xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

64a4b01a94777368b7f0e1dcc327811b6c88828b

202

py

Python

tests/fixtures/task_specs/simple_with_list.py

ludwigschubert/flow-simulator

24e7813d8544fd16e760cbe8c482818e06c3a55b

[ "Apache-2.0" ]

null

tests/fixtures/task_specs/simple_with_list.py

ludwigschubert/flow-simulator

24e7813d8544fd16e760cbe8c482818e06c3a55b

[ "Apache-2.0" ]

null

tests/fixtures/task_specs/simple_with_list.py

ludwigschubert/flow-simulator

24e7813d8544fd16e760cbe8c482818e06c3a55b

[ "Apache-2.0" ]

null

x = [1,2,3] name = "/tests/fixtures/data/names/{name_id}.txt" output = "/tests/fixtures/data/salutations/{name_id}-{x}.txt" def main(): return "Hello {name} for the {x} time!".format(name=name, x=x)

28.857143

64

0.663366

35

202

3.771429

0.6

0.19697

0.257576

0

0.016667

0.108911

202

6

65

33.666667

0.716667

0

0.594059

0.445545

0

1

0.2

false

0

0.2

0.4

0

null

0

1

0

1

0

1

null

0

1

0

4

64b461278cb4633f672ff5f36c5d4bfdf622f82e

662

py

Python

libkge/embedding/interfaces.py

samehkamaleldin/libkge

c80e82a894ddc4160cc03034206e3c1f05d32b42

[ "Apache-2.0" ]

12

2019-10-08T08:42:04.000Z

2021-12-16T06:50:17.000Z

benchmarking/libkge/libkge/embedding/interfaces.py

hpi-sam/GNN-Effectants

e1204cb78bb91ffe3126df62d2d14b20da950694

[ "MIT" ]

null

benchmarking/libkge/libkge/embedding/interfaces.py

hpi-sam/GNN-Effectants

e1204cb78bb91ffe3126df62d2d14b20da950694

[ "MIT" ]

3

2020-03-11T02:34:38.000Z

2021-01-24T15:09:44.000Z

from abc import ABCMeta, abstractmethod class IExportable(ABCMeta): def __init__(self): """ """ pass @abstractmethod def export_to_file(self, filepath): """ Export model to file. Parameters ---------- filepath: str file path Returns ------- """ raise NotImplementedError("Not implemented") @abstractmethod def import_from_file(self, filepath): """ Import model from file Parameters ---------- filepath Returns ------- """ raise NotImplementedError("Not implemented")

16.974359

52

0.509063

51

662

6.45098

0.470588

0.103343

0.097264

0.206687

0.273556

0

0.371601

662

39

53

16.974359

0.790865

0.243202

0

0.4

0

0.082192

0

1

0.3

false

0.1

0.2

0

0.6

0

null

0

1

0

null

0

1

0

1

0

1

0

4

b37a890f8c43c83abd02e550ce7b0400d326f7a2

205

py

Python

aioclustermanager/nomad/node.py

sunbit/aioclustermanager

f5a2f4ba7936a75c7748cff9f77c3bfff1a3a61d

[ "BSD-3-Clause" ]

1

2020-03-24T16:15:56.000Z

aioclustermanager/nomad/node.py

bloodbare/aioclustermanager

9abe7e9db7140854709c8044128e0153debe6971

[ "BSD-3-Clause" ]

8

2018-03-12T20:40:23.000Z

2018-06-05T18:35:16.000Z

aioclustermanager/nomad/node.py

onna/aioclustermanager

9abe7e9db7140854709c8044128e0153debe6971

[ "BSD-3-Clause" ]

2

2020-05-21T17:32:23.000Z

2021-05-11T12:17:56.000Z

from aioclustermanager.node import Node class NomadNode(Node): @property def id(self): return self._raw['Name'] @property def hostname(self): raise NotImplementedError()

17.083333

39

0.658537

22

205

6.090909

0.727273

0.164179

0

0.24878

205

11

40

18.636364

0.87013

0

0.25

0

0.019512

0

1

0.25

false

0

0.125

0.625

0

1

0

null

0

null

0

1

0

1

0

4

b3aeacedbebbed82f1a9504fbdd8167daef35ff6

138

py

Python

src/util/__init__.py

WardenAllen/Uranus

0d20cac631320b558254992c17678ddd1658587b

[ "MIT" ]

null

src/util/__init__.py

WardenAllen/Uranus

0d20cac631320b558254992c17678ddd1658587b

[ "MIT" ]

null

src/util/__init__.py

WardenAllen/Uranus

0d20cac631320b558254992c17678ddd1658587b

[ "MIT" ]

null

# !/usr/bin/python # -*- coding: utf-8 -*- # @Time : 2020/11/22 15:17 # @Author : WardenAllen # @File : __init__.py # @Brief :

17.25

29

0.536232

18

138

3.888889

1

0

0.125

0.246377

138

7

30

19.714286

0.548077

0.891304

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

4

b3b3d1a744670c96895cc0b69581d3bd819d434b

178

py

Python

samples/python/KafkaTriggerMany/main.py

mym-kingbob/azure-functions-kafka-extension

7e1cb155b8ebe7533c910b7999e5f281021b5f0a

[ "MIT" ]

74

2019-04-14T16:48:25.000Z

2022-03-30T04:11:23.000Z

samples/python/KafkaTriggerMany/main.py

mym-kingbob/azure-functions-kafka-extension

7e1cb155b8ebe7533c910b7999e5f281021b5f0a

[ "MIT" ]

175

2019-04-10T20:55:27.000Z

2022-03-31T18:20:10.000Z

samples/python/KafkaTriggerMany/main.py

mym-kingbob/azure-functions-kafka-extension

7e1cb155b8ebe7533c910b7999e5f281021b5f0a

[ "MIT" ]

52

2019-04-10T19:54:39.000Z

2022-03-10T22:53:12.000Z

import logging import typing from azure.functions import KafkaEvent def main(kevents : typing.List[KafkaEvent]): for event in kevents: logging.info(event.get_body())

25.428571

44

0.752809

24

178

5.541667

0.708333

0

0.162921

178

7

45

25.428571

0.892617

0

1

0.166667

false

0

0.5

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

b3ca1083aab324ba9772f1660e6a94bdfdc8f094

216

py

Python

mri_works/NodeEditor/modules/Yaml_Json/Json_tools.py

montigno/mri_works

8ec6ff1500aa34d3540e44e4b0148023cf821f61

[ "CECILL-B" ]

2

2020-08-20T21:00:53.000Z

2021-08-16T15:28:51.000Z

mri_works/NodeEditor/modules/Yaml_Json/Json_tools.py

montigno/mri_works

8ec6ff1500aa34d3540e44e4b0148023cf821f61

[ "CECILL-B" ]

3

2020-09-24T06:50:43.000Z

2020-12-15T11:02:04.000Z

mri_works/NodeEditor/modules/Yaml_Json/Json_tools.py

montigno/mri_works

8ec6ff1500aa34d3540e44e4b0148023cf821f61

[ "CECILL-B" ]

1

2020-08-20T21:00:59.000Z

class outJson(): def __init__(self, json_file='path'): import json with open(json_file) as f: self.outJson = json.load(f) def dict_json(self: 'dict'): return self.outJson

24

41

0.587963

29

216

4.137931

0.551724

0.133333

0

0.296296

216

8

42

27

0.789474

0

0.037037

0

1

0.285714

false

0

0.142857

0.714286

0

1

0

null

0

1

0

null

0

1

0

1

0

4

b6032427d8611decfc555b3496a3920f461d7a92

80

py

Python

start_timing.py

gholamw/DNS-QUIC

c020cbf69b3067eb5862a206a317b48feca798fe

[ "MIT" ]

null

start_timing.py

gholamw/DNS-QUIC

c020cbf69b3067eb5862a206a317b48feca798fe

[ "MIT" ]

null

start_timing.py

gholamw/DNS-QUIC

c020cbf69b3067eb5862a206a317b48feca798fe

[ "MIT" ]

null

import timeit start = timeit.timeit() #end = timeit.timeit() #print end - start

16

23

0.7125

11

80

5.181818

0.454545

0.421053

0

0.15

80

5

24

16

0.838235

0.475

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

4

b613ea2ec5028bdeeab9d2b99f503f0fe942ede9

1,810

py

Python

hypha/apply/determinations/migrations/0004_change_labels.py

maxpearl/hypha

e181ebadfb744aab34617bb766e746368d6f2de0

[ "BSD-3-Clause" ]

20

2021-04-08T16:38:49.000Z

2022-02-09T20:05:57.000Z

hypha/apply/determinations/migrations/0004_change_labels.py

maxpearl/hypha

e181ebadfb744aab34617bb766e746368d6f2de0

[ "BSD-3-Clause" ]

1,098

2017-12-15T11:23:03.000Z

2020-01-24T07:58:07.000Z

hypha/apply/determinations/migrations/0004_change_labels.py

maxpearl/hypha

e181ebadfb744aab34617bb766e746368d6f2de0

[ "BSD-3-Clause" ]

17

2020-02-07T14:55:54.000Z

2021-04-04T19:32:38.000Z

# Generated by Django 2.0.2 on 2018-06-22 14:23 from django.db import migrations, models import wagtail.core.fields class Migration(migrations.Migration): dependencies = [ ('determinations', '0003_message_template_settings'), ] operations = [ migrations.AlterField( model_name='determination', name='outcome', field=models.IntegerField(choices=[(0, 'Dismissed'), (1, 'Needs more info'), (2, 'Approved')], default=1, verbose_name='Determination'), ), migrations.AlterField( model_name='determinationmessagesettings', name='concept_accepted', field=wagtail.core.fields.RichTextField(verbose_name='Approved'), ), migrations.AlterField( model_name='determinationmessagesettings', name='concept_rejected', field=wagtail.core.fields.RichTextField(verbose_name='Dismissed'), ), migrations.AlterField( model_name='determinationmessagesettings', name='proposal_accepted', field=wagtail.core.fields.RichTextField(verbose_name='Approved'), ), migrations.AlterField( model_name='determinationmessagesettings', name='proposal_rejected', field=wagtail.core.fields.RichTextField(verbose_name='Dismissed'), ), migrations.AlterField( model_name='determinationmessagesettings', name='request_accepted', field=wagtail.core.fields.RichTextField(verbose_name='Approved'), ), migrations.AlterField( model_name='determinationmessagesettings', name='request_rejected', field=wagtail.core.fields.RichTextField(verbose_name='Dismissed'), ), ]

36.2

148

0.631492

153

1,810

7.320261

0.333333

0.06875

0.10625

0.18125

0.700893

0.607143

0.550893

0

0.017088

0.256354

1,810

49

149

36.938776

0.815007

0.024862

0

0.604651

1

0

0.241634

0.112309

0

1

0

false

0

0.046512

0

0.116279

0

null

0

1

0

1

0

1

0

null

0

4

37327305a29fe5cec6cb7af42edec8ee28b7409b

62

py

Python

rplugin/python3/nvim_diary_template/__init__.py

CrossR/nvim_notes

355bfec2339d1c6a612e1a89e6e4a3aed9e70f43

[ "MIT" ]

6

2019-01-11T23:09:32.000Z

2021-03-04T04:22:04.000Z

rplugin/python3/nvim_diary_template/__init__.py

CrossR/nvim_notes

355bfec2339d1c6a612e1a89e6e4a3aed9e70f43

[ "MIT" ]

20

2018-07-22T16:20:56.000Z

2019-11-10T14:11:05.000Z

rplugin/python3/nvim_diary_template/__init__.py

CrossR/nvim_notes

355bfec2339d1c6a612e1a89e6e4a3aed9e70f43

[ "MIT" ]

null

# pylint: disable=all from .plugin import DiaryTemplatePlugin

20.666667

39

0.822581

7

62

7.285714

1

0

0.112903

62

2

40

31

0.927273

0.306452

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

374bd394d895835f411cf34bf7c2f178eb17241a

140

py

Python

akika_venv/lib/python3.6/site-packages/django_seo_js/middleware/__init__.py

laetitia123/akikatest

812f26155b6e3d003ac7e48c08c16df406e11086

[ "MIT" ]

183

2015-01-02T09:02:21.000Z

2022-02-24T05:09:08.000Z

akika_venv/lib/python3.6/site-packages/django_seo_js/middleware/__init__.py

laetitia123/akikatest

812f26155b6e3d003ac7e48c08c16df406e11086

[ "MIT" ]

31

2015-02-03T21:15:53.000Z

2022-03-22T15:07:01.000Z

akika_venv/lib/python3.6/site-packages/django_seo_js/middleware/__init__.py

laetitia123/akikatest

812f26155b6e3d003ac7e48c08c16df406e11086

[ "MIT" ]

55

2015-02-03T04:00:55.000Z

2022-02-24T05:09:10.000Z

from .escaped_fragment import EscapedFragmentMiddleware from .hashbang import HashBangMiddleware from .useragent import UserAgentMiddleware

35

55

0.892857

13

140

9.538462

0.692308

0

0.085714

140

3

56

46.666667

0.96875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

805ee2b54933550aeb3bc8b8cbada3b82ae02435

688

py

Python

datawinners/submission/location.py

ICT4H/dcs-web

fb0f53fad4401cfac1c1789ff28b9d5bda40c975

[ "Apache-2.0" ]

1

2015-11-02T09:11:12.000Z

datawinners/submission/location.py

ICT4H/dcs-web

fb0f53fad4401cfac1c1789ff28b9d5bda40c975

[ "Apache-2.0" ]

null

datawinners/submission/location.py

ICT4H/dcs-web

fb0f53fad4401cfac1c1789ff28b9d5bda40c975

[ "Apache-2.0" ]

null

from datawinners.location.LocationTree import get_location_tree, get_location_hierarchy class LocationBridge(object): def __init__(self,location_tree=None,get_loc_hierarchy=None): self.location_tree = location_tree or get_location_tree() self.get_location_hierarchy = get_loc_hierarchy or get_location_hierarchy def get_location_hierarchy_for_geocode(self, lat, long): return self.location_tree.get_location_hierarchy_for_geocode( lat, long) def get_centroid(self, location, level): return self.location_tree.get_centroid(location,level) def get_location_hierarchy(self,lowest_level): self.get_location_hierarchy(lowest_level)

40.470588

87

0.792151

91

688

5.56044

0.274725

0.195652

0.27668

0.090909

0.274704

0

0.140988

688

16

88

43

0.856176

0

1

0.363636

false

0

0.090909

0.181818

0.727273

0

null

0

1

0

null

0

1

0

1

0

4

807b838282fb150e31445f38c3530dc0a69fc4e1

397

py

Python

letters/forms.py

deppiedave64/elternbrief

605c2eb455ec5d42a912198ed2c9663206924646

[ "Apache-2.0" ]

2

2019-05-26T16:43:52.000Z

2019-08-19T13:37:01.000Z

letters/forms.py

deppiedave64/elternbrief

605c2eb455ec5d42a912198ed2c9663206924646

[ "Apache-2.0" ]

1

2019-05-01T11:44:22.000Z

2019-05-06T18:06:52.000Z

letters/forms.py

deppiedave64/elternbrief

605c2eb455ec5d42a912198ed2c9663206924646

[ "Apache-2.0" ]

1

2019-05-06T12:57:49.000Z

"""Form for the elternbrief application""" from django import forms class UserImportForm(forms.Form): """Simple form for uploading csv files for importing users.""" parents_file = forms.FileField(required=True, widget=forms.FileInput(attrs={'class': 'custom-file-input'})) students_file = forms.FileField(required=True, widget=forms.FileInput(attrs={'class': 'custom-file-input'}))

39.7

112

0.743073

50

397

5.86

0.56

0.047782

0.122867

0.177474

0.511945

0

0.11335

397

9

113

44.111111

0.832386

0.234257

0

0.150171

0

1

0

false

0

0.5

0

1.25

0

null

0

1

0

1

0

null

0

1

0

1

0

4

808ae77129fa40da302f8269d77898d8b75826d7

236

py

Python

frappe-bench/apps/erpnext/erpnext/patches/v10_0/update_hub_connector_domain.py

Semicheche/foa_frappe_docker

a186b65d5e807dd4caf049e8aeb3620a799c1225

[ "MIT" ]

null

frappe-bench/apps/erpnext/erpnext/patches/v10_0/update_hub_connector_domain.py

Semicheche/foa_frappe_docker

a186b65d5e807dd4caf049e8aeb3620a799c1225

[ "MIT" ]

null

frappe-bench/apps/erpnext/erpnext/patches/v10_0/update_hub_connector_domain.py

Semicheche/foa_frappe_docker

a186b65d5e807dd4caf049e8aeb3620a799c1225

[ "MIT" ]

null

import frappe def execute(): if frappe.db.table_exists("Data Migration Connector"): frappe.db.sql(""" UPDATE `tabData Migration Connector` SET hostname = 'https://hubmarket.org' WHERE connector_name = 'Hub Connector' """)

26.222222

55

0.707627

29

236

5.689655

0.758621

0.09697

0

0.15678

236

9

56

26.222222

0.829146

0

0.637131

0

1

0.125

true

0

0.125

0

0.25

0

1

0

null

0

1

0

1

0

null

0

1

0

4

809a619f8c01da372e073aa7979ece38d58690dc

172

py

Python

towel/resources/__init__.py

enterstudio/towel

6892788527b8a111cbf5963e909964aabc96d740

[ "BSD-3-Clause" ]

null

towel/resources/__init__.py

enterstudio/towel

6892788527b8a111cbf5963e909964aabc96d740

[ "BSD-3-Clause" ]

null

towel/resources/__init__.py

enterstudio/towel

6892788527b8a111cbf5963e909964aabc96d740

[ "BSD-3-Clause" ]

null

# flake8: noqa from .base import (ModelResourceView, ListView, DetailView, FormView, AddView, EditView, LiveUpdateAfterEditMixin, LiveFormView, PickerView, DeleteView)

43

78

0.802326

15

172

9.2

1

0

0.006579

0.116279

172

3

79

57.333333

0.901316

0.069767

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

80a02d1625ef1cf572d03d09eb611ca20271d7eb

1,514

py

Python

src/sentry/plugins/bases/data_forwarding.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

[ "BSD-3-Clause" ]

1

2019-10-17T17:46:16.000Z

src/sentry/plugins/bases/data_forwarding.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

[ "BSD-3-Clause" ]

null

src/sentry/plugins/bases/data_forwarding.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import from sentry import tsdb, ratelimits from sentry.api.serializers import serialize from sentry.plugins.base import Plugin from sentry.plugins.base.configuration import react_plugin_config from sentry.plugins.status import PluginStatus class DataForwardingPlugin(Plugin): status = PluginStatus.BETA def configure(self, project, request): return react_plugin_config(self, project, request) def has_project_conf(self): return True def get_rate_limit(self): # number of requests, number of seconds (window) return (50, 1) def forward_event(self, payload): """ Forward the event and return a boolean if it was successful. """ raise NotImplementedError def get_event_payload(self, event): return serialize(event) def get_plugin_type(self): return "data-forwarding" def post_process(self, event, **kwargs): rl_key = u"{}:{}".format(self.conf_key, event.project.organization_id) # limit segment to 50 requests/second limit, window = self.get_rate_limit() if limit and window and ratelimits.is_limited(rl_key, limit=limit, window=window): return payload = self.get_event_payload(event) success = self.forward_event(event, payload) if success is False: # TODO(dcramer): record failure pass tsdb.incr(tsdb.models.project_total_forwarded, event.project.id, count=1)

31.541667

90

0.688243

189

1,514

5.349206

0.433862

0.049456

0.050445

0.041543

0

0.005159

0.231836

1,514

47

91

32.212766

0.864144

0.114927

0

0.015221

0

0.021277

0

1

0.233333

false

0.033333

0.2

0.166667

0.7

0

null

0

null

0

1

0

1

0

1

0

4

80db2ddc24af6510b5417ef8b83124fa15c9333f

21

py

Python

solutions/00_04.py

glemaitre/IBIOM-M2-deep-learning

001bf7834e57a7357326087d31049fc91ab8967f

[ "MIT" ]

null

solutions/00_04.py

glemaitre/IBIOM-M2-deep-learning

001bf7834e57a7357326087d31049fc91ab8967f

[ "MIT" ]

null

solutions/00_04.py

glemaitre/IBIOM-M2-deep-learning

001bf7834e57a7357326087d31049fc91ab8967f

[ "MIT" ]

null

digits["data"].shape

10.5

20

0.714286

3

21

5

1

0

0.047619

21

1

21

0.75

0

0.190476

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

4

80e7a85efb2c01a96d4e101b019122a6120fe0ef

55

py

Python

tests/__init__.py

oijkn/pyproject

381c810f4ec2f295ab2799220a560619bf07d03b

[ "MIT" ]

6

2021-04-15T11:49:34.000Z

2022-01-09T16:47:25.000Z

tests/__init__.py

oijkn/pyproject

381c810f4ec2f295ab2799220a560619bf07d03b

[ "MIT" ]

1

2021-11-24T17:07:46.000Z

tests/__init__.py

oijkn/pyproject

381c810f4ec2f295ab2799220a560619bf07d03b

[ "MIT" ]

1

2021-11-24T17:09:23.000Z

"""Automated testing gives you a good nights sleep."""

