Saravanan666/pypi-org huggingface dataset:
Package: pyreadline-ais
This a branch of the pyreadline project. This branch implements several
functions that were missing in the mainline version of pyreadline.The pyreadline package is a python implementation of GNU readline functionality
it is based on the ctypes based UNC readline package by Gary Bishop.
It is not complete. It has been tested for use with windows 2000 and windows xp.Version 2.0 runs on Python 2.6, 2.7, and 3.2 using the same code.Features:keyboard text selection and copy/pasteShift-arrowkeys for text selectionControl-c can be used for copy activate with allow_ctrl_c(True) in config fileDouble tapping ctrl-c will raise a KeyboardInterrupt, use ctrl_c_tap_time_interval(x)
where x is your preferred tap time window, default 0.3 s.paste pastes first line of content on clipboard.ipython_paste, pastes tab-separated data as list of lists or numpy array if all data is numericpaste_mulitline_code pastes multi line code, removing any empty lines.The latest development version is always available at the IPython githubrepository.
Package: pyreadpartitions
pyreadpartitionsRead MBR and GPT partitions directly in python.Examples:frompyreadpartitionsimportget_disk_partitions_infowithopen('/dev/sda','rb')asfp:info=get_disk_partitions_info(fp)print(info.mbr)print(info.gpt)frompyreadpartitionsimportshow_disk_partitions_infowithopen('/dev/sda','rb')asfp:show_disk_partitions_info(fp)A console script is also available:$sudocat/dev/sda|pyreadpartitionsor if you already have access to a file or directly to the disk:# pyreadpartitions /dev/sda
Package: pyreal
An open source project from Data to AI Lab at MIT.PyrealLibrary for generating useful and usable machine learning explanations.License: MITDocumentation:https://pyreal.gitbook.io/pyrealAPI Documentation:https://sibyl-ml.dev/pyreal/api_reference/index.htmlHomepage:https://sibyl-ml.dev/OverviewPyrealwraps the complete machine learning explainability pipeline into RealApp objects, which seamlessly
provide usable explanations in a low-code manner.InstallRequirementsPyrealhas been developed and tested onPython 3.9, 3.10, and 3.11The library uses Poetry for package management.Install from PyPIWe recommend usingpipin order to installPyreal:pip install pyrealThis will pull and install the latest stable release fromPyPI.Install from sourceIf you do not havepoetryinstalled, please head topoetry installation guideand install poetry according to the instructions.Run the following command to make sure poetry is activated. You may need to close and reopen the terminal.poetry --versionFinally, you can clone this repository and install it from
source by runningpoetry install, with the optionalexamplesextras if you'd like to run our tutorial scripts.git clone https://github.com/sibyl-dev/pyreal.git
cd pyreal
poetry installInstall for DevelopmentIf you want to contribute to the project, a few more steps are required to make the project ready
for development.Please head to theContributing Guidefor more details about this process.QuickstartIn this short tutorial we will guide you through a series of steps that will help you
getting started withPyreal. We will get an explanation for a prediction on whether a
passenger on the Titanic would have survived.For a more detailed version of this tutorial, seeour documentation.Load in the demo data and application>>> import pyreal.sample_applications.titanic as titanic

>>> real_app = titanic.load_app()
>>> sample_data = titanic.load_data(n_rows=300)Predict and produce explanation>>> predictions = real_app.predict(sample_data)

>>> explanation = real_app.produce_feature_contributions(sample_data)Visualize explanation for one passengerpassenger_id = 1
feature_bar_plot(explanation[passenger_id], prediction=predictions[passenger_id], show=False)The output will be a bar plot showing the most contributing features, by absolute value.We can see here that the input passenger's predicted chance of survival was greatly reduced
because of their sex (male) and ticket class (3rd class).TroubleshootFor macOS users, an error regardinglightgbmmight arise when running thetitanictutorial due to lack of lightgbm installation.
