ALAI.m

%
%%% --- GUI builder, from the MATLAB editor
%

function varargout = ALAI(varargin)
% ALAI MATLAB code for
% ALAI.fig
%      ALAI, by itself, creates a new
%      ALAI or raises the existing singleton*.
%
%      H = ALAI returns the handle to a new
%      ALAI or the handle to the existing
%      singleton*.
%
%      ALAI('CALLBACK',hObject,eventData,handles
%      calls the local function named CALLBACK in
%      ALAI.M with the given input arguments.
%
%      ALAI('Property','Value',...) creates a
%      new ALAI or raises the existing
%      singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before ALAI_OpeningFcn
%      gets called.  An unrecognized property name or invalid value makes
%      property application stop.  All inputs are passed to
%      ALAI_OpeningFcn via varargin.
%
%      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one
%      instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES

% Edit the above text to modify the response to help
% ALAI

% Last Modified by GUIDE v2.5 04-Jun-2018 23:19:14

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct(		'gui_Name',       mfilename, ...
        'gui_Singleton',  gui_Singleton, ...
        'gui_OpeningFcn', @ALAI_OpeningFcn, ...
        'gui_OutputFcn',  @ALAI_OutputFcn, ...
        'gui_LayoutFcn',  [] , ...
        'gui_Callback',   []);
if nargin && ischar(varargin{1})
    gui_State.gui_Callback = str2func(varargin{1});
end

if nargout
    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
    gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT

% --- Executes just before ALAI is made visible.
function ALAI_OpeningFcn(hObject, eventdata,...
	handles, varargin)
% This function has no output args, see OutputFcn. hObject    handle to
% figure eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA) varargin
% command line arguments to ALAI (see VARARGIN)

% Choose default command line output for ALAI
handles.output = hObject;

% Update handles structure
guidata(hObject, handles);

% UIWAIT makes ALAI wait for user response (see
% UIRESUME) uiwait(handles.figure1);

% --- Outputs from this function are returned to the command line.
function varargout = ALAI_OutputFcn(hObject, ...
	eventdata, handles) 
% varargout  cell array for returning output args (see VARARGOUT); hObject
% handle to figure eventdata  reserved - to be defined in a future version
% of MATLAB handles    structure with handles and user data (see GUIDATA)

% Get default command line output from handles structure
varargout{1} = handles.output;

% --- Executes on selection change in popupmenu1.
function popupmenu1_Callback(hObject, eventdata, handles)

global A
global AFMMetaData
global FileName
global IBWFiles
global Space

Space = ' '; %Just a space used in glueing together strings throughout 
	...the program!

% The lines below take important information from the AFM file, such as
% file name, Scan Size (physical and # pixels), and Imaging mode, and save
% them for calculation or later use on the graphs to be plotted
[FileName, PathName, filterindex] = uigetfile('*.ibw',... 
	'Select the ibw file', 'MultiSelect','on');
if ischar( FileName ), FileName = { FileName };  % for uigetfile,
end										% single file read-in is trouble
             
A = length(FileName);        % A variable we use in for loops to follow
IBWFiles = cell(1,A);       % This will hold the image data itself!!!

for i= 1:A                % Look through all of the files selected above
    
    Q = FileName{i};        % Assign the filename to variable Q  
    P = IBWread(Q);         % This fcn reads in and assigns ibw files to P
    IBWFiles{i} = P;        % Creates a cell array of all the read-in files
    tempWaveNotes = P.WaveNotes;  % this
    
    AFMMetaData{i} = textscan(tempWaveNotes, '%s','delimiter', '\n');
    
%     assignin('base','IBWFiles',IBWFiles);       % Adds the cell array
%     just created to the workspace assignin('base','FileName',FileName);
end

%    assignin('base','AFMMetaData',AFMMetaData);

if isequal(FileName,0) % in case no file is selected, a msg is displayed
    disp('User did not select files')

else                    % the filenames of the files read in are displayed
    disp(['user selected the .ibw file(s):', fullfile(PathName, FileName)])
end


% --- Executes during object creation, after setting all properties.
function popupmenu1_CreateFcn(hObject, eventdata, handles)

if ispc && isequal(get(hObject,'BackgroundColor'), ... 
		get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end


function edit2_Callback(hObject, eventdata, handles)

get(hObject,'String');


% --- Executes during object creation, after setting all properties.
function edit2_CreateFcn(hObject, eventdata, handles)

if ispc && isequal(get(hObject,'BackgroundColor'), ... 
		get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end


function edit3_Callback(hObject, eventdata, handles)

get(hObject,'String');


% --- Executes during object creation, after setting all properties.
function edit3_CreateFcn(hObject, eventdata, handles)

if ispc && isequal(get(hObject,'BackgroundColor'), ...
		get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function edit5_Callback(hObject, eventdata, handles)

get(hObject,'String');

% --- Executes during object creation, after setting all properties.
function edit5_CreateFcn(hObject, eventdata, handles)

if ispc && isequal(get(hObject,'BackgroundColor'), ...
		get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end

%
%%% ROUGHNESS STATISTICS AND ROUGHNESS PARAMETER CALCULATION AND FILE
%%% EXPORT
%
function pushbutton1_Callback(hObject, eventdata, handles)

format longeng    % makes it so we can see numbers which are different ...
					% by orders of magnitude

global A
global AllImageStats
global AFMMetaData
global AllExpFitCoefficients
global AllLinFitCoefficients
global AllRoughnessParameters
global DataForFitting
global FileName
global FirstExpFit
global FirstExpFitOptions
global IBWFiles
global LastLinearPoint
global LinFit
global PhysicalScanSize
global Pixels
global SquareRootofLastWeight
global y0Start

AllImageStats = cell(1,A);     % pre-allocates a cell array which will 
								% store the calculated length/RMS matrices
DataFileHeader = cell(1,3); %This will be the header for the 
							%"____statistics.txt" export text file
DataFileHeader = {'Length(A)','RMS Roughness(A)','STD of Roughness(A)'};
ImageSelect = get(handles.edit2, 'string');
ImageSelect = str2double(ImageSelect);
Pixels = zeros(1,A);
VarianceOfLogRMSValues = 1;

disp '***Roughness Analysis Has Begun***'

for i = [1:A]                       
    tempCELL = IBWFiles{i};       % picks out a single image file-set 
									% from the cell array
    
	PickHeight = tempCELL.y; % picks out all of the height data 
							 % (for all of the available types, 
							 %phase, amplitude, height, etc) from the file
    
