Saravanan666/pypi-org huggingface dataset:
Package: keller-protocol
keller-protocol - The KELLER Protocol (Python)General Pre-requirement:A KELLER device is needed that can communicate with the Serial Port....with a USB-COM converter cable (eg.K114 series) AND the needed FTDI
driver (https://www.ftdichip.com/Drivers/VCP.htm)...or directly with a cable of the K-10X seriesInstallationpip install keller-protocolSeehttps://pypi.org/project/keller-protocol/UsageSeehttps://keller-druck.com/en/downloads?categories=Software.Communication-Protocolsfor the Communication Protocol.Import the Keller Protocol (keller_protocol.py) in your Python Project:fromkeller_protocolimportkeller_protocolaskpDefine Communication settings like port, baud-rate (Default 9600), timeout (Default 0.3s) and echo (Default True):transmitter=kp.KellerProtocol(port="COM17",baud_rate=9600,timeout=0.3,echo=True)To be able to communicate with the transmitter you will have to use F48 first to initialize.
Otherwise, an exception occurs. F48 will return the firmware version. Make sure the device address is correct. (Default 1).
If only one transmitter is connected, you can use the "transparent" address 250.device_address=1firmware=transmitter.f48(device_address)Read out current pressure value from the transmitter. You will receive the pressure value P1 with channel 1.p1=transmitter.f73(device_address,1)Usage Example with X Lineimportkeller_protocol.keller_protocolaskpimporttimeclassXLine:def__init__(self,port,baud_rate,address,timeout,echo=True):self.bus=kp.KellerProtocol(port,baud_rate,timeout,echo)self.address=addressself.serial_number=Noneself.f73_channels={"CH0":0,"P1":1,"P2":2,"T":3,"TOB1":4,"TOB2":5,"ConTc":10,"ConRaw":11,}self.init_f48()definit_f48(self)->str:"""Initialise and release"""answer=self.bus.f48(self.address)print(f" Init of Device Address:{self.address}with Firmware:{answer}")defget_serial(self)->int:"""Get Serial Number from X-Line:returns Serial Number"""self.serial_number=self.bus.f69(self.address)returnself.serial_numberdefget_address(self)->int:returnself.addressdefset_address(self,new_address:int)->int:"""Change the Device address. -> Has to be unique on the RS485 bus:param new_address: New address of the Device:return: If successful return new_address otherwise old address and throw exception"""self.address=self.bus.f66(self.address,new_address)returnself.addressdefmeasure_tob1(self)->float:"""Get temperature TOB1:return: temperature"""temperature=self.bus.f73(self.address,self.f73_channels["TOB1"])returntemperaturedefmeasure_p1(self)->float:"""Get pressure P1:return: pressure"""pressure=self.bus.f73(self.address,self.f73_channels["P1"])returnpressureif__name__=="__main__":# Example usage:# Init transmittertransmitter=XLine(port="COM17",baud_rate=9600,address=1,timeout=0.3,echo=True)serial_number=transmitter.get_serial()print(f"Transmitter serial number:{serial_number}")print("Press CTRL + C to quit")whileTrue:try:p1=transmitter.measure_p1()tob1=transmitter.measure_tob1()print(f"Pressure P1:{p1:.3f}Temperature TOB1:{tob1:.3f}")time.sleep(1)exceptKeyboardInterrupt:print("Quit!")break
Package: kelley-portfolio-optimization
Kelley OptimizationInstallationRun the following to install:pipinstallkelley_portfolio_optimizationUsageimport numpy as np
returns = np.array([0.0476, 0.004]) # mu
varcov = np.matrix([[2.12, 1.03], # sigma_ij
                [1.03, 1.89]])DevelopmentTo install kelley_portfolio_optimization, along with the tools you need to develop and run tests, run the following in your virtualenv:$pipinstall-e.[dev]
Package: kellog
kellogExtremely easy to use pythonprint()replacement with coloured output and status icons.InstallationpipinstallkellogRequires python version 3.7 or above.UsageIt is very very simple to start using this right away:fromkellogimportdebug,info,warning,error,criticaldebug("Debug message")info(f"Five plus six is{5+6}")warning("f-strings are better than","comma-separated","arguments")error("Exception")critical("DEFCON 1")kellogwraps the built-inlogginglibrary, so it can be controlled in the same ways.