If this occurs, please run the following line in your terminal to installlightgbm.brew install lightgbmTerminologyPyreal introduces specific terms and naming schemes to refer to different feature spaces and
transformations. TheTerminology User Guideprovides an introduction to these terms.What's next?For more details aboutPyrealand all its possibilities
and features, please check thedocumentation site.
Package: pyRealEstate
pyRealEstatepyRealEstate is a library designed for data scientists working in the real estate industry. pyRealEstate is still currently under development but is aimed at providing functions to assist in the development and evaluation of AVM's. Below are some instructions on how to get started with pyRealEstate and some helpful links to descriptions and examples of all of PyRealEstates functionality.InstallationThe pyRealEstate package is available onPyPi. Simply run:pip install pyRealEstateAVM Evaluation MetricspyRealEstate can provide metrics on the evaluation of your AVM (Automated Valuation Model) such as the weighted mean sale ratio, COD (Coefficient Of Disspersion), and PRD (Price Related Differential) please visit the wiki for detailed documentationpyRealEstate Evaluation.Data Pre ProcessingIn addition to the evaluation metrics for the models, pyRealEstate also offers a multitude of functions to help with the pre processing of data. Please visit the wiki for detailed documentationpyRealEstate Pre Processing.Time Trending for AVM'sIn creating Automated Valuation Models (AVM's) for real estate, it is key to capture time trends in the market. There are typically two major approaches; one is to model the time trend and then to adjust the sales based on the time adjustment rate. The other option is to include time directly in the model. If one wishes to take the first approach this pyRealEstate is to assist in the finding of the time adjustment rates. Please visit the wiki for detailed documentationpyRealEstate Time Trending.
Package: pyRealParser
pyRealParserA Python package to readsongs in the iRealPro format.Introduction & UsageHere's an example of how to import an iReal link and print the information it contains:>frompyRealParserimportTune>my_tune=Tune.parse_ireal_url('irealb://%44%65%61%72<link shortened>')[0]>print(my_tune)<pyRealParser.Tuneobjectat0x107305e80>Title:DearOldStockholmComposer:TraditionalStyle:MediumSwingKey:D-Transpose:NoneCompstyle:0BPM:0Repeats:NoneTimesignature:4/4Chordstring:*A{T44D-|Eh7A7b9|G-7C7|F^7|Eh7A7b9|D-|Eh7|A7b9|D-7|D-6|D-7|D-6}*B[F^7|G-7C7|F^7|Eh7A7b9]*C[D-|Eh7A7b9|G-7C7|F^7|Eh7A7b9|D-|C7sus|x|C7sus|x|x|x|C7susA7b9|D-|xFlattenedmeasures:|D-|Eh7A7b9|G-7C7|F^7||Eh7A7b9|D-|Eh7|A7b9||D-7|D-6|D-7|D-6||D-|Eh7A7b9|G-7C7|F^7||Eh7A7b9|D-|Eh7|A7b9||D-7|D-6|D-7|D-6||F^7|G-7C7|F^7|Eh7A7b9||D-|Eh7A7b9|G-7C7|F^7||Eh7A7b9|D-|C7sus|C7sus||C7sus|C7sus|C7sus|C7sus||C7susA7b9|D-|D-|parse_ireal_urlreturns a list ofTuneobjects, each representing a song contained in the input url. They contain a number of member variables, containing the chords as well as some meta information:chord_string: A single string that has all chords of the tune, with bar lines, repeat markers, endings, codas etc:>print(my_tune.chord_string)*A{T44D-|Eh7A7b9|G-7C7|F^7|Eh7A7b9|D-|Eh7|A7b9|D-7|D-6|D-7|D-6}*B[F^7|G-7C7|F^7|Eh7A7b9]*C[D-|Eh7A7b9|G-7C7|F^7|Eh7A7b9|D-|C7sus|x|C7sus|x|x|x|C7susA7b9|D-|xmeasures_as_strings: A list, for which every element corresponds to a single bar, containing the chords in string form. Repeats, codas etc. have been flattened.>print(my_tune.measures_as_strings)*['D-','Eh7A7b9','G-7C7','F^7','Eh7A7b9','D-','Eh7','A7b9','D-7','D-6','D-7','D-6','D-','Eh7A7b9','G-7C7','F^7','Eh7A7b9','D-','Eh7','A7b9','D-7','D-6','D-7','D-6','F^7','G-7C7','F^7','Eh7A7b9','D-','Eh7A7b9','G-7C7','F^7','Eh7A7b9','D-','C7sus','C7sus','C7sus','C7sus','C7sus','C7sus','C7susA7b9','D-','D-']title: The titlecomposer: The composerstyle: The style (e.g. 