Short answer: Create 16 view arrays, each with a stride of 4 in both dimensions. Test them against the conditions and combine the tests with an |= operator. Thus you replace the nested loop with one that has only 16 iterations. Or reshape to 3 dimensions, the last with length 4, and you can do the same with only four view arrays.
Sturla Sendt fra min iPad Den 6. feb. 2012 kl. 20:16 skrev "Moroney, Catherine M (388D)" <catherine.m.moro...@jpl.nasa.gov>: > Hello, > > I have to write a code to downsample an array in a specific way, and I am > hoping that > somebody can tell me how to do this without the nested do-loops. Here is the > problem > statement: Segment a (MXN) array into 4x4 squares and set a flag if any of > the pixels > in that 4x4 square meet a certain condition. > > Here is the code that I want to rewrite avoiding loops: > > shape_out = (data_in.shape[0]/4, data_in.shape[1]/4) > found = numpy.zeros(shape_out).astype(numpy.bool) > > for i in xrange(0, shape_out[0]): > for j in xrange(0, shape_out[1]): > > excerpt = data_in[i*4:(i+1)*4, j*4:(j+1)*4] > mask = numpy.where( (excerpt >= t1) & (excerpt <= t2), True, False) > if (numpy.any(mask)): > found[i,j] = True > > Thank you for any hints and education! > > Catherine > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
