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Bayesian_regularization.m
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Bayesian_regularization.m
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% Copyright (C) 2010 - 2019, Sabass Lab
%
% This program is free software: you can redistribute it and/or modify it
% under the terms of the GNU General Public License as published by the Free
% Software Foundation, either version 3 of the License, or (at your option)
% any later version. This program is distributed in the hope that it will be
% useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
% Public License for more details. You should have received a copy of the
% GNU General Public License along with this program.
% If not, see <http://www.gnu.org/licenses/>.
function varargout = Bayesian_regularization(varargin)
%BAYESIAN_REGULARIZATION MATLAB code file for Bayesian_regularization.fig
% BAYESIAN_REGULARIZATION, by itself, creates a new BAYESIAN_REGULARIZATION or raises the existing
% singleton*.
%
% H = BAYESIAN_REGULARIZATION returns the handle to a new BAYESIAN_REGULARIZATION or the handle to
% the existing singleton*.
%
% BAYESIAN_REGULARIZATION('Property','Value',...) creates a new BAYESIAN_REGULARIZATION using the
% given property value pairs. Unrecognized properties are passed via
% varargin to Bayesian_regularization_OpeningFcn. This calling syntax produces a
% warning when there is an existing singleton*.
%
% BAYESIAN_REGULARIZATION('CALLBACK') and BAYESIAN_REGULARIZATION('CALLBACK',hObject,...) call the
% local function named CALLBACK in BAYESIAN_REGULARIZATION.M with the given input
% arguments.
%
% *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 Bayesian_regularization
% Last Modified by GUIDE v2.5 26-Nov-2019 22:02:00
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @Bayesian_regularization_OpeningFcn, ...
'gui_OutputFcn', @Bayesian_regularization_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 Bayesian_regularization is made visible.
function Bayesian_regularization_OpeningFcn(hObject, eventdata, handles, varargin)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Setting initial parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
handles.data = varargin{1};
%set figure units
set(handles.figure1, 'units', 'normalized');
%get image file names and display them in the drop down menu
dir_struct = vertcat(dir(fullfile(handles.data.imagedir_name,'*.tif*')),dir(fullfile(handles.data.imagedir_name,'*.jpg*')));
[sorted_names,sorted_index] = sortrows({dir_struct.name}');
if ~isempty(sorted_names)
set(handles.preview_image,'String',sorted_names,'Value',1)
else
set(handles.preview_image,'String','No image available','Value',1)
end
%get dataset numbers
for i = 1:length(handles.data.displacement)
hilf_cell{i,1} = num2str(i);
end
set(handles.preview_frame,'String',hilf_cell,'Value',1);
% use the first nonempty dataset to calculate a grid spacing that is
% suggested in the menu
data_nonempty = handles.data.data_nonempty;
first_index = find(data_nonempty(:,1),1);
if ~isempty(first_index)
first_useful_frame = data_nonempty(first_index,2);
else
errordlg('The dataset only contains empty or too small dispacement fields.','Error');
end
max_eck(1:2) = [max(handles.data.displacement(first_useful_frame).pos(:,1)), max(handles.data.displacement(first_useful_frame).pos(:,2))];
min_eck(1:2) = [min(handles.data.displacement(first_useful_frame).pos(:,1)), min(handles.data.displacement(first_useful_frame).pos(:,2))];
handles.data.meshsize = round(sqrt((max_eck(1)-min_eck(1))*(max_eck(2)-min_eck(2))/size(handles.data.displacement(first_useful_frame).pos,1)));
set(handles.meshsize_win,'String',num2str(handles.data.meshsize));
%initialize variables
handles.data.pos = [];
handles.data.bild = [];
handles.data.selected_region_with_noise_xy = [];
handles.data.selected_region_with_noise_frame = [];
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes Bayesian_regularization wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = Bayesian_regularization_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 button press in abort.
function abort_Callback(hObject, eventdata, handles)
delete(handles.figure1);
return;
% --- Executes on button press in analyze.
