametrize("value", positive_float_values+negative_float_values_and_zero) def test_isNaNValidator_wrong_values(value): """Check if NaN value is validated properly.""" # exception is expected with pytest.raises(Invalid) as excinfo: isNaNValidator(value) @pytest.mark.parametrize("value", not_float_type) def test_isNaNValidator_wrong_types(value): """Check if NaN value is validated properly.""" # exception is expected with pytest.raises(Invalid) as excinfo: isNaNValidator(value) def test_isNotNaNValidator(): """Check if NaN value is validated properly.""" # exception is expected with pytest.raises(Invalid) as excinfo: isNotNaNValidator(math.nan) @pytest.mark.parametrize("value", positive_float_values+negative_float_values_and_zero) def test_isNotNaNValidator_wrong_values(value): """Check if NaN value is validated properly.""" # exception is not expected isNotNaNValidator(value) @pytest.mark.parametrize("value", not_float_type) def test_isNotNaNValidator_wrong_types(value): """Check if NaN value is validated properly.""" # exception is expected with pytest.raises(Invalid) as excinfo: isNotNaNValidator(value) @pytest.mark.parametrize("value", string_values) def test_stringTypeValidator_correct_values(value): """Check if proper string values are validated.""" # no exception is expected stringTypeValidator(value) @pytest.mark.parametrize("value", not_string_type) def test_stringTypeValidator_incorrect_values(value): """Check if improper values (with wrong type) are validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: stringTypeValidator(value) def test_emptyStringValidator_correct_value(): """Check if proper empty string value is validated.""" # no exception is expected emptyStringValidator("") @pytest.mark.parametrize("value", non_empty_string_values) def test_emptyStringValidator_correct_values(value): """Check if improper empty string values are validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: emptyStringValidator(value) @pytest.mark.parametrize("value", non_empty_string_values) def test_notEmptyStringValidator_correct_values(value): """Check if proper non empty string values are validated.""" # no exception is expected notEmptyStringValidator(value) def test_notEmptyStringValidator_incorrect_value(): """Check if improper non empty string values are validated.""" # exception is expected with pytest.raises(Invalid) as excinfo: notEmptyStringValidator("") @pytest.mark.parametrize("value", positive_int_values_in_string + negative_int_values_in_string) def test_intInStringValidator_correct_values(value): """Check the parsing and validating integers stored in string.""" # no exception is expected intInStringValidator(value) @pytest.mark.parametrize("value", positive_float_values_in_string) def test_intInStringValidator_incorrect_values(value): """Check the parsing and validating integers stored in string.""" # exception is expected with pytest.raises(ValueError) as excinfo: intInStringValidator(value) @pytest.mark.parametrize("value", positive_int_values_in_string) def test_posIntInStringValidator_correct_values(value): """Check the parsing and validating positive integers stored in string.""" # no exception is expected posIntInStringValidator(value) @pytest.mark.parametrize("value", negative_int_values_and_zero_in_string) def test_posIntInStringValidator_incorrect_values(value): """Check the parsing and validating positive integers stored in string.""" # exception is expected with pytest.raises(Invalid) as excinfo: posIntInStringValidator(value) @pytest.mark.parametrize("value", positive_int_values_and_zero_in_string) def test_posIntOrZeroInStringValidator_correct_values(value): """Check the parsing and validating positive integers or a zero stored in string.""" # no exception is expected posIntOrZeroInStringValidator(value) @pytest.mark.parametrize("value", negative_int_values_in_string) def test_posIntOrZeroInStringValidator_incorrect_values(value): """Check the parsing and validating positive integers or a zero stored in string.""" # exception is expected with pytest.raises(Invalid) as excinfo: posIntInStringValidator(value) @pytest.mark.parametrize("value", negative_int_values_in_string) def test_negIntInStringValidator_correct_values(value): """Check the parsing and validating negative integers stored in string.""" # no exception is expected negIntInStringValidator(value) @pytest.mark.parametrize("value", positive_int_values_and_zero_in_string) def test_negIntInStringValidator_incorrect_values(value): """Check the parsing and validating negative integers stored in string.""" # exception is expected with pytest.raises(Invalid) as excinfo: negIntInStringValidator(value) @pytest.mark.parametrize("value", negative_int_values_and_zero_in_string) def test_negIntOrZeroInStringValidator_correct_values(value): """Check the parsing and validating negative integers or a zero stored in string.""" # no exception is expected negIntOrZeroInStringValidator(value) @pytest.mark.parametrize("value", positive_int_values_in_string) def test_negIntOrZeroInStringValidator_incorrect_values(value): """Check the parsing and validating negative integers or a zero stored in string.""" # exception is expected with pytest.raises(Invalid) as excinfo: negIntInStringValidator(value) @pytest.mark.parametrize("value", positive_float_values_in_string) def test_posFloatInStringValidator_correct_values(value): """Check the parsing and validating positive floats stored in string.""" # no exception is expected posFloatInStringValidator(value) @pytest.mark.parametrize("value", negative_float_values_and_zero_in_string) def test_posFloatInStringValidator_incorrect_values(value): """Check the parsing and validating positive floats stored in string.""" # exception is expected with pytest.raises(Invalid) as excinfo: posFloatInStringValidator(value) @pytest.mark.parametrize("value", positive_float_values_and_zero_in_string) def test_posFloatOrZeroInStringValidator_correct_values(value): """Check the parsing and validating positive floats or a zero stored in string.""" # no exception is expected posFloatOrZeroInStringValidator(value) @pytest.mark.parametrize("value", negative_float_values_in_string) def test_posFloatOrZeroInStringValidator_incorrect_values(value): """Check the parsing and validating positive floats or a zero stored in string.""" # exception is expected with pytest.raises(Invalid) as excinfo: posFloatInStringValidator(value) @pytest.mark.parametrize("value", negative_float_values_in_string) def test_negFloatInStringValidator_correct_values(value): """Check the parsing and validating negative floats stored in string.""" # no exception is expected negFloatInStringValidator(value) @pytest.mark.parametrize("value", positive_float_values_and_zero_in_string) def test_negFloatInStringValidator_incorrect_values(value): """Check the parsing and validating negative floats stored in string.""" # exception is expected with pytest.raises(Invalid) as excinfo: negFloatInStringValidator(value) @pytest.mark.parametrize("value", negative_float_values_and_zero_in_string) def test_negFloatOrZeroInStringValidator_correct_values(value): """Check the parsing and validating negative floats or a zero stored in string.""" # no exception is expected negFloatOrZeroInStringValidator(value) @pytest.mark.parametrize("value", positive_float_values_in_string) def test_negFloatOrZeroInStringValidator_incorrect_values(value): """Check the parsing and validating negative floats or a zero stored in string.""" # exception is expected with pytest.raises(Invalid) as excinfo: negFloatInStringValidator(value) @pytest.mark.parametrize("value", hexa32_strings) def test_hexaString32Validator_correct_values(value): """Check the parsing and validating strings with 32 hexa characters.""" # no exception is expected hexaString32Validator(value) @pytest.mark.parametrize("value", not_hexa32_strings) def test_hexaString32Validator_incorrect_values(value): """Check the parsing and validating strings with 32 hexa characters.""" # exception is expected with pytest.raises(Invalid) as excinfo: hexaString32Validator(value)