'Swing', 'Bossa', 'Blues' etc.)key: The key (e.g. 'A', 'F#' etc)transpose: How many semitones to transposecomp_style: Accompaniment style (usually empty)bpm: Tempo in BPM (usually empty)repeats: How many repeats (usually empty)time_signature: Time signature as a tuple (e.g. (3,4), (4, 4), (5, 8) etc.)Notice, that some of these meta-data fields might be empty, depending on the input url.Tuneobjects are designed to have a nice textual representation in Jupyter notebooks, but can be used outside of a notebook perfectly well.For more documentation, please read the code.Contributions are welcome, please submit a PR.Installationpip install pyRealParserAcknowledgementsKudos to@pianosnakeand@ironssfor figuring out the iReal url format!
Package: pyrealpro
PyRealProThe pyrealpro module provides classes that can be used to build a representation of a song, and render it in the import
URL format used by the iRealPro app.  It assumes that you have a passing familiarity with theiRealPro import format, but hopefully makes it easier to programmatically
construct an iRealPro song than resorting to brute-force string concatenation.ConceptsTheSongclass is used to define a song that you wish to import into iRealPro. In addition to basic information like
title, composer, key, and style, a song'smeasuresproperty may contain a list ofMeasureobjects.AMeasureobject'schordsproperty will have one or more chord names, as well as additional properties that describe
time signature (as aTimeSignatureobject), barlines, endings, rehearsal marks, and text to be displayed underneath.TheTimeSignatureclass provides simple objects used by theMeasureclass to properly format the output ultimately
used by theSongclass to generate an iRealPro URL.Examplefrom pyrealpro import Song, Measure, TimeSignature

s = Song(title="Automation Blues", composer="pyrealpro", key='G', style='New Orleans Swing',
                 composer_name_first="Otto",
                 composer_name_last="Matonne")

s.measures.append(Measure(chords='G7', barline_open='{', staff_text='Generated by pyrealpro'))
s.measures.append(Measure(chords='G7'))
s.measures.append(Measure(chords='G7'))
s.measures.append(Measure(chords='G7', barline_close=']'))

s.measures.append(Measure(chords='C7', barline_open='['))
s.measures.append(Measure(chords='C7'))
s.measures.append(Measure(chords='G7'))
s.measures.append(Measure(chords='G7', barline_close=']'))

s.measures.append(Measure(chords='D7', barline_open='['))
s.measures.append(Measure(chords='C7'))

s.measures.append(Measure(chords='G7', ending='N1'))
s.measures.append(Measure(chords='D7', barline_close='}'))

s.measures.append(Measure(chords='G7', ending='N2'))
s.measures.append(Measure(chords='G7', barline_close='Z'))

print(s.url())The above program will output the following URL, which can be imported into iRealPro:irealbook://Automation%20Blues=Matonne%20Otto=New%20Orleans%20Swing=G=n=%7BT44%3CGenerated%20by%20pyrealpro%3EG7%20%20%20%7CG7%20%20%20%7CG7%20%20%20%7CG7%20%20%20%5D%5BC7%20%20%20%7CC7%20%20%20%7CG7%20%20%20%7CG7%20%20%20%5D%5BD7%20%20%20%7CC7%20%20%20%7CN1G7%20%20%20%7CD7%20%20%20%7DN2G7%20%20%20%7CG7%20%20%20ZiReal Pro File Format Referencehttps://irealpro.com/ireal-pro-file-format/
Package: pyrealsense2
The python wrapper for Intel RealSense SDK 2.0 provides the C++ to Python binding required to access the SDK.Quick startimport pyrealsense2 as rs
pipe = rs.pipeline()
profile = pipe.start()
try:
  for i in range(0, 100):
    frames = pipe.wait_for_frames()
    for f in frames:
      print(f.profile)
finally:
    pipe.stop()
