numpy create 2D mask from list of indices [+ then draw from masked array]

Question

I have a 2-D array of values and need to mask certain elements of that array (with indices taken from a list of ~ 100k tuple-pairs) before drawing random samples from the remaining elements without replacement.

I need something that is both quite fast/efficient (hopefully avoiding for loops) and has a small memory footprint because in practice the master array is ~ 20000 x 20000.

For now I'd be content with something like (for illustration):

xys=[(1,2),(3,4),(6,9),(7,3)]

gxx,gyy=numpy.mgrid[0:100,0:100]
mask = numpy.where((gxx,gyy) not in set(xys)) # The bit I can't get right

# Now sample the masked array
draws=numpy.random.choice(master_array[mask].flatten(),size=40,replace=False)

Fortunately for now I don't need the x,y coordinates of the drawn fluxes - but bonus points if you know an efficient way to do this all in one step (ie it would be acceptable for me to identify those coordinates first and then use them to fetch the corresponding master_array values; the illustration above is a shortcut).

Thanks!

Linked questions:

Numpy mask based on if a value is in some other list

Mask numpy array based on index

Implementation of numpy in1d for 2D arrays?

Answer 1

You can do it efficently using sparse coo matrix

from scipy import sparse
xys=[(1,2),(3,4),(6,9),(7,3)]

coords = zip(*xys)
mask = sparse.coo_matrix((numpy.ones(len(coords[0])), coords ), shape= master_array.shape, dtype=bool)
draws=numpy.random.choice( master_array[~mask.toarray()].flatten(), size=10)