Saravanan666/pypi-org huggingface dataset:
Package: anncheck
anncheckCheck for missing annotations in Python. It's super fast compared to mypy, checking for missing
annotations insympygave the following results:BenchmarkQueryResultExecution timeMyPymypy --disallow-untyped-calls --disallow-untyped-defs --disallow-incomplete-defs sympyFound 31037 errors in 1171 files (checked 1405 source files)88.32 sAnncheckanncheck sympy -rFound 128059 variables missing annotation(s) in 1197 files47.28 sAnncheck Compact modeanncheck sympy -r -cFound 128059 variables missing annotation(s) in 1197 files4.47 sIt also has more options for annotation checking.Installationpip install anncheckUsageUsage: anncheck [OPTIONS] SRC...

Options:
  -a, --include-asterisk    Include variables starting with '*'.  [default:
                            False]
  -c, --compact             Compact mode, displays file name and number of
                            missing annotations on a single line.  [default:
                            False]
  -d, --include-docstrings  Anncheck doesn't check for functions inside
                            triple-quotes by default, set flag to do.
                            [default: False]
  -e, --exclude-return      Exclude return annotations.  [default: False]
  -n, --new-line            Set flag to separate functions by an empty line.
                            [default: False]
  -r, --recursive           Set flag to recursively go into folders.
                            [default: False]
  -m, --exclude-main        Exclude functions defined in 'if __name__ ==
                            "__main__": ...'  [default: False]
  -p, --padding INTEGER     Padding for line number.  [default: 3]
  --exclude-dunder          Exclude dunder functions.  [default: False]
  --init-return             Set flag to show if __init__ is missing a return
                            annotation.  [default: False]
  --match-function TEXT     Only search functions matching regex. Note: Put
                            regex in quotes.
  --match-variable TEXT     Match variables with regex. Note: Put regex in
                            quotes.
  --help                    Show this message and exit.
Package: anncorra
Tags of AnnCorraIndian Language Machine Translation (ILMT) project has taken the task of annotating corpora(AnnCorra)of several Indian languages and came up with tags which have been defined for the tagging schemes for POS (part of speech) tagging.This repository would explain thePOS(Part Of Speech) Tags along with examples.RequirementsPackage requires the following to run:python(preferable version 3+)InstallationUse the package managerpipto install foobar.pipinstallanncorraorgitclonehttps://github.com/kuldip-barot/anncorra.gitcdanncorra
pythonsetup.pyinstallUsageImport the package after installation.>>>importanncorra>>>anncorra.explain('NN')The output of above command:POS Tags :  NN
Full form :  Noun
Desription :  The tag NN tag set makes a distinction between noun singular (NN) and noun plural (NNS).
Example :
yaha bAta  galI_NN galI_RDP meM  phEla gayI
 'this' 'talk'  'lane'      'lane'         'in'    'spread' 'went'
 “The word was spread in every lane”.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.ReferencesAnnCorra : Annotating Corpora; Guidelines For POS And Chunk Annotation For Indian Languages
Package: anndata
