Saravanan666/pypi-org huggingface dataset:
Package: coriolis-eda
Coriolis Open EDA ToolCoriolis is a free database, placement and routing tool for VLSI design.This project is hosted at:https://github.com/lip6/coriolisMain documentation is available at:https://coriolis.lip6.fr/Development discussion can be foundon our Matrix Channeland in ourGitHub Discussions.Coriolis is part of a set of three repositories that are closely relateds.AllianceA venerable EDA toolchain, working only on symbolic layout. Some of it’s
checking tools (LVS, DRC) are used for the regression tests in Alliance Check Toolkit.https://github.com/lip6/allianceAlliance Check ToolkitA wide set of examples of designs and scripts to use by Coriolis.https://github.com/lip6/alliance-check-toolkitPurposeCoriolis provides several tools to perform the layout of VLSI circuits.  Its
main components are the Hurricane database, the Etesian placer and the Katana
router, but other tools can use the Hurricane database and the parsers
provided.The user interfacecgtis the prefered way to run Coriolis, but all
Coriolis tools are Python modules and thus scriptable.Building CoriolisTo build Coriolis, ensure the following prerequisites are met:A C++11 compliant compiler.Python 3.make or ninja.cmake.bzip2boost.bison & flex.Qt 5 & Qt 5 Svg.Qwtlibxml2.RapidJSON,http://rapidjson.org/.Eigen 3,http://eigen.tuxfamily.org.Lemon,http://lemon.cs.elte.hu/trac/lemon.doxygenpelicanThe build system relies on a fixed directory tree from the root
of the user currently building it. Thus first step is to get a clone of
the repository in the right place. Proceed as follow:ego@home:~$mkdir-p~/coriolis-2.x/src/ego@home:~$cd~/coriolis-2.x/srcego@home:src$gitclone--recurse-submoduleshttps://github.com/lip6/coriolisego@home:src$cdcoriolisThen, build the tool:ego@home:coriolis$makeinstallCoriolis gets installed at the root of the following tree:~/coriolis-2.x/OS.DISTRIB/Release.Shared/install/WhereOSis the name of your operating system andDISTRIByour
distribution (mileage may vary).Using CoriolisPrior to using Coriolis, you need to setup your UNIX environment by running
the following command:ego@home:~$eval`~/coriolis-2.x/OS.DISTRIB/Release.Shared/install/etc/coriolis2/coriolisEnv.py`Then you can launch the GUI:ego@home:~$cgt-V
Package: corker
![TravisCI Status](https://api.travis-ci.org/jd-boyd/corker.png)# AboutI wanted a framework that was based around[routes](https://github.com/bbangert/routes) and[webob](http://webob.org/) without opinionation about database andtemplating.Some of the specifics of this are inspired/copied from: http://www.ianbicking.org/blog/2010/03/12/a-webob-app-example/index.html# Installation`pip install corker`# UsageThe basics are that you create controllers (classes that subclass`BaseController`).  In a controller, you label methods with the `@route`decorator to expose them.  You then invoke your controller's`setup_routes` method (inheritted from `BaseController`), passing it aroutes mapper to add itself to.  Then, to create the actual wsgi app,you create an `Application` and pass it the mapper.In a controller, `self.request` gives you the current WebOb requestobject.  The controller can return a string containing HTML (in whichcase it will be given a status code of 200), or a WebOb response object.Additionally, it is safe to raise any[WebOb exception](http://webob.readthedocs.org/en/latest/modules/exceptions.html).On an exposed method, the arguments (after self) are from routespositional/regex arguments.  GET and POST arguments are accessed via`self.request`.The arguments for the `@route` decorator largely match the arguments forroute's `mapper.connect` method.  The decorated method is automaticallyused as the action argument to `mapper.connect` and all other argumentsto `@route` are passed through to `mapper.connect` as is.Arguments passed to `Application` after the mapper are inserted intocontrollers with the same name.  So, is the `Application` wasinstantiated with `Application(mapper, x=5)`, then in an exposed methodon the controller, `self.x == 5` would be `True`.## Example```pythonfrom routes import Mapperfrom corker.controller import BaseController, routefrom corker.app import Applicationfrom webob import Responseclass Index(BaseController):@route('')def index(self, request):return 'Hi index!\n'@route('view/{item}')def view(self, request, item):return 'Hi view %d!\n' % int(item)class Sub(BaseController):def __init__(self, request, arg1):self.request = requestself.arg1 = arg1@route('')def index(self, request):return Response('Hi sub!\n' + self.arg1)mapper = Mapper()Index.setup_routes(mapper)with mapper.submapper(path_prefix='/sub') as sub:Sub.setup_routes(sub, config={"arg1": "arg string"})example_app = Application(mapper)# At that point `example_app` is a wsgi app ready to be mounted by the# server of your choice.  For example with `wsgiref`:from wsgiref.util import setup_testing_defaultsfrom wsgiref.simple_server import make_serverhttpd = make_server('', 8000, example_app)print "Serving on port 8000..."httpd.serve_forever()```# Bugs/Feature RequestsSee [github issues](https://github.com/jd-boyd/corker/issues).# CopyrightThis is distributed as BSD.  Copyright Joshua D. Boyd
