xszheng2020/the_stack_dedup_python_hits_3 · Datasets at Hugging Face

2d33a5df6de81ffccc1cc28e6ed456c15bb617a5

771

py

Python

image_repo/repo/models.py

HollisHolmes/Image_Repository

4ec3060f79d0e09ea78f515e2bf6665572dd5f60

[ "MIT" ]

1

2021-05-11T00:03:28.000Z

image_repo/repo/models.py

HollisHolmes/image_repository

4ec3060f79d0e09ea78f515e2bf6665572dd5f60

[ "MIT" ]

null

image_repo/repo/models.py

HollisHolmes/image_repository

4ec3060f79d0e09ea78f515e2bf6665572dd5f60

[ "MIT" ]

null

from django.db import models from django.contrib.auth.models import User # Create your models here. class Tag(models.Model): tag = models.CharField(max_length=20) def __str(self): return f'{self.id}: {self.tag}' class Item(models.Model): name = models.CharField(max_length=50) image_url = models.URLField(max_length=400) num_reviews = models.IntegerField(default=0) price = models.FloatField(default=10.99) user = models.ForeignKey(User, on_delete=models.CASCADE, related_name='inventory', blank=True, null=True) tags = models.ManyToManyField(Tag, blank=True, related_name='items') def __str__(self): return f'{self.id}: {self.name} | ${self.price} | {self.image_url}' def is_valid_item(self): pass

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2d44e27056004631c8787e68bd9ce63961504ea5

1,174

py

Python

tests/test_cisco_simple.py

mostau1/netmiko

5b5463fb01e39e771be553281748477a48c7391c

[ "MIT" ]

1

2021-11-16T11:37:00.000Z

tests/test_cisco_simple.py

mostau1/netmiko

5b5463fb01e39e771be553281748477a48c7391c

[ "MIT" ]

null

tests/test_cisco_simple.py

mostau1/netmiko

5b5463fb01e39e771be553281748477a48c7391c

[ "MIT" ]

1

2016-10-03T08:57:35.000Z

#!/usr/bin/env python from netmiko import ConnectHandler from getpass import getpass #ip_addr = raw_input("Enter IP Address: ") pwd = getpass() ip_addr = '184.105.247.70' telnet_device = { 'device_type': 'cisco_ios_telnet', 'ip': ip_addr, 'username': 'pyclass', 'password': pwd, 'port': 23, } ssh_device = { 'device_type': 'cisco_ios_ssh', 'ip': ip_addr, 'username': 'pyclass', 'password': pwd, 'port': 22, } print "telnet" net_connect1 = ConnectHandler(**telnet_device) print "telnet prompt: {}".format(net_connect1.find_prompt()) print "send_command: " print '-' * 50 print net_connect1.send_command_timing("show arp") print '-' * 50 print '-' * 50 print net_connect1.send_command("show run") print '-' * 50 print print "SSH" net_connect2 = ConnectHandler(**ssh_device) print "SSH prompt: {}".format(net_connect2.find_prompt()) print "send_command: " print '-' * 50 print net_connect2.send_command("show arp") print '-' * 50 print '-' * 50 print net_connect1.send_command("show run") print '-' * 50 print #output = net_connect.send_command_expect("show version") #print #print '#' * 50 #print output #print '#' * 50 #print

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7423eec73eba4ebbb15b1bb00e9a238b23da68de

1,265

py

Python

setup.py

qyliu-hkust/fasthaversine

5ec8f96d827369705cf313ec363a035b14952673

[ "MIT" ]

null

setup.py

qyliu-hkust/fasthaversine

5ec8f96d827369705cf313ec363a035b14952673

[ "MIT" ]

null

setup.py

qyliu-hkust/fasthaversine

5ec8f96d827369705cf313ec363a035b14952673

[ "MIT" ]

1

2022-01-26T16:10:59.000Z

from setuptools import setup setup( name='fasthaversine', version='0.1.1', description='A fast vectorized version of haversine distance calculation.', include_package_data=True, install_requires=['numpy'], long_description=open('README.md').read(), long_description_content_type="text/markdown", author='Qiyu Liu', author_email='keiyuk.liu@gmail.com', maintainer='Qiyu Liu', maintainer_email='keiyuk.liu@gmail.com', url='https://github.com/qyliu-hkust/fasthaversine', download_url='https://github.com/qyliu-hkust/fasthaversine/archive/0.1.1.tar.gz', packages=['fasthaversine'], license=['MIT'], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.1', 'Programming Language :: Python :: 3.2', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Topic :: Scientific/Engineering :: Mathematics' ], )

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null

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1

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3

7443b2e010d0c7d8af95a0c0a62c63ce9d36dec4

330

py

Python

MonitorFolders_test.py

judysu1983/PythonMBSi

9481bf1409a888c3f8511bcd05718ea81a063fa1

[ "bzip2-1.0.6" ]

null

MonitorFolders_test.py

judysu1983/PythonMBSi

9481bf1409a888c3f8511bcd05718ea81a063fa1

[ "bzip2-1.0.6" ]

null

MonitorFolders_test.py

judysu1983/PythonMBSi

9481bf1409a888c3f8511bcd05718ea81a063fa1

[ "bzip2-1.0.6" ]

null

import sys import time, os import logging from watchdog.observers import Observer from watchdog.events import LoggingEventHandler from watchdog.events import RegexMatchingEventHandler def D365Shell: #sync souce depot master files os.chdir(C:\\Depots\\MBSI\\Projects\\D365Shell\\UI\\Master\\Source\\Portal\\lcl)

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3

7450b1e06fd88c0dee4fbbcb7b5f06feb4416c38

624

bzl

Python

source/bazel/deps/big_integer_cpp/get.bzl

luxe/unilang

6c8a431bf61755f4f0534c6299bd13aaeba4b69e

[ "MIT" ]

33

2019-05-30T07:43:32.000Z

2021-12-30T13:12:32.000Z

source/bazel/deps/big_integer_cpp/get.bzl

luxe/unilang

6c8a431bf61755f4f0534c6299bd13aaeba4b69e

[ "MIT" ]

371

2019-05-16T15:23:50.000Z

2021-09-04T15:45:27.000Z

source/bazel/deps/big_integer_cpp/get.bzl

luxe/unilang

6c8a431bf61755f4f0534c6299bd13aaeba4b69e

[ "MIT" ]

6

2019-08-22T17:37:36.000Z

2020-11-07T07:15:32.000Z

# Do not edit this file directly. # It was auto-generated by: code/programs/reflexivity/reflexive_refresh load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") def bigIntegerCpp(): http_archive( name="big_integer_cpp" , build_file="//bazel/deps/big_integer_cpp:build.BUILD" , sha256="1c9505406accb1216947ca60299ed70726eade7c9458c7c7f94ca2aea68d288e" , strip_prefix="BigIntegerCPP-79e7b023bf5157c0f8d308d3791cf3b081d1e156" , urls = [ "https://github.com/Unilang/BigIntegerCPP/archive/79e7b023bf5157c0f8d308d3791cf3b081d1e156.tar.gz", ], )

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3

7451d7877239c61cee4b8ef27730e2958d28e2a9

1,406

py

Python

xrpl/models/transactions/offer_create.py

mDuo13/xrpl-py

70f927dcd2dbb8644b3e210b0a8de2a214e71e3d

[ "0BSD" ]

null

xrpl/models/transactions/offer_create.py

mDuo13/xrpl-py

70f927dcd2dbb8644b3e210b0a8de2a214e71e3d

[ "0BSD" ]

null

xrpl/models/transactions/offer_create.py

mDuo13/xrpl-py

70f927dcd2dbb8644b3e210b0a8de2a214e71e3d

[ "0BSD" ]

null

""" Represents an OfferCreate transaction on the XRP Ledger. An OfferCreate transaction is effectively a limit order. It defines an intent to exchange currencies, and creates an Offer object if not completely fulfilled when placed. Offers can be partially fulfilled. `See OfferCreate <https://xrpl.org/offercreate.html>`_ """ from dataclasses import dataclass, field from typing import Optional from xrpl.models.amounts import Amount from xrpl.models.required import REQUIRED from xrpl.models.transactions.transaction import Transaction, TransactionType from xrpl.models.utils import require_kwargs_on_init @require_kwargs_on_init @dataclass(frozen=True) class OfferCreate(Transaction): """ Represents an OfferCreate transaction on the XRP Ledger. An OfferCreate transaction is effectively a limit order. It defines an intent to exchange currencies, and creates an Offer object if not completely fulfilled when placed. Offers can be partially fulfilled. `See OfferCreate <https://xrpl.org/offercreate.html>`_ """ #: This field is required. taker_gets: Amount = REQUIRED # type: ignore #: This field is required. taker_pays: Amount = REQUIRED # type: ignore expiration: Optional[int] = None offer_sequence: Optional[int] = None transaction_type: TransactionType = field( default=TransactionType.OFFER_CREATE, init=False, )

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1

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1

0

1

0

3

746431f999141fab0a1700aa2226e12544bdeba5

7,294

py

Python

trait/test.py

zhao-embassy/learn_language

2719f27dea1877ba1da32f1c1d53ebedd5a81fdf

[ "FSFAP" ]

48

2015-02-05T15:25:47.000Z

2022-01-07T05:52:03.000Z

trait/test.py

zhao-embassy/learn_language

2719f27dea1877ba1da32f1c1d53ebedd5a81fdf

[ "FSFAP" ]

null

trait/test.py

zhao-embassy/learn_language

2719f27dea1877ba1da32f1c1d53ebedd5a81fdf

[ "FSFAP" ]

21

2015-02-12T23:50:10.000Z

2019-12-12T08:25:35.000Z

from coderunner import * header("Instantiation") test(Scala, """ trait Foo{ def foo() = println("foo!") } new Foo // error """, """ ...tmp.scala:5: error: trait Foo is abstract; cannot be instantiated new Foo // error ^ one error found """) test(Squeak, """ Trait named: #Foo uses: {} category: #MyCategory. print value:(Foo new). """, """ a Foo """) comment('Oops, trait in Squeak can be instanciated...') test(Ruby, """ module Foo end Foo new """, """ tmp.rb:3:in `<main>': undefined local variable or method `new' for main:Object (NameError) """) header('Single inheritance') test(Scala, """ trait Foo{ def foo() = println("foo!") } class C extends Foo{} new C().foo """, """ foo! """) test(Squeak, """ Trait named: #Foo uses: {} category: #MyCategory. Foo compile: ' foo ^''foo'' '. Object subclass: #C uses: Foo instanceVariableNames: '' classVariableNames: '' poolDictionaries: '' category: #MyCategory. print value: (C new foo). """, """ foo """) test(Ruby, """ module Foo def foo puts "foo" end end class C include Foo end C.new.foo """, """ foo """) header('Multiple inheritance') test(Scala, """ trait Foo{ def foo() = println("foo!") } trait Bar{ def bar() = println("bar!") } class C extends Foo with Bar{} new C().foo new C().bar """, """ foo! bar! """) test(Squeak, """ Trait named: #Foo uses: {} category: #MyCategory. Foo compile: ' foo ^''foo'' '. Trait named: #Bar uses: {} category: #MyCategory. Bar compile: ' bar ^''bar'' '. Object subclass: #C uses: Foo + Bar instanceVariableNames: '' classVariableNames: '' poolDictionaries: '' category: #MyCategory. print value: (C new foo). print value: (C new bar). """, """ foo bar """) test(Ruby, """ module Foo def foo puts "foo" end end module Bar def bar puts "bar" end end class C include Foo include Bar end C.new.foo C.new.bar """, """ foo bar """) header('Conflicting name') test(Scala, """ trait Foo{ def hello() = println("foo!") } trait Bar{ def hello() = println("bar!") } class C extends Foo with Bar{} """, """ ...tmp.scala:9: error: class C inherits conflicting members: method hello in trait Foo of type ()Unit and method hello in trait Bar of type ()Unit (Note: this can be resolved by declaring an override in class C.) class C extends Foo with Bar{} ^ one error found """) test(Squeak, """ Trait named: #Foo uses: {} category: #MyCategory. Foo compile: ' hello ^''foo'' '. Trait named: #Bar uses: {} category: #MyCategory. Bar compile: ' hello ^''bar'' '. Object subclass: #C uses: Foo + Bar instanceVariableNames: '' classVariableNames: '' poolDictionaries: '' category: #MyCategory. [ print value: (C new hello). ] on: Exception do: printException. """, """ Error: A class or trait does not properly resolve a conflict between multiple traits it uses. """) comment('error occurs when you send a message, not when you define a class') test(Ruby, """ module Foo def hello puts "foo" end end module Bar def hello puts "bar" end end class C include Foo include Bar end C.new.hello """, """ bar """) comment("Ruby silently overrides conflicting methods") header('Choose one of the methods') test(Scala, """ trait Foo{ def hello() = println("foo!") } trait Bar{ def hello() = println("bar!") } class C extends Foo with Bar{ override def hello() = super[Bar].hello } new C().hello """, """ bar! """) test(Squeak, """ Trait named: #Foo uses: {} category: #MyCategory. Foo compile: ' hello ^''foo'' '. Trait named: #Bar uses: {} category: #MyCategory. Bar compile: ' hello ^''bar'' '. Object subclass: #C uses: Foo - {#hello} + Bar instanceVariableNames: '' classVariableNames: '' poolDictionaries: '' category: #MyCategory. print value: (C new hello). """, """ bar """) test(Ruby, """ module Foo def hello puts "foo" end end module Bar def hello puts "bar" end end class C include Bar include Foo def hello Bar.instance_method(:hello).bind(self).call end end C.new.hello """, """ bar """) header('Use both of the methods') test(Scala, """ trait Foo{ def hello() = println("foo!") } trait Bar{ def hello() = println("bar!") } class C extends Foo with Bar{ override def hello() = { // use both super[Foo].hello super[Bar].hello } } new C().hello """, """ foo! bar! """) test(Squeak, """ Trait named: #Foo uses: {} category: #MyCategory. Foo compile: ' hello ^''foo'' '. Trait named: #Bar uses: {} category: #MyCategory. Bar compile: ' hello ^''bar'' '. Object subclass: #C uses: (Foo @ {#foo -> #hello} - {#hello} + Bar @ {#bar -> #hello} - {#hello}) instanceVariableNames: '' classVariableNames: '' poolDictionaries: '' category: #MyCategory. C compile: ' hello ^(self foo , self bar) '. print value: (C new hello). """, """ foobar """) test(Ruby, """ module Foo def hello puts "foo" end end module Bar def hello puts "bar" end end class C include Foo include Bar def hello Foo.instance_method(:hello).bind(self).call Bar.instance_method(:hello).bind(self).call end end C.new.hello """, """ foo bar """) header('required trait(self type annotation of Scala)') test(Scala, """ trait HaveFoo{ def foo() : String = "foo" } trait NeedFoo{ self : HaveFoo => def hello() = println(foo()) } // error: NeedFoo should be with HaveFoo class C extends NeedFoo{} """, """ ...tmp.scala:11: error: illegal inheritance; self-type this.C does not conform to this.NeedFoo's selftype this.NeedFoo with this.HaveFoo class C extends NeedFoo{} ^ one error found """) test(Scala, """ trait HaveFoo{ def foo() : String = "foo" } trait NeedFoo{ self : HaveFoo => def hello() = println(foo()) } class C extends NeedFoo with HaveFoo{} new C().hello """, """ foo """) header('conflict between parent class and trait') test(Scala, """ trait Foo{ def hello() = println("foo!") } class ParentClass{ def hello() = println("parent class!") } class C extends ParentClass with Foo{} """, """ ...tmp.scala:9: error: class C inherits conflicting members: method hello in class ParentClass of type ()Unit and method hello in trait Foo of type ()Unit (Note: this can be resolved by declaring an override in class C.) class C extends ParentClass with Foo{} ^ one error found """) test(Squeak, """ Trait named: #Foo uses: {} category: #MyCategory. Foo compile: ' hello ^''foo'' '. Object subclass: #ParentClass instanceVariableNames: '' classVariableNames: '' poolDictionaries: '' category: #MyCategory. ParentClass compile: ' hello ^''parent class'' '. ParentClass subclass: #C uses: Foo instanceVariableNames: '' classVariableNames: '' poolDictionaries: '' category: #MyCategory. print value: (C new hello). """, """ foo """) test(Ruby, """ module Foo def hello puts "foo" end end class ParentClass def hello puts "parent class" end end class C < ParentClass include Foo end C.new.hello """, """ foo """) main()

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746f6002d2b7353a1d48482a81945178f62ef005

1,354

py

Python

project/apps/core/templatetags/coretags.py

havencruise/emberjam

38153f84dc09f84100f4c0f2c9523be905c16762

[ "MIT" ]

1

2016-04-04T05:43:05.000Z

project/apps/core/templatetags/coretags.py

havencruise/emberjam

38153f84dc09f84100f4c0f2c9523be905c16762

[ "MIT" ]

null

project/apps/core/templatetags/coretags.py

havencruise/emberjam

38153f84dc09f84100f4c0f2c9523be905c16762

[ "MIT" ]

null

from django import template register = template.Library() @register.filter('field_type') def field_type(field): """ Get the name of the field class. """ if hasattr(field, 'field'): field = field.field s = (type(field.widget).__name__).replace('Input', '').lower() return s @register.filter('get_form_field') def get_form_field(form, field): return form[field] @register.filter('all_fields_hidden') def all_fields_hidden(form): return all([field.is_hidden for field in form]) @register.inclusion_tag('core/form_fieldset_fields.html') def form_as_fieldset_fields(form, fieldsets): """ Render the form as a fieldset form. Example usage in template with 'myform' and 'myfieldsets as context attributes: {% form_as_fieldset_fields myform myfieldsets %} Sample fieldset: MY_FIELDSETS = ( ( 'info', ('first_name', 'middle_name', 'last_name', 'is_published') ), ( 'image', ('profile_image', 'avatar_image', 'profile_image_crop') ), ( 'profile', ('title', 'location', 'profile_full', 'profile_brief', 'website_url', 'average_artwork_cost', 'born_year', 'deceased_year') ), ) """ return {'form': form, 'fieldsets' : fieldsets}

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7483b320b336f62d749d65e13237175c0e96a262

506

py

Python

apps/goods/migrations/0009_auto_20180727_1713.py

lianxiaopang/camel-store-api

b8021250bf3d8cf7adc566deebdba55225148316

[ "Apache-2.0" ]

12

2020-02-01T01:52:01.000Z

2021-04-28T15:06:43.000Z

apps/goods/migrations/0009_auto_20180727_1713.py

lianxiaopang/camel-store-api

b8021250bf3d8cf7adc566deebdba55225148316

[ "Apache-2.0" ]

5

2020-02-06T08:07:58.000Z

2020-06-02T13:03:45.000Z

apps/goods/migrations/0009_auto_20180727_1713.py

lianxiaopang/camel-store-api

b8021250bf3d8cf7adc566deebdba55225148316

[ "Apache-2.0" ]

11

2020-02-03T13:07:46.000Z

2020-11-29T01:44:06.000Z

# Generated by Django 2.0.7 on 2018-07-27 09:13 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('goods', '0008_auto_20180727_1203'), ] operations = [ migrations.RenameField( model_name='goods', old_name='num', new_name='asset_ratio_1', ), migrations.RenameField( model_name='goods', old_name='num2', new_name='asset_ratio_2', ), ]

21.083333

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1

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null

0

3

776c877396570b98ec8d30903006cdcceed3ffb5

2,661

py

Python

hikcamerabot/registry.py

tropicoo/hik-camera-bot

a7108c08a8e009e7361bbb9904c3a71f3226afd5

[ "MIT" ]

1

2019-02-09T20:08:50.000Z

hikcamerabot/registry.py

SirNoish/hikvision-camera-bot

a7108c08a8e009e7361bbb9904c3a71f3226afd5

[ "MIT" ]

3

2019-02-10T12:42:10.000Z

2019-02-16T00:33:29.000Z

hikcamerabot/registry.py

SirNoish/hikvision-camera-bot

a7108c08a8e009e7361bbb9904c3a71f3226afd5

[ "MIT" ]

null

"""Registry module.""" import logging from collections import defaultdict from typing import Iterator from hikcamerabot.camera import HikvisionCam RegistryValue = dict[str, HikvisionCam | dict | str] CamRegistryType = dict[str, RegistryValue] class CameraRegistry: """Registry class with camera meta information.""" def __init__(self) -> None: self._log = logging.getLogger(self.__class__.__name__) self._cam_registry: CamRegistryType = {} self._group_registry = defaultdict(dict) self._group_command_alias: dict[str, str] = {} def __repr__(self) -> str: return str(self._cam_registry) def add( self, cam: HikvisionCam, commands: dict, commands_presentation: str ) -> None: """Add metadata to teh registry.""" self._cam_registry[cam.id] = { 'cam': cam, 'cmds': commands, 'cmds_presentation': commands_presentation, } self._add_to_group_registry(cam) def _add_to_group_registry(self, cam: HikvisionCam) -> None: try: key = self._group_command_alias[cam.group] except KeyError: key = f'group_{len(self._group_registry) + 1}' try: self._group_registry[key]['cams'].append(cam) except KeyError: self._group_command_alias[cam.group] = key self._group_registry[key] = { 'name': cam.group, 'cams': [cam], } def get_commands(self, cam_id: str) -> dict: """Get camera commands.""" return self._cam_registry[cam_id]['cmds'] def get_commands_presentation(self, cam_id: str) -> dict: """Get camera commands presentation string.""" return self._cam_registry[cam_id]['cmds_presentation'] def get_instance(self, cam_id: str) -> HikvisionCam: return self._cam_registry[cam_id]['cam'] def get_meta(self, cam_id: str) -> RegistryValue: return self._cam_registry[cam_id] def get_instances(self) -> Iterator[HikvisionCam]: return (v['cam'] for v in self._cam_registry.values()) def get_all(self) -> CamRegistryType: """Return raw registry metadata dict.""" return self._cam_registry def count(self) -> int: """Get cameras count.""" return len(self._cam_registry) def get_instances_by_group(self, group_name: str) -> list[HikvisionCam]: return self._group_registry.get(group_name, []) def get_groups_registry(self) -> dict: return self._group_registry def get_group(self, group_id: str) -> dict: return self._group_registry[group_id]

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76

0.637354

312

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5.121795

0.214744

0.070088

0.093867

0.05632

0.197747

0.163955

0.078849

0.041302

0

0.0005

0.248779

2,661

83

77

32.060241

0.798899

0.07779

0

0.071429

0

0.041356

0.013234

0

1

0.25

false

0

0.071429

0.125

0.535714

0

null

0

null

0

1

0

1

0

3

777dad77b3e1c97e085e619432a2765455de4b49

223

py

Python

invoices/context_processors.py

GDGSNF/My-Business

792bb13a5b296260e5de7e03fba6445a13922851

[ "MIT" ]

21

2020-08-29T14:32:13.000Z

2021-08-28T21:40:32.000Z

invoices/context_processors.py

GDGSNF/My-Business

792bb13a5b296260e5de7e03fba6445a13922851

[ "MIT" ]

1

2020-10-11T21:56:15.000Z

invoices/context_processors.py

yezz123/My-Business

792bb13a5b296260e5de7e03fba6445a13922851

[ "MIT" ]

5

2021-09-11T23:31:10.000Z

2022-03-06T20:29:59.000Z

from django.db.models import Q from invoices.models import Invoice def review_invoices_processor(request): invoices = Invoice.objects.filter(Q(status=2) | Q(status=0) | Q(status=1)) return {"invoices": invoices}

24.777778

78

0.73991

32

223

5.09375

0.59375

0.128834

0

0.015625

0.139013

223

8

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27.875

0.833333

0

0.035874

0

1

0.2

false

0

0.4

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0.8

0

null

0

1

0

null

0

1

0

1

0

3

7793cca5bbff4cb83222a737628d6f02856214fe

66,156

py

Python

pyboto3/elasticloadbalancingv2.py

thecraftman/pyboto3

653a0db2b00b06708334431da8f169d1f7c7734f

[ "MIT" ]

null

pyboto3/elasticloadbalancingv2.py

thecraftman/pyboto3

653a0db2b00b06708334431da8f169d1f7c7734f

[ "MIT" ]

null

pyboto3/elasticloadbalancingv2.py

thecraftman/pyboto3

653a0db2b00b06708334431da8f169d1f7c7734f

[ "MIT" ]

