Saravanan666/pypi-org huggingface dataset:
Package: mango-coding-test-pl-sept20
# mango_testPackage containing Peter Ling’s solutions to the Mango Python coding test, September 2020.## InstallationThe package can be installed from PyPI via the command line by typingpip install mango_coding_test_pl_sept20.## UsageThe solutions can then be used in a Python 3 environment after using the import statementfrom mango_test import mango_test as mt#### Functions
The ‘Functions’ part of the exercise is called with therandom_draw(nsamples, dist, **params)function, whereparamsrelate to the given distribution.##### Examples
To return a 10 random samples (as a numpy array) from a Normal distribution with mean 100 and standard deviation 10:mt.random_draw(10, ‘normal’, mean=100, sd=10)To return a 20 random samples from a Binomial distribution with _n_=100, _p_=0.5:mt.random_draw(20, ‘binomial’, n=100, p=0.5)To return a 100 random samples from a Poisson distribution with $lambda$=100:mt.random_draw(100, ‘poisson’, lam=100)#### Object-oriented Programming
The ‘Object-oriented Programming’ part of the exercise is called with theSampleclass.##### Example
To create an instance of theSampleclass, using the Normal distribution:s = mt.Sample(‘normal’)To set the parameters ofs(mean and standard deviation):s.mean = 100
s.sd = 100To draw a 1000 samples froms, then summarise:s.draw(1000)
s.summarise()
Package: mangodb
mangodbMangoDB is a very very thin wrapper around sync/async python mongo driver encapsulating
all details necessary for temporary file storage with zero maintenance.Capped collection  - when mongo collection is capped, old data is delete when disk space is exhaustedTTL index - when an object has ttl index, mongo automatically deletes after ttl expiresChunked storage - as mongo max document size is capped at 16Mb we have to chunkinate the documents
in the same manner GridFS does, but contrary to gridfs, we store metadata in the same collection as chunks,
this way it will be deleted along with the chunks.
Package: mangodistributions
Readme for Mango_distInstallationRunpip install -i https://test.pypi.org/simple/ mangodistributionsin the command lineImporting functionalityImport into a python script withimport mangodistributions as mgExample functionalityCreate a random sample of 10 values from Normal distribution with a mean of 5 and a standard deviation of 1:import mangodistributions as mg

normal_dist = mg.Normal(n = 10, mean = 5, sd=1)
sample = normal_dist.draw()Print summary metrics on your sampleimport mangodistributions as mg

normal_dist = mg.Normal(n = 10, mean = 5, sd=1)
sample = normal_dist.draw()
normal_dist.summarise()
Package: mangoml
mangomlmango (MAchine learniNG Orchestra) is a python API for quick machine learning experimentation. The idea is to read data from a CSV file and compare different machine learning models using cross-validation.Much of the operations used to read the data and test machine learning models are repeated for different experiments and the goal here is to avoid this repetition by implementing wrappers of object from the main python libraries used in data science (e.g.scikit-learn,pandasandxgboost).Most of the configuration needed is made through a JSON file. The JSON objects are read by wrappers for objects from different APIs. The parameters configured through JSON are the same used by scikit/pandas/xgboost objects so you don't need to code everything again.I've created this API to make it easier to participate inKagglecompetitions. I used the classicTitanic Competitionas an example of mangoml utilization.Any suggestions can be send to my email:gustavohas@outlook.com
