xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

bdd0b5a2c7eccc4bf7ae664e464e4eb1b7daf026

1,772

py

Python

carpyncho1/skdjango/management/commands/skshell.py

carpyncho/yeolde_carpyncho

fba72ebf9d4a3e4e4ea18160310058c6812a0457

[ "BSD-3-Clause" ]

null

carpyncho1/skdjango/management/commands/skshell.py

carpyncho/yeolde_carpyncho

fba72ebf9d4a3e4e4ea18160310058c6812a0457

[ "BSD-3-Clause" ]

2

2020-06-05T19:37:26.000Z

2020-06-05T19:40:38.000Z

carpyncho1/skdjango/management/commands/skshell.py

carpyncho/yeolde_carpyncho

fba72ebf9d4a3e4e4ea18160310058c6812a0457

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- #============================================================================== # DOCS #============================================================================== """Move data """ #============================================================================== # IMPORTS #============================================================================== import logging import numpy as np import matplotlib.pyplot as plt import scipy.stats as stats import pandas as pd from django_extensions.management import shells from django_extensions.management.shells import import_objects as _oio from ... import extra_stats as estats #============================================================================== # PATCH IMPORT OBJECTS #============================================================================== def sk_import_objects(*args, **kwargs): data = _oio(*args, **kwargs) data.update(np=np, plt=plt, stats=stats, estats=estats, pd=pd) return data shells.import_objects = sk_import_objects #============================================================================== # LOGGER #============================================================================== logger = logging.getLogger("carpyncho") #============================================================================== # COMMAND #============================================================================== from django_extensions.management.commands import shell_plus class Command(shell_plus.Command): pass #============================================================================== # MAIN #============================================================================== if __name__ == "__main__": print(__doc__)

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da38a5ca9427cc212f0b791e31276318e0da9206

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Python

main.py

AkshayRaul/Rise2Code_PackHackers

1caecfd7a9335b37e4c10ef6cbe7f202adaa3941

[ "MIT" ]

null

main.py

AkshayRaul/Rise2Code_PackHackers

1caecfd7a9335b37e4c10ef6cbe7f202adaa3941

[ "MIT" ]

null

main.py

AkshayRaul/Rise2Code_PackHackers

1caecfd7a9335b37e4c10ef6cbe7f202adaa3941

[ "MIT" ]

null

import relevance import main import requests from bs4 import BeautifulSoup, SoupStrainer import urllib3 import urllib from lxml import etree import html2text import re

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py

Python

main_app/admin.py

donavonelli/Wayfarer

703207f37aabfac4469929f59bd86fb3f80cc559

[ "MIT" ]

1

2020-11-19T16:07:27.000Z

main_app/admin.py

qmsparks/Wayfarer

cd67e8548131c8777632290fd89b69e7e47d0354

[ "MIT" ]

1

2020-10-14T21:36:54.000Z

main_app/admin.py

qmsparks/Wayfarer

cd67e8548131c8777632290fd89b69e7e47d0354

[ "MIT" ]

2

2020-10-14T19:45:55.000Z

2020-11-30T14:41:52.000Z

from django.contrib import admin from .models import City, Profile, Post # Register your models here. admin.site.register(City) admin.site.register(Profile) admin.site.register(Post)

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py

Python

simulation/src/simulation_evaluation/src/state_machine/__init__.py

LeonardII/KitCarFork

b2802c5b08cc8250446ce3731cb622af064db4ca

[ "MIT" ]

13

2020-06-30T17:18:28.000Z

2021-07-20T16:55:35.000Z

simulation/src/simulation_evaluation/src/state_machine/__init__.py

LeonardII/KitCarFork

b2802c5b08cc8250446ce3731cb622af064db4ca

[ "MIT" ]

1

2020-11-10T20:15:42.000Z

2020-12-25T18:27:56.000Z

simulation/src/simulation_evaluation/src/state_machine/__init__.py

LeonardII/KitCarFork

b2802c5b08cc8250446ce3731cb622af064db4ca

[ "MIT" ]

3

2020-07-20T09:09:08.000Z

2021-07-20T17:00:37.000Z

"""The StateMachineNode handels multiple state machines. It subscribes to the speaker, parses the messages to the state machines and publishes it's changes. """

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e5784c159c8aa4b5dcd479aaf551f232e404bfb4

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py

Python

st_library/utils/api_client/__init__.py

vartagg/dataprovider-py

e392af3dab21c99c51a32345710fcd0dc4023462

[ "Apache-2.0" ]

null

st_library/utils/api_client/__init__.py

vartagg/dataprovider-py

e392af3dab21c99c51a32345710fcd0dc4023462

[ "Apache-2.0" ]

2

2018-03-27T11:06:46.000Z

2020-10-27T20:48:51.000Z

st_library/utils/api_client/__init__.py

vartagg/dataprovider-py

e392af3dab21c99c51a32345710fcd0dc4023462

[ "Apache-2.0" ]

4

2018-02-26T08:12:39.000Z

2018-05-18T06:01:01.000Z

from .api_client import ApiClient

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e5b2b393dd5789680dc4cdb5b2b003b2a18e9876

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py

Python

custom/ilsgateway/migrations/0002_auto__add_organizationsummary__add_productavailabilitydata__add_supply.py

dslowikowski/commcare-hq

ad8885cf8dab69dc85cb64f37aeaf06106124797

[ "BSD-3-Clause" ]

1

2017-02-10T03:14:51.000Z

custom/ilsgateway/migrations/0002_auto__add_organizationsummary__add_productavailabilitydata__add_supply.py

dslowikowski/commcare-hq

ad8885cf8dab69dc85cb64f37aeaf06106124797

[ "BSD-3-Clause" ]

null

custom/ilsgateway/migrations/0002_auto__add_organizationsummary__add_productavailabilitydata__add_supply.py

dslowikowski/commcare-hq

ad8885cf8dab69dc85cb64f37aeaf06106124797

[ "BSD-3-Clause" ]

null

# encoding: utf-8 import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'OrganizationSummary' db.create_table(u'ilsgateway_organizationsummary', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('supply_point', self.gf('django.db.models.fields.CharField')(max_length=100, db_index=True)), ('create_date', self.gf('django.db.models.fields.DateTimeField')()), ('update_date', self.gf('django.db.models.fields.DateTimeField')()), ('external_id', self.gf('django.db.models.fields.PositiveIntegerField')(null=True, db_index=True)), ('date', self.gf('django.db.models.fields.DateTimeField')()), ('total_orgs', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), ('average_lead_time_in_days', self.gf('django.db.models.fields.FloatField')(default=0)), )) db.send_create_signal(u'ilsgateway', ['OrganizationSummary']) # Adding model 'ProductAvailabilityData' db.create_table(u'ilsgateway_productavailabilitydata', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('supply_point', self.gf('django.db.models.fields.CharField')(max_length=100, db_index=True)), ('create_date', self.gf('django.db.models.fields.DateTimeField')()), ('update_date', self.gf('django.db.models.fields.DateTimeField')()), ('external_id', self.gf('django.db.models.fields.PositiveIntegerField')(null=True, db_index=True)), ('date', self.gf('django.db.models.fields.DateTimeField')()), ('product', self.gf('django.db.models.fields.CharField')(max_length=100, db_index=True)), ('total', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), ('with_stock', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), ('without_stock', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), ('without_data', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), )) db.send_create_signal(u'ilsgateway', ['ProductAvailabilityData']) # Adding model 'SupplyPointWarehouseRecord' db.create_table(u'ilsgateway_supplypointwarehouserecord', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('supply_point', self.gf('django.db.models.fields.CharField')(max_length=100, db_index=True)), ('create_date', self.gf('django.db.models.fields.DateTimeField')()), )) db.send_create_signal(u'ilsgateway', ['SupplyPointWarehouseRecord']) # Adding model 'Alert' db.create_table(u'ilsgateway_alert', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('supply_point', self.gf('django.db.models.fields.CharField')(max_length=100, db_index=True)), ('create_date', self.gf('django.db.models.fields.DateTimeField')()), ('update_date', self.gf('django.db.models.fields.DateTimeField')()), ('external_id', self.gf('django.db.models.fields.PositiveIntegerField')(null=True, db_index=True)), ('date', self.gf('django.db.models.fields.DateTimeField')()), ('type', self.gf('django.db.models.fields.CharField')(max_length=50, null=True, blank=True)), ('number', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), ('text', self.gf('django.db.models.fields.TextField')()), ('url', self.gf('django.db.models.fields.CharField')(max_length=100, null=True, blank=True)), ('expires', self.gf('django.db.models.fields.DateTimeField')()), )) db.send_create_signal(u'ilsgateway', ['Alert']) # Adding model 'GroupSummary' db.create_table(u'ilsgateway_groupsummary', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('org_summary', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['ilsgateway.OrganizationSummary'])), ('title', self.gf('django.db.models.fields.CharField')(max_length=50, null=True, blank=True)), ('total', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), ('responded', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), ('on_time', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), ('complete', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), ('external_id', self.gf('django.db.models.fields.PositiveIntegerField')(null=True, db_index=True)), )) db.send_create_signal(u'ilsgateway', ['GroupSummary']) # Adding field 'DeliveryGroupReport.external_id' db.add_column(u'ilsgateway_deliverygroupreport', 'external_id', self.gf('django.db.models.fields.PositiveIntegerField')(null=True, db_index=True), keep_default=False) # Changing field 'DeliveryGroupReport.report_date' db.alter_column(u'ilsgateway_deliverygroupreport', 'report_date', self.gf('django.db.models.fields.DateTimeField')()) # Adding field 'SupplyPointStatus.external_id' db.add_column(u'ilsgateway_supplypointstatus', 'external_id', self.gf('django.db.models.fields.PositiveIntegerField')(null=True, db_index=True), keep_default=False) def backwards(self, orm): # Deleting model 'OrganizationSummary' db.delete_table(u'ilsgateway_organizationsummary') # Deleting model 'ProductAvailabilityData' db.delete_table(u'ilsgateway_productavailabilitydata') # Deleting model 'SupplyPointWarehouseRecord' db.delete_table(u'ilsgateway_supplypointwarehouserecord') # Deleting model 'Alert' db.delete_table(u'ilsgateway_alert') # Deleting model 'GroupSummary' db.delete_table(u'ilsgateway_groupsummary') # Deleting field 'DeliveryGroupReport.external_id' db.delete_column(u'ilsgateway_deliverygroupreport', 'external_id') # Changing field 'DeliveryGroupReport.report_date' db.alter_column(u'ilsgateway_deliverygroupreport', 'report_date', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True)) # Deleting field 'SupplyPointStatus.external_id' db.delete_column(u'ilsgateway_supplypointstatus', 'external_id') models = { u'ilsgateway.alert': { 'Meta': {'object_name': 'Alert'}, 'create_date': ('django.db.models.fields.DateTimeField', [], {}), 'date': ('django.db.models.fields.DateTimeField', [], {}), 'expires': ('django.db.models.fields.DateTimeField', [], {}), 'external_id': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'db_index': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'number': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'supply_point': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}), 'text': ('django.db.models.fields.TextField', [], {}), 'type': ('django.db.models.fields.CharField', [], {'max_length': '50', 'null': 'True', 'blank': 'True'}), 'update_date': ('django.db.models.fields.DateTimeField', [], {}), 'url': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}) }, u'ilsgateway.deliverygroupreport': { 'Meta': {'ordering': "('-report_date',)", 'object_name': 'DeliveryGroupReport'}, 'delivery_group': ('django.db.models.fields.CharField', [], {'max_length': '1'}), 'external_id': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'db_index': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'message': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}), 'quantity': ('django.db.models.fields.IntegerField', [], {}), 'report_date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2014, 10, 16, 9, 25, 21, 907582)'}), 'supply_point': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}) }, u'ilsgateway.groupsummary': { 'Meta': {'object_name': 'GroupSummary'}, 'complete': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'external_id': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'db_index': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'on_time': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'org_summary': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['ilsgateway.OrganizationSummary']"}), 'responded': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '50', 'null': 'True', 'blank': 'True'}), 'total': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}) }, u'ilsgateway.ilsmigrationcheckpoint': { 'Meta': {'object_name': 'ILSMigrationCheckpoint'}, 'api': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'date': ('django.db.models.fields.DateTimeField', [], {'null': 'True'}), 'domain': ('django.db.models.fields.CharField', [], {'max_length': '100'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'limit': ('django.db.models.fields.PositiveIntegerField', [], {}), 'offset': ('django.db.models.fields.PositiveIntegerField', [], {}) }, u'ilsgateway.organizationsummary': { 'Meta': {'object_name': 'OrganizationSummary'}, 'average_lead_time_in_days': ('django.db.models.fields.FloatField', [], {'default': '0'}), 'create_date': ('django.db.models.fields.DateTimeField', [], {}), 'date': ('django.db.models.fields.DateTimeField', [], {}), 'external_id': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'db_index': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'supply_point': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}), 'total_orgs': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'update_date': ('django.db.models.fields.DateTimeField', [], {}) }, u'ilsgateway.productavailabilitydata': { 'Meta': {'object_name': 'ProductAvailabilityData'}, 'create_date': ('django.db.models.fields.DateTimeField', [], {}), 'date': ('django.db.models.fields.DateTimeField', [], {}), 'external_id': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'db_index': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'product': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}), 'supply_point': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}), 'total': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'update_date': ('django.db.models.fields.DateTimeField', [], {}), 'with_stock': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'without_data': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'without_stock': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}) }, u'ilsgateway.supplypointstatus': { 'Meta': {'ordering': "('-status_date',)", 'object_name': 'SupplyPointStatus'}, 'external_id': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'db_index': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'status_date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.utcnow'}), 'status_type': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'status_value': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'supply_point': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}) }, u'ilsgateway.supplypointwarehouserecord': { 'Meta': {'object_name': 'SupplyPointWarehouseRecord'}, 'create_date': ('django.db.models.fields.DateTimeField', [], {}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'supply_point': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}) } } complete_apps = ['ilsgateway']

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Python

notes/admin.py

JekyllAndHyde8999/LetsNote

5ebbfbf16d95d85ed475daadf39ad3a850ac1079

[ "Apache-2.0" ]

5

2019-10-29T17:58:01.000Z

2021-01-08T09:07:43.000Z

notes/admin.py

JekyllAndHyde8999/LetsNote

5ebbfbf16d95d85ed475daadf39ad3a850ac1079

[ "Apache-2.0" ]

1

2020-06-05T20:15:04.000Z

notes/admin.py

JekyllAndHyde8999/LetsNote

5ebbfbf16d95d85ed475daadf39ad3a850ac1079

[ "Apache-2.0" ]

2

2019-04-07T00:21:49.000Z

2020-09-25T15:40:56.000Z

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

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f9233fc30e155121175590715648df755ecb95d4

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py

Python

mini/admin.py

VGichuki/minimusic

1092b42ea5b4b3f291a64814543675cdb118dde3

[ "MIT", "Unlicense" ]

null

mini/admin.py

VGichuki/minimusic

1092b42ea5b4b3f291a64814543675cdb118dde3

[ "MIT", "Unlicense" ]

null

mini/admin.py

VGichuki/minimusic

1092b42ea5b4b3f291a64814543675cdb118dde3

[ "MIT", "Unlicense" ]

null

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

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006d2fd56e6d4ba9804d6b1040cfbb3c1ddd8295

623

py

Python

units/energy/kilowatt_hours.py

putridparrot/PyUnits

4f1095c6fc0bee6ba936921c391913dbefd9307c

[ "MIT" ]

null

units/energy/kilowatt_hours.py

putridparrot/PyUnits

4f1095c6fc0bee6ba936921c391913dbefd9307c

[ "MIT" ]

null

units/energy/kilowatt_hours.py

putridparrot/PyUnits

4f1095c6fc0bee6ba936921c391913dbefd9307c

[ "MIT" ]

null

# <auto-generated> # This code was generated by the UnitCodeGenerator tool # # Changes to this file will be lost if the code is regenerated # </auto-generated> def to_kilojoules(value): return value * 3600.0 def to_kilocalories(value): return value * 860.421 def to_joules(value): return value * 3.6e+6 def to_btu(value): return value * 3412.14 def to_calories(value): return value * 860421.0 def to_u_s_therms(value): return value / 29.3001 def to_watt_hours(value): return value * 1000.0 def to_foot_pounds(value): return value / 0.00000037662 def to_electronvolts(value): return value * 2.246943e+25

23.961538

62

0.743178

102

623

4.411765

0.5

0.1

0.32

0

0.113027

0.162119

623

25

63

24.92

0.749042

0.239165

0

1

0

1

0.5

false

0

0.5

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

00a50acd477e90bcae1974f45e00734af562cb35

103

py

Python

src/lobber/share/admin.py

SUNET/lobber

2ba707ebd8a6513bff7236262930a24f5e0e9492

[ "BSD-2-Clause-FreeBSD" ]

1

2015-11-10T17:08:57.000Z

src/lobber/share/admin.py

SUNET/lobber

2ba707ebd8a6513bff7236262930a24f5e0e9492

[ "BSD-2-Clause-FreeBSD" ]

null

src/lobber/share/admin.py

SUNET/lobber

2ba707ebd8a6513bff7236262930a24f5e0e9492

[ "BSD-2-Clause-FreeBSD" ]

null

from lobber.share.models import Torrent from django.contrib import admin admin.site.register(Torrent)

20.6

39

0.834951

15

103

5.733333

0.733333

0

0.097087

103

4

40

25.75

0.924731

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

00d8a57eaf241d04957bc49f12bd88c73a191ccb

35

py

Python

modules/http_requests/__init__.py

WMDA/weapy

11eb6e24f22915116fd81da398305d9b3af79299

[ "MIT" ]

1

2021-11-17T09:49:48.000Z

modules/http_requests/__init__.py

WMDA/weapy

11eb6e24f22915116fd81da398305d9b3af79299

[ "MIT" ]

null

modules/http_requests/__init__.py

WMDA/weapy

11eb6e24f22915116fd81da398305d9b3af79299

[ "MIT" ]

null

from modules.http_requests import *

35

0.857143

5

35

5.8

1

0

0.085714

35

1

35

0.90625

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

00dddb52edfac7ff3af9c06f1bf03384e281ce92

189

py

Python

Patient/admin.py

s0hailAnsari/ARCIT

b1d6a0596efaa887a498c518e6a387adc7ec12c6

[ "MIT" ]

null

Patient/admin.py

s0hailAnsari/ARCIT

b1d6a0596efaa887a498c518e6a387adc7ec12c6

[ "MIT" ]

20

2021-04-19T11:31:48.000Z

2021-09-07T07:51:10.000Z

Patient/admin.py

s0hailAnsari/ARCIT

b1d6a0596efaa887a498c518e6a387adc7ec12c6

[ "MIT" ]

null

from django.contrib import admin from .models import Patient, PatientHistory, Appointment admin.site.register(Patient) admin.site.register(PatientHistory) admin.site.register(Appointment)

27

56

0.84127

23

189

6.913043

0.478261

0.169811

0.320755

0

0.074074

189

6

57

31.5

0.908571

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

00f5e1455ed96092abe4bbd02875e150c56b000f

1,980

py

Python

src/server/proxyServer.py

josexy/proxyGet

4f770b176f83969a173bdf0c63846fa3ae8bcd2d

[ "MIT" ]

null

src/server/proxyServer.py

josexy/proxyGet

4f770b176f83969a173bdf0c63846fa3ae8bcd2d

[ "MIT" ]

null

src/server/proxyServer.py

josexy/proxyGet

4f770b176f83969a173bdf0c63846fa3ae8bcd2d

[ "MIT" ]

null

class ProxyServerBase(object): def __init__(self,url=None): super().__init__() self._url=url def url(): return self._url def __str__(self): return self._url def proxy_types(self): return ['common','high_anonymous','http','https'] class XilaProxy(ProxyServerBase): def __init__(self,url='http://www.xiladaili.com'): super().__init__(url=url) def commmon_proxy(self): return self._url+"/putong" def high_anonymous_proxy(self): return self._url+"/gaoni" def http_proxy(self): return self._url+"/http" def https_proxy(self): return self._url+"/https" class NimaProxy(ProxyServerBase): def __init__(self, url='http://www.nimadaili.com'): super().__init__(url=url) def commmon_proxy(self): return self._url+"/putong" def high_anonymous_proxy(self): return self._url+"/gaoni" def http_proxy(self): return self._url+"/http" def https_proxy(self): return self._url+"/https" class XiciProxy(ProxyServerBase): def __init__(self, url='https://www.xicidaili.com'): super().__init__(url=url) def commmon_proxy(self): return self._url+"/nt" def high_anonymous_proxy(self): return self._url+"/nn" def http_proxy(self): return self._url+"/wt" def https_proxy(self): return self._url+"/wn" class KuaiProxy(ProxyServerBase): def __init__(self, url='https://www.kuaidaili.com'): super().__init__(url=url) def commmon_proxy(self): return self._url+"/free/intr" def high_anonymous_proxy(self): return self._url+"/free/inha" def proxy_types(self): return ['common','high_anonymous'] class Yip7Proxy(ProxyServerBase): def __init__(self, url='https://www.7yip.cn'): super().__init__(url=url) def http_proxy(self): return self._url+'/free' def proxy_types(self): return ['http']

30

57

0.633838

250

1,980

4.648

0.168

0.144578

0.190189

0.234079

0.796041

0.763339

0.753012

0.519793

0.3821

0

0.0013

0.222727

1,980

65

58

30.461538

0.753086

0

0.559322

0

0.128918

0

null

0

null

0

null

0

1

0

1

0

null

0

1

0

5

dab9908cd93e9d06a08384046b4e376d768aeebe

53

py

Python

src/models/products/__init__.py

nnecklace/webi-shoppi

140d1e6ea8d019aa10ee2104e1bbd2baf0b9aa0f

[ "MIT" ]

null

src/models/products/__init__.py

nnecklace/webi-shoppi

140d1e6ea8d019aa10ee2104e1bbd2baf0b9aa0f

[ "MIT" ]

2

2020-06-02T13:55:02.000Z

2020-06-16T17:58:55.000Z

src/models/products/__init__.py

nnecklace/webi-shoppi

140d1e6ea8d019aa10ee2104e1bbd2baf0b9aa0f

[ "MIT" ]

null

from . import products from .products import Product

17.666667

29

0.811321

7

53

6.142857

0.571429

0

0.150943

53

2

30

26.5

0.955556

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

daba124b9b1a6ff9d4ccb535ab01c9fbf24b2fd2

37

py

Python

__init__.py

snbcypher/yolov4

2b680a928142e4edb0b2ca901554c5f8871e966a

[ "Apache-2.0" ]

null

__init__.py

snbcypher/yolov4

2b680a928142e4edb0b2ca901554c5f8871e966a

[ "Apache-2.0" ]

null

__init__.py

snbcypher/yolov4

2b680a928142e4edb0b2ca901554c5f8871e966a

[ "Apache-2.0" ]

null

import sys sys.path.append("yolov4")

12.333333

25

0.756757

6

37

4.666667

0.833333

0

0.029412

0.081081

37

3

25

12.333333

0.794118

0

0.157895

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

dac27f670b0b59c0b303e50a528e73c9dbd13a4f

91

py

Python

test_community_repos/generative_collections/__run.py

esc/builder

5af4e79729213d683df3638c1444da09c9fffe68

[ "BSD-2-Clause" ]

null

test_community_repos/generative_collections/__run.py

esc/builder

5af4e79729213d683df3638c1444da09c9fffe68

[ "BSD-2-Clause" ]

null

test_community_repos/generative_collections/__run.py

esc/builder

5af4e79729213d683df3638c1444da09c9fffe68

[ "BSD-2-Clause" ]

null

import matplotlib.pyplot matplotlib.pyplot.switch_backend('agg') import main main.main()

13

39

0.802198

12

91

6

0.583333

0.444444

0

0.087912

91

6

40

15.166667

0.86747

0

0.033333

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

dacc54c0030fc8a40133142293aafa272d0da3ec

43

py

Python

test/test_celery.py

Niracler/display-back-end

e84e95c70bc0713f29eb5ea7be70f706c7c1e746

[ "MIT" ]

null

test/test_celery.py

Niracler/display-back-end

e84e95c70bc0713f29eb5ea7be70f706c7c1e746

[ "MIT" ]

7

2020-02-12T02:37:08.000Z

2021-06-09T18:19:44.000Z

test/test_celery.py

game-news/display-back-end

e84e95c70bc0713f29eb5ea7be70f706c7c1e746

[ "MIT" ]

1

2019-08-12T00:40:11.000Z

from test.tasks import add add.delay(4, 4)

14.333333

26

0.744186

9

43

3.555556

0.777778

0

0.054054

0.139535

43

3

27

14.333333

0.810811

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

97165a060e2f57bca7f9a57987dd92189a83c951

55

py

Python

reactopya/templates/_other/NewWidget/__init__.py

flatironinstitute/reactopy

ec78d0ca6628e959017c58a6b6a09ef172de6d96

[ "Apache-2.0" ]

7

2020-03-01T22:39:49.000Z

2021-11-17T01:14:15.000Z

reactopya/templates/_other/NewWidget/__init__.py

flatironinstitute/reactopy

ec78d0ca6628e959017c58a6b6a09ef172de6d96

[ "Apache-2.0" ]

3

2019-11-29T07:12:54.000Z

2019-12-04T18:43:41.000Z

reactopya/templates/_other/NewWidget/__init__.py

flatironinstitute/reactopy

ec78d0ca6628e959017c58a6b6a09ef172de6d96

[ "Apache-2.0" ]

2

2019-12-04T18:32:59.000Z

2021-09-23T01:07:06.000Z

from .{{ NewWidget.type }} import {{ NewWidget.type }}

27.5

54

0.654545

6

55

6

0.666667

0.722222

0

0.145455

55

1

55

0.765957

0

null

0

1

null

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

973dc2a4aa1bba85e4ef4d5e3da1dbe14ccd81c6

62

py

Python

playlist.py

dylanrees/djqueue

b9809811e0b170d8e9af9184b64eee1937d6522c

[ "Unlicense" ]

null

playlist.py

dylanrees/djqueue

b9809811e0b170d8e9af9184b64eee1937d6522c

[ "Unlicense" ]

null

playlist.py

dylanrees/djqueue

b9809811e0b170d8e9af9184b64eee1937d6522c

[ "Unlicense" ]

null

#this will be the script that actually generates the playlist

31

61

0.822581

10

62

5.1

0.9

0

0.16129

62

1

62

0.980769

0.967742

0

null

1

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

1

0

null

0

1

0

5

97591de792b555ae0413ec22a1fbf809ffccc97a

120

py

Python

fastapi_rss/__init__.py

elreydetoda/fastapi_rss

94539937fed408a6918ab45a378e46a6cf179bde

[ "MIT" ]

8

2021-03-23T10:37:12.000Z

2022-02-05T07:47:12.000Z

fastapi_rss/__init__.py

elreydetoda/fastapi_rss

94539937fed408a6918ab45a378e46a6cf179bde

[ "MIT" ]

1

2022-03-25T23:26:55.000Z

2022-03-31T19:50:18.000Z

fastapi_rss/__init__.py

elreydetoda/fastapi_rss

94539937fed408a6918ab45a378e46a6cf179bde

[ "MIT" ]

