Saravanan666/pypi-org huggingface dataset:
Package: limix
limixGenomic analyses require flexible models that can be adapted to the needs of the user.Limix is a flexible and efficient linear mixed model library with interfaces to Python.
It includes methods forSingle-variant association and interaction testingVariance decompostion analysis with linear mixed modelsAssociation and interaction set testsDifferent utils for statistical analysis, basic i/o, and plotting.We have an extensivedocumentationof the library.
If you need further help or want to discuss anything related to limix, please, join ourforum💬 and have a chat with us 😃.
In case you have found a bug, please, report it creating anissue.InstallNOTE: We will be maintaining limix 2.0.x for a while, in case you find some
missing feature in limix 3.0.x. If that is the case, please, typepip install "limix <3,>=2"in your terminal.Installation is easy and works on macOS, Linux, and Windows:pipinstalllimixIf you already have Limix but want to upgrade it to the latest version:pipinstalllimix--upgradeInteractive tutorialseQTL(requires limix 2.0.x)Struct-LMM(requires limix 2.0.x)Running testsAfter installation, you can test itpython-c"import limix; limix.test()"as long as you havepytest.AuthorsChristoph LippertDanilo HortaFrancesco Paolo CasaleOliver StegleLicenseThis project is licensed under theApache License License.
Package: limix-core
Fast inference for Linear Mixed Models and Gaussian Proccesses. Together
withglimix-coreit
implements a flexible framework for Linear Mixed Model inference.See also:https://github.com/limix/limix/InstallThe recommended way of installing it is viapippipinstalllimix-coreUsagePlease, refer to thedocumentation.ProblemsIf you encounter any issue, please,report
it.AuthorsChristoph Lippert-https://github.com/clippertDanilo Horta-https://github.com/HortaFranceso Paolo Casale-https://github.com/fpcasaleOliver Stegle-https://github.com/ostegleLicenseThis project is licensed under the Apache License (Version 2.0, January
2004) - see theLICENSEfile for details
Package: limix-genetics
Genetic-related tools for Limix.InstallThe recommended way of installing it is viacondacondainstall-cconda-forgelimix-geneticsAn alternative way would be via pippipinstalllimix-geneticsRefer todocumentationfor
more information.Running the testsAfter installation, you can test itpython -c "import limix_genetics; limix_genetics.test()"as long as you havepytest.AuthorsDanilo Horta-https://github.com/HortaLicenseThis project is licensed under the MIT License - see theLICENSEfile for details
Package: limix-inference
Fast inference for Generalized Linear Mixed Models.InstallThe recommended way of installing it is viacondacondainstall-cconda-forgelimix-inferenceAn alternative way would be viapip. First you need to installliknormlibrary and thenpipinstalllimix-inferenceRefer todocumentationfor
more information.Running the testsAfter installation, you can test itpython -c "import limix_inference; limix_inference.test()"as long as you havepytest.AuthorsDanilo Horta-https://github.com/HortaLicenseThis project is licensed under the MIT License - see theLICENSEfile for details
Package: limix-legacy
This is a deprecated package meant to provide backward compatibility forlimixpackage.InstallThe recommended way of installing it is viapippipinstalllimix-legacyProblemsIf you encounter any issue, please,submit
it.AuthorsChristoph Lippert-https://github.com/clippertDanilo Horta-https://github.com/HortaFranceso Paolo Casale-https://github.com/fpcasaleOliver Stegle-https://github.com/ostegleLicenseThis project is licensed under the Apache License (Version 2.0, January
2004) - see theLICENSEfile for details
Package: limix-lmm
limix-lmmThis a standalone module that implements the basic functionalities of LMM-based models for genome-wide association studies.
