Saravanan666/pypi-org huggingface dataset:
Package: pyCallBy
pyCallByAuxilary classes to implement call by reference.Python does not allow a user to distinguish betweencall-by-valueandcall-by-referenceparameter passing. Python's standard types are passed by-value to a function or
method. Instances of a class are passed by-reference (pointer) to a function or
method.By implementing a wrapper-classCallByRefParam, any types value can be
passed by-reference. In addition, standard types likeintorboolcan be handled by derived wrapper-classes.Example# define a call-by-reference parameter for integer valuesmyInt=CallByRefIntParam()# a function using a call-by-reference parameterdeffunc(param:CallByRefIntParam):param<<=3# call the function and pass the wrapper objectfunc(myInt)print(myInt.value)ContributorsPatrick Lehmann(Maintainer)and more...LicenseThis Python package (source code) licensed underApache License 2.0.The accompanying documentation is licensed underCreative Commons - Attribution 4.0 (CC-BY 4.0).SPDX-License-Identifier: Apache-2.0
Package: pycallgraph
Welcome! Python Call Graph is aPythonmodule that createscall graphvisualizations for Python applications.ScreenshotsClick on the images below to see a larger version and the source code that generated them.Project StatusThe latest version is1.0.1which was released on 2013-09-17, and is a backwards incompatbile from the previous release.Theproject lives on GitHub, where you canreport issues, contribute to the project byforking the projectthen creating apull request, or justbrowse the source code.The documentation needs some work stiil. Feel free to contribute :)FeaturesSupport for Python 2.7+ and Python 3.3+.Static visualizations of the call graph using various tools such as Graphviz and Gephi.Execute pycallgraph from the command line or import it in your code.Customisable colors. You can programatically set the colors based on number of calls, time taken, memory usage, etc.Modules can be visually grouped together.Easily extendable to create your own output formats.Quick StartInstallation is easy as:pip install pycallgraphYou can either use thecommand-line interfacefor a quick visualization of your Python script, or thepycallgraph modulefor more fine-grained settings.The following examples specify graphviz as the outputter, so it’s required to be installed. They will generate a file calledpycallgraph.png.The command-line method of running pycallgraph is:$ pycallgraph graphviz -- ./mypythonscript.pyA simple use of the API is:from pycallgraph import PyCallGraph
from pycallgraph.output import GraphvizOutput

with PyCallGraph(output=GraphvizOutput()):
    code_to_profile()DocumentationFeel free to browse thedocumentation of pycallgraphfor theusage guideandAPI reference.
Package: pycallgraph2
Python Call GraphNote: This is a fork of the originalpycallgraphsince it became unmaintained.Welcome! pycallgraph2 is aPythonmodule that createscall graphvisualizations for Python applications.Project StatusThe project lives onGitHub, where you canreport issues, contribute to the project byforking the projectthen creating apull request, or just browse the source code.The fork needs documentation. Feel free to contribute :)License:GNU GPLv2FeaturesSupport for Python 2.7+ and Python 3.3+.Static visualizations of the call graph using various tools such as Graphviz and Gephi.Execute pycallgraph from the command line or import it in your code.Customisable colors. You can programatically set the colors based on number of calls, time taken, memory usage, etc.Modules can be visually grouped together.Easily extendable to create your own output formats.Quick StartOS dependencies:Graphviz is open source software and can be installed on Ubuntu/Debian viaapt install graphviz, or equivalent on other distributions.See here for more information.Installation is easy as:pip install pycallgraph2The following examples specify graphviz as the outputter, so it's required to be installed. They will generate a file calledpycallgraph.png.The command-line method of running pycallgraph is::$ pycallgraph graphviz -- ./mypythonscript.pyA simple use of the API is::from pycallgraph2 import PyCallGraph
from pycallgraph2.output import GraphvizOutput

with PyCallGraph(output=GraphvizOutput()):
    code_to_profile()DocumentationDocumentation for the fork is a work in progress.
