I am trying to use the function as_strided from numpy.lib.stride_tricks to extract sub series from a larger 2D array, but I struggled to find the right thing to write for the strides argument.
Let's say I have a matrix m which contains 5 1D array of length (a=)10. I want to extract sub 1D arrays of length (b=)4 for each 1D array in m.
import numpy
from numpy.lib.stride_tricks import as_strided
a, b = 10, 4
m = numpy.array([range(i,i+a) for i in range(5)])
# first try
sub_m = as_strided(m, shape=(m.shape[0], m.shape[1]-b+1, b))
print sub_m.shape # (5,7,4) which is what i expected
print sub_m[-1,-1,-1] # Some unexpected strange number: 8227625857902995061
# second try with strides argument
sub_m = as_strided(m, shape=(m.shape[0], m.shape[1]-b+1, b), strides=(m.itemize,m.itemize,m.itemize))
# gives error, see below
AttributeError: 'numpy.ndarray' object has no attribute 'itemize'
As you can see I succeed to get the right shape for sub_m in my first try. However I can't find what to write in strides=()
For information:
m = [[ 0 1 2 3 4 5 6 7 8 9]
[ 1 2 3 4 5 6 7 8 9 10]
[ 2 3 4 5 6 7 8 9 10 11]
[ 3 4 5 6 7 8 9 10 11 12]
[ 4 5 6 7 8 9 10 11 12 13]]
Expected output:
sub_n = [
[[0 1 2 3] [1 2 3 4] ... [5 6 7 8] [6 7 8 9]]
[[1 2 3 4] [2 3 4 5] ... [6 7 8 9] [7 8 9 10]]
[[2 3 4 5] [3 4 5 6] ... [7 8 9 10] [8 9 10 11]]
[[3 4 5 6] [4 5 6 7] ... [8 9 10 11] [9 10 11 12]]
[[4 5 6 7] [5 6 7 8] ... [9 10 11 12] [10 11 12 13]]
]
edit: I have much more data, that's the reason why I want to use as_strided (efficiency)