Saravanan666/pypi-org huggingface dataset:
Package: pymangaplus
PyMangaPlusPyMangaPlus is a Python client for the MangaPlus API, allowing you to access various manga-related information and
features. This library provides a convenient way to interact with the MangaPlus API and retrieve data such as manga
details, chapters, comments, rankings, and more.FeaturesRetrieve manga details including title, author, description, cover image, and more.Get a list of available chapters for a specific manga.View manga chapters and read them online.Add or remove manga titles from your bookmarks.Access publisher news for specific publishers.View featured manga titles.Retrieve and update user profile information.View the home page with recommended manga titles.Like or unlike comments on manga chapters.InstallationYou can install PyMangaPlus using pip:pipinstallpymangaplusUsageHere's an example of how to use the PyMangaPlus library:importsecretsfrompymangaplus.clientimportClientfrompymangaplus.constantsimportQualityif__name__=="__main__":title_id=100191# OSHI NO KOchapter_id=1014090# Chapter 86: ASSISTANT DIRECTORandroid_id=secrets.token_bytes(8).hex()client=Client()# Register new deviceclient.register(android_id)# Home titlescontent=client.home()print(content)# View manga chapterscontent=client.manga_viewer(chapter_id,quality=Quality.SUPER_HIGH)print(content)For more information on how to use PyMangaPlus, please refer to
thedocumentation.DisclaimerPyMangaPlus is an unofficial library and is not affiliated with or endorsed by MangaPlus or Shueisha. The library is
provided "as is" without any warranty, and the usage of this library is at your own risk. Make sure to comply with the
terms and conditions of the MangaPlus service while using this library.LicenseThis project is licensed under theGPL v3 License.
Package: pymangle
pymangleSimple python code to read and work with Mangle masks.The underlying code is in C for speed, and is based on some C++ code written by
Martin White.  Martin has posted his C++ code athttps://github.com/martinjameswhite/litemangleCurrently supports ascii mangle polygon files, with and without pixelization.
The code works in python2 and python3Authors: Erin Sheldon.  Long double support added by Eli Rykoff.examplesimportpymangle# read a mangle polygon filem=pymangle.Mangle("mask.ply")# test an ra,dec point against the maskgood=m.contains(200.0,0.0)# test arrays of ra,dec points against the maskra=numpy.array([200.0,152.7])dec=numpy.array([0.0,-15.0])good=m.contains(ra,dec)# get the polygon idsids=m.polyid(ra,dec)# get the weightsweights=m.weight(ra,dec)# get poth polyids and weightsids,weights=m.polyid_and_weight(ra,dec)# generate random pointsra_rand,dec_rand=m.genrand(1000)# generate randoms from the mask and with the additional constraint that they# are within the specified rectangle.  This speeds things up if your mask is# relatively small compared to the full sphere; choose the box just big enough# to contain the mask.ra_min=200.0ra_max=210.0dec_min=0.0dec_max=10.0ra_rand,dec_rand=m.genrand_range(1000,ra_min,ra_max,dec_min,dec_max)# get the polygon weightsweights=m.weights# read in a new set of weightsm.read_weightfile(weightfile)# set the weightsm.weights=weight_arraybuild and install python library# using pippipinstallpymangle# from sourcepythonsetup.pyinstall--prefix=/some/pathtestspython-c'importpymangle;pymangle.test.test()
Package: pymania-controller
pymaniaA minimalistic server controller for the popular Trackmania racing games.FeaturesCentralized configuration for all pluginsAlready contains plugins for out-of-the-box useAbility to reload configuration and plugin code during executionAsynchronous/parallel plugin loading (order and parallelism determined from inter-dependencies)Dedicated server handler ID allocation does not create overlaps and is therefore suitable for high-load environmentsCompletely dynamic and fine-grained control over privilegesScalable to a certain extent, despite dedicated server limitations
Package: pymanifest
PyManifestDescriptionEasily build a list of files to process with file, directory, and pattern
