Saravanan666/pypi-org huggingface dataset:
Package: pcstf
#pcstfA library for solving the prize-collecting Steiner forest (PCSF) problem on graphs.InstallationOption 1: pippip install pcstfOption 2: ManualThe core library has no dependencies besides a basic build system for C++11.
Both g++ and clang are currently supported.
The Python wrapper requires a functioning Python build system.Compile the python wrapper with the supplied makefile:make pcst_fast_pyYou can then import the package viaimport pcst_fast.UsageThepcst_fastpackage contains the following function:vertices, edges = pcst_fast(edges, prizes, costs, root, num_clusters, pruning, verbosity_level)The parameters are:edges: a 2D int64 array. Each row (of length 2) specifies an undirected edge in the input graph. The nodes are labeled 0 to n-1, where n is the number of nodes.prizes: the node prizes as a 1D float64 array.costs: the edge costs as a 1D float64 array.root: the root note for rooted PCST. For the unrooted variant, this parameter should be -1.num_clusters: the number of connected components in the output.pruning: a string value indicating the pruning method. Possible values are'none','simple','gw', and'strong'(all literals are case-insensitive).'none'and'simple'return intermediate stages of the algorithm and do not have approximation guarantees. They are only intended for development. The standard GW pruning method is'gw', which is also the default.'strong'uses "strong pruning", which was introduced in[JMP00]. It has the same theoretical guarantees as GW pruning but better empirical performance in some cases. For the PCSF problem, the output of strong pruning is at least as good as the output of GW pruning.verbosity_level: an integer indicating how much debug output the function should produce.The output variables are:vertices: the vertices in the solution as a 1D int64 array.edges: the edges in the output as a 1D int64 array. The list contains indices into the list of edges passed into the function.
Package: pcst-fast
pcst_fastA library for solving theprize-collecting Steiner forest (PCSF)problem on graphs.
The underlying algorithm is based on the classical Goemans-Williamson approximation scheme.
Our variant provably runs in nearly-linear time and has a factor-2 approximation guarantee.
The following paper contains details about the algorithm:A Nearly-Linear Time Framework for Graph-Structured SparsityChinmay Hegde, Piotr Indyk, Ludwig Schmidt
ICML 2015InstallationOption 1: pippip install pcst_fastThis may not work for some versions of python on some operating systems.Option 2: manualThe core C++ library has no dependencies besides a basic build system for C++11.
Both g++ and clang are currently supported.
The Python wrapper requires a functioning Python build system.Clone the repository and compile the python wrapper with the supplied makefile:make pcst_fast_pyYou can then import the package viaimport pcst_fast.UsageThepcst_fastpackage contains the following function:vertices, edges = pcst_fast(edges, prizes, costs, root, num_clusters, pruning, verbosity_level)The parameters are:edges: a 2D int64 array. Each row (of length 2) specifies an undirected edge in the input graph. The nodes are labeled 0 to n-1, where n is the number of nodes.prizes: the node prizes as a 1D float64 array.costs: the edge costs as a 1D float64 array.root: the root note for rooted PCST. For the unrooted variant, this parameter should be -1.num_clusters: the number of connected components in the output.pruning: a string value indicating the pruning method. Possible values are'none','simple','gw', and'strong'(all literals are case-insensitive).'none'and'simple'return intermediate stages of the algorithm and do not have approximation guarantees. They are only intended for development. The standard GW pruning method is'gw', which is also the default.'strong'uses "strong pruning", which was introduced in[JMP00]. It has the same theoretical guarantees as GW pruning but better empirical performance in some cases. For the PCSF problem, the output of strong pruning is at least as good as the output of GW pruning.verbosity_level: an integer indicating how much debug output the function should produce.The output variables are:vertices: the vertices in the solution as a 1D int64 array.edges: the edges in the output as a 1D int64 array. The list contains indices into the list of edges passed into the function.PerformanceThe following paper contains many results on standard PCST benchmark instances:A Fast, Adaptive Variant of the Goemans-Williamson Scheme for the Prize-Collecting Steiner Tree ProblemChinmay Hegde, Piotr Indyk, Ludwig Schmidt
Workshop of the 11th DIMACS Implementation Challenge: Steiner Tree Problems, 2014On instances with up to 350,000 edges, the algorithm typically runs in under 2 seconds on a standard laptop computer from 2010.
