Saravanan666/pypi-org huggingface dataset:
Package: celery-types
celery-typesType stubs for celery related projects:celerydjango-celery-resultsamqpkombubilliardvineepheminstallpipinstallcelery-typesYou'll also need to monkey patchTaskso generic params can be provided:fromcelery.app.taskimportTaskTask.__class_getitem__=classmethod(lambdacls,*args,**kwargs:cls)# type: ignore[attr-defined]devinitial setup# install poetry (https://python-poetry.org/docs/)curl-sSLhttps://install.python-poetry.org|python3-# install node# install yarnnpminstall--globalyarn# install node dependenciesyarnregular developmentpoetryconfigvirtualenvs.in-projecttruepoetryinstall# run formatting, linting, and typecheckings/lint# build and publishpoetrypublish--buildrelatedhttps://github.com/sbdchd/django-typeshttps://github.com/sbdchd/djangorestframework-typeshttps://github.com/sbdchd/mongo-typeshttps://github.com/sbdchd/msgpack-types
Package: celery-with-beanstalk
Version:3.0ContentsThis bundle installs the following packages:['celery>=3.0,<4.0', 'beanstalk']What is this?This is a bundle of several packages that you can use as a shortcut in the
requirements lists of your applications.  Bundles are used to follow a
common group of packages, or a package with an optional extension feature.You can install all of the packages in this bundle by,using pip:$ pip install -U celery-with-beanstalkorusing easy_install:$ easy_install -U celery-with-beanstalkOr if you want to add this bundle as a dependency in your application, you
can add the following identifier in yoursetup.py’s requires list or
in your pip requirements files:celery-with-beanstalkYou can also specify a specific version:celery-with-beanstalk>=3.0
Package: celery-with-couchdb
Version:3.0ContentsThis bundle installs the following packages:['celery>=3.0,<4.0', 'couchdb']What is this?This is a bundle of several packages that you can use as a shortcut in the
requirements lists of your applications.  Bundles are used to follow a
common group of packages, or a package with an optional extension feature.You can install all of the packages in this bundle by,using pip:$ pip install -U celery-with-couchdborusing easy_install:$ easy_install -U celery-with-couchdbOr if you want to add this bundle as a dependency in your application, you
can add the following identifier in yoursetup.py’s requires list or
in your pip requirements files:celery-with-couchdbYou can also specify a specific version:celery-with-couchdb>=3.0
Package: celery-with-mongodb
Version:3.0ContentsThis bundle installs the following packages:['celery>=3.0,<4.0', 'pymongo']What is this?This is a bundle of several packages that you can use as a shortcut in the
requirements lists of your applications.  Bundles are used to follow a
common group of packages, or a package with an optional extension feature.You can install all of the packages in this bundle by,using pip:$ pip install -U celery-with-mongodborusing easy_install:$ easy_install -U celery-with-mongodbOr if you want to add this bundle as a dependency in your application, you
can add the following identifier in yoursetup.py’s requires list or
in your pip requirements files:celery-with-mongodbYou can also specify a specific version:celery-with-mongodb>=3.0
Package: celery-with-redis
Version:3.0ContentsThis bundle installs the following packages:['celery>=3.0,<4.0', 'redis>=2.4.4']What is this?This is a bundle of several packages that you can use as a shortcut in the
requirements lists of your applications.  Bundles are used to follow a
common group of packages, or a package with an optional extension feature.You can install all of the packages in this bundle by,using pip:$ pip install -U celery-with-redisorusing easy_install:$ easy_install -U celery-with-redisOr if you want to add this bundle as a dependency in your application, you
can add the following identifier in yoursetup.py’s requires list or
in your pip requirements files:celery-with-redisYou can also specify a specific version:celery-with-redis>=3.0
Package: celery-yaml
