Saravanan666/pypi-org huggingface dataset:
Package: olsapy
OLSApyOLSApy: Orthogonal Linear Separation Analysis in PythonOLSA is an analysis method of omics data to decompose the complex effects of a perturbagen into basic components.OLSApy is a package for OLSA in python.OLSA can be applied to any kinds of omics data such as RNA-seq, proteome, and so on.Dependencypython >= 3.6requirements: numpy, pandas, scipySetuppip install olsapyFor conda, please use conda skeletonUsagefrom olsapy import olsa

df = <a dataframe of interest (feature x sample)>

### calculation
dat = olsa.OLSA()
dat.load_df(df)
dat.olsa(accumulation=0.6) # accumulation determines the vectors subjected to varimax rotation
rsm,rvm,ts,contribution = dat.get_res()
### visualization
dat.plot(focus=[<sample names of interest>])

### visualization of already prepared data
dat.plot(rsm,focus=[<sample names of interest>])LicenceThis software is released under the MIT License, see LICENSE.AuthorsSetsuo Kinoshita, Shotaro Maedera, and Tadahaya MizunoReferencesSci Rep. 2019 Feb 12;9(1):1824. doi: 10.1038/s41598-019-38528-4.http://www.ilincs.org/ilincs/Bug ReportIf you would like to report any bugs about olsapy, don't hesitate to create an issue on github here, or email me:tadahaya@gmail.comtadahaya@mol.f.u-tokyo.ac.jp
Package: olscheck
OlsCheckolscheckis a Python library designed to check and visualize the assumptions of Ordinary Least Squares (OLS). This tool streamlines the process of model diagnostics, offering an all-in-one solution for analyzing residuals, leverage, and multi-collinearity.Sample of Residuals vs Fitted values visualizationFeaturesResiduals vs Fitted Plot: Visualize potential non-linear patterns.QQ-Plot: Check the normality of residuals.Scale-Location Plot: Confirm homoscedasticity.Leverage Plot: Identify influential cases.VIF Test: Assess multi-collinearity among predictors.BackgroundMuch of the code for this library is inspired byStatsmodels. However, the Statsmodels implementation is tightly coupled with its own components, making it challenging to use if you are employing OLS from a different library.OlsCheckprovides a straightforward alternative for generating key diagnostic plots that assess the assumptions underlying Ordinary Least Squares (OLS) regression. The library internally calculates residuals, leverage, and studentized residuals to deliver these insights.Installationpipinstallolscheck
Package: ols-client
OLS ClientA client to the EBI Ontology Lookup Service (OLS) and other instances.💪 Getting StartedOne of several OLS clients can be instantiated and accessed with the same API:fromols_clientimportEBIClientebi_client=EBIClient()ontologies=ebi_client.get_ontologies()Further documentation can be found atols-client.readthedocs.io.🚀 InstallationThe most recent release can be installed fromPyPIwith:$pipinstallols_clientThe most recent code and data can be installed directly from GitHub with:$pipinstallgit+https://github.com/cthoyt/ols-client.git👐 ContributingContributions, whether filing an issue, making a pull request, or forking, are appreciated. SeeCONTRIBUTING.mdfor more information on getting involved.👋 Attribution⚖️ LicenseThe code in this package is licensed under the MIT License.RelatedSince the creation of this repository, the EBI has also generated their
own client that can be found athttps://github.com/Ensembl/ols-clientand
on PyPI asebi-ols-client.🍪 CookiecutterThis package was created with@audreyfeldroy'scookiecutterpackage using@cthoyt'scookiecutter-snekpacktemplate.🛠️ For DevelopersSee developer instructionsThe final section of the README is for if you want to get involved by making a code contribution.Development InstallationTo install in development mode, use the following:$gitclonegit+https://github.com/cthoyt/ols-client.git
$cdols-client
$pipinstall-e.🥼 TestingAfter cloning the repository and installingtoxwithpip install tox, the unit tests in thetests/folder can be
run reproducibly with:$toxAdditionally, these tests are automatically re-run with each commit in aGitHub Action.📖 Building the DocumentationThe documentation can be built locally using the following:$gitclonegit+https://github.com/cthoyt/ols-client.git
$cdols-client
$tox-edocs
$opendocs/build/html/index.htmlThe documentation automatically installs the package as well as thedocsextra specified in thesetup.cfg.sphinxplugins
liketexextcan be added there. Additionally, they need to be added to theextensionslist indocs/source/conf.py.📦 Making a ReleaseAfter installing the package in development mode and installingtoxwithpip install tox, the commands for making a new release are contained within thefinishenvironment
intox.ini. Run the following from the shell:$tox-efinishThis script does the following:UsesBump2Versionto switch the version number in thesetup.cfg,src/ols_client/version.py, anddocs/source/conf.pyto not have the-devsuffixPackages the code in both a tar archive and a wheel usingbuildUploads to PyPI usingtwine. Be sure to have a.pypircfile configured to avoid the need for manual input at this
stepPush to GitHub. You'll need to make a release going with the commit where the version was bumped.Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can
usetox -e bumpversion minorafter.
