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

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.

[Valerio Ficcadenti]

I have a very similar issue.

The areas from the same set of points are different!

Not sure what to do!