Fitting data with python

From LPTMS Wiki
Jump to: navigation, search

Curve fitting

Preparing noisy data:

Npoints = 30
x = np.linspace(1,10,100)
xb = np.linspace(1,10,Npoints)
f = lambda x: np.sin(x)
yb = f(xb) + 0.3*np.random.normal(size=len(xb))

Using a polynomial fit that is based on generalized linear regression algorithm, solving a linear system.

from numpy.polynomial import polynomial as P
coeff, stats = P.polyfit(xb,yb,9,full=True)
fitpoly = P.Polynomial(coeff)
print stats

fitpoly is a function and coeff are the coefficients of the optimal polynomial.

Using curve-fit that calls *leastsq* algorithm, taking a step-by-step search for the minimum.

fitfunc = lambda x, a, b: a*np.sin(b*x)
p, pcov = curve_fit(fitfunc,xb,yb,p0 = [1.0,1.0])
print p, np.sqrt(np.diag(pcov))

The last lines provides the found optimal parameters and their uncertainties. It is worth trying several guesses p0.

Plotting the results:

import matplotlib.pyplot as plt

Using the least-square function directly

The basic syntax is the following:


from scipy import optimize
from numpy import array

# your data as lists
x = [0.0, 1.0, 2.0,  3.0]
y = [1.0, 0.5, 0.0, -1.0]
# define a fitting function and the corresponding error function that must be minimized
# use the lambda shortcut or define standard functions with def fit():
# p is the list of parameters
fit = lambda p, x: p[0] + p[1]*(x) + p[2]*(x)**2
err = lambda p, x, y: fit(p,x) - y
# initial guess
p0 = [1.0,1.0,1.0]
# calls optimize.leastsq to find optimal parameters, converts lists into numpy.array on the fly
p, success = optimize.leastsq(err, p0, args=(array(x), array(y)), ftol=5e-9, xtol=5e-9)
# some info about convergence is in success and the optimized parameters in p