Package: pyrealsense2-aarch64
The python wrapper for Intel RealSense SDK 2.0 provides the C++ to Python binding required to access the SDK.Quick startimport pyrealsense2 as rs
pipe = rs.pipeline()
profile = pipe.start()
try:
  for i in range(0, 100):
    frames = pipe.wait_for_frames()
    for f in frames:
      print(f.profile)
finally:
    pipe.stop()
Package: pyrealsense2-mac
The python wrapper for Intel RealSense SDK 2.0 provides the C++ to Python binding required to access the SDK.Quick startimport pyrealsense2 as rs
pipe = rs.pipeline()
profile = pipe.start()
try:
  for i in range(0, 100):
    frames = pipe.wait_for_frames()
    for f in frames:
      print(f.profile)
finally:
    pipe.stop()
Package: pyrealsense2-macosx
The python wrapper for Intel RealSense SDK 2.0 provides the C++ to Python binding required to access the SDK.Quick startimport pyrealsense2 as rs
pipe = rs.pipeline()
profile = pipe.start()
try:
  for i in range(0, 100):
    frames = pipe.wait_for_frames()
    for f in frames:
      print(f.profile)
finally:
    pipe.stop()
Package: pyRealtor
pyRealtor is a python package that provides fast and easy way to extract Multiple Listing Service (MLS) details from REALTOR.CA, such as house sale/rent price, number of bedrooms, stories, ammenities nearby etc. of any city / region within Canada. The library provides functionality to easily store the extracted data in excel sheet for further analysis.pyRealtor can be used toAnalyze and extract all real estate listing in a specific area.Find onlyOpen Houseon a specifc day in a particular area.InstallingThe easiest way to install the library is to execute (preferably in a virtualenv) the command:pipinstallpyRealtorFrom the source codegitclonehttps://github.com/rachitt96/pyRealtor.gitcdpyRealtor
pythonsetup.pyinstallUsageTo get all real estate properties for sale in specific areaimportpyRealtorhouse_obj=pyRealtor.HousesFacade()house_obj.search_save_houses(search_area='Barrhaven',report_file_name='barrhaven_all_listings.xlsx')RELTOR.CA API has the rate limit, and it may block IP in case you trigger API multiple times in short period. In that case, you can useuse_proxyargument.importpyRealtorhouse_obj=pyRealtor.HousesFacade()house_obj.search_save_houses(search_area='Barrhaven',report_file_name='barrhaven_all_listings.xlsx',use_proxy=True)To get only Open House listings in a specific areaimportpyRealtorhouse_obj=pyRealtor.HousesFacade()house_obj.search_save_houses(search_area='CITY/SUBURB',report_file_name='FILES_TO_LOAD_LISTINGS.xlsx',open_house_date='MM/DD/YYYY')importpyRealtorhouse_obj=pyRealtor.HousesFacade()house_obj.search_save_houses(search_area='Barrhaven',report_file_name='barrhaven_all_listings.xlsx',open_house_date='10/29/2023')Terms of UsepyRealtor is a tool to extract, filter and analyze publicly accessible MLS listings provided by REALTOR.CA. More information on the use of this data can be found on REALTOR.CA website:https://www.realtor.ca/terms-of-use"REALTOR.CA website/database is a copyright-protected work which is owned by CREA. Part of the contents of REALTOR.CA website/database, including all real estate listings and related information, images and photographs (collectively, "Listing Content"), are also protected by copyright, which is owned by the CREA members who supplied the content and/or by third parties, and is reproduced in REALTOR.CA website/database under license. The contents of REALTOR.CA website/database, including the Listing Content, are intended for the private, non-commercial use by individuals. Any commercial use of the website/database, including the Listing Content, in whole or in part, directly or indirectly, is specifically forbidden except with the prior written authority of the owner of the copyright."
