Import the SCN Core Support.

Author: zebdelk

@canlab_dataset/add_var.m

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+% Not complete yet. Please edit me
+% 
+% Function for adding a variable to a dataset in a systematic way.
+% - Checks IDs of subjects to make sure data is added in the correct order.
+% - Values for missing data are coded as missing values, as specified in dat.Description.Missing_Values
+% - Handles Subject_Level or Event_Level data
+
+varname = 'ValenceType';
+var_to_add = SETUP.data.X;
+
+% get indices
+[subjn, ia, ib] = intersect(dat.Subj_Level.id, subjname2, 'stable');
+
+wh_subjs = false(length(dat.Subj_Level.id));
+wh_subjs(ia) = true;
+
+n = length(dat.Subj_Level.id);
+    
+
+% set missing value
+missingval = dat.Description.Missing_Values;
+if ischar(missingval), missingval = str2num(missingval);
+    
+
+nmissing = length(dat.Subj_Level.id) - length(ia);
+fprintf('Adding variable %s:\n\tSubjects with missing data: %d\n', varname, nmissing);
+
+nextra = length(subjname2) - length(ia);
+fprintf('\tSubjects not in dataset (will not be added): %d\n', nextra);
+
+is_subj_level = iscell(var_to_add);
+
+% Subject level
+% simplest case
+% --------------------------------------------------------------------
+if is_subj_level
+    
+    newvar = repmat(missingval, n, 1);
+    
+    newvar(ia) = var_to_add(ib);
+    
+    return
+    
+end
+
+% Event level
+% --------------------------------------------------------------------
+
+for i = 1:n
+    
+    
+    
+end
+
+
+str2num('NaN')
+

