rasterio

Fast and direct raster I/O for use with Numpy and SciPy

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Project links

Homepage

Project description

Rasterio reads and writes geospatial raster data.

https://app.travis-ci.com/rasterio/rasterio.svg?branch=master

https://coveralls.io/repos/github/mapbox/rasterio/badge.svg?branch=master

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 1.3 works with Python 3.8+, Numpy 1.18+, and GDAL 3.1+. Official binary packages for Linux, macOS, and Windows with most built-in format drivers plus HDF5, netCDF, and OpenJPEG2000 are available on PyPI. Unofficial binary packages for Windows are available through other channels.

Read the documentation for more details: https://rasterio.readthedocs.io/.

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:

http://farm6.staticflickr.com/5501/11393054644_74f54484d9_z_d.jpg

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 read/write windows (like extended array slices) given georeferenced coordinates.

with rasterio.open('tests/data/RGB.byte.tif') as src:
    window = src.window(*src.bounds)
    print(window)
    print(src.read(window=window).shape)

# Printed:
# Window(col_off=0.0, row_off=0.0, width=791.0000000000002, height=718.0)
# (3, 718, 791)

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.crt

Dependencies

Rasterio has a C library dependency: GDAL >= 3.1. GDAL itself depends on some other libraries provided by most major operating systems and also depends on the non standard GEOS and PROJ libraries. How to meet these requirement will be explained below.

Rasterio’s Python dependencies are (see the package metadata file):

affine
attrs
certifi
click>=4.0
cligj>=0.5
numpy>=1.18
snuggs>=1.4.1
click-plugins
setuptools

[all]
hypothesis
pytest-cov>=2.2.0
matplotlib
boto3>=1.2.4
numpydoc
pytest>=2.8.2
shapely
ipython>=2.0
sphinx
packaging
ghp-import
sphinx-rtd-theme

[docs]
ghp-import
numpydoc
sphinx
sphinx-rtd-theme

[ipython]
ipython>=2.0

[plot]
matplotlib

[s3]
boto3>=1.2.4

[test]
boto3>=1.2.4
hypothesis
packaging
pytest-cov>=2.2.0
pytest>=2.8.2
shapely

Development requires Cython and other packages.

Binary Distributions

Use a binary distribution that directly or indirectly provides GDAL if possible.

The rasterio wheels on PyPI include GDAL and its own dependencies.

Rasterio	GDAL
1.2.3	3.2.2
1.2.4+	3.3.0

Linux

Rasterio distributions are available from UbuntuGIS and Anaconda’s conda-forge channel.

Manylinux1 wheels are available on PyPI.

OS X

Binary distributions with GDAL, GEOS, and PROJ4 libraries included are available for OS X versions 10.9+. 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, adjusting for your Python version.

$ pip install -U pip
$ pip install GDAL-3.1.4-cp39-cp39‑win_amd64.whl
$ pip install rasterio‑1.1.8-cp39-cp39-win_amd64.whl

You can also install rasterio with conda using Anaconda’s conda-forge channel.

$ conda install -c conda-forge rasterio

Source 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 rasterio

Adapt 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-binary rasterio

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. You will also need to specify the installed gdal version through the GDAL_VERSION environment variable.

$ python setup.py build_ext -I<path to gdal include files> -lgdal_i -L<path to gdal library> install

With pip

$ pip install --no-use-pep517 --global-option -I<path to gdal include files> -lgdal_i -L<path to gdal library> .

Note: --no-use-pep517 is required as pip currently hasn’t implemented a way for optional arguments to be passed to the build backend when using PEP 517. See here for more details.

Alternatively environment variables (e.g. INCLUDE and LINK) used by MSVC compiler can be used to point to include directories and library files.

We 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 and gdal-data directory need to be in your Windows PATH otherwise rasterio will fail to work.

Support

The primary forum for questions about installation and usage of Rasterio is https://rasterio.groups.io/g/main. The authors and other users will answer questions when they have expertise to share and time to explain. Please take the time to craft a clear question and be patient about responses.

Please do not bring these questions to Rasterio’s issue tracker, which we want to reserve for bug reports and other actionable issues.

Development and Testing

See CONTRIBUTING.rst.

Documentation

See docs/.

License

See LICENSE.txt.

Authors

The rasterio project was begun at Mapbox and was transferred to the rasterio Github organization in October 2021.

See AUTHORS.txt.

Changes

See CHANGES.txt.

Who is Using Rasterio?

See here.

