diff options
| author | CoprDistGit <infra@openeuler.org> | 2023-05-05 15:31:08 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-05-05 15:31:08 +0000 |
| commit | a07aae6c7cb52d7e42903b6047a5b46e852fade6 (patch) | |
| tree | fa0abadd44b0b102057e4d44a50610ba35f29231 | |
| parent | 1646b7e26e785316013422b76c1fdbc9160f1488 (diff) | |
automatic import of python-pyrosmopeneuler20.03
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-pyrosm.spec | 531 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 533 insertions, 0 deletions
@@ -0,0 +1 @@ +/pyrosm-0.6.1.tar.gz diff --git a/python-pyrosm.spec b/python-pyrosm.spec new file mode 100644 index 0000000..6b20629 --- /dev/null +++ b/python-pyrosm.spec @@ -0,0 +1,531 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pyrosm +Version: 0.6.1 +Release: 1 +Summary: A Python tool to parse OSM data from Protobuf format into GeoDataFrame. +License: MIT +URL: https://pyrosm.readthedocs.io/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/bb/63/c23648e1d9d4eebd69fc2d1aac6819807598996a0541f4c668c7c31fdaaa/pyrosm-0.6.1.tar.gz +BuildArch: noarch + + +%description +# Pyrosm +[](https://anaconda.org/conda-forge/pyrosm/) +[](https://badge.fury.io/py/pyrosm) +[](https://travis-ci.org/HTenkanen/pyrosm) +[](https://pyrosm.readthedocs.io/en/latest/?badge=latest) +[](https://codecov.io/gh/HTenkanen/pyrosm) +[](https://pypistats.org/packages/pyrosm) +[](https://doi.org/10.5281/zenodo.4279527) +[](https://anaconda.org/conda-forge/pyrosm/) + + +**Pyrosm** is a Python library for reading OpenStreetMap data from Protocolbuffer Binary Format -files (`*.osm.pbf`) into Geopandas GeoDataFrames. +Pyrosm makes it easy to extract various datasets from OpenStreetMap pbf-dumps including e.g. road networks, buildings, +Points of Interest (POI), landuse and natural elements. Also fully customized queries are supported which makes it possible +to parse the data from OSM with more specific filters. + + +**Pyrosm** is easy to use and it provides a somewhat similar user interface as [OSMnx](https://github.com/gboeing/osmnx). +The main difference between pyrosm and OSMnx is that OSMnx reads the data over internet using OverPass API, whereas pyrosm reads the data from local OSM data dumps +that can be downloaded e.g. from [GeoFabrik's website](http://download.geofabrik.de/). This makes it possible to read data faster thus +allowing e.g. parsing street networks for the whole country fairly efficiently (however, see [caveats](#caveats)). + + +The library has been developed by keeping performance in mind, hence, it is mainly written in Cython (*Python with C-like performance*) +which makes it probably faster than any other Python alternatives for parsing OpenStreetMap data. +Pyrosm is built on top of another Cython library called [Pyrobuf](https://github.com/appnexus/pyrobuf) which is a faster Cython alternative +to Google's Protobuf library: It provides 2-4x boost in performance for deserializing the protocol buffer messages compared to +Google's version with C++ backend. Google's Protocol Buffers is a commonly used and efficient method to serialize and compress structured data +which is also used by OpenStreetMap contributors to distribute the OSM data in PBF format (Protocolbuffer Binary Format). + +**Documentation** is available at [https://pyrosm.readthedocs.io](https://pyrosm.readthedocs.io/en/latest/). + +## Current features + + - download PBF data easily from hundreds of locations across the world + - read street networks (separately for driving, cycling, walking and all-combined) + - read buildings from PBF + - read Points of Interest (POI) from PBF + - read landuse from PBF + - read "natural" from PBF + - read boundaries from PBF (+ allow searching by name) + - read any other data from PBF by using a custom user-defined filter + - filter data based on bounding box + - export networks as a directed graph to `igraph`, `networkx` and `pandana` + +## Roadmap + + - add possibility to optimize memory usage (see #87) + - add possibility to simplify graph (see #89) + - add possibility to crop PBF and save a subset into new PBF. + - add Cython specific tests + +## Install + +Pyrosm is distributed via PyPi and conda-forge. + +The recommended