Unverified Commit 1f89a990 authored by stephen scott tucker's avatar stephen scott tucker Committed by GitHub
Browse files

Merge pull request #9 from BUNPC/feature-sstucker-1 

1.21.4 --GUI disabled on startup, data saving, processing
--Homer checks npc website for updates once a week
--Params can be saved and loaded via the ProcStreamEditGUI
--setpaths has new "diffnames" option, which checks for conflicts on the path intended for developers to maintain compatibility with AtlasViewer environment
--Minor GUI fixes
parents 975ff157 e24b7099

AppSettings.cfg

+6 −0
Original line number Diff line number Diff line
@@ -14,4 +14,10 @@ SNIRF 
% Logging

On



% Last Checked For Update

12-Mar-1998 17:03:23



% Update Refused

0



% END

DataTree/ProcStream/ProcResultClass.m

+6 −1
Original line number Diff line number Diff line
@@ -670,9 +670,10 @@ classdef ProcResultClass < handle 
            tblcells = TableCell.empty();

            if isa(obj.dcAvg, 'DataClass')

                dataTimeSeries = obj.dcAvg(iBlk).GetDataTimeSeries('');

                

                h = waitbar_improved(0, sprintf('Generating table cells for export ... 0%% complete.'));

                % Data rows

                for iCond = 1:length(CondNames)

                    waitbar_improved(iCond/length(CondNames), h, sprintf('Generating table cells for export ... %d%% complete.', uint32(100 * iCond/length(CondNames))));

                    measListIdxs = obj.dcAvg(iBlk).GetMeasurementListIdxs(iCond);

                    for iCh = measListIdxs

                        iT = (obj.dcAvg.time >= trange(1)) & (obj.dcAvg.time <= trange(2));
@@ -685,6 +686,7 @@ classdef ProcResultClass < handle 
                        tblcells(iCond, mod(iCh-1, length(measListIdxs))+1) = TableCell(cname, width);

                    end

                end

                close(h);

            end

        end
@@ -710,7 +712,9 @@ classdef ProcResultClass < handle 
                end

                

                % Data rows

                h = waitbar_improved(0, sprintf('Generating table cells for export ... 0%% complete.'));

                for t = 1:size(dataTimeSeries,1)

                    waitbar_improved(t/size(dataTimeSeries,1), h, sprintf('Generating table cells for export ... %d%% complete.', uint32(100 * t/size(dataTimeSeries,1))));

                    for iCh = 1:length(measList)

                        if isnan(dataTimeSeries(t,iCh))

                            cname  = 'NaN';
@@ -720,6 +724,7 @@ classdef ProcResultClass < handle 
                        tblcells(t+2,iCh) = TableCell(cname, 12);

                    end

                end

                close(h);

            else

                

            end

DataTree/ProcStream/ProcStreamClass.m

+5 −0
Original line number Diff line number Diff line
@@ -178,6 +178,8 @@ classdef ProcStreamClass < handle 
                filename = '';

            end

            

            MainGUI_EnableDisableGUI('off');

            

            % loop over functions

            FcallsIdxs = obj.GetFcallsIdxs();

            nFcall = length(FcallsIdxs);
@@ -248,6 +250,9 @@ classdef ProcStreamClass < handle 
            

            obj.input.misc = [];

            close(hwait);

            

            MainGUI_EnableDisableGUI('on');

            

        end

FuncRegistry/UserFunctions/hmrR_GLM.m

+14 −14
Original line number Diff line number Diff line
@@ -25,47 +25,47 @@ 
%           only relevant when flagNuisanceRMethod = 3 (tCCA regressors).

% trange - defines the range for the block average [tPre tPost]

% glmSolveMethod - this specifies the GLM solution method to use

%            1 - use ordinary least squares (Ye et al (2009). NeuroImage, 44(2), 428?447.)

