xszheng2020/the_stack_dedup_python_hits_3 · Datasets at Hugging Face

d64f415897e749c6aa56a242b90bd89c895e2511

87

py

Python

protocolws/__init__.py

panmpan17/ProtocolWebsocket

05893ab47883fec4356f2f617213093eb7d9b4df

[ "MIT" ]

null

protocolws/__init__.py

panmpan17/ProtocolWebsocket

05893ab47883fec4356f2f617213093eb7d9b4df

[ "MIT" ]

null

protocolws/__init__.py

panmpan17/ProtocolWebsocket

05893ab47883fec4356f2f617213093eb7d9b4df

[ "MIT" ]

null

from .server import WebsocketServer, ErrMsg name = "protocolws" __version__ = "0.2.1"

17.4

43

0.747126

11

87

5.545455

1

0

0.04

0.137931

87

4

44

21.75

0.773333

0

0.172414

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

c38ac90ca808d2dd0ca78ec136aaa0e419c7ab9a

88

py

Python

CodeUP/Python basic 100/6081.py

cmsong111/NJ_code

2df6176d179e168a2789a825ddeb977a82eb8d97

[ "MIT" ]

null

CodeUP/Python basic 100/6081.py

cmsong111/NJ_code

2df6176d179e168a2789a825ddeb977a82eb8d97

[ "MIT" ]

null

CodeUP/Python basic 100/6081.py

cmsong111/NJ_code

2df6176d179e168a2789a825ddeb977a82eb8d97

[ "MIT" ]

null

n = int(input(),16) for i in range(1,16): print("%X"%n,"*%X"%i,"=%X"%(n*i),sep="")

17.6

44

0.454545

19

88

2.105263

0.631579

0.1

0

0.066667

0.147727

88

4

45

22

0.466667

0

0.090909

0

1

0

false

0

0.333333

1

0

null

0

1

0

1

0

null

0

3

c392ee111fc105d1add3bfe72b6ea6451c22de9b

71

py

Python

scanapi/__init__.py

marcuxyz/scanapi

e42bcde18d4219fc603b0b9ee8f0b67d4085ec63

[ "MIT" ]

2

2020-08-26T01:54:19.000Z

2021-07-23T14:06:34.000Z

scanapi/__init__.py

marcuxyz/scanapi

e42bcde18d4219fc603b0b9ee8f0b67d4085ec63

[ "MIT" ]

null

scanapi/__init__.py

marcuxyz/scanapi

e42bcde18d4219fc603b0b9ee8f0b67d4085ec63

[ "MIT" ]

null

name = "scanapi" from scanapi.__main__ import main __all__ = ["main"]

14.2

33

0.71831

9

71

4.777778

0.666667

0

0.15493

71

4

34

17.75

0.716667

0

0.15493

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

c39f85552fb76ed1c43748aba7b313d0e063acf8

243

py

Python

BasicCache/BasicCacheModule.py

prodProject/WorkkerAndConsumerServer

95496f026109279c9891e08af46040c7b9487c81

[ "MIT" ]

null

BasicCache/BasicCacheModule.py

prodProject/WorkkerAndConsumerServer

95496f026109279c9891e08af46040c7b9487c81

[ "MIT" ]

null

BasicCache/BasicCacheModule.py

prodProject/WorkkerAndConsumerServer

95496f026109279c9891e08af46040c7b9487c81

[ "MIT" ]

null

from werkzeug.contrib.cache import SimpleCache class BasicCache: cache = SimpleCache() def set(self, key, value): self.cache.set(key, value, timeout=50 * 1000) def get(self, key): return self.cache.get(key=key)

20.25

53

0.658436

33

243

4.848485

0.545455

0.0875

0

0.031915

0.226337

243

11

54

22.090909

0.819149

0

1

0.285714

false

0

0.142857

0.857143

0

null

0

null

0

1

0

1

0

3

c3b239f366d0aa2e3fa43d5373d1a03a579fb69c

1,104

py

Python

tests/test_iter_ij_in_block.py

kalekundert/wellmap

05a9029807276ec29aea63db10c664ad2ede093c

[ "MIT" ]

7

2020-05-29T21:14:49.000Z

2022-01-25T15:35:17.000Z

tests/test_iter_ij_in_block.py

kalekundert/wellmap

05a9029807276ec29aea63db10c664ad2ede093c

[ "MIT" ]

24

2020-06-09T14:29:03.000Z

2022-03-25T22:43:24.000Z

tests/test_iter_ij_in_block.py

kalekundert/wellmap

05a9029807276ec29aea63db10c664ad2ede093c

[ "MIT" ]

null

#!/usr/bin/env python3 import pytest from wellmap import * @pytest.mark.parametrize( 'args, expected', [ (((0,0), 0, 0), []), (((0,0), 0, 1), []), (((0,0), 1, 0), []), (((0,0), 1, 1), [(0,0)]), (((0,0), 2, 1), [(0,0), (0,1)]), (((0,0), 1, 2), [(0,0), (1,0)]), (((0,0), 2, 2), [(0,0), (0,1), (1,0), (1,1)]), (((0,1), 0, 0), []), (((0,1), 0, 1), []), (((0,1), 1, 0), []), (((0,1), 1, 1), [(0,1)]), (((0,1), 2, 1), [(0,1), (0,2)]), (((0,1), 1, 2), [(0,1), (1,1)]), (((0,1), 2, 2), [(0,1), (0,2), (1,1), (1,2)]), (((1,0), 0, 0), []), (((1,0), 0, 1), []), (((1,0), 1, 0), []), (((1,0), 1, 1), [(1,0)]), (((1,0), 2, 1), [(1,0), (1,1)]), (((1,0), 1, 2), [(1,0), (2,0)]), (((1,0), 2, 2), [(1,0), (1,1), (2,0), (2,1)]), ], ) def test_iter_ij_in_block(args, expected): print(args) assert set(iter_ij_in_block(*args)) == set(expected)

30.666667

58

0.272645

171

1,104

1.719298

0.140351

0.204082

0.153061

0.108844

0.55102

0.367347

0.221088

0.061224

0

0.197724

0.363225

1,104

35

59

31.542857

0.220484

0.019022

0

0.012939

0

0.033333

1

0.033333

false

0

0.066667

0

0.1

0.033333

0

1

null

1

0

1

0

null

0

3

c3b44be37f288d5b48b6f0177fd4a4019cc7b623

215

py

Python

py-server/src/enum/index.py

Jonnytoshen/wind-layer

514e9b9c76b6d72faac21543fd5fb1c43e6bd9b5

[ "BSD-3-Clause" ]

285

2017-12-16T13:29:27.000Z

2022-03-28T02:59:08.000Z

py-server/src/enum/index.py

Jonnytoshen/wind-layer

514e9b9c76b6d72faac21543fd5fb1c43e6bd9b5

[ "BSD-3-Clause" ]

104

2018-01-01T01:40:13.000Z

2022-03-26T18:20:45.000Z

py-server/src/enum/index.py

Jonnytoshen/wind-layer

514e9b9c76b6d72faac21543fd5fb1c43e6bd9b5

[ "BSD-3-Clause" ]

97

2017-12-18T08:05:21.000Z

2022-03-28T15:49:38.000Z

from enum import Enum class noaa_site_label(Enum): DIRECTORY = 'directory:' FILE_LIST = '\n**NEW** Select one file only ' SURFACES = 'select the levels desired:' VARIABLES = 'select the variables desired:'

26.875

47

0.716279

29

215

5.206897

0.689655

0.119205

0

0.176744

215

7

48

30.714286

0.853107

0

0.446512

0

1

0

false

0

0.166667

0

1

0

1

0

null

0

1

0

null

0

1

0

3

c3d5b63b4aec63e711dc906333d611d89cd9fa71

1,360

py

Python

app/data.py

leecrowe/bandersnatch-app

575394a7b7c47b8525d182edd4d7cf22de985d18

[ "MIT" ]

null

app/data.py

leecrowe/bandersnatch-app

575394a7b7c47b8525d182edd4d7cf22de985d18

[ "MIT" ]

null

app/data.py

leecrowe/bandersnatch-app

575394a7b7c47b8525d182edd4d7cf22de985d18

[ "MIT" ]

null

from os import getenv from typing import Iterator, Dict, Iterable from pymongo import MongoClient import pandas as pd from dotenv import load_dotenv class Data: """ MongoDB Data Model """ load_dotenv() db_url = getenv("DB_URL") db_name = getenv("DB_NAME") db_table = getenv("DB_TABLE") def connect(self): return MongoClient(self.db_url)[self.db_name][self.db_table] def find(self, query_obj: Dict) -> Dict: return self.connect().find_one(query_obj) def insert(self, insert_obj: Dict): self.connect().insert_one(insert_obj) def find_many(self, query_obj: Dict, limit=0) -> Iterator[Dict]: return self.connect().find(query_obj, limit=limit) def insert_many(self, insert_obj: Iterable[Dict]): self.connect().insert_many(insert_obj) def update(self, query: Dict, data_update: Dict): self.connect().update_one(query, {"$set": data_update}) def delete(self, query_obj: Dict): self.connect().delete_many(query_obj) def reset_db(self): self.connect().delete_many({}) def get_df(self, limit=0) -> pd.DataFrame: return pd.DataFrame(self.find_many({}, limit=limit)) def get_count(self, query_obj: Dict) -> int: return self.connect().count_documents(query_obj) def __str__(self): return f"{self.get_df()}"

28.333333

68

0.668382

193

1,360

4.487047

0.243523

0.073903

0.055427

0.073903

0.057737

0

0.001842

0.201471

1,360

47

69

28.93617

0.79558

0.013235

0

0.029985

0

1

0.34375

false

0

0.15625

0.1875

0.8125

0

null

0

null

0

1

0

1

0

3

c3dc65c6f95112e170d27e15fb7aca74b4ab07cd

11,253

py

Python

lib/fcn/test_common.py

LeiYangJustin/UnseenObjectClustering

177d67c47bfd973d46b816e6b68bf20660f4b71d

[ "BSD-Source-Code" ]

101

2020-12-13T22:20:12.000Z

2022-03-22T07:58:58.000Z

lib/fcn/test_common.py

LeiYangJustin/UnseenObjectClustering

177d67c47bfd973d46b816e6b68bf20660f4b71d

[ "BSD-Source-Code" ]

6

2021-01-13T13:33:04.000Z

2022-03-08T02:13:27.000Z

lib/fcn/test_common.py

LeiYangJustin/UnseenObjectClustering

177d67c47bfd973d46b816e6b68bf20660f4b71d

[ "BSD-Source-Code" ]

19

2021-01-21T18:19:42.000Z

2022-03-07T14:17:21.000Z

# Copyright (c) 2020 NVIDIA Corporation. All rights reserved. # This work is licensed under the NVIDIA Source Code License - Non-commercial. Full # text can be found in LICENSE.md import torch import time import sys, os import numpy as np import cv2 import matplotlib.pyplot as plt from fcn.config import cfg from utils.mask import visualize_segmentation def normalize_descriptor(res, stats=None): """ Normalizes the descriptor into RGB color space :param res: numpy.array [H,W,D] Output of the network, per-pixel dense descriptor :param stats: dict, with fields ['min', 'max', 'mean'], which are used to normalize descriptor :return: numpy.array normalized descriptor """ if stats is None: res_min = res.min() res_max = res.max() else: res_min = np.array(stats['min']) res_max = np.array(stats['max']) normed_res = np.clip(res, res_min, res_max) eps = 1e-10 scale = (res_max - res_min) + eps normed_res = (normed_res - res_min) / scale return normed_res def _vis_features(features, labels, rgb, intial_labels, selected_pixels=None): num = features.shape[0] height = features.shape[2] width = features.shape[3] fig = plt.figure() start = 1 m = np.ceil((num * 4 ) / 8.0) n = 8 im_blob = rgb.cpu().numpy() for i in range(num): if i < m * n / 4: # show image im = im_blob[i, :3, :, :].copy() im = im.transpose((1, 2, 0)) * 255.0 im += cfg.PIXEL_MEANS im = im[:, :, (2, 1, 0)] im = np.clip(im, 0, 255) im = im.astype(np.uint8) ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(im) ax.set_title('image') plt.axis('off') ''' if selected_pixels is not None: selected_indices = selected_pixels[i] for j in range(len(selected_indices)): index = selected_indices[j] y = index / width x = index % width plt.plot(x, y, 'ro', markersize=1.0) ''' im = torch.cuda.FloatTensor(height, width, 3) for j in range(3): im[:, :, j] = torch.sum(features[i, j::3, :, :], dim=0) im = normalize_descriptor(im.detach().cpu().numpy()) im *= 255 im = im.astype(np.uint8) ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(im) ax.set_title('features') plt.axis('off') ax = fig.add_subplot(m, n, start) start += 1 label = labels[i].detach().cpu().numpy() plt.imshow(label) ax.set_title('labels') plt.axis('off') ax = fig.add_subplot(m, n, start) start += 1 label = intial_labels[i].detach().cpu().numpy() plt.imshow(label) ax.set_title('intial labels') plt.axis('off') plt.show() def _vis_minibatch_segmentation_final(image, depth, label, out_label=None, out_label_refined=None, features=None, ind=None, selected_pixels=None, bbox=None): if depth is None: im_blob = image.cpu().numpy() else: im_blob = image.cpu().numpy() depth_blob = depth.cpu().numpy() num = im_blob.shape[0] height = im_blob.shape[2] width = im_blob.shape[3] if label is not None: label_blob = label.cpu().numpy() if out_label is not None: out_label_blob = out_label.cpu().numpy() if out_label_refined is not None: out_label_refined_blob = out_label_refined.cpu().numpy() m = 2 n = 3 for i in range(num): # image im = im_blob[i, :3, :, :].copy() im = im.transpose((1, 2, 0)) * 255.0 im += cfg.PIXEL_MEANS im = im[:, :, (2, 1, 0)] im = np.clip(im, 0, 255) im = im.astype(np.uint8) fig = plt.figure() start = 1 ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(im) ax.set_title('image') plt.axis('off') # depth if depth is not None: depth = depth_blob[i][2] ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(depth) ax.set_title('depth') plt.axis('off') # feature if features is not None: im_feature = torch.cuda.FloatTensor(height, width, 3) for j in range(3): im_feature[:, :, j] = torch.sum(features[i, j::3, :, :], dim=0) im_feature = normalize_descriptor(im_feature.detach().cpu().numpy()) im_feature *= 255 im_feature = im_feature.astype(np.uint8) ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(im_feature) ax.set_title('feature map') plt.axis('off') # initial seeds if selected_pixels is not None: ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(im) ax.set_title('initial seeds') plt.axis('off') selected_indices = selected_pixels[i] for j in range(len(selected_indices)): index = selected_indices[j] y = index / width x = index % width plt.plot(x, y, 'ro', markersize=2.0) # intial mask mask = out_label_blob[i, :, :] im_label = visualize_segmentation(im, mask, return_rgb=True) ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(im_label) ax.set_title('initial label') plt.axis('off') # refined mask if out_label_refined is not None: mask = out_label_refined_blob[i, :, :] im_label = visualize_segmentation(im, mask, return_rgb=True) ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(im_label) ax.set_title('refined label') plt.axis('off') elif label is not None: # show gt label mask = label_blob[i, 0, :, :] im_label = visualize_segmentation(im, mask, return_rgb=True) ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(im_label) ax.set_title('gt label') plt.axis('off') if ind is not None: mng = plt.get_current_fig_manager() mng.resize(*mng.window.maxsize()) plt.pause(0.001) # plt.show(block=False) filename = 'output/images/%06d.png' % ind fig.savefig(filename) plt.close() else: plt.show() def _vis_minibatch_segmentation(image, depth, label, out_label=None, out_label_refined=None, features=None, ind=None, selected_pixels=None, bbox=None): if depth is None: im_blob = image.cpu().numpy() m = 2 n = 3 else: im_blob = image.cpu().numpy() depth_blob = depth.cpu().numpy() m = 3 n = 3 num = im_blob.shape[0] height = im_blob.shape[2] width = im_blob.shape[3] if label is not None: label_blob = label.cpu().numpy() if out_label is not None: out_label_blob = out_label.cpu().numpy() if out_label_refined is not None: out_label_refined_blob = out_label_refined.cpu().numpy() for i in range(num): # image im = im_blob[i, :3, :, :].copy() im = im.transpose((1, 2, 0)) * 255.0 im += cfg.PIXEL_MEANS im = im[:, :, (2, 1, 0)] im = np.clip(im, 0, 255) im = im.astype(np.uint8) ''' if out_label_refined is not None: mask = out_label_refined_blob[i, :, :] visualize_segmentation(im, mask) #''' # show image fig = plt.figure() start = 1 ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(im) ax.set_title('image') plt.axis('off') ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(im) plt.axis('off') if bbox is not None: boxes = bbox[i].numpy() for j in range(boxes.shape[0]): x1 = boxes[j, 0] y1 = boxes[j, 1] x2 = boxes[j, 2] y2 = boxes[j, 3] plt.gca().add_patch( plt.Rectangle((x1, y1), x2-x1, y2-y1, fill=False, edgecolor='g', linewidth=3)) if selected_pixels is not None: selected_indices = selected_pixels[i] for j in range(len(selected_indices)): index = selected_indices[j] y = index / width x = index % width plt.plot(x, y, 'ro', markersize=1.0) if im_blob.shape[1] == 4: label = im_blob[i, 3, :, :] ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(label) ax.set_title('initial label') if depth is not None: depth = depth_blob[i] ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(depth[0]) ax.set_title('depth X') plt.axis('off') ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(depth[1]) ax.set_title('depth Y') plt.axis('off') ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(depth[2]) ax.set_title('depth Z') plt.axis('off') # show label if label is not None: label = label_blob[i, 0, :, :] ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(label) ax.set_title('gt label') plt.axis('off') # show out label if out_label is not None: label = out_label_blob[i, :, :] ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(label) ax.set_title('out label') plt.axis('off') # show out label refined if out_label_refined is not None: label = out_label_refined_blob[i, :, :] ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(label) ax.set_title('out label refined') plt.axis('off') if features is not None: im = torch.cuda.FloatTensor(height, width, 3) for j in range(3): im[:, :, j] = torch.sum(features[i, j::3, :, :], dim=0) im = normalize_descriptor(im.detach().cpu().numpy()) im *= 255 im = im.astype(np.uint8) ax = fig.add_subplot(m, n, start) start += 1 plt.imshow(im) ax.set_title('features') plt.axis('off') if ind is not None: mng = plt.get_current_fig_manager() plt.show() filename = 'output/images/%06d.png' % ind fig.savefig(filename) plt.show()

31.085635

98

0.50902

1,473

11,253

3.758995

0.130346

0.0419

0.035759

0.05689

0.707062

0.702547

0.660827

0.646018

0.631389

0

0.022877

0.366836

11,253

361

99

31.171745

0.754246

0.055185

0

0.718412

0

0.029385

0.004368

0

1

0.01444

false

0

0.028881

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0.046931

0

null

0

1

0

1

0

1

0

null

0

3

c3e63aa7608692e9e7131de69634555c66e2658b

123

py

Python

mantraml/templates/projects/default/settings.py

cclauss/mantra

19e2f72960da8314f11768d9acfe7836629b817c

[ "Apache-2.0" ]

330

2018-09-04T19:07:51.000Z

2021-09-14T11:21:05.000Z

mantraml/templates/projects/default/settings.py

cclauss/mantra

19e2f72960da8314f11768d9acfe7836629b817c

[ "Apache-2.0" ]

13

2018-09-06T06:08:16.000Z

2018-12-01T17:04:38.000Z

mantraml/templates/projects/default/settings.py

cclauss/mantra

19e2f72960da8314f11768d9acfe7836629b817c

[ "Apache-2.0" ]

20

2018-09-06T11:56:07.000Z

2021-12-03T19:48:21.000Z

# AWS SETTINGS AWS_AMI_IMAGE_ID = 'ami-6356761c' AWS_INSTANCE_TYPE = 'p2.xlarge' S3_BUCKET_NAME = 'default-s3-bucket-name'

30.75

41

0.780488

20

123

4.45

0.7

0.179775

0.269663

0

0.09009

0.097561

123

4

41

30.75

0.711712

0.097561

0

0.390909

0.2

0

1

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false

0

1

0

null

0

1

0

1

0

null

0

3

c3f41031d68e0a2d6308085aa0e246d0f490465c

968

py

Python

lct/tasks/taskstore.py

pathbreak/linode-cluster-toolkit

280257436105703c9a122e7ed111a72efa79adfc

[ "MIT" ]

11

2017-07-19T15:25:39.000Z

2021-12-02T20:03:21.000Z

lct/tasks/taskstore.py

pathbreak/linode-cluster-toolkit

280257436105703c9a122e7ed111a72efa79adfc

[ "MIT" ]

null

lct/tasks/taskstore.py

pathbreak/linode-cluster-toolkit

280257436105703c9a122e7ed111a72efa79adfc

[ "MIT" ]

1

2021-12-02T20:03:22.000Z

class TaskStore(object): ''' All task execution plans are stored and tracked in a TaskStore. Both are crucual for supporting pause and resume of operations. The interface to be implemented by all providers that provide task storage capabilities. ''' def initialize(self): ''' Initialization opportunity for providers. This is called when the toolkit itself and all services, including the Task Service, are initializing themselves. ''' raise NotImplementedError('subclasses should override this') def save_execution_plan(self, task_plan): ''' Save or update the execution plan. ''' raise NotImplementedError('subclasses should override this') def close(self): ''' Provider should release its resources here. ''' raise NotImplementedError('subclasses should override this')

28.470588

74

0.636364

101

968

6.069307

0.623762

0.117455

0.166395

0.195759

0.264274

0.179445

0

0.303719

968

33

75

29.333333

0.909496

0.469008

0

0.428571

0

0.234257

0

1

0.428571

false

0

0.571429

0

null

0

1

0

null

0

1

0

1

0

3

c3f677ecc5dfde9410fda55667fad516d6d7ddf7

827

py

Python

catweazle/applog.py

upendar245/CatWeazle

58e4e37a71c61b998c6a3adae5e16343db50aff5

[ "MIT" ]

1

2020-12-17T04:23:32.000Z

catweazle/applog.py

upendar245/CatWeazle

58e4e37a71c61b998c6a3adae5e16343db50aff5

[ "MIT" ]

null

catweazle/applog.py

upendar245/CatWeazle

58e4e37a71c61b998c6a3adae5e16343db50aff5

[ "MIT" ]

1

2020-04-24T10:17:03.000Z

import logging import aiotask_context as context class AppLogging: def __init__(self): self.log = logging.getLogger('application') self.context = context def info(self, msg): self.log.info('{0} {1}'.format(self.context.get('X-Request-ID'), msg)) def warning(self, msg): self.log.warning('{0} {1}'.format(self.context.get('X-Request-ID'), msg)) def error(self, msg): self.log.error('{0} {1}'.format(self.context.get('X-Request-ID'), msg)) def critical(self, msg): self.log.critical('{0} {1}'.format(self.context.get('X-Request-ID'), msg)) def fatal(self, msg): self.log.fatal('{0} {1}'.format(self.context.get('X-Request-ID'), msg)) def debug(self, msg): self.log.debug('{0} {1}'.format(self.context.get('X-Request-ID'), msg))

29.535714

82

0.610641

122

827

4.098361

0.221311

0.098

0.132

0.168

0.45

0

0.017804

0.185006

827

27

83

30.62963

0.724036

0

0.151149

0

1

0.388889

false

0

0.111111

0

0.555556

0

null

0

1

0

null

0

1

0

1

0

3

7f17a75bcf20017304a34f189d671e3f94815fb9

387

py

Python

service/models/employer/employer_filter.py

CyberArkForTheCommunity/jobli-backend

2309c9ac33993cb89a8e1581630d99b46f8d55aa

[ "MIT" ]

null

service/models/employer/employer_filter.py

CyberArkForTheCommunity/jobli-backend

2309c9ac33993cb89a8e1581630d99b46f8d55aa

[ "MIT" ]

1

2021-12-23T13:36:43.000Z

service/models/employer/employer_filter.py

CyberArkForTheCommunity/jobli-backend

2309c9ac33993cb89a8e1581630d99b46f8d55aa

[ "MIT" ]

null

from pydantic import BaseModel, Field from typing import Optional from service.lambdas.employer.constants import EmployerConstants class EmployerFilter(BaseModel): employer_id: Optional[str] business_name: Optional[str] city: Optional[str] last_pagination_key: Optional[str] limit_per_page: Optional[int] = Field(default=EmployerConstants.LIMITS_PER_EMPLOYER_PAGE)

32.25

93

0.803618

47

387

6.425532

0.595745

0.145695

0

0.126615

387

11

94

35.181818

0.893491

0

1

0

true

0

0.333333

0

1

0

null

0

null

0

1

0

1

0

1

0

3

7f1abb5060e39a368256be6fdccc62d3097395a3

185

py

Python

user_guide/urls.py

jmcriffey/django-user-guide

f4a1c462d2f7bf8569576f757e2f106b565e3e40

[ "MIT" ]

null

user_guide/urls.py

jmcriffey/django-user-guide

f4a1c462d2f7bf8569576f757e2f106b565e3e40

[ "MIT" ]

null

user_guide/urls.py

jmcriffey/django-user-guide

f4a1c462d2f7bf8569576f757e2f106b565e3e40

[ "MIT" ]

null

from django.conf.urls import patterns, url from user_guide import views urlpatterns = patterns( '', url(r'^seen/?$', views.GuideSeenView.as_view(), name='user_guide.seen') )

18.5

75

0.702703

25

185

5.08

0.68

0.173228

0

0.151351

185

9

76

20.555556

0.808917

0

0.124324

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

61696efc02baa4bd76628d5a785149b2ecf4ac45

11,300

py

Python

018-crackme-z3/asdf.py

gynvael/stream

2d1a3f25b2f83241b39dab931d9ff03fca81d26e

[ "MIT" ]

152

2016-02-04T10:40:46.000Z

2022-03-03T18:25:54.000Z

018-crackme-z3/asdf.py

gynvael/stream

2d1a3f25b2f83241b39dab931d9ff03fca81d26e

[ "MIT" ]

4

2016-03-11T23:49:46.000Z

2017-06-16T18:58:53.000Z

018-crackme-z3/asdf.py

gynvael/stream

2d1a3f25b2f83241b39dab931d9ff03fca81d26e

[ "MIT" ]