27.5

54

0.727273

8

55

5

1

0

0.145455

55

1

55

0.851064

0.872727

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

4

037e083024be95b238bd46911192b241364d3418

126

py

Python

simpletextgenerator/training_status.py

thtroyer/simple-text-generator

2b84d5fc5efa6311331210cabfb74e4305fcf947

[ "MIT" ]

1

2018-08-04T02:01:15.000Z

simpletextgenerator/training_status.py

thtroyer/simple-text-generator

2b84d5fc5efa6311331210cabfb74e4305fcf947

[ "MIT" ]

21

2020-09-25T22:52:32.000Z

2021-07-07T01:40:27.000Z

simpletextgenerator/training_status.py

thtroyer/simple-text-generator

2b84d5fc5efa6311331210cabfb74e4305fcf947

[ "MIT" ]

1

2019-01-11T21:00:26.000Z

class TrainingStatus: NEW = "NEW" NEW_LOAD_MODEL = "NEW_LOAD_MODEL" STARTED = "STARTED" FINISHED = "FINISHED"

21

37

0.65873

14

126

5.642857

0.5

0.151899

0.303797

0

0.238095

126

5

38

25.2

0.822917

0

0.253968

0

1

0

false

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

4

039c7891ccf5605eb88947c555b3f748ba52b68b

72

py

Python

bot/extensions.py

HarkonenBade/discord-bot

3321186b5df3d3453166db6d3c01e59d9860b4ed

[ "MIT" ]

null

bot/extensions.py

HarkonenBade/discord-bot

3321186b5df3d3453166db6d3c01e59d9860b4ed

[ "MIT" ]

null

bot/extensions.py

HarkonenBade/discord-bot

3321186b5df3d3453166db6d3c01e59d9860b4ed

[ "MIT" ]

null

import discord discord.Member.__str__ = lambda self: self.display_name

18

55

0.819444

10

72

5.4

0.8

0

0.111111

72

3

56

24

0.84375

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

03b9226ae73738344bdc298309bfbc2fd7f0026a

968

py

Python

app_hotornot/migrations/0002_auto_20180910_1810.py

Audiotuete/backend_hotornot_api

0ef7025e0beed60420b2fcc048321e24cd2fb10a

[ "MIT" ]

null

app_hotornot/migrations/0002_auto_20180910_1810.py

Audiotuete/backend_hotornot_api

0ef7025e0beed60420b2fcc048321e24cd2fb10a

[ "MIT" ]

null

app_hotornot/migrations/0002_auto_20180910_1810.py

Audiotuete/backend_hotornot_api

0ef7025e0beed60420b2fcc048321e24cd2fb10a

[ "MIT" ]

null

# Generated by Django 2.0.8 on 2018-09-10 18:10 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('app_hotornot', '0001_initial'), ] operations = [ migrations.AlterModelOptions( name='question', options={'ordering': ('order',)}, ), migrations.AlterModelOptions( name='questionmultiple', options={'ordering': ('order',)}, ), migrations.AlterModelOptions( name='questionopen', options={'ordering': ('order',)}, ), migrations.AlterModelOptions( name='questionyesorno', options={'ordering': ('order',)}, ), migrations.AddField( model_name='question', name='order', field=models.PositiveIntegerField(db_index=True, default=0, editable=False), preserve_default=False, ), ]

26.888889

88

0.549587

76

968

6.934211

0.565789

0.204934

0.235294

0.227704

0.290323

0

0.030211

0.316116

968

35

89

27.657143

0.765861

0.046488

0

0.448276

1

0

0.152009

0

1

0

false

0

0.034483

0

0.137931

0

null

1

0

1

0

null

0

4

03f237798612410728d1392302fdbcf5340d700b

58

py

Python

hotsparser/__init__.py

HoTSStuff/HotSParser

b3cb39be68dad7864c28e14830af1987992da19e

[ "Apache-2.0" ]

null

hotsparser/__init__.py

HoTSStuff/HotSParser

b3cb39be68dad7864c28e14830af1987992da19e

[ "Apache-2.0" ]

null

hotsparser/__init__.py

HoTSStuff/HotSParser

b3cb39be68dad7864c28e14830af1987992da19e

[ "Apache-2.0" ]

null

""" A bottle app to parse Heroes of the Storm Replays """

14.5

49

0.689655

10

58

4

1

0

0.206897

58

3

50

19.333333

0.869565

0.844828

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

4

ff0b580c63ee3c2764fbb4f3b5b9de17f93ae82c

106

py

Python

meeting_rooms_ii/interval.py

mvgiacomello/leetcode-solutions

7b204131a6b381e8c0879bcda58c18ccb45a36f8

[ "Apache-2.0" ]

null

meeting_rooms_ii/interval.py

mvgiacomello/leetcode-solutions

7b204131a6b381e8c0879bcda58c18ccb45a36f8

[ "Apache-2.0" ]

null

meeting_rooms_ii/interval.py

mvgiacomello/leetcode-solutions

7b204131a6b381e8c0879bcda58c18ccb45a36f8

[ "Apache-2.0" ]

null

class Interval: def __init__(self, start=0, end=0): self.start = start self.end = end

21.2

39

0.584906

15

106

3.866667

0.533333

0.310345

0

0.027027

0.301887

106

4

40

26.5

0.756757

0

1

0.25

false

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

4

ff1892ac3b18a1fed71e89982330d87fe89022fb

1,456

py

Python

ejercicio1/ej1.py

mariaSerrabona/ejercicioPOO

989726294e5614e57a510511802dbbcfe94e966a

[ "Apache-2.0" ]

null

ejercicio1/ej1.py

mariaSerrabona/ejercicioPOO

989726294e5614e57a510511802dbbcfe94e966a

[ "Apache-2.0" ]

null

ejercicio1/ej1.py

mariaSerrabona/ejercicioPOO

989726294e5614e57a510511802dbbcfe94e966a

[ "Apache-2.0" ]

null

class Libro: #definimos todos los atributos que caracteizan a un libro isbn=0 #texto autor=0 titito=0 #texto año_de_publicacion=0 #texto idioma=0 #texto editor=0 ejemplares=0 #texto #ahora creamos el constructor def __init__(self, isbn,autor, titulo, año_de_publicacion, idioma,editor, ejemplares): self._isbn=isbn self._autor=autor self._titulo=titulo self._año_de_publicacion=año_de_publicacion self._idioma=idioma self._editor=editor self._ejemplares=ejemplares def getISBN(self): return self._isbn def setISBN(self, isbn): self._isbn=isbn def getAutor(self): return self._autor def setAutor(self, autor): self._autor=autor def getTitulo(self): return self._titulo def setTitulo(self, titulo): self._titulo=titulo def getAñoPublicacion(self): return self._año_de_publicacion def setAñoPublicacion(self, año_de_publicacion): self._año_de_publicacion=año_de_publicacion def getIdidoma(self): return self._idioma def setIdioma(self, idioma): self._idioma=idioma def getEditor(self): return self._editor def setEditor(self, editor): self._editor=editor def getEjemplares(self): return self._ejemplares def setEjemplares(self, ejemplares): self._ejemplares=ejemplares

24.266667

90

0.659341

172

1,456

5.343023

0.255814

0.043526

0.139282

0.087051

0.078346

0

0.006548

0.265797

1,456

59

91

24.677966

0.853134

0.074863

0

0.318182

0

0.016949

0

1

0.340909

false

0

0.159091

0.681818

0

null

0

null

0

1

0

1

0

1

0

4

ff18ee4e98b9d4de5b39e4cd8b584c99f340c38b

3,723

py

Python

autorequests/lib/url.py

Hexiro/autorequests

53923e6f089a34f5cc0babeed305c9b63f8f489b

[ "MIT" ]

29

2021-05-28T20:13:45.000Z

2022-03-24T22:26:07.000Z

autorequests/lib/url.py

Hexiro/autorequests

53923e6f089a34f5cc0babeed305c9b63f8f489b

[ "MIT" ]

5

2021-06-19T12:51:56.000Z

2021-10-17T01:43:18.000Z

autorequests/lib/url.py

Hexiro/autorequests

53923e6f089a34f5cc0babeed305c9b63f8f489b

[ "MIT" ]

3

2021-06-07T16:27:06.000Z

2021-07-20T20:49:38.000Z

import urllib.parse from typing import Dict, Optional, Tuple, Union, Any from ..utilities import parse_url_encoded class URL: def __init__(self, url: str): """ Uniform Resource Locator (URL) as per <scheme>://<net_loc>/<path>;<params>?<query>#<fragment> """ # https://www.rfc-editor.org/rfc/rfc1808.html#section-2.1 # urlsplit automatically appends `params` to the end of path parsed = urllib.parse.urlsplit(url) self._protocol: str = parsed.scheme self._path: str = parsed.path self._query: Dict[str, str] = parse_url_encoded(parsed.query) self._fragment: str = parsed.fragment # <user>:<password>@<host>:<port> # https://www.rfc-editor.org/rfc/rfc1738#section-3.1 self._network_location: str = parsed.netloc self._username: Optional[str] = None self._password: Optional[str] = None self._domain: str self._domain_name: str self._subdomain: Optional[str] = None # it would also make sense to default this to 80 # None means that there is none set explicitly in the url, however self._port: Optional[int] = None self._username, self._password, host = self._credentials(self._network_location) self._domain, self._port = self._domain_and_port(host) # subdomain, domain (this might break with domains like .co.uk) if self._domain.count(".") >= 2: self._subdomain, self._domain = self._domain.split(".", maxsplit=1) # domain name self._domain_name = self._domain.split(".")[0] def __repr__(self) -> str: return f"<URL {self.url}>" def __str__(self) -> str: return self.url def __eq__(self, other: Any) -> bool: if not isinstance(other, URL): return NotImplemented return self.url == other.url def __hash__(self) -> int: return hash(self.url) @staticmethod def _domain_and_port(host: str) -> Tuple[str, Optional[int]]: if ":" not in host: return host, None split = host.split(":", maxsplit=1) domain = split[0] port = int(split[1]) if split[1].isdigit() else None return domain, port @staticmethod def _credentials(network_location: str) -> Union[Tuple[Optional[str], Optional[str], str]]: if "@" not in network_location: return None, None, network_location credentials, host = network_location.split("@", maxsplit=1) username, password = credentials.split(":", maxsplit=1) return username, password, host @property def url(self) -> str: """url without query string params""" return f"{self.protocol}://{self.network_location}{self.path}{'#'+self.fragment if self.fragment else ''}" @property def protocol(self) -> str: return self._protocol @property def path(self) -> str: return self._path @property def query(self) -> Dict[str, str]: return self._query @property def fragment(self) -> str: return self._fragment @property def network_location(self) -> str: return self._network_location @property def username(self) -> Optional[str]: return self._username @property def password(self) -> Optional[str]: return self._password @property def domain(self) -> str: return self._domain @property def domain_name(self) -> str: return self._domain_name @property def subdomain(self) -> Optional[str]: return self._subdomain @property def port(self) -> Optional[int]: return self._port

30.268293

114

0.61617

452

3,723

4.900442

0.243363

0.058691

0.06456

0.053725

0.075395

0.020767

0

0.0084

0.264572

3,723

122

115

30.516393

0.800584

0.136449

0

0.168675

0

0.012048

0.037867

0.022089

0

1

0.228916

false

0.072289

0.036145

0.168675

0.53012

0

null

0

null

0

1

0

1

0

1

0

4

2066b92f0892bbd77a5f681cd411e3e398849986

146

py

Python

modder/utils/__init__.py

JokerQyou/ModderZ

a7ca9945a7010dac8f44c4c74659ce82e4fb20ff

[ "MIT" ]

1

2018-05-22T05:14:12.000Z

modder/utils/__init__.py

JokerQyou/ModderZ

a7ca9945a7010dac8f44c4c74659ce82e4fb20ff

[ "MIT" ]

1

2017-10-22T15:34:48.000Z

2017-10-22T15:36:14.000Z

modder/utils/__init__.py

JokerQyou/ModderZ

a7ca9945a7010dac8f44c4c74659ce82e4fb20ff

[ "MIT" ]

null

# coding: utf-8 from .log_utils import get_logger from .desktop_notification import desktop_notify __all__ = ['get_logger', 'desktop_notify', ]

20.857143

48

0.780822

20

146

5.2

0.65

0.173077

0

0.007813

0.123288

146

6

49

24.333333

0.804688

0.089041

0

0.183206

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

206af13aebc968fd12aa51512f1c5756217c335d

105

py

Python

tests/modules/module1.py

rhhayward/py_load_modules

e5ad3e5f10a1512f19cb4364e9dfbae452a6fee0

[ "MIT" ]

null

tests/modules/module1.py

rhhayward/py_load_modules

e5ad3e5f10a1512f19cb4364e9dfbae452a6fee0

[ "MIT" ]

null

tests/modules/module1.py

rhhayward/py_load_modules

e5ad3e5f10a1512f19cb4364e9dfbae452a6fee0

[ "MIT" ]

null

from test_loading import SuperClass class SubClassOne(SuperClass): def __init__(self): pass

17.5

35

0.733333

12

105

6

0.916667

0

0.209524

105

5

36

21

0.86747

0

1

0.25

false

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

2078f7148228a23275a8f3ba53ecebe4c8d03ed6

70

py

Python

man.py

MiltFra/HackTheBurgh2020

80b80c8ddf4d587555ccc40c07456e7b7270901b

[ "MIT" ]

null

man.py

MiltFra/HackTheBurgh2020

80b80c8ddf4d587555ccc40c07456e7b7270901b

[ "MIT" ]

null

man.py

MiltFra/HackTheBurgh2020

80b80c8ddf4d587555ccc40c07456e7b7270901b

[ "MIT" ]

null

import autotrader autotrader.send_order("SP-FUTURE", "BUY", 2933,197)

23.333333

51

0.771429

10

70

5.3

0.9

0

0.107692

0.071429

70

3

51

23.333333

0.707692

0

0.169014

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

2082eba95848e8bc35846d0d92262cf32f8e8b14

31,277

py

Python

tests/test_models.py

p768lwy3/torecsys

2251366268b4fbe6f8c3ab1628fa72a0db043dcd

[ "MIT" ]

92

2019-08-15T11:03:50.000Z

2022-03-12T01:21:05.000Z

tests/test_models.py

p768lwy3/torecsys

2251366268b4fbe6f8c3ab1628fa72a0db043dcd

[ "MIT" ]

3

2020-03-11T08:57:50.000Z

2021-01-06T01:39:47.000Z

tests/test_models.py

p768lwy3/torecsys

2251366268b4fbe6f8c3ab1628fa72a0db043dcd

[ "MIT" ]

16

2019-10-12T11:28:53.000Z

2022-03-28T14:04:12.000Z

import unittest from functools import partial import torch import torch.nn as nn from parameterized import parameterized from torchinfo import summary from torecsys.miners import UniformBatchMiner from torecsys.models import * from torecsys.utils.operations import inner_product_similarity device = 'cuda:0' if torch.cuda.is_available() else 'cpu' class AttentionalFactorizationMachineModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): model = AttentionalFactorizationMachineModel( embed_size=embed_size, num_fields=num_fields, attn_size=16, use_bias=True, dropout_p=0.9 ) model = model.to(device) # Generate inputs for the layer feat_inp = torch.rand(batch_size, num_fields, 1) feat_inp.names = ('B', 'N', 'E',) feat_inp_size = feat_inp.size() emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[feat_inp_size, emb_inp_size], device=device, dtypes=[torch.float, torch.float]) # Forward outputs = model.forward(feat_inp, emb_inp) self.assertEqual(outputs.size(), (batch_size, 1)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class DeepAndCrossNetworkModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): output_size = 1 model = DeepAndCrossNetworkModel( inputs_size=embed_size, num_fields=num_fields, deep_output_size=4, deep_layer_sizes=[32, 16, 8], cross_num_layers=4, output_size=output_size, deep_dropout_p=[0.9, 0.9, 0.9], deep_activation=nn.ReLU6() ) model = model.to(device) # Generate inputs for the layer emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[emb_inp_size], device=device, dtypes=[torch.float]) # Forward outputs = model.forward(emb_inp) self.assertEqual(outputs.size(), (batch_size, output_size)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class DeepFieldAwareFactorizationMachineModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): model = DeepFieldAwareFactorizationMachineModel( embed_size=embed_size, num_fields=num_fields, deep_output_size=4, deep_layer_sizes=[32, 16, 8], ffm_dropout_p=0.9, deep_dropout_p=[0.9, 0.9, 0.9], deep_activation=nn.ReLU6() ) model = model.to(device) # Generate inputs for the layer field_emb_inp = torch.rand(batch_size, num_fields ** 2, embed_size) field_emb_inp.names = ('B', 'N', 'E',) field_emb_inp_size = field_emb_inp.size() summary(model, input_size=[field_emb_inp_size], device=device, dtypes=[torch.float]) # Forward outputs = model.forward(field_emb_inp) self.assertEqual(outputs.size(), (batch_size, 1)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class DeepFactorizationMachineModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): model = DeepFactorizationMachineModel( embed_size=embed_size, num_fields=num_fields, deep_layer_sizes=[16, 16, 16], fm_dropout_p=0.9, deep_dropout_p=[0.9, 0.9, 0.9], deep_activation=nn.ReLU() ) model = model.to(device) # Generate inputs for the layer feat_inp = torch.rand(batch_size, num_fields, 1) feat_inp.names = ('B', 'N', 'E',) feat_inp_size = feat_inp.size() emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[feat_inp_size, emb_inp_size], device=device, dtypes=[torch.float, torch.float]) # Forward outputs = model.forward(feat_inp, emb_inp) self.assertEqual(outputs.size(), (batch_size, 1)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class DeepMatchingCorrelationPredictionModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 16, 128), (16, 6, 32, 64), (32, 12, 64, 8) ]) def test_forward(self, batch_size: int, user_num_fields: int, item_num_fields: int, embed_size: int): model = DeepMatchingCorrelationPredictionModel( embed_size=embed_size, user_num_fields=user_num_fields, item_num_fields=item_num_fields, corr_output_size=8, match_output_size=8, corr_layer_sizes=[16, 16, 16], match_layer_sizes=[16, 16, 16], pred_layer_sizes=[16, 16, 16], corr_dropout_p=[0.9, 0.9, 0.9], match_dropout_p=[0.9, 0.9, 0.9], pred_dropout_p=[0.9, 0.9, 0.9], corr_activation=nn.ReLU(), match_activation=nn.ReLU(), pred_activation=nn.ReLU() ) model = model.to(device) # Generate inputs for the layer user_emb_inp = torch.rand(batch_size, user_num_fields, embed_size) user_emb_inp.names = ('B', 'N', 'E',) user_emb_inp_size = user_emb_inp.size() content_emb_inp = torch.rand(batch_size, item_num_fields, embed_size) content_emb_inp.names = ('B', 'N', 'E',) content_emb_inp_size = content_emb_inp.size() pos_emb_inp = torch.rand(batch_size, item_num_fields, embed_size) pos_emb_inp.names = ('B', 'N', 'E',) pos_emb_inp_size = pos_emb_inp.size() # num_neg_samples = 16 neg_emb_inp = torch.rand(batch_size, item_num_fields, embed_size) neg_emb_inp.names = ('B', 'N', 'E',) neg_emb_inp_size = neg_emb_inp.size() summary(model, input_size=[user_emb_inp_size, content_emb_inp_size, pos_emb_inp_size, neg_emb_inp_size], device=device, dtypes=[torch.float, torch.float, torch.float, torch.float]) # Forward y_pred, y_match, y_corr_pos, y_corr_neg = model.forward(user_emb_inp, content_emb_inp, pos_emb_inp, neg_emb_inp) self.assertEqual(y_pred.size(), (batch_size, 1)) self.assertEqual(y_match.size(), (batch_size, 1)) self.assertEqual(y_corr_pos.size(), (batch_size, 1)) self.assertEqual(y_corr_neg.size(), (batch_size, 1)) print(f'y_pred Size: {y_pred.size()},\n' f'y_match Size: {y_match.size()},\n' f'y_corr_pos Size: {y_corr_pos.size()},\n' f'y_corr_neg Size: {y_corr_neg.size()},\n') class DeepMixtureOfExpertsModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): model = DeepMixtureOfExpertsModel( embed_size=embed_size, num_fields=num_fields, num_experts=4, moe_layer_sizes=[16, 16, 16], deep_layer_sizes=[16, 16, 16], deep_dropout_p=[0.9, 0.9, 0.9], deep_activation=nn.ReLU() ) model = model.to(device) # Generate inputs for the layer emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[emb_inp_size], device=device, dtypes=[torch.float]) # Forward outputs = model.forward(emb_inp) self.assertEqual(outputs.size(), (batch_size, 1)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class ElaboratedEntireSpaceSupervisedMultiTaskModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): model = ElaboratedEntireSpaceSupervisedMultiTaskModel( num_fields=num_fields, layer_sizes=[16, 16, 16], dropout_p=[0.9, 0.9, 0.9], activation=nn.ReLU() ) model = model.to(device) # Generate inputs for the layer emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[emb_inp_size], device=device, dtypes=[torch.float]) # Forward prob_impress_to_click, prob_impress_to_d_action, prob_impress_to_buy = model.forward(emb_inp) self.assertEqual(prob_impress_to_click.size(), (batch_size, 1)) self.assertEqual(prob_impress_to_d_action.size(), (batch_size, 1)) self.assertEqual(prob_impress_to_buy.size(), (batch_size, 1)) print(f'Prob impress to click Size: {prob_impress_to_click.size()},\n' f'Prob impress to d action Size: {prob_impress_to_d_action.size()},\n' f'Prob impress to buy Size: {prob_impress_to_buy.size()},\n') class EntireSpaceMultiTaskModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): model = EntireSpaceMultiTaskModel( num_fields=num_fields, layer_sizes=[16, 16, 16], dropout_p=[0.9, 0.9, 0.9], activation=nn.ReLU() ) model = model.to(device) # Generate inputs for the layer emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[emb_inp_size], device=device, dtypes=[torch.float]) # Forward pcvr, pctr = model.forward(emb_inp) self.assertEqual(pcvr.size(), (batch_size, 1)) self.assertEqual(pctr.size(), (batch_size, 1)) print(f'pcvr Size: {pcvr.size()}\n' f'pctr Size: {pctr.names}') class FactorizationMachineModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): model = FactorizationMachineModel(dropout_p=0.9) model = model.to(device) # Generate inputs for the layer feat_inp = torch.rand(batch_size, num_fields, 1) feat_inp.names = ('B', 'N', 'E',) feat_inp_size = feat_inp.size() emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[feat_inp_size, emb_inp_size], device=device, dtypes=[torch.float, torch.float]) # Forward outputs = model.forward(feat_inp, emb_inp) self.assertEqual(outputs.size(), (batch_size, 1)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class FactorizationMachineSupportedNeuralNetworkModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): output_size = 4 model = FactorizationMachineSupportedNeuralNetworkModel( embed_size=embed_size, num_fields=num_fields, deep_output_size=output_size, deep_layer_sizes=[32, 32, 16], fm_dropout_p=0.9, deep_dropout_p=[0.9, 0.9, 0.9], deep_activation=nn.ReLU() ) model = model.to(device) # Generate inputs for the layer feat_inp = torch.rand(batch_size, num_fields, 1) feat_inp.names = ('B', 'N', 'E',) feat_inp_size = feat_inp.size() emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[feat_inp_size, emb_inp_size], device=device, dtypes=[torch.float, torch.float]) # Forward outputs = model.forward(feat_inp, emb_inp) self.assertEqual(outputs.size(), (batch_size, output_size)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class FieldAttentiveDeepFieldAwareFactorizationMachineModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): output_size = 16 model = FieldAttentiveDeepFieldAwareFactorizationMachineModel( embed_size=embed_size, num_fields=num_fields, deep_output_size=output_size, deep_layer_sizes=[32, 32, 16], reduction=2, ffm_dropout_p=0.9, deep_dropout_p=[0.9, 0.9, 0.9], deep_activation=nn.ReLU() ) model = model.to(device) # Generate inputs for the layer field_emb_inp = torch.rand(batch_size, num_fields ** 2, embed_size) field_emb_inp.names = ('B', 'N', 'E',) field_emb_inp_size = field_emb_inp.size() summary(model, input_size=[field_emb_inp_size], device=device, dtypes=[torch.float]) # Forward outputs = model.forward(field_emb_inp) self.assertEqual(outputs.size(), (batch_size, output_size)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class FieldAwareFactorizationMachineModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): model = FieldAwareFactorizationMachineModel( num_fields=num_fields, dropout_p=0.9 ) model = model.to(device) # Generate inputs for the layer feat_inp = torch.rand(batch_size, num_fields, 1) feat_inp.names = ('B', 'N', 'E',) feat_inp_size = feat_inp.size() field_emb_inp = torch.rand(batch_size, num_fields ** 2, embed_size) field_emb_inp.names = ('B', 'N', 'E',) field_emb_inp_size = field_emb_inp.size() summary(model, input_size=[feat_inp_size, field_emb_inp_size], device=device, dtypes=[torch.float, torch.float]) # Forward outputs = model.forward(feat_inp, field_emb_inp) self.assertEqual(outputs.size(), (batch_size, 1)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class FeatureImportanceAndBilinearFeatureInteractionNetworkTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): output_size = 1 model = FeatureImportanceAndBilinearFeatureInteractionNetwork( embed_size=embed_size, num_fields=num_fields, senet_reduction=4, deep_output_size=output_size, deep_layer_sizes=[16, 16, 16], bilinear_type='all', bilinear_bias=True, deep_dropout_p=[0.9, 0.9, 0.9], deep_activation=nn.ReLU6() ) model = model.to(device) # Generate inputs for the layer emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[emb_inp_size], device=device, dtypes=[torch.float]) # Forward outputs = model.forward(emb_inp) self.assertEqual(outputs.size(), (batch_size, output_size)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class LearningToRankWrapperTestCase(unittest.TestCase): @parameterized.expand([ (8, 128), (16, 64), (32, 8) ]) def test_forward(self, batch_size: int, embed_size: int): sample_size = 10 miner = UniformBatchMiner(sample_size=sample_size) model = MatrixFactorizationModel() wrapped = LearningToRankWrapper(model=model) wrapped = wrapped.to(device) # Generate inputs for the layer emb_inp = torch.rand(batch_size, 2, embed_size) # mine negative samples with miner # outputs: p, shape = (B, N, E) # outputs: n, shape = (B * N Neg, N, E) p, n = miner(emb_inp[:, 0], emb_inp[:, 1]) p.names = ('B', 'N', 'E',) n.names = ('B', 'N', 'E',) # p_size = p.size() # n_size = n.size() # summary(wrapped, input_size=[p_size, n_size], device=device, dtypes=[torch.float, torch.float]) # Forward outputs = wrapped.forward(pos_inputs={'emb_inputs': p}, neg_inputs={'emb_inputs': n}) self.assertEqual(outputs['pos_outputs'].size(), (batch_size, 1)) self.assertEqual(outputs['neg_outputs'].size(), (batch_size * sample_size, 1)) print(f'Pos Output Size: {outputs["pos_outputs"].size()},\n' f'Pos Output Dimensions: {outputs["pos_outputs"].names},\n' f'Neg Output Size: {outputs["neg_outputs"].size()},\n' f'NegOutput Dimensions: {outputs["neg_outputs"].names}') class LogisticRegressionModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): output_size = 1 model = LogisticRegressionModel( inputs_size=num_fields * embed_size, output_size=output_size ) model = model.to(device) # Generate inputs for the layer emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[emb_inp_size], device=device, dtypes=[torch.float]) # Forward outputs = model.forward(emb_inp) self.assertEqual(outputs.size(), (batch_size, output_size)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class MatrixFactorizationModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 128), (16, 64), (32, 8) ]) def test_forward(self, batch_size: int, embed_size: int): model = MatrixFactorizationModel() model = model.to(device) # Generate inputs for the layer emb_inp = torch.rand(batch_size, 2, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[emb_inp_size], device=device, dtypes=[torch.float]) # Forward outputs = model.forward(emb_inp) self.assertEqual(outputs.size(), (batch_size, 1)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class MultiGateMixtureOfExpertsModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): model = MultiGateMixtureOfExpertsModel( embed_size=embed_size, num_fields=num_fields, num_tasks=4, num_experts=4, expert_output_size=1, expert_layer_sizes=[16, 16, 16], deep_layer_sizes=[16, 16, 16], expert_dropout_p=[0.9, 0.9, 0.9], deep_dropout_p=[0.9, 0.9, 0.9], expert_activation=nn.ReLU6(), deep_activation=nn.ReLU6() ) model = model.to(device) # Generate inputs for the layer emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[emb_inp_size], device=device, dtypes=[torch.float]) # Forward outputs = model.forward(emb_inp) self.assertEqual(outputs.size(), (batch_size, 1)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class NeuralCollaborativeFilteringModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 128), (16, 64), (32, 8) ]) def test_forward(self, batch_size: int, embed_size: int): model = NeuralCollaborativeFilteringModel( embed_size=embed_size, deep_output_size=8, deep_layer_sizes=[16, 16, 16], deep_dropout_p=[0.9, 0.9, 0.9], deep_activation=nn.ReLU6() ) model = model.to(device) # Generate inputs for the layer emb_inp = torch.rand(batch_size, 2, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[emb_inp_size], device=device, dtypes=[torch.float]) # Forward outputs = model.forward(emb_inp) self.assertEqual(outputs.size(), (batch_size, 1)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class NeuralFactorizationMachineModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): model = NeuralFactorizationMachineModel( embed_size=embed_size, deep_layer_sizes=[16, 16, 16], use_bias=True, fm_dropout_p=0.9, deep_dropout_p=[0.9, 0.9, 0.9], deep_activation=nn.ReLU() ) model = model.to(device) # Generate inputs for the layer feat_inp = torch.rand(batch_size, num_fields, 1) feat_inp.names = ('B', 'N', 'E',) feat_inp_size = feat_inp.size() emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[feat_inp_size, emb_inp_size], device=device, dtypes=[torch.float, torch.float]) # Forward outputs = model.forward(feat_inp, emb_inp) self.assertEqual(outputs.size(), (batch_size, 1)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class PersonalizedReRankingModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, length: int, embed_size: int): model = PersonalizedReRankingModel( embed_size=embed_size, max_num_position=length, encoding_size=16, num_heads=4, num_layers=2, use_bias=True, dropout=0.9, fnn_dropout_p=0.9, fnn_activation=nn.ReLU() ) model = model.to(device) # Generate inputs for the layer feat_inp = torch.randint(0, length, (batch_size, length, embed_size)) feat_inp.names = ('B', 'L', 'E',) feat_inp_size = feat_inp.size() summary(model, input_size=[feat_inp_size], device=device, dtypes=[torch.int]) # Forward outputs = model.forward(feat_inp) self.assertEqual(outputs.size(), (batch_size, length)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class PositionBiasAwareLearningFrameworkModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): max_num_position = 32 pctr_model = NeuralFactorizationMachineModel( embed_size=embed_size, deep_layer_sizes=[16, 16, 16], use_bias=True, fm_dropout_p=0.9, deep_dropout_p=[0.9, 0.9, 0.9], deep_activation=nn.ReLU() ) model = PositionBiasAwareLearningFrameworkModel( pctr_model=pctr_model, output_size=1, max_num_position=max_num_position, layer_sizes=[16, 16, 16], dropout_p=[0.9, 0.9, 0.9], activation=nn.ReLU() ) model = model.to(device) # Generate inputs for the layer feat_inp = torch.rand(batch_size, num_fields, 1) feat_inp.names = ('B', 'N', 'E',) # feat_inp_size = feat_inp.size() emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) # emb_inp_size = emb_inp.size() pos_inp = torch.randint(0, max_num_position, (batch_size,)) pos_inp.names = ('B',) # summary(model, input_size=[feat_inp_size, emb_inp_size], device=device, dtypes=[torch.float, torch.float]) # Forward outputs = model.forward({'feat_inputs': feat_inp, 'emb_inputs': emb_inp}, pos_inp) self.assertEqual(outputs.size(), (batch_size, 1)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class ProductNeuralNetworkModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): output_size = 1 model = ProductNeuralNetworkModel( embed_size=embed_size, num_fields=num_fields, deep_layer_sizes=[64, 32, 16], output_size=output_size, prod_method='outer', use_bias=True, deep_dropout_p=[0.9, 0.9, 0.9], deep_activation=nn.ReLU6(), kernel_type='mat' ) model = model.to(device) # Generate inputs for the layer feat_inp = torch.rand(batch_size, num_fields, 1) feat_inp.names = ('B', 'N', 'E',) feat_inp_size = feat_inp.size() emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[feat_inp_size, emb_inp_size], device=device, dtypes=[torch.float, torch.float]) # Forward outputs = model.forward(feat_inp, emb_inp) self.assertEqual(outputs.size(), (batch_size, output_size)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class StarSpaceModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 128), (16, 64), (32, 8) ]) def test_forward(self, batch_size: int, embed_size: int): num_neg = 16 model = StarSpaceModel( embed_size=embed_size, num_neg=num_neg, similarity=partial(inner_product_similarity, dim=2) ) model = model.to(device) # Generate inputs for the layer samples_size = batch_size * (1 + num_neg) context_inp = torch.rand(samples_size, 1, embed_size) context_inp.names = ('B', 'N', 'E',) context_inp_size = context_inp.size() target_inp = torch.rand(samples_size, 1, embed_size) target_inp.names = ('B', 'N', 'E',) target_inp_size = target_inp.size() summary(model, input_size=[context_inp_size, target_inp_size], device=device, dtypes=[torch.float, torch.float]) # Forward outputs = model.forward(context_inp, target_inp) self.assertEqual(outputs.size(), (samples_size, 1)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class WideAndDeepModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): output_size = 1 model = WideAndDeepModel( embed_size=embed_size, num_fields=num_fields, deep_layer_sizes=[64, 32, 16], out_dropout_p=None, wide_dropout_p=None, deep_dropout_p=[0.9, 0.9, 0.9], deep_activation=nn.ReLU() ) model = model.to(device) # Generate inputs for the layer feat_inp = torch.rand(batch_size, num_fields, 1) feat_inp.names = ('B', 'N', 'E',) feat_inp_size = feat_inp.size() emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[feat_inp_size, emb_inp_size], device=device, dtypes=[torch.float, torch.float]) # Forward outputs = model.forward(feat_inp, emb_inp) self.assertEqual(outputs.size(), (batch_size, output_size)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') class XDeepFactorizationMachineModelTestCase(unittest.TestCase): @parameterized.expand([ (8, 4, 128), (16, 6, 64), (32, 12, 8) ]) def test_forward(self, batch_size: int, num_fields: int, embed_size: int): output_size = 1 model = XDeepFactorizationMachineModel( embed_size=embed_size, num_fields=num_fields, cin_layer_sizes=[64, 32, 16], deep_layer_sizes=[64, 32, 16], cin_is_direct=False, cin_use_bias=True, cin_use_batchnorm=True, cin_activation=nn.ReLU6(), deep_dropout_p=[0.9, 0.9, 0.9], deep_activation=nn.ReLU() ) model = model.to(device) # Generate inputs for the layer feat_inp = torch.rand(batch_size, num_fields, 1) feat_inp.names = ('B', 'N', 'E',) feat_inp_size = feat_inp.size() emb_inp = torch.rand(batch_size, num_fields, embed_size) emb_inp.names = ('B', 'N', 'E',) emb_inp_size = emb_inp.size() summary(model, input_size=[feat_inp_size, emb_inp_size], device=device, dtypes=[torch.float, torch.float]) # Forward outputs = model.forward(feat_inp, emb_inp) self.assertEqual(outputs.size(), (batch_size, output_size)) print(f'Output Size: {outputs.size()}, Output Dimensions: {outputs.names}') if __name__ == '__main__': unittest.main()