However, we recommend using this module usingLIMIX2.InstallFrom terminal, it can be installed usingpip:pipinstalllimix-lmmDocumentationThe public interface and a quick start in python are available athttp://limix-lmm.readthedocs.io/.ProblemsIf you encounter any problem, please, consider submitting anew issue.AuthorsFrancesco Paolo Casale-https://github.com/fpcasaleDanilo Horta-https://github.com/hortaRachel Moore-https://github.com/rm18Oliver Stegle-https://github.com/ostegleLicenseThis project is licensed under the Apache License (Version 2.0, January 2004) -
see theLICENSEfile for details
Package: limmat
LimmatI sat by the river and thought about WSGI...Limmat is yet another Python web framework building on top of WSGI. Limmat defines itself by the features it lacks.Lacking FeaturesNo request objectNo response objectNo template engineNo built in WSGI serverNo routerNo Websocket supportNo plugin systemNo support for multiple apps or blueprintsFormal AimsSingle file libraryAs small as possibleClean library namespaceThread-safeWSGI
Package: limmbo
LiMMBoLiMMBo enables multivariate analysis of high-dimensional phenotypes based on
linear mixed models with bootstrapping (LiMMBo). LiMMBo is available as an open
source Python module. It builds on and can be used in combination withLIMIX, a flexible and efficient linear mixed
model library with interfaces to Python.A description of the public interface can be foundhereInstallLiMMBo is available on thePython Package Index.
LiMMBo is dependent on LimixLIMIX, however
the latest Limix release does not include multi-variate models (seeissue 7).
While waiting for the updated Limix release please install as follows:Install LIMIX with multi-variate support (v1.0.18):pip3install"limix<2"After successful installation of LIMIX, simply install LiMMBo viapip3installlimmboRecently, an R wrapper package for limmbo was created - this independent project
can be foundhere.ProblemsIf you encounter any issue, please,submit them.LicenseThis project is licensed under the Apache License (Version 2.0, January 2004) -
see theLICENSEfile for details
Package: limnoria-attackprotector
This plugin aims to provide a highly configurable protection against flood
and spam.AttackProtectorDetection typesThere are two kind of detections:Individual floods: are the most common types of flood, involving floods
from one user. Options controlling these detections exist in the
configuration as the name of the flood type (e.g.config plugins.attackprotector.{join,message,etc.}).grouped floods: are floods from several nicks. Their names in the
configuration are the flood type, prepended by ‘group’ (e.g.config plugins.attackprotector.group{join,message,etc.}).Of course, the detection value of group floods should be greater than the
individual floods’.Punishment typesFor each flood type, you can define a punishment. More common areban,kick, orkban. You also can define modes, such as the default
punishment for group joins:mode+i, which sets the mode+i. You can
also remove a mode, with the syntaxmode-i, or set/unset modes to the
user, withmode-v’ or ‘mode+v.
For a complete list of available modes, see your IRC network’s help pages
or try/quote help.Forbanandkban, you can also add a timeout this way:ban+Xandkban+X, whereXis the number of seconds.
Package: limnoria-linkrelay
LinkRelay is an alternative to the official Relay plugin. As Relay can
relay only between channels with the same name, LinkRelay is highly
configurable, and can be used to relay from any channel to any channel.More over, LinkRelay offers unique features, such as coloring the
non-PRIVMSG messages (i.e. joins, parts, quits, kicks, nick changes, …),
non-reciprocal relays, or messages filtering (by regexp).
Package: limnoria-webstats
WebStats is a plugin that provide a web access to channel based statistics.This plugin requires Limnoria.It is designed to be used on active channels, so it implements a cache system.
