It looks like you are creating a coastline mask (or a coastline mask + some other mask), and computing the ratio of two quantities in a particular window around each point. If your coastline covers a sufficiently large portion of the image, you may get quite a bit of mileage using an efficient convolution instead of summing the windows directly. For example, you could use scipy.signal.convolve2d with inputs being (nsidc_copy != NSIDC_COASTLINE_MIXED), (nsidc_copy == NSIDC_SEAICE_LOW & nsdic_copy == NSIDC_FRESHSNOW) for the frst array, and a (2*radius x 2*radius) array of ones for the second. You may have to center the block of ones in an array of zeros the same size as nsdic_copy, but I am not sure about that.
Another option you may want to try is implementing your window movement more efficiently. If you step your window center along using an algorithm like flood-fill, you can insure that there will be very large overlap between successive steps (even if there is a break in the coastline). That means that you can reuse most of the data you've extracted. You will only need to subtract off the non-overlapping portion of the previous window and add in the non-overlapping portion of the updated window. If radius is 16, giving you a 32x32 window, you go from summing ~1000 pixels per quantity of interest, to summing only ~120 if the window moves along a diagonal, and only 64 if it moves vertically or horizontally. While an algorithm like this will probably give you the greatest boost, it is a pain to implement. If I had to guess, this looks like L2 processing for a multi-spectral instrument. If you don't mind me asking, what mission is this for? I'm working on space-looking detectors at the moment, but have spent many years on the L0, L1b and L1 portions of the GOES-R ground system. - Joe On Wed, Mar 28, 2018 at 9:43 PM, Eric Wieser <wieser.eric+nu...@gmail.com> wrote: > Well, one tip to start with: > > numpy.where(some_comparison, True, False) > > is the same as but slower than > > some_comparison > > Eric > > On Wed, 28 Mar 2018 at 18:36 Moroney, Catherine M (398E) > <catherine.m.moro...@jpl.nasa.gov> wrote: >> >> Hello, >> >> >> >> I have the following sample code (pretty simple algorithm that uses a >> rolling filter window) and am wondering what the best way is of speeding it >> up. I tried rewriting it in Cython by pre-declaring the variables but that >> didn’t buy me a lot of time. Then I rewrote it in Fortran (and compiled it >> with f2py) and now it’s lightning fast. But I would still like to know if I >> could rewrite it in pure python/numpy/scipy or in Cython and get a similar >> speedup. >> >> >> >> Here is the raw Python code: >> >> >> >> def mixed_coastline_slow(nsidc, radius, count, mask=None): >> >> >> >> nsidc_copy = numpy.copy(nsidc) >> >> >> >> if (mask is None): >> >> idx_coastline = numpy.where(nsidc_copy == NSIDC_COASTLINE_MIXED) >> >> else: >> >> idx_coastline = numpy.where(mask & (nsidc_copy == >> NSIDC_COASTLINE_MIXED)) >> >> >> >> for (irow0, icol0) in zip(idx_coastline[0], idx_coastline[1]): >> >> >> >> rows = ( max(irow0-radius, 0), min(irow0+radius+1, >> nsidc_copy.shape[0]) ) >> >> cols = ( max(icol0-radius, 0), min(icol0+radius+1, >> nsidc_copy.shape[1]) ) >> >> window = nsidc[rows[0]:rows[1], cols[0]:cols[1]] >> >> >> >> npoints = numpy.where(window != NSIDC_COASTLINE_MIXED, True, >> False).sum() >> >> nsnowice = numpy.where( (window >= NSIDC_SEAICE_LOW) & (window <= >> NSIDC_FRESHSNOW), \ >> >> True, False).sum() >> >> >> >> if (100.0*nsnowice/npoints >= count): >> >> nsidc_copy[irow0, icol0] = MISR_SEAICE_THRESHOLD >> >> >> >> return nsidc_copy >> >> >> >> and here is my attempt at Cython-izing it: >> >> >> >> import numpy >> >> cimport numpy as cnumpy >> >> cimport cython >> >> >> >> cdef int NSIDC_SIZE = 721 >> >> cdef int NSIDC_NO_SNOW = 0 >> >> cdef int NSIDC_ALL_SNOW = 100 >> >> cdef int NSIDC_FRESHSNOW = 103 >> >> cdef int NSIDC_PERMSNOW = 101 >> >> cdef int NSIDC_SEAICE_LOW = 1 >> >> cdef int NSIDC_SEAICE_HIGH = 100 >> >> cdef int NSIDC_COASTLINE_MIXED = 252 >> >> cdef int NSIDC_SUSPECT_ICE = 253 >> >> >> >> cdef int MISR_SEAICE_THRESHOLD = 6 >> >> >> >> def mixed_coastline(cnumpy.ndarray[cnumpy.uint8_t, ndim=2] nsidc, int >> radius, int count): >> >> >> >> cdef int irow, icol, irow1, irow2, icol1, icol2, npoints, nsnowice >> >> cdef cnumpy.ndarray[cnumpy.uint8_t, ndim=2] nsidc2 \ >> >> = numpy.empty(shape=(NSIDC_SIZE, NSIDC_SIZE), dtype=numpy.uint8) >> >> cdef cnumpy.ndarray[cnumpy.uint8_t, ndim=2] window \ >> >> = numpy.empty(shape=(2*radius+1, 2*radius+1), dtype=numpy.uint8) >> >> >> >> nsidc2 = numpy.copy(nsidc) >> >> >> >> idx_coastline = numpy.where(nsidc2 == NSIDC_COASTLINE_MIXED) >> >> >> >> for (irow, icol) in zip(idx_coastline[0], idx_coastline[1]): >> >> >> >> irow1 = max(irow-radius, 0) >> >> irow2 = min(irow+radius+1, NSIDC_SIZE) >> >> icol1 = max(icol-radius, 0) >> >> icol2 = min(icol+radius+1, NSIDC_SIZE) >> >> window = nsidc[irow1:irow2, icol1:icol2] >> >> >> >> npoints = numpy.where(window != NSIDC_COASTLINE_MIXED, True, >> False).sum() >> >> nsnowice = numpy.where( (window >= NSIDC_SEAICE_LOW) & (window >> <= NSIDC_FRESHSNOW), \ >> >> True, False).sum() >> >> >> >> if (100.0*nsnowice/npoints >= count): >> >> nsidc2[irow, icol] = MISR_SEAICE_THRESHOLD >> >> >> >> return nsidc2 >> >> >> >> Thanks in advance for any advice! >> >> >> >> Catherine >> >> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@python.org >> https://mail.python.org/mailman/listinfo/numpy-discussion > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
