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|---|---|---|---|---|---|---|---|---|
github
|
philippboehmsturm/antx-master
|
xml2struct.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/xml2struct.m
| 8,612 |
utf_8
|
3d883353ccb551f2dcd554835610a565
|
function [ s ] = xml2struct( file )
%Convert xml file into a MATLAB structure
% [ s ] = xml2struct( file )
%
% A file containing:
% <XMLname attrib1="Some value">
% <Element>Some text</Element>
% <DifferentElement attrib2="2">Some more text</DifferentElement>
% <DifferentElement attrib3="2" attrib4="1">Even more text</DifferentElement>
% </XMLname>
%
% Used to produce:
% s.XMLname.Attributes.attrib1 = "Some value";
% s.XMLname.Element.Text = "Some text";
% s.XMLname.DifferentElement{1}.Attributes.attrib2 = "2";
% s.XMLname.DifferentElement{1}.Text = "Some more text";
% s.XMLname.DifferentElement{2}.Attributes.attrib3 = "2";
% s.XMLname.DifferentElement{2}.Attributes.attrib4 = "1";
% s.XMLname.DifferentElement{2}.Text = "Even more text";
%
% Will produce (gp: to matche the output of xml2struct in XML4MAT, but note that Element(2) is empty):
% Element: Some text
% DifferentElement:
% attrib2: 2
% DifferentElement: Some more text
% attrib1: Some value
%
% Element:
% DifferentElement:
% attrib3: 2
% attrib4: 1
% DifferentElement: Even more text
% attrib1:
%
% Note the characters : - and . are not supported in structure fieldnames and
% are replaced by _
%
% Written by W. Falkena, ASTI, TUDelft, 21-08-2010
% Attribute parsing speed increased by 40% by A. Wanner, 14-6-2011
% 2011/12/14 giopia: changes in the main function to make more similar to xml2struct of the XML4MAT toolbox, bc it's used by fieldtrip
% 2012/04/04 roboos: added the original license clause, see also http://bugzilla.fcdonders.nl/show_bug.cgi?id=645#c11
% 2012/04/04 roboos: don't print the filename that is being read
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Copyright (c) 2010, Wouter Falkena
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if (nargin < 1)
clc;
help xml2struct
return
end
%check for existance
if (exist(file,'file') == 0)
%Perhaps the xml extension was omitted from the file name. Add the
%extension and try again.
if (isempty(strfind(file,'.xml')))
file = [file '.xml'];
end
if (exist(file,'file') == 0)
error(['The file ' file ' could not be found']);
end
end
%fprintf('xml2struct reading %s\n', file); % gp 11/12/15
%read the xml file
xDoc = xmlread(file);
%parse xDoc into a MATLAB structure
s = parseChildNodes(xDoc);
fn = fieldnames(s); s = s.(fn{1}); % gp 11/12/15: output is compatible with xml2struct of xml2mat
end
% ----- Subfunction parseChildNodes -----
function [children,ptext] = parseChildNodes(theNode)
% Recurse over node children.
children = struct;
ptext = [];
if theNode.hasChildNodes
childNodes = theNode.getChildNodes;
numChildNodes = childNodes.getLength;
for count = 1:numChildNodes
theChild = childNodes.item(count-1);
[text,name,attr,childs] = getNodeData(theChild);
if (~strcmp(name,'#text') && ~strcmp(name,'#comment'))
%XML allows the same elements to be defined multiple times,
%put each in a different cell
if (isfield(children,name))
% if 0 % numel(children) > 1 % gp 11/12/15: (~iscell(children.(name)))
% %put existsing element into cell format
% children.(name) = {children.(name)};
% end
index = length(children)+1; % gp 11/12/15: index = length(children.(name))+1;
else
index = 1; % gp 11/12/15: new field
end
%add new element
children(index).(name) = childs;
if isempty(attr)
if(~isempty(text))
children(index).(name) = text;
end
else
fn = fieldnames(attr);
for f = 1:numel(fn)
children(index).(name)(1).(fn{f}) = attr.(fn{f}); % gp 11/12/15: children.(name){index}.('Attributes') = attr;
end
if(~isempty(text))
children(index).(name).(name) = text; % gp 11/12/15: children.(name){index}.('Text') = text;
end
end
% else % gp 11/12/15: cleaner code, don't reuse the same code
% %add previously unknown new element to the structure
% children.(name) = childs;
% if(~isempty(text))
% children.(name) = text; % gp 11/12/15: children.(name).('Text') = text;
% end
% if(~isempty(attr))
% children.('Attributes') = attr; % gp 11/12/15 children.(name).('Attributes') = attr;
% end
% end
elseif (strcmp(name,'#text'))
%this is the text in an element (i.e. the parentNode)
if (~isempty(regexprep(text,'[\s]*','')))
if (isempty(ptext))
ptext = text;
else
%what to do when element data is as follows:
%<element>Text <!--Comment--> More text</element>
%put the text in different cells:
% if (~iscell(ptext)) ptext = {ptext}; end
% ptext{length(ptext)+1} = text;
%just append the text
ptext = [ptext text];
end
end
end
end
end
end
% ----- Subfunction getNodeData -----
function [text,name,attr,childs] = getNodeData(theNode)
% Create structure of node info.
%make sure name is allowed as structure name
name = regexprep(char(theNode.getNodeName),'[-:.]','_');
attr = parseAttributes(theNode);
if (isempty(fieldnames(attr)))
attr = [];
end
%parse child nodes
[childs,text] = parseChildNodes(theNode);
if (isempty(fieldnames(childs)))
%get the data of any childless nodes
try
%faster then if any(strcmp(methods(theNode), 'getData'))
text = char(theNode.getData);
catch
%no data
end
end
end
% ----- Subfunction parseAttributes -----
function attributes = parseAttributes(theNode)
% Create attributes structure.
attributes = struct;
if theNode.hasAttributes
theAttributes = theNode.getAttributes;
numAttributes = theAttributes.getLength;
for count = 1:numAttributes
%attrib = theAttributes.item(count-1);
%attr_name = regexprep(char(attrib.getName),'[-:.]','_');
%attributes.(attr_name) = char(attrib.getValue);
%Suggestion of Adrian Wanner
str = theAttributes.item(count-1).toString.toCharArray()';
k = strfind(str,'=');
attr_name = regexprep(str(1:(k(1)-1)),'[-:.]','_');
attributes.(attr_name) = str((k(1)+2):(end-1));
end
end
end
|
github
|
philippboehmsturm/antx-master
|
nanstd.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/nanstd.m
| 231 |
utf_8
|
0ff62b1c345b5ad76a9af59cf07c2983
|
% NANSTD provides a replacement for MATLAB's nanstd that is almost
% compatible.
%
% For usage see STD. Note that the three-argument call with FLAG is not
% supported.
function Y = nanstd(varargin)
Y = sqrt(nanvar(varargin{:}));
|
github
|
philippboehmsturm/antx-master
|
read_eeglabdata.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_eeglabdata.m
| 3,193 |
utf_8
|
33c4ab48f49c3929347fee8295589fbc
|
% read_eeglabdata() - import EEGLAB dataset files
%
% Usage:
% >> dat = read_eeglabdata(filename);
%
% Inputs:
% filename - [string] file name
%
% Optional inputs:
% 'begtrial' - [integer] first trial to read
% 'endtrial' - [integer] last trial to read
% 'chanindx' - [integer] list with channel indices to read
% 'header' - FILEIO structure header
%
% Outputs:
% dat - data over the specified range
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, 2008-
%123456789012345678901234567890123456789012345678901234567890123456789012
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD, [email protected]
%
% 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 2 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, write to the Free Software
% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
function dat = read_eeglabdata(filename, varargin);
if nargin < 1
help read_eeglabdata;
return;
end;
header = ft_getopt(varargin, 'header');
begsample = ft_getopt(varargin, 'begsample');
endsample = ft_getopt(varargin, 'endsample');
begtrial = ft_getopt(varargin, 'begtrial');
endtrial = ft_getopt(varargin, 'endtrial');
chanindx = ft_getopt(varargin, 'chanindx');
if isempty(header)
header = read_eeglabheader(filename);
end
if ischar(header.orig.data)
if strcmpi(header.orig.data(end-2:end), 'set'),
header.ori = load('-mat', filename);
else
% assuming that the data file is in the current directory
fid = fopen(header.orig.data);
% assuming the .dat and .set files are located in the same directory
if fid == -1
pathstr = fileparts(filename);
fid = fopen(fullfile(pathstr, header.orig.data));
end
if fid == -1
fid = fopen(fullfile(header.orig.filepath, header.orig.data)); %
end
if fid == -1, error(['Cannot not find data file: ' header.orig.data]); end;
% only read the desired trials
if strcmpi(header.orig.data(end-2:end), 'dat')
dat = fread(fid,[header.nSamples*header.nTrials header.nChans],'float32')';
else
dat = fread(fid,[header.nChans header.nSamples*header.nTrials],'float32');
end;
dat = reshape(dat, header.nChans, header.nSamples, header.nTrials);
fclose(fid);
end;
else
dat = header.orig.data;
dat = reshape(dat, header.nChans, header.nSamples, header.nTrials);
end;
if isempty(begtrial), begtrial = 1; end;
if isempty(endtrial), endtrial = header.nTrials; end;
if isempty(begsample), begsample = 1; end;
if isempty(endsample), endsample = header.nSamples; end;
dat = dat(:,begsample:endsample,begtrial:endtrial);
if ~isempty(chanindx)
% select the desired channels
dat = dat(chanindx,:,:);
end
|
github
|
philippboehmsturm/antx-master
|
readbdf.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/readbdf.m
| 3,632 |
utf_8
|
9c94d90dc0c8728b0b46e5276747e847
|
% readbdf() - Loads selected Records of an EDF or BDF File (European Data Format
% for Biosignals) into MATLAB
% Usage:
% >> [DAT,signal] = readedf(EDF_Struct,Records,Mode);
% Notes:
% Records - List of Records for Loading
% Mode - 0 Default
% 1 No AutoCalib
% 2 Concatenated (channels with lower sampling rate
% if more than 1 record is loaded)
% Output:
% DAT - EDF data structure
% signal - output signal
%
% Author: Alois Schloegl, 03.02.1998, updated T.S. Lorig Sept 6, 2002 for BDF read
%
% See also: openbdf(), sdfopen(), sdfread(), eeglab()
% Version 2.11
% 03.02.1998
% Copyright (c) 1997,98 by Alois Schloegl
% [email protected]
% 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 2
% 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, write to the Free Software
% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This program has been modified from the original version for .EDF files
% The modifications are to the number of bytes read on line 53 (from 2 to
% 3) and to the type of data read - line 54 (from int16 to bit24). Finally the name
% was changed from readedf to readbdf
% T.S. Lorig Sept 6, 2002
%
% Header modified for eeglab() compatibility - Arnaud Delorme 12/02
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [DAT,S]=readbdf(DAT,Records,Mode)
if nargin<3 Mode=0; end;
EDF=DAT.Head;
RecLen=max(EDF.SPR);
S=nan(RecLen,EDF.NS);
DAT.Record=zeros(length(Records)*RecLen,EDF.NS);
DAT.Valid=uint8(zeros(1,length(Records)*RecLen));
DAT.Idx=Records(:)';
for nrec=1:length(Records),
NREC=(DAT.Idx(nrec)-1);
if NREC<0 fprintf(2,'Warning READEDF: invalid Record Number %i \n',NREC);end;
fseek(EDF.FILE.FID,(EDF.HeadLen+NREC*EDF.AS.spb*3),'bof');
[s, count]=fread(EDF.FILE.FID,EDF.AS.spb,'bit24');
try,
S(EDF.AS.IDX2)=s;
catch,
error('File is incomplete (try reading begining of file)');
end;
%%%%% Test on Over- (Under-) Flow
% V=sum([(S'==EDF.DigMax(:,ones(RecLen,1))) + (S'==EDF.DigMin(:,ones(RecLen,1)))])==0;
V=sum([(S(:,EDF.Chan_Select)'>=EDF.DigMax(EDF.Chan_Select,ones(RecLen,1))) + ...
(S(:,EDF.Chan_Select)'<=EDF.DigMin(EDF.Chan_Select,ones(RecLen,1)))])==0;
EDF.ERROR.DigMinMax_Warning(find(sum([(S'>EDF.DigMax(:,ones(RecLen,1))) + (S'<EDF.DigMin(:,ones(RecLen,1)))]')>0))=1;
% invalid=[invalid; find(V==0)+l*k];
if floor(Mode/2)==1
for k=1:EDF.NS,
DAT.Record(nrec*EDF.SPR(k)+(1-EDF.SPR(k):0),k)=S(1:EDF.SPR(k),k);
end;
else
DAT.Record(nrec*RecLen+(1-RecLen:0),:)=S;
end;
DAT.Valid(nrec*RecLen+(1-RecLen:0))=V;
end;
if rem(Mode,2)==0 % Autocalib
DAT.Record=[ones(RecLen*length(Records),1) DAT.Record]*EDF.Calib;
end;
DAT.Record=DAT.Record';
return;
|
github
|
philippboehmsturm/antx-master
|
warning_once.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/warning_once.m
| 3,832 |
utf_8
|
07dc728273934663973f4c716e7a3a1c
|
function [ws warned] = warning_once(varargin)
%
% Use as one of the following
% warning_once(string)
% warning_once(string, timeout)
% warning_once(id, string)
% warning_once(id, string, timeout)
% where timeout should be inf if you don't want to see the warning ever
% again. The default timeout value is 60 seconds.
%
% It can be used instead of the MATLAB built-in function WARNING, thus as
% s = warning_once(...)
% or as
% warning_once(s)
% where s is a structure with fields 'identifier' and 'state', storing the
% state information. In other words, warning_once accepts as an input the
% same structure it returns as an output. This returns or restores the
% states of warnings to their previous values.
%
% It can also be used as
% [s w] = warning_once(...)
% where w is a boolean that indicates whether a warning as been thrown or not.
%
% Please note that you can NOT use it like this
% warning_once('the value is %d', 10)
% instead you should do
% warning_once(sprintf('the value is %d', 10))
% Copyright (C) 2012, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: warning_once.m 7123 2012-12-06 21:21:38Z roboos $
persistent stopwatch previous
if nargin < 1
error('You need to specify at least a warning message');
end
warned = false;
if isstruct(varargin{1})
warning(varargin{1});
return;
end
% put the arguments we will pass to warning() in this cell array
warningArgs = {};
if nargin==3
% calling syntax (id, msg, timeout)
warningArgs = varargin(1:2);
timeout = varargin{3};
fname = [warningArgs{1} '_' warningArgs{2}];
elseif nargin==2 && isnumeric(varargin{2})
% calling syntax (msg, timeout)
warningArgs = varargin(1);
timeout = varargin{2};
fname = warningArgs{1};
elseif nargin==2 && ~isnumeric(varargin{2})
% calling syntax (id, msg)
warningArgs = varargin(1:2);
timeout = 60;
fname = [warningArgs{1} '_' warningArgs{2}];
elseif nargin==1
% calling syntax (msg)
warningArgs = varargin(1);
timeout = 60; % default timeout in seconds
fname = [warningArgs{1}];
end
if isempty(timeout)
error('Timeout ill-specified');
end
if isempty(stopwatch)
stopwatch = tic;
end
if isempty(previous)
previous = struct;
end
now = toc(stopwatch); % measure time since first function call
fname = decomma(fixname(fname)); % make a nice string that is allowed as structure fieldname
if length(fname) > 63 % MATLAB max name
fname = fname(1:63);
end
if ~isfield(previous, fname) || ...
(isfield(previous, fname) && now>previous.(fname).timeout)
% warning never given before or timed out
ws = warning(warningArgs{:});
previous.(fname).timeout = now+timeout;
previous.(fname).ws = ws;
warned = true;
else
% the warning has been issued before, but has not timed out yet
ws = previous.(fname).ws;
end
end % function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function name = decomma(name)
name(name==',')=[];
end % function
|
github
|
philippboehmsturm/antx-master
|
ft_hastoolbox.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/ft_hastoolbox.m
| 21,701 |
utf_8
|
7141791b922e3b46334b4d5888532adf
|
function [status] = ft_hastoolbox(toolbox, autoadd, silent)
% FT_HASTOOLBOX tests whether an external toolbox is installed. Optionally
% it will try to determine the path to the toolbox and install it
% automatically.
%
% Use as
% [status] = ft_hastoolbox(toolbox, autoadd, silent)
%
% autoadd = 0 means that it will not be added
% autoadd = 1 means that give an error if it cannot be added
% autoadd = 2 means that give a warning if it cannot be added
% autoadd = 3 means that it remains silent if it cannot be added
%
% silent = 0 means that it will give some feedback about adding the toolbox
% silent = 1 means that it will not give feedback
% Copyright (C) 2005-2012, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_hastoolbox.m 7172 2012-12-13 11:50:49Z roboos $
% this function is called many times in FieldTrip and associated toolboxes
% use efficient handling if the same toolbox has been investigated before
% persistent previous previouspath
%
% if ~isequal(previouspath, path)
% previous = [];
% end
%
% if isempty(previous)
% previous = struct;
% elseif isfield(previous, fixname(toolbox))
% status = previous.(fixname(toolbox));
% return
% end
% this points the user to the website where he/she can download the toolbox
url = {
'AFNI' 'see http://afni.nimh.nih.gov'
'DSS' 'see http://www.cis.hut.fi/projects/dss'
'EEGLAB' 'see http://www.sccn.ucsd.edu/eeglab'
'NWAY' 'see http://www.models.kvl.dk/source/nwaytoolbox'
'SPM99' 'see http://www.fil.ion.ucl.ac.uk/spm'
'SPM2' 'see http://www.fil.ion.ucl.ac.uk/spm'
'SPM5' 'see http://www.fil.ion.ucl.ac.uk/spm'
'SPM8' 'see http://www.fil.ion.ucl.ac.uk/spm'
'MEG-PD' 'see http://www.kolumbus.fi/kuutela/programs/meg-pd'
'MEG-CALC' 'this is a commercial toolbox from Neuromag, see http://www.neuromag.com'
'BIOSIG' 'see http://biosig.sourceforge.net'
'EEG' 'see http://eeg.sourceforge.net'
'EEGSF' 'see http://eeg.sourceforge.net' % alternative name
'MRI' 'see http://eeg.sourceforge.net' % alternative name
'NEUROSHARE' 'see http://www.neuroshare.org'
'BESA' 'see http://www.megis.de, or contact Karsten Hoechstetter'
'EEPROBE' 'see http://www.ant-neuro.com, or contact Maarten van der Velde'
'YOKOGAWA' 'this is deprecated, please use YOKOGAWA_MEG_READER instead'
'YOKOGAWA_MEG_READER' 'see http://www.yokogawa.com/me/me-login-en.htm'
'BEOWULF' 'see http://robertoostenveld.nl, or contact Robert Oostenveld'
'MENTAT' 'see http://robertoostenveld.nl, or contact Robert Oostenveld'
'SON2' 'see http://www.kcl.ac.uk/depsta/biomedical/cfnr/lidierth.html, or contact Malcolm Lidierth'
'4D-VERSION' 'contact Christian Wienbruch'
'SIGNAL' 'see http://www.mathworks.com/products/signal'
'OPTIM' 'see http://www.mathworks.com/products/optim'
'IMAGE' 'see http://www.mathworks.com/products/image' % Mathworks refers to this as IMAGES
'SPLINES' 'see http://www.mathworks.com/products/splines'
'DISTCOMP' 'see http://www.mathworks.nl/products/parallel-computing/'
'COMPILER' 'see http://www.mathworks.com/products/compiler'
'FASTICA' 'see http://www.cis.hut.fi/projects/ica/fastica'
'BRAINSTORM' 'see http://neuroimage.ucs.edu/brainstorm'
'FILEIO' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'PREPROC' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'FORWARD' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'INVERSE' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'SPECEST' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'REALTIME' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'PLOTTING' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'SPIKE' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'CONNECTIVITY' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'PEER' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'PLOTTING' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'DENOISE' 'see http://lumiere.ens.fr/Audition/adc/meg, or contact Alain de Cheveigne'
'BCI2000' 'see http://bci2000.org'
'NLXNETCOM' 'see http://www.neuralynx.com'
'DIPOLI' 'see ftp://ftp.fcdonders.nl/pub/fieldtrip/external'
'MNE' 'see http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php'
'TCP_UDP_IP' 'see http://www.mathworks.com/matlabcentral/fileexchange/345, or contact Peter Rydesaeter'
'BEMCP' 'contact Christophe Phillips'
'OPENMEEG' 'see http://gforge.inria.fr/projects/openmeeg and http://gforge.inria.fr/frs/?group_id=435'
'PRTOOLS' 'see http://www.prtools.org'
'ITAB' 'contact Stefania Della Penna'
'BSMART' 'see http://www.brain-smart.org'
'PEER' 'see http://fieldtrip.fcdonders.nl/development/peer'
'FREESURFER' 'see http://surfer.nmr.mgh.harvard.edu/fswiki'
'SIMBIO' 'see https://www.mrt.uni-jena.de/simbio/index.php/Main_Page'
'VGRID' 'see http://www.rheinahrcampus.de/~medsim/vgrid/manual.html'
'FNS' 'see http://hhvn.nmsu.edu/wiki/index.php/FNS'
'GIFTI' 'see http://www.artefact.tk/software/matlab/gifti'
'XML4MAT' 'see http://www.mathworks.com/matlabcentral/fileexchange/6268-xml4mat-v2-0'
'SQDPROJECT' 'see http://www.isr.umd.edu/Labs/CSSL/simonlab'
'BCT' 'see http://www.brain-connectivity-toolbox.net/'
'CCA' 'see http://www.imt.liu.se/~magnus/cca or contact Magnus Borga'
'EGI_MFF' 'see http://www.egi.com/ or contact either Phan Luu or Colin Davey at EGI'
'TOOLBOX_GRAPH' 'see http://www.mathworks.com/matlabcentral/fileexchange/5355-toolbox-graph or contact Gabriel Peyre'
'NETCDF' 'see http://www.mathworks.com/matlabcentral/fileexchange/15177'
'MYSQL' 'see http://www.mathworks.com/matlabcentral/fileexchange/8663-mysql-database-connector'
'ISO2MESH' 'see http://iso2mesh.sourceforge.net/cgi-bin/index.cgi?Home or contact Qianqian Fang'
'DATAHASH' 'see http://www.mathworks.com/matlabcentral/fileexchange/31272'
'IBTB' 'see http://www.ibtb.org'
'ICASSO' 'see http://www.cis.hut.fi/projects/ica/icasso'
'XUNIT' 'see http://www.mathworks.com/matlabcentral/fileexchange/22846-matlab-xunit-test-framework'
'PLEXON' 'available from http://www.plexon.com/assets/downloads/sdk/ReadingPLXandDDTfilesinMatlab-mexw.zip'
};
if nargin<2
% default is not to add the path automatically
autoadd = 0;
end
if nargin<3
% default is not to be silent
silent = 0;
end
% determine whether the toolbox is installed
toolbox = upper(toolbox);
% set fieldtrip trunk path, used for determining ft-subdirs are on path
fttrunkpath = unixpath(fileparts(which('ft_defaults')));
switch toolbox
case 'AFNI'
status = (exist('BrikLoad') && exist('BrikInfo'));
case 'DSS'
status = exist('denss', 'file') && exist('dss_create_state', 'file');
case 'EEGLAB'
status = exist('runica', 'file');
case 'NWAY'
status = exist('parafac', 'file');
case 'SPM'
status = exist('spm.m'); % any version of SPM is fine
case 'SPM99'
status = exist('spm.m') && strcmp(spm('ver'),'SPM99');
case 'SPM2'
status = exist('spm.m') && strcmp(spm('ver'),'SPM2');
case 'SPM5'
status = exist('spm.m') && strcmp(spm('ver'),'SPM5');
case 'SPM8'
status = exist('spm.m') && strncmp(spm('ver'),'SPM8', 4);
case 'SPM12'
status = exist('spm.m') && strncmp(spm('ver'),'SPM12', 5);
case 'MEG-PD'
status = (exist('rawdata') && exist('channames'));
case 'MEG-CALC'
status = (exist('megmodel') && exist('megfield') && exist('megtrans'));
case 'BIOSIG'
status = (exist('sopen') && exist('sread'));
case 'EEG'
status = (exist('ctf_read_res4') && exist('ctf_read_meg4'));
case 'EEGSF' % alternative name
status = (exist('ctf_read_res4') && exist('ctf_read_meg4'));
case 'MRI' % other functions in the mri section
status = (exist('avw_hdr_read') && exist('avw_img_read'));
case 'NEUROSHARE'
status = (exist('ns_OpenFile') && exist('ns_SetLibrary') && exist('ns_GetAnalogData'));
case 'BESA'
status = (exist('readBESAtfc') && exist('readBESAswf'));
case 'EEPROBE'
status = (exist('read_eep_avr') && exist('read_eep_cnt'));
case 'YOKOGAWA'
status = hasyokogawa('16bitBeta6');
case 'YOKOGAWA12BITBETA3'
status = hasyokogawa('12bitBeta3');
case 'YOKOGAWA16BITBETA3'
status = hasyokogawa('16bitBeta3');
case 'YOKOGAWA16BITBETA6'
status = hasyokogawa('16bitBeta6');
case 'YOKOGAWA_MEG_READER'
status = hasyokogawa('1.4');
case 'BEOWULF'
status = (exist('evalwulf') && exist('evalwulf') && exist('evalwulf'));
case 'MENTAT'
status = (exist('pcompile') && exist('pfor') && exist('peval'));
case 'SON2'
status = (exist('SONFileHeader') && exist('SONChanList') && exist('SONGetChannel'));
case '4D-VERSION'
status = (exist('read4d') && exist('read4dhdr'));
case {'STATS', 'STATISTICS'}
status = license('checkout', 'statistics_toolbox'); % also check the availability of a toolbox license
case {'OPTIM', 'OPTIMIZATION'}
status = license('checkout', 'optimization_toolbox'); % also check the availability of a toolbox license
case {'SPLINES', 'CURVE_FITTING'}
status = license('checkout', 'curve_fitting_toolbox'); % also check the availability of a toolbox license
case 'SIGNAL'
status = license('checkout', 'signal_toolbox'); % also check the availability of a toolbox license
case 'IMAGE'
status = license('checkout', 'image_toolbox'); % also check the availability of a toolbox license
case {'DCT', 'DISTCOMP'}
status = license('checkout', 'distrib_computing_toolbox'); % also check the availability of a toolbox license
case 'COMPILER'
status = license('checkout', 'compiler'); % also check the availability of a toolbox license
case 'FASTICA'
status = exist('fpica', 'file');
case 'BRAINSTORM'
status = exist('bem_xfer');
case 'DENOISE'
status = (exist('tsr', 'file') && exist('sns', 'file'));
case 'CTF'
status = (exist('getCTFBalanceCoefs') && exist('getCTFdata'));
case 'BCI2000'
status = exist('load_bcidat');
case 'NLXNETCOM'
status = (exist('MatlabNetComClient', 'file') && exist('NlxConnectToServer', 'file') && exist('NlxGetNewCSCData', 'file'));
case 'DIPOLI'
status = exist('dipoli.maci', 'file');
case 'MNE'
status = (exist('fiff_read_meas_info', 'file') && exist('fiff_setup_read_raw', 'file'));
case 'TCP_UDP_IP'
status = (exist('pnet', 'file') && exist('pnet_getvar', 'file') && exist('pnet_putvar', 'file'));
case 'BEMCP'
status = (exist('bem_Cij_cog', 'file') && exist('bem_Cij_lin', 'file') && exist('bem_Cij_cst', 'file'));
case 'OPENMEEG'
status = exist('om_save_tri.m', 'file');
case 'PRTOOLS'
status = (exist('prversion', 'file') && exist('dataset', 'file') && exist('svc', 'file'));
case 'ITAB'
status = (exist('lcReadHeader', 'file') && exist('lcReadData', 'file'));
case 'BSMART'
status = exist('bsmart');
case 'FREESURFER'
status = exist('MRIread', 'file') && exist('vox2ras_0to1', 'file');
case 'FNS'
status = exist('elecsfwd', 'file');
case 'SIMBIO'
status = exist('calc_stiff_matrix_val', 'file') && exist('sb_transfer', 'file');
case 'VGRID'
status = exist('vgrid.m', 'file');
case 'GIFTI'
status = exist('gifti', 'file');
case 'XML4MAT'
status = exist('xml2struct.m', 'file') && exist('xml2whos.m', 'file');
case 'SQDPROJECT'
status = exist('sqdread.m', 'file') && exist('sqdwrite.m', 'file');
case 'BCT'
status = exist('macaque71.mat', 'file') && exist('motif4funct_wei.m', 'file');
case 'CCA'
status = exist('ccabss.m', 'file');
case 'EGI_MFF'
status = exist('mff_getObject.m', 'file') && exist('mff_getSummaryInfo.m', 'file');
case 'TOOLBOX_GRAPH'
status = exist('toolbox_graph');
case 'NETCDF'
status = exist('netcdf');
case 'MYSQL'
status = exist(['mysql.' mexext], 'file'); % this only consists of a single mex file
case 'ISO2MESH'
status = exist('vol2surf.m', 'file') && exist('qmeshcut.m', 'file');
case 'QSUB'
status = exist('qsubfeval.m', 'file') && exist('qsubcellfun.m', 'file');
case 'ENGINE'
status = exist('enginefeval.m', 'file') && exist('enginecellfun.m', 'file');
case 'DATAHASH'
status = exist('DataHash.m', 'file');
case 'IBTB'
status = exist('make_ibtb.m', 'file') && exist('binr.m', 'file');
case 'ICASSO'
status = exist('icassoEst.m', 'file');
case 'XUNIT'
status = exist('initTestSuite.m', 'file') && exist('runtests.m', 'file');
case 'PLEXON'
status = exist('plx_adchan_gains.m', 'file') && exist('mexPlex');
% the following are fieldtrip modules/toolboxes
case 'FILEIO'
status = (exist('ft_read_header', 'file') && exist('ft_read_data', 'file') && exist('ft_read_event', 'file') && exist('ft_read_sens', 'file'));
case 'FORWARD'
status = (exist('ft_compute_leadfield', 'file') && exist('ft_prepare_vol_sens', 'file'));
case 'PLOTTING'
status = (exist('ft_plot_topo', 'file') && exist('ft_plot_mesh', 'file') && exist('ft_plot_matrix', 'file'));
case 'PEER'
status = exist('peerslave', 'file') && exist('peermaster', 'file');
case 'CONNECTIVITY'
status = exist('ft_connectivity_corr', 'file') && exist('ft_connectivity_granger', 'file');
case 'SPIKE'
status = exist('ft_spiketriggeredaverage.m', 'file') && exist('ft_spiketriggeredspectrum.m', 'file');
% these were missing, added them using the below style, see bug 1804 - roevdmei
case 'INVERSE'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/inverse'], 'once')); % INVERSE is not added above, consider doing it there -roevdmei
case 'REALTIME'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/realtime'], 'once')); % REALTIME is not added above, consider doing it there -roevdmei
case 'SPECEST'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/specest'], 'once')); % SPECEST is not added above, consider doing it there -roevdmei
case 'PREPROC'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/preproc'], 'once')); % PREPROC is not added above, consider doing it there -roevdmei
% the following are not proper toolboxes, but only subdirectories in the fieldtrip toolbox
% these are added in ft_defaults and are specified with unix-style forward slashes
case 'COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/compat'], 'once'));
case 'STATFUN'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/statfun'], 'once'));
case 'TRIALFUN'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/trialfun'], 'once'));
case 'UTILITIES/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/utilities/compat'], 'once'));
case 'FILEIO/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/fileio/compat'], 'once'));
case 'PREPROC/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/preproc/compat'], 'once'));
case 'FORWARD/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/forward/compat'], 'once'));
case 'PLOTTING/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/plotting/compat'], 'once'));
case 'TEMPLATE/LAYOUT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/layout'], 'once'));
case 'TEMPLATE/ANATOMY'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/anatomy'], 'once'));
case 'TEMPLATE/HEADMODEL'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/headmodel'], 'once'));
case 'TEMPLATE/ELECTRODE'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/electrode'], 'once'));
case 'TEMPLATE/NEIGHBOURS'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/neighbours'], 'once'));
case 'TEMPLATE/SOURCEMODEL'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/sourcemodel'], 'once'));
otherwise
if ~silent, warning('cannot determine whether the %s toolbox is present', toolbox); end
status = 0;
end
% it should be a boolean value
status = (status~=0);
% try to determine the path of the requested toolbox
if autoadd>0 && ~status
% for core fieldtrip modules
prefix = fileparts(which('ft_defaults'));
if ~status
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
end
% for external fieldtrip modules
prefix = fullfile(fileparts(which('ft_defaults')), 'external');
if ~status
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
licensefile = [lower(toolbox) '_license'];
if status && exist(licensefile, 'file')
% this will execute openmeeg_license and mne_license
% which display the license on screen for three seconds
feval(licensefile);
end
end
% for contributed fieldtrip extensions
prefix = fullfile(fileparts(which('ft_defaults')), 'contrib');
if ~status
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
licensefile = [lower(toolbox) '_license'];
if status && exist(licensefile, 'file')
% this will execute openmeeg_license and mne_license
% which display the license on screen for three seconds
feval(licensefile);
end
end
% for linux computers in the Donders Centre for Cognitive Neuroimaging
prefix = '/home/common/matlab';
if ~status && isunix
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
end
% for windows computers in the Donders Centre for Cognitive Neuroimaging
prefix = 'h:\common\matlab';
if ~status && ispc
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
end
% use the matlab subdirectory in your homedirectory, this works on linux and mac
prefix = fullfile(getenv('HOME'), 'matlab');
if ~status && isdir(prefix)
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
end
if ~status
% the toolbox is not on the path and cannot be added
sel = find(strcmp(url(:,1), toolbox));
if ~isempty(sel)
msg = sprintf('the %s toolbox is not installed, %s', toolbox, url{sel, 2});
else
msg = sprintf('the %s toolbox is not installed', toolbox);
end
if autoadd==1
error(msg);
elseif autoadd==2
warning(msg);
else
% fail silently
end
end
end
% this function is called many times in FieldTrip and associated toolboxes
% use efficient handling if the same toolbox has been investigated before
if status
previous.(fixname(toolbox)) = status;
end
% remember the previous path, allows us to determine on the next call
% whether the path has been modified outise of this function
previouspath = path;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function status = myaddpath(toolbox, silent)
if isdeployed
warning('cannot change path settings for %s in a compiled application', toolbox);
elseif exist(toolbox, 'dir')
if ~silent,
ws = warning('backtrace', 'off');
warning('adding %s toolbox to your Matlab path', toolbox);
warning(ws); % return to the previous warning level
end
addpath(toolbox);
status = 1;
else
status = 0;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function path = unixpath(path)
path(path=='\') = '/'; % replace backward slashes with forward slashes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function status = hasfunction(funname, toolbox)
try
% call the function without any input arguments, which probably is inapropriate
feval(funname);
% it might be that the function without any input already works fine
status = true;
catch
% either the function returned an error, or the function is not available
% availability is influenced by the function being present and by having a
% license for the function, i.e. in a concurrent licensing setting it might
% be that all toolbox licenses are in use
m = lasterror;
if strcmp(m.identifier, 'MATLAB:license:checkouterror')
if nargin>1
warning('the %s toolbox is available, but you don''t have a license for it', toolbox);
else
warning('the function ''%s'' is available, but you don''t have a license for it', funname);
end
status = false;
elseif strcmp(m.identifier, 'MATLAB:UndefinedFunction')
status = false;
else
% the function seems to be available and it gave an unknown error,
% which is to be expected with inappropriate input arguments
status = true;
end
end
|
github
|
philippboehmsturm/antx-master
|
read_yokogawa_data_new.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_yokogawa_data_new.m
| 5,691 |
utf_8
|
0f379a92d07b29f6a231c357f4f02920
|
function [dat] = read_yokogawa_data_new(filename, hdr, begsample, endsample, chanindx)
% READ_YOKAGAWA_DATA_NEW reads continuous, epoched or averaged MEG data
% that has been generated by the Yokogawa MEG system and software
% and allows that data to be used in combination with FieldTrip.
