Saravanan666/pypi-org huggingface dataset:
Package: mahadevadd
A sample Python projectA sample project that exists as an aid to thePython Packaging User
Guide'sTutorial on Packaging and Distributing
Projects.This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.The source for this project is available here.The metadata for a Python project is defined in thepyproject.tomlfile,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.This is the README file for the project.The file should use UTF-8 encoding and can be written usingreStructuredTextormarkdownwith the appropriatekey set. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.
Package: maharashtra-forts
Maharashtra FortsThis Python package provides information about the forts in the state of Maharashtra, India. The data has been collected from various sources and compiled into a convenient package for easy access and use.InstallationTo install Maharashtra Forts, simply usepip:pip install maharashtra-fortsUsageTo use Maharashtra Forts, first import the package:import maharashtra_fortsThen, you can access the information about the forts using theget_forts()function:all_forts = maharashtra_forts.all_forts()print(all_forts)This will print a list of dictionaries, with each dictionary containing information about a fort, including its name, location, and historical significance.DataThe data in Maharashtra Forts has been collected from various sources, including historical texts, government archives, and local historians. We have done our best to ensure the accuracy of the data, but please note that some information may be incomplete or outdated.ContributingIf you would like to contribute to Maharashtra Forts, please fork the repository and submit a pull request with your changes. We welcome contributions of all kinds, including bug fixes, new features, and additional data.LicenseMaharashtra Forts is distributed under the MIT license. Please see the LICENSE file for more information.ContactIf you have any questions, comments, or concerns about Maharashtra Forts, please feel free to contact us atmaharashtra_forts@gmail.com
Package: mahi-gui
mahi-gui Python bindingsThis module enables you to createmahi-guiapplications withDear ImGuiandImPlotusing Python. Note that there currently are no bindings for NanoVG.Usage is similar to mahi-gui, inherit your Application class frommahi_gui.Applicationand overwrite the methods_update(),_draw_opengl()(nameddraw()in C++ interface). To get you started have a look into theexamplesfolder.Almost all ImGui and ImPlot functions are available, please consult their documentations. You can even call OpenGL functions inside_draw_opengl()to draw custom stuff. If a functionality you require is not mapped feel free to request it or create a pull request.The bindings are maintained by hand to get the best performance, especially when plotting large datasets.InstallationThe project is available in the Python Package Index PyPi, just run the following command:pip install mahi-guiIf you are on a platform where no pre-compiled binaries are available or want to build from source you may need to resolve missing build dependencies.
Package: mahinmorsetext
Mahin morsetextencoderlaunch Screen is a Python library for creating launching (loading) screen.InstallationUse the package managerpipto install foobar.pipinstallmahinmorsetextUsagefrommahinmorsetextimport*t='Normal String'm='-- .- .... .. -.'#morse stringprint(texttomorse.gen(t))print(morsetotext.gen(m))ContributingPlease make sure to update tests as appropriate.LicenseMIT##ContactMy FacebookMy Github
Package: mahirodos2
mahirodos2 is a Python package that provides a simple yet powerful capability to perform DoS (Denial-of-Service) attacks. Please note that launching DoS attacks is illegal and unethical unless you have explicit permission from the target site’s owner and are using it for legitimate purposes such as security testing. It’s important to use such tools responsibly and within the boundaries of the law.—### How to useImport thespamreqfunction from themahirodos2package:`python from mahirodos2 import spamreq `Call thespamreqfunction with the target site URL as the argument:`pythonspamreq("<TARGETSITEHERE>")`Replace<TARGET SITE HERE>with the URL of the site you want to perform the DoS attack on. Make sure you have proper authorization and follow legal and ethical guidelines.—For more information, please refer to the package documentation and guidelines on responsible use.
Package: mahjong
This library can calculate hand cost (han, fu with details, yaku, and scores) for riichi mahjong (Japanese version).Also calculating of shanten is supported.The code was validated on tenhou.net phoenix replays in total on11,120,125 hands.So, we can say that our hand calculator works the same way that tenhou.net hand calculation.How to installpipinstallmahjongSupported rules and usage examplesYou can find usage examples and information about all supported rules variations in thewiki
Package: mahon
Mahon linear regressionThe goal of this project is to provide a python package
that allows to calculate linear regressions on data sets with uncertainties.
Linear regressions can be performed for correlated and uncorrelated uncertainties.
The calculations follow the methodology published byMahon (1996).
