Dear Matlab communities:
I am running a spm script for analyizing neuroimaging data, the first script here (ModelSpecificationBinaryMask.m) is to loop another job file ModelSpecificationBinaryMask_job.m over the two subjects while assigning the regressor files that correspond to each of the runs for one subject.
However, I am seeing the follwing errors:
Brace indexing is not supported for variables of this type.

Error in spm_jobman>canonicalise_jobs (line 415)
comp(i) = comp(i) && any(strcmp(fieldnames(job{i}{j}), …

Error in spm_jobman (line 152)
mljob = canonicalise_jobs(jobs);

Error in ModelSpecificationBinaryMask (line 50)
spm_jobman(‘run’, jobs, inputs);

ModelSpecificationBinaryMask.m

clear
close all
clc

% Created by GB on 9/9/19.
% Edited by LC on 8/19/24.

for sub = [101,102]
jobfile = {‘/Volumes/PUMPKIN/scripts/GabbyScripts/ModelSpecificationBinaryMaskTHRESHOFF_job.m’};
nrun = 1; % Assuming nrun should be 1 since we are processing one subject at a time
ncue = 8; % 8 runs for cue task
jobs = repmat(jobfile, 1, nrun);
inputs = cell(25, 1); % Initialize cell array for inputs (25 because you have 3 inputs per run and 8 runs)

% Create a homepath, a base location of all the data
homepath = ‘/Volumes/PUMPKIN/Preprocessed/groupA/Volumes’;
evpath = ‘/Volumes/PUMPKIN/Regressors’;

for n = 1:nrun
sub_path = fullfile(‘/Volumes/PUMPKIN/halfpipeXspm/sub-‘, num2str(sub(n)), ‘/1stLevel’);
%mkdir(sub_path);
inputs{1, n} = {sub_path}; % fMRI model specification: Directory – cfg_files

% Loop through each run for the subject
for run = 1:ncue
run_dir = fullfile(homepath, strcat(‘sub_’, num2str(sub(n))), ‘-‘, num2str(run));

% List all ‘vol_*.nii’ files in the current folder
files = dir(fullfile(run_dir, ‘vol_*.nii’));

if ~isempty(files)
% Now assign images
inputs{2+(run-1)*3, n} = {fullfile(run_dir, {files.name})}; % fMRI model specification: Scans – cfg_files

% Insert multiple condition files (i.e., evs)
ev_file = dir(fullfile(evpath, strcat(‘sub’, num2str(sub(n)), ‘run’, num2str(run), ‘_exev_CS_faceCue.mat’)));
inputs{3+(run-1)*3, n} = {fullfile(evpath, ev_file.name)};

% Insert multiple regressors (6 rigid body motion parameters)
rp_file = dir(fullfile(run_dir, ‘rp_af*.txt’));
inputs{4+(run-1)*3, n} = {fullfile(run_dir, rp_file.name)};
end

clear files rp_file
end

clear currfold
end

% Run the job using SPM
spm_jobman(‘run’, jobs, inputs);
end

% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %

% ModelSpecificationBinaryMask_job.m

%———————————————————————–
% Job saved on 17-Jun-2016 14:21:03 by cfg_util (rev $Rev: 6134 $)
% spm SPM – SPM12 (6225)
% cfg_basicio BasicIO – Unknown
%———————————————————————–
disp(‘Script is running’);