%
% Use as
% [dat] = read_yokogawa_data_new(filename, hdr, begsample, endsample, chanindx)
%
% This is a wrapper function around the function
% getYkgwData
%
% See also READ_YOKOGAWA_HEADER_NEW, READ_YOKOGAWA_EVENT
% Copyright (C) 2005, Robert Oostenveld and 2010, Tilmann Sander-Thoemmes
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: read_yokogawa_data_new.m 7123 2012-12-06 21:21:38Z roboos $
if ~ft_hastoolbox('yokogawa_meg_reader')
error('cannot determine whether Yokogawa toolbox is present');
end
% hdr = read_yokogawa_header(filename);
hdr = hdr.orig; % use the original Yokogawa header, not the FieldTrip header
% default is to select all channels
if nargin<5
chanindx = 1:hdr.channel_count;
end
handles = definehandles;
switch hdr.acq_type
case handles.AcqTypeEvokedAve
% dat is returned as double
start_sample = begsample - 1; % samples start at 0
sample_length = endsample - begsample + 1;
epoch_count = 1;
start_epoch = 0;
dat = getYkgwData(filename, start_sample, sample_length);
case handles.AcqTypeContinuousRaw
% dat is returned as double
start_sample = begsample - 1; % samples start at 0
sample_length = endsample - begsample + 1;
epoch_count = 1;
start_epoch = 0;
dat = getYkgwData(filename, start_sample, sample_length);
case handles.AcqTypeEvokedRaw
% dat is returned as double
begtrial = ceil(begsample/hdr.sample_count);
endtrial = ceil(endsample/hdr.sample_count);
if begtrial<1
error('cannot read before the begin of the file');
elseif endtrial>hdr.actual_epoch_count
error('cannot read beyond the end of the file');
end
epoch_count = endtrial-begtrial+1;
start_epoch = begtrial-1;
% read all the neccessary trials that contain the desired samples
dat = getYkgwData(filename, start_epoch, epoch_count);
if size(dat,2)~=epoch_count*hdr.sample_count
error('could not read all epochs');
end
rawbegsample = begsample - (begtrial-1)*hdr.sample_count;
rawendsample = endsample - (begtrial-1)*hdr.sample_count;
sample_length = rawendsample - rawbegsample + 1;
% select the desired samples from the complete trials
dat = dat(:,rawbegsample:rawendsample);
otherwise
error('unknown data type');
end
if size(dat,1)~=hdr.channel_count
error('could not read all channels');
elseif size(dat,2)~=(endsample-begsample+1)
error('could not read all samples');
end
% select only the desired channels
dat = dat(chanindx,:);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% this defines some usefull constants
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function handles = definehandles
handles.output = [];
handles.sqd_load_flag = false;
handles.mri_load_flag = false;
handles.NullChannel = 0;
handles.MagnetoMeter = 1;
handles.AxialGradioMeter = 2;
handles.PlannerGradioMeter = 3;
handles.RefferenceChannelMark = hex2dec('0100');
handles.RefferenceMagnetoMeter = bitor( handles.RefferenceChannelMark, handles.MagnetoMeter );
handles.RefferenceAxialGradioMeter = bitor( handles.RefferenceChannelMark, handles.AxialGradioMeter );
handles.RefferencePlannerGradioMeter = bitor( handles.RefferenceChannelMark, handles.PlannerGradioMeter );
handles.TriggerChannel = -1;
handles.EegChannel = -2;
handles.EcgChannel = -3;
handles.EtcChannel = -4;
handles.NonMegChannelNameLength = 32;
handles.DefaultMagnetometerSize = (4.0/1000.0); % Square of 4.0mm in length
handles.DefaultAxialGradioMeterSize = (15.5/1000.0); % Circle of 15.5mm in diameter
handles.DefaultPlannerGradioMeterSize = (12.0/1000.0); % Square of 12.0mm in length
handles.AcqTypeContinuousRaw = 1;
handles.AcqTypeEvokedAve = 2;
handles.AcqTypeEvokedRaw = 3;
handles.sqd = [];
handles.sqd.selected_start = [];
handles.sqd.selected_end = [];
handles.sqd.axialgradiometer_ch_no = [];
handles.sqd.axialgradiometer_ch_info = [];
handles.sqd.axialgradiometer_data = [];
handles.sqd.plannergradiometer_ch_no = [];
handles.sqd.plannergradiometer_ch_info = [];
handles.sqd.plannergradiometer_data = [];
handles.sqd.eegchannel_ch_no = [];
handles.sqd.eegchannel_data = [];
handles.sqd.nullchannel_ch_no = [];
handles.sqd.nullchannel_data = [];
handles.sqd.selected_time = [];
handles.sqd.sample_rate = [];
handles.sqd.sample_count = [];
handles.sqd.pretrigger_length = [];
handles.sqd.matching_info = [];
handles.sqd.source_info = [];
handles.sqd.mri_info = [];
handles.mri = [];
|
github
|
philippboehmsturm/antx-master
|
read_plexon_nex.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_plexon_nex.m
| 7,633 |
utf_8
|
64b3b2124d2af848afb0d1746f422818
|
function [varargout] = read_plexon_nex(filename, varargin)
% READ_PLEXON_NEX reads header or data from a Plexon *.nex file, which
% is a file containing action-potential (spike) timestamps and waveforms
% (spike channels), event timestamps (event channels), and continuous
% variable data (continuous A/D channels).
%
% LFP and spike waveform data that is returned by this function is
% expressed in microVolt.
%
% Use as
% [hdr] = read_plexon_nex(filename)
% [dat] = read_plexon_nex(filename, ...)
% [dat1, dat2, dat3, hdr] = read_plexon_nex(filename, ...)
%
% Optional arguments should be specified in key-value pairs and can be
% header structure with header information
% feedback 0 or 1
% tsonly 0 or 1, read only the timestamps and not the waveforms
% channel number, or list of numbers (that will result in multiple outputs)
% begsample number (for continuous only)
% endsample number (for continuous only)
%
% See also READ_PLEXON_PLX, READ_PLEXON_DDT
% Copyright (C) 2007, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: read_plexon_nex.m 7123 2012-12-06 21:21:38Z roboos $
% parse the optional input arguments
hdr = ft_getopt(varargin, 'header');
channel = ft_getopt(varargin, 'channel');
feedback = ft_getopt(varargin, 'feedback', false);
tsonly = ft_getopt(varargin, 'tsonly', false);
begsample = ft_getopt(varargin, 'begsample', 1);
endsample = ft_getopt(varargin, 'endsample', inf);
% start with empty return values and empty data
varargout = {};
% read header info from file, use Matlabs for automatic byte-ordering
fid = fopen(filename, 'r', 'ieee-le');
fseek(fid, 0, 'eof');
siz = ftell(fid);
fseek(fid, 0, 'bof');
if isempty(hdr)
if feedback, fprintf('reading header from %s\n', filename); end
% a NEX file consists of a file header, followed by a number of variable headers
% sizeof(NexFileHeader) = 544
% sizeof(NexVarHeader) = 208
hdr.FileHeader = NexFileHeader(fid);
if hdr.FileHeader.NumVars<1
error('no channels present in file');
end
hdr.VarHeader = NexVarHeader(fid, hdr.FileHeader.NumVars);
end
for i=1:length(channel)
chan = channel(i);
vh = hdr.VarHeader(chan);
clear buf
fseek(fid, vh.DataOffset, 'bof');
switch vh.Type
case 0
% Neurons, only timestamps
buf.ts = fread(fid, [1 vh.Count], 'int32=>int32');
case 1
% Events, only timestamps
buf.ts = fread(fid, [1 vh.Count], 'int32=>int32');
case 2
% Interval variables
buf.begs = fread(fid, [1 vh.Count], 'int32=>int32');
buf.ends = fread(fid, [1 vh.Count], 'int32=>int32');
case 3
% Waveform variables
buf.ts = fread(fid, [1 vh.Count], 'int32=>int32');
if ~tsonly
buf.dat = fread(fid, [vh.NPointsWave vh.Count], 'int16');
% convert the AD values to miliVolt, subsequently convert from miliVolt to microVolt
buf.dat = buf.dat * (vh.ADtoMV * 1000);
end
case 4
% Population vector
error('population vectors are not supported');
case 5
% Continuously recorded variables
buf.ts = fread(fid, [1 vh.Count], 'int32=>int32');
buf.indx = fread(fid, [1 vh.Count], 'int32=>int32');
if vh.Count>1 && (begsample~=1 || endsample~=inf)
error('reading selected samples from multiple AD segments is not supported');
end
if ~tsonly
numsample = min(endsample - begsample + 1, vh.NPointsWave);
fseek(fid, (begsample-1)*2, 'cof');
buf.dat = fread(fid, [1 numsample], 'int16');
% convert the AD values to miliVolt, subsequently convert from miliVolt to microVolt
buf.dat = buf.dat * (vh.ADtoMV * 1000);
end
case 6
% Markers
ts = fread(fid, [1 vh.Count], 'int32=>int32');
for j=1:vh.NMarkers
buf.MarkerNames{j,1} = fread(fid, [1 64], 'uint8=>char');
for k=1:vh.Count
buf.MarkerValues{j,k} = fread(fid, [1 vh.MarkerLength], 'uint8=>char');
end
end
otherwise
error('incorrect channel type');
end % switch channel type
% return the data of this channel
varargout{i} = buf;
end % for channel
% always return the header as last
varargout{end+1} = hdr;
fclose(fid);
return
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function hdr = NexFileHeader(fid);
hdr.NexFileHeader = fread(fid,4,'uint8=>char')'; % string NEX1
hdr.Version = fread(fid,1,'int32');
hdr.Comment = fread(fid,256,'uint8=>char')';
hdr.Frequency = fread(fid,1,'double'); % timestamped freq. - tics per second
hdr.Beg = fread(fid,1,'int32'); % usually 0
hdr.End = fread(fid,1,'int32'); % maximum timestamp + 1
hdr.NumVars = fread(fid,1,'int32'); % number of variables in the first batch
hdr.NextFileHeader = fread(fid,1,'int32'); % position of the next file header in the file, not implemented yet
Padding = fread(fid,256,'uint8=>char')'; % future expansion
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function hdr = NexVarHeader(fid, numvar);
for varlop=1:numvar
hdr(varlop).Type = fread(fid,1,'int32'); % 0 - neuron, 1 event, 2- interval, 3 - waveform, 4 - pop. vector, 5 - continuously recorded
hdr(varlop).Version = fread(fid,1,'int32'); % 100
hdr(varlop).Name = fread(fid,64,'uint8=>char')'; % variable name
hdr(varlop).DataOffset = fread(fid,1,'int32'); % where the data array for this variable is located in the file
hdr(varlop).Count = fread(fid,1,'int32'); % number of events, intervals, waveforms or weights
hdr(varlop).WireNumber = fread(fid,1,'int32'); % neuron only, not used now
hdr(varlop).UnitNumber = fread(fid,1,'int32'); % neuron only, not used now
hdr(varlop).Gain = fread(fid,1,'int32'); % neuron only, not used now
hdr(varlop).Filter = fread(fid,1,'int32'); % neuron only, not used now
hdr(varlop).XPos = fread(fid,1,'double'); % neuron only, electrode position in (0,100) range, used in 3D
hdr(varlop).YPos = fread(fid,1,'double'); % neuron only, electrode position in (0,100) range, used in 3D
hdr(varlop).WFrequency = fread(fid,1,'double'); % waveform and continuous vars only, w/f sampling frequency
hdr(varlop).ADtoMV = fread(fid,1,'double'); % waveform continuous vars only, coeff. to convert from A/D values to Millivolts
hdr(varlop).NPointsWave = fread(fid,1,'int32'); % waveform only, number of points in each wave
hdr(varlop).NMarkers = fread(fid,1,'int32'); % how many values are associated with each marker
hdr(varlop).MarkerLength = fread(fid,1,'int32'); % how many characters are in each marker value
Padding = fread(fid,68,'uint8=>char')';
end
|
github
|
philippboehmsturm/antx-master
|
read_bti_m4d.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_bti_m4d.m
| 5,505 |
utf_8
|
1c319e059794d81ea2fe302a09a2ba73
|
function [msi] = read_bti_m4d(filename)
% READ_BTI_M4D
%
% Use as
% msi = read_bti_m4d(filename)
% Copyright (C) 2007, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: read_bti_m4d.m 7123 2012-12-06 21:21:38Z roboos $
[p, f, x] = fileparts(filename);
if ~strcmp(x, '.m4d')
% add the extension of the header
filename = [filename '.m4d'];
end
fid = fopen(filename, 'r');
if fid==-1
error(sprintf('could not open file %s', filename));
end
% start with an empty header structure
msi = struct;
% these header elements contain strings and should be converted in a cell-array
strlist = {
'MSI.ChannelOrder'
};
% these header elements contain numbers and should be converted in a numeric array
% 'MSI.ChannelScale'
% 'MSI.ChannelGain'
% 'MSI.FileType'
% 'MSI.TotalChannels'
% 'MSI.TotalEpochs'
% 'MSI.SamplePeriod'
% 'MSI.SampleFrequency'
% 'MSI.FirstLatency'
% 'MSI.SlicesPerEpoch'
% the conversion to numeric arrays is implemented in a general fashion
% and all the fields above are automatically converted
numlist = {};
line = '';
msi.grad.label = {};
msi.grad.coilpos = zeros(0,3);
msi.grad.coilori = zeros(0,3);
while ischar(line)
line = cleanline(fgetl(fid));
if isempty(line) || (length(line)==1 && all(line==-1))
continue
end
sep = strfind(line, ':');
if length(sep)==1
key = line(1:(sep-1));
val = line((sep+1):end);
elseif length(sep)>1
% assume that the first separator is the relevant one, and that the
% next ones are part of the value string (e.g. a channel with a ':' in
% its name
sep = sep(1);
key = line(1:(sep-1));
val = line((sep+1):end);
elseif length(sep)<1
% this is not what I would expect
error('unexpected content in m4d file');
end
if ~isempty(strfind(line, 'Begin'))
sep = strfind(key, '.');
sep = sep(end);
key = key(1:(sep-1));
% if the key ends with begin and there is no value, then there is a block
% of numbers following that relates to the magnetometer/gradiometer information.
% All lines in that Begin-End block should be treated seperately
val = {};
lab = {};
num = {};
ind = 0;
while isempty(strfind(line, 'End'))
line = cleanline(fgetl(fid));
if isempty(line) || (length(line)==1 && all(line==-1)) || ~isempty(strfind(line, 'End'))
continue
end
ind = ind+1;
% remember the line itself, and also cut it into pieces
val{ind} = line;
% the line is tab-separated and looks like this
% A68 0.0873437 -0.075789 0.0891512 0.471135 -0.815532 0.336098
sep = find(line==9); % the ascii value of a tab is 9
sep = sep(1);
lab{ind} = line(1:(sep-1));
num{ind} = str2num(line((sep+1):end));
end % parsing Begin-End block
val = val(:);
lab = lab(:);
num = num(:);
num = cell2mat(num);
% the following is FieldTrip specific
if size(num,2)==6
msi.grad.label = [msi.grad.label; lab(:)];
% the numbers represent position and orientation of each magnetometer coil
msi.grad.coilpos = [msi.grad.coilpos; num(:,1:3)];
msi.grad.coilori = [msi.grad.coilori; num(:,4:6)];
else
error('unknown gradiometer design')
end
end
% the key looks like 'MSI.fieldname.subfieldname'
fieldname = key(5:end);
% remove spaces from the begin and end of the string
val = strtrim(val);
% try to convert the value string into something more usefull
if ~iscell(val)
% the value can contain a variety of elements, only some of which are decoded here
if ~isempty(strfind(key, 'Index')) || ~isempty(strfind(key, 'Count')) || any(strcmp(key, numlist))
% this contains a single number or a comma-separated list of numbers
val = str2num(val);
elseif ~isempty(strfind(key, 'Names')) || any(strcmp(key, strlist))
% this contains a comma-separated list of strings
val = tokenize(val, ',');
else
tmp = str2num(val);
if ~isempty(tmp)
val = tmp;
end
end
end
% assign this header element to the structure
msi = setsubfield(msi, fieldname, val);
end % while ischar(line)
% each coil weighs with a value of 1 into each channel
msi.grad.tra = eye(size(msi.grad.coilpos,1));
msi.grad.unit = 'm';
fclose(fid);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION to remove spaces from the begin and end
% and to remove comments from the lines
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function line = cleanline(line)
if isempty(line) || (length(line)==1 && all(line==-1))
return
end
comment = findstr(line, '//');
if ~isempty(comment)
line(min(comment):end) = ' ';
end
line = strtrim(line);
|
github
|
philippboehmsturm/antx-master
|
read_asa.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_asa.m
| 3,857 |
utf_8
|
bd6525da96c296723f6a29b027b2445e
|
function [val] = read_asa(filename, elem, format, number, token)
% READ_ASA reads a specified element from an ASA file
%
% val = read_asa(filename, element, type, number)
%
% where the element is a string such as
% NumberSlices
% NumberPositions
% Rows
% Columns
% etc.
%
% and format specifies the datatype according to
% %d (integer value)
% %f (floating point value)
% %s (string)
%
% number is optional to specify how many lines of data should be read
% The default is 1 for strings and Inf for numbers.
%
% token is optional to specifiy a character that separates the values from
% anything not wanted.
% Copyright (C) 2002-2012, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: read_asa.m 7123 2012-12-06 21:21:38Z roboos $
fid = fopen(filename, 'rt');
if fid==-1
error(sprintf('could not open file %s', filename));
end
if nargin<4
if strcmp(format, '%s')
number = 1;
else
number = Inf;
end
end
if nargin<5
token = '';
end
val = [];
elem = strtrim(lower(elem));
while (1)
line = fgetl(fid);
if ~isempty(line) && isequal(line, -1)
% prematurely reached end of file
fclose(fid);
return
end
line = strtrim(line);
lower_line = lower(line);
if strmatch(elem, lower_line)
data = line((length(elem)+1):end);
break
end
end
while isempty(data)
line = fgetl(fid);
if isequal(line, -1)
% prematurely reached end of file
fclose(fid);
return
end
data = strtrim(line);
end
if strcmp(format, '%s')
if number==1
% interpret the data as a single string, create char-array
val = detoken(strtrim(data), token);
if val(1)=='='
val = val(2:end); % remove the trailing =
end
fclose(fid);
return
end
% interpret the data as a single string, create cell-array
val{1} = detoken(strtrim(data), token);
count = 1;
% read the remaining strings
while count<number
line = fgetl(fid);
if ~isempty(line) && isequal(line, -1)
fclose(fid);
return
end
tmp = sscanf(line, format);
if isempty(tmp)
fclose(fid);
return
else
count = count + 1;
val{count} = detoken(strtrim(line), token);
end
end
else
% interpret the data as numeric, create numeric array
count = 1;
data = sscanf(detoken(data, token), format)';
if isempty(data),
fclose(fid);
return
else
val(count,:) = data;
end
% read remaining numeric data
while count<number
line = fgetl(fid);
if ~isempty(line) && isequal(line, -1)
fclose(fid);
return
end
data = sscanf(detoken(line, token), format)';
if isempty(data)
fclose(fid);
return
else
count = count+1;
val(count,:) = data;
end
end
end
fclose(fid);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [out] = detoken(in, token)
if isempty(token)
out = in;
return;
end
[tok rem] = strtok(in, token);
if isempty(rem)
out = in;
return;
else
out = strtok(rem, token);
return
end
|
github
|
philippboehmsturm/antx-master
|
nanmean.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/nanmean.m
| 165 |
utf_8
|
e6c473a49d8be6e12960af55ced45e54
|
% NANMEAN provides a replacement for MATLAB's nanmean.
%
% For usage see MEAN.
function y = nanmean(x, dim)
N = sum(~isnan(x), dim);
y = nansum(x, dim) ./ N;
end
|
github
|
philippboehmsturm/antx-master
|
ft_checkdata.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/ft_checkdata.m
| 68,446 |
utf_8
|
826e5a3878076ce68bb166d5afbfe9fd
|
function [data] = ft_checkdata(data, varargin)
% FT_CHECKDATA checks the input data of the main FieldTrip functions, e.g. whether
% the type of data strucure corresponds with the required data. If neccessary
% and possible, this function will adjust the data structure to the input
% requirements (e.g. change dimord, average over trials, convert inside from
% index into logical).
%
% If the input data does NOT correspond to the requirements, this function
% is supposed to give a elaborate warning message and if applicable point
% the user to external documentation (link to website).
%
% Use as
% [data] = ft_checkdata(data, ...)
%
% Optional input arguments should be specified as key-value pairs and can include
% feedback = yes, no
% datatype = raw, freq, timelock, comp, spike, source, dip, volume, segmentation, parcellation
% dimord = any combination of time, freq, chan, refchan, rpt, subj, chancmb, rpttap, pos
% senstype = ctf151, ctf275, ctf151_planar, ctf275_planar, neuromag122, neuromag306, bti148, bti248, bti248_planar, magnetometer, electrode
% inside = logical, index
% ismeg = yes, no
% hastrials = yes, no
% hasunits = yes, no
% hassampleinfo = yes, no, ifmakessense (only applies to raw data)
% hascumtapcnt = yes, no (only applies to freq data)
% hasdim = yes, no
% hasdof = yes, no
% cmbrepresentation = sparse, full (applies to covariance and cross-spectral density)
% fsample = sampling frequency to use to go from SPIKE to RAW representation
% segmentationstyle = indexed, probabilistic (only applies to segmentation)
% parcellationstyle = indexed, probabilistic (only applies to parcellation)
% hasbrain = yes, no (only applies to segmentation)
%
% For some options you can specify multiple values, e.g.
% [data] = ft_checkdata(data, 'senstype', {'ctf151', 'ctf275'}), e.g. in megrealign
% [data] = ft_checkdata(data, 'datatype', {'timelock', 'freq'}), e.g. in sourceanalysis
% Copyright (C) 2007-2012, Robert Oostenveld
% Copyright (C) 2010-2012, Martin Vinck
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_checkdata.m 7394 2013-01-23 14:33:30Z jorhor $
% in case of an error this function could use dbstack for more detailled
% user feedback
%
% this function should replace/encapsulate
% fixdimord
% fixinside
% fixprecision
% fixvolume
% data2raw
% raw2data
% grid2transform
% transform2grid
% fourier2crsspctrm
% freq2cumtapcnt
% sensortype
% time2offset
% offset2time
% fixsens -> this is kept a separate function because it should also be
% called from other modules
%
% other potential uses for this function:
% time -> offset in freqanalysis
% average over trials
% csd as matrix
% get the optional input arguments
feedback = ft_getopt(varargin, 'feedback', 'no');
dtype = ft_getopt(varargin, 'datatype'); % should not conflict with the ft_datatype function
dimord = ft_getopt(varargin, 'dimord');
stype = ft_getopt(varargin, 'senstype'); % senstype is a function name which should not be masked
ismeg = ft_getopt(varargin, 'ismeg');
inside = ft_getopt(varargin, 'inside'); % can be 'logical' or 'index'
hastrials = ft_getopt(varargin, 'hastrials');
hasunits = ft_getopt(varargin, 'hasunits');
hassampleinfo = ft_getopt(varargin, 'hassampleinfo', 'ifmakessense');
hasdimord = ft_getopt(varargin, 'hasdimord', 'no');
hasdim = ft_getopt(varargin, 'hasdim');
hascumtapcnt = ft_getopt(varargin, 'hascumtapcnt');
hasdof = ft_getopt(varargin, 'hasdof', 'no');
haspow = ft_getopt(varargin, 'haspow', 'no');
cmbrepresentation = ft_getopt(varargin, 'cmbrepresentation');
channelcmb = ft_getopt(varargin, 'channelcmb');
sourcedimord = ft_getopt(varargin, 'sourcedimord');
sourcerepresentation = ft_getopt(varargin, 'sourcerepresentation');
fsample = ft_getopt(varargin, 'fsample');
segmentationstyle = ft_getopt(varargin, 'segmentationstyle'); % this will be passed on to the corresponding ft_datatype_xxx function
parcellationstyle = ft_getopt(varargin, 'parcellationstyle'); % this will be passed on to the corresponding ft_datatype_xxx function
hasbrain = ft_getopt(varargin, 'hasbrain');
% check whether people are using deprecated stuff
depHastrialdef = ft_getopt(varargin, 'hastrialdef');
if (~isempty(depHastrialdef))
warning_once('ft_checkdata option ''hastrialdef'' is deprecated; use ''hassampleinfo'' instead');
hassampleinfo = depHastrialdef;
end
if (~isempty(ft_getopt(varargin, 'hasoffset')))
warning_once('ft_checkdata option ''hasoffset'' has been removed and will be ignored');
end
% determine the type of input data
% this can be raw, freq, timelock, comp, spike, source, volume, dip
israw = ft_datatype(data, 'raw');
isfreq = ft_datatype(data, 'freq');
istimelock = ft_datatype(data, 'timelock');
iscomp = ft_datatype(data, 'comp');
isspike = ft_datatype(data, 'spike');
isvolume = ft_datatype(data, 'volume');
issegmentation = ft_datatype(data, 'segmentation');
isparcellation = ft_datatype(data, 'parcellation');
issource = ft_datatype(data, 'source');
isdip = ft_datatype(data, 'dip');
ismvar = ft_datatype(data, 'mvar');
isfreqmvar = ft_datatype(data, 'freqmvar');
ischan = ft_datatype(data, 'chan');
% FIXME use the istrue function on ismeg and hasxxx options
if ~isequal(feedback, 'no')
if iscomp
% it can be comp and raw at the same time, therefore this has to go first
ncomp = length(data.label);
nchan = length(data.topolabel);
fprintf('the input is component data with %d components and %d original channels\n', ncomp, nchan);
elseif israw
nchan = length(data.label);
ntrial = length(data.trial);
fprintf('the input is raw data with %d channels and %d trials\n', nchan, ntrial);
elseif istimelock
nchan = length(data.label);
ntime = length(data.time);
fprintf('the input is timelock data with %d channels and %d timebins\n', nchan, ntime);
elseif isfreq
nchan = length(data.label);
nfreq = length(data.freq);
if isfield(data, 'time'), ntime = num2str(length(data.time)); else ntime = 'no'; end
fprintf('the input is freq data with %d channels, %d frequencybins and %s timebins\n', nchan, nfreq, ntime);
elseif isspike
nchan = length(data.label);
fprintf('the input is spike data with %d channels\n', nchan);
elseif isvolume
if issegmentation
subtype = 'segmented volume';
else
subtype = 'volume';
end
fprintf('the input is %s data with dimensions [%d %d %d]\n', subtype, data.dim(1), data.dim(2), data.dim(3));
clear subtype
elseif issource
nsource = size(data.pos, 1);
if isparcellation
subtype = 'parcellated source';
else
subtype = 'source';
end
if isfield(data, 'dim')
fprintf('the input is %s data with %d positions on a [%d %d %d] grid\n', subtype, nsource, data.dim(1), data.dim(2), data.dim(3));
elseif isfield(data, 'tri')
fprintf('the input is %s data with %d vertex positions and %d triangles\n', subtype, nsource, size(data.tri, 1));
else
fprintf('the input is %s data with %d positions\n', subtype, nsource);
end
clear subtype
elseif isdip
fprintf('the input is dipole data\n');
elseif ismvar
fprintf('the input is mvar data\n');
elseif isfreqmvar
fprintf('the input is freqmvar data\n');
elseif ischan
nchan = length(data.label);
fprintf('the input is chan data\n');
end
end % give feedback
if issource && isvolume
% it should be either one or the other: the choice here is to
% represent it as volume description since that is simpler to handle
% the conversion is done by remove the grid positions
data = rmfield(data, 'pos');
issource = false;
end
% the ft_datatype_XXX functions ensures the consistency of the XXX datatype
% and provides a detailed description of the dataformat and its history
if isfreq
data = ft_datatype_freq(data);
elseif istimelock
data = ft_datatype_timelock(data);
elseif isspike
data = ft_datatype_spike(data);
elseif iscomp % this should go before israw
data = ft_datatype_comp(data, 'hassampleinfo', hassampleinfo);
elseif israw
data = ft_datatype_raw(data, 'hassampleinfo', hassampleinfo);
elseif issegmentation % this should go before isvolume
data = ft_datatype_segmentation(data, 'segmentationstyle', segmentationstyle, 'hasbrain', hasbrain);
elseif isvolume
data = ft_datatype_volume(data);
elseif isparcellation % this should go before issource
data = ft_datatype_parcellation(data, 'parcellationstyle', parcellationstyle);
elseif issource
data = ft_datatype_source(data);
elseif isdip
data = ft_datatype_dip(data);
elseif ismvar || isfreqmvar
data = ft_datatype_mvar(data);
end
if ~isempty(dtype)
if ~isa(dtype, 'cell')
dtype = {dtype};
end
okflag = 0;
for i=1:length(dtype)
% check that the data matches with one or more of the required ft_datatypes
switch dtype{i}
case 'raw'
okflag = okflag + israw;
case 'freq'
okflag = okflag + isfreq;
case 'timelock'
okflag = okflag + istimelock;
case 'comp'
okflag = okflag + iscomp;
case 'spike'
okflag = okflag + isspike;
case 'volume'
okflag = okflag + isvolume;
case 'source'
okflag = okflag + issource;
case 'dip'
okflag = okflag + isdip;
case 'mvar'
okflag = okflag + ismvar;
case 'freqmvar'
okflag = okflag + isfreqmvar;
case 'chan'
okflag = okflag + ischan;
case 'segmentation'
okflag = okflag + issegmentation;
case 'parcellation'
okflag = okflag + isparcellation;
end % switch dtype
end % for dtype
if ~okflag
% try to convert the data
for iCell = 1:length(dtype)
if isequal(dtype(iCell), {'source'}) && isvolume
data = volume2source(data);
data = ft_datatype_source(data);
isvolume = 0;
issource = 1;
okflag = 1;
elseif isequal(dtype(iCell), {'volume'}) && issource
data = source2volume(data);
data = ft_datatype_volume(data);
isvolume = 1;
issource = 0;
okflag = 1;
elseif isequal(dtype(iCell), {'raw'}) && issource
data = data2raw(data);
data = ft_datatype_raw(data, 'hassampleinfo', hassampleinfo);
issource = 0;
israw = 1;
okflag = 1;
elseif isequal(dtype(iCell), {'raw'}) && istimelock
data = timelock2raw(data);
data = ft_datatype_raw(data, 'hassampleinfo', hassampleinfo);
istimelock = 0;
israw = 1;
okflag = 1;
elseif isequal(dtype(iCell), {'timelock'}) && iscomp % this should go before israw
data = comp2raw(data);
data = raw2timelock(data);
data = ft_datatype_timelock(data);
iscomp = 0;
israw = 0;
istimelock = 1;
okflag = 1;
elseif isequal(dtype(iCell), {'timelock'}) && israw
data = raw2timelock(data);
data = ft_datatype_timelock(data);
israw = 0;
istimelock = 1;
okflag = 1;
elseif isequal(dtype(iCell), {'raw'}) && isfreq
data = freq2raw(data);
data = ft_datatype_raw(data, 'hassampleinfo', hassampleinfo);
isfreq = 0;
israw = 1;
okflag = 1;
elseif isequal(dtype(iCell), {'raw'}) && iscomp
% this is never executed, because when iscomp==true, then also israw==true
data = comp2raw(data);
data = ft_datatype_raw(data, 'hassampleinfo', hassampleinfo);
iscomp = 0;
israw = 1;
okflag = 1;
elseif isequal(dtype(iCell), {'timelock'}) && ischan
data = chan2timelock(data);
data = ft_datatype_timelock(data);
ischan = 0;
istimelock = 1;
okflag = 1;
elseif isequal(dtype(iCell), {'freq'}) && ischan
data = chan2freq(data);
data = ft_datatype_freq(data);
ischan = 0;
isfreq = 1;
okflag = 1;
elseif isequal(dtype(iCell), {'spike'}) && israw
data = raw2spike(data);
data = ft_datatype_spike(data);
israw = 0;
isspike = 1;
okflag = 1;
elseif isequal(dtype(iCell), {'raw'}) && isspike
data = spike2raw(data,fsample);
data = ft_datatype_raw(data, 'hassampleinfo', hassampleinfo);
isspike = 0;
israw = 1;
okflag = 1;
end
end % for iCell
end % if okflag
if ~okflag
% construct an error message
if length(dtype)>1
str = sprintf('%s, ', dtype{1:(end-2)});
str = sprintf('%s%s or %s', str, dtype{end-1}, dtype{end});
else
str = dtype{1};
end
str = sprintf('This function requires %s data as input.', str);
error(str);
end % if okflag
end
if ~isempty(dimord)
if ~isa(dimord, 'cell')
dimord = {dimord};
end
if isfield(data, 'dimord')
okflag = any(strcmp(data.dimord, dimord));
else
okflag = 0;
end
if ~okflag
% construct an error message
if length(dimord)>1
str = sprintf('%s, ', dimord{1:(end-2)});
str = sprintf('%s%s or %s', str, dimord{end-1}, dimord{end});
else
str = dimord{1};
end
str = sprintf('This function requires data with a dimord of %s.', str);
error(str);
end % if okflag
end
if ~isempty(stype)
if ~isa(stype, 'cell')
stype = {stype};
end
if isfield(data, 'grad') || isfield(data, 'elec')
if any(strcmp(ft_senstype(data), stype));
okflag = 1;
else
okflag = 0;
end
else
okflag = 0;
end
if ~okflag
% construct an error message
if length(stype)>1
str = sprintf('%s, ', stype{1:(end-2)});
str = sprintf('%s%s or %s', str, stype{end-1}, stype{end});
else
str = stype{1};
end
str = sprintf('This function requires %s data as input, but you are giving %s data.', str, ft_senstype(data));
error(str);
end % if okflag
end
if ~isempty(ismeg)
if isequal(ismeg, 'yes')
okflag = isfield(data, 'grad');
elseif isequal(ismeg, 'no')
okflag = ~isfield(data, 'grad');
end
if ~okflag && isequal(ismeg, 'yes')
error('This function requires MEG data with a ''grad'' field');
elseif ~okflag && isequal(ismeg, 'no')
error('This function should not be given MEG data with a ''grad'' field');
end % if okflag
end
if ~isempty(inside)
% TODO absorb the fixinside function into this code
data = fixinside(data, inside);
okflag = isfield(data, 'inside');
if ~okflag
% construct an error message
error('This function requires data with an ''inside'' field.');
end % if okflag
end
%if isvolume
% % ensure consistent dimensions of the volumetric data
% % reshape each of the volumes that is found into a 3D array
% param = parameterselection('all', data);
% dim = data.dim;
% for i=1:length(param)
% tmp = getsubfield(data, param{i});
% tmp = reshape(tmp, dim);
% data = setsubfield(data, param{i}, tmp);
% end
%end
if isequal(hasunits, 'yes') && ~isfield(data, 'units')
% calling convert_units with only the input data adds the units without converting
data = ft_convert_units(data);
end
if issource || isvolume,
% the following section is to make a dimord-consistent representation of
% volume and source data, taking trials, time and frequency into account
if isequal(hasdimord, 'yes') && (~isfield(data, 'dimord') || ~strcmp(data.dimord,sourcedimord))
% determine the size of the data
if isfield(data, 'dimord'),
dimtok = tokenize(data.dimord, '_');
if ~isempty(strmatch('time', dimtok)), Ntime = length(data.time); else Ntime = 1; end
if ~isempty(strmatch('freq', dimtok)), Nfreq = length(data.freq); else Nfreq = 1; end
else
Nfreq = 1;
Ntime = 1;
end
%convert old style source representation into new style
if isfield(data, 'avg') && isfield(data.avg, 'mom') && (isfield(data, 'freq') || isfield(data, 'frequency')) && strcmp(sourcedimord, 'rpt_pos'),
%frequency domain source representation convert to single trial power
Npos = size(data.pos,1);
Nrpt = size(data.cumtapcnt,1);
tmpmom = zeros(Npos, size(data.avg.mom{data.inside(1)},2));
tmpmom(data.inside,:) = cat(1,data.avg.mom{data.inside});
tmppow = zeros(Npos, Nrpt);
tapcnt = [0;cumsum(data.cumtapcnt)];
for k = 1:Nrpt
Ntap = tapcnt(k+1)-tapcnt(k);
tmppow(data.inside,k) = sum(abs(tmpmom(data.inside,(tapcnt(k)+1):tapcnt(k+1))).^2,2)./Ntap;
end
data.pow = tmppow';
data = rmfield(data, 'avg');
if strcmp(inside, 'logical'),
data = fixinside(data, 'logical');
data.inside = repmat(data.inside(:)',[Nrpt 1]);
end
elseif isfield(data, 'avg') && isfield(data.avg, 'mom') && (isfield(data, 'freq') || isfield(data, 'frequency')) && strcmp(sourcedimord, 'rpttap_pos'),
%frequency domain source representation convert to single taper fourier coefficients
Npos = size(data.pos,1);
Nrpt = sum(data.cumtapcnt);
data.fourierspctrm = complex(zeros(Nrpt, Npos), zeros(Nrpt, Npos));
data.fourierspctrm(:, data.inside) = transpose(cat(1, data.avg.mom{data.inside}));
data = rmfield(data, 'avg');
elseif isfield(data, 'avg') && isfield(data.avg, 'mom') && isfield(data, 'time') && strcmp(sourcedimord, 'pos_time'),
Npos = size(data.pos,1);
Nrpt = 1;
tmpmom = zeros(Npos, size(data.avg.mom{data.inside(1)},2));
tmpmom(data.inside,:) = cat(1,data.avg.mom{data.inside});
data.mom = tmpmom;
if isfield(data.avg, 'noise'),
tmpnoise = data.avg.noise(:);
data.noise = tmpnoise(:,ones(1,size(tmpmom,2)));
end
data = rmfield(data, 'avg');
Ntime = length(data.time);
elseif isfield(data, 'trial') && isfield(data.trial(1), 'mom') && isfield(data, 'time') && strcmp(sourcedimord, 'rpt_pos_time'),
Npos = size(data.pos,1);
Nrpt = length(data.trial);
Ntime = length(data.time);
tmpmom = zeros(Nrpt, Npos, Ntime);
for k = 1:Nrpt
tmpmom(k,data.inside,:) = cat(1,data.trial(k).mom{data.inside});
end
data = rmfield(data, 'trial');
data.mom = tmpmom;
elseif isfield(data, 'trial') && isstruct(data.trial)
Nrpt = length(data.trial);
else
Nrpt = 1;
end
% start with an initial specification of the dimord and dim
if (~isfield(data, 'dim') || ~isfield(data, 'dimord'))
if issource
% at least it should have a Nx3 pos
data.dim = size(data.pos, 1);
data.dimord = 'pos';
elseif isvolume
% at least it should have a 1x3 dim
data.dim = data.dim;
data.dimord = 'dim1_dim2_dim3';
end
end
% add the additional dimensions
if Nfreq>1
data.dimord = [data.dimord '_freq'];
data.dim = [data.dim Nfreq];
end
if Ntime>1
data.dimord = [data.dimord '_time'];
data.dim = [data.dim Ntime];
end
if Nrpt>1 && strcmp(sourcedimord, 'rpt_pos'),
data.dimord = ['rpt_' data.dimord];
data.dim = [Nrpt data.dim ];
elseif Nrpt>1 && strcmp(sourcedimord, 'rpttap_pos'),
data.dimord = ['rpttap_' data.dimord];
data.dim = [Nrpt data.dim ];
end
% the nested trial structure is not compatible with dimord
if isfield(data, 'trial') && isstruct(data.trial)
param = fieldnames(data.trial);
for i=1:length(param)
if isa(data.trial(1).(param{i}), 'cell')
concat = cell(data.dim(1), prod(data.dim(2:end)));
else
concat = zeros(data.dim(1), prod(data.dim(2:end)));
end
for j=1:length(data.trial)
tmp = data.trial(j).(param{i});
concat(j,:) = tmp(:);
end % for each trial
data.trial = rmfield(data.trial, param{i});
data.(param{i}) = reshape(concat, data.dim);
end % for each param
data = rmfield(data, 'trial');
end
end
% ensure consistent dimensions of the source reconstructed data
% reshape each of the source reconstructed parameters
if issource && isfield(data, 'dim') && prod(data.dim)==size(data.pos,1)
dim = [prod(data.dim) 1];
%elseif issource && any(~cellfun('isempty',strfind(fieldnames(data), 'dimord')))
% dim = [size(data.pos,1) 1]; %sparsely represented source structure new style
elseif isfield(data, 'dim'),
dim = [data.dim 1];
elseif issource && ~isfield(data, 'dimord')
dim = [size(data.pos,1) 1];
elseif isfield(data, 'dimord'),
%HACK
dimtok = tokenize(data.dimord, '_');
for i=1:length(dimtok)
if strcmp(dimtok(i), 'pos')
dim(1,i) = size(getsubfield(data,dimtok{i}),1);
elseif strcmp(dimtok(i), 'rpt')
dim(1,i) = nan;
else
dim(1,i) = length(getsubfield(data,dimtok{i}));
end
end
i = find(isnan(dim));
if ~isempty(i)
n = fieldnames(data);
for ii=1:length(n)
numels(1,ii) = numel(getfield(data,n{ii}));
end
nrpt = numels./prod(dim(setdiff(1:length(dim),i)));
nrpt = nrpt(nrpt==round(nrpt));
dim(i) = max(nrpt);
end
if numel(dim)==1, dim(1,2) = 1; end;
end
% these fields should not be reshaped
exclude = {'cfg' 'fwhm' 'leadfield' 'q' 'rough' 'pos'};
if ~isempty(inside) && ~strcmp(inside, 'logical')
% also exclude the inside/outside from being reshaped
exclude = cat(2, exclude, {'inside' 'outside'});
end
param = setdiff(parameterselection('all', data), exclude);
for i=1:length(param)
if any(param{i}=='.')