Package: mango-odm
Mango🥭 带有类型提示的 Python 异步 MongoDB 对象文档映射器查看演示·错误报告·功能请求简体中文·English·日本語🔖 目录目录简介核心特性安装PIPPoetry示例贡献鸣谢支持许可证📖 简介Mango 是一个带有类型提示的 Python 异步 MongoDB 对象文档映射器(ODM)，它构建在Motor和Pydantic之上。Mango 使得数据建模和查询变得非常容易，帮助您关注应用程序中真正重要的部分。✨ 核心特性：完善的类型标注：利用静态分析来减少运行时问题简洁流畅的 API：更易于学习和使用，提高开发效率优雅的编辑器支持：自动完成无处不在，从对象创建到查询结果[⬆回到顶部]🚀 安装PIPpipinstallmango-odmPoetrypoetryaddmango-odm[⬆回到顶部]🌟 示例importasynciofrommangoimportDocument,EmbeddedDocument,Field,Mango# 嵌入式文档classAuthor(EmbeddedDocument):name:strprofile:str|None=None# Mango 文档模型classBook(Document):name:str=Field(primary_key=True)# 将字段设置为主键，如果不显式指定主键，则会自动创建 id 字段作为主键summary:str|None=Noneauthor:Author# 嵌入文档price:int=Field(index=True)# 为字段添加索引asyncdefmain():# 初始化 Mango，它会创建连接并初始化文档模型，你可以传入 db 或者 uri 参数来指定连接awaitMango.init()# 像 pydantic 的模型一样使用book=Book(name="book",author=Author(name="author"),price=10)# 将它插入到数据库中awaitbook.save()# Mango 提供了丰富的查询语言，允许您使用 Python 表达式来查询ifbook:=awaitBook.find(Book.price<=20,Book.author.name=="author").get():# 更新文档的 summary 字段book.summary="summary"awaitbook.update()if__name__=="__main__":asyncio.run(main())[⬆回到顶部]🤝 贡献想为这个项目做出一份贡献吗？点击这里阅读并了解如何贡献。🎉 鸣谢感谢以下开发者对该项目做出的贡献：[⬆回到顶部]💖 支持喜欢这个项目？请点亮 star 并分享它！[⬆回到顶部]📝 许可证在MIT许可证下分发。请参阅LICENSE以获取更多信息。[⬆回到顶部]
Package: mangopi
# mangopi[![Build Status](https://travis-ci.org/jiaweihli/mangopi.png)](https://travis-ci.org/jiaweihli/mangopi)
[![Coverage Status](https://coveralls.io/repos/jiaweihli/mangopi/badge.png?branch=master)](https://coveralls.io/r/jiaweihli/mangopi?branch=master)mangopi aims to be an easy-to-use, easy-to-extend manga api.  Adding a new site should be as simple
as filling in a few regexes - everything else is taken care of.## InstallationRunpip install mangopi, or clone the repo and runpython setup.py install.## Layouthelpercontains some shared, non-application-specific code.  (Or it will, after the Util class
is removed.)sitecontains the library logic to retrieve data from the websites.  The hierarchy:MangaSiteNoez
- MangaFox
- MangaHereAftv
- MangaPanda
- MangaReaderNoez and Aftv appear to be the parent companies of the sites that exist under them.## UsageQ: What’s the image url for the first page of the first chapter of ‘Toriko’?>>> from mangopi.site.mangafox import MangaFox
>>> MangaFox.series('toriko').chapters[0].pages[0].image.url
'http://z.mfcdn.net/store/manga/3660/01-001.0/compressed/toriko_v01_c01_01.jpg'Alternatively, use MetaSite, which allows aggregation of multiple sites (there are some performance
issues related to error correction that are being worked out however):>>> from mangopi.metasite import MetaSite
>>> search = MetaSite([MangaFox, MangaHere, MangaPanda, MangaReader])
>>> from mangopi.site.mangafox import MangaFox
>>> from mangopi.site.mangahere import MangaHere
>>> from mangopi.site.mangapanda import MangaPanda
>>> from mangopi.site.mangareader import MangaReader
>>> search.series('death note').chapters['22'].pages[0].image.url
'http://i39.mangapanda.com/death-note/22/death-note-1678383.jpg'Take a look at themangopi.testsmodule for further examples.## Version SupportSupport is maintained on Python 2.7, 3.3, and 3.4.## TestingRunpython setup.py testornosetests.
