run setting sample attr

run_up:

run_setting_sample_attr

%% Dataset Basics (setting sample attributes)
%
%
% #   For CoSMoMVPA's copyright information and license terms,   #
% #   see the COPYING file distributed with CoSMoMVPA.           #

% Set the targets and the chunks
%
% There are 10 runs with 6 volumes per run. The runs are vertically stacked one
% above the other. The six volumes in each run correspond to the stimuli:
% 'monkey','lemur','mallard','warbler','ladybug','lunamoth', in that order. Add
% numeric targets labels (samples attribute) such that 1 corresponds to 'monkey',
% 2 corresponds to 'lemur', etc. Then add numeric chunks (another samples
% attribute) so that 1 corresponds to run1, 2 corresponds to run2, etc.

config = cosmo_config();
data_path = fullfile(config.tutorial_data_path, 'ak6', 's01');

%% Load the dataset 'glm_T_stats_perrun.nii' masked with 'brain_mask.nii'
mask_fn = fullfile(data_path, 'brain_mask.nii');
data_fn = fullfile(data_path, 'glm_T_stats_perrun.nii');
ds = cosmo_fmri_dataset(data_fn, 'mask', mask_fn);
%% set targets
% remember that targets are part of ds.sa and that they are stored in a
% column vector
ds.sa.targets = repmat(1:6, [1, 10])'; % 10 times labels 1 to 6, column vector
%% set chunks
% remember that chunks are part of ds.sa and that they are stored in a
% column vector
chunks = repmat(1:10, [6, 1]);
chunks = chunks(:); % flatten matrix to a column vector
ds.sa.chunks = chunks;

%% Show the results

%% print the dataset
fprintf('\nDataset:\n');
cosmo_disp(ds);

%% print the sample attributes
fprintf('\nSample attributes (in full):\n');
cosmo_disp(ds.sa, 'edgeitems', Inf); % 'edgeitems determine how much of a
% matrix is displayed. Try different values.

%% print targets and chunks next to each other
fprintf('\nTargets and chunks attributes (in full):\n');
nsamples = size(ds.samples, 1);
fprintf('sample #   target   chunk\n');
index_target_chunks = [(1:nsamples)', ds.sa.targets, ds.sa.chunks];
cosmo_disp(index_target_chunks, 'edgeitems', Inf);