Saravanan666/pypi-org huggingface dataset:
Package: rattledin
A Python library to communicate with Linkedin using an asynchronous interfaceEnvironmentDependenciesNecessary for use:Python >= 3.6PipGit (development)Make (development)Python Venv (development)It’s recommended the use of Python virtual environments, to use only needed dependencies.
With thepython venvinstalled, to create an environment, just do:$ python3.6 -m venv venv$ . venv /bin/activateThe bash will start using the virtual environment for now on.BuildThis library can be imported to be used in another applications. So, after the git clone
to install the library inside the virtual environment:$ make deployThis way, the application will be completely installed, and all it’s dependencies.
When finished, the library can be imported insidepythonor a.pyfile with:from rattledin import LinkedinCoreThis library depends onLinkedin API, that can be found [here](https://github.com/jabolina/linkedin-api),
so it’s recommended to install this before Rattledin installation, it’s better to clone the repo
because the PyPi version will not always be in the latest version. Install theLinkedin APIinside
the same virtual environment. Withmake deploythe library will be cloned and installed.ExamplesIn theexamplesfolder exists and example on how to use the library to communicate with Linkedin.
At the moment only exists message methods, but someday, I’ll extends this. If you want to create more
methods, fell free to contribute and create a pull request.
Package: rattlepy
Rattle.py is a pure python templating library for html.
And this library has no special notation like Django or Jinja.
For example:<html><head><title>Hello, Rattle.py!</title></head><body><h1class="heading">Hello, Rattle.py!</h1></body></html>The above HTML equals to below Python code with rattle.py:greeting="Hello, Rattle.py!"withhtml()ashtml:withhead():withtitle():text(greeting)withbody():withh1(className="heading"):text(greeting)# show as HTMLprint(html)And then, you can also make reusable components by yourself:defgreet(name):withnode("div",className="greet-wrapper")ascomponent:withnode("h1"):text(f"Hello,{name}=san")withnode("button",className="ok-btn"):text("ok!")returncomponent# and using:withgreet("User"):passEnjoy!
Package: rattleSNP
Table of ContentsAbout RattleSNPAuthorsLicenseAbout RattleSNPHomepage:https://rattleSNP.readthedocs.io/en/latest/The Best snakemake mapping pipeline !!!AuthorsSébastien RAVEL(CIRAD).LicenseLicencied underCeCill-Cand GPLv3.Intellectual property belongs to CIRAD and authors.
Package: rattlesume
RattlesumeA tool for building resumes by combining markdown snippets into a single
document usingyamldefinitions, then transformed into a PDFpandoc. Allows
developers to store their resumes in git, and present them in a common format.
Different yaml definitions can be used to craft bespoke resumes from a collection
of markdown snippets.InstallInstall viapip(orpipx).pip install rattlesumeUsageSnippetsTo start, create acontentdirectory with a subdirectory
to organize a class of snippets. example/content/
├──  experience/
│  └──  FooBar.md
└──  projects/
   └──  Rattlusume.mdEach of these markdown files are called 'snippets'. They are
typically a short summary & include relevant information such as
dates, job titles, etc.---filename: FooBar.md---# FooBar Inc.## Sr. Foobar Enigneer, March 5th, 2015 - PresentResponsible for announcing the zero-modulate of the counting numbers greater than
zero with respect to three and five. Saved FooBar Inc. $15 annually by implementing
an`O(n)`solution.Snippets can contain an optional slug at the very top, denoted by---.
This slug must be valid yaml. Currently, it is parsed, but not used.DefinitionsEach unique resume has a corresponding definition file.
There are currently two required top-level values.header,
andresume.headerA list of raw markdown. Each entry in the list is placed on its own line.
