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sakul-45
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EPR_script.m
217
EPR_script.m
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% high-spin S=1 simulation
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% Inputs requested in command line at certain points
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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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% specifiy excel-file for saving parameters
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table_path = 'example_results.xlsx';
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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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% get name of dataset
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dataname = string(extractBefore(extractAfter(path,asManyOfPattern(wildcardPattern + "/")),'.'));
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% using Parallel Computing toolbox to speed up (check with "gpuDevice" if you can use this)
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gpuData = gpuArray(Data);
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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(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, 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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% 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_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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% 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_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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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_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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% 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',position2)
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set(gcf,'InvertHardcopy','off','Color',[1 1 1])
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set(0,'DefaultAxesFontSize', 12,'DefaultAxesLineWidth',2)
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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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if cont_or_surf == 1
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% contour plot: add the time and field axes
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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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subplot(2,1,1)
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% plot the spectrum
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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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%% Simulation section
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Exp.mwFreq = params.mwFreq; % GHz
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Exp.nPoints = length(params.Field_Vector);
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Exp.CenterSweep = 0.1*[params.Field_Center params.Field_Sweep]; % mT (converted from Gauss)
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Exp.Harmonic = 0; % zeroth harmonic
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init_proceed = 'n';
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while init_proceed == 'n'
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% populations of the triplet sub-levels
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% these need to be varied manually to get the right shape
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Exp.Temperature = input('Input population of triplett sublevels as [T_x T_y T_z]: ');
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% initial simulation settings
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Sys.S = 1; % Total Spin
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Sys.g = input('g value: '); % needs to be optimised
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Sys.D = input('D and E value as [D E]: '); % mT, The D and E values need to be optimised
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Sys.lw = input('Isotropic line broadening at FWHM as [Gaussian Lorentzian]: '); % mT, linewidth needs to be optimised
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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(3)
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set (gcf,'PaperUnits','centimeters')
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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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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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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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% 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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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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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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%% printing figures
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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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%% saving parameters
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% concatenate data to existing table
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table_old = readtable(table_path);
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table_old.Properties.VariableNames = {'filename', 'date', 'pre-trigger', ...
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'baseline_points', 'max_area_left', 'max_area_right', 'T_x', 'T_y', 'T_z', ...
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'sim. g-value', 'sim_D', 'sim_E', 'sim_lw_gauss', 'sim_lw_lorentz'};
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% new data as table
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table_new = table(dataname, string(datestr(clock)), pre_trigger, baseline_points, ...
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max_region(1), max_region(end), Exp.Temperature(1), Exp.Temperature(2), Exp.Temperature(3), ...
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BestSys.g, BestSys.D(1), BestSys.D(2), BestSys.lw(1), BestSys.lw(2), ...
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'VariableNames', {'filename', 'date', 'pre-trigger', 'baseline_points', ...
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'max_area_left', 'max_area_right', 'T_x', 'T_y', 'T_z', 'sim. g-value', ...
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'sim_D', 'sim_E', 'sim_lw_gauss', 'sim_lw_lorentz'});
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table_conc = [table_old;table_new];
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writetable(table_conc,table_path)
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@ -1,7 +0,0 @@
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function [outputArg1,outputArg2] = double_2D_3D_plot(inputArg1,inputArg2)
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%DOUBLE_2D_3D_PLOT Summary of this function goes here
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% Detailed explanation goes here
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outputArg1 = inputArg1;
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outputArg2 = inputArg2;
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end
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