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I have a 3-D dataframe representing family of curves: yi=f(xi) which are also depending on a third variable lets say zi (yi=g(xi,zi)) as shown in the graphic below:

Family Of Curves

The yi data are measurements and each curve is valid on its own.

Now I would like to find out a proper way to fit a plane or surface to this data in order to avoid 2-D bilinear data interpolation.

In general I am trying to do a similar approach as in the link below:

2D polynomial in Python

With the difference that my x and y data do not have the same dimensions. What I am looking for is more a solution for this kind of problem:

2D data fitting Matlab

Bab
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    Hi, try to think of a good way of encapsulating your data then write a residual function and use `scipy.optimize.leastsq`. If you have programming problems post them. – mikuszefski Jun 29 '20 at 09:58
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    Thanks. I already solves this problem by myself. Sometimes it helps just du write down the question. – Bab Jul 08 '20 at 20:36

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