Saravanan666/pypi-org huggingface dataset:
Package: polyendtracker-midi-export
Polyend Tracker MIDI Export toolMIDI conversion tool for Polyend Tracker project files written in Python.If you just want to convert your Tracker project to MIDI you can use the Web Service:https://polyend-tracker-midi-export.onrender.com/DisclaimerI am in no way affiliated with Polyend. This is a hobby project.Installationpipinstallpolyendtracker-midi-exportUsagePolyend Tracker MIDI Export tool can be used as a command line utility or as a library that can be imported in your python projects.Converting an entire Tracker project to MIDIJust point the script to your project's directory:$polymidiexport./my-tracker-project/Alternatively point it to a project file:$polymidiexport./my-tracker-project/project-file.mtConverting an individual Tracker pattern file to MIDIConverting Polyend Tracker*.mtppattern file to midi (pattern files are nested in project folders underpatterns):$polymidiexport./my-tracker-project/patterns/pattern_02.mtpSpecifying custom output file name:$polymidiexport./my-tracker-project/patterns/pattern_02.mtp./my-midi-file.midConverting Polyend Tracker*.mtppattern file to a text file (outputs a table view of the
pattern similar to how you see it in Tracker UI)::$pythonpolytracker2text.py./my-tracker-project/patterns/pattern_02.mtpYou can see an example of pattern text representationhereUsage in python projectsImport lib:importpolytrackermidifrompolytrackermidi.parsersimportconstants,project,patterns,arps,chordsfrompolytrackermidi.exportersimportmidi#todo: describe API usageReverse EngineeringPattern *.mtp filesTODOsPattern file parsingRender names for All FX typesSupport rendering of all possible ranges of values for FXsPattern MIDI exportbasic exportsupport for velocity (volume FX)support for chord FXmake sure that all chord interval formulas are correctsupport for arp FXsupport for microtiming (micromove,m) FXsupport for microtuningMfx (do midi files support that?)cli tool for converting filessupport for panning (it seemsto be supported by midi )Song arrangement MIDI exportexportextract BPMcli tool for converting filesassign instrument names to midi tracks from instrument project filesPyPi packageconversion web service–https://polyend-tracker-midi-export.onrender.com/
Package: polyenum
The files of this repository allows one to enumerate polyominoes based on
Jensen’s algorithm. The code is written in Python, but it has been optimized
using Cython.DependenciesPythonGCCPython module dependenciesCythonsetuptoolsRoadmapUpdate the docstring in polyenum package (enumerators, data_structures
modules).Enhance –north-snake option implementation. The criterion for exluding
invalid north snake candidate can be enhanced using the fact that for any
row of a north snake the patternx...0...xcannot be found. Since the
algorithm builds polyominoes column after column, it would be a good idea to
make all of the “left snakes” and rotate the results.Creates a test suite.Implements Knuth’s algorithm for the minimum connectivity problem (see comment
in source code). This will improve significantly the speed when enumerating
polyominoes.Takes into account symmetry to improve speed of algorithm. It will also allow
to generate free/fixed/one-sided polyominoes. Currently, only fixed
polyominoes are generated.Add other types of installers (MacPorts, Homebrew, Windows, Debian package,
ArchLinux).Add a script for extracting statistics and classifying.Add a script for drawing.Improve speed for enumerating partially directed snakes (it uses a Python
boolean list instead of a bool*). Moreover, the enumeration columnwise could
discard more configurations than by looking only at the pillars.Howto useThe scriptpolyenumis used to call the enumeration code. You can./scripts/polyenum--helpfor help.From git repositoryIn order for the script to run properly, the Cython library has to be compiled.
It’s done by doing:$ ./configure
$ makeand then,polyenumwill be available under./scriptsdirectory.ExampleEnumerating all polyominoes of given areaLet the area of the polyominoes be4. Finding all the polyominoes (not
inscribed in a particular rectangle) of area 4 is done as follows:$ ./polyenum -a 4 --fixed
Fixed polyomino enumeration.
