Saravanan666/pypi-org huggingface dataset:
Package: pydocstyle
pydocstyleis a static analysis tool for checking compliance with Python
docstring conventions.pydocstylesupports most ofPEP 257out of the box, but it
should not be considered a reference implementation.pydocstylesupports Python 3.6+.Quick StartInstallpip install pydocstyleRun$ pydocstyle test.py
test.py:18 in private nested class `meta`:
        D101: Docstring missing
test.py:27 in public function `get_user`:
    D300: Use """triple double quotes""" (found '''-quotes)
test:75 in public function `init_database`:
    D201: No blank lines allowed before function docstring (found 1)
...DevelopYou can use Gitpod to run pre-configured dev environment in the cloud right from your browser -Before submitting a PR make sure that you runmake all.LinksRead the full documentation here.Fork pydocstyle on GitHub.PyPI project page.
Package: pydocstyle-gitlab-code-quality
pydocstyle-gitlab-code-qualityGenerateGitLab Code Quality reportfrom an output ofpydocstyle.Usage# passing pydocstyle output through stdin (output printed to stdout)pydocstylemain.py|pydocstyle-gitlab-code-quality>codequality.json# orpydocstyle-gitlab-code-quality<pydocstyle_out.txt>codequality.json# using CLI flags (output printed directly to a file)pydocstyle-gitlab-code-quality--inputpydocstyle_out.txt--outputcodequality.jsonCLI configurationpydocstyle-gitlab-code-qualityallows for the following CLI arguments:flagexampledefaultdescription--minor <CODE>...--minor=D100,D101emptyError codes to be displayed with MINOR severity.--major <CODE>...--major=D102,D103emptyError codes to be displayed with MAJOR severity.--critical <CODE>...--critical=D104,D105emptyError codes to be displayed with CRITICAL severity.-i, --ignore <CODE>...--ignore=D106,D107emptyError codes to be omitted from Code Quality report.-f, --file, --input <FILE>-f pydocstyle_out.txtemptyPath to the file with pydocstyle output.-o, --output <FILE>-o codequality.jsonemptyPath to the file where the Code Quality report will be saved.--no-stdoutN/AFalseDo not print the Code Quality report to stdout.--log-file <FILE>--log-file latest.logpgcq_latest.logPath to the file where the log will be saved.--enable-loggingN/AFalseEnable logging to a file. For debugging purposes only.By default, all error codes are reported with INFO severity.In case the same error code frompydocstylehas been provided to many severity options, the highest severity level takes precedence.Example.gitlab-ci.ymlfileimage:python:alpinevariables:PIP_CACHE_DIR:"$CI_PROJECT_DIR/.cache/pip"cache:paths:-.cache/pip/-venv/before_script:-python --version# For debugging-python -m venv venv-. venv/bin/activatecodequality:script:-pip install pydocstyle pydocstyle-gitlab-code-quality-pydocstyle program.py > pydocstyle-out.txt-pydocstyle-gitlab-code-quality --input pydocstyle-out.txt --output codequality.jsonartifacts:when:alwaysreports:codequality:codequality.jsonallow_failure:trueCreditsThis script was inspired bymypy-gitlab-code-quality. Thanks!LicenseThe project is licensed under MIT - a free and open-source license. For more information, please seethe license file.
Package: pydocusign
pydocusignpydocusignis a Python client forDocuSignsignature SAAS platform.Project statuspydocusignhas just been created to provide Python bindings for DocuSign’s
API. The project is not mature yet, but authors already use it! It means that,
while API and implementation may change (improve!) a bit, authors do care of
the changes.Also, help is welcome! Feel free to report issues, request features or
refactoring!ResourcesDocumentation:https://pydocusign.readthedocs.orgPyPI page:https://pypi.python.org/pypi/pydocusignBugtracker:https://github.com/peopledoc/pydocusign/issuesChangelog:https://pydocusign.readthedocs.org/en/latest/about/changelog.htmlCode repository:https://github.com/peopledoc/pydocusignContinuous integration:https://travis-ci.org/peopledoc/pydocusign
Package: pydocverter
Python client forDocverterserviceDocverteris a hosted service
that can do convert documents from one format to another (usingpandoc).
