Luca - 1 year ago 145

Python Question

I have a bit of python code which currently is hard-wired with two-dimensional arrays as follows:

`import numpy as np`

data = np.random.rand(5, 5)

width = 3

for y in range(0, data.shape[1] - W + 1):

for x in range(0, data.shape[0] - W + 1):

block = data[x:x+W, y:y+W]

# Do something with this block

Now, this is hard coded for a 2-dimensional array and I would like to extend this to 3D and 4D arrays. I could, of course, write more functions for other dimensions but I was wondering if there is a python/numpy trick to generate these sub-blocks without having to replicate this function for multidimensional data.

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Answer Source

Here is my wack at this problem. The idea behind the code below is to find the "starting indices" for each slice of data. So for 4x4x4 sub-arrays of a 5x5x5 array, the starting indices would be `(0,0,0), (0,0,1), (0,1,0), (0,1,1), (1,0,0), (1,0,1), (1,1,1)`

, and the slices along each dimension would be of length 4.

To get the sub-arrays, you just need to iterate over the different tuples of slice objects and pass them to the array.

```
import numpy as np
from itertools import product
def iterslice(data_shape, width):
# check for invalid width
assert(all(sh>=width for sh in data_shape),
'all axes lengths must be at least equal to width')
# gather all allowed starting indices for the data shape
start_indices = [range(sh-width+1) for sh in data_shape]
# create tuples of all allowed starting indices
start_coords = product(*start_indices)
# iterate over tuples of slice objects that have the same dimension
# as data_shape, to be passed to the vector
for start_coord in start_coords:
yield tuple(slice(coord, coord+width) for coord in start_coord)
# create 5x5x5 array
arr = np.arange(0,5**3).reshape(5,5,5)
# create the data slice tuple iterator for 3x3x3 sub-arrays
data_slices = iterslice(arr.shape, 3)
# the sub-arrays are a list of 3x3x3 arrays, in this case
sub_arrays = [arr[ds] for ds in data_slices]
```

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