Easy Configuration For Celery App Using a Yaml Filecelery-yamlis a library to inject a --yaml option to thecelery workercommand in order to inject its configuration.It also handle help to configurate this application for Pyramid application.UsageWith Celery 4celeryworker-Amy_application.module_containing_my.app--yamldevelopment.yaml...With Celery 5celery-Amy_application.module_containing_my.appworker--yamldevelopment.yaml...This will configure the applicationmy_applicationcontaining an applicationappin a submodulemodule_containing_my.The celery app must register the--yamlusing theadd_yaml_optionon the
app instance, this way:fromceleryimportCeleryfromcelery_yamlimportadd_yaml_optionapp=Celery()add_yaml_option(app)Yaml formatcelery:broker_url:'amqp://guest:guest@localhost:5672//'result_backend:'rpc://'imports:-my_application.tasks# see all settings in the celery docs:# https://docs.celeryproject.org/en/stable/userguide/configuration.htmllogging:version:1# dictConfig format# https://docs.python.org/3/library/logging.config.html#logging-config-dictschemaNOTEThe broker_url can also be override by an environment variableCELERY_BROKER_URLto avoid password in the configuration file.Using Celery in a Pyramid App.The extras "pyramid" must be added to install the extras depencencies.With poetry[tool.poetry.dependencies]celery-yaml={version="^0.1.3",extras=["pyramid"]}Then some entry_points have to configure, such as:[tool.poetry.plugins."paste.app_factory"]main="pyramid_app.wsgi:main"[tool.poetry.plugins."celery_yaml.app"]main="pyramid_app.backend:app"[tool.poetry.plugins."plaster.loader_factory"]"file+yaml"="plaster_yaml:Loader"thepaste.app_factoryis used byPyramiditself to build the WSGI
sergivice but we add aplaster.loader_factoryto configure the usage
of a yaml file instead of aninifile to configure it.Then thecelery_yaml.appis used bycelery-yamlas an entrypoint to
the celery app.Then, in the configuration file,celery:&celeryresult_backend:'rpc://'imports:-pyramid_app.tasksapp:"use":"egg:pyramid_app""pyramid.includes":["celery_yaml"]"celery":<<:*celery"use":"egg:pyramid_app"More configurationif the celery app as a methodon_yaml_loadedthen the function
is called with the data and the filepath in parameter.
It may be used to get some config.ExamplefromceleryimportCeleryasCeleryBaseclassCelery(CeleryBase):defon_yaml_loaded(self,data:Dict[str,Any],config_path:str):...See full example in the examples directory:Pyramid with Celery 4https://github.com/mardiros/celery-yaml/tree/master/examples/pyramid-appPyramid with Celery 5https://github.com/mardiros/celery-yaml/tree/master/examples/pyramid-app5
Package: celery-zabbix
Sends task execution metrics to Zabbix: how many tasks were started and have
completed successfully or with failure, and how many tasks are still in the
queues (supported only for broker redis). Tested against Zabbix-3.0.Inspired byhttps://gitlab.com/kalibrr/celery-prometheusUsageRunbin/celery zabbix--zabbix-nodenamemyhost.example.com--zabbix-serverzabbix.example.com.
(Alternatively you can pass--zabbix-agent-config=/etc/zabbix/zabbix_agentd.conf, then the values for server+nodename will be read from there.)Import the correspondingZabbix Templateto set up the matching items.ItemsThe following items will be sent every 60 seconds (pass--dump-interval=xto configure):celery.task.startedcelery.task.succeeded (with trigger >{$WARN_TASKS_FAILED})celery.task.failedcelery.task.retriedThese are counted from the time the monitoring process started,
so you’ll need to process them as delta on the Zabbix server.celery.task.queuetime (only iftask_send_sent_eventis enabled)celery.task.runtimeThese are the median values and use the item type “Numeric (float)”.Queue lengthsIf you pass--queuelength-interval=xthen every x seconds the queue lengths will be checked (NOTE: this only works with redis as the broker), and the following items will also be sent:celery.queue[myqueuename]These are gauge values, i.e. they contain the length as it was retrieved each
time, so they can go up and down.celery.discover.queuesThis is an auto-discovery item, together with theZabbix Templatethis will create an item and trigger for each queue. On your host, create macros of the form{$WARN_QUEUE_LENGTH:myqueuename}to determine the trigger threshold.Run testsUsingtoxandpy.test. Maybe installtox(e.g. viapip install tox)
and then simply runtox.celery_zabbix changes1.1.0 (2019-11-28)Make Python-3 compatible.