Package: pycallibri-ecg
Mathematical library for working with ECG data from the Callibri sensor.The main functionality is the calculation of cardio-interval lengths, heart rate and Stress Index (SI).During the first 6 seconds the algorithm is learning, if no 5 RR-intervals are found in the signal
5 RR-intervals are not found, the training is repeated. Further work with the library is iterative (adding new data, calculating indicators).InitializationDetermine the basic parametersRaw signal sampling frequency. Integer type. The allowed values are 250 or 1000.Data processing window size. Integer type. Valid values of sampling_rate / 4 or sampling_rate / 2.Number of windows to calculate SI. Integer type. Allowable values [20...50].The averaging parameter of the IN calculation. Default value is 6.Creating a library instanceFirstly you need to determine lybrary parameters and then put them to library. Tne next step is initialize the filters. In the current version the filters are built-in and clearly defined: Butterworth 2nd order BandPass 5_15 Hz.You can initialize averaging for SI calculation. It is optional value.Python# 1. Raw signal sampling frequencysamplingRate=250# 2. Data processing window sizedataWindow=samplingRate/2# 3. Number of windows to calculate SInwinsForPressureIndex=30callibri_math=CallibriMath(samplingRate,dataWindow,nwinsForPressureIndex)callibri_math.init_filter()# optional# 4. The averaging parameter of the IN calculation. Default value is 6.pressureIndexAverage=6callibri_math.set_pressure_average(pressureIndexAverage)Initializing a data array for transfer to the library:The size of the transmitted array has to be of a certain length:25 values for a signal frequency of 250 Hz100 values for a signal frequency of 1000 HzPythonrawData=[float(x)forxinrange(25)]# orrawData=[float(x)forxinrange(100)]Optional functions (not necessary for the library to work)Check for initial signal corruption. This method should be used if you want to detect and notify of a distorted signal explicitly.Pythonifcallibri_math.initial_signal_corrupted():# Signal corrupted!!!Work with the libraryAdding and process data:Pythoncallibri_math.push_data(samples)callibri_math.process_data_arr()Getting the results:Pythonifcallibri_math.rr_detected():# check for a new peak in the signal# RR-interval lengthrr=math.get_rr()# HRhr=math.get_hr()# SIpi=math.get_pressure_index()# Modamoda=math.get_moda()# Amplitude of modeamplModa=math.get_ampl_moda()# Variation rangevariationDist=math.get_variation_dist()callibri_math.set_rr_checked()Finishing work with the library:Pythondelcallibri_math
Package: pycallingcards
PycallingcardsPycallingcardsis a package for calling cards data analysis developed and maintained byMitra Labat Washington University in St. Louis.Calling cards is a sequencing technology to assay TF binding which could be done in vitro and in vivo at both bulk and single-cell level. To know more about calling cards technology, please checkMoudgil et alandWang et al.Pycallingcards is composed of five different part: datasets, reading (rd), preprocessing (pp), tools (tl) and plotting (pl).
For single-cell calling cards anaysis, Pycallingcards interacts withScanpyand the main structure of Pycallingcards also follows the Scanpy.Datasetscontains four main published datasets for callingcards data.Reading (rd)includes several functions to read and save qbed and peak data.Preprocessing (pp)helps to preprocess data from qbed data to call peaks, make annotation, make Anndata object and filter peaks.Tools (tl)calls motif of the peaks, completes differential peaks and pair differential peaks with gene expression,Plotting (pl)proveides an allround plottting system. It could plot genome areas, link withWashU Epigenome Browser, show signal comparison with Chip-seq(BigWig file), display differential peaks, demonstrate potenial binding-gene expression relationships.Documentation.Please checkherefor detailed documentation.InstallationpipinstallpycallingcardsDevelopmentUse pre-commit to format code atgit commit.pipinstallpre-commit
pre-commitinstall
Package: pycallrail
Python wrapper for the CallRail API.DescriptionThis library provides a Python intefrace for interacting with the Callrail API.
