numpy ndarray切片和迭代

Question

I'm trying to slice and iterate over a multidimensional array at the same time. I have a solution that's functional, but it's kind of ugly, and I bet there's a slick way to do the iteration and slicing that I don't know about. Here's the code:

import numpy as np
x = np.arange(64).reshape(4,4,4)
y = [x[i:i+2,j:j+2,k:k+2] for i in range(0,4,2) 
                          for j in range(0,4,2) 
                          for k in range(0,4,2)]
y = np.array(y)
z = np.array([np.min(u) for u in y]).reshape(y.shape[1:])

Answer 1

Your last reshape doesn't work, because y has no shape defined. Without it you get:

>>> x = np.arange(64).reshape(4,4,4)
>>> y = [x[i:i+2,j:j+2,k:k+2] for i in range(0,4,2) 
...                           for j in range(0,4,2) 
...                           for k in range(0,4,2)]
>>> z = np.array([np.min(u) for u in y])
>>> z
array([ 0,  2,  8, 10, 32, 34, 40, 42])

But despite that, what you probably want is reshaping your array to 6 dimensions, which gets you the same result as above:

>>> xx = x.reshape(2, 2, 2, 2, 2, 2)
>>> zz = xx.min(axis=-1).min(axis=-2).min(axis=-3)
>>> zz
array([[[ 0,  2],
        [ 8, 10]],

       [[32, 34],
        [40, 42]]])
>>> zz.ravel()
array([ 0,  2,  8, 10, 32, 34, 40, 42])

Answer 2

It's hard to tell exactly what you want in the last mean, but you can use stride_tricks to get a "slicker" way. It's rather tricky.

import numpy.lib.stride_tricks

# This returns a view with custom strides, x2[i,j,k] matches y[4*i+2*j+k]
x2 = numpy.lib.stride_tricks(
        x, shape=(2,2,2,2,2,2),
        strides=(numpy.array([32,8,2,16,4,1])*x.dtype.itemsize))

z2 = z2.min(axis=-1).min(axis=-2).min(axis=-3)

Still, I can't say this is much more readable. (Or efficient, as each min call will make temporaries.)

Note, my answer differs from Jaime's because I tried to match your elements of y. You can tell if you replace the min with max .