function analyze_Callback(hObject, eventdata, handles)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Performs Bayesian TFM calculations for the data sequence and save result
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% IMPORTANT: for strain energy calculations, we crop the grid by
%Crop_rim_perc percent from all sides to avoid boundary effects. Change if desired
Crop_rim_perc = 15; %value between 0 [%] and 50 [%]
%With Bayesian TFM, we assume for simplicity z=0. This can be
%changed, but functions need to be adapted
handles.data.zdepth = 0.0;
%get all the data sets that contain nonempty displacement fields
framenumber = length(handles.data.displacement);
data_nonempty = handles.data.data_nonempty;
indices = find(data_nonempty(:,1));
%init
reg_parameter = []; %regularization parameter
grid_mat = []; %reguar grid
beta = zeros(framenumber,1); %inverse noise variance
% Select source of noise data
blah = get(handles.noiseway, 'SelectedObject');
noise_way = get(blah,'Tag');
switch noise_way
case 'inputfile'
noise_from_inputfile = 1;
noise_framenumber = length(handles.data.noise);
otherwise
noise_from_inputfile = 0;
end
%calculate the noise variance in the whole data sequence
for frame = 1:framenumber
if ~data_nonempty(frame,1) %%% skip if no displacement data available
continue;
end
if frame == indices(1) %see if we are dealing with the first frame
first_frame = 1;
else
first_frame = 0;
end
if noise_from_inputfile %noise comes from input data
if ~isfield(handles.data,'noise') || isempty(handles.data.noise)
%display error message if 'noise from input file'
%was selected, but input does not contain noise
%sample
errordlg('The input file contains no noise data. Select noise sample manually.','Error');
return;
else
%check each frame length of noise
size_noise_pos = size(handles.data.noise(frame).pos);
size_noise_vec = size(handles.data.noise(frame).vec);
end
if first_frame %is this the first dataset?
%search for first dataset containing noise data
first_noise_frame_index = 0;
frame2 = frame;
while (first_noise_frame_index == 0) && (frame2 <= noise_framenumber)
size_noise_vec1 = size(handles.data.noise(frame2).vec);
%does the variable really contain enough data?
if (size_noise_vec1(1) >= 1) && (size_noise_vec1(2) >= 2) && length(handles.data.noise(frame2).vec) > 2
%This index is used to calculate noise variance
%for frames that have no noise data
first_noise_frame_index = frame2;
end
frame2 = frame2 +1;
end
if first_noise_frame_index==0
%display error message if 'noise from input file'
%was selected, but input does not contain noise
%sample
errordlg('The input file contains no noise data. Select noise sample manually.','Error');
return;
end
end
if (frame > noise_framenumber) || size_noise_pos(1) < 1 || size_noise_pos(2) < 2 || size_noise_vec(1) < 1 || size_noise_vec(2) < 2
disp(['NOTICE: No noise sample in dataset number ', num2str(frame),'. Using noise in dataset number ', num2str(first_noise_frame_index)]);
current_noise_frame_index = first_noise_frame_index;
else
current_noise_frame_index = frame;
end
used_noise_sample = handles.data.noise(current_noise_frame_index).vec(:,1:2);
beta(frame) = 1/var(used_noise_sample(:));
else %noise comes from manually selected region
if ~isempty(handles.data.selected_region_with_noise_xy) %Region of interest for noise has been specified
%select the noise sample in the polygonal roi that was
%manually chosen
pxy = handles.data.selected_region_with_noise_xy;
indata = inpolygon(handles.data.displacement(frame).pos(:,1),handles.data.displacement(frame).pos(:,2),pxy(:,1),pxy(:,2));
if isempty(indata) ||(nnz(indata) <2)
errordlg('In dataset no. ', num2str(frame),': The ROI for noise selection contains to few displacements.','Error');
return;
end
used_noise_sample = handles.data.displacement(frame).vec(indata,1:2);
beta(frame) = 1/var(used_noise_sample(:));
else %Region of interest for noise is not yet determined. Must select Roi manually now.
noise_frame = get(handles.preview_frame,'value'); %%frame for noise selection is the current one in the GUI.