It allows for CRUD operations on objects and resources within your CallRail account and to integrate
with your own applications.InstallationInstall from PyPI using pip, a package manager for Python.# Linux/macOSpython3-mpipinstallpycallrail# Windowspy-3-mpipinstallpycallrailRequirements & DependenciesPython 3.6+requestsujsonpython-dateutiltypeguardUsageimportpycallrail.callrailasclrl# init the api clientapi=clrl.CallRail('your_api_key')# list accounts associated with your api keyaccounts=api.list_accounts(# optional kwargs, more info on supported kwargs at endpoint docs)# list calls associated with your accountcalls=accounts[0].list_calls(# optional kwargs, more info on supported kwargs at endpoint docs)print(calls[0].id)# prints the id of the first call in the list# >>> 123456789Links & ContactDocumentationQuestions
Package: pycalphad
Note: Unsolicited pull requests are _happily_ accepted!pycalphad is a free and open-source Python library for
designing thermodynamic models, calculating phase diagrams and
investigating phase equilibria within the CALPHAD method. It
provides routines for reading Thermo-Calc TDB files and for
solving the multi-component, multi-phase Gibbs energy
minimization problem.The purpose of this project is to provide any interested people
the ability to tinker with and improve the nuts and bolts of
CALPHAD modeling without having to be a computer scientist or
expert programmer.For assistance in setting up your Python environment and/or
collaboration opportunities, please contact the author
by e-mail or using the issue tracker on GitHub.pycalphad is licensed under the MIT License.
See LICENSE.txt for details.Required Dependencies:Python 3.7+matplotlib, numpy, scipy, symengine, xarray, pyparsing, tinydbInstallationSeeInstallation Instructions.ExamplesJupyter notebooks with examples are available onNBViewerandpycalphad.org.DocumentationSee the documentation onpycalphad.org.Getting HelpQuestions about installing and using pycalphad can be addressed in thepycalphad Google Group.
Technical issues and bugs should be reported on onGitHub.
A public chat channel is available onGitter.CitingIf you use pycalphad in your research, please consider citing the following work:Otis, R. & Liu, Z.-K., (2017). pycalphad: CALPHAD-based Computational Thermodynamics in Python. Journal of Open Research Software. 5(1), p.1. DOI:http://doi.org/10.5334/jors.140AcknowledgementsDevelopment has been made possible in part through NASA Space Technology Research Fellowship (NSTRF) grant NNX14AL43H, and is supervised byProf. Zi-Kui Liuin theDepartment of Materials Science and Engineeringat thePennsylvania State University.
We would also like to acknowledge technical assistance on array computations from Denis Lisov.
Package: pycalphad-xml
pycalphad-xmlXML database plugin for PyCalphadThis package (including the RelaxNG-based schemas that support it) are considered experimental and are likely to change.
We welcome any feedback and encourage you to report issues or suggestions to us on ourGitHub issues page!Installingpipinstallpycalphad-xmlUsagePyCalphad version 0.10.1 or later will automatically detect this packge as a plugin and load register the XML reader and writer with PyCalphad'sDatabaseinterface.Databases can be loaded and used as any other supported format:frompycalphadimportDatabasedbf=Database("my_db.xml")# load from filedbf.to_file("out.xml")# write to a fileDevelopment versionsTo install the development version ofpycalphad-xml, clone the repository and install it in editable mode withpip:gitclonegit@github.com:pycalphad/pycalphad-xml.git
pipinstall-e.
Package: pycaltransfer
Calibration transfer for chemometrics and spectral data applicationsThis package contains methods to perform calibration transfer based on bilinear models, mainly Partial Least Squares Regression.