3

2021-04-13T06:16:05.000Z

2022-01-13T03:38:33.000Z

# flake8: noqa __version__ = '0.1.3' from fastapi_rss.models import * from fastapi_rss.rss_response import RSSResponse

20

48

0.791667

18

120

4.888889

0.722222

0.25

0.318182

0

0.038095

0.125

120

6

48

20

0.8

0.1

0

0.046729

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

5

9773ab4e72d5cab55966870576ff7d9fc9c2d3b2

99

py

Python

datagets/__init__.py

gaford/datagets

afb34620f06d3cf82f8a67f8aefd5ebccd2b3313

[ "MIT" ]

null

datagets/__init__.py

gaford/datagets

afb34620f06d3cf82f8a67f8aefd5ebccd2b3313

[ "MIT" ]

null

datagets/__init__.py

gaford/datagets

afb34620f06d3cf82f8a67f8aefd5ebccd2b3313

[ "MIT" ]

null

""" Datagets: A collection of data science gadgets and utilities. """ from .evaluators import *

14.142857

62

0.717172

12

99

5.916667

1

0

0.181818

99

6

63

16.5

0.876543

0.626263

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

977f7fe3fd8a2b45706374c2b86af0221d50e764

126

py

Python

spexxy/grid/__init__.py

thusser/spexxy

14a8d121076b9e043bdf2e27222a65088f771ff9

[ "MIT" ]

4

2019-05-13T21:36:31.000Z

2021-09-06T01:56:36.000Z

spexxy/grid/__init__.py

thusser/spexxy

14a8d121076b9e043bdf2e27222a65088f771ff9

[ "MIT" ]

2

2020-02-12T14:36:39.000Z

2020-07-14T11:43:10.000Z

spexxy/grid/__init__.py

thusser/spexxy

14a8d121076b9e043bdf2e27222a65088f771ff9

[ "MIT" ]

1

2019-11-08T09:26:23.000Z

from .grid import Grid, GridAxis from .files import FilesGrid from .values import ValuesGrid from .synspec import SynspecGrid

25.2

32

0.825397

17

126

6.117647

0.588235

0

0.134921

126

4

33

31.5

0.954128

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

978b913bc38db978e5409fc2f729bc2a75f5181a

124

py

Python

nesmdb/vgm/__init__.py

youngmg1995/NES-Music-Maker

aeda10a541cfd439cfa46c45e63411e0d98e41c1

[ "MIT" ]

3

2020-06-26T22:02:35.000Z

2021-11-20T19:24:33.000Z

nesmdb/vgm/__init__.py

youngmg1995/NES-Music-Maker

aeda10a541cfd439cfa46c45e63411e0d98e41c1

[ "MIT" ]

null

nesmdb/vgm/__init__.py

youngmg1995/NES-Music-Maker

aeda10a541cfd439cfa46c45e63411e0d98e41c1

[ "MIT" ]

null

from .ndr_ndf import ndf_to_ndr from .vgm_ndr import ndr_to_vgm from .vgm_to_wav import vgm_to_wav, load_vgmwav, save_vgmwav

41.333333

60

0.854839

26

124

3.615385

0.384615

0.148936

0.170213

0

0.104839

124

3

60

41.333333

0.846847

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

97add447032a21e98a8c4c2ba4411315b379f5c7

125

py

Python

pkg/sub2/relative2.py

wweiradio/pkg-relative-import

c4e733c1dfd17fcf41927499f3eca07822059005

[ "Apache-2.0" ]

12

2018-04-12T20:09:59.000Z

2021-04-22T10:13:20.000Z

pkg/sub2/relative2.py

wweiradio/pkg-relative-import

c4e733c1dfd17fcf41927499f3eca07822059005

[ "Apache-2.0" ]

null

pkg/sub2/relative2.py

wweiradio/pkg-relative-import

c4e733c1dfd17fcf41927499f3eca07822059005

[ "Apache-2.0" ]

2

2018-07-10T12:36:46.000Z

2020-09-07T21:50:34.000Z

#! /usr/bin/env python # -*- coding: utf-8 __author__ = 'THINK' import parent from .. import parent print "in sub2 relative2"

20.833333

25

0.704

18

125

4.666667

0.888889

0.285714

0

0.028302

0.152

125

6

25

20.833333

0.764151

0.312

0

0.258824

0

null

0

0.5

null

0.25

1

0

null

1

0

1

0

null

0

1

0

1

0

5

c10c2a5c49d8f361efb3166b7ea2259e7440589c

55

py

Python

module.py

BinhMinhs10/pyEncryptor

8c99135a6486bcdd542d8e69d39940999c204885

[ "MIT" ]

null

module.py

BinhMinhs10/pyEncryptor

8c99135a6486bcdd542d8e69d39940999c204885

[ "MIT" ]

null

module.py

BinhMinhs10/pyEncryptor

8c99135a6486bcdd542d8e69d39940999c204885

[ "MIT" ]

null

def hello_world(): print("How Minh Secure code!")

13.75

34

0.654545

8

55

4.375

1

0

0.2

55

3

35

18.333333

0.795455

0

0.388889

0

1

0.5

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

c1701707e764f66c00d9872a054d0a3145aa9908

132

py

Python

bank_accounts/admin.py

mathemaat/afscheck

2899cca2d759c6433fceda8482c7400b161d7b3b

[ "MIT" ]

null

bank_accounts/admin.py

mathemaat/afscheck

2899cca2d759c6433fceda8482c7400b161d7b3b

[ "MIT" ]

4

2019-02-25T17:24:09.000Z

2019-02-25T17:25:05.000Z

bank_accounts/admin.py

mathemaat/afscheck

2899cca2d759c6433fceda8482c7400b161d7b3b

[ "MIT" ]

null

from django.contrib import admin from .models import Bank, BankAccount admin.site.register(Bank) admin.site.register(BankAccount)

18.857143

37

0.818182

18

132

6

0.555556

0.166667

0.314815

0

0.098485

132

6

38

22

0.907563

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

e72bd1bc4bc61dfa38a37ab4434efe9c27eee898

52

py

Python

FoodScan/ShopSync/__init__.py

danielBreitlauch/FoodScan

cf84209c4da84a8cb56deccdbde9c305eee1b8c3

[ "MIT" ]

1

2017-03-16T00:59:01.000Z

FoodScan/ShopSync/__init__.py

danielBreitlauch/FoodScan

cf84209c4da84a8cb56deccdbde9c305eee1b8c3

[ "MIT" ]

null

FoodScan/ShopSync/__init__.py

danielBreitlauch/FoodScan

cf84209c4da84a8cb56deccdbde9c305eee1b8c3

[ "MIT" ]

null

from .shopSync import ShopSync from .Shops import *

17.333333

30

0.788462

7

52

5.857143

0.571429

0

0.153846

52

2

31

26

0.931818

0

1

0

true

0

1

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0

null

0

1

0

1

0

1

0

5

e72d1dea5732f4c154237fe1311d12ff542f79e7

27

py

Python

constants/ip_addr_constants.py

chrisruenes1/Collisions-I

176a14884c483a422e1e457efa61d79140f77893

[ "MIT" ]

null

constants/ip_addr_constants.py

chrisruenes1/Collisions-I

176a14884c483a422e1e457efa61d79140f77893

[ "MIT" ]

null

constants/ip_addr_constants.py

chrisruenes1/Collisions-I

176a14884c483a422e1e457efa61d79140f77893

[ "MIT" ]

null

BLUE_ROBOT_IP = "10.0.0.28"

27

0.703704

7

27

2.428571

0.857143

0

0.24

0.074074

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1

27

0.44

0

0.321429

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1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

e770c4214b843d2bbfbe39e9560e68bfdd5f2610

216

py

Python

mmdet2trt/core/bbox/coder/__init__.py

jackweiwang/mmdetection-to-tensorrt

f988ba8e923764fb1173385a1c7160b8f8b5bd99

[ "Apache-2.0" ]

1

2021-08-23T10:09:37.000Z

mmdet2trt/core/bbox/coder/__init__.py

gcong18/mmdetection-to-tensorrt

c31c32ee4720ff56010bcda77bacf3a110d0526c

[ "Apache-2.0" ]

null

mmdet2trt/core/bbox/coder/__init__.py

gcong18/mmdetection-to-tensorrt

c31c32ee4720ff56010bcda77bacf3a110d0526c

[ "Apache-2.0" ]

null

from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoderWraper from .tblr_bbox_coder import TBLRBBoxCoderWraper from .yolo_bbox_coder import YOLOBBoxCoderWraper from .bucketing_bbox_coder import BucketingBBoxCoderWraper

54

59

0.912037

25

216

7.52

0.52

0.191489

0.319149

0

0.069444

216

4

60

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0

1

0

true

0

1

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1

0

null

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1

0

1

0

null

0

1

0

1

0

5

e7a303338ad99255f27bb5aaa44e6a6835b389bb

29

py

Python

NBA_News/NBA_Scrapy/__init__.py

papagorgio23/NBA_News_Spiders

ca5c12bf50e1a8b422b0afc315a6b61ba3b67588

[ "MIT" ]

3

2020-07-20T22:10:02.000Z

2022-02-09T22:04:37.000Z

NBA_News/NBA_Scrapy/__init__.py

papagorgio23/NBA_News_Spiders

ca5c12bf50e1a8b422b0afc315a6b61ba3b67588

[ "MIT" ]

null

NBA_News/NBA_Scrapy/__init__.py

papagorgio23/NBA_News_Spiders

ca5c12bf50e1a8b422b0afc315a6b61ba3b67588

[ "MIT" ]

null

# get this show on the road!

14.5

28

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6

29

3.333333

1

0

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29

1

29

0.909091

0.896552

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

e7d72316584d5596fbf9c84f3f3ab4d1e0ec6288

431

py

Python

python/anyascii/_data/_003.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

null

python/anyascii/_data/_003.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

null

python/anyascii/_data/_003.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

null

b=" a e i o u c d h m r t v x H h S s ' , W w i s s. s. ? J ' \"' A ; E I I O Y O i A V G D E Z I Th I K L M N X O P R S T Y F Ch Ps O I Y a e i i y a v g d e z i th i k l m n x o p r s s t y f ch ps o i y o y o & b th Y Y Y ph p & Q q St st W w Q q S s Sh sh F f X x H h J j Q q Ti ti k r s j Th e e Sh sh S S s r. S S. S."

431

0.366589

141

431

1.120567

0.234043

0.113924

0.056962

0.050633

0.373418

0.240506

0

0.591647

431

1

431

0.897727

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1

0.37963

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1

0

false

0

1

null

0

1

0

1

0

1

0

1

0

null

0

5

99b302145b2f99f826e2f83ee226d68206e803b3

148

py

Python

pylinkage/output_mode.py

Drumato/pylinkage

2033112c95a15722efcd9271c08fd919df635eae

[ "MIT" ]

null

pylinkage/output_mode.py

Drumato/pylinkage

2033112c95a15722efcd9271c08fd919df635eae

[ "MIT" ]

null

pylinkage/output_mode.py

Drumato/pylinkage

2033112c95a15722efcd9271c08fd919df635eae

[ "MIT" ]

null

from __future__ import annotations import enum class OutputMode(enum.Enum): YAML = enum.auto() SCRIPT = enum.auto() NONE = enum.auto()

18.5

34

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19

148

5.157895

0.578947

0.244898

0

0.202703

148

8

35

18.5

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1

0

false

0

0.333333

0

1

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1

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null

1

0

1

0

null

0

1

0

1

0

5

82070ed867d1d9a4e1075b7b286b9218ff029629

31

py

Python

src/Products/Five/viewlet/__init__.py

tseaver/Zope-RFA

08634f39b0f8b56403a2a9daaa6ee4479ef0c625

[ "ZPL-2.1" ]

2

2015-12-21T10:34:56.000Z

2017-09-24T11:07:58.000Z

src/Products/Five/viewlet/__init__.py

MatthewWilkes/Zope

740f934fc9409ae0062e8f0cd6dcfd8b2df00376

[ "ZPL-2.1" ]

null

src/Products/Five/viewlet/__init__.py

MatthewWilkes/Zope

740f934fc9409ae0062e8f0cd6dcfd8b2df00376

[ "ZPL-2.1" ]

null

# A package for viewlet support

31

0.806452

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31

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1

0

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1

31

0.961538

0.935484

0

null

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1

null

true

0

null

1

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null

0

1

0

1

0

null

0

1

0

5

823e6abb06792e637e8b7c0d170702fadef6de2b

32

py

Python

yusheng_shuai/play.py

YoungRainy/HY_coorprate

89661ad737863fd2b65cb8731f5c613e0ed13b99

[ "MIT" ]

null

yusheng_shuai/play.py

YoungRainy/HY_coorprate

89661ad737863fd2b65cb8731f5c613e0ed13b99

[ "MIT" ]

null

yusheng_shuai/play.py

YoungRainy/HY_coorprate

89661ad737863fd2b65cb8731f5c613e0ed13b99

[ "MIT" ]

null

import numpy as np np.random(3)

10.666667

18

0.75

7

32

3.428571

0.857143

0

0.037037

0.15625

32

2

19

16

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true

0

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null

0

1

0

null

0

1

0

1

0

5

415d1fcdfb8105ca358bbfd0c1e023264f35dd57

87

py

Python

nodemcu_kernel/__init__.py

nealmcb/nodemcu_kernel

ccd97a4fccb99d3c05ed7e22b15c1e28218676fe

[ "MIT" ]

4

2017-02-18T19:28:33.000Z

2020-12-10T07:48:21.000Z

nodemcu_kernel/__init__.py

nealmcb/nodemcu_kernel

ccd97a4fccb99d3c05ed7e22b15c1e28218676fe

[ "MIT" ]

1

2018-01-05T08:15:30.000Z

nodemcu_kernel/__init__.py

nealmcb/nodemcu_kernel

ccd97a4fccb99d3c05ed7e22b15c1e28218676fe

[ "MIT" ]

2

2019-04-12T14:18:51.000Z

2019-09-25T16:39:58.000Z

'''A Jupyter kernel for MicroPython on the NodeMcu''' from .kernel import __version__

21.75

53

0.770115

12

87

5.25

0.916667

0

0.149425

87

3

54

29

0.851351

0.54023

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

41905ca3c4bf79bb076c8c99081c5b0beb636d6a

506

py

Python

studio/houdini/scripts/hurl_resolver.py

astips/tk-astips-app-url-resolver

fd1a5d49d1ef1590a05ad640fb4f74a9579721ab

[ "MIT" ]

null

studio/houdini/scripts/hurl_resolver.py

astips/tk-astips-app-url-resolver

fd1a5d49d1ef1590a05ad640fb4f74a9579721ab

[ "MIT" ]

null

studio/houdini/scripts/hurl_resolver.py

astips/tk-astips-app-url-resolver

fd1a5d49d1ef1590a05ad640fb4f74a9579721ab

[ "MIT" ]

3

2018-06-07T14:26:51.000Z

2021-11-30T12:49:18.000Z

# -*- coding: utf-8 -*- ########################################################################################### # # Author: astips (animator.well) # # Date: 2017.05 # # Url: https://github.com/astips # # Description: Houdini url resolver scripts # ########################################################################################### from studiourl import StudioUrl def hurl_checker(burl): return StudioUrl(burl).is_valid def hurl_helper(burl): return StudioUrl(burl).real_path

22

91

0.440711

42

506

5.214286

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0.063927

0.173516

0.210046

0

0.015625

0.114625

506

22

92

23

0.473214

0.274704

0

1

0.4

false

0

0.2

0.4

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

41bd908258cd815839aeadd7e01142ec8cefcea3

376

py

Python

hb_quant/huobi/model/subuser/__init__.py

wenli135/Binance-volatility-trading-bot

75a03ad61df0e95492128fb6f1f419d4dc256ab3

[ "MIT" ]

611

2019-07-10T08:17:50.000Z

2022-03-21T18:56:39.000Z

hb_quant/huobi/model/subuser/__init__.py

wenli135/Binance-volatility-trading-bot

75a03ad61df0e95492128fb6f1f419d4dc256ab3

[ "MIT" ]

105

2019-07-12T03:43:41.000Z

2022-03-30T10:33:06.000Z

hb_quant/huobi/model/subuser/__init__.py

wenli135/Binance-volatility-trading-bot

75a03ad61df0e95492128fb6f1f419d4dc256ab3

[ "MIT" ]

325

2019-07-12T02:46:54.000Z

2022-03-21T18:56:41.000Z

from huobi.model.subuser.subuser_creation import SubuserCreation from huobi.model.subuser.subuser_transferability import SubuserTransferability from huobi.model.subuser.subuser_apikey_generation import SubuserApikeyGeneration from huobi.model.subuser.user_apikey_info import UserApikeyInfo from huobi.model.subuser.subuser_apikey_modification import SubuserApikeyModification

62.666667

85

0.906915

43

376

7.744186

0.395349

0.135135

0.21021

0.315315

0.372372

0.204204

0

0.053191

376

5

86

75.2

0.935393

0

1

0

true

0

1

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null

0

1

0

1

0

null

0

1

0

1

0

5

41bf043e80501a4aa68eec55a77c5887d106c190

29

py

Python

src/UnitTests/TestData/Grammar/IncompleteMemberExpr.py

jamesralstin/python-language-server

53eb5886776c9e75590bf2f5a787ba4015879c4d

[ "Apache-2.0" ]

695

2019-05-06T23:49:37.000Z

2022-03-30T01:56:00.000Z

src/UnitTests/TestData/Grammar/IncompleteMemberExpr.py

jamesralstin/python-language-server

53eb5886776c9e75590bf2f5a787ba4015879c4d

[ "Apache-2.0" ]

1,043

2019-05-07T02:24:11.000Z

2022-03-31T22:21:24.000Z

src/UnitTests/TestData/Grammar/IncompleteMemberExpr.py

jamesralstin/python-language-server

53eb5886776c9e75590bf2f5a787ba4015879c4d

[ "Apache-2.0" ]

131

2019-05-09T15:34:23.000Z

2022-03-23T17:52:35.000Z

a. #comment x = 1 b. x = 2 c.

5.8

11

0.482759

8

29

1.75

0.875

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0.1

0.310345

29

5

12

5.8

0.6

0.241379

0

null

0

null

0

1

null

0

1

0

null

0

1

0

5

68c5c52d6cf678a03286021eed6b143f70715bdf

85

py

Python

tests/deep_cnn/cifar/__init__.py

neil-tan/utensor_cgen

ffaf692bf6d1f8572039ad7e82e695f97b050cd2

[ "Apache-2.0" ]

null

tests/deep_cnn/cifar/__init__.py

neil-tan/utensor_cgen

ffaf692bf6d1f8572039ad7e82e695f97b050cd2

[ "Apache-2.0" ]

null

tests/deep_cnn/cifar/__init__.py

neil-tan/utensor_cgen

ffaf692bf6d1f8572039ad7e82e695f97b050cd2

[ "Apache-2.0" ]

null

# -*- coding: utf8 -*- from __future__ import absolute_import from ._cifar import *

17

38

0.717647

10

85

5.5

0.7

0

0.014085

0.164706

85

4

39

21.25

0.760563

0.235294

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

ec1208166e8e97248d1e4a35ce8299c1e22adf35

42

py

Python

tests/__init__.py

thejeffreyli/pySimpleMask

8c7155acaf413b4eca78f812a9d038e2a366341a

[ "MIT" ]

null

tests/__init__.py

thejeffreyli/pySimpleMask

8c7155acaf413b4eca78f812a9d038e2a366341a

[ "MIT" ]

null

tests/__init__.py

thejeffreyli/pySimpleMask

8c7155acaf413b4eca78f812a9d038e2a366341a

[ "MIT" ]

1

2021-11-03T16:11:57.000Z

"""Unit test package for pysimplemask."""

21

41

0.714286

5

42

6

1

0

0.119048

42

1

42

0.810811

0.833333

0

null

0

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0

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1

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true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

ec260dced77a95f205ac8e41358ea0b35544eb96

181

py

Python

pydantabase/__init__.py

tombulled/pydantable

32c36f5f03395c26997861de1a8b7c26cf2a96e5

[ "MIT" ]

1

2022-01-07T01:09:07.000Z

pydantabase/__init__.py

tombulled/pydantable

32c36f5f03395c26997861de1a8b7c26cf2a96e5

[ "MIT" ]

null

pydantabase/__init__.py

tombulled/pydantable

32c36f5f03395c26997861de1a8b7c26cf2a96e5

[ "MIT" ]

null

from tinydb import Query from .database import Database from .document import Document from .mixins import ModelMixin from .models import BaseModel from .table import Table

22.625

32

0.79558

24

181

6

0.458333

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181

7

33

25.857143

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true

0

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null

0

1

0

1

0

1

0

5

ec47d853a5b3a0ce331ed362f9a8d1afbf9b79fc

12,617

py

Python

sim/cocotb_sim/stc0_tests.py

russellfriesenhahn/stc0

0fa75db9109a528c4751cf78987575f1eede39ae

[ "BSD-3-Clause" ]

null

sim/cocotb_sim/stc0_tests.py

russellfriesenhahn/stc0

0fa75db9109a528c4751cf78987575f1eede39ae

[ "BSD-3-Clause" ]

null

sim/cocotb_sim/stc0_tests.py

russellfriesenhahn/stc0

0fa75db9109a528c4751cf78987575f1eede39ae

[ "BSD-3-Clause" ]

