{ "info": { "author": "Brian Galey", "author_email": "bkgaley@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: BSD License", "Natural Language :: English", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5" ], "description": "Greenwich\n=========\n\n.. image:: https://travis-ci.org/bkg/greenwich.svg?branch=master\n :target: https://travis-ci.org/bkg/greenwich\n.. image:: https://coveralls.io/repos/bkg/greenwich/badge.png?branch=master\n :target: https://coveralls.io/r/bkg/greenwich?branch=master \n\nAdding Python conveniences to the wonderful world of `GDAL `_.\n\nGreenwich provides a wrapper for the GDAL SWIG Python bindings. The focus here is on\nproviding some higher level behavior mainly to the raster side of the GDAL/OGR fence.\n\nInstallation\n------------\n\nThe GDAL Python bindings need a little assistance in finding the pertinent\nheaders when building from within a virtualenv. If the usual ``pip install greenwich``\nfails, specify the GDAL header directory wherever that may be for you such as:\n\n.. code-block:: sh\n\n CFLAGS=-I/usr/include/gdal pip install greenwich\n\nBasic Usage\n-----------\n\nOpen any raster data set you have lying around, perhaps some climate data from\n`WorldClim `_.\n\n.. code-block:: python\n\n from greenwich import Geometry, Raster\n from greenwich.io import MemFileIO\n\n with Raster('cc85tn701.tif') as tmax:\n # Save as a NetCDF file.\n tmax.save('cc85tn701.nc')\n geom = Geometry(\n wkt='POLYGON((-123 47,-123 48,-122 49,-121 48,-121 47,-123 47))',\n srs=4326)\n # Clip the raster with a geometry and save the result as a GeoTIFF.\n with tmax.clip(geom) as clipped:\n clipped.save('clipped.tif')\n\n # Return a NumPy MaskedArray using nodata values for a given bounding box.\n m = tmax.masked_array((-120, 38, -118, 44))\n\n # Convert to an Erdas Imagine file in memory.\n imgio = MemFileIO(suffix='.img')\n tmax.save(imgio)\n imgdata = imgio.read()\n imgio.close()\n\n # Iterate over bands and retrieve the maximum pixel values.\n maxvals = [band.GetMaximum() for band in tmax]\n\nRetrieve a NumPy array for a specific area by providing the extent as a 4-tuple of min/max x, y coordinates::\n\n arr = tmax.array((-120, 38, -118, 44))\n\nReproject the raster to another coordinate system. 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