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.gitattributes

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+* text=auto: This tells Git to automatically handle EOL conversion for files it detects as text.

README.md

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-# AOCompObserver
-Computational observer calculations for our AO psychopysical experiments
+# AOMicroRepeat
+AO psychophysics analysis

code/CheckDataSnippet.m

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+
+% Load a QUEST data file and run this to find maximum trial intensity with
+% incorrect response. Intensity is raw log10, not LUT corrected.
+index = find(response_matrix == 0 & ~isnan(theThreshold) );
+max(trial_matrix(index))
+
+% Load a MOCS data file and run this to find maximum trial intensity with
+% incorrect response.  Intensity is raw log10, not LUT corrected.
+index = find(response_vector);
+max(trial_vector(index))
+unique(trial_vector)

code/CombineTrialsDHB.m

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+%% CombineTrials
+%
+% Combine all of the psychophysical data files into one big .mat
+% file.  We do this to simplify the other programs, since the logic
+% here involves lots of checks that everything is as it should be.
+% 
+% Also identify catch trials in QUEST runs, which are somewhat obscurely
+% indicated, and fix them up so later programs don't have to worry about
+% this.
+%
+% Although this might look like it is splitting the data into two halves, that
+% actually happens elsewhere, and here all the data are stored under all the splits.
+% This is a vestige from the time this code was part of the full data analysis loop, and
+% if we were better people we'd skip this step here.  Maybe we will become better at some
+% point.
+%
+% The output of this program is a mat file 'CombinedData.mat' that gets stored in the
+% analysis output tree, and that contains one big cell array with all of the data.
+%
+% See also: FitTrials, FigureN
+
+%% Initialize
+close all; clear all;
+
+%% Get path to data
+%
+% Path to data tree on this machine
+%
+% This is set up by TbTb local hook file, but
+% you can also use
+%  setpath('AOMicroRepeat','dataDir',theDataDir)
+% to do this, where theDataDir is the path to the
+% files.
+%
+% In the code example I got, this was
+%  path = 'W:\Data\11125\20231103\AO_Psychophysics\MOCS\8\group2';
+% but that does not match the actuall data tree I received for 11002.
+dataDir = getpref('AOMicroRepeat','dataDir');
+
+%% Also analysis output dir, same idea
+analysisDir = getpref('AOMicroRepeat','analysisDir');
+
+%% Some parameters
+log0Value = -3.5;
+
+%% Define what data we are analyzing here
+%
+% Generally speaking, this routine loops over everything and does its thing
+%
+% Define subjects
+theParticipants = {'11002' '11108' '11118' '11119' '11125'};
+
+% Define sessions (1 or 2)
+theSessions = [1 2];
+
+% Define session splits
+theSplits = {'All', 'FirstHalf', 'SecondHalf'};
+
+% Define sizes (8 or 43)
+theDiameters = [8 43];
+
+% Define methods ('MOCS' or 'QUEST')
+%
+% When we create COMBINED data below, we count
+% on this being as it is.  Don't change wihtout care.
+theMethods = {'MOCS' 'QUEST', 'COMBINED'};
+
+%% Get the AOM lookup table info.
+%
+% This is loaded and saved here with the data, so that the programs that
+% read the output of this program don't have to go and find it.
+%
+% As far as I can guess
+%   Column 1: nominal linear intensities
+%   Column 2: LUT linear intensities
+%   Column 3: nominal log10 intensities
+%   Column 4: looks like a non-linear mapping from 10 to 8 bits
+AOM = load('green_AOM_LUT_processing');
+
+%% Loop over everything
+tableRow = 1;
+for pp = 1:length(theParticipants)
+    for dd = 1:length(theDiameters)
+        for ss = 1:length(theSessions)
+            for hh = 1:length(theSplits)
+                checkSessionDate = [];
+                MOCSFileTimes = [];
+                QUESTFileTimes = [];
+                for mm = 1:length(theMethods)
+
+                    % Store info for what we are analyzing in this run
+                    theMethod{tableRow,1} = theMethods{mm};
+                    theSubject{tableRow,1} = theParticipants{pp};
+                    theDiameter(tableRow,1) = theDiameters(dd);
+                    theSession(tableRow,1) = theSessions(ss);
+                    theSplit{tableRow,1} = theSplits{hh};
+
+                    % Handle MOCS and QUEST from the data
+                    if (strcmp(theMethods{mm},'MOCS') | strcmp(theMethods{mm},'QUEST'))
+
+                        % Form path the the directory with the data sitting in it
+                        pathToData = fullfile(dataDir,theSubject{tableRow},['Session' num2str(theSession(tableRow))],['Size' num2str(theDiameter(tableRow))],theMethod{tableRow});
+
+                        % Get list of data files
+                        dirOffset = 1;
+                        trial_videos = dir(fullfile(pathToData,'*.mat'));
