Saravanan666/pypi-org huggingface dataset:
Package: pyKard
Kard Private ApiPython wrapper of Kard Private ApiMade with the help of the awesome open-source toolHTTP Toolkitfor trafic sniffing ❤️This project is a refactored version ofkard-private-apiInstallationpip install pyKardUsagefromkard_private_apiimportKardmyKard=Kard()myKard.authenticate(forceApiAuth=False)...That's all! You're all setup to play with Kard private API.I recommend you to start with some of the exampleshere.FAQHow does it login to my Kard account?On your first (or clean) login, the library will generate a unique vendor identifier that will be linked to your session.Then, a request will be made to Kard, which in return will either ask for an OTP code or your PIN code.Once successfully authenticated, we have access to anaccessTokenand arefreshTokenwhich are your account sesame. These as well as your vendor identifier are saved on your computer for later use.Where my credentials are saved?By default, they are saved in a file named.kard-login_YOURPHONENUMBER-settings.jsonlocated in your home directory.(C:\\Users\%username%\on Windows or~on Linux/MacOs)LicenseThis repository and all of its content is under theGNU GPLv3license.DisclaimerThis software is provided as is, I shall not, and will not be liable for any misuse or unauthorised use, leading or not to damage to any third-party.
Package: pykart
pykart ModuleThis module will be helpful for Web scraping data of product from an E-commerce site [flipkart].
Product data like name,rating,price,discount.
This way data gathering for product will become easy.
Need 3 extra module :request,bs4,prettytable to get successfull output.InstallationRun the following to install:pipinstallpykart##Usageimportpykartasfkproduct=input("Product name : ")fop=fk.pykart(product)#flipkart_opiffop:fop.pg_title()c=fop.pg_count()print("\nResults found per page : ",c[1],"to",c[3]," Total matches :",c[5])#data into table format on command linefop.product_details()#create control for next,previous page momentswhileTrue:pg_no=input("[Next Page] n | [previous pg] b | [save scrap code] save :")ifpg_no.lower()=="n":fop.next_pg()elifpg_no.lower()=="b":fop.prev_pg()elifpg_no.lower()=="save":fop.save_code()else:print("end")break#product data in list#the data you see from product_details only those products are present in listfop.pg_ls()Developing pykartTo install pykart,along with the tools you need to develop and run tests,run the following in your virtualenv:$pipinstall-e.[dev]License(c) 2021 Adesh Dangi
GNU GENERAL PUBLIC LICENSE 2007This repository is licensed under the GNU license,
See LICENSE for details.
Package: pykaryote
Pykaryote is a set of tools for modeling and studying the evolution of
biological complexity.Documentation is hosted at Read The Docs:https://pykaryote.readthedocs.org/en/latest/InstallationPykaryote requires a number of dependencies, including Python and Open MPI. For installation instructions, see the documentation:https://pykaryote.readthedocs.org/en/latest/tut/installing.htmlOnce the dependencies are installed, Pykaryote can be installed with:pip install pykaryoteUsageThe main front end to Pykaryote is petri. To run a simulation with the default settings, run:mpirun -np 2 petri runThis will save data and graphs to ~/pykaryote/comparisonFor more information, see the Pykaryote documentation.
Package: pykasso
