Saravanan666/pypi-org huggingface dataset:
Package: mafia-rl-discord-bot
Rocket League Mafia Discord BotA discord bot to help you facilitate Mafia games on Rocket LeagueThe RulesInspired bySunless Khan's Rocket League Mafia.Each player will be assigned a role,VillagerorMafia. The goal forVillagers is to win the Rocket League match whileMafiatries to lose the match, while doing their best to be undetected. At the end of the game, everyone votes on who isMafia.Mafiais defeated if a plurality of votes are against him/her.The points:VillagerPointsWins the game1Correctly guesses mafia1Kill the mafia2MafiaPointsLoses the game2Not killed1Has no votes against2Wins the gamex0How ToAll commands can be viewed with$help.In order to start a new game:$mafia new <number of Mafia>this will check your voice channel and split the members on the VC into the two teams.If you want to specify the players:$mafia with <number of Mafia> @player1 @player2 @player3This can also be used to generate teams, without mafia:$game new$game with @player1 @player2 @player3InstallThe bot needs to run on your machine. The package is available inPyPIand can be installed viapip.What I didThis is what I did to set up the bot. (If you're comfortable with python, you can do whatever works for you.)Set up avirtual environment. (Note which directory you call this method, the environment will be in that location. I created a folder for the bot specifically.)~/personalWorkspace/mafia_bot $ python3 -m venv env
~/personalWorkspace/mafia_bot $ source env/bin/activate
(env) ~/personalWorkspace/mafia_bot $Installed the package(env) ~/personalWorkspace/mafia_bot $ pip install mafia-rl-discord-bot
(env) ~/personalWorkspace/mafia_bot $ source env/bin/activateRan the package(env) ~/personalWorkspace/mafia_bot $ mafia_rl_discord_botIf this is your first time, it will ask for your bot token(env) ~/personalWorkspace/mafia_bot $ mafia_rl_discord_bot 
Enter your bot token:Create a bot token, and paste it here. It will be saved if you start it up in the future. Once entered, you should see:Logged in as
<something>
<something>
------You're good to go!Tear DownTo leave the environment(env) ~/personalWorkspace/mafia_bot $ deactivate 
~/personalWorkspace/mafia_bot $Once you leave the environment you cannot start the bot. The bot was only installed in the virtual environment.~/personalWorkspace/mafia_bot $ mafia_rl_discord_bot
zsh: command not found: mafia_rl_discord_botTo start it back up, you need to run the activate command wherever you set it up~/personalWorkspace/mafia_bot $ source env/bin/activate
(env) ~/personalWorkspace/mafia_bot $or~ $ source ~/personalWorkspace/mafia_bot/env/bin/activate
(env) ~ $
Package: mafic
MaficA properly typehinted lavalink client for discord.py, nextcord, disnake and py-cord.InstallationpipinstallmaficNoteUsepython -m,py -m,python3 -mor similar if that is how you install packages.
Generally windows usespy -m pipand linux usespython3 -m pipDiscord ServerJoin the Discord Serverfor support and updates.DocumentationRead the docs.FeaturesFully customisable node balancing.Multi-node support.Filters.Full API coverage.Properly typehinted for Pyright strict.UsageGo to theLavalink Repositoryto set up a Lavalink node.importosimportmaficimportnextcordfromnextcord.extimportcommandsclassMyBot(commands.Bot):def__init__(self,*args,**kwargs):super().__init__(*args,**kwargs)self.pool=mafic.NodePool(self)self.loop.create_task(self.add_nodes())asyncdefadd_nodes(self):awaitself.pool.create_node(host="127.0.0.1",port=2333,label="MAIN",password="<password>",)bot=MyBot(intents=nextcord.Intents(guilds=True,voice_states=True))@bot.slash_command(dm_permission=False)asyncdefplay(inter:nextcord.Interaction,query:str):ifnotinter.guild.voice_client:player=awaitinter.user.voice.channel.connect(cls=mafic.Player)else:player=inter.guild.voice_clienttracks=awaitplayer.fetch_tracks(query)ifnottracks:returnawaitinter.send("No tracks found.")track=tracks[0]awaitplayer.play(track)awaitinter.send(f"Playing{track.title}.")bot.run(...)
