Masks ... nodata ... nulls
The attached pdf will serve for now. I will add additional documentation here on how to work with rasters with nodata areas here soon.
A simple example here, using Matplotlib to do the mapping. The -- cells are nodata values
Raster/array values | Sample properties |
---|---|
>>> print(c.filled()) # -9 is nodata [[ 3 1 3 -9 3 2 -9 -9 -9 2 2 2 3 -9 1 -9 3 3 -9 1] [ 1 3 2 3 -9 -9 -9 3 1 3 3 -9 3 2 3 3 3 3 1 1] [ 2 2 2 -9 1 2 2 -9 2 2 3 -9 3 -9 3 -9 2 2 1 3] [ 3 1 3 2 -9 -9 2 -9 2 -9 -9 1 2 1 1 2 1 3 3 -9] [-9 2 -9 2 -9 -9 1 2 1 -9 -9 2 2 1 1 1 3 1 3 3] [-9 2 3 3 1 2 2 3 -9 1 1 3 1 -9 -9 -9 2 1 3 -9] [-9 -9 -9 2 1 1 -9 2 2 1 1 2 2 3 2 3 2 2 2 -9] [ 3 -9 3 -9 -9 2 3 -9 3 2 2 2 1 -9 3 2 -9 2 2 1] [ 1 -9 2 2 1 -9 2 1 2 2 -9 -9 3 -9 2 2 -9 1 -9 1] [ 1 1 -9 -9 -9 2 -9 2 3 2 -9 1 2 1 3 1 -9 -9 1 3] [ 1 -9 -9 1 2 1 -9 1 -9 -9 -9 -9 1 -9 -9 -9 -9 2 -9 3] [-9 3 -9 -9 -9 2 3 -9 -9 1 2 1 1 2 1 1 3 2 3 2] [ 3 3 3 -9 3 1 3 -9 -9 -9 3 2 -9 -9 3 2 3 -9 1 -9] [-9 2 2 3 3 1 3 1 -9 -9 2 3 3 1 1 1 1 1 -9 1] [-9 1 -9 3 1 1 -9 2 -9 1 1 2 2 1 -9 2 2 3 -9 3] [ 3 2 -9 2 2 -9 -9 2 1 2 1 2 -9 3 2 1 1 1 3 1] [-9 -9 3 2 2 -9 2 2 1 2 -9 3 1 2 2 -9 3 3 2 1] [ 1 -9 1 -9 2 2 3 -9 3 2 2 -9 1 2 3 -9 -9 -9 3 3] [ 3 1 1 1 2 1 2 -9 -9 2 2 1 2 -9 -9 -9 2 -9 3 2] [-9 2 3 1 -9 1 1 -9 2 -9 1 1 1 2 1 2 3 -9 -9 3]] | >>> c.mean() 1.96028880866426 >>> c.min() = 1 >>> c.max() = 3 >>> np.histogram(c, bins=[1,2,3,4]) (array([ 92, 104, 81]), array([1, 2, 3, 4])) |
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