Saravanan666/pypi-org huggingface dataset:
Package: prenoms
prenomsGénérateur de prénomsInstallationLe script est disponible sur PyPI. Pour installer avec pip :pipinstallprenomsUtilisationprenomspeut être utilisé via la ligne de commande ou importé comme paquet Python.Utilisation depuis la ligne de commandePour utiliserprenomsdepuis la ligne de commande :$prenoms
JeanDupontUtilisation du paquet PythonQuelques exemples d'utilisation :>>>importprenoms>>>prenoms.get_nom_complet()'Jean Bernard'>>>prenoms.get_prenom()'Lucie'>>>prenoms.get_nom()'Lefevre'>>>prenoms.get_prenom(originality=Originality.VERY_RARE,gender=Gender.MALE,year=1995)'Hortensius'>>>prenoms.get_nom(originality=Originality.VERY_RARE,year=1995)'Wazner'Le paramètreoriginalitypermet de spécifier l'originalité du nom ou du prénom. Quatre options sont disponibles :COMMONUNCOMMONRAREVERY_RARELe paramètregenderpermet de spécifier le genre du prénom, les options sontMALEouFEMALE.Le paramètreyearpermet de spécifier la décennie de recherche pour un nom ou un prénom. Si ce paramètre est égal àNone, l'entièreté des données est utilisée.Détails sur les donnéesLes prénoms sont ceux de personnes nées entre 1900 et 2018 en France (hors Mayotte), regroupés par décennies. Ils doivent avoir été donnés au moins 3 fois par an pour figurer dans la liste globale, et 10 fois par décénnie pour apparaître dans les listes décennales.Les noms de famille (anciennement noms patronymiques) sont ceux de personnes nées entre 1891 et 2000 en France métropolitaine (sauf celles nées avant 1946 et décédées avant 1972) et celles nées entre 1900 et 2000 dans un département d'outremer. Un nom doit apparaître 10 fois par décénnie pour apparaître dans les listes décennales.Un nom ou un prénom est commun s'il apparaît plus de 500 fois dans une liste, peu commun s'il apparaît plus de 100 fois, rare s'il apparaît plus de 50 fois, et très rare sinon.LicenceCe projet est sous licence MIT.Les données dans le dossierprenoms/dataproviennent de l'INSEE et appartiennent au domaine public.
Package: pre-notifier
NotifierThis tool provides you ability to send yourself information about looong executed command when it is done. Information will be sent using Telegram Bot, which you can generate for yourself with Telegram BotFather.PrerequisitesFirst of all register your bot at@BotFather. He will give you back your bot Telegram TOKEN. We'll use it laterGet your Telegram ID. You can get it from@userinfobot. Save it too.Installation and running>>>pipinstallpre_notifier
>>>pre_notifierconfig--telegram_id=YOUR_TELEGRAM_ID--token=YOUR_BOT_TOKEN
>>>pre_notifiernotify[your_command_here]
Package: prentr
prentr :anger:ANTI PARENTHESES GANG :pinching_hand:The..... one-liner behind it (Technically)WhitepaperAs you've seen so far, this project took a lot of time and effort to maximize its performance and abilities.
    And behind every great project, there are some genius individuals that worked hard for a really long time to
    ship this project to the rest of the world. If you got a minute please check this whitepaper that took us a
    couple of months to write and perfect, to make it easy for you and the rest of the people to understand.Whitepaper Link:WebsiteInstallationpip install git+https://github.com/DmarZX2/prentr.gitUse CasesSay goodbye to parentheses :nauseated_face:What is this
Package: prenv
prenvportablereproducibleenvironments for Linux applicationsWhat is prenv?prenv is a tool that allows regular users to deploy applications in a portable and reproducible way.prenv does not require root privileges to be installed or used.
