cosmo unflatten hdrΒΆ

function [arr, dim_labels, dim_values]=cosmo_unflatten(ds, dim, varargin)
% unflattens a dataset from 2 to (1+K) dimensions.
%
% [arr, dim_labels, dim_values]=cosmo_unflatten(ds, [dim, ][,...])
%
% Inputs:
%   ds                 dataset structure, with fields:
%      .samples        PxQ for P samples and Q features.
%      .a.Xdim.labels  1xK cell with string labels for each dimension,
%                      with X='s' for samples (dim=1) or X='f' for features
%                      (dim=2).
%      .a.Xdim.values  1xK cell, with S_J values (J in 1:K) corresponding
%                      to the labels in each of the K dimensions.
%      .Xa.(label)     for each label in a.Xdim.labels it contains the
%                      sub-indices for the K dimensions. It is required
%                      that for every dimension J in 1:K, all values in
%                      ds.fa.(a.fdim.labels{J}) are in the range 1:S_K, and
%                      that every combination across labels is unique.
%   dim                dimension to be unflattened, either 1 (for samples)
%                      or 2 (for features; default)
%   'set_missing_to',s value to set missing values to (default: 0)
%   'matrix_labels',m  Allow labels in the cell string m to be matrices
%                      rather than vectors. Currently the only use case is
%                      the 'pos' attribute for MEEG source space data.
%
% Returns:
%   arr                S_1 x ... x S_K x Q array if (dim==1), or
%                      P x S_1 x ... x S_K array if (dim==2), where
%                      Q=prod(S_*) if dim==1 and P=prod(S_*) if dim==2
%   dim_labels         the value of .a.Xdim.labels
%   dim_values         the value of .a.Xdim.values
%
% Example:
%     % ds is an FMRI dataset with 6 samples, volumes are 3 x 2 x 5 voxels
%     ds=cosmo_synthetic_dataset('size','normal','type','fmri');
%     size(ds.samples)
%     %|| [ 6 30 ]
%     cosmo_disp(ds.a.fdim)
%     %|| .labels
%     %||   { 'i'  'j'  'k' }
%     %|| .values
%     %||   { [ 1 2 3 ]  [ 1 2 ]  [ 1 2 3 4 5 ] }
%     %
%     % flatten the dataset
%     [unfl,labels,values]=cosmo_unflatten(ds);
%     %
%     % the unflattened dataset is of size 6 x 3 x 2 x 5
%     size(unfl)
%     %|| [ 6 3 2 5 ]
%     cosmo_disp(labels)
%     %|| { 'i'  'j'  'k' }
%     cosmo_disp(values)
%     %|| { [ 1 2 3 ]  [ 1 2 ]  [ 1 2 3 4 5 ] }
%
%     % ds is a small dataset with 2 classes
%     ds=cosmo_synthetic_dataset();
%     %
%     % compute all (2x2) split-half correlation values
%     res=cosmo_correlation_measure(ds,'output','raw',...
%                                       'post_corr_func',[]);
%     cosmo_disp(res)
%     %|| .samples
%     %||   [  0.363
%     %||     -0.404
%     %||     -0.447
%     %||      0.606 ]
%     %|| .sa
%     %||   .half1
%     %||     [ 1
%     %||       2
%     %||       1
%     %||       2 ]
%     %||   .half2
%     %||     [ 1
%     %||       1
%     %||       2
%     %||       2 ]
%     %|| .a
%     %||   .sdim
%     %||     .labels
%     %||       { 'half1'  'half2' }
%     %||     .values
%     %||       { [ 1    [ 1
%     %||           2 ]    2 ] }
%     %
%     % reshape the correlations into a square matrix
%     [unfl,labels,values]=cosmo_unflatten(res,1);
%     %
%     % yields a 2x2x1 matrix (matlab omits the last, singleton dimension)
%     cosmo_disp(unfl)
%     %|| [  0.363    -0.447
%     %||   -0.404     0.606 ]
%     %
%     cosmo_disp(labels)
%     %|| { 'half1'  'half2' }
%     %
%     cosmo_disp(values)
%     %|| { [ 1    [ 1
%     %||     2 ]    2 ] }
%
%
% Notes:
%   - A typical use case is mapping an fMRI or MEEG dataset struct
%     back to a 3D or 4D array.
%   - This function is the inverse of cosmo_flatten.
%
% See also: cosmo_flatten, cosmo_map2fmri, cosmo_map2meeg
%
% #   For CoSMoMVPA's copyright information and license terms,   #
% #   see the COPYING file distributed with CoSMoMVPA.           #