Package: mafio
MAFIOA minimalist Python package for reading and writingMAFfiles.InstallationPackage can be installed using pip.>pipinstallmafioUsageReadfrommafioimportMAFReaderreader=MAFReader("test.maf")# for reading compressed filereader=MAFReader("test.maf",compression='gzip')data=reader.read()print(reader.headers)# ['col1', 'col2', 'col3']print(next(data))# [[1,2,3], [4,5,6], [7,8,9]]# return only certain columnsdata=reader.read(use_cols=['col1','col3'])print(reader.headers)# ['col1', 'col3']print(next(data))# [[1,3], [4,6], [7,9]]# for big files use chunksdata=reader.read(chunk_size=2)print(next(data))#[[1,2,3], [4,5,6]]print(next(data))#[[7,8,9]]Writefrommafioimportwritecols=['col1','col2','col3']row1=[1,2,3]row2=[4,5,6]row3=[7,8,9]data=[cols,row1,row2,row3]# Creates file if not existswrite("test.maf",data)# Appending the file instead of overwritingwrite("test.maf",data,append=True)# gzip the filewrite("test.maf.gz",data)TestsClone the library.>gitclonehttps://github.com/zeyad-kay/mafio.gitInstall test dependencies.pipinstall-rtest-requirements.txtRun tests.>pytestmafio/tests
Package: mafipy
mafipyDescriptionmathmatical finance in python.
Supported methods are belows:replication methodQuantoCMSAnalytic formulaBlack ScholesBlackSABRQuasi Monte Carlosobol sequence (cython) up to 21202 dimensionInstallationpipinstallmafipyRun examplesgitclonehttps://github.com/i05nagai/mafipy.gitcdmafipypythonexamples/plot_smile_curve_sabr.pyYou will see the following drawing.DocumentationAPI documentBenchmarksContributingSet up development environment.gitclonehttps://github.com/i05nagai/mafipy.gitcdmafipygitsubmoduleinitgitsubmoduleupdatepipinstall-rrequirements.txtBuildsTo generate c files,pythonsetup.pybuild_extTestTo run tests, you additionally need to installpytest.pipinstall-rrequirements-dev.txtpythonsetup.pytestBenchmarksasvis required to execute benchmarks.
You can installasvbypip.pipinstallasvThen# execute benchmarkspythonsetup.pybenchmark# generate html file from the resultspythonsetup.pybenchmark_publish# preview the generated html through local serverpythonsetup.pybenchmark_previewReleaseSuppose that we create the versionv.X.Y.Z.
We followPEP0440compatible versioning rule.In Circle CI, every built creates release files inmasterbrnach.
Make sure that the release files are properly created before releasing.Create git tag locallygittag-avX.Y.Zmaster# for testing purpose, in this case, the package will be uploaded to test.pypi.orggittag-avX.Y.ZdevmasterPush git tag to remotegitpushoriginvX.Y.ZCircle CI build and run tests. After all tests passed, Circle CI make a release to GitHub and upload wheels to PyPI.Related ProjectsGitHub - lballabio/QuantLib: The QuantLib C++ libraryOne of the best library for mathmatical finance.
The library is written in C++.
There are many wrapper projects of the QuantLib.GitHub - finmath/finmath-lib: Mathematical Finance Library: Algorithms and methodologies related to mathematical finance.Mathematical Finance Library: Algorithms and methodologies related to mathematical finance.
The library is written in Java.
Package: maflib
Table of ContentsInstallationUsageLicenseMAFLib is a library for reading and manipulating Mozilla Archive Format (MAF) files.InstallationPackage is uploaded onPyPI.You can install it with pip:$ pip install maflibUsageOpen a .maff filefd=maflib.MAF("test.maff")Open a file within the .maff filefd2=fd.open("image.jpg")Open the index file in the .maff filefd2=fd.open_index()Return the content of the index file in the .maff filecontent=fd.read_index()Show a .maff file in a browser windowfd.show()LicenseMAFLib is released under the GNU Lesser General Public License, Version 3.
Package: mafs
MagicFS (mafs)MagicFS is an easy-to-use library that allows anyone to easily create virtual
filesystems using FUSE.MagicFS allows you to redirect file requests, so instead of the request going to
an underlying storage medium like a hard drive, the request goes to a program
that you've written.If you like the idea of playing around with virtual filesystems, but have been
put off by the complexity of it all, then this library could be for you. You can
easily create whole, feature-complete filesystems in just a few lines of code.