Package: corkus.py
Corkus.py ⚙️Asynchronous, feature-rich and easy to use Python wrapper forPublic Wynncraft API.Key FeaturesModern asynchronous API usingasync/awaitsyntax.Easy to use with an object oriented design usingfetchand helper functions.100% coverage of the Wynncraft API.Proper rate limit handling that prevents 429s.Responses caching to improve speed.InstallationPython 3.8+ or higher is requiredpipinstallcorkus.pyOr install latest development version:pipinstall--upgradegit+https://github.com/MrBartusek/corkus.py@mainSeedocumentationfor more information.Quick ExampleUsingContext Manager:importasynciofromcorkusimportCorkusasyncdefplayer_stats():asyncwithCorkus()ascorkus:player=awaitcorkus.player.get("MrBartusekXD")character=player.best_characterprint(f"username:{player.username}")print(f"best character:{character.display_name}({character.combat.level}lv)")loop=asyncio.get_event_loop()loop.run_until_complete(player_stats())Without Context Manager:importasynciofromcorkusimportCorkusasyncdefplayer_stats():corkus=Corkus()awaitcorkus.start()player=awaitcorkus.player.get("MrBartusekXD")character=player.best_characterprint(f"username:{player.username}")print(f"best character:{character.display_name}({character.combat.level}lv)")awaitcorkus.close()loop=asyncio.get_event_loop()loop.run_until_complete(player_stats())Output:username: MrBartusek
best character: Mage (102lv)ContributingWant to contribute to the project?First of all, thanks! Checkcontributing guidelinesfor more information.LinksDocumentationForum PostPyPi Package
Package: corm
cormData structures relationships made easycorm is in early development. API can change.HelpSeedocumentationfor more details.Installationpip install cormRationaleсложно помнить ключи словарей, если структура данных большаяорганизация двусторонних связей между сущностями, особенно иерархических структурах данныхвозможность разделить безнес-логику и технические детали связывания сущностей друг с другом и поиска связанной сущности
Package: corn
System information discovery and asset trackingINSTALLATIONpip install cornINSTALLATION FOR DEVELOPMENTClone thecornrepo and install with pip:git clone https://gitlab.com/saltstack/pop/corn.git
pip install -e cornEXECUTIONAfter installation thecorncommand should now be available.The command will have no output until it is vertically extended (details below).TESTINGinstallrequirements-test.txtwith pip and run pytest:pip install -r corn/requirements-test.txt
pytest corn/testsVERTICAL APP-MERGINGInstructions for extending cornInstall pop:pip install --upgrade popCreate a new directory for the project:mkdir corn_{project_name}
cd corn_{project_name}Usepop-seedto generate the structure of a project that extendscorn:pop-seed -t v corn_{project_name} -d corn“-t v” specifies that this is a vertically app-merged project“-d corn” says that we want to implement the dynamic name of “corn”Notice that some structure has been created for you.
We especially care about the new directory incorn_{project_name}/cornAdd “corn” to the requirements.txt:echo "corn" >> requirements.txtCREATING CORNSCreate a new file in “corn_{project_name}/corn”The directory should already have been created bypop-seed.The file name is arbitrary.Every file in this directory, then it’s subdirectories,  will be parsed “simultaneously” (as far as that makes sense for asyncio).Corns that are dependant on each other should be assigned in the same function.Do NOT rely on the collector’s recursion strategy for corns to depend on each other.Usehub.corn.init.wait_for(“corn”)to wait for corns to be generated by another subAdd your new project to the python pathalternatively, run “pip install -e .” from your project’s root directoryyou only need to do this onceCreate an async function in this file with a descriptive yet arbitrary nameBy convention it should start with “load”Make it async unless you have a really really really good reason.Grains that depend on each other already belong in the same function, don’t depend on synchronous programming for determinismCorns can be accessed and assigned like a dictionary, but our convention is to use the namespaceExample:async def load_my_corn(hub):
    hub.corn.CORN.new_corn = "Hello World!"And that’s it! Now verify that your corn is collected from the command line:corn new_cornOutput:new_corn:
    Hello World!