filters.RequirementsPython 3.0+PipInstallpip install pymanifestExamples# Help commandpython-mpymanifest-h# List all files within current directory and all subdirectories ending# in .txtpython-mpymanifest--recurse-directory.--pattern*.txt# List all files within current directory and all subdirectories# excluding those ending in .pypython-mpymanifest--recurse-directory.--exclude-pattern*.py# Use directly in code to add all arguments listed from '-h' to your own# argparse parserap=ArgumentParser()pymanifest.add_args(ap)# ... Add more args ...args=ap.parse_args()files=pymanifest.process_from_args(args)# Use argument mapping to customize command line parameters to better fit# your naming scheme.  In this example, all command line parameters will# appear with '--custom' prepended.  But you can map the argument names# to anything you like!arg_map={'--file':'--custom-file','--directory':'--custom-directory','--recurse-directory':'--custom-recurse-directory','--manifest':'--custom-manifest','--exclude-file':'--custom-exclude-file','--exclude-directory':'--custom-exclude-directory','--exclude-recurse-directory':'--custom-exclude-recurse-directory','--exclude-manifest':'--custom-exclude-manifest','--pattern':'--custom-pattern','--exclude-pattern':'--custom-exclude-pattern',}ap=ArgumentParser()pymanifest.add_args(ap,arg_map)# ... Add more args ...args=ap.parse_args()# be sure to pass back the arg_mapfiles=pymanifest.process_from_args(args,arg_map)
Package: pymanifesto
pymanifestopymanifesto connects you to the Manifesto Project’s database using Python.Free software: GNU General Public License v3Documentation:https://pymanifesto.readthedocs.io.About the Manifesto ProjectQuoting from the projectwebsite:The Manifesto Project provides the scientific community with parties’
policy positions derived from a content analysis of parties’ electoral
manifestos. It covers over 1000 parties from 1945 until today in over
50 countries on five continents. The DFG-funded MARPOR project continues
the work of the Manifesto Research Group (MRG) and the Comparative
Manifestos Project (CMP). On this website you find the Manifesto Project
Dataset containing the parties’ policy preferences generated by the project.
You also find coded and uncoded election manifestos of the parties in
the dataset as well as information and links to many applications for
the dataset, related projects and publications etc.Featurespymanifestois a Python interface to theManifesto Project API.The following API endpoints are implemented:api_list_core_versionsThe following API endpoints will be implemented:api_list_metadata_versionsapi_get_coreapi_metadataapi_texts_and_annotationsapi_get_core_codebookapi_get_core_citationapi_get_corpus_citationCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.2.1 (2019-11-30)Fix issues with version numbering insetup.py0.2.0 (2019-11-30)Implement basic API client.Implement API routeapi_list_core_versionsas method on API client.Update documentation.Deprecate Python 3.5 and 3.6 (because f-strings and data classes!)0.1.0 (2019-11-28)First release on PyPI.
Package: pymanip
pymanipDescription and goalspymanip is the main package that we use for data acquisition and
monitoring of our experimental systems in the Convection group atLaboratoire de physique de l’ENS de
Lyon. It can be seen as an
extension of theFluidLabmodule, which
it heavily uses. It is available freely under the FrenchCECILL-B
licensein
the hope that it can be useful to others. But it is provided AS IS,
without any warranty as to its commercial value, its secured, safe,
innovative or relevant nature.Unlike FluidLab, pymanip does not garantee any long term stability, and
may change the API in the future without warning. However, some parts of
the pymanip module may eventually be integrated into FluidLab, once they
are stable enough.The pymanip module is a set of tools for data acquisition and data
management. Its goals are the following:management of experimental “sessions”, for storing and retriving
data, and useful live tools for experimental monitoring over long
times, such as live plot, automated emails, and remote access of the
live data, and also simple interrupt signal management;simplify access to FluidLab instrument classes;experimental implementation of asynchroneous video acquisition and
DAQ acquisition;experimental extension of FluidLab interface and instrument classes
with asynchroneous methods;miscellaneous CLI tools for saved session introspection, live video
preview, live oscilloscope and spectrum analyser-style DAQ preview,
and VISA/GPIB scanning.DocumentationThe documentation is available atreadthedocs.InstallationThe package can be installed frompypiusingpip:python -m pip install pymanipor can be installed from githubgit clone https://github.com/jsalort/pymanip.git
cd pymanip
python -m pip install -e .
