Saravanan666/pypi-org huggingface dataset:
Package: pecrs
Pythonic Entity Collision Resolution Systempecrs is a pure Python 2D physics system with a focus on top-down games and simple platformers.Pure Python makes pecrs portable and easy to modify to suit your own needs.Focused use-case makes pecrs simple to learn and use.Seamless integrationwithPygletInstallationVia pippython3 -m pip install pecrsQuickstartfrompecrsimport*space=Space()rectA=Rect(0,0,32,32)rectB=Rect(10,0,32,32)space.add(rectA)space.add(rectB)collision=space.check(rectA)print(f"Is something colliding with rectA?{collision}")collisions=space.collisions_with(rectB)print(f"Who is colliding with rectB?{collisions}")space.place(rectB,100,0)collision=space.check_two(rectA,rectB)print(f"Are rectA and rectB still colliding?{collision}")Structual OverviewBase types of the system are Shapes and Bodies.
Shapes have a position and dimensions which describe its physical properties.
Bodies are Shapes with an id, direction, speed, and movement state.Core functionality is providied by the Collider, which detects collisions between Shapes or Shape-like Objects.The Space handles positioning of Shapes and optimizes collision handling.The Controller provides high-level object oriented control over Bodies in a Space.Real-world Usagefrompecrsimport*importpygletclassWorld(Space):def__init__(self):super().__init__()self.window=pyglet.window.Window(400,300)self.batch=pyglet.graphics.Batch()self.red_image=pyglet.resource.image("red_rect.png")self.blue_image=pyglet.resource.image("blue_rect.png")spriteA=pyglet.sprite.Sprite(self.blue_image,x=0,y=150,batch=self.batch)spriteB=pyglet.sprite.Sprite(self.blue_image,x=300,y=150,batch=self.batch)self.add(spriteA,moving=True)self.turn(spriteA,(150,0))self.add(spriteB)pyglet.clock.schedule_interval(self.run,1.0/60)pyglet.app.run()defon_collision(self,shape,collisions):shape.image=self.red_imagedefrun(self,delta):self.step(delta)self.window.clear()self.batch.draw()world=World()Documentationhttps://solidsmokesoftware.github.io/pecrs-py/Demonstrationhttps://github.com/solidsmokesoftware/solconomyRequirementsTested with Python3.6.9
Package: pecuzal-embedding
PECUZAL PythonWe introduce the PECUZAL automatic embedding of time series method for Python. It is solely based
on the paper[kraemer2021](Open Source), where the functionality is explained in detail. Here we
give an introduction to its easy usage in three examples. Enjoy Embedding!Getting startedInstall fromPyPIby simply typingpip install pecuzal-embeddingin your console.NOTEThis implementation is not profiled well. We recommend to use the implementation
in theJulia languageor
inMatlab,
in order to get fast results, especially in the multivariate case. Moreover,
it is well documented and embedded in theDynamicalSystems.jlecosystem.
For instance, the compuations made in theUnivariate exampleand theMultivariate examplein this documentation took approximately800s(approx. 13 mins) and4700s(approx. 1 hour and 10 mins!), respectively, even when
using theeconoption in the function call, for an accelerated computation. In the Julia implementation
the exact same computation took4sand25s, respectively! (running on a 2.8GHz Quad-Core i7,  16GB 1600 MHz DDR3)DocumentationThere is adocumentation availableincluding some basic usage examples.Citing and referenceIf you enjoy this tool and find it valuable for your research please cite[kraemer2021]Kraemer et al., “A unified and automated approach to attractor reconstruction”, New Journal of Physics 23(3), 033017,10.1088/1367-2630/abe336, 2021.or as BiBTeX-entry:@article{Kraemer2021,
    doi = {10.1088/1367-2630/abe336},
    url = {https://doi.org/10.1088/1367-2630/abe336},
    year = 2021,
    month = {mar},
    publisher = {{IOP} Publishing},
    volume = {23},
    number = {3},
    pages = {033017},
    author = {K H Kraemer and G Datseris and J Kurths and I Z Kiss and J L Ocampo-Espindola and N Marwan},
    title = {A unified and automated approach to attractor reconstruction},
    journal = {New Journal of Physics},
    abstract = {We present a fully automated method for the optimal state space reconstruction from univariate and multivariate time series. The proposed methodology generalizes the time delay embedding procedure by unifying two promising ideas in a symbiotic fashion. Using non-uniform delays allows the successful reconstruction of systems inheriting different time scales. In contrast to the established methods, the minimization of an appropriate cost function determines the embedding dimension without using a threshold parameter. Moreover, the method is capable of detecting stochastic time series and, thus, can handle noise contaminated input without adjusting parameters. The superiority of the proposed method is shown on some paradigmatic models and experimental data from chaotic chemical oscillators.}
}LicenceThis is program is free software and runs underMIT Licence.