You’ll need a version ofzbxsendthat is py3 compatible as well,
e.g. fromhttps://github.com/pistolero/zbxsend/pull/91.0.4 (2019-06-26)Allow using agent config file and still overriding values on the commandline
(Patch by Peter Radcliffe)1.0.3 (2018-03-23)Close redis connection we use for queue length checking after useCatch exceptions in the dump_stats and check_queue_length threads1.0.2 (2018-03-21)Be defensive about missingtask.runtime1.0.1 (2018-03-09)Fix typo in agent config parsing1.0.0 (2018-03-06)Initial release
Package: celescope
IntroductionCeleScope is a collection of bioinfomatics analysis pipelines to process single cell sequencing data generated with Singleron products. These pipelines take paired-end FASTQ files as input and generate output files which can be used for downstream data analysis as well as a summary of QC criteria.DocumentationUser guideSupportPlease use the issue tracker for questions and malfunctions.AboutCeleScope is currently developed by Singleron Biotechnologies.www.singleron.bio
Package: celestia-node
Celestia Node RPC SDKThis Python SDK provides a convenient way to interact with theCelestia Node RPC.It is currently in development and will be updated as more endpoints are added.Supported Endpointshttps://node-rpc-docs.celestia.org/?version=v0.12.1BlobDas(Coming Soon)Fraud(Coming Soon)Header(Coming Soon)Node(Coming Soon)P2P(Coming Soon)Share(Coming Soon)State(Coming Soon)InstallationTo install this sdk use thepip package manager:pip install celestia_nodeUsageimport celestia_node

# Create a Celestia Node client
client = celestia_node.Client()For more details please check thesample.py-files
Package: celestine
AboutA python framework for desktop applications.
Featuring support for multiple graphical user interfaces.
Localized for members of the European Union.The only requirement is Python.
However, you can install additional packages for extended functionality.This has also been designed to run as aBlender Add-on!
Note that Blender is an isolated environment, so other packages can not be used with it.CommandsApplicationsBuilt in application to try out.ApplicationCommandInformationCleancelestine -a cleanRuns several code formatting  tools to get ready for publishing.Democelestine -a demoTest application for changing screens with buttons.Translatorcelestine -a translatorTranslate the default language file into the 24 other languages.Viewercelestine -a viewerTest application for viewing a limited number of images.Caution: By default the viewer application will try to load every image it finds begining with the current working directory.Graphical User InterfacesInterfaceSourcecelestinepip installblender[1]Blender Foundation-i blendercursesPython Standard Library-i curseswindows-curses[2]dearpyguiPython Package Index-i dearpyguidearpyguipygamePython Package Index-i pygamepygametkinterPython Standard Library-i tkinterNote:[1]Blender interface can only be run when this is installed as a Blender addon.[2]Package only needed on Windows. Unix and Linix already have Curses.Natural LanguagesLanguages translated from English using the Microsoft Azure Translator.LanguageTranslationCommandBulgarianбългарскиcelestine -l bgCzechčeštinacelestine -l csDanishdanskcelestine -l daGermanDeutschcelestine -l deGreekελληνικάcelestine -l elEnglishEnglishcelestine -l enSpanishespañolcelestine -l esEstonianeesticelestine -l etFinnishsuomicelestine -l fiFrenchfrançaiscelestine -l frIrishGaeilgecelestine -l gaCroatianhrvatskicelestine -l hrHungarianmagyarcelestine -l huItalianitalianocelestine -l itLithuanianlietuviųcelestine -l ltLatvianlatviešucelestine -l lvMalteseMalticelestine -l mtDutchNederlandscelestine -l nlPolishpolskicelestine -l plPortugueseportuguêscelestine -l ptRomanianromânăcelestine -l roSlovakslovenčinacelestine -l skSlovenianslovenščinacelestine -l slSwedishsvenskacelestine -l svOptional DependenciesPackagepip installInformationdearpyguidearpyguiDearPyGui: A simple Python GUI Toolkit.pygamepygamePython Game Development.windows-curseswindows-cursesSupport for the standard curses module on Windows.PillowPillowPython Imaging Library (Fork)LicencesThe licence forCascadia Codeis theSIL Open Font License.The licence forcelestineis theEuropean Union Public Licence.Project LinksDiscordDocumentationEmailLibraries.ioPyPISourceTracker
Package: celiagg
celiagg provides a simple, stateless canvas object that uses
Anti-Grain Geometry 2.4 <svn://svn.code.sf.net/p/agg/svn> with Cython to
render directly into a Numpy array.A respectably modern C++ compiler is required to build celiaggDocumentation!Installationpip install celiaggshould do the trick.Building from source with the Freetype font library on macOS requires
thepkg-configtool which can be installed via Homebrew, MacPorts, or
other macOS package management systems.To build without text support, set theCELIAGG_NO_TEXT_RENDERINGenvironment variable while building, eg.CELIAGG_NO_TEXT_RENDERING=1 pip install celiaggDependenciesNumpyCython (build-time only)Freetype2 (optional)ContributingWe encourage contributions to celiagg!  If you would like to contribute, just
fork the repository on GitHub, make your changes, and issue a pull request.Historyceliagg started as pyagg (https://github.com/erikhvatum/pyagg) but was renamed
to avoid clashing with PyAgg (https://github.com/karimbahgat/PyAgg).celiagg is a play on Celiac (Anti grain…) and AGG (Anti-Grain Geometry).