48

2016-01-31T19:13:36.000Z

2021-09-03T19:50:17.000Z

from z3 import * def movsx(v): return ZeroExt(32 - 8, v) def imul(a, b, c = None): if c is None: return a * b return b * c def xor_(r, v): return r ^ v def or_(r, v): return r | v def mov(_, r2): return r2 def shr(r1, c): return LShR(r1, c) def shl(r1, c): return r1 << c def calc(): esp_0x10 = BitVec("esp_0x10", 8) esp_0x11 = BitVec("esp_0x11", 8) esp_0x12 = BitVec("esp_0x12", 8) esp_0x13 = BitVec("esp_0x13", 8) esp_0x14 = BitVec("esp_0x14", 8) esp_0x15 = BitVec("esp_0x15", 8) esp_0x16 = BitVec("esp_0x16", 8) esp_0x17 = BitVec("esp_0x17", 8) esp_0x18 = BitVec("esp_0x18", 8) esp_0x19 = BitVec("esp_0x19", 8) esp_0x1A = BitVec("esp_0x1A", 8) esp_0x1B = BitVec("esp_0x1B", 8) esp_0x1C = BitVec("esp_0x1C", 8) esp_0x1D = BitVec("esp_0x1D", 8) esp_0x1E = BitVec("esp_0x1E", 8) esp_0x1F = BitVec("esp_0x1F", 8) eax = BitVec("eax", 32) ebx = BitVec("ebx", 32) ecx = BitVec("ecx", 32) edx = BitVec("edx", 32) esi = BitVec("esi", 32) edi = BitVec("edi", 32) ebp = BitVec("ebp", 32) edi = movsx(esp_0x10) edx = imul(edx, edi, 0x3039) edx = xor_(edx, 0x93E6BBCF) ebx = imul(ebx, edi, 0x0AEDCE) ebx = xor_(ebx, 0x2ECBBAE2) ecx = imul(ecx, edi, 0x2EF8F) ecx = xor_(ecx, 0x0A0A2A282) edi = imul(edi, 0x0DEDC7) edi = xor_(edi, 0x9BDFE6F7) eax = mov(eax, edx) eax = shr(eax, 3) edx = shl(edx, 3) eax = or_(eax, edx) edx = movsx(esp_0x11) esi = imul(esi, edx, 0x3039) eax = xor_(eax, esi) esi = mov(esi, ebx) esi = shr(esi, 5) ebx = shl(ebx, 5) esi = or_(esi, ebx) ebx = imul(ebx, edx, 0x0AEDCE) esi = xor_(esi, ebx) ebx = mov(ebx, ecx) ebx = shr(ebx, 7) ecx = shl(ecx, 7) ebx = or_(ebx, ecx) ecx = imul(ecx, edx, 0x2EF8F) ebx = xor_(ebx, ecx) ecx = mov(ecx, edi) ecx = shr(ecx, 9) edi = shl(edi, 9) ecx = or_(ecx, edi) edx = imul(edx, 0x0DEDC7) ecx = xor_(ecx, edx) edx = mov(edx, eax) edx = shr(edx, 3) eax = shl(eax, 3) edx = or_(edx, eax) edi = movsx(esp_0x12) eax = imul(eax, edi, 0x3039) edx = xor_(edx, eax) eax = mov(eax, esi) eax = shr(eax, 5) esi = shl(esi, 5) eax = or_(eax, esi) esi = imul(esi, edi, 0x0AEDCE) eax = xor_(eax, esi) esi = mov(esi, ebx) esi = shr(esi, 7) ebx = shl(ebx, 7) esi = or_(esi, ebx) ebx = imul(ebx, edi, 0x2EF8F) esi = xor_(esi, ebx) ebx = mov(ebx, ecx) ebx = shr(ebx, 9) ecx = shl(ecx, 9) ebx = or_(ebx, ecx) edi = imul(edi, 0x0DEDC7) ebx = xor_(ebx, edi) ecx = mov(ecx, edx) ecx = shr(ecx, 3) edx = shl(edx, 3) ecx = or_(ecx, edx) edi = movsx(esp_0x13) edx = imul(edx, edi, 0x3039) ecx = xor_(ecx, edx) edx = mov(edx, eax) edx = shr(edx, 5) eax = shl(eax, 5) edx = or_(edx, eax) eax = imul(eax, edi, 0x0AEDCE) edx = xor_(edx, eax) eax = mov(eax, esi) eax = shr(eax, 7) esi = shl(esi, 7) eax = or_(eax, esi) esi = imul(esi, edi, 0x2EF8F) eax = xor_(eax, esi) esi = mov(esi, ebx) esi = shr(esi, 9) ebx = shl(ebx, 9) esi = or_(esi, ebx) edi = imul(edi, 0x0DEDC7) esi = xor_(esi, edi) ebx = mov(ebx, ecx) ebx = shr(ebx, 3) ecx = shl(ecx, 3) ebx = or_(ebx, ecx) edi = movsx(esp_0x14) ecx = imul(ecx, edi, 0x3039) ebx = xor_(ebx, ecx) ecx = mov(ecx, edx) ecx = shr(ecx, 5) edx = shl(edx, 5) ecx = or_(ecx, edx) edx = imul(edx, edi, 0x0AEDCE) ecx = xor_(ecx, edx) edx = mov(edx, eax) edx = shr(edx, 7) eax = shl(eax, 7) edx = or_(edx, eax) eax = imul(eax, edi, 0x2EF8F) edx = xor_(edx, eax) eax = mov(eax, esi) eax = shr(eax, 9) esi = shl(esi, 9) eax = or_(eax, esi) edi = imul(edi, 0x0DEDC7) eax = xor_(eax, edi) esi = mov(esi, ebx) esi = shr(esi, 3) ebx = shl(ebx, 3) esi = or_(esi, ebx) edi = movsx(esp_0x15) ebx = imul(ebx, edi, 0x3039) esi = xor_(esi, ebx) ebx = mov(ebx, ecx) ebx = shr(ebx, 5) ecx = shl(ecx, 5) ebx = or_(ebx, ecx) ecx = imul(ecx, edi, 0x0AEDCE) ebx = xor_(ebx, ecx) ecx = mov(ecx, edx) ecx = shr(ecx, 7) edx = shl(edx, 7) ecx = or_(ecx, edx) edx = imul(edx, edi, 0x2EF8F) ecx = xor_(ecx, edx) edx = mov(edx, eax) edx = shr(edx, 9) eax = shl(eax, 9) edx = or_(edx, eax) edi = imul(edi, 0x0DEDC7) edx = xor_(edx, edi) eax = mov(eax, esi) eax = shr(eax, 3) esi = shl(esi, 3) eax = or_(eax, esi) edi = movsx(esp_0x16) esi = imul(esi, edi, 0x3039) eax = xor_(eax, esi) esi = mov(esi, ebx) esi = shr(esi, 5) ebx = shl(ebx, 5) esi = or_(esi, ebx) ebx = imul(ebx, edi, 0x0AEDCE) esi = xor_(esi, ebx) ebx = mov(ebx, ecx) ebx = shr(ebx, 7) ecx = shl(ecx, 7) ebx = or_(ebx, ecx) ecx = imul(ecx, edi, 0x2EF8F) ebx = xor_(ebx, ecx) ecx = mov(ecx, edx) ecx = shr(ecx, 9) edx = shl(edx, 9) ecx = or_(ecx, edx) edi = imul(edi, 0x0DEDC7) ecx = xor_(ecx, edi) edx = mov(edx, eax) edx = shr(edx, 3) eax = shl(eax, 3) edx = or_(edx, eax) edi = movsx(esp_0x17) eax = imul(eax, edi, 0x3039) edx = xor_(edx, eax) eax = mov(eax, esi) eax = shr(eax, 5) esi = shl(esi, 5) eax = or_(eax, esi) esi = imul(esi, edi, 0x0AEDCE) eax = xor_(eax, esi) esi = mov(esi, ebx) esi = shr(esi, 7) ebx = shl(ebx, 7) esi = or_(esi, ebx) ebx = imul(ebx, edi, 0x2EF8F) esi = xor_(esi, ebx) ebx = mov(ebx, ecx) ebx = shr(ebx, 9) ecx = shl(ecx, 9) ebx = or_(ebx, ecx) edi = imul(edi, 0x0DEDC7) ebx = xor_(ebx, edi) ecx = mov(ecx, edx) ecx = shr(ecx, 3) edx = shl(edx, 3) ecx = or_(ecx, edx) edi = movsx(esp_0x18) edx = imul(edx, edi, 0x3039) ecx = xor_(ecx, edx) edx = mov(edx, eax) edx = shr(edx, 5) eax = shl(eax, 5) edx = or_(edx, eax) eax = imul(eax, edi, 0x0AEDCE) edx = xor_(edx, eax) eax = mov(eax, esi) eax = shr(eax, 7) esi = shl(esi, 7) eax = or_(eax, esi) esi = imul(esi, edi, 0x2EF8F) eax = xor_(eax, esi) esi = mov(esi, ebx) esi = shr(esi, 9) ebx = shl(ebx, 9) esi = or_(esi, ebx) edi = imul(edi, 0x0DEDC7) esi = xor_(esi, edi) ebx = mov(ebx, ecx) ebx = shr(ebx, 3) ecx = shl(ecx, 3) ebx = or_(ebx, ecx) edi = movsx(esp_0x19) ecx = imul(ecx, edi, 0x3039) ebx = xor_(ebx, ecx) ecx = mov(ecx, edx) ecx = shr(ecx, 5) edx = shl(edx, 5) ecx = or_(ecx, edx) edx = imul(edx, edi, 0x0AEDCE) ecx = xor_(ecx, edx) edx = mov(edx, eax) edx = shr(edx, 7) eax = shl(eax, 7) edx = or_(edx, eax) eax = imul(eax, edi, 0x2EF8F) edx = xor_(edx, eax) eax = mov(eax, esi) eax = shr(eax, 9) esi = shl(esi, 9) eax = or_(eax, esi) edi = imul(edi, 0x0DEDC7) eax = xor_(eax, edi) esi = mov(esi, ebx) esi = shr(esi, 3) ebx = shl(ebx, 3) esi = or_(esi, ebx) edi = movsx(esp_0x1A) ebx = imul(ebx, edi, 0x3039) esi = xor_(esi, ebx) ebx = mov(ebx, ecx) ebx = shr(ebx, 5) ecx = shl(ecx, 5) ebx = or_(ebx, ecx) ecx = imul(ecx, edi, 0x0AEDCE) ebx = xor_(ebx, ecx) ecx = mov(ecx, edx) ecx = shr(ecx, 7) edx = shl(edx, 7) ecx = or_(ecx, edx) edx = imul(edx, edi, 0x2EF8F) ecx = xor_(ecx, edx) edx = mov(edx, eax) edx = shr(edx, 9) eax = shl(eax, 9) edx = or_(edx, eax) edi = imul(edi, 0x0DEDC7) edx = xor_(edx, edi) eax = mov(eax, esi) eax = shr(eax, 3) esi = shl(esi, 3) eax = or_(eax, esi) esi = movsx(esp_0x1B) edi = imul(edi, esi, 0x3039) eax = xor_(eax, edi) edi = mov(edi, ebx) edi = shr(edi, 5) ebx = shl(ebx, 5) edi = or_(edi, ebx) ebx = imul(ebx, esi, 0x0AEDCE) edi = xor_(edi, ebx) ebx = mov(ebx, ecx) ebx = shr(ebx, 7) ecx = shl(ecx, 7) ebx = or_(ebx, ecx) ecx = imul(ecx, esi, 0x2EF8F) ebx = xor_(ebx, ecx) ecx = mov(ecx, edx) ecx = shr(ecx, 9) edx = shl(edx, 9) ecx = or_(ecx, edx) esi = imul(esi, 0x0DEDC7) ecx = xor_(ecx, esi) edx = mov(edx, eax) edx = shr(edx, 3) eax = shl(eax, 3) edx = or_(edx, eax) esi = movsx(esp_0x1C) eax = imul(eax, esi, 0x3039) edx = xor_(edx, eax) eax = mov(eax, edi) eax = shr(eax, 5) edi = shl(edi, 5) eax = or_(eax, edi) edi = imul(edi, esi, 0x0AEDCE) eax = xor_(eax, edi) edi = mov(edi, ebx) edi = shr(edi, 7) ebx = shl(ebx, 7) edi = or_(edi, ebx) ebx = imul(ebx, esi, 0x2EF8F) edi = xor_(edi, ebx) ebx = mov(ebx, ecx) ebx = shr(ebx, 9) ecx = shl(ecx, 9) ebx = or_(ebx, ecx) esi = imul(esi, 0x0DEDC7) ebx = xor_(ebx, esi) ecx = mov(ecx, edx) ecx = shr(ecx, 3) edx = shl(edx, 3) ecx = or_(ecx, edx) esi = movsx(esp_0x1D) edx = imul(edx, esi, 0x3039) ecx = xor_(ecx, edx) edx = mov(edx, eax) edx = shr(edx, 5) eax = shl(eax, 5) edx = or_(edx, eax) eax = imul(eax, esi, 0x0AEDCE) edx = xor_(edx, eax) eax = mov(eax, edi) eax = shr(eax, 7) edi = shl(edi, 7) eax = or_(eax, edi) edi = imul(edi, esi, 0x2EF8F) eax = xor_(eax, edi) edi = mov(edi, ebx) edi = shr(edi, 9) ebx = shl(ebx, 9) edi = or_(edi, ebx) esi = imul(esi, 0x0DEDC7) edi = xor_(edi, esi) ebx = mov(ebx, ecx) ebx = shr(ebx, 3) ecx = shl(ecx, 3) ebx = or_(ebx, ecx) esi = movsx(esp_0x1E) ecx = imul(ecx, esi, 0x3039) ebx = xor_(ebx, ecx) ecx = mov(ecx, edx) ecx = shr(ecx, 5) edx = shl(edx, 5) ecx = or_(ecx, edx) edx = imul(edx, esi, 0x0AEDCE) ecx = xor_(ecx, edx) edx = mov(edx, eax) edx = shr(edx, 7) eax = shl(eax, 7) edx = or_(edx, eax) eax = imul(eax, esi, 0x2EF8F) edx = xor_(edx, eax) eax = mov(eax, edi) eax = shr(eax, 9) edi = shl(edi, 9) eax = or_(eax, edi) esi = imul(esi, 0x0DEDC7) eax = xor_(eax, esi) esi = mov(esi, ebx) esi = shr(esi, 3) ebx = shl(ebx, 3) esi = or_(esi, ebx) edi = movsx(esp_0x1F) ebx = mov(ebx, ecx) ebx = shr(ebx, 5) ecx = shl(ecx, 5) ebx = or_(ebx, ecx) ecx = imul(ecx, edi, 0x0AEDCE) ebx = xor_(ebx, ecx) ecx = mov(ecx, edx) ecx = shr(ecx, 7) edx = shl(edx, 7) ecx = or_(ecx, edx) edx = imul(edx, edi, 0x2EF8F) ecx = xor_(ecx, edx) edx = mov(edx, eax) edx = shr(edx, 9) eax = shl(eax, 9) edx = or_(edx, eax) eax = imul(eax, edi, 0x0DEDC7) edx = xor_(edx, eax) edi = imul(edi, 0x3039) esi = xor_(esi, edi) #print simplify(esi) s = Solver() s.add(esi == 0xFFF4A1CE) s.add(ebx == 0xB5A4A9A7) s.add(ecx == 0xF05A945C) s.add(edx == 0x9504A82D) s.add(esp_0x10 >= 32, esp_0x10 <= 126) s.add(esp_0x11 >= 32, esp_0x11 <= 126) s.add(esp_0x12 >= 32, esp_0x12 <= 126) s.add(esp_0x13 >= 32, esp_0x13 <= 126) s.add(esp_0x14 >= 32, esp_0x14 <= 126) s.add(esp_0x15 >= 32, esp_0x15 <= 126) s.add(esp_0x16 >= 32, esp_0x16 <= 126) s.add(esp_0x17 >= 32, esp_0x17 <= 126) s.add(esp_0x18 >= 32, esp_0x18 <= 126) s.add(esp_0x19 >= 32, esp_0x19 <= 126) s.add(esp_0x1A >= 32, esp_0x1A <= 126) s.add(esp_0x1B >= 32, esp_0x1B <= 126) s.add(esp_0x1C >= 32, esp_0x1C <= 126) s.add(esp_0x1D >= 32, esp_0x1D <= 126) s.add(esp_0x1E >= 32, esp_0x1E <= 126) s.add(esp_0x1F >= 32, esp_0x1F <= 126) s.check() print s.model() calc()

24.197002

43

0.544867

1,926

11,300

3.089304

0.03946

0.035294

0.024202

0.02521

0.725882

0.689076

0.684706

0.656134

0

0.086348

0.285664

11,300

466

44

24.248927

0.650768

0.001681

0

0.739421

0

0.01378

0

0.050402

0

null

0

0.002227

null

0.002227

0

null

0

1

0

1

0

null

0

1

0

3

617acae417a764a9156239c61c9cc59f10cf60e7

4,725

py

Python

tests/models/torch/q_functions/test_qr_q_function.py

ningyixue/AIPI530_Final_Project

b95353ffd003692a37a59042dfcd744a18b7e802

[ "MIT" ]

565

2020-08-01T02:44:28.000Z

2022-03-30T15:00:54.000Z

tests/models/torch/q_functions/test_qr_q_function.py

ningyixue/AIPI530_Final_Project

b95353ffd003692a37a59042dfcd744a18b7e802

[ "MIT" ]

144

2020-08-01T03:45:10.000Z

2022-03-30T14:51:16.000Z

tests/models/torch/q_functions/test_qr_q_function.py

ningyixue/AIPI530_Final_Project

b95353ffd003692a37a59042dfcd744a18b7e802

[ "MIT" ]

103

2020-08-26T13:27:34.000Z

2022-03-31T12:24:27.000Z

import numpy as np import pytest import torch from d3rlpy.models.torch import ContinuousQRQFunction, DiscreteQRQFunction from d3rlpy.models.torch.q_functions.qr_q_function import _make_taus from d3rlpy.models.torch.q_functions.utility import ( pick_quantile_value_by_action, ) from ..model_test import ( DummyEncoder, check_parameter_updates, ref_quantile_huber_loss, ) @pytest.mark.parametrize("feature_size", [100]) @pytest.mark.parametrize("action_size", [2]) @pytest.mark.parametrize("n_quantiles", [200]) @pytest.mark.parametrize("batch_size", [32]) @pytest.mark.parametrize("gamma", [0.99]) def test_discrete_qr_q_function( feature_size, action_size, n_quantiles, batch_size, gamma ): encoder = DummyEncoder(feature_size) q_func = DiscreteQRQFunction(encoder, action_size, n_quantiles) # check output shape x = torch.rand(batch_size, feature_size) y = q_func(x) assert y.shape == (batch_size, action_size) # check taus taus = _make_taus(encoder(x), n_quantiles) step = 1 / n_quantiles for i in range(n_quantiles): assert np.allclose(taus[0][i].numpy(), i * step + step / 2.0) # check compute_target action = torch.randint(high=action_size, size=(batch_size,)) target = q_func.compute_target(x, action) assert target.shape == (batch_size, n_quantiles) # check compute_target with action=None targets = q_func.compute_target(x) assert targets.shape == (batch_size, action_size, n_quantiles) # check quantile huber loss obs_t = torch.rand(batch_size, feature_size) act_t = torch.randint(action_size, size=(batch_size,)) rew_tp1 = torch.rand(batch_size, 1) q_tp1 = torch.rand(batch_size, n_quantiles) ter_tp1 = torch.randint(2, size=(batch_size, 1)) # shape check loss = q_func.compute_error( obs_t, act_t, rew_tp1, q_tp1, ter_tp1, reduction="none" ) assert loss.shape == (batch_size, 1) # mean loss loss = q_func.compute_error(obs_t, act_t, rew_tp1, q_tp1, ter_tp1) target = rew_tp1.numpy() + gamma * q_tp1.numpy() * (1 - ter_tp1.numpy()) y = pick_quantile_value_by_action( q_func._compute_quantiles(encoder(obs_t), taus), act_t ) reshaped_target = np.reshape(target, (batch_size, -1, 1)) reshaped_y = np.reshape(y.detach().numpy(), (batch_size, 1, -1)) reshaped_taus = np.reshape(taus, (1, 1, -1)) ref_loss = ref_quantile_huber_loss( reshaped_y, reshaped_target, reshaped_taus, n_quantiles ) assert np.allclose(loss.cpu().detach(), ref_loss.mean()) # check layer connection check_parameter_updates(q_func, (obs_t, act_t, rew_tp1, q_tp1, ter_tp1)) @pytest.mark.parametrize("feature_size", [100]) @pytest.mark.parametrize("action_size", [2]) @pytest.mark.parametrize("n_quantiles", [200]) @pytest.mark.parametrize("batch_size", [32]) @pytest.mark.parametrize("gamma", [0.99]) def test_continuous_qr_q_function( feature_size, action_size, n_quantiles, batch_size, gamma ): encoder = DummyEncoder(feature_size, action_size, concat=True) q_func = ContinuousQRQFunction(encoder, n_quantiles) # check output shape x = torch.rand(batch_size, feature_size) action = torch.rand(batch_size, action_size) y = q_func(x, action) assert y.shape == (batch_size, 1) # check taus taus = _make_taus(encoder(x, action), n_quantiles) step = 1 / n_quantiles for i in range(n_quantiles): assert np.allclose(taus[0][i].numpy(), i * step + step / 2.0) target = q_func.compute_target(x, action) assert target.shape == (batch_size, n_quantiles) # check quantile huber loss obs_t = torch.rand(batch_size, feature_size) act_t = torch.rand(batch_size, action_size) rew_tp1 = torch.rand(batch_size, 1) q_tp1 = torch.rand(batch_size, n_quantiles) ter_tp1 = torch.randint(2, size=(batch_size, 1)) # check shape loss = q_func.compute_error( obs_t, act_t, rew_tp1, q_tp1, ter_tp1, reduction="none" ) assert loss.shape == (batch_size, 1) # mean loss loss = q_func.compute_error(obs_t, act_t, rew_tp1, q_tp1, ter_tp1) target = rew_tp1.numpy() + gamma * q_tp1.numpy() * (1 - ter_tp1.numpy()) y = q_func._compute_quantiles(encoder(obs_t, act_t), taus).detach().numpy() reshaped_target = target.reshape((batch_size, -1, 1)) reshaped_y = y.reshape((batch_size, 1, -1)) reshaped_taus = taus.reshape((1, 1, -1)) ref_loss = ref_quantile_huber_loss( reshaped_y, reshaped_target, reshaped_taus, n_quantiles ) assert np.allclose(loss.cpu().detach(), ref_loss.mean()) # check layer connection check_parameter_updates(q_func, (obs_t, act_t, rew_tp1, q_tp1, ter_tp1))

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null

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3

619ba5260e66d9abf9514a7f5139f96e733a0a46

4,085

py

Python

src/lybica/loader.py

protone/lybica-runner

185b3fc4ffc7a6e066baa6c2f030e109db97b4b1

[ "MIT" ]

null

src/lybica/loader.py

protone/lybica-runner

185b3fc4ffc7a6e066baa6c2f030e109db97b4b1

[ "MIT" ]

3

2015-11-04T09:43:35.000Z

2015-11-12T09:41:21.000Z

src/lybica/loader.py

protone/lybica-runner

185b3fc4ffc7a6e066baa6c2f030e109db97b4b1

[ "MIT" ]

null

from .executor import ScriptExecutor import logging SCRIPT_CONFIG = [ # health check script before install package { "name": 'install_package_pre_check', "search_path": ["${TESTCASE_PATH}/AreaCI/${PID}/install_package_pre_check", "${BRANCH_ROOT}/PlatformCI/${PID}/${PRODUCT}/${TEST_TYPE}/install_package_pre_check", "${BRANCH_ROOT}/PlatformCI/${PID}/${PRODUCT}/install_package_pre_check", "${BRANCH_ROOT}/PlatformCI/${PID}/install_package_pre_check", "${BRANCH_ROOT}/PlatformCI/system/install_package_pre_check",], "failed_actions": ['action_pate_to_maintaining', 'stop_actions'], "success_actions": ['no_action', ], }, # health check script after install package { "name": 'install_package_post_check', "search_path": ["${TESTCASE_PATH}/AreaCI/${PID}/install_package_post_check", "${BRANCH_ROOT}/PlatformCI/${PID}/${PRODUCT}/${TEST_TYPE}/install_package_post_check", "${BRANCH_ROOT}/PlatformCI/${PID}/${PRODUCT}/install_package_post_check", "${BRANCH_ROOT}/PlatformCI/${PID}/install_package_post_check", "${BRANCH_ROOT}/PlatformCI/system/install_package_post_check", ], "failed_actions": ['action_pate_to_maintaining', 'stop_actions'], "success_actions": ['no_action', ], }, { "name": 'health_check_before_crt', "search_path": ["${TESTCASE_PATH}/AreaCI/${PID}/health_check_before_crt", "${BRANCH_ROOT}/PlatformCI/${PID}/${PRODUCT}/${TEST_TYPE}/health_check_before_crt", "${BRANCH_ROOT}/PlatformCI/${PID}/${PRODUCT}/health_check_before_crt", "${BRANCH_ROOT}/PlatformCI/${PID}/health_check_before_crt", "${BRANCH_ROOT}/PlatformCI/system/health_check_before_crt", ], "failed_actions": ['action_pate_to_maintaining', 'stop_actions'], "expired_actions": ['action_pkg_to_expired', 'stop_actions'], "success_actions": ['no_action', ], }, { "name": 'health_check_after_crt', "search_path": ["${TESTCASE_PATH}/AreaCI/${PID}/health_check_after_crt", "${BRANCH_ROOT}/PlatformCI/${PID}/${PRODUCT}/${TEST_TYPE}/health_check_after_crt", "${BRANCH_ROOT}/PlatformCI/${PID}/${PRODUCT}/health_check_after_crt", "${BRANCH_ROOT}/PlatformCI/${PID}/health_check_after_crt", "${BRANCH_ROOT}/PlatformCI/system/health_check_after_crt", ], "failed_actions": ['action_pate_to_maintaining_and_task_error' ], "expired_actions": ['action_pkg_to_expired', 'stop_actions'], "success_actions": ['no_action', ], }, { "name": 'health_check_after_run_case', "search_path": ["${TESTCASE_PATH}/AreaCI/${PID}/health_check_after_run_case", "${BRANCH_ROOT}/PlatformCI/${PID}/${PRODUCT}/health_check_after_run_case", "${BRANCH_ROOT}/PlatformCI/${PID}/health_check_after_run_case", "${BRANCH_ROOT}/PlatformCI/system/health_check_after_run_case", ], "failed_actions": ['no_action', ], "expired_actions": ['no_action', ], "success_actions": ['no_action', ], }, ] class ScriptWraper(object): def __init__(self, configed_scripts=[]): self.configed_scripts = [] def run_script(self, context, name, param={}, check_in_config=True): if check_in_config and not name in self.configed_scripts: logging.info("The external scripts '%s' does not configured to run." % name) return if not hasattr(self, name): logging.info("The external scripts '%s' does not supported by ipaci." % name) return return getattr(self, name)(context, param) def load_scripts(): wrapper = ScriptWraper() for param in SCRIPT_CONFIG: name = param['name'] setattr(wrapper, name, ScriptExecutor(param)) return wrapper

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4,085

5.408257

0.172018

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0.136556

0.749788

0.716709

0.656064

0.457591

0.227311

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0.228641

4,085

85

108

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0

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0

null

0

1

0

1

null

0

3

61c7129583ba4b43eb7ad3e93b2aa91fac2d4fa7

372

py

Python

djangae/contrib/locking/kinds.py

bocribbz/djangae

8a118d755cd707e6a452593050c25d790edde944

[ "BSD-3-Clause" ]

467

2015-01-02T22:35:37.000Z

2022-02-22T23:13:36.000Z

djangae/contrib/locking/kinds.py

bocribbz/djangae

8a118d755cd707e6a452593050c25d790edde944

[ "BSD-3-Clause" ]

743

2015-01-02T15:55:34.000Z

2021-01-29T09:43:19.000Z

djangae/contrib/locking/kinds.py

bocribbz/djangae

8a118d755cd707e6a452593050c25d790edde944

[ "BSD-3-Clause" ]

154

2015-01-01T17:05:59.000Z

2021-12-09T06:40:07.000Z

class LOCK_KINDS(object): """ The different kinds of lock which you can use. WEAK is not guaranteed to be robust, but can be used for situations where avoiding simultaneous code execution is preferable but not critical. STRONG is for where preventing simultaneous code execution is *required*. """ WEAK = 'weak' STRONG = 'strong'

33.818182

90

0.69086

50

372

5.12

0.64

0.125

0.195313

0.210938

0

0.252688

372

10

91

37.2

0.920863

0.706989

0

0.140845

0

1

0

false

0

1

0

null

0

1

0

null

0

1

0

3

f6036eaf498e2f4f6785b62692e626d23ee553a9

1,075

py

Python

indy_common/test/auth/multi_sig/test_auth_multi_sig_for_1_owner.py

NeolithEra/indy-node

c1f5ee8643a19d84b06cbb16347df845fa60bdb0

[ "Apache-2.0" ]

null

indy_common/test/auth/multi_sig/test_auth_multi_sig_for_1_owner.py

NeolithEra/indy-node

c1f5ee8643a19d84b06cbb16347df845fa60bdb0

[ "Apache-2.0" ]

1

2019-02-07T18:11:15.000Z

2019-02-07T18:14:06.000Z

indy_common/test/auth/multi_sig/test_auth_multi_sig_for_1_owner.py

NeolithEra/indy-node

c1f5ee8643a19d84b06cbb16347df845fa60bdb0

[ "Apache-2.0" ]

null

import pytest from indy_common.authorize.auth_constraints import AuthConstraint, IDENTITY_OWNER @pytest.fixture(scope='module') def write_auth_req_validator(write_auth_req_validator, key): write_auth_req_validator.auth_cons_strategy.get_auth_constraint = lambda a: AuthConstraint(IDENTITY_OWNER, 1) return write_auth_req_validator def test_claim_def_adding_success_1_owner(write_request_validation, req, identity_owners, key): req.signatures = {identity_owners[0]: "signature"} assert write_request_validation(req, [key]) def test_claim_def_adding_success_2_owner(write_request_validation, req, identity_owners, key): req.signatures = {idr: "signature" for idr in identity_owners[:2]} assert write_request_validation(req, [key]) def test_claim_def_adding_fail_1_trustee(write_request_validation, req, trustees, key): req.signatures = {trustees[0]: "signature"} assert not write_request_validation(req, [key])

38.392857

113

0.713488

131

1,075

5.442748

0.351145

0.100982

0.185133

0.210379

0.41094

0.371669

0.322581

0

0.008245

0.210233

1,075

27

114

39.814815

0.831567

0

0.222222

0

0.030698

0

0.166667

1

0.222222

false

0

0.111111

0

0.388889

0

null

0

1

0

null

0

1

0

3

f603fa533fa7372d6f36ecb1fb1141ca4cbdca2b

53

py

Python

code/answer_4-2-5.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

1

2022-03-29T13:50:12.000Z

code/answer_4-2-5.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

null

code/answer_4-2-5.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

null

S = input() print(S+"s" if S[-1] != "s" else S+"es")

17.666667

40

0.471698

12

53

2.083333

0.583333

0

0.023256

0.188679

53

2

41

26.5

0.55814

0

0.075472

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

3

f6044ea76ca13f4f72c527f9840f77894b5d9897

693

py

Python

computer-truevalue.py

yjnanan/Model_Free_Prediction

135762a330ce6879973f005f370c886e53491922

[ "Apache-2.0" ]

null

computer-truevalue.py

yjnanan/Model_Free_Prediction

135762a330ce6879973f005f370c886e53491922

[ "Apache-2.0" ]

null

computer-truevalue.py

yjnanan/Model_Free_Prediction

135762a330ce6879973f005f370c886e53491922

[ "Apache-2.0" ]

null

import numpy as np def state_value_function(p,r,v): gamma=0.9 while True: value=r.T+gamma*p*v if(value==v).all(): break v=value return v if __name__ =='__main__': #c1 c2 c3 pass pub fb sleep #probability matrix P_matrix=np.mat([[0,0,0,0,0,0,0], [0.5,0,0.5,0,0,0,0], [0,0.5,0,0.5,0,0,0], [0,0,0.5,0,0.5,0,0], [0,0,0,0.5,0,0.5,0], [0,0,0,0,0.5,0,0.5], [0,0,0,0,0,0,0]]) R_matrix=np.mat([0,0,0,0,0,0,1]) v_function=np.mat(np.zeros((7,1))) print(state_value_function(P_matrix,R_matrix,v_function))

28.875

61

0.464646

135

693

2.251852

0.281481

0.282895

0.305921

0.315789

0.286184

0.282895

0.184211

0

0.159737

0.340548

693

24

61

28.875

0.50547

0.063492

0

0.012346

0

1

0.05

false

0

0.05

0

0.15

0.05

0

null

1

0

null

0

3

f607b3974a5c6166e58b2e10c377a1e3e486d89a

363

py

Python

users/models.py

AhteshamSid/Blog-Post-Django

12cfe49f9909b3f35decda396626bcd010fabc74

[ "MIT" ]

null

users/models.py

AhteshamSid/Blog-Post-Django

12cfe49f9909b3f35decda396626bcd010fabc74

[ "MIT" ]

null

users/models.py

AhteshamSid/Blog-Post-Django

12cfe49f9909b3f35decda396626bcd010fabc74

[ "MIT" ]

null

from django.db import models from django.contrib.auth.models import User from PIL import Image # Create your models here. class Profile(models.Model): user = models.OneToOneField(User,on_delete=models.CASCADE) image = models.ImageField(default='default.jpg', upload_to='profile_pics') def __str__(self): return self.user.username

30.25

79

0.732782

49

363

5.285714

0.632653

0.07722

0

0.173554

363

11

80

33

0.863333

0.066116

0

0.070769

0

1

0.125

false

0

0.375

0.125

1

0

null

0

null

0

1

0

3

f60fa01fcca07216f2bcd4f1876c31853730ce6d

1,151

py

Python

visual_mpc/envs/offline_env.py

Asap7772/visual_foresight

13c631dc76ca1b61d7159473b3f2207ce2a3da04

[ "MIT" ]

null

visual_mpc/envs/offline_env.py

Asap7772/visual_foresight

13c631dc76ca1b61d7159473b3f2207ce2a3da04

[ "MIT" ]

null

visual_mpc/envs/offline_env.py

Asap7772/visual_foresight

13c631dc76ca1b61d7159473b3f2207ce2a3da04

[ "MIT" ]

null

from visual_mpc.envs.base_env import BaseEnv class OfflineSawyerEnv(BaseEnv): """ Emulates a real-image Sawyer Env without access to robot, only works together with the Offline Agent! """ def __init__(self, env_params_dict, reset_state=None): self._hp = self._default_hparams() self._adim, self._sdim = 4, 4 def _default_hparams(self): default_dict = {} parent_params = super()._default_hparams() for k in default_dict.keys(): parent_params.add_hparam(k, default_dict[k]) return parent_params def step(self, action): """ return None, the offline agent will append loaded observations :param action: :return: """ return None def reset(self): return self.step(None), None def valid_rollout(self): return True @property def adim(self): return self._adim @property def sdim(self): return self._sdim @property def ncam(self): return 1 @property def num_objects(self): return 1

