pygeos 0.4

GEOS wrapped in numpy ufuncs

PyGEOS is a C/Python library with vectorized geometry functions. The geometry operations are done in the open-source geometry library GEOS. PyGEOS wraps these operations in NumPy ufuncs providing a performance improvement when operating on arrays of geometries.

Why ufuncs?

A universal function (or ufunc for short) is a function that operates on n-dimensional arrays in an element-by-element fashion, supporting array broadcasting. The for-loops that are involved are fully implemented in C diminishing the overhead of the Python interpreter.

The Geometry object

The pygeos.Geometry object is a container of the actual GEOSGeometry object. A C pointer to this object is stored in a static attribute of the Geometry object. This keeps the python interpreter out of the ufunc inner loop. The Geometry object keeps track of the underlying GEOSGeometry and allows the python garbage collector to free memory when it is not used anymore.

Geometry objects are immutable. Construct them as follows:

>>> from pygeos import Geometry

>>> geometry = Geometry.from_wkt("POINT (5.2 52.1)")

Or using one of the provided (vectorized) functions:

>>> from pygeos import points

>>> point = points(5.2, 52.1)

Examples

Compare an grid of points with a polygon:

>>> geoms = points(*np.indices((4, 4)))
>>> polygon = box(0, 0, 2, 2)

>>> contains(polygon, geoms)

  array([[False, False, False, False],
         [False,  True, False, False],
         [False, False, False, False],
         [False, False, False, False]])

Compute the area of all possible intersections of two lists of polygons:

>>> from pygeos import box, area, intersection

>>> polygons_x = box(range(5), 0, range(10, 15), 10)
>>> polygons_y = box(0, range(5), 10, range(10, 15))

>>> area(intersection(polygons_x[:, np.newaxis], polygons_y[np.newaxis, :]))

array([[100.,  90.,  80.,  70.,  60.],
     [ 90.,  81.,  72.,  63.,  54.],
     [ 80.,  72.,  64.,  56.,  48.],
     [ 70.,  63.,  56.,  49.,  42.],
     [ 60.,  54.,  48.,  42.,  36.]])

Installation using conda

Pygeos requires the presence of NumPy and GEOS >= 3.5. It is recommended to install these using Anaconda from the conda-forge channel (which provides pre-compiled binaries):

$ conda install numpy geos pygeos --channel conda-forge

Installation using system GEOS

On Linux:

$ sudo apt install libgeos-dev

On OSX:

$ brew install geos

Make sure geos-config is available from you shell; append PATH if necessary:

$ export PATH=$PATH:/path/to/dir/having/geos-config
$ pip install pygeos

Installation for developers

Ensure you have numpy and GEOS installed (either using conda or using system GEOS, see above).

Clone the package:

$ git clone https://github.com/caspervdw/pygeos.git

Install it using pip:

$ pip install -e .[test]

Run the unittests:

$ pytest

If GEOS is installed, normally the geos-config command line utility will be available, and pip install will find GEOS automatically. But if needed, you can specify where PyGEOS should look for the GEOS library before installing it:

On Linux / OSX:

$ export GEOS_INCLUDE_PATH=$CONDA_PREFIX/Library/include
$ export GEOS_LIBRARY_PATH=$CONDA_PREFIX/Library/lib

On windows (assuming you are in a Visual C++ shell):

$ set GEOS_INCLUDE_PATH=%CONDA_PREFIX%\Library\include
$ set GEOS_LIBRARY_PATH=%CONDA_PREFIX%\Library\lib

References

• GEOS: http://trac.osgeo.org/geos

• Shapely: https://shapely.readthedocs.io/en/latest/

• Numpy ufuncs: https://docs.scipy.org/doc/numpy/reference/ufuncs.html

• Joris van den Bossche’s blogpost: https://jorisvandenbossche.github.io/blog/2017/09/19/geopandas-cython/

• Matthew Rocklin’s blogpost: http://matthewrocklin.com/blog/work/2017/09/21/accelerating-geopandas-1

Copyright & License

Copyright (c) 2019, Casper van der Wel. BSD 3-Clause license.