Saravanan666/pypi-org huggingface dataset:
Package: koha-sync
Koha synchronizerEste modulo permite sincronizar registros de Koha al sistema estadistico Lookproxy.InstalacionPara instalar el modulokoha-syncdebe seguir estos pasos:Requerimientospython 3.7+El servidor desde el cual ejecuta el modulo, debe tener conexion con el servidor KohaLa instalacion la puede realizar en unhost dedicadoo en unhost compartidohost dedicado: significa que el modulo se ejecuta en un servidor exclusivamente para este proceso. El servidor
puede ser de 512 MB o 1 GB de RAM.host compartido: significa  que el modulo de ejecuta en el mismo servidor del Koha.Crear directorio raiz>mkdir/opt/koha-syncInstalar y crear sesion de tmux>apt-getinstalltmux
>tmuxnew-skoha-syncpara salirse de la sesion, debe presionarctrl + b + dInstalar Python3.7UbuntuCentOs 8CentOs 7Al finalizar la instalacion, verifique que corresponda a la3.7>aliaspython='python3.7'>python-V|_______3.7Instalar pip3UbuntuCentOs 8CentOs 7Al finalizar la instalacion, verifique que corresponda a la20+>pip3-V|______pip20.1from/usr/local/lib/python3.5/dist-packages/pip(python3.7)Instalar setuptools>python3.7-mpipinstallsetuptoolsInstalar y activar virtaulenv (solo si la instalacion es compartida)>python3.7-mpipinstallvirtualenv
>cd/opt/koha-sync&virtualenvvenv
>source/opt/koha-sync/venv/bin/activateInstalar el modulo koha-sync>python-mpipinstallkoha-syncCargar al servidor el config.yml enviado por Referencistasls|__config.ymlEjecutar el comandokoha_syncrhonizer --help

    Options:
     -V, --version           print current cli version and stops execution
     -c, --config-file TEXT  path to configuration file  [required]
     -v, --verbose           activate verbosity output
     -r, --run-once          synchronize once and stops execution
     -t, --test              run validations and stops before synchronize
     --help                  Show this message and exit.Copyright Â©Referencistas
Package: kohilites
KOHilitesA lightweight command line tool to extract and save highlights and notes made onKOReader.KOHilites takesmetadata.*.luafiles, extracts the content of each highlight or note, and saves them as formatted markdown files.InstallationInstall this package from PyPi usingpip install kohilitesQuick StartKOHilites is run from the command line. It can import and process multiple metadatafiles from a directory or single files if a specific path is given. The options available are:--input: Path to a single document's metadata or a directory containing metadata from several documents.--output: Directory to save parsed document metadata. If not given, outputs are saved to the root input directory. Defaults to blank.ExamplesWhen given a directory input, KOHilites will search that directory and all its subdirectories for metadata files and parse them. It will save all of the outputs to the output directory (not copying any nested structure).$kohilites--inputdirectory/containing/multiple/metadata/files--outputdirectory/to/save/highlightsWhen given the path to a file, KOHilites will parse only that file. If no output directory is given, the output is saved to the same directory that contained the input file.$kohilites--inputpath/to/metatdata.epub.lua
Package: kohler
kohler-pythonPython library for talking to Kohler devices (DTV+)Installationpip3 install kohlerUsagefromkohlerimportKohlerkohler=Kohler(bondIp='192.168.1.50')kohler.lightOn(1,50)kohler.musicOn()kohler.quickShower()kohler.stopShower()kohler.steamOn()kohler.steamOff()
Package: koho
koho (TM)koho(Hawaiian word for ‘to estimate’) is aDecision ForestC++ librarywith ascikit-learncompatiblePython interface.ClassificationNumerical (dense) dataMissing values (Not Missing At Random (NMAR))Class balancingMulti-ClassMulti-Output (single model)Build order: depth firstImpurity criteria: ginin Decision Trees with soft votingSplit a. features: best over k (incl. all) random featuresSplit b. thresholds: 1 random or all thresholdsStop criteria: max depth, (pure, no improvement)Bagging (Bootstrap AGGregatING) with out-of-bag estimatesImportant FeaturesExport GraphReadTheDocsNew BSD LicenseChange Log:1.1.0 Multi-Output (single model)
1.0.0 Missing Values (NMAR) : Python, Cython(bindings), C++
0.0.2 Criterion implemented in Cython
0.0.1 Classification : Python onlyCopyright 2019,AI Werkstatt (TM). All rights reserved.
Package: kohokoho
kohokoho (को हो को हो !)Installationpip install kohokohoInstallation (from source)Clone the repositorygit clone https://github.com/ayushsubedi/kohokohoCD into the cloned directory and create a virtualenvpython -m venv envEnable virtualenv.\env\Scripts\activateInstall dependency packages from requirements.txtpip install -r requirements.txtRun the apppython kohokoho.py --csv={location of csv file}
Package: kohonen
# py-kohonenThis module contains some basic implementations of Kohonen-style vector
quantizers: Self-Organizing Map (SOM), Neural Gas, and Growing Neural Gas.
