I have a matlab file that I can't post here (3000 lines) which contains a lot of functions which are used from a GUI.
I am working with matlab file that contains the 3000 lines which has so many functions for design GUI
when I am using Function A that function which are related to uses the several other functions and make it as for loop that run many time function A (1600-2000) times of iterations through taking a long time.
when I reached at 400-500 Matlab gives me
error : "Error while evaluation UIcontrol callback"
I must to kill the existing process and then exit Matlab and run again from the previous iteration which give the error. So my problem is not based on the function call but it may comes based on memory or may be temporary memory.
Does it possible to increase the temporary memory uses by Matlab ?
I increase the preference "Java heat memory" at maximum but this preference change nothing to my problem.
Is there any way to solve this issue ?
A part of the script :
function CalculateManyOffset % It's Function A on this topic
mainfig = FigHandle;
parameters = get(mainfig,'UserData');
dbstop if error
NumberofProfiles = str2double(get(parameters.NumberofProfilesBox,'string'));
step = str2double(get(parameters.DistBetweenProfilesBox,'string'));
Alphabet=('A':'Z').';
[I,J] = meshgrid(1:26,1:26);
namered = [Alphabet(I(:)), Alphabet(J(:))];
namered = strvcat(namered)';
nameblue = [Alphabet(I(:)), Alphabet(J(:))];
nameblue = strvcat(nameblue)';
apostrophe = '''';
SaveNameDisplacementFile = [get(parameters.SaveNamebox,'string'),'.txt'];
a=0;
icounter = 0;
StartBlue = str2double(get(parameters.bluelinebox,'String'));
EndBlue = StartBlue + NumberofProfiles;
StartRed = str2double(get(parameters.redlinebox,'String'));
EndRed = StartRed + NumberofProfiles-15;
for i = StartBlue:step:EndBlue;
icounter = icounter +1;
jcounter = 0;
for j=StartRed:step:EndRed;
jcounter = jcounter +1;
opthorz = [];
maxGOF = [];
a=[a(1)+1 length(StartRed:step:EndRed)*length(StartBlue:step:EndBlue)]
%
if a(1) >= 0 && a(1) <= 20000
BlueLineDist = 1*i;
parameters.bluelinedist = i;
RedLineDist = 1*j;
parameters.redlinedist = j;
parameters.i = icounter;
parameters.j = jcounter;
set(mainfig,'UserData',parameters,'HandleVisibility','callback'); % To update variable parameters for the function which use them (downside : BlueLine, RedLine, GetBlueProfile, GetRedProfile, CalculateOffset)
BlueLine;
RedLine;
GetBlueProfile;
GetRedProfile;
CalculateOffset;
% Now, reload variable parameters with new value calculate on previous functions
mainfig = FigHandle;
parameters = get(mainfig,'UserData');
opthorz = parameters.opthorz;
name = [num2str(namered(:,jcounter)'),num2str(nameblue(:,icounter)'),apostrophe];
namefid2 = [num2str(namered(:,jcounter)'),' - ',num2str(nameblue(:,icounter)'),apostrophe];
Distance = [num2str(RedLineDist),' - ',num2str(BlueLineDist)];
maxGOF = parameters.maxGOF;
% Create file with all displacements
if a(1) == 1;
fid2 = fopen(SaveNameDisplacementFile,'w');
fprintf(fid2,['Profile red - blue\t','Distance (m) between profile red - blue with fault\t','Optimal Displacement\t','Goodness of Fit\t','20%% from Goodness of Fit\t','Minimal Displacement\t','Maximal Displacement \n']);
fprintf(fid2,[namefid2,'\t',Distance,'\t',num2str(opthorz),'\t',num2str(maxGOF),'\t',num2str(parameters.ErrorGOF),'\t',num2str(parameters.ErrorDisp(1,1)),'\t',num2str(parameters.ErrorDisp(1,2)),'\n']);
elseif a(1) ~= b(end);
fid2 = fopen(SaveNameDisplacementFile,'a');
fprintf(fid2,[namefid2,'\t',Distance,'\t',num2str(opthorz),'\t',num2str(maxGOF),'\t',num2str(parameters.ErrorGOF),'\t',num2str(parameters.ErrorDisp(1,1)),'\t',num2str(parameters.ErrorDisp(1,2)),'\n']);
else
fid2 = fopen(SaveNameDisplacementFile,'a');
fprintf(fid2,[namefid2,'\t',Distance,'\t',num2str(opthorz),'\t',num2str(maxGOF),'\t',num2str(parameters.ErrorGOF),'\t',num2str(parameters.ErrorDisp(1,1)),'\t',num2str(parameters.ErrorDisp(1,2))]);
fclose(fid2);
end
end
end
end
Related
When I ran the code below, it gave me this error. Any thoughts?