The focus of prenv is to provide a tool that can be used to run applications in a reproducible way, without the need to install dependencies on the host system.prenv doesNOTaim to provide full isolation between environemtns and the host, for such kind case please consider using Docker or Podman.Which applications can I run with prenv?Currently you can run applications available to install from the following distributions:DistroStatusCommandExec. timeNotesAlpine Linux✓prenv run alpine:3.17 true2openSUSE✓prenv run opensuse:tumbleweed true5Void Linux✓prenv run voidlinux true5Amazon Linux✓prenv run amazonlinux true6Springdale Linux✓prenv run springdalelinux:9 true8AlmaLinuxOS✓prenv run almalinux:9 true10CentoOS✓prenv run centos:9-Stream true10Fedora Linux✓prenv run fedora:37 true10Rocky Linux✓prenv run rockylinux:9 true10Arch Linux✓prenv run archlinux true16The Linux images are sourced fromhttps://linuxcontainers.org/and are based on the official images from the respective distributions.System RequirementsLinux or WSL2 (Kernel version >=4.18)Python 3.8, 3.9, 3.10 or 3.11How to installpipinstallprenvHow to useList all the distributions available from the LXC project# Check the list of supported distro names in the table aboveprenvlxclistRun an in-memory single run containerRun a shell in an Alpine Linux containerprenvrunalpine:3.17Check the apk version:prenvrunalpine:3.17"apk --version"Create an in-memory multi run containerprenvcreatealpine:3.17Execute a command in a containerprenvexecutealpine:3.17"apk --version"
Package: preon
preon (PREcision Oncology Normalization)preon is a fuzzy search tool for medical entities.InstallationYou can install preon with PyPi:python -m pip install preonExamplesLet's first import the normalizer and EBI drug names with CHEMBL ids.>>>frompreon.normalizationimportPrecisionOncologyNormalizer>>>frompreon.drugimportstore_ebi_drugs,load_ebi_drugsPlease download theEBI compound CSV fileand store it as a local resource. This step only has to be performed when the resource file is created or updated.>>>store_ebi_drugs("/Users/Username/Downloads/compounds.csv")Next, we can fit the normalizer with the drug names and ids as its reference data.>>>drug_names,chembl_ids=load_ebi_drugs()>>>normalizer=PrecisionOncologyNormalizer().fit(drug_names,chembl_ids)We can now search for drug names and retrieve their CHEMBL ids. Let's search for the cancer drug "Avastin".>>>normalizer.query("Avastin")(['avastin'],[['CHEMBL1201583']],{'match_type':'exact'})As a result for our query, we get list of matching normalized drug names (in this case['avastin']), a list of associated CHEMBL ids for every returned drug name[['CHEMBL1201583']]and some meta information about the matching{'match_type': 'exact'}. We can also search for multi-token drug names like "Ixabepilone Epothilone B analog" and find CHEMBL ids for the relevant tokens.>>>normalizer.query("Ixabepilone Epothilone B analog")(['ixabepilone'],[['CHEMBL1201752']],{'match_type':'substring'})We find the relevant drug name['ixabepilone']and preon provides the meta information that the matching is based on a substring. On default, preon only looks for 1 matching token. It can also look for n-grams by setting then_gramsparameter in the query method. Let's take a harder example, say "Isavuconazonium", but misspell it as "Isavuconaconium".>>>normalizer.query("Isavuconaconium")(['isavuconazonium'],[['CHEMBL1183349']],{'match_type':'partial','edit_distance':0.067})preon finds the correct drug "Isavuconazonium" and provides the meta information that it is a partial match with 7% distance. It returns drug names with a distance smaller than 20% on default. In order to change this parameter, set thethresholdargument in the query method.In a similar fashion you can normalize cancer types or genes. we provide gold standards for preon with which we test it. For more detail, see the examplenotebooks. We also use preon in practice to normalize and integrate medical data in thePREDICTproject.CitationThe preon package is actively maintained, updated and intended for application. If you use it in your scientific publication, we would appreciate the followingcitation:@article {preon2023,
	author = {Arik Ermshaus and Michael Piechotta and Gina R{\"u}ter and Ulrich Keilholz and Ulf Leser and Manuela Benary},
	title = {preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology},
	year = {2023},
	doi = {10.1101/2023.05.22.540912},
	journal = {bioRxiv}
}
Package: prep2dbt