anndata - Annotated dataanndata is a Python package for handling annotated data matrices in memory and on disk, positioned between pandas and xarray. anndata offers a broad range of computationally efficient features including, among others, sparse data support, lazy operations, and a PyTorch interface.Discuss development onGitHub.Read thedocumentation.Ask questions on thescverse Discourse.Install viapip install anndataorconda install anndata -c conda-forge.SeeScanpy's documentationfor usage related to single cell data. anndata was initially built for Scanpy.anndata is part of the scverse project (website,governance) and is fiscally sponsored byNumFOCUS.
Please consider making a tax-deductibledonationto help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.CitationIf you useanndatain your work, please cite theanndatapre-print as follows:anndata: Annotated dataIsaac Virshup, Sergei Rybakov, Fabian J. Theis, Philipp Angerer, F. Alexander WolfbioRxiv2021 Dec 19. doi:10.1101/2021.12.16.473007.You can cite the scverse publication as follows:The scverse project provides a computational ecosystem for single-cell omics data analysisIsaac Virshup, Danila Bredikhin, Lukas Heumos, Giovanni Palla, Gregor Sturm, Adam Gayoso, Ilia Kats, Mikaela Koutrouli, Scverse Community, Bonnie Berger, Dana Pe’er, Aviv Regev, Sarah A. Teichmann, Francesca Finotello, F. Alexander Wolf, Nir Yosef, Oliver Stegle & Fabian J. TheisNat Biotechnol.2023 Apr 10. doi:10.1038/s41587-023-01733-8.
Package: anndata2ri
AnnData ↭ SingleCellExperimentRPy2 converter fromAnnDatatoSingleCellExperimentand back. (Fordetails about conversionsee thedocs)You can for example use it to process your data using bothScanpyandSeurat, as described in thisexample notebookInstallationpipinstallanndata2ri# orcondainstall-cbiocondaanndata2riTroubleshootingIf you have problems installing or importing anndata2ri,
please make sure you first:Check the stack trace:
If the error happens while installing or importing a dependency such asrpy2,
report your problem in that project’s bug tracker.Search theissues.
At the time of writing 17 of the 29 bugs (60%) are invalid or rpy2 bugs / install problems.Make sure you have a compatible R version: rpy2 requires R ≥ 3.6.Usage from PythonEither use the converter manually …importanndata2rifromrpy2.robjectsimportrfromrpy2.robjects.conversionimportlocalconverterwithlocalconverter(anndata2ri.converter):adata=r('as(some_data, "SingleCellExperiment")')… or activate it globally:importanndata2rifromrpy2.robjectsimportranndata2ri.activate()adata=r('as(some_data, "SingleCellExperiment")')Usage from JupyterActivate the conversion before you load the extension:importanndata2rianndata2ri.activate()%load_extrpy2.ipythonNow you can move objects from Python to R …importscanpy.datasetsasscdadata_paul=scd.paul15()%%R-iadata_pauladata_paul# class: SingleCellExperiment ...… and back:%%R-oadata_allendata(allen,package='scRNAseq')adata_allen<-as(allen,'SingleCellExperiment')print(adata_allen)# AnnData object with ...
Package: anneal
AnnealThis is apythonpackage for simulated annealing (and quenching) in all its
many guises. The design decisions are described in the correspondingArXiV preprint.Developmentpdmis used throughout.micromambacreate-fenvironment.yml
micromambaactivateanneal-devContributingAll contributions are welcome!!We follow theNumPy commit guidelines.Please runpdm alland ensure no linting or test errors existCo-author commitsgenerouslyLicenseMIT.
Package: annex
SummaryAnnex provides assistance with developing plugin-based tools.With Annex you can load and reload plugins from various python modules
without the requirement that they exist on the PYTHONPATH.Example UsageIn your project you would define a base class from which all plugins for
project would subclass.base_plugin.pyclass BaseTestPlugin(object):
    def run(self, *args, **kwargs):
        raise NotImplementedError()example_plugin.pyfrom base_plugin import BaseTestPlugin