Package: pecyn
PecynSimple, fast, compact Python object serialization for humans.EXPERIMENTAL: Use at your own risk.Requires Python 3.5 or better.LegalCopyright 2018 by individual and corporate contributors as
noted in the individual source files.Licensed under the Apache License, Version 2.0 (the "License"); you may
not use any portion of this software except in compliance with
the License. Contributors agree to license their work under the same
License. You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: ped
Quickly open Python modules in your text editor.$peddjango$peddjango.core.urlresolvers$peddjango.views.generic.TemplateView# Partial name matching$peddjango.http.respEditingdjango.http.response......Done.# Specify which editor to use$PED_EDITOR=vimpeddjango.shortcutspedwill find your modules in the currently-active virtual environment.Get it nowFrom PyPI:$ pip install pedOr, run it withpipx:$ pipx run ped --helpChanging the default editorpedwill try to use your favorite text editor. If you want to override the editorpeduses, set thePED_EDITORenvironment variable.# .zshrc or .bashrc
# Use vim with pedexportPED_EDITOR=vimOpening directoriesBy default,pedwill open__init__.pyfiles when a package name is passed.
If you would rather open the package’s directory, set thePED_OPEN_DIRECTORIESenvironment variable.# .zshrc or .bashrc
# Open package directories instead of __init__.pyexportPED_OPEN_DIRECTORIES=1Tab-completionThe ped package contains tab-completion scripts for bash and zsh. Place these files in your system’s completion directories. Theped.install_completionmodule can be run as a script to output the files to a given location. It determines the correct completion file from
the$SHELLenvironment variable.Bash completionTo install bash completion, run:# The path given here will depend on your OS
$ python -m ped.install_completion > /usr/local/etc/bash_completion.dZsh completionTo install zsh completion, run:# The path given here will depend on your OS
$ python -m ped.install_completion > /usr/local/share/zsh/site-functionsEditor integrationsvim-pedKudosThis was inspired byIPython’s%editmagic.Changelog3.0.0 (2024-01-18)Publish type information.Test against Python 3.8-3.12. Older versions of Python are no longer supported.Backwards-incompatible: Removeped.__version__attribute.
Useimportlib.metadata.version("ped")instead.2.1.0 (2020-03-18)SetPED_OPEN_DIRECTORIES=1to open package directories instead of
opening__init__.pyfiles. ThanksAlex Nordin.2.0.1 (2018-01-27)Bug fixes:Properly handle imports that don’t correspond to a file.2.0.0 (2019-01-22)Drop support for Python 2.7 and 3.5. Only Python>=3.6 is supported.pedcan be run its own virtual environment separate from the
user’s virtual environment. Therefore, ped can be installed with
pipsi or pipx.install_completionscript writes tostdoutand detemrmines
script from$SHELL.1.6.0 (2019-01-14)Test against Python 3.7.Note: This is the last version to support Python 2.1.5.1Minor code cleanups.Test against Python 2.7, 3.5, and 3.6. Support for older versions is dropped.1.5.0Support tab-completion in bash and zsh. ThanksThomas Kluyver.1.4.0Add--infoargument for outputting name, file path, and line number of modules/functions/classes.Fix: Support line numbers in gvim.1.3.0If a class or function is passed, the editor will open up the file at the correct line number (for supported editors).1.2.1Fix for Py2 compatibility.1.2.0Add partial name matching.1.1.0Add support for editing functions and classes.1.0.2Fix for editing subpackages, e.g.ped pkg.subpkg.