	PickImage = PickHeight(:,:,ImageSelect);  % selects the 6th image, 
			  %in my case the modified height, THIS NEEDS TO BE USER-INPUT
    
	Pixels(1,i) = length(PickImage);     % # of pixels for a square image 
										 % in EACH DIMENSION
    
	N = log(Pixels(1,i))/log(2);         % This will control the outer-most
										 % for loop for each image, below
    
    tempStringforPhysicalScanSize= AFMMetaData{i}{1}{1};  
	% This will go into the AFM-file meta-data to find physical image size
	
	clippedString = strrep(tempStringforPhysicalScanSize,'ScanSize: ','');
    
	PhysicalScanSize = 10e9*str2double(clippedString); 
    % Gives the square-dimension of the image in Angstroms
	
    AngstromLength = zeros(N,1);    
		% pre-allocate a matrix to hold length values in Angstroms
		
	RMSValues = zeros(N,1);         
		% pre-allocate a matrix to hold RMS Roughness values for each length-scale
		
	LogAngstromLength = zeros(N,1);
    
	LogRMSValues = zeros(N,1);
    
	LogRMSWeights = zeros(N,1);
    
	LogSTD = zeros(N,1);
    
	STD = zeros(N,1);               
		% pre-allocate a matrix to hold the uncertainty for each RMS Roughness value
		
    for j = [1:N]                 
        % this outer loop will reset for each image selected
		
        PixelsPerSquare = 2^j;      
		% determines the number of pixels (for each dimension) to examine
		
        NumberOfValuesToAVG = (Pixels(1,i)/PixelsPerSquare)^2;    
		% With each new length scale, the number of values changes as x^2
		
        RMSHolder = zeros(NumberOfValuesToAVG,1);       
		% pre-allocates a matrix in which the STD from each small square
		
        m = 1; % is saved until they can be averaged and STD calculated                                                 
   
        ADD = 2^j-1;        
        % ADD is an incrementer which adjusts to the size of the box being
		%  used for STD calculations, below
		
        for k = [1:PixelsPerSquare:(Pixels(1,i)-PixelsPerSquare+1)]          
        %in the x-direction, this loop takes us across the pixels
		%by bits = to the square size defined prev as "pixel per square"
			 incrementk = k + ADD;                                                     
            
            for l = [1:PixelsPerSquare:(Pixels(1,i)-PixelsPerSquare+1)]          
            % runs through the y values    
				incrementl = l + ADD;                
                RMSHolder(m) = std2(PickImage(k:incrementk,l:incrementl));    
                % The heart of things: finds the std for each small square
				m = m + 1;
				%increments the list of STDs forward to save the next value
             end
        end
        
        StdOfRMS = std(RMSHolder)*1e10;         
		% Finds the STD/uncertainty for all the little-square calculations
        RMSMean = mean(RMSHolder)*1e10;         
		% averages the measurements from each small square
        STD(j) = StdOfRMS;                      
		% Stores the avg STD value in a vector for calculation of LogSTD 
		% in the next line
        LogSTD(j) = STD(j)/(RMSMean*log(10));   
		% This is the fractional uncertainty of LogRMSValues, below   
        RMSValues(j) = RMSMean;                 
		%Adds to the average from above to a list which saves 
		% the values across length scales, for each image
        LogRMSValues(j) = log10(RMSMean);       
		% this will be the y-value we use for "roughness statistics" and 
		%calculating roughness parameters
       
        tempAngstromLength =PhysicalScanSize*(PixelsPerSquare/Pixels(1,i));  
		% need to turn pixel values into real length values!
        AngstromLength(j) = tempAngstromLength;                               
		% creates a list of the real lengths we have just calculated
        LogAngstromLength(j) = log10(tempAngstromLength);                     
		% we want log-log for roughness parameter calculations, 
		% this is the x-values vector
        
        if j ~= N
            VarianceOfLogRMSValues = LogSTD(j)*LogSTD(j);  
			% We calculate the weights for each point for the final fit
            LogRMSWeights(j) = 1 / VarianceOfLogRMSValues; 
			% The weight, for each data point, is  1 / variance
        else
            tempFit = fitlm(LogAngstromLength,LogSTD);   
			% so... above we could find the weights for fitting quite easily
            
			tempSlope = tempFit.Coefficients{2,1};       
			% but the last RMS roughness measurement, of the ENTIRE AFM image
            
			tempy0 = tempFit.Coefficients{1,1};          
			% produces only ONE value. Thus, there is no obvious 
			% STD/uncertainty to use!
            
			SquareRootofLastWeight = tempSlope(1,1)*LogAngstromLength(j)+tempy0(1,1); 
            
			LogSTD(j) = SquareRootofLastWeight;          
			% So, here we fit the uncertainties of all of the 
			% smaller RMS roughness values
            
			LastWeight = SquareRootofLastWeight^2;       
			% which were calculated, used to extrapolate what might expect
           
			LogRMSWeights(j) = 1 / LastWeight;           
			% The uncertainty of the last single RMS roughness measurements
			% to be
        end
        
        XandYandZ = [AngstromLength RMSValues STD LogAngstromLength ...
			LogRMSValues LogSTD LogRMSWeights]; 
			% creates a 6-column matrix with important values
        y0Start = LogRMSValues(end);         
		% this is  used to give the exponential fit a place to begin 
		% its search for a y-intercept

    end
    
    OutputTemp1 = FileName{i};
    OutputTemp1 = OutputTemp1(1:end-4);
    NameForTextFile = sprintf('%s_Roughness_Statistics.txt',OutputTemp1);
    AllImageStats{1,i} = {NameForTextFile; DataFileHeader; XandYandZ};              
	% Adds the 3-column list to a cell array to store for all selected
	% images
    
	%assignin('base','AllImageStats',AllImageStats); % DIAGNOSTIC: Saves
    %the above stat lists for all of the images to the workspace
	% useful as a diagnostic tool
end

LS = 'Length Scale [Angstroms]', RR = 'RMS Roughness [Angstroms]', ...
	STDRR = 'STD of RMS Roughness [Angstroms]';
LOGLS = 'log(Length Scale)', LOGRR = 'log(RMS Roughness)', ...
	STDLOGRR = 'STD of log(RMS Roughness)';

for i = [1:A]
    tempName = AllImageStats{i}{1};
    tempData = AllImageStats{i}{3};
    
    tempRoughnessStatisticsExportFile = fopen(tempName,'w');
    fprintf(tempRoughnessStatisticsExportFile, '%s\n', tempName);  
    fprintf(tempRoughnessStatisticsExportFile, ...
		'%s %4s %4s %4s %4s %4s\n',LS,RR,STDRR,LOGLS,LOGRR,STDLOGRR);
    fprintf(tempRoughnessStatisticsExportFile, ...
		'%2.1f %20.1f %30.1f %10.1f %10.1f %10.1f\n',tempData');
    fclose(tempRoughnessStatisticsExportFile);
end

disp '**Done Computing Roughness Statistics**'