+                        num_trial_videos = length(trial_videos(dirOffset:end));
+                        if (num_trial_videos ~= 4)
+                            error('Expect 4 data files');
+                        end
+
+                        % Read and concatenate the data
+                        all_trials{pp,dd,ss,hh,mm} = {};
+                        all_trials_unpacked{pp,dd,ss,hh,mm} = [];                
+                        fprintf('\tReadng videos\n');
+                        for i = 1:num_trial_videos
+                            % Get check date from filename.  This should be the
+                            % same for all the MOCS and QUEST files from the
+                            % same subject session.  The method is the inner
+                            % loop variable, so we can do the check here.
+                            if (isempty(checkSessionDate))
+                                checkSessionDate = trial_videos(i+dirOffset-1).name(7:16);
+                            else
+                                if (~strcmp(checkSessionDate,trial_videos(i+dirOffset-1).name(7:16)))
+                                    error('Not all data files from same session are on the same date');
+                                end
+                            end
+
+                            % Check that QUEST file times are later than last
+                            % MOCS time.  This code assumes that it runs to
+                            % completion on the same calendar day on which it
+                            % was started, which will almost always be true.
+                            %
+                            % Count underscores to find start of the time
+                            % string.
+                            nUnderscore = 0;
+                            for cc = 1:length(trial_videos(i+dirOffset-1).name)
+                                if (trial_videos(i+dirOffset-1).name(cc) == '_')
+                                    nUnderscore = nUnderscore + 1;
+                                end
+                                if (nUnderscore == 4)
+                                    timeStartIndex = cc + 1;
+                                    break;
+                                end
+                            end
+                            fileTimeStr = trial_videos(i+dirOffset-1).name(timeStartIndex:timeStartIndex+4);
+
+                            % Handle way single digit time numbers get written
+                            % in the filename.  Brute force this. Ugh.
+                            if (fileTimeStr(2) == '_')
+                                fileTimeStr = ['0' fileTimeStr];
+                            end
+                            if (fileTimeStr(5) == '_')
+                                fileTimeStr(5) = fileTimeStr(4);
+                                fileTimeStr(4) = '0';
+                            end
+                            fileTimeStr = fileTimeStr(1:5);
+
+                            % Convert to format MATLAB can operate on
+                            fileTime = datetime(fileTimeStr,'InputFormat','HH_mm');
+                            if (strcmp(theMethod(tableRow),'MOCS'))
+                                MOCSFileTimes = [MOCSFileTimes ; fileTime];
+                            else
+                                QUESTFileTimes = [QUESTFileTimes ; fileTime];
+                            end
+
+                            % Read the data file
+                            % fprintf('Reading file %s\n',fullfile(pathToData,trial_videos(i+dirOffset-1).name));
+                            loadedData{pp,dd,ss,hh,mm,i} = load(fullfile(pathToData,trial_videos(i+dirOffset-1).name));
+
+                            % Grab the trial data we need.  Also do some
+                            % MOCS and QUEST specific reality checks
+                            if (isfield(loadedData{pp,dd,ss,hh,mm,i},'trial_vector'))
+                                % It's a MOCS data file by what's in it
+                                if (~strcmp(theMethod{tableRow},'MOCS'))
+                                    fprintf('File %s\n\tUnder QUEST, appears to be MOCS\n\tTrials: %d\n',fullfile(pathToData,trial_videos(i+dirOffset-1).name),length(loadedData{pp,dd,ss,hh,mm,i}.trial_vector));
+                                end
+                                if (length(loadedData{pp,dd,ss,hh,mm,i}.trial_vector) ~= 90 & length(loadedData{pp,dd,ss,hh,mm,i}.trial_vector) ~= 100)
+                                    fprintf('File %s: Wrong number of trials in MOCS data file\n');
+                                end
+                                all_trials{pp,dd,ss,hh,mm}{i,1} = loadedData{pp,dd,ss,hh,mm,i}.trial_vector;
+                                all_trials{pp,dd,ss,hh,mm}{i,2} = loadedData{pp,dd,ss,hh,mm,i}.response_vector;
+
+                                % Reality check on MOCS stimulus levels.
+                                % This should be the same across runs