null

# Simple tests for an adder module import cocotb #from cocotb.triggers import Timer from cocotb.triggers import * from cocotb.result import TestFailure from cocotb.clock import Clock import random from ft245 import FT245 import numpy import sys sys.path.append("../../sw") sys.path.append("../../modules/housekeeper/sw") from hk import * from stc0 import * from stc0SIMcocotb import * from lfsr32 import * from crc32 import * async def reset_dut(reset_n, duration_ns): reset_n <= 1 await Timer(duration_ns, units='ns') reset_n <= 0 reset_n._log.debug("Reset complete") CLK_PERIOD_NS = 10 def setup_dut(dut): cocotb.fork(Clock(dut.Clk, CLK_PERIOD_NS, units='ns').start()) @cocotb.test(skip = False) def stc0_load_tw(dut): """ This test loads the Twiddle RAM up with LFSRY data, then reads it back out verifying the ability to load and read the Twiddle factors. """ numpy.set_printoptions(formatter={'int':lambda x:hex(int(x))}) dut.ARst <= 1 stc0 = stc0SIMcocotb(dut, CLK_PERIOD_NS) cocotb.fork(Clock(dut.Clk, CLK_PERIOD_NS, units='ns').start()) yield Timer(CLK_PERIOD_NS * 10, units='ns') dut.ARst <= 0 yield Timer(CLK_PERIOD_NS * 10, units='ns') yield stc0.hk.reset() seedValA = 0x1 seedValB = 0x2 crcValA = 0x0 crcValB = 0x0 yield stc0.hk.set_sfifoWrSrc(hk.HW_SFFWRSRC_INGRESS) bf0Ctrl = (stc0.HW_CTRL_BF0 << stc0.HW_RB_CTRL_ADDR) | (0x1 << stc0.HW_RB_BFCTRL_TWWR) yield stc0.hk.send_write_command(0x1, stc0.HW_FA_CTRL_CTRLWORD, [bf0Ctrl]) esCtrl = (stc0.HW_CTRL_ES << stc0.HW_RB_CTRL_ADDR) | (0x0 << stc0.HW_RB_EGRESSCTRL_OUTPUTEN) | (0x1 << stc0.HW_RB_EGRESSCTRL_CRCBYPASS)|(0x2 << stc0.HW_RB_EGRESSCTRL_OUTPUTMUX) #yield stc0.hk.send_write_command(0x1, (stc0.HW_RM_CTRL << stc0.HW_RML) | (stc0.HW_RA_CTRL_CTRLWORD << stc0.HW_RAL), [esCtrl]) yield stc0.hk.send_write_command(0x1, stc0.HW_FA_CTRL_CTRLWORD, [esCtrl]) yield stc0.configAndStartLFSRs(0x1, 512, 0x0, 0x1, 0x2) yield Timer(CLK_PERIOD_NS * 1050, units='ns') print(hex(bf0Ctrl)) bf0Ctrl ^= (0x1 << stc0.HW_RB_BFCTRL_TWWR) print(hex(bf0Ctrl)) bf0Ctrl |= (0x1 << stc0.HW_RB_BFCTRL_TWRD) print(hex(bf0Ctrl)) yield stc0.hk.send_write_command(0x1, stc0.HW_FA_CTRL_CTRLWORD, [bf0Ctrl]) esCtrl |= (0x1 << stc0.HW_RB_EGRESSCTRL_OUTPUTEN) esCtrl |= (0x2 << stc0.HW_RB_EGRESSCTRL_OUTPUTMUX) yield stc0.hk.send_write_command(0x1, stc0.HW_FA_CTRL_CTRLWORD, [esCtrl]) yield stc0.configAndStartLFSRs(0x4, 512, 0x0, 0x0, 0x0) yield Timer(CLK_PERIOD_NS * 530 * 4, units='ns') #yield Timer(CLK_PERIOD_NS * 30, units='ns') yield stc0.hk.send_write_command(stc0.hk.HW_HK_WRITE, stc0.hk.HW_ADDR_SFFRB_NUMBYTES, [512*4]) yield Timer(CLK_PERIOD_NS * 5, units='ns') a = yield stc0.hk.ft245m.read_bytes(512*4) yield Timer(CLK_PERIOD_NS * 40, units='ns') data = numpy.arange(512) for i in range(0,512): data[i] = seedValB crcValB = crc32(crcValB, seedValB) seedValB = lfsr32(seedValB, 1) #print(data) #print(a) print("crcA is " + hex(crcValA)) print("crcB is " + hex(crcValB)) if numpy.array_equal(data, a) is False: # Fail raise TestFailure("Readback data does not match") @cocotb.test(skip = False) def stc0_both_crc_test(dut): """Verify CRC operations. The LFSRs' data goes to the CRC blocks. Only the two final CRC values are transmitted from the DUT for comparison """ numpy.set_printoptions(formatter={'int':lambda x:hex(int(x))}) dut.ARst <= 1 stc0 = stc0SIMcocotb(dut, CLK_PERIOD_NS) cocotb.fork(Clock(dut.Clk, CLK_PERIOD_NS, units='ns').start()) yield Timer(CLK_PERIOD_NS * 10, units='ns') dut.ARst <= 0 yield Timer(CLK_PERIOD_NS * 10, units='ns') yield stc0.hk.reset() seedValA = 0x1 seedValB = 0x2 crcValA = 0x0 crcValB = 0x0 yield stc0.hk.set_sfifoWrSrc(hk.HW_SFFWRSRC_INGRESS) yield stc0.hk.send_write_command(0x1, (stc0.HW_RM_CTRL << stc0.HW_RML) | (stc0.HW_RA_CTRL_CTRLWORD << stc0.HW_RAL), [(stc0.HW_CTRL_BF0 << stc0.HW_RB_CTRL_ADDR) | (0x1 << stc0.HW_RB_BFCTRL_BFBYPASS)]) yield stc0.hk.send_write_command(0x1, (stc0.HW_RM_CTRL << stc0.HW_RML) | (stc0.HW_RA_CTRL_CTRLWORD << stc0.HW_RAL), [(stc0.HW_CTRL_ES << stc0.HW_RB_CTRL_ADDR) | (0x0 << stc0.HW_RB_EGRESSCTRL_OUTPUTEN) | (0x0 << stc0.HW_RB_EGRESSCTRL_CRCBYPASS)|(0x0 << stc0.HW_RB_EGRESSCTRL_OUTPUTMUX) ]) yield stc0.configAndStartLFSRs(0x1, 0x10, 0x0, 0x1, 0x2) yield Timer(CLK_PERIOD_NS * 30, units='ns') yield stc0.hk.send_write_command(0x1, (stc0.HW_RM_CTRL << stc0.HW_RML) | (stc0.HW_RA_CTRL_CTRLWORD << stc0.HW_RAL), [(stc0.HW_CTRL_ES << stc0.HW_RB_CTRL_ADDR) | (0x1 << stc0.HW_RB_EGRESSCTRL_OUTPUTEN) | (0x0 << stc0.HW_RB_EGRESSCTRL_CRCBYPASS)|(0x0 << stc0.HW_RB_EGRESSCTRL_OUTPUTMUX) ]) yield stc0.hk.send_write_command(stc0.hk.HW_HK_WRITE, stc0.hk.HW_ADDR_SFFRB_NUMBYTES, [8]) yield Timer(CLK_PERIOD_NS * 5, units='ns') a = yield stc0.hk.ft245m.read_bytes(8) yield Timer(CLK_PERIOD_NS * 40, units='ns') data = numpy.arange(32) for i in range(0,32,2): data[i] = seedValA data[i+1] = seedValB crcValA = crc32(crcValA, seedValA) seedValA = lfsr32(seedValA, 1) crcValB = crc32(crcValB, seedValB) seedValB = lfsr32(seedValB, 1) print(a) print("crcA is " + hex(crcValA)) print("crcB is " + hex(crcValB)) if numpy.array_equal([crcValA,crcValB], a) is False: # Fail raise TestFailure("Readback data does not match") @cocotb.test(skip = False) def stc0_both_lfsr_test(dut): """Verifies both LFSRs and transmitting data from both streams. The LFSRs are run and LFSR values are streamed out for comparison. Due to the egress setup, the LFSR values are interleaved. """ numpy.set_printoptions(formatter={'int':lambda x:hex(int(x))}) dut.ARst <= 1 stc0 = stc0SIMcocotb(dut, CLK_PERIOD_NS) cocotb.fork(Clock(dut.Clk, CLK_PERIOD_NS, units='ns').start()) yield Timer(CLK_PERIOD_NS * 10, units='ns') dut.ARst <= 0 yield Timer(CLK_PERIOD_NS * 10, units='ns') yield stc0.hk.reset() seedValA = 0x1 seedValB = 0x2 yield stc0.hk.set_sfifoWrSrc(hk.HW_SFFWRSRC_INGRESS) yield stc0.hk.send_write_command(0x1, (stc0.HW_RM_CTRL << stc0.HW_RML) | (stc0.HW_RA_CTRL_CTRLWORD << stc0.HW_RAL), [(stc0.HW_CTRL_BF0 << stc0.HW_RB_CTRL_ADDR) | (0x1 << stc0.HW_RB_BFCTRL_BFBYPASS)]) yield stc0.hk.send_write_command(0x1, (stc0.HW_RM_CTRL << stc0.HW_RML) | (stc0.HW_RA_CTRL_CTRLWORD << stc0.HW_RAL), [(stc0.HW_CTRL_ES << stc0.HW_RB_CTRL_ADDR) | (0x1 << stc0.HW_RB_EGRESSCTRL_OUTPUTEN) | (0x1 << stc0.HW_RB_EGRESSCTRL_CRCBYPASS)|(0x0 << stc0.HW_RB_EGRESSCTRL_OUTPUTMUX) ]) yield stc0.configAndStartLFSRs(0x8, 0x10, 0x0, 0x1, 0x2) yield stc0.hk.send_write_command(stc0.hk.HW_HK_WRITE, stc0.hk.HW_ADDR_SFFRB_NUMBYTES, [128]) yield Timer(CLK_PERIOD_NS * 5, units='ns') a = yield stc0.hk.ft245m.read_bytes(128) yield Timer(CLK_PERIOD_NS * 40, units='ns') data = numpy.arange(32) for i in range(0,32,2): data[i] = seedValA data[i+1] = seedValB seedValA = lfsr32(seedValA, 1) seedValB = lfsr32(seedValB, 1) print(data) print(a) if numpy.array_equal(data, a) is False: # Fail raise TestFailure("Readback data does not match") @cocotb.test(skip = False) def stc0_lfsr_test(dut): """Verifies basic LFSR X operation. The LFSR is run and LFSR values are streamed out for comparison. """ numpy.set_printoptions(formatter={'int':lambda x:hex(int(x))}) dut.ARst <= 1 stc0 = stc0SIMcocotb(dut, CLK_PERIOD_NS) cocotb.fork(Clock(dut.Clk, CLK_PERIOD_NS, units='ns').start()) yield Timer(CLK_PERIOD_NS * 10, units='ns') dut.ARst <= 0 yield Timer(CLK_PERIOD_NS * 10, units='ns') yield stc0.hk.reset() seedVal = 0x1 yield stc0.hk.set_sfifoWrSrc(hk.HW_SFFWRSRC_INGRESS) yield stc0.hk.send_write_command(0x1, (stc0.HW_RM_CTRL << stc0.HW_RML) | (stc0.HW_RA_CTRL_CTRLWORD << stc0.HW_RAL), [(stc0.HW_CTRL_BF0 << stc0.HW_RB_CTRL_ADDR) | (0x1 << stc0.HW_RB_BFCTRL_BFBYPASS)]) yield stc0.hk.send_write_command(0x1, (stc0.HW_RM_CTRL << stc0.HW_RML) | (stc0.HW_RA_CTRL_CTRLWORD << stc0.HW_RAL), [(stc0.HW_CTRL_ES << stc0.HW_RB_CTRL_ADDR) | (0x1 << stc0.HW_RB_EGRESSCTRL_OUTPUTEN) | (0x1 << stc0.HW_RB_EGRESSCTRL_CRCBYPASS)|(0x1 << stc0.HW_RB_EGRESSCTRL_OUTPUTMUX) ]) yield stc0.configAndStartLFSRs(0x4, 0x10, 0x0, 0x1, 0x1) #yield stc0.hk.send_write_command(0x1, (stc0.HW_RM_CTRL << stc0.HW_RML) | (stc0.HW_RA_CTRL_MUXCTRL << stc0.HW_RAL), [0x1]) #yield stc0.hk.send_write_command(0x1, (stc0.HW_RM_CTRL << stc0.HW_RML) | (stc0.HW_RA_CTRL_LFSRS_STRIDE << stc0.HW_RAL), [0x3]) #yield stc0.hk.send_write_command(0x1, (stc0.HW_RM_CTRL << stc0.HW_RML) | (stc0.HW_RA_CTRL_LFSRS_ITERATIONS << stc0.HW_RAL), [0x10]) #yield stc0.hk.send_write_command(0x1, (stc0.HW_RM_CTRL << stc0.HW_RML) | (stc0.HW_RA_CTRL_LFSRX_SEED << stc0.HW_RAL), [seedVal]) #yield stc0.hk.send_write_command(0x1, (stc0.HW_RM_CTRL << stc0.HW_RML) | (stc0.HW_RA_CTRL_LFSRS_CTRL << stc0.HW_RAL), [0x1]) yield stc0.hk.send_write_command(stc0.hk.HW_HK_WRITE, stc0.hk.HW_ADDR_SFFRB_NUMBYTES, [64]) yield Timer(CLK_PERIOD_NS * 5, units='ns') a = yield stc0.hk.ft245m.read_bytes(64) yield Timer(CLK_PERIOD_NS * 30, units='ns') data = numpy.arange(16) for i in range(16): data[i] = seedVal seedVal = lfsr32(seedVal, 1) print(data) print(a) if numpy.array_equal(data, a) is False: # Fail raise TestFailure("Readback data does not match") # Test the stc0 simulation class # This test no longer works because it expects the output of the HK FPGA to be # looped back @cocotb.test(skip = True) def stc0_basic_test(dut): """Tests Housekeeper sending and receiving data. This test requires an external loopback which is no longer present due to the actual DUT wired up to the Housekeeper FPGA """ dut.ARst <= 1 stc0 = stc0SIMcocotb(dut, CLK_PERIOD_NS) cocotb.fork(Clock(dut.Clk, CLK_PERIOD_NS, units='ns').start()) yield Timer(CLK_PERIOD_NS * 10, units='ns') dut.ARst <= 0 yield Timer(CLK_PERIOD_NS * 10, units='ns') yield stc0.hk.reset() data = numpy.arange(10) data[0] = 0xA5B6C7D8 data[8] = 0x12345678 data[9] = 0xDEADC0DE yield stc0.hk.set_sfifoWrSrc(hk.HW_SFFWRSRC_INGRESS) yield stc0.hk.send_write_command(0x1, 0x1, data) yield Timer(CLK_PERIOD_NS * 20, units='ns') yield stc0.hk.send_write_command(stc0.hk.HW_HK_WRITE, stc0.hk.HW_ADDR_SFFRB_NUMBYTES, [40]) yield Timer(CLK_PERIOD_NS * 5, units='ns') a = yield stc0.hk.ft245m.read_bytes(40) numpy.set_printoptions(formatter={'int':lambda x:hex(int(x))}) print(data) print(a) if numpy.array_equal(data, a) is False: # Fail raise TestFailure("Readback data does not match") # Test the housekeeper simulation class @cocotb.test() def stc0_housekeeper_test(dut): """Tests basic Housekeeper FPGA functionality using internal loopback. FPGA is configured for loopback and data streamed in. """ dut.ARst <= 1 hk = hkSIMcocotb(dut, CLK_PERIOD_NS) cocotb.fork(Clock(dut.Clk, CLK_PERIOD_NS, units='ns').start()) yield Timer(CLK_PERIOD_NS * 10, units='ns') dut.ARst <= 0 yield Timer(CLK_PERIOD_NS * 10, units='ns') yield hk.reset() data = numpy.arange(10) data[0] = 0xA5B6C7D8 data[8] = 0x12345678 data[9] = 0xDEADC0DE yield hk.set_sfifoWrSrc(hk.HW_SFFWRSRC_LOOPBACK) yield hk.send_write_command(0x1, 0x1, data) yield Timer(CLK_PERIOD_NS * 20, units='ns') yield hk.send_write_command(hk.HW_HK_WRITE, hk.HW_ADDR_SFFRB_NUMBYTES, [52]) yield Timer(CLK_PERIOD_NS * 5, units='ns') a = yield hk.ft245m.read_bytes(52) numpy.set_printoptions(formatter={'int':lambda x:hex(int(x))}) #vhex = numpy.vectorize(hex) #print(vhex(a)) print(sys.path) print(data) print(a[2:-1]) if numpy.array_equal(data, a[2:-1]) is False: # Fail raise TestFailure("Readback data does not match") #yield hk.set_sfifoWrSrc(hk.HW_SFFWRSRC_INGRESS) #yield hk.send_write_command(0x1, 0x1, data) #yield hk.send_write_command(hk.HW_HK_WRITE, hk.HW_ADDR_SFFRB_NUMBYTES, [40]) #a = yield hk.ft245m.read_bytes(40) #print(data) #print(a) #if numpy.array_equal(data, a) is False: ## Fail #raise TestFailure("Readback data does not match")

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bot/utilities/__init__.py

Bellyria/monkalot

1ae6117b7989dcf692ac77acb23f00a63e658a06

[ "MIT" ]

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2017-09-08T21:13:38.000Z

2022-01-29T03:24:13.000Z

bot/utilities/__init__.py

Bellyria/monkalot

1ae6117b7989dcf692ac77acb23f00a63e658a06

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2017-08-20T17:46:14.000Z

2021-11-18T22:54:59.000Z

bot/utilities/__init__.py

Bellyria/monkalot

1ae6117b7989dcf692ac77acb23f00a63e658a06

[ "MIT" ]

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2017-08-19T01:13:41.000Z

2021-08-07T08:45:30.000Z

"""Contains a variety of utility functions."""

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Python

src/test/data/pa3/sample/call.py

Leo-Enrique-Wu/chocopy_compiler_code_generation

4606be0531b3de77411572aae98f73169f46b3b9

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2021-08-28T18:20:45.000Z

2022-02-01T07:35:59.000Z

src/test/data/pa3/sample/call.py

Leo-Enrique-Wu/chocopy_compiler_code_generation

4606be0531b3de77411572aae98f73169f46b3b9

[ "BSD-2-Clause" ]

4

2020-05-18T01:06:15.000Z

2020-06-12T19:33:14.000Z

src/test/data/pa3/sample/call.py

Leo-Enrique-Wu/chocopy_compiler_code_generation

4606be0531b3de77411572aae98f73169f46b3b9

[ "BSD-2-Clause" ]

5

2019-11-27T05:11:05.000Z

2021-06-29T14:31:14.000Z

def f() -> int: print("start f") g() print("end f") return 42 def g() -> object: print("start g") h() print("end g") def h() -> object: print("start h") print("end h") print(f())

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Python

venv/Lib/site-packages/apiclient/__init__.py

nfuster2017/AmazonWebCrawler

d45e2dec826b5cadd632ed8a94c2c4c127430000

[ "MIT" ]

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2015-02-20T11:23:53.000Z

2022-03-08T22:10:40.000Z

venv/Lib/site-packages/apiclient/__init__.py

nfuster2017/AmazonWebCrawler

d45e2dec826b5cadd632ed8a94c2c4c127430000

[ "MIT" ]

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2015-04-23T18:01:37.000Z

2021-08-16T14:08:06.000Z

venv/Lib/site-packages/apiclient/__init__.py

nfuster2017/AmazonWebCrawler

d45e2dec826b5cadd632ed8a94c2c4c127430000

[ "MIT" ]

20

2015-01-16T19:57:53.000Z

2022-02-12T16:17:27.000Z

from .base import APIClient, APIClient_SharedSecret from .ratelimiter import RateLimiter

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py

Python

edge/space/__init__.py

Data-Science-in-Mechanical-Engineering/edge

586eaba2f0957e75940f4f19fa774603f57eae89

[ "MIT" ]

null

edge/space/__init__.py

Data-Science-in-Mechanical-Engineering/edge

586eaba2f0957e75940f4f19fa774603f57eae89

[ "MIT" ]

null

edge/space/__init__.py

Data-Science-in-Mechanical-Engineering/edge

586eaba2f0957e75940f4f19fa774603f57eae89

[ "MIT" ]

null

from .space import Space, DiscretizableSpace, ProductSpace from .stateaction_space import StateActionSpace from .box import Segment, Box from .discrete import Discrete __all__ = ['Segment', 'Box', 'StateActionSpace', 'Space', 'DiscretizableSpace', 'ProductSpace']

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py

Python

test/test_state_manager.py

xray/py-tic-tac-toe

d3375d90d4464a2aae6d6ead5e115efd908d9581

[ "Unlicense" ]

null

test/test_state_manager.py

xray/py-tic-tac-toe

d3375d90d4464a2aae6d6ead5e115efd908d9581

[ "Unlicense" ]

null

test/test_state_manager.py

xray/py-tic-tac-toe

d3375d90d4464a2aae6d6ead5e115efd908d9581

[ "Unlicense" ]

null

from game.state_manager import StateManager def test_new_state(): sm = StateManager() new_state = sm.create({"game_id": "debug_testing"}) assert new_state.board == {'size': 3, 'status': [[0, 0, 0], [0, 0, 0], [0, 0, 0]]} assert new_state.player_turn == 1 assert new_state.player_count == 2 assert new_state.history == [] def test_update_state(): sm = StateManager() new_state = sm.create({"game_id": "debug_testing"}) updated_state = sm.update(new_state, {"coordinates": [1, 1]}) assert updated_state.board == {'size': 3, 'status': [[0, 0, 0], [0, 1, 0], [0, 0, 0]]} assert updated_state.player_turn == 2 def test_update_state_three_times(): sm = StateManager() new_state = sm.create({"game_id": "debug_testing"}) updated_state = sm.update(new_state, {"coordinates": [1, 1]}) assert updated_state.board == {'size': 3, 'status': [[0, 0, 0], [0, 1, 0], [0, 0, 0]]} assert updated_state.player_turn == 2 updated_state = sm.update(updated_state, {"coordinates": [0, 1]}) assert updated_state.board == {'size': 3, 'status': [[0, 2, 0], [0, 1, 0], [0, 0, 0]]} assert updated_state.player_turn == 1 def test_update_board(): sm = StateManager() new_state = sm.create({"game_id": "debug_testing"}) updated_board = sm.update_board(new_state, [1,1]) assert updated_board["board"] == {'size': 3, 'status': [[0, 0, 0], [0, 1, 0], [0, 0, 0]]} def test_dynamic_board(): sm = StateManager() new_state = sm.create({"board_size": 5, "game_id": "debug_testing"}) assert new_state.board["status"] == [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]] def test_board_size(): sm = StateManager() assert sm.regulate_board_size(5) == 5 def test_board_size_too_big(): sm = StateManager() assert sm.regulate_board_size(10) == 9 def test_win_top_row(): sm = StateManager() class MockState: def __init__(self): self.board = { "status": [[1, 1, 1], [0, 0, 0], [0, 0, 0]], "size": 3 } assert sm.is_game_complete(MockState()) == (True, False) def test_win_left_column(): sm = StateManager() class MockState: def __init__(self): self.board = { "status": [[1, 0, 0], [1, 0, 0], [1, 0, 0]], "size": 3 } assert sm.is_game_complete(MockState()) == (True, False) def test_win_middle_column(): sm = StateManager() class MockState: def __init__(self): self.board = { "status": [[0, 1, 0], [0, 1, 0], [0, 1, 0]], "size": 3 } assert sm.is_game_complete(MockState()) == (True, False) def test_incomplete_left_column(): sm = StateManager() class MockState: def __init__(self): self.board = { "status": [[1, 0, 0], [1, 0, 0], [0, 0, 0]], "size": 3 } assert sm.is_game_complete(MockState()) == (False, False) def test_incomplete_left_column_one_move(): sm = StateManager() class MockState: def __init__(self): self.board = { "status": [[1, 0, 0], [0, 0, 0], [0, 0, 0]], "size": 3 } assert sm.is_game_complete(MockState()) == (False, False) def test_diagonal_left_to_right(): sm = StateManager() class MockState: def __init__(self): self.board = { "status": [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], "size": 4 } assert sm.is_game_complete(MockState()) == (True, False) def test_diagonal_right_to_left(): sm = StateManager() class MockState: def __init__(self): self.board = { "status": [[0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0]], "size": 4 } assert sm.is_game_complete(MockState()) == (True, False) def test_cats_game(): sm = StateManager() class MockState: def __init__(self): self.board = { "status": [[1, 2, 1, 2], [2, 1, 2, 1], [2, 1, 2, 1], [1, 2, 1, 2]], "size": 4 } assert sm.is_game_complete(MockState()) == (True, True) def test_check_identical_values_all_ones(): sm = StateManager() test_array = [1, 1, 1, 1] assert sm.check_identical_values(test_array) == True def test_check_identical_values_all_twos(): sm = StateManager() test_array = [2, 2, 2, 2] assert sm.check_identical_values(test_array) == True def test_check_identical_values_all_zeros(): sm = StateManager() test_array = [0, 0, 0, 0] assert sm.check_identical_values(test_array) == False

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null

0

1

0

5

6bcd12429f7db4d55c222f6416b5e5a8fcee3fdc

28

py

Python

khed/api/__init__.py

bnu123/Khed

dd7d53bf400e9ba59ac623aba8bb6c4f96347117

[ "MIT" ]

14

2019-02-02T17:35:04.000Z

2021-07-07T12:13:50.000Z

khed/api/__init__.py

bnu123/Khed

dd7d53bf400e9ba59ac623aba8bb6c4f96347117

[ "MIT" ]

3

2019-02-02T20:05:15.000Z

2019-05-03T17:44:35.000Z

khed/api/__init__.py

bnu123/Khed

dd7d53bf400e9ba59ac623aba8bb6c4f96347117

[ "MIT" ]

4

2019-02-02T15:02:13.000Z

2021-12-30T11:09:30.000Z

from .sites import ChiaAnime

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true

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null

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5

d4419fe2518510b496a89a310bcf0c4b3bbc8fe0

71

py

Python

Contributions/example.py

sattwik21/Hacktoberfest2021-1

74c8edd54f9c967c0f301f74dec31526dffa8222

[ "MIT" ]

215

2021-10-01T08:18:16.000Z

2022-03-29T04:12:03.000Z

Contributions/example.py

sattwik21/Hacktoberfest2021-1

74c8edd54f9c967c0f301f74dec31526dffa8222

[ "MIT" ]

51

2021-10-01T08:16:42.000Z

2021-10-31T13:51:51.000Z

Contributions/example.py

sattwik21/Hacktoberfest2021-1

74c8edd54f9c967c0f301f74dec31526dffa8222

[ "MIT" ]

807

2021-10-01T08:11:45.000Z

2021-11-21T18:57:09.000Z

print("This is an example file showing how to contribute to the repo")

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null

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1

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d477367fee5d3e85c1153489b13953d3d09b36ae

244

py

Python

tests/data.py

lmmx/range-streams

4d7385e0f71c57486e990af5ae9b94e8ed43c626

[ "MIT" ]

4

2021-07-29T08:34:13.000Z

2022-02-13T22:33:55.000Z

tests/data.py

lmmx/range-streams

4d7385e0f71c57486e990af5ae9b94e8ed43c626

[ "MIT" ]

40

2021-07-01T22:16:16.000Z

2021-12-18T20:53:16.000Z

tests/data.py

lmmx/range-streams

4d7385e0f71c57486e990af5ae9b94e8ed43c626

[ "MIT" ]

null

EXAMPLE_URL = "https://raw.githubusercontent.com/lmmx/range-streams/master/data/example_text_file.txt" EXAMPLE_FILE_LENGTH = 11 EXAMPLE_SMALL_PNG_URL = ( "https://raw.githubusercontent.com/lmmx/range-streams/master/data/red_square.png" )

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1

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null

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5

d48569d73ac4ac9a5b2c739d68037081fdf95359

187

py

Python

tests/conftest.py

chomechome/charamel

f5d664f19b8be70c2361f4037a4f065959e050bd

[ "Apache-2.0" ]

37

2020-05-12T05:58:17.000Z

2022-03-04T14:43:11.000Z

tests/conftest.py

chomechome/charamel

f5d664f19b8be70c2361f4037a4f065959e050bd

[ "Apache-2.0" ]

5

2020-05-11T15:45:55.000Z

2021-03-01T11:52:50.000Z

tests/conftest.py

chomechome/charamel

f5d664f19b8be70c2361f4037a4f065959e050bd

[ "Apache-2.0" ]

1

2020-05-28T04:59:22.000Z

from typing import Any def pytest_make_parametrize_id(val: Any, argname: str): """ Format argument for `pytest.mark.parametrize` test item """ return f'{argname}={val}'

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null

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5

00fdae49d3898f49f3138f67c8bf631b007cf46a

112

py

Python

pyopversion/__init__.py

pcaston/pyopversion

f95ad6bdd1cd188620bc957acd8b9052a1cf4859

[ "MIT" ]

null

pyopversion/__init__.py

pcaston/pyopversion

f95ad6bdd1cd188620bc957acd8b9052a1cf4859

[ "MIT" ]

21

2021-07-19T06:08:27.000Z

2022-03-29T06:08:08.000Z

pyopversion/__init__.py

pcaston/pyopversion

f95ad6bdd1cd188620bc957acd8b9052a1cf4859

[ "MIT" ]

null

"""pyopversion package.""" from .consts import OpVersionChannel, OpVersionSource from .version import OpVersion

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112

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null

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1

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1

0

1

0

5

2e0a72cc1734b45f278aee7e1f94bc50c841bbc2

52

py

Python

KD_Lib/KD/vision/DML/__init__.py

PiaCuk/KD_Lib

153299d484e4c6b33793749709dbb0f33419f190

[ "MIT" ]

null

KD_Lib/KD/vision/DML/__init__.py

PiaCuk/KD_Lib

153299d484e4c6b33793749709dbb0f33419f190

[ "MIT" ]

null

KD_Lib/KD/vision/DML/__init__.py

PiaCuk/KD_Lib

153299d484e4c6b33793749709dbb0f33419f190

[ "MIT" ]

null

from .dml import DML from .dml_e import DMLEnsemble

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null

0

1

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1

0

5

2e70dc629b5231bc5de19bb43e63c3f798e36ed3

35

py

Python

makelove/__main__.py

jiaaro/makelove

3d74e60b48623fb1c6cfee2c45e7004e44810829

[ "MIT" ]

56

2020-02-21T20:46:19.000Z

2022-03-30T11:54:19.000Z

makelove/__main__.py

jiaaro/makelove

3d74e60b48623fb1c6cfee2c45e7004e44810829

[ "MIT" ]

22

2020-02-20T23:10:11.000Z

2022-03-29T01:58:23.000Z

makelove/__main__.py

jiaaro/makelove

3d74e60b48623fb1c6cfee2c45e7004e44810829

[ "MIT" ]

6

2020-05-22T17:04:26.000Z

2021-12-12T20:00:42.000Z

from .makelove import main main()

8.75

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35

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27

11.666667

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true

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null

0

1

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1

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5

2e772784b56947a6954538607cefd32e43347128

98

py

Python

pattern_matching/core/__init__.py

Xython/pattern-matching

17ccdb68189353f1c63032013f5ef6f1ca4c0902

[ "MIT" ]

20

2017-12-31T05:45:47.000Z

2021-05-15T22:08:21.000Z

pattern_matching/core/__init__.py

Xython/Destruct.py

17ccdb68189353f1c63032013f5ef6f1ca4c0902

[ "MIT" ]

null

pattern_matching/core/__init__.py

Xython/Destruct.py

17ccdb68189353f1c63032013f5ef6f1ca4c0902

[ "MIT" ]

1

2018-01-12T04:54:19.000Z

from .match import Match, Overload, when, overwrite from .pattern import var, T, t, match_err, _

24.5

51

0.744898

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4.733333

0.666667

0

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3

52

32.666667

0.865854

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true

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1

0

1

0

1

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5

5cebd97512ca45d1fcf596012ceefaadbf3be1db

65

py

Python

sklift/utils/__init__.py

rishawsingh/scikit-uplift

a46f11d24025f8489577640271abfc4d847d0334

[ "MIT" ]

403

2019-12-21T09:36:57.000Z

2022-03-30T09:36:56.000Z

sklift/utils/__init__.py

fspofficial/scikit-uplift

c9dd56aa0277e81ef7c4be62bf2fd33432e46f36

[ "MIT" ]

100

2020-02-29T11:52:21.000Z

2022-03-29T23:14:33.000Z

sklift/utils/__init__.py

fspofficial/scikit-uplift

c9dd56aa0277e81ef7c4be62bf2fd33432e46f36

[ "MIT" ]