Typographical errors that were made in that work have been corrected.
A method to allow calculating a linear regression through a fixed point,
avoiding previously made errors,
is also provided.InstallationThe package can be installed frompypivia:pip install mahonUsageBelow is an example on how to use the package.>>>importnumpyasnp>>>frommahonimportLinReg>>># some data>>>xdata=np.array([1,2,3.1,4.9])>>>ydata=np.array([1.1,1.9,3,5.5])>>># some uncertainty and correlation>>>xunc=0.05*xdata>>>yunc=0.073*ydata>>>rho=np.zeros_like(xdata)+0.5>>># do regression>>>my_reg=LinReg(xdata,xunc,ydata,yunc,rho)>>># print out the parameters and their uncertainties>>>my_reg.slope(0.9983617286559998,0.0684389236571533)>>>my_reg.intercept(0.05545339135826666,0.11811730191506546)>>>my_reg.mswd2.5105964767246842Detailed example on how to use the class for fitting and plotting the results
can be foundin this Jupyter notebook.Development & ContributingIf you would like to contribute,
clone the GitHub repo and then install the package locally from within the folder via:pip install -e .[dev]If you also want to install the full test environment,
run:pip install -e .[dev,test]Code auto formatting is implemented usingpre-commithooks.
Full testing of the package can be done withnox.Please feel free to raise issues on GitHub
and open pull requests if you have a feature to be added.
Tests and adequate docstrings should be provided along with your new code.
Package: mahos
MAHOS: Measurement Automation Handling and Orchestration System.This package currently includes the following.Base system for distributed measurement automation.Implementations of microscopy / optically detected magnetic resonance (ODMR) system
for solid-state color center research, based on above.DocumentationDocumentation is browsable here.You can also browse the documentation locally bymake browseor
openingdocsdirectory with a web browser.InstallRead theInstallation guide.In short, we recommend editable installation with cloned repository
because this library is under development:Clone this repo somewhere.Install the requirements:pip install-rrequirements.txtInstall themahospackage:pip install-e.RunTo use the mahos-based system, you have to write a tomlconfiguration filefirst.
With your config, use thecommand line interfaceto start the nodes and interact with them.Thetutorialand correspondingexamplesare provided to get used to these concepts.There is anexample configfor the unit test too.
Here you can observe main built-in measurement logics and GUIs with mock instruments
(microscopy / ODMR system for color centers, for the time being).TestpytestLicenseThe mahos project is licensed under the3-Clause BSD License.RedistributionTheGUI themeis taken fromBreezeStyleSheetsproject,
which is licensed under theMIT license: Copyright 2013-2014 Colin Duquesnoy and 2015-2016 Alex Huszagh.Afileincludes a function from thepyqtgraphproject,
which is licensed under theMIT license: Copyright 2012 Luke Campagnola, University of North Carolina at Chapel Hill.ContributingPlease check outContribution Guidelines.
Package: mahsocket
What iswebsockets?websockets is a library for buildingWebSocketservers and clients in Python
with a focus on correctness, simplicity, robustness, and performance.Built on top ofasyncio, Python’s standard asynchronous I/O framework, the
default implementation provides an elegant coroutine-based API.An implementation on top ofthreadingand a Sans-I/O implementation are also
available.Documentation is available on Read the Docs.Here’s an echo server with theasyncioAPI:#!/usr/bin/env pythonimportasynciofromwebsockets.serverimportserveasyncdefecho(websocket):asyncformessageinwebsocket:awaitwebsocket.send(message)asyncdefmain():asyncwithserve(echo,"localhost",8765):awaitasyncio.get_running_loop().create_future()# run foreverasyncio.run(main())Here’s how a client sends and receives messages with thethreadingAPI:#!/usr/bin/env pythonfromwebsockets.sync.clientimportconnectdefhello():withconnect("ws://localhost:8765")aswebsocket:websocket.send("Hello world!")message=websocket.recv()print(f"Received:{message}")hello()Does that look good?Get started with the tutorial!Why should I usewebsockets?The development ofwebsocketsis shaped by four principles:Correctness:websocketsis heavily tested for compliance withRFC 6455. Continuous integration fails under 100% branch coverage.Simplicity: all you need to understand ismsg = await ws.recv()andawait ws.send(msg).websocketstakes care of managing connections
so you can focus on your application.Robustness:websocketsis built for production. For example, it was
the only library tohandle backpressure correctlybefore the issue
became widely known in the Python community.Performance: memory usage is optimized and configurable. A C extension
accelerates expensive operations. It’s pre-compiled for Linux, macOS and
Windows and packaged in the wheel format for each system and Python version.Documentation is a first class concern in the project. Head over toRead the
Docsand see for yourself.Why shouldn’t I usewebsockets?If you prefer callbacks over coroutines:websocketswas created to
provide the best coroutine-based API to manage WebSocket connections in
Python. Pick another library for a callback-based API.If you’re looking for a mixed HTTP / WebSocket library:websocketsaims
at being an excellent implementation ofRFC 6455: The WebSocket Protocol
andRFC 7692: Compression Extensions for WebSocket. Its support for HTTP
is minimal — just enough for an HTTP health check.If you want to do both in the same server, look at HTTP frameworks that
build on top ofwebsocketsto support WebSocket connections, likeSanic.What else?Bug reports, patches and suggestions are welcome!To report a security vulnerability, please use theTidelift security
contact. Tidelift will coordinate the fix and disclosure.For anything else, please open anissueor send apull request.Participants must uphold theContributor Covenant code of conduct.websocketsis released under theBSD license.