For example, it can be used to convertMarkdowndocuments toreStructuredText.
This is very useful if you prefer to write yourREADMEin Markdown,
but want to publish your package to PyPI,
which only knows how to do nice rendering of descriptions
written in reStructuredText.This module is a Python client to the Docverter service.It has a very similar API to that ofpypandoc, so that you can do stuff like:try:importpypandocasconverterexceptImportError:importpydocverterasconverterconverter.convert('somefile.md','rst')
Package: pyDocxReport
pyDocxReportEase the docx report generation using templates and importing featuresDataBridgeDataBridge class manages resources and match them with keyword set in a template docx file.All keywords in the template so referenced ar replaced by the appropriate content.An example of use with a yml file as a matchs dictionary is given below:bridge = DataBridge('path/to/template.docx')
matchs['_keyword2_'] = {
                            'type': 'table',
                            'replacement': df1,
                            'header': False
                        }
bridge.match(matchs)
bridge.save('path/to/output.docx')where matchs is defined as a yml file like below:_keyword1_:
    type: string
    replacement: text1
_myimage1set_:
    type: images
    replacement: 
        - path/to/image1.png
        - path/to/image2.jpg
    width: 120
_logo_:
    type: images
    replacement: path/to/logo.png
    height: 10
_keyword2_:
    type: table
    replacement: table1
    header: false               # if header is true, the column names of the DataFrame are used as header. Otherwiser no header. Default is no header
_text2_:
    type: string
    replacement: text2Seeteststo see this example implemented.DocxTemplateThe DocxTemplate class makes use of python-docx to modify a word document.
Use DataBridge for a standard operation and use DocxTemplate when you need to tune some replacements.
Package: pydocxrunner
PydocxrunnerIsWordorLibre Office Writeryou favorite IDE? Do you recognizedocxas a superior file format? And are you tired of the high effort required to execute the code stored in.docxfiles? Then don't worry,pydocxrunneris the tool of your dreams.pydocxrunnerprovides a simple command to make a.docxfile executable like any normal.pyfile.FeaturesMake.docxfiles executable via thepythoncommand. Once the file is made executable,pydocxrunneris no longer required.The.docxfile can then be executed on any device withpythoninstalled.What else do you need?ExampleAssuming you have a file calledprintquad.docxwith the following content:foriinrange(10):print(i)Then you can make it executable usingpydocxrunner:$pydocxrunnerprintquad.docxAnd execute it (on any device havingpython3installed):$python3printquad.docx0149162536496481How it worksUnknown to most,.docxfiles are.zipfiles with a fancy name. Also unknown to most,pythoncan execute.zipfiles.pydocxrunnerwrites a simple__main__.pyfile as well aspython-docx2txtinto the.docxfile. The__main__.pyscript will be executed by the python interpreter and read the contents of the.docxfile. This content is then passed to anexec()call.CaveatsThis is not tested beyond a few simple statementsI do not recommend using this  tool for productionAn internet connection is required when runningpydocxrunneraspython-docx2txtneeds to be downloaded. No connection is required to actually execute the.docxfile.You still have to use'and"for strings,„and“will not work.TODOMake this work with other types of documentsMake this work with other languagesFind a better solution for installingpython-docx2txt
Package: pydocxs3upload
OverviewAn mixin for PyDocX that uploads the images to an S3 bucket instead of returning them as Data URIs when converting from .docx to html.RequirementsPython 2.7Works on Linux, Windows, Mac OSX, BSDInstallThe quick way:pip install pydocxs3uploadUsageFirst of all, to use this mixin, you need to create you own amazon s3 bucket and give public permission so that uploaded images
can be accessed from generated html files.Hereyou can find a guide
how to do that. Once we have the bucket created, we need to generate a singed request that will be used to upload data to s3.
