Hi there,
I have a 2D array of size, lets say 40000 * 512, which I need to downsample
by a step of 4 in the y direction, and step 3 in the x direction, so I would
get an array of 10000 * 170 (or 171, the edges do not matter much). But what
I need to retain are the maxima and minima in every 4*3 window.
I have a solution that works by first clipping off the edges of the array,
so that its shape is divisible by 4*3, and then applying a two step process
that will compute the min and max in strips --first in the x direction and
then in the y-direction through reshaping and reduce (not super elegant, but
it seems to work):
def downsampleArray1D(data, step, nullValue):
print 'downsampling %d' %step
if data.shape[1] % step != 0:
xlen = data.shape[1] - data.shape[1] % step
data = data[...,:xlen]
data1d = data.ravel()
assert(data1d.shape[0] % step == 0)
data1d.shape = (len(data1d)/step, step)
maxdata = numpy.maximum.reduce(data1d,1)
mindata = numpy.minimum.reduce(data1d,1)
mindata.shape = ((data.shape[0],int(data.shape[1] / step)))
maxdata.shape = ((data.shape[0],int(data.shape[1] / step)))
return mindata, maxdata
def downsampleArray2D(data, yStep, xStep, nullValue):
# first adjust the data to the step size by ignoring edges
if data.shape[0] % yStep != 0:
ylen = data.shape[0] - data.shape[0] % yStep
data = data[:ylen,...]
if data.shape[1] % xStep != 0:
xlen = data.shape[1] - data.shape[1] % xStep
data = data[...,:xlen]
minx, maxx = downsampleArray1D(data, xStep, nullValue)
minxt = numpy.transpose(minx)
minxt1, dummy = downsampleArray1D(minxt, yStep, nullValue)
maxxt = numpy.transpose(maxx)
dummy, maxxt1 = downsampleArray1D(maxxt, yStep, nullValue)
minimum = numpy.transpose(minxt1)
maximum = numpy.transpose(maxxt1)
return minimum, maximum
Now I need a solution that will ignore null values, i.e. that any value that
is equivalent to e.g. -999 ignored in the min and max computation, but if
all values in a 4*3 window are nullvalues, then min and max should be set to
the null value.
Any suggestions?
Ludwig
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