[gdal-dev] GDAL and numpy, masking arrays

[Armin Burger]

I guess I found it, looks like the function
filled(c, 255)
does this.

Armin

On 12/11/2008 21:54, Armin Burger wrote:
> Chris
> 
> thanks a lot for the quick and very helpful explanations. Is there a 
> possibility to reset the masked values '--' after the calculation to a 
> special value?
> 
> Best regards
> 
> Armin
> 
> On 12/11/2008 21:27, Christopher Barker wrote:
>> 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
>>
>>
>>
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