xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

e55615aa05e1ce1ce7853e73fc177018f9fa1a7b

99

py

Python

back-end/app/api/__init__.py

liaoherui/flask-vue-microblog

e34672d834013fc9a4919607bbfdc103048581a4

[ "Apache-2.0" ]

null

back-end/app/api/__init__.py

liaoherui/flask-vue-microblog

e34672d834013fc9a4919607bbfdc103048581a4

[ "Apache-2.0" ]

null

back-end/app/api/__init__.py

liaoherui/flask-vue-microblog

e34672d834013fc9a4919607bbfdc103048581a4

[ "Apache-2.0" ]

null

from flask import Blueprint bp=Blueprint('api',__name__) from app.api import ping, users, tokens

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e584a93589ba6b55b1e87d927419a05ec2621581

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py

Python

dynamicRooms/__init__.py

MichaelBoshell/RSCBot

6a77a76e7beab073bc40e8cab300b3031279298b

[ "MIT" ]

12

2018-12-19T17:00:00.000Z

2021-06-10T13:27:01.000Z

dynamicRooms/__init__.py

MichaelBoshell/RSCBot

6a77a76e7beab073bc40e8cab300b3031279298b

[ "MIT" ]

37

2020-03-10T18:42:29.000Z

2021-09-29T19:36:42.000Z

dynamicRooms/__init__.py

MichaelBoshell/RSCBot

6a77a76e7beab073bc40e8cab300b3031279298b

[ "MIT" ]

14

2018-12-31T02:12:18.000Z

2021-11-13T01:49:53.000Z

from .dynamicRooms import DynamicRooms def setup(bot): bot.add_cog(DynamicRooms(bot))

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5

e589d52bda020ef4213d6f06e65a6e58ffa9fcdc

641

py

Python

ee/clickhouse/views/session_recordings.py

lalitkale/posthog

25163d0bdbef22fb254cd10f0cd7afd6a3cdc346

[ "MIT" ]

null

ee/clickhouse/views/session_recordings.py

lalitkale/posthog

25163d0bdbef22fb254cd10f0cd7afd6a3cdc346

[ "MIT" ]

null

ee/clickhouse/views/session_recordings.py

lalitkale/posthog

25163d0bdbef22fb254cd10f0cd7afd6a3cdc346

[ "MIT" ]

null

from ee.clickhouse.queries.session_recordings.session_recording import ClickhouseSessionRecording from ee.clickhouse.queries.session_recordings.session_recording_list import ClickhouseSessionRecordingList from posthog.api.session_recording import SessionRecordingViewSet class ClickhouseSessionRecordingViewSet(SessionRecordingViewSet): def _get_session_recording_list(self, filter): return ClickhouseSessionRecordingList(filter=filter, team=self.team).run() def _get_session_recording(self, session_recording_id): return ClickhouseSessionRecording(team=self.team, session_recording_id=session_recording_id).run()

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e59c0465392b25fc9b9fb4ba747a681387bab4d4

2,768

py

Python

resources/dot_PyCharm/system/python_stubs/-762174762/PySide/QtCore/QXmlStreamWriter.py

basepipe/developer_onboarding

05b6a776f8974c89517868131b201f11c6c2a5ad

[ "MIT" ]

1

2020-04-20T02:27:20.000Z

resources/dot_PyCharm/system/python_stubs/cache/16012662ddca113c1f50140f9e0d3bd290a511015767475cf362e5267760f062/PySide/QtCore/QXmlStreamWriter.py

basepipe/developer_onboarding

05b6a776f8974c89517868131b201f11c6c2a5ad

[ "MIT" ]

null

resources/dot_PyCharm/system/python_stubs/cache/16012662ddca113c1f50140f9e0d3bd290a511015767475cf362e5267760f062/PySide/QtCore/QXmlStreamWriter.py

basepipe/developer_onboarding

05b6a776f8974c89517868131b201f11c6c2a5ad

[ "MIT" ]

null

# encoding: utf-8 # module PySide.QtCore # from C:\Python27\lib\site-packages\PySide\QtCore.pyd # by generator 1.147 # no doc # imports import Shiboken as __Shiboken class QXmlStreamWriter(__Shiboken.Object): # no doc def autoFormatting(self, *args, **kwargs): # real signature unknown pass def autoFormattingIndent(self, *args, **kwargs): # real signature unknown pass def codec(self, *args, **kwargs): # real signature unknown pass def device(self, *args, **kwargs): # real signature unknown pass def hasError(self, *args, **kwargs): # real signature unknown pass def setAutoFormatting(self, *args, **kwargs): # real signature unknown pass def setAutoFormattingIndent(self, *args, **kwargs): # real signature unknown pass def setCodec(self, *args, **kwargs): # real signature unknown pass def setDevice(self, *args, **kwargs): # real signature unknown pass def writeAttribute(self, *args, **kwargs): # real signature unknown pass def writeAttributes(self, *args, **kwargs): # real signature unknown pass def writeCDATA(self, *args, **kwargs): # real signature unknown pass def writeCharacters(self, *args, **kwargs): # real signature unknown pass def writeComment(self, *args, **kwargs): # real signature unknown pass def writeCurrentToken(self, *args, **kwargs): # real signature unknown pass def writeDefaultNamespace(self, *args, **kwargs): # real signature unknown pass def writeDTD(self, *args, **kwargs): # real signature unknown pass def writeEmptyElement(self, *args, **kwargs): # real signature unknown pass def writeEndDocument(self, *args, **kwargs): # real signature unknown pass def writeEndElement(self, *args, **kwargs): # real signature unknown pass def writeEntityReference(self, *args, **kwargs): # real signature unknown pass def writeNamespace(self, *args, **kwargs): # real signature unknown pass def writeProcessingInstruction(self, *args, **kwargs): # real signature unknown pass def writeStartDocument(self, *args, **kwargs): # real signature unknown pass def writeStartElement(self, *args, **kwargs): # real signature unknown pass def writeTextElement(self, *args, **kwargs): # real signature unknown pass def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(S, *more): # real signature unknown; restored from __doc__ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """ pass

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e5e3a93573493fafca9b274491e85e7facbe8ae7

86

py

Python

pdat/templates/__init__.py

Christine8888/PulsarDataToolbox

d4c4a508f90fec77cc60be48776bf8300e98aeaa

[ "MIT" ]

1

2019-02-07T20:05:15.000Z

pdat/templates/__init__.py

Christine8888/PulsarDataToolbox

d4c4a508f90fec77cc60be48776bf8300e98aeaa

[ "MIT" ]

2

2017-09-29T22:31:44.000Z

2017-10-12T07:41:13.000Z

pdat/templates/__init__.py

Hazboun6/pypsrfits

d4c4a508f90fec77cc60be48776bf8300e98aeaa

[ "MIT" ]

null

""" __init__.py file to make templates a module""" from .template import get_template

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1

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5

f906a8b803ebf94ab8ab014071ba16a935e9c0c9

57

py

Python

gym_gridworld/envs/__init__.py

utilForever/2021-AIFrenz-RLEnv

f6936ef0ca753a63af0133102d0c2b840d8d299b

[ "MIT" ]

6

2021-05-11T12:07:17.000Z

2021-06-18T03:46:02.000Z

gym_gridworld/envs/__init__.py

utilForever/2021-AIFrenz-RLEnv

f6936ef0ca753a63af0133102d0c2b840d8d299b

[ "MIT" ]

null

gym_gridworld/envs/__init__.py

utilForever/2021-AIFrenz-RLEnv

f6936ef0ca753a63af0133102d0c2b840d8d299b

[ "MIT" ]

5

2021-05-11T11:42:57.000Z

2022-03-22T04:49:22.000Z

from gym_gridworld.envs.env_gridworld import GridworldEnv

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5

00db727d31e9e3c1771f2742ff8d3a494951402e

64

py

Python

apps/api/v2/mixins/__init__.py

blockomat2100/vulnman

835ff3aae1168d8e2fa5556279bc86efd2e46472

[ "MIT" ]

3

2021-12-22T07:02:24.000Z

2022-01-27T20:19:11.000Z

apps/api/v2/mixins/__init__.py

vulnman/vulnman

d48ee022bc0e4368060a990a527b1c7a5e437504

[ "MIT" ]

44

2021-12-14T07:24:29.000Z

2022-03-23T07:01:16.000Z

apps/api/v2/mixins/__init__.py

blockomat2100/vulnman

835ff3aae1168d8e2fa5556279bc86efd2e46472

[ "MIT" ]

1

2022-01-21T16:29:56.000Z

from apps.api.v2.mixins.testcase import VulnmanAPITestCaseMixin

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5

da9ee415e998b955293bc0ab4637d217cf655b3b

183

py

Python

show_news/admin.py

QuocHung52/daily_news

b051dfad82bc1b53b30ddf7895473b732ff3ad24

[ "MIT" ]

null

show_news/admin.py

QuocHung52/daily_news

b051dfad82bc1b53b30ddf7895473b732ff3ad24

[ "MIT" ]

null

show_news/admin.py

QuocHung52/daily_news

b051dfad82bc1b53b30ddf7895473b732ff3ad24

[ "MIT" ]

null

from django.contrib import admin from .models import * # Register your models here. admin.site.register(Articles) admin.site.register(Source_Of_News) admin.site.register(Skip_List)

20.333333

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5

dadff7f18518c720199f9f12d36fe88ab13c3e70

118

py

Python

scripts/programa-pedidos.py

pblocz/programa-pedidos-san-cecilio

8af64c4988dcfc0cdfb9d92867e4650b268a547a

[ "CC-BY-3.0" ]

null

scripts/programa-pedidos.py

pblocz/programa-pedidos-san-cecilio

8af64c4988dcfc0cdfb9d92867e4650b268a547a

[ "CC-BY-3.0" ]

null

scripts/programa-pedidos.py

pblocz/programa-pedidos-san-cecilio

8af64c4988dcfc0cdfb9d92867e4650b268a547a

[ "CC-BY-3.0" ]

null

#! /usr/bin/python2 import sys import hospital.hospital_gui as gui if __name__ == "__main__": sys.exit(gui.main())

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5

daee65e5f32990e09418f0c544685e2b2f60a5d7

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py

Python

electionnight/models/__init__.py

The-Politico/politico-civic-election-night

a8aaf5be43872a7b84d2b0d7c2b6151d32d4d8b6

[ "MIT" ]

null

electionnight/models/__init__.py

The-Politico/politico-civic-election-night

a8aaf5be43872a7b84d2b0d7c2b6151d32d4d8b6

[ "MIT" ]

55

2018-03-19T20:56:04.000Z

2018-10-10T21:28:26.000Z

electionnight/models/__init__.py

The-Politico/politico-civic-election-night

a8aaf5be43872a7b84d2b0d7c2b6151d32d4d8b6

[ "MIT" ]

null

# flake8: noqa from .candidate_color_order import CandidateColorOrder from .page_content_block import PageContentBlock from .page_content_type import PageContentType from .page_content import PageContent from .page_type import PageType

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9704a9e23821654ac78f093771c9d60e001972e5

37

py

Python

tests/__init__.py

MustardForBreakfast/safetywrap

170f836e12df455aed9b6dce5e7c634f6b9e8f87

[ "Apache-2.0" ]

21

2019-10-31T17:43:18.000Z

2022-03-19T13:46:05.000Z

tests/__init__.py

MustardForBreakfast/safetywrap

170f836e12df455aed9b6dce5e7c634f6b9e8f87

[ "Apache-2.0" ]

null

tests/__init__.py

MustardForBreakfast/safetywrap

170f836e12df455aed9b6dce5e7c634f6b9e8f87

[ "Apache-2.0" ]

3

2019-11-01T17:50:07.000Z

2021-12-15T07:23:21.000Z

"""Test modules for result types."""

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5

97382fbebb7181474fda91ae6ab830ba41edb89e

322

py

Python

datasets/transformations/utils/__init__.py

bytedance/Hammer

388ed20b3d9b34f33f5357d75f8fe5d726782ec8

[ "MIT" ]

97

2022-02-08T09:00:57.000Z

2022-03-23T05:33:35.000Z

datasets/transformations/utils/__init__.py

bytedance/Hammer

388ed20b3d9b34f33f5357d75f8fe5d726782ec8

[ "MIT" ]

null

datasets/transformations/utils/__init__.py

bytedance/Hammer

388ed20b3d9b34f33f5357d75f8fe5d726782ec8

[ "MIT" ]

7

2022-02-08T15:13:02.000Z

2022-03-19T19:11:13.000Z

# python3.7 """Collects dataset related utility functions.""" from .affine_transform import generate_affine_transformation from .polygon import generate_polygon_contour from .polygon import generate_polygon_mask __all__ = [ 'generate_affine_transformation', 'generate_polygon_contour', 'generate_polygon_mask' ]

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5

9740447e71ae7bc5fec243575d6796817504f535

104

py

Python

megumi/db/__init__.py

dev-techmoe/megumi

67d5626b168dada5f42671a81eca46ea8b7a603c

[ "MIT" ]

4

2020-10-28T08:37:49.000Z

2022-03-30T05:39:27.000Z

megumi/db/__init__.py

dev-techmoe/megumi

67d5626b168dada5f42671a81eca46ea8b7a603c

[ "MIT" ]

null

megumi/db/__init__.py

dev-techmoe/megumi

67d5626b168dada5f42671a81eca46ea8b7a603c

[ "MIT" ]

null

from .db import db, get_db, clean_db from .dao import DAO __all__ = ['db', 'get_db', 'DAO', 'clean_db']

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5

97539a1cd265a0c047da45d1965dcd6eba244899

24

py

Python

cryopicls/autorefine/__init__.py

kttn8769/cryopicls

29a8b4d6cc43c592576ded781462ca84276fd4c1

[ "MIT" ]

null

cryopicls/autorefine/__init__.py

kttn8769/cryopicls

29a8b4d6cc43c592576ded781462ca84276fd4c1

[ "MIT" ]

null

cryopicls/autorefine/__init__.py

kttn8769/cryopicls

29a8b4d6cc43c592576ded781462ca84276fd4c1

[ "MIT" ]

null

from . import cryosparc

12

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3

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6.333333

1

0

0.166667

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1

24

0.95

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true

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0

1

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null

0

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0

1

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5

976005978200d1c46755b983d1fbc74222f383c8

58

py

Python

gym_tictactoe/envs/__init__.py

LudwigStumpp/gym-tic-tac-toe

2c41f14249d0336b35467010be1957d3b018ae71

[ "MIT" ]

4

2020-07-24T11:47:16.000Z

2020-10-10T18:44:44.000Z

gym_tictactoe/envs/__init__.py

LudwigStumpp/gym-tic-tac-toe

2c41f14249d0336b35467010be1957d3b018ae71

[ "MIT" ]

null

gym_tictactoe/envs/__init__.py

LudwigStumpp/gym-tic-tac-toe

2c41f14249d0336b35467010be1957d3b018ae71

[ "MIT" ]

null

from gym_tictactoe.envs.tictactoe_env import TictactoeEnv

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6.25

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1

0

1

0

5

97ab313baa15535a569f83dbcb95e7f7fe5cf452

20,167

py

Python

models/vgg_pytorch.py

AffectAnalysisGroup/AUNets

dc3c6ad937b4ced5564a4b002e8adc7e36979e13

[ "MIT" ]

2

2021-04-12T09:57:39.000Z

2021-11-30T16:42:48.000Z

models/vgg_pytorch.py

AffectAnalysisGroup/AUNets

dc3c6ad937b4ced5564a4b002e8adc7e36979e13

[ "MIT" ]

null

models/vgg_pytorch.py

AffectAnalysisGroup/AUNets

dc3c6ad937b4ced5564a4b002e8adc7e36979e13

[ "MIT" ]

null

import torch.nn as nn import torch.utils.model_zoo as model_zoo import math import torch import glob, pdb """ We provide pre-trained models, using the PyTorch :mod:`torch.utils.model_zoo`. These can be constructed by passing ``pretrained=True``: .. code:: python import torchvision.models as models vgg16 = models.vgg16(pretrained=True) All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using ``mean = [0.485, 0.456, 0.406]`` and ``std = [0.229, 0.224, 0.225]``. You can use the following transform to normalize:: normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) An example of such normalization can be found in the imagenet example `here <https://github.com/pytorch/examples/blob/\ 42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101>`_ """ __all__ = ['VGG', 'vgg16'] model_urls = { 'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth', } # ========================================================================# # ========================================================================# # ========================================================================# def make_layers(cfg, in_channels=3, batch_norm=False): layers = [] for v in cfg: if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) if batch_norm: layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] else: layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v return nn.Sequential(*layers) cfg = { 'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'D': [ 64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M' ], 'E': [ 64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M' ], } # ========================================================================# class VGG(nn.Module): def __init__(self, features, num_classes=2): super(VGG, self).__init__() self.features = features self.classifier = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, num_classes), ) self._initialize_weights() def forward(self, x): x = self.features(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.weight.data.normal_(0, 0.01) m.bias.data.zero_() # ========================================================================# class VGG_ALONE(nn.Module): def __init__(self, features, num_classes=2): super(VGG_ALONE, self).__init__() self.features = features self.classifier = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, num_classes), ) self._initialize_weights() def forward(self, x, OF=None): OF = self.features(OF) OF = OF.view(OF.size(0), -1) OF = self.classifier(OF) return OF def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.weight.data.normal_(0, 0.01) m.bias.data.zero_() # ========================================================================# class VGG_IMAGE(nn.Module): def __init__(self, features, num_classes=2, OF_option='horizontal'): # img = torch.from_numpy(np.zeros((4,3,448,224), dtype=np.float32)) super(VGG_IMAGE, self).__init__() self.OF_option = OF_option self.features = features self.classifier = nn.Sequential( nn.Linear(512 * 14 * 7, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, num_classes), ) self._initialize_weights() def forward(self, x, OF=None): if self.OF_option.lower() == 'horizontal': dim = 3 else: dim = 2 img_of = torch.cat([x, OF], dim=dim) x = self.features(img_of) x = x.view(x.size(0), -1) x = self.classifier(x) return x def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.weight.data.normal_(0, 0.01) m.bias.data.zero_() # ========================================================================# class VGG_CHANNELS(nn.Module): def __init__(self, features, num_classes=2): # img = torch.from_numpy(np.zeros((4,6,224,224), dtype=np.float32)) super(VGG_CHANNELS, self).__init__() self.features = features self.classifier = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, num_classes), ) self._initialize_weights() def forward(self, x, OF=None): img_of = torch.cat([x, OF], dim=1) out = self.features(img_of) out = out.view(out.size(0), -1) out = self.classifier(out) return out def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.weight.data.normal_(0, 0.01) m.bias.data.zero_() # ========================================================================# class VGG_CONV(nn.Module): def __init__(self, features_rgb, features_of, num_classes=2): # img = torch.from_numpy(np.zeros((4,3,224,224), dtype=np.float32)) super(VGG_CONV, self).__init__() self.features_rgb = features_rgb self.features_of = features_of self.classifier = nn.Sequential( nn.Linear(1024 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, num_classes), ) self._initialize_weights() def forward(self, x, OF=None): conv_rgb = self.features_rgb(x) conv_of = self.features_of(OF) conv_out = torch.cat([conv_rgb, conv_of], dim=1) conv_out = conv_out.view(conv_out.size(0), -1) out = self.classifier(conv_out) return out def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.weight.data.normal_(0, 0.01) m.bias.data.zero_() # ========================================================================# class VGG_FC6(nn.Module): def __init__(self, features_rgb, features_of, num_classes=2): super(VGG_FC6, self).__init__() self.features_rgb = features_rgb self.features_of = features_of self.classifier_rgb = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), ) self.classifier_of = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), ) self.classifier = nn.Sequential( nn.Linear(8192, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, num_classes), ) self._initialize_weights() def forward(self, x, OF=None): conv_rgb = self.features_rgb(x) conv_rgb = conv_rgb.view(conv_rgb.size(0), -1) fc6_rgb = self.classifier_rgb(conv_rgb) conv_of = self.features_of(OF) conv_of = conv_of.view(conv_of.size(0), -1) fc6_of = self.classifier_of(conv_of) fc_cat = torch.cat([fc6_rgb, fc6_of], dim=1) out = self.classifier(fc_cat) return out def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.weight.data.normal_(0, 0.01) m.bias.data.zero_() # ========================================================================# class VGG_FC7(nn.Module): def __init__(self, features_rgb, features_of, num_classes=2): super(VGG_FC7, self).__init__() self.features_rgb = features_rgb self.features_of = features_of self.classifier_rgb = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), ) self.classifier_of = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), ) self.classifier = nn.Sequential(nn.Linear(8192, num_classes), ) self._initialize_weights() def forward(self, x, OF=None): conv_rgb = self.features_rgb(x) conv_rgb = conv_rgb.view(conv_rgb.size(0), -1) fc7_rgb = self.classifier_rgb(conv_rgb) conv_of = self.features_of(OF) conv_of = conv_of.view(conv_of.size(0), -1) fc7_of = self.classifier_rgb(conv_of) fc_cat = torch.cat([fc7_rgb, fc7_of], dim=1) out = self.classifier(fc_cat) return out def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.weight.data.normal_(0, 0.01) m.bias.data.zero_() # ========================================================================# # ========================================================================# # ========================================================================# # ========================================================================# def vgg16(pretrained='', OF_option='None', model_save_path='', **kwargs): """VGG 16-layer model (configuration "D") Args: pretrained (str): If '', returns a model pre-trained on ImageNet """ # ========================================================================# # ========================================================================# # pdb.set_trace() if pretrained == 'ImageNet': model_zoo_ = model_zoo.load_url(model_urls['vgg16']) model_zoo_ = {k.encode("utf-8"): v for k, v in model_zoo_.iteritems()} elif pretrained == 'emotionnet' and OF_option == 'None': emo_file = sorted( glob.glob('/home/afromero/datos2/EmoNet/snapshot/models/\ EmotionNet/normal/fold_all/Imagenet/*.pth'))[-1] pddb.set_trace() model_zoo_ = torch.load(emo_file) # print("Finetuning from: "+emo_file) model_zoo_ = { k.replace('model.', ''): v for k, v in model_zoo_.iteritems() } elif pretrained == 'emotionnet' and OF_option != 'None': au_rgb_file = sorted( glob.glob(model_save_path.replace(OF_option, 'None') + '/*.pth'))[-1] model_zoo_ = torch.load(au_rgb_file) # print("Finetuning from: "+os.path.abspath(au_rgb_file)) model_zoo_ = { k.replace('model.', ''): v for k, v in model_zoo_.iteritems() } # ========================================================================# # ========================================================================# if OF_option == 'None': model = VGG(make_layers(cfg['D']), **kwargs) if pretrained: model.load_state_dict(model_zoo_) # ========================================================================# elif OF_option == 'Alone': model = VGG_ALONE(make_layers(cfg['D']), **kwargs) if pretrained: model.load_state_dict(model_zoo_) # ========================================================================# elif OF_option == 'Vertical' or OF_option == 'Horizontal': #pdb.set_trace() model = VGG_IMAGE(make_layers(cfg['D']), **kwargs) if pretrained: model_zoo_['classifier.0.weight'] = model_zoo_[ 'classifier.0.weight'].repeat(1, 2) model.load_state_dict(model_zoo_) # ========================================================================# elif OF_option == 'Channels': model = VGG_CHANNELS(make_layers(cfg['D'], in_channels=6), **kwargs) if pretrained: model_zoo_['features.0.weight'] = model_zoo_[ 'features.0.weight'].repeat(1, 2, 1, 1) model.load_state_dict(model_zoo_) # ========================================================================# elif OF_option == 'Conv': model = VGG_CONV( make_layers(cfg['D']), make_layers(cfg['D']), **kwargs) if pretrained: model_zoo_2 = {} model_zoo_2['classifier.0.weight'] = model_zoo_[ 'classifier.0.weight'].repeat(1, 2) conv_rgb_params = { k.replace('features', 'features_rgb'): v for k, v in model_zoo_.iteritems() if 'features' in k } model_zoo_2.update(conv_rgb_params) conv_of_params = { k.replace('features', 'features_of'): v for k, v in model_zoo_.iteritems() if 'features' in k } model_zoo_2.update(conv_of_params) fc_params = { k: v for k, v in model_zoo_.iteritems() if 'classifier' in k and 'classifier.0.weight' not in k } model_zoo_2.update(fc_params) model.load_state_dict(model_zoo_2) # ========================================================================# elif OF_option == 'FC6': model = VGG_FC6(make_layers(cfg['D']), make_layers(cfg['D']), **kwargs) if pretrained: model_zoo_2 = {} conv_rgb_params = { k.replace('features', 'features_rgb'): v for k, v in model_zoo_.iteritems() if 'features' in k } model_zoo_2.update(conv_rgb_params) fc_rgb_params = { k.replace('classifier', 'classifier_rgb'): v for k, v in model_zoo_.iteritems() if 'classifier.0' in k } model_zoo_2.update(fc_rgb_params) conv_of_params = { k.replace('features', 'features_of'): v for k, v in model_zoo_.iteritems() if 'features' in k } model_zoo_2.update(conv_of_params) fc_of_params = { k.replace('classifier', 'classifier_of'): v for k, v in model_zoo_.iteritems() if 'classifier.0' in k } model_zoo_2.update(fc_of_params) model_zoo_2['classifier.0.weight'] = model_zoo_[ 'classifier.3.weight'].repeat(1, 2) model_zoo_2['classifier.0.bias'] = model_zoo_['classifier.3.bias'] model_zoo_2['classifier.3.weight'] = model_zoo_[ 'classifier.6.weight'] model_zoo_2['classifier.3.bias'] = model_zoo_['classifier.6.bias'] model.load_state_dict(model_zoo_2) # ========================================================================# elif OF_option == 'FC7': model = VGG_FC7(make_layers(cfg['D']), make_layers(cfg['D']), **kwargs) if pretrained: model_zoo_2 = {} conv_rgb_params = { k.replace('features', 'features_rgb'): v for k, v in model_zoo_.iteritems() if 'features' in k } model_zoo_2.update(conv_rgb_params) fc_rgb_params = { k.replace('classifier', 'classifier_rgb'): v for k, v in model_zoo_.iteritems() if 'classifier.0' in k or 'classifier.3' in k } model_zoo_2.update(fc_rgb_params) conv_of_params = { k.replace('features', 'features_of'): v for k, v in model_zoo_.iteritems() if 'features' in k } model_zoo_2.update(conv_of_params) fc_of_params = { k.replace('classifier', 'classifier_of'): v for k, v in model_zoo_.iteritems() if 'classifier.0' in k or 'classifier.3' in k } model_zoo_2.update(fc_of_params) model_zoo_2['classifier.0.weight'] = model_zoo_[ 'classifier.6.weight'].repeat(1, 2) model_zoo_2['classifier.0.bias'] = model_zoo_['classifier.6.bias'] model.load_state_dict(model_zoo_2) return model