The format of this value is still in flux.resumeTheresumesection has arbitrary sub-key corresponding to the
names of the subdirectories the snippets are located in.Each of these directories takes two values,headerandsnippets.snippetsis a list of values, which are ether file names or special values.headerchanges the default header from the sub-key to the given value.If all other keys are omitted thesnippetscan be listed directly under the
directory name.For example, the aforementioned 'FooBar.md' could be defined as follows:header:...resume:experience:-"---"-"FooBar.md"# ORexperience:header:"WorkExperience"snippets:-"---"-"FooBar.md"There is currently only one special value,"---", which denotes a horizontal rule.
Package: ratus
RatusRatus is a simple expression language that can be used to easily extend Python
applications with an embedded expression language. Evaluating basic expressions
is as simple as:fromratusimportEvaluatorevaluator=Evaluator()evaluator.evaluate("1 + 1")# => 1evaluator.evaluate("1 > 1")# => Falseevaluator.evaluate("if(1 < 2, 10, 5)")#  => 5For more information, please check out thedocsWhat's in a name?When I first started this project I did a bit of research around parsing
techniques as this is really the most complex part. One idea that that really
stuck out to me waspackratparsing. This
technique allows for linear time parsing, as opposed to the usual exponential
time of backtracking parsing.Currently packrat parsing isn't implemented forratusbut I'm working on it!Roadmapv1.0.0Fully document all featuresAPIGrammarFix known bugsDon't require functions to be the root of an expressione.g.pow(2, 2) + 1should be allowed
Package: ratvec
This tool generates low-dimensional, continuous, distributed vector representations for non-numeric entities such as
text or biological sequences (e.g. DNA or proteins) via kernel PCA with rational kernels.The current implementation accepts any input dataset that can be read as a list of strings.InstallationRatVec can be installed on Python 3.6+ fromPyPIwith the following code in
your favorite terminal:$pipinstallratvecor from the latest code onGitHubwith:$pipinstallgit+https://github.com/ratvec/ratvec.gitIt can be installed in development mode with:$gitclonehttps://github.com/ratvec/ratvec.git$cdratvec$pipinstall-e.The-edynamically links the code in the git repository to the Python site-packages so your changes get
reflected immediately.How to Useratvecautomatically installs a command line interface. Check it out with:$ratvec--helpRatVec has three main commands:generate,train, andevaluate:Generate. Downloads and prepare the SwissProt data set that is showcased in the RatVec paper.$ratvecgenerateTrain. Compute KPCA embeddings on a given data set. Please run the following command to see the arguments:$ratvectrain--helpEvaluate. Evaluate and optimize KPCA embeddings. Please run the following command to see the arguments:$ratvecevaluate--helpShowcase DatasetThe application presented in the paper (SwissProt dataset[1]used by Boutetet al.[2]) can be downloaded directly
fromhereor running the following
command:$ratvecgenerateReferences[1]Boutet, E.et al.(2016).UniProtKB/Swiss-Prot, the manually annotated section of the UniProt KnowledgeBase:
how to use the entry view.. Plant Bioinformatics (pp. 23-54).[2]Asgari, E., & Mofrad, M. R. (2015).Continuous distributed representation of biological sequences for deep
proteomics and genomics. PloS one, 10(11), e0141287.
