Saravanan666/pypi-org huggingface dataset:
Package: pmc-id-converter
ID converter between PMID, PMCID and DOIhttps://www.ncbi.nlm.nih.gov/pmc/tools/id-converter-api/Installationpython3-mpipinstallpmc-id-converterUsageCommand Linepmc_idconv--help# PMIDpmc_idconv30003000# PMCIDpmc_idconvPMC6039336# DOIpmc_idconv10.1007/s13205-018-1330-z# Multiple IDspmc_idconv300030003000300130003002# Output to a filepmc_idconv300030003000300130003002-oout.jsonPythonfrompmc_id_converterimportAPIAPI.idconv('PMC3531190')API.idconv('PMC3531190','PMC3531191123','PMC3531191')API.idconv('23193287')API.idconv('10.1093/nar/gks1195')
Package: pmcli
PM- Password ManagerDiscontinued. Now replaced by passrackpm: A simple CLI for managing passwordsHave important passwords to store? Use pmcli to encrypt your passwords into files and decrypt them whenever you want!Installpip3 install pmcliDependenciesclickclick_help_colorspyfigletcryptocodeBuilt withPython 3.9.5Supported Platforms:Operating System = Cross-PlatformHow to useOpen powershell for Windows or Terminal for Linux/Mac and  and typepmIf this result comes, then you have successfully installed pmcli on your systemPM: Encrypt, decrypt and save your passwords

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  clear     Clear existing data
  config    Set your configuration for pm
  decrypt   Decrypt any of your passwords
  decryptf  Decrypt all your passwords
  encrypt   Encrypt your message
  info      Information about PMCLI
  init      Initialize pmcli for you to get started with itelse, try the above steps again!SetupFirst you need to setup pm for yourselfProcedure:Runpm initto initialize pm for your directoryThere should be no outputRunpm config -ps <password>to set your password for PM. It will ask you for your OLD PASSWORDNow if you haven't configured your passowrd before, enterfetcherin the OLD PASSWORD INPUT which is the default passwordNOTE: THIS STORES YOUR PASSWORD FOR PM AND DOES NOT INDICATE A PASSWORD FOR PASSWORD MANAGEMENTNow you are set up to use PMEncryption:pm encrypt {password} {note(optional)}For example:pm encrypt 'Welcome982' -n googleHere the passwordWelcome982will be stored in encrypted format and stored in your device.In the above example, 'google' is an ID that gives the encrypted data and identity.You can even use--noteinstead of-nto add an IDGiving an ID is completely optional, but highly recommended for every user. This helps you decrypt your messages easilyDecryptionThere are two methods of decrypting/obtaining your passwordsSPECIFIC DECRYPTIONpm decrypt -n {note}For example:pm decrypt -n googleThis gives you the stored password identified bygoogleNOTE/ID.MASS DECRYPTIONpm decryptfThis gives you all of your stored passwordspm decryptfNOTE: EVERY DECRYPTION METHOD NEEDS YOUT PM PASSWORD, HENCE IF YOU HAVE NOT SETUP YOUR PM, DECRYPTION WONT WORKCLEARpm clearcleans the data from data fileRelease NotesCurrent Release- 0.2.4 (Major Update)What's new?Double Encryption makes passwords safe and secure!A beautiful TUI appliedFaster PerformanceThe Files are encrypted, and stored in your device, hence it's so secure that even you can't access them without pmDevelopersAvanindra ChakrabortyArghya SarkarShravan AsatiDeveloper ToolsVisual Studio CodePython 3.9.5GitPython PoetryLicenseLicense © 2021-Present Avanindra ChakrabortyThis repository is licensed under the MIT license. SeeLICENSEfor details.Special NotesContribution is appreciated! Visit the contribution guide inContribution GuideIf you see anything uncomfortable or not working, file an issue inthe issue page. Issues aren't ignored by the developersThanks for seeing my project!