35.868119

120

0.606548

3,950

31,277

4.552152

0.051392

0.051054

0.041711

0.009343

0.777209

0.752961

0.722596

0.712085

0.690618

0.676381

0

0.032283

0.266138

31,277

871

121

35.9093

0.7511

0.043866

0

0.678733

0

0.072477

0.009215

0

0.048265

1

0.037707

false

0

0.016591

0

0.092006

0.037707

0

null

0

1

0

1

0

null

0

4

209b4832e26e988eb960b624c76045725c96386e

19

py

Python

ox_ui/assets/css/__init__.py

emin63/ox_ui

207c9acebb1f2ed90cb0142355601800bab13d4c

[ "BSD-2-Clause" ]

2

2019-10-08T04:06:37.000Z

2021-12-20T18:35:31.000Z

ox_ui/assets/css/__init__.py

emin63/ox_ui

207c9acebb1f2ed90cb0142355601800bab13d4c

[ "BSD-2-Clause" ]

4

2022-03-15T19:08:37.000Z

2022-03-31T17:39:43.000Z

ox_ui/assets/css/__init__.py

emin63/ox_ui

207c9acebb1f2ed90cb0142355601800bab13d4c

[ "BSD-2-Clause" ]

null

"""CSS assets. """

6.333333

14

0.473684

2

19

4.5

1

0

0.157895

19

2

15

9.5

0.5625

0.578947

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

20aece9f220b001ef699f0cf1c72a7216e8e517e

12,261

py

Python

api/python/tests/test_ml_featurizers.py

Nitin0309/Nitin-indigo-bugs

71f16ec2930fde8d46a6e6a0481b94e291c80f79

[ "Apache-2.0" ]

null

api/python/tests/test_ml_featurizers.py

Nitin0309/Nitin-indigo-bugs

71f16ec2930fde8d46a6e6a0481b94e291c80f79

[ "Apache-2.0" ]

null

api/python/tests/test_ml_featurizers.py

Nitin0309/Nitin-indigo-bugs

71f16ec2930fde8d46a6e6a0481b94e291c80f79

[ "Apache-2.0" ]

null

import torch # type: ignore from indigo.ml.mpp.featurizers import ( # type: ignore acid_pka_values, aromatic_bonds, atom_in_ring, atomic_charges, atomic_degrees, atomic_isotopes, atomic_masses, atomic_number, atomic_radicals, atomic_valences, basic_pka_values, bond_order, bond_stereo, implicit_hydrogens, stereocenter_types, topologies, ) from tests import TestIndigoBase class TestIndigoFeaturizers(TestIndigoBase): def assertTensorEqual(self, input: dict, expected: dict): for input_key, expected_key in zip(input, expected): assert input_key == expected_key assert torch.equal(input[input_key], expected[expected_key]) def test_node_featurizers(self): m1 = self.indigo.loadMolecule("c1ccccc1") m2 = self.indigo.loadMolecule("[2H][H]") m3 = self.indigo.loadMolecule("C[CH2]") m4 = self.indigo.loadMolecule("C1CC[C@H]([C@H](C1)Cl)Br") self.assertTensorEqual( atomic_number(m1), {"atomic": torch.tensor([6, 6, 6, 6, 6, 6]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_number(m2), {"atomic": torch.tensor([1, 1]).unsqueeze(1)} ) self.assertTensorEqual( atomic_number(m3), {"atomic": torch.tensor([6, 6]).unsqueeze(1)} ) self.assertTensorEqual( atomic_number(m4), {"atomic": torch.tensor([6, 6, 6, 6, 6, 6, 17, 35]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_degrees(m1), {"degrees": torch.tensor([2, 2, 2, 2, 2, 2]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_degrees(m2), {"degrees": torch.tensor([1, 1]).unsqueeze(1)} ) self.assertTensorEqual( atomic_degrees(m3), {"degrees": torch.tensor([1, 1]).unsqueeze(1)} ) self.assertTensorEqual( atomic_degrees(m4), {"degrees": torch.tensor([2, 2, 2, 3, 3, 2, 1, 1]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_isotopes(m1), {"isotopes": torch.tensor([0, 0, 0, 0, 0, 0]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_isotopes(m2), {"isotopes": torch.tensor([2, 0]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_isotopes(m3), {"isotopes": torch.tensor([0, 0]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_isotopes(m4), {"isotopes": torch.tensor([0, 0, 0, 0, 0, 0, 0, 0]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_charges(m1), {"charges": torch.tensor([0, 0, 0, 0, 0, 0]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_charges(m2), {"charges": torch.tensor([0, 0]).unsqueeze(1)} ) self.assertTensorEqual( atomic_charges(m3), {"charges": torch.tensor([0, 0]).unsqueeze(1)} ) self.assertTensorEqual( atomic_charges(m4), {"charges": torch.tensor([0, 0, 0, 0, 0, 0, 0, 0]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_valences(m1), {"valences": torch.tensor([4, 4, 4, 4, 4, 4]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_valences(m2), {"valences": torch.tensor([1, 1]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_valences(m3), {"valences": torch.tensor([4, 4]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_valences(m4), {"valences": torch.tensor([4, 4, 4, 4, 4, 4, 1, 1]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_radicals(m1), {"radicals": torch.tensor([0, 0, 0, 0, 0, 0]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_radicals(m2), {"radicals": torch.tensor([0, 0]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_radicals(m3), {"radicals": torch.tensor([0, 1]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_radicals(m4), {"radicals": torch.tensor([0, 0, 0, 0, 0, 0, 0, 0]).unsqueeze(1)}, ) self.assertTensorEqual( atom_in_ring(m1), { "in_aromatic_ring": torch.tensor([1, 1, 1, 1, 1, 1]).unsqueeze( 1 ) }, ) self.assertTensorEqual( atom_in_ring(m2), {"in_aromatic_ring": torch.tensor([0, 0]).unsqueeze(1)}, ) self.assertTensorEqual( atom_in_ring(m3), {"in_aromatic_ring": torch.tensor([0, 0]).unsqueeze(1)}, ) self.assertTensorEqual( atom_in_ring(m4), { "in_aromatic_ring": torch.tensor( [0, 0, 0, 0, 0, 0, 0, 0] ).unsqueeze(1) }, ) self.assertTensorEqual( stereocenter_types(m1), { "stereocenter_types": torch.tensor( [0, 0, 0, 0, 0, 0] ).unsqueeze(1) }, ) self.assertTensorEqual( stereocenter_types(m2), {"stereocenter_types": torch.tensor([0, 0]).unsqueeze(1)}, ) self.assertTensorEqual( stereocenter_types(m3), {"stereocenter_types": torch.tensor([0, 0]).unsqueeze(1)}, ) self.assertTensorEqual( stereocenter_types(m4), { "stereocenter_types": torch.tensor( [0, 0, 0, 1, 1, 0, 0, 0] ).unsqueeze(1) }, ) self.assertTensorEqual( implicit_hydrogens(m1), { "implicit_hydrogens": torch.tensor( [1, 1, 1, 1, 1, 1] ).unsqueeze(1) }, ) self.assertTensorEqual( implicit_hydrogens(m2), {"implicit_hydrogens": torch.tensor([0, 0]).unsqueeze(1)}, ) self.assertTensorEqual( implicit_hydrogens(m3), {"implicit_hydrogens": torch.tensor([3, 2]).unsqueeze(1)}, ) self.assertTensorEqual( implicit_hydrogens(m4), { "implicit_hydrogens": torch.tensor( [2, 2, 2, 1, 1, 2, 0, 0] ).unsqueeze(1) }, ) self.assertTensorEqual( acid_pka_values(m1), { "acid_pka_values": torch.tensor( [100.0, 100.0, 100.0, 100.0, 100.0, 100.0] ).unsqueeze(1) }, ) self.assertTensorEqual( acid_pka_values(m2), {"acid_pka_values": torch.tensor([100.0, 100.0]).unsqueeze(1)}, ) self.assertTensorEqual( acid_pka_values(m3), {"acid_pka_values": torch.tensor([100.0, 100.0]).unsqueeze(1)}, ) self.assertTensorEqual( acid_pka_values(m4), { "acid_pka_values": torch.tensor( [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0] ).unsqueeze(1) }, ) self.assertTensorEqual( basic_pka_values(m1), { "basic_pka_values": torch.tensor( [-100.0, -100.0, -100.0, -100.0, -100.0, -100.0] ).unsqueeze(1) }, ) self.assertTensorEqual( basic_pka_values(m2), {"basic_pka_values": torch.tensor([-100.0, -100.0]).unsqueeze(1)}, ) self.assertTensorEqual( basic_pka_values(m3), {"basic_pka_values": torch.tensor([-100.0, -100.0]).unsqueeze(1)}, ) self.assertTensorEqual( basic_pka_values(m4), { "basic_pka_values": torch.tensor( [ -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, ] ).unsqueeze(1) }, ) self.assertTensorEqual( atomic_masses(m1), { "atomic_masses": torch.tensor( [12.0107, 12.0107, 12.0107, 12.0107, 12.0107, 12.0107] ).unsqueeze(1) }, ) self.assertTensorEqual( atomic_masses(m2), {"atomic_masses": torch.tensor([2.0141, 1.0079]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_masses(m3), {"atomic_masses": torch.tensor([12.0107, 12.0107]).unsqueeze(1)}, ) self.assertTensorEqual( atomic_masses(m4), { "atomic_masses": torch.tensor( [ 12.0107, 12.0107, 12.0107, 12.0107, 12.0107, 12.0107, 35.4530, 79.9040, ] ).unsqueeze(1) }, ) def test_edge_featurizers(self): m1 = self.indigo.loadMolecule("c1ccccc1") m2 = self.indigo.loadMolecule("[2H][H]") m3 = self.indigo.loadMolecule("C[CH2]") m4 = self.indigo.loadMolecule("C1CC[C@H]([C@H](C1)Cl)Br") self.assertTensorEqual( bond_order(m1), { "orders": torch.tensor( [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4] ).unsqueeze(1) }, ) self.assertTensorEqual( bond_order(m2), {"orders": torch.tensor([1, 1]).unsqueeze(1)} ) self.assertTensorEqual( bond_order(m3), {"orders": torch.tensor([1, 1]).unsqueeze(1)} ) self.assertTensorEqual( bond_order(m4), { "orders": torch.tensor( [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] ).unsqueeze(1) }, ) self.assertTensorEqual( topologies(m1), { "topologies": torch.tensor( [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10] ).unsqueeze(1) }, ) self.assertTensorEqual( topologies(m2), {"topologies": torch.tensor([9, 9]).unsqueeze(1)} ) self.assertTensorEqual( topologies(m3), {"topologies": torch.tensor([9, 9]).unsqueeze(1)} ) self.assertTensorEqual( topologies(m4), { "topologies": torch.tensor( [10, 10, 10, 10, 10, 10, 9, 9, 10, 10, 10, 10, 10, 10, 9, 9] ).unsqueeze(1) }, ) self.assertTensorEqual( aromatic_bonds(m1), { "is_aromatic": torch.tensor( [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] ).unsqueeze(1) }, ) self.assertTensorEqual( aromatic_bonds(m2), {"is_aromatic": torch.tensor([0, 0]).unsqueeze(1)} ) self.assertTensorEqual( aromatic_bonds(m3), {"is_aromatic": torch.tensor([0, 0]).unsqueeze(1)} ) self.assertTensorEqual( aromatic_bonds(m4), { "is_aromatic": torch.tensor( [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ).unsqueeze(1) }, ) self.assertTensorEqual( bond_stereo(m1), { "bond_stereo": torch.tensor( [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ).unsqueeze(1) }, ) self.assertTensorEqual( bond_stereo(m2), {"bond_stereo": torch.tensor([0, 0]).unsqueeze(1)} ) self.assertTensorEqual( bond_stereo(m3), {"bond_stereo": torch.tensor([0, 0]).unsqueeze(1)} ) self.assertTensorEqual( bond_stereo(m4), { "bond_stereo": torch.tensor( [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ).unsqueeze(1) }, )

30.806533

79

0.473942

1,241

12,261

4.566479

0.070911

0.037762

0.04235

0.047291

0.844186

0.819305

0.756308

0.712017

0.643727

0.573143

0

0.094257

0.382187

12,261

397

80

30.884131

0.653861

0.002039

0

0.354667

0

0.06556

0.003924

0

0.178667

1

0.008

false

0

0.008

0

0.018667

0

null

0

1

0

null

0

4

20d19b350695aa68323ad69977b7e4d8c17c64f6

97

py

Python

labellines/__init__.py

Bobo529019686/matplotlib-label-lines

9cf37e99a8581f0477f6495433f09d284cc452e0

[ "MIT" ]

null

labellines/__init__.py

Bobo529019686/matplotlib-label-lines

9cf37e99a8581f0477f6495433f09d284cc452e0

[ "MIT" ]

null

labellines/__init__.py

Bobo529019686/matplotlib-label-lines

9cf37e99a8581f0477f6495433f09d284cc452e0

[ "MIT" ]

null

from .core import labelLine, labelLines __all__ = [labelLine, labelLines] __version__ = "0.5.1"

19.4

39

0.752577

12

97

5.416667

0.833333

0.584615

0

0.035714

0.134021

97

4

40

24.25

0.738095

0

0.051546

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

4

20d2c58e63ca574b2e12f2c195f937788b2a893f

255

py

Python

Interface/admin.py

kemilio/eScavenge

8c2330f6f3f8fa50384a66ceed7c821fd6e19e08

[ "MIT" ]

null

Interface/admin.py

kemilio/eScavenge

8c2330f6f3f8fa50384a66ceed7c821fd6e19e08

[ "MIT" ]

null

Interface/admin.py

kemilio/eScavenge

8c2330f6f3f8fa50384a66ceed7c821fd6e19e08

[ "MIT" ]

null

from django.contrib import admin from .models import HuntUser, HuntCommand, Landmark, Penalty, Game admin.site.register(HuntUser) admin.site.register(HuntCommand) admin.site.register(Landmark) admin.site.register(Penalty) admin.site.register(Game)

31.875

67

0.803922

33

255

6.212121

0.393939

0.219512

0.414634

0

0.094118

255

8

68

31.875

0.887446

0

1

0

true

0

0.285714

0

0.285714

0

null

1

0

1

0

null

0

1

0

4

20ea2459f45b615725d2cc572285e074dbe1130a

162

py

Python

tasks/urls.py

QizaiMing/ergo-project-manager

2b02b2ab6d9e48bfccbbca8c05180b07177dcb77

[ "MIT" ]

null

tasks/urls.py

QizaiMing/ergo-project-manager

2b02b2ab6d9e48bfccbbca8c05180b07177dcb77

[ "MIT" ]

3

2020-11-01T22:08:38.000Z

2022-03-12T00:49:00.000Z

tasks/urls.py

QizaiMing/ergo-project-manager

2b02b2ab6d9e48bfccbbca8c05180b07177dcb77

[ "MIT" ]

2

2021-01-03T07:17:16.000Z

2021-05-29T17:27:11.000Z

from django.urls import path from .views import ( tasks_list_view) app_name = 'tasks' urlpatterns = [ path('', tasks_list_view, name='tasks_list_view') ]

20.25

53

0.716049

23

162

4.73913

0.521739

0.247706

0.357798

0

0.166667

162

8

54

20.25

0.807407

0

0.122699

0

1

0

false

0

0.285714

0

0.285714

0

1

0

null

1

0

1

0

null

0

4

454949320a83f3a4604a7a6e6c6f6fb72dc42bef

112

py

Python

2019/glen.py

nyanthanya/Contoh-Program

924d79c34a92e77374228f1605a1d37b0fe37c70

[ "Unlicense" ]