Package: py-mannerism
Python LibraryThis project consists of code for creating minimal python library and deploying it to Pypi.Operating on This LibraryInstalling and Using LibraryInstallingYou can simply run this command to install this library:pip install py-mannerismor add the library to requirements.txt:py-mannerism=0.0.1Using the libraryYou can use the library with this code (remember, that this is not real solving-problem library).frompylib.commonsimportMannerismmannerism=Mannerism('Unis Badri')print(mannerism.create())it will show this text:Hi Unis Badri, how are you today?Developing with LibraryThis library is intended to fasten the library development, you can look at this as starter kit for library development. So you can add you own structure and library and doing your own library development on it.These are some useful information when developing library with this repository.Running Unit TestpytestRunning Code Coveragepytest --cov-report html --cov-report term --cov=pylib tests/Running Static AnalysisTo run static analysis using flake8, run this command:flake8 --config=.flake8 --count --statistics pylib/Adding Status Check for Pull RequestYou can add status check to the target branch on Pull Request in the .github/workflows/onpush-ci.yml. The name of the workflow Status Checks will be:build-ci (3.6)
build-ci (3.7)
build-ci (3.8)Please refer to github documentation to create status check in github repository.Other Library ProjectI create other library projects in various programming language. The purpose of these projects are to ease engineers to create their own library.Python LibraryRuby LibraryJava LibraryNode LibraryPHP LibraryGo LibraryRust LibraryElixir LibraryAny suggestions can be sent to my email atunis.badri@elementcreativestudio.comoruzumaki.unis@gmail.com.
Package: pymanopt
PymanoptA Python toolbox for optimization on Riemannian manifolds with support for
automatic differentiation.OverviewLatest versionDownloadsBuild statusCoverageCode qualityCommunityPlease refer to thedocumentationand thisJMLR paperto get started with
optimization on manifolds using Pymanopt.
If you wish to extend Pymanopt's functionality and/or contribute to the project
please refer to thecontributing guide.We encourage users and developers to report problems, request features,
ask for help, or leave general comments either here on github or ongitter.Pymanopt is distributed under the3-clause BSD license.
Package: pymantic
Quick Start>>> from pymantic.rdf import *
>>> from pymantic.parsers import turtle_parser
>>> import requests
>>> Resource.prefixes['foaf'] = Prefix('http://xmlns.com/foaf/0.1/')
>>> graph = turtle_parser.parse(requests.get('https://raw.github.com/norcalrdf/pymantic/master/examples/foaf-bond.ttl').text)
>>> bond_james = Resource(graph, 'http://example.org/stuff/Bond')
>>> print("%s knows:" % (bond_james.get_scalar('foaf:name'),))
>>> for person in bond_james['foaf:knows']:
        print(person.get_scalar('foaf:name'))Requirementspymanticrequires Python 3.9 or higher.larkis used for the Turtle and NTriples parser.
Therequestslibrary is used for HTTP requests and the SPARQL client.lxmlandrdflibare required by the SPARQL client as well.Install$ pip install pymanticThis will installpymanticand all its dependencies.DocumentationGenerating a local copy of the documentation requires Sphinx:$ pip install Sphinx
Package: py-maori-stemmer
Snowball stemmer for Maori language, implementing PorterStemmer (https://snowballstem.org/projects.html)Maori snowball syntax written by kshepherd (https://github.com/kshepherd/maoristemmer)Installpipinstallpy_maori_stemmerUsageTo stem one word:frompy_maori_stemmerimportMaoriStemmerstemmer=MaoriStemmer()stemmer.stemWord('waihangatia')# waihangaTo stem a sentence:frompy_maori_stemmerimportMaoriStemmerstemmer=MaoriStemmer()stemmer.stemWord('i waihangatia mō ngā akomanga kaupapa')# ['i', 'waihanga', 'mō', 'ngā', 'akoma', 'kaupap']Testnosetests--nocapture-v
Package: pymap2d
pymap2dpymap2d is a Cython-based fast toolbox for 2d grid maps.The CMap2D class provides:simple xy <-> ij coordinate conversionsimplementation of the dijkstra / fastmarch algorithmfast 2D distance transform (ESDF)conversions:to/from polygon verticesfrom ROS occupancy map or lidar scan messageserialization to/from dictNote: rather than carefully designed, this codebase was chaotically grown.
