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# blk_diag

## (Toolbox/blk_diag.m in BrainStorm 2.0 (Alpha))

### Function Synopsis

    bd = blk_diag(A,n);

### Help Text

    BLK_DIAG - Make or extract a sparse block diagonal matrix
     function bd = blk_diag(A,n);
     If A is not sparse, then
     returns a sparse block diagonal "bd", diagonalized from the
     elements in "A".
     "A" is ma x na, comprising bdn=(na/"n") blocks of submatrices.
     Each submatrix is ma x "n", and these submatrices are
     placed down the diagonal of the matrix.

     If A is already sparse, then the operation is reversed, yielding a block
     row matrix, where each set of n columns corresponds to a block element
     from the block diagonal.

     Routine uses NO for-loops for speed considerations.

### Cross-Reference Information

This function is called by

    cross_mat                          C:\BrainStorm_2001\Toolbox\cross_mat.m
    montreal_data_create               C:\BrainStorm_2001\Toolbox\montreal_data_create.m
    multidipole_simulation_eeg_data_create  C:\BrainStorm_2001\Toolbox\multidipole_simulation_eeg_data_create.m
    os_meg                             C:\BrainStorm_2001\Toolbox\os_meg.m
    simulator                          C:\BrainStorm_2001\Toolbox\simulator.m

### Listing of function C:\BrainStorm_2001\Toolbox\blk_diag.m

    function bd = blk_diag(A,n);
    %BLK_DIAG - Make or extract a sparse block diagonal matrix
    % function bd = blk_diag(A,n);
    % If A is not sparse, then
    % returns a sparse block diagonal "bd", diagonalized from the
    % elements in "A".
    % "A" is ma x na, comprising bdn=(na/"n") blocks of submatrices.
    % Each submatrix is ma x "n", and these submatrices are
    % placed down the diagonal of the matrix.
    %
    % If A is already sparse, then the operation is reversed, yielding a block
    % row matrix, where each set of n columns corresponds to a block element
    % from the block diagonal.
    %
    % Routine uses NO for-loops for speed considerations.

    %<autobegin> ---------------------- 14-Jun-2004 17:09:48 -----------------------
    % --------- Automatically Generated Comments Block Using AUTO_COMMENTS ---------
    %
    % CATEGORY: Utility - Numeric
    %
    % At Check-in: $Author: Mosher $  $Revision: 15 $  $Date: 6/14/04 3:37p $
    %
    % This software is part of BrainStorm Toolbox Version 2.0 (Alpha) 14-Jun-2004
    %
    % Principal Investigators and Developers:
    % ** Richard M. Leahy, PhD, Signal & Image Processing Institute,
    %    University of Southern California, Los Angeles, CA
    % ** John C. Mosher, PhD, Biophysics Group,
    %    Los Alamos National Laboratory, Los Alamos, NM
    % ** Sylvain Baillet, PhD, Cognitive Neuroscience & Brain Imaging Laboratory,
    %    CNRS, Hopital de la Salpetriere, Paris, France
    %
    % See BrainStorm website at http://neuroimage.usc.edu for further information.
    %
    % Copyright (c) 2004 BrainStorm by the University of Southern California
    % This software distributed  under the terms of the GNU General Public License
    % as published by the Free Software Foundation. Further details on the GPL
    % license can be found at http://www.gnu.org/copyleft/gpl.html .
    %
    % FOR RESEARCH PURPOSES ONLY. THE SOFTWARE IS PROVIDED "AS IS," AND THE
    % UNIVERSITY OF SOUTHERN CALIFORNIA AND ITS COLLABORATORS DO NOT MAKE ANY
    % WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF
    % MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, NOR DO THEY ASSUME ANY
    % LIABILITY OR RESPONSIBILITY FOR THE USE OF THIS SOFTWARE.
    %<autoend> ------------------------ 14-Jun-2004 17:09:48 -----------------------

    % ----------------------------- Script History ---------------------------------
    % July 29, 1993 Author
    % September 28, 1993 JCM Conversion to sparse
    % July 27, 1995 JCM inverse block diagonal added
    % JCM 19-May-2004  Comments cleaning
    % ----------------------------- Script History ---------------------------------

    if(~issparse(A)),        % then make block sparse
      [ma,na] = size(A);
      bdn = na/n;             % number of submatrices

      if(bdn - fix(bdn)),
        error('Width of matrix must be even multiple of n');
      end

    if(0)
      i = [1:ma]';
      i = i(:,ones(1,n));
      i = i(:);             % row indices first submatrix

      ml = length(i);         % ma*n

      % ndx = [0:(bdn-1)]*ma;     % row offsets per submatrix
      ndx = [0:ma:(ma*(bdn-1))];     % row offsets per submatrix

      i = i(:,ones(1,bdn)) + ndx(ones(ml,1),:);
    else
      tmp = reshape([1:(ma*bdn)]',ma,bdn);
      i = zeros(ma*n,bdn);
      for iblock = 1:n,
        i((iblock-1)*ma+[1:ma],:) = tmp;
      end
    end

      i = i(:);             % row indices foreach sparse bd

      j = [1:na];
      j = j(ones(ma,1),:);
      j = j(:);             % column indices foreach sparse bd

      bd = sparse(i,j,A(:));

    else                 % already is sparse, unblock it

      [mA,na] = size(A);        % matrix always has na columns
      % how many entries in the first column?
      bdn = na/n;            % number of blocks
      ma = mA/bdn;            % rows in first block

      % blocks may themselves contain zero entries.  Build indexing as above
    if(0)
      i = [1:ma]';
      i = i(:,ones(1,n));
      i = i(:);             % row indices first submatrix

      ml = length(i);         % ma*n

      % ndx = [0:(bdn-1)]*ma;     % row offsets per submatrix
      ndx = [0:ma:(ma*(bdn-1))];     % row offsets per submatrix

      i = i(:,ones(1,bdn)) + ndx(ones(ml,1),:);
    else
      tmp = reshape([1:(ma*bdn)]',ma,bdn);
      i = zeros(ma*n,bdn);
      for iblock = 1:n,
        i((iblock-1)*ma+[1:ma],:) = tmp;
      end
    end

      i = i(:);             % row indices foreach sparse bd

    if(0)
      j = [1:na];
      j = j(ones(ma,1),:);
      j = j(:);             % column indices foreach sparse bd

      % so now we have the complete two dimensional indexing. Convert to
      % one dimensional

      i = i + (j-1)*mA;
    else
      j = [0:mA:(mA*(na-1))];
      j = j(ones(ma,1),:);
      j = j(:);

      i = i + j;
    end

      bd = full(A(i));     % column vector
      bd = reshape(bd,ma,na);    % full matrix
    end

    return

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