@canlab_dataset/bars.m

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+function [dat, descrip, colors, h1, s1] = bars(obj, varnames, varargin)
+%
+%
+% Takes any optional inputs to barplot_colored.m
+%
+% Bar plot for canlab_dataset object
+%
+% Usage:
+% -------------------------------------------------------------------------
+% [dat, descrip, colors, h1, s1] = bars(obj, varnames, varargin)
+% Takes any optional inputs to barplot_colored.m
+%
+% Author and copyright information:
+% -------------------------------------------------------------------------
+%     Copyright (C) 2013 Tor Wager
+%
+%     This program is free software: you can redistribute it and/or modify
+%     it under the terms of the GNU General Public License as published by
+%     the Free Software Foundation, either version 3 of the License, or
+%     (at your option) any later version.
+%
+%     This program is distributed in the hope that it will be useful,
+%     but WITHOUT ANY WARRANTY; without even the implied warranty of
+%     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+%     GNU General Public License for more details.
+%
+%     You should have received a copy of the GNU General Public License
+%     along with this program.  If not, see <http://www.gnu.org/licenses/>.
+%
+% Inputs:
+% -------------------------------------------------------------------------
+% obj           canlab_dataset object
+% varnames      Cell string of variable names to plot
+%
+% Takes any optional inputs to barplot_colored.m
+%
+% Outputs:
+% -------------------------------------------------------------------------
+% dat           data matrix for each variable
+%
+% Examples:
+% -------------------------------------------------------------------------
+%
+% create_figure('NPS values - All subjects');
+% 
+% varnames = {'15' '13' '11' ' 9' '16' '14' '12' '10'};
+% xvals = [1 2 4 5 8 9 11 12];
+% colors = {[1 0 0] [0 1 0] [1 0 0] [0 1 0] [1 0 0] [0 1 0] [1 0 0] [0 1 0]};
+% bars(LevoNPS, varnames, 'x', xvals, 'colors', colors, 'XTickLabels', varnames, 'within', 'nofig');
+%
+%
+% See also:
+% * list other functions related to this one, and alternatives*
+
+% Programmers' notes:
+% List dates and changes here, and author of changes
+
+n = length(varnames);
+colors = scn_standard_colors(n);
+
+
+% -------------------------------------------------------------------------
+% DEFAULTS AND INPUTS
+% -------------------------------------------------------------------------
+
+% optional inputs with default values
+% -----------------------------------
+% - allowable_args is a cell array of argument names
+% - avoid spaces, special characters, and names of existing functions
+% - variables will be assigned based on these names
+%   i.e., if you use an arg named 'cl', a variable called cl will be
+%   created in the workspace
+
+allowable_args = {'colors' 'nofig'};
+
+default_values = {colors [0]};
+
+% define actions for each input
+% -----------------------------------
+% - cell array with one cell for each allowable argument
+% - these have special meanings in the code below
+% - allowable actions for inputs in the code below are: 'assign_next_input' or 'flag_on'
+
+actions = {'assign_next_input' 'flag_on'};
+
+% logical vector and indices of which inputs are text
+textargs = cellfun(@ischar, varargin);
+whtextargs = find(textargs);
+
+for i = 1:length(allowable_args)
+    
+    % assign default
+    % -------------------------------------------------------------------------
+    
+    eval([allowable_args{i} ' =  default_values{i};']);
+    
+    wh = strcmp(allowable_args{i}, varargin(textargs));
+    
+    if any(wh)
+        % Optional argument has been entered
+        % -------------------------------------------------------------------------
+        
+        wh = whtextargs(wh);
+        if length(wh) > 1, warning(['input ' allowable_args{i} ' is duplicated.']); end
+        
+        switch actions{i}
+            case 'assign_next_input'
+                eval([allowable_args{i} ' = varargin{wh(1) + 1};']);
+                varargin{wh(1) + 1} = [];
+                
+            case 'flag_on'
+                eval([allowable_args{i} ' = 1;']);
+                
+            otherwise
+                error(['Coding bug: Illegal action for argument ' allowable_args{i}])
+        end
+        
+    end % argument is input
+end
+
+% END DEFAULTS AND INPUTS
+% -------------------------------------------------------------------------
+
+% REST OF BAR PLOT
+
+
+[dat, ~, ~, descrip] = get_var(obj, varnames, varargin{:});
+
+descrip = cat(1, descrip{:});
+
+
+colors = cat(1, colors{:});
+
+xvals = 1:n;
+
+if nofig
+else
+    
+create_figure('bars');
+end
+
+[h1, s1] = barplot_colored(dat, varargin{:});
+% set(h2, 'BarWidth', .9)
+colormap(colors)
+
+fprintf('T-tests against zero\n');
+fprintf('Description\tt(%3.0f)\tp\n', size(dat, 1) - 1);
+
+% Print table - t-test against zero
+for i = 1:n
+    [h, p, ci, stats] = ttest(dat(:, i));
+    if p < .001, str = '***';
+    elseif p < .01, str = '**';
+    elseif p < .05, str = '*';
+    elseif p < .10, str = '+';
+    else str = ' ';
+    end
+    
+    fprintf('%s\t%3.2f\t%3.6f\t%s\n', descrip{i}, stats.tstat, p, str);
+end
+
+
+end
+