Numpy and Sci-Kit Learn are mandatory dependenciesThe methods included are:(Piecewise) Direct standardization (PDS, DS) (Wang 1991, Bouveresse1996)Orthogonal projection (EPO transfer) (Zeaiter 2006, Roger 2003)Domain invariant PLS (Nikzad-Langerodi 2018, 2020)Joint Y PLS (Folch-Fortuny 2017, Garcia Munoz 2005)Spectral Space Transformation (SST) (W. Du, 2011)Transfer by orthogonal projection (TOP) (A. Andrew and T. Fearn, 2004)Dynamic orthogonal projection (DOP) (Zeater, et al 2006)Transfer component analysis (TCA) (Pan, et at 2011)Unsupervised dynamic orthogonal projection (uDOP) (Fonseca Diaz, et al 2022)Installation optionsOption 1. Install via pippipinstallpycaltransferOption 2. Clone repositorygit clone https://gitlab.com/chemosoftware/python/pycaltransfer.gitTo start using this package and get the documentation of the methods, do:importpycaltransfer.caltransferascaltransferhelp(caltransfer.ds_pc_transfer_fit)help(caltransfer.pds_pls_transfer_fit)help(caltransfer.epo_fit)help(caltransfer.jointypls_regression)help(caltransfer.slope_bias_correction)help(caltransfer.dipals)help(caltransfer.sst)help(caltransfer.top)help(caltransfer.dop)help(caltransfer.linear_tca)help(caltransfer.udop)
Package: pycamel
InstallAccording to your version of pip type in consolepip3 install pycamelorpip install pycamelQuick startFirst, init CamelConfig in the maintests/conftest.pyfilefrompycamelimportCamelConfigCamelConfig(host='https://localhost/')This host will be used as the main project url.For example, if you have a lot of services in your infrastructuredata-serviceimage-serviceall of them will have the same host, but different paths according to the services and their api versionshttp://localhost/data-service/v1/http://localhost/image-service/v1/http://localhost/image-service/v2/We recommend you to create a separate sub folder for each service under the tests folder and init API maker for themtests/data_service/conftest.pyfrompycamelimportRouterMakerdata_service_maker_v1=RouterMaker('/data-service/v1')The same code should be used for another services. For cases with different API versions (v1, v2, etc.) it is up to you
to create different folders or to make one for both of them but with a router maker for each version.So, for now we are ready to make some tests ^_^ Let’s test endpoint on the data-service.Add some code into ourtests/data_service/conftest.pytests/data_service/conftest.pyimportpytestfrompycamelimportRouterMakerdata_service_maker_v1=RouterMaker('/data-service/v1')cats_statistic=data_service_maker_v1.make_router('/cats-statistic')@pytest.fixture(scope='session')defstatistic_route():returncats_statisticIn the tests below we will check only status codes. More information about the validation you can find in
the example project or in the official documentation.Create a file for our tests.tests/data_service/test_statistic.pyimportpytestdeftest_getting_statistic(statistic_route):response=statistic_route.get()response.assert_status_code([200])@pytest.mark.parametrize("page",[1,2,3])deftest_getting_statistic_with_pagination(page):response=statistic_route.set_filters({"page":page}).get()response.assert_status_code([200])ExamplesIn the project you can findexamplesof using the framework in test cases.PylintCheck if your code doesn’t have any pylint errors.Submit your pull requestIn a pull request, describe your feature as clearly as possible and submit it, please.Learn automation with usHere you can find some youtube lessons about automation on python with
a common pytest framework and with pycamel. Enjoy it :)https://www.youtube.com/c/SolveMeChannelIt is ready to use backend API where you can practice with automation case writing.https://send-request.me/Contact usEmail:solveme.solutions@gmail.comTelegram:https://t.me/automation_testing_with_solvemeDonationFor people who would like to support us. God bless U ^_^BSC200x3EC81929e06950322d5125d8e6CA834F3d9B21f8DOGE | BNB | CAKE | ADA | BUSD | TRX | MATIC | AVAX | ATOM | DIA | DOT
Package: pycamera