XXXX

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X--
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XX
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-X-
XXX

-X
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-XX
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-X
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--X
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-X
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-XX

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XX

Time elapsed: 0.000782012939453125 seconds
Number of polyominoes: 19More examples can be found reading polyenum’s man page (man ./doc/polyenum.1).InstallingFrom PyPi dist releaseSimply do:python2.7 setup.py installFrom git repositoryThe script can be installed on your system. This is useful since it provides
bash tab completion capability for the script. To install, simply do:$ ./configure
$ make
# make installNOTE: Most of the installation process is done by python. On some platforms,
python may install scripts under a directory which is not in your $PATH bash
environnement variable.Bash CompletionIn order to install bash completion, do the following$ BASH_COMPLETION_DIR=$p ./configure
$ make
# make installwhere$pis the path to the directory where bash-completion scripts are
installed on your system.
Package: polyfactory
ProjectStatusCI/CDQualityPackageCommunityMetaPolyfactoryPolyfactory is a simple and powerful mock data generation library, based around type
hints and supporting dataclasses, typed-dicts, pydantic models, msgspec structs and more.Polyfactory part of the Litestar project and as such actively maintained by a community of maintainers and contributors.Examplefromdataclassesimportdataclassfrompolyfactory.factoriesimportDataclassFactory@dataclassclassPerson:name:strage:floatheight:floatweight:floatclassPersonFactory(DataclassFactory[Person]):...deftest_is_person()->None:person_instance=PersonFactory.build()assertisinstance(person_instance,Person)That's it - with almost no work, we are able to create a mock data object fitting thePersonclass model definition.This is possible because of the typing information available on the dataclass, which are used as a
source of truth for data generation.The factory parses the information stored in the dataclass and generates a dictionary of kwargs that are passed toPerson.DocumentationUsage and API reference documentation is available onhttps://polyfactory.litestar.dev/.InstallationpipinstallpolyfactoryRelation to Pydantic-FactoriesPrior to version 2, this library was known aspydantic-factories, a name
under which it gained quite a bit of popularity.
A main motivator for the 2.0 release was that we wanted to support more than just Pydantic models, something which also
required a change to its core architecture. As this library would no longer be directly tied to Pydantic,polyfactorywas chosen as its new name to reflect its capabilities; It can generate mock data for dataclasses, typed-dicts,
Pydantic, odmantic, and beanie ODM models, as well as custom factories.ContributingThis library is a community driven open source project. We welcome and encourage contributions. Please checkout the
GitHub issues, read the contribution guide (at the repository's root), and you're always welcome
tojoin our discord server.Contributors ✨Thanks goes to these wonderful people (emoji key):Na'aman Hirschfeld🚧🚇⚠️💻📖Jacob Coffee🚧📖⚠️Janek Nouvertné🚧📖⚠️💻Peter Schutt🚧⚠️💻📖Marek Czaplicki💻Piotr Przybyło💻sygutss🐛💻chrisbeardy📖guacs💻Vadim💻Simske💻Sondre Lillebø Gundersen💻Mateusz Ciszczoń💻Pedro Bernardes💻Martin Roy💻Matthew Aylward💻Elton H.Y. Chou💻Thang💻Daan💻Lyz💻Thorin Schiffer💻Iipin💻avihai-yosef💻anthonyh209💻Roman Reznikov💻gigelu💻Rodrigo Medina💻Gerrit Egnew💻danielkatzan📖This project follows theall-contributorsspecification.
Contributions of any kind welcome!
Package: polyfamily-tree
polyfamily_treePython package for structuredly managing directional relationship between objects.Though it uses allegory of 'family tree', it doesn't implement 'tree data structure'.Alternatively, relationship in default is drawn by organizing Directed Acyclic Graph(DAG).This means that there is a loop-less hierarchy in between members.Not like tree, member of this data structure can have multiple parentsthus the package is named 'poly'familyGraph organizing members is not a concrete object.All participants store its vicinity relationship in itself.