81

2019-12-26T08:28:44.000Z

2022-03-22T09:08:54.000Z

from .utils import check_is_binary __all__ = ['check_is_binary']

21.666667

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0.7

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3

35

21.666667

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1

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null

0

1

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5

cf074165abba699349e8af47fc5501e8a3899103

52,869

py

Python

numerblox/evaluation.py

crowdcent/numerblox

e014a30eb22ce64cdc590e32d776367a7132cb39

[ "Apache-2.0" ]

30

2022-03-17T03:23:20.000Z

2022-03-30T15:20:19.000Z

numerblox/evaluation.py

crowdcent/numerblox

e014a30eb22ce64cdc590e32d776367a7132cb39

[ "Apache-2.0" ]

8

2022-03-18T10:31:44.000Z

2022-03-31T15:43:46.000Z

numerblox/evaluation.py

crowdcent/numerblox

e014a30eb22ce64cdc590e32d776367a7132cb39

[ "Apache-2.0" ]

5

2022-03-18T10:24:38.000Z

2022-03-30T14:40:08.000Z

# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/07_evaluation.ipynb (unless otherwise specified). __all__ = ['FNCV3_FEATURES', 'MEDIUM_FEATURES', 'BaseEvaluator', 'NumeraiClassicEvaluator', 'NumeraiSignalsEvaluator'] # Cell import time import json import numpy as np import pandas as pd from tqdm.auto import tqdm import matplotlib.pyplot as plt from typing import Tuple, Union from numerapi import SignalsAPI from rich import print as rich_print from .numerframe import NumerFrame, create_numerframe from .postprocessing import FeatureNeutralizer from .key import Key # Cell # hide FNCV3_FEATURES = ["feature_honoured_observational_balaamite", "feature_polaroid_vadose_quinze", "feature_untidy_withdrawn_bargeman", "feature_genuine_kyphotic_trehala", "feature_unenthralled_sportful_schoolhouse", "feature_divulsive_explanatory_ideologue", "feature_ichthyotic_roofed_yeshiva", "feature_waggly_outlandish_carbonisation", "feature_floriated_amish_sprite", "feature_iconoclastic_parietal_agonist", "feature_demolished_unfrightened_superpower", "feature_styloid_subdermal_cytotoxin", "feature_ironfisted_nonvintage_chlorpromazine", "feature_torose_unspiritualised_kylie", "feature_tearing_unkingly_adulthood", "feature_stylolitic_brown_spume", "feature_ferial_incumbent_engraving", "feature_litigant_unsizable_rhebok", "feature_floatiest_quintuplicate_carpentering", "feature_tuberculate_patelliform_paging", "feature_cuddlesome_undernamed_incidental", "feature_loony_zirconic_hoofer", "feature_indign_tardier_borough", "feature_fair_papal_vinaigrette", "feature_attack_unlit_milling", "feature_froggier_unlearned_underworkman", "feature_peninsular_pulsatile_vapor", "feature_midmost_perspiratory_hubert", "feature_laminable_unspecified_gynoecium", "feature_bally_bathymetrical_isadora", "feature_skim_unmeant_bandsman", "feature_ungenuine_sporophytic_evangelist", "feature_supercelestial_telic_dyfed", "feature_inconsiderate_unbooted_ricer", "feature_inured_conservable_forcer", "feature_glibber_deficient_jakarta", "feature_morbific_irredentist_interregnum", "feature_conjoint_transverse_superstructure", "feature_tingling_large_primordiality", "feature_phyllopod_unconstrainable_blubberer", "feature_deformable_unitary_schistosity", "feature_unprovisioned_aquatic_deuterogamy", "feature_equipped_undoubted_athanasian", "feature_inflammable_numb_anticline", "feature_kinky_benzal_holotype", "feature_ruptured_designing_interpolator", "feature_hierologic_expectable_maiolica", "feature_boiling_won_rama", "feature_lovelorn_aided_limiter", "feature_soviet_zibeline_profiler", "feature_altimetrical_muddled_symbolism", "feature_bratty_disrespectable_bookstand", "feature_unshaken_ahorse_wehrmacht", "feature_mightier_chivalric_kana", "feature_gambrel_unblessed_gigantomachy", "feature_ethiopic_anhedonic_stob", "feature_overstrung_dysmenorrheal_ingolstadt", "feature_rose_buttoned_dandy", "feature_recipient_perched_dendrochronologist", "feature_spikier_ordinate_taira", "feature_mercian_luddite_aganippe", "feature_faint_consociate_rhytidectomy", "feature_unpressed_mahratta_dah", "feature_maxillary_orphic_despicability", "feature_clasping_fast_menstruation", "feature_obeliscal_bewildered_reviewer", "feature_babist_moribund_myna", "feature_underdressed_tanagrine_prying", "feature_corniest_undue_scall", "feature_reduplicative_appalling_metastable", "feature_wrathful_prolix_colotomy", "feature_limonitic_issuable_melancholy", "feature_approximal_telautographic_sharkskin", "feature_fribble_gusseted_stickjaw", "feature_spec_subversive_plotter", "feature_unsinkable_dumbstruck_octuplet", "feature_integrative_reviviscent_governed", "feature_tamil_grungy_empathy", "feature_canopic_exigible_schoolgirl", "feature_plumular_constantinian_repositing", "feature_serpentiform_trinary_imponderability", "feature_gyroidal_embowed_pilcher", "feature_unlivable_armenian_wedge", "feature_flawed_demonological_toady", "feature_pruinose_raploch_roubaix", "feature_seediest_ramshackle_reclamation", "feature_hagiological_refer_vitamin", "feature_alcibiadean_lumpier_origan", "feature_encased_unamiable_hasidism", "feature_evocable_woollen_guarder", "feature_hunchbacked_unturning_meditation", "feature_circumnavigable_naughty_retranslation", "feature_testicular_slashed_ventosity", "feature_potential_subsessile_disconnection", "feature_unswaddled_inenarrable_goody", "feature_stellular_paler_centralisation", "feature_angevin_fitful_sultan", "feature_subinfeudatory_brainy_carmel", "feature_simpatico_cadential_pup", "feature_esculent_erotic_epoxy", "feature_milliary_hyperpyretic_medea", "feature_coraciiform_sciurine_reef", "feature_weightiest_protozoic_brawler", "feature_cooled_perkiest_electrodeposition", "feature_differing_peptizing_womaniser", "feature_gleaming_monosyllabic_scrod", "feature_unyielding_dismal_divertissement", "feature_rankine_meaty_port", "feature_southernmost_unhuman_arbiter", "feature_singhalese_cerographical_ego", "feature_malignant_campodeid_pluton", "feature_dure_jaspery_mugging", "feature_educational_caustic_mythologisation", "feature_diverted_astral_dunghill", "feature_degenerate_diaphragmatic_literalizer", "feature_laced_scraggly_grimalkin", "feature_wheezier_unjaundiced_game", "feature_unimpressed_uninflected_theophylline", "feature_shiite_overfed_mense", "feature_irritant_reciprocal_pelage", "feature_bricky_runed_bottleful", "feature_phyletic_separate_genuflexion", "feature_peckish_impetrative_kanpur", "feature_unshrinking_semiarid_floccule", "feature_heartier_salverform_nephew", "feature_geostrophic_adaptative_karla", "feature_navigational_enured_condensability", "feature_confusable_pursy_plosion", "feature_clenched_wayward_coelostat", "feature_developed_arbitrary_traditionalist", "feature_unnameable_abysmal_net", "feature_completive_pedantical_sinecurist", "feature_witchy_orange_muley", "feature_misfeatured_sometime_tunneler", "feature_agaze_lancinate_zohar", "feature_subservient_wedged_limping", "feature_urticant_ultracentrifugal_wane", "feature_pulverized_unified_dupery", "feature_stoichiometric_unanswerable_leveller", "feature_cyanophyte_emasculated_turpin", "feature_unruly_salian_impetuosity", "feature_ataractic_swept_rubeola", "feature_pansophical_agitato_theatricality", "feature_recreational_homiletic_nubian", "feature_burning_phrygian_axinomancy", "feature_protractive_moral_forswearing", "feature_certificated_putrescent_godship", "feature_dietetic_unscholarly_methamphetamine", "feature_vegetable_manlier_macaco", "feature_anthropoid_pithy_newscast", "feature_verifying_imagism_sublease", "feature_deckled_exaggerative_algol", "feature_songful_intercostal_frightener", "feature_additive_untrustworthy_hierologist", "feature_translative_quantitative_eschewer", "feature_coseismic_surpassable_invariance", "feature_blubbery_octahedral_bushfire", "feature_continued_conjugated_natalia", "feature_dissident_templed_shippon", "feature_wally_unrotted_eccrinology", "feature_unforgivable_airtight_reinsurance", "feature_unrelenting_intravascular_mesenchyme", "feature_linear_scummiest_insobriety", "feature_ovine_bramblier_leaven", "feature_uninforming_predictable_pepino", "feature_pluviometrical_biannual_saiga", "feature_affettuoso_taxidermic_greg", "feature_lateral_confervoid_belgravia", "feature_coalier_hircine_brokerage", "feature_undiverted_analyzed_accidie", "feature_favourable_swankiest_tympanist", "feature_refractory_topped_dependance", "feature_bustled_fieriest_doukhobor", "feature_isobilateral_olden_nephron", "feature_circassian_leathern_impugner", "feature_signed_ringent_sunna", "feature_cornute_potentiometric_tinhorn", "feature_veristic_parklike_halcyon", "feature_geochemical_unsavoury_collection", "feature_guerrilla_arrested_flavine", "feature_undependable_stedfast_donegal", "feature_bijou_penetrant_syringa", "feature_lamarckian_tarnal_egestion", "feature_horticultural_footworn_superscription", "feature_unwithered_personate_dilatation", "feature_wrought_muckier_temporality", "feature_rival_undepraved_countermarch", "feature_irrevocable_unlawful_oral", "feature_flawy_caller_superior", "feature_elohistic_totalitarian_underline", "feature_unrecognisable_waxier_paging", "feature_paraffinoid_flashiest_brotherhood", "feature_depauperate_armipotent_decentralisation", "feature_palpebral_univalve_pennoncel", "feature_received_veiniest_tamarix", "feature_scissile_dejected_kainite", "feature_narcotized_collectivist_evzone", "feature_jamesian_scutiform_ionium", "feature_gambogian_feudalist_diocletian", "feature_moneyed_mesophytic_lester", "feature_purblind_autarkic_pyrenoid", "feature_paleolithic_myalgic_lech", "feature_fortyish_neptunian_catechumenate", "feature_tricksiest_pending_voile", "feature_forcipate_laced_greenlet", "feature_overjoyed_undriven_sauna", "feature_small_cumulative_graywacke", "feature_incertain_catchable_zibet", "feature_unsustaining_chewier_adnoun", "feature_ruthenic_peremptory_truth", "feature_blind_concordant_tribalist", "feature_strigose_rugose_interjector", "feature_binding_lanky_rushing", "feature_carolean_tearable_smoothie", "feature_nappiest_unportioned_readjustment", "feature_sarmatia_foldable_eutectic", "feature_plum_anemometrical_guessing", "feature_gubernacular_liguloid_frankie", "feature_castigatory_hundredfold_hearthrug", "feature_pennsylvanian_sibylic_chanoyu", "feature_unreaving_intensive_docudrama", "feature_relinquished_incognizable_batholith", "feature_indusiate_canned_cosh", "feature_maglemosian_kittle_coachbuilding", "feature_unreeling_homeothermic_macedonia", "feature_asteriated_invigorated_penitence", "feature_anucleate_knotted_nonage", "feature_shrinelike_unreplaceable_nitrogenization", "feature_lacerable_backmost_vaseline", "feature_unreceipted_latest_lesser", "feature_unimaginable_sec_kaka", "feature_goidelic_gobelin_ledge", "feature_incondite_undisappointing_telephotograph", "feature_concoctive_symmetric_abulia", "feature_anglophobic_unformed_maneuverer", "feature_gravimetric_ski_enigma", "feature_balmiest_spinal_roundelay", "feature_required_bibliological_tonga", "feature_amoroso_wimpish_maturing", "feature_exertive_unmodernised_scaup", "feature_rude_booziest_ilium", "feature_uncompelled_curvy_amerindian", "feature_septuple_bonapartean_sanbenito", "feature_tottery_unmetalled_codder", "feature_tachygraphical_sedimentological_mesoderm", "feature_adsorbed_blizzardy_burlesque", "feature_wistful_tussive_cycloserine", "feature_superjacent_grubby_axillary", "feature_biological_caprine_cannoneer", "feature_unreversed_fain_jute", "feature_unexalted_rebel_kofta", "feature_doggish_mouthwatering_abelard", "feature_forfeit_contributing_joinder", "feature_necked_moresque_lowell", "feature_footling_unpuckered_lophophore", "feature_thorniest_laughable_hindustani", "feature_hotter_cattish_aridity", "feature_developing_behind_joan", "feature_ectodermal_mandaean_saffian", "feature_crimpier_gude_housedog", "feature_probationary_readying_roundelay", "feature_inserted_inconvertible_functioning", "feature_manifold_melodramatic_girl", "feature_drizzling_refrigerative_imperfection", "feature_sardonic_primary_shadwell", "feature_monocyclic_galliambic_par", "feature_smutty_prohibited_sullivan", "feature_productile_auriform_fil", "feature_accommodable_crinite_cleft", "feature_clipped_kurdish_grainer", "feature_dustproof_unafraid_stampede", "feature_neutered_postpositive_writ", "feature_twelve_haphazard_pantography", "feature_riskier_ended_typo", "feature_smaller_colored_immurement", "feature_snatchy_xylic_institution", "feature_conchal_angriest_oophyte", "feature_multiseriate_oak_benzidine", "feature_gobioid_transhuman_interconnection", "feature_reservable_peristomal_emden", "feature_inestimable_unmoral_extraversion", "feature_nubby_sissified_value", "feature_incorporating_abominable_daily", "feature_herbaged_brownish_consubstantialist", "feature_solemn_wordier_needlework", "feature_evangelistic_cruel_dissimilitude", "feature_impetratory_shuttered_chewer", "feature_referenced_biliteral_chiropody", "feature_eleatic_fellow_auctioneer", "feature_malpighian_vaporized_biogen", "feature_expiscatory_wriest_colportage", "feature_yelled_hysteretic_eath", "feature_bitterish_buttocked_turtleneck", "feature_percipient_atelectatic_cinnamon", "feature_gobony_premonitory_twinkler", "feature_twittery_tai_attainment", "feature_crooked_wally_lobation", "feature_crookback_workable_infringement", "feature_brawling_unpeppered_comedian", "feature_glyphographic_reparable_empyrean", "feature_noctilucent_subcortical_proportionality", "feature_guardian_frore_rolling", "feature_denuded_typed_wattmeter", "feature_unreachable_neritic_saracen", "feature_enzymatic_poorest_advocaat", "feature_wariest_vulnerable_unmorality", "feature_guttering_half_spondee", "feature_distressed_bloated_disquietude", "feature_leaky_overloaded_rhodium", "feature_unsapped_anionic_catherine", "feature_kissable_forfeit_egotism", "feature_unsizable_ancestral_collocutor", "feature_healthier_unconnected_clave", "feature_cirsoid_buddhism_vespa", "feature_rid_conveyable_cinchonization", "feature_donsie_folkish_renitency", "feature_agee_sold_microhabitat", "feature_newfangled_huddled_gest", "feature_clandestine_inkiest_silkworm", "feature_unutterable_softening_roper", "feature_balaamitical_electropositive_exhaustibility", "feature_unvalued_untangled_keener", "feature_undisturbing_quadrifid_reinhardt", "feature_bucked_costume_malagasy", "feature_joint_unreturning_basalt", "feature_coordinate_shyer_evildoing", "feature_carunculate_discursive_hectare", "feature_cynic_unreckonable_feoffment", "feature_cnidarian_micrologic_sousaphone", "feature_unperceivable_unrumpled_appendant", "feature_dissolvable_chrismal_obtund", "feature_choosier_uncongenial_coachwood", "feature_grimmest_prostate_doctrinaire", "feature_granulative_uncritical_agostini", "feature_convalescence_deuteranopic_lemuroid", "feature_disintegrable_snakier_zion", "feature_thoughtful_accommodable_lack", "feature_basophil_urdy_matzo", "feature_repellant_unwanted_clarinetist", "feature_antimonarchist_ordainable_quarterage", "feature_hardback_saturnalian_cyclometer", "feature_mythic_florentine_psammite", "feature_serpentiform_incomplete_bessarabia", "feature_unappeasable_employed_photoelectron", "feature_seaboard_adducent_polynesian", "feature_genoese_uncreditable_subregion", "feature_dexter_unstifled_snoring", "feature_protonematal_springtime_varioloid", "feature_orchitic_reported_coloration", "feature_stelliform_curling_trawler", "feature_athenian_pragmatism_isomorphism", "feature_abating_unadaptable_weakfish", "feature_instructional_desensitized_symmetallism", "feature_disarrayed_rarefactive_trisulphide", "feature_partible_amphibrachic_classicism", "feature_ecstatic_foundational_crinoidea", "feature_unimproved_courtliest_uncongeniality", "feature_cosy_microtonal_cedar", "feature_heedful_argyle_russianization", "feature_unhonoured_detested_xenocryst", "feature_sicker_spelaean_endplay", "feature_coordinated_astir_vituperation", "feature_stratocratic_aerodynamic_herero", "feature_uneasy_unaccommodating_immortality", "feature_professional_platonic_marten", "feature_detrital_respected_parlance", "feature_contraceptive_cartelist_beast", "feature_tapestried_madding_acclimatiser", "feature_optic_mycelial_whimper", "feature_liftable_direful_polyploid", "feature_objective_micro_langton", "feature_entopic_interpreted_subsidiary", "feature_saclike_hyphal_postulator", "feature_recent_shorty_preferment", "feature_strip_honoured_trail", "feature_unsheltered_doughtiest_episiotomy", "feature_acclimatisable_unfeigned_maghreb", "feature_galactopoietic_luckiest_protecting", "feature_scarcest_vaporized_max", "feature_spicier_unstripped_initial", "feature_hooly_chekhovian_phytogeographer", "feature_smouldering_underground_wingspan", "feature_phantasmal_extenuative_britain", "feature_sciurine_stibial_lintwhite", "feature_eucharistic_widowed_misfeasance", "feature_libratory_seizable_orlando", "feature_brackish_obstructed_almighty", "feature_translucid_neuroanatomical_sego", "feature_unheeded_stylar_planarian", "feature_preceptive_rushed_swedenborgian", "feature_sumerian_descendible_kalpa", "feature_jazziest_spellbinding_philabeg", "feature_dormie_sodden_steed", "feature_directoire_propositional_clydebank", "feature_triangled_rubber_skein", "feature_vendean_thwartwise_resistant", "feature_preoral_tonsorial_souk", "feature_virescent_telugu_neighbour", "feature_prefigurative_downstream_transvaluation", "feature_undepreciated_partitive_ipomoea", "feature_coactive_bandoleered_trogon", "feature_southerly_assonant_amicability", "feature_cortical_halt_catcher", "feature_queenliest_childing_ritual", "feature_antarthritic_syzygial_wonderland", "feature_revitalizing_rutilant_swastika", "feature_holy_chic_cali", "feature_hermitical_stark_serfhood", "feature_deformable_productile_piglet", "feature_lentissimo_ducky_quadroon", "feature_happening_tristful_yodeling", "feature_guardant_giocoso_natterjack", "feature_bootleg_clement_joe", "feature_thousandth_hierarchal_plight", "feature_unhoped_hex_ventriloquism", "feature_unappreciated_humiliated_misapprehension", "feature_cragged_sacred_malabo", "feature_idled_unwieldy_improvement", "feature_censorial_leachier_rickshaw", "feature_carbuncled_athanasian_ampul"] MEDIUM_FEATURES = ["feature_abstersive_emotional_misinterpreter", "feature_accessorial_aroused_crochet", "feature_acerb_venusian_piety", "feature_affricative_bromic_raftsman", "feature_agile_unrespited_gaucho", "feature_agronomic_cryptal_advisor", "feature_alkaline_pistachio_sunstone", "feature_altern_unnoticed_impregnation", "feature_ambisexual_boiled_blunderer", "feature_amoebaean_wolfish_heeler", "feature_amygdaloidal_intersectional_canonry", "feature_antipathetical_terrorful_ife", "feature_antipodal_unable_thievery", "feature_antisubmarine_foregoing_cryosurgery", "feature_apomictical_motorized_vaporisation", "feature_apophthegmatical_catechetical_millet", "feature_apostate_impercipient_knighthood", "feature_appraisive_anagrammatical_tentacle", "feature_arillate_nickelic_hemorrhage", "feature_armoured_finable_skywriter", "feature_assenting_darn_arthropod", "feature_assertive_worsened_scarper", "feature_atlantic_uveal_incommunicability", "feature_attuned_southward_heckle", "feature_autarkic_constabulary_dukedom", "feature_autodidactic_gnarlier_pericardium", "feature_axillary_reluctant_shorty", "feature_aztecan_encomiastic_pitcherful", "feature_barest_kempt_crowd", "feature_basaltic_arid_scallion", "feature_base_ingrain_calligrapher", "feature_beady_unkind_barret", "feature_belgravian_salopian_sheugh", "feature_biannual_maleficent_thack", "feature_bifacial_hexastyle_hemialgia", "feature_bleeding_arabesque_pneuma", "feature_bloodied_twinkling_andante", "feature_brawny_confocal_frail", "feature_brickier_heterostyled_scrutiny", "feature_built_reincarnate_sherbet", "feature_bushwhacking_unaligned_imperturbability", "feature_busty_unfitted_keratotomy", "feature_buxom_curtained_sienna", "feature_caecilian_unexperienced_ova", "feature_caespitose_unverifiable_intent", "feature_cairned_fumiest_ordaining", "feature_calceolate_pudgy_armure", "feature_calculating_unenchanted_microscopium", "feature_calefactive_anapaestic_jerome", "feature_calycled_living_birmingham", "feature_camphorated_spry_freemartin", "feature_caressive_cognate_cubature", "feature_casemated_ibsenian_grantee", "feature_castrated_presented_quizzer", "feature_casuistic_barbarian_monochromy", "feature_centric_shaggier_cranko", "feature_cerebrovascular_weeny_advocate", "feature_chafed_undenominational_backstitch", "feature_chaldean_vixenly_propylite", "feature_chaotic_granitoid_theist", "feature_chartered_conceptual_spitting", "feature_cheering_protonemal_herd", "feature_chelonian_pyknic_delphi", "feature_chopfallen_fasciate_orchidologist", "feature_christadelphian_euclidean_boon", "feature_chuffier_analectic_conchiolin", "feature_churrigueresque_talc_archaicism", "feature_clawed_unwept_adaptability", "feature_clerkish_flowing_chapati", "feature_coalier_typhoid_muntin", "feature_collective_stigmatic_handfasting", "feature_commensurable_industrial_jungfrau", "feature_communicatory_unrecommended_velure", "feature_conceding_ingrate_tablespoonful", "feature_confiscatory_triennial_pelting", "feature_congealed_lee_steek", "feature_congenial_transmigrant_isobel", "feature_congenital_conched_perithecium", "feature_conjugal_postvocalic_rowe", "feature_consecrate_legislative_cavitation", "feature_contaminative_intrusive_tagrag", "feature_continuate_unprocurable_haversine", "feature_contused_festal_geochemistry", "feature_coordinated_undecipherable_gag", "feature_covalent_methodological_brash", "feature_covalent_unreformed_frogbit", "feature_crablike_panniered_gloating", "feature_criticisable_authentical_deprecation", "feature_croupiest_shaded_thermotropism", "feature_ctenoid_moaning_fontainebleau", "feature_culinary_pro_offering", "feature_curling_aurorean_iseult", "feature_curtained_gushier_tranquilizer", "feature_cyrenaic_unschooled_silurian", "feature_decent_solo_stickup", "feature_degenerate_officinal_feasibility", "feature_demisable_expiring_millepede", "feature_demure_groutiest_housedog", "feature_dendritic_prothallium_sweeper", "feature_dentilingual_removed_osmometer", "feature_descendent_decanal_hon", "feature_desiderative_commiserative_epizoa", "feature_designer_notchy_epiploon", "feature_dichasial_hammier_spawner", "feature_dipped_sent_giuseppe", "feature_discrepant_ventral_shicker", "feature_dismaying_chaldean_tallith", "feature_dispiriting_araeostyle_jersey", "feature_diverticular_punjabi_matronship", "feature_doggish_whacking_headscarf", "feature_dovetailed_winy_hanaper", "feature_draconic_contractible_romper", "feature_emmetropic_heraclitean_conducting", "feature_encompassing_skeptical_salience", "feature_endangered_unthreaded_firebrick", "feature_enlightening_mirthful_laurencin", "feature_epicurean_fetal_seising", "feature_epidermic_scruffiest_prosperity", "feature_epitaxial_loathsome_essen", "feature_eruptive_seasoned_pharmacognosy", "feature_escutcheoned_timocratic_kotwal", "feature_euterpean_frazzled_williamsburg", "feature_exacerbating_presentationism_apagoge", "feature_expressed_abhominable_pruning", "feature_extractable_serrulate_swing", "feature_fake_trident_agitator", "feature_faltering_tergal_tip", "feature_farcical_spinal_samantha", "feature_faustian_unventilated_lackluster", "feature_favoring_prescript_unorthodoxy", "feature_festering_controvertible_hostler", "feature_fierier_goofier_follicle", "feature_fissirostral_multifoliate_chillon", "feature_flakiest_fleecy_novelese", "feature_flavourful_seismic_erica", "feature_fleshly_bedimmed_enfacement", "feature_foamy_undrilled_glaciology", "feature_fragrant_fifteen_brian", "feature_frequentative_participial_waft", "feature_fumarolic_known_sharkskin", "feature_fustiest_voiced_janet", "feature_galvanometric_sturdied_billingsgate", "feature_ganoid_osiered_mineralogy", "feature_generative_honorific_tughrik", "feature_glare_factional_assessment", "feature_glyptic_unrubbed_holloway", "feature_gone_honduran_worshipper", "feature_gossamer_placable_wycliffite", "feature_grazed_blameful_desiderative", "feature_greedier_favorable_enthymeme", "feature_groggy_undescried_geosphere", "feature_gullable_sanguine_incongruity", "feature_gutta_exploitive_simpson", "feature_haematoid_runaway_nightjar", "feature_hawkish_domiciliary_duramen", "feature_headhunting_unsatisfied_phenomena", "feature_hellenistic_scraggly_comfort", "feature_helpable_chanciest_fractionisation", "feature_hemispherical_unabsolved_aeolipile", "feature_hendecagonal_deathly_stiver", "feature_hexametric_ventricose_limnology", "feature_hibernating_soritic_croupe", "feature_highland_eocene_berean", "feature_hillier_unpitied_theobromine", "feature_himyarite_tetragonal_deceit", "feature_horizontal_snug_description", "feature_hotfoot_behaviorist_terylene", "feature_huskiest_compartmental_jacquerie", "feature_hydrologic_cymric_nyctophobia", "feature_hypermetropic_unsighted_forsyth", "feature