Saravanan666/pypi-org huggingface dataset:
Package: kerningvalidator
Kerning ValidatorCompares kerning defined in.glyphssource with kerning defined in binary font.It reads all kerning pairs of all masters of a Variable Font from the.glyphssource
and shapes the pairs using the binary font withvharfbuzz, once with and once without
kerning enabled, then compares the two values.To catch absolutely all kerning, it gathers all unicodes associated with a kerning class
and cross-checks all possible combinations. This is necessary because members of a kerning class
may be part of different writing scripts and may or may not be included in the font based on their script.The shaped kerning is lazily read from thevharfbuzzbuffer through thexvalue ofbuf.glyph_positionsby simply comparing the highestxvalue of each composition.InvocationFrom the command line:kerningvalidator font.glyphs font.ttfFrom within Python:fromkerningvalidatorimportmissing_kerning# Get missing kerningmissing_kerning=missing_kerning("font.glyphs","font.ttf")# Success means en ampty listassertlen(missing_kerning)==0LimitationsThis tool was written with Variable Fonts in mind that contain bothLTRandRTLkerning pairsThis tool currently only consumes.glyphssources, no UFOsThe kerning comparisons are currently limited to encoded glyphs onlyThevharfbuzzshaping has shown a difference of up to 1 font unit compared to the kerning defined in the.glyphssource. The tool therefore allows for 1 unit differenceThere are currently no unit tests for this toolIt takes forever for a large fontPackageTo create a new package, install twine viapip install twine, thencdtoLib/and then:python3 setup.py sdisttwine upload dist/*
Package: kernreg
KernRegIntroductionKernRegprovides a pure-Python routine for local polynomial kernel regression based onWand & Jones (1995)and their accompanyingRpackageKernSmooth. In addition,KernRegcomes with an automatic bandwidth selection procedure that minimizes the residual squares criterion proposed byFan & Gijbels (1996).KernRegallows for the estimation of a regression function as well as theirvth derivatives. The degree of the polynomialpmust be equal tov + 1,v + 3,v + 5, orv + 7.Local polynomial fitting provides a simple way of finding a functional relationship between two variables (where X typically denotes the predictor, and Y the response variable)  without the imposition of a parametric model. It is a natural extension of local mean smoothing, as described byNadaraya (1964)andWatson (1964). Instead of fitting a local mean, local polynomial smooting involves fitting a localpth-order polynomial via locally weighted least-squares. The Nadaraya–Watson estimator is thus equivalent to fitting a local polynomial of degree zero. Local polynomials of higher order have better bias properties and, in general, do not require bias adjustment at the boundaries of the regression space.InstallationInstallKernRegvia PyPI.$pipinstallkernregQuick-Startimportkernregaskrmotorcycle=kr.get_example_data()x,y=motorcycle["time"],motorcycle["accel"]# By default, only x and y need to be provided.# Derivative = 0 is chosen by default# and hence the polynomial degree = 0 + 1.rslt_default=kr.locpoly(x,y)kr.plot(x,y,rslt_default,"motorcycle_default_fit.png")# We can improve on the default specification by# choosing a higher order polynomialrslt_user=kr.locpoly(x,y,degree=3)kr.plot(x,y,rslt_user,"motorcycle_user_fit.png")ReferencesFan, J. and Gijbels, I. (1996).Local Polynomial Modelling and Its Applications.Monographs on Statistics and Applied Probability, 66. Chapman & Hall.Wand, M.P. & Jones, M.C. (1995).Kernel Smoothing.Monographs on Statistics and Applied Probability, 60. Chapman & Hall.Wand, M.P. and Ripley, B. D. (2015).KernSmooth:  Functions for Kernel Smoothing for Wand and Jones (1995).Rpackage version 2.23-18.*The image is taken from futurist illustratorArthur Radebaugh's (1906–1974)Sunday comic stripCloser Than We Think!, which was published by the Chicago Tribune - New York News Syndicate from 1958 to 1963.