null

''' The MIT License (MIT) Copyright (c) 2016 WavyCloud Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ''' def add_tags(ResourceArns=None, Tags=None): """ Adds the specified tags to the specified resource. You can tag your Application Load Balancers and your target groups. Each tag consists of a key and an optional value. If a resource already has a tag with the same key, AddTags updates its value. To list the current tags for your resources, use DescribeTags . To remove tags from your resources, use RemoveTags . See also: AWS API Documentation Examples This example adds the specified tags to the specified load balancer. Expected Output: :example: response = client.add_tags( ResourceArns=[ 'string', ], Tags=[ { 'Key': 'string', 'Value': 'string' }, ] ) :type ResourceArns: list :param ResourceArns: [REQUIRED] The Amazon Resource Name (ARN) of the resource. (string) -- :type Tags: list :param Tags: [REQUIRED] The tags. Each resource can have a maximum of 10 tags. (dict) --Information about a tag. Key (string) -- [REQUIRED]The key of the tag. Value (string) --The value of the tag. :rtype: dict :return: {} :returns: (dict) -- """ pass def can_paginate(operation_name=None): """ Check if an operation can be paginated. :type operation_name: string :param operation_name: The operation name. This is the same name as the method name on the client. For example, if the method name is create_foo, and you'd normally invoke the operation as client.create_foo(**kwargs), if the create_foo operation can be paginated, you can use the call client.get_paginator('create_foo'). """ pass def create_listener(LoadBalancerArn=None, Protocol=None, Port=None, SslPolicy=None, Certificates=None, DefaultActions=None): """ Creates a listener for the specified Application Load Balancer. You can create up to 10 listeners per load balancer. To update a listener, use ModifyListener . When you are finished with a listener, you can delete it using DeleteListener . If you are finished with both the listener and the load balancer, you can delete them both using DeleteLoadBalancer . For more information, see Listeners for Your Application Load Balancers in the Application Load Balancers Guide . See also: AWS API Documentation Examples This example creates an HTTP listener for the specified load balancer that forwards requests to the specified target group. Expected Output: This example creates an HTTPS listener for the specified load balancer that forwards requests to the specified target group. Note that you must specify an SSL certificate for an HTTPS listener. You can create and manage certificates using AWS Certificate Manager (ACM). Alternatively, you can create a certificate using SSL/TLS tools, get the certificate signed by a certificate authority (CA), and upload the certificate to AWS Identity and Access Management (IAM). Expected Output: :example: response = client.create_listener( LoadBalancerArn='string', Protocol='HTTP'|'HTTPS', Port=123, SslPolicy='string', Certificates=[ { 'CertificateArn': 'string' }, ], DefaultActions=[ { 'Type': 'forward', 'TargetGroupArn': 'string' }, ] ) :type LoadBalancerArn: string :param LoadBalancerArn: [REQUIRED] The Amazon Resource Name (ARN) of the load balancer. :type Protocol: string :param Protocol: [REQUIRED] The protocol for connections from clients to the load balancer. :type Port: integer :param Port: [REQUIRED] The port on which the load balancer is listening. :type SslPolicy: string :param SslPolicy: The security policy that defines which ciphers and protocols are supported. The default is the current predefined security policy. :type Certificates: list :param Certificates: The SSL server certificate. You must provide exactly one certificate if the protocol is HTTPS. (dict) --Information about an SSL server certificate deployed on a load balancer. CertificateArn (string) --The Amazon Resource Name (ARN) of the certificate. :type DefaultActions: list :param DefaultActions: [REQUIRED] The default action for the listener. (dict) --Information about an action. Type (string) -- [REQUIRED]The type of action. TargetGroupArn (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the target group. :rtype: dict :return: { 'Listeners': [ { 'ListenerArn': 'string', 'LoadBalancerArn': 'string', 'Port': 123, 'Protocol': 'HTTP'|'HTTPS', 'Certificates': [ { 'CertificateArn': 'string' }, ], 'SslPolicy': 'string', 'DefaultActions': [ { 'Type': 'forward', 'TargetGroupArn': 'string' }, ] }, ] } """ pass def create_load_balancer(Name=None, Subnets=None, SecurityGroups=None, Scheme=None, Tags=None, IpAddressType=None): """ Creates an Application Load Balancer. When you create a load balancer, you can specify security groups, subnets, IP address type, and tags. Otherwise, you could do so later using SetSecurityGroups , SetSubnets , SetIpAddressType , and AddTags . To create listeners for your load balancer, use CreateListener . To describe your current load balancers, see DescribeLoadBalancers . When you are finished with a load balancer, you can delete it using DeleteLoadBalancer . You can create up to 20 load balancers per region per account. You can request an increase for the number of load balancers for your account. For more information, see Limits for Your Application Load Balancer in the Application Load Balancers Guide . For more information, see Application Load Balancers in the Application Load Balancers Guide . See also: AWS API Documentation Examples This example creates an Internet-facing load balancer and enables the Availability Zones for the specified subnets. Expected Output: This example creates an internal load balancer and enables the Availability Zones for the specified subnets. Expected Output: :example: response = client.create_load_balancer( Name='string', Subnets=[ 'string', ], SecurityGroups=[ 'string', ], Scheme='internet-facing'|'internal', Tags=[ { 'Key': 'string', 'Value': 'string' }, ], IpAddressType='ipv4'|'dualstack' ) :type Name: string :param Name: [REQUIRED] The name of the load balancer. This name must be unique per region per account, can have a maximum of 32 characters, must contain only alphanumeric characters or hyphens, and must not begin or end with a hyphen. :type Subnets: list :param Subnets: [REQUIRED] The IDs of the subnets to attach to the load balancer. You can specify only one subnet per Availability Zone. You must specify subnets from at least two Availability Zones. (string) -- :type SecurityGroups: list :param SecurityGroups: The IDs of the security groups to assign to the load balancer. (string) -- :type Scheme: string :param Scheme: The nodes of an Internet-facing load balancer have public IP addresses. The DNS name of an Internet-facing load balancer is publicly resolvable to the public IP addresses of the nodes. Therefore, Internet-facing load balancers can route requests from clients over the Internet. The nodes of an internal load balancer have only private IP addresses. The DNS name of an internal load balancer is publicly resolvable to the private IP addresses of the nodes. Therefore, internal load balancers can only route requests from clients with access to the VPC for the load balancer. The default is an Internet-facing load balancer. :type Tags: list :param Tags: One or more tags to assign to the load balancer. (dict) --Information about a tag. Key (string) -- [REQUIRED]The key of the tag. Value (string) --The value of the tag. :type IpAddressType: string :param IpAddressType: The type of IP addresses used by the subnets for your load balancer. The possible values are ipv4 (for IPv4 addresses) and dualstack (for IPv4 and IPv6 addresses). Internal load balancers must use ipv4 . :rtype: dict :return: { 'LoadBalancers': [ { 'LoadBalancerArn': 'string', 'DNSName': 'string', 'CanonicalHostedZoneId': 'string', 'CreatedTime': datetime(2015, 1, 1), 'LoadBalancerName': 'string', 'Scheme': 'internet-facing'|'internal', 'VpcId': 'string', 'State': { 'Code': 'active'|'provisioning'|'failed', 'Reason': 'string' }, 'Type': 'application', 'AvailabilityZones': [ { 'ZoneName': 'string', 'SubnetId': 'string' }, ], 'SecurityGroups': [ 'string', ], 'IpAddressType': 'ipv4'|'dualstack' }, ] } :returns: (string) -- """ pass def create_rule(ListenerArn=None, Conditions=None, Priority=None, Actions=None): """ Creates a rule for the specified listener. Each rule can have one action and one condition. Rules are evaluated in priority order, from the lowest value to the highest value. When the condition for a rule is met, the specified action is taken. If no conditions are met, the default action for the default rule is taken. For more information, see Listener Rules in the Application Load Balancers Guide . To view your current rules, use DescribeRules . To update a rule, use ModifyRule . To set the priorities of your rules, use SetRulePriorities . To delete a rule, use DeleteRule . See also: AWS API Documentation Examples This example creates a rule that forwards requests to the specified target group if the URL contains the specified pattern (for example, /img/*). Expected Output: :example: response = client.create_rule( ListenerArn='string', Conditions=[ { 'Field': 'string', 'Values': [ 'string', ] }, ], Priority=123, Actions=[ { 'Type': 'forward', 'TargetGroupArn': 'string' }, ] ) :type ListenerArn: string :param ListenerArn: [REQUIRED] The Amazon Resource Name (ARN) of the listener. :type Conditions: list :param Conditions: [REQUIRED] A condition. Each condition specifies a field name and a single value. If the field name is host-header , you can specify a single host name (for example, my.example.com). A host name is case insensitive, can be up to 128 characters in length, and can contain any of the following characters. Note that you can include up to three wildcard characters. A-Z, a-z, 0-9 . (matches 0 or more characters) ? (matches exactly 1 character) If the field name is path-pattern , you can specify a single path pattern. A path pattern is case sensitive, can be up to 128 characters in length, and can contain any of the following characters. Note that you can include up to three wildcard characters. A-Z, a-z, 0-9 _ - . $ / ~ ' ' @ : + (using amp;) (matches 0 or more characters) ? (matches exactly 1 character) (dict) --Information about a condition for a rule. Field (string) --The name of the field. The possible values are host-header and path-pattern . Values (list) --The condition value. If the field name is host-header , you can specify a single host name (for example, my.example.com). A host name is case insensitive, can be up to 128 characters in length, and can contain any of the following characters. Note that you can include up to three wildcard characters. A-Z, a-z, 0-9 . (matches 0 or more characters) ? (matches exactly 1 character) If the field name is path-pattern , you can specify a single path pattern (for example, /img/*). A path pattern is case sensitive, can be up to 128 characters in length, and can contain any of the following characters. Note that you can include up to three wildcard characters. A-Z, a-z, 0-9 _ - . $ / ~ ' ' @ : + (using amp;) (matches 0 or more characters) ? (matches exactly 1 character) (string) -- :type Priority: integer :param Priority: [REQUIRED] The priority for the rule. A listener can't have multiple rules with the same priority. :type Actions: list :param Actions: [REQUIRED] An action. Each action has the type forward and specifies a target group. (dict) --Information about an action. Type (string) -- [REQUIRED]The type of action. TargetGroupArn (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the target group. :rtype: dict :return: { 'Rules': [ { 'RuleArn': 'string', 'Priority': 'string', 'Conditions': [ { 'Field': 'string', 'Values': [ 'string', ] }, ], 'Actions': [ { 'Type': 'forward', 'TargetGroupArn': 'string' }, ], 'IsDefault': True|False }, ] } :returns: A-Z, a-z, 0-9 . (matches 0 or more characters) ? (matches exactly 1 character) """ pass def create_target_group(Name=None, Protocol=None, Port=None, VpcId=None, HealthCheckProtocol=None, HealthCheckPort=None, HealthCheckPath=None, HealthCheckIntervalSeconds=None, HealthCheckTimeoutSeconds=None, HealthyThresholdCount=None, UnhealthyThresholdCount=None, Matcher=None): """ Creates a target group. To register targets with the target group, use RegisterTargets . To update the health check settings for the target group, use ModifyTargetGroup . To monitor the health of targets in the target group, use DescribeTargetHealth . To route traffic to the targets in a target group, specify the target group in an action using CreateListener or CreateRule . To delete a target group, use DeleteTargetGroup . For more information, see Target Groups for Your Application Load Balancers in the Application Load Balancers Guide . See also: AWS API Documentation Examples This example creates a target group that you can use to route traffic to targets using HTTP on port 80. This target group uses the default health check configuration. Expected Output: :example: response = client.create_target_group( Name='string', Protocol='HTTP'|'HTTPS', Port=123, VpcId='string', HealthCheckProtocol='HTTP'|'HTTPS', HealthCheckPort='string', HealthCheckPath='string', HealthCheckIntervalSeconds=123, HealthCheckTimeoutSeconds=123, HealthyThresholdCount=123, UnhealthyThresholdCount=123, Matcher={ 'HttpCode': 'string' } ) :type Name: string :param Name: [REQUIRED] The name of the target group. This name must be unique per region per account, can have a maximum of 32 characters, must contain only alphanumeric characters or hyphens, and must not begin or end with a hyphen. :type Protocol: string :param Protocol: [REQUIRED] The protocol to use for routing traffic to the targets. :type Port: integer :param Port: [REQUIRED] The port on which the targets receive traffic. This port is used unless you specify a port override when registering the target. :type VpcId: string :param VpcId: [REQUIRED] The identifier of the virtual private cloud (VPC). :type HealthCheckProtocol: string :param HealthCheckProtocol: The protocol the load balancer uses when performing health checks on targets. The default is the HTTP protocol. :type HealthCheckPort: string :param HealthCheckPort: The port the load balancer uses when performing health checks on targets. The default is traffic-port , which indicates the port on which each target receives traffic from the load balancer. :type HealthCheckPath: string :param HealthCheckPath: The ping path that is the destination on the targets for health checks. The default is /. :type HealthCheckIntervalSeconds: integer :param HealthCheckIntervalSeconds: The approximate amount of time, in seconds, between health checks of an individual target. The default is 30 seconds. :type HealthCheckTimeoutSeconds: integer :param HealthCheckTimeoutSeconds: The amount of time, in seconds, during which no response from a target means a failed health check. The default is 5 seconds. :type HealthyThresholdCount: integer :param HealthyThresholdCount: The number of consecutive health checks successes required before considering an unhealthy target healthy. The default is 5. :type UnhealthyThresholdCount: integer :param UnhealthyThresholdCount: The number of consecutive health check failures required before considering a target unhealthy. The default is 2. :type Matcher: dict :param Matcher: The HTTP codes to use when checking for a successful response from a target. The default is 200. HttpCode (string) -- [REQUIRED]The HTTP codes. You can specify values between 200 and 499. The default value is 200. You can specify multiple values (for example, '200,202') or a range of values (for example, '200-299'). :rtype: dict :return: { 'TargetGroups': [ { 'TargetGroupArn': 'string', 'TargetGroupName': 'string', 'Protocol': 'HTTP'|'HTTPS', 'Port': 123, 'VpcId': 'string', 'HealthCheckProtocol': 'HTTP'|'HTTPS', 'HealthCheckPort': 'string', 'HealthCheckIntervalSeconds': 123, 'HealthCheckTimeoutSeconds': 123, 'HealthyThresholdCount': 123, 'UnhealthyThresholdCount': 123, 'HealthCheckPath': 'string', 'Matcher': { 'HttpCode': 'string' }, 'LoadBalancerArns': [ 'string', ] }, ] } :returns: (string) -- """ pass def delete_listener(ListenerArn=None): """ Deletes the specified listener. Alternatively, your listener is deleted when you delete the load balancer it is attached to using DeleteLoadBalancer . See also: AWS API Documentation Examples This example deletes the specified listener. Expected Output: :example: response = client.delete_listener( ListenerArn='string' ) :type ListenerArn: string :param ListenerArn: [REQUIRED] The Amazon Resource Name (ARN) of the listener. :rtype: dict :return: {} """ pass def delete_load_balancer(LoadBalancerArn=None): """ Deletes the specified Application Load Balancer and its attached listeners. You can't delete a load balancer if deletion protection is enabled. If the load balancer does not exist or has already been deleted, the call succeeds. Deleting a load balancer does not affect its registered targets. For example, your EC2 instances continue to run and are still registered to their target groups. If you no longer need these EC2 instances, you can stop or terminate them. See also: AWS API Documentation Examples This example deletes the specified load balancer. Expected Output: :example: response = client.delete_load_balancer( LoadBalancerArn='string' ) :type LoadBalancerArn: string :param LoadBalancerArn: [REQUIRED] The Amazon Resource Name (ARN) of the load balancer. :rtype: dict :return: {} """ pass def delete_rule(RuleArn=None): """ Deletes the specified rule. See also: AWS API Documentation Examples This example deletes the specified rule. Expected Output: :example: response = client.delete_rule( RuleArn='string' ) :type RuleArn: string :param RuleArn: [REQUIRED] The Amazon Resource Name (ARN) of the rule. :rtype: dict :return: {} """ pass def delete_target_group(TargetGroupArn=None): """ Deletes the specified target group. You can delete a target group if it is not referenced by any actions. Deleting a target group also deletes any associated health checks. See also: AWS API Documentation Examples This example deletes the specified target group. Expected Output: :example: response = client.delete_target_group( TargetGroupArn='string' ) :type TargetGroupArn: string :param TargetGroupArn: [REQUIRED] The Amazon Resource Name (ARN) of the target group. :rtype: dict :return: {} """ pass def deregister_targets(TargetGroupArn=None, Targets=None): """ Deregisters the specified targets from the specified target group. After the targets are deregistered, they no longer receive traffic from the load balancer. See also: AWS API Documentation Examples This example deregisters the specified instance from the specified target group. Expected Output: :example: response = client.deregister_targets( TargetGroupArn='string', Targets=[ { 'Id': 'string', 'Port': 123 }, ] ) :type TargetGroupArn: string :param TargetGroupArn: [REQUIRED] The Amazon Resource Name (ARN) of the target group. :type Targets: list :param Targets: [REQUIRED] The targets. If you specified a port override when you registered a target, you must specify both the target ID and the port when you deregister it. (dict) --Information about a target. Id (string) -- [REQUIRED]The ID of the target. Port (integer) --The port on which the target is listening. :rtype: dict :return: {} :returns: (dict) -- """ pass def describe_account_limits(Marker=None, PageSize=None): """ Describes the current Elastic Load Balancing resource limits for your AWS account. For more information, see Limits for Your Application Load Balancer in the Application Load Balancer Guide . See also: AWS API Documentation :example: response = client.describe_account_limits( Marker='string', PageSize=123 ) :type Marker: string :param Marker: The marker for the next set of results. (You received this marker from a previous call.) :type PageSize: integer :param PageSize: The maximum number of results to return with this call. :rtype: dict :return: { 'Limits': [ { 'Name': 'string', 'Max': 'string' }, ], 'NextMarker': 'string' } :returns: application-load-balancers listeners-per-application-load-balancer rules-per-application-load-balancer target-groups targets-per-application-load-balancer """ pass def describe_listeners(LoadBalancerArn=None, ListenerArns=None, Marker=None, PageSize=None): """ Describes the specified listeners or the listeners for the specified Application Load Balancer. You must specify either a load balancer or one or more listeners. See also: AWS API Documentation Examples This example describes the specified listener. Expected Output: :example: response = client.describe_listeners( LoadBalancerArn='string', ListenerArns=[ 'string', ], Marker='string', PageSize=123 ) :type LoadBalancerArn: string :param LoadBalancerArn: The Amazon Resource Name (ARN) of the load balancer. :type ListenerArns: list :param ListenerArns: The Amazon Resource Names (ARN) of the listeners. (string) -- :type Marker: string :param Marker: The marker for the next set of results. (You received this marker from a previous call.) :type PageSize: integer :param PageSize: The maximum number of results to return with this call. :rtype: dict :return: { 'Listeners': [ { 'ListenerArn': 'string', 'LoadBalancerArn': 'string', 'Port': 123, 'Protocol': 'HTTP'|'HTTPS', 'Certificates': [ { 'CertificateArn': 'string' }, ], 'SslPolicy': 'string', 'DefaultActions': [ { 'Type': 'forward', 'TargetGroupArn': 'string' }, ] }, ], 'NextMarker': 'string' } """ pass def describe_load_balancer_attributes(LoadBalancerArn=None): """ Describes the attributes for the specified Application Load Balancer. See also: AWS API Documentation Examples This example describes the attributes of the specified load balancer. Expected Output: :example: response = client.describe_load_balancer_attributes( LoadBalancerArn='string' ) :type LoadBalancerArn: string :param LoadBalancerArn: [REQUIRED] The Amazon Resource Name (ARN) of the load balancer. :rtype: dict :return: { 'Attributes': [ { 'Key': 'string', 'Value': 'string' }, ] } """ pass def describe_load_balancers(LoadBalancerArns=None, Names=None, Marker=None, PageSize=None): """ Describes the specified Application Load Balancers or all of your Application Load Balancers. To describe the listeners for a load balancer, use DescribeListeners . To describe the attributes for a load balancer, use DescribeLoadBalancerAttributes . See also: AWS API Documentation Examples This example describes the specified load balancer. Expected Output: :example: response = client.describe_load_balancers( LoadBalancerArns=[ 'string', ], Names=[ 'string', ], Marker='string', PageSize=123 ) :type LoadBalancerArns: list :param LoadBalancerArns: The Amazon Resource Names (ARN) of the load balancers. You can specify up to 20 load balancers in a single call. (string) -- :type Names: list :param Names: The names of the load balancers. (string) -- :type Marker: string :param Marker: The marker for the next set of results. (You received this marker from a previous call.) :type PageSize: integer :param PageSize: The maximum number of results to return with this call. :rtype: dict :return: { 'LoadBalancers': [ { 'LoadBalancerArn': 'string', 'DNSName': 'string', 'CanonicalHostedZoneId': 'string', 'CreatedTime': datetime(2015, 1, 1), 'LoadBalancerName': 'string', 'Scheme': 'internet-facing'|'internal', 'VpcId': 'string', 'State': { 'Code': 'active'|'provisioning'|'failed', 'Reason': 'string' }, 'Type': 'application', 'AvailabilityZones': [ { 'ZoneName': 'string', 'SubnetId': 'string' }, ], 'SecurityGroups': [ 'string', ], 'IpAddressType': 'ipv4'|'dualstack' }, ], 'NextMarker': 'string' } :returns: (string) -- """ pass def describe_rules(ListenerArn=None, RuleArns=None, Marker=None, PageSize=None): """ Describes the specified rules or the rules for the specified listener. You must specify either a listener or one or more rules. See also: AWS API Documentation Examples This example describes the specified rule. Expected Output: :example: response = client.describe_rules( ListenerArn='string', RuleArns=[ 'string', ], Marker='string', PageSize=123 ) :type ListenerArn: string :param ListenerArn: The Amazon Resource Name (ARN) of the listener. :type RuleArns: list :param RuleArns: The Amazon Resource Names (ARN) of the rules. (string) -- :type Marker: string :param Marker: The marker for the next set of results. (You received this marker from a previous call.) :type PageSize: integer :param PageSize: The maximum number of results to return with this call. :rtype: dict :return: { 'Rules': [ { 'RuleArn': 'string', 'Priority': 'string', 'Conditions': [ { 'Field': 'string', 'Values': [ 'string', ] }, ], 'Actions': [ { 'Type': 'forward', 'TargetGroupArn': 'string' }, ], 'IsDefault': True|False }, ], 'NextMarker': 'string' } :returns: A-Z, a-z, 0-9 . (matches 0 or more characters) ? (matches exactly 1 character) """ pass def describe_ssl_policies(Names=None, Marker=None, PageSize=None): """ Describes the specified policies or all policies used for SSL negotiation. For more information, see Security Policies in the Application Load Balancers Guide . See also: AWS API Documentation Examples This example describes the specified policy used for SSL negotiation. Expected Output: :example: response = client.describe_ssl_policies( Names=[ 'string', ], Marker='string', PageSize=123 ) :type Names: list :param Names: The names of the policies. (string) -- :type Marker: string :param Marker: The marker for the next set of results. (You received this marker from a previous call.) :type PageSize: integer :param PageSize: The maximum number of results to return with this call. :rtype: dict :return: { 'SslPolicies': [ { 'SslProtocols': [ 'string', ], 'Ciphers': [ { 'Name': 'string', 'Priority': 123 }, ], 'Name': 'string' }, ], 'NextMarker': 'string' } :returns: (string) -- """ pass def describe_tags(ResourceArns=None): """ Describes the tags for the specified resources. You can describe the tags for one or more Application Load Balancers and target groups. See also: AWS API Documentation Examples This example describes the tags assigned to the specified load balancer. Expected Output: :example: response = client.describe_tags( ResourceArns=[ 'string', ] ) :type ResourceArns: list :param ResourceArns: [REQUIRED] The Amazon Resource Names (ARN) of the resources. (string) -- :rtype: dict :return: { 'TagDescriptions': [ { 'ResourceArn': 'string', 'Tags': [ { 'Key': 'string', 'Value': 'string' }, ] }, ] } """ pass def describe_target_group_attributes(TargetGroupArn=None): """ Describes the attributes for the specified target group. See also: AWS API Documentation Examples This example describes the attributes of the specified target group. Expected Output: :example: response = client.describe_target_group_attributes( TargetGroupArn='string' ) :type TargetGroupArn: string :param TargetGroupArn: [REQUIRED] The Amazon Resource Name (ARN) of the target group. :rtype: dict :return: { 'Attributes': [ { 'Key': 'string', 'Value': 'string' }, ] } """ pass def describe_target_groups(LoadBalancerArn=None, TargetGroupArns=None, Names=None, Marker=None, PageSize=None): """ Describes the specified target groups or all of your target groups. By default, all target groups are described. Alternatively, you can specify one of the following to filter the results: the ARN of the load balancer, the names of one or more target groups, or the ARNs of one or more target groups. To describe the targets for a target group, use DescribeTargetHealth . To describe the attributes of a target group, use DescribeTargetGroupAttributes . See also: AWS API Documentation Examples This example describes the specified target group. Expected Output: :example: response = client.describe_target_groups( LoadBalancerArn='string', TargetGroupArns=[ 'string', ], Names=[ 'string', ], Marker='string', PageSize=123 ) :type LoadBalancerArn: string :param LoadBalancerArn: The Amazon Resource Name (ARN) of the load balancer. :type TargetGroupArns: list :param TargetGroupArns: The Amazon Resource Names (ARN) of the target groups. (string) -- :type Names: list :param Names: The names of the target groups. (string) -- :type Marker: string :param Marker: The marker for the next set of results. (You received this marker from a previous call.) :type PageSize: integer :param PageSize: The maximum number of results to return with this call. :rtype: dict :return: { 'TargetGroups': [ { 'TargetGroupArn': 'string', 'TargetGroupName': 'string', 'Protocol': 'HTTP'|'HTTPS', 'Port': 123, 'VpcId': 'string', 'HealthCheckProtocol': 'HTTP'|'HTTPS', 'HealthCheckPort': 'string', 'HealthCheckIntervalSeconds': 123, 'HealthCheckTimeoutSeconds': 123, 'HealthyThresholdCount': 123, 'UnhealthyThresholdCount': 123, 'HealthCheckPath': 'string', 'Matcher': { 'HttpCode': 'string' }, 'LoadBalancerArns': [ 'string', ] }, ], 'NextMarker': 'string' } :returns: (string) -- """ pass def describe_target_health(TargetGroupArn=None, Targets=None): """ Describes the health of the specified targets or all of your targets. See also: AWS API Documentation Examples This example describes the health of the targets for the specified target group. One target is healthy but the other is not specified in an action, so it can't receive traffic from the load balancer. Expected Output: This example describes the health of the specified target. This target is healthy. Expected Output: :example: response = client.describe_target_health( TargetGroupArn='string', Targets=[ { 'Id': 'string', 'Port': 123 }, ] ) :type TargetGroupArn: string :param TargetGroupArn: [REQUIRED] The Amazon Resource Name (ARN) of the target group. :type Targets: list :param Targets: The targets. (dict) --Information about a target. Id (string) -- [REQUIRED]The ID of the target. Port (integer) --The port on which the target is listening. :rtype: dict :return: { 'TargetHealthDescriptions': [ { 'Target': { 'Id': 'string', 'Port': 123 }, 'HealthCheckPort': 'string', 'TargetHealth': { 'State': 'initial'|'healthy'|'unhealthy'|'unused'|'draining', 'Reason': 'Elb.RegistrationInProgress'|'Elb.InitialHealthChecking'|'Target.ResponseCodeMismatch'|'Target.Timeout'|'Target.FailedHealthChecks'|'Target.NotRegistered'|'Target.NotInUse'|'Target.DeregistrationInProgress'|'Target.InvalidState'|'Elb.InternalError', 'Description': 'string' } }, ] } :returns: Elb.RegistrationInProgress - The target is in the process of being registered with the load balancer. Elb.InitialHealthChecking - The load balancer is still sending the target the minimum number of health checks required to determine its health status. """ pass def generate_presigned_url(ClientMethod=None, Params=None, ExpiresIn=None, HttpMethod=None): """ Generate a presigned url given a client, its method, and arguments :type ClientMethod: string :param ClientMethod: The client method to presign for :type Params: dict :param Params: The parameters normally passed to ClientMethod. :type ExpiresIn: int :param ExpiresIn: The number of seconds the presigned url is valid for. By default it expires in an hour (3600 seconds) :type HttpMethod: string :param HttpMethod: The http method to use on the generated url. By default, the http method is whatever is used in the method's model. """ pass def get_paginator(operation_name=None): """ Create a paginator for an operation. :type operation_name: string :param operation_name: The operation name. This is the same name as the method name on the client. For example, if the method name is create_foo, and you'd normally invoke the operation as client.create_foo(**kwargs), if the create_foo operation can be paginated, you can use the call client.get_paginator('create_foo'). :rtype: L{botocore.paginate.Paginator} """ pass def get_waiter(): """ """ pass def modify_listener(ListenerArn=None, Port=None, Protocol=None, SslPolicy=None, Certificates=None, DefaultActions=None): """ Modifies the specified properties of the specified listener. Any properties that you do not specify retain their current values. However, changing the protocol from HTTPS to HTTP removes the security policy and SSL certificate properties. If you change the protocol from HTTP to HTTPS, you must add the security policy and server certificate. See also: AWS API Documentation Examples This example changes the default action for the specified listener. Expected Output: This example changes the server certificate for the specified HTTPS listener. Expected Output: :example: response = client.modify_listener( ListenerArn='string', Port=123, Protocol='HTTP'|'HTTPS', SslPolicy='string', Certificates=[ { 'CertificateArn': 'string' }, ], DefaultActions=[ { 'Type': 'forward', 'TargetGroupArn': 'string' }, ] ) :type ListenerArn: string :param ListenerArn: [REQUIRED] The Amazon Resource Name (ARN) of the listener. :type Port: integer :param Port: The port for connections from clients to the load balancer. :type Protocol: string :param Protocol: The protocol for connections from clients to the load balancer. :type SslPolicy: string :param SslPolicy: The security policy that defines which protocols and ciphers are supported. For more information, see Security Policies in the Application Load Balancers Guide . :type Certificates: list :param Certificates: The SSL server certificate. (dict) --Information about an SSL server certificate deployed on a load balancer. CertificateArn (string) --The Amazon Resource Name (ARN) of the certificate. :type DefaultActions: list :param DefaultActions: The default actions. (dict) --Information about an action. Type (string) -- [REQUIRED]The type of action. TargetGroupArn (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the target group. :rtype: dict :return: { 'Listeners': [ { 'ListenerArn': 'string', 'LoadBalancerArn': 'string', 'Port': 123, 'Protocol': 'HTTP'|'HTTPS', 'Certificates': [ { 'CertificateArn': 'string' }, ], 'SslPolicy': 'string', 'DefaultActions': [ { 'Type': 'forward', 'TargetGroupArn': 'string' }, ] }, ] } """ pass def modify_load_balancer_attributes(LoadBalancerArn=None, Attributes=None): """ Modifies the specified attributes of the specified Application Load Balancer. If any of the specified attributes can't be modified as requested, the call fails. Any existing attributes that you do not modify retain their current values. See also: AWS API Documentation Examples This example enables deletion protection for the specified load balancer. Expected Output: This example changes the idle timeout value for the specified load balancer. Expected Output: This example enables access logs for the specified load balancer. Note that the S3 bucket must exist in the same region as the load balancer and must have a policy attached that grants access to the Elastic Load Balancing service. Expected Output: :example: response = client.modify_load_balancer_attributes( LoadBalancerArn='string', Attributes=[ { 'Key': 'string', 'Value': 'string' }, ] ) :type LoadBalancerArn: string :param LoadBalancerArn: [REQUIRED] The Amazon Resource Name (ARN) of the load balancer. :type Attributes: list :param Attributes: [REQUIRED] The load balancer attributes. (dict) --Information about a load balancer attribute. Key (string) --The name of the attribute. access_logs.s3.enabled - Indicates whether access logs stored in Amazon S3 are enabled. The value is true or false . access_logs.s3.bucket - The name of the S3 bucket for the access logs. This attribute is required if access logs in Amazon S3 are enabled. The bucket must exist in the same region as the load balancer and have a bucket policy that grants Elastic Load Balancing permission to write to the bucket. access_logs.s3.prefix - The prefix for the location in the S3 bucket. If you don't specify a prefix, the access logs are stored in the root of the bucket. deletion_protection.enabled - Indicates whether deletion protection is enabled. The value is true or false . idle_timeout.timeout_seconds - The idle timeout value, in seconds. The valid range is 1-3600. The default is 60 seconds. Value (string) --The value of the attribute. :rtype: dict :return: { 'Attributes': [ { 'Key': 'string', 'Value': 'string' }, ] } :returns: access_logs.s3.enabled - Indicates whether access logs stored in Amazon S3 are enabled. The value is true or false . access_logs.s3.bucket - The name of the S3 bucket for the access logs. This attribute is required if access logs in Amazon S3 are enabled. The bucket must exist in the same region as the load balancer and have a bucket policy that grants Elastic Load Balancing permission to write to the bucket. access_logs.s3.prefix - The prefix for the location in the S3 bucket. If you don't specify a prefix, the access logs are stored in the root of the bucket. deletion_protection.enabled - Indicates whether deletion protection is enabled. The value is true or false . idle_timeout.timeout_seconds - The idle timeout value, in seconds. The valid range is 1-3600. The default is 60 seconds. """ pass def modify_rule(RuleArn=None, Conditions=None, Actions=None): """ Modifies the specified rule. Any existing properties that you do not modify retain their current values. To modify the default action, use ModifyListener . See also: AWS API Documentation Examples This example modifies the condition for the specified rule. Expected Output: :example: response = client.modify_rule( RuleArn='string', Conditions=[ { 'Field': 'string', 'Values': [ 'string', ] }, ], Actions=[ { 'Type': 'forward', 'TargetGroupArn': 'string' }, ] ) :type RuleArn: string :param RuleArn: [REQUIRED] The Amazon Resource Name (ARN) of the rule. :type Conditions: list :param Conditions: The conditions. (dict) --Information about a condition for a rule. Field (string) --The name of the field. The possible values are host-header and path-pattern . Values (list) --The condition value. If the field name is host-header , you can specify a single host name (for example, my.example.com). A host name is case insensitive, can be up to 128 characters in length, and can contain any of the following characters. Note that you can include up to three wildcard characters. A-Z, a-z, 0-9 . (matches 0 or more characters) ? (matches exactly 1 character) If the field name is path-pattern , you can specify a single path pattern (for example, /img/*). A path pattern is case sensitive, can be up to 128 characters in length, and can contain any of the following characters. Note that you can include up to three wildcard characters. A-Z, a-z, 0-9 _ - . $ / ~ ' ' @ : + (using amp;) (matches 0 or more characters) ? (matches exactly 1 character) (string) -- :type Actions: list :param Actions: The actions. (dict) --Information about an action. Type (string) -- [REQUIRED]The type of action. TargetGroupArn (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the target group. :rtype: dict :return: { 'Rules': [ { 'RuleArn': 'string', 'Priority': 'string', 'Conditions': [ { 'Field': 'string', 'Values': [ 'string', ] }, ], 'Actions': [ { 'Type': 'forward', 'TargetGroupArn': 'string' }, ], 'IsDefault': True|False }, ] } :returns: A-Z, a-z, 0-9 . (matches 0 or more characters) ? (matches exactly 1 character) """ pass def modify_target_group(TargetGroupArn=None, HealthCheckProtocol=None, HealthCheckPort=None, HealthCheckPath=None, HealthCheckIntervalSeconds=None, HealthCheckTimeoutSeconds=None, HealthyThresholdCount=None, UnhealthyThresholdCount=None, Matcher=None): """ Modifies the health checks used when evaluating the health state of the targets in the specified target group. To monitor the health of the targets, use DescribeTargetHealth . See also: AWS API Documentation Examples This example changes the configuration of the health checks used to evaluate the health of the targets for the specified target group. Expected Output: :example: response = client.modify_target_group( TargetGroupArn='string', HealthCheckProtocol='HTTP'|'HTTPS', HealthCheckPort='string', HealthCheckPath='string', HealthCheckIntervalSeconds=123, HealthCheckTimeoutSeconds=123, HealthyThresholdCount=123, UnhealthyThresholdCount=123, Matcher={ 'HttpCode': 'string' } ) :type TargetGroupArn: string :param TargetGroupArn: [REQUIRED] The Amazon Resource Name (ARN) of the target group. :type HealthCheckProtocol: string :param HealthCheckProtocol: The protocol to use to connect with the target. :type HealthCheckPort: string :param HealthCheckPort: The port to use to connect with the target. :type HealthCheckPath: string :param HealthCheckPath: The ping path that is the destination for the health check request. :type HealthCheckIntervalSeconds: integer :param HealthCheckIntervalSeconds: The approximate amount of time, in seconds, between health checks of an individual target. :type HealthCheckTimeoutSeconds: integer :param HealthCheckTimeoutSeconds: The amount of time, in seconds, during which no response means a failed health check. :type HealthyThresholdCount: integer :param HealthyThresholdCount: The number of consecutive health checks successes required before considering an unhealthy target healthy. :type UnhealthyThresholdCount: integer :param UnhealthyThresholdCount: The number of consecutive health check failures required before considering the target unhealthy. :type Matcher: dict :param Matcher: The HTTP codes to use when checking for a successful response from a target. HttpCode (string) -- [REQUIRED]The HTTP codes. You can specify values between 200 and 499. The default value is 200. You can specify multiple values (for example, '200,202') or a range of values (for example, '200-299'). :rtype: dict :return: { 'TargetGroups': [ { 'TargetGroupArn': 'string', 'TargetGroupName': 'string', 'Protocol': 'HTTP'|'HTTPS', 'Port': 123, 'VpcId': 'string', 'HealthCheckProtocol': 'HTTP'|'HTTPS', 'HealthCheckPort': 'string', 'HealthCheckIntervalSeconds': 123, 'HealthCheckTimeoutSeconds': 123, 'HealthyThresholdCount': 123, 'UnhealthyThresholdCount': 123, 'HealthCheckPath': 'string', 'Matcher': { 'HttpCode': 'string' }, 'LoadBalancerArns': [ 'string', ] }, ] } :returns: (string) -- """ pass def modify_target_group_attributes(TargetGroupArn=None, Attributes=None): """ Modifies the specified attributes of the specified target group. See also: AWS API Documentation Examples This example sets the deregistration delay timeout to the specified value for the specified target group. Expected Output: :example: response = client.modify_target_group_attributes( TargetGroupArn='string', Attributes=[ { 'Key': 'string', 'Value': 'string' }, ] ) :type TargetGroupArn: string :param TargetGroupArn: [REQUIRED] The Amazon Resource Name (ARN) of the target group. :type Attributes: list :param Attributes: [REQUIRED] The attributes. (dict) --Information about a target group attribute. Key (string) --The name of the attribute. deregistration_delay.timeout_seconds - The amount time for Elastic Load Balancing to wait before changing the state of a deregistering target from draining to unused . The range is 0-3600 seconds. The default value is 300 seconds. stickiness.enabled - Indicates whether sticky sessions are enabled. The value is true or false . stickiness.type - The type of sticky sessions. The possible value is lb_cookie . stickiness.lb_cookie.duration_seconds - The time period, in seconds, during which requests from a client should be routed to the same target. After this time period expires, the load balancer-generated cookie is considered stale. The range is 1 second to 1 week (604800 seconds). The default value is 1 day (86400 seconds). Value (string) --The value of the attribute. :rtype: dict :return: { 'Attributes': [ { 'Key': 'string', 'Value': 'string' }, ] } :returns: deregistration_delay.timeout_seconds - The amount time for Elastic Load Balancing to wait before changing the state of a deregistering target from draining to unused . The range is 0-3600 seconds. The default value is 300 seconds. stickiness.enabled - Indicates whether sticky sessions are enabled. The value is true or false . stickiness.type - The type of sticky sessions. The possible value is lb_cookie . stickiness.lb_cookie.duration_seconds - The time period, in seconds, during which requests from a client should be routed to the same target. After this time period expires, the load balancer-generated cookie is considered stale. The range is 1 second to 1 week (604800 seconds). The default value is 1 day (86400 seconds). """ pass def register_targets(TargetGroupArn=None, Targets=None): """ Registers the specified targets with the specified target group. By default, the load balancer routes requests to registered targets using the protocol and port number for the target group. Alternatively, you can override the port for a target when you register it. The target must be in the virtual private cloud (VPC) that you specified for the target group. If the target is an EC2 instance, it must be in the running state when you register it. To remove a target from a target group, use DeregisterTargets . See also: AWS API Documentation Examples This example registers the specified instances with the specified target group. Expected Output: This example registers the specified instance with the specified target group using multiple ports. This enables you to register ECS containers on the same instance as targets in the target group. Expected Output: :example: response = client.register_targets( TargetGroupArn='string', Targets=[ { 'Id': 'string', 'Port': 123 }, ] ) :type TargetGroupArn: string :param TargetGroupArn: [REQUIRED] The Amazon Resource Name (ARN) of the target group. :type Targets: list :param Targets: [REQUIRED] The targets. The default port for a target is the port for the target group. You can specify a port override. If a target is already registered, you can register it again using a different port. (dict) --Information about a target. Id (string) -- [REQUIRED]The ID of the target. Port (integer) --The port on which the target is listening. :rtype: dict :return: {} :returns: (dict) -- """ pass def remove_tags(ResourceArns=None, TagKeys=None): """ Removes the specified tags from the specified resource. To list the current tags for your resources, use DescribeTags . See also: AWS API Documentation Examples This example removes the specified tags from the specified load balancer. Expected Output: :example: response = client.remove_tags( ResourceArns=[ 'string', ], TagKeys=[ 'string', ] ) :type ResourceArns: list :param ResourceArns: [REQUIRED] The Amazon Resource Name (ARN) of the resource. (string) -- :type TagKeys: list :param TagKeys: [REQUIRED] The tag keys for the tags to remove. (string) -- :rtype: dict :return: {} :returns: (dict) -- """ pass def set_ip_address_type(LoadBalancerArn=None, IpAddressType=None): """ Sets the type of IP addresses used by the subnets of the specified Application Load Balancer. See also: AWS API Documentation :example: response = client.set_ip_address_type( LoadBalancerArn='string', IpAddressType='ipv4'|'dualstack' ) :type LoadBalancerArn: string :param LoadBalancerArn: [REQUIRED] The Amazon Resource Name (ARN) of the load balancer. :type IpAddressType: string :param IpAddressType: [REQUIRED] The IP address type. The possible values are ipv4 (for IPv4 addresses) and dualstack (for IPv4 and IPv6 addresses). Internal load balancers must use ipv4 . :rtype: dict :return: { 'IpAddressType': 'ipv4'|'dualstack' } """ pass def set_rule_priorities(RulePriorities=None): """ Sets the priorities of the specified rules. You can reorder the rules as long as there are no priority conflicts in the new order. Any existing rules that you do not specify retain their current priority. See also: AWS API Documentation Examples This example sets the priority of the specified rule. Expected Output: :example: response = client.set_rule_priorities( RulePriorities=[ { 'RuleArn': 'string', 'Priority': 123 }, ] ) :type RulePriorities: list :param RulePriorities: [REQUIRED] The rule priorities. (dict) --Information about the priorities for the rules for a listener. RuleArn (string) --The Amazon Resource Name (ARN) of the rule. Priority (integer) --The rule priority. :rtype: dict :return: { 'Rules': [ { 'RuleArn': 'string', 'Priority': 'string', 'Conditions': [ { 'Field': 'string', 'Values': [ 'string', ] }, ], 'Actions': [ { 'Type': 'forward', 'TargetGroupArn': 'string' }, ], 'IsDefault': True|False }, ] } :returns: A-Z, a-z, 0-9 _ - . $ / ~ " ' @ : + (using amp;) (matches 0 or more characters) ? (matches exactly 1 character) """ pass def set_security_groups(LoadBalancerArn=None, SecurityGroups=None): """ Associates the specified security groups with the specified load balancer. The specified security groups override the previously associated security groups. See also: AWS API Documentation Examples This example associates the specified security group with the specified load balancer. Expected Output: :example: response = client.set_security_groups( LoadBalancerArn='string', SecurityGroups=[ 'string', ] ) :type LoadBalancerArn: string :param LoadBalancerArn: [REQUIRED] The Amazon Resource Name (ARN) of the load balancer. :type SecurityGroups: list :param SecurityGroups: [REQUIRED] The IDs of the security groups. (string) -- :rtype: dict :return: { 'SecurityGroupIds': [ 'string', ] } :returns: (string) -- """ pass def set_subnets(LoadBalancerArn=None, Subnets=None): """ Enables the Availability Zone for the specified subnets for the specified load balancer. The specified subnets replace the previously enabled subnets. See also: AWS API Documentation Examples This example enables the Availability Zones for the specified subnets for the specified load balancer. Expected Output: :example: response = client.set_subnets( LoadBalancerArn='string', Subnets=[ 'string', ] ) :type LoadBalancerArn: string :param LoadBalancerArn: [REQUIRED] The Amazon Resource Name (ARN) of the load balancer. :type Subnets: list :param Subnets: [REQUIRED] The IDs of the subnets. You must specify at least two subnets. You can add only one subnet per Availability Zone. (string) -- :rtype: dict :return: { 'AvailabilityZones': [ { 'ZoneName': 'string', 'SubnetId': 'string' }, ] } """ pass