For instance, logging to file and configuring the output format.class Kellog(
	path: Path | None,
	name: str,
	prefixes: dict[str, str] | None,
	append: bool,
)

Parameters
----------
path, optional
  Output file, by default None
name, optional
  Reset the logger name to this, by default "kellog"
prefixes, optional
  Set the log message prefixes (does not automatically add separating whitespace), by default None.
  The dictionary keys to set are "debug", "info", "warning", "error", "critical".
  Setting to None will use the default unicode prefixes, from codicons:      .
append, optional
  If False, delete the contents of `path` first, by default TrueTestsInstall this package with:pipinstall-e.[dev]Then run the tests:maketests
Package: kelluwen
KelluwenOpen AI library for research and education designed to be easy to use and develop with.InstallationpipinstallkelluwenUsagefromkelluwen.functions.transformsimportgenerate_affine,apply_affinefromkelluwen.functions.toolsimportshow_midplanesfromtorchimportrand# Create image tensor of shape BxCxDxHxWimg=rand(2,3,100,100,100)# Define parameters for affine transform. These transforms can be independent for each channel. If there is no channel dimension, the same transform will be applied for all channels.parameters=dict(parameter_translation=rand(2,3),parameter_rotation=rand(2,3),parameter_scaling=rand(2,3),type_rotation="euler_xyz",transform_order="trs",)# Generate affine transformtransform=generate_affine(**parameters)# Apply affine transform to image tensorimg_transformed=apply_affine(image=img,**transform)# Show midplanes of volume. In this example we're using the RAS coordinate system, and we're scaling the features using a min-max method.show_midplanes(image=img_transformed,title="Example midplanes",show=True,type_coordinates="ras",type_scaling="min_max")# ```
Package: kelly-criterion
Kelly Criterion===============Money management strategy based on Kelly J. L.'s formula described in "A New Interpretation of Information Rate" [1].The formula was adopted to gambling and stock market by Ed Thorp, et al., see:"The Kelly Criterion in Blackjack Sports Betting, and the Stock Market" [2].This program calculates the optimal capital allocation for the provided portfolio of securities with the formula:`f_i = m_i / s_i^2`where* `f_i` is the calculated leverage of the i-th security from the portfolio* `m_i` is the mean of the return of the i-th security from the portfolio* `s_i` is the standard deviation of the return of the i-th security from the portfolioassuming that the strategies for the securuties are all statistically independent.The stock quotes are downloaded from Yahoo Finance using Pandas.Reference (Matlab) implementation was taken from Ernie Chan's Quantitative Trading book [3].Installation------------`pip install kelly_criterion`Usage-----`kelly_criterion [--risk-free-rate=<pct>] <start-date> <end-date> <security>...`Example-------```$ kelly_criterion --risk-free-rate 0.04 2001-02-26 2014-12-28 IBB VDE SPYKelly Criterion calculationArguments: risk-free-rate=0.04 start-date=2001-02-26 end-date=2014-12-28 securities=['IBB', 'VDE', 'SPY']Leverages per security:IBB: 3.61SPY: -2.73VDE: 1.04Sum leverage: 1.92```Dependencies------------* Python 2.7* [Numpy](http://www.numpy.org/)* [Pandas](http://pandas.pydata.org/)* [Docopt](http://docopt.org/)References----------* [1]: [A New Interpretation of Information Rate](http://ieeexplore.ieee.org/stamp/stamp.jsp?reload=true&tp=&arnumber=6771227)* [2]: [The Kelly Criterion in Blackjack Sports Betting, and the Stock Market](http://www.edwardothorp.com/sitebuildercontent/sitebuilderfiles/beatthemarket.pdf)* [3]: [Ernest P. Chan: Quantitative Trading (ISBN 978-0470284889)](http://www.amazon.com/Quantitative-Trading-Build-Algorithmic-Business/dp/0470284889)
Package: kelp
kelpPhotometric phase curves of exoplanets.Read the docsfor more info.LicenseThis project is Copyright (c) Brett M. Morris and licensed under
the terms of the BSD 3-Clause license. This package is based upon
theAstropy package templatewhich is licensed under the BSD 3-clause license. See the licenses folder for
more information.ContributingWe love contributions! kelp is open source,
built on open source, and we’d love to have you hang out in our community.