AllExpFitCoefficients = cell(2,A);  
% We will need to store and re-use the fitting parameters
AllLinFitCoefficients = cell(1,A);  
% for calculations of Roughness Parameters as well as 
AllRoughnessParameters = cell(1,A); 
% plotting figures in functions to follow

FirstExpFit = fittype('y0+a*exp(b*x)','independent','x','dependent','y','coefficients',{'y0','a','b'});

FirstExpFitOptions = fitoptions(FirstExpFit);
%FirstExpFitOptions.Algorithm = 'Levenberg-Marquardt';
%FirstExpFitOptions.Robust = 'LAR';
FirstExpFitOptions.StartPoint = [y0Start -150 0.1];   
% the start point, and lower upper bounds for fitting

FirstExpFitOptions.Lower = [0 -300 -5];               
% were determined by using OriginPro to fit several plots of the Roughness Statistics data                                                    

FirstExpFitOptions.Upper = [5  0 0];
FirstExpFitOptions.MaxFunEvals = 50000;
FirstExpFitOptions.MaxIter = 50000;

LastLinearPoint = zeros(A,1);  
% pre-allocates a vector to hold the points where the fit should 
% switch over from linear to exponential, for different AFM images

for i = [1:A]
     
    BREAK = 0;
    N = AllImageStats{i}{3};    
	% this is for convenience so the global variable 
	% does not keep getting called
    tempLength = length(N(:,1));  
	% we previously calculated the roughness 
	% statistics with # rows = tempLength
    testOnesVector = ones(tempLength,1); 
	%diagnostic vector for tinkering with weights 4 the fitting procedures
    
    DataForFitting = zeros(tempLength,2); 
	% pre-allocate a matrix of width 3 and length tempLength 
    
	DataForFitting(:,1) = N(:,4);  
	% this column = Log (Length Scale [A])
    
	DataForFitting(:,2) = N(:,5); 
	% this column = Log (RMS Roughness [A])
    
    ExpFitAdjRHolder = zeros(tempLength,1);
    
    for k = [2:tempLength-2] 
      
         %FirstExpFitOptions.Weights = testOnesVector(end-k-1:end,1);
         FirstExpFitOptions.Weights = AllImageStats{1,i}{3}(end-k:end,7);
         [ExpFit, GoF] = fit(DataForFitting(end-k:end,1), ...
			 DataForFitting(end-k:end,2), FirstExpFit, FirstExpFitOptions);
         AdjR2 = GoF.adjrsquare;
         ExpFitAdjRHolder(k) = AdjR2;

        if ExpFitAdjRHolder(k) < ExpFitAdjRHolder(k-1)
                       
            FirstExpFitOptions.Weights = AllImageStats{1,i}{3}(end-k-1:end,7);       
            [ExpFit, GoF2] = fit(DataForFitting(end-k-1:end,1), ...
				DataForFitting(end-k-1:end,2), FirstExpFit, FirstExpFitOptions);
            AdjR2_2 = GoF2.adjrsquare;
            ExpFitAdjRHolder(k+1) = AdjR2_2;
            
            if AdjR2_2 < ExpFitAdjRHolder(k-1)

                 FirstExpFitOptions.Weights = ...
					 AllImageStats{1,i}{3}(end-k:end,7);
                 [ExpFit, GoF] = fit(DataForFitting(end-k:end,1), ...
					 DataForFitting(end-k:end,2), FirstExpFit, ...
					 FirstExpFitOptions);
                 BREAK = 1;          
            end
        end
        
        if BREAK == 1
            break
        end
            
            FirstExpPoint = tempLength - k + 1;
            LastLinearPoint(i,1) = FirstExpPoint;
            temp4 = tempLength - FirstExpPoint + 1;
    end
    
    ExpFitCoefficients = coeffvalues(ExpFit); 
	% Here we pull out the coeffcients from the exponential fit, above
    y0 = ExpFitCoefficients(1);
    a = ExpFitCoefficients(2);
    b = ExpFitCoefficients(3);
    ExpFitError = GoF.sse;
    
    ExpRange = temp4;
    ExpFitConfidenceInterval = confint(ExpFit);
    BOTTOM = ExpFitConfidenceInterval(1,1);
    TOP = ExpFitConfidenceInterval(2,1);
    ConfidenceIntervalRange = TOP - BOTTOM;
    tvalue = tinv(0.95,ExpRange);
          
    ExpFitYIntercept = ExpFitCoefficients(1);    
    ExpFitYInterceptError = sqrt(ExpRange)*ConfidenceIntervalRange/tvalue;
    y0error = ExpFitYInterceptError;    
    ExpFitYInterceptPartialError=(ExpFitYInterceptError/ExpFitYIntercept)^2;
    
    AllExpFitCoefficients {1,i} = {y0;a;b}; 
	% We now store the coefficients from the exponential 
	% fit for later calculation and plotting
    
	AllExpFitCoefficients {2,i} = {y0error;ExpFitError};
    
	assignin('base','AllExpFitCoefficients',AllExpFitCoefficients); 
	% DIAGNOSTIC
   
    LinFit = fitlm(DataForFitting(1:LastLinearPoint(i,1),1),...
		DataForFitting(1:LastLinearPoint(i,1),2),'Weight', ... 
		AllImageStats{1,i}{3}(1:LastLinearPoint(i,1),7));
  
    LinearYIntercept = LinFit.Coefficients{1,1};
    LinearYInterceptError = LinFit.Coefficients{1,2};
    LinearYInterceptPartialError = ...
		(LinearYInterceptError/LinearYIntercept)^2;
    
    LinearSlope = LinFit.Coefficients{2,1};
    LinearSlopeError = LinFit.Coefficients{2,2};
    LinearSlopePartialError = (LinearSlopeError/LinearSlope)^2;
    AllLinFitCoefficients{1,i} = {LinearSlope; LinearSlopeError ; ...
		LinearYIntercept ; LinearYInterceptError};
    % assignin('base','AllLinFitCoefficients', AllLinFitCoefficients);
        
