Fit a sine using least squares

fitsine.py

#!/usr/bin/env python
import numpy as np
import scipy.optimize as optimize
import matplotlib.pylab as plt
import csv
import argparse


def main():
    parser = argparse.ArgumentParser(description='iris - sine fitter')
    parser.add_argument('data', type=str)

    args = parser.parse_args()
    x = []
    y = []
    with open(args.data, 'rb') as csvfile:
        reader = csv.reader(csvfile, delimiter=',', quotechar='#')
        for row in reader:
            x.append(float(row[0]))
            y.append(float(row[1]))

    x = np.array(x)
    y = np.array(y)

    def func(p, x):
        return p[2] + p[0] * np.sin(x - p[1])

    def fitfunc(p, x, y):
        return y - func(p, x)

    kd,cov,infodict,mesg,ier = optimize.leastsq(fitfunc, (0.2, 1.9, 0.67), args=(x, y), full_output=True)
    #print (kd, cov, infodict, mesg, ier)
    print(kd)
    x_uq = np.unique(x)
    y_fit = func(kd, x_uq)

    plt.scatter(x, y,  color='dodgerblue', label='data')
    plt.plot(x_uq, y_fit, 'k--')

    plt.show()



if __name__ == '__main__':
    main()