Saravanan666/pypi-org huggingface dataset:
Package: maesters-nwp
Maesters - Numercial Weather PredictionA package focus on fecth open-source global numerical weather prediction product in a elegant way.The following data sources are supported.✔︎Deutscher Wetterdientst-ICON✔︎European Centre for Medium-Range Weather Forecasts-OPER/ENFO✔︎Canadian Meteorological Center-GEM/GEPSThe following data sources support is coming. 🚀🚀🚀❏National Oceanic and Atmospheric Adminstration- GFS❏Met Office- MOGREPS(not open-source anymore)How to installmaesters-nwp depends on cdo. And as cdo is not supported on Windows platform, maesters-nwp fail to install on Windows.Instal via conda (Recommended)condainstall-cconda-forgemaesters-nwpInstall via pipInstall dependence cdo,curl (installcdo,curl)condainstall-cconda-forgecdocurlInstall maesters-nwppipinstallmaesters-nwpUsagefrommaesterimportMaestersec=Maester(source='ecmwf',product='oper',batch='2022-06-29 12:00',hour=[6,30],varname='TP_SFC')# get xarray objectec.xarray()# or only download (if lcoal_dir is not given, default download to current dir)ec.download(local_dir='./')# or operation download all data of the newest batch, default download to $HOME/data/{source}/{product}/{batch:%Y%m%d%H0000}ec.operation(local_dir='./')Variable NameSourceVariableecmwfvariablesdwdvariablescmcvariablesProblem ListP1:pyporj instal fail on M1 chipS1:brewinstallproj
pipinstallpyprojCitationIf this package give helps to your research or work, it will be a enjoyable thing to the contributors of this package. And if you are willing to cite the contribution of this package in your publication, you can find the DOI information athttps://doi.org/10.5281/zenodo.6796046.
Package: maestro
multimodal-maestro👋 helloMultimodal-Maestro gives you more control over large multimodal models to get the
outputs you want. With more effective prompting tactics, you can get multimodal models
to do tasks you didn't know (or think!) were possible. Curious how it works? Try our
HFspace!🚧 The project is still under construction and the API is prone to change.💻 install⚠️ Our package has been renamed tomaestro. Install package in a3.11>=Python>=3.8environment.pipinstallmaestro🚀 examplesGPT-4 VisionFind dog.

