Saravanan666/pypi-org huggingface dataset:
Package: cableloss
cablelosscablelossis a Python 3.3+ module that calculates the cable loss for a given
cable type and length.RequirementsnumpyContributingContributions are welcome! To contribute please:Fork the repositoryCreate a feature branchCodeSubmit apull requestVirtualenvUse the following commands to create a Python 3.9.9 virtualenv using [pyenv][]
and [pyenv-virtualenv][], install the requirements in the virtualenv namedsdfascii, and list the available [Invoke][] tasks.$pyenvvirtualenv3.9.9sdfascii
$pyenvactivatesdfascii
$pipinstall-rrequirements.txt
$inv-lSubmitting Pull Requests[keysight][] is developed using [Scott Chacon][]'s [GitHub Flow][]. To
contribute, fork [sdfascii][], create a feature branch, and then submit
a pull request.  [GitHub Flow][] is summarized as:Anything in themasterbranch is deployableTo work on something new, create a descriptively named branch off ofmaster(e.g.,new-oauth2-scopes)Commit to that branch locally and regularly push your work to the same
named branch on the serverWhen you need feedback or help, or you think the brnach is ready for
merging, open apull request.After someone else has reviewed and signed off on the feature, you can
merge it into master.Once it is merged and pushed tomaster, you can andshoulddeploy
immediately.Licensecablelossis released under the MIT license. Please see theLICENSE.txtfile for more information.
Package: cablemap.core
The core package provides utilities which can be used to extract information
from diplomatic cables:>>> from cablemap.core import cable_by_id
>>> from cablemap.core.utils import cable_to_json
>>> # Fetches the cable with the reference id '09BERLIN1167' from the Internet
>>> # and converts it into a Cable object
>>> cable = cable_by_id('09BERLIN1167')
>>> cable.reference_id
u'09BERLIN1167'
>>> cable.subject
u'DATA PRIVACY TRUMPS SECURITY: IMPLICATIONS OF A FDP VICTORY ON COUNTERTERRORISM COOPERATION'
>>> json = cable_to_json(cable)
>>>The core package has no Topic Maps related code and can be used independently
of the other packages.For other examples see:https://github.com/heuer/cablemap/tree/master/examples
Package: cabocha-python
cabocha-pythonThis is a Python wrapper for CaboCha Japanese Dependency Structure Analyzer.If you encounter “library not found” in install, set libraries directory to $CPLUS_INCLUDE_PATH or $CPATH and retry.NOTE: It does not sopport Windows Python 64bit version.Japanese documentis available.USAGE>>>importCaboCha>>>c=CaboCha.Parser()>>>sentence="太郎はこの本を二郎を見た女性に渡した。">>>print(c.parseToString(sentence))太郎は-----------Dこの-D|本を---D|二郎を-D|見た-D|女性に-D渡した。EOS>>>tree=c.parse(sentence)>>>print(tree.toString(CaboCha.FORMAT_TREE))太郎は-----------Dこの-D|本を---D|二郎を-D|見た-D|女性に-D渡した。EOS>>>print(tree.toString(CaboCha.FORMAT_LATTICE))*06D0/1-2.457381太郎名詞,固有名詞,人名,名,*,*,太郎,タロウ,タローは助詞,係助詞,*,*,*,*,は,ハ,ワ*12D0/01.488413この連体詞,*,*,*,*,*,この,コノ,コノ*24D0/10.091699本名詞,一般,*,*,*,*,本,ホン,ホンを助詞,格助詞,一般,*,*,*,を,ヲ,ヲ*34D0/12.266072二郎名詞,固有名詞,人名,名,*,*,二郎,ジロウ,ジローを助詞,格助詞,一般,*,*,*,を,ヲ,ヲ*45D0/11.416783見動詞,自立,*,*,一段,連用形,見る,ミ,ミた助動詞,*,*,*,特殊・タ,基本形,た,タ,タ*56D0/1-2.457381女性名詞,一般,*,*,*,*,女性,ジョセイ,ジョセイに助詞,格助詞,一般,*,*,*,に,ニ,ニ*6-1D0/10.000000渡し動詞,自立,*,*,五段・サ行,連用形,渡す,ワタシ,ワタシた助動詞,*,*,*,特殊・タ,基本形,た,タ,タ。