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@@ -0,0 +1 @@ +/commonroad_io-2023.1.tar.gz diff --git a/python-commonroad-io.spec b/python-commonroad-io.spec new file mode 100644 index 0000000..97715bf --- /dev/null +++ b/python-commonroad-io.spec @@ -0,0 +1,505 @@ +%global _empty_manifest_terminate_build 0 +Name: python-commonroad-io +Version: 2023.1 +Release: 1 +Summary: Python tool to read, write, and visualize CommonRoad scenarios and solutions for automated vehicles. +License: BSD +URL: https://commonroad.in.tum.de +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b6/8a/92e275aba2def65257c8bf485dc7e0477332773344417f5df13ebd104529/commonroad_io-2023.1.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-numpy +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-scipy +Requires: python3-shapely +Requires: python3-matplotlib +Requires: python3-lxml +Requires: python3-networkx +Requires: python3-Pillow +Requires: python3-iso3166 +Requires: python3-commonroad-vehicle-models +Requires: python3-rtree +Requires: python3-protobuf +Requires: python3-omegaconf +Requires: python3-tqdm + +%description +# CommonRoad +[](https://svgshare.com/i/Zhy.svg) +[](https://svgshare.com/i/ZjP.svg) +[](https://svgshare.com/i/ZhY.svg) +[](https://pypi.python.org/pypi/commonroad-io/) +[](https://pypi.python.org/pypi/commonroad-io/) +[](https://pypi.python.org/pypi/commonroad-io/) +[](https://pypi.python.org/pypi/commonroad-io/) +[](https://pypi.python.org/pypi/commonroad-io/) +[](http://commonroad-io.readthedocs.io/?badge=latest) + + +Numerical experiments for motion planning of road vehicles require numerous ingredients: vehicle dynamics, +a road network, static obstacles, dynamic obstacles and their movement over time, goal regions, a cost function, etc. +Providing a description of the numerical experiment precise enough to reproduce it might require several pages of +information. +Thus, only key aspects are typically described in scientific publications, making it impossible to reproduce +results - yet, reproducibility is an important asset of good science. + +Composable benchmarks for motion planning on roads (CommonRoad) are proposed so that numerical experiments are fully +defined by a unique ID; all required information to reconstruct the experiment can be found on [commonroad.in.tum.de](https://commonroad.in.tum.de/). +Each benchmark is composed of a [vehicle model](https://gitlab.lrz.de/tum-cps/commonroad-vehicle-models/blob/master/vehicleModels_commonRoad.pdf), +a [cost function](https://gitlab.lrz.de/tum-cps/commonroad-cost-functions/blob/master/costFunctions_commonRoad.pdf), +and a [scenario](https://commonroad.in.tum.de/scenarios/) (including goals and constraints). +The scenarios are partly recorded from real traffic and partly hand-crafted to create dangerous situations. +Solutions to the benchmarks can be uploaded and ranked on the CommonRoad Website. +Learn more about the scenario specification [here](https://gitlab.lrz.de/tum-cps/commonroad-scenarios/blob/master/documentation/XML_commonRoad_2020a.pdf). + +# commonroad-io + +The commonroad-io package provides methods to read, write, and visualize CommonRoad scenarios and planning problems. Furthermore, it can be used as a framework for implementing motion planning algorithms to solve CommonRoad Benchmarks and is the basis for other tools of the CommonRoad Framework. +With commonroad-io, those solutions can be written to xml-files for uploading them on [commonroad.in.tum.de](https://commonroad.in.tum.de/). + +commonroad-io 2023.1 is compatible with CommonRoad scenarios in version 2020a and supports reading 2018b scenarios. + +The software is written in Python and tested on Linux for the Python 3.7, 3.8, 3.9, 3.10, and 3.11. + + +## Documentation + +The full documentation of the API and introducing examples can be found under [commonroad.in.tum.de](https://commonroad-io.readthedocs.io/en/latest/). + +For getting started, we recommend our [tutorials](https://commonroad.in.tum.de/commonroad-io). + +## Additional Tools +Based on commonroad-io, we have developed a list of tools supporting the development of motion-planning algorithms: + +* [Drivability Checker](https://commonroad.in.tum.de/tools/drivability-checker) +* [CommonRoad-SUMO Interface](https://commonroad.in.tum.de/tools/sumo-interface) +* [Scenario Designer](https://commonroad.in.tum.de/tools/scenario-designer) +* [Vehicle Models](https://commonroad.in.tum.de/tools/model-cost-functions) +* [Dateset Converters](https://gitlab.lrz.de/tum-cps/dataset-converters) +* [Interactive