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Fast and direct raster I/O for Python programmers who use Numpy

Project description

Clean and fast and geospatial raster I/O for Python programmers who use Numpy.

This library is designed for developers who want to read raster datasets into Numpy arrays or buffers, operate on them in Python (or Cython), and write the results out to new GeoTIFF files.

Rasterio employs GDAL under the hood for file I/O and raster formatting. It aims to let you get more done with less code and fewer bugs than you can with other GDAL interfaces.

Example

Here’s an example of the basic features rasterio provides. Three bands are read from an image and summed to produce something like a panchromatic band. This new band is then written to a new single band TIFF.

import numpy
import rasterio
import subprocess

# Register format drivers with a context manager

with rasterio.drivers():

    # Read raster bands directly to Numpy arrays.
    #
    with rasterio.open('rasterio/tests/data/RGB.byte.tif') as src:
        b, g, r = map(src.read_band, (1, 2, 3))

    # Combine arrays using the 'iadd' ufunc. Expecting that the sum
    # will exceed the 8-bit integer range, initialize it as 16-bit.
    # Adding other arrays to it in-place converts those arrays up
    # and preserves the type of the total array.

    total = numpy.zeros(r.shape, dtype=rasterio.uint16)
    for band in (r, g, b):
        total += band
    total /= 3
    assert total.dtype == rasterio.uint16

    # Write the product as a raster band to a new 8-bit file. For
    # keyword arguments, 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.

    kwargs = src.meta
    kwargs.update(
        dtype=rasterio.uint8,
        count=1,
        compress='lzw')

    with rasterio.open('example-total.tif', 'w', **kwargs) as dst:
        dst.write_band(1, total.astype(rasterio.uint8))

# At the end of the ``with rasterio.drivers()`` block, context
# manager exits and all drivers are de-registered.

# Dump out gdalinfo's report card and open the image.

info = subprocess.check_output(
    ['gdalinfo', '-stats', 'example-total.tif'])
print(info)
subprocess.call(['open', 'example-total.tif'])
http://farm6.staticflickr.com/5501/11393054644_74f54484d9_z_d.jpg

The rasterio.drivers() function and context manager are new in 0.5. The example above shows the way to use it to register and de-register drivers in a deterministic and efficient way. Code written for rasterio 0.4 will continue to work: opened raster datasets may manage the global driver registry if no other manager is present.

Simple access is provided to properties of a geospatial raster file.

with rasterio.drivers():

    with rasterio.open('rasterio/tests/data/RGB.byte.tif') as src:
        print(src.width, src.height)
        print(src.crs)
        print(src.transform)
        print(src.count)
        print(src.indexes)

# Output:
# (791, 718)
# {u'units': u'm', u'no_defs': True, u'ellps': u'WGS84', u'proj': u'utm', u'zone': 18}
# [101985.0, 300.0379266750948, 0.0, 2826915.0, 0.0, -300.041782729805]
# 3
# [1, 2, 3]

Rasterio also affords conversion of GeoTIFFs, on copy, to other formats.

with rasterio.drivers():

    rasterio.copy(
        'example-total.tif',
        'example-total.jpg',
        driver='JPEG')

subprocess.call(['open', 'example-total.jpg'])

Dependencies

C library dependecies:

  • GDAL

Python package dependencies (see also requirements.txt):

  • Numpy
  • setuptools

Development also requires (see requirements-dev.txt)

  • Cython
  • nose

Installation

Rasterio is a C extension and there are not yet any binary releases. You’ll need a working compiler (XCode on OS X, etc).

$ pip install Numpy
$ pip install rasterio

The Numpy headers are required to run the rasterio setup script. Numpy has to be installed first.

Testing

From the repo directory, run nosetests.

$ nosetests

License

See LICENSE.txt

Authors

See AUTHORS.txt

Changes

See CHANGES.txt

Project details


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