prep2dbtTableau Prepで作成されたフローをdbtモデルに変換するツールです。ドキュメントhttps://hogehogeソースコードhttps://github.com/t0momi219/prep2dbtサンプル（Superstoreを本ツールでdbtに変換）https://t0momi219.github.io/prep2dbt-demo/#!/overviewモチベーションTableau PrepはGUIで簡易なデータパイプラインを作成・操作することに焦点が当てられたツールです。フローが複雑化してきた場合や複数人でフローを利用する場合には、これを維持・管理するために大きな労力を支払わなければなりません。一方、dbtはSQLを中核にしたデータパイプラインを構築するためのツールとして知られています。コードのモジュール化、テスト、自動ドキュメント作成など、共同でパイプラインを管理するために必要となる様々な機能が提供されます。このツールは、Tableau Prepの処理自体をdbtへ移植する、あるいはドキュメント化だけをdbtに頼るといった部分的な利用を支援するために作りました。機能dbtモデル変換フローファイルをdbtモデル（SQLファイルとYML構成ファイル）に変換します。[!NOTE]dbtプロジェクト自体はこのツールでは作成しないため、プロジェクトは別で用意してください。初めてdbtを利用する方は、クイックスタートでプロジェクトの作り方を確認できます。[!caution]Tableau Prepのフローファイルからは、変換に十分な情報が取得できない場合があります。
（例えば、データソースがどのようなカラムを持っているのかといった情報がファイルに残っていません。）
よって、変換後のSQLが完璧に動作する保証はありません。また、多くの場合でTableau PrepのフローをそのままSQLにマッピングするだけのモデルは、パフォーマンスや再利用性の観点から推奨しません
あくまで開発の土台として利用してください。統計情報の出力フローが利用している機能の一覧と、統計情報を出力します。これはフロー移行前に難易度や見積もりの測定を行ったり、リファクタリングのヒントとして活用するために利用できます。インストールpipからインストールできます。$pipinstallprep2dbtまたは、このプロジェクトをローカルでパッケージとして実行します。$gitclonehttps://github.com/t0momi219/prep2dbt.git
$python-mprep2dbtconvert-f/path/to/file.tfl
Package: prepaid-sim-py-client-xethhung12
Prepaid Sim Py ClientPython package to retrieve prepaid sim remaining data usage.Examplefromprepaid_sim_py_client_xethhung12.hk.cslimportremainingAmountOfDataif__name__=='__main__':mobileNo="{mobileNo}"password="{password}"print(remainingAmountOfData(mobileNo,password))Buildrm-frdist
python-mbuildUploadrm-frdist
python-mbuild
twineuploaddist/*
Package: prepare-assignment
Prepare assignmentPrepare assignment is a GitHub Actions inspired helper tool to prepare assignments at Fontys Venlo. The goal is to define jobs inside theprepare.ymlthat indicate how to convert a solution project into a student project.DependenciesGitPython >=3.8ExampleFirst we need to have tasks available that can be executed. Take for example a look at theremovetask.The tests use atestproject, which contains an example of aprepare.yml, see below for convenience.name:Test projectjobs:prepare:-name:remove outuses:removewith:input:-"out"-"out.txt"force:truerecursive:true-name:codestripperid:codestripperuses:codestripperwith:include:-"**/*.java"-"pom.xml"working-directory:"solution"verbosity:5-name:Test a run command with substitutionrun:echo '${{ tasks.codestripper.outputs.stripped-files }}' > out.txtFor people familiar with GitHub Actions this should look very familiar. We have jobs that indicate what should happen to prepare an assignment. The tasks are defined in their own repositories, if theusestag doesn't have a username/organization, it will default toprepare-assignment. So for example theremovetask uses the following repository:prepare-assignment/removeTasksThere are three different kind of tasks available:Run tasks: these execute a shell command (for now only bash is supported)Python tasks: these execute a python scriptComposite tasks: these combine multiple tasks into oneCustom tasksIt is possible to create custom (python/composite) tasks.Create a repositoryDefine the properties of the task intask.yml, these includeid*: unique identifiername*: name of the taskdescription*: short descriptionruns*: whether it is a python or composite taskinputs: the inputs for the taskoutputs: the outputs that get set by the taskValidate that the task definition is correct against thejson schemaIf python task, create a script that implements desired functionality
Package: prepare-commit-msg
prepare-commit-msgYet another tool for theprepare-commit-msggit hook. To use withpre-commitFor pre-commit: seehttps://github.com/pre-commit/pre-commitWhat's all aboutIn many projects some sort of ticket system is used. The commit messages are used for updating the status of the ticket by "linking" the ticket name, which it is nothing more than mentioning the ticket name, in the commit message.This allows for using a jinja2 template for formatting your commit message.You can configure this with the following commandline options:-t/--template- absolute path to the template. Default value:
name of bundled template:prepare_commit_msg_append.j2file-b/--branch- may be specified multiple times to exclude branches
from formatting the commit message. Default values:main, master-p/--pattern- may be specified multiple times to include branches
for formatting the commit message. Default value:(?<=feature/).*Usage# .pre-commit-config.yaml filedefault_install_hook_types:-pre-commit-prepare-commit-msgrepos:-repo:https://github.com/davidmpaz/prepare-commit-msgrev:v0.0.1hooks:-id:prepare-commit-msgname:Prepare Commit Messagedescription:Add statistics and a message template to the commit message.stages:[prepare-commit-msg]args:[-t,prepare_commit_msg_prepend.j2,-b,main,-b,master,-b,test,-b develop,-p,'(?<=feature/).*',-p,'(?<=release/).*']or if having the package installed via pip:# .pre-commit-config.yaml filedefault_install_hook_types:-pre-commit-prepare-commit-msgrepos:-repo:localhooks:-id:prepare-commit-msgname:Prepare Commit Messagedescription:Add statistics and a message template to the commit message.stages:[prepare-commit-msg]entry:prepare-commit-msgargs:[-t,prepare_commit_msg_prepend.j2,-b,main,-b,master,-b,test,-b develop,-p,'(?<=feature/).*',-p,'(?<=release/).*']
