from matplotlib import pylab as pl
import numpy as np


#First, we use the NumPy loadtxt function to read our previously saved file containing corresponding values of Tc and lattice size 
YC_Data_Tc = np.loadtxt("YC_Data_Tc.txt")

#Obtain required data from file
#Our equation contains the term '1/L'  - so we determine the inverse of lattice size:
Lattice_size_inverse= (YC_Data_Tc[:,0])**(-1)


Tc = YC_Data_Tc[:,1]

#Plot Data Points
pl.plot(Lattice_size_inverse, Tc, 'ro', label='Data Points Recorded')


fit = np.polyfit(Lattice_size_inverse, Tc, 1) 
#fit a 1 order polynomial (as needed)

L_inverse_max = np.max(Lattice_size_inverse)
L_inverse_min = np.min(Lattice_size_inverse)


L_range = np.linspace(L_inverse_min, L_inverse_max, 1000) #generate 1000 evenly spaced points between L_min and L_max

fitted_Tc_values = np.polyval(fit, L_range) # use the fit object to generate the corresponding values of Tc


#Plotting 'fitted' data
pl.plot(L_range,fitted_Tc_values,'b-', label = 'Fitted curve') 

pl.title('Plot of Curie Temperature vs Inverse of Lattice Size')
pl.ylabel("Curie Temperature")
pl.xlabel("Inverse of Lattice Size")

pl.legend(loc='best')


pl.show()

print (np.polyval(fit,0))