class PrinterPlugin(BaseTestPlugin):
    def run(self, *args, **kwargs):
        print args, kwargsfoo.pyfrom base_plugin import BaseTestPlugin
from annex import Annex

plugins = Annex(BaseTestPlugin, ["/path/to/plugins"])

for plugin in plugins:
    plugin.run("foo", bar="baz")
Package: annex-dataproxy
annex_dataproxyThis git annex external remote extension usesAnnexRemoteto
talk toEBRAINS Data Proxyso that you can use EBRAINS Collaboratory buckets as
Datalad siblings.Installpip install annex_dataproxy.Usagecreate a dataset$dataladcreatepdfdata[INFO]Creatinganewannexrepoat/tmp/pdfdata[INFO]scanningforunlockedfiles(thismaytakesometime)create(ok):/tmp/pdfdata(dataset)$cdpdfdata/
$rsync-ra$HOME/PDFs/./
$dataladsave
add(ok):1107.0903MontbrioPazo-StuartLandau.pdf(file)[199similarmessageshavebeensuppressed;disablewithdatalad.ui.suppress-similar-results=off]save(ok):.(dataset)actionsummary:add(ok:209)save(ok:1)tell the dataproxy remote our token and what bucket & prefix to use$exportEBRAINS_TOKEN=$EBRAINS_TOKEN$exportDATAPROXY_PATH=insference/pdfscreate the annex remote anddatalad push --toit$ git annex initremote pdfs type=external externaltype=dataproxy encryption=none
initremote pdfs ok
(recording state in git...)
$ datalad push --to pdfs
copy(ok): 15009.full.pdf (file) [to pdfs...]                                                   
  [193 similar messages have been suppressed; disable with datalad.ui.suppress-similar-results=off]  
action summary:                                                                                      
  copy (notneeded: 6, ok: 203)Statussloppy proof of conceptgit annex testremotepassesPyPI package for easier installbetter mechanism for specifying bucket & prefix
Package: annexlang
The Annex language is a markup language for protocols. Its current
(only) feature is to create protocol descriptions that can be used in
LaTeX documents.Annex is based on YAML. As of now, there is no further description of
the language except for the example files which make use of most
language features.To convert a file from Annex to TeX, just runannex-convert infile.yml outfile.tex
Package: ann-gsea
annsigAn API for annotating single-cell AnnData with Molecular Signatures fromMSigDBTo use:Step 1. InstallTo install with the latest release fromPYPI:pipinstallannsigalternatively, install the development version:gitclonehttps://github.com/mvinyard/annsigcdannsig;pipinstall-e.Step 2. Register and download MSigDBlinkDeveloped with:"msigdb.v7.4.symbols.gmt"(Currently the latest version)Step 3. Example usageimportann_gseaasgseadb=gsea.MSigDB()db.load()db.search()db.fetch()linkDeveloped with:"msigdb.v7.4.symbols.gmt"(Currently the latest version)Instructions from the MSigDB website on how to cite their resource:To cite your use of the Molecular Signatures Database (MSigDB), a joint project of UC San Diego and Broad Institute, please reference Subramanian, Tamayo, et al. (2005, PNAS) and one or more of the following as appropriate: Liberzon, et al. (2011, Bioinformatics), Liberzon, et al. (2015, Cell Systems), and also the source for the gene set as listed on the gene set page.
Package: anngtf
anngtfLift annotations from agtfto youradataobject.InstallationTo install viapip:pipinstallanngtfTo install the development version:gitclonehttps://github.com/mvinyard/anngtf.gitcdanngtf;pipinstall-e.Example usageParsing a.gtffileimportanngtfgtf_filepath="/path/to/ref/hg38/refdata-cellranger-arc-GRCh38-2020-A-2.0.0/genes/genes.gtf"If this is your first time usinganngtf, run:gtf=anngtf.parse(path=gtf_filepath,genes=False,force=False,return_gtf=True)Running this function will create two.csvfiles from the given.gtffiles - one containing all feature types and one containing only genes. Both of these files are smaller than a.gtfand can be loaded into memory much faster usingpandas.read_csv()(shortcut implemented in the next function). Additionally, this function leaves a paper trail foranngtfto find the newly-created.csvfiles again in the future such that one does not need to pass a path to the gtf.In the scenario in which you've already run the above function, run:gtf=anngtf.load()# no path necessary!Updating theadata.vartable.importanndataasaimportanngtfadata=anndata.read_h5ad("/path/to/singlecell/data/adata.h5ad")gtf=anngtf.load(genes=True)anngtf.add(adata,gtf)Since theanngtfdistribution already knows where the.csv / .gtffiles are, we could directly annotateadatawithout first specifcyinggtfas a DataFrame, saving a step but I think it's more user-friendly to see what each one looks like, first.Working advantageLet's take a look at the time difference of loading a.gtfinto memory as apandas.DataFrame:importanngtfimportgtfparseimporttimestart=time.time()gtf=gtfparse.read_gtf("/home/mvinyard/ref/hg38/refdata-cellranger-arc-GRCh38-2020-A-2.0.0/genes/genes.gtf")stop=time.time()print("baseline loading time:{:.2f}s".format(stop-start),end='\n\n')start=time.time()gtf=anngtf.load()stop=time.time()print("anngtf loading time:{:.2f}s".format(stop-start))baseline loading time: 87.54s