Package: kelpie-personal-trainer
Kelpie Personal TrainerKelpie Personal Trainer is a Python library designed to help users exercise at home with customized workout plans based on their body type. Additionally, the library provides a free body type checker utilizing computer vision techniques to analyze body types from photos.FeaturesBody Type Checker: Utilize computer vision algorithms to analyze body types by uploading photos.Exercise Recommendations: Receive exercise recommendations based on the detected body type.Accuracy and Counter: Utilize computer vision techniques and machine learning to help count the amount of reps and move accuracyInstallationTo install the library, use the following command:pipinstallkelpie-personal-trainerUsage1. Body Type CheckerThebody_typemodule allows users to check their body type by providing a photo. Here's an example of how to use it:fromkelpie_personal_trainerimportbody_type# Load the photophoto_path="path/to/photo.jpg"my_body_type=body_type_checker.check_body(photo_path,gender='male')# Print the resultprint("Detected body type:",my_body_type[0])2. Workout Plan GenerationThebody_typealso module helps users generate personalized workout plans based on their body type. Here's an example of how to use it:fromkelpie_personal_trainerimportbody_type# Get the body typemy_body_type=body_type_checker.check_body(photo_path,gender='male')my_body_type=my_body_type[0]# Automaticly detect body type# Generate the workout planworkout_plan=body_type.get_menu(my_body_type)# Print the workout planprint(json.dumps(workout_plan,indent=4))3. Accuracy and Movement CounterTheworkout_plan_generatormodule helps users generate personalized workout plans based on their body type. Here's an example of how to use it:fromkelpie_personal_trainerimportaccuracy_and_counter# Get the video inputvideo_path="./video.mp4"accuracy_and_counter.check_move('pushup',video)#processing the video inputContributingContributions to the Kelpie Personal Trainer library are welcome! If you find any issues or have suggestions for improvements, please feel free to submit a pull request or open an issue on the GitHub repository.LicenseThis library is released under theMIT License.ContactFor any inquiries or questions, please contact us atarillesmana2001@gmail.com.Visit our website:https://www.kelpiepersonaltrainer.com
Package: kelvin-python-sdk
READMEThis README would normally document whatever steps are necessary to get your application up and running.What is this repository for?Quick summaryVersionLearn MarkdownHow do I get set up?Summary of set upConfigurationDependenciesDatabase configurationHow to run testsDeployment instructionsContribution guidelinesWriting testsCode reviewOther guidelinesWho do I talk to?Repo owner or adminOther community or team contact
Package: kem4ksp
KEMKem is a Python 3 based Programm to manage your Mods.
You can add and remove mods to your libary and install and uninstall them afterwards.When you uninstall a mod it will only remove files which are only in the dependencys of this mod.
so you can easily uninstall mods without watching out for dependencies of other mods.https://github.com/kontiko/Kerbal-Extension-ManagerStep by step tutorial:a) use "kme add <modname> <file1> <file2>" with all files which are needed for this mod as argument*
b) use "kme add <modname>" without an argument to open the folder and drag all files in there*
*if the mod depends on another mod put it also into thereuse "kme install <modname>" to install itcommands:add <modname> <file1> <file2> ... -  adds mod with these filesadd <modname> - adds mods and open modfolder in your Fileexplorerremove <modname> - removes modinstall <modname>uninstall <modname>list - list all added modsinstalled - list all installed mods
Package: kemksp
KEMKem is a Python 3 based Programm to manage your Mods.
You can add and remove mods to your libary and install and uninstall them afterwards.When you uninstall a mod it will only remove files which are only in the dependencys of this mod.
so you can easily uninstall mods without watching out for dependencies of other mods.https://github.com/kontiko/Kerbal-Extension-ManagerStep by step tutorial:a) use "kme add <modname> <file1> <file2>" with all files which are needed for this mod as argument*
b) use "kme add <modname>" without an argument to open the folder and drag all files in there*
*if the mod depends on another mod put it also into thereuse "kme install <modname>" to install itcommands:add <modname> <file1> <file2> ... -  adds mod with these filesadd <modname> - adds mods and open modfolder in your Fileexplorerremove <modname> - removes modinstall <modname>uninstall <modname>list - list all added modsinstalled - list all installed mods
Package: kemmering
KemmeringKemmeringmakes generating, templating, and composing little snippets of HTML
or other XMLish markup easy.For full documentation seehttp://kemmering.readthedocs.org/.1.0.3 (2017-08-08)Added cdata. (Not documented.)1.0.2 (2017-08-07)Properly escape text.1.0 (2016-02-06)First release.