% the parameter is nested in a substructure, which can have multiple elements (e.g. source.trial(1).pow, source.trial(2).pow, ...)
% loop over the substructure array and reshape for every element
tok = tokenize(param{i}, '.');
sub1 = tok{1}; % i.e. this would be 'trial'
sub2 = tok{2}; % i.e. this would be 'pow'
tmp1 = getfield(data, sub1);
for j=1:numel(tmp1)
tmp2 = getfield(tmp1(j), sub2);
if prod(dim)==numel(tmp2)
tmp2 = reshape(tmp2, dim);
end
tmp1(j) = setfield(tmp1(j), sub2, tmp2);
end
data = setfield(data, sub1, tmp1);
else
tmp = getfield(data, param{i});
if prod(dim)==numel(tmp)
tmp = reshape(tmp, dim);
end
data = setfield(data, param{i}, tmp);
end
end
end
if isequal(hastrials, 'yes')
okflag = isfield(data, 'trial');
if ~okflag && isfield(data, 'dimord')
% instead look in the dimord for rpt or subj
okflag = ~isempty(strfind(data.dimord, 'rpt')) || ...
~isempty(strfind(data.dimord, 'rpttap')) || ...
~isempty(strfind(data.dimord, 'subj'));
end
if ~okflag
error('This function requires data with a ''trial'' field');
end % if okflag
end
if isequal(hasdim, 'yes') && ~isfield(data, 'dim')
data.dim = pos2dim(data.pos);
elseif isequal(hasdim, 'no') && isfield(data, 'dim')
data = rmfield(data, 'dim');
end % if hasdim
if isequal(hascumtapcnt, 'yes') && ~isfield(data, 'cumtapcnt')
error('This function requires data with a ''cumtapcnt'' field');
elseif isequal(hascumtapcnt, 'no') && isfield(data, 'cumtapcnt')
data = rmfield(data, 'cumtapcnt');
end % if hascumtapcnt
if isequal(hasdof, 'yes') && ~isfield(data, 'hasdof')
error('This function requires data with a ''dof'' field');
elseif isequal(hasdof, 'no') && isfield(data, 'hasdof')
data = rmfield(data, 'cumtapcnt');
end % if hasdof
if ~isempty(cmbrepresentation)
if istimelock
data = fixcov(data, cmbrepresentation);
elseif isfreq
data = fixcsd(data, cmbrepresentation, channelcmb);
elseif isfreqmvar
data = fixcsd(data, cmbrepresentation, channelcmb);
else
error('This function requires data with a covariance, coherence or cross-spectrum');
end
end % cmbrepresentation
if issource && ~isempty(sourcerepresentation)
data = fixsource(data, 'type', sourcerepresentation);
end
if issource && ~strcmp(haspow, 'no')
data = fixsource(data, 'type', sourcerepresentation, 'haspow', haspow);
end
if isfield(data, 'grad')
% ensure that the gradiometer balancing is specified
if ~isfield(data.grad, 'balance') || ~isfield(data.grad.balance, 'current')
data.grad.balance.current = 'none';
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% represent the covariance matrix in a particular manner
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function data = fixcov(data, desired)
if isfield(data, 'cov') && ~isfield(data, 'labelcmb')
current = 'full';
elseif isfield(data, 'cov') && isfield(data, 'labelcmb')
current = 'sparse';
else
error('Could not determine the current representation of the covariance matrix');
end
if isequal(current, desired)
% nothing to do
elseif strcmp(current, 'full') && strcmp(desired, 'sparse')
% FIXME should be implemented
error('not yet implemented');
elseif strcmp(current, 'sparse') && strcmp(desired, 'full')
% FIXME should be implemented
error('not yet implemented');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% represent the cross-spectral density matrix in a particular manner
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [data] = fixcsd(data, desired, channelcmb)
% FIXCSD converts univariate frequency domain data (fourierspctrm) into a bivariate
% representation (crsspctrm), or changes the representation of bivariate frequency
% domain data (sparse/full/sparsewithpow, sparsewithpow only works for crsspctrm or
% fourierspctrm)
% Copyright (C) 2010, Jan-Mathijs Schoffelen, Robert Oostenveld
if isfield(data, 'crsspctrm') && isfield(data, 'powspctrm')
current = 'sparsewithpow';
elseif isfield(data, 'powspctrm')
current = 'sparsewithpow';
elseif isfield(data, 'fourierspctrm') && ~isfield(data, 'labelcmb')
current = 'fourier';
elseif ~isfield(data, 'labelcmb')
current = 'full';
elseif isfield(data, 'labelcmb')
current = 'sparse';
else
error('Could not determine the current representation of the %s matrix', param);
end
% first go from univariate fourier to the required bivariate representation
if strcmp(current, 'fourier') && strcmp(desired, 'fourier')
% nothing to do
elseif strcmp(current, 'fourier') && strcmp(desired, 'sparsewithpow')
dimtok = tokenize(data.dimord, '_');
if ~isempty(strmatch('rpttap', dimtok)),
nrpt = size(data.cumtapcnt,1);
flag = 0;
else
nrpt = 1;
end
if ~isempty(strmatch('freq', dimtok)), nfrq=length(data.freq); else nfrq = 1; end
if ~isempty(strmatch('time', dimtok)), ntim=length(data.time); else ntim = 1; end
fastflag = all(data.cumtapcnt(:)==data.cumtapcnt(1));
flag = nrpt==1; % needed to truncate the singleton dimension upfront
%create auto-spectra
nchan = length(data.label);
if fastflag
% all trials have the same amount of tapers
powspctrm = zeros(nrpt,nchan,nfrq,ntim);
ntap = data.cumtapcnt(1);
for p = 1:ntap
powspctrm = powspctrm + abs(data.fourierspctrm(p:ntap:end,:,:,:,:)).^2;
end
powspctrm = powspctrm./ntap;
else
% different amount of tapers
powspctrm = zeros(nrpt,nchan,nfrq,ntim)+i.*zeros(nrpt,nchan,nfrq,ntim);
sumtapcnt = [0;cumsum(data.cumtapcnt(:))];
for p = 1:nrpt
indx = (sumtapcnt(p)+1):sumtapcnt(p+1);
tmpdat = data.fourierspctrm(indx,:,:,:);
powspctrm(p,:,:,:) = (sum(tmpdat.*conj(tmpdat),1))./data.cumtapcnt(p);
end
end
%create cross-spectra
if ~isempty(channelcmb),
ncmb = size(channelcmb,1);
cmbindx = zeros(ncmb,2);
labelcmb = cell(ncmb,2);
for k = 1:ncmb
ch1 = find(strcmp(data.label, channelcmb(k,1)));
ch2 = find(strcmp(data.label, channelcmb(k,2)));
if ~isempty(ch1) && ~isempty(ch2),
cmbindx(k,:) = [ch1 ch2];
labelcmb(k,:) = data.label([ch1 ch2])';
end
end
crsspctrm = zeros(nrpt,ncmb,nfrq,ntim)+i.*zeros(nrpt,ncmb,nfrq,ntim);
if fastflag
for p = 1:ntap
tmpdat1 = data.fourierspctrm(p:ntap:end,cmbindx(:,1),:,:,:);
tmpdat2 = data.fourierspctrm(p:ntap:end,cmbindx(:,2),:,:,:);
crsspctrm = crsspctrm + tmpdat1.*conj(tmpdat2);
end
crsspctrm = crsspctrm./ntap;
else
for p = 1:nrpt
indx = (sumtapcnt(p)+1):sumtapcnt(p+1);
tmpdat1 = data.fourierspctrm(indx,cmbindx(:,1),:,:);
tmpdat2 = data.fourierspctrm(indx,cmbindx(:,2),:,:);
crsspctrm(p,:,:,:) = (sum(tmpdat1.*conj(tmpdat2),1))./data.cumtapcnt(p);
end
end
data.crsspctrm = crsspctrm;
data.labelcmb = labelcmb;
end
data.powspctrm = powspctrm;
data = rmfield(data, 'fourierspctrm');
if ntim>1,
data.dimord = 'chan_freq_time';
else
data.dimord = 'chan_freq';
end
if nrpt>1,
data.dimord = ['rpt_',data.dimord];
end
if flag, siz = size(data.crsspctrm); data.crsspctrm = reshape(data.crsspctrm, siz(2:end)); end
elseif strcmp(current, 'fourier') && strcmp(desired, 'sparse')
if isempty(channelcmb), error('no channel combinations are specified'); end
dimtok = tokenize(data.dimord, '_');
if ~isempty(strmatch('rpttap', dimtok)),
nrpt = size(data.cumtapcnt,1);
flag = 0;
else
nrpt = 1;
end
if ~isempty(strmatch('freq', dimtok)), nfrq=length(data.freq); else nfrq = 1; end
if ~isempty(strmatch('time', dimtok)), ntim=length(data.time); else ntim = 1; end
flag = nrpt==1; % flag needed to squeeze first dimension if singleton
ncmb = size(channelcmb,1);
cmbindx = zeros(ncmb,2);
labelcmb = cell(ncmb,2);
for k = 1:ncmb
ch1 = find(strcmp(data.label, channelcmb(k,1)));
ch2 = find(strcmp(data.label, channelcmb(k,2)));
if ~isempty(ch1) && ~isempty(ch2),
cmbindx(k,:) = [ch1 ch2];
labelcmb(k,:) = data.label([ch1 ch2])';
end
end
sumtapcnt = [0;cumsum(data.cumtapcnt(:))];
fastflag = all(data.cumtapcnt(:)==data.cumtapcnt(1));
if fastflag && nrpt>1
ntap = data.cumtapcnt(1);
% compute running sum across tapers
siz = [size(data.fourierspctrm) 1];
for p = 1:ntap
indx = p:ntap:nrpt*ntap;
if p==1.
tmpc = zeros(numel(indx), size(cmbindx,1), siz(3), siz(4)) + ...
1i.*zeros(numel(indx), size(cmbindx,1), siz(3), siz(4));
end
for k = 1:size(cmbindx,1)
tmpc(:,k,:,:) = data.fourierspctrm(indx,cmbindx(k,1),:,:).* ...
conj(data.fourierspctrm(indx,cmbindx(k,2),:,:));
end
if p==1
crsspctrm = tmpc;
else
crsspctrm = tmpc + crsspctrm;
end
end
crsspctrm = crsspctrm./ntap;
else
crsspctrm = zeros(nrpt, ncmb, nfrq, ntim);
for p = 1:nrpt
indx = (sumtapcnt(p)+1):sumtapcnt(p+1);
tmpdat1 = data.fourierspctrm(indx,cmbindx(:,1),:,:);
tmpdat2 = data.fourierspctrm(indx,cmbindx(:,2),:,:);
crsspctrm(p,:,:,:) = (sum(tmpdat1.*conj(tmpdat2),1))./data.cumtapcnt(p);
end
end
data.crsspctrm = crsspctrm;
data.labelcmb = labelcmb;
data = rmfield(data, 'fourierspctrm');
data = rmfield(data, 'label');
if ntim>1,
data.dimord = 'chan_freq_time';
else
data.dimord = 'chan_freq';
end
if nrpt>1,
data.dimord = ['rpt_',data.dimord];
end
if flag, siz = size(data.crsspctrm); data.crsspctrm = reshape(data.crsspctrm, siz(2:end)); end
elseif strcmp(current, 'fourier') && strcmp(desired, 'full')
% this is how it is currently and the desired functionality of prepare_freq_matrices
dimtok = tokenize(data.dimord, '_');
if ~isempty(strmatch('rpttap', dimtok)),
nrpt = size(data.cumtapcnt, 1);
flag = 0;
else
nrpt = 1;
flag = 1;
end
if ~isempty(strmatch('rpttap',dimtok)), nrpt=size(data.cumtapcnt, 1); else nrpt = 1; end
if ~isempty(strmatch('freq', dimtok)), nfrq=length(data.freq); else nfrq = 1; end
if ~isempty(strmatch('time', dimtok)), ntim=length(data.time); else ntim = 1; end
if any(data.cumtapcnt(1,:) ~= data.cumtapcnt(1,1)), error('this only works when all frequencies have the same number of tapers'); end
nchan = length(data.label);
crsspctrm = zeros(nrpt,nchan,nchan,nfrq,ntim);
sumtapcnt = [0;cumsum(data.cumtapcnt(:,1))];
for k = 1:ntim
for m = 1:nfrq
for p = 1:nrpt
%FIXME speed this up in the case that all trials have equal number of tapers
indx = (sumtapcnt(p)+1):sumtapcnt(p+1);
tmpdat = transpose(data.fourierspctrm(indx,:,m,k));
crsspctrm(p,:,:,m,k) = (tmpdat*tmpdat')./data.cumtapcnt(p);
clear tmpdat;
end
end
end
data.crsspctrm = crsspctrm;
data = rmfield(data, 'fourierspctrm');
if ntim>1,
data.dimord = 'chan_chan_freq_time';
else
data.dimord = 'chan_chan_freq';
end
if nrpt>1,
data.dimord = ['rpt_',data.dimord];
end
% remove first singleton dimension
if flag || nrpt==1, siz = size(data.crsspctrm); data.crsspctrm = reshape(data.crsspctrm, siz(2:end)); end
elseif strcmp(current, 'fourier') && strcmp(desired, 'fullfast'),
dimtok = tokenize(data.dimord, '_');
nrpt = size(data.fourierspctrm, 1);
nchn = numel(data.label);
nfrq = numel(data.freq);
if ~isempty(strmatch('time', dimtok)), ntim=numel(data.time); else ntim = 1; end
data.fourierspctrm = reshape(data.fourierspctrm, [nrpt nchn nfrq*ntim]);
data.fourierspctrm(~isfinite(data.fourierspctrm)) = 0;
crsspctrm = complex(zeros(nchn,nchn,nfrq*ntim));
for k = 1:nfrq*ntim
tmp = transpose(data.fourierspctrm(:,:,k));
n = sum(tmp~=0,2);
crsspctrm(:,:,k) = tmp*tmp'./n(1);
end
data = rmfield(data, 'fourierspctrm');
data.crsspctrm = reshape(crsspctrm, [nchn nchn nfrq ntim]);
if isfield(data, 'time'),
data.dimord = 'chan_chan_freq_time';
else
data.dimord = 'chan_chan_freq';
end
if isfield(data, 'trialinfo'), data = rmfield(data, 'trialinfo'); end;
if isfield(data, 'sampleinfo'), data = rmfield(data, 'sampleinfo'); end;
if isfield(data, 'cumsumcnt'), data = rmfield(data, 'cumsumcnt'); end;
if isfield(data, 'cumtapcnt'), data = rmfield(data, 'cumtapcnt'); end;
end % convert to the requested bivariate representation
% from one bivariate representation to another
if isequal(current, desired)
% nothing to do
elseif (strcmp(current, 'full') && strcmp(desired, 'fourier')) || ...
(strcmp(current, 'sparse') && strcmp(desired, 'fourier')) || ...
(strcmp(current, 'sparsewithpow') && strcmp(desired, 'fourier'))
% this is not possible
error('converting the cross-spectrum into a Fourier representation is not possible');
elseif strcmp(current, 'full') && strcmp(desired, 'sparsewithpow')
error('not yet implemented');
elseif strcmp(current, 'sparse') && strcmp(desired, 'sparsewithpow')
% convert back to crsspctrm/powspctrm representation: useful for plotting functions etc
indx = labelcmb2indx(data.labelcmb);
autoindx = indx(indx(:,1)==indx(:,2), 1);
cmbindx = setdiff([1:size(indx,1)]', autoindx);
if strcmp(data.dimord(1:3), 'rpt')
data.powspctrm = data.crsspctrm(:, autoindx, :, :);
data.crsspctrm = data.crsspctrm(:, cmbindx, :, :);
else
data.powspctrm = data.crsspctrm(autoindx, :, :);
data.crsspctrm = data.crsspctrm(cmbindx, :, :);
end
data.label = data.labelcmb(autoindx,1);
data.labelcmb = data.labelcmb(cmbindx, :);
if isempty(cmbindx)
data = rmfield(data, 'crsspctrm');
data = rmfield(data, 'labelcmb');
end
elseif strcmp(current, 'full') && strcmp(desired, 'sparse')
dimtok = tokenize(data.dimord, '_');
if ~isempty(strmatch('rpt', dimtok)), nrpt=size(data.cumtapcnt,1); else nrpt = 1; end
if ~isempty(strmatch('freq', dimtok)), nfrq=numel(data.freq); else nfrq = 1; end
if ~isempty(strmatch('time', dimtok)), ntim=numel(data.time); else ntim = 1; end
nchan = length(data.label);
ncmb = nchan*nchan;
labelcmb = cell(ncmb, 2);
cmbindx = zeros(nchan, nchan);
k = 1;
for j=1:nchan
for m=1:nchan
labelcmb{k, 1} = data.label{m};
labelcmb{k, 2} = data.label{j};
cmbindx(m,j) = k;
k = k+1;
end
end
% reshape all possible fields
fn = fieldnames(data);
for ii=1:numel(fn)
if numel(data.(fn{ii})) == nrpt*ncmb*nfrq*ntim;
if nrpt>1,
data.(fn{ii}) = reshape(data.(fn{ii}), nrpt, ncmb, nfrq, ntim);
else
data.(fn{ii}) = reshape(data.(fn{ii}), ncmb, nfrq, ntim);
end
end
end
% remove obsolete fields
data = rmfield(data, 'label');
try, data = rmfield(data, 'dof'); end
% replace updated fields
data.labelcmb = labelcmb;
if ntim>1,
data.dimord = 'chancmb_freq_time';
else
data.dimord = 'chancmb_freq';
end
if nrpt>1,
data.dimord = ['rpt_',data.dimord];
end
elseif strcmp(current, 'sparsewithpow') && strcmp(desired, 'sparse')
% this representation for sparse data contains autospectra
% as e.g. {'A' 'A'} in labelcmb
if isfield(data, 'crsspctrm'),
dimtok = tokenize(data.dimord, '_');
catdim = match_str(dimtok, {'chan' 'chancmb'});
data.crsspctrm = cat(catdim, data.powspctrm, data.crsspctrm);
data.labelcmb = [data.label(:) data.label(:); data.labelcmb];
data = rmfield(data, 'powspctrm');
else
data.crsspctrm = data.powspctrm;
data.labelcmb = [data.label(:) data.label(:)];
data = rmfield(data, 'powspctrm');
end
data = rmfield(data, 'label');
elseif strcmp(current, 'sparse') && strcmp(desired, 'full')
dimtok = tokenize(data.dimord, '_');
if ~isempty(strmatch('rpt', dimtok)), nrpt=size(data.cumtapcnt,1); else nrpt = 1; end
if ~isempty(strmatch('freq', dimtok)), nfrq=numel(data.freq); else nfrq = 1; end
if ~isempty(strmatch('time', dimtok)), ntim=numel(data.time); else ntim = 1; end
if ~isfield(data, 'label')
% ensure that the bivariate spectral factorization results can be
% processed. FIXME this is experimental and will not work if the user
% did something weird before
for k = 1:numel(data.labelcmb)
tmp = tokenize(data.labelcmb{k}, '[');
data.labelcmb{k} = tmp{1};
end
data.label = unique(data.labelcmb(:));
end
nchan = length(data.label);
ncmb = size(data.labelcmb,1);
cmbindx = zeros(nchan,nchan);
for k = 1:size(data.labelcmb,1)
ch1 = find(strcmp(data.label, data.labelcmb(k,1)));
ch2 = find(strcmp(data.label, data.labelcmb(k,2)));
if ~isempty(ch1) && ~isempty(ch2),
cmbindx(ch1,ch2) = k;
end
end
complete = all(cmbindx(:)~=0);
fn = fieldnames(data);
for ii=1:numel(fn)
if numel(data.(fn{ii})) == nrpt*ncmb*nfrq*ntim;
if nrpt==1,
data.(fn{ii}) = reshape(data.(fn{ii}), [nrpt ncmb nfrq ntim]);
end
tmpall = nan(nrpt,nchan,nchan,nfrq,ntim);
for j = 1:nrpt
for k = 1:ntim
for m = 1:nfrq
tmpdat = nan(nchan,nchan);
indx = find(cmbindx);
if ~complete
% this realizes the missing combinations to be represented as the
% conjugate of the corresponding combination across the diagonal
tmpdat(indx) = reshape(data.(fn{ii})(j,cmbindx(indx),m,k),[numel(indx) 1]);
tmpdat = ctranspose(tmpdat);
end
tmpdat(indx) = reshape(data.(fn{ii})(j,cmbindx(indx),m,k),[numel(indx) 1]);
tmpall(j,:,:,m,k) = tmpdat;
end % for m
end % for k
end % for j
% replace the data in the old representation with the new representation
if nrpt>1,
data.(fn{ii}) = tmpall;
else
data.(fn{ii}) = reshape(tmpall, [nchan nchan nfrq ntim]);
end
end % if numel
end % for ii
% remove obsolete fields
try, data = rmfield(data, 'powspctrm'); end
try, data = rmfield(data, 'labelcmb'); end
try, data = rmfield(data, 'dof'); end
if ntim>1,
data.dimord = 'chan_chan_freq_time';
else
data.dimord = 'chan_chan_freq';
end
if nrpt>1,
data.dimord = ['rpt_',data.dimord];
end
elseif strcmp(current, 'sparse') && strcmp(desired, 'fullfast')
dimtok = tokenize(data.dimord, '_');
if ~isempty(strmatch('rpt', dimtok)), nrpt=size(data.cumtapcnt,1); else nrpt = 1; end
if ~isempty(strmatch('freq', dimtok)), nfrq=numel(data.freq); else nfrq = 1; end
if ~isempty(strmatch('time', dimtok)), ntim=numel(data.time); else ntim = 1; end
if ~isfield(data, 'label')
data.label = unique(data.labelcmb(:));
end
nchan = length(data.label);
ncmb = size(data.labelcmb,1);
cmbindx = zeros(nchan,nchan);
for k = 1:size(data.labelcmb,1)
ch1 = find(strcmp(data.label, data.labelcmb(k,1)));
ch2 = find(strcmp(data.label, data.labelcmb(k,2)));
if ~isempty(ch1) && ~isempty(ch2),
cmbindx(ch1,ch2) = k;
end
end
complete = all(cmbindx(:)~=0);
fn = fieldnames(data);
for ii=1:numel(fn)
if numel(data.(fn{ii})) == nrpt*ncmb*nfrq*ntim;
if nrpt==1,
data.(fn{ii}) = reshape(data.(fn{ii}), [nrpt ncmb nfrq ntim]);
end
tmpall = nan(nchan,nchan,nfrq,ntim);
for k = 1:ntim
for m = 1:nfrq
tmpdat = nan(nchan,nchan);
indx = find(cmbindx);
if ~complete
% this realizes the missing combinations to be represented as the
% conjugate of the corresponding combination across the diagonal
tmpdat(indx) = reshape(nanmean(data.(fn{ii})(:,cmbindx(indx),m,k)),[numel(indx) 1]);
tmpdat = ctranspose(tmpdat);
end
tmpdat(indx) = reshape(nanmean(data.(fn{ii})(:,cmbindx(indx),m,k)),[numel(indx) 1]);
tmpall(:,:,m,k) = tmpdat;
end % for m
end % for k
% replace the data in the old representation with the new representation
if nrpt>1,
data.(fn{ii}) = tmpall;
else
data.(fn{ii}) = reshape(tmpall, [nchan nchan nfrq ntim]);
end
end % if numel
end % for ii
% remove obsolete fields
try, data = rmfield(data, 'powspctrm'); end
try, data = rmfield(data, 'labelcmb'); end
try, data = rmfield(data, 'dof'); end
if ntim>1,
data.dimord = 'chan_chan_freq_time';
else
data.dimord = 'chan_chan_freq';
end
elseif strcmp(current, 'sparsewithpow') && strcmp(desired, 'full')
% this is how is currently done in prepare_freq_matrices
data = ft_checkdata(data, 'cmbrepresentation', 'sparse');
data = ft_checkdata(data, 'cmbrepresentation', 'full');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% convert to new source representation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [output] = fixsource(input, varargin)
% FIXSOURCE converts old style source structures into new style source structures and the
% other way around
%
% Use as:
% fixsource(input, type)
% where input is a source structure,
%
% Typically, old style source structures contain
% avg.XXX or trial.XXX fields
%
% The new style source structure contains:
% source.pos
% source.dim (optional, if the list of positions describes a 3D volume
% source.XXX the old style subfields in avg/trial
% source.XXXdimord string how to interpret the respective XXX field:
% e.g. source.leadfield = cell(1,Npos), source.leadfielddimord = '{pos}_chan_ori'
% source.mom = cell(1,Npos), source.momdimord = '{pos}_ori_rpttap'
type = ft_getopt(varargin, 'type');
haspow = ft_getopt(varargin, 'haspow');
if isempty(type), type = 'old'; end
if isempty(haspow), haspow = 'no'; end
fnames = fieldnames(input);
tmp = cell2mat(strfind(fnames, 'dimord')); %get dimord like fields
if any(tmp>1),
current = 'new';
elseif any(tmp==1),
%don't know what to do yet data is JM's own invention
current = 'old';
else
current = 'old';
end
if strcmp(current, type),
%do nothing
output = input;
%return
elseif strcmp(current, 'old') && strcmp(type, 'new'),
%go from old to new
if isfield(input, 'avg'),
stuff = getfield(input, 'avg');
output = rmfield(input, 'avg');
elseif isfield(input, 'trial'),
stuff = getfield(input, 'trial');
output = rmfield(input, 'trial');
else
%this could occur later in the pipeline, e.g. when doing group statistics using individual subject
%descriptive statistics
error('the input does not contain an avg or trial field');
end
%-------------------------------------------------
%remove and rename the specified fields if present
removefields = {'xgrid';'ygrid';'zgrid';'method'};
renamefields = {'frequency' 'freq'; 'csdlabel' 'orilabel'};
fnames = fieldnames(output);
for k = 1:numel(fnames)
ix = strmatch(fnames{k}, removefields);
if ~isempty(ix),
output = rmfield(output, fnames{k});
end
ix = strmatch(fnames{k}, renamefields(:,1), 'exact');
if ~isempty(ix),
output = setfield(output, renamefields{ix,2}, ...
