% script file polyfit_diode.m
%
% Sets arrays of measured data and uses polyfit function
%  to find best values for a linear regression model of 
%  ln(i) vs v.
%
% The motivation for the linear regression model is the
%  Shockley's Law diode model:
%   i = Is (exp(v/vT) - 1)
%  where Is = reverse saturation current
%        vT = kT/q is thermal voltage
%         T = temperature in degrees K

% Define physical constants.
k = 1.38e-23;  % J/degK Boltzmann constant
q = 1.602e-19; % C electron charge

% Create arrays containing measured data.
v_diode = [1.3 1.35 1.4 1.41];
i_diode = [0.0006 0.0022 0.0136 0.0211];

x=v_diode;
y=log([0.6e-3, 2.2e-3, 13.6e-3, 21.1e-3]);

a = polyfit(x,y,1);

% Calculate model predictions for the measured voltages.
pred_lni_diode = a(2) + a(1) * v_diode;
pred_i_diode = exp(pred_lni_diode)

% Plot the measured and model values on the same plot.
axes('FontSize',14)
% hold off
plot(v_diode, y, 'bo')
hold on
xlabel('LED voltage (V)','FontSize',14);
ylabel('log LED current log(A)','FontSize',14);
plot(v_diode, pred_lni_diode, 'r-');
legend('data','linear fit','Location','Best');
% hold off