Package: peda
PEDA - Python Exploit Development Assistance for GDBVersion: 1.0Release: special public release, Black Hat USA 2012CreditsHuy Phan (pdah) for reviewing code1. Introduction
PEDA is a Python GDB script with many handy commands to help speed up
exploit development process on Linux/Unix. It is also a framework for
writing custom interactive Python GDB commands.RequirementsPEDA 1.0 is only support LinuxGDB 7.xPython 2.6+Utilities: nasm, readelf, objdumpInstallationDownload$ wgethttp://ropshell.com/peda/peda.tar.gzUnpack to HOME directory$ tar zxvf peda.tar.gzAppend a line to ~/.gdbinit to load PEDA when GDB starts$ echo “source ~/peda/peda.py” >> ~/.gdbinitUsageList of available commands:gdb-peda$ peda helpSearch for some commands:gdb-peda$ apropos <keyword>
gdb-peda$ help <keyword>Get usage manual of specific command:gdb-peda$ phelp <command>
gdb-peda$ help <command>Get/set config option:gdb-peda$ pshow option
gdb-peda$ pset option <name> <value>
Package: pedal
PedalA collection of tools to analyze student’s work in a pipeline.
Pedal not only provides some of these tools, but it provides aframeworkaround those tools.InstallationInstall from PyPi:pip install pedalOr install from thehttps://github.com/acbart/pedalrepositoryImportant ConceptsPedal revolves around providingFeedback Functionsthat can be called in an
Instructor Control Script to generateFeedbackfor aSubmission, which are
all attached to aReport. AResolvercan then transform that Feedback into
something that anEnvironmentcan hand off to a learner (or other interested
party). These Feedback Functions are organized intoTools.One of our major goals is to attach metadata to feedback to enable easier
analysis, versioning, and evaluation. Although Instructor Control Scripts can
be written very imperatively to specify very complex (or simple) grading logic,
we are trying to reach an elegant, declarative style. This will enable tooling
to automatically generate reports on occurrences of feedback, connect to
datasets like those in the ProgSnap format, and allow us to “unit test our
unit tests”.One of our other goals for this project is to categorize Feedbacks’ Conditions
and Responses, using concepts established by Narciss 2006. For example, we
say some Kinds of Responses are “hints” instead of “mistakes”. We also say
that Conditions can be Categorized as being from “Specifications” or
“Runtime”.For more information, check out the docs forPedal.
Package: pedalboard-pluginary
Pedalboard PluginaryPedalboard Pluginaryis an independent Python-based package and command-line tool that scans and lists VST-3 plugins on macOS and Windows, and Audio Unit (AU) plugins on macOS. It’s intended as a companion for thePedalboardPython library by Spotify, but it’s not affiliated withPedalboardor Spotify.FeaturesWithPedalboard Pluginary, you can scan and list VST-3 and AU audio plugins installed on your machine, including their default parameters.It automatically scans and catalogs VST-3 and AU plugins installed on your system.Provides a command-line interface (CLI) for quick access to your plugin library.Saves the plugin information in a JSON file. This file has the information about the plugin parameters and their default values.Works on Windows and macOS (Windows is currently untested).It bundles anignores.jsonfile, which “blacklists” some plugins that are known to cause issues with Pedalboard. It will not scan these, and will not include them in the cache. If you find that some plugins are not working with Pedalboard, you can add them to yourignores.jsonfile. See “Contributing” section below.Future plansI plan to extend the package with another functionality, “jobs”, which will allow to load a stack of plugins with their parameter values from a dictionary or JSON file, and run them in a batch using Pedalboard.InstallationTo installPedalboard Pluginary, run:python3-mpipinstall--upgradepedalboard-pluginaryFor the current development version:python3-mpipinstall--upgradegit+https://github.com/twardoch/pedalboard-pluginaryCommand-line usageAfter installation, you can usepbpluginaryfrom the command line.Commands:pbpluginary listdisplays the plugin information stored in the cache, as a JSON. If no cache exists, it will scan your system and create the cache.pbpluginary scanscans all available plugins, and caches the information. Run this if you’ve installed or upgraded some VST-3 or AU plugins.Python usageYou can usePedalboard Pluginaryas a library in your Python scripts. Here's a quick example:frompedalboard_pluginaryimportPedalboardPluginarypluginary=PedalboardPluginary()print(pluginary.list_plugins())This snippet will list all plugins that have been scanned and cached, as a JSON.Changesv1.1.0: AddedupdateCLI command which only scans plugins that aren’t cached yet. Not perfect. AddedjsonandyamlCLI commands. Additional refactorings.v1.0.0: Initial release with basic scanning and listing of both VST-3 and AU plugins, and command-line interface for easy interaction.LicensePedalboard Pluginaryis written by Adam Twardoch, with assistance from GPT-4.Copyright (c) 2023 Adam Twardoch.Licensed under theApache-2.0 license.Pedalboard Pluginaryis not affiliated withPedalboardor Spotify.ContributingIf you encounter any issues or have suggestions, feel free to open anissueon GitHub.If you find that some plugins are not working with Pedalboard, open an issue that lists the key, which is the plugin type and the base filename, like"aufx/CoreAudio"or"vst3/RX 10 Connect". You can also modify thedefault_ignores.jsonfile, and submit a pull request.If you want to contribute code, please open a pull request.