It is in dire need of refactoring / documentation. I hope it still proves useful.Dependency: Cython$ pip install numpy CythonInstallation:Inside this project root folder:$ pip install pymap2dHow toCreating a mapfromCMap2DimportCMap2D# empty mapmymap=CMap2D()# from an arraymymap.from_array(array,origin,resolution)# from a pgm filemymap=CMap2D("folder","filename")# from a ROS messagemymap.from_msg(msg)Accessing occupancy data, origin, resolution (read-only)# occupancy as 2d arraymymap.occupancy()# origin: (x, y) coordinates of point (i, j) = (0, 0)mymap.origin_xy()# resolution: size of grid cell [meters]mymap.resolution()Converting between grid and spatial coordinateslist_of_xy_points=np.array([[1.3,2.3],[-1.1,-4.0],[6.4,2.3]])in_ij_coordinates=mymap.xy_to_floatij(list_of_xy_points)as_indices=mymap.xy_to_ij(list_of_xy_points,clip_if_outside=True)gridshow is a convenience function, which wraps plt.imshow to intuitively visualize 2d array contents.
It makes the first array dimension x axis and uses grayscale by default.fromCMap2Dimportgridshowgridshow(mymap.occupancy())For more examples, seetest/example_*.py
Package: pymapd
A wrapper for the pyomnisci libararyhttp://github.com/omnisci/pyomnisci, maintained for backwards compatibility.Existing scripts should be migrated to pyomnisci from pymapd, this library will
not be updated moving forward.Quick Install (CPU)Packages are available on conda-forge and PyPI:conda install -c conda-forge pymapd

pip install pymapdQuick Install (GPU)We recommend creating a fresh conda 3.7 or 3.8 environment when installing
pymapd with GPU capabilities.To install pymapd and cudf for GPU Dataframe support (conda-only):conda create -n omnisci-gpu -c rapidsai -c nvidia -c conda-forge \
 -c defaults cudf=0.15 python=3.7 cudatoolkit=10.2 pymapd
Package: pymape
Distributed and decentralized MonitorAnalyzePlanExecute-Knowledge loops frameworkFramework to support the development and deployment of Autonomous (Self-Adaptive) SystemsSource Code:https://github.com/elbowz/PyMAPEDocumentation:https://elbowz.github.io/PyMAPE-WIPInstallpipinstallpymapeExamplesImplementation of the 5 decentralized (and distributed) MAPE patterns described in the paper:"On Patterns for Decentralized Control in Self-Adaptive Systems", Danny WeynsAmbulance-Car Emergency(Information Sharing and Coordinated Control)Average Speed Enforcement(Master/Slave)Dynamic Carriageway(Regional Planning)Cruise Control with Distance Hold(Hierarchical Control)If you want try some examples (pathexamples/), refer to section# CLI EXAMPLESinside the source code of each one.Slide - Introduction to PyMAPEwith examplesThe examples need furthers requirements, please seepyproject.tomlor use poetry toinstall them.You also need a Redis and InfluxDB instance running, for example:dockerrun--namemape-redis-p6379:6379\-v$(pwd)/docker/redis:/usr/local/etc/redis\--rmredisredis-server/usr/local/etc/redis/redis.confdockerrun--namemape-influxdb-p8086:8086\-v$(pwd)/docker/influxdb/data:/var/lib/influxdb2\-v$(pwd)/docker/influxdb/conf:/etc/influxdb2\-eDOCKER_INFLUXDB_INIT_MODE=setup\-eDOCKER_INFLUXDB_INIT_USERNAME=user\-eDOCKER_INFLUXDB_INIT_PASSWORD=qwerty123456\-eDOCKER_INFLUXDB_INIT_ORG=univaq\-eDOCKER_INFLUXDB_INIT_BUCKET=mape\-eDOCKER_INFLUXDB_INIT_RETENTION=1w\-eDOCKER_INFLUXDB_INIT_ADMIN_TOKEN=<GENERATE_OR_TAKE_FROM_CONFIG_YAML>\--rminfluxdb:2.0See source for more information.
