Contents

close all
clear variables

Dataset specification and parameters

fitType = 2 % choose 1 for autoGaussianSurf, 2 for gaussian2DFit (use 2)

crop = true;
shouldSaveResults = 1;
format short e

for dataset = 30% input('Select a dataset: \n') % select the dataset to fit

    %Remove previously added images paths to avoid confusion among files
    warning('off','all')
    rmpath(genpath('Pump-Side\'));
    rmpath(genpath('Back Propagation of 1550nm\'));
    rmpath(genpath('After Beam Reducer\'));
    warning('on','all')


if dataset == 1 % pump-side after flip mirror 1
    type = "Pump-side"
    z=[75, 240,500,790,1040,1205]'.*1e-3;
    dataPath = 'Pump-Side\After first Flip-Mirror'
    names = {'75.PNG','240.PNG','500.PNG','790.PNG','1040.PNG','1205.PNG'}
    downsamplingFactor = 8
    waistGuess = 3
end
if dataset == 2 %pump-side after M3
    type = "Pump-side"
    z=[150,250 350,450,550,650,750,850,950]'.*1e-3;
    dataPath = 'Pump-Side\After M3 3-7-20'
    names = {'15.PNG','25.PNG','35.PNG','45.PNG','55.PNG','65.PNG','75.PNG','85.PNG','95.PNG'};
    downsamplingFactor = 8
    waistGuess = 3
    crop = 0
    guess = [1e-3,0]
end

if dataset == 3 % backpropagation after the sencond mirror before the collimator
    type = "Back-propagation"
    z = [1,2,3,4,6,7,8,9]'.*1e-1; % distances of the camera for each picture from arbitrary origin
    dataPath = 'Back Propagation of 1550nm\set3'
    names = {'10.pgm','20.pgm','30.pgm','40.pgm','60.pgm','70.pgm','80.pgm','90.pgm'};
    downsamplingFactor = 8
    waistGuess = 30
end

if dataset == 4 % pump-side at crystal 3/9/20
    type = "Pump-side"
    dataPath = 'Pump-Side\At crystal\3-9-20\'
    names = {'Gaussian mode.png'};
    downsamplingFactor = 1
    waistGuess = 1
end
if dataset == 5 % pump-side at crystal 4/9/20
    type = "Pump-side"
    dataPath = 'Pump-Side\At crystal\4-9-20\'
    names = {'1.bmp', '2.bmp','3.bmp','4.bmp','5.bmp'};
    downsamplingFactor = 1
    waistGuess = 15
end

if dataset == 6 % pump-side at crystal 7/9/20 (moved the lens 10 cm further away from MIRA)
    type = "Pump-side"
    dataPath = 'Pump-Side\At crystal\7-9-20\10cmMore'
    names = {'1.png', '2.png','3.png','4.png','5.png'};
    downsamplingFactor = 1
    waistGuess = 15
end

if dataset == 7 % pump-side at crystal 7/9/20 (moved the lens 30 cm further away from MIRA)
    type = "Pump-side"
    dataPath = 'Pump-Side\At crystal\7-9-20\30cmMore'
    names = {'1.png', '2.png','3.png','4.png','5.png'};
    downsamplingFactor = 1
    waistGuess = 15
end
if dataset == 8 % pump-side at crystal 7/9/20 (moved the lens 50 cm further away from MIRA)
    type = "Pump-side"
    dataPath = 'Pump-Side\At crystal\7-9-20\50cmMore'
    names = {'1.png', '2.png','3.png','4.png','5.png'};
    downsamplingFactor = 1
    waistGuess = 15
end

if dataset == 9 % pump-side at crystal 7/9/20 (moved the lens 55 cm further away from MIRA)
    type = "Pump-side"
    dataPath = 'Pump-Side\At crystal\7-9-20\55cmMore'
    names = {'1.png', '2.png','3.png','4.png','5.png'};
    downsamplingFactor = 1
    waistGuess = 15
end