35.376766

100

0.753583

2,667

22,535

6.173228

0.073116

0.031584

0.072704

0.088435

0.822157

0.766825

0.714954

0.660107

0.632957

0.604045

0

0.042321

0.15487

22,535

636

101

35.43239

0.822106

0.305392

0

0.46875

0

0.082714

0.044842

0

1

0.225694

false

0

0.010417

0

0.236111

0

null

0

1

0

1

0

null

0

1

0

5

3765ce8cd1f60d3055422275882cbfe67f68b23c

309

py

Python

app/admin/views.py

AdminWhaleFall/Flask-CheckStudent

67810f5b1f939a44ea19e89350565354d04f245c

[ "Apache-2.0" ]

1

2022-01-06T10:52:06.000Z

app/admin/views.py

AdminWhaleFall/Flask-CheckStudent

67810f5b1f939a44ea19e89350565354d04f245c

[ "Apache-2.0" ]

null

app/admin/views.py

AdminWhaleFall/Flask-CheckStudent

67810f5b1f939a44ea19e89350565354d04f245c

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # @Time : 2022/1/8 16:45 # @Author : WhaleFall # @Site : # @File : view.py # @Software: PyCharm # 系统后台视图 from . import admin # from app import models # 导入模块 from flask import render_template @admin.route('/admin/') def admin_index(): return render_template('admin/login.html')