So, because of this signed request you have full control over the uploaded images. Usingthis gistyou can simple generate such signature using boto library.Here is an example of mixin usage:frompydocx.exportimportPyDocXHTMLExporterfrompydocxs3uploadimportS3ImageUploadMixinclassPyDocXHTMLExporterS3ImageUpload(S3ImageUploadMixin,PyDocXHTMLExporter):passdocx_path='path/to/file/doc.docx'signed_request='<signed json string>'exporter=PyDocXHTMLExporterS3ImageUpload(docx_path,s3_upload=signed_request)html=exporter.parsedNote that you can use the same signed request to convert any docs you want. So, in order to avoid uploading images with the same name and
overriding previous one, each image is appended a timestamp which makes it unique. If, for some reasons, you don’t want this feature by default
you can change it by passing parameter'unique_filename': False.exporter=PyDocXHTMLExporterS3ImageUpload(docx_path,s3_upload=signed_request,unique_filename=False)In this case all images are named asimage1, image2,...,imagen.
Package: pydocxtpl
pydocxtplA docx templater.How to installpipinstallpydocxtplHow to useTo use pydocxtpl, you need to be familiar with thesyntax of jinja2 template.code samplefrompydocxtplimportDocxWriterperson_info={'address':u'','name':u'','pic':'1.jpg'}person_info2={'address':u'Somewhere over the rainbow','name':u'Hello Wizard','pic':'0.jpg'}persons=[person_info,person_info2]payload={'persons':persons}writer=DocxWriter('test.docx')writer.render(payload)writer.save('test_result.docx')Seeexamples.
Package: pyDOE
ThepyDOEpackage is designed to help thescientist, engineer, statistician,etc., to construct appropriateexperimental designs.CapabilitiesThe package currently includes functions for creating designs for any
number of factors:Factorial DesignsGeneral Full-Factorial(fullfact)2-level Full-Factorial(ff2n)2-level Fractional Factorial(fracfact)Plackett-Burman(pbdesign)Response-Surface DesignsBox-Behnken(bbdesign)Central-Composite(ccdesign)Randomized DesignsLatin-Hypercube(lhs)See thepackage homepagefor details on usage and other notesWhat’s NewIn this release, an incorrect indexing variable in the internal LHS function_pdisthas been corrected so point-distances are now calculated accurately.RequirementsNumPySciPyInstallation and downloadSee thepackage homepagefor helpful hints relating to downloading
and installing pyDOE.Source CodeThe latest, bleeding-edge but workingcodeanddocumentation sourceare
availableon GitHub.ContactAny feedback, questions, bug reports, or success stores should
be sent to theauthor. I’d love to hear from you!CreditsThis code was originally published by the following individuals for use with
Scilab:Copyright (C) 2012 - 2013 - Michael BaudinCopyright (C) 2012 - Maria ChristopoulouCopyright (C) 2010 - 2011 - INRIA - Michael BaudinCopyright (C) 2009 - Yann ColletteCopyright (C) 2009 - CEA - Jean-Marc MartinezWebsite: forge.scilab.org/index.php/p/scidoe/sourcetree/master/macrosMuch thanks goes to these individuals.And thanks goes out to the following for finding and offering solutions for
bugs:Ashmeet SinghLicenseThis package is provided under two licenses:TheBSD License(3-clause)Any other that the author approves (just ask!)ReferencesFactorial designsPlackett-Burman designsBox-Behnken designsCentral composite designsLatin-Hypercube designs
Package: pyDOE2
pyDOE2: An experimental design package for pythonpyDOE2is a fork of thepyDOEpackage
that is designed to help the scientist, engineer, statistician, etc., to
construct appropriate experimental designs.This fork came to life to solve bugs and issues that remained unsolved in the
original package.CapabilitiesThe package currently includes functions for creating designs for any
number of factors:Factorial DesignsGeneral Full-Factorial (fullfact)2-level Full-Factorial (ff2n)2-level Fractional Factorial (fracfact)Plackett-Burman (pbdesign)Generalized Subset Designs (gsd)Response-Surface DesignsBox-Behnken (bbdesign)Central-Composite (ccdesign)Randomized DesignsLatin-Hypercube (lhs)See the originalpyDOE homepagefor details
on usage and other notes.What's new?Generalized Subset DesignsIn pyDOE2 version 1.1 theGeneralized Subset Design (GSD)is introduced. GSD is a generalization of traditional fractional factorial
designs to problems where factors can have more than two levels.In many application problems factors can have categorical or quantitative
factors on more than two levels. Previous reduced designs have not been
able to deal with such types of problems. Full multi-level factorial
designs can handle such problems but are however not economical regarding
the number of experiments.The GSD provide balanced designs in multi-level experiments with the number
of experiments reduced by a user-specified reduction factor. Complementary
reduced designs are also provided analogous to fold-over in traditional
fractional factorial designs.GSD is available in pyDOE2 as:import pyDOE2

levels = [2, 3, 4]  # Three factors with 2, 3 or 4 levels respectively.
reduction = 3       # Reduce the number of experiment to approximately a third.