way to install pyrosm is using `conda` package manager: + +`$ conda install -c conda-forge pyrosm` + +You can also install the package with pip: + +`$ pip install pyrosm` + +### Troubleshooting + +Notice that `pyrosm` requires geopandas to work. +On Linux and Mac installing geopandas with `pip` should work without a problem, which is handled automatically when installing pyrosm. + +However, on Windows installing geopandas with pip is likely to cause issues, hence, it is recommended to install Geopandas before installing +`pyrosm`. See instructions from [Geopandas website](https://geopandas.org/install.html#installation). + +## When should I use Pyrosm? + +Pyrosm can of course be used whenever you need to parse data from OSM into geopandas GeoDataFrames. +However, `pyrosm` is better suited for situations where you want to fetch data for whole city or larger regions (even whole country). + +If you are interested to fetch OSM data for smaller areas such as neighborhoods, or search data around a specific location/address, +we recommend using [OSMnx](https://github.com/gboeing/osmnx) which is more flexible in terms of specifying the area of interest. +That being said, it is also possible to extract neighborhood level information with pyrosm and filter data based on a bounding box +(see [docs](https://pyrosm.readthedocs.io/en/latest/basics.html#filtering-data-based-on-bounding-box)). + +## How to use? + +Using `pyrosm` is straightforward. See [docs](https://pyrosm.readthedocs.io/en/latest/basics.html) +for instructions how to use the library. + +## Performance + +See [docs](https://pyrosm.readthedocs.io/en/latest/benchmarking.html) for more comprehensive benchmarking tests. Reading all drivable roads in Helsinki Region (approx. 85,000 roads) +takes approximately **12 seconds** (laptop with 16GB memory, SSD drive, and Intel Core i5-8250U CPU 1.6 GHZ). And the result looks something like: + + + +Parsing all buildings from the same area (approx. 180,000) takes approximately **17 seconds**. And the result looks something like: + + + +Parsing all Points of Interest (POIs) with defaults elements (amenities, shops and tourism) +takes approximately **14 seconds** (approx. 32,000 features). +And the result looks something like: + + + +## Get in touch + contributions + +If you find a bug from the tool, have question, or would like to suggest a new feature to it, you can [make a new issue here](https://github.com/HTenkanen/pyrosm/issues). + +We warmly welcome contributions to `pyrosm` to make it better. If you are interested in contributing to the library, +please check the [contribution guidelines](https://pyrosm.readthedocs.io/en/latest/contributions.html). + +## Development + +You can install a local development version of the tool by 1) installing necessary packages with conda and 2) building pyrosm from source: + + 1. install conda-environment for Python 3.7 or 3.8 by: + + - Python 3.7 (you might want to modify the env-name which is `test` by default): `$ conda env create -f ci/37-conda.yaml` + - Python 3.8: `$ conda env create -f ci/38-conda.yaml` + + 2. build pyrosm development version from master (activate the environment first): + + - `pip install -e .` + +You can run tests with `pytest` by executing: + + `$ pytest -v` + + +## License and copyright + +Pyrosm is licensed under MIT (see [license](LICENSE)). + +The OSM data is downloaded from two sources: + +[](https://download.geofabrik.de/) +[](https://download.bbbike.org/osm/) + +Data © [Geofabrik GmbH](http://www.geofabrik.de/), [BBBike](https://download.bbbike.org/) and [OpenStreetMap Contributors](http://www.openstreetmap.org/) + +All data from the [OpenStreetMap](https://www.openstreetmap.org) is licensed under the [OpenStreetMap License](https://www.openstreetmap.org/copyright). + +## Caveats + +### Filtering large files by bounding box + +Although `pyrosm` provides possibility to filter even larger data files based on bounding box, +this process can slow down the reading process significantly (1.5-3x longer) due to necessary lookups when parsing the data. +This might not be an issue with smaller files (up to ~100MB) but with larger data dumps this can take longer than necessary. + +Hence, a recommended