Saravanan666/pypi-org huggingface dataset:
Package: libtakiyasha
LibTakiyashaLibTakiyasha 是一个 Python 音频加密/解密工具库（当然也可用于加密非音频数据），支持多种加密文件格式。LibTakiyasha 不提供任何命令行或图形界面支持。使用前必读本项目是以学习和技术研究的初衷创建的，修改、再分发时请遵循License。本项目的设计灵感，以及部分解密方案，来源于同类项目：Unlock Music Project - CLI Editionparakeet-rs/libparakeet本项目不内置任何密钥，要正常打开/保存任何类型的加密文件，你需要提供正确的对应的密钥。你需要自行寻找解密所需密钥或加密参数，在调用时作为参数传入。如果你要解密别人提供的文件，你可以从提供者处索要密钥，或者寻求同类项目和他人的帮助。LibTakiyasha 对输出数据的可用性（是否可以识别、播放等）不做任何保证。新变化？请参阅变更记录。（如果你是在 PyPI 上浏览本 README，以上链接是无效的；变更记录可能会出现在页面的底部。）特性使用纯 Python 代码编写兼容 Python 3.8 及后续版本，兼容多种 Python 解释器实现可在此处查看具体兼容哪些实现易于阅读，方便 Python 爱好者学习（包括依赖库）无任何 C/C++ 扩展模块，跨平台性强支持四种加密文件：网易云音乐加密文件.ncmQQ 音乐加密文件 QMCv1.qmc[0-9]、.qmcflac、.qmcogg、.qmcra等QQ 音乐加密文件 QMCv2.mflac[0-9]、.mgg[0-9]等酷狗音乐加密文件 KGM/VPR.kgm、.vpr不支持创建新加密文件酷我音乐加密文件.kwm更多信息，请参见此处注意：LibTakiyasha 的所有操作都不是线程安全的。尽量不要尝试在多线程环境下使用 LibTakiyasha 的任何功能。性能表现参见此处。安装可用的最新版本：2.1.1，GitHub 发布页面，PyPI安装方式使用pip，通过 PyPI 安装最新版本：python -m pip install -U libtakiyasha如果你要下载其他版本：PyPI：https://pypi.org/project/libtakiyasha/#history，挑选自己所需的版本，下载安装包，手动安装。或者使用 pip 安装：python -m pip install -U libtakiyasha==<你所需的版本>前往发布页面挑选自己所需的版本，下载安装包，手动安装。依赖项LibTakiyasha 依赖以下包，均可从 PyPI 获取：pyaes- 用于加解密 NCM 文件内嵌的主密钥和元数据mutagen- 用于以mutagen可接受的形式导出 NCM 文件内嵌的元数据如何使用？在这里可以找到使用方法和示例。同时，在本项目的 Wiki 主页可以找到其他一些可能对你有用的东西。常见问题为什么 2.x 打开文件需要密钥，而 1.x 版本不需要？这是出于以下考虑：LibTakiyasha 是一个加解密库，当然需要为用户提供自定义密钥的权利为了保护本项目不受美国数字千年版权法Digital Millennium Copyright Act（DMCA）影响，避免仓库被误杀因此，本仓库所有 1.x 及更早版本的提交和发布版本都已删除。变更记录版本 2.1.1.post1修复了import libtakiyasha时因为缺少数据文件而抛出FileNotFoundError，从而导致libtakiyasha彻底无法使用的问题将libtakiyasha.ncm.CloudmusicIdentifier加入顶层__init__.py的导入内容在这里查看更详细的变更记录。版本 2.1.1修复了各个模块的文件探测函数probe_*()的类型提示，现在PyCharm、Pylance等 IDE 和 LSP 应该会正确识别和显示它们的参数类型和返回类型修改了libtakiyasha.__init__中导入的内容：不再导入常用模块ncm、qmc、kgmvpr、kwm、pkgmetadata，而是直接导入这些模块中的常用函数、方法和类，可以在只导入了libtakiyasha包后直接使用。可以在这里看到libtakiyasha.__init__中导入的内容。为各个模块都添加了另一个文件探测函数probeinfo_*()，此函数与probe_*()不同：probeinfo_*()仅在输入文件是受支持的文件类型时才会返回探测结果（*FileInfo对象），否则为None；probe_*()始终返回一个 2 元组：如果输入了文件路径，第一个元素是输入的路径使用pathlib.Path转换过的路径对象；如果输入了文件对象，第一个元素就是输入的文件对象如果输入文件是受支持的文件类型，第二个元素是探测结果（*FileInfo对象）；否则为Nonelibtakiyasha.qmc中作为文件探测信息容器的QMCv1FileInfo和QMCv2FileInfo已被删除，它们的作用被QMCFileInfo取代。这意味着不应该再使用内置函数isinstance()判断 QMC 文件版本，转而通过访问探测结果的version属性获得。在这里查看更详细的变更记录。版本 2.1.0减少了一些重复代码的使用，删除了大量不再使用的代码优化了判断是否为 QMCv1 文件的逻辑、QMCv2 文件的主密钥探测逻辑libtakiyasha.qmc.QMCv2的open()和save()现在可接受多个混淆密钥，通过关键字参数garble_keys在需要时传入。因此，上述方法中原来的关键字参数garble_key1和garble_key2已经被干掉了，请及时修改你的工具链。如果提供此参数，需要提供一个产生至少一个混淆密钥（类字节对象）的可迭代对象（例如列表），且混淆密钥的顺序必须正确。在这里查看更详细的变更记录。版本 2.0.1 至 2.1.0rc2在这里查看更详细的变更记录。
Package: libtaxii
A Python library for handlingTrusted Automated eXchange of Indicator Information (TAXII™)v1.x Messages and invoking TAXII Services.Source:https://github.com/TAXIIProject/libtaxii/Documentation:https://libtaxii.readthedocs.io/Information:https://taxiiproject.github.io/Download:https://pypi.python.org/pypi/libtaxii/OverviewA primary goal of libtaxii is to remain faithful to both the TAXII
specifications and to customary Python practices. libtaxii is designed to be