pyDOE2.gsd(levels, reduction)RequirementsNumPySciPyInstallation and downloadThrough pip:pip install pyDOE2CreditspyDOEoriginal code was originally converted from code by the following
individuals for use with Scilab:Copyright (C) 2012 - 2013 - Michael BaudinCopyright (C) 2012 - Maria ChristopoulouCopyright (C) 2010 - 2011 - INRIA - Michael BaudinCopyright (C) 2009 - Yann ColletteCopyright (C) 2009 - CEA - Jean-Marc MartinezWebsite: forge.scilab.org/index.php/p/scidoe/sourcetree/master/macrospyDOEwas converted to Python by the following individual:Copyright (c) 2014, Abraham D. LeeThe following individuals forked and works onpyDOE2:Copyright (C) 2018 - Rickard Sjögren and Daniel SvenssonLicenseThis package is provided under two licenses:TheBSD License(3-clause)Any other that the author approves (just ask!)ReferencesFactorial designsPlackett-Burman designsBox-Behnken designsCentral composite designsLatin-Hypercube designsSurowiec, Izabella, Ludvig Vikström, Gustaf Hector, Erik Johansson,
Conny Vikström, and Johan Trygg. “Generalized Subset Designs in Analytical
Chemistry.” Analytical Chemistry 89, no. 12 (June 20, 2017): 6491–97.https://doi.org/10.1021/acs.analchem.7b00506.
Package: pyDOE3
pyDOE3: An experimental design package for pythonpyDOE3is a fork of thepyDOE2package
that is designed to help the scientist, engineer, statistician, etc., to
construct appropriate experimental designs.This fork came to life to solve bugs and issues that remained unsolved in the
original package.CapabilitiesThe package currently includes functions for creating designs for any
number of factors:Factorial DesignsGeneral Full-Factorial (fullfact)2-level Full-Factorial (ff2n)2-level Fractional Factorial (fracfact)Plackett-Burman (pbdesign)Generalized Subset Designs (gsd)Response-Surface DesignsBox-Behnken (bbdesign)Central-Composite (ccdesign)Randomized DesignsLatin-Hypercube (lhs)SeeDocumentation.RequirementsNumPySciPyInstallationpip install pyDOE3CreditspyDOEoriginal code was originally converted from code by the following
individuals for use with Scilab:Copyright (C) 2012 - 2013 - Michael BaudinCopyright (C) 2012 - Maria ChristopoulouCopyright (C) 2010 - 2011 - INRIA - Michael BaudinCopyright (C) 2009 - Yann ColletteCopyright (C) 2009 - CEA - Jean-Marc MartinezWebsite: forge.scilab.org/index.php/p/scidoe/sourcetree/master/macrospyDOEwas converted to Python by the following individual:Copyright (c) 2014, Abraham D. Lee & timsimstThe following individuals forked and work onpyDOE2:Copyright (C) 2018 - Rickard Sjögren and Daniel SvenssonLicenseThis package is provided under theBSD License(3-clause)ReferencesFactorial designsPlackett-Burman designsBox-Behnken designsCentral composite designsLatin-Hypercube designsSurowiec, Izabella, Ludvig Vikström, Gustaf Hector, Erik Johansson,
Conny Vikström, and Johan Trygg. “Generalized Subset Designs in Analytical
Chemistry.” Analytical Chemistry 89, no. 12 (June 20, 2017): 6491–97.https://doi.org/10.1021/acs.analchem.7b00506.
