Saravanan666/pypi-org huggingface dataset:
Package: cldfofflinebrowser
cldfofflinebrowserProvides a cldfbench plugin to create offline-browseable representations
of the data in a CLDF dataset.Notes on offline mapscldfbench create --with-tiles […]allows you to predownload the world map at
different zoom levels for offline viewing.  These tiles are provided by the kind
folk atOpenStreetMap– free of charge, no less.  But obviously they're
not wizards who can make bandwidth appear out of thin air.  That's why we should
all be nice to them andplay by their rulesto reduce strain on
their servers.This is why we would like to ask you to follow the following guidelines:Don't delete youroffline/tilesfolder unless you really, really have
to!If you do you'll just end up re-downloading the same map tiles all over
again, which is a waste of everybody's time and bandwidth.Keep yourLanguageTableclean!Make sure it only contains languages that you have actual data points for,
otherwise you'll end up downloading map tiles for regions that nobody will
look at.Set--max-zoomto a reasonably low zoom level!This is especially true if your language sample spans over great distances.
A map of that covers like half the planet will result intens if not
hundreds of thousands of tile downloadsat higher zoom levels (10, 11, 12).
So it's better to find a zoom-level that makes your data comfortable to look
at and not go any deeper than that.Bulk-downloading map tiles for zoom level 13 or higher is forbidden!At these zoom levels, if you try to download more than 250 tiles at once, OSM
will straight-up refuse your request.  End of story.Don't runcldfbench offline.createmultiple times at once!Whatever download speed you get is probably either your bandwidth limit or
the server's.  Either way, battering the server with more download requests
isn't gonna make anything faster.FAQQ:How to add sound files and transcriptions to an offline dataset?A:If you want to make a new sound file and transcription browseable in an offline version:Determine relevant language (LID) and parameter (PID) (aka concept) IDs.Copy the sound file to./parameter-<PID>/<LID>.mp3.Add a corresponding row tocldf/media.csv.Add a row tocldf/forms.csv.Re-runcldfbench offline.create ....
Package: cldr
cldrChangeLog Driven Releases (CLDR)...project status badges:version badges:Installation 🗹Usingpipxto Install (preferred)This packagecouldbe installed using pip like any other Python package (in
fact, see the section below this one for instructions on how to do just that).
Given that we only need this package's entry points, however, we recommend thatpipxbe used instead:# install and setup pipxpython3-mpipinstall--userpipx
python3-mpipxensurepath# install cldrpipxinstallcldrUsingpipto InstallTo installcldrusingpip, run the following
commands in your terminal:python3-mpipinstall--usercldr# install cldrIf you don't have pip installed, thisPython installation guidecan guide
you through the process.Useful Links 🔗API Reference: A developer's reference of the API exposed by this
project.cc-python: Thecookiecutterthat was used to generate this project.
Changes made to this cookiecutter are periodically synced with this project
usingcruft.CHANGELOG.md: We use this file to document all notable changes made to
this project.CONTRIBUTING.md: This document contains guidelines for developers
interested in contributing to this project.Create a New Issue: Create a new GitHub issue for this project.Documentation: This project's full documentation.
Package: cldr-language-helpers
cldr-language-helpersBasically this projects was designed to provide a map of UTF8 Block
Ranges to the locale/language code.It uses cldr.unicode.org project as its reference.BuildClone the latest cldr release:svncohttp://www.unicode.org/repos/cldr/tags/release-34/./cldr-release-34Testspipinstall-rrequirements/dev.txtThen run test:pytestTests will also populate thecldr_language_helpers/datadirectory.