Package: cornea
CorneaA PyPI package to extract insightful information about an image using Machine IntelligenceRequirementsPython >=3.7InstallationThe recommended method to install this package is by usingpiporpip3pip3installcornea
or
python3-mpipinstallcorneaSetupThe package requires that you specify your Microsoft Azure API credentials as environment variables. Type the following commands in your terminal. ALternatively, you can also add the following lines at the end of.bashrcor.bash_profile.exportAZURE_API_KEY={{{YOUR_API_KEY}}}exportAZURE_API_BASE_ENDPOINT={{{YOUR_API_REGION_BASE_URL}}}exportAZURE_API_VERSION={{{AZURE_API_VERSION}}}Example:exportAZURE_API_KEY=QmQZdzdQXftzrOOvCUC7tjx8wzZed8WkexportAZURE_API_BASE_ENDPOINT=https://eastus.api.cognitive.microsoft.comexportAZURE_API_VERSION=v2.0UsagefromcorneaimportdescImgFromURLanalysis=descImgFromURL("https://upload.wikimedia.org/wikipedia/commons/b/b9/CyprusShorthair.jpg")print(analysis)Output{'categories':[{'name':'animal_cat','score':0.99609375}],'color':{'dominantColorForeground':'Grey','dominantColorBackground':'Grey','dominantColors':['Grey'],'accentColor':'5C4E3C','isBwImg':False,'isBWImg':False},'description':{'tags':['cat','indoor','laying','bed','sitting','white','brown','lying','small','large','sleeping'],'captions':[{'text':'a cat lying on a bed','confidence':0.9512938154506553}]},'requestId':'ef807a48-ecbf-4c30-883b-64a7d25683f2','metadata':{'width':1024,'height':768,'format':'Jpeg'}}
Package: cornelius
corneliusCornelius allows you to control mouse and keyboard inputs in a simple way.Currently only supportsWindows.See full documentationhereUsageInstall thePyPI packagepipinstallcorneliusor clone the repositorygitclonehttps://github.com/manucabral/cornelius.gitExampleUsing cursor modulefromcorneliusimportCursormouse=Cursor()mouse.set(500,500)mouse.left_click()Using keyboard modulefromcorneliusimportKeyboardkeyboard=Keyboard()keyboard.send('a')You can see more examplesherePlanned featuresMouse scrollingDetect multiple clicksWait for a key/mouse inputKeyboard hotkeys
Package: cornellGrading
gradingRoutines for semi-automated grading of MATLAB coding assignments and interaction with Canvas and Qualtrics.By Dmitry Savransky with contributions by Guy Hoffman and Brian Kirby. Thanks also to Hadas Ritz for extensive testing and QA.Please note: the Canvas routines have the potential to bork your gradebook and (unlikely but possibly) whole course site.Use at your own risk.cornellGrading InstallationTo install from PyPI:pip install --user cornellGradingOr, with optional dependencies required to push LaTeX into Canvas HTML:pip install --user cornellGrading[latex2html]To install system-wide, omit the--useroption.NOTEThelatex2htmloption requires the pandoc executable to be installed and in the system PATH.  For detailed pandoc installation instructions see here:https://pandoc.org/installing.htmlIf cloning from github, in the cloned grading directory:pip install --user .or, to install in developer mode:pip install --user -e .In order to also install requirements needed push LaTeX into Canvas HTML, do:pip install --user -e .[latex2html]cornellGrading DocumentationDocumentation is available here:https://grading.readthedocs.io/Docstrings:https://grading.readthedocs.io/en/latest/cornellGrading.html#module-cornellGrading.cornellGradingAcknowledgementscornellGrading usesUCF/Open_'scanvasapiand theblackcode formatter.TestimonialsI love pagescript.py. So easy to add my notes, matlab, powerpoint to module right after class. Bless you.--Hadas Ritz