pyKasso: a stochastic karst network simulation toolpyKasso is a python3 open-source package intended to simulate easily and quickly karst networks using a geological model, hydrogeological, and structural data. It relies on a pseudo-genetic methodology where stochastic data and fast-marching methods are combined to perform thousands of simulations rapidly. The method is based on the stochastic karst simulator developed by Borghi et al (2012). It has been extended to account for anisotropy allowing to simplify the algorithm while accounting better for the geological structure following the method presented in Fandel et al. (2022). Statistical geometrical and topological metrics are computed on the simulated networks and compared with the same statistics computed on real karst network to evaluate the plausibility of the simulations.InstallationCurrently, pyKasso is only working with Python 3.9.Using condaDownloadenvironment.yml. From source:conda env create --name pykasso --file=environment.ymlThen:pip install -e pykasso[analysis, visualization]DocumentationWork in progress.ExamplesSome basic examples are avaible here :notebooks/geometry/ContactF. MivilleProf. C. FandelProf. P. RenardPublicationsFandel, C., Miville, F., Ferré, T. et al. 2022: The stochastic simulation of karst conduit network structure using anisotropic fast marching, and its application to a geologically complex alpine karst system. Hydrogeol J 30, 927–946,https://doi.org/10.1007/s10040-022-02464-xBorghi, A., Renard, P., Jenni, S. 2012: A pseudo-genetic stochastic model to generate karstic networks, Journal of Hydrology, 414–415,https://doi.org/10.1016/j.jhydrol.2011.11.032.
Package: pykattis
pykattisA CLI tool for solvingKattisproblems with python.Installationpip install pykattisUsageTo create a solution for a problem, run:pykattis create {problem_id}whereproblem_idis the Kattis problem ID.
This creates the directoryproblems/{problem_id}in the current working directory with the filessolution.py, an__init__.pyfile, and downloads the sample input-anwer pairs to asamples.jsonfile.Thesolution.py-file is where you will be writing the solution to the problem.
Inside it is a functionsolve(input_: str) -> str:, which you will fill out with your program as you see fit.
This function is called by the commandskattis runandkattis test.To simply run the program as a script, run:pykattis run {problem_id}and to test it on the sample input-answer pairs defined insamples.json, run:pykattis test {problem_id}To upload the solution to Kattis, you can submit the problem'ssolution.pyfile through Kattis's web form.
However, this is a temporary solution.
In a future version, you will be able to publish your solution to Kattis directly through pykattis.If you, for some reason, just want to download a problem's samples, you can run:pykattis download_samples {problem_id}It is recommended to use a VCS, likegit, to keep track of your solutions.
Package: pykauriid
Kauri ID Python LibraryA (reference) library of tools to support work with the Kauri ID data
structures and protocols.Installation/UsageTo install use pip:$ pip install pykauriidOr clone the repo:$ git clone https://gitlab.com/kauriid/pykauriid.git
$ python setup.py installSet up and activate for Python 3:virtualenv ${HOME}/.virtualenvs/pykauriid \
           --system-site-packages --python=/usr/bin/python3
source ${HOME}/.virtualenvs/pykauriid/bin/activateInstall required packages:pip install -e .For installing the additional development, testing or documentation
dependencies, add a qualifier with one or more of these commands:pip install -e .[dev]           # Development dependencies
pip install -e .[test]          # Testing dependencies
pip install -e .[doc]           # Documentation dependencies
pip install -e .[dev,test,doc]  # All dependencies togetherContributingTBDExampleTBDLicenceCopyright 2018-2019 by SingleSource Limited, Auckland, New ZealandThis work is licensed under the Apache 2.0 open source licence.
Terms and conditions apply.
Package: pykawaii
PykawaiiPython api wrapper for the waifu.pics api made by:okimii#0434Key FeaturesObject Oriented wrapperType-safety measures.InstallingPython 3.7 or higher is required.To install the library, you can just run thefollowing command:# Linux/macOSpython3-mpipinstall-Upykawaii# Windowspy-3-mpipinstall-UpykawaiiQuick ExampleBasic Client Exampleimportpykawaiiimportasynciokawaii=pykawaii.Client()asyncdefmain()->None:sfw_img_url=awaitkawaii.sfw.waifu()nsfw_img_url=awaitkawaii.nsfw.waifu()print(sfw_img_url)print(nsfw_img_url)if__name__=="__main__":asyncio.run(main())