Package: kern-smooth
Porting popular R library KernSmooth to python.Functions for Kernel Smoothing and Density Estimation.Transformed R and Fortran functions into Python(2,3) code.Attention:Please use kern-smooth 1.1.0 or newer.
Reason: found not needed log10 density transformation.Installation:pipinstallkern-smoothMain function of the module:defdensCols(x,y=None,nbin=128,bandwidth=None)Produces a vector of numbers which encode the local densities at each point in dataset.x, y : 1D numpy array with coordinates of the points density will be estimated onnbin[optional] int or [int, int] - number of bins along each axis(in case of single value - [nbin, nbin] will be used). Default value 128.bandwidth : [optional] numeric vector (len of 1 or 2) of smoothing bandwidth.Returns: numpy array with numerical representation (in range [0,1]) of point densities.Attention: For return value numpy.nan values are allowed in case of nan / infinite values in original datasetSource: R::grDevices::densColsUsageGenerate data for plotting (make sure Matplotlib is installed)frommatplotlibimportpyplotaspltfrommatplotlibimportcmimportnumpyasnpnp.random.seed(0)# create two 'bulbs' with normal distributionsmean1=[0,0]cov1=[[5,0],[0,30]]# diagonal covariancex1,y1=np.random.multivariate_normal(mean1,cov1,50000).Tmean2=[5,17]cov2=[[30,0],[0,5]]# diagonal covariancex2,y2=np.random.multivariate_normal(mean2,cov2,50000).Tx=np.hstack([x1,x2])y=np.hstack([y1,y2])Generate point densities:fromkern_smoothimportdensColsdensities=densCols(x,y,nbin=128)Plot the resultsc=plt.scatter(x,y,c=densities,s=15,edgecolors='none',alpha=0.75,cmap=cm.jet)plt.colorbar(sc)plt.show()Result![Result](https://github.com/AlexanderButyaev/kern_smooth/blob/master/example_density.png)AuthorAlexander Butyaev
Package: keros
Artificial Neural Network for brain tumor detectionIndexEste é um pacote destinado a implementação dos conceitos de visão computacional e processamento de imagens.🚧  Status 🚀 Em construção...  🚧InfosEste pacote contém uma rede neural implementada do zero, somente usando o numpy. Também contém funcionalidades para o processamento de imagens e extração de atributos.Pré-requerimentsAs depêndencias deste pacote são:numpyscikit-imagescikit-learnmatplotlibpickle
Package: kervi
# kervi
Easy Python framework for robotic and automation projects.
The framework runs on all platforms that supports Python and have hardware support for Raspberry pi.
Wire up sensors, controllers and other devices to your Raspberry PI and link them to web based dashboards
and internal application logic.Knowledge about web servers, html or web programming is not needed.
UI configuration is done in python code and the framework creates the web ui.Features of the framework are:
* Python classes for handling sensors, displays, motors, gpio and other hardware.
* Python classes for handling user interactions from controllers on dashboards.
* Integrated camera handling (Raspberry PI camera at the moment)
* Device driver library for common devices
* Very easy dashboard setup.
* Dashboard UI is responsive and scales from mobile phones to desktop.
* Multi process oriented by spreading over more than one core.
* Multi device oriented by connecting multiple Raspberry Pi’s in one large application.
* No web development knowledge is required as dashboard layout is handled in python.[Read the full documentation](https://kervi.org/)<img src=”https://kervi.github.io/_images/dashboard_browser.png” width=”500”> <img src=”https://kervi.github.io/_images/dashboard_mobile.png” width=”300”><img src=”https://kervi.github.io/_images/dashboard_cam.png” width=”500”> <img src=”https://kervi.github.io/_images/panel_cam.png” width=”300”>Install Kervi via pip:On Raspberry Pi` sudo pip3 install kervi[rpi] `On other systems like windows and linux:` pip3 install kervi `After Kervi is installed, a commandline tool calledkerviis available.
