可以使用Ivan的detect_peaks函数对任意维度的数组找到局部极小值的位置，并进行少量修改：
import numpy as np
import scipy.ndimage.filters as filters
import scipy.ndimage.morphology as morphology
def detect_local_minima(arr):
# https://stackoverflow.com/questions/3684484/peak-detection-in-a-2d-array/3689710#3689710
"""
Takes an array and detects the troughs using the local maximum filter.
Returns a boolean mask of the troughs (i.e. 1 when
the pixel's value is the neighborhood maximum, 0 otherwise)
"""
# define an connected neighborhood
# http://www.scipy.org/doc/api_docs/SciPy.ndimage.morphology.html#generate_binary_structure
neighborhood = morphology.generate_binary_structure(len(arr.shape),2)
# apply the local minimum filter; all locations of minimum value
# in their neighborhood are set to 1
# http://www.scipy.org/doc/api_docs/SciPy.ndimage.filters.html#minimum_filter
local_min = (filters.minimum_filter(arr, footprint=neighborhood)==arr)
# local_min is a mask that contains the peaks we are
# looking for, but also the background.
# In order to isolate the peaks we must remove the background from the mask.
#
# we create the mask of the background
background = (arr==0)
#
# a little technicality: we must erode the background in order to
# successfully subtract it from local_min, otherwise a line will
# appear along the background border (artifact of the local minimum filter)
# http://www.scipy.org/doc/api_docs/SciPy.ndimage.morphology.html#binary_erosion
eroded_background = morphology.binary_erosion(
background, structure=neighborhood, border_value=1)
#
# we obtain the final mask, containing only peaks,
# by removing the background from the local_min mask
detected_minima = local_min ^ eroded_background
return np.where(detected_minima)
您可以这样使用：
arr=np.array([[[0,0,0,-1],[0,0,0,0],[0,0,0,0],[0,0,0,0],[-1,0,0,0]],
[[0,0,0,0],[0,-1,0,0],[0,0,0,0],[0,0,0,-1],[0,0,0,0]]])
local_minima_locations = detect_local_minima(arr)
print(arr)
# [[[ 0 0 0 -1]
# [ 0 0 0 0]
# [ 0 0 0 0]
# [ 0 0 0 0]
# [-1 0 0 0]]
# [[ 0 0 0 0]
# [ 0 -1 0 0]
# [ 0 0 0 0]
# [ 0 0 0 -1]
# [ 0 0 0 0]]]
这表示最小值出现在索引[0,0,3]，[0,4,0]，[1,1,1]和[1,3,3]处：
print(local_minima_locations)
# (array([0, 0, 1, 1]), array([0, 4, 1, 3]), array([3, 0, 1, 3]))
print(arr[local_minima_locations])
# [-1 -1 -1 -1]
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