By default relationship is build bidirectional - parent knows about children whilechildren knows about parent. But this can be manually tweaked usinglower level methods likefm_appendfm_remove.Methods use prefix 'fm_' to be distinguished.InstallationUsepipto install the package.pip install polyfamily_treeUsagefrompolyfamily_treeimport*classMyMember(FamilyMember):# inherit `FamilyMember`def__init__(self):super().__init__()# vital for initiating required attributes# your __init__ code below...a,b=MyMember(),MyMember()a.fm_append_member(parent=a,child=b)# build relationshipprint(b.fm_get_parents())ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.https://github.com/grasshopperTrainer/polyfamily_treeAlternative contectFor any questions :grasshoppertrainer@gmail.comLicenceMIT
Package: polyfempy
polyfem-pythonThe python bindings are in alpha. Expect a lot of API changes and possible bugs. Use at your own peril!I am making efforts to provide a simple python interface to Polyfem.For doing so I am maintaining  acondapackage which can be easily installedhttps://anaconda.org/conda-forge/polyfempy.Note that the conda deployment is slow and this tutorial will follow the deployment version.If you hare in a hurry for the juicy latest feature you can clone the repositoryPolyfem-pythonand usepipto install:python setup.py developandpython setup.py testfor testing.For python documentationhttps://polyfem.github.io/python/.For full documentation seehttps://polyfem.github.io/.
Package: polyfiller-g4
PolyFillerPython module for polygon filling on images.InstallationPackage Installation from PyPi$pipinstallpolyfiller-g4Package Installation from Source CodeThe source code is available onGitHub.Download and install the package:$gitclonehttps://github.com/Genzo4/polyfiller
$cdpolyfiller
$pipinstall-rrequirements.txt
$pipinstall.Basic usageImport:frompolyfiller_g4importPolyFillerCreate instance:Create an instance of the PolyFiller. You can specify additional options:ext - extension to add to the output file.
Default value: fill.color - filling color.
Default value: 0 (black).pf=PolyFiller(ext='add_ext',color=(255,0,0))Add filling polygons (0 or many)pf.addPolygon([[0,0],[1919,0],[1919,682],[1277,385],[951,374],[0,615]])pf.addPolygon([[100,100],[200,100],[150,150]])Filling framepf.fill('frame_1.png')pf.fill('frame_2.png')...pf.fill('frame_n.png')Output files are created with the extension added.See the example.py file for an example of usage.ChangelogPolyFillerPython модуль для заливки многоугольника\ов на изображении однотонным цветом.УстановкаУстановка пакета с PyPi$pipinstallpolyfiller-g4Установка пакета из исходного кодаИсходный код размещается наGitHub.Скачайте его и установите пакет:$gitclonehttps://github.com/Genzo4/polyfiller
$cdpolyfiller
$pipinstall-rrequirements.txt
$pipinstall.ИспользованиеПодключаем:frompolyfiller_g4importPolyFillerСоздаём экземплярСоздаём экземпляр PolyFiller. Можно указать дополнительные параметры:ext - расширение, добавляемое к выходному файлу.
Значение по умолчанию: fill.color - цвет заливки.