if dataset == 10 % pump-side at crystal 7/9/20 (moved the lens 60 cm further away from MIRA)
    type = "Pump-side"
    dataPath = 'Pump-Side\At crystal\7-9-20\60cmMore'
    names = {'1.png', '2.png','3.png','4.png','5.png'};
    downsamplingFactor = 1
    waistGuess = 15
end

if dataset == 11 % pump-side at crystal 7/9/20 (moved the lens 65 cm further away from MIRA)
    type = "Pump-side"
    dataPath = 'Pump-Side\At crystal\7-9-20\65cmMore'
    names = {'1.png', '2.png','3.png','4.png','5.png'};
    downsamplingFactor = 1
    waistGuess = 15
end

if dataset == 12 % backpropagation at crystal 8-9-20
    type = "Back-propagation"
    dataPath = 'Back Propagation of 1550nm\At crystal\8-9-20\'
    names = {'6c.png'};
    downsamplingFactor = 2
    waistGuess = 30
end

if dataset == 13 % backpropagation at crystal 11-9-20
    type = "Back-propagation"
    dataPath = 'Back Propagation of 1550nm\At crystal\11-9-20\'
    names = {'31.png','32.png','33.png'};
    downsamplingFactor = 1
    waistGuess = 30
end

if dataset == 14 % pump-side at crystal 7/9/20 (moved the lens 65 cm further away from MIRA)
    type = "Pump-side"
    dataPath = 'Pump-Side\At crystal\11-9-20\'
    names = {'15.png'};
    downsamplingFactor = 1
    waistGuess = 10 % in pixels
end

if dataset == 15 % backpropagation after collimator
    type = "Back-propagation"
    dataPath = 'Back Propagation of 1550nm\Collimator f=15.29mm\25-9-20\'
    names = {'1.png','2.png'};
    downsamplingFactor = 4
    waistGuess = 30
end


if dataset == 16 % backpropagation at crystal 11-9-20
    type = "Back-propagation"
    dataPath = 'Back Propagation of 1550nm\At crystal\28-9-20\Dichroic at 8cm\'
    names = {'85mm.png','86mm.png','90mm.png','105mm.png'};
    downsamplingFactor = 2
    waistGuess = 30
end

if dataset == 17 % backpropagation at crystal 28-9-20
    type = "Back-propagation"
    dataPath = 'Back Propagation of 1550nm\At crystal\28-9-20\f=15cm lens\Dichroic at 15cm\'
    names = {'95mm.png','96mm.png','105mm.png'};
    z=[95,96,105]'*1e-3
    downsamplingFactor = 2
    waistGuess = 50
end

if dataset == 18 % backpropagation at crystal 28-9-20
    type = "Back-propagation"
    dataPath = 'Back Propagation of 1550nm\At crystal\28-9-20\f=125mm lens\Dichroic at 15cm\'
    names = {'65mm.png','70mm.png','75mm.png'};
    downsamplingFactor = 2
    waistGuess = 30
end

if dataset == 19 % backpropagation at crystal 28-9-20
    type = "Back-propagation"
    dataPath = 'Back Propagation of 1550nm\At crystal\28-9-20\f=125mm lens\Dichroic at 125mm\'
    names = {'70mm.png','75mm.png'};
    downsamplingFactor = 1
    waistGuess = 30
end

if dataset == 20 % backpropagation at crystal 28-9-20
    type = "Back-propagation"
    dataPath = 'Back Propagation of 1550nm\At crystal\28-9-20\f=125mm lens\Dichroic at 80mm\'
    names = {'75.png','77.png','80.png'};
    downsamplingFactor = 1
    waistGuess = 30
    crop = false;
end

if dataset == 21 % pump-side at crystal 28/9/20 new configuration
    type = "Pump-side"
    dataPath = 'Pump-Side\At crystal\28-9-20\'
    names = {'85mm.png','150mm.png'};
    downsamplingFactor = 1
    waistGuess = 10 % in pixels
end