getfield(output, renamefields{ix,1}));
output = rmfield(output, fnames{k});
end
end
%----------------------------------------------------------------------
%put the stuff originally in avg or trial one level up in the structure
fnames = fieldnames(stuff(1));
npos = size(input.pos,1);
nrpt = numel(stuff);
for k = 1:numel(fnames)
if nrpt>1,
%multiple trials
%(or subjects FIXME not yet implemented, nor tested)
tmp = getfield(stuff(1), fnames{k});
siz = size(tmp);
if isfield(input, 'cumtapcnt') && strcmp(fnames{k}, 'mom')
%pcc based mom is orixrpttap
%tranpose to keep manageable
for kk = 1:numel(input.inside)
indx = input.inside(kk);
tmp{indx} = permute(tmp{indx}, [2 1 3]);
end
nrpttap = sum(input.cumtapcnt);
sizvox = [size(tmp{input.inside(1)}) 1];
sizvox = [nrpttap sizvox(2:end)];
elseif strcmp(fnames{k}, 'mom'),
%this is then probably not a frequency based mom
nrpttap = numel(stuff);
sizvox = [size(tmp{input.inside(1)}) 1];
sizvox = [nrpttap sizvox];
elseif iscell(tmp)
nrpttap = numel(stuff);
sizvox = [size(tmp{input.inside(1)}) 1];
sizvox = [nrpttap sizvox];
end
if siz(1) ~= npos && siz(2) ==npos,
tmp = transpose(tmp);
end
if iscell(tmp)
%allocate memory for cell-array
tmpall = cell(npos,1);
for n = 1:numel(input.inside)
tmpall{input.inside(n)} = zeros(sizvox);
end
else
%allocate memory for matrix
tmpall = zeros([npos nrpt siz(2:end)]);
end
cnt = 0;
for m = 1:nrpt
tmp = getfield(stuff(m), fnames{k});
siz = size(tmp);
if siz(1) ~= npos && siz(2) ==npos,
tmp = transpose(tmp);
end
if ~iscell(tmp),
tmpall(:,m,:,:,:) = tmp;
else
for n = 1:numel(input.inside)
indx = input.inside(n);
tmpdat = tmp{indx};
if isfield(input, 'cumtapcnt') && strcmp(fnames{k}, 'mom'),
if n==1, siz1 = size(tmpdat,2); end
else
if n==1, siz1 = 1; end
end
tmpall{indx}(cnt+[1:siz1],:,:,:,:) = tmpdat;
if n==numel(input.inside), cnt = cnt + siz1; end
end
end
end
output = setfield(output, fnames{k}, tmpall);
newdimord = createdimord(output, fnames{k}, 1);
if ~isempty(newdimord)
output = setfield(output, [fnames{k},'dimord'], newdimord);
end
else
tmp = getfield(stuff, fnames{k});
siz = size(tmp);
if isfield(input, 'cumtapcnt') && strcmp(fnames{k}, 'mom')
%pcc based mom is orixrpttap
%tranpose to keep manageable
for kk = 1:numel(input.inside)
indx = input.inside(kk);
tmp{indx} = permute(tmp{indx}, [2 1 3]);
end
end
if siz(1) ~= npos && siz(2) ==npos,
tmp = transpose(tmp);
end
output = setfield(output, fnames{k}, tmp);
newdimord = createdimord(output, fnames{k});
if ~isempty(newdimord)
output = setfield(output, [fnames{k},'dimord'], newdimord);
end
end
end
if isfield(output, 'csdlabel')
output = setfield(output, 'orilabel', getfield(output, 'csdlabel'));
output = rmfield(output, 'csdlabel');
end
if isfield(output, 'leadfield')
% add dimord to leadfield as well. since the leadfield is not in
% the original .avg or .trial field it has not yet been taken care of
output.leadfielddimord = createdimord(output, 'leadfield');
end
if isfield(output, 'ori')
% convert cell-array ori into matrix
ori = nan(3,npos);
try,
ori(:,output.inside) = cat(2, output.ori{output.inside});
catch
%when oris are in wrong orientation (row rather than column)
for k = 1:numel(output.inside)
ori(:,output.inside(k)) = output.ori{output.inside(k)}';
end
end
output.ori = ori;
end
current = 'new';
elseif strcmp(current, 'new') && strcmp(type, 'old')
%go from new to old
error('not implemented yet');
end
if strcmp(current, 'new') && strcmp(haspow, 'yes'),
%----------------------------------------------
%convert mom into pow if requested and possible
convert = 0;
if isfield(output, 'mom') && size(output.mom{output.inside(1)},2)==1,
convert = 1;
else
warning('conversion from mom to pow is not possible, either because there is no mom in the data, or because the dimension of mom>1. in that case call ft_sourcedescriptives first with cfg.projectmom');
end
if isfield(output, 'cumtapcnt')
convert = 1 & convert;
else
warning('conversion from mom to pow will not be done, because cumtapcnt is missing');
end
if convert,
npos = size(output.pos,1);
nrpt = size(output.cumtapcnt,1);
tmpmom = cat(2,output.mom{output.inside});
tmppow = zeros(npos, nrpt);
tapcnt = [0;cumsum(output.cumtapcnt(:))];
for k = 1:nrpt
ntap = tapcnt(k+1)-tapcnt(k);
tmppow(output.inside,k) = sum(abs(tmpmom((tapcnt(k)+1):tapcnt(k+1),:)).^2,1)./ntap;
end
output.pow = tmppow;
output.powdimord = ['pos_rpt_freq'];
end
elseif strcmp(current, 'old') && strcmp(haspow, 'yes')
warning('construction of single trial power estimates is not implemented here using old style source representation');
end
%--------------------------------------------------------
function [dimord] = createdimord(output, fname, rptflag);
if nargin==2, rptflag = 0; end
tmp = getfield(output, fname);
dimord = '';
dimnum = 1;
hasori = isfield(output, 'ori'); %if not, this is probably singleton and not relevant at the end
if iscell(tmp) && (size(output.pos,1)==size(tmp,dimnum) || size(output.pos,1)==size(tmp,2))
dimord = [dimord,'{pos}'];
dimnum = dimnum + 1;
elseif ~iscell(tmp) && size(output.pos,1)==size(tmp,dimnum)
dimord = [dimord,'pos'];
dimnum = dimnum + 1;
end
switch fname
case 'cov'
if hasori, dimord = [dimord,'_ori_ori']; end;
case 'csd'
if hasori, dimord = [dimord,'_ori_ori']; end;
case 'csdlabel'
dimord = dimord;
case 'filter'
dimord = [dimord,'_ori_chan'];
case 'leadfield'
%if hasori,
dimord = [dimord,'_chan_ori'];
%else
% dimord = [dimord,'_chan'];
%end
case 'mom'
if isfield(output, 'cumtapcnt') && sum(output.cumtapcnt)==size(tmp{output.inside(1)},1)
if hasori,
dimord = [dimord,'_rpttap_ori'];
else
dimord = [dimord,'_rpttap'];
end
elseif isfield(output, 'time')
if rptflag,
dimord = [dimord,'_rpt'];
dimnum = dimnum + 1;
end
if numel(output.time)==size(tmp{output.inside(1)},dimnum)
dimord = [dimord,'_ori_time'];
end
end
if isfield(output, 'freq') && numel(output.freq)>1,
dimord = [dimord,'_freq'];
end
case 'nai'
if isfield(output, 'freq') && numel(output.freq)==size(tmp,dimnum)
dimord = [dimord,'_freq'];
end
case 'noise'
if isfield(output, 'freq') && numel(output.freq)==size(tmp,dimnum)
dimord = [dimord,'_freq'];
end
case 'noisecsd'
if hasori, dimord = [dimord,'_ori_ori']; end
case 'ori'
dimord = '';
case 'pow'
if isfield(output, 'cumtapcnt') && size(output.cumtapcnt,1)==size(tmp,dimnum)
dimord = [dimord,'_rpt'];
dimnum = dimnum + 1;
end
if isfield(output, 'freq') && numel(output.freq)>1 && numel(output.freq)==size(tmp,dimnum)
dimord = [dimord,'_freq'];
dimnum = dimnum+1;
end
if isfield(output, 'time') && numel(output.time)>1 && numel(output.time)==size(tmp,dimnum)
dimord = [dimord,'_time'];
dimnum = dimnum+1;
end
otherwise
warning('skipping unknown fieldname %s', fname);
%error(sprintf('unknown fieldname %s', fname));
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% convert between datatypes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function data = comp2raw(data)
% remove the fields that are specific to the comp representation
fn = fieldnames(data);
fn = intersect(fn, {'topo' 'topolabel' 'unmixing'});
data = rmfield(data, fn);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% convert between datatypes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function data = volume2source(data)
if isfield(data, 'dimord')
% it is a modern source description
else
% it is an old-fashioned source description
xgrid = 1:data.dim(1);
ygrid = 1:data.dim(2);
zgrid = 1:data.dim(3);
[x y z] = ndgrid(xgrid, ygrid, zgrid);
data.pos = warp_apply(data.transform, [x(:) y(:) z(:)]);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% convert between datatypes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function data = source2volume(data)
if isfield(data, 'dimord')
% it is a modern source description
%this part depends on the assumption that the list of positions is describing a full 3D volume in
%an ordered way which allows for the extraction of a transformation matrix
%i.e. slice by slice
try,
if isfield(data, 'dim'),
data.dim = pos2dim(data.pos, data.dim);
else
data.dim = pos2dim(data);
end
catch
end
end
if isfield(data, 'dim') && length(data.dim)>=3,
% it is an old-fashioned source description, or the source describes a regular 3D volume in pos
xgrid = 1:data.dim(1);
ygrid = 1:data.dim(2);
zgrid = 1:data.dim(3);
[x y z] = ndgrid(xgrid, ygrid, zgrid);
ind = [x(:) y(:) z(:)]; % these are the positions expressed in voxel indices along each of the three axes
pos = data.pos; % these are the positions expressed in head coordinates
% represent the positions in a manner that is compatible with the homogeneous matrix multiplication,
% i.e. pos = H * ind
ind = ind'; ind(4,:) = 1;
pos = pos'; pos(4,:) = 1;
% recompute the homogeneous transformation matrix
data.transform = pos / ind;
end
% remove the unwanted fields
if isfield(data, 'pos'), data = rmfield(data, 'pos'); end
if isfield(data, 'xgrid'), data = rmfield(data, 'xgrid'); end
if isfield(data, 'ygrid'), data = rmfield(data, 'ygrid'); end
if isfield(data, 'zgrid'), data = rmfield(data, 'zgrid'); end
% make inside a volume
data = fixinside(data, 'logical');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% convert between datatypes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function data = freq2raw(freq)
if strcmp(freq.dimord, 'rpt_chan_freq_time')
dat = freq.powspctrm;
elseif strcmp(freq.dimord, 'rpttap_chan_freq_time')
warning('converting fourier representation into raw data format. this is experimental code');
dat = freq.fourierspctrm;
else
error('this only works for dimord=''rpt_chan_freq_time''');
end
nrpt = size(dat,1);
nchan = size(dat,2);
nfreq = size(dat,3);
ntime = size(dat,4);
data = [];
% create the channel labels like "MLP11@12Hz""
k = 0;
for i=1:nfreq
for j=1:nchan
k = k+1;
data.label{k} = sprintf('%s@%dHz', freq.label{j}, freq.freq(i));
end
end
% reshape and copy the data as if it were timecourses only
for i=1:nrpt
data.time{i} = freq.time;
data.trial{i} = reshape(dat(i,:,:,:), nchan*nfreq, ntime);
if any(isnan(data.trial{i}(1,:))),
tmp = data.trial{i}(1,:);
begsmp = find(isfinite(tmp),1, 'first');
endsmp = find(isfinite(tmp),1, 'last' );
data.trial{i} = data.trial{i}(:, begsmp:endsmp);
data.time{i} = data.time{i}(begsmp:endsmp);
end
end
nsmp = cellfun('size',data.time,2);
seln = find(nsmp>1,1, 'first');
if isfield(freq, 'trialinfo'), data.trialinfo = freq.trialinfo; end;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% convert between datatypes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [data] = raw2timelock(data)
nsmp = cellfun('size',data.time,2);
data = ft_checkdata(data, 'hassampleinfo', 'yes');
ntrial = numel(data.trial);
nchan = numel(data.label);
if ntrial==1
data.time = data.time{1};
data.avg = data.trial{1};
data = rmfield(data, 'trial');
data.dimord = 'chan_time';
else
begtime = cellfun(@min,data.time);
endtime = cellfun(@max,data.time);
% this part is just about the number of samples, not about the time-axis
for i = 1:ntrial
time = data.time{i};
mint = min([ 0, begtime(i)]);
maxt = max([-max(abs(2*endtime)) * eps, endtime(i)]);
% extrapolate so that we get near 0
if (mint==0)
tmptime = -1*(fliplr(-maxt:mean(diff(time)):-mint));
else
tmptime = mint:mean(diff(time)):maxt;
end
ix(i) = sum(tmptime<0); % number of samples pre-zero
iy(i) = sum(tmptime>=0); % number of samples post-zero
% account for strictly positive or negative time-axes by removing those
% elements that are near 0 but should not be in the time-axis
if ix(i)==0
ix(i) = 1-nearest(tmptime, begtime(i));
end
if iy(i)==0
iy(i) = nearest(tmptime, endtime(i))-length(tmptime);
end
end
[mx,ix2] = max(ix);
[my,iy2] = max(iy);
nsmp = mx+my;
% create temporary time-axis
time = linspace(min(begtime), max(endtime), nsmp);
% remove any time-points before 0 iff not needed - see bug 1477
time(nearest(time, max(endtime))+1:end) = [];
% concatenate all trials
tmptrial = nan(ntrial, nchan, length(time));
for i=1:ntrial
begsmp(i) = nearest(time, data.time{i}(1));
endsmp(i) = nearest(time, data.time{i}(end));
tmptrial(i,:,begsmp(i):endsmp(i)) = data.trial{i};
end
% update the sampleinfo
begpad = begsmp - min(begsmp);
endpad = max(endsmp) - endsmp;
if isfield(data, 'sampleinfo')
data.sampleinfo = data.sampleinfo + [-begpad(:) endpad(:)];
end
% construct the output timelocked data
% data.avg = reshape(nanmean(tmptrial, 1), nchan, length(tmptime));
% data.var = reshape(nanvar (tmptrial, [], 1), nchan, length(tmptime))
% data.dof = reshape(sum(~isnan(tmptrial), 1), nchan, length(tmptime));
data.trial = tmptrial;
data.time = time;
data.dimord = 'rpt_chan_time';
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% convert between datatypes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [data] = timelock2raw(data)
try
nsmp = cellfun('size',data.time,2);
catch
nsmp = size(data.time,2);
end
switch data.dimord
case 'chan_time'
data.trial{1} = data.avg;
data.time = {data.time};
data = rmfield(data, 'avg');
seln = find(nsmp>1,1, 'first');
case 'rpt_chan_time'
tmptrial = {};
tmptime = {};
ntrial = size(data.trial,1);
nchan = size(data.trial,2);
ntime = size(data.trial,3);
for i=1:ntrial
tmptrial{i} = reshape(data.trial(i,:,:), [nchan, ntime]);
tmptime{i} = data.time;
end
data = rmfield(data, 'trial');
data.trial = tmptrial;
data.time = tmptime;
seln = find(nsmp>1,1, 'first');
case 'subj_chan_time'
tmptrial = {};
tmptime = {};
ntrial = size(data.individual,1);
nchan = size(data.individual,2);
ntime = size(data.individual,3);
for i=1:ntrial
tmptrial{i} = reshape(data.individual(i,:,:), [nchan, ntime]);
tmptime{i} = data.time;
end
data = rmfield(data, 'individual');
data.trial = tmptrial;
data.time = tmptime;
seln = find(nsmp>1,1, 'first');
otherwise
error('unsupported dimord');
end
% remove the unwanted fields
if isfield(data, 'avg'), data = rmfield(data, 'avg'); end
if isfield(data, 'var'), data = rmfield(data, 'var'); end
if isfield(data, 'cov'), data = rmfield(data, 'cov'); end
if isfield(data, 'dimord'), data = rmfield(data, 'dimord'); end
if isfield(data, 'numsamples'), data = rmfield(data, 'numsamples'); end
if isfield(data, 'dof'), data = rmfield(data, 'dof'); end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% convert between datatypes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [data] = chan2freq(data)
data.dimord = [data.dimord '_freq'];
data.freq = 0;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% convert between datatypes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [data] = chan2timelock(data)
data.dimord = [data.dimord '_time'];
data.time = 0;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% convert between datatypes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [spike] = raw2spike(data)
fprintf('converting raw data into spike data\n');
nTrials = length(data.trial);
[spikelabel] = detectspikechan(data);
spikesel = match_str(data.label, spikelabel);
nUnits = length(spikesel);
if nUnits==0
error('cannot convert raw data to spike format since the raw data structure does not contain spike channels');
end
trialTimes = zeros(nTrials,2);
for iUnit = 1:nUnits
unitIndx = spikesel(iUnit);
spikeTimes = []; % we dont know how large it will be, so use concatenation inside loop
trialInds = [];
for iTrial = 1:nTrials
% read in the spike times
[spikeTimesTrial] = getspiketimes(data, iTrial, unitIndx);
nSpikes = length(spikeTimesTrial);
spikeTimes = [spikeTimes; spikeTimesTrial(:)];
trialInds = [trialInds; ones(nSpikes,1)*iTrial];
% get the begs and ends of trials
hasNum = find(~isnan(data.time{iTrial}));
if iUnit==1, trialTimes(iTrial,:) = data.time{iTrial}([hasNum(1) hasNum(end)]); end
end
spike.label{iUnit} = data.label{unitIndx};
spike.waveform{iUnit} = [];
spike.time{iUnit} = spikeTimes(:)';
spike.trial{iUnit} = trialInds(:)';
if iUnit==1, spike.trialtime = trialTimes; end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% sub function for detection channels
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [spikelabel, eeglabel] = detectspikechan(data)
maxRate = 2000; % default on what we still consider a neuronal signal: this firing rate should never be exceeded
% autodetect the spike channels
ntrial = length(data.trial);
nchans = length(data.label);
spikechan = zeros(nchans,1);
for i=1:ntrial
for j=1:nchans
hasAllInts = all(isnan(data.trial{i}(j,:)) | data.trial{i}(j,:) == round(data.trial{i}(j,:)));
hasAllPosInts = all(isnan(data.trial{i}(j,:)) | data.trial{i}(j,:)>=0);
T = nansum(diff(data.time{i})); % total time
fr = nansum(data.trial{i}(j,:)) ./ T;
spikechan(j) = spikechan(j) + double(hasAllInts & hasAllPosInts & fr<=maxRate);
end
end
spikechan = (spikechan==ntrial);
spikelabel = data.label(spikechan);
eeglabel = data.label(~spikechan);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [spikeTimes] = getspiketimes(data, trial, unit)
spikeIndx = logical(data.trial{trial}(unit,:));
spikeCount = data.trial{trial}(unit,spikeIndx);
spikeTimes = data.time{trial}(spikeIndx);
if isempty(spikeTimes), return; end
multiSpikes = find(spikeCount>1);
% get the additional samples and spike times, we need only loop through the bins
[addSamples, addTimes] = deal([]);
for iBin = multiSpikes(:)' % looping over row vector
addTimes = [addTimes ones(1,spikeCount(iBin))*spikeTimes(iBin)];
addSamples = [addSamples ones(1,spikeCount(iBin))*spikeIndx(iBin)];
end
% before adding these times, first remove the old ones
spikeTimes(multiSpikes) = [];
spikeTimes = sort([spikeTimes(:); addTimes(:)]);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% convert between datatypes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [data] = spike2raw(spike, fsample)
if nargin<2 || isempty(fsample)
timeDiff = abs(diff(sort([spike.time{:}])));
fsample = 1/min(timeDiff(timeDiff>0));
warning('Desired sampling rate for spike data not specified, automatically resampled to %f', fsample);
end
% get some sizes
nUnits = length(spike.label);
nTrials = size(spike.trialtime,1);
% preallocate
data.trial(1:nTrials) = {[]};
data.time(1:nTrials) = {[]};
for iTrial = 1:nTrials
% make bins: note that the spike.time is already within spike.trialtime
x = [spike.trialtime(iTrial,1):(1/fsample):spike.trialtime(iTrial,2)];
timeBins = [x x(end)+1/fsample] - (0.5/fsample);
time = (spike.trialtime(iTrial,1):(1/fsample):spike.trialtime(iTrial,2));
% convert to continuous
trialData = zeros(nUnits,length(time));
for iUnit = 1:nUnits
% get the timestamps and only select those timestamps that are in the trial
ts = spike.time{iUnit};
hasTrial = spike.trial{iUnit}==iTrial;
ts = ts(hasTrial);
N = histc(ts,timeBins);
if isempty(N)
N = zeros(1,length(timeBins)-1);
else
N(end) = [];
end
% store it in a matrix
trialData(iUnit,:) = N;
end
data.trial{iTrial} = trialData;
data.time{iTrial} = time;
end
% create the associated labels and other aspects of data such as the header
data.label = spike.label;
data.fsample = fsample;
if isfield(spike,'hdr'), data.hdr = spike.hdr; end
if isfield(spike,'cfg'), data.cfg = spike.cfg; end
|
github
|
philippboehmsturm/antx-master
|
nanvar.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/nanvar.m
| 1,093 |
utf_8
|
d9641af3bba1e2c6e3512199221e686c
|
% NANVAR provides a replacement for MATLAB's nanvar that is almost
% compatible.
%
% For usage see VAR. Note that the weight-vector is not supported. If you
% need it, please file a ticket at our bugtracker.
function Y = nanvar(X, w, dim)
switch nargin
case 1
% VAR(x)
% Normalize by n-1 when no dim is given.
Y = nanvar_base(X);
n = nannumel(X);
w = 0;
case 2
% VAR(x, 1)
% VAR(x, w)
% In this case, the default of normalizing by n is expected.
Y = nanvar_base(X);
n = nannumel(X);
case 3
% VAR(x, w, dim)
% if w=0 normalize by n-1, if w=1 normalize by n.
Y = nanvar_base(X, dim);
n = nannumel(X, dim);
otherwise
error ('Too many input arguments!')
end
% Handle different forms of normalization:
if numel(w) == 0
% empty weights vector defaults to 0
w = 0;
end
if numel(w) ~= 1
error('Weighting vector w is not implemented! Please file a bug.');
end
if ~isreal(X)
Y = real(Y) + imag(Y);
n = real(n);
end
if w == 1
Y = Y ./ n;
end
if w == 0
Y = Y ./ max(1, (n - 1)); % don't divide by zero!
end
|
github
|
philippboehmsturm/antx-master
|
read_yokogawa_data.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_yokogawa_data.m
| 11,038 |
utf_8
|
aa2c8a06c417ada7e9af353f2307443e
|
function [dat] = read_yokogawa_data(filename, hdr, begsample, endsample, chanindx)
% READ_YOKAGAWA_DATA reads continuous, epoched or averaged MEG data
% that has been generated by the Yokogawa MEG system and software
% and allows that data to be used in combination with FieldTrip.
%
% Use as
% [dat] = read_yokogawa_data(filename, hdr, begsample, endsample, chanindx)
%
% This is a wrapper function around the functions
% GetMeg160ContinuousRawDataM
% GetMeg160EvokedAverageDataM
% GetMeg160EvokedRawDataM
%
% See also READ_YOKOGAWA_HEADER, READ_YOKOGAWA_EVENT
% Copyright (C) 2005, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: read_yokogawa_data.m 7123 2012-12-06 21:21:38Z roboos $
if ~ft_hastoolbox('yokogawa')
error('cannot determine whether Yokogawa toolbox is present');
end
% hdr = read_yokogawa_header(filename);
hdr = hdr.orig; % use the original Yokogawa header, not the FieldTrip header
% default is to select all channels
if nargin<5
chanindx = 1:hdr.channel_count;
end
handles = definehandles;
fid = fopen(filename, 'rb', 'ieee-le');
switch hdr.acq_type
case handles.AcqTypeEvokedAve
% Data is returned by double.
start_sample = begsample - 1; % samples start at 0
sample_length = endsample - begsample + 1;
epoch_count = 1;
start_epoch = 0;
dat = double(GetMeg160EvokedAverageDataM( fid, start_sample, sample_length ));
% the first extra sample is the channel number
channum = dat(:,1);
dat = dat(:,2:end);
case handles.AcqTypeContinuousRaw
% Data is returned by int16.
start_sample = begsample - 1; % samples start at 0
sample_length = endsample - begsample + 1;
epoch_count = 1;
start_epoch = 0;
dat = double(GetMeg160ContinuousRawDataM( fid, start_sample, sample_length ));
% the first extra sample is the channel number
channum = dat(:,1);
dat = dat(:,2:end);
case handles.AcqTypeEvokedRaw
% Data is returned by int16.
begtrial = ceil(begsample/hdr.sample_count);
endtrial = ceil(endsample/hdr.sample_count);
if begtrial<1
error('cannot read before the begin of the file');
elseif endtrial>hdr.actual_epoch_count
error('cannot read beyond the end of the file');
end
epoch_count = endtrial-begtrial+1;
start_epoch = begtrial-1;
% read all the neccessary trials that contain the desired samples
dat = double(GetMeg160EvokedRawDataM( fid, start_epoch, epoch_count ));
% the first extra sample is the channel number
channum = dat(:,1);
dat = dat(:,2:end);
if size(dat,2)~=epoch_count*hdr.sample_count
error('could not read all epochs');
end
rawbegsample = begsample - (begtrial-1)*hdr.sample_count;
rawendsample = endsample - (begtrial-1)*hdr.sample_count;
sample_length = rawendsample - rawbegsample + 1;
% select the desired samples from the complete trials
dat = dat(:,rawbegsample:rawendsample);
otherwise
error('unknown data type');
end
fclose(fid);
if size(dat,1)~=hdr.channel_count
error('could not read all channels');
elseif size(dat,2)~=(endsample-begsample+1)
error('could not read all samples');
end
% Count of AxialGradioMeter
ch_type = hdr.channel_info(:,2);
index = find(ch_type==[handles.AxialGradioMeter]);
axialgradiometer_index_tmp = index;
axialgradiometer_ch_count = length(index);
% Count of PlannerGradioMeter
ch_type = hdr.channel_info(:,2);
index = find(ch_type==[handles.PlannerGradioMeter]);
plannergradiometer_index_tmp = index;
plannergradiometer_ch_count = length(index);
% Count of EegChannel
ch_type = hdr.channel_info(:,2);
index = find(ch_type==[handles.EegChannel]);
eegchannel_index_tmp = index;
eegchannel_ch_count = length(index);
% Count of NullChannel
ch_type = hdr.channel_info(:,2);
index = find(ch_type==[handles.NullChannel]);
nullchannel_index_tmp = index;
nullchannel_ch_count = length(index);
%%% Pulling out AxialGradioMeter and value conversion to physical units.
if ~isempty(axialgradiometer_index_tmp)
% Acquisition of channel information
axialgradiometer_index = axialgradiometer_index_tmp;
ch_info = hdr.channel_info;
axialgradiometer_ch_info = ch_info(axialgradiometer_index, :);
% Value conversion
% B = ( ADValue * VoltRange / ADRange - Offset ) * Sensitivity / FLLGain
calib = hdr.calib_info;
amp_gain = hdr.amp_gain(1);
tmp_ch_no = channum(axialgradiometer_index, 1);
tmp_data = dat(axialgradiometer_index, 1:sample_length);
tmp_offset = calib(axialgradiometer_index, 3) * ones(1,sample_length);
ad_range = 5/2^(hdr.ad_bit-1);
tmp_data = ( tmp_data * ad_range - tmp_offset );
clear tmp_offset;
tmp_gain = calib(axialgradiometer_index, 2) * ones(1,sample_length);
tmp_data = tmp_data .* tmp_gain / amp_gain;
dat(axialgradiometer_index, 1:sample_length) = tmp_data;
clear tmp_gain;
% Deletion of Inf row
index = find(axialgradiometer_ch_info(1,:) == Inf);
axialgradiometer_ch_info(:,index) = [];
% Deletion of channel_type row
axialgradiometer_ch_info(:,2) = [];
% Outputs to the global variable
handles.sqd.axialgradiometer_ch_info = axialgradiometer_ch_info;
handles.sqd.axialgradiometer_ch_no = tmp_ch_no;
handles.sqd.axialgradiometer_data = [ tmp_ch_no tmp_data];
clear tmp_data;
end
%%% Pulling out PlannerGradioMeter and value conversion to physical units.
if ~isempty(plannergradiometer_index_tmp)
% Acquisition of channel information
plannergradiometer_index = plannergradiometer_index_tmp;
ch_info = hdr.channel_info;
plannergradiometer_ch_info = ch_info(plannergradiometer_index, :);
% Value conversion
% B = ( ADValue * VoltRange / ADRange - Offset ) * Sensitivity / FLLGain
calib = hdr.calib_info;
amp_gain = hdr.amp_gain(1);
tmp_ch_no = channum(plannergradiometer_index, 1);
tmp_data = dat(plannergradiometer_index, 1:sample_length);
tmp_offset = calib(plannergradiometer_index, 3) * ones(1,sample_length);
ad_range = 5/2^(hdr.ad_bit-1);
tmp_data = ( tmp_data * ad_range - tmp_offset );
clear tmp_offset;
tmp_gain = calib(plannergradiometer_index, 2) * ones(1,sample_length);
tmp_data = tmp_data .* tmp_gain / amp_gain;
dat(plannergradiometer_index, 1:sample_length) = tmp_data;
clear tmp_gain;
% Deletion of Inf row
index = find(plannergradiometer_ch_info(1,:) == Inf);
plannergradiometer_ch_info(:,index) = [];
% Deletion of channel_type row
plannergradiometer_ch_info(:,2) = [];
% Outputs to the global variable
handles.sqd.plannergradiometer_ch_info = plannergradiometer_ch_info;
handles.sqd.plannergradiometer_ch_no = tmp_ch_no;
handles.sqd.plannergradiometer_data = [ tmp_ch_no tmp_data];
clear tmp_data;
end
%%% Pulling out EegChannel Channel and value conversion to Volt units.
if ~isempty(eegchannel_index_tmp)
% Acquisition of channel information
eegchannel_index = eegchannel_index_tmp;
% Value conversion
% B = ADValue * VoltRange / ADRange
tmp_ch_no = channum(eegchannel_index, 1);
tmp_data = dat(eegchannel_index, 1:sample_length);
ad_range = 5/2^(hdr.ad_bit-1);
tmp_data = tmp_data * ad_range;
dat(eegchannel_index, 1:sample_length) = tmp_data;
% Outputs to the global variable
handles.sqd.eegchannel_ch_no = tmp_ch_no;
handles.sqd.eegchannel_data = [ tmp_ch_no tmp_data];
clear tmp_data;
end
%%% Pulling out Null Channel and value conversion to Volt units.
if ~isempty(nullchannel_index_tmp)
% Acquisition of channel information
nullchannel_index = nullchannel_index_tmp;
% Value conversion
% B = ADValue * VoltRange / ADRange
tmp_ch_no = channum(nullchannel_index, 1);
tmp_data = dat(nullchannel_index, 1:sample_length);
ad_range = 5/2^(hdr.ad_bit-1);
tmp_data = tmp_data * ad_range;
dat(nullchannel_index, 1:sample_length) = tmp_data;
% Outputs to the global variable
handles.sqd.nullchannel_ch_no = tmp_ch_no;
handles.sqd.nullchannel_data = [ tmp_ch_no tmp_data];
clear tmp_data;
end
% select only the desired channels
dat = dat(chanindx,:);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% this defines some usefull constants
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function handles = definehandles
handles.output = [];
handles.sqd_load_flag = false;
handles.mri_load_flag = false;
handles.NullChannel = 0;
handles.MagnetoMeter = 1;
handles.AxialGradioMeter = 2;
handles.PlannerGradioMeter = 3;
handles.RefferenceChannelMark = hex2dec('0100');
handles.RefferenceMagnetoMeter = bitor( handles.RefferenceChannelMark, handles.MagnetoMeter );
handles.RefferenceAxialGradioMeter = bitor( handles.RefferenceChannelMark, handles.AxialGradioMeter );
handles.RefferencePlannerGradioMeter = bitor( handles.RefferenceChannelMark, handles.PlannerGradioMeter );
handles.TriggerChannel = -1;
handles.EegChannel = -2;
handles.EcgChannel = -3;
handles.EtcChannel = -4;
handles.NonMegChannelNameLength = 32;
handles.DefaultMagnetometerSize = (4.0/1000.0); % Square of 4.0mm in length
handles.DefaultAxialGradioMeterSize = (15.5/1000.0); % Circle of 15.5mm in diameter
handles.DefaultPlannerGradioMeterSize = (12.0/1000.0); % Square of 12.0mm in length
handles.AcqTypeContinuousRaw = 1;
handles.AcqTypeEvokedAve = 2;
handles.AcqTypeEvokedRaw = 3;
handles.sqd = [];
handles.sqd.selected_start = [];
handles.sqd.selected_end = [];
handles.sqd.axialgradiometer_ch_no = [];
handles.sqd.axialgradiometer_ch_info = [];
handles.sqd.axialgradiometer_data = [];
handles.sqd.plannergradiometer_ch_no = [];
handles.sqd.plannergradiometer_ch_info = [];
handles.sqd.plannergradiometer_data = [];
handles.sqd.eegchannel_ch_no = [];
handles.sqd.eegchannel_data = [];
handles.sqd.nullchannel_ch_no = [];
handles.sqd.nullchannel_data = [];
handles.sqd.selected_time = [];
handles.sqd.sample_rate = [];
handles.sqd.sample_count = [];
handles.sqd.pretrigger_length = [];
handles.sqd.matching_info = [];
handles.sqd.source_info = [];
handles.sqd.mri_info = [];
handles.mri = [];
|
github
|
philippboehmsturm/antx-master
|
read_biff.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_biff.m
| 5,857 |
utf_8
|
d71281c0e6be7868430597d71c166b5d
|
function [this] = read_biff(filename, opt)
% READ_BIFF reads data and header information from a BIFF file
%
% This is a attemt for a reference implementation to read the BIFF
% file format as defined by the Clinical Neurophysiology department of
% the University Medical Centre, Nijmegen.
%
% read all data and information
% [data] = read_biff(filename)
% or read a selected top-level chunk
% [chunk] = read_biff(filename, chunkID)
%
% known top-level chunk id's are
% data : measured data (matrix)
% dati : information on data (struct)
% expi : information on experiment (struct)
% pati : information on patient (struct)
% evnt : event markers (struct)
% Copyright (C) 2000, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: read_biff.m 7123 2012-12-06 21:21:38Z roboos $
define_biff;
this = [];
fid = fopen(filename, 'r');
fseek(fid,0,'eof');
eof = ftell(fid);
fseek(fid,0,'bof');
[id, siz] = chunk_header(fid);
switch id
case 'SEMG'
child = subtree(BIFF, id);
this = read_biff_chunk(fid, id, siz, child);
case 'LIST'
fprintf('skipping unimplemented chunk id="%s" size=%4d\n', id, siz);
case 'CAT '
fprintf('skipping unimplemented chunk id="%s" size=%4d\n', id, siz);
otherwise
fprintf('skipping unrecognized chunk id="%s" size=%4d\n', id, siz);
fseek(fid, siz, 'cof');
end % switch
fclose(fid); % close file
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION read_biff_chunk
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function this = read_biff_chunk(fid, id, siz, chunk);
% start with empty structure
this = [];
if strcmp(id, 'null') % this is an empty chunk
fprintf('skipping empty chunk id="%s" size=%4d\n', id, siz);
assert(~feof(fid));
fseek(fid, siz, 'cof');
elseif isempty(chunk) % this is an unrecognized chunk
fprintf('skipping unrecognized chunk id="%s" size=%4d\n', id, siz);
assert(~feof(fid));
fseek(fid, siz, 'cof');
else
eoc = ftell(fid) + siz;
name = char(chunk.desc(2));
type = char(chunk.desc(3));
fprintf('reading chunk id= "%s" size=%4d name="%s"\n', id, siz, name);
switch type
case 'group'
while ~feof(fid) & ftell(fid)<eoc
% read all subchunks
[id, siz] = chunk_header(fid);
child = subtree(chunk, id);
if ~isempty(child)
% read data and add subchunk data to chunk structure
name = char(child.desc(2));
val = read_biff_chunk(fid, id, siz, child);
this = setfield(this, name, val);
else
fprintf('skipping unrecognized chunk id="%s" size=%4d\n', id, siz);
fseek(fid, siz, 'cof');
end
end % while
case 'string'
this = char(fread(fid, siz, 'uchar')');
case {'char', 'uchar', 'int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'float32', 'float64'}
this = fread(fid, 1, type);
case {'char', 'uchar', 'int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'float32', 'float64'}
this = fread(fid, 1, type);
case {'int8vec', 'int16vec', 'int32vec', 'int64vec', 'uint8vec', 'uint16vec', 'uint32vec', 'float32vec', 'float64vec'}
ncol = fread(fid, 1, 'uint32');
this = fread(fid, ncol, type(1:(length(type)-3)));
case {'int8mat', 'int16mat', 'int32mat', 'int64mat', 'uint8mat', 'uint16mat', 'uint32mat', 'float32mat', 'float64mat'}
nrow = fread(fid, 1, 'uint32');
ncol = fread(fid, 1, 'uint32');
this = fread(fid, [nrow, ncol], type(1:(length(type)-3)));
otherwise
fseek(fid, siz, 'cof'); % skip this chunk
sprintf('unimplemented data type "%s" in chunk "%s"', type, id);
% warning(ans);
end % switch chunk type
end % else
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION subtree
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function child = subtree(parent, id);
blank = findstr(id, ' ');
while ~isempty(blank)
id(blank) = '_';
blank = findstr(id, ' ');
end
elem = fieldnames(parent); % list of all subitems
num = find(strcmp(elem, id)); % number in parent tree
if size(num) == [1,1]
child = getfield(parent, char(elem(num))); % child subtree
else
child = [];
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION chunk_header
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [id, siz] = chunk_header(fid);
id = char(fread(fid, 4, 'uchar')'); % read chunk ID
siz = fread(fid, 1, 'uint32'); % read chunk size
if strcmp(id, 'GRP ') | strcmp(id, 'BIFF')
id = char(fread(fid, 4, 'uchar')'); % read real chunk ID
siz = siz - 4; % reduce size by 4
end
|
github
|
philippboehmsturm/antx-master
|
read_eeglabheader.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_eeglabheader.m
| 2,255 |
utf_8
|
fe4446f32b250441d57acff9ebe691a2
|
% read_eeglabheader() - import EEGLAB dataset files
%
% Usage:
% >> header = read_eeglabheader(filename);
%
% Inputs:
% filename - [string] file name
%
% Outputs:
% header - FILEIO toolbox type structure
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, 2008-
%123456789012345678901234567890123456789012345678901234567890123456789012
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD, [email protected]
%
% 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 2 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, write to the Free Software
% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
function header = read_eeglabheader(filename)
if nargin < 1
help read_eeglabheader;
return;
end;
if ~isstruct(filename)
load('-mat', filename);
else
EEG = filename;
end;
header.Fs = EEG.srate;
header.nChans = EEG.nbchan;
header.nSamples = EEG.pnts;
header.nSamplesPre = -EEG.xmin*EEG.srate;
header.nTrials = EEG.trials;
try
header.label = { EEG.chanlocs.labels }';
catch
warning('creating default channel names');
for i=1:header.nChans
header.label{i} = sprintf('chan%03d', i);
end
end
ind = 1;
for i = 1:length( EEG.chanlocs )
if isfield(EEG.chanlocs(i), 'X') && ~isempty(EEG.chanlocs(i).X)
header.elec.label{ind, 1} = EEG.chanlocs(i).labels;
% this channel has a position
header.elec.pnt(ind,1) = EEG.chanlocs(i).X;
header.elec.pnt(ind,2) = EEG.chanlocs(i).Y;
header.elec.pnt(ind,3) = EEG.chanlocs(i).Z;
ind = ind+1;
end;
end;
% remove data
% -----------
%if isfield(EEG, 'datfile')
% if ~isempty(EEG.datfile)
% EEG.data = EEG.datfile;
% end;
%else
% EEG.data = 'in set file';
%end;
EEG.icaact = [];
header.orig = EEG;
|
github
|
philippboehmsturm/antx-master
|
read_ctf_svl.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_ctf_svl.m
| 3,812 |
utf_8
|
d3442d0a013cf5e0a8d4277d99e45206
|
% [data, hdr] = opensvl(filename)
%
% Reads a CTF SAM (.svl) file.
function [data, hdr] = read_ctf_svl(filename)
fid = fopen(filename, 'rb', 'ieee-be', 'ISO-8859-1');
if fid <= 0
error('Could not open SAM file: %s\n', filename);
end
% ----------------------------------------------------------------------
% Read header.
hdr.identity = fread(fid, 8, '*char')'; % 'SAMIMAGE'
hdr.version = fread(fid, 1, 'int32'); % SAM file version.
hdr.setName = fread(fid, 256, '*char')'; % Dataset name.
hdr.numChans = fread(fid, 1, 'int32');
hdr.numWeights = fread(fid, 1, 'int32'); % 0 for static image.
if(hdr.numWeights ~= 0)
warning('hdr.numWeights ~= 0');
end
fread(fid,1,'int32'); % Padding to next 8 byte boundary.
hdr.xmin = fread(fid, 1, 'double'); % Bounding box coordinates (m).
hdr.xmax = fread(fid, 1, 'double');
hdr.ymin = fread(fid, 1, 'double');
hdr.ymax = fread(fid, 1, 'double');
hdr.zmin = fread(fid, 1, 'double');
hdr.zmax = fread(fid, 1, 'double');
hdr.stepSize = fread(fid, 1, 'double'); % m
hdr.hpFreq = fread(fid, 1, 'double'); % High pass filtering frequency (Hz).
hdr.lpFreq = fread(fid, 1, 'double'); % Low pass.
hdr.bwFreq = fread(fid, 1, 'double'); % Bandwidth
hdr.meanNoise = fread(fid, 1, 'double'); % Sensor noise (T).
hdr.mriName = fread(fid, 256, '*char')';
hdr.fiducial.mri.nas = fread(fid, 3, 'int32'); % CTF MRI voxel coordinates?
hdr.fiducial.mri.rpa = fread(fid, 3, 'int32');
hdr.fiducial.mri.lpa = fread(fid, 3, 'int32');
hdr.SAMType = fread(fid, 1, 'int32'); % 0: image, 1: weights array, 2: weights list.
hdr.SAMUnit = fread(fid, 1, 'int32');
% Possible values: 0 coefficients Am/T, 1 moment Am, 2 power (Am)^2, 3 Z,
% 4 F, 5 T, 6 probability, 7 MUSIC.
fread(fid, 1, 'int32'); % Padding to next 8 byte boundary.
if hdr.version > 1
% Version 2 has extra fields.
hdr.fiducial.head.nas = fread(fid, 3, 'double'); % CTF head coordinates?
hdr.fiducial.head.rpa = fread(fid, 3, 'double');
hdr.fiducial.head.lpa = fread(fid, 3, 'double');
hdr.SAMUnitName = fread(fid, 32, '*char')';
% Possible values: 'Am/T' SAM coefficients, 'Am' source strength,
% '(Am)^2' source power, ('Z', 'F', 'T') statistics, 'P' probability.
end
% ----------------------------------------------------------------------
% Read image data.
data = fread(fid, inf, 'double');
fclose(fid);
% Raw image data is ordered as a C array with indices: [x][y][z], meaning
% z changes fastest and x slowest. These x, y, z axes point to ALS
% (anterior, left, superior) respectively in real world coordinates,
% which means the voxels are in SLA order.