No need for painstakingly dealing with folder structures and buffers, mafs
manages all the low-level details, provides sane defaults, and lets you focus on
the functionality.InstallationMagicFS is available onpypi, and can be
easily installed with pip.$ pip3 install mafsExamplesAll of the examples are listed inexamples/. Here's a demo of running theplaces.pyexample.$ mkdir fs
$ python3 examples/places.py fs
$ ls fs
place  shortcut
$ ls fs/place
here  there
$ cat fs/place/here
this is here
$ cat fs/place/there
this is there
$ cat fs/place/anywhere
this is anywhere!
$ fusermount -u fsDevelopmentTo download MagicFS for development, execute the following commands:$ git clone https://github.com/jedevc/mafs.git
$ cd mafs
$ pip3 install -r requirements.txtTo launch mafs with an example, execute the following:$ PYTHONPATH=. python3 examples/places.py fs -fgNote the use of thePYTHONPATHenvironment variable to include the
library, and the use of the-fgflag to run mafs in the foreground for
easier debugging.TestsTo run the tests for MagicFS, install nose, and then use it to run the tests.$ pip install nose
$ nosetestsIf you make any changes, please run the tests before you commit to ensure that
you haven't broken anything.
Package: mag2exp
mag2expSamuel Holt1,2,Martin Lang1, James Loudon3, Thomas Hicken4,Swapneel Amit Pathak1,5,David Cortés Ortuño6,Marijan Beg7,1, andHans Fangohr1,5,81Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK2Department of Physics, University of Warwick, Coventry CV4 7AL, UK3Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, CB3 0FS, United Kingdom4Department of Physics, Durham University, Durham, DH1 3LE, United Kingdom5Max Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg, Germany6Paleomagnetic Laboratory Fort Hoofddijk, Utrecht University, Utrecht, 3584 CD, Netherlands7Department of Earth Science and Engineering, Imperial College London, London SW7 2AZ, UK8Center for Free-Electron Laser Science, Luruper Chaussee 149, 22761 Hamburg, GermanyDescriptionBadgeTestsLintingReleasesCoverageDocumentationYouTubeBinderPlatformsDownloadsLicenseDOIAboutmag2expis a Python package, integrated with Jupyter, providing:simulations of experimental techniques perfomed on magnetisation textures,the ability to perform "virtual" experiments,the functionality to compare of real experimental results agaist simulated experiments.It is available on Windows, MacOS, and Linux. It requires Python 3.8+.DocumentationAPIs and tutorials are available in the documentation. To access the documentation, use the badge in the table above.Installation, testing, and upgradeWe recommend installation usingcondapackage manager. Instructions can be found in thedocumentation.BinderThis package can be used in the cloud via Binder. To access Binder, use the badge in the table above.YouTubeYouTube video tutorials are available on theUbermag channel.SupportIf you require support, have questions, want to report a bug, or want to suggest an improvement, please raise an issue inubermag/helprepository.ContributionsAll contributions are welcome, however small they are. If you would like to contribute, please fork the repository and create a pull request. If you are not sure how to contribute, please contact us by raising an issue inubermag/helprepository, and we are going to help you get started and assist you on the way.Contributors:LicenseLicensed under the BSD 3-Clause "New" or "Revised" License. For details, please refer to theLICENSEfile.How to citeM. Beg, M. Lang, and H. Fangohr. Ubermag: Towards more effective micromagnetic workflows.IEEE Transactions on Magnetics58, 7300205(2022).M. Beg, R. A. Pepper, and H. Fangohr. User interfaces for computational science: A domain specific language for OOMMF embedded in Python.AIP Advances7, 56025(2017).Samuel Holt, Martin Lang, James Loudon, Thomas Hicken, Swapneel Amit Pathak, David Cortés Ortuño, Marijan Beg, and Hans Fangohr. mag2exp: Simulate experimental measurements for magnetic materials DOI:10.5281/zenodo.3539461(2023).AcknowledgementsOpenDreamKit– Horizon 2020 European Research Infrastructure project (676541)EPSRC Programme Grant onSkyrmionics(EP/N032128/1)
Package: magali
Modelling and inversion of magnetic microscopy data.Documentation(latest)•Documentation(main branch)•CompGeoLabAboutProject goalsProject statusMagali is in early stages of development.We welcome feedback and ideas!This is a great time to bring new ideas on
how we can improve the project.
Submitissues on GitHub.LicenseThis is free software: you can redistribute it and/or modify it under the terms
of theMIT License. A copy of this license is provided inLICENSE.txt.
Package: magame
MaGameMaGame is a package currently involving two maze games: Mappy and Pac-man. In the game Mappy, a mouse cop is searching for the criminal cat in the maze, while in the other game Pac-man, the character is trying to eat all dots in the maze.Once you installed the package with thepipcommand, you may follow steps below to run the game.Step 1: import the game and mazes from the MaGame packagefrommagameimportmappy# Import the Mappy gamefrommagameimportpacman# Import the Pac-man gamefrommagameimportmazes# Import the pre-defined mazesThe variablemazesis a tuple, containing four nested lists representing the mazes, where:Maze wall is indicated by "1".Open path is indicated by "0".The target in the Mappy game is indicated by "-1". It is treated as "0" in the Pac-man game.One example is given below, and you may create your own maze following the same format.mazes[0][[0, 0, 0, 1, 1, 0, 0, 0, 1, 1],
 [0, 0, 1, 0, 1, 0, 0, 0, 0, 1],
 [1, 0, 1, 0, 1, 0, 0, 1, 0, 0],
 [1, 0, 0, 0, 1, 0, 0, 1, 0, 0],
 [1, 1, 0, 1, 1, 1, 0, 1, 1, 0],
 [0, 1, 0, 0, 1, 0, 0, 1, 0, 0],
 [0, 1, 0, 1, 1, 0, 1, 1, 1, 0],
 [0, 1, 0, 1, 0, 0, 1, 0, 0, 0],
 [0, 0, 0, 1, 0, 1, 1, 0, 1, 1],
 [1, 1, 0, 0, 0, 1, 0, 0, -1, 1]]Step 2: create your own function that determines the movement in the mazeThe user-defined function must follow the format below.defmaze_move(maze,position,memory):"""Your code here"""returnmove,memoryThe input arguments of the function are:mazeas a nested list representing the maze.positionis atuplewith two integers:v_indexandh_index. The first integerv_indexindicates the vertical index, and the second integerh_indexindicates the horizontal index, as shown by the picture below.memorycan be any data type. Users may use it to keep a track of all previous steps or previous moves. The initial value of memory is given asNone.The outputs are:moveis atuplewith two integers:v_moveandh_move. It is used to indicate how the character moves in the maze, as shown by the following picture.memoryis the updated memory, such as all previous steps or previous moves of the character in the maze.The function receives the maze information, the current position in the maze, and previous step, moves, or other forms of memory; then decide the next move and update the memory.Step 3: apply your function to play the gameThe game can be played by simply calling theplay()function of themappyorpacmanmappy.play(maze,maze_move)pacman.play(maze,maze_move)There are two input arguments for theplay()function:mazeis the nested list as the maze information.maze_moveis the name of the function you created. It can be any valid Python function name.If your function works correctly, you will find the character moving in the maze.
Package: magani
MaganiAPI Testing ToolDeveloped in pure PythonREST API testing softwareCreate test cases and test in project level as well as test case level. Only Public API is supporting. You can use inside private network without authentication.Testing methodsGET : ImplementedPOST : ImplementedPUT : ImplementedDELETE : ImplementedAuthorizationBasic Auth : Not ImplementedReportsExport Project wise CSV fileExport Project wise Excel fileFuture SupportExport All test case results in pdf, docs.Basic auth supportSetupTo use the software python is required.Open the terminal & follow the instructionsType to install Magani$ pip install maganiType to run the Magani software$ maganiNote:It will support for Windows & LinuxDeveloper SetupClone it$ git clone https://github.com/Magani-Stack/magani.gitRun$ python magani_app.pyContributionWe welcome contributor in the category as followsContribute Code ChangesContributing by Reviewing ChangesContributing Documentation ChangesContributing Bug ReportsContributing by Testing ReleasesCode Style GuidePlease follow the style of the existing codebase.For Python code, Magani follows PEP 8 with one exception: lines can be up to 120 characters in length, not 79.Learn MoreMIT License : Copyright (c) 2020 Magani-Stack
Package: magCalEM
#MagCalibrationThis is a program to calibrate the effective pixel size for cryoEM data collected on gold foils.
Documentation and source code is availbale atwww.mrc-lmb.cam.ac.uk/crusso/If the program is useful in your work, please cite:
Joshua L. Dickerson, Erin Leahy, Mathew J. Peet, Katerina Naydenova, Christopher J. Russo. "Accurate magnification determination for cryoEM using gold". Ultramicroscopy 2023:113883.
Package: mag-cli
Magasin Command Line Interface (mag)Command line interface client that helps to manage the different modules ofmagasinInstallpipinstallmag-cliUsagemag
Usage:mag[OPTIONS]COMMAND[ARGS]...magasinclientisthegluebetweenmagasincomponents,itmakeseasiercommontasks

Options:-v,--verbose--version--helpShowthismessageandexit.

Commands:dagster(d)Dagstercommandsdaskhub(dh)Daskhub/Jupyterhubcommandsdrill(dr)ApacheDrillcommandsminio(m)MinIOcommandssuperset(ss)ApacheSupersetcommandsDevelopmentClone the repogitclonehttps://github.com/unicef/magasinCreate and activate the virtual environmentcdmagasin/mag-cli
python-mvenvvenvsourcevenv/bin/activateInstall module in editing modepipinstall-e'.[dev]'Start coding!
Package: magcoilcalc
magcoilcalc - Axisymmetric circular current loops (coils) magnetic field calculatorIntroductionHighly uniform magnetic field with minimal lateral field gradient is required for the generation and preservation of the spin-polarized state of3He and129Xe gasses and other noble gas species with non-zero nuclear spin.magcoilcalcis designed to be useful in the design, simulation and optimization of magnet systems used to generate an uniform magnetic field intended to support such systems.Installationpip install magcoilcalc. Requires Python >= 3.4,numpyandmatplotlib.LimitationsBefore you get your hopes up,magcoilcalcis only for circular axisymmetric current loops with no support for non-unity relative permeability. This geometry is versatile and useful, but this package won't get you very far for problems other than building spin-polarizer magnets. For proper 2D EM FEM software checkFEMMout.magcoilcalcis useful for quickly mapping out a large parameter space, or for getting a quick answer typing on one hand while holding that magnet wire mid-winding with the other.Support for infinitely thin cylindrical current sheets are also being worked on. You can spawn one withmagcoilcalc.CurrentSheetwith the usual parameters, but these are not vectorized so can be slow on a large mesh.Mu-metal shieldingmagcoilcalcin incapable of dealing with non-unity relative permeability. Simulate mu-metal shields inFEMMupon your finished design inmagcoilcalc.UsageUsers are encouraged to refer to cookbook examples for usage examples with line-by-line comments.Found a bug?The author still discovers yet another misconception in his understanding of elementary electromagnetism once in a while. If you find something not quite right, you are probably right - open an issue or drop me an email!
Package: magcolloid
magcolloisThis is a set of routines to start and read simulations in lammps of colloidal particles interacting through dipolar and Lennard-Jones potentials. It includes functionality to set up walls to confine the colloids and biharmonic traps. It's mostly a wrapper for lammps, although that might change in the future. It is made to work on windows, and it therefore interfaces with lammps through input scripts and dump files, instead of using the python interface.PrerequisitesMost things are tested using numpy 1.13.1, scipy 1.0.0 and pandas 0.22.0. A lammps executable is needed, which includes the dipole package, and a superparamagnetic atom type. The source code for this can be found inhttps://github.com/aortiza/lammpswhich is a branch of the original lammpshttp://lammps.sandia.gov/. Executables for Windows, Intel Mac and Ubuntu can be found in the folder lammps_executables/AuthorsAntonio Ortiz-AmbrizLicenseThis project is licensed under the GPL License. In fact, most of the code is licenced under MIT licence, but LAMMPS is GPL.
Package: magcoordpy
MagCoordPyA python package for working with magnetic coordinate transformations.
The documentation is available athttps://magcoordpy.readthedocs.io/en/latest/.Installationpip install magcoordpyExample usagefrom magcoordpy import coord_transforms
long_geo = np.arange(-180, 190, 10)
lati_geo = np.zeros(len(long_array))
alti_geo = np.zeros(len(long_array))
lat_cd, lon_cd, r_cd = coord_transforms.geodetic2cd(lati_geo, long_geo, alti_geo, year=2021.0)It includes the following functions (not exhaustive list):geodetic2cd --> transformation from geodetic to centered dipolecd2geodetic --> transformation from centered dipole to geodeticAuthorsGiorgio Savastano (giorgiosavastano@gmail.com)Please use github issues to make bug reports and request new functionality. Contributions are always welcome.ReferencesLaundal, K.M., Richmond, A.D. Magnetic Coordinate Systems. Space Sci Rev 206, 27–59 (2017).https://doi.org/10.1007/s11214-016-0275-y