Package: py-realty
PY-RealtyThis is a library designed to scrape publicly available Real Estate listing information.Specifically, all this library does is send the bare minimum/near bare minimum requests to the public web servers of supported listing websites. Meaning that is is effectively the same as visiting the website (such as Zillow) manually with a web browser with the exception that withPY-Realtyyou can avoid loading unnecessary JavaScript and media (images, etc.).That being said scrapping large number of listings with no delays may result in the source (Zillow, Realtor.com, etc.) blocking your IP address (Zillow can probably figure out you're not manually looking at listings if you're querying thousands a minute.), although this has not been tested.StatusVERSION:0.1.0Early development. The library is still in early development and is missing key features and may not be working entirely as expected.This library is now somewhat usable (mileage may vary) and can be installed (and built) via pip, see installation instructions below.Road MapZillowZillow Search QueryImplementationManual TestingUnit TestingZillow Listing Details ParsingProperty Sale ListingImplementationManual TestingUnit TestingProperty Rental ListingImplementationManual TestingUnit TestingApartment Rental ListingImplementationManual TestingUnit TestingRealtor.comRealtor.com Sale Search QueryImplementationManual TestingUnit TestingRealtor.com Rental Search QueryImplementationManual TestingUnit TestingProperty Sale ListingImplementationManual TestingUnit TestingProperty Rental ListingImplementationManual TestingUnit TestingLandWatchLandWatch search QueryImplementationManual TestingUnit TestingLandWatch Listing Details ParsingImplementationManual TestingUnit TestingApartments.comApartments.com Search QueryImplementationManual TestingUnit TestingApartments.com Listing Details ParsingImplementationManual TestingUnit TestingRegarding Unit TestsDue to the nature of the data, it is impractical to have Unit tests check if data is extracted correctly. If testing against a source of Truth, say a listing, that listing may change over time. Additionally even though Zillow and Realtor.com may have similar data, if there is a mismatch it does not necessarily mean that scraping is done incorrectly, could just be a mismatch between the two websites.Therefore Unit tests will check against unexpected behavior as opposed to extraction correctness. This meaning that they will check against exceptions and check if value types are correct. That being said the Unit Test criteria still needs to ensure reasonable robustness.UsageMore detailed documentation will follow as development continues. However attributes and functions are well documented withing class and function doc-strings.ZillowInsidezillowtheQueryclass is used to construct and send query request to Zillow's public API. Once theQueryis configured as desired, theget_responsemethod will return the search results.The search results can then be scraped via the functionsdetails.crapes_listing,details.scrape_listings, anddetails.lazy_scrape_listings.InstallationClone this repositoryWithin the repository folder, runpip install .
Package: pyreap
This module provides a subprocess.Popen()-compatible reap.Popen() function that
has the nice property that if the parent process dies the child process will
also be reaped.Normally, orphaned child processes will end up re-parented to the init(1)
process and then live on until they finally complete of their own volition.
