Saravanan666/pypi-org huggingface dataset:
Package: py-import-search
py-import-search================Utility to search Python source files for imports matching given patterns.Installation------------```pip install py-import-search```Usage-----```usage: py-import-search [-h] [-p --pattern PATTERN] [-d --dir DIR][-r --recursive] [-e --exclude-module MODULE]Search Python source files for importsoptional arguments:-h, --help            show this help message and exit-p --pattern PATTERN  pattern for matching imports (multiple allowed).-d --dir DIR          path of directory containing Python source files.-r --recursive        read all source files under each directory,recursively.-e --exclude-module MODULEexclude module (multiple allowed).```Examples--------Print all imports for source files in a directory:```$ py-import-search -d ..../src/py-import-search/setup.py: from setuptools import find_packages.../src/py-import-search/setup.py: from setuptools import setup.../src/py-import-search/setup.py: import os```Print all imports for source files in a directory all subdirectories:```$ py-import-search -d . -r.../src/py-import-search/setup.py: from setuptools import find_packages.../src/py-import-search/setup.py: from setuptools import setup.../src/py-import-search/setup.py: import os.../src/py-import-search/src/pyimportsearch/__init__.py: import argparse.../src/py-import-search/src/pyimportsearch/__init__.py: import ast.../src/py-import-search/src/pyimportsearch/__init__.py: import os.../src/py-import-search/src/pyimportsearch/__init__.py: import re```Print imports matching 'setup':```$ py-import-search -d . -p setup.../src/py-import-search/setup.py: from setuptools import find_packages.../src/py-import-search/setup.py: from setuptools import setup```Print imports matching 'setup$':```$ py-import-search -d . -p 'setup$'.../src/py-import-search/setup.py: from setuptools import setup```Print imports matching 'setup$':```$ py-import-search -d . -p 'setup$'.../src/py-import-search/setup.py: from setuptools import setup```Print all imports for source files in a directory excluding os module:```$ py-import-search -d . -e os.../src/py-import-search/setup.py: from setuptools import find_packages.../src/py-import-search/setup.py: from setuptools import setup```
Package: pyimprove
PyimproveAutomated program repair for Python using Genetic Improvement.InstallationThis project is avaliable through pip:pip install pyimproveAnd run the project using:python -m pyimprove ./bugs/custom/factorial.py ./bugs/custom/factorial_test.pyFuture developmentThis project was made during University and there were a lot of features that were dropped due to time constraints. There are also a raft of changes I would make in hindsight after completing the first version. Please checkout the issue board to see future intended development.
Package: pyimpspec
pyimpspecA package for parsing, validating, analyzing, and simulating impedance spectra.Table of contentsAboutContributingLicenseChangelogAboutPyimpspec is a Python package that provides an application programming interface (API) and a command-line interface (CLI) for working with impedance spectra.
The target audience is researchers who use electrochemical impedance spectroscopy (EIS) though the program may also be useful in educational settings.
Those looking for a program with a graphical user interface may wish to instead useDearEIS, which is based on pyimpspec.The interfaces of pyimpspec implement features such as:reading certain data formats and parsing the experimental data contained withinvalidation of impedance spectra using linear Kramers-Kronig tests or the Z-HIT algorithmconstruction of circuits by, e.g., parsing circuit description codes (CDC)support for user-defined circuit elementsestimation of the distribution of relaxation times (DRT)complex non-linear least squares fitting of equivalent circuitssimulation of the impedance spectra of circuitsvisualization of impedance spectra and various analysis resultsSee theofficial documentationfor instructions on how to install pyimpspec, examples of how to use the API, and the API reference.ChangelogSeeCHANGELOG.mdfor details.ContributingIf you wish to contribute to the further development of pyimpspec, then there are several options available to you depending on your ability and the amount of time that you can spare.If you find bugs, wish some feature was added, or find the documentation to be lacking, then please open an issue onGitHub.If you wish to contribute code, then start by cloning the repository:git clone https://github.com/vyrjana/pyimpspec.gitThe development dependencies can be installed from within the repository directory using pip:pip install -r ./dev-requirements.txtCreate a new branch based on either themainbranch or the most recent development branch (e.g.,dev-*), and submit your changes as a pull request.Code contributions should, if it is applicable, also include unit tests, which should be implemented in files placed in thetestsfolder found in the root of the repository along with any assets required by the tests.
