Jupyter Snippet CB2nd 06_stride_tricks

4.6. Using stride tricks with NumPy

import numpy as np

def aid(x):
    # This function returns the memory
    # block address of an array.
    return x.__array_interface__['data'][0]

x = np.zeros(10)
x.strides

(8,)

y = np.zeros((10, 10))
y.strides

(80, 8)

n = 1000
a = np.arange(n)

b = np.lib.stride_tricks.as_strided(a, (n, n), (0, 8))

b

array([[  0,   1,   2, ..., 997, 998, 999],
       [  0,   1,   2, ..., 997, 998, 999],
       [  0,   1,   2, ..., 997, 998, 999],
       ...,
       [  0,   1,   2, ..., 997, 998, 999],
       [  0,   1,   2, ..., 997, 998, 999],
       [  0,   1,   2, ..., 997, 998, 999]])

b.size, b.shape, b.nbytes

(1000000, (1000, 1000), 8000000)

%timeit b * b.T

766 µs ± 2.59 µs per loop (mean ± std. dev. of 7 runs,
1000 loops each)

%%timeit
np.tile(a, (n, 1)) * np.tile(a[:, np.newaxis], (1, n))

5.55 ms ± 9.1 µs per loop (mean ± std. dev. of 7 runs,
100 loops each)