Package: pydoer
PYDOER - CLI ApplicationPyDoer is a CLI application aiming to automate executing multiple commands in different
terminal applications.Source Code:https://github.com/boromir674/doerFeaturingTerminal Configuration as codeDesign Menu and commands using json configLaunch/Close commandsOverviewFree software: GNU General Public License v3.0PrerequisitesYou need to have Python, Bash and gnome-terminal installed.InstallationInstall PyDoer in virtual environment:Get the codegit clonegit@github.com:boromir674/doer.gitInstall in a python virtual environmentcd doervirtualenv env –python=python3
source env/bin/activatepip install python-doerMake pydoer cli available in pathAssuming ~/.local/bin is in $PATHln -s $PWD/env/bin/pydoer ~/.local/bin/pydoer(Optional) Define useful aliasesAssuming you cloned the code in directory ‘/data/repos/doer’alias doer='/data/repos/doer/env/bin/pydoer menu /data/repos/doer/python-doer/menu_entries.json'
alias close-doing='/data/repos/doer/env/bin/pydoer close-doing'Install PyDoer for user:Get the codegit clonegit@github.com:boromir674/doer.gitInstall for usercd doerpip install –user python-doerThe pydoer cli should now be (automatically) in $PATH(Optional) Define useful aliasesalias doer='pydoer menu /data/repos/doer/python-doer/menu_entries.json'
alias close-doing='pydoer close-doing'UsageTo run, simply execute (either from within the virtual env or if you installed with user/global scope):pydoerBasically you have 2 commands:show interactive menupydoer menu </path/to/menu.json>The program parses the entries defined in json formatted file defined by the user json’ file and renders
an interactive “Menu” in the terminal, waiting for the user to make a selection.
Each selection, generates a ‘do’ script which is responsible for opening/spawning one or more terminal applications.
For each terminal application, a ‘launch’ script is generated which is responsible for running certain commands on that terminal.close windows spawned from previous activitypydoer close-doing
Package: pydogceo
PyDogCeoPyDogCeo is a thin wrapper on top of theDogCEO-API.DOG-CEO on GITHUBTo Start...To install pydogceo:pipinstallpydogceoUsing it...All methods return a requests Response Object representing the raw response from the API. Any HTTP errors that are returned by the API are
raised as HTTPError exceptions from the Requests library.frompydogceoimportPyDogCeodog=PyDogCeo()r=dog.breeds.listAll()Available Methods/ Endpointsdog.breeds.listAll()dog.breeds.listAllRandom()dog.breeds.listAllRandomNumber(10)dog.breeds.listMaster()dog.breeds.listMasterRandom()dog.breeds.listMasterRandomNumber(3)dog.breeds.image.random()dog.breeds.image.randomNumber(3)dog.breed.subbreed("hound")dog.breed.subbreedRandom("hound")dog.breed.subbreedRandomNumber("hound",3)dog.breed.image("hound")dog.breed.images.random("hound")dog.breed.images.randomNumber("hound",4)dog.breed.images.subbread("hound","afghan")dog.breed.images.subbreadRandom("hound","afghan")dog.breed.images.subbreadRandomNumber("hound","afghan",10)Changelogv0.0.1- 28/08/2022First Release :)
Package: pydogs
pyDOGS4For Imperial Collage collaboratorpyDOGSA derivative-free global optimization algorithm based on Delaunay triangulation designed to solve efficiently the problems whose function evaluations process is inaccurate, but its accuracy can be improved by increasing its computational cost. This package is particularly well-suited when the objective function is the infinite time-averaged value of a statistics of a stationary and ergodic process. Moreover, a new UQ method is developed to quantify the expected squared averaging error which is multiscale, meaning that it is based on an autocorrelation model that is tuned to the data to fit the statistic of interest at a large range of different timescales. In addition a transient detector scheme is developed to delete the transient part of the signal.OutputOptimization code generetes a file called allpoints/pts_to_eval.dat which has  the format of:
flagin=1
IDin=3
Awin=0.75
lambdain=0.5
fangle=0.5Moreover, it reads surr_J_new.dat from the current directory.