Значение по умолчанию: 0 (чёрный цвет).pf=PolyFiller(ext='add_ext',color=(255,0,0))Добавляем полигоны для заливки (0 или много)pf.addPolygon([[0,0],[1919,0],[1919,682],[1277,385],[951,374],[0,615]])pf.addPolygon([[100,100],[200,100],[150,150]])Заливка изображенийpf.fill('frame_1.png')pf.fill('frame_2.png')...pf.fill('frame_n.png')Создаются выходные файлы с добавленным расширением.Пример использования см. в файле example.pyChangelog
Package: polyfinder
polyfinderGiven a single-line address,polyfinderreturns geoenabled
data from ESRI ArcGIS REST services. In its default configuration,
it uses geocoding services hosted by ESRI (primary)
and MapQuest (fallback).Currently the only useful method available is to get ward districts
in Philadelphia. However, this could be easily adapted to work
with other endpoints.Formal documentation has not yet been developed, so check
the docstrings and comments in the source.Here’s a simple example:>>> from polyfinder import PolyFinder
>>> pf = PolyFinder('226 Ripka St, 19127')
>>> pf.ward_div()
{u'ADDRESS': u'201 FOUNTAIN ST',
 u'CENTROID_X': -75.23040009,
 u'CENTROID_Y': 40.03039932,
 u'DIVISION': u'08',
 u'ID': u'2108',
 u'LOCATION': u'Y',
 u'MATCH_ADDR': u'201 FOUNTAIN ST',
 u'OBJECTID': 438,
 u'OBJECTID_1': 1253,
 u'OBJECTID_12': 1253,
 u'PERIMETER': 1.0217016,
 u'POLLING_PL': u'HILLSIDE RECREATION CENTER',
 u'POLLING_X': -75.229599,
 u'POLLING_Y': 40.03350067,
 u'SHAPE.AREA': 1501706.65864654,
 u'SHAPE.LEN': 5394.584423083521,
 u'SHAPE_LENG': 5394.5844231,
 u'WARD': u'21'}
Package: polyfit
PolyfitScikit learn compatible constrained and robust polynomial regression in Python.Mostly developed for educational purposes, polyfit enables fitting scikit learn compatible polynomial regression models under shape constraints. Under the hood polynomial coefficients are optimized viacvxpy'sexcellent convex optimizers.Installation:pipinstallpolyfitDocumentationCheck theonline documentationfor an example and API reference.Why?Often human intuition or prior knowledge gives us an idea that relationships between variables should be monotonic or follow certain asymptotic behaviour. In this example the monotonic fit is visually much more convincing than an unconstrained fit.ExampleSimple example to fit a polynomial of degree 3 which is monotonically increasing for the first feature:frompolyfitimportPolynomRegressor,Constraintspolyestimator=PolynomRegressor(deg=3,regularization=None,lam=0)monotone_constraint=Constraints(monotonicity='inc')polyestimator.fit(X,y,loss='l2',constraints={0:monotone_constraint})MethodThe constraints are enforced by imposing inequality constraints upon the polynomial coefficients. For example, if the resulting one dimensional polynomial is required to be monotonically increasing, its first derivative must be greater than 0. Enforcing this for an interval is not possible but enforcing it for a reasonable number of points within an interval (default: 20) is usually enough to guarantee the monotonicity for this interval. Given the predictor vector x, target vector y and the Vandermonde matrix V the polynomial coefficients p are then estimated by the following optimization problem:Warning: by default, the polynomial is only monotonic or convex/concave for the interval of the input data!
Package: polyflin
PolyFlin: A Powerful Pyhton Module for Multiconfiguration Polymer Structure GenerationPolyFlin can generate polymer structure in any shape for molecular modeling and simulations. It is only limited to your imagination.Latest update (version 2.0.3):LigParGen is now seamlessly integrated within PolyFlin, eliminating the need for separate installations.More output options supported by OpenBabel.Introducing a new output parameter (obabel_func) for enhanced control over file conversion.Visit our homepage here:https://etflin.com/software/PolyFlin
Package: polyform
PolyformProof of ConceptPolyforms are functions-as-a-service which include DataOps, specifically oriented
for running AI systems:are designed for native AI functionalityhave an explicit expectation of the shape of data sent to themsupport different sub-forms for variant purposes (such as training, vs intersect)support transformation of data in flight, as well as storing back to the data universecome with the implicit security of the Data MultiverseData Expectations describe the shape of the data as needed, and draw it in on
