Armin Burger wrote:
maybe someone on this list has some experience using numpy in
combination with GDAL/Python and could give me some advice.
For further questions about numpy, the numpy list is very helpful.
difference can be easily calculated after reading both images into an
array and substract one from the other.
The problem is that both images can contain clouds or snow that have
predefined pixel values (252,253).
Does anybody know if this is possible and how to perform it?
yep.
Looking through the numpy docs I was not able to identify required
methods or functions for this. There is something like 'masked
arrays' but I have not understood if this could be used for my purpose.
This is exactly the kind of thing masked arrays are for. Yu can create
a masked array out of your data with something like:
>>> a
array([ 1, 2, 3, 4, 5, 6, 3, 4, 67, 4, 3, 5, 6, 7])
#a regular array
>>> import numpy.ma as ma
# create a masked array with the mask set at all elements with a value
of 3:
>>> a = ma.masked_values(a, 3)
>>> a
masked_array(data = [1 2 -- 4 5 6 -- 4 67 4 -- 5 6 7],
mask = [False False True False False False True False False
False True False
False False],
fill_value=3)
#another one:
>>> b = np.array((1,3,4,2,4,7,4,5,23,5,7,3,8,5))
>>> b = ma.masked_values(b, 3)
>>> b
masked_array(data = [1 -- 4 2 4 7 4 5 23 5 7 -- 8 5],
mask = [False True False False False False False False False
False False True
False False],
fill_value=3)
add them together:
>>> c = a+b
>>> c
masked_array(data = [2 -- -- 6 9 13 -- 9 90 9 -- -- 14 12],
mask = [False True True False False False True False False
False True True
False False],
fill_value=3)
If your values are Floating Point, then Another option would be to
replace all the "cloud" values with NaN:
>>> a = np.array((1,2,3,4,5,6,3,4,67,4,3,5,6,7), dtype=np.float)
>>> a[a==3] = np.nan
>>> a
array([ 1., 2., NaN, 4., 5., 6., NaN, 4., 67., 4., NaN,
5., 6., 7.])
>>> b = np.array((1,3,4,2,4,7,4,5,23,5,7,3,8,5), dtype=np.float)
>>> b[b==3] = np.nan
>>> b
array([ 1., NaN, 4., 2., 4., 7., 4., 5., 23., 5., 7.,
NaN, 8., 5.])
>>> a+b
array([ 2., NaN, NaN, 6., 9., 13., NaN, 9., 90., 9., NaN,
NaN, 14., 12.])
-Chris