    XIntercept = ((y0)-LinearYIntercept)/LinearSlope;
    XInterceptError = XIntercept*sqrt(LinearSlopePartialError + ...
		LinearYInterceptPartialError + ExpFitYInterceptPartialError);

    %BELOW WE CALCULATE THE THREE ROUGHNESS PARAMETERS
    
    FractalDimension = 3 - LinearSlope;
    FractalDimensionError = FractalDimension*(LinearSlopeError/LinearSlope);
      
    SaturationRoughness = 10^(y0);
    SaturationRoughnessError = SaturationRoughness * ...
		(ExpFitYInterceptError / ExpFitYIntercept);
    
    CorrelationLength = 10^XIntercept;
    CorrelationLengthError = CorrelationLength * ...
		(XInterceptError / XIntercept);
    
    ThreeParameters = [FractalDimension SaturationRoughness ...
		CorrelationLength];
    ThreeParametersError = [FractalDimensionError SaturationRoughnessError ...
		CorrelationLengthError];
    %assignin('base','ThreeRoughnessParameters',ThreeParameters);
    
    OutputTemp2 = FileName{i};
    OutputTemp2 = OutputTemp2(1:end-4);
    RoughnessParameterFileName = sprintf('%s_Roughness_Parameters.txt',...
		OutputTemp2);
    AllRoughnessParameters{1,i} = {RoughnessParameterFileName; ...
		ThreeParameters; ThreeParametersError};
    
    %assignin('base','DataForFitting',DataForFitting) % DIAGNOSTICS
    %assignin('base','LinFit',LinFit) assignin('base','ExpFit',ExpFit)
end  

FD = 'Fractal Dimension', SR = 'Saturation Roughness [Angstroms]', ...
	CL = 'Correlation Length [Angstroms]';

for i = [1:A]
    
    tempName2 = AllRoughnessParameters{i}{1};
    tempData2 = AllRoughnessParameters{i}{2};

    tempRoughnessParameterExportFile = fopen(tempName2,'w'); 
    fprintf(tempRoughnessParameterExportFile, '%s\n', tempName2);  
    fprintf(tempRoughnessParameterExportFile, '%s %2s %2s\n',FD,SR,CL); 
    fprintf(tempRoughnessParameterExportFile, '%.1f %20.1f %30.1f',...
		tempData2);
    fclose(tempRoughnessParameterExportFile);
end
disp '***Done Computing Roughness Parameters***'

%
%%% --- Display each AFM image's Roughness Report in an individual figure
%

function pushbutton3_Callback(hObject, eventdata, handles)

global A
global AFMMetaData
global AllExpFitCoefficients
global AllImageStats
global AllLinFitCoefficients
global AllRoughnessParameters
global FileName
global LastLinearPoint
global Pixels
global Space
global y0

clippedString = '';

for i = [1:A]
    
    N = AllImageStats{i}{3}; 
    tempLength = length(N(:,1));
    
    DataForFitting2 = zeros(tempLength,3);
    DataForFitting2(:,1) = N(:,4);
    DataForFitting2(:,2) = N(:,5);
    DataForFitting2(:,3) = N(:,6);    
    
    LinSlope = AllLinFitCoefficients{1,i}{1};
    LinYIntercept = AllLinFitCoefficients{1,i}{3};
       
    y0 = AllExpFitCoefficients{1,i}{1,1}; a = ...
	AllExpFitCoefficients{1,i}{2,1}; b = AllExpFitCoefficients{1,i}{3,1};

    XforLinear = DataForFitting2(1:LastLinearPoint(i,1),1);
    YforLinear = LinYIntercept + LinSlope*XforLinear;

    XforExp = DataForFitting2(LastLinearPoint(i,1):end,1);
    YforExp = y0 + a*exp(b*XforExp);

    %%%Now we plot our data!
    
    figure();
    tempName2 = FileName{i};
    tempName2 = tempName2(1:end-4); 
    
    % Below is a text box which will display the title of the sample/image
    GraphTitle = cat(2,'Roughness Analysis for: ',tempName2,'');
    TitleNotes = annotation('textbox',...
    [0.195 0.84 0.64 0.075],...
    'String',{GraphTitle},...
    'FontSize',14,...
    'FontName','Constantina',...
    'LineStyle','-',...
    'EdgeColor',[0 0 0],...
    'LineWidth',0.01,...
    'BackgroundColor',[1 1 1],...
    'Color',[0.1 0.1 0.1],...
    'Interpreter', 'none',...
    'FontWeight', 'bold',...
    'HorizontalAlignment','center',...
    'FitBoxToText','on');
    
    FractalDimension = AllRoughnessParameters{1,i}{2,1}(1,1);
    FractalDimension = num2str(FractalDimension);
    FractalDimension = FractalDimension(1:end-2);
    
    FractalDimensionError = AllRoughnessParameters{1,i}{3,1}(1,1);
    FractalDimensionError = ceil(FractalDimensionError/10^floor...
		(log10(FractalDimensionError)))...
		*10^floor(log10(FractalDimensionError)); % salt to suit...
	
    FractalErrorInfo = num2str(FractalDimensionError);
    FractalErrorInfo = cat(2,'(',FractalErrorInfo,')');
    FractalInfo = 'Fractal Dimension: ';
    FractalInfo = ...
		cat(2,FractalInfo, FractalDimension,Space,FractalErrorInfo);
    
    SatRuffError = AllRoughnessParameters{1,i}{3,1}(1,2);
    SatRuffError = ceil(SatRuffError/10^floor(log10(SatRuffError)))...
		*10^floor(log10(SatRuffError)); % salt to suit...
	