_hypersonic_volcanological_footwear", "feature_hypogastric_effectual_sunlight", "feature_hypothetic_distressing_endemic", "feature_hysteric_mechanized_recklinghausen", "feature_iconomatic_boozier_age", "feature_illiterate_stomachal_terpene", "feature_impractical_endorsed_tide", "feature_incitant_trochoidal_oculist", "feature_incommensurable_diffused_curability", "feature_indefatigable_enterprising_calf", "feature_indentured_communicant_tulipomania", "feature_indirect_concrete_canaille", "feature_induplicate_hoarse_disbursement", "feature_inexpugnable_gleg_candelilla", "feature_inflexed_lamaism_crit", "feature_inhabited_pettier_veinlet", "feature_inhibited_snowiest_drawing", "feature_inseminated_filarial_mesoderm", "feature_insociable_exultant_tatum", "feature_instrumentalist_extrovert_cassini", "feature_integrated_extroversive_ambivalence", "feature_intended_involute_highbinder", "feature_intercalative_helvetian_infirmarian", "feature_interdental_mongolian_anarchism", "feature_intermontane_vertical_moo", "feature_interrogatory_isohyetal_atacamite", "feature_intersubjective_juristic_sagebrush", "feature_intertwined_leeriest_suffragette", "feature_introvert_symphysial_assegai", "feature_intrusive_effluent_hokkaido", "feature_invalid_chromatographic_cornishman", "feature_invalid_extortionary_titillation", "feature_iridic_unpropertied_spline", "feature_irresponsive_compositive_ramson", "feature_irritant_euphuistic_weka", "feature_isotopic_hymenial_starwort", "feature_jerkwater_eustatic_electrocardiograph", "feature_jiggish_tritheist_probity", "feature_juvenalian_paunchy_uniformitarianism", "feature_kerygmatic_splashed_ziegfeld", "feature_koranic_rude_corf", "feature_leaky_maroon_pyrometry", "feature_learned_claustral_quiddity", "feature_leggiest_slaggiest_inez", "feature_leisurable_dehortatory_pretoria", "feature_leukemic_paler_millikan", "feature_levigate_kindly_dyspareunia", "feature_liege_unexercised_ennoblement", "feature_limitable_astable_physiology", "feature_lipogrammatic_blowsier_seismometry", "feature_log_unregenerate_babel", "feature_lordly_lamellicorn_buxtehude", "feature_loricate_cryptocrystalline_ethnology", "feature_lost_quirky_botel", "feature_loyal_fishy_pith", "feature_malacological_differential_defeated", "feature_malagasy_abounding_circumciser", "feature_massed_nonracial_ecclesiologist", "feature_mattery_past_moro", "feature_maximal_unobserving_desalinisation", "feature_mazy_superrefined_punishment", "feature_merovingian_tenebrism_hartshorn", "feature_methylated_necrophilic_serendipity", "feature_midget_noncognizable_plenary", "feature_migrant_reliable_chirurgery", "feature_mined_game_curse", "feature_misanthropic_knurliest_freebooty", "feature_more_hindoo_diageotropism", "feature_mucky_loanable_gastrostomy", "feature_multilinear_sharpened_mouse", "feature_myographic_gawkier_timbale", "feature_naval_edified_decarbonize", "feature_nebule_barmier_bibliomania", "feature_nubblier_plosive_deepening", "feature_nucleophilic_uremic_endogen", "feature_obeisant_vicarial_passibility", "feature_offshore_defamatory_catalog", "feature_outdated_tapered_speciation", "feature_outsized_admonishing_errantry", "feature_oversea_permed_insulter", "feature_ovular_powered_neckar", "feature_padded_peripteral_pericranium", "feature_palatalized_unsucceeded_induration", "feature_palmy_superfluid_argyrodite", "feature_pansophic_merino_pintado", "feature_paraffinoid_irreplevisable_ombu", "feature_paramagnetic_complex_gish", "feature_passerine_ultraist_neon", "feature_patristical_analysable_langouste", "feature_peaty_vulgar_branchia", "feature_peculiar_sheenier_quintal", "feature_peltate_okay_info", "feature_perceivable_gasiform_psammite", "feature_perigean_bewitching_thruster", "feature_periscopic_thirteenth_cartage", "feature_permanent_cottony_ballpen", "feature_pert_performative_hormuz", "feature_petitionary_evanescent_diallage", "feature_phellogenetic_vibrational_jocelyn", "feature_piffling_inflamed_jupiter", "feature_planar_unessential_bride", "feature_planned_superimposed_bend", "feature_plexiform_won_elk", "feature_polaroid_squalliest_applause", "feature_precooled_inoperable_credence", "feature_puberulent_nondescript_laparoscope", "feature_publishable_apiarian_rollick", "feature_quadratic_untouched_liberty", "feature_questionable_diplex_caesarist", "feature_quinsied_increased_braincase", "feature_ratlike_matrilinear_collapsability", "feature_recidivism_petitory_methyltestosterone", "feature_reclaimed_fallibilist_turpentine", "feature_reclinate_cruciform_lilo", "feature_reconciling_dauby_database", "feature_reduplicate_conoid_albite", "feature_refreshed_untombed_skinhead", "feature_reminiscent_unpained_ukulele", "feature_renegade_undomestic_milord", "feature_reported_slimy_rhapsody", "feature_reserved_cleanable_soldan", "feature_restricted_aggregately_workmanship", "feature_resuscitative_communicable_brede", "feature_retinoscopy_flinty_wool", "feature_revealable_aeonian_elvira", "feature_revitalizing_dashing_photomultiplier", "feature_rheumy_epistemic_prancer", "feature_rimmed_conditional_archipelago", "feature_roasting_slaked_reposition", "feature_roiling_trimeric_kurosawa", "feature_rowable_unshod_noise", "feature_rubblier_chlorotic_stogy", "feature_ruffianly_uncommercial_anatole", "feature_rural_inquisitional_trotline", "feature_rusted_unassisting_menaquinone", "feature_ruthenian_uncluttered_vocalizing", "feature_salian_suggested_ephemeron", "feature_sallowish_cognisant_romaunt", "feature_scenic_cormophytic_bilirubin", "feature_scenographical_dissentient_trek", "feature_scorbutic_intellectualism_mongoloid", "feature_scrobiculate_unexcitable_alder", "feature_seamier_jansenism_inflator", "feature_seclusive_emendatory_plangency", "feature_seemlier_reorient_monandry", "feature_severe_tricky_pinochle", "feature_sixteen_inbreed_are", "feature_sludgy_implemental_sicily", "feature_smoggy_niftiest_lunch", "feature_smugger_hydroponic_farnesol", "feature_softish_unseparated_caudex", "feature_sorted_ignitable_sagitta", "feature_spagyric_echt_alum", "feature_spookiest_expedite_overnighter", "feature_springlike_crackjaw_bheesty", "feature_squishiest_unsectarian_support", "feature_stelar_balmiest_pellitory", "feature_stereotypic_ebracteate_louise", "feature_strychnic_structuralist_chital", "feature_stylistic_honduran_comprador", "feature_subapostolic_dungy_fermion", "feature_subdued_spiffier_kano", "feature_subglobular_unsalable_patzer", "feature_substandard_permissible_paresthesia", "feature_sudsy_polymeric_posteriority", "feature_supergene_legible_antarthritic", "feature_synoptic_botryose_earthwork", "feature_syrian_coital_counterproof", "feature_tarry_meet_chapel", "feature_telephonic_shakable_bollock", "feature_terrific_epigamic_affectivity", "feature_tittering_virgilian_decliner", "feature_together_suppositive_aster", "feature_tonal_graptolitic_corsac", "feature_tortured_arsenical_arable", "feature_torturesome_estimable_preferrer", "feature_tossing_denominative_threshing", "feature_trabeate_eutherian_valedictory", "feature_tranquilizing_abashed_glyceria", "feature_transmontane_clerkly_value", "feature_travelled_semipermeable_perruquier", "feature_tribal_germinable_yarraman", "feature_trim_axial_suffocation", "feature_unaimed_yonder_filmland", "feature_unamazed_tumular_photomicrograph", "feature_unapplicable_jerkiest_klemperer", "feature_unbeaten_orological_dentin", "feature_unbreakable_nosological_comedian", "feature_unburied_exponent_pace", "feature_uncertified_myrmecological_nagger", "feature_uncharged_unovercome_smolder", "feature_unco_terefah_thirster", "feature_uncomplimentary_malignant_scoff", "feature_uncompromising_fancy_kyle", "feature_uncurtailed_translucid_coccid", "feature_undescribed_methylic_friday", "feature_undetermined_idle_aftergrowth", "feature_undirected_perdu_ylem", "feature_undisguised_whatever_gaul", "feature_undivorced_unsatisfying_praetorium", "feature_undrossy_serpentiform_sack", "feature_unextinct_smectic_isa", "feature_uninclosed_handcrafted_springing", "feature_univalve_abdicant_distrail", "feature_unknown_reusable_cabbage", "feature_unlawful_superintendent_brunet", "feature_unlivable_morbific_traveling", "feature_unliving_bit_bengaline", "feature_unluckiest_mulley_benzyl", "feature_unmalleable_resistant_kingston", "feature_unmodernized_vasodilator_galenist", "feature_unmoved_alt_spoonerism", "feature_unnetted_bay_premillennialist", "feature_unnourishing_indiscreet_occiput", "feature_unperfect_implemental_cellarage", "feature_unrated_intact_balmoral", "feature_unrelieved_rawish_cement", "feature_unrequired_waxing_skeptic", "feature_unscheduled_malignant_shingling", "feature_unsparing_moralistic_commissary", "feature_unsparred_scarabaeid_anthologist", "feature_unspotted_practiced_gland", "feature_unstacked_trackable_blizzard", "feature_unsurveyed_boyish_aleph", "feature_unsurveyed_chopped_feldspathoid", "feature_untellable_penal_allegorization", "feature_untouchable_unsolvable_agouti", "feature_untrimmed_monaxial_accompanist", "feature_untumbled_histologic_inion", "feature_unvaried_social_bangkok", "feature_unweary_congolese_captain", "feature_uretic_seral_decoding", "feature_urochordal_swallowed_curn", "feature_vedic_mitral_swiz", "feature_venatic_intermetallic_darling", "feature_vestmental_hoofed_transpose", "feature_vizierial_courtlier_hampton", "feature_volitional_ascensive_selfhood", "feature_voltairean_consolidative_parallel", "feature_voltairean_dyslogistic_epagoge", "feature_vulcanological_sepulchral_spean", "feature_wale_planned_tolstoy", "feature_westering_immunosuppressive_crapaud", "feature_whistleable_unbedimmed_chokey", "feature_whitened_remanent_blast", "feature_whopping_eminent_attempter", "feature_wieldable_defiled_aperitive", "feature_wombed_reverberatory_colourer", "feature_zarathustrian_albigensian_itch", "feature_zymotic_varnished_mulga"] # Cell class BaseEvaluator: """ Evaluation functionality that is relevant for both Numerai Classic and Numerai Signals. :param era_col: Column name pointing to eras. \n Most commonly "era" for Numerai Classic and "friday_date" for Numerai Signals. \n :param fast_mode: Will skip compute intensive metrics if set to True, namely max_exposure, feature neutral mean, TB200 and TB500. """ def __init__(self, era_col: str = "era", fast_mode=False): self.era_col = era_col self.fast_mode = fast_mode def full_evaluation( self, dataf: NumerFrame, example_col: str, pred_cols: list = None, target_col: str = "target", ) -> pd.DataFrame: """ Perform evaluation for each prediction column in the NumerFrame against give target and example prediction column. """ val_stats = pd.DataFrame() cat_cols = dataf.get_feature_data.select_dtypes(include=['category']).columns.to_list() if cat_cols: rich_print(f":warning: WARNING: Categorical features detected that cannot be used for neutralization. Removing columns: '{cat_cols}' for evaluation. :warning:") dataf.loc[:, dataf.feature_cols] = dataf.get_feature_data.select_dtypes(exclude=['category']) dataf = dataf.fillna(0.5) pred_cols = dataf.prediction_cols if not pred_cols else pred_cols for col in tqdm(pred_cols, desc="Evaluation: "): col_stats = self.evaluation_one_col( dataf=dataf, pred_col=col, target_col=target_col, example_col=example_col, ) val_stats = pd.concat([val_stats, col_stats], axis=0) return val_stats def evaluation_one_col( self, dataf: NumerFrame, pred_col: str, target_col: str, example_col: str, ): """ Perform evaluation for one prediction column against given target and example prediction column. """ col_stats = pd.DataFrame() # Compute stats val_corrs = self.per_era_corrs( dataf=dataf, pred_col=pred_col, target_col=target_col ) mean, std, sharpe = self.mean_std_sharpe(era_corrs=val_corrs) max_drawdown = self.max_drawdown(era_corrs=val_corrs) apy = self.apy(era_corrs=val_corrs) example_corr = self.example_correlation( dataf=dataf, pred_col=pred_col, example_col=example_col ) mmc_mean, mmc_std, mmc_sharpe = self.mmc( dataf=dataf, pred_col=pred_col, target_col=target_col, example_col=example_col, ) col_stats.loc[pred_col, "target"] = target_col col_stats.loc[pred_col, "mean"] = mean col_stats.loc[pred_col, "std"] = std col_stats.loc[pred_col, "sharpe"] = sharpe col_stats.loc[pred_col, "max_drawdown"] = max_drawdown col_stats.loc[pred_col, "apy"] = apy col_stats.loc[pred_col, "mmc_mean"] = mmc_mean col_stats.loc[pred_col, "mmc_std"] = mmc_std col_stats.loc[pred_col, "mmc_sharpe"] = mmc_sharpe col_stats.loc[pred_col, "corr_with_example_preds"] = example_corr # Compute intensive stats if not self.fast_mode: max_feature_exposure = self.max_feature_exposure( dataf=dataf, pred_col=pred_col ) fn_mean, fn_std, fn_sharpe = self.feature_neutral_mean_std_sharpe( dataf=dataf, pred_col=pred_col, target_col=target_col ) tb200_mean, tb200_std, tb200_sharpe = self.tbx_mean_std_sharpe( dataf=dataf, pred_col=pred_col, target_col=target_col, tb=200 ) tb500_mean, tb500_std, tb500_sharpe = self.tbx_mean_std_sharpe( dataf=dataf, pred_col=pred_col, target_col=target_col, tb=500 ) ex_diss = self.exposure_dissimilarity( dataf=dataf, pred_col=pred_col, example_col=example_col ) col_stats.loc[pred_col, "max_feature_exposure"] = max_feature_exposure col_stats.loc[pred_col, "feature_neutral_mean"] = fn_mean col_stats.loc[pred_col, "feature_neutral_std"] = fn_std col_stats.loc[pred_col, "feature_neutral_sharpe"] = fn_sharpe col_stats.loc[pred_col, "tb200_mean"] = tb200_mean col_stats.loc[pred_col, "tb200_std"] = tb200_std col_stats.loc[pred_col, "tb200_sharpe"] = tb200_sharpe col_stats.loc[pred_col, "tb500_mean"] = tb500_mean col_stats.loc[pred_col, "tb500_std"] = tb500_std col_stats.loc[pred_col, "tb500_sharpe"] = tb500_sharpe col_stats.loc[pred_col, "exposure_dissimilarity"] = ex_diss return col_stats def per_era_corrs( self, dataf: pd.DataFrame, pred_col: str, target_col: str ) -> pd.Series: """Correlation between prediction and target for each era.""" return dataf.groupby(dataf[self.era_col]).apply( lambda d: self._normalize_uniform(d[pred_col].fillna(0.5)).corr( d[target_col] ) ) def mean_std_sharpe( self, era_corrs: pd.Series ) -> Tuple[np.float64, np.float64, np.float64]: """ Average, standard deviation and Sharpe ratio for correlations per era. """ mean = pd.Series(era_corrs.mean()).item() std = pd.Series(era_corrs.std(ddof=0)).item() sharpe = mean / std return mean, std, sharpe @staticmethod def max_drawdown(era_corrs: pd.Series) -> np.float64: """Maximum drawdown per era.""" # Arbitrarily large window rolling_max = ( (era_corrs + 1).cumprod().rolling(window=9000, min_periods=1).max() ) daily_value = (era_corrs + 1).cumprod() max_drawdown = -((rolling_max - daily_value) / rolling_max).max() return max_drawdown @staticmethod def apy(era_corrs: pd.Series, stake_compounding_lag: int = 4) -> np.float64: """ Annual percentage yield. :param era_corrs: Correlation scores by era :param stake_compounding_lag: Compounding lag for Numerai rounds (4 for Numerai Classic) """ payout_scores = era_corrs.clip(-0.25, 0.25) payout_daily_value = (payout_scores + 1).cumprod() apy = ( ((payout_daily_value.dropna().iloc[-1]) ** (1 / len(payout_scores))) ** ( 52 - stake_compounding_lag ) # 52 weeks of compounding minus n for stake compounding lag - 1 ) * 100 return apy def example_correlation( self, dataf: Union[pd.DataFrame, NumerFrame], pred_col: str, example_col: str ): """Correlations with example predictions.""" return self.per_era_corrs( dataf=dataf, pred_col=pred_col, target_col=example_col, ).mean() def max_feature_exposure( self, dataf: Union[pd.DataFrame, NumerFrame], pred_col: str ) -> np.float64: """Maximum exposure over all features.""" max_per_era = dataf.groupby(self.era_col).apply( lambda d: d[dataf.feature_cols].corrwith(d[pred_col]).abs().max() ) max_feature_exposure = max_per_era.mean(skipna=True) return max_feature_exposure def feature_neutral_mean_std_sharpe( self, dataf: Union[pd.DataFrame, NumerFrame], pred_col: str, target_col: str, feature_names: list = None ) -> Tuple[np.float64, np.float64, np.float64]: """ Feature neutralized mean performance. More info: https://docs.numer.ai/tournament/feature-neutral-correlation """ fn = FeatureNeutralizer(pred_name=pred_col, feature_names=feature_names, proportion=1.0) neutralized_dataf = fn(dataf=dataf) neutral_corrs = self.per_era_corrs( dataf=neutralized_dataf, pred_col=f"{pred_col}_neutralized_1.0", target_col=target_col, ) mean, std, sharpe = self.mean_std_sharpe(era_corrs=neutral_corrs) return mean, std, sharpe def tbx_mean_std_sharpe( self, dataf: pd.DataFrame, pred_col: str, target_col: str, tb: int = 200 ) -> Tuple[np.float64, np.float64, np.float64]: """ Calculate Mean, Standard deviation and Sharpe ratio when we focus on the x top and x bottom predictions. :param tb: How many of top and bottom predictions to focus on. TB200 and TB500 are the most common situations. """ tb_val_corrs = self._score_by_date( dataf=dataf, columns=[pred_col], target=target_col, tb=tb ) return self.mean_std_sharpe(era_corrs=tb_val_corrs) def mmc( self, dataf: pd.DataFrame, pred_col: str, target_col: str, example_col: str ) -> Tuple[np.float64, np.float64, np.float64]: """ MMC Mean, standard deviation and Sharpe ratio. More info: https://docs.numer.ai/tournament/metamodel-contribution """ mmc_scores = [] corr_scores = [] for _, x in dataf.groupby(self.era_col): series = self._neutralize_series( self._normalize_uniform(x[pred_col]), (x[example_col]) ) mmc_scores.append(np.cov(series, x[target_col])[0, 1] / (0.29 ** 2)) corr_scores.append(self._normalize_uniform(x[pred_col]).corr(x[target_col])) val_mmc_mean = np.mean(mmc_scores) val_mmc_std = np.std(mmc_scores) corr_plus_mmcs = [c + m for c, m in zip(corr_scores, mmc_scores)] corr_plus_mmc_sharpe = np.mean(corr_plus_mmcs) / np.std(corr_plus_mmcs) return val_mmc_mean, val_mmc_std, corr_plus_mmc_sharpe def exposure_dissimilarity(self, dataf: NumerFrame, pred_col: str, example_col: str) -> np.float32: """ Model pattern of feature exposure to the example column. See TC details forum post: https://forum.numer.ai/t/true-contribution-details/5128/4 """ U = dataf.get_feature_data.corrwith(dataf[pred_col]).values E = dataf.get_feature_data.corrwith(dataf[example_col]).values exp_dis = 1 - np.dot(U, E) / np.dot(E, E) return exp_dis @staticmethod def _neutralize_series(series, by, proportion=1.0): scores = series.values.reshape(-1, 1) exposures = by.values.reshape(-1, 1) # this line makes series neutral to a constant column so that it's centered and for sure gets corr 0 with exposures exposures = np.hstack( (exposures, np.array([np.mean(series)] * len(exposures)).reshape(-1, 1)) ) correction = proportion * ( exposures.dot(np.linalg.lstsq(exposures, scores, rcond=None)[0]) ) corrected_scores = scores - correction neutralized = pd.Series(corrected_scores.ravel(), index=series.index) return neutralized def _score_by_date( self, dataf: pd.DataFrame, columns: list, target: str, tb: int = None ): """ Get era correlation based on given TB (x top and bottom predictions). :param tb: How many of top and bottom predictions to focus on. TB200 is the most common situation. """ unique_eras = dataf[self.era_col].unique() computed = [] for u in unique_eras: df_era = dataf[dataf[self.era_col] == u] era_pred = np.float64(df_era[columns].values.T) era_target = np.float64(df_era[target].values.T) if tb is None: ccs = np.corrcoef(era_target, era_pred)[0, 1:] else: tbidx = np.argsort(era_pred, axis=1) tbidx = np.concatenate([tbidx[:, :tb], tbidx[:, -tb:]], axis=1) ccs = [ np.corrcoef(era_target[idx], pred[idx])[0, 1] for idx, pred in zip(tbidx, era_pred) ] ccs = np.array(ccs) computed.append(ccs) return pd.DataFrame( np.array(computed), columns=columns, index=dataf[self.era_col].unique() ) @staticmethod def _normalize_uniform(df: pd.DataFrame) -> pd.Series: """Normalize predictions uniformly using ranks.""" x = (df.rank(method="first") - 0.5) / len(df) return pd.Series(x, index=df.index) def plot_correlations( self, dataf: NumerFrame, pred_cols: list = None, target_col: str = "target", roll_mean: int = 20, ): """ Plot per era correlations over time. :param roll_mean: How many eras should be averaged to compute a rolling score. """ validation_by_eras = pd.DataFrame() pred_cols = dataf.prediction_cols if not pred_cols else pred_cols for pred_col in pred_cols: per_era_corrs = self.per_era_corrs( dataf, pred_col=pred_col, target_col=target_col ) validation_by_eras.loc[:, pred_col] = per_era_corrs validation_by_eras.rolling(roll_mean).mean().plot( kind="line", marker="o", ms=4, title=f"Rolling Per Era Correlation Mean (rolling window size: {roll_mean})", figsize=(15, 5), ) plt.legend( loc="upper center", bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=1, ) plt.axhline(y=0.0, color="r", linestyle="--") plt.show() validation_by_eras.cumsum().plot( title="Cumulative Sum of Era Correlations", figsize=(15, 5) ) plt.legend( loc="upper center", bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=1, ) plt.axhline(y=0.0, color="r", linestyle="--") plt.show() return # Cell class NumeraiClassicEvaluator(BaseEvaluator): """Evaluator for all metrics that are relevant in Numerai Classic.""" def __init__(self, era_col: str = "era", fast_mode=False): super().__init__(era_col=era_col, fast_mode=fast_mode) self.fncv3_features = FNCV3_FEATURES self.medium_features = MEDIUM_FEATURES def full_evaluation( self, dataf: NumerFrame, example_col: str, pred_cols: list = None, target_col: str = "target", ) -> pd.DataFrame: val_stats = pd.DataFrame() dataf = dataf.fillna(0.5) pred_cols = dataf.prediction_cols if not pred_cols else pred_cols # Check if sufficient columns are present in dataf to compute FNCv3 if set(self.fncv3_features).issubset(set(dataf.columns)): valid_features = self.fncv3_features elif set(self.medium_features).issubset(set(dataf.columns)): print("WARNING: 'v4/fncv3_features' are not present in the DataFrame. Falling back on 'v3/medium' features.") valid_features = self.medium_features else: print("WARNING: neither 'v4/fncv3_features' nor 'v3/medium' features are defined in DataFrame. Skipping calculation of v3 metrics.") valid_features = [] for col in tqdm(pred_cols, desc="Evaluation: "): # Metrics that can be calculated for both Numerai Classic and Signals col_stats = self.evaluation_one_col( dataf=dataf, pred_col=col, target_col=target_col, example_col=example_col, ) # Numerai Classic specific metrics if not self.fast_mode and valid_features: fnc_v3, fn_std_v3, fn_sharpe_v3 = self.feature_neutral_mean_std_sharpe( dataf=dataf, pred_col=col, target_col=target_col, feature_names=valid_features ) col_stats.loc[col, "feature_neutral_mean_v3"] = fnc_v3 col_stats.loc[col, "feature_neutral_std_v3"] = fn_std_v3 col_stats.loc[col, "feature_neutral_sharpe_v3"] = fn_sharpe_v3 val_stats = pd.concat([val_stats, col_stats], axis=0) return val_stats def __load_json(self, json_path: str) -> dict: with open(json_path, 'r') as f: data = json.load(f) return data # Cell class NumeraiSignalsEvaluator(BaseEvaluator): """Evaluator for all metrics that are relevant in Numerai Signals.""" def __init__(self, era_col: str = "friday_date", fast_mode=False): super().__init__(era_col=era_col, fast_mode=fast_mode) def get_neutralized_corr(self, val_dataf: pd.DataFrame, model_name: str, key: Key, timeout_min: int = 2) -> pd.Series: """ Retrieved neutralized validation correlation by era. \n Calculated on Numerai servers. \n :param val_dataf: A DataFrame containing prediction, friday_date, era and data_type columns. \n data_type column should contain 'validation' instances. \n :param model_name: Any model name for which you have authentication credentials. \n :param key: Key object to authenticate upload of diagnostics. \n :param timeout_min: How many minutes to wait on diagnostics processing on Numerai servers before timing out. \n 2 minutes by default. \n :return: Pandas Series with era as index and neutralized validation correlations (validationCorr). """ api = SignalsAPI(public_id=key.pub_id, secret_key=key.secret_key) model_id = api.get_models()[model_name] api.upload_diagnostics(df=val_dataf, model_id=model_id) data = self.__await_diagnostics(api=api, model_id=model_id, timeout_min=timeout_min) dataf = pd.DataFrame(data['perEraDiagnostics']).set_index("era")['validationCorr'] dataf.index = pd.to_datetime(dataf.index) return dataf @staticmethod def __await_diagnostics(api: SignalsAPI, model_id: str, timeout_min: int, interval_sec: int = 15): """ Wait for diagnostics to be uploaded. Try every 'interval_sec' seconds until 'timeout_min' minutes have passed. """ timeout = time.time() + 60 * timeout_min data = {"status": "not_done"} while time.time() < timeout: data = api.diagnostics(model_id=model_id)[0] if data['status'] == 'done': break else: print(f"Diagnostics not processed yet. Sleeping for another {interval_sec} seconds.") time.sleep(interval_sec) if not data['status'] == 'done': raise Exception(f"Diagnostics couldn't be retrieved within {timeout_min} minutes after uploading. Check if Numerai API is offline.") return data