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8af737b707cc932c68d03ea6384465730e8864e7

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Python

CURSO PYTHON/Modulos/aula 01.py

Sabrinaparussoli/PYTHON

77436608ffd799e9e2bbe4fa5084443fb7382793

[ "MIT" ]

null

CURSO PYTHON/Modulos/aula 01.py

Sabrinaparussoli/PYTHON

77436608ffd799e9e2bbe4fa5084443fb7382793

[ "MIT" ]

null

CURSO PYTHON/Modulos/aula 01.py

Sabrinaparussoli/PYTHON

77436608ffd799e9e2bbe4fa5084443fb7382793

[ "MIT" ]

null

from modulos import teste teste(10)

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c17144a4f4bc8516961f7b80a8aa761bb5b7d088

120

py

Python

tensornet/model/__init__.py

Pandinosaurus/Depth-Estimation-Segmentation

2eea883c96bf106774ea94464fc16c6baea86a95

[ "MIT" ]

4

2020-06-18T13:07:19.000Z

2022-01-07T10:51:10.000Z

tensornet/model/__init__.py

Pandinosaurus/Depth-Estimation-Segmentation

2eea883c96bf106774ea94464fc16c6baea86a95

[ "MIT" ]

1

2021-07-31T04:34:46.000Z

2021-08-11T05:55:57.000Z

tensornet/model/__init__.py

Pandinosaurus/Depth-Estimation-Segmentation

2eea883c96bf106774ea94464fc16c6baea86a95

[ "MIT" ]

2

2020-07-21T18:41:58.000Z

2021-05-28T09:40:02.000Z

from .basicnet import BasicNet from .resnet import ResNet18 from .masknet import MaskNet from .dsresnet import DSResNet

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c171d481e58e1ea708b2a8074b860068241212d9

42

py

Python

wrappers/weather/__init__.py

Yat-o/Aoi

51215c2f9fa2049ceee224ab8997e7673cb6e3d6

[ "MIT" ]

5

2020-10-18T02:25:47.000Z

2021-07-01T04:58:58.000Z

wrappers/weather/__init__.py

Yat-o/Aoi

51215c2f9fa2049ceee224ab8997e7673cb6e3d6

[ "MIT" ]

58

2020-09-26T03:16:23.000Z

2021-11-01T18:41:56.000Z

wrappers/weather/__init__.py

Yat-o/Aoi

51215c2f9fa2049ceee224ab8997e7673cb6e3d6

[ "MIT" ]

5

2020-11-25T09:07:11.000Z

2021-08-21T10:25:31.000Z

from .api import * from .helpers import *

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c1b0d2c7f17e16af5e574640d4ece6e4c5723af7

107

py

Python

src/utils/common.py

rustam-azimov/CFPQ_PyAlgo

1f40c300a2dfeded5297ca48d0ddde26cfa8887c

[ "Apache-2.0" ]

11

2020-08-16T15:29:32.000Z

2022-01-26T12:45:39.000Z

src/utils/common.py

rustam-azimov/CFPQ_PyAlgo

1f40c300a2dfeded5297ca48d0ddde26cfa8887c

[ "Apache-2.0" ]

4

2021-02-10T13:35:54.000Z

2021-06-04T07:14:32.000Z

src/utils/common.py

rustam-azimov/CFPQ_PyAlgo

1f40c300a2dfeded5297ca48d0ddde26cfa8887c

[ "Apache-2.0" ]

3

2021-02-23T16:08:38.000Z

2021-12-10T12:47:06.000Z

def chunkify(xs, chunk_size): for i in range(0, len(xs), chunk_size): yield xs[i:i+chunk_size]

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null

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5

c1d7580d805161f5fb129f384e690308b48c38ef

132

py

Python

danesfield/core/apps.py

girder/Danesfield

04b0e991cae52bda758de4ee3f7e04dab45f3ff9

[ "Apache-2.0" ]

null

danesfield/core/apps.py

girder/Danesfield

04b0e991cae52bda758de4ee3f7e04dab45f3ff9

[ "Apache-2.0" ]

24

2021-10-29T21:03:34.000Z

2022-03-18T02:07:57.000Z

danesfield/core/apps.py

girder/Danesfield

04b0e991cae52bda758de4ee3f7e04dab45f3ff9

[ "Apache-2.0" ]

1

2022-01-26T09:31:48.000Z

from django.apps import AppConfig class CoreConfig(AppConfig): name = 'danesfield.core' verbose_name = 'Danesfield: Core'

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c1f627ad1cd0e0d45610bee4b71f0a5cff58fceb

83

py

Python

cloudbridge/providers/gcp/__init__.py

MosheFriedland/cloudbridge

af7644322044863d401645311c0d1f2556bccb63

[ "MIT" ]

61

2018-07-10T18:32:43.000Z

2022-03-06T04:50:20.000Z

cloudbridge/providers/gcp/__init__.py

MosheFriedland/cloudbridge

af7644322044863d401645311c0d1f2556bccb63

[ "MIT" ]

134

2018-07-02T16:46:29.000Z

2022-02-03T17:05:43.000Z

cloudbridge/providers/gcp/__init__.py

MosheFriedland/cloudbridge

af7644322044863d401645311c0d1f2556bccb63

[ "MIT" ]

23

2018-08-07T17:33:16.000Z

2021-12-25T01:44:20.000Z

""" Exports from this provider """ from .provider import GCPCloudProvider # noqa

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1

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1

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5

de0342e65a2ea6393f5513c040f5f5393d5c686d

141

py

Python

mfa/__init__.py

limeburst/mfa

d33c9bd801a4aa7b2c494d3c17fb9933c93e313d

[ "MIT" ]

36

2015-02-09T17:18:51.000Z

2022-01-29T05:51:50.000Z

mfa/__init__.py

limeburst/mfa

d33c9bd801a4aa7b2c494d3c17fb9933c93e313d

[ "MIT" ]

2

2015-03-06T04:04:17.000Z

2019-07-22T05:51:00.000Z

mfa/__init__.py

limeburst/mfa

d33c9bd801a4aa7b2c494d3c17fb9933c93e313d

[ "MIT" ]

9

2015-06-26T15:43:40.000Z

2021-12-06T04:11:15.000Z

""":mod:`mfa` --- Multi-factor authentication on your command line. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """

28.2

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141

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141

4

68

35.25

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0

null

0

1

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5

a9c8ab242d1191351b0b33fb852cfad70d7648e7

174

py

Python

src/walker/topicmodeling.py

ucd-plse/func2vec-fse2018-artifact

a17e22f247a03b77931751dd55d429f26c8f293c

[ "BSD-3-Clause" ]

7

2019-06-01T18:34:53.000Z

2020-07-17T04:11:45.000Z

src/walker/topicmodeling.py

ucd-plse/func2vec-fse2018-artifact

a17e22f247a03b77931751dd55d429f26c8f293c

[ "BSD-3-Clause" ]

1

2018-12-05T14:17:13.000Z

2020-02-24T14:11:01.000Z

src/walker/topicmodeling.py

ucd-plse/func2vec-fse2018-artifact

a17e22f247a03b77931751dd55d429f26c8f293c

[ "BSD-3-Clause" ]

7

2018-07-19T05:49:22.000Z

2021-01-07T01:26:48.000Z

from gensim.models import KeyedVectors from gensim.models.ldamodel import LdaModel def lda(walks_list, numtopics): lda = LdaModel(walks_list, numtopics) return lda

21.75

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null

0

1

0

1

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5

e702745099e21ab935185238ebb77951ab53dedf

36

py

Python

alexa_skills/__init__.py

johnyob/Alexa-Skills

3679a887bb519042511a16fbb848254dc0ee43a0

[ "MIT" ]

null

alexa_skills/__init__.py

johnyob/Alexa-Skills

3679a887bb519042511a16fbb848254dc0ee43a0

[ "MIT" ]

null

alexa_skills/__init__.py

johnyob/Alexa-Skills

3679a887bb519042511a16fbb848254dc0ee43a0

[ "MIT" ]

null

from alexa_skills.Skill import Skill

36

0.888889

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null

0

1

0

1

0

5

e7a87c82fdc6bd23aa71134c05ef471d7b7ccc5e

4,101

py

Python

tests/graph-algo/test_path_selection.py

yonch/fastpass

07e620d6e9a16af731fc5e267c04ac03c5286f94

[ "MIT" ]

61

2015-01-16T00:19:50.000Z

2022-01-25T03:05:56.000Z

tests/graph-algo/test_path_selection.py

yonch/fastpass

07e620d6e9a16af731fc5e267c04ac03c5286f94

[ "MIT" ]

3

2016-09-13T22:51:45.000Z

2019-03-24T16:53:48.000Z

tests/graph-algo/test_path_selection.py

yonch/fastpass

07e620d6e9a16af731fc5e267c04ac03c5286f94

[ "MIT" ]

26

2015-01-18T17:35:43.000Z

2022-01-29T04:17:15.000Z

''' Created on January 3, 2014 @author: aousterh ''' import random import sys import unittest sys.path.insert(0, '../../bindings/graph-algo') sys.path.insert(0, '../../src/graph-algo') from graph_util import graph_util import pathselection import structures class Test(unittest.TestCase): def test_regular_graph(self): """Basic test involving graphs that are already regular.""" generator = graph_util() num_experiments = 10 n_nodes = 256 # network with 8 racks of 32 nodes each n_racks = n_nodes / structures.MAX_NODES_PER_RACK for i in range(num_experiments): # generate admitted traffic g_p = generator.generate_random_regular_bipartite(n_nodes, 1) admitted = structures.create_admitted_traffic() admitted_copy = structures.create_admitted_traffic() for edge in g_p.edges_iter(): structures.insert_admitted_edge(admitted, edge[0], edge[1] - n_nodes) structures.insert_admitted_edge(admitted_copy, edge[0], edge[1] - n_nodes) # select paths pathselection.select_paths(admitted, n_racks) # check that path assignments are valid self.assertTrue(pathselection.paths_are_valid(admitted, n_racks)) # check that src addrs and lower bits of destination addrs are unchanged for e in range(admitted.size): edge = structures.get_admitted_edge(admitted, e) edge_copy = structures.get_admitted_edge(admitted_copy, e) self.assertEqual(edge.src, edge_copy.src) self.assertEqual(edge.dst & pathselection.PATH_MASK, edge_copy.dst & pathselection.PATH_MASK) # clean up structures.destroy_admitted_traffic(admitted) pass def test_irregular_graph(self): """Tests graphs that are not necessarily regular - some number of sources and destinations have no edges.""" generator = graph_util() num_experiments = 100 n_nodes = 256 # network with 8 racks of 32 nodes each n_racks = n_nodes / structures.MAX_NODES_PER_RACK for i in range(num_experiments): # generate admitted traffic g_p = generator.generate_random_regular_bipartite(n_nodes, 1) # choose a number of edges to remove num_edges_to_remove = random.randint(1, 256) # remove edges for j in range(num_edges_to_remove): while (True): # choose an edge index at random index = random.randint(0, n_nodes - 1) edge = g_p.edges(index) if edge != []: edge_tuple = edge[0] g_p.remove_edge(edge_tuple[0], edge_tuple[1]) break admitted = structures.create_admitted_traffic() admitted_copy = structures.create_admitted_traffic() for edge in g_p.edges_iter(): structures.insert_admitted_edge(admitted, edge[0], edge[1] - n_nodes) structures.insert_admitted_edge(admitted_copy, edge[0], edge[1] - n_nodes) # select paths pathselection.select_paths(admitted, n_racks) # check that path assignments are valid self.assertTrue(pathselection.paths_are_valid(admitted, n_racks)) # check that src addrs and lower bits of destination addrs are unchanged for e in range(admitted.size): edge = structures.get_admitted_edge(admitted, e) edge_copy = structures.get_admitted_edge(admitted_copy, e) self.assertEqual(edge.src, edge_copy.src) self.assertEqual(edge.dst & pathselection.PATH_MASK, edge_copy.dst & pathselection.PATH_MASK) # clean up structures.destroy_admitted_traffic(admitted) pass if __name__ == "__main__": unittest.main()

37.281818

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4,101

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0

1

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null

0

5

e7a9ab8229d01671bcaa878bf8e1d456d4cf245a

73

py

Python

social/pipeline/partial.py

raccoongang/python-social-auth

81c0a542d158772bd3486d31834c10af5d5f08b0

[ "BSD-3-Clause" ]

1,987

2015-01-01T16:12:45.000Z

2022-03-29T14:24:25.000Z

social/pipeline/partial.py

raccoongang/python-social-auth

81c0a542d158772bd3486d31834c10af5d5f08b0

[ "BSD-3-Clause" ]

731

2015-01-01T22:55:25.000Z

2022-03-10T15:07:51.000Z

virtual/lib/python3.6/site-packages/social/pipeline/partial.py

dennismwaniki67/awards

80ed10541f5f751aee5f8285ab1ad54cfecba95f

[ "MIT" ]

1,082

2015-01-01T16:27:26.000Z

2022-03-22T21:18:33.000Z

from social_core.pipeline.partial import save_status_to_session, partial

36.5

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73

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0

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1

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1

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1

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5

99eff6c8829b9ff6b97475129e35549be7e980c7

700

py

Python

Day12/art.py

Abubutt/My100DaysOfCode

d049185547f0101f5b97517399efdbbb3a5c6496

[ "MIT" ]

null

Day12/art.py

Abubutt/My100DaysOfCode

d049185547f0101f5b97517399efdbbb3a5c6496

[ "MIT" ]

null

Day12/art.py

Abubutt/My100DaysOfCode

d049185547f0101f5b97517399efdbbb3a5c6496

[ "MIT" ]

null

logo = """ _ _ _ ____ _____ ____ _____ _ _____ ____ ____ _ _ _____ _____ ____ _ _____ / \ /|/ \ /\/ \__/|/ _ \/ __// __\ / __// \ /\/ __// ___\/ ___\/ \/ \ /|/ __/ / __// _ \/ \__/|/ __/ | |\ ||| | ||| |\/||| | //| \ | \/| | | _| | ||| \ | \| \| || |\ ||| | _ | | _| / \|| |\/||| \ | | \||| \_/|| | ||| |_\\| /_ | / | |_//| \_/|| /_ \___ |\___ || || | \||| |_// | |_//| |-||| | ||| /_ \_/ \|\____/\_/ \|\____/\____\\_/\_\ \____\\____/\____\\____/\____/\_/\_/ \|\____\ \____\\_/ \|\_/ \|\____\ """

77.777778

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8

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null

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1

0

1

0

null

0

5

821154455e8342cf8f08350dac88cb6979093d5f

92

py

Python

enthought/mayavi/filters/metadata.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/mayavi/filters/metadata.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/mayavi/filters/metadata.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from mayavi.filters.metadata import *

23

38

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12

92

6

0.75

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0.119565

92

3

39

30.666667

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0

1

0

true

0

1

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0

null

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null

0

1

0

1

0

1

0

5

824066770568ab07e20c47702f44f04c2546b72c

176

py

Python

data_vis/tests.py

jneuendorf/dkb_pdf2csv

836257403054242fe2971fb3e9c0dfd909b2d199

[ "MIT" ]

null

data_vis/tests.py

jneuendorf/dkb_pdf2csv

836257403054242fe2971fb3e9c0dfd909b2d199

[ "MIT" ]

null

data_vis/tests.py

jneuendorf/dkb_pdf2csv

836257403054242fe2971fb3e9c0dfd909b2d199

[ "MIT" ]

null

from django.test import TestCase # Create your tests here. # class ExampleTestCase(TestCase): # # def test_upper(self): # self.assertEqual('foo'.upper(), 'FOO')

17.6

48

0.670455

21

176

5.571429

0.761905

0

0.193182

176

9

49

19.555556

0.823944

0.732955

0

1

0

true

0

1

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1

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1

0

null

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1

0

null

0

1

0

1

0

1

0

5

41b2c886fdc9f2ffc4472718657a28d9a397bfe3

798

py

Python

record/cd/migrations/0002_auto_20210603_2030.py

Brayton-Han/Brayton-s-Record

67cb6f7b17d8cb2c5f428079afb091f12b015f5c

[ "MIT" ]

null

record/cd/migrations/0002_auto_20210603_2030.py

Brayton-Han/Brayton-s-Record

67cb6f7b17d8cb2c5f428079afb091f12b015f5c

[ "MIT" ]

null

record/cd/migrations/0002_auto_20210603_2030.py

Brayton-Han/Brayton-s-Record

67cb6f7b17d8cb2c5f428079afb091f12b015f5c

[ "MIT" ]

null

# Generated by Django 3.2.3 on 2021-06-03 12:30 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('cd', '0001_initial'), ] operations = [ migrations.AlterField( model_name='cd', name='cost', field=models.FloatField(), ), migrations.AlterField( model_name='cd', name='price', field=models.FloatField(), ), migrations.AlterField( model_name='vinyl', name='cost', field=models.FloatField(), ), migrations.AlterField( model_name='vinyl', name='price', field=models.FloatField(), ), ]

23.470588

48

0.487469

67

798

5.731343

0.462687

0.208333

0.260417

0.302083

0.645833

0.565104

0.46875

0

0.039337

0.394737

798

33

49

24.181818

0.755694

0.056391

0

0.740741

1

0

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false

0

0.037037

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0

null

1

0

1

0

null

0

5

41b3393f5dd1c4462f3b4e54d191975c9014f11b

85

py

Python

sec.py

rikamble/pythonpro

9c1c1be561b9cd9b8f562f52733138bdc3d7a9fa

[ "MIT" ]

1

2019-05-01T08:22:37.000Z

sec.py

rikamble/pythonpro

9c1c1be561b9cd9b8f562f52733138bdc3d7a9fa

[ "MIT" ]

1

2019-05-01T16:46:57.000Z

sec.py

rikamble/pythonpro

9c1c1be561b9cd9b8f562f52733138bdc3d7a9fa

[ "MIT" ]

null

print("Enter number") a=input() print("Enter B") b=input() print('enter C') print(c)

12.142857

21

0.658824

15

85

3.733333

0.466667

0.535714

0

0.105882

85

6

22

14.166667

0.736842

0

0.305882

0

1

0

false

0

0.666667

1

0

null

1

0

1

0

null

0

1

0

5

68b7c49c9cadc7311f3ab163266476fd80ec8c9b

801

py

Python

Lib/fontParts/fontshell/__init__.py

sanjaymsh/fontParts

dda5b23336d0d04f2ba5ffa371813991de20635d

[ "MIT" ]

66

2019-01-17T13:50:12.000Z

2022-03-19T15:57:43.000Z

Lib/fontParts/fontshell/__init__.py

sanjaymsh/fontParts

dda5b23336d0d04f2ba5ffa371813991de20635d

[ "MIT" ]

311

2016-03-03T19:52:56.000Z

2019-01-15T12:44:59.000Z

Lib/fontParts/fontshell/__init__.py

sanjaymsh/fontParts

dda5b23336d0d04f2ba5ffa371813991de20635d

[ "MIT" ]

28

2019-02-21T01:54:19.000Z

2022-03-10T09:29:48.000Z

from fontParts.base.errors import FontPartsError from fontParts.fontshell.font import RFont from fontParts.fontshell.info import RInfo from fontParts.fontshell.groups import RGroups from fontParts.fontshell.kerning import RKerning from fontParts.fontshell.features import RFeatures from fontParts.fontshell.lib import RLib from fontParts.fontshell.layer import RLayer from fontParts.fontshell.glyph import RGlyph from fontParts.fontshell.contour import RContour from fontParts.fontshell.point import RPoint from fontParts.fontshell.segment import RSegment from fontParts.fontshell.bPoint import RBPoint from fontParts.fontshell.component import RComponent from fontParts.fontshell.anchor import RAnchor from fontParts.fontshell.guideline import RGuideline from fontParts.fontshell.image import RImage