Package: mangoplateapi
Fast and effective Instagram Private API wrapper (public+private requests and challenge resolver).Use the most recent version of the API from Instagram.Features:Performs Public API (web, anonymous) or Private API (mobile app, authorized)
requests depending on the situation (to avoid Instagram limits)Challenge Resolver have Email (as well as recipes for automating receive a code from email) and SMS handlersSupport upload a Photo, Video, IGTV, Clips (Reels), Albums and StoriesSupport work with User, Media, Insights, Collections, Location (Place), Hashtag and Direct objectsLike, Follow, Edit account (Bio) and much more elseInsights by account, posts and storiesBuild stories with custom background, font animation, swipe up link and mention usersIn the next release, account registration and captcha passing will appear
Package: mangopy
This repository contains code for processing all-sky images generated by the Mid-latitude All-sky-imager Network for Geospace Observations (MANGO), a project funded by the US National Science Foundation (NSF) (Grant # AGS-1452357 and AGS-1933013). MANGO has a network of 7 all-sky (wide-angle) cameras across the continental United States to observe the ionospheric airglow occurring at the heights of 250-300km and 9 proposed cameras observing the airglow at 90-100km. More information at the MANGO website.The mangopy project is a python package for accessing and visualizing MANGO data. The project has several authors as mentioned in each code header. This code can be applied to image processing of other all-sky data (i.e. systems with wide-angle cameras pointed vertically towards the sky). The code was developed under three NSF funded projects, MANGO (Grant # AGS-1452357 and AGS-1933013) and InGeO (Grant # OAC-1835573).Installing cartopyCartopyis used for visualizing MANGO data and must be installed before mangopy.  Please refer to thecartopy installation instructionsand make sure cartopy is successfully installed on your system before attempting to install mangopy.Installing mangopyMangopy can be installed with pip:pip install mangopyThe mangopy developers encourage community contributions!  Please create apull requestif you’ve fixed a problem or created a new feature that would be useful to the community!For basic usage examples, please refer to the mangopy tutorial jupyter notebook,mangopy tutorial.Full mangopy documentation
Package: mangosort
mangosort: Sort list by multiple keys and directionsmangosortis a small module that helps you sort lists of dictionaries by multiple keysHot to useSort by multiple directions:To sort by different directions at same time you need to pass a list of dictionaries with keys you want to order and valuesascordesc.from mangosort import mangosort

my_list =  [{'code': 'beta', 'number': 3}, 
            {'code': 'delta', 'number': 2},
            {'code': 'delta', 'number': 3}]

mangosort.sort_by_key(my_list, [{'code': 'desc'}, {'number': 'asc'}])

[{'code': 'delta', 'number': 2}, 
{'code': 'delta', 'number': 3}, 
{'code': 'beta', 'number': 3}]If you need or prefer to pass bool, in this case, think reverseTrue(desc) orFalse(asc).# Reverse True or False
mangosort.sort_by_key(my_list, [{'code': True}, {'number': 0}])

[{'code': 'delta', 'number': 2}, 
{'code': 'delta', 'number': 3}, 
{'code': 'beta', 'number': 3}]Sort ASC:from mangosort import mangosort

my_list =  [{'code': 'beta', 'number': 3}, 
            {'code': 'delta', 'number': 2},
            {'code': 'delta', 'number': 3}]
mangosort.sort_by_key_asc(my_list, ['code', 'number'])

[{'code': 'beta', 'number': 3}, 
{'code': 'delta', 'number': 2}, 
{'code': 'delta', 'number': 3}]Sort DESC:from mangosort import mangosort

my_list =  [{'code': 'beta', 'number': 3}, 
            {'code': 'delta', 'number': 2},
            {'code': 'delta', 'number': 3}]
mangosort.sort_by_key_asc(my_list, ['code', 'number'])

[{'code': 'delta', 'number': 3}, 
{'code': 'delta', 'number': 2}, 
{'code': 'beta', 'number': 3}]How to InstallInstall via PyPipip install mangosortContributingI’m accepting pull requests that improve speed and legibility of the code or anything else you think will be good.LicenseThis module is under MIT License, but you can do what you want. It's just a bunch of code.Just tell your friends about it.Communism will win!
