Saravanan666/pypi-org huggingface dataset:
Package: koko-keywords
Koko Keyword Python ClientA python client  for theKoko Keywords API. The client handles caching to ensure very low latency.Installpip install koko_keywordsUsageSet theKOKO_KEYWORDS_AUTHenvironment to the authentication string provided
by Koko. To get an api key, complete oursign up form.export KOKO_KEYWORDS_AUTH=username:passwordImport the libraryimportkoko_keywordsThen use thematchfunction to check whether a query prompt matches a risky
keywordkoko_keywords.match("sewerslide")There is one optional param,filter.FilterFilter the keyword based on the taxonomy using a colon delimited list of “dimension=value” filters. Omitting a dimension does not filter by that dimension e.g.koko_keywords.match("sewerslide","category=eating,parenting:confidence=1,2")This matches "sewerslide" against eating eating and parenting, with a confidence of 1 and 2 and any intensity (as intensity was omitted).PerformanceThe underlying library is written in Rust and cross-compiled to the four major CPU targets. Regexes are cached based on the cache expiration headers (currently set to an hour). This ensures very low latency and overhead (< 1μs/req).Error HandlingA RuntimeError will be raised with hints on how to resolve the issue. No exception handling should be necessary.LoggingMinimal log messages are logged to STDERRLicenseWWWWWW||WWWWWW
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       (__|__|(__|__|(The MIT License)Copyright (c) 2017 Koko AI Inc.us@kokocares.orgPermission 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.
Package: kokoroio
What is thisThis is a client forkokoro.iowhich is a chat service (maybe going to be) best ever.I know that you want to join as soon as possible, please askmeorsupermomonga-sanabout how to create account.RequirementsPython3.6+kokoro.io account(using your access token)Install$pipinstallkokoroioConfigureYou must set an access token to sign in to kokoro.io by three way like follows.Set environ path namedKOKOROIO_ACCESS_TOKEN.Set your access token to any file your choice and pass it’s file name toKokoroioconstructor asenv_path.Pass directlyKokoroioconstructor asaccess_token.UsageOnce you createKokoroioinstance, you can request from two way syncronous or asyncronous.fromkokoroioimportKokoroioclient=Kokoroio(access_token='xxxxxxx')client.channels.get()# You received a response!If an endpoint requires path parameters, you need to pass path parameters as arguments for request method.# Assuming you want to post a message to channel whose channel_id is 'hogehoge'.# In this case, url is `channels/hogehoge`.o=client.channels.send_message(channle_id='hogehoge')# So you can request with a payload.o(message='hi')And you can find every client’s methods.clients.channels.method_namesclients.channels.methodsSyncronous requestYou will getrequests's HttpResponseobject.If you want to know about response object, please referrequests doc.Exampleclient.channels.get()Asyncronous requestDifferent from syncronous version, request method hasaprefix likechannels.aget()and you don’t need to create new event loop at every request.You will getaiohttp's ClientResponseobject because usingaiohttpinternally.If you want to know aboutaiohttp, please referaiohttp doc.Exampleclient.channels.aget()TestSorry now I have no test😭About kokoro.io’s API detailYou can find all of current API detail fromthe official apidoc.
Package: kola
kolaa PythonPolarsInterface to kdb+/qBasic Data Type MapDeserializationAtomk typensizepython typenoteboolean11boolguid216strbyte41intshort52intint64intlong78intreal84floatfloat98floatchar101strstring101strsymbol11*strtimestamp128datetimemonth134-date144date0001.01.01 - 9999.12.31datetime158datetimetimespan168timedeltaminute174time00:00 - 23:59second184time00:00:00 - 23:59:59time194time00:00:00.000 - 23:59:59.999Composite Data Typek typensizepython typeboolean list11pl.Booleanguid list216pl.List(pl.Binary(16))byte list41pl.Uint8short list52pl.Int16int list64pl.Int32long list78pl.Int64real list84pl.Float32float list98pl.Float64char list101pl.Utf8string list101pl.Utf8symbol list11*pl.Categoricaltimestamp list128pl.Datetimemonth list134-date list144pl.Datedatetime list158pl.Datetimetimespan list168pl.Durationminute list174pl.Timesecond list184pl.Timetime list194pl.Timetable98*pl.DataFramedictionary99*-keyed table99*pl.DataFrameperformance is impacted by converting guid to string, deserialize the uuid to 16 fixed binary list, use .hex() to convert binary to string if requiredreal/float 0n is mapped to Polars null not NaNdf.with_columns([
