Metadata-Version: 1.1
Name: rasterio
Version: 0.5
Summary: Fast and direct raster I/O for Python programmers who use Numpy
Home-page: https://github.com/sgillies/rasterio
Author: Sean Gillies
Author-email: sean@mapbox.com
License: BSD
Description: rasterio
        ========
        
        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.
        
        .. code-block:: python
        
            import numpy
            import rasterio
            import subprocess
            
            # Register format drivers with a context manager
            
            with fiona.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'])
        
        .. image:: http://farm6.staticflickr.com/5501/11393054644_74f54484d9_z_d.jpg
           :width: 640
           :height: 581
        
        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.
        
        .. code-block:: python
            
            with rasterio.open():
        
                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.
        
        .. code-block:: python
            
            with rasterio.open():
        
                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).
        
        .. code-block:: console
        
            $ 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.
        
        .. code-block:: console
        
            $ nosetests
        
        License
        -------
        
        See LICENSE.txt
        
        Authors
        -------
        
        See AUTHORS.txt
        
        Changes
        -------
        
        See CHANGES.txt
        
        
Keywords: raster gdal
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: C
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.0
Classifier: Programming Language :: Python :: 3.1
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Topic :: Multimedia :: Graphics :: Graphics Conversion
Classifier: Topic :: Scientific/Engineering :: GIS