% ----------------------------------------------------------------------
% Post processing.
% Change from m to mm.
hdr.xmin = hdr.xmin * 1000;
hdr.ymin = hdr.ymin * 1000;
hdr.zmin = hdr.zmin * 1000;
hdr.xmax = hdr.xmax * 1000;
hdr.ymax = hdr.ymax * 1000;
hdr.zmax = hdr.zmax * 1000;
hdr.stepSize = hdr.stepSize * 1000;
% Number of voxels in each dimension.
hdr.dim = [round((hdr.xmax - hdr.xmin)/hdr.stepSize) + 1, ...
round((hdr.ymax - hdr.ymin)/hdr.stepSize) + 1, ...
round((hdr.zmax - hdr.zmin)/hdr.stepSize) + 1];
data = reshape(data, hdr.dim([3, 2, 1]));
% Build transformation matrix from raw voxel coordinates (indexed from 1)
% to head coordinates in mm. Note that the bounding box is given in
% these coordinates (in m, but converted above).
% Apply scaling.
hdr.transform = diag([hdr.stepSize * ones(1, 3), 1]);
% Reorder directions.
hdr.transform = hdr.transform(:, [3, 2, 1, 4]);
% Apply translation.
hdr.transform(1:3, 4) = [hdr.xmin; hdr.ymin; hdr.zmin] - hdr.stepSize;
% -step is needed since voxels are indexed from 1.
end
|
github
|
philippboehmsturm/antx-master
|
read_erplabevent.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_erplabevent.m
| 1,786 |
utf_8
|
40ece49ff6bd2afd6024b46f210e65fa
|
% read_erplabevent() - import ERPLAB dataset events
%
% Usage:
% >> event = read_erplabevent(filename, ...);
%
% Inputs:
% filename - [string] file name
%
% Optional inputs:
% 'header' - FILEIO structure header
%
% Outputs:
% event - FILEIO toolbox event structure
%
% Modified from read_eeglabevent
%123456789012345678901234567890123456789012345678901234567890123456789012
%
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD, [email protected]
%
% 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 2 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, write to the Free Software
% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
function event = read_erplabevent(filename, varargin)
if nargin < 1
help read_erplabheader;
return;
end;
hdr = ft_getopt(varargin, 'header');
if isempty(hdr)
hdr = read_erplabheader(filename);
end
event = []; % these will be the output in FieldTrip format
oldevent = hdr.orig.bindescr; % these are in ERPLAB format
for index = 1:length(oldevent)
event(end+1).type = 'trial';
event(end ).sample = (index-1)*hdr.nSamples + 1;
event(end ).value = oldevent{index};
event(end ).offset = -hdr.nSamplesPre;
event(end ).duration = hdr.nSamples;
end;
|
github
|
philippboehmsturm/antx-master
|
read_yokogawa_header_new.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_yokogawa_header_new.m
| 8,959 |
utf_8
|
654f373c60405e9a27c40d65ab0147dd
|
function hdr = read_yokogawa_header_new(filename)
% READ_YOKOGAWA_HEADER_NEW reads the header information from continuous,
% epoched or averaged MEG data that has been generated by the Yokogawa
% MEG system and software and allows that data to be used in combination
% with FieldTrip.
%
% Use as
% [hdr] = read_yokogawa_header_new(filename)
%
% This is a wrapper function around the functions
% getYkgwHdrSystem
% getYkgwHdrChannel
% getYkgwHdrAcqCond
% getYkgwHdrCoregist
% getYkgwHdrDigitize
% getYkgwHdrSource
%
% See also READ_YOKOGAWA_DATA_NEW, READ_YOKOGAWA_EVENT
% **
% Copyright (C) 2005, Robert Oostenveld and 2010, Tilmann Sander-Thoemmes
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: read_yokogawa_header_new.m 7123 2012-12-06 21:21:38Z roboos $
% FIXED
% txt -> m
% fopen iee-le
if ~ft_hastoolbox('yokogawa_meg_reader')
error('cannot determine whether Yokogawa toolbox is present');
end
handles = definehandles;
sys_info = getYkgwHdrSystem(filename);
id = sys_info.system_id;
ver = sys_info.version;
rev = sys_info.revision;
sys_name = sys_info.system_name;
model_name = sys_info.model_name;
clear('sys_info'); % remove structure as local variables are collected in the end
channel_info = getYkgwHdrChannel(filename);
channel_count = channel_info.channel_count;
acq_cond = getYkgwHdrAcqCond(filename);
acq_type = acq_cond.acq_type;
% these depend on the data type
sample_rate = [];
sample_count = [];
pretrigger_length = [];
averaged_count = [];
actual_epoch_count = [];
switch acq_type
case handles.AcqTypeContinuousRaw
sample_rate = acq_cond.sample_rate;
sample_count = acq_cond.sample_count;
if isempty(sample_rate) | isempty(sample_count)
error('invalid sample rate or sample count in ', filename);
return;
end
pretrigger_length = 0;
averaged_count = 1;
case handles.AcqTypeEvokedAve
sample_rate = acq_cond.sample_rate;
sample_count = acq_cond.frame_length;
pretrigger_length = acq_cond.pretrigger_length;
averaged_count = acq_cond.average_count;
if isempty(sample_rate) | isempty(sample_count) | isempty(pretrigger_length) | isempty(averaged_count)
error('invalid sample rate or sample count or pretrigger length or average count in ', filename);
return;
end
if acq_cond.multi_trigger.enable
error('multi trigger mode not supported for ', filename);
return;
end
case handles.AcqTypeEvokedRaw
sample_rate = acq_cond.sample_rate;
sample_count = acq_cond.frame_length;
pretrigger_length = acq_cond.pretrigger_length;
actual_epoch_count = acq_cond.average_count;
if isempty(sample_rate) | isempty(sample_count) | isempty(pretrigger_length) | isempty(actual_epoch_count)
error('invalid sample rate or sample count or pretrigger length or epoch count in ', filename);
return;
end
if acq_cond.multi_trigger.enable
error('multi trigger mode not supported for ', filename);
return;
end
otherwise
error('unknown data type');
end
clear('acq_cond'); % remove structure as local variables are collected in the end
coregist = getYkgwHdrCoregist(filename);
digitize = getYkgwHdrDigitize(filename);
source = getYkgwHdrSource(filename);
% put all local variables into a structure, this is a bit unusual matlab programming style
tmp = whos;
orig = [];
for i=1:length(tmp)
if isempty(strmatch(tmp(i).name, {'tmp', 'ans', 'handles'}))
orig = setfield(orig, tmp(i).name, eval(tmp(i).name));
end
end
% convert the original header information into something that FieldTrip understands
hdr = [];
hdr.orig = orig; % also store the original full header information
hdr.Fs = orig.sample_rate; % sampling frequency
hdr.nChans = orig.channel_count; % number of channels
hdr.nSamples = []; % number of samples per trial
hdr.nSamplesPre = []; % number of pre-trigger samples in each trial
hdr.nTrials = []; % number of trials
switch orig.acq_type
case handles.AcqTypeEvokedAve
hdr.nSamples = orig.sample_count;
hdr.nSamplesPre = orig.pretrigger_length;
hdr.nTrials = 1; % only the average, which can be considered as a single trial
case handles.AcqTypeContinuousRaw
hdr.nSamples = orig.sample_count;
hdr.nSamplesPre = 0; % there is no fixed relation between triggers and data
hdr.nTrials = 1; % the continuous data can be considered as a single very long trial
case handles.AcqTypeEvokedRaw
hdr.nSamples = orig.sample_count;
hdr.nSamplesPre = orig.pretrigger_length;
hdr.nTrials = orig.actual_epoch_count;
otherwise
error('unknown acquisition type');
end
% construct a cell-array with labels of each channel
for i=1:hdr.nChans
% this should be consistent with the predefined list in ft_senslabel,
% with yokogawa2grad_new and with ft_channelselection
if hdr.orig.channel_info.channel(i).type == handles.NullChannel
prefix = '';
elseif hdr.orig.channel_info.channel(i).type == handles.MagnetoMeter
prefix = 'M';
elseif hdr.orig.channel_info.channel(i).type == handles.AxialGradioMeter
prefix = 'AG';
elseif hdr.orig.channel_info.channel(i).type == handles.PlannerGradioMeter
prefix = 'PG';
elseif hdr.orig.channel_info.channel(i).type == handles.RefferenceMagnetoMeter
prefix = 'RM';
elseif hdr.orig.channel_info.channel(i).type == handles.RefferenceAxialGradioMeter
prefix = 'RAG';
elseif hdr.orig.channel_info.channel(i).type == handles.RefferencePlannerGradioMeter
prefix = 'RPG';
elseif hdr.orig.channel_info.channel(i).type == handles.TriggerChannel
prefix = 'TRIG';
elseif hdr.orig.channel_info.channel(i).type == handles.EegChannel
prefix = 'EEG';
elseif hdr.orig.channel_info.channel(i).type == handles.EcgChannel
prefix = 'ECG';
elseif hdr.orig.channel_info.channel(i).type == handles.EtcChannel
prefix = 'ETC';
end
hdr.label{i} = sprintf('%s%03d', prefix, i);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% this defines some usefull constants
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function handles = definehandles;
handles.output = [];
handles.sqd_load_flag = false;
handles.mri_load_flag = false;
handles.NullChannel = 0;
handles.MagnetoMeter = 1;
handles.AxialGradioMeter = 2;
handles.PlannerGradioMeter = 3;
handles.RefferenceChannelMark = hex2dec('0100');
handles.RefferenceMagnetoMeter = bitor( handles.RefferenceChannelMark, handles.MagnetoMeter );
handles.RefferenceAxialGradioMeter = bitor( handles.RefferenceChannelMark, handles.AxialGradioMeter );
handles.RefferencePlannerGradioMeter = bitor( handles.RefferenceChannelMark, handles.PlannerGradioMeter );
handles.TriggerChannel = -1;
handles.EegChannel = -2;
handles.EcgChannel = -3;
handles.EtcChannel = -4;
handles.NonMegChannelNameLength = 32;
handles.DefaultMagnetometerSize = (4.0/1000.0); % Square of 4.0mm in length
handles.DefaultAxialGradioMeterSize = (15.5/1000.0); % Circle of 15.5mm in diameter
handles.DefaultPlannerGradioMeterSize = (12.0/1000.0); % Square of 12.0mm in length
handles.AcqTypeContinuousRaw = 1;
handles.AcqTypeEvokedAve = 2;
handles.AcqTypeEvokedRaw = 3;
handles.sqd = [];
handles.sqd.selected_start = [];
handles.sqd.selected_end = [];
handles.sqd.axialgradiometer_ch_no = [];
handles.sqd.axialgradiometer_ch_info = [];
handles.sqd.axialgradiometer_data = [];
handles.sqd.plannergradiometer_ch_no = [];
handles.sqd.plannergradiometer_ch_info = [];
handles.sqd.plannergradiometer_data = [];
handles.sqd.eegchannel_ch_no = [];
handles.sqd.eegchannel_data = [];
handles.sqd.nullchannel_ch_no = [];
handles.sqd.nullchannel_data = [];
handles.sqd.selected_time = [];
handles.sqd.sample_rate = [];
handles.sqd.sample_count = [];
handles.sqd.pretrigger_length = [];
handles.sqd.matching_info = [];
handles.sqd.source_info = [];
handles.sqd.mri_info = [];
handles.mri = [];
|
github
|
philippboehmsturm/antx-master
|
ft_datatype_raw.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/ft_datatype_raw.m
| 10,365 |
utf_8
|
8e666a629bcfec8802a49fc7d1eb4d8c
|
function data = ft_datatype_raw(data, varargin)
% FT_DATATYPE_RAW describes the FieldTrip MATLAB structure for raw data
%
% The raw datatype represents sensor-level time-domain data typically
% obtained after calling FT_DEFINETRIAL and FT_PREPROCESSING. It contains
% one or multiple segments of data, each represented as Nchan X Ntime
% arrays.
%
% An example of a raw data structure with 151 MEG channels is
%
% label: {151x1 cell} the channel labels (e.g. 'MRC13')
% time: {1x266 cell} the timeaxis [1*Ntime double] per trial
% trial: {1x266 cell} the numeric data [151*Ntime double] per trial
% sampleinfo: [266x2 double] the begin and endsample of each trial relative to the recording on disk
% trialinfo: [266x1 double] optional trigger or condition codes for each trial
% hdr: [1x1 struct] the full header information of the original dataset on disk
% grad: [1x1 struct] information about the sensor array (for EEG it is called elec)
% cfg: [1x1 struct] the configuration used by the function that generated this data structure
%
% Required fields:
% - time, trial, label
%
% Optional fields:
% - sampleinfo, trialinfo, grad, elec, hdr, cfg
%
% Deprecated fields:
% - fsample
%
% Obsoleted fields:
% - offset
%
% Revision history:
%
% (2011/latest) The description of the sensors has changed, see FT_DATATYPE_SENS
% for further information.
%
% (2010v2) The trialdef field has been replaced by the sampleinfo and
% trialinfo fields. The sampleinfo corresponds to trl(:,1:2), the trialinfo
% to trl(4:end).
%
% (2010v1) In 2010/Q3 it shortly contained the trialdef field which was a copy
% of the trial definition (trl) is generated by FT_DEFINETRIAL.
%
% (2007) It used to contain the offset field, which correcponds to trl(:,3).
% Since the offset field is redundant with the time axis, the offset field is
% from now on not present any more. It can be recreated if needed.
%
% (2003) The initial version was defined
%
% See also FT_DATATYPE, FT_DATATYPE_COMP, FT_DATATYPE_TIMELOCK, FT_DATATYPE_FREQ,
% FT_DATATYPE_SPIKE, FT_DATATYPE_SENS
% Copyright (C) 2011, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_datatype_raw.m 7217 2012-12-17 19:45:35Z roboos $
% get the optional input arguments, which should be specified as key-value pairs
version = ft_getopt(varargin, 'version', 'latest');
hassampleinfo = ft_getopt(varargin, 'hassampleinfo', 'ifmakessense'); % can be yes/no/ifmakessense
hastrialinfo = ft_getopt(varargin, 'hastrialinfo', 'ifmakessense'); % can be yes/no/ifmakessense
if isequal(hassampleinfo, 'ifmakessense')
hassampleinfo = 'yes';
if isfield(data, 'sampleinfo') && size(data.sampleinfo,1)~=numel(data.trial)
% it does not make sense, so don't keep it
hassampleinfo = 'no';
end
if isfield(data, 'sampleinfo')
numsmp = data.sampleinfo(:,2)-data.sampleinfo(:,1)+1;
for i=1:length(data.trial)
if size(data.trial{i},2)~=numsmp(i);
% it does not make sense, so don't keep it
hassampleinfo = 'no';
break;
end
end
end
if strcmp(hassampleinfo, 'no')
% the actual removal will be done further down
warning('removing inconsistent sampleinfo');
end
end
if isequal(hastrialinfo, 'ifmakessense')
hastrialinfo = 'yes';
if isfield(data, 'trialinfo') && size(data.trialinfo,1)~=numel(data.trial)
% it does not make sense, so don't keep it
hastrialinfo = 'no';
end
if strcmp(hastrialinfo, 'no')
% the actual removal will be done further down
warning('removing inconsistent sampleinfo');
end
end
% convert it into true/false
hassampleinfo = istrue(hassampleinfo);
hastrialinfo = istrue(hastrialinfo);
if strcmp(version, 'latest')
version = '2011';
end
if isempty(data)
return;
end
switch version
case '2011'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if isfield(data, 'grad')
% ensure that the gradiometer balancing is specified
if ~isfield(data.grad, 'balance') || ~isfield(data.grad.balance, 'current')
data.grad.balance.current = 'none';
end
% ensure the new style sensor description
data.grad = ft_datatype_sens(data.grad);
end
if isfield(data, 'elec')
data.elec = ft_datatype_sens(data.elec);
end
if ~isfield(data, 'fsample')
data.fsample = 1/mean(diff(data.time{1}));
end
if isfield(data, 'offset')
data = rmfield(data, 'offset');
end
% the trialdef field should be renamed into sampleinfo
if isfield(data, 'trialdef')
data.sampleinfo = data.trialdef;
data = rmfield(data, 'trialdef');
end
if (hassampleinfo && ~isfield(data, 'sampleinfo')) || (hastrialinfo && ~isfield(data, 'trialinfo'))
% try to reconstruct the sampleinfo and trialinfo
data = fixsampleinfo(data);
end
if ~hassampleinfo && isfield(data, 'sampleinfo')
data = rmfield(data, 'sampleinfo');
end
if ~hastrialinfo && isfield(data, 'trialinfo')
data = rmfield(data, 'trialinfo');
end
case '2010v2'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~isfield(data, 'fsample')
data.fsample = 1/mean(diff(data.time{1}));
end
if isfield(data, 'offset')
data = rmfield(data, 'offset');
end
% the trialdef field should be renamed into sampleinfo
if isfield(data, 'trialdef')
data.sampleinfo = data.trialdef;
data = rmfield(data, 'trialdef');
end
if (hassampleinfo && ~isfield(data, 'sampleinfo')) || (hastrialinfo && ~isfield(data, 'trialinfo'))
% try to reconstruct the sampleinfo and trialinfo
data = fixsampleinfo(data);
end
if ~hassampleinfo && isfield(data, 'sampleinfo')
data = rmfield(data, 'sampleinfo');
end
if ~hastrialinfo && isfield(data, 'trialinfo')
data = rmfield(data, 'trialinfo');
end
case {'2010v1' '2010'}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~isfield(data, 'fsample')
data.fsample = 1/mean(diff(data.time{1}));
end
if isfield(data, 'offset')
data = rmfield(data, 'offset');
end
if ~isfield(data, 'trialdef') && hascfg
% try to find it in the nested configuration history
data.trialdef = ft_findcfg(data.cfg, 'trl');
end
case '2007'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~isfield(data, 'fsample')
data.fsample = 1/mean(diff(data.time{1}));
end
if isfield(data, 'offset')
data = rmfield(data, 'offset');
end
case '2003'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~isfield(data, 'fsample')
data.fsample = 1/mean(diff(data.time{1}));
end
if ~isfield(data, 'offset')
data.offset = zeros(length(data.time),1);
for i=1:length(data.time);
data.offset(i) = round(data.time{i}(1)*data.fsample);
end
end
otherwise
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
error('unsupported version "%s" for raw datatype', version);
end
% Numerical inaccuracies in the binary representations of floating point
% values may accumulate. The following code corrects for small inaccuracies
% in the time axes of the trials. See http://bugzilla.fcdonders.nl/show_bug.cgi?id=1390
data = fixtimeaxes(data);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function data = fixtimeaxes(data)
if ~isfield(data, 'fsample')
fsample = 1/mean(diff(data.time{1}));
else
fsample = data.fsample;
end
begtime = zeros(1, length(data.time));
endtime = zeros(1, length(data.time));
numsample = zeros(1, length(data.time));
for i=1:length(data.time)
begtime(i) = data.time{i}(1);
endtime(i) = data.time{i}(end);
numsample(i) = length(data.time{i});
end
% compute the differences over trials and the tolerance
tolerance = 0.01*(1/fsample);
begdifference = abs(begtime-begtime(1));
enddifference = abs(endtime-endtime(1));
% check whether begin and/or end are identical, or close to identical
begidentical = all(begdifference==0);
endidentical = all(enddifference==0);
begsimilar = all(begdifference < tolerance);
endsimilar = all(enddifference < tolerance);
% Compute the offset of each trial relative to the first trial, and express
% that in samples. Non-integer numbers indicate that there is a slight skew
% in the time over trials. This works in case of variable length trials.
offset = fsample * (begtime-begtime(1));
skew = abs(offset - round(offset));
% try to determine all cases where a correction is needed
% note that this does not yet address all possible cases where a fix might be needed
needfix = false;
needfix = needfix || ~begidentical && begsimilar;
needfix = needfix || ~endidentical && endsimilar;
needfix = needfix || ~all(skew==0) && all(skew<0.01);
% if the skew is less than 1% it will be corrected
if needfix
warning_once('correcting numerical inaccuracy in the time axes');
for i=1:length(data.time)
% reconstruct the time axis of each trial, using the begin latency of
% the first trial and the integer offset in samples of each trial
data.time{i} = begtime(1) + ((1:numsample(i)) - 1 + round(offset(i)))/fsample;
end
end
|
github
|
philippboehmsturm/antx-master
|
loadcnt.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/loadcnt.m
| 23,166 |
utf_8
|
3629cdf2a890ef76f423f15fff965b3a
|
% loadcnt() - Load a Neuroscan continuous signal file.
%
% Usage:
% >> cnt = loadcnt(file, varargin)
%
% Inputs:
% filename - name of the file with extension
%
% Optional inputs:
% 't1' - start at time t1, default 0. Warning, events latency
% might be innacurate (this is an open issue).
% 'sample1' - start at sample1, default 0, overrides t1. Warning,
% events latency might be innacurate.
% 'lddur' - duration of segment to load, default = whole file
% 'ldnsamples' - number of samples to load, default = whole file,
% overrides lddur
% 'scale' - ['on'|'off'] scale data to microvolt (default:'on')
% 'dataformat' - ['int16'|'int32'] default is 'int16' for 16-bit data.
% Use 'int32' for 32-bit data.
% 'blockread' - [integer] by default it is automatically determined
% from the file header, though sometimes it finds an
% incorect value, so you may want to enter a value manually
% here (1 is the most standard value).
% 'memmapfile' - ['memmapfile_name'] use this option if the .cnt file
% is too large to read in conventially. The suffix of
% the memmapfile_name must be .fdt. The memmapfile
% functions process files based on their suffix, and an
% error will occur if you use a different suffix.
%
% Outputs:
% cnt - structure with the continuous data and other informations
% cnt.header
% cnt.electloc
% cnt.data
% cnt.tag
%
% Authors: Sean Fitzgibbon, Arnaud Delorme, 2000-
%
% Note: function original name was load_scan41.m
%
% Known limitations:
% For more see http://www.cnl.salk.edu/~arno/cntload/index.html
% Copyright (C) 2000 Sean Fitzgibbon, <[email protected]>
% Copyright (C) 2003 Arnaud Delorme, Salk Institute, [email protected]
%
% 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 2 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, write to the Free Software
% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
function [f,lab,ev2p] = loadcnt(filename,varargin)
if ~isempty(varargin)
r=struct(varargin{:});
else r = [];
end;
try, r.t1; catch, r.t1=0; end
try, r.sample1; catch, r.sample1=[]; end
try, r.lddur; catch, r.lddur=[]; end
try, r.ldnsamples; catch, r.ldnsamples=[]; end
try, r.scale; catch, r.scale='on'; end
try, r.blockread; catch, r.blockread = []; end
try, r.dataformat; catch, r.dataformat = 'auto'; end
try, r.memmapfile; catch, r.memmapfile = ''; end
sizeEvent1 = 8 ; %%% 8 bytes for Event1
sizeEvent2 = 19 ; %%% 19 bytes for Event2
sizeEvent3 = 19 ; %%% 19 bytes for Event3
type='cnt';
if nargin ==1
scan=0;
end
fid = fopen(filename,'r', 'l');
disp(['Loading file ' filename ' ...'])
h.rev = fread(fid,12,'char');
h.nextfile = fread(fid,1,'long');
h.prevfile = fread(fid,1,'ulong');
h.type = fread(fid,1,'char');
h.id = fread(fid,20,'char');
h.oper = fread(fid,20,'char');
h.doctor = fread(fid,20,'char');
h.referral = fread(fid,20,'char');
h.hospital = fread(fid,20,'char');
h.patient = fread(fid,20,'char');
h.age = fread(fid,1,'short');
h.sex = fread(fid,1,'char');
h.hand = fread(fid,1,'char');
h.med = fread(fid,20, 'char');
h.category = fread(fid,20, 'char');
h.state = fread(fid,20, 'char');
h.label = fread(fid,20, 'char');
h.date = fread(fid,10, 'char');
h.time = fread(fid,12, 'char');
h.mean_age = fread(fid,1,'float');
h.stdev = fread(fid,1,'float');
h.n = fread(fid,1,'short');
h.compfile = fread(fid,38,'char');
h.spectwincomp = fread(fid,1,'float');
h.meanaccuracy = fread(fid,1,'float');
h.meanlatency = fread(fid,1,'float');
h.sortfile = fread(fid,46,'char');
h.numevents = fread(fid,1,'int');
h.compoper = fread(fid,1,'char');
h.avgmode = fread(fid,1,'char');
h.review = fread(fid,1,'char');
h.nsweeps = fread(fid,1,'ushort');
h.compsweeps = fread(fid,1,'ushort');
h.acceptcnt = fread(fid,1,'ushort');
h.rejectcnt = fread(fid,1,'ushort');
h.pnts = fread(fid,1,'ushort');
h.nchannels = fread(fid,1,'ushort');
h.avgupdate = fread(fid,1,'ushort');
h.domain = fread(fid,1,'char');
h.variance = fread(fid,1,'char');
h.rate = fread(fid,1,'ushort'); % A USER CLAIMS THAT SAMPLING RATE CAN BE
h.scale = fread(fid,1,'double'); % FRACTIONAL IN NEUROSCAN WHICH IS
h.veogcorrect = fread(fid,1,'char'); % OBVIOUSLY NOT POSSIBLE HERE (BUG 606)
h.heogcorrect = fread(fid,1,'char');
h.aux1correct = fread(fid,1,'char');
h.aux2correct = fread(fid,1,'char');
h.veogtrig = fread(fid,1,'float');
h.heogtrig = fread(fid,1,'float');
h.aux1trig = fread(fid,1,'float');
h.aux2trig = fread(fid,1,'float');
h.heogchnl = fread(fid,1,'short');
h.veogchnl = fread(fid,1,'short');
h.aux1chnl = fread(fid,1,'short');
h.aux2chnl = fread(fid,1,'short');
h.veogdir = fread(fid,1,'char');
h.heogdir = fread(fid,1,'char');
h.aux1dir = fread(fid,1,'char');
h.aux2dir = fread(fid,1,'char');
h.veog_n = fread(fid,1,'short');
h.heog_n = fread(fid,1,'short');
h.aux1_n = fread(fid,1,'short');
h.aux2_n = fread(fid,1,'short');
h.veogmaxcnt = fread(fid,1,'short');
h.heogmaxcnt = fread(fid,1,'short');
h.aux1maxcnt = fread(fid,1,'short');
h.aux2maxcnt = fread(fid,1,'short');
h.veogmethod = fread(fid,1,'char');
h.heogmethod = fread(fid,1,'char');
h.aux1method = fread(fid,1,'char');
h.aux2method = fread(fid,1,'char');
h.ampsensitivity = fread(fid,1,'float');
h.lowpass = fread(fid,1,'char');
h.highpass = fread(fid,1,'char');
h.notch = fread(fid,1,'char');
h.autoclipadd = fread(fid,1,'char');
h.baseline = fread(fid,1,'char');
h.offstart = fread(fid,1,'float');
h.offstop = fread(fid,1,'float');
h.reject = fread(fid,1,'char');
h.rejstart = fread(fid,1,'float');
h.rejstop = fread(fid,1,'float');
h.rejmin = fread(fid,1,'float');
h.rejmax = fread(fid,1,'float');
h.trigtype = fread(fid,1,'char');
h.trigval = fread(fid,1,'float');
h.trigchnl = fread(fid,1,'char');
h.trigmask = fread(fid,1,'short');
h.trigisi = fread(fid,1,'float');
h.trigmin = fread(fid,1,'float');
h.trigmax = fread(fid,1,'float');
h.trigdir = fread(fid,1,'char');
h.autoscale = fread(fid,1,'char');
h.n2 = fread(fid,1,'short');
h.dir = fread(fid,1,'char');
h.dispmin = fread(fid,1,'float');
h.dispmax = fread(fid,1,'float');
h.xmin = fread(fid,1,'float');
h.xmax = fread(fid,1,'float');
h.automin = fread(fid,1,'float');
h.automax = fread(fid,1,'float');
h.zmin = fread(fid,1,'float');
h.zmax = fread(fid,1,'float');
h.lowcut = fread(fid,1,'float');
h.highcut = fread(fid,1,'float');
h.common = fread(fid,1,'char');
h.savemode = fread(fid,1,'char');
h.manmode = fread(fid,1,'char');
h.ref = fread(fid,10,'char');
h.rectify = fread(fid,1,'char');
h.displayxmin = fread(fid,1,'float');
h.displayxmax = fread(fid,1,'float');
h.phase = fread(fid,1,'char');
h.screen = fread(fid,16,'char');
h.calmode = fread(fid,1,'short');
h.calmethod = fread(fid,1,'short');
h.calupdate = fread(fid,1,'short');
h.calbaseline = fread(fid,1,'short');
h.calsweeps = fread(fid,1,'short');
h.calattenuator = fread(fid,1,'float');
h.calpulsevolt = fread(fid,1,'float');
h.calpulsestart = fread(fid,1,'float');
h.calpulsestop = fread(fid,1,'float');
h.calfreq = fread(fid,1,'float');
h.taskfile = fread(fid,34,'char');
h.seqfile = fread(fid,34,'char');
h.spectmethod = fread(fid,1,'char');
h.spectscaling = fread(fid,1,'char');
h.spectwindow = fread(fid,1,'char');
h.spectwinlength = fread(fid,1,'float');
h.spectorder = fread(fid,1,'char');
h.notchfilter = fread(fid,1,'char');
h.headgain = fread(fid,1,'short');
h.additionalfiles = fread(fid,1,'int');
h.unused = fread(fid,5,'char');
h.fspstopmethod = fread(fid,1,'short');
h.fspstopmode = fread(fid,1,'short');
h.fspfvalue = fread(fid,1,'float');
h.fsppoint = fread(fid,1,'short');
h.fspblocksize = fread(fid,1,'short');
h.fspp1 = fread(fid,1,'ushort');
h.fspp2 = fread(fid,1,'ushort');
h.fspalpha = fread(fid,1,'float');
h.fspnoise = fread(fid,1,'float');
h.fspv1 = fread(fid,1,'short');
h.montage = fread(fid,40,'char');
h.eventfile = fread(fid,40,'char');
h.fratio = fread(fid,1,'float');
h.minor_rev = fread(fid,1,'char');
h.eegupdate = fread(fid,1,'short');
h.compressed = fread(fid,1,'char');
h.xscale = fread(fid,1,'float');
h.yscale = fread(fid,1,'float');
h.xsize = fread(fid,1,'float');
h.ysize = fread(fid,1,'float');
h.acmode = fread(fid,1,'char');
h.commonchnl = fread(fid,1,'uchar');
h.xtics = fread(fid,1,'char');
h.xrange = fread(fid,1,'char');
h.ytics = fread(fid,1,'char');
h.yrange = fread(fid,1,'char');
h.xscalevalue = fread(fid,1,'float');
h.xscaleinterval = fread(fid,1,'float');
h.yscalevalue = fread(fid,1,'float');
h.yscaleinterval = fread(fid,1,'float');
h.scaletoolx1 = fread(fid,1,'float');
h.scaletooly1 = fread(fid,1,'float');
h.scaletoolx2 = fread(fid,1,'float');
h.scaletooly2 = fread(fid,1,'float');
h.port = fread(fid,1,'short');
h.numsamples = fread(fid,1,'ulong');
h.filterflag = fread(fid,1,'char');
h.lowcutoff = fread(fid,1,'float');
h.lowpoles = fread(fid,1,'short');
h.highcutoff = fread(fid,1,'float');
h.highpoles = fread(fid,1,'short');
h.filtertype = fread(fid,1,'char');
h.filterdomain = fread(fid,1,'char');
h.snrflag = fread(fid,1,'char');
h.coherenceflag = fread(fid,1,'char');
h.continuoustype = fread(fid,1,'char');
h.eventtablepos = fread(fid,1,'ulong');
h.continuousseconds = fread(fid,1,'float');
h.channeloffset = fread(fid,1,'long');
h.autocorrectflag = fread(fid,1,'char');
h.dcthreshold = fread(fid,1,'uchar');
for n = 1:h.nchannels
e(n).lab = deblank(char(fread(fid,10,'char')'));
e(n).reference = fread(fid,1,'char');
e(n).skip = fread(fid,1,'char');
e(n).reject = fread(fid,1,'char');
e(n).display = fread(fid,1,'char');
e(n).bad = fread(fid,1,'char');
e(n).n = fread(fid,1,'ushort');
e(n).avg_reference = fread(fid,1,'char');
e(n).clipadd = fread(fid,1,'char');
e(n).x_coord = fread(fid,1,'float');
e(n).y_coord = fread(fid,1,'float');
e(n).veog_wt = fread(fid,1,'float');
e(n).veog_std = fread(fid,1,'float');
e(n).snr = fread(fid,1,'float');
e(n).heog_wt = fread(fid,1,'float');
e(n).heog_std = fread(fid,1,'float');
e(n).baseline = fread(fid,1,'short');
e(n).filtered = fread(fid,1,'char');
e(n).fsp = fread(fid,1,'char');
e(n).aux1_wt = fread(fid,1,'float');
e(n).aux1_std = fread(fid,1,'float');
e(n).senstivity = fread(fid,1,'float');
e(n).gain = fread(fid,1,'char');
e(n).hipass = fread(fid,1,'char');
e(n).lopass = fread(fid,1,'char');
e(n).page = fread(fid,1,'uchar');
e(n).size = fread(fid,1,'uchar');
e(n).impedance = fread(fid,1,'uchar');
e(n).physicalchnl = fread(fid,1,'uchar');
e(n).rectify = fread(fid,1,'char');
e(n).calib = fread(fid,1,'float');
end
% finding if 32-bits of 16-bits file
% ----------------------------------
begdata = ftell(fid);
if strcmpi(r.dataformat, 'auto')
r.dataformat = 'int16';
if (h.nextfile > 0)
fseek(fid,h.nextfile+52,'bof');
is32bit = fread(fid,1,'char');
if (is32bit == 1)
r.dataformat = 'int32';
end;
fseek(fid,begdata,'bof');
end;
end;
enddata = h.eventtablepos; % after data
if strcmpi(r.dataformat, 'int16')
nums = (enddata-begdata)/h.nchannels/2;
else nums = (enddata-begdata)/h.nchannels/4;
end;
% number of sample to read
% ------------------------
if ~isempty(r.sample1)
r.t1 = r.sample1/h.rate;
else
r.sample1 = r.t1*h.rate;
end;
if strcmpi(r.dataformat, 'int16')
startpos = r.t1*h.rate*2*h.nchannels;
else startpos = r.t1*h.rate*4*h.nchannels;
end;
if isempty(r.ldnsamples)
if ~isempty(r.lddur)
r.ldnsamples = round(r.lddur*h.rate);
else r.ldnsamples = nums;
end;
end;
% FIELDTRIP BUGFIX #1412
% In some cases, the orig.header.numsamples = 0, and the output number of samples is wrong.
% In the previous version of loadcnt.m the orig.header.nums field was used (instead of numsamples), which was changed in r5380 to fix bug #1348.
% This bug (1348) was due to loadcnt.m being updated to the most recent version (from neuroscan), which removed the nums field in favor of using numsamples.