anngtf loading time: 12.46s~ 7x speed improvement.Note: This is not meant to criticize or comment on anything related togtfparse- in fact, this library relies solely ongtfparsefor the actual parsing of a.gtffile into memory aspandas.DataFrame.
Package: annhub-python
ANNHUB Python libraryMain backend module, which is used for developing web-app logic and deploying AI model by just a few lines of code.UsageWe develop a RESTful web controller into a reusable library between many AI models. With these functionalities:Input model,Define data input,logging,exception handler.InstallingDelivering and versioning as aPyPipackage.
Install and update usingpip:$ pip install annhub-pythonA simple examplefromannhub_pythonimportPyAnnapp=PyAnn()# Define the expected AI modelapp.set_model("D:\ARI\ANSCENTER\TrainedModel_c++.ann")# Define which model ID will be usedapp.set_model_id(5122020)# Define the input corresponding to the choosen modelapp.set_input_length(4)if__name__=="__main__":app.run()APIThe library will product two APIs:health checking,predictingas well as aSwagger UIfor API documentation.GET: /api/v1/health
POST: /api/v1/predictDetailed ExampleIris Prediction serverIn this example, we illustrate how to develop a server by using AI model powered by ANNHUB with only few steps. You can use thislinkto access our code.
The procedure of using our library to server AI model is as follows:Put a trained model into your project folder.Create main.py file, where some key information will be determined such as model path, model id, input length,...Create Dockerfile to containerize your application. (We recommend to reuse ourDockerfile).Create docker-compose.yml file, which will construct your docker container by a simple command line. (We also recommend to use as ourinstruction)Run your application be a simple command line:docker-compose up -dWith default settings, your AI can be used athttp://localhost:8080. You can accesshttp://localhost:8080/docsto use your Swagger UI documentation.
Package: annict
python-annictAnnict APIwrapper for Pythonpython-annictofficially supports Python 3.6 or higher.InstallationpipinstallannictQuickstartAuthenticationAcquire the URL for authentication code.>>>fromannict.authimportOAuthHandler>>>handler=OAuthHandler(client_id='Your client ID',client_secret='Your client secret')>>>url=handler.get_authorization_url(scope='read write')>>>print(url)Open the browser and access the URL you obtained, the authentication code will be displayed.
It will be passed to thehandler.authenticate()'s argument to get the access token.>>>handler.authenticate(code='Authentication code')>>>print(handler.get_access_token())Note that this authentication flow is unnecessary when issuing a personal access token on Annict and using it.See:Annict API: 個人用アクセストークンが発行できるようになりましたHello world>>>fromannict.apiimportAPI>>>annict=API('Your access token')>>>results=annict.works(filter_title="Re:ゼロから始める異世界生活")>>>print(results[0].title)Re:ゼロから始める異世界生活CacheFor now, we do not have our own cache system. However, caching is also important to reduce the load on AnnictAPI.So I introduce a cache plugin forrequestslibrary calledrequests_cache.Install with pip.pipinsallrequests_cacherequests_cacheis very easy to use.>>>importrequests_cache>>>requests_cache.install_cache(cache_name='annict',backend='memory',expire_after=300)>>># At first, from Annict API.>>>api.me()>>># You can get results from cache, if it is within the expiration time.>>>api.me()For more information:Requests-cache documentationDocumentationThis library's documentationAnnict Docs(Japanese)
Package: annin-dofu
author: laplaciannin102(Kosuke Asada)Table of Contentsannin_dofuTable of ContentsHow to install概要annin_dofuの由来内容機能How to installpipinstallannin_dofu概要annin_dofuの由来「analyzenatureindataofuniverse」の略「宇宙のデータで自然を分析する」内容laplaciannin102の自作ライブラリ群です.機能詳しくはドキュメント参照modulescalcmatrixparallelstatsutilsresponse_surface_methodology応答曲面法関連stocking_quantity_optimization在庫最適化関連
Package: annize
TODO Anise is a Python-based execution engine for automation tasks.Automation tasks exist in software development, and probably all kinds of other sectors. They typicallyrequire the execution of different smaller and larger tools.Complex tasks often need a sequence of many steps to execute, with some steps having dependenciesto each other.Manually triggering all these steps in the graphical interfaces of all the involved tools ispossible in theory, but will generate errors and frustration after some cycles.The automation interfaces of those tools are sometimes easier, but sometimesthey are error-prone. Some tasks may also need to ask the user for some information in an interactive way.Some smaller parts might also be machine-specific (e.g. filesystem paths or the code how toaccess a password vault), while the entire task must be runnable on some different machines.In some situations, this can lead to a rather intransparent forest of different tools, with uniqueoddnesses and special conventions. As the number of different project increases, you will see more and moredifferent tools, often doing a similar job, but for different platforms or frameworks and, of course,with different usage conventions. Spontaneously written glue scripts help in the beginning, butwill explode as the complexity exceeds some threshold.Typical tasks in software development could be:- Generating documentation- Testing- Automatic code generation- Creating packages- Creating a homepage, automatically built from the available  version information, the packages, the documentation and so on- Deploying this homepage to a web server- Handling version information  - e.g. print it in the manual- and many moreThe Anise framework allows you to implement all those tasks in a structured but generic way in a combination of XML andPython code. Once you have created this stuff at a defined place in your project, Anise lets you easily execute yourtasks from command line (or from any editor if you embed it somehow). This gives you a common and easy interfaceto all your 'tool glue' code.The Anise engine executes arbitrary Python sourcecode and provides some additional services like logging, parameter passing from command line, basic graphical userinterface support, a plugin interface, a flexible event system, injecting code and data from other place,dependencies between code fragments, and more.On top of this engine, Anise comes with a bunch of implementations that fulfill tasks (or parts of them) ofsoftware development. There is a testing module, a documentation- and homepage-generator, some package buildingmethods and a lot more. The implementations use the event system in many places in order to allow customization ina somewhat technical but very flexible way. Even so, those implementations are rather specific and it depends onthe particular case, if, and how many of those implementations are useful.
Package: ann-nmf
AnnData wrapper of theARD-NMF modulefromSignatureAnalyzer-GPU.InstallationInstall usingpippipinstallann_nmfAlternatively, install the development version:gitclonehttps://github.com/mvinyard/ann_nmf.git;cdann_nmf;pipinstall-e.API overviewImport libraries and get some dataimportann_nmfimportscanpyasscadata=sc.datasets.pbmc3k()ann_nmf.ut.preprocess_raw_counts(adata)Key class:nmf=ann_nmf.NMF(adata,outdir="nmf_results/pbmc3k")# saves .h5 filenmf.run(n_runs=10,K0=20,max_iter=2000)SignatureAnalyzer visualization:nmf.cluster()nmf.signatures()Conceptual background and foundational workARD-NMF theoryArxivSignatureAnalyzer (GitHub)SignatureAnalyzer-GPU (GitHub)AcknowledgementsMost of the code to wrap SignatureAnalyzer in an AnnData-friendly API was borrowed directly (and shamelessly) from Shankara Anand (@shankara-a) with only slight refactoring for more flexibility with fewer dependencies on install.
Package: annodize
Annodize: Python Annotations that are shockingly usefulUsing PEP 593 (Python 3.9+) Annotated Types for good... or for evil.Example usesData type conversion.Dataframe validation.(Your use case here!)See AlsoCheck outfuture-typing, which
lets you use Python 3.10 UnionType types in any Python version:# -*- coding: future_typing -*-foo:int|str
Package: annofab-3dpc-editor-cli
annofab-3dpc-editor-cliAnnofabの3次元プロジェクトを操作するためのCLIです。Install$ pip install annofab-3dpc-editor-cliコマンドサンプルhttps://annofab-3dpc-editor-cli.readthedocs.io/ja/latest/user_guide/command_sample.html参照バージョンの確認方法$ anno3d version
annofab-3dpc-editor-cli 0.2.1開発環境poetryPoetry version 1.0.5python 3.8開発環境初期化poetryのインストール手順は、このファイル下部のpoetryのインストール手順を参照poetry installpoetryのインストール手順poetryのインストール手順一例を以下に示す2020/05/21 ubuntu 18.04 にて確認ローカルの環境に以下の手順でインストールする以外に，
python 3.8 および poetry の導入がなされたdocker/Dockerfileを用いても良い．pyenvシステムにpython 3.8を直接インストールして使うならpyenvは要らないsudo apt-get update

sudo apt-get install build-essential libssl-dev zlib1g-dev libbz2-dev \
libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev \
xz-utils tk-dev libffi-dev liblzma-dev python-openssl gitcurl https://pyenv.run | bashコンソールに、以下のような設定すべき内容が出力されるので.bashrcなどに設定export PATH="/home/vagrant/.pyenv/bin:$PATH"
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)"pyenv install 3.8.3
pyenv global 3.8.3pipx直接 poetry をインストールするなら要らないpython -m pip install --user pipx
python -m pipx ensurepathcompletionを効かせたいときは、pipx completionsの実行結果に従って設定する$ pipx completions

Add the appropriate command to your shell's config file
so that it is run on startup. You will likely have to restart
or re-login for the autocompletion to start working.

bash:
    eval "$(register-python-argcomplete pipx)"

zsh:
    To activate completions for zsh you need to have
    bashcompinit enabled in zsh:

    autoload -U bashcompinit
    bashcompinit

    Afterwards you can enable completion for pipx:

    eval "$(register-python-argcomplete pipx)"

tcsh:
    eval `register-python-argcomplete --shell tcsh pipx`

fish:
    register-python-argcomplete --shell fish pipx | .poetrypipx install poetry
poetry completions bash | sudo tee /etc/bash_completion.d/poetry.bash-completionPyPIへの公開$ make publish
Package: annofabapi-3dpc-extensions
annofabapi-3dpc-extensionsannofabapiの3次元アノテーション用の拡張機能です。InstallPython 3.8+Install$ pip install annofabapi-3dpc-extensionsUsagecuboidアノテーションやセグメントアノテーションに対応したデータクラスを利用できます。fromannofabapi.parserimportSimpleAnnotationDirParserfromannofab_3dpc.annotationimport(CuboidAnnotationDetailDataV2,EulerAnglesZXY,SegmentAnnotationDetailData,SegmentData,convert_annotation_detail_data,)parser=SimpleAnnotationDirParser("tests/data/task1/input1.json")result=parser.parse(convert_annotation_detail_data)segment_annotation_data=result.details[0].datacuboid_annotation_data=result.details[1].dataasserttype(segment_annotation_data)==SegmentAnnotationDetailDataasserttype(cuboid_annotation_data)==CuboidAnnotationDetailDataV2### cuboid annotationprint(cuboid_annotation_data)# => CuboidAnnotationDetailDataV2(shape=CuboidShapeV2(dimensions=Size(width=6.853874863204751, height=0.2929844409227371, depth=4.092537841193188), location=Location(x=-11.896872014598989, y=-3.0571381239812996, z=0.3601047024130821), rotation=EulerAnglesZXY(x=0, y=0, z=0), direction=CuboidDirection(front=Vector3(x=1, y=0, z=0), up=Vector3(x=0, y=0, z=1))), kind='CUBOID', version='2')# オイラー角をクォータニオンに変換print(cuboid_annotation_data.shape.rotation.to_quaternion())# => [1.0, 0.0, 0.0, 0.0]# クォータニオンからオイラー角に変換print(EulerAnglesZXY.from([1.0,0.0,0.0,0.0]))# => EulerAnglesZXY(x=-0.0, y=0.0, z=0.0)### segment annotationprint(segment_annotation_data)# => SegmentAnnotationDetailData(data_uri='./input1/7ba51c15-f07a-4e29-8584-a4eaf3a6812a')# セグメント情報が格納されたファイルを読み込むwithparser.open_outer_file(Path(segment_annotation_data.data_uri).name)asf:dict_segmenta_data=json.load(f)segment_data=SegmentData.from_dict(dict_segmenta_data)asserttype(segment_data)==SegmentDataassertlen(segment_data.points)>0print(segment_data.points)# => [130439, 130442, ... ]