105

2019-12-09T07:27:43.000Z

2022-01-28T16:34:37.000Z

2019/glen.py

nyanthanya/Contoh-Program

924d79c34a92e77374228f1605a1d37b0fe37c70

[ "Unlicense" ]

1

2021-12-11T21:25:47.000Z

2021-12-12T21:21:35.000Z

2019/glen.py

nyanthanya/Contoh-Program

924d79c34a92e77374228f1605a1d37b0fe37c70

[ "Unlicense" ]

9

2020-12-06T01:00:11.000Z

2021-12-14T00:48:43.000Z

def glen(generator): """ len implementation for generators. """ return sum(1 for _ in generator)

22.4

38

0.633929

13

112

5.384615

0.846154

0

0.012048

0.258929

112

5

39

22.4

0.831325

0.303571

0

1

0.5

false

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

4551a82bc0b4253a0313f98511d86093f37048ea

9,746

py

Python

terrascript/resource/openstack.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

terrascript/resource/openstack.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

terrascript/resource/openstack.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

# terrascript/resource/openstack.py import terrascript class openstack_blockstorage_quotaset_v2(terrascript.Resource): pass class openstack_blockstorage_quotaset_v3(terrascript.Resource): pass class openstack_blockstorage_volume_v1(terrascript.Resource): pass class openstack_blockstorage_volume_v2(terrascript.Resource): pass class openstack_blockstorage_volume_v3(terrascript.Resource): pass class openstack_blockstorage_volume_attach_v2(terrascript.Resource): pass class openstack_blockstorage_volume_attach_v3(terrascript.Resource): pass class openstack_compute_flavor_v2(terrascript.Resource): pass class openstack_compute_flavor_access_v2(terrascript.Resource): pass class openstack_compute_instance_v2(terrascript.Resource): pass class openstack_compute_interface_attach_v2(terrascript.Resource): pass class openstack_compute_keypair_v2(terrascript.Resource): pass class openstack_compute_secgroup_v2(terrascript.Resource): pass class openstack_compute_servergroup_v2(terrascript.Resource): pass class openstack_compute_quotaset_v2(terrascript.Resource): pass class openstack_compute_floatingip_v2(terrascript.Resource): pass class openstack_compute_floatingip_associate_v2(terrascript.Resource): pass class openstack_compute_volume_attach_v2(terrascript.Resource): pass class openstack_containerinfra_clustertemplate_v1(terrascript.Resource): pass class openstack_containerinfra_cluster_v1(terrascript.Resource): pass class openstack_db_instance_v1(terrascript.Resource): pass class openstack_db_user_v1(terrascript.Resource): pass class openstack_db_configuration_v1(terrascript.Resource): pass class openstack_db_database_v1(terrascript.Resource): pass class openstack_dns_recordset_v2(terrascript.Resource): pass class openstack_dns_zone_v2(terrascript.Resource): pass class openstack_fw_firewall_v1(terrascript.Resource): pass class openstack_fw_policy_v1(terrascript.Resource): pass class openstack_fw_rule_v1(terrascript.Resource): pass class openstack_identity_endpoint_v3(terrascript.Resource): pass class openstack_identity_project_v3(terrascript.Resource): pass class openstack_identity_role_v3(terrascript.Resource): pass class openstack_identity_role_assignment_v3(terrascript.Resource): pass class openstack_identity_service_v3(terrascript.Resource): pass class openstack_identity_user_v3(terrascript.Resource): pass class openstack_identity_application_credential_v3(terrascript.Resource): pass class openstack_images_image_v2(terrascript.Resource): pass class openstack_images_image_access_v2(terrascript.Resource): pass class openstack_images_image_access_accept_v2(terrascript.Resource): pass class openstack_lb_member_v1(terrascript.Resource): pass class openstack_lb_monitor_v1(terrascript.Resource): pass class openstack_lb_pool_v1(terrascript.Resource): pass class openstack_lb_vip_v1(terrascript.Resource): pass class openstack_lb_loadbalancer_v2(terrascript.Resource): pass class openstack_lb_listener_v2(terrascript.Resource): pass class openstack_lb_pool_v2(terrascript.Resource): pass class openstack_lb_member_v2(terrascript.Resource): pass class openstack_lb_monitor_v2(terrascript.Resource): pass class openstack_lb_l7policy_v2(terrascript.Resource): pass class openstack_lb_l7rule_v2(terrascript.Resource): pass class openstack_networking_floatingip_v2(terrascript.Resource): pass class openstack_networking_floatingip_associate_v2(terrascript.Resource): pass class openstack_networking_network_v2(terrascript.Resource): pass class openstack_networking_port_v2(terrascript.Resource): pass class openstack_networking_rbac_policy_v2(terrascript.Resource): pass class openstack_networking_port_secgroup_associate_v2(terrascript.Resource): pass class openstack_networking_qos_bandwidth_limit_rule_v2(terrascript.Resource): pass class openstack_networking_qos_dscp_marking_rule_v2(terrascript.Resource): pass class openstack_networking_qos_minimum_bandwidth_rule_v2(terrascript.Resource): pass class openstack_networking_qos_policy_v2(terrascript.Resource): pass class openstack_networking_quota_v2(terrascript.Resource): pass class openstack_networking_router_v2(terrascript.Resource): pass class openstack_networking_router_interface_v2(terrascript.Resource): pass class openstack_networking_router_route_v2(terrascript.Resource): pass class openstack_networking_secgroup_v2(terrascript.Resource): pass class openstack_networking_secgroup_rule_v2(terrascript.Resource): pass class openstack_networking_subnet_v2(terrascript.Resource): pass class openstack_networking_subnet_route_v2(terrascript.Resource): pass class openstack_networking_subnetpool_v2(terrascript.Resource): pass class openstack_networking_addressscope_v2(terrascript.Resource): pass class openstack_networking_trunk_v2(terrascript.Resource): pass class openstack_objectstorage_container_v1(terrascript.Resource): pass class openstack_objectstorage_object_v1(terrascript.Resource): pass class openstack_objectstorage_tempurl_v1(terrascript.Resource): pass class openstack_orchestration_stack_v1(terrascript.Resource): pass class openstack_vpnaas_ipsec_policy_v2(terrascript.Resource): pass class openstack_vpnaas_service_v2(terrascript.Resource): pass class openstack_vpnaas_ike_policy_v2(terrascript.Resource): pass class openstack_vpnaas_endpoint_group_v2(terrascript.Resource): pass class openstack_vpnaas_site_connection_v2(terrascript.Resource): pass class openstack_sharedfilesystem_securityservice_v2(terrascript.Resource): pass class openstack_sharedfilesystem_sharenetwork_v2(terrascript.Resource): pass class openstack_sharedfilesystem_share_v2(terrascript.Resource): pass class openstack_sharedfilesystem_share_access_v2(terrascript.Resource): pass class openstack_keymanager_secret_v1(terrascript.Resource): pass class openstack_keymanager_container_v1(terrascript.Resource): pass __all__ = [ "openstack_blockstorage_quotaset_v2", "openstack_blockstorage_quotaset_v3", "openstack_blockstorage_volume_v1", "openstack_blockstorage_volume_v2", "openstack_blockstorage_volume_v3", "openstack_blockstorage_volume_attach_v2", "openstack_blockstorage_volume_attach_v3", "openstack_compute_flavor_v2", "openstack_compute_flavor_access_v2", "openstack_compute_instance_v2", "openstack_compute_interface_attach_v2", "openstack_compute_keypair_v2", "openstack_compute_secgroup_v2", "openstack_compute_servergroup_v2", "openstack_compute_quotaset_v2", "openstack_compute_floatingip_v2", "openstack_compute_floatingip_associate_v2", "openstack_compute_volume_attach_v2", "openstack_containerinfra_clustertemplate_v1", "openstack_containerinfra_cluster_v1", "openstack_db_instance_v1", "openstack_db_user_v1", "openstack_db_configuration_v1", "openstack_db_database_v1", "openstack_dns_recordset_v2", "openstack_dns_zone_v2", "openstack_fw_firewall_v1", "openstack_fw_policy_v1", "openstack_fw_rule_v1", "openstack_identity_endpoint_v3", "openstack_identity_project_v3", "openstack_identity_role_v3", "openstack_identity_role_assignment_v3", "openstack_identity_service_v3", "openstack_identity_user_v3", "openstack_identity_application_credential_v3", "openstack_images_image_v2", "openstack_images_image_access_v2", "openstack_images_image_access_accept_v2", "openstack_lb_member_v1", "openstack_lb_monitor_v1", "openstack_lb_pool_v1", "openstack_lb_vip_v1", "openstack_lb_loadbalancer_v2", "openstack_lb_listener_v2", "openstack_lb_pool_v2", "openstack_lb_member_v2", "openstack_lb_monitor_v2", "openstack_lb_l7policy_v2", "openstack_lb_l7rule_v2", "openstack_networking_floatingip_v2", "openstack_networking_floatingip_associate_v2", "openstack_networking_network_v2", "openstack_networking_port_v2", "openstack_networking_rbac_policy_v2", "openstack_networking_port_secgroup_associate_v2", "openstack_networking_qos_bandwidth_limit_rule_v2", "openstack_networking_qos_dscp_marking_rule_v2", "openstack_networking_qos_minimum_bandwidth_rule_v2", "openstack_networking_qos_policy_v2", "openstack_networking_quota_v2", "openstack_networking_router_v2", "openstack_networking_router_interface_v2", "openstack_networking_router_route_v2", "openstack_networking_secgroup_v2", "openstack_networking_secgroup_rule_v2", "openstack_networking_subnet_v2", "openstack_networking_subnet_route_v2", "openstack_networking_subnetpool_v2", "openstack_networking_addressscope_v2", "openstack_networking_trunk_v2", "openstack_objectstorage_container_v1", "openstack_objectstorage_object_v1", "openstack_objectstorage_tempurl_v1", "openstack_orchestration_stack_v1", "openstack_vpnaas_ipsec_policy_v2", "openstack_vpnaas_service_v2", "openstack_vpnaas_ike_policy_v2", "openstack_vpnaas_endpoint_group_v2", "openstack_vpnaas_site_connection_v2", "openstack_sharedfilesystem_securityservice_v2", "openstack_sharedfilesystem_sharenetwork_v2", "openstack_sharedfilesystem_share_v2", "openstack_sharedfilesystem_share_access_v2", "openstack_keymanager_secret_v1", "openstack_keymanager_container_v1", ]

22.251142

79

0.812846

1,107

9,746

6.625113

0.084914

0.225389

0.269703

0.324516

0.738751

0.626807

0.554541

0.273384

0.038178

0

0.020631

0.124667

9,746

437

80

22.30206

0.839058

0.003386

0

0.329502

0

0.281125

0.27093

0

1

0

false

0.329502

0.003831

0

0.333333

0

null

1

0

null

0

1

0

4

45869b62262449d169739e0d6cce932a0fec63d1

22

py

Python

bonobo/_version.py

winsmith/bonobo

6fb9f52bec43a23feac2db968dd4315d75d69910

[ "Apache-2.0" ]

243

2020-05-12T01:15:46.000Z

2022-03-21T22:07:57.000Z

bonobo/_version.py

winsmith/bonobo

6fb9f52bec43a23feac2db968dd4315d75d69910

[ "Apache-2.0" ]

495

2020-05-12T06:45:12.000Z

2022-03-31T07:14:02.000Z

bonobo/_version.py

winsmith/bonobo

6fb9f52bec43a23feac2db968dd4315d75d69910

[ "Apache-2.0" ]

37

2020-05-12T02:16:07.000Z

2021-08-11T06:00:16.000Z

__version__ = '0.5.2'

11

21

0.636364

4

22

2.5

1

0

0.157895

0.136364

22

1

22

0.368421

0

0.227273

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

45977dc03a8340f1326eacee3de3d8e48c8410ae

607

py

Python

evalml/pipelines/components/estimators/regressors/__init__.py

ObinnaObeleagu/evalml

3b5bf62b08a5a5bc6485ba5387a08c32e1857473

[ "BSD-3-Clause" ]

1

2021-07-28T14:20:35.000Z

evalml/pipelines/components/estimators/regressors/__init__.py

ObinnaObeleagu/evalml

3b5bf62b08a5a5bc6485ba5387a08c32e1857473

[ "BSD-3-Clause" ]

null

evalml/pipelines/components/estimators/regressors/__init__.py

ObinnaObeleagu/evalml

3b5bf62b08a5a5bc6485ba5387a08c32e1857473

[ "BSD-3-Clause" ]

null

from .elasticnet_regressor import ElasticNetRegressor from .linear_regressor import LinearRegressor from .lightgbm_regressor import LightGBMRegressor from .rf_regressor import RandomForestRegressor from .catboost_regressor import CatBoostRegressor from .xgboost_regressor import XGBoostRegressor from .et_regressor import ExtraTreesRegressor from .baseline_regressor import BaselineRegressor from .decision_tree_regressor import DecisionTreeRegressor from .time_series_baseline_estimator import TimeSeriesBaselineEstimator from .svm_regressor import SVMRegressor from .arima_regressor import ARIMARegressor

46.692308

71

0.901153

63

607

8.444444

0.47619

0.31015

0

0.079077

607

12

72

50.583333

0.951699

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

4

459828ee46d9573e11d51c1a80022f445e80fd24

4,970

py

Python

topics/grammars/hats/abs/getkwd.py

grammarware/slps

a39bb0f8454de8508269d4467f2501badbb2cc4a

[ "BSD-3-Clause" ]

19

2015-01-18T13:50:02.000Z

2021-11-08T11:23:22.000Z

topics/grammars/hats/abs/getkwd.py

grammarware/slps

a39bb0f8454de8508269d4467f2501badbb2cc4a

[ "BSD-3-Clause" ]

null

topics/grammars/hats/abs/getkwd.py

grammarware/slps

a39bb0f8454de8508269d4467f2501badbb2cc4a

[ "BSD-3-Clause" ]

13

2015-01-18T13:50:07.000Z

2020-05-26T10:10:18.000Z

kwds = ''' "module" { return sym(Terminals.MODULE); } "import" { return sym(Terminals.IMPORT); } "export" { return sym(Terminals.EXPORT); } "from" { return sym(Terminals.FROM); } "class" { return sym(Terminals.CLASS); } "interface" { return sym(Terminals.INTERFACE); } "extends" { return sym(Terminals.EXTENDS); } "data" { return sym(Terminals.DATA); } "def" { return sym(Terminals.DEF); } "implements" { return sym(Terminals.IMPLEMENTS); } "delta" { return sym(Terminals.DELTA); } "adds" { return sym(Terminals.ADDS); } "modifies" { return sym(Terminals.MODIFIES); } "removes" { return sym(Terminals.REMOVES); } "hasField" { return sym(Terminals.HASFIELD); } "hasMethod" { return sym(Terminals.HASMETHOD); } "hasInterface" { return sym(Terminals.HASINTERFACE); } "productline" { return sym(Terminals.PRODUCTLINE); } "features" { return sym(Terminals.OPTFEATURES); } "after" { return sym(Terminals.AFTER); } "when" { return sym(Terminals.WHEN); } "product" { return sym(Terminals.PRODUCT); } "while" { return sym(Terminals.WHILE); } "return" { return sym(Terminals.RETURN); } "skip" { return sym(Terminals.SKIP); } "get" { return sym(Terminals.GET); } "null" { return sym(Terminals.NULL); } "await" { return sym(Terminals.AWAIT); } "if" { return sym(Terminals.IF); } "then" { return sym(Terminals.THEN); } "else" { return sym(Terminals.ELSE); } "suspend" { return sym(Terminals.SUSPEND); } "duration" { return sym(Terminals.DURATION); } "new" { return sym(Terminals.NEW); } "this" { return sym(Terminals.THIS); } "core" { return sym(Terminals.CORE); } "original" { return sym(Terminals.ORIGINAL); } ".original" { return sym(Terminals.DOTORIGINAL); } "case" { return sym(Terminals.CASE); } "let" { return sym(Terminals.LET); } "in" { return sym(Terminals.IN); } "cog" { return sym(Terminals.COG); } "type" { return sym(Terminals.TYPE); } "assert" { return sym(Terminals.ASSERT); } "builtin" { return sym(Terminals.BUILTIN); } // "root" { return sym(Terminals.ROOT); } "extension" { return sym(Terminals.EXTENSION); } "group" { return sym(Terminals.GROUP); } "opt" { return sym(Terminals.OPT); } "oneof" { return sym(Terminals.ONEOF); } "allof" { return sym(Terminals.ALLOF); } //"Int" { return sym(Terminals.INT); } //"Bool" { return sym(Terminals.BOOL); } //"in" { return sym(Terminals.IN); } "ifin" { return sym(Terminals.IFIN); } "ifout" { return sym(Terminals.IFOUT); } "exclude" { return sym(Terminals.EXCLUDE); } "require" { return sym(Terminals.REQUIRE); } //"excludes" { return sym(Terminals.EXCLUDE); } //"requires" { return sym(Terminals.REQUIRE); } //"true" { return sym(Terminals.TRUE); } //"tt" { return sym(Terminals.TRUE); } //"false" { return sym(Terminals.FALSE); } //"ff" { return sym(Terminals.FALSE); } "(" { return sym(Terminals.LPAREN); } ")" { return sym(Terminals.RPAREN); } "{" { return sym(Terminals.LBRACE); } "}" { return sym(Terminals.RBRACE); } "[" { return sym(Terminals.LBRACKET); } "]" { return sym(Terminals.RBRACKET); } "," { return sym(Terminals.COMMA); } ";" { return sym(Terminals.SEMICOLON); } ":" { return sym(Terminals.COLON); } "?" { return sym(Terminals.QMARK); } ".." { return sym(Terminals.UNTIL); } "." { return sym(Terminals.DOT); } "!" { return sym(Terminals.BANG); } "=" { return sym(Terminals.ASSIGN); } "&" { return sym(Terminals.GUARDAND); } "==" { return sym(Terminals.EQEQ); } "!=" { return sym(Terminals.NOTEQ); } "=>" { return sym(Terminals.RARROW); } "->" { return sym(Terminals.IMPLIES); } "<->" { return sym(Terminals.EQUIV); } "+" { return sym(Terminals.PLUS); } "-" { return sym(Terminals.MINUS); } "*" { return sym(Terminals.MULT); } "/" { return sym(Terminals.DIV); } "%" { return sym(Terminals.MOD); } "&&" { return sym(Terminals.ANDAND); } "||" { return sym(Terminals.OROR); } "|" { return sym(Terminals.BAR); } "~" { return sym(Terminals.NEGATION); } "<" { return sym(Terminals.LT); } ">" { return sym(Terminals.GT); } "<=" { return sym(Terminals.LTEQ); } ">=" { return sym(Terminals.GTEQ); } "_" { return sym(Terminals.USCORE); } "'" { return sym(Terminals.PRIME); } ''' defs = {} for line in kwds.split('\n'): line = line.strip() if line.startswith('//'): continue elif line == '': continue else: name = line.split('Terminals.')[1].split(')')[0] defs[name] = line.split('"')[1] for a in defs: print 'lexical',a,'=', '"'+defs[a]+'"',';'

41.764706

54

0.568813

500

4,970

5.652

0.234

0.315287

0.630573

0.018401

0.01557

0

0.000796

0.241247

4,970

119

55

41.764706

0.748608

0

0.017544

0

0.946087

0.269966

0

0.008772

0

null

0

0.008772

null

0.008772

0

null

1

0

1

0

null

0

1

0

4

45b84e7b8d6c88d2a0e164fbc5faecc624b17979

982

py

Python

object_detection/vantara/lumada/config/communication_channel_config.py

cardosov/Hackathon2018ObjectDetector

bd71aead2e58e8cf9756d5b06b751b60f69b0165

[ "MIT" ]

null

object_detection/vantara/lumada/config/communication_channel_config.py

cardosov/Hackathon2018ObjectDetector

bd71aead2e58e8cf9756d5b06b751b60f69b0165

[ "MIT" ]

null

object_detection/vantara/lumada/config/communication_channel_config.py

cardosov/Hackathon2018ObjectDetector

bd71aead2e58e8cf9756d5b06b751b60f69b0165

[ "MIT" ]

null

""" Copyright (c) by Hitachi Data Systems, 2017. All rights reserved. """ from lumada.utils.validator import Validator class CommunicationChannelConfig: def __init__(self, hostname=None, username=None, password=None, requires_secure=True, trust_certs=False): self._hostname = Validator.validate_param(hostname, 'hostname') self._username = Validator.validate_param(username, 'username') self._password = Validator.validate_param(password, 'password') self._requires_secure = requires_secure self._trust_certs = trust_certs self._exchange = 'lumada' def get_hostname(self): return self._hostname def get_username(self): return self._username def get_password(self): return self._password def get_requires_secure(self): return self._requires_secure def get_trust_certs(self): return self._trust_certs def get_exchange(self): return self._exchange

28.882353

109

0.704684

113

982

5.814159

0.309735

0.054795

0.127854

0

0.005161

0.210794

982

33

110

29.757576

0.842581

0.066191

0

0.033149

0

1

0.333333

false

0.190476

0.047619

0.285714

0.714286

0

null

0

null

0

1

0

1

0

1

0

4

45cefedb86b69c73fec685a2cb19f6427cc74f62

192

py

Python

superai/__init__.py

mysuperai/superai-sdk

796c411c6ab69209600bf727e8fd08c20f4d67b1

[ "Apache-2.0" ]

1

2020-12-03T18:18:16.000Z

superai/__init__.py

mysuperai/superai-sdk

796c411c6ab69209600bf727e8fd08c20f4d67b1

[ "Apache-2.0" ]

13

2021-02-22T18:27:58.000Z

2022-02-10T08:14:10.000Z

superai/__init__.py

mysuperai/superai-sdk

796c411c6ab69209600bf727e8fd08c20f4d67b1

[ "Apache-2.0" ]

1

2021-04-27T12:38:47.000Z

from __future__ import absolute_import, division, print_function, unicode_literals __version__ = "0.1.0.beta2" # Client comes first from .client import * from superai.config import settings

24

82

0.807292

26

192

5.538462

0.730769

0

0.02381

0.125

192

7

83

27.428571

0.833333

0.09375

0

0.063953

0

1

0

false

0

0.75

0

0.75

0.25

1

0

null

0

1

0

null

0

1

0

1

0

4

b3214988f1409f29b6861fa43db207605ffda099

93

py

Python

komparasi/apps.py

yudhapatria96/sidang

67252d8ec11791444cfd2ed5330391775372afc6

[ "bzip2-1.0.6" ]

null

komparasi/apps.py

yudhapatria96/sidang

67252d8ec11791444cfd2ed5330391775372afc6

[ "bzip2-1.0.6" ]

6

2019-12-05T00:12:52.000Z

2022-02-10T09:47:41.000Z

komparasi/apps.py

yudhapatria96/sidang

67252d8ec11791444cfd2ed5330391775372afc6

[ "bzip2-1.0.6" ]

null

from django.apps import AppConfig class KomparasiConfig(AppConfig): name = 'komparasi'