Package: cell
Version:0.0.3Synopsiscellis an actor framework forKombu.InstallationYou can installcelleither via the Python Package Index (PyPI)
or from source.To install usingpip,:$ pip install cellTo install usingeasy_install,:$ easy_install cellIf you have downloaded a source tarball you can install it
by doing the following,:$ python setup.py build
# python setup.py install # as rootGetting HelpMailing listJoin thecarrot-usersmailing list.Bug trackerIf you have any suggestions, bug reports or annoyances please report them
to our issue tracker athttp://github.com/celery/cell/issues/ContributingDevelopment ofcellhappens at Github:http://github.com/celery/cellYou are highly encouraged to participate in the development. If you don’t
like Github (for some reason) you’re welcome to send regular patches.LicenseThis software is licensed under theNew BSD License. See theLICENSEfile in the top distribution directory for the full license text.CopyrightCopyright (C) 2011 VMware, Inc.
Package: cell2cell
Inferring cell-cell interactions from transcriptomes withcell2cell:book: Getting startedFor tutorials and documentation, visitcell2cell ReadTheDocsor ourcell2cell website.:wrench: InstallationStep 1: Install Anaconda:snake:First,install Anaconda following this tutorialStep 2: Create and Activate a New Conda Environment:computer:# Create a new conda environment
conda create -n cell2cell -y python=3.7 jupyter

# Activate the environment
conda activate cell2cellStep 3: Install cell2cell:arrow_down:pip install cell2cell:bulb: Examplescell2cell ExamplesTensor-cell2cell Examples-Step-by-step Pipeline-Interaction Pipeline for Bulk Data-Interaction Pipeline for Single-Cell Data-Whole Body ofC. elegans-Obtaining patterns of cell-cell communication-Downstream 1: Factor-specific analyses-Downstream 2: Patterns to functions (GSEA)-Tensor-cell2cell in Google Colab (GPU)-Communication patterns inSpatial TranscriptomicsReproducible runs of the analyses in theTensor-cell2cell paperare available atCodeOcean.com:link: LIANA & Tensor-cell2cellExplore our tutorials for using Tensor-cell2cell withLIANAatccc-protocols.readthedocs.io.:question: Common IssuesMemory Errors with Tensor-cell2cell:If you encounter memory errors when performing tensor factorizations, try replacinginit='svd'withinit='random'.:dna: Ligand-Receptor PairsFind a curated list of ligand-receptor pairs for your analyses at ourGitHub Repository.:bookmark_tabs: CitationPlease cite our work using the following references:cell2cell:Inferring a spatial code of cell-cell interactions across a whole animal body.PLOS Computational Biology, 2022Tensor-cell2cell:Context-aware deconvolution of cell-cell communication with Tensor-cell2cell.Nature Communications, 2022.LIANA & Tensor-cell2cell tutorials:Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.bioRxiv, 2023
Package: cell2sentence
cell2sentenceReframing cells as sentences of genes, ordered by expression. Please
read the manuscript on bioRxiv for methodological details and examples.(https://www.biorxiv.org/content/10.1101/2022.09.18.508438)Stable SetupInstallcell2sentencefrom PyPI withpip install cell2sentenceConvert Anndata Object to Cell SentencesAfter your data is loaded into a standard AnnDataadataobject, you may
create a cell2sentence object with:import cell2sentence as cs

csdata = cs.transforms.csdata_from_adata(adata)and generate a list of cell sentences with:sentences = csdata.create_sentence_lists()A tutorial script showing how to use pretrained word vectors to analyze
thepbmc3kdataset used by Seurat and scanpy in their guided clustering
tutorials is available attutorials/pbmc3k_cell_sentences.pyDevelopment SetupCreate a conda environment usingpython3usinganacondawith:conda create -n cell2sentence python=3.8and activate the environment withconda activate cell2sentencefinally, you can install the latest development version ofcell2sentenceby
runningmake installwhich simply usespip -e.Loading DataAll data used in the bioRxiv manuscript are publicly available, and details
are outlined in theDATA.mdfile in this repository.
