Saravanan666/pypi-org huggingface dataset:
Package: pumpp
pumpppractically universal music pre-processorpumpp up the jamsThe goal of this package is to make it easy to convert pairs of(audio, jams)into data that can
be easily consumed by statistical algorithms.  Some desired features:Converting tags to sparse encoding vectorsSampling(start, end, label)to frame-level annotations at a specific frame rateExtracting input features (eg, Mel spectra or CQT) from audioConverting between annotation spaces for a given taskExample usage>>>importjams>>>importpumpp>>>audio_f='/path/to/audio/myfile.ogg'>>>jams_f='/path/to/annotations/myfile.jamz'>>># Set up sampling and frame rate parameters>>>sr,hop_length=44100,512>>># Create a feature extraction object>>>p_cqt=pumpp.feature.CQT(name='cqt',sr=sr,hop_length=hop_length)>>># Create some annotation extractors>>>p_beat=pumpp.task.BeatTransformer(sr=sr,hop_length=hop_length)>>>p_chord=pumpp.task.SimpleChordTransformer(sr=sr,hop_length=hop_length)>>># Collect the operators in a pump>>>pump=pumpp.Pump(p_cqt,p_beat,p_chord)>>># Apply the extractors to generate training data>>>data=pump(audio_f=audio_f,jam=jams_fjams_f)>>># Or test data>>>test_data=pump(audio_f='/my/test/audio.ogg')>>># Or in-memory>>>y,sr=librosa.load(audio_f)>>>test_data=pump(y=y,sr=sr)
Package: pumpwood-viewutils
Extends Django and Rest FrameWork exceptions in order to give a better detail over model miss-especification and permit serialization of the error data to facilitate front-end debug.Quick startAdd “pumpwood_viewutils” to your Django INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'pumpwood_viewutils',
]DescriptionEach model exposed by a PumpWood View have nine basic end-poitns exposed througth PumpWood router.list, served as POST: {slugfied model_name}/listlist_without_pag, served as POST: {slugfied model_name}/list-without-pagretrieve, served as POST: {slugfied model_name}/retrieve/(?pk)delete, served as DELETE: {slugfied model_name}/retrieve/(?pk)save, served as POST, PUT: {slugfied model_name}/save/list_actions, served as GET {slugfied model_name}/actions/execute_action, served as POST {slugfied model_name}/execute_action/(?pk)list_search_options, served as GET {slugfied model_name}/optionslist_options, served as POST {slugfied model_name}/optionslistReturns a list of objects acording with the filter dict passed as msg body. List is retricted with the maximum
length defined on REST_FRAMEWORK[[‘PAGINATE_BY’]]list-without-pagReturns a list of objects acording with the filter dict passed as msg body, but without length limitretrieveReturns the object as a dictionary, serialized by retrive_serializer. Usualy, when an object is retrieved it have more
information than when it is listed.deleteDelete de object. For security reason, the url is maped but the code for deleting the object is not implemented (it raises an error, must be overrided).saveSave and updates an object acording to msg body dictionary. If msg body have a pk the object with this pk will be updated, other wise it will be createdlist_actionsList Class functions that have been marked with @rest_function wrapper. Returns a list with the path of the action, a short description, an indication for an
icon to be used in the front end and necessary parameter, optional parameter and what the function will return.execute_actionExecute an action over an object, if pk was filled, or a class method. Returns a dictionary with the results, the action name, parameters used in the action and the object
on which the action was performed.list_search_optionsReturn options to be user in dropdowns and other filter to be used in the search of the object.list_optionsList the options on the object creation according to parcial filment of the object.This project is on beta phase, use it with carefull.
Package: pumuckl
pumuckl(Puppet Module Version Checker) is a simple script to check
a Puppetfile if there are newer module versions on the forge.RequirementsPython >=3.3pip(3)InstallationTo install system-wide run:sudo pip3 install pumucklAlternatively you may create a virtualenv and install it there.UsageEither runpumucklin a folder containing aPuppetfile.Runpumuckl path/to/PuppetfileContributingIf you find a bug or wish to have a new feature simply open an issue.Otherwise if you are really motivated pull requests are always
welcome, too.CreditsCreated byRobin GlosterNamed byTristan HelmichLicenseGPLv3, seeLICENSEfor details.
