Week 5 - Curve fitting

# A program to measure fitness characteristics for the linear and cubic polynomial for the Cp data:
import math
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit

# Curve fit function
# Linear function
def function_0(t,a,b):
	return a*t + b 
# Cubic function	
def function_2(t,a,b,c,d):
	return a*pow(t,3) + b*pow(t,2) + c*t +d
	

# Reading thermodynamic data file
def read_file():
	temperature = []
	cp = []
	for line in open('data','r'):
		values = line.split(',')
		temperature.append(float(values[0]))
		cp.append(float(values[1]))

	return [temperature , cp]

# Main Program
temperature , cp = read_file()

popt, pcov = curve_fit(function_0,temperature,cp)
fit_cp_l = function_0(np.array(temperature), *popt)
plt.figure(1)
plt.plot(temperature,cp,'k--')
plt.plot(temperature,fit_cp_l,color='red',linewidth = 1)
plt.legend(['Actual data','Curve fit'])
plt.xlabel('Temperature (Kevlin)')
plt.ylabel('Cp')
plt.title('Linear Curve fitting')
plt.show()

Answer

1.popt is cofficient store arry & pcov is variance of that coefficients

2.np.arry(tempture) is command converts each value of temperature in the data file into an array using numpy module.

3.popt is coefficients store in arry