Use non-linear least squares to fit a function, f, to data.
Assumes ydata = f(xdata, *params) + eps
Parameters : | f : callable
xdata : An N-length sequence or an (k,N)-shaped array
ydata : N-length sequence
p0 : None, scalar, or M-length sequence
sigma : None or N-length sequence
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Returns : | popt : array
pcov : 2d array
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Notes
The algorithm uses the Levenburg-Marquardt algorithm: scipy.optimize.leastsq. Additional keyword arguments are passed directly to that algorithm.
Examples
>>> import numpy as np
>>> from scipy.optimize import curve_fit
>>> def func(x, a, b, c):
... return a*np.exp(-b*x) + c
>>> x = np.linspace(0,4,50)
>>> y = func(x, 2.5, 1.3, 0.5)
>>> yn = y + 0.2*np.random.normal(size=len(x))
>>> popt, pcov = curve_fit(func, x, yn)