Package: raudio-bot
raudio-botThis project is a Discord bot written in Python as a client for the Raudio
server. At the moment, it just a proof of concept in order to be an example
for one of many clients you could build Raudio.InstallationInstall the package, optionally in a virtual environment:pip install raudio-botCreate the appropriate configuration files. SeeSetting your tokenandCreating a bot configurationbelow.Finally, run the bot:python3 -m raudio_botSetting up a development environmentIn order to develop for the Raudio bot, first you must obtain a token from
Discord for authenticating your bot. You can do this by visiting theDiscord
Developer Portaland creating an
application. From there you can generate a token for the bot.Downloading the projectClone the repository with the following:$gitclonegit@github.com:raudio-project/raudio-bot.git
$cdraudio-bot
$python3-mvenvvenv# Create a python virtual environment$sourcevenv/bin/activate# Activate the dev environment$pip3install-rrequirements.txt# Install dependencies for the projectSetting your tokenNow, specify the token by creating a.envfile inside of theraudio-botfolder.echo"BOT_TOKEN=your_token_goes_here">>.envCreating a bot configurationThird, you must create a configuration for the bot. Create araudio_config.jsonwith the following:{"stream_url":"http://momo.campus.nd.edu:5000/stream","authenticated":[]}Currently, thestream_urlfield corresponds to the URL where the raudio
server is set to. You may, of course, host your own, but feel free to use
the hosted version. This URL is only available to University of Notre Dame
students signed into theeduroamnetwork, due to the university firewall.Running the botNow you should be all set up! Simply run the following$ python3 raudioLicenseGPL-3.0
Package: raudio-server
The open platform for hosting and streaming music.InstallationInstall the package, optionally in a virtual environment:pip install raudio-serverRun the HLS streaming backend:python3 -m raudio_server.hls /path/to/file.mp3Run the HTTP server in a separate terminal:python3 -m raudio_server.serverSetting a up a development environment$gitclonegit@github.com:raudio-project/raudio-server.git
$cdraudio-server
$python3-mvenvvenv# Create a Python virtual environment$sourcevenv/bin/activate# Activate the enviornment$pip3install.# Install the package
Package: raumfeld
A pythonic library for discovering and controlling Teufel Raumfeld
devices.Tested with a Raumfeld One. Hardware donations to improve the library
are welcome :smile:Supports Python >2.7, 3.xInstallationpip install raumfeldQuickstartimportraumfeld# discovery returns a list of RaumfeldDevicesdevices=raumfeld.discover(timeout=1,retries=1)iflen(devices)>0:speaker=devices[0]# now you can control your raumfeld speakerspeaker.mute=True# muteprint(speaker.volume)# print current volumespeaker.volume=50# set volumespeaker.pause()speaker.play()else:print('No devices found.')
Package: rav
ravA cross-platform Python CLI to shortcut to command-line commands. Inspired by Makefiles and npm scripts.InstallIt's recommended that you use a virtual environment withrav.python3 -m pip install ravMinimum python version is 3.7Start new projectBasic Usage:cd~/path/to/projectravnewRun through the setup wizard to createrav.yamlorManually createrav.yamlscripts:echo:echo hello worldUse:rav run echoManage the Rav projectrav.yamlfileThe configuration block is flexible. Userav,scripts, orcommandsas the top-level key.rav.yamlname:rav-in-sixty-secondsscripts:echo:echo "this is awesome"server:venv/bin/python -m http.serverOr if on windows:rav.yamlname:rav-in-sixty-secondsscripts:echo:echo this is awesomewin-server:venv\Scripts\python -m http.serverThe following all work and will run in this exact order (ravfirst,scriptssecond,commandslast)rav:echo:echo "this is awesome"server:venv/bin/python -m http.serverscripts:echo:echo "this is awesome"server:venv/bin/python -m http.servercommands:echo:echo "this is awesome"server:venv/bin/python -m http.serverBasic Syntaxrav run <command>rav.yaml:scripts:
    <command>: echo "this is a command"Basic Examplerav.yaml:scripts:
    hello: echo hello world!rav run helloBy default,rav runwill look for arav.yamlfile in the current directory.  You can customize it, with-fas explainedbelow.Try the built-in Samplerav sampleThis will outputrav.sample.yamlin the current directory.rav run -f rav.sample.yaml echo-fis used to specify a custom rav file as documentedbelow.Run a commandrav run echoOrrav run serverOr (if windows):rav run win-server 8080Custom Rav FileRav supports custom yaml files by default. The yaml declaration needs to be any of the following:ravscriptscommandsproject.yamlrav:sweet:echo "this is working"echo:echo "so is this"rav.basic.yamlscripts:sweet:echo "this is working"echo:echo "so is this"rav run -f project.yaml sweetorrav run --file rav.other.yaml echoHere's a few rules for custom files:-for--fileis used to specify a custom rav file-for--filemust be used prior to the command shortcut name (e.g.rav run -f <your-new-file> <your-command>)Multiple Commands at Oncerav.yamlscripts:multi:-echo this is-echo awesome-echo simple-echo and-echo easyRun with:rav run multiThis is the same as running:echo this is && echo awesome && echo simple && echo and && echo easy
Package: ravand-plotting
backtrader_plottingLibrary to add extended plotting capabilities tobacktrader(https://www.backtrader.com/). Currently the only available backend isBokeh(https://bokeh.org/).FeaturesInteractive plotsInteractivebacktraderoptimization result browser (only supported for single-strategy runs)Highly configurableDifferent skinnable themesEasy to useFeel free to test it and play with it. I am happy about feedback, critics and ideas on backtrader forum (and also in GitHub issues):https://community.backtrader.com/topic/813/bokeh-integration-interactive-webbrowser-plottingNeeds Python >= 3.6.Demoshttps://verybadsoldier.github.io/backtrader_plotting/Installationpip install backtrader_plottingQuickstartfrombacktrader_plottingimportBokehfrombacktrader_plotting.schemesimportTradimo<yourbacktradercode>b=Bokeh(style='bar',plot_mode='single',scheme=Tradimo())cerebro.plot(b)Minimal Exampleimportdatetimeimportbacktraderasbtfrombacktrader_plottingimportBokehclassTestStrategy(bt.Strategy):params=(('buydate',21),('holdtime',6),)defnext(self):iflen(self.data)==self.p.buydate:self.buy(self.datas[0],size=None)iflen(self.data)==self.p.buydate+self.p.holdtime:self.sell(self.datas[0],size=None)if__name__=='__main__':cerebro=bt.Cerebro()cerebro.addstrategy(TestStrategy,buydate=3)data=bt.feeds.YahooFinanceCSVData(dataname="datas/orcl-1995-2014.txt",# Do not pass values before this datefromdate=datetime.datetime(2000,1,1),# Do not pass values after this datetodate=datetime.datetime(2001,2,28),reverse=False,)cerebro.adddata(data)cerebro.run()b=Bokeh(style='bar',plot_mode='single')cerebro.plot(b)Plotting Optimization ResultsAnother way to use this package is to use theOptBrowserto browse abacktraderoptimization result:...cerebro.optstrategy(TestStrategy,buydate=range(1,10,1))cerebro.addanalyzer(bt.analyzers.SharpeRatio)...res=cerebro.run()bo=Bokeh()browser=OptBrowser(bo,result)browser.start()This will start a Bokeh application (standalone webserver) displaying all optimization results. Different results can be selected and viewed.It is possible possible to add further user-provided columns.
When dealing with huge amounts of optimization results the number of results can be limited and the remaining results can be sorted by a user-provided function to allow for simple selection of the best results.DocumentationPlease refert to the Wiki for further documentation:https://github.com/verybadsoldier/backtrader_plotting/wiki
Package: raveberry-visualization
Testcargo runCrosscompile for the PiSetup# install crosscompiling for raspberry pi
sudo apt-get install arm-linux-gnueabihf-gcc
# add target
rustup target add armv7-unknown-linux-gnueabihf
# copy python sysconfig (required for pyo3 cross compilation)
mkdir lib