Package: pcstools
pcstools - exploit toolkit for the PCS coursepcstools is an exploit development library heavily inspired byhttps://pwntools.com/.It supports both Python 2.7 and Python 3.3+.DocumentationOur documentation is available atpcstools.readthedocs.ioInstallationPcstools is pure Python library and should be supported on most
platforms. It should even work on non-linux platforms. However
Pcstools is currently being developed exclusively on debian-based
systems, so bugs might be encountered. If this happens, be sure to
file an issue!To install on debian-based systems, do the following:# For usage with Python 2.7aptupdate
aptinstallpython-pip
pip2install--upgradepcstools# For usage with Python 3.3+aptupdate
aptinstallpython3-pip
pip3install--upgradepcstoolsHow is this project related to pwntoolsPcstools is not directly related to pwntools (though some of the developers
overlap), however the goals of the two projects are very different.Pwntools tries to be the best possible exploitation library with a particular
focus on CTFs. While itdoesprioritize readability, learnability and doing
things the "normal python way", those values are occationally sacrifized in the
name of productivity (for the exploit developer), performance or advanced
features.This is not the case for Pcstools. Pcstools tries to be a normal python library
that iseasy to learnandeasy to understand. The target user is
someone new to exploitation that just wants to avoid too much boilerplate. We
have deliberately omitted many features, as we believe that they should not be
introduced until the basics have been covered.Why the name "Pcstools"?Pcstools has been developed with a particular target audience in mind: The
students taking the course "Proactive Computer Security" at the Department of
Computer Science at Copenhagen University.
Package: pcs-to-vmix
Play Cricket Scorer to vMix & Companion interfaceReadsPCS (Play Cricket Scorer)data files and updatesvMixtitle data for both legacy titles and GT Designer titlesIntro to GT videoStingersRC:https://test.pypi.org/project/pcs-to-vmix/Formal:https://pypi.org/project/pcs-to-vmix/python interfaceInstallingThis should do the job for you:runme.cmdInstall & ConfigurationInstallAddinghttps://test.pypi.org/legacy/to install rc rather than formal:pip3 config list -vSimply install withpip install pcs-to-vmixor from test PyPi withpip install -i https://test.pypi.org/simple/ pcs-to-vmix
Package: pcs-utils
OverviewSelenium scraper for fetching information from the ProCyclingStats page.SetupSetting up virtualenvpython3 -m pip install virtualenv
virtualenv .
source bin/activateInstalling requirementspip install -r requirements.txt
sudo apt-get install xvfb
sudo apt-get install -y chromium-chromedriverUsageimport pcs_scraper

pcs_scraper.scrap_teams_by_year(2021)
pcs_scraper.scrap_stage('tour-de-france', 2020, 2)
pcs_scraper.scrap_stage_gc('tour-de-france', 2020, 2)
pcs_scraper.scrap_final_gc('tour-de-france', 2021)
pcs_scraper.scrap_final_points('tour-de-france', 2021)
pcs_scraper.scrap_final_kom('tour-de-france', 2021)
pcs_scraper.scrap_final_youth('tour-de-france', 2021)
pcs_scraper.scrap_final_teams('tour-de-france', 2021)Publishing to PyPyhttps://packaging.python.org/en/latest/tutorials/packaging-projects/
Package: pcSVM
pcSVM is a framwork for support vector machines. Support Vector Machines is a
new generation of learning algorithms based on recent advances in statistical
learning theory, and applied to large number of real-world applications, such as
text categorization, hand-written character recognition. Support Vector Machines
outperform other classifiers as artificial neural networks in most situations.
The core of the framework is written in c++, I used boost.python for python support.
Package: pctl-scale
pctl_scaleScale a variable into an open interval (0,1) whereas values within a given lower and upper percentile maintain a linear relation, and outlier saturate towards the interval limits.This scaling functions was developed withratio-scaledata in mind
(i.e. it has a natural zero value).Table of ContentsInstallationUsageCommandsSupportContributingInstallationThepctl_scalegit repois available asPyPi packagepip install pctl_scaleUsageCheck theexamplesfolder for notebooks.CommandsCheck syntax:flake8 --ignore=F401Run Unit Tests:python -W ignore -m unittest discoverRemove.pycfiles:find . -type f -name "*.pyc" | xargs rmRemove__pycache__folders:find . -type d -name "__pycache__" | xargs rm -rfUpload to PyPi with twine:python setup.py sdist && twine upload -r pypi dist/*DebuggingNotebooks to profile python code are in theprofilefolderSupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow. Create a branch, add commits, andopen a pull request.