matlabbatch(1).spm.stats.fmri_spec.dir = {‘/Volumes/PUMPKIN/halfpipeXspm/sub-*/1stLevel’};
matlabbatch(1).spm.stats.fmri_spec.timing.units = ‘secs’;
matlabbatch(1).spm.stats.fmri_spec.timing.RT = 2;
matlabbatch(1).spm.stats.fmri_spec.timing.fmri_t = 32;
matlabbatch(1).spm.stats.fmri_spec.timing.fmri_t0 = 1;
matlabbatch(1).spm.stats.fmri_spec.sess(1).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(1).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(1).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(1).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(1).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(1).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(2).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(2).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(2).multi ='<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(2).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(2).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(2).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(3).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(3).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(3).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(3).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(3).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(3).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(4).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(4).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(4).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(4).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(4).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(4).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(5).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(5).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(5).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(5).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(5).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(5).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(6).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(6).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(6).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(6).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(6).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(6).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(7).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(7).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(7).multi ='<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(7).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(7).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(7).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(8).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(8).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(8).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(8).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(8).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(8).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.fact = struct(‘name’, {}, ‘levels’, {});
matlabbatch(1).spm.stats.fmri_spec.bases.hrf.derivs = [0 0];
matlabbatch(1).spm.stats.fmri_spec.volt = 1;
matlabbatch(1).spm.stats.fmri_spec.global = ‘None’;
matlabbatch(1).spm.stats.fmri_spec.mthresh = 0.0; %no voxels are thresholded so all voxels are being used%
matlabbatch(1).spm.stats.fmri_spec.mask = {‘/Volumes/PUMPKIN/scripts/LancyScripts/peripheral/C1-101-117/binarygreymattermask.nii’};
matlabbatch(1).spm.stats.fmri_spec.cvi = ‘AR(1)’;

disp(‘Script completed’);Dear Matlab communities:
I am running a spm script for analyizing neuroimaging data, the first script here (ModelSpecificationBinaryMask.m) is to loop another job file ModelSpecificationBinaryMask_job.m over the two subjects while assigning the regressor files that correspond to each of the runs for one subject.
However, I am seeing the follwing errors:
Brace indexing is not supported for variables of this type.

Error in spm_jobman>canonicalise_jobs (line 415)
comp(i) = comp(i) && any(strcmp(fieldnames(job{i}{j}), …

Error in spm_jobman (line 152)
mljob = canonicalise_jobs(jobs);

Error in ModelSpecificationBinaryMask (line 50)
spm_jobman(‘run’, jobs, inputs);

ModelSpecificationBinaryMask.m

clear
close all
clc

% Created by GB on 9/9/19.
% Edited by LC on 8/19/24.

for sub = [101,102]
jobfile = {‘/Volumes/PUMPKIN/scripts/GabbyScripts/ModelSpecificationBinaryMaskTHRESHOFF_job.m’};
nrun = 1; % Assuming nrun should be 1 since we are processing one subject at a time
ncue = 8; % 8 runs for cue task
jobs = repmat(jobfile, 1, nrun);
inputs = cell(25, 1); % Initialize cell array for inputs (25 because you have 3 inputs per run and 8 runs)

% Create a homepath, a base location of all the data
homepath = ‘/Volumes/PUMPKIN/Preprocessed/groupA/Volumes’;
evpath = ‘/Volumes/PUMPKIN/Regressors’;

for n = 1:nrun
sub_path = fullfile(‘/Volumes/PUMPKIN/halfpipeXspm/sub-‘, num2str(sub(n)), ‘/1stLevel’);
%mkdir(sub_path);
inputs{1, n} = {sub_path}; % fMRI model specification: Directory – cfg_files

% Loop through each run for the subject
for run = 1:ncue
run_dir = fullfile(homepath, strcat(‘sub_’, num2str(sub(n))), ‘-‘, num2str(run));

% List all ‘vol_*.nii’ files in the current folder
files = dir(fullfile(run_dir, ‘vol_*.nii’));

if ~isempty(files)
% Now assign images
inputs{2+(run-1)*3, n} = {fullfile(run_dir, {files.name})}; % fMRI model specification: Scans – cfg_files

% Insert multiple condition files (i.e., evs)
ev_file = dir(fullfile(evpath, strcat(‘sub’, num2str(sub(n)), ‘run’, num2str(run), ‘_exev_CS_faceCue.mat’)));
inputs{3+(run-1)*3, n} = {fullfile(evpath, ev_file.name)};

