I'm given an array with an arbitrary number of axes, and I want to iterate over, say the first 'd' of them. How do I do this?
Initially I thought I would make an array containing all the indices I want to loop through, using
i = np.indices(a.shape[:d])
indices = np.transpose(np.asarray([x.flatten() for x in i]))
for idx in indices:
a[idx]
But apparently I cannot index an array like that, i.e. using another array containing the index.
You can use ndindex:
d = 2
a = np.random.random((2,3,4))
for i in np.ndindex(a.shape[:d]):
print i, a[i]
Output:
(0, 0) [ 0.72730488 0.2349532 0.36569509 0.31244037]
(0, 1) [ 0.41738425 0.95999499 0.63935274 0.9403284 ]
(0, 2) [ 0.90690468 0.03741634 0.33483221 0.61093582]
(1, 0) [ 0.06716122 0.52632369 0.34441657 0.80678942]
(1, 1) [ 0.8612884 0.22792671 0.15628046 0.63269415]
(1, 2) [ 0.17770685 0.47955698 0.69038541 0.04838387]
You could reshape a to compress the 1st d dimensions into one:
for x in a.reshape(-1,*a.shape[d:]):
print x
or
aa=a.reshape(-1,*a.shape[d:])
for i in range(aa.shape[0]):
print aa[i]
We really need to know more about what you need to do with a[i].
shx2 uses np.ndenumerate. The doc for that function mentions ndindex. That could be used as:
for i in np.ndindex(a.shape[:d]):
print i
print a[i]
Where i is a tuple. It's instructive to look at the Python code for these functions. ndindex for example uses nditer.
Write a simple recursive function:
import numpy as np
data = np.random.randint(0,10,size=4).reshape((1,1,1,1,2,2))
def recursiveIter(d, level=0):
print level
if level <= 2:
for item in d:
recursiveIter(item, level+1)
else:
print d
recursiveIter(data)
outputs:
0
1
2
3
[[[2 5]
[6 0]]]