Package: pct-processamento-imagens
Digital Innovation OnePacoteimage-processing-package:MÃ³dulo Processing:CorrespondÃªncia de histograma;Similaridade estrutural;Redimensionar imagem;MÃ³dulo Utils:Ler imagem;Salvar imagem;Plotar imagem;Resultado do grÃ¡fico;Plotar histograma;AutorJoÃ£o Vitor
Package: pc-tracker
PC tracker - clientAboutPC tracker - client is project that focuses on recording activity that happens on PC Windows OS.Currently it trackslocal ip address of PC in usename of user that has logged inwhat application were in usetitle of those applicationshow much time did user spend on per applicationhow much time did user spend in idle state during that time on app and idle periods (time and date + duration)Data can be savedon local PC where data is gatheringinside local network on some shared network foldercurrently it doesn't support saving activity data on remote pc that is not part of your local networkInstallationIf you don not have Python 3 installed, please do that first. I'm using 3.7 version at this moment.Install virtualenv library if you didn't already. You can do that by writing following line in command linepip3.7 install virtualenv. Be sure to change the version number on pip to the version number of python that you have installed.Deploy virtual environmentpy -3.7 -m virtualenv Envs/myproject. By default location should beC:\Users\NameOfUserThatIsLoggedIn\Envs.Activate virtual environment that should be on location where you deployed virtual environment. it should be something likeC:\Users\NameOfUserThatIsLoggedIn\Envs\myproject\Scripts\activate.bat.Install pc_tracker usingpip install pc-trackerSetup - for AdministratorRun CMD as administratorActivate virtual environment same way it is explained in Installation section in step 4.Enter commandmanage.py config. As admin you will have to fill down 2 options:path where the data of all pc users on local network will save data.path to main settings file where all pc users will pull latest settings admin had setSave form data and you are ready for next step.Setup - ClientRun CMDActivate virtual environment same way it is explained in Installation section in step 4.Enter commandmanage.py config. After you run command and if option for specifying path to save data is visible then that user has admin privileges.
If you do not want to override existing path and settings you should pressCTRL+Cand then write commandmanage.py configclClient with no admin privileges should be able to see only form with option to specify directory path where the global settings are located.After specifying path save form data.Run activity monitoringRepeat steps 1 and 2 from Setup sectionEnter commandmanage.py runclientAnd that's it! Data is now storing on specified location and it is ready for further processing
Package: pcu-io
# pcu_io (IO management for PCU project)IO management for PCU project.
From the path of a file (PDF or JSON), parse it in order to get its textual content.[Check PCU project][pcu].[pcu]:https://github.com/zevio/pcu_core## Usage in another projectIf you wish to import this module in another Python project, please install it :pip install pcu-ioThen, add this import line at the beginning of your Python file :from pcu_io import pcu_ioYou can now use pcu_io’s functions, for example :pcu_io.getEquivalentTextfile(path/to/json/or/pdf/file)## TestTo test your installation, go to pcu_io/ directory and execute the Makefile with the following command line :make test
Package: pcu-json
# pcu_json (JSON parser for PCU project)JSON parser component for PCU project.
From the path of a json file, get its textual content.[Check PCU project][pcu].[pcu]:https://github.com/zevio/pcu_core## Usage in another projectIf you wish to import this module in another Python project, please install it :pip install pcu-jsonThen, add this import line at the beginning of your Python file :from pcu_json import json_parserYou can now use pcu_nlp’s functions, for example :json_parser.JSONParser(path/to/json/file)## TestTo test your installation, go to pcu_json/ directory and execute the Makefile with the following command line :make test
Package: pcu-keyphrase
pcu_keyphrase (Keyphrase extraction for PCU project)Keyphrase extraction component (kleis) for PCU project.
From a text, extract its keyphrases.Based on the keyphrase extraction algorithmkleis.Check PCU project.InstallationTo install requirements, go to pcu_keyphrase/ directory and execute the Makefile with the following command line :make initUsage in another projectIf you wish to import this module in another Python project, please install it :pip install pcu-keyphraseThen, add this import line at the beginning of your Python file :from pcu_keyphrase import pcu_keyphraseYou can now use pcu_keyphrase's functions, for example :pcu_keyphrase.extractKeyphrases(text)TestTo test your installation, go to pcu_keyphrase/ directory and execute the Makefile with the following command line :make test
Package: pcu-language
# pcu_language
Language detection component (langdetect) for PCU project.