    SatRuffErrorInfo = num2str(SatRuffError);
    SatRuffErrorInfo = cat(2,'(',SatRuffErrorInfo,')');
    SatRuff = AllRoughnessParameters{1,i}{2,1}(1,2);
    SatRuff = num2str(SatRuff);
    SatRuff = SatRuff(1:end-3);
    SatRuffInfo = 'Saturation Roughness: ';
    SatRuffInfo = ...
		cat(2,SatRuffInfo, SatRuff,Space,SatRuffErrorInfo,' [A]');
    
    CorrLengthError = AllRoughnessParameters{1,i}{3,1}(1,3);
    CorrLengthError = ceil(CorrLengthError/10^floor...
		(log10(CorrLengthError)))*10^floor(log10(CorrLengthError)); 
    CorrLengthErrorInfo = num2str(CorrLengthError);
    CorrLengthErrorInfo = cat(2,'(',CorrLengthErrorInfo,')');
    CorrLength = AllRoughnessParameters{1,i}{2,1}(1,3);
    CorrLength = num2str(CorrLength);
    CorrLength = CorrLength(1:end-4);
    CorrLengthInfo = 'Correlation Length: ';
    CorrLengthInfo = cat(2, CorrLengthInfo, CorrLength, ...
		Space, CorrLengthErrorInfo,' [A]'); 
   
    [PhysicalImageSize,ImageResolution] = ImageSizeProperties(Pixels,...
		AFMMetaData);
    
    %%%
    ImageNotes = annotation('textbox',...
    [0.62 0.08 0.1 0.3],...
    'String',{PhysicalImageSize ImageResolution Space ...
		FractalInfo SatRuffInfo CorrLengthInfo },...
    'FontSize',14,...
    'FontName','Constantina',...
    'LineStyle','-',...
    'EdgeColor',[0 0 0],...
    'LineWidth',0.1,...
    'BackgroundColor',[1 1 1],...
    'Color',[0.1 0.1 0.1],...
    'FitBoxToText','on');
    
    plot(XforLinear, DataForFitting2(1:LastLinearPoint(i,1),2),'o'); ...
		hold on;
    plot(XforLinear,YforLinear,'b'), hold on;  
    plot(XforExp, YforExp,'g'), hold on;
    plot(XforExp, DataForFitting2(LastLinearPoint(i,1):end,2),'x');
    
    errorbar(DataForFitting2(:,1),DataForFitting2(:,2), ...
		DataForFitting2(:,3),'r.');
    
    grid on;
    axis([1.5 ceil(DataForFitting2(end,1)*10)/10 0 ...
		DataForFitting2(end,2)+DataForFitting2(end,2)/4]);
    xlabel('Log( LengthScale [A] )','FontWeight','bold','FontName',...
		'Constantina','FontSize',16); 
    ylabel('Log( RMS Roughness [A] )','FontWeight','bold','FontName',...
		'Constantina','FontSize',16);
end

% --- Plot Data from ALL AFM IMAGES SELECTED on a Single Figure
%
function pushbutton4_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton4 (see GCBO) eventdata  reserved - to be
% defined in a future version of MATLAB handles    structure with handles
% and user data (see GUIDATA)

global A
global AFMMetaData
global AllLinFitCoefficients
global AllExpFitCoefficients
global AllImageStats
global AllRoughnessParameters
global LastLinearPoint
global Pixels
global Space
global y0


SumOfWeightedSatRuffs = 0;
SumOfSatRuffWeights = 0;
 
SumOfWeightedCorrLengths = 0;
SumOfCorrLengthWeights = 0;

SumOfWeightedFractalDimensions = 0;
SumOfFractalDimensionWeights = 0;


clippedString = '';
CorrLengthError = 0;
CurrentCorrLength = 0;
CurrentCorrLengthError = 0;
CurrentCorrLengthWeight = 0;
CurrentDf = 0;
CurrentDfError = 0;
CurrentDfWeight = 0;
CurrentSatRuff = 0;
CurrentSatRuffError = 0;
CurrentSatRuffWeight = 0;
DfError = 0;
FractalDimensionWeightedAVGError = 0;
SatRuff = 0;
SatRuffError = 0;
sqrtA = sqrt(A);
HolderForSumOfCorrLengthWeights = 0;
SumOfDfWeights = 0;
SumOfSatRuffWeights = 0;
SumOfFractalDimensions = 0;
SumOfCorrLengths = 0;
SumOfCorrLengthsError = 0;
TotalLinSlope = 0;
TotalLinSlopeError = 0;
TotalLinYIntercept = 0;
TotalLinYInterceptError = 0;
SumOfSatRuffs = 0;
SumOfSatRuffsError = 0;
TotalYDataForFitting = zeros(length(AllImageStats{1}{3}),1);
TotalYErrorDataForFitting = zeros(length(AllImageStats{1}{3}),1);
SumOfCorrLengthWeights = 0;
HolderForSumOfCorrLengths = 0;
SumOfFractalDimensionWeights = 0;
CL = 0;
CLW = 0;

for i = [1:A]
    
    N = AllImageStats{i}{3}; 
    tempLength = length(N(:,1));

    DataForFitting2 = zeros(tempLength,3);
    DataForFitting2(:,1) = N(:,4);
    DataForFitting2(:,2) = N(:,5);
    DataForFitting2(:,3) = N(:,6);    

    LinSlope = AllLinFitCoefficients{1,i}{1};
    TotalLinSlope = TotalLinSlope + LinSlope;
    LinSlopeError = AllLinFitCoefficients{1,i}{2};
    TotalLinSlopeError = TotalLinSlopeError + LinSlopeError;
    
    LinYIntercept = AllLinFitCoefficients{1,i}{3};
    TotalLinYIntercept = TotalLinYIntercept + LinYIntercept;
    LinYInterceptError = AllLinFitCoefficients{1,i}{4};
    TotalLinYInterceptError = TotalLinYInterceptError + LinYInterceptError;
    
    y0 = AllExpFitCoefficients{1,i}{1,1}, ...
		a = AllExpFitCoefficients{1,i}{2,1}, ...
		b = AllExpFitCoefficients{1,i}{3,1};

    XforLinear = DataForFitting2(1:LastLinearPoint(i,1),1);
    YforLinear = LinYIntercept + LinSlope*XforLinear;
    
    XforExp = DataForFitting2(LastLinearPoint(i,1):end,1);
    YforExp = y0 + a*exp(b*XforExp);
    
    [CL,CLW] = SumOfWeightedCorrelationLengthsAndWeights ...
		(i, AllRoughnessParameters)
    SumOfWeightedCorrLengths = SumOfWeightedCorrLengths + CL
    SumOfCorrLengthWeights = SumOfCorrLengthWeights + CLW
    
    [FD,FDW] = SumOfWeightedFractalDimensionsAndWeights ...
		(i, AllRoughnessParameters)
    SumOfWeightedFractalDimensions = SumOfWeightedFractalDimensions + FD
    SumOfFractalDimensionWeights = SumOfFractalDimensionWeights + FDW
    
    [SR,SRW] = SumOfWeightedSaturationRuffsAndWeights ...
		(i, AllRoughnessParameters)
    SumOfWeightedSatRuffs = SumOfWeightedSatRuffs + SR
    SumOfSatRuffWeights = SumOfSatRuffWeights + SRW
    
