On Sun, Jun 16, 2013 at 3:24 AM, Thomas Robitaille < thomas.robitaille@gmail.com> wrote:
Hi everyone,
I have a question regarding the output from the scipy.optimize.curve_fit function - in the following example:
""" In [1]: import numpy as np
In [2]: from scipy.optimize import curve_fit
In [3]: f = lambda x, a, b: a * x + b
In [4]: x = np.array([0., 1., 2.])
In [5]: y = np.array([1.2, 4.6, 7.8])
In [6]: e = np.array([1., 1., 1.])
In [7]: curve_fit(f, x, y, sigma=e) Out[7]: (array([ 3.3 , 1.23333333]), array([[ 0.00333333, -0.00333333], [-0.00333333, 0.00555556]]))
In [8]: curve_fit(f, x, y, sigma=e * 100) Out[8]: (array([ 3.3 , 1.23333333]), array([[ 0.00333333, -0.00333333], [-0.00333333, 0.00555556]])) """
it's clear that the covariance matrix does not take into account the uncertainties on the data points. If I do:
""" popt, pcov = curve_fit(...) """
Then pcov[0,0]**0.5 is therefore not the uncertainty on the parameter, so I was wondering how this should be scaled to give the actual uncertainty on the parameter?
There was a long discussion by email and then github on this: http://mail.scipy.org/pipermail/scipy-user/2011-August/030412.html https://github.com/scipy/scipy/pull/448 The open pull request has the code to do the scaling you want. - Tom
Thanks! Tom _______________________________________________ SciPy-User mailing list SciPy-User@scipy.org http://mail.scipy.org/mailman/listinfo/scipy-user