intuitive both to Python developers and XML developers.InstallationUse pip to install or upgrade libtaxii:$ pip install libtaxii [--upgrade]For more information, see theInstallation instructions.Getting StartedRead theGetting Started guide.LayoutThe libtaxii repository has the following layout:docs/- Used to build thedocumentation.libtaxii/- The main libtaxii source.libtaxii/tests/- libtaxii tests.xsd/- A copy of the TAXII XML Schemas, used by libtaxii for validation.VersioningReleases of libtaxii are givenmajor.minor.revisionversion numbers, wheremajorandminorcorrespond to the TAXII version being supported.  Therevision`number is used to indicate new versions of libtaxii.FeedbackPlease provide feedback and/or comments on open issues totaxii@mitre.org.
Package: libtcd
This is a set ofctypesbindings tolibtcd.so.
The raw bindings are inlibtcd._libtcd, and a higher level interface to these can be found inlibtcd.api.Libtcdis the library used to read and write files containing harmonic
constituents and secondary station offsets used by thextideprogram to predict tidal heights and currents.RequirementsYou must havelibtcd.so.0, the shared library forlibtcdinstalled
on your system.This code has been tested under CPython 2.6, 2.7, 3.2 and 3.4.DevelopmentDevelopment takes place athttps://github.com/dairiki/python-libtcdAuthorJeff Dairiki <dairiki@dairiki.org>HistoryNext ReleaseNo changes.
Package: libtecnoan
PyToolsMÃ³dulo para exemplificar construÃ§Ã£o de projetos Python no curso PyToolsNesse curso Ã© ensinado como contribuir com projetos de cÃ³digo abertoLink para o curso [Python Pro] (https://www.python.pro.br/)Suportada versÃ£o 3 de PythonPara instalar:python3 -m venv .venvsource .venv/bin/activatepip install -r requeriments-dev.txtPara conferir qualidade do cÃ³digo:flake8
Package: libtele
This is a simple library to get the TV guide fromhttp://tele.at/.DocumentationYou can use Python’s builtinhelpor use the Sphinx documentation online
athttp://kaini.github.com/libtele/. You may also want to look at the tests or
the source code itself.IssuesIf you find any issues or have got any wishes feel free to report them athttps://github.com/kaini/libtele.Dependencieslxml (python-lxml)twisted (python-twisted)pytz (python-tz)setuptools (python-setuptools): for installation onlyInstallationUbuntu:sudo add-apt-repository ppa:kaini/tele
sudo apt-get install python-libteleQuick and easy:easy_install libteleIf you have got the source tarball:python setup.py easy_installChangelog0.3
- Fixes a bug that may occour if the servers are very unstable0.2
- Minor bugfixes, seegit log0.1
- Initial
Package: libtemplate
Template for python packages generated with cookiecutter-pylibraryInstallationpip install libtemplateYou can also install the in-development version with:pip install https://github.com/jpardovega/libtemplate/archive/master.zipDocumentationhttps://libtemplate.readthedocs.io/DevelopmentTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.0 (2021-04-05)First release on PyPI.