% Below is a workaround for when numsamples is incorrect (bug 1412). The reason is unknown (it looks like a neuroscan data-file specific bug).
% I re-added the nums field to loadcnt.m so that it can be used in ft_read_header.m.
% -roevdmei
h.nums = nums;
% channel offset
% --------------
if ~isempty(r.blockread)
h.channeloffset = r.blockread;
end;
if h.channeloffset > 1
fprintf('WARNING: reading data in blocks of %d, if this fails, try using option "''blockread'', 1"\n', ...
h.channeloffset);
end;
disp('Reading data .....')
if type == 'cnt'
% while (ftell(fid) +1 < h.eventtablepos)
%d(:,i)=fread(fid,h.nchannels,'int16');
%end
fseek(fid, startpos, 0);
% **** This marks the beginning of the code modified for reading
% large .cnt files
% Switched to r.memmapfile for continuity. Check to see if the
% variable exists. If it does, then the user has indicated the
% file is too large to be processed in memory. If the variable
% is blank, the file is processed in memory.
if (~isempty(r.memmapfile))
% open a file for writing
foutid = fopen(r.memmapfile, 'w') ;
% This portion of the routine reads in a section of the EEG file
% and then writes it out to the harddisk.
samples_left = h.nchannels * r.ldnsamples ;
% the size of the data block to be read is limited to 4M
% samples. This equates to 16MB and 32MB of memory for
% 16 and 32 bit files, respectively.
data_block = 4000000 ;
max_rows = data_block / h.nchannels ;
%warning off ;
max_written = h.nchannels * uint32(max_rows) ;
%warning on ;
% This while look tracks the remaining samples. The
% data is processed in chunks rather than put into
% memory whole.
while (samples_left > 0)
% Check to see if the remaining data is smaller than
% the general processing block by looking at the
% remaining number of rows.
to_read = max_rows ;
if (data_block > samples_left)
to_read = samples_left / h.nchannels ;
end ;
% Read data in a relatively small chunk
temp_dat = fread(fid, [h.nchannels to_read], r.dataformat) ;
% The data is then scaled using the original routine.
% In the original routine, the entire data set was scaled
% after being read in. For this version, scaling occurs
% after every chunk is read.
if strcmpi(r.scale, 'on')
disp('Scaling data .....')
%%% scaling to microvolts
for i=1:h.nchannels
bas=e(i).baseline;sen=e(i).senstivity;cal=e(i).calib;
mf=sen*(cal/204.8);
temp_dat(i,:)=(temp_dat(i,:)-bas).*mf;
end
end
% Write out data in float32 form to the file name
% supplied by the user.
written = fwrite (foutid, temp_dat, 'float32') ;
if (written ~= max_written)
samples_left = 0 ;
else
samples_left = samples_left - written ;
end ;
end ;
fclose (foutid) ;
% Set the dat variable. This gets used later by other
% EEGLAB functions.
dat = r.memmapfile ;
% This variable tracks how the data should be read.
bReadIntoMemory = false ;
else
% The memmapfile variable is empty, read into memory.
bReadIntoMemory = true ;
end
% This ends the modifications made to read large files.
% Everything contained within the following if statement is the
% original code.
if (bReadIntoMemory == true)
if h.channeloffset <= 1
dat=fread(fid, [h.nchannels Inf], r.dataformat);
if size(dat,2) < r.ldnsamples
dat=single(dat);
r.ldnsamples = size(dat,2);
else
dat=single(dat(:,1:r.ldnsamples));
end;
else
h.channeloffset = h.channeloffset/2;
% reading data in blocks
dat = zeros( h.nchannels, r.ldnsamples, 'single');
dat(:, 1:h.channeloffset) = fread(fid, [h.channeloffset h.nchannels], r.dataformat)';
counter = 1;
while counter*h.channeloffset < r.ldnsamples
dat(:, counter*h.channeloffset+1:counter*h.channeloffset+h.channeloffset) = ...
fread(fid, [h.channeloffset h.nchannels], r.dataformat)';
counter = counter + 1;
end;
end ;
% ftell(fid)
if strcmpi(r.scale, 'on')
disp('Scaling data .....')
%%% scaling to microvolts
for i=1:h.nchannels
bas=e(i).baseline;sen=e(i).senstivity;cal=e(i).calib;
mf=sen*(cal/204.8);
dat(i,:)=(dat(i,:)-bas).*mf;
end % end for i=1:h.nchannels
end; % end if (strcmpi(r.scale, 'on')
end ;
ET_offset = (double(h.prevfile) * (2^32)) + double(h.eventtablepos); % prevfile contains high order bits of event table offset, eventtablepos contains the low order bits
fseek(fid, ET_offset, 'bof');
disp('Reading Event Table...')
eT.teeg = fread(fid,1,'uchar');
eT.size = fread(fid,1,'ulong');
eT.offset = fread(fid,1,'ulong');
if eT.teeg==2
nevents=eT.size/sizeEvent2;
if nevents > 0
ev2(nevents).stimtype = [];
for i=1:nevents
ev2(i).stimtype = fread(fid,1,'ushort');
ev2(i).keyboard = fread(fid,1,'char');
temp = fread(fid,1,'uint8');
ev2(i).keypad_accept = bitand(15,temp);
ev2(i).accept_ev1 = bitshift(temp,-4);
ev2(i).offset = fread(fid,1,'long');
ev2(i).type = fread(fid,1,'short');
ev2(i).code = fread(fid,1,'short');
ev2(i).latency = fread(fid,1,'float');
ev2(i).epochevent = fread(fid,1,'char');
ev2(i).accept = fread(fid,1,'char');
ev2(i).accuracy = fread(fid,1,'char');
end
else
ev2 = [];
end;
elseif eT.teeg==3 % type 3 is similar to type 2 except the offset field encodes the global sample frame
nevents=eT.size/sizeEvent3;
if nevents > 0
ev2(nevents).stimtype = [];
if r.dataformat == 'int32'
bytes_per_samp = 4; % I only have 32 bit data, unable to check whether this is necessary,
else % perhaps there is no type 3 file with 16 bit data
bytes_per_samp = 2;
end
for i=1:nevents
ev2(i).stimtype = fread(fid,1,'ushort');
ev2(i).keyboard = fread(fid,1,'char');
temp = fread(fid,1,'uint8');
ev2(i).keypad_accept = bitand(15,temp);
ev2(i).accept_ev1 = bitshift(temp,-4);
os = fread(fid,1,'ulong');
ev2(i).offset = os * bytes_per_samp * h.nchannels;
ev2(i).type = fread(fid,1,'short');
ev2(i).code = fread(fid,1,'short');
ev2(i).latency = fread(fid,1,'float');
ev2(i).epochevent = fread(fid,1,'char');
ev2(i).accept = fread(fid,1,'char');
ev2(i).accuracy = fread(fid,1,'char');
end
else
ev2 = [];
end;
elseif eT.teeg==1
nevents=eT.size/sizeEvent1;
if nevents > 0
ev2(nevents).stimtype = [];
for i=1:nevents
ev2(i).stimtype = fread(fid,1,'ushort');
ev2(i).keyboard = fread(fid,1,'char');
% modified by Andreas Widmann 2005/05/12 14:15:00
%ev2(i).keypad_accept = fread(fid,1,'char');
temp = fread(fid,1,'uint8');
ev2(i).keypad_accept = bitand(15,temp);
ev2(i).accept_ev1 = bitshift(temp,-4);
% end modification
ev2(i).offset = fread(fid,1,'long');
end;
else
ev2 = [];
end;
else
disp('Skipping event table (tag != 1,2,3 ; theoritically impossible)');
ev2 = [];
end
fseek(fid, -1, 'eof');
t = fread(fid,'char');
f.header = h;
f.electloc = e;
f.data = dat;
f.Teeg = eT;
f.event = ev2;
f.tag=t;
% Surgical addition of number of samples
f.ldnsamples = r.ldnsamples ;
%%%% channels labels
for i=1:h.nchannels
plab=sprintf('%c',f.electloc(i).lab);
if i>1
lab=str2mat(lab,plab);
else
lab=plab;
end
end
%%%% to change offest in bytes to points
if ~isempty(ev2)
if r.sample1 ~= 0
fprintf(2,'Warning: events imported with a time shift might be innacurate\n');
end;
ev2p=ev2;
ioff=900+(h.nchannels*75); %% initial offset : header + electordes desc
if strcmpi(r.dataformat, 'int16')
for i=1:nevents
ev2p(i).offset=(ev2p(i).offset-ioff)/(2*h.nchannels) - r.sample1; %% 2 short int end
end
else % 32 bits
for i=1:nevents
ev2p(i).offset=(ev2p(i).offset-ioff)/(4*h.nchannels) - r.sample1; %% 4 short int end
end
end;
f.event = ev2p;
end;
frewind(fid);
fclose(fid);
end
|
github
|
philippboehmsturm/antx-master
|
read_yokogawa_header.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_yokogawa_header.m
| 8,340 |
utf_8
|
c1392a52ad7bb86127e7a704c5abce9d
|
function hdr = read_yokogawa_header(filename)
% READ_YOKOGAWA_HEADER reads the header information from continuous,
% epoched or averaged MEG data that has been generated by the Yokogawa
% MEG system and software and allows that data to be used in combination
% with FieldTrip.
%
% Use as
% [hdr] = read_yokogawa_header(filename)
%
% This is a wrapper function around the functions
% GetMeg160SystemInfoM
% GetMeg160ChannelCountM
% GetMeg160ChannelInfoM
% GetMeg160CalibInfoM
% GetMeg160AmpGainM
% GetMeg160DataAcqTypeM
% GetMeg160ContinuousAcqCondM
% GetMeg160EvokedAcqCondM
%
% See also READ_YOKOGAWA_DATA, READ_YOKOGAWA_EVENT
% this function also calls
% GetMeg160MriInfoM
% GetMeg160MatchingInfoM
% GetMeg160SourceInfoM
% but I don't know whether to use the information provided by those
% Copyright (C) 2005, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: read_yokogawa_header.m 7123 2012-12-06 21:21:38Z roboos $
% FIXED
% txt -> m
% fopen iee-le
if ~ft_hastoolbox('yokogawa')
error('cannot determine whether Yokogawa toolbox is present');
end
handles = definehandles;
fid = fopen(filename, 'rb', 'ieee-le');
% these are always present
[id ver rev sys_name] = GetMeg160SystemInfoM(fid);
channel_count = GetMeg160ChannelCountM(fid);
channel_info = GetMeg160ChannelInfoM(fid);
calib_info = GetMeg160CalibInfoM(fid);
amp_gain = GetMeg160AmpGainM(fid);
acq_type = GetMeg160DataAcqTypeM(fid);
ad_bit = GetMeg160ADbitInfoM(fid);
% these depend on the data type
sample_rate = [];
sample_count = [];
pretrigger_length = [];
averaged_count = [];
actual_epoch_count = [];
switch acq_type
case handles.AcqTypeContinuousRaw
[sample_rate, sample_count] = GetMeg160ContinuousAcqCondM(fid);
if isempty(sample_rate) | isempty(sample_count)
fclose(fid);
return;
end
pretrigger_length = 0;
averaged_count = 1;
case handles.AcqTypeEvokedAve
[sample_rate, sample_count, pretrigger_length, averaged_count] = GetMeg160EvokedAcqCondM( fid );
if isempty(sample_rate) | isempty(sample_count) | isempty(pretrigger_length) | isempty(averaged_count)
fclose(fid);
return;
end
case handles.AcqTypeEvokedRaw
[sample_rate, sample_count, pretrigger_length, actual_epoch_count] = GetMeg160EvokedAcqCondM( fid );
if isempty(sample_rate) | isempty(sample_count) | isempty(pretrigger_length) | isempty(actual_epoch_count)
fclose(fid);
return;
end
otherwise
error('unknown data type');
end
% these are always present
mri_info = GetMeg160MriInfoM(fid);
matching_info = GetMeg160MatchingInfoM(fid);
source_info = GetMeg160SourceInfoM(fid);
fclose(fid);
% put all local variables into a structure, this is a bit unusual matlab programming style
tmp = whos;
orig = [];
for i=1:length(tmp)
if isempty(strmatch(tmp(i).name, {'tmp', 'fid', 'ans', 'handles'}))
orig = setfield(orig, tmp(i).name, eval(tmp(i).name));
end
end
% convert the original header information into something that FieldTrip understands
hdr = [];
hdr.orig = orig; % also store the original full header information
hdr.Fs = orig.sample_rate; % sampling frequency
hdr.nChans = orig.channel_count; % number of channels
hdr.nSamples = []; % number of samples per trial
hdr.nSamplesPre = []; % number of pre-trigger samples in each trial
hdr.nTrials = []; % number of trials
switch orig.acq_type
case handles.AcqTypeEvokedAve
hdr.nSamples = orig.sample_count;
hdr.nSamplesPre = orig.pretrigger_length;
hdr.nTrials = 1; % only the average, which can be considered as a single trial
case handles.AcqTypeContinuousRaw
hdr.nSamples = orig.sample_count;
hdr.nSamplesPre = 0; % there is no fixed relation between triggers and data
hdr.nTrials = 1; % the continuous data can be considered as a single very long trial
case handles.AcqTypeEvokedRaw
hdr.nSamples = orig.sample_count;
hdr.nSamplesPre = orig.pretrigger_length;
hdr.nTrials = orig.actual_epoch_count;
otherwise
error('unknown acquisition type');
end
% construct a cell-array with labels of each channel
for i=1:hdr.nChans
% this should be consistent with the predefined list in ft_senslabel,
% with yokogawa2grad and with ft_channelselection
if hdr.orig.channel_info(i, 2) == handles.NullChannel
prefix = '';
elseif hdr.orig.channel_info(i, 2) == handles.MagnetoMeter
prefix = 'M';
elseif hdr.orig.channel_info(i, 2) == handles.AxialGradioMeter
prefix = 'AG';
elseif hdr.orig.channel_info(i, 2) == handles.PlannerGradioMeter
prefix = 'PG';
elseif hdr.orig.channel_info(i, 2) == handles.RefferenceMagnetoMeter
prefix = 'RM';
elseif hdr.orig.channel_info(i, 2) == handles.RefferenceAxialGradioMeter
prefix = 'RAG';
elseif hdr.orig.channel_info(i, 2) == handles.RefferencePlannerGradioMeter
prefix = 'RPG';
elseif hdr.orig.channel_info(i, 2) == handles.TriggerChannel
prefix = 'TRIG';
elseif hdr.orig.channel_info(i, 2) == handles.EegChannel
prefix = 'EEG';
elseif hdr.orig.channel_info(i, 2) == handles.EcgChannel
prefix = 'ECG';
elseif hdr.orig.channel_info(i, 2) == handles.EtcChannel
prefix = 'ETC';
end
hdr.label{i} = sprintf('%s%03d', prefix, i);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% this defines some usefull constants
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function handles = definehandles;
handles.output = [];
handles.sqd_load_flag = false;
handles.mri_load_flag = false;
handles.NullChannel = 0;
handles.MagnetoMeter = 1;
handles.AxialGradioMeter = 2;
handles.PlannerGradioMeter = 3;
handles.RefferenceChannelMark = hex2dec('0100');
handles.RefferenceMagnetoMeter = bitor( handles.RefferenceChannelMark, handles.MagnetoMeter );
handles.RefferenceAxialGradioMeter = bitor( handles.RefferenceChannelMark, handles.AxialGradioMeter );
handles.RefferencePlannerGradioMeter = bitor( handles.RefferenceChannelMark, handles.PlannerGradioMeter );
handles.TriggerChannel = -1;
handles.EegChannel = -2;
handles.EcgChannel = -3;
handles.EtcChannel = -4;
handles.NonMegChannelNameLength = 32;
handles.DefaultMagnetometerSize = (4.0/1000.0); % Square of 4.0mm in length
handles.DefaultAxialGradioMeterSize = (15.5/1000.0); % Circle of 15.5mm in diameter
handles.DefaultPlannerGradioMeterSize = (12.0/1000.0); % Square of 12.0mm in length
handles.AcqTypeContinuousRaw = 1;
handles.AcqTypeEvokedAve = 2;
handles.AcqTypeEvokedRaw = 3;
handles.sqd = [];
handles.sqd.selected_start = [];
handles.sqd.selected_end = [];
handles.sqd.axialgradiometer_ch_no = [];
handles.sqd.axialgradiometer_ch_info = [];
handles.sqd.axialgradiometer_data = [];
handles.sqd.plannergradiometer_ch_no = [];
handles.sqd.plannergradiometer_ch_info = [];
handles.sqd.plannergradiometer_data = [];
handles.sqd.eegchannel_ch_no = [];
handles.sqd.eegchannel_data = [];
handles.sqd.nullchannel_ch_no = [];
handles.sqd.nullchannel_data = [];
handles.sqd.selected_time = [];
handles.sqd.sample_rate = [];
handles.sqd.sample_count = [];
handles.sqd.pretrigger_length = [];
handles.sqd.matching_info = [];
handles.sqd.source_info = [];
handles.sqd.mri_info = [];
handles.mri = [];
|
github
|
philippboehmsturm/antx-master
|
encode_nifti1.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/encode_nifti1.m
| 4,870 |
utf_8
|
9cf92a03587c511a5cec2c8c76a3c2c3
|
function blob = encode_nifti1(H)
%function blob = encode_nifti1(H)
%
% Encodes a NIFTI-1 header (=> raw 348 bytes (uint8)) from a Matlab structure
% that matches the C struct defined in nifti1.h.
%
% WARNING: This function currently ignores endianness !!!
% (C) 2010 S.Klanke
blob = uint8(zeros(1,348));
if ~isstruct(H)
error 'Input must be a structure';
end
% see nift1.h for information on structure
sizeof_hdr = int32(348);
blob(1:4) = typecast(sizeof_hdr, 'uint8');
blob = setString(blob, 5, 14, H, 'data_type');
blob = setString(blob, 15, 32, H, 'db_name');
blob = setInt32( blob, 33, 36, H, 'extents');
blob = setInt16( blob, 37, 38, H, 'session_error');
blob = setInt8( blob, 39, 39, H, 'regular');
blob = setInt8( blob, 40, 40, H, 'dim_info');
dim = int16(H.dim(:)');
ndim = numel(dim);
if ndim<1 || ndim>7
error 'Field "dim" must have 1..7 elements';
end
dim = [int16(ndim) dim];
blob(41:(42+2*ndim)) = typecast(dim,'uint8');
blob = setSingle(blob, 57, 60, H, 'intent_p1');
blob = setSingle(blob, 61, 64, H, 'intent_p2');
blob = setSingle(blob, 65, 68, H, 'intent_p3');
blob = setInt16( blob, 69, 70, H, 'intent_code');
blob = setInt16( blob, 71, 72, H, 'datatype');
blob = setInt16( blob, 73, 74, H, 'bitpix');
blob = setInt16( blob, 75, 76, H, 'slice_start');
blob = setSingle(blob, 77, 80, H, 'qfac');
if isfield(H,'pixdim')
pixdim = single(H.pixdim(:)');
ndim = numel(pixdim);
if ndim<1 || ndim>7
error 'Field "pixdim" must have 1..7 elements';
end
blob(81:(80+4*ndim)) = typecast(pixdim,'uint8');
end
blob = setSingle(blob, 109, 112, H, 'vox_offset');
blob = setSingle(blob, 113, 116, H, 'scl_scope');
blob = setSingle(blob, 117, 120, H, 'scl_inter');
blob = setInt16( blob, 121, 122, H, 'slice_end');
blob = setInt8( blob, 123, 123, H, 'slice_code');
blob = setInt8( blob, 124, 124, H, 'xyzt_units');
blob = setSingle(blob, 125, 128, H, 'cal_max');
blob = setSingle(blob, 129, 132, H, 'cal_min');
blob = setSingle(blob, 133, 136, H, 'slice_duration');
blob = setSingle(blob, 137, 140, H, 'toffset');
blob = setInt32( blob, 141, 144, H, 'glmax');
blob = setInt32( blob, 145, 148, H, 'glmin');
blob = setString(blob, 149, 228, H, 'descrip');
blob = setString(blob, 229, 252, H, 'aux_file');
blob = setInt16( blob, 253, 254, H, 'qform_code');
blob = setInt16( blob, 255, 256, H, 'sform_code');
blob = setSingle(blob, 257, 260, H, 'quatern_b');
blob = setSingle(blob, 261, 264, H, 'quatern_c');
blob = setSingle(blob, 265, 268, H, 'quatern_d');
blob = setSingle(blob, 269, 272, H, 'quatern_x');
blob = setSingle(blob, 273, 276, H, 'quatern_y');
blob = setSingle(blob, 277, 280, H, 'quatern_z');
blob = setSingle(blob, 281, 296, H, 'srow_x');
blob = setSingle(blob, 297, 312, H, 'srow_y');
blob = setSingle(blob, 313, 328, H, 'srow_z');
blob = setString(blob, 329, 344, H, 'intent_name');
if ~isfield(H,'magic')
blob(345:347) = uint8('ni1');
else
blob = setString(blob, 345, 347, H, 'magic');
end
function blob = setString(blob, begidx, endidx, H, fieldname)
if ~isfield(H,fieldname)
return
end
F = getfield(H, fieldname);
ne = numel(F);
mx = endidx - begidx +1;
if ne > 0
if ~ischar(F) || ne > mx
errmsg = sprintf('Field "data_type" must be a string of maximally %i characters.', mx);
error(errmsg);
end
blob(begidx:(begidx+ne-1)) = uint8(F(:)');
end
% set 32-bit integers (check #elements)
function blob = setInt32(blob, begidx, endidx, H, fieldname)
if ~isfield(H,fieldname)
return
end
F = int32(getfield(H, fieldname));
ne = numel(F);
sp = (endidx - begidx +1) / 4;
if ne~=sp
errmsg = sprintf('Field "data_type" must be an array with exactly %i elements.', sp);
error(errmsg);
end
blob(begidx:(begidx+4*ne-1)) = typecast(F(:)', 'uint8');
% set 16-bit integers (check #elements)
function blob = setInt16(blob, begidx, endidx, H, fieldname)
if ~isfield(H,fieldname)
return
end
F = int16(getfield(H, fieldname));
ne = numel(F);
sp = (endidx - begidx +1) / 2;
if ne~=sp
errmsg = sprintf('Field "data_type" must be an array with exactly %i elements.', sp);
error(errmsg);
end
blob(begidx:(begidx+2*ne-1)) = typecast(F(:)', 'uint8');
% just 8-bit integers (check #elements)
function blob = setInt8(blob, begidx, endidx, H, fieldname)
if ~isfield(H,fieldname)
return
end
F = int8(getfield(H, fieldname));
ne = numel(F);
sp = (endidx - begidx +1);
if ne~=sp
errmsg = sprintf('Field "data_type" must be an array with exactly %i elements.', sp);
error(errmsg);
end
blob(begidx:(begidx+ne-1)) = typecast(F(:)', 'uint8');
% single precision floats
function blob = setSingle(blob, begidx, endidx, H, fieldname)
if ~isfield(H,fieldname)
return
end
F = single(getfield(H, fieldname));
ne = numel(F);
sp = (endidx - begidx +1) / 4;
if ne~=sp
errmsg = sprintf('Field "data_type" must be an array with exactly %i elements.', sp);
error(errmsg);
end
blob(begidx:(begidx+4*ne-1)) = typecast(F(:)', 'uint8');
|
github
|
philippboehmsturm/antx-master
|
avw_hdr_read.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/avw_hdr_read.m
| 16,668 |
utf_8
|
7cb599cd5b75177fed96189188822304
|
function [ avw, machine ] = avw_hdr_read(fileprefix, machine, verbose)
% avw_hdr_read - read Analyze format data header (*.hdr)
%
% [ avw, machine ] = avw_hdr_read(fileprefix, [machine], [verbose])
%
% fileprefix - string filename (without .hdr); the file name
% can be given as a full path or relative to the
% current directory.
%
% machine - a string, see machineformat in fread for details.
% The default here is 'ieee-le' but the routine
% will automatically switch between little and big
% endian to read any such Analyze header. It
% reports the appropriate machine format and can
% return the machine value.
%
% avw.hdr - a struct, all fields returned from the header.
% For details, find a good description on the web
% or see the Analyze File Format pdf in the
% mri_toolbox doc folder or read this .m file.
%
% verbose - the default is to output processing information to the command
% window. If verbose = 0, this will not happen.
%
% This function is called by avw_img_read
%
% See also avw_hdr_write, avw_hdr_make, avw_view_hdr, avw_view
%
% $Revision: 7123 $ $Date: 2009/01/14 09:24:45 $
% Licence: GNU GPL, no express or implied warranties
% History: 05/2002, [email protected]
% The Analyze format and c code below is copyright
% (c) Copyright, 1986-1995
% Biomedical Imaging Resource, Mayo Foundation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~exist('verbose','var'), verbose = 1; end
if verbose,
version = '[$Revision: 7123 $]';
fprintf('\nAVW_HDR_READ [v%s]\n',version(12:16)); tic;
end
if ~exist('fileprefix','var'),
msg = sprintf('...no input fileprefix - see help avw_hdr_read\n\n');
error(msg);
end
if ~exist('machine','var'), machine = 'ieee-le'; end
if findstr('.hdr',fileprefix),
% fprintf('...removing .hdr extension from ''%s''\n',fileprefix);
fileprefix = strrep(fileprefix,'.hdr','');
end
if findstr('.img',fileprefix),
% fprintf('...removing .img extension from ''%s''\n',fileprefix);
fileprefix = strrep(fileprefix,'.img','');
end
file = sprintf('%s.hdr',fileprefix);
if exist(file),
if verbose,
fprintf('...reading %s Analyze format',machine);
end
fid = fopen(file,'r',machine);
avw.hdr = read_header(fid,verbose);
avw.fileprefix = fileprefix;
fclose(fid);
if ~isequal(avw.hdr.hk.sizeof_hdr,348),
if verbose, fprintf('...failed.\n'); end
% first try reading the opposite endian to 'machine'
switch machine,
case 'ieee-le', machine = 'ieee-be';
case 'ieee-be', machine = 'ieee-le';
end
if verbose, fprintf('...reading %s Analyze format',machine); end
fid = fopen(file,'r',machine);
avw.hdr = read_header(fid,verbose);
avw.fileprefix = fileprefix;
fclose(fid);
end
if ~isequal(avw.hdr.hk.sizeof_hdr,348),
% Now throw an error
if verbose, fprintf('...failed.\n'); end
msg = sprintf('...size of header not equal to 348 bytes!\n\n');
error(msg);
end
else
msg = sprintf('...cannot find file %s.hdr\n\n',file);
error(msg);
end
if verbose,
t=toc; fprintf('...done (%5.2f sec).\n',t);
end
return
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ dsr ] = read_header(fid,verbose)
% Original header structures - ANALYZE 7.5
%struct dsr
% {
% struct header_key hk; /* 0 + 40 */
% struct image_dimension dime; /* 40 + 108 */
% struct data_history hist; /* 148 + 200 */
% }; /* total= 348 bytes*/
dsr.hk = header_key(fid);
dsr.dime = image_dimension(fid,verbose);
dsr.hist = data_history(fid);
return
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [hk] = header_key(fid)
% The required elements in the header_key substructure are:
%
% int sizeof_header Must indicate the byte size of the header file.
% int extents Should be 16384, the image file is created as
% contiguous with a minimum extent size.
% char regular Must be 'r' to indicate that all images and
% volumes are the same size.
% Original header structures - ANALYZE 7.5
% struct header_key /* header key */
% { /* off + size */
% int sizeof_hdr /* 0 + 4 */
% char data_type[10]; /* 4 + 10 */
% char db_name[18]; /* 14 + 18 */
% int extents; /* 32 + 4 */
% short int session_error; /* 36 + 2 */
% char regular; /* 38 + 1 */
% char hkey_un0; /* 39 + 1 */
% }; /* total=40 bytes */
fseek(fid,0,'bof');
hk.sizeof_hdr = fread(fid, 1,'*int32'); % should be 348!
hk.data_type = fread(fid,10,'*char')';
hk.db_name = fread(fid,18,'*char')';
hk.extents = fread(fid, 1,'*int32');
hk.session_error = fread(fid, 1,'*int16');
hk.regular = fread(fid, 1,'*char')'; % might be uint8
hk.hkey_un0 = fread(fid, 1,'*uint8')';
% check if this value was a char zero
if hk.hkey_un0 == 48,
hk.hkey_un0 = 0;
end
return
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ dime ] = image_dimension(fid,verbose)
%struct image_dimension
% { /* off + size */
% short int dim[8]; /* 0 + 16 */
% /*
% dim[0] Number of dimensions in database; usually 4.
% dim[1] Image X dimension; number of *pixels* in an image row.
% dim[2] Image Y dimension; number of *pixel rows* in slice.
% dim[3] Volume Z dimension; number of *slices* in a volume.
% dim[4] Time points; number of volumes in database
% */
% char vox_units[4]; /* 16 + 4 */
% char cal_units[8]; /* 20 + 8 */
% short int unused1; /* 28 + 2 */
% short int datatype; /* 30 + 2 */
% short int bitpix; /* 32 + 2 */
% short int dim_un0; /* 34 + 2 */
% float pixdim[8]; /* 36 + 32 */
% /*
% pixdim[] specifies the voxel dimensions:
% pixdim[1] - voxel width, mm
% pixdim[2] - voxel height, mm
% pixdim[3] - slice thickness, mm
% pixdim[4] - volume timing, in msec
% ..etc
% */
% float vox_offset; /* 68 + 4 */
% float roi_scale; /* 72 + 4 */
% float funused1; /* 76 + 4 */
% float funused2; /* 80 + 4 */
% float cal_max; /* 84 + 4 */
% float cal_min; /* 88 + 4 */
% int compressed; /* 92 + 4 */
% int verified; /* 96 + 4 */
% int glmax; /* 100 + 4 */
% int glmin; /* 104 + 4 */
% }; /* total=108 bytes */
dime.dim = fread(fid,8,'*int16')';
dime.vox_units = fread(fid,4,'*char')';
dime.cal_units = fread(fid,8,'*char')';
dime.unused1 = fread(fid,1,'*int16');
dime.datatype = fread(fid,1,'*int16');
dime.bitpix = fread(fid,1,'*int16');
dime.dim_un0 = fread(fid,1,'*int16');
dime.pixdim = fread(fid,8,'*float')';
dime.vox_offset = fread(fid,1,'*float');
dime.roi_scale = fread(fid,1,'*float');
dime.funused1 = fread(fid,1,'*float');
dime.funused2 = fread(fid,1,'*float');
dime.cal_max = fread(fid,1,'*float');
dime.cal_min = fread(fid,1,'*float');
dime.compressed = fread(fid,1,'*int32');
dime.verified = fread(fid,1,'*int32');
dime.glmax = fread(fid,1,'*int32');
dime.glmin = fread(fid,1,'*int32');
if dime.dim(1) < 4, % Number of dimensions in database; usually 4.
if verbose,
fprintf('...ensuring 4 dimensions in avw.hdr.dime.dim\n');
end
dime.dim(1) = int16(4);
end
if dime.dim(5) < 1, % Time points; number of volumes in database
if verbose,
fprintf('...ensuring at least 1 volume in avw.hdr.dime.dim(5)\n');
end
dime.dim(5) = int16(1);
end
return
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ hist ] = data_history(fid)
% Original header structures - ANALYZE 7.5
%struct data_history
% { /* off + size */
% char descrip[80]; /* 0 + 80 */
% char aux_file[24]; /* 80 + 24 */
% char orient; /* 104 + 1 */
% char originator[10]; /* 105 + 10 */
% char generated[10]; /* 115 + 10 */
% char scannum[10]; /* 125 + 10 */
% char patient_id[10]; /* 135 + 10 */
% char exp_date[10]; /* 145 + 10 */
% char exp_time[10]; /* 155 + 10 */
% char hist_un0[3]; /* 165 + 3 */
% int views /* 168 + 4 */
% int vols_added; /* 172 + 4 */
% int start_field; /* 176 + 4 */
% int field_skip; /* 180 + 4 */
% int omax; /* 184 + 4 */
% int omin; /* 188 + 4 */
% int smax; /* 192 + 4 */
% int smin; /* 196 + 4 */
% }; /* total=200 bytes */
hist.descrip = fread(fid,80,'*char')';
hist.aux_file = fread(fid,24,'*char')';
hist.orient = fread(fid, 1,'*uint8'); % see note below on char
hist.originator = fread(fid,10,'*char')';
hist.generated = fread(fid,10,'*char')';
hist.scannum = fread(fid,10,'*char')';
hist.patient_id = fread(fid,10,'*char')';
hist.exp_date = fread(fid,10,'*char')';
hist.exp_time = fread(fid,10,'*char')';
hist.hist_un0 = fread(fid, 3,'*char')';
hist.views = fread(fid, 1,'*int32');
hist.vols_added = fread(fid, 1,'*int32');
hist.start_field = fread(fid, 1,'*int32');
hist.field_skip = fread(fid, 1,'*int32');
hist.omax = fread(fid, 1,'*int32');
hist.omin = fread(fid, 1,'*int32');
hist.smax = fread(fid, 1,'*int32');
hist.smin = fread(fid, 1,'*int32');
% check if hist.orient was saved as ascii char value
switch hist.orient,
case 48, hist.orient = uint8(0);
case 49, hist.orient = uint8(1);
case 50, hist.orient = uint8(2);
case 51, hist.orient = uint8(3);
case 52, hist.orient = uint8(4);
case 53, hist.orient = uint8(5);
end
return
% Note on using char:
% The 'char orient' field in the header is intended to
% hold simply an 8-bit unsigned integer value, not the ASCII representation
% of the character for that value. A single 'char' byte is often used to
% represent an integer value in Analyze if the known value range doesn't
% go beyond 0-255 - saves a byte over a short int, which may not mean
% much in today's computing environments, but given that this format
% has been around since the early 1980's, saving bytes here and there on
% older systems was important! In this case, 'char' simply provides the
% byte of storage - not an indicator of the format for what is stored in
% this byte. Generally speaking, anytime a single 'char' is used, it is
% probably meant to hold an 8-bit integer value, whereas if this has
% been dimensioned as an array, then it is intended to hold an ASCII
% character string, even if that was only a single character.
% Denny <[email protected]>
% Comments
% The header format is flexible and can be extended for new
% user-defined data types. The essential structures of the header
% are the header_key and the image_dimension.
%
% The required elements in the header_key substructure are:
%
% int sizeof_header Must indicate the byte size of the header file.
% int extents Should be 16384, the image file is created as
% contiguous with a minimum extent size.
% char regular Must be 'r' to indicate that all images and
% volumes are the same size.
%
% The image_dimension substructure describes the organization and
% size of the images. These elements enable the database to reference
% images by volume and slice number. Explanation of each element follows:
%
% short int dim[ ]; /* Array of the image dimensions */
%
% dim[0] Number of dimensions in database; usually 4.
% dim[1] Image X dimension; number of pixels in an image row.
% dim[2] Image Y dimension; number of pixel rows in slice.
% dim[3] Volume Z dimension; number of slices in a volume.
% dim[4] Time points; number of volumes in database.
% dim[5] Undocumented.
% dim[6] Undocumented.
% dim[7] Undocumented.
%
% char vox_units[4] Specifies the spatial units of measure for a voxel.
% char cal_units[8] Specifies the name of the calibration unit.
% short int unused1 /* Unused */
% short int datatype /* Datatype for this image set */
% /*Acceptable values for datatype are*/
% #define DT_NONE 0
% #define DT_UNKNOWN 0 /*Unknown data type*/
% #define DT_BINARY 1 /*Binary ( 1 bit per voxel)*/
% #define DT_UNSIGNED_CHAR 2 /*Unsigned character ( 8 bits per voxel)*/
% #define DT_SIGNED_SHORT 4 /*Signed short (16 bits per voxel)*/
% #define DT_SIGNED_INT 8 /*Signed integer (32 bits per voxel)*/
% #define DT_FLOAT 16 /*Floating point (32 bits per voxel)*/
% #define DT_COMPLEX 32 /*Complex (64 bits per voxel; 2 floating point numbers)/*
% #define DT_DOUBLE 64 /*Double precision (64 bits per voxel)*/
% #define DT_RGB 128 /*A Red-Green-Blue datatype*/
% #define DT_ALL 255 /*Undocumented*/
%
% short int bitpix; /* Number of bits per pixel; 1, 8, 16, 32, or 64. */
% short int dim_un0; /* Unused */
%
% float pixdim[]; Parallel array to dim[], giving real world measurements in mm and ms.
% pixdim[0]; Pixel dimensions?
% pixdim[1]; Voxel width in mm.
% pixdim[2]; Voxel height in mm.