Don't worry if you have enabled statistics and nothing happens on the Web
interface in the following hour.You need pygraphviz (python-pygraphviz in Debian) for the "links" graph.Reverse proxiesapacheIf you want to use Apache as a proxy for your WebStats instance, you can
use this sample configuration:<VirtualHost0.0.0.0:80>ServerNamestats.supybot.fr.crServerAliasstats.supybot-fr.tk<Location/>ProxyPasshttp://localhost:8080/SetEnvforce-proxy-request-1.01SetEnvproxy-nokeepalive1</Location></VirtualHost>nginxCreate a new site for your bot/etc/nginx/sites-enabled/bot:server{listen80;listen[::]:80;server_namestats.supybot.fr.cr;location/{proxy_passhttp://localhost:8080/;}}
Package: limoe
LiMoEImplementation of the "the first large-scale multimodal mixture of experts models." from the paper: "Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts".CLICK HERE FOR THE PAPER LINK:installpip install limoeusageimporttorchfromlimoe.mainimportLiMoE# Text tokens (batch, sequence length)text=torch.randint(0,100,(1,64))# image (batch, channels, height, width)image=torch.randn(1,3,224,224)# Create an instance of LiMoE with the specified parametersmodel=LiMoE(dim=64,# Dimension of the input and output tensorsdepth=3,# Number of layers in the encoderheads=8,# Number of attention headsnum_tokens=100,# Number of tokens in the vocabularyseq_length=64,# Length of the input sequencenum_experts=4,# Number of experts in the mixture-of-experts layerdim_head=64,# Dimension of each attention headdropout=0.1,# Dropout rateff_mult=4,# Multiplier for the dimension of the feed-forward layerpatch_size=16,# Patch sizeimage_size=224,# Image sizechannels=3,# Number of image channelsdense_encoder_depth=5,)# Pass the input tensor through the model and print the outputout=model(text,image)# Printprint(out)LicenseMITCitation@misc{mustafa2022multimodal,title={Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts},author={Basil Mustafa and Carlos Riquelme and Joan Puigcerver and Rodolphe Jenatton and Neil Houlsby},year={2022},eprint={2206.02770},archivePrefix={arXiv},primaryClass={cs.CV}}
Package: limonade
LimonadeLimonade is a library meant to simplify handling of list mode data from different sources. To make this possible a
framework of data storage is defined. All details of the data are defined by configuration of the detector and the plot
using configuration files written in json.Once in Limonade format, the data can be retrieved and histogrammed using powerful selection tools in the plot module.
With Limonade you canSelect events by time intervalDefine extra data, such as coordinates or boolean flagsDefine gates for any data for any channel and define (anti)coincidence logic for them.Chain-load multiple data filesCreate 1d- and 2d-histograms of any data.InstallationInstall withpip install limonadepreferably using virtual environment or --user flag to prevent any clashes with permissions.After installation one needs to set the configuration and data directories using commandsset-limonade-config path/to/config-dir
    set-limonade-data path/data/dirNow you need to make a detector. Next command creates a basic detector configuration with a dummy calibration and basic
plots for each channel. The automatically generated configuration is very basic. You may want to modify it manually
later.add-limonade-detector detname 2 g4where 2 is the number of channels and standard is a type of data (name of loader) defined in
limonade.loaders.loader_dict.
Package: limonade-plot-utils
Limonade-plot-utilsLimonade is a library meant to simplify handling of list mode data from different sources. The scripts
provided in limonade-plot-utils are example scripts on how to plot and sort list-mode data with limonade
and matplotlib.Included are:read_list_data:Load and plot list mode data using specific plot configuration.By default the data is loaded with the
setup frozen in the data directory, but different detector configuration can be given.Only part of the data can be plotted by using time-slices.Histograms can be exported to comma separated ASCII-files.Histograms can be exported to .phd files with command line inputs for efficiency calibration files and data
collection information.read_hist_data:Load and plot comma separated ASCII-files saved with read_list_data. The metadata contains all
the necessary information.create-plots:Create simple plot configurations for a given detector.fix-data:Experimental. Truncate data to smallest data file length. Used to recover data after daq crash.InstallationInstall withpip install limonade-plot-utilspreferably using virtual environment or --user flag to prevent any clashes with permissions.After installation one can make basic plots for a given limonade detector with commandcreate-plots det_namePlot files are then automatically created in limonade config dir.
Package: limonado
Limonado: Tornado API ToolsContentsLimonado: Tornado API ToolsIntroductionDependenciesDocumentationLicenseSource codeAuthorsInstall with pipCompile from sourceIntroductionDependenciesDocumentationThe latest documentation can be found athttp://limonado.readthedocs.io/en/latest/LicenseMIT LicenseSource codehttps://github.com/ferretgo/limonado/AuthorsAndrii Gakhov @gakhovJean Vancoppenolle @jvcopInstall with pipInstallation requires a working build environment.$pip3install-UlimonadoWhen using pip it is generally recommended to install packages in avirtualenvto avoid modifying system state:$virtualenv.env-ppython3--no-site-packages$source.env/bin/activate$pip3install-UlimonadoCompile from sourceThe other way to install Limonado is to clone itsGitHub repositoryand build it from
source.$gitclonehttps://github.com/ferretgo/limonado.git$cdlimonado$makebuild$bin/pip3install-rrequirements-dev.txt$maketests
Package: limonata
limonataInstallUsingpip:pipinstalllimonataHow to useYou can currently set a “nature” or a “city” palette. These are palettes
we created from pictures we took. Use the following functions to set a
palette:fromlimonataimportset_city_paletteset_city_palette("TuebingenClassic")Or you can see which palettes are available with the following
functions:fromlimonataimportshow_nature_palettesshow_nature_palettes()See the documentationherefor
more details and examples.