Package: kemux
Kemux - Kafka DemultiplexerKemuxis a Kafka demultiplexer that allows to route messages from one topic to another based on a set of rules.How it worksKemux is a Python library that uses Robinhood'sFaustto process messages from one topic and route them to another topic based on a set of rules.The rules are defined within a Stream object, that defines input and output topics, schema of their messages, as well as a set of filters and transformations that are applied to messages.InstallationKemux can be installed viapip:pipinstallkemuxUsageRunning as a standalone applicationKemux can be run as a standalone application, using theManagerclass:fromkemux.managerimportManagerkemux_manager=Manager(...)kemux_manager.start()Running as a Docker containerKemux can be deployed as a standalone Docker container. The process is described in theDocker sectionof the documentation.Further readingFor more information about Kemux, please refer to thedocumentation.
Package: kendall-w
kendall-wAuthor:Ugo LoobuyckOverviewComputes Kendall's coefficient of concordance for inter-annotator agreement
in the case of item ranking between more than two annotators.InstallationTo install use pip:$ pip install kendall-wOr clone the repo:$ git clone https://github.com/ugolbck/kendall-w.git
$ python setup.py installUsageimportkendall_waskwannotations=[[1,1,1,2],[2,2,2,3],[3,3,3,1],]W=kw.compute_w(annotations)# returns 0.4375 (fair overall agreement)ContributionsAll contributions are welcome.How to help?Fork this repository to your GitHub accountClone the forked repositery to localCode something and push to your branchCreate a pull request from your repositoryTODO:Handlepandas.DataFrameas an input with the instructions inthe main fileUse numpy for faster computation?
Package: kendama
kendama国税庁が提供するインボイス制度適格請求書発行事業者公表システムWeb-APIを使用するためのクライアントAPI(https://www.invoice-kohyo.nta.go.jp/web-api/index.html)This Python package is automatically generated by theOpenAPI Generatorproject:API version: 1.0.0Package version: 0.1.3Build package: org.openapitools.codegen.languages.PythonClientCodegenRequirements.Python 3.7+Installation & Usagepip installRequests is available on PyPI:pipinstallkendamaIf the python package is hosted on a repository, you can install directly using:pipinstallgit+https://github.com/uichi/kendama-python.git(you may need to runpipwith root permission:sudo pip install git+https://github.com/uichi/kendama-python.git)Then import the package:importkendamaSetuptoolsInstall viaSetuptools.pythonsetup.pyinstall--user(orsudo python setup.py installto install the package for all users)Then import the package:importkendamaTestsExecutepytestto run the tests.Getting StartedPlease follow theinstallation procedureand then run the following:importtimeimportkendamafromkendama.restimportApiExceptionfrompprintimportpprint# Defining the host is optional and defaults to https://kensyo.invoice-kohyo.nta.go.jp# See configuration.py for a list of all supported configuration parameters.configuration=kendama.Configuration(host="https://kensyo.invoice-kohyo.nta.go.jp")# Enter a context with an instance of the API clientwithkendama.ApiClient(configuration)asapi_client:# Create an instance of the API classapi_instance=kendama.V1Api(api_client)id='id_example'# str |var_from='Tue Oct 01 09:00:00 JST 2024'# date |to='Tue Oct 01 09:00:00 JST 2024'# date |type='21'# str |  (default to '21')division='1'# str |  (optional) (default to '1')divide='1'# str |  (optional) (default to '1')try:# 取得期間を指定して情報を取得api_response=api_instance.get_announcement_by_diff(id,var_from,to,type,division=division,divide=divide)print("The response of V1Api->get_announcement_by_diff:\n")pprint(api_response)exceptApiExceptionase:print("Exception when calling V1Api->get_announcement_by_diff:%s\n"%e)Documentation for API EndpointsAll URIs are relative tohttps://kensyo.invoice-kohyo.nta.go.jpClassMethodHTTP requestDescriptionV1Apiget_announcement_by_diffGET/1/diff取得期間を指定して情報を取得V1Apiget_announcement_by_numberGET/1/num登録番号を指定して情報を取得V1Apiget_announcement_by_validGET/1/valid登録番号と日付を指定して情報を取得Documentation For ModelsAnnouncementResponseBodyDocumentation For AuthorizationEndpoints do not require authorization.Authoruichi
Package: kendryte-caffe
CaffeCaffe is a deep learning framework made with expression, speed, and modularity in mind.