111.303158

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null

0

1

0

1

0

1

null

0

5

cf095d1cc1c0a7aeb1bfa725fcbb0567a3a1ff93

100

py

Python

votedperceptron/__init__.py

bmgee/votedperceptron

166aa1fd581fa1df05bdb3608c6eff8012ec3b20

[ "Apache-2.0" ]

1

2021-03-16T03:05:35.000Z

votedperceptron/__init__.py

bmgee/votedperceptron

166aa1fd581fa1df05bdb3608c6eff8012ec3b20

[ "Apache-2.0" ]

null

votedperceptron/__init__.py

bmgee/votedperceptron

166aa1fd581fa1df05bdb3608c6eff8012ec3b20

[ "Apache-2.0" ]

2

2020-03-06T15:59:46.000Z

2022-01-04T13:47:11.000Z

from .votedperceptron import VotedPerceptron from .multiclassclassifier import MulticlassClassifier

33.333333

54

0.9

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100

11.25

0.5

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0

null

0

1

0

1

0

5

cf401b682e08d73e9b854d1617ebcf53e2b8b846

46

py

Python

msbd/varieta/__init__.py

mnslarcher/metodi-statistici-big-data

4587b4e4104557e50d09d028259d6c42c44d2814

[ "MIT" ]

1

2019-02-17T09:28:04.000Z

msbd/varieta/__init__.py

mnslarcher/metodi-statistici-big-data

4587b4e4104557e50d09d028259d6c42c44d2814

[ "MIT" ]

null

msbd/varieta/__init__.py

mnslarcher/metodi-statistici-big-data

4587b4e4104557e50d09d028259d6c42c44d2814

[ "MIT" ]

null

from .curva_principale import CurvaPrincipale

23

45

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5

46

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1

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1

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null

0

1

0

null

0

1

0

1

0

5

cf57147f53c8c4411cbef0b3146294daf662edb7

370

py

Python

lliregistration_back/api/views/__init__.py

ydang5/final-project-back

ae8b0ff2b340b521b70e3b0c25ab8cb4b64ac453

[ "BSD-3-Clause" ]

null

lliregistration_back/api/views/__init__.py

ydang5/final-project-back

ae8b0ff2b340b521b70e3b0c25ab8cb4b64ac453

[ "BSD-3-Clause" ]

null

lliregistration_back/api/views/__init__.py

ydang5/final-project-back

ae8b0ff2b340b521b70e3b0c25ab8cb4b64ac453

[ "BSD-3-Clause" ]

null

from api.views.homepage.views import GetVersion from api.views.gateway.views import UserLogoutAPI from api.views.file_upload.views import LLIStudentMasterSheetUploadAPIView from api.views.data_organizer.views import ImmiStatusValidCheckAPI from api.views.data_organizer.views import PaymentValidCheckAPI from api.views.data_organizer.views import InsuranceValidCheckAPI

52.857143

74

0.886486

46

370

7.043478

0.347826

0.12963

0.222222

0.148148

0.333333

0

0.064865

370

6

75

61.666667

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true

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1

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1

0

null

0

1

0

1

0

1

0

5

cf66d235f27e1bdf651245e9001d38bc8109f3f6

67

py

Python

models/fpnssd/__init__.py

lihaojia24/pytorch-dt

0a8bda73d2055e960ac4840c651b5dff61bc4f5f

[ "MIT" ]

null

models/fpnssd/__init__.py

lihaojia24/pytorch-dt

0a8bda73d2055e960ac4840c651b5dff61bc4f5f

[ "MIT" ]

null

models/fpnssd/__init__.py

lihaojia24/pytorch-dt

0a8bda73d2055e960ac4840c651b5dff61bc4f5f

[ "MIT" ]

null

from .net import FPNSSD512 from .box_coder import FPNSSDBoxCoder

22.333333

38

0.820896

9

67

6

0.777778

0

0.052632

0.149254

67

2

39

33.5

0.894737

0

1

0

true

0

1

0

1

0

1

0

null

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1

0

null

0

1

0

1

0

1

0

5

cf7faef5ea7fed012acfa5a75de0f4402ca3848c

93

py

Python

gie/tests/test_modules/basic.py

Kerdek/gie

ebcd1aec6dc34de46145e4013afd6d5dad194a9f

[ "BSD-3-Clause-Clear" ]

57

2019-06-21T21:15:03.000Z

2022-03-30T18:17:56.000Z

gie/tests/test_modules/basic.py

Kerdek/gie

ebcd1aec6dc34de46145e4013afd6d5dad194a9f

[ "BSD-3-Clause-Clear" ]

2

2020-08-04T05:45:03.000Z

2021-02-26T10:21:16.000Z

gie/tests/test_modules/basic.py

Kerdek/gie

ebcd1aec6dc34de46145e4013afd6d5dad194a9f

[ "BSD-3-Clause-Clear" ]

8

2019-11-24T07:57:46.000Z

2021-05-05T07:58:29.000Z

def to_string(x: int) -> str: return str(x) def to_int(x: str) -> int: return int(x)

18.6

29

0.591398

18

93

2.944444

0.388889

0.188679

0

0.236559

93

5

30

18.6

0.746479

0

1

0.5

false

0

0.5

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1

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null

0

1

0

null

0

1

0

1

0

5

d85427ebe2efa0c5a204f78dd43cf6bf56bd6a11

208

py

Python

main/academy/admin.py

UsamaKashif/studentutor

7aa5407ac81134a49e474726220e48beaadc9390

[ "MIT" ]

7

2021-01-17T23:10:15.000Z

2021-02-01T21:35:36.000Z

main/academy/admin.py

DiveshTheReal/studentutor

0d3ef57887bde4dd2ee40d68015598f9c8052ffd

[ "MIT" ]

7

2021-01-17T15:10:47.000Z

2022-03-12T00:53:49.000Z

main/academy/admin.py

DiveshTheReal/studentutor

0d3ef57887bde4dd2ee40d68015598f9c8052ffd

[ "MIT" ]

3

2021-01-18T09:36:16.000Z

2021-01-20T16:29:40.000Z

from django.contrib import admin from .models import Academy, PostAnAd, Invitations # Register your models here. admin.site.register(Academy) admin.site.register(PostAnAd) admin.site.register(Invitations)

20.8

50

0.8125

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208

6.259259

0.481481

0.159763

0.301775

0

0.100962

208

9

51

23.111111

0.903743

0.125

0

1

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true

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1

0

1

0

null

0

1

0

1

0

5

d874aeea6ae50f04b5a49191f30e4996a02bd636

284

py

Python

singUpSystem/scheme.py

xodbox/prediccionf1

0f20931493947d3c655a9d50fe9290dfdd722eb9

[ "MIT" ]

null

singUpSystem/scheme.py

xodbox/prediccionf1

0f20931493947d3c655a9d50fe9290dfdd722eb9

[ "MIT" ]

null

singUpSystem/scheme.py

xodbox/prediccionf1

0f20931493947d3c655a9d50fe9290dfdd722eb9

[ "MIT" ]

null

from google.appengine.ext import db class UserInfo(db.Model): username = db.StringProperty(required = True) password = db.StringProperty(required = True) email = db.StringProperty def query(*q): if len(q) == 1: return db.GqlQuery(q[0]) else: return db.GqlQuery(q[0], q[1])

21.846154

46

0.707746

43

284

4.674419

0.581395

0.238806

0.278607

0.179104

0

0.016529

0.147887

284

12

47

23.666667

0.81405

0

1

0.1

false

0.1

0

0.8

0

null

1

0

null

0

1

0

1

0

5

d8767fc661f736407fd24243a091071e0d120a95

56

py

Python

scivision/io/__init__.py

RaoOfPhysics/scivision

880914f0606c51743794fa69a667b181929b5c21

[ "BSD-3-Clause" ]

null

scivision/io/__init__.py

RaoOfPhysics/scivision

880914f0606c51743794fa69a667b181929b5c21

[ "BSD-3-Clause" ]

null

scivision/io/__init__.py

RaoOfPhysics/scivision

880914f0606c51743794fa69a667b181929b5c21

[ "BSD-3-Clause" ]

null

from .reader import load_pretrained_model, load_dataset

28

55

0.875

8

56

5.75

0.875

0

0.089286

56

1

56

0.901961

0

1

0

true

0

1

0

1

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1

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null

0

1

0

null

0

1

0

1

0

1

0

5

d87c975e8fdd557e379e81e17a5ab0904535773e

84

py

Python

andes/models/vcomp/__init__.py

cuihantao/Andes

6cdc057986c4a8382194ef440b6e92b8dfb77e25

[ "Apache-2.0" ]

16

2017-06-16T14:21:04.000Z

2018-08-18T08:52:27.000Z

andes/models/vcomp/__init__.py

cuihantao/Andes

6cdc057986c4a8382194ef440b6e92b8dfb77e25

[ "Apache-2.0" ]

1

2017-12-12T07:51:16.000Z

andes/models/vcomp/__init__.py

cuihantao/Andes

6cdc057986c4a8382194ef440b6e92b8dfb77e25

[ "Apache-2.0" ]

7

2017-12-10T07:32:36.000Z

2018-09-19T16:38:30.000Z

""" Voltage compensators. """ from andes.models.vcomp.ieeevc import IEEEVC # NOQA

14

52

0.72619

10

84

6.1

0.9

0

0.142857

84

5

53

16.8

0.847222

0.321429

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d87e4a321437c9a315d69e11baa4c92fee9ca7cc

18,841

py

Python

examples/python/oracle_arb_finder/core/smartcontracts.py

edd34/OrFeed

25d22ef79817861d7f7acef333cf1f9db395fffc

[ "Apache-2.0" ]

null

examples/python/oracle_arb_finder/core/smartcontracts.py

edd34/OrFeed

25d22ef79817861d7f7acef333cf1f9db395fffc

[ "Apache-2.0" ]

null

examples/python/oracle_arb_finder/core/smartcontracts.py

edd34/OrFeed

25d22ef79817861d7f7acef333cf1f9db395fffc

[ "Apache-2.0" ]

null

import os from dotenv import load_dotenv load_dotenv() orfeed_contract_address_mainnet = "0x8316b082621cfedab95bf4a44a1d4b64a6ffc336" registry_contract_address_mainnet = "0x74b5CE2330389391cC61bF2287BDC9Ac73757891" aave_liquidity_provider = "0x3dfd23a6c5e8bbcfc9581d2e864a68feb6a076d3" registry_abi_mainnet = [ { "constant": False, "inputs": [ {"name": "name", "type": "string"}, {"name": "newOrSameOracleAddress", "type": "address"}, ], "name": "editOracleAddress", "outputs": [{"name": "", "type": "bool"}], "payable": True, "stateMutability": "payable", "type": "function", }, { "constant": True, "inputs": [ {"name": "selectedOracle", "type": "string"}, {"name": "fromParam", "type": "string"}, {"name": "toParam", "type": "string"}, {"name": "side", "type": "string"}, {"name": "amount", "type": "uint256"}, ], "name": "getPriceFromOracle", "outputs": [{"name": "", "type": "uint256"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": False, "inputs": [], "name": "withdrawBalance", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": True, "inputs": [], "name": "getAllOracles", "outputs": [{"name": "", "type": "string[]"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": False, "inputs": [{"name": "newFee", "type": "uint256"}], "name": "changeFee", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [ {"name": "name", "type": "string"}, {"name": "requestedAddress", "type": "address"}, {"name": "info", "type": "string"}, ], "name": "registerOracle", "outputs": [{"name": "", "type": "bool"}], "payable": True, "stateMutability": "payable", "type": "function", }, { "constant": True, "inputs": [{"name": "nameReference", "type": "string"}], "name": "getOracleInfo", "outputs": [{"name": "", "type": "string"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": True, "inputs": [{"name": "nameReference", "type": "string"}], "name": "getOracleOwner", "outputs": [{"name": "", "type": "address"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": False, "inputs": [ {"name": "name", "type": "string"}, {"name": "info", "type": "string"}, ], "name": "editOracleInfo", "outputs": [{"name": "", "type": "bool"}], "payable": True, "stateMutability": "payable", "type": "function", }, { "constant": False, "inputs": [{"name": "newOwner", "type": "address"}], "name": "changeOwner", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": True, "inputs": [{"name": "nameReference", "type": "string"}], "name": "getOracleAddress", "outputs": [{"name": "", "type": "address"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": False, "inputs": [ {"name": "name", "type": "string"}, {"name": "toAddress", "type": "address"}, ], "name": "transferOracleName", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "inputs": [], "payable": True, "stateMutability": "payable", "type": "constructor", }, ] orfeed_abi_mainnet = [ { "constant": False, "inputs": [ {"name": "symb", "type": "string"}, {"name": "tokenAddress", "type": "address"}, {"name": "byteCode", "type": "bytes32"}, ], "name": "addFreeCurrency", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": True, "inputs": [ {"name": "fromSymbol", "type": "string"}, {"name": "toSymbol", "type": "string"}, {"name": "venue", "type": "string"}, {"name": "amount", "type": "uint256"}, {"name": "referenceId", "type": "string"}, ], "name": "requestAsyncExchangeRateResult", "outputs": [{"name": "", "type": "uint256"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": True, "inputs": [ {"name": "eventName", "type": "string"}, {"name": "source", "type": "string"}, {"name": "referenceId", "type": "string"}, ], "name": "getAsyncEventResult", "outputs": [{"name": "", "type": "string"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": False, "inputs": [ {"name": "newDiv", "type": "uint256"}, {"name": "newMul", "type": "uint256"}, ], "name": "updateMulDivConverter2", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [ {"name": "synth", "type": "bytes32"}, {"name": "token", "type": "address"}, {"name": "inputAmount", "type": "uint256"}, ], "name": "getSynthToTokenOutputAmount", "outputs": [{"name": "", "type": "uint256"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [ {"name": "symb", "type": "string"}, {"name": "tokenAddress", "type": "address"}, ], "name": "addFreeToken", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "_a", "type": "string"}, {"name": "_b", "type": "string"}], "name": "compare", "outputs": [{"name": "", "type": "int256"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "newOracle", "type": "address"}], "name": "updateForexOracleAddress", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "_a", "type": "string"}, {"name": "_b", "type": "string"}], "name": "equal", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": True, "inputs": [ {"name": "eventName", "type": "string"}, {"name": "source", "type": "string"}, ], "name": "getEventResult", "outputs": [{"name": "", "type": "string"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": False, "inputs": [{"name": "newOracle", "type": "address"}], "name": "updateSynthAddress", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [ {"name": "newDiv", "type": "uint256"}, {"name": "newMul", "type": "uint256"}, ], "name": "updateMulDivConverter1", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [ {"name": "newDiv", "type": "uint256"}, {"name": "newMul", "type": "uint256"}, ], "name": "updateMulDivConverter3", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": True, "inputs": [ {"name": "fromSymbol", "type": "string"}, {"name": "toSymbol", "type": "string"}, {"name": "venue", "type": "string"}, {"name": "amount", "type": "uint256"}, ], "name": "getExchangeRate", "outputs": [{"name": "", "type": "uint256"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": False, "inputs": [{"name": "symb", "type": "string"}], "name": "removeFreeToken", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "newOracle", "type": "address"}], "name": "updateEthTokenAddress", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [ {"name": "fundsReturnToAddress", "type": "address"}, {"name": "liquidityProviderContractAddress", "type": "address"}, {"name": "tokens", "type": "string[]"}, {"name": "amount", "type": "uint256"}, {"name": "exchanges", "type": "string[]"}, ], "name": "arb", "outputs": [{"name": "", "type": "bool"}], "payable": True, "stateMutability": "payable", "type": "function", }, { "constant": False, "inputs": [{"name": "newOracle", "type": "address"}], "name": "updatePremiumSubOracleAddress", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [ {"name": "_haystack", "type": "string"}, {"name": "_needle", "type": "string"}, ], "name": "indexOf", "outputs": [{"name": "", "type": "int256"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "symb", "type": "string"}], "name": "removeFreeCurrency", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "newOracle", "type": "address"}], "name": "updateAsyncOracleAddress", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "venueToCheck", "type": "string"}], "name": "isFreeVenueCheck", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "symToCheck", "type": "string"}], "name": "isFree", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "newAddress", "type": "address"}], "name": "updateArbContractAddress", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "newOwner", "type": "address"}], "name": "changeOwner", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "newOracle", "type": "address"}], "name": "updateAsyncEventsAddress", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": True, "inputs": [{"name": "tokenAddress", "type": "address"}], "name": "getTokenDecimalCount", "outputs": [{"name": "", "type": "uint256"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": True, "inputs": [{"name": "a", "type": "string"}, {"name": "b", "type": "string"}], "name": "compareStrings", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": False, "inputs": [ {"name": "eventName", "type": "string"}, {"name": "source", "type": "string"}, ], "name": "requestAsyncEvent", "outputs": [{"name": "", "type": "string"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": True, "inputs": [{"name": "symbol", "type": "string"}], "name": "getTokenAddress", "outputs": [{"name": "", "type": "address"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": False, "inputs": [ {"name": "token", "type": "address"}, {"name": "synth", "type": "bytes32"}, {"name": "inputAmount", "type": "uint256"}, ], "name": "getTokenToSynthOutputAmount", "outputs": [{"name": "", "type": "uint256"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "source", "type": "string"}], "name": "stringToBytes32", "outputs": [{"name": "result", "type": "bytes32"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [ {"name": "fromSymbol", "type": "string"}, {"name": "toSymbol", "type": "string"}, {"name": "venue", "type": "string"}, {"name": "amount", "type": "uint256"}, ], "name": "requestAsyncExchangeRate", "outputs": [{"name": "", "type": "string"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "newOracle", "type": "address"}], "name": "updateTokenOracleAddress2", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "newOracle", "type": "address"}], "name": "updateSyncEventsAddress", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": True, "inputs": [{"name": "symbol", "type": "string"}], "name": "getSynthBytes32", "outputs": [{"name": "", "type": "bytes32"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": False, "inputs": [ {"name": "fromSymb", "type": "string"}, {"name": "toSymb", "type": "string"}, {"name": "amount", "type": "uint256"}, ], "name": "getFreeExchangeRate", "outputs": [{"name": "", "type": "uint256"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [{"name": "newOracle", "type": "address"}], "name": "updateTokenOracleAddress", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": False, "inputs": [ {"name": "newDiv", "type": "uint256"}, {"name": "newMul", "type": "uint256"}, ], "name": "updateMulDivConverter4", "outputs": [{"name": "", "type": "bool"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "constant": True, "inputs": [{"name": "symbol", "type": "string"}], "name": "getForexAddress", "outputs": [{"name": "", "type": "address"}], "payable": False, "stateMutability": "view", "type": "function", }, { "constant": False, "inputs": [ {"name": "param1", "type": "string"}, {"name": "param2", "type": "string"}, {"name": "param3", "type": "string"}, {"name": "param4", "type": "string"}, ], "name": "callExtraFunction", "outputs": [{"name": "", "type": "string"}], "payable": False, "stateMutability": "nonpayable", "type": "function", }, { "inputs": [], "payable": True, "stateMutability": "payable", "type": "constructor", }, {"payable": True, "stateMutability": "payable", "type": "fallback"}, ] my_smartcontracts = {} if os.getenv("NETWORK") == "mainnet": my_smartcontracts["orfeed"] = { "address": orfeed_contract_address_mainnet, "abi": orfeed_abi_mainnet, } my_smartcontracts["registry"] = { "address": registry_contract_address_mainnet, "abi": registry_abi_mainnet, }

31.090759

87

0.452046

1,268

18,841

6.690852

0.109621

0.074257

0.092409

0.105728

0.772159

0.733498

0.731141

0.716525

0.711693

0

0.013689

0.317605

18,841

605

88

31.142149

0.646185

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0.003333

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0.003333

0

null

0

1

0

null

0

5

d8ae9c65994b7b29b7ea692215d948759739b279

1,982

py

Python

mak/libs/pyxx/cxx/grammar/declaration/specifier/type/simple.py

bugengine/BugEngine

1b3831d494ee06b0bd74a8227c939dd774b91226

[ "BSD-3-Clause" ]

4

2015-05-13T16:28:36.000Z

2017-05-24T15:34:14.000Z

mak/libs/pyxx/cxx/grammar/declaration/specifier/type/simple.py

bugengine/BugEngine

1b3831d494ee06b0bd74a8227c939dd774b91226

[ "BSD-3-Clause" ]

null

mak/libs/pyxx/cxx/grammar/declaration/specifier/type/simple.py

bugengine/BugEngine

1b3831d494ee06b0bd74a8227c939dd774b91226

[ "BSD-3-Clause" ]

1

2017-03-21T08:28:07.000Z

""" simple-type-specifier: nested-name-specifier? type-name nested-name-specifier template simple-template-id decltype-specifier placeholder-type-specifier nested-name-specifier? template-name char char8_t char16_t char32_t wchar_t bool short int long signed unsigned float double void type-name: class-name enum-name typedef-name """ import glrp from .....parser import cxx98 from be_typing import TYPE_CHECKING @glrp.rule('simple-type-specifier : nested-name-specifier? type-name') @glrp.rule('simple-type-specifier : nested-name-specifier "template" simple-template-id') @glrp.rule('simple-type-specifier : decltype-specifier') @glrp.rule('simple-type-specifier : placeholder-type-specifier') @glrp.rule('simple-type-specifier[split] : nested-name-specifier? template-name') @glrp.rule('simple-type-specifier : [split]"char"') @glrp.rule('simple-type-specifier : [split]"char8_t"') @glrp.rule('simple-type-specifier : [split]"char16_t"') @glrp.rule('simple-type-specifier : [split]"char32_t"') @glrp.rule('simple-type-specifier : [split]"wchar_t"') @glrp.rule('simple-type-specifier : [split]"bool"') @glrp.rule('simple-type-specifier : [split]"short"') @glrp.rule('simple-type-specifier : [split]"int"') @glrp.rule('simple-type-specifier : [split]"long"') @glrp.rule('simple-type-specifier : [split]"signed"') @glrp.rule('simple-type-specifier : [split]"unsigned"') @glrp.rule('simple-type-specifier : [split]"float"') @glrp.rule('simple-type-specifier : [split]"double"') @glrp.rule('simple-type-specifier : [split]"void"') @cxx98 def simple_type_specifier(self, p): # type: (CxxParser, glrp.Production) -> None pass @glrp.rule('type-name : class-name') @glrp.rule('type-name : enum-name') @glrp.rule('type-name : typedef-name') @cxx98 def type_name(self, p): # type: (CxxParser, glrp.Production) -> None pass if TYPE_CHECKING: from .....parser import CxxParser

28.724638

89

0.701312

263

1,982

5.231939

0.171103

0.217297

0.289971

0.248547

0.700581

0.62936

0.308866

0.164244

0

0.009254

0.127649

1,982

69

90

28.724638

0.786582

0.25328

0

0.121212

0

0.610054

0.337636

0

1

0.060606

false

0.060606

0.121212

0

0.181818

0

null

1

0

1

0

null

0

1

0

5

2b0e76299e6b3309a09aa302c08a25d5f283cba1

138

py

Python

sqller/exceptions.py

VoIlAlex/sqller

93cd15a6d6eab195fa12e52d1e83e214405cfd35

[ "MIT" ]

1

2020-12-13T20:25:44.000Z

sqller/exceptions.py

VoIlAlex/sqller

93cd15a6d6eab195fa12e52d1e83e214405cfd35

[ "MIT" ]

1

2020-03-13T23:31:45.000Z

sqller/exceptions.py

VoIlAlex/sqller

93cd15a6d6eab195fa12e52d1e83e214405cfd35

[ "MIT" ]

null

class ConventionViolationError(Exception): pass class SQLError(Exception): pass class CustomSQLBuildError(SQLError): pass

12.545455

42

0.753623

12

138

8.666667

0.5

0.25

0.346154

0

0.181159

138

10

43

13.8

0.920354

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

null

0

1

0

5

2b281ab08bac2e0164ab0eb7ac71cbb696d61dc4

178

py

Python

data-storage-manager/src/simcore_service_dsm/rest/generated_code/models/__init__.py

mguidon/aiohttp-dsm

612e4c7f6f73df7d6752269965c428fda0276191

[ "MIT" ]

null

data-storage-manager/src/simcore_service_dsm/rest/generated_code/models/__init__.py

mguidon/aiohttp-dsm

612e4c7f6f73df7d6752269965c428fda0276191

[ "MIT" ]

null

data-storage-manager/src/simcore_service_dsm/rest/generated_code/models/__init__.py

mguidon/aiohttp-dsm

612e4c7f6f73df7d6752269965c428fda0276191

[ "MIT" ]

null

# coding: utf-8 # flake8: noqa from __future__ import absolute_import # import models into model package from .error_model import ErrorModel from .health_info import HealthInfo

22.25

38

0.814607

25

178

5.52

0.72

0

0.013072

0.140449

178

7

39

25.428571

0.888889

0.331461

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

9924b0e1f6dc69d0994fcd13ad12ea4d63ea6722

12

py

Python

py1.py

saksitdGtec/pygit

80759eaac3500eb62761641eb4f94b77a52872a5

[ "MIT" ]

null

py1.py

saksitdGtec/pygit

80759eaac3500eb62761641eb4f94b77a52872a5

[ "MIT" ]

null

py1.py

saksitdGtec/pygit

80759eaac3500eb62761641eb4f94b77a52872a5

[ "MIT" ]

null

print("tst")

12

0.666667

2

12

4

1

0

12

1

12

0.666667

0

0.230769

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

995bedc3206b3dfaae418d3f810fa960e09c82a8

56

py

Python

src/compare-qrels/__init__.py

giguru/compare-qrels

dfc119c3b1403748333a48ba880c7e7372055eeb

[ "MIT" ]

null

src/compare-qrels/__init__.py

giguru/compare-qrels

dfc119c3b1403748333a48ba880c7e7372055eeb

[ "MIT" ]

null

src/compare-qrels/__init__.py

giguru/compare-qrels

dfc119c3b1403748333a48ba880c7e7372055eeb

[ "MIT" ]

null

from .compare_qrels import CompareData, compute_qrels_df

56

0.892857

8

56

5.875

0.875

0

0.071429

56

1

56

0.903846

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

9967fb395b4df0cd22f3e65736785a0fc1fb1b13

124

py

Python

fulmo/core/__init__.py

jexio/fulmo

daa4bd4f1cf3b8bd785a9024a413db9a0238f10c

[ "MIT" ]

null

fulmo/core/__init__.py

jexio/fulmo

daa4bd4f1cf3b8bd785a9024a413db9a0238f10c

[ "MIT" ]

80

2021-07-13T12:58:25.000Z

2022-03-24T03:17:08.000Z

fulmo/core/__init__.py

jexio/fulmo

daa4bd4f1cf3b8bd785a9024a413db9a0238f10c

[ "MIT" ]

null

from .datamodule import BaseDataModule, BaseDataModuleParameters # noqa: F401 from .module import BaseModule # noqa: F401

41.333333

78

0.806452

13

124

7.692308

0.692308

0.16

0

0.056075

0.137097

124

2

79

62

0.878505

0.169355

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

41e0383f02d8496f17b9fcb2474615635ee15eef

72

py

Python

stubs/3/django/core/management/base.py

ucdstudent95618/pyre-check

032c67c5b75d573e0f645545b01c0f0f30475ed7

[ "MIT" ]

null

stubs/3/django/core/management/base.py

ucdstudent95618/pyre-check

032c67c5b75d573e0f645545b01c0f0f30475ed7

[ "MIT" ]

null

stubs/3/django/core/management/base.py

ucdstudent95618/pyre-check

032c67c5b75d573e0f645545b01c0f0f30475ed7

[ "MIT" ]

null

from typing import TextIO class BaseCommand: stdout: TextIO = ...

