cpmorphology.maximum_position gives inconsistent results based on values outside the region of interest

[afermg]

This was detected on cp_measure, and I'm pretty sure it's a scipy issue, as I could reproduce the behaviour by directly calling scipy's maximum_position.

import numpy as np
from centrosome.cpmorphology import distance_to_edge, maximum_position_of_labels

masks = np.zeros((240, 240), dtype=int)
masks[50:100, 50:100] = 1  # First square 50x50
masks[80:120, 90:120] = 1  # Major asymmetries on bottom right edge
masks[150:200, 150:200] = 2  # Second square 50x50
masks[175:180, 180:210] = 2  # Minor asymmetries on bottom right edge

masks1 = np.zeros_like(masks)
masks1[50:100, 50:100] = 1  # First square 50x50
masks1[80:120, 90:120] = 1  # Major asymmetries on bottom right edge

masks2 = np.zeros_like(masks)
masks2[150:200, 150:200] = 1  # Second square 50x50
masks2[175:180, 180:210] = 1  # Minor asymmetries on bottom right edge

res = []
for m in (masks, masks1, masks2):
    uniq = np.unique(m)
    d_to_edge = distance_to_edge(m)
    maxpos = maximum_position_of_labels(d_to_edge, m, uniq[uniq > 0])
    res.append(maxpos)

np.testing.assert_array_equal(
    res[0], np.concatenate((res[1], res[2]), axis=1)
)
"""
AssertionError: 
Arrays are not equal

Max absolute difference: 1
Max relative difference: 0.01351351
 x: array([[ 75, 175],
       [ 75, 175]])
 y: array([[ 75, 174],
       [ 74, 175]])
"""

The behaviour seems to change when there are other values outside the region, even if they belong to another label, the maximum value has a one-off error.

import numpy as np
from scipy.ndimage import maximum_position
from centrosome.cpmorphology import distance_to_edge

masks = np.zeros((240, 240), dtype=int)
masks[50:100, 50:100] = 1  # First square 50x50
masks[80:120, 90:120] = 1  # Major asymmetries on bottom right edge
masks[150:200, 150:200] = 2  # Second square 50x50
masks[175:180, 180:210] = 2  # Minor asymmetries on bottom right edge

d_to_edge = distance_to_edge(masks)
d_to_edge_one_object = d_to_edge.copy()
d_to_edge_one_object[masks == 2] = 0
np.testing.assert_array_equal(
    maximum_position(d_to_edge, masks, [1]),
    maximum_position(d_to_edge_one_object, masks, [1]),
)
"""
AssertionError: 
Arrays are not equal

Mismatched elements: 1 / 2 (50%)
Max absolute difference: 1
Max relative difference: 0.01351351
 x: array([[75, 75]])
 y: array([[75, 74]])
"""

I'll check the code of scipy.ndimage.maximum_position to see if I can figure it out, but I wanted to record this in an issue here.