approach with large data files is to **first** filter the protobuf file based on bounding box into a +smaller subset by using a dedicated open source Java tool called [Osmosis](https://wiki.openstreetmap.org/wiki/Osmosis) +which is available for all operating systems. Detailed installation instructions are [here](https://wiki.openstreetmap.org/wiki/Osmosis/Installation), +and instructions how to filter data based on bounding box are [here](https://wiki.openstreetmap.org/wiki/Osmosis/Examples#Extract_administrative_Boundaries_from_a_PBF_Extract). + +%package -n python3-pyrosm +Summary: A Python tool to parse OSM data from Protobuf format into GeoDataFrame. +Provides: python-pyrosm +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-pyrosm +# Pyrosm +[](https://anaconda.org/conda-forge/pyrosm/) +[](https://badge.fury.io/py/pyrosm) +[](https://travis-ci.org/HTenkanen/pyrosm) +[](https://pyrosm.readthedocs.io/en/latest/?badge=latest) +[](https://codecov.io/gh/HTenkanen/pyrosm) +[](https://pypistats.org/packages/pyrosm) +[](https://doi.org/10.5281/zenodo.4279527) +[](https://anaconda.org/conda-forge/pyrosm/) + + +**Pyrosm** is a Python library for reading OpenStreetMap data from Protocolbuffer Binary Format -files (`*.osm.pbf`) into Geopandas GeoDataFrames. +Pyrosm makes it easy to extract various datasets from OpenStreetMap pbf-dumps including e.g. road networks, buildings, +Points of Interest (POI), landuse and natural elements. Also fully customized queries are supported which makes it possible +to parse the data from OSM with more specific filters. + + +**Pyrosm** is easy to use and it provides a somewhat similar user interface as [OSMnx](https://github.com/gboeing/osmnx). +The main difference between pyrosm and OSMnx is that OSMnx reads the data over internet using OverPass API, whereas pyrosm reads the data from local OSM data dumps +that can be downloaded e.g. from [GeoFabrik's website](http://download.geofabrik.de/). This makes it possible to read data faster thus +allowing e.g. parsing street networks for the whole country fairly efficiently (however, see [caveats](#caveats)). + + +The library has been developed by keeping performance in mind, hence, it is mainly written in Cython (*Python with C-like performance*) +which makes it probably faster than any other Python alternatives for parsing OpenStreetMap data. +Pyrosm is built on top of another Cython library called [Pyrobuf](https://github.com/appnexus/pyrobuf) which is a faster Cython alternative +to Google's Protobuf library: It provides 2-4x boost in performance for deserializing the protocol buffer messages compared to +Google's version with C++ backend. Google's Protocol Buffers is a commonly used and efficient method to serialize and compress structured data +which is also used by OpenStreetMap contributors to distribute the OSM data in PBF format (Protocolbuffer Binary Format). + +**Documentation** is available at [https://pyrosm.readthedocs.io](https://pyrosm.readthedocs.io/en/latest/). + +## Current features + + - download PBF data easily from hundreds of locations across the world + - read street networks (separately for driving, cycling, walking and all-combined) + - read buildings from PBF + - read Points of Interest (POI) from PBF + - read landuse from PBF + - read "natural" from PBF + - read boundaries from PBF (+ allow searching by name) + - read any other data from PBF by using a custom user-defined filter + - filter data based on bounding box + - export networks as a directed graph to `igraph`, `networkx` and `pandana` + +## Roadmap + + - add possibility to optimize memory usage (see #87) + - add possibility to simplify graph (see #89) + - add possibility to crop PBF and save a subset into new PBF. + - add Cython specific tests + +## Install + +Pyrosm is distributed via PyPi and conda-forge. + +The recommended way to install pyrosm is using `conda` package manager: + +`$ conda install -c conda-forge pyrosm` + +You can also install the package with pip: + +`$ pip install pyrosm` + +### Troubleshooting + +Notice that `pyrosm` requires geopandas to work. +On Linux and Mac installing geopandas with `pip` should work without a problem, which is handled automatically when