Package: cornel-movie-dialogs-corpus-storm
A set of python modules for cornel movie-dialogs corpus with storm.AbstractThis module include some classes extendingstormORM forcornel movie-dialogs
corpusdata.Installpip install storm                # if you not
pip install cornel-movie-dialogs-corpus-stormSetupdownload corpus and unzipgenerate database and insert withgenerate-mdcorpus-database.pyfor example:generate-mdcorpus-database.py --corpus-dir "cornell movie-dialogs corpus" corpus.dbUsagefrom mdcorpus.orm import *
from mdcorpus.parser import *

...Class ListMovieTitlesMetadataGenreMovieGenreLineMovieCharactersMetadataMovieConversationMovieLineRawScriptUrlCorpus ProblemThis is memo when I dealt with corpus problems.movie_titles_metadata.txtI ignored analphabetfollowing year.for example, line 34,1989/II ignoredduplicationfor genre data.line 58,['horror', 'mystery', 'mystery','sci-fi','sci-fi']Code ProblemI usePython2.7and I don’t know how to usecodecsmodule.(Unicode HOWTO — Python 2.7ja1
documentation)mimeconvert text-code toutf-8withMibeforecornell movie-dialogs corpus$ file --mime {(ls)}
README.txt:                    text/plain; charset=iso-8859-1
chameleons.pdf:                application/pdf; charset=binary
movie_characters_metadata.txt: text/plain; charset=iso-8859-1
movie_conversations.txt:       text/plain; charset=us-ascii
movie_lines.txt:               text/plain; charset=us-ascii
movie_titles_metadata.txt:     text/plain; charset=iso-8859-1
raw_script_urls.txt:           text/plain; charset=iso-8859-1aftercornell movie-dialogs corpus$ file --mime {(ls)}
README.txt:                    text/plain; charset=utf-8
chameleons.pdf:                application/pdf; charset=binary
movie_characters_metadata.txt: text/plain; charset=utf-8
movie_conversations.txt:       text/plain; charset=us-ascii
movie_lines.txt:               text/plain; charset=us-ascii
movie_titles_metadata.txt:     text/plain; charset=utf-8
raw_script_urls.txt:           text/plain; charset=utf-8movie_titles_metadata.txtline 115,léonmovie_characters_metadata.txtline 1727 - 1736,léonresultsqlite> select * from movie_titles_metadata where title = 'léon';
sqlite> select * from movie_titles_metadata where title = 'l駮n';
114|l駮n|1994|8.6|204901
Package: corner
corner.pyReadthe documentationIf you make use of this code, please citethe JOSS paper:@article{corner,
  doi ={10.21105/joss.00024},
  url ={https://doi.org/10.21105/joss.00024},
  year  ={2016},
  month ={jun},
  publisher ={The Open Journal},
  volume ={1},
  number ={2},
  pages ={24},
  author ={Daniel Foreman-Mackey},
  title ={corner.py: Scatterplot matrices in Python},
  journal ={The Journal of Open Source Software}}
Package: cornerhex
cornerhexcornerhexis a package to visualize multidimensional data in matrix corner plots, for example the results of Markov Chain Monte Carlo (MCMC) methods. Instead of 2d histograms or scatter plots it usesmatplotlib.pyplot.hexbin.cornerhexcan be easily costumized with different color schemes.Installationcornerhexcan be installed via PyPI.pip install cornerhexDocumentationFor the usage ofcornerhexplease have a look at itsdocumentation.1. Quickstart2. CustomizationsA. Contributing / Bug reports / Features requestsAcknowledgementscornerhexis free to use and modify. Please acknowledge this repository when usingcornerhexin a publication.cornerhexhas been inspired bycorner.py(Foreman-Mackey, 2016).