>>> The dog is prominently featured in the center of the image with the label [9].👉 read moreload imageimportcv2image=cv2.imread("...")create and refine marksimportmaestroasmmgenerator=mm.SegmentAnythingMarkGenerator(device='cuda')marks=generator.generate(image=image)marks=mm.refine_marks(marks=marks)visualize marksmark_visualizer=mm.MarkVisualizer()marked_image=mark_visualizer.visualize(image=image,marks=marks)promptprompt="Find dog."response=mm.prompt_image(api_key=api_key,image=marked_image,prompt=prompt)>>> "The dog is prominently featured in the center of the image with the label [9]."extract related marksmasks=mm.extract_relevant_masks(text=response,detections=refined_marks)>>> {'6': array([
...     [False, False, False, ..., False, False, False],
...     [False, False, False, ..., False, False, False],
...     [False, False, False, ..., False, False, False],
...     ...,
...     [ True,  True,  True, ..., False, False, False],
...     [ True,  True,  True, ..., False, False, False],
...     [ True,  True,  True, ..., False, False, False]])
... }🚧 roadmapDocumentation page.Segment Anything guided marks generation.Non-Max Suppression marks refinement.LLaVA demo.💜 acknowledgementSet-of-Mark Prompting Unleashes Extraordinary Visual Grounding
in GPT-4Vby Jianwei Yang, Hao Zhang, Feng Li, Xueyan
Zou, Chunyuan Li, Jianfeng Gao.🦸 contributionWe would love your help in making this repository even better! If you noticed any bug,
or if you have any suggestions for improvement, feel free to open anissueor submit apull request.
Package: maestro-app
![Project logo](https://github.com/urosjarc/maestro/blob/master/docs/media/header.png?raw=true)<br>[![pip][pip]][pip-url]
[![travis][travis]][travis-url]
[![coverage][coverage]][coverage-url]
[![dependencies][dep]][dep-url]
[![codestyle][codestyle]][codestyle-url]
[![docs][docs]][docs-url]
[![chat][chat]][chat-url]> Music practice on steroids.[![Patreon User][support]][support-url]
[![Twitter User][twitter]][twitter-url][pip]:https://img.shields.io/npm/v/generator-generator2.svg[pip-url]:https://www.npmjs.com/package/generator-generator2[travis]:https://img.shields.io/travis/urosjarc/maestro.svg[travis-url]:https://travis-ci.org/urosjarc/maestro[coverage]:https://img.shields.io/codacy/coverage/71b26bbc68de46ed9b6ad037d821b635.svg[coverage-url]:https://www.codacy.com/app/urosjarc/maestro[codestyle]:https://img.shields.io/codacy/grade/71b26bbc68de46ed9b6ad037d821b635.svg[codestyle-url]:https://www.codacy.com/app/urosjarc/maestro[dep]:https://img.shields.io/david/urosjarc/maestro.svg[dep-url]:https://david-dm.org/urosjarc/maestro[docs]:http://inch-ci.org/github/urosjarc/maestro.svg?style=shields[docs-url]:http://inch-ci.org/github/urosjarc/maestro[chat]:https://badges.gitter.im/Join%20Chat.svg[chat-url]:https://gitter.im/urosjarc/maestro?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge[support]:https://img.shields.io/badge/patreon-urosjarc-green.svg?style=social[support-url]:https://patreon.com/urosjarc/[twitter]:https://img.shields.io/twitter/follow/urosjarc.svg?style=social&label=follow[twitter-url]:https://twitter.com/intent/follow?screen_name=urosjarc<br>## <font color=red>Warning</font>
Project is still in development!
Please see project [milestones](https://github.com/urosjarc/maestro/milestones)
to see when the first production ready version will come out.<br>## DescriptionMaestro will help you develop similar skill as blind typing on computer
keyboard but on your music instrument. Maestro practice will be onmovingmusic
note track with generated random notes. While player will try to
play those notes, Maestro will check frequency of the sound and compare
it with notes on music track (notes must be calibrated before). Base on that checking Maestro will make
final score at the end of playing session and create players statistics and weak spots
which player can fix and practice on.<br>## Homepage
For more informations please visit projects official homepage.
> [https://urosjarc.github.io/maestro](https://urosjarc.github.io/maestro)<br>## License
Copyright © 2016 Uroš JarcMIT License
Package: maestro-cli
Launch Maestro JobRequest with ease.Exemple :>>> from maestro_cli import Maestro
>>>
>>> maestro = Maestro()
>>> maestro.launch_jobrequest(82, {'A': 1, 'B': 3, 'C': 2})
<JobRequest #1877>>>> maestro = Maestro('http://localhost:8003', '9354a3fe-e1ec-499d-9402-bbef0d700487', 'BeHive')
>>> request = maestro.get_jobrequest(414)
>>> request.progress
45.0
>>> request.status_label
En cours
>>> request.cancel()
>>> 200
Package: maestro-music-player
MaestroA keyboard-centric configurable music playerDesign principlesfocus on user interaction:maestro is a only a front-end application, playing the actual audio is delegated to well-know applications like mpv or vlckeyboard driven:all interaction with maestro is done through keyboard shortcuts and command lines, which allows for a clean and minimalist interfaceconfigurable:the modular command system and the various configuration options allows you to adapt maestro to your workflow (and not the opposite)RequirementsMaestro is made with python. You needpython>=3.10to run maestro.You also need one of the supported audio backend to play the music.
Currently supported backends arempvandvlc.
Install whichever you like or whichever works best for you.As an optional dependency, you may installxdotool, which is used to properly set theWM_CLASSattribute of the window. This will help you if you want to add specific rules for maestro in your window manager.InstallationClone the repository and run theinstallscript.git clone https://framagit.org/Blaireau/maestro.git
cd maestro
./installThis will install maestro on your system (setup desktop file and icons and put maestro on your path).After installation, you will need to setup a few things before you can use maestro.
Seemaestro help setup.DocumentationAll the documentation is included with the application.
Simply typemaestro helpto get started.Support: getting help or reporting bugsPlease use theissue trackeror contactrabbitstemplate@disroot.org.Reinstalling, uninstallingIn case you don’t want to use maestro anymore, or want to uninstall it before reinstalling it, you can run the following commands.cd maestro