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77a99dda069f9f7f48cb48d772b1f2807a59d8bd

381

py

Python

apple_problems/problem_6.py

loftwah/Daily-Coding-Problem

0327f0b4f69ef419436846c831110795c7a3c1fe

[ "MIT" ]

129

2018-10-14T17:52:29.000Z

2022-01-29T15:45:57.000Z

apple_problems/problem_6.py

loftwah/Daily-Coding-Problem

0327f0b4f69ef419436846c831110795c7a3c1fe

[ "MIT" ]

2

2019-11-30T23:28:23.000Z

2020-01-03T16:30:32.000Z

apple_problems/problem_6.py

loftwah/Daily-Coding-Problem

0327f0b4f69ef419436846c831110795c7a3c1fe

[ "MIT" ]

60

2019-02-21T09:18:31.000Z

2022-03-25T21:01:04.000Z

"""This problem was asked by Apple. Gray code is a binary code where each successive value differ in only one bit, as well as when wrapping around. Gray code is common in hardware so that we don't see temporary spurious values during transitions. Given a number of bits n, generate a possible gray code for it. For example, for n = 2, one gray code would be [00, 01, 11, 10]. """

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541

py

Python

modules/apps/PrefabTfChain.py

Jumpscale/rsal9

e7ff7638ca53dafe872ce3030a379e8b65cb4831

[ "Apache-2.0" ]

1

2017-06-07T08:11:57.000Z

modules/apps/PrefabTfChain.py

Jumpscale/rsal9

e7ff7638ca53dafe872ce3030a379e8b65cb4831

[ "Apache-2.0" ]

106

2017-05-10T18:16:31.000Z

2019-09-18T15:09:07.000Z

modules/apps/PrefabTfChain.py

Jumpscale/rsal9

e7ff7638ca53dafe872ce3030a379e8b65cb4831

[ "Apache-2.0" ]

5

2018-01-26T16:11:52.000Z

2018-08-22T15:12:52.000Z

from js9 import j app = j.tools.prefab._getBaseAppClass() class PrefabTfChain(app): NAME = "tfchain" def build(self, reset=False): """Get/Build the binaries of tfchain (tfchaid and tfchainc) Keyword Arguments: reset {bool} -- reset the build process (default: {False}) """ self.prefab.blockchain.tfchain.build(reset=reset) def install(self, reset=False): """ Install the tftchain binaries """ self.prefab.blockchain.tfchain.install(reset=reset)

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null

0

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null

0

1

0

1

0

3

77cf32aec4f7cf325652a9d249291b4c344fb703

2,419

py

Python

Test/test.py

Tim232/Python-Things

05f0f373a4cf298e70d9668c88a6e3a9d1cd8146

[ "MIT" ]

2

2020-12-05T07:42:55.000Z

2021-01-06T23:23:18.000Z

Test/test.py

Tim232/Python-Things

05f0f373a4cf298e70d9668c88a6e3a9d1cd8146

[ "MIT" ]

null

Test/test.py

Tim232/Python-Things

05f0f373a4cf298e70d9668c88a6e3a9d1cd8146

[ "MIT" ]

null

import numpy as np a = np.array([[['a11', 'a12', 'a13', 'a21', 'a22', 'a23', 'a31', 'a32', 'a33', 'a41', 'a42', 'a43']]]) b = np.array([[['b11', 'b12', 'b13', 'b21', 'b22', 'b23', 'b31', 'b32', 'b33', 'b41', 'b42', 'b43']]]) c = np.concatenate([a, b], axis=1) with open(file='d:/result.txt', mode='w') as f: for (h1, w1, c1) in [[1, 1, 24], [1, 2, 12], [1, 3, 8], [1, 4, 6], [1, 6, 4], [1, 8, 3], [1, 12, 2], [1, 24, 1], [2, 1, 12], [2, 2, 6], [2, 3, 4], [2, 4, 3], [2, 6, 2], [2, 12, 1], [3, 1, 8], [3, 2, 4], [3, 4, 2], [3, 8, 1], [4, 1, 6], [4, 2, 3], [4, 3, 2], [4, 6, 1], [6, 1, 4], [6, 2, 2], [6, 4, 1], [8, 1, 3], [8, 3, 1], [12, 1, 2], [12, 2, 1], [24, 1, 1]]: for (h2, w2, c2) in [[0, 1, 2], [0, 2, 1], [1, 0, 2], [1, 2, 0], [2, 0, 1], [2, 1, 0]]: for (h3, w3, c3) in [[1, 1, 24], [1, 2, 12], [1, 3, 8], [1, 4, 6], [1, 6, 4], [1, 8, 3], [1, 12, 2], [1, 24, 1], [2, 1, 12], [2, 2, 6], [2, 3, 4], [2, 4, 3], [2, 6, 2], [2, 12, 1], [3, 1, 8], [3, 2, 4], [3, 4, 2], [3, 8, 1], [4, 1, 6], [4, 2, 3], [4, 3, 2], [4, 6, 1], [6, 1, 4], [6, 2, 2], [6, 4, 1], [8, 1, 3], [8, 3, 1], [12, 1, 2], [12, 2, 1], [24, 1, 1]]: for (h4, w4, c4) in [[0, 1, 2], [0, 2, 1], [1, 0, 2], [1, 2, 0], [2, 0, 1], [2, 1, 0]]: c = c.reshape([h1, w1, c1]) c = c.transpose([h2, w2, c2]) c = c.reshape([h3, w3, c3]) c = c.transpose([h4, w4, c4]) shape = c.shape f.write('(' + str(h1) + ',' + str(w1) + ',' + str(c1) + '), ' + '(' + str(h2) + ',' + str(w2) + ',' + str(c2) + '), ' + '(' + str(h3) + ',' + str(w3) + ',' + str(c3) + '), ' + '(' + str(h4) + ',' + str(w4) + ',' + str(c4) + ')\n') for line in c: f.write(str(line) + '\n') f.write('-- (' + str(shape[0]) + ',' + str(shape[1]) + ',' + str(shape[2]) + ') --' + '\n') f.write('==============================================\n\n')

62.025641

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1

null

0

1

0

1

0

null

0

3

77d3a8f1fd26a35e1791558751a04930ebe0761e

157

py

Python

aiocloudflare/api/accounts/tunnels/connections/connections.py

Stewart86/aioCloudflare

341c0941f8f888a8b7e696e64550bce5da4949e6

[ "MIT" ]

2

2021-09-14T13:20:55.000Z

2022-02-24T14:18:24.000Z

aiocloudflare/api/accounts/tunnels/connections/connections.py

Stewart86/aioCloudflare

341c0941f8f888a8b7e696e64550bce5da4949e6

[ "MIT" ]

46

2021-09-08T08:39:45.000Z

2022-03-29T12:31:05.000Z

aiocloudflare/api/accounts/tunnels/connections/connections.py

Stewart86/aioCloudflare

341c0941f8f888a8b7e696e64550bce5da4949e6

[ "MIT" ]

1

2021-12-30T23:02:23.000Z

from aiocloudflare.commons.auth import Auth class Connections(Auth): _endpoint1 = "accounts" _endpoint2 = "tunnels" _endpoint3 = "connections"

19.625

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0.726115

15

157

7.4

0.8

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0.184713

157

7

44

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0.165605

0

1

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null

0

1

0

null

0

1

0

3

ae009ca6027718bbda68ea736eb2d58c56c75e2a

231

py

Python

examples/python/gpu/tensors/tensor_copy_02.py

kant/ocean-tensor-package

fb3fcff8bba7f4ef6cd8b8d02f0e1be1258da02d

[ "Apache-2.0" ]

27

2018-08-16T21:32:49.000Z

2021-11-30T10:31:08.000Z

examples/python/gpu/tensors/tensor_copy_02.py

kant/ocean-tensor-package

fb3fcff8bba7f4ef6cd8b8d02f0e1be1258da02d

[ "Apache-2.0" ]

null

examples/python/gpu/tensors/tensor_copy_02.py

kant/ocean-tensor-package

fb3fcff8bba7f4ef6cd8b8d02f0e1be1258da02d

[ "Apache-2.0" ]

13

2018-08-17T17:33:16.000Z

2021-11-30T10:31:09.000Z

import ocean a = ocean.gpu[0](12345) b = ocean.tensor([], ocean.float, ocean.gpu[0]) b.copy(a) print(b) b = ocean.double(b) print(b) b = ocean.cdouble(b) print(b) b.copy(54321) print(b) b.copy(ocean.gpu[0](1.2345)) print(b)

11

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0.645022

46

231

3.23913

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0.201342

0.187919

0.161074

0

0.09

0.134199

231

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0.384615

0

null

1

0

null

0

3

7ac312598521cfaa89198f4c3b86a054006966b2

6,929

py

Python

pcap.py

ephracis/pylibpcap

bcab6cd2b27eeae0be1b3899cc1edb7b1f487136

[ "BSD-3-Clause" ]

3

2018-12-08T11:41:40.000Z

2021-09-21T04:59:38.000Z

pcap.py

ephracis/pylibpcap

bcab6cd2b27eeae0be1b3899cc1edb7b1f487136

[ "BSD-3-Clause" ]

2

2016-04-28T18:21:10.000Z

2021-04-05T09:15:44.000Z

pcap.py

ephracis/pylibpcap

bcab6cd2b27eeae0be1b3899cc1edb7b1f487136

[ "BSD-3-Clause" ]

1

2019-03-05T02:08:36.000Z

# This file was automatically generated by SWIG (http://www.swig.org). # Version 2.0.10 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. from sys import version_info if version_info >= (2,6,0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_pcap', [dirname(__file__)]) except ImportError: import _pcap return _pcap if fp is not None: try: _mod = imp.load_module('_pcap', fp, pathname, description) finally: fp.close() return _mod _pcap = swig_import_helper() del swig_import_helper else: import _pcap del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self,class_type,name,value,static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name,None) if method: return method(self,value) if (not static): self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self,class_type,name,value): return _swig_setattr_nondynamic(self,class_type,name,value,0) def _swig_getattr(self,class_type,name): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name,None) if method: return method(self) raise AttributeError(name) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object : pass _newclass = 0 __doc__ = _pcap.__doc__ for dltname, dltvalue in _pcap.DLT.items(): globals()[dltname] = dltvalue del dltname, dltvalue class pcapObject(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, pcapObject, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, pcapObject, name) __repr__ = _swig_repr def __init__(self): import sys if int(sys.version[0])>=2: self.datalink.im_func.__doc__ = _pcap.pcapObject_datalink.__doc__ self.activate.im_func.__doc__ = _pcap.pcapObject_activate.__doc__ self.dispatch.im_func.__doc__ = _pcap.pcapObject_dispatch.__doc__ self.setnonblock.im_func.__doc__ = _pcap.pcapObject_setnonblock.__doc__ self.set_promisc.im_func.__doc__ = _pcap.pcapObject_set_promisc.__doc__ self.minor_version.im_func.__doc__ = _pcap.pcapObject_minor_version.__doc__ self.stats.im_func.__doc__ = _pcap.pcapObject_stats.__doc__ self.create.im_func.__doc__ = _pcap.pcapObject_create.__doc__ self.open_live.im_func.__doc__ = _pcap.pcapObject_open_live.__doc__ self.next.im_func.__doc__ = _pcap.pcapObject_next.__doc__ self.dump_open.im_func.__doc__ = _pcap.pcapObject_dump_open.__doc__ self.snapshot.im_func.__doc__ = _pcap.pcapObject_snapshot.__doc__ self.is_swapped.im_func.__doc__ = _pcap.pcapObject_is_swapped.__doc__ self.open_offline.im_func.__doc__ = _pcap.pcapObject_open_offline.__doc__ self.set_snaplen.im_func.__doc__ = _pcap.pcapObject_set_snaplen.__doc__ self.fileno.im_func.__doc__ = _pcap.pcapObject_fileno.__doc__ self.datalinks.im_func.__doc__ = _pcap.pcapObject_datalinks.__doc__ self.set_rfmon.im_func.__doc__ = _pcap.pcapObject_set_rfmon.__doc__ self.major_version.im_func.__doc__ = _pcap.pcapObject_major_version.__doc__ self.getnonblock.im_func.__doc__ = _pcap.pcapObject_getnonblock.__doc__ self.open_dead.im_func.__doc__ = _pcap.pcapObject_open_dead.__doc__ self.set_timeout.im_func.__doc__ = _pcap.pcapObject_set_timeout.__doc__ self.loop.im_func.__doc__ = _pcap.pcapObject_loop.__doc__ self.setfilter.im_func.__doc__ = _pcap.pcapObject_setfilter.__doc__ this = _pcap.new_pcapObject() try: self.this.append(this) except: self.this = this __swig_destroy__ = _pcap.delete_pcapObject __del__ = lambda self : None; def create(self, *args): return _pcap.pcapObject_create(self, *args) def set_snaplen(self, *args): return _pcap.pcapObject_set_snaplen(self, *args) def set_promisc(self, *args): return _pcap.pcapObject_set_promisc(self, *args) def set_rfmon(self, *args): return _pcap.pcapObject_set_rfmon(self, *args) def set_timeout(self, *args): return _pcap.pcapObject_set_timeout(self, *args) def activate(self): return _pcap.pcapObject_activate(self) def open_live(self, *args): return _pcap.pcapObject_open_live(self, *args) def open_dead(self, *args): return _pcap.pcapObject_open_dead(self, *args) def open_offline(self, *args): return _pcap.pcapObject_open_offline(self, *args) def dump_open(self, *args): return _pcap.pcapObject_dump_open(self, *args) def setnonblock(self, *args): return _pcap.pcapObject_setnonblock(self, *args) def getnonblock(self): return _pcap.pcapObject_getnonblock(self) def setfilter(self, *args): return _pcap.pcapObject_setfilter(self, *args) def loop(self, *args): return _pcap.pcapObject_loop(self, *args) def dispatch(self, *args): return _pcap.pcapObject_dispatch(self, *args) def next(self): return _pcap.pcapObject_next(self) def datalink(self): return _pcap.pcapObject_datalink(self) def datalinks(self): return _pcap.pcapObject_datalinks(self) def snapshot(self): return _pcap.pcapObject_snapshot(self) def is_swapped(self): return _pcap.pcapObject_is_swapped(self) def major_version(self): return _pcap.pcapObject_major_version(self) def minor_version(self): return _pcap.pcapObject_minor_version(self) def stats(self): return _pcap.pcapObject_stats(self) def fileno(self): return _pcap.pcapObject_fileno(self) pcapObject_swigregister = _pcap.pcapObject_swigregister pcapObject_swigregister(pcapObject) def lookupdev(): return _pcap.lookupdev() lookupdev = _pcap.lookupdev def findalldevs(unpack=1): return _pcap.findalldevs(unpack) findalldevs = _pcap.findalldevs def lookupnet(*args): return _pcap.lookupnet(*args) lookupnet = _pcap.lookupnet def aton(*args): return _pcap.aton(*args) aton = _pcap.aton def ntoa(*args): return _pcap.ntoa(*args) ntoa = _pcap.ntoa # This file is compatible with both classic and new-style classes.

42.25

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0

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6,929

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false

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0.072464

0.217391

0.463768

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null

0

1

0

null

0

1

0

1

0

3

7ac35c04d628c2286a5b5b5c085bff755c3f4ad5

107

py

Python

artefact_nca/utils/utils.py

Ackey-code/3d-artefacts-nca

b13228d5dd30519ad885d2400061be2adf6cfc3c

[ "MIT" ]

37

2021-05-26T03:41:07.000Z

2022-02-03T21:24:26.000Z

artefact_nca/utils/utils.py

Ackey-code/3d-artefacts-nca

b13228d5dd30519ad885d2400061be2adf6cfc3c

[ "MIT" ]

1

2021-12-01T21:43:33.000Z

artefact_nca/utils/utils.py

Ackey-code/3d-artefacts-nca

b13228d5dd30519ad885d2400061be2adf6cfc3c

[ "MIT" ]

4

2021-06-07T17:29:13.000Z

2021-12-18T16:30:50.000Z

import os def makedirs(path): if not os.path.exists(path): os.makedirs(path) return path

13.375

32

0.635514

16

107

4.25

0.5625

0.352941

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0.261682

107

7

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15.285714

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0

null

1

0

1

0

null

0

3

7ad630a85cfa9d6e9ad12e494c49b6ff546ab0f7

113

py

Python

dymouse/driver/old/scale.py

x4rMa/dymouse

0304837f4af362ec33ec6da09c7eb6a9840dcca6

[ "MIT" ]

2

2020-11-02T17:52:01.000Z

2021-02-25T14:34:24.000Z

dymouse/driver/old/scale.py

x4rMa/dymouse

0304837f4af362ec33ec6da09c7eb6a9840dcca6

[ "MIT" ]

null

dymouse/driver/old/scale.py

x4rMa/dymouse

0304837f4af362ec33ec6da09c7eb6a9840dcca6

[ "MIT" ]

null

from DataRecorder import DataRecorder z = DataRecorder(plot=False) z.make_record() import pdb; pdb.set_trace()

16.142857

37

0.787611

16

113

5.4375

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0

0.115044

113

6

38

18.833333

0.87

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

7aeb3440f9c37924a5a0f17a54bef250befdcf4a

380

py

Python

src/manage.py

graipher/TeaRoom

0beb8c2c889b8685d5b7a463206de41947c2d669

[ "MIT" ]

null

src/manage.py

graipher/TeaRoom

0beb8c2c889b8685d5b7a463206de41947c2d669

[ "MIT" ]

15

2015-05-20T12:55:13.000Z

2022-03-11T23:26:40.000Z

src/manage.py

graipher/TeaRoom

0beb8c2c889b8685d5b7a463206de41947c2d669

[ "MIT" ]

2

2016-11-17T11:07:41.000Z

2017-07-07T11:18:36.000Z

#!/bin/sh """": exec /usr/bin/env python -W ignore::DeprecationWarning $0 $@ """ import os import sys import warnings warnings.simplefilter("ignore", DeprecationWarning) if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "TeaRoom.settings") from django.core.management import execute_from_command_line execute_from_command_line(sys.argv)

20

71

0.752632

47

380

5.744681

0.659574

0.177778

0.133333

0.162963

0

0.003003

0.123684

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18

72

21.111111

0.807808

0.189474

0

0.173333

0.073333

0

1

0

true

0

0.5

0

0.5

0

null

0

1

0

null

0

1

0

1

0

3

7aed08ca73b10656f07d2319c9ccc9e4e6cf6e2f

366

py

Python

dashboard/utils.py

riparias/early-warning-webapp

702691a6ecbabc7865e5f232e125c8dee28a7f2e

[ "MIT" ]

null

dashboard/utils.py

riparias/early-warning-webapp

702691a6ecbabc7865e5f232e125c8dee28a7f2e

[ "MIT" ]

124

2021-09-02T06:53:33.000Z

2022-03-31T12:46:51.000Z

dashboard/utils.py

riparias/early-warning-webapp

702691a6ecbabc7865e5f232e125c8dee28a7f2e

[ "MIT" ]

null

import subprocess def readable_string(input_string: str) -> str: """Remove multiple whitespaces and \n to make a long string more readable""" return " ".join(input_string.replace("\n", "").split()) def human_readable_git_version_number() -> str: return subprocess.check_output( ["git", "describe", "--always"], encoding="UTF-8" ).strip()

28.153846

80

0.672131

46

366

5.173913

0.717391

0.092437

0

0.003289

0.169399

366

12

81

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0.779605

0.185792

0

0.093103

0

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0.285714

false

0

0.142857

0.714286

0

null

0

null

0

1

0

1

0

3

7af4117944ceb99cc978659fcc0837bce72c1297

219

py

Python

backoffice/web/core/models.py

uktrade/trade-access-program

8fb565e96de7d7bb0bde31255aef0f291063e93c

[ "MIT" ]

1

2021-03-04T15:24:12.000Z

backoffice/web/core/models.py

uktrade/trade-access-program

8fb565e96de7d7bb0bde31255aef0f291063e93c

[ "MIT" ]

7

2020-08-24T13:27:02.000Z

2021-06-09T18:42:31.000Z

backoffice/web/core/models.py

uktrade/trade-access-program

8fb565e96de7d7bb0bde31255aef0f291063e93c

[ "MIT" ]

1

2021-05-20T07:40:00.000Z

from django.db import models class Image(models.Model): file = models.ImageField(upload_to='images/') uploaded_at = models.DateTimeField(auto_now_add=True) def __str__(self): return self.file.url

21.9

57

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30

219

4.966667

0.833333

0

0.178082

219

9

58

24.333333

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0

0.166667

1

0

null

0

1

0

null

0

1

0

3

7afb6ecd4cc96c4178d2c21f873246fe6ff0cebe

157

py

Python

companies/tests/test_home.py

vitorpvcampos/comp-emp

de9ceeda510e1c484316b52be409347fad59515d

[ "MIT" ]

null

companies/tests/test_home.py

vitorpvcampos/comp-emp

de9ceeda510e1c484316b52be409347fad59515d

[ "MIT" ]

134

2020-11-23T12:16:08.000Z

2022-03-20T13:42:11.000Z

companies/tests/test_home.py

vitorpvcampos/comp-emp

de9ceeda510e1c484316b52be409347fad59515d

[ "MIT" ]

null

from django.test import Client def test_admin_home(client: Client): resp = client.get('/admin/login/?next=/admin/') assert resp.status_code == 200

22.428571

51

0.713376

23

157

4.73913

0.695652

0

0.022556

0.152866

157

6

52

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0

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0

0.25

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1

0

null

0

1

0

null

0

1

0

3

249821db5e9c11db98858f44ee541b1f3d480d14

315

py

Python

07-User-authentication/00-Pasword-hashing/bcrypt_basics.py

alehpineda/flask_bootcamp

7310bf093be61f33567c6d8ffd710a29e35004cd

[ "MIT" ]

null

07-User-authentication/00-Pasword-hashing/bcrypt_basics.py

alehpineda/flask_bootcamp

7310bf093be61f33567c6d8ffd710a29e35004cd

[ "MIT" ]

3

2021-02-08T20:38:44.000Z

2021-06-02T00:46:15.000Z

07-User-authentication/00-Pasword-hashing/bcrypt_basics.py

alehpineda/flask_bootcamp

7310bf093be61f33567c6d8ffd710a29e35004cd

[ "MIT" ]

null

from flask_bcrypt import Bcrypt bcrypt = Bcrypt() password = 'supersecretpassword' hashed = bcrypt.generate_password_hash(password=password) print(hashed) check = bcrypt.check_password_hash(hashed, 'wrongpassword') print(check) check = bcrypt.check_password_hash(hashed, 'supersecretpassword') print(check)

17.5

65

0.8

36

315

6.805556

0.333333

0.146939

0.130612

0.195918

0.277551

0

0.101587

315

17

66

18.529412

0.865724

0

0.222222

1

0

0.161905

0

1

0

false

0.444444

0.111111

0

0.111111

0.333333

0

null

0

1

0

1

0

null

0

1

0

3

24a060f1df6df17b3e24a4be05becff92382bbc8

256

py

Python

demo_built-in/demo_queue.py

Ethan16/python_misc

29cf2fdbd7529a05bcf35768e0244e634fe2ae7a

[ "Apache-2.0" ]

1

2019-05-04T09:26:29.000Z

demo_built-in/demo_queue.py

Ethan16/python_misc

29cf2fdbd7529a05bcf35768e0244e634fe2ae7a

[ "Apache-2.0" ]

null

demo_built-in/demo_queue.py

Ethan16/python_misc

29cf2fdbd7529a05bcf35768e0244e634fe2ae7a

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ @version: 1.0 @author: James @license: Apache Licence @contact: euler52201044@sina.com @file: demo_queue.py @time: 2019/4/7 下午12:26 @description: """ from random import randint from time import sleep from queue import Queue

16

32

0.710938

38

256

4.763158

0.815789

0

0.09633

0.148438

256

15

33

17.066667

0.733945

0.664063

0

1

0

true

0

1

0

1

0

null

0

null

0

1

0

1

0

1

0

3

24a524313a1a72a486aecbd1a883179629ead6a3

215

py

Python

src/sum.py

yxtay/data-structures-algorithms

a28f0b7a727192c121579ed51d44d00e09cc1b9a

[ "MIT" ]

1

2020-06-23T16:08:51.000Z

src/sum.py

yxtay/data-structures-algorithms

a28f0b7a727192c121579ed51d44d00e09cc1b9a

[ "MIT" ]

null

src/sum.py

yxtay/data-structures-algorithms

a28f0b7a727192c121579ed51d44d00e09cc1b9a

[ "MIT" ]

null

def sum_iter(numbers): total = 0 for n in numbers: total = total + n return total def sum_rec(numbers): if len(numbers) == 0: return 0 return numbers[0] + sum_rec(numbers[1:])

16.538462

44

0.581395

32

215

3.8125

0.4375

0.098361

0.213115

0

0.033784

0.311628

215

12

45

17.916667

0.790541

0

1

0.222222

false

0

0.555556

0

null

0

1

0

null

0

1

0

1

0

3

24acf2855092bbd0ac5af08ee88d6c322bebbc76

584

py

Python

library/flotilla/touch.py

sonntagsgesicht/flotilla-python

3b2535d9ff41c99b3104e2b67f3cd46639e3be0b

[ "MIT" ]

22

2016-01-26T14:08:25.000Z

2022-01-17T01:43:26.000Z

library/flotilla/touch.py

sonntagsgesicht/flotilla-python

3b2535d9ff41c99b3104e2b67f3cd46639e3be0b

[ "MIT" ]

19

2016-01-09T19:53:30.000Z

2022-02-10T17:19:46.000Z

library/flotilla/touch.py

sonntagsgesicht/flotilla-python

3b2535d9ff41c99b3104e2b67f3cd46639e3be0b

[ "MIT" ]

18

2015-12-16T18:13:36.000Z

2021-11-14T15:26:44.000Z

from .module import Module class Touch(Module): name = 'touch' @property def one(self): if len(self.data) > 0: return int(self.data[0]) == 1 return False @property def two(self): if len(self.data) > 1: return int(self.data[1]) == 1 return False @property def three(self): if len(self.data) > 2: return int(self.data[2]) == 1 return False @property def four(self): if len(self.data) > 3: return int(self.data[3]) == 1 return False

19.466667

41

0.510274

77

584

3.87013

0.298701

0.214765

0.120805

0.174497

0.459732

0

0.03252

0.368151

584

29

42

20.137931

0.775068

0

0.347826

0

0.008562

0

1

0.173913

false

0

0.043478

0

0.652174

0

null

1

0

1

0

null

0

1

0

3

24b0da5ec1a158caa10fe4e900a7386b6e50688d

485

py

Python

07/7.py

cjm00/project-euler

10f186aafda2ed13bf93cf3e3ba6cff63c85fbd0

[ "CC-BY-3.0" ]

1

2015-08-16T20:30:40.000Z

07/7.py

cjm00/project-euler

10f186aafda2ed13bf93cf3e3ba6cff63c85fbd0

[ "CC-BY-3.0" ]

1

2016-08-11T13:06:12.000Z

07/7.py

cjm00/project-euler

10f186aafda2ed13bf93cf3e3ba6cff63c85fbd0

[ "CC-BY-3.0" ]

null

#wow such code reuse #this is a very slow sieve known_prime_list = [] desired_prime = 10001 index = 2 def IsPrime(input): for prime in known_prime_list: if input % prime == 0: return False return True while len(known_prime_list) < desired_prime: if not known_prime_list: #Empty lists are False known_prime_list.append(index) index += 1 continue if IsPrime(index): known_prime_list.append(index) index += 1 known_prime_list.sort() print known_prime_list[-1]

16.724138

48

0.736082

78

485

4.346154

0.487179

0.235988

0.330383

0.123894

0.336283

0.182891

0

0.025189

0.181443

485

28

49

17.321429

0.828715

0.134021

0

0.222222

0

null

0

null

0.055556

0

null

1

0

null

0

1

0

3

24d02a0cf495cba1b4884a5173b78251ca064ae5

590

py

Python

sample_api_app/main.py

ShankarChavan/FastAPI_ML_demo

8f635231c10dbe2310aa6ae89316f931140f630a

[ "MIT" ]

null

sample_api_app/main.py

ShankarChavan/FastAPI_ML_demo

8f635231c10dbe2310aa6ae89316f931140f630a

[ "MIT" ]

null

sample_api_app/main.py

ShankarChavan/FastAPI_ML_demo

8f635231c10dbe2310aa6ae89316f931140f630a

[ "MIT" ]

null

from fastapi import FastAPI from pydantic import BaseModel app=FastAPI() db=[] class city(BaseModel): name: str time_zone: str @app.get('/') def index(): return {'healthcheck':'True'} @app.get('/cities') def get_cities(): return db @app.get('/cities/{city_id}') def get_city(city_id:int): return db[city_id-1] @app.post('/cities') def create_city(city:city): db.append(city.dict()) return db[-1] @app.delete('/cities/{city_id}') def delete_city(cityid:int): db.pop(city_id-1) return {} #uvicorn main:app --reload

17.352941

34

0.623729

85

590

4.211765

0.388235

0.083799

0.067039

0.083799

0

0.006424

0.208475

590

34

35

17.352941

0.760171

0.042373

0

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0

1

0.208333

false

0

0.083333

0.125

0.625

0

null

0

null

0

1

0

1

0

3

24dc1f4e948216aa3249ac5e8455041bece65ace

1,153

py

Python

dataset/tfrecords/base/writer.py

AltumTek/deep-koalarization

b5a16751a40484ca4990e0b9c005fe31c3301812

[ "MIT" ]

23

2018-08-16T12:50:01.000Z

2021-12-27T13:13:10.000Z

dataset/tfrecords/base/writer.py

Kriztoper/deep-koalarization

7d45895272ac457cc2b4df836ff23598d889d49c

[ "MIT" ]

22

2018-08-27T04:49:57.000Z

2022-03-11T23:43:18.000Z

dataset/tfrecords/base/writer.py

Kriztoper/deep-koalarization

7d45895272ac457cc2b4df836ff23598d889d49c

[ "MIT" ]

7

2018-08-27T15:32:59.000Z

2020-04-20T14:48:40.000Z

from os.path import join import tensorflow as tf compression = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE) class RecordWriter(tf.python_io.TFRecordWriter): """ A commodity subclass of TFRecordWriter that adds the methods to easily serialize different data types. """ def __init__(self, tfrecord_name, dest_folder=''): self.path = join(dest_folder, tfrecord_name) super().__init__(self.path, options=compression) @staticmethod def _bytes_feature(value): return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) @staticmethod def _int64(single_int): return tf.train.Feature(int64_list=tf.train.Int64List(value=[single_int])) @staticmethod def _int64_list(list_of_int): return tf.train.Feature(int64_list=tf.train.Int64List(value=list_of_int)) @staticmethod def _float32(single_float): return tf.train.Feature(float_list=tf.train.FloatList(value=[single_float])) @staticmethod def _float32_list(list_of_floats): return tf.train.Feature(float_list=tf.train.FloatList(value=list_of_floats))

31.162162

85

0.732003

150

1,153

5.36

0.373333

0.087065

0.080846

0.124378

0.256219

0

0.016632

0.165655

1,153

36

86

32.027778

0.819127

0.088465

0

0.227273

0

1

0.272727

false

0

0.090909

0.227273

0.636364

0

null

0

null

0

1

0

1

0

3

24dfd375a51b9e03b227d47163839bc164e5d9f8

104

py

Python

wsgi.py

MagedMYoussef/corona-bel3raby

4558f6ddc936f01e21f51800a73779ad855707b9

[ "MIT" ]

null

wsgi.py

MagedMYoussef/corona-bel3raby

4558f6ddc936f01e21f51800a73779ad855707b9

[ "MIT" ]

null

wsgi.py

MagedMYoussef/corona-bel3raby

4558f6ddc936f01e21f51800a73779ad855707b9

[ "MIT" ]

1

2020-04-10T20:36:24.000Z

from src.main.api import create_app app = create_app("prod") if __name__ == "__main__": app.run()

14.857143

35

0.682692

16

104

3.8125

0.6875

0.295082

0

0.173077

104

6

36

17.333333

0.709302

0

0.115385

0

1

0

false

0

0.25

0

0.25

0

1

0

null

1

0

1

0

null

0

3

24e0d81fe6021edda199097b214798a302e13107

744

py

Python

qcloudsdkvpc/SubnetUnBindBmNatGatewayRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkvpc/SubnetUnBindBmNatGatewayRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkvpc/SubnetUnBindBmNatGatewayRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from qcloudsdkcore.request import Request class SubnetUnBindBmNatGatewayRequest(Request): def __init__(self): super(SubnetUnBindBmNatGatewayRequest, self).__init__( 'vpc', 'qcloudcliV1', 'SubnetUnBindBmNatGateway', 'vpc.api.qcloud.com') def get_natId(self): return self.get_params().get('natId') def set_natId(self, natId): self.add_param('natId', natId) def get_subnetIds(self): return self.get_params().get('subnetIds') def set_subnetIds(self, subnetIds): self.add_param('subnetIds', subnetIds) def get_vpcId(self): return self.get_params().get('vpcId') def set_vpcId(self, vpcId): self.add_param('vpcId', vpcId)

26.571429

83

0.663978

85

744

5.576471

0.329412

0.037975

0.088608

0.107595

0.164557

0

0.003373

0.202957

744

27

84

27.555556

0.795953

0.028226

0

0.130374

0.033287

0

1

0.411765

false

0

0.058824

0.176471

0.705882

0

null

0

null

0

1

0

1

0

3

24e80ea6fdf7d90f65f728e2d718804aa6303f31

2,490

py

Python

py/legacypipe/runs.py

legacysurvey/pipeline

76dc2a9fc94e7b94fcd41af77e7c0423b62d693a

[ "BSD-3-Clause" ]

null

py/legacypipe/runs.py

legacysurvey/pipeline

76dc2a9fc94e7b94fcd41af77e7c0423b62d693a

[ "BSD-3-Clause" ]

