% high-spin S=1 simulation
% Go through the script line by line. Every "return" stops it.
% When you have completed the task, comment out that "return"
% so the script runs to the next "return"
% An important part of your task is to understand the script.
% I will be asking you about this in the exam.
% This will be easier for those of you who attended the simulation session.

clear all
close all

%% Baseline Correcting
load '..\..\Messdaten\TR_EPR\EMX_Aufbau\HU_F2\HU_F2_435nm.mat' % check this matches the name of your data file

whos % what variables have been loaded
params % what information is contained in the structure called 'params'

 % plot the raw data & check the number of points before the signal (pre-trigger)
% plot(Data)
% return

 % substract the pre-trigger. Check how many points from the plot above
signal_baseline_field = bsxfun(@minus, Data, mean(Data(1:1100,:)));
% plot(signal_baseline_field) % plot the corrected data set
% return

 % plot the transpose and check the number of points to the lower and higher fields of the signal
% plot(signal_baseline_field')
% use the smaller value below
% return

 % BASLINE correction
time_points = 140; % number of points from the plot above
l1 = mean(signal_baseline_field(:,1:time_points),2); % calculate the mean on the left along the time axis
l2 = mean(signal_baseline_field(:,end-time_points:end),2); %calculate the mean on the right along the time axis
baseline_time = (l1 +l2)/2; %take the average

signal_baseline_field_time = bsxfun(@minus, signal_baseline_field, baseline_time); % subtract the background in the time-domain 

 % plot the corrected data set
% plot(signal_baseline_field_time')
% return

 % plot the transpose to find the region of maximum signal. Use this below
% plot(signal_baseline_field_time)
% return

 % contour plot: The index gives the number of contours
% contourf(signal_baseline_field_time,6)
% return

%%
figure(1)
set(gcf,'PaperUnits','centimeters')
set(gcf,'Position',[0,0,750, 750])
set(gcf,'InvertHardcopy','off','Color',[1 1 1])
set(0,'DefaultAxesFontSize', 14,'DefaultAxesLineWidth',2)

 % contour plot: add the time and field axes
subplot(2,1,2)
contourf(0.1*params.Field_Vector, TimeBase*1e6 ,signal_baseline_field_time,'LineColor','none')

 % contour plot: add the time and field axes
% surf(0.1*params.Field_Vector, TimeBase*1e6 ,signal_baseline_field_time)
% colormap default
% shading interp

xlabel('Magnetic Field / mT')
ylabel('Time / \mus')
% colorbar
% print('TR_EPR_Chichibabin_80K_frozen_solution_532_01_3D' , '-dpng', '-r300')
% return

subplot(2,1,1)

 % take the mean over the maxium region. You can decide how wide it is
signal_baseline_field_time_mean = (mean(signal_baseline_field_time(1300:1390,:)));
 % normalise the amplitude to 1
signal_baseline_field_time_mean_norm = signal_baseline_field_time_mean/max(signal_baseline_field_time_mean);
 % plot the spectrum
plot(0.1*params.Field_Vector,signal_baseline_field_time_mean_norm,'LineWidth',2)

xlabel('Magnetic Field / mT')

axis('tight')
box off
return

% print('TR_EPR_Chichibabin_80K_frozen_solution_570_01' , '-dpng', '-r300')
return
%% Simulation section. Use the "Run Section" button to avoid running the previous section every time

Exp.mwFreq = params.mwFreq; % GHz
Exp.nPoints = length(params.Field_Vector);
Exp.CenterSweep = 0.1*[params.Field_Center params.Field_Sweep]; % mT (converted from Gauss)
Exp.Harmonic = 0; % zeroth harmonic

Exp.Temperature = [0 0.67 0.33]; % populations of the triplet sub-levels. These need to be varied manually to get the right shape

Sys.S = 1; % Total Spin
Sys.g = 1.9951; % needs to be optimised
Sys.D = [2148.02 75.35]; % mT; The D and E values need to be optimised
Sys.lw = [8.1034 0]; % mT; linewidth needs to be optimised

Vary.g = 0.01; 
Vary.D = [10 10];
Vary.lw = [1 0];

FitOpt.Method = 'simplex fcn';
FitOpt.Scaling = 'lsq';

 % When you have got a good fit by eye, use esfit to optimise
% esfit('pepper',signal_baseline_field_time_mean_norm,Sys,Vary,Exp,[],FitOpt);%fitting route
% return

[bfield,spec] = pepper(Sys,Exp); % perform a simulation with the parameters above
spec_norm = spec/max(spec); % normalize the simulation

figure(2)
set (gcf,'PaperUnits','centimeters')
set (gcf,'Position',[-900,100,800,400]) % set the position, size and shape of the plot
set (gcf,'InvertHardcopy','off','Color',[1 1 1])
set(0,'DefaultAxesFontSize', 16,'DefaultAxesLineWidth',1.5)

plot(0.1*params.Field_Vector,signal_baseline_field_time_mean_norm,'r', bfield,spec_norm,'b','LineWidth',1);
axis('tight')

legend('experimental','simulation')
legend boxoff
xlabel('Magnetic Field / mT')
ylabel('EPR signal / A. U.')
set(gca,'Box','Off','XMinorTick','On',...
    'YMinorTick','On','TickDir','Out','YColor','k')
return

set(gcf,'Units','Inches');
pos = get(gcf,'Position');
set(gcf,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)]);
print(gcf,'..\Abbildungen\Regression5','-dpdf','-r0');