Metadata-Version: 1.1
Name: rasterio
Version: 0.21.0
Summary: Fast and direct raster I/O for use with Numpy and SciPy
Home-page: https://github.com/mapbox/rasterio
Author: Sean Gillies
Author-email: sean@mapbox.com
License: BSD
Description: ========
        Rasterio
        ========
        
        Rasterio reads and writes geospatial raster datasets.
        
        .. image:: https://travis-ci.org/mapbox/rasterio.png?branch=master
           :target: https://travis-ci.org/mapbox/rasterio
        
        .. image:: https://coveralls.io/repos/mapbox/rasterio/badge.png
           :target: https://coveralls.io/r/mapbox/rasterio
        
        Rasterio employs GDAL under the hood for file I/O and raster formatting. Its
        functions typically accept and return Numpy ndarrays. Rasterio is designed to
        make working with geospatial raster data more productive and more fun.
        
        Rasterio is pronounced raw-STEER-ee-oh.
        
        Example
        =======
        
        Here's a simple 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 GDAL format drivers and configuration options with a
            # context manager.
            
            with rasterio.drivers(CPL_DEBUG=True):
                
                # Read raster bands directly to Numpy arrays.
                #
                with rasterio.open('tests/data/RGB.byte.tif') as src:
                    b, g, r = src.read()
                
                # Combine arrays in place. Expecting that the sum will 
                # temporarily 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
        
                # 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.
        
        API Overview
        ============
        
        Simple access is provided to properties of a geospatial raster file.
        
        .. code-block:: python
            
            with rasterio.drivers():
        
                with rasterio.open('tests/data/RGB.byte.tif') as src:
                    print(src.width, src.height)
                    print(src.crs)
                    print(src.affine)
                    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}
            # Affine(300.0379266750948, 0.0, 101985.0,
            #        0.0, -300.041782729805, 2826915.0)
            # 3
            # [1, 2, 3]
        
        Rasterio also affords conversion of GeoTIFFs to other formats.
        
        .. code-block:: python
            
            with rasterio.drivers():
        
                rasterio.copy(
                    'example-total.tif',
                    'example-total.jpg', 
                    driver='JPEG')
            
            subprocess.call(['open', 'example-total.jpg'])
        
        Rasterio CLI
        ============
        
        Rasterio's command line interface, named "rio", is documented at `cli.rst
        <https://github.com/mapbox/rasterio/blob/master/docs/cli.rst>`__. Its ``rio
        insp`` command opens the hood of any raster dataset so you can poke around
        using Python.
        
        .. code-block:: pycon
        
            $ rio insp tests/data/RGB.byte.tif
            Rasterio 0.10 Interactive Inspector (Python 3.4.1)
            Type "src.meta", "src.read_band(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)
        
            >>> b.min(), b.max(), b.mean()
            (1, 255, 44.434478650699106)
        
        Installation
        ============
        
        Dependencies
        ------------
        
        Rasterio has one C library dependency: GDAL >=1.9. GDAL itself depends on a
        number of other libraries provided by most major operating systems and also
        depends on the non standard GEOS and PROJ4 libraries.
        
        Python package dependencies (see also requirements.txt): affine, cligj (and
        click), enum34, numpy.
        
        Development also requires (see requirements-dev.txt) Cython and other packages.
        
        Rasterio binaries for OS X
        --------------------------
        
        Binary wheels with the GDAL, GEOS, and PROJ4 libraries included are available
        for OS X versions 10.7+ starting with Rasterio version 0.17. To install, just
        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-use-wheel`` 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).
        
        Binary wheels for other operating systems will be available in a future
        release.
        
        Installing from the source distribution
        ---------------------------------------
        
        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
        <https://github.com/mapbox/rasterio/blob/master/.travis.yml>`__ for more
        guidance.
        
        Linux
        -----
        
        The following commands are adapted from Rasterio's Travis-CI configuration.
        
        .. code-block:: console
        
            $ sudo add-apt-repository ppa:ubuntugis/ppa
            $ sudo apt-get update
            $ sudo apt-get install python-numpy libgdal1h gdal-bin libgdal-dev
            $ pip install rasterio
        
        Adapt them as necessary for your Linux system.
        
        OS X
        ----
        
        For a Homebrew based Python environment, do the following.
        
        .. code-block:: console
        
            $ brew install gdal
            $ pip install rasterio
        
        Windows
        -------
        
        Windows binary packages created by Christoph Gohlke are available `here
        <http://www.lfd.uci.edu/~gohlke/pythonlibs/#rasterio>`_.
        
        Testing
        -------
        
        From the repo directory, run py.test
        
        .. code-block:: console
        
            $ py.test
        
        Documentation
        -------------
        
        See https://github.com/mapbox/rasterio/tree/master/docs.
        
        License
        -------
        
        See LICENSE.txt
        
        Authors
        -------
        
        See AUTHORS.txt
        
        Changes
        -------
        
        See CHANGES.txt
        
Keywords: raster gdal
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
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 :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Topic :: Multimedia :: Graphics :: Graphics Conversion
Classifier: Topic :: Scientific/Engineering :: GIS
