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I have a number of points in 3d space (cartesian x,y,z) and would like to fit a ellipsoid to that in order to determine the axis ratios. The issue here is that I have a distribution of points (not points on a surface), and the solutions to this problem mainly consider the points on a surface. Also would this fit be iterative (like some optimize or mcmc type method), I work in Python.

The code i am using was given in this answer: Python: fit 3D ellipsoid (oblate/prolate) to 3D points

But this does not work for me ( I think it was meant for points on the surface of an ellipsoid). But I have more density distribution of points rather than surface points.

Warrenmovic
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    Hi Warrenmovic. Could you help us by giving us a little more of context? Your series of points, and the code you have so far? It will maximize the help you can get from the community. – ohe Mar 24 '20 at 13:30
  • without more informaion about your probelm its hard to give you an answer but here's a couple links [leaast squares fitting of ellipsoid](http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1982-21702015000200329) or depending on your data [Principle Component Analysis](https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60) might be a better match. I have used a variant of PCA for ellipsoid fitting in the past but the data set was particularly suited to it. – DrBwts Mar 24 '20 at 16:12

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