Convolution along one axis only

You can use np.apply_along_axis to apply np.convolve along the desired axis. Here is an example of applying a boxcar filter to a 2d array:

import numpy as npa = np.arange(10)a = np.vstack((a,a)).Tfilt = np.ones(3)np.apply_along_axis(lambda m: np.convolve(m, filt, mode='full'), axis=0, arr=a)

This is an easy way to generalize many functions that don't have an axis argument.

With ndimage.convolve1d, you can specify the axis...

np.apply_along_axis won't really help you, because you're trying to iterate over two arrays. Effectively, you'd have to use a loop, as described here.

Now, loops are fine if your arrays are small, but if N and P are large, then you probably want to use FFT to convolve instead.

However, you need to appropriately zero pad your arrays first, so that your "full" convolution has the expected shape:

M, N, P = 4, 10, 20A = np.random.randn(M, N)B = np.random.randn(M, P)A_ = np.zeros((M, N+P-1), dtype=A.dtype)A_[:, :N] = AB_ = np.zeros((M, N+P-1), dtype=B.dtype)B_[:, :P] = BA_fft = np.fft.fft(A_, axis=1)B_fft = np.fft.fft(B_, axis=1)C_fft = A_fft * B_fftC = np.real(np.fft.ifft(C_fft))# TestC_test = np.zeros((M, N+P-1))for i in range(M):    C_test[i, :] = np.convolve(A[i, :], B[i, :], 'full')assert np.allclose(C, C_test)