Something like this: m,n = data.shape x = data.reshape((m,n//4,4)) z = (x[0::4,...] >= t1) & (x[0::4,...] <= t1) z |= (x[1::4,...] >= t1) & (x[1::4,...] <= t1) z |= (x[2::4,...] >= t1) & (x[2::4,...] <= t1) z |= (x[3::4,...] >= t1) & (x[3::4,...] <= t1) found = np.any(z, axis=2)
Sturla Sendt fra min iPad Den 6. feb. 2012 kl. 21:57 skrev Sturla Molden <stu...@molden.no>: > 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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