20.6

46

0.666667

44

309

4.613636

0.75

0.137931

0.187192

0

0.043137

0.174757

309

14

47

22.071429

0.752941

0.472492

0

0.150327

0

1

0.2

true

0

0.4

0.2

0.8

0

null

0

1

0

null

0

1

0

1

0

5

8067a0c5a780acdbe7fc84fd152e417b5ab77bbd

183

py

Python

unix/__main__.py

tabboud/mpkernel

fa2ba20dea4864b5d5658bfbd43c6d7963056654

[ "MIT" ]

2

2020-05-11T17:17:58.000Z

2020-07-08T06:26:49.000Z

unix/__main__.py

tabboud/mpkernel

fa2ba20dea4864b5d5658bfbd43c6d7963056654

[ "MIT" ]

null

unix/__main__.py

tabboud/mpkernel

fa2ba20dea4864b5d5658bfbd43c6d7963056654

[ "MIT" ]

null

""" Launch MPKernelUnix """ from ipykernel.kernelapp import IPKernelApp from .unix import MPKernelUnix # Launch the unix port IPKernelApp.launch_instance(kernel_class=MPKernelUnix)

22.875

54

0.819672

21

183

7.047619

0.619048

0

0.10929

183

7

55

26.142857

0.907975

0.229508

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

80ac5a168bf65c3fed963bb141c8083c0168965f

3,347

py

Python

notebook/numpy_fancy_indexing.py

vhn0912/python-snippets

80b2e1d6b2b8f12ae30d6dbe86d25bb2b3a02038

[ "MIT" ]

174

2018-05-30T21:14:50.000Z

2022-03-25T07:59:37.000Z

notebook/numpy_fancy_indexing.py

vhn0912/python-snippets

80b2e1d6b2b8f12ae30d6dbe86d25bb2b3a02038

[ "MIT" ]

5

2019-08-10T03:22:02.000Z

2021-07-12T20:31:17.000Z

notebook/numpy_fancy_indexing.py

vhn0912/python-snippets

80b2e1d6b2b8f12ae30d6dbe86d25bb2b3a02038

[ "MIT" ]

