Merge branch 'develop' into 804_pspm_display

doc/PsPM_release_checklist.md

@@ -2,10 +2,10 @@
 This file contains the steps required for finalising a PsPM release. Many items at the beginning don't have to be followed sequentially. However, it is important that there aren't any revisions (commits) that implement/fix something new in the release branch, because we don't merge these branches back to trunk. Therefore, it is sensible to create the release branch after making absolutely sure that no new stuff will be implemented.
 
 - [ ] Update version number & date in
-	 - [ ] `pspm_msg`
+	 - [ ] `pspm_msg.txt`
 	 - [ ] `pspm_quit`
 	 - [ ] `pspm_ui`
-	 - [ ] `pspm.fig`: Load `pspm.fig` into MATLAB using `openfig`, update `fig.Children(9).String` and save back to `pspm.fig`
+	 - [ ] `pspm_ui_app`
 - [ ] Manual and Developers Guide: front pages
 - [ ] Make sure both manuals are updated
 - [ ] Add release notes section of the new version to manual (at the end) and release\_notes.tex
@@ -23,3 +23,5 @@ This file contains the steps required for finalising a PsPM release. Many items
 - [ ] Add release message to GitHub
 - [ ] Change release number on lab webpage (gh-pages branch)
 - [ ] Add release message to lab webpage (gh-pages branch)
+- [ ] Update `PsPM_installer` in `/src/helper` with the new version number and download link, and merge this pull request on the official release date.
+

src/f_SCR.m

@@ -18,14 +18,14 @@
 % ● Format
 %   [fx, dfdx, dfdP] = f_SCR(Xt,Theta,ut,in)
 % ● Arguments
-%   Theta:  4 ER constants (3 ODE params + time)
+% * Theta:  4 ER constants (3 ODE params + time)
 %           3 SF constants (3 ODE params)
 %           3 values per aSCR (invsigma(peaktime), invsigma(std),
 %           log(amplitude))
 %           1 value  per eSCR (log(amplitude))
 %           2 values per SF (invsigma(peaktime), log(amplitude))
 %           2 values per SCL change (invsigma(time), amplitude)
-%      ut:  row 1 - time (after cue onset)
+% *    ut:  row 1 - time (after cue onset)
 %           row 2 - number of aSCR
 %           row 3 - number of eSCR
 %           row 4 - number of SF
@@ -89,15 +89,15 @@
         [s, dsdx(k)]  = sigm(aTheta(k, 2), sig);
         aTheta(k, 2) = s + sigma_offset;
         aTheta(k, 3) = exp(aTheta(k, 3));
-    end;
+    end
     if any(isinf(aTheta(:, 3)))
         aTheta(isinf(aTheta(:, 3)), 3) = 1e200; % an arbitrary value way below realmax
-    end;
+    end
     clear sig m s
 else
     aTheta = [];
     aSCR_s = 5;
-end;
+end
 
 % - event-related responses
 if ut(3) > 0
@@ -109,11 +109,11 @@
     eTheta(:, 3) = exp(Theta((Theta_n + 3 * ut(2)) + (1:ut(3))));
     if any(isinf(eTheta(:, 3)))
         eTheta(isinf(eTheta(:, 3)), 3) = 1e200;
-    end;
+    end
 else
     eTheta = [];
     eSCR_o = aSCR_s(end);
-end;
+end
 
 % - spontaneous fluctuations
 if ut(4) > 0
@@ -125,13 +125,13 @@
         sig.G0 = ut(SF_ub(k)) - ut(SF_lb(k));
         [t, dtdx(k)] = sigm(Theta(Theta_n + 3 * ut(2) + ut(3) + (k - 1) * 2 + 1), sig);
         sfTheta(k, 1) = ut(SF_lb(k)) + t; % lower bound plus parameter vaue
-    end;
+    end
     sfTheta(:, 2) = sigma;
     sfTheta(:, 3) = exp(Theta((Theta_n + 3 * ut(2) + ut(3)) + (2:2:(2 * ut(4)))));
 else
     sfTheta = [];
     SF_ub = eSCR_o;
-end;
+end
 
