numpy write with two masks-CodePudding

If I have an array a with 100 elements that I want to conditionally update. I have the first mask m which selects elements of a that will be tried to update. Out of a[m] (say, 50 elements), I want to update a subset some elements, but leaves others. So the second mask m2 has 50=m.sum() elements, only some of which are True.

For completeness, a minimal example:

a = np.random.random(size=100)
m = a > 0
m2 = np.random.random(size=m.sum()) < 0
newvalues = -np.random.randint(size=m2.sum())

Then if I were to do

a[m][m2] = newvalues

This does not change the values of a, because fancy indexing a[m] makes a copy here. using indices (with where) has the same behaviour.

Instead, this works:

m12 = m.copy()
m12[m] = m2

a[m12] = newvalues

However, this is verbose and difficult to read.

Is there a more elegant way to update a subset of a subset of an array?

CodePudding user response：

First compute the indices of elements to update:

indices = np.array(range(100))
indices = indices[m1][m2]

then use indices to update array a:

a[indices] = newvalue

CodePudding user response：

You can potentially first compute the "final index" of interest and then use those indexes to update. One way to achieve this in a more "numpy" way is to mask the first index array, which is computed based on the first mask array.

final_mask = np.where(m)[0][m2]
a[final_mask] = newvalues