Or you can use just run populate tests:pytest-smgenerateAPIfromcldr_language_helpers.annotatorimportStringAnnotatorassertStringAnnotator('123').char_types_by_index==[{'numbers'},{'numbers'},{'numbers'}]assert'ru_RU'inStringAnnotator('ф').langs_by_index[0]assert{'ru_RU','en_US','en','ru'}.issubset(StringAnnotator('йцу 123 qwe LOL').all_langs)stats=StringAnnotator('qwe йцу').lang_statsassertstats['ru_RU']==3assertstats['ru']==3assertstats['en']==3assertstats['space']==1assert'en'inStringAnnotator('somesortof123').langs_intersectionassertStringAnnotator('somesortof123').char_types_intersection==set()assertStringAnnotator('somesortof').char_types_intersection=={'auxiliary','main'}assertStringAnnotator(' ').char_types_intersection=={'space'}assertStringAnnotator('что-то everything как-то lol !').split_by_lang_intersection()==\['что-то',' ','everything',' ','как-то',' ','lol',' ','!']assertStringAnnotator('somesortof123').split_by_lang_intersection()==['somesortof123']assertStringAnnotator('qwe, 123!!!').split_by_char_type()==\['qwe',',',' ','123','!!!']assertStringAnnotator('йцу 123 qwe LOL').has_langs('ru','en')assertStringAnnotator().has_langs_throughout('ru')isFalseassertStringAnnotator('йцу 123').has_langs_throughout('ru')isTrueassertStringAnnotator('йцу 123').has_langs_throughout('ru_RU')isTrue
Package: clds_pipeline
CLDS PipelineCross Language Document Similarity Pipeline for Master’s ThesisFree software: GNU General Public License v3Documentation:https://clds-pipeline.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.3 (2017-07-08)Update extractors to allow chained filteringMake sure results are always returned in English0.1.0 (2017-04-28)First release on PyPI.
Package: clea
Clea - A lightweight framework for creating CLI applications in pythonClea is uses type annotations to generate the command/group defintions and parse the arguments at the runtime. To start with clea runpip3 install cleaDefine you first command usingfromtyping_extensionsimportAnnotatedfromclea.paramsimportIntegerfromclea.wrappersimportcommand@commanddefadd(n1:Annotated[int,Integer()],n2:Annotated[int,Integer()],)->None:"""Add two numbers"""print(f"Total{n1+n2}")Invoke the command at runtime usingfromclea.runnerimportrunif__name__=="__main__":run(cli=add)The example is taken fromadd.pyin the examples folder.You can check the command definition using$pythonadd.py--help

Usage:add[OPTIONS]N1N2Addtwonumbers

Options:--helpShowhelpandexit.Execute the command using$pythonadd.py23Total5Read more about the usage of clea in thedocs
Package: cleague-bot
Commander League Discord BotA discord bot to handle the different aspects of a tournament:Registration:!register player_nickname deck_hash deck_linkResults:!result player_1 result_1-result_2 player_2Performance during one split:!statsKnowing who is selected for the end-of-split invitational:!invitationalContributeContributions are welcome !Hosting the botIf you need to host a new version of the bot yourself,Python 3is required, as well as an
environment variableDISCORD_TOKEN.
The token can be found on yourDiscord applications page.The preferred way to run the bot is to use a python virtualenv:/usr/bin/python3-mvenvvenvsourcevenv/bin/activate
pipinstallleague-botDISCORD_TOKEN=discord_token_of_your_bot
league-botAsystemdunit can be used
to configure the bot as a system service:[Unit]Description=league-botAfter=network-online.target[Service]Type=simpleRestart=alwaysWorkingDirectory=directory_where_krcg_is_installedEnvironment=DISCORD_TOKEN=discord_token_of_your_botExecStart=/bin/bash -c 'source venv/bin/activate && league-bot'[Install]WantedBy=multi-user.targetFor development, the environment variableDISCORD_TOKENcan be provided
by a personal.envfile at the root of the league-bot folder (ignored by git):exportDISCORD_TOKEN="discord_token_of_your_bot"
Package: clean