if dataset == 22 % backpropagation at crystal 28-9-20
    type = "Back-propagation"
    dataPath = 'Back Propagation of 1550nm\At crystal\28-9-20\f=125mm lens\Dichroic at 135mm\'
    names = {'70mm.png','75mm.png','80mm.png'};
    downsamplingFactor = 1
    waistGuess = 30
end
if dataset == 23 % backpropagation at crystal 30-9-20
    type = "Back-propagation";
    dataPath = 'Back Propagation of 1550nm\At crystal\30-9-20\'
    names = {'f=75a.png','f=75b.png','f=75c.png'};
    downsamplingFactor = 1
    waistGuess = 30
end

if dataset == 24 %collimated backpropagation 06-10-20
    type = "Back-propagation";
    dataPath = 'Back Propagation of 1550nm\Collimator f=6.24mm\6-10-20\old'
    names = {'205.png','365.png','620.png','830.png'};
    z = [205,365,620,830]'.*1e-3;
    downsamplingFactor = 8
    waistGuess = 200
    guess = [4e-4 0.4]  % [w_0 z_i]
end

if dataset == 25 % collimated backpropagation 06-10-20
    type = "Back-propagation";
    dataPath = 'Back Propagation of 1550nm\Collimator f=6.24mm\6-10-20\8'
    names = {'65.png','120.png','225.png','335.png','795.png','1035.png'};
    z = [65,120,225,335,795,1035]'.*1e-3;
    downsamplingFactor = 4
    waistGuess = 100
    %guess = [4e-4 0.4]  % [w_0 z_i]
end
if dataset == 26 %collimated backpropagation 06-10-20
    type = "Back-propagation";
    dataPath = 'Back Propagation of 1550nm\Collimator f=6.24mm\6-10-20\35'
    names = {'95.pgm','350.pgm','820.pgm','1030.pgm'};
    z = [95,350,820,1030]'.*1e-3;
    downsamplingFactor = 8
    waistGuess = 100 %in pixels
    guess = [4e-4 0.4]  % [w_0 z_i] in meters
end

if dataset == 27 % backpropagation at crystal 06-10-20
    type = "Back-propagation";
    dataPath = 'Back Propagation of 1550nm\At crystal\6-10-20\'
    names = {'a.png','b.png','c.png'};
    downsamplingFactor =1
    waistGuess = 30
end

if dataset == 28 % backpropagation at crystal 06-10-20
    type = "Back-propagation";
    dataPath = 'Back Propagation of 1550nm\At crystal\8-10-20\'
    names = {'8a.png','8b.png','8c.png'};
    downsamplingFactor =1
    waistGuess = 30
end

if dataset == 29 % backpropagation at crystal 08-10-20,
    % Test of the influence of the camera gain on waist estimation.
    % Result: no influence if no saturation (then widening)
    type = "Back-propagation";
    dataPath = 'Back Propagation of 1550nm\At crystal\8-10-20\diffgain'
    names = {'-5db.png','0db.png','5db.png','10db.png','20dbSat.png'};
    downsamplingFactor =1
    waistGuess = 30
end

if dataset == 30 % backpropagation f=11m collimator test 12-10-20,
    type = "Back-propagation";
    dataPath = 'Back Propagation of 1550nm\Collimator f=11mm\12-10-20\Set1\'
    names = {'150.png','218.png','487.png','614.png','645.png','732.png','753.png','818.png','90.png'};
    z= [150,218,487,614,645,732,753,818,90]'*1e-3
    downsamplingFactor = 6
    waistGuess = 300
    %Result: nice gaussian shape, but the output waist is virtually before
    %the lens so the collimator must be placed further away from the fiber
end