 % - SCL changes
 if ut(5) > 0
@@ -143,12 +143,12 @@
         sig.G0 = ut(SCL_ub(k)) - ut(SCL_lb(k));
         [t, dtscldx(k)] = sigm(Theta(Theta_n + 3 * ut(2) + ut(3) + 2 * ut(4) + (k - 1) * 2 + 1), sig);
         SCLtheta(k, 1) = ut(SCL_lb(k)) + t; % lower bound plus parameter vaue
-    end;
+    end
     SCLtheta(:, 2) = sigma_SCL;
     SCLtheta(:, 3) = Theta((Theta_n + 3 * ut(2) + ut(3)) + 2 * ut(4)  + (2:2:(2 * ut(5))));
 else
     SCLtheta = [];
-end;
+end
 
 % ODE
 % ------------------------------------------------------------------------
@@ -190,33 +190,33 @@
      Jp(3, 7 + (1:3:(3 * ut(2))))  = gu(ut(1), aTheta, 0) .* (ut(1) - aTheta(:, 1))    .* (aTheta(:, 2)).^-2 .* dmdx;
      Jp(3, 7 + (2:3:(3 * ut(2))))  = gu(ut(1), aTheta, 0) .* (ut(1) - aTheta(:, 1)).^2 .* (aTheta(:, 2)).^-3 .* dsdx;
      Jp(3, 7 + (3:3:(3 * ut(2))))  = gu(ut(1), aTheta, 0);
- end;
+ end
 
 if ~isempty(eTheta)
     Jp(3, (7 + 3 * ut(2)) + (1:ut(3)))  = gu(ut(1), eTheta, 0);
-end;
+end
 
 if ~(isempty(eTheta) && isempty(aTheta))
     allTheta = [eTheta; aTheta];
     Jp(3, 4) = sum(gu(ut(1), allTheta, 0) .* 1./(allTheta(:, 2).^2) .* (ut(1) - allTheta(:, 1)) .* exp(Theta(4)));
-end;
+end
 
 if ~isempty(sfTheta)
     Jp(6, (7 + 3 * ut(2) + ut(3)) + (1:2:(2 * ut(4)))) = gu(ut(1), sfTheta, 0) .* (ut(1) - sfTheta(:, 1)) .* sfTheta(:, 2).^-2 .* dtdx;
     Jp(6, (7 + 3 * ut(2) + ut(3)) + (2:2:(2 * ut(4)))) = gu(ut(1), sfTheta, 0);
-end;
+end
 
 if ~isempty(SCLtheta)
     Jp(7, (7 + 3 * ut(2) + ut(3)) + 2 * ut(4) + (1:2:(2 * ut(5)))) = gu(ut(1), SCLtheta, 0) .* 1./(sigma_SCL.^2) .* (ut(1) - SCLtheta(:, 1)) .* dtscldx;
     SCLtheta(:, 3) = 1; % we don't take the exp(amp) here, so dSCLdamp = gu for unit amplitude
     Jp(7, (7 + 3 * ut(2) + ut(3)) + 2 * ut(4) + (2:2:(2 * ut(5)))) = gu(ut(1), SCLtheta, 0);
-end;
+end
 
 dfdP = dt .* Jp';
 
-if any(isweird(fx(:))), error('Weird values in f_SCR'); end;
-if any(isweird(dfdx(:))), error('Weird values in f_SCR'); end;
-if any(isweird(dfdP(:))), error('Weird values in f_SCR'); end;
+if any(isweird(fx(:))), error('Weird values in f_SCR'); end
+if any(isweird(dfdx(:))), error('Weird values in f_SCR'); end
+if any(isweird(dfdP(:))), error('Weird values in f_SCR'); end
 
 
 return;
@@ -237,10 +237,10 @@
     gu = a .* exp(-(ut - mu).^2 ./ (2 .* sigma.^2));
     if f
         gu = sum(gu);
-    end;
+    end
 else
     gu = 0;
-end;
+end
 return;
 
 % =========================================================================

src/f_SF.m

@@ -13,17 +13,17 @@
 % ● Format
 %   [fx, dfdx] = f_SF(Xt,Theta,ut,in)
 % ● Arguments
-%   Theta:  3 general constants
-%           2 value  per SF (time, log(amplitude))
-%      ut:  row 1 - time (after cue onset)
-%           row 2 - number of SF
+%   * Theta: 3 general constants
+%            2 value  per SF (time, log(amplitude))
+%   *    ut: row 1 - time (after cue onset)
+%            row 2 - number of SF
 % ● History
 %   Introduced in PsPM 3.0
 %   Written in 2008-2015 by Dominik R Bach (Wellcome Trust Centre for Neuroimaging)
 
