#!/usr/bin/env python

#from scipy import optimize, special
from numpy import *
import matplotlib.pyplot as plt
from scipy.optimize import leastsq

## Parametric function: 'v' is the parameter vector, 'x' the independent varible
fp = lambda v, x: v[0]/(x**v[1])*sin(v[2]*x)

## Noisy function (used to generate data to fit)
v_real = [1.5, 0.1, 2.]
fn = lambda x: fp(v_real, x)

## Error function
e = lambda v, x, y: (fp(v,x)-y)

## Generating noisy data to fit
n = 30
xmin = 0.1
xmax = 5
x = linspace(xmin,xmax,n)
y = fn(x) + random.rand(len(x))*0.2*(fn(x).max()-fn(x).min())

## Initial parameter value
v0 = [3., 1, 4.]

## Fitting
v, success = leastsq(e, v0, args=(x,y), maxfev=10000)

## Plot
def plot_fit():
    print 'Estimater parameters: ', v
    print 'Real parameters: ', v_real
    X = linspace(xmin,xmax,n*5)
    plt.plot(x,y,'ro', X, fp(v,X))
    plt.legend(['Orig','LeastSq'], loc=4)

plot_fit()

plt.savefig('fig_leastSquare.png',dpi=200)
plt.show()