Contents
Dataset basics
- For CoSMoMVPA's copyright information and license terms, #
- see the COPYING file distributed with CoSMoMVPA. #
% Set data path, load dataset, set targets and chunks, and add labels as % sample attributes % Set the data path config = cosmo_config(); data_path = fullfile(config.tutorial_data_path, 'ak6', 's01'); % Set the filename to the glm_T_stats_perrun NIFTI file; assign % the filename to a variable named 'fn' % >@@> fn = fullfile(data_path, 'glm_T_stats_perrun.nii'); % <@@< % Load data using cosmo_fmri_dataset, and assign the result to a variable % named 'ds' % >@@> ds = cosmo_fmri_dataset(fn); % <@@< % set ds.sa.targets (trial conditions) to the 60x1 column vector: % [ 1 2 3 4 5 6 1 2 3 ... 5 6 ]' % >@@> targets = repmat((1:6)', 10, 1); ds.sa.targets = targets; % <@@< % sanity check cosmo_check_dataset(ds); % set ds.sa.chunks (acquisition run number) to the 60x1 column vector: % [ 1 1 1 1 1 1 2 2 2 ... 10 10 ]' % >@@> chunks = zeros(60, 1); for i = 1:10 idxs = (i - 1) * 6 + (1:6); chunks(idxs) = i; end ds.sa.chunks = chunks; % ( alternative: ds.sa.chunks = ceil((1:60)'/6); ) % <@@< % sanity check cosmo_check_dataset(ds); % Add ds.sa.labels as sample attributes as a 60x1 cell with strings % The order is {monkey, lemur, mallard, warbler, ladybug, and lunamoth}, % repeated 10 times % >@@> labels = repmat({ 'monkey' 'lemur' 'mallard' 'warbler' 'ladybug' 'lunamoth'}, 10, 1); ds.sa.labels = labels; % <@@< % sanity check cosmo_check_dataset(ds);
Overview of the dataset
fprintf('\nOverview of dataset:\n');
cosmo_disp(ds);
Overview of dataset:
.a
.vol
.mat
[ -3 0 0 121
0 3 0 -114
0 0 3 -11.1
0 0 0 1 ]
.xform
'scanner_anat'
.dim
[ 80 80 43 ]
.fdim
.labels
{ 'i'
'j'
'k' }
.values
{ [ 1 2 3 ... 78 79 80 ]@1x80
[ 1 2 3 ... 78 79 80 ]@1x80
[ 1 2 3 ... 41 42 43 ]@1x43 }
.sa
.targets
[ 1
2
3
:
4
5
6 ]@60x1
.chunks
[ 1
1
1
:
10
10
10 ]@60x1
.labels
{ 'monkey'
'lemur'
'mallard'
:
'warbler'
'ladybug'
'lunamoth' }@60x1
.samples
[ 0 0 0 ... 0 0 0
0 0 0 ... 0 0 0
0 0 0 ... 0 0 0
: : : : : :
0 0 0 ... 0 0 0
0 0 0 ... 0 0 0
0 0 0 ... 0 0 0 ]@60x275200
.fa
.i
[ 1 2 3 ... 78 79 80 ]@1x275200
.j
[ 1 1 1 ... 80 80 80 ]@1x275200
.k
[ 1 1 1 ... 43 43 43 ]@1x275200
Apply mask to dataset
fn_mask = fullfile(data_path, 'vt_mask.nii'); % Load mask using cosmo_fmri_dataset and store in ds_mask % >@@> ds_mask = cosmo_fmri_dataset(fn_mask); % <@@< % sanity check: ensure the same feature attributes assert(isequal(ds_mask.fa, ds.fa)); % Now slice the dataset using cosmo_slice, where the third argument must be % 2 to indicate the slicing of features (not samples). % Assign the result to a variable 'ds_masked'. % >@@> mask_indices = find(ds_mask.samples); ds_masked = cosmo_slice(ds, mask_indices, 2); cosmo_disp(ds_masked); % <@@<
.a
.vol
.mat
[ -3 0 0 121
0 3 0 -114
0 0 3 -11.1
0 0 0 1 ]
.xform
'scanner_anat'
.dim
[ 80 80 43 ]
.fdim
.labels
{ 'i'
'j'
'k' }
.values
{ [ 1 2 3 ... 78 79 80 ]@1x80
[ 1 2 3 ... 78 79 80 ]@1x80
[ 1 2 3 ... 41 42 43 ]@1x43 }
.sa
.targets
[ 1
2
3
:
4
5
6 ]@60x1
.chunks
[ 1
1
1
:
10
10
10 ]@60x1
.labels
{ 'monkey'
'lemur'
'mallard'
:
'warbler'
'ladybug'
'lunamoth' }@60x1
.samples
[ 0 0 0 ... 0.669 1.78 2.42
0 0 0 ... 1.06 1.61 0.943
0 0 0 ... 1.51 1.75 3.08
: : : : : :
0 0 0 ... 0.491 1.1 1.78
0 0 0 ... 0.687 1.88 2.72
0 0 0 ... 2.86 4.04 4.28 ]@60x400
.fa
.i
[ 51 53 54 ... 28 29 29 ]@1x400
.j
[ 24 24 24 ... 25 25 26 ]@1x400
.k
[ 2 2 2 ... 9 9 9 ]@1x400
Now use cosmo_fmri_dataset with all input parameters
% We do exactly the same as above, but shorter: % Load the dataset again using cosmo_fmri_dataset, but now use the % 'mask','targets','chunks' parameters. % Assign the result to ds_masked_alt % >@@> ds_masked_alt = cosmo_fmri_dataset(fn, 'mask', fn_mask, ... 'targets', targets, ... 'chunks', chunks); % <@@< % set labels again ds_masked_alt.sa.labels = labels; % check ds_masked and ds_masked_alt are the same assert(isequal(ds_masked_alt, ds_masked));