44.5

52

0.872659

102

801

6.852941

0.382353

0.316166

0.503577

0

0.084894

801

17

53

47.117647

0.953615

0

1

0

true

0

1

0

1

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null

1

0

null

0

1

0

1

0

1

0

5

68f2cc228e870d82a31ea289243abe094754a082

74

py

Python

libs/src/evalib/gradcam/__init__.py

gantir/eva4-2

e95d7f614d21931150d4c0b6b5437c90a742d408

[ "Apache-2.0" ]

null

libs/src/evalib/gradcam/__init__.py

gantir/eva4-2

e95d7f614d21931150d4c0b6b5437c90a742d408

[ "Apache-2.0" ]

null

libs/src/evalib/gradcam/__init__.py

gantir/eva4-2

e95d7f614d21931150d4c0b6b5437c90a742d408

[ "Apache-2.0" ]

1

2021-04-10T05:03:53.000Z

from . import gradcam from . import utils __all__ = ["gradcam", "utils"]

14.8

30

0.689189

9

74

5.222222

0.555556

0.425532

0

0.175676

74

4

31

18.5

0.770492

0

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0

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0

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0.666667

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1

0

1

0

null

0

1

0

1

0

5

ec0f2a56854cf4ac0c3aa9892f5e73b4dfc1fd6f

128

py

Python

hm/main.py

owl2/kaggle

f02e1bb12ffed143ef801821d1c9b3e75ad45dab

[ "Unlicense" ]

null

hm/main.py

owl2/kaggle

f02e1bb12ffed143ef801821d1c9b3e75ad45dab

[ "Unlicense" ]

null

hm/main.py

owl2/kaggle

f02e1bb12ffed143ef801821d1c9b3e75ad45dab

[ "Unlicense" ]

null

from preparation.utils import spark_daily_sales df = spark_daily_sales(begin="2018-09-24", end="2020-09-01") print(df.head())

21.333333

60

0.757813

22

128

4.227273

0.772727

0.215054

0.322581

0

0.136752

0.085938

128

5

61

25.6

0.65812

0

0.15625

0

1

0

false

0

0.333333

0

0.333333

1

0

null

1

0

1

0

null

0

1

0

5

ec26c086690d2319f26d731d9920aedd5489801e

155

py

Python

scrapy/utils/multipart.py

HyunTruth/scrapy

9bc5fab870aaee23905057002276fc0e1a48485f

[ "BSD-3-Clause" ]

32

2019-11-14T07:49:33.000Z

2022-02-16T00:49:22.000Z

scrapy/utils/multipart.py

HyunTruth/scrapy

9bc5fab870aaee23905057002276fc0e1a48485f

[ "BSD-3-Clause" ]

48

2018-11-08T01:31:33.000Z

2019-03-08T01:18:18.000Z

scrapy/utils/multipart.py

HyunTruth/scrapy

9bc5fab870aaee23905057002276fc0e1a48485f

[ "BSD-3-Clause" ]

16

2019-06-25T13:26:43.000Z

2022-03-07T07:29:12.000Z

""" Transitional module for moving to the w3lib library. For new code, always import from w3lib.form instead of this module """ from w3lib.form import *

19.375

66

0.754839

24

155

4.875

0.708333

0.153846

0.222222

0

0.023438

0.174194

155

7

67

22.142857

0.890625

0.774194

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

ec320917b255dd78d9649c2aceef62b39e47b79e

388

py

Python

Grass.py

KRHS-GameProgramming-2015/Adlez

8912da1ee4b3c7b105851dbcc00579ff0c3cf33e

[ "BSD-2-Clause" ]

null

Grass.py

KRHS-GameProgramming-2015/Adlez

8912da1ee4b3c7b105851dbcc00579ff0c3cf33e

[ "BSD-2-Clause" ]

4

2016-04-01T15:12:31.000Z

2016-04-18T15:05:29.000Z

Grass.py

KRHS-GameProgramming-2015/Adlez

8912da1ee4b3c7b105851dbcc00579ff0c3cf33e

[ "BSD-2-Clause" ]

null

from SoftBlock import * class Grass(SoftBlock): def __init__(self, pos=[0,0], blockSize = 25): image = "Block/Block Images/grass.png" SoftBlock.__init__(self, image, pos, blockSize) class BigGrass(SoftBlock): def __init__(self, pos=[0,0], blockSize = 25): image = "Block/Block Images/grass5x5.png" SoftBlock.__init__(self, image, pos, blockSize)

32.333333

55

0.667526

49

388

4.959184

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0.164609

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0.032258

0.201031

388

11

56

35.272727

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0

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0

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0

null

0

1

0

1

0

5

ec36b8d62f218dae6f33e34434273fb85e662b88

225

py

Python

08-read files.py

ghost098/LearnPythonTheHardWay

93fd4f116e09d15ecea637a74f8216be135d3af8

[ "MIT" ]

null

08-read files.py

ghost098/LearnPythonTheHardWay

93fd4f116e09d15ecea637a74f8216be135d3af8

[ "MIT" ]

null

08-read files.py

ghost098/LearnPythonTheHardWay

93fd4f116e09d15ecea637a74f8216be135d3af8

[ "MIT" ]

null

from sys import argv script, filename = argv txt = open(filename) print "Here's your file %r: " % filename print txt.read() print "Type the filname again:" filename = raw_input("> ") txt = open(filename) print txt.read()

16.071429

40

0.697778

34

225

4.588235

0.617647

0.25

0.192308

0.25641

0

0.173333

225

14

41

16.071429

0.83871

0

0.444444

0

0.20354

0

null

0

0.111111

null

0.444444

0

null

1

0

null

0

1

0

1

0

5

6b5d82e7835eee4277e0b4828006b4b4a9b7535a

91

py

Python

molsysmt/demo_systems/__init__.py

uibcdf/MolSysMT

9866a6fb090df9fff36af113a45164da4b674c09

[ "MIT" ]

3

2020-06-02T03:55:52.000Z

2022-03-21T04:43:52.000Z

molsysmt/demo_systems/__init__.py

uibcdf/MolSysMT

9866a6fb090df9fff36af113a45164da4b674c09

[ "MIT" ]

28

2020-06-24T00:55:53.000Z

2021-07-16T22:09:19.000Z

molsysmt/demo_systems/__init__.py

uibcdf/MolSysMT

9866a6fb090df9fff36af113a45164da4b674c09

[ "MIT" ]

1

2021-06-17T18:55:25.000Z

from .files import files from .classes import metenkephalin, pentalanine, pentalanine_traj

30.333333

65

0.846154

11

91

6.909091

0.636364

0

0.10989

91

2

66

45.5

0.938272

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

6b65dc7cd40f3862b2a75200df06cd6db593cfbb

152

py

Python

tests/web_platform/CSS2/positioning/test_bottom_applies_to.py

jonboland/colosseum

cbf974be54fd7f6fddbe7285704cfaf7a866c5c5

[ "BSD-3-Clause" ]

71

2015-04-13T09:44:14.000Z

2019-03-24T01:03:02.000Z

tests/web_platform/CSS2/positioning/test_bottom_applies_to.py

jonboland/colosseum

cbf974be54fd7f6fddbe7285704cfaf7a866c5c5

[ "BSD-3-Clause" ]

35

2019-05-06T15:26:09.000Z

2022-03-28T06:30:33.000Z

tests/web_platform/CSS2/positioning/test_bottom_applies_to.py

jonboland/colosseum

cbf974be54fd7f6fddbe7285704cfaf7a866c5c5

[ "BSD-3-Clause" ]

139

2015-05-30T18:37:43.000Z

2019-03-27T17:14:05.000Z

from tests.utils import W3CTestCase class TestBottomAppliesTo(W3CTestCase): vars().update(W3CTestCase.find_tests(__file__, 'bottom-applies-to-'))

25.333333

73

0.789474

17

152

6.764706

0.823529

0

0.021739

0.092105

152

5

74

30.4

0.811594

0

0.118421

0

1

0

true

0

0.333333

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0.666667

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null

0

1

0

null

0

1

0

1

0

1

0

5

6b6cdb72c0cc6662595b6cd1a85b9ae6f37952ed

111

py

Python

main.py

tgb20/RemoteEV3

2188caab742a915251c58a539a836b28cc6384c5

[ "MIT" ]

null

main.py

tgb20/RemoteEV3

2188caab742a915251c58a539a836b28cc6384c5

[ "MIT" ]

null

main.py

tgb20/RemoteEV3

2188caab742a915251c58a539a836b28cc6384c5

[ "MIT" ]

null

#!/usr/bin/env python3 from ev3dev2.sound import Sound sound = Sound() sound.speak('This is a test project!')

18.5

38

0.72973

18

111

4.5

0.777778

0.37037

0

0.03125

0.135135

111

5

39

22.2

0.8125

0.189189

0

0.258427

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

5

6b888081c89dd94fc6bc6d58af5b9d9cadfbe614

36

py

Python

app/__init__.py

smuggy/webapp

4fa62af5788157be171bf457a8e1a9b617057c78

[ "MIT" ]

null

app/__init__.py

smuggy/webapp

4fa62af5788157be171bf457a8e1a9b617057c78

[ "MIT" ]

1

2021-06-02T00:37:38.000Z

app/__init__.py

smuggy/webapp

4fa62af5788157be171bf457a8e1a9b617057c78

[ "MIT" ]

null

from app.webapp import appcontainer

18

35

0.861111

5

36

6.2

1

0

0.111111

36

1

36

0.96875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

6b8ac3ba59afed063eb9cb193d2ce0c3e349cde9

158

py

Python

micromelon/_robot_comms/ble/__init__.py

timmyhadwen/mm-pymodule

da14b5a77bf58fa364274f8722d51e5affe2e7df

[ "MIT" ]

3

2021-04-15T10:02:41.000Z

2021-12-01T00:22:51.000Z

micromelon/_robot_comms/ble/__init__.py

timmyhadwen/mm-pymodule

da14b5a77bf58fa364274f8722d51e5affe2e7df

[ "MIT" ]

1

2021-05-24T02:06:51.000Z

micromelon/_robot_comms/ble/__init__.py

timmyhadwen/mm-pymodule

da14b5a77bf58fa364274f8722d51e5affe2e7df

[ "MIT" ]

1

2021-05-21T10:34:12.000Z

from ._ble_controller import BleController from ._ble_controller_threadwrapped import BleControllerThread __all__ = ["BleController", "BleControllerThread"]

31.6

62

0.85443

14

158

9

0.571429

0.111111

0.269841

0

0.082278

158

4

63

39.5

0.868966

0

0.202532

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

6bf1b967c34bf9a5818e41f978a8c7350f8eb485

75

py

Python

src/addnn/benchmark/cli.py

MatthiasJReisinger/addnn

1d68648e81faf478cfb1f7d9a3f944a014fa3867

[ "MIT" ]

4

2022-01-25T23:09:24.000Z

2022-03-30T20:57:18.000Z

src/addnn/benchmark/cli.py

MatthiasJReisinger/addnn

1d68648e81faf478cfb1f7d9a3f944a014fa3867

[ "MIT" ]

null

src/addnn/benchmark/cli.py

MatthiasJReisinger/addnn

1d68648e81faf478cfb1f7d9a3f944a014fa3867

[ "MIT" ]

1

2022-01-25T23:08:50.000Z

from addnn.cli import cli @cli.group() def benchmark() -> None: pass

10.714286

25

0.653333

11

75

4.454545

0.818182

0

0.213333

75

6

26

12.5

0.830508

0

1

0.25

true

0.25

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

d41018b19f891ff7eca84279a7ede44cafdd960b

91

py

Python

2935.py

dayaelee/baekjoon

cf0e2b8b29dcd759c90f4736f6c26dd1982c72a3

[ "MIT" ]

null

2935.py

dayaelee/baekjoon

cf0e2b8b29dcd759c90f4736f6c26dd1982c72a3

[ "MIT" ]

null

2935.py

dayaelee/baekjoon

cf0e2b8b29dcd759c90f4736f6c26dd1982c72a3

[ "MIT" ]

null

a=int(input()) b=input() c=int(input()) if b=='*': print(a*c) elif b=='+': print(a+c)

10.111111

14

0.505495

18

91

2.555556

0.444444

0.347826

0.304348

0.347826

0

0.153846

91

8

15

11.375

0.597403

0

0.021978

0

1

0

false

0

0.285714

1

0

null

1

0

1

0

null

0

5

d456f8ff96108b887d8bdf3ca5f7420aac8a6308

222

py

Python

src/suton/toncontrol/mqueue/azureservicebus/core.py

jarig/suton

a946779bdee61f62de28da666ce01dba03ab9128

[ "MIT" ]

4

2020-08-24T21:20:21.000Z

2021-02-20T16:53:11.000Z

src/suton/toncontrol/mqueue/azureservicebus/core.py

jarig/suton

a946779bdee61f62de28da666ce01dba03ab9128

[ "MIT" ]

null

src/suton/toncontrol/mqueue/azureservicebus/core.py

jarig/suton

a946779bdee61f62de28da666ce01dba03ab9128

[ "MIT" ]

null

from mqueue.interfaces.tonqueue import TonControllQueueAbstract class ServiceBusQueueProvider(TonControllQueueAbstract): pass class QueueProvider(ServiceBusQueueProvider): # entry-point for TonControl pass

20.181818

63

0.81982

18

222

10.111111

0.777778

0

0.135135

222

10

64

22.2

0.947917

0.117117

0

0.4

0

1

0

true

0.4

0.2

0

0.6

0

1

0

1

null

0

null

0

1

0

1

0

5

d4640b28d50ba8805cb61d807a2811da66ef8c84

78

py

Python

Python-3/multiprocessing_examples/multiprocessing_cpu_count.py

ghiloufibelgacem/jornaldev

b9b27f9f7da595892520314b4ed1d2675556310a

[ "MIT" ]

1,139

2018-05-09T11:54:36.000Z

2022-03-31T06:52:50.000Z

Python-3/multiprocessing_examples/multiprocessing_cpu_count.py

ghiloufibelgacem/jornaldev

b9b27f9f7da595892520314b4ed1d2675556310a

[ "MIT" ]

56

2018-06-20T03:52:53.000Z

2022-02-09T22:57:41.000Z

Python-3/multiprocessing_examples/multiprocessing_cpu_count.py

ghiloufibelgacem/jornaldev

b9b27f9f7da595892520314b4ed1d2675556310a

[ "MIT" ]

2,058

2018-05-09T09:32:17.000Z

2022-03-29T13:19:42.000Z

import multiprocessing print("Number of cpu : ", multiprocessing.cpu_count())

26

54

0.782051

9

78

6.666667

0.777778

0

0.102564

78

3

54

26

0.857143

0

0.202532

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d47953986653c2e087bc786b9ba5bc7fcf307651

3,445

py

Python

contacts-api/Errors/errors.py

GibranHL0/contacts-api

dfb81cc08ed5a9cc7cd45c1d1b663dbeacfb1d25

[ "MIT" ]

null

contacts-api/Errors/errors.py

GibranHL0/contacts-api

dfb81cc08ed5a9cc7cd45c1d1b663dbeacfb1d25

[ "MIT" ]

null

contacts-api/Errors/errors.py

GibranHL0/contacts-api

dfb81cc08ed5a9cc7cd45c1d1b663dbeacfb1d25

[ "MIT" ]

1

2021-08-23T17:46:44.000Z

"""Define contact-api defined exceptions.""" from abc import ABC from http import HTTPStatus class Error(ABC, Exception): """Base error class raised when a custom error happened.""" def __init__( self, message='', code=HTTPStatus.INTERNAL_SERVER_ERROR, ) -> None: """ Initialize the exception. Args: message: Explanation of the error. code: HTTP code error. """ self.msg = message self.code = code super().__init__(self.msg) class EmailNotValid(Error): """Exception raised when the email is not valid.""" def __init__( self, message='Email is not valid', code=HTTPStatus.PARTIAL_CONTENT, ) -> None: """ Inititalize the exception. Args: message: Explanation of the error. code: HTTP code error. """ self.msg = message self.code = code super().__init__(self.msg, self.code) class EmailNotFound(Error): """Exception raised when the email is not found in the DB.""" def __init__( self, message='Email not found', code=HTTPStatus.PARTIAL_CONTENT, ) -> None: """ Inititalize the exception. Args: message: Explanation of the error. code: HTTP code error. """ self.msg = message self.code = code super().__init__(self.msg, self.code) class EmailAlreadyExists(Error): """Exception raised when the email is already in the Contacts list.""" def __init__( self, message='Email already exists', code=HTTPStatus.PARTIAL_CONTENT, ) -> None: """ Initialize the exception. Args: message: Explation of the error. code: HTTP error code. """ self.msg = message self.code = code super().__init__(self.msg, self.code) class NameNotValid(Error): """Exception raised when the name is not in the appropiate format.""" def __init__( self, message='Name is not valid', code=HTTPStatus.PARTIAL_CONTENT, ) -> None: """ Initialize the exception. Args: message: Explanation of the error. code: HTTP error code. """ self.msg = message self.code = code super().__init__(self.msg, self.code) class LastNameNotValid(Error): """Exception raised when the last name is not in the appropiate format.""" def __init__( self, message='Last name is not valid', code=HTTPStatus.PARTIAL_CONTENT, ) -> None: """ Initialize the exception. Args: message: Explanation of the error. code: HTTP error code. """ self.msg = message self.code = code super().__init__(self.msg, self.code) class InternalError(Error): """Exception raised when something unexpected happened.""" def __init__( self, message='Something wrong happened', code=HTTPStatus.INTERNAL_SERVER_ERROR, ) -> None: """ Initialize the exception. Args: message: Explanation of the error. code: HTTP error code. """ self.msg = message self.code = code super().__init__(self.msg, self.code)

22.966667

78

0.561974

363

3,445

5.15427

0.170799

0.059861

0.041154

0.067344

0.804917

0.711384

0.693212

0.648316

0

0.340493

3,445

149

79

23.120805

0.823504

0.330334

0

0.738462

0

0.059886

0

1

0.107692

false

0

0.030769

0

0.246154

0

null

0

1

0

1

0

1

0

null

0

5

2e0a0f07cb94391a1874202ddac98cbd9bbd0e56

637

py

Python

components/studio/controller/tasks.py

aitmlouk/stackn

c8029394a15b03796a4864938f9db251b65c7354

[ "Apache-2.0" ]

25

2020-05-08T22:24:54.000Z

2022-03-11T18:16:58.000Z

components/studio/controller/tasks.py

aitmlouk/stackn

c8029394a15b03796a4864938f9db251b65c7354

[ "Apache-2.0" ]

75

2020-05-08T22:15:59.000Z

2021-11-22T10:00:04.000Z

components/studio/controller/tasks.py

aitmlouk/stackn

c8029394a15b03796a4864938f9db251b65c7354

[ "Apache-2.0" ]

12

2020-11-04T13:09:46.000Z

2022-03-14T16:22:40.000Z

from registrar.celery import app def on_alliance_save_spawn_aggregator(): pass def on_alliance_delete_destroy_aggregator(): pass def on_model_save_notify_aggregator(): pass def on_member_save_notify_aggregator(): pass def on_endpoint_save_notify_aggregator(): pass def member_request_contribution(): pass def member_request_validation(): pass from registrar.celery import app #TODO remove? @app.task def train_remote(node_id): import subprocess import os cmd = "python3 train.py" args = "--node-id={}".format(node_id) cwd = os.cwd() subprocess.run(cmd, args, cwd=cwd)

14.813953

44

0.723705

87

637

4.988506

0.425287

0.096774

0.195853

0.175115

0.324885

0.133641

0

0.001934

0.188383

637

42

45

15.166667

0.837524

0.018838

0

0.375

0

0.044872

0

0.02381

0

1

0.333333

false

0.291667

0.166667

0

0.5

0

null

0

1

0

null

0

1

0

1

0

1

0

5

2e3e2f73c069e240f4a688b1f94825ffba31b88a

160

py

Python

leap/leap.py

vietanhtran2710/python-exercism

1f88dfca56928276ab81a274e8259ce465a2d425

[ "MIT" ]

null

leap/leap.py

vietanhtran2710/python-exercism

1f88dfca56928276ab81a274e8259ce465a2d425

[ "MIT" ]

null

leap/leap.py

vietanhtran2710/python-exercism

1f88dfca56928276ab81a274e8259ce465a2d425

[ "MIT" ]

null

""" Leap year exercise """ from calendar import isleap def leap_year(year): """ Check if a year is a leap year """ return isleap(year)

14.545455

38

0.59375

22

160

4.272727

0.590909

0.255319

0

0.3

160

10

39

16

0.839286

0.30625

0

1

0.333333

false

0

0.333333

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

5

2e747880330e004a3dd94874a2544f68d7a4626c

231

py

Python

wandb/xgboost/__init__.py

borisgrafx/client

c079f7816947a3092b500751eb920fda3866985f

[ "MIT" ]

null

wandb/xgboost/__init__.py

borisgrafx/client

c079f7816947a3092b500751eb920fda3866985f

[ "MIT" ]

1

2021-11-15T10:15:16.000Z

2021-11-17T10:01:59.000Z

wandb/xgboost/__init__.py

borisgrafx/client

c079f7816947a3092b500751eb920fda3866985f

[ "MIT" ]

1

2022-01-03T16:19:52.000Z

""" Compatibility xgboost module. In the future use: from wandb.integration.xgboost import wandb_callback """ from wandb.integration.xgboost import wandb_callback, WandbCallback __all__ = ["wandb_callback", "WandbCallback"]

21

67

0.78355

26

231

6.692308

0.538462

0.224138

0.229885

0.310345

0.528736

0

0.125541

231

10

68

23.1

0.861386

0.458874

0

0.230769

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

null

0

1

0

5

cf22cadee1d3a97a68dff68d5dbef591542aa8e9

1,844

py

Python

pyoop/5/1.py

yc19890920/Learn

3990e75b469225ba7b430539ef9a16abe89eb863

[ "Apache-2.0" ]

1

2021-01-11T06:30:44.000Z

pyoop/5/1.py

yc19890920/Learn

3990e75b469225ba7b430539ef9a16abe89eb863

[ "Apache-2.0" ]

23

2020-02-12T02:35:49.000Z

2022-02-11T03:45:40.000Z

pyoop/5/1.py

yc19890920/Learn

3990e75b469225ba7b430539ef9a16abe89eb863

[ "Apache-2.0" ]