if dataset == 31 % backpropagation f=11m collimator test 12-10-20,
    type = "Back-propagation";
    dataPath = 'Back Propagation of 1550nm\Collimator f=11mm\13-10-20\Set2\'
    names = {'145.png', '324.png', '485.png', '518.png', '697.png', '780.png'};
    z= [145,324,485,518,697,780]'*1e-3
    downsamplingFactor = 6
    waistGuess = 300
    %Result: The output waist is 5 cm after the lens so the collimator must
    % be placedcloser to the fiber
end

if dataset == 32 % backpropagation f=11m collimator test 13-10-20,
    type = "Back-propagation";
    dataPath = 'Back Propagation of 1550nm\Collimator f=11mm\13-10-20\Set3\'
    names = {'166.png', '366.png', '498.png', '601.png', '691.png', '780.png'};
    z= [166,366,498,601,691,780]'*1e-3
    downsamplingFactor = 6
    waistGuess = 300
    %Result:
end
end

fitType =
     2
dataPath =
    'Back Propagation of 1550nm\Collimator f=11mm\12-10-20\Set1\'
z =
   1.5000e-01
   2.1800e-01
   4.8700e-01
   6.1400e-01
   6.4500e-01
   7.3200e-01
   7.5300e-01
   8.1800e-01
   9.0000e-02
downsamplingFactor =
     6
waistGuess =
   300

Publish or execute only

shouldPublish = 1
firstPublish = load("firstPublish.mat");
firstPublish = firstPublish.firstPublish;
publishPath =  strcat('C:\Users\franc\Box\Dottorato\Projects\SECRET\BeamWidth\Pusblished html\',strrep(dataPath,'\','_'));
if (shouldPublish && firstPublish)
    firstPublish = 0;
    save("firstPublish.mat","firstPublish")
    publish('GaussianBeamFit.m','outputDir',publishPath);
    firstPublish = 1;
    save("firstPublish.mat","firstPublish");
    web(strcat(publishPath,"\GaussianBeamFit.html"));
    close all;
else

Sort captures by distance if provided

    if exist('z','var')
        [z,ordered_ind] = sort(z);
        names = names(ordered_ind);
    end

Image Loading

    addpath(dataPath);

    if (type == "Back-propagation")
        lambda = 1550e-9
        pixel_size = 3.75 *10^(-6) % = 3.75 micron ; for CMLN-13S2C-CS (1550 nm)
    elseif(type == "Pump-side")
        lambda = 775e-9
        pixel_size = 5.3e-6 % = 5.3 micron ; for UI-3240LE-NIR-GL of IDS (775 nm)
    end

    n= length(names)
    images = cell(n,1);


    for i = 1:n
        rawData1 = importdata(names{i});
        [~,name] = fileparts(names{i});
        newData1.(matlab.lang.makeValidName(name)) = rawData1;
        vars = fieldnames(newData1);
        for j = 1:length(vars)
            assignin('base', vars{j}, newData1.(vars{j})); %newData1.(vars{j}.cdata
            images{i}= sum(double(eval(vars{j})),3);
        end
    end
    results1 = cell(n);
    results2 = cell(n,1);

lambda =
   1.5500e-06
pixel_size =
   3.7500e-06
n =
     9

Show pictures

    if 1
        for i= 1:length(images)
            figure
            image(images{i});
            title(sprintf('Picture n° %i, dataset n°%d', [i,dataset] ));
        end
    end

Pre-process pictures

    for i = 1:n
        img = images{i};
        [old_l,old_m] = size(img);

        %img = smoothdata(img,'movmedian',10); %smoothing

        img = imresize(img, 1/downsamplingFactor,'bicubic');

        %     if not(downsamplingFactor == 1)
        %         decimated_hor= zeros(old_l,ceil(old_m/downsamplingFactor));
        %         decimated = zeros(ceil(old_l/downsamplingFactor),ceil(old_m/downsamplingFactor));
        %         for r=1:old_l
        %             decimated_hor(r,:) = decimate(img(r,:),downsamplingFactor);
        %         end
        %         for c=1:ceil(old_m/downsamplingFactor)
        %             decimated(:,c) = decimate(decimated_hor(:,c),downsamplingFactor);
        %         end
        %         img = decimated;
        %         clear decimated_hor decimated;
        %     end