Project details

These details have not been verified by PyPI

Project links

Homepage

Release history Release notifications | RSS feed

1.4.2

Oct 30, 2024

1.4.1

Oct 1, 2024

1.4.0

Sep 26, 2024

1.4.0rc2 pre-release

Sep 10, 2024

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Sep 9, 2024

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Aug 11, 2024

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Apr 16, 2024

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Mar 3, 2024

1.4a1 pre-release

Feb 16, 2024

This version

1.3.11

Sep 4, 2024

1.3.10

Apr 12, 2024

1.3.9

Oct 19, 2023

1.3.8.post2

Oct 12, 2023

1.3.8.post1

Oct 10, 2023

1.3.8

Jun 28, 2023

1.3.7

May 22, 2023

1.3.6

Feb 13, 2023

1.3.5.post1

Feb 3, 2023

1.3.5

Jan 25, 2023

1.3.4

Nov 16, 2022

1.3.3

Oct 19, 2022

1.3.2

Aug 19, 2022

1.3.1

Aug 18, 2022

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1.2.9

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Sep 9, 2021

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Jun 22, 2021

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May 31, 2021

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Jun 6, 2019

1.0.23

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Feb 28, 2019

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1.0.18

Feb 7, 2019

1.0.17

Feb 6, 2019

1.0.16

Feb 5, 2019

1.0.15

Jan 28, 2019

1.0.14

Jan 25, 2019

1.0.13

Dec 15, 2018

1.0.12

Dec 11, 2018

1.0.11

Dec 1, 2018

1.0.10

Nov 17, 2018

1.0.9

Oct 26, 2018

1.0.8

Oct 3, 2018

1.0.7

Sep 26, 2018

1.0.6

Sep 24, 2018

1.0.5

Sep 19, 2018

1.0.4

Sep 17, 2018

1.0.3.post1

Sep 8, 2018

1.0.3

Sep 7, 2018

1.0.2

Jul 27, 2018

1.0.1

Jul 23, 2018

1.0.0

Jul 12, 2018

1.0rc5 pre-release

Jul 9, 2018

1.0rc4 pre-release

Jul 5, 2018

1.0rc3 pre-release

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1.0rc2 pre-release

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1.0b4 pre-release

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1.0b3 pre-release

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1.0b2 pre-release

Jun 19, 2018

1.0b1 pre-release

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1.0a12 pre-release

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1.0a11 pre-release

Oct 31, 2017

1.0a10 pre-release

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1.0a9 pre-release

Jun 2, 2017

1.0a8 pre-release

Mar 28, 2017

1.0a7 pre-release

Feb 14, 2017

0.36.0

Jun 14, 2016

0.35.1

May 6, 2016

0.35.0.post1

May 4, 2016

0.35.0

May 4, 2016

0.34.0

Apr 5, 2016

0.33.0

Apr 1, 2016

0.32.0.post1

Mar 28, 2016

0.32.0

Mar 23, 2016

0.31.0

Dec 18, 2015

0.30.0

Nov 17, 2015

0.29.0

Oct 23, 2015

0.28.0

Oct 6, 2015

0.27.0

Sep 25, 2015

0.26.0

Aug 12, 2015

0.25.0

Jul 17, 2015

0.24.1

Jul 1, 2015

0.24.0

May 27, 2015

0.23.0

May 8, 2015

0.22.0

May 1, 2015

0.21.0

Apr 22, 2015

0.20.0

Apr 8, 2015

0.19.1

Mar 30, 2015

0.19.0

Mar 26, 2015

0.18

Feb 10, 2015

0.17.1

Jan 20, 2015

0.17

Jan 15, 2015

0.16

Dec 16, 2014

0.15.1

Nov 4, 2014

0.15

Oct 11, 2014

0.14.1

Oct 2, 2014

0.14

Oct 1, 2014

0.13.2

Sep 23, 2014

0.13.1

Sep 13, 2014

0.13

Sep 10, 2014

0.12.1

Sep 2, 2014

0.12

Sep 2, 2014

0.11.1

Aug 19, 2014

0.11

Aug 6, 2014

0.10.1

Jul 21, 2014

0.10

Jul 19, 2014

0.9

Jul 16, 2014

0.8

Mar 31, 2014

0.7.3

Mar 22, 2014

0.7.2

Mar 20, 2014

0.7.1

Mar 15, 2014

0.7

Mar 15, 2014

0.6

Feb 10, 2014

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Feb 2, 2014

0.5

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Dec 20, 2013

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Dec 16, 2013

0.2

Nov 24, 2013

0.1

Nov 19, 2013

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