    
% Below is a text box which will display the title of the sample/image
    figure(99)
    
    TitleForManyPlotsFigure = get(handles.edit3, 'string');
    TitleNotes = annotation('textbox',...
    [0.195 0.84 0.64 0.075],...
    'String',{TitleForManyPlotsFigure},...
    'FontSize',14,...
    'FontName','Constantina',...
    'LineStyle','-',...
    'EdgeColor',[0 0 0],...
    'LineWidth',0.01,...
    'BackgroundColor',[1 1 1],...
    'Color',[0.1 0.1 0.1],...
    'Interpreter', 'none',...
    'FontWeight', 'bold',...
    'HorizontalAlignment','center',...
    'FitBoxToText','on');

    plot(XforLinear, DataForFitting2(1:LastLinearPoint(i,1),2),'o'); ...
		hold on;
    plot(XforLinear,YforLinear,'b'), hold on;  
    
    plot(XforExp, YforExp,'g'), hold on;
    plot(XforExp, DataForFitting2(LastLinearPoint(i,1):end,2),'x'); ...
		hold on;

    errorbar(DataForFitting2(:,1),DataForFitting2(:,2), ...
		DataForFitting2(:,3),'r.');   
    
    grid on;
    axis([1.5 ceil(DataForFitting2 ...
		(end,1)*10)/10 0 DataForFitting2(end,2)+DataForFitting2(end,2)/4]);
    xlabel('Log( LengthScale [A] )','FontWeight','bold','FontName',...
		'Constantina','FontSize',16); 
    ylabel('Log( RMS Roughness [A] )','FontWeight','bold','FontName',...
		'Constantina','FontSize',16);
end



if A == 1
    CorrLengthWeightedAVGError = AllRoughnessParameters{1,i}{3,1}(1,3);
    FractalDimensionWeightedAVGError = AllRoughnessParameters{1,i}{3,1}(1,1);
    SatRuffWeightedAVGError = AllRoughnessParameters{1,i}{3,1}(1,2);;
else
    CorrLengthWeightedAVGError = 1 / sqrt(SumOfCorrLengthWeights);    
    FractalDimensionWeightedAVGError = 1 / sqrt(SumOfFractalDimensionWeights);
    SatRuffWeightedAVGError = 1 / sqrt(SumOfSatRuffWeights);
end



CorrelationLengthStuff = CorrelationLengthWeightedAVGInfo...
	(SumOfWeightedCorrLengths, SumOfCorrLengthWeights,...
		CorrLengthWeightedAVGError);
FractalDimensionStuff = FractalDimensionWeightedAVGInfo ...
	(SumOfWeightedFractalDimensions, SumOfFractalDimensionWeights, ...
		FractalDimensionWeightedAVGError);
SaturationRoughnessStuff = SaturationRoughnessWeightedAVGInfo ...
	(SumOfWeightedSatRuffs, SumOfSatRuffWeights, SatRuffWeightedAVGError);
  
[PhysicalSizeOfAFMImage, AMFImageResolution] = ...
	ImageSizeProperties(Pixels, AFMMetaData);

% Below is a textbox which will show the important information about the
% sample
ImageNotes = annotation('textbox',...
    [0.62 0.08 0.1 0.3],...
    'String',{PhysicalSizeOfAFMImage AMFImageResolution Space ...
		FractalDimensionStuff SaturationRoughnessStuff ...
			CorrelationLengthStuff },...
    'FontSize',14,...
    'FontName','Constantina',...
    'LineStyle','-',...
    'EdgeColor',[0 0 0],...
    'LineWidth',0.1,...
    'BackgroundColor',[1 1 1],...
    'Color',[0.1 0.1 0.1],...
    'FitBoxToText','on');
   

% --- Average and then plot all data, as a single set, on a Single Figure
function pushbutton5_Callback(hObject, eventdata, handles)

global A
global AFMMetaData
global AllLinFitCoefficients
global AllImageStats
global AllRoughnessParameters
global FirstExpFit
global FirstExpFitOptions
global LastLinearPoint
global Pixels
global Space
                     
clippedString = '';  
CorrLengthWeightedAVG = 0; 
CorrLengthWeightedAVGError = 0;
FractalDimensionWeightedAVG = 0;
FractalDimensionWeightedAVGError = 0;
SatRuffWeightedAVG = 0;
SatRuffWeightedAVGError = 0;
sqrtA = sqrt(A);
SumOfCorrLengthWeights = 0;
SumOfSatRuffWeights = 0;
SumOfDfWeights = 0;
SumOfWeightedSatRuffs = 0;
SumOfSatRuffWeights = 0;
SumOfWeightedCorrLengths = 0;
SumOfCorrLengthWeights = 0;
SumOfWeightedFractalDimensions = 0;
SumOfFractalDimensionWeights = 0; 
Totala = 0;
Totalb = 0;
SumOfCorrLengths = 0;
SumOfCorrLengthsError = 0;
SumOfFractalDimensions = 0;
SumOfSatRuffs = 0;
SumOfSatRuffsError = 0;
TotalLinSlope = 0;
TotalLinSlopeError = 0;
TotalLinYIntercept = 0;
TotalLinYInterceptError = 0;
Totaly0 = 0;
y0 = 0;

TotalYDataForFitting = zeros(length(AllImageStats{1}{3}),1);
TotalYErrorDataForFitting = zeros(length(AllImageStats{1}{3}),1);
SumOfWeightForYDataForFitting = zeros(length(AllImageStats{1}{3}),1);

for i = [1:A]
    
    N = AllImageStats{i}{3};
    tempLength = length(N(:,1));

    DataForFitting2 = zeros(tempLength,3);
    DataForFitting2(:,1) = N(:,4);
    DataForFitting2(:,2) = N(:,5);
    DataForFitting2(:,3) = N(:,6);
    
    XforLinear = DataForFitting2(1:LastLinearPoint(i,1),1);
    
    for j = [1:tempLength]
    
        WeightForYDataForFitting(j,1) = 1 / DataForFitting2(j,3)^2;
    end

    TotalYDataForFitting = TotalYDataForFitting + DataForFitting2(:,2) ...
		.* WeightForYDataForFitting(:,1);
    TotalYErrorDataForFitting = TotalYErrorDataForFitting + ...
		DataForFitting2(:,3);
    SumOfWeightForYDataForFitting = SumOfWeightForYDataForFitting + ...
		WeightForYDataForFitting(:,1);
    
