基于值以特定顺序迭代numpy数组

Question

I want to iterate over a numpy array starting at the index of the highest value working through to the lowest value

import numpy as np #imports numpy package

elevation_array = np.random.rand(5,5) #creates a random array 5 by 5

print elevation_array # prints the array out

ravel_array = np.ravel(elevation_array)
sorted_array_x = np.argsort(ravel_array)
sorted_array_y = np.argsort(sorted_array_x)

sorted_array = sorted_array_y.reshape(elevation_array.shape)

for index, rank in np.ndenumerate(sorted_array):
    print index, rank

I want it to print out:

index of the highest value index of the next highest value index of the next highest value etc

Answer 1

Try this:

from operator import itemgetter

>>> a = np.array([[2, 7], [1, 4]])
array([[2, 7],
       [1, 4]])

>>> sorted(np.ndenumerate(a), key=itemgetter(1), reverse=True)
[((0, 1), 7), 
 ((1, 1), 4), 
 ((0, 0), 2), 
 ((1, 0), 1)]

you can iterate this list if you so wish. Essentially I am telling the function sorted to order the elements of np.ndenumerate(a) according to the key itemgetter(1) . This function itemgetter gets the second (index 1) element from the tuples ((0, 1), 7), ((1, 1), 4), ... (ie the values) generated by np.ndenumerate(a) .

Answer 2

If you want numpy doing the heavy lifting, you can do something like this:

>>> a = np.random.rand(100, 100)
>>> sort_idx = np.argsort(a, axis=None)
>>> np.column_stack(np.unravel_index(sort_idx[::-1], a.shape))
array([[13, 62],
       [26, 77],
       [81,  4],
       ..., 
       [83, 40],
       [17, 34],
       [54, 91]], dtype=int64)

You first get an index that sorts the whole array, and then convert that flat index into pairs of indices with np.unravel_index . The call to np.column_stack simply joins the two arrays of coordinates into a single one, and could be replaced by the Python zip(*np.unravel_index(sort_idx[::-1], a.shape)) to get a list of tuples instead of an array.