2

2020-04-08T15:39:46.000Z

2020-10-10T10:13:09.000Z

import collections.abc class Power(collections.abc.Callable): def __call__(self, x, n): if n==0: return 1 elif n%2==1: return self.__call__(x, n-1)*x else: t = self.__call__(x, n//2) return t*t # p = Power() class Power2(collections.abc.Callable): _caches = {} def __call__(self, x, n): if (x, n) not in self._caches: if n==0: self._caches[x,n] = 1 elif n%2==1: self._caches[x,n] = self.__call__(x, n-1)*x else: t = self.__call__(x, n//2) self._caches[x,n] = t*t return self._caches[x,n] # p = Power2() # print(p(5,20)) from functools import lru_cache @lru_cache(maxsize=128) def power(x, n): if n==0: return 1 elif n%2==1: return power(x, n-1)*x else: t = power(x, n//2) return t*t # print(power(5,20)) import timeit it1 = timeit.timeit("power(2, 128)", """ from functools import lru_cache @lru_cache(maxsize=5) def power(x, n): if n==0: return 1 elif n%2==1: return power(x, n-1)*x else: t = power(x, n//2) return t*t """, number=1000000) it2 = timeit.timeit("p(2, 1024)", """ import collections.abc class Power2(collections.abc.Callable): _caches = {} def __call__(self, x, n): if (x, n) not in self._caches: if n==0: self._caches[x,n] = 1 elif n%2==1: self._caches[x,n] = self.__call__(x, n-1)*x else: t = self.__call__(x, n//2) self._caches[x,n] = t*t return self._caches[x,n] p = Power2() """, number=1000000) print(it1, it2)

21.952381

60

0.47885

267

1,844

3.11236

0.146067

0.060168

0.105897

0.115523

0.761733

0.738869

0.637786

0

0.061578

0.374729

1,844

83

61

22.216867

0.65915

0.031453

0

0.854839

0

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null

0

1

0

1

0

1

0

null

0

5

cf2b1f36ecbcac575e62a40f4338e775bd2fa34b

333

py

Python

ib_tws_server/codegen/__init__.py

ncpenke/ib_wrapper_py

62af6170dcf80a1491e0f6a1bef3f98e62da5d79

[ "MIT" ]

1

2022-03-06T19:24:13.000Z

ib_tws_server/codegen/__init__.py

ncpenke/ib_wrapper_py

62af6170dcf80a1491e0f6a1bef3f98e62da5d79

[ "MIT" ]

null

ib_tws_server/codegen/__init__.py

ncpenke/ib_wrapper_py

62af6170dcf80a1491e0f6a1bef3f98e62da5d79

[ "MIT" ]

3

2021-07-30T10:49:16.000Z

2021-08-29T06:20:43.000Z

from ib_tws_server.codegen.asyncio_client_generator import AsyncioClientGenerator from ib_tws_server.codegen.response_types_generator import ResponseTypesGenerator from ib_tws_server.codegen.graphql_schema_generator import GraphQLSchemaGenerator from ib_tws_server.codegen.graphql_resolver_generator import GraphQLResolverGenerator

55.5

85

0.924925

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333

7.3

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0.123288

0.205479

0.349315

0.19863

0

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5

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0

1

0

true

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1

0

null

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1

0

1

0

1

0

5

cf564632cf94ebcff92bafed9e4769f7b8290f38

331

py

Python

api/barriers/serializers/__init__.py

uktrade/market-access-api

850a59880f8f62263784bcd9c6b3362e447dbc7a

[ "MIT" ]

null

api/barriers/serializers/__init__.py

uktrade/market-access-api

850a59880f8f62263784bcd9c6b3362e447dbc7a

[ "MIT" ]

51

2018-05-31T12:16:31.000Z

2022-03-08T09:36:48.000Z

api/barriers/serializers/__init__.py

uktrade/market-access-api

850a59880f8f62263784bcd9c6b3362e447dbc7a

[ "MIT" ]

2

2019-12-24T09:47:42.000Z

2021-02-09T09:36:51.000Z

from .barriers import BarrierDetailSerializer, BarrierListSerializer # noqa from .csv import BarrierCsvExportSerializer # noqa from .data_workspace import DataWorkspaceSerializer # noqa from .public_barriers import PublicBarrierSerializer, PublishedVersionSerializer # noqa from .reports import BarrierReportSerializer # noqa

55.166667

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331

9.655172

0.551724

0.114286

0

0.111782

331

5

89

66.2

0.952381

0.072508

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

cf688d74d5a82b61eead435f24493b41776d696f

54

py

Python

boilerplate/app/controllers/__init__.py

davideasaf/effortless_rest_flask

ee96069614aa670837152db36616b847f1cb5f73

[ "MIT" ]

6

2019-10-31T17:10:06.000Z

2020-07-01T15:18:46.000Z

boilerplate/app/controllers/__init__.py

davideasaf/effortless_rest_flask

ee96069614aa670837152db36616b847f1cb5f73

[ "MIT" ]

1

2019-11-07T20:31:27.000Z

boilerplate/app/controllers/__init__.py

pydatacharlotte/effortless_rest_flask

4691d2ffda3f4eebae2ba1f089fdce087750c984

[ "MIT" ]

2

2019-11-07T20:26:02.000Z

2019-12-09T01:29:32.000Z

from .user import user_api from .iris import iris_api

18

26

0.814815

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54

4.2

0.5

0

0.148148

54

2

27

0.913043

0

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0

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1

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null

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1

0

1

0

1

0

5

cf6b1988b6da793372876436ca4e213715d3d6c8

1,939

py

Python

fullwavepy/__init__.py

kmch/FullwavePy

3c704b9b6ae2c6c585adb61e57991caf30ab240e

[ "MIT" ]

2

2020-12-24T01:02:16.000Z

2021-02-17T10:00:58.000Z

fullwavepy/__init__.py

kmch/FullwavePy

3c704b9b6ae2c6c585adb61e57991caf30ab240e

[ "MIT" ]

null

fullwavepy/__init__.py

kmch/FullwavePy

3c704b9b6ae2c6c585adb61e57991caf30ab240e

[ "MIT" ]

null

import numpy as np # import pandas as pd # pd.set_option('display.max_columns', 50) import matplotlib.pyplot as plt # from matplotlib.gridspec import GridSpec # from mpl_toolkits.mplot3d import Axes3D # import cmocean.cm as cm # import plotly.express as px # import plotly.graph_objects as go # from ipywidgets import (interactive, interact, interact_manual, fixed, # IntSlider, FloatSlider, BoundedIntText, Dropdown, # SelectMultiple, Checkbox, # Layout, TwoByTwoLayout) # from fullwavepy.config.logging import * # from fullwavepy.dsp.su import su_filter # from fullwavepy.generic.system import * # from fullwavepy.generic.parse import * # from fullwavepy.numeric.generic import * # from fullwavepy.numeric.funcs import * # from fullwavepy.ioapi.generic import save_txt, read_txt, read_any # from fullwavepy.ioapi.fw3d import TtrFile, VtrFile, read_vtr, save_vtr from fullwavepy.ioapi.memmap import read_mmp, save_mmp # from fullwavepy.ioapi.segy import SgyFile # from fullwavepy.ioapi.su import * # from fullwavepy.ndat.arrays import * # from fullwavepy.ndat.manifs import * # from fullwavepy.ndat.points import * # from fullwavepy.plot.generic import * # from fullwavepy.plot.plt1d import * # from fullwavepy.plot.plt2d import * # from fullwavepy.plot.plt3d import * # from fullwavepy.plot.misc import time_freq from fullwavepy.project.types.basic import * # from fullwavepy.project.types.deriv import * # from fullwavepy.project.types.extra import * from fullwavepy.seismic.data import Dat, DataSet from fullwavepy.seismic.proteus import PROTEUS # from fullwavepy.seismic.metadata import * from fullwavepy.seismic.misc import BoxFactory, Box3d from fullwavepy.seismic.models import * # from fullwavepy.seismic.srcrec import * # from fullwavepy.seismic.wavefields import * # from fullwavepy.seismic.wavelets import * from fullwavepy.utils import *

32.316667

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1,939

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1,939

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1

0

null

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1

0

1

0

1

0

5

cf7e571c28038496a4a821ecfae55d47ec102baf

408

py

Python

src/Diagnostics.AIProjects/SearchAPI/SearchModule/Exceptions.py

hannaatmsft/Azure-AppServices-Diagnostics

d5dc281219bdd56e6c832b927e961ddbd1a1469f

[ "MIT" ]

41

2018-03-21T01:58:38.000Z

2022-03-17T01:16:30.000Z

src/Diagnostics.AIProjects/SearchAPI/SearchModule/Exceptions.py

hannaatmsft/Azure-AppServices-Diagnostics

d5dc281219bdd56e6c832b927e961ddbd1a1469f

[ "MIT" ]

138

2018-03-21T16:52:32.000Z

2022-03-21T18:36:18.000Z

src/Diagnostics.AIProjects/SearchAPI/SearchModule/Exceptions.py

hannaatmsft/Azure-AppServices-Diagnostics

d5dc281219bdd56e6c832b927e961ddbd1a1469f

[ "MIT" ]

35

2018-07-26T23:35:52.000Z

2022-03-14T19:44:04.000Z

class ModelDownloadFailed(Exception): pass class ModelFileConfigFailed(Exception): pass class ModelFileVerificationFailed(Exception): pass class ModelFileLoadFailed(Exception): pass class ResourceConfigDownloadFailed(Exception): pass class ModelRefreshException(Exception): pass class CopySourceFolderNotFoundException(Exception): pass class CopyTaskException(Exception): pass

25.5

51

0.806373

32

408

10.28125

0.34375

0.316109

0.382979

0

0.134804

408

16

52

25.5

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0

0.5

0

1

0

true

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0

0.5

0

1

null

1

0

null

0

1

0

5

d886241ffa64152533038b7fc80f0249a3a9cb54

21

py

Python

rbql_core/rbql/__init__.py

neilsustc/vscode_rainbow_csv

025db053200355f33f2dc57d757f7033840c5d73

[ "MIT" ]

2

2020-04-28T07:50:54.000Z

2021-01-23T00:56:14.000Z

rbql_core/rbql/__init__.py

neilsustc/vscode_rainbow_csv

025db053200355f33f2dc57d757f7033840c5d73

[ "MIT" ]

null

rbql_core/rbql/__init__.py

neilsustc/vscode_rainbow_csv

025db053200355f33f2dc57d757f7033840c5d73

[ "MIT" ]

null

from .rbql import *

7

19

0.666667

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21

4.666667

1

0

0.238095

21

2

20

10.5

0.875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

d8962a845d6e664181f64cbb879f7445de722438

51

py

Python

quantum_computer_simulator/helpers/Exceptions.py

johnyob/Quantum-Computer-Simulator

9dfe219f855da61ac21ca27db1b10385b77f235e

[ "MIT" ]

2

2019-05-26T15:26:33.000Z

2021-03-19T02:37:49.000Z

quantum_computer_simulator/helpers/Exceptions.py

johnyob/Quantum-Computer-Simulator

9dfe219f855da61ac21ca27db1b10385b77f235e

[ "MIT" ]

null

quantum_computer_simulator/helpers/Exceptions.py

johnyob/Quantum-Computer-Simulator

9dfe219f855da61ac21ca27db1b10385b77f235e

[ "MIT" ]

null

class QuantumRegisterException(Exception): pass

25.5

42

0.823529

4

51

10.5

1

0

0.117647

51

2

43

25.5

0.933333

0

1

0

true

0.5

0

0.5

0

1

0

1

null

0

1

0

null

0

1

0

5

d8a603012281743973b71f72151f716cf5ce7a2b

115

py

Python

mastering/log/__init__.py

julian-medve/Audio-Mastering

5c79118902db5d2f2053f0d1f3b740fc4cf708b6

[ "Apache-2.0" ]

null

mastering/log/__init__.py

julian-medve/Audio-Mastering

5c79118902db5d2f2053f0d1f3b740fc4cf708b6

[ "Apache-2.0" ]

null

mastering/log/__init__.py

julian-medve/Audio-Mastering

5c79118902db5d2f2053f0d1f3b740fc4cf708b6

[ "Apache-2.0" ]

null

from .codes import Code from .handlers import warning, info, debug, debug_line from .exceptions import ModuleError

28.75

54

0.817391

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115

5.8125

0.6875

0

0.130435

115

3

55

38.333333

0.93

0

1

0

true

0

1

0

1

0

1

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null

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1

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null

0

1

0

1

0

1

0

5

d8b4a65e3f6a3ca74a0f4ac63b0823e5344e70b7

92

py

Python

dates.py

abkumarggn/python-learning-1

df45396cd14f5762053728760953b3806d0069b6

[ "Apache-2.0" ]

null

dates.py

abkumarggn/python-learning-1

df45396cd14f5762053728760953b3806d0069b6

[ "Apache-2.0" ]

null

dates.py

abkumarggn/python-learning-1

df45396cd14f5762053728760953b3806d0069b6

[ "Apache-2.0" ]

null

import datetime print (datetime.date.today().strftime("%w-%d-%m-%Y-Day:%A")) print("Hello")

23

60

0.673913

15

92

4.133333

0.866667

0

0.054348

92

4

61

23

0.712644

0

0.247312

0

1

0

true

0

0.333333

0

0.333333

0.666667

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

2b05a6b2295bc8dbe8281ed6deccbed1eb4f1cf7

252

py

Python

ibmcli/tests/func/test_cli.py

powellquiring/pycli

ecc5beec84833b978dfa41259ab3ac306617fc55

[ "Apache-2.0" ]

null

ibmcli/tests/func/test_cli.py

powellquiring/pycli

ecc5beec84833b978dfa41259ab3ac306617fc55

[ "Apache-2.0" ]

2

2021-04-06T18:19:56.000Z

2021-06-02T03:28:55.000Z

ibmcli/tests/func/test_cli.py

powellquiring/pycli

ecc5beec84833b978dfa41259ab3ac306617fc55

[ "Apache-2.0" ]

null

import ibmcli def test_cli(): #ibmcli.uninstall_plugins(True) # dryrun True #ibmcli.log_help_commands(True, 'cs') # dryrun True #ibmcli.sl_vs_cancel() #ibmcli.resource_service_instances_delete() ibmcli.docker_ps_a_delete() pass

28

55

0.730159

34

252

5.058824

0.705882

0.116279

0.186047

0

0.162698

252

9

56

28

0.815166

0.615079

0

1

0.25

true

0.25

0

0.5

0

null

0

1

0

1

0

null

0

1

0

5

2b0f1796babd3ad6b14af9ba6394455470934e60

18

py

Python

app.py

monkeyhjy/HjyOwn

c2d387d701066a5f45e108fd314179c5c54272f7

[ "MIT" ]

null

app.py

monkeyhjy/HjyOwn

c2d387d701066a5f45e108fd314179c5c54272f7

[ "MIT" ]

null

app.py

monkeyhjy/HjyOwn

c2d387d701066a5f45e108fd314179c5c54272f7

[ "MIT" ]

null

# This is app.py.

9

17

0.611111

4

18

2.75

1

0

0.222222

18

1

18

0.785714

0.833333

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

2b29d0a86dee7fb2c34e11ca1249abd796e4db24

15

py

Python

tap_csv/__init__.py

MeltanoLabs/tap-csv

0994dbe2181071163fd63358961d5877c9b9f0df

[ "Apache-2.0" ]

3

2021-11-02T20:57:30.000Z

2022-03-05T09:36:12.000Z

tap_csv/__init__.py

MeltanoLabs/tap-csv

0994dbe2181071163fd63358961d5877c9b9f0df

[ "Apache-2.0" ]

25

2022-01-24T19:46:51.000Z

2022-03-28T18:18:50.000Z

tap_csv/__init__.py

MeltanoLabs/tap-csv

0994dbe2181071163fd63358961d5877c9b9f0df

[ "Apache-2.0" ]

1

2022-03-07T10:33:34.000Z

"""Tap-csv."""

7.5

14

0.4

2

15

3

1

0

0.066667

15

1

15

0.428571

0.533333

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

2b2aa07cfa78da15b49ac25f23577b3de5e960b6

103

py

Python

pyEX/caching/tests/test_common.py

timkpaine/pyEX-caching

966201687c99cc42d6ecbc31079cf8df44ae3abd

[ "Apache-2.0" ]

8

2020-09-23T15:43:05.000Z

2022-03-09T04:22:03.000Z

pyEX/caching/tests/test_common.py

timkpaine/pyEX-caching

966201687c99cc42d6ecbc31079cf8df44ae3abd

[ "Apache-2.0" ]

10

2019-06-24T16:57:50.000Z

2020-12-29T17:32:20.000Z

pyEX/caching/tests/test_common.py

timkpaine/pyEX-caching

966201687c99cc42d6ecbc31079cf8df44ae3abd

[ "Apache-2.0" ]

1

2019-11-27T17:32:36.000Z

# for Coverage from mock import patch, MagicMock class TestAll: def test_all(self): pass

12.875

33

0.679612

14

103

4.928571

1

0

0.262136

103

7

34

14.714286

0.907895

0.116505

0

1

0.25

false

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

2b4b22a1c32be4f7cd8ff64c0f9f942a403e3671

117

py

Python

funniest_ieee/joke.py

axel-sirota/IEEE-CICD

3a3e65af4d9c8267b1e4967fe4f372ac1ac8ba87

[ "MIT" ]

null

funniest_ieee/joke.py

axel-sirota/IEEE-CICD

3a3e65af4d9c8267b1e4967fe4f372ac1ac8ba87

[ "MIT" ]

null

funniest_ieee/joke.py

axel-sirota/IEEE-CICD

3a3e65af4d9c8267b1e4967fe4f372ac1ac8ba87

[ "MIT" ]

null

"""Joke module inside funniest""" def joke(): """Funniest joke in the world""" return 'Habia una vez truz'

16.714286

36

0.632479

16

117

4.625

0.8125

0

0.222222

117

6

37

19.5

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0

0.346154

0

1

0.5

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

2b5818dd07599780b3280c497979eaf65926c4f4

6,061

py

Python

silo/benchmarks/results/istc3-7-31-13.py

anshsarkar/TailBench

25845756aee9a892229c25b681051591c94daafd

[ "MIT" ]

274

2015-01-23T16:24:09.000Z

2022-02-22T03:16:14.000Z

silo/benchmarks/results/istc3-7-31-13.py

anshsarkar/TailBench

25845756aee9a892229c25b681051591c94daafd

[ "MIT" ]

3

2015-03-17T11:52:36.000Z

2019-07-22T23:04:25.000Z

silo/benchmarks/results/istc3-7-31-13.py

anshsarkar/TailBench

25845756aee9a892229c25b681051591c94daafd

[ "MIT" ]

94

2015-01-07T06:55:36.000Z

2022-01-22T08:14:15.000Z

RESULTS = [({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 1, 'name': 'scale_tpcc', 'numa_memory': '4G', 'persist': True, 'threads': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(29172.5, 29172.5, 0.0341824, 43.0112, 0.0), (29398.3, 29398.3, 0.0339165, 42.9486, 0.0), (28847.7, 28847.7, 0.0345633, 42.8973, 0.0)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 1, 'name': 'scale_tpcc', 'numa_memory': '4G', 'persist': False, 'threads': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(30468.6, 30468.6, 0.0327431, 0.0, 0.0), (29992.4, 29992.4, 0.0332567, 0.0, 0.0), (31170.2, 31170.2, 0.0320002, 0.0, 0.0)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 4, 'name': 'scale_tpcc', 'numa_memory': '16G', 'persist': True, 'threads': 4, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(103422.0, 103422.0, 0.0385643, 85.2116, 4.06392), (105666.0, 105666.0, 0.0377509, 60.8138, 3.93148), (104045.0, 104045.0, 0.0383381, 69.674, 4.63118)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 4, 'name': 'scale_tpcc', 'numa_memory': '16G', 'persist': False, 'threads': 4, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(113136.0, 113136.0, 0.0352708, 0.0, 4.78329), (114626.0, 114626.0, 0.0348127, 0.0, 4.7333), (114772.0, 114772.0, 0.0347643, 0.0, 5.1833)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 8, 'name': 'scale_tpcc', 'numa_memory': '32G', 'persist': True, 'threads': 8, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(197185.0, 197185.0, 0.0404586, 94.891, 8.41167), (197842.0, 197842.0, 0.0403221, 115.439, 7.94573), (197553.0, 197553.0, 0.0402832, 145.298, 8.4924)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 8, 'name': 'scale_tpcc', 'numa_memory': '32G', 'persist': False, 'threads': 8, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(216956.0, 216956.0, 0.0367886, 0.0, 9.64994), (216944.0, 216944.0, 0.0367856, 0.0, 9.98321), (215150.0, 215150.0, 0.0370956, 0.0, 9.59995)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 12, 'name': 'scale_tpcc', 'numa_memory': '48G', 'persist': True, 'threads': 12, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(296988.0, 296988.0, 0.0402945, 167.935, 12.127), (297616.0, 297616.0, 0.0402009, 213.303, 12.1242), (295502.0, 295502.0, 0.0404885, 258.694, 12.6107)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 12, 'name': 'scale_tpcc', 'numa_memory': '48G', 'persist': False, 'threads': 12, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(316547.0, 316547.0, 0.0377822, 0.0, 13.4998), (321822.0, 321822.0, 0.0371894, 0.0, 13.4833), (318126.0, 318126.0, 0.037625, 0.0, 13.4665)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 16, 'name': 'scale_tpcc', 'numa_memory': '64G', 'persist': True, 'threads': 16, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(391746.0, 391746.0, 0.0407244, 169.879, 16.0217), (386561.0, 386561.0, 0.041277, 181.483, 15.2751), (389939.0, 389939.0, 0.0409258, 162.949, 15.6935)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 16, 'name': 'scale_tpcc', 'numa_memory': '64G', 'persist': False, 'threads': 16, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(423632.0, 423632.0, 0.0376798, 0.0, 18.3165), (425391.0, 425391.0, 0.0375244, 0.0, 18.3499), (422392.0, 422392.0, 0.0377958, 0.0, 17.9999)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 20, 'name': 'scale_tpcc', 'numa_memory': '80G', 'persist': True, 'threads': 20, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(477401.0, 477401.0, 0.0417748, 99.8102, 18.5246), (484175.0, 484175.0, 0.0411903, 195.776, 19.9578), (485011.0, 485011.0, 0.0411185, 265.607, 20.8213)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 20, 'name': 'scale_tpcc', 'numa_memory': '80G', 'persist': False, 'threads': 20, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(536948.0, 536948.0, 0.0371455, 0.0, 22.483), (538513.0, 538513.0, 0.0370509, 0.0, 23.383), (532447.0, 532447.0, 0.0374746, 0.0, 22.3997)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 24, 'name': 'scale_tpcc', 'numa_memory': '96G', 'persist': True, 'threads': 24, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(573385.0, 573385.0, 0.0413281, 431.492, 23.0567), (570228.0, 570228.0, 0.041975, 220.776, 22.7373), (573395.0, 573395.0, 0.0417394, 145.422, 23.4863)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 24, 'name': 'scale_tpcc', 'numa_memory': '96G', 'persist': False, 'threads': 24, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(632495.0, 632495.0, 0.0378555, 0.0, 27.383), (638658.0, 638658.0, 0.0374896, 0.0, 26.983), (631381.0, 631381.0, 0.0379224, 0.0, 26.5997)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 28, 'name': 'scale_tpcc', 'numa_memory': '112G', 'persist': True, 'threads': 28, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(644206.0, 644206.0, 0.0427002, 1196.09, 26.5088), (635368.0, 635368.0, 0.0431587, 940.665, 26.4838), (645459.0, 645459.0, 0.0421841, 1584.15, 25.8248)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 28, 'name': 'scale_tpcc', 'numa_memory': '112G', 'persist': False, 'threads': 28, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(728954.0, 728954.0, 0.0383087, 0.0, 31.2495), (730546.0, 730546.0, 0.0382386, 0.0, 30.0829), (731333.0, 731333.0, 0.0381883, 0.0, 32.0495)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 32, 'name': 'scale_tpcc', 'numa_memory': '128G', 'persist': True, 'threads': 32, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(681638.0, 681638.0, 0.0464162, 935.341, 28.1929), (683559.0, 683559.0, 0.0464315, 731.81, 29.2552), (678029.0, 678029.0, 0.0468788, 612.582, 27.1238)]), ({'par_load': False, 'bench_opts': '', 'retry': False, 'scale_factor': 32, 'name': 'scale_tpcc', 'numa_memory': '128G', 'persist': False, 'threads': 32, 'db': 'ndb-proto2', 'bench': 'tpcc'}, [(799321.0, 799321.0, 0.0399277, 0.0, 34.0809), (799648.0, 799648.0, 0.0399073, 0.0, 35.415), (803230.0, 803230.0, 0.0397377, 0.0, 34.6826)])]