        [l,m] = size(img);
        downsamplingFactor = sqrt((old_m*old_l)/(l*m));


        %Find the maximum (beam center)
        imgSmoothed = smoothdata(img,'movmedian',20); %smoothing
        % figure
        % image(imgSmoothed)
        % imgMean= mean(mean(imgSmoothed));
        [colMax, row] = max(imgSmoothed);
        [imgMax, col] = max(colMax);
        row = row(col);

        if (crop)
            %Crop and center arround the beam

            width = round( waistGuess / downsamplingFactor) * 2 * 4;
            height = round(waistGuess / downsamplingFactor) * 2 * 4;
            images{i} = img(1+subplus(row-width/2-1):min(row+width/2,l),1+subplus(col-height/2-1):min(col+height/2,m));

        else
            images{i} = img;
        end

        % Normalise amplitude
        images{i} = images{i}/max(max(images{i}))*100;

    end
    clear imgMax imgMean colMax row col

Show pre-processed pictures

    for i= 1:length(images)
        images{i} = double(images{i});
        figure
        image(images{i})
        title(sprintf('Pre-processed picture n° %i, dataset n°%d', [i,dataset] ));
    end

Fit Gaussian bell

    rsquare_bells = zeros(1,n);
    for i= 1:length(images)
        clear opts
        disp("Fitting image n°" + int2str(i))
        img = images{i};
        sz = size(img);
        x_ax = 1:sz(2);
        y_ax = 1:sz(1);
        [x,y] = meshgrid(1:sz(2),1:sz(1));
        if fitType == 1
            opts.tilted= true;
            results1{i} = autoGaussianSurf(x,y,img, opts);
        end
        if fitType ==2
            opts = fitoptions( 'Method', 'NonlinearLeastSquares', 'Robust', 'LAR');
            opts.Display = 'Off';
            opts.StartPoint = [1 100 waistGuess/downsamplingFactor sz(1)/2 sz(2)/2];
            [bellFit, bellGof] = gaussian2DFit(y,x,img,opts);
            %bellGof
            rsquare_bells(i)= bellGof.rsquare;
            results2{i} = bellFit;
            clear fit
        end
    end

Fitting image n°1
Fitting image n°2
Fitting image n°3
Fitting image n°4
Fitting image n°5
Fitting image n°6
Fitting image n°7
Fitting image n°8
Fitting image n°9

Compute and show residuals

    for i= 1:length(images)
        sz = size(images{i});
        [x,y] = meshgrid(1:sz(2),1:sz(1));
        if fitType == 1
            r = results1{i};
            if (opts.tilted == false)
                r.angle = 0;
            end
            xip = (x-r.x0)*cos(r.angle) + (y-r.y0)*sin(r.angle);
            yip =-(x-r.x0)*sin(r.angle) + (y-r.y0)*cos(r.angle);
            zfit = r.a*exp(-(xip.^2/2/r.sigmax^2 + ...
                yip.^2/2/r.sigmay^2)) + r.b;
        end
        if fitType == 2
            bellFit = results2{i};
            zfit= abs(bellFit.A)*exp(-2*((x-abs(bellFit.x0)).^2+(y-abs(bellFit.y0)).^2)/(bellFit.W^2))+bellFit.B;
            clear fit
        end
        if false %% show fit
            figure
            image(zfit./max(max(zfit)).*100)
            title(sprintf('Fit n° %i, dataset n°%d', [i,dataset] ));
        end
        figure
        image(abs(images{i}-zfit))
        title(sprintf('Residuals n° %i, dataset n°%d', [i,dataset] ));
    end