demand.This code is a combination of a very rough implementation (prototype)
to prove the concept, combined with CI/CD automation for ease of use.It is targeted towards the data science industry, enabling deep learning to be
more easily managed as production ready services.pip3install'git+ssh://git@github.com/srevenant/polyform.git@master#egg=polyform'Runpolywith no args for a breakdown of optionsTutorial coming soon
Package: polyfoto
PolyfotoCreate image mosaics.Showcase"Before and After Science" by Brian Eno"Scott 3" by Scott WalkerInstallationClone this repository to your local machine with git, then install with
Python.gitclonehttps://github.com/shanedrabing/polyfoto.gitcdpolyfoto
pythonsetup.pyinstallGetting StartedRun the program with Python.pythonpolyfoto.py-finput.png-dsources-ooutput.png-n16Required arguments-f: Input file name. This is the target to recreate.-d: Input folder name. Contains images used to recreate the target.-o: Output file name. Name of the rendered canvas.-n: Number of rows. How many rows of images should be used in recreation?Optional arguments-p: Proportional center of construction. 0 being the top of the canvas, 1
being the bottom.-c: Proportional chance to consume image. 0 means no image is consumed, 1 each image always consumed-s: Rescaling factor. Normally 4x target size.-t: Pixel height of thumbnails. Used for the math operations, 16px height
by default.-x: Output folder name for thumbnails. "tmp" by default.LicenseMIT
Package: polygenerator
polygeneratorGenerates random simple polygons. This can be useful to test computational geometry
algorithms or to generate maps.Installation$ pip install polygeneratorAPIThere are 3 functions and each returns a list of (x, y) tuples:random_convex_polygon(num_points)random_polygon(num_points)random_star_shaped_polygon(num_points)All polygons are generated to be counterclockwise. You can reverse the order
outside if you need the points in clockwise order.The generated polygon is made to fit the bounding box (0.0, 0.0) ... (1.0, 1.0)
and you can then scale and translate it to where you need it.Examplefrompolygeneratorimport(random_polygon,random_star_shaped_polygon,random_convex_polygon,)# these two are only for demonstrationimportmatplotlib.pyplotaspltimportrandomdefplot_polygon(polygon,out_file_name):plt.figure()plt.gca().set_aspect("equal")fori,(x,y)inenumerate(polygon):plt.text(x,y,str(i),horizontalalignment="center",verticalalignment="center")# just so that it is plotted as closed polygonpolygon.append(polygon[0])xs,ys=zip(*polygon)plt.plot(xs,ys,"r-",linewidth=0.4)plt.savefig(out_file_name,dpi=300)plt.close()# this is just so that you can reproduce the same resultsrandom.seed(5)polygon=random_polygon(num_points=20)print(polygon)# [(0.752691110661913, 0.948158571633034), (0.7790276993942304, 0.05437135270534656), ..., (0.633385213909564, 0.7365967958574935)]plot_polygon(polygon,"random_polygon.png")polygon=random_star_shaped_polygon(num_points=20)plot_polygon(polygon,"random_star_shaped_polygon.png")polygon=random_convex_polygon(num_points=20)plot_polygon(polygon,"random_convex_polygon.png")NotesFor the generation of a concave/general polygon, algorithms with better
scaling exist but this was good enough for me since for testing I did not
need polygons with more than 100 points. Improvements welcome.
Package: polygeohasher
polygeohasherpolygeohasher is a python package to implement polygon to geohash and vice versa with optimisation of geohash levels as per the user requirement, with error rate being controlled by the user.InstallationUse the package managerpipto install polygeohasher.pipinstallpolygeohasher#install all dependenciespip3install-rrequirements.txtUsagefrompolygeohasherimportpolygeohasherimportgeopandasasgpdgdf=gpd.read_file("your geospatial file format")# read your geometry file hereprimary_df=polygeohasher.create_geohash_list(gdf,geohash_level,inner=False)# returns a dataframe with list of geohashes for each geometrysecondary_df=polygeohasher.geohash_optimizer(primary_df,largest_gh_size,smallest_gh_size,gh_input_level)# returns optimized list of geohashpolygeohasher.optimization_summary(primary_df,secondary_df)#creates