23.489796

106

0.588184

135

1,151

4.8

0.474074

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0.064815

0

0.005181

0.329279

1,151

48

107

23.979167

0.834197

0.163336

0

0.206897

0

1

0.310345

false

0

0.034483

0.206897

0.655172

0

null

0

null

0

1

0

1

0

3

f62b5eeb8c988346be283fc9dfbe7ef972666e1e

713

py

Python

src/controllers/out.py

marijadebe/lab

b3b213116b25fc89db374f6e61a2513508d00934

[ "MIT" ]

null

src/controllers/out.py

marijadebe/lab

b3b213116b25fc89db374f6e61a2513508d00934

[ "MIT" ]

null

src/controllers/out.py

marijadebe/lab

b3b213116b25fc89db374f6e61a2513508d00934

[ "MIT" ]

null

import sys import re from models.types import Types def out(treechildren, variables): print("") for j in range(len(treechildren.children)): if treechildren.getChild(j).getToken().getType() == Types.STRING: sys.stdout.write(treechildren.getChild(j).getToken().getValue()) elif treechildren.getChild(j).getToken().getType() == Types.IDENTIFIER: sys.stdout.write(variables[treechildren.getChild(j).getToken().getValue()]) elif treechildren.getChild(j).getToken().getType() == Types.ARGUMENT: val = re.findall('[0-9]+',treechildren.getChild(j).getToken().getValue()) val = int(val[0]) sys.stdout.write(str(sys.argv[val+2]))

44.5625

87

0.653576

84

713

5.547619

0.428571

0.257511

0.270386

0.373391

0.519313

0.439914

0.351931

0

0.006861

0.182328

713

16

88

44.5625

0.792453

0

0.008403

0

1

0.071429

false

0

0.214286

0

0.285714

0.071429

0

null

1

0

null

0

3

f63d7e029bf5a5228b9983d3d3b72e181cfc36c8

2,313

py

Python

tests/test_prometheus_querybuilder.py

m-chrome/prometheus-query-builder

14c391346b99742909265fc00b0e620fc0d6134c

[ "MIT" ]

null

tests/test_prometheus_querybuilder.py

m-chrome/prometheus-query-builder

14c391346b99742909265fc00b0e620fc0d6134c

[ "MIT" ]

null

tests/test_prometheus_querybuilder.py

m-chrome/prometheus-query-builder

14c391346b99742909265fc00b0e620fc0d6134c

[ "MIT" ]

null

from prometheus_query_builder.query import Query import pytest def test_query(): """ Test query with only metric name """ query = Query("http_requests_total") assert str(query) == "http_requests_total" def test_query_with_label(): query = Query("http_requests_total") query.add_label("environment", "production") assert str(query) == 'http_requests_total{environment="production"}' def test_query_with_label_operators(): query = Query("http_requests_total") query.add_label("environment", "production", "!=") assert str(query) == 'http_requests_total{environment!="production"}' def test_query_with_unsupported_operator(): query = Query("http_requests_total") with pytest.raises(ValueError): query.add_label("environment", "production", "!===") def test_query_with_labels(): query = Query("http_requests_total") query.add_label("environment", "production") query.add_label("method", "GET") assert str(query) == 'http_requests_total{environment="production",method="GET"}' def test_query_with_label_update(): query = Query("http_requests_total") query.add_label("environment", "production") query.add_label("environment", "stage") assert str(query) == 'http_requests_total{environment="stage"}' def test_query_remove_label(): query = Query("http_requests_total") query.add_label("environment", "production") assert len(query.labels) == 1 query.remove_label("environment") assert len(query.labels) == 0 def test_query_with_time_duration(): query = Query("http_requests_total") query.add_label("environment", "production") query.add_time_duration("5m") assert str(query) == 'http_requests_total{environment="production"}[5m]' def test_query_with_offset(): query = Query("http_requests_total") query.add_offset("5m") assert str(query) == 'http_requests_total offset 5m' def test_query_with_at_modifier(): query = Query("http_requests_total") query.add_at_modifier("1609746000") assert str(query) == 'http_requests_total @ 1609746000' def test_query_with_time_modifiers(): query = Query("http_requests_total") query.add_offset("5m") query.add_at_modifier("1609746000") assert str(query) == 'http_requests_total offset 5m @ 1609746000'

30.434211

85

0.722006

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2,313

5.461806

0.149306

0.114431

0.216147

0.27972

0.78131

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0.621742

0.576605

0.48061

0.425938

0

0.02423

0.143537

2,313

75

86

30.84

0.769813

0.013835

0

0.392157

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0.344919

0.104707

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0.215686

false

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0.039216

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0.254902

0

null

0

1

0

null

0

1

0

3

f6682ef64e0abbac8e8a75420b895edbc5772431

250

py

Python

OOPs/inheritance.py

Shivams9/pythoncodecamp

e6cd27f4704a407ee360414a8c9236b254117a59

[ "MIT" ]

6

2021-08-04T08:15:22.000Z

2022-02-02T11:15:56.000Z

OOPs/inheritance.py

Maurya232Abhishek/Python-repository-for-basics

3dcec5c529a0847df07c9dcc1424675754ce6376

[ "MIT" ]

14

2021-08-02T06:28:00.000Z

2022-03-25T10:44:15.000Z

OOPs/inheritance.py

Maurya232Abhishek/Python-repository-for-basics

3dcec5c529a0847df07c9dcc1424675754ce6376

[ "MIT" ]

6

2021-07-16T04:56:41.000Z

2022-02-16T04:40:06.000Z

class A: def f1(self): print("F1 in A") class B: def f2(self): print("F2 in B") def f1(self): print("F1 in B") class C(A,B): def f(self): A().f1() B().f1() c = C() c.f1() c.f2() c.f()

13.157895

24

0.404

44

250

2.295455

0.25

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0.178218

0.277228

0.356436

0

0.065359

0.388

250

18

25

13.888889

0.594771

0

0.125

0

0.084

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1

0.25

false

0

0.4375

0.1875

0

null

1

0

1

0

null

0

1

0

3

9c9d85a146755f67b9bdd4855fe645173983f2c8

223

py

Python

tests/run_tests.py

in-toto/layout-web-tool

cbadd131a692e8457d548da2d37012306ba4a7b9

[ "MIT" ]

1

2020-04-01T15:05:54.000Z

tests/run_tests.py

in-toto/layout-web-tool

cbadd131a692e8457d548da2d37012306ba4a7b9

[ "MIT" ]

42

2017-05-23T17:19:19.000Z

2021-04-26T12:28:47.000Z

tests/run_tests.py

in-toto/layout-web-tool

cbadd131a692e8457d548da2d37012306ba4a7b9

[ "MIT" ]

7

2017-05-04T02:13:07.000Z

2020-07-09T10:56:03.000Z

from unittest import defaultTestLoader, TextTestRunner import sys suite = defaultTestLoader.discover(start_dir=".") result = TextTestRunner(verbosity=2, buffer=True).run(suite) sys.exit(0 if result.wasSuccessful() else 1)

31.857143

60

0.802691

28

223

6.357143

0.785714

0

0.014778

0.089686

223

6

61

37.166667

0.862069

0

0.004484

0

1

0

false

0

0.4

0

0.4

0

1

0

null

0

1

0

null

0

1

0

3

9cbc11953dd73fb6f511c74d730fad379ee2138f

91

py

Python

config.py

agithug/fan-controller

046ecd9120c3a3fa0c581cbcd3dc89bd50a7373c

[ "MIT" ]

null

config.py

agithug/fan-controller

046ecd9120c3a3fa0c581cbcd3dc89bd50a7373c

[ "MIT" ]

null

config.py

agithug/fan-controller

046ecd9120c3a3fa0c581cbcd3dc89bd50a7373c

[ "MIT" ]

null

#used as a configuration file def fan: pin = 4 dependencies = [“python3”,”gpiozero”]

13

37

0.67033

12

91

5.083333

1

0

0.027778

0.208791

91

6

38

15.166667

0.819444

0.307692

0

null

0

null

0

1

0

null

0

1

0

null

0

1

0

3

9cc696533b798f11c9e94346fe3734171f19a56d

328

py

Python

api/common/JQSDK_Connect.py

abcdcamey/stock-data

bfdc67e60b7d4de59c66dbb52159574b4e0a5e51

[ "MIT" ]

1

2019-04-10T09:07:59.000Z

api/common/JQSDK_Connect.py

abcdcamey/stock-data

bfdc67e60b7d4de59c66dbb52159574b4e0a5e51

[ "MIT" ]

2

2021-06-01T23:39:34.000Z

2021-12-13T19:58:31.000Z

api/common/JQSDK_Connect.py

abcdcamey/stock-data

bfdc67e60b7d4de59c66dbb52159574b4e0a5e51

[ "MIT" ]

null

#coding=utf-8 from jqdatasdk import * class JQSDK_Connect: def __init__(self): #auth('18818273592','Chenmin585858') #账号是申请时所填写的手机号；密码为聚宽官网登录密码，新申请用户默认为手机号后6位 auth('15821295829', 'Niuniuniu2020') #auth('13816896174', '896174') def get_query_count(self): return get_query_count()

16.4

86

0.670732

33

328

6.393939

0.787879

0.075829

0.123223

0

0.196911

0.210366

328

19

87

17.263158

0.617761

0.356707

0

0.119403

0

1

0.333333

false

0

0.166667

0.833333

0

null

0

null

0

1

0

1

0

3

9cd61c204b329ca886ff80f063d2527cb83d49ab

462

py

Python

sanskrit_parser/generator/test/test_ajanta_stri.py

avinashvarna/sanskrit_parser

79338213128b29927fe2f06031379bb1e3864a83

[ "MIT" ]

54

2017-06-30T09:11:53.000Z

2022-03-22T15:35:41.000Z

sanskrit_parser/generator/test/test_ajanta_stri.py

avinashvarna/sanskrit_parser

79338213128b29927fe2f06031379bb1e3864a83

[ "MIT" ]

159

2017-06-30T07:04:36.000Z

2021-06-17T17:03:43.000Z

sanskrit_parser/generator/test/test_ajanta_stri.py

avinashvarna/sanskrit_parser

79338213128b29927fe2f06031379bb1e3864a83

[ "MIT" ]

18

2017-08-17T13:22:00.000Z

2022-01-20T01:08:58.000Z

from sanskrit_parser.generator.pratyaya import * # noqa: F403, F401 from sanskrit_parser.generator.dhatu import * # noqa: F403, F401 from sanskrit_parser.generator.pratipadika import * # noqa: F403, F401 from sanskrit_parser.base.sanskrit_base import DEVANAGARI from sanskrit_parser.generator.sutras_yaml import sutra_list from conftest import run_test def test_vibhakti_ajanta_stri(ajanta_stri): run_test(ajanta_stri, sutra_list, encoding=DEVANAGARI)

38.5

71

0.824675

64

462

5.703125

0.390625

0.164384

0.246575

0.29589

0.345205

0.246575

0

0.043796

0.11039

462

11

72

42

0.844282

0.108225

0

1

0.125

false

0

0.75

0

0.875

0

null

0

1

0

null

0

1

0

3

9cec9b5ef5691da4f654da65ea5fb44ee14018a8

63

py

Python

certifi/__init__.py

andrew-aladev/certifi-shim

efbda777bfbdcf0676a789b15010b43a54e4da1a

[ "CC0-1.0" ]

null

certifi/__init__.py

andrew-aladev/certifi-shim

efbda777bfbdcf0676a789b15010b43a54e4da1a

[ "CC0-1.0" ]

null

certifi/__init__.py

andrew-aladev/certifi-shim

efbda777bfbdcf0676a789b15010b43a54e4da1a

[ "CC0-1.0" ]

null

from certifi.core import contents, where __version__ = "9999"

15.75

40

0.777778

8

63

5.625

1

0

0.074074

0.142857

63

3

41

21

0.759259

0

0.063492

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

9cf34c2b4493cdfb7aff2d968769acb0ab4ee469

578

py

Python

song_generator/tests.py

gorbulus/song_generator

ba527e7a0151177f794995d0d79fdeffff45b7fb

[ "MIT" ]

null

song_generator/tests.py

gorbulus/song_generator

ba527e7a0151177f794995d0d79fdeffff45b7fb

[ "MIT" ]

null

song_generator/tests.py

gorbulus/song_generator

ba527e7a0151177f794995d0d79fdeffff45b7fb

[ "MIT" ]

null

''' # tests.py # William Ponton # 5.9.21 # Tests for the song_generator project ''' # import pacagkes import song_generator.generator as gen import song_generator.song_data as s_data import song_generator.string_literals as s_lit from song_generator.SongClass import Song # Song class # test output from each import file def test_import_files(): print("\n...testing...") print("Hello world ~ from main.py") gen.test_output() s_data.test_output() Song.test_output() s_lit.test_output() print("Hello world ~ from tests.py") return print("...testing completed...")

26.272727

54

0.743945

88

578

4.693182

0.431818

0.157385

0.138015

0.09201

0

0.008032

0.138408

578

22

55

26.272727

0.821285

0.237024

0

0.210648

0

1

0.076923

true

0

0.384615

0

0.538462

0.307692

0

null

0

null

0

1

0

1

0

1

0

3

1404d7c78ab63ab6c44fb7aef71aaac733eb3dc8

578

py

Python

install_imports.py

surajssd/kube-hunter

0157ac83ce6a735d4a431e9e3a6d4ec847aa6fc1

[ "Apache-2.0" ]

1

2019-09-25T12:31:33.000Z

install_imports.py

surajssd/kube-hunter

0157ac83ce6a735d4a431e9e3a6d4ec847aa6fc1

[ "Apache-2.0" ]

null

install_imports.py

surajssd/kube-hunter

0157ac83ce6a735d4a431e9e3a6d4ec847aa6fc1

[ "Apache-2.0" ]

1

2020-08-17T16:05:45.000Z

from os.path import basename import glob def get_py_files(path): for py_file in glob.glob("{}*.py".format(path)): if not py_file.endswith("__init__.py"): yield basename(py_file)[:-3] def install_static_imports(path): with open("{}__init__.py".format(path), 'w') as init_f: for pf in get_py_files(path): init_f.write("from .{} import *\n".format(pf)) install_static_imports("src/modules/discovery/") install_static_imports("src/modules/hunting/") install_static_imports("src/modules/report/") install_static_imports("plugins/")

32.111111

59

0.695502

85

578

4.411765

0.435294

0.173333

0.266667

0.184

0.24

0

0.002037

0.150519

578

17

60

34

0.761711

0

0.205882

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0

1

0.142857

false

0

0.571429

0

0.714286

0

null

0

1

0

null

0

1

0

3

142c7611071a9367cf6a272e8d29329b7ce52109

193

py

Python

api/serializers/server.py

lndba/apasa_backend

e0bb96e22a22f6e2a5a2826f225388113473e7e2

[ "Apache-2.0" ]

1

2019-08-06T07:31:40.000Z

api/serializers/server.py

lndba/apasa_backend

e0bb96e22a22f6e2a5a2826f225388113473e7e2

[ "Apache-2.0" ]

null

api/serializers/server.py

lndba/apasa_backend

e0bb96e22a22f6e2a5a2826f225388113473e7e2

[ "Apache-2.0" ]

null

from rest_framework import serializers from api.models import * class ServerListSerializers(serializers.ModelSerializer): class Meta: model = Server fields = "__all__"

16.083333

57

0.720207

19

193

7.052632

0.789474

0

0.222798

193

11

58

17.545455

0.893333

0

0.036842

0

1

0

false

0

0.333333

0

0.666667

0

1

0

null

0

null

0

1

0

1

0

3

143f866b2d562d6e58d70545f8b51ba4cdbd1e78

486

py

Python

gnosis/eth/oracles/__init__.py

titandac/gnosis-py

cf0af4f25e64b22256eabb415d0f3fe3a6180b14

[ "MIT" ]

64

2018-09-26T19:56:50.000Z

2022-03-18T21:45:59.000Z

gnosis/eth/oracles/__init__.py

zhanghao-ic/gnosis-py

d2a5912547b7d1b576c826909f4c1d0155db536f

[ "MIT" ]

151

2018-09-10T21:42:05.000Z

2022-03-31T12:33:31.000Z

gnosis/eth/oracles/__init__.py

zhanghao-ic/gnosis-py

d2a5912547b7d1b576c826909f4c1d0155db536f

[ "MIT" ]

50

2018-12-13T20:43:46.000Z

2022-03-30T09:32:32.000Z

# flake8: noqa F401 from .oracles import ( AaveOracle, BalancerOracle, CannotGetPriceFromOracle, ComposedPriceOracle, CreamOracle, CurveOracle, EnzymeOracle, InvalidPriceFromOracle, KyberOracle, MooniswapOracle, OracleException, PoolTogetherOracle, PriceOracle, PricePoolOracle, SushiswapOracle, UnderlyingToken, UniswapOracle, UniswapV2Oracle, UsdPricePoolOracle, YearnOracle, ZerionComposedOracle, )

19.44

29

0.711934

27

486

12.814815

1

0

0.013441

0.234568

486

24

30

20.25

0.916667

0.034979

0

1

0

true

0

0.043478

0

0.043478

0

1

0

1

null

0

null

0

1

0

3

145854982248ea84d9abe0f478e5e22274de2489

142

py

Python

vet_care/doc_events/patient_appointment.py

neerajvkn/vet_care

14914b22e7a83265d736f9f9dc5186271ae62d66

[ "MIT" ]

2

2020-11-23T11:14:32.000Z

2021-02-03T06:40:33.000Z

vet_care/doc_events/patient_appointment.py

neerajvkn/vet_care

14914b22e7a83265d736f9f9dc5186271ae62d66

[ "MIT" ]

null

vet_care/doc_events/patient_appointment.py

neerajvkn/vet_care

14914b22e7a83265d736f9f9dc5186271ae62d66

[ "MIT" ]

7

2019-11-16T14:36:33.000Z

2021-08-25T07:54:51.000Z

import frappe def validate(doc, method): customer = frappe.db.get_value('Patient', doc.patient, 'customer') doc.vc_owner = customer

20.285714

70

0.711268

19

142

5.210526

0.684211

0

0.161972

142

6

71

23.666667

0.831933

0

0.105634

0

1

0.25

false

0

0.25

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

1459e47e29f07327f77b66aae27e1f15b3c747d9

2,587

py

Python

PyObjCTest/test_nsjavasetup.py

Khan/pyobjc-framework-Cocoa

f8b015ea2a72d8d78be6084fb12925c4785b8f1f

[ "MIT" ]

132

2015-01-01T10:02:42.000Z

2022-03-09T12:51:01.000Z

mac/pyobjc-framework-Cocoa/PyObjCTest/test_nsjavasetup.py

mba811/music-player

7998986b34cfda2244ef622adefb839331b81a81

[ "BSD-2-Clause" ]

6

2015-01-06T08:23:19.000Z

2019-03-14T12:22:06.000Z

mac/pyobjc-framework-Cocoa/PyObjCTest/test_nsjavasetup.py

mba811/music-player

7998986b34cfda2244ef622adefb839331b81a81

[ "BSD-2-Clause" ]

27

2015-02-23T11:51:43.000Z

2022-03-07T02:34:18.000Z

from PyObjCTools.TestSupport import * import os from Foundation import * try: unicode except NameError: unicode = str class TestNSJavaSetup (TestCase): @max_os_level('10.5') def testConstants(self): self.assertIsInstance(NSJavaClasses, unicode) self.assertIsInstance(NSJavaRoot, unicode) self.assertIsInstance(NSJavaPath, unicode) self.assertIsInstance(NSJavaUserPath, unicode) self.assertIsInstance(NSJavaLibraryPath, unicode) self.assertIsInstance(NSJavaOwnVirtualMachine, unicode) self.assertIsInstance(NSJavaPathSeparator, unicode) self.assertIsInstance(NSJavaWillSetupVirtualMachineNotification, unicode) self.assertIsInstance(NSJavaDidSetupVirtualMachineNotification, unicode) self.assertIsInstance(NSJavaWillCreateVirtualMachineNotification, unicode) self.assertIsInstance(NSJavaDidCreateVirtualMachineNotification, unicode) @max_os_level('10.5') def testFunctions(self): v = NSJavaNeedsVirtualMachine({}) self.assertIs(v, False) v = NSJavaProvidesClasses({}) self.assertIs(v, False) v = NSJavaNeedsToLoadClasses({}) self.assertIs(v, False) vm = NSJavaSetup({}) self.assertIsInstance(vm, objc.objc_object) v = NSJavaSetupVirtualMachine() self.assertIsInstance(v, objc.objc_object) v = NSJavaObjectNamedInPath("java.lang.Object", None) self.assertIsInstance(v, objc.objc_object) v, vm = NSJavaClassesFromPath(None, ['java.lang.Object'], True, None) self.assertIsInstance(v, NSArray) self.assertEqual(len(v), 1) self.assertIsInstance(vm, objc.objc_object) v, vm = NSJavaClassesForBundle(NSBundle.mainBundle(), True, None) self.assertIsInstance(v, NSArray) self.assertEqual(len(v), 0) self.assertIsInstance(vm, objc.objc_object) vm = NSJavaBundleSetup(NSBundle.mainBundle(), {}) self.assertIsInstance(vm, objc.objc_object) # FIXME: NSJavaBundleCleanup gives an exception # This seems to be related to the way we call these APIs and I don't # plan to fix is (there is no problem with PyObjC or the Foundation # wrappers) fd = os.dup(2) x = os.open('/dev/null', os.O_WRONLY) os.dup2(x, 2) os.close(x) try: try: NSJavaBundleCleanup(NSBundle.mainBundle(), {}) except ValueError: pass finally: os.dup2(fd, 2) if __name__ == "__main__": main()

38.044118

82

0.665636

251

2,587

6.784861

0.426295

0.223136

0.158544

0.061069

0.236054

0.211392

0.150323

0.064592

0

0.006602

0.238887

2,587

67

83

38.61194

0.858304

0.072671

0

0.275862

0

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0

0.014925

0.413793

1

0.034483

false

0.017241

0.051724

0

0.103448

0

null

1

0

null

0

1

0

3

145dd3d7b020a6d88ed30bd57eb630dd062a950e

378

py

Python

DataStructures and Algorithms/Ammortization onArrays/Gameentry.py

abhishekratnam/Datastructuresandalgorithmsinpython

9339319f441755797f4d2818ac9cf742a63ab5ea

[ "MIT" ]

null

DataStructures and Algorithms/Ammortization onArrays/Gameentry.py

abhishekratnam/Datastructuresandalgorithmsinpython

9339319f441755797f4d2818ac9cf742a63ab5ea

[ "MIT" ]

null

DataStructures and Algorithms/Ammortization onArrays/Gameentry.py

abhishekratnam/Datastructuresandalgorithmsinpython

9339319f441755797f4d2818ac9cf742a63ab5ea

[ "MIT" ]

null

class Gameentry: """Represents one entry of a list of high scores""" def __init__(self,name,score): self._name = name self._score = score def get_name(self): return self._name def get_score(self): return self._score def __str__(self): return ' ({0},{1})'.format(self._name,self._score)#(bob,98)

23.625

68

0.574074

49

378

4.102041

0.469388

0.159204

0.129353

0

0.015209

0.304233

378

15

69

25.2

0.749049

0.142857

0

0.033003

0

1

0.4

false

0

0.3

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0

null

0

null

0

1

0

1

0

3

148ceb1fb000771c1bef8a1a38a56aebb024d47d

1,457

py

Python

route/__init__.py

zyguan/pyroute

16527374419780672f7ceae6f2a123a336603572

[ "MIT" ]

null

route/__init__.py

zyguan/pyroute

16527374419780672f7ceae6f2a123a336603572

[ "MIT" ]

null

route/__init__.py

zyguan/pyroute

16527374419780672f7ceae6f2a123a336603572

[ "MIT" ]

null

import _route VINCENTY = 1 HAVERSINE = 2 def distance(lat1, lng1, lat2, lng2, formula=VINCENTY, iterlimit=20): """ Calculate distance between two points. :param lat1: latitude of point 1 in degrees :param lng1: longitude of point 1 in degrees :param lat2: latitude of point 2 in degrees :param lng2: longitude of point 2 in degrees :param formula: distance formula to use :param iterlimit: max iterations, used by vincenty's formula :return: the distance between (lat1, lng1) and (lat2, lng2) in meter """ return _route.distance(lat1, lng1, lat2, lng2, formula, iterlimit) def distance_vincenty(lat1, lng1, lat2, lng2): return _route.distance(lat1, lng1, lat2, lng2, VINCENTY) def distance_haversine(lat1, lng1, lat2, lng2): return _route.distance(lat1, lng1, lat2, lng2, HAVERSINE) def measure(ps, formula=VINCENTY, iterlimit=20): """ Calculate cumulative distances of a route. :param ps: points in route, eg: [(lng1, lat1), (lng2, lat2), ...] :param formula: distance formula to use :param iterlimit: max iterations, used by vincenty's formula :return: the cumulative distance array `ds` where ds[j]-ds[i] is the distance from (lat_i, lng_i) to (lat_j, lng_j) in meter. """ return _route.measure(ps, formula, iterlimit) def measure_vincenty(ps): return _route.measure(ps, VINCENTY) def measure_haversine(ps): return _route.measure(ps, HAVERSINE)

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0.095238

0.540675

0.434524

0.314484

0.279762

0

0.039316

0.19698

1,457

46

73

31.673913

0.822222

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1

0.4

false

0

0.066667

0.266667

0.866667

0

null

0

null

0

1

0

1

0

3

149096338c91567b2635fcffc0b59e5d56c47ad5

1,958

py

Python

TMTool/Scripts/Scoring/owasp_rr.py

tmart234/TMTool

054d055d73fa5e76be22bf04cf1136b88cb7efb9

[ "MIT" ]

7

2021-04-23T23:13:02.000Z

2022-03-21T03:35:47.000Z

TMTool/Scripts/Scoring/owasp_rr.py

tmart234/TMTool

054d055d73fa5e76be22bf04cf1136b88cb7efb9

[ "MIT" ]

1

2022-03-21T03:45:45.000Z

TMTool/Scripts/Scoring/owasp_rr.py

tmart234/TMTool

054d055d73fa5e76be22bf04cf1136b88cb7efb9

[ "MIT" ]

null

## based on OWASP risk rating # clculates impact or likihood def calc_scores(_dict): # set Numerical Score _dict["Numerical Score"] = int(0) for key,value in _dict: if "Score" in key: continue else: _dict["Numerical Score"] = value + _dict["Numerical Score"] # set Categorical Score if _dict["Numerical Score"] < 3: _dict["Categorical Score"] = "Low" elif _dict["Numerical Score"] < 6: _dict["Categorical Score"] = "Medium" elif _dict["Numerical Score"] < 9: _dict["Categorical Score"] = "High" else: _dict["Categorical Score"] = None return # translating the risk matrix def calc_risk(_likihood,_impact): if _likihood["Categorical Score"] == "Low" and _impact["Categorical Score"] == "Low": return "Note" elif (_likihood["Categorical Score"] == "Low" and _impact["Categorical Score"] == "Medium") or \ (_likihood["Categorical Score"] == "Medium" and _impact["Categorical Score"] == "Low"): return "Low" elif (_likihood["Categorical Score"] == "Low" and _impact["Categorical Score"] == "High") or \ (_likihood["Categorical Score"] == "High" and _impact["Categorical Score"] == "Low") or \ (_likihood["Categorical Score"] == "Medium" and _impact["Categorical Score"] == "Medium"): return "Medium" elif (_likihood["Categorical Score"] == "High" and _impact["Categorical Score"] == "Medium") or \ (_likihood["Categorical Score"] == "Medium" and _impact["Categorical Score"] == "High"): return "High" elif _likihood["Categorical Score"] == "High" and _impact["Categorical Score"] == "High": return "Critical" else: return None likihood = dict.fromkeys("SL","M","O","S","ED","EE","Aw","ID","Numerical Score","Categorical Score") impact = dict.fromkeys("C","I","A","Ac","FD","RD","NCD","PV", "Numerical Score","Categorical Score")

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0.61287

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1,958

5.373272

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1,958

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false

0

0.257143

0

null

1

0

null

0

3

14b10ba84092552ef5446166e93acde05dd54b70

376

py

Python

nbdev_calc/core.py

alinaselega/nbdev_calc

4e7cffb81a08770aea7652c10481e41d5d13a05e

[ "Apache-2.0" ]

null

nbdev_calc/core.py

alinaselega/nbdev_calc

4e7cffb81a08770aea7652c10481e41d5d13a05e

[ "Apache-2.0" ]

null

nbdev_calc/core.py

alinaselega/nbdev_calc

4e7cffb81a08770aea7652c10481e41d5d13a05e

[ "Apache-2.0" ]

null

# AUTOGENERATED! DO NOT EDIT! File to edit: 00_core.ipynb (unless otherwise specified). __all__ = ['add', 'multiply', 'subtract', 'divide'] # Cell def add(x, y): "Add x and y" return x+y def multiply(x, y): "Multiply x and y" return x*y def subtract(x, y): "Subtract y from x" return add(x, -y) def divide(x, y): "Divide x by y" return x/y

18.8

87

0.609043

64

376

3.5

0.390625

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0.107143

0.120536

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0.007067

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376

20

88

18.8

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0

1

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0.288732

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0.307692

false

0

0.615385

0

null

0

1

0

null

0

1

0

1

0

3

14b48aef525713da7768110b19cc3d3319051874

456

py

Python

mainApp/Serializers.py

MSNP1381/rayanRooyeshCityBack

f3e8224538949c4951d4b25e1f14e4fcc02fdc53

[ "MIT" ]

null

mainApp/Serializers.py

MSNP1381/rayanRooyeshCityBack

f3e8224538949c4951d4b25e1f14e4fcc02fdc53

[ "MIT" ]

null

mainApp/Serializers.py

MSNP1381/rayanRooyeshCityBack

f3e8224538949c4951d4b25e1f14e4fcc02fdc53

[ "MIT" ]

null

from rest_framework import serializers from .models import Transaction,Sections class TransactionSerializer(serializers.ModelSerializer): class Meta: model = Transaction fields = '__all__' def create(self, data): print(100*"%") print(data) return Transaction.objects.create(**data) class SectionSerializer(serializers.ModelSerializer): class Meta: model = Sections fields = '__all__'