An easier solution to computer vision.What is pycamera?PyCamera is a computer vision library for people who, don't know how to use OpenCV, don't want to use OpenCV for such a simple project, or want something easier!ExamplesSave Picturefrompycameraimportcameracam=camera.Camera(0)# Choosing a camerasnap=cam.snap()# Snapping a picture from that camerasnap.save("output.jpg")# Save picture to output.jpgEditing with PillowfrompycameraimportcamerafromPILimportImageDrawcam=camera.Camera(0)# Choosing a camerasnap=cam.snap()# Snap photoimage=snap.to_pillow()# Convert pycamera image to Pillow imagedraw=ImageDraw.Draw(image)white=(255,255,255)draw.ellipse([(10,10),(100,100)],width=3,fill=white,outline=white)# Draw circleimage.show()Live Viewimportpycamerafrompycameraimportcameracam=camera.Camera(0)# Choosing a camerawhileTrue:snap=cam.read()# (reading is better for loops)snap.show()pycamera.waitForKey()# Wait until key is pressed (default key is Escape)
Package: pycamgeom
pycamgeomPackage for projective camera and associated classesCamera: projective camera class
Frame: represents a image and its pose in world coordinates
AABBtree: used to accelerate searches for world entities visible in camera image
Projector: projects faces and vertices from mesh into frame to identify visible mesh elementsfor uploading to pypi:python3 -m build from root dir (/pycamgeom)python3 -m twine upload --repository pypi dist/*for local install (development testing)- couldnt' get pip install -e . option to workpip uninstall pycamgeompip install . (from root: ie. /pycamgeom)
Package: pycaML
pycaML is an easy machine learning model comparison tool with optimization. It allows to generate a table comparing multiple machine learning models, to see which one is best for your data. The unique feature of pycaML is built-in hyperparameters tuning using Bayesian Optimization. It also supports meta-models like Stacking and Voting ensembles. You can setup and optimize 25 models with one line of code.
Package: pyCamRecord
Calculate differential power of each frame in webcam stream and record while it is bigger than the presetting threshold.importpyCamRecord#filename format: "%Y%m%d%H%M%S.mp4"  ex:20210630115959.mp4#==>2021 06/30 11:59:59pyCamRecord.record(maxDuration=0.1)#==>Auto record to file every 0.1 minuteimportpyCamRecord#Record while the differential power is bigger than threshold (TH = 15000)pyCamRecord.recordMove(showPower=True)# print differential power frame by frame
Package: pyCamSet
pyCamSetpyCamSet is a python library for multi-camera calibration for MVS systems and instrumentation.Table of contentsAboutGetting startedContributingReporting issuesContributorsAcknowledgementsAboutGetting startedPre-requisitesPython versions:3.11Installing via PyPIHere is thelinkto our project on PyPIpip install pyCamSetReporting issuesTo report an issue or suggest a new feature, please use theissues page.
Please check existing issues before submitting a new one.ContributorsAcknowledgements
Package: pycamunda
PyCamundaPyCamunda is a Python REST api client for the workflow and decision automation engine Camunda.PyCamunda takes care of Camunda-specific characteristics and gives you an intuitive Python interface so you can focus on developing your actual business application. So PyCamunda deals with http requests, nested dictionaries, camelCase keys,  datetime formatting and more for you.Simply.... create an instance of the PyCamunda class corresponding to a Camunda endpoint,.. configure it using keyword arguments and methods,.. send the request and get the response serialized in a Python data class.importpycamunda.processinsturl='http://localhost/engine-rest'get_instances=pycamunda.processinst.GetList(url,process_definition_key='MyProcess')instances=get_instances()forinstanceininstances:print('Process instance id:',instance.id_)Output:Process instance id: 174cb832-a8a7-11ea-8129-0242ac110002Process instance id: 1a70a8e9-a7ed-11ea-8129-0242ac110002...InstallationPyCamunda requires Python >= 3.7 and can be installed from the officalPython Package Index.$ pip install pycamundaDocumentationThe latest documentation can be foundhere.