Package: pmc-mapper
pmc-mapperA Python Library that map PubMed Central XML to Python object.installationpip install pmc-mapperusagelibraryfrompmc_mapperimportArticle# parse fileat1=Article.from_file('PMC8353774.nxml')at2=Article.from_pmc_id('PMC8353774')# journalprint(at2.journal.title)# output# BMC Bioinformatics# article idsforat_idinat2.ids:print(f'type:{at_id.type}, value:{at_id.value}')# output:# type: pmid, value: 34376148# type: pmc, value: 8353774# type: publisher-id, value: 4312# type: doi, value: 10.1186/s12859-021-04312-3command linepmc-mapper -i PMC8353774.nxml -o PMC8353774.jl
Package: pmcpy
pmcpy: A Python package for PartMC post-processingpmcpy is aPythonpackage forPartMCpost-processing.Author:Dr. Zhonghua ZhengInstallationStep 1: create an environment:$ conda create -n pmcpy python=3.8
$ conda activate pmcpy
$ conda install -c conda-forge numpy pandas xarray netcdf4Step 2: install using pip:$ pip install pmcpy(optional) install from source:$ git clone https://github.com/zzheng93/pmcpy.git
$ cd pmcpy
$ python setup.py installHow to use it?You may reproduce the jupyter notebook example onBinder.Please checkonline documentation(orRead the Docsversion) for more information.How to ask for helpTheGitHub issue trackeris the primary place for bug reports.
Package: pmd
Polymer Molecular Dynamics toolkit, easy to learn, fast to code, ready for polymer property productionDocumentation:https://polymer-molecular-dynamics.netlify.appSource Code:https://github.com/kevinshen56714/Polymer-Molecular-DynamicsPMD is a modern, fast, python framework for building LAMMPS input and data files for predicting polymer propertiesThe key properties are:Glass transition temperature (Tg)- [Guide] [Scripts]Gas diffusivity- [Guide] [Scripts]Solvent diffusivity- [Guide] [Scripts]Viscosity- [Guide] [Scripts]Young's modulusandtensile strengths- [Guide] [Scripts]Thermal conductivity- [Scripts]InstallationpipinstallpmdExampleBelow are examples of using PMD to generate LAMMPS data and input files:From the command linepmd-templatehttps://github.com/kevinshen56714/Polymer-Molecular-Dynamics/assets/11501902/ba892b38-d735-4109-bb66-e4bd7b967245From a python scriptexample.pyimportpmd# A list of polymer SMILES strings to create simulations forsmiles_list=['*CC*','*CC(*)CC','*CC(*)CCCC','*CC(*)c1ccccc1']forsmilesinsmiles_list:# Define polymer and system specssyst=pmd.System(smiles=smiles,density=0.8,natoms_total=5000,natoms_per_chain=150,builder=pmd.EMC(force_field='pcff'))# Customize LAMMPS simulationlmp=pmd.Lammps(read_data_from=syst,procedures=[pmd.Minimization(min_style='cg'),pmd.Equilibration(Teq=600,Tmax=800),pmd.TgMeasurement(Tinit=600,Tfinal=200)])# Create job scheduler settingsjob=pmd.Torque(run_lammps=lmp,jobname=smiles,project='Your-project-id',nodes=2,ppn=24,walltime='48:00:00')# Generate all necessary files at each SMILES folderrun=pmd.Pmd(system=syst,lammps=lmp,job=job)run.create(output_dir=smiles,save_config=True)From a YAML filePMD can generate config file in YAML format out of the box, which helps you keep track of all the parameters used for each simulation. At the same time, you can build PMD systems directly via the config file from the command line. For example, run thepmd-loadcommand with the followingconfig.yamlto get exact same setup as the above example python script (but only for '*CC*').$pmd-loadconfig.yaml[-ooutput_dir]config.yamlpmd.System:smiles:"*CC*"density:0.8builder:pmd.EMC:force_field:pcffnatoms_total:5000natoms_per_chain:150data_fname:data.lmpspmd.Lammps:read_data:data.lmps# This file name has to match data_fname if build from a yaml fileget_functional_form_from:# A PMD Builder has to be provided if build from a yaml filepmd.EMC:force_field:pcffprocedures:-pmd.Minimization:min_style:cg-pmd.Equilibration:Teq:600Tmax:800-pmd.TgMeasurement:Tinit:600Tfinal:200lmp_input_fname:lmp.inpmd.Torque:run_lammps:lmp.in# This file name has to match the above lmp_input_fname if build from a yaml filejobname:"*CC*"project:Your-project-idnodes:2ppn:24walltime:"48:00:00"job_fname:job.pbs
