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+numpy@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.Moroney@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
>>
>>
>>
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>
>
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