15.5

33

0.763441

10

93

7.1

0.9

0

0.16129

93

5

34

18.6

0.910256

0

0.096774

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

b3279b12e44d1448099bfbdcf12507c1ef978788

11,674

py

Python

userbot/modules/ShivamCredits.py

theshashankk/javes-3.0

9b16914be1350f7f6ac034bd30e33992035301b9

[ "MIT" ]

null

userbot/modules/ShivamCredits.py

theshashankk/javes-3.0

9b16914be1350f7f6ac034bd30e33992035301b9

[ "MIT" ]

null

userbot/modules/ShivamCredits.py

theshashankk/javes-3.0

9b16914be1350f7f6ac034bd30e33992035301b9

[ "MIT" ]

null

from userbot.events import javes05 from userbot import CMD_HELP, bot as javes, LOGS, JAVES_NAME from userbot.javes_main.commands import rekcah05 from telethon.events import ChatAction #made by shivam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam from userbot import bot as javes, CMD_HELP #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam from userbot import TEMP_DOWNLOAD_DIRECTORY #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam import os,re, bs4, requests, io #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Shivam#Made#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam from telethon import events #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam from pathlib import Path #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Sh1vam from os import remove #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam from bs4 import BeautifulSoup #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam from re import findall #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam from urllib.parse import quote_plus #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam from requests import get #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam from selenium import webdriver from selenium.webdriver.chrome.options import Options #Made by Sh1vam #Made by Sh1vam#Made by Sh1vam#Made#Made by Sh1vam#Made by Sh1vam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam from PIL import Image #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam from telethon.tl.types import MessageMediaPhoto #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam import urllib from userbot import bot as borg import os #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam from bs4 import BeautifulSoup #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam opener = urllib.request.build_opener() ; useragent = 'Mozilla/5.0 (Linux; Android 9; SM-G960F Build/PPR1.180610.011; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/78.0.3904.70 Mobile Safari/537.36' ; opener.addheaders = [('User-agent', useragent)] #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam JAVES_NNAME = str(JAVES_NAME) if JAVES_NAME else str(JAVES_MSG) WAFU_CHATID=int(os.environ.get("WAFU_CHATID",-1001230114424)) async def ParseSauce(googleurl): source = opener.open(googleurl).read() soup = BeautifulSoup(source, 'html.parser') results = {'similar_images': '', 'best_guess': ''} try: for similar_image in soup.findAll('input', {'class': 'gLFyf'}): url = 'https://www.google.com/search?tbm=isch&q=' + \ urllib.parse.quote_plus(similar_image.get('value')) results['similar_images'] = url except BaseException: pass for best_guess in soup.findAll('div', attrs={'class': 'r5a77d'}): results['best_guess'] = best_guess.get_text() return results #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam async def scam(results, lim): single = opener.open(results['similar_images']).read() decoded = single.decode('utf-8') imglinks = [] counter = 0 pattern = r'^,\[\"(.*[!png|!jpg|!jpeg])\",[0-9]+,[0-9]+\]$' oboi = re.findall(pattern, decoded, re.I | re.M) for imglink in oboi: counter += 1 if not counter >= int(lim): imglinks.append(imglink) else: break return imglinks #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam async def chrome(chrome_options=None): if chrome_options is None: chrome_options = await options() if not os.path.isdir(TEMP_DOWNLOAD_DIRECTORY): os.mkdir(TEMP_DOWNLOAD_DIRECTORY) prefs = {'download.default_directory': TEMP_DOWNLOAD_DIRECTORY} chrome_options.add_experimental_option('prefs', prefs) driver = webdriver.Chrome(executable_path=CHROME_DRIVER, options=chrome_options) return driver #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam #Made by Shivam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam @javes.on(events.NewMessage(incoming=True)) async def on_new_message(event): name = event.raw_text snip = """appeared! Add them to your harem by sending /protecc character name""" pattern = r"( |^|[^\w])" + re.escape(snip) + r"( |$|[^\w])" if re.search(pattern, name, flags=re.IGNORECASE): try: photo = io.BytesIO() await event.client.download_media(event.media, photo) image = Image.open(photo) name = "okgoogle.png" image.save(name, "PNG") image.close() searchUrl = 'https://www.google.com/searchbyimage/upload' multipart = { 'encoded_image': (name, open(name, 'rb')), 'image_content': '' } response = requests.post(searchUrl, files=multipart, allow_redirects=False) fetchUrl = response.headers['Location'] match = await ParseSauce(fetchUrl +"&preferences?hl=en&fg=1#languages") guess = match['best_guess'] guesss = guess[12:] #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam try: from userbot.modules.sql_helper.autowafu_sql import get_current_wafu_settings from userbot.modules.sql_helper.autowafu_sql import update_previous_wafu except AttributeError: return cws = get_current_wafu_settings(event.chat_id) if cws: await event.reply( f"/protecc {guesss}") else: await borg.send_message( WAFU_CHATID,f"/protecc {guesss}") except Exception as e: pass #Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made by Sh1vam#Made#Made by Shivam #Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam#Made by Shivam '''@javes.on(ChatAction) async def wafu_to_chat(event): try: from userbot.modules.sql_helper.autowafu_sql import get_current_wafu_settings from userbot.modules.sql_helper.autowafu_sql import update_previous_wafu except AttributeError: return cws = get_current_wafu_settings(event.chat_id) if cws:''' @javes05(outgoing=True, pattern=r"^!savewafu(?: |$)(.*)") async def save_wafu(event): try: from userbot.modules.sql_helper.autowafu_sql import add_wafu_setting except AttributeError: return await event.edit("`Running on Non-SQL mode!`") string = """appeared! Add them to your harem by sending /protecc character name""" msg_id = None if add_wafu_setting(event.chat_id, 0,string, msg_id) is True: await event.edit('Auto wafu mode on') else: await event.edit(f"`{JAVES_NNAME}`: **auto wafu already present**") @javes05(outgoing=True, pattern="^!checkwafu$") async def show_wafu(event): try: from userbot.modules.sql_helper.autowafu_sql import get_current_wafu_settings except AttributeError: await event.edit("`Running on Non-SQL mode!`") return cws = get_current_wafu_settings(event.chat_id) if not cws: await event.edit(f"`{JAVES_NNAME}`: **auto wafu not on.**") return else: await event.edit(f"`{JAVES_NNAME}`: **auto wafu on.**") @javes05(outgoing=True, pattern="^!clearwafu$") async def del_wafu(event): try: from userbot.modules.sql_helper.autowafu_sql import rm_wafu_setting except AttributeError: await event.edit("`Running on Non-SQL mode!`") return if rm_wafu_setting(event.chat_id) is True: await event.edit(f"`{JAVES_NNAME}`: **auto wafu stops**") else: await event.edit(f"`{JAVES_NNAME}`: ** no auto wafu on. **")

49.676596

361

0.667295

1,724

11,674

4.453596

0.145592

0.243813

0.276635

0.337588

0.66137

0.652383

0.648476

0.644699

0.631805

0.622167

0

0.028834

0.245417

11,674

234

362

49.888889

0.842774

0.375193

0

0.224638

0

0.007246

0.166927

0.019831

0

1

0

false

0.014493

0.195652

0

0.253623

0

null

1

0

1

0

null

0

4

b331c8f070df33554e94442c41951503ebfacd22

796

py

Python

chibi_command/rabbitmq.py

dem4ply/chibi_command

49efc3070bdf40e5f27146379487345b1accd427

[ "WTFPL" ]

null

chibi_command/rabbitmq.py

dem4ply/chibi_command

49efc3070bdf40e5f27146379487345b1accd427

[ "WTFPL" ]

null

chibi_command/rabbitmq.py

dem4ply/chibi_command

49efc3070bdf40e5f27146379487345b1accd427

[ "WTFPL" ]

null

from chibi_command import Command class Rabbitmqctl( Command ): command = 'rabbitmqctl' captive = False @classmethod def add_user( cls, user, password ): return cls( 'add_user', user, password )() @classmethod def delete_user( cls, user ): return cls( 'delete_user', user )() @classmethod def set_user_tags( cls, user, tag ): return cls( 'set_user_tags', user, tag )() @classmethod def set_permissions( cls, vhost, user, conf='.*', write='.*', read='.*' ): return cls( 'set_permissions', '-p', vhost, user, conf, write, read )() @classmethod def add_vhost( cls, vhost ): return cls( 'add_vhost', vhost )() @classmethod def list_user( cls ): return cls( 'list_users', captive=True )()

25.677419

79

0.605528

93

796

5.021505

0.301075

0.179872

0.072805

0.077088

0.094218

0

0.256281

796

30

80

26.533333

0.788851

0

0.272727

0

0.106784

0

1

0.272727

false

0.090909

0.045455

0.272727

0.727273

0

null

0

null

0

1

0

1

0

1

0

4

b337abc868321e8eead65370e433751da76b70c6

222

py

Python

Python/Courses/Python-Tutorials.Telusko/01.Object-Oriented-Programming/16.05-Abstract-Class-and-Abstract-Method.py

shihab4t/Books-Code

b637b6b2ad42e11faf87d29047311160fe3b2490

[ "Unlicense" ]

null

Python/Courses/Python-Tutorials.Telusko/01.Object-Oriented-Programming/16.05-Abstract-Class-and-Abstract-Method.py

shihab4t/Books-Code

b637b6b2ad42e11faf87d29047311160fe3b2490

[ "Unlicense" ]

null

Python/Courses/Python-Tutorials.Telusko/01.Object-Oriented-Programming/16.05-Abstract-Class-and-Abstract-Method.py

shihab4t/Books-Code

b637b6b2ad42e11faf87d29047311160fe3b2490

[ "Unlicense" ]

null

from abc import ABC, abstractmethod class Computer(ABC): @abstractmethod def process(self): pass class Laptop(Computer): def process(self): print("Running") com1 = Laptop() com1.process()

13.058824

35

0.648649

25

222

5.76

0.56

0.236111

0.194444

0

0.011905

0.243243

222

16

36

13.875

0.845238

0

0.2

0

0.031532

0

1

0.2

false

0.1

0

0.5

0.1

1

0

null

1

0

null

0

1

0

4

b33f475eb9a71b36667eea6f4a18c6a31d6ad7f9

240

py

Python

Project Πολυδιάστατες Δομές Δεδομένων/Quad_Tree/rebalance.py

DimosthenisMich/UndergraduateCeidProjects

9f99f2c44e41d06020f3a5e9aacc0cd4357ee833

[ "MIT" ]

6

2021-02-10T18:31:22.000Z

2022-03-03T17:49:30.000Z

Project Πολυδιάστατες Δομές Δεδομένων/Quad_Tree/rebalance.py

DimosthenisMich/UndergraduateCeidProjects

9f99f2c44e41d06020f3a5e9aacc0cd4357ee833

[ "MIT" ]

1

2020-09-30T19:16:39.000Z

Project Πολυδιάστατες Δομές Δεδομένων/Quad_Tree/rebalance.py

DimitrisKostorrizos/UndergraduateCeidProjects

9f99f2c44e41d06020f3a5e9aacc0cd4357ee833

[ "MIT" ]

5

2021-11-24T21:34:15.000Z

2022-01-23T22:37:35.000Z

import build import gatherTreeNodes def rebalance(node,maxNodesPerQuad): gatherTreeNodes.gather_tree_nodes(node) # Gather all points root = build.build(gatherTreeNodes.general_list, maxNodesPerQuad) # Rebuild tree return root

26.666667

84

0.795833

27

240

6.962963

0.62963

0

0.141667

240

8

85

30

0.912621

0.125

0

1

0.166667

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

2fa7c394b57ed817b7cda1d4223f3e5256ef77eb

301

py

Python

src/helperz.py

DreamingRaven/Serverus

3f15dcef92813809f9df1387d38c3d0a244d8cde

[ "MIT" ]

1

2018-11-07T16:21:09.000Z

src/helperz.py

DreamingRaven/Serverus

3f15dcef92813809f9df1387d38c3d0a244d8cde

[ "MIT" ]

null

src/helperz.py

DreamingRaven/Serverus

3f15dcef92813809f9df1387d38c3d0a244d8cde

[ "MIT" ]

null

# @Author: George Onoufriou <georgeraven> # @Date: 2018-11-04 # @Filename: helpers.py # @Last modified by: georgeraven # @Last modified time: 2018-11-04 # @License: Please see LICENSE in project root. # @Copyright: George Onoufriou import os, sys def placeholder(): print("placeholder")

17.705882

47

0.700997

38

301

5.552632

0.736842

0.14218

0.075829

0

0.064516

0.17608

301

16

48

18.8125

0.78629

0.734219

0

0.152778

0

1

0.333333

true

0

0.333333

0

0.666667

0.333333

0

null

0

null

0

1

0

1

0

1

0

4

2fe6078967ade1448ad2286d3a611ec4a18da886

65

py

Python

python/args-test.py

honux77/practice

f92481740190b20ef352135c392c8a9bea58dcc7

[ "MIT" ]

152

2015-01-12T07:40:53.000Z

2022-03-20T15:51:35.000Z

python/args-test.py

Brielle-Choi/practice

f92481740190b20ef352135c392c8a9bea58dcc7

[ "MIT" ]

11

2015-01-12T07:45:54.000Z

2021-09-02T02:46:52.000Z

python/args-test.py

Brielle-Choi/practice

f92481740190b20ef352135c392c8a9bea58dcc7

[ "MIT" ]

32

2015-01-12T09:10:04.000Z

2022-03-02T09:18:17.000Z

a = [1, 2, 3, 4, 5,] print(*a) for i in a: print(i, end=' ')

13

21

0.430769

15

65

1.866667

0.733333

0

0.108696

0.292308

65

5

21

13

0.5

0

0.015152

0

1

0

false

0

0.5

1

0

1

null

0

1

0

null

0

1

0

4

64074775bd7ba09effd8d3a675c5e7c447cb833f

166

py

Python

mysite/templatetags/extras.py

gbriones1/django-skelleton

ee067594e3994f1bac5bf754f618d365bb5248d8

[ "BSD-3-Clause" ]

null

mysite/templatetags/extras.py

gbriones1/django-skelleton

ee067594e3994f1bac5bf754f618d365bb5248d8

[ "BSD-3-Clause" ]

10

2020-06-05T16:38:25.000Z

2022-03-11T23:12:12.000Z

mysite/templatetags/extras.py

gbriones1/django-skelleton

ee067594e3994f1bac5bf754f618d365bb5248d8

[ "BSD-3-Clause" ]

null

from django import template register = template.Library() def debug_var(value): # import pdb; pdb.set_trace() return "" register.filter('debug', debug_var)

18.444444

35

0.716867

22

166

5.272727

0.681818

0.137931

0

0.162651

166

9

35

18.444444

0.834532

0.162651

0

0.036232

0

1

0.2

false

0

0.2

0.6

0

1

0

null

0

1

0

null

0

1

0

4

640ebd486650c9dea4fd6667129222adfe8519ec

879

py

Python

sslic/models/ressl.py

gergopool/ssl

676d01b88a50acea9a3b6c406fc4b82de74ec7b3

[ "MIT" ]

1

2022-02-05T13:49:17.000Z

sslic/models/ressl.py

gergopool/ssl

676d01b88a50acea9a3b6c406fc4b82de74ec7b3

[ "MIT" ]

null

sslic/models/ressl.py

gergopool/ssl

676d01b88a50acea9a3b6c406fc4b82de74ec7b3

[ "MIT" ]

null

from ..losses.ressl import ReSSLLoss from .momentum_model import MomentumModel __all__ = ['ressl_model'] class ReSSL(MomentumModel): default_loss = ReSSLLoss @classmethod def imagenet(cls, *args, **kwargs) -> MomentumModel: return super().imagenet(*args, dim=512, hidden_dim=4096, momentum=0.999, **kwargs) @classmethod def tiny_imagenet(cls, *args, **kwargs) -> MomentumModel: return super().tiny_imagenet(*args, dim=128, hidden_dim=128, momentum=0.996, **kwargs) @classmethod def cifar10(cls, *args, **kwargs) -> MomentumModel: return super().cifar10(*args, dim=128, hidden_dim=128, momentum=0.99, **kwargs) @classmethod def cifar100(cls, *args, **kwargs) -> MomentumModel: return super().cifar100(*args, dim=128, hidden_dim=128, momentum=0.99, **kwargs) def ressl_model() -> ReSSL: return ReSSL

30.310345

94

0.680319

107

879

5.457944

0.299065

0.061644

0.089041

0.178082

0.467466

0.340753

0.186644

0.133562

0

0.06768

0.176337

879

29

95

30.310345

0.73895

0

0.210526

0

0.0125

0

1

0.263158

false

0

0.105263

0.263158

0.736842

0

null

0

1

0

null

0

1

0

1

0

4

641d038ea53c8ff229981826df21fc7ad8c23c9e

153

py

Python

app/admin.py

LaurierCS/Pod5

2e5b9298ff2c5d7820993b9bcf980919a99c1474

[ "MIT" ]

null

app/admin.py

LaurierCS/Pod5

2e5b9298ff2c5d7820993b9bcf980919a99c1474

[ "MIT" ]

null

app/admin.py

LaurierCS/Pod5

2e5b9298ff2c5d7820993b9bcf980919a99c1474

[ "MIT" ]

null

# Imports from django.contrib import admin from .models import * # Register your models here. admin.site.register()#Model to register in the Admin site)

25.5

58

0.777778

23

153

5.173913

0.652174

0.151261

0

0.143791

153

6

58

25.5

0.908397

0.457516

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

ff5cf802ec37d7d08aa5f0fcc21ae7c4e767196e

795

py

Python

itasca/__init__.py

drozdovgrigoriy/itasca-python

edeb54a72699d10bab141c1eb47f5726312f3093

[ "BSD-3-Clause" ]

1

2022-03-11T15:09:25.000Z

itasca/__init__.py

drozdovgrigoriy/itasca-python

edeb54a72699d10bab141c1eb47f5726312f3093

[ "BSD-3-Clause" ]

null

itasca/__init__.py

drozdovgrigoriy/itasca-python

edeb54a72699d10bab141c1eb47f5726312f3093

[ "BSD-3-Clause" ]

null

"""Python connectivity for Itasca software. This library implements a connection via sockets between Python and the numerical modeling software from Itasca Consulting Group. Functions are provided to read and write files in the Itasca FISH binary format. itascacg.com/software FLAC, FLAC3D, PFC2D, PFC3D, UDEC & 3DEC See https://github.com/jkfurtney/itasca-python for more information. """ __version__ = "2018.08.20" from .main import FLAC3D_Connection from .main import PFC3D_Connection from .main import FishBinaryReader from .main import FishBinaryWriter from .main import FLAC_Connection from .main import UDEC_Connection from .main import threeDEC_Connection from .main import UDECFishBinaryReader from .main import UDECFishBinaryWriter from .main import p2pLinkClient, p2pLinkServer

29.444444

68

0.822642

106

795

6.084906

0.54717

0.124031

0.217054

0.186047

0

0.023055

0.127044

795

26

69

30.576923

0.90634

0.485535

0

0.024876

0

1

0

false

0

0.909091

0

0.909091

0

null

0

1

0

null

0

1

0

1

0

4

ff61abdf725b555db884ba406e0ef3417bbcccca

73

py

Python

dtgui/__init__.py

LaurentRDC/dtgui

e0bd5ce0db0d0bbaff71b5f338506a6915bbf68e

[ "MIT" ]

null

dtgui/__init__.py

LaurentRDC/dtgui

e0bd5ce0db0d0bbaff71b5f338506a6915bbf68e

[ "MIT" ]

null

dtgui/__init__.py

LaurentRDC/dtgui

e0bd5ce0db0d0bbaff71b5f338506a6915bbf68e

[ "MIT" ]

3

2018-07-05T14:11:03.000Z

2021-04-07T19:54:38.000Z

# Force use of PyQt5 import os os.environ["PYQTGRAPH_QT_LIB"] = "PyQt5"

14.6

40

0.726027

12

73

4.25

0.833333

0

0.032258

0.150685

73

4

41

18.25

0.790323

0.246575

0

0.396226

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

ff68a309dfbfe9f27713ea3b6a0c9fa8cc94726c

68

py

Python

src/api/app_configs.py

EduardManzhula/stats_and_gender_prediction

2b661b3cd331c6672580462273c7c746b135adfd

[ "MIT" ]

null

src/api/app_configs.py

EduardManzhula/stats_and_gender_prediction

2b661b3cd331c6672580462273c7c746b135adfd

[ "MIT" ]

null

src/api/app_configs.py

EduardManzhula/stats_and_gender_prediction

2b661b3cd331c6672580462273c7c746b135adfd

[ "MIT" ]

null

DB_URL = "mysql://guest:relational@relational.fit.cvut.cz:3306/ftp"

34

67

0.764706

11

68

4.636364

0.909091

0

0.061538

0.044118

68

1

68

0.723077

0

0.823529

0

1

0

false

0

1

0

null

0

1

0

1

null

0

4

ff6b368cd5d41f6824080fe084193d59e33e673f

75

py

Python

go/src/infra/tools/vpython/vpythonsmoketest/testdata/child2/child.py

allaparthi/monorail

e18645fc1b952a5a6ff5f06e0c740d75f1904473

[ "BSD-3-Clause" ]

2

2021-04-13T21:22:18.000Z

2021-09-07T02:11:57.000Z

go/src/infra/tools/vpython/vpythonsmoketest/testdata/child2/child.py

allaparthi/monorail

e18645fc1b952a5a6ff5f06e0c740d75f1904473

[ "BSD-3-Clause" ]

21

2020-09-06T02:41:05.000Z

2022-03-02T04:40:01.000Z

go/src/infra/tools/vpython/vpythonsmoketest/testdata/child2/child.py

allaparthi/monorail

e18645fc1b952a5a6ff5f06e0c740d75f1904473

[ "BSD-3-Clause" ]

null

#!/usr/bin/env vpython import sys import six print "Child2", sys.argv[1]

10.714286

27

0.706667

13

75

4.076923

0.846154

0

0.03125

0.146667

75

6

28

12.5

0.796875

0.28

0

0.113208

0

null

0

0.666667

null

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

4

ff9312f5cd3b40d0705d8e80e5a5c2d5c620aec6

11,547

py

Python

api/python/src/jbw/environments.py

NishanthVAnand/jelly-bean-world

a43c603f8b9a07dc2d0bc0b04db1b42be451023f

[ "Apache-2.0" ]

57

2019-08-07T15:20:35.000Z

2022-02-28T11:57:55.000Z

api/python/src/jbw/environments.py

NishanthVAnand/jelly-bean-world

a43c603f8b9a07dc2d0bc0b04db1b42be451023f

[ "Apache-2.0" ]

5

2020-03-31T16:00:28.000Z

2021-10-05T05:34:06.000Z

api/python/src/jbw/environments.py

NishanthVAnand/jelly-bean-world

a43c603f8b9a07dc2d0bc0b04db1b42be451023f

[ "Apache-2.0" ]