It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.Check out theproject sitefor all the details likeDIY Deep Learning for Vision with CaffeTutorial DocumentationBAIR reference modelsand thecommunity model zooInstallation instructionsand step-by-step examples.Custom distributionsIntel Caffe(Optimized for CPU and support for multi-node), in particular Intel® Xeon processors.OpenCL Caffee.g. for AMD or Intel devices.Windows CaffeCommunityPlease join thecaffe-users grouporgitter chatto ask questions and talk about methods and models.
Framework development discussions and thorough bug reports are collected onIssues.Happy brewing!License and CitationCaffe is released under theBSD 2-Clause license.
The BAIR/BVLC reference models are released for unrestricted use.Please cite Caffe in your publications if it helps your research:@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}
Package: kenexar-core
Kenexar-CoreWelcome to the Kenexar Team's base package for Python projects. This project is a collection of our most commonly used code snippets, designed to make your development process more efficient and streamlined.Explore the docs »Report Bug·Request FeatureInstallationTo install this package, follow the instructions below:On Windows:pip install kenexar-coreOn Linux:python3 -m pip install kenexar-coreOnce the package is installed, you can start using it in your Python project.ContributingIf you would like to contribute to this project, please feel free to open issues or pull requests on our GitHub repository.To set up your development environment, follow these steps:Clone the project using the following command:git clone https://github.com/kenexar/kenexar-coreIf you are using Linux or OSX, or have a Windows Subsystem for Linux (WSL) installed on Windows, you can run thesetup.shscript to set up your environment:./setup.shIf thesetup.shscript does not work for you, you can set up your environment manually by following these steps:Create a virtual environment with the following command:python3 -m venv venv/Activate the virtual environment:venv/Scripts/activateInstall the required dependencies:pip install -r reqOnce your environment is set up, you can start developing and contributing to the project!LicenseThis project is licensed under the MIT license. For more information, see theLICENSEfile in this repository.Project byexersalza "Julian J."
Package: kenghwee
kenghweeA package where a meme becomes a reality.DescriptionAs of now, thekenghweepackage supports four things:solution: Living up the memefrom kenghwee import solutionmission04: You must be truly desperate to come to this for help...rmrf:sudo rm -rf ~/, goodbye!cs1010s: Very random, trust meInstallationNormal installationpipinstallkenghweeDevelopment installationgitclonehttps://github.com/RussellDash332/kenghwee.gitcdkenghwee
pipinstall--editable.Usage>>>importkenghwee>>>help(kenghwee)# help, Keng Hwee!>>>fromkenghweeimportsolutionContributingMake a pull request, that's it :)Change log[1.1] - 2022-05-27Addedkenghwee.rmrfkenghwee.cs1010sChangedSlight changes onkenghwee.solutionRefactorkenghwee.mission04Modify dependenciesRemoved[1.0] - 2022-05-26Addedkenghwee.solutionkenghwee.mission04ChangedRemoved
Package: kengine-opentelemetry
Python Kengine OpenTelemetry SDKInstrument your Python applications with OpenTelemetry and send the traces toKengineGetting StartedCheck out thedocumentation.Examplepoetryaddkengine-opentelemetryKENGINE_API_KEY=<YOUR_API_KEY>poetryrunopentelemetry-instrumentpythonmyapp.pyLicense© Kengine Limited, 2023Distributed under MIT License (The MIT License).SeeLICENSEfor more information.
Package: kenjutsu
kenjutsuPython utility functions for slices.Free software: BSD 3-ClauseDocumentation:https://kenjutsu.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand thenanshe-org/nanshe-cookiecutterproject template.History0.1.0 (2016-09-08)First release on PyPI.