% pixdim[3]; Slice thickness in mm.
% pixdim[4]; timeslice in ms (ie, TR in fMRI).
% pixdim[5]; Undocumented.
% pixdim[6]; Undocumented.
% pixdim[7]; Undocumented.
%
% float vox_offset; Byte offset in the .img file at which voxels start. This value can be
% negative to specify that the absolute value is applied for every image
% in the file.
%
% float roi_scale; Specifies the Region Of Interest scale?
% float funused1; Undocumented.
% float funused2; Undocumented.
%
% float cal_max; Specifies the upper bound of the range of calibration values.
% float cal_min; Specifies the lower bound of the range of calibration values.
%
% int compressed; Undocumented.
% int verified; Undocumented.
%
% int glmax; The maximum pixel value for the entire database.
% int glmin; The minimum pixel value for the entire database.
%
%
|
github
|
philippboehmsturm/antx-master
|
read_stl.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_stl.m
| 4,072 |
utf_8
|
f8ab163555c079a78445be6bc53cac39
|
function [pnt, tri, nrm] = read_stl(filename);
% READ_STL reads a triangulation from an ascii or binary *.stl file, which
% is a file format native to the stereolithography CAD software created by
% 3D Systems.
%
% Use as
% [pnt, tri, nrm] = read_stl(filename)
%
% The format is described at http://en.wikipedia.org/wiki/STL_(file_format)
%
% See also WRITE_STL
% Copyright (C) 2006-2011, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: read_stl.m 7123 2012-12-06 21:21:38Z roboos $
fid = fopen(filename, 'rt');
% read a small section to determine whether it is ascii or binary
% a binary STL file has an 80 byte asci header, followed by non-printable characters
section = fread(fid, 160, 'uint8');
fseek(fid, 0, 'bof');
if printableascii(section)
% the first 160 characters are printable ascii, so assume it is an ascii format
% solid testsphere
% facet normal -0.13 -0.13 -0.98
% outer loop
% vertex 1.50000 1.50000 0.00000
% vertex 1.50000 1.11177 0.05111
% vertex 1.11177 1.50000 0.05111
% endloop
% endfacet
% ...
ntri = 0;
while ~feof(fid)
line = fgetl(fid);
ntri = ntri + ~isempty(findstr('facet normal', line));
end
fseek(fid, 0, 'bof');
tri = zeros(ntri,3);
nrm = zeros(ntri,3);
pnt = zeros(ntri*3,3);
line = fgetl(fid);
name = sscanf(line, 'solid %s');
for i=1:ntri
line1 = fgetl(fid);
line2 = fgetl(fid); % outer loop
line3 = fgetl(fid);
line4 = fgetl(fid);
line5 = fgetl(fid);
line6 = fgetl(fid); % endloop
line7 = fgetl(fid); % endfacet
i1 = (i-1)*3+1;
i2 = (i-1)*3+2;
i3 = (i-1)*3+3;
tri(i,:) = [i1 i2 i3];
dum = sscanf(strtrim(line1), 'facet normal %f %f %f'); nrm(i,:) = dum(:)';
dum = sscanf(strtrim(line3), 'vertex %f %f %f'); pnt(i1,:) = dum(:)';
dum = sscanf(strtrim(line4), 'vertex %f %f %f'); pnt(i2,:) = dum(:)';
dum = sscanf(strtrim(line5), 'vertex %f %f %f'); pnt(i3,:) = dum(:)';
end
else
% reopen the file in binary mode, which does not make a difference on
% UNIX but it does on windows
fclose(fid);
fid = fopen(filename, 'rb');
fseek(fid, 80, 'bof'); % skip the ascii header
ntri = fread(fid, 1, 'uint32');
tri = zeros(ntri,3);
nrm = zeros(ntri,3);
pnt = zeros(ntri*3,3);
attr = zeros(ntri,1);
for i=1:ntri
i1 = (i-1)*3+1;
i2 = (i-1)*3+2;
i3 = (i-1)*3+3;
tri(i,:) = [i1 i2 i3];
nrm(i,:) = fread(fid, 3, 'float32');
pnt(i1,:) = fread(fid, 3, 'float32');
pnt(i2,:) = fread(fid, 3, 'float32');
pnt(i3,:) = fread(fid, 3, 'float32');
attr(i) = fread(fid, 1, 'uint16'); % Attribute byte count, don't know what it is
end % for each triangle
end
fclose(fid);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function retval = printableascii(num)
% Codes 20hex (32dec) to 7Ehex (126dec), known as the printable characters,
% represent letters, digits, punctuation marks, and a few miscellaneous
% symbols. There are 95 printable characters in total.
num = double(num);
num(num==double(sprintf('\n'))) = double(sprintf(' '));
num(num==double(sprintf('\r'))) = double(sprintf(' '));
num(num==double(sprintf('\t'))) = double(sprintf(' '));
retval = all(num>=32 & num<=126);
|
github
|
philippboehmsturm/antx-master
|
read_itab_mhd.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_itab_mhd.m
| 12,499 |
utf_8
|
3f00166bb7197b9d65c619cfe0ac954d
|
function mhd = read_itab_mhd(filename)
fid = fopen(filename, 'rb');
% Name of structure
mhd.stname = fread(fid, [1 10], 'uint8=>char'); % Header identifier (VP_BIOMAG)
mhd.stver = fread(fid, [1 8], 'uint8=>char'); % Header version
mhd.stendian = fread(fid, [1 4], 'uint8=>char'); % LE (little endian) or BE (big endian) format
% Subject's INFOs
mhd.first_name = fread(fid, [1 32], 'uint8=>char'); % Subject's first name
mhd.last_name = fread(fid, [1 32], 'uint8=>char'); % Subject's family name
mhd.id = fread(fid, [1 32], 'uint8=>char'); % Subject's id
mhd.notes = fread(fid, [1 256], 'uint8=>char'); % Notes on measurement
% Other subj infos
mhd.subj_info.sex = fread(fid, [1 1], 'uint8=>char'); % Sex (M or F)
pad = fread(fid, [1 5], 'uint8');
mhd.subj_info.notes = fread(fid, [1 256], 'uint8=>char'); % Notes on subject
mhd.subj_info.height = fread(fid, [1 1], 'float'); % Height in cm
mhd.subj_info.weight = fread(fid, [1 1], 'float'); % Weight in kg
mhd.subj_info.birthday = fread(fid, [1 1], 'int32'); % Birtday (1-31)
mhd.subj_info.birthmonth = fread(fid, [1 1], 'int32'); % Birthmonth (1-12)
mhd.subj_info.birthyear = fread(fid, [1 1], 'int32'); % Birthyear (1900-2002)
% Data acquisition INFOs
mhd.time = fread(fid, [1 12], 'uint8=>char'); % time (ascii)
mhd.date = fread(fid, [1 16], 'uint8=>char'); % date (ascii)
mhd.nchan = fread(fid, [1 1], 'int32'); % total number of channels
mhd.nelech = fread(fid, [1 1], 'int32'); % number of electric channels
mhd.nelerefch = fread(fid, [1 1], 'int32'); % number of electric reference channels
mhd.nmagch = fread(fid, [1 1], 'int32'); % number of magnetic channels
mhd.nmagrefch = fread(fid, [1 1], 'int32'); % number of magnetic reference channels
mhd.nauxch = fread(fid, [1 1], 'int32'); % number of auxiliary channels
mhd.nparamch = fread(fid, [1 1], 'int32'); % number of parameter channels
mhd.ndigitch = fread(fid, [1 1], 'int32'); % number of digit channels
mhd.nflagch = fread(fid, [1 1], 'int32'); % number of flag channels
mhd.data_type = fread(fid, [1 1], 'int32'); % 0 - BE_SHORT (HP-PA, big endian)
% 1 - BE_LONG (HP-PA, big endian)
% 2 - BE_FLOAT (HP-PA, big endian)
% 3 - LE_SHORT (Intel, little endian)
% 4 - LE_LONG (Intel, little endian)
% 5 - LE_FLOAT (Intel, little endian)
% 6 - RTE_A_SHORT (HP-A900, big endian)
% 7 - RTE_A_FLOAT (HP-A900, big endian)
% 8 - ASCII
mhd.smpfq = fread(fid, [1 1], 'single'); % sampling frequency in Hz
mhd.hw_low_fr = fread(fid, [1 1], 'single'); % hw data acquisition low pass filter
mhd.hw_hig_fr = fread(fid, [1 1], 'single'); % hw data acquisition high pass filter
mhd.hw_comb = fread(fid, [1 1], 'int32'); % hw data acquisition 50 Hz filter (1-TRUE)
mhd.sw_hig_tc = fread(fid, [1 1], 'single'); % sw data acquisition high pass time constant
mhd.compensation= fread(fid, [1 1], 'int32'); % 0 - no compensation 1 - compensation
mhd.ntpdata = fread(fid, [1 1], 'int32'); % total number of time points in data
mhd.no_segments = fread(fid, [1 1], 'int32'); % Number of segments described in the segment structure
% INFOs on different segments
for i=1:5
mhd.sgmt(i).start = fread(fid, [1 1], 'int32'); % Starting time point from beginning of data
mhd.sgmt(i).ntptot = fread(fid, [1 1], 'int32'); % Total number of time points
mhd.sgmt(i).type = fread(fid, [1 1], 'int32');
mhd.sgmt(i).no_samples = fread(fid, [1 1], 'int32');
mhd.sgmt(i).st_sample = fread(fid, [1 1], 'int32');
end
mhd.nsmpl = fread(fid, [1 1], 'int32'); % Overall number of samples
% INFOs on different samples
for i=1:4096
mhd.smpl(i).start = fread(fid, [1 1], 'int32'); % Starting time point from beginning of data
mhd.smpl(i).ntptot = fread(fid, [1 1], 'int32'); % Total number of time points
mhd.smpl(i).ntppre = fread(fid, [1 1], 'int32'); % Number of points in pretrigger
mhd.smpl(i).type = fread(fid, [1 1], 'int32');
mhd.smpl(i).quality = fread(fid, [1 1], 'int32');
end
mhd.nrefchan = fread(fid, [1 1], 'int32'); % number of reference channels
mhd.ref_ch = fread(fid, [1 640], 'int32'); % reference channel list
mhd.ntpref = fread(fid, [1 1], 'int32'); % total number of time points in reference
% Header INFOs
mhd.raw_header_type = fread(fid, [1 1], 'int32'); % 0 - Unknown header
% 2 - rawfile (A900)
% 3 - GE runfile header
% 31 - ATB runfile header version 1.0
% 41 - IFN runfile header version 1.0
% 51 - BMDSys runfile header version 1.0
mhd.header_type = fread(fid, [1 1], 'int32'); % 0 - Unknown header
% 2 - rawfile (A900)
% 3 - GE runfile header
% 4 - old header
% 10 - 256ch normal header
% 11 - 256ch master header
% 20 - 640ch normal header
% 21 - 640ch master header
% 31 - ATB runfile header version 1.0
% 41 - IFN runfile header version 1.0
% 51 - BMDSys runfile header version 1.0
mhd.conf_file = fread(fid, [1 64], 'uint8=>char'); % Filename used for data acquisition configuration
mhd.header_size = fread(fid, [1 1], 'int32'); % sizeof(header) at the time of file creation
mhd.start_reference = fread(fid, [1 1], 'int32'); % start reference
mhd.start_data = fread(fid, [1 1], 'int32'); % start data
mhd.rawfile = fread(fid, [1 1], 'int32'); % 0 - not a rawfile 1 - rawfile
mhd.multiplexed_data = fread(fid, [1 1], 'int32'); % 0 - FALSE 1 - TRUE
mhd.isns = fread(fid, [1 1], 'int32'); % sensor code 1 - Single channel
% 28 - Original Rome 28 ch.
% 29 - ..............
% .. - ..............
% 45 - Updated Rome 28 ch. (spring 2009)
% .. - ..............
% .. - ..............
% 55 - Original Chieti 55 ch. flat
% 153 - Original Chieti 153 ch. helmet
% 154 - Chieti 153 ch. helmet from Jan 2002
% Channel's INFOs
for i=1:640
mhd.ch(i).type = fread(fid, [1 1], 'uint8');
pad = fread(fid, [1 3], 'uint8');
% type 0 - unknown
% 1 - ele
% 2 - mag
% 4 - ele ref
% 8 - mag ref
% 16 - aux
% 32 - param
% 64 - digit
% 128 - flag
mhd.ch(i).number = fread(fid, [1 1], 'int32'); % number
mhd.ch(i).label = fixstr(fread(fid, [1 16], 'uint8=>char')); % label
mhd.ch(i).flag = fread(fid, [1 1], 'uint8');
pad = fread(fid, [1 3], 'uint8');
% on/off flag 0 - working channel
% 1 - noisy channel
% 2 - very noisy channel
% 3 - broken channel
mhd.ch(i).amvbit = fread(fid, [1 1], 'float'); % calibration from LSB to mV
mhd.ch(i).calib = fread(fid, [1 1], 'float'); % calibration from mV to unit
mhd.ch(i).unit = fread(fid, [1 6], 'uint8=>char'); % unit label (fT, uV, ...)
pad = fread(fid, [1 2], 'uint8');
mhd.ch(i).ncoils = fread(fid, [1 1], 'int32'); % number of coils building up one channel
mhd.ch(i).wgt = fread(fid, [1 10], 'float'); % weight of coils
% position and orientation of coils
for j=1:10
mhd.ch(i).position(j).r_s = fread(fid, [1 3], 'float');
mhd.ch(i).position(j).u_s = fread(fid, [1 3], 'float');
end
end
% Sensor position INFOs
mhd.r_center = fread(fid, [1 3], 'float'); % sensor position in convenient format
mhd.u_center = fread(fid, [1 3], 'float');
mhd.th_center = fread(fid, [1 1], 'float'); % sensor orientation as from markers fit
mhd.fi_center= fread(fid, [1 1], 'float');
mhd.rotation_angle= fread(fid, [1 1], 'float');
mhd.cosdir = fread(fid, [3 3], 'float'); % for compatibility only
mhd.irefsys = fread(fid, [1 1], 'int32'); % reference system 0 - sensor reference system
% 1 - Polhemus
% 2 - head3
% 3 - MEG
% Marker positions for MRI integration
mhd.num_markers = fread(fid, [1 1], 'int32'); % Total number of markers
mhd.i_coil = fread(fid, [1 64], 'int32'); % Markers to be used to find sensor position
mhd.marker = fread(fid, [3 64], 'int32'); % Position of all the markers
mhd.best_chi = fread(fid, [1 1], 'float'); % Best chi_square value obtained in finding sensor position
mhd.cup_vertex_center = fread(fid, [1 1], 'float'); % dist anc sensor cente vertex (as entered
% from keyboard)
mhd.cup_fi_center = fread(fid, [1 1], 'float'); % fi angle of sensor center
mhd.cup_rotation_angle = fread(fid, [1 1], 'float'); % rotation angle of sensor center axis
mhd.dist_a1_a2 = fread(fid, [1 1], 'float'); % head informations
% (used to find subject's head dimensions)
mhd.dist_inion_nasion = fread(fid, [1 1], 'float');
mhd.max_circ = fread(fid, [1 1], 'float');
mhd.nasion_vertex_inion = fread(fid, [1 1], 'float');
% Data analysis INFOs
mhd.security = fread(fid, [1 1], 'int32'); % security flag
mhd.ave_alignement = fread(fid, [1 1], 'int32'); % average data alignement 0 - FALSE
% 1 - TRUE
mhd.itri = fread(fid, [1 1], 'int32'); % trigger channel number
mhd.ntpch = fread(fid, [1 1], 'int32'); % no. of time points per channel
mhd.ntppre = fread(fid, [1 1], 'int32'); % no. of time points of pretrigger
mhd.navrg = fread(fid, [1 1], 'int32'); % no. of averages
mhd.nover = fread(fid, [1 1], 'int32'); % no. of discarded averages
mhd.nave_filt = fread(fid, [1 1], 'int32'); % no. of applied filters
% Filters used before average
for i=1:15
mhd.ave_filt(i).type = fread(fid, [1 1], 'int32'); % type 0 - no filter
% 1 - bandpass - param[0]: highpass freq
% - param[1]: lowpass freq
% 2 - notch - param[0]: notch freq 1
% param[1]: notch freq 2
% param[2]: notch freq 3
% param[3]: notch freq 4
% param[4]: span
% 3 - artifact - param[0]: True/False
% 4 - adaptive - param[0]: True/False
% 5 - rectifier - param[0]: True/False
% 6 - heart - param[0]: True/False
% 7 - evoked - param[0]: True/False
% 8 - derivate - param[0]: True/False
% 9 - polarity - param[0]: True/False
mhd.ave_filt(i).param = fread(fid, [1 5], 'float'); % up to 5 filter parameters
end
mhd.stdev = fread(fid, [1 1], 'int32'); % 0 - not present
mhd.bas_start = fread(fid, [1 1], 'int32'); % starting data points for baseline
mhd.bas_end = fread(fid, [1 1], 'int32'); % ending data points for baseline
mhd.source_files = fread(fid, [1 32], 'int32'); % Progressive number of files (if more than one)
% template INFOs
mhd.ichtpl = fread(fid, [1 1], 'int32');
mhd.ntptpl = fread(fid, [1 1], 'int32');
mhd.ifitpl = fread(fid, [1 1], 'int32');
mhd.corlim = fread(fid, [1 1], 'float');
% Filters used before template
for i=1:15
mhd.tpl_filt(i).type = fread(fid, [1 1], 'int32'); % type 0 - no filter
% 1 - bandpass - param[0]: highpass freq
% - param[1]: lowpass freq
% 2 - notch - param[0]: notch freq 1
% param[1]: notch freq 2
% param[2]: notch freq 3
% param[3]: notch freq 4
% param[4]: span
% 3 - artifact - param[0]: True/False
% 4 - adaptive - param[0]: True/False
% 5 - rectifier - param[0]: True/False
% 6 - heart - param[0]: True/False
% 7 - evoked - param[0]: True/False
% 8 - derivate - param[0]: True/False
% 9 - polarity - param[0]: True/False
mhd.tpl_filt(i).param = fread(fid, [1 5], 'float'); % up to 5 filter parameters
end
% Just in case info
mhd.dummy = fread(fid, [1 64], 'int32');
% there seems to be more dummy data at the end...
fclose(fid);
function str = fixstr(str)
sel = find(str==0, 1, 'first');
if ~isempty(sel)
str = str(1:sel-1);
end
|
github
|
philippboehmsturm/antx-master
|
read_plexon_plx.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_plexon_plx.m
| 19,936 |
utf_8
|
a5f213ea449dd4e5bb51d240302e6364
|
function [varargout] = read_plexon_plx(filename, varargin)
% READ_PLEXON_PLX reads header or data from a Plexon *.plx file, which
% is a file containing action-potential (spike) timestamps and waveforms
% (spike channels), event timestamps (event channels), and continuous
% variable data (continuous A/D channels).
%
% Use as
% [hdr] = read_plexon_plx(filename)
% [dat] = read_plexon_plx(filename, ...)
% [dat1, dat2, dat3, hdr] = read_plexon_plx(filename, ...)
%
% Optional input arguments should be specified in key-value pairs
% 'header' = structure with header information
% 'memmap' = 0 or 1
% 'feedback' = 0 or 1
% 'ChannelIndex' = number, or list of numbers (that will result in multiple outputs)
% 'SlowChannelIndex' = number, or list of numbers (that will result in multiple outputs)
% Copyright (C) 2007, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: read_plexon_plx.m 7123 2012-12-06 21:21:38Z roboos $
% parse the optional input arguments
hdr = ft_getopt(varargin, 'header');
memmap = ft_getopt(varargin, 'memmap', false);
feedback = ft_getopt(varargin, 'feedback', true);
ChannelIndex = ft_getopt(varargin, 'ChannelIndex');
SlowChannelIndex = ft_getopt(varargin, 'SlowChannelIndex');
EventIndex = ft_getopt(varargin, 'EventIndex'); % not yet used
needhdr = isempty(hdr);
% start with empty return values
varargout = {};
% the datafile is little endian, hence it may be neccessary to swap bytes in
% the memory mapped data stream depending on the CPU type of this computer
if littleendian
swapFcn = @(x) x;
else
swapFcn = @(x) swapbytes(x);
end
% read header info from file, use Matlabs for automatic byte-ordering
fid = fopen(filename, 'r', 'ieee-le');
fseek(fid, 0, 'eof');
siz = ftell(fid);
fseek(fid, 0, 'bof');
if needhdr
if feedback, fprintf('reading header from %s\n', filename); end
% a PLX file consists of a file header, channel headers, and data blocks
hdr = PL_FileHeader(fid);
for i=1:hdr.NumDSPChannels
hdr.ChannelHeader(i) = PL_ChannelHeader(fid);
end
for i=1:hdr.NumEventChannels
hdr.EventHeader(i) = PL_EventHeader(fid);
end
for i=1:hdr.NumSlowChannels
hdr.SlowChannelHeader(i) = PL_SlowChannelHeader(fid);
end
hdr.DataOffset = ftell(fid);
if memmap
% open the file as meory mapped object, note that byte swapping may be needed
mm = memmapfile(filename, 'offset', hdr.DataOffset, 'format', 'int16');
end
dum = struct(...
'Type', [],...
'UpperByteOf5ByteTimestamp', [],...
'TimeStamp', [],...
'Channel', [],...
'Unit', [],...
'NumberOfWaveforms', [],...
'NumberOfWordsInWaveform', [] ...
);
% read the header of each data block and remember its data offset in bytes
Nblocks = 0;
offset = hdr.DataOffset; % only used when reading from memmapped file
hdr.DataBlockOffset = [];
hdr.DataBlockHeader = dum;
while offset<siz
if Nblocks>=length(hdr.DataBlockOffset);
% allocate another 1000 elements, this prevents continuous reallocation
hdr.DataBlockOffset(Nblocks+10000) = 0;
hdr.DataBlockHeader(Nblocks+10000) = dum;
if feedback, fprintf('reading DataBlockHeader %4.1f%%\n', 100*(offset-hdr.DataOffset)/(siz-hdr.DataOffset)); end
end
Nblocks = Nblocks+1;
if memmap
% get the header information from the memory mapped file
hdr.DataBlockOffset(Nblocks) = offset;
hdr.DataBlockHeader(Nblocks) = PL_DataBlockHeader(mm, offset-hdr.DataOffset, swapFcn);
% skip the header (16 bytes) and the data (int16 words)
offset = offset + 16 + 2 * double(hdr.DataBlockHeader(Nblocks).NumberOfWordsInWaveform * hdr.DataBlockHeader(Nblocks).NumberOfWaveforms);
else
% read the header information from the file the traditional way
hdr.DataBlockOffset(Nblocks) = offset;
hdr.DataBlockHeader(Nblocks) = PL_DataBlockHeader(fid, [], swapFcn);
fseek(fid, 2 * double(hdr.DataBlockHeader(Nblocks).NumberOfWordsInWaveform * hdr.DataBlockHeader(Nblocks).NumberOfWaveforms), 'cof'); % data consists of short integers
offset = ftell(fid);
end % if memmap
end
% this prints the final 100%
if feedback, fprintf('reading DataBlockHeader %4.1f%%\n', 100*(offset-hdr.DataOffset)/(siz-hdr.DataOffset)); end
% remove the allocated space that was not needed
hdr.DataBlockOffset = hdr.DataBlockOffset(1:Nblocks);
hdr.DataBlockHeader = hdr.DataBlockHeader(1:Nblocks);
end % if needhdr
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% read the spike channel data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~isempty(ChannelIndex)
if feedback, fprintf('reading spike data from %s\n', filename); end
if memmap
% open the file as meory mapped object, note that byte swapping may be needed
mm = memmapfile(filename, 'offset', hdr.DataOffset, 'format', 'int16');
end
type = [hdr.DataBlockHeader.Type];
chan = [hdr.DataBlockHeader.Channel];
ts = [hdr.DataBlockHeader.TimeStamp];
for i=1:length(ChannelIndex)
% determine the data blocks with continuous data belonging to this channel
sel = (type==1 & chan==hdr.ChannelHeader(ChannelIndex(i)).Channel);
sel = find(sel);
if isempty(sel)
warning('spike channel %d contains no data', ChannelIndex(i));
varargin{end+1} = [];
continue;
end
% the number of samples can potentially be different in each block
num = double([hdr.DataBlockHeader(sel).NumberOfWordsInWaveform]) .* double([hdr.DataBlockHeader(sel).NumberOfWaveforms]);
% check whether the number of samples per block makes sense
if any(num~=num(1))
error('spike channel blocks with diffent number of samples');
end
% allocate memory to hold the data
buf = zeros(num(1), length(sel), 'int16');
if memmap
% get the header information from the memory mapped file
datbeg = double(hdr.DataBlockOffset(sel) - hdr.DataOffset)/2 + 8 + 1; % expressed in 2-byte words, minus the file header, skip the 16 byte block header
datend = datbeg + num - 1;
for j=1:length(sel)
buf(:,j) = mm.Data(datbeg(j):datend(j));
end
% optionally swap the bytes to correct for the endianness
buf = swapFcn(buf);
else
% read the data from the file in the traditional way
offset = double(hdr.DataBlockOffset(sel)) + 16; % expressed in bytes, skip the 16 byte block header
for j=1:length(sel)
fseek(fid, offset(j), 'bof');
buf(:,j) = fread(fid, num(j), 'int16');
end
end % if memmap
% remember the data for this channel
varargout{i} = buf;
end %for ChannelIndex
end % if ChannelIndex
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% read the continuous channel data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~isempty(SlowChannelIndex)
if feedback, fprintf('reading continuous data from %s\n', filename); end
dat = {};
if memmap
% open the file as meory mapped object, note that byte swapping may be needed
mm = memmapfile(filename, 'offset', hdr.DataOffset, 'format', 'int16');
end
type = [hdr.DataBlockHeader.Type];
chan = [hdr.DataBlockHeader.Channel];
ts = [hdr.DataBlockHeader.TimeStamp];
for i=1:length(SlowChannelIndex)
% determine the data blocks with continuous data belonging to this channel
sel = (type==5 & chan==hdr.SlowChannelHeader(SlowChannelIndex(i)).Channel);
sel = find(sel);
if isempty(sel)
error(sprintf('Continuous channel %d contains no data', SlowChannelIndex(i)));
% warning('Continuous channel %d contains no data', SlowChannelIndex(i));
% varargin{end+1} = [];
% continue;
end
% the number of samples can be different in each block
num = double([hdr.DataBlockHeader(sel).NumberOfWordsInWaveform]) .* double([hdr.DataBlockHeader(sel).NumberOfWaveforms]);
cumnum = cumsum([0 num]);
% allocate memory to hold the data
buf = zeros(1, cumnum(end), 'int16');
if memmap
% get the header information from the memory mapped file
datbeg = double(hdr.DataBlockOffset(sel) - hdr.DataOffset)/2 + 8 + 1; % expressed in 2-byte words, minus the file header, skip the 16 byte block header
datend = datbeg + num - 1;
for j=1:length(sel)
bufbeg = cumnum(j)+1;
bufend = cumnum(j+1);
% copy the data from the memory mapped file into the continuous buffer
buf(bufbeg:bufend) = mm.Data(datbeg(j):datend(j));
end
% optionally swap the bytes to correct for the endianness
buf = swapFcn(buf);
else
% read the data from the file in the traditional way
offset = double(hdr.DataBlockOffset(sel)) + 16; % expressed in bytes, skip the 16 byte block header
for j=1:length(sel)
bufbeg = cumnum(j)+1;
bufend = cumnum(j+1);
% copy the data from the file into the continuous buffer
fseek(fid, offset(j), 'bof');
buf(bufbeg:bufend) = fread(fid, num(j), 'int16');
end
end % if memmap
% remember the data for this channel
varargout{i} = buf;
end %for SlowChannelIndex
end % if SlowChannelIndex
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% read the event channel data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~isempty(EventIndex)
type = [hdr.DataBlockHeader.Type];
chan = [hdr.DataBlockHeader.Channel];
ts = [hdr.DataBlockHeader.TimeStamp];
for i=1:length(EventIndex)
% determine the data blocks with continuous data belonging to this channel
sel = (type==0 & chan==hdr.EventHeader(EventIndex(i)).Channel);
sel = find(sel);
if isempty(sel)
warning('event channel %d contains no data', EventIndex(i));
varargin{end+1} = [];
continue;
end
% this still has to be implemented
keyboard
end % for EventIndex
end % if EventIndex
fclose(fid);
% always return the header as last
varargout{end+1} = hdr;
return
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTIONS for reading the different header elements
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function hdr = PL_FileHeader(fid)
hdr.MagicNumber = fread(fid, 1, 'uint32=>uint32'); % = 0x58454c50;
hdr.Version = fread(fid, 1, 'int32' ); % Version of the data format; determines which data items are valid
hdr.Comment = fread(fid, [1 128], 'uint8=>char' ); % User-supplied comment
hdr.ADFrequency = fread(fid, 1, 'int32' ); % Timestamp frequency in hertz
hdr.NumDSPChannels = fread(fid, 1, 'int32' ); % Number of DSP channel headers in the file
hdr.NumEventChannels = fread(fid, 1, 'int32' ); % Number of Event channel headers in the file
hdr.NumSlowChannels = fread(fid, 1, 'int32' ); % Number of A/D channel headers in the file
hdr.NumPointsWave = fread(fid, 1, 'int32' ); % Number of data points in waveform
hdr.NumPointsPreThr = fread(fid, 1, 'int32' ); % Number of data points before crossing the threshold
hdr.Year = fread(fid, 1, 'int32' ); % Time/date when the data was acquired
hdr.Month = fread(fid, 1, 'int32' );
hdr.Day = fread(fid, 1, 'int32' );
hdr.Hour = fread(fid, 1, 'int32' );
hdr.Minute = fread(fid, 1, 'int32' );
hdr.Second = fread(fid, 1, 'int32' );
hdr.FastRead = fread(fid, 1, 'int32' ); % reserved
hdr.WaveformFreq = fread(fid, 1, 'int32' ); % waveform sampling rate; ADFrequency above is timestamp freq
hdr.LastTimestamp = fread(fid, 1, 'double'); % duration of the experimental session, in ticks
% The following 6 items are only valid if Version >= 103
hdr.Trodalness = fread(fid, 1, 'char' ); % 1 for single, 2 for stereotrode, 4 for tetrode
hdr.DataTrodalness = fread(fid, 1, 'char' ); % trodalness of the data representation
hdr.BitsPerSpikeSample = fread(fid, 1, 'char' ); % ADC resolution for spike waveforms in bits (usually 12)
hdr.BitsPerSlowSample = fread(fid, 1, 'char' ); % ADC resolution for slow-channel data in bits (usually 12)
hdr.SpikeMaxMagnitudeMV = fread(fid, 1, 'uint16'); % the zero-to-peak voltage in mV for spike waveform adc values (usually 3000)
hdr.SlowMaxMagnitudeMV = fread(fid, 1, 'uint16'); % the zero-to-peak voltage in mV for slow-channel waveform adc values (usually 5000); Only valid if Version >= 105 (usually either 1000 or 500)
% The following item is only valid if Version >= 105
hdr.SpikePreAmpGain = fread(fid, 1, 'uint16'); % so that this part of the header is 256 bytes
hdr.Padding = fread(fid, 46, 'char' ); % so that this part of the header is 256 bytes
% Counters for the number of timestamps and waveforms in each channel and unit.
% Note that these only record the counts for the first 4 units in each channel.
% channel numbers are 1-based - array entry at [0] is unused
hdr.TSCounts = fread(fid, [5 130], 'int32' ); % number of timestamps[channel][unit]
hdr.WFCounts = fread(fid, [5 130], 'int32' ); % number of waveforms[channel][unit]
% Starting at index 300, the next array also records the number of samples for the
% continuous channels. Note that since EVCounts has only 512 entries, continuous
% channels above channel 211 do not have sample counts.
hdr.EVCounts = fread(fid, 512, 'int32' ); % number of timestamps[event_number]
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function hdr = PL_ChannelHeader(fid)
hdr.Name = fread(fid, [1 32], 'uint8=>char' ); % Name given to the DSP channel
hdr.SIGName = fread(fid, [1 32], 'uint8=>char' ); % Name given to the corresponding SIG channel
hdr.Channel = fread(fid, 1, 'int32' ); % DSP channel number, 1-based
hdr.WFRate = fread(fid, 1, 'int32' ); % When MAP is doing waveform rate limiting, this is limit w/f per sec divided by 10
hdr.SIG = fread(fid, 1, 'int32' ); % SIG channel associated with this DSP channel 1 - based
hdr.Ref = fread(fid, 1, 'int32' ); % SIG channel used as a Reference signal, 1- based
hdr.Gain = fread(fid, 1, 'int32' ); % actual gain divided by SpikePreAmpGain. For pre version 105, actual gain divided by 1000.
hdr.Filter = fread(fid, 1, 'int32' ); % 0 or 1
hdr.Threshold = fread(fid, 1, 'int32' ); % Threshold for spike detection in a/d values
hdr.Method = fread(fid, 1, 'int32' ); % Method used for sorting units, 1 - boxes, 2 - templates
hdr.NUnits = fread(fid, 1, 'int32' ); % number of sorted units
hdr.Template = fread(fid, [64 5], 'int16' ); % Templates used for template sorting, in a/d values
hdr.Fit = fread(fid, 5, 'int32' ); % Template fit
hdr.SortWidth = fread(fid, 1, 'int32' ); % how many points to use in template sorting (template only)
hdr.Boxes = reshape(fread(fid, 4*2*5, 'int16' ), [4 2 5]); % the boxes used in boxes sorting
hdr.SortBeg = fread(fid, 1, 'int32' ); % beginning of the sorting window to use in template sorting (width defined by SortWidth)
hdr.Comment = fread(fid, [1 128], 'uint8=>char' );
hdr.Padding = fread(fid, 11, 'int32' );
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function hdr = PL_EventHeader(fid)
hdr.Name = fread(fid, [1 32], 'uint8=>char' ); % name given to this event
hdr.Channel = fread(fid, 1, 'int32' ); % event number, 1-based
hdr.Comment = fread(fid, [1 128], 'uint8=>char' );
hdr.Padding = fread(fid, 33, 'int32' );
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function hdr = PL_SlowChannelHeader(fid)
hdr.Name = fread(fid, [1 32], 'uint8=>char' ); % name given to this channel
hdr.Channel = fread(fid, 1, 'int32' ); % channel number, 0-based
hdr.ADFreq = fread(fid, 1, 'int32' ); % digitization frequency
hdr.Gain = fread(fid, 1, 'int32' ); % gain at the adc card
hdr.Enabled = fread(fid, 1, 'int32' ); % whether this channel is enabled for taking data, 0 or 1
hdr.PreAmpGain = fread(fid, 1, 'int32' ); % gain at the preamp
% As of Version 104, this indicates the spike channel (PL_ChannelHeader.Channel) of
% a spike channel corresponding to this continuous data channel.
% <=0 means no associated spike channel.
hdr.SpikeChannel = fread(fid, 1, 'int32' );
hdr.Comment = fread(fid, [1 128], 'uint8=>char' );
hdr.Padding = fread(fid, 28, 'int32' );
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function hdr = PL_DataBlockHeader(fid, offset, swapFcn)
% % this is the conventional code, it has been replaced by code that works
% % with both regular and memmapped files
% hdr.Type = fread(fid, 1, 'int16=>int16' ); % Data type; 1=spike, 4=Event, 5=continuous
% hdr.UpperByteOf5ByteTimestamp = fread(fid, 1, 'uint16=>uint16' ); % Upper 8 bits of the 40 bit timestamp
% hdr.TimeStamp = fread(fid, 1, 'uint32=>uint32' ); % Lower 32 bits of the 40 bit timestamp
% hdr.Channel = fread(fid, 1, 'int16=>int16' ); % Channel number
% hdr.Unit = fread(fid, 1, 'int16=>int16' ); % Sorted unit number; 0=unsorted
% hdr.NumberOfWaveforms = fread(fid, 1, 'int16=>int16' ); % Number of waveforms in the data to folow, usually 0 or 1
% hdr.NumberOfWordsInWaveform = fread(fid, 1, 'int16=>int16' ); % Number of samples per waveform in the data to follow
if isa(fid, 'memmapfile')
mm = fid;
datbeg = offset/2 + 1; % the offset is in bytes (minus the file header), the memory mapped file is indexed in int16 words
datend = offset/2 + 8;
buf = mm.Data(datbeg:datend);
else
buf = fread(fid, 8, 'int16=>int16');
end
hdr.Type = swapFcn(buf(1));
hdr.UpperByteOf5ByteTimestamp = swapFcn(uint16(buf(2)));
hdr.TimeStamp = swapFcn(typecast(buf([3 4]), 'uint32'));
hdr.Channel = swapFcn(buf(5));
hdr.Unit = swapFcn(buf(6));
hdr.NumberOfWaveforms = swapFcn(buf(7));
hdr.NumberOfWordsInWaveform = swapFcn(buf(8));
|
github
|
philippboehmsturm/antx-master
|
read_neurosim_evolution.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_neurosim_evolution.m
| 4,562 |
utf_8
|
fef2b6110659fbc86ec755b093316cc2
|
function [hdr, dat] = read_neurosim_evolution(filename, varargin)
% READ_NEUROSIM_EVOLUTION reads the "evolution" file that is written
% by Jan van der Eerden's NeuroSim software. When a directory is used
% as input, the default filename 'evolution' is read.