installing pyrosm. + +However, on Windows installing geopandas with pip is likely to cause issues, hence, it is recommended to install Geopandas before installing +`pyrosm`. See instructions from [Geopandas website](https://geopandas.org/install.html#installation). + +## When should I use Pyrosm? + +Pyrosm can of course be used whenever you need to parse data from OSM into geopandas GeoDataFrames. +However, `pyrosm` is better suited for situations where you want to fetch data for whole city or larger regions (even whole country). + +If you are interested to fetch OSM data for smaller areas such as neighborhoods, or search data around a specific location/address, +we recommend using [OSMnx](https://github.com/gboeing/osmnx) which is more flexible in terms of specifying the area of interest. +That being said, it is also possible to extract neighborhood level information with pyrosm and filter data based on a bounding box +(see [docs](https://pyrosm.readthedocs.io/en/latest/basics.html#filtering-data-based-on-bounding-box)). + +## How to use? + +Using `pyrosm` is straightforward. See [docs](https://pyrosm.readthedocs.io/en/latest/basics.html) +for instructions how to use the library. + +## Performance + +See [docs](https://pyrosm.readthedocs.io/en/latest/benchmarking.html) for more comprehensive benchmarking tests. Reading all drivable roads in Helsinki Region (approx. 85,000 roads) +takes approximately **12 seconds** (laptop with 16GB memory, SSD drive, and Intel Core i5-8250U CPU 1.6 GHZ). And the result looks something like: + + + +Parsing all buildings from the same area (approx. 180,000) takes approximately **17 seconds**. And the result looks something like: + + + +Parsing all Points of Interest (POIs) with defaults elements (amenities, shops and tourism) +takes approximately **14 seconds** (approx. 32,000 features). +And the result looks something like: + + + +## Get in touch + contributions + +If you find a bug from the tool, have question, or would like to suggest a new feature to it, you can [make a new issue here](https://github.com/HTenkanen/pyrosm/issues). + +We warmly welcome contributions to `pyrosm` to make it better. If you are interested in contributing to the library, +please check the [contribution guidelines](https://pyrosm.readthedocs.io/en/latest/contributions.html). + +## Development + +You can install a local development version of the tool by 1) installing necessary packages with conda and 2) building pyrosm from source: + + 1. install conda-environment for Python 3.7 or 3.8 by: + + - Python 3.7 (you might want to modify the env-name which is `test` by default): `$ conda env create -f ci/37-conda.yaml` + - Python 3.8: `$ conda env create -f ci/38-conda.yaml` + + 2. build pyrosm development version from master (activate the environment first): + + - `pip install -e .` + +You can run tests with `pytest` by executing: + + `$ pytest -v` + + +## License and copyright + +Pyrosm is licensed under MIT (see [license](LICENSE)). + +The OSM data is downloaded from two sources: + +[](https://download.geofabrik.de/) +[](https://download.bbbike.org/osm/) + +Data © [Geofabrik GmbH](http://www.geofabrik.de/), [BBBike](https://download.bbbike.org/) and [OpenStreetMap Contributors](http://www.openstreetmap.org/) + +All data from the [OpenStreetMap](https://www.openstreetmap.org) is licensed under the [OpenStreetMap License](https://www.openstreetmap.org/copyright). + +## Caveats + +### Filtering large files by bounding box + +Although `pyrosm` provides possibility to filter even larger data files based on bounding box, +this process can slow down the reading process significantly (1.5-3x longer) due to necessary lookups when parsing the data. +This might not be an issue with smaller files (up to ~100MB) but with larger data dumps this can take longer than necessary. + +Hence, a recommended approach with large data files is to **first** filter the protobuf file based on bounding box into a +smaller subset by using a dedicated open source Java tool called [Osmosis](https://wiki.openstreetmap.org/wiki/Osmosis) +which is available for all operating systems. Detailed installation instructions are [here](https://wiki.openstreetmap.org/wiki/Osmosis/Installation), +and