Package: punch-clock
⏰ Punch clockPython script to keep track of your working hours. Automatically pushes in local .git repo.🏗️ Requirementspipinstallpunch-clockclick
gitconfig--globaluser.name"Your Name"gitconfig--globaluser.email"youremail@email.com"gitconfig--globalcredential.helperstore⚔️ UsageCopypunch.pyto your repoRunpython3 punch.py -inorpython3 punch.py -out🔍 ExampleSeeexample.mdfile.
Package: punchline
punchlineCLI tool to generate SVG punch cards (stripes with melodies) for a DIY music box from MIDI melodies.Some basics:SVGis a vector format of images, meaning that you can scale the image as much as you want and print it on any printer,pen plotter, orlaser cutter.MIDIis a sound format that instead of exact sounds describes which musical instruments should play which note at which moment. You can find a free MIDI file for any popular melody online.DIY music boxis a type of music box which instead of playing a predefined melody can play any melody from a paper stripe with holes in it. You can buy one online for a pretty low price.Features:Written on modern Python.Has friendly CLI.Well documented source code (if you really want to tweak something).Highly configurable.Has only necessary dependencies.Automatically finds the best transposition.Finds the best fit for notes that aren't present on the music box.Supports all kinds of music boxes.Works offline.Detects notes that might be silent.Laser cutter friendly output.InstallationYou'll need:Pythonversion 3.9 or laterpipKnowledge of how to useterminal.python3.9-mpipinstallpunchlineUsageGet help:python3.9-mpunchline--helpGenerate punch cards from 3rd track inSevenNationArmy.midfor 20-notes music box that supports sharp notes and save the result into "7nation" directory:python3.9-mpunchline--inputSevenNationArmy.mid--tracks3--sharps--notes-count20--output7nationThe only required argument is--input. By default, it will generate stripes from all tracks for 15-notes non-sharp music box and store the result in the "output" directory.You can find a few more examples inpresets.md.Editing tipsYou can useInkscapeto edit generated SVG images. It's free and powerful.To mass-edit a specific type of element:Select an element of this typeRight-click on it.Open "Select same" menu.Select "Fill and stroke"Red circles indicate sounds that aren't perfectly fit on the music box. They are either in a different octave comparing to most of the other sounds or they are semitones (sharp notes) when your music box doesn't support semitones. Punchline tries its best to place them where it will sound OK but you may want to edit them or remove altogether.Pay attention to the outlined circles (filled with white instead of a color). They indicate sounds that are too close to the previous one on the same line. They might be silent because of limitations of the music box. You may want to decrease the--speedfor the whole melody or manually move (or remove) colliding sounds. The minimal distance between circles (their centers) is configured by--min-distance.AcknowledgmentsThe initial implementation is based onpunchbox.midois used to read MIDI files.svg.pyis used to generate SVG files.
Package: puncia
The Panthera(P.)uncia of CybersecuritySubdomain & Exploit Hunter powered by AIPuncia utilizes two of our intelligent APIs -Subdomain Center&Exploit Observer, to gather the results.Please note that although these results can sometimes be pretty inaccurate & unreliable, they can greatly differ from time to time due to their self-improvement capabilities.InstallationFrom PyPi -pip3 install punciaFrom Source -pip3 install .UsageSubdomain -puncia subdomain <domain> <output>Exploit -puncia exploit <keyword> <output>Noteworthy MentionsPUNCIA — The Panthera(P.)uncia of CybersecuritySubdomain Enumeration Tool Face-off - 2023 EditionMore fromA.R.P. SyndicateAttack Surface ManagementOSINT ResourcesSubdomain CenterExploit Observer
Package: pun-cli
Pun CLIpun-cliis a Python-based command-line tool that generates a random pun based on a given keyword.InstallationYou can install pun-cli via pip:pipinstallpun-cliUsagepun-cli[-h][--keywordKEYWORD][--version][--add]OptionalArguments--keyword(-k):Akeywordtosearchforpuns.Bydefault,pun-cligeneratesarandompun.--version(-v):Showtheversionnumberandexit.--add(-a):Addapuntotherepository.Examples$pun-cli
Whywasthemathbooksad?Becauseithadtoomanyproblems.

