Suppose you have a 3D array:
arr = np.zeros((9,9,9))
a[2:7,2:7,2:7] = np.random.randint(5, size=(5,5,5))
How can you sort all occurring values in this array (not along an axis like with e.g. np.sort) and show all indices of those values?
Output should be something like:
0 at [0,0,0], [0,1,0], [0,2,1], ...etc.
1 at [5,5,5], [5,7,6], ...etc
2 at [4,5,5], ...etc
3 at ...etc
and so on
A very simple method for getting your grouped values would be defaultdict:
from collections import defaultdict
grouped = defaultdict(list)
for position, v in np.ndenumerate(arr):
grouped[v].append(position)
for v, positions in grouped.items():
print('{0} is at {1}'.format(v, positions))
import numpy as np
arr = np.zeros((9,9,9))
arr[2:7,2:7,2:7] = np.random.randint(5, size=(5,5,5))
S = np.sort(arr,axis=None)
I = np.argsort(arr, axis=None)
print np.array([S] + list( np.unravel_index(I, arr.shape))).T
This should give you more or less the result you are looking for; the essence here is in unravel_index. If you insist on obtaining your results in a manner grouped by array value, you can search stackoverflow for grouping in numpy.
This would work (not very efficient though):
arr = np.zeros((9,9,9))
arr[2:7,2:7,2:7] = np.random.randint(5, size=(5,5,5))
arr = arr.flatten () # Flatten the array, arr is now a (9 * 9 * 9) vector
arr.sort () # Sort the now 1-d array
arr.reshape ((9, 9, 9)) # Reshape it
for i in range(0, 5):
id = np.array(np.where (arr == i)).T
print('{} at {}'.format(i, ', '.join(map(str, c))))
