>>> x = arange(0,6e-2,6e-2/30)
>>> A,k,theta = 10, 1.0/3e-2, pi/6
>>> y_true = A*sin(2*pi*k*x+theta)
>>> y_meas = y_true + 2*randn(len(x))
>>> def residuals(p, y, x):
A,k,theta = p
err = y-A*sin(2*pi*k*x+theta)
return err
>>> def peval(x, p):
return p[0]*sin(2*pi*p[1]*x+p[2])
>>> p0 = [8, 1/2.3e-2, pi/3]
>>> print array(p0)
[ 8. 43.4783 1.0472]
>>> from scipy.optimize import leastsq
>>> plsq = leastsq(residuals, p0, args=(y_meas, x))
>>> print plsq[0]
[ 10.9437 33.3605 0.5834]
>>> print array([A, k, theta])
[ 10. 33.3333 0.5236]
>>> from xplt import * # Only on X-windows systems
>>> plot(x,peval(x,plsq[0]),x,y_meas,'o',x,y_true)
>>> title('Least-squares fit to noisy data')
>>> legend(['Fit', 'Noisy', 'True'])
>>> gist.eps('leastsqfit') # Make epsi file.