12

25

0.694444

8

72

6.25

0.875

0

0.222222

72

5

26

14.4

0.892857

0

1

0

true

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

41e7f1d33b3fd6289352c62c825975bc458e8969

31

py

Python

client.py

Li-Pro/P2P-Remote_Party

120144e5fdacb30c77981e59d9d242e541178b89

[ "Apache-2.0" ]

1

2020-04-10T10:15:53.000Z

client.py

Li-Pro/P2P-Remote-Party

7aff94c3bf4dea8327b2b49a1f7dd5abe3c60bfe

[ "Apache-2.0" ]

null

client.py

Li-Pro/P2P-Remote-Party

7aff94c3bf4dea8327b2b49a1f7dd5abe3c60bfe

[ "Apache-2.0" ]

null

import p2prp p2prp.runClient()

10.333333

17

0.806452

4

31

6.25

0.75

0

0.071429

0.096774

31

3

17

10.333333

0.821429

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

5138e61620fec34db702857307274dc51dde32e5

45

py

Python

corehq/ex-submodules/soil/exceptions.py

johan--/commcare-hq

86ee99c54f55ee94e4c8f2f6f30fc44e10e69ebd

[ "BSD-3-Clause" ]

null

corehq/ex-submodules/soil/exceptions.py

johan--/commcare-hq

86ee99c54f55ee94e4c8f2f6f30fc44e10e69ebd

[ "BSD-3-Clause" ]

1

2022-03-12T01:03:25.000Z

corehq/ex-submodules/soil/exceptions.py

johan--/commcare-hq

86ee99c54f55ee94e4c8f2f6f30fc44e10e69ebd

[ "BSD-3-Clause" ]

null

class TaskFailedError(Exception): pass

9

33

0.733333

4

45

8.25

1

0

0.2

45

4

34

11.25

0.916667

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

513cbfc7811faa01881ceb4a2b4633891c1f1611

181

py

Python

mp_sort/virtenv/lib/python3.6/site-packages/transcrypt/development/automated_tests/warnings/autotest.py

ang-jason/fip_powerx_mini_projects-foxtrot

37e3671969b516369e2d1c7cab5890b75c489f56

[ "MIT" ]

2,200

2016-10-12T16:47:13.000Z

2022-03-30T16:40:35.000Z

mp_sort/virtenv/lib/python3.6/site-packages/transcrypt/development/automated_tests/warnings/autotest.py

ang-jason/fip_powerx_mini_projects-foxtrot

37e3671969b516369e2d1c7cab5890b75c489f56

[ "MIT" ]

672

2016-10-12T16:36:48.000Z

2022-03-25T00:57:04.000Z

mp_sort/virtenv/lib/python3.6/site-packages/transcrypt/development/automated_tests/warnings/autotest.py

ang-jason/fip_powerx_mini_projects-foxtrot

37e3671969b516369e2d1c7cab5890b75c489f56

[ "MIT" ]

230

2016-10-20T14:31:40.000Z

2022-03-16T15:57:15.000Z

import org.transcrypt.autotester import basic_tests autoTester = org.transcrypt.autotester.AutoTester () autoTester.run( basic_tests, "basic_tests" ) autoTester.done()

18.1

53

0.762431

20

181

6.75

0.4

0.222222

0.340741

0

0.143646

181

9

54

20.111111

0.870968

0

0.064327

0

1

0

false

0

0.4

0

0.4

0

1

0

null

1

0

1

0

null

0

1

0

5

513df9df3b51c7e82927b77a25aa7b9f0bc47f92

1,237

py

Python

syslinkats/data_file_retrievers.py

stick152/SystemLink-Python-ATS

82b0fac9bae22b808ba519fa4425a931ff3c77aa

[ "MIT" ]

null

syslinkats/data_file_retrievers.py

stick152/SystemLink-Python-ATS

82b0fac9bae22b808ba519fa4425a931ff3c77aa

[ "MIT" ]

null

syslinkats/data_file_retrievers.py

stick152/SystemLink-Python-ATS

82b0fac9bae22b808ba519fa4425a931ff3c77aa

[ "MIT" ]

null

""" data_file_retrievers.py This module contains methods for retrieving paths to all configuration .json files. """ import pkg_resources def ats_config_file(): return pkg_resources.resource_filename( 'syslinkats', 'tests/default_conf.json') def installation_config_file(): return pkg_resources.resource_filename( 'syslinkats', 'tests/setup/installation/data/config.json') def instance_config_file(): return pkg_resources.resource_filename( 'syslinkats', 'tests/setup/instances/data/config.json') def systemlink_server_config_file(): return pkg_resources.resource_filename( 'syslinkats', 'tests/setup/systemlink_server/data/config.json') def user_config_file(): return pkg_resources.resource_filename( 'syslinkats', 'tests/setup/users/data/config.json') def workspaces_config_file(): return pkg_resources.resource_filename( 'syslinkats', 'tests/setup/workspaces/data/config.json') def security_config_file(): return pkg_resources.resource_filename( 'syslinkats', 'tests/setup/security/data/config.json' ) def mongo_config_file(): return pkg_resources.resource_filename( 'syslinkats', 'tests/setup/mongo/data/config.json')

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null

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0

1

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5

5159d57992cfe8e0f9134838535f34f4a3d105a3

212

py

Python

discordbot/src/helpers/__init__.py

knabb215/discord-masz

a1b8434ca8e6e31cb61a8a6069338fdd34698ea2

[ "MIT" ]

null

discordbot/src/helpers/__init__.py

knabb215/discord-masz

a1b8434ca8e6e31cb61a8a6069338fdd34698ea2

[ "MIT" ]

null

discordbot/src/helpers/__init__.py

knabb215/discord-masz

a1b8434ca8e6e31cb61a8a6069338fdd34698ea2

[ "MIT" ]

null

from .console import console from .create_whois_embed import create_whois_embed from .parse_timedeltas import parse_delta from .get_prefix import get_prefix from .create_modcase_embed import create_modcase_embed

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5aa920ed6ae0a3641b45fac1985c9cd5005d2b94

351

py

Python

example/tutorial.py

vyahello/python-package-template

a0133915d1ead210eef87e421f880812f6035986

[ "MIT" ]

null

example/tutorial.py

vyahello/python-package-template

a0133915d1ead210eef87e421f880812f6035986

[ "MIT" ]

null

example/tutorial.py

vyahello/python-package-template

a0133915d1ead210eef87e421f880812f6035986

[ "MIT" ]

null

class Tutorial: AUTHOR: str = "Volodymyr Yahello" def __init__(self, foo: str, bar: str) -> None: self._foo: str = foo self._bar: str = bar def foo(self) -> str: return self._foo def bar(self) -> str: return self._bar def meta(self) -> str: return f"Packaging tutorial by {self.AUTHOR}"

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null

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5

5ab9a6ca71b1c1e60e2ac3819acce22a329d418e

215

py

Python

omnisci_olio/ipython/__init__.py

omnisci/omnisci-olio.py

e8b33d660b49bc7677d82845ed384e57582ef0f8

[ "Apache-2.0" ]

2

2022-03-16T20:46:26.000Z

2022-03-16T20:46:28.000Z

omnisci_olio/ipython/__init__.py

heavyai/heavyai-olio.py

e8b33d660b49bc7677d82845ed384e57582ef0f8

[ "Apache-2.0" ]

1

2022-02-05T12:16:09.000Z

omnisci_olio/ipython/__init__.py

omnisci/omnisci-olio.py

e8b33d660b49bc7677d82845ed384e57582ef0f8

[ "Apache-2.0" ]

null

"""OmniSciDB SQL magic""" # https://ipython.readthedocs.io/en/stable/config/custommagics.html from .magic import OmniSciSqlMagic def load_ipython_extension(ipython): ipython.register_magics(OmniSciSqlMagic)

21.5

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215

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215

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null

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1

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1

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1

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5

5afd2a647e51a760adaede709c477c6c09b74021

95

py

Python

analysisstore/test/test_api_smoke.py

JunAishima/analysisstore

d38d17a1ad9dff15b51740893d811b61312609b7

[ "BSD-3-Clause" ]

1

2016-05-18T22:04:26.000Z

analysisstore/test/test_api_smoke.py

JunAishima/analysisstore

d38d17a1ad9dff15b51740893d811b61312609b7

[ "BSD-3-Clause" ]

15

2015-10-16T19:50:34.000Z

2022-01-27T23:19:28.000Z

analysisstore/test/test_api_smoke.py

JunAishima/analysisstore

d38d17a1ad9dff15b51740893d811b61312609b7

[ "BSD-3-Clause" ]

7

2015-10-28T18:48:33.000Z

2021-11-24T23:20:08.000Z

from ..client.commands import AnalysisClient def test_client_api(): cli = AnalysisClient

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null

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1

0

1

0

1

0

5

85035d9092c4d2ae1d7fc2010d0e75ccb767ccd6

96

py

Python

addresses/admin.py

DKMDebugin/ecommerce

427d18f19cabd128fe21c716d965e85b8e91a169

[ "MIT" ]

null

addresses/admin.py

DKMDebugin/ecommerce

427d18f19cabd128fe21c716d965e85b8e91a169

[ "MIT" ]

null

addresses/admin.py

DKMDebugin/ecommerce

427d18f19cabd128fe21c716d965e85b8e91a169

[ "MIT" ]

null

from django.contrib import admin from .models import Addresses admin.site.register(Addresses)

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0

1

0

5

852662684c00e2024b215119e385ce6dedf675ec

12,945

py

Python

benchmarks/cifar_exp/plot_time_space.py

KhelmholtzR/ProgLearn

f5177c720e53d2f5936272998b94e0746135a3b9

[ "MIT" ]

18

2020-05-17T21:56:36.000Z

2020-09-18T17:39:26.000Z

benchmarks/cifar_exp/plot_time_space.py

KhelmholtzR/ProgLearn

f5177c720e53d2f5936272998b94e0746135a3b9

[ "MIT" ]

209

2020-06-05T19:08:51.000Z

2020-10-03T16:49:39.000Z

benchmarks/cifar_exp/plot_time_space.py

KhelmholtzR/ProgLearn

f5177c720e53d2f5936272998b94e0746135a3b9

[ "MIT" ]

33

2020-06-10T23:12:09.000Z

2020-09-28T05:09:44.000Z

#%% import pickle import matplotlib.pyplot as plt from matplotlib import rcParams rcParams.update({"figure.autolayout": True}) import numpy as np from itertools import product import seaborn as sns ### MAIN HYPERPARAMS ### ntrees = 10 slots = 1 shifts = 6 task_num = 10 model = "uf" ######################## #%% def unpickle(file): with open(file, "rb") as fo: dict = pickle.load(fo, encoding="bytes") return dict def get_fte_bte(err, single_err, ntrees): bte = [[] for i in range(10)] te = [[] for i in range(10)] fte = [] for i in range(10): for j in range(i, 10): # print(err[j][i],j,i) bte[i].append(err[i][i] / err[j][i]) te[i].append(single_err[i] / err[j][i]) for i in range(10): # print(single_err[i],err[i][i]) fte.append(single_err[i] / err[i][i]) return fte, bte, te def calc_mean_bte(btes, task_num=10, reps=6): mean_bte = [[] for i in range(task_num)] for j in range(task_num): tmp = 0 for i in range(reps): tmp += np.array(btes[i][j]) tmp = tmp / reps mean_bte[j].extend(tmp) return mean_bte def calc_mean_te(tes, task_num=10, reps=6): mean_te = [[] for i in range(task_num)] for j in range(task_num): tmp = 0 for i in range(reps): tmp += np.array(tes[i][j]) tmp = tmp / reps mean_te[j].extend(tmp) return mean_te def calc_mean_fte(ftes, task_num=10, reps=6): fte = np.asarray(ftes) return list(np.mean(np.asarray(fte), axis=0)) def calc_mean_err(err, task_num=10, reps=6): mean_err = [[] for i in range(task_num)] for j in range(task_num): tmp = 0 for i in range(reps): tmp += np.array(err[i][j]) tmp = tmp / reps # print(tmp) mean_err[j].extend([tmp]) return mean_err def calc_mean_multitask_time(multitask_time, task_num=10, reps=6): mean_multitask_time = [[] for i in range(task_num)] for j in range(task_num): tmp = 0 for i in range(reps): tmp += np.array(multitask_time[i][j]) tmp = tmp / reps # print(tmp) mean_multitask_time[j].extend([tmp]) return mean_multitask_time def calc_mean_multitask_space(multitask_space, task_num=10, reps=6): mean_multitask_space = [[] for i in range(task_num)] for j in range(task_num): tmp = 0 for i in range(reps): tmp += np.array(multitask_space[i][j]) tmp = tmp / reps # print(tmp) mean_multitask_space[j].extend([tmp]) return mean_multitask_space #%% reps = slots * shifts btes = [[] for i in range(task_num)] ftes = [[] for i in range(task_num)] tes = [[] for i in range(task_num)] err_ = [[] for i in range(task_num)] te_tmp = [[] for _ in range(reps)] bte_tmp = [[] for _ in range(reps)] fte_tmp = [[] for _ in range(reps)] err_tmp = [[] for _ in range(reps)] train_time_tmp = [[] for _ in range(reps)] single_task_inference_time_tmp = [[] for _ in range(reps)] multitask_inference_time_tmp = [[] for _ in range(reps)] multitask_inference_space_tmp = [[] for _ in range(reps)] count = 0 for slot in range(slots): for shift in range(shifts): filename = ( "result/result/increased_sample_" + model + str(ntrees) + "_" + str(shift + 1) + "_" + str(slot) + ".pickle" ) multitask_df, single_task_df = unpickle(filename) err = [[] for _ in range(10)] multitask_inference_times = [[] for _ in range(10)] for ii in range(10): err[ii].extend( 1 - np.array( multitask_df[multitask_df["base_task"] == ii + 1]["accuracy"] ) ) multitask_inference_times[ii].extend( np.array( multitask_df[multitask_df["base_task"] == ii + 1][ "multitask_inference_times" ] ) ) single_err = 1 - np.array(single_task_df["accuracy"]) fte, bte, te = get_fte_bte(err, single_err, ntrees) err_ = [[] for i in range(task_num)] for i in range(task_num): for j in range(task_num - i): # print(err[i+j][i]) err_[i].append(err[i + j][i]) train_time_tmp[count].extend(np.array(single_task_df["train_times"])) single_task_inference_time_tmp[count].extend( np.array(single_task_df["single_task_inference_times"]) ) multitask_inference_time_tmp[count].extend(multitask_inference_times) multitask_inference_space_tmp[count].extend( np.array(single_task_df["model_size"]) / 1024 ) te_tmp[count].extend(te) bte_tmp[count].extend(bte) fte_tmp[count].extend(fte) err_tmp[count].extend(err_) count += 1 te = calc_mean_te(te_tmp, reps=reps) bte = calc_mean_bte(bte_tmp, reps=reps) fte = calc_mean_fte(fte_tmp, reps=reps) error = calc_mean_err(err_tmp, reps=reps) train_time = np.mean(train_time_tmp, axis=0) single_task_inference_time = np.mean(single_task_inference_time_tmp, axis=0) multitask_inference_time = calc_mean_multitask_time(multitask_inference_time_tmp) multitask_inference_time = [ np.mean(multitask_inference_time[i]) for i in range(len(multitask_inference_time)) ] multitask_inference_space = calc_mean_multitask_space(multitask_inference_space_tmp) #%% btes = [[] for i in range(task_num)] ftes = [[] for i in range(task_num)] tes = [[] for i in range(task_num)] err_ = [[] for i in range(task_num)] te_tmp = [[] for _ in range(reps)] bte_tmp = [[] for _ in range(reps)] fte_tmp = [[] for _ in range(reps)] err_tmp = [[] for _ in range(reps)] train_time_tmp = [[] for _ in range(reps)] single_task_inference_time_tmp = [[] for _ in range(reps)] multitask_inference_time_tmp = [[] for _ in range(reps)] multitask_inference_space_tmp = [[] for _ in range(reps)] count = 0 for slot in range(slots): for shift in range(shifts): filename = ( "result/result/increased_sample_dnn0" + "_" + str(shift + 1) + "_" + str(slot) + ".pickle" ) multitask_df, single_task_df = unpickle(filename) err = [[] for _ in range(10)] multitask_inference_times = [[] for _ in range(10)] for ii in range(10): err[ii].extend( 1 - np.array( multitask_df[multitask_df["base_task"] == ii + 1]["accuracy"] ) ) multitask_inference_times[ii].extend( np.array( multitask_df[multitask_df["base_task"] == ii + 1][ "multitask_inference_times" ] ) ) single_err = 1 - np.array(single_task_df["accuracy"]) fte, bte, te = get_fte_bte(err, single_err, ntrees) err_ = [[] for i in range(task_num)] for i in range(task_num): for j in range(task_num - i): # print(err[i+j][i]) err_[i].append(err[i + j][i]) train_time_tmp[count].extend(np.array(single_task_df["train_times"])) single_task_inference_time_tmp[count].extend( np.array(single_task_df["single_task_inference_times"]) ) multitask_inference_time_tmp[count].extend(multitask_inference_times) multitask_inference_space_tmp[count].extend( np.array(single_task_df["model_size"]) / 1024 ) te_tmp[count].extend(te) bte_tmp[count].extend(bte) fte_tmp[count].extend(fte) err_tmp[count].extend(err_) count += 1 te_ = calc_mean_te(te_tmp, reps=reps) bte_ = calc_mean_bte(bte_tmp, reps=reps) fte_ = calc_mean_fte(fte_tmp, reps=reps) error_ = calc_mean_err(err_tmp, reps=reps) train_time_ = np.mean(train_time_tmp, axis=0) single_task_inference_time_ = np.mean(single_task_inference_time_tmp, axis=0) multitask_inference_time_ = calc_mean_multitask_time(multitask_inference_time_tmp) multitask_inference_time_ = [ np.mean(multitask_inference_time_[i]) for i in range(len(multitask_inference_time_)) ] multitask_inference_space_ = calc_mean_multitask_space(multitask_inference_space_tmp) #%% sns.set_context("talk") n_tasks = 10 clr = ["#e41a1c", "#a65628", "#377eb8", "#4daf4a", "#984ea3", "#ff7f00", "#CCCC00"] # c = sns.color_palette(clr, n_colors=len(clr)) fontsize = 22 ticksize = 20 fig, ax = plt.subplots(3, 2, figsize=(24, 15)) # fig.suptitle('ntrees = '+str(ntrees),fontsize=25) ax[0][0].plot( np.arange(1, n_tasks + 1), fte, label="L2F", c="red", marker=".", markersize=14, linewidth=3, ) ax[0][0].plot( np.arange(1, n_tasks + 1), fte_, label="L2N", c="blue", marker=".", markersize=14, linewidth=3, ) ax[0][0].hlines(1, 1, n_tasks, colors="grey", linestyles="dashed", linewidth=1.5) ax[0][0].tick_params(labelsize=ticksize) ax[0][0].set_xlabel("Number of tasks seen", fontsize=fontsize) ax[0][0].set_ylabel("FTE", fontsize=fontsize) ax[0][0].legend(fontsize=22) for i in range(n_tasks): et = np.asarray(bte[i]) et_ = np.asarray(bte_[i]) ns = np.arange(i + 1, n_tasks + 1) ax[0][1].plot(ns, et, c="red", label="L2F", linewidth=2.6) ax[0][1].plot(ns, et_, c="blue", label="L2N", linewidth=2.6) ax[0][1].set_xlabel("Number of tasks seen", fontsize=fontsize) ax[0][1].set_ylabel("BTE", fontsize=fontsize) # ax[0][1].set_xticks(np.arange(1,10)) ax[0][1].tick_params(labelsize=ticksize) ax[0][1].hlines(1, 1, n_tasks, colors="grey", linestyles="dashed", linewidth=1.5) for i in range(n_tasks): et = np.asarray(te[i]) et_ = np.asarray(te_[i]) ns = np.arange(i + 1, n_tasks + 1) ax[1][0].plot(ns, et, c="red", linewidth=2.6) ax[1][0].plot(ns, et_, c="blue", linewidth=2.6) ax[1][0].set_xlabel("Number of tasks seen", fontsize=fontsize) ax[1][0].set_ylabel("Transfer Efficiency", fontsize=fontsize) # ax[1][0].set_xticks(np.arange(1,10)) ax[1][0].tick_params(labelsize=ticksize) ax[1][0].hlines(1, 1, n_tasks, colors="grey", linestyles="dashed", linewidth=1.5) """for rep in range(reps): _, single_task_df = unpickle('./result/'+model+str(ntrees)+'__'+str(rep+1)+'.pickle') single_err = 1 - np.array(single_task_df['accuracy']) for i in range(n_tasks): et = np.asarray(err_tmp[rep][i]) ns = np.arange(i + 1, n_tasks + 1) ax[1][1].plot(i+1, 1-single_err[i], marker='o',c=c[rep]) if i==0: ax[1][1].plot(ns, 1-et, c=c[rep], label='rep '+str(rep+1) ,linewidth = 2.6) else: ax[1][1].plot(ns, 1-et, c=c[rep], linewidth = 2.6) """ for i in range(n_tasks): et = np.asarray(error[i][0]) et_ = np.asarray(error_[i][0]) ns = np.arange(i + 1, n_tasks + 1) ax[1][1].plot(ns, 1 - et, c="red", linewidth=2.6) ax[1][1].plot(ns, 1 - et_, c="blue", linewidth=2.6) # ax[1][1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=22) ax[1][1].set_xlabel("Number of tasks seen", fontsize=fontsize) ax[1][1].set_ylabel("Accuracy", fontsize=fontsize) # ax[1][1].set_yticks([.4,.6,.8,.9,1, 1.1,1.2]) # ax[1][1].set_xticks(np.arange(1,10)) # ax[1][1].set_ylim(0.89, 1.15) ax[1][1].tick_params(labelsize=ticksize) ax[2][0].plot( range(1, len(multitask_inference_time) + 1), multitask_inference_time / multitask_inference_time[0], c="red", linewidth=3, linestyle="solid", label="Multi-Task Inference Time", ) ax[2][0].plot( range(1, len(multitask_inference_time_) + 1), multitask_inference_time_ / multitask_inference_time_[0], c="blue", linewidth=3, linestyle="solid", label="Multi-Task Inference Time", ) ax[2][0].set_yscale("log") ax[2][0].set_xlabel("Number of Tasks Seen", fontsize=fontsize) ax[2][0].set_ylabel("Time (seconds)", fontsize=fontsize) ax[2][0].tick_params(labelsize=ticksize) # plt.savefig('./result/figs/fig_trees'+str(ntrees)+"__"+model+'.pdf',dpi=300) # plt.close() ax[2][1].plot( range(1, len(multitask_inference_space) + 1), np.array(multitask_inference_space) / multitask_inference_space[0], c="red", linewidth=3, linestyle="solid", label="Multi-Task Inference Time", ) ax[2][1].plot( range(1, len(multitask_inference_space_) + 1), np.array(multitask_inference_space_) / multitask_inference_space_[0], c="blue", linewidth=3, linestyle="solid", label="Multi-Task Inference Time", ) ax[2][1].set_yscale("log") ax[2][1].set_xlabel("Number of Tasks Seen", fontsize=fontsize) ax[2][1].set_ylabel("Size of the model (kB)", fontsize=fontsize) ax[2][1].tick_params(labelsize=ticksize) plt.savefig("./result/figs/space_time_efficiency2.pdf") # %%

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1

0

1

0

null

0

5

852a65045e396235f50c4184e16172c4412e3463

741

py

Python

detection/export/persistence.py

adamivora/ecg_arrhythmia_classification

70f9a79c45c5b0315b496057dd8be6cf1f57b66a

[ "MIT" ]

3

2020-07-19T07:01:36.000Z

2021-12-06T06:29:54.000Z

detection/export/persistence.py

adamivora/ecg_arrhythmia_classification

70f9a79c45c5b0315b496057dd8be6cf1f57b66a

[ "MIT" ]

null

detection/export/persistence.py

adamivora/ecg_arrhythmia_classification

70f9a79c45c5b0315b496057dd8be6cf1f57b66a

[ "MIT" ]

null

from os import path from detection.utils.filesystem import ensure_directory_exists def trained_model_exists(model, dataset, models_dir): return path.isfile(get_model_fullname(model, dataset, models_dir)) def get_model_fullname(model, dataset, models_dir): return path.join(models_dir, f'{dataset.name()}_{model.name()}.gz') def save_model(model, dataset, models_dir): ensure_directory_exists(models_dir) model.save(get_model_fullname(model, dataset, models_dir)) def load_model(model, dataset, models_dir): try: return model.load(get_model_fullname(model, dataset, models_dir)) except Exception as e: print(f'[ERROR] Cannot load trained model. Original exception: {e}.') return model

29.64

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5.086538

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null

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1

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5

5187febde7b45a31670d5effdc737b237081210c

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py

Python

build/firefox_dev.py

geajack/Language-Assistant

63c5b12383ab2324799d15d14460f5fe8ef4da66

[ "MIT" ]

19

2018-07-27T17:31:14.000Z

2022-03-19T12:48:28.000Z

build/firefox_dev.py

geajack/Language-Assistant

63c5b12383ab2324799d15d14460f5fe8ef4da66

[ "MIT" ]

15

2018-07-28T23:02:50.000Z

2021-03-18T03:57:01.000Z

build/firefox_dev.py

geajack/Language-Assistant

63c5b12383ab2324799d15d14460f5fe8ef4da66

[ "MIT" ]

6

2018-08-16T15:26:20.000Z

2021-03-18T04:43:17.000Z

from build import copy if __name__ == "__main__": copy("manifest-firefox-dev.json", "../Builds/2/Development")

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51bc00c04ccbc14cab06954798f8d4610d1c8691

170

py

Python

auditinater/views.py

uktrade/auditinater

405042c3bfa1fa00136e095d61baf267be35a02d

[ "MIT" ]

null

auditinater/views.py

uktrade/auditinater

405042c3bfa1fa00136e095d61baf267be35a02d

[ "MIT" ]

3

2021-06-29T15:05:17.000Z

2021-09-23T16:32:21.000Z

auditinater/views.py

uktrade/auditinater

405042c3bfa1fa00136e095d61baf267be35a02d

[ "MIT" ]

null

from django.http import HttpResponse def index(request): """A super basic site root rather than 400 bad request. """ return HttpResponse("🚀 auditinater 🚀")