./uninstallThis will remove maestro and all it’s associated files from your system, except for configuration files and the cloned repository, you will have to remove those manually.ContributingYou are welcome to contribute to the project !
You can report bugs, improve the documentation, suggest new features…
For that, use theissue trackeror send an email torabbitstemplate@disroot.org.LicenseMaestro isfree software, released under GPLv3 with the exception of a few assets which are under their respective license.CreditsA huge thanks to all the tools this project relies on:the programming languagepythonthe graphical interface librarykivympvandvlcfor the audio backendAnd on specific points:For how to handle the recycle view:https://github.com/FilipeMarch/Smart-RecycleView-KivyFor the monospaced font:https://fontlibrary.org/en/font/natural-mono
Package: maetl-administration
Polls is a Django app to conduct web-based polls. For each question,
visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “polls” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'polls',
]Include the polls URLconf in your project urls.py like this:path('polls/', include('polls.urls')),Runpython manage.py migrateto create the polls models.Start the development server and visithttp://127.0.0.1:8000/admin/to create a poll (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/polls/to participate in the poll.
Package: maetl-custom
Polls is a Django app to conduct web-based polls. For each question,
visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “polls” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'polls',
]Include the polls URLconf in your project urls.py like this:path('polls/', include('polls.urls')),Runpython manage.py migrateto create the polls models.Start the development server and visithttp://127.0.0.1:8000/admin/to create a poll (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/polls/to participate in the poll.
Package: maetl-custom-development
Polls is a Django app to conduct web-based polls. For each question,
visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “polls” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'polls',
]Include the polls URLconf in your project urls.py like this:path('polls/', include('polls.urls')),Runpython manage.py migrateto create the polls models.Start the development server and visithttp://127.0.0.1:8000/admin/to create a poll (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/polls/to participate in the poll.
Package: maetl-development
Polls is a Django app to conduct web-based polls. For each question,
visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “polls” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'polls',
]Include the polls URLconf in your project urls.py like this:path('polls/', include('polls.urls')),Runpython manage.py migrateto create the polls models.Start the development server and visithttp://127.0.0.1:8000/admin/to create a poll (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/polls/to participate in the poll.
Package: maetl-employee
Polls is a Django app to conduct web-based polls. For each question,
visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “polls” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'polls',
]Include the polls URLconf in your project urls.py like this:path('polls/', include('polls.urls')),Runpython manage.py migrateto create the polls models.Start the development server and visithttp://127.0.0.1:8000/admin/to create a poll (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/polls/to participate in the poll.
Package: maetl-main
Polls is a Django app to conduct web-based polls. For each question,
visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “polls” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'polls',
]Include the polls URLconf in your project urls.py like this:path('polls/', include('polls.urls')),Runpython manage.py migrateto create the polls models.Start the development server and visithttp://127.0.0.1:8000/admin/to create a poll (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/polls/to participate in the poll.
Package: maetl-population
population is a Django app to conduct web-based population. For each question,
visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “population” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'population',
]Include the polls URLconf in your project urls.py like this:path('population/', include('polls.urls')),Runpython manage.py migrateto create the polls models.Start the development server and visithttp://127.0.0.1:8000/admin/to create a poll (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/polls/to participate in the poll.
Package: maetl-report
Polls is a Django app to conduct web-based polls. For each question,
visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “polls” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'polls',
]Include the polls URLconf in your project urls.py like this:path('polls/', include('polls.urls')),Runpython manage.py migrateto create the polls models.Start the development server and visithttp://127.0.0.1:8000/admin/to create a poll (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/polls/to participate in the poll.
Package: maexpa
MaExPa(__Ma__thematical __Ex__pression __Pa__rser) is library providing a simple algebraic expression analyser, with callbacks for variable replacement and function calls, and support for implicit support of NumPy arrays.AimsThe goal of this library is to provide an easy way to do custom calculations on an existing dataset. If a dataset provides several fields, this library allows the end user to combine and transform fields to suit their whishes. Fields are exposed as variables in the expression, and are retrived by means of a callback function where the user-provided expression requests a mathematical variable.UsageMaExPa provides on main class,maexpa.Expressionthat compiles and executes user-provided mathematical expressions. A very simple use is this:import maexpa

