Saravanan666/pypi-org huggingface dataset:
Package: cement-complete-fig
Autogenerate Completions for CLI tools built withcementThis package generatesFig
completionsfor CLI tools built
onCement.InstallationInstall the integration as a dependency using pip:pipinstallcement_complete_figUsageChoose one of the methods below to add thegenerate-fig-specsubcommand to the top level of your CLI tool.Method 1: Load as a Cement extensionThis package provides aCement
extensionfor convenience called'cement_complete_fig.ext.ext_complete_fig'which can be loaded in any of the ways cited in the linked documentation.Repeated here for convenience you can do one of the following:Add the extension to theMetaof yourAppobject:fromcementimportAppclassMyApp(App):classMeta:label='myapp'extensions=[...,'cement_complete_fig.ext.ext_complete_fig']Manually load the extension before running your app:fromcementimportAppwithApp('myapp')asapp:app.ext.load_extension('cement_complete_fig.ext.ext_complete_fig')app.run()Add the extension to your app configuration file (e.g..myapp.conf):[myapp]exensions=cement_complete_fig.ext.ext_complete_figMethod 2: Bind the controller manuallyYou can also add theGenerateFigSpecControllermanually as a handler to your
app:fromcementimportAppfromcement_complete_figimportGenerateFigSpecControllerclassMyApp(App):classMeta:label='myapp'handlers=[...,GenerateFigSpecController]withMyApp()asapp:app.run()Generating the spec fileOnce you've added the integration you will have new subcommand nested
under your base handler.  When this subcommand is invoked it will print
a Fig spec.To save your completion spec skeleton to a file, run the following:myappgenerate-fig-spec>myapp.ts
Package: cementstarter
A cement boilerplateInstallation$ pip install -r requirements.txt

$ pip install setup.pyDevelopmentThis project includes a number of helpers in theMakefileto streamline common development tasks.Environment SetupThe following demonstrates setting up and working with a development environment:### create a virtualenv for development

$ make virtualenv

$ source env/bin/activate


### run cementstarter cli application

$ cementstarter --help


### run pytest / coverage

$ make testReleasing to PyPiBefore releasing to PyPi, you must configure your login credentials:~/.pypirc:[pypi]
username = YOUR_USERNAME
password = YOUR_PASSWORDThen use the included helper function via theMakefile:$ make dist

$ make dist-uploadDeploymentsDockerIncluded is a basicDockerfilefor building and distributingStarter project Using Cemnet framework,
and can be built with the includedmakehelper:$ make docker