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5

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py

Python

ileco1_fin/ileco1_fin/doctype/voucher_legacy_series/test_voucher_legacy_series.py

josephalbaph/ileco1_fin

a0d2e332da59500306631dd671a0f00d52354901

[ "MIT" ]

null

ileco1_fin/ileco1_fin/doctype/voucher_legacy_series/test_voucher_legacy_series.py

josephalbaph/ileco1_fin

a0d2e332da59500306631dd671a0f00d52354901

[ "MIT" ]

null

ileco1_fin/ileco1_fin/doctype/voucher_legacy_series/test_voucher_legacy_series.py

josephalbaph/ileco1_fin

a0d2e332da59500306631dd671a0f00d52354901

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2020, Joseph Marie M. Alba and Contributors # See license.txt from __future__ import unicode_literals # import frappe import unittest class TestVoucherLegacySeries(unittest.TestCase): pass

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null

0

1

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1

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cf8d2c50821700327a107004b70beaaac97f89fd

235

py

Python

app/views/sobre.py

yasminbraga/ufopa-reports

6d8b213eb0dfce6775d0bb0fd277e8dc09da041c

[ "MIT" ]

null

app/views/sobre.py

yasminbraga/ufopa-reports

6d8b213eb0dfce6775d0bb0fd277e8dc09da041c

[ "MIT" ]

null

app/views/sobre.py

yasminbraga/ufopa-reports

6d8b213eb0dfce6775d0bb0fd277e8dc09da041c

[ "MIT" ]

2

2019-11-24T13:30:35.000Z

2022-01-12T11:47:11.000Z

from flask import Blueprint, render_template sobre_bp = Blueprint('sobre', __name__, url_prefix='/') @sobre_bp.route('/sobre') def index_sobre(): return render_template('sobre/index.html')

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0

null

1

0

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0

null

0

1

0

5

cf8fff7602243de743b3fd76aef7ff324282e396

356

py

Python

testing/regression/mcas/argparse_cfg_mapstore.py

omriarad/mcas

f47aab12754c91ebd75b0e1881c8a7cc7aa81278

[ "Apache-2.0" ]

60

2020-04-28T08:15:07.000Z

2022-03-08T10:35:15.000Z

testing/regression/mcas/argparse_cfg_mapstore.py

omriarad/mcas

f47aab12754c91ebd75b0e1881c8a7cc7aa81278

[ "Apache-2.0" ]

66

2020-09-03T23:40:48.000Z

2022-03-07T20:34:52.000Z

testing/regression/mcas/argparse_cfg_mapstore.py

omriarad/mcas

f47aab12754c91ebd75b0e1881c8a7cc7aa81278

[ "Apache-2.0" ]

13

2019-11-02T06:30:36.000Z

2022-01-26T01:56:42.000Z

#!/usr/bin/python3 from argparse_cfg_ipaddr import argparse_cfg_ipaddr class argparse_cfg_mapstore(argparse_cfg_ipaddr): def __init__(self, description='Generate a JSON document for mapstore testing.'): argparse_cfg_ipaddr.__init__(self, description) self.add_argument("--core", type=int, default=0, help="base of CPUs cores to use")

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0

null

0

1

0

5

cfcf99dad78a7cf4f5197f791a6f5eebf63ffe2f

226

py

Python

esg_leipzig_homepage_2015/context_processors.py

ESG-Leipzig/Homepage-2015

6b77451881031dcb640d2e61ce862617d634f9ac

[ "MIT" ]

null

esg_leipzig_homepage_2015/context_processors.py

ESG-Leipzig/Homepage-2015

6b77451881031dcb640d2e61ce862617d634f9ac

[ "MIT" ]

4

2015-03-31T22:37:09.000Z

2015-10-22T21:37:17.000Z

esg_leipzig_homepage_2015/context_processors.py

ESG-Leipzig/Homepage-2015

6b77451881031dcb640d2e61ce862617d634f9ac

[ "MIT" ]

3

2015-02-03T10:23:24.000Z

2018-04-11T12:29:23.000Z

from .models import FlatPage def flatpages(request): """ Adds a queryset of all root flatpages (without parents) to the template context. """ return {'flatpages': FlatPage.objects.filter(parent_id=None)}

22.6

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1

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5

cfd136a26576b089e4d815b1cc817883629e1405

817

py

Python

src/dot/entities/dotheart.py

alisonbento/steering-all

99797f99180dd64189ea5ed85ff71b66bfd9cf6f

[ "MIT" ]

3

2016-10-10T18:34:55.000Z

2017-08-02T15:18:28.000Z

src/dot/entities/dotheart.py

alisonbento/steering-all

99797f99180dd64189ea5ed85ff71b66bfd9cf6f

[ "MIT" ]

null

src/dot/entities/dotheart.py

alisonbento/steering-all

99797f99180dd64189ea5ed85ff71b66bfd9cf6f

[ "MIT" ]

null

import src.dot.dotentity class DotHeart(src.dot.dotentity.DotEntity): def __init__(self): res = [ "assets/img/red-brick.png", "assets/img/black-brick.png" ] grid = [ [0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] ] src.dot.dotentity.DotEntity.__init__(self, grid, res) def setSmall(self): self.setDotScale(0.5) def setMedium(self): self.setDotScale(0.75) def setLarge(self): self.setDotScale(1)

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1

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5

5c6a08aa00deddeab8243da9b32381db956dab0c

4,960

py

Python

src/genie/libs/parser/iosxe/tests/ShowIpv6PimNeighborDetail/cli/equal/golden_output_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

204

2018-06-27T00:55:27.000Z

2022-03-06T21:12:18.000Z

src/genie/libs/parser/iosxe/tests/ShowIpv6PimNeighborDetail/cli/equal/golden_output_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

468

2018-06-19T00:33:18.000Z

2022-03-31T23:23:35.000Z

src/genie/libs/parser/iosxe/tests/ShowIpv6PimNeighborDetail/cli/equal/golden_output_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

309

2019-01-16T20:21:07.000Z

2022-03-30T12:56:41.000Z

expected_output = { "vrf": { "default": { "interfaces": { "Port-channel1.100": { "address_family": { "ipv6": { "neighbors": { "secondary_address": ["2001::1:1"], "FE80::21A:30FF:FE47:6EC0": { "up_time": "3w3d", "dr_priority": 1, "expiration": "00:01:37", "interface": "Port-channel1.100", "genid_capable": True, "bidir_capable": True, }, } } } }, "Port-channel1.101": { "address_family": { "ipv6": { "neighbors": { "secondary_address": ["2001:1::1:1"], "FE80::21A:30FF:FE47:6EC0": { "up_time": "3w3d", "dr_priority": 1, "expiration": "00:01:38", "interface": "Port-channel1.101", "genid_capable": True, "bidir_capable": True, }, } } } }, "GigabitEthernet0/2/3.100": { "address_family": { "ipv6": { "neighbors": { "secondary_address": ["2001::4:2"], "FE80::2D7:8FFF:FECB:8602": { "up_time": "3w3d", "designated_router": True, "dr_priority": 1, "expiration": "00:01:25", "interface": "GigabitEthernet0/2/3.100", "genid_capable": True, "bidir_capable": True, }, } } } }, "GigabitEthernet0/2/0.101": { "address_family": { "ipv6": { "neighbors": { "FE80::21A:30FF:FE47:6E01": { "up_time": "3w3d", "dr_priority": 1, "expiration": "00:01:24", "interface": "GigabitEthernet0/2/0.101", "genid_capable": True, "bidir_capable": True, }, "secondary_address": ["2001:1::1"], } } } }, "GigabitEthernet0/2/3.101": { "address_family": { "ipv6": { "neighbors": { "secondary_address": ["2001:1::4:2"], "FE80::2D7:8FFF:FECB:8602": { "up_time": "3w3d", "designated_router": True, "dr_priority": 1, "expiration": "00:01:42", "interface": "GigabitEthernet0/2/3.101", "genid_capable": True, "bidir_capable": True, }, } } } }, "GigabitEthernet0/2/0.100": { "address_family": { "ipv6": { "neighbors": { "FE80::21A:30FF:FE47:6E01": { "up_time": "3w3d", "dr_priority": 1, "expiration": "00:01:33", "interface": "GigabitEthernet0/2/0.100", "genid_capable": True, "bidir_capable": True, }, "secondary_address": ["2001::1"], } } } }, } } } }

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null

0

5

5c72bf022b5dbe080ddf71661592315dcdc57f9b

44

py

Python

__init__.py

nwu63/pyhyp

d29715f509c7c460d6705183301eda14da217755

[ "Apache-2.0" ]

null

__init__.py

nwu63/pyhyp

d29715f509c7c460d6705183301eda14da217755

[ "Apache-2.0" ]

null

__init__.py

nwu63/pyhyp

d29715f509c7c460d6705183301eda14da217755

[ "Apache-2.0" ]

null

from .python.pyHyp import pyHyp, pyHypMulti

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null

0

1

0

1

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5

5ce25790cfff82975729caf1a1e15b9d91ef1c58

211

py

Python

shortener_app.py

hedythedev/hello-flask

c196dd20133d96a994767def1ee79861e00df7a5

[ "MIT" ]

null

shortener_app.py

hedythedev/hello-flask

c196dd20133d96a994767def1ee79861e00df7a5

[ "MIT" ]

null

shortener_app.py

hedythedev/hello-flask

c196dd20133d96a994767def1ee79861e00df7a5

[ "MIT" ]

null

from app import app, db from app.models import ShortURL from app.shortener import shorten @app.shell_context_processor def make_shell_context(): return {'db': db, 'ShortURL': ShortURL, 'shorten': shorten}

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null

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5

7a7836af1f65c9759c853fc6bda0becfafca30ff

802

py

Python

sdk/python/v0.1-rc.4/opentelematicsapi/controllers/__init__.py

nmfta-repo/nmfta-opentelematics-prototype

729e9391879e273545a4818558677b2e47261f08

[ "Apache-2.0" ]

2

2021-12-15T08:37:03.000Z

2022-02-11T20:40:42.000Z

sdk/python/v0.1-rc.4/opentelematicsapi/controllers/__init__.py

nmfta-repo/nmfta-opentelematics-prototype

729e9391879e273545a4818558677b2e47261f08

[ "Apache-2.0" ]

8

2019-12-04T22:56:46.000Z

2022-02-10T08:23:29.000Z

sdk/python/v0.1-rc.4/opentelematicsapi/controllers/__init__.py

nmfta-repo/nmfta-opentelematics-prototype

729e9391879e273545a4818558677b2e47261f08

[ "Apache-2.0" ]

null

__all__ = [ 'base_controller', 'open_telematics_data_model_controller', 'use_case_check_provider_state_of_health_controller', 'use_case_data_export_controller', 'use_case_driver_availability_controller', 'use_case_driver_route_directions_communication_controller', 'use_case_driver_route_directions_start_controller', 'use_case_driver_route_and_directions_done_controller', 'use_case_driver_messaging_by_geo_location_controller', 'use_case_vehicle_location_time_history_tracking_controller', 'use_case_human_resources_process_payroll_controller', 'use_case_carrier_custom_business_intelligence_controller', 'use_case_compliance_and_safety_monitoring_controller', 'use_case_in_field_maintenance_repair_controller', 'localization_controller', ]

47.176471

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1

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7a9168c22495440214de525928886eba9960f1ad

6,021

py

Python

tests/test_foss_cli_logger.py

thsetz/fossology-python

1c7394624f8bf2deb0aece6ef0db443cf10c791b

[ "MIT" ]

12

2019-12-10T09:57:27.000Z

2022-01-05T19:09:34.000Z

tests/test_foss_cli_logger.py

thsetz/fossology-python

1c7394624f8bf2deb0aece6ef0db443cf10c791b

[ "MIT" ]

66

2019-12-11T12:22:33.000Z

2022-03-01T02:53:09.000Z

tests/test_foss_cli_logger.py

thsetz/fossology-python

1c7394624f8bf2deb0aece6ef0db443cf10c791b

[ "MIT" ]

9

2020-05-08T19:45:29.000Z

2022-01-05T19:09:24.000Z

__doc__ = """Test the logging of the foss_cli foss_cli distinguishes the verbosity levels 0,1,2 defined in foss_cli.py FOSS_LOGGING_MAP = {0: logging.WARNING, 1: logging.INFO, 2: logging.DEBUG} and set with: logger.setLevel(FOSS_LOGGING_MAP.get(verbose, logging.DEBUG)) in the cli main command. The Log command uses: Log --log-level 0 ==> logger.debug Log --log-level 1 ==> logger.info Log --log-level 2 ==> logger.warning """ import os from fossology import foss_cli TEST_MESSAGE = "This is a Test Message." TEST_LOG_FILE_NAME = "my.log" TEST_RESULT_DIR = "test_result_dir" def test_with_verbosity_0(runner, click_test_dict): # Should be seen on console d = click_test_dict result = runner.invoke( foss_cli.cli, ["log", "--log_level", "2", "--message_text", TEST_MESSAGE], obj=d, ) assert result.exit_code == 0 assert TEST_MESSAGE in result.output # Should not be seen on console result = runner.invoke( foss_cli.cli, ["log", "--log_level", "1", "--message_text", TEST_MESSAGE], obj=d, ) assert result.exit_code == 0 assert TEST_MESSAGE not in result.output # Should not be seen on console result = runner.invoke( foss_cli.cli, ["log", "--log_level", "0", "--message_text", TEST_MESSAGE], obj=d, ) assert result.exit_code == 0 assert TEST_MESSAGE not in result.output def test_with_verbosity_1(runner, click_test_dict): # Should be seen on console d = click_test_dict result = runner.invoke( foss_cli.cli, ["-v", "log", "--log_level", "2", "--message_text", TEST_MESSAGE], obj=d, ) assert result.exit_code == 0 assert TEST_MESSAGE in result.output # Should be seen on console result = runner.invoke( foss_cli.cli, ["-v", "log", "--log_level", "1", "--message_text", TEST_MESSAGE], obj=d, ) assert result.exit_code == 0 assert TEST_MESSAGE in result.output # Should not be seen on console result = runner.invoke( foss_cli.cli, ["-v", "log", "--log_level", "0", "--message_text", TEST_MESSAGE], obj=d, ) assert result.exit_code == 0 assert TEST_MESSAGE not in result.output def test_with_verbosity_2(runner, click_test_dict): # Should be seen on console d = click_test_dict result = runner.invoke( foss_cli.cli, ["-vv", "log", "--log_level", "2", "--message_text", TEST_MESSAGE], obj=d, ) assert result.exit_code == 0 assert TEST_MESSAGE in result.output # Should be seen on console result = runner.invoke( foss_cli.cli, ["-vv", "log", "--log_level", "1", "--message_text", TEST_MESSAGE], obj=d, ) assert result.exit_code == 0 assert TEST_MESSAGE in result.output # Should be seen on console result = runner.invoke( foss_cli.cli, ["-vv", "log", "--log_level", "0", "--message_text", TEST_MESSAGE], obj=d, ) assert result.exit_code == 0 assert TEST_MESSAGE in result.output # As console and filehandler work the same way corresponding to verbosity, it suffices to test the # --log_to_file/log_file_name conceirning output to the correct file and dir. def test_log_to_default_file(runner, click_test_dict): d = click_test_dict with runner.isolated_filesystem(): result = runner.invoke( foss_cli.cli, [ "--log_to_file", "-vv", "log", "--log_level", "2", "--message_text", TEST_MESSAGE, ], obj=d, ) assert result.exit_code == 0 filename = os.path.join( foss_cli.DEFAULT_RESULT_DIR, foss_cli.DEFAULT_LOG_FILE_NAME ) assert os.path.exists(filename) assert TEST_MESSAGE in open(filename).read() def test_log_to_userdefined_file(runner, click_test_dict): d = click_test_dict with runner.isolated_filesystem(): result = runner.invoke( foss_cli.cli, [ "--log_to_file", "-vv", "--log_file_name", TEST_LOG_FILE_NAME, "log", "--log_level", "2", "--message_text", TEST_MESSAGE, ], obj=d, ) assert result.exit_code == 0 filename = os.path.join(foss_cli.DEFAULT_RESULT_DIR, TEST_LOG_FILE_NAME) assert os.path.isdir(foss_cli.DEFAULT_RESULT_DIR) assert os.path.exists(filename) assert TEST_MESSAGE in open(filename).read() def test_log_to_userdefined_file_in_userdefined_result_dir(runner, click_test_dict): d = click_test_dict with runner.isolated_filesystem(): result = runner.invoke( foss_cli.cli, [ "--log_to_file", "-vv", "--result_dir", TEST_RESULT_DIR, "--log_file_name", TEST_LOG_FILE_NAME, "log", "--log_level", "2", "--message_text", TEST_MESSAGE, ], obj=d, ) assert result.exit_code == 0 filename = os.path.join(TEST_RESULT_DIR, TEST_LOG_FILE_NAME) assert os.path.isdir(TEST_RESULT_DIR) assert os.path.exists(filename) assert TEST_MESSAGE in open(filename).read() def test_debug_and_verbosity_is_captured_in_context(runner, click_test_dict): with runner.isolated_filesystem(): d = click_test_dict result = runner.invoke(foss_cli.cli, ["-vv", "--debug", "log",], obj=d,) assert result.exit_code == 0 assert d["VERBOSE"] == 2 assert d["DEBUG"]

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US_Crime_Analytics/analysis/rates.py

salma-shaik/research-projects-new

3bc0efb58e18d13bb614ec48f139dfbac46e5904

[ "MIT" ]

null

US_Crime_Analytics/analysis/rates.py

salma-shaik/research-projects-new

3bc0efb58e18d13bb614ec48f139dfbac46e5904

[ "MIT" ]

null

US_Crime_Analytics/analysis/rates.py

salma-shaik/research-projects-new

3bc0efb58e18d13bb614ec48f139dfbac46e5904

[ "MIT" ]

null

import pandas as pd import numpy as np main_df = pd.read_csv('/Users/salma/Studies/Research/Criminal_Justice/research_projects/US_Crime_Analytics/data/analysis/final_main.csv') main_df['black_count_county'] = main_df[['blackmale_count_county', 'blackfemale_count_county']].sum(axis=1) main_df['white_count_county'] = main_df[['whitemale_count_county', 'whitefemale_count_county']].sum(axis=1) main_df['hispanic_count_county'] = main_df[['hispmale_count_county', 'hispfem_count_county']].sum(axis=1) """ Create rates for the below crime variables murder manslaughter rape robbery gun_robbery knife_robbery aggravated_assault gun_assault knife_assault simple_assault burglary larceny auto_theft officers_assaulted officers_killed_by_felony officers_killed_by_accident total_crime violent_crime property_crime crimes_against_officers population - crime pop """ def create_rates(var_list, pop_var=None, var_group=None): rate_multiplier = 10000 if var_group == 'crime': rate_multiplier = 100000 for rate_var in var_list: main_df[f'{rate_var}_rate'] = (main_df[f'{rate_var}']/main_df['population'])*rate_multiplier # # Drop the crime count columns # main_df.drop(var_list, axis=1, inplace=True) # Create crime rates crime_vars = ['murder', 'manslaughter', 'rape', 'robbery', 'gun_robbery', 'knife_robbery', 'aggravated_assault', 'gun_assault', 'knife_assault', 'simple_assault','burglary', 'larceny', 'auto_theft', 'officers_assaulted', 'officers_killed_by_felony','officers_killed_by_accident', 'total_crime', 'violent_crime','property_crime', 'crimes_against_officers'] create_rates(crime_vars, 'population', 'crime') """ Create rates for the below arrests variables agg_assault_tot_arrests agg_assault_tot_black agg_assault_tot_white all_other_tot_arrests all_other_tot_black all_other_tot_white arson_tot_arrests arson_tot_black arson_tot_white burglary_tot_arrests burglary_tot_black burglary_tot_white mtr_veh_theft_tot_arrests mtr_veh_theft_tot_black mtr_veh_theft_tot_white murder_tot_arrests murder_tot_black murder_tot_white rape_tot_arrests rape_tot_black rape_tot_white robbery_tot_arrests robbery_tot_black robbery_tot_white sale_cannabis_tot_arrests sale_cannabis_tot_black sale_cannabis_tot_white sale_drug_total_tot_arrests sale_drug_total_tot_black sale_drug_total_tot_white weapons_tot_arrests weapons_tot_black weapons_tot_white poss_cannabis_tot_arrests poss_cannabis_tot_black poss_cannabis_tot_white poss_drug_total_tot_arrests poss_drug_total_tot_black poss_drug_total_tot_white disorder_arrests_tot_index disorder_arrests_black_index disorder_arrests_white_index larceny_theft_arrests_tot larceny_theft_arrests_black larceny_theft_arrests_white arrests_vars = ['agg_assault_tot_arrests','agg_assault_tot_black','agg_assault_tot_white','all_other_tot_arrests','all_other_tot_black','all_other_tot_white', 'arson_tot_arrests','arson_tot_black','arson_tot_white','burglary_tot_arrests','burglary_tot_black','burglary_tot_white','mtr_veh_theft_tot_arrests', 'mtr_veh_theft_tot_black','mtr_veh_theft_tot_white','murder_tot_arrests','murder_tot_black','murder_tot_white','rape_tot_arrests','rape_tot_black', 'rape_tot_white','robbery_tot_arrests','robbery_tot_black','robbery_tot_white','sale_cannabis_tot_arrests','sale_cannabis_tot_black','sale_cannabis_tot_white', 'sale_drug_total_tot_arrests','sale_drug_total_tot_black','sale_drug_total_tot_white','weapons_tot_arrests','weapons_tot_black','weapons_tot_white', 'poss_cannabis_tot_arrests','poss_cannabis_tot_black','poss_cannabis_tot_white','poss_drug_total_tot_arrests','poss_drug_total_tot_black', 'poss_drug_total_tot_white','disorder_arrests_tot_index','disorder_arrests_black_index','disorder_arrests_white_index','larceny_theft_arrests_tot', 'larceny_theft_arrests_black','larceny_theft_arrests_white'] """ """ Create rates for the below arrests total variables agg_assault_tot_arrests all_other_tot_arrests arson_tot_arrests burglary_tot_arrests mtr_veh_theft_tot_arrests murder_tot_arrests rape_tot_arrests robbery_tot_arrests sale_cannabis_tot_arrests sale_drug_total_tot_arrests weapons_tot_arrests poss_cannabis_tot_arrests poss_drug_total_tot_arrests disorder_arrests_tot_index larceny_theft_arrests_tot """ arrests_total_vars = ['agg_assault_tot_arrests','all_other_tot_arrests','arson_tot_arrests','burglary_tot_arrests','mtr_veh_theft_tot_arrests', 'murder_tot_arrests','rape_tot_arrests','robbery_tot_arrests','sale_cannabis_tot_arrests','sale_drug_total_tot_arrests', 'weapons_tot_arrests','poss_cannabis_tot_arrests','poss_drug_total_tot_arrests','disorder_arrests_tot_index','larceny_theft_arrests_tot'] create_rates(arrests_total_vars) """ Create rates for the below arrests black variables agg_assault_tot_black all_other_tot_black arson_tot_black burglary_tot_black mtr_veh_theft_tot_black murder_tot_black rape_tot_black robbery_tot_black sale_cannabis_tot_black sale_drug_total_tot_black weapons_tot_black poss_cannabis_tot_black poss_drug_total_tot_black disorder_arrests_black_index larceny_theft_arrests_black """ main_df['drug_arrests_black'] = main_df[['sale_drug_total_tot_black', 'poss_drug_total_tot_black']].sum(axis=1) arrests_black_vars = ['agg_assault_tot_black','all_other_tot_black','arson_tot_black','burglary_tot_black','mtr_veh_theft_tot_black', 'murder_tot_black','rape_tot_black','robbery_tot_black','drug_arrests_black','sale_cannabis_tot_black','sale_drug_total_tot_black', 'weapons_tot_black','poss_cannabis_tot_black','poss_drug_total_tot_black','disorder_arrests_black_index','larceny_theft_arrests_black'] # create_rates(arrests_black_vars, 'Black_count') """ Create rates for the below arrests white variables agg_assault_tot_white all_other_tot_white arson_tot_white burglary_tot_white mtr_veh_theft_tot_white murder_tot_white rape_tot_white robbery_tot_white sale_cannabis_tot_white sale_drug_total_tot_white weapons_tot_white poss_cannabis_tot_white poss_drug_total_tot_white disorder_arrests_white_index larceny_theft_arrests_white """ main_df['drug_arrests_white'] = main_df[['sale_drug_total_tot_white', 'poss_drug_total_tot_white']].sum(axis=1) arrests_white_vars = ['agg_assault_tot_white','all_other_tot_white','arson_tot_white','burglary_tot_white','mtr_veh_theft_tot_white', 'murder_tot_white','rape_tot_white','robbery_tot_white','drug_arrests_white','sale_cannabis_tot_white','sale_drug_total_tot_white', 'weapons_tot_white','poss_cannabis_tot_white','poss_drug_total_tot_white','disorder_arrests_white_index','larceny_theft_arrests_white'] # create_rates(arrests_white_vars, 'White_count') """ Create rates for the below incarceration variables total_jail_pop black_jail_pop latino_jail_pop white_jail_pop total_prison_pop black_prison_pop latino_prison_pop white_prison_pop """ #incarc_tot_vars = ['total_jail_pop', 'total_prison_pop'] # create_rates(incarc_tot_vars, 'county_pop_final') #incarc_black_vars = ['black_jail_pop', 'black_prison_pop'] # create_rates(incarc_black_vars, 'black_count_county') #incarc_white_vars = ['white_jail_pop', 'white_prison_pop'] # create_rates(incarc_white_vars, 'white_count_county') #incarc_hispanic_vars = ['latino_jail_pop', 'latino_prison_pop'] # create_rates(incarc_hispanic_vars, 'hispanic_count_county') # some populations are zero so divide by zero gets infinity so replace them with 0 main_df.replace(np.inf, 0, inplace=True) main_df.to_csv('/Users/salma/Studies/Research/Criminal_Justice/research_projects/US_Crime_Analytics/data/analysis/final_main_rates.csv', index=False)

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__init__.py

ab-ten/tornado_graceful_terminator

5cd8884daff30c886611b55100e716a0f48bea63

[ "MIT" ]

null

__init__.py

ab-ten/tornado_graceful_terminator

5cd8884daff30c886611b55100e716a0f48bea63

[ "MIT" ]

null

__init__.py

ab-ten/tornado_graceful_terminator

5cd8884daff30c886611b55100e716a0f48bea63

[ "MIT" ]

null

# -*- coding: utf-8-unix; -*- from .graceful_terminator import GracefulTerminator

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aim/sdk/configs.py

fairhopeweb/aim

f17b309e0e415e8798b6330b9ee71436a1b3994e

[ "Apache-2.0" ]

null

aim/sdk/configs.py

fairhopeweb/aim

f17b309e0e415e8798b6330b9ee71436a1b3994e

[ "Apache-2.0" ]

null

aim/sdk/configs.py

fairhopeweb/aim

f17b309e0e415e8798b6330b9ee71436a1b3994e

[ "Apache-2.0" ]

null

AIM_REPO_NAME = '.aim' AIM_ENABLE_TRACKING_THREAD = '__AIM_ENABLE_TRACKING_THREAD__'

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