Package: limoo-sdk
Limoo Python SDKA Python SDK for Limoo.Wondering what Limoo is? Visithttps://limoo.imGive Limoo a try:https://web.limoo.imInstallpip install limoo-sdkExampleThe following is a simple echo bot. It listens to every conversation that it is
a member of and will reply to every message that it receives in that
conversation by echoing it back.importasyncioimportcontextlibfromlimooimportLimooDriverasyncdefrespond(event):# We only process events that inform us of new messages being created.# We have to make sure that the created message is not a system message and# that it was not created by us. Non-system messages have "null" as their# "type".if(event['event']=='message_created'andnot(event['data']['message']['type']orevent['data']['message']['user_id']==self['id'])):message_id=event['data']['message']['id']thread_root_id=event['data']['message']['thread_root_id']direct_reply_message_id=event['data']['message']['thread_root_id']andevent['data']['message']['id']# If the received message is part of a thread, it will have# thread_root_id set and we need to reuse that thread_root_id so that# our message ends up in the same thread. We also set# direct_reply_message_id to the id of the message so our message is# sent as a reply to the received message. If however, the received# message does not have thread_root_id set, we will create a new thread# by setting thread_root_id to the id of the received message. In this# case, we must set direct_reply_message_id to None.response=awaitld.messages.create(event['data']['workspace_id'],event['data']['message']['conversation_id'],event['data']['message']['text'],thread_root_id=thread_root_idormessage_id,direct_reply_message_id=thread_root_idandmessage_id)asyncdeflisten(ld):forever=asyncio.get_running_loop().create_future()# The given event_handler will be called on the event loop thread for each# event received from the WebSocket. Also it must be a normal function and# not a coroutine therefore we create our own task so that our coroutine# gets executed.ld.set_event_handler(lambdaevent:asyncio.create_task(respond(event)))awaitforeverasyncdefmain():globalld,selfasyncwithcontextlib.AsyncExitStack()asstack:ld=LimooDriver('web.limoo.im','botusername','botpassword')stack.push_async_callback(ld.close)# Calling ld.users.get without any arguments gets information# about the currently logged in userself=awaitld.users.get()awaitlisten(ld)asyncio.run(main())APILimoo APIThe current version of the Limoo API is available athttps://web.limoo.im/Limonad/api_reference/index.htmlwhich maps out the
structure of the WebSocket events and the objects received from the methods in
this SDK.SDKRight now the API is not yet stable so you should probably thoroughly test your
code to make sure it still works with newer versions. Also we don't yet have
a proper documentation but try and stick with what is documented in this file as
everything else will probably not be part of the API.RequirementsPython >= 3.7aiohttp~= 3.7Bot creationIn order to create a bot, send the following command in your direct conversation with Limoo Bot:/ساخت-بات my_bot bot_nicknameNote that only admins of the workspace can create bots.