Package: py_canalyzer
Aboutpy_canalyzerPython 🐍 Package for controlling Vector CANalyzer Toolforkthis repoand create pull request to contribute back to this project.GitHub Releases 👉linkPyPi Package 👉linkPrerequisiteslinkPython environment setup instructionslinkInstallpy_canalyzerpackagepip install py_canalyzer --upgradeDocumentation Linksexample use cases 👉linkpackage reference doc 👉linkSample Example# Import CANalyzer modulefrompy_canalyzerimportCANalyzer# create CANalyzer objectcanalyzer_inst=CANalyzer()# open CANalyzer configuration. Replace CANalyzer_cfg with yours.canalyzer_inst.open(canalyzer_cfg=r'tests\demo_cfg\CANMainDemo\CANMainDemo.cfg')# print installed CANalyzer application versioncanalyzer_inst.get_canalyzer_version_info()# Start CANalyzer measurementcanalyzer_inst.start_measurement()# get signal value. Replace arguments with your message and signal data.sig_val=canalyzer_inst.get_signal_value('CAN',1,'EngineData','EngSpeed')print(sig_val)# Stop CANalyzer Measurementcanalyzer_inst.stop_measurement()# Quit / Close CANalyzer configurationcanalyzer_inst.quit()
Package: pyCANape
pyCANapeThis is a pythonic wrapper around the VECTOR CANape API.
The documentation is availablehere.Example usageOpen and close CANapeimportpycanapecanape=pycanape.CANape(project_path="C:\\Users\\Public\\Documents\\Vector CANape 17\\Examples\\XCPDemo",modal_mode=True,)canape.exit(close_canape=True)Create ModulefrompycanapeimportDriverType,Channels# Create XCPsim modulexcpsim=canape.create_module(module_name="XCPSim",database_filename=r"C:\Users\Public\Documents\Vector CANape 17\Examples\XCPDemo\XCPsim.a2l",driver=DriverType.ASAP3_DRIVER_XCP,channel=Channels.DEV_CAN1,go_online=True,)Calibration# get scalar (0D) calibration objectscalar_obj=xcpsim.get_calibration_object("map1Counter")# read scalar valueprint(scalar_obj.value)# set scalar valuescalar_obj.value=2.0# get axis (1D) calibration objectaxis_obj=xcpsim.get_calibration_object("Curve1")# read axis lengthaxis_dim=axis_obj.dimension# read axis valuesprint(axis_obj.axis)# set axis valuesaxis_obj.axis=[0]*axis_dimHow to contributeSetup your development environment:# clone the repositorygitclonehttps://github.com/zariiii9003/pycanape.git# install pyCANape in editable mode with all development dependenciespipinstall-e.[dev,doc]# install the pre-commit hookpre-commitinstallAfter you implement your changes you should run the static code analysis and check the documentation:# install pycanape in editable modepython-mpipinstall-e.[dev,doc]# format the code with blackblack.# run the linterruffcheck--fixsrc# run the type checkermypy.# run the testspython-mpytest--cov-config=./pyproject.toml--cov=pycanape--cov-report=term--cov-report=html# build the documentationpython-msphinx-Wan--keep-goingdocsbuild
Package: py-canberra
py-canberra is a Python interface to thelibcanberrasound-playing library for Linux.Full documentation is availablehereDependenciesThe only dependency is libcanberra, which can usually be installed from your package repository aslibcanberra.# Ubuntuaptinstalllibcanberra0# RHEL / CentOSyuminstalllibcanberra# Archpacman-SlibcanberraInstallationpy-canberra includes binary distributions for many platforms throughPyPIpipinstallpy-canberraQuickstartOftentimes, if libcanberra is installed, some default system sounds will be installed to/usr/share/sounds, including a bell soundimportcanberracanberra.play(event_id='bell')importtimetime.sleep(0.5)# wait for the sound to finish playingThis plays/usr/share/sounds/freedesktop/stereo/bell.ogaon the default output device.