Spencer Hill Spencer Hill - 1 year ago 119
Python Question

Numpy averaging with multi-dimensional weights along an axis

I have a numpy array,

. I want to take the weighted average of
along the first axis using the weights in array
. So the output should be a numpy array of shape

I know this can be done with a list comprehension:

np.array([np.average(a[i], weights=b) for i in range(48)])

But I'd like to avoid having to convert from a list back to a numpy array.

Can anyone help? I'm sure this is possible using numpy functions and slicing, but I'm stuck. Thanks!

Answer Source

In a single line:

np.average(a.reshape(48, -1), weights=b.ravel()), axis=1)

You can test it with:

a = np.random.rand(48, 90, 144)
b = np.random.rand(90,144)
np.testing.assert_almost_equal(np.average(a.reshape(48, -1),
                                          weights=b.ravel(), axis=1),
                                                    weights=b) for i in range(48)]))
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