Package: limpopo
limpopois a framework that allows you to create an application for conducting surveys in the following messengers:TelegramViberInstallingInstall and update usingpip:pip install limpopoA Simple Exampleimportenvimportenvparse# External dependenciesimportimportfromlimpopo.questionimportQuestionfromlimpopo.servicesimportTelegramServicefromlimpopo.storagesimportFakeStoragehow_are_you_question=Question(topic="How are you?",choices=["fine"],strict_choose=False)awaitdefquiz(dialog):how_are_you_answer=awaitdialog.ask(how_are_you_question)ifhow_are_you_answer.text!="fine":awaitdialog.tell("Ohh")defmain():settings={'api_id':env("TELEGRAM_API_ID",cast=int),'api_hash':env("TELEGRAM_API_HASH",cast=str),'token':env("TELEGRAM_BOT_TOKEN",cast=str)}storage=FakeStorage()service=TelegramService(quiz=quiz,storage=storage,settings=settings)service.run_forever()Designlimpopo provides the following entities, by which an poll-application is created:Service (limpopo providesTelegramService,ViberService)Storage (limpopo providesPostgreStorage,FakeStorage)DialogQuestionDevelopmentHow to lint and format the codeWe are usingpre-committool,
seeinstallation guides.pre-commit install
pre-commit run -a
Package: limri
Package that provides tools for brain Lithium MRI pre-processing.Home-page:https://github.com/rlink7/rlink_7limriAuthor:
Limri developersAuthor-email:antoine.grigis@cea.frLicense: CeCILL-B
Description: Package that provides tools for brain Lithium MRI pre-processing.Platform: OS Independent
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: X11 Applications :: Qt
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Utilities
Package: lim-sample
Spectral Analysis for Modal Parameter Linear EstimatePython package with tools for spectral analysis and modal parameters estimateTable of ContentsSpectral Analysis for Modal Parameter Linear EstimateTable of ContentsInstallGUIWindowsPythonDocumentationSource CodeNotebooksScriptsReferencesInstallWe recommend installing in a virtual environment. For how to create virtual environments, please, refer to the official documentation forvenvorconda.You can install thesamplepackage fromPyPIvia pip.pip install lim-sampleAvailable extras areplots: for plotting utilitiesnotebooks: for running notebooksgui: for running the GUIGUIIf you don't want write code to use SAMPLE,
you can use the graphical user interfaceWindowsFor Windows, a stand-alone executable is available. You can download the
latest version from GitHub:Go tohttps://github.com/limunimi/sample/releasesDownload the zip file from the latest release (SAMPLE_win_<version>.zip)Unzip theSAMPLE.exefileThat's it, you can run it!PythonYou can install the GUI from the command line with Python via pip.We recommend toinstall in a virtual environmentpip install lim-sample[gui]To run the GUI from the command line, runpython -m sample.guiDocumentationAPI documentation can be found online here:https://limunimi.github.io/SAMPLESource CodeSource code is available on GitHubhttps://github.com/limunimi/sampleNotebooksFor learning to use the package, you can refer to the interactive
notebooks in thenotebooksfolderScriptsIn thescriptsfolder, there are Python scripts for the reproducibility of experimentsReferencesReferences are available both as aBibTeXand aCITATION.cfffile.If you use this software in your research, please, consider citing the following itemsThe SMC 2020 paper"Spectral Analysis for Modal Parameters Linear Estimate"TheSAMPLEpackage for Python
Package: limsmock
limsmockClarity LIMS system mainly consist of a postgress database, a REST API and a web interface.The REST API is the fundamental data access interface using XML over HTTP. Its data structure looks as below.<ri:index xmlns:ri="http://genologics.com/ri">
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/artifactgroups" rel="artifactgroups"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/artifacts" rel="artifacts"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/configuration/automations" rel="configuration/automations"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/configuration/protocols" rel="protocols"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/configuration/udfs" rel="udfs"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/configuration/udts" rel="udts"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/configuration/workflows" rel="workflows"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/containers" rel="containers"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/containertypes" rel="containertypes"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/controltypes" rel="controltypes"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/files" rel="files"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/instruments" rel="instruments"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/labs" rel="labs"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/permissions" rel="permissions"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/processes" rel="processes"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/processtemplates" rel="processtemplates"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/processtypes" rel="processtypes"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/projects" rel="projects"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/reagentkits" rel="reagentkits"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/reagentlots" rel="reagentlots"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/reagenttypes" rel="reagenttypes"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/researchers" rel="researchers"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/roles" rel="roles"/>
<link uri="https://clinical-lims-stage.scilifelab.se/api/v2/samples" rel="samples"/>
</ri:index>This package serves as a LIMS REST API mock for testing purposes.Some examples of how it can be used can be fount in tests directory.