$ docker run -it cementstarter --help
Package: cemir-print
fromcemir_print.ccprintimportccprintccprint("Hello, World!")ccprint([1,2,3,4,5,6,{"name":"İzmir","plaka":35}])ccprint(42)ccprint(False)ccprint({"name":"muslu yüksektepe","age":30,"city":"İstanbul","whereareyoufrom":"Türkiye","are_you_ok":True,"listem":[1,3.14,3,"ceylan","emir","muslu","cem",{"age":35,"number":{"float":35.58}}]})
Package: ceml
CEML is a Python toolbox for computing counterfactuals. Counterfactuals can be used to explain the predictions of machine learing models.It supports many common machine learning frameworks:scikit-learn (1.3.1)PyTorch (2.0.1)Keras & Tensorflow (2.13.1)Furthermore, CEML is easy to use and can be extended very easily. See the following user guide for more information on how to use and extend CEML.InstallationNote: Python 3.8 is required!PyPIpipinstallcemlNote: The package hosted on PyPI uses the cpu only. If you want to use the gpu, you have to install CEML manually - see next section.GitDownload or clone the repository:gitclonehttps://github.com/andreArtelt/ceml.gitcdcemlInstall all requirements (listed inrequirements.txt):pipinstall-rrequirements.txtNote: If you want to use a gpu/tpu, you have to install the gpu version of jax, tensorflow and pytorch manually. Do not usepip install-rrequirements.txt.Install the toolbox itself:pipinstall.Quick example#!/usr/bin/env python3# -*- coding: utf-8 -*-fromsklearn.datasetsimportload_irisfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportaccuracy_scorefromsklearn.treeimportDecisionTreeClassifierfromceml.sklearnimportgenerate_counterfactualif__name__=="__main__":# Load dataX,y=load_iris(return_X_y=True)X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.33,random_state=4242)# Whitelist of features - list of features we can change/use when computing a counterfactualfeatures_whitelist=None# We can use all features# Create and fit modelmodel=DecisionTreeClassifier(max_depth=3)model.fit(X_train,y_train)# Select data point for explaining its predictionx=X_test[1,:]print("Prediction on x:{0}".format(model.predict([x])))# Compute counterfactualprint("\nCompute counterfactual ....")print(generate_counterfactual(model,x,y_target=0,features_whitelist=features_whitelist))DocumentationDocumentation is available on readthedocs:https://ceml.readthedocs.io/en/latest/LicenseMIT license - SeeLICENSEHow to cite?You can cite CEML by using the following BibTeX entry:@misc{ceml,
        author = {André Artelt},
        title = {CEML: Counterfactuals for Explaining Machine Learning models - A Python toolbox},
        year = {2019 - 2023},
        publisher = {GitHub},
        journal = {GitHub repository},
        howpublished = {\url{https://www.github.com/andreArtelt/ceml}}
    }Third party componentsnumpyscipyjaxcvxpyscikit-learnsklearn-lvqPyTorchtensorflow
Package: cems-nuclei
NUCLEIThis repository is created byCEMS BVand helps the user to access and process API calls to
theNUCLEIenvironment.InstallationTo install this package, including theNucleiClientlibrary and its dependencies, run:pipinstallcems-nuclei[client]To skip the installation of theNucleiClientlibrary, in case you do not need it (e.g. only use pure requests), run:pipinstallcems-nucleiENV VARSTo usenucleiadd the follow ENV vars to your environment. Or provide them when asked.* NUCLEI_TOKEN
    - Your NUCLEI user tokenYou can obtain yourNUCLEI_TOKENonNUCLEI.
Go topersonal-access-tokensand create a new user token.ContributionEnvironmentWe recommend developing in Python3.9 with a clean virtual environment (usingvirtualenvorconda), installing the
requirements from the requirements.txt file:Example usingvirtualenvandpipto install the dependencies in a new environment .env on Linux:python-mvenv.envsourceactivate.env/bin/activate
python-mpipinstall--upgradepipsetuptools
pipinstall-rrequirements.txt
pipinstall-e.DocumentationBuild the docs:python-mpipinstall--upgradepipsetuptools
pipinstall-rrequirements.txt
pipinstall.
sphinx-build-bhtmldocspublicNote: You'll need to set theNUCLEI_TOKENenvironmental variable.
You can get your token at:https://cemsbv.crux-nuclei.comFormatWe format our code with black and isort.black--config"pyproject.toml"src/nucleitests
isort--settings-path"pyproject.toml"src/nucleitestsLintTo maintain code quality we use the GitHub super-linter.To run the linters locally, run therun_super_linters.shbash script from the root directory.UnitTestTest the software with the use of coverage:python-mpipinstall--upgradepipsetuptools
pipinstall-rrequirements.txt
pipinstall.
coveragerun-mpytestRequirementsRequirements are autogenerated bypip-compilewith python 3.9pip-compile--extra=test--extra=docs--extra=lint--extra=client--output-file=requirements.txtpyproject.tomlTo update the requirements within the defined ranges, run:pip-compile--upgrade--extra=test--extra=docs--extra=lint--extra=client--output-file=requirements.txtpyproject.toml
Package: cen-dev
cen-command-line-toolКонсольная утилита cen для внутреннего использованияОснова для работы как command line tool отhttps://github.com/google/python-fire/Deployhttps://johnfraney.ca/blog/create-publish-python-package-poetry/Заливается вhttps://pypi.org/из подtech@centra.ai. Логин пароль у менеджмента в ключницеpoetry config repositories.testpypi https://test.pypi.org/legacy/
poetry build
poetry publish -u __token__ -p '{TOKEN}'
Package: cenerator
CeneratorA Minecraft data pack creation utilityFeaturesProcedurally generate Minecraft data packs with PythonAs this is just a fancy way of writing to files, it is fully customizable, for example with functionsSome convenient shorthand featuresOutputs everything in a data pack, ready to goInstallationInstalled with pip, something likepip3 install -U ceneratorOr perhaps on Windowspy -m pip install -U ceneratorExample usageSeeexamples/directory for more examples.Hello, world!importceneratorp=cenerator.Pack('hello_world',default_namespace='hello_world',description='A hello world datapack made with cenerator',)@p.func(tags=['minecraft:load'])defhello_world(c):c('say Hello, world!')This outputs a data pack which executes the commandsay Hello, world!on load.MacrosThe following example showcases use of functions that takecand use it to output commands (referred to as "macros")defsay_with_all(c,format_str,values):forvinvalues:c(f'say{format_str.format(v)}')@p.func(tags=['minecraft:load'])defmacro(c):values=['John','Joe','Jane','Jill']say_with_all(c,'Hello,{}!',values)say_with_all(c,'Goodbye,{}!',values)This results in amcfunctionfile with some otherwise tedious-to-write code:sayHello,John!sayHello,Joe!sayHello,Jane!sayHello,Jill!sayGoodbye,John!sayGoodbye,Joe!sayGoodbye,Jane!sayGoodbye,Jill!This is extremely useful for situations where a lot of code must be repeated with different but constant parameters.
Package: cenfind
CenFindCenfindis a command line interface written in Python to detect and assign centrioles in immunofluorescence images of human cells.
Specifically, it orchestrates the detection of centrioles, the detection of the nuclei and the assignment of the centrioles to the nearest nucleus.Getting startedInstall cenfind from PyPI:pipinstallcenfindYou need to download it fromhttps://figshare.com/articles/software/Cenfind_model_weights/21724421Collect all images in a project folder inside a projections folder (<project_name>/projection/).Runscorewith the path to the project, the path to the model, the index of the nuclei channel (usually 0 or 3),
the channel to score:cenfindscore/path/to/dataset/path/to/model/-n0-c123Check that the predictions in the foldersvisualisations/andstatistics/For more information, please check the documentation (https://cenfind.readthedocs.io).CitationWe appreciate citations as they help us obtain grant funding and let us discover its application range.To cite Cenfind in publications, please use:Bürgy, L., Weigert, M., Hatzopoulos, G. et al. CenFind: a deep-learning pipeline for efficient centriole detection in
microscopy datasets. BMC Bioinformatics 24, 120 (2023).https://doi.org/10.1186/s12859-023-05214-2
Package: ceng
Tool for Coding Tasks Specific to Civil EngineeringTools to make - may or may not get around to doing all (many?) of these.General CEFactor of Safety ChecksLRFD ChecksLoad Combinations (COMPLETED)Material Properties (steel, aluminum, concrete, plastic...)Simple 1d curve and multi 1d curve interpolation from charts, tables (COMPLETED)MechanicsStaticsMechanicsStructural AnalysisGeotech engSoil classificationsSoil MechanicsBearing CapacityShear StrengthPressure IncreaseLateral PressureWalls DesignASCE 7 Equations (multiple versions)Seismic analysisWind loadingSnow loadingAASHTO equations (multiple versions)Chapter 12Reinforced concreteLive loadingSeismic loadingOther CodesPV1PV2NCSPANDSADMACIAISCAISIStructural DesignPilesFootingsMat FoundationsWallsEndwallsAbutmentsRelief SlabsCollarsRiser Slabs
Package: cenotes
CENotesC(ryptographical) E(xpendable) NotesFree software: GNU General Public License v3DemoSource code:BackendFrontendCLILibrariesDocumentationDesignRelease historyWhat is this?Abackendproject to support encryption/decryption of expendable
notesAn example using this backend can be found athttps://cenot.esWhat this isn'tUI/Frontend. This is abackendproject. Frontend solutions will be
different projects. The reason for this is to allow flexibility in
frontend choice and to avoid huge bundle projects.Afrontendproject that communicates with thebackendcan be
foundhereAcliproject to test the encrypt/decrypt actions offline can be foundhere. Backend project uses this package
for every crypto actionFeaturesSymmetric encryption of notes using thepynaclprojectOn the fly encryption/decryptionNotes can be encrypted/decrypted on the fly without storing
anything on the serverExpiration date notesAfter that date, the notes are deleted and cannot be
retrieved (default is never)Notes that are deleted after N visitsAfter N retrievals of a note, the note is deleted (default
is 1)Duress key for immediate note deletionUsing the duress key instead of the real decryption key
will delete the note and respond as if the note didn't exist
(to avoid indicating the use of the duress key)Persistent visit notesNotes can be marked as "persistent visit" so that that they
