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.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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