The last t1 on each lineis of course t2. Sorry for the typo. Hard to code on an ipad ;-)
Sturla Sendt fra min iPad Den 6. feb. 2012 kl. 22:12 skrev Sturla Molden <stu...@molden.no>: > > 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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