記号,句点,*,*,*,*,。,。,。EOSLicenseCaboCha is copyrighted free software by Taku Kudo <taku@chasen.org> is released under any of the the LGPL (see the file LGPL) or the BSD License (see the file BSD).CHANGES0.69.3 (2022-08-17)Avoid the compilation error: python-config file not foundLook up $CPLUS_INCLUDE_PATH and $CPATHUpdate codes by swig 4.0.20.69.1 (2019-09-21)Avoid the compilation error: ‘Python.h’ file not foundUpdate codes by swig 4.0.10.69 (2019-03-04)First release to PyPI
Package: cabot-alert-dingding
==========================
Cabot Dingding Alert PluginA plugin forCabot_ service monitoring that will post alerts to a URL.The plugin will POST a payload like the following to a user-defined endpoint::payload = {
        "msgtype": "text", 
        "text": {
            "content": message
        }, 
        "at": {
            "atMobiles": [
              service.name
            ], 
            "isAtAll": True
        }
    }InstallationInstall from pip::$ pip install cabot-alert-dingdingEditconf/*.env::# add cabot_alert_dingding to your comma separated list
CABOT_PLUGINS_ENABLED=cabot_alert_dingdingRun migrations and restart cabot::$ cabot migrate.. _Cabot:https://cabotapp.com
Package: cabot-alert-rocketchat
Based on Cabot Hipechat Plugin (https://github.com/cabotapp/cabot-alert-hipchat).This is an alert plugin for the cabot service monitoring tool. It allows you to alert users in a RocketChat channel.Installationpippip install cabot-alert-rocketchatDev environmentTODOConfigurationThe plugin use Rocketchat webhooks (https://rocket.chat/docs/administrator-guides/integrations/)Enable rocketchat plugin on cabot:#PluginstobeloadedatlaunchCABOT_PLUGINS_ENABLED=...,cabot_alert_rocketchatConfiguration available:Common:#ChannelusedtopublishmessagesROCKETCHAT_CHANNEL=#support#WebhookURLwithtokenROCKETCHAT_WEBHOOK_URL=http://rocketchat.example.org/hooks/TOKEN#Usernameformessages.Thisusernamedoesnotneedto#existonyourRocketchat,thisisanalias.ROCKETCHAT_USERNAME=CabotMessage format:#CollapseServicepartROCKETCHAT_COLLAPSED_SERVICE=False#CollapseCheckspartROCKETCHAT_COLLAPSED_CHECKS=False#CollapseAlertpartROCKETCHAT_COLLAPSED_ALERT=FalseExample
Package: cabot_alert_telegram
Based on:https://github.com/lblasc/cabot-alert-slackThis is an alert plugin for the cabot service monitoring tool. It allows you to alert users using aTelegramchat room.InstallationEnter the cabot virtual environment:$ pip install cabot_alert_telegram
$ foreman stopor:$ pip install git+git://github.com/codesyntax/cabot_alert_telegram.git
$ foreman stopEditconf/*.env:CABOT_PLUGINS_ENABLED=cabot_alert_telegram=0.4
...
TELEGRAM_BOT_TOKEN=bot_token
TELEGRAM_CHAT_ID=id of the chat where messages will be sentAdd cabot_alert_telegram to the installed apps in settings.py:$ foreman run python manage.py syncdb
$ foreman startAdd manually an instance of the Alert Plugin (sometimes it is not added automatically):$ ssh ubuntu@server
$ source venv/bin/activate
$ cd cabot
$ foreman run -e conf/production.env python manage.py shell # point to the correct production.env file
Python 2.7.3 (default, Dec 18 2014, 19:10:20)
[GCC 4.6.3] on linux2
Type "help", "copyright", "credits" or "license" for more information.
(InteractiveConsole)
>>> from cabot_alert_telegram.models import TelegramAlert
>>> ta = TelegramAlert()
>>> ta.title = u'Telegram'
>>> ta.save()
>>> (Ctrl-D to exit)Telegram BotYou need to create a Telegram bot which will be used to send the messages to the users. To create a new Telegram Bot check the official documentation athttps://core.telegram.org/botsYou need to create a new Group Chat, add all your users to that chatroom and add also the Bot there.To get the chat id, openhttps://telegram.meand select the group chat you previously created. Check the URL, it will be something like this:https://web.telegram.org/#/im?p=g99999999You need to not the value of p parameter (g99999999) and exchangegwith a-:-99999999This is a bit hacky, but I don’t know any other way to get the group id.0.6 (2018-05-29)Include migrations [ex0ns]0.5 (2016-11-07)Simpler alert message [garaolaza]0.4 (2016-01-21)rework readme [Mikel Larreategi <mlarreategi@codesyntax.com>]remove unneeded slack test [Mikel Larreategi <mlarreategi@codesyntax.com>]add whitespace to make links correctly clickable [Mikel Larreategi <mlarreategi@codesyntax.com>]document telegram install [Mikel Larreategi <mlarreategi@codesyntax.com>]bot documentation [Mikel Larreategi <mlarreategi@codesyntax.com>]remove [Mikel Larreategi <mlarreategi@codesyntax.com>]0.3 (2015-07-02)bump [Mikel Larreategi <mlarreategi@codesyntax.com>]alert text change [Mikel Larreategi <mlarreategi@codesyntax.com>]0.2 (2015-07-02)document the bot [Mikel Larreategi <mlarreategi@codesyntax.com>]reformat message [Mikel Larreategi <mlarreategi@codesyntax.com>]reformat [Mikel Larreategi <mlarreategi@codesyntax.com>]docs [Mikel Larreategi <mlarreategi@codesyntax.com>]Merge remote-tracking branch ‘origin/master’ [Mikel Larreategi <mlarreategi@codesyntax.com>]Update README.md [Aitzol <anaberan@codesyntax.com>]0.1 (2015-07-02)history! [Mikel Larreategi <mlarreategi@codesyntax.com>]version [Mikel Larreategi <mlarreategi@codesyntax.com>]
Package: cabot-alert-typetalk
Cabot Alert TypetalkThis is an alert plugin for Cabot. It will send notifications to Typetalk.InstallEnter the cabot virtual environment:$ pip install cabot-alert-typetalkor:$ pip install git+git://github.com/is2ei/cabot-alert-typetalk.gitEnable pluginEdit conf/*.envCABOT_PLUGINS_ENABLED=cabot_alert_typetalk
...
TYPETALK_TOKEN={YOUR_TYPETALK_TOKEN}
TYPETALK_TOPIC_ID={YOUR_TYPETALK_TOPIC_ID}
Package: cabot-alert-webhook
A plugin forCabotservice monitoring that will post alerts to a URL.The plugin will POST a payload like the following to a user-defined endpoint:{
  'service': 'my_service_name',
  'status': 'ERROR',
  'old_status': 'PASSING',
}InstallationInstall from pip:$ pip install cabot-alert-webhook
$ foreman stopEditconf/*.env:# add cabot_alert_webhook to your comma separated list
CABOT_PLUGINS_ENABLED=cabot_alert_webhookRun migrations and restart cabot:$ foreman run python manage.py migrate
$ foreman start
Package: cabot-alert-webhooks
A plugin forCabotservice monitoring that will post alerts to a URL.The plugin will POST a payload like the following to a user-defined endpoint:{
  'service': 'my_service_name',
  'status': 'ERROR',
  'old_status': 'PASSING',
}InstallationInstall from pip:$ pip install cabot-alert-webhook
$ foreman stopEditconf/*.env:# add cabot_alert_webhook to your comma separated list
CABOT_PLUGINS_ENABLED=cabot_alert_webhookRun migrations and restart cabot:$ foreman run python manage.py migrate
$ foreman start
Package: cabrillo-decoder
CabrilloDecoderPythonDecode File Cabrillo to dictionary Python.InstallationTo install this package, you have to write like this:pipinstallcabrillo-decoderAdd an example fileMake sure that you have created a Python-extension file and write like this:fromcabrillo_decoderimportdecoderfile=open("cq-ww-cw.cbr")cabrillo=decoder.Cabrillo(file)print(cabrillo.decode())
Package: cabu
CabuCabu is a simple microservice framework to remotely crawl websites.
It’s built on Flask and Selenium, contains a virtual display wrapper and few methods.Full documentation hereUsage@app.route('/gizmodo_last_articles_links')defgizmodo_last_articles():app.webdriver.get('http://www.gizmodo.com')articles_links=[i.get_attribute('href')foriinapp.webdriver.find_elements_by_css_selector('h1.headline>a')]returnjsonify({'articles':articles_links})Installing$pipinstallcabuFeaturesSelenium configuration out of the boxFlask wrappingCrawling methods includedAWS S3 ExportFTP / FTPSCookies persistenceLink extractorProxy configurationHeadless optional for local debugDocker pre-configured distributed environmentDatabase handlerCompatible with most Flask extensions (Flask-Admin, Flask-Mail, Flask-OAuth, …)12 Factors compliance(Likely to come soon)CouchDB supportCouchbase supportMobile driversSFTPHtmlUnit web driverRemote webdriver wrapperParallelizationNeural Network pluginsTestingAll tests were written using Docker services instead of Mocks.
Alternative mocks will be added soon ;)$pipinstall-rrequirements-dev.txt$py.testcabu/testsContributingPlease see theContribute page.CopyrightCabu is an open source project byThéotime Lévèque.
Package: ca-bundle
CA-bundleNo moreSSLErrors because of unsetREQUESTS_CA_BUNDLE.This package searches through common locations of SLL certificates on linux and sets
the first existing location toREQUESTS_CA_BUNDLEandHTTPLIB2_CA_CERTSenvironment variables.Installationpipinstallca-bundleUsageimportca_bundleca_bundle.install()Inspired byGo's implementation.
Package: cac
What's CACThis repository contains a library of Content-Aware Computing (CAC) by Fujitsu.CAC is a software technology that aims at easy, high-speed, lightweight, and accurate deep learning processing.Contents1. Gradient-SkipGradient-Skip is an approach for CNNs to skip backward calculations for layers that enouch converged.This reduces calculations in backward and communications of gradient.You can use Gradient-Skip by simply replacing the optimizer with our SGD.Python SourceExample2. Automatic PrunerAutomatic Pruner is a pruning tool for neural networks, which can determine the pruning rate of each layer automatically.Python SourceExample3. Synchronous-RelaxationRelaxed Synchronization technique removes slow processes from the group of distributed training and prevent limiting overall training speed due to slow processes.Python SourceExampleRequirementsPython 3.6 or laterCUDA 10 or laterPyTorch 1.6 or laterApexQuick StartLinuxgit clone https://github.com/FujitsuLaboratories/CAC.git
cd CAC
python setup.py installCAC will be registered in PyPI at a later date and will be able to pip installpip install -v --disable-pip-version-check --no-cache-dir ./
Package: cacahuate
The process virtual machineThis project defines storage for an abstract _process_ in a company, and
implements a virtual machine that keeps track of the execution of instances of
the process.DevelopYou will need theredisandmongodatabases, andrabbitmqfor this
to work. I recommend usingpipenvorvirtualenvin your python projetcs ;)clone the repoinstall the requirements listed inrequirements.txtrun the tests (pytest)you can control your cacahuate installation using this three environment
variables:CACAHUATE_SETTINGS: absolute path to a settings fileFLASK_APP: import path to wsgi module (cacahuate.http.wsgi)FLASK_DEBUG: set to1to turn on flask’s debuggingInstallationpipinstallcacahuateCacahuatedThis is the daemon in charge of moving pointers in the process, run with:cacahuatedThe Cacahuate REST APIIn this same repository you will find a flask application that exposes a REST
api for controling Cacahuate.How to runFLASK_APP=cacahuate.http.wsgiflaskrunYou can use any wsgi-compliant server, like gunicorn, to run this:gunicorncacahuate.http.wsgi:appThe docsDocs are built usingsphinxand published inhttps://tracsa.github.io/cacahuate/index.html. To build a local copy of the docs
navigate to thedocs/directory and run:makehtmlFor more options just runmakeby itself.Release`bash ./release.sh cacahuate/version.txt [major|minor|patch] `
Package: cacao
Cacao frameworkA library for dynamic simulation/ optimization.Installationpip install cacaoGet startedSimulation of gravity drained tank, initially filed with water 10 meters high. The tank has constant cross-section (16 m^2) and an orifice at the bottom with 5 cm^2 area. Example extracted from:Himmelblau, D. M., & Riggs, J. B. (2006). Basic principles and calculations in chemical engineering. FT press.# importsimportnumpyasnpimportmatplotlib.pyplotaspltfromcacaoimportComposite,SimulationProblemfromcacao.componentsimportTank,Orifice,Material,Contentmodel=Composite()model.time=np.linspace(0,8e4,50)# tankwater=Material(rho=1000)area=16# m^2initial_volume=10*areacontent_tank=Content(water,volume=initial_volume)tank=Tank(model.time,area,content_tank)# orificeA_orifice=5e-4# m2c=0.62# orifice flow coefficientorifice=Orifice(model.time,A_orifice,c)model.tank=tankmodel.orifice=orificemodel.connect(tank,orifice)sim=SimulationProblem(model)result=sim.run()model.change_inputs(result.x)plt.plot(model.time,model.tank1.height())plt.show()
Package: cacao-accounting
Cacao AccountingAplicacion contable para el control de cuentas por cobrar, cuentas por pagar, inventarios y tesoreria.Nota:Este es un proyecto en etapa temprana de desarrollo no apta para uso en producción.Hemos definido una serie demetas y objetivospara el proyecto.Este proyecto surge de la necesidad observada en Nicaragua de contar con una solucion de software para que micro, pequeñas y medianas empresas (Pymes) cuenten con una herramienta para el procesamiento contable de sus actividades.
Dado el costo de soluciones de software ya existentes muchas Pymes obtan por usar software pirata que casi de forma
inevitable termina en una falla del software utilizado y perdida de datos. Otras empresas asumen que al contratar a
un profesional contable este debe de contar por defecto con una herramienta para llevar la contabilidad de la entidad,
el objetivo principal de este proyecto es proveeder de una herramienta confiable para el procesamiento contable de las
operaciones de una o varias entidades.Reconocemos que ya existen opciones de soluciones de software contable robustas desarrolladas en python como lo sonOdooyERPNext. Sin embargo estas herramientas requieren o
el pago de una suscripción o la instalación de un servidor web, por lo que mayoria de profesionales contables no
podrian implementarlos sin requerir asistencia de un administrador de sistemas, por esto uno de los objetivos de
este proyecto es ofrecer tanto una solución de escritorio para un solo usuario así como una solución web que
pueder ser usada por multiples usuarios al mismo tiempo.Consideramos que una ves el proyecto sea un producto usable puede tener potencial economico, no mediante el modelo
de negocios de licenciamiento, si no con servicios conexos como implementación, capacitación, hospedaje en la nube y mantenimientoParticipar en el Proyecto:Todos los aportes son bienvenidos, ver el archivoCONTRIBUTINGpara mas detalles.Licencia.Derechos de autor 2020 William José Moreno ReyesAutorizado en virtud de la Licencia de Apache, Versión 2.0 (la "Licencia"); se
prohíbe utilizar este archivo excepto en cumplimiento de la Licencia. Podrá
obtener una copia de la Licencia en:http://www.apache.org/licenses/LICENSE-2.0A menos que lo exijan las leyes pertinentes o se haya establecido por escrito,
el software distribuido en virtud de la Licencia se distribuye “TAL CUAL”, SIN
GARANTÍAS NI CONDICIONES DE NINGÚN TIPO, ya sean expresas o implícitas. Véase
la Licencia para consultar el texto específico relativo a los permisos y
limitaciones establecidos en la Licencia.
Package: cacca
caccaA workflow framework, easy as shit.ContentsInstallationUsageResourcesContributingSupportLicenseInstallationFollow the steps below to make use of Cacca.Clone this repository:pipinstallcaccaUsageThis section is under construction but here you go with an"Hello, Toilet!"workflow:fromcacca.libimportCaccacacca=Cacca()cacca.insert({"action":print})cacca.run(0,{"action":"Hello, Toilet!"})ResourcesDocumentation: Project documentation.Flaticon: Collection of assets.ContributingContributions and suggestions about how to improve this project are welcome!
Please followour contribution guidelines.SupportIf you want to support my work you can do it by following me, leaving a star, sharing my projects or also donating at the links below.Choose what you find more suitable for you:LicenseThis repository is licensed underMIT License.
Package: ca-certs-locater
$ uname -aLinux miku 3.11.7-hardened-r1 #5 SMP Thu Feb 27 18:27:34 CST 2014 x86_64 Intel(R) Core(TM) i3-2120 CPU @ 3.30GHz GenuineIntel GNU/Linux|=----------------------------------------------------------------------------=||=---------------------------=| ca-certs-locater |=---------------------------=||=----------------------------------------------------------------------------=||=-------------------------=| aleiphoenix@gmail.com |=------------------------=||=----------------------------------------------------------------------------=|--[ 0x00 How to useThis is used with httplib2[1], which help locate CA certification files.import ca_certs_locaterca_certs_locater.get = lambda : '/path/to/cacerts.txt'import httplib2# ...[1]: https://github.com/jcgregorio/httplib2--[ 0x01 LicenseMIT
Package: cacgmm-gpu
GPU-based CACGMM trainer in CuPyThis package adapts the complex angular GMM model and trainer frompb_bssusingCuPyfor accelerated inference on the GPU.At the moment, it is meant to be used with theGSStoolkit, but
it can also be used as a general CACGMM trainer tool.Installation>pipinstallcupy-cuda102# modify according to your CUDA version (https://docs.cupy.dev/en/stable/install.html#installing-cupy)>pipinstallcacgmm-gpuUsagefromcacgmm.cacgmm_trainerimportCACGMMTrainerimportcupyascpsource_activity=cp.random.rand(2,1000)source_activity=source_activity/cp.sum(initialization,keepdims=True,axis=0)initialization=cp.repeat(source_activity[None,...],513,axis=0)# F x K x Tsource_active_mask=cp.repeat(source_activity[None,...],513,axis=0)X=cp.random.rand(4,1000,513)# D x T x FcacGMM=CACGMMTrainer()cur=cacGMM.fit(y=X.T,initialization=initialization,iterations=10,source_activity_mask=source_active_mask,)affiliation=cur.predict(X.T,source_activity_mask=source_active_mask)#posterior=affiliation.transpose(1,2,0)# K x T x F
Package: cacha
CachaThe simplest Python cache for Data Scientist:cache on disk, use between runs,use at function call, not definition.ExampleIf you don't want to wait for a givencompute()function to complete
each time you run the script, you can cache it:importcacharesult=compute(data)# regular usage, slowresult=cacha.cache(compute,(data,))# usage with cacheThe cached data is stored in$HOME/.cacha/. Each time you run the
function with identical input arguments, the output data will be loaded,
instead of being computed.It can be easily used with popular data structures likepandas.DataFrameornumpy.array. In case of complicated python objects that can't be easily
hashed, you can use an additionalkeyparameter that forces saving the cache
based on thekeyvalue.importcacharesult=cacha.cache(compute,(data,),key="compute-v3")FAQHow is it different other caching packages?In contrary to many other tools,cacha:is used at the function call, not definition. Many packages implement
the@cachedecorator that has to be used before definition of
a function that is not easy enough to use.it stores the cache on disk which means you can use cache between runs.
This is convenient in data science work.How can I clear the cache?Just delete the$HOME/.cacha/directory. You can also callcacha.clean()which has the same effect.Why does it require thepandas,numpyand other libraries?To properly cache the objects from specific packages, it is necessary
to have access to the functions they provide in that regard.The main goal of cache is not to be lightweight but to provide the best
developer experience.However most of the required packages are usually
used in Machine Learning projects anyway.
Package: cachalot
CachalotCachalot is a minimal persistent memoization cache. It provides a decorator, that stores function result for future use. Perfect for heavy computations and I/O operation (such as web requests). On backend, it uses TinyDB for storage.FeaturesSimple usage via decoratorPersistent cachingKey expirationMaximum cache size, to prevent bloatInstallationCachalot requires Python 3.5 or newer to run.Python packageYou can easily install Cachalot using pip:pip3 install cachalotManualAlternatively, to get the latest development version, you can clone this repository and then manually install it:git clone git@gitlab.com:radek-sprta/cachalot.git
cd cachalot
python3 setup.py installUsagefromcachalotimportCache@Cache()defexpensive_function():returnexpensive_calculation()Advanced usagefromcachalotimportCache@Cache(path='cache.json',timeout=3600,size=5e3,filesize=1e6,retry=True,renew_on_read=True)defexpensive_function():returnexpensive_calculation()path: Path to the database file. Defaults to .cache.json.timeout: How long should the data be cached in seconds. Defaults to 0 (infinite).size: Maximum number of keys cached. Defaults to 0 (infinite).filesize: Maximum size of database file in bytes. Defaults to 0 (infinite).retry: Retry if result is blank. Defaults to False.renew_on_read: Renew the entry, i.e refresh the entry timestamp on reads. Defaults to TrueManually deleting entriesIf you want to manually invalidate an entry, you can calculate the hash of the function call and then pass it theremovemethod.key=cache.calculate_hash(len)('teststring')cache.remove(key)For more information, seedocumentation.ContributingFor information on how to contribute to the project, please check theContributor's GuideContactmail@radeksprta.euincoming+radek-sprta/cachalot@gitlab.comLicenseMIT LicenseCreditsThis package was created withCookiecutterand thepython-cookiecutterproject template. Inspired byCashier