 %% initialise
 global settings;
-if isempty(settings), pspm_init; end;
+if isempty(settings), pspm_init; end
 % settings
 Theta_n = 3;  % number of parameters for the peripheral function (the rest is for the neural function)
 try
@@ -45,10 +45,10 @@
         sfTheta(n, 1) = Theta((n - 1) * 2 + Theta_n + 1);
         sfTheta(n, 2) = sigma;
         sfTheta(n, 3) = exp(Theta((n - 1) * 2 + Theta_n + 2));
-    end;
+    end
 else
     sfTheta = [];
-end;
+end
 % ODE 3rd order + gaussian
 xdot = [Xt(2)
         Xt(3)
@@ -72,8 +72,8 @@
     gu = a .* exp(-(ut - mu).^2 ./ (2 .* sigma.^2));
     if f
         gu = sum(gu);
-    end;
+    end
 else
     gu = 0;
-end;
+end
 return;

src/g_SCR.m

@@ -1,20 +1,24 @@
 function [gx,dgdx,dgdPhi] = g_SCR(Xt,Phi,ut,inG)
 % ● Description
-%   TBA.
+%   This is the SCR observation function required by the VBA toolbox. It 
+%   adds the three generative processes (phasic SCR, SF, SCL) modelled in
+%   f_SCR.
+% ● Format
+%   [gx,dgdx,dgdPhi] = g_SCR(Xt,Phi,ut,inG)
 % ● Arguments
-%     Xt:
-%    Phi:
-%     ut:
-%    inG:
+%     Xt: a 7-element vector
+%    Phi: input required by VBA, ignored
+%     ut: input required by VBA, ignored
+%    inG: input required by VBA, ignored
 % ● History
 %   Introduced in PsPM 3.0
 %   Written in 2008-2015 by Dominik R Bach (Wellcome Trust Centre for Neuroimaging)
 
 global settings;
-if isempty(settings), pspm_init; end;
+if isempty(settings), pspm_init; end
 gx = Xt(1) + Xt(4) + Xt(7);
 dgdx = zeros(size(Xt,1),1);
 dgdx([1;4;7]) = 1;
 dgdPhi = [];
-if any(isnan(gx)|isinf(gx)), keyboard; end;
-if any(isnan(dgdx)|isinf(dgdx)), keyboard; end;
+if any(isnan(gx)|isinf(gx)), keyboard; end
+if any(isnan(dgdx)|isinf(dgdx)), keyboard; end

src/helper/PsPM_installer.m

@@ -1,7 +1,7 @@
 % This is a convenience PsPM installer. As an alternative, download the
 % lastest release from https://github.com/bachlab/PsPM, or clone the
 % develop branch.
-% Uzay Gökay & Dominik Bach, 2024
+% Uzay Gökay & Dominik Bach, 2025
 
 fprintf('Welcome to the PsPM installer.\n');
 [indx,tf] = listdlg('ListString',{'Latest PsPM release', 'GLM tutorial dataset', 'DCM tutorial dataset'}, ...
@@ -20,9 +20,9 @@
 if ismember(1, indx)
 
     % GitHub release URL for PsPM and the desired version
-    githubReleaseURL = 'https://github.com/bachlab/PsPM/releases/download';
-    version = 'v6.1.2';
-    packageName = 'PsPM_v6.1.2';
+    githubReleaseURL = 'https://github.com/bachlab/PsPM/releases/download'; 
+    version = 'v7.0.0';
+    packageName = 'PsPM_v7.0.0';
 