Package: maia-hdl
maia-hdlInstallationIt is not necessary to install maia-hdl in order to generate the Vivado IP cores
or projects, or to run the tests and simulations. These can be run as indicated
below.The current checkout of this source tree can be installed withpip install .Currently it is not possible to install maia-hdl as a Python package fromPyPI, because maia-hdl depends onamaranth, and the latest amaranth
package in PyPI is too old.Building the Vivado IP cores and projectsThe build system for the Vivado IP cores and projects is based on Makefiles. The
IP cores can be built by runningmakefrom theipdirectory. A specific IP
core can be built by runningmakefrom its subdirectory inside theipdirectory.The Vivado projects can be built by runningmakefrom theprojectsdirectory. A specific project cna be built by runningmakefrom its
subdirectory inside theprojectsdirectory. This will create the Vivado
project and block diagram, run synthesis and place and route, and finally
generate the bitstream.TestingPure Amaranth tests can be run withpython3 -m unittestMixed Amaranth/Verilog tests usecocotband a Verilog
simulator such asIcarus Verilog. Verilog code is
generated from the Amaranth code, so the simulation involves only Verilog code
and the Python code running from cocotb. The test execution of cocotb is based
on Makefiles. Tests can be run by runningmakefrom thetest_cocotbdirectory. It is possible to check which tests failed withgrep --include=results.xml -r -e failure .LicenseLicensed under MIT license (LICENSE-MITorhttp://opensource.org/licenses/MIT).ContributionUnless you explicitly state otherwise, any contribution intentionally submitted
for inclusion in the work by you, shall be licensed as above, without any
additional terms or conditions.
Package: maibenchai
maibench Python SDKPython toolings to help run evaluations on large langauge model chains.Getting startedFirst, install the package.pip install maibenchaiNext, create a completion function. This maps different inputs your LLM application response. Here's an example.from maibench import CompletionFn

 class ExampleCompletionFn(CompletionFn):
    @staticmethod
    def complete(**kwargs) -> str:
        return "This is an example response from an LLM application."Next, create some text cases. These test cases should each contain the input kwargs of your completion function in additon to an expected output and a grade_fn. Supported grade functions are currentlymatch,includes,fuzzy_match,not_match,not_includes, andnot_fuzzy_match. More complex grading functions and model-based grading is coming soon.EXAMPLE_TEST_CASES = [
    {
        "input": "This is an example input.",
        "expected": "This is an example response from an LLM application.",
        "grade_fn": "match",
    },
    {
        "input": "This is an example input 2.",
        "expected": "This is an example response from an LLM application.",
        "grade_fn": "match",
    },
]Create and run.EXAMPLE_TEST_SUITE = TestSuite(name="maibench.example_test_suite", completion_fn=ExampleCompletionFn, test_cases=EXAMPLE_TEST_CASES)
results, id = EXAMPLE_TEST_SUITE.run()See results athttps://maibench.ai/individual-result?id=[INSERTID]We hope you enjoy!Contactsupport@maibench.aiwith any questions.
Package: maicos
MAICoSis the acronym for Molecular Analysis for Interfacial and Confined Systems.
It is an object-oriented python toolkit for analysing the structure and dynamics of
interfacial and confined fluids from molecular simulations. Combined withMDAnalysis,
MAICoS can be used to extract density profiles, dielectric constants, structure factors,
or transport properties from trajectories files, including LAMMPS, GROMACS, CHARMM or
NAMD data. MAICoS is open source and is released under the GNU general public license
v3.0.MAICoS is a tool for beginners of molecular simulations with no prior Python experience.
For these users MAICoS provides a descriptive command line interface. Also experienced
users can use the Python API for their day to day analysis or use the provided
infrastructure to build their own analysis for interfacial and confined systems.Keep up to date with MAICoS news by following us onTwitter. If you find an issue, you
can report it onGitlab. You can also join the developer team onDiscordto discuss
possible improvements and usages of MAICoS.Basic exampleThis is a simple example showing how to use MAICoS to extract the density profile from a
molecular dynamics simulation. The filesconf.groandtraj.trrcorrespond to
simulation files from aGROMACSsimulation package. In a Python environment, type:importMDAnalysisasmdaimportmaicosu=mda.Universe("conf.gro","traj.trr")dplan=maicos.DensityPlanar(u.atoms).run()The density profile can be accessed fromdplan.results.profileand the position of
the bins fromdplan.results.bin_pos.DocumentationFor details, tutorials, and examples, please have a look at ourdocumentation. If you
are using an older version of MAICoS, you can access the corresponding documentation onReadTheDocs.InstallationInstall MAICoS usingpipwith:pip install maicosor usingcondawith:conda install -c conda-forge maicosList of analysis modulesCurrently, MAICoS supports the following analysis modules:Module NameDescriptionDensityPlanarCompute cartesian partial density profilesDensityCylinderCompute cylindrical partial densitiy profilesDensitySphereCompute spherical partial density profilesTemperaturePlanarCompute temperature profiles in a cartesian geometryDielectricPlanarCompute planar dielectric profilesDielectricCylinderCompute cylindrical dielectric profilesDielectricSphereCompute spherical dielectric profilesDielectricSpectrumCompute the linear dielectric spectrumSaxsCompute small angle X-Ray scattering intensities (SAXS)DiporderPlanarCompute planar dipolar order parametersDiporderCylinderCompute cylindrical dipolar order parametersDiporderSphereCompute spherical dipolar order parametersPDFPlanarCompute slab-wise planar 2D pair distribution functionsPDFCylinderCompute cylindrical shell-wise 1D pair distribution functionsDipoleAngleCompute angle timeseries of dipole momentsKineticEnergyCompute the timeseries of energiesVelocityPlanarCompute the velocity profile in a cartesian geometryVelocityCylinderCompute the cartesian velocity profile across a cylinder
Package: maid
MaidA package manager, written in Python 3.7, for Windows.Status:AlphaInstallationViapippipinstallmaidCurrently, Maid is depends onpipto update. In the future, Maid will be able to do self-update.Download pre-built binarySee:ReleasesNote: Pre-built binary requires Microsoft Visual C++ 2017 Redistributable, which Windows 10 comes with by default. For older version of Windows, please search online for download links.Run source codegit clonethis repository.python -r requirements.txtto install all dependenciesInvokepython -m maid -h.
Package: maida
maida目前：用于发送邮件小说图片转文字安装可以直接pip安装pip install maidamailEmailSender基于smtplib的发送邮件脚本1. 可以发送文本
2. 可以发送图片
3. 可以发送附件（图片、文件等），附件文件名可以包含中文
4. 可以群发邮件
5. 可以设置邮件优先级（如标红）demofrom maida import EmailSender

email_sender = EmailSender(email_host='smtp.qq.com', email_port=465, from_addr='xxx@qq.com', email_pass='xxx')
content = '这是 maida 发送过来的邮件。请注意查收！'
email_sender.attach_text(text=content)
email_sender.attach_file(r'C:\xxx\xx.jpg')
email_sender.attach_file(r'C:\xxx\xx.txt')
email_sender.send(to_addrs=['xxx@qq.com'], subject='测-试')ocr【可选】安装方式pip install maida[ocr]win 平台安装时若报python-levenshtein安装失败请根据提示安装相应环境或在https://www.lfd.uci.edu/~gohlke/pythonlibs/#python-levenshtein使用 whl 安装img_2_cn基于cnocr的小说图片转中文文字脚本这里提供了一个来源于网上的小说图片样例可以看出识别效果还是不错的demofrom maida.ocr import img_2_cn

print(img_2_cn('xxx.gif'))版本历程详见history.md
Package: maidenhair
A plugin based data load and manimupulation library.InstallationUsepiplike:$ pip install maidenhairUsageAssume that there are three kinds of samples and each samples have 5 indipendent
experimental results.
All filenames are written as the following format:sample-type<type number>.<experiment number>.txtAnd files are saved indatadirectory like:+- data
    |
    +- sample-type1.001.txt
    +- sample-type1.002.txt
    +- sample-type1.003.txt
    +- sample-type1.004.txt
    +- sample-type1.005.txt
    +- sample-type2.001.txt
    +- sample-type2.002.txt
    +- sample-type2.003.txt
    +- sample-type2.004.txt
    +- sample-type2.005.txt
    +- sample-type3.001.txt
    +- sample-type3.002.txt
    +- sample-type3.003.txt
    +- sample-type3.004.txt
    +- sample-type3.005.txtThen, the code for plotting the data will be:>>> import matplotlib.pyplot as plt
>>> import maidenhair
>>> import maidenhair.statistics
>>> dataset = []
>>> dataset += maidenhair.load('data/sample-type1.*.txt', unite=True)
>>> dataset += maidenhair.load('data/sample-type2.*.txt', unite=True)
>>> dataset += maidenhair.load('data/sample-type3.*.txt', unite=True)
>>> nameset = ['Type1', 'Type2', 'Type3']
>>> for name, (x, y) in zip(nameset, dataset):
...     xa = maidenhair.statistics.average(x)
...     ya = maidenhair.statistics.average(y)
...     ye = maidenhair.statistics.confidential_interval(y)
...     plt.errorbar(xa, ya, yerr=ye, label=name)
...
>>> plt.show()
Package: maidenhead
Maidenhead <-> Lat/Lonmaidenheadprovides a simple, yet effective location hashing algorithm.
Maidenhead allows global location precision down to 750mMaidenhead provides 4 levels of increasing accuracyLevelPrecision1World2Regional3Metropolis4CityInstallpipinstallmaidenheador for development versiongitclonehttps://github.com/space-physics/maidenhead

pipinstall-emaidenheadExamplesAll examples assume first doingimportmaidenheadasmhlat lon to Maidenhead locatormh.to_maiden(lat,lon,level)returns a char (len = lvl*2)Maidenhead locator to lat lonmh.to_location('AB01cd')takes Maidenhead location string and returns top-left lat, lon of Maidenhead grid square.Thecenter=Trueoption outputs lat lon of the center of provided maidenhead grid square, instead of the default southwest corner.Command LineThe command line interface takes either decimal degrees for "latitude longitude" or the Maidenhead locator string:python-mmaidenhead65.0-148.0BP65aapython-mmaidenheadBP65aa1265.0083 -147.9917The "python -m" CLI is also available:python-mmaidenhead65.0-148.0The--centeroption outputs lat lon of the center of provided maidenhead grid square, instead of the default southwest corner.AlternativesOpen Location Codes a.k.a Plus Codes are inPython code by Google.
Package: maid-manga-id
Maid Manga ID WrapperMaid Manga Indonesia wrapper using Python BeautifulSoup.
You can contact me onLINEorTelegram.Instalation$pipinstallmaid_manga_id--upgradeor$pythonsetup.pyinstallFeaturesSearch mangaGet mangaDownload manga for each chapter (still development)and more.UsageGet manga informationfrommaid_mangaimportMaidMangaimportasyncioif__name__=='__main__':maid=MaidManga()asyncdefmain():manga=awaitmaid.info('kanojo mo kanojo')print(manga)asyncio.get_event_loop().run_until_complete(main())Search mangafrommaid_mangaimportMaidMangaimportasyncioif__name__=='__main__':maid=MaidManga()asyncdefmain():manga=awaitmaid.search('kanojo mo kanojo')print(manga)asyncio.get_event_loop().run_until_complete(main())Download manga each chapterfrommaid_mangaimportMaidMangaimportasyncioif__name__=='__main__':maid=MaidManga()asyncdefmain():manga=awaitmaid.info('kanojo mo kanojo')chapter=1print(awaitmanga.chapters[chapter-1].download())asyncio.get_event_loop().run_until_complete(main())Release note:This project is under development, any meaningful pull request are opened.DISCLAIMER:OOP (Object Oriented Programming) design is inspired fromthis library