expr = maexpa.Expression( "3*5" )
print( expr() )A more complicated use case with a replacement for variables is:import numpy
import maexpa

expr = maexpa.Expression( "item/total*100." )

def vars_callback( name ):
	if name == "item":
		return numpy.asarray( [ 1., 2., 3. ] )

	if name == "total":
		return numpy.asarray( [ 10., 10., 10. ] )

	raise Exception( "Unknown variable {:s}".format( name ) )

print( expr( var = vars_callback ) )which will show[10. 20. 30.].LicenseThe library is licensed under version 2.0 of the Apache License, see theLICENSEfile for the full terms and conditions.
Package: mafaextractor
MAFAExtractorThis is a tool for extracting data from theMAFA Dataset. It succesfully extracts all labels and data from the dataset's provided MATLAB files into a Pandas DataFrame.The primary function isextract_mafa()which is all you really need if you're just extracting the data into Pandas. If the filename isn't the originalLabelTrainAll.matorLabelTestAll.mat, then you have to provide thedataset_typewhich can be either"train"or"test". You can also choose whether you want the dataframe to be cleaned to have more readable and processed values by thecleanparameter (which by default is True), or can be set to False if you  require the dataset's original headings.InstallationInstall mafaextractor by either running pip install into your environment using:pipinstallmafaextractoror by cloning the github repository into your working directory:gitclonehttps://github.com/DhyeyLalseta/MAFAExtractorUsage:frommafaextractorimportextract_mafadf=extract_mafa("path/to/LabelTrainAll.mat <or> LabelTestAll.mat")# differing file namesdf=extract_mafa("path/to/IChangedTheTestingSetsFileName.mat",dataset_type="test")# no cleaningdf=extract_mafa("path/to/TestingSet.mat",dataset_type="test",clean=False)If you run into any bugs or have any concerns feel free to contact me via e-mail atdhyeyl1@outlook.com!LicenseThis project is licensed under the terms of the MIT license.
Package: mafe
Music Audio Feature ExtractorInstallation$pipinstallmafeTypical usages# scan music tracks to extract raw set of features$mafe-tscanned.csv.bz2scan-f[MUSIC_DIR]# run normalization on extracted features$mafe-tscanned.csv.bz2-onormalized.csv.bz2normalize# create table of distances between tracks$mafe-tnormalized.csv.bz2-odistances.csv.bz2distance# find clusters of similar tracks$mafe-tnormalized.csv.bz2-oclustered.csv.bz2cluster-n4# run dimensionality reduction, keeping only the most distinctive features$mafe-tnormalized.csv.bz2-oreduced.csv.bz2pca# run clustering on distinct features, creating a visualization of the clusters$mafe-treduced.csv.bz2-oclustered_reduced.csv.bz2cluster-n4-V-Icluster.pngCommand line options$mafe--help
Usage:mafe[OPTIONS]COMMAND[ARGS]...

Options:-t,--tracks-csvTEXTCSVfilecontainingtracks[required]-o,--outputTEXTCSVfilecontainingdistancesbetweenthetracks[required]--helpShowthismessageandexit.