Via the commandline interface it is possible scaffold Kervi apps and
setup image and video handling on Raspberry PI.Create your  first app in an empty folder by executing:` kervi create application myapp "My first app" `Start your new app by executing:` python myapp.py `The following output should be displayed:
(example)` Starting kervi application, please wait load sensors load controllers load cameras init IPC Your Kervi application is ready athttp://192.168.0.120:8080`Goto the url specified
Package: kesacco
KESACCO: Keen Event Simulation and Analysis for CTA Cluster ObservationsThis repository contains a software dedicated to the simulation of CTA event files
corresponding to the observation of galaxy clusters. The event files can then be analyzed
to produce CTA analysis results.clustpipe.py :
Main code that defines the class ClusterPipe.clustpipe_common.py :
Subclass that defines common tools.clustpipe_sim.py :
Subclass that deal with event simulation.clustpipe_ana.py :
Subclass that deal with observation analysis.clustpipe_ana_plot.py :
Sub-modules dedicated to run automatic plots related to analysis.clustpipe_title.py :
Title for the software.Tools :
Repository that gather several useful libraries and run ctools scripts.
It also contain the compact source, background, and observation setup
classes.notebook :
Repository where to find Jupyter notebook used for development/example.Overview of the codeFigure 1. Overview of the KESACCO software.EnvironmentThe code works with python 3. It should work with python 2, although several features may fail. Please make sure that you are in the correct environment when you run the code.InstallationYou can use pip to install the package:pip install kesaccoDependenciesThe software depends on standard python package (non-exhaustive list):astropymatplotlibrandomnumpyoscopypickleBut also:gammalib (http://cta.irap.omp.eu/gammalib/)ctools (http://cta.irap.omp.eu/ctools/)emcee (https://emcee.readthedocs.io/en/stable/)corner (https://corner.readthedocs.io/en/latest/)minot (seehttps://github.com/remi-adam/minot)
Package: kes-RHDHV
Kes Python ClientDescriptionThis repository contains a python client which can be used to programatically access and modify your activity data through an abstraction called tables. Thedocumentationexplains this in in more detail.Generally speaking you would first create your data model in Kes and, publish it, download the Python representation of it and use that in your own Python code by creating a client like this:config = Config(kes_service_address='localhost:50051')
client = Client(config)
project = client.open_project("Preview Python client example")RequirementsPython 3.10 and higher.InstallationInstall using pip:pip install kes-RHDHVYou will need a token which you can obtain from the project page in Kes. Theexampleshows how to use the token.Each project has its own unique tokens for your user account in Kes.ExamplesSee theexamplesDocumentationSee theonline documentationLicenseThis library is distributed under theMIT license
Package: kessel
kesselWhat this isMinimal dependency python wsgi-compatible web framework with the tried
and tested fluid-container-inspired name. This is pretty crude, so I decided
to go with 'kessel', german for 'cauldron'.DISCLAIMERThis project is mainly a learning effort and should not be used in any
reliability- oder security-critical context.Featuresminimal web framework, can be anything you want.token auth withpyJWTmodularisation can be achieved withrecipesthread-local app context for handlingcurrent_user,current_request, etc.small and readable codebaseinstallationpipinstallkesselusageWIPseeexamples/app.pyandexamples/run.pyrecipesTODOseeexamples/foo/fooController.pyEncoding & Decoding Tokens with RS256 (RSA)TODOmkdirtest-app&&\opensslgenrsa-outtest-app/private.pem2048Acknowledgementsobviously heavily inspired byflaskclassHeadersfromheaders.pyis a modified version ofwerkzeug's
Package: kessel_sqladmin