%
% Use as
% [hdr, dat] = read_neurosim_evolution(filename, ...)
% where additional options should come in key-value pairs and can include
% Vonly = 0 or 1, only give the membrane potentials as output
% headerOnly = 0 or 1, only read the header information (skip the data), automatically set to 1 if nargout==1
%
% See also FT_READ_HEADER, FT_READ_DATA
% Copyright (C) 2012 Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: read_neurosim_evolution.m 7327 2013-01-16 08:26:23Z bargip $
if isdir(filename)
filename = fullfile(filename, 'evolution');
end
Vonly = ft_getopt(varargin, 'Vonly',0);
headerOnly = ft_getopt(varargin, 'headerOnly',0);
if nargout<2 % make sure that when only one output is requested the header is returned
headerOnly=true;
end
label = {};
orig = {};
fid = fopen(filename, 'rb');
% read the header
line = '#';
ishdr=1;
while ishdr==1
% find temporal information
if strfind(lower(line),'start time')
dum= regexp(line, 'time\s+(\d+.\d+E[+-]\d+)', 'tokens');
hdr.FirstTimeStamp = str2double(dum{1}{1});
end
if strfind(lower(line),'time bin')
dum= regexp(line, 'bin\s+(\d+.\d+E[+-]\d+)', 'tokens');
dt=str2double(dum{1}{1});
hdr.Fs= 1e3/dt;
hdr.TimeStampPerSample=dt;
end
if strfind(lower(line),'end time')
dum= regexp(line, 'time\s+(\d+.\d+E[+-]\d+)', 'tokens');
hdr.LastTimeStamp = str2double(dum{1}{1});
hdr.nSamples=int64((hdr.LastTimeStamp-hdr.FirstTimeStamp)/dt+1);
end
% parse the content of the line, determine the label for each column
colid = sscanf(line, '# column %d:', 1);
if ~isempty(colid)
label{colid} = [num2str(colid) rmspace(line(find(line==':'):end))];
end
offset = ftell(fid); % remember the file pointer position
line = fgetl(fid); % get the next line
if ~isempty(line) && line(1)~='#' && ~isempty(str2num(line))
% the data starts here, rewind the last line
fseek(fid, offset, 'bof');
line = [];
ishdr=0;
else
orig{end+1} = line;
end
end
timelab=find(~cellfun('isempty',regexp(lower(label), 'time', 'match')));
if ~headerOnly
% read the complete data
dat = fscanf(fid, '%f', [length(label), inf]);
hdr.nSamples = length(dat(timelab, :)); %overwrites the value written in the header with the actual number of samples found
hdr.LastTimeStamp = dat(1,end);
end
fclose(fid);
% only extract V_membrane if wanted
if Vonly
matchLab=regexp(label,'V of (\S+) neuron','start');
idx=find(~cellfun(@isempty,matchLab));
if isempty(idx) % most likely a multi compartment simulation
matchLab=regexp(label,'V\S+ of (\S+) neuron','start');
idx=find(~cellfun(@isempty,matchLab));
end
if ~headerOnly
dat=dat([timelab idx],:);
end
label=label([timelab idx]);
for n=2:length(label) % renumbering of the labels
label{n}=[num2str(n) label{n}(regexp(label{n},': V'):end)];
end
end
% convert the header into fieldtrip style
hdr.label = label(:);
hdr.nChans = length(label);
hdr.nSamplesPre = 0;
hdr.nTrials = 1;
% also store the original ascii header details
hdr.orig = orig(:);
[hdr.chanunit hdr.chantype] = deal(cell(length(label),1));
hdr.chantype(:) = {'evolution (neurosim)'};
hdr.chanunit(:) = {'unknown'};
function y=rmspace(x)
% remove double spaces from string
% (c) Bart Gips 2012
y=strtrim(x);
[sbeg send]=regexp(y,' \s+');
for n=1:length(sbeg)
y(sbeg(n):send(n)-1)=[];
end
|
github
|
philippboehmsturm/antx-master
|
read_eeglabevent.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_eeglabevent.m
| 4,191 |
utf_8
|
67bd5db5f42d6abb30df32d9db8277eb
|
% read_eeglabevent() - import EEGLAB dataset events
%
% Usage:
% >> event = read_eeglabevent(filename, ...);
%
% Inputs:
% filename - [string] file name
%
% Optional inputs:
% 'header' - FILEIO structure header
%
% Outputs:
% event - FILEIO toolbox event structure
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, 2008-
%123456789012345678901234567890123456789012345678901234567890123456789012
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD, [email protected]
%
% 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 2 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, write to the Free Software
% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
function event = read_eeglabevent(filename, varargin)
if nargin < 1
help read_eeglabheader;
return;
end;
hdr = ft_getopt(varargin, 'header');
if isempty(hdr)
hdr = read_eeglabheader(filename);
end
event = []; % these will be the output in FieldTrip format
oldevent = hdr.orig.event; % these are in EEGLAB format
missingFieldFlag=false;
if ~isfield(oldevent,'code') && ~isfield(oldevent,'value') && ~isfield(oldevent,'setname')
disp('Warning: No ''value'' field in the events structure.');
missingFieldFlag=true;
end;
if ~isfield(oldevent,'type')
disp('Warning: No ''type'' field in the events structure.');
missingFieldFlag=true;
end;
if missingFieldFlag
if ~isfield(oldevent,'setname') %accommodate Widmann's pop_grandaverage function
disp('EEGlab data files should have both a ''value'' field');
disp('to denote the generic type of event, as in ''trigger'', and a ''type'' field');
disp('to denote the nature of this generic event, as in the condition of the experiment.');
disp('Note also that this is the reverse of the FieldTrip convention.');
end;
end;
for index = 1:length(oldevent)
if isfield(oldevent,'code')
type = oldevent(index).code;
elseif isfield(oldevent,'value')
type = oldevent(index).value;
else
type = 'trigger';
end;
% events can have a numeric or a string value
if isfield(oldevent,'type')
value = oldevent(index).type;
else
value = 'default';
end;
% this is the sample number of the concatenated data to which the event corresponds
sample = oldevent(index).latency;
% a non-zero offset only applies to trial-events, i.e. in case the data is
% segmented and each data segment needs to be represented as event. In
% that case the offset corresponds to the baseline duration (times -1).
offset = 0;
if isfield(oldevent, 'duration')
duration = oldevent(index).duration;
else
duration = 0;
end;
% add the current event in fieldtrip format
event(index).type = type; % this is usually a string, e.g. 'trigger' or 'trial'
event(index).value = value; % in case of a trigger, this is the value
event(index).sample = sample; % this is the sample in the datafile at which the event happens
event(index).offset = offset; % some events should be represented with a shifted time-axix, e.g. a trial with a baseline period
event(index).duration = duration; % some events have a duration, such as a trial
end;
if hdr.nTrials>1
% add the trials to the event structure
for i=1:hdr.nTrials
event(end+1).type = 'trial';
event(end ).sample = (i-1)*hdr.nSamples + 1;
if isfield(oldevent,'setname') && (length(oldevent) == hdr.nTrials)
event(end ).value = oldevent(i).setname; %accommodate Widmann's pop_grandaverage function
else
event(end ).value = [];
end;
event(end ).offset = -hdr.nSamplesPre;
event(end ).duration = hdr.nSamples;
end
end
|
github
|
philippboehmsturm/antx-master
|
read_bti_ascii.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/read_bti_ascii.m
| 2,301 |
utf_8
|
1a848485b63c5722f7013276c80d4dab
|
function [file] = read_bti_ascii(filename);
% READ_BTI_ASCII reads general data from a BTI configuration file
%
% The file should be formatted like
% Group:
% item1 : value1a value1b value1c
% item2 : value2a value2b value2c
% item3 : value3a value3b value3c
% item4 : value4a value4b value4c
%
% Copyright (C) 2004, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: read_bti_ascii.m 7123 2012-12-06 21:21:38Z roboos $
fid = fopen(filename, 'r');
if fid==-1
error(sprintf('could not open file %s', filename));
end
line = '';
while ischar(line)
line = cleanline(fgetl(fid))
if isempty(line) | line==-1 | isempty(findstr(line, ':'))
continue
end
% the line is not empty, which means that we have encountered a chunck of information
if findstr(line, ':')~=length(line)
[item, value] = strtok(line, ':');
value(1) = ' '; % remove the :
value = strtrim(value);
item = strtrim(item);
item(findstr(item, '.')) = '_';
item(findstr(item, ' ')) = '_';
if ischar(item)
eval(sprintf('file.%s = ''%s'';', item, value));
else
eval(sprintf('file.%s = %s;', item, value));
end
else
subline = cleanline(fgetl(fid));
error, the rest has not been implemented (yet)
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function line = cleanline(line)
if isempty(line) | line==-1
return
end
comment = findstr(line, '//');
if ~isempty(comment)
line(min(comment):end) = ' ';
end
line = strtrim(line);
|
github
|
philippboehmsturm/antx-master
|
openbdf.m
|
.m
|
antx-master/xspm8/external/fieldtrip/fileio/private/openbdf.m
| 6,812 |
utf_8
|
cb49358a2a955b165a5c50127c25e3d8
|
% openbdf() - Opens an BDF File (European Data Format for Biosignals) in MATLAB (R)
%
% Usage:
% >> EDF=openedf(FILENAME)
%
% Note: About EDF -> www.biosemi.com/faq/file_format.htm
%
% Author: Alois Schloegl, 5.Nov.1998
%
% See also: readedf()
% Copyright (C) 1997-1998 by Alois Schloegl
% [email protected]
% Ver 2.20 18.Aug.1998
% Ver 2.21 10.Oct.1998
% Ver 2.30 5.Nov.1998
%
% For use under Octave define the following function
% function s=upper(s); s=toupper(s); end;
% V2.12 Warning for missing Header information
% V2.20 EDF.AS.* changed
% V2.30 EDF.T0 made Y2K compatible until Year 2090
% 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 2
% 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, write to the Free Software
% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
% Name changed for bdf files Sept 6,2002 T.S. Lorig
% Header updated for EEGLAB format (update web link too) - Arnaud Delorme 14 Oct 2002
function [DAT,H1]=openbdf(FILENAME)
SLASH='/'; % defines Seperator for Subdirectories
BSLASH=char(92);
cname=computer;
if cname(1:2)=='PC' SLASH=BSLASH; end;
fid=fopen(FILENAME,'r','ieee-le');
if fid<0
fprintf(2,['Error LOADEDF: File ' FILENAME ' not found\n']);
return;
end;
EDF.FILE.FID=fid;
EDF.FILE.OPEN = 1;
EDF.FileName = FILENAME;
PPos=min([max(find(FILENAME=='.')) length(FILENAME)+1]);
SPos=max([0 find((FILENAME=='/') | (FILENAME==BSLASH))]);
EDF.FILE.Ext = FILENAME(PPos+1:length(FILENAME));
EDF.FILE.Name = FILENAME(SPos+1:PPos-1);
if SPos==0
EDF.FILE.Path = pwd;
else
EDF.FILE.Path = FILENAME(1:SPos-1);
end;
EDF.FileName = [EDF.FILE.Path SLASH EDF.FILE.Name '.' EDF.FILE.Ext];
H1=char(fread(EDF.FILE.FID,256,'char')'); %
EDF.VERSION=H1(1:8); % 8 Byte Versionsnummer
%if 0 fprintf(2,'LOADEDF: WARNING Version EDF Format %i',ver); end;
EDF.PID = deblank(H1(9:88)); % 80 Byte local patient identification
EDF.RID = deblank(H1(89:168)); % 80 Byte local recording identification
%EDF.H.StartDate = H1(169:176); % 8 Byte
%EDF.H.StartTime = H1(177:184); % 8 Byte
EDF.T0=[str2num(H1(168+[7 8])) str2num(H1(168+[4 5])) str2num(H1(168+[1 2])) str2num(H1(168+[9 10])) str2num(H1(168+[12 13])) str2num(H1(168+[15 16])) ];
% Y2K compatibility until year 2090
if EDF.VERSION(1)=='0'
if EDF.T0(1) < 91
EDF.T0(1)=2000+EDF.T0(1);
else
EDF.T0(1)=1900+EDF.T0(1);
end;
else ;
% in a future version, this is hopefully not needed
end;
EDF.HeadLen = str2num(H1(185:192)); % 8 Byte Length of Header
% reserved = H1(193:236); % 44 Byte
EDF.NRec = str2num(H1(237:244)); % 8 Byte # of data records
EDF.Dur = str2num(H1(245:252)); % 8 Byte # duration of data record in sec
EDF.NS = str2num(H1(253:256)); % 8 Byte # of signals
EDF.Label = char(fread(EDF.FILE.FID,[16,EDF.NS],'char')');
EDF.Transducer = char(fread(EDF.FILE.FID,[80,EDF.NS],'char')');
EDF.PhysDim = char(fread(EDF.FILE.FID,[8,EDF.NS],'char')');
EDF.PhysMin= str2num(char(fread(EDF.FILE.FID,[8,EDF.NS],'char')'));
EDF.PhysMax= str2num(char(fread(EDF.FILE.FID,[8,EDF.NS],'char')'));
EDF.DigMin = str2num(char(fread(EDF.FILE.FID,[8,EDF.NS],'char')')); %
EDF.DigMax = str2num(char(fread(EDF.FILE.FID,[8,EDF.NS],'char')')); %
% check validity of DigMin and DigMax
if (length(EDF.DigMin) ~= EDF.NS)
fprintf(2,'Warning OPENEDF: Failing Digital Minimum\n');
EDF.DigMin = -(2^15)*ones(EDF.NS,1);
end
if (length(EDF.DigMax) ~= EDF.NS)
fprintf(2,'Warning OPENEDF: Failing Digital Maximum\n');
EDF.DigMax = (2^15-1)*ones(EDF.NS,1);
end
if (any(EDF.DigMin >= EDF.DigMax))
fprintf(2,'Warning OPENEDF: Digital Minimum larger than Maximum\n');
end
% check validity of PhysMin and PhysMax
if (length(EDF.PhysMin) ~= EDF.NS)
fprintf(2,'Warning OPENEDF: Failing Physical Minimum\n');
EDF.PhysMin = EDF.DigMin;
end
if (length(EDF.PhysMax) ~= EDF.NS)
fprintf(2,'Warning OPENEDF: Failing Physical Maximum\n');
EDF.PhysMax = EDF.DigMax;
end
if (any(EDF.PhysMin >= EDF.PhysMax))
fprintf(2,'Warning OPENEDF: Physical Minimum larger than Maximum\n');
EDF.PhysMin = EDF.DigMin;
EDF.PhysMax = EDF.DigMax;
end
EDF.PreFilt= char(fread(EDF.FILE.FID,[80,EDF.NS],'char')'); %
tmp = fread(EDF.FILE.FID,[8,EDF.NS],'char')'; % samples per data record
EDF.SPR = str2num(char(tmp)); % samples per data record
fseek(EDF.FILE.FID,32*EDF.NS,0);
EDF.Cal = (EDF.PhysMax-EDF.PhysMin)./ ...
(EDF.DigMax-EDF.DigMin);
EDF.Off = EDF.PhysMin - EDF.Cal .* EDF.DigMin;
tmp = find(EDF.Cal < 0);
EDF.Cal(tmp) = ones(size(tmp));
EDF.Off(tmp) = zeros(size(tmp));
EDF.Calib=[EDF.Off';(diag(EDF.Cal))];
%EDF.Calib=sparse(diag([1; EDF.Cal]));
%EDF.Calib(1,2:EDF.NS+1)=EDF.Off';
EDF.SampleRate = EDF.SPR / EDF.Dur;
EDF.FILE.POS = ftell(EDF.FILE.FID);
if EDF.NRec == -1 % unknown record size, determine correct NRec
fseek(EDF.FILE.FID, 0, 'eof');
endpos = ftell(EDF.FILE.FID);
EDF.NRec = floor((endpos - EDF.FILE.POS) / (sum(EDF.SPR) * 2));
fseek(EDF.FILE.FID, EDF.FILE.POS, 'bof');
H1(237:244)=sprintf('%-8i',EDF.NRec); % write number of records
end;
EDF.Chan_Select=(EDF.SPR==max(EDF.SPR));
for k=1:EDF.NS
if EDF.Chan_Select(k)
EDF.ChanTyp(k)='N';
else
EDF.ChanTyp(k)=' ';
end;
if findstr(upper(EDF.Label(k,:)),'ECG')
EDF.ChanTyp(k)='C';
elseif findstr(upper(EDF.Label(k,:)),'EKG')
EDF.ChanTyp(k)='C';
elseif findstr(upper(EDF.Label(k,:)),'EEG')
EDF.ChanTyp(k)='E';
elseif findstr(upper(EDF.Label(k,:)),'EOG')
EDF.ChanTyp(k)='O';
elseif findstr(upper(EDF.Label(k,:)),'EMG')
EDF.ChanTyp(k)='M';
end;
end;
EDF.AS.spb = sum(EDF.SPR); % Samples per Block
bi=[0;cumsum(EDF.SPR)];
idx=[];idx2=[];
for k=1:EDF.NS,
idx2=[idx2, (k-1)*max(EDF.SPR)+(1:EDF.SPR(k))];
end;
maxspr=max(EDF.SPR);
idx3=zeros(EDF.NS*maxspr,1);
for k=1:EDF.NS, idx3(maxspr*(k-1)+(1:maxspr))=bi(k)+ceil((1:maxspr)'/maxspr*EDF.SPR(k));end;
%EDF.AS.bi=bi;
EDF.AS.IDX2=idx2;
%EDF.AS.IDX3=idx3;
DAT.Head=EDF;
DAT.MX.ReRef=1;
%DAT.MX=feval('loadxcm',EDF);
return;
|
github
|
philippboehmsturm/antx-master
|
ft_trialfun_general.m
|
.m
|
antx-master/xspm8/external/fieldtrip/trialfun/ft_trialfun_general.m
| 13,271 |
utf_8
|
0cfa2157a643d65e98d581477165b7b5
|
function [trl, event] = ft_trialfun_general(cfg)
% FT_TRIALFUN_GENERAL determines trials/segments in the data that are
% interesting for analysis, using the general event structure returned
% by read_event. This function is independent of the dataformat
%
% The trialdef structure can contain the following specifications
% cfg.trialdef.eventtype = 'string'
% cfg.trialdef.eventvalue = number, string or list with numbers or strings
% cfg.trialdef.prestim = latency in seconds (optional)
% cfg.trialdef.poststim = latency in seconds (optional)
%
% If you want to read all data from a continous file in segments, you can specify
% cfg.trialdef.triallength = duration in seconds (can be Inf)
% cfg.trialdef.ntrials = number of trials
%
% If you specify
% cfg.trialdef.eventtype = '?'
% a list with the events in your datafile will be displayed on screen.
%
% See also FT_DEFINETRIAL, FT_PREPROCESSING
% Copyright (C) 2005-2012, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_trialfun_general.m 7123 2012-12-06 21:21:38Z roboos $
% some events do not require the specification a type, pre or poststim period
% in that case it is more convenient not to have them, instead of making them empty
if isfield(cfg.trialdef, 'eventvalue') && isempty(cfg.trialdef.eventvalue ), cfg.trialdef = rmfield(cfg.trialdef, 'eventvalue' ); end
if isfield(cfg.trialdef, 'prestim') && isempty(cfg.trialdef.prestim ), cfg.trialdef = rmfield(cfg.trialdef, 'prestim' ); end
if isfield(cfg.trialdef, 'poststim') && isempty(cfg.trialdef.poststim ), cfg.trialdef = rmfield(cfg.trialdef, 'poststim' ); end
if isfield(cfg.trialdef, 'triallength') && isempty(cfg.trialdef.triallength ), cfg.trialdef = rmfield(cfg.trialdef, 'triallength'); end
if isfield(cfg.trialdef, 'ntrials') && isempty(cfg.trialdef.ntrials ), cfg.trialdef = rmfield(cfg.trialdef, 'ntrials' ); end
if isfield(cfg.trialdef, 'triallength')
% reading all segments from a continuous file is incompatible with any other option
try, cfg.trialdef = rmfield(cfg.trialdef, 'eventvalue'); end
try, cfg.trialdef = rmfield(cfg.trialdef, 'prestim' ); end
try, cfg.trialdef = rmfield(cfg.trialdef, 'poststim' ); end
if ~isfield(cfg.trialdef, 'ntrials')
if isinf(cfg.trialdef.triallength)
cfg.trialdef.ntrials = 1;
else
cfg.trialdef.ntrials = inf;
end
end
end
% default rejection parameter
if ~isfield(cfg, 'eventformat'), cfg.eventformat = []; end
if ~isfield(cfg, 'headerformat'), cfg.headerformat = []; end
if ~isfield(cfg, 'dataformat'), cfg.dataformat = []; end
% read the header, contains the sampling frequency
hdr = ft_read_header(cfg.headerfile, 'headerformat', cfg.headerformat);
% read the events
if isfield(cfg, 'event')
fprintf('using the events from the configuration structure\n');
event = cfg.event;
else
try
fprintf('reading the events from ''%s''\n', cfg.headerfile);
event = ft_read_event(cfg.headerfile, 'headerformat', cfg.headerformat, 'eventformat', cfg.eventformat, 'dataformat', cfg.dataformat);
catch
% ensure that it has the correct fields, even if it is empty
event = struct('type', {}, 'value', {}, 'sample', {}, 'offset', {}, 'duration', {});
end
end
% for the following, the trials do not depend on the events in the data
if isfield(cfg.trialdef, 'triallength')
if isinf(cfg.trialdef.triallength)
% make one long trial with the complete continuous data in it
trl = [1 hdr.nSamples*hdr.nTrials 0];
elseif isinf(cfg.trialdef.ntrials)
% cut the continous data into as many segments as possible
nsamples = round(cfg.trialdef.triallength*hdr.Fs);
trlbeg = 1:nsamples:(hdr.nSamples*hdr.nTrials - nsamples + 1);
trlend = trlbeg + nsamples - 1;
offset = zeros(size(trlbeg));
trl = [trlbeg(:) trlend(:) offset(:)];
else
% make the pre-specified number of trials
nsamples = round(cfg.trialdef.triallength*hdr.Fs);
trlbeg = (0:(cfg.trialdef.ntrials-1))*nsamples + 1;
trlend = trlbeg + nsamples - 1;
offset = zeros(size(trlbeg));
trl = [trlbeg(:) trlend(:) offset(:)];
end
return
end
sel = [];
trl = [];
val = [];
if strcmp(cfg.trialdef.eventtype, '?')
% no trials should be added, show event information using subfunction and exit
show_event(event);
return
end
if strcmp(cfg.trialdef.eventtype, 'gui') || (isfield(cfg.trialdef, 'eventvalue') && length(cfg.trialdef.eventvalue)==1 && strcmp(cfg.trialdef.eventvalue, 'gui'))
cfg.trialdef = select_event(event, cfg.trialdef);
usegui = 1;
else
usegui = 0;
end
if ~isfield(cfg.trialdef, 'eventvalue')
cfg.trialdef.eventvalue = [];
elseif ischar(cfg.trialdef.eventvalue)
% convert single string into cell-array, otherwise the intersection does not work as intended
cfg.trialdef.eventvalue = {cfg.trialdef.eventvalue};
end
% select all events of the specified type and with the specified value
if ~isempty(cfg.trialdef.eventtype)
sel = ismember({event.type}, cfg.trialdef.eventtype);
else
sel = true(size(event));
end
if ~isempty(cfg.trialdef.eventvalue)
% this cannot be done robustly in a single line of code
if ~iscell(cfg.trialdef.eventvalue)
valchar = ischar(cfg.trialdef.eventvalue);
valnumeric = isnumeric(cfg.trialdef.eventvalue);
else
valchar = ischar(cfg.trialdef.eventvalue{1});
valnumeric = isnumeric(cfg.trialdef.eventvalue{1});
end
for i=1:numel(event)
if (ischar(event(i).value) && valchar) || (isnumeric(event(i).value) && valnumeric)
sel(i) = sel(i) & ~isempty(intersect(event(i).value, cfg.trialdef.eventvalue));
end
end
end
% convert from boolean vector into a list of indices
sel = find(sel);
if usegui
% Checks whether offset and duration are defined for all the selected
% events and/or prestim/poststim are defined in trialdef.
if (any(cellfun('isempty', {event(sel).offset})) || ...
any(cellfun('isempty', {event(sel).duration}))) && ...
~(isfield(cfg.trialdef, 'prestim') && isfield(cfg.trialdef, 'poststim'))
% If at least some of offset/duration values and prestim/poststim
% values are missing tries to ask the user for prestim/poststim
answer = inputdlg({'Prestimulus latency (sec)','Poststimulus latency (sec)'}, 'Enter borders');
if isempty(answer) || any(cellfun('isempty', answer))
error('The information in the data and cfg is insufficient to define trials.');
else
cfg.trialdef.prestim=str2double(answer{1});
cfg.trialdef.poststim=str2double(answer{2});
if isnan(cfg.trialdef.prestim) || isnan(cfg.trialdef.poststim)
error('Illegal input for trial borders');
end
end
end % if specification is not complete
end % if usegui
for i=sel
% catch empty fields in the event table and interpret them meaningfully
if isempty(event(i).offset)
% time axis has no offset relative to the event
event(i).offset = 0;
end
if isempty(event(i).duration)
% the event does not specify a duration
event(i).duration = 0;
end
% determine where the trial starts with respect to the event
if ~isfield(cfg.trialdef, 'prestim')
trloff = event(i).offset;
trlbeg = event(i).sample;
else
% override the offset of the event
trloff = round(-cfg.trialdef.prestim*hdr.Fs);
% also shift the begin sample with the specified amount
trlbeg = event(i).sample + trloff;
end
% determine the number of samples that has to be read (excluding the begin sample)
if ~isfield(cfg.trialdef, 'poststim')
trldur = max(event(i).duration - 1, 0);
else
% this will not work if prestim was not defined, the code will then crash
trldur = round((cfg.trialdef.poststim+cfg.trialdef.prestim)*hdr.Fs) - 1;
end
trlend = trlbeg + trldur;
% add the beginsample, endsample and offset of this trial to the list
% if all samples are in the dataset
if trlbeg>0 && trlend<=hdr.nSamples.*hdr.nTrials,
trl = [trl; [trlbeg trlend trloff]];
if isnumeric(event(i).value),
val = [val; event(i).value];
elseif ischar(event(i).value) && (event(i).value(1)=='S'|| event(i).value(1)=='R')
% on brainvision these are called 'S 1' for stimuli or 'R 1' for responses
val = [val; str2double(event(i).value(2:end))];
else
val = [val; nan];
end
end
end
% append the vector with values
if ~isempty(val) && ~all(isnan(val))
trl = [trl val];
end
if usegui
% This complicated line just computes the trigger times in seconds and
% converts them to a cell array of strings to use in the GUI
eventstrings = cellfun(@num2str, mat2cell((trl(:, 1)- trl(:, 3))./hdr.Fs , ones(1, size(trl, 1))), 'UniformOutput', 0);
% Let us start with handling at least the completely unsegmented case
% semi-automatically. The more complicated cases are better left
% to the user.
if hdr.nTrials==1
selected = find(trl(:,1)>0 & trl(:,2)<=hdr.nSamples);
else
selected = find(trl(:,1)>0);
end
indx = select_channel_list(eventstrings, selected , 'Select events');
trl=trl(indx, :);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION that shows event table
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function show_event(event)
if isempty(event)
fprintf('no events were found in the datafile\n');
return
end
eventtype = unique({event.type});
Neventtype = length(eventtype);
if Neventtype==0
fprintf('no events were found in the datafile\n');
else
fprintf('the following events were found in the datafile\n');
for i=1:Neventtype
sel = find(strcmp(eventtype{i}, {event.type}));
try
eventvalue = unique({event(sel).value}); % cell-array with string value
eventvalue = sprintf('''%s'' ', eventvalue{:}); % translate into a single string
catch
eventvalue = unique(cell2mat({event(sel).value})); % array with numeric values or empty
eventvalue = num2str(eventvalue); % translate into a single string
end
fprintf('event type: ''%s'' ', eventtype{i});
fprintf('with event values: %s', eventvalue);
fprintf('\n');
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION that allows the user to select an event using gui
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function trialdef=select_event(event, trialdef)
if isempty(event)
fprintf('no events were found in the datafile\n');
return
end
if strcmp(trialdef.eventtype, 'gui')
eventtype = unique({event.type});
else
eventtype ={trialdef.eventtype};
end
Neventtype = length(eventtype);
if Neventtype==0
fprintf('no events were found in the datafile\n');
else
% Two lists are built in parallel
settings={}; % The list of actual values to be used later
strsettings={}; % The list of strings to show in the GUI
for i=1:Neventtype
sel = find(strcmp(eventtype{i}, {event.type}));
emptyval=find(cellfun('isempty', {event(sel).value}));
if all(cellfun(@isnumeric, {event(sel).value}))
[event(sel(emptyval)).value]=deal(Inf);
eventvalue = unique([event(sel).value]);
else
if ~isempty(strmatch('Inf', {event(sel).value},'exact'))
% It's a very unlikely scenario but ...
warning('Event value''Inf'' cannot be handled by GUI selection. Mistakes are possible.')
end
[event(sel(emptyval)).value]=deal('Inf');
eventvalue = unique({event(sel).value});
if ~iscell(eventvalue)
eventvalue={eventvalue};
end
end
for j=1:length(eventvalue)
if (ischar(eventvalue(j)) && ~strcmp(eventvalue(j), 'Inf')) || ...
(isnumeric(eventvalue(j)) && eventvalue(j)~=Inf)
settings=[settings; [eventtype(i), eventvalue(j)]];
else
settings=[settings; [eventtype(i), {[]}]];
end
if isa(eventvalue, 'numeric')
strsettings=[strsettings; {['Type: ' eventtype{i} ' ; Value: ' num2str(eventvalue(j))]}];
else
strsettings=[strsettings; {['Type: ' eventtype{i} ' ; Value: ' eventvalue{j}]}];
end
end
end
if isempty(strsettings)
fprintf('no events of the selected type were found in the datafile\n');
return
end
[selection ok]= listdlg('ListString',strsettings, 'SelectionMode', 'single', 'Name', 'Select event', 'ListSize', [300 300]);
if ok
trialdef.eventtype=settings{selection,1};
trialdef.eventvalue=settings{selection,2};
end
end
|
github
|
philippboehmsturm/antx-master
|
ft_trialfun_realtime.m
|
.m
|
antx-master/xspm8/external/fieldtrip/trialfun/ft_trialfun_realtime.m
| 4,591 |
utf_8
|
643acf8caae74037ece5c0f0d5ca7057
|
function trl = ft_trialfun_realtime(cfg)
% FT_TRIALFUN_REALTIME can be used to segment a continuous stream of
% data in real-time. Trials are defined as [begsample endsample offset
% condition]
%
% The configuration structure can contain the following specifications
% cfg.minsample = the last sample number that was already considered (passed from rt_process)
% cfg.blocksize = in seconds. In case of events, offset is
% wrt the trigger.
% cfg.offset = the offset wrt the 0 point. In case of no events, offset is wrt
% prevSample. E.g., [-0.9 1] will read 1 second blocks with
% 0.9 second overlap
% cfg.bufferdata = {'first' 'last'}. If 'last' then only the last block of
% interest is read. Otherwise, all well-defined blocks are read (default = 'first')
% Copyright (C) 2009, Marcel van Gerven
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_trialfun_realtime.m 7123 2012-12-06 21:21:38Z roboos $
if ~isfield(cfg,'minsample'), cfg.minsample = 0; end
if ~isfield(cfg,'blocksize'), cfg.blocksize = 0.1; end
if ~isfield(cfg,'offset'), cfg.offset = 0; end
if ~isfield(cfg,'bufferdata'), cfg.bufferdata = 'first'; end
if ~isfield(cfg,'triggers'), cfg.triggers = []; end
% blocksize and offset in terms of samples
cfg.blocksize = round(cfg.blocksize * cfg.hdr.Fs);
cfg.offset = round(cfg.offset * cfg.hdr.Fs);
% retrieve trials of interest
if isempty(cfg.event) % asynchronous mode
trl = trialfun_asynchronous(cfg);
else % synchronous mode
trl = trialfun_synchronous(cfg);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function trl = trialfun_asynchronous(cfg)
trl = [];
prevSample = cfg.minsample;
if strcmp(cfg.bufferdata, 'last') % only get last block
% begsample starts blocksize samples before the end
begsample = cfg.hdr.nSamples*cfg.hdr.nTrials - cfg.blocksize;
% begsample should be offset samples away from the previous read
if begsample >= (prevSample + cfg.offset)
endsample = cfg.hdr.nSamples*cfg.hdr.nTrials;
if begsample < endsample && begsample > 0
trl = [begsample endsample 0 nan];
end
end
else % get all blocks
while true
% see whether new samples are available
newsamples = (cfg.hdr.nSamples*cfg.hdr.nTrials-prevSample);
% if newsamples exceeds the offset plus length specified in blocksize
if newsamples >= (cfg.offset+cfg.blocksize)
% we do not consider samples < 1
begsample = max(1,prevSample+cfg.offset);
endsample = max(1,prevSample+cfg.offset+cfg.blocksize);
if begsample < endsample && endsample <= cfg.hdr.nSamples*cfg.hdr.nTrials
trl = [trl; [begsample endsample 0 nan]];
end
prevSample = endsample;
else
break;
end
end
end
end % function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function trl = trialfun_synchronous(cfg)
trl = [];
% process all events
for j=1:length(cfg.event)
if isempty(cfg.triggers)
curtrig = cfg.event(j).value;
else
[m1,curtrig] = ismember(cfg.event(j).value,cfg.triggers);
end
if isempty(curtrig), curtrig = nan; end
if isempty(cfg.triggers) || (~isempty(m1) && m1)
% catched a trigger of interest
% we do not consider samples < 1
begsample = max(1,cfg.event(j).sample + cfg.offset);
endsample = max(1,begsample + cfg.blocksize);
trl = [trl; [begsample endsample cfg.offset curtrig]];
end
end
end % function
|
github
|
philippboehmsturm/antx-master
|
select_channel_list.m
|
.m
|
antx-master/xspm8/external/fieldtrip/trialfun/private/select_channel_list.m
| 5,910 |
utf_8
|
51149b83e6eca7c0510e5a740c7f87e7
|
function [select] = select_channel_list(label, select, titlestr);
% SELECT_CHANNEL_LIST presents a dialog for selecting multiple elements
% from a cell array with strings, such as the labels of EEG channels.
% The dialog presents two columns with an add and remove mechanism.
%
% select = select_channel_list(label, initial, titlestr)
%
% with
% initial indices of channels that are initially selected
% label cell array with channel labels (strings)
% titlestr title for dialog (optional)
% and
% select indices of selected channels
%
% If the user presses cancel, the initial selection will be returned.
% Copyright (C) 2003, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: select_channel_list.m 7123 2012-12-06 21:21:38Z roboos $
if nargin<3
titlestr = 'Select';
end
pos = get(0,'DefaultFigurePosition');
pos(3:4) = [290 300];
dlg = dialog('Name', titlestr, 'Position', pos);
select = select(:)'; % ensure that it is a row array
userdata.label = label;
userdata.select = select;
userdata.unselect = setdiff(1:length(label), select);
set(dlg, 'userdata', userdata);
uicontrol(dlg, 'style', 'text', 'position', [ 10 240+20 80 20], 'string', 'unselected');
uicontrol(dlg, 'style', 'text', 'position', [200 240+20 80 20], 'string', 'selected ');
uicontrol(dlg, 'style', 'listbox', 'position', [ 10 40+20 80 200], 'min', 0, 'max', 2, 'tag', 'lbunsel')
uicontrol(dlg, 'style', 'listbox', 'position', [200 40+20 80 200], 'min', 0, 'max', 2, 'tag', 'lbsel')
uicontrol(dlg, 'style', 'pushbutton', 'