Saravanan666/pypi-org huggingface dataset:
Package: productml
Product-mlA package built by DevsDoData for product-level inference.Includes functionality for ML lifecycle including data preparation and model training.Used to sync key workflows across platforms locally, cloud (Colab) notebooks and deployment.Setup VenvGoogle Colab uses Python version3.7.13and we need to match that. Otherwise it would cause issues.sudoaptupdate&&sudoaptinstallpython3.7
sudoaptinstallpython3.7-distutils
pipenvinstall--python="/usr/bin/python3.7"And then install everything withpython3-mpipenvinstall\loguruwandbhydra-core\fuzzywuzzy\torchvision\opencv-pythonscikit-learnnumpymatplotlib\pytest\twineDistributing the packageSpecify the dependencies and build the packagepipenvrequirements|sed1d>requirements.txt

python3./productml/setup.pyinstallsdistbdist_wheelThen create a new dir for testing.cd../&&mkdirtest-dir&&cdtest-dir

touchactivate.sh<echo". ./venv/bin/activate"pip3installvirtualenv&&virtualenvvenvsourceactivate.sh

touchtest.py&&test.py<echo"from productml.data.labelling import text_matching"python3test.pyAnd you should not get anImportErroror any other error for that matter.
Package: product-ouroboros
A wrapper for the moviepy library.Required -Python 3._ -moviepyCommon Errors EncounteredImageIO and FFMPEG ErrorOpen an interactive python shell by running:python3Within the interactive shell run the commands:import imageio
imageio.plugins.ffmpeg.download()This will fix the issue.Not Valid Win32 Application.If you get the error:'osError: [WinError 193] %1 is not a valid Win32 application'then your ffmpeg doesn’t match your python install, 32-bit or 64-bit. A
manual install of ffmeg can fix this.
Package: product.recaptcha-invisible
Recaptcha Invisible support for Plone. Provides a field and a widget ready to be used in zope schemasFeaturesFieldWidgetValidatorTranslationsThis product has been translated intoCatalanSpanishEnglishInstallationInstall product.recaptcha_invisible by adding it to your buildout:[buildout]

...

eggs =
    product.recaptcha_invisibleand then runningbin/buildoutClassifiersFrameworkPlone 5.2Programming LanguagePython 3.6Python 3.7LicenseThe project is licensed under the GPLv2.Contributorscsanahuja,csanahuja10@gmail.comChangelog2.0 (11/04/2023)Update widget to Boostrap 5 and Plone 6 [csanahuja]1.3 (21/03/2023)Fixes:Fix widget for easyform when more than one button with submit-widget class exists
[csanahuja]1.2 (13/02/2023)Fixes:Fix widget input submitting form with submit-widget input if exists
[csanahuja]1.1 (09/09/2021)Fixes:Fix widget input for easyform fields view.
[csanahuja]1.0 (09/09/2021)New features:Initial release.
[csanahuja]1.0a1 (unreleased)Initial release.
[csanahuja]
Package: products-crawler
ProductCrawler介绍ProductCrawler是一个Scrapy项目，目的是从购物网站收集商品信息，它包含一系列爬虫，爬虫均以商品的品牌来命名。该项目的创建是兴趣使然，目的是学习Scarpy框架，可能存在一些Bug和莫名奇妙的代码...正在生产环境中使用的爬虫有:supreme停止维护且不知道能否正常运行的爬虫有:kapitalgallianolandornikebearbrickunited-arrows online storetravis scott正在开发的爬虫有：等待支持的品牌有：humanmadewtaps依赖Python3.7+为了使用 nike 爬虫，你还需要：Chrome 浏览器和相应版本的 ChromeDriver。缺少它们不会影响其他爬虫的使用。安装pip install products_crawler用法crawl -h
usage: crawl [-h] {bearbrick,glld,kapital,nike,supreme,ts,uastore} start_urls [start_urls ...]

positional arguments:
  {bearbrick,glld,kapital,nike,supreme,ts,uastore}
  start_urls

optional arguments:
  -h, --help            show this help message and exit试着执行下面这条命令，当前工作目录下会创建product目录，所有爬取到的商品图片和信息都会出现在里面。crawl supreme https://www.supremecommunity.com/season/spring-summer2020/droplist/2020-02-27/示例Supreme爬取某一季所有周的商品crawl supreme https://www.supremecommunity.com/season/spring-summer2020/droplists/爬取某一周所有的商品crawl supreme https://www.supremecommunity.com/season/spring-summer2020/droplist/2020-02-27/Kapital爬取某一分类下的所有商品crawl kapital https://www.kapital-webshop.jp/category/W_COAT/Nike爬取当前搜索款式的商品（包括所有颜色）crawl nike https://www.nike.com/cn/w?q=CU6525&vst=CU6525BearBrick爬取当前分类的所有商品crawl bearbrick http://www.bearbrick.com/product/12_0已知问题：BearBrickLoader的category_in无法达到预期的行为。United Arrows Online Shop爬取当前商品crawl uastore https://store.united-arrows.co.jp/shop/mt/goods.html?gid=52711245Travis Scott爬取所有商品crawl ts https://shop.travisscott.com/
Package: productsup-py
ProductsUpPyThis is a simple Python wrapper around the ProductsUp platform API. It allows you to easily interact with the ProductsUp platform using Python code.FeaturesEasy to use interface
Support most ProductsUp platform API endpoints
Exception handling for common error casesInstallationYou can install the package using pip:pip install productsup-pyUsageimportproductsup_pyaspu# First, create a ProductUpAuth objectpu_auth=pu.ProductUpAuth(1234,'mknjbhvgcd')# Next, create a Projects objectexample_project=pu.Projects(pu_auth)project=example_project.get_project(28532)print("Project ID:",project.project_id)print("Project Name:",project.name)print("Project Creation Date:",project.created_at)Output:Project ID: 28532Project Name: TestProject Creation Date: 2015-08-20 14:19:00SupportingIn case of any issues or for feature request, please raise an issue on the GitHub repository.LicenseCopyright (c) 2023, Lyes Tarzalt
All rights reserved.This source code is licensed under the BSD-style license found in the
LICENSE file in the root directory of this source tree.
Package: product-sync
Connect Product SynchronizerIntroductionCloudBlue Connect is a supply automation platform that manages your products and services, contracts,
ordering and fulfillment, usage and subscriptions.It supports any product, from physical goods to cloud products, as well as any channel, including your
direct and indirect sales channels and internal procurement.With its flexible APIs, it can connect to any commerce platform.Vendors can leverage CloudBlue Connect to:Reduce the total cost of ownership for homegrown technology supporting their indirect channelStandardize integrations with partnersIncrease efficiencies and minimize redundancies by bridging their direct and indirect sales channelsService providers can use CloudBlue Connect to:Define, manage and distribute any type of product (omni-product) through any channel (omni-channel)Transform perpetual licensed products into a subscription modelOnboard new products into their portfolio quickly to build and deliver unique solutions to end customersproduct-syncallow users to export/synchronize the items of a product to/from an Excel workbook.InstallUsing a virtualenvTo useproduct-syncyou need any *nix system with python 3.6 or later installed.The preferred way to installproduct-syncis using avirtualenv:$ virtualenv psync
    $ source pysync/bin/activate
    $ pip install product-synchronizerBinary distributionsA single executable binary distribution is available for both linux and mac osx (amd64).
You can it from theGithub Releasespage.To install under linux:$ curl -O -J https://github.com/cloudblue/product-sync/releases/download/1.2/product-sync_1.2_linux_amd64.tar.gz
    $ tar xvfz product-sync_1.2_linux_amd64.tar.gz
    $ sudo cp dist/ccli /usr/local/bin/ccliTo install under Mac OSX:$ curl -O -J https://github.com/cloudblue/product-sync/releases/download/1.2/product-sync_1.2_osx_amd64.tar.gz
    $ tar xvfz product-sync_1.2_linux_amd64.tar.gz
    $ sudo cp dist/ccli /usr/local/bin/ccliIf your user is not a sudoer, you can copy theccliexecutable from the dist directory to a directory of your choice
that is listed in thePATHvariable.UsageConfigureFirst of all you need to configure theproduct-syncwith the CloudBlue Connect APIendpointandkey.$ ccli configure --url https://api.connect.cloudblue.com/public/v1 --key "ApiKey XXXXX:YYYYY"Dump products to ExcelTo dump products to Excel run:$ ccli product dump PRD-000-000-000 PRD-000-000-001 PRD-000-000-002 --out my_products.xlsxSynchronize productsTo sync products from Excel run:$ ccli product sync --in my_products.xlsxLicenseproduct-syncis released under theApache License Version 2.0.
Package: product_tagger
IntroductionThe package aims at findng core terms, brand, and descriptions from a product title from e-commerce websites.> Example: product title: ‘Makibes Unisex Red LED Digital Band Wrist Watch’)product core term:  ‘watch’
product brand name: ‘makibes’
Descriptions: ‘unisex red led digital band wrist’Usage$ python
>>> from product_tagger import tag_product
>>> tag_product(‘Makibes Unisex Red LED Digital Band Wrist Watch’)
(u’watch’, u’makibes’, u’unisex red led digital band wrist’)The tagger can also be called in a shell$ cd product_tagger
$ python product_tagger.py ‘Makibes Unisex Red LED Digital Band Wrist Watch’
===========
(‘Core term: ‘, u’watch’)
(‘Brand: ‘, u’makibes’)
(‘Discriptions: ‘, u’unisex red led digital band wrist’)RequirementsPython &gt;=2.7 or &gt;=3.3nltk &gt;=3.2.1requests &gt;= 2.8.1lxmlxlrd
Package: produttore-e-consumatore2
produttore_e_consumatore2DescriptionProducer and consumer with multiprocessing.
You can use this tool to get the files which are in a selected folder/ subfolder.Requiredchoco/ aptInstallationchoco (Windows) (as Administartor)choco install produttore_e_consumatore2 --version=01.01Ubuntu using apt:sudo add-apt-repository ppa:castellanidavide/school -y; sudo apt update; sudo apt install produttoreconsumatore2 -yUpdateWindows (using choco):choco upgrade produttore_e_consumatore2Ubuntu using apt:sudo apt update; sudo apt upgradeDelateWindows (using choco):choco remove produttore_e_consumatore2Ubuntu using apt:sudo apt remove produttoreconsumatore2Usagewith GUIFollow the labels (only folder one is obbligatory).without GUIproduttore_e_consumatore2 [--batch | -b] - disable GUI modeproduttore_e_consumatore2 [--verbose] - verbose mode (if not in GUI mode)produttore_e_consumatore2 [--csv] - enable csv output (if not in GUI mode)produttore_e_consumatore2 [--folder] - enable csv output (if not in GUI mode)produttore_e_consumatore2 [--url=... --token=... --table=...] - enable the upload to HarperDB (Replace "..." with the value(s)) (if not in GUI mode)Directories structure.gitignoresetup.pyLICENSE.md.githubISSUE_TEMPLATEbug_report.mdfeature-request.mdworkflowson-push.ymlon-release.ymlchocoset.txtproduttore_e_consumatore2.nuspectoolschocolateyinstall.ps1chocolateyuninstall.ps1LICENSE.txtVERIFICATION.txtdebianproduttoreconsumatore2.1produttoreconsumatore2.cMakefilerequirements.indebianchangelogcompatcontrolcopyrightpostinstpostrmpreinstrulessourcedocslogo.png*.mdflussi (example output(s))net.csvOS.csvlog (example log(s))trace.logrequirementsrequirements.txtproduttore_e_consumatore2__init__.pyExecution examplesproduttore_e_consumatore2Output location*.csv (if enabled) in the location where the code was lauched*.logC:/Program Files/produttore_e_consumatore2/* on Windows~/* on linuxcurrent location (if you didn't lauch the code with the correct rights)Made by Castellani Davide
If you have any problem please contact me:help@castellanidavide.itIssue
Package: prody-rhapsody
RhapsodyPython program, based on ProDy, for pathogenicity prediction of human
missense variants.Install latest published version using pipRhapsody is published onPyPI. To install Rhapsody,
please use pip in the terminal:$pipinstall-Uprody-rhapsodyIt might be necessary to manually install the DSSP program, for instance
by typing on Linux:$sudoaptinstalldsspInstall from sourceRhapsody is written in pure Python so no local compilation is needed.To install all needed dependencies, we strongly suggest to use Conda and create
a new environment with:$condacreate-nrhapsodypython=3numpyscikit-learnrequestspyparsingmatplotlibbiopython$condaactivaterhapsody$pipinstallprody$condainstall-csalilabdsspAfter cloning/forking the Rhapsody repository, you can permanently add the
repository path to the conda environment with:$condadeveloppath/to/local/repositoryIf not using Conda, you can manually install all dependencies and then add
the repository location to thePYTHONPATHenvironmental variable. For
example, on Linux simply add the following line to your~/.bashrc:export PYTHONPATH="path/to/local/repository/:$PYTHONPATH"If you are running on Windows, please follow thistutorial.Running initial setupAfter installation, please run:import rhapsody as rdrd.initialSetup()
Package: profane
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
[![Worfklow](https://github.com/andrewyates/profane/workflows/pytest/badge.svg)](https://github.com/andrewyates/profane/actions)
[![PyPI version fury.io](https://badge.fury.io/py/profane.svg)](https://pypi.python.org/pypi/profane/)# OverviewProfaneis a library for creating complex experimental pipelines. Profane pipelines are based on two key ideas:
1. An experiment is afunction of its configuration. In other words, an experiment should be deterministic given a set of experimental parameters (random seed, specific algorithms to run, etc).
2. An experiment is described as aDAGrepresenting modules’ (nodes’) dependencies in which thestate of a node is independent of its parent’s state. That is, anode’s operation is a function of its configuration and the configurations of its children. This means that a node may not modify the configuration (or state) of its children (or descendants).These allow for the construction of a flexible pipeline with automatic caching. Each node’s configuration can be modified to change experimental parameters, and a node’s output can be safely cached in a path derived from its configuration and the configurations of its children. These nodes are called modules.This library is heavily inspired by the excellent [sacred](https://sacred.readthedocs.io/en/stable/) library. Among other differences, profane imposes a specific structure on the pipeline and leverages this to allow profane modules to be dynamically configured (which would be similar to dynamic sacred ingredients). Profane was developed based on experiences using sacred with a heavily modified pipeline initialization step.## Example
Theexample/directory contains a module graph similar to that used in Capreolus. Run it with therun.shscript.
Package: profane-detector
ProfanityDetectorA Python library for profanity detection in text, providing a customizable ProfaneDetector class. Easily integrate into your projects to filter or flag content.Installingpipinstallprofane-detectorUsage examplesfromprofane_detectorimportProfaneDetectorprofane_detector=ProfaneDetector()to_detect="fuck you!"language="en"did_detect=profane_detector.detect_api(language,to_detect)print(did_detect)
Package: profanity
profanity=========A Python library to check for (and clean) profanity in strings.##Installationpip install profanity##Usagefrom profanity import profanityprofanity.contains_profanity("You smell like shit.")Out: Trueprofanity.censor("You smell like shit.")Out: 'You smell like !@#$.'## Features* Load your own wordlist, or use the bundled one.* Censors words using standard censor characters (!@#$%), or load your owncensor characters.* Uses a pool of censor characters to create a more natural censor string.* Censors all instances of a given word with the same censor string - foreasy correlation.### I love Forks, Pull Requests, and Bugs!I wrote this to satisfy my needs. Please feel free to contribute if you haveother needs that you think others would benefit from!Feel free to contact me: http://www.bugben.com
Package: profasta
ProFASTAIntroductionProFASTA is a Python library for working with FASTA files containing protein records. Unlike other packages, ProFASTA prioritizes simplicity, while aiming to provide a set of useful features required in the field of proteomics based mass spectrometry.The library is still in early development and the interface might change over time. At the current stage ProFASTA provides functionality for parsing and writing FASTA files, as well as for providing access to protein records imported from FASTA files.ProFASTA is developed as part of the computational toolbox for theMass Spectrometry Facilityat the Max Perutz Labs (University of Vienna).Similar projectsIf ProFASTA doesn't meet your requirements, consider exploring these alternative Python packages with a focus on protein-containing FASTA files:fastapyis a lightweight package with no dependencies that offers FASTA reading functionality.protfastais another library with no dependencies that provides reading functionality along with basic validation (e.g., duplicate headers, conversion of non-canonical amino acids). The library also allows writing FASTA files with the ability to specify the sequence line length.pyteomicsis a feature-rich package that provides tools to handle various sorts of proteomics data. It provides functions for FASTA reading, automatic parsing of headers (in various formats defined at uniprot.org), writing, and generation of decoy entries. Note that pyteomics is a large package with many dependencies.Usage exampleThe following code snippet shows how to import a FASTA file containing UniProt protein entries, retrieve a protein record by its UniProt accession number and print its gene name:>>>importprofasta>>>>>>fasta_path="./example_data/uniprot_hsapiens_10entries.fasta">>>db=profasta.db.ProteinDatabase()>>>db.add_fasta(fasta_path,header_parser="uniprot")>>>protein_record=db["O75385"]>>>print(protein_record.header_fields["gene_name"])ULK1RequirementsPython >= 3.9InstallationThe following command will install the latest version of ProFASTA and its dependencies from PyPi, the Python Packaging Index:pip install profastaTo uninstall the ProFASTA library use:pip uninstall profastaPlanned featuresMain requirementsparse FASTA fileparse FASTA headerbuilt-in parser that never failsbuilt-in parser for uniprot formatallow user defined parserwrite FASTA file
-[x] allow custom FASTA header generationAdditional featuresread multiple FASTA files and write a combined fileadd protein records to an existing FASTA filegenerate decoy protein records by reversing the sequenceadd decoy protein records to an existing FASTA filevalidate FASTA file / FASTA records
Package: prof-checker
pro-checkerProf-checkerThis is wrapper overprofanity-checkbyvzhou842prof-checkeris meant to work the same way as other python linters work. Depending on how big your codebase
is, it might take quite a bit of time to run this. I would not recommend running it on every PR.Installation$ pip install prof-checkerRough benchmarksAs this is wrapper aroundprofanity-checkthe performance greatly depends on the package. These benchmarks
are only rought estimates.FilesLoCTime (ms)500250K7230224.3K12007020.6K1510UsageFor helpprof_checker -hTo checktest.py:prof_checker test.pyTo check all files in directory:find . -name \*.py | xargs prof_checkerTo override threshold:prof_checker -P 0.5 test.py
Package: profcomff-parse-lib
ОписаниеЭтот модуль предназначен для парсинга расписания с сайта физфака и загрузки его в базу данных.ИспользованиеСперва нужно отредактировать файл.env. Параметр token - токен авторизации в тестовую БД. Затем указать параметры соеднинения (host, database ...) для взятия данных
из таблицы, имитирующей сайт расписания (это временная мера, поскольку летом расписание физфака пусто).token="some_token"host="localhost"database="postgres"user="postgres"password="postgres"Далее нужно отредактировать сам скриптmain.py, указав дату начала периода загрузки и конца:be=datetime.datetime.now()begin=be.strftime("%m/%d/%Y")en=datetime.datetime.now()+datetime.timedelta(days=2)end=en.strftime("%m/%d/%Y")После этогоmain.pyможно запускать.
Package: professional-python-exercises-2-githubcli
Exercise2: GitHub Command line interface (CLI)PurposeThis is the second exercise for the professional python course. The purpose is to build a proper CLI using the github API.
The task is (copied from the professional python course):Create a python program to interact with GitHub and retrieve data.
Add the following commands:Count all stars of all repos of yourself, a specified user or an organization.Print out details about yourself, a user or organization.
Allow a nicely printed format as default and offer output as json too.One of the following:(easy) Modify your user description by adding a tea emoji and a heart.(difficult) Set your user status (top-right when clicking on username)
to a tea emoji with the message "Drinking Tea".Focus points:End-users will use your program so focus on usabilityIntegrate previous lessons as much as it makes senseQuickstartInstallingThis repository uses poetry to install the dependencies and taskfile to execute the most basic functionality.
After having installed taskfile onto your system you can simply usetask installto install the necessary dependendcies into a virtual environment. 4
Besides the dependencies you will need a GitHub Access Token to use this program. Get ithere.UsagePromttaskto get a list of the tasks availabe and go for one of the options:$task
task:Availabletasksforthisproject:
*build:Buildstheputhonpackage
*docs-publish:Publishthedocumentationtogh-pages
*docs-serve:Servethedocumentationlocally
*getdetails:Getdetailsofagivenuserandprintittoconsole
*getdetailsjson:Getdetailsofagivenuserinjson
*install:Installsthedependeciesbasedonthepoetryfile
*lint:Runsformattingandlinting
*sbom:GeneratetheSoftwareBillofMaterials
*stars:Getthenumberofstarsofthegivenuser's repositories* starsjson:            Get the number of stars of the given user'srepositoriesinjsonformat
*status:Setthestatusofyourusertosomethingdelicous
*test:RunstestsonthecodeThe other way of using this tool is directly with calling etiher poetry or native python to start on of the functions, likepoetryrunpythonprofessional_python_exercises_2_githubcli/github_cli.pysetstatus
Package: professionals
Professional Software Development KitData structures and functions to work with professional data.
Built for use withprofessional.appand associated APIs.
Using this SDK, you will be able to reflect professional.app API queries directly into usable data structures. You'll also be able to build data analytics pipelines withesam.Built with andeonsusing theeons build systemInstallationpip install professionalsUsageCheck out theesam docson how to do data analysis witheons.More examples coming soon.
Package: professore
Installpipinstall.Upgradepipinstall-U.Usageusage:professor.py[-h]-uURL[-c][-dDOWNLOAD]optionalarguments:-h,--helpshowthishelpmessageandexit-uURL,--urlURLUrl-c,--codePrinttagsinntertext-dDOWNLOAD,--downloadDOWNLOADDownloadremotelibs,mustbeapathScanprofessore--urlhttps://github.comShow<link>and<script>tags htmlprofessore--urlhttps://github.com--codeDownload remote linksFile url link is used as filename, replacing/with#professore--urlhttps://github.com--download/tmp/github
Package: profet
profetA Python 3protein structurefetcher. Retrieves the cif or pdb files from either the RCSB Protein Data Bank (PDB, usingpypdb) orAlphafoldusing theUniprotID.InstallationInstall using pip:$ pip install profetTo install the development version, which contains the latest features and fixes, install directly from GitHub using$ pip install git+git://github.com/alan-turing-institute/profetTest the installation, navigate to the root directory and run$ pytestThis code has been designed and tested for Python 3.UsageThis package can be used to retrieve the available protein structure from any Uniprot ID.TheFetcherclass can search the IDs in both PDB and Alphafold, and saves the search results in a dictionary.get_filereturns the structure corresponding touniprot_idin the definedfiletype:(default as'pdb', option as'cif'), searching first in the defaulted databasedb(default as'pdb', option as'alphafold').
The files can be saved to a local file withfilesave.set_default_dbchanges the default database into the given one between'pdb'and'alphafold'.Runsearch_history()to see the search history of the fetcher.Issues and Feature RequestsIf you run into an issue, or if you find a workaround for an existing issue, we would very much appreciate it if you could post your question or code as aGitHub issue.