% Insert multiple regressors (6 rigid body motion parameters)
rp_file = dir(fullfile(run_dir, ‘rp_af*.txt’));
inputs{4+(run-1)*3, n} = {fullfile(run_dir, rp_file.name)};
end

clear files rp_file
end

clear currfold
end

% Run the job using SPM
spm_jobman(‘run’, jobs, inputs);
end

% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %

% ModelSpecificationBinaryMask_job.m

%———————————————————————–
% Job saved on 17-Jun-2016 14:21:03 by cfg_util (rev $Rev: 6134 $)
% spm SPM – SPM12 (6225)
% cfg_basicio BasicIO – Unknown
%———————————————————————–
disp(‘Script is running’);

matlabbatch(1).spm.stats.fmri_spec.dir = {‘/Volumes/PUMPKIN/halfpipeXspm/sub-*/1stLevel’};
matlabbatch(1).spm.stats.fmri_spec.timing.units = ‘secs’;
matlabbatch(1).spm.stats.fmri_spec.timing.RT = 2;
matlabbatch(1).spm.stats.fmri_spec.timing.fmri_t = 32;
matlabbatch(1).spm.stats.fmri_spec.timing.fmri_t0 = 1;
matlabbatch(1).spm.stats.fmri_spec.sess(1).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(1).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(1).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(1).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(1).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(1).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(2).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(2).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(2).multi ='<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(2).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(2).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(2).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(3).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(3).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(3).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(3).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(3).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(3).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(4).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(4).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(4).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(4).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(4).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(4).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(5).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(5).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(5).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(5).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(5).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(5).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(6).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(6).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(6).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(6).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(6).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(6).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(7).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(7).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(7).multi ='<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(7).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(7).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(7).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(8).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(8).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(8).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(8).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(8).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(8).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.fact = struct(‘name’, {}, ‘levels’, {});
matlabbatch(1).spm.stats.fmri_spec.bases.hrf.derivs = [0 0];
matlabbatch(1).spm.stats.fmri_spec.volt = 1;
matlabbatch(1).spm.stats.fmri_spec.global = ‘None’;
matlabbatch(1).spm.stats.fmri_spec.mthresh = 0.0; %no voxels are thresholded so all voxels are being used%
matlabbatch(1).spm.stats.fmri_spec.mask = {‘/Volumes/PUMPKIN/scripts/LancyScripts/peripheral/C1-101-117/binarygreymattermask.nii’};
matlabbatch(1).spm.stats.fmri_spec.cvi = ‘AR(1)’;

disp(‘Script completed’); Dear Matlab communities:
I am running a spm script for analyizing neuroimaging data, the first script here (ModelSpecificationBinaryMask.m) is to loop another job file ModelSpecificationBinaryMask_job.m over the two subjects while assigning the regressor files that correspond to each of the runs for one subject.
However, I am seeing the follwing errors:
Brace indexing is not supported for variables of this type.

Error in spm_jobman>canonicalise_jobs (line 415)
comp(i) = comp(i) && any(strcmp(fieldnames(job{i}{j}), …

Error in spm_jobman (line 152)
mljob = canonicalise_jobs(jobs);

Error in ModelSpecificationBinaryMask (line 50)
spm_jobman(‘run’, jobs, inputs);

ModelSpecificationBinaryMask.m

clear
close all
clc

% Created by GB on 9/9/19.
% Edited by LC on 8/19/24.

for sub = [101,102]
jobfile = {‘/Volumes/PUMPKIN/scripts/GabbyScripts/ModelSpecificationBinaryMaskTHRESHOFF_job.m’};
nrun = 1; % Assuming nrun should be 1 since we are processing one subject at a time
ncue = 8; % 8 runs for cue task
jobs = repmat(jobfile, 1, nrun);
inputs = cell(25, 1); % Initialize cell array for inputs (25 because you have 3 inputs per run and 8 runs)

% Create a homepath, a base location of all the data
homepath = ‘/Volumes/PUMPKIN/Preprocessed/groupA/Volumes’;
evpath = ‘/Volumes/PUMPKIN/Regressors’;

for n = 1:nrun
sub_path = fullfile(‘/Volumes/PUMPKIN/halfpipeXspm/sub-‘, num2str(sub(n)), ‘/1stLevel’);
%mkdir(sub_path);
inputs{1, n} = {sub_path}; % fMRI model specification: Directory – cfg_files

% Loop through each run for the subject
for run = 1:ncue
run_dir = fullfile(homepath, strcat(‘sub_’, num2str(sub(n))), ‘-‘, num2str(run));

% List all ‘vol_*.nii’ files in the current folder
files = dir(fullfile(run_dir, ‘vol_*.nii’));

if ~isempty(files)
% Now assign images
inputs{2+(run-1)*3, n} = {fullfile(run_dir, {files.name})}; % fMRI model specification: Scans – cfg_files

% Insert multiple condition files (i.e., evs)
ev_file = dir(fullfile(evpath, strcat(‘sub’, num2str(sub(n)), ‘run’, num2str(run), ‘_exev_CS_faceCue.mat’)));
inputs{3+(run-1)*3, n} = {fullfile(evpath, ev_file.name)};

% Insert multiple regressors (6 rigid body motion parameters)
rp_file = dir(fullfile(run_dir, ‘rp_af*.txt’));
inputs{4+(run-1)*3, n} = {fullfile(run_dir, rp_file.name)};
end

clear files rp_file
end

clear currfold
end

% Run the job using SPM
spm_jobman(‘run’, jobs, inputs);
end

% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %

% ModelSpecificationBinaryMask_job.m

%———————————————————————–
% Job saved on 17-Jun-2016 14:21:03 by cfg_util (rev $Rev: 6134 $)
% spm SPM – SPM12 (6225)
% cfg_basicio BasicIO – Unknown
%———————————————————————–
disp(‘Script is running’);

matlabbatch(1).spm.stats.fmri_spec.dir = {‘/Volumes/PUMPKIN/halfpipeXspm/sub-*/1stLevel’};
matlabbatch(1).spm.stats.fmri_spec.timing.units = ‘secs’;
matlabbatch(1).spm.stats.fmri_spec.timing.RT = 2;
matlabbatch(1).spm.stats.fmri_spec.timing.fmri_t = 32;
matlabbatch(1).spm.stats.fmri_spec.timing.fmri_t0 = 1;
matlabbatch(1).spm.stats.fmri_spec.sess(1).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(1).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(1).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(1).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(1).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(1).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(2).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(2).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(2).multi ='<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(2).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(2).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(2).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(3).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(3).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(3).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(3).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(3).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(3).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(4).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(4).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(4).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(4).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(4).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(4).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(5).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(5).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(5).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(5).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(5).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(5).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(6).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(6).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(6).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(6).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(6).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(6).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(7).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(7).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(7).multi ='<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(7).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(7).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(7).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.sess(8).scans = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(8).cond = struct(‘name’, {}, ‘onset’, {}, ‘duration’, {}, ‘tmod’, {}, ‘pmod’, {}, ‘orth’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(8).multi = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(8).regress = struct(‘name’, {}, ‘val’, {});
matlabbatch(1).spm.stats.fmri_spec.sess(8).multi_reg = ‘<UNDEFINED>’;
matlabbatch(1).spm.stats.fmri_spec.sess(8).hpf = 128;
matlabbatch(1).spm.stats.fmri_spec.fact = struct(‘name’, {}, ‘levels’, {});
matlabbatch(1).spm.stats.fmri_spec.bases.hrf.derivs = [0 0];
matlabbatch(1).spm.stats.fmri_spec.volt = 1;
matlabbatch(1).spm.stats.fmri_spec.global = ‘None’;
matlabbatch(1).spm.stats.fmri_spec.mthresh = 0.0; %no voxels are thresholded so all voxels are being used%
matlabbatch(1).spm.stats.fmri_spec.mask = {‘/Volumes/PUMPKIN/scripts/LancyScripts/peripheral/C1-101-117/binarygreymattermask.nii’};
matlabbatch(1).spm.stats.fmri_spec.cvi = ‘AR(1)’;

disp(‘Script completed’); spm, image processing, error MATLAB Answers — New Questions

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I’m currently trying to use a laser alongside a VariSpec Liquid Crystal Tunable Filter (LCTF) for fluorescent imaging microscopy. Long story short, the software that goes with the microscope is Micro-Manager and the software for the LCTF is VSGUI (or VariSpecGUI).

I want to write code that will effectively carry out a sequence of events on the VSGUI and MM software for me using MATLAB, automatically. From what I’ve read, this is fairly straightforward to do for the Micro-Manager software as there’s some pre-written .m files / script that can be used for the operation of MM and if push comes to shove there is also the Microscopy Toolbox. However, with the VariSpec VSGUI software, I’m struggling to find any files relating to it. I know they exist because the manual for the LCTF states "CRi provides a set of MATLAB files to control VariSpec filters. There is a core support .DLL file and a series of .m files"

So great, the files exist and I don’t need to start from scratch, however, this CRi company does not seem to exist (think it might have been bought over?) and there are no websites that I can find with the files.

Long shot, but does anyone have any of the files that I’m talking about to operate the VariSpec filters?
I don’t think I’m fully capable of writing out a full script from scratch.I’m currently trying to use a laser alongside a VariSpec Liquid Crystal Tunable Filter (LCTF) for fluorescent imaging microscopy. Long story short, the software that goes with the microscope is Micro-Manager and the software for the LCTF is VSGUI (or VariSpecGUI).

I want to write code that will effectively carry out a sequence of events on the VSGUI and MM software for me using MATLAB, automatically. From what I’ve read, this is fairly straightforward to do for the Micro-Manager software as there’s some pre-written .m files / script that can be used for the operation of MM and if push comes to shove there is also the Microscopy Toolbox. However, with the VariSpec VSGUI software, I’m struggling to find any files relating to it. I know they exist because the manual for the LCTF states "CRi provides a set of MATLAB files to control VariSpec filters. There is a core support .DLL file and a series of .m files"

So great, the files exist and I don’t need to start from scratch, however, this CRi company does not seem to exist (think it might have been bought over?) and there are no websites that I can find with the files.

Long shot, but does anyone have any of the files that I’m talking about to operate the VariSpec filters?
I don’t think I’m fully capable of writing out a full script from scratch. I’m currently trying to use a laser alongside a VariSpec Liquid Crystal Tunable Filter (LCTF) for fluorescent imaging microscopy. Long story short, the software that goes with the microscope is Micro-Manager and the software for the LCTF is VSGUI (or VariSpecGUI).

I want to write code that will effectively carry out a sequence of events on the VSGUI and MM software for me using MATLAB, automatically. From what I’ve read, this is fairly straightforward to do for the Micro-Manager software as there’s some pre-written .m files / script that can be used for the operation of MM and if push comes to shove there is also the Microscopy Toolbox. However, with the VariSpec VSGUI software, I’m struggling to find any files relating to it. I know they exist because the manual for the LCTF states "CRi provides a set of MATLAB files to control VariSpec filters. There is a core support .DLL file and a series of .m files"

So great, the files exist and I don’t need to start from scratch, however, this CRi company does not seem to exist (think it might have been bought over?) and there are no websites that I can find with the files.

Long shot, but does anyone have any of the files that I’m talking about to operate the VariSpec filters?
I don’t think I’m fully capable of writing out a full script from scratch. varispec, .m files, vsgui, lctf, software MATLAB Answers — New Questions

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dear collegaes, please let me ask your help to find a solution for my data analisys.

I have a P0300.txt file that contains a long strip.

here is a portion of P0300.txt:
59 04 03 01 00 65 01 3E 51 09 00 00 51 1C 00 E1
I need to find sets of data, sets could be 2 or 4 digits.
lets say I need to find 01 3E
DATA = regexp(fileread(‘P0300.txt’), ‘r?n’, ‘split’)’;
>> A = string(DATA);
>> B = strfind(A,’01 3E’);
B gives me the position where 01 3E is, in this case B = 19
after this I need to extract the 6 digits on the rigth 51 09 00 <– this values can change depend on test conditions

Any idea on how can I do that?
your feedback will be highly appreciateddear collegaes, please let me ask your help to find a solution for my data analisys.