36

2015-06-26T20:39:44.000Z

2015-07-03T03:36:54.000Z

py/legacypipe/runs.py

legacysurvey/pipeline

76dc2a9fc94e7b94fcd41af77e7c0423b62d693a

[ "BSD-3-Clause" ]

null

from legacypipe.survey import LegacySurveyData class DecamSurvey(LegacySurveyData): def filter_ccd_kd_files(self, fns): return [fn for fn in fns if 'decam' in fn] def filter_ccds_files(self, fns): return [fn for fn in fns if 'decam' in fn] def filter_annotated_ccds_files(self, fns): return [fn for fn in fns if 'decam' in fn] def get_default_release(self): return 9008 class NinetyPrimeMosaic(LegacySurveyData): def filter_ccd_kd_files(self, fns): return [fn for fn in fns if ('90prime' in fn) or ('mosaic' in fn)] def filter_ccds_files(self, fns): return [fn for fn in fns if ('90prime' in fn) or ('mosaic' in fn)] def filter_annotated_ccds_files(self, fns): return [fn for fn in fns if ('90prime' in fn) or ('mosaic' in fn)] def get_default_release(self): return 9009 class M33SurveyData(DecamSurvey): def ccds_for_fitting(self, brick, ccds): import numpy as np from astrometry.libkd.spherematch import match_radec I, _, _ = match_radec(ccds.ra, ccds.dec, np.array(23.462121), np.array(30.659925), 0.55, nearest=True) #I = np.delete(I, np.where((ccds.filter[I] == 'g') * (ccds.expnum[I] != 661055))[0]) #I = np.delete(I, np.where((ccds.filter[I] == 'z') * (ccds.expnum[I] != 790242))[0]) return I class OdinData(LegacySurveyData): #def filter_ccd_kd_files(self, fns): # return [fn for fn in fns if ('90prime' in fn) or ('mosaic' in fn)] def filter_ccds_files(self, fns): return [fn for fn in fns if ('odin' in fn)] def filter_annotated_ccds_files(self, fns): return [fn for fn in fns if ('odin' in fn)] def get_default_release(self): return 200 class HscData(LegacySurveyData): #def filter_ccd_kd_files(self, fns): # return [fn for fn in fns if ('90prime' in fn) or ('mosaic' in fn)] def filter_ccds_files(self, fns): return [fn for fn in fns if ('hsc' in fn)] def filter_annotated_ccds_files(self, fns): return [fn for fn in fns if ('hsc' in fn)] def get_default_release(self): return 200 runs = { 'decam': DecamSurvey, '90prime-mosaic': NinetyPrimeMosaic, 'south': DecamSurvey, 'north': NinetyPrimeMosaic, 'm33': M33SurveyData, 'odin': OdinData, 'hsc': HscData, None: LegacySurveyData, } def get_survey(name, **kwargs): survey_class = runs[name] survey = survey_class(**kwargs) return survey

37.164179

110

0.648594

370

2,490

4.232432

0.197297

0.043423

0.091954

0.137931

0.637292

0.60281

0.528736

0

0.03396

0.231325

2,490

66

111

37.727273

0.784222

0.151004

0

0.490566

0

0.050759

0

1

0.301887

false

0

0.056604

0.264151

0.754717

0

null

0

null

0

1

0

1

0

3

24fe3ca21a2fe9e00f892dde54ef4ec5b5835aed

572

py

Python

app/src/main/python/hello.py

hbszlf/Python

009d66e50eb227b6d428f29fc09a32a53abcfd98

[ "MIT" ]

null

app/src/main/python/hello.py

hbszlf/Python

009d66e50eb227b6d428f29fc09a32a53abcfd98

[ "MIT" ]

null

app/src/main/python/hello.py

hbszlf/Python

009d66e50eb227b6d428f29fc09a32a53abcfd98

[ "MIT" ]

null

from java import jclass def greet(name): print("--- hello,%s ---" % name) def add(a, b): return a + b def sub(count, a=0, b=0, c=0): return count - a - b - c def get_list(a, b, c, d): return [a, b, c, d] def print_list(data): print(type(data)) # 遍历Java的ArrayList对象 for i in range(data.size()): print(data.get(i)) # python调用Java类 def get_java_bean(): JavaBean = jclass("com.mn.python.bean.JavaBean") # 用自己的包名 jb = JavaBean("python") jb.setData("json") jb.setData("xml") jb.setData("xhtml") return jb

16.342857

62

0.583916

89

572

3.707865

0.460674

0.030303

0.027273

0.024242

0

0.006961

0.246504

572

34

63

16.823529

0.758701

0.068182

0

0.115312

0.05104

0

1

0.3

false

0

0.05

0.15

0.55

0.2

0

null

0

null

0

1

0

1

0

3

701d54dd2464556f581913269546783a689d21de

2,795

py

Python

networks/utils.py

shirgur/UMIS

d3be2fe6adc05843aa3d5dde2733ca43b3e5b149

[ "Apache-2.0" ]

67

2019-08-19T06:14:41.000Z

2022-01-18T02:04:18.000Z

networks/utils.py

shirgur/UMIS

d3be2fe6adc05843aa3d5dde2733ca43b3e5b149

[ "Apache-2.0" ]

8

2019-10-31T13:11:26.000Z

2022-02-21T14:53:43.000Z

networks/utils.py

shirgur/UMIS

d3be2fe6adc05843aa3d5dde2733ca43b3e5b149

[ "Apache-2.0" ]

13

2019-10-06T14:05:24.000Z

2020-04-30T08:46:15.000Z

import torch import torch.nn as nn import torch.nn.functional as F class GradXYZ(nn.Module): def __init__(self): super(GradXYZ, self).__init__() self.padding = 1 self.register_buffer('dX', torch.Tensor([[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [-1 / 2, 0, 1 / 2], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]] ] ).unsqueeze(0).unsqueeze(0)) self.register_buffer('dY', torch.Tensor([[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, -1 / 2, 0], [0, 0, 0], [0, 1 / 2, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]] ] ).unsqueeze(0).unsqueeze(0)) self.register_buffer('dZ', torch.Tensor([[[0, 0, 0], [0, -1 / 2, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 1 / 2, 0], [0, 0, 0]] ] ).unsqueeze(0).unsqueeze(0)) def forward(self, x): dx = F.conv3d(x, self.dX, padding=self.padding).abs() dy = F.conv3d(x, self.dY, padding=self.padding).abs() dz = F.conv3d(x, self.dZ, padding=self.padding).abs() return dx + dy + dz def norm_ip(img, min, max): out = torch.clamp(img, min=min, max=max) out = (out - min) / (max - min + 1e-5) return out def norm_range(t, range=None): if range is not None: return norm_ip(t, range[0], range[1]) else: return norm_ip(t, float(t.min()), float(t.max()))

45.080645

76

0.243649

235

2,795

2.834043

0.195745

0.198198

0.261261

0.3003

0.324324

0.307808

0.277778

0

0.100698

0.641145

2,795

62

77

45.080645

0.56331

0

0.407407

0

0.002146

0

1

0.074074

false

0

0.055556

0

0.222222

0

null

0

1

0

1

0

null

0

3

702e0552af3f2797b0caa011efffe0bc1a961fb3

1,186

py

Python

test/lmp/model/_lstm_2002/test_forward.py

ProFatXuanAll/char-RNN

531f101b3d1ba20bafd28ca060aafe6f583d1efb

[ "Beerware" ]

null

test/lmp/model/_lstm_2002/test_forward.py

ProFatXuanAll/char-RNN

531f101b3d1ba20bafd28ca060aafe6f583d1efb

[ "Beerware" ]

null

test/lmp/model/_lstm_2002/test_forward.py

ProFatXuanAll/char-RNN

531f101b3d1ba20bafd28ca060aafe6f583d1efb

[ "Beerware" ]

null

"""Test forward pass and tensor graph. Test target: - :py:meth:`lmp.model._lstm_2002.LSTM2002.forward`. """ import torch from lmp.model._lstm_2002 import LSTM2002 def test_forward_path( lstm_2002: LSTM2002, batch_cur_tkids: torch.Tensor, batch_next_tkids: torch.Tensor, ) -> None: """Parameters used during forward pass must have gradients.""" # Make sure model has zero gradients at the begining. lstm_2002 = lstm_2002.train() lstm_2002.zero_grad() loss = lstm_2002(batch_cur_tkids=batch_cur_tkids, batch_next_tkids=batch_next_tkids) loss.backward() assert loss.size() == torch.Size([]) assert loss.dtype == torch.float assert hasattr(lstm_2002.emb.weight, 'grad') assert hasattr(lstm_2002.h_0, 'grad') assert hasattr(lstm_2002.c_0, 'grad') assert hasattr(lstm_2002.proj_e2cg[1].weight, 'grad') assert hasattr(lstm_2002.proj_e2cg[1].bias, 'grad') assert hasattr(lstm_2002.proj_h2cg.weight, 'grad') assert hasattr(lstm_2002.proj_c2ig, 'grad') assert hasattr(lstm_2002.proj_c2fg, 'grad') assert hasattr(lstm_2002.proj_c2og, 'grad') assert hasattr(lstm_2002.proj_h2e[1].weight, 'grad') assert hasattr(lstm_2002.proj_h2e[1].bias, 'grad')

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null

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1

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3

7046caf4da8dad46dadb1c6a0c82033c5a73e184

882

py

Python

accelbyte_py_sdk/api/eventlog/operations/event_registry/__init__.py

AccelByte/accelbyte-python-sdk

dcd311fad111c59da828278975340fb92e0f26f7

[ "MIT" ]

null

accelbyte_py_sdk/api/eventlog/operations/event_registry/__init__.py

AccelByte/accelbyte-python-sdk

dcd311fad111c59da828278975340fb92e0f26f7

[ "MIT" ]

1

2021-10-13T03:46:58.000Z

accelbyte_py_sdk/api/eventlog/operations/event_registry/__init__.py

AccelByte/accelbyte-python-sdk

dcd311fad111c59da828278975340fb92e0f26f7

[ "MIT" ]

null

# Copyright (c) 2021 AccelByte Inc. All Rights Reserved. # This is licensed software from AccelByte Inc, for limitations # and restrictions contact your company contract manager. # # Code generated. DO NOT EDIT! # template file: justice_py_sdk_codegen/__main__.py """Auto-generated package that contains models used by the justice-event-log-service.""" __version__ = "" __author__ = "AccelByte" __email__ = "dev@accelbyte.net" # pylint: disable=line-too-long from .get_registered_event_id_f55558 import GetRegisteredEventIDHandler from .get_registered_events_b_671cec import GetRegisteredEventsByEventTypeHandler from .get_registered_events_handler import GetRegisteredEventsHandler from .register_event_handler import RegisterEventHandler from .unregister_event_id_handler import UnregisterEventIDHandler from .update_event_registry_handler import UpdateEventRegistryHandler

38.347826

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null

0

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null

0

1

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1

0

3

704a4567ef4aa02871e79c6f3827a11331f0876b

1,313

py

Python

src/elastic/azext_elastic/generated/_client_factory.py

haroonf/azure-cli-extensions

61c044d34c224372f186934fa7c9313f1cd3a525

[ "MIT" ]

207

2017-11-29T06:59:41.000Z

2022-03-31T10:00:53.000Z

src/elastic/azext_elastic/generated/_client_factory.py

haroonf/azure-cli-extensions

61c044d34c224372f186934fa7c9313f1cd3a525

[ "MIT" ]

4,061

2017-10-27T23:19:56.000Z

2022-03-31T23:18:30.000Z

src/elastic/azext_elastic/generated/_client_factory.py

haroonf/azure-cli-extensions

61c044d34c224372f186934fa7c9313f1cd3a525

[ "MIT" ]

802

2017-10-11T17:36:26.000Z

2022-03-31T22:24:32.000Z

# -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- def cf_elastic_cl(cli_ctx, *_): from azure.cli.core.commands.client_factory import get_mgmt_service_client from azext_elastic.vendored_sdks.elastic import MicrosoftElastic return get_mgmt_service_client(cli_ctx, MicrosoftElastic) def cf_monitor(cli_ctx, *_): return cf_elastic_cl(cli_ctx).monitors def cf_monitored_resource(cli_ctx, *_): return cf_elastic_cl(cli_ctx).monitored_resources def cf_deployment_info(cli_ctx, *_): return cf_elastic_cl(cli_ctx).deployment_info def cf_tag_rule(cli_ctx, *_): return cf_elastic_cl(cli_ctx).tag_rules def cf_vm_host(cli_ctx, *_): return cf_elastic_cl(cli_ctx).vm_host def cf_vm_ingestion(cli_ctx, *_): return cf_elastic_cl(cli_ctx).vm_ingestion def cf_vm_collection(cli_ctx, *_): return cf_elastic_cl(cli_ctx).vm_collection

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null

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null

0

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1

0

3

706a2528f0b32dcb1eea25e23dc9f8521d82c8f6

643

py

Python

NakovBook/ConditionalStatements/Trip.py

LuGeorgiev/PythonSelfLearning

db8fcff2c2df8946d6acf2a2e5677eccf2bbe5dc

[ "MIT" ]

null

NakovBook/ConditionalStatements/Trip.py

LuGeorgiev/PythonSelfLearning

db8fcff2c2df8946d6acf2a2e5677eccf2bbe5dc

[ "MIT" ]

null

NakovBook/ConditionalStatements/Trip.py

LuGeorgiev/PythonSelfLearning

db8fcff2c2df8946d6acf2a2e5677eccf2bbe5dc

[ "MIT" ]

null

budget = float(input()) season = input() if budget <= 100: destination = 'Bulgaria' money_spent = budget * 0.7 info = f'Hotel - {money_spent:.2f}' if season == 'summer': money_spent = budget * 0.3 info = f'Camp - {money_spent:.2f}' elif budget <= 1000: destination = 'Balkans' money_spent = budget * 0.8 info = f'Hotel - {money_spent:.2f}' if season == 'summer': money_spent = budget * 0.4 info = f'Camp - {money_spent:.2f}' else: destination = 'Europe' money_spent = budget * 0.9 info = f'Hotel - {money_spent:.2f}' print('Somewhere in ' + destination) print(info)

26.791667

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643

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0

0.090909

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null

1

0

null

0

3

707f0d663a3caecf9f5e630b1f04356d33c4fc0e

31

py

Python

__init__.py

cclough715/Budget-Calculator

a71eeb71a04ed3003936f9de03f00cfb0289dff4

[ "BSD-2-Clause" ]

null

__init__.py

cclough715/Budget-Calculator

a71eeb71a04ed3003936f9de03f00cfb0289dff4

[ "BSD-2-Clause" ]

7

2019-12-12T04:18:11.000Z

2021-06-02T00:47:19.000Z

__init__.py

cclough715/Budget-Calculator

a71eeb71a04ed3003936f9de03f00cfb0289dff4

[ "BSD-2-Clause" ]

null

__all__ = {'budget_calculator'}

31

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null

0

1

0

null

0

3

709c9e7cb9b011564273f2373b8748030f76a174

94

py

Python

examples/blackpink_logo.py

jeremia50/TextProme

2e4d1ecde2287b7153bb259759e01a1771857e7a

[ "MIT" ]

2

2021-01-07T07:40:42.000Z

2021-01-07T15:04:36.000Z

examples/blackpink_logo.py

jeremia50/TextProme

2e4d1ecde2287b7153bb259759e01a1771857e7a

[ "MIT" ]

1

2021-01-08T01:13:11.000Z

2021-01-08T03:07:10.000Z

examples/blackpink_logo.py

jeremia50/TextProme

2e4d1ecde2287b7153bb259759e01a1771857e7a

[ "MIT" ]

1

2021-01-21T13:26:06.000Z

from textprome import TextProMe textpro = TextProMe() print(textpro.style_blackpink("Minato"))

31.333333

40

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94

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31.333333

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0

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0

null

0

1

0

null

0

1

0

3

709e9338763ff3f7257569f63f1bd38e20e150cb

182

py

Python

python全栈/day20/day20-8 静态方法.py

Ringo-li/python_exercise_100

2c6c42b84a88ffbbac30c67ffbd7bad3418eda14

[ "MIT" ]

null

python全栈/day20/day20-8 静态方法.py

Ringo-li/python_exercise_100

2c6c42b84a88ffbbac30c67ffbd7bad3418eda14

[ "MIT" ]

null

python全栈/day20/day20-8 静态方法.py

Ringo-li/python_exercise_100

2c6c42b84a88ffbbac30c67ffbd7bad3418eda14

[ "MIT" ]

null

# 1.定义类：定义静态方法 class Dog(object): @staticmethod def info_print(): print('这是一个静态方法') # 2.创建对象 wangcai = Dog() # 3.用类和实例分别调用静态方法 wangcai.info_print() Dog.info_print()

15.166667

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0.664835

24

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4.916667

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0

0.02027

0.186813

182

12

26

15.166667

0.777027

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0

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0.142857

false

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1

0

null

1

0

null

0

1

0

3

709fe118e3f4b9d0c9bc0aed0c4186cbf2f46192

26,357

py

Python

dataPre_Postprocess/calculate_nearestPts.py

dddtqshmpmz/IHC

c5d8f34a901bc0196666250b86f08a5a1f47ced5

[ "Apache-2.0" ]

1

2020-09-28T07:16:15.000Z

dataPre_Postprocess/calculate_nearestPts.py

dddtqshmpmz/IHC

c5d8f34a901bc0196666250b86f08a5a1f47ced5

[ "Apache-2.0" ]

1

2021-03-16T09:45:10.000Z

dataPre_Postprocess/calculate_nearestPts.py

dddtqshmpmz/IHC

c5d8f34a901bc0196666250b86f08a5a1f47ced5

[ "Apache-2.0" ]

null

import numpy as np import cv2 import csv import os import pandas as pd import time def calcuNearestPtsDis2(ptList1): ''' Find the nearest point of each point in ptList1 & return the mean min_distance Parameters ---------- ptList1: numpy array points' array, shape:(x,2) Return ---------- mean_Dis: float the mean value of the minimum distances ''' if len(ptList1)<=1: print('error!') return 'error' minDis_list = [] for i in range(len(ptList1)): currentPt = ptList1[i,0:2] ptList2 = np.delete(ptList1,i,axis=0) disMat = np.sqrt(np.sum(np.asarray(currentPt - ptList2)**2, axis=1).astype(np.float32) ) minDis = disMat.min() minDis_list.append(minDis) minDisArr = np.array(minDis_list) mean_Dis = np.mean(minDisArr) return mean_Dis def calcuNearestPtsDis(ptList1, ptList2): ''' Find the nearest point of each point in ptList1 from ptList2 & return the mean min_distance Parameters ---------- ptList1: numpy array points' array, shape:(x,2) ptList2: numpy array points' array, shape:(x,2) Return ---------- mean_Dis: float the mean value of the minimum distances ''' if (not len(ptList2)) or (not len(ptList1)): print('error!') return 'error' minDis_list = [] for i in range(len(ptList1)): currentPt = ptList1[i,0:2] disMat = np.sqrt(np.sum(np.asarray(currentPt - ptList2)**2, axis=1).astype(np.float32) ) minDis = disMat.min() minDis_list.append(minDis) minDisArr = np.array(minDis_list) mean_Dis = np.mean(minDisArr) return mean_Dis def calcuNearestPts(csvName1, csvName2): ptList1_csv = pd.read_csv(csvName1,usecols=['x_cord', 'y_cord']) ptList2_csv = pd.read_csv(csvName2,usecols=['x_cord', 'y_cord']) ptList1 = ptList1_csv.values[:,:2] ptList2 = ptList2_csv.values[:,:2] minDisInd_list = [] for i in range(len(ptList1)): currentPt = ptList1[i,0:2] disMat = np.sqrt(np.sum(np.asarray(currentPt - ptList2)**2, axis=1)) minDisInd = np.argmin(disMat) minDisInd_list.append(minDisInd) minDisInd = np.array(minDisInd_list).reshape(-1,1) ptList1_csv = pd.concat([ptList1_csv, pd.DataFrame( columns=['nearestInd'],data = minDisInd)], axis=1) ptList1_csv.to_csv(csvName1,index=False) return minDisInd def drawDisPic(picInd): picName = 'patients_dataset/image/'+ picInd +'.png' img = cv2.imread(picName) csvName1='patients_dataset/data_csv/'+picInd+'other_tumour_pts.csv' csvName2='patients_dataset/data_csv/'+picInd+'other_lymph_pts.csv' ptList1_csv = pd.read_csv(csvName1) ptList2_csv = pd.read_csv(csvName2) ptList1 = ptList1_csv.values ptList2 = ptList2_csv.values for i in range(len(ptList1)): img = cv2.circle(img, tuple(ptList1[i,:2]), 3 , (0, 0, 255), -1 ) img = cv2.line(img, tuple(ptList1[i,:2]) , tuple(ptList2[ ptList1[i,2] ,:2]), (0,255,0), 1) for i in range(len(ptList2)): img = cv2.circle(img, tuple(ptList2[i,:2]), 3 , (255, 0, 0), -1 ) cv2.imwrite( picInd+'_dis.png',img) def drawDistancePic(disName1, disName2, picID): ''' Draw & save the distance pics Parameters ---------- disName1,disName2: str such as 'positive_lymph', 'all_tumour' picID: str the patient's ID ''' cellName_color = {'other_lymph': (255, 0, 0), 'positive_lymph': (255, 255, 0), 'other_tumour': (0, 0, 255), 'positive_tumour': (0, 255, 0)} ptline_color = {'positive_lymph': (0,0,255), 'positive_tumour': (0,0,255), 'ptumour_plymph': (51, 97, 235), 'other_tumour': (0, 255, 0)} if (disName1 == 'all_tumour' and disName2 == 'all_lymph') or (disName1 == 'all_tumour' and disName2 == 'positive_lymph'): line_color = (0,255,255) elif disName1 == 'positive_tumour' and disName2 == 'positive_lymph': line_color = (51, 97, 235) else: line_color = ptline_color[disName1] csv_dir = '/data/Datasets/MediImgExp/data_csv' img_dir = '/data/Datasets/MediImgExp/image' if disName1 == 'all_tumour' and disName2 == 'positive_lymph': dis1_csv = pd.read_csv(csv_dir + '/' + picID + 'positive_tumour' + '_pts.csv', usecols=['x_cord', 'y_cord']) dis2_csv = pd.read_csv(csv_dir + '/' + picID + 'other_tumour' + '_pts.csv', usecols=['x_cord', 'y_cord']) dis3_csv = pd.read_csv(csv_dir + '/' + picID + 'positive_lymph' + '_pts.csv', usecols=['x_cord', 'y_cord']) ptList1 = dis1_csv.values[:,:2] ptList2 = dis2_csv.values[:,:2] ptList3 = dis3_csv.values[:,:2] # positive tumour: find the nearest lymph cell minDisInd_list = [] for i in range(len(ptList1)): currentPt = ptList1[i,:] disMat = np.sqrt(np.sum(np.asarray(currentPt - ptList3)**2, axis=1)) minDisInd = np.argmin(disMat) minDisInd_list.append(minDisInd) minDisInd = np.array(minDisInd_list).reshape(-1,1) dis1_csv = pd.concat([dis1_csv, pd.DataFrame(columns=['nearestInd'], data=minDisInd)], axis=1) # other tumour: find the nearest lymph cell minDisInd_list = [] for i in range(len(ptList2)): currentPt = ptList2[i,:] disMat = np.sqrt(np.sum(np.asarray(currentPt - ptList3)**2, axis=1)) minDisInd = np.argmin(disMat) minDisInd_list.append(minDisInd) minDisInd = np.array(minDisInd_list).reshape(-1,1) dis2_csv = pd.concat([dis2_csv, pd.DataFrame(columns=['nearestInd'], data=minDisInd)], axis=1) img = cv2.imread(img_dir + '/' + picID + '.jpg') ptList1 = dis1_csv.values for i in range(len(ptList1)): img = cv2.line(img, tuple(ptList1[i,:2]), tuple(ptList3[ptList1[i, 2],:2]), line_color, 1) ptList2 = dis2_csv.values for i in range(len(ptList2)): img = cv2.line(img, tuple(ptList2[i,:2]), tuple(ptList3[ptList2[i, 2],:2]), line_color, 1) for i in range(len(ptList1)): img = cv2.circle(img, tuple(ptList1[i,:2]), 4, (0, 255, 0), -1) for i in range(len(ptList2)): img = cv2.circle(img, tuple(ptList2[i,:2]), 4, (0, 0, 255), -1) for i in range(len(ptList3)): img = cv2.circle(img, tuple(ptList3[i,:2]), 4, (255, 255, 0), -1) cv2.imwrite(picID + disName1 + '_' + disName2 + '_dis.png', img) elif disName1 == 'all_tumour' and disName2 == 'all_lymph': dis1_csv = pd.read_csv(csv_dir + '/' + picID + 'positive_tumour' + '_pts.csv', usecols=['x_cord', 'y_cord']) dis2_csv = pd.read_csv(csv_dir + '/' + picID + 'other_tumour' + '_pts.csv', usecols=['x_cord', 'y_cord']) dis3_csv = pd.read_csv(csv_dir + '/' + picID + 'positive_lymph' + '_pts.csv', usecols=['x_cord', 'y_cord']) dis4_csv = pd.read_csv(csv_dir + '/' + picID + 'other_lymph' + '_pts.csv', usecols=['x_cord', 'y_cord']) ptList1 = dis1_csv.values[:,:2] ptList2 = dis2_csv.values[:,:2] ptList3 = dis3_csv.values[:,:2] ptList4 = dis4_csv.values[:,:2] ptList6 = np.concatenate((ptList3, ptList4), axis=0) minDisInd_list = [] for i in range(len(ptList1)): currentPt = ptList1[i,:] disMat = np.sqrt(np.sum(np.asarray(currentPt - ptList6)**2, axis=1)) minDisInd = np.argmin(disMat) minDisInd_list.append(minDisInd) minDisInd = np.array(minDisInd_list).reshape(-1,1) dis1_csv = pd.concat([dis1_csv, pd.DataFrame(columns=['nearestInd'], data=minDisInd)], axis=1) minDisInd_list = [] for i in range(len(ptList2)): currentPt = ptList2[i,:] disMat = np.sqrt(np.sum(np.asarray(currentPt - ptList6)**2, axis=1)) minDisInd = np.argmin(disMat) minDisInd_list.append(minDisInd) minDisInd = np.array(minDisInd_list).reshape(-1,1) dis2_csv = pd.concat([dis2_csv, pd.DataFrame(columns=['nearestInd'], data=minDisInd)], axis=1) img = cv2.imread(img_dir + '/' + picID + '.jpg') ptList1 = dis1_csv.values for i in range(len(ptList1)): img = cv2.line(img, tuple(ptList1[i,:2]), tuple(ptList6[ptList1[i, 2],:2]), line_color, 1) ptList2 = dis2_csv.values for i in range(len(ptList2)): img = cv2.line(img, tuple(ptList2[i,:2]), tuple(ptList6[ptList2[i, 2],:2]), line_color, 1) for i in range(len(ptList1)): img = cv2.circle(img, tuple(ptList1[i,:2]), 4, (0, 255, 0), -1) for i in range(len(ptList2)): img = cv2.circle(img, tuple(ptList2[i,:2]), 4, (0, 0, 255), -1) for i in range(len(ptList3)): img = cv2.circle(img, tuple(ptList3[i,:2]), 4, (255, 255, 0), -1) for i in range(len(ptList4)): img = cv2.circle(img, tuple(ptList4[i,:2]), 4, (255, 0, 0), -1) cv2.imwrite(picID + disName1 + '_' + disName2 + '_dis.png', img) elif disName1 != disName2: dis1_csv = pd.read_csv(csv_dir + '/' + picID + disName1 + '_pts.csv', usecols=['x_cord', 'y_cord']) dis2_csv = pd.read_csv(csv_dir + '/' + picID + disName2 + '_pts.csv', usecols=['x_cord', 'y_cord']) ptList1 = dis1_csv.values[:,:2] ptList2 = dis2_csv.values[:,:2] minDisInd_list = [] for i in range(len(ptList1)): currentPt = ptList1[i,:] disMat = np.sqrt(np.sum(np.asarray(currentPt - ptList2)**2, axis=1)) minDisInd = np.argmin(disMat) minDisInd_list.append(minDisInd) minDisInd = np.array(minDisInd_list).reshape(-1,1) dis1_csv = pd.concat([dis1_csv, pd.DataFrame( columns=['nearestInd'],data = minDisInd)], axis=1) img = cv2.imread(img_dir + '/' + picID + '.jpg') img[:,:, 0] = 255 img[:,:, 1] = 255 img[:,:, 2] = 255 ptList1 = dis1_csv.values for i in range(len(ptList1)): img = cv2.line(img, tuple(ptList1[i,:2]) , tuple(ptList2[ ptList1[i,2] ,:2]), line_color, 1) for i in range(len(ptList1)): img = cv2.circle(img, tuple(ptList1[i,:2]), 5, cellName_color[disName1], -1) for i in range(len(ptList2)): img = cv2.circle(img, tuple(ptList2[i,:2]), 5, cellName_color[disName2], -1) cv2.imwrite(picID + disName1 + '_' + disName2 + '_dis.png', img) elif disName1 == disName2: dis1_csv = pd.read_csv(csv_dir + '/' + picID + disName1 + '_pts.csv', usecols=['x_cord', 'y_cord']) ptList1 = dis1_csv.values[:,:2] minDisInd_list = [] for i in range(len(ptList1)): currentPt = ptList1[i, :2] disMat = np.sqrt(np.sum(np.asarray(currentPt - ptList1)** 2, axis=1).astype(np.float32)) minDisInd = np.argmin(disMat) disMat[minDisInd] = 1000.0 minDisInd = np.argmin(disMat) minDisInd_list.append(minDisInd) minDisInd = np.array(minDisInd_list).reshape(-1,1) dis1_csv = pd.concat([dis1_csv, pd.DataFrame( columns=['nearestInd'],data = minDisInd)], axis=1) img = cv2.imread(img_dir + '/' + picID + '.jpg') img[:,:, 0] = 255 img[:,:, 1] = 255 img[:,:, 2] = 255 ptList1 = dis1_csv.values for i in range(len(ptList1)): img = cv2.line(img, tuple(ptList1[i,:2]), tuple(ptList1[ptList1[i, 2],:2]), line_color, 1) for i in range(len(ptList1)): img = cv2.circle(img, tuple(ptList1[i,:2]), 5, cellName_color[disName1], -1) cv2.imwrite(picID + disName1 + '_dis.png', img) def getAllPicsDisCSV(): ''' Get all distance data from the saved csv files (get from the above functions) ''' base_dir = '/data/Datasets/MediImgExp' f = open( base_dir + '/' + 'AllDisData.csv','w',encoding='utf-8',newline="") csv_writer = csv.writer(f) csv_writer.writerow([ 'Ind','PosiTumourRatio','PosiLymphRatio', 'DisTumourLymph','DisPosiTumour','DisPosiLymph', 'DisPosiTumourPosiLymph','DisTumourPosiLymph']) process_dir = base_dir + '/process' csv_dir = base_dir + '/data_csv' pic_name = os.listdir(process_dir) picIDList = [] for pic_name_ in pic_name: picIDList.append( pic_name_.split('_')[0] ) for picID in picIDList: list_data = [] list_data.append(picID) # PosiTumourRatio PosiTumourCsv = pd.read_csv( csv_dir+'/'+ picID +'positive_tumour_pts.csv') OtherTumourCsv = pd.read_csv( csv_dir+'/'+ picID +'other_tumour_pts.csv') Num_PosiTumour = PosiTumourCsv.shape[0] Num_OtherTumour = OtherTumourCsv.shape[0] if (Num_PosiTumour + Num_OtherTumour)!=0 : PosiTumourRatio = Num_PosiTumour / (Num_PosiTumour + Num_OtherTumour) else: PosiTumourRatio = 'error' list_data.append(PosiTumourRatio) # PosiLymphRatio PosiLymphCsv = pd.read_csv( csv_dir+'/'+ picID +'positive_lymph_pts.csv') OtherLymphCsv = pd.read_csv( csv_dir+'/'+ picID +'other_lymph_pts.csv') Num_PosiLymph = PosiLymphCsv.shape[0] Num_OtherLymph = OtherLymphCsv.shape[0] if (Num_PosiLymph + Num_OtherLymph)!=0 : PosiLymphRatio = Num_PosiLymph / (Num_PosiLymph + Num_OtherLymph) else: PosiLymphRatio = 'error' list_data.append(PosiLymphRatio) # DisTumourLymph ptList1_csv = pd.read_csv(csv_dir+'/'+ picID +'positive_tumour_pts.csv',usecols=['x_cord', 'y_cord']) ptList2_csv = pd.read_csv(csv_dir+'/'+ picID +'positive_lymph_pts.csv',usecols=['x_cord', 'y_cord']) ptList1 = ptList1_csv.values[:,:2] ptList2 = ptList2_csv.values[:,:2] ptList3_csv = pd.read_csv(csv_dir+'/'+ picID +'other_tumour_pts.csv',usecols=['x_cord', 'y_cord']) ptList4_csv = pd.read_csv(csv_dir+'/'+ picID +'other_lymph_pts.csv',usecols=['x_cord', 'y_cord']) ptList3 = ptList3_csv.values[:,:2] ptList4 = ptList4_csv.values[:,:2] ptList1 = np.concatenate((ptList1,ptList3), axis=0) ptList2 = np.concatenate((ptList2,ptList4), axis=0) DisTumourLymph = calcuNearestPtsDis(ptList1, ptList2) list_data.append(DisTumourLymph) # DisPosiTumour ptList1_csv = pd.read_csv(csv_dir+'/'+ picID +'positive_tumour_pts.csv',usecols=['x_cord', 'y_cord']) ptList1 = ptList1_csv.values[:,:2] DisPosiTumour = calcuNearestPtsDis2(ptList1) list_data.append(DisPosiTumour) # DisPosiLymph ptList1_csv = pd.read_csv(csv_dir+'/'+ picID +'positive_lymph_pts.csv',usecols=['x_cord', 'y_cord']) ptList1 = ptList1_csv.values[:,:2] DisPosiLymph = calcuNearestPtsDis2(ptList1) list_data.append(DisPosiLymph) # DisPosiTumourPosiLymph ptList1_csv = pd.read_csv(csv_dir+'/'+ picID +'positive_tumour_pts.csv',usecols=['x_cord', 'y_cord']) ptList2_csv = pd.read_csv(csv_dir+'/'+ picID +'positive_lymph_pts.csv',usecols=['x_cord', 'y_cord']) ptList1 = ptList1_csv.values[:,:2] ptList2 = ptList2_csv.values[:,:2] DisPosiTumourPosiLymph = calcuNearestPtsDis(ptList1, ptList2) list_data.append(DisPosiTumourPosiLymph) # DisTumourPosiLymph ptList1_csv = pd.read_csv(csv_dir+'/'+ picID +'positive_tumour_pts.csv',usecols=['x_cord', 'y_cord']) ptList2_csv = pd.read_csv(csv_dir+'/'+ picID +'positive_lymph_pts.csv',usecols=['x_cord', 'y_cord']) ptList1 = ptList1_csv.values[:,:2] ptList2 = ptList2_csv.values[:,:2] ptList3_csv = pd.read_csv(csv_dir+'/'+ picID +'other_tumour_pts.csv',usecols=['x_cord', 'y_cord']) ptList3 = ptList3_csv.values[:,:2] ptList1 = np.concatenate((ptList1,ptList3), axis=0) DisTumourPosiLymph = calcuNearestPtsDis(ptList1, ptList2) list_data.append(DisTumourPosiLymph) csv_writer.writerow(list_data) def adjustToMultiCSV(): ''' Divide the AllDisData.csv into 6+1=7 csv ''' base_dir = '/data/Datasets/MediImgExp' alldata = pd.read_csv( base_dir + '/' + 'AllDisData.csv' ) IndData = alldata['Ind'].values patient_Ind = [] for IndName in IndData: patient_Ind.append(IndName.split('-')[0]) patient_Ind = np.unique(patient_Ind) patient_Ind = sorted( list(map(int,patient_Ind)) ) column_name = ['Ind','2D','3D','GAL9','LAG3','MHC','OX40','OX40L','PD1','PDL1','TIM3'] # stage 1 calculate the 6 csv (10 cols for each csv) DisPosiTumour = pd.DataFrame(columns=column_name,index= patient_Ind) DisPosiTumour['Ind'] = patient_Ind patient_Id = patient_Ind column_names = ['2D','3D','GAL9','LAG3','MHC','OX40','OX40L','PD1','PDL1','TIM3'] for patient in patient_Id: for column in column_names: combine_name = str(patient) + '-' + column exist_flag = (alldata['Ind'].str[0:len(combine_name)]== combine_name).any() if not exist_flag: continue valid_slice = alldata[ alldata['Ind'].str[0:len(combine_name)]== combine_name ] arr = valid_slice['DisTumourPosiLymph'].values if arr.__contains__('error'): arr = np.setdiff1d(arr, ['error']) if not arr.shape[0]: continue valid_slice_mean = np.mean( arr.astype(np.float32)) DisPosiTumour.loc[ patient ,column ] = valid_slice_mean DisPosiTumour.to_csv( base_dir + '/' + 'DisTumourPosiLymph.csv',index=False ) # stage 2 add the outputs (4 cols) all_data_name = base_dir + '/' + 'alldata2.csv' all_data = pd.read_csv(all_data_name) all_data.index = all_data['Ind'] valid_columns = ['RELAPSE','RFS','DEATH','OS'] valid_slice = all_data.loc[ patient_Ind, valid_columns ] DisPosiTumour = pd.read_csv( base_dir + '/' + 'PosiTumourRatio.csv',index_col=0) DisPosiTumour = pd.concat([DisPosiTumour,valid_slice],axis = 1) DisPosiTumour.to_csv( base_dir + '/' + 'PosiTumourRatio.csv' ) # stage 3 calculate DisTumourLymph (use all markers' mean values) DisTumourLymph = pd.DataFrame(columns=['mean_10markers'],index= patient_Ind) patient_Id = patient_Ind column_names = [ 'mean_10markers'] for patient in patient_Id: for column in column_names: combine_name = str(patient) + '-' exist_flag = (alldata['Ind'].str[0:len(combine_name)]== combine_name).any() if not exist_flag: continue valid_slice = alldata[ alldata['Ind'].str[0:len(combine_name)]== combine_name ] arr = valid_slice['DisTumourLymph'].values if arr.__contains__('error'): arr = np.setdiff1d(arr, ['error']) if not arr.shape[0]: continue valid_slice_mean = np.mean( arr.astype(np.float32)) DisTumourLymph.loc[ patient ,column ] = valid_slice_mean DisTumourLymph.to_csv( base_dir + '/' + 'DisTumourLymph.csv' ) all_data_name = base_dir + '/' + 'alldata2.csv' all_data = pd.read_csv(all_data_name) all_data.index = all_data['Ind'] valid_columns = ['RELAPSE','RFS','DEATH','OS'] valid_slice = all_data.loc[ patient_Ind, valid_columns] DisTumourLymph = pd.concat([DisTumourLymph,valid_slice],axis = 1) DisTumourLymph.to_csv( base_dir + '/' + 'DisTumourLymph.csv') def getAllFeatureCSV(): base_dir = '/data/Datasets/MediImgExp/csv' alldata = pd.read_csv( base_dir + '/' + 'AllDisData.csv' ) oridata = pd.read_csv( base_dir + '/' + 'alldata2.csv',index_col=0 ) ori_columns = oridata.columns.values ori_columns = ori_columns[4:-4] # original 40 feature names csv_name = [ 'DisPosiLymph','DisPosiTumour', 'DisPosiTumourPosiLymph', 'DisTumourPosiLymph','PosiLymphRatio','PosiTumourRatio', 'DisTumourLymph'] meaningful_feature_OS = { 'DisPosiLymph_PD1':24,'DisPosiLymph_OX40L':48.14,'DisPosiLymph_OX40':98.33,'DisPosiLymph_3D':13.44, 'DisPosiTumour_TIM3':546.86, 'DisPosiTumour_GAL9':85.97,'DisPosiTumour_2D':24.22, 'DisPosiTumourPosiLymph_3D':20.88, 'DisPosiTumourPosiLymph_MHC':18.68,'DisPosiTumourPosiLymph_OX40L':40.02, 'DisTumourPosiLymph_OX40L':173.56, 'DisTumourPosiLymph_2D':223.71,'DisTumourPosiLymph_3D':21.19,'DisTumourPosiLymph_OX40':445.89, 'PosiLymphRatio_3D':0.97, 'PosiLymphRatio_OX40':0.44,'PosiLymphRatio_GAL9':0.23,'PosiLymphRatio_2D':0.37, 'PosiTumourRatio_MHC':0.93,'PosiTumourRatio_GAL9':0.41,'PosiTumourRatio_3D':1.0 } IndData = alldata['Ind'].values patient_Ind = [] for IndName in IndData: patient_Ind.append(IndName.split('-')[0]) patient_Ind = np.unique(patient_Ind) patient_Ind = sorted( list(map(int,patient_Ind)) ) # create a super_csv including all features 61+40=101 features / 4 outputs super_csv = pd.DataFrame(index= patient_Ind) super_csv_copy = pd.DataFrame(index= patient_Ind) super_csv['Ind'] = patient_Ind for column in ori_columns: # 40 features super_csv[column] = oridata.loc[ patient_Ind,column] for i in range(0,7): csvName = os.path.join(base_dir,csv_name[i] + '.csv') csvData = pd.read_csv(csvName,index_col=0) if i == 6: # 1 feature column_name = csvData.columns.values[:1] super_csv[ csv_name[i]+'_'+column_name[0] ] = csvData.loc[ patient_Ind, column_name[0] ] output_name = csvData.columns.values[-4:] for k in range(0,4): super_csv[ output_name[k] ] = csvData.loc[ patient_Ind,output_name[k] ] break column_name = csvData.columns.values[:10] for j in range(0,10): # 6*10 = 60 features super_csv[ csv_name[i]+'_'+column_name[j] ] = csvData.loc[ patient_Ind, column_name[j] ] for key, values in meaningful_feature_OS.items(): super_csv_copy[key] = super_csv.loc[patient_Ind, key ] super_csv_copy.loc[ super_csv_copy[key] <= values, key] = 0 super_csv_copy.loc[ super_csv_copy[key] > values, key ] = 1 for k in range(0,4): super_csv_copy[ output_name[k] ] = csvData.loc[ patient_Ind,output_name[k] ] super_csv_copy = super_csv_copy.dropna(axis=0,how='any') #super_csv.to_csv(base_dir+'/'+ 'super1.csv',index =False) #super_csv.to_csv(base_dir+'/'+ 'super2.csv',index =False) #super_csv_copy.to_csv(base_dir+'/'+ 'super3.csv',index =True) #super_csv_copy.to_csv(base_dir+'/'+ 'super4.csv',index =True) def getAllFeatureCSV2(): base_dir = '/data/Datasets/MediImgExp/csv' alldata = pd.read_csv( base_dir + '/' + 'AllDisData.csv' ) oridata = pd.read_csv( base_dir + '/' + 'alldata2.csv',index_col=0 ) ori_columns = oridata.columns.values ori_columns = np.concatenate(( ori_columns[19:32],ori_columns[44:48] ) ,axis=0 ) # original 40 feature names csv_name = [ 'DisPosiLymph','DisPosiTumour', 'DisPosiTumourPosiLymph', 'DisTumourPosiLymph','PosiLymphRatio','PosiTumourRatio', 'DisTumourLymph'] meaningful_feature_OS = { 'DisPosiLymph_OX40L':61.55, 'DisPosiTumour_GAL9':119.40, 'DisPosiTumourPosiLymph_3D':20.28, 'DisPosiTumourPosiLymph_OX40L':40.02, 'DisTumourPosiLymph_3D':21.19, 'DisTumourPosiLymph_MHC':135.56, 'PosiLymphRatio_3D':0.97, 'PosiLymphRatio_OX40':0.52,'PosiLymphRatio_GAL9':0.2 } IndData = alldata['Ind'].values patient_Ind = [] for IndName in IndData: patient_Ind.append(IndName.split('-')[0]) patient_Ind = np.unique(patient_Ind) patient_Ind = sorted( list(map(int,patient_Ind)) ) # create a super_csv including all features 61+xx features / 4 outputs super_csv = pd.DataFrame(index= patient_Ind) super_csv_copy = pd.DataFrame(index= patient_Ind) super_csv['Ind'] = patient_Ind for column in ori_columns: # 40 features super_csv[column] = oridata.loc[ patient_Ind,column] for i in range(0,7): csvName = os.path.join(base_dir,csv_name[i] + '.csv') csvData = pd.read_csv(csvName,index_col=0) if i == 6: # 1 feature column_name = csvData.columns.values[:1] super_csv[ csv_name[i]+'_'+column_name[0] ] = csvData.loc[ patient_Ind, column_name[0] ] output_name = csvData.columns.values[-4:] for k in range(0,4): super_csv[ output_name[k] ] = csvData.loc[ patient_Ind,output_name[k] ] break column_name = csvData.columns.values[:10] for j in range(0,10): # 6*10 = 60 features super_csv[ csv_name[i]+'_'+column_name[j] ] = csvData.loc[ patient_Ind, column_name[j] ] for key, values in meaningful_feature_OS.items(): super_csv_copy[key] = super_csv.loc[patient_Ind, key ] super_csv_copy.loc[ super_csv_copy[key] <= values, key] = 0 super_csv_copy.loc[ super_csv_copy[key] > values, key ] = 1 for k in range(0,4): super_csv_copy[ output_name[k] ] = csvData.loc[ patient_Ind,output_name[k] ] #super_csv_copy = super_csv_copy.dropna(axis=0,how='any') #super_csv.to_csv(base_dir+'/rfs_csv'+'/'+ 'super1.csv',index =False) #super_csv.to_csv(base_dir+'/'+ 'super2.csv',index =False) super_csv_copy.to_csv(base_dir+'/rfs_csv'+'/'+ 'super3.csv',index =True) #super_csv_copy.to_csv(base_dir+'/'+ 'super4.csv',index =True) if __name__ == '__main__': getAllPicsDisCSV() adjustToMultiCSV() getAllFeatureCSV2() # draw the distance pics drawDistancePic(disName1='all_tumour', disName2='positive_lymph', picID='0-GAL9-1')