Package: libtest51
Libtest51FeaturesTODORequirementsTODOInstallationYou can installLibtest51viapipfromPyPI:pip install libtest51UsagePlease see theReference Guidefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,Libtest51is free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated fromStatistics Norway'sSSB PyPI Template.
Package: libTesteHGF
libpythonproMÃ³dulo para exemplificar a construÃ§Ã£o de projetos em Python no curso PyToolsNesse curso Ã© ensinado como contribuir com projetos de cÃ³digo abertoLink para o cursoPython ProUtilizei a versÃ£o 3.9.6 do PythonPara instalar:abra o console no windowsnavegue atÃ© o diretÃ³rio com o conteÃºdo clonadodigite para criar o ambiente virtual: py -3 -m venv .venvdigite para ativar o ambiente virtual: .venv/scripts/activate.batPara Desenvolvedoresdigite para instalar as bibliotecas: pip install -r requirements-dev.txtPara UsuÃ¡riosdigite para instalar as bibliotecas: pip install -r requirements.txt
Package: libtestingtools
dev_tools_testsDeveloper tools testing repositoryRepositÃ³rio para testes e aprendisagem de ferramentas que auxiliam o desenvolvedor.TÃ³picos de estudo:1. Git
2. Virtualenv
3. Pip
4. Rrquests
5. Flake8
6. Travis CI
7. PublicaÃ§Ã£o de pacote PyPi
8. Pytest
9. CodecovInstalaÃ§Ã£o (Windows)python -m venv venvvenv\Scripts\activatepip install -r requirements-dev.txtConferir Qualidadeflake8ðŸš§  RepositÃ³rio ðŸš€ Em construÃ§Ã£o...  ðŸš§
Package: libtetrabz
libtetrabzLibtetrabz is a library which perform efficiently the Brillouin-zone
integration in the electronic structure calculation in a solid by
using the tetrahedron method.Quick start guideTo experience libtetrabz, please open the following python notebook with jupyter, Google colab, etc.https://github.com/mitsuaki1987/libtetrabz/blob/main/python/src/libtetrabz_tutorial.ipynbManualhttps://mitsuaki1987.github.io/libtetrabz/python/_build/html/index.htmlFor citing libtetrabzWe would appreciate if you cite the following article in your
research with libtetrabz."Improved tetrahedron method for the Brillouin-zone integration applicable to response functions",M. Kawamura, Y. Gohda, and S. Tsuneyuki, Phys. Rev. B 89, 094515 (2014).
arXiv:2203.15648https://journals.aps.org/prb/abstract/10.1103/PhysRevB.89.094515https://arxiv.org/abs/2203.15648Git repositoryhttps://github.com/mitsuaki1987/libtetrabz
Package: libtextworker
libtextworkerUsageAvailable packages from Pypi:libtextworker: Base library, contains all codes but no other deps installedlibtextworker[tkinter]: Tkinter supportlibtextworker[wx]: wxPython supportlibtextworker[autocolor]: (since 0.1.3) GUI auto color effect (depends on system theme)libtextworker[all]: Everything aboveDocuments are stored tov<version>-docstag, normally the document onhttps://lebao3105.github.io/libtextworkeris the latest stable version of libtextworker.
Package: libtf
ThreshingFloor CLI Quickstart Guide================================The ThreshingFloor CLI helps you separate the signal from the noise in your logfiles. If you are running a service that faces the internet, you likely see thousands of scans, bots, and brute force attempts every day. These scans clog up your log files, and make it hard to find legitimate events of interest.The ThreshingFloor CLI is a utility that leverages the ThreshingFloor API to reduce noisy entries from your log files. This tool is currently in ALPHA.