11

2020-02-23T02:19:56.000Z

2022-03-02T18:35:03.000Z

# Copyright 2019, The Jelly Bean World Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of # the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations under # the License. """Collection of JBW environments for OpenAI gym.""" from __future__ import absolute_import, division, print_function import numpy as np try: from gym.envs.registration import register modules_loaded = True except: modules_loaded = False from .agent import Agent from .direction import RelativeDirection from .item import * from .simulator import * from .visualizer import MapVisualizer, pi def make_config(): # specify the item types items = [] items.append(Item("banana", [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1, 0, 0, 0], [0, 0, 0, 0], False, 0.0, intensity_fn=IntensityFunction.CONSTANT, intensity_fn_args=[-5.3], interaction_fns=[ [InteractionFunction.PIECEWISE_BOX, 10.0, 200.0, 0.0, -6.0], # parameters for interaction between item 0 and item 0 [InteractionFunction.PIECEWISE_BOX, 200.0, 0.0, -6.0, -6.0], # parameters for interaction between item 0 and item 1 [InteractionFunction.PIECEWISE_BOX, 10.0, 200.0, 2.0, -100.0], # parameters for interaction between item 0 and item 2 [InteractionFunction.ZERO] # parameters for interaction between item 0 and item 3 ])) items.append(Item("onion", [1.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0, 1, 0, 0], [0, 0, 0, 0], False, 0.0, intensity_fn=IntensityFunction.CONSTANT, intensity_fn_args=[-5.0], interaction_fns=[ [InteractionFunction.PIECEWISE_BOX, 200.0, 0.0, -6.0, -6.0], # parameters for interaction between item 1 and item 0 [InteractionFunction.ZERO], # parameters for interaction between item 1 and item 1 [InteractionFunction.PIECEWISE_BOX, 200.0, 0.0, -100.0, -100.0], # parameters for interaction between item 1 and item 2 [InteractionFunction.ZERO] # parameters for interaction between item 1 and item 3 ])) items.append(Item("jellybean", [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0, 0, 0, 0], [0, 0, 0, 0], False, 0.0, intensity_fn=IntensityFunction.CONSTANT, intensity_fn_args=[-5.3], interaction_fns=[ [InteractionFunction.PIECEWISE_BOX, 10.0, 200.0, 2.0, -100.0], # parameters for interaction between item 2 and item 0 [InteractionFunction.PIECEWISE_BOX, 200.0, 0.0, -100.0, -100.0], # parameters for interaction between item 2 and item 1 [InteractionFunction.PIECEWISE_BOX, 10.0, 200.0, 0.0, -6.0], # parameters for interaction between item 2 and item 2 [InteractionFunction.ZERO] # parameters for interaction between item 2 and item 3 ])) items.append(Item("wall", [0.0, 0.0, 0.0], [0.5, 0.5, 0.5], [0, 0, 0, 1], [0, 0, 0, 0], True, 0.0, intensity_fn=IntensityFunction.CONSTANT, intensity_fn_args=[0.0], interaction_fns=[ [InteractionFunction.ZERO], # parameters for interaction between item 3 and item 0 [InteractionFunction.ZERO], # parameters for interaction between item 3 and item 1 [InteractionFunction.ZERO], # parameters for interaction between item 3 and item 2 [InteractionFunction.CROSS, 10.0, 15.0, 20.0, -200.0, -20.0, 1.0] # parameters for interaction between item 3 and item 3 ])) # construct the simulator configuration return SimulatorConfig(max_steps_per_movement=1, vision_range=5, allowed_movement_directions=[ActionPolicy.ALLOWED, ActionPolicy.DISALLOWED, ActionPolicy.DISALLOWED, ActionPolicy.DISALLOWED], allowed_turn_directions=[ActionPolicy.DISALLOWED, ActionPolicy.DISALLOWED, ActionPolicy.ALLOWED, ActionPolicy.ALLOWED], no_op_allowed=False, patch_size=32, mcmc_num_iter=4000, items=items, agent_color=[0.0, 0.0, 1.0], agent_field_of_view=2*pi, collision_policy=MovementConflictPolicy.FIRST_COME_FIRST_SERVED, decay_param=0.4, diffusion_param=0.14, deleted_item_lifetime=2000) def make_v1_config(): """ This config file matches the config """ # specify the item types items = [] # ANOTHER discrepancy in paper. Paper lists interaction with wall, whereas Configurations.swift # lists interaction with tree. Maybe it's a wrong index? Maybe the paper is listed incorrectly? items.append(Item("banana", [1.92, 1.76, 0.40], [0.96, 0.88, 0.20], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], False, 0.0, intensity_fn=IntensityFunction.CONSTANT, intensity_fn_args=[1.5], interaction_fns=[ [InteractionFunction.PIECEWISE_BOX, 10.0, 100.0, 0.0, -6.0], [InteractionFunction.ZERO], [InteractionFunction.PIECEWISE_BOX, 10.0, 100.0, 2.0, -100.0], [InteractionFunction.ZERO], [InteractionFunction.PIECEWISE_BOX, 50.0, 100.0, -100.0, -100.0], [InteractionFunction.ZERO] ])) # Onion has a discrepancy in intensity - in the paper it's listed as +1.5. items.append(Item("onion", [0.68, 0.01, 0.99], [0.68, 0.01, 0.99], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], False, 0.0, intensity_fn=IntensityFunction.CONSTANT, intensity_fn_args=[-3.0], interaction_fns=[ [InteractionFunction.ZERO], [InteractionFunction.ZERO], [InteractionFunction.ZERO], [InteractionFunction.ZERO], [InteractionFunction.ZERO], [InteractionFunction.ZERO] ])) items.append(Item("jellybean", [1.64, 0.54, 0.40], [0.82, 0.27, 0.20], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], False, 0.0, intensity_fn=IntensityFunction.CONSTANT, intensity_fn_args=[1.5], interaction_fns=[ [InteractionFunction.PIECEWISE_BOX, 10.0, 100.0, 2.0, -100.0], [InteractionFunction.ZERO], [InteractionFunction.PIECEWISE_BOX, 10.0, 100.0, 0.0, -6.0], [InteractionFunction.ZERO], [InteractionFunction.PIECEWISE_BOX, 50.0, 100.0, -100.0, -100.0], [InteractionFunction.ZERO] ])) items.append(Item("wall", [0.0, 0.0, 0.0], [0.20, 0.47, 0.67], [0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0], True, 0.0, intensity_fn=IntensityFunction.CONSTANT, intensity_fn_args=[-12.0], interaction_fns=[ [InteractionFunction.ZERO], [InteractionFunction.ZERO], [InteractionFunction.ZERO], [InteractionFunction.CROSS, 20.0, 40.0, 8.0, -1000.0, -1000.0, -1.0], [InteractionFunction.ZERO], [InteractionFunction.ZERO] ])) items.append(Item("tree", [0.00, 0.47, 0.06], [0.00, 0.47, 0.06], [0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0], False, 0.0, intensity_fn=IntensityFunction.CONSTANT, intensity_fn_args=[2.0], interaction_fns=[ [InteractionFunction.ZERO], [InteractionFunction.ZERO], [InteractionFunction.ZERO], [InteractionFunction.ZERO], [InteractionFunction.PIECEWISE_BOX, 100.0, 500.0, 0.0, -0.1], [InteractionFunction.ZERO] ])) items.append(Item("truffle", [8.40, 4.80, 2.60], [0.42, 0.24, 0.13], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], False, 0.0, intensity_fn=IntensityFunction.CONSTANT, intensity_fn_args=[0.0], interaction_fns=[ [InteractionFunction.ZERO], # parameters for interaction between item 3 and item 0 [InteractionFunction.ZERO], # parameters for interaction between item 3 and item 1 [InteractionFunction.ZERO], # parameters for interaction between item 3 and item 2 [InteractionFunction.ZERO], # parameters for interaction between item 3 and item 2 [InteractionFunction.PIECEWISE_BOX, 4.0, 200.0, 2.0, 0.0], [InteractionFunction.PIECEWISE_BOX, 30.0, 1000.0, -0.3, -1.0], ])) # construct the simulator configuration return SimulatorConfig(max_steps_per_movement=1, vision_range=5, allowed_movement_directions=[ActionPolicy.ALLOWED, ActionPolicy.DISALLOWED, ActionPolicy.DISALLOWED, ActionPolicy.DISALLOWED], allowed_turn_directions=[ActionPolicy.DISALLOWED, ActionPolicy.DISALLOWED, ActionPolicy.ALLOWED, ActionPolicy.ALLOWED], no_op_allowed=False, patch_size=32, mcmc_num_iter=4000, items=items, agent_color=[0.0, 0.0, 1.0], agent_field_of_view=2*pi, collision_policy=MovementConflictPolicy.FIRST_COME_FIRST_SERVED, decay_param=0.4, diffusion_param=0.14, deleted_item_lifetime=2000) def case1_reward_fn(prev_items, items): """ Reference for item indicies: 0 - Banana: 0 reward 1 - Onion: -1 reward for every one collected 2 - JellyBean: +1 reward for every one collected 3 - Wall: 0 reward, cannot collect 4 - Tree: 0 reward, cannot collect 5 - Truffle: 0 reward """ reward_array = np.array([0, -1, 1, 0, 0, 0]) diff = items - prev_items return (diff * reward_array).sum().astype(np.float32) if modules_loaded: # Construct the simulator configuration. sim_config = make_config() # Create a reward function. reward_fn = lambda prev_items, items: len(items) - len(prev_items) register( id='JBW-v0', entry_point='jbw.environment:JBWEnv', kwargs={ 'sim_config': sim_config, 'reward_fn': reward_fn, 'render': False}) register( id='JBW-render-v0', entry_point='jbw.environment:JBWEnv', kwargs={ 'sim_config': sim_config, 'reward_fn': reward_fn, 'render': True}) sim_v1_config = make_v1_config() register( id='JBW-v1', entry_point='jbw.environment:JBWEnv', kwargs={ 'sim_config': sim_v1_config, 'reward_fn': case1_reward_fn, 'render': False}) register( id='JBW-render-v1', entry_point='jbw.environment:JBWEnv', kwargs={ 'sim_config': sim_v1_config, 'reward_fn': case1_reward_fn, 'render': True})

56.326829

156

0.585607

1,430

11,547

4.618182

0.172727

0.049364

0.052241

0.049061

0.739098

0.726832

0.70427

0.687311

0.670503

0.669897

0

0.083272

0.299039

11,547

204

157

56.602941

0.732641

0.209232

0

0.707792

0

0.031547

0.009741

0

1

0.019481

false

0

0.051948

0

0.090909

0.006494

0

null

0

1

0

1

0

1

0

null

0

4

ffa92e0fa2e78a9d89d347670bfb23f1b622a1aa

255

py

Python

app/mod_home/forms.py

BrianLusina/tweetstream

6b60d779b3d4f5b615c62dc1ccc93e722ec6f9b9

[ "Apache-2.0" ]

null

app/mod_home/forms.py

BrianLusina/tweetstream

6b60d779b3d4f5b615c62dc1ccc93e722ec6f9b9

[ "Apache-2.0" ]

null

app/mod_home/forms.py

BrianLusina/tweetstream

6b60d779b3d4f5b615c62dc1ccc93e722ec6f9b9

[ "Apache-2.0" ]

null

from flask_wtf import FlaskForm from wtforms import StringField, SubmitField from wtforms.validators import DataRequired class TopicsForm(FlaskForm): topic_name = StringField(validators=[DataRequired()]) submit_field = SubmitField("Get Topics")

28.333333

57

0.803922

28

255

7.214286

0.642857

0.108911

0

0.12549

255

8

58

31.875

0.90583

0

0.039216

0

1

0

false

0

0.5

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

440964465b5b3aa3ebddf95a03e130315379ce88

56

py

Python

minerva/utils.py

Samrat-Nakarmi/Minerva

4dd87ef102e4c83d26cdf8b6f12bad9d3fed4c7a

[ "MIT" ]

1

2021-02-03T12:54:48.000Z

minerva/utils.py

Samrat-Nakarmi/Minerva

4dd87ef102e4c83d26cdf8b6f12bad9d3fed4c7a

[ "MIT" ]

1

2022-02-14T01:31:13.000Z

edavids/utils/get_filename.py

me-edavids/profile

388758dcfd67da7f974fa3ebcef51c740a07ec60

[ "MIT" ]

null

def get_filename(filename): return filename.upper()

18.666667

27

0.75

7

56

5.857143

0.714286

0

0.142857

56

2

28

0.854167

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

440ec22366e2b0c35c8fb7e60522bdb4e0a71e9c

207

py

Python

atest/resources/testlibs/cache_error.py

hugovk/SeleniumLibrary

489178c1beb16a4b90747ed35bad7dac80a1cc24

[ "ECL-2.0", "Apache-2.0" ]

792

2015-09-28T15:22:48.000Z

2022-03-27T21:31:34.000Z

atest/resources/testlibs/cache_error.py

hugovk/SeleniumLibrary

489178c1beb16a4b90747ed35bad7dac80a1cc24

[ "ECL-2.0", "Apache-2.0" ]

710

2015-08-20T13:31:20.000Z

2022-03-24T15:33:20.000Z

atest/resources/testlibs/cache_error.py

hugovk/SeleniumLibrary

489178c1beb16a4b90747ed35bad7dac80a1cc24

[ "ECL-2.0", "Apache-2.0" ]

429

2016-10-26T08:26:09.000Z

2022-03-28T23:19:42.000Z

from robot.libraries.BuiltIn import BuiltIn def invalidate_driver(): sl = BuiltIn().get_library_instance("SeleniumLibrary") sl.register_driver(None, "tidii") sl.register_driver(None, "foobar")

25.875

58

0.748792

25

207

6

0.68

0.133333

0.213333

0.266667

0

0.130435

207

7

59

29.571429

0.833333

0

0.125604

0

1

0.2

false

0

0.2

0

0.4

0

1

0

null

0

1

0

1

0

null

0

4

442b719a9d48cf361365c5c8b9bad366d527f63f

161

py

Python

problem0243.py

kmarcini/Project-Euler-Python

d644e8e1ec4fac70a9ab407ad5e1f0a75547c8d3

[ "BSD-3-Clause" ]

null

problem0243.py

kmarcini/Project-Euler-Python

d644e8e1ec4fac70a9ab407ad5e1f0a75547c8d3

[ "BSD-3-Clause" ]

null

problem0243.py

kmarcini/Project-Euler-Python

d644e8e1ec4fac70a9ab407ad5e1f0a75547c8d3

[ "BSD-3-Clause" ]

null

########################### # # #243 Resilience - Project Euler # https://projecteuler.net/problem=243 # # Code by Kevin Marciniak # ###########################

17.888889

38

0.465839

13

161

5.769231

0.923077

0

0.041958

0.111801

161

8

39

20.125

0.482517

0.565217

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

4

442c2729bfaa47af98f92fee0fb2fd949a4b8fa4

457

py

Python

test/test_utils.py

contentful-labs/contentful.py

d9eb4a68abcad33e4766e2be8c7b35e605210b5a

[ "Apache-2.0" ]

10

2015-12-01T07:18:43.000Z

2018-07-10T13:56:18.000Z

test/test_utils.py

contentful-labs/contentful.py

d9eb4a68abcad33e4766e2be8c7b35e605210b5a

[ "Apache-2.0" ]

11

2015-12-17T13:36:47.000Z

2018-10-11T22:19:07.000Z

test/test_utils.py

contentful-labs/contentful.py

d9eb4a68abcad33e4766e2be8c7b35e605210b5a

[ "Apache-2.0" ]

9

2015-12-15T16:02:25.000Z

2020-04-29T20:09:04.000Z

from contentful.cda import utils from contentful.cda.resources import Asset, ContentType, Entry, Space from test import BaseTestCase class UtilsTestCase(BaseTestCase): def test_class_for_type(self): self.assertIs(utils.class_for_type('Asset'), Asset) self.assertIs(utils.class_for_type('ContentType'), ContentType) self.assertIs(utils.class_for_type('Entry'), Entry) self.assertIs(utils.class_for_type('Space'), Space)

38.083333

71

0.752735

59

457

5.644068

0.322034

0.12012

0.18018

0.264264

0.348348

0

0.142232

457

11

72

41.545455

0.84949

0

0.056893

0

0.444444

1

0.111111

false

0

0.333333

0

0.555556

0

null

0

1

0

null

0

1

0

1

0

4

446741a21fd75f8a3618ae1c31645eb5797f3bed

842

py

Python

chain/crypto/bytebuffer.py

tsifrer/ark

c678091e226d79fabe4a2c554e1d8e704a5b5cec

[ "MIT" ]

5

2019-02-01T01:22:27.000Z

2019-05-24T12:20:38.000Z

chain/crypto/bytebuffer.py

tsifrer/ark

c678091e226d79fabe4a2c554e1d8e704a5b5cec

[ "MIT" ]

15

2019-03-29T13:12:10.000Z

2019-08-25T19:19:35.000Z

chain/crypto/bytebuffer.py

tsifrer/ark

c678091e226d79fabe4a2c554e1d8e704a5b5cec

[ "MIT" ]

4

2019-01-31T13:52:03.000Z

2020-08-12T02:12:03.000Z

from binary.unsigned_integer import read_bit32, read_bit64, read_bit8 # TODO: Put this into binary package class ByteBuffer(bytearray): def read_uint8(self): return read_bit8(self) def read_uint32(self): return read_bit32(self) def read_uint64(self): return read_bit64(self) def read_bytes(self, num_bytes, offset=0): return bytes(self[offset : offset + num_bytes]) def pop_uint8(self): data = read_bit8(self) del self[:1] return data def pop_uint32(self): data = read_bit32(self) del self[:4] return data def pop_uint64(self): data = read_bit64(self) del self[:8] return data def pop_bytes(self, num_bytes): data = self[:num_bytes] del self[:num_bytes] return bytes(data)

22.756757

69

0.622328

115

842

4.356522

0.286957

0.07984

0.095808

0

0.048414

0.288599

842

36

70

23.388889

0.78798

0.04038

0

0.115385

0

0.027778

0

1

0.307692

false

0

0.038462

0.153846

0.692308

0

null

0

null

0

1

0

1

0

1

0

4

447669b7dcf510c0e1956405d495be48a8000b8d

2,144

py

Python

tests/fundings/validation_service_tests_.py

cbn-alpin/gefiproj-api

35e3f00dd71bcdd9ad751307ac379aa78d1545cf

[ "MIT" ]

2

2020-10-15T15:16:08.000Z

2020-11-06T10:41:13.000Z

tests/fundings/validation_service_tests_.py

cbn-alpin/gefiproj-api

35e3f00dd71bcdd9ad751307ac379aa78d1545cf

[ "MIT" ]

1

2020-11-14T19:40:14.000Z

tests/fundings/validation_service_tests_.py

cbn-alpin/gefiproj-api

35e3f00dd71bcdd9ad751307ac379aa78d1545cf

[ "MIT" ]

null

import unittest from src.api.fundings.validation_service import FundingValidationService class ProjectValidationServiceTestCase(unittest.TestCase): def test_validate_post(self): funding = {'id_f': 1, 'id_p': 1, 'id_financeur': 1, 'montant_arrete_f': 4, 'statut_f': 'ANTR', 'date_solde_f': None} validation_errors = FundingValidationService.validate_post(funding) self.assertEqual(len(validation_errors), 0) funding['statut_f'] = 'SOLDE' validation_errors = FundingValidationService.validate_post(funding) self.assertEqual(len(validation_errors), 1) self.assertEqual(validation_errors[0]['field'], 'statut_f') funding['statut_f'] = 'ANTR' del funding['id_p'] validation_errors = FundingValidationService.validate_post(funding) self.assertEqual(len(validation_errors), 1) self.assertEqual(validation_errors[0]['field'], 'id_p') funding['id_p'] = 1 del funding['id_financeur'] validation_errors = FundingValidationService.validate_post(funding) self.assertEqual(len(validation_errors), 1) self.assertEqual(validation_errors[0]['field'], 'id_financeur') funding['id_financeur'] = 1 del funding['montant_arrete_f'] validation_errors = FundingValidationService.validate_post(funding) self.assertEqual(len(validation_errors), 1) self.assertEqual(validation_errors[0]['field'], 'montant_arrete_f') funding['montant_arrete_f'] = 10 del funding['statut_f'] validation_errors = FundingValidationService.validate_post(funding) self.assertEqual(len(validation_errors), 1) self.assertEqual(validation_errors[0]['field'], 'statut_f') funding['statut_f'] = 'ANTR' funding['statut_f'] = 'NO' validation_errors = FundingValidationService.validate_post(funding) self.assertEqual(len(validation_errors), 1) self.assertEqual(validation_errors[0]['field'], 'statut_f') funding['statut_f'] = 'ANTR' if __name__ == '__main__': unittest.main()

42.039216

82

0.679571

228

2,144

6.096491

0.184211

0.230216

0.201439

0.241727

0.690647

0

0.012274

0.201959

2,144

50

83

42.88

0.800117

0

0.475

0

0.132929

0

0.325

1

0.025

false

0

0.05

0

0.1

0

null

1

0

1

0

null

0

4

44903506cec8d7e6cfc37c9d9943b2a097543315

85

py

Python

lfd_hw7/hw7_q3.py

MahmutOsmanovic/machine-learning-mooc-caltech

deca978e13f6d6950f06417c4d520e71904962d7

[ "MIT" ]

null

lfd_hw7/hw7_q3.py

MahmutOsmanovic/machine-learning-mooc-caltech

deca978e13f6d6950f06417c4d520e71904962d7

[ "MIT" ]

null

lfd_hw7/hw7_q3.py

MahmutOsmanovic/machine-learning-mooc-caltech

deca978e13f6d6950f06417c4d520e71904962d7

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Thu Jun 3 17:25:24 2021 @author: Mahmu """

10.625

35

0.552941

14

85

3.357143

1

0

0.181818

0.223529

85

7

36

12.142857

0.530303

0.870588

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

4

922cc89db64e69f44ee82b8546bb344f730ca11e

15

py

Python

tackle/providers/system/tests/listdir/dirs/dir1/things.py

geometry-labs/tackle-box

83424a10416955ba983f0c14ec89bd79673a4282

[ "BSD-3-Clause" ]

1

2021-04-13T23:10:11.000Z

tackle/providers/system/tests/listdir/dir/things.py

geometry-labs/tackle-box

83424a10416955ba983f0c14ec89bd79673a4282

[ "BSD-3-Clause" ]

4

2021-01-27T00:06:12.000Z

2021-02-12T01:20:32.000Z

tackle/providers/system/tests/listdir/dirs/dir2/things.py

geometry-labs/tackle-box

83424a10416955ba983f0c14ec89bd79673a4282

[ "BSD-3-Clause" ]

1

2021-05-07T05:07:29.000Z

"""Fixture."""

7.5

14

0.466667

1

15

7

1

0

0.066667

15

1

15

0.5

0.533333

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

927ff146253bab5b542604ef8da3df3f7b17770b

190

py

Python

graphql_persist/renderers/__init__.py

flavors/django-graphql-persist

ff9acf108961f68831126c0f2d6e31b8b562390d

[ "MIT" ]

24

2018-04-14T03:07:11.000Z

2021-11-08T12:04:09.000Z

graphql_persist/renderers/__init__.py

urantialife/django-graphql-persist

ff9acf108961f68831126c0f2d6e31b8b562390d

[ "MIT" ]

1

2018-10-02T18:45:49.000Z

graphql_persist/renderers/__init__.py

urantialife/django-graphql-persist

ff9acf108961f68831126c0f2d6e31b8b562390d

[ "MIT" ]

5

2018-07-02T07:03:54.000Z

2020-06-17T01:42:25.000Z

from .base import BaseRenderer, BaseStripTagsRenderer from .relay import StripRelayTagsRenderer __all__ = [ 'BaseRenderer', 'BaseStripTagsRenderer', 'StripRelayTagsRenderer', ]

21.111111

53

0.768421

13

190

10.923077

0.615385

0.464789

0

0.152632

190

8

54

23.75

0.881988

0

0.289474

0.226316

0

1

0

false

0

0.285714

0

0.285714

0

1

0

1

null

1

0

1

0

null

0

4

929425d121fe8df77c3f3566e1e783da33e87651

90

py

Python

previous_programs/pyseg1.py

omar115/code_for_Kids

3f50ffb1d492c6ea5aa09688944aa01a0cadf1fd

[ "MIT" ]

null

previous_programs/pyseg1.py

omar115/code_for_Kids

3f50ffb1d492c6ea5aa09688944aa01a0cadf1fd

[ "MIT" ]

null

previous_programs/pyseg1.py

omar115/code_for_Kids

3f50ffb1d492c6ea5aa09688944aa01a0cadf1fd

[ "MIT" ]

2

2021-01-08T03:52:46.000Z

2021-04-01T19:16:12.000Z

import pygame #importing pygame library pygame.init() #initialize the pygame modules

30

47

0.777778

11

90

6.363636

0.727273

0

0.166667

90

3

47

30

0.933333

0.588889

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

92b0b1a69c9e990068464ba8568699743e6e28b1

35

py

Python

FileReader/__init__.py

hanscje/PySearch

22d551f5fa5c41cc82e9e8205f0dcea78ede9338

[ "MIT" ]

null

FileReader/__init__.py

hanscje/PySearch

22d551f5fa5c41cc82e9e8205f0dcea78ede9338

[ "MIT" ]

null

FileReader/__init__.py

hanscje/PySearch

22d551f5fa5c41cc82e9e8205f0dcea78ede9338

[ "MIT" ]

null

''' Leser filer gitt filnavn '''

5.833333

24

0.6

4

35

5.25

1

0

0.228571

35

5

25

7

0.777778

0.685714

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

2b9b0e371b366f805709054e52f39ef6d571d30d

296

py

Python

toydl/nn/__init__.py

CharlesPikachu/pytoydl

861ebce6c7c40d155a6575330c08abbf07c9477d

[ "Apache-2.0" ]

18

2022-03-26T15:56:01.000Z

2022-03-30T11:31:27.000Z

toydl/nn/__init__.py

CharlesPikachu/pytoydl

861ebce6c7c40d155a6575330c08abbf07c9477d

[ "Apache-2.0" ]

null

toydl/nn/__init__.py

CharlesPikachu/pytoydl

861ebce6c7c40d155a6575330c08abbf07c9477d

[ "Apache-2.0" ]

1

2022-03-27T08:08:05.000Z

'''initialize''' from .linear import Linear from .module import Module from .flatten import Flatten from .convolution import Conv2d from .sequential import Sequential from .criterion import MSELoss, CrossEntropy from .activation import Softmax, Sigmoid, Tanh, ReLU, LeakyReLU, ELU, SELU, Softplus