Package: pmda
Ready to use analysis and buildings blocks to write parallel analysis algorithms
usingMDAnalysiswithdask.WarningThis project isalpha softwareand not API stable. It will and
should rapidly evolve to test different approaches to implementing
parallel analysis in a seamless and intuitive fashion.For example, run a rmsd analysis on all available cores:importMDAnalysisasmdafrompmdaimportrmsu=mda.Universe(top,traj)ref=mda.Universe(top,traj)rmsd_ana=rms.RMSD(u.atoms,ref.atoms).run(n_jobs=-1)print(rmsd_ana.rmsd)By default PMDA use the multiprocessing scheduler ofdask. This is sufficient
if you want to run your simulation on a single machine. If your analysis takes
a very long time (>30 min) you can also spread it to several nodes using thedistributedscheduler. To do this you can pass aschedulerkeyword
argument to therunmethod.To write your own parallel algorithms you can subclass thepmda.parallel.ParallelAnalysisBaseclass.License and source codePMDA is released under theGNU General Public License, version 2(see the
files AUTHORS and LICENSE for details).Source code is available in the public GitHub repositoryhttps://github.com/MDAnalysis/pmda/.InstallationInstall a release withpipThe latest release is available fromhttps://pypi.org/project/pmda/and can be installed withpippipinstall--upgradepmdaDevelopment version from sourceTo install the latest development version from source, rungitclonegit@github.com:MDAnalysis/pmda.gitcdpmdapythonsetup.pyinstallGetting helpHelpis also available through theMDAnalysis mailing listhttps://groups.google.com/group/mdnalysis-discussionPlease reportbugs and feature requestsfor PMDA through theIssue
Tracker.ContributingPMDA welcomes new contributions. Please drop by theMDAnalysis developer
mailing listto discuss and ask questions.To contribute code, submit apull requestagainst the master branch in thePMDA repository.CitationIf you use PMDA in published work please cite[Fan2019].[Fan2019]Shujie Fan, Max Linke, Ioannis Paraskevakos, Richard J. Gowers,
Michael Gecht, and Oliver Beckstein. PMDA — Parallel Molecular Dynamics
Analysis. In Chris Calloway, David Lippa, Dillon Niederhut, and David Shupe,
editors, Proceedings of the 18th Python in Science Conference,
pages 134-142, Austin, TX, 2019.
doi:10.25080/Majora-7ddc1dd1-013
Package: pmd-beamphysics
openPMD-beamphysicsTools for analyzing and viewing particle data in the openPMD standard, extension beamphysics.https://github.com/openPMD/openPMD-standard/blob/upcoming-2.0.0/EXT_BeamPhysics.mdInstalling openpmd-beamphysicsInstallingopenpmd-beamphysicsfrom theconda-forgechannel can be achieved by addingconda-forgeto your channels with:conda config --add channels conda-forgeOnce theconda-forgechannel has been enabled,openpmd-beamphysicscan be installed with:conda install openpmd-beamphysicsIt is possible to list all of the versions ofopenpmd-beamphysicsavailable on your platform with:conda search openpmd-beamphysics --channel conda-forge
Package: pmdownloader
