notebook.community

Edit and run



In [1]:

    
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

Create data



In [2]:

    
xdata = np.array([-2,-1.64,-1.33,-0.7,0,0.45,1.2,1.64,2.32,2.9])
ydata = np.array([0.699369,0.700462,0.695354,1.03905,1.97389,2.41143,1.91091,0.919576,-0.730975,-1.42001])

Show data points



In [3]:

    
plt.plot(xdata,ydata,'*')
plt.xlabel('xdata')
plt.ylabel('ydata');

Define fit function



In [4]:

    
def func(x, p1,p2):
  return p1*np.cos(p2*x) + p2*np.sin(p1*x)

Calculate and show fit parameters. Use a starting guess of p1=1 and p2=0.2



In [5]:

    
popt, pcov = curve_fit(func, xdata, ydata,p0=(1.0,0.2))
popt









    Out[5]:





array([ 1.88184732,  0.70022901])

Calculate and show sum of squares of residuals since it's not given by the curve_fit function



In [6]:

    
p1 = popt[0]
p2 = popt[1]
residuals = ydata - func(xdata,p1,p2)
fres = sum(residuals**2)
fres









    Out[6]:





0.053812696547933969

Plot fitted curve along with data



In [7]:

    
curvex=np.linspace(-2,3,100)
curvey=func(curvex,p1,p2)
plt.plot(xdata,ydata,'*')
plt.plot(curvex,curvey,'r')
plt.xlabel('xdata')
plt.ylabel('ydata');