37

84

0.807432

37

296

6.459459

0.567568

0

0.003846

0.121622

296

8

84

37

0.915385

0.033784

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

2bb02e03aab91ab30e71668763a2af6ba9d109a0

62

py

Python

python/testData/formatter/attributeAlignmentInClassPatterns.py

06needhamt/intellij-community

63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b

[ "Apache-2.0" ]

null

python/testData/formatter/attributeAlignmentInClassPatterns.py

06needhamt/intellij-community

63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b

[ "Apache-2.0" ]

null

python/testData/formatter/attributeAlignmentInClassPatterns.py

06needhamt/intellij-community

63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b

[ "Apache-2.0" ]

null

match x: case Class(1, foo=2, bar=3): pass

12.4

17

0.451613

10

62

2.8

1

0

0.083333

0.419355

62

5

18

12.4

0.694444

0

1

0

true

0.2

0

1

0

null

0

1

0

null

0

1

0

4

2bd2163bb16f5245de973785c7060666025eb36e

125

py

Python

versiongrid/__main__.py

rsnyman/versiongrid

0870f320f2b53f1071282692816fcbba1f9a0346

[ "MIT" ]

null

versiongrid/__main__.py

rsnyman/versiongrid

0870f320f2b53f1071282692816fcbba1f9a0346

[ "MIT" ]

null

versiongrid/__main__.py

rsnyman/versiongrid

0870f320f2b53f1071282692816fcbba1f9a0346

[ "MIT" ]

null

#!/usr/bin/env python3 from versiongrid import get_app if __name__ == "__main__": get_app().run(port=8080, debug=True)

17.857143

40

0.712

19

125

4.157895

0.894737

0.151899

0

0.046729

0.144

125

6

41

20.833333

0.691589

0.168

0

0.07767

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

4

920362c8f520a333f3e3d28070877f35947421ec

157

py

Python

survol/sources_types/nmap/__init__.py

rchateauneu/survol

ba66d3ec453b2d9dd3a8dabc6d53f71aa9ba8c78

[ "BSD-3-Clause" ]

9

2017-10-05T23:36:23.000Z

2021-08-09T15:40:03.000Z

survol/sources_types/nmap/__init__.py

rchateauneu/survol

ba66d3ec453b2d9dd3a8dabc6d53f71aa9ba8c78

[ "BSD-3-Clause" ]

21

2018-01-02T09:33:03.000Z

2018-08-27T11:09:52.000Z

survol/sources_types/nmap/__init__.py

rchateauneu/survol

ba66d3ec453b2d9dd3a8dabc6d53f71aa9ba8c78

[ "BSD-3-Clause" ]

4

2018-06-23T09:05:45.000Z

2021-01-22T15:36:50.000Z

""" Scripts using nmap """ # TODO: Check if nmap is accessible. def Usable(entity_type, entity_ids_arr): """Nmap must be accessible""" return True

15.7

40

0.681529

22

157

4.727273

0.818182

0

0.197452

157

9

41

17.444444

0.825397

0.496815

0

0.111111

0

1

0.5

false

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

9215733498c13f3dc57c03ffa0560576a94c6889

3,686

py

Python

env/Lib/site-packages/OpenGL/GLES1/OES/required_internalformat.py

5gconnectedbike/Navio2

8c3f2b5d8bbbcea1fc08739945183c12b206712c

[ "BSD-3-Clause" ]

210

2016-04-09T14:26:00.000Z

2022-03-25T18:36:19.000Z

env/Lib/site-packages/OpenGL/GLES1/OES/required_internalformat.py

5gconnectedbike/Navio2

8c3f2b5d8bbbcea1fc08739945183c12b206712c

[ "BSD-3-Clause" ]

72

2016-09-04T09:30:19.000Z

2022-03-27T17:06:53.000Z

env/Lib/site-packages/OpenGL/GLES1/OES/required_internalformat.py

5gconnectedbike/Navio2

8c3f2b5d8bbbcea1fc08739945183c12b206712c

[ "BSD-3-Clause" ]

64

2016-04-09T14:26:49.000Z

2022-03-21T11:19:47.000Z

'''OpenGL extension OES.required_internalformat This module customises the behaviour of the OpenGL.raw.GLES1.OES.required_internalformat to provide a more Python-friendly API Overview (from the spec) The ES 1.1 API allows an implementation to store texture data internally with arbitrary precision, regardless of the format and type of the data supplied by the application. Similarly, ES allows an implementation to choose an arbitrary precision for the internal storage of image data allocated by glRenderbufferStorageOES. While this allows flexibility for implementations, it does mean that an application does not have a reliable means to request the implementation maintain a specific precision or to find out what precision the implementation will maintain for a given texture or renderbuffer image. For reference, "Desktop" OpenGL uses the <internalformat> argument to glTexImage*, glCopyTexImage* and glRenderbufferStorageEXT as a hint, defining the particular base format and precision that the application wants the implementation to maintain when storing the image data. Further, the application can choose an <internalformat> with a different base internal format than the source format specified by <format>. The implementation is not required to exactly match the precision specified by <internalformat> when choosing an internal storage precision, but it is required to match the base internal format of <internalformat>. In addition, ES 1.1 does not allow an implementation to fail a request to glTexImage2D for any of the legal <format> and <type> combinations listed in Table 3.4, even if the implementation does not natively support data stored in that external <format> and <type>. However, there are no additional requirements placed on the implementation. The ES implementation is free to store the texture data with lower precision than originally specified, for instance. Further, since ES removes the ability to query the texture object to find out what internal format it chose, there is no way for the application to find out that this has happened. This extension addresses the situation in two ways: 1) This extension introduces the ability for an application to specify the desired "sized" internal formats for texture image allocation. 2) This extension guarantees to maintain at least the specified precision of all available sized internal formats. An implementation that exports this extension is committing to support all of the legal values for <internalformat> in Tables 3.4, 3.4.x, and 3.4.y, subject to the extension dependencies described herein. That is to say, the implementation is guaranteeing that choosing an <internalformat> argument with a value from these tables will not cause an image allocation request to fail. Furthermore, it is guaranteeing that for any sized internal format, the renderbuffer or texture data will be stored with at least the precision prescribed by the sized internal format. The official definition of this extension is available here: http://www.opengl.org/registry/specs/OES/required_internalformat.txt ''' from OpenGL import platform, constant, arrays from OpenGL import extensions, wrapper import ctypes from OpenGL.raw.GLES1 import _types, _glgets from OpenGL.raw.GLES1.OES.required_internalformat import * from OpenGL.raw.GLES1.OES.required_internalformat import _EXTENSION_NAME def glInitRequiredInternalformatOES(): '''Return boolean indicating whether this extension is available''' from OpenGL import extensions return extensions.hasGLExtension( _EXTENSION_NAME ) ### END AUTOGENERATED SECTION

50.493151

78

0.801139

531

3,686

5.54049

0.374765

0.040449

0.042488

0.017335

0.046567

0.033311

0

0.006165

0.163863

3,686

73

79

50.493151

0.94841

0.975041

0

1

0.111111

true

0

0.777778

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

a61594f632b0d3d0c910eeb641612e237e504366

1,904

py

Python

orbeon_xml_api/tests/controls/test_yesno_input.py

euroblaze/orbeon-xml-api

de8bdafdb0964dc7521f6cfbdb6e7c1350d0e0fd

[ "MIT" ]

2

2017-10-03T21:01:59.000Z

2018-11-25T14:56:56.000Z

orbeon_xml_api/tests/controls/test_yesno_input.py

euroblaze/orbeon-xml-api

de8bdafdb0964dc7521f6cfbdb6e7c1350d0e0fd

[ "MIT" ]

15

2017-06-21T22:03:10.000Z

2020-01-24T14:41:58.000Z

orbeon_xml_api/tests/controls/test_yesno_input.py

bobslee/orbeon_xml_api

de8bdafdb0964dc7521f6cfbdb6e7c1350d0e0fd

[ "MIT" ]

5

2018-01-19T07:39:18.000Z

2022-02-05T18:45:58.000Z

# -*- coding: utf-8 -*- # Copyright 2017-2018 Bob Leers (http://www.novacode.nl) # See LICENSE file for full licensing details. from . import CommonTestCase from ..controls import BooleanControl class YesnoInputTestCase(CommonTestCase): def setUp(self): super(YesnoInputTestCase, self).setUp() self.control = self.builder.controls['yesno-input'] def test_control(self): self.assertIsInstance(self.control, BooleanControl) def test_builder_bind(self): self.assertEqual(self.control._bind.id, 'yesno-input-bind') self.assertEqual(self.control._bind.name, 'yesno-input') def test_builder_parent(self): self.assertEqual(self.control._parent._bind.id, 'selection-controls-bind') self.assertEqual(self.control._parent._bind.name, 'selection-controls') self.assertEqual(self.control._parent._resource_element.label, 'Selection Controls') def test_builder_form(self): self.assertEqual(self.control._resource_element.label, 'Yes/No Answer') self.assertEqual(self.control._resource_element.hint, None) self.assertEqual(self.control.label, 'Yes/No Answer') self.assertEqual(self.control.hint, None) def test_builder_form_default_value(self): self.assertEqual(self.control.default_raw_value, 'false') self.assertEqual(self.control.default_value, False) def test_runner_value(self): self.assertEqual(self.runner.get_raw_value('yesno-input').text, 'true') self.assertEqual(self.runner.get_value('yesno-input'), True) def test_runner_form(self): self.assertEqual(self.runner.form.yesnoinput.label, 'Yes/No Answer') self.assertEqual(self.runner.form.yesnoinput.choice_label, 'Yes') self.assertEqual(self.runner.form.yesnoinput.choice_value, True) self.assertEqual(self.runner.form.yesnoinput.choice, {'Yes': True})

39.666667

92

0.720063

234

1,904

5.705128

0.260684

0.191011

0.241948

0.214232

0.504869

0.301124

0.17603

0.062921

0

0.005597

0.155462

1,904

47

93

40.510638

0.824627

0.06355

0

0.097246

0.012929

0

0.580645

1

0.258065

false

0

0.064516

0

0.354839

0

null

0

1

0

null

0

1

0

1

0

4

a61a78e1cab1f03c9d0461743ce06ad3357c0c65

88

py

Python

deepanimebot/wsgi.py

jmuddappa/DeepClassificationBot

70aaa6787cf02e8a6b49a913af6496bc0f288b35

[ "MIT" ]

null

deepanimebot/wsgi.py

jmuddappa/DeepClassificationBot

70aaa6787cf02e8a6b49a913af6496bc0f288b35

[ "MIT" ]

null

deepanimebot/wsgi.py

jmuddappa/DeepClassificationBot

70aaa6787cf02e8a6b49a913af6496bc0f288b35

[ "MIT" ]

null

# -*- coding: utf-8 -*- from deepanimebot.webapp import create_app app = create_app()

14.666667

42

0.693182

12

88

4.916667

0.75

0.305085

0

0.013514

0.159091

88

5

43

17.6

0.783784

0.238636

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

4

a63ae79dab43a45f9796020acdffd2b083f3767f

237

py

Python

ex006.py

thaisouza30/Exercicios-Python3-Curso-em-Video

ec9ccf57fae7bd86ec7a80efb1df779dd2128154

[ "Apache-2.0" ]

1

2021-02-01T17:22:11.000Z

ex006.py

thaisouza30/Exercicios-Python3-Curso-em-Video

ec9ccf57fae7bd86ec7a80efb1df779dd2128154

[ "Apache-2.0" ]

null

ex006.py

thaisouza30/Exercicios-Python3-Curso-em-Video

ec9ccf57fae7bd86ec7a80efb1df779dd2128154

[ "Apache-2.0" ]

null

n = int(input('Digite um número:')) d = n * 2 t = n * 3 r = n ** (1/2) print('O dobro de {} é igual a {}'.format(n,d)) print('O triplo de {} é igual a {}'.format(n,t)) print('A raiz quadrada de {} é igual a {:.3f}'.format(n,r))

26.333333

60

0.535865

48

237

2.645833

0.5

0.070866

0.188976

0.212598

0.251969

0

0.027322

0.227848

237

8

61

29.625

0.666667

0

0.473684

0

1

0

false

0

0.428571

0

null

0

1

0

1

0

null

0

1

0

4

a653a0a0f95f45f0410d7198d1c7494621ed44b5

4,643

py

Python

python/src/all_sql_leave_one_out.py

antlr/groom

909c04b386c6d384344cd0d060dd1e3b4bde77a2

[ "BSD-2-Clause" ]

408

2016-04-21T09:40:08.000Z

2022-03-22T02:05:29.000Z

python/src/all_sql_leave_one_out.py

antlr/groom

909c04b386c6d384344cd0d060dd1e3b4bde77a2

[ "BSD-2-Clause" ]

25

2016-01-24T17:28:49.000Z

2021-05-05T19:17:55.000Z

python/src/all_sql_leave_one_out.py

antlr/groom

909c04b386c6d384344cd0d060dd1e3b4bde77a2

[ "BSD-2-Clause" ]

78

2016-02-14T07:22:21.000Z

2022-02-10T08:23:12.000Z

# # AUTO-GENERATED FILE. DO NOT EDIT # CodeBuff 1.4.15 'Wed May 18 13:26:32 PDT 2016' # import matplotlib.pyplot as plt sqlite_noisy_dist = [0.09836066, 0.022033898, 0.17368421, 0.2141707, 0.18779589, 0.14686924, 0.18441814, 0.054758802, 0.124442354, 0.3275449, 0.30130294, 0.18965517, 0.25156575, 0.17710997, 0.15145873, 0.07841709, 0.13931298, 0.05945604, 0.12756215, 0.17483108, 0.25382832, 0.1692503, 0.050943397, 0.29538462, 0.29448563, 0.096440874, 0.17417678, 0.10140562, 0.42797562, 0.18231541, 0.2881356, 0.12930804, 0.13706197, 0.4048535, 0.25200784, 0.2426739] sqlite_noisy_err = [0.13043478, 0.05154639, 0.16666667, 0.19277108, 0.21613833, 0.16488223, 0.20158103, 0.23076923, 0.3004695, 0.1767442, 0.2877698, 0.35341364, 0.14965986, 0.17948718, 0.13783784, 0.16071428, 0.2375, 0.0954142, 0.14021571, 0.3480663, 0.18771331, 0.11507192, 0.12962963, 0.25609756, 0.082474224, 0.17699115, 0.18137255, 0.12195122, 0.2031746, 0.20421052, 0.15662651, 0.24232633, 0.16241299, 0.15104167, 0.23387873, 0.20476191] sqlite_dist = [0.051136363, 0.12647554, 0.13404508, 0.1653944, 0.100965105, 0.13206628, 0.12976141, 0.053326294, 0.1389049, 0.29259777, 0.08171206, 0.073387094, 0.12825397, 0.16488846, 0.21766561, 0.17828201, 0.06295051, 0.12, 0.017766498, 0.08073557, 0.15343915, 0.1861004, 0.23136717, 0.042955328, 0.09806695, 0.04945904, 0.14314449, 0.045454547, 0.01772264, 0.3630448, 0.24878557, 0.06834991, 0.13519663, 0.23031302, 0.29133984, 0.20620084] sqlite_err = [0.02173913, 0.13095239, 0.09322034, 0.13978495, 0.112526536, 0.12707183, 0.20600858, 0.09163347, 0.1446281, 0.08108108, 0.06081081, 0.08365019, 0.088652484, 0.103896104, 0.13819095, 0.12345679, 0.086448595, 0.105882354, 0.033492822, 0.096352376, 0.11954766, 0.15254237, 0.14150943, 0.07826087, 0.109725684, 0.06410257, 0.120440066, 0.045454547, 0.02793296, 0.13432837, 0.14677104, 0.070336394, 0.11606218, 0.11358025, 0.16992882, 0.13303168] tsql_noisy_dist = [0.0726257, 0.022033898, 0.23188406, 0.18421052, 0.1662283, 0.14548802, 0.11126827, 0.16565247, 0.09313155, 0.39137214, 0.30509746, 0.26384366, 0.302714, 0.056074765, 0.11660079, 0.09351145, 0.066521004, 0.1731419, 0.09747056, 0.2146606, 0.29583976, 0.33452633, 0.035849057, 0.13214473, 0.09190809, 0.058553386, 0.15247019, 0.32157692, 0.23518687, 0.13145539, 0.12927757, 0.14373498, 0.3435682, 0.16183333, 0.2810994, 0.25643808] tsql_noisy_err = [0.13333334, 0.05102041, 0.20731707, 0.19318181, 0.24431819, 0.18859649, 0.2746479, 0.1983471, 0.22891566, 0.23041475, 0.39112905, 0.23404256, 0.21917808, 0.16788322, 0.16666667, 0.2125, 0.12280702, 0.35359117, 0.12654321, 0.19186492, 0.26993865, 0.07020548, 0.083333336, 0.25, 0.11873351, 0.112068966, 0.1407767, 0.13370998, 0.2079832, 0.087628864, 0.18769231, 0.24183007, 0.11627907, 0.13879408, 0.20209724, 0.21345165] tsql_dist = [0.051136363, 0.0994941, 0.107947804, 0.14492753, 0.09844054, 0.086391434, 0.13415655, 0.39455307, 0.040126715, 0.1723343, 0.089516126, 0.050583657, 0.09372893, 0.12512124, 0.29865205, 0.13614263, 0.047573283, 0.086045824, 0.01427372, 0.08557879, 0.106541604, 0.18301158, 0.04639175, 0.21465969, 0.054929577, 0.012269938, 0.11664296, 0.12520953, 0.024550635, 0.22519083, 0.117156476, 0.18596148, 0.118186206, 0.19769357, 0.19796954, 0.12833889] tsql_err = [0.02173913, 0.11764706, 0.075630255, 0.14893617, 0.08310992, 0.11612903, 0.13983051, 0.10358566, 0.09589041, 0.1125, 0.11026616, 0.046979867, 0.075, 0.07692308, 0.13, 0.083333336, 0.07339449, 0.11126374, 0.023809524, 0.10192024, 0.11556982, 0.09677419, 0.060869563, 0.1577287, 0.07263923, 0.007575758, 0.047826085, 0.10996564, 0.083798885, 0.14563107, 0.069536425, 0.07230769, 0.08527919, 0.1127451, 0.11852502, 0.07671601] language_data = [sqlite_dist, sqlite_err, sqlite_noisy_dist, sqlite_noisy_err, tsql_dist, tsql_err, tsql_noisy_dist, tsql_noisy_err] labels = ["sqlite\nn=36", "sqlite_err\nn=36", "sqlite_noisy\nn=36", "sqlite_noisy_err\nn=36", "tsql\nn=36", "tsql_err\nn=36", "tsql_noisy\nn=36", "tsql_noisy_err\nn=36"] fig = plt.figure() ax = plt.subplot(111) ax.boxplot(language_data, whis=[10, 90], # 10 and 90 % whiskers widths=.35, labels=labels) ax.set_xticklabels(labels, rotation=60, fontsize=8) plt.xticks(range(1,len(labels)+1), labels, rotation=60) ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5) ax.set_xlabel("Grammar and corpus size") ax.set_ylabel("Edit distance / size of file") ax.set_title("Leave-one-out Validation Using Edit Distance / Error Rate\nBetween Formatted and Original File") plt.tight_layout() fig.savefig('images/all_sql_leave_one_out.pdf', format='pdf') plt.show()

140.69697

456

0.7437

774

4,643

4.405685

0.483204

0.009384

0.008211

0.008798

0

0.625564

0.093474

4,643

32

457

145.09375

0.184604

0.021538

0

1

0

0.071192

0.011902

0

1

0

false

0

0.038462

0

0.038462

0

null

0

1

0

1

0

1

0

null

0

4

a6917d30d39c576a95090cfd10f0cc82205a48d3

285

py

Python

activitysim/abm/tables/__init__.py

SEMCOG/SEMCOG_ActSim

cc18cce84b2e4b5f380f58c7919953d2cd03ee73

[ "BSD-3-Clause" ]

null

activitysim/abm/tables/__init__.py

SEMCOG/SEMCOG_ActSim

cc18cce84b2e4b5f380f58c7919953d2cd03ee73

[ "BSD-3-Clause" ]

1

2021-06-30T23:39:37.000Z

activitysim/abm/tables/__init__.py

SEMCOG/SEMCOG_ActSim

cc18cce84b2e4b5f380f58c7919953d2cd03ee73

[ "BSD-3-Clause" ]

null

# ActivitySim # See full license in LICENSE.txt. from . import households from . import persons from . import landuse from . import skims from . import tours from . import size_terms from . import trips from . import time_windows from . import shadow_pricing from . import table_dict

20.357143

34

0.778947

41

285

5.317073

0.536585

0.458716

0

0.17193

285

13

35

21.923077

0.923729

0.154386

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

4

a6dbdc89446022940fa3d39d8a5ae4dddbf2cb20

122

py

Python

src/columnize/menu/apps.py

StepanBakshayev/columnize

b4189ea1cb65635924f87fb61ebc8c069f6b023f

[ "MIT" ]

null

src/columnize/menu/apps.py

StepanBakshayev/columnize

b4189ea1cb65635924f87fb61ebc8c069f6b023f

[ "MIT" ]

null

src/columnize/menu/apps.py

StepanBakshayev/columnize

b4189ea1cb65635924f87fb61ebc8c069f6b023f

[ "MIT" ]

null

from django.apps import AppConfig class MenuConfig(AppConfig): name = 'columnize.menu' label = 'columnize_menu'

17.428571

33

0.729508

14

122

6.285714

0.785714

0.295455

0

0.180328

122

6

34

20.333333

0.88

0

0.229508

0

1

0

false

0

0.25

0

1

0

1

0

null

1

0

1

0

null

0

1

0

4

4708f05dc6e6de5209c8415b368eeb890cf68118

1,319

py

Python

ampel/template/ChannelWithProcsTemplate.py

mafn/Ampel-core

744acbf36f0a2ceae7230ceab1350236c1501b57

[ "BSD-3-Clause" ]

null

ampel/template/ChannelWithProcsTemplate.py

mafn/Ampel-core

744acbf36f0a2ceae7230ceab1350236c1501b57

[ "BSD-3-Clause" ]

null

ampel/template/ChannelWithProcsTemplate.py

mafn/Ampel-core

744acbf36f0a2ceae7230ceab1350236c1501b57

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # File: Ampel-core/ampel/template/ChannelWithProcsTemplate.py # License: BSD-3-Clause # Author: valery brinnel <firstname.lastname@gmail.com> # Date: 16.10.2019 # Last Modified Date: 05.01.2022 # Last Modified By: valery brinnel <firstname.lastname@gmail.com> from ampel.log.AmpelLogger import AmpelLogger from typing import Any from ampel.config.builder.FirstPassConfig import FirstPassConfig from ampel.abstract.AbsChannelTemplate import AbsChannelTemplate from ampel.model.ChannelModel import ChannelModel class ChannelWithProcsTemplate(AbsChannelTemplate): """ Convenience class allowing channel definitions to include processes. """ # Note: not using list[ProcessModel] on purpose since embedded processes # might need template processing as well process: list[dict[str, Any]] def get_channel(self, logger: AmpelLogger) -> dict[str, Any]: return self.dict(include=ChannelModel.get_model_keys()) def get_processes(self, logger: AmpelLogger, first_pass_config: FirstPassConfig) -> list[dict[str, Any]]: # Note: not enforcing channel selection for t3 processes # as these could require template processing first return [ self.transfer_channel_parameters(p) for p in self.process ]

36.638889

106

0.74602

162

1,319

6.024691

0.561728

0.036885

0.030738

0.061475

0.077869

0

0.017257

0.165277

1,319

35

107

37.685714

0.86921

0.480667

0

1

0.142857

false

0.142857

0.357143

0.142857

0.785714

0

null

0

null

0

1

0

4

4711e0c79a281f5fadf51d0a70146c25fa121cb6

117

py

Python

simplarchiver/example/file/__init__.py

yindaheng98/simple-archiver

9563679a455491734899eeaf6226f066da6dfc88

[ "MIT" ]

2

2021-10-01T10:37:33.000Z

2021-11-15T09:39:56.000Z

simplarchiver/example/file/__init__.py

yindaheng98/simple-archiver

9563679a455491734899eeaf6226f066da6dfc88

[ "MIT" ]

null

simplarchiver/example/file/__init__.py

yindaheng98/simple-archiver

9563679a455491734899eeaf6226f066da6dfc88

[ "MIT" ]

null

from .update import CentralizedUpdateDownloader from .file import FileFeeder, DirFeeder, WalkFeeder, ExtFilterFeeder

