Inspired by Roll rows of a matrix independently’s solution, here’s a vectorized one based on np.lib.stride_tricks.as_strided
–
from skimage.util.shape import view_as_windows as viewW
def strided_indexing_roll(a, r):
# Concatenate with sliced to cover all rolls
p = np.full((a.shape[0],a.shape[1]-1),np.nan)
a_ext = np.concatenate((p,a,p),axis=1)
# Get sliding windows; use advanced-indexing to select appropriate ones
n = a.shape[1]
return viewW(a_ext,(1,n))[np.arange(len(r)), -r + (n-1),0]
Sample run –
In [76]: a
Out[76]:
array([[4, 0, 0],
[1, 2, 3],
[0, 0, 5]])
In [77]: r
Out[77]: array([ 2, 0, -1])
In [78]: strided_indexing_roll(a, r)
Out[78]:
array([[nan, nan, 4.],
[ 1., 2., 3.],
[ 0., 5., nan]])