Package: cellacdc
Cell-ACDCA GUI-based Python framework forsegmentation,tracking,cell cycle annotationsandquantificationof microscopy dataWritten in Python 3 byFrancesco PadovaniandBenedikt Mairhoermann.Overview of pipeline and GUIOverviewLet’s face it, when dealing with segmentation of microscopy data we
often do not have time to check thateverything is correct, because
it is atediousandvery time consuming process. Cell-ACDC comes
to the rescue! We combined the currentlybest available neural network
models(such asSegment Anything Model
(SAM),YeaZ,cellpose,StarDist,YeastMate,omnipose,delta,DeepSea, etc.) and we
complemented them with afast and intuitive GUI.We developed and implemented several smart functionalities such asreal-time continuous tracking,automatic propagationof error
correction, and several tools to facilitate manual correction, from
simple yet usefulbrushanderaserto more complex flood fill
(magic wand) and Random Walker segmentation routines.See belowhow it comparesto other popular tools available (Table 1
of
ourpublication).Is it only about segmentation?Of course not! Cell-ACDC automatically computesseveral single-cell
numerical featuressuch as cell area and cell volume, plus the mean,
max, median, sum and quantiles of any additional fluorescent channel’s
signal. It even performs background correction, to compute theprotein
amount and concentration.You can load and analyse single2D images,3D data(3D z-stacks
or 2D images over time) and even4D data(3D z-stacks over time).Finally, we provide Jupyter notebooks tovisualizeand interactivelyexplorethe data produced.Bidirectional microscopy shift error correctionIs every second line in your files from your bidirectional microscopy
shifted? Lookherefor further information on how to correct your data.ResourcesPlease find a complete user guidehereInstallation guideUser manualPublicationof Cell-ACDCForumfor discussions (feel free toask any question)Report issues, request a feature or ask questionsby opening a new issuehereXthreadCiting Cell-ACDC and the available modelsIf you find Cell-ACDC useful, please cite it as follows:Padovani, F., Mairhörmann, B., Falter-Braun, P., Lengefeld, J. &
Schmoller, K. M. Segmentation, tracking and cell cycle analysis of live-cell
imaging data with Cell-ACDC.BMC Biology20, 174 (2022).
DOI:10.1186/s12915-022-01372-6IMPORTANT: when citing Cell-ACDC make sure to also cite the paper of the
segmentation models and trackers you used!
Seeherefor a list of models currently available in Cell-ACDC.ContactDo not hesitate to contact ushere on GitHub (by opening an issue)
or directly at the emailpadovaf@tcd.iefor any problem and/or feedback
on how to improve the user experience!ContributingAt Cell-ACDC we encourage contributions to the code! Please read ourcontributing guideto get started.
Package: cell-analysis-tools
CELL ANALYSIS TOOLSA library for loading, processing and summarizing single cell imaging data. This library was originally created for the Skala lab at
the Morgridge Institute for Research/Univeristy of Wisconsin-Madison to process and analyze 2-photon data from time correlated single photon counting.
Many functions and implementations can be reused for different image types. It also has examples of how to use some of these functions along with
example data.More documentationDocumentation on ReadTheDocsDependenciesnumpytifffilefrom pathlib import Pathread_roiosmatplotlibreskimagepandas as pdInstallationThe library can be install through pippip install cell-analysis-toolsor the latest version can be installed by cloning the repository, changeing directory to the root of the cell_analysis_tools folder then execute:$ pip install -e .you should then be able to import it into your scriptimport cell_analysis_toolsCaveat: It currently only supports python < 3.12.x and >= 3.9.x.Cite cell-analysis-tools@inproceedings{10.1117/12.2647280,
author = {Emmanuel Contreras Guzman and Peter R. Rehani and Melissa C. Skala},
title = {{Cell analysis tools: an open-source library for single-cell analysis of multi-dimensional microscopy images}},
volume = {12383},
booktitle = {Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXI},
editor = {Attila Tarnok and Jessica P. Houston},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {123830G},
keywords = {Single-cell, image analysis, data analysis, open-source software, python},
year = {2023},
doi = {10.1117/12.2647280},
URL = {https://doi.org/10.1117/12.2647280}
}Emmanuel Contreras Guzman, Peter R. Rehani, Melissa C. Skala, "Cell analysis tools: an open-source library for single-cell analysis of multi-dimensional microscopy images," Proc. SPIE 12383, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXI, 123830G (15 March 2023);https://doi.org/10.1117/12.2647280
Package: cellannotation
# cell-annotationThis package provides functionalities to annotate data with labels based on the
Mann-Whitney U test and Hypergeometric test. Currently, we provide examples of
the fast annotation of gene expression data with the cell types based on marker
genes.## Build the documentationFor building the documentation one needs to have sphinx python package
installed.cd docs
make hmtlAfter build the documentation is available in the _build directory.