Package: ols-py
ols-pyDocumentation:https://ahida-development.github.io/ols-pySource Code:https://github.com/ahida-development/ols-pyPyPI:https://pypi.org/project/ols-py/Python client for theOntology Lookup ServiceCurrent status:Now that OLS4 is released and OLS3 is no longer updated, this client will only
support OLS4 going forwardSome API endpoints may not be implemented, or schemas not fully described -
pleaseopen an issueif
you want anything added.Features:Type annotated so you know which parameters can be used for each endpointResponses validated and parsed withpydanticfor
easy access to response dataExample usagefromols_py.clientimportOls4Clientclient=Ols4Client()resp=client.search("MC1R",params={"ontology":"go"})term=resp.response.docs[0]print(term)# SearchResultItem(#   id=None, annotations=None, annotations_trimmed=None,#   description=['A rhodopsin-like G-protein ... gamma-melanocyte-stimulating hormone.'],#   iri='http://purl.obolibrary.org/obo/PR_000001146', label='melanocortin receptor',#   obo_id='PR:000001146', ontology_name='go', ontology_prefix='GO', subset=None,#   short_form='PR_000001146', synonyms=None, type='class'#)print(term.iri)# http://purl.obolibrary.org/obo/PR_000001146Installationpipinstallols-pyDevelopmentClone this repositoryRequirements:PoetryPython 3.10+Create a virtual environment and install the dependenciespoetryinstallActivate the virtual environmentpoetryshellTestingpytestDocumentationThe documentation is automatically generated from the content of thedocs directoryand from the docstrings
of the public signatures of the source code. The documentation is updated and published as aGithub project pageautomatically as part each release.ReleasingTrigger theDraft release workflow(pressRun workflow). This will update the changelog & version and create a GitHub release which is inDraftstate.Find the draft release from theGitHub releasesand publish it. When
a release is published, it'll triggerreleaseworkflow which creates PyPI
release and deploys updated documentation.Pre-commitPre-commit hooks run all the auto-formatters (e.g.black,isort), linters (e.g.mypy,flake8), and other quality
checks to make sure the changeset is in good shape before a commit/push happens.You can install the hooks with (runs for each commit):pre-commitinstallOr if you want them to run only for each push:pre-commitinstall-tpre-pushOr if you want e.g. want to run all checks manually for all files:pre-commitrun--all-filesThis project was generated using thewolt-python-package-cookiecuttertemplate.