Half diameter computation

    variance = zeros(length(images),1);
    for i= 1:n
        if fitType == 1
            variance(i) = results1{i}.sigmax * results1{i}.sigmay;
        end
        if fitType == 2
            variance(i) = results2{i}.W.^2 ./4;
        end
    end

    allW=(4*variance).^(1/2)*pixel_size*downsamplingFactor; % half diameter at 1/e^2

    if not(exist('z','var'))
        figure1 = figure;
        axes1 = axes('Parent',figure1);
        plot(allW,'*')
        ylabel 'W [m]'
        title(sprintf('1/e^2 radius fit of gaussian profiles, dataset n°%d',dataset))
        set(axes1,'XTick',1:1:length(allW));
    end

Select only images which were fitted correctly

    min_rsquare=0.9;
    indices = 1:length(images);
    %chosenIndices = indices(rsquare_bells > min_rsquare);
    chosenIndices = indices;

    W=allW(chosenIndices);

Gaussian beam and waist fit

    if exist('z','var')

        z_selected = z(chosenIndices);
        if not(exist('guess','var'))
            [smallestW,ind]= min(W)
            guess= [smallestW,z_selected(ind)]
        end

        LAMBDA = num2str(lambda);

        modelsTypes = ["ideal gaussian beam model", "imperfect gaussian gaussian beam model (with M^2)"];
        for use_real = [0,1]
            disp(strcat("Fitting ", modelsTypes(use_real+1)))
            if use_real
                model= strcat('w_0*M*sqrt(    1+ ( (z-z_i)./( pi*w_0.^2./', LAMBDA,') ).^2     )') %non ideal gaussian beam with M^2 different from 1
                %following  <https://www.gentec-eo.com/blog/laser-beam-quality-measurement-m2>
                if exist('guess','var')
                    guess = [1 guess] % [M w_0 z_i]
                end
            else
                model = strcat('w_0*sqrt(    1+ ( (z-z_i)./( pi*w_0.^2./', LAMBDA,') ).^2     )')
            end

            ft = fittype( model, 'independent', 'z', 'dependent', 'y' );
            opts = fitoptions( 'Method', 'NonlinearLeastSquares'); % ,'robust','LAR'
            opts.DiffMaxChange = 1;
            opts.DiffMinChange = 1e-09;
            opts.Display = 'Off';
            opts.MaxIter = 4000;
            opts.StartPoint = guess;

            % Fit model to data.
            [wfit, wgof] = fit( z_selected, W, ft, opts );
            rsquare_beam= wgof.rsquare

            %Get fitted parameters
            W_0 =  wfit.w_0;                         % waist 1/e^2 radius
            z_i = wfit.z_i;                          % waist position
            z_R = pi*(wfit.w_0).^2./lambda     ;          % rayleigh range
            theta_0_deg = lambda./pi./wfit.w_0 *(180/pi); % divergence angle (degrees)

            if use_real
                M = wfit.M;
                theta_0_deg= theta_0_deg * M;
            end
            % Create a figure for the plots.
            beamFigure = figure( 'Name', 'Gaussian beam fit' );
            hold on
            zstart = -1.5*max([z_R,abs(z_selected(1)-z_i),abs(z_selected(end)-z_i)])+z_i;
            zstop = -zstart+2*z_i;
            range= zstart:(zstop-zstart)/100:zstop;
            plot(z_selected,W,'b*')
            plot( range, wfit(range),'r');
            plot(range,abs(range-wfit.z_i)*sind(theta_0_deg))
            plot(z_selected, wfit(z_selected),'+r')
            %%ylim([min(W)*0.8,max(W)*1.2])
            xlim([zstart,zstop])
            vline(wfit.z_i,'k--','waist')
            vline(z_i-z_R,'k--','-z_R')
            vline(z_i+z_R,'k--','+z_R')
            legend('Data', 'NLLS fit','Linear asymptote', 'Location', 'NorthWest' );
            xlabel 'z'' [m]'
            ylabel '1/e^2 radius [m]'
            if use_real
                title(sprintf('Non-ideal gaussian beam fit, dataset n°%d',dataset))
            else
                title(sprintf('Ideal gaussian beam fit, dataset n°%d',dataset))
            end
            grid on
            set(beamFigure, 'HandleVisibility', 'off');