Package: cornerplots
CornerplotsSimple library to create corner plots inmatplotlib. Corner plots provide a simple way to visualize multidimensional data, with each dimension plotted against every other dimension. The main goal is to let the user specify data and/or model predictions in arbitrary subsets of the multi-dimensional space and let the code take care of where to plot these values. Originally inspired bycorner.py.Simple exampleYou might have one set of (w,x,y) data, another set of (x,y,z) data, and a model predicting (w,x,y,z) trajectories.importnumpyasnpt=np.linspace(0,1)model=dict(w=3*t+1,x=5*t,y=7*t**.5,z=3*t**1.5,)red_data=dict(w=np.array([1,2,3,4]),x=np.array([0,2,4,5]),y=np.array([1,4,6,8]),)blue_data=dict(x=np.array([1,2,5]),y=np.array([3,5,7]),z=np.array([0,1,3]),)In that case, you could plot everything by simply calling:fromcornerplotsimportCornerplotsimportmatplotlib.pyplotaspplfig=ppl.figure(figsize=(5,5))cp=Cornerplots(fields=['w','x','y','z'],labels=['W-value','X-value','Y-value','Z-value'],grid_kwargs={'alpha':0.25,'lw':0.7},)cp.plot(model,'k-',lw=1,dashes=(6,2,2,2),label='model')cp.plot(red_data,'ws',mec='r',mew=1,ms=5,label='red data')cp.plot(blue_data,'wD',mec='b',mew=1,ms=5,label='blue data')cp.legend()ppl.savefig('example.png')
Package: cornershop
The Unofficial Python wrapper for Cornershop by Uber's APICornershop is a grocery delivery service owned by Uber.This API allows the user to search for all available stores near a given zip code.Please, check their official website at:https://cornershopapp.com/Instalationpip install cornershopDependenciesPython 3.6 or higherrequestsLicenseMITEven though this is just a wrapper, use it with caution, since the data fetched is Uber's property.Getting StartedFirst, import and instanciate the Cornershop API model using your zip code as locality.>>>fromcornershopimportCornershop>>>cs=Cornershop(...locality='00000000',...country='BR'...)Then, call the following methods to fetch data from branches near your locality.>>>cs.search_branches(query='...')>>>cs.search_branch(branch_id='00000',query='...')>>>cs.search_branch_group()>>>cs.search_countries()Set thejson_formatargument asTrueto return a json-style dictionaty instead of acornershopmodel.>>>cs=Cornershop(...locality='00000000',...country='BR',...json_format=True...)Check theexamples folderfor more details aboutworking with methodsandworking with models.
Package: cornerstone
cornerstone/kɔrˈneːrston/n.an indispensable and fundamental basiscornerstoneis a state-of-the-art repository setup to start a python project hosted on Github.FeaturesMarkdownREADME.mdfile used for both pypi and github project homepagesMarkdown documentation hosted by readthedocsCI/CD automation using Github action :codecov.ymlupload coverage to Codecovpython-buildlint and run unit testspython-semantic-release.ymlautomatic changelog generation and versioning via Python Semantic Release and one click deployment to PypiGetting startedgit clone git@github.com:Kraymer/cornerstone.git YOUR_PROJECT
sed -i 's/cornerstone/YOUR_PROJECT/g' **/*setup*Please readdocumentationfor further instructions.
Package: corneto
CORNETO: An unified network learning framework from biological prior knowledgeCORNETO (CORe NETwork Optimization) is an unified network inference framework implemented in Python library that models biological network inference problems using convex and combinatorial optimization. It offers a comprehensive framework that facilitates the design and formulation of common optimization problems related to inference of biological networks from omics data. The library leverages domain-specific language frameworks, such asCVXPYorPICOS, to translate high-level problem specifications in a clear manner and solves the problem using a wide range of supported free and commercial solvers.NOTE: This is an early preview of the library, which includes a very limited subset of methods for signalling, and an early version of the API to build optimization problems. We're currently working towards having a final version including additional and novel methods.InstallationThe library will be uploaded to pypi once the API is stable. Meanwhile, it can be installed by downloading the wheel file from the repository. It's recommended to use also conda to create a new environment, although it's not mandatory.Recommended setupCORNETO does not include any backend nor solver by default to avoid issues with architectures for which some of the required binaries are not available. The recommended setup for CORNETO requires CVXPY and Gurobi:pipinstallcornetocvxpygurobipyPlease note thatGUROBI is a commercial solver which offers free academic licenses. If you have an academic email, this step is very easy to do in just few minutes:https://www.gurobi.com/features/academic-named-user-license/.
Alternatively, it is possible to use CORNETO with any free solver, such as HIGHS, included in Scipy. To install CORNETO with support for HIGHs, you only need to install the latest version of scipy:pipinstallcornetocvxpyscipy:warning: Please note that without any backend, you can't do much with CORNETO. There are two supported backends right now:PICOSandCVXPY. Both backends allow symbolic manipulation of expressions in matrix notation.AcknowledgementsCORNETO is developed at theInstitute for Computational Biomedicine(Heidelberg University). The development of this project is supported by European Union's Horizon 2020 Programme under
PerMedCoE project (permedcoe.eu) agreement no. 951773.