a summary of first and second output'''--------------------------------------------------OPTIMIZATION SUMMARY--------------------------------------------------Total Counts of Initial Geohashes :  2597Total Counts of Final Geohashes   :  837Percent of optimization           :  67.77 %--------------------------------------------------'''geo_df=polygeohasher.geohashes_to_geometry(secondary_df,"geohash_column_name")# return geometry for a DataFrame with a column - `opitimized_geohash_list` (output from above)geo_df.to_file("your write path.format",driver="GeoJSON")#write file in your favorite spatial file formatSome visualisationsStudy are consist of division of City of Bengaluru in India.Primary Output of geohashes without any optimisation.Final Output of geohashes with optimization of number of geohashes at different levels to cover an area.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseApache License, Version 2.0
Package: polygeom
polygeomA python package for fast and simple geometry..first of allspecifics:writen and owned by: Shahaf Frank-Shapirall the rights are saved for: Shahaf Frank-Shapirprogramming languages: python 3.9.12 (100%)before we startdescription:visit the docs to learn more about this project and how to develop with it.dependencies:opening:
For this is a complex program, which uses a lot of modules, there are required dependencies needed
in order to run the program. keep in mined the program was writen in python 3.9, so any python version lower
than 3.9 might not work properly.install app dependencies by writing the "-r" option to install the requirements
writen in a file, and write the following line in the project directory:pip install -r requirements.txtrun a testrun from windows command line (inside the project directory)run with python by writing to the command line in the project directory:python test.py
Package: polyglot-code
Clone me!Clone or download the project:gitclonehttps://github.com/cosasdepuma/polyglot-code.gitPolyglot[or]gitclonehttps://gitlab.com/cosasdepuma/polyglot-code.gitPolyglotcdPolyglotTest it!Run all the unitary tests through NodeJS:cdjavascriptnpminpmtestRun all the unitary tests using Python:cdpythonpython2test/test.pypython3test/test.pyRequirementsPolyglot does not require anything!UsageImportPolyglotinto your NodeJS file:require('polyglot-code')ImportPolyglotinto your Python file:importpolyglotAvailable Languages!JavaScript// No new-line logsprintf('C')IO.write('Elixir')fmt.Printf('Go')putStr('Haskell')io.write('Lua')fprintf('Matlab')Write('Pascal')write('Pike')// New-line logstrace('ActionScript')WriteF('AmigaE')Response.Write('ASP')putschar('B')puts('C')Console.println('C#')Console.WriteLine('C#')writeln('D')std.stdio.writeln('D')print('Dart')Writeln('Delphi')IO.puts('Elixir')fmt.Println('Go')putStrLn('Haskell')System.out.println('Java')console.log('JavaScript')println('Kotlin')disp('Matlab')echo('Nim')NSLog('Objetive-C')writeln('Pascal')writeln('Pike')println('Processing')PrintN('PureBasic')print('Python')p('Ruby')Python# No new-line logsprintf("C")cout<<"C++"IO.write("Elixir")fmt.Print("Go")fmt.Printf("Go")putStr("Haskell")System.out.printf("Java")io.write("Lua")fprintf("Matlab")write("Pike")System.Console.Write("VB.NET")# New-line logsprint("Python")trace("ActionScript")Ada.Text_IO.Put_Line("Ada")WriteF("AmigaE")Response.Write("ASP")putchar("B")puts("C")Console.println("C#")Console.WriteLine("C#")cout<<"C++"<<endlQout("Clipper")writeln("D")std.stdio.writeln("D")Writeln("Delphi")IO.puts("Elixir")fmt.Println("Go")putStrLn("Haskell")System.out.println("Java")console.log("JavaScript")println("Kotlin")Print["Mathematica"]disp("Matlab")echo("Nim")NSLog("Objetive-C")writeln("Pascal")writeln("Pike")PrintN("PureBasic")p("Ruby")Debug.Log("Unity3D")System.Console.WriteLine("VB.NET")Support the developer!Everything I do and publish can be used for free whenever I receive my
corresponding merit.Scheme of contentsPolyglot-Code
 < Repository >
|__ .gitignore
|__ .repository
  |__ odd.jpeg
|__ LICENSE
|__ README.md
< JavaScript >
|__ javascript
  |__ package.json
  |__ lib
    |__ polyglot.js
  |__ test
    |__ test.js
< Python >
|__ python
  |__ lib
    |__ __init__.py
    |__ polyglot.py
  |__ test
    |__ test.pyPlease contact withKike Pumaif
you need more information.