Fast and direct raster I/O for use with Numpy and SciPy
Project description
Rasterio reads and writes geospatial raster data.
Geographic information systems use GeoTIFF and other formats to organize and store gridded, or raster, datasets. Rasterio reads and writes these formats and provides a Python API based on N-D arrays.
Rasterio supports Python 2.7 and 3.3-3.6 on Linux and Mac OS X.
Read the documentation for more details: https://rasterio.readthedocs.io/en/latest/.
Example
Here’s an example of some basic features that Rasterio provides. Three bands are read from an image and averaged to produce something like a panchromatic band. This new band is then written to a new single band TIFF.
import numpy as np
import rasterio
# Read raster bands directly to Numpy arrays.
#
with rasterio.open('tests/data/RGB.byte.tif') as src:
r, g, b = src.read()
# Combine arrays in place. Expecting that the sum will
# temporarily exceed the 8-bit integer range, initialize it as
# a 64-bit float (the numpy default) array. Adding other
# arrays to it in-place converts those arrays "up" and
# preserves the type of the total array.
total = np.zeros(r.shape)
for band in r, g, b:
total += band
total /= 3
# Write the product as a raster band to a new 8-bit file. For
# the new file's profile, we start with the meta attributes of
# the source file, but then change the band count to 1, set the
# dtype to uint8, and specify LZW compression.
profile = src.profile
profile.update(dtype=rasterio.uint8, count=1, compress='lzw')
with rasterio.open('example-total.tif', 'w', **profile) as dst:
dst.write(total.astype(rasterio.uint8), 1)The output:
API Overview
Rasterio gives access to properties of a geospatial raster file.
with rasterio.open('tests/data/RGB.byte.tif') as src:
print(src.width, src.height)
print(src.crs)
print(src.transform)
print(src.count)
print(src.indexes)
# Printed:
# (791, 718)
# {u'units': u'm', u'no_defs': True, u'ellps': u'WGS84', u'proj': u'utm', u'zone': 18}
# Affine(300.0379266750948, 0.0, 101985.0,
# 0.0, -300.041782729805, 2826915.0)
# 3
# [1, 2, 3]A rasterio dataset also provides methods for getting extended array slices given georeferenced coordinates.
with rasterio.open('tests/data/RGB.byte.tif') as src:
print src.window(**src.window_bounds(((100, 200), (100, 200))))
# Printed:
# ((100, 200), (100, 200))Rasterio CLI
Rasterio’s command line interface, named “rio”, is documented at cli.rst. Its rio insp command opens the hood of any raster dataset so you can poke around using Python.
$ rio insp tests/data/RGB.byte.tif
Rasterio 0.10 Interactive Inspector (Python 3.4.1)
Type "src.meta", "src.read(1)", or "help(src)" for more information.
>>> src.name
'tests/data/RGB.byte.tif'
>>> src.closed
False
>>> src.shape
(718, 791)
>>> src.crs
{'init': 'epsg:32618'}
>>> b, g, r = src.read()
>>> b
masked_array(data =
[[-- -- -- ..., -- -- --]
[-- -- -- ..., -- -- --]
[-- -- -- ..., -- -- --]
...,
[-- -- -- ..., -- -- --]
[-- -- -- ..., -- -- --]
[-- -- -- ..., -- -- --]],
mask =
[[ True True True ..., True True True]
[ True True True ..., True True True]
[ True True True ..., True True True]
...,
[ True True True ..., True True True]
[ True True True ..., True True True]
[ True True True ..., True True True]],
fill_value = 0)
>>> np.nanmin(b), np.nanmax(b), np.nanmean(b)
(0, 255, 29.94772668847656)Rio Plugins
Rio provides the ability to create subcommands using plugins. See cli.rst for more information on building plugins.
See the plugin registry for a list of available plugins.
Installation
Please install Rasterio in a virtual environment so that its requirements don’t tamper with your system’s Python.