I have a P0300.txt file that contains a long strip.

here is a portion of P0300.txt:
59 04 03 01 00 65 01 3E 51 09 00 00 51 1C 00 E1
I need to find sets of data, sets could be 2 or 4 digits.
lets say I need to find 01 3E
DATA = regexp(fileread(‘P0300.txt’), ‘r?n’, ‘split’)’;
>> A = string(DATA);
>> B = strfind(A,’01 3E’);
B gives me the position where 01 3E is, in this case B = 19
after this I need to extract the 6 digits on the rigth 51 09 00 <– this values can change depend on test conditions

Any idea on how can I do that?
your feedback will be highly appreciated dear collegaes, please let me ask your help to find a solution for my data analisys.

I have a P0300.txt file that contains a long strip.

here is a portion of P0300.txt:
59 04 03 01 00 65 01 3E 51 09 00 00 51 1C 00 E1
I need to find sets of data, sets could be 2 or 4 digits.
lets say I need to find 01 3E
DATA = regexp(fileread(‘P0300.txt’), ‘r?n’, ‘split’)’;
>> A = string(DATA);
>> B = strfind(A,’01 3E’);
B gives me the position where 01 3E is, in this case B = 19
after this I need to extract the 6 digits on the rigth 51 09 00 <– this values can change depend on test conditions

Any idea on how can I do that?
your feedback will be highly appreciated strings, text, data MATLAB Answers — New Questions

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Hi, all!
I desperately need help. As the title says, I generated multiple surrogate models on Regression Learner; now I need to use these models in a gradient-based optimization process.
I’ve tried Export>Generate Function & Export>Export Model / Export Model For Deployment. None of them work, I don’t understand how anything works. If I can convert the surrogate models into matlab functions, these matlab functions are supposed to go into the ‘select problem data>Objective function’? How do I define lower/upper bounds with just one value for each when the surrogate models have multiple inputs?? How do I define non-linear constraints??
I put an image just to explain my problem better.
Thank you….Hi, all!
I desperately need help. As the title says, I generated multiple surrogate models on Regression Learner; now I need to use these models in a gradient-based optimization process.
I’ve tried Export>Generate Function & Export>Export Model / Export Model For Deployment. None of them work, I don’t understand how anything works. If I can convert the surrogate models into matlab functions, these matlab functions are supposed to go into the ‘select problem data>Objective function’? How do I define lower/upper bounds with just one value for each when the surrogate models have multiple inputs?? How do I define non-linear constraints??
I put an image just to explain my problem better.
Thank you…. Hi, all!
I desperately need help. As the title says, I generated multiple surrogate models on Regression Learner; now I need to use these models in a gradient-based optimization process.
I’ve tried Export>Generate Function & Export>Export Model / Export Model For Deployment. None of them work, I don’t understand how anything works. If I can convert the surrogate models into matlab functions, these matlab functions are supposed to go into the ‘select problem data>Objective function’? How do I define lower/upper bounds with just one value for each when the surrogate models have multiple inputs?? How do I define non-linear constraints??
I put an image just to explain my problem better.
Thank you…. optimization MATLAB Answers — New Questions

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I’m trying to build code via rtwbuild and am being met with the error Selected subsystem is linked to a library with unsaved changes. Please save the library.

I’ve been manually looking through the model and can’t see anything with unsaved changes or broken links. And there seems to be no easy way to find the library with unsaved changes in question. Am I missing something or is there a way to find these libraries? I’m working with a very big model.I’m trying to build code via rtwbuild and am being met with the error Selected subsystem is linked to a library with unsaved changes. Please save the library.

I’ve been manually looking through the model and can’t see anything with unsaved changes or broken links. And there seems to be no easy way to find the library with unsaved changes in question. Am I missing something or is there a way to find these libraries? I’m working with a very big model. I’m trying to build code via rtwbuild and am being met with the error Selected subsystem is linked to a library with unsaved changes. Please save the library.

