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|
%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
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