Unable to perform assignment because dot indexing is not supported for variables of this type.
The fundamental basis of this piece of is to filter or parse through a series of tracking data (generated via ImageJ/Fiji), one named Spots and one named Tracks, to remove undesired particle tracks - those that do not share a starting time of 0 and have different durations.
Batch Process Control
%batch parse TrackMate V.7 output to individual x y and t .csv files
clear all; clc;
%specify directory
directory = '/Volumes/GoogleDrive/Shared drives/Jessica_Michael/Data/matlab/20220715_noise_floor/';
addpath(directory)
%find and store all imgs in the directory to cell array
dirspots = dir([directory '*Spots_statistics.csv']);
dirtracks = dir([directory '*Tracks_statistics.csv']);
spotsfnames = {};
tracksfnames = {};
numfiles = length(dirspots);
[spotsfnames{1:numfiles}] = dirspots(:).name;
[tracksfnames{1:numfiles}] = dirtracks(:).name;
for fnum = 1:numfiles
spots_file = spotsfnames{fnum};
tracks_file = tracksfnames{fnum};
[x_temp, y_temp, t_temp] = parse_file(tracks_file,spots_file);
x_filtered{fnum}=x_temp;
y_filtered{fnum}=y_temp;
t_filtered{fnum}=t_temp;
end
%% read track statistics
function [x_filtered,y_filtered,t_filtered]=parse_file(tracks_file,spots_file)
trackStatistics = readtable(tracks_file);
lengthTrack = length(trackStatistics.TRACK_START); %returns number of rows/tracks
trackStatistics_filtered = [];
spotsStatistics_filtered = [];
x_filtered = [];
y_filtered = [];
t_filtered = [];
spotsStatistics = readtable(spots_file);
lengthSpots = length(spotsStatistics.ID); %returns number of rows/spots
max_frame = max(spotsStatistics.FRAME)+1; %TrackMate frame number starts from 0
max_timelapse = max(trackStatistics.TRACK_DURATION);
for i = 1 : lengthTrack
if trackStatistics.TRACK_START(i) == 0 && trackStatistics.TRACK_DURATION(i) == max_timelapse
trackStatistics_filtered(end+1) = trackStatistics.TRACK_ID(i);
end
end
lengthStatistics_filtered = length(trackStatistics_filtered);
for j = 1 : lengthStatistics_filtered
for i = 1 : lengthSpots
if spotsStatistics.TRACK_ID(i) == trackStatistics_filtered(j)
% if there is a trackID equal to one in the filtered list,
% append that spotsStatistics to the *END* of the row
spotsStatistics_filtered = [spotsStatistics_filtered; spotsStatistics(i,:)];
end
end
% at the end of each x_, y_, and t_filtered, append spotsStatistics.
% x_filtered, y_filtered and t_filtered will grow in size till for loop
% ends.
x_filtered = [x_filtered spotsStatistics_filtered.POSITION_X(end-max_frame+1:end)];
y_filtered = [y_filtered spotsStatistics_filtered.POSITION_Y(end-max_frame+1:end)];
t_filtered = [t_filtered spotsStatistics_filtered.POSITION_T(end-max_frame+1:end)];
end
end
Any recommendations?