Package: pykbart
pykbart is a library for dealing with data created according to the [KBART standard](http://www.niso.org/workrooms/kbart, “Kbart page on NISO”). It should work under Python 2.7 or 3.x, and should also work for KBART Recommended Practice 1 and 2. It is mostly a convenience wrapper for reading and representing TSV files containing knowledge base data, but can also be used to modify item data in bulk or create KBART files of data from other formats.
Package: pykbi
pykbi: Python module to work on radial distribution functions and Kirkwood-Buff integralsKirkwood-Buff theory bridges microscopic structure and macroscopic properties
of isotropic liquids. The classical version of the theory deals with open and infinite
systems[1], while the new correction allows us to work on closed and finite
systems[2]. The code contains modules to correct finite-size effects in Radial
Distribution functions (RDFs) from Molecular Simulations, and integrate RDFs
obtained from both open and closed ensembles.The methods in this module is included in the references below.pykbiworks
with radial distribution functions obtained from various softwares, so it is up
to the user to read in and make radial distance and the pair correlation
function available as vectors. The integral can be done for both open and
closed systems.  In addition, it has included correction methods to correct for
finite-size effects.This package accompanies the paper "Kirkwood-Buff integrals from molecular simulation"
[3].InstallationThe program can be installed from pip, using:pip3installpykbior it can be installed directly from github:pip3installgit+git://github.com/sondresc/pykbi.gitor you can clone the code, and install manually:gitclonehttps://github.com/sondresc/pykbicdpykbi
pythonsetup.pyinstallReferencesJ. G. Kirkwood and F. P. Buff,J. Chem. Phys.19, 774(1951).P. Kruger, S. K. Schnell, D. Bedeaux, S. Kjelstrup, T. J. H. Vlugt, J.-M. Simon,J. Phys. Chem. Lett.4, 2(2013).N. Dawass, P. Kruger, S. K. Schnell, J.-M. Simon, T. J. H. Vlugt,Fluid Phase Equilibria486, (2019).
Package: pykbtcal
This is a simple library that performs
basic python arithmetic operations.Installationpip install pykbtcalUsage:A guide to using the add_numbers function is as followsimport kbtcal
add_num = kbtcal.add_numbers(3, 7) # where 3 and 7 are the number you want to add
print(add_num)The functions in this library are:add_numberssubtract_numbersmultiply_numbersdivide_numbers_floatdivide_numbers_floorpower_numbersmode_numbersVideo Usage GuideA video usage guide is provided via this linkYouTube LinkLicenseCopyright 2021 KenBroTechThis repository is licensed under MIT license.
See LICENSE for details.Change Log1.0 (26/07/2021)
Package: pykc
This Python library provides high-level access to a KC thread and/or post (including all available metadata) by parsing the HTML source, additionally various endpoints of the KC AJAX API can be called.Minimal Requirements:Core:Python >= 2.6Modules:lxmlenum34requestsBeautifulSoup4Features:Checkpykc/core.pyandpykc.pyor the example code below. :-)frompykcimportKrautchan# Create a Krautchan instance.## If you happen to be a mod/admin try the following:# kc = Krautchan(username='foo', password='bar')## Congratulations, you now have access to some nifty special features! :3kc=Krautchan()# Search for a specific file using the AJAX API.print(kc.search_file(name='rfk'))# Parse a complete thread and return a thread object.thread=kc.get_thread('prog','1698')print(thread.__dict__)# You can even JSONify the object! :)print(thread.to_json())Credits:Main author:buckketLicense:DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE
                   Version 2, December 2004