3,030.5

6,060

0.619205

991

6,061

3.696266

0.264379

0.045864

0.058968

0.083538

0.519247

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6,061

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0

null

0

1

0

1

0

null

0

5

2b79923752433d3b8c359916f39a4d19417fba80

621

py

Python

Projects/PlantMaker/archive/20100516/src/schedule.py

fredmorcos/attic

0da3b94aa525df59ddc977c32cb71c243ffd0dbd

[ "Unlicense" ]

2

2021-01-24T09:00:51.000Z

2022-01-23T20:52:17.000Z

Projects/PlantMaker/archive/20100516/src/schedule.py

fredmorcos/attic

0da3b94aa525df59ddc977c32cb71c243ffd0dbd

[ "Unlicense" ]

6

2020-02-29T01:59:03.000Z

2022-02-15T10:25:40.000Z

Projects/PlantMaker/archive/20100516/src/schedule.py

fredmorcos/attic

0da3b94aa525df59ddc977c32cb71c243ffd0dbd

[ "Unlicense" ]

1

2019-03-22T14:41:21.000Z

class Schedule(object): def __init__(self): self.schedule = [] self.finishTime = [] self.report = {} self.fitness = None def representation(self): return (self.schedule, self.finishTime) def __repr__(self): return str((self.schedule, self.finishTime)) + str(self.fitness) def sort(self, func): self.schedule.sort(func) def __getitem__(self, key): return self.schedule[key] def __setitem__(self, key, value): self.schedule[key] = value def __eq__(self, s): for i in s.schedule: for j in self.schedule: if i[0] == j[0]: if i[1] != j[1]: return False return True

20.7

66

0.652174

88

621

4.375

0.352273

0.218182

0.124675

0.202597

0

0.008114

0.206119

621

29

67

21.413793

0.772819

0

1

0.304348

false

0

0.130435

0.565217

0

null

1

0

1

0

null

0

1

0

1

0

5

9959898705f0e89b4b0bd4433dbfcbf25d2daf20

53

py

Python

storage/__init__.py

moehrenzahn/worktimer

ab92e8625652d94987c7da8ccdbf29be72bf3612

[ "MIT" ]

3

2018-07-29T20:48:15.000Z

2019-03-29T10:42:19.000Z

storage/__init__.py

moehrenzahn/worktimer

ab92e8625652d94987c7da8ccdbf29be72bf3612

[ "MIT" ]

null

storage/__init__.py

moehrenzahn/worktimer

ab92e8625652d94987c7da8ccdbf29be72bf3612

[ "MIT" ]

null

from storage.json import * from storage.yaml import *

26.5

26

0.792453

8

53

5.25

0.625

0.52381

0

0.132075

53

2

27

26.5

0.913043

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

99accd22d07f7a211447c2d236b9c8e8f074dac1

42

py

Python

wsgi.py

vurl/vurl-webapi

77fb7ed95c17355d5e98e5ca5318335a3eb93962

[ "MIT" ]

3

2020-01-30T16:22:58.000Z

2020-02-05T00:53:45.000Z

wsgi.py

vurl/vurl-webapi

77fb7ed95c17355d5e98e5ca5318335a3eb93962

[ "MIT" ]

null

wsgi.py

vurl/vurl-webapi

77fb7ed95c17355d5e98e5ca5318335a3eb93962

[ "MIT" ]

null

from vurlwebapi import app app.ready()

7

26

0.738095

6

42

5.166667

0.833333

0

0.190476

42

5

27

8.4

0.911765

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

51060fabeaaea50a79715f830eb5e11974a14149

275

py

Python

models/backbones/__init__.py

jhaochenz/spectral_contrastive_learning

ee431bdba9bb62ad00a7e55792213ee37712784c

[ "MIT" ]

null

models/backbones/__init__.py

jhaochenz/spectral_contrastive_learning

ee431bdba9bb62ad00a7e55792213ee37712784c

[ "MIT" ]

null

models/backbones/__init__.py

jhaochenz/spectral_contrastive_learning

ee431bdba9bb62ad00a7e55792213ee37712784c

[ "MIT" ]

null

from .cifar_resnet_1 import resnet18 as resnet18_cifar_variant1 from .cifar_resnet_2 import ResNet18 as resnet18_cifar_variant2 from .cifar_resnet_1_mlp_norelu import resnet18_cifar_variant1_mlp1000_norelu from .resnet_mlp_norelu_3layer import resnet50_mlp8192_norelu_3layer

55

77

0.912727

42

275

5.452381

0.380952

0.117904

0.196507

0.139738

0.253275

0

0.109804

0.072727

275

4

78

68.75

0.788235

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

5120e77cd6904e2ad60a433ce6bbe8c1bba1a88b

3,407

py

Python

utils/twitter_api.py

kazumasa-kusaba/TwitterCrawler

ef17e907093908448e6137f273c47a03461caa63

[ "MIT" ]

null

utils/twitter_api.py

kazumasa-kusaba/TwitterCrawler

ef17e907093908448e6137f273c47a03461caa63

[ "MIT" ]

1

2022-02-13T15:49:06.000Z

utils/twitter_api.py

kazumasa-kusaba/TwitterCrawler

ef17e907093908448e6137f273c47a03461caa63

[ "MIT" ]

null

# -*- coding: utf-8 -*- import sys import logging import json import time import datetime from requests_oauthlib import OAuth1Session class TwitterApi(): def __init__(self, access_token, access_token_secret, consumer_key, consumer_secret, logging_level): self.oauth = OAuth1Session(consumer_key, consumer_secret, access_token, access_token_secret) log_handler = logging.StreamHandler(sys.stdout) log_handler.setFormatter(logging.Formatter('[%(asctime)s][%(levelname)s] %(message)s')) self.logger = logging.getLogger(__name__) self.logger.addHandler(log_handler) self.logger.setLevel(logging_level) def retrieve_user_timeline(self, screen_name, count): params = {"screen_name":screen_name, "count":count} response = self.oauth.get("https://api.twitter.com/1.1/statuses/user_timeline.json", params=params) if "X-Rate-Limit-Remaining" in response.headers: rate_limit_remaining = response.headers["X-Rate-Limit-Remaining"] self.logger.debug("rate_limit_remaining: %s" % rate_limit_remaining) wait_sec = int(int(response.headers["X-Rate-Limit-Reset"]) - time.time()) + 10 self.logger.debug("wait_sec: %d" % wait_sec) if rate_limit_remaining == "0": self.logger.warning("twitter api rate-limit error occured. wait %s seconds for rate-limit be lifted. " % wait_sec) time.sleep(wait_sec) if "status" in response.headers: if response.headers["status"] != "200 OK": self.logger.error("status: %s" % response.headers["status"]) return None json_dict = json.loads(response.text) if "errors" in json_dict: for error in json_dict["errors"]: self.logger.critical("message: %s, code: %d" % (error["message"], error["code"])) self.logger.critical("check if the access_token infomartion in config.json is correct") sys.exit(1) return json_dict def retrieve_favorites(self, screen_name, count): params = {"screen_name":screen_name, "count":count} response = self.oauth.get("https://api.twitter.com/1.1/favorites/list.json", params=params) if "X-Rate-Limit-Remaining" in response.headers: rate_limit_remaining = response.headers["X-Rate-Limit-Remaining"] self.logger.debug("rate_limit_remaining: %s" % rate_limit_remaining) wait_sec = int(int(response.headers["X-Rate-Limit-Reset"]) - time.time()) + 10 self.logger.debug("wait_sec: %d" % wait_sec) if rate_limit_remaining == "0": self.logger.warning("twitter api rate-limit error occured. wait %s seconds for rate-limit be lifted. " % wait_sec) time.sleep(wait_sec) if "status" in response.headers: if response.headers["status"] != "200 OK": self.logger.error("status: %s" % response.headers["status"]) return None json_dict = json.loads(response.text) if "errors" in json_dict: for error in json_dict["errors"]: self.logger.critical("message: %s, code: %d" % (error["message"], error["code"])) self.logger.critical("check if the access_token infomartion in config.json is correct") sys.exit(1) return json_dict

46.671233

130

0.638098

428

3,407

4.915888

0.226636

0.076996

0.102662

0.036122

0.769962

0.743346

0

0.008077

0.236865

3,407

72

131

47.319444

0.801154

0.006164

0

0.689655

0

0.240615

0.046704

0

1

0.051724

false

0

0.103448

0

0.241379

0

null

0

1

0

null

0

5

516897a00f40c69433ae0251d2bfcdd3a824275f

121

py

Python

TRDWLL/settings/__init__.py

trdwll/TRDWLL.com

b4b2bbf3178d42ce8f854518d6d09274c8af8fc4

[ "MIT" ]

1

2020-06-15T19:54:06.000Z

TRDWLL/settings/__init__.py

trdwll/TRDWLL.com

b4b2bbf3178d42ce8f854518d6d09274c8af8fc4

[ "MIT" ]

null

TRDWLL/settings/__init__.py

trdwll/TRDWLL.com

b4b2bbf3178d42ce8f854518d6d09274c8af8fc4

[ "MIT" ]

null

from TRDWLL.settings.base import * if os.environ['TRDWLL'] == 'prod': from .prod import * else: from .dev import *

20.166667

34

0.661157

17

121

4.705882

0.647059

0

0.190083

121

6

35

20.166667

0.816327

0

0.081967

0

1

0

true

0

0.6

0

0.6

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

5a8f4e1c4de3709059240970c573a20741a029ae

10,227

py

Python

2020_3/projeto2/antlr4-python3-runtime-4.7.2/src/autogen/GrammarLexer.py

danperazzo/compilers-cin

c23dfe637175be8fe3d23312cb8a28f714aabfee

[ "MIT" ]

null

2020_3/projeto2/antlr4-python3-runtime-4.7.2/src/autogen/GrammarLexer.py

danperazzo/compilers-cin

c23dfe637175be8fe3d23312cb8a28f714aabfee

[ "MIT" ]

null

2020_3/projeto2/antlr4-python3-runtime-4.7.2/src/autogen/GrammarLexer.py

danperazzo/compilers-cin

c23dfe637175be8fe3d23312cb8a28f714aabfee

[ "MIT" ]

null

# Generated from antlr4-python3-runtime-4.7.2/src/autogen/Grammar.g4 by ANTLR 4.7.2 from antlr4 import * from io import StringIO from typing.io import TextIO import sys def serializedATN(): with StringIO() as buf: buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2*") buf.write("\u00fb\b\1\4\2\t\2\4\3\t\3\4\4\t\4\4\5\t\5\4\6\t\6\4\7") buf.write("\t\7\4\b\t\b\4\t\t\t\4\n\t\n\4\13\t\13\4\f\t\f\4\r\t\r") buf.write("\4\16\t\16\4\17\t\17\4\20\t\20\4\21\t\21\4\22\t\22\4\23") buf.write("\t\23\4\24\t\24\4\25\t\25\4\26\t\26\4\27\t\27\4\30\t\30") buf.write("\4\31\t\31\4\32\t\32\4\33\t\33\4\34\t\34\4\35\t\35\4\36") buf.write("\t\36\4\37\t\37\4 \t \4!\t!\4\"\t\"\4#\t#\4$\t$\4%\t%") buf.write("\4&\t&\4\'\t\'\4(\t(\4)\t)\3\2\3\2\3\3\3\3\3\4\3\4\3\5") buf.write("\3\5\3\5\3\6\3\6\3\7\3\7\3\b\3\b\3\b\3\b\3\b\3\t\3\t\3") buf.write("\t\3\t\3\n\3\n\3\13\3\13\3\f\3\f\3\f\3\r\3\r\3\r\3\16") buf.write("\3\16\3\16\3\17\3\17\3\17\3\20\3\20\3\20\3\21\3\21\3\21") buf.write("\3\22\3\22\3\23\3\23\3\24\3\24\3\25\3\25\3\26\3\26\3\27") buf.write("\3\27\3\30\3\30\3\30\3\31\3\31\3\31\3\32\3\32\3\32\3\33") buf.write("\3\33\3\33\3\34\3\34\3\35\3\35\3\36\3\36\3\36\3\36\3\37") buf.write("\3\37\3\37\3\37\3\37\3\37\3 \3 \3 \3 \3 \3!\3!\3!\3!\3") buf.write("!\3!\3!\3\"\3\"\3\"\3\"\7\"\u00b6\n\"\f\"\16\"\u00b9\13") buf.write("\"\3\"\3\"\3\"\3\"\3#\3#\3#\3#\7#\u00c3\n#\f#\16#\u00c6") buf.write("\13#\3#\3#\3#\3#\3#\3$\3$\7$\u00cf\n$\f$\16$\u00d2\13") buf.write("$\3$\3$\3$\3$\3%\3%\7%\u00da\n%\f%\16%\u00dd\13%\3&\6") buf.write("&\u00e0\n&\r&\16&\u00e1\3\'\6\'\u00e5\n\'\r\'\16\'\u00e6") buf.write("\3\'\3\'\6\'\u00eb\n\'\r\'\16\'\u00ec\3(\3(\7(\u00f1\n") buf.write("(\f(\16(\u00f4\13(\3(\3(\3)\3)\3)\3)\6\u00b7\u00c4\u00d0") buf.write("\u00f2\2*\3\3\5\4\7\5\t\6\13\7\r\b\17\t\21\n\23\13\25") buf.write("\f\27\r\31\16\33\17\35\20\37\21!\22#\23%\24\'\25)\26+") buf.write("\27-\30/\31\61\32\63\33\65\34\67\359\36;\37= ?!A\"C#E") buf.write("$G%I&K\'M(O)Q*\3\2\6\5\2C\\aac|\6\2\62;C\\aac|\3\2\62") buf.write(";\5\2\13\f\17\17\"\"\2\u0102\2\3\3\2\2\2\2\5\3\2\2\2\2") buf.write("\7\3\2\2\2\2\t\3\2\2\2\2\13\3\2\2\2\2\r\3\2\2\2\2\17\3") buf.write("\2\2\2\2\21\3\2\2\2\2\23\3\2\2\2\2\25\3\2\2\2\2\27\3\2") buf.write("\2\2\2\31\3\2\2\2\2\33\3\2\2\2\2\35\3\2\2\2\2\37\3\2\2") buf.write("\2\2!\3\2\2\2\2#\3\2\2\2\2%\3\2\2\2\2\'\3\2\2\2\2)\3\2") buf.write("\2\2\2+\3\2\2\2\2-\3\2\2\2\2/\3\2\2\2\2\61\3\2\2\2\2\63") buf.write("\3\2\2\2\2\65\3\2\2\2\2\67\3\2\2\2\29\3\2\2\2\2;\3\2\2") buf.write("\2\2=\3\2\2\2\2?\3\2\2\2\2A\3\2\2\2\2C\3\2\2\2\2E\3\2") buf.write("\2\2\2G\3\2\2\2\2I\3\2\2\2\2K\3\2\2\2\2M\3\2\2\2\2O\3") buf.write("\2\2\2\2Q\3\2\2\2\3S\3\2\2\2\5U\3\2\2\2\7W\3\2\2\2\tY") buf.write("\3\2\2\2\13\\\3\2\2\2\r^\3\2\2\2\17`\3\2\2\2\21e\3\2\2") buf.write("\2\23i\3\2\2\2\25k\3\2\2\2\27m\3\2\2\2\31p\3\2\2\2\33") buf.write("s\3\2\2\2\35v\3\2\2\2\37y\3\2\2\2!|\3\2\2\2#\177\3\2\2") buf.write("\2%\u0081\3\2\2\2\'\u0083\3\2\2\2)\u0085\3\2\2\2+\u0087") buf.write("\3\2\2\2-\u0089\3\2\2\2/\u008b\3\2\2\2\61\u008e\3\2\2") buf.write("\2\63\u0091\3\2\2\2\65\u0094\3\2\2\2\67\u0097\3\2\2\2") buf.write("9\u0099\3\2\2\2;\u009b\3\2\2\2=\u009f\3\2\2\2?\u00a5\3") buf.write("\2\2\2A\u00aa\3\2\2\2C\u00b1\3\2\2\2E\u00be\3\2\2\2G\u00cc") buf.write("\3\2\2\2I\u00d7\3\2\2\2K\u00df\3\2\2\2M\u00e4\3\2\2\2") buf.write("O\u00ee\3\2\2\2Q\u00f7\3\2\2\2ST\7=\2\2T\4\3\2\2\2UV\7") buf.write("}\2\2V\6\3\2\2\2WX\7\177\2\2X\b\3\2\2\2YZ\7k\2\2Z[\7h") buf.write("\2\2[\n\3\2\2\2\\]\7*\2\2]\f\3\2\2\2^_\7+\2\2_\16\3\2") buf.write("\2\2`a\7g\2\2ab\7n\2\2bc\7u\2\2cd\7g\2\2d\20\3\2\2\2e") buf.write("f\7h\2\2fg\7q\2\2gh\7t\2\2h\22\3\2\2\2ij\7?\2\2j\24\3") buf.write("\2\2\2kl\7.\2\2l\26\3\2\2\2mn\7-\2\2no\7?\2\2o\30\3\2") buf.write("\2\2pq\7/\2\2qr\7?\2\2r\32\3\2\2\2st\7,\2\2tu\7?\2\2u") buf.write("\34\3\2\2\2vw\7\61\2\2wx\7?\2\2x\36\3\2\2\2yz\7-\2\2z") buf.write("{\7-\2\2{ \3\2\2\2|}\7/\2\2}~\7/\2\2~\"\3\2\2\2\177\u0080") buf.write("\7/\2\2\u0080$\3\2\2\2\u0081\u0082\7-\2\2\u0082&\3\2\2") buf.write("\2\u0083\u0084\7,\2\2\u0084(\3\2\2\2\u0085\u0086\7\61") buf.write("\2\2\u0086*\3\2\2\2\u0087\u0088\7>\2\2\u0088,\3\2\2\2") buf.write("\u0089\u008a\7@\2\2\u008a.\3\2\2\2\u008b\u008c\7>\2\2") buf.write("\u008c\u008d\7?\2\2\u008d\60\3\2\2\2\u008e\u008f\7@\2") buf.write("\2\u008f\u0090\7?\2\2\u0090\62\3\2\2\2\u0091\u0092\7?") buf.write("\2\2\u0092\u0093\7?\2\2\u0093\64\3\2\2\2\u0094\u0095\7") buf.write("#\2\2\u0095\u0096\7?\2\2\u0096\66\3\2\2\2\u0097\u0098") buf.write("\7]\2\2\u00988\3\2\2\2\u0099\u009a\7_\2\2\u009a:\3\2\2") buf.write("\2\u009b\u009c\7k\2\2\u009c\u009d\7p\2\2\u009d\u009e\7") buf.write("v\2\2\u009e<\3\2\2\2\u009f\u00a0\7h\2\2\u00a0\u00a1\7") buf.write("n\2\2\u00a1\u00a2\7q\2\2\u00a2\u00a3\7c\2\2\u00a3\u00a4") buf.write("\7v\2\2\u00a4>\3\2\2\2\u00a5\u00a6\7x\2\2\u00a6\u00a7") buf.write("\7q\2\2\u00a7\u00a8\7k\2\2\u00a8\u00a9\7f\2\2\u00a9@\3") buf.write("\2\2\2\u00aa\u00ab\7t\2\2\u00ab\u00ac\7g\2\2\u00ac\u00ad") buf.write("\7v\2\2\u00ad\u00ae\7w\2\2\u00ae\u00af\7t\2\2\u00af\u00b0") buf.write("\7p\2\2\u00b0B\3\2\2\2\u00b1\u00b2\7\61\2\2\u00b2\u00b3") buf.write("\7\61\2\2\u00b3\u00b7\3\2\2\2\u00b4\u00b6\13\2\2\2\u00b5") buf.write("\u00b4\3\2\2\2\u00b6\u00b9\3\2\2\2\u00b7\u00b8\3\2\2\2") buf.write("\u00b7\u00b5\3\2\2\2\u00b8\u00ba\3\2\2\2\u00b9\u00b7\3") buf.write("\2\2\2\u00ba\u00bb\7\f\2\2\u00bb\u00bc\3\2\2\2\u00bc\u00bd") buf.write("\b\"\2\2\u00bdD\3\2\2\2\u00be\u00bf\7\61\2\2\u00bf\u00c0") buf.write("\7,\2\2\u00c0\u00c4\3\2\2\2\u00c1\u00c3\13\2\2\2\u00c2") buf.write("\u00c1\3\2\2\2\u00c3\u00c6\3\2\2\2\u00c4\u00c5\3\2\2\2") buf.write("\u00c4\u00c2\3\2\2\2\u00c5\u00c7\3\2\2\2\u00c6\u00c4\3") buf.write("\2\2\2\u00c7\u00c8\7,\2\2\u00c8\u00c9\7\61\2\2\u00c9\u00ca") buf.write("\3\2\2\2\u00ca\u00cb\b#\2\2\u00cbF\3\2\2\2\u00cc\u00d0") buf.write("\7%\2\2\u00cd\u00cf\13\2\2\2\u00ce\u00cd\3\2\2\2\u00cf") buf.write("\u00d2\3\2\2\2\u00d0\u00d1\3\2\2\2\u00d0\u00ce\3\2\2\2") buf.write("\u00d1\u00d3\3\2\2\2\u00d2\u00d0\3\2\2\2\u00d3\u00d4\7") buf.write("\f\2\2\u00d4\u00d5\3\2\2\2\u00d5\u00d6\b$\2\2\u00d6H\3") buf.write("\2\2\2\u00d7\u00db\t\2\2\2\u00d8\u00da\t\3\2\2\u00d9\u00d8") buf.write("\3\2\2\2\u00da\u00dd\3\2\2\2\u00db\u00d9\3\2\2\2\u00db") buf.write("\u00dc\3\2\2\2\u00dcJ\3\2\2\2\u00dd\u00db\3\2\2\2\u00de") buf.write("\u00e0\t\4\2\2\u00df\u00de\3\2\2\2\u00e0\u00e1\3\2\2\2") buf.write("\u00e1\u00df\3\2\2\2\u00e1\u00e2\3\2\2\2\u00e2L\3\2\2") buf.write("\2\u00e3\u00e5\t\4\2\2\u00e4\u00e3\3\2\2\2\u00e5\u00e6") buf.write("\3\2\2\2\u00e6\u00e4\3\2\2\2\u00e6\u00e7\3\2\2\2\u00e7") buf.write("\u00e8\3\2\2\2\u00e8\u00ea\7\60\2\2\u00e9\u00eb\t\4\2") buf.write("\2\u00ea\u00e9\3\2\2\2\u00eb\u00ec\3\2\2\2\u00ec\u00ea") buf.write("\3\2\2\2\u00ec\u00ed\3\2\2\2\u00edN\3\2\2\2\u00ee\u00f2") buf.write("\7$\2\2\u00ef\u00f1\13\2\2\2\u00f0\u00ef\3\2\2\2\u00f1") buf.write("\u00f4\3\2\2\2\u00f2\u00f3\3\2\2\2\u00f2\u00f0\3\2\2\2") buf.write("\u00f3\u00f5\3\2\2\2\u00f4\u00f2\3\2\2\2\u00f5\u00f6\7") buf.write("$\2\2\u00f6P\3\2\2\2\u00f7\u00f8\t\5\2\2\u00f8\u00f9\3") buf.write("\2\2\2\u00f9\u00fa\b)\2\2\u00faR\3\2\2\2\13\2\u00b7\u00c4") buf.write("\u00d0\u00db\u00e1\u00e6\u00ec\u00f2\3\b\2\2") return buf.getvalue() class GrammarLexer(Lexer): atn = ATNDeserializer().deserialize(serializedATN()) decisionsToDFA = [ DFA(ds, i) for i, ds in enumerate(atn.decisionToState) ] T__0 = 1 T__1 = 2 T__2 = 3 T__3 = 4 T__4 = 5 T__5 = 6 T__6 = 7 T__7 = 8 T__8 = 9 T__9 = 10 T__10 = 11 T__11 = 12 T__12 = 13 T__13 = 14 T__14 = 15 T__15 = 16 T__16 = 17 T__17 = 18 T__18 = 19 T__19 = 20 T__20 = 21 T__21 = 22 T__22 = 23 T__23 = 24 T__24 = 25 T__25 = 26 T__26 = 27 T__27 = 28 INT = 29 FLOAT = 30 VOID = 31 RETURN = 32 COMMENT = 33 MULTILINE_COMMENT = 34 DIRECTIVE = 35 IDENTIFIER = 36 INTEGER = 37 FLOATING = 38 STRING = 39 WHITESPACE = 40 channelNames = [ u"DEFAULT_TOKEN_CHANNEL", u"HIDDEN" ] modeNames = [ "DEFAULT_MODE" ] literalNames = [ "<INVALID>", "';'", "'{'", "'}'", "'if'", "'('", "')'", "'else'", "'for'", "'='", "','", "'+='", "'-='", "'*='", "'/='", "'++'", "'--'", "'-'", "'+'", "'*'", "'/'", "'<'", "'>'", "'<='", "'>='", "'=='", "'!='", "'['", "']'", "'int'", "'float'", "'void'", "'return'" ] symbolicNames = [ "<INVALID>", "INT", "FLOAT", "VOID", "RETURN", "COMMENT", "MULTILINE_COMMENT", "DIRECTIVE", "IDENTIFIER", "INTEGER", "FLOATING", "STRING", "WHITESPACE" ] ruleNames = [ "T__0", "T__1", "T__2", "T__3", "T__4", "T__5", "T__6", "T__7", "T__8", "T__9", "T__10", "T__11", "T__12", "T__13", "T__14", "T__15", "T__16", "T__17", "T__18", "T__19", "T__20", "T__21", "T__22", "T__23", "T__24", "T__25", "T__26", "T__27", "INT", "FLOAT", "VOID", "RETURN", "COMMENT", "MULTILINE_COMMENT", "DIRECTIVE", "IDENTIFIER", "INTEGER", "FLOATING", "STRING", "WHITESPACE" ] grammarFileName = "Grammar.g4" def __init__(self, input=None, output:TextIO = sys.stdout): super().__init__(input, output) self.checkVersion("4.7.2") self._interp = LexerATNSimulator(self, self.atn, self.decisionsToDFA, PredictionContextCache()) self._actions = None self._predicates = None