46.485009

138

0.609933

3,365

26,357

4.576226

0.089153

0.027924

0.022794

0.020716

0.770245

0.740308

0.711085

0.688616

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0

0.044614

0.243123

26,357

567

139

46.485009

0.727305

0.073036

0

0.652681

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0.124319

0.03485

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1

0.020979

false

0

0.013986

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0.04662

0.004662

0

null

0

1

0

1

0

null

0

3

70a6436bede7b65ea73d086ce97303034229e00b

261

py

Python

zhangzhen/20180402/text6.py

python20180319howmework/homework

c826d7aa4c52f8d22f739feb134d20f0b2c217cd

[ "Apache-2.0" ]

null

zhangzhen/20180402/text6.py

python20180319howmework/homework

c826d7aa4c52f8d22f739feb134d20f0b2c217cd

[ "Apache-2.0" ]

null

zhangzhen/20180402/text6.py

python20180319howmework/homework

c826d7aa4c52f8d22f739feb134d20f0b2c217cd

[ "Apache-2.0" ]

null

''' 6. 有这样一个字典d = {"chaoqian":87, “caoxu”:90, “caohuan”:98, “wuhan”:82, “zhijia”:89} 1)将以上字典按成绩排名 ''' d = {"chaoqian":87, "caoxu":90, "caohuan":98,"wuhan":82,"zhijia":89} print(sorted(d.items(),key = lambda x :x[1],reverse =True))

7.909091

80

0.547893

36

261

3.972222

0.638889

0.13986

0.20979

0.237762

0.573427

0

0.110048

0.199234

261

32

81

8.15625

0.574163

0.367816

0

0.238462

0

1

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false

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0.5

0

null

0

1

0

null

0

1

0

3

5626dcdb8caf54becd357267cec05d7721358da0

44

py

Python

version.py

AlanAyy/unify-bot

b28c02a0ca7921ef7bc80a5ac86ec1981230177c

[ "MIT" ]

null

version.py

AlanAyy/unify-bot

b28c02a0ca7921ef7bc80a5ac86ec1981230177c

[ "MIT" ]

null

version.py

AlanAyy/unify-bot

b28c02a0ca7921ef7bc80a5ac86ec1981230177c

[ "MIT" ]

null

version = 0.1 # View README for v0.1 details

22

30

0.727273

9

44

3.555556

0.888889

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0.111111

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0

1

0

null

0

1

0

null

0

3

563b3c79a91f2f21a08bf694d976c8064abbbbcb

237

py

Python

cryptocurrency/celery.py

deepanshu-jain1999/cryptocurrencytracking

1feb8f14e7615406b0658138d23314188f8f0e8b

[ "Apache-2.0" ]

null

cryptocurrency/celery.py

deepanshu-jain1999/cryptocurrencytracking

1feb8f14e7615406b0658138d23314188f8f0e8b

[ "Apache-2.0" ]

null

cryptocurrency/celery.py

deepanshu-jain1999/cryptocurrencytracking

1feb8f14e7615406b0658138d23314188f8f0e8b

[ "Apache-2.0" ]

null

import os from celery import Celery os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'cryptocurrency.settings') app = Celery('cryptocurrency') app.config_from_object('django.conf:settings', namespace='CELERY') app.autodiscover_tasks()

23.7

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237

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26.333333

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0.333333

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null

0

1

0

null

0

1

0

3

5654560040bcea100546e70954c0b6426dc1457f

16,931

py

Python

insurance-app.py

alliwene/gb-november-grp2-health-insurance

9f2699f9a7a533b33ea431e9bb7cb95c25654599

[ "MIT" ]

null

insurance-app.py

alliwene/gb-november-grp2-health-insurance

9f2699f9a7a533b33ea431e9bb7cb95c25654599

[ "MIT" ]

null

insurance-app.py

alliwene/gb-november-grp2-health-insurance

9f2699f9a7a533b33ea431e9bb7cb95c25654599

[ "MIT" ]

null

# import libraries import base64 import os import uuid import re import streamlit as st import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import pickle from PIL import Image import numpy as np from sklearn.ensemble import RandomForestClassifier from matplotlib.backends.backend_agg import RendererAgg _lock = RendererAgg.lock plt.style.use('seaborn-notebook') sns.set(context='paper', font='monospace', font_scale=3) def main(): page = st.sidebar.selectbox('Choose a page',['About App','Prediction and Evaluation']) if page == 'About App': st.title('Analysis and Prediction of Health Insurance Subscription in Nigeria') image = Image.open('images/GB.png') st.image(image) st.markdown(""" This app predicts whether an individual would take up a health insurance policy or not leveraging a machine learning classification model. We would also investigate factors that most likely influence taking up a health insurance policy by an individual using the trained model. Data obtained from Individual Recode section of the 2018 Nigerian Demographic and Health Survey [DHS](https://dhsprogram.com/data/dataset/Nigeria_Standard-DHS_2018.cfm) . """) st.markdown("## Meet the Data Scientists") col1,mid,col2 = st.beta_columns(3) with col1: st.image('images/ope.jpg',width=300) html = f"Opeyemi Idris <a href='https://github.com/hardcore05' alt='GitHub'><img height='20' src='data:image/png;base64,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'></a>" st.markdown(html, unsafe_allow_html=True) with col2: st.image('images/shakir.jpg',width=300) html = f"Shakiru Muraina <a href='https://github.com/Debare' alt='GitHub'><img height='20' src='data:image/png;base64,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'></a>" st.markdown(html, unsafe_allow_html=True) col1,mid,col2 = st.beta_columns(3) with col1: st.image('images/bolu.jpg',width=300) html = f"Boluwatife Adewale <a href='https://github.com/BBLinus' alt='GitHub'><img height='20' src='data:image/png;base64,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'></a>" st.markdown(html, unsafe_allow_html=True) with col2: st.image('images/uthman.jpg',width=300) html = f"Uthman Allison <a href='https://github.com/alliwene' alt='GitHub'><img height='20' src='data:image/png;base64,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'></a>" st.markdown(html, unsafe_allow_html=True) if page == 'Prediction and Evaluation': st.title('Predicting Health Insurance Subscription') st.sidebar.header('User Input Features') # st.sidebar.markdown(""" # [Example CSV input file](https://raw.githubusercontent.com/alliwene/gb-november-grp2-health-insurance/main/data/data_sample.csv) # """) def download_button(object_to_download, download_filename, button_text): """ Generates a link to download the given object_to_download. Params: ------ object_to_download: The object to be downloaded. download_filename (str): filename and extension of file. e.g. mydata.csv button_text (str): Text to display on download button (e.g. 'click here to download file') Returns: ------- (str): the anchor tag to download object_to_download """ try: # some strings <-> bytes conversions necessary here b64 = base64.b64encode(object_to_download.encode()).decode() except AttributeError as e: b64 = base64.b64encode(object_to_download).decode() button_uuid = str(uuid.uuid4()).replace('-', '') button_id = re.sub('\d+', '', button_uuid) custom_css = f""" <style> #{button_id} {{ background-color: rgb(255, 255, 255); color: rgb(38, 39, 48); padding: 0.25em 0.38em; position: relative; text-decoration: none; border-radius: 4px; border-width: 1px; border-style: solid; border-color: rgb(230, 234, 241); border-image: initial; }} #{button_id}:hover {{ border-color: rgb(246, 51, 102); color: rgb(246, 51, 102); }} #{button_id}:active {{ box-shadow: none; background-color: rgb(246, 51, 102); color: white; }} </style> """ dl_link = custom_css + f'<a download="{download_filename}" id="{button_id}" href="data:file/txt;base64,{b64}">{button_text}</a><br></br>' return dl_link def file_selector(folder_path='data'): filenames = os.listdir(folder_path) selected_filename = 'data_sample.csv' return os.path.join(folder_path, selected_filename) filename = file_selector() # Load selected file with open(filename, 'rb') as f: s = f.read() download_button_str = download_button(s, filename, 'Download sample input CSV file') st.sidebar.markdown(download_button_str, unsafe_allow_html=True) # Load cleaned dataset insurance_clean = pd.read_csv('data/data_clean.csv') insurance = insurance_clean.drop(columns=['target']) # Collects user input features into dataframe uploaded_file = st.sidebar.file_uploader("Upload your input CSV file", type=["csv"]) if uploaded_file is not None: input_df = pd.read_csv(uploaded_file) internet = st.sidebar.selectbox('Use of internet',('Yes, last 12 months', 'Never', 'Yes, before last 12 months')) bank_acount = st.sidebar.selectbox('Account in bank',('Yes', 'No')) attainment = st.sidebar.selectbox("Husband/partner's educational attainment", ('Complete secondary', 'No education', 'Higher', 'Complete primary', 'Incomplete secondary', "Don't know", 'Incomplete primary')) internet_freq = st.sidebar.selectbox('Internet use frequency',('At least once a week', 'Almost every day', 'Not at all', 'Less than once a week')) literacy = st.sidebar.selectbox('Literacy', ('Able to read whole sentence', 'Cannot read at all', 'Able to read only parts of sentence', 'Blind/visually impaired', 'No card with required language')) wealth_index = st.sidebar.slider('Wealth index', insurance['Wealth index factor score for urban/rural (5 decimals)'].min(), insurance['Wealth index factor score for urban/rural (5 decimals)'].max(), float(input_df['Wealth index factor score for urban/rural (5 decimals)'][0])) toilet = st.sidebar.selectbox('Type of toilet facility', ('Flush to piped sewer system', 'Flush to septic tank', 'Flush to pit latrine', 'Pit latrine with slab', 'Not a dejure resident', 'Pit latrine without slab/open pit', 'No facility/bush/field', 'Ventilated Improved Pit latrine (VIP)', 'Flush to somewhere else', 'Bucket toilet', 'Other', 'Composting toilet', "Flush, don't know where", 'Hanging toilet/latrine')) medical_help = st.sidebar.selectbox('Getting money needed for treatment', ('Not a big problem', 'Big problem')) residence = st.sidebar.selectbox('Type of place of residence', ('Urban', 'Rural')) medical_visit = st.sidebar.selectbox('Visited health facility last 12 months', ('Yes', 'No')) tv_watch = st.sidebar.selectbox('Frequency of watching television', ('At least once a week', 'Not at all', 'Less than once a week')) edu_year = st.sidebar.selectbox('Highest year of education', ('3.0', '4.0', '2.0', '6.0', '1.0', 'No years completed at level V106', '5.0', '8.0', '7.0')) data = {'Use of internet': internet, 'Has an account in a bank or other financial institution': bank_acount, "Husband/partner's educational attainment": attainment, 'Frequency of using internet last month': internet_freq, 'Literacy': literacy, 'Wealth index factor score for urban/rural (5 decimals)': wealth_index, 'Type of toilet facility': toilet, 'Getting medical help for self: getting money needed for treatment': medical_help, 'Type of place of residence': residence, 'Visited health facility last 12 months': medical_visit, 'Frequency of watching television': tv_watch, 'Highest year of education': edu_year, } # Replace some values in input_ using data for key, value in data.items(): input_df[key] = value # Combines user input features with cleaned dataset # This will be useful for the encoding phase df = pd.concat([input_df,insurance],axis=0,ignore_index=True) @st.cache() # one hot encode categorical features def one_hot_encode(df): # get categorical features of df cat_feat = df.select_dtypes(exclude = np.number).columns for feat in cat_feat: dummy = pd.get_dummies(df[feat], prefix=feat) df = pd.concat([df,dummy], axis=1) del df[feat] input_df = df[:1] # Selects only the first row (the user input data) # remove duplicate columns input_df = input_df.loc[:,~input_df.columns.duplicated()] return input_df input_df = one_hot_encode(df) st.subheader('User Input features') st.write(input_df) # Reads in saved classification model load_clf = pickle.load(open('model/insurance_rf.pkl', 'rb')) # Apply model to make predictions prediction = load_clf.predict(input_df) prediction_proba = load_clf.predict_proba(input_df) st.subheader('Prediction') output = np.array(['No','Yes']) st.write(output[prediction]) st.subheader('Prediction Probability') st.write(prediction_proba) # make feature importance plot st.subheader('Feature Importance Plot') st.markdown(''' The top $30$ factors that most likely influence taking up an health insurance policy by an individual is plotted. Values of some of these factors would be moved around to investigate its effect on our prediction. ''') feat_imp = pd.DataFrame(sorted(zip(load_clf.feature_importances_,input_df.columns)), columns=['Value','Feature']) imp_data = feat_imp.sort_values(by="Value", ascending=False) with _lock: fig = plt.figure(figsize=(20,15)) sns.barplot(x="Value", y="Feature", data=imp_data.iloc[:30]) plt.ylabel('Feature Importance Score') st.pyplot(fig) # Displays the user input features if uploaded_file is not None: st.write(' ') else: st.subheader('User Input features') st.write('Awaiting CSV file to be uploaded...') if __name__=="__main__": main()

64.132576

1,381

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1,540

16,931

7.531169

0.324675

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0.473013

0.468702

0.461114

0

0.06531

0.222255

16,931

263

1,382

64.376426

0.815462

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0.092391

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0.032609

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1

0

1

0.021739

false

0

0.086957

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null

0

1

0

1

0

null

1

0

3

5672ebf88e6a636aad5b677603743bec5d2a56a3

361

py

Python

hello.py

whiterabbitsource/pyhello

7268780aab969d43aadf166ce199e454230bed73

[ "MIT" ]

null

hello.py

whiterabbitsource/pyhello

7268780aab969d43aadf166ce199e454230bed73

[ "MIT" ]

null

hello.py

whiterabbitsource/pyhello

7268780aab969d43aadf166ce199e454230bed73

[ "MIT" ]

null

# Hello! World! print("Hello, World!") # Learning Strings my_string = "This is a string" ## Make string uppercase my_string_upper = my_string.upper() print(my_string_upper) # Determine data type of string print(type(my_string)) # Slicing strings [python is zero-based and starts at 0 and not 1] print(my_string[0:4]) print(my_string[:1]) print(my_string[0:14])

25.785714

66

0.750693

62

361

4.209677

0.467742

0.245211

0.199234

0.10728

0.114943

0

0.025316

0.124654

361

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1

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false

0

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0

null

1

0

null

0

1

0

3

567532ef07dca51337f49b0b35ebbb473c9ee7c1

6,456

py

Python

crescent/policy/actions/kinesis.py

mpolatcan/zepyhrus

2fd0b1b9b21613b5876a51fe8b5f9e3afbec1b67

[ "Apache-2.0" ]

1

2020-03-26T19:20:03.000Z

crescent/policy/actions/kinesis.py

mpolatcan/zepyhrus

2fd0b1b9b21613b5876a51fe8b5f9e3afbec1b67

[ "Apache-2.0" ]

null

crescent/policy/actions/kinesis.py

mpolatcan/zepyhrus

2fd0b1b9b21613b5876a51fe8b5f9e3afbec1b67

[ "Apache-2.0" ]

null

from crescent.resources.kinesis import StreamArn, StreamConsumerArn from .action import Action, AccessLevelAllActions from typing import Union class KinesisAction(Action): __SERVICE_KINESIS = "kinesis" def __init__(self, action_name, **definable_resources): super(KinesisAction, self).__init__(self.__SERVICE_KINESIS, action_name, **definable_resources) def Stream(self, stream: Union[str, StreamArn]): return self._set_resource(self.Stream.__name__, stream) def Consumer(self, consumer: Union[str, StreamConsumerArn]): return self._set_resource(self.Consumer.__name__, consumer) # ----------------------------------------------- class KinesisAccessLevelAllActions(AccessLevelAllActions): def __init__(self, access_level): super(KinesisAccessLevelAllActions, self).__init__(access_level) def Stream(self, stream: Union[str, StreamArn]): return self._set_all_actions_resources(self.Stream.__name__, stream) def Consumer(self, consumer: Union[str, StreamConsumerArn]): return self._set_all_actions_resources(self.Consumer.__name__, consumer) # ----------------------------------------------- class Actions: class Tagging: @staticmethod def AddTagsToStream(): return KinesisAction(Actions.Tagging.AddTagsToStream.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def RemoveTagsFromStream(): return KinesisAction(Actions.Tagging.RemoveTagsFromStream.__name__, required=[KinesisAction.Stream.__name__]) class Write: @staticmethod def CreateStream(): return KinesisAction(Actions.Write.CreateStream.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def DecreaseStreamRetentionPeriod(): return KinesisAction(Actions.Write.DecreaseStreamRetentionPeriod.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def DeleteStream(): return KinesisAction(Actions.Write.DeleteStream.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def DeregisterStreamConsumer(): return KinesisAction(Actions.Write.DeregisterStreamConsumer.__name__, required=[KinesisAction.Consumer.__name__]) @staticmethod def DisableEnhancedMonitoring(): return KinesisAction(Actions.Write.DisableEnhancedMonitoring.__name__) @staticmethod def EnableEnhancedMonitoring(): return KinesisAction(Actions.Write.EnableEnhancedMonitoring.__name__) @staticmethod def IncreaseStreamRetentionPeriod(): return KinesisAction(Actions.Write.IncreaseStreamRetentionPeriod.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def MergeShards(): return KinesisAction(Actions.Write.MergeShards.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def PutRecord(): return KinesisAction(Actions.Write.PutRecord.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def PutRecords(): return KinesisAction(Actions.Write.PutRecords.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def RegisterStreamConsumer(): return KinesisAction(Actions.Write.RegisterStreamConsumer.__name__, required=[KinesisAction.Consumer.__name__]) @staticmethod def SplitShard(): return KinesisAction(Actions.Write.SplitShard.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def UpdateShardCount(): return KinesisAction(Actions.Write.UpdateShardCount.__name__) class Read: @staticmethod def DescribeLimits(): return KinesisAction(Actions.Read.DescribeLimits.__name__) @staticmethod def DescribeStream(): return KinesisAction(Actions.Read.DescribeStream.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def DescribeStreamConsumer(): return KinesisAction(Actions.Read.DescribeStreamConsumer.__name__, required=[KinesisAction.Consumer.__name__]) @staticmethod def DescribeStreamSummary(): return KinesisAction(Actions.Read.DescribeStreamSummary.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def GetRecords(): return KinesisAction(Actions.Read.GetRecords.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def GetShardIterator(): return KinesisAction(Actions.Read.GetShardIterator.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def ListTagsForStream(): return KinesisAction(Actions.Read.ListTagsForStream.__name__, required=[KinesisAction.Stream.__name__]) @staticmethod def UpdateShardCount(): return KinesisAction(Actions.Write.UpdateShardCount.__name__) class List: @staticmethod def ListShards(): return KinesisAction(Actions.List.ListShards.__name__) @staticmethod def ListStreamConsumers(): return KinesisAction(Actions.List.ListStreamConsumers.__name__) @staticmethod def ListStreams(): return KinesisAction(Actions.List.ListStreams.__name__) @staticmethod def TaggingAll(): return KinesisAccessLevelAllActions(Actions.Tagging) @staticmethod def WriteAll(): return KinesisAccessLevelAllActions(Actions.Write) @staticmethod def ReadAll(): return KinesisAccessLevelAllActions(Actions.Read) @staticmethod def ListAll(): return KinesisAccessLevelAllActions(Actions.List) All = "kinesis:*"

43.328859

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6,456

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0.142241

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0.17595

0.121031

0.366216

0.338886

0.131182

0

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6,456

148

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43.621622

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0.014715

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0

null

0

null

0

1

0

1

0

3

568a1aa337f57bc7c4cda92b824694bbeb5808eb

824

py

Python

ACME/geometry/laplacian.py

mauriziokovacic/ACME

2615b66dd4addfd5c03d9d91a24c7da414294308

[ "MIT" ]

3

2019-10-23T23:10:55.000Z

2021-09-01T07:30:14.000Z

ACME/geometry/laplacian.py

mauriziokovacic/ACME-Python

2615b66dd4addfd5c03d9d91a24c7da414294308

[ "MIT" ]

null

ACME/geometry/laplacian.py

mauriziokovacic/ACME-Python

2615b66dd4addfd5c03d9d91a24c7da414294308

[ "MIT" ]

1

2020-07-11T11:35:43.000Z

import torch from ..topology.laplacian import * from .adjacency import * def combinatorial_Laplacian(P, T): """ Computes the combinatorial laplacian matrix for a given mesh. Parameters ---------- P : Tensor the input points set tensor T : LongTensor the topology tensor Returns ------- Tensor the laplacian matrix """ return laplacian(Adjacency(T, P=P, type='std')) def cotangent_Laplacian(P, T): """ Computes the cotangent weights laplacian matrix for a given triangle mesh. Parameters ---------- P : Tensor the input points set tensor T : LongTensor the topology tensor Returns ------- Tensor the laplacian matrix """ return laplacian(Adjacency(T, P=P, type='cot'))

18.727273

78

0.59466

91

824

5.362637

0.340659

0.122951

0.045082

0.077869

0.737705

0.54918

0

0.300971

824

43

79

19.162791

0.847222

0.538835

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0.285714

false

0

0.428571

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null

0

null

0

1

0

1

0

1

0

3

569570cd854476e56a289049d62849a9b1e02716

263

py

Python

light_test/light_test/doctype/audit_keith/test_audit_keith.py

kwatkinsLexul/light_test

048937aac2d2b13af7d55b92fc6f7437f74f4c04

[ "MIT" ]

null

light_test/light_test/doctype/audit_keith/test_audit_keith.py

kwatkinsLexul/light_test

048937aac2d2b13af7d55b92fc6f7437f74f4c04

[ "MIT" ]

null

light_test/light_test/doctype/audit_keith/test_audit_keith.py

kwatkinsLexul/light_test

048937aac2d2b13af7d55b92fc6f7437f74f4c04

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2015, Keith and Contributors # See license.txt from __future__ import unicode_literals import frappe import unittest # test_records = frappe.get_test_records('Audit Keith') class TestAuditKeith(unittest.TestCase): pass

20.230769

55

0.768061

34

263

5.705882

0.794118

0.113402

0

0.02193

0.13308

263

12

56

21.916667

0.828947

0.509506

0

1

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true

0.2

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0

0.8

0

null

0

null

0

1

0

3

56a8778cd78552434dd56f302aa163ca4702f8d3

211

py

Python

yt/frontends/art/api.py

Xarthisius/yt

321643c3abff64a6f132d98d0747f3558f7552a3

[ "BSD-3-Clause-Clear" ]