Scenarios](https://gitlab.lrz.de/tum-cps/commonroad-interactive-scenarios) +* [Apollo Interface](https://gitlab.lrz.de/tum-cps/commonroad-apollo-interface) + +## Requirements + +The required dependencies for running commonroad-io are: + +* numpy>=1.13 +* scipy>=1.5.2 +* shapely>=2.0.1 +* matplotlib>=2.2.2 +* lxml>=4.2.2 +* networkx>=2.2 +* Pillow>=7.0.0 +* commonroad-vehicle-models>=2.0.0 +* rtree>=0.8.3 +* protobuf==3.20.1 + +## Installation + +commonroad-io can be installed with:: + + pip install commonroad-io + +Alternatively, clone from our gitlab repository:: + + git clone https://gitlab.lrz.de/tum-cps/commonroad_io.git + +and add the folder commonroad-io to your Python environment. + +## Changelog +Compared to version 2022.3, the following features have been added or changed: + +### Added +- Support for shapely>=2.0.0 + +### Fixed + +- Writing scenarios without location to protobuf +- Dashed lanelet boundaries with fixed dash position +- Default plot limits for focused obstacle +- Use dt from scenario as default for video creation +- Apply axis visible-option also for video creation +- Protobuf FileReader marking road network related IDs as used +- State attribute comparison + +### Changed + +- Name of SIDEWALK and BUSLANE traffic signs to PEDESTRIAN_SIDEWALK and BUS_LANE +- Packaging and dependency management using poetry + + +## Authors +Contribution (in alphabetic order by last name): Yannick Ballnath, Behtarin Ferdousi, Luis Gressenbuch, Moritz Klischat, +Markus Koschi, Sebastian Maierhofer, Stefanie Manzinger, Christina Miller, Christian Pek, Anna-Katharina Rettinger, +Simon Sagmeister, Moritz Untersperger, Murat Üste, Xiao Wang + +## Credits +We gratefully acknowledge partial financial support by + +* DFG (German Research Foundation) Priority Program SPP 1835 Cooperative Interacting Automobiles +* BMW Group within the Car@TUM project +* German Federal Ministry of Economics and Technology through the research initiative Ko-HAF + +## Citation +**If you use our code for research, please consider to cite our paper:** +``` +@inproceedings{Althoff2017a, + author = {Althoff, Matthias and Koschi, Markus and Manzinger, Stefanie}, + title = {CommonRoad: Composable benchmarks for motion planning on roads}, + booktitle = {Proc. of the IEEE Intelligent Vehicles Symposium}, + year = {2017}, + abstract = {Numerical experiments for motion planning of road vehicles require numerous components: vehicle + dynamics, a road network, static obstacles, dynamic obstacles and their movement over time, goal + regions, a cost function, etc. Providing a description of the numerical experiment precise enough to + reproduce it might require several pages of information. Thus, only key aspects are typically described + in scientific publications, making it impossible to reproduce results—yet, re- producibility is an + important asset of good science. Composable benchmarks for motion planning on roads (CommonRoad) are + proposed so that numerical experiments are fully defined by a unique ID; all information required to + reconstruct the experiment can be found on the CommonRoad website. Each benchmark is composed by a + vehicle model, a cost function, and a scenario (including goals and constraints). The scenarios are + partly recorded from real traffic and partly hand-crafted to create dangerous situations. We hope that + CommonRoad saves researchers time since one does not have to search for realistic parameters of vehicle + dynamics or realistic traffic situations, yet provides the freedom to compose a benchmark that fits + one’s needs.}, +} +``` + + +%package -n python3-commonroad-io +Summary: Python tool to read, write, and visualize CommonRoad scenarios and solutions for automated vehicles. +Provides: python-commonroad-io +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-commonroad-io +# CommonRoad +[](https://svgshare.com/i/Zhy.svg) +[](https://svgshare.com/i/ZjP.svg) +[](https://svgshare.com/i/ZhY.svg) +[](https://pypi.python.org/pypi/commonroad-io/) +[](https://pypi.python.org/pypi/commonroad-io/) +[](https://pypi.python.org/pypi/commonroad-io/) +[](https://pypi.python.org/pypi/commonroad-io/) +[](https://pypi.python.org/pypi/commonroad-io/) +[](http://commonroad-io.readthedocs.io/?badge=latest) + + +Numerical experiments for motion planning of road vehicles require numerous ingredients: vehicle dynamics, +a road network, static obstacles, dynamic obstacles and their movement over time, goal regions, a cost function, etc. +Providing a description of the numerical experiment precise enough to reproduce it might require