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1

null

0

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5ab131a6cbe1f93b08cd0ceee134b07c3b009b9c

72

py

Python

widgets/MainWindow/__init__.py

ChineseWriter/MiddleSchool

a27525564574b083aff751a3bc16dea08b9eca8b

[ "MIT" ]

null

widgets/MainWindow/__init__.py

ChineseWriter/MiddleSchool

a27525564574b083aff751a3bc16dea08b9eca8b

[ "MIT" ]

null

widgets/MainWindow/__init__.py

ChineseWriter/MiddleSchool

a27525564574b083aff751a3bc16dea08b9eca8b

[ "MIT" ]

null

# coding = UTF-8 from widgets.MainWindow.Controller import MainWindow

14.4

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72

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0.138889

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1

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1

0

1

0

5

85100ed15ec5ed3b243c31ca7227cff2d666a964

106

py

Python

tests/pyutgenerator/data/pattern02.py

shigeshige/py-ut-generator

95faba39418a2ac52f58433c0f980a5e03ac29cf

[ "MIT" ]

2

2021-11-03T09:46:09.000Z

2021-12-28T12:48:58.000Z

tests/pyutgenerator/data/pattern02.py

shigeshige/py-ut-generator

95faba39418a2ac52f58433c0f980a5e03ac29cf

[ "MIT" ]

16

2020-05-01T13:25:56.000Z

2021-11-25T13:24:30.000Z

tests/pyutgenerator/data/pattern02.py

shigeshige/py-ut-generator

95faba39418a2ac52f58433c0f980a5e03ac29cf

[ "MIT" ]

1

2022-01-31T07:44:56.000Z

""" test pattern 02 """ def p01(): x = f01(f01(f01)) return x def f01(prm1): return prm1

7.571429

21

0.528302

16

106

3.5

0.5625

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0.191781

0.311321

106

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null

1

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null

0

1

0

1

0

5

a400abb0b8a5b39df8d2ef685f74bf0712a0e506

211

py

Python

app/config/wsgi.py

FormatMemory/django_api_backend

690439ad612598c86c22a837bc0f2e5bea74f2d2

[ "MIT" ]

null

app/config/wsgi.py

FormatMemory/django_api_backend

690439ad612598c86c22a837bc0f2e5bea74f2d2

[ "MIT" ]

8

2021-03-18T23:26:33.000Z

2022-03-11T23:44:22.000Z

app/config/wsgi.py

FormatMemory/django_api_backend

690439ad612598c86c22a837bc0f2e5bea74f2d2

[ "MIT" ]

null

import os from django.core.wsgi import get_wsgi_application from config.environment import SETTINGS_MODULE os.environ.setdefault("DJANGO_SETTINGS_MODULE", SETTINGS_MODULE) application = get_wsgi_application()

26.375

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1

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1

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null

0

1

0

5

cfa7fb2a8417b2a702287f5232fe7df3a4151cdc

227

py

Python

orchestration/dashboard/mpi.py

monkey-H/nap-core

50d23b0431682f276990db04527deae3b6d84661

[ "Apache-2.0" ]

null

orchestration/dashboard/mpi.py

monkey-H/nap-core

50d23b0431682f276990db04527deae3b6d84661

[ "Apache-2.0" ]

null

orchestration/dashboard/mpi.py

monkey-H/nap-core

50d23b0431682f276990db04527deae3b6d84661

[ "Apache-2.0" ]

null

from orchestration import config from orchestration.nap_api import create_from_table def create_mpi(username, password, mpi_name, slaves): args = ['slaves':slaves] create_from_table(username, password, mpi_name, args)

32.428571

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null

0

1

0

1

0

5

cfea60984b1153d39b3b7323a0972b9adc017114

9,773

py

Python

tests/sklearn_data/datasets.py

wmonteiro92/xmoai-examples

0286d57e15cb60693f57cdff386cbb246787442b

[ "MIT" ]

1

2021-03-22T11:31:00.000Z

tests/sklearn_data/datasets.py

wmonteiro92/xmoai-examples

0286d57e15cb60693f57cdff386cbb246787442b

[ "MIT" ]

null

tests/sklearn_data/datasets.py

wmonteiro92/xmoai-examples

0286d57e15cb60693f57cdff386cbb246787442b

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Thu Jul 2 21:37:05 2020 @author: wmont """ from sklearn.datasets import load_breast_cancer, load_boston, load_diabetes, \ load_digits, load_iris, load_wine, fetch_california_housing import numpy as np def get_dataset(dataset_name='breast_cancer'): """Retrieve one of the standard datasets in sklearn. :param dataset_name: the dataset name to use from sklearn. Valid values are `breast_cancer`, `digits`, `iris`, `wine` for classification and `boston`, `diabetes` and `california` for regression. Default is `breast_cancer`. :type dataset_name: str :return: Five variables are returned. First is the dataset itself without the target values; second includes the target values; third has all the categorical columns; fourth has all the integer columns and the last informs if it is a classification problem (True) or a regression problem (False). :rtype: np.array, np.array, np.array, np.array, Boolean """ if dataset_name == 'breast_cancer': # loading the dataset X, y = load_breast_cancer(return_X_y=True) # informing categorical columns and their available values categorical_columns = {} integer_columns = [] is_classification = True elif dataset_name == 'digits': # loading the dataset X, y = load_digits(return_X_y=True) # informing categorical columns and their available values categorical_columns = {} integer_columns = list(range(64)) is_classification = True elif dataset_name == 'iris': # loading the dataset X, y = load_iris(return_X_y=True) # informing categorical columns and their available values categorical_columns = {} integer_columns = [] is_classification = True elif dataset_name == 'wine': # loading the dataset X, y = load_wine(return_X_y=True) # informing categorical columns and their available values categorical_columns = {} integer_columns = [4, 12] is_classification = True elif dataset_name == 'boston': X, y = load_boston(return_X_y=True) # informing categorical columns and their available values categorical_columns = {3: [0, 1]} integer_columns = [8, 9] is_classification = False elif dataset_name == 'diabetes': # loading the dataset X, y = load_diabetes(return_X_y=True) # informing categorical columns and their available values categorical_columns = {1: [ 0.05068012, -0.04464164]} integer_columns = [] is_classification = False elif dataset_name == 'california': # loading the dataset X, y = fetch_california_housing(return_X_y=True) # informing categorical columns and their available values categorical_columns = {} integer_columns = [1, 4] is_classification = False return X, y, categorical_columns, integer_columns, is_classification def get_instance_from_dataset(X, index, dataset_name='breast_cancer'): """Retrieve one of the instances from the dataset to generate the counterfactuals. :param X: the input samples. :type X: np.array :param index: the index relative to the sample to be retrieved. :type index: Integer :param dataset_name: the dataset name to use from sklearn. Valid values are `breast_cancer`, `digits`, `iris`, `wine` for classification and `boston`, `diabetes` and `california` for regression. Default is `breast_cancer`. :type dataset_name: str :return: Six variables are returned. First is the instance from the dataset in reference to the index provided; second is the list of columns that cannot be modified; third and fourth are the upper and lower bounds for each variable, respectively; fifth includes the acceptable range to be considered with the desired target; sixth is the desired target (outcome). :rtype: np.array, np.array, np.array, np.array, np.array, Integer """ # get a instance in the i-th row X_current = X[index, :].flatten() if dataset_name == 'breast_cancer': count_class = 2 # define which columns must remain untouched immutable_column_indexes = [] # defining how much can we modify the input values upper_bounds = np.max(X, axis=0) lower_bounds = np.min(X, axis=0) # defining what are the tolerable output values y_desired = 1 y_acceptable_range = np.array([1.0/count_class, 1.0]) elif dataset_name == 'boston': # define which columns must remain untouched immutable_column_indexes = [1, 5] # defining how much can we modify the input values upper_bounds = np.max(X, axis=0) lower_bounds = np.min(X, axis=0) # defining what are the tolerable output values y_desired = 30 y_acceptable_range = np.array([y_desired * 0.98, y_desired * 1.02]) elif dataset_name == 'diabetes': # define which columns must remain untouched immutable_column_indexes = [1, 4, 5, 6] # defining how much can we modify the input values upper_bounds = np.max(X, axis=0) lower_bounds = np.min(X, axis=0) # defining what are the tolerable output values y_desired = 200 y_acceptable_range = np.array([y_desired * 0.95, y_desired * 1.05]) elif dataset_name == 'digits': count_class = 10 # define which columns must remain untouched immutable_column_indexes = [2, 5, 10, 20, 30, 40, 50] # defining how much can we modify the input values upper_bounds = np.max(X, axis=0) lower_bounds = np.min(X, axis=0) # defining what are the tolerable output values y_desired = 9 y_acceptable_range = np.array([1.0/count_class, 1.0]) elif dataset_name == 'iris': count_class = 3 # define which columns must remain untouched immutable_column_indexes = [] # defining how much can we modify the input values upper_bounds = np.max(X, axis=0) lower_bounds = np.min(X, axis=0) # defining what are the tolerable output values y_desired = 2 y_acceptable_range = np.array([1.0/count_class, 1.0]) elif dataset_name == 'wine': count_class = 3 # define which columns must remain untouched immutable_column_indexes = [7, 8, 9] # defining how much can we modify the input values upper_bounds = np.max(X, axis=0) lower_bounds = np.min(X, axis=0) # defining what are the tolerable output values y_desired = 2 y_acceptable_range = np.array([1.0/count_class, 1.0]) elif dataset_name == 'california': # define which columns must remain untouched immutable_column_indexes = [0, 1, 2] # defining how much can we modify the input values upper_bounds = np.max(X, axis=0) lower_bounds = np.min(X, axis=0) # defining what are the tolerable output values y_desired = 1.5 y_acceptable_range = np.array([y_desired * 0.95, y_desired * 1.05]) return X_current, immutable_column_indexes, \ upper_bounds, lower_bounds, y_acceptable_range, y_desired def load_sample_from_dataset(index, dataset_name='breast_cancer'): """Retrieve one of the instances from the dataset to generate the counterfactuals as well as other dataset metadata and multiobjective optimization (MOO) design space info relative to the sample. :param index: the index relative to the sample to be retrieved. :type index: Integer :param dataset_name: the dataset name to use from sklearn. Valid values are `breast_cancer`, `digits`, `iris`, `wine` for classification and `boston`, `diabetes` and `california` for regression. Default is `breast_cancer`. :type dataset_name: str :return: Ten variables are returned. First is the dataset itself without the target values; second includes the target values; third is the instance from the dataset in reference to the index provided; fourth is the desired target (outcome); fifth is the list of columns that cannot be modified; sixth and seventh are the upper and lower bounds for each variable, respectively; eigth includes the acceptable range to be considered with the desired target; ninth has all the categorical columns and tenth has all the integer columns. :rtype: np.array, np.array, np.array, Integer, np.array, np.array, np.array, np.array, np.array, np.array """ # get a dataset X, y, categorical_columns, integer_columns, \ is_classification = get_dataset(dataset_name) # get a instance from the dataset in the i-th row (defined in index) # as well as its predicted output X_current, immutable_column_indexes, upper_bounds, lower_bounds, \ y_acceptable_range, y_desired = get_instance_from_dataset(X, index, dataset_name) return X, y, X_current, y_desired, immutable_column_indexes, \ upper_bounds, lower_bounds, y_acceptable_range, categorical_columns, \ integer_columns

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1

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null

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5

5cca29ceec24e8b5613cc69d9e6612e21966849c

147

py

Python

constants.py

gleybersonandrade/TOA

42482671fda780f18441bb47f1946feabae5ccb8

[ "MIT" ]

null

constants.py

gleybersonandrade/TOA

42482671fda780f18441bb47f1946feabae5ccb8

[ "MIT" ]

null

constants.py

gleybersonandrade/TOA

42482671fda780f18441bb47f1946feabae5ccb8

[ "MIT" ]

null

"""Traffic Occurrence Analyzer constants.""" MAIN_DESC = "Traffic Occurrence Analyzer" METHOD_DESC = "method to be executed (construct, execute)"

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6.529412

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0.306306

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0.122449

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0.669903

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0

null

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1

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null

0

5

7a392977f22caa70283b7ea112ea3ad834067e2d

77

py

Python

snippets/python-number-minmax.py

district10/snippet-manager

bebe45a601368947168e3ee6e6ab8c1fc2ee2055

[ "MIT" ]

7

2018-08-04T09:28:19.000Z

2020-10-19T17:46:34.000Z

snippets/python-number-minmax.py

district10/snippet-manager

bebe45a601368947168e3ee6e6ab8c1fc2ee2055

[ "MIT" ]

null

snippets/python-number-minmax.py

district10/snippet-manager

bebe45a601368947168e3ee6e6ab8c1fc2ee2055

[ "MIT" ]

2

2018-07-31T04:14:55.000Z

2020-04-02T01:22:39.000Z

# min max for python float sys.float_info.max (> 0) sys.float_info.min (> 0)

19.25

26

0.701299

15

77

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0.307692

0.461538

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0.030769

0.155844

77

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27

25.666667

0.769231

0.311688

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null

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null

1

0

1

0

null

0

1

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5

7a446ae9c0317bb31bf7eb0af37453002ecf04f6

160

py

Python

Escolas/Curso em Video/Back-End/Curso de Python/Mundos/Mundo 01/Exercicio_21.py

c4st1lh0/Projetos-de-Aula

e8abc9f4bce6cc8dbc6d7fb5da0f549ac8ef5302

[ "MIT" ]

null

Escolas/Curso em Video/Back-End/Curso de Python/Mundos/Mundo 01/Exercicio_21.py

c4st1lh0/Projetos-de-Aula

e8abc9f4bce6cc8dbc6d7fb5da0f549ac8ef5302

[ "MIT" ]

null

Escolas/Curso em Video/Back-End/Curso de Python/Mundos/Mundo 01/Exercicio_21.py

c4st1lh0/Projetos-de-Aula

e8abc9f4bce6cc8dbc6d7fb5da0f549ac8ef5302

[ "MIT" ]

null

import pygame pygame.init() pygame.mixer.music.load('Exercicio_21.mp3') pygame.mixer.music.set_volume(0.1) pygame.mixer.music.play() input() pygame.event.wait()

22.857143

43

0.78125

26

160

4.730769

0.653846

0.268293

0.390244

0

0.03268

0.04375

160

7

44

22.857143

0.771242

0

0.099379

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1

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true

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null

1

0

1

0

null

0

1

0

5

7a5f724dc558f7d7c014a18512388c6218d42656

81

py

Python

Python/advanced_tree.py

jiangzhengshen/AlgorithmCollection

beac5c39bd91c3686db3db533e6e601598e7e730

[ "MIT" ]

null

Python/advanced_tree.py

jiangzhengshen/AlgorithmCollection

beac5c39bd91c3686db3db533e6e601598e7e730

[ "MIT" ]

null

Python/advanced_tree.py

jiangzhengshen/AlgorithmCollection

beac5c39bd91c3686db3db533e6e601598e7e730

[ "MIT" ]

null

class Trie: pass class BalanceTree: pass class RedBlackTree: pass

8.1

19

0.666667

9

81

6

0.555556

0.333333

0

0.296296

81

9

20

9

0.947368

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

7aa44a25b12f8ab1561dc9d1aca2159d62ceeccf

65

py

Python

lscom/__init__.py

joshschmelzle/lscom

7c83b6f685278210293e1b5f5dd2d1b5a7982e6d

[ "MIT" ]

null

lscom/__init__.py

joshschmelzle/lscom

7c83b6f685278210293e1b5f5dd2d1b5a7982e6d

[ "MIT" ]

null

lscom/__init__.py

joshschmelzle/lscom

7c83b6f685278210293e1b5f5dd2d1b5a7982e6d

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # | _ _ _ ._ _ # | _> (_ (_) | | |

13

23

0.276923

3

65

3.333333

1

0

0.025

0.384615

65

4

24

16.25

0.225

0.861538

0

null

0

null

0

null

0

null

1

null

true

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null

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1

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1

0

null

0

1

0

5

8f86e0354bd3437ae38eb026e1eac42e281da2e4

102

py

Python

Tools/MassSpectrometry/__init__.py

deaconjs/SPADE

da28cb927ae14f60aaf847591f81a86c9796d95e

[ "BSD-3-Clause" ]

3

2017-09-26T03:09:14.000Z

2022-03-20T11:12:34.000Z

Tools/MassSpectrometry/__init__.py

deaconjs/SPADE

da28cb927ae14f60aaf847591f81a86c9796d95e

[ "BSD-3-Clause" ]

null

Tools/MassSpectrometry/__init__.py

deaconjs/SPADE

da28cb927ae14f60aaf847591f81a86c9796d95e

[ "BSD-3-Clause" ]

1

2020-01-15T03:05:36.000Z

import MassSpecWindow import ExperimentalConditionViewer import ModifyAAViewer import PeakRationalizer

25.5

34

0.931373

8

102

11.875

0.625

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102

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35

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1

0

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0

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1

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5

8fa78e8f6f40556373d535021bb95c85d35c38f9

18

py

Python

pyscript/torch/optimizer/__init__.py

takuto0831/Competition-utils

c738e199c6a771a0c58b9cd237660bb76b4be4fb

[ "MIT" ]

105

2019-04-09T21:57:51.000Z

2022-03-12T11:39:55.000Z

pyscript/torch/optimizer/__init__.py

takuto0831/Competition-utils

c738e199c6a771a0c58b9cd237660bb76b4be4fb

[ "MIT" ]

5

2020-01-10T09:08:05.000Z

2022-02-08T23:14:40.000Z

pyscript/torch/optimizer/__init__.py

takuto0831/Competition-utils

c738e199c6a771a0c58b9cd237660bb76b4be4fb

[ "MIT" ]

18

2020-01-12T06:50:41.000Z

2022-02-27T02:21:41.000Z

from .sam import *

18

0.722222

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18

4.333333

1

0

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1

18

0.866667

0

1

0

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null

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0

null

0

1

0

1

0

5

8f53dbcade00af34eeefad61b6d0abbeecfb3ce8

327

py

Python

rapid/__init__.py

limetreeleon/RAPID

6f922496ccbad84a8594af83cc63e8c7535cc804

[ "MIT" ]

2

2021-01-14T04:44:51.000Z

2021-01-14T13:43:38.000Z

rapid/__init__.py

limetreeleon/RAPID

6f922496ccbad84a8594af83cc63e8c7535cc804

[ "MIT" ]

null

rapid/__init__.py

limetreeleon/RAPID

6f922496ccbad84a8594af83cc63e8c7535cc804

[ "MIT" ]

1

2020-11-09T02:25:50.000Z

"""RAPID (Robustness Analysis Producing Intelligent Decisions) This software package contains two sub-packages: 1. robustness - contains sub-packages for calculation and analysis of robustness values. 2. examples - examples of the use of the RAPID software package. """ from . import robustness from . import examples