From the path of a text file, detect the main language/all languages used within the text.Based on [langdetect][langdetect].[Check PCU project][pcu].[langdetect]:https://pypi.org/project/langdetect/[pcu]:https://github.com/zevio/pcu_core## InstallationTo install requirements, go to pcu_language/ directory and execute the Makefile with the following command line :make init## TestTo test your installation, go to pcu_language/ directory and execute the Makefile with the following command line :make test
Package: pcu-nlp
# pcu_nlpNLP pipeline component (spacy.io) for PCU projectBased on [spacy.io][spacy].[Check PCU project][pcu].[spacy]:https://spacy.io[pcu]:https://github.com/zevio/pcu_core## InstallationTo install requirements, go to pcu_nlp/ directory and execute the Makefile with the following command line :make init## Usage in another projectIf you wish to import this module in another Python project, please install it :pip install pcu-nlpThen, add this import line at the beginning of your Python file :from pcu_nlp import pcu_nlpYou can now use pcu_nlp’s functions, for example :pcu_nlp.spacyPipeline(text)## TestTo test your installation, go to pcu_nlp/ directory and execute the Makefile with the following command line :make test
Package: pcu-pdf
# pcu_pdf (Apache Tika parser for PCU project)PDF parser component (Apache Tika) for PCU project.
From the path of a PDF file, get its textual content.[Check PCU project][pcu].[pcu]:https://github.com/zevio/pcu_core## Usage in another projectIf you wish to import this module in another Python project, please install it :pip install pcu-pdfThen, add this import line at the beginning of your Python file :from pcu_pdf import pcu_pdfYou can now use pcu_pdf’s functions, for example :pcu_pdf.PDFParser(“path/to/pdf/file”)## TestTo test your installation, go to pcu_pdf/ directory and execute the Makefile with the following command line :make test
Package: pcurvepy
pcurvepyPrincipal curves implementation (Hastie '89) in PythonExample:data=pd.read_csv('test_data.csv')x=data.loc[:,('X1','X2')].to_numpy()# transform data to have zero meanx=x-np.mean(x,0)index=np.arange(0,len(x))p=pcurve.PrincipalCurve(k=5)p.fit(x)plt.scatter(x[:,0],x[:,1],alpha=0.25,c=index)plt.plot(p.p[:,0],p.p[:,1],c='k')
Package: pcurvepy2
pcurvepyPrincipal CurvesThis is an implementation of the Principal Curves (Hastie '89) algorithm in Python.It is a fork of thezsteve/pcurvepypackage where
the projection indices are selected according to our
translation of the R/C++princurvepackage.Installation:pip install pcurvepy2Example:importpandasaspdimportmatplotlib.pyplotaspltimportnumpyasnpimportpcurvedata=pd.read_csv('test_data.csv')x=data.loc[:,('X1','X2')].to_numpy()# transform data to have zero meanx=x-np.mean(x,0)index=np.arange(0,len(x))curve=pcurve.PrincipalCurve(k=5)curve.fit(x)plt.scatter(x[:,0],x[:,1],alpha=0.25,c=index)plt.plot(curve.points[:,0],curve.points[:,1],c='k')# get interpolation indicespseudotime_interp,point_interp,order=curve.unpack_params()
Package: pcvs
PCVS DocumentationParallel Computing Validation System (PCVS) is a Validation Orchestrator
designed by and for software at scale. Its primary target is HPC applications &
runtimes but can flawlessly address smaller use cases. PCVS can help users to
create their test scenarios and reuse them among multiples implementations, an
high value when it comes to validating Programmation standards (like APIs &
ABIs). No matter the number of programs, benchmarks, languages, or tech
non-regression bases use, PCVS gathers in a single execution, and, with a focus
on interacting with HPC batch managers efficiently, run jobs concurrently to
reduce the time-to-result overall. Through basic YAML-based configuration files,
PCVS handles more than hundreds of thousands of tests and helps developers to
ensure code production is moving forward.Quick installation guideA more detailed guide to install PCVS can be found in the appropriate
documentation, here is a quick overview to set up and test the framework.# considering python3.7+
$ pip3 install -r requirements.txt
# for dev/testing purposes, use:
$ pip3 install -r requirements-dev.txt
$ pip3 install pcvs
# basic tests:
$ tox
# OR
$ coverage runComplete documentationPCVS documentation is currently in active progress. Feel free to redistribute
comments and/or notes to the dev team about what should be more covered.
