# Load Python Modules
from numpy import *  
from matplotlib.pyplot import *
from scipy.stats import linregress

# Enter Data here, between [ ] brackets separated by comma ','
Lambda = [ 420e-9,  576e-9, 615e-9, 700e-9]
Va = [  2.6, 1.85, 1.9, 1.7 ]

# Error on Va
#error = 0.0003

e = 1.60217662e-19 # [C]
c = 299792458 # [m/s]

# Define x and y values
x = c/array(Lambda)
y = e*array(Va)

# Fit x/y Data using a linear regression
slope, intercept, r_value, p_value, err = linregress(x,y)

# Print the fit coeffs
print('Slope, Error, Intercept, Correlation Coefficient:')
print(slope, err, intercept, r_value**2)

# Calculate Plancks Constant
print("Plancks Constant h:", slope, "+/-", err, "Js")

# Calculate Plancks Constant
print("Plancks Constant h: %1.0e +/- %1.0e Js" %(slope,err))

# Create the fit data
xfit = linspace(1e13, 8e14) # define the x-data using a start and end point
yfit = slope*xfit + intercept

# Plot Fit results
plot(x,y,'o', label='Measured')
plot(xfit,yfit, 'r-', label='Linear Fit')

# Label Axes
# Put maths code between $ $ symbols
xlabel('$c/ \lambda$ [s$^{-1}$]')
ylabel('$eV_a$ [J]')

# Add legend
legend()
# Save Figure
savefig('PlanckResults.png', dpi=300, format='png', bbox_inches='tight')