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0.682018

43

456

7.023256

0.55814

0.172185

0.205298

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0.264901

0

0.008596

0.234649

456

19

58

24

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0.285714

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0.032823

0

1

0.071429

false

0

0.142857

0

0.571429

0.142857

0

null

0

1

0

null

0

1

0

3

1ad8843aee0e7aba85c685628079d06bf297c82d

2,064

py

Python

src/leetcode_384_shuffle_an_array.py

sungho-joo/leetcode2github

ce7730ef40f6051df23681dd3c0e1e657abba620

[ "MIT" ]

null

src/leetcode_384_shuffle_an_array.py

sungho-joo/leetcode2github

ce7730ef40f6051df23681dd3c0e1e657abba620

[ "MIT" ]

null

src/leetcode_384_shuffle_an_array.py

sungho-joo/leetcode2github

ce7730ef40f6051df23681dd3c0e1e657abba620

[ "MIT" ]

null

# @l2g 384 python3 # [384] Shuffle an Array # Difficulty: Medium # https://leetcode.com/problems/shuffle-an-array # # Given an integer array nums,design an algorithm to randomly shuffle the array. # All permutations of the array should be equally likely as a result of the shuffling. # Implement the Solution class: # # Solution(int[] nums) Initializes the object with the integer array nums. # int[] reset() Resets the array to its original configuration and returns it. # int[] shuffle() Returns a random shuffling of the array. # # # Example 1: # # Input # ["Solution", "shuffle", "reset", "shuffle"] # [[[1, 2, 3]], [], [], []] # Output # [null, [3, 1, 2], [1, 2, 3], [1, 3, 2]] # # Explanation # Solution solution = new Solution([1, 2, 3]); # solution.shuffle(); // Shuffle the array [1,2,3] and return its result. # // Any permutation of [1,2,3] must be equally likely to be returned. # // Example: return [3, 1, 2] # solution.reset(); // Resets the array back to its original configuration [1,2,3].Return [1,2,3] # solution.shuffle(); // Returns the random shuffling of array [1,2,3]. Example: return [1, 3, 2] # # # # Constraints: # # 1 <= nums.length <= 200 # -10^6 <= nums[i] <= 10^6 # All the elements of nums are unique. # At most 5 * 10^4 calls in total will be made to reset and shuffle. # # import random from typing import List class Solution: def __init__(self, nums: List[int]): self.nums = nums def reset(self) -> List[int]: """ Resets the array to its original configuration and return it. """ return self.nums def shuffle(self) -> List[int]: """ Returns a random shuffling of the array. """ return random.sample(self.nums, len(self.nums)) # Your Solution object will be instantiated and called as such: # obj = Solution(nums) # param_1 = obj.reset() # param_2 = obj.shuffle() if __name__ == "__main__": import os import pytest pytest.main([os.path.join("tests", "test_384.py")])

27.52

102

0.630814

297

2,064

4.333333

0.353535

0.01554

0.018648

0.060606

0.146076

0.118104

0.066822

0

0.040328

0.231105

2,064

74

103

27.891892

0.770636

0.734012

0

0.052288

0

1

0.230769

false

0

0.307692

0

0.769231

0

null

0

null

0

1

0

1

0

1

0

3

1af37e5031f27006259d50bd37911e3e99b40d74

771

py

Python

nest_py/knoweng/data_types/public_gene_sets.py

KnowEnG/platform

7356fabf5e2db4171ef1f910514436b69ecaa701

[ "MIT" ]

2

2020-02-12T22:20:51.000Z

2020-07-31T03:19:51.000Z

nest_py/knoweng/data_types/public_gene_sets.py

KnowEnG/platform

7356fabf5e2db4171ef1f910514436b69ecaa701

[ "MIT" ]

1

2021-06-02T00:29:02.000Z

nest_py/knoweng/data_types/public_gene_sets.py

KnowEnG/platform

7356fabf5e2db4171ef1f910514436b69ecaa701

[ "MIT" ]

1

2018-01-03T22:56:27.000Z

""" A collection of public gene sets. """ from nest_py.core.data_types.tablelike_schema import TablelikeSchema COLLECTION_NAME = 'public_gene_sets' def generate_schema(): schema = TablelikeSchema(COLLECTION_NAME) schema.add_categoric_attribute('set_id', valid_values=None) schema.add_categoric_attribute('set_name', valid_values=None) schema.add_numeric_attribute('species_id') schema.add_numeric_attribute('gene_count') schema.add_categoric_attribute('collection', valid_values=None) schema.add_categoric_attribute('edge_type_name', valid_values=None) schema.add_categoric_attribute('supercollection', valid_values=None) schema.add_categoric_attribute('url', valid_values=None) schema.add_index(['set_id']) return schema

33.521739

72

0.787289

99

771

5.747475

0.363636

0.142355

0.189807

0.28471

0.45167

0.351494

0.295255

0

0.111543

771

22

73

35.045455

0.830657

0.042802

0

1

0

0.134431

0

1

0.071429

false

0

0.071429

0

0.214286

0

null

0

1

0

1

0

null

0

3

1af6926099c1d7d07976357c858e6720fbdd0e0b

565

py

Python

server/utils/cors.py

amirdib/dapp_dashboard

e3f556b20ea2d866300e7501f8c720dc0eb56f4b

[ "MIT" ]

null

server/utils/cors.py

amirdib/dapp_dashboard

e3f556b20ea2d866300e7501f8c720dc0eb56f4b

[ "MIT" ]

null

server/utils/cors.py

amirdib/dapp_dashboard

e3f556b20ea2d866300e7501f8c720dc0eb56f4b

[ "MIT" ]

null

from bottle import response, request def enable_cors(func): def _enable_cors(*args, **kwargs): # Set CORS headers response.headers['Access-Control-Allow-Origin'] = '*' response.headers['Access-Control-Allow-Methods'] = 'GET, POST, PUT, OPTIONS' response.headers['Access-Control-Allow-Headers'] = 'Origin, Accept, Content-Type, X-Requested-With, X-CSRF-Token' if request.method != 'OPTIONS': # actual request; reply with the actual response return func(*args, **kwargs) return _enable_cors

35.3125

121

0.654867

67

565

5.447761

0.537313

0.082192

0.172603

0.230137

0.271233

0

0.217699

565

15

122

37.666667

0.825792

0.111504

0

0.348697

0.166333

0

1

0.222222

false

0

0.111111

0

0.555556

0

null

0

1

0

null

0

1

0

1

0

3

1af6c73c4393c2fe75719524db1ca5b86014c72d

2,367

py

Python

TerminalStory/Jack.py

TomlinsonJ03/4006CEM-Mini-Project

ac2ebcfa5793475d7bfb074034b5aa23cfd1858e

[ "CC0-1.0" ]

null

TerminalStory/Jack.py

TomlinsonJ03/4006CEM-Mini-Project

ac2ebcfa5793475d7bfb074034b5aa23cfd1858e

[ "CC0-1.0" ]

null

TerminalStory/Jack.py

TomlinsonJ03/4006CEM-Mini-Project

ac2ebcfa5793475d7bfb074034b5aa23cfd1858e

[ "CC0-1.0" ]

null

# Entering the Basement def basement(): print(""" I will walk into the basement, there seems to be a robot there, working hard it seems. They don’t see the things the way I do, it exclaimed. I wander what it means, see what? Maybe I will approach it and find out. It seems to be working on some kind of budgeting, the finances of the family perhaps. However, it is scribbling the words free will, programming. Perhaps the robot’s have some sort of sentience, Maybe the robot in the garden has a passion for that sort of thing, maybe robots love to cook. But then again, this one doesn’t seem to love what it’s doing, more like, it knows that it’s simply a slave who isn’t allowed to do as it pleases. Maybe it ignored me because it hopes that I will make a choice, will I save the robot or leave it be. No surely not, a robot has no free thought, it’s just an AI with programming, no-one would program a robot to hate what it is doing, do they have such emotions? How do you make it so a robot can experience such things, would it be considered real if so? I suppose I must make a choice. """) print("1: Unplug the robot") print("2: Leave the robot to suffer") choice = int(input("Which option will you choose? ")) if choice == 1: unplugRobot() elif choice == 2: robotSuffer() # Unplug the robot def unplugRobot(): print(""" I ponder the overall necessity for the robot and weight in the possible consequences that could unravel if it was no longer active. None surpassed a high enough level of degree to garner concern, I lay my finger upon a circular button illuminated by a warm white led, I press it and enjoy a moment of silence as its internal components hum until still. I then unplug the robot and take the power cord with me in hope of the robot never being activated again. """) # Let the robot suffer def robotSuffer(): print(""" I notice the robot in despair and locate the unorganized box of components and stripped wires, I turn of the robot and begin swapping out parts, refitting incorrect capacitors and resistors, mismatch the organized wiring with a mesh of unsafe wires and leave the existing body panels in front of its visual sensor to gaze upon until next reset. """)

62.289474

167

0.709759

402

2,367

4.179104

0.5

0.057143

0.025

0

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2,367

38

168

62.289474

0.93579

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0

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0

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0

1

0.096774

false

0.064516

0

0.096774

0.16129

0

null

0

1

0

null

0

1

0

3

210693b9ec3b9f3189d565efa51e605d1e16d8d3

3,311

py

Python

src/genie/libs/parser/iosxe/tests/ShowBgpAllDetail/cli/equal/golden_output4_expected.py

jmedina0911/genieparser

2fcd6d3e44891551af4b9d05e2c053218ee25c32

[ "Apache-2.0" ]

null

src/genie/libs/parser/iosxe/tests/ShowBgpAllDetail/cli/equal/golden_output4_expected.py

jmedina0911/genieparser

2fcd6d3e44891551af4b9d05e2c053218ee25c32

[ "Apache-2.0" ]

null

src/genie/libs/parser/iosxe/tests/ShowBgpAllDetail/cli/equal/golden_output4_expected.py

jmedina0911/genieparser

2fcd6d3e44891551af4b9d05e2c053218ee25c32

[ "Apache-2.0" ]

null

expected_output = { "instance": { "default": { "vrf": { "vrf1": { "address_family": { "": { "default_vrf": "vrf1", "prefixes": { "0.0.0.0/0": { "available_path": "2", "best_path": "1", "index": { 1: { "community": "163:43242 2002:8 2002:35 2002:53 2002:100 2002:1000", "ext_community": "RT:65002:3014", "gateway": "9.2.2.2", "localpref": 950, "next_hop": "9.2.2.2", "next_hop_via": "vrf vrf1", "origin_codes": "i", "originator": "9.207.128.21", "recipient_pathid": "0", "refresh_epoch": 2, "route_info": "304 13", "status_codes": "*>", "transfer_pathid": "0x0", "update_group": 64624, }, 2: { "community": "163:43242 2002:8 2002:35 2002:53 2002:100 2002:1000", "ext_community": "RT:65002:3014", "gateway": "9.91.117.38", "imported_path_from": "9.91.117.38:3014:0.0.0.0/0 (global)", "localpref": 950, "metric": 0, "next_hop": "9.91.117.38", "next_hop_igp_metric": "11", "next_hop_via": "default", "origin_codes": "i", "originator": "9.91.117.38", "recipient_pathid": "0", "refresh_epoch": 17, "route_info": "64624", "status_codes": "* i", "transfer_pathid": "0", "update_group": 64624, }, }, "paths": "2 available, best #1, table vrf1", "table_version": "74438", } }, "route_distinguisher": "9.1.1.1:3014", } } } } } } }

53.403226

111

0.231048

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3,311

3.909091

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0

null

0

1

0

1

0

null

0

3

214a07d8cc0215d98c308b5d7c5428fd93ac98c9

355

py

Python

cvat_reader/video_reader/dummy_reader.py

eyedl/cvat_reader

9f4837d82b3bb7946f6fb03b3c8fb0991a6d929f

[ "BSD-3-Clause" ]

null

cvat_reader/video_reader/dummy_reader.py

eyedl/cvat_reader

9f4837d82b3bb7946f6fb03b3c8fb0991a6d929f

[ "BSD-3-Clause" ]

5

2021-12-03T12:56:38.000Z

2022-01-18T20:35:01.000Z

cvat_reader/video_reader/dummy_reader.py

eyedl/cvat_reader

9f4837d82b3bb7946f6fb03b3c8fb0991a6d929f

[ "BSD-3-Clause" ]

2

2022-02-09T08:53:35.000Z

2022-03-16T10:21:14.000Z

from typing import Tuple, Any from .base import VideoReader class DummyVideoReader(VideoReader): def __init__(self): self.frame_id = 0 def read_frame(self) -> Tuple[int, Any]: frame_id = self.frame_id self.frame_id += 1 return frame_id, None def seek(self, frame_id: int): self.frame_id = frame_id

20.882353

44

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false

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0

null

1

0

null

0

1

0

3

2158eb73536fe9b11e0d8fd0dd4f2a37c52f7ad1

458

py

Python

services/divnik/src/api/migrations/0005_auto_20200503_0933.py

pomo-mondreganto/innoctf-final-10-05-2020

8d31bb43b543c6948b1e09d9816728a10125c4be

[ "WTFPL" ]

1

2021-01-04T23:52:34.000Z

services/divnik/src/api/migrations/0005_auto_20200503_0933.py

pomo-mondreganto/innoctf-final-10-05-2020

8d31bb43b543c6948b1e09d9816728a10125c4be

[ "WTFPL" ]

null

services/divnik/src/api/migrations/0005_auto_20200503_0933.py

pomo-mondreganto/innoctf-final-10-05-2020

8d31bb43b543c6948b1e09d9816728a10125c4be

[ "WTFPL" ]

2

2020-10-22T12:27:25.000Z

2020-10-22T12:27:28.000Z

# Generated by Django 3.0.3 on 2020-05-03 09:33 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('api', '0004_auto_20200502_1946'), ] operations = [ migrations.AlterModelOptions( name='course', options={'ordering': ('-id',)}, ), migrations.AlterModelOptions( name='user', options={'ordering': ('-id',)}, ), ]

21.809524

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1

0

1

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null

0

3

21686e2c5a6fb53bb4999288166075c2a704a954

7,152

py

Python

food_reference_listing/database/models.py

bfssi-forest-dussault/food_reference_listing

85372a81a9201dda02797ab0c11b1bd710f9b70d

[ "MIT" ]

null

food_reference_listing/database/models.py

bfssi-forest-dussault/food_reference_listing

85372a81a9201dda02797ab0c11b1bd710f9b70d

[ "MIT" ]

null

food_reference_listing/database/models.py

bfssi-forest-dussault/food_reference_listing

85372a81a9201dda02797ab0c11b1bd710f9b70d

[ "MIT" ]

null

from django.db import models from simple_history.models import HistoricalRecords class Language(models.Model): language_id = models.CharField(max_length=2, blank=True, null=True, unique=True) description_en = models.CharField(max_length=16, blank=True, null=True, unique=True) description_fr = models.CharField(max_length=16, blank=True, null=True, unique=True) updated = models.DateTimeField() # Historical record of previous DB history = HistoricalRecords() def __str__(self): try: return f'{self.language_id}: {self.description_en}' except TypeError as e: return '' class AcronymType(models.Model): acronym_type_id = models.CharField(max_length=16, null=True, blank=True, unique=True) description_en = models.CharField(max_length=16, null=True, blank=True) description_fr = models.CharField(max_length=16, null=True, blank=True) updated = models.DateTimeField() # Historical record of previous DB history = HistoricalRecords() class Acronym(models.Model): acronym_id = models.CharField(max_length=8, null=True, blank=True, unique=True) language = models.ForeignKey(Language, related_name="acronyms", on_delete=models.SET_NULL, null=True) description_en = models.CharField(max_length=64, blank=True, null=True) description_fr = models.CharField(max_length=64, blank=True, null=True) acronym_type = models.ForeignKey(AcronymType, related_name="acronyms", on_delete=models.SET_NULL, null=True) updated = models.DateTimeField() # Historical record of previous DB history = HistoricalRecords() class Category(models.Model): category_id = models.IntegerField(null=True, blank=True, unique=True) header_en = models.CharField(max_length=256, blank=True, null=True) header_fr = models.CharField(max_length=256, blank=True, null=True) note_en = models.TextField(blank=True, null=True) note_fr = models.TextField(blank=True, null=True) updated = models.DateTimeField() # Historical record of previous DB history = HistoricalRecords() def __str__(self): try: return f'{self.category_id}: {self.header_en}' except TypeError as e: return '' class Meta: verbose_name = 'Category' verbose_name_plural = 'Categories' class Country(models.Model): country_id = models.IntegerField(null=True, blank=True, unique=True) description_en = models.CharField(max_length=256, null=True, blank=True) description_fr = models.CharField(max_length=256, null=True, blank=True) updated = models.DateTimeField() # Historical record of previous DB history = HistoricalRecords() class ProvinceState(models.Model): province_state_id = models.IntegerField(null=True, blank=True, unique=True) province_state_code = models.CharField(max_length=8, unique=True, null=True, blank=True) description_en = models.CharField(max_length=256, null=True, blank=True) description_fr = models.CharField(max_length=256, null=True, blank=True) country = models.ForeignKey(Country, related_name="provincestates", on_delete=models.CASCADE) updated = models.DateTimeField() # Historical record of previous DB history = HistoricalRecords() class City(models.Model): city_id = models.IntegerField(null=True, blank=True, unique=True) description_en = models.CharField(max_length=256, null=True, blank=True) description_fr = models.CharField(max_length=256, null=True, blank=True) province_state = models.ForeignKey(ProvinceState, on_delete=models.SET_NULL, blank=True, null=True) country = models.ForeignKey(Country, related_name="cities", on_delete=models.SET_NULL, null=True, blank=True) updated = models.DateTimeField() # Historical record of previous DB history = HistoricalRecords() class Company(models.Model): company_id = models.CharField(max_length=16, unique=True, blank=True, null=True) name_en = models.CharField(max_length=256, blank=True, null=True) name_fr = models.CharField(max_length=256, blank=True, null=True) language = models.ForeignKey(Language, related_name="companies", on_delete=models.SET_NULL, null=True) city = models.ForeignKey(City, related_name="companies", on_delete=models.SET_NULL, null=True) postal_code = models.CharField(max_length=16, blank=True, null=True) updated = models.DateTimeField() # Historical record of previous DB history = HistoricalRecords() def __str__(self): try: return f'{self.company_id}: {self.name_en}' except TypeError as e: return '' class Meta: verbose_name = 'Company' verbose_name_plural = 'Companies' class Subcategory(models.Model): subcategory_id = models.IntegerField(null=True, blank=True, unique=True) subcategory_code = models.CharField(max_length=8, null=True, blank=True, unique=True) topic_en = models.CharField(max_length=256, null=True, blank=True) topic_fr = models.CharField(max_length=256, null=True, blank=True) long_topic_en = models.TextField(null=True, blank=True) long_topic_fr = models.TextField(null=True, blank=True) condition_use_en = models.TextField(null=True, blank=True) condition_use_fr = models.TextField(null=True, blank=True) category = models.ForeignKey(Category, related_name="subcategories", on_delete=models.CASCADE) updated = models.DateTimeField() # Historical record of previous DB history = HistoricalRecords() def __str__(self): try: return f'{self.subcategory_code}: {self.topic_en}' except TypeError as e: return '' class Meta: verbose_name = 'Subcategory' verbose_name_plural = 'Subcategories' class Product(models.Model): """ Corresponds with extremely poorly named 'Final Web Update' table --> might need to combine with original Products table, not sure how these datasets overlap though """ product_code = models.CharField(max_length=16, unique=True, blank=True, null=True) product_name_en = models.CharField(max_length=256, unique=False, blank=True, null=True) product_name_fr = models.CharField(max_length=256, unique=False, blank=True, null=True) language = models.ForeignKey(Language, on_delete=models.SET_NULL, null=True, blank=True) acceptance_date = models.DateTimeField(blank=True, null=True) company = models.ForeignKey(Company, related_name="products", on_delete=models.CASCADE) subcategory = models.ForeignKey(Subcategory, related_name="products", on_delete=models.CASCADE, blank=True, null=True) updated = models.DateTimeField(blank=True, null=True) # Historical record of previous DB history = HistoricalRecords() @property def acceptance_date_pretty(self): return f'{self.acceptance_date.strftime("%Y-%m-%d")}' def __str__(self): try: return f'{self.product_code}: {self.product_name_en}' except TypeError as e: return '' class Meta: verbose_name = 'Product' verbose_name_plural = 'Products'

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null

0

1

0

null

0

1

0

3

dcc424db20a8d7965b1e60f57bf13d6c0c24b2c1

308

py

Python

GrAm/forms.py

claudianjeri/Instagram

93b16495c0ec62eb4ab0327d18e3155b82f1b4bd

[ "MIT" ]

3

2018-06-14T12:07:49.000Z

2020-06-16T02:44:14.000Z

GrAm/forms.py

claudianjeri/Instagram

93b16495c0ec62eb4ab0327d18e3155b82f1b4bd

[ "MIT" ]

null

GrAm/forms.py

claudianjeri/Instagram

93b16495c0ec62eb4ab0327d18e3155b82f1b4bd

[ "MIT" ]

null

from .models import Image, Profile, Comment from django import forms class EditProfileForm(forms.ModelForm): class Meta: model = Profile exclude = ['user'] class UploadForm(forms.ModelForm): class Meta: model = Image exclude = ['user','likes','upload_date','profile']

28

58

0.662338

34

308

5.970588

0.558824

0.137931

0.187192

0.226601

0.275862

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308

11

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null

0

1

0

null

0

1

0

3

dcc7cc7a40b45ab40e2a51260b50373958a8da92

187

py

Python

data_collection/gazette/spiders/sc_tangara.py

Jefersonalves/diario-oficial

9a4bdfe2e31414c993d88831a67160c49a5ee657

[ "MIT" ]

3

2021-08-18T17:50:31.000Z

2021-11-12T23:36:33.000Z

data_collection/gazette/spiders/sc_tangara.py

Jefersonalves/diario-oficial

9a4bdfe2e31414c993d88831a67160c49a5ee657

[ "MIT" ]

4

2021-02-10T02:36:48.000Z

2022-03-02T14:55:34.000Z

data_collection/gazette/spiders/sc_tangara.py

Jefersonalves/diario-oficial

9a4bdfe2e31414c993d88831a67160c49a5ee657

[ "MIT" ]

null

from gazette.spiders.base import FecamGazetteSpider class ScTangaraSpider(FecamGazetteSpider): name = "sc_tangara" FECAM_QUERY = "cod_entidade:268" TERRITORY_ID = "4217907"

23.375

51

0.764706

20

187

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0.95

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0.063291

0.15508

187

7

52

26.714286

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1

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1

0

null

0

1

0

null

0

1

0

3

dcefdb99fe2524931fcc166cd7b76a8ef691d120

216

py

Python

djconnectwise/management/commands/delete_callback.py

kti-cameron/django-connectwise

e3438d6f303163cd482ce31a98aca5abf15ec932

[ "MIT" ]

null

djconnectwise/management/commands/delete_callback.py

kti-cameron/django-connectwise

e3438d6f303163cd482ce31a98aca5abf15ec932

[ "MIT" ]

null

djconnectwise/management/commands/delete_callback.py

kti-cameron/django-connectwise

e3438d6f303163cd482ce31a98aca5abf15ec932

[ "MIT" ]

null

from django.utils.translation import ugettext_lazy as _ from . import _callback class Command(_callback.Command): help = str(_('Deletes the callback from the target connectwise system.')) ACTION = 'delete'

27

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0.75463

27

216

5.851852

0.740741

0

0.162037

216

7

78

30.857143

0.872928

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0.287037

0

1

0

false

0

0.4

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1

0

1

0

null

0

1

0

null

0

1

0

3

dcfb3f72e395046eb90ede6f87900476cf79be0a

43

py

Python

conftest.py

sergei-bondarenko/exchange-simulator

a5bba00c38016823a5d18471da01fd4e04319b8e

[ "Unlicense" ]

1

2021-03-16T06:04:12.000Z

conftest.py

sergei-bondarenko/exchange-simulator

a5bba00c38016823a5d18471da01fd4e04319b8e

[ "Unlicense" ]

null

conftest.py

sergei-bondarenko/exchange-simulator

a5bba00c38016823a5d18471da01fd4e04319b8e

[ "Unlicense" ]

null

pytest_plugins = [ 'xchg.tests.data', ]

10.75

21

0.627907

5

43

5.2

1

0

0.186047

43

3

22

14.333333

0.742857

0

0.348837

0

1

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false

0

1

0

null

0

1

0

null

0

3

0d096ee6b4123c7c0c3a2ec91a6b9b448d9095f1

131

py

Python

code/webhookhandler/urls/hooks.py

superfluidity/RDCL3D

3c5717941bd4046aa1be178e9004db1dc1c469a0

[ "Apache-2.0" ]

8

2017-03-13T16:34:28.000Z

2021-11-16T11:35:56.000Z

code/webhookhandler/urls/hooks.py

superfluidity/RDCL3D

3c5717941bd4046aa1be178e9004db1dc1c469a0

[ "Apache-2.0" ]

null

code/webhookhandler/urls/hooks.py

superfluidity/RDCL3D

3c5717941bd4046aa1be178e9004db1dc1c469a0

[ "Apache-2.0" ]

3

2017-03-28T09:26:40.000Z

2020-12-08T14:16:12.000Z

from django.conf.urls import url from webhookhandler import views urlpatterns = [ url(r'^$', views.webhook, name='webhook'), ]

21.833333

46

0.717557

17

131

5.529412

0.705882

0

0.145038

131

6

47

21.833333

0.839286

0

0.068182

0

1

0

false

0

0.4

0

0.4

0

1

0

null

0

1

0

null

0

1

0

3

0d17348f8f1d18cb7ea86e8791e3c59ce2012a91

240

py

Python

kohtaaminen/__main__.py

sthagen/kohtaaminen

ce0784ccd8be109164d63f2b5dcea128bd6f4534

[ "MIT" ]

1

2021-11-13T10:57:55.000Z

kohtaaminen/__main__.py

sthagen/kohtaaminen

ce0784ccd8be109164d63f2b5dcea128bd6f4534

[ "MIT" ]

4

2021-11-14T15:12:06.000Z

2021-11-30T13:54:47.000Z

kohtaaminen/__main__.py

sthagen/kohtaaminen

ce0784ccd8be109164d63f2b5dcea128bd6f4534

[ "MIT" ]

null

# -*- coding: utf-8 -*- # pylint: disable=expression-not-assigned,line-too-long,missing-module-docstring import sys from kohtaaminen.cli import app if __name__ == '__main__': sys.exit(app(prog_name='kohtaaminen')) # pragma: no cover

26.666667

80

0.725

33

240

5

0.848485

0

0.004762

0.125

240

8

81

30

0.780952

0.4875

0

0.159664

0

1

0

true

0

0.5

0

0.5

0

null

0

1

0

null

0

1

0

1

0

3

0d2236c09fb61de0facb0db8ff5d2c14d3843c5c

608

py

Python

Virtualenv/Env/src/GoTravel/Home/models.py

Anoop01234/Go-Travel

aa91f1a4ce7e7ed78de8eadc55e6a25d1a73bdd8

[ "MIT" ]

null

Virtualenv/Env/src/GoTravel/Home/models.py

Anoop01234/Go-Travel

aa91f1a4ce7e7ed78de8eadc55e6a25d1a73bdd8

[ "MIT" ]

null

Virtualenv/Env/src/GoTravel/Home/models.py

Anoop01234/Go-Travel

aa91f1a4ce7e7ed78de8eadc55e6a25d1a73bdd8

[ "MIT" ]

1

2021-12-21T17:27:34.000Z

from django.db import models class Chef(models.Model): name=models.CharField(max_length=50) position=models.CharField(max_length=50) description=models.TextField() photo=models.ImageField(upload_to='chef/') class Meta: verbose_name ='Chef' verbose_name_plural='Chefs' def __str__(self): return self.name class Slider(models.Model): image=models.ImageField(upload_to='slider/') name=models.CharField(max_length=50) class Meta: verbose_name='Slider' verbose_name_plural='Sliders' def __str__(self): return self.name

23.384615

48

0.685855

76

608

5.236842

0.407895

0.110553

0.135678

0.180905

0.336683

0.271357

0

0.012422

0.205592

608

25

49

24.32

0.811594

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0.421053

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false

0

0.052632

0.105263

0.789474

0

null

0

1

0

null

0

1

0

3

0d2792d0fd44769568c671a62acca11f98fc94a4

305

py

Python

iotapp/Classes/httprequest.py

fcarbah/IOT

6b323c75844d0a6744df12059466d6c54bd0b242

[ "MIT" ]

null

iotapp/Classes/httprequest.py

fcarbah/IOT

6b323c75844d0a6744df12059466d6c54bd0b242

[ "MIT" ]

null

iotapp/Classes/httprequest.py

fcarbah/IOT

6b323c75844d0a6744df12059466d6c54bd0b242

[ "MIT" ]

null

import urllib3 class HttpRequest: __http=None def __init__(self): self.__http = urllib3.PoolManager() def post(self,url,data={},headers={}): self.__http.request('POST',url,data,headers) def get(self,url,headers={}): self.__http.request('get',url,{},headers)

17.941176

52

0.629508

37

305

4.864865

0.432432

0.133333

0.155556

0.244444

0

0.008264

0.206557

305

17

53

17.941176

0.735537

0

0.022876

0

1

0.333333

false

0

0.111111

0

0.666667

0

null

0

1

0

null

0

1

0

1

0

3

0d2b31d40272b35f2023f3820d8bcc5908033214

1,795

py

Python

UnityEngine/MatchTargetWeightMask/__init__.py

Grim-es/udon-pie-auto-completion

c2cd86554ed615cdbbb01e19fa40665eafdfaedc

[ "MIT" ]

null

UnityEngine/MatchTargetWeightMask/__init__.py

Grim-es/udon-pie-auto-completion

c2cd86554ed615cdbbb01e19fa40665eafdfaedc

[ "MIT" ]

null

UnityEngine/MatchTargetWeightMask/__init__.py

Grim-es/udon-pie-auto-completion

c2cd86554ed615cdbbb01e19fa40665eafdfaedc

[ "MIT" ]

null

from UdonPie import System from UdonPie import UnityEngine from UdonPie.Undefined import * class MatchTargetWeightMask: def __new__(cls, arg1=None): ''' :returns: MatchTargetWeightMask :rtype: UnityEngine.MatchTargetWeightMask ''' pass @staticmethod def ctor(arg1, arg2): ''' :param arg1: Vector3 :type arg1: UnityEngine.Vector3 :param arg2: Single :type arg2: System.Single or float :returns: MatchTargetWeightMask :rtype: UnityEngine.MatchTargetWeightMask ''' pass @staticmethod def get_positionXYZWeight(): ''' :returns: Vector3 :rtype: UnityEngine.Vector3 ''' pass @staticmethod def set_positionXYZWeight(arg1): ''' :param arg1: Vector3 :type arg1: UnityEngine.Vector3 ''' pass @staticmethod def get_rotationWeight(): ''' :returns: Single :rtype: System.Single ''' pass @staticmethod def set_rotationWeight(arg1): ''' :param arg1: Single :type arg1: System.Single or float ''' pass @staticmethod def Equals(arg1): ''' :param arg1: Object :type arg1: System.Object :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def ToString(): ''' :returns: String :rtype: System.String ''' pass @staticmethod def GetHashCode(): ''' :returns: Int32 :rtype: System.Int32 ''' pass @staticmethod def GetType(): ''' :returns: Type :rtype: System.Type ''' pass

19.725275

49

0.528691

144

1,795

6.534722

0.256944

0.153029

0.181722

0.093518

0.327311

0.2678

0.178533

0

0.023235

0.376602

1,795

90

50

19.944444

0.817694

0.359889

0

0.575758

0

1

0.30303

false

0.30303

0.090909

0

0.424242

0

null

0

1

0

null

0

1

0

1

0

3

0d2c7e6cf904b313acc9aca26daeafb4a2b97695

107

py

Python

teagles_advent_2021/day_13/pt2.py

teagles/teagles-advent-2021

d49d842663d6382bd0d4d93198ccd66ab68e681b

[ "MIT" ]

null

teagles_advent_2021/day_13/pt2.py

teagles/teagles-advent-2021

d49d842663d6382bd0d4d93198ccd66ab68e681b

[ "MIT" ]

null

teagles_advent_2021/day_13/pt2.py

teagles/teagles-advent-2021

d49d842663d6382bd0d4d93198ccd66ab68e681b

[ "MIT" ]

null

import sys from .lib import parse_input def main(): print() if __name__ == '__main__': main()