Multiple documentation can be generated from this repo:the CLI is managed and documented throughclick. The manpages can be
automatically built with the third-party toolclick-man(not a dep,
should be installed manually). Note that these manpages may not contain more
information than the content of each--helpcommand.The general documentation (readthedocs.io-formatted) throughsphinx, able
to generate multiple formats. A built version can be foundhere.ContributionsA guide about contributing to PCVS can be found in theCONTRIBUTINGsection of the documentation.AuthorsThis work is currently supported by the French Alternative Energies and Atomic
Energy Commission (CEA). For any question and/or remarks, please contact :Julien JAEGERjulien.jaeger@cea.frJulien ADAMjulien.adam@paratools.comLicensingPCVS is released under theCeCILL-C Free Software
License..
Package: pcwvalidator
PCW ValidatorThis small program can be used to validate a batch csv file sent by price comparison websites against a series of operators.Base Operators are defined and then combined together to form operators which are executed on a specified column in the CSV file.Usagepip install pcwvalidatorfrom pcwvalidator.validate import execute

execute('/full/path/to/csvfile')This will return return True if that CSV is validCommand line Usageexport PYTHONPATH=$(pwd)Then in the command line:python run.py -f { csv_file }Use full paths for each element, i.e:/PATH/bin/python /full/path/src/run.py -f /full/path/src/{ csv_file }AuthorAlexander Isherwood
Package: pc-zap-scrapper
Zap Scrapper - Imoveis em Poços de CaldasScrapper para obter dados de imóveis na cidade de Poços de Caldas, MG. O aplicativo roda o scrapper, formata os dados e faz o load numa base de dados privada do PostgreSQL.1. InstalaçãoA instalação é dada através dopip:$pipinstallpc_zap_scrapper2. Configurando a conexão com o bancoNa etapa de load do banco de dados, é necessário fornecer as credenciais do banco de dados. Serão necessárias as informações:DB_USERNAMEDB_PASSWORDDB_NAMEDB_HOSTDB_PORTEsses dados podem ser passados manualmente ou através de arquivo.env2.a Configuração manual das credenciaisBasta rodar:$zapscrapconfigure-ppath/to/.enve fornecer cada uma das informações requeridas.2.b Configuração através do arquivo.envalternativamente, pode-se definir o.envcom as informações necessárias.# Arquivo .env para conexão com banco de dados PostgreSQLDB_USERNAME=nome_do_usuarioDB_PASSWORD=admin123DB_NAME=nome_da_baseDB_HOST=esse_e_meu.hostDB_PORT=0000Salve esse arquivo em qualquer lugar; por exemplo, empath/to/.env. Depois, rode o comando$zapscrapconfigure-ppath/to/.env3. UtilizaçãoO scrapping, seguido da sanitização dos dados e carregamento no banco de dados é feito simplesmente com o comando:# Exemplo do uso do comando 'zapscrap'$zapscrapApós sua chamada, você deverá ver um barra de progresso indicado a evolução do processo de raspagem de dados.É também possível executar individualmente cada etapa dessa ETL. Isso está documentado nas seções posteriores3.1 Webscrapping para extração de dadosPara executar o Scrapping, basta utilizar o comandozapscrap search. Esse comando tem quatro argumentos básicos:action(-aou--action): Define se você está procurando por imóveis a venda ou para aluguel. Por padrão, está configurado como "venda".# Exemplo do uso do argumento 'action'$zapscrapsearch-avendaestate_type(-tou--estate_type): Define se você vai procurar por casas, apartamentos ou ambos. Por padrão, esta configurado com o  valor "imoveis", que representa casas e apartamentos.# Exemplo do uso do argumento 'estate_type'$zapscrapsearch-timoveisestate_type(-lou--location): Define o local onde procurar. O formato deve ser uf+nome-da-cidade. Por exemplo, para São Paulo capital de ve ficar como sp+sao-paulo.# Exemplo do uso do argumento 'location'$zapscrapsearch-lmg+pocos-de-caldasmax_pages(-mou--max_pages): Define o alcance da paginação. Por exemplo, se for escolhido p valor 3 para esse parâmetro, apenas as três primeiras páginas serão retornadas. Por padrão, é atribuido a ele o valorNoneque indica ao scrapper para trazer todas as páginas.# Exemplo do uso do argumento 'max_pages'$zapscrapsearch-m2Após o scrapping, o programa irá manter os dados na memórias da forma como foram consultados3.2 Formatação dos dadosEm seguida, deve-se formatar os dados para o esquema necessário na ingestão.# Exemplo do uso do comando 'format-data'$zapscrapformat-data3.3 Ingestão no bancoPor último, já com a base sanitizado, deve-se executar a ingestão de fato:# Exemplo do uso do comando 'db-ingest'$zapscrapdb-ingest