Kohonen-style vector quantizers use some sort of explicitly specified topology
to encourage good separation among prototype “neurons”.Vector quantizers are useful for learning discrete representations of a
distribution over continuous space, based solely on samples drawn from the
distribution. This process is also generally known as density estimation.The source distribution includes an interactive test module that uses PyGTK and
Cairo to render a set of quantizers that move around in real time as samples are
drawn from a known distribution and fed to the quantizers. Run this test with :python kohonen_test.pyHave fun !
Package: kohonen-som
Kohonen's Self-Organizing Map (SOM)BackgroundThe originalpaperwritten by Teuvo Kohonen in 1990 was one of the first neural network model capable of unsupervised learning.Out of the different implementations of the algorithm, this one follows almost entirely the original paper. The update function is defined aswhereandis the current epoch.Also, each neuron is connected to all the other ones, hence the map is acomplete graph, whereis the number of neurons.Example
Package: kohtaaminen
kohtaaminenMeeting, rendezvous, confluence (Finnish kohtaaminen) mark up, down, and up again.Given a zip file containing a tree of html and media files following certain conventions,
the tool produces a markdown media tree belowkohtaaminen-md.License: MITDocumentationUser and developerdocumentation of kohtaaminen.Bug TrackerFeature requests and bug reports are bested entered in thetodos of kohtaaminen.Primary Source repositoryThe primary source repository ofkohtaaminen is at sourcehuta collection of tools useful for software development.StatusExperimental.Note: The default branch isdefault.
Package: koi
# koi(西湖锦鲤)A third-party (and incomplete) client library for aliyun.Why reinvent the wheel when there is official SDK?- Aliyun's Python SDK is poor maintained.- It doesn't support Python3.- It's design is weird, sugar methods are to rescue.Why not AWS, why the hell do anyone want Aliyun over AWS?- AWS only has one region in China, which is not as flexible as Aliyun when your business is very sensitive to network location.- AWS sucks on ICP备案 what is the key prerequisite to all internet services within China.- AWS China is not the same thing as AWS Global. For example, it lacks spot instances; its pricing is weird, too.## Get Stared```pyfrom koi.ecs import ECSClientc = ECSClient(ACCESS_KEY_ID, ACCESS_KEY_SECRET)c.describe_regions()```## What's Next?There's no docs, read the source for more details, yay!Read Aliyun docs may also help: https://docs.aliyun.com/#/pub/ecs/open-api/summary
Package: koii
KOIIPython module for displaying routes in a flask application. This is a python variant forKoii. It's a simple to use flask middleware that simply prints all the routes in a Flask application on the console.InstallationKoii is easy to install - it currently can be installed using thepiporpipenvcommand.pipinstallkoiipipenvinstallkoiiUsageTo make use of Koii, ensure you initialize the middleware by passing the instance of your flaks app into the class or theinit_appmethod. Ensure the class is initialized after your routes have been defined. If you're making use of blueprints, the same also applies, place the initialisation of the class after your blue print registration.fromkoiiimportKoiiapp=Flask(__name__)Koii(app)# alternatively Koii.init_app(app)An example file can befound here.
Package: koine-nlp
AboutA library for common NLP-related tasks for Koine
Greek.The fullest use of the library comes in the normalize() function,
which combines all of the available methods to produce an end result
which is suitable for indexing.View thefull documentation online.Authors© 2015, 2016 Nathan D. Smith <nathan@smithfam.info>Licensekoine-nlp is released under the terms of the The MIT License
(MIT). See COPYING for full license text.
Package: koinonikos
Koinonikos: A Python Library for Social Science ResearchDocumentation:https://koinonikos.entelecheia.aiGitHub:https://github.com/entelecheia/koinonikosPyPI:https://pypi.org/project/koinonikosKoinonikos is a versatile and comprehensive Python library designed to facilitate social science research by providing advanced tools and techniques for data collection, analysis, and visualization. The name Koinonikos is derived from the Greek word κοινωνικός, meaning "social," reflecting the library's focus on empowering researchers in the social sciences.FeaturesKoinonikos offers a wide range of features that cater to both beginners and experienced social science researchers:Data Collection: The library includes modules for collecting data from various sources, such as APIs, web scraping, and online databases, making it easy to gather and curate research data.Data Cleaning and Preprocessing: Koinonikos provides tools for data cleaning, normalization, and transformation, ensuring that datasets are ready for analysis.Statistical Analysis: The library supports a variety of statistical methods, including descriptive statistics, hypothesis testing, regression analysis, and multivariate techniques, enabling researchers to explore and analyze their data thoroughly.Network Analysis: Koinonikos offers tools for analyzing social networks and other complex systems, including graph algorithms, centrality measures, and community detection techniques.Text Analysis: The library incorporates natural language processing (NLP) techniques to analyze textual data, such as sentiment analysis, topic modeling, and keyword extraction.Geospatial Analysis: Koinonikos includes geospatial analysis tools, enabling researchers to explore spatial patterns, relationships, and trends in their data.Visualization: The library provides a range of visualization options for presenting data and results, including bar charts, line charts, scatter plots, heatmaps, and interactive maps.Interoperability: Koinonikos is designed to work seamlessly with popular Python libraries and frameworks, allowing researchers to integrate their findings into existing workflows and applications.InstallationYou can install Koinonikos using pip:pipinstallkoinonikosGetting StartedTo get started with Koinonikos, visit theofficial documentationand theGitHub repositoryfor examples, tutorials, and more information.ChangelogSee theCHANGELOGfor more information.ContributingContributions are welcome! Please see thecontributing guidelinesfor more information.LicenseThis project is released under theMIT License.
Package: koios-python
Koios PythonOverviewKoios Pythonis Python wrapper which allow interacting with all information and parameters stored on the Cardano blockchain usingKoios REST APIWhat is Koios Python?Koios Pythonis a library based onKoiosElastic Query Layer forCardano NodebyCardano Community Guild Operators.Koiosis best described as a Decentralized and Elastic RESTful query layer for exploring data on Cardano blockchain to consume within applications/wallets/explorers/etc.Koiosis really useful for developers because resource and maintenance requirements for Cardano blockchain components (e.g. cardano-node, cardano-db-sync) are ever-growing. It also simplifies how to query complex information from the blockchain.This library allows getting data from the Cardano Blockchain using a simple syntaxis in your Python code. All the querys follow Koios API REST operations.Required Python Moduleshttps://pypi.python.org/pypi/requestsInstallationpipinstallkoios_pythonUpgrade to the last Versionpipinstall--upgradekoios_pythonUsageImport to your python file this library:importkoios_pythonYou can read all info about how works this library in ourWikiTODOAdding Pagination for all queries where makes sense (At the moment, there are a bunch with this feature)Managing errorsInside functionsPaginationUser InputsTimeoutsAdding Vertical FilteringAdding Async methodsFeaturesSupported REST Services:NetworkChain TipGenesis InfoHistorical Tokenomic StatisticsEpochEpoch InformationEpoch's Protocol ParametersEpoch Blocks ProtocolBlockBlock ListBlock InformationBlock TransactionsTransactionsTransaction InformationTransaction UTxOsTransaction MetadataTransaction Metadata LabelsTransaction SubmissionTransaction Status (Block Confirmations)AddressAddress InformationAddress TransactionsTransactions from Payment CredentialsAddress AssetsAccountAccount ListAccount InformationAccount Information CachedAccount RewardsAccount Updates (History)Account AddressesAccount AssetsAccount HistoryAssetAsset ListAsset Address ListAsset InformationAsset HistoryAsset Policy InformationAsset SummaryAsset Transaction HistoryPoolPool ListPool InformationStake SnapshotPool Delegators ListPool BlocksPool Stake, Block and Reward HistoryPool Updates (History)Pool RelaysPool MetadataScriptNative Script ListPlutus Script ListScript RedeemersDatum Information
Package: koishi
koishi.py[Documentation][Installation][Testing][Credits][Contributing][License]Wrapper library for the unofficial scraped API of the satori testing system.Example usage>>>fromkoishiimportKoishi>>>foo=Koishi("login","password")>>>foo[4]<Contest6460771approved"Liga Informatyczna V LO, 2020/21">>>>foo[4].problems[<Problem6629872X1Promocja>,<Problem6629900X2MagazynPrezentow>,...<Problem6473321PIZPizza>,<Problem6473327TRATramwaje>]>>>foo[4][6]<Problem6501411A01Meteory>>>>foo[4][6].submit("print(\"hehe\")","py")6851966>>>foo[4].result<Result6851966A012021-04-0322:45:10QUE>>>>foo[4][6].submit("print(\"trolling\")",...lang="py",...callback=lambdax:print(x.srcx.details.tests,sep="\n")...)print("trolling")[ResultTest(name='0',status=<RStatus.ANS:1>,time='0.004')]installation$pipinstallkoishiCreditsMasFlam/SatoriCLI.jl- for helping with multipart problem submit requests.zielmicha/satori-cli- for inspiration / idea.u/plasticparakeet- Koishi Komeiji icon.ContributingForkthis repoCreate your feature branch (git checkout -b feature/foobar)Commit your changes         (git commit -am 'Add some foobar')Push to the branch              (git push origin feature/foobar)Create a new Pull RequestLicensekoishi is Free Software: You can use, study share and improve it at your will. Specifically you can redistribute and/or modify it under the terms and conditions of the MIT license.
