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how to get the same output of voronoin of MATLAB by scipy.spatial.Voronoi of Python

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Kyohei
Kyohei on 28 Nov 2019
I use voronoin of MATLAB to judge the connection between cells.
When I use scipy.spatial.Voronoi of Python, the output is a little different.
For example, I used the same input for MATLAB and Python as you can see in the next code.
MATLAB:
seed = [ 17.746 -0.37283 -0.75523;
6.1704 1.3404 7.0341;
-7.7211 5.4282 4.5016;
5.8014 2.1252 -6.2491;
-16.047 -2.8472 -0.024795;
-2.2967 -6.7334 0.60707]
[vvern_mat, vceln_mat] = voronoin(seed);
Python:
import numpy as np
from scipy.spatial import Voronoi
seed = np.array([[ 17.746 , -0.37283 , -0.75523 ],
[ 6.1704 , 1.3404 , 7.0341 ],
[ -7.7211 , 5.4282 , 4.5016 ],
[ 5.8014 , 2.1252 , -6.2491 ],
[-16.047 , -2.8472 , -0.024795],
[ -2.2967 , -6.7334 , 0.60707 ]])
vor = Voronoi(kariseed)
vvern_py = vor.vertices
vceln_py = vor.regions
The output is as followed.
vvern_mat =
Inf Inf Inf
-6.9386 1.7980 -7.7861
-15.9902 -20.8031 50.1840
29.5016 106.3690 5.9214
8.6816 -6.5899 -0.1741
-0.2027 2.1210 0.5874
vceln_mat =
1 4 5
1 3 4 5 6
1 2 3 4 6
1 2 4 5 6
1 2 3
1 2 3 5 6
vvern_py =
array([[ -6.93864391, 1.79801934, -7.78610533],
[-15.9902125 , -20.80310202, 50.1840397 ],
[ 29.501584 , 106.36899584, 5.92137852],
[ 8.68156407, -6.58985621, -0.17410448],
[ -0.20266123, 2.12100225, 0.58735065]])
vceln_py =
[[],
[-1, 0, 2, 3, 4],
[-1, 2, 3],
[-1, 0, 1],
[-1, 0, 1, 2, 4],
[-1, 1, 2, 3, 4],
[-1, 0, 1, 3, 4]]
When, you focus on vceln, you would notice the values are the same between MATLAB and Python because you can get vceln_mat by adding two to vceln_mat.
However, the line order is different, and I had difficulty converting vceln_py to vceln_mat.
I thought I could solve this problem by applying Qhull options of MATLAB to Python, but I couldn't get the same output. (About options of voronoin: https://jp.mathworks.com/help/matlab/ref/voronoin.html?lang=en#bqurvsm-1)
If anyone can solve this problem, I would really appreciate.

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