Package: libthither
Thither.Direct library for python============================Installation------------pip install libthither##  THE LIBRARY+ APIs are:+ Flow Metrics Statistics Client+ push_single+ push_list+ push_csv_data+ get_definitions+ get_stats+ (more to come)+ Utils:+ (still to come)Creating a Flow Metrics Statistics Client------------------------.. code-block:: pythonfrom libthither.api.fms import FlowMetricsStatisticsClientclient = FlowMetricsStatisticsClient('YourFlowID',pass_phrase='YourPassPhrase',keep_alive=True,# https=False,# cipher='AES',# json=False,# version='v201807',# requests_args={},)# after initiated, client can be called with:client.push_single(YourMetricId, DateAndTime, Value)client.push_list([[Metric ID, DateTime, Value],])client.push_csv_data("mid,dt,v\nYourMetricId,DateAndTime,Value")More information at:https://thither.direct/information/services/commercial/indexhttps://github.com/thither/Thither.Direct-client-libraries/tree/master/python
Package: libthumbor
libthumbor allows easy usage ofthumborin Python. Check the docs for django integration.This version is compliant with the new URL generation schema (thumbor 3.0.0 and up).Using itfromlibthumborimportCryptoURLcrypto=CryptoURL(key='my-security-key')encrypted_url=crypto.generate(width=300,height=200,smart=True,image_url='/path/to/my/image.jpg')DocsCheck the wiki for more information on using libthumbor.ContributionsBernardo HeynemannGeneric URL encryptionRafael CaricioDjango Generic View and URLFábio CostaDjango Generic View and URL
Package: libtimed
libtimedA python library to interact with the JSON API oftimed. It authenticates using a custom OIDC browser flow, locally on your machine.ConfigurationYour timed OIDC client must allow the following redirect URI for the example to work:http://localhost:5000/timedctl/authThis is work in progress.Usage / ExamplesThere are examples in./examples, run them withpoetry run ./examples/<EXAMPLE>.py. If you have suggestions or additions, please open an issue or a pull request.LicenseCode released under theGNU Affero General Public License v3.0.
Package: libtimetag
AboutLibtimetag is a library for opening time-correlated single-photon counting data stored in the "small simple time-tagged" (SSTT) file format, and also provides algorithms to process these data, such as fast cross-correlation functions. These functions are fast enough that anti-bunching and fluorescence-correlation spectroscopy (FCS) cross-correlation curves can be visualized live; no hardware correlator is required.The library was made for two use cases: (i) as a supporting library for other (C/C++/...) applications (e.g.,PHAST), and (ii) as Python library for data processing and analysis. It is used on a daily basis for data acquisition, processing, and analysis at the lab ofFreddy Rabouwat Utrecht University. The library was originally developed by Stijn Hinterding.Installing as Python moduleSimply install from pypi using pip:pip install libtimetagUsage as Python modulePlease see thepython notebook.Installing as C++ libraryLibtimetag was developed on Windows usingMSYS2(more specifically, the MSYS2 MinGW 64-bit toolchain). The following should work under Windows (MSYS2), and Linux-like OS's. Inside a shell, situated in the project root directory, execute:mkdir build
cd build
cmake ..
make
make installAcknowledgementsThis work was supported by The Netherlands Center for Multiscale Catalytic Energy Conversion (MCEC), an NWO Gravitation Programme funded by the Ministry of Education, Culture and Science of the government of The Netherlands.LicenseThis project is licensed under the MIT license. See the LICENSE file in the project root.