It should be possible to run the tests by executing therun_tests.shscript, which uses the test discovery process built into theunittestmodule that is included with Python.If it is possible, then delay the importing of dependencies until they are needed in order to reduce the overhead associated with just importing pyimpspec.
Ignoring this guideline may, e.g., make using the CLI slower than it needs to be.SeeCONTRIBUTORSfor a list of people who have contributed to the pyimpspec project.LicenseCopyright 2023 pyimpspec developersPyimpspec is licensed under theGPLv3 or later.The licenses of pyimpspec's dependencies and/or sources of portions of code are included in the LICENSES folder.
Package: pyims
PyIMSA simple python wrapper around the IMS (It Makes Sense) word-sense disambiguation toolkit, that integrates it with NLTK's WordNet interface. Requires IMS to be downloaded separately, which requires a working Java installation. Also requires NLTK with WordNet downloaded. Only tested in Linux, might not work in other OSes. Built for python 3.6+InstallationFirst, follow the instructions inSection 3 of the IMS Readmeto install the components of IMS into some directory; extract the models fromhere(step 3.e) into the same directory as in steps 3.b and 3.c. Then, run:pip install pyimsUsagefrompyimsimportPyIMSwsd=PyIMS("path/to/ims","modelsDirName")print(wsd.disambiguate("I am interested in the interest rates at the bank.",probs=True,synsets=False))# If probs=True, returns a list of (token, probability_distribution) tuples where probability_distribution is a map of lemma to its probability# If probs=False (default False), returns a list of (token, lemma) where lemma is the most probable word-sense in WordNet for the given token# If synsets=True, lemmas are replaced with the Synsets to which they belong# If synsets=False (default False), to access a lemma's synset, call lemma.synset()ReferencesZhong, Zhi and Ng, Hwee Tou. 2010. It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text. In Proceedings of the ACL 2010 System Demonstrations, pages 78--83, Uppsala, Sweden
Package: py-imu-mpu6050
py_imu_mpu6050Inertial Measurement Unit (IMU) Driver based on on theMPU6050IntroductionThis module is able to communicate to the MPU6050 throught I2C. The hardware support is very wide since the code is using Adafruit-Blinka and adafruit-circuitpython-busdevice that provides neccesary configuration for different platform, as shown in the following list:FEATHER_HUZZAHNODEMCUany_raspberry_pi_40_pinany_raspberry_pi_cmRASPBERRY_PI_A or RASPBERRY_PI_B_REV1RASPBERRY_PI_B_REV2BEAGLEBONE_BLACKBEAGLEBONE_GREENBEAGLEBONE_BLACK_INDUSTRIALBEAGLEBONE_GREEN_WIRELESSBEAGLEBONE_BLACK_WIRELESSBEAGLEBONE_POCKETBEAGLEORANGE_PI_PCORANGE_PI_R1ORANGE_PI_ZEROORANGE_PI_ONEORANGE_PI_PC_PLUSORANGE_PI_PC_PLUSORANGE_PI_PC_PLUSORANGE_PI_PC_PLUSORANGE_PI_PC_PLUSORANGE_PI_PC_PLUSORANGE_PI_PC_PLUSORANGE_PI_PC_PLUSORANGE_PI_LITEORANGE_PI_PLUS_2EGIANT_BOARDJETSON_TX1JETSON_TX2JETSON_XAVIERJETSON_NANOJETSON_NXODROID_C2ODROID_N2DRAGONBOARD_410CBINHO_NOVAMICROCHIP_MCP2221SIFIVE_UNLEASHEDPINE64DependenciesThis driver depends on:"Adafruit-Blinka"
"adafruit-circuitpython-busdevice"
"adafruit-circuitpython-register"The Hardware is the MPU6050Installing from PyPIOn supported GNU/Linux systems like the RRaspberry pi, Beaglebone, Jetson Boards, etc. you can install the driver locally fromPyPI.