Package: olunicodenormalizer
olunicodenormalizerᱚᱞ-ᱪᱦᱤᱠᱤ Unicode Normalization for word normalizationinstallpipinstallolunicodenormalizeruseageinitialization and cleaning# importfromolunicodenormalizerimportNormalizerfrompprintimportpprint# initializebnorm=Normalizer()# normalizeword='ᱡᱚᱦᱟᱨ'result=bnorm(word)print(f"Non-norm:{word}; Norm:{result['normalized']}")print("--------------------------------------------------")pprint(result)outputNon-norm:ᱡᱚᱦᱟᱨ; Norm:ᱡᱚᱦᱟᱨ
--------------------------------------------------
{'given': 'ᱡᱚᱦᱟᱨ', 'normalized': 'ᱡᱚᱦᱟᱨ', 'ops': []}# initialize without english (default)norm=Normalizer()print("without english:",norm("ASD123")["normalized"])# --> returns Nonenorm=Normalizer(allow_english=True)print("with english:",norm("ASD123")["normalized"])outputwithout english: None
with english: ASD123Change Log0.0.5 (9/03/2022)added details for execution mapcheckop typo correction0.0.6 (9/03/2022)broken diacritics op addition0.0.7 (11/03/2022)assemese replacementword op and unicode op mappingmodifier list modificationdoc string for call and initializationverbosity removaltypo correction for operationunit test updates'এ' replacement correctionNonGylphUnicodesLegacy symbols optionlegacy mapper addedadded bn:bd declaration0.0.8 (14/03/2022)MultipleConsonantDiacritics handling changeto+hosonto correctioninvalid hosonto correction0.0.9 (15/04/2022)base normalizerlanguage classolchiki extensioncomplex root normalization0.0.10 (15/04/2022)added conjuctsexception for english words0.0.11 (15/04/2022)fixed no space char issue for olchiki0.0.12 (26/04/2022)fixed consonants orders0.0.13 (26/04/2022)fixed non char followed by diacritics0.0.14 (01/05/2022)word based normalizationencoding fix0.0.15 (02/05/2022)import correction0.0.16 (02/05/2022)local variable issue0.0.17 (17/05/2022)nukta mod break0.0.18 (08/06/2022)no space chars fix0.0.19 (15/06/2022)no space chars further fixbase_olchiki_compose to avoid false op flagsadded foreign conjuncts0.0.20 (01/08/2022)এ্যা replacement correction0.0.21 (01/08/2022)"য","ব" + hosonto combination correctionadded 'ব্ল্য' in conjuncts0.0.22 (22/08/2022)\u200d combination limiting0.0.23 (23/08/2022)\u200d condition change0.0.24 (26/08/2022)\u200d error handling0.0.25 (10/09/22)removed unnecessary operations: fixRefOrder,fixOrdersForCCadded conjuncts: 'র্ন্ত','ঠ্য','ভ্ল'0.1.0 (20/10/22)added indic parserfixed language class0.1.1 (21/10/22)added nukta and diacritic maps for indicscleaned conjucts for nowfixed issues with no-space and connector0.1.2 (10/12/22)allow halant ending for indic language except olchiki0.1.3 (10/12/22)broken char break cases for halant0.1.4 (01/01/23)added sylhetinagri0.1.5 (01/01/23)cleaned panjabi double quotes in diac map0.0.1 (26/08/23)added olchiki punctuations
Package: olutils
olutilsIntroductionAboutThe moduleolutilsprovide common tools to simplify daily coding. It includes:object storing (.csv, .json, .pickle, .txt)convenient collection (deep defaultdict, lazy list, identity/prod functions)conversion functions (for datetime, dictionaries, errors)sequencing helpers (iteration with progress display, wait until predicate)parameter managementand more...InstallationOne can install olutils using pip install command:pip install olutilsUsageObject StoringStorage:saveandloadimportolutilsmy_dict={'key_1':"value_1",'key_2':2}my_rows=[{'col_1':11,'col_2':21},{'col_1':21,'col_2':22}]# Saving objects in output directory (automatically created)olutils.save(my_dict,"output/my_dict.json")olutils.save(my_rows,"output/my_rows.csv")olutils.save(my_rows,"output/my_rows.unknown",mthd="json")# Loading objects from save outputsmy_loaded_dict=olutils.load("output/my_dict.json")my_loaded_rows=olutils.load("output/my_rows.csv")my_loaded_rows_=olutils.load("output/my_rows.unknown",mthd="json")Collectionsdeepdefaultdictimportolutilsfromolutils.conversionimportstr2dt# Building a deep default dict with datetimes as valuesflights=olutils.deepdefaultdict(lambdax:None,depth=2)# Filling itflights['Paris-NY']['departure']=str2dt("2019-01-15 08:00+01:00")flights['Paris-NY']['arrival']=str2dt("2019-01-15 10:30-05:00")flights['NY-Paris']['departure']=str2dt("2019-01-17 