            disp('Results in meters:')
            W_0                                     % waist 1/e^2 radius
            z_i                                     % waist position
            z_R                                     % rayleigh range
            theta_0_deg                             % divergence angle (degrees)
            if use_real
                M2 = M^2
                W_real = W_0 * M
            end
        end

smallestW =
   6.9498e-04
ind =
     1
guess =
   6.9498e-04   9.0000e-02
Fitting ideal gaussian beam model
model =
    'w_0*sqrt(    1+ ( (z-z_i)./( pi*w_0.^2./1.55e-06) ).^2     )'
rsquare_beam =
   8.7356e-01
Results in meters:
W_0 =
   6.9682e-04
z_i =
  -3.3928e-01
z_R =
   9.8414e-01
theta_0_deg =
   4.0568e-02
Fitting imperfect gaussian gaussian beam model (with M^2)
model =
    'w_0*M*sqrt(    1+ ( (z-z_i)./( pi*w_0.^2./1.55e-06) ).^2     )'
guess =
   1.0000e+00   6.9498e-04   9.0000e-02
rsquare_beam =
   8.5535e-01
Results in meters:
W_0 =
   6.8263e-04
z_i =
  -3.0894e-01
z_R =
   9.4448e-01
theta_0_deg =
   4.2356e-02
M2 =
   1.0461e+00
W_real =
   6.9821e-04

Linear fit

        linearFit = false;
        if linearFit
            pol_fit = fit(z,W,'poly1')
            figure
            hold on
            plot(z,W,'+')
            plot(z,pol_fit(z),'-')
            %ylim([1.1,1.2])
            xlabel('z'' [m]')
            ylabel('W [m]')
            title('Polynomial fit')
            legend('data','fit')
            grid on

            theta_lin = atan(pol_fit.p1)
            W_0_lin = lambda./pi/theta_lin

            z_R_lin = pi*(W_0_lin).^2./lambda               % rayleigh range
            theta_0_lin_deg = lambda./pi./W_0_lin *(180/pi) % divergence angle (degrees)
        end

    end

Other expressions (for gaussian beams)

    W_chosen = W                            % Half Width at 1/(e^2)
    STD = W_chosen./2;                       % Gaussian distrib standard deviation
    FWHM = W_chosen*sqrt(2*log(2));          % Full Width at Half Maximum
    e2_diam = sqrt(2)*FWHM ./ sqrt(log(2));  % Full Width at 1/(e^2)

W_chosen =
   6.9498e-04
   7.1862e-04
   7.4658e-04
   8.5855e-04
   9.2301e-04
   9.6149e-04
   1.0030e-03
   1.0094e-03
   1.0489e-03

Goodness of fits

    rsquare_bells
    if exist('z','var')
        rsquare_beam
        if use_real
            M2
        end
    end

rsquare_bells =
  Columns 1 through 6
   9.9956e-01   9.9961e-01   9.9960e-01   9.9856e-01   9.9741e-01   9.9682e-01
  Columns 7 through 9
   9.9460e-01   9.9745e-01   9.9373e-01
rsquare_beam =
   8.5535e-01
M2 =
   1.0461e+00

Saving results

    if shouldSaveResults
        outputPath = strcat('Results\',strrep(dataPath,'\','_'))
        save(outputPath,'W_chosen','W_0','z_R','z_i','theta_0_deg','results2');
    end

outputPath =
    'Results\Back Propagation of 1550nm_Collimator f=11mm_12-10-20_Set1_'

end

shouldPublish =
     1

Published with MATLAB® R2018a