Update EPR_script.m
Finished requests on correcting and plotting section. Also moved normalising into "Baseline correcting" section.
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EPR_script.m
107
EPR_script.m
@ -4,88 +4,107 @@
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clear variables
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close all
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%% Baseline Correcting
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% loading Data
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% window positions (currently optimised for dual WQHD with main on right)
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% Give desired position of figure window as number of pixels [pos_x pos_y size_x size_y]:
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position = [-1250,50,1200,800];
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% Give desired position of figure(2) window (will have two stacked subplots)
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% as number of pixels [pos_x pos_y size_x size_y]:
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position2 = [-2000,50,700,800];
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%% loading Data
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path = input('Path to dataset: ','s');
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load(path)
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whos % what variables have been loaded
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params % what information is contained in the structure called 'params'
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%% Baseline Correcting
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% plot the raw data & check the number of points before the signal (pre-trigger)
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plot(Data)
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title('raw data')
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set(gcf,'Position',position)
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% substract the pre-trigger
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pre_trigger = input('Number of pre-trigger points: ');
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signal_baseline_field = bsxfun(@minus, Data, mean(Data(1:pre_trigger,:)));
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plot(signal_baseline_field) % plot the corrected data set
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title('corrected data')
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return
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signal_baseline_time = bsxfun(@minus, Data, mean(Data(1:pre_trigger,:)));
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plot(signal_baseline_time) % plot the corrected data set
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title('time corrected data')
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set(gcf,'Position',position)
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% figure(gcf) % bring figure to foreground
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ready = input('Proceed?');
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% plot the transpose and check the number of points to the lower and higher fields of the signal
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% plot(signal_baseline_field')
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% use the smaller value below
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% return
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plot(signal_baseline_time')
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title('transposed time corrected data')
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set(gcf,'Position',position)
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% figure(gcf) % bring figure to foreground
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% BASLINE correction
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time_points = 140; % number of points from the plot above
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l1 = mean(signal_baseline_field(:,1:time_points),2); % calculate the mean on the left along the time axis
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l2 = mean(signal_baseline_field(:,end-time_points:end),2); %calculate the mean on the right along the time axis
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% BASELINE correction
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baseline_points = input('Number of baseline points (use smaller value from left and right): ');
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l1 = mean(signal_baseline_time(:,1:baseline_points),2); % calculate the mean on the left along the time axis
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l2 = mean(signal_baseline_time(:,end-baseline_points:end),2); %calculate the mean on the right along the time axis
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baseline_time = (l1 +l2)/2; %take the average
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signal_baseline_field_time = bsxfun(@minus, signal_baseline_field, baseline_time); % subtract the background in the time-domain
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signal_baseline_time_field = bsxfun(@minus, signal_baseline_time, baseline_time); % subtract the background in the time-domain
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% plot the corrected data set
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% plot(signal_baseline_field_time')
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% return
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plot(signal_baseline_time_field')
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title('transposed fully corrected data')
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set(gcf,'Position',position)
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% figure(gcf) % bring figure to foreground
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clear ready
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ready = input('Proceed?');
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% plot the transpose to find the region of maximum signal. Use this below
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% plot(signal_baseline_field_time)
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% return
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plot(signal_baseline_time_field)
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title('fully corrected data')
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set(gcf,'Position',position)
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% figure(gcf) % bring figure to foreground
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% contour plot: The index gives the number of contours
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% contourf(signal_baseline_field_time,6)
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% return
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%%
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figure(1)
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% NORMALISING
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max_region = input('Region of the maximum signal as [x1:x2]: ');
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% take the mean over the maxium region. You can decide how wide it is
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signal_baseline_time_field_mean = (mean(signal_baseline_time_field(max_region,:)));
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% normalise the amplitude to 1
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signal_baseline_time_field_mean_norm = signal_baseline_time_field_mean/max(signal_baseline_time_field_mean);
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%% Creating figure with two subplots
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figure(2)
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set(gcf,'PaperUnits','centimeters')
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set(gcf,'Position',[0,0,750, 750])
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set(gcf,'Position',position2)
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set(gcf,'InvertHardcopy','off','Color',[1 1 1])
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set(0,'DefaultAxesFontSize', 14,'DefaultAxesLineWidth',2)
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set(0,'DefaultAxesFontSize', 12,'DefaultAxesLineWidth',2)
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% contour plot: add the time and field axes
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cont_or_surf = input('Should lower subplot be contour(1) or surface(2) plot? (1/2): ');
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subplot(2,1,2)
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contourf(0.1*params.Field_Vector, TimeBase*1e6 ,signal_baseline_field_time,'LineColor','none')
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if cont_or_surf == 1
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% contour plot: add the time and field axes
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% surf(0.1*params.Field_Vector, TimeBase*1e6 ,signal_baseline_field_time)
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% colormap default
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% shading interp
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contourf(0.1*params.Field_Vector, TimeBase*1e6 ,signal_baseline_time_field,'LineColor','none')
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elseif cont_or_surf == 2
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% surface plot: add the time and field axes
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surf(0.1*params.Field_Vector, TimeBase*1e6 ,signal_baseline_time_field)
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colormap default
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shading interp
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end
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xlabel('Magnetic Field / mT')
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ylabel('Time / \mus')
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% colorbar
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% print('TR_EPR_Chichibabin_80K_frozen_solution_532_01_3D' , '-dpng', '-r300')
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% return
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subplot(2,1,1)
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% take the mean over the maxium region. You can decide how wide it is
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signal_baseline_field_time_mean = (mean(signal_baseline_field_time(1300:1390,:)));
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% normalise the amplitude to 1
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signal_baseline_field_time_mean_norm = signal_baseline_field_time_mean/max(signal_baseline_field_time_mean);
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% plot the spectrum
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plot(0.1*params.Field_Vector,signal_baseline_field_time_mean_norm,'LineWidth',2)
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plot(0.1*params.Field_Vector,signal_baseline_time_field_mean_norm,'LineWidth',2)
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xlabel('Magnetic Field / mT')
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axis('tight')
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box off
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return
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% print('TR_EPR_Chichibabin_80K_frozen_solution_570_01' , '-dpng', '-r300')
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return
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%% Simulation section. Use the "Run Section" button to avoid running the previous section every time
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@ -115,13 +134,13 @@ FitOpt.Scaling = 'lsq';
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[bfield,spec] = pepper(Sys,Exp); % perform a simulation with the parameters above
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spec_norm = spec/max(spec); % normalize the simulation
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figure(2)
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figure(3)
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set (gcf,'PaperUnits','centimeters')
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set (gcf,'Position',[-900,100,800,400]) % set the position, size and shape of the plot
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set (gcf,'Position',position) % set the position, size and shape of the plot
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set (gcf,'InvertHardcopy','off','Color',[1 1 1])
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set(0,'DefaultAxesFontSize', 16,'DefaultAxesLineWidth',1.5)
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plot(0.1*params.Field_Vector,signal_baseline_field_time_mean_norm,'r', bfield,spec_norm,'b','LineWidth',1);
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plot(0.1*params.Field_Vector,signal_baseline_time_field_mean_norm,'r', bfield,spec_norm,'b','LineWidth',1);
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axis('tight')
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legend('experimental','simulation')
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