23:00-05:00")flights['NY-Paris']['arrival']=str2dt("2019-01-15 11:00+01:00")flights.pprint()LazyListimportolutilsprint(olutils.LazyList(range(10000),10))functions:prodandidentityimportolutils# Operationsassertolutils.prod([2,7])==14assertolutils.identity(1)==1singleton:importolutilsclassMyClass(metaclass=olutils.Singleton):passinstance=MyClass()assertinstanceisMyClass()MoreExplicit iterationimportolutilsprint("Iterating on very long iterable:")foreleminolutils.countiter(range(int(10e6)),vbatch=100,prefix=". "):# One-Line display of progress every 100 iterationpassCompareimportolutils# Comparisonl1=[1,2,"hi","bye"]l2=[3,"bye","bye bye",2]assertolutils.content_diff(l1,l2)=={'common':{2,"bye"},'minus':{1,"hi"},'plus':{3,"bye bye"},}Pretty displaysimportolutilsassertolutils.err2str(ValueError("Message"))=="ValueError - Message"l=[1,2,3,4,5]imp_l=olutils.lazy_content(l,4)assertstr(imp_l)=='[1, 2, Ellipsis, 5]'dic={'values':l,'info':{'name':"Some example",'also':"This is awesome (kinda)",}}print(f"Dictionary used:{dic}")print(f"Dictionary to pretty string:")defleafconv(x):return(str(olutils.lazy_content(x,5))ifisinstance(x,list)elsestr(x))print(olutils.dict2str(dic,leafconv=leafconv))
Package: olxApi
Description'olxapi' Is a python module designed to scrape with OLX website. It enable user programmatically search for products and retrieve a sorted list by ID, Title, Price, Thumbnail and Link of the ads."instalationpip install olxapiFeatures-Search for products on OLX.
-Retrieve a list of ads with details like thumbnail, title, price, and permalink.
-Limit the number of pages to scrape.How to Usefrom olxapi import OlxApiproduct = "Macbook pro"page_limit = 2olx = OlxApi(page_limit)ads = olx.get_list_product(product)print(ads)
Package: olxcrapper
OLXCRAPPERIntroduçãoScript em Python + Beautiful Soup que realiza consultas exaustivas em categorias da OLX em busca de anúncios novos,
ao encontrar um novo anúncio nessa categoria, ele usa as credenciais de login do GMail fornecidas nas variáveis do script
para enviar para o próprio usuário um Email com informações sobre o novo produto da categoria.RequisitosEsse script depende do Python e dos módulos: bs4, requests, lxml, argparse.O usuário tem que usar uma conta do GMail, por enquanto é o unico servidor suportado.Instalação e usopipinstallolxcrapper
olxcrapper-gseuemail@gmail.com-ssenhadogmail-uhttps://sp.olx.com.br/celulares-t60Substitua os dados de login acima com suas credenciais do gmail e com o link da categoria desejada.Quanto a conta do GMail, sugiro criar uma nova conta pra uso desse script para que sua caixa de entrada
não seja floodada com emails do tipo, é importante que na conta do GMail utilizada esteja habilitada
a opção de acesso de apps menos confiáveis.Caso deseje mais segurança ao utilizar sua conta, habilite
a opção de autenticação de dois fatores e crie uma senha exclusiva para o uso desse script, esse tipo de senha
é gerada automaticamente pela conta da google e tem 16 caracteres, ex: "gzhupbrkrdbfhpiy", você pode gerar
uma senha de app aqui:https://security.google.com/settings/security/apppasswordsQuanto ao link da categoria da olx desejada, para obter um link utilizável, acesse a categoria da olx desejada
escolha seu estado e/ou cidade e copie o link do navegador pra substituir no comando exemplificado acima.O script irá executar continuamente e irá atualizar a lista a cada -t segundos, sempre que encontrar um
anúncio novo na categoria referida pelo usuário, o mesmo enviará um email para a caixa de entrada do GMail indicado,
esse script só enviará email caso haja um anuncio novo, assim evitando floodar a caixa de entrada.OBS: é importante inserir os dados de login corretamente, caso estejam incorretos, o script executará, mostrará
a listagem de anúncios mas não conseguirá notificar por email. Também é interessante deixar o argumento -t com valor 60
pois a listagem da olx só atualiza a cada 1 minuto, valores menores que 60 segundos pode causar mau comportamento do script.AutorYago Lima LinsGithub:@yagolimalinsContribua com o projetoContribuições, bugs e pedidos de novas funcionalidades são bem vindos!