8.916667

28

0.626168

14

107

4.142857

0.785714

0

0.252336

107

11

29

9.727273

0.725

0

0.074766

0

1

0.166667

true

0

0.333333

0

0.5

0.166667

1

0

null

0

null

0

1

0

1

0

3

b49c01b7f4d4caa36d93674286e808354fe98984

278

py

Python

drogher/package/dhl.py

jbittel/drogher

9a01a19aa6575f82949ef83a3e3b8834807d9cc9

[ "BSD-3-Clause" ]

13

2017-04-24T07:49:30.000Z

2020-09-22T13:13:13.000Z

drogher/package/dhl.py

jbittel/drogher

9a01a19aa6575f82949ef83a3e3b8834807d9cc9

[ "BSD-3-Clause" ]

null

drogher/package/dhl.py

jbittel/drogher

9a01a19aa6575f82949ef83a3e3b8834807d9cc9

[ "BSD-3-Clause" ]

4

2018-09-08T05:31:57.000Z

2022-02-10T17:42:31.000Z

from .base import Package class DHL(Package): barcode_pattern = r'^\d{10}$' shipper = 'DHL' @property def valid_checksum(self): chars, check_digit = self.tracking_number[:-1], self.tracking_number[-1] return int(chars) % 7 == int(check_digit)

23.166667

80

0.643885

37

278

4.675676

0.702703

0.115607

0.208092

0.219653

0

0.023041

0.219424

278

11

81

25.272727

0.774194

0

0.039568

0

1

0.125

false

0

0.125

0

0.75

0

null

0

1

0

null

0

1

0

3

b4bee4983a1e8f4098187f57c27bfb93f46aeb4b

338

py

Python

src/addongen/src/addongen/__object__.py

KAIYOHUGO/addon-gen

65c7db176bdef8c8746eb6db50b684f8cce23bd6

[ "MIT" ]

null

src/addongen/src/addongen/__object__.py

KAIYOHUGO/addon-gen

65c7db176bdef8c8746eb6db50b684f8cce23bd6

[ "MIT" ]

null

src/addongen/src/addongen/__object__.py

KAIYOHUGO/addon-gen

65c7db176bdef8c8746eb6db50b684f8cce23bd6

[ "MIT" ]

null

class component: __type__ = str() data = object() class query: __type__ = str() query_id = int() class itemStack: __identifier__ = str() __type__ = str() count = str() item = str() class block: __identifier__ = str() __type__ = str() block_position = object() ticking_area = object()

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3

b4c44cfa861a3cb2f7399a87693dea11f5c7ba5f

481

py

Python

search_service/models/base.py

verdan/amundsensearchlibrary

ca57770d82f7956339376655aaae736870671a66

[ "Apache-2.0" ]

null

search_service/models/base.py

verdan/amundsensearchlibrary

ca57770d82f7956339376655aaae736870671a66

[ "Apache-2.0" ]

null

search_service/models/base.py

verdan/amundsensearchlibrary

ca57770d82f7956339376655aaae736870671a66

[ "Apache-2.0" ]

null

from abc import ABCMeta, abstractmethod from typing import Set class Base(metaclass=ABCMeta): """ A base class for ES model """ @abstractmethod def get_id(cls) -> str: # return a document id in ES pass @abstractmethod def get_attrs(cls) -> Set: # return a set of attributes for the class pass @staticmethod @abstractmethod def get_type() -> str: # return a type string for the class pass

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1

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1

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370211a081a208a56f69edfdacecf44b58bc1ce6

26,188

py

Python

airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py

arezamoosavi/airflow

c3c81c3144386d1de535c1c5e777270e727bb69e

[ "Apache-2.0" ]

2

2016-08-23T14:22:15.000Z

2017-09-28T19:45:26.000Z

airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py

arezamoosavi/airflow

c3c81c3144386d1de535c1c5e777270e727bb69e

[ "Apache-2.0" ]

4

2019-01-24T11:01:17.000Z

2022-02-28T04:28:07.000Z

airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py

arezamoosavi/airflow

c3c81c3144386d1de535c1c5e777270e727bb69e

[ "Apache-2.0" ]

6

2018-04-09T07:46:05.000Z

2019-07-16T00:13:15.000Z

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # """This module contains Google Vertex AI operators.""" from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union from google.api_core.exceptions import NotFound from google.api_core.retry import Retry from google.cloud.aiplatform import datasets from google.cloud.aiplatform.models import Model from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline from airflow.models import BaseOperator from airflow.providers.google.cloud.hooks.vertex_ai.auto_ml import AutoMLHook from airflow.providers.google.cloud.links.vertex_ai import VertexAIModelLink, VertexAITrainingPipelinesLink if TYPE_CHECKING: from airflow.utils.context import Context class AutoMLTrainingJobBaseOperator(BaseOperator): """The base class for operators that launch AutoML jobs on VertexAI.""" def __init__( self, *, project_id: str, region: str, display_name: str, labels: Optional[Dict[str, str]] = None, training_encryption_spec_key_name: Optional[str] = None, model_encryption_spec_key_name: Optional[str] = None, # RUN training_fraction_split: Optional[float] = None, test_fraction_split: Optional[float] = None, model_display_name: Optional[str] = None, model_labels: Optional[Dict[str, str]] = None, sync: bool = True, gcp_conn_id: str = "google_cloud_default", delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.project_id = project_id self.region = region self.display_name = display_name self.labels = labels self.training_encryption_spec_key_name = training_encryption_spec_key_name self.model_encryption_spec_key_name = model_encryption_spec_key_name # START Run param self.training_fraction_split = training_fraction_split self.test_fraction_split = test_fraction_split self.model_display_name = model_display_name self.model_labels = model_labels self.sync = sync # END Run param self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain self.hook = None # type: Optional[AutoMLHook] def on_kill(self) -> None: """ Callback called when the operator is killed. Cancel any running job. """ if self.hook: self.hook.cancel_auto_ml_job() class CreateAutoMLForecastingTrainingJobOperator(AutoMLTrainingJobBaseOperator): """Create AutoML Forecasting Training job""" template_fields = [ 'region', 'impersonation_chain', ] operator_extra_links = (VertexAIModelLink(),) def __init__( self, *, dataset_id: str, target_column: str, time_column: str, time_series_identifier_column: str, unavailable_at_forecast_columns: List[str], available_at_forecast_columns: List[str], forecast_horizon: int, data_granularity_unit: str, data_granularity_count: int, optimization_objective: Optional[str] = None, column_specs: Optional[Dict[str, str]] = None, column_transformations: Optional[List[Dict[str, Dict[str, str]]]] = None, validation_fraction_split: Optional[float] = None, predefined_split_column_name: Optional[str] = None, weight_column: Optional[str] = None, time_series_attribute_columns: Optional[List[str]] = None, context_window: Optional[int] = None, export_evaluated_data_items: bool = False, export_evaluated_data_items_bigquery_destination_uri: Optional[str] = None, export_evaluated_data_items_override_destination: bool = False, quantiles: Optional[List[float]] = None, validation_options: Optional[str] = None, budget_milli_node_hours: int = 1000, **kwargs, ) -> None: super().__init__(**kwargs) self.dataset_id = dataset_id self.target_column = target_column self.time_column = time_column self.time_series_identifier_column = time_series_identifier_column self.unavailable_at_forecast_columns = unavailable_at_forecast_columns self.available_at_forecast_columns = available_at_forecast_columns self.forecast_horizon = forecast_horizon self.data_granularity_unit = data_granularity_unit self.data_granularity_count = data_granularity_count self.optimization_objective = optimization_objective self.column_specs = column_specs self.column_transformations = column_transformations self.validation_fraction_split = validation_fraction_split self.predefined_split_column_name = predefined_split_column_name self.weight_column = weight_column self.time_series_attribute_columns = time_series_attribute_columns self.context_window = context_window self.export_evaluated_data_items = export_evaluated_data_items self.export_evaluated_data_items_bigquery_destination_uri = ( export_evaluated_data_items_bigquery_destination_uri ) self.export_evaluated_data_items_override_destination = ( export_evaluated_data_items_override_destination ) self.quantiles = quantiles self.validation_options = validation_options self.budget_milli_node_hours = budget_milli_node_hours def execute(self, context: "Context"): self.hook = AutoMLHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) model = self.hook.create_auto_ml_forecasting_training_job( project_id=self.project_id, region=self.region, display_name=self.display_name, dataset=datasets.TimeSeriesDataset(dataset_name=self.dataset_id), target_column=self.target_column, time_column=self.time_column, time_series_identifier_column=self.time_series_identifier_column, unavailable_at_forecast_columns=self.unavailable_at_forecast_columns, available_at_forecast_columns=self.available_at_forecast_columns, forecast_horizon=self.forecast_horizon, data_granularity_unit=self.data_granularity_unit, data_granularity_count=self.data_granularity_count, optimization_objective=self.optimization_objective, column_specs=self.column_specs, column_transformations=self.column_transformations, labels=self.labels, training_encryption_spec_key_name=self.training_encryption_spec_key_name, model_encryption_spec_key_name=self.model_encryption_spec_key_name, training_fraction_split=self.training_fraction_split, validation_fraction_split=self.validation_fraction_split, test_fraction_split=self.test_fraction_split, predefined_split_column_name=self.predefined_split_column_name, weight_column=self.weight_column, time_series_attribute_columns=self.time_series_attribute_columns, context_window=self.context_window, export_evaluated_data_items=self.export_evaluated_data_items, export_evaluated_data_items_bigquery_destination_uri=( self.export_evaluated_data_items_bigquery_destination_uri ), export_evaluated_data_items_override_destination=( self.export_evaluated_data_items_override_destination ), quantiles=self.quantiles, validation_options=self.validation_options, budget_milli_node_hours=self.budget_milli_node_hours, model_display_name=self.model_display_name, model_labels=self.model_labels, sync=self.sync, ) result = Model.to_dict(model) model_id = self.hook.extract_model_id(result) VertexAIModelLink.persist(context=context, task_instance=self, model_id=model_id) return result class CreateAutoMLImageTrainingJobOperator(AutoMLTrainingJobBaseOperator): """Create Auto ML Image Training job""" template_fields = [ 'region', 'impersonation_chain', ] operator_extra_links = (VertexAIModelLink(),) def __init__( self, *, dataset_id: str, prediction_type: str = "classification", multi_label: bool = False, model_type: str = "CLOUD", base_model: Optional[Model] = None, validation_fraction_split: Optional[float] = None, training_filter_split: Optional[str] = None, validation_filter_split: Optional[str] = None, test_filter_split: Optional[str] = None, budget_milli_node_hours: Optional[int] = None, disable_early_stopping: bool = False, **kwargs, ) -> None: super().__init__(**kwargs) self.dataset_id = dataset_id self.prediction_type = prediction_type self.multi_label = multi_label self.model_type = model_type self.base_model = base_model self.validation_fraction_split = validation_fraction_split self.training_filter_split = training_filter_split self.validation_filter_split = validation_filter_split self.test_filter_split = test_filter_split self.budget_milli_node_hours = budget_milli_node_hours self.disable_early_stopping = disable_early_stopping def execute(self, context: "Context"): self.hook = AutoMLHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) model = self.hook.create_auto_ml_image_training_job( project_id=self.project_id, region=self.region, display_name=self.display_name, dataset=datasets.ImageDataset(dataset_name=self.dataset_id), prediction_type=self.prediction_type, multi_label=self.multi_label, model_type=self.model_type, base_model=self.base_model, labels=self.labels, training_encryption_spec_key_name=self.training_encryption_spec_key_name, model_encryption_spec_key_name=self.model_encryption_spec_key_name, training_fraction_split=self.training_fraction_split, validation_fraction_split=self.validation_fraction_split, test_fraction_split=self.test_fraction_split, training_filter_split=self.training_filter_split, validation_filter_split=self.validation_filter_split, test_filter_split=self.test_filter_split, budget_milli_node_hours=self.budget_milli_node_hours, model_display_name=self.model_display_name, model_labels=self.model_labels, disable_early_stopping=self.disable_early_stopping, sync=self.sync, ) result = Model.to_dict(model) model_id = self.hook.extract_model_id(result) VertexAIModelLink.persist(context=context, task_instance=self, model_id=model_id) return result class CreateAutoMLTabularTrainingJobOperator(AutoMLTrainingJobBaseOperator): """Create Auto ML Tabular Training job""" template_fields = [ 'region', 'impersonation_chain', ] operator_extra_links = (VertexAIModelLink(),) def __init__( self, *, dataset_id: str, target_column: str, optimization_prediction_type: str, optimization_objective: Optional[str] = None, column_specs: Optional[Dict[str, str]] = None, column_transformations: Optional[List[Dict[str, Dict[str, str]]]] = None, optimization_objective_recall_value: Optional[float] = None, optimization_objective_precision_value: Optional[float] = None, validation_fraction_split: Optional[float] = None, predefined_split_column_name: Optional[str] = None, timestamp_split_column_name: Optional[str] = None, weight_column: Optional[str] = None, budget_milli_node_hours: int = 1000, disable_early_stopping: bool = False, export_evaluated_data_items: bool = False, export_evaluated_data_items_bigquery_destination_uri: Optional[str] = None, export_evaluated_data_items_override_destination: bool = False, **kwargs, ) -> None: super().__init__(**kwargs) self.dataset_id = dataset_id self.target_column = target_column self.optimization_prediction_type = optimization_prediction_type self.optimization_objective = optimization_objective self.column_specs = column_specs self.column_transformations = column_transformations self.optimization_objective_recall_value = optimization_objective_recall_value self.optimization_objective_precision_value = optimization_objective_precision_value self.validation_fraction_split = validation_fraction_split self.predefined_split_column_name = predefined_split_column_name self.timestamp_split_column_name = timestamp_split_column_name self.weight_column = weight_column self.budget_milli_node_hours = budget_milli_node_hours self.disable_early_stopping = disable_early_stopping self.export_evaluated_data_items = export_evaluated_data_items self.export_evaluated_data_items_bigquery_destination_uri = ( export_evaluated_data_items_bigquery_destination_uri ) self.export_evaluated_data_items_override_destination = ( export_evaluated_data_items_override_destination ) def execute(self, context: "Context"): self.hook = AutoMLHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) model = self.hook.create_auto_ml_tabular_training_job( project_id=self.project_id, region=self.region, display_name=self.display_name, dataset=datasets.TabularDataset(dataset_name=self.dataset_id), target_column=self.target_column, optimization_prediction_type=self.optimization_prediction_type, optimization_objective=self.optimization_objective, column_specs=self.column_specs, column_transformations=self.column_transformations, optimization_objective_recall_value=self.optimization_objective_recall_value, optimization_objective_precision_value=self.optimization_objective_precision_value, labels=self.labels, training_encryption_spec_key_name=self.training_encryption_spec_key_name, model_encryption_spec_key_name=self.model_encryption_spec_key_name, training_fraction_split=self.training_fraction_split, validation_fraction_split=self.validation_fraction_split, test_fraction_split=self.test_fraction_split, predefined_split_column_name=self.predefined_split_column_name, timestamp_split_column_name=self.timestamp_split_column_name, weight_column=self.weight_column, budget_milli_node_hours=self.budget_milli_node_hours, model_display_name=self.model_display_name, model_labels=self.model_labels, disable_early_stopping=self.disable_early_stopping, export_evaluated_data_items=self.export_evaluated_data_items, export_evaluated_data_items_bigquery_destination_uri=( self.export_evaluated_data_items_bigquery_destination_uri ), export_evaluated_data_items_override_destination=( self.export_evaluated_data_items_override_destination ), sync=self.sync, ) result = Model.to_dict(model) model_id = self.hook.extract_model_id(result) VertexAIModelLink.persist(context=context, task_instance=self, model_id=model_id) return result class CreateAutoMLTextTrainingJobOperator(AutoMLTrainingJobBaseOperator): """Create Auto ML Text Training job""" template_fields = [ 'region', 'impersonation_chain', ] operator_extra_links = (VertexAIModelLink(),) def __init__( self, *, dataset_id: str, prediction_type: str, multi_label: bool = False, sentiment_max: int = 10, validation_fraction_split: Optional[float] = None, training_filter_split: Optional[str] = None, validation_filter_split: Optional[str] = None, test_filter_split: Optional[str] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.dataset_id = dataset_id self.prediction_type = prediction_type self.multi_label = multi_label self.sentiment_max = sentiment_max self.validation_fraction_split = validation_fraction_split self.training_filter_split = training_filter_split self.validation_filter_split = validation_filter_split self.test_filter_split = test_filter_split def execute(self, context: "Context"): self.hook = AutoMLHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) model = self.hook.create_auto_ml_text_training_job( project_id=self.project_id, region=self.region, display_name=self.display_name, dataset=datasets.TextDataset(dataset_name=self.dataset_id), prediction_type=self.prediction_type, multi_label=self.multi_label, sentiment_max=self.sentiment_max, labels=self.labels, training_encryption_spec_key_name=self.training_encryption_spec_key_name, model_encryption_spec_key_name=self.model_encryption_spec_key_name, training_fraction_split=self.training_fraction_split, validation_fraction_split=self.validation_fraction_split, test_fraction_split=self.test_fraction_split, training_filter_split=self.training_filter_split, validation_filter_split=self.validation_filter_split, test_filter_split=self.test_filter_split, model_display_name=self.model_display_name, model_labels=self.model_labels, sync=self.sync, ) result = Model.to_dict(model) model_id = self.hook.extract_model_id(result) VertexAIModelLink.persist(context=context, task_instance=self, model_id=model_id) return result class CreateAutoMLVideoTrainingJobOperator(AutoMLTrainingJobBaseOperator): """Create Auto ML Video Training job""" template_fields = [ 'region', 'impersonation_chain', ] operator_extra_links = (VertexAIModelLink(),) def __init__( self, *, dataset_id: str, prediction_type: str = "classification", model_type: str = "CLOUD", training_filter_split: Optional[str] = None, test_filter_split: Optional[str] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.dataset_id = dataset_id self.prediction_type = prediction_type self.model_type = model_type self.training_filter_split = training_filter_split self.test_filter_split = test_filter_split def execute(self, context: "Context"): self.hook = AutoMLHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) model = self.hook.create_auto_ml_video_training_job( project_id=self.project_id, region=self.region, display_name=self.display_name, dataset=datasets.VideoDataset(dataset_name=self.dataset_id), prediction_type=self.prediction_type, model_type=self.model_type, labels=self.labels, training_encryption_spec_key_name=self.training_encryption_spec_key_name, model_encryption_spec_key_name=self.model_encryption_spec_key_name, training_fraction_split=self.training_fraction_split, test_fraction_split=self.test_fraction_split, training_filter_split=self.training_filter_split, test_filter_split=self.test_filter_split, model_display_name=self.model_display_name, model_labels=self.model_labels, sync=self.sync, ) result = Model.to_dict(model) model_id = self.hook.extract_model_id(result) VertexAIModelLink.persist(context=context, task_instance=self, model_id=model_id) return result class DeleteAutoMLTrainingJobOperator(BaseOperator): """Deletes an AutoMLForecastingTrainingJob, AutoMLImageTrainingJob, AutoMLTabularTrainingJob, AutoMLTextTrainingJob, or AutoMLVideoTrainingJob. """ template_fields = ("region", "project_id", "impersonation_chain") def __init__( self, *, training_pipeline_id: str, region: str, project_id: str, retry: Optional[Retry] = None, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.training_pipeline = training_pipeline_id self.region = region self.project_id = project_id self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain def execute(self, context: "Context"): hook = AutoMLHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) try: self.log.info("Deleting Auto ML training pipeline: %s", self.training_pipeline) training_pipeline_operation = hook.delete_training_pipeline( training_pipeline=self.training_pipeline, region=self.region, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) hook.wait_for_operation(timeout=self.timeout, operation=training_pipeline_operation) self.log.info("Training pipeline was deleted.") except NotFound: self.log.info("The Training Pipeline ID %s does not exist.", self.training_pipeline) class ListAutoMLTrainingJobOperator(BaseOperator): """Lists AutoMLForecastingTrainingJob, AutoMLImageTrainingJob, AutoMLTabularTrainingJob, AutoMLTextTrainingJob, or AutoMLVideoTrainingJob in a Location. """ template_fields = [ "region", "project_id", "impersonation_chain", ] operator_extra_links = [ VertexAITrainingPipelinesLink(), ] def __init__( self, *, region: str, project_id: str, page_size: Optional[int] = None, page_token: Optional[str] = None, filter: Optional[str] = None, read_mask: Optional[str] = None, retry: Optional[Retry] = None, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.region = region self.project_id = project_id self.page_size = page_size self.page_token = page_token self.filter = filter self.read_mask = read_mask self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain def execute(self, context: "Context"): hook = AutoMLHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) results = hook.list_training_pipelines( region=self.region, project_id=self.project_id, page_size=self.page_size, page_token=self.page_token, filter=self.filter, read_mask=self.read_mask, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) VertexAITrainingPipelinesLink.persist(context=context, task_instance=self) return [TrainingPipeline.to_dict(result) for result in results]

41.967949

107

0.688941

2,883

26,188

5.855706

0.097121

0.035422

0.033764

0.042649

0.778699

0.730364

0.674446

0.652411

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0.000754

0.240568

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108

42.035313

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null

0

1

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1

0

1

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null

0

3

370239612665db191d987f35efe965057710194c

18

py

Python

ret_benchmark/utils/log_info.py

alibaba-edu/Ranking-based-Instance-Selection

06e7fa2061d42f5e8181f7181fe591fc3d294f8d

[ "MIT" ]

20

2021-04-15T09:15:28.000Z

2022-03-30T02:31:20.000Z

vit_retri/utils/log_info.py

ludics/ViT-Retri

4a17ae8392a0f8145a2f5ee37854e76503c26009

[ "MIT" ]

1

2021-06-03T05:51:52.000Z

2021-06-19T05:52:33.000Z

vit_retri/utils/log_info.py

ludics/ViT-Retri

4a17ae8392a0f8145a2f5ee37854e76503c26009

[ "MIT" ]

5

2021-05-17T09:05:38.000Z

2022-02-28T10:10:50.000Z

log_info = dict()

9

17

0.666667

3

18

3.666667

1

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0.166667

18

1

18

0.733333

0

1

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false

0

1

0

null

0

1

0

null

0

3

371c11466bbee73e5a5eb806d61588dcac84b8ca

106

py

Python

start.py

fahimfarhan/python-Package-Demo

ce4fe79dd0fe9494892baf620aca9d88107894a7

[ "MIT" ]

null

start.py

fahimfarhan/python-Package-Demo

ce4fe79dd0fe9494892baf620aca9d88107894a7

[ "MIT" ]

null

start.py

fahimfarhan/python-Package-Demo

ce4fe79dd0fe9494892baf620aca9d88107894a7

[ "MIT" ]

null

from measure import metrics, norms if __name__ == "__main__": metrics.metric(1) norms.norms()

11.777778

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0.669811

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4.846154

0.769231

0

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0.216981

106

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1

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null

0

1

0

3

2ec18eb4c9d609c45f211bbba5ede5409418c196

173

py

Python

crawl_cvs/handlers/my_func.py

MacHu-GWU/crawl_cvs-project

1cf63254ea1d7c8026a9d0e0543aaa2d7b7f4918

[ "MIT" ]

1

2020-06-19T09:45:20.000Z

crawl_cvs/handlers/my_func.py

MacHu-GWU/crawl_cvs-project

1cf63254ea1d7c8026a9d0e0543aaa2d7b7f4918

[ "MIT" ]

1

2019-12-27T18:41:21.000Z

crawl_cvs/handlers/my_func.py

MacHu-GWU/crawl_cvs-project

1cf63254ea1d7c8026a9d0e0543aaa2d7b7f4918

[ "MIT" ]

1

2018-08-22T01:27:32.000Z

# -*- coding: utf-8 -*- def handler(event, context): if event.get("name"): return "Hello {}!".format(event.get("name")) else: return "Hello World!"

21.625

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0.549133

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4.52381

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null

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null

0

3

2ecbc23da4493780a4b43db3b26245823e15d32e

5,925

py

Python

bsm/paradag/__init__.py

bsmsoft/bsm

e45ec5442de39e5f948023cd5b4c6181073cf9a2

[ "MIT" ]

3

2019-06-12T17:19:12.000Z

2022-01-07T02:10:06.000Z

bsm/paradag/__init__.py

bsmsoft/bsm

e45ec5442de39e5f948023cd5b4c6181073cf9a2

[ "MIT" ]

null

bsm/paradag/__init__.py

bsmsoft/bsm

e45ec5442de39e5f948023cd5b4c6181073cf9a2

[ "MIT" ]

null

''' This package comes from https://github.com/xianghuzhao/paradag It can also be installed by "pip install paradag" ''' import random class DagVertexNotFoundError(Exception): pass class DagEdgeNotFoundError(Exception): pass class DagCycleError(Exception): pass class DagData(object): def __init__(self): self.__graph = {} self.__graph_reverse = {} def vertice(self): return set(self.__graph.keys()) def add_vertex(self, vertex): if vertex not in self.__graph: self.__graph[vertex] = set() self.__graph_reverse[vertex] = set() def add_edge(self, v_from, v_to): self.__graph[v_from].add(v_to) self.__graph_reverse[v_to].add(v_from) def remove_edge(self, v_from, v_to): self.__graph[v_from].remove(v_to) self.__graph_reverse[v_to].remove(v_from) def successors(self, vertex): return self.__graph[vertex] def predecessors(self, vertex): return self.__graph_reverse[vertex] class Dag(object): def __init__(self): self.__data = DagData() def __validate_vertex(self, *vertice): for vertex in vertice: if vertex not in self.__data.vertice(): raise DagVertexNotFoundError('Vertex "{0}" does not belong to DAG'.format(vertex)) def __has_path_to(self, v_from, v_to): if v_from == v_to: return True for v in self.__data.successors(v_from): if self.__has_path_to(v, v_to): return True return False def vertice(self): return self.__data.vertice() def add_vertex(self, *vertice): for vertex in vertice: self.__data.add_vertex(vertex) def add_edge(self, v_from, *v_tos): self.__validate_vertex(v_from, *v_tos) for v_to in v_tos: if self.__has_path_to(v_to, v_from): raise DagCycleError('Cycle if add edge from "{0}" to "{1}"'.format(v_from, v_to)) self.__data.add_edge(v_from, v_to) def remove_edge(self, v_from, v_to): self.__validate_vertex(v_from, v_to) if v_to not in self.__data.successors(v_from): raise DagEdgeNotFoundError('Edge not found from "{0}" to "{1}"'.format(v_from, v_to)) self.__data.remove_edge(v_from, v_to) def vertex_size(self): return len(self.__data.vertice()) def edge_size(self): size = 0 for vertex in self.__data.vertice(): size += self.outdegree(vertex) return size def successors(self, vertex): self.__validate_vertex(vertex) return self.__data.successors(vertex) def predecessors(self, vertex): self.__validate_vertex(vertex) return self.__data.predecessors(vertex) def indegree(self, vertex): return len(self.predecessors(vertex)) def outdegree(self, vertex): return len(self.successors(vertex)) def __endpoints(self, degree_callback): endpoints = set() for vertex in self.__data.vertice(): if degree_callback(vertex) == 0: endpoints.add(vertex) return endpoints def all_starts(self): return self.__endpoints(self.indegree) def all_terminals(self): return self.__endpoints(self.outdegree) class SingleSelector(object): def select(self, running, idle): return [next(iter(idle))] class FullSelector(object): def select(self, running, idle): return list(idle) class RandomSelector(object): def select(self, running, idle): return [random.choice(list(idle))] class ShuffleSelector(object): def select(self, running, idle): idle_list = list(idle) random.shuffle(idle_list) return idle_list class NullProcessor(object): def process(self, vertice, executor): return [(vertex, None) for vertex in vertice] # TODO: report_*, deliver, abort should be optional class NullExecutor(object): def param(self, vertex): return None def execute(self, param_vertex): return None def report_start(self, vertice): pass def report_finish(self, vertice_results): pass def report_running(self, vertice): pass def deliver(self, vertex, result): pass def abort(self, vertice): pass # TODO: Use Vertice as core. Processor, Selector and Executor should all surround Vertice def dag_run(dag, selector=None, processor=None, executor=None): if selector is None: selector = FullSelector() if processor is None: processor = NullProcessor() if executor is None: executor = NullExecutor() indegree_dict = {} for vertex in dag.vertice(): indegree_dict[vertex] = dag.indegree(vertex) vertice_final = [] vertice_processing = set() vertice_zero_indegree = dag.all_starts() while vertice_zero_indegree: vertice_to_run = selector.select(vertice_processing, vertice_zero_indegree-vertice_processing) executor.report_start(vertice_to_run) executor.report_running(set(vertice_to_run) | vertice_processing) vertice_processed_results = processor.process(vertice_to_run, executor) executor.report_finish(vertice_processed_results) vertice_processed = [result[0] for result in vertice_processed_results] vertice_processing |= set(vertice_to_run) vertice_processing -= set(vertice_processed) vertice_final += vertice_processed vertice_zero_indegree -= set(vertice_processed) for vertex, result in vertice_processed_results: for v_to in dag.successors(vertex): executor.deliver(v_to, result) indegree_dict[v_to] -= 1 if indegree_dict[v_to] == 0: vertice_zero_indegree.add(v_to) return vertice_final

27.430556

102

0.649451

743

5,925

4.889637

0.158816

0.018993

0.019818

0.02202

0.316818

0.224883

0.138178

0.069915

0.033031

0

0.002266

0.25519

5,925

215

103

27.55814

0.820983

0.042363

0

0.217687

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0.018711

0

0.004651

0

1

0.244898

false

0.054422

0.006803

0.102041

0.489796

0

null

0

null

0

1

0

1

0

1

0

3

2c0aca15167ff5ae1f5d402750c284f46ad21e27

1,585

py

Python

models/metrics_and_losses.py

marioviti/nn_segmentation

b754b38cd1898c0746e383ecd32d9d4c33c60b33

[ "MIT" ]

null

models/metrics_and_losses.py

marioviti/nn_segmentation

b754b38cd1898c0746e383ecd32d9d4c33c60b33

[ "MIT" ]

null

models/metrics_and_losses.py

marioviti/nn_segmentation

b754b38cd1898c0746e383ecd32d9d4c33c60b33

[ "MIT" ]

null

from keras import backend as K from skimage.transform import resize import tensorflow as tf K.set_image_data_format('channels_last') def true_pos(y_true, y_pred): return K.sum(y_true * K.round(y_pred)) def false_pos(y_true, y_pred): return K.sum(y_true * (1. - K.round(y_pred))) def false_neg(y_true, y_pred): return K.sum((1. - y_true) * K.round(y_pred)) def precision(y_true, y_pred): return true_pos(y_true, y_pred) / \ (true_pos(y_true, y_pred) + false_pos(y_true, y_pred)) def PSNR(y_true, y_pred): shape = y_pred.get_shape() return K.sum((y_true - K.round(y_pred))) def dice_coef(y_true, y_pred): """ Attention: y_true can be weighted to modify learning therefore apply sign to get back to labels y_pred have to be rounded to nearest integer to obtain labels. """ smooth = 1. y_true_f = K.flatten(K.sign(y_true)) y_pred_f = K.flatten(K.round(y_pred)) intersection = K.sum(y_true_f * y_pred_f) return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth) def dice_coef_loss(y_true, y_pred): return -dice_coef(y_true, y_pred) def softmax_categorical_crossentropy(target, output): """Categorical crossentropy between an output tensor and a target tensor. # Arguments target: A tensor of the same shape as `output`. output: result of a softmax, or is a tensor of logits. # Returns Output tensor. """ # manual computation of crossentropy return - tf.reduce_sum(target * tf.log(output),len(output.get_shape())-1)