Package: libtlda
libTLDA: library of transfer learning and domain adaptation classifiers.This package contains the following classifiers:Importance-weighted classifier, with weight estimators:Kernel density estimationRatio of Gaussians(Shimodaira, 2000)Logistic discrimination(Bickel et al., 2009)Kernel Mean Matching(Huang et al., 2006)Nearest-neighbour-based weighting(Loog, 2015)Transfer Component Analysis(Pan et al, 2009)Subspace Alignment(Fernando et al., 2013)Structural Correspondence Learning(Blitzer et al., 2006)Robust Bias-Aware(Liu & Ziebart, 2014)Feature-Level Domain Adaptation(Kouw et al., 2016)Python-specific classifiers:Target Contrastive Pessimistic Risk(Kouw et al., 2017)Matlab-specific classifiers:Geodesic Flow Kernel(Gong et al., 2012)PythonInstallationInstallation can be done through pip:pipinstalllibtldaEnvironment management is generally a good idea. To create acondaenvironment, run the following commands:conda env create -f environment.yml
source activate libtldaUsageLibtlda follows a similar logic asscikit-learn. Each type of adaptive classifier is a submodule, from which the classifiers can be imported:fromlibtlda.iwimportImportanceWeightedClassifierfromlibtlda.tcaimportTransferComponentClassifierfromlibtlda.subaimportSubspaceAlignedClassifierfromlibtlda.sclimportStructuralCorrespondenceClassifierfromlibtlda.rbaimportRobustBiasAwareClassifierfromlibtlda.fldaimportFeatureLevelDomainAdaptiveClassifierfromlibtlda.tcprimportTargetContrastivePessimisticClassifierFrom there on, training is a matter of calling thefitmethod on your labeled source dataset(X,y)and unlabeled target datasetZ. For example:classifier=ImportanceWeightedClassifier().fit(X,y,Z)Predictions can be made by calling thepredictmethod:y_pred=classifier.predict(Z)Documentation will be improved soon. For now, have a look at theexample.pyscript. It shows a couple of options for training adaptive classifiers.MatlabInstallation:First clone the repository and change directory to matlab:gitclonehttps://github.com/wmkouw/libTLDAcdlibTLDA/matlab/In the matlab command window, call the installation script. It downloads all dependencies (minFunc,libsvm) and adds them - along withlibtlda- to your path:install.mUsageThere is an example script that can be edited to test the different classifiers:example.mContact:Questions, comments and bugs can be submitted in theissues tracker.
Package: lib-toggl
lib-toggllib-togglis a Python library for interacting with theTogglAPI.
It exists because I figured it'd be easier to start fresh instead of updateTogglPyto use the newv9API and the other changes needed for better Home Assistant integration.Notelib-togglis currently in a very early stage of development and is meant primarily for use in withha-toggl-track.
There are other, better Python based API clients for toggl track.
You should probably use one of those instead.Seriously, the number of#TODO:comments in this code is ridiculous.As this library is mostly meant for use with Home Assistant, it's written to be compatible with Python 3.11+ and is mostlyasyncbased with strong-ish typing.
It isabsolutelynot meant to be a complete implementation of the Toggl API; it does just enough forha-toggl-trackand that's about it.UsingDevpre-commitdoctocTODOUsingMore formal docsto be added.
Tests are also needed!For now, see thescriptsdirectory for a quick example of how to use the library.DevI use VSCode for development so there's a.vscodedirectory with some settings that I use.
Most of the extensions and configuration directives there should be easy to port to your preferred editor / IDE as needed.Pretty simple setup; it's allpoetrydriven...Create a virtual environment for the project.❯poetryenvuse--python3
Creatingvirtualenvtogglpyin/home/karl/projects/ha-dev/TogglPy/.venv
Usingvirtualenv:/home/karl/projects/ha-dev/TogglPy/.venvUseautoenvto automatically activate the virtual environment when you enter the project directory.❯cdTogglPy
Switchingvirtualenv:.venv[🐍Python3.11.6]❯whichpython
/home/karl/projects/ha-dev/TogglPy/.venv/bin/pythonDon't forget toinstallpre-commithooks.pre-commitAssuming you'veinstalled thepre-committool, just run the following to install the git hooks:❯pre-commitinstall
pre-commitinstalledat.git/hooks/pre-commitdoctocThe table of contents inreadme.mdis updated withdoctoc❯dockerrun--rm-v"$(pwd)":/apppeterdavehello/npm-doctocdoctoc/app/README.md
<...>TODOpre-commit hooksTestsCI/CD/GHA automation for testing and pypi releaseDevcontainers (for VSCode) so the dev environment is more portable / easier to set up