Sinta-se a vontade para conferir:issues page.Apoie o autorConsidere fazer uma contribuição no patreon caso esse script seja útil pra você :)https://www.patreon.com/yagolimalinsLicençaCopyright © 2020Yago Lima Lins.
This project isGNUlicensed.
Package: olx-utils
OLX UtilitiesA set of tools to facilitate courseware development using theOpen
Learning
XML(OLX) format.OLX is sometimes tediously repetitive, and this package enables
courseware authors to apply theDRYprinciple when writing OLX content. It allows you to create templates
(usingMako), which in turn enable
you todefine OLX fragments only once, to reuse them as often as you want
(this comes in very handy in using thehastexo
XBlock),write courseware content inMarkdown,do anything else you would like to do using your own plugins.InstallInstall theolx-utilspackage from PyPI:pipinstallolx-utilsApply templates to a courseIn order to create a new course run namednewrun, starting on May 1,
2017 and ending on October 31, 2017, simply change into your courseware
checkout and run:olxnew-run-bnewrun2019-01-012019-12-31The-boption causes your rendered OLX to be added to a new Git
branch namedrun/newrun, which you can then import into your Open
edX content store.You can also invokeolxnew-runasnew_run.py. However, this
is deprecated and its use is discouraged.new_run.pywill go
away in a future release.LicenseThis package is licensed under theGNU Affero
GPL; seeLICENSEfor details.
Package: olx-web-scraper
Olx Web ScraperCLI to Scrape OLX for Ad listing (Optimized for Car/Motocycle ads)Table of ContentsInstallationUsageInstallationCreate a virtual enviroment for the repo & activate itpython-mvenvvenvcdvenvScripts\activate.batInstall the scraperpipinstallolx-web-scraperUsageTo get scraping, simple run the following on your prefered terminal:olx_web_scraper<filenamewithoutextension>Eg:python olx_web_scraper scraped_dataNext, input the URL to scrapeOLX Web Scraper!
URL: https://www.olx.in/kerala_g2001160/q-interceptor-650This will then scrape all the listing available and save to a file named scraped_data.csv in your root folder.
Package: oly
Oly - CLIOly is a command line tool that allows developers to manage services.Supported PlatformsOly has been tested and is known to run on Linux (Ubuntu), Mac OS X
(10.4, 10.5, 10.6), and Solaris (10 for Intel) and FreeBSD 6.1.  It will
likely work fine on most UNIX systems.Windows not tested yet...Oly is intended to work on Python 3 version 3.* or later
and on Python 2 version >2.7.Documentation--Reporting Bugs, and Viewing the Source RepositoryPlease report bugs in theGithub issue trackerYou can view the source repository for oly viahttps://github.com/glikaj/olyContributingIf you'd like to contribute to oly directly, please contact me directly
Package: olymp
Olympus: a benchmarking framework for noisy optimization and experiment planningOlympusprovides a consistent and easy-to-useframework for benchmarking optimization algorithms. Witholympusyou can:Access a suite of18 experiment planning algortihmsvia a simple and consistent interfaceEasily integrate custom optimization algorithmsAccess10 experimentally-derived benchmarksemulated with probabilistic models, and23 analytical test functionsfor optimizationEasily integrate custom datasets, which can be used to train models for custom benchmarksYou can find more details in thedocumentation.InstallationOlympuscan be installed withpip:pip install olympDependenciesThe installation only requires:python >= 3.6numpypandasAdditional libraries are required to use specific modules and objects.Olympuswill alert you about these requirements as you try access the related functionality.CitationOlympusis research software. If you make use of it in scientific publications, please cite the following article:@misc{olympus,
      title={Olympus: a benchmarking framework for noisy optimization and experiment planning}, 
      author={Florian Häse and Matteo Aldeghi and Riley J. Hickman and Loïc M. Roch and Melodie Christensen and Elena Liles and Jason E. Hein and Alán Aspuru-Guzik},
      year={2020},
      eprint={2010.04153},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}LicenseOlympusis distributed under an MIT License.