Package: prepareImage
PrepareImage - Custom Your Our Image DatasetFlexible and Easy. We perform a package that help you build your own image dataset. Specially, it's easy to add your own prepare functionInstallpip instal prepareImageHow to use1. Fork this repo and custom it!2. Class datasetComing soon!This is what make this package powerful.3. Run directly on fileprepare_data.pyRun on TerminalIf you already installed the package:python3-mprepareImage.prepare_dataor download fileprepare_data.pyfrom this repo and run:python3prepare_data.pymodein["split", "combine", "line", "pos", "all", "gray", "look_alike", "without_edge"]:combine: combine input, output images, then split to train/validation/test setline: add line to input imagesall: line then combineExample:python prepare_data.py --mode all \
--image_dir ../training_data/lhq_256 \ 
--caffemodel hed_pretrained_bsds.caffemodel \
--pos2_dir ../training_data/new_data/pos2 \
--pos 2 \
--grayscale_dir ../training_data/new_data/grayscale \
--line_dir ../training_data/new_data/edge \
--train_dir ../training_data/new_data/train \
--test_dir ../training_data/new_data/test \
--val_dir ../training_data/new_data/val \
--output_dir ../training_data/new_data/grayscale \
--combine_dir ../training_data/new_data/combineSplit Data:python prepare_data.py --mode split \
--train_dir ../training_data/new_data/train \
--test_dir ../training_data/new_data/test \
--val_dir ../training_data/new_data/val \
--combine_dir ../training_data/new_data/combineRun on Jupyter Notebook%%bashpython3prepare_data.py
Package: preparenovonix
Preparing Novonix Datapreparenovonixis a Python package that handles common issues encountered in data files generated with a range of software versions from theNovonixbattery-testers. This package can also add extra information that makes easier coulombic counting and relating a measurement to the experimental protocol. The package provides a master function,prepare_novonix, that can run at once the cleaning and adding derived information, with flexibility to choose only some features. There is a separate function to simply read a column by its given name.ExampleTheexample.pyruns over the given example data, producing a new file and a plot that compares the original and the prepared data. To run this
example, simply type:python example.py.Requirements and InstallationThis code has been developed in Python 3.7.1 and it is compatible with Python above 3.5 versions. The code has been tested to run in Windows, OSX and Linux operating systems.This code uses numpy as specified in docs/requirements.txt. The ploting routine from theexample.pyalso requires the use of matplotlib.The code can be run directly from a cloned GitHubrepositoryor it can also be installed as a pythonpackagethrough pip:pip install preparenovonixThe functions in the package can be used after importing novonix_add, for example as follows:importpreparenovonix.novonix_addasprepThe code has been tested within Matlab R2018a.Runningpreparenovonixcode from MatLabTo run the code from Matlab, Python will need to be installed including the packages: numpy, pathlib and preparenovonix (see details above). Ensure that Matlab can see your installation of Python by runningpyversion. If this is not the case then: (i) find where your Python executable is (within a python terminal you can do this by typing:import os, sys ; os.path.dirname(sys.executable)), (ii) type  within your MatLab interpreterpyversion [path to python executable]and (iii) check that now the path to Python is recognised withpyversion. Make sure thatIn your code you can add the following lines that will call the master function from the package, catching exceptions:trypy.preparenovonix.novonix_prep.prepare_novonix(file_to_open,...pyargs('addstate','True',...'lprotocol','True',...'overwrite','True',...'verbose','False'));catchee.messageif(isa(e,'matlab.exception.PyException'))e.ExceptionObjectendendCompatibilityThis code has been tested with data generated by different versions of
theNovonixsoftware. If you encounter issues running the code for
any version of Novonix software report an issue. Note that an example
file will be needed in order to improve the code. List of theNovonixsoftware. If you encounter issues running the code for any version of Novonix software report an issue. Note that an example file will be needed in order to improve the code.