Package: mangowg
MangoWGMango Website Generator PyPi PackagePython Venvpython -m venv venvvenv\Scripts\activate.batpip install -r requirements.txtpip freeze > requirements.txtUsageGet startedpython mangowg.py --helpTo use command selection rather than typing the command name, usepython mangowg.py cutthen select the command to be executed.Run the commands in orderstart >  new > updateWholeProfile > getGithubInfo > getRepos > selectRepos > updateTemplate > pushPyPi publishPublish new packageinstall poetrypoetry initpoetry buildpoetry publish -u USERNAME -p PASSWORDuse pypi password for this stepTo update version number:poetry version 0.1.2poetry buildpoetry publish -u USERNAME -p PASSWORDuse github password for this step
Package: mangrove-surface-sdk
===========================Mangrove Surface Python SDK===========================.. image:: https://circleci.com/gh/mangroveai/mangrove-surface-python-sdk.svg?style=svg:target: https://circleci.com/gh/mangroveai/mangrove-surface-python-sdk.. image:: https://readthedocs.org/projects/mangrove-surface-python-sdk/badge/?version=stable:target: https://mangrove-surface-python-sdk.readthedocs.io/en/latest/?badge=stable:alt: Documentation Status.. _documentation: http://mangrove-surface-python-sdk.readthedocs.io/Documentation-------------A complete documentation is available there: documentation_Installation------------Install the python SDK package:.. code-block:: pythonpip install mangrove-surfaceor using the git repository::git clone https://github.com/mangroveai/mangrove-surface-python-sdkpython setup.py install(*optional*) Setup your environment variables---------------------------------------------You can used the SDK with an explicit configuration of url instance and tokenor you can provide them as environment variables: ``SURFACE_URL`` and``SURFACE_TOKEN``.For example on unix-like system:.. code-block:: shell$ export SURFACE_URL=http://your_mangrove.ai_url/api$ export SURFACE_TOKEN='eyJ0eXAiOiJKV1QiLCJhbGciOiJ...'.. warning:: Mangrove Surface URL have to end with ``/api``... note:: The explicit configuration overrides the implicit one... note:: Token can be provided by the administrator using GUI (seedocumentation) or using the SDK (see:meth:`mangrove.Surface._Admin.create_token`:)Test your installation----------------------You can run tests with the following python lines:- Test if it is properly installed:.. code-block:: python>>> import mangrove_surface>>> mangrove_surface.__version__'2.0.0'The python SDK is properly connected to your Mangrove Surface:.. code-block:: python>>> from mangrove_surface import SurfaceClient>>> # if environment variables are setup>>> client = SurfaceClient()>>> # otherwise>>> # client = SurfaceClient(url="...", token="...")>>> client.admin.versions()[{'name': 'atlas','version': '1.0.0'}, {'name': 'license_authority','version': u'1.5.0'}, {'name': 'dmgr','version': '1.0.0'}, {'name': 'modeler','version': '1.0.0'}, {'name': 'exporter','version': '1.0.0'}, {'name': 'mangrove-surface-sdk','version': '1.0.0'}]It is well configured! Congratulation!Let's begin with :doc:`surface`.Support-------Please refer to :doc:`logger` section if you have any unexpected behavior usingthe SDK.
Package: mangum
MangumMangum is an adapter for runningASGIapplications in AWS Lambda to handle Function URL, API Gateway, ALB, and Lambda@Edge events.Documentation:https://mangum.io/FeaturesEvent handlers for API GatewayHTTPandRESTAPIs,Application Load Balancer,Function URLs, andCloudFront Lambda@Edge.Compatibility with ASGI application frameworks, such asStarlette,FastAPI,QuartandDjango.Support for binary media types and payload compression in API Gateway using GZip or Brotli.Works with existing deployment and configuration tools, includingServerless FrameworkandAWS SAM.Startup and shutdownlifespanevents.RequirementsPython 3.7+InstallationpipinstallmangumExamplefrommangumimportMangumasyncdefapp(scope,receive,send):awaitsend({"type":"http.response.start","status":200,"headers":[[b"content-type",b"text/plain; charset=utf-8"]],})awaitsend({"type":"http.response.body","body":b"Hello, world!"})handler=Mangum(app,lifespan="off")Or using a framework:fromfastapiimportFastAPIfrommangumimportMangumapp=FastAPI()@app.get("/")defread_root():return{"Hello":"World"}@app.get("/items/{item_id}")defread_item(item_id:int,q:str=None):return{"item_id":item_id,"q":q}handler=Mangum(app,lifespan="off")
Package: mangum-cli