Hello I want to use parallelize my MATLAB code to run High computing server. It is code to make image database for Deep learning. To parallelize the code I found the I have to for parfor loop. But I used that with the first loop or with the second loop it shows me error parfor cannot be run due to the variable imdb and image_counter. Anyone please help me to change the code to work with parfor
for i = 1:length(cur_images)
X = sprintf('image Numb: %d ',i);
disp(X)
cur_image = load(cur_images{i,:});
cur_image=(cur_image.Image.crop);
%----------------------------------------------
cur_image = imresize(cur_image, image_size);
if(rgb < 1)
imdb.images.data(:,:,1,image_counter) = cur_image;
else
imdb.images.data(:,:,1,image_counter) = cur_image(:,:,1);
imdb.images.data(:,:,2,image_counter) = cur_image(:,:,2);
imdb.images.data(:,:,3,image_counter) = cur_image(:,:,3);
imdb.images.data(:,:,4,image_counter) = cur_image(:,:,4);
imdb.images.data(:,:,5,image_counter) = cur_image(:,:,5);
imdb.images.data(:,:,6,image_counter) = cur_image(:,:,6);
imdb.images.data(:,:,7,image_counter) = cur_image(:,:,7);
imdb.images.data(:,:,8,image_counter) = cur_image(:,:,8);
imdb.images.data(:,:,9,image_counter) = cur_image(:,:,9);
imdb.images.data(:,:,10,image_counter) = cur_image(:,:,10);
end
imdb.images.set( 1,image_counter) = set;
image_counter = image_counter + 1;
end
The main problem here is that you can't assign to fields of a structure inside parfor in the way that you're trying to do. Also, your outputs need to be indexed by the loop variable to qualify as "sliced" - i.e. don't use image_counter. Putting this together, you need something more like:
% Make a numeric array to store the output.
data_out = zeros([image_size, 10, length(cur_images)]);
parfor i = 1:length(cur_images)
cur_image = load(cur_images{i, :});
cur_image=(cur_image.Image.crop);
cur_image = imresize(cur_image, image_size);
% Now, assign into 'data_out'. A little care needed
% here.
if rgb < 1
data_tmp = zeros([image_size, 10]);
data_tmp(:, :, 1) = cur_image;
else
data_tmp = cur_image;
end
data_out(:, :, :, i) = data_tmp;
end
imdb.images.data = data_out;
When I run the following code in matlab, I get the "Not enough input arguments."
It is not clear to me what I am doing wrong. I have checked it over an over again, but obviously what I intend and what the computer says I intend do not match up.
tau = 50e-3;
b= 1;
dt = .2;
time=1:dt:100;
w = 1; W= w;
inte = zeros(1, length(time));
inte(1) = 0;
Ds = #(s) (w*s-s+b)*(1/tau);
for i=1:length(time)
inte(i) = integral(Ds,0,time(i));
end
My hope was that the loop would integrate with different values of time from the time array. Instead, it appears to be looping through and only integrating once.
Any and all help is appreciated.
Update to include full error message:
% Validate the first three inputs.
narginchk(3,inf);
if ~isa(fun,'function_handle')
error(message('MATLAB:integral:funArgNotHandle'));
end
if ~(isscalar(a) && isfloat(a) && isscalar(b) && isfloat(b))
error(message('MATLAB:integral:invalidEndpoint'));
end
opstruct = integralParseArgs(varargin{:});
Q = integralCalc(fun,a,b,opstruct);
I have tried to generate a square pulsed clock. But it gives error. I tried this:
function pll( block)
setup(block);
function setup(block)
% Register number of ports
block.NumInputPorts = 1;
block.NumOutputPorts = 1;
% Override input port properties
block.InputPort(1).Dimensions = 1;
block.InputPort(1).DatatypeID = 8; % boolean
block.InputPort(1).Complexity = 'Real';
block.InputPort(1).DirectFeedthrough = false;
% Override output port properties
block.OutputPort(1).Dimensions = 1;
block.OutputPort(1).DatatypeID = 0; %double
block.OutputPort(1).Complexity = 'Real';
block.NumDialogPrms = 1;
block.DialogPrmsTunable = 0;
ts = 1/24000000'; %'
block.sample times= [ts 0];
block.SimStateCompliance = 'DefaultSimState'
function Outputs(block)
t = [0:1/(24000000):0.000001];
l = 0.1*exp(-6);
c = 220*exp(-9) + 60*exp(-9);
f = 1/(2*pi*sqrt(l*c));
block.OutputPort(1).Data = square(2*pi*f*t);
function Terminate(block)
But it gives me the error
"Error evaluating registered method 'Outputs' of M-S-Function 'pll' in
'untitled/Level-2 M-file S-Function'. Invalid assignment in
'untitled/Level-2 M-file S-Function': attempt to assign a vector of
width 24001 to a vector of width 1."
the error indicates on the line
block.OutputPort(1).Data = square(2*pi*f*t);
so what can be done to overcome this error?