Commands:clusterdistancenormalizepcascan
Package: maff
# maff
Python convenience functions that I wish were in the math module, as well as some that I agree
probably shouldn’t be.## Quick usageimport maff
print(maff.ease(0.1))The first time you importmaff, it will also pollute themathmodule with its own functions, so
you can call them viamathon any module that importsmath.import maff, math
print(math.ease(0.1))## To installDownloadmaff.pyand put it in your source directory. To install from command line:curlhttps://raw.githubusercontent.com/cosmologicon/maff/master/maff.py> my-source-directory/maff.py## Usage noteAs the name hopefully implies, this is a bit of a frivolous module and not recommended for serious
use. I plan to use it for game jams.## Constantsmaff.tau: the circle constant equal to 2pi.maff.phiandmaff.Phi: golden ratio and reciprocal golden ratio.## Functions taken from GLSLmaff.sign(x): sign of x.maff.clamp(x, a, b): clamps x to range [a, b].maff.mix(x, y, a): mix the values x and y with mixing factor a.maff.step(edge, x): Heaviside step function of x with given edge.maff.smoothstep(edge0, edge1, x): step function with Hermite interpolation between edge0 and edge1.maff.length(v): length of vector v. Can be any iterable.maff.distance(v0, v1): distance between vectors v0 and v1maff.dot(v0, v1): dot productmaff.norm(v)ormaff.normalize(v): normalize vector to length 1. Does not fail on zero vector.maff.norm(v, a)ormaff.normalize(v, a): normalize vector to length a.## Fade functionsmaff.ease(x): Hermite interpolation of x in range (0, 1). Equal tomaff.smoothstep(0, 1, x).maff.fade(x, x0, dx): fade from 0 to 1 starting at x = x0 for a fade interval of dx.maff.smoothfade(x, x0, dx): likefadewith Hermite interpolation.maff.dfade(x, x0, x1, dx): double-fade from 0 to 1 starting at x0, then back from 1 to 0 ending at
x1, with a fade interval of dx on both ends.maff.dsmoothfade(x, x0, x1, dx): double-fade with Hermite interpolation.## Approach functionsmaff.approach(x, y, dx): increase or decrease x by amount dx in the direction of y. If the
distance between x and y is less than dx, then return y instead. x and y can be vectors.maff.softapproach(x, y, dlogx): increase or decrease x by an amount determined by dlogx. dlogx is
a unitless parameter between 0 and infinity. If dlogx is 0, then x will be returned. For
sufficiently large dlogx, then y will be returned. Calling this repeatedly and updating the value of
x will give exponential decay toward y, with a timescale factor of 1 / dlogx.maff.softapproach(x, y, dlogx, dxmax=inf, dymin=0.1): After calculating the approach distance dx,
it’s compared with dxmax. If it exceeds dxmax then it’s capped at dxmax. If the resulting is a
distance less than dymin away from y, then y is returned. This is because with exponential decay
it’s impossible to ever exactly reach y.## Other functionsmaff.CS(theta): 2-tuple of cos(theta), sin(theta).maff.CS(theta, r): 2-tuple of r cos(theta), r sin(theta).maff.CSround(ntheta): Produces ntheta 2-tuples of cos(theta), sin(theta) distributed around the
unit circle.maff.CSround(ntheta, r=1, jtheta0=0): specify radius and offset.
Package: mafiagg
MafiaggBotUsagepipinstall-Umafiaggpipinstall-Upython-dotenvpython-dotenvlets you pass a.envfile with your mafia.gg credentialsMAFIA_USERNAME=username
MAFIA_PASSWORD=passwordExamplefrommafiagg.clientimportBotfrommafiagg.credential_managerimportCredentialManagerfromsysimportexitauth=CredentialManager()bot=Bot(auth=auth,command_prefix="$")try:bot.run()exceptKeyboardInterrupt:exit(0)When you run the bot for the first time, it will download some metadata it a./data/folder.FeaturesRoomsList roomsMake private/publicBecome player/spectatorDo afk checkDo afk checkDo ready checkEdit room optionsRename roomCreate new roomOnly create room when game endDecksGet deck by nameSet deck by nameUse random deckSetupsGet setup by nameSet setup by codeSet setup by nameGet current setup codeRolesGet role by nameCleanup role descriptions, alignment with validatorCommandsCustom command namesCommand docsBot ClientAuthenticationHelp commandFormatted / multi-message help commandHost only commandsHost can add adminsTasklistDo not allow creation of new room unless game is overLookup in game ItemsEdit room optionsImplement conversion of role dictionary to setup codesReturn character info (for decks in next message)Better commands (sub-commands!)Better doc strings using decoratorsJoin other rooms on requestAllow forsuper().__init__callsGracefully end session (end event loops, close websocket connection)When exiting alls DELETE onuser-session(cookie invalidation)DevelopmentClone the repogitclonehttps://github.com/blacksmithop/MafiaggBotcdMafiaggBotInstall the dependenciespython3-mpipinstall-rrequirements.txt