message = sprintf(['Choose a ROI containing noise in the current dataset no. ', num2str(noise_frame)]);
uiwait(msgbox(message, 'Notice'));
% check if data is provided
if ~data_nonempty(noise_frame,1)
errordlg('Can not select noise sample. The currently selected data set has too few displacements.','Error');
return;
end
bild_dateien_1 = get(handles.preview_image,'string');
bild_datei_index_1 = get(handles.preview_image,'value');
% Transfer information to Select_noise.mat
setappdata(0,'frame_1', noise_frame);
setappdata(0,'bild_datei_index_1', bild_datei_index_1 );
setappdata(0,'bild_dateien_1', bild_dateien_1 );
%choose the ROI with the mouse
Select_noise(handles.data)
try
[xx,yy] = getline('closed');
catch
errordlg('The selected noise data is empty.','Error');
return;
end
%check if ROI contains data
if isempty(xx) || isempty(yy)
errordlg('The selected noise data is empty.','Error');
delete(Select_noise(handles.data));
return;
end
indata = inpolygon(handles.data.displacement(noise_frame).pos(:,1),handles.data.displacement(noise_frame).pos(:,2),xx,yy);
if (length(xx)<3) ||(nnz(indata) <2)
errordlg('The selected area contains to few displacements.','Error');
delete(Select_noise(handles.data));
return;
end
delete(Select_noise(handles.data));
%save the polygon to select noise in other frames
handles.data.selected_region_with_noise_xy = [xx yy];
handles.data.selected_region_with_noise_frame= noise_frame;
%get the noise sample and calculate the variance
used_noise_sample = handles.data.displacement(noise_frame).vec(indata,1:2);
beta(frame) = 1/var(used_noise_sample(:));
end
end
end
E = 1; %set Young modulus to 1 in calculations
E_rescale = handles.data.young; %Young modulus for final rescaling
s = handles.data.poisson; %Poisson's modulus
meshsize = handles.data.meshsize;
haha = waitbar(0,'Please wait while data is being assembled..','WindowStyle','modal');
for frame = 1:framenumber
waitbar(frame/framenumber);
if ~data_nonempty(frame,1) %%% displacement data available?
continue;
end
%calculate Green's function and fourier transformed
%displacement field
orig_pos = handles.data.displacement(frame).pos(:,1:2);
vec = handles.data.displacement(frame).vec(:,1:2);
[grid_mat,i_max,j_max, X,fuu,Ftux,Ftuy,u] = fourier_X_u(orig_pos,vec, meshsize, E, s,grid_mat);
%%%Calculate the optimal regularization parameter
[L,~,~] = optimal_lambda(beta(frame),fuu,Ftux,Ftuy,...
E,s,handles.data.meshsize,i_max, j_max,X,1);
%calculate traction forces
[TFM_results(frame).pos,TFM_results(frame).traction,TFM_results(frame).traction_magnitude,f_n_m,~,~] = ...
reg_fourier_TFM(Ftux,Ftuy,L,E,s, handles.data.meshsize,...
i_max,j_max, grid_mat,handles.data.pix_durch_my, handles.data.zdepth);
%%% save TFM_results
TFM_results(frame).traction = E_rescale*TFM_results(frame).traction;
TFM_results(frame).traction_magnitude = E_rescale*TFM_results(frame).traction_magnitude;
%calculate strain energy. Note that we crop the outer rim of the field by 'bnd' grid
%units to avoid edge effects
f_mat=E_rescale.*f_n_m;
bnd = floor(min(size(u(:,:,1)))*Crop_rim_perc/100+1);
TFM_results(frame).energy = 1/2*sum(sum(u(bnd:end-bnd+1,bnd:end-bnd+1,1).*f_mat(bnd:end-bnd+1,bnd:end-bnd+1,1) +...
u(bnd:end-bnd+1,bnd:end-bnd+1,2).*f_mat(bnd:end-bnd+1,bnd:end-bnd+1,2)))*(handles.data.meshsize)^2*...
handles.data.pix_durch_my^3/10^6;
u_reshape(:,1) = reshape(u(:,:,1),i_max*j_max,1);
u_reshape(:,2) = reshape(u(:,:,2),i_max*j_max,1);
TFM_results(frame).displacement = u_reshape;
reg_parameter(frame,1) = L;
end
%%% save TFM_settings
TFM_settings.poisson = handles.data.poisson;
TFM_settings.young = handles.data.young;
TFM_settings.micrometer_per_pix = handles.data.pix_durch_my;
TFM_settings.regularization_parameter = reg_parameter;
TFM_settings.meshsize = handles.data.meshsize;
TFM_settings.zdepth = handles.data.zdepth;
TFM_settings.i_max = i_max;
TFM_settings.j_max = j_max;
%document the noise source in the saved settings
if noise_from_inputfile
TFM_settings.type_noise = ('Noise from input_file'); %% save type of noise
else
TFM_settings.type_noise = ['Region of interest for noise selected manually']; %% save type of noise
end
close(haha);
[filepath,name,ext] = fileparts(handles.data.strain_noise_file_name);
handles.data.targetdir_name = filepath;
savefile_name = fullfile(handles.data.targetdir_name,['Bay-FTTC_results_',datestr(now, 'dd-mm-yy'),'.mat']);
if exist(savefile_name)
button = questdlg('The file exists already. Overwrite?','Error','Yes');
if strcmpi(button,'No') || strcmpi(button,'')
return;
end
end
save(savefile_name,'TFM_results','TFM_settings','-mat');
msgbox('Calculation completed.');
%optional, if we want to pass data back after calculations
%guidata(hObject, handles);
return;
% --- Executes on button press in show_vectors.