Mangum CLIThis package provides a command-line interface for generating AWS Lambda & API Gateway deployments used withMangum.Note: This is a work-in-progress. PRs welcomed.Requirements: Python3.7+Installationpipinstallmangum-cliCommandsmangum init- Create a new deployment configuration.mangum build- Create a local build.mangum deploy- Deploy the packaged project.mangum package-  Package the local build.mangum all-build,package, anddeploy.mangum describe-  Retrieve the endpoints for the deployment.mangum validate- Validate the AWS CloudFormation template.mangum delete- Delete the CloudFormation stack.TutorialThe steps below outline a basicFastAPIdeployment, however you should be able to use any ASGI framework/application with the adapter.Step 1 - Create a local projectFirst, create a new directoryapp/, this is the folder that will contain the main application code and function handler.Then create a fileasgi.pywith the following:frommangumimportMangumfromfastapiimportFastAPIapp=FastAPI()@app.post("/items/")defcreate_item(item_id:int):return{"id":item_id}@app.get("/items/")deflist_items():items=[{"id":i}foriinrange(10)]returnitems@app.get("/")defread_root():return{"Hello":"World!"}handler=Mangum(app)This demonstrates a basic FastAPI application, the most relevant part is:handler=Mangum(app)Thehandlervariable will be used as the handler name defined in the CloudFormation template to be generated later.Lastly, create arequirements.txtfile to include Mangum and FastAPI in the build:mangum
fastapiStep 2 - Create a new deployment configurationRun the following command with a name for the project (required) and optionally include the name of an S3 bucket, the region and the runtime version (these values can be changed later):manguminit<name>[bucket-name][region-name][runtime]After defining the configuration amangum.ymlfile will be generated, the current directory should now look this:├──app
│└──asgi.py
├──mangum.yml
└──requirements.txtStep 3 - Create a local buildRun the following command to create a local application build:mangumbuildThis will create abuild/directory containing the application code and any dependencies included inrequirements.txt.Step 4 - Package the local buildRun the following command to package the local build:mangumpackageThis wraps the AWS CLI'spackagecommand, it uses the definitions inmangum.ymlto produce apackaged.ymlfile and atemplate.ymlfile.Step 5 - Deploy the packaged buildRun the following command to deploy the packaged build:mangumdeployThis wraps the AWS CLI'sdeploycommand. It may take a few minutes to complete. If successful, the endpoints for the deployed application will be displayed in the console.Step 6 - Delete CloudFormation stackRun the following command to delete the CloudFormation stack:mangumdeleteAppendix.A - Enable shell completion.You can enable shell completion by running the install option.mangumcomplementbashCandidates can be displayed by pressing the tab key.$mangum[TAB][TAB]allcomplementdeletedescribepackage
buildcreate-bucketdeployinitvalidateAppendix.B -build,package, anddeploy.If you want to execute build, package, and deploy sequentially, do as follows:mangumall
Package: mangum-extra-handlers
Mangum extra handlersAn extra set ofMangumhandlers for AWS SQS, SNS and EventBridge services.Installation$pipinstallmangum-extra-handlersExamplefrommangum_extra_handlersimportEventBridgeHandler,SNSHandler,SQSHandler...handler=Mangum(app,lifespan="off",custom_handlers=[SNSHandler,SQSHandler,EventBridgeHandler])
Package: mangust228
ProxyManager()При активном использовании прокси во время парсинга возникает проблема постоянного перекидывания прокси из проекта в проект.Для использования данного модуля: необходимо развернуть АПИ на сервере.использованное apiУстановка:pipinstallmangust228Пример получения актуальных прокси:frommangust228importProxyManagerproxies=ProxyManager('your_token','your_url')proxies.get('string')# [{http://user:pass@127.0.0.1:8000},...]proxies.get('dict[str,str]')# [{'server':'http://127.0.0.1:8000','username':'user','password':'pass'}, ...]proxies.get('playwright')# [{'proxy':{'server':'http://127.0.0.1:8000','username':'user','password':'pass'}},...]Пример получения списка всех прокси (включая просроченные)proxies=ProxyManager('your_token','your_url')proxies.get_full()Можно указать путь (только csv!), тогда результат будет сохранен вcsvфайлproxies.get_full('all_proxies.csv')Пример добавления проксиdata=[{'server':'127.0.0.1','port':8000,'username':'user','password':'pass','expire':'2023-12-31','service':'example.service.com'},...]proxies=ProxyManager(token,url)proxies.post(data=data)Можно добавлять из файлов excel или csvproxies.post(path='example.csv')Пример удаления проксиproxies.delete(id)Пример изменения проксиdata={'id':1,'username':'John'}proxies.put(data)CaptchaAi()Разгадывалка капчи с сервиса: captchaai.comДокументацияНа данный момент реализовано только разгадывание картинок.Пример использования:# synccaptcher=CaptchaAi(token)result=captcher.solve_picture(image)# asynccaptcher=CaptchaAi(token,async_=True)result=awaitcaptcher.solve_picture(image)Параметры которые можно передать при инициализации CaptchaAi():tokenтокен который получаем от сервисаasync_передаем, если надо вызывать в асинхронном кодеthreadsсколько допустимо параллельно запросов(зависит от тарифа). В данный момент не реализовано.Параметры метода solve_picture():timeoutвремя между отправкой изображения на сервис и получения данных (default=5)retriesсколько раз попытаться получить ответ от сервера(default=3)phraseсм.документациюlinkregsenseсм.документациюlinknumericсм.документациюlinkcalcсм.документациюlinkmin_lenсм.документациюlinkmax_lenсм.документациюlinklanguageсм.документациюlinklangсм.документациюlinkПараметрыjson&methodНЕ ПОДДЕРЖИВАЮТСЯ.
