Metadata-Version: 1.1
Name: greenwich
Version: 0.4.1
Summary: A GDAL wrapper with Python conveniences
Home-page: https://github.com/bkg/greenwich
Author: Brian Galey
Author-email: bkgaley@gmail.com
License: BSD
Description: Greenwich
        =========
        
        .. image:: https://travis-ci.org/bkg/greenwich.svg?branch=master
            :target: https://travis-ci.org/bkg/greenwich
        .. image:: https://coveralls.io/repos/bkg/greenwich/badge.png?branch=master
            :target: https://coveralls.io/r/bkg/greenwich?branch=master 
        
        Adding Python conveniences to the wonderful world of `GDAL <http://www.gdal.org/>`_.
        
        Greenwich provides a wrapper for the GDAL SWIG Python bindings. The focus here is on
        providing some higher level behavior mainly to the raster side of the GDAL/OGR fence.
        
        Installation
        ------------
        
        The GDAL Python bindings need a little assistance in finding the pertinent
        headers when building from within a virtualenv. If the usual ``pip install greenwich``
        fails, specify the GDAL header directory wherever that may be for you such as:
        
        .. code-block:: sh
        
            CFLAGS=-I/usr/include/gdal pip install greenwich
        
        Basic Usage
        -----------
        
        Open any raster data set you have lying around, perhaps some climate data from
        `WorldClim <http://worldclim.org/CMIP5>`_.
        
        .. code-block:: python
        
            from greenwich import Geometry, Raster
            from greenwich.io import MemFileIO
        
            with Raster('cc85tn701.tif') as tmax:
                # Save as a NetCDF file.
                tmax.save('cc85tn701.nc')
                geom = Geometry(
                    wkt='POLYGON((-123 47,-123 48,-122 49,-121 48,-121 47,-123 47))',
                    srs=4326)
                # Clip the raster with a geometry and save the result as a GeoTIFF.
                with tmax.clip(geom) as clipped:
                    clipped.save('clipped.tif')
        
                # Return a NumPy MaskedArray using no data values for a given bounding box.
                m = tmax.masked_array((-120, 38, -118, 44))
        
                # Convert to an Erdas Imagine file in memory.
                imgio = MemFileIO(suffix='.img')
                tmax.save(imgio)
                imgdata = imgio.read()
                imgio.close()
        
                # Iterate over bands and retrieve the maximum pixel values.
                maxvals = [band.GetMaximum() for band in tmax]
        
        Retrieve a NumPy array for a specific area by providing the extent as a 4-tuple of min/max x, y coordinates::
        
            arr = tmax.array((-120, 38, -118, 44))
        
        Reproject the raster to another coordinate system. You may pass EPSG codes, WKT,
        proj4 formatted projections, or a SpatialReference instance as an argument.::
        
            warped = tmax.warp(3857)
        
        Perhaps you would like to resample your image to a new resolution which can be
        achieved with::
        
            resampled = tmax.resample((100, 100))
        
        Raster instances still behave like a gdal.Dataset.::
        
            meta = tmax.GetMetadata()
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