Python Media DownloaderA very small and simple python package to download media files. This will save you some time if you need a downloader for webscraping or related project.RequirementsPython 3.6+RequestsInstallationpipinstallpmdownloaderTested with:Audio:MP3M4AWAVAACVideo:MP4MKVMOVAVIWEBMNote: Not tested with large filesUsageDefault filename and filepath:Call the function, and provide the URL of your media file.importpmdownloaderurl='https://i.imgur.com/pVzZERs.mp4'print(pmdownloader.download(url))Returns:Downloaded: pVzZERs.mp4Custom filename, Default filepath:Call the function, and provide the URL, and filename.importpmdownloaderurl='https://i.imgur.com/pVzZERs.mp4'print(pmdownloader.download(url,file_name='video.mp4'))Returns:Downloaded: video.mp4Default filename, custom filepath:Call the function, and provide the URL, and filename.importpmdownloaderurl='https://skyline.github.com/_nuxt/assets/sound/music-807dfe09ce23793891674eb022b38c1b.mp3'print(pmdownloader.download(url,path='D:/saves/'))# Use absolute path.Returns:Downloaded: music-807dfe09ce23793891674eb022b38c1b.mp3Custom filename, custom filepath:Call the function, and provide the URL, provide path and filename.importpmdownloaderurl='https://skyline.github.com/_nuxt/assets/sound/music-807dfe09ce23793891674eb022b38c1b.mp3'print(pmdownloader.download(url,path='D:/saves/',file_name='audio.mp3'))# Use absolute path.Returns:Downloaded: audio.mp3
Package: pmdsky-debug-py
Autogenerated and statically check-ablepmdsky-debugsymbol definitions
for Python.To use the symbols in your projects, you can install the package from PyPi:pip install pmdsky-debug-pySymbols are grouped by regions. Each region implements thepmdsky_debug_py.SymbolsProtocol.To get a symbol, you can query it like this:pmdsky_debug_py.eu.functions.InitMemAllocTable.addressSee the source code and the symbol definitions ofpmdsky-debugfor more information.See theREADME.rston the root of the repository for additional information.
Package: pmef
## PMEF
## Programando el Método de Elementos FinitosPMEF es una librería desarrollada para enseñar a Programar el Método de Elementos Finitos mediante el curso online de JPI Ingeniería (https://jpi-ingenieria.com).Códigos creados en Python en el año 2020 por Julian Palacios (jpalaciose@uni.pe), con la colaboración de:Jorge Lulimachi (jlulimachis@uni.pe)Luis Baldeon (luis.baldeon.c@uni.pe)Humberto Rojas (humberto.rojas.h@uni.pe)Cabe resaltar que parte de los códigos se basó en las rutinas de matlab FEMCode realizado por Garth N .Wells (2005) para la clase CT5123 en TU Delft, Países Bajos.
Package: pmemkv
pmemkv-pythonPython bindings for pmemkv. Currently functionally equal to pmemkv in version 1.0.
Some of the new functionalities (from pmemkv 1.1) are not yet available.All known issues and limitations are logged as GitHub issues or are described
in pmemkv's man pages.DependenciesPython 3.6 or lateralong with python3-setuptoolspython3-dev(el) - header files and a static library for Pythonlibpmemkv-dev(el) - at least in version 1.0 - native key/value libraryInstallationStart by installingpmemkv(currently at best in version1.0.2or1.1) in your system.gitclonehttps://github.com/pmem/pmemkv-pythoncdpmemkv-pythonIf pmemkv is installed in default directory (e.g. /usr):sudopython3setup.pyinstallor to rather install it locally (in '/home/user_name/.local/lib/python3.X/site-packages'):python3setup.pyinstall--userIf pmemkv is in some other directory:python3setup.pybuild_ext--library-dirs=<path_to_pmemkv_lib_dir>--include-dirs=<path_to_pmemkv_include_dir>
python3setup.pyinstall--userTestingPython bindings includes automated test cases.
Use following command to run test cases:cdtests
python3-mpytest-vpmemkv_tests.pyExamplesWe are using/dev/shmtoemulate persistent memoryin examples.They can be found within this repository inexamples directory.
To execute examples:PMEM_IS_PMEM_FORCE=1python3basic_example.pyPMEM_IS_PMEM_FORCE=1python3restAPI/run_example.pyDocumentationAfter installation, docs can be generated using sphinx (to install, run:pip3 install sphinx) by executing commands:cddoc
makehtml
Package: pmemo
PmemoPmemois a command-line memo editor designed for seamless editing directly in the terminal environment. It offers a range of features to enhance your memo-taking experience and leverages the power of OpenAI's ChatGPT.FeaturesRebindctrl-oto request toChatGPTRebindctrl-tto quickly access frequently used registered promptsCUI memo application that allows seamless editing directly in the terminalNo fullscreen mode, keeping your workflow within the terminalEfficient search functionality for your memosEasily customizable to fit your preferencesExecute code blocks written by you or ChatGPT immediately (Python only)Installation$pipinstallpmemoUsagecommanddescriptionpmorpm newcreate new memopm editedit memopm removeremove memopm listlist all memospm previewpreview memo(markdown) on terminalpm preferenceplease refer to thePreference sectionpm templatecreate a new prompt template for completion usingctrl-tpm template -eedit an existing prompt template.pm runexecute code blocks within your memos.Preferencenamedefaultdescriptionout_dir$HOME/.pmemospecifies the directory where Pmemo saves memosmemo_preference.max_title_length30sets the maximum length of a memo titleeditor_preference.prompt_spaces4defines the number of spaces used for line numbering in the editoreditor_preference.style_name"github-dark"sets the style of the editoreditor_preference.indentation_spaces4sets the number of spaces for indentation (tab size)openai_preference.api_keyNoneThe OpenAI API uses API keys for authenticationopenai_preference.model"gpt-3.5-turbo"ID of the model to useopenai_preference.max_tokens16the maximum number of tokens to generate in the completionopenai_preference.temperature0what sampling temperature to use, between 0 and 2openai_preference.n1how many completions to generate for each promptNote: To enable ChatGPT functionality, make sure to set your OpenAI API key as an environment variable or preference.VersioningThis repo usesSemantic Versioning.Licensepmemois released under the MIT License. SeeLICENSEfor additional details.
Package: pmenu-lib
pmenuSleek dmenu alternative written in Python and powered by curses.Comes in different flavors:ThepmenuCLI, for your terminal and shell scripts.[📂 GitHub]Thepmenu_libpackage, for your Python projects.[📦 PyPi][📂 GitHub]You are now looking at thepmenu_libPython package.UsageThepmenu(list_of_options)function will display a menu and return the selected option as astr, orNoneif the menu is closed without selecting an option.frompmenu_libimportpmenuselected_option=pmenu(list_of_options)Menu bindingsUp arrow: Highlight the previous menu entry.Down arrow: Highlight the next menu entry.Enter: Select the highlighted entry.Esc: Close the menu.
Package: pmenv
pmenvIntroduction to thepmenvPackageThe pmenv is a reinforcement learning environment for the Portfolio Rebalancing Task. It loads multiple stock market data using tickers and synchronizes them into a 3D tensor format suitable for learning. It is an efficient environment for implementing and testing various reinforcement learning algorithms.OverviewThe pmenv is composed of two modules, DataManager and Environment:The Environment module plays a role in simulating the stock market environment and handling the transitions caused by price changes and transactions. The observation is a vector of Open, High, Low, Close price and Volume and the action space is $[-1,1]^N$ where $N$ is a number of risky assets in the portfolio. Portfolio is a vector of weights of all assets and the first entry always represents cash weight.The DataManager module is responsible for loading multiple stock market data using tickers and synchronizing them into a unified format suitable for learning.The mathematical details of the environment are implemented based on the following paper. Please refer to the paper for further information.An intelligent financial portfolio trading strategy using deep Q-learning, ESWA, 2020Installpip install pmenvBasic Usageimportpmenvdatamanager=pmenv.DataManager()# Tickers and Periodtickers=["AAPL","INCY","REGN"]train_start="2019-01-02"train_end="2021-06-30"# Load stock OHLCV dataframe with a tickerdata=datamanager.get_data(tickers[0],train_start,train_end)# Load stock data tensor with ticker listdata_tensor=datamanager.get_data_tensor(tickers,train_start,train_end)environment=pmenv.Environment(data_tensor)# Set transaction costconfig={"CHARGE":0.0025,"TEX":0.001}environment.config=config# Initialize the environment with balancebalance=5000observation=environment.reset(balance)# Stepaction=np.random.random(size=(len(tickers))next_observation,reward,done=env.step(action)Tutorial with RLPortfolio Rebalancing with Deep Q-LearningPortfolio Rebalancing with DDPGPortfolio Rebalancing with Dirichlet Distribution Trader
Package: pmeshlab
pmeshlabModules•Installing the application•Makefile commands•Environments•Running the applicationRessourcespmeshlab is a Python package that provides two high-level features:A simple Mesh processorA list of tool to convert mesh files into point cloudYou can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend ZakuroCache integration.ModulesAt a granular level, pmeshlab is a library that consists of the following components:ComponentDescriptionpmeshlabContains the implementation of pmeshlabInstalling the applicationTo clone and run this application, you'll need the following installed on your computer:GitDocker DesktopInstall Docker Desktop on MacInstall Docker Desktop on WindowsInstall Docker Desktop on LinuxPythonInstall the package:# Clone this repository and install the codegitclonehttps://github.com/JeanMaximilienCadic/pmeshlab# Go into the repositorycdpmeshlabMakefile commandsExhaustive list of make commands:install_wheels
sandbox_cpu
sandbox_gpu
build_sandbox
push_environment
push_container_sandbox
push_container_vanilla
pull_container_vanilla
pull_container_sandbox
build_vanilla
build_wheels
auto_branchEnvironmentsDockerNoteRunning this application by using Docker is recommended.To build and run the docker imagemake build
make sandboxPythonEnvWarningRunning this application by using PythonEnv is possible butnotrecommended.make install_wheelsRessourcesVanilla:https://en.wikipedia.org/wiki/Vanilla_softwareSandbox:https://en.wikipedia.org/wiki/Sandbox_(software_development)All you need is docker:https://www.theregister.com/2014/05/23/google_containerization_two_billion/Dev in containers :https://code.visualstudio.com/docs/remote/containersDelta lake partitions:https://k21academy.com/microsoft-azure/data-engineer/delta-lake/
Package: pmf-searchbox
This is a simple example package.
You can have a Searchbox in tkinter using this package for your tkinter apps.
In Searchbox, if you type part of an item and then click on the button, you will only see the items which contain that text and you can find items easily. Another item which is added to attributes, is the sort attribute. If you use sort=True, the result items which are shown are sorted, otherwise, they will appear in the same order where you have listed them in the values attribute.
Package: pmg
PMG ToolboxThis package contains functions I've found myself using over and over in various projects. If you find them useful, please go ahead and use them. Much appreciated if you let me know. If you find better ways of accomplishing the same task (or spot a bug!), please let me know even more... I'm always eager to learn.This is an ecclectic collection of functions. I've worked on several projects using LaTeX and SQLite, which is why there are specific helper functions for these technologies. One of the main goals of thepmgpackage is to help me ensure consistency in how I solve problems, especially when working with technologies that aren't used every day.
Package: pmine
pmineExample code in Python 2.7+ / Python 3.6+ using only the standard library.This is a terminal app that is installed as pmine. It requires support for
UTF-8 and curses. Entry point is in minesweeper_cli.py. Use the-hoption
to print help.Usage: pmine [difficulty [width [height]]] [-h] [-d] [-a]

  difficulty  Integer between 0 and 40 inclusive.
  width       Integer greater than 1.
  height      Integer greater than 1.

Options:
  -h  HelpDisplays this help message.
  -d  DebugDisplays complete call stacks on errors.
  -a  AutoEnables AI player to play 20 boards.

In game:
  Use the arrow keys or [hjkl] to move.
  Press [f] to flag a mine under cursor.
  Press [space] to reveal from under the cursor.
  Press [q] to quit the game.

There are no commands when AI player is enabled.

Example:
> pmine 3 32 16 -a

  This will have the AI player attempt 32x16 boards moderately packed
  with mines.