Copyright (C) 2004 Sam Hocevar <sam@hocevar.net>

Everyone is permitted to copy and distribute verbatim or modified
copies of this license document, and changing it is allowed as long
as the name is changed.

           DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE
  TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION

 0. You just DO WHAT THE FUCK YOU WANT TO.
Package: pykCSD
Kernel Current Source Density is a recently developed method for estimating the density of trans-membrane current sources, which can be used for a detailed study of neuronal synaptic dynamics.It can estimate current source density from potentials measured with irregularly placed linear, planar and spatial electrodes.Free software: BSD licenseDocumentation:http://pykCSD.rtfd.org.FeaturesEstimation of potentials and CSD in 1D, 2D, 3D case for both static and dynamic recordingsVisualization of the estimated quantitiesTODOtracking the unitsmanagement of big datasetsGUIHistory
Package: pykda
PyKDApykdais a Python package for the Kemeny Decomposition Algorithm (KDA) which
allows to decompose a Markov chain into clusters of states, where states within
a cluster are relatively more connected to each other compared to states outside
the cluster. KDA was developed in the paper fromBerkhout and Heidergott (2019)and uses the Kemeny constant as a connectivity measure. The Kemeny constant
is equal to the expected number of steps it takes to go from an arbitrary state
to a random state drawn according to the stationary distribution of the Markov
chain.The package also contains Markov chain tooling for the calculations, for more
details about these calculations and the theory, please refer to the book fromKemeny and Snell (1976).InstallationUse the package managerpipto install PyKDA.pipinstallpykdaUsagefrompykda.Markov_chainimportMarkovChainimportnumpyasnpP=np.array([[0.0,0.52493781,0.47506219],[0.0,0.66666667,0.33333333],[0.06666667,0.33333333,0.6],])MC=MarkovChain(P)print(MC.stationary_distribution.T)print(MC.ergodic_projector)print(MC.mean_first_passage_matrix)ContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.
Please make sure to update tests as appropriate.How to citepykda?If you usepykdain your research, please consider citing the following paper:Joost Berkhout, Bernd F. Heidergott (2019).
Analysis of Markov influence graphs.Operations Research, 67(3):892-904.https://doi.org/10.1287/opre.2018.1813Or, using the following BibTeX entry:@article{Berkhout2016a,title={Analysis of {Markov} influence graphs},volume={67},number={3},journal={Operations Research},author={Berkhout, J. and Heidergott, B. F.},year={2019},pages={892--904},}
Package: pyKDC101
pyKDC101Functions and examples to control Thorlabs KDC101 through serial.
Mainly for Python, additionally some basic usage for MATLAB.
Here, the controller is used to move a Thorlabs rotation stage (PRM1-Z8).
The interface offered by Thorlabs does not work for unix systems (afaik). Therefore these functions use the low-level serial functionality that is documented inAPT_Communications_Protocol.pdf.UsageSimply runpip install pyKDC101Then in your Python script invoke the functions byfrom pyKDC101.pyKDC101 import KDC
kdc = KDC()Withkdcyou can then call all functions and methods of the controllers.Look atexample.py. There are a few more commands implemented than shown in the example but they appear not to be too helpful for most applications.PythonOffers the most used commands (move here,move there,go home) in a simple structure, shown inexample.py. Additionally offers a lower-level command structure in which you can send most basic serial commands and read the reply.MATLABI created basic functionalities to test it until it worked but then switched to Python. Still, MATLABthusiasts may find the essential bits in thematlab_KDC101.m-file as guideline to build their own command structure.Serial portsBy default theopenstage()-function will use all the ports found under/dev/serial/by-id/*Thorlabs_Brushed_Motor_Controller*(Windows: COM[0-255],not tested) and try to connect through serial.You need write-access to the tty-port. Either run as the admin or change the permissions or the ownership on the serial port, preferably with a udev rule, e.g. something like/etc/udev/rules.d/80-usb-serial.rulesSUBSYSTEM=="tty", MODE="0666"(After changing run as root:udevadm control --reload-rules && udevadm trigger)CommandsThere are a lot of tricks in getting this to work. I try to document most of it in the code.Communication basicsThe address '01' is the host (usually the computer)The address '50' refers to generic USB deviceWhen using commands with parameters, '50' needs to be OR'd with '80' leading to 'D0'While we create the commands in hex, they are converted to integers and sent in binary formatIf you are not familiar with serial: be advised to add a delay between sending a command and receiving data (e.g. 50 ms)Angle to EncCnt conversionThe position parameter structure is encoded in 4 Byte signed little endian, e.g. "64 00 10 00"To get the number, you have to flip the byte sequence and then convert it:00 10 00 64 = 0000 0000  0001 0000  0000 0000  0110 0100 =2^20 + 2^6 + 2^5 + 2^2 = 1048676Divide this by EncCnt/deg yields rotation in degree: 1048676/1920 = 546 degreeHence to calculate the needed parameter, you take your angle, multiply it withthe EncCnt/deg, convert it to binary (filling up to 4 bytes). Then youflip the sequence of the four bytes.LicenseThis project is licensed under the MIT license.AcknowledgementThis is under development but I hope it will be helpful for fellow linux-users. Comments and suggestions highly welcome.
Package: pykdeconnect
A work-in-progress implementation of the KDEConnect protocol in python.
Discovery and pairing works already, but not much else does.MIT LicenseCopyright (c) 2022 Adrian FreundPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: pykdgrav3_utils
pykdgrav3_utilsThis package contains modules to handle IO of PKDGRAV3.
They can be used to read/write snapshots, FoF data or unit conversions.However, this package is not intended to provide any plotting routine.InstallFrom pippython3-mpippykdgrav3_utilsFrom sourcegitclonegit@bitbucket.org:iacsimgro/pykdgrav3_utils.gitcdpykdgrav3_utils
python3-mpipinstall.Development (using poetry)makepoetry-download
makeinstall
makepre-commit-install
makecodestyle
makepublish
Package: pykdumpfile
# pykdumpfileBindings to the libkdumpfile shared library.## Links[libkdumpfile](https://github.com/ptesarik/libkdumpfile)## LicenseThis program is free software; you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation; either version 2 of the License, or (at your option) any later
version.
Package: pykea
An Example of using LineFinder Packageimprt the package(s)from pykea.LineFinder import LineFinderinstantiate an objectlf = LineFinder(hsv_v_max=20,display_contour=True, 