It seems from your example that you're not really familiar with the way Simulink works. At each time step, each block in a Simulink model outputs a value (i.e the block's output value) corresponding to the current simulation time. In your case, within the block.Output function you are trying to output all time points at every simulation time step.
It appears that what you really want is to replace
t = [0:1/(24000000):0.000001];
with
t = block.CurrentTime;
And replace
block.OutputPort(1).Data = square(2*pi*f*t);
with
block.OutputPort(1).Data = sign(sin(2*pi*f*t));
Also, some other things to consider:
you don't seem to be registering the block's output method using:
block.RegBlockMethod('Outputs',#Output);
Why have you defined the block to have an input when it doesn't seem to require one?
Why are you doing this in an S-Function when a From Workspace block (or one of the many other ways to get data into a model) would seem to make more sense?
I'm unable to execute the following code:
parallel_mode = true; %To process multiple images at the same time
parallel_degree = 4; %Number of threads that will be created
if parallel_mode
if matlabpool('size') == 0
matlabpool(parallel_degree);
elseif matlabpool('size') ~= parallel_degree
matlabpool close;
matlabpool(parallel_degree);
end
end
%Loading dictionary
try
load(dictionary_path);
catch
error(['Was impossible to load the dictionary in path: ' dictionary_path ', Please, check the path. ' ...
'Maybe you should use dictionary_training function to create it.'])
end
%% Processing test images
test_images = dir([test_im_path, pattern]);
num_test_images = size(test_images,1);
%Pre-allocating memory to speed-up
estimated_count = zeros(1,num_test_images);
true_count = zeros(1,num_test_images);
estimated_upper_count = zeros(1,num_test_images);
estimated_lower_count = zeros(1,num_test_images);
true_upper_count = zeros(1,num_test_images);
true_lower_count = zeros(1,num_test_images);
%Calculating dimensions of the image subregion where we can count
im_test = imread([test_im_path test_images(1).name]);
dis = round(patch_size/2);
dim_x = dis:size(im_test,2)-dis+1;
dim_y = dis:size(im_test,1)-dis+1;
toGaussian = fspecial('gaussian', hsize, sigma);
parfor a=1:num_test_images
disp(['Processing image #' num2str(a) ' of ' num2str(num_test_images) '...']);
im_test = imread([test_im_path test_images(a).name]);
[~, name, extension] = fileparts(test_images(a).name);
im_ground_truth = imread([ground_truth_path name 'dots' extension]);
disp('Extracting features...');
features = extract_features(im_test, features_type, dic_signal, sparsity, patch_size, mean_rem_flag);
features = full(features); %ND-sparse arrays are not supported.
%Re-arranging features
features = reshape(features', size(dim_y,2), size(dim_x,2), dic_size);
%Normalizing features
max_factors_3D = repmat(max_factors_depth, [size(features,1), size(features,2)]);
max_offset_3D = repmat(max_offset_depth, [size(features,1), size(features,2)]);
features = (features-max_offset_3D)./max_factors_3D;
%%Some stuff
....
end
When i execute it i get:
Undefined function or variable 'dic_signal'.
when it arrives to the extract_features function. However, in the single thread version (with for instead of parfor) it works correctly.
Someone can give me any hint?.
Thank you.
EDIT:
dic_signal is defined and correctly loaded in load(dictionary_path);
I suspect the load command doesn't load the variables in the workers workspace, only on your MATLAB instance workspace, which is why it works in a normal for loop, but not with a parfor loop. You might want to try instead:
pctRunOnAll load(dictionary_path)
to ensure the correct data is loaded into the workspace of each of the workers.