To install for current user:pip3installpy-imu-mpu6050To install system-wide (this may be required in some cases):sudopip3installpy-imu-mpu6050To install in a virtual environment in your current project:mkdirproject-folder-namecdproject-folder-name
python3-mvenv.envsource.env/bin/activate
pip3installpy-imu-mpu6050Usage ExamplesSee:examples/MPU6050_SimpleRead_test.pyfor a simple read demo.examples/MPU6050_CSV_IMU_test.pyfor a writing IMU data into a csv file.ContributingContributions are welcome! Please read our Code of Conduct before contributing to help this project stay welcoming.FutureAdd support to the magnetometer I2C device that can be connected at AUX_SCL and AUX_SDA pins
Package: pyIMX233
This package provides class to control the GPIO on Olinuxino Maxi.Current release does no support any peripheral functions.Example=======Typical usage::#!/usr/bin/env pythonimport IMX233_GPIO as GPIO#init moduleGPIO.init()#configure moduleGPIO.setcfg(GPIO.PIN#, GPIO.OUT)GPIO.setcfg(GPIO.PIN#, GPIO.IN)#read the current GPIO configurationconfig = GPIO.getcfg(GPIO.PIN#)#set GPIO highGPIO.output(GPIO.PIN#, GPIO.HIGH)#set GPIO lowGPIO.output(GPIO.PIN#, GPIO.LOW)#read inputstate = GPIO.input(GPIO.PIN#)#cleanupGPIO.cleanup()Warning=======
Package: pyinapp
pyinappIn-app purchase validation has never been so friendly and convenient!Installationpip install pyinappUsageCurrently pyinapp supports Google Play and App Store receipts validation.Google Play:frompyinappimportGooglePlayValidator,InAppValidationErrorbundle_id='com.yourcompany.yourapp'api_key='API key from the developer console'validator=GooglePlayValidator(bundle_id,api_key)try:purchases=validator.validate('receipt','signature')process_purchases(purchases)exceptInAppValidationError:""" handle validation error """App Store:frompyinappimportAppStoreValidator,InAppValidationErrorbundle_id='com.yourcompany.yourapp'validator=AppStoreValidator(bundle_id)try:purchases=validator.validate('receipt')process_purchases(purchases)exceptInAppValidationError:""" handle validation error """Important!If your version is under 0.1.3, you need to check the type of purchases. For the sake of convenience you can process purchases this way:defprocess_purchases(purchases):process(*purchases)ifisinstance(purchases,list)elseprocess(purchases)defprocess(*purchases):forpinpurchases:""" for instance, save p to db and add a player some coins for it """This approach allows to process both Google Play and App Store purchases the same way.PurchasePurchase is a universal wrapper for Google Play and App Store receipts. It contains the following fields:transaction_id: id of the purchase (transaction_idfor App Store andorderIdfor Google Play);product_id: what product has been purchased (product_idfor App Store andproductIdfor Google Play);quantity: how many products have been purchased (quantityfor App Store and always1for Google Play - there’s no such field in Google Play receipt);purchased_at: when the product has been purchased, UNIX timestamp (purchase_datefor App Store andpurchaseTimefor Google Play).response: (App Store only) the response (in JSON format) from the App Store.ContributingTo run tests, you’ll needtox. After installing, simply run it:tox
Package: pyinaturalist-convert
pyinaturalist-convertThis package provides tools to convert iNaturalist observation data to and from a wide variety of
useful formats. This is mainly intended for use with the iNaturalist API
viapyinaturalist, but also works with other data sources.Complete project documentation can be found atpyinaturalist-convert.readthedocs.io.FormatsImportCSV (From eitherAPI resultsor theiNaturalist export tool)JSON (from API results)pyinaturalist.ObservationobjectsDataframes, Feather, Parquet, and anything else supported bypandasiNaturalist GBIF ArchiveiNaturalist Taxonomy ArchiveiNaturalist Open Data on AmazonNote: seeAPI Recommended Practicesfor details on which data sources are best suited to different use casesExportCSV, Excel, and anything else supported bytablibDataframes, Feather, Parquet, and anything else supported bypandasDarwin CoreGeoJSONGPXSQLiteSQLite + FTS5 text search for taxonomyInstallationInstall with pip:pipinstallpyinaturalist-convertOr with conda:condainstall-cconda-forgepyinaturalist-convertTo keep things modular, many format-specific dependencies are not installed by default, so you may
need to install some more packages depending on which features you want. Each module's docs lists