32.346939

86

0.68265

269

1,585

3.776952

0.301115

0.103346

0.070866

0.11811

0.307087

0.25

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0.097441

0

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0.206309

1,585

49

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0.264353

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0.011722

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false

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0.115385

0.192308

0.730769

0

null

0

null

0

1

0

1

0

3

2c0ca091ba8017a6ea07dedf32869a44838d16fc

537

py

Python

basics01/esacpechar.py

DevAnuragGarg/Python-Learning-Basics

2c58c82ba79bf2c7c3317628222554133dd50713

[ "Apache-2.0" ]

1

2020-06-09T09:49:02.000Z

basics01/esacpechar.py

DevAnuragGarg/Python-Learning-Basics

2c58c82ba79bf2c7c3317628222554133dd50713

[ "Apache-2.0" ]

null

basics01/esacpechar.py

DevAnuragGarg/Python-Learning-Basics

2c58c82ba79bf2c7c3317628222554133dd50713

[ "Apache-2.0" ]

null

split_string = "This string has been \nsplit over\nseveral\nlines" print(split_string) tabbed_string = "1\t2\t3\t" print(tabbed_string) print('Hello what\'s the situation like. He\'s is not responding') print("Hello what's the situation like. He's is not responding") print("""Hello what's the situation like. He's is not responding""") print("""Hello the thing is this is going to next line""") print("""Hello \ the thing is \ this is going \ to next line""") print("C:\\users\\abc\\tim\\python") print(r"C:\users\abc\tim\python")

23.347826

68

0.713222

93

537

4.075269

0.387097

0.131926

0.110818

0.118734

0.712401

0.617414

0

0.006424

0.130354

537

22

69

24.409091

0.805139

0

0.117647

0

0.640596

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false

0

0.529412

0

null

0

1

0

1

0

null

0

1

0

3

2c0ca4433555acb470f71744f8f25a30302d9742

299

py

Python

tests/app/libs/test_utils.py

pricem14pc/eq-questionnaire-runner

54cc2947ba181a2673ea1fb7cf6b4acdd609e06b

[ "MIT" ]

null

tests/app/libs/test_utils.py

pricem14pc/eq-questionnaire-runner

54cc2947ba181a2673ea1fb7cf6b4acdd609e06b

[ "MIT" ]

null

tests/app/libs/test_utils.py

pricem14pc/eq-questionnaire-runner

54cc2947ba181a2673ea1fb7cf6b4acdd609e06b

[ "MIT" ]

null

from app.helpers.uuid_helper import is_valid_uuid from app.libs.utils import convert_tx_id def test_convert_tx_id(): tx_id_to_convert = "bc26d5ef-8475-4710-ac82-753a0a150708" assert is_valid_uuid(tx_id_to_convert) assert convert_tx_id(tx_id_to_convert) == "BC26 - D5EF - 8475 - 4710"

29.9

73

0.782609

51

299

4.176471

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0.112676

0.15493

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299

9

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0

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null

0

1

0

1

0

null

0

1

0

3

2c16360d424581c214b60779daa9baad492484f7

2,311

py

Python

tests/unit/bokeh/models/test_filters.py

teresafds/bokeh

95b2a74ff463cfabdf9e3390951fa380166e6691

[ "BSD-3-Clause" ]

null

tests/unit/bokeh/models/test_filters.py

teresafds/bokeh

95b2a74ff463cfabdf9e3390951fa380166e6691

[ "BSD-3-Clause" ]

null

tests/unit/bokeh/models/test_filters.py

teresafds/bokeh

95b2a74ff463cfabdf9e3390951fa380166e6691

[ "BSD-3-Clause" ]

null

#----------------------------------------------------------------------------- # Copyright (c) 2012 - 2022, Anaconda, Inc., and Bokeh Contributors. # All rights reserved. # # The full license is in the file LICENSE.txt, distributed with this software. #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Boilerplate #----------------------------------------------------------------------------- from __future__ import annotations # isort:skip import pytest ; pytest #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Module under test import bokeh.models.filters as bmf # isort:skip #----------------------------------------------------------------------------- # Setup #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # General API #----------------------------------------------------------------------------- def test_Filter_set_operators() -> None: f0 = ~bmf.BooleanFilter() assert isinstance(f0, bmf.InversionFilter) f1 = bmf.BooleanFilter() & bmf.IndexFilter() assert isinstance(f1, bmf.IntersectionFilter) assert len(f1.operands) == 2 f2 = bmf.BooleanFilter() | bmf.IndexFilter() assert isinstance(f2, bmf.UnionFilter) assert len(f2.operands) == 2 f3 = bmf.BooleanFilter() - bmf.IndexFilter() assert isinstance(f3, bmf.DifferenceFilter) assert len(f3.operands) == 2 f4 = bmf.BooleanFilter() ^ bmf.IndexFilter() assert isinstance(f4, bmf.SymmetricDifferenceFilter) assert len(f4.operands) == 2 #----------------------------------------------------------------------------- # Dev API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Private API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Code #-----------------------------------------------------------------------------

37.885246

78

0.320208

129

2,311

5.682171

0.51938

0.109141

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0.163711

0.251023

0

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0.09087

2,311

60

79

38.516667

0.336506

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false

0

0.166667

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null

0

1

0

1

0

null

0

1

0

3

2c17df50a8d83000fd4e766f46fb4bd0e6305963

755

py

Python

tests/test_mhk_models_no_db.py

time-link/timelink-py

60d51bfedb64688aa7603f074d7bbc7432b5e841

[ "MIT" ]

null

tests/test_mhk_models_no_db.py

time-link/timelink-py

60d51bfedb64688aa7603f074d7bbc7432b5e841

[ "MIT" ]

3

2021-08-02T13:25:46.000Z

2022-03-27T11:17:59.000Z

tests/test_mhk_models_no_db.py

time-link/timelink-py

60d51bfedb64688aa7603f074d7bbc7432b5e841

[ "MIT" ]

null

""" Test models with requiring a db connection """ import pytest from timelink.mhk.models import base # noqa from timelink.mhk.models.entity import Entity # noqa from timelink.mhk.models.pom_som_mapper import PomSomMapper from timelink.mhk.models.base_class import Base from timelink.mhk.models.db import TimelinkDB def test_entity_subclasses(): scl = list(Entity.get_subclasses()) sc1 = len(scl) class SubEntity(Entity): pass scl2 = list(Entity.get_subclasses()) sc2 = len(scl2) assert sc2 == sc1 + 1, "wrong direct subclasses of Entity" class SubSubEntity(SubEntity): pass scl3 = list(Entity.get_subclasses()) sc3 = len(scl3) assert sc3 == sc2 + 1, "wrong recursive subclasses of Entity"

25.166667

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5.186275

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0.141777

0.198488

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false

0.105263

0.315789

0

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0

null

0

1

0

null

0

1

0

3

2c1890bcbe415de6b707f1f914bd82dec46e3744

218

py

Python

twitter_problems/problem_5.py

loftwah/Daily-Coding-Problem

0327f0b4f69ef419436846c831110795c7a3c1fe

[ "MIT" ]

129

2018-10-14T17:52:29.000Z

2022-01-29T15:45:57.000Z

twitter_problems/problem_5.py

loftwah/Daily-Coding-Problem

0327f0b4f69ef419436846c831110795c7a3c1fe

[ "MIT" ]

2

2019-11-30T23:28:23.000Z

2020-01-03T16:30:32.000Z

twitter_problems/problem_5.py

loftwah/Daily-Coding-Problem

0327f0b4f69ef419436846c831110795c7a3c1fe

[ "MIT" ]

60

2019-02-21T09:18:31.000Z

2022-03-25T21:01:04.000Z

"""This problem was asked by Twitter. Given a list of numbers, create an algorithm that arranges them in order to form the largest possible integer. For example, given [10, 7, 76, 415], you should return 77641510."""

54.5

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0.752294

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0.088398

0.169725

218

4

112

54.5

0.81768

0.972477

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1

0

1

0

null

0

1

0

3

2c2b3898833c53843db57d3da758950386a9aad9

7,592

py

Python

languages/python/cp857_5x7.py

ercanersoy/font-library

7d71b41bddea9d87c230afbaec1a92412ebd7ad9

[ "CC0-1.0" ]

1

2019-03-30T13:34:24.000Z

languages/python/cp857_5x7.py

ercanersoy/font-library

7d71b41bddea9d87c230afbaec1a92412ebd7ad9

[ "CC0-1.0" ]

null

languages/python/cp857_5x7.py

ercanersoy/font-library

7d71b41bddea9d87c230afbaec1a92412ebd7ad9

[ "CC0-1.0" ]

null

# cp857_5x7.py - CP857 5x7 font file for Python # # Copyright (c) 2019-2022 Ercan Ersoy # This file is written by Ercan Ersoy. # This file is licensed under CC0-1.0 Universal License. cp857_5x7 = [ 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5F, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x14, 0x7F, 0x14, 0x7F, 0x14, 0x04, 0x2A, 0x7F, 0x2A, 0x10, 0x23, 0x13, 0x08, 0x64, 0x62, 0x36, 0x49, 0x55, 0x22, 0x50, 0x00, 0x04, 0x03, 0x00, 0x00, 0x00, 0x3E, 0x41, 0x00, 0x00, 0x00, 0x00, 0x41, 0x3E, 0x00, 0x00, 0x0A, 0x07, 0x0A, 0x00, 0x08, 0x08, 0x3E, 0x08, 0x08, 0x00, 0x20, 0x18, 0x00, 0x00, 0x00, 0x08, 0x08, 0x08, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x40, 0x30, 0x08, 0x06, 0x01, 0x3E, 0x61, 0x5D, 0x43, 0x3E, 0x00, 0x44, 0x42, 0x7F, 0x40, 0x72, 0x49, 0x49, 0x49, 0x46, 0x22, 0x49, 0x49, 0x49, 0x36, 0x08, 0x0C, 0x0A, 0x7F, 0x08, 0x27, 0x49, 0x49, 0x49, 0x31, 0x3E, 0x49, 0x49, 0x49, 0x32, 0x41, 0x21, 0x11, 0x09, 0x07, 0x36, 0x49, 0x49, 0x49, 0x36, 0x26, 0x49, 0x49, 0x49, 0x3E, 0x00, 0x00, 0x14, 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257e05fcff394b8322ba4dd1554e86692b4c8292

3,918

py

Python

editline/tests/test_editline.py

mark-nicholson/python-editline

c23f1071c4b832a92f66e2f49142e5c5f00e500d

[ "BSD-3-Clause" ]

4

2017-10-05T19:34:32.000Z

2021-05-18T23:29:44.000Z

editline/tests/test_editline.py

mark-nicholson/python-editline

c23f1071c4b832a92f66e2f49142e5c5f00e500d

[ "BSD-3-Clause" ]

2

2018-03-30T22:38:17.000Z

2018-03-30T22:39:13.000Z

editline/tests/test_editline.py

mark-nicholson/python-editline

c23f1071c4b832a92f66e2f49142e5c5f00e500d

[ "BSD-3-Clause" ]

null

""" Unit testing for parts of the editline and _editline modules. """ import os import sys import unittest import subprocess from test.support import import_module # too bad this thing moved ... try: if sys.version_info[0] >= 3 and sys.version_info[1] >= 5: from test.support.script_helper import assert_python_ok else: from test.script_helper import assert_python_ok have_assert_python_ok = True except ImportError: have_assert_python_ok = False def check_test_support(): return have_assert_python_ok def check_nose_runner(): """Certain situations prevent NOSE from running tests -- it appears that nose does not allow access to the terminal.""" return 'nose' not in sys.modules.keys() class TestEditline(unittest.TestCase): def load_assert_python_ok(self): if sys.version_info[0] >= 3 and sys.version_info[1] >= 5: from test.support.script_helper import assert_python_ok else: from test.script_helper import assert_python_ok def test_001_import_pkg(self): _editline = import_module('editline') def test_002_import__el(self): _editline = import_module('editline._editline') def test_002_import_el(self): editline = import_module('editline.editline') @unittest.skipUnless(check_nose_runner(), "nose cannot run this test") def test_003_build_instance(self): editline = import_module('editline.editline') el = editline.EditLine("testcase", sys.stdin, sys.stdout, sys.stderr) self.assertIsNotNone(el) @unittest.skipUnless(check_test_support(), "no script_helper") def test_100_import_pkg(self): self.load_assert_python_ok() #from test.support.script_helper import assert_python_ok rc, stdout, stderr = assert_python_ok('-c', 'import editline') self.assertEqual(stdout, b'') self.assertEqual(rc, 0) @unittest.skipUnless(check_test_support(), "no script_helper") def test_100_import_module(self): self.load_assert_python_ok() #from test.support.script_helper import assert_python_ok rc, stdout, stderr = assert_python_ok( '-c', 'from editline import editline') self.assertEqual(stdout, b'') self.assertEqual(rc, 0) @unittest.skipUnless(check_test_support(), "no script_helper") def test_100_import_class(self): self.load_assert_python_ok() #from test.support.script_helper import assert_python_ok rc, stdout, stderr = assert_python_ok( '-c', 'from editline.editline import EditLine') self.assertEqual(stdout, b'') self.assertEqual(rc, 0) @unittest.skipUnless(check_test_support(), "no script_helper") def test_101_init(self): # Issue #19884: Ensure that the ANSI sequence "\033[1034h" is not # written into stdout when the readline module is imported and stdout # is redirected to a pipe. self.load_assert_python_ok() #from test.support.script_helper import assert_python_ok rc, stdout, stderr = assert_python_ok( '-c', 'from editline.editline import EditLine', TERM='xterm-256color') self.assertEqual(stdout, b'') self.assertEqual(rc, 0) @unittest.skipUnless(check_nose_runner(), "nose cannot run this test") def test_200_terminal_size(self): rows = int(subprocess.check_output(['tput', 'lines']).decode()) columns = int(subprocess.check_output(['tput', 'cols']).decode()) self.assertNotEqual(columns, 0) editline = import_module('editline.editline') el = editline.EditLine("testcase", sys.stdin, sys.stdout, sys.stderr) el_cols = el.gettc('co') self.assertEqual(el_cols, columns) if __name__ == "__main__": unittest.main()

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null

0

1

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1

0

3

2584851a4d17126b2fb8d6436c754e69633733dc

724

py

Python

validator/rules_src/dict.py

Varun-22/Validator

2c5caa9323aef35e2796813a357e5a7d6d4a80ba

[ "MIT" ]

41

2020-05-07T15:35:12.000Z

2021-11-01T03:57:09.000Z

validator/rules_src/dict.py

Varun-22/Validator

2c5caa9323aef35e2796813a357e5a7d6d4a80ba

[ "MIT" ]

83

2020-05-09T22:11:26.000Z

2022-03-10T19:06:46.000Z

validator/rules_src/dict.py

Varun-22/Validator

2c5caa9323aef35e2796813a357e5a7d6d4a80ba

[ "MIT" ]

25

2020-05-27T22:46:01.000Z

2022-03-04T01:36:11.000Z

from validator.rules_src import Rule class Dict(Rule): """ The field under validation must be a dictionary (Python map) Examples: >>> from validator import validate >>> reqs = {"data" : {"key1" : "val1", "key2" : "val2"} } >>> rule = {"data" : "dict"} >>> validate(reqs, rule) True >>> reqs = {"data" : ["val1", "val2", "val3", "val4"]} >>> rule = {"data" : "dict"} >>> validate(reqs, rule) False """ def __init__(self): Rule.__init__(self) def check(self, arg): if isinstance(arg, dict): return True self.set_error(f"Expected type dict, Got:{type(arg)}") return False def __from_str__(self): pass

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1

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1

0

1

0

3

2585e87eaaff2fdd76309cfdbd38bd7f7c95efca

935

py

Python

setup.py

DiegoLing33/ling-simple-api

d4b3789cf6110afa3c2db822ff6593079927eca1

[ "MIT" ]

2

2020-10-28T14:00:05.000Z

2020-10-30T11:55:27.000Z

setup.py

DiegoLing33/ling-simple-api

d4b3789cf6110afa3c2db822ff6593079927eca1

[ "MIT" ]

null

setup.py

DiegoLing33/ling-simple-api

d4b3789cf6110afa3c2db822ff6593079927eca1

[ "MIT" ]

null

# ██╗░░░░░██╗███╗░░██╗░██████╗░░░░██████╗░██╗░░░░░░█████╗░░█████╗░██╗░░██╗ # ██║░░░░░██║████╗░██║██╔════╝░░░░██╔══██╗██║░░░░░██╔══██╗██╔══██╗██║░██╔╝ # ██║░░░░░██║██╔██╗██║██║░░██╗░░░░██████╦╝██║░░░░░███████║██║░░╚═╝█████═╝░ # ██║░░░░░██║██║╚████║██║░░╚██╗░░░██╔══██╗██║░░░░░██╔══██║██║░░██╗██╔═██╗░ # ███████╗██║██║░╚███║╚██████╔╝░░░██████╦╝███████╗██║░░██║╚█████╔╝██║░╚██╗ # ╚══════╝╚═╝╚═╝░░╚══╝░╚═════╝░░░░╚═════╝░╚══════╝╚═╝░░╚═╝░╚════╝░╚═╝░░╚═╝ # # Developed by Yakov V. Panov (C) Ling • Black 2020 # @site http://ling.black import setuptools setuptools.setup( name="ling-simple-api", version="0.0.1", author="Yakov V. Ling", author_email="diegoling33@yandex.ru", packages=setuptools.find_packages(), classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], python_requires='>=3.8', )

35.961538

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0

0.014815

0.13369

935

25

76

37.4

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0.554011

0

0.408867

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1

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0.071429

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0

1

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null

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0

3

25a38ec4aea7b8a56416a4d31b2980a2324c2705

1,460

py

Python

digsby/src/util/plistutil.py

ifwe/digsby

f5fe00244744aa131e07f09348d10563f3d8fa99

[ "Python-2.0" ]

35

2015-08-15T14:32:38.000Z

2021-12-09T16:21:26.000Z

digsby/src/util/plistutil.py

niterain/digsby

16a62c7df1018a49eaa8151c0f8b881c7e252949

[ "Python-2.0" ]

4

2015-09-12T10:42:57.000Z

2017-02-27T04:05:51.000Z

digsby/src/util/plistutil.py

niterain/digsby

16a62c7df1018a49eaa8151c0f8b881c7e252949

[ "Python-2.0" ]

15

2015-07-10T23:58:07.000Z

2022-01-23T22:16:33.000Z

''' Some tools to convert a .plist into python objects. Incomplete. TODO: add load(s)/dump(s) functions to match the 'data shelving' interfaces of pickle, simplejson, pyyaml, etc. ''' #_type_map = dict(real = (float, 'cdata'), # integer = (int, 'cdata'), # true = (lambda x: True, 'cdata'), # false = (lambda x: False, 'cdata'), # data = (_from_data, 'cdata'), # array = (_to_plist, 'children'), # dict = # ) def plisttype_to_pytype(plist): type = plist._name transformer = globals().get('plist_to_%s' % type, None) if transformer is not None: return transformer(plist) else: return plist def _from_data(cdata): return cdata.decode('base64') def _to_data(data): return data.encode('base64') def plist_to_real(plist): return float(plist._cdata) def plist_to_string(plist): return unicode(plist._cdata) def plist_to_array(plist): return map(plisttype_to_pytype, plist._children) def plist_to_dict(plist): result = {} key = None value = None for child in plist._children: if child._name == 'key': key = child._cdata else: value = plisttype_to_pytype(child) result[key] = value key = value = None return result

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1

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25ac8194d8ccbd9a09f074296efd798e9bbe2bfd

132

py

Python

PyTrinamic/ic/TMC2041/TMC2041_register_variant.py

trinamic-AA/PyTrinamic

b054f4baae8eb6d3f5d2574cf69c232f66abb4ee

[ "MIT" ]

37

2019-01-13T11:08:45.000Z

2022-03-25T07:18:15.000Z

PyTrinamic/ic/TMC2041/TMC2041_register_variant.py

AprDec/PyTrinamic

a9db10071f8fbeebafecb55c619e5893757dd0ce

[ "MIT" ]

56

2019-02-25T02:48:27.000Z

2022-03-31T08:45:34.000Z

PyTrinamic/ic/TMC2041/TMC2041_register_variant.py

AprDec/PyTrinamic

a9db10071f8fbeebafecb55c619e5893757dd0ce

[ "MIT" ]

26

2019-01-14T05:20:16.000Z

2022-03-08T13:27:35.000Z

''' Created on 24.10.2019 @author: JM ''' class TMC2041_register_variant: " ===== TMC2041 register variants ===== " "..."

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null

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null

0

1

0

3

25afbfde05c67785ccd3dc5ab3cf731469a73398

23,741

py

Python

shirp/handler.py

cedricg92/event-manager

d07fafe88dca06a4d1a07e2a368122b0fb18ccfd

[ "Apache-2.0" ]

null

shirp/handler.py

cedricg92/event-manager

d07fafe88dca06a4d1a07e2a368122b0fb18ccfd

[ "Apache-2.0" ]

null

shirp/handler.py

cedricg92/event-manager

d07fafe88dca06a4d1a07e2a368122b0fb18ccfd

[ "Apache-2.0" ]