several pages of +information. +Thus, only key aspects are typically described in scientific publications, making it impossible to reproduce +results - yet, reproducibility is an important asset of good science. + +Composable benchmarks for motion planning on roads (CommonRoad) are proposed so that numerical experiments are fully +defined by a unique ID; all required information to reconstruct the experiment can be found on [commonroad.in.tum.de](https://commonroad.in.tum.de/). +Each benchmark is composed of a [vehicle model](https://gitlab.lrz.de/tum-cps/commonroad-vehicle-models/blob/master/vehicleModels_commonRoad.pdf), +a [cost function](https://gitlab.lrz.de/tum-cps/commonroad-cost-functions/blob/master/costFunctions_commonRoad.pdf), +and a [scenario](https://commonroad.in.tum.de/scenarios/) (including goals and constraints). +The scenarios are partly recorded from real traffic and partly hand-crafted to create dangerous situations. +Solutions to the benchmarks can be uploaded and ranked on the CommonRoad Website. +Learn more about the scenario specification [here](https://gitlab.lrz.de/tum-cps/commonroad-scenarios/blob/master/documentation/XML_commonRoad_2020a.pdf). + +# commonroad-io + +The commonroad-io package provides methods to read, write, and visualize CommonRoad scenarios and planning problems. Furthermore, it can be used as a framework for implementing motion planning algorithms to solve CommonRoad Benchmarks and is the basis for other tools of the CommonRoad Framework. +With commonroad-io, those solutions can be written to xml-files for uploading them on [commonroad.in.tum.de](https://commonroad.in.tum.de/). + +commonroad-io 2023.1 is compatible with CommonRoad scenarios in version 2020a and supports reading 2018b scenarios. + +The software is written in Python and tested on Linux for the Python 3.7, 3.8, 3.9, 3.10, and 3.11. + + +## Documentation + +The full documentation of the API and introducing examples can be found under [commonroad.in.tum.de](https://commonroad-io.readthedocs.io/en/latest/). + +For getting started, we recommend our [tutorials](https://commonroad.in.tum.de/commonroad-io). + +## Additional Tools +Based on commonroad-io, we have developed a list of tools supporting the development of motion-planning algorithms: + +* [Drivability Checker](https://commonroad.in.tum.de/tools/drivability-checker) +* [CommonRoad-SUMO Interface](https://commonroad.in.tum.de/tools/sumo-interface) +* [Scenario Designer](https://commonroad.in.tum.de/tools/scenario-designer) +* [Vehicle Models](https://commonroad.in.tum.de/tools/model-cost-functions) +* [Dateset Converters](https://gitlab.lrz.de/tum-cps/dataset-converters) +* [Interactive Scenarios](https://gitlab.lrz.de/tum-cps/commonroad-interactive-scenarios) +* [Apollo Interface](https://gitlab.lrz.de/tum-cps/commonroad-apollo-interface) + +## Requirements + +The required dependencies for running commonroad-io are: + +* numpy>=1.13 +* scipy>=1.5.2 +* shapely>=2.0.1 +* matplotlib>=2.2.2 +* lxml>=4.2.2 +* networkx>=2.2 +* Pillow>=7.0.0 +* commonroad-vehicle-models>=2.0.0 +* rtree>=0.8.3 +* protobuf==3.20.1 + +## Installation + +commonroad-io can be installed with:: + + pip install commonroad-io + +Alternatively, clone from our gitlab repository:: + + git clone https://gitlab.lrz.de/tum-cps/commonroad_io.git + +and add the folder commonroad-io to your Python environment. + +## Changelog +Compared to version 2022.3, the following features have been added or changed: + +### Added +- Support for shapely>=2.0.0 + +### Fixed + +- Writing scenarios without location to protobuf +- Dashed lanelet boundaries with fixed dash position +- Default plot limits for focused obstacle +- Use dt from scenario as default for video creation +- Apply axis visible-option also for video creation +- Protobuf FileReader marking road network related IDs as used +- State attribute comparison + +### Changed + +- Name of SIDEWALK and BUSLANE traffic signs to PEDESTRIAN_SIDEWALK and BUS_LANE +- Packaging and dependency management using poetry + + +## Authors +Contribution (in alphabetic order by last name): Yannick Ballnath, Behtarin Ferdousi, Luis Gressenbuch, Moritz Klischat, +Markus Koschi, Sebastian Maierhofer, Stefanie Manzinger, Christina Miller, Christian Pek, Anna-Katharina Rettinger, +Simon Sagmeister, Moritz Untersperger, Murat Üste, Xiao Wang + +## Credits +We gratefully acknowledge partial financial support by + +* DFG (German Research Foundation) Priority Program SPP 1835 Cooperative Interacting Automobiles +* BMW Group within the Car@TUM project +* German