Metadata-Version: 2.1
Name: pyrosm
Version: 0.4.0
Summary: A Python tool to parse OSM data from Protobuf format into GeoDataFrame.
Home-page: https://github.com/htenkanen/pyrosm
Author: Henrikki Tenkanen
Author-email: h.tenkanen@ucl.ac.uk
License: MIT
Project-URL: Issue Tracker, https://github.com/htenkanen/pyrosm/issues
Description: # Pyrosm 
        [![PyPI version](https://badge.fury.io/py/pyrosm.svg)](https://badge.fury.io/py/pyrosm)[![build status](https://api.travis-ci.org/HTenkanen/pyrosm.svg?branch=master)](https://travis-ci.org/HTenkanen/pyrosm)[![Coverage Status](https://codecov.io/gh/HTenkanen/pyrosm/branch/master/graph/badge.svg)](https://codecov.io/gh/HTenkanen/pyrosm)
        
        **Pyrosm** is a Python library for reading OpenStreetMap from `protobuf` 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 much faster thus 
        allowing e.g. parsing street networks for whole country in a matter of minutes instead of hours (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). 
        
        
        ## Current features
        
         - 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 any other data from PBF by using a custom user-defined filter
         - filter data based on bounding box
         
         
        ## Roadmap
        
         - improve docs and make simple website
         - run benchmarks against other tools
         - add possibility to crop PBF and save a subset into new PBF.
         - automate PBF downloading from Geofabrik (?)
        
        ## Install
        
        Pyrosm is distributed via PyPi and it can be installed 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).
        
        ## How to use?
        
        Using `pyrosm` is straightforward. To read drivable street networks from OpenStreetMap protobuf file (package includes a small test protobuf file), simply:
        
        ```python
        from pyrosm import OSM
        from pyrosm import get_path
        fp = get_path("test_pbf")
        # Initialize the OSM parser object
        osm = OSM(fp)
        
        # Read all drivable roads
        # =======================
        drive_net = osm.get_network(network_type="driving")
        drive_net.head()
        ...
          access bridge  ...        id                                           geometry
        0   None   None  ...   4732994  LINESTRING (26.94310 60.52580, 26.94295 60.525...
        1   None   None  ...   5184588  LINESTRING (26.94778 60.52231, 26.94717 60.522...
        2   None    yes  ...   5184589  LINESTRING (26.94891 60.52181, 26.94778 60.52231)
        3   None   None  ...   5184590  LINESTRING (26.94310 60.52580, 26.94452 60.525...
        4   None   None  ...  22731285  LINESTRING (26.93072 60.52252, 26.93094 60.522...
        
        [5 rows x 14 columns]
        
        # Read all residential and retail buildings
        # =========================================
        custom_filter = {'building': ['residential', 'retail']}
        buildings = osm.get_buildings(custom_filter=custom_filter)
        buildings.head()
        ...
              building  ...                                           geometry
        0       retail  ...  POLYGON ((26.94511 60.52322, 26.94487 60.52314...
        1       retail  ...  POLYGON ((26.95093 60.53644, 26.95083 60.53642...
        2  residential  ...  POLYGON ((26.96536 60.52540, 26.96528 60.52539...
        3  residential  ...  POLYGON ((26.93920 60.53257, 26.93940 60.53254...
        4  residential  ...  POLYGON ((26.96578 60.52129, 26.96569 60.52137...
        
        # Read POIs such as shops and amenities 
        # =====================================
        custom_filter = {'amenity': True, 'shop': True }
        pois = osm.get_pois(custom_filter=custom_filter)
        pois.head()
        ...
           changeset   timestamp        lon  version  ...  phone  building landuse parking
        0        0.0  1461601534  26.951475        2  ...    NaN       NaN     NaN     NaN
        1        0.0  1310921959  26.945166        3  ...    NaN       NaN     NaN     NaN
        2        0.0  1347308819  26.932177        2  ...    NaN       NaN     NaN     NaN
        3        0.0  1310921960  26.949650        2  ...    NaN       NaN     NaN     NaN
        4        0.0  1307995246  26.959021        1  ...    NaN       NaN     NaN     NaN
        
        [5 rows x 22 columns]
        ```   
        
        To get further information how to use the tool, you can use good old `help`:
        
        ```python
        
        help(osm.get_network)
        
        ...
        
        Help on method get_network in module pyrosm.pyrosm:
        
        get_network(network_type='walking') method of pyrosm.pyrosm.OSM instance
            Reads data from OSM file and parses street networks
            for walking, driving, and cycling.
            
            Parameters
            ----------
            
            network_type : str
                What kind of network to parse. Possible values are: 'walking' | 'cycling' | 'driving' | 'all'.
        
        ```
        
        ## Examples
        
        For further usage examples (for now), take a look at the tests, such as:
          - [test_network_parsing.py](tests/test_network_parsing.py)
          - [test_building_parsing.py](tests/test_building_parsing.py)
        
        ## Performance
        
        Proper benchmarking results are on their way, but to give some idea, 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:
        
        ![Helsinki_driving_net](resources/img/Helsinki_driving_net.PNG)
        
        Parsing all buildings from the same area (approx. 180,000) takes approximately **17 seconds**. And the result looks something like:
        
        ![Helsinki_building_footprints](resources/img/Helsinki_building_footprints.png)
        
        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:
        
        ![Helsinki_POIs](resources/img/Helsinki_POIs_amenity_shop_tourism.png)
        
        
        ## Get in touch
        
        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).
        
        ## 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` 
        
        ## 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).
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Utilities
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Description-Content-Type: text/markdown