SQLAlchemy Admin for Starlette/FastAPISQLAdmin is a flexible Admin interface for SQLAlchemy models.Main features include:SQLAlchemysync/async enginesStarletteintegrationFastAPIintegrationSQLModelsupportModern UI usingTablerDocumentation:https://aminalaee.dev/sqladminSource Code:https://github.com/aminalaee/sqladminOnline Demo:DemoInstallation$pipinstallsqladminQuickstartLet's define an example SQLAlchemy model:fromsqlalchemyimportColumn,Integer,String,create_enginefromsqlalchemy.ext.declarativeimportdeclarative_baseBase=declarative_base()engine=create_engine("sqlite:///example.db",connect_args={"check_same_thread":False},)classUser(Base):__tablename__="users"id=Column(Integer,primary_key=True)name=Column(String)Base.metadata.create_all(engine)# Create tablesIf you want to useSQLAdminwithFastAPI:fromfastapiimportFastAPIfromsqladminimportAdmin,ModelViewapp=FastAPI()admin=Admin(app,engine)classUserAdmin(ModelView,model=User):column_list=[User.id,User.name]admin.add_view(UserAdmin)Or if you want to useSQLAdminwithStarlette:fromsqladminimportAdmin,ModelViewfromstarlette.applicationsimportStarletteapp=Starlette()admin=Admin(app,engine)classUserAdmin(ModelView,model=User):column_list=[User.id,User.name]admin.add_view(UserAdmin)Now visiting/adminon your browser you can see theSQLAdmininterface.Related projects and inspirationsFlask-AdminAdmin interface for Flask supporting different database backends and ORMs. This project has inspired SQLAdmin extensively and most of the features and configurations are implemented the same.FastAPI-AdminAdmin interface for FastAPI which works withTortoiseORM.DashboardAdmin interface for ASGI frameworks which works with theormpackage.
Package: kessler-av
kessler-avPython library for controlling aKramermedia switch that usesProtocol 2000over a TCP connection.It's primarily intended for use as a device driver for aHome Assistantintegration.USAGEfromkessleravimportget_media_switchdevice_url='10.0.0.1'media_switch=get_media_switch(device_url)media_switch.select_source(3)# Change to input 3media_switch.lock()# Lock front panelmedia_switch.unlock()# Unlock front panelmedia_switch.update()# Refreshes device stateSeesrc/kesslerav/media_switch.pyfor fullMediaSwitchcapabilities.Device URL formatThe URL takes the form of<scheme>://<host>:<port>#<protocol>with all
buthostbeing optional.Default scheme istcp, with a default port of5000.Default protocol is Protocol 2000 (identifier:protocol2k.)Examples:10.0.0.1->
Scheme:tcp, Host:10.0.0.1, Port:5000, Protocol:protocol2klocalhost:1337->
Scheme:tcp, Host:localhost, Port:1337, Protocol:protocol2klocalhost:1337#protocol2000->
Scheme:tcp, Host:localhost, Port:1337, Protocol:protocol2ktcp://10.0.0.1->
Scheme:tcp, Host:10.0.0.1, Port:5000, Protocol:protocol2ktcp://switch.local:8080->
Scheme:tcp, Host:switch.local, Port:8080, Protocol:protocol2ktcp://localhost:8080#protocol2kScheme:tcp, Host:localhost, Port:8080, Protocol:protocol2kLimitationsThe library was tested and developed using a KramerVS-161HDMI switch,
butshouldwork for any Kramer switch using Protocol 2000.It doesnotcurrently support:Matrixswitch operations, since I don't have a device to test withUDP communication, since I don't have a device to test withSerial communication, since TCP is the more likely control mechanism for home
automation purposesThe library has extension points for adding the support above should an
opportunity or need to do so arise.Development workflowPython environmentWorkflow scripts assume a working Python environment, includingpip.Remember to be kind to yourself and use a virtual environment.python3-mvenvenv
env/bin/activateSetupInstall development and runtime dependencies. This also installs the library as an
editable path, so that it can be loaded in the REPL andpytest.script/setupTestsRun unit tests:script/testTests can also be continuously run while developing with:ptw.BuildTo build distributables:script/buildBuild artifacts will be placed in thedistdirectory.PublishingBuild the distribution.script/buildPublish the library to TestPyPI.script/publish_testPublish the library to PyPI.script/publishWhykessler-av?InSeinfeld, the characterCosmo Kramerwas based on comedianKenny
Kramer. In the shows pilot episode, the producers hadn't gotten permission
to use Kramer's name, so the character was called Kessler instead.This library controlsKramer A/V'smedia switches, but doesn't have
permission to usetheirtrademarked name. As a result, it seemed fitting to
name itkessler-av.
