![]() You can visualize the results by using polyval: y_new = N.polyval(fit,x) Not bad, but not exactly there due to the introduced errors. In my case, the fit array contained: array()Ĭompare with our initial values of. Now to fit a degree 3 polynomial is quite easy: fit = N.polyfit(x,y,3) Y is in this case a cubic polynomial with coefficients, with the added complexity of some random numbers. First, let's get some random data: import numpy as N We will fit the data arrays x, y against a polynomial (in this case degree 3). A more complicated to use but more complete option is to use scipy's odrpack (Orthogonal Distance Regression). So why would you want more? Well, one reason is that if you want the errors for the fitted coefficients. The quick and easy way to do it in python is using numpy's polyfit. Polynomial fitting is one of the simplest cases, and one used often. Here I cover numpy's polyfit and scipy's least squares and orthogonal distance regression functions. As with many other things in python and scipy, fitting routines are scattered in many places and not always easy to find or learn to use. ![]() This page deals with fitting in python, in the sense of least-squares fitting (but not limited to).
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