+                                % within session, I think, but are not
+                                % always. This doesn't error out, but does
+                                % report the unexpected cases.
+                                for j = i-1:-1:1  
+                                    if (any(unique(loadedData{pp,dd,ss,hh,mm,j}.trial_vector) ~= unique(loadedData{pp,dd,ss,hh,mm,i}.trial_vector)))
+                                        fprintf('\t\tMOCS mismatch in trial levels across runs\n')
+                                        fprintf('\t\t%s, session %d, size %d, run loaded #%d vs run loaded #%d\n',theParticipants{pp},theSessions(ss), theDiameters(dd),i,j);
+                                        fprintf('\t\tFile loaded #%d: %s\n',i,fullfile(trial_videos(i+dirOffset-1).name));
+                                        fprintf('\t\tFile loaded #%d: %s\n',j,fullfile(trial_videos(j+dirOffset-1).name));
+                                    end
+                                end
+
+                            elseif (isfield(loadedData{pp,dd,ss,hh,mm,i},'trial_matrix'))
+                                % It's a QUEST data file by what's in it
+                                if (~strcmp(theMethod{tableRow},'QUEST'))
+                                    fprintf('File %s\n\tUnder MOCS, appears to be QUEST\n\tTrials: %d\n',fullfile(pathToData,trial_videos(i+dirOffset-1).name),length(loadedData{pp,dd,ss,hh,mm,i}.trial_matrix));
+                                end
+                                if (length(loadedData{pp,dd,ss,hh,mm,i}.trial_matrix) ~= 44)
+                                    fprintf('File %s: Wrong number of trials in QUEST data file\n');
+                                end
+                                all_trials{pp,dd,ss,hh,mm}{i,1} = loadedData{pp,dd,ss,hh,mm,i}.trial_matrix;
+                                questCatchTrialIndex = find(isnan(loadedData{pp,dd,ss,hh,mm,i}.theThreshold));
+                                if (length(questCatchTrialIndex) ~= 4)
+                                    fprintf('Wrong number of catch trials in QUEST data file');
+                                end
+                                all_trials{pp,dd,ss,hh,mm}{i,1}(questCatchTrialIndex) = log0Value;
+                                all_trials{pp,dd,ss,hh,mm}{i,2} = loadedData{pp,dd,ss,hh,mm,i}.response_matrix;
+                            else
+                                error('Do not understand data format');
+                            end
+
+                            % Get/check number of trials and make sure they
+                            % are the same for each run within
+                            % method/session.
+                            if (i == 1)
+                                nTrialsPerSession = length(all_trials{pp,dd,ss,hh,mm}{i,1});
+                            else
+                                if (length(all_trials{pp,dd,ss,hh,mm}{i,1}) ~= nTrialsPerSession)
+                                    error('Trial mismatch across runs');
+                                end
+                            end
+
+                            % Concatenate across runs into one long pair of
+                            % vectors.
+                            all_trials_unpacked{pp,dd,ss,hh,mm} = [all_trials_unpacked{pp,dd,ss,hh,mm} ; [all_trials{pp,dd,ss,hh,mm}{i,1} all_trials{pp,dd,ss,hh,mm}{i,2}] ];
+                        end
+
+                        if (strcmp(theMethod{tableRow},'MOCS') & size(MOCSFileTimes,1) ~= 4)
+                            error('Wrong number of MOCS files somewhere in data tree');
+                        end
+                        if (strcmp(theMethod{tableRow},'QUEST')  & size(QUESTFileTimes,1) ~= 4)
+                            error('Wrong number of QUEST files somewhere in data tree');
+                        end
+                        if (strcmp(theMethod{tableRow},'QUEST'))
+                            QUESTFileTimesSorted = sort(QUESTFileTimes);
+                            MOCSFileTimesSorted = sort(MOCSFileTimes);
+                            for ff = 1:size(MOCSFileTimes,1)
+                                if (QUESTFileTimesSorted(ff) < MOCSFileTimesSorted(ff))
+                                    fprintf('File time order error\n');
+                                end
+                            end
+                        end
+                    else
+                        % Concatenate MOCS and QUEST data into COMBINED
+                        all_trials_unpacked{pp,dd,ss,hh,mm} = [all_trials_unpacked{pp,dd,ss,hh,1} ; all_trials_unpacked{pp,dd,ss,hh,2}];
+                    end
+
+                    % Bump table row
+                    tableRow = tableRow + 1;
+                end
+            end
+        end
+    end
+end
+
+% Save out one nice big combined file
+save(fullfile(analysisDir,'combinedData.mat'),'all_trials','all_trials_unpacked','log0Value','theParticipants','theDiameters','theSessions','theSplits','theMethods','AOM','-v7.3');
+
+