# Clean.py[![Build Status](https://travis-ci.org/HibikineKage/clean.svg?branch=master)](https://travis-ci.org/HibikineKage/clean) [![Coverage Status](https://coveralls.io/repos/github/HibikineKage/clean/badge.svg?branch=master)](https://coveralls.io/github/HibikineKage/clean?branch=master) [![PEP8](https://img.shields.io/badge/code%20style-pep8-orange.svg)](https://www.python.org/dev/peps/pep-0008/) [![Scrutinizer Code Quality](https://scrutinizer-ci.com/g/HibikineKage/clean/badges/quality-score.png?b=master)](https://scrutinizer-ci.com/g/HibikineKage/clean/?branch=master) [![Code Intelligence Status](https://scrutinizer-ci.com/g/HibikineKage/clean/badges/code-intelligence.svg?b=master)](https://scrutinizer-ci.com/code-intelligence) [![Dependency Status](https://www.versioneye.com/user/projects/5ab5f5a60fb24f0ac49c2b9e/badge.svg?style=flat-square)](https://www.versioneye.com/user/projects/5ab5f5a60fb24f0ac49c2b9e) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/91755cd5aab24d07bc707d85a97a2d96)](https://www.codacy.com/app/HibikineKage/clean?utm_source=github.com&utm_medium=referral&utm_content=HibikineKage/clean&utm_campaign=Badge_Grade)Glob files sorter (alpha!!!!!)## UsagePlease type `clean.sh --help````bash$ clean.sh cwd/home/kage/$ clean.sh add foo*.txt ~/bar$ clean.sh listfoo*.txt => ~/bar$ clean.sh run/home/kage/foo1.txt => ~/bar/foo1.txt/home/kage/foo2.txt => ~/bar/foo2.txt/home/kage/foobar.txt => ~/bar/foobar.txt$ cat ~/.cleanrc{"path": [{"path": "~/bar", "glob": "foo*.txt"}]}```## LicenseMIT
Package: cleanapi
CleanAPIPretty tornado wrapper for making lightweight REST API servicesInstallation:pip install cleanapiExample:Project folders structure:.
├── handlers
│   └── simple_handler.py
├── log
├── ssl
│   ├── ca.csr
│   └── ca.key
├── static_html
│   └── index.html
└── server_example.pyserver_example.pyfromcleanapiimportserverif__name__=='__main__':# uses http protocolserver.start('http',8080,'/','./handlers','./static_html')# # uses https protocol# server.start('https', 8443, '/', './handlers', './static_html',#              path_to_ssl='./ssl', ssl_certfile_name='ca.csr', ssl_keyfile_name='ca.key')simple_handler.pyfromcleanapi.serverimportBaseHandlerurl_tail='/example.json'# noinspection PyAbstractClassclassHandler(BaseHandler):"""Test API request handler"""asyncdefget(self):self.set_status(200)self.write({'status':'working'})index.html<!DOCTYPE html><html><head><metacharset="utf-8"><title>Cleanapy demo</title><linkrel="icon"href="/favicon.ico"type="image/x-icon"/></head><body><h1>Cleanapy demo page</h1><p>Everything OK</p></body></html>You also may put 'favicon.ico' file to the 'static_html' folder, but it is not necessary.Then you can test server responses onhttp://localhost:8080andhttp://localhost:8080/example.jsonSee log/cleanapi.log for information about externel access to the serverExample with PydanticHandler:pydantic_handler.pyfromcleanapi.serverimportPydanticHandlerfrompydanticimportBaseModel,validator,NonNegativeIntfromtypingimportOptional,Listurl_tail='/pydantic.json'classPydanticRequest(BaseModel):"""Pydantic dataclass for request"""foo:NonNegativeIntbar:NonNegativeInt@validator('foo','bar')def_validate_foo_bar(cls,val:str):ifval==666:raiseValueError(f'Values of foo and bar should not be equal to 666')returnvalclassPydanticResponse(BaseModel):"""Pydantic dataclass for response"""summ:Optional[NonNegativeInt]errors:Optional[List[dict]]# noinspection PyAbstractClassclassHandler(PydanticHandler):"""Example of using PydanticHandler"""request_dataclass=PydanticRequestresult_dataclass=PydanticResponse# noinspection PyUnusedLocaldefprocess(self,request:request_dataclass)->result_dataclass:"""What the handler should do:param request: incoming request:type request: request_dataclass:return: processing result:type: result_data class"""result=PydanticResponse(summ=request.foo+request.bar,errors=[])ifresult.summ>1000:raiseValueError('The sum of foo and bar is more than 1000')returnresultdefif_exception(self,errors:list)->None:"""What to do if an exception was thrown:param errors: list of errors:type errors: list"""self.set_status(400)self.write({'errors':errors})returnYou can not test it with a browser because of POST method using. You have to use a program like Postman or some custom util like my pynger.py
Package: clean-architecture
Clean ArchitectureThis project is described a clean architecture written by Robert C. Martine.
Architecture is fellow aclean architectureTable of contentsFeaturesDirectory StructureInstallationExampleTestingDirectoryStructure|--clean_architecture||--domain|||-request_object.py|||-response_object.py||`-use_case.py||--entity||`-entity.py||--exception|||-exception.py||`-*_exception.py||--serializer|`-serializer.pyInstallationpipinstallclean-architectureExampleEntityfromclean_architecture.entityimportEntityclassUserEntity(Entity):def__init__(self,user_id,name,created_at,updated_at):self.id=user_idself.name=nameself.created_at=created_atself.updated_at=updated_at@classmethoddeffrom_dict(cls,adict):user=UserEntity(user_id=adict.get("id"),name=adict.get("name"),created_at=adict.get("created_at"),updated_at=adict.get("updated_at"))returnuserDomainValidRequestObjectfromclean_architecture.domainimport(ValidRequestObject,InvalidRequestObject,UseCase,ResponseSuccess)classCreateUserRequestObject(ValidRequestObject):def__init__(self,user):self.user=userifinvalid_req.has_errors():returninvalid_reqreturnCreateUserRequestObject(user=UserEntity.from_dict(adict))UseCaseclassCreatedUser(UseCase):def__init__(self,user_repository):self.user_repository=user_repositorydefprocess_request(self,request_object):user=self.user_repository.create(user=request_object.user)returnResponseSuccess(201,user)SerializerfromschemaimportSchemafromclean_architecture.serializerimportSerializerclassCreateUserEncoder(Serializer):schema=Schema({"name":str},ignore_extra_keys=True)Testing$pytest
Package: cleanassist
Clean AssistClean Assist is a simple library designed to help data scientists observe a summary of any DataFrame they would like to clean.
This library also displays charts to view the normal approximation of your variables.Clean Assist is composed of 2 functions:clean_assist.table(df, n_rows, n_round)Displays relevant features to help you on data cleaning and analysis.Parametersdf               : DataFrame you would like to analyzen_rows    : Number of variables to displayn_round   :  Number of decimals to round calculationsclean_assist.normality(df, list_var, print_img, size_x, size_y, font_size)Displays histograms to compare the your variables to a normal distribution.Parametersdf                 : DataFrame you would like to analyzelist_var    : Name of columns to analyze in a list formatprint_img    : input 'y' to print image or 'n' to not printsize_x         :  width of the image outputsize_y         :  height of the image outputfont_size    :  font size of the titles and headersTo import the library: copy paste the green colored code to your python code:- Note: Delete the plus(+) signs after pasting code+ import requests+ url = 'https://raw.githubusercontent.com/juanduranc/Clean-Assist/master/library'+ exec(requests.get(url).text)+ help(clean_assist)Example of library usage and interpretation:1. The following table is a sample of an output form the function: clean_assist.table(df, n_rows, n_round)VARIABLESNULLSCOUNTTYPESMEANMEDIANUNIQUESSAMPLE_________________________________Outlierspval(Norm)AVG_CLICKS_PER_VISIT01946int6413.513.015[11, 13, 12, 13, 13, 17, 10, 13, 12, 12][6,0]0.03MEDIAN_MEAL_RATING471899int642.83.05[3, 3, 3, 3, 3, 2, 4, 3, 3, 3][0,13]3e-06REVENUE01946float642107.31740.0859[1880, 1495, 2572.5, 1647, 1923, 1250][0,82]1e-21TOTAL_PHOTOS_VIEWED01946int64106.40.0371[0, 90, 0, 0, 253, 0, 705, 0, 0, 0][0,120]5e-90CROSS_SELL_SUCCESS01946int640.71.02[1, 1, 1, 0, 1, 1, 0, 1, 1, 1]1e-159Examples of findings:AVG_CLICKS_PER_VISIT has a similar mean and mean, it aproximates a normal distribution and has 6 lower outliers.MEDIAN_MEAL_RATING has 47 nulls which need imputation.Revenue is the only float variables, the rest are integer.TOTAL_PHOTOS_VIEWED has a median of 0 and 120 upper outliers. This means most people dont look view photos.CROSS_SELL_SUCCESS has 2 unique values. From the column named sample you can see only ones and zeros. This is a binary or boolean column.2. Next, a sample output from the function: clean_assist.normality(df, list_var, print_img, size_x, size_y, font_size)Histograms' interpretation:MEDIAN_MEAL_RATING has interger values and it mimisc a normal distribution.AVG_CLICKS_PER_VISIT is the colsest variable to a normal distribution with a p value of 0.03.REVENUE is right skewed with 82 upper outliers.TOTAL_PHOTOS_VIEWED has too many zero values. It is also right skewed and far from being a normal distribution.
Package: cleanbug
cleanbugCLI tool for managing and easily converting the code into comments and vice versa. Define multiple modes and execute the command to swap between them. Comments all flags that are not the current mode and uncomment the flag that is the current mode.PrerequisitesPython 3.6 or higherInstallationpipinstallcleanbugUsagecleanbug<file_name>[<output_file_name>]--mode<flag>--config<config_file>Config FileCan contain the parameters FLAGS, INLINE_COMMENT, MULTILINE_COMMENT. The starting and ending symbols of MULTILINE_COMMENT should be separated by spaces.
Eg:FLAGS=DEBUGDEVPRODINLINE_COMMENT=//MULTI_COMMENT=/**/FLAGS=DEBUGDEVPRODBUILDINLINE_COMMENT=#MULTILINE_COMMENT can also be broken into MULTILINE_COMMENT_START and MULTILINE_COMMENT_END.Input FileThe flags should be written in the following format:.<flag_name_1>[.<flag_name_2>...<flag_name_n>]with each flag separated by a dot. The flags must be written in a single line, preceded and succeeded only by the comment symbol and whitespace.Multiple LinesTo mark multiple lines under a single flag line, use a>sybmol as the first flag in the line. The end of the lines marked under those flags is marked by another flag line using the<symbol. The flag lines between the>and<symbols are marked under the same flag.ExampleOriginal Code:voidmain(){//.DEVprintf("Hello World!\n");//>.PRODprintf("Hello There, World!\n");printf("Nice to meet you! \n");//<.PROD//.PROD.DEVprintf("I'm Good!\n");printf("Nice to meet you! \n");}FLAG: DEVcleanbug test/test.c --config test/cleanbug.config --mode DEVvoidmain(){//.DEVprintf("Hello World!\n");//>.PROD/*printf("Hello There, World!\n");printf("Nice to meet you! \n");*///<.PROD//.PROD.DEVprintf("I'm Good!\n");printf("Nice to meet you! \n");}FLAG: PRODcleanbug test/test.c --config test/cleanbug.config --mode PRODvoidmain(){//.DEV//printf("Hello World!\n");//>.PRODprintf("Hello There, World!\n");printf("Nice to meet you! \n");//<.PROD//.PROD.DEVprintf("I'm Good!\n");printf("Nice to meet you! \n");}FLAG: DEBUGcleanbug test/test.c --config test/cleanbug.config --mode DEBUGvoidmain(){//.DEV//printf("Hello World!\n");//>.PROD/*printf("Hello There, World!\n");printf("Nice to meet you! \n");*///<.PROD//.PROD.DEV//printf("I'm Good!\n");printf("Nice to meet you! \n");}Prevent Conversion from Comments to CodeThe conversion from comment to code can be prevented by inserting a!!symbol after the comment symbol (separated only by whitespaces).
Eg:# !! NOTE: This is a note.This allows for comment lines within the code to be ignored by the tool.Output FileIf the output file is not specified, the input file is overwritten with the converted code. If the output file is specified, the converted code is written to the output file.
Package: cleancc
数据清洗--cleancccleancc快速清洗数据内容可以项目地址(欢迎star):https://github.com/Amiee-well/clean使用方法pip install cleanccimport cleancc共有五个函数调用：1.第一个函数为punct：[​	去除标点并让所有字母小写​    :param pop_list:所要处理的的列表格式​    :param lower:是否转小写，默认是​    :return all_comment:处理后的结果-字符串格式]2.第二个函数为statistics：[​	词频统计​    :param pop_list:所要处理的的列表格式​    :param symbol:是否去除标点，默认是​    :param lower:是否转小写，默认是​    :return wordCount_dict:统计结果-字典格式]3.第三个函数为stop_words：[​	删除词频统计中的停顿词​    :param statis:是否选择词频清理​    :param pop_list:所要处理的的列表格式​    :param symbol:是否去除标点，默认是​    :param lower:是否转小写，默认是​    :param wordCount_dict:词频统计结果-字典​    :return wordCount_dict:清除后结果-字典格式]4.第四个函数为Count_Sort：[​	字典排名数目排序​    :param wordCount_dict:词频统计结果-字典​    :param choices_number:返回前choices_number个字典个数​    :return keyword_list:出现的单词-列表格式​    :return value_list:单词对应的词频-列表格式]5.第五个函数为word_all：[​	调用全部函数​    :param pop_list:所要处理的的列表格式​    :param choices_number:返回前choices_number个字典个数​    :param symbol:是否去除标点，默认是​    :param lower:是否转小写，默认是​    :return keyword_list:出现的单词-列表格式​    :return value_list:单词对应的词频-列表格式]注意事项注意:处理数据参数类型为列表，需要pandas转换为列表后进行调用！使用示例:importpandasaspdfromcleanccimportcleanfrombs4importBeautifulSoupdf=pd.read_csv("label.csv",sep='\t',escapechar='\\')review_list=df['review'].tolist()comment_list=[BeautifulSoup(k,'lxml').textforkinreview_list]print(comment_list)keyword_list,value_list=clean.word_all(comment_list,150)print(keyword_list,value_list)
Package: cleanchausie
CleanChausieCleanChausie is a data validation and transformation library for Python. It is a successor to CleanCat.Check out thedocs!Interested in working on projects like this?Closeis looking forgreat engineersto join our team.Key features:Operate on/with type-checked objects that have good IDE/autocomplete supportAnnotation-based declarations for simple fieldsComposable/reusable fields and field validation logicSupport (but not require) passing around a context (to avoid global state)Context pattern is compatible with explicit sqlalchemy-based session management. i.e. pass in a session when validatingCleanly support intra-schema field dependencies (i.e. one field can depend on the validated value of another)Explicit nullability/omission parametersErrors returned for multiple fields at a time, with field attributionCleanChausie by exampleThis is a short example of how a schema might be used to support a flask
endpoint. More detailed examples can be found in thedocs.fromtypingimportListfromcleanchausieimport(clean,ListField,URLField,EmailField,field,ValidationError,Schema)fromflaskimportapp,request,jsonifyclassJobApplication(Schema):first_name:strlast_name:stremail:str=field(EmailField())urls:List[str]=field(ListField(URLField(default_scheme='http://')))@app.route('/job_application',methods=['POST'])deftest_view():result=clean(JobApplication,request.json)ifisinstance(result,ValidationError):returnjsonify(result.serialize()),400# Now "result" has the validated data, in the form of a `JobApplication` instance.assertisinstance(result,JobApplication)name=f'{result.first_name}{result.last_name}'Release processMake sure to thoroughly review and test the code changes.Prepare for a new releaseUpdate the package version withincleanchausie/__init__.py.Add a changelog entry for the new version.Merge to masterDispatch a new "build and release" workflow action within the github actions tab.The resulting workflow will build and publish the new version to PyPi.