Package: peddler
Peddler is an open source, container-based, OpenCart distribution fromAlto9.It’s written in Python and Click and is designed to make launching an OpenCart instance as easy as possible.Product DocumentationDownload Compiled VersionDeploy using docker-composepeddler local quickstartDeploy using Kubernetespeddler k8s quickstartLicenseSee theLICENSE.txtfor license rights and limitations.Get the Latest NewsTwitter-https://twitter.com/alto9cloudBlog-https://alto9.com/blog/Facebook-https://www.facebook.com/Alto9IncLinkedIn-https://www.linkedin.com/company/alto9ContributingPlease seeCONTRIBUTING.md.Any other questions, mail us atinfo@alto9.com. We’d love to chat!
Package: pedemath
PedeMath - A Python Vector and Math Library====================================================Introduction------------PedeMath is a python vector and math library.Status - In Development------It includes classes for vectors, matrices, and quaternions including<br/>Vec3, Vec2, Matrix44, Rect, and Quat.Notes:<br/>Apis for newer classes such as Matrix44 and Quaternions are still in flux<br/>and in need of more unittests.<br/>Most classes are pure Python, but Matrix44 requires numpy for now.
Package: pedesign
PE-DesignerPrime editing tools that consist of a reverse transcriptase linked with Cas9 nickase are capable of generating targeted insertions, deletions, and base conversions without producing DNA double strand breaks or requiring any donor DNA.usagepython3 pe-designer.py reference_fasta induced_fasta PAM_sequence reference_genome_directoryExamplepython3 pe-designer.py example_ref.fasta example_ind.fasta NGG /directory/of/reference/genome/
Package: pe-designer
PE-DesignerPrime editing tools that consist of a reverse transcriptase linked with Cas9 nickase are capable of generating targeted insertions, deletions, and base conversions without producing DNA double strand breaks or requiring any donor DNA.usagepython3 pe-designer.py reference_fasta induced_fasta PAM_sequence reference_genome_directoryExamplepython3 pe-designer.py example_ref.fasta example_ind.fasta NGG /directory/of/reference/genome/
Package: pedestal-inference
pedestal-inferenceA collection of python tools for Bayesian analysis of plasma-edge Thomson-scattering data.Thomson-scattering synthetic diagnostic modelPedestal-inference provides a model for polychromator-based Thomson-scattering systems
which predicts the expected profiles of spectral intensity in each wavelength band given
a pair of electron temperature and density profiles defined by one of the edge profile
models.Edge profile modelsA collection ofedge profile modelsis available, including the standard 'mtanh' model.DocumentationPackage documentation is available atpedestal-inference.readthedocs.io
Package: pedestrian-prediction
pedestrian_predictionRealtime, confidence-varying trajectory prediction for "Probabilistically Safe Robot Planning with Confidence-Based Human Predictions". RSS '18Installationgit clone https://github.com/sirspinach/pedestrian_prediction.git
cd pedestrian_prediction
pip install -e .Module contentspp.mdp: Data structures and value iteration algorithms for two types of GridWorlds -- a standard GridWorld with only lateral and diagonal movement, and a GridWorldExpanded in which actions operate in "gridless" continuous space but are snapped to the nearest grid cell.pp.inference: Algorithms for inferring destinations, occupancies, and states given a GridWorld, a list of destinations, and the pedestrian's trajectory so far.pp.util: Utilities for parsing and trajectory manipulation.pp.plots: Plot simulated trajectories, overlayed with heatmaps of predicted states. Produce some test plots by executingpython -m pp.plot. (Requires plotly)
Package: pedia-cloud