function show_vectors_Callback(hObject, eventdata, handles)
if ~isempty(handles.data.pos)
axes(handles.axes2);
cla; axis equal; hold on;
if ~isempty(handles.data.bild)
imagesc(handles.data.bild);
end
if (get(hObject,'Value') == get(hObject,'Max'))
quiver(handles.data.pos(:,1),handles.data.pos(:,2),handles.data.force(:,1),handles.data.force(:,2),2,'r');
end
hold off;
end
% Hint: get(hObject,'Value') returns toggle state of show_vectors
% --- Executes on selection change in preview_image.
function preview_image_Callback(hObject, eventdata, handles)
% hObject handle to preview_image (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns preview_image contents as cell array
% contents{get(hObject,'Value')} returns selected item from preview_image
% --- Executes during object creation, after setting all properties.
function preview_image_CreateFcn(hObject, eventdata, handles)
if ispc
set(hObject,'BackgroundColor','white');
else
set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
end
% --- Executes on selection change in preview_frame.
function preview_frame_Callback(hObject, eventdata, handles)
% hObject handle to preview_frame (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes during object creation, after setting all properties.
function preview_frame_CreateFcn(hObject, eventdata, handles)
if ispc
set(hObject,'BackgroundColor','white');
else
set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
end
% --- Executes on button press in preview.
function preview_Callback(hObject, eventdata, handles)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Preview the Bayesian TFM results for individual frames
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%for the Bayesian method, we only assume z=0 for simplicity
handles.data.zdepth = 0.0;
%check if we have enough data in the dataset number
frame = get(handles.preview_frame,'value');
if ~handles.data.data_nonempty(frame,1)
errordlg('The dataset number refers to an empty or too small displacement field.','Error');
return;
end
E_rescale = handles.data.young; % initial Young modulus
E = 1; %%% scale the Young modulus to 1
Orig_pos = handles.data.displacement(frame).pos(:,1:2); %% position of displacement
vec = handles.data.displacement(frame).vec(:,1:2); %% value of displacement
meshsize = handles.data.meshsize;
s = handles.data.poisson;
grid_mat = [];
%construct Green's function and Fourier transform the displacements
[grid_mat,i_max,j_max, X,fuu,Ftux,Ftuy,~] = fourier_X_u(Orig_pos,vec, meshsize,E, s, grid_mat);
%select source for noise data
blah = get(handles.noiseway, 'SelectedObject');
noise_way = get(blah,'Tag');
switch noise_way
case 'inputfile'
if ~isfield(handles.data,'noise') || isempty(handles.data.noise) || (frame > length(handles.data.noise)) || isempty(handles.data.noise(frame).vec) || size(handles.data.noise(frame).vec,2) < 2 || size(handles.data.noise(frame).vec,2) < 2
errordlg(['Current dataset no. ', num2str(frame), ' contains no noise data. Use manual noise selection.'],'Error');
return;
end
used_noise_sample = handles.data.noise(frame).vec(:,1:2);
case 'noisedraw'
bild_dateien_1 = get(handles.preview_image,'string');
bild_datei_index_1 = get(handles.preview_image,'value');
% Transfer information to Select_noise.mat
setappdata(0,'frame_1', frame);
setappdata(0,'bild_datei_index_1', bild_datei_index_1 );
setappdata(0,'bild_dateien_1', bild_dateien_1 );
Select_noise(handles.data)
try
[xx,yy] = getline('closed');
catch
errordlg('The selected noise data is empty.','Error');
delete(Select_noise(handles.data));
return;
end
delete(Select_noise(handles.data)); %% delete the Select_noise GUI
%check if ROI contains displacement noise information
if isempty(xx) || isempty(yy)
errordlg('The selected noise data is empty.','Error');
return;
end
indata = inpolygon(handles.data.displacement(frame).pos(:,1),handles.data.displacement(frame).pos(:,2),xx,yy);
if (length(xx)<3) || (nnz(indata) <2)
errordlg('The selected area contains to few displacements.','Error');
return;
end
%get the displacement data in the ROI
used_noise_sample = handles.data.displacement(frame).vec(indata,1:2);
%%% save the polygonal area containing the noise data
handles.data.selected_region_with_noise_xy = [xx yy];
handles.data.selected_region_with_noise_frame = frame;
end
beta = 1/var(used_noise_sample(:));
% Main calculation starts here, show waitbar
wait_f = waitbar(0.5,'Please wait...');
%Calculate optimal regularization parameter L
[L evidencep evidence_one] = optimal_lambda(beta,fuu,Ftux,Ftuy,...
