On Tue, Oct 12, 2010 at 11:14 PM, Pinner, Luke <
[email protected]> wrote:
> I'm working with some MODIS satellite imagery. MODIS data includes a
> quality flag mask. For the particular dataset I'm working with, this is
> a two dimensional unsigned 16 bit integer array. The quality flags are
> stored as one or more bits in each integer value:
>
> Bits are numbered from 0 (least significant bit)
> Bit Long name Key
> 0-1 MODLAND_QA
> 00=VI produced, good quality
> 01=VI produced, but check other QA
> 10=Pixel produced, but most probably
> cloudy
> 11=Pixel not produced due to other
> reasons
> than clouds
> 2-5 VI usefulness
> 0000=Highest quality
> 0001=Lower quality
> 0010=Decreasing quality
> 0100=Decreasing quality
> 1000=Decreasing quality
> 1001=Decreasing quality
> 1010=Decreasing quality
> 1100=Lowest quality
> 1101=Quality so low that it is not
> useful
> 1110=L1B data faulty
> 1111=Not useful for any other reason/not
> processed
> ...<SNIP>...
> 15 Possible shadow
> 0=No
> 1=Yes
>
>
> Some typical values are:
> arr=numpy.array([51199,37013,36885,36889,34841,2062,34837,2061,35033,349
> 61,2185,37013,36885,2185,4109,4233], dtype=numpy.uint16)
>
> How would I extract groups of/individual bit values from such an array?
>
You could use the shift (>>) and bitwise 'and' (&) operators:
In [50]: arr
Out[50]:
array([51199, 37013, 36885, 36889, 34841, 2062, 34837, 2061, 35033,
34961, 2185, 37013, 36885, 2185, 4109, 4233], dtype=uint16)
In [51]: qa = arr & 3
In [52]: qa
Out[52]: array([3, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
dtype=uint16)
In [53]: usefulness = (arr >> 2) & 15
In [54]: usefulness
Out[54]: array([15, 5, 5, 6, 6, 3, 5, 3, 6, 4, 2, 5, 5, 2, 3,
2], dtype=uint16)
In [55]: shadow = arr >> 15
In [56]: shadow
Out[56]: array([1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0],
dtype=uint16)
Warren
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