Pedia Cloud APIA third-party Python API forPedia Cloud DictionaryInstallpipinstallpedia-cloudUsageGet all words and its meaning>>>frompedia_cloudimportPediaDictionary>>>PediaDictionary.get_all("早")[早,ㄗㄠˇ,meaningscount:11]>>>PediaDictionary.get_all("好")[好,ㄏㄠˇ,meaningscount:14,好,ㄏㄠˋ,meaningscount:3]>>>PediaDictionary.get_all("早")[0].meanings[{'type':'[名]','def':'天剛亮的時候...'},...]Get all possible part-of-speech>>>frompedia_cloudimportPediaDictionary>>>PediaDictionary.get_all_poss("好")Counter({'副':7,'形':3,'動':3,'歎':2,'名':2})A word>>>frompedia_cloudimportPediaDictionary>>>word=PediaDictionary.get_one("好")>>>word好,ㄏㄠˇ,meaningscount:14>>>word.text'好'>>>word.zuyin'ㄏㄠˇ'>>>word.annotations['美、善，理想的。','友愛的。',...]>>>word.meanings[{'type':'[形]','def':'美、善，...>>>word.filter_by_pos(["副"])[{'type':'[副]','def':'很、非常。表示程度深...>>>word=PediaDictionary.get_one("囫")>>>word.has_meaningFalse>>>word.annotations['完整、整個。']>>>word.meanings[{'def':'參見「囫圇」條。'}]>>>word.meta{'bopomofo':'ㄏㄨˊ','pinyin':'hú','definitions':[{'def':'參見「囫圇」條。'}]}A meaning>>>meaning=word.meanings[0]>>>meaning{'type':'[形]','def':'美、善 ...>>>meaning.pos'形'>>>meaning.full'美、善，理想的。如：「好東西」...>>>meaning.annotation'美、善，理想的。'Dictionary based idiom segmentationI implement it by using pedia-cloud dictionary.This will be slow for long sentence due to api call.>>>frompedia_cloudimportPediaDictionary>>>PediaDictionary.segment("一鳴驚人")['一','鳴','驚人']>>>PediaDictionary.segment("匯集了世界各地的新聞來源")['匯集','了','世界','各','地','的','新聞來源']
Package: pedigree
pedigreetakes a [toml][] file like that inexamples/example.toml([source][]) and outputs a
few messy visualizations that do not favor patriliny over matriliny.  Seeexamples/for example output.![Screenshot 1](media/screenshot1.png)
![Screenshot 2](media/screenshot2.png)Example:For a quick example, generate the example .toml filepedigree -f new_relations.tomlthen generate output based on itpedigree -f new_relations.toml generateOutputs:.htmlfile: a [d3][] visualization that can be opened in a web browser.svgfile: a “Sugiyama style” tree that can be opened in a web browser.dotfile: the [dot][] file used to generate the.svgfileInstallation:pip3 install pedigreepedigree –helpwill tell you your options.You need to installgraphvizform your package manager if you want to
generate .svg outputBugs:Please create an [issue](https://github.com/jbaber/pedigree/issues) for any bugs you find.If you’re a developer, the bugs are actually being kept in [git-bug](https://github.com/MichaelMure/git-bug), so feel free to work with them there.Caveats:Don’t put all your genealogical data in one text file that you manipulate via a python script written by some idiot on the internet.  At least make copies of the one text file.This can only recognize the relationsx is the mother of y,x is the father of y, andx is the spouse of y.  It has no concept of siblings or partial siblings.[toml]:https://en.wikipedia.org/wiki/TOML[d3]:http://d3js.org/[dot]:https://en.wikipedia.org/wiki/Graphviz[source]:https://en.wikipedia.org/wiki/Template:Flintstones_family_tree
Package: pedon
pedonfrom Greek: πέδον, pedon -> soilPython package for (unsaturated) soil properties including pedotransfer functions. This package takes an object-oriented approach to soils, soil samples and soil models. Soil models that are available in this package are:Mualem-Van GenuchtenBrooks-CoreyFredlund & XingGardnerPanday (Modflow-USG)This package can fit soil water retention curves and  using least squares (same asRETC) to measurements.Measurements for different soil properties and parameters are available from these datasets:HYDRUSVS2DStaring Series (2001&2018)Additionaly, there are pedotransfer functions implemented such as:Van Genuchten:WöstenVan Genuchten:Staring SeriesBrooks-Corey:CosbyInstallationTo get the latest stable version install using:pip install pedonTo get the development version download the GitHub code to your computer. Use cd to get to the download directory and install using:pip install -e .TodoRosetta Pedotransfer FunctionOther soil models such as Haverkamp