Update EPR_script.m

Finished requests on correcting and plotting section. Also moved normalising into "Baseline correcting" section.

EPR_script.m

@@ -4,88 +4,107 @@
 clear variables
 close all
 
-%% Baseline Correcting
+ % window positions (currently optimised for dual WQHD with main on right)
-% loading Data
+% Give desired position of figure window as number of pixels [pos_x pos_y size_x size_y]:
+position = [-1250,50,1200,800];
+% Give desired position of figure(2) window (will have two stacked subplots)
+% as number of pixels [pos_x pos_y size_x size_y]: 
+position2 = [-2000,50,700,800];
+
+%% loading Data
 path = input('Path to dataset: ','s');
 load(path)
 
 whos % what variables have been loaded
 params % what information is contained in the structure called 'params'
 
+%% Baseline Correcting
 % plot the raw data & check the number of points before the signal (pre-trigger)
 plot(Data)
 title('raw data')
+set(gcf,'Position',position)
 
 % substract the pre-trigger
 pre_trigger = input('Number of pre-trigger points: ');
-signal_baseline_field = bsxfun(@minus, Data, mean(Data(1:pre_trigger,:)));
+signal_baseline_time = bsxfun(@minus, Data, mean(Data(1:pre_trigger,:)));
-plot(signal_baseline_field) % plot the corrected data set
+plot(signal_baseline_time) % plot the corrected data set
-title('corrected data')
+title('time corrected data')
-return
+set(gcf,'Position',position)
+% figure(gcf) % bring figure to foreground
+
+ready = input('Proceed?');
+
 
  % plot the transpose and check the number of points to the lower and higher fields of the signal
-% plot(signal_baseline_field')
+plot(signal_baseline_time')
-% use the smaller value below
+title('transposed time corrected data')
-% return
+set(gcf,'Position',position)
+% figure(gcf) % bring figure to foreground
 
- % BASLINE correction
+
-time_points = 140; % number of points from the plot above
+ % BASELINE correction
-l1 = mean(signal_baseline_field(:,1:time_points),2); % calculate the mean on the left along the time axis
+baseline_points = input('Number of baseline points (use smaller value from left and right): ');
-l2 = mean(signal_baseline_field(:,end-time_points:end),2); %calculate the mean on the right along the time axis
+l1 = mean(signal_baseline_time(:,1:baseline_points),2); % calculate the mean on the left along the time axis
+l2 = mean(signal_baseline_time(:,end-baseline_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 
+signal_baseline_time_field = bsxfun(@minus, signal_baseline_time, baseline_time); % subtract the background in the time-domain 
 
  % plot the corrected data set
-% plot(signal_baseline_field_time')
+plot(signal_baseline_time_field')
-% return
+title('transposed fully corrected data')
+set(gcf,'Position',position)
+% figure(gcf) % bring figure to foreground
+
+clear ready
+ready = input('Proceed?');
 
  % plot the transpose to find the region of maximum signal. Use this below
-% plot(signal_baseline_field_time)
+plot(signal_baseline_time_field)
-% return
+title('fully corrected data')
+set(gcf,'Position',position)
+% figure(gcf) % bring figure to foreground
 
  % contour plot: The index gives the number of contours
 % contourf(signal_baseline_field_time,6)
-% return
 
-%%
+ % NORMALISING
-figure(1)
+max_region = input('Region of the maximum signal as [x1:x2]: ');
+% take the mean over the maxium region. You can decide how wide it is
+signal_baseline_time_field_mean = (mean(signal_baseline_time_field(max_region,:)));
+% normalise the amplitude to 1
+signal_baseline_time_field_mean_norm = signal_baseline_time_field_mean/max(signal_baseline_time_field_mean);
+
+%% Creating figure with two subplots
+figure(2)
 set(gcf,'PaperUnits','centimeters')
-set(gcf,'Position',[0,0,750, 750])
+set(gcf,'Position',position2)
 set(gcf,'InvertHardcopy','off','Color',[1 1 1])
-set(0,'DefaultAxesFontSize', 14,'DefaultAxesLineWidth',2)
+set(0,'DefaultAxesFontSize', 12,'DefaultAxesLineWidth',2)
 
- % contour plot: add the time and field axes
+cont_or_surf = input('Should lower subplot be contour(1) or surface(2) plot? (1/2): ');
 subplot(2,1,2)
-contourf(0.1*params.Field_Vector, TimeBase*1e6 ,signal_baseline_field_time,'LineColor','none')
+if cont_or_surf == 1
-
     % contour plot: add the time and field axes
-% surf(0.1*params.Field_Vector, TimeBase*1e6 ,signal_baseline_field_time)
+    contourf(0.1*params.Field_Vector, TimeBase*1e6 ,signal_baseline_time_field,'LineColor','none')
-% colormap default
+elseif cont_or_surf == 2
-% shading interp
+    % surface plot: add the time and field axes
+    surf(0.1*params.Field_Vector, TimeBase*1e6 ,signal_baseline_time_field)
+    colormap default
+    shading interp
+end
 
 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)
+plot(0.1*params.Field_Vector,signal_baseline_time_field_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
 
@@ -115,13 +134,13 @@ FitOpt.Scaling = 'lsq';
 [bfield,spec] = pepper(Sys,Exp); % perform a simulation with the parameters above
 spec_norm = spec/max(spec); % normalize the simulation
 
-figure(2)
+figure(3)
 set (gcf,'PaperUnits','centimeters')
-set (gcf,'Position',[-900,100,800,400]) % set the position, size and shape of the plot
+set (gcf,'Position',position) % 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);
+plot(0.1*params.Field_Vector,signal_baseline_time_field_mean_norm,'r', bfield,spec_norm,'b','LineWidth',1);
 axis('tight')
 
 legend('experimental','simulation')