Padding Numpy Rolling Window Operations Using Strides

I have a function f that I would like to efficiently compute in a sliding window. def efficient_f(x): # do stuff wSize=50 return another_f(rolling_window_using_strides(x,

Solution 1:

Here's one way to solve it with np.lib.stride_tricks.as_strided -

def strided_axis0(a, fillval, L): # a is 1D array
    a_ext = np.concatenate(( np.full(L-1,fillval) ,a))
    n = a_ext.strides[0]
    strided = np.lib.stride_tricks.as_strided     
    returnstrided(a_ext, shape=(a.shape[0],L), strides=(n,n))

Sample run -

In [95]: np.random.seed(0)

In [96]: a = np.random.rand(8,1)

In [97]: a
Out[97]: 
array([[ 0.55],
       [ 0.72],
       [ 0.6 ],
       [ 0.54],
       [ 0.42],
       [ 0.65],
       [ 0.44],
       [ 0.89]])

In [98]: strided_axis0(a[:,0], fillval=np.nan, L=3)
Out[98]: 
array([[  nan,   nan,  0.55],
       [  nan,  0.55,  0.72],
       [ 0.55,  0.72,  0.6 ],
       [ 0.72,  0.6 ,  0.54],
       [ 0.6 ,  0.54,  0.42],
       [ 0.54,  0.42,  0.65],
       [ 0.42,  0.65,  0.44],
       [ 0.65,  0.44,  0.89]])