On 07/19/2010 10:14 PM, Friedrich Romstedt wrote: > 2010/7/19 sandric ionut<sandricio...@yahoo.com>: >> For land-use a class would be for example forest, other would be orchard >> etc. For Slope gradient I would have values which<3 and between 3 and 7 >> etc. So, I will have 2 raster data with, let's say, 3 classes each: forest, >> orchards and built-up area and for slope gradient: 0-3, 3-15, 15-35. The >> cross-tabulation analysis should give me a table like: >> >> forest orchards built-up >> 0-3 10 20 15 >> 3-15 5 10 20 >> 15-35 5 15 15 >> >> where the numbers represents all the common cells, for example: 10 cells >> with forest correspond to 10 cells with 0-3 slope gradient interval and so >> on >> (by cells I mean the pixel from a raster data) > > Okay everything is clear now. I would suggestest looping over the > cells of the table. E.g. when: > > landuse_catmap > slope_map > > are the respective maps, you can categorise the slope_map with > > slope_catmap = 0 * (0<= slope_map) * (slope_map< 3) + 1 * (3<= > slope_map) * (slope_map< 15) + 2 * (15<= slope_map) > > to get categories 0, 1, and 2, where the zero case is included here > only for illustration, since it's just zero it can be omitted. > > Then you are maybe supposed to do: > > table = numpy.zeros((N_slope_cats, N_landuse_cats)) > > and finally the looping over its elements, where the looping over the > map cells is done entirely by numpy: > > for slope_cat in xrange(0, N_slope_cats): > for landuse_cat in xrange(0, N_landuse_cats): > table[slope_cat, landuse_cat] = \ > ((slope_catmap == slope_cat) * \ > (landuse_catmap == landuse_cat)).sum() > > I believe there is some slightly faster but more obscure way doing > this table creation in one single large step by putting the maps into > a hyperdimensianal array but I also believe this is beyond our scope > here if this is already fast enough.
That 'slightly faster but more obscure way' would be histogram2d :-) Especially when you have many landuse and/or slope classes over which to loop, histogram2d will be more than just slightly faster. About obscure, well, I leave that to the user. Probably depends on your background. Personally I use it all the time. Loops are evil :-) your 'more obscure' code would become something like this: slope_bin_edges = [0, 3, 15, 35] landuse_bin_edges = [0, 1, 2, 3] crosstab = numpy.histogram2d(landuse, slope, bins=(landuse_bin_edges, slope_bin_edges)) VS. > > Friedrich _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