A_w is the same parameter as the current ‘Aw’ and the two other parameters would be replaced by b_1 and b_2.The range for these parameters would be:
A : [0.05 ; 0.15]  (à same as current Aw)
B_1 : [0 ; 0.2]      (à in place of lambda)
B_2 : [0 ; 0.13]   (à in place of fangle)
Package: pyDOI
pyDOI -- Python DOI ResolverWrapper for theDOI system Proxy Server REST API(see alsohere).Happy to receive any help in the form of bug reports, suggestions and/or pull requests!
Especially, suggestions to enhance programming style (and also setup of a test suite)
are highly welcome.UsageIn[1]:importpydoiGet full responseIn[2]:pydoi.resolve("10.1002/chem.202000622")Out[2]:{'responseCode':1,'handle':'10.1002/chem.202000622','values':[{'index':1,'type':'URL','data':{'format':'string','value':'https://onlinelibrary.wiley.com/doi/10.1002/chem.202000622'},'ttl':86400,'timestamp':'2020-09-25T16:02:07Z'},{'index':700050,'type':'700050','data':{'format':'string','value':'2020100503563800217'},'ttl':86400,'timestamp':'2020-10-05T12:25:43Z'},{'index':100,'type':'HS_ADMIN','data':{'format':'admin','value':{'handle':'0.na/10.1002','index':200,'permissions':'111111110010'}},'ttl':86400,'timestamp':'2020-03-30T02:01:43Z'}]}Get URLIn[3]:pydoi.get_url("10.1016/j.chempr.2020.04.016")Out[3]:'https://linkinghub.elsevier.com/retrieve/pii/S2451929420301844'Get single URL or list of URLs from "10320/loc" type recordsIn[4]:pydoi.get_url("10.6567/IFToMM.14TH.WC.OS3.029")Out[4]:'https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=P20150909001-201510-201510260026-201510260026-672-680'In[5]:pydoi.get_url("10.6567/IFToMM.14TH.WC.OS3.029",allow_multi=True)Out[5]:['http://www.airitilibrary.cn/Publication/alDetailedMesh?DocID=P20150909001-201510-201510260026-201510260026-672-680','https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=P20150909001-201510-201510260026-201510260026-672-680']pyDOI supports the use of query parametersIn[6]:pydoi.resolve("10.1002/anie.201804551",params=[("type","URL")])Out[6]:{'responseCode':1,'handle':'10.1002/anie.201804551','values':[{'index':1,'type':'URL','data':{'format':'string','value':'https://onlinelibrary.wiley.com/doi/abs/10.1002/anie.201804551'},'ttl':86400,'timestamp':'2020-03-19T11:37:54Z'}]}In[7]:pydoi.resolve("10.1002/anie.201804551",params=[("type",["HS_ADMIN","700050"])])Out[7]:{'responseCode':1,'handle':'10.1002/anie.201804551','values':[{'index':700050,'type':'700050','data':{'format':'string','value':'2020031904142600770'},'ttl':86400,'timestamp':'2020-03-19T11:37:54Z'},{'index':100,'type':'HS_ADMIN','data':{'format':'admin','value':{'handle':'0.na/10.1002','index':200,'permissions':'111111110010'}},'ttl':86400,'timestamp':'2018-05-15T11:31:52Z'}]}InstallationInstallable withpip.pip install pyDOILicenseGNU General Public License v3.0 or laterSeeLICENSEfor the full text.
Package: pyDOID
pyDOIDPython package with utilities for managing the Human Disease Ontology.pyDOIDwas created to provide functionality that is currently unavailable (or difficult to create) inDO.utilsthe more extensive R package designed with the same purpose.The functionality provided bypyDOIDis encapsulated in three primary classes:TheDOrepoclass is designed specifically for access and manipulation of the Disease Ontology's git repository and the files within it.DOrepo:Inherits from thegit.repo.Repoclass and extends it with methods to check out individual tags, iteratively execute code across a range of tags, and to capture/restore the state of the git repo.Wraps thepyDOID.owlclasses (outlined below) for specific files in the repository (owl.functional: doid-edit.owl;owl.xml: doid.owl and doid-merged.owl).Theowl.functionalclass provides a single method to extract class axioms from OWL files in the functional format.Theowl.xmlclass provides methods to load and execute SPARQL queries against OWL files in the XML format.