360

2017-04-24T05:06:04.000Z

2022-03-31T10:47:07.000Z

yt/frontends/art/api.py

Xarthisius/yt

321643c3abff64a6f132d98d0747f3558f7552a3

[ "BSD-3-Clause-Clear" ]

2,077

2017-04-20T20:36:07.000Z

2022-03-31T16:39:43.000Z

yt/frontends/art/api.py

stonnes/yt

aad3cfa3b4ebab7838352ab467275a27c26ff363

[ "BSD-3-Clause-Clear" ]

257

2017-04-19T20:52:28.000Z

2022-03-29T12:23:52.000Z

from . import tests from .data_structures import ( ARTDataset, ARTDomainFile, ARTDomainSubset, ARTIndex, DarkMatterARTDataset, ) from .fields import ARTFieldInfo from .io import IOHandlerART

19.181818

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0.748815

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7.85

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0.199052

211

10

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21.1

0.928994

0

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0

true

0

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null

0

null

0

1

0

1

0

3

3b14a8a90a9b966384d4097207eed24c289af924

9,786

py

Python

src/configs/project_configs.py

PawelRosikiewicz/SkinDiagnosticAI

7cc7b7a9ccd4103095a7548e7b99de4988858356

[ "MIT" ]

1

2021-05-15T09:57:25.000Z

src/configs/project_configs.py

PawelRosikiewicz/SkinDiagnosticAI

7cc7b7a9ccd4103095a7548e7b99de4988858356

[ "MIT" ]

null

src/configs/project_configs.py

PawelRosikiewicz/SkinDiagnosticAI

7cc7b7a9ccd4103095a7548e7b99de4988858356

[ "MIT" ]

null

# ********************************************************************************** # # # # Project: SkinAnaliticAI # # Author: Pawel Rosikiewicz # # Contact: prosikiewicz_gmail.com # # # #. This notebook is a part of Skin AanaliticAI development kit, created # #. for evaluation of public datasets used for skin cancer detection with # #. large number of AI models and data preparation pipelines. # # # # License: MIT # #. Copyright (C) 2021.01.30 Pawel Rosikiewicz # # https://opensource.org/licenses/MIT # # # # ********************************************************************************** # #!/usr/bin/env python # -*- coding: utf-8 -*- # config, ........................................................................................... PROJECT_NAME = "Skin_cancer_detection_and_classyfication" # config, ........................................................................................... # CLASS_DESCRIPTION #. "key" : str, class name used in original dataset downloaded form databse # "original_name" : str, same as the key, but you can introduce other values in case its necessarly # "class_full_name" : str, class name used on images, saved data etc, (more descriptive then class names, or sometimes the same according to situation) # "class_group" : str, group of classes, if the classes are hierarchical, # "class_description" : str, used as notes, or for class description available for the user/client # "links" : list, with link to more data, on each class, CLASS_DESCRIPTION = { 'akiec':{ "original_name":'akiec', "class_full_name": "squamous_cell_carcinoma", # prevoisly called "Actinic_keratoses" in my dataset, but ths name is easier to find in online resourses, noth names are correct, "class_group": "Tumour_Benign", "class_description": "Class that contains two subclasses:(A) Actinic_Keratoses or (B) Bowen’s disease. Actinic Keratoses (Solar Keratoses) and Intraepithelial Carcinoma (Bowen’s disease) are common non-invasive, variants of squamous cell carcinoma that can be treated locally without surgery. These lesions may progress to invasive squamous cell carcinoma – which is usually not pigmented. Both neoplasms commonly show surface scaling and commonly are devoid of pigment, Actinic keratoses are more common on the face and Bowen’s disease is more common on other body sites. Because both types are induced by UV-light the surrounding skin is usually typified by severe sun damaged except in cases of Bowen’s disease that are caused by human papilloma virus infection and not by UV. Pigmented variants exist for Bowen’s disease and for actinic keratoses", "links":["https://dermoscopedia.org/Actinic_keratosis_/_Bowen%27s_disease_/_keratoacanthoma_/_squamous_cell_carcinoma"] }, 'bcc':{ "original_name":'bcc', "class_full_name": "Basal_cell_carcinoma", "class_group": "Tumour_Benign", "class_description": "Basal cell carcinoma (BCC) is the most common type of skin cancer in the world that rarely metastasizes but grows destructively if untreated. It appears in different morphologic variants (flat, nodular, pigmented, cystic). There are multiple histopathologic subtypes of BCC including superficial, nodular, morpheaform/sclerosing/infiltrative, fibroepithelioma of Pinkus, microcytic adnexal and baso-squamous cell BCC. Each subtype can be clinically pigmented or non-pigmented. It is not uncommon for BCCs to display pigment on dermoscopy with up to 30% of clinically non-pigmented BCCs revealing pigment on dermoscopy. Based on the degree of pigmentation, some BCCs can mimic melanomas or other pigmented skin lesions. Depending on the subtype of BCC and the degree of pigmentation, the clinical differential diagnosis can be quite broad ranging from benign inflammatory lesions to melanoma. Fortunately, the dermoscopic criteria for BCC are visible irrespective of the size of the tumor and can be well distiguished using dermatoscopy", "links":["https://dermoscopedia.org/Basal_cell_carcinoma"] }, 'bkl':{ "original_name":'bkl', "class_full_name": "Benign_keratosis", "class_group": "Tumour_Benign", "class_description": "Benign keratosis is a generic group that includes three typesy of non-carcinogenig lesions: (A) seborrheic keratoses (senile wart), (B) solar lentigo - which can be regarded a flat variant of seborrheic keratosis, (C) and lichen-planus like keratoses (LPLK), which corresponds to a seborrheic keratosis or a solar lentigo with inflammation and regression. The three subgroups may look different dermatoscopically, but we grouped them together because they are similar biologically and often reported under the same generic term histopathologically. Briefly: Seborrheic keratoses (A) are benign epithelial lesions that can appear on any part of the body except for the mucous membranes, palms, and soles. The lesions are quite prevalent in people older than 30 years. Early seborrheic keratoses are light - to dark brown oval macules with sharply demarcated borders. As the lesions progress, they transform into plaques with a waxy or stuck-on appearance, often with follicular plugs scattered over their surfaces. The size of the lesions varies from a few millimeters to a few centimeters. Solar lentigines (B) are sharply circumscribed, uniformly pigmented macules that are located predominantly on the sun-exposed areas of the skin, such as the dorsum of the hands, the shoulders, and the scalp. Lentigines are a result of hyperplasia of keratinocytes and melanocytes, with increased accumulation of melanin in the keratinocytes. They are induced by ultraviolet light exposure. Unlike freckles, solar lentigines persist indefinitely. Nearly 90% of Caucasians over the age of 60 years have these lesions. LPLK (C), is one of the common benign neoplasms of the skin, and it is highly variable in its appearance, Some LPKL can show morphologic features mimicking melanoma and are often biopsied or excised for diagnostic reasons", "links": ["https://dermoscopedia.org/Solar_lentigines_/_seborrheic_keratoses_/_lichen_planus-like_keratosis"] }, 'df': { "original_name":'df', "class_full_name": "Dermatofibroma", "class_group": "Tumour_Benign", "class_description": "Dermatofibromas (DFs) are prevalent cutaneous lesions that most frequently affect young to middle-aged adults, with a slight predominance in females. Clinically, dermatofibromas appear as firm, single or multiple papules/nodules with a relatively smooth surface and predilection for the lower extremities. Characteristically, upon lateral compression of the skin surrounding dermatofibromas, the tumors tend to pucker inward producing a dimple-like depression in the overlying skin; a feature known as the dimple or Fitzpatrick’s sign. Dermatofibroma is a benign skin lesion regarded as either a benign proliferation or an inflammatory reaction to minimal trauma. The most common dermatoscopic presentation is reticular lines at the periphery with a central white patch denoting fibrosis", "links": ["https://dermoscopedia.org/Dermatofibromas"] }, 'nv': { "original_name":'nv', "class_full_name": "Melanocytic_nevus", "class_group": "Tumour_Benign", "class_description": "Melanocytic nevi are benign neoplasms of melanocytes and appear in a myriad of variants, which all were included in train data used for diagnosis. The variants may differ significantly from a dermatoscopic point of view. Unlike, melanoma they are usually symmetric with regard to the distribution of color and structure", "links":["https://dermoscopedia.org/Benign_Melanocytic_lesions"] }, "mel": { "original_name":'mel', "class_full_name": "Melanoma", "class_group": "Tumour_Malignant", "class_description": "Melanoma is a malignant neoplasm derived from melanocytes that may appear in different variants. If excised in an early stage it can be cured by simple surgical excision. Melanomas can be invasive or non-invasive (in situ). Melanomas are usually, albeit not always, chaotic, and some melanoma specific criteria depend on anatomic site, All variants of melanoma including melanoma in situ, except for non-pigmented, subungual, ocular or mucosal melanoma were included in train dataset used for diagnosis", "linkss": ["https://dermoscopedia.org/Melanoma"] }, 'vasc':{ "original_name":'vasc', "class_full_name": "Vascular_skin_lesions", "class_group": "Vascular_skin_lesions", "class_description": "Angiomas are dermatoscopically characterized by red or purple color and solid, well circumscribed structures known as red clods or lacunes.Data Used for training for diagnosis: Vascular skin lesions in the dataset range from cherry angiomas to angiokeratomas and pyogenic granulomas. Hemorrhage is also included in this category", "links": ["https://dermoscopedia.org/Vascular_lesions"] } }

109.955056

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9,786

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109.955056

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0

null

0

1

0

1

0

null

0

3

3b150c9ed8bd6f6ef6cb2c14cefd89505c76a9dd

152

py

Python

backend/config/pagination.py

hnthh/foodgram-project-react

3383c6a116fded11b4a764b95e6ca4ead03444f3

[ "MIT" ]

1

2022-02-09T10:42:45.000Z

backend/config/pagination.py

hnthh/foodgram

3383c6a116fded11b4a764b95e6ca4ead03444f3

[ "MIT" ]

null

backend/config/pagination.py

hnthh/foodgram

3383c6a116fded11b4a764b95e6ca4ead03444f3

[ "MIT" ]

null

from rest_framework.pagination import PageNumberPagination class LimitQueryParamPagination(PageNumberPagination): page_size_query_param = 'limit'

25.333333

58

0.855263

14

152

9

0.928571

0

0.098684

152

5

59

30.4

0.919708

0

0.032895

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

3

3b42179ab00c78174874156f3d536008a8c28bea

258

py

Python

phyton/Exercicio026_Quantidade_de_Letras_A.py

felipebaloneker/Practice

6c4f9b9f91c872350b566927fe9df10aed6930be

[ "MIT" ]

null

phyton/Exercicio026_Quantidade_de_Letras_A.py

felipebaloneker/Practice

6c4f9b9f91c872350b566927fe9df10aed6930be

[ "MIT" ]

null

phyton/Exercicio026_Quantidade_de_Letras_A.py

felipebaloneker/Practice

6c4f9b9f91c872350b566927fe9df10aed6930be

[ "MIT" ]

null

frase = str(input('Digite uma frase?').lower().strip()) print('Na Frase há: {} letra A.'.format(frase.count('a'))) print('Ela aparece pela primeira vez em {}.'.format(frase.find('a')+1)) print(' Ela aparece pela ultima vez em {}'.format(frase.rfind('a')+1))

51.6

71

0.662791

42

258

4.071429

0.571429

0.192982

0.175439

0.222222

0

0.008621

0.100775

258

4

72

64.5

0.728448

0

0.44186

0

1

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false

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0.75

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null

0

1

0

1

0

null

0

1

0

3

3b48b93d3bf4369cb18e3ae1122381bb59ebc4f4

571

py

Python

pynars/Narsese/_py/Interval.py

AIxer/PyNARS

443b6a5e1c9779a1b861df1ca51ce5a190998d2e

[ "MIT" ]

null

pynars/Narsese/_py/Interval.py

AIxer/PyNARS

443b6a5e1c9779a1b861df1ca51ce5a190998d2e

[ "MIT" ]

null

pynars/Narsese/_py/Interval.py

AIxer/PyNARS

443b6a5e1c9779a1b861df1ca51ce5a190998d2e

[ "MIT" ]

null

from typing import Type from .Term import Term class Interval(Term): is_interval: bool = True def __init__(self, interval, do_hashing=False, word_sorted=None, is_input=False) -> None: super().__init__("+"+str(interval), do_hashing=do_hashing, word_sorted=word_sorted, is_input=is_input) self.interval = int(interval) def __repr__(self) -> str: return f'<Interval: {str(self)}>' def __int__(self) -> int: return self.interval def __add__(self, o: Type['Interval']): return Interval(int(self)+int(o))

31.722222

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0.658494

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571

4.493506

0.363636

0.104046

0.098266

0

0.210158

571

18

111

31.722222

0.767184

0

0.055944

0

1

0.307692

false

0

0.153846

0.230769

0.846154

0

null

0

null

0

1

0

1

0

3

3b526aed7d086bae704ca9ac633d49d7f0033d5e

162

py

Python

py_tdlib/constructors/page_block_related_articles.py

Mr-TelegramBot/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

24

2018-10-05T13:04:30.000Z

2020-05-12T08:45:34.000Z

py_tdlib/constructors/page_block_related_articles.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

3

2019-06-26T07:20:20.000Z

2021-05-24T13:06:56.000Z

py_tdlib/constructors/page_block_related_articles.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

5

2018-10-05T14:29:28.000Z

2020-08-11T15:04:10.000Z

from ..factory import Type class pageBlockRelatedArticles(Type): header = None # type: "RichText" articles = None # type: "vector<pageBlockRelatedArticle>"

23.142857

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7.5625

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0.132231

0

0.148148

162

6

60

27

0.876812

0.345679

0

1

0

false

0

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1

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0

null

0

1

0

null

0

1

0

3

3b6599b100e35ef2c8d864695349bf3c844cd6ac

151

py

Python

src/webshot/utils.py

hostilex00/webshot

55e3d866af9136dc5e37eccb19ba2507346bd598

[ "MIT" ]

null

src/webshot/utils.py

hostilex00/webshot

55e3d866af9136dc5e37eccb19ba2507346bd598

[ "MIT" ]

null

src/webshot/utils.py

hostilex00/webshot

55e3d866af9136dc5e37eccb19ba2507346bd598

[ "MIT" ]

null

from pathlib import Path def mkdirs(directories: list): for directory in directories: Path(directory).mkdir(parents=True, exist_ok=True)

21.571429

58

0.735099

20

151

5.5

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0

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6

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25.166667

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false

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null

0

1

0

null

0

1

0

3

8e5c752e8092ec0210ade846c9f8fa29bd9e3eb9

174

py

Python

setup.py

neureal/gym-zmq

9df9adcc2cedecebca9556ace13ba0729add902f

[ "MIT" ]

null

setup.py

neureal/gym-zmq

9df9adcc2cedecebca9556ace13ba0729add902f

[ "MIT" ]

null

setup.py

neureal/gym-zmq

9df9adcc2cedecebca9556ace13ba0729add902f

[ "MIT" ]

null

from setuptools import setup setup(name='gym_zmq', version='0.0.1', install_requires=['gym>=0.10.9', 'pyzmq>=17.1.2'] # libzmq5 v4.1.4 )

21.75

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8e601e1d6371060deac4d7262c1a8ed8ff0d7958

256

py

Python

finance/admin.py

NSYT0607/DONGKEY

83f926f22a10a28895c9ad71038c9a27d200e231

[ "MIT" ]

1

2018-04-10T11:47:16.000Z

finance/admin.py

NSYT0607/DONGKEY

83f926f22a10a28895c9ad71038c9a27d200e231

[ "MIT" ]

null

finance/admin.py

NSYT0607/DONGKEY

83f926f22a10a28895c9ad71038c9a27d200e231

[ "MIT" ]

null

from django.contrib import admin from .models import ( Accounting, Classification, Income, Expenditure ) admin.site.register(Accounting) admin.site.register(Classification) admin.site.register(Income) admin.site.register(Expenditure)

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null

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8e7570909604fc79b6b2cfa734263ec181f0833e

241

py

Python

posix_checkapi/TRACES/POT/ut_lind_fs_statfs.py

JustinCappos/checkapi

2508c414869eda3479e1384b1bea65ec1e749d3b

[ "Apache-2.0" ]

null

posix_checkapi/TRACES/POT/ut_lind_fs_statfs.py

JustinCappos/checkapi

2508c414869eda3479e1384b1bea65ec1e749d3b

[ "Apache-2.0" ]

null

posix_checkapi/TRACES/POT/ut_lind_fs_statfs.py

JustinCappos/checkapi

2508c414869eda3479e1384b1bea65ec1e749d3b

[ "Apache-2.0" ]

null

import lind_test_server from lind_fs_constants import * lind_test_server._blank_fs_init() # / should exist. statfsdict = lind_test_server.statfs_syscall('/') assert(statfsdict['f_type']==0xBEEFC0DE) assert(statfsdict['f_bsize']==4096)

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null

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null

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3

8e9220eee6986672fd8ba4788d066506d5b28c4d

2,828

py

Python

src/api/datamanage/pro/datamodel/application/jobs/console.py

Chromico/bk-base

be822d9bbee544a958bed4831348185a75604791

[ "MIT" ]

84

2021-06-30T06:20:23.000Z

2022-03-22T03:05:49.000Z

src/api/datamanage/pro/datamodel/application/jobs/console.py

Chromico/bk-base

be822d9bbee544a958bed4831348185a75604791

[ "MIT" ]

7

2021-06-30T06:21:16.000Z

2022-03-29T07:36:13.000Z

src/api/datamanage/pro/datamodel/application/jobs/console.py

Chromico/bk-base

be822d9bbee544a958bed4831348185a75604791

[ "MIT" ]

40

2021-06-30T06:21:26.000Z

2022-03-29T12:42:26.000Z

# -*- coding: utf-8 -*- """ Tencent is pleased to support the open source community by making BK-BASE 蓝鲸基础平台 available. Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved. BK-BASE 蓝鲸基础平台 is licensed under the MIT License. License for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from datamanage.pro.datamodel.application.jobs.orchestrator import achieved_job_system class Console(object): job_system = None def __init__(self, data_scope, job_type='AssembleSql'): """ 任务面板初始化 :param data_scope: 数据域 { "node_type": (string) dim/fact/indicator, "node_instance": } :param job_type: 任务类型 AssembleSql - 构建sql任务 """ if job_type in achieved_job_system: self.job_system = achieved_job_system[job_type](data_scope=data_scope) if self.job_system is None: # Todo: XXX任务系统还未实现 raise def build(self, *args, **kwargs): """ 构建任务 :return: mixed None - 无执行内容; 非None - 执行结果 """ plan = self.job_system.gen_plan(command='build') return plan.execute(*args, **kwargs) def destroy(self): """ 销毁任务 :return: mixed None - 无执行内容; 非None - 执行结果 """ plan = self.job_system.gen_plan(command='destroy') return plan.execute() def start(self): """ 启动任务 :return: mixed None - 无执行内容; 非None - 执行结果 """ plan = self.job_system.gen_plan(command='start') return plan.execute() def stop(self): """ 停止任务 :return: mixed None - 无执行内容; 非None - 执行结果 """ plan = self.job_system.gen_plan(command='stop') return plan.execute()

35.35

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1

0

1

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3

8e9f1c6c567e138d25c328267947ccd37f8edfc2

201

py

Python

examples/create_frames.py

axju/blurring

2f2e50b7f47d8556be2d74687f34ac6eabd8d235

[ "MIT" ]

15

2019-09-20T14:20:53.000Z

2022-01-06T13:31:17.000Z

examples/create_frames.py

axju/blurring

2f2e50b7f47d8556be2d74687f34ac6eabd8d235

[ "MIT" ]

null

examples/create_frames.py

axju/blurring

2f2e50b7f47d8556be2d74687f34ac6eabd8d235

[ "MIT" ]

2

2019-09-21T05:37:30.000Z

2019-09-22T04:53:57.000Z

import os from blurring.utils import create_frames root = os.path.dirname(os.path.abspath(__file__)) src = os.path.join(root, 'video.mp4') dest = os.path.join(root, 'frames') create_frames(src, dest)

25.125

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33

201

4.393939

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0.165517

0.137931

0.193103

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0.005525

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201

7

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null

0

1

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1

0

null

0

1

0

3

8eb2a20778f7cf51025f1dd897c1a4cb3894ebca

327

py

Python

app/routes/main.py

selutin99/flask-template

76afd4544433bad6052a82c927696808c5821979

[ "Apache-2.0" ]

1

2022-01-04T11:21:57.000Z

app/routes/main.py

selutin99/flask-template

76afd4544433bad6052a82c927696808c5821979

[ "Apache-2.0" ]

null

app/routes/main.py

selutin99/flask-template

76afd4544433bad6052a82c927696808c5821979

[ "Apache-2.0" ]

null

from flask import jsonify, make_response from flask import render_template, Blueprint main = Blueprint('main', __name__, template_folder='templates') @main.route('/') def index(): return render_template('main/index.html') @main.route('/json') def json(): return make_response(jsonify(response='Hello world'), 200)

21.8

63

0.737003

42

327

5.52381

0.52381

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0.12931

0

0.010453

0.122324

327

14

64

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0

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false

0

0.222222

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0.222222

0

null

0

null

0

1

0

1

0

3

8ebad60b678a7665a77bbce9a9153f1b6af1fbe6

915

py

Python

pyobs/events/log.py

pyobs/pyobs-core

e3401e63eb31587c2bc535f7346b7e4ef69d64ab

[ "MIT" ]

4

2020-02-14T10:50:03.000Z

2022-03-25T04:15:06.000Z

pyobs/events/log.py

pyobs/pyobs-core

e3401e63eb31587c2bc535f7346b7e4ef69d64ab

[ "MIT" ]

60

2020-09-14T09:10:20.000Z

2022-03-25T17:51:42.000Z

pyobs/events/log.py

pyobs/pyobs-core

e3401e63eb31587c2bc535f7346b7e4ef69d64ab

[ "MIT" ]

2

2020-10-14T09:34:57.000Z

2021-04-27T09:35:57.000Z

from .event import Event class LogEvent(Event): """Event for log entries.""" __module__ = 'pyobs.events' def __init__(self, time=None, level=None, filename=None, function=None, line=None, message=None): Event.__init__(self) self.data = { 'time': time, 'level': level, 'filename': filename, 'function': function, 'line': line, 'message': message } @property def time(self): return self.data['time'] @property def level(self): return self.data['level'] @property def filename(self): return self.data['filename'] @property def function(self): return self.data['function'] @property def line(self): return self.data['line'] @property def message(self): return self.data['message'] __all__ = ['LogEvent']

20.333333

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0

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915

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null

0

1

0

1

0

3

8ee7284d8bcc58529f5312577823cdb140b81117

856

py

Python

steps/step-01/main.py

sfeir-open-source/sfeir-school-python

7ae95b74cc9867d1dbcc90559ca0d47edb0b0883

[ "Apache-2.0" ]

5

2020-04-29T13:26:28.000Z

2022-03-17T13:02:35.000Z

steps/step-01/main.py

sfeir-open-source/sfeir-school-python

7ae95b74cc9867d1dbcc90559ca0d47edb0b0883

[ "Apache-2.0" ]

12

2020-07-24T10:08:26.000Z

2022-03-15T08:10:25.000Z

steps/step-01/main.py

sfeir-open-source/sfeir-school-python

7ae95b74cc9867d1dbcc90559ca0d47edb0b0883

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ To-Do application """ def add(todos): """ Add a task """ pass def delete(todos, index=None): """ Delete one or all tasks """ pass def get_printable_todos(todos): """ Get formatted tasks """ pass def toggle_done(todos, index): """ Toggle a task """ pass def view(todos, index): """ Print tasks """ print('\nTo-Do list') print('=' * 40) def main(): """ Main function """ print('Add New tasks...') # TODO Add 3 tasks & print print('\nThe Second one is toggled') # TODO Toggle the second task & print print('\nThe last one is removed') # TODO Remove only the third task & print print('\nAll the todos are cleaned.') # TODO Remove all the tasks & print if __name__ == '__main__': main()

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1

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1

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1

0

3

d9031eb28f7c4001d26f8afc819693851750038e

265

py

Python

test/test.py

gosia1138/MontyHall

21d6a79bb857e1820c715d44a72af9eee248a215

[ "MIT" ]

null

test/test.py

gosia1138/MontyHall

21d6a79bb857e1820c715d44a72af9eee248a215

[ "MIT" ]

null

test/test.py

gosia1138/MontyHall

21d6a79bb857e1820c715d44a72af9eee248a215

[ "MIT" ]

null

#!/usr/bin/env python3 import PIL import os import unittest from io import StringIO from unittest.mock import patch from .context import * class TestBasic(unittest.TestCase): def test_first(self): pass if __name__ == '__main__': unittest.main()

15.588235

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16

36

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0

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0

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0

null

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null

0

1

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1

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3

d90ad2223de74fc247f1ab04f905c51abb4a6c4c

333

py

Python

app/core/models/profile.py

rdurica/example

733420f955b679d34adfb6bffa35b17177e086f6

[ "MIT" ]

null

app/core/models/profile.py

rdurica/example

733420f955b679d34adfb6bffa35b17177e086f6

[ "MIT" ]

1

2022-03-15T22:42:58.000Z

2022-03-15T23:05:30.000Z

app/core/models/profile.py

rdurica/example

733420f955b679d34adfb6bffa35b17177e086f6

[ "MIT" ]

null

from django.contrib.auth.models import User from django.db import models class Profile(models.Model): user = models.OneToOneField(User, on_delete=models.CASCADE) def __str__(self) -> str: return f"{self.user.username}" def __repr__(self) -> str: return f"--id: {self.id} --name: {self.user.username}"

25.615385

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64

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0

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false

0

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1

0

null

0

null

0

1

0

1

0

3

d90d534a5142bd3ecc5d983e901a22b900394171

164

py

Python

ex015.py

mateusguida/ExerciciosPython

70f2df0a2a7bfd152205bcce228e2161c11f5888

[ "MIT" ]

null

ex015.py

mateusguida/ExerciciosPython

70f2df0a2a7bfd152205bcce228e2161c11f5888

[ "MIT" ]

null

ex015.py

mateusguida/ExerciciosPython

70f2df0a2a7bfd152205bcce228e2161c11f5888

[ "MIT" ]

null

dias = int(input("Quantos dias alugados? ")) km = float(input("Quantos Kms rodados: ")) preco = 60 * dias + 0.15 * km print(f'O total a pagar é de R${preco:.2f}')

27.333333

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3.62069

0.793103

0.228571

0

0.044444

0.176829

164

6

45

27.333333

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0

0.472727

0

1

0

false

0

0.25

1

0

null

1

0

1

0

null

0

3

d9102bf14ddafe216862a4391d0aa3a6ce4ed657

293

py

Python

src/test/stack_growth.py

yuyichao/rr

18f2ae57eee76e50c216066ad9163a90d0dfddb5

[ "BSD-1-Clause" ]

2

2020-10-29T02:10:54.000Z

2021-06-20T00:00:26.000Z

src/test/stack_growth.py

yuyichao/rr

18f2ae57eee76e50c216066ad9163a90d0dfddb5

[ "BSD-1-Clause" ]

4

2018-07-14T23:44:05.000Z

2018-11-28T00:04:30.000Z

src/test/stack_growth.py

yuyichao/rr

18f2ae57eee76e50c216066ad9163a90d0dfddb5

[ "BSD-1-Clause" ]

6

2018-06-07T02:28:36.000Z

2019-09-02T07:36:30.000Z

from rrutil import * send_gdb('break breakpoint') expect_gdb('Breakpoint 1') send_gdb('c') expect_gdb('Breakpoint 1') send_gdb('finish') send_gdb('watch -l buf[100]') expect_gdb('Hardware[()/a-z ]+watchpoint 2') send_gdb('c') expect_gdb('Old value = 0') expect_gdb('New value = 100') ok()

17.235294

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4.083333

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16

45

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0

null

0

1

0

null

0

1

0

3

d91c3821ef45ca7747d2fb1419586c778cda6068

4,691

py

Python

4/tests.py

remihuguet/aoc2020

c313c5b425dda92d949fd9ca4f18ff66f452794f

[ "MIT" ]

null

4/tests.py

remihuguet/aoc2020

c313c5b425dda92d949fd9ca4f18ff66f452794f

[ "MIT" ]

null

4/tests.py

remihuguet/aoc2020

c313c5b425dda92d949fd9ca4f18ff66f452794f

[ "MIT" ]

null

import passport filename = '4/test_input.txt' def test_parse_batch_file_properly(): passports = passport.read_batch(filename) assert 4 == len(passports) assert "ecl:gry pid:860033327 eyr:2020 hcl:#fffffd byr:1937 iyr:2017 cid:147 hgt:183cm" == passports[0] def test_passport_is_valid_againt_fields(): passports = passport.read_batch(filename) assert passport.is_valid(passports[0]) assert not passport.is_valid(passports[1]) assert passport.is_valid(passports[2]) assert not passport.is_valid(passports[3]) def test_count_valids(): assert 2 == passport.count_valids(filename) def test_validate_byr(): assert passport.is_byr_valid('byr:2002') assert not passport.is_byr_valid('byr:2003') assert not passport.is_byr_valid('byr:2dsdsdsds') assert passport.is_byr_valid('byr:1920') assert not passport.is_byr_valid('byr:1919') assert not passport.is_byr_valid('byr:192') assert not passport.is_byr_valid('byr:20022') assert passport.is_byr_valid('byr:2002 ') assert passport.is_byr_valid('dskdlms byr:2002 hgt:fdiksdlkd') def test_validate_hgt(): assert passport.is_hgt_valid('dsdsd hgt:60in fkmdslkfml') assert passport.is_hgt_valid('hgt:59in') assert passport.is_hgt_valid('dsdsd hgt:76in fkmdslkfml') assert not passport.is_hgt_valid('dsdsd hgt:77in fkmdslkfml') assert not passport.is_hgt_valid('hgt:190in') assert not passport.is_hgt_valid('hgt:58in') assert passport.is_hgt_valid('dfsdsd hgt:150cm ') assert passport.is_hgt_valid('dfsdsd hgt:190cm ') assert passport.is_hgt_valid('dfsdsd hgt:193cm ') assert not passport.is_hgt_valid('dfsdsd hgt:194cm ') assert not passport.is_hgt_valid('hgt:149cm') assert not passport.is_hgt_valid('hgt:190') assert not passport.is_hgt_valid('hgt:1919') def test_validate_iyr(): assert passport.is_iyr_valid('iyr:2010') assert not passport.is_iyr_valid('iyr:2008') assert not passport.is_iyr_valid('iyr:2dsdsdsds') assert passport.is_iyr_valid('iyr:2020') assert passport.is_iyr_valid('iyr:2012') def test_validate_eyr(): assert passport.is_eyr_valid('eyr:2020') assert not passport.is_eyr_valid('eyr:2018') assert not passport.is_eyr_valid('eyr:2dsdsdsds') assert passport.is_eyr_valid('eyr:2030') assert not passport.is_eyr_valid('eyr:2032') def test_validate_hcl(): assert passport.is_hcl_valid('hcl:#12ac45') assert not passport.is_hcl_valid('hcl:#12ac45dd') assert not passport.is_hcl_valid('hcl:12ac45') assert not passport.is_hcl_valid('hcl:#12ac4') assert not passport.is_hcl_valid('hcl:#12ac4!') def test_validate_ecl(): assert passport.is_ecl_valid('ecl:amb') assert passport.is_ecl_valid('ecl:blu') assert passport.is_ecl_valid('ecl:brn') assert passport.is_ecl_valid('ecl:gry') assert passport.is_ecl_valid('ecl:grn') assert passport.is_ecl_valid('ecl:hzl') assert passport.is_ecl_valid('ecl:oth') assert not passport.is_ecl_valid('ecl:amc') assert not passport.is_ecl_valid('ecl:bla') assert not passport.is_ecl_valid('ecl:brnaaa') assert not passport.is_ecl_valid('ecl:1112323') def test_validate_pid(): assert passport.is_pid_valid('pid:012345678') assert passport.is_pid_valid('pid:458289043') assert not passport.is_pid_valid('pid:akdfmlkf') assert not passport.is_pid_valid('pid:45828904312323232') assert not passport.is_pid_valid('pid:4232') def test_are_fields_valid(): p = 'pid:087499704 hgt:74in ecl:grn iyr:2012 eyr:2030 byr:1980 hcl:#623a2f' assert passport.are_fields_valid(p) def test_are_fields_invalid(): p = 'eyr:1972 cid:100 hcl:#18171d ecl:amb hgt:170 pid:186cm iyr:2018 byr:1926' assert not passport.are_fields_valid(p) def test_invalidate_passports(): passports = [ 'eyr:1972 cid:100 hcl:#18171d ecl:amb hgt:170 pid:186cm iyr:2018 byr:1926', 'iyr:2019 hcl:#602927 eyr:1967 hgt:170cm ecl:grn pid:012533040 byr:1946', 'hcl:dab227 iyr:2012 ecl:brn hgt:182cm pid:021572410 eyr:2020 byr:1992 cid:277', 'hgt:59cm ecl:zzz eyr:2038 hcl:74454a iyr:2023 pid:3556412378 byr:2007' ] assert 0 == passport.count_valid_passports(passports) def test_validate_passports(): passports = [ 'pid:087499704 hgt:74in ecl:grn iyr:2012 eyr:2030 byr:1980 hcl:#623a2f', 'eyr:2029 ecl:blu cid:129 byr:1989 iyr:2014 pid:896056539 hcl:#a97842 hgt:165cm', 'hcl:#888785 hgt:164cm byr:2001 iyr:2015 cid:88 pid:545766238 ecl:hzl eyr:2022', 'iyr:2010 hgt:158cm hcl:#b6652a ecl:blu byr:1944 eyr:2021 pid:093154719' ] assert 4 == passport.count_valid_passports(passports)