53

2018-04-27T05:26:35.000Z

2022-03-25T07:59:37.000Z

import numpy as np a = np.arange(10) * 10 print(a) # [ 0 10 20 30 40 50 60 70 80 90] print(a[5]) # 50 print(a[8]) # 80 print(a[[5, 8]]) # [50 80] print(a[[5, 4, 8, 0]]) # [50 40 80 0] print(a[[5, 5, 5, 5]]) # [50 50 50 50] idx = np.array([[5, 4], [8, 0]]) print(idx) # [[5 4] # [8 0]] print(a[idx]) # [[50 40] # [80 0]] # print(a[[[5, 4], [8, 0]]]) # IndexError: too many indices for array print(a[[[[5, 4], [8, 0]]]]) # [[50 40] # [80 0]] a_2d = np.arange(12).reshape((3, 4)) print(a_2d) # [[ 0 1 2 3] # [ 4 5 6 7] # [ 8 9 10 11]] print(a_2d[0]) # [0 1 2 3] print(a_2d[2]) # [ 8 9 10 11] print(a_2d[[2, 0]]) # [[ 8 9 10 11] # [ 0 1 2 3]] print(a_2d[[2, 2, 2]]) # [[ 8 9 10 11] # [ 8 9 10 11] # [ 8 9 10 11]] print(a_2d[:, 1]) # [1 5 9] print(a_2d[:, 3]) # [ 3 7 11] print(a_2d[:, 1:2]) # [[1] # [5] # [9]] print(a_2d[:, [3, 1]]) # [[ 3 1] # [ 7 5] # [11 9]] print(a_2d[:, [3, 3, 3]]) # [[ 3 3 3] # [ 7 7 7] # [11 11 11]] print(a_2d[0, 1]) # 1 print(a_2d[2, 3]) # 11 print(a_2d[[0, 2], [1, 3]]) # [ 1 11] # index # [[0, 1] [2, 3]] # print(a_2d[[0, 2, 1], [1, 3]]) # IndexError: shape mismatch: indexing arrays could not be broadcast together with shapes (3,) (2,) print(a_2d[[[0, 0], [2, 2]], [[1, 3], [1, 3]]]) # [[ 1 3] # [ 9 11]] # index # [[0, 1] [0, 3] # [2, 1] [2, 3]] print(a_2d[[[0], [2]], [1, 3]]) # [[ 1 3] # [ 9 11]] idxs = np.ix_([0, 2], [1, 3]) print(idxs) # (array([[0], # [2]]), array([[1, 3]])) print(type(idxs)) # <class 'tuple'> print(type(idxs[0])) # <class 'numpy.ndarray'> print(idxs[0]) # [[0] # [2]] print(idxs[1]) # [[1 3]] print(a_2d[np.ix_([0, 2], [1, 3])]) # [[ 1 3] # [ 9 11]] print(a_2d[np.ix_([2, 0], [3, 3, 3])]) # [[11 11 11] # [ 3 3 3]] print(a_2d[[0, 2]][:, [1, 3]]) # [[ 1 3] # [ 9 11]] a_2d = np.arange(12).reshape((3, 4)) print(a_2d) # [[ 0 1 2 3] # [ 4 5 6 7] # [ 8 9 10 11]] a_2d[np.ix_([0, 2], [1, 3])] = 100 print(a_2d) # [[ 0 100 2 100] # [ 4 5 6 7] # [ 8 100 10 100]] a_2d[np.ix_([0, 2], [1, 3])] = [100, 200] print(a_2d) # [[ 0 100 2 200] # [ 4 5 6 7] # [ 8 100 10 200]] a_2d[np.ix_([0, 2], [1, 3])] = [[100, 200], [300, 400]] print(a_2d) # [[ 0 100 2 200] # [ 4 5 6 7] # [ 8 300 10 400]] print(a_2d[[0, 2]][:, [1, 3]]) # [[100 200] # [300 400]] a_2d[[0, 2]][:, [1, 3]] = 0 print(a_2d) # [[ 0 100 2 200] # [ 4 5 6 7] # [ 8 300 10 400]] a_2d = np.arange(12).reshape((3, 4)) print(a_2d) # [[ 0 1 2 3] # [ 4 5 6 7] # [ 8 9 10 11]] a_2d[[2, 0]] = [[100, 200, 300, 400], [500, 600, 700, 800]] print(a_2d) # [[500 600 700 800] # [ 4 5 6 7] # [100 200 300 400]] a_2d[[2, 2]] = [[-1, -2, -3, -4], [-5, -6, -7, -8]] print(a_2d) # [[500 600 700 800] # [ 4 5 6 7] # [ -5 -6 -7 -8]] a_2d = np.arange(12).reshape((3, 4)) print(a_2d) # [[ 0 1 2 3] # [ 4 5 6 7] # [ 8 9 10 11]] a_fancy = a_2d[np.ix_([0, 2], [1, 3])] print(a_fancy) # [[ 1 3] # [ 9 11]] a_fancy[0, 0] = 100 print(a_fancy) # [[100 3] # [ 9 11]] print(a_2d) # [[ 0 1 2 3] # [ 4 5 6 7] # [ 8 9 10 11]] a_2d = np.arange(12).reshape((3, 4)) print(a_2d) # [[ 0 1 2 3] # [ 4 5 6 7] # [ 8 9 10 11]] print(a_2d[[2, 0], ::-1]) # [[11 10 9 8] # [ 3 2 1 0]] print(a_2d[::2, [3, 0, 1]]) # [[ 3 0 1] # [11 8 9]]

15.008969

100

0.432029

701

3,347

1.982882

0.091298

0.097122

0.189928

0.116547

0.659712

0.597842

0.539568

0.471223

0.389209

0.358273

0

0.288526

0.286525

3,347

222

101

15.076577

0.293551

0.461309

0

0.323077

0

1

0

false

0

0.015385

0

0.015385

0.738462

0

1

null

0

1

0

1

0

1

0

null

0

1

0

5

80c6061ade4caa0af383ea75ecad239c88589f55

99

py

Python

src/petpeeve/pip_internal/locations.py

uranusjr/petpeeve

66cbec32ffd9b8bd81cf0992a3021931d88df825

[ "ISC" ]

null

src/petpeeve/pip_internal/locations.py

uranusjr/petpeeve

66cbec32ffd9b8bd81cf0992a3021931d88df825

[ "ISC" ]

null

src/petpeeve/pip_internal/locations.py

uranusjr/petpeeve

66cbec32ffd9b8bd81cf0992a3021931d88df825

[ "ISC" ]

1

2019-01-21T12:39:38.000Z

from ._not import from_pip_import USER_CACHE_DIR = from_pip_import('locations', 'USER_CACHE_DIR')

24.75

63

0.818182

16

99

4.5

0.5

0.194444

0.361111

0

0.090909

99

3

64

33

0.8

0

0.232323

0

1

0

false

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5