Package: pymapee
Welcome to pymapeeA Simple Python package to pre-processing satellite-based data using Google Earth Engine.pymapeeis a simple Python package that aims to provide common functionalities to pre-process or calculate vegetation drought indices using Google Earth Engine. Currently, this package supports cloud masking (MODIS, Landsat, Sentinel-2), composite (monthly), and calculation of vegetation anomaly index (VAI) and vegetation condition index (VCI). It also supports to download an image collection (e.g., time-series NDVI or LST) or an image. This package is under active development, and changes are expected over time.GitHub repo:https://github.com/tuyenhavan/pymapeeFree software: MIT licenseFeaturesMasking cloud-related pixels (e.g., MODIS, Landsat, and Sentinel-2)Making monthly compositeCalculating monthly vegetation anomaly index (VAI) and vegetation condition index (VCI).Scaling dataDownloading an image or image collection (e.g., time-series NDVI)Examples are providedhere, and it will be regularly updated.InstallationInstallpymapeeusing pippip install pymapeeor install from Github to get the latest update.pip install git+https://github.com/tuyenhavan/pymapee.gitCreditsThis package was created withCookiecutterand thegiswqs/pypackageproject template.
Package: pymapf
PyMAPF✨ A Python toolbox for Multi-Agents Planning (Centralized and Decentralized) ✨Report Bug·Request FeatureLoved the project? Please considerdonatingto help it improve!Features 🌱✨ Built to be expanded: easy to add new planners🖥️ Supported on Ubuntu🎌 Built with Python🔎 Reactive Distributed Planners (Nonlinear Model Predictive Control, Velocity Obstacles)🧭 Centralized Planners (Incoming...)📊 Benchmark Tools (Incoming...)🍻 Maintained (Incoming: Enhanced Conflict-Based Search, Cooperative A*, Local-Repair A*, Replanning RRT*...)Dependencies 🖇️Install Python (3.7.5 is the tested version)Install Pip:sudo apt install python3-pipUpgrade Pip:python3 -m pip install --upgrade pipUsing the repository 💾Clone the repo:git clone https://github.com/apla-toolbox/pymapfCd into the repocd pymapfInstall requirements:python3 -m pip install -r requirements.txtUsing the pip package 📦Install the package:python3 -m pip install pymapfUsage 📑Scripts 💨Launch hub switch scripts using:python3 scripts/switch_positions_nmpc.pypython3 scripts/switch_positions_vel_obstacles.py(broken)More to come...Library 🗺️frompymapf.decentralizedimportMultiAgentNMPCfrompymapf.decentralized.positionimportPositionimportnumpyasnpsim=MultiAgentNMPC()sim.register_agent("r2d2",Position(0,3),Position(10,7))sim.register_agent("bb8",Position(0,7),Position(5,10))sim.register_agent("c3po",Position(10,7),Position(5,0))sim.register_obstacle(2,np.pi/4,Position(0,0))sim.run_simulation()sim.visualize("filename_test",10,10)frompymapf.decentralized.velocity_obstacleimportMultiAgentVelocityObstaclefrompymapf.decentralized.positionimportPositionsim=MultiAgentVelocityObstacle(simulation_time=8.0)sim.register_agent("r2d2",Position(0,3),Position(10,7))sim.register_agent("bb8",Position(0,7),Position(5,10))sim.register_agent("c3po",Position(10,7),Position(5,0))sim.run_simulation()sim.visualize("filename_test_2",10,10)Cite 📰If you use the project in your work, please consider citing it with:@misc{pymapf,
  author = {Erwin Lejeune},
  title = {PyMAPF: a python library for multi-agent pathfinding},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/APLA-Toolbox/pymapf}}