null

import datetime import fnmatch import gzip import shutil import tarfile import time import croniter import hdfs from watchdog.events import PatternMatchingEventHandler, FileCreatedEvent, FileModifiedEvent, FileMovedEvent, os, \ FileSystemEvent def add_log_str(log_pattern, strlog): """Add log in logfile :param log_pattern: Pattern of log :type log_pattern: str :param strlog: Log message :type strlog: str """ logfile = log_pattern logfile = logfile.replace("%Y", time.strftime("%Y")) logfile = logfile.replace("%m", time.strftime("%m")) logfile = logfile.replace("%d", time.strftime("%d")) logfile = logfile.replace("%H", time.strftime("%H")) logfile = logfile.replace("%M", time.strftime("%M")) logfile = logfile.replace("%S", time.strftime("%S")) with open(logfile, "ab+") as myfile: myfile.write(strlog) myfile.close() def add_log(log_pattern, event, filename, destination, event_type, event_subtype, exec_program, args, return_val): """Add log in filelog :param log_pattern: Pattern of log :type log_pattern: str :param event: Event Name :param filename: File name :param destination: Destination :param event_type: Event type :param event_subtype: Event sub type :param exec_program: executable :param args: Arguments :param return_val: Return Value """ log = time.strftime("%Y-%m-%d %H:%M:%S") log += "|" + str(event) log += "|" + str(filename) log += "|" + str(destination) log += "|" + str(event_type) log += "|" + str(event_subtype) log += "|" + str(exec_program) log += "|" + str(args) if return_val: log += "|" + str(0) else: log += "|" + str(1) log += os.linesep add_log_str(log_pattern, log) class ExecHandler(PatternMatchingEventHandler): """Class ExecHandler """ FILE_LOG = "" def __init__(self, event_conf): """ :param event_conf: EventConf :type event_conf: em.event.EventConf :return: """ super(self.__class__, self).__init__(["*"], ["*.err"], True, False) self._patterns = event_conf.patterns self.event_conf = event_conf def is_scheduled(self): """Check if the event handler is scheduled :return: True if the event handler is scheduled :rtype: bool """ return self.event_conf.is_scheduled() def on_created(self, event): """Function called when file is created :param event: File created event :type event: FileCreatedEvent """ if isinstance(event, FileCreatedEvent): self.process(event) def on_modified(self, event): """Function called when file is modified :param event: File modified event :type event: FileModifiedEvent """ if isinstance(event, FileModifiedEvent): self.process(event) def on_moved(self, event): """Function called when file is moved :param event: File moved event :type event: FileMovedEvent """ if isinstance(event, FileMovedEvent): self.process(event) def process(self, event): """Function process :param event: Event file :type event: FileSystemEvent :return: Return value (True success, False error) :rtype: bool """ if self.event_conf.enabled == 0: return 0 args = str(self.event_conf.get_context_value("execArgs")) args = args.replace("%filenale", event.src_path) args = args.replace("%destination", self.event_conf.destination) exec_dir = os.path.dirname(self.event_conf.get_context_value("execProgram")) exec_app = os.path.dirname(self.event_conf.get_context_value("execProgram")) ret = os.system("cd "+exec_dir+";./"+exec_app+" "+args) add_log(self.FILE_LOG, self.event_conf.name, event.src_path, self.event_conf.destination, self.event_conf.type, self.event_conf.subtype, self.event_conf.get_context_value("execProgram"), args, ret) if ret is False: os.rename(event.src_path, event.src_path + ".err") else: os.remove(event.src_path) return ret def check_schedule(self, now): """Check if the event should be launched :param now: Actual date and time :type now: datetime.datetime :return: True if the event should be launched :rtype: bool """ cron = croniter.croniter(self.event_conf.get_cron(), now) current_exec_datetime = cron.get_current(datetime.datetime) return (current_exec_datetime.year == now.year and current_exec_datetime.month == now.month and current_exec_datetime.day == now.day and current_exec_datetime.hour == now.hour and current_exec_datetime.minute == now.minute) class FsHandler(PatternMatchingEventHandler): """File System Handler """ FILE_LOG = "" TYPE_MOVE = 1 TYPE_ARCHIVE = 2 TYPE_COMPRESS = 3 TYPE_UNARCHIVE = 4 TYPE_UNCOMPRESS = 5 STR_TYPE_MOVE = "move" STR_TYPE_ARCHIVE = "archive" STR_TYPE_COMPRESS = "compress" STR_TYPE_UNARCHIVE = "unarchive" STR_TYPE_UNCOMPRESS = "uncompress" def __init__(self, event_conf, fs_type): """ :param event_conf: ExecConf :type event_conf: em.event.EventConf :param fs_type: Process type of Fs handler :type fs_type: str """ super(self.__class__, self).__init__(["*"], ["*.tmp", "*.err", "*.run"], True, False) self.event_conf = event_conf self.fs_type = fs_type self.delimiter = os.path.sep def is_scheduled(self): """Check if the event handler is scheduled :return: True if the event handler is scheduled :rtype: bool """ return self.event_conf.is_scheduled() def on_any_event(self, event): if not event.src_path.endswith(".tmp"): for pattern in self.event_conf.patterns: if fnmatch.fnmatch(os.path.basename(event.src_path), pattern): print(event) def on_created(self, event): """Handler listener on creation of file :param event: File created event :type event: FileCreatedEvent :return: True if the process run correctly :rtype: bool """ if isinstance(event, FileCreatedEvent): return self.process(event.src_path) return False def on_modified(self, event): """Handler listener on modification of file :param event: File modified event :type event: FileModifiedEvent :return: True if the process run correctly :rtype: bool """ if isinstance(event, FileModifiedEvent): return self.process(event.src_path) return False def on_moved(self, event): """Handler listener on move of file :param event: File moved event :type event: FileMovedEvent :return: True if the process run correctly :rtype: bool """ if isinstance(event, FileMovedEvent): return self.process(event.dest_path) return False def process(self, full_filename): """Function process :param full_filename: Full path of filename :type full_filename: str :return: True if the process run correctly :rtype: bool """ if self.event_conf.enabled == 0: return True if not os.path.exists(full_filename): return False if os.path.dirname(full_filename) != self.event_conf.directory: return False res = False filename = os.path.basename(full_filename) matched = False for pattern in self.event_conf.patterns: if fnmatch.fnmatch(filename, pattern): matched = True break if not matched: return False os.rename(full_filename, full_filename + ".run") if self.fs_type == FsHandler.TYPE_MOVE or self.fs_type == FsHandler.STR_TYPE_MOVE: res = self.process_move(filename, "run") elif self.fs_type == FsHandler.TYPE_ARCHIVE or self.fs_type == FsHandler.STR_TYPE_ARCHIVE: res = self.process_archive(filename, "run", "tmp") elif self.fs_type == FsHandler.TYPE_COMPRESS or self.fs_type == FsHandler.STR_TYPE_COMPRESS: res = self.process_compress(filename, "run", "tmp") elif self.fs_type == FsHandler.TYPE_UNCOMPRESS or self.fs_type == FsHandler.STR_TYPE_UNCOMPRESS: res = self.process_uncompress(filename, "run", "tmp") elif self.fs_type == FsHandler.TYPE_UNARCHIVE or self.fs_type == FsHandler.STR_TYPE_UNARCHIVE: res = self.process_unarchive(filename, "run") add_log(self.FILE_LOG, self.event_conf.name, full_filename, self.event_conf.destination, self.event_conf.type, self.event_conf.subtype, "", "", res) if not res: os.rename(full_filename + ".run", full_filename + ".err") return res def process_move(self, filename, extension): """Move file to destination directory -> File -> destination :param filename: Filename :type filename: str :param extension: Extention of file (run) :type extension: str :return: True if the process run correctly :rtype: bool """ if os.path.exists(self.event_conf.destination + self.delimiter + filename): os.remove(self.event_conf.destination + self.delimiter + filename) os.rename(self.event_conf.directory + self.delimiter + filename + "." + extension, self.event_conf.destination + self.delimiter + filename) return os.path.exists(self.event_conf.destination + self.delimiter + filename) def process_archive(self, filename, extension, tmp_extension): """Create archive file (tar) :param filename: Filename :type filename: str :param extension: Extension of file (run) :type extension: str :param tmp_extension: Tmp extension :type tmp_extension: str :return: True if the process run correctly :rtype: bool """ if os.path.exists(self.event_conf.destination + self.delimiter + filename + ".tar" + "." + tmp_extension): os.remove(self.event_conf.destination + self.delimiter + filename + ".tar" + "." + tmp_extension) if os.path.exists(self.event_conf.destination + self.delimiter + filename + ".tar"): os.remove(self.event_conf.destination + self.delimiter + filename + ".tar") tar = tarfile.open(self.event_conf.destination + self.delimiter + filename + ".tar" + "." + tmp_extension, "w") tar.add(self.event_conf.directory + self.delimiter + filename + "." + extension, filename) tar.close() os.remove(self.event_conf.directory + self.delimiter + filename + "." + extension) os.rename(self.event_conf.destination + self.delimiter + filename + ".tar" + "." + tmp_extension, self.event_conf.destination + self.delimiter + filename + ".tar") return os.path.exists(self.event_conf.destination + self.delimiter + filename + ".tar") def process_compress(self, filename, extension, tmp_extension): """Compress file (gzip) :param filename: Filename :type filename: str :param extension: Extension of file (run) :type extension: str :param tmp_extension: Tmp extension :type tmp_extension: str :return: True if the process run correctly :rtype: bool """ if os.path.exists(self.event_conf.destination + self.delimiter + filename + ".gz"): os.remove(self.event_conf.destination + self.delimiter + filename + ".gz") if os.path.exists(self.event_conf.destination + self.delimiter + filename + ".gz" + "." + tmp_extension): os.remove(self.event_conf.destination + self.delimiter + filename + ".gz" + "." + tmp_extension) with open(self.event_conf.directory + self.delimiter + filename + "." + extension, 'rb') as f_in, \ gzip.open(self.event_conf.destination + self.delimiter + filename + ".gz" + "." + tmp_extension, 'wb') as f_out: shutil.copyfileobj(f_in, f_out) f_in.close() f_out.close() os.remove(self.event_conf.directory + self.delimiter + filename + "." + extension) os.rename(self.event_conf.destination + self.delimiter + filename + ".gz" + "." + tmp_extension, self.event_conf.destination + self.delimiter + filename + ".gz") return os.path.exists(self.event_conf.destination + self.delimiter + filename + ".gz") def process_uncompress(self, filename, extension, tmp_extension): """Uncompress file (gunzip) :param filename: Filename :type filename: str :param extension: Extension of file (run) :type extension: str :param tmp_extension: Tmp extension :type tmp_extension: str :return: True if the process run correctly :rtype: bool """ if os.path.exists(self.event_conf.destination + self.delimiter + filename.replace(".gz", "") + "." + tmp_extension): os.remove(self.event_conf.destination + self.delimiter + filename.replace(".gz", "") + "." + tmp_extension) if os.path.exists(self.event_conf.destination + self.delimiter + filename.replace(".gz", "")): os.remove(self.event_conf.destination + self.delimiter + filename.replace(".gz", "")) with gzip.open(self.event_conf.directory + self.delimiter + filename + "." + extension) as f_in, \ open(self.event_conf.destination + self.delimiter + filename.replace(".gz", "") + "." + tmp_extension, "w") as f_out: f_out.write(f_in.read()) os.rename(self.event_conf.destination + self.delimiter + filename.replace(".gz", "") + "." + tmp_extension, self.event_conf.destination + self.delimiter + filename.replace(".gz", "")) os.remove(self.event_conf.directory + self.delimiter + filename + "." + extension) return os.path.exists(self.event_conf.destination + self.delimiter + filename.replace(".gz", "")) def process_unarchive(self, filename, extension): """Unarchive file (untar) :param filename: Filename :type filename: str :param extension: Extension of file (run) :type extension: str :return: True if the process run correctly :rtype: bool """ try: tar = tarfile.open(self.event_conf.directory + self.delimiter + filename + "." + extension) tar.extractall(self.event_conf.destination) tar.close() os.remove(self.event_conf.directory + self.delimiter + filename + "." + extension) return True except Exception: return False def run_schedule(self): """Run scheduled event :return: None """ for filename in os.listdir(self.event_conf.directory): for pattern in self.event_conf.patterns: if fnmatch.fnmatch(filename, pattern): exit(self.process(self.event_conf.directory + self.delimiter + filename)) exit(1) def check_schedule(self, now): """Check if the event should be launched :param now: Actual date and time :type now: datetime.datetime :return: True if the event should be launched :rtype: bool """ cron = croniter.croniter(self.event_conf.get_cron(), now) current_exec_datetime = cron.get_current(datetime.datetime) return (current_exec_datetime.year == now.year and current_exec_datetime.month == now.month and current_exec_datetime.day == now.day and current_exec_datetime.hour == now.hour and current_exec_datetime.minute == now.minute) class HDFSHandler(PatternMatchingEventHandler): """HDFS handler """ FILE_LOG = "" TYPE_PUT = 1 TYPE_GET = 2 STR_TYPE_PUT = "put" STR_TYPE_GET = "get" def __init__(self, event_conf, hdfs_type): """ :param self: :param event_conf: ExecConf :type event_conf: em.event.EventConf :return: """ super(self.__class__, self).__init__(["*"], ["*.tmp", "*.err", "*.run"], True, False) self.event_conf = event_conf self.hdfs_type = hdfs_type self.delimiter = os.path.sep def is_scheduled(self): """Check if the event handler is scheduled :return: True if the event handler is scheduled :rtype: bool """ if not self.event_conf.is_scheduled() and\ (self.event_conf.subtype == self.STR_TYPE_GET or self.event_conf.subtype == self.TYPE_GET): raise ValueError("HDFSHandler: Subtype error - get should be scheduled !") return self.event_conf.is_scheduled() def on_any_event(self, event): if not event.src_path.endswith((".tmp", ".err", ".run")): for pattern in self.event_conf.patterns: if fnmatch.fnmatch(os.path.basename(event.src_path), pattern): print(event) def on_created(self, event): """Handler listener on creation of file :param event: File created event :type event: FileCreatedEvent :return: True if the process run correctly :rtype: bool """ if isinstance(event, FileCreatedEvent): return self.process(event.src_path) return False def on_modified(self, event): """Handler listener on modification of file :param event: File modified event :type event: FileModifiedEvent :return: True if the process run correctly :rtype: bool """ if isinstance(event, FileModifiedEvent): return self.process(event.src_path) return False def on_moved(self, event): """Handler listener on move of file :param event: File moved event :type event: FileMovedEvent :return: True if the process run correctly :rtype: bool """ if isinstance(event, FileMovedEvent): return self.process(event.dest_path) return False def process(self, full_filename): """Function process :param full_filename: Full path of filename :type full_filename: str :return: True if the process run correctly :rtype: bool """ ret = False if self.event_conf.enabled == 0: return False filename = os.path.basename(full_filename) matched = False for pattern in self.event_conf.patterns: if fnmatch.fnmatch(filename, pattern): matched = True break if not matched: return False if self.event_conf.is_fs_directory(): os.rename(full_filename, full_filename + ".run") if self.hdfs_type == self.TYPE_PUT or self.hdfs_type == self.STR_TYPE_PUT: ret = self.process_put(filename, "run", "err") if self.hdfs_type == self.TYPE_GET or self.hdfs_type == self.STR_TYPE_GET: ret = self.process_get(filename) add_log(self.FILE_LOG, self.event_conf.name, full_filename, self.event_conf.destination, self.event_conf.type, self.event_conf.subtype, self.event_conf.get_context_value("hdfsUrl"), self.event_conf.get_context_value("hdfsUser"), ret) return ret def process_put(self, filename, extension, err_extension): """Put file to HDFS :param filename: Filename :type filename: str :param extension: extension of file (run) :type extension: str :param err_extension: error extension (err) :type err_extension: str :return: True if the process run correctly :rtype: bool """ res = None try: client = hdfs.InsecureClient(self.event_conf.get_context_value("hdfsUrl"), self.event_conf.get_context_value("hdfsUser")) client.upload(self.event_conf.destination + "/" + filename, self.event_conf.directory + self.delimiter + filename + "." + extension, overwrite=True) res = client.status(self.event_conf.destination + "/" + filename, False) except Exception as e: print(e) ret = False if res is None: os.rename(self.event_conf.directory + self.delimiter + filename + "." + extension, self.event_conf.directory + self.delimiter + filename + "." + extension + "." + err_extension) else: ret = True os.remove(self.event_conf.directory + self.delimiter + filename + "." + extension) return ret def process_get(self, filename): """Get file from HDFS :param filename: Filename :type filename: str :return: True if the process run correctly :rtype: bool """ ret = False try: client = hdfs.InsecureClient(self.event_conf.get_context_value("hdfsUrl"), self.event_conf.get_context_value("hdfsUser")) res = client.download(self.event_conf.directory + "/" + filename, self.event_conf.destination, True) ret = res == (self.event_conf.destination + os.path.sep + os.path.basename(self.event_conf.directory + "/" + filename)) if ret: client.delete(self.event_conf.directory + "/" + filename) except Exception as e: print(e.message) return ret def run_schedule(self): """Run scheduled event :return: None """ if self.event_conf.subtype == self.STR_TYPE_GET or self.event_conf.subtype == self.TYPE_GET: client = hdfs.InsecureClient(self.event_conf.get_context_value("hdfsUrl"), self.event_conf.get_context_value("hdfsUser")) files = client.list(self.event_conf.directory) for filename in files: for pattern in self.event_conf.patterns: if fnmatch.fnmatch(filename, pattern): exit(self.process(self.event_conf.directory + "/" + filename)) else: for filename in os.listdir(self.event_conf.directory): for pattern in self.event_conf.patterns: if fnmatch.fnmatch(filename, pattern): exit(self.process(self.event_conf.directory + self.delimiter + filename)) exit(1) def check_schedule(self, now): """Check if the event should be launched :param now: Actual date and time :type now: datetime.datetime :return: True if the event should be launched :rtype: bool """ cron = croniter.croniter(self.event_conf.get_cron(), now) current_exec_datetime = cron.get_current(datetime.datetime) return (current_exec_datetime.year == now.year and current_exec_datetime.month == now.month and current_exec_datetime.day == now.day and current_exec_datetime.hour == now.hour and current_exec_datetime.minute == now.minute)

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0

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null

0

1

0

1

0

null

0

3

25b0c94c1b7162ba39eb9ea90001ad594220bda8

2,417

py

Python

tests/test_status.py

tbartelmess/python-nomad

3ddbf904af2947992722a9dcf58c23eb0078fc8f

[ "MIT" ]

null

tests/test_status.py

tbartelmess/python-nomad

3ddbf904af2947992722a9dcf58c23eb0078fc8f

[ "MIT" ]

null

tests/test_status.py

tbartelmess/python-nomad

3ddbf904af2947992722a9dcf58c23eb0078fc8f

[ "MIT" ]

null

import pytest import tests.common as common import nomad import sys @pytest.fixture def nomad_setup(): n = nomad.Nomad(host=common.IP, port=common.NOMAD_PORT, verify=False, token=common.NOMAD_TOKEN) return n # integration tests requires nomad Vagrant VM or Binary running def test_get_leader(nomad_setup): if int(sys.version[0]) == 3: assert isinstance(nomad_setup.status.leader.get_leader(), str) == True else: assert isinstance( nomad_setup.status.leader.get_leader(), unicode) == True def test_get_peers(nomad_setup): assert isinstance(nomad_setup.status.peers.get_peers(), list) == True def test_peers_dunder_getitem_exist(nomad_setup): n = nomad_setup.status.peers["{IP}:4647".format(IP=common.IP)] if int(sys.version[0]) == 3: assert isinstance(n, str) else: assert isinstance(n, unicode) def test_peers_dunder_getitem_not_exist(nomad_setup): with pytest.raises(KeyError): p = nomad_setup.status.peers["{IP}:4647".format(IP="172.16.10.100")] def test_peers_dunder_contain_exists(nomad_setup): assert "{IP}:4647".format(IP=common.IP) in nomad_setup.status.peers def test_peers_dunder_contain_not_exist(nomad_setup): assert "{IP}:4647".format( IP="172.16.10.100") not in nomad_setup.status.peers def test_leader_dunder_contain_exists(nomad_setup): assert "{IP}:4647".format(IP=common.IP) in nomad_setup.status.leader def test_leader_dunder_contain_not_exist(nomad_setup): assert "{IP}:4647".format( IP="172.16.10.100") not in nomad_setup.status.leader def test_dunder_str(nomad_setup): assert isinstance(str(nomad_setup.status), str) assert isinstance(str(nomad_setup.status.leader), str) assert isinstance(str(nomad_setup.status.peers), str) def test_dunder_repr(nomad_setup): assert isinstance(repr(nomad_setup.status), str) assert isinstance(repr(nomad_setup.status.leader), str) assert isinstance(repr(nomad_setup.status.peers), str) def test_dunder_getattr(nomad_setup): with pytest.raises(AttributeError): d = nomad_setup.status.does_not_exist def test_peers_dunder_iter(nomad_setup): assert hasattr(nomad_setup.status.peers, '__iter__') for p in nomad_setup.status.peers: pass def test_dunder_len(nomad_setup): assert len(nomad_setup.status.leader) >= 0 assert len(nomad_setup.status.peers) >= 0

28.435294

99

0.733968

357

2,417

4.72549

0.193277

0.201541

0.189686

0.124481

0.658566

0.518672

0.471844

0.362774

0.186129

0

0.029254

0.151427

2,417

84

100

28.77381

0.793272

0.025238

0

0.113208

0

0.042906

0

0.339623

1

0.264151

false

0.018868

0.075472

0

0.358491

0

null

1

0

null

0

1

0

3

25b158d89271f5b65a9c02d9dbc2083e03cdfb9a

154

py

Python

enums/Matiere.py

ythepaut/visi301_univdefender

13f0e564ad3265aabf77544755873b8584d296de

[ "CC-BY-3.0" ]

7

2019-10-08T15:48:18.000Z

2021-07-06T13:20:54.000Z

enums/Matiere.py

ythepaut/visi301_univdefender

13f0e564ad3265aabf77544755873b8584d296de

[ "CC-BY-3.0" ]

null

enums/Matiere.py

ythepaut/visi301_univdefender

13f0e564ad3265aabf77544755873b8584d296de

[ "CC-BY-3.0" ]

3

2019-10-08T15:47:55.000Z

2019-12-13T23:28:12.000Z

"""Module Enum:Matiere""" from enum import Enum class Matiere(Enum): """Enum : Matiere""" HISTOIRE = 0 MATHS = 1 INFO = 2 SPORT = 3

14

25

0.571429

20

154

4.4

0.7

0.25

0

0.036697

0.292208

154

10

26

15.4

0.770642

0.220779

0

1

0

false

0

0.166667

0

1

0

1

0

null

1

0

null

0

1

0

3

25b171c31a40f6d8668db31079821a30e464b8d6

155

py

Python

tests/plugins/viz/conftest.py

andrsd/otter

3d47d4ad433b5e7bd1a1d5fed45b4950de14c25f

[ "MIT" ]

null

tests/plugins/viz/conftest.py

andrsd/otter

3d47d4ad433b5e7bd1a1d5fed45b4950de14c25f

[ "MIT" ]

30

2020-02-25T13:29:59.000Z

2022-01-07T20:19:05.000Z

tests/plugins/viz/conftest.py

andrsd/otter

3d47d4ad433b5e7bd1a1d5fed45b4950de14c25f

[ "MIT" ]

null

import pytest @pytest.fixture def viz_plugin(qtbot): from otter.plugins.viz.VizPlugin import VizPlugin plugin = VizPlugin(None) yield plugin

17.222222

53

0.748387

20

155

5.75

0.65

0

0.180645

155

8

54

19.375

0.905512

0

1

0.166667

false

0

0.333333

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

25d27198f145805ad47007d54d3831652ac2959c

207

py

Python

AlgoMethod/full_search/multi_array_full_serach/01.py

Nishi05/Competitive-programming

e59a6755b706d9d5c1f359f4511d92c114e6a94e

[ "MIT" ]

null

AlgoMethod/full_search/multi_array_full_serach/01.py

Nishi05/Competitive-programming

e59a6755b706d9d5c1f359f4511d92c114e6a94e

[ "MIT" ]

null

AlgoMethod/full_search/multi_array_full_serach/01.py

Nishi05/Competitive-programming

e59a6755b706d9d5c1f359f4511d92c114e6a94e

[ "MIT" ]

null

n, m = map(int, input().split()) a_lst = list(map(int, input().split())) b_lst = list(map(int, input().split())) cnt = 0 for i in a_lst: for j in b_lst: if i > j: cnt += 1 print(cnt)

20.7

39

0.531401

39

207

2.717949

0.487179

0.169811

0.311321

0.45283

0.433962

0

0.013245

0.270531

207

9

40

23

0.688742

0

1

0

false

0

0.111111

0

null

0

1

0

1

0

null

0

3

25fc5f6e0c01ba67a5687398c8403a5380a5ac0e

2,176

py

Python

videostreams.py

agnanp/multithreading-videostream

c3d47f2f68d4d238f0923909c5cce5de42ae35a4

[ "MIT" ]

null

videostreams.py

agnanp/multithreading-videostream

c3d47f2f68d4d238f0923909c5cce5de42ae35a4

[ "MIT" ]

null

videostreams.py

agnanp/multithreading-videostream

c3d47f2f68d4d238f0923909c5cce5de42ae35a4

[ "MIT" ]

null

from threading import Thread import datetime import cv2 class FPS: """docstring for FPS""" def __init__(self): self._start = None self._end = None self._numFrames = 0 def start(self): self._start = datetime.datetime.now() return self def stop(self): self._end = datetime.datetime.now() def update(self): self._numFrames += 1 def elapsed(self): return (self._end - self._start).total_seconds() def fps(self): return self._numFrames / self.elapsed() class videoStream: def __init__(self, src=0, name="videoStream", fps=None): self.stream = cv2.VideoCapture(src) (self.grabbed, self.frame) = self.stream.read() if fps: self.stream.set(cv2.CAP_PROP_FPS, fps) self.fokus = 0 self.name = name self.stopped = False def start(self): t = Thread(target=self.update, name=self.name, args=()) t.daemon = True t.start() return self def update(self): while True: if self.stopped: return (self.grabbed, self.frame) = self.stream.read() def read(self): return self.grabbed, self.frame def stop(self): self.stopped = True def getWidth(self): return int(self.stream.get(cv2.CAP_PROP_FRAME_WIDTH)) def getHeight(self): return int(self.stream.get(cv2.CAP_PROP_FRAME_HEIGHT)) def getFPS(self): return int(self.stream.get(cv2.CAP_PROP_FPS)) def isOpen(self): return self.stream.isOpened() def setFramePosition(self, framePos): self.stream.set(cv2.CAP_PROP_POS_FRAMES, framePos) def getFramePosition(self): return int(self.stream.get(cv2.CAP_PROP_POS_FRAMES)) def getFrameCount(self): return int(self.stream.get(cv2.CAP_PROP_FRAME_COUNT)) def setFocus(self): self.stream.set(cv2.CAP_PROP_AUTOFOCUS, 0) self.stream.set(cv2.CAP_PROP_FOCUS, 0) class multiCamera(videoStream): camAddrList = [] def __init__(self,camAddrList, setFps=None): self.camAddr = camAddrList self._cams = [] self._frames = [] for cam_Addr in self.camAddr: cs = videoStream(src=cam_Addr, fps=setFps).start() self._cams.append(cs) def capture(self): self._frames = [] for cm in self._cams: ret, frame = cm.read() if ret: self._frames.append(frame) return self._frames

20.72381

57

0.705423

319

2,176

4.642633

0.247649

0.087779

0.06077

0.057394

0.273464

0.239703

0.177583

0.131668

0.083052

0

0.009341

0.163603

2,176

104

58

20.923077

0.804396

0.007813

0

0.16

0

0.005109

0

1

0.28

false

0

0.04

0.12

0.546667

0

null

0

null

0

1

0

1

0

3

d30aa2b8ad3b8e1e64e065adc63f6c47c9d9af5b

391

py

Python

rl/tools/normalizers/__init__.py

haamoon/librl

f9fc633b79c1bec1cd1b1a275834eb5221cffff3

[ "MIT" ]

3

2019-08-11T23:25:17.000Z

2021-08-24T13:24:01.000Z

rl/tools/normalizers/__init__.py

haamoon/librl

f9fc633b79c1bec1cd1b1a275834eb5221cffff3

[ "MIT" ]

6

2020-01-28T22:45:38.000Z

2022-02-10T00:13:42.000Z

rl/tools/normalizers/__init__.py

haamoon/librl

f9fc633b79c1bec1cd1b1a275834eb5221cffff3

[ "MIT" ]

3

2019-07-03T21:18:27.000Z

2019-10-03T04:51:55.000Z

from .normalizer import OnlineNormalizer, NormalizerStd, NormalizerMax, NormalizerId from .tf_normalizer import tfNormalizer, tfNormalizerMax, tfNormalizerStd, tfNormalizerId def create_build_nor_from_str(nor_cls_str, nor_kwargs): nor_cls = globals()[nor_cls_str] def build_nor(shape): return nor_cls(shape, unscale=False, unbias=False, **nor_kwargs) return build_nor

35.545455

89

0.792839

49

391

6.020408

0.510204

0.081356

0.061017

0

0.132992

391

10

90

39.1

0.870206

0

1

0.285714

false

0

0.285714

0.142857

0.857143

0

null

0

null

0

1

0

1

0

3

d32d9dc00142a808ab4884c037d719b88a753e99

176

py

Python

openiPrototype/openiPrototype/APIS/Products_and_Services/Shop/admin.py

OPENi-ict/ntua_demo

104118fbe1f54db35386ca96286317ceb64cb658

[ "Apache-2.0" ]

null

openiPrototype/openiPrototype/APIS/Products_and_Services/Shop/admin.py

OPENi-ict/ntua_demo

104118fbe1f54db35386ca96286317ceb64cb658

[ "Apache-2.0" ]

null

openiPrototype/openiPrototype/APIS/Products_and_Services/Shop/admin.py

OPENi-ict/ntua_demo

104118fbe1f54db35386ca96286317ceb64cb658

[ "Apache-2.0" ]

null

__author__ = 'mpetyx' from django.contrib import admin from .models import OpeniShop class ShopAdmin(admin.ModelAdmin): pass admin.site.register(OpeniShop, ShopAdmin)

14.666667

41

0.778409

21

176

6.333333

0.714286

0

0.142045

176

11

42

16

0.880795

0

0.034091

0

1

0

false

0.166667

0.333333

0

0.5

0

1

0

null

0

null

0

1

0

3

d3314a32508d50103f80a9ed56866ebaa49e1a0f

755

py

Python

src/solver.py

geraldzakwan/katla-helper

c4f7d655fe00dc07bf1eee656b1e64684b5eded7

[ "MIT" ]

null

src/solver.py

geraldzakwan/katla-helper

c4f7d655fe00dc07bf1eee656b1e64684b5eded7

[ "MIT" ]

null

src/solver.py

geraldzakwan/katla-helper

c4f7d655fe00dc07bf1eee656b1e64684b5eded7

[ "MIT" ]

null

from abc import ABC, abstractmethod from src.utils import read_dictionary class Solver(ABC): def __init__(self, word_dict_filepath, hist_dict_filepath): self.word_dict = read_dictionary(word_dict_filepath) self.hist_dict = read_dictionary(hist_dict_filepath) def is_in_dictionary(self, word): if word in self.word_dict: return True return False def is_used_previously(self, word): if word in self.hist_dict: return True return False @abstractmethod def set_important_consonants(self, num_important_consonants): pass @abstractmethod def get_starters(self): pass @abstractmethod def get_guesses(self, states): pass

22.205882

65

0.678146

94

755

5.138298

0.361702

0.082816

0.074534

0.057971

0.186335

0.082816

0

0.263576

755

33

66

22.878788

0.868705

0

0.434783

0

1

0.26087

false

0.130435

0

0.608696

0

null

0

null

0

1

0

1

0

1

0

3

d35834e4ac51677b11ebf3a3403d9007c865a7d7

69

py

Python

pymiddy/__init__.py

godvsdeity/pymiddy

5126110a3f9e673f062e4248301796b5de10507d

[ "MIT" ]

1

2021-01-15T06:17:50.000Z

pymiddy/__init__.py

godvsdeity/pymiddy

5126110a3f9e673f062e4248301796b5de10507d

[ "MIT" ]

null

pymiddy/__init__.py

godvsdeity/pymiddy

5126110a3f9e673f062e4248301796b5de10507d

[ "MIT" ]

null

__name__ = 'pymiddy' __version__ = '0.1.0' from .middy import Middy

13.8

24

0.710145

10

69

4.1

0.8

0

0.051724

0.15942

69

4

25

17.25

0.655172

0

0.173913

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

d374f59d67c6fe97e2ccc0a2b608d4637cc76e57

168

py

Python

search.py

urplatshubham/arr_deal

a3b0563e78b9f1fa267bb934f3cf0ff922dc35f2

[ "MIT" ]

null

search.py

urplatshubham/arr_deal

a3b0563e78b9f1fa267bb934f3cf0ff922dc35f2

[ "MIT" ]

null

search.py

urplatshubham/arr_deal

a3b0563e78b9f1fa267bb934f3cf0ff922dc35f2

[ "MIT" ]

null

class searching: def lin_search(self, arr, num): for i in range(len(arr)): if arr[i] == num: return i return -1

21

36

0.458333

22

168

3.454545

0.727273

0

0.010638

0.440476

168

7

37

24

0.797872

0

1

0.166667

false

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

3

d385ca7241bd26e2e1fb9eb4af0ac1bee221e31c

1,465

py

Python

sandbox/lib/jumpscale/JumpscaleLibs/clients/gitea/GiteaOrgForMember.py

threefoldtech/threebot_prebuilt

1f0e1c65c14cef079cd80f73927d7c8318755c48

[ "Apache-2.0" ]

2

2019-05-09T07:21:25.000Z

2019-08-05T06:37:53.000Z

sandbox/lib/jumpscale/JumpscaleLibs/clients/gitea/GiteaOrgForMember.py

threefoldtech/threebot_prebuilt

1f0e1c65c14cef079cd80f73927d7c8318755c48

[ "Apache-2.0" ]

664

2018-12-19T12:43:44.000Z

2019-08-23T04:24:42.000Z

sandbox/lib/jumpscale/JumpscaleLibs/clients/gitea/GiteaOrgForMember.py

threefoldtech/threebot_prebuilt

1f0e1c65c14cef079cd80f73927d7c8318755c48

[ "Apache-2.0" ]

7

2019-05-03T07:14:37.000Z

2019-08-05T12:36:52.000Z

from .GiteaOrg import GiteaOrg class GiteaOrgForMember(GiteaOrg): def save(self, commit=True): is_valid, err = self._validate(create=True) if not commit or not is_valid: self._log_debug(err) return is_valid try: resp = self.user.client.api.admin.adminCreateOrg(data=self.data, username=self.user.username) org = resp.json() for k, v in org.items(): setattr(self, k, v) return True except Exception as e: self._log_debug(e.response.content) return False def update(self, commit=True): is_valid, err = self._validate(update=True) if not commit or not is_valid: self._log_debug(err) return is_valid try: resp = self.user.client.api.orgs.orgEdit(data=self.data, org=self.username) return True except Exception as e: self._log_debug(e.response.content) return False # def delete(self, commit=True): # is_valid, err = self._validate(delete=True) # # if not commit or not is_valid: # self._log_debug(err) # return is_valid # try: # resp = self.user.client.api.admin.adminDeleteUser(username=self.username) # return True # except Exception as e: # self._log_debug(e.response.content) # return False

31.847826

105

0.574061

180

1,465

4.538889

0.277778

0.077111

0.088127

0.058752

0.717258

0.585067

0

0.335836

1,465

45

106

32.555556

0.839671

0.251877

0

0.592593

0

1

0.074074

false

0

0.037037

0

0.37037

0

null

0

1

0

null

0

3

d3894fa9b5b91661bd5eeb0871ee8e2e049cf34c

1,486

py

Python

crits/emails/urls.py

dutrow/crits

6b357daa5c3060cf622d3a3b0c7b41a9ca69c049

[ "MIT" ]

738

2015-01-02T12:39:55.000Z

2022-03-23T11:05:51.000Z

crits/emails/urls.py

dutrow/crits

6b357daa5c3060cf622d3a3b0c7b41a9ca69c049

[ "MIT" ]

605

2015-01-01T01:03:39.000Z

2021-11-17T18:51:07.000Z

crits/emails/urls.py

dutrow/crits

6b357daa5c3060cf622d3a3b0c7b41a9ca69c049

[ "MIT" ]

316

2015-01-07T12:35:01.000Z

2022-03-30T04:44:30.000Z

from django.conf.urls import url from . import views urlpatterns = [ url(r'^search/$', views.email_search, name='crits-emails-views-email_search'), url(r'^delete/(?P<email_id>\w+)/$', views.email_del, name='crits-emails-views-email_del'), url(r'^upload/attach/(?P<email_id>\w+)/$', views.upload_attach, name='crits-emails-views-upload_attach'), url(r'^details/(?P<email_id>\w+)/$', views.email_detail, name='crits-emails-views-email_detail'), url(r'^new/fields/$', views.email_fields_add, name='crits-emails-views-email_fields_add'), url(r'^new/outlook/$', views.email_outlook_add, name='crits-emails-views-email_outlook_add'), url(r'^new/raw/$', views.email_raw_add, name='crits-emails-views-email_raw_add'), url(r'^new/yaml/$', views.email_yaml_add, name='crits-emails-views-email_yaml_add'), url(r'^new/eml/$', views.email_eml_add, name='crits-emails-views-email_eml_add'), url(r'^edit/(?P<email_id>\w+)/$', views.email_yaml_add, name='crits-emails-views-email_yaml_add'), url(r'^update_header_value/(?P<email_id>\w+)/$', views.update_header_value, name='crits-emails-views-update_header_value'), url(r'^indicator_from_header_field/(?P<email_id>\w+)/$', views.indicator_from_header_field, name='crits-emails-views-indicator_from_header_field'), url(r'^list/$', views.emails_listing, name='crits-emails-views-emails_listing'), url(r'^list/(?P<option>\S+)/$', views.emails_listing, name='crits-emails-views-emails_listing'), ]

70.761905

151

0.718708

234

1,486

4.320513

0.17094

0.178042

0.207715

0.276954

0.531157

0.363007

0.205737

0.104847

0

0.07537

1,486

20

152

74.3

0.735808

0

0.519515

0.469717

0

1

0

false

0

0.111111

0

0.111111

0

null

0

1

0

1

null

0

3

d39d87163c302a5224242e13bcd3a257bf4c8bac

22,731

py

Python

lib/extraction/XRR.py

ArnaudHemmerle/JupyLabBook

1975eabc85e73e28432514fea2199fddd033ecfc

[ "MIT" ]

null

lib/extraction/XRR.py

ArnaudHemmerle/JupyLabBook

1975eabc85e73e28432514fea2199fddd033ecfc

[ "MIT" ]