instructions how to filter data based on bounding box are [here](https://wiki.openstreetmap.org/wiki/Osmosis/Examples#Extract_administrative_Boundaries_from_a_PBF_Extract). + +%package help +Summary: Development documents and examples for pyrosm +Provides: python3-pyrosm-doc +%description help +# Pyrosm +[](https://anaconda.org/conda-forge/pyrosm/) +[](https://badge.fury.io/py/pyrosm) +[](https://travis-ci.org/HTenkanen/pyrosm) +[](https://pyrosm.readthedocs.io/en/latest/?badge=latest) +[](https://codecov.io/gh/HTenkanen/pyrosm) +[](https://pypistats.org/packages/pyrosm) +[](https://doi.org/10.5281/zenodo.4279527) +[](https://anaconda.org/conda-forge/pyrosm/) + + +**Pyrosm** is a Python library for reading OpenStreetMap data from Protocolbuffer Binary Format -files (`*.osm.pbf`) into Geopandas GeoDataFrames. +Pyrosm makes it easy to extract various datasets from OpenStreetMap pbf-dumps including e.g. road networks, buildings, +Points of Interest (POI), landuse and natural elements. Also fully customized queries are supported which makes it possible +to parse the data from OSM with more specific filters. + + +**Pyrosm** is easy to use and it provides a somewhat similar user interface as [OSMnx](https://github.com/gboeing/osmnx). +The main difference between pyrosm and OSMnx is that OSMnx reads the data over internet using OverPass API, whereas pyrosm reads the data from local OSM data dumps +that can be downloaded e.g. from [GeoFabrik's website](http://download.geofabrik.de/). This makes it possible to read data faster thus +allowing e.g. parsing street networks for the whole country fairly efficiently (however, see [caveats](#caveats)). + + +The library has been developed by keeping performance in mind, hence, it is mainly written in Cython (*Python with C-like performance*) +which makes it probably faster than any other Python alternatives for parsing OpenStreetMap data. +Pyrosm is built on top of another Cython library called [Pyrobuf](https://github.com/appnexus/pyrobuf) which is a faster Cython alternative +to Google's Protobuf library: It provides 2-4x boost in performance for deserializing the protocol buffer messages compared to +Google's version with C++ backend. Google's Protocol Buffers is a commonly used and efficient method to serialize and compress structured data +which is also used by OpenStreetMap contributors to distribute the OSM data in PBF format (Protocolbuffer Binary Format). + +**Documentation** is available at [https://pyrosm.readthedocs.io](https://pyrosm.readthedocs.io/en/latest/). + +## Current features + + - download PBF data easily from hundreds of locations across the world + - read street networks (separately for driving, cycling, walking and all-combined) + - read buildings from PBF + - read Points of Interest (POI) from PBF + - read landuse from PBF + - read "natural" from PBF + - read boundaries from PBF (+ allow searching by name) + - read any other data from PBF by using a custom user-defined filter + - filter data based on bounding box + - export networks as a directed graph to `igraph`, `networkx` and `pandana` + +## Roadmap + + - add possibility to optimize memory usage (see #87) + - add possibility to simplify graph (see #89) + - add possibility to crop PBF and save a subset into new PBF. + - add Cython specific tests + +## Install + +Pyrosm is distributed via PyPi and conda-forge. + +The recommended way to install pyrosm is using `conda` package manager: + +`$ conda install -c conda-forge pyrosm` + +You can also install the package with pip: + +`$ pip install pyrosm` + +### Troubleshooting + +Notice that `pyrosm` requires geopandas to work. +On Linux and Mac installing geopandas with `pip` should work without a problem, which is handled automatically when installing pyrosm. + +However, on Windows installing geopandas with pip is likely to cause issues, hence, it is recommended to install Geopandas before installing +`pyrosm`. See instructions from [Geopandas website](https://geopandas.org/install.html#installation). + +## When should I use Pyrosm? + +Pyrosm can of course be used whenever you need to parse data from OSM into geopandas GeoDataFrames. +However, `pyrosm` is better suited for