any extra dependencies needed, and a full list can be found inpyproject.toml.For getting started, it's recommended to install all optional dependencies:pipinstallpyinaturalist-convert[all]UsageExportGet your own observations and save to CSV:frompyinaturalistimportget_observationsfrompyinaturalist_convertimport*observations=get_observations(user_id='my_username')to_csv(observations,'my_observations.csv')Or any other supported format:to_dwc(observations,'my_observations.dwc')to_excel(observations,'my_observations.xlsx')to_feather(observations,'my_observations.feather')to_geojson(observations,'my_observations.geojson')to_gpx(observations,'my_observations.gpx')to_hdf(observations,'my_observations.hdf')to_json(observations,'my_observations.json')to_parquet(observations,'my_observations.parquet')df=to_dataframe(observations)ImportMost file formats can be loaded viapyinaturalist_convert.read():observations=read('my_observations.csv')observations=read('my_observations.xlsx')observations=read('my_observations.feather')observations=read('my_observations.hdf')observations=read('my_observations.json')observations=read('my_observations.parquet')DownloadDownload the complete research-grade observations dataset:download_dwca_observations()And load it into a SQLite database:load_dwca_observations()And do the same with the complete taxonomy dataset:download_dwca_taxa()load_dwca_taxa()Load taxonomy data into a full text search database:load_taxon_fts_table(languages=['english','german'])And get lightning-fast autocomplete results from it:ta=TaxonAutocompleter()ta.search('aves')ta.search('flughund',language='german')FeedbackIf you have any problems, suggestions, or questions about pyinaturalist-convert, you are welcome tocreate an issueordiscussion. Also,PRs are welcome!
Package: py_inception
py-inceptionDescriptionPython introspection tool.Generate html page with information about object and classes.Installationpip install py-inceptionUsagepy_inception.extract(varlist, out=filename)varlist- list of variables for inspectfilename- output file name (must ends with ".html")example:import py_inception
py_inception.extract([__builtins__,], out="test.html")
Package: pyinclusion
py_inclusionThis package provides an easy way to generate the inclusion flowchart generation for participant inclusion for medical and epidemiology analyses. It will reduce the possibility of erroneous reporting in epi research as often the inclusion conditions may vary as the research progresses. Manual flowchart generation is neither efficient or reliable.How to installpipinstallpy_inclusionHow to usesample_flowchart.ipynbhas a nice example and the specific format required. We expect the input to be aPandasdataframe of with at least a subject id column calledPID. All the other columns should contain bolean variables, whehter a particular subject has met the condition based on that criteriasample_df.head()
pid	calibration     summary	    duration
248	True	        True	    True
884	True	        True	    False
793	True	        True	    True
699	False	        True	    True
319	True	        True	    TrueTo generate the flowchartfrompyinclusion.coreimportgen_graphmy_order=['calibration','duration','summary']# optional. The default ordering will be used if not specifiedgen_flowchart(sample_df,condition_order=my_order)It will generate a script like this:st79=>start: This is not working Ok
cond81=>condition: Fulfilled condtions 
 n=72
cond83=>condition: Fulfilled condtions 
 n=72
cond85=>condition: Fulfilled condtions 
 n=72
e80=>end: Included subjects
op86=>operation: Excluded due to duration
 n= 0
op84=>operation: Excluded due to summary
 n= 0
op82=>operation: Excluded due to calibration
 n= 0

st79->cond81
cond81->
cond81->
cond81(yes)->cond83
cond83->
cond83->
cond83(yes)->cond85
cond85->
cond85->
cond85(yes)->e80
cond85(no)->op86
cond83(no)->op84
cond81(no)->op82The generated graph is based onflowchart.js, which can be used to render images in the web.
You can then copy and paste the generated script intoflowchart.jsto generate SVG for your
work. To download svg, you can useSVG Exporton from to easily to that.A sample figure will look like:
Package: py-incomepropertyevaluatorkit
# py-incomepropertyevaluatorkit
## Build Status
[![Build Status](https://travis-ci.org/MikaSoftware/py-incomepropertyevaluatorkit.svg?branch=master)](https://travis-ci.org/MikaSoftware/py-incomepropertyevaluatorkit)
[![Coverage Status](https://coveralls.io/repos/github/MikaSoftware/py-incomepropertyevaluatorkit/badge.svg?branch=master)](https://coveralls.io/github/MikaSoftware/py-incomepropertyevaluatorkit?branch=master)This library is currently being developed and is not production quality ready!## Overview
Python library for performing rental and income property calculations.## Minimum Requirements
* Python 3.6## Installation`bash pip installpy-incomepropertyevaluatorkit`## Usage
### Development
Here is an example of using the using the library in your code.`python #TODO: Writeexample...`### Quality Assurance
#### Unit Tests
If you want to run the unit tests, you can run the following.Here is how you run the unit tests.`bash python test_realestatekit.py `#### Code Coverage
Here is how you run code coverage. The first command runs the code coverage
and the second command provides a report. If you would like to know more aboutcoveragethen click to [here to read](http://coverage.readthedocs.io/en/latest/).`bash coverage run test_analyzer.py coverage report-mtest_analyzer.py `## License
This library is licensed under theBSDlicense. See [LICENSE.md](LICENSE.md) for more information.
