Update EPR_script.m
Added: - parallel computing to baseline correction - main simultation loop with decisions at its end - printing decision for fig(3) - extracting name of dataset for further use (printing figures, fir values in excel, ...)
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sakul-45
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EPR_script.m
83
EPR_script.m
@ -5,11 +5,18 @@ clear variables
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close all
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% window positions (currently optimised for dual WQHD with main on right)
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% also uncomment all figure(gcf) when working with single monitor
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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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% Give desired position of figure(3) window (will contain EPR spectrum and
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% simulation) as number of pixels [pos_x pos_y size_x size_y]:
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position3 = [-1250,50,1200,600];
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% specify dir for printing figures
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figdir = './';
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%% loading Data
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path = input('Path to dataset: ','s');
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@ -18,15 +25,20 @@ 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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% get name of dataset
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dataname = extractBefore(extractAfter(path,asManyOfPattern(wildcardPattern + "/")),'.');
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gpuData = gpuArray(Data); % using Parallel Computing toolbox to speed up
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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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plot(gpuData)
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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_time = bsxfun(@minus, Data, mean(Data(1:pre_trigger,:)));
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signal_baseline_time = bsxfun(@minus, gpuData, mean(gpuData(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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@ -34,9 +46,8 @@ set(gcf,'Position',position)
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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_time')
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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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@ -51,7 +62,7 @@ baseline_time = (l1 +l2)/2; %take the average
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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_time_field')
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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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@ -128,7 +139,7 @@ while init_proceed == 'n'
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figure(3)
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set (gcf,'PaperUnits','centimeters')
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set (gcf,'Position',position) % set the position, size and shape of the plot
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set (gcf,'Position',position3) % 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_time_field_mean_norm,'r', bfield,spec_norm,'b','LineWidth',1);
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@ -138,41 +149,51 @@ while init_proceed == 'n'
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xlabel('Magnetic Field / mT')
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ylabel('EPR signal / A. U.')
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set(gca,'Box','Off', 'XMinorTick','On', 'YMinorTick','On', 'TickDir','Out', 'YColor','k')
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pause(2);
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init_proceed = input('Spectrum shape manually fitted? [y/n]: ','s');
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end
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return
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% variation settings for simulation
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Vary.g = 0.01;
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Vary.D = [10 10];
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Vary.lw = [1 0];
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% further setup
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FitOpt.Method = 'simplex fcn';
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FitOpt.Scaling = 'lsq';
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% When you have got a good fit by eye, use esfit to optimise
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% esfit('pepper',signal_baseline_field_time_mean_norm,Sys,Vary,Exp,[],FitOpt);%fitting route
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% return
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simu_proceed = 'n';
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while simu_proceed == 'n'
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% fitting routine
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[BestSys,BestSpc] = esfit('pepper',signal_baseline_time_field_mean_norm,Sys,Vary,Exp,[],FitOpt);
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% plot best fit
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figure(3)
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plot(0.1*params.Field_Vector,signal_baseline_time_field_mean_norm,'r',...
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0.1*params.Field_Vector,BestSpc,'b','LineWidth',1);
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axis('tight')
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legend('experimental','simulation')
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legend boxoff
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xlabel('Magnetic Field / mT')
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ylabel('EPR signal / A. U.')
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set(gca,'Box','Off', 'XMinorTick','On', 'YMinorTick','On', 'TickDir','Out', 'YColor','k')
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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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simu_proceed = input('Did the simulation converge? [y/n]: ','s');
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if simu_proceed == 'n'
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simu_val = input('Do you want to repeat the simulation with new best values? [y/n]: ','s');
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if simu_val == 'y'
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Sys.g = BestSys.g;
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Sys.D = BestSys.D;
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Sys.lw = BestSys.lw;
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end
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end
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end
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figure(3)
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set (gcf,'PaperUnits','centimeters')
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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_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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legend boxoff
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xlabel('Magnetic Field / mT')
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ylabel('EPR signal / A. U.')
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set(gca,'Box','Off','XMinorTick','On',...
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'YMinorTick','On','TickDir','Out','YColor','k')
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% set(gcf,'Units','Inches');
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% pos = get(gcf,'Position');
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% set(gcf,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)]);
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% print(gcf,'..\Abbildungen\Regression5','-dpdf','-r0');
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%% printing figures and saving parameters
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printing = input('Do you want to print figure(3)? [y/n]: ','s');
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if printing == 'y'
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figure(3)
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set(gcf,'Units','Inches');
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pos = get(gcf,'Position');
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set(gcf,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)]);
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print(gcf,strcat(figdir,dataname),'-dpdf','-r0');
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end
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