     % Create the destination folder if it does not exist
     if ~exist(destinationFolder, 'dir')

src/pspm.m

@@ -1,7 +1,7 @@
 function pspm(varargin)
 % ● Description
 %   pspm.m handles the main GUI for PsPM
-% ● Developer's notes
+% ● Developer
 %   PsPM will no longer support GUIDE and only use App Designer for UI.
 %   App Designer is available for MATLAB that is later than version R2016a (9.0).
 % ● History

src/pspm_align_channels.m

@@ -9,7 +9,7 @@
 % ● Arguments
 %   *                data : [struct] the input data to be processed, in PsPM data format.
 %   *          induration : [double] the duration of the input data.
-% ● Copyright
+% ● History
 %   Introduced in PsPM 3.1
 %   Written    in 2008-2016 by Dominik R Bach (Wellcome Trust Centre for Neuroimaging)
 %   Maintained in 2022      by Teddy

src/pspm_bf_FIR.m

@@ -17,29 +17,29 @@
 %% Check input arguments
 if nargin==0
   errmsg='No sampling interval stated'; warning('ID:invalid_input', errmsg); return;
-end;
+end
 n = 30;
 d = 1;
 td = varargin{1}(1);
 if nargin == 1 && numel(varargin{1}) > 1
   n = varargin{1}(2);
   if numel(varargin{1}) >= 3
     d = varargin{1}(3);
-  end;
+  end
 
 elseif nargin > 1
   if nargin >= 2
     n = varargin{2}(1);
-  end;
+  end
   if nargin >= 3
     d = varargin{3}(1);
-  end;
-end;
+  end
+end
 if td > d
   warning('ID:invalid_input', 'Time resolution is larger than duration of the function.'); return;
 elseif td == 0
   warning('ID:invalid_input', 'Time resolution must be larger than 0.'); return;
-end;
+end
 % initialise FIR
 FIR = [zeros(1, n); zeros(round((d*n/td)), n);];
 % generate timestamps
@@ -50,5 +50,5 @@
   stops=(d*reg)/td;
   FIR(starts:stops, reg)=1;
   starts=stops+1;
-end;
+end
 return

src/pspm_bf_hprf_e.m

@@ -8,7 +8,7 @@
 % ● Arguments
 %   *       td : time resolution in second.
 %   *        b : number of basis functions. Default as 1:6.
-% ● Developer's Notes
+% ● Developer
 %   Basis functions will be orthogonalized using spm_orth by default. Onsets
 %   pspm_glm must be shifted by 5 s to account for the pre-event epoch.
 %   Put in values 1:6 for b in order to get following basis functions:
@@ -25,10 +25,10 @@
 
 %% input checks
 global settings;
-if isempty(settings), pspm_init; end;
+if isempty(settings), pspm_init; end
 if nargin < 1
   errmsg = 'No sampling interval stated'; warning('ID:invalid_input', errmsg); return;
-end;
+end
 varargin = cell2mat(varargin);
 if length(varargin)==1
   b=1:6;
@@ -48,7 +48,7 @@
   warning('ID:invalid_input', 'Time resolution is larger than duration of the function.'); return;
 elseif td == 0
   warning('ID:invalid_input', 'Time resolution must be larger than 0.'); return;
-end;
+end
 
 x = (0:td:50-td);
 bf=[];

src/pspm_bf_hprf_fc_f.m

@@ -12,13 +12,13 @@
 
 %% initialise
 global settings;
-if isempty(settings), pspm_init; end;
+if isempty(settings), pspm_init; end
 if nargin < 1
   errmsg = 'No sampling interval stated'; warning('ID:invalid_input',errmsg); return;
-end;
+end
 if nargin < 2
   soa = 3.5;
-end;
+end
 if nargin < 3
   % table 2 row 3 in Castegnetti et al. 2016
   %p=[43.2180170215633,0.195621916215104,-6.9671,81.0383536117737];
@@ -30,7 +30,7 @@
   % before
   % amplitude does not matter because it will be downscaled to 1 by the
   % calling function
-end;
+end
 x0 = p(3);
 b = p(2);
 a = p(1);
@@ -49,7 +49,7 @@
 elseif soa > 8
   warning(['SOA longer than 8s is not recommended. ', ...
     'Use at own risk.']);
-end;
+end
 shift = soa + x0;
 x = (start:td:stop-td)';
 % try not to use stats toolbox, but stats toolbox has very good

src/pspm_bf_hprf_rew.m

@@ -7,7 +7,7 @@
 %   [bs, x] = pspm_bf_hprf_fc(TD)
 % ● Arguments
 %   *    td : time resolution in second.
-% ● References:
+% ● References
 %   GLM for reward-conditioned bradycardia:
 %   Xia Y, Liu H, Kälin OK, Gerster S, Bach DR (2024). Measuring
 %   human Pavlovian appetitive conditioning and memory retention.