Package: pyincore
pyIncoreis a component of IN-CORE. It is a python package
consisting of two primary components: 1) a set of service classes to
interact with the IN-CORE web services described below, and 2) IN-CORE
analyses. The pyIncore allows users to apply various hazards to infrastructure
in selected areas, propagating the effect of physical infrastructure damage
and loss of functionality to social and economic impacts.Installation with condaInstallingpyincorewith Conda is officially supported by IN-CORE development team.To addconda-forgechannel to your environment, runconda config –-add channels conda-forgeTo installpyincorepackage, runconda install -c in-core pyincoreTo updatepyIncore, runconda update -c in-core pyincoreYou can find detail information at theInstallationsection at IN-CORE manual.Installation with pipInstallingpyincorewith pip isNOT supportedby IN-CORE development team.
Please use pip for installing pyincore at your discretion.Installing pyincore with pip is only tested on linux environment.PrerequisiteGDAL C library must be installed to install pyincore. (for Ubuntu,gdal-binandlibgdal-dev)ipopt executable must be installed to run some analyses such as seaside CGE, joplin CGE, etc.To installpyincorepackage, runpip install pyincoreTesting and RunningPlease read theTesting and
Runningsection at IN-CORE manual.DocumentationpyIncoredocumentation can be found athttps://incore.ncsa.illinois.edu/doc/incore/pyincore.htmlpyIncoretechnical reference (API) can be found athttps://incore.ncsa.illinois.edu/doc/pyincore/.AcknowledgementThis work herein was supported by the National Institute of Standards
and Technology (NIST) (Award No. 70NANB15H044). This support is
gratefully acknowledged. The views expressed in this work are those of
the authors and do not necessarily reflect the views of NIST.
Package: pyincore-data
pyIncore-datais a component of IN-CORE. It is a python package containing utilities to prepare data for IN-CORE analyses.Installation with condaInstallingpyincore-datawith Conda is officially supported by IN-CORE development team.To addconda-forgechannel to your environment, runconda config –-add channels conda-forgeTo installpyincore-datapackage, runconda install -c in-core pyincore-dataTo updatepyIncore-data, runconda update -c in-core pyincore-dataInstallation with pipInstallingpyincore-datawith pip isNOT supportedby IN-CORE development team.
Please use pip for installing pyincore-data at your discretion.Installing pyincore-data with pip is only tested on linux environment.To installpyincore-datapackage, runpip install pyincore-data
Package: pyincore-viz
pyincore-vizis a component of IN-CORE that provides visualization and other utilities for use withpyincore.Mac specific notesWe usematplotliblibrary to create graphs. There is a Mac specific installation issue addressed athere (1)andhere (2).
In a nutshell, insert line:backend : Agginto~/.matplotlib/matplotlibrcfile.Installation with condaInstallingpyincore-vizwith Conda is officially supported by IN-CORE development team.To addconda-forgechannel to your environment, runconda config –-add channels conda-forgeTo installpyincore-vizpackage, runconda install -c in-core pyincore-vizTo updatepyIncore-viz, runconda update -c in-core pyincore-vizInstallation with pipInstallingpyincore-vizwith pip isNOT supportedby IN-CORE development team.
Please use pip for installing pyincore-viz at your discretion.Installing pyincore-viz with pip is only tested on linux environment.PrerequisiteGDAL C library must be installed to install pyincore-viz. (for Ubuntu,gdal-binandlibgdal-dev)To installpyincore-vizpackage, runpip install pyincore-vizDocumentation ContainterTo build the container with the documentation you can use:docker build -t doc/pyincore-viz -f Dockerfile.docs .
docker run -ti -p 8000:80 doc/pyincore-vizThen check the documentation athttp://localhost:8000/doc/pyincore_viz/.