@canlab_dataset/canlab_dataset.m

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+% D = canlab_dataset();
+%
+% Constructs a new, empty instance of a canlab_dataset object.
+% The fields of this object can subsequently be assigned data.
+%
+% The dataset (D) The dataset (D) is a way of collecting behavioral data 
+% and/or meta-data about fMRI (and other) studies in a standardized format.
+% It has entries for experiment description, Subject-level
+% variables, Event-level variables (e.g., for fMRI events or trials in a
+% behavioral experiment) and other fields for sub-event level  data (e.g., 
+% physio or continuous ratings within trials).
+% 
+% See D.Description for some basic info.
+%
+% Dataset methods include:
+%
+% write_text    -> writes a flat text file (data across all subjects)
+% concatenate   -> returns flat matrix of data across all subjects to the workspace
+% get_var       -> get event-level data from one variable and return in matrix and cell formats
+% scatterplot   -> standard or multi-line scatterplot of relationship between two variables
+% scattermatrix -> Scatterplot matrix of pairwise event-level variables
+%
+% - type methods(canlab_dataset) for a list of all methods
+%
+% Copyright Tor Wager, 2013
+
+classdef canlab_dataset
+    
+    properties
+        
+        % Build a dataset containing the key variables
+        
+        
+        Description = struct('Experiment_Name', [], 'Missing_Values', [], 'Subj_Level', [], 'Event_Level', []);
+        
+        Subj_Level = struct('id', cell(1), 'names', cell(1), 'type', cell(1), 'units', cell(1), 'descrip', cell(1), 'data', []);
+        
+        Event_Level = struct('names', cell(1), 'type', cell(1), 'units', cell(1), 'descrip', cell(1), 'data', cell(1));
+        
+        Sub_Event_Level = struct('names', cell(1), 'type', cell(1), 'units', cell(1), 'descrip', cell(1), 'data', cell(1));
+        
+        Continuous = struct('names', cell(1), 'type', cell(1), 'units', cell(1), 'descrip', cell(1), 'data', cell(1));
+        
+        wh_keep = struct();
+        
+    end % properties
+    
+    %% Subject-level data
+    
+    methods
+        
+        % Class constructor
+        function obj = canlab_dataset(varargin)
+            
+            obj.Description.Subj_Level{1, 1} = 'id: Unique text identifier for each subject (e.g., subject code)';
+            
+            obj.Description.Subj_Level{2, 1} = 'data: cell array, one cell per subject, with rectangular data matrix.';
+            obj.Description.Subj_Level{3, 1} = '      Each row is one event, each column a variable, with names in .names';
+            
+            obj.Description.Event_Level{1, 1} = 'data: cell array, one cell per subject, with rectangular data matrix.';
+            obj.Description.Event_Level{2, 1} = '      Each row is one event, each column a variable, with names in .names';
+            
+            
+        end % constructor function
+        
+    end % methods
+    
+    
+end % classdef
+
+
+

@canlab_dataset/concatenate.m

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+function [names, ids, dat, descrips] = concatenate(D, varargin)
+%
+% Concatenates Subject-level and Event-level data across all subjects
+%
+% [names ids dat] = concatenate(D)
+% 
+%  INPUT:
+%     D:  canlab_dataset
+%     varargin:  currently accepts a wh_keep (logical array)
+%
+%  OUTPUT:
+% Names: cell array of variable names
+%
+% Descrip: cell array of variable descriptions
+%
+% ids:   subject IDs matching data rows in dat
+%
+% dat:   subjects*events x variables matrix
+%
+%           - subject number, event number are included
+%           - all subject-level and event-level data are included
+%           - this format appropriate for, e.g., SAS/HLM
+%
+% Examples:
+% [names, ids, flatdat] = concatenate(D);
+% id_numbers = flatdat(:, 1);
+%
+% wh_subjs = true(size(D.Subj_Level.id));
+% wh_subjs([13 18 19]) = false;
+% [names, ids, dat] = concatenate(D, wh_subjs);
+%
+% % Copyright Tor Wager, 2013
+
+% select subjects
+wh_ids = true(length(D.Subj_Level.id), 1);
+if length(varargin) > 0, wh_ids = logical(varargin{1}); end
+whfind = find(wh_ids);
+
+names = {'subj_num' D.Subj_Level.names{:} 'Event_number' D.Event_Level.names{:}};
+descrips = {'subj_num' D.Subj_Level.descrip{:} 'Event_number' D.Event_Level.descrip{:}};
+
+
+n = length(D.Subj_Level.id(wh_ids));
+
+x = D.Event_Level.data(wh_ids);
+
+
+for i = 1:n
+
+    e = size(D.Event_Level.data{whfind(i)}, 1);
+    
+    ids{i} = repmat(D.Subj_Level.id{whfind(i)}, e, 1);
+    
+    %           subj ID                     subj level data              trialID  event data
+    x{i} = [repmat(i, e, 1) repmat(D.Subj_Level.data(whfind(i), :), e, 1) (1:e)' x{i}];
+    
+end
+
+ids = strvcat(ids{:});
+
+dat = cat(1, x{:});
+
+end % function