List of theNovonixsoftware versions the code has been tested against:3.0.2.33.0.2.1TO2.0.0.71.9.4.0
Package: preparepack
PreparePackRepository that prepares directories and files for programming and uploading Python packages.Installation:$ pip install preparepackUsage:$ prepack packageNameReplace packageName with the name of your package.
It will create the package directory named "packageName_package",
the "setup.py" file, a subdirectory named "packageName" containing
the __init__.py file and the package file named "packageName.py" .$ buildpackUsing this command you build the needed files before uploading your repository, such as .tar.gz and .whl .
When you use this command, you must be in the same directory where the setup.py file is located.$ uploadpypiCalling this command, you start the procedure for uploading a package on Pypi.
Make sure to be in the the directory which contains the dist folder.
Make also sure to have a valid Pypi account, to remember the credentials and that your package
has a valid name (not taken yet) and a valid version (not uploaded yet).All commands have "--help" option.
Package: prepare-toolbox
ToolboxThis repository contains helper functions for:Interacting with a run inprepare_assignmentRetrieving inputSetting outputFinding files based on globsCreating zip filesInteracting withprepare_assignmentTo help aprepare_assignmentaction interact with a run, a couple of helper functions are defined incore.py.To retrieve input use theget_inputfunction, this has the following parameters:key: str: The key of the input to retrieve, this is the same as the key as defined in theaction.ymlrequired: bool= False: if true and the key is not present it will raise an exceptiontrim_whitespace: bool: if true it will automatically trim whitespace from the retrieved valueTo set output that can be used by other actions in the run, use theset_outputfunction. This function has the following parameters:name: str: the key that other actions can use to retrieve the valuevalue: Any: the valueFurthermore it defines helper methods for logging:set_failed: log exception/message with levelERRORand exit the processset_error: log exception/message with levelERRORset_warning: log message with levelWARNINGinfo: log message with levelINFOdebug: log message with levelDEBUGFinding filesA common task for actions is to find files based on a glob. To make this repetitive task easier a helper function is defined infile.py. The functionget_matching_filesreturns a list of paths (as strings) that match the files, given the parameters. The following parameters are available:included: Union[str, List[str]]: Glob(s) that should be matchedexcluded: Union[str, List[str], None]: Glob(s) that should be excluded from being matched. I.e. if a path matches theincludedglob, it should not be processed if it also matches theexcludedglob. Default:Nonerelative_to: : Union[str, None]: Set relative path from where the globs should be matched. IfNonethe current working directory is used. Default:Noneallow_outside_working_dir: bool: Allowrelative_toto be outside the current working directory. Allow the matched glob(s) to be outside therelative_todirectory. Default:Falserecursive: bool: If true the glob should recurse directories. Default:TrueCreating zip filesTo help with creating zip files thezip.pyadds a helper methodcreate_zipto easily create a simple zip archive. It takes the following parameters:name: str: name of the archivefiles: List[str]: paths to the files to includeoutput: Optional[str]: the output directory to write to (default to current working directory). Default:None
Package: prependnewline
prependnewlineThis extension is a third party extension for python markdown. This was developed after an experience with python markdown
where some lists are ignored if an empty line or paragraph isn't inserted before the list.The extension prepends a new line to a list if marked as a paragraph by the markdown.See the python markdown documentation for more information.
Use it in any personal or commercial project you want.Current Version: 1.2Example Markdown:This is a paragraph
* Run through albums.Output:This is a paragraphRun through albums.Usage / Setup:pip install prependnewlinemd = markdown.Markdown(extensions=['prependnewline'])converted_text = md.convert(text)