E,s,meshsize,i_max, j_max,X);
%Calculate tractions
[handles.data.pos,f_nm_2,traction_magnitude,f_n_m,~,~] = ...
reg_fourier_TFM(Ftux,Ftuy,L,E,s,meshsize,...
i_max,j_max, grid_mat,handles.data.pix_durch_my, handles.data.zdepth);
handles.data.force = E_rescale.*f_nm_2;
f_mat = E_rescale.*f_n_m;
% Show results on the GUI
%plot evidence curve
cla(handles.axes1,'reset');
axes(handles.axes1)
hold on;
plot(evidencep(1,:), evidencep(2,:),'o');
set(gca,'FontSize',9);
xlabel('\lambda_2E^2','FontSize',9);
ylabel('Log(Evidence)','FontSize',9);
plot(L, evidence_one,'r*','MarkerSize',10);
ps_t=[L evidence_one];
strValues = num2str(ps_t(1),'%.4f');
lamtexth =text(ps_t(1), 1*ps_t(2),['\lambda_2E^2=', strValues],'FontSize',9,'VerticalAlignment','bottom');
Txpos = get(lamtexth,'Extent');
ayLims = get(gca,'YLim');
if Txpos(2)+Txpos(4) >= ayLims(2)
set(gca,'YLim', [ayLims(1), Txpos(2)+Txpos(4)*1.5]);
end
hold off;
%Plot cel image and vectors
%first gather information from the GUI
bild_datei_index = get(handles.preview_image,'value');
bild_dateien = get(handles.preview_image,'string');
%If we do not get a cell array, we need to make one (eg if only one entry)
if ~isempty(bild_dateien) && ~iscellstr(bild_dateien)
bild_dateien =cellstr(bild_dateien);
end
no_image = false;
if ~isempty(bild_dateien) && (length(bild_dateien) >= bild_datei_index) && ~strcmp(bild_dateien{bild_datei_index},'No image available')
bild_datei = bild_dateien{bild_datei_index};
try
handles.data.bild = imread(fullfile(handles.data.imagedir_name, bild_datei));
catch
no_image = true;
handles.data.bild = [];
end
else
no_image = true;
end
%plot images and traction vectors
axes(handles.axes2);
cla; axis equal, colormap(gca,gray); hold on;
if ~no_image
imagesc(handles.data.bild);
end
hilf = get(handles.show_vectors,'Value');
if hilf
quiver(handles.data.pos(:,1),handles.data.pos(:,2),handles.data.force(:,1),handles.data.force(:,2),2,'r');
end
set(gca, 'DataAspectRatio', [1,1,50],'YDir','reverse','XTick',[],'YTick',[],'YColor','w','XColor','w'),hold off;
%plot the heatmap for visualizing traction magnitude
axes(handles.axes3);
colorbar off;
cla; hold on; colormap(gca,jet);
fnorm = (f_mat(:,:,2).^2 + f_mat(:,:,1).^2).^0.5;
surf(grid_mat(:,:,1), grid_mat(:,:,2),fnorm),view(0,90),shading interp, axis equal;
set(gca, 'DataAspectRatio', [1,1,50],'YDir','reverse','XTick',[],'YTick',[],'YColor','w','XColor','w');
hilf = get(handles.showColorbar,'Value');
if hilf
colorbar('location','East','YColor','w','XColor','w');
end
hold off;
%close waitbar
close(wait_f);
%pass variables back
guidata(hObject, handles);
function meshsize_win_Callback(hObject, eventdata, handles)
written = str2double(get(hObject,'String'));
if ~isnan(written) && written > 0
handles.data.meshsize = written;
else
errordlg('The mesh size must be given as a positive number.','Error');
set(handles.meshsize_win,'String', num2str(handles.data.meshsize));
end
guidata(hObject, handles);
% --- Executes during object creation, after setting all properties.
function meshsize_win_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in noisedraw.
function noisedraw_Callback(hObject, eventdata, handles)
% --- Executes on button press in inputfile.
function inputfile_Callback(hObject, eventdata, handles)
% hObject handle to inputfile (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in showColorbar.
function showColorbar_Callback(hObject, eventdata, handles)
% hObject handle to showColorbar (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of showColorbar
axes(handles.axes3);
if (get(hObject,'Value') == get(hObject,'Max'))
hold on;
colorbar('location','East','YColor','w','XColor','w');
hold off;
else
colorbar off;
end