are not deleted based on visit countHow does this work?SeedesignHow to runSeehow to runHow to deploySeedeploymentFeatures to be added sometimeModification of a note's settingsZero visit countChange max visits optionChange expiration datePublic key encryption and user databaseQ&ASeeQuestions and answers
Package: cenotes-cli
C(ryptographical) E(xpendable) NotesCommand Line InterfaceFree software: GNU General Public License v3Backend & Frontend DemoSource code:BackendFrontendCLILibrariesDocumentationBackend DesignWhat is this?This is acliproject to support encryption/decryption
of expendable notesWhat this isn’tUI/Frontend/Backend. This is acliproject. Frontend and
backend solutions are different projects. The reason for this is to
allow flexibility in frontend / backend choice and to avoid huge bundle
projects.Abackendproject that uses these libraries can be foundhereAfrontendproject that communicates with thebackendcan be
foundhereFeaturesSymmetric encryption of notes using thepynaclprojectLocal encryption & decryption of a noteSupport to store a note to a cenotes server instanceSupport to fetch a note from a cenotes server instanceSupport for custom cenotes server urlHow does this work?SeedesignHow to runYou will need python >= 3.3Cloning the repoClone the repogit clonehttps://github.com/cenotes/cenotes-cli.gitInstall the requirements with pipenvpip install pipenvpipenv installSet yourPYTHONPATHto include the projectFor linux:exportPYTHONPATH=<path-to-the-cloned-repo>:$PYTHONPATHSee available optionspython cenotes_cli/cli.py--helpInstalling the python packageIdeally inside a virtualenvpip installcenotes-cliSee available optionscenotes-cli--helpHistory0.5.0 (2017-12-20)Add support to upload a note to a cenotes instanceAdd support to download a note from a cenotes instanceAdd support to define custom cenotes instance urlExtract actions to different modules (crypto/remote)Add dependency for requests & dateutil0.4.0 (2017-12-01)Remove internal cenotes-lib and use the new external cenotes-lib package0.3.0 (2017-12-01)Add support for custom exceptions insafe_decryptiondecorator0.2.1 (2017-12-01)Fix imports to be relative inside lib package0.2.0 (2017-12-01)Expose only library packagesSet status Pre-Alpha -> BetaAdd python 3.6 support in package0.1.1 (2017-11-30)Fix invalid packaged files.0.1.0 (2017-11-30)First release on PyPI.
Package: cenotes-lib
C(ryptographical) E(xpendable) Notes libraryFree software: GNU General Public License v3Backend & Frontend DemoSource code:BackendFrontendCLIDocumentationBackend DesignWhat is this?This is alibraryproject to support encryption/decryption
of expendable notesAn example of a backend that uses the libraries provided here can be
found athttps://cenot.esWhat this isn’tUI/Frontend/Backend/CLI. This is alibraryproject. Frontend and
backend solutions are different projects. The reason for this is to
allow flexibility in frontend / backend choice and to avoid huge bundle
projects.Abackendproject that uses these libraries can be foundhereAfrontendproject that communicates with thebackendcan be
foundhereAcliproject that uses these libraries can be foundhereFeaturesSymmetric encryption of notes using thepynaclprojectHow does cenotes work?SeedesignHow to useYou will need python >= 3.4Installing the python packageIdeally inside a virtualenv:pip installcenotes-libThen in your project:import cenotes_libHistory0.2.4 (2018-09-24)Add test discovery and inclusion to setup.py0.2.3 (2018-09-18)Fix bug when encrypting with encryption options0.2.2 (2018-05-29)Add flake8 to Pipfile0.2.1 (2018-05-29)Fix issues with travis builds0.2.0 (2018-05-29)Add support for different algorithm and algorithm hardnes picking.Add benchmark function as a helper for benchmarking each algorithm option.0.1.1 (2017-12-14)First release on PyPI.
Package: censaurus
Not just another Census data tool.Seriously.censaurusis a comprehensive, feature-rich, and user-oriented Python package that wraps the U.S. Census Bureau’s Data and Geographic APIs.DocumentationCheck out thecensaurusdocumentation. There you’ll find anintroductionto the package, awalkthroughof acensaurusworkflow, and fullAPI reference.Installationcensaurusis available on thePython Package Index, so it can be installed by running the following in the command line:pip install censaurusGet InvolvedAll contributions are welcomed! Please feel free to submit apull requestor point out anissue.
Package: censius
To Install Main Packagepip install censius -UTo Install Dev Packagepip install --trusted-host pypi-dev.eastus.azurecontainer.io:8080 \
 --index-url http://pypi-dev.eastus.azurecontainer.io:8080/simple/ censius -UAPI Reference to execute operationhttps://documentation.censius.ai/api-referenceTestingbuild the package with below commandpython setup.py sdistyour package will be available in dist folderinstall package in local venvpip install your_package_name
Package: censoredzz
Censoredzzcensor your profane words in Nepali-(Roman) textInstallationYou can install the package using pip:pipinstallcensoredzzusagefromcensoredzzimportcensor,profanityTo censor the word.
input: "text".
output: "censored text"censor.censor_text("ta badword ho")output:
"ta ****** ho"To check the profane word.
input: "text".
output: "1/0"profanity.has_profanity("ta badword ho")output:
1
Package: censor-fix
CensorFixExample Notebook and API and documentation are availablehereA library for multiple imputation of censored data.This software performs multiple imputation on censored data using the probabilistic programming langauge Stan.
The library fits a distributions to censored data and produces imputations based on the results.How to use:The data is assumed to have a normal distribution unless stated otherwise but there are options for the data to be uniform,t-distributed or exponential.Consider preprocessing using a power transform such as sklearn.reprocessing.power_transform before applying the algorithm.
Package: censor-this
censor-thiscensor-this is a Python command line tool that allows you to censor words in an image.InstallationInstall Tesseracthttps://github.com/tesseract-ocr/tessdoc/blob/main/Installation.md#introductionpipinstallcensor-thisCommandsaddcensor-thisadd[word]Adds a "censor word".Argsword - The "censor word" that will be added.[required]removecensor-thisremove[word]Removes acensor word.Argsword - Thecensor wordthat will be removed.[required]clearcensor-thisclearRemoves allcensor words.censor-wordscensor-thiscensor-wordsPrints allcensor words.imagecensor-thisimage[path]Censors allcensor wordsfrom an image or directory of images with blur, then outputs the results to./censor-this-output.Argspath - The path to the image/directory.[example: this/is/the/path/to/the/image.png] [required]--censor-all | --all | -a -  Censor all words in an image.[example: --censor-all]--min-conf | --conf | -m - The minimum confidence that will be required for a word to be censored.[example: --min-conf=40.0] [default: 90.0] FLOAT--bar-color | --color | -c - The color the censor bar will be.  When not present censor bar will default to blur.  Must be inhexformat.[example: --bar-color=#FFA200 ] [default: None] TEXTExampleAdd words that you want to censor.censor-thisaddbest# adding best to censor wordscensor-thisaddworst# adding worst to censor wordscensor-thisaddage# adding age to censor wordscensor-thisaddfoolishness# adding foolishness to censor wordsCensor the words in the image.censor-thisimage./imgs/example.png--bar-color=#FFA200Retrieve output from./censor-this-output.
Package: censosine21
censosine21Python library to interact with the API of the National Institute of Statistics (INE) of Spain that offers the dataDissemination System of the Population and Housing Censuses 2021 (SDC21)Read this inSpanishInstallationThecensosine21package is available as an open source library published atPyPI.
You can install censosine2021 by simply executing this command:pipinstallcensosine21If you already have installedcensosine21, you can upgrade it using:pipinstallcensosine21--upgradeKick offThe first step to usecensosine21is creating theCensosINE21object.fromcensosine21importCensosINE21hogares=CensosINE21()hogares.post("hog",'SHOGARES',['ID_RESIDENCIA_N2','ID_TENEN_VIV'],"ES")At this first moment, the object's attributes have no data. Using thepost()method, the library will make the request to the API according to the indicated parameters (table, metrics, variables and language) and will download the query data (metadata and data).hogares.post("hog",'SHOGARES',['ID_RESIDENCIA_N2','ID_TENEN_VIV'],"ES")Objet attributestable: Data set available from SDC21metrics: Unit of measurevariables: Variableslanguage: Language used for the resultsparams: JSON of parameters to make the POST request to the APIapi_url: official URL of the INE APImetadata: Metadata of the response JSONdata: Response JSON dataConversion to CSV format and filtersOnce the query is made, the class has theto_csv()method to save the data from the data attribute in CSV format.hogares.to_csv('csv/nhorares_regimen_provincias.csv')It is also possible to filter the dataset by indicating the key-value pair from the variable and the filter text using thefilter()method.hogares_filter_asturias=hogares.filter('ID_RESIDENCIA_N2','asturias')This same filtering option is also available in theto_csv()method.hogares.to_csv('csv/nhorares_regimen_asturias.csv','ID_RESIDENCIA_N2','33 Asturias')Show API entities infoThe library has a set of static methods to access the information from the tables, units of measurement and API variables and their description in Spanish and English.Being static methods, it is not necessary to instantiate an object to use it. We just use the class and the query methodfromcensosine21importCensosINE21CensosINE21.show_tables()CensosINE21.show_metrics()CensosINE21.show_variables()CensosINE21.show_languajes()Information about metrics and variables can be filtered by table.CensosINE21.show_metrics('hog')CensosINE21.show_variables('nuc')Looking for more examples?Get the source code from thegithub repository.There are more examples in the/examplesdirectory.LinksYou can obtain more information about how the INE API works on theINE official documentation pageLicenseThecensosine21library is developed byPatricio Soriano@sigdeletrasunder licenseMIT License.