Package: libtorrentx
LibtorrentXA python wrapper for libtorrent, makes it easy to manage torrents. Built in support for state management and state recovery after restart.Easily pause and resume your torrent streams.Optimized settings for faster download speeds.Install via pippipinstalllibtorrentxSimple demo appfromlibtorrentximportLibTorrentSessionimporttimemagnet="magnet:?xt=urn:btih:4C9B41D664D7B6B23F0BF39AE185858CBADDA3FF"output_dir="./downloads"session=LibTorrentSession()handle=session.add_torrent(magnet,output_dir)whileTrue:props=handle.props()ifnotprops.ok:print("waiting for torrent to start...")time.sleep(1)continueprint(props.string)ifprops.is_finished:breaktime.sleep(1)or use CLIpython -m libtorrentx -m magnet:?xt=urn:btih:4C9B41D664D7B6B23F0BF39AE185858CBADDA3FFOutputSpider-Man.No.Way.Home.2022.1080p.BluRay.1600MB.DD5.1.x264-GalaxyRG[TGx],1.20MB/s,19%
Spider-Man.No.Way.Home.2022.1080p.BluRay.1600MB.DD5.1.x264-GalaxyRG[TGx],12.00MB/s,19%
Spider-Man.No.Way.Home.2022.1080p.BluRay.1600MB.DD5.1.x264-GalaxyRG[TGx],28.11MB/s,77%
Spider-Man.No.Way.Home.2022.1080p.BluRay.1600MB.DD5.1.x264-GalaxyRG[TGx],29.00MB/s,100%You can stop the execution and restart again, the download will resume from previous state.Install via Dockerdockerbuild.-tlibtorrentxdockerrun--rm-it-v$(pwd)/downloads:/app/downloadslibtorrentx-mmagnet:?xt=urn:btih:4C9B41D664D7B6B23F0BF39AE185858CBADDA3FF
Package: libtraci
Eclipse SUMO - Simulation of Urban MObilityWhat is SUMO"Simulation of Urban MObility" (SUMO)is an open source,
highly portable, microscopic traffic simulation package designed to handle
large road networks and different modes of transport.It is mainly developed by employees of theInstitute of Transportation Systems
at the German Aerospace Center.Where to get itYou can download SUMO via ourdownloads site.As the program is still under development and is extended continuously, we advice you to
use the latest sources from our GitHub repository. Using a command line client
the following command should work:git clone --recursive https://github.com/eclipse-sumo/sumoContactTo stay informed, we have a mailing list for SUMO, whichyou can subscribeto.
Messages to the list can be sent tosumo-user@eclipse.org.
SUMO announcements will be made through thesumo-announce@eclipse.orglist;you can subscribeto it as well.
For further contact information, have a look atthis page.Build and InstallationFor Windows we provide pre-compiled binaries and CMake files to generate Visual Studio projects.
If you want to develop under Windows, please also clone the dependent libraries usinggit clone --recursive https://github.com/DLR-TS/SUMOLibrariesIf you're using Linux, you should have a look whether your distribution already contains sumo.
There is also appa for ubuntu usersand anopen build service instance.
If you want to build sumo yourself, the steps for ubuntu are:sudo apt-get install cmake python g++ libxerces-c-dev libfox-1.6-dev libgdal-dev libproj-dev libgl2ps-dev swig
cd <SUMO_DIR> # please insert the correct directory name here
export SUMO_HOME="$PWD"
mkdir build/cmake-build && cd build/cmake-build
cmake ../..
make -j$(nproc)Fordetailed build instructions, have a look at our Documentation.Getting startedTo get started with SUMO, take a look at the docs/tutorial and examples directories,
which contain some example networks with routing data and configuration files.
There is also user documentation provided in the docs/ directory and on the
homepage.DocumentationThe main documentation is atsumo.dlr.de/docs. Note, that this tracks thedevelopment version.A mirror of the main documentation is atsumo.sourceforge.net/docs.An offline version of the documentation is part of every release and can be accessed viadocs/userdoc/index.html.Improving SUMOPlease use theGitHub bug tracking toolfor bugs and requests,
or file them to the listsumo-user@eclipse.org. Before
filing a bug, please consider to check with a current repository checkout
whether the problem has already been fixed.We welcome patches, pull requests and other contributions! For details seeour contribution guidelines.We useWeblate for translating SUMO. If you
want to add translation strings or a language, seeour contribution guidelines.LicenseSUMO is licensed under theEclipse Public License Version 2.
For the licenses of the different libraries and supplementary code information is in the
subdirectories and theDocumentation.