20

2020-05-07T16:47:14.000Z

2021-04-01T10:15:12.000Z

lib/extraction/XRR.py

ArnaudHemmerle/JupyLabBook

1975eabc85e73e28432514fea2199fddd033ecfc

[ "MIT" ]

null

# custom libraries from lib.extraction.common import PyNexus as PN from lib.extraction.common import Check_dead_pixels from lib.extraction import PilatusSum as PilatusSum import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import matplotlib.colors as colors import os import sys def Treat(nxs_filename, recording_dir, direct_nxs_filename, ROIx0, ROIy0, ROIsizex, ROIsizey, m4pitch0, wavelength, force_direct=True, fdirect=1., working_dir='', plot_gains=False, plot_XRR_m4pitch=False, plot_XRR_qz=False, save=False, verbose=False): ''' Call functions for extracting, plotting, and saving a series of XRR scans. Parameters ---------- nxs_filename : str nexus filename recording_dir : str directory where the nexus file is stored direct_nxs_filename : str nexus filename of the direct scan ROIx0 : int x0 of the integrated ROI (for the direct and the XRR scans) ROIy0 : int y0 of the integrated ROI (for the direct and the XRR scans) ROIsizex : int sizex of the integrated ROI (for the direct and the XRR scans) ROIsizey : int sizey of the integrated ROI (for the direct and the XRR scans) m4pitch0 : float value of m4pitch0 (m4pitch aligned with the beam) in deg wavelength : float wavelength in nm force_direct : bool, optional enforce the value of fdirect (not extracted from the direct scan) fdirect : float, optional value of the normalisation to be used if force_direct is True working_dir : str, optional directory where the treated files will be stored plot_gains : bool, optional plot the intensity of the chamber for each gain plot_XRR_m4pitch : bool, optional plot the XRR as a function of m4pitch plot_XRR_qz : bool, optional plot the XRR as a function of qz save : bool, optional save the XRR verbose : bool, optional verbose mode Returns ------- array_like m4pitch, an array containing the list of m4pitch array_like theta, an array containing the list of theta (i.e. 2.*theta/2.) array_like qz, an array containing the list of qz array_like gains, an array of arrays, each containing the list of intensities for a specific gain of the chamber array_like I0, an array containing the intensities of the chamber normalized by its gain array_like I, an array containing the values of the integrated ROI on the XRR scans array_like Inorm, an array containing the values I normalized by I0 and the direct (i.e. the reflectivity) Raises ------ SystemExit('XRR_files.dat not found') when XXX_XRR_files.dat file not found SystemExit('Sensor not found') when a particular sensor is not found in the nxs SystemExit('direct_gains.dat not found') when XXX_direct_gains.dat file not found ''' # Check if the direct should be extracted from a file, or is given by the user if force_direct: print('Direct is forced to the value: direct=%g'%fdirect) direct = fdirect else: direct = extract_direct(direct_nxs_filename, ROIx0, ROIy0, ROIsizex, ROIsizey, recording_dir, verbose) print('Direct extracted from %s: direct=%g'%(direct_nxs_filename,direct)) m4pitch, theta, qz, gains, I0, I, Inorm = \ Extract(nxs_filename, ROIx0, ROIy0, ROIsizex, ROIsizey, m4pitch0, wavelength, direct, recording_dir, verbose) if (plot_gains or plot_XRR_m4pitch or plot_XRR_qz): Plot(m4pitch, theta, qz, gains, I0, Inorm, nxs_filename, plot_gains, plot_XRR_m4pitch, plot_XRR_qz) if save: Save(m4pitch, theta, qz, I0, I, direct, Inorm, nxs_filename, working_dir, verbose) return m4pitch, theta, qz, gains, I0, I, Inorm def Extract(nxs_filename, ROIx0, ROIy0, ROIsizex, ROIsizey, m4pitch0, wavelength, direct, recording_dir, verbose): ''' Extract the nexus scan and companion files and return useful quantities for XRR. Parameters ---------- nxs_filename : str nexus filename ROIx0 : int x0 of the integrated ROI (for the direct and the XRR scans) ROIy0 : int y0 of the integrated ROI (for the direct and the XRR scans) ROIsizex : int sizex of the integrated ROI (for the direct and the XRR scans) ROIsizey : int sizey of the integrated ROI (for the direct and the XRR scans) m4pitch0 : float value of m4pitch0 (m4pitch aligned with the beam) in deg wavelength : float wavelength in nm direct : float value of the normalisation working_dir : str directory where the treated files will be stored verbose : bool verbose mode Returns ------- array_like m4pitch, an array containing the list of m4pitch array_like theta, an array containing the list of theta (i.e. 2.*theta/2.) array_like qz, an array containing the list of qz array_like gains, an array of arrays, each containing the list of intensities for a specific gain of the chamber array_like I0, an array containing the intensities of the chamber normalized by its gain array_like I, an array containing the values of the integrated ROI on the XRR scans array_like Inorm, an array containing the values I normalized by I0 and the direct (i.e. the reflectivity) Raises ------ SystemExit('XRR_files.dat not found') when XXX_XRR_files.dat file not found SystemExit('Sensor not found') when a particular sensor is not found in the nxs SystemExit('direct_gains.dat not found') when XXX_direct_gains.dat file not found ''' # Extract the intensity of the ionization chamber for the different gains # The nxs_filename should be the first XRR, with a companion file XXX_XRR_files.dat files_path = recording_dir+nxs_filename[:-4]+'_XRR_files.dat' if not os.path.isfile(files_path): print(PN._RED+'The file %s seems not to exist in recording directory'% (nxs_filename[:-4]+'_XRR_files.dat')+PN._RESET) print('\t\t recording directory: %s'%recording_dir) sys.exit('XRR_files.dat not found') else: file_list = np.genfromtxt(recording_dir+nxs_filename[:-4]+'_XRR_files.dat',dtype='U') ######################### # Extraction of the gains gain1 = np.array([]) gain2 = np.array([]) gain3 = np.array([]) gain4 = np.array([]) gain5 = np.array([]) gain6 = np.array([]) for file in file_list: file += '_XRR_gains.dat' if verbose: print('Extracting I0 for different gains from file %s'%file) gain1_temp = np.genfromtxt(recording_dir+file)[1] gain2_temp = np.genfromtxt(recording_dir+file)[2] gain3_temp = np.genfromtxt(recording_dir+file)[3] gain4_temp = np.genfromtxt(recording_dir+file)[4] gain5_temp = np.genfromtxt(recording_dir+file)[5] gain6_temp = np.genfromtxt(recording_dir+file)[6] gain1 = np.append(gain1, gain1_temp) gain2 = np.append(gain2, gain2_temp) gain3 = np.append(gain3, gain3_temp) gain4 = np.append(gain4, gain4_temp) gain5 = np.append(gain5, gain5_temp) gain6 = np.append(gain6, gain6_temp) gains = [gain1, gain2, gain3, gain4, gain5, gain6] # Identify saturated values g1s = np.where(gain1<9.9, gain1, -1) g2s = np.where(gain2<9.9, gain2, -1) g3s = np.where(gain3<9.9, gain3, -1) g4s = np.where(gain4<9.9, gain4, -1) g5s = np.where(gain5<9.9, gain5, -1) g6s = np.where(gain6<9.9, gain6, -1) # Construct the final curve g_temp = np.where(g6s<0, g5s/1e4, g6s/1e5) g_temp = np.where(g_temp<0, g4s/1e3, g_temp) g_temp = np.where(g_temp<0, g3s/1e2, g_temp) g_temp = np.where(g_temp<0, g2s/1e1, g_temp) I0 = np.where(g_temp<0, g1s, g_temp) I = np.array([]) m4pitch = np.array([]) ######################### # Extraction of XRR for file in file_list: nxs_filename = file+'.nxs' # Extract the sum of all images image, _, _ =PilatusSum.Extract(nxs_filename, recording_dir, show_data_stamps=False, verbose=verbose) # Extract the ROI containing reflected beams # Full image: ROI = [0, 0, 981, 1043] ROI = [ROIx0, ROIy0, ROIsizex, ROIsizey] #Apply the ROI image_ROI = image[ROI[1]:ROI[1]+ROI[3], ROI[0]:ROI[0]+ROI[2]] # Extract info from nexus file nexus = PN.PyNexusFile(recording_dir+nxs_filename, fast=True) stamps0D, data0D = nexus.extractData("0D") nbpts=int(nexus.get_nbpts()) sensor_list = [stamps0D[i][0] if stamps0D[i][1]== None else stamps0D[i][1] for i in range(len(stamps0D))] if 'm4pitch' in sensor_list: m4pitchArg = sensor_list.index('m4pitch') else: print(PN._RED+'\t Sensor %s is not in the sensor list'%('m4pitch')+PN._RESET) sys.exit('Sensor not found') if 'integration_time' in sensor_list: integration_timeArg = sensor_list.index('integration_time') else: print(PN._RED+'\t Sensor %s is not in the sensor list'%('integration_time')+PN._RESET) sys.exit('Sensor not found') m4pitch = np.append(m4pitch, np.mean(data0D[m4pitchArg])) integration_time = np.mean(data0D[integration_timeArg]) # Sum the ROI and normalize with the integration time and the number of images integrated_ROI = image_ROI.sum(axis=0).sum(axis=0)/integration_time/nbpts I = np.append(I, integrated_ROI) # Convert to qz theta = np.abs(2*(m4pitch-m4pitch0)*np.pi/180.) #Incident angle in rad qz = 4*np.pi/wavelength*np.sin(theta) #qz in nm-1 # Normalize by I0 and direct (Inorm is the reflectivity) Inorm = I/I0/direct return m4pitch, theta, qz, gains, I0, I, Inorm def Plot(m4pitch, theta, qz, gains, I0, Inorm, nxs_filename, plot_gains, plot_XRR_m4pitch, plot_XRR_qz): ''' Plot the intensity of the ion chamber for each gain and the XRR. Parameters ---------- nxs_filename : str nexus filename m4_pitch : array of float list of m4pitch theta : array of float list of theta (i.e. 2.*theta/2.) qz : array of float list of qz gains : array of array of float list of intensities for a specific gain of the chamber I0 : array of float intensities of the chamber normalized by its gain I : array of float values of the integrated ROI on the XRR scans Inorm : array of float alues I normalized by I0 and the direct (i.e. the reflectivity) plot_gains : bool plot the intensity of the chamber for each gain plot_XRR_m4pitch : bool plot the XRR as a function of m4pitch plot_XRR_qz : bool plot the XRR as a function of qz ''' # Unpack the gains [gain1, gain2, gain3, gain4, gain5, gain6] = gains if plot_gains: # Plot gains not normalized fig = plt.figure(figsize=(12,5)) ax=fig.add_subplot(111) plt.yscale('log') plt.plot(m4pitch, gain1, 'ro-', label = 'gain 1') plt.plot(m4pitch, gain2, 'cx-', label = 'gain 2') plt.plot(m4pitch, gain3, 'bv-', label = 'gain 3') plt.plot(m4pitch, gain4, 'y^-', label = 'gain 4') plt.plot(m4pitch, gain5, 'ms-', label = 'gain 5') plt.plot(m4pitch, gain6, 'g*-', label = 'gain 6') plt.ylabel('I', fontsize=16) plt.legend() ax.set_title('I0 not normalized', fontsize=16) ax.set_xlabel('m4pitch', fontsize=16) ax.set_ylabel('I0', fontsize=16) fig.subplots_adjust(top=0.85) fig.suptitle('First scan: '+nxs_filename.split('\\')[-1], fontsize=16) ax.tick_params(labelsize=16) ax.yaxis.offsetText.set_fontsize(16) plt.show() # Plot the final curve fig = plt.figure(figsize=(12,5)) ax=fig.add_subplot(111) plt.yscale('log') plt.plot(m4pitch, gain1, 'ro-') plt.plot(m4pitch, gain2/1e1, 'cx-') plt.plot(m4pitch, gain3/1e2, 'bv-') plt.plot(m4pitch, gain4/1e3, 'y^-') plt.plot(m4pitch, gain5/1e4, 'ms-') plt.plot(m4pitch, gain6/1e5, 'g*-') plt.plot(m4pitch, I0, 'k-', lw = 3, label='Final I0') plt.legend() ax.set_title('I0 normalized', fontsize=16) ax.set_xlabel('m4pitch', fontsize=16) ax.set_ylabel('I0 norm.', fontsize=16) fig.subplots_adjust(top=0.85) ax.tick_params(labelsize=16) ax.yaxis.offsetText.set_fontsize(16) plt.show() if plot_XRR_m4pitch: fig = plt.figure(figsize=(12,5)) ax=fig.add_subplot(111) plt.yscale('log') plt.plot(m4pitch, Inorm,'x-k') ax.set_xlabel('m4pitch', fontsize=16) ax.set_ylabel('R', fontsize=16) fig.subplots_adjust(top=0.9) fig.suptitle('First scan: '+nxs_filename.split('\\')[-1], fontsize=16) ax.tick_params(labelsize=16) ax.yaxis.offsetText.set_fontsize(16) plt.show() if plot_XRR_qz: fig = plt.figure(figsize=(12,5)) ax=fig.add_subplot(111) plt.yscale('log') plt.plot(qz, Inorm,'x-k') ax.set_xlabel('qz (nm-1)', fontsize=16) ax.set_ylabel('R', fontsize=16) fig.subplots_adjust(top=0.9) fig.suptitle('First scan: '+nxs_filename.split('\\')[-1], fontsize=16) ax.tick_params(labelsize=16) ax.yaxis.offsetText.set_fontsize(16) plt.show() def Save(m4pitch, theta, qz, I0, I, direct, Inorm, nxs_filename, working_dir, verbose): ''' Save. XXX_XRR.dat: different parameters and XRR for each point of m4pitch. Parameters ---------- m4_pitch : array of float list of m4pitch theta : array of float list of theta (i.e. 2.*theta/2.) qz : array of float list of qz I0 : array of float intensities of the chamber normalized by its gain I : array of float values of the integrated ROI on the XRR scans direct: float value of the direct Inorm : array of float alues I normalized by I0 and the direct (i.e. the reflectivity) nxs_filename : str nexus filename working_dir : str directory where the treated files will be stored verbose : bool verbose mode ''' # Create Save Name savename=working_dir+nxs_filename[:nxs_filename.rfind('.nxs')] # Save XRR tobesaved = [m4pitch, theta, qz, I0, I, len(I)*[direct], Inorm] np.savetxt(savename+'_XRR.dat', np.transpose(tobesaved), delimiter = '\t', comments='', header ='#m4pitch(deg)\t#theta(rad)\t#qz(nm-1)\t#I0\t#I\t#direct\t#R') if verbose: print('\t. XRR saved in:') print('\t%s_XRR.dat'%savename) def extract_direct(nxs_filename, ROIx0, ROIy0, ROIsizex, ROIsizey, recording_dir, verbose): ''' Extract the nexus scan of the direct beam and companion files and return the value of the direct. Parameters ---------- nxs_filename : str nexus filename ROIx0 : int x0 of the integrated ROI (for the direct and the XRR scans) ROIy0 : int y0 of the integrated ROI (for the direct and the XRR scans) ROIsizex : int sizex of the integrated ROI (for the direct and the XRR scans) ROIsizey : int sizey of the integrated ROI (for the direct and the XRR scans) recording_dir : str directory where the nexus file is stored verbose : bool verbose mode Returns ------- float direct, value of the direct Raises ------ SystemExit('direct_gains.dat not found') when XXX_direct_gains.dat file not found SystemExit('Sensor not found') when a particular sensor is not found in the nxs ''' file_path = recording_dir+nxs_filename[:-4]+'_direct_gains.dat' import os import sys if not os.path.isfile(file_path): print(PN._RED+'The file %s seems not to exist in recording directory'% (nxs_filename[:-4]+'_direct_gains.dat')+PN._RESET) print('\t\t recording directory: %s'%recording_dir) sys.exit('direct_gains.dat not found') else: file = nxs_filename[:-4]+'_direct_gains.dat' ######################### # Extraction of the gains gain1 = np.array([]) gain2 = np.array([]) gain3 = np.array([]) gain4 = np.array([]) gain5 = np.array([]) gain6 = np.array([]) if verbose: print('Extracting I0 for different gains from file %s'%file) gain1_temp = np.genfromtxt(recording_dir+file)[0] gain2_temp = np.genfromtxt(recording_dir+file)[1] gain3_temp = np.genfromtxt(recording_dir+file)[2] gain4_temp = np.genfromtxt(recording_dir+file)[3] gain5_temp = np.genfromtxt(recording_dir+file)[4] gain6_temp = np.genfromtxt(recording_dir+file)[5] gain1 = np.append(gain1, gain1_temp) gain2 = np.append(gain2, gain2_temp) gain3 = np.append(gain3, gain3_temp) gain4 = np.append(gain4, gain4_temp) gain5 = np.append(gain5, gain5_temp) gain6 = np.append(gain6, gain6_temp) gains = [gain1, gain2, gain3, gain4, gain5, gain6] # Identify saturated values g1s = np.where(gain1<9.9, gain1, -1) g2s = np.where(gain2<9.9, gain2, -1) g3s = np.where(gain3<9.9, gain3, -1) g4s = np.where(gain4<9.9, gain4, -1) g5s = np.where(gain5<9.9, gain5, -1) g6s = np.where(gain6<9.9, gain6, -1) # Construct the final curve g_temp = np.where(g6s<0, g5s/1e4, g6s/1e5) g_temp = np.where(g_temp<0, g4s/1e3, g_temp) g_temp = np.where(g_temp<0, g3s/1e2, g_temp) g_temp = np.where(g_temp<0, g2s/1e1, g_temp) I0 = np.where(g_temp<0, g1s, g_temp)[0] ######################### # Extraction of direct # Extract the sum of all images image, _, _ =PilatusSum.Extract(nxs_filename, recording_dir, show_data_stamps=False, verbose=verbose) # Extract the ROI containing reflected beams # Full image: ROI = [0, 0, 981, 1043] ROI = [ROIx0, ROIy0, ROIsizex, ROIsizey] #Apply the ROI image_ROI = image[ROI[1]:ROI[1]+ROI[3], ROI[0]:ROI[0]+ROI[2]] # Extract info from nexus file nexus = PN.PyNexusFile(recording_dir+nxs_filename, fast=True) nbpts=int(nexus.get_nbpts()) stamps0D, data0D = nexus.extractData("0D") sensor_list = [stamps0D[i][0] if stamps0D[i][1]== None else stamps0D[i][1] for i in range(len(stamps0D))] if 'integration_time' in sensor_list: integration_timeArg = sensor_list.index('integration_time') else: print(PN._RED+'\t Sensor %s is not in the sensor list'%('integration_time')+PN._RESET) sys.exit('Sensor not found') integration_time = np.mean(data0D[integration_timeArg]) # Sum the ROI and normalize with the integration time, the number of images image_ROI_sum = image_ROI.sum(axis=0).sum(axis=0) direct = image_ROI_sum/integration_time/I0/nbpts return direct def Calib(calib_XRR_data, distance): ''' Fit and plot the values from the calibration, to give the user the coefficient to be used in the XRR scripts. Parameters ---------- calib_XRR_data : array array with the values of the calibration for m4pitch, c10tablepitch, gamma and zs distance : float the distance pilatus-trough in mm ''' # c10tablepitch vs m4pitch fig=plt.figure(1, figsize=(10,5)) ax=fig.add_subplot(111) xfit=calib_XRR_data[:,0] yfit=calib_XRR_data[:,1] ax.plot(xfit,yfit, 'bo') B_c10, A_c10 = np.polyfit(xfit, yfit, 1) ax.plot(xfit, A_c10+B_c10*xfit, 'r-', lw=2) ax.set_title('Calibration c10tablepitch') ax.set_xlabel('m4pitch (deg)', fontsize=16) ax.set_ylabel('c10tablepitch (step)', fontsize=16) ax.tick_params(labelsize=16) ax.yaxis.offsetText.set_fontsize(16) fig.text(0.15, .8, "coeff_c10tablepitch = %3.5g (steps per deg)"%(B_c10), fontsize=14) plt.show() # zs vs m4pitch fig=plt.figure(1, figsize=(10,5)) ax=fig.add_subplot(111) xfit=calib_XRR_data[:,0] yfit=calib_XRR_data[:,3] ax.plot(xfit,yfit, 'bo') B_zs, A_zs = np.polyfit(xfit, yfit, 1) ax.plot(xfit, A_zs+B_zs*xfit, 'r-', lw=2) ax.set_title('Calibration zs') ax.set_xlabel('m4pitch (deg)', fontsize=16) ax.set_ylabel('zs (mm)', fontsize=16) ax.tick_params(labelsize=16) ax.yaxis.offsetText.set_fontsize(16) fig.text(0.15, .8, "coeff_zs = %3.5f (mm per deg)"%(B_zs), fontsize=14) plt.show() # gamma vs m4pitch fig=plt.figure(1, figsize=(10,5)) ax=fig.add_subplot(111) xfit=calib_XRR_data[:,0] yfit=calib_XRR_data[:,2] ax.plot(xfit,yfit, 'bo') B_gamma, A_gamma = np.polyfit(xfit, yfit, 1) coeff_gamma = (B_gamma+2)*np.pi*distance/(180.*B_zs) ax.plot(xfit, A_gamma + B_gamma*xfit, 'r-', lw=2) ax.set_title('Calibration gamma') ax.set_xlabel('m4pitch (deg)', fontsize=16) ax.set_ylabel('gamma (deg)', fontsize=16) ax.tick_params(labelsize=16) ax.yaxis.offsetText.set_fontsize(16) #fig.text(0.2, .8, "%3.5g deg per deg"%(B_gamma), fontsize=14) fig.text(0.15, .8, "coeff_gamma = %3.2f "%(coeff_gamma), fontsize=14) plt.show()

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1

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null

0

3

d3a0e130e8b9d7d7c42e0164502452cf061d2603

499

py

Python

app/controllers/__init__.py

s1hofmann/MrHyde2.0-Backbone

d1d95cb8ba3e2db60c75218625453b915e87e1bf

[ "MIT" ]

3

2017-05-14T20:11:49.000Z

2018-04-12T03:56:06.000Z

app/controllers/__init__.py

s1hofmann/MrHyde2.0-Backbone

d1d95cb8ba3e2db60c75218625453b915e87e1bf

[ "MIT" ]

null

app/controllers/__init__.py

s1hofmann/MrHyde2.0-Backbone

d1d95cb8ba3e2db60c75218625453b915e87e1bf

[ "MIT" ]

null

from flask import Blueprint from app import current_config jekyll = Blueprint('jekyll', __name__, static_folder=current_config.STATICDIR, template_folder=current_config.TEMPLATEDIR) status = Blueprint('status', __name__, static_folder=current_config.STATICDIR, template_folder=current_config.TEMPLATEDIR) from .status_controller import status from .jekyll_controller import jekyll

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31.1875

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0

0.5

0

0.024048

0

1

0

false

0

0.333333

0

0.333333

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0

null

1

0

null

0

1

0

3

d3a1dc062b30267f8b6a7cc3486fac2bf169ce7d

1,808

py

Python

tests/test_util.py

BrechtBa/knxpy

9e486f4a4623f586091e72cc6472441f3efbdd72

[ "MIT" ]

null

tests/test_util.py

BrechtBa/knxpy

9e486f4a4623f586091e72cc6472441f3efbdd72

[ "MIT" ]

null

tests/test_util.py

BrechtBa/knxpy

9e486f4a4623f586091e72cc6472441f3efbdd72

[ "MIT" ]

null

#!/usr/bin/env/ python ################################################################################ # Copyright (c) 2016 Daniel Matuschek # This file is part of knxpy. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. ################################################################################ import unittest import asyncio import knxpy class TestUtil(unittest.TestCase): def test_encode_ga(self): self.assertEqual( knxpy.util.encode_ga('1/1/71'), 2375 ) def test_decode_ga(self): self.assertEqual( knxpy.util.decode_ga(2375), '1/1/71' ) def test_encode_dpt_1(self): self.assertEqual( knxpy.util.encode_dpt(0,'1'), [0] ) def test_decode_dpt_1(self): self.assertEqual( knxpy.util.decode_dpt(1,'1'), 1 ) def test_encode_dpt_5(self): self.assertEqual( knxpy.util.encode_dpt(140,'5'), [0,140] ) def test_decode_dpt_5(self): self.assertEqual( knxpy.util.decode_dpt(b'\x8c','5'), 140 ) def test_encode_dpt_9(self): self.assertEqual( knxpy.util.encode_dpt(22.64,'9'), b'\x00\x0cl' ) def test_decode_dpt_9(self): self.assertEqual( knxpy.util.decode_dpt(b'\x0cl','9'), 22.64 ) if __name__ == '__main__': unittest.main()

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244

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0

0.039946

0.183075

1,808

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81

35.45098

0.701422

0.380531

0

0.046463

0

0.363636

1

0.363636

false

0

0.136364

0

0.545455

0

null

0

1

0

null

0

1

0

1

0

3

d3a8fa7d4f538e6d4f3b23579f159cc8cfadaa69

480

py

Python

4/oop6.py

ikramulkayes/Python_season2

d057460d07c5d2d218ecd52e08c1d355add44df2

[ "MIT" ]

null

4/oop6.py

ikramulkayes/Python_season2

d057460d07c5d2d218ecd52e08c1d355add44df2

[ "MIT" ]

null

4/oop6.py

ikramulkayes/Python_season2

d057460d07c5d2d218ecd52e08c1d355add44df2

[ "MIT" ]

null

class Vehicle: def __init__(self): self.lst = [0,0] def moveUp(self): self.lst[1] += 1 def moveDown(self): self.lst[1] -= 1 def moveRight(self): self.lst[0] += 1 def moveLeft(self): self.lst[0] -= 1 def print_position(self): print(tuple(self.lst)) car = Vehicle() car.print_position() car.moveUp() car.print_position() car.moveLeft() car.print_position() car.moveDown() car.print_position() car.moveRight()

20

30

0.604167

67

480

4.19403

0.238806

0.149466

0.19573

0.270463

0.227758

0

0.027322

0.2375

480

24

31

20

0.740437

0

0.181818

0

1

0.272727

false

0

0.318182

0.272727

0

null

0

1

0

null

0

1

0

3

6c92d27382dba0aad5c7403248ee9b4f5c933459

85

py

Python

ABC_A/ABC143_A.py

ryosuke0825/atcoder_python

185cdbe7db44ecca1aaf357858d16d31ce515ddb

[ "MIT" ]

null

ABC_A/ABC143_A.py

ryosuke0825/atcoder_python

185cdbe7db44ecca1aaf357858d16d31ce515ddb

[ "MIT" ]

null

ABC_A/ABC143_A.py

ryosuke0825/atcoder_python

185cdbe7db44ecca1aaf357858d16d31ce515ddb

[ "MIT" ]

null

a, b = map(int, input().split()) if b * 2 >= a: print(0) else: print(a-b*2)

12.142857

32

0.482353

17

85

2.411765

0.647059

0.097561

0

0.048387

0.270588

85

6

33

14.166667

0.612903

0

1

0

true

0

0.4

1

0

null

0

1

0

null

0

1

0

3

6c9b3c5cc62a2d5ac5dd14196042eed862b77aa0

858

py

Python

recruitment_agency_api/agency_api/views.py

swingthrough/recruitment-agency-storage-api-task

ab388cdfb21cf5611e9fb00a0e7dfc20c125c5c3

[ "MIT" ]

null

recruitment_agency_api/agency_api/views.py

swingthrough/recruitment-agency-storage-api-task

ab388cdfb21cf5611e9fb00a0e7dfc20c125c5c3

[ "MIT" ]

null

recruitment_agency_api/agency_api/views.py

swingthrough/recruitment-agency-storage-api-task

ab388cdfb21cf5611e9fb00a0e7dfc20c125c5c3

[ "MIT" ]

null

from rest_framework import generics from rest_framework import mixins from . import models from . import serializers # Create your views here. # class JobCandidateView( # mixins.CreateModelMixin, # mixins.RetrieveModelMixin, # mixins.ListModelMixin, # mixins.UpdateModelMixin, # mixins.DestroyModelMixin, # generics.GenericAPIView, # ): # queryset = models.JobCandidate.objects.all() # serializer_class = serializers.JobCandidateSerializer # def get_queryset(self): # return super().get_queryset() # class JobAdView( # mixins.CreateModelMixin, # mixins.RetrieveModelMixin, # mixins.ListModelMixin, # mixins.UpdateModelMixin, # mixins.DestroyModelMixin, # generics.GenericAPIView, # ): # queryset = models.JobAd.objects.all() # serializer_class = serializers.JobAdSerializer

26.8125

59

0.719114

74

858

8.256757

0.459459

0.026187

0.055646

0.075286

0.599018

0.481178

0

0.187646

858

32

60

26.8125

0.876614

0.799534

0

1

0

true

0

1

0

1

0

null

0

null

0

1

0

1

0

1

0

3

6ca3c3072f88c49c10e934740064094359f1f4b8

999

py

Python

kalasearch/http_client.py

oeddyo/kalasearch-python-sdk

bbb0cd44bd19d23ee4f1a3ea0aa5599e3e1fd38b

[ "MIT" ]

9

2020-08-19T06:48:25.000Z

2022-02-05T07:24:30.000Z

kalasearch/http_client.py

oeddyo/kalasearch-python-sdk

bbb0cd44bd19d23ee4f1a3ea0aa5599e3e1fd38b

[ "MIT" ]

1

2020-08-24T00:55:32.000Z

kalasearch/http_client.py

oeddyo/kalasearch-python-sdk

bbb0cd44bd19d23ee4f1a3ea0aa5599e3e1fd38b

[ "MIT" ]

4

2021-11-09T20:41:13.000Z

2022-03-22T09:13:54.000Z

import requests class HttpClient(): config = None headers = {} def __init__(self, config): self.config = config self.headers = { "X-Kalasearch-Id": self.config.appId, "X-Kalasearch-Key": self.config.apiKey, "Content-Type": "application/json" } def send_request(self, http_method, path, body=None): endpoint = self.config.domain + "/" + path if body is None: request = http_method(endpoint, headers=self.headers) else: request = http_method(endpoint, headers=self.headers, json=body) return request.json() def get(self, path): return self.send_request(requests.get, path) def post(self, path, body=None): return self.send_request(requests.post, path, body) def put(self, path, body=None): return self.send_request(requests.put, path, body) def delete(self, path): return self.send_request(requests.delete, path)

28.542857

76

0.613614

120

999

5.008333

0.3

0.083195

0.093178

0.139767

0.415973

0.14975

0

0.27027

999

34

77

29.382353

0.824417

0

0.06006

0

1

0.230769

false

0

0.038462

0.153846

0.576923

0

null

0

null

0

1

0

1

0

3

6ca943dcea2e6106828fb2f413dbeb83826f198e

171

py

Python

tests/pyb/pyb_f405.py

TG-Techie/circuitpython

390295dd218fb705fe652de77132dea472adf1ed

[ "MIT", "BSD-3-Clause", "MIT-0", "Unlicense" ]

3

2020-01-09T21:50:22.000Z

2020-01-15T08:27:48.000Z

tests/pyb/pyb_f405.py

TG-Techie/circuitpython

390295dd218fb705fe652de77132dea472adf1ed

[ "MIT", "BSD-3-Clause", "MIT-0", "Unlicense" ]

null

tests/pyb/pyb_f405.py

TG-Techie/circuitpython

390295dd218fb705fe652de77132dea472adf1ed

[ "MIT", "BSD-3-Clause", "MIT-0", "Unlicense" ]

1

2020-01-11T12:42:41.000Z

# test pyb module on F405 MCUs import os, pyb if not "STM32F405" in os.uname().machine: print("SKIP") raise SystemExit print(pyb.freq()) print(type(pyb.rng()))

15.545455

41

0.672515

27

171

4.259259

0.777778

0

0.057143

0.181287

171

10

42

17.1

0.764286

0.163743

0

0.092199

0

1

0

true

0

0.166667

0

0.166667

0.5

1

0

null

0

null

0

1

0

1

0

3