$pun-cli-kcheese
I’mreadingabookaboutanti-gravity.It’simpossibletoputdown.Adding a PunTo add a pun to the repository, follow these steps:Fork thepun-cli repository.Clone your fork to your local machine.Add your pun to pun_repository.txt file.Commit your changes and push to your fork.Create a pull request to merge your changes into the main pun-cli repository.ContributingIf you'd like to contribute topun-cli, please open an issue or a pull request on the GitHub repository.Licensepun-cliis licensed under the MIT License.
Package: puncover
puncoverAnalyzes C/C++ binaries for code size, static variables and stack usages. It
creates a report with disassembler and call-stack analysis per directory, file,
or function.Installation and UsageInstall with pip:pipinstallpuncoverRun it by passing the binary to analyze:puncover--elf_fileproject.elf
...
*Runningonhttp://127.0.0.1:5000/(PressCTRL+Ctoquit)Open the link in your browser to view the analysis.Running Tests LocallySetupTo run the tests locally, you need to install the development dependencies:install pyenv:https://github.com/pyenv/pyenvcurlhttps://pyenv.run|bashinstall all the python environments, using this bashism (this can take a few
minutes):for_pyin$(<.python-version);dopyenvinstall${_py};doneinstall the development dependencies:pipinstall-rrequirements-dev.txtRunning TestsThen you can run the tests with:toxor, to target only the currentpythonon$PATH:tox-epyPublishing ReleaseRelease ScriptSeerelease.shfor a script that automates the above steps. This example will
work with the PyPi tokens (now required):PUNCOVER_VERSION=0.3.5TWINE_PASSWORD="<pypi token>"TWINE_USERNAME=__token__./release.shManual StepsOnly for reference, the release script should take care of all of this.Click to expandUpdate the version inpuncover/__version__.py.Commit the version update:gitadd.&&gitcommit-m"Bump version to x.y.z"Create an annotated tag:gittag-a{-m=,}x.y.zPush the commit and tag:gitpush&&gitpush--tagsEither wait for the GitHub Action to complete and download the release
artifact for uploading:https://github.com/HBehrens/puncover/actionsOR Build
the package locally:python setup.py sdist bdist_wheelUpload the package to PyPI:twineuploaddist/*Create GitHub releases:gh release create --generate-notes x.y.zattach the artifacts to the release too:gh release upload x.y.z dist/*ContributingContributions are welcome! Please open an issue or pull request on GitHub.
Package: puncover-riscv
puncoverAnalyzes RISCV C/C++ binaries for code size, static variables and stack usages. It
creates a report with disassembler and call-stack analysis per directory, file,
or function.This project based onhttps://github.com/HBehrens/puncover, but only support arch riscv.
Add -fstask-usage to your gcc build flag. By Heiko Behrens - MIT license, copyright © 2014-2017Installation and UsageInstall with pip:pipinstallpuncover_riscvRun it by passing the binary to analyze:puncover_riscv--elf_fileproject.elf...*Runningonhttp://127.0.0.1:5000/(PressCTRL+Ctoquit)Open the link in your browser to view the analysis.Running Tests LocallyTo run the tests locally, you need to install the development dependencies:install pyenv:https://github.com/pyenv/pyenvcurlhttps://pyenv.run|bashinstall all the python environments, using this bashism (this can take a few
minutes):for_pyin$(<.python-version);dopyenvinstall${_py};doneinstall the development dependencies:pipinstall-rrequirements-dev.txtThen you can run the tests with:toxPublishing ReleaseUpdate the version inpuncover_riscv/__version__.py.Commit the version update:
..  code-block:: bashgit add . && git commit -m “Bump version to x.y.z”Create an annotated tag:
..  code-block:: bashgit tag -a {-m=,}x.y.zPush the commit and tag:
..  code-block:: bashgit push && git push –tagsEither wait for the GitHub Action to complete and download the release
artifact for uploading:https://github.com/Egahp/puncover_riscv/actionsOR Build
the package locally:python setup.py sdist bdist_wheelUpload the package to PyPI:
..  code-block:: bashtwine upload dist/*Create GitHub releases:
-gh release create--generate-notesx.y.z- attach the artifacts to the release too:gh release upload x.y.z dist/*Release ScriptSeescripts/release.shfor a script that automates the above steps.ContributingContributions are welcome! Please open an issue or pull request on GitHub.