    LinSlope = AllLinFitCoefficients{1,i}{1};
    TotalLinSlope = TotalLinSlope + LinSlope;
    LinSlopeError = AllLinFitCoefficients{1,i}{2};
    TotalLinSlopeError = TotalLinSlopeError + LinSlopeError;
    
    LinYIntercept = AllLinFitCoefficients{1,i}{3};
    TotalLinYIntercept = TotalLinYIntercept + LinYIntercept;
    LinYInterceptError = AllLinFitCoefficients{1,i}{4};
    TotalLinYInterceptError = TotalLinYInterceptError + LinYInterceptError;

    [CL,CLW] = SumOfWeightedCorrelationLengthsAndWeights ...
		(i, AllRoughnessParameters);
    SumOfWeightedCorrLengths = SumOfWeightedCorrLengths + CL;
    SumOfCorrLengthWeights = SumOfCorrLengthWeights + CLW;
    
    [FD,FDW] = SumOfWeightedFractalDimensionsAndWeights ...
		(i, AllRoughnessParameters);
    SumOfWeightedFractalDimensions = SumOfWeightedFractalDimensions + FD;
    SumOfFractalDimensionWeights = SumOfFractalDimensionWeights + FDW;
    
    [SR,SRW] = SumOfWeightedSaturationRuffsAndWeights ...
		(i, AllRoughnessParameters);
    SumOfWeightedSatRuffs = SumOfWeightedSatRuffs + SR;
    SumOfSatRuffWeights = SumOfSatRuffWeights + SRW;
    
end

XDataForFitting = N(:,4);
WeightedYDataForFitting = ...
	TotalYDataForFitting./SumOfWeightForYDataForFitting;
meanYErrorDataForFitting = ...
	TotalYErrorDataForFitting./ sqrt(SumOfWeightForYDataForFitting);

meanLinSlope = TotalLinSlope/A;  
meanLinYIntercept = TotalLinYIntercept/A; 
MeanYforLinear = meanLinYIntercept + meanLinSlope*XforLinear;

FirstExpFitOptions = fitoptions(FirstExpFit);
FirstExpFitOptions.StartPoint = [WeightedYDataForFitting(end) -150 0.1];
% the start point, and lower upper bounds for fitting

[AVGExpFitForWeightedAVG, GoFExpFitForWeightedAVG] = ...
	fit(XDataForFitting(LastLinearPoint:end), ...
		WeightedYDataForFitting(LastLinearPoint:end), ...
			FirstExpFit, FirstExpFitOptions);
ExpFitCoefficients = coeffvalues(AVGExpFitForWeightedAVG);      
% Here we pull out the coeffcients from the exponential fit, above

y0 = ExpFitCoefficients(1), a = ExpFitCoefficients(2), ...
	b = ExpFitCoefficients(3);

WeightedYforExp = y0 + a*exp(b*XDataForFitting(LastLinearPoint(i,1):end));

if A == 1
    FractalDimensionWeightedAVGError = ...
		AllRoughnessParameters{1,i}{3,1}(1,1);
    CorrLengthWeightedAVGError = AllRoughnessParameters{1,i}{3,1}(1,3);
    SatRuffWeightedAVGError = AllRoughnessParameters{1,i}{3,1}(1,2);  
else
    FractalDimensionWeightedAVGError = 1 / sqrt(SumOfFractalDimensionWeights);
    SatRuffWeightedAVGError = 1 / sqrt(SumOfSatRuffWeights);
    CorrLengthWeightedAVGError = 1 / sqrt(SumOfCorrLengthWeights);
end

[PhysicalSizeOfAFMImage, AMFImageResolution] = ...
	ImageSizeProperties(Pixels, AFMMetaData);

CorrelationLengthStuff = CorrelationLengthWeightedAVGInfo ...
	(SumOfWeightedCorrLengths, SumOfCorrLengthWeights, ...
		CorrLengthWeightedAVGError);
FractalDimensionStuff = FractalDimensionWeightedAVGInfo ...
	(SumOfWeightedFractalDimensions, SumOfFractalDimensionWeights, ...
		FractalDimensionWeightedAVGError);
SaturationRoughnessStuff = SaturationRoughnessWeightedAVGInfo ...
	(SumOfWeightedSatRuffs, SumOfSatRuffWeights, SatRuffWeightedAVGError);

figure();

    % Below is a text box which will display the title of the sample/image
    TitleForAverageFigure = get(handles.edit5, 'string');
    TitleNotes = annotation('textbox',...
    [0.195 0.84 0.64 0.075],...
    'String',{TitleForAverageFigure},...
    'FontSize',14,...
    'FontName','Constantina',...
    'LineStyle','-',...
    'EdgeColor',[0 0 0],...
    'LineWidth',0.01,...
    'BackgroundColor',[1 1 1],...
    'Color',[0.1 0.1 0.1],...
    'Interpreter', 'none',...
    'FontWeight', 'bold',...
    'HorizontalAlignment','center',...
    'FitBoxToText','on');
    
    % Below a textbox which will show the most important information (i.e.,
    % Fractal Dimension, Saturation Roughness, and Correlation Length) is
    % created
    ImageNotes = annotation('textbox',...
    [0.62 0.08 0.1 0.3],...
    'String',{PhysicalSizeOfAFMImage AMFImageResolution Space ...
		FractalDimensionStuff SaturationRoughnessStuff ...
			CorrelationLengthStuff },...
    'FontSize',14,...
    'FontName','Constantina',...
    'LineStyle','-',...
    'EdgeColor',[0 0 0],...
    'LineWidth',0.1,...
    'BackgroundColor',[1 1 1],...
    'Color',[0.1 0.1 0.1],...
    'FitBoxToText','on');
   
    plot(XDataForFitting(1:LastLinearPoint(i,1)), MeanYforLinear,'b'), ...
		hold on;  
    plot(XDataForFitting(1:LastLinearPoint(i,1)), ...
		WeightedYDataForFitting(1:LastLinearPoint(i,1)),'o'); hold on;

    plot(XDataForFitting(LastLinearPoint(i,1):end), WeightedYforExp,'g'),...
		hold on;
    plot(XDataForFitting(LastLinearPoint(i,1):end), ...
		WeightedYDataForFitting(LastLinearPoint(i,1):end),'x'); hold on;
    
    errorbar(XDataForFitting(:), WeightedYDataForFitting(:), ...
		meanYErrorDataForFitting(:), 'r.'); 
    
    axis([1.5 ceil(XDataForFitting(end)*10)/10 0 ...
		WeightedYDataForFitting(end) + WeightedYDataForFitting(end)/4]);
    grid on; 
    xlabel('Log( LengthScale [A] )','FontWeight','bold','FontName',...
		'Constantina','FontSize',16); 
    ylabel('Log( RMS Roughness [A] )','FontWeight','bold','FontName',...
		'Constantina','FontSize',16);