Package: kojak
kojakPython projects analyzerkojak is a development tools that help you to get informations
about a given python file and to get a big picture of this one
like all the module classes and all the module classes functions members.kojak retrieve informations from python projects by using the pythonabstract
tree syntax (AST).WarningReally young project with poor functionalities for now.FeaturesGet classes and methodsGet functions from python modulesGet list of importsInstall or Update kojak$pipinstall-UkojakUsage$kojak<pathtoyourpythonfile>

Listimportsbythemodule:-Importargparse-Fromsysimportexit-Fromkojak.utilsimportget_classes

Listclassesbythemodule:
Class1-method11-method12-method13
Class2-method21-method22-method23Future improvementsadd the possibility to do a recursive of the whole files of a given projectinspect from the stdinadd the possibility to scoping an object or a given method or functionContributeIf you want to contribute to kojakplease first read the contribution guidelinesLicenceThis project is under the MIT License.See the license file for more details
Package: koji
koji - RPM building and tracking systemKoji is an RPM-based build system. The Fedora Project uses Koji fortheir build system, as doseveral other projects.Koji's goal is to provide a flexible, secure, and reproducible way to build software.Key features:New buildroot for each buildRobust XML-RPC APIs for easy integration with other toolsWeb interface with SSL and Kerberos authenticationThin, portable command line clientUsers can create local buildrootsBuildroot contents are tracked in the databaseVersioned dataCommunicateComments, questions, bugs, feedback, ideas, help requests? We'd love to hear from you.Mailing lists:Development:koji-devel AT lists.fedorahosted.orgUser discussion and Fedora-specific topics:buildsys AT lists.fedoraproject.orgIRC chat: #koji onlibera.chatBugs/RFEsIf you have found a bug or would like to request a new feature, pleasereport an issue in Pagure.DownloadThe koji source code can be downloaded with git via:git clone https://pagure.io/koji.gitYou may browse code athttps://pagure.io/kojiArchived releases can be found athttps://pagure.io/koji/releasesDocumentationSee:https://docs.pagure.org/koji/Related SoftwareMock: The tool Koji uses to generate buildrootsYumPungi: Use Pungi to "compose" Koji builds into highly customizable Yum repositories.Koji Tools: Various utilities for KojiKojiji: Koji Java Interfacetxkoji: Async interface to Koji, using Twistedkoji-hs: Koji Haskell Interface
Package: koji-wrapper
Koji WrapperHelper library to work with areas of koji that are not well supported in that project’s api.Free software: MIT licenseFeaturesTODONotesThis library only supports python 3. Some features may still work with python
2.7 but not all of the syntax and features my be compatible.DevelopmentThere are several dependencies needed to build and work on koji_wrapper.  Using
your distribution’s package manager, install these system packages:openssl-devel python3-devel rpm-devel krb5-devel make gcc findutils whichkoji_wrapper uses the upcoming standard of Pipfiles via pipenv.  This is integrated
into our Makefile and once you have the above dependencies, you can simply run:make devThis will install our dev environment for the package via pipenv.  It is installed
with –user, so it does not affect your site-packages.  Pipenv creates a unique virtualenv
for us, which you can activate via:pipenv shellSee thepipenv documentationfor more detail.DocumentationTo build the documentation on your checkout, simply run:make docsWe plan to get this published in the near future, and this README will be
updated when that happens.ContributionsAll new code should include tests that excercise the code and prove that it
works, or fixes the bug you are trying to fix.  Any Pull Request without tests
will not be accepted.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-02-22)First release on PyPI.