36.333333

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6.047619

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0.11811

0

0.007326

0.165138

327

8

93

40.875

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0.828746

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null

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1

0

null

0

1

0

1

0

1

0

5

56b6d75741dc0ba325f311d67defe7903b7c8606

26

py

Python

StatsTest/__init__.py

Nickroll/SALMetrics

3b346013516a6e25761cdabee1d6ff389901951c

[ "MIT" ]

null

StatsTest/__init__.py

Nickroll/SALMetrics

3b346013516a6e25761cdabee1d6ff389901951c

[ "MIT" ]

null

StatsTest/__init__.py

Nickroll/SALMetrics

3b346013516a6e25761cdabee1d6ff389901951c

[ "MIT" ]

null

from . import SalMetrics

8.666667

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3

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6.666667

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0

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26

2

25

13

0.952381

0

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true

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null

0

1

0

null

0

1

0

1

0

5

56cfb8d7509268d6340a2d1c24b712ad540cb4b2

27,499

py

Python

tests/resource_tests/generator_tests/test_consent_metrics.py

all-of-us/raw-data-repository

d28ad957557587b03ff9c63d55dd55e0508f91d8

[ "BSD-3-Clause" ]

39

2017-10-13T19:16:27.000Z

2021-09-24T16:58:21.000Z

tests/resource_tests/generator_tests/test_consent_metrics.py

all-of-us/raw-data-repository

d28ad957557587b03ff9c63d55dd55e0508f91d8

[ "BSD-3-Clause" ]

312

2017-09-08T15:42:13.000Z

2022-03-23T18:21:40.000Z

tests/resource_tests/generator_tests/test_consent_metrics.py

all-of-us/raw-data-repository

d28ad957557587b03ff9c63d55dd55e0508f91d8

[ "BSD-3-Clause" ]

19

2017-09-15T13:58:00.000Z

2022-02-07T18:33:20.000Z

# # This file is subject to the terms and conditions defined in the # file 'LICENSE', which is part of this source code package. # from datetime import datetime, date from tests.helpers.unittest_base import BaseTestCase from rdr_service.dao.resource_dao import ResourceDataDao from rdr_service.model.consent_file import ConsentSyncStatus, ConsentType, ConsentOtherErrors import rdr_service.resource.generators class ConsentMetricGeneratorTest(BaseTestCase): def setUp(self, *args, **kwargs) -> None: super(ConsentMetricGeneratorTest, self).setUp(*args, **kwargs) self.resource_data_dao = ResourceDataDao() def _create_participant_with_all_consents_authored(self, **kwargs): """ Populate a participant_summary record with provided data """ defaults = { 'consentForStudyEnrollmentAuthored': datetime.strptime('2020-01-01 01:00:00', "%Y-%m-%d %H:%M:%S"), 'consentForStudyEnrollmentFirstYesAuthored': datetime.strptime('2020-01-01 01:00:00', "%Y-%m-%d %H:%M:%S"), 'consentForCABoRAuthored': datetime.strptime('2020-01-01 02:00:00', "%Y-%m-%d %H:%M:%S"), 'consentForElectronicHealthRecordsAuthored': datetime.strptime('2020-01-01 03:00:00', "%Y-%m-%d %H:%M:%S"), 'consentForElectronicHealthRecordsFirstYesAuthored': \ datetime.strptime('2020-01-01 03:00:00', "%Y-%m-%d %H:%M:%S"), 'consentForGenomicsRORAuthored': datetime.strptime('2020-01-01 04:00:00', "%Y-%m-%d %H:%M:%S"), 'participantOrigin': 'vibrent' } # Merge the kwargs and defaults dicts; kwargs values take precedence over default values for key in defaults.keys(): if key not in kwargs.keys(): kwargs = dict(**{key: defaults[key]}, **kwargs) participant = self.data_generator.create_database_participant_summary(**kwargs) return participant def _create_participant_with_custom_primary_consent_authored(self, authored, **kwargs): participant = self.data_generator.create_database_participant_summary( consentForStudyEnrollmentAuthored=authored, consentForStudyEnrollmentFirstYesAuthored=authored, **kwargs ) return participant @staticmethod def _create_expected_metrics_dict(participant, consent_type=ConsentType.PRIMARY, consent_status=ConsentSyncStatus.READY_FOR_SYNC, expected_errors=[]): """ Set up a dictionary of values to compare against resource data dictionary from ConsentMetricGenerator; does not include created, modified, or id (auto-generated values) """ expected_values_dict = {'hpo_id': participant.hpoId, 'organization_id': participant.organizationId, 'participant_id': f'P{participant.participantId}', 'consent_type': str(consent_type), 'consent_type_id': int(consent_type), 'sync_status': str(consent_status), 'sync_status_id': int(consent_status), 'missing_file': ('missing_file' in expected_errors), 'signature_missing': ('signature_missing' in expected_errors), 'invalid_signing_date': ('invalid_signing_date' in expected_errors), 'checkbox_unchecked': ('checkbox_unchecked' in expected_errors), 'non_va_consent_for_va': ('non_va_consent_for_va' in expected_errors), 'va_consent_for_non_va': ('va_consent_for_non_va' in expected_errors), 'invalid_dob': ('invalid_dob' in expected_errors), 'invalid_age_at_consent': ('invalid_age_at_consent' in expected_errors) } return expected_values_dict def test_consent_metrics_generator_no_errors(self): """ Test the consent_metrics generator with no error conditions """ # Use a valid datOfBirth for participant summary data participant = self._create_participant_with_all_consents_authored( dateOfBirth=datetime.date(datetime.strptime('1999-01-01', '%Y-%m-%d')), ) # Create consent_file record with no error conditions consent_file_rec = self.data_generator.create_database_consent_file( type=ConsentType.PRIMARY, sync_status=ConsentSyncStatus.READY_FOR_SYNC, participant_id=participant.participantId, signing_date=participant.consentForStudyEnrollmentFirstYesAuthored.date(), expected_sign_date=date(year=2020, month=1, day=1), file_exists=1, is_signature_valid=1, is_signing_date_valid=1 ) self.assertIsNotNone(consent_file_rec.id) res_gen = rdr_service.resource.generators.ConsentMetricGenerator() resource_data = res_gen.make_resource(consent_file_rec.id).get_data() # No expected_errors provided, all error conditions default to False expected = self._create_expected_metrics_dict(participant, expected_errors=[]) generated = {k: v for k, v in resource_data.items() if k in expected} self.assertDictEqual(generated, expected) # Also check that the authored date matches the date from the participant_summary record self.assertEqual(resource_data.get('consent_authored_date', None), datetime.date(participant.consentForElectronicHealthRecordsFirstYesAuthored)) def test_consent_metrics_generator_dob_invalid(self): """ invalid_dob error calculated from participant_summary data, sync_status can still be READY_TO_SYNC """ # Create participant summary data with (1) DOB missing, and (2) DOB > 124 years from primary consent authored participant_1 = self._create_participant_with_all_consents_authored(dateOfBirth=None) participant_2 = self._create_participant_with_all_consents_authored( participantOrigin='example', dateOfBirth=datetime.date(datetime.strptime('1895-12-31', '%Y-%m-%d')) ) # Create consent_file records for each participant's primary consent with no other error conditions consent_file_rec_1 = self.data_generator.create_database_consent_file( type=ConsentType.PRIMARY, sync_status=ConsentSyncStatus.READY_FOR_SYNC, participant_id=participant_1.participantId, signing_date=participant_1.consentForStudyEnrollmentFirstYesAuthored.date(), expected_sign_date=date(year=2020, month=1, day=1), file_exists=1, is_signature_valid=1, is_signing_date_valid=1 ) consent_file_rec_2 = self.data_generator.create_database_consent_file( type=ConsentType.PRIMARY, sync_status=ConsentSyncStatus.READY_FOR_SYNC, participant_id=participant_2.participantId, signing_date=participant_2.consentForStudyEnrollmentFirstYesAuthored.date(), expected_sign_date=date(year=2020, month=1, day=1), file_exists=1, is_signature_valid=1, is_signing_date_valid=1 ) self.assertIsNotNone(consent_file_rec_1.id) self.assertIsNotNone(consent_file_rec_2.id) res_gen = rdr_service.resource.generators.ConsentMetricGenerator() # Expected: Invalid DOB because DOB is missing resource_data = res_gen.make_resource(consent_file_rec_1.id).get_data() expected = self._create_expected_metrics_dict(participant_1, expected_errors=['invalid_dob']) generated = {k: v for k, v in resource_data.items() if k in expected} self.assertDictEqual(generated, expected) # Expected: Invalid DOB because DOB is > 124 years before primary consent authored date resource_data = res_gen.make_resource(consent_file_rec_2.id).get_data() expected = self._create_expected_metrics_dict(participant_2, expected_errors=['invalid_dob']) generated = {k: v for k, v in resource_data.items() if k in expected} self.assertDictEqual(generated, expected) def test_consent_metrics_generator_invalid_age_at_consent(self): """ invalid_age_at_consent errors come from participant_summary data, sync_status can still be READY_TO_SYNC """ # Create participant summary data with a DOB less than 18 years from primary consent authored date participant = self._create_participant_with_all_consents_authored( dateOfBirth=datetime.date(datetime.strptime('2014-01-01', '%Y-%m-%d')), ) # Create consent_file record with no other error conditions consent_file_rec = self.data_generator.create_database_consent_file( type=ConsentType.PRIMARY, sync_status=ConsentSyncStatus.READY_FOR_SYNC, participant_id=participant.participantId, signing_date=participant.consentForStudyEnrollmentFirstYesAuthored.date(), expected_sign_date=date(year=2020, month=1, day=1), file_exists=1, is_signature_valid=1, is_signing_date_valid=1 ) self.assertIsNotNone(consent_file_rec.id) res_gen = rdr_service.resource.generators.ConsentMetricGenerator() # Expected: invalid_age_at_consent (less than 18 years of age) resource_data = res_gen.make_resource(consent_file_rec.id).get_data() expected = self._create_expected_metrics_dict(participant, expected_errors=['invalid_age_at_consent']) generated = {k: v for k, v in resource_data.items() if k in expected} self.assertDictEqual(generated, expected) def test_consent_metrics_generator_missing_file(self): """ Consent metrics missing_file error based on consent_file having file_exists = 0 """ # Create participant summary data (valid DOB) participant = self._create_participant_with_all_consents_authored( dateOfBirth=datetime.date(datetime.strptime('1999-01-01', '%Y-%m-%d')), ) # Create consent_file record with file_exists set to false, status NEEDS_CORRECTING consent_file_rec = self.data_generator.create_database_consent_file( type=ConsentType.PRIMARY, sync_status=ConsentSyncStatus.NEEDS_CORRECTING, participant_id=participant.participantId, signing_date=participant.consentForStudyEnrollmentFirstYesAuthored.date(), expected_sign_date=date(year=2020, month=1, day=1), file_exists=0, is_signature_valid=0, is_signing_date_valid=0 ) self.assertIsNotNone(consent_file_rec.id) res_gen = rdr_service.resource.generators.ConsentMetricGenerator() # Expected: invalid_age_at_consent (less than 18 years of age) resource_data = res_gen.make_resource(consent_file_rec.id).get_data() # Note: if file is missing, neither the signature_missing or invalid_signing_date errors should be set expected = self._create_expected_metrics_dict(participant, consent_status=ConsentSyncStatus.NEEDS_CORRECTING, expected_errors=['missing_file']) generated = {k: v for k, v in resource_data.items() if k in expected} self.assertDictEqual(generated, expected) def test_consent_metrics_generator_dob_and_file_errors(self): """ Consent metrics signature_missing error + invalid_age_at_consent error from primary consent """ # Create participant summary data (DOB < 18 years from primary consent authored date) participant = self._create_participant_with_all_consents_authored( dateOfBirth=datetime.date(datetime.strptime('2004-01-01', '%Y-%m-%d')), ) # Create consent_file record with file_exists set to false, status NEEDS_CORRECTING consent_file_rec = self.data_generator.create_database_consent_file( type=ConsentType.PRIMARY, sync_status=ConsentSyncStatus.NEEDS_CORRECTING, participant_id=participant.participantId, signing_date=participant.consentForStudyEnrollmentFirstYesAuthored.date(), expected_sign_date=date(year=2020, month=1, day=1), file_exists=1, is_signature_valid=0, # Because there wasn't a signature detected, this downstream signing date error is ignored in metrics code is_signing_date_valid=0 ) self.assertIsNotNone(consent_file_rec.id) res_gen = rdr_service.resource.generators.ConsentMetricGenerator() # Expected: invalid_age_at_consent and signature_missing errors resource_data = res_gen.make_resource(consent_file_rec.id).get_data() expected = self._create_expected_metrics_dict(participant, consent_status=ConsentSyncStatus.NEEDS_CORRECTING, expected_errors=['invalid_age_at_consent', 'signature_missing']) generated = {k: v for k, v in resource_data.items() if k in expected} self.assertDictEqual(generated, expected) def test_consent_metrics_generator_other_errors(self): """ Consent metrics errors that are extracted from the consent_file other_errors string field """ # Create participant summary data (valid DOB) participant = self._create_participant_with_all_consents_authored( dateOfBirth=datetime.date(datetime.strptime('1999-01-01', '%Y-%m-%d')), ) # Create consent_file record with missing check error, status NEEDS_CORRECTING consent_file_rec_1 = self.data_generator.create_database_consent_file( type=ConsentType.GROR, sync_status=ConsentSyncStatus.NEEDS_CORRECTING, participant_id=participant.participantId, signing_date=participant.consentForStudyEnrollmentFirstYesAuthored.date(), expected_sign_date=date(year=2020, month=1, day=1), file_exists=1, is_signature_valid=1, is_signing_date_valid=1, other_errors=ConsentOtherErrors.MISSING_CONSENT_CHECK_MARK ) # Create consent_file record with non-veteran consent for veteran participant error consent_file_rec_2 = self.data_generator.create_database_consent_file( type=ConsentType.EHR, sync_status=ConsentSyncStatus.NEEDS_CORRECTING, participant_id=participant.participantId, signing_date=participant.consentForStudyEnrollmentFirstYesAuthored.date(), expected_sign_date=date(year=2020, month=1, day=1), file_exists=1, is_signature_valid=1, is_signing_date_valid=1, other_errors=ConsentOtherErrors.VETERAN_CONSENT_FOR_NON_VETERAN ) self.assertIsNotNone(consent_file_rec_2.id) # Create consent_file record with both missing check mark and non-veteran consent for veteran participant error consent_file_rec_3 = self.data_generator.create_database_consent_file( type=ConsentType.EHR, sync_status=ConsentSyncStatus.NEEDS_CORRECTING, participant_id=participant.participantId, signing_date=participant.consentForStudyEnrollmentFirstYesAuthored.date(), expected_sign_date=date(year=2020, month=1, day=1), file_exists=1, is_signature_valid=1, is_signing_date_valid=1, other_errors=", ".join([ConsentOtherErrors.NON_VETERAN_CONSENT_FOR_VETERAN, ConsentOtherErrors.MISSING_CONSENT_CHECK_MARK]) ) res_gen = rdr_service.resource.generators.ConsentMetricGenerator() # Expected: checkbox_unchecked error for consent_file_rec_1 resource_data = res_gen.make_resource(consent_file_rec_1.id).get_data() expected = self._create_expected_metrics_dict(participant, consent_type=ConsentType.GROR, consent_status=ConsentSyncStatus.NEEDS_CORRECTING, expected_errors=\ ['checkbox_unchecked'] ) generated = {k: v for k, v in resource_data.items() if k in expected} self.assertDictEqual(generated, expected) # Also validate the resource data consent_authored_date matches the participant_summary GROR authored date self.assertEqual(resource_data.get('consent_authored_date', None), datetime.date(participant.consentForGenomicsRORAuthored)) # Expected: non_va_consent_for_va for consent_file_rec_2 resource_data = res_gen.make_resource(consent_file_rec_2.id).get_data() expected = self._create_expected_metrics_dict(participant, consent_type=ConsentType.EHR, consent_status=ConsentSyncStatus.NEEDS_CORRECTING, expected_errors=\ ['va_consent_for_non_va'] ) generated = {k: v for k, v in resource_data.items() if k in expected} self.assertDictEqual(generated, expected) # Also validate the resource data consent_authored_date matches the participant_summary EHR authored date self.assertEqual(resource_data.get('consent_authored_date', None), datetime.date(participant.consentForElectronicHealthRecordsFirstYesAuthored)) # Expected: checkbox_unchecked, non_va_for_va_consent for consent_file_rec_3 resource_data=res_gen.make_resource(consent_file_rec_3.id).get_data() expected = self._create_expected_metrics_dict(participant, consent_type=ConsentType.EHR, consent_status=ConsentSyncStatus.NEEDS_CORRECTING, expected_errors=\ ['checkbox_unchecked', 'non_va_consent_for_va'] ) generated = {k: v for k, v in resource_data.items() if k in expected} self.assertDictEqual(generated, expected) # Also validate the resource data consent_authored_date matches the participant_summary EHR authored date self.assertEqual(resource_data.get('consent_authored_date', None), datetime.date(participant.consentForElectronicHealthRecordsFirstYesAuthored)) def test_consent_metrics_generator_resolved_date(self): """ For OBSOLETE sync status, confirm the resolved date equals the last modified date from consent_file record """ # Create participant summary data (valid DOB) participant = self._create_participant_with_all_consents_authored( dateOfBirth=datetime.date(datetime.strptime('1999-01-01', '%Y-%m-%d')), ) # Create consent_file record with file_exists set to false, status NEEDS_CORRECTING consent_file_rec = self.data_generator.create_database_consent_file( type=ConsentType.EHR, sync_status=ConsentSyncStatus.OBSOLETE, participant_id=participant.participantId, expected_sign_date=date(year=2020, month=1, day=1), file_exists=1, is_signature_valid=1, is_signing_date_valid=1, other_errors=ConsentOtherErrors.MISSING_CONSENT_CHECK_MARK ) self.assertIsNotNone(consent_file_rec.id) res_gen = rdr_service.resource.generators.ConsentMetricGenerator() # Expected: checkbox_unchecked error resource_data = res_gen.make_resource(consent_file_rec.id).get_data() expected = self._create_expected_metrics_dict(participant, consent_type=ConsentType.EHR, consent_status=ConsentSyncStatus.OBSOLETE, expected_errors=['checkbox_unchecked']) generated = {k: v for k, v in resource_data.items() if k in expected} self.assertDictEqual(generated, expected) # Confirm the consent authored date matches the date from the participant_summary record, and that the # resolved date matches the consent_file record modified date self.assertEqual(resource_data.get('consent_authored_date', None), datetime.date(participant.consentForElectronicHealthRecordsFirstYesAuthored)) self.assertEqual(resource_data.get('resolved_date', None), datetime.date(consent_file_rec.modified)) def test_consent_metrics_generator_signature_missing_error_filtered(self): """ Ignore known potential false positives for missing signatures, for consents authored before 2018-07-13 """ # Create participant summary data with a primary consent authored date before the false positive cutoff participant = self._create_participant_with_custom_primary_consent_authored( datetime.strptime('2018-01-01 00:00:00', '%Y-%m-%d %H:%M:%S'), dateOfBirth=datetime.date(datetime.strptime('1999-01-01', '%Y-%m-%d')), ) # Create consent_file record with file_exists set to false, status NEEDS_CORRECTING consent_file_rec = self.data_generator.create_database_consent_file( type=ConsentType.PRIMARY, sync_status=ConsentSyncStatus.NEEDS_CORRECTING, participant_id=participant.participantId, expected_sign_date=date(year=2018, month=1, day=1), file_exists=1, is_signature_valid=0, ) self.assertIsNotNone(consent_file_rec.id) res_gen = rdr_service.resource.generators.ConsentMetricGenerator() resource_data = res_gen.make_resource(consent_file_rec.id).get_data() # Confirm this record's ignore flag was set due to filtering the signature_missing error self.assertEqual(resource_data['ignore'], True) def test_consent_metrics_generator_special_sync_status_filtered(self): """ Ignore consent records whose current sync_status is a special case status such as UNKNOWN or DELAYING_SYNC """ # Create participant summary data with a primar consent authored date before the false positive cutoff participant = self._create_participant_with_all_consents_authored( consentForStudyEnrollmentAuthored=datetime.strptime('2018-01-01 00:00:00', '%Y-%m-%d %H:%M:%S'), consentForStudyEnrollmentFirstYesAuthored=datetime.strptime('2018-01-01 00:00:00', '%Y-%m-%d %H:%M:%S'), dateOfBirth=datetime.date(datetime.strptime('1999-01-01', '%Y-%m-%d')) ) # Create consent_file record with file_exists set to false, status NEEDS_CORRECTING consent_file_rec = self.data_generator.create_database_consent_file( type=ConsentType.PRIMARY, sync_status=ConsentSyncStatus.DELAYING_SYNC, participant_id=participant.participantId, expected_sign_date=date(year=2018, month=1, day=1), file_exists=1, is_signature_valid=1, is_signing_date_valid=1 ) self.assertIsNotNone(consent_file_rec.id) res_gen = rdr_service.resource.generators.ConsentMetricGenerator() resource_data = res_gen.make_resource(consent_file_rec.id).get_data() # Confirm this record's ignore flag was set due to filtering on the special sync_status self.assertEqual(resource_data['ignore'], True) def test_consent_metrics_generator_va_consent_for_non_va_filtered(self): """ Ignore va_consent_for_non_va errors if that's the only error and participant's current pairing is to the VA HPO """ va_hpo = self.data_generator.create_database_hpo(hpoId=2000, name='VA') participant = self._create_participant_with_all_consents_authored( consentForStudyEnrollmentAuthored=datetime.strptime('2018-01-01 00:00:00', '%Y-%m-%d %H:%M:%S'), consentForStudyEnrollmentFirstYesAuthored=datetime.strptime('2018-01-01 00:00:00', '%Y-%m-%d %H:%M:%S'), dateOfBirth=datetime.date(datetime.strptime('1999-01-01', '%Y-%m-%d')), hpoId=va_hpo.hpoId ) # Create consent_file record with file_exists set to false, status NEEDS_CORRECTING consent_file_rec = self.data_generator.create_database_consent_file( type=ConsentType.PRIMARY, sync_status=ConsentSyncStatus.NEEDS_CORRECTING, participant_id=participant.participantId, expected_sign_date=date(year=2018, month=1, day=1), file_exists=1, is_signature_valid=1, is_signing_date_valid=1, other_errors=ConsentOtherErrors.VETERAN_CONSENT_FOR_NON_VETERAN ) self.assertIsNotNone(consent_file_rec.id) res_gen = rdr_service.resource.generators.ConsentMetricGenerator() resource_data = res_gen.make_resource(consent_file_rec.id).get_data() # Confirm this record's ignore flag was set due to filtering the va_consent_for_non_va error self.assertEqual(resource_data['ignore'], True) def test_consent_metrics_generator_test_participant(self): """ Confirm test_participant flag is set by generator if participant is paired to TEST hpo """ test_hpo = self.data_generator.create_database_hpo(hpoId=2000, name='TEST') participant = self._create_participant_with_all_consents_authored( consentForStudyEnrollmentFirstYesAuthored=datetime.strptime('2018-01-01 00:00:00', '%Y-%m-%d %H:%M:%S'), consentForStudyEnrollmentAuthored=datetime.strptime('2018-01-01 00:00:00', '%Y-%m-%d %H:%M:%S'), dateOfBirth=datetime.date(datetime.strptime('1999-01-01', '%Y-%m-%d')), hpoId=test_hpo.hpoId ) # Create consent_file record with file_exists set to false, status NEEDS_CORRECTING consent_file_rec = self.data_generator.create_database_consent_file( type=ConsentType.PRIMARY, sync_status=ConsentSyncStatus.READY_FOR_SYNC, participant_id=participant.participantId, expected_sign_date=date(year=2018, month=1, day=1), file_exists=1, is_signature_valid=1, is_signing_date_valid=1 ) self.assertIsNotNone(consent_file_rec.id) res_gen = rdr_service.resource.generators.ConsentMetricGenerator() resource_data = res_gen.make_resource(consent_file_rec.id).get_data() self.assertTrue(resource_data.get('test_participant'))