%            2 - use iterative weighted least squares (Barker,

%            1. use ordinary least squares (Ye et al (2009). NeuroImage, 44(2), 428?447.)

%            2. use iterative weighted least squares (Barker,

%               Aarabi, Huppert (2013). Biomedical optics express, 4(8), 1366?1379.)

%               Note that we suggest driftOrder=0 for this method as

%               otherwise it can produce spurious results.

% idxBasis - this specifies the type of basis function to use for the HRF

%            1 - a consecutive sequence of gaussian functions

%            2 - a modified gamma function convolved with a square-wave of

%            1. a consecutive sequence of gaussian functions

%            2. a modified gamma function convolved with a square-wave of

%                duration T. Set T=0 for no convolution.

%                The modified gamma function is

%                (exp(1)*(t-tau).^2/sigma^2) .* exp(-(tHRF-tau).^2/sigma^2)

%            3 - a modified gamma function and its derivative convolved

%            3. a modified gamma function and its derivative convolved

%                with a square-wave of duration T. Set T=0 for no convolution.

%			 4-  GAM function from 3dDeconvolve AFNI convolved with

%			 4.  GAM function from 3dDeconvolve AFNI convolved with

%                a square-wave of duration T. Set T=0 for no convolution.

% 			         (t/(p*q))^p * exp(p-t/q)

%                Defaults: p=8.6 q=0.547

%                The peak is at time p*q.  The FWHM is about 2.3*sqrt(p)*q.

% paramsBasis - Parameters for the basis function depends on idxBasis

%               idxBasis=1 - [stdev step] where stdev is the width of the

%               idxBasis=1 [stdev step] where stdev is the width of the

%                  gaussian and step is the temporal spacing between

%                  consecutive gaussians

%               idxBasis=2 - [tau sigma T] applied to both HbO and HbR

%               idxBasis=2. [tau sigma T] applied to both HbO and HbR

%                  or [tau1 sigma1 T1 tau2 sigma2 T2]

%                  where the 1 (2) indicates the parameters for HbO (HbR).

%               idxBasis=3 - [tau sigma T] applied to both HbO and HbR

%               idxBasis=3 [tau sigma T] applied to both HbO and HbR

%                  or [tau1 sigma1 T1 tau2 sigma2 T2]

%                  where the 1 (2) indicates the parameters for HbO (HbR).

%               idxBasis=4 - [p q T] applied to both HbO and HbR

%               idxBasis=4 [p q T] applied to both HbO and HbR

%                  or [p1 q1 T1 p2 q2 T2]

%                  where the 1 (2) indicates the parameters for HbO (HbR).

% rhoSD_ssThresh - max distance for a short separation measurement. Set =0

%          if you do not want to regress the short separation measurements.

%          Follows the static estimate procedure described in Gagnon et al (2011).

%          NeuroImage, 56(3), 1362?1371.

% flagNuisanceRMethod - 0 if short separation regression is performed with the nearest

% flagNuisanceRMethod - 0. if short separation regression is performed with the nearest

%               short separation channel.

%            1 if performed with the short separation channel with the

%            1. if performed with the short separation channel with the

%               greatest correlation.

%            2 if performed with average of all short separation channels.

%            3 uses tCCA regressors for nuisance regression, in Aaux,

%            2. if performed with average of all short separation channels.

%            3. uses tCCA regressors for nuisance regression, in Aaux,

%            mapped by rcMap, provided by hmr_tCCA()

% driftOrder - Polynomial drift correction of this order

% flagMotionCorrect - set to 1 to baseline correct between motion epochs indicated in tIncAuto, otherwise set to 0

Homer3.m

+1 −1
Original line number Diff line number Diff line
@@ -14,7 +14,7 @@ logger = Logger('Homer3'); 


logger.CurrTime();



if ~exist('groupDirs','var') || isempty(groupDirs)

if nargin==0

    groupDirs = convertToStandardPath(pwd);

end

if nargin<2

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