            h_start=380,

            h_end=480,

            w_start=0,

            w_end=640,

            min_area=500,

            max_area=90000)start processinglf.processget cam datalf.line_center_xy()lf.line_angle()lf.line_area()
Package: pykebabs
pybabsMachine Learning algorithms already support our daily life in many areas of application.
Be it self-driving cars, intelligent robots, or the field of computational biology. Identi-
fying specific features from biological sequences is essential in understanding the world
surrounding us and its complex connections. Forensics, the development of new drugs,
and tools for predicting certain Illnesses based on our DNA are important subareas of
this Topic. One standard method to do so is in the form of Support Vector Machines
(SVM). SVMs are well suited for this task for several reasons, but the biggest profit
comes from their ability to use the so-called kernels for their calculation. They convert
the alphabetical nature of biological sequences via similarity analysis into their own
numerical similarity Matrices, called Kernel. This Kernel can then be used for internal
calculations. In the case of biological sequences, this special feature clearly distinguishes
the support vector machines from other machine learning algorithms
This thesis is based on the KEBABS Package from Johannes Palme, which was im-
plemented in R. Hence, the user base of R is declining, and the functionalities of the
Kebabs Package are more and more sought after in other Programming languages.
Hence Python is the go-to language for processing biological Data, it makes sense to
provide an easy-to-use framework for manipulating and processing biological Data in
this well-supported platform. The Package offers two Kernels for Kernel-Based Sequence
analysis: the spectrum kernel and the gappy pair Kernel. Both can be used with DNA,
RNA, and Aminoacids sequences. The Package also explicitly represents the calculated
Kernel Matrix as a whole or in the sparse format. The Package also works seamlessly
with multiple existing SVM Frameworks in the Python landscape, like Libsvm and the
prevalent scikit learning library.
The Package also provides Cross-validation, grid search, and an unbiased model se-
lection. For better biological interpretability, the weights used for the calculations can
easily be extracted. Also, prediction profiles have been implemented to understand bet-
ter how some parts of a sequence contribute to the overall result.
Package: pykeboola
pykeboolaPython package for interacting with Keboola. I have just started this and will build it as needed for future development.
For now it includes only queueing jobs and checking on their statuses and listing all tables available to the token.How to useYou can use theClientclass to gateway into individual functionalities:from pykeboola import Client
client = Client('url', 'token')For queueing jobs and checking their statuses you can use E.g.:client.jobs.check_job_status(id_of_the_job)