scp raveberry.local:/usr/lib/python3.9/_sysconfigdata__arm-linux-gnueabihf.py lib/
export PYO3_CROSS_LIB_DIR=$(pwd)/lib
# for building wheels
pip install maturin
# copy shaders and images to the pi
scp -r raveberry_visualization raveberry.local:BuildBinary:# --release to reduce binary size, speeding up scp
cargo build --target armv7-unknown-linux-gnueabihf --release
scp target/armv7-unknown-linux-gnueabihf/release/raveberry-visualization raveberry.local:Wheel:# optional: add --release --strip
maturin build --target armv7-unknown-linux-gnueabihf
scp target/wheels/*.whl raveberry.local:Buildsudo apt-get install python3.8 python3.8-venv python3.9 python3.9-venv python3.10 python3.10-venv
for python in python3.8 python3.9 python3.10; do
	rm -rf .venv
	$python -m venv .venv
	. .venv/bin/activate
	pip install setuptools-rust wheel
	# plat-name obtained via "auditwheel show <wheel_file>"
	python setup.py bdist_wheel --plat-name manylinux_2_27_x86_64
done
# on the pi
pip install setuptools-rust wheel
python setup.py sdist bdist_wheel --plat-name manylinux_2_28_armv7l
# upload
twine upload dist/*
Package: rave-dl
Rave-DLRips video and audio (with metadata) from music mixing websiteRaveDJ. Support the creators of the website onPatreon.Install withpython3 -m pip install rave-dlorpoetry add rave-dl.Example UsageApplication Programming Interfacefromrave-dlimportRaveDJrave=RaveDJ('https://rave.dj/sZJr6b9MG6vpPA')rave.scraper(filetype="mp3+mp4",metadata=True,output="hello_world")Command Line Interfacepython3-mrave-dl"https://rave.dj/sZJr6b9MG6vpPA"-m-fmp3+mp4-ohello_worldAPIclass RaveDJAttributes:datastore (list): Contains information on multiple videosArgs:url (str): URL of RaveDJ videodef scraper(filetype="mp4", metadata=False, output="output")Download music videos from RaveDJ.Args:filetype (str, optional): Choose to download MP3s (mp3), MP4s (mp4), or both (mp3+mp4). Defaults to "mp4".metadata (bool, optional): Adds metadata (title, artist, album art) to MP3s. Defaults to False.output (str, optional): Folder files are downloaded to. Defaults to "output".CLIInvoke it withpython3 -m rave-dlorpython3 rave-dl.py.usage:rave-dl[-h][-m][-fFILETYPE][-oOUTPUT][-nb][-v]URL

Ripsvideoandaudio(withmetadata)frommusicmixingwebsiteRaveDJ.(v1.0)positionalarguments:URLlinktoRaveDJvideo

optionalarguments:-h,--helpshowthishelpmessageandexit-m,--metadataAddsmetadata(title,artist,albumart)toMP3s.-fFILETYPE,--filetypeFILETYPEChoosetodownloadMP3s[mp3],MP4s[mp4],orboth[mp3+mp4].-oOUTPUT,--outputOUTPUTFoldertodownloadfilesto.-nb,--nobarDonotdisplaybarwhendownloading.-v,--versionShowversionnumberandexit.
Package: ravegen
RaveGen: Telegram bot generatorProgram for generate, create and deploy telegram bots in a readable way. You do not need to know how the Telegram API works or how or where you are going to deploy the bot, you just have to worry about what the bot is going to reply.RaveGen usespython-telegram-botfor connect withTelegram Bot APIand you can deploy the bot on Heroku or Google App EngineInstallingRequirementsDepending on where you want to deploy the bot:Heroku Cli:$sudosnapinstallheroku--classicGoogle App Engine:You need to follow the steps inhttps://cloud.google.com/sdk/docs/quickstart-debian-ubuntuRaveGen installationYou can install RaveGen with:$sudopipinstallravegen--upgradeORclone this repository and runsudo make installCreate and deploy a bot in three steps!Step 1Get the Token from@BotFather. If you need help, you can read theTutorial for Create a Telegram Bot in BotFatherStep 2Run the follow command and paste the Token:$ravegeninit-mStep 3Run this command and follow the indications:$ravegendeploy-dEureka!Now find your bot on Telegram and try to tell it something.Advanced usageIf you need help about the commands run:$ravegenhelpOr if you want to know more see theRaveGen Wiki
Package: ravegen-dev