I’ve been manually looking through the model and can’t see anything with unsaved changes or broken links. And there seems to be no easy way to find the library with unsaved changes in question. Am I missing something or is there a way to find these libraries? I’m working with a very big model. simulink MATLAB Answers — New Questions

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Dear experts –
I want to create a continuous 1D surface heatmap for every data point, with Distance (ft) in the X-direction and Temperature in the Y-direction. I have no idea how to do it. Please, can someone helps me with that.
Here I attached a sample of my data.
Thank you!Dear experts –
I want to create a continuous 1D surface heatmap for every data point, with Distance (ft) in the X-direction and Temperature in the Y-direction. I have no idea how to do it. Please, can someone helps me with that.
Here I attached a sample of my data.
Thank you! Dear experts –
I want to create a continuous 1D surface heatmap for every data point, with Distance (ft) in the X-direction and Temperature in the Y-direction. I have no idea how to do it. Please, can someone helps me with that.
Here I attached a sample of my data.
Thank you! heatmap MATLAB Answers — New Questions

​

Hi,
I’m encountering a problem while trying to adjust the bathymetry in my MATLAB code. I have successfully plotted the artificial reef structure. However, I’m having difficulty modifying the bathymetry to reflect a 4-meter depth for the structure instead of the initial 5 meters. I am using the inpolygon function, but it seems to be causing some issues and not detecting the whole structure.
The code plots correctly up to line 77, but I’m experiencing problems with the polygon section. I’ve attached the MATLAB code for review.
Could you please help me identify what might be going wrong?
Thank you!Hi,
I’m encountering a problem while trying to adjust the bathymetry in my MATLAB code. I have successfully plotted the artificial reef structure. However, I’m having difficulty modifying the bathymetry to reflect a 4-meter depth for the structure instead of the initial 5 meters. I am using the inpolygon function, but it seems to be causing some issues and not detecting the whole structure.
The code plots correctly up to line 77, but I’m experiencing problems with the polygon section. I’ve attached the MATLAB code for review.
Could you please help me identify what might be going wrong?
Thank you! Hi,
I’m encountering a problem while trying to adjust the bathymetry in my MATLAB code. I have successfully plotted the artificial reef structure. However, I’m having difficulty modifying the bathymetry to reflect a 4-meter depth for the structure instead of the initial 5 meters. I am using the inpolygon function, but it seems to be causing some issues and not detecting the whole structure.
The code plots correctly up to line 77, but I’m experiencing problems with the polygon section. I’ve attached the MATLAB code for review.
Could you please help me identify what might be going wrong?
Thank you! inpolygon MATLAB Answers — New Questions

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לק"י

Hi guys,
I have a cell array in 64X61X21 size.
I want to make a matrix of the values (no vectors) within specific area of the cell array – 15:21,2:61,1:21.
First I make a copy of the cell array:
cellarrayneeded=analysisdayaWT(8:15, 2:60, 1:21);
cellarrayneeded=cell2mat(cellarrayneeded);

Error message I get:

Error using cat
Dimensions of arrays being concatenated are not consistent.

Error in cell2mat (line 118)
ct{mref{:}} = cat(cdim+1,c{mref{:},:});

When I try something else:

cellarrayneeded=cell2mat(analysisdataWT{8:15, 2:60, 1:21});

I get:
Error using cell2mat
Too many input arguments.
What am I doing wrong?

Thanks!לק"י

Hi guys,
I have a cell array in 64X61X21 size.
I want to make a matrix of the values (no vectors) within specific area of the cell array – 15:21,2:61,1:21.
First I make a copy of the cell array:
cellarrayneeded=analysisdayaWT(8:15, 2:60, 1:21);
cellarrayneeded=cell2mat(cellarrayneeded);

Error message I get:

Error using cat
Dimensions of arrays being concatenated are not consistent.

