one other note: As a rule, using numpy array operations from Cython doesn't buy you much, as you discovered. YOu need to use numpy arrays as n-d containers, and write the loops yourself.
You may want to check out numba as another alternative -- it DOES optimize numpy operations. -CHB On Wed, Mar 28, 2018 at 5:56 PM, 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 > > -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov
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