SSL certs
The Linux wheels on PyPI are built on CentOS and libcurl expects certs to be in /etc/pki/tls/certs/ca-bundle.crt. Ubuntu’s certs, for example, are in a different location. You may need to use the CURL_CA_BUNDLE environment variable to specify the location of SSL certs on your computer. On an Ubuntu system set the variable as shown below.
$ export CURL_CA_BUNDLE=/etc/ssl/certs/ca-certificates.crtDependencies
Rasterio has a C library dependency: GDAL >=1.11. GDAL itself depends on some other libraries provided by most major operating systems and also depends on the non standard GEOS and PROJ4 libraries. How to meet these requirement will be explained below.
Rasterio’s Python dependencies are listed in its requirements.txt file.
Development also requires (see requirements-dev.txt) Cython and other packages.
Binary Distributions
Use a binary distributions that directly or indirectly provide GDAL if possible.
Linux
Rasterio distributions are available from UbuntuGIS and Anaconda’s conda-forge channel.
Manylinux1 distributions may be available in the future.
OS X
Binary distributions with GDAL, GEOS, and PROJ4 libraries included are available for OS X versions 10.7+ starting with Rasterio version 0.17. To install, run pip install rasterio. These binary wheels are preferred by newer versions of pip.
If you don’t want these wheels and want to install from a source distribution, run pip install rasterio --no-binary rasterio instead.
The included GDAL library is fairly minimal, providing only the format drivers that ship with GDAL and are enabled by default. To get access to more formats, you must build from a source distribution (see below).
Windows
Binary wheels for rasterio and GDAL are created by Christoph Gohlke and are available from his website.
To install rasterio, simply download both binaries for your system (rasterio and GDAL) and run something like this from the downloads folder:
$ pip install -U pip
$ pip install GDAL-2.0.2-cp27-none-win32.whl
$ pip install rasterio-0.34.0-cp27-cp27m-win32.whlSource Distributions
Rasterio is a Python C extension and to build you’ll need a working compiler (XCode on OS X etc). You’ll also need Numpy preinstalled; the Numpy headers are required to run the rasterio setup script. Numpy has to be installed (via the indicated requirements file) before rasterio can be installed. See rasterio’s Travis configuration for more guidance.
Linux
The following commands are adapted from Rasterio’s Travis-CI configuration.
$ sudo add-apt-repository ppa:ubuntugis/ppa
$ sudo apt-get update
$ sudo apt-get install gdal-bin libgdal-dev
$ pip install -U pip
$ pip install rasterioAdapt them as necessary for your Linux system.
OS X
For a Homebrew based Python environment, do the following.
$ brew update
$ brew install gdal
$ pip install -U pip
$ pip install --no-use-wheel rasterioAlternatively, you can install GDAL binaries from kyngchaos. You will then need to add the installed location /Library/Frameworks/GDAL.framework/Programs to your system path.
Windows
You can download a binary distribution of GDAL from here. You will also need to download the compiled libraries and headers (include files).
When building from source on Windows, it is important to know that setup.py cannot rely on gdal-config, which is only present on UNIX systems, to discover the locations of header files and libraries that rasterio needs to compile its C extensions. On Windows, these paths need to be provided by the user. You will need to find the include files and the library files for gdal and use setup.py as follows.
$ python setup.py build_ext -I<path to gdal include files> -lgdal_i -L<path to gdal library>
$ python setup.py installWe have had success compiling code using the same version of Microsoft’s Visual Studio used to compile the targeted version of Python (more info on versions used here.).
Note: The GDAL dll (gdal111.dll) and gdal-data directory need to be in your Windows PATH otherwise rasterio will fail to work.
Development and Testing
See CONTRIBUTING.rst.
Documentation
See docs/.
License
See LICENSE.txt.
Changes
See CHANGES.txt.
Who is Using Rasterio?
See here.
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