RaveGen: Telegram bot generatorProgram for generate, create and deploy telegram bots readable way. You do not need to know how the Telegram API works or how or where you are going to deploy the bot, you just have to worry about what the bot is going to reply.RaveGen usespython-telegram-botfor connect withTelegram Bot APIand you can deploy the bot on Heroku or Google App EngineInstallingRequirementsDepending on where you want to deploy the bot:Heroku Cli:$sudosnapinstallheroku--classicGoogle App Engine:You need to follow the steps inhttps://cloud.google.com/sdk/docs/quickstart-debian-ubuntuRaveGen installationYou can install RaveGen with:$sudopipinstallravegen--upgradeORclone this repository and runsudo make installCreate and deploy a bot in three steps!Step 1Get the Token from@BotFather. If you need help, you can read theTutorial for Create a Telegram Bot in BotFatherStep 2Run the follow command and paste the Token:$ravegeninit-mStep 3Run this command and follow the indications:$ravegendeploy-dEureka!Now find your bot on Telegram and try to tell it something.Advanced usageIf you need help about the commands run:$ravegenhelpOr if you want to know more see theRaveGen Wiki
Package: raven-aiohttp
.. raw:: html<p align="center">.. image:: https://sentry-brand.storage.googleapis.com/sentry-logo-black.png:target: https://sentry.io:align: center:width: 116:alt: Sentry website.. raw:: html</p>===========================================================Raven-Aiohttp - Asyncio Transport for the Sentry Python SDK===========================================================.. image:: https://img.shields.io/pypi/v/raven-aiohttp.svg:target: https://pypi.python.org/pypi/raven-aiohttp:alt: PyPi page link -- version.. image:: https://travis-ci.org/getsentry/raven-aiohttp.svg?branch=master:target: https://travis-ci.org/getsentry/raven-aiohttp.. image:: https://img.shields.io/pypi/l/raven-aiohttp.svg:target: https://pypi.python.org/pypi/raven-aiohttp:alt: PyPi page link -- BSD licence.. image:: https://img.shields.io/pypi/pyversions/raven-aiohttp.svg:target: https://pypi.python.org/pypi/raven-aiohttp:alt: PyPi page link -- Python versionsA transport for the `Sentry Python SDK`_ which supports Python 3's asyncio interface.For more information about Sentry and the python SDK, see our `Python Documentation`_ for framework integrationsand other goodies.Requirements============- `raven-python>=5.4.0`- `python>=3.4.2`- `aiohttp>=2.0`Usage=====`raven-aiohttp` ships two asyncio based transports for `raven.Client`: `AioHttpTransport` and `QueuedAioHttpTransport`.AioHttpTransport----------------All messages to the sentry server will be produced by "Fire And Forget"Each new message spawns it owns `asyncio.Task`, amount of them is not limited.. code-block:: pythonimport asynciofrom raven import Clientfrom raven_aiohttp import AioHttpTransportclient = Client(transport=AioHttpTransport)try:1 / 0except ZeroDivisionError:client.captureException()# graceful shutdown waits until all pending messages are sendloop = asyncio.get_event_loop()loop.run_until_complete(client.remote.get_transport().close())QueuedAioHttpTransport----------------------All messages to the sentry server will be produced by queue systemWhen transport is created it spawns limited amount of `asyncio.Task`which sends messages one by one from internal `asyncio.Queue`.. code-block:: pythonimport asynciofrom functools import partialfrom raven import Clientfrom raven_aiohttp import QueuedAioHttpTransportclient = Client(transport=partial(QueuedAioHttpTransport, workers=5, qsize=1000))try:1 / 0except ZeroDivisionError:client.captureException()# graceful shutdown waits until internal queue is emptyloop = asyncio.get_event_loop()loop.run_until_complete(client.remote.get_transport().close())Resources=========* `Sentry`_* `Python Documentation`_* `Issue Tracker`_* `IRC Channel`_ (irc.freenode.net, #sentry).. _Sentry: https://getsentry.com/.. _Sentry Python SDK: https://github.com/getsentry/raven-python.. _Python Documentation: https://docs.getsentry.com/hosted/clients/python/.. _Issue Tracker: https://github.com/getsentry/raven-aiohttp/issues.. _IRC Channel: irc://irc.freenode.net/sentry