Package: kojo-fan-art
kojo-fan-art日本語版 (In Japanese)This is a fan art project for "Lonely Castle in the Mirror."Installation$pipinstallkojo-fan-artUsageThis tool shows what day of the week it is in each character's parallel world from "Lonely Castle in the Mirror" in JSON format.$kojo-daykokoroakifuka{"kokoro":"Friday","aki":"Monday","fuka":"Monday"}$# pretty print with jq$kojo-daykokoroakifuka|jq.{"kokoro":"Friday","aki":"Monday","fuka":"Monday"}For help message, typekojo-day -h.Development environment$gitclonegit@github.com:ftnext/the-solitary-castle-in-the-mirror-cli.git
$cdthe-solitary-castle-in-the-mirror-cli

$python3.11-mvenvvenv--upgrade-deps
$sourcevenv/bin/activate(venv)$pipinstall-e'.[dev]'(venv)$tasktest⚠️The implementation reveals the secrets of "Lonely Castle in the Mirror."
Package: kokex
kokex한국어 문서 집합에서 키워드를 추출하는 라이브러리입니다.
구문분석을 통해 사전 없이도 고유명사, 복합명사, 신조어에 강건한 키워드 추출기를 만들고자 합니다.간단한 사용예는 아래와 같습니다.importkokexkeywords=kokex.keywords(['첫 번째 문서입니다. 여러 문장을 포함할 수 있습니다.','두 번째 문서입니다. 여러 문서를 포함할 수 있습니다.'])print(keywords)# {'번째': 2, '문서': 3, '문장': 1, '포함': 2}더 자세한 사용법은문서를 참고해주세요.설치설치는pip를 이용하는 방법과docker를 이용하는 방법이 있습니다.pippip install kokex이 라이브러리는 konlpy 와 Mecab 형태소 분석기에 의존성이 있습니다.
konlpy 의설치 안내에 따라 Mecab 을 설치해주시기 바랍니다. 예를 들어, Mac 환경에서는 아래의 명령을 입력하시기 바랍니다.$ bash <(curl -s https://raw.githubusercontent.com/konlpy/konlpy/master/scripts/mecab.sh)docker도커를 사용한다면, 모든 의존성이 설치된 상태로 로컬 API 서버를 구동할 수 있습니다.export KOKEX_PORT=80
export KOKEX_VERSION=0.0.11

docker pull kokex/server:${KOKEX_VERSION}
docker run -d -p ${KOKEX_PORT}:8081 --name kokex-server --rm kokex/server:${KOKEX_VERSION}서버를 종료할 때는docker stop kokex-server를 입력하세요.서버를 실행하면 아래와 같이 http 요청을 처리할 수 있습니다.curl -X POST \
  http://localhost/keywords \
  -d '{
	"docs": [
		"첫 번째 문서입니다. 여러 문장을 포함할 수 있습니다.", 
		"두 번째 문서입니다. 여러 문서를 포함할 수 있습니다."
	]
}'그리고http://localhost/docs에 접속하면 API 문서를 확인할 수 있습니다.참여모든 논의는 이슈를 통해 이루어지면 좋겠습니다.API 사용자kokex api 를 사용하는 분들이라면 새로운 문장에 대한 분석 요청을 하실 수 있습니다.
테스트 문장과 함께 원하는 분석결과를 담아 이슈를 생성하실 수 있도록 템플릿(test_suggest)을 준비해두었으니 참고해주세요.API 개발자kokex api 개발에 관심이 있으시다면, 이슈를 생성 후 논의를 진행하고, 수정한 사항을 PR 해주시면 됩니다.
소스 코드를 수정하신다면, isort 와 black 으로 포맷팅을 해주시기 바랍니다. 저장소 복제 후 아래와 같이 입력하시면 이를 자동으로 체크할 수 있습니다.pip install -r requirements.txt
pre-commit install그리고bin/run_test.sh를 실행하여 기존 테스트 문장의 분석 결과를 해치지 않도록 해야합니다.
테스트 문장은,test디렉토리의 test_{API_NAME}.py 파일에서 찾아보실 수 있습니다.
Package: kokiy
kokiycontains utilities to work on 2D and 3D structured grids. Grids can be, among others, cartesian planes (CartShell) or axi-cylindrical extrusions from x-r splines (AxiShell).ThickShellare 3D shells built by extruding any of the available 2d objects. Check out the API reference for an extensive list of all available geometries.Main features include:create 2D and 3D structured grids (including axi-cylindrical objects)interpolate a solutionoperate (e.g. average) a solution over a directiondump shells for fields visualization inParaViewexport mesh in any format available inyamio(e.g.hip-friendly.hdf5,xdfm,dolfin-xml)export.geofiles for visualization withtiny-3d-engineInstallationInstall withpipinstallkokiy