% function y = SaturationRoughnessWeightedAVGInfo(SumOfWeightedSatRuffs, ...
% 	SumOfSatRuffWeights, SatRuffWeightedAVGError)
%     
%     SatRuffWeightedAVG = SumOfWeightedSatRuffs / SumOfSatRuffWeights;
%     
% 	SatRuffWeightedAVGError = ceil(SatRuffWeightedAVGError/10^floor(log10 ...
% 		(SatRuffWeightedAVGError)))...
% 			*10^floor(log10(SatRuffWeightedAVGError)); 
% 			% salt to suit...   
%     
% 	SatRuffErrorInfoAVG = num2str(SatRuffWeightedAVGError);
%     
% 	SatRuffErrorInfoAVG = cat(2,'(',SatRuffErrorInfoAVG,')');
%     
% 	SatRuffWeightedAVG = num2str(SatRuffWeightedAVG);
%     
% 	SatRuffWeightedAVG = SatRuffWeightedAVG(1:end-2);
%     
% 	y = 'Saturation Roughness: ';
%     
% 	y = cat(2,y, SatRuffWeightedAVG,' ',SatRuffErrorInfoAVG,' [A]');
    
% function y = CorrelationLengthWeightedAVGInfo(SumOfWeightedCorrLengths, ...
% 	SumOfCorrLengthWeights, CorrLengthWeightedAVGError)
%    
%     CorrLengthWeightedAVG = SumOfWeightedCorrLengths / ...
% 		SumOfCorrLengthWeights;
%     CorrLengthWeightedAVGError = ceil(CorrLengthWeightedAVGError ...
% 		/10^floor(log10(CorrLengthWeightedAVGError))) ...
% 			*10^floor(log10(CorrLengthWeightedAVGError)); % salt to suit... 
%     CorrLengthErrorInfoAVG = num2str(CorrLengthWeightedAVGError);
%     CorrLengthErrorInfoAVG = cat(2,'(',CorrLengthErrorInfoAVG,')');
%     CorrLengthWeightedAVG = num2str(CorrLengthWeightedAVG);
%     CorrLengthWeightedAVG = CorrLengthWeightedAVG(1:end-4);
%     y = 'Correlation Length: ';
%     y = cat(2, y, CorrLengthWeightedAVG,' ', CorrLengthErrorInfoAVG,' [A]'); 
% 
% function y = FractalDimensionWeightedAVGInfo ...
% 	(SumOfWeightedFractalDimensions, SumOfFractalDimensionWeights,...
% 		FractalDimensionWeightedAVGError)
%  
%     FractalDimensionWeightedAVG = SumOfWeightedFractalDimensions / ...
% 		SumOfFractalDimensionWeights;
%    % FractalDimensionWeightedAVG = round(FractalDimensionWeightedAVG, 5);
%    % ^ was trying to force an extra decimal place on the D_f printed out...
%     FractalDimensionWeightedAVGError = ...
% 		ceil(FractalDimensionWeightedAVGError/10^floor ...
% 			(log10(FractalDimensionWeightedAVGError)))*10^floor ...
% 				(log10(FractalDimensionWeightedAVGError)); % salt to suit...
%     FractalErrorInfoAVG = num2str(FractalDimensionWeightedAVGError);
%     FractalErrorInfoAVG = cat(2,'(',FractalErrorInfoAVG,')');
%     FractalDimensionWeightedAVG = num2str(FractalDimensionWeightedAVG);
%     FractalDimensionWeightedAVG = FractalDimensionWeightedAVG(1:end-2);
%     y = 'Fractal Dimension: ';
%     y = cat(2, y, FractalDimensionWeightedAVG,' ',FractalErrorInfoAVG);
    
% function [y,z] = ImageSizeProperties(Pixels, AFMMetaData)
%     
%     tempStringforPhysicalImageSize= AFMMetaData{1}{1}{1};
%     clippedString = strrep(tempStringforPhysicalImageSize,'ScanSize: ','');
%     clippedString1 = 10e9*str2num(clippedString); 
%     clippedString = num2str(clippedString1);
%     y = cat(2, 'Scan Size: ', ' ', clippedString, ' [A]'); 
%     
%     ImageResolution = clippedString1/Pixels(1,1);
%     ImageResolution = ceil(ImageResolution/10^floor ...
% 		(log10(ImageResolution)))*10^floor(log10(ImageResolution));
%     ImageResolution = num2str(ImageResolution);
%     z = cat(2, 'Image Resolution: ', ImageResolution, ' [A/pixel]');

% function [y,z] = SumOfWeightedFractalDimensionsAndWeights ...
% 	(i, AllRoughnessParameters)
%     
%     CurrentDf = AllRoughnessParameters{1,i}{2,1}(1,1);
%     CurrentDfError = AllRoughnessParameters{1,i}{3,1}(1,1);
%     CurrentDfWeight = 1 / (CurrentDfError)^2;    
%     y = CurrentDf*CurrentDfWeight;
%     z = CurrentDfWeight;
    

    
% function [y,z] = SumOfWeightedSaturationRuffsAndWeights ...
% 	(i, AllRoughnessParameters)
%     
%     CurrentSatRuff = AllRoughnessParameters{1,i}{2,1}(1,2);
%     CurrentSatRuffError = AllRoughnessParameters{1,i}{3,1}(1,2);
%     CurrentSatRuffWeight = 1 / (CurrentSatRuffError)^2;
%     y = CurrentSatRuff*CurrentSatRuffWeight;
%     z = CurrentSatRuffWeight;
    
% function [y,z] = SumOfWeightedCorrelationLengthsAndWeights ...
% 	(i, AllRoughnessParameters)
%         
%     CurrentCorrLength = AllRoughnessParameters{1,i}{2,1}(1,3);
%     CurrentCorrLengthError = AllRoughnessParameters{1,i}{3,1}(1,3);
%     CurrentCorrLengthWeight = 1 / (CurrentCorrLengthError)^2;
%     y = CurrentCorrLength*CurrentCorrLengthWeight;
%     z = CurrentCorrLengthWeight;