Add multiple np.newaxis as needed?
Question:
I would like to pairwise compare (with <=) all elements of two NumPy ndarrays A and B, where both arrays can have arbitrary dimensions m and n, such that the result is an array of dimension m + n.
I know how to do it for given dimension of B.
-
scalar: A <= B
-
one-dimensional: A[..., np.newaxis] <= B
-
two-dimensional: A[..., np.newaxis, np.newaxis] <= B
Basically, I’m looking for a way to insert as many np.newaxis as there are dimensions in the second array.
Is there a syntax like np.newaxis * B.ndim, or another way?
Answers:
There’s builtin for that –
np.less_equal.outer(A,B)
Another way would be with reshaping to accomodate new axes –
A.reshape(list(A.shape)+[1]*B.ndim) <= B
The accepted answer solves OP’s problem, but does not address the question in the title in the optimal way. To add multiple np.newaxis, you can do
A[(...,) + (np.newaxis,) * B.ndim]
which is maybe more readable than the reshape solution.
I would like to pairwise compare (with <=) all elements of two NumPy ndarrays A and B, where both arrays can have arbitrary dimensions m and n, such that the result is an array of dimension m + n.
I know how to do it for given dimension of B.
-
scalar:
A <= B -
one-dimensional:
A[..., np.newaxis] <= B -
two-dimensional:
A[..., np.newaxis, np.newaxis] <= B
Basically, I’m looking for a way to insert as many np.newaxis as there are dimensions in the second array.
Is there a syntax like np.newaxis * B.ndim, or another way?
There’s builtin for that –
np.less_equal.outer(A,B)
Another way would be with reshaping to accomodate new axes –
A.reshape(list(A.shape)+[1]*B.ndim) <= B
The accepted answer solves OP’s problem, but does not address the question in the title in the optimal way. To add multiple np.newaxis, you can do
A[(...,) + (np.newaxis,) * B.ndim]
which is maybe more readable than the reshape solution.