Package: kesslergame
# Kessler## Getting startedKessler is a simulation environment loosely modeled after our internal project PsiBee and the external project [Fuzzy Asteroids](https://github.com/xfuzzycomp/FuzzyAsteroids).
The game has ships that shoot bullets at asteroids to gain score. Ships can collide with asteroids and lose lives.
If the ship runs out of lives, the game terminates. In multi-ship scenarios, ships can collide with each other as well,
but cannot shoot each other.Kessler can be built as python extension for install using pip, or used as a local package by copying thesrc/kessler_gamedirectory to your project. Wheels are provided in
[releases](https://github.com/ThalesGroup/kessler-game/releases) for install using:` pip install <path tokessler_game-#.#.#-py3-none-any.whl>`Kessler has two primary graphics modules. The first uses Python’s Tkinter UI library to display the game. The second
utilizes a separate executable process called kessler_graphics made in Unreal Engine 5. Data is sent to the
kessler_graphics instance using UDP protocol on a local machine.## Using the UE5 graphics engine
Under kessler_graphics is an Unreal Engine 5 project for receiving simulation data from the Kessler Python process and
displaying it in a 3d environment. To contribute to the UE5 project, you will need to do the following.
- Install Visual Studio 2019 v16.11.5 or later with “Game Development with C++” selected on install
- Install the .NET Core 3.1 Runtime from [here](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/runtime-3.1.30-windows-x64-installer?cid=getdotnetcore)
- Install Unreal Engine 5.0.x from the [Epic Games Launcher](https://store.epicgames.com/en-US/download)
- Grab the latest release of the UDP-Unreal plugin from [here](https://github.com/getnamo/UDP-Unreal/releases)
- Follow installation instructions for UDP-Unreal from its included README
- Right clickkessler_graphics.uprojectunder thekessler_graphicsdirectory and select “Generate Visual Studio Project Files” from the context menu
- Launch the project by double-clicking onkessler_graphics.uproject, and select “Yes” if prompted to rebuild engine modules## DocumentationDocumentation is not yet available for Kessler. If you would like to contribute to documentation, check out
[CONTRIBUTING.md](CONTRIBUTING.md) for info on how to get started## ContributingIf you are interested in contributing to the Kessler project, start by reading the [Contributing guide](/CONTRIBUTING.md).## LicenseKessler is licensed under the Apache 2.0 license. Please read [LICENSE](LICENSE) for more information.
Package: ket
ketket is a command-line tool, that makes interaction withBitbucketrepositories easier.You probably knowhubforGitHuband/orlabforGitLab.
Well, ket is trying to accomplish the same goal, but forBitbucket.ket is currently only capable of managing pull requests. More to come!Installation$ pip install ketConfigurationIn order to interact with a Bitbucket repository, ket needs to authenticate to
Bitbucket with a username and an API key. The username is usually your
Bitbucket username. For the API key you should use the so-called app password.
More on app passwords, and how to create themhere.Configuration fileket tries to read its configuration from the file$HOME/.config/ket, which
must be formatted as follows:[bitbucket]
username = Ch00k
api_key = <app_password>Running$ ket
Usage: ket [OPTIONS] COMMAND [ARGS]...

  Bitbucket in your command-line

Options:
  --help  Show this message and exit.

Commands:
  pull-request  Pull request operations$ ket pull-request
Usage: ket pull-request [OPTIONS] COMMAND [ARGS]...

  Pull request operations

Options:
  --help  Show this message and exit.