39

68

0.863248

11

117

9.181818

0.818182

0

0.094017

117

2

69

58.5

0.95283

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

4721810ac69033766220e843cfad2f07c8f3ab95

257

py

Python

web/web/views.py

jnvilo/django-bootstrap-template

fb62caf32ae9a43d77d1da5a19c9834168d8f095

[ "MIT" ]

null

web/web/views.py

jnvilo/django-bootstrap-template

fb62caf32ae9a43d77d1da5a19c9834168d8f095

[ "MIT" ]

4

2021-03-30T14:15:36.000Z

2021-09-22T19:31:48.000Z

web/web/views.py

jnvilo/django-bootstrap-template

fb62caf32ae9a43d77d1da5a19c9834168d8f095

[ "MIT" ]

null

from django.views import View from django import http from django import shortcuts class BaseView(View): template_name = "base.html" def get(self, request, **kwargs): return shortcuts.render(request, self.template_name, )

23.363636

62

0.684825

32

257

5.4375

0.625

0.172414

0.183908

0

0.233463

257

11

63

23.363636

0.883249

0

0.034884

0

1

0.142857

false

0

0.428571

0.142857

1

0

null

0

1

0

null

0

1

0

4

5b67e0d6dd2ae8a5694f4afff74385028cc70f2b

97

py

Python

python/testData/addImport/localImport.after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/addImport/localImport.after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/addImport/localImport.after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def func(): try: import module module # <ref> except: pass

12.125

21

0.42268

9

97

4.555556

0.888889

0

0.494845

97

7

22

13.857143

0.836735

0.051546

0

1

0.166667

true

0.166667

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

4

5b76749e536646e392ae50b2df5848066ff99fd7

2,680

py

Python

forte/data/ontology/test/test_outputs/ft/onto/stanfordnlp_ontology.py

tcl326/forte

d0d7b8b97da5e1d507dfa7cd4ec51d96067770b8

[ "Apache-2.0" ]

null

forte/data/ontology/test/test_outputs/ft/onto/stanfordnlp_ontology.py

tcl326/forte

d0d7b8b97da5e1d507dfa7cd4ec51d96067770b8

[ "Apache-2.0" ]

13

2019-12-01T04:51:38.000Z

2020-02-11T23:55:11.000Z

forte/data/ontology/test/test_outputs/ft/onto/stanfordnlp_ontology.py

tcl326/forte

d0d7b8b97da5e1d507dfa7cd4ec51d96067770b8

[ "Apache-2.0" ]

null

# ***automatically_generated*** # flake8: noqa # mypy: ignore-errors # pylint: skip-file """ Automatically generated file. Do not change manually. """ import forte.data.data_pack import forte.data.ontology.top import ft.onto import typing __all__ = [] __all__.extend('Token') class Token(forte.data.ontology.top.Annotation): def __init__(self, pack: forte.data.base_pack.PackType, begin: int, end: int): super().__init__(pack, begin, end) self._lemma: typing.Optional[str] = None self._pos_tag: typing.Optional[str] = None self._upos: typing.Optional[str] = None self._xpos: typing.Optional[str] = None @property def lemma(self): return self._lemma def set_lemma(self, lemma: typing.Optional[str]): self.set_fields(_lemma=lemma) @property def pos_tag(self): return self._pos_tag def set_pos_tag(self, pos_tag: typing.Optional[str]): self.set_fields(_pos_tag=pos_tag) @property def upos(self): return self._upos def set_upos(self, upos: typing.Optional[str]): self.set_fields(_upos=upos) @property def xpos(self): return self._xpos def set_xpos(self, xpos: typing.Optional[str]): self.set_fields(_xpos=xpos) __all__.extend('Sentence') class Sentence(forte.data.ontology.top.Annotation): def __init__(self, pack: forte.data.base_pack.PackType, begin: int, end: int): super().__init__(pack, begin, end) self._tokens: typing.Optional[typing.List[ft.onto.stanfordnlp_ontology.Token]] = None @property def tokens(self): return self._tokens def set_tokens(self, tokens: typing.Optional[typing.List[ft.onto.stanfordnlp_ontology.Token]]): self.set_fields(_tokens=[item.tid for item in tokens]) __all__.extend('Document') class Document(forte.data.ontology.top.Annotation): def __init__(self, pack: forte.data.base_pack.PackType, begin: int, end: int): super().__init__(pack, begin, end) __all__.extend('Dependency') class Dependency(forte.data.ontology.top.Link): parent_type: ft.onto.stanfordnlp_ontology.Token = None child_type: ft.onto.stanfordnlp_ontology.Token = None def __init__(self, pack: forte.data.base_pack.PackType, parent: typing.Optional[forte.data.ontology.core.Entry] = None, child: typing.Optional[forte.data.ontology.core.Entry] = None): super().__init__(pack, parent, child) self._rel_type: typing.Optional[str] = None @property def rel_type(self): return self._rel_type def set_rel_type(self, rel_type: typing.Optional[str]): self.set_fields(_rel_type=rel_type)

26.534653

187

0.691045

362

2,680

4.81768

0.18232

0.112385

0.097477

0.057339

0.584862

0.53211

0.37328

0.329702

0.279243

0.256307

0

0.000457

0.183955

2,680

100

188

26.8

0.796982

0.050373

0

0.20339

1

0

0.012234

0

1

0.271186

false

0

0.067797

0.101695

0.542373

0

null

0

1

0

null

0

1

0

1

0

4

5bd7b4a61816559713cb77b56ff1187cf244f36d

47

py

Python

src/images/__init__.py

bartoszcholewa/django-learning

cd1e2c7f4b9753c9930cb83d350f8e84b4d3837b

[ "MIT" ]

3

2017-04-25T10:19:02.000Z

2017-06-07T12:50:30.000Z

src/images/__init__.py

bartoszcholewa/django-learning

cd1e2c7f4b9753c9930cb83d350f8e84b4d3837b

[ "MIT" ]

null

src/images/__init__.py

bartoszcholewa/django-learning

cd1e2c7f4b9753c9930cb83d350f8e84b4d3837b

[ "MIT" ]

null

default_app_config = 'images.apps.ImagesConfig'

47

0.851064

6

47

6.333333

1

0

0.042553

47

1

47

0.844444

0

0.5

0

1

0

false

0

1

0

null

0

1

0

1

null

0

4

5be1ef5846bea87e71e1fa0aa314def275bb9883

24

py

Python

customjson/cards/__init__.py

nikrolls/customjson

643eee2b44e066ba5dd26d640fa47800782cd5a6

[ "MIT" ]

5

2019-01-06T22:06:25.000Z

2020-12-23T08:41:24.000Z

customjson/cards/__init__.py

nikrolls/customjson

643eee2b44e066ba5dd26d640fa47800782cd5a6

[ "MIT" ]

3

2019-01-12T23:12:07.000Z

2019-06-02T19:01:55.000Z

customjson/cards/__init__.py

nikrolls/customjson

643eee2b44e066ba5dd26d640fa47800782cd5a6

[ "MIT" ]

6

2019-04-05T01:37:23.000Z

2019-05-22T21:59:00.000Z

"""Initialize cards."""

12

23

0.625

2

24

7.5

1

0

0.083333

24

1

24

0.681818

0.708333

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

5be751c2aa284ed959be440cb855440f3af5471a

135

py

Python

python/11172.py

ThePeeps191/online-judge-solutions

1cc7d26233c7bd2da23b82ac0fd1d4132cf8d0ad

[ "MIT" ]

1

2022-03-14T22:53:44.000Z

python/11172.py

ThePeeps191/online-judge-solutions

1cc7d26233c7bd2da23b82ac0fd1d4132cf8d0ad

[ "MIT" ]

null

python/11172.py

ThePeeps191/online-judge-solutions

1cc7d26233c7bd2da23b82ac0fd1d4132cf8d0ad

[ "MIT" ]

null

for _ in range(int(input())): a, b = [int(i) for i in input().split()] if a > b: print(">") elif a < b: print("<") else: print("=")

27

41

0.518519

24

135

2.875

0.541667

0.086957

0.202899

0

0.2

135

5

42

27

0.638889

0

0.022059

0

1

0

false

0

0.6

1

0

null

0

1

0

1

0

null

0

1

0

4

7513193e8171fb30cae7c4519e34b970306737a8

108

py

Python

fornecedor/admin.py

Moisestuli/karrata

962ce0c573214bfc83720727c9cacae823a8c372

[ "MIT" ]

null

fornecedor/admin.py

Moisestuli/karrata

962ce0c573214bfc83720727c9cacae823a8c372

[ "MIT" ]

null

fornecedor/admin.py

Moisestuli/karrata

962ce0c573214bfc83720727c9cacae823a8c372

[ "MIT" ]

null

from django.contrib import admin from fornecedor.models import Fornecedor admin.site.register(Fornecedor)

18

40

0.842593

14

108

6.5

0.642857

0

0.101852

108

5

41

21.6

0.938144

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

7555dd7722fe313b28f794803113b47431edad05

108

py

Python

flat_old_style_app/apps.py

thinkAmi-sandbox/Django_AppConfig-sample

14e2018dcaf31c6a615e615fb4b1ae713ea56416

[ "Unlicense" ]

null

flat_old_style_app/apps.py

thinkAmi-sandbox/Django_AppConfig-sample

14e2018dcaf31c6a615e615fb4b1ae713ea56416

[ "Unlicense" ]

null

flat_old_style_app/apps.py

thinkAmi-sandbox/Django_AppConfig-sample

14e2018dcaf31c6a615e615fb4b1ae713ea56416

[ "Unlicense" ]

null

from django.apps import AppConfig class FlatOldStyleAppConfig(AppConfig): name = 'flat_old_style_app'

18

39

0.796296

13

108

6.384615

0.923077

0

0.138889

108

5

40

21.6

0.892473

0

0.166667

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

f3a6f7bd0d8c5e8a391a053dc772107e01d24263

245

py

Python

azure_monitor/src/azure_monitor/__init__.py

victoraugustolls/opentelemetry-exporters-python

301ebb4cc7268def80e39a3978cdcf249e9c38dd

[ "MIT" ]

null

azure_monitor/src/azure_monitor/__init__.py

victoraugustolls/opentelemetry-exporters-python

301ebb4cc7268def80e39a3978cdcf249e9c38dd

[ "MIT" ]

null

azure_monitor/src/azure_monitor/__init__.py

victoraugustolls/opentelemetry-exporters-python

301ebb4cc7268def80e39a3978cdcf249e9c38dd

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. from azure_monitor.trace import AzureMonitorSpanExporter from azure_monitor.version import __version__ # noqa __all__ = ["AzureMonitorSpanExporter"]

35

59

0.820408

27

245

7.074074

0.740741

0.094241

0.167539

0

0.118367

245

6

60

40.833333

0.884259

0.383673

0

0.163265

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

f3b3e70f61f0566d39f93d1f3973257411161204

74

py

Python

xeden/toolchain/__init__.py

flieger19/xEDEN

18eb090f6a9c91b7a891a2572ae43a3c2c691e5a

[ "MIT" ]

1

2020-09-18T18:40:40.000Z

xeden/toolchain/__init__.py

flieger19/xEDEN

18eb090f6a9c91b7a891a2572ae43a3c2c691e5a

[ "MIT" ]

null

xeden/toolchain/__init__.py

flieger19/xEDEN

18eb090f6a9c91b7a891a2572ae43a3c2c691e5a

[ "MIT" ]

null

""" Documentation, License etc. @package X-EDEN Toolchain generation """

12.333333

36

0.72973

8

74

6.75

1

0

0.135135

74

5

37

14.8

0.84375

0.878378

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

4

f3ba28052bd18e41c206180f704398c723bf2635

1,411

py

Python

tests/029.py

abawchen/leetcode

41d3b172a7694a46a860fbcb0565a3acccd000f2

[ "MIT" ]

null

tests/029.py

abawchen/leetcode

41d3b172a7694a46a860fbcb0565a3acccd000f2

[ "MIT" ]

null

tests/029.py

abawchen/leetcode

41d3b172a7694a46a860fbcb0565a3acccd000f2

[ "MIT" ]

null

import unittest from operator import truediv import sys sys.path.append('./') solutions = __import__('solutions.029_divide_two_integers', fromlist='*') class Test029(unittest.TestCase): def test_divide(self): s = solutions.Solution() dividend, divisor = 49, 7 self.assertEqual(s.divide(dividend, divisor), self._divide(dividend, divisor)) dividend, divisor = 55, 7 self.assertEqual(s.divide(dividend, divisor), self._divide(dividend, divisor)) dividend, divisor = 56, 7 self.assertEqual(s.divide(dividend, divisor), self._divide(dividend, divisor)) dividend, divisor = 56+29, 7 self.assertEqual(s.divide(dividend, divisor), self._divide(dividend, divisor)) dividend, divisor = -1020450018, 2091335377 self.assertEqual(s.divide(dividend, divisor), self._divide(dividend, divisor)) dividend, divisor = -2147483648, -1 self.assertEqual(s.divide(dividend, divisor), 2147483647) dividend, divisor = -999511578, -2147483648 self.assertEqual(s.divide(dividend, divisor), self._divide(dividend, divisor)) dividend, divisor = -2147483648, 1 self.assertEqual(s.divide(dividend, divisor), self._divide(dividend, divisor)) def _divide(self, dividend, divisor): return int(truediv(dividend, divisor)) if __name__ == '__main__': unittest.main()

31.355556

86

0.678242

155

1,411

6.019355

0.264516

0.401929

0.337621

0.188639

0.633441

0

0.080674

0.200567

1,411

44

87

32.068182

0.746454

0

0.25

0

0.031184

0.023388

0

0.285714

1

0.071429

false

0

0.142857

0.035714

0.285714

0

null

1

0

1

0

null

0

4

f3bb3c9fead4d4ec5ed7df66887d32939402e380

259

py

Python

wsrpc_aiohttp/websocket/decorators.py

dizballanze/wsrpc-aiohttp

db75acfd331cceda420f4a6142399951eff13656

[ "MIT" ]

25

2017-09-25T19:45:24.000Z

2022-02-09T23:37:57.000Z

wsrpc_aiohttp/websocket/decorators.py

dizballanze/wsrpc-aiohttp

db75acfd331cceda420f4a6142399951eff13656

[ "MIT" ]

19

2017-08-08T08:55:40.000Z

2022-02-28T15:02:24.000Z

wsrpc_aiohttp/websocket/decorators.py

dizballanze/wsrpc-aiohttp

db75acfd331cceda420f4a6142399951eff13656

[ "MIT" ]

13

2017-09-13T11:01:41.000Z

2021-05-11T19:59:17.000Z

from functools import partial class ProxyBase(partial): pass class NoProxyFunction(ProxyBase): pass class ProxyFunction(ProxyBase): pass def noproxy(func): return NoProxyFunction(func) def proxy(func): return ProxyFunction(func)

11.772727

33

0.72973

28

259

6.75

0.5

0.095238

0

0.196911

259

21

34

12.333333

0.908654

0

0.272727

0

1

0.181818

false

0.272727

0.090909

0.181818

0.727273

0

1

0

null

0

null

0

1

0

1

0

4

f3c98f7f235397c5ca72e4826c1e22bdbfcbb84f

28

py

Python

config.py

Daniil-Pozdnyakov/Refresh_bot

5666b6abe12f5687fc8665cbf19477ef70510278

[ "MIT" ]

null

config.py

Daniil-Pozdnyakov/Refresh_bot

5666b6abe12f5687fc8665cbf19477ef70510278

[ "MIT" ]

null

config.py

Daniil-Pozdnyakov/Refresh_bot

5666b6abe12f5687fc8665cbf19477ef70510278

[ "MIT" ]

null

SLACK_API_KEY="Your API key"

28

0.821429

6

28

3.5

0.666667

0.571429

0

0.071429

28

1

28

0.807692

0

0.413793

0

1

0

false

0

1

0

null

1

0

1

0

null

0

4

45f096891383e10b64b3cecf4de511fc60e5e163

162

py

Python

example_snippets/multimenus_snippets/Snippets/SciPy/Integration and ODE solvers/Integrate given function object/General-purpose integration.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

null

example_snippets/multimenus_snippets/Snippets/SciPy/Integration and ODE solvers/Integrate given function object/General-purpose integration.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

null

example_snippets/multimenus_snippets/Snippets/SciPy/Integration and ODE solvers/Integrate given function object/General-purpose integration.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

1

2021-02-04T04:51:48.000Z

from scipy import integrate def f(x, a, b): return a * x + b integral,error = integrate.quad(f, 0, 4.5, args=(2,1)) # integrates 2*x+1 print(integral, error)

32.4

74

0.660494

31

162

3.451613

0.677419

0.242991

0

0.052632

0.179012

162

5

75

32.4

0.75188

0.098765

0

1

0.2

false

0

0.2

0.6

0.2

0

null

1

0

1

0

null

0

1

0

4

3401dab7126e0239b87ef3cd02922d203eb8d4f5

134

py

Python

api/urls.py

kneeraazon01/Site-monitoring-System

ac4e6e960556814a15e83b5d46c0421416be9da5

[ "Apache-2.0" ]

2

2021-04-10T19:16:00.000Z

2021-04-10T19:40:42.000Z

api/urls.py

kneeraazon01/Site-monitoring-System

ac4e6e960556814a15e83b5d46c0421416be9da5

[ "Apache-2.0" ]

null

api/urls.py

kneeraazon01/Site-monitoring-System

ac4e6e960556814a15e83b5d46c0421416be9da5

[ "Apache-2.0" ]

null

from django.urls import path from . import views urlpatterns = [path("", views.apiOverview, name="home"), path("apis/", views.Apis)]

26.8

83

0.708955

18

134

5.277778

0.611111

0

0.119403

134

4

84

33.5

0.805085

0

0.067164

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

343898183c100b807de21653a2d1334cdb4f6aac

33

py

Python

notebooks/_solutions/13-raster-processing16.py

jorisvandenbossche/DS-python-geospatial

893a12edc5c203a75815f6dcb5f1e18c577c8cd5

[ "BSD-3-Clause" ]

58

2020-10-09T10:10:59.000Z

2022-03-07T14:58:07.000Z

notebooks/_solutions/13-raster-processing16.py

jorisvandenbossche/DS-python-geospatial

893a12edc5c203a75815f6dcb5f1e18c577c8cd5

[ "BSD-3-Clause" ]

24

2020-09-30T19:57:14.000Z

2021-10-05T07:21:09.000Z

notebooks/_solutions/13-raster-processing16.py

jorisvandenbossche/DS-python-geospatial

893a12edc5c203a75815f6dcb5f1e18c577c8cd5

[ "BSD-3-Clause" ]

19

2020-10-05T09:32:18.000Z

2022-03-20T00:09:14.000Z

land_use.plot.imshow(robust=True)

33

0.848485

6

33

4.5

1

0

33

1

33

0.818182

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

4

34438e125ee685228c3b316b55ec9be33f7bd0f4

183

py

Python

venv/bin/django-admin.py

rupaltotale/To-do-list-webapp

1416077f71a703cca86ad59fda03fe7f77eddc96

[ "MIT" ]

1

2021-03-28T01:44:59.000Z

venv/bin/django-admin.py

rupaltotale/To-do-list-webapp

1416077f71a703cca86ad59fda03fe7f77eddc96

[ "MIT" ]

5

2021-03-30T14:05:33.000Z

2021-09-22T19:28:39.000Z

venv/bin/django-admin.py

rupaltotale/list-it

1416077f71a703cca86ad59fda03fe7f77eddc96

[ "MIT" ]

null

#!/Users/rupalt/Desktop/Personal Projects/To-do-list-webapp/venv/bin/python3 from django.core import management if __name__ == "__main__": management.execute_from_command_line()

30.5

76

0.79235

25

183

5.36

0.92

0

0.005988

0.087432

183

5

77

36.6

0.796407

0.409836

0

0.074766

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

4

cab698f605df0fbdcf1a69fb6feb5aa18468f1a3

497

py

Python

code-exercises-etc/playtime/lesson02_99bottles.py

hannahkwarren/CLaG-Sp2016

a75862d187176d9f2f1778eb6300056364292b44

[ "MIT" ]

null

code-exercises-etc/playtime/lesson02_99bottles.py

hannahkwarren/CLaG-Sp2016

a75862d187176d9f2f1778eb6300056364292b44

[ "MIT" ]

null

code-exercises-etc/playtime/lesson02_99bottles.py

hannahkwarren/CLaG-Sp2016

a75862d187176d9f2f1778eb6300056364292b44

[ "MIT" ]

null

# Can you make Python print out the song for 99 bottles of beer on the wall? # Helpful mnemonic: range(start, stop, step) for bottles in range(99, 1, -1): print "({0} bottles of beer on the wall, {0} bottles of beer...".format(bottles) print "Take one down, toss it around, {0} bottles of beer on the wall!".format(bottles - 1) #Stolen from KristenLinkLogan print "2 bottles of beer on the wall, 2 bottles of beer..." print "Take one down, toss it around, 1 bottle of beer on the wall!"

45.181818

95

0.704225

89

497

3.932584

0.404494

0.12

0.222857

0.157143

0.46

0.417143

0.291429

0

0.032419

0.193159

497

10

96

49.7

0.840399

0.291751

0

0.66092

0

null

0

null

0.8

0

null

0

1

0

1

0

null

0

1

0

1

0

4

cad7a29a35ff7662c5765d29a1820c57fb122363

23

py

Python

codes/sse/FirstPY.py

hiperwe/Grade1

e987d37cbae73d8ad1cb60007fc1497b08c4fa01

[ "MIT" ]

1

2019-07-21T16:41:22.000Z

codes/sse/FirstPY.py

hiperwe/Grade1

e987d37cbae73d8ad1cb60007fc1497b08c4fa01

[ "MIT" ]

null

codes/sse/FirstPY.py

hiperwe/Grade1

e987d37cbae73d8ad1cb60007fc1497b08c4fa01

[ "MIT" ]

null

a = 5 b = 4 print(a+ b)

7.666667

11

0.478261

7

23

1.571429

0.714286

0

0.125

0.304348

23

3

11

7.666667

0.5625

0

1

0

false

0

0.333333

1

null

0

1

0

null

0

4

cade8a0e9598a83757dca205fcaf0046d85b424c

874

py

Python

gridaurora/zglow.py

scivision/gridaurora

89ae1b41826f971dd0e9a1329eb723116c9459c6

[ "BSD-2-Clause" ]

null

gridaurora/zglow.py

scivision/gridaurora

89ae1b41826f971dd0e9a1329eb723116c9459c6

[ "BSD-2-Clause" ]

null

gridaurora/zglow.py

scivision/gridaurora

89ae1b41826f971dd0e9a1329eb723116c9459c6

[ "BSD-2-Clause" ]

null

import numpy as np """ these are altitudes hard-coded into the old version of NCAR GLOW. """ def glowalt() -> np.ndarray: # z = range(80,110+1,1) z = np.arange(30.0, 110 + 1.0, 1.0) z = np.append(z, [111.5, 113.0, 114.5, 116.0]) z = np.append(z, np.arange(118, 150 + 2, 2.0)) z = np.append(z, np.arange(153, 168 + 3, 3.0)) z = np.append(z, np.arange(172, 180 + 4, 4.0)) z = np.append(z, np.arange(185, 205 + 5, 5)) z = np.append(z, np.arange(211, 223 + 6, 6)) z = np.append(z, np.arange(230, 244 + 7, 7)) z = np.append(z, np.arange(252, 300 + 8, 8)) z = np.append(z, np.arange(309, 345 + 9, 9)) z = np.append(z, np.arange(355, 395 + 10, 10)) z = np.append(z, np.arange(406, 428 + 11, 11)) z = np.append(z, [440.0, 453, 467, 482, 498, 515, 533, 551]) z = np.append(z, np.arange(570, 950 + 20, 20)) return z

33.615385

65

0.551487

176

874

2.738636

0.420455

0.155602

0.242739

0.26971

0.441909

0.419087

0.157676

0

0.232733

0.237986

874

25

66

34.96

0.490991

0.024027

0

1

0.058824

false

0

0.058824

0

0.176471

0

null

0

1

0

1

0

1

0

null

0

4