Error in cell2mat (line 118)
ct{mref{:}} = cat(cdim+1,c{mref{:},:});

When I try something else:

cellarrayneeded=cell2mat(analysisdataWT{8:15, 2:60, 1:21});

I get:
Error using cell2mat
Too many input arguments.
What am I doing wrong?

Thanks! לק"י

Hi guys,
I have a cell array in 64X61X21 size.
I want to make a matrix of the values (no vectors) within specific area of the cell array – 15:21,2:61,1:21.
First I make a copy of the cell array:
cellarrayneeded=analysisdayaWT(8:15, 2:60, 1:21);
cellarrayneeded=cell2mat(cellarrayneeded);

Error message I get:

Error using cat
Dimensions of arrays being concatenated are not consistent.

Error in cell2mat (line 118)
ct{mref{:}} = cat(cdim+1,c{mref{:},:});

When I try something else:

cellarrayneeded=cell2mat(analysisdataWT{8:15, 2:60, 1:21});

I get:
Error using cell2mat
Too many input arguments.
What am I doing wrong?

Thanks! cell arrays, matrix, cell2mat MATLAB Answers — New Questions

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For example, what is the easiest way to turn a 1*1 cell ‘a b c d e’ into a cell array {‘a’} {‘b’} {‘c’} {‘d’} {‘e’}? Please! Anyone can help me?For example, what is the easiest way to turn a 1*1 cell ‘a b c d e’ into a cell array {‘a’} {‘b’} {‘c’} {‘d’} {‘e’}? Please! Anyone can help me? For example, what is the easiest way to turn a 1*1 cell ‘a b c d e’ into a cell array {‘a’} {‘b’} {‘c’} {‘d’} {‘e’}? Please! Anyone can help me? cell, cell array, split MATLAB Answers — New Questions

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I’m working on a project where I use Simulink to interface with ROS and Gazebo. My setup involves communicating with an Iris drone that uses the PX4 Autopilot, and I am using MAVROS for this communication. Typically, I launch ROS and Gazebo using the following commands:
HEADLESS=1 make px4_sitl gazebo
roslaunch mavros px4.launch fcu_url:="udp://:14540@127.0.0.1:14540"
However, I’m having difficulty accessing ROS topics from within the Simulink environment. I believe I need to install the Gazebo plugin for Simulink, but I’m unsure of the exact steps to do this.
Could someone guide me through the process of installing and configuring the Gazebo plugin in Simulink? Any specific instructions for ensuring that I can properly access ROS topics from Simulink would be greatly appreciated.I’m working on a project where I use Simulink to interface with ROS and Gazebo. My setup involves communicating with an Iris drone that uses the PX4 Autopilot, and I am using MAVROS for this communication. Typically, I launch ROS and Gazebo using the following commands:
HEADLESS=1 make px4_sitl gazebo
roslaunch mavros px4.launch fcu_url:="udp://:14540@127.0.0.1:14540"
However, I’m having difficulty accessing ROS topics from within the Simulink environment. I believe I need to install the Gazebo plugin for Simulink, but I’m unsure of the exact steps to do this.
Could someone guide me through the process of installing and configuring the Gazebo plugin in Simulink? Any specific instructions for ensuring that I can properly access ROS topics from Simulink would be greatly appreciated. I’m working on a project where I use Simulink to interface with ROS and Gazebo. My setup involves communicating with an Iris drone that uses the PX4 Autopilot, and I am using MAVROS for this communication. Typically, I launch ROS and Gazebo using the following commands:
HEADLESS=1 make px4_sitl gazebo
roslaunch mavros px4.launch fcu_url:="udp://:14540@127.0.0.1:14540"
However, I’m having difficulty accessing ROS topics from within the Simulink environment. I believe I need to install the Gazebo plugin for Simulink, but I’m unsure of the exact steps to do this.
Could someone guide me through the process of installing and configuring the Gazebo plugin in Simulink? Any specific instructions for ensuring that I can properly access ROS topics from Simulink would be greatly appreciated. simulink, ros, gazebo, px4, mavros MATLAB Answers — New Questions

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