Commands:
  approve    Approve a pull request
  checkout   Checkout the branch of a pull request
  close      Close (decline) a pull request
  create     Create a pull request
  diff       Show the diff of a pull request
  list       List pull requests
  merge      Merge (accept) a pull request
  show       Show pull request details
  unapprove  Unapprove a pull requestTab completionTo enable tab completion, executeeval "$(_KET_COMPLETE=source ket)"
Package: ketama
Python bindings forKetama consistent hash library.ExamplefromketamaimportKetamawithopen("ketama.conf","w")asfp:fp.write("127.0.0.1:1\t400\n")fp.write("127.0.0.1:2\t600\n")fp.write("127.0.0.1:3\t600\n")defmain():k=Ketama("ketama.conf")key_hash,server=k.get_server("1")assertserver=="127.0.0.1:3"key_hash,server=k.get_server("3")assertserver!="127.0.0.1:1"if__name__=='__main__':main()
Package: ketamine
ketamine: A robust python library for colour conversion.A python library for colour conversion between colour models.Supported Colour ModelsRGB, RGBA, hexHSLHSVCMYKCIELABCIEXYZInstallationpipinstallketamineor install it from source:gitclonehttps://github.com/sertdfyguhi/ketamine/cdketamine
python3-mbuild
pipinstalldist/*.whlExample UsagefromketamineimportColourwhite=Colour.from_rgb(255,255,255)print(white.to_hex())# #ffffffprint(white.to_hsl())# (0, 0.0, 1.0)hsv=Colour.from_hsv(184,0.85,1.0)print(hsv.to_rgb())# (38.25000000000001, 240.54999999999995, 255.0)# round RGB values firsthsv.round()print(hsv.to_rgb())# (38, 241, 255)TodoFinish CIELAB colour conversionCorrectly implement to_labFinish CMYK colour conversionPublish to PyPI
Package: ketaway
Ketaway Library (ketaway)สวัสดครับ ไลบรารี่นี้ใช้สำหรับฝึกสร้างไลบรารี่เป็นของตัวเอง ความสามารถคือแสดงชื่อภาษาอังกฤษแสดงชื่อภาษาไทยวิธีติดตั้งแสนง่ายเราจะติดตั้งผ่านเจ้า pippipinstallketawayง่ายไหมล่ะวิธีใช้ก็ง่ายมากเปิด Python แล้วพิพม์ตามนี้เลยfromketawayimportKetawaymylib=Ketaway()print(help(mylib.WhoIAm))print(mylib)print(mylib.name)print(mylib.lastname)print(mylib.nickname)mylib.WhoIAm()print(mylib.email)Visitแวะเข้าไปเยี่ยมชมกันได้เนื้อหาลิ้งค์githubhttps://github.com/ketawayFacebookhttps://www.facebook.com/ketaway------------สร้างไฟล์ READMEhttps://dillinger.io/
Package: keter
KeterKeter (keh-ter) is a highly-scalable lab environment for coronavirus data science. This project
is informed by lessons learned from past B.Next projects.The goals of this project:Support distributed computing using an archiecture based on a job queue and workers.Workers can run on heterogeneous hardware and started and stopped on demand without issue.Support Kubernetes as the first class or preferred orchestration solution.Use machine learning techniques that build on state-of-the-art.Antiviral subsystem will build on a ChEMBL foundation.Forecasting UI that is mostly or entirely static and can scale to massive amounts of users.This project is in the planning phases – not even pre-alpha.AcknowledgmentA project ofB.Next.
Package: ketl
kETLkonfigurable ETLFree software: MIT licenseDocumentation:https://ketl.readthedocs.io.IntroductionkETL is a konfigurable ETL library. Its job is to simplify the process of extracting, transforming,
and loading files from the internet. The goal of kETL is not to be all things to all use cases,
but rather to maintain a simple but useful set of features intended to get your data from wherever
it lives to some place where you can do stuff with it.FeaturesStrict separation of extraction, transformation, and loading concernsEasily configurable for most use cases; derive subclasses to define your own behaviorsSimple API to use with or without pipelining logicCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-10-18)First release on PyPI.