Package: punctfix
Punctuation restorationAdds punctuation and capitalization for a given text without punctuation.Works on Danish, German and English.Models hosted on huggingface! ❤️  🤗Status with python 3.8Installationpip install punctfixUsageIts quite simple to use!>>>frompunctfiximportPunctFixer>>>fixer=PunctFixer(language="da")>>>example_text="mit navn det er rasmus og jeg kommer fra firmaet alvenir det er mig som har trænet denne lækre model">>>print(fixer.punctuate(example_text))'Mit navn det er Rasmus og jeg kommer fra firmaet Alvenir. Det er mig som har trænet denne lækre model.'>>>example_text="en dag bliver vi sku glade for, at vi nu kan sætte punktummer og kommaer i en sætning det fungerer da meget godt ikke">>>print(fixer.punctuate(example_text))'En dag bliver vi sku glade for, at vi nu kan sætte punktummer og kommaer i en sætning. Det fungerer da meget godt, ikke?'Note that, per default, the input text will be normalied. See next section for more details.Parameters for PunctFixerPassdevice="cuda"ordevice="cpu"to indicate where to run inference. Default isdevice="cpu"To handle long sequences, we use a chunk size and an overlap. These can be modified. For higher speed but
lower acuracy use a chunk size of 150-200 and very little overlap i.e. 5-10. These parameters are set with
default valuesword_chunk_size=100,word_overlap=70which makes it run a bit slow. The default parameters
will be updated when we have some results on variations.Supported languages are "en" for English, "da" for Danish and "de" for German. Default islanguage="da".Note that the fixer has been trained on normalized text (lowercase letters and numbers) and will per default normalize input text. You can instantiate the model withskip_normalization=Trueto disable this but this might yield errors on some input text.To raise warnings every time the input is normalied, setwarn_on_normalization=True.ContributeIf you encounter issues, feel free to open issues in the repo and then we will fix. Even better, create issue and
then a PR that fixes the issue! ;-)Happy punctuating!
Package: punctilious
punctiliousA human-friendly and developer-friendly math proof assistant library.AboutPunctiliousis amath proof assistantopen-source project developed in python. In straightforward language, it
intends to
facilitate the study and development of mathematical proofs by human beings.There exists multiple math proof assistants, including:CoqIsabelleLean...and many more. Theproof assistantpage on Wikipedia contains a
general-purpose comparison of these.In contrast,punctiliousis a ridiculousLilliputianwith
neither the length, nor the breadth of these time-proven systems.So why develop yet another math proof assistant?Besides being a fun and thrilling learning experience, the focus points ofpunctiliousare:Human-friendly and developer-friendly inputs (i.e.: write math as we learn it)Ease of useHuman-friendly and developer-friendly outputs (i.e.: readable)Punctiliousdoes not pay much attention to:Performance (i.e. for large-scale proof applications)Source code repositoryThe source code of thepunctiliouspackage lives onGitHubhere:https://github.com/daviddoret/punctiliousPackage directoryThepunctiliouspackage is available for download from thePython Package Index (PyPi)here:https://pypi.org/project/punctilious/PrerequisitesPython version 3.11 or later.InstallationInstall the latest version of thepunctiliouspackage, or upgrade it to the latest version if already installed:pip install punctilious --upgradeGetting startedA series of samples will be provided soon once we will have published our documentation on ReadTheDocs. Please wait...DocumentationThe documentation of thepunctiliouspackage is hosted onReadTheDocshere:https://punctilious.readthedocs.io/en/latest/More linksRoadmapContributingProjectLicenseAcknowledgmentsWe express our gratitude to the wonderful projects we depend on, and their amazing contributors:Project dependencies
Package: punctual
punctualis an uncomplicated dotfile manager that works on common Linux fundamentals and simplistic JSON configuration files.InstallationFromPyPi, with pip:pip install punctualWith this you will have an executable namedpcl.UsageMain:$ pcl --help
Usage: pcl [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  delete
  install
  listInstall:$ pcl install --help
Usage: pcl install [OPTIONS] PACKAGE_NAME

Options:
  --force  Remove any existing files
  --help   Show this message and exit.DocumentationAvailable on ReadTheDocs!
