Currently the SciPy’s scipy.signal.correlate function supports computing the cross-correlation between two 1D arrays. However, there is no direct support for axis-wise cross-correlation between two 2D arrays of the same shape. For example, computing the cross-correlation between each row (or column) pair across two 2D arrays currently requires implementing a Python for loop. This limitation becomes frustrating when dealing with large datasets, as loops are computationally inefficient, and workarounds can be non-intuitive for many users.

Currently the SciPy’s scipy.signal.correlate function supports computing the cross-correlation between two 1D arrays. However, there is no direct support for axis-wise cross-correlation between two 2D arrays of the same shape. For example, computing the cross-correlation between each row (or column) pair across two 2D arrays currently requires implementing a Python for loop. This limitation becomes frustrating when dealing with large datasets, as loops are computationally inefficient, and workarounds can be non-intuitive for many users.

Currently the SciPy’s scipy.signal.correlate function supports computing the cross-correlation between two 1D arrays. However, there is no direct support for axis-wise cross-correlation between two 2D arrays of the same shape. For example, computing the cross-correlation between each row (or column) pair across two 2D arrays currently requires implementing a Python for loop. This limitation becomes frustrating when dealing with large datasets, as loops are computationally inefficient, and workarounds can be non-intuitive for many users.