Package: manhattan
Manhattan - Robust Server-Side AnalyticsScott Torborg -Cart LogicManhattan is a Python infrastructure block to provide basic server-side
analytics and multivariate testing. It is:Easyto deploy and develop onScalableNot slow, in-band request latency < 1ms and can process > 2k events/secCustomizableand flexible to varying rollup needsRobustto server failures, migrating between cluster topologies, and
backend reconfigurationIt is also:2 oz rye whiskey&frac12; oz sweet vermouth2 dashes bitters1 cherryInstallationInstall with pip:$ pip install manhattanDocumentationManhattan hasextensive documentation here.LicenseManhattan is licensed under an MIT license. Please see the LICENSE file for
more information.Code StandardsManhattan has a comprehensive test suite with 100% line and branch coverage, as
reported by the excellentcoveragemodule. To run the tests, simply run in
the top level of the repo:$ nosetestsThere are noPEP8orPyflakeswarnings in the codebase. To
verify that:$ pip install pep8 pyflakes
$ pep8 .
$ pyflakes .Any pull requests must maintain the sanctity of these three pillars.
Package: manhattandistance
Manhattan DistanceDefinition: The distance between two points measured along axes at right angles.This library allows you to calculate the Manhattan Distance between two points using their coordinates.Usage:
from manhattandistance import utils
utils.mandist(lat_from, lon_from, lat_to, lon_to)
lat = integer or floatManhattan DistanceUpdated: 15/DEC/2020v1.1.1:Added the Manhattan Distance formula.Fixed bugs.
Package: manhuagui
manhuaguimanhuagui 漫画下载UsageTODO：目前只实现了下载第 i 话下载 别当欧尼酱了 第一话https://www.manhuagui.com/comic/23552manhuagui--id=23552[--vol=1]# 默认只下载第一话manhuagui--id=23552--vol=1# 下载第一话manhuagui--id=23552--vol=i# 下载第 i 话manhuagui--id=23552--vol=1,2,3,4# 下载（1,2,3,4）前 4 话注意：不要作死下载过多话，虽然提供下载所有话的命令，但这样容易被封 ipmanhuagui--id=23552--vol=all# 下载所有话webpwebp 是 Google 的图像存储格式，Google Chrome 等浏览器可以识别和浏览。TODO feature: 漫画图片文件夹下自动生成了 index.html 模板，可以直接打开，里面实现了简单的图片浏览器。AcknowledgementThanks to [RicterZ@Github](https://github.com/RicterZ)’scode in [nhentai](https://github.com/RicterZ/nhentai), his code is very pretty and inspiring, and, very useful.And thanks to this repository [manhuagui-dl](https://github.com/ysc3839/manhuagui-dl)
Package: manic-entropy-storm
version number: 0.0.1
author: Duane NielsenOverviewA python package that can be installed with pip.Installation / UsageTo install use pip:$ pip install manic-entropy-stormOr clone the repo:$ git clonehttps://github.com/duanenielsen/manic-entropy-storm.git$ python setup.py installContributingTBDExampleTBD