This often (usually?) isn’t the desired behavior and it can be really handy to
ensure that when a parent process dies all the child processes will be
automatically cleaned up.The reap module accomplishes this by inserting a small intermediate process
between the parent and the child.  This intermediate process periodically
checks to see if its parent PID has changed (indicating that the parent died
and we have been reparented to init(1) or some other init(1)-like process), and
if a change is detected then it kills the child before itself exiting.  This
ensures that any child processes you create will not later live on as orphaned
processes.Using pyreap is as easy as using subprocess.Popen(), and in fact all arguments
are simply passed to subprocess.Popen(); the reap.Popen() function also returns
a subprocess.Popen object that you can use just like you normally would.The following demonstrates how subprocess.Popen() behaves with an orphaned
process (note the orphaned ‘sleep 1000’ lives on even after the Python
interpreter has closed):~$ python3
Python 3.8.5 (default, Jul 21 2020, 10:48:26)
[Clang 11.0.3 (clang-1103.0.32.62)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import subprocess
>>> subprocess.Popen(['/usr/bin/env', 'sleep', '1000'])
<subprocess.Popen object at 0x10fc67d30>
>>>
~$ ps aux | grep sleep
greent7          85310   0.0  0.0  4268280    656 s026  S+    4:57pm   0:00.01 grep sleep
greent7          85308   0.0  0.0  4268156    504 s026  S     4:57pm   0:00.01 sleep 1000The following demonstrates how reap.Popen() solves the orphan issue:greent7@avocado:~$ python3
Python 3.8.5 (default, Jul 21 2020, 10:48:26)
[Clang 11.0.3 (clang-1103.0.32.62)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import reap
>>> reap.Popen(['/usr/bin/env', 'sleep', '1000'])
<subprocess.Popen object at 0x10bac9d30>
>>>
greent7@avocado:~$ ps aux | grep sleep
greent7          85319   0.0  0.0  4287736    704 s026  S+    4:57pm   0:00.01 grep sleepNote how the ‘sleep 1000’ process is reaped after the Python interpreter shuts
down.
Package: pyreaqtive
pyreaqtivepyreaqtive is a framework for developing applications in pyqt/pyside with reactive programming. It enables to write applications in a declarative form, that is focused on data and functionality and that does not bloat code with gui callbacks.The library provides several models to hold data, and many prebuilt widgets and layouts to present the data from the models to the user, making the separation from data and visualization, and the adequate link between them, a simple task.All reactive widgets and layouts use qt built-in widgets, which makes them fully compatible with qt without loosing functionality. Reactive and non-reactive parts can be mixed into an application, and non-reactive components can be turned to reactive ones without necessarily changing codebases.It was originally developed for PyQt5, but through the QtPy package it is also compatible with to PyQt6, PySide2 and PySide6.Documentationhttps://pyreaqtive.readthedocs.io/en/latest/
Package: pyreason
PyReasonAn explainable inference software supporting annotated, real valued, graph based and temporal logic.Links📃 Paper📽️ Video🌐 Website🏋️‍♂️ PyReason GymCheck out thePyReason Hello Worldprogram if you're new, or want get get a feel for the software.Table of ContentsIntroductionInstallInstall as a Python LibraryInstall as a command line toolUsageBibtexLicenseContact1. IntroductionPyReason makes use of 4 files:A GraphMl file containing the graph (required)A YAML file containing the pyreason rules (required)A YAML file containing the pyreason facts (optional but recommended)A YAML file containing the pyreason labels (optional but recommended)A YAML file containing the pyreason ipl (inconsistent predicate list) (optional)The format of these files is very important. Please refer to theexample YAML files providedwhen making your own rules/facts/labels/ipl. TODO: make doc for each format.2. InstallPyReason can be installed as a python library (recommended) or as a command line tool2.1 Install as a Python LibraryWe import pyreason to initialize it for the first time, this may take a few minutespipinstallpyreason
python
importpyreason2.2 Install as a Command Line Toolgitclonehttps://github.com/lab-v2/pyreasoncdpyreason
pipinstall-rrequirements.txt
pythoninitialize.py3. UsagePlease refer to the documentation that is relevant to youUsage as Python LibraryUsage as a Command Line Tool4. BibtexIf you used this software in your work please cite our paperBibtex:@inproceedings{aditya_pyreason_2023,
title = {{PyReason}: Software for Open World Temporal Logic},
booktitle = {{AAAI} Spring Symposium},
author = {Aditya, Dyuman and Mukherji, Kaustuv and Balasubramanian, Srikar and Chaudhary, Abhiraj and Shakarian, Paulo},
year = {2023}}5. LicenseThis repository is licensed underBSD-3-Clause6. ContactDyuman Aditya -dyuman.aditya@asu.eduKaustuv Mukherji -kmukher2@asu.eduPaulo Shakarian -pshak02@asu.edu