Numpy Rolling Window Over 2d Array, As A 1d Array With Nested Array As Data Values

When using np.lib.stride_tricks.as_strided, how can I manage 2D a array with the nested arrays as data values? Is there a preferable efficient approach? Specifically, if I have a 2

Solution 1:

IIUC you could do something like this -

defrolling_window2D(a,n):
    # a: 2D Input array # n: Group/sliding window lengthreturn a[np.arange(a.shape[0]-n+1)[:,None] + np.arange(n)]

Sample run -

In [110]: a
Out[110]: 
array([[ 1,  2],
       [ 3,  4],
       [ 5,  6],
       [ 7,  8],
       [ 9, 10]])

In [111]: rolling_window2D(a,3)
Out[111]: 
array([[[ 1,  2],
        [ 3,  4],
        [ 5,  6]],

       [[ 3,  4],
        [ 5,  6],
        [ 7,  8]],

       [[ 5,  6],
        [ 7,  8],
        [ 9, 10]]])

Solution 2:

What was wrong with your as_strided trial? It works for me.

In [28]: x=np.arange(1,11.).reshape(5,2)
In [29]: x.shape
Out[29]: (5, 2)
In [30]: x.strides
Out[30]: (16, 8)
In [31]: np.lib.stride_tricks.as_strided(x,shape=(3,3,2),strides=(16,16,8))
Out[31]: 
array([[[  1.,   2.],
        [  3.,   4.],
        [  5.,   6.]],

       [[  3.,   4.],
        [  5.,   6.],
        [  7.,   8.]],

       [[  5.,   6.],
        [  7.,   8.],
        [  9.,  10.]]])

On my first edit I used an int array, so had to use (8,8,4) for the strides.

Your shape could be wrong. If too large it starts seeing values off the end of the data buffer.

[[  7.00000000e+000,   8.00000000e+000],
    [  9.00000000e+000,   1.00000000e+001],
    [  8.19968827e-257,   5.30498948e-313]]])

Here it just alters the display method, the 7, 8, 9, 10 are still there. Writing those those slots could be dangerous, messing up other parts of your code. as_strided is best if used for read-only purposes. Writes/sets are trickier.

Solution 3:

You task is similar to this one. So I slightly changed it.

# Rolling window for2D arrays in NumPy
import numpy as np

def rolling_window(a, shape):  # rolling window for2D array
    s = (a.shape[0] - shape[0] + 1,) + (a.shape[1] - shape[1] + 1,) + shape
    strides = a.strides + a.strides
    return np.lib.stride_tricks.as_strided(a, shape=s, strides=strides)

x = np.array([[1,2],[3,4],[5,6],[7,8],[9,10],[3,4],[5,6],[7,8],[11,12]])
y = np.array([[3,4],[5,6],[7,8]])
found = np.all(np.all(rolling_window(x, y.shape) == y, axis=2), axis=2)
print(found.nonzero()[0])