situations where you want to fetch data for whole city or larger regions (even whole country). + +If you are interested to fetch OSM data for smaller areas such as neighborhoods, or search data around a specific location/address, +we recommend using [OSMnx](https://github.com/gboeing/osmnx) which is more flexible in terms of specifying the area of interest. +That being said, it is also possible to extract neighborhood level information with pyrosm and filter data based on a bounding box +(see [docs](https://pyrosm.readthedocs.io/en/latest/basics.html#filtering-data-based-on-bounding-box)). + +## How to use? + +Using `pyrosm` is straightforward. See [docs](https://pyrosm.readthedocs.io/en/latest/basics.html) +for instructions how to use the library. + +## Performance + +See [docs](https://pyrosm.readthedocs.io/en/latest/benchmarking.html) for more comprehensive benchmarking tests. Reading all drivable roads in Helsinki Region (approx. 85,000 roads) +takes approximately **12 seconds** (laptop with 16GB memory, SSD drive, and Intel Core i5-8250U CPU 1.6 GHZ). And the result looks something like: + + + +Parsing all buildings from the same area (approx. 180,000) takes approximately **17 seconds**. And the result looks something like: + + + +Parsing all Points of Interest (POIs) with defaults elements (amenities, shops and tourism) +takes approximately **14 seconds** (approx. 32,000 features). +And the result looks something like: + + + +## Get in touch + contributions + +If you find a bug from the tool, have question, or would like to suggest a new feature to it, you can [make a new issue here](https://github.com/HTenkanen/pyrosm/issues). + +We warmly welcome contributions to `pyrosm` to make it better. If you are interested in contributing to the library, +please check the [contribution guidelines](https://pyrosm.readthedocs.io/en/latest/contributions.html). + +## Development + +You can install a local development version of the tool by 1) installing necessary packages with conda and 2) building pyrosm from source: + + 1. install conda-environment for Python 3.7 or 3.8 by: + + - Python 3.7 (you might want to modify the env-name which is `test` by default): `$ conda env create -f ci/37-conda.yaml` + - Python 3.8: `$ conda env create -f ci/38-conda.yaml` + + 2. build pyrosm development version from master (activate the environment first): + + - `pip install -e .` + +You can run tests with `pytest` by executing: + + `$ pytest -v` + + +## License and copyright + +Pyrosm is licensed under MIT (see [license](LICENSE)). + +The OSM data is downloaded from two sources: + +[](https://download.geofabrik.de/) +[](https://download.bbbike.org/osm/) + +Data © [Geofabrik GmbH](http://www.geofabrik.de/), [BBBike](https://download.bbbike.org/) and [OpenStreetMap Contributors](http://www.openstreetmap.org/) + +All data from the [OpenStreetMap](https://www.openstreetmap.org) is licensed under the [OpenStreetMap License](https://www.openstreetmap.org/copyright). + +## Caveats + +### Filtering large files by bounding box + +Although `pyrosm` provides possibility to filter even larger data files based on bounding box, +this process can slow down the reading process significantly (1.5-3x longer) due to necessary lookups when parsing the data. +This might not be an issue with smaller files (up to ~100MB) but with larger data dumps this can take longer than necessary. + +Hence, a recommended approach with large data files is to **first** filter the protobuf file based on bounding box into a +smaller subset by using a dedicated open source Java tool called [Osmosis](https://wiki.openstreetmap.org/wiki/Osmosis) +which is available for all operating systems. Detailed installation instructions are [here](https://wiki.openstreetmap.org/wiki/Osmosis/Installation), +and instructions how to filter data based on bounding box are [here](https://wiki.openstreetmap.org/wiki/Osmosis/Examples#Extract_administrative_Boundaries_from_a_PBF_Extract). + +%prep +%autosetup -n pyrosm-0.6.1 + +%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-pyrosm -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.1-1 +- Package Spec generated @@ -0,0 +1 @@ +5c0cae34fef645cb609812c5af26e119 pyrosm-0.6.1.tar.gz |