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Python

test_src/test_proj/wapp.py

FloThinksPi-Forks/vstutils

eeb4d7a4d280cb8b844d9c9ab212e88f7bbe5d38

[ "Apache-2.0" ]

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2018-05-29T22:55:45.000Z

2021-11-18T22:59:29.000Z

test_src/test_proj/wapp.py

FloThinksPi-Forks/vstutils

eeb4d7a4d280cb8b844d9c9ab212e88f7bbe5d38

[ "Apache-2.0" ]

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2020-03-05T01:31:52.000Z

2022-01-21T08:22:19.000Z

test_src/test_proj/wapp.py

FloThinksPi-Forks/vstutils

eeb4d7a4d280cb8b844d9c9ab212e88f7bbe5d38

[ "Apache-2.0" ]

10

2018-07-30T10:14:30.000Z

2022-01-08T12:07:20.000Z

from vstutils.environment import get_celery_app app = get_celery_app()

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py

Python

fitlins/utils/__init__.py

poldracklab/fitlins

3f6cea2f18db176cbd471419313b974e2bcd52ed

[ "Apache-2.0" ]

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2018-03-05T17:14:07.000Z

2022-03-25T22:08:57.000Z

fitlins/utils/__init__.py

poldracklab/fitlins

3f6cea2f18db176cbd471419313b974e2bcd52ed

[ "Apache-2.0" ]

292

2018-03-07T16:28:22.000Z

2022-03-30T12:56:01.000Z

fitlins/utils/__init__.py

poldracklab/fitlins

3f6cea2f18db176cbd471419313b974e2bcd52ed

[ "Apache-2.0" ]

34

2018-03-02T17:15:22.000Z

2021-10-02T18:48:19.000Z

from .strings import snake_to_camel, to_alphanum from .collections import dict_intersection

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71011230c980b33e94eee9255a08aa515ffb124a

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py

Python

backend/apps/projects/__init__.py

wuchaofan1654/tester

ff38d42e06cbdfa04882e8e95ada2dd93e6609f2

[ "MIT" ]

null

backend/apps/projects/__init__.py

wuchaofan1654/tester

ff38d42e06cbdfa04882e8e95ada2dd93e6609f2

[ "MIT" ]

null

backend/apps/projects/__init__.py

wuchaofan1654/tester

ff38d42e06cbdfa04882e8e95ada2dd93e6609f2

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Create by sandy at 15:54 09/12/2021 Description: ToDo """

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712d00e6f98e5c3d535be7658e02f8ce98d609c6

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py

Python

build/lib/rsnapsim/ssa_cpp/benchmark_ssas.py

MunskyGroup/rSNAPsim

af3e496d5252e1d2e1da061277123233a5d609b4

[ "MIT" ]

1

2022-01-28T18:17:37.000Z

rsnapsim/defunct/ssa_cpp/benchmark_ssas.py

MunskyGroup/rSNAPsim

af3e496d5252e1d2e1da061277123233a5d609b4

[ "MIT" ]

null

rsnapsim/defunct/ssa_cpp/benchmark_ssas.py

MunskyGroup/rSNAPsim

af3e496d5252e1d2e1da061277123233a5d609b4

[ "MIT" ]

1

2020-12-02T06:36:17.000Z

# -*- coding: utf-8 -*- """ Created on Thu May 21 16:02:42 2020 @author: willi """ import numpy as np import ssa_translation_lowmem import ssa_translation_lowmem_leaky import ssa_translation_lowmem_nostats import ssa_translation import matplotlib.pyplot as plt import time import os os.chdir('..') from rss import ProbeVectorFactory as pvf from rss import PropensityFactory as pff os.chdir('ssa_cpp') # load the elongation kelong = np.loadtxt('elongationrates.txt') kbind = kelong[0] kcompl = kelong[-1] kelong = kelong[1:-1] ncolor = 2 t_array = np.array([0,10,20,30,50,100,250,500],dtype=np.float64) t0 = 15 t_array = np.linspace(0,1000,1000,dtype=np.float64) N_rib = 200 result = np.zeros((len(t_array)*ncolor),dtype=np.int32 ) #kelong = np.array([3.1,3.2,3.3,3.4,3.5,3.1,3.2,3.3,3.4,3.5],dtype=np.float64) n_trajectories = 100 #preallocated arrays here all_results = np.zeros((n_trajectories,len(t_array),ncolor),dtype=np.int32) lenfrap = len(np.intersect1d(np.where(t_array>0)[0],np.where(t_array<20)[0])) all_frapresults = np.zeros((n_trajectories,N_rib*len(t_array)),dtype=np.int32) all_ribtimes = np.zeros((n_trajectories,400),dtype=np.float64) all_coltimes = np.zeros((n_trajectories,400),dtype=np.int32) nribs = np.array([0],dtype=np.int32) all_ribs = np.zeros((n_trajectories,1)) seeds = np.random.randint(0,0x7FFFFFF,n_trajectories) x0 = np.zeros((N_rib),dtype=np.int32) pl = np.zeros((len(kelong),ncolor), dtype=np.int32) pl[ [10,20,30,100,120,140],0 ] = 1 #pl[ [10,140],1 ] = 1 pl = np.cumsum(pl,axis=0) pl = pl.T.copy(order='C') print('-----------------------') print('GENERATING REPORT') print('-----------------------') print('1 cpu core, 2 color') print('{0} base pairs'.format(len(kelong)-2)) print('-----------------------') all_col_points = [] start = time.time() for i in range(n_trajectories): result = np.zeros((ncolor,len(t_array)),dtype=np.int32) frapresult = np.zeros((len(t_array)*N_rib),dtype=np.int32) ribtimes = np.zeros((400),dtype=np.float64) coltimes = np.zeros((400),dtype=np.int32) colpointsx = np.zeros(len(kelong)*400,dtype=np.int32) colpointst = np.zeros(len(kelong)*400,dtype=np.float64) ssa_translation_lowmem.run_SSA(result,ribtimes,coltimes,colpointsx,colpointst, kelong,frapresult,t_array,.03,kcompl, 1,0,300, seeds[i],nribs,x0,9, pl,2) all_results[i,:,:] = result.T all_frapresults[i,:] = frapresult all_coltimes[i,:] = coltimes all_ribtimes[i,:] = ribtimes all_ribs[i,:] = nribs[0] endcolrec = np.where(colpointsx == 0)[0][0] colpoints = np.vstack((colpointsx[:endcolrec],colpointst[:endcolrec])) all_col_points.append(colpoints.T) print('low memory w/recording_stats: time for {0} trajectories {1}'.format(n_trajectories,time.time()-start)) #plt.hist(result[result>0]) #plt.show() #traj = result.reshape((N_rib,len(t_array))).T ##print('The result is \n {0}'.format(result.reshape((N_rib,len(t_array))).T)) #plt.plot(traj[-1,:]) #plt.show() plt.plot(result.T,'--') start = time.time() for i in range(n_trajectories): result = np.zeros((ncolor,len(t_array)),dtype=np.int32) frapresult = np.zeros((len(t_array)*N_rib),dtype=np.int32) ssa_translation_lowmem_nostats.run_SSA(result, kelong,frapresult,t_array,.03,kcompl, 1,0,300, seeds[i],x0,9, pl,2) all_results[i,:,:] = result.T all_frapresults[i,:] = frapresult print('Low memory w/o recording stats: time for {0} trajectories {1}'.format(n_trajectories,time.time()-start)) plt.plot(result.T) pl_2color = np.atleast_2d(pl) probe_loc = (pl_2color[:,1:]-pl_2color[:,:-1] > 0).astype(int) inds = pff.intellegent_bin(np.atleast_2d(probe_loc),100) bpv,bpl = pvf.bin_probe_vecs(probe_loc,inds) kelong_2color = kelong k_bin = pff.bin_k(kelong, inds) start = time.time() for i in range(n_trajectories): result = np.zeros((ncolor,len(t_array)),dtype=np.int32) frapresult = np.zeros((len(t_array)*N_rib),dtype=np.int32) ssa_translation_lowmem_nostats.run_SSA(result, k_bin,frapresult,t_array,.03,kcompl, 1,0,300, seeds[i],x0,9, bpl,2) #all_results[i,:,:] = result.T #all_frapresults[i,:] = frapresult print('Low memory 100 bins: time for {0} trajectories {1}'.format(n_trajectories,time.time()-start)) plt.plot(result.T,'.') plt.legend(['w/ stats color 1','w/ stats color 2','w/o stats color 1','w/o stats color 2']) plt.xlabel('time') plt.ylabel('intensity') print('-----------------------') print('1 cpu core, 1 color') print('-----------------------') all_results = np.zeros((n_trajectories,N_rib*len(t_array)),dtype=np.int32) lenfrap = len(np.intersect1d(np.where(t_array>0)[0],np.where(t_array<20)[0])) all_frapresults = np.zeros((n_trajectories,N_rib*len(t_array)),dtype=np.int32) all_ribtimes = np.zeros((n_trajectories,400),dtype=np.float64) all_coltimes = np.zeros((n_trajectories,400),dtype=np.int32) nribs = np.array([0],dtype=np.int32) all_ribs = np.zeros((n_trajectories,1)) seeds = np.random.randint(0,0x7FFFFFF,n_trajectories) x0 = np.zeros((N_rib),dtype=np.int32) pv = np.loadtxt('probe_design.txt') all_col_points = [] start = time.time() for i in range(n_trajectories): result = np.zeros((len(t_array)*N_rib),dtype=np.int32) frapresult = np.zeros((len(t_array)*N_rib),dtype=np.int32) ribtimes = np.zeros((400),dtype=np.float64) coltimes = np.zeros((400),dtype=np.int32) colpointsx = np.zeros(len(kelong)*400,dtype=np.int32) colpointst = np.zeros(len(kelong)*400,dtype=np.float64) print(result.shape) print(kelong.shape) print(frapresult.shape) print(ribtimes.shape) print(coltimes.shape) print(colpointsx.shape) print(colpointst.shape) ssa_translation.run_SSA(result,ribtimes,coltimes,colpointsx,colpointst, kelong,frapresult,t_array,.03,kcompl, 1,0,300, seeds[i],nribs,x0,9,N_rib) all_results[i,:] = result all_frapresults[i,:] = frapresult all_coltimes[i,:] = coltimes all_ribtimes[i,:] = ribtimes all_ribs[i,:] = nribs[0] endcolrec = np.where(colpointsx == 0)[0][0] colpoints = np.vstack((colpointsx[:endcolrec],colpointst[:endcolrec])) all_col_points.append(colpoints.T) ntimes = len(t_array) intensity_vec = np.zeros(ntimes) tstart = 0 I = np.zeros((n_trajectories,ntimes-tstart)) for i in range(n_trajectories): traj = all_results[i,:].reshape((N_rib,len(t_array))).T for j in range(tstart,ntimes): temp_output = traj[j,:] I[i,j] = np.sum(pv[temp_output[temp_output>0]-1]) print('Full SSA with Recording: time for {0} trajectories {1}'.format(n_trajectories,time.time()-start)) plt.figure() plt.plot(I[-1,:],'x') pl = np.atleast_2d(pv.astype(int)) ncolor=1 all_results = np.zeros((n_trajectories,len(t_array),ncolor),dtype=np.int32) lenfrap = len(np.intersect1d(np.where(t_array>0)[0],np.where(t_array<20)[0])) all_frapresults = np.zeros((n_trajectories,N_rib*len(t_array)),dtype=np.int32) all_ribtimes = np.zeros((n_trajectories,400),dtype=np.float64) all_coltimes = np.zeros((n_trajectories,400),dtype=np.int32) nribs = np.array([0],dtype=np.int32) all_ribs = np.zeros((n_trajectories,1)) #seeds = np.random.randint(0,0x7FFFFFF,n_trajectories) x0 = np.zeros((N_rib),dtype=np.int32) all_col_points = [] start = time.time() for i in range(n_trajectories): result = np.zeros((ncolor,len(t_array)),dtype=np.int32) frapresult = np.zeros((len(t_array)*N_rib),dtype=np.int32) ribtimes = np.zeros((400),dtype=np.float64) coltimes = np.zeros((400),dtype=np.int32) colpointsx = np.zeros(len(kelong)*400,dtype=np.int32) colpointst = np.zeros(len(kelong)*400,dtype=np.float64) ssa_translation_lowmem.run_SSA(result,ribtimes,coltimes,colpointsx,colpointst, kelong,frapresult,t_array,.03,kcompl, 1,0,300, seeds[i],nribs,x0,9, pl,1) all_results[i,:] = result.T all_frapresults[i,:] = frapresult all_coltimes[i,:] = coltimes all_ribtimes[i,:] = ribtimes all_ribs[i,:] = nribs[0] endcolrec = np.where(colpointsx == 0)[0][0] colpoints = np.vstack((colpointsx[:endcolrec],colpointst[:endcolrec])) all_col_points.append(colpoints.T) print('Low memory w/recording_stats: time for {0} trajectories {1}'.format(n_trajectories,time.time()-start)) #plt.hist(result[result>0]) #plt.show() #traj = result.reshape((N_rib,len(t_array))).T ##print('The result is \n {0}'.format(result.reshape((N_rib,len(t_array))).T)) #plt.plot(traj[-1,:]) #plt.show() plt.plot(result.T,'o') start = time.time() for i in range(n_trajectories): result = np.zeros((ncolor,len(t_array)),dtype=np.int32) frapresult = np.zeros((len(t_array)*N_rib),dtype=np.int32) ssa_translation_lowmem_nostats.run_SSA(result, kelong,frapresult,t_array,.03,kcompl, 1,0,300, seeds[i],x0,9, pl,1) all_results[i,:] = result.T all_frapresults[i,:] = frapresult print('Low memory w/o recording stats: time for {0} trajectories {1}'.format(n_trajectories,time.time()-start)) plt.plot(result.T) pl = pl.flatten() probe_loc = (np.where(pv[1:]-pv[:-1] > 0)[0]+1).astype(np.int32) k_probe = .2 all_results = np.zeros((n_trajectories,len(t_array)),dtype=np.int32) lenfrap = len(np.intersect1d(np.where(t_array>0)[0],np.where(t_array<20)[0])) all_frapresults = np.zeros((n_trajectories,N_rib*len(t_array)),dtype=np.int32) all_ribtimes = np.zeros((n_trajectories,400),dtype=np.float64) all_coltimes = np.zeros((n_trajectories,400),dtype=np.int32) nribs = np.array([0],dtype=np.int32) all_ribs = np.zeros((n_trajectories,1)) seeds = np.random.randint(0,0x7FFFFFF,n_trajectories) x0 = np.zeros((N_rib),dtype=np.int32) start = time.time() for i in range(n_trajectories): result = np.zeros((len(t_array)),dtype=np.int32) frapresult = np.zeros((len(t_array)*N_rib),dtype=np.int32) ribtimes = np.zeros((400),dtype=np.float64) coltimes = np.zeros((400),dtype=np.int32) colpointsx = np.zeros(len(kelong)*400,dtype=np.int32) colpointst = np.zeros(len(kelong)*400,dtype=np.float64) ssa_translation_lowmem_leaky.run_SSA(result,ribtimes,coltimes,colpointsx,colpointst, kelong,frapresult,t_array,.03,kcompl, 1,0,300, seeds[i],nribs,x0,9, pl,k_probe,probe_loc ) all_results[i,:] = result all_frapresults[i,:] = frapresult all_coltimes[i,:] = coltimes all_ribtimes[i,:] = ribtimes all_ribs[i,:] = nribs[0] endcolrec = np.where(colpointsx == 0)[0][0] colpoints = np.vstack((colpointsx[:endcolrec],colpointst[:endcolrec])) all_col_points.append(colpoints.T) print('Low memory w/ leaky probes: time for {0} trajectories {1}'.format(n_trajectories,time.time()-start)) plt.plot(result.T) plt.legend(['full ssa','lowmem w/ stats','lowmem w/o stats','lowmem leaky']) plt.xlabel('time') plt.ylabel('intensity')

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71431221493b1d8b33028e167a2e6fae4494a823

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py

Python

tests/__init__.py

stsievert/scikeras

9a17b476f34809d5a436c048a5d145a8c55e7b55

[ "MIT" ]

null

tests/__init__.py

stsievert/scikeras

9a17b476f34809d5a436c048a5d145a8c55e7b55

[ "MIT" ]

null

tests/__init__.py

stsievert/scikeras

9a17b476f34809d5a436c048a5d145a8c55e7b55

[ "MIT" ]

1

2021-05-21T12:46:23.000Z

"""Unit test package for scikeras."""

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41

py

Python

experimentor/views/exceptions.py

aquilesC/experimentor

1a70760912ef40f0e2aaee44ed1a1e5594fd5b45

[ "MIT" ]

4

2020-05-15T04:07:25.000Z

2020-09-30T22:20:46.000Z

experimentor/views/exceptions.py

aquilesC/experimentor

1a70760912ef40f0e2aaee44ed1a1e5594fd5b45

[ "MIT" ]

null

experimentor/views/exceptions.py

aquilesC/experimentor

1a70760912ef40f0e2aaee44ed1a1e5594fd5b45

[ "MIT" ]

null

class ViewException(Exception): pass

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859740999b00b35376f5da7f09e91d0dba7b8e39

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py

Python

lib/systems/l-lysine.py

pulsar-chem/BPModule

f8e64e04fdb01947708f098e833600c459c2ff0e

[ "BSD-3-Clause" ]

null

lib/systems/l-lysine.py

pulsar-chem/BPModule

f8e64e04fdb01947708f098e833600c459c2ff0e

[ "BSD-3-Clause" ]

null

lib/systems/l-lysine.py

pulsar-chem/BPModule

f8e64e04fdb01947708f098e833600c459c2ff0e

[ "BSD-3-Clause" ]

null

import pulsar as psr def load_ref_system(): """ Returns l-lysine as found in the IQMol fragment library. All credit to https://github.com/nutjunkie/IQmol """ return psr.make_system(""" N 1.1027 -2.1223 0.2103 C 0.8799 -0.9837 -0.7101 C 2.1653 -0.5233 -1.3940 O 2.0015 0.1019 -2.5808 C 0.2612 0.2130 0.0260 C -1.1667 -0.0696 0.4666 C -1.7674 1.1389 1.1684 C -3.1924 0.8435 1.6233 N -3.7558 1.9940 2.3635 O 3.3165 -0.6536 -1.0164 H 1.6562 -1.8371 0.9909 H 1.5630 -2.8629 -0.2743 H 0.1669 -1.3483 -1.4928 H 2.8473 0.3657 -2.9306 H 0.2731 1.0970 -0.6439 H 0.8809 0.4945 0.9025 H -1.1933 -0.9499 1.1397 H -1.7864 -0.3461 -0.4106 H -1.7516 2.0153 0.4883 H -1.1378 1.4238 2.0355 H -3.2011 -0.0264 2.3108 H -3.8121 0.5488 0.7478 H -4.6963 1.7877 2.6259 H -3.7507 2.8057 1.7814 """)

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py

Python

server/src/controller/verify.py

y-yu/qrand

b041c3c9cccaf20ee24a0ad90c81b89d3dc753bf

[ "MIT" ]

3

2020-02-02T09:04:21.000Z

2020-02-09T07:25:59.000Z

server/src/controller/verify.py

y-yu/qrand

b041c3c9cccaf20ee24a0ad90c81b89d3dc753bf

[ "MIT" ]

null

server/src/controller/verify.py

y-yu/qrand

b041c3c9cccaf20ee24a0ad90c81b89d3dc753bf

[ "MIT" ]

null

from ..service import verify from flask import json, jsonify class VerifyQRandController: def __init__(self, verify_service_impl: verify.VerifyQRandService): self.verify_service_impl = verify_service_impl # クライアントはサーバーに対して公開した`a`と`x`と # サーバーのセッションに保存された1 qubitの測定結果を元に # コイントスが正常に行なわれたかを判定する。 def post_ax(self, a: int, x: int) -> json: return jsonify( {'is_valid': self.verify_service_impl.verify(a, x)} )

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234

py

Python

sturn/utils.py

m32/sturn

ffc252db2a434daef33c5e819444b1d929a8599b

[ "MIT" ]

2

2021-07-11T21:24:37.000Z

2021-12-23T18:30:50.000Z

sturn/utils.py

m32/sturn

ffc252db2a434daef33c5e819444b1d929a8599b

[ "MIT" ]

null

sturn/utils.py

m32/sturn

ffc252db2a434daef33c5e819444b1d929a8599b

[ "MIT" ]

1

2021-12-24T01:07:21.000Z

import hashlib def saslprep(string): #TODO return string def ha1(username, realm, password): data = b':'.join((username.encode('utf-8'), realm, saslprep(password.encode('utf-8')))) return hashlib.md5(data).digest()

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a4393d3c37b2e742985f737029320ead5079d8fa

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py

Python

src/__init__.py

nirvanesque/streaming-ML-benchmark

689e818da070b6f48c51b17cac6be69a0669f277

[ "Apache-2.0" ]

null

src/__init__.py

nirvanesque/streaming-ML-benchmark

689e818da070b6f48c51b17cac6be69a0669f277

[ "Apache-2.0" ]

null

src/__init__.py

nirvanesque/streaming-ML-benchmark

689e818da070b6f48c51b17cac6be69a0669f277

[ "Apache-2.0" ]

1

2018-11-12T10:22:34.000Z

# -*- coding: utf-8 -*- # ------------------------------------------------------------------ # Author : Baruch AMOUSSOU-DJANGBAN # Data Scientist # ------------------------------------------------------------------

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5

a4423f45b5632f29a9a1736775d6f4fd96838cc8

48

py

Python

test.py

lzqlzzq/GinPlum

a19cd9d84d37c11426ba87a0ea51d8382ab0525c

[ "MIT" ]

null

test.py

lzqlzzq/GinPlum

a19cd9d84d37c11426ba87a0ea51d8382ab0525c

[ "MIT" ]

null

test.py

lzqlzzq/GinPlum

a19cd9d84d37c11426ba87a0ea51d8382ab0525c

[ "MIT" ]

null

from index import * from api import service

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