Package: punditkit
PunditKitSimplify. Visualise. Learn.PunditKit is a free toolkit for exploratory data analysis and modelling of
tabular data - such as a database or a spreadsheet - with a simple interface,
visual diagnostics, and the choice of a large number of different models.Included Features:Machine learning models usingscikit-learnInterpretable explanations of predictions usinglimeExploratory data summaries for checking datasetsModel diagnostics for evaluating the effectiveness of different modelsFeature importance: identify key drivers of the responsePartial dependence plots: understand relationships with a particular feature.PunditKit is under active development. The goal is to develop an opinionated
modelling framework with best practice modelling and visualisation techniques.
If you encounter any problems or have any feature requests, please consider
raising anissue.Installation (via pip)PunditKit is developed using Python. First download and install a Python 3.x
distribution such asAnacondaPunditKit can then be installed usingpipfrom the command line (ifpipis added to PATH during installation) or using Anaconda prompt.pip install punditkitThis adds thepunditkitcommand.Modelling a datasetSuppose you have a file callediris.csvthat you would like to model.To use punditkit on the dataset, run:punditkit iris.csvCurrently only Comma Separated Values (CSV) datasets are supported. Datasets
within Excel spreadsheets can be analysed using punditkit, use Save As CSV from
within Excel to convert to the right format.
Package: punica
Punica Python is a dApp development command line tool which has (almost) everything you need to start developing your dApp based on Ontology network.InstallationThere are a few technical requirements before we start. Please install the following:Python 3.7GitThen, execute the following command to install your tool:pipinstallpunicaDocumentationRead a beautiful documentation inhere.LicenseThe Ontology library (i.e. all code outside of the cmd directory) is licensed under the MIT License, also included in our repository in the License file.
Package: punica-neo-boa
punica-neo-boaOverviewThepunica-neo-boacompiler is a tool fork from the branch ofontioand simplifies the project to supportpython-punica.The compiler supports a subset of the Python language ( in the same way that aboa constrictoris a subset of the Python snake species)What does it currently doCompiles a subset of the Python language to the.avmformat for use in theOntologyWorks for Python 3.6+supports dictionariesWhat will it doCompile a larger subset of the Python languageInstallationInstallation requires a Python 3.6 or later environment.pip install obox-neo-boaLicenseOpen-sourceMIT.Main author is@localhuman.DonationsAccepted atATEMNPSjRVvsXmaJW4ZYJBSVuJ6uR2mjQU
Package: punictest
PunicPunic is a lightweight and user-friendly unit testing framework for Python. It provides an easy and efficient way to write and execute tests for your Python code.FeaturesEasy Integration:Punic can be easily integrated into your Python projects.Simple Assertion:The framework provides anassert_method for making assertions on expected and actual values.Output Formatting:Test results are displayed in a clear and readable format.Customizable:Punic is flexible and customizable to suit your specific requirements.Getting StartedInstall Punic:pip install punictestWrite your tests using theassert_method:frompunicimportPunicp=Punic()p.assert_(4,lambda:2+2,"Addition")Run your test program.ReferenceThe Punic class provides methods for defining and executing tests, configuring behavior, and callbacks:assert_(assertion, function, named="Unnamed"): Defines a test case.exit_on_failure(on_exit): Configures whether the program should exit on a test failure.when_failed(action): Sets a callback function to be executed when a test case fails.when_passed(action): Sets a callback function to be executed when a test case passes.ContributionsContributions to Punic are welcome! Please open an issue or submit a pull request for bug fixes, new features, or enhancements.LicensePunic is released under the MIT License. See the LICENSE file for more details.
Package: punits
punitsA simple utility to convert values between different units.InstallationYou need Python>=3.6 to use punits.It can be installed from PyPI with pip usingpip install punitsCommand line usagepython3 -m punits <choice> <source-unit> <target-unit> <value-list>where choice is the kind of units. Allowed values for  are:masslengthvolumedatatemperature.For example, you can usepython3 -m punits mass lb kg 23 46.2 12.46to get10.43 20.96 5.65Usepython3 -m punits --helpfor more information.Usage as moduleThepunits()function can be used.Its signature is:def punits(measure: str,
           src_unit: str,
           target_unit: str,
           values: List[float],
           params: Optional[dict] = None) -> List[float]where theparamsargument can be used to pass additional parameters that may be needed (likedpivalue when conversion is from or to pixesl).For example,>>> punits.punits('temperature', 'K', 'C', [235, 123.2])
[-38.14999999999998, -149.95]or>>> punits.punits('length', 'px', 'mm', [235, 123.2], {'dpi': 200})
[29.844999999999995, 15.646400000000002]