On Wed, Dec 8, 2010 at 12:12 PM, Gregory, Matthew matt.gregory@oregonstate.edu wrote:

Hi all,

Likely a very newbie type of question.  I'm using numpy with GDAL to calculate zonal statistics on images.  The basic approach is that I have a zone raster and a value raster which are aligned spatially and I am storing each zone's corresponding values in a dictionary, then calculating the statistics on that population.  (I'm well aware that this approach may have memory issues with large rasters ...)

GDAL ReadAsArray gives you a chunk of raster data as a numpy array.  Currently I'm iterating over rows and columns of that chunk, but I'm guessing there's a better (and more numpy-like) way.

zone_stats = {} zone_block = zone_band.ReadAsArray(x_off, y_off, x_size, y_size) value_block = value_band.ReadAsArray(x_off, y_off, x_size, y_size) for row in xrange(y_size):    for col in xrange(x_size):        zone = zone_block[row][col]        value = value_block[row][col]        try:            zone_stats[zone].append(value)        except KeyError:            zone_stats[zone] = [value]

# Then calculate stats per zone ...

Just a thought since I'm not doing spatial statistics.

If you can create (integer) labels that assigns each point to a zone, then you can treat it essentially as a 1d grouped data, and you could use np.bincount to calculate some statistics, or alternatively scipy.ndimage.measurements for some additional statistics.

This would avoid any python loop, but require a full label array.

Josef

Thanks for all suggestions on how to make this better, especially if the initial approach I'm taking is flawed.

matt

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