Federal Ministry of Economics and Technology through the research initiative Ko-HAF + +## Citation +**If you use our code for research, please consider to cite our paper:** +``` +@inproceedings{Althoff2017a, + author = {Althoff, Matthias and Koschi, Markus and Manzinger, Stefanie}, + title = {CommonRoad: Composable benchmarks for motion planning on roads}, + booktitle = {Proc. of the IEEE Intelligent Vehicles Symposium}, + year = {2017}, + abstract = {Numerical experiments for motion planning of road vehicles require numerous components: vehicle + dynamics, a road network, static obstacles, dynamic obstacles and their movement over time, goal + regions, a cost function, etc. Providing a description of the numerical experiment precise enough to + reproduce it might require several pages of information. Thus, only key aspects are typically described + in scientific publications, making it impossible to reproduce results—yet, re- producibility is an + important asset of good science. Composable benchmarks for motion planning on roads (CommonRoad) are + proposed so that numerical experiments are fully defined by a unique ID; all information required to + reconstruct the experiment can be found on the CommonRoad website. Each benchmark is composed by a + vehicle model, a cost function, and a scenario (including goals and constraints). The scenarios are + partly recorded from real traffic and partly hand-crafted to create dangerous situations. We hope that + CommonRoad saves researchers time since one does not have to search for realistic parameters of vehicle + dynamics or realistic traffic situations, yet provides the freedom to compose a benchmark that fits + one’s needs.}, +} +``` + + +%package help +Summary: Development documents and examples for commonroad-io +Provides: python3-commonroad-io-doc +%description help +# CommonRoad +[](https://svgshare.com/i/Zhy.svg) +[](https://svgshare.com/i/ZjP.svg) +[](https://svgshare.com/i/ZhY.svg) +[](https://pypi.python.org/pypi/commonroad-io/) +[](https://pypi.python.org/pypi/commonroad-io/) +[](https://pypi.python.org/pypi/commonroad-io/) +[](https://pypi.python.org/pypi/commonroad-io/) +[](https://pypi.python.org/pypi/commonroad-io/) +[](http://commonroad-io.readthedocs.io/?badge=latest) + + +Numerical experiments for motion planning of road vehicles require numerous ingredients: vehicle dynamics, +a road network, static obstacles, dynamic obstacles and their movement over time, goal regions, a cost function, etc. +Providing a description of the numerical experiment precise enough to reproduce it might require several pages of +information. +Thus, only key aspects are typically described in scientific publications, making it impossible to reproduce +results - yet, reproducibility is an important asset of good science. + +Composable benchmarks for motion planning on roads (CommonRoad) are proposed so that numerical experiments are fully +defined by a unique ID; all required information to reconstruct the experiment can be found on [commonroad.in.tum.de](https://commonroad.in.tum.de/). +Each benchmark is composed of a [vehicle model](https://gitlab.lrz.de/tum-cps/commonroad-vehicle-models/blob/master/vehicleModels_commonRoad.pdf), +a [cost function](https://gitlab.lrz.de/tum-cps/commonroad-cost-functions/blob/master/costFunctions_commonRoad.pdf), +and a [scenario](https://commonroad.in.tum.de/scenarios/) (including goals and constraints). +The scenarios are partly recorded from real traffic and partly hand-crafted to create dangerous situations. +Solutions to the benchmarks can be uploaded and ranked on the CommonRoad Website. +Learn more about the scenario specification [here](https://gitlab.lrz.de/tum-cps/commonroad-scenarios/blob/master/documentation/XML_commonRoad_2020a.pdf). + +# commonroad-io + +The commonroad-io package provides methods to read, write, and visualize CommonRoad scenarios and planning problems. Furthermore, it can be used as a framework for implementing motion planning algorithms to solve CommonRoad Benchmarks and is the basis for other tools of the CommonRoad Framework. +With commonroad-io, those solutions can be written to xml-files for uploading them on [commonroad.in.tum.de](https://commonroad.in.tum.de/). + +commonroad-io 2023.1 is compatible with CommonRoad scenarios in version 2020a and supports reading 2018b scenarios. + +The software is written in Python and tested on Linux for the Python 3.7, 3.8, 3.9, 3.10, and 3.11. + + +## Documentation + +The full documentation of the API and introducing examples can be found under [commonroad.in.tum.de](https://commonroad-io.readthedocs.io/en/latest/). + +For getting started, we recommend our [tutorials](https://commonroad.in.tum.de/commonroad-io). + +## Additional Tools +Based on commonroad-io, we have developed a list of tools supporting the development of motion-planning algorithms: + +* [Drivability Checker](https://commonroad.in.tum.de/tools/drivability-checker) +* [CommonRoad-SUMO Interface](https://commonroad.in.tum.de/tools/sumo-interface) +* [Scenario Designer](https://commonroad.in.tum.de/tools/scenario-designer) +* [Vehicle Models](https://commonroad.in.tum.de/tools/model-cost-functions) +* [Dateset Converters](https://gitlab.lrz.de/tum-cps/dataset-converters) +* [Interactive Scenarios](https://gitlab.lrz.de/tum-cps/commonroad-interactive-scenarios) +* [Apollo Interface](https://gitlab.lrz.de/tum-cps/commonroad-apollo-interface) + +## Requirements + +The required dependencies for running commonroad-io are: + +* numpy>=1.13 +* scipy>=1.5.2 +* shapely>=2.0.1 +* matplotlib>=2.2.2 +* lxml>=4.2.2 +* networkx>=2.2 +* Pillow>=7.0.0 +* commonroad-vehicle-models>=2.0.0 +* rtree>=0.8.3 +* protobuf==3.20.1 + +## Installation + +commonroad-io can be installed with:: + + pip install commonroad-io + +Alternatively, clone from our gitlab repository:: + + git clone https://gitlab.lrz.de/tum-cps/commonroad_io.git + +and add the folder commonroad-io to your Python environment. + +## Changelog +Compared to version 2022.3, the following features have been added or changed: + +### Added +- Support for shapely>=2.0.0 + +### Fixed + +- Writing scenarios without location to protobuf +- Dashed lanelet boundaries with fixed dash position +- Default plot limits for focused obstacle +- Use dt from scenario as default for video creation +- Apply axis visible-option also for video creation +- Protobuf FileReader marking road network related IDs as used +- State attribute comparison + +### Changed + +- Name of SIDEWALK and BUSLANE traffic signs to PEDESTRIAN_SIDEWALK and BUS_LANE +- Packaging and dependency management using poetry + + +## Authors +Contribution (in alphabetic order by last name): Yannick Ballnath, Behtarin Ferdousi, Luis Gressenbuch, Moritz Klischat, +Markus Koschi, Sebastian Maierhofer, Stefanie Manzinger, Christina Miller, Christian Pek, Anna-Katharina Rettinger, +Simon Sagmeister, Moritz Untersperger, Murat Üste, Xiao Wang + +## Credits +We gratefully acknowledge partial financial support by + +* DFG (German Research Foundation) Priority Program SPP 1835 Cooperative Interacting Automobiles +* BMW Group within the Car@TUM project +* German Federal Ministry of Economics and Technology through the research initiative Ko-HAF + +## Citation +**If you use our code for research, please consider to cite our paper:** +``` +@inproceedings{Althoff2017a, + author = {Althoff, Matthias and Koschi, Markus and Manzinger, Stefanie}, + title = {CommonRoad: Composable benchmarks for motion planning on roads}, + booktitle = {Proc. of the IEEE Intelligent Vehicles Symposium}, + year = {2017}, + abstract = {Numerical experiments for motion planning of road vehicles require numerous components: vehicle + dynamics, a road network, static obstacles, dynamic obstacles and their movement over time, goal + regions, a cost function, etc. Providing a description of the numerical experiment precise enough to + reproduce it might require several pages of information. Thus, only key aspects are typically described + in scientific publications, making it impossible to reproduce results—yet, re- producibility is an + important asset of good science. Composable benchmarks for motion planning on roads (CommonRoad) are + proposed so that numerical experiments are fully defined by a unique ID; all information required to + reconstruct the experiment can be found on the CommonRoad website. Each benchmark is composed by a + vehicle model, a cost function, and a scenario (including goals and constraints). The scenarios are + partly recorded from real traffic and partly hand-crafted to create dangerous situations. We hope that + CommonRoad saves researchers time since one does not have to search for realistic parameters of vehicle + dynamics or realistic traffic situations, yet provides the freedom to compose a benchmark that fits + one’s needs.}, +} +``` + + +%prep +%autosetup -n commonroad-io-2023.1 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-commonroad-io -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 2023.1-1 +- Package Spec generated @@ -0,0 +1 @@ +a378a27349d24e6067e7107e1b41311b commonroad_io-2023.1.tar.gz |