36.364341

107

0.731614

728

4,691

4.498626

0.196429

0.177099

0.160916

0.174046

0.656183

0.603969

0.42229

0.137405

0.102595

0

0.122869

0.149861

4,691

128

108

36.648438

0.698345

0

0.0625

0

0.114583

0.29951

0.004477

0

0.666667

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0.145833

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0.75

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0.15625

0

null

0

1

0

null

0

1

0

1

0

3

d92389f85c2992190e44f393f493834251373728

224

py

Python

arc/utils.py

jtguibas/arc

e9df473ce5051f2b9f3981ef219b6a02076bdb42

[ "MIT" ]

null

arc/utils.py

jtguibas/arc

e9df473ce5051f2b9f3981ef219b6a02076bdb42

[ "MIT" ]

null

arc/utils.py

jtguibas/arc

e9df473ce5051f2b9f3981ef219b6a02076bdb42

[ "MIT" ]

null

import numpy as np def softmax(x): stable_logits = x - np.amax(x, axis=1, keepdims=True) # Shift for stabilitity exp_logits = np.exp(stable_logits) return exp_logits / np.sum(exp_logits, axis=1, keepdims=True)

32

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0.709821

37

224

4.162162

0.540541

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0.168831

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0.178571

224

6

83

37.333333

0.826087

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0

1

0.2

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0

0.2

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0.6

0

null

0

1

0

1

0

null

0

1

0

3

d93440e440d253ffe90c7fa95e0e58ebe127eff8

611

py

Python

util/color.py

IVRL/AdversaryLossLandscape

73456788b38bebeac40b833f2ac5d6cb2f1530ea

[ "MIT" ]

3

2022-02-22T18:44:22.000Z

2022-02-24T01:20:14.000Z

util/color.py

IVRL/AdversaryLossLandscape

73456788b38bebeac40b833f2ac5d6cb2f1530ea

[ "MIT" ]

null

util/color.py

IVRL/AdversaryLossLandscape

73456788b38bebeac40b833f2ac5d6cb2f1530ea

[ "MIT" ]

null

import random global_color_map = {} def get_color(color_idx): if color_idx in global_color_map: return global_color_map[color_idx] base_color = ['b', 'y', 'c', 'm', 'g', 'r'] if color_idx < 6: global_color_map[color_idx] = base_color[color_idx] return base_color[color_idx] else: dex = ['0','1','2','3','4','5','6','7','8','9','a','b','c','d','e','f'] ret_color = '#' for _ in range(6): token_idx = random.randint(0,15) ret_color += dex[token_idx] global_color_map[color_idx] = ret_color return ret_color

30.55

79

0.564648

94

611

3.361702

0.425532

0.202532

0.221519

0.18038

0.265823

0.196203

0

0.033113

0.258592

611

20

80

30.55

0.664459

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0

0.058824

0

0.294118

0

null

1

0

null

0

3

d9453ad89c8316f6505aa2d0df85f0eba9d94487

363

py

Python

instance/manage.py

ESA-VirES/eoxserver-magnetism

89746756d80f3cfea05305ee0f373c7a2742cde1

[ "MIT" ]

1

2017-11-21T22:23:45.000Z

instance/manage.py

ESA-VirES/eoxserver-magnetism

89746756d80f3cfea05305ee0f373c7a2742cde1

[ "MIT" ]

null

instance/manage.py

ESA-VirES/eoxserver-magnetism

89746756d80f3cfea05305ee0f373c7a2742cde1

[ "MIT" ]

null

#!/usr/bin/env python import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "instance.settings") from django.core.management import execute_from_command_line # Initialize the EOxServer component system. import eoxserver.core eoxserver.core.initialize() execute_from_command_line(sys.argv)

24.2

72

0.752066

45

363

5.711111

0.622222

0.085603

0.140078

0.171206

0

0.15978

363

14

73

25.928571

0.842623

0.173554

0

0.157718

0.073826

0

1

0

true

0

0.5

0

0.5

0

null

0

1

0

null

0

1

0

1

0

3

d94a47d514957df17bba72e0b18cc8681b80c71c

3,103

py

Python

user.py

12moi/password-locker

44e7e9cbf2008ee42dbdf80e28f5ba1e5c2be28c

[ "MIT" ]

null

user.py

12moi/password-locker

44e7e9cbf2008ee42dbdf80e28f5ba1e5c2be28c

[ "MIT" ]

null

user.py

12moi/password-locker

44e7e9cbf2008ee42dbdf80e28f5ba1e5c2be28c

[ "MIT" ]

null

from collections import UserList class User: ''' class that generates a new user instance ''' # Empty user list array user_list=[] def __init__(self,firstname,lastname, username, userpassword): self.username=username self.firstname=firstname self.lastname=lastname self.password=userpassword def save_user(self): ''' save_user method saves a new user objects to the user_list ''' User.user_list.append(self) @classmethod def diplay_user(cls): return cls.user_list def delete_user(self): ''' A method that deletes a saved account from the list ''' UserList.user_list.remove(self) def verify_user(cls, username,password): ''' A method that very the user if the user exist in the user_list ''' a_user="" for user in user.user_list: if(username==username and password==password): a_user=username return a_user class Credentials(): ''' Create credentials class to help create new objects of credentials ''' acounts = [] @classmethod def __init__(self,accountname,accountusername, accountpassword): ''' a method that defines the user credentials to saved ''' self.accountname=accountname self.accountusername=accountusername self.accountpassword=accountpassword def save_account(self): ''' this is a method that saves Accounts information ''' Credentials.acounts.append(self) def delete_account(self): ''' Deletes saved account credentials ''' Credentials.acounts.remove(self) @classmethod def display_accounts(cls): ''' this method returns the accounts list ''' for acount in cls.acounts: return cls.acounts @classmethod def find_by_username(cls,username): ''' This method takes in a number and finds a contact that matches the number ''' for account in cls.acounts: if account.accountusername==username: return account def save_credentials(self): ''' save_user method saves a new user objects to the user_list ''' Credentials.credentials_list.append(self) def delete_credentils(self): ''' A method that deletes a saved account from the list ''' Credentials.credentials_list.remove(self) @classmethod def find_credentials(cls, account): ''' method that take account and retrieves password for the account ''' for credential in cls.credentials_list: if credential.account==account: return credential @classmethod def display_credentials(cls): ''' A method that returns all the items in the credentials list ''' return cls.credentials_list

25.024194

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0.594586

332

3,103

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0.33387

3,103

123

82

25.227642

0.874214

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1

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0.113208

0.018868

0.471698

0

null

0

1

0

null

0

1

0

1

0

3

d971c3a2420c667305fce359ce9cbfcdcbe1b5e6

150

py

Python

test_codes/deneme6.py

Akerdogmus/ake_python_toolkit

f4228b611584a9311a5b08068b75c7486182a15f

[ "MIT" ]

null

test_codes/deneme6.py

Akerdogmus/ake_python_toolkit

f4228b611584a9311a5b08068b75c7486182a15f

[ "MIT" ]

null

test_codes/deneme6.py

Akerdogmus/ake_python_toolkit

f4228b611584a9311a5b08068b75c7486182a15f

[ "MIT" ]

null

def solution(n): sum = 0 print(list(str(n))) for i, j in enumerate(list(str(n))): sum+=int(j) return sum print(solution(11))

16.666667

40

0.56

25

150

3.36

0.64

0.095238

0.190476

0

0.027273

0.266667

150

9

41

16.666667

0.736364

0

1

0.142857

false

0

0.285714

1

0

null

0

1

0

1

0

null

0

3

d9885225dd9f0070ac668d692d08d6fc3c85815d

295

py

Python

sprinkler/dict_def.py

shanisma/plant-keeper

3ca92ae2d55544a301e1398496a08a45cca6d15b

[ "CC0-1.0" ]

1

2020-04-12T22:00:17.000Z

sprinkler/dict_def.py

shanisma/plant-keeper

3ca92ae2d55544a301e1398496a08a45cca6d15b

[ "CC0-1.0" ]

null

sprinkler/dict_def.py

shanisma/plant-keeper

3ca92ae2d55544a301e1398496a08a45cca6d15b

[ "CC0-1.0" ]

null

from typing import TypedDict class SprinklerCtrlDict(TypedDict): wtl: str # water tag link wvs: int # water_valve_signal fwv: int # force_water_valve fwvs: int # force_water_valve_signal hmin: float # soil_moisture_min_level hmax: float # soil_moisture_max_level

26.818182

42

0.732203

40

295

5.075

0.65

0.147783

0.157635

0.17734

0

0.216949

295

10

43

29.5

0.878788

0.420339

0

1

0

true

0

0.125

0

1

0

null

0

1

0

null

0

1

0

1

0

3

7959489d41e9f1311f07cc1728e0d73086e3d127

602

py

Python

src/PYnative/exercise/Random Data Generation/Q6.py

c-w-m/learning_python

8f06aa41faf9195d978a7d21cbb329280b0d3200

[ "CNRI-Python" ]

null

src/PYnative/exercise/Random Data Generation/Q6.py

c-w-m/learning_python

8f06aa41faf9195d978a7d21cbb329280b0d3200

[ "CNRI-Python" ]

null

src/PYnative/exercise/Random Data Generation/Q6.py

c-w-m/learning_python

8f06aa41faf9195d978a7d21cbb329280b0d3200

[ "CNRI-Python" ]

null

# Generate a random Password which meets the following conditions # Password length must be 10 characters long. # It must contain at least 2 upper case letter, 2 digits, and 2 special symbols. # My Solution import random import string source = string.ascii_letters + string.digits + string.punctuation password = random.choices(string.ascii_uppercase, k=2) password += random.choices(string.digits, k=2) password += random.choices(string.punctuation, k=2) for i in range(4): password += random.choice(source) random.SystemRandom().shuffle(password) password = ''.join(password) print(password)

30.1

80

0.769103

85

602

5.423529

0.564706

0.121475

0.136659

0.175705

0.125813

0

0.017241

0.13289

602

19

81

31.684211

0.8659

0.328904

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false

0.636364

0.181818

0

0.181818

0.090909

0

null

0

1

0

1

0

null

0

1

0

3

7960b1bb22655a09ba0d11b6cd22f4da89ea0a74

619

py

Python

tests/test_CoreCli.py

pbianchi/climatic

dc0fa65640e9b8161d07b10c73245c33244124b9

[ "MIT" ]

12

2021-03-08T13:22:13.000Z

2022-02-10T01:02:41.000Z

tests/test_CoreCli.py

pbianchi/climatic

dc0fa65640e9b8161d07b10c73245c33244124b9

[ "MIT" ]

1

2022-02-03T22:59:33.000Z

2022-02-03T23:46:42.000Z

tests/test_CoreCli.py

pbianchi/climatic

dc0fa65640e9b8161d07b10c73245c33244124b9

[ "MIT" ]

2

2021-10-18T01:38:31.000Z

2022-01-26T23:19:21.000Z

import pexpect import pytest import re from expects import * from unittest.mock import MagicMock from unittest.mock import Mock from climatic.CoreCli import CoreCli def test_core_cli_constructor_destructor(core_cli): connection = Mock() cmd = core_cli(connection) del cmd connection.connect.assert_called_once() connection.disconnect.assert_called_once() @pytest.fixture def core_cli(): class CoreCliExtension(CoreCli): def login(self): pass def logout(self): pass def _get_prompt_size(self): return 3 return CoreCliExtension

21.344828

51

0.705977

75

619

5.64

0.493333

0.066194

0.07565

0.104019

0

0.002105

0.232633

619

28

52

22.107143

0.888421

0

0.086957

0

0.086957

1

0.217391

false

0.086957

0.304348

0.043478

0.652174

0

null

0

null

0

1

0

1

0

3

79677c524299a769c716b175a2f8065776e0f73a

390

py

Python

Flask/NationalEducationRadio/NationalEducationRadio/models/form/CaptionForm.py

Jessieluu/WIRL_national_education_radio

edb8b63c25bc7bd5a9a7d074173f02913971f8a7

[ "MIT" ]

null

Flask/NationalEducationRadio/NationalEducationRadio/models/form/CaptionForm.py

Jessieluu/WIRL_national_education_radio

edb8b63c25bc7bd5a9a7d074173f02913971f8a7

[ "MIT" ]

null

Flask/NationalEducationRadio/NationalEducationRadio/models/form/CaptionForm.py

Jessieluu/WIRL_national_education_radio

edb8b63c25bc7bd5a9a7d074173f02913971f8a7

[ "MIT" ]

null

from flask.ext.wtf import Form from wtforms import StringField, HiddenField from wtforms.validators import DataRequired from wtforms.widgets import TextArea class CaptionForm(Form): """ 傳送關鍵字用的表單 """ caption_id = HiddenField(validators=[DataRequired(message="不能沒有 ID ")]) caption_content = StringField(widget=TextArea(), validators=[DataRequired(message="請匯入逐字稿")])

26

97

0.75641

42

390

6.97619

0.547619

0.112628

0.197952

0

0.141026

390

14

98

27.857143

0.874627

0.023077

0

0.038462

0

1

0

false

0

0.571429

0

1

0

null

0

1

0

null

0

1

0

1

0

3

797c03669cc0f2003183b6b24c272805b0656fb0

30

py

Python

ChatConnector/Config/discordConfig.py

Micadurp/SHODAN

c54b3d1f58c9f54dd6ba3031ef4a1d30032be5f7

[ "MIT" ]

null

ChatConnector/Config/discordConfig.py

Micadurp/SHODAN

c54b3d1f58c9f54dd6ba3031ef4a1d30032be5f7

[ "MIT" ]

null

ChatConnector/Config/discordConfig.py

Micadurp/SHODAN

c54b3d1f58c9f54dd6ba3031ef4a1d30032be5f7

[ "MIT" ]

null

#!/usr/bin/python3 TOKEN = ''

10

18

0.6

4

30

4.5

1

0

0.038462

0.133333

30

2

19

15

0.653846

0.566667

0

1

0

false

0

1

0

null

0

1

0

null

0

3

798014170008bb2859089c82cd78b35c6631add4

627

py

Python

python_helpers/opcode_7xxx.py

RandomBananazz/chip8mc

0e184c392a523c82dbc945325aa2cb9e5487e5e7

[ "MIT" ]

3

2020-09-28T17:50:49.000Z

2020-12-30T18:23:46.000Z

python_helpers/opcode_7xxx.py

RandomBananazz/chip8mc

0e184c392a523c82dbc945325aa2cb9e5487e5e7

[ "MIT" ]

null

python_helpers/opcode_7xxx.py

RandomBananazz/chip8mc

0e184c392a523c82dbc945325aa2cb9e5487e5e7

[ "MIT" ]

null

for x in range(16): with open(f'..\\data\\cpu\\functions\\opcode_switch\\opcode_7xxx\\opcode_7xxx_{x}.mcfunction', 'w') as f: f.write(f'scoreboard players operation Global V{hex(x)[2:].upper()} += Global PC_nibble_4\n') f.write(f'execute if score Global V{hex(x)[2:].upper()} matches 256.. run scoreboard players remove Global V{hex(x)[2:].upper()} 256\n') """ for x in range(16): with open('..\\data\\cpu\\functions\\opcode_switch\\opcode_7xxx.mcfunction', 'a') as f: f.write(f'execute if score Global PC_nibble_2 matches {x} run function cpu:opcode_switch/opcode_7xxx/opcode_7xxx_{x}\n') """

57

144

0.677831

106

627

3.877358

0.367925

0.121655

0.131387

0.160584

0.666667

0.635037

0.525547

0

0.0369

0.135566

627

10

145

62.7

0.721402

0

0.25

0.754617

0.377309

0

1

0

false

0

null

0

1

0

1

0

null

0

3

798e80c7ea92312be89cba8f05d60d3199c9d711

793

py

Python

cluster/silhouette.py

BMI203-2022/project5

c2361a1b9e3a7295068205fecee39c405de324bb

[ "MIT" ]

null

cluster/silhouette.py

BMI203-2022/project5

c2361a1b9e3a7295068205fecee39c405de324bb

[ "MIT" ]

null

cluster/silhouette.py

BMI203-2022/project5

c2361a1b9e3a7295068205fecee39c405de324bb

[ "MIT" ]

20

2022-01-31T20:09:57.000Z

2022-02-15T03:17:27.000Z

import numpy as np from scipy.spatial.distance import cdist class Silhouette: def __init__(self, metric: str = "euclidean"): """ inputs: metric: str the name of the distance metric to use """ def score(self, X: np.ndarray, y: np.ndarray) -> np.ndarray: """ calculates the silhouette score for each of the observations inputs: X: np.ndarray A 2D matrix where the rows are observations and columns are features. y: np.ndarray a 1D array representing the cluster labels for each of the observations in `X` outputs: np.ndarray a 1D array with the silhouette scores for each of the observations in `X` """

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79a4ab842f79f3c8da6ebe9646fe6b242e9a039d

3,760

py

Python

common/migrations/0031_auto_20210805_1214.py

jordanm88/Django-CRM

5faf22acb30aeb32f5830898fd5d8ecd1ac0bbd8

[ "MIT" ]

1,334

2017-06-04T07:47:14.000Z

2022-03-30T17:12:37.000Z

common/migrations/0031_auto_20210805_1214.py

AhmedDoudou/Django-CRM-1

5faf22acb30aeb32f5830898fd5d8ecd1ac0bbd8

[ "MIT" ]

317

2017-06-04T07:48:13.000Z

2022-03-29T19:24:26.000Z

common/migrations/0031_auto_20210805_1214.py

AhmedDoudou/Django-CRM-1

5faf22acb30aeb32f5830898fd5d8ecd1ac0bbd8

[ "MIT" ]

786

2017-06-06T09:18:48.000Z

2022-03-29T01:29:29.000Z

# Generated by Django 3.2.5 on 2021-08-05 06:44 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('common', '0030_alter_user_role'), ] operations = [ migrations.RenameField( model_name='user', old_name='type', new_name='user_type', ), migrations.AlterField( model_name='address', name='address_line', field=models.CharField(blank=True, max_length=255, null=True, verbose_name='Address'), ), migrations.AlterField( model_name='address', name='city', field=models.CharField(blank=True, max_length=255, null=True, verbose_name='City'), ), migrations.AlterField( model_name='address', name='postcode', field=models.CharField(blank=True, max_length=64, null=True, verbose_name='Post/Zip-code'), ), migrations.AlterField( model_name='address', name='state', field=models.CharField(blank=True, max_length=255, null=True, verbose_name='State'), ), migrations.AlterField( model_name='address', name='street', field=models.CharField(blank=True, max_length=55, null=True, verbose_name='Street'), ), migrations.AlterField( model_name='apisettings', name='website', field=models.URLField(max_length=255, null=True), ), migrations.AlterField( model_name='comment_files', name='comment_file', field=models.FileField(null=True, upload_to='comment_files', verbose_name='File'), ), migrations.AlterField( model_name='company', name='address', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='company', name='name', field=models.CharField(blank=True, max_length=100, null=True), ), migrations.AlterField( model_name='document', name='title', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='google', name='dob', field=models.CharField(max_length=50, null=True), ), migrations.AlterField( model_name='google', name='email', field=models.CharField(db_index=True, max_length=200, null=True), ), migrations.AlterField( model_name='google', name='family_name', field=models.CharField(max_length=200, null=True), ), migrations.AlterField( model_name='google', name='gender', field=models.CharField(max_length=10, null=True), ), migrations.AlterField( model_name='google', name='given_name', field=models.CharField(max_length=200, null=True), ), migrations.AlterField( model_name='google', name='google_id', field=models.CharField(max_length=200, null=True), ), migrations.AlterField( model_name='google', name='google_url', field=models.TextField(null=True), ), migrations.AlterField( model_name='google', name='name', field=models.CharField(max_length=200, null=True), ), migrations.AlterField( model_name='google', name='verified_email', field=models.CharField(max_length=200, null=True), ), ]

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1

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null

0

3

79a54dbb12e5a05fd4c9284d99241f5be375fd77

2,495

py

Python

tests/operators/test_defuzz_som.py

amirrr/pyfuzzy

97e88f7b014e9791fb0a3d07d0727867d27ea9d3

[ "Apache-2.0" ]

9

2019-04-11T07:03:04.000Z

2021-05-12T13:01:53.000Z

tests/operators/test_defuzz_som.py

amirrr/pyfuzzy

97e88f7b014e9791fb0a3d07d0727867d27ea9d3

[ "Apache-2.0" ]

null

tests/operators/test_defuzz_som.py

amirrr/pyfuzzy

97e88f7b014e9791fb0a3d07d0727867d27ea9d3

[ "Apache-2.0" ]

13

2019-04-07T19:19:03.000Z

2019-08-20T11:53:23.000Z

import unittest from pyfuzzy.operators import defuzz_som class DefuzzLomTestCase(unittest.TestCase): # Test input type - Input argument should be a dictionary. def test_defuzz_som_1(self): test = [1, 2, 3] self.assertRaises(TypeError, lambda: defuzz_som.defuzz_som(test)) # Test input type - Input argument should be a dictionary. def test_defuzz_som_2(self): test = [[1], [2], [3]] self.assertRaises(TypeError, lambda: defuzz_som.defuzz_som(test)) # Test input type - Input argument should be a dictionary. def test_defuzz_som_3(self): test = 0.1 self.assertRaises(TypeError, lambda: defuzz_som.defuzz_som(test)) # Test input size - Dictionary should have at least one set. def test_defuzz_som_4(self): test = {} self.assertRaises(ValueError, lambda: defuzz_som.defuzz_som(test)) # Test key type - Key of dictionary should be a int. def test_defuzz_som_5(self): test = {1.0: 0.1, 2.0: 0.2, 3.0: 0.3} self.assertRaises(TypeError, lambda: defuzz_som.defuzz_som(test)) # Test value type - Value of dictionary should be a float or int. def test_defuzz_som_6(self): test = {1: '0.1', 2: '0.2', 3: '0.3'} self.assertRaises(TypeError, lambda: defuzz_som.defuzz_som(test)) # Test value type - Value of dictionary should be a float or int. def test_defuzz_som_7(self): test = {1: [0.2], 2: [0.2], 3: [0.1]} self.assertRaises(TypeError, lambda: defuzz_som.defuzz_som(test)) # Test value range - Value should be between 0 or 1. def test_defuzz_som_8(self): test = {1: 2, 2: 3.5, 3: -1} self.assertRaises(ValueError, lambda: defuzz_som.defuzz_som(test)) # Test 1 - return smallest item with largest value def test_defuzz_som_9(self): test = {1: 0.5, 2: 0.3, 3: 0.85, 4: 0.35} self.assertEqual(defuzz_som.defuzz_som(test), 3) # Test 2 - return smallest item with largest value def test_defuzz_som_10(self): test = {0: 0, 1: 0.3, 2: 0.3, 3: 0.3, 4: 0.5, 5: 0.5, 6: 1, 7: 1, 8: 0} self.assertEqual(defuzz_som.defuzz_som(test), 6) # Test 3 - return smallest item with largest value def test_defuzz_som_11(self): test = {0: 0, 1: 0.8, 2: 0.2, 3: 0.8, 4: 0.8, 5: 0.5, 6: 0.5, 7: 0.2, 8: 0.2, 9: 0.2, 10: 0, 11: 0.8} self.assertEqual(defuzz_som.defuzz_som(test), 1) # Run all unittests if __name__ == '__main__': unittest.main()

37.238806

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412

2,495

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0.093403

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0

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2,495

66

110

37.80303

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null

0

1

0

1

0

null

0

1

0

3

79b70ecee41b06171313bee26027ac119e9c3b38

738

py

Python

atom_deb-latest.py

pierreduchemin/install_scripts

87dd42ab54aa36378fc05486b6c43d4fe02ecfd7

[ "Apache-2.0" ]

1

2017-03-21T13:10:23.000Z

atom_deb-latest.py

pierreduchemin/install_scripts

87dd42ab54aa36378fc05486b6c43d4fe02ecfd7

[ "Apache-2.0" ]

null

atom_deb-latest.py

pierreduchemin/install_scripts

87dd42ab54aa36378fc05486b6c43d4fe02ecfd7

[ "Apache-2.0" ]

2

2018-10-11T11:57:22.000Z

2021-10-07T13:45:18.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import urllib.request import os, stat import subprocess print("Downloading atom...") urllib.request.urlretrieve("https://atom.io/download/deb", "/tmp/atom-amd64.deb") os.chmod("/tmp/atom-amd64.deb", 755) subprocess.call(["sudo", "-S", "dpkg", "-i", "/tmp/atom-amd64.deb"]) os.remove("/tmp/atom-amd64.deb") print("Installing atom plugins...") subprocess.call(["apm", "install", "pretty-json"]) subprocess.call(["apm", "install", "platformio-ide-terminal"]) subprocess.call(["apm", "install", "pandoc-convert"]) subprocess.call(["apm", "install", "language-javascript-jsx"]) subprocess.call(["apm", "install", "atom-typescript"]) subprocess.call(["apm", "install", "intellij-idea-keymap"])

35.142857

81

0.688347

96

738

5.291667

0.510417

0.192913

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0.283465

0.066929

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738

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null

0

1

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null

0

1

0

3

79cb802a0934fb13913b1168d3ece2407767d505

50

py

Python

hmr/__init__.py

Mr-Milk/python-hmr

1a71f3413ea2374afc27919031db02e09f0f6b75

[ "MIT" ]

8

2021-01-20T13:28:23.000Z

2021-08-20T21:35:46.000Z

hmr/__init__.py

Mr-Milk/python-hmr

1a71f3413ea2374afc27919031db02e09f0f6b75

[ "MIT" ]

5

2022-02-07T14:54:50.000Z

2022-03-01T20:19:19.000Z

hmr/__init__.py

Mr-Milk/python-hmr

1a71f3413ea2374afc27919031db02e09f0f6b75

[ "MIT" ]

null

__all__ = ["Reloader"] from .api import Reloader

12.5

25

0.72

6

50

5.333333

0.833333

0

0.16

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3

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16.666667

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null

0

1

0

3

8dbb579a608c0e6cabd5b059b9d8539d9c061fa1

294

py

Python

app/api/api.py

ridhanf/python-fastapi-challenge

041a2156c222dbd84805d6b6ee1d9b88b8227db3

[ "MIT" ]

null

app/api/api.py

ridhanf/python-fastapi-challenge

041a2156c222dbd84805d6b6ee1d9b88b8227db3

[ "MIT" ]

null

app/api/api.py

ridhanf/python-fastapi-challenge

041a2156c222dbd84805d6b6ee1d9b88b8227db3

[ "MIT" ]

null

from fastapi import APIRouter from app.api.endpoints import user_controller, course_controller api_router = APIRouter() api_router.include_router(user_controller.router, prefix="/users", tags=["users"]) api_router.include_router(course_controller.router, prefix="/courses", tags=["courses"])

36.75

88

0.809524

38

294

6.026316

0.421053

0.117904

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0.19214

0

0.068027

294

7

89

42

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0.4

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null

0

1

0

1

0

null

0

1

0

3

8de949adc97a823bbea1b557333b0752027c0066

457

py

Python

drone-wrapper/users/views.py

2022-capstone-design-KPUCS/capstone-drone-backend

b56155a44eb7d0353806d534cb4c5220363a68b0

[ "MIT" ]

null

drone-wrapper/users/views.py

2022-capstone-design-KPUCS/capstone-drone-backend

b56155a44eb7d0353806d534cb4c5220363a68b0

[ "MIT" ]

null

drone-wrapper/users/views.py

2022-capstone-design-KPUCS/capstone-drone-backend

b56155a44eb7d0353806d534cb4c5220363a68b0

[ "MIT" ]

2

2022-02-11T09:02:41.000Z

2022-02-20T12:50:59.000Z

from rest_framework.decorators import action from rest_framework import viewsets from rest_framework.permissions import AllowAny, IsAuthenticated from rest_framework.response import Response from .models import User from .permissions import IsUserOrReadOnly from .serializers import UserSerializer class UserViewSet(viewsets.ModelViewSet): queryset = User.objects.all() serializer_class = UserSerializer permission_class = (IsUserOrReadOnly,)

32.642857

64

0.835886

50

457

7.52

0.48

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0.180851

0

0.118162

457

14

65

32.642857

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0

1

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false

0

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0

1

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null

0

1

0

null

0

1

0

1

0

3

8df969118d302f79d52bd1fab665ec1b756561e7

731

py

Python

setup.py

PropertyGuard/psycopg2_numpy_ext

c42acd97e3ac5c784b205555d8828a8c6efd6eab

[ "MIT" ]

4

2015-08-20T16:48:56.000Z

2019-12-17T16:14:26.000Z

setup.py

PropertyGuard/psycopg2_numpy_ext

c42acd97e3ac5c784b205555d8828a8c6efd6eab

[ "MIT" ]

null

setup.py

PropertyGuard/psycopg2_numpy_ext

c42acd97e3ac5c784b205555d8828a8c6efd6eab

[ "MIT" ]

1

2019-12-17T16:15:24.000Z

import setuptools setuptools.setup( name="psycopg2_numpy_ext", version="0.1.0", url="https://github.com/musically-ut/psycopg2-numpy-ext", author="Utkarsh Upadhyay", author_email="musically.ut@gmail.com", description="Adapters for Numpy's types for Psycopg2.", long_description=open('README.rst').read(), packages=setuptools.find_packages(), install_requires=['numpy', 'psycopg2'], classifiers=[ 'Development Status :: 2 - Pre-Alpha', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', ], )

27.074074

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5.797468

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0.207424

0.272926

0.113537

0

0.024263

0.21067

731

26

62

28.115385

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0.030096

0

1

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true

0

0.05

0

0.05

0

null

1

0

null

0

1

0

3

5c169c237c698e84466c60958aec7d8ede0d8b54

30,090

py

Python

dd_pseudolabels.py

Ophir-Gal/ssl-descent

3bff63a3d00cd2d63c549551c1a086f689a94ed6

[ "MIT" ]

null

dd_pseudolabels.py

Ophir-Gal/ssl-descent

3bff63a3d00cd2d63c549551c1a086f689a94ed6

[ "MIT" ]

null

dd_pseudolabels.py

Ophir-Gal/ssl-descent

3bff63a3d00cd2d63c549551c1a086f689a94ed6

[ "MIT" ]