Package: olympic
Olympic PyTorchOlympic implements a Keras-like API for PyTorch.The goal of Olympic is to combine the joy of Pytorch's dynamic graph execution with the joy of Keras's high level abstractions
for training. Concretely, Olympic contains:Theolympic.fit()function. This implements a very similar API
to Keras'smodel.fitandmodel.fit_generatormethods in a
more functional and less object-oriented fashion and spares you
the effort of "hand-rolling" your own training loop.Callbackobjects that perform functionality common to most deep
learning training pipelines such as learning rate scheduling, model
checkpointing and csv logging. These integrate intoolympic.fit()and spare you the effort of writing boilerplate code.Some helpful utility functions such as common metrics and some
convenience layers from Keras that are missing in PyTorch.Documentation is availablehereThis package is available on PyPi.pip install olympic
Package: olympic-sports
olympic_sports_toolsThis repo contains two scripts that help parsing the Olympic Sports dataset, from the following publication:Juan Carlos Niebles, Chih-Wei Chen and Li Fei-Fei, "Modeling Temporal Structure of Decomposable Motion Segments for Activity Classification", ECCV 2010Dataset's paper :http://vision.stanford.edu/documents/NieblesChenFei-Fei_ECCV2010.pdfDataset website :http://vision.stanford.edu/Datasets/OlympicSports/The scripts are the following:parse_attributes.py (used to save a numpy array with some class-related attributes)convert_Olympic.py (used to convert the videos from .seq to .avi format)Parsing class-related attributesThe class-related attributes are taken from the following publication:J. Liu, B. Kuipers, S. Savarese, "Recognizing Human Actions by Attributes", CVPR 2011Publication link :http://cvgl.stanford.edu/papers/cvpr11_liu_a.pdfTechnical report :http://www.cs.ucf.edu/%7Eliujg/papers/cvpr11_liu_a_tech_report.pdfThe unprocessed attributes are copied from Table 3 in the paper's technical report, and pasted in this file :Olympic_Attributes.txtWe have 16 classes of the Olympic Sports dataset, and 40 attributes.Then, we executefromolympic_sports.parse_attributesimportread_attributesattributes,class_list,attribute_list=read_attributes()and save the following 3 variables in the 'attributes.npz' file:attributes:
The attributes are stored in a numpy array of size 16x40, containing ones and zeros.Values of 1 (integer) in the cell (i,j), mean that the i-th class has the j-th attribute.Values of 0 (integer) mean that the i-th class does not have the j-th attribute.attribute_names:
List of the attribute namesclass_names:
List of the class namesYou can see the correspondences in the saved Attributes_Olympic.png image.Converting the videos from .seq to .avi formatTo convert the videos of the dataset from .seq file format to .avi, we execute:convert-olympic[path-seq-files]This script will need a file named "video_Olympic.list", containing all the videos. This file already exists in this repo.
If you want to create this on your own for a modified version of the dataset, you can execute the following command:ls /path/to/your/dataset/*/*.seq > video_Olympic.listThen using a text editor you can simply find and replace the substring of the exported directories, so that you only keep the class names and the video names in your final file (as in the already saved file). The folder location should be in the repo underolympic_sports/resourcesor in the enviroment directory.Credits go to @psycharo, for his useful gist to read .seq files, that is used in this repo.https://gist.github.com/psycharo/7e6422a491d93e1e3219/