}List of publications & preprints usingpymapf(please open a pull request to add missing entries):Survey of MAPF solutions(January 2021)Contribute 🆘Open an issue to state clearly the contribution you want to make. Upon aproval send in a PR with the Issue referenced. (Implement Issue #No / Fix Issue #No).Maintainers Ⓜ️Erwin LejeuneSampreet Sarkar
Package: pymapme
PyMapMePyMapMeis a tiny library for mappingpydanticmodels, it might be useful when you have some model and want to represent it using different structure.Supported functionality:nested mappingusing helper functions with the access to InModel (see_get_full_name)using context data, when you need to extend your model with some extra dataGetting startedfromtypingimportAnyfrompydanticimportBaseModel,Fieldfrompymapme.models.mappingimportMappingModelclassPerson(BaseModel):name:strsurname:strclassProfile(BaseModel):nickname:strperson:PersonclassUser(MappingModel):nickname:str=Field(source='nickname')first_name:str=Field(source='person.name')surname:str=Field(source='person.surname')full_name:str=Field(source_func='_get_full_name')age:int=Field(source_func='_get_age_from_context')@staticmethoddef_get_full_name(model:Profile,default:Any):returnmodel.person.name+' '+model.person.surname@staticmethoddef_get_age_from_context(model:Profile,default:Any,age:int):returnageextra={'age':35}profile=Profile(nickname='baobab',person=Person(name='John',surname='Smith'))user=User.build_from_model(profile,context=extra)print(user.dict())# {'nickname': 'baobab', 'first_name': 'John', 'surname': 'Smith', 'full_name': 'John Smith', 'age': 35}DevelopmentRun tests:make run-unit-testsRun static analysis:make run-static-analysisBuild package:make build-packageInstallationIt is recommended to use Poetry:poetry add pymapme
Package: py-mapper
py-mapperPython Mapper LibraryHow to use this library1. Mapping Flat DictionaryfrompymapperimportMappermapper=Mapper({'dest_1':'$source_1','dest_2':'$source_2'})result=mapper.map({'source_1':1,'source_2':'Lorem ipsum','source_3':3.4})# result = {'dest_1': 1, 'dest_2': 'Lorem ipsum'}2. Mapping Nested DictionaryfrompymapperimportMappermapper=Mapper({'dest_1':{'dest_2':'$source_1.source_2'},'dest_2':'$source_1.source_3.source_4'})result=mapper.map({'source_1':{'source_2':[1,2,3],'source_3':{'source_4':5}},})# result = {'dest_1': {'dest_2': [1, 2, 3]}, 'dest_2': 5}3. Mapping a List of DictionariesfrompymapperimportMappermapper=Mapper({'dest_1':{'dest_2':'$source_1.source_2'},'dest_2':'$source_1.source_3.source_4'})result=mapper.map([{'source_1':{'source_2':[1,2,3],'source_3':{'source_4':5}}},{'source_1':{'source_2':[4,5,6],'source_3':{'source_4':7}}}])# result = [{'dest_1': {'dest_2': [1, 2, 3]}, 'dest_2': 5}, {'dest_1': {'dest_2': [4, 5, 6]}, 'dest_2': 7}]
Package: pymapping
Mapping finite element data between meshesThis package is a handy Python (re-)wrapper ofMEDCoupling. It can be used to transfer finite element data defined on nodes (P1 fields) or on cells (P0 fields) between twomeshio-compatible meshes.Some notebook examples can be found inexamples.Documentation is availablehere.InstallationTo installpymapping, you are invited to usepipand its associated optionspip install -U pymappingThe MEDCoupling library is a strong dependency of this package. Currently by installing via the previouspipcommand it will automatically install themedcouplingPython package (a repackaging of the official library).TestingTo run thepymappingunit tests, check out this repository and typepytestLicensepymappingis published under theMIT license.