null

# -*- coding: utf-8 -*- """DD-pseudolabels.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1eYKQIIS4i7RPTeAFX5SgIZmG3S2jtnr3 ## Double Descent Risk Curve for Neural Networks The goal of this experiment is to analyze error/loss curve for Resnet18 trained using pseudo-labeling method with CIFAR-10. - models: resnet18 - dataset: CIFAR-10 - learning algorithm: standard supervised, semi-supervised(pseudo-labeling) - output : model complexity (number of parameters, epochs) vs. test error/loss --- - hypothesis: in both supervised and semi-supervised, we should get Double Descent phenomenon, which is defined by - having a U-shaped curve before the interpolation threshold (under-parameterized) - peaking at the threshold - decreasing again in the over-parameterized regime """ # Commented out IPython magic to ensure Python compatibility. from google.colab import drive drive.mount('/gdrive') # %matplotlib inline import torch from torch.utils.data import DataLoader, Subset, random_split from torch import nn import torch.nn.functional as F from torchvision import datasets, transforms from torchvision.models.resnet import ResNet, BasicBlock from torchvision.datasets import CIFAR10, MNIST from torchvision.transforms import ToTensor import numpy as np import pickle, time, json import matplotlib.pyplot as plt from PIL import Image torch.manual_seed(0) np.random.seed(0) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False """Define some functions""" def alpha_weight(epoch, T1, T2, af): """ calculate value of alpha used in loss based on the epoch params: - epoch: your current epoch - T1: threshold for training with only labeled data - T2: threshold for training with only unlabeled data - af: max alpha value """ if epoch < T1: return 0.0 elif epoch > T2: return af else: return ((epoch - T1) / (T2 - T1)) * af def evaluate(model, data_loader, b): """ evaluate the loss and accuracy of the trained network on test data returns: - (test_accuracy, test_loss) params: - model: - test_loader: - b: """ correct = 0 total = 0 running_loss = 0 model.eval() with torch.no_grad(): for data, labels in data_loader: data = data.to(device) labels = labels.to(device) output = model(data) predicted = torch.max(output, 1)[1] correct += (predicted == labels).sum() total += data.shape[0] loss = loss_fn(output, labels, b) running_loss += loss.item() test_error = 1 - correct/total return test_error, running_loss/len(data_loader) def loss_fn(outputs, labels, b): criterion = nn.CrossEntropyLoss() return (criterion(outputs, labels) - b).abs() + b def train_semisuper(epochs, model, optimizer, train_loader, unlabeled_loader, test_loader, b): """train model on labeled and unlabeled data using pseudo-labels/self-training returns: - metrics: params: - epochs: total epochs for training - model: - optimizer: - train_loader: - unlabeled_loader: - test_loader: - b: """ device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') metrics = [] start_ts = time.time() T1 = 20 # no contribution of unlabeled data T2 = 150 # less contribution of unlabeled data af = 3.1 # Instead of using current epoch we use a "step" variable to calculate alpha_weight. This helps the model converge faster # step 1: pre-train teacher model for T1 epochs only on labeled data print("START SUPERVISED LEARNING") train_super(T1, model, optimizer, train_loader, test_loader, b) model.train() print("START SEMI-SUPERVISED LEARNING") # step 2: train model only on unlabeled data for (T2-T1) epochs and both for epoch in range(epochs): running_loss = 0 unlabeled_correct = 0 unlabeled_total = 0 labeled_correct = 0 labeled_total = 0 # generate pseudo labels and train model on unlabeled data for (x_unlabeled, y_unlabeled), (x_labeled, y_labeled) in zip(unlabeled_loader, train_loader): # genereate the pseudo labels (changed by every weight update) for unlabeled images x_unlabeled, y_unlabeled = x_unlabeled.to(device), y_unlabeled.to(device) x_labeled, y_labeled = x_labeled.to(device), y_labeled.to(device) output_unlabeled = model(x_unlabeled) _, pseudo_labels = torch.max(output_unlabeled, 1) # even if the absolute confidence is low, the max class becomes 1 unlabeled_loss = loss_fn(output_unlabeled, pseudo_labels, b) unlabeled_total += x_unlabeled.shape[0] unlabeled_correct += (pseudo_labels == y_unlabeled).sum() output_labeled = model(x_labeled) _, predicted = torch.max(output_labeled, 1) labeled_loss = loss_fn(output_labeled, y_labeled, b) labeled_total += x_labeled.shape[0] labeled_correct += (predicted == y_labeled).sum() total_loss = alpha_weight(epoch, T1, T2, af)*unlabeled_loss + labeled_loss optimizer.zero_grad() total_loss.backward() optimizer.step() running_loss += total_loss.item() print('pseudo label accuracy={}'.format(unlabeled_correct/unlabeled_total)) train_loss = running_loss/len(train_loader) correct = unlabeled_correct + labeled_correct total = unlabeled_total + labeled_total train_error = 1 - correct/total test_error, test_loss = evaluate(model, test_loader, b) metrics.append([train_error.item(), test_error.item(), train_loss, test_loss]) print('Epoch: {} : Alpha Weights : {:.3f}, Train Error : {:.3f}, Train Loss : {:.3f} | Test Error : {:.3f}, Test Loss : {:.3f} '.format(epoch+1, alpha_weight(epoch, T1, T2, af), train_error, train_loss, test_error, test_loss)) model.train() print("minutes elapsed: {:.3f}".format((time.time() - start_ts)/60)) return metrics def train_super(epochs, model, optimizer, train_loader, test_loader, b): """train model in a standard supervised way returns: - metrics: params: - epochs: total epochs for training - model: - optimizer: - train_loader: - test_loader: - b: flooding level """ device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') metrics = [] start_ts = time.time() model.train() # for each epoch for epoch in range(epochs): running_loss = 0 total = 0 correct = 0 # for each batch for batch_idx, (data, labels) in enumerate(train_loader): data, labels = data.to(device), labels.to(device) output = model(data) _, predicted = torch.max(output, 1) correct += (predicted == labels).sum() total += data.shape[0] loss = loss_fn(output, labels, b) running_loss += loss.item() optimizer.zero_grad() loss.backward() optimizer.step() train_loss = running_loss/len(train_loader) train_error = 1 - correct/total test_error, test_loss = evaluate(model, test_loader, b) metrics.append([train_error.item(), test_error.item(), train_loss, test_loss]) print('Epoch: {} : Train Error : {:.3f}, Train Loss : {:.3f} | Test Error : {:.3f}, Test Loss : {:.3f} '.format(epoch+1, train_error, train_loss, test_error, test_loss)) model.train() print("minutes elapsed: {:.3f}".format((time.time() - start_ts)/60)) return metrics """Define your neural net model""" class PreActBlock(nn.Module): expansion = 1 def __init__(self, in_planes, planes, stride=1, **kwargs): super(PreActBlock, self).__init__() self.bn1 = nn.BatchNorm2d(in_planes) self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) if stride != 1 or in_planes != self.expansion * planes: self.shortcut = nn.Sequential(nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False)) def forward(self, x): out = F.relu(self.bn1(x)) shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x out = self.conv1(out) out = self.conv2(F.relu(self.bn2(out))) out += shortcut return out class PreActResNet(nn.Module): def __init__(self, block, num_blocks, num_classes=10, init_channels=64): super(PreActResNet, self).__init__() self.in_planes = init_channels c = init_channels self.conv1 = nn.Conv2d(3, c, kernel_size=3, stride=1, padding=1, bias=False) self.layer1 = self._make_layer(block, c, num_blocks[0], stride=1) self.layer2 = self._make_layer(block, 2*c, num_blocks[1], stride=2) self.layer3 = self._make_layer(block, 4*c, num_blocks[2], stride=2) self.layer4 = self._make_layer(block, 8*c, num_blocks[3], stride=2) self.dropout = nn.Dropout(0.5) self.linear = nn.Linear(8 * c * block.expansion, num_classes) def _make_layer(self, block, planes, num_blocks, stride): # eg: [2, 1, 1, ..., 1]. Only the first one downsamples. strides = [stride] + [1] * (num_blocks-1) layers = [] for stride in strides: layers.append(block(self.in_planes, planes, stride)) self.in_planes = planes * block.expansion return nn.Sequential(*layers) def forward(self, x): out = self.conv1(x) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = F.avg_pool2d(out, 4) out = out.view(out.size(0), -1) out = self.dropout(out) out = self.linear(out) return out class MNISTResNet(PreActResNet): def __init__(self, n_classes, k): super(MNISTResNet, self).__init__(PreActBlock, [2, 2, 2, 2], num_classes=n_classes, init_channels=k) class CIFARResNet(PreActResNet): def __init__(self, n_classes, k): super(CIFARResNet, self).__init__(PreActBlock, [2, 2, 2, 2], num_classes=n_classes, init_channels=k) """#Supervised Experiments ### Experiment 1 - Model-wise DD (Finished!) """ basic_setting = { 'k': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 40, 48, 56, 64], 'epochs': 300, 'label_noise': 0.15, 'n_batch': 128, 'n_classes': 10, 'lr': 1e-4, 'b': 0.15, 'n_labeled': (10000, 40000), 'augmentation': True } super_results = {} device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') file_name = '/gdrive/My Drive/CMSC 828W Research/Code (Won & Amartya)/Supervised Experiments/super_model_64.json' open_file = open(file_name, "w") # define transformations for training and test set if basic_setting['augmentation']: transform_cifar = transforms.Compose([transforms.ToTensor(),transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip()]) else: transform_cifar = transforms.Compose([transforms.ToTensor()]) transform_test = transforms.Compose([transforms.ToTensor()]) # load either MNIST or CIFAR-10 train = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_cifar) test = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) total_samples = len(train.targets) # assign random labels to (label_noise)% of the training set (needed for semi-supervised learning) rands = np.random.choice(total_samples, int(basic_setting['label_noise']*total_samples), replace=False) for rand in rands: train.targets[rand] = torch.randint(high=10, size=(1,1)).item() n_labeled, n_unlabeled = basic_setting['n_labeled'] # split training data into labeled and unlabeled train, val = random_split(train, [n_labeled, n_unlabeled]) print(len(train), len(val)) train_loader = DataLoader(train, batch_size=basic_setting['n_batch'], shuffle=True, num_workers=2) test_loader = DataLoader(test, batch_size=basic_setting['n_batch'], shuffle=True, num_workers=2) print(basic_setting) for k in basic_setting['k']: model = CIFARResNet(basic_setting['n_classes'], k) # define model with the number of parameter model.to(device) total_params = sum(p.numel() for p in model.parameters()) print("number of model parameters = {} when k={}".format(total_params, k)) optimizer = torch.optim.Adam(model.parameters(), lr=basic_setting['lr']) # optimizer = torch.optim.SGD(model.parameters(), lr=lr) # standard supervised training error_metrics = train_super(basic_setting['epochs'], model, optimizer, train_loader, test_loader, basic_setting['b']) super_results[str(k)] = error_metrics # semi-supervised training using pseudo-labels # error_metrics = train_semisuper(basic_setting['epochs'], model, optimizer, train_loader, unlabeled_loader, test_loader, basic_setting['b']) # save list to pickle file with open(file_name, 'w') as f: json.dump(super_results, f) """### Experiment 2 - Epoch-wise DD vs. n_samples""" basic_setting = { 'k': 64, 'epochs': 300, 'label_noise': 0.2, 'n_batch': 128, 'n_classes': 10, 'lr': 1e-4, 'b': 0.15, 'n_labeled': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8] 'augmentation': True } super_results = {} semisuper_results = {} device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') file_name = '/gdrive/My Drive/CMSC 828W Research/Code (Won & Amartya)/super_epoch_n_samples.json' open_file = open(file_name, "ab") # define transformations for training and test set if basic_setting['augmentation']: transform_cifar = transforms.Compose([transforms.ToTensor(),transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip()]) else: transform_cifar = transforms.Compose([transforms.ToTensor()]) transform_test = transforms.Compose([transforms.ToTensor()]) print(basic_setting) n_labeled, n_unlabeled = basic_setting['n_labeled'] for ratio in basic_setting['n_labeled']: train = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_cifar) test = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) total_samples = len(train) # assign random labels to (label_noise)% of the training set (needed for semi-supervised learning) rands = np.random.choice(total_samples, int(basic_setting['label_noise']*total_samples), replace=False) for rand in rands: train.targets[rand] = torch.randint(high=10, size=(1,1)).item() # split training data into labeled and unlabeled train, val = random_split(train, [n_labeled, n_unlabeled]) print("number of labeled: {}, number of unlabeled: {}\n".format(len(train), len(val))) train_loader = DataLoader(train, batch_size=basic_setting['n_batch'], shuffle=True, num_workers=2) test_loader = DataLoader(test, batch_size=basic_setting['n_batch'], shuffle=True, num_workers=2) model = CIFARResNet(basic_setting['n_classes'], basic_setting['k']) # define model with the number of parameter model.to(device) optimizer = torch.optim.Adam(model.parameters(), lr=basic_setting['lr']) # optimizer = torch.optim.SGD(model.parameters(), lr=lr) # standard supervised training error_metrics = train_super(basic_setting['epochs'], model, optimizer, train_loader, test_loader, basic_setting['b']) super_results[str(ratio)] = error_metrics # save list to pickle file with open(file_name, 'w') as f: json.dump(super_results, f) """### Experiment 3 - Epoch-wise DD vs. flooding (Finished!)""" basic_setting = { 'k': 64, 'epochs': 200, 'label_noise': 0.2, 'n_batch': 128, 'n_classes': 10, 'lr': 1e-4, 'b': [0.1, 0.15, 0.2], 'n_labeled': (50000, 0), 'augmentation': True } super_results = {} device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') file_name = '/gdrive/My Drive/CMSC 828W Research/Code (Won & Amartya)/Supervised Experiments/super_epoch_flooding.json' open_file = open(file_name, "ab") # define transformations for training and test set if basic_setting['augmentation']: transform_cifar = transforms.Compose([transforms.ToTensor(),transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip()]) else: transform_cifar = transforms.Compose([transforms.ToTensor()]) transform_test = transforms.Compose([transforms.ToTensor()]) print(basic_setting) train = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_cifar) test = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) total_samples = len(train) # assign random labels to (label_noise)% of the training set (needed for semi-supervised learning) rands = np.random.choice(total_samples, int(basic_setting['label_noise']*total_samples), replace=False) for rand in rands: train.targets[rand] = torch.randint(high=10, size=(1,1)).item() # split training data into labeled and unlabeled n_labeled, n_unlabeled = basic_setting['n_labeled'] train, val = random_split(train, [n_labeled, n_unlabeled]) print("number of labeled: {}, number of unlabeled: {}\n".format(len(train), len(val))) train_loader = DataLoader(train, batch_size=basic_setting['n_batch'], shuffle=True, num_workers=2) test_loader = DataLoader(test, batch_size=basic_setting['n_batch'], shuffle=True, num_workers=2) for flood in basic_setting['b']: model = CIFARResNet(basic_setting['n_classes'], basic_setting['k']) # define model with the number of parameter model.to(device) optimizer = torch.optim.Adam(model.parameters(), lr=basic_setting['lr']) # optimizer = torch.optim.SGD(model.parameters(), lr=lr) # standard supervised training error_metrics = train_super(basic_setting['epochs'], model, optimizer, train_loader, test_loader, flood) super_results[str(flood)] = error_metrics # save list to pickle file with open(file_name, 'w') as f: json.dump(super_results, f) """### Experiment 4 - Epoch-wise DD vs. label noise (Finished!)""" basic_setting = { 'k': 64, 'epochs': 200, 'noise': [0.1, 0.15, 0.2], 'n_batch': 128, 'n_classes': 10, 'lr': 1e-4, 'b': 0.1, 'n_labeled': (20000, 30000), 'augmentation': True } super_results = {} semisuper_results = {} device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') file_name = '/gdrive/My Drive/CMSC 828W Research/Code (Won & Amartya)/Supervised Experiments/super_epoch_label_noise.json' open_file = open(file_name, "ab") # define transformations for training and test set if basic_setting['augmentation']: transform_cifar = transforms.Compose([transforms.ToTensor(),transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip()]) else: transform_cifar = transforms.Compose([transforms.ToTensor()]) transform_test = transforms.Compose([transforms.ToTensor()]) print(basic_setting) n_labeled, n_unlabeled = basic_setting['n_labeled'] for noise in basic_setting['noise']: # train = datasets.MNIST(root='./data', train=True, download=True, transform=transform_cifar) # test = datasets.MNIST(root='./data', train=False, download=True, transform=transform_test) train = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_cifar) test = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) total_samples = len(train) # assign random labels to (label_noise)% of the training set (needed for semi-supervised learning) rands = np.random.choice(total_samples, int(noise*total_samples), replace=False) for rand in rands: train.targets[rand] = torch.randint(high=10, size=(1,1)).item() # split training data into labeled and unlabeled train, val = random_split(train, [n_labeled, n_unlabeled]) print("number of labeled: {}, number of unlabeled: {}\n".format(len(train), len(val))) train_loader = DataLoader(train, batch_size=basic_setting['n_batch'], shuffle=True, num_workers=2) test_loader = DataLoader(test, batch_size=basic_setting['n_batch'], shuffle=True, num_workers=2) model = CIFARResNet(basic_setting['n_classes'], basic_setting['k']) # define model with the number of parameter model.to(device) optimizer = torch.optim.Adam(model.parameters(), lr=basic_setting['lr']) # optimizer = torch.optim.SGD(model.parameters(), lr=lr) # standard supervised training error_metrics = train_super(basic_setting['epochs'], model, optimizer, train_loader, test_loader, basic_setting['b']) super_results[str(noise)] = error_metrics # save list to pickle file with open(file_name, 'w') as f: json.dump(super_results, f) """# Semi-Supervised Experiments ### Experiment 1 - Epoch-wise DD vs. labeled ratio """ basic_setting = { 'k': 64, 'epochs': 200, 'n_batch': 128, 'n_classes': 10, 'lr': 1e-4, 'b': 0.1, 'n_labeled': [(20000, 30000),(10000, 40000)], 'augmentation': True } # We observe all forms of double descent most strongly in settings with label noise in the train set (as is often the case when collecting train data in the real-world). # lr = (n_unlabeled)**(-0.5) # SGD learning rate print(basic_setting) semisuper_results = {} device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') file_name = '/gdrive/My Drive/CMSC 828W Research/Code (Won & Amartya)/Semi-supervised Experiments/semisuper_epoch_ratio.json' open_file = open(file_name, "ab") # define transformations for training and test set if basic_setting['augmentation']: transform_cifar = transforms.Compose([transforms.ToTensor(),transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip()]) else: transform_cifar = transforms.Compose([transforms.ToTensor()]) transform_test = transforms.Compose([transforms.ToTensor()]) # load either MNIST or CIFAR-10 # train = datasets.MNIST(root='./data', train=True, download=True, transform=transform_mnist) # test = datasets.MNIST(root='./data', train=False, download=True, transform=transform_mnist) for n_labeled, n_unlabeled in basic_setting['n_labeled']: train = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_cifar) test = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) train, val = random_split(train, [n_labeled, n_unlabeled]) print("number of labeled: {}, number of unlabeled: {}\n".format(len(train), len(val))) train_loader = DataLoader(train, batch_size=int(len(train)/basic_setting['n_batch']), shuffle=True, num_workers=2) unlabeled_loader = DataLoader(val, batch_size=int(len(val)/basic_setting['n_batch']), shuffle=True, num_workers=2) test_loader = DataLoader(test, batch_size=basic_setting['n_batch'], shuffle=True, num_workers=2) model = CIFARResNet(basic_setting['n_classes'], basic_setting['k']) # define model with the number of parameter model.to(device) # total_params = sum(p.numel() for p in model.parameters()) # print("number of model parameters = {} when k={}".format(total_params, basic_setting['k'])) optimizer = torch.optim.Adam(model.parameters(), lr=basic_setting['lr']) # optimizer = torch.optim.SGD(model.parameters(), lr=0.1) # semi-supervised training using pseudo-labels error_metrics = train_semisuper(basic_setting['epochs'], model, optimizer, train_loader, unlabeled_loader, test_loader, basic_setting['b']) semisuper_results = {} semisuper_results[str(n_labeled)] = error_metrics # save list to pickle file with open(file_name, 'w') as f: json.dump(semisuper_results, f) """### Experiment 2 - Model-wise DD""" basic_setting = { 'k': 64, 'epochs': 300, 'n_batch': 128, 'n_classes': 10, 'lr': 1e-4, 'b': 0.15, 'n_labeled': [0.6, 0.4, 0.2], 'augmentation': True } # We observe all forms of double descent most strongly in settings with label noise in the train set (as is often the case when collecting train data in the real-world). # lr = (n_unlabeled)**(-0.5) # SGD learning rate print(basic_setting) semisuper_results = {} device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') file_name = '/gdrive/My Drive/CMSC 828W Research/Code (Won & Amartya)/semisuper_epoch.json' open_file = open(file_name, "ab") # define transformations for training and test set if basic_setting['augmentation']: transform_cifar = transforms.Compose([transforms.ToTensor(),transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip()]) else: transform_cifar = transforms.Compose([transforms.ToTensor()]) transform_test = transforms.Compose([transforms.ToTensor()]) # load either MNIST or CIFAR-10 # train = datasets.MNIST(root='./data', train=True, download=True, transform=transform_mnist) # test = datasets.MNIST(root='./data', train=False, download=True, transform=transform_mnist) for ratio in basic_setting['n_labeled']: train = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_cifar) test = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) # split training data into labeled and unlabeled n_labeled = int(total_samples*ratio) n_unlabeled = int(total_samples*(1-ratio)) train, val = random_split(train, [n_labeled, n_unlabeled]) print("number of labeled: {}, number of unlabeled: {}\n".format(len(train), len(val))) train_loader = DataLoader(train, batch_size=int(len(train)/basic_setting['n_batch']), shuffle=True, num_workers=2) unlabeled_loader = DataLoader(val, batch_size=int(len(val)/basic_setting['n_batch']), shuffle=True, num_workers=2) test_loader = DataLoader(test, batch_size=basic_setting['n_batch'], shuffle=True, num_workers=2) model = CIFARResNet(basic_setting['n_classes'], basic_setting['k']) # define model with the number of parameter model.to(device) # total_params = sum(p.numel() for p in model.parameters()) # print("number of model parameters = {} when k={}".format(total_params, basic_setting['k'])) optimizer = torch.optim.Adam(model.parameters(), lr=basic_setting['lr']) # optimizer = torch.optim.SGD(model.parameters(), lr=lr) # semi-supervised training using pseudo-labels error_metrics = train_semisuper(basic_setting['epochs'], model, optimizer, train_loader, unlabeled_loader, test_loader, basic_setting['b']) semi_super_results[str(ratio)] = error_metrics # save list to pickle file with open(file_name, 'w') as f: json.dump(semi_super_results, f) """# Plotting""" # matplotlib.rcParams.update({'font.size': 25}) def plot_modelwise(results, fname): titles = ['error', 'loss'] colors = ['blue', 'lime'] labels = ['train', 'test'] k = [] # fixed x-axis metrics =. [] exp_type = fname.split('/')[-2] exp_name = fname.split('/')[-1].split('.')[0] # extract model size and metrics into separate lists for model_size, metric in results.items(): k.append(int(model_size)) metrics.append(metric[-1]) metrics = np.array(metrics) # test/train error and loss for i, title in enumerate(titles): fig, axes = plt.subplots(figsize=(8, 6), dpi=300) axes.grid() axes.plot(k, metrics[:, 2*i], label=labels[0], color=colors[0]) # train axes.plot(k, metrics[:, 2*i+1], label=labels[1], color=colors[1]) # test axes.set_xlabel('resnet width=k') axes.set_ylabel(title) axes.set_title("model width vs. {}".format(title)) axes.legend(loc='upper right', prop={'size': 15}) fig.savefig('/gdrive/My Drive/CMSC 828W Research/Code (Won & Amartya)/{}/{}_{}.png'.format(exp_type, exp_name, title), dpi=300) def plot_epochwise(results, fname): titles = ['error', 'loss'] colors = ['red', 'lime', 'blue'] labels = ['train', 'test'] exp_type = fname.split('/')[-2] exp_name = fname.split('/')[-1].split('.')[0] for key in results.keys(): epochs = np.arange(1, len(results[key])+1) # fixed x-axis for i, title in enumerate(titles): fig, axes = plt.subplots(figsize=(8, 6)) for j, (param, metrics) in enumerate(results.items()): metrics = np.array(results[param]) axes.plot(epochs, metrics[:, ], label=param, color=colors[0]) axes.plot(epochs, metrics[:, ], label=param, color=colors[1]) axes.set_xlabel('Epochs') axes.set_ylabel(title) axes.set_title('Epochs vs. {}'.format(title)) axes.legend(loc='upper right', prop={'size': 15}) fig.savefig('/gdrive/My Drive/CMSC 828W Research/Code (Won & Amartya)/{}/{}_{}.png'.format(exp_type, exp_name, title), dpi=300) file_name = '/gdrive/My Drive/CMSC 828W Research/Code (Won & Amartya)/Semi-supervised Experiments/semisuper_epoch_ratio.json' with open(file_name) as json_file: data = json.load(json_file) print(data) #plot_modelwise(data, file_name) plot_epochwise(data, file_name)

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30b786cd5fa20d1e7b694de361c426d6baeb53cd

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py

Python

attic/operator/factorial/factorial.py

matteoshen/example-code

b54c22a1b8cee3fc53d1473cb38ca46eb179b4c3

[ "MIT" ]

5,651

2015-01-06T21:58:46.000Z

2022-03-31T13:39:07.000Z

attic/operator/factorial/factorial.py

matteoshen/example-code

b54c22a1b8cee3fc53d1473cb38ca46eb179b4c3

[ "MIT" ]

42

2016-12-11T19:17:11.000Z

2021-11-23T19:41:16.000Z

attic/operator/factorial/factorial.py

matteoshen/example-code

b54c22a1b8cee3fc53d1473cb38ca46eb179b4c3

[ "MIT" ]

2,394

2015-01-18T10:57:38.000Z

2022-03-31T11:41:12.000Z

def factorial(n): return 1 if n < 2 else n * factorial(n-1) if __name__=='__main__': for i in range(1, 26): print('%s! = %s' % (i, factorial(i))) """ output: 1! = 1 2! = 2 3! = 6 4! = 24 5! = 120 6! = 720 7! = 5040 8! = 40320 9! = 362880 10! = 3628800 11! = 39916800 12! = 479001600 13! = 6227020800 14! = 87178291200 15! = 1307674368000 16! = 20922789888000 17! = 355687428096000 18! = 6402373705728000 19! = 121645100408832000 20! = 2432902008176640000 21! = 51090942171709440000 22! = 1124000727777607680000 23! = 25852016738884976640000 24! = 620448401733239439360000 25! = 15511210043330985984000000 """

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1

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null

0

1

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3

30c78f1e4b38cbf4328632d869f83cd49884978b

310

py

Python

claripy/vsa/__init__.py

embg/claripy

1a5e0ca61d3f480e541226f103900e983f025e4a

[ "BSD-2-Clause" ]

211

2015-08-06T23:25:01.000Z

2022-03-26T19:34:49.000Z

claripy/vsa/__init__.py

embg/claripy

1a5e0ca61d3f480e541226f103900e983f025e4a

[ "BSD-2-Clause" ]

175

2015-09-03T11:09:18.000Z

2022-03-09T20:24:33.000Z

claripy/vsa/__init__.py

embg/claripy

1a5e0ca61d3f480e541226f103900e983f025e4a

[ "BSD-2-Clause" ]

99

2015-08-07T10:30:08.000Z

2022-03-26T10:32:09.000Z

from .valueset import RegionAnnotation, ValueSet from .strided_interval import StridedInterval, CreateStridedInterval from .discrete_strided_interval_set import DiscreteStridedIntervalSet from .abstract_location import AbstractLocation from .bool_result import BoolResult, TrueResult, FalseResult, MaybeResult

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30db2a3cdf832309b396aca6c28e5c404c8bc106

740

py

Python

src/cdev/default/environment.py

cdev-framework/cdev-sdk

06cd7b40936ab063d1d8fd1a7d9f6882750e8a96

[ "BSD-3-Clause-Clear" ]

2

2022-02-28T02:51:59.000Z

2022-03-24T15:23:18.000Z

src/cdev/default/environment.py

cdev-framework/cdev-sdk

06cd7b40936ab063d1d8fd1a7d9f6882750e8a96

[ "BSD-3-Clause-Clear" ]

null

src/cdev/default/environment.py

cdev-framework/cdev-sdk

06cd7b40936ab063d1d8fd1a7d9f6882750e8a96

[ "BSD-3-Clause-Clear" ]

null

from core.constructs.workspace import ( Workspace, initialize_workspace, load_workspace, ) from ..constructs.environment import Environment, environment_info class local_environment(Environment): """ A logically isolated instance of a project. """ def __init__(self, info: environment_info) -> None: self.name = info.name self.workspace_info = info.workspace_info self._loaded_workspace = load_workspace(self.workspace_info) def get_name(self) -> str: return self.name def get_workspace(self) -> Workspace: return self._loaded_workspace def initialize_environment(self) -> None: initialize_workspace(self._loaded_workspace, self.workspace_info)

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null

0

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30e545d65916ca66dbe988165aec9d0884329b7f

249

py

Python

awsscripts/sketches/ca.py

vbmacher/aws-scripts

d3ae9d862c9d388dc8326bba244f4805b2599b91

[ "MIT" ]

null

awsscripts/sketches/ca.py

vbmacher/aws-scripts

d3ae9d862c9d388dc8326bba244f4805b2599b91

[ "MIT" ]

12

2021-09-10T09:23:15.000Z

2022-01-05T09:09:07.000Z

awsscripts/sketches/ca.py

vbmacher/aws-scripts

d3ae9d862c9d388dc8326bba244f4805b2599b91

[ "MIT" ]

null

from awsscripts.sketches.sketchitem import SketchItem class CodeArtifactSketchItem(SketchItem): def generate(self): return { 'repository': 'TODO', 'domain': 'TODO', 'domain-owner': 'TODO' }

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249

11

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1

0

null

0

1

0

null

0

1

0

3

30f4a3ae5ea95d1940574c95c6eca484a3fb1354

222

py

Python

agent/serializers.py

Serafim-End/bearAI

32d9896ddcac8a7509f2e32a75923e17f29a9af8

[ "Unlicense" ]

1

2017-06-05T10:37:43.000Z

agent/serializers.py

Serafim-End/bearAI

32d9896ddcac8a7509f2e32a75923e17f29a9af8

[ "Unlicense" ]

null

agent/serializers.py

Serafim-End/bearAI

32d9896ddcac8a7509f2e32a75923e17f29a9af8

[ "Unlicense" ]

null

from rest_framework import serializers from agent.models import Agent class AgentSerializer(serializers.ModelSerializer): class Meta: model = Agent fields = ('developer', 'username', 'date_joined')

20.181818

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222

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222

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false

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0

null

0

null

0

1

0

1

0

3

30f58c391509423a4bee8d65a9cde3b8ff49837e

2,930

py

Python

downloads-generation/data_pdb/make_pdb_query.py

openvax/mhc2flurry

914dddfd708801a83615d0cc3d41dd3b19e45919

[ "Apache-2.0" ]

1

2021-11-09T11:34:25.000Z

downloads-generation/data_pdb/make_pdb_query.py

openvax/mhc2flurry

914dddfd708801a83615d0cc3d41dd3b19e45919

[ "Apache-2.0" ]

null

downloads-generation/data_pdb/make_pdb_query.py

openvax/mhc2flurry

914dddfd708801a83615d0cc3d41dd3b19e45919

[ "Apache-2.0" ]

1

2021-11-11T16:02:54.000Z

# Just print a JSON PDB query to stdout # Doing this in a python script so we have comments. import json sequences = [] # DRA1*01:01 sequences.append( "MAISGVPVLGFFIIAVLMSAQESWAIKEEHVIIQAEFYLNPDQSGEFMFDFDGDEIFHVDMAKKETVWRLEEFGRF" "ASFEAQGALANIAVDKANLEIMTKRSNYTPITNVPPEVTVLTNSPVELREPNVLICFIDKFTPPVVNVTWLRNGKP" "VTTGVSETVFLPREDHLFRKFHYLPFLPSTEDVYDCRVEHWGLDEPLLKHWEFDAPSPLPETTENVVCALGLTVGL" "VGIIIGTIFIIKGVRKSNAAERRGPL") # DRB1*01:01 sequences.append( "MVCLKLPGGSCMTALTVTLMVLSSPLALAGDTRPRFLWQLKFECHFFNGTERVRLLERCIYNQEESVRFDSDVGEY" "RAVTELGRPDAEYWNSQKDLLEQRRAAVDTYCRHNYGVGESFTVQRRVEPKVTVYPSKTQPLQHHNLLVCSVSGFY" "PGSIEVRWFRNGQEEKAGVVSTGLIQNGDWTFQTLVMLETVPRSGEVYTCQVEHPSVTSPLTVEWRARSESAQSKM" "LSGVGGFVLGLLFLGAGLFIYFRNQKGHSGLQPTGFLS") # DRB3*01:01 sequences.append( "MVCLKLPGGSSLAALTVTLMVLSSRLAFAGDTRPRFLELRKSECHFFNGTERVRYLDRYFHNQEEFLRFDSDVGEY" "RAVTELGRPVAESWNSQKDLLEQKRGRVDNYCRHNYGVGESFTVQRRVHPQVTVYPAKTQPLQHHNLLVCSVSGFY" "PGSIEVRWFRNGQEEKAGVVSTGLIQNGDWTFQTLVMLETVPRSGEVYTCQVEHPSVTSALTVEWRARSESAQSKM" "LSGVGGFVLGLLFLGAGLFIYFRNQKGHSGLQPTGFLS") # DRB4*01:01 sequences.append( "MVCLKLPGGSCMAALTVTLTVLSSPLALAGDTQPRFLEQAKCECHFLNGTERVWNLIRYI" "YNQEEYARYNSDLGEYQAVTELGRPDAEYWNSQKDLLERRRAEVDTYCRYNYGVVESFTV" "QRRVQPKVTVYPSKTQPLQHHNLLVCSVNGFYPGSIEVRWFRNSQEEKAGVVSTGLIQNG" "DWTFQTLVMLETVPRSGEVYTCQVEHPSMMSPLTVQWSARSESAQSKMLSGVGGFVLGLL" "FLGTGLFIYFRNQKGHSGLQPTGLLS") # DRB5*01:01 sequences.append( "MVCLKLPGGSYMAKLTVTLMVLSSPLALAGDTRPRFLQQDKYECHFFNGTERVRFLHRDIYNQEEDLRFDSDVGEY" "RAVTELGRPDAEYWNSQKDFLEDRRAAVDTYCRHNYGVGESFTVQRRVEPKVTVYPARTQTLQHHNLLVCSVNGFY" "PGSIEVRWFRNSQEEKAGVVSTGLIQNGDWTFQTLVMLETVPRSGEVYTCQVEHPSVTSPLTVEWRAQSESAQSKM" "LSGVGGFVLGLLFLGAGLFIYFKNQKGHSGLHPTGLVS") # HLA-DQB1*02:01 sequences.append( "MSWKKALRIPGGLRAATVTLMLSMLSTPVAEGRDSPEDFVYQFKGMCYFTNGTERVRLVS" "RSIYNREEIVRFDSDVGEFRAVTLLGLPAAEYWNSQKDILERKRAAVDRVCRHNYQLELR" "TTLQRRVEPTVTISPSRTEALNHHNLLVCSVTDFYPAQIKVRWFRNDQEETAGVVSTPLI" "RNGDWTFQILVMLEMTPQRGDVYTCHVEHPSLQSPITVEWRAQSESAQSKMLSGIGGFVL" "GLIFLGLGLIIHHRSQKGLLH") # HLA-DPB1*01:01 sequences.append( "MMVLQVSAAPRTVALTALLMVLLTSVVQGRATPENYVYQGRQECYAFNGTQRFLERYIYN" "REEYARFDSDVGEFRAVTELGRPAAEYWNSQKDILEEKRAVPDRVCRHNYELDEAVTLQR" "RVQPKVNVSPSKKGPLQHHNLLVCHVTDFYPGSIQVRWFLNGQEETAGVVSTNLIRNGDW" "TFQILVMLEMTPQQGDVYICQVEHTSLDSPVTVEWKAQSDSAQSKTLTGAGGFVLGLIIC" "GVGIFMHRRSKKVQRGSA") # Should be distinct assert len(sequences) == len(set(sequences)) def node_from_sequence(sequence): return { "type": "terminal", "service": "sequence", "parameters": { "evalue_cutoff": 10, "identity_cutoff": 0.5, "target": "pdb_protein_sequence", "value": sequence, } } query = { "query": { "type": "group", "logical_operator": "or", "nodes": [node_from_sequence(sequence) for sequence in sequences], }, "request_options": { "return_all_hits": True }, "return_type": "entry" } print(json.dumps(query))

32.921348

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0.015625

0

1

null

0

1

null

0

3

eb4af68fe6e2b37d9387e8cfbe2a09723fbfe4cc

222

py

Python

setup.py

jthop/MQTTPlugin

74b3701f810051ee115899d6ed0b63767bbc2507

[ "MIT" ]

null

setup.py

jthop/MQTTPlugin

74b3701f810051ee115899d6ed0b63767bbc2507

[ "MIT" ]

null

setup.py

jthop/MQTTPlugin

74b3701f810051ee115899d6ed0b63767bbc2507

[ "MIT" ]

null

from setuptools import setup setup(name='MQTTPlugin', version='0.1', py_modules=['MQTTPlugin'], install_requires=['setuptools', 'paho-mqtt'], entry_points={'pynx584': ['mqtt_plugin=src.MQTTPlugin:MQTTBridge']}, )

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0

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0

true

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1

0

null

0

1

0

null

0

1

0

3

eb72acbdcd2a4abc2840da625f8f608321da3ff4

1,329

py

Python

api/database/table.py

dhinakg/BitSTAR

f2693c5a0612e58e337511023f8f9e4f25543e33

[ "Apache-2.0" ]

6

2017-04-29T03:45:56.000Z

2018-05-27T02:03:13.000Z

api/database/table.py

dhinakg/BitSTAR

f2693c5a0612e58e337511023f8f9e4f25543e33

[ "Apache-2.0" ]

18

2017-04-12T20:26:05.000Z

2018-06-23T18:11:55.000Z

api/database/table.py

dhinakg/BitSTAR

f2693c5a0612e58e337511023f8f9e4f25543e33

[ "Apache-2.0" ]

16

2017-04-30T05:04:15.000Z

2019-08-15T04:59:09.000Z

# Copyright 2017 Starbot Discord Project # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from api.database import DAL from api.database.db import DB from api.database.DAL import SQLite class Table: name = None table_type = None def __init__(self, name_in, type_in): self.name = name_in self.table_type = type_in DAL.db_create_table(DB, self.name) def insert(self, dataDict): return DAL.db_insert(DB, self, dataDict) def search(self, searchTerm, searchFor): return SQLite.db_search(DB, self, searchTerm, searchFor) def getContents(self, rows): return DAL.db_get_contents_of_table(DB, self, rows) def getLatestID(self): return DAL.db_get_latest_id(DB, self) class TableTypes: pServer = 1 pGlobal = 2

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