    (pl.col("uuid").apply(lambda u: u.hex()))
    ])SerializationBasic Data Typepython typek typenoteboolbooleanintlongfloatfloatstrsymbolbytesstringdatetimetimestampdatedate0001.01.01 - 9999.12.31datetimedatetimetimedeltatimespantimetime00:00:00.000 - 23:59:59.999Dictionary, Series and DataFramepython typek typedictdictpl.Booleanbooleanpl.List(pl.Binary(16))guidpl.Uint8bytepl.Int16shortpl.Int32intpl.Int64longpl.Float32realpl.Float64floatpl.Utf8charpl.Categoricalsymbolpl.Datetimetimestamppl.Datedatepl.Datetimedatetimepl.Durationtimespanpl.Timetimepl.DataFrametableLimited Support for dictionary as arguments, pythonstringas keys and PythonBasic Data Typesandpl.Seriesas values.Quick StartCreate a Connectionimportpolarsasplimportkolaq=kola.Q('localhost',1800)Connect(Optional)Automatically connect when querying q processq.connect()DisconnectAutomatically disconnect if any IO errorq.disconnect()String Queryq.sync("select from trade where date=last date")Functional QueryFor functional query,kolasupports PythonBasic Data Type,pl.Series,pl.DataFrameand Python Dictionary with string keys and PythonBasic Data Typeandpl.Seriesvalues.fromdatetimeimportdate,timeq.sync(".gw.query","table",{"date":date(2023,11,21),"syms":pl.Series("",["sym0","sym1"],pl.Categorical),# 09:00"startTime":time(9),# 11:30"endTime":time(11,30),},)Send DataFrame# pl_df is a Polars DataFrameq.sync("upsert","table",pl_df)# pd_df is a Pandas DataFrame, use pl.DateFrame to cast Pandas DataFrameq.sync("upsert","table",pl.DataFrame(pd_df))Async Query# pl_df is a Polars DataFrameq.asyn("upsert","table",pl_df)Polars DocumentationsRefer toUser GuideAPI Reference
Package: kolada_scraper
This is a scraper for statistical data fromhttp://api.kolada.se/v2/built on top of theStatscraper package <https://github.com/jplusplus/statscraper>.Installpip install -r requirements.txtExample usagefromkoladaimportKoladaScraperscraper=KoladaScraper()dataset=scraper.items["N00002"]# pass a KPI id# Query by yeardata=dataset.fetch({'period':[2016,2015],})# ...or by municipalitytowns=[x.valueforxindataset.dimensions['municipality'].allowed_values]data=dataset.fetch({'municipality':towns[:5],})# ... or by bothdata=dataset.fetch({'period':[2016,2015],'municipality':towns[:5],})# And then do something with the results.print(data.pandas)TODOAdd more allowed valuesImplement errors when unallowed values are passedImplement regionsUpdatechunkify()function, to make url building better
Package: kolak
Universal RSS Parser.extra features:work with any rss and atom feed format.smart cachemultiple rss supportparallel parserparse rss podcast.Installationpip install kolakUsage ExamplefromkolakimportParserurl=""rss=Parser(url)items=rss.parse()print("title:",rss.title)print("description:",rss.description)print("permalink:",rss.permalink)print("updated:",rss.updated)print("author:",rss.author)print("-"*10)foriteminitems:print("title:",item["title"])print("date:",item["date"])print("description:",item["description"])print("link:",item["link"])print("link_image",item["link_image"])print("link_podcast:",item["link_podcast"])print("-"*10)# linemultiple rss processingurl1 = ""
url2 = ""
rss1 = Parser(url1)
items1 = rss1.parse()
rss2 = Parser(url2)
items2 = rss2.parse()
items = items1+items2also you can pass raw xml feed as url input inParser().parse optionrss.parse(limit=0,debug=False,parallel=False)"""limit: limitable items parse, 0 = nolimiteddebug: activate debug mode. when no model is match, you will see what happened on kolak.logparallel: use multiprocessing to parse items (experimental)"""
Package: koldar-utils
IntroductionGeneric django providing models and function used to better developer other apps.
In itself, this isnota django apppip install django-koldar-utilsCapabilitiesAbstractModelMetaclasswhich can be used to create django models with abc capabilities;AbstractPermissionMixinwhich does not rely ongroupsandpermissionsrelationships;Ormclass that provides the creation of popular django fields, just for your convenience;ArrowAuditMixinfor providingcreated_atandupdated_atfields usingarrowproject;convenience class to easily create django backends, middlesware, validators;ArrowFieldimplementation (taken fromdjango-arrow-field);convenience methods to interact with django framework without remembering how to do certain tasks;classes used to create graphql CRUD operation easily;Upload new versionUse setup.py directly:# create .pypirc in your home directory
python setup.py update_version_patch bdist_wheel uploadGenerate new docpython setup.py build_sphinxOrpmake:# pmakeup is a Massimo Bono project used to automatize the build; per se it is not necessary
pip install pmakeup
# add in TWINE_PYPI_PASSWORD the pypi password
pmakeup update-version-patch build upload-to-pypi
Package: kolena
Kolenais a comprehensive machine learning testing and debugging platform to surface hidden
model behaviors and take the mystery out of model development. Kolena helps you:Perform high-resolution model evaluationUnderstand and track behavioral improvements and regressionsMeaningfully communicate model capabilitiesAutomate model testing and deployment workflowsThiskolenapackage contains the Python client library for programmatic interaction with the Kolena ML testing
platform.Installwith your favorite Python package manager andget started in minutes:pip install kolenaQuick LinksDeveloper GuideQuickstartAPI ReferenceExamplesTutorial and usage documentationSet up rigorous and repeatable model testing in minutesDetailedkolenatyping and function documentationReference integrations for different machine learning workflows
Package: kolena-client
Kolenais a comprehensive machine learning testing and debugging platform to surface hidden
model behaviors and take the mystery out of model development. Kolena helps you:Perform high-resolution model evaluationUnderstand and track behavioral improvements and regressionsMeaningfully communicate model capabilitiesAutomate model testing and deployment workflowsThiskolenapackage contains the Python client library for programmatic interaction with the Kolena ML testing
platform.Installwith your favorite Python package manager andget started in minutes:pip install kolenaQuick LinksDeveloper GuideQuickstartAPI ReferenceExamplesTutorial and usage documentationSet up rigorous and repeatable model testing in minutesDetailedkolenatyping and function documentationReference integrations for different machine learning workflows
Package: kolibri2zim
kolibri2zimkolibr2zimallows you to create aZIM filefrom a Kolibri Channel.It downloads the video (webmormp4extension – optionnaly
recompress them in lower-quality, smaller size), the thumbnails, the
subtitles and the authors' profile pictures ; then, it create a static
HTML files folder of it before creating a ZIM off of it.Requirementsffmpegfor video transcoding (only used with--use-webmor--low-quality).curlandunzipto install Javascript dependencies. Seeget_js_deps.shif you want to do it manually.InstallationVirtualenvkolibri2zimis a Python3 software. If you are not using theDockerimage, you are advised to use it in a
virtual environment to avoid installing software dependencies on your system.python3-mvenvenv# Create virtualenvsourceenv/bin/Activate# Activate the virtualenvpip3installkolibri2zim# Install dependencieskolibri2zim--help# Display kolibri2zim helpCalldeactivateto quit the virtual environment.Seerequirements.txtfor the list of python dependencies.Dockerdockerrun-vmy_dir:/outputghcr.io/openzim/kolibri2zimkolibri2zim--helpUsagekolibri2zimworks off achannel-idthat you must provide. This is a 32-characters long ID that you can find in the URL of the channel you want, either fromKolibri Studioor theKolibri CatalogDevelopmentkolibri2zim adheres to openZIM'sContribution Guidelines.kolibri2zim has implemented openZIM'sPython bootstrap, conventions and policiesv1.0.0.Before contributing be sure to check out theCONTRIBUTING.mdguidelines.To test epubs and pdfs rendering, a potential usefull command is:kolibri2zim--name"Biblioteca Elejandria"--output/output--tmp-dir/tmp--zim-fileBiblioteca_Elejandria.zim--channel-id"fed29d60e4d84a1e8dcfc781d920b40e"--node-ids'd92c07655128458f8248416154b18a68,89fe2f86ee3f4fbaa7fb2bf9bd56d088,75f99e6b97d14b14a4e74762ad77391f,89fe2f86ee3f4fbaa7fb2bf9bd56d088'LicenseGPLv3or later, seeLICENSEfor more details.
Package: kolibri-app-desktop-xdg-plugin
Kolibri Linux desktop app integration pluginThis is a Kolibri plugin which generates Linux desktop launchers and icons for Kolibri channels. These files are created automatically in$KOLIBRI_HOME/content/xdg/share. This plugin is intended to be used inside a Kolibri desktop application such ashttps://flathub.org/apps/details/org.learningequality.Kolibri.UsageInstall the plugin from pypi:pip install kolibri-app-desktop-xdg-pluginEnable the plugin in Kolibri and generate launchers:kolibri plugin enable kolibri_app_desktop_xdg_plugin
kolibri manage app_desktop_xdg_update_launchersAdditional desktop launchers will be generated automatically when channels are added or removed. For these launchers to appear on your desktop, you will need to add their location to theXDG_DATA_DIRSenvironment variable. To do this in most Linux distributions, create a file named/usr/lib/systemd/user-environment-generators/61-kolibri-app-desktop-xdg-pluginwith the following contents, then log out and log in again:#!/bin/bash
XDG_DATA_DIRS="$HOME/.kolibri/content/xdg/share:${XDG_DATA_DIRS:-/usr/local/share:/usr/share}"
echo "XDG_DATA_DIRS=$XDG_DATA_DIRS"You can substitute the path to Kolibri's home directory for$HOME/.kolibri.
Package: kolibri-desktop-auth-plugin
kolibri-desktop-auth-pluginAuthentication backend that allows an HTTP client to authenticate by providing
a desktop user tokenTestingThis is a kolibri plugin, you need to install the python package in the kolibri
python environment using pip and then enable the plugin..../kolibri $ pip install -e ../kolibri-desktop-auth-plugin
 .../kolibri $ kolibri plugin enable kolibri_desktop_auth_pluginTo test you can launch the fake user info dbus daemon included in this
repository:./fake-dbus-daemon.pyAfter enabling the fake dbus daemon you can try to autologin in your kolibri
with the plugin opening the following url in your browser:http://localhost:8000/kolibri_desktop_auth_plugin/login/TOKEN1You can change the TOKEN at the end of the url above to test with different
user data:TOKEN1: adminuser, user with admin rightsTOKEN2: eosuser, user without admin rightsYou can test also with a non existent token that should redirect to the login
page.It's also possible to redirect after the authentication passing the next url as
a get parameter to the url, for example:http://localhost:8000/kolibri_desktop_auth_plugin/login/TOKEN1?next=/learnDevelopmentSetup the Python environment:pipenv installSetup pre-commit:pipenv run pre-commit installEnter the Python environment for development:pipenv shellReleasingCreating a releaseIf you are releasing a new version, usebump-versionwith withmajor,minor, orpatch. For example:yarn bump-version patchThis creates a new commit and a git tag. Push this to the remote:git push
git push --tagsCreate a.whlfile:python setup.py bdist_wheel --universalFinally, upload the.whlfile to PyPi:twine check -s dist/*
twine upload -s dist/*
Package: kolibri-dynamic-collections-plugin
Kolibri Dynamic Collections pluginThis is a Kolibri plugin for dynamic collections, which are a new way to
bundle together content across multiple Kolibri channels. The plugin includes
database models and utilities to manage dynamic collections, as well as an
editor to create collections from a selection of channels and content nodes.UsageInstall a release from pypi:pip install kolibri-dynamic-collections-pluginEnable the plugin in Kolibri:kolibri plugin enable kolibri_dynamic_collections_pluginDevelopmentGetting startedCreate a pipenv shell and then install additional dependencies usingbootstrap.sh:pipenv shell
./scripts/bootstrap.shInstall kolibri-dynamic-collections-plugin in editable mode:pip install -e .To build front end code:yarn buildRefer to theUsage instructionsto enable the plugin.Submitting changesBefore submitting changes, please be sure to run the pre-commit checks:pre-commit runIf you can configure git to run these checks automatically:pre-commit install -f --install-hooksCreating a releaseIf you are releasing a new version, usebump-versionwith withmajor,minor, orpatch. For example:yarn bump-version patchThis creates a new commit and a git tag. Push this to the remote:git push
git push --tagsCreate a.whlfile:yarn distThe file will be placed in thedist/directory.Finally, upload the.whlfile to PyPi:yarn release
Package: kolibri-light
KolibriA comprehensive multi modal machine learning platformContents├── README.md
├── configREADME.md: this documentationSetting up PyFreeLing in Mac OS SierraThere are 4 types of dependencies:Homebrew, a package manager to install software in MacFreeLing, an open source language analysis tool suitePython 3lxml, a Python library to work with XMLlibxml2andlibxsltC libraries which are dependencies oflxmlInstall HomebrewFollowNeil Gee's guideto install and set up homebrew for Mac OS SierraInstall FreeLingUse Homebrew to install FreeLing by running this command:brewinstallfreelingHomebrew will take care of any dependencies.Install Python 3You can install Python 3 with Homebrew following the instructions fromThe Hitchhiker's Guide to Pythonor following the very completeLisa Tagliaferri's guide.Basically:brewinstallpython3Install libxml2 and libxsltmacOS Sierra already provideslibxml2andlibxslt. They can be installed through Homebrew though:brewinstalllibxml2
brewinstalllibxsltInstall lxmlNow, you are ready to installlxml:pip3installlxmlOnce you have installed FreeLing and all the dependencies, you will always need to start a FreeLing analyzer in server modeStarting a FreeLing analyzer in server modeanalyze-fen.cfg--server--port50005&Install pyaudioinstall pyaudio in WindowsIntalling pyaudiocan be tricky in window platform and generally 'pip install pyaudio' will not work.
see the following stackoverflow response if you have any problem.https://stackoverflow.com/questions/52283840/i-cant-install-pyaudio-on-windows-how-to-solve-error-microsoft-visual-c-14Install pyaudio in mac os xFirst install portaudiobrewinstallportaudiothe run the following commandpipinstall--global-option='build_ext'--global-option='-I/usr/local/include'--global-option='-L/usr/local/lib'pyaudioinstall pocketsphinxInstaaling this library could be difficult on some systems.
If the command:pipinstallpocketsphinxdid not work for you then try the following stepsgitclone--recursivehttps://github.com/bambocher/pocketsphinx-pythoncdpocketsphinx-pythonEdit file pocketsphinx-python/deps/sphinxbase/src/libsphinxad/ad_openal.c.Change#include <al.h>
#include <alc.h>to#include <OpenAL/al.h>
#include <OpenAL/alc.h>and finally:pythonsetup.pyinstall
Package: kolibri-ml
KolibriA comprehensive multi modal machine learning platformContents├── README.md
├── configREADME.md: this documentationSetting up PyFreeLing in Mac OS SierraThere are 4 types of dependencies:Homebrew, a package manager to install software in MacFreeLing, an open source language analysis tool suitePython 3lxml, a Python library to work with XMLlibxml2andlibxsltC libraries which are dependencies oflxmlInstall HomebrewFollowNeil Gee's guideto install and set up homebrew for Mac OS SierraInstall FreeLingUse Homebrew to install FreeLing by running this command:brewinstallfreelingHomebrew will take care of any dependencies.Install Python 3You can install Python 3 with Homebrew following the instructions fromThe Hitchhiker's Guide to Pythonor following the very completeLisa Tagliaferri's guide.Basically:brewinstallpython3Install libxml2 and libxsltmacOS Sierra already provideslibxml2andlibxslt. They can be installed through Homebrew though:brewinstalllibxml2
brewinstalllibxsltInstall lxmlNow, you are ready to installlxml:pip3installlxmlOnce you have installed FreeLing and all the dependencies, you will always need to start a FreeLing analyzer in server modeStarting a FreeLing analyzer in server modeanalyze-fen.cfg--server--port50005&Install pyaudioinstall pyaudio in WindowsIntalling pyaudiocan be tricky in window platform and generally 'pip install pyaudio' will not work.
see the following stackoverflow response if you have any problem.https://stackoverflow.com/questions/52283840/i-cant-install-pyaudio-on-windows-how-to-solve-error-microsoft-visual-c-14Install pyaudio in mac os xFirst install portaudiobrewinstallportaudiothe run the following commandpipinstall--global-option='build_ext'--global-option='-I/usr/local/include'--global-option='-L/usr/local/lib'pyaudioinstall pocketsphinxInstaaling this library could be difficult on some systems.
If the command:pipinstallpocketsphinxdid not work for you then try the following stepsgitclone--recursivehttps://github.com/bambocher/pocketsphinx-pythoncdpocketsphinx-pythonEdit file pocketsphinx-python/deps/sphinxbase/src/libsphinxad/ad_openal.c.Change#include <al.h>
#include <alc.h>to#include <OpenAL/al.h>
#include <OpenAL/alc.h>and finally:pythonsetup.pyinstall
Package: kolibri-oidc-client-plugin
Kolibriis the offline learning platform
fromLearning Equality.OpenID Connect (OIDC) is a simple identity layer on top of the OAuth 2.0 protocol. It allows Clients to verify the identity of the End-User based on the authentication performed by an Authorization Server, as well as to obtain basic profile information about the End-User in an interoperable and REST-like manner.).This package provides Kolibri users with the ability to authenticate against an OpenID provider. This is usually a need when integrating it with another applications sharing a Single Sign On (SSO) authentication.
Package: kolibri-oidc-provider-plugin
Kolibriis the offline learning platform
fromLearning Equality.OpenID Connect (OIDC) is a simple identity layer on top of the OAuth 2.0 protocol. It allows providers to verify the identity of the End-User based on the authentication performed by an Authorization Server, as well as to obtain basic profile information about the End-User in an interoperable and REST-like manner.).This package provides a plugin to convert a  Kolibri server into a OIDC provider that can be use by external applications to authenticate, thus Kolibri becomes the source of truth when integrating it with another applications needing a single-sign-on (SSO) authentication.