Saravanan666/pypi-org huggingface dataset:
Package: artefacts
artefactsA deserialization library for dbt artifacts.pip install artefactsTheartefacts.apimodule aims to provide simple, easy to use python objects for interacting with your dbt project. Here's an example that identifies models in your project that are missing tests or descriptions.>>>importartefacts.api>>>formodelinartefacts.api.models():...ifmodel.descriptionisNoneorlen(model.tests)==0:...print(f"Incomplete model:{model.name}")UsageAfter installing artefacts, you first need tocompileyour dbt project.dbt compileYou can then start using the api.>>>importartefacts.apiDocsDocumentation for the project is available on the Github Pages site.https://tjwaterman99.github.io/artefacts/All methods exposed by the api are documented with usage examples in the API section.https://tjwaterman99.github.io/artefacts/api.htmlReferences for the objects returned by the api are available in the References section.https://tjwaterman99.github.io/artefacts/reference.htmlDevelopment SetupOpen this repository in a Github Codespace. (Click the greenCodebutton in the repository'sGithub pageand selectNew Codespace).Testingpoetry run pytestBy default, pytest will test against the dbt project located atARTEFACTS_DBT_PROJECT_DIR. To test against a different dbt project, update that environment variable and build the project.export ARTEFACTS_DBT_PROJECT_DIR=$PWD/dbt_projects/dbt-starter-project
poetry run dbt build --project-dir $ARTEFACTS_DBT_PROJECT_DIR
poetry run pytestTo run the doctests use the--doctest-modulesflag. Note that the doctests are intended to pass only when using thepoffertjes_shopproject.poetry run pytest --doctest-modulesDocumentation siteUsesphinx-livereloadto run the docs site on port8000.poetry run sphinx-autobuild docs/ docs/_build --watch artefacts
Package: artefactscomparison
Artefacts Comparisonartefactscomparisonis a Python package to compare and report on two artefacts summaries.Anartefact summaryis a CSV listing files' paths and SHA512 sum. Assuming artefacts are saved under theresults/directory, anartefact summaryis generated as follows:$findresults/-typef`# list files in the 'results/' directory…`\-execsha512sum{}\;`# … and compute their SHA-512 sum`\|sed's/  /,/'`# use comma — instead of double space — as separator`\|cat<(echo'sha512,file_name')-`# add header to CSV`sha512,file_name
<SHA512>,results/artefact_1
<SHA512>,results/path/to/artefact_2Theartefactscomparisonpackage provides a CLI entry point that outputs a diff report on two artefacts summaries:$ artefacts_comparison --base base_artefact_summary.csv --head head_artefact_summary.csv@@ 3 file(s) added @@+ path/to/new_file_1+ path/to/new_file_2+ path/to/new_file_3@@ 1 file(s) deleted @@- path/to/removed_file.csv@@ 2 file(s) renamed @@! path/to/to_rename_1 ￫ path/to/renamed_1! path/to/to_rename_2 ￫ path/to/renamed_2# 2 other file(s) remain unmodifiedUser QuickstartInstallationYou can installartefactscomparisonfrom PyPI:pipinstallartefactscomparisonHow to useFrom the command line:artefacts_comparison--headhead_artefact_summary.csv--basebase_artefact_summary.csvwhere:head_artefact_summary.csvis the artefact summary of the branch you want to merge,base_artefact_summary.csvis the artefact summary of the branch your PR points to (i.e.main,master, etc.).Development QuickstartThis project adheres toSemantic Versioning, and releases descriptions can be found inCHANGELOG.md.Use your own environment management preferenceForpyvenv:python-mvenv.venv/source.venv/bin/activateInstall this packagegitclonegit@github.com:EBoisseauSierra/artefacts_comparison.gitcdartefacts_comparison
pipinstall--upgradepip
pipinstall-e'.[dev,test]'Initialise pre-commit hooksThepre-commit hooksdefined in this repo ensure that code formating and linting is applied on any piece of code committed. This should enable a cleaner code base and less “formatting noise” in commits.To install the hooks, simply run:pre-commitinstallContributingFork this repo (https://github.com/EBoisseauSierra/artefacts_comparison/fork)Create 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 RequestLicenceDistributed under the MIT License. SeeLICENSEfor more information.ReferencesAdd comments on multi-line shell commands:https://stackoverflow.com/a/12797512/5433628Compute the SHA sum of all files in a directory:https://askubuntu.com/a/1091369Prefix the stdout:https://stackoverflow.com/a/33139133/5433628
Package: artelebot
ttbotIt's a beautiful project that helps developers program.Installation :pip install tambotapiLogan Usagefromtambotapiimport*## Follow us on social media[![Github](https://img.shields.io/badge/Github-NovaTools-orange?style=for-the-badge&logo=github)](https://github.com/NovaTools4/)[![Telegram](https://img.shields.io/badge/Telegram-NovaTools-orange?style=for-the-badge&logo=Telegram)](https://t.me/VZX_TEAM)
Package: artembay-logger
Just another logger for pythonThis logger has been writen by Artem Bay. Can used in any projectmain.py:fromartembay_loggerimportLoggerif__name__=="__main__":logger=Logger(log_file="log.txt",log_level=3,log_timezone="Europe/Moscow",log_colors=True)logger.info(f"All log levels will writen in file but print only selectd and lower Now log level is{logger.log_level}\n")logger.debug("Debug [Log level 1]")logger.info("Info [Log level 2]")logger.warning("Warning [Log level 3]")logger.error("Error [Log level 4]")logger.critical("Critical [Log level 5]")log.txt:2022-11-03 05:21:52 |INFO    | All log levels will writen in file but print only selectd and lower Now log level is 3 
 
2022-11-03 05:21:52 |DEBUG   | Debug [Log level 1] 
2022-11-03 05:21:52 |INFO    | Info [Log level 2] 
2022-11-03 05:21:52 |WARNING | Warning [Log level 3] 
2022-11-03 05:21:52 |ERROR   | Error [Log level 4] 
2022-11-03 05:21:52 |CRITICAL| Critical [Log level 5]Dm me in discord for help. Artem Bay#0547Made with ❤️ by Artem Bay and Github Copilot
Package: artemis2tumonline
Artemis2TUMOnlineAfter conducting an exam withArtemis, you can download a CSV file with the grades of the students.
The problem is that the format of the Artemis export isNOTthe format that TUMOnline is expecting.
Thus, you cannot directly upload the CSV to TUMOnline.This small tool can help you to create the CSV for TUMOnline.InstallationThe easiest way to install the package is to use pip.pipinstallartemis2tumonlineAttention: On macOS,pipis usually the installer of the Python2 instance.
Please usepip3orpip3.xin this case.If you want to work on the software, you can install the dependencies viapoetry.poetryinstallUsageartemis2tumonlinecreate-final-results--help
Usage:artemis2tumonlinecreate-final-results[OPTIONS]ReadsaTUMOnlineregistrationandaArtemisexportfile.CreatesanTUMOnlinefilewiththegradesofthestudents.

Options:-t,--tumonline-registration-fileFILETheregistrationfile.YoucangetthisfilefromTUMOnline.Usually,thisisthesamefileyouusetoregisterthestudentsfortheexam.-a,--artemis-export-fileFILETheCSVfileyoucandownloadfromArtemis.-o,--output-fileFILETheresultingCSVfile.ThisfilecontainsthenecessaryinformationfromtheTUMOnlineRegistrationfileandthegradesfromtheArtemisexport.YoucanuploadthisfiletoTUMOnline.[default:/path/to/cwd/tumonline.csv]--install-completion[bash|zsh|fish|powershell|pwsh]Installcompletionforthespecifiedshell.--show-completion[bash|zsh|fish|powershell|pwsh]Showcompletionforthespecifiedshell,tocopyitorcustomizetheinstallation.--helpShowthismessageandexit.Example$poetryrunartemis2tumonline--tumonline-registration-file./test/Modulpruefung_29072021-0800_IN2178_FA_SecurityEngineering.csv--artemis-export-filetest/Final_exam__Security_EngineeringResults.csv
WeloadtheTUMonlinefile/path/to/cwd/test/Modulpruefung_29072021-0800_IN2178_FA_SecurityEngineering.csv
...andtheArtemisfile/path/to/cwd/test/Final_exam__Security_EngineeringResults.csv
...andwritetheresultsin/path/to/cwd/tumonline.csvMiscIf you have any question, just writemean email.
Package: artemis7
ARTEMIS7Analytics, Reporting, and Tools for Exploratory Measures in StatisticsThis library is pre-released. you can install artemis from PyPI withpip:pipinstallartemis7datageneratorvirtual_drift_generator.pymyplotMyPlot.pystatsbinning.pySturges' Formula :Sturges, H. A. (1926)Doane's Formula : Doane DP (1976)Scott's normal reference rule :Scott, David W. (1979)Freedman-Diaconis' choice :Freedman, David; Diaconis, P. (1981)frechet_inception_distance.pypiecewise_rejection_sampling.py :Creditmultivariate_sampling.pyutilcolorprint.pyfuntion_list.pymake_movie.pyprogressbar.py
Package: artemis.bookmarks
bookmarksa small bookmark apiQuickstart$ python -m pip install artemis.bookmarks
$ export FLASK_APP=bookmarks.app
$ python -m flask db:init
$ python -m flask runConfigurationBy default, the DB is configured to stay in RAM, so it simply won't work.The configuration file's name isbookmarks.toml, and it can reside in the following paths../bookmarks.toml~/.config/bookmarks/bookmarks.toml~/.config/bookmarks.toml/etc/bookmarks/bookmarks.toml/etc/bookmarks.tomlExample configurationSQLALCHEMY_DATABASE_URI='sqlite:////usr/share/bookmarks.db'SERVER_NAME='bookmarks.example.com'You need to check your WSGI runner's configuration to configure its listen interface.
Package: artemis-utils
ARTEMIS is an open-source tool, that implements a defense approach against BGP prefix hijacking attacks.This package holds all the utility functions that are shared between the different modules.Read more atbgpartemis.organd thedocs.Instructions on publishing a package update:# follow instructions to build a correct $HOME/.pypirc for artemis-utils
https://docs.gitlab.com/ee/user/packages/pypi_repository/
# generate pypi token
https://pypi.org/manage/account/token/
# update the package (code, version, files, etc.)
python3 setup.py sdist bdist_wheel
twine upload dist/*
Package: arte_plus7
ArtePlus7ArtePlus7 is a python script aimed at downloading the mp4 videos from
ArtePlus7 using their url.It’s based on BeautifulSoup4.UsageThe following commands will return the videos urls found# The page dedicated to the video

./arte_plus_7.py -u http://www.arte.tv/guide/fr/034049-007/karambolage

# Direct access to some pre-stored programs

./arte_plus_7.py -p tracksTo actually download the video, add a--qualiy<QUAL>for the one
you want from the list# Download 'tracks' with selected quality

./arte_plus_7.py -p tracks --quality <MQ|HQ|EQ|SQ>Changelog2.2.1 - 2.2.2 - 2.2.3: Updates for Pypi2.2.3: convertREADMEto reStructuredText2.2.3: add a changelog inCHANGELOG.rst2.2.3: addCHANGELOG.rsttolong_description2.2.2: fixes issues while releasing previous version on pypi2.2.1: changes for pypi2.2.0: Update to the new search resultsFix search handlingImprove ‘program’ to exclude not matching included search resultsAdd a ‘–version’ optionRemove unused ‘–keep-artifacts’ optionPylint/pep8 cleanuptox.ini: update to fail on pylint/pep8 errors.2.1.0: New ArtePlus7 search handlingHandle new way of presenting search resultsFix num-progs for-1to use all entriesNicely handle some errors and add debug logging2.0.0: New ArtePlus7 site and new optionsAdapt to new ArtePlus7 interface, main JSON has been removed and now a
search page should be parsed to get search resultsAllow downloading multiple videosAdd a--searchoption to search using arte search engineAdd also a--verboseoption
Package: arteria-python
Arteria – Python client libraryReadarteria docsfor all integration details.Installationpip install arteria-pythonAuthenticationTo configure manually your credentials:importarteriaARTERIA_API_KEY='PKxxxx'ARTERIA_API_SECRET='yyyyyy'arteria.configure(api_key=ARTERIA_API_KEY,api_secret=ARTERIA_API_SECRET)TransfersCreate transferimportarteriaarteria.configure(sandbox=True)# if using sandboxlocal_transfer_id='078efdc20bab456285437309c4b75673'transfer=arteria.Transfer.create(recipient_name='Benito Juárez',account_number='646180157042875763',# CLABE or card numberamount=12345,# Mx$123.45descriptor='sending money',# As it'll appear for the customeridempotency_key=local_transfer_id)# To get updated statustransfer.refresh()Retrieve byidimportarteriatransfer=arteria.Transfer.retrieve('tr_123')Query byidempotency_key,account_numberandstatusResults are always returned in descending order ofcreated_atThe methods that can be used:one()- returns a single result. RaisesNoResultFoundif there are no
results andMultipleResultsFoundif there are more than onefirst()- returns the first result orNoneif there aren't anyall()- returns a generator of all matching results. Pagination is handled
automatically as you iterate over the responsecount()- returns an integer with the count of the matching resultsimportarteria# find the unique transfer using the idempotency keylocal_transfer_id='078efdc20bab456285437309c4b75673'transfer=arteria.Transfer.one(idempotency_key=local_transfer_id)# returns a generator of all succeeded transfers to the specific accounttransfers=arteria.Transfer.all(account_number='646180157000000004',status='succeeded')# the total number of succeeded transferscount=arteria.Transfer.count(status='succeeded')Api KeysCreate newApiKeyand deactivate oldimportarteria# Create new ApiKeynew=arteria.ApiKey.create()# Have to use the new key to deactivate the old keyold_id=arteria.session.auth[0]arteria.session.configure(new.id,new.secret)arteria.ApiKey.deactivate(old_id,60)# revoke prior API key in an hour
Package: artes
Atmospheric Radiative Transfer for Exoplanet ScienceARTES is a 3D Monte Carlo radiative transfer code specifically designed for (polarized) scattered light simulations of planetary atmospheres. Computations of both reflected stellar light and thermal emission from the planet are possible for an arbitrary atmospheric structure and distribution of opacity sources. Multiple scattering, absorption, and polarization are fully treated and the output includes an image, spectrum, or phase curve. Several tools are included to create opacities and scattering matrices for molecules and clouds.DocumentationDocumentation can be found athttp://artes.readthedocs.io, including installation instructions, a description of the requires input files, and several examples.AttributionPlease citeStolker et al., 2017, A&A, 607, A42whenever results from ARTES are used in a publication.LicenseCopyright 2018-2020 Tomas StolkerARTES is free software and distributed under the GNU General Public License v3.
Package: art-fid
ArtFID: Quantitative Evaluation of Neural Style Transfer (GCPR Oral 2022)ArtFID: Quantitative Evaluation of Neural Style TransferMatthias WrightandBjörn Ommer.Installation>pipinstallart-fidUsageCUDA_VISIBLE_DEVICES=0python-mart_fid--style_imagespath/to/style-images--content_imagespath/to/content-images--stylized_imagespath/to/stylized-imagesThe content images and the corresponding stylized images are compared in pairs. In order to ensure that a content image is matched up with the correct stylized image, both the content images and the stylized images are processed in lexicographical order. A simple way of pairing the content images and the stylized images is to use the name of content image for the corresponding stylized image.Arguments--batch_size- Batch size for computing activations.--num_workers- Number of threads used for data loading.--mode- Evaluate ArtFID or ArtFID_infinity, choices = ['art_fid', 'art_fid_inf'].--content_metric- Content metric, choices = ['lpips', 'vgg', 'alexnet'].--device- Device to use, choices = ['cuda', 'cpu'].--style_images- Path to style images.--content_images- Path to content images.--stylized_images- Path to stylized images.DataThe dataset is contained inartfid_dataset.csv. It consists of 250k labeled artworks.AcknowledgmentsThe implementation of the FID is based onmseitzer/pytorch-fid.The implementation of the FID_infinity is taken frommchong6/FID_IS_infinity.The implementation of the Inception network is taken frompytorch/vision.The checkpoint is hosted on theHuggingface Model Hub.Citation@article{wright_gcpr_2022,
    title={ArtFID: Quantitative Evaluation of Neural Style Transfer},
    author={Matthias Wright and Bj{\"o}rn Ommer},
    journal={GCPR},
    year={2022}
}
Package: artfight-api
Artfight ApiA module for retrieving data fromartfight.net.Powered bybeautifulsoup4Artfight Api scrapes the normal frontend to retrieve data from Artfight. As a result any changes to the UI are likely to break this module. If you encounter any errors please be sure to report them to the repository'sGitHub issues page.QuickstartInstall the module:pip install artfight-apiFetch all attacks by a user:importasynciofromartfightimportArtfightClientasyncdefmain():asyncwithArtfightClient("USERNAME","PASSWORD")asclient:asyncforattackinclient.get_user("Takaia").fetch_attacks():fetched=awaitattack.fetch()print(fetched.title)asyncio.run(main())
Package: artfornormies
ArtfornormiesGenerate data visualizations - Pypi versionInstructions:Install:pip install artfornormiesGenerate a visual using one of the listed functions:from artfornormies import functions as art

#
out_name = "example_venn.png"
fig = art.create_venn_2()
fig.write_image(out_name)TO DO - AUTHOR:Rework readme - add explanationsadd docs for each fnAdd images for each bannerexplore test for each fnrefactor colorscheme variables
Package: artgenerator
# ArtgeneratorA simple image manipulation package that will turn images into randomly generated effects.# Installations`console pip install autogen `other installations will include:`consoleopencv-pythonnumpy `# UsageColor Parameters: (file path,
RBG color for background,
amount of iterations,
refresh rate,
downscaling of image for speed,
upscaling of image for display
)Effects Parameters: (file path,
Effect type (pixel, color, line),
amount of iterations,
refresh rate,
downscaling of image for speed,
upscaling of image for display
)Change Log0.0.1 (08/03/2022)First Release
Package: arthub-api
Arthub SDK for pythonThis python project provides a series of API interfaces to access the ArtHub serverInstallingYou can install via pip.pip install arthub_apior build from sourcegit clone https://git.woa.com/arthub/arthub-python-sdk.git
cd arthub-python-sdk
python setup.py installTestingWe provide unit tests in ./test, you can use them with pytestpytest ./testsYou can test under different domain by passing the parameter 'env'pytest ./tests --env=oa_test
pytest ./tests --env=qq_test
pytest ./tests --env=oa
pytest ./tests --env=qqUsing the SDKPlease refer to the SDK usage guide:Usage GuideIf you have any questions, please contact joeyding on WeChat WorkFor DeveloperDeveloper
Package: arthur
========arthur========.. image:: https://dl.dropbox.com/u/38476311/Logos/arthur.pngThis is the client for the game-shaped exercises which are part ofCrypto 101, the introductory book on cryptography by lvh_.Here's a screenshot of the client:.. image:: https://dl.dropboxusercontent.com/u/38476311/hacks.pngTesting and documentation=========================The short version: use tox_.The long version: see the extra notes for merlin_.Whence the name?================The server side to this is called merlyn_, because the step-by-steporacle-like model reminded me of Merlin in the AM complexity class and`Arthur-Merlin protocols`_. (It's not spelled the usual Merlin,because that primo PyPI real estate was already taken up by some weirdsetuptools fork.)Since Arthur is the person who performs the protocol together withMerlin, it only made sense to name this project `arthur`. Finally,clarent_, named after king Arthur's ceremonial sword, holds commontools... _lvh: https://twitter.com/lvh/.. _tox: https://testrun.org/tox/.. _merlyn: https://github.com/crypto101/merlyn.. _clarent: https://github.com/crypto101/clarent.. _`Arthur-Merlin protocols`: https://en.wikipedia.org/wiki/Merlin-Arthur_protocol
Package: arthurai
Arthur SDKArthuris the platform for centralized monitoring of production models.
We help data scientists, product owners, and business leaders accelerate model operations to optimize
for accuracy, explainability, and fairness. As a model- and infrastructure-agnostic platform, Arthur
adds a layer of intelligence to your AI stack and scales with your deployments.Our SDK makes it easy to integrate your models with the Arthur platform. For help getting started or
using the SDK, check outour documentation.
Package: arthur-bench
BenchBench is a tool for evaluating LLMs for production use cases. Whether you are comparing different LLMs, considering different prompts, or testing generation hyperparameters like temperature and # tokens, Bench provides one touch point for all your LLM performance evaluation.If you have encountered a need for any of the following in your LLM work, then Bench can help with your evaluation:to standardize the workflow of LLM evaluation with a common interface across tasks and use casesto test whether open source LLMs can do as well as the top closed-source LLM API providers on your specific datato translate the rankings on LLM leaderboards and benchmarks into scores that you care about for your actual use caseJoin the bench community onDiscord.For bug fixes and feature requests, please file a Github issue.Package installationInstall Bench to your python environment with optional dependencies for serving results locally (recommended):pip install 'arthur-bench[server]'Alternatively, install Bench to your python environment with minimum dependencies:pip install arthur-benchFor further setup instructions visit ourinstallation guideUsing BenchFor a more in-depth walkthrough of using bench, visit ourquickstart walkthroughand ourtest suite creation guideon our docs.To make sure you can run test suites in bench, you can run the following code snippets to create a test suite and run it to give a score to candidate outputs.fromarthur_bench.run.testsuiteimportTestSuitesuite=TestSuite("bench_quickstart","exact_match",input_text_list=["What year was FDR elected?","What is the opposite of down?"],reference_output_list=["1932","up"])suite.run("quickstart_run",candidate_output_list=["1932","up is the opposite of down"])Saved test suites can be loaded later on to benchmark test performance over time, without needing to re-prepare reference data:existing_suite=TestSuite("bench_quickstart","exact_match")existing_suite.run("quickstart_new_run",candidate_output_list=["1936","up"])To view the results for these runs in the local UI that comes with thebenchpackage, runbenchfrom the command line (this requires the bench optional server dependencies to be installed):benchViewing examples in the bench UI will look something like this:Running Bench from sourceTo launch Bench from source:Install the dependenciespip install -e '.[server]'Build the Front Endcd arthur_bench/server/jsnpm inpm run buildLaunch the serverbenchBecause the server was installed withpip -e, local changes will be picked up. However, the server will need to be restarted between
changes in order for those changes to be picked up.
Package: arthur-client
Arthur ClientThis library provides a lightweight Python API for interacting withArthur's REST API.Example: Fetch ModelsfromtypingimportListfromarthur.client.restimportArthurClientfromarthur.client.rest.models.modelsimportModelResponse# create clientclient=ArthurClient(url="https://app.arthur.ai",login="<my_username>")# enter your password when prompted# fetch first page of modelsmodels_p1:List[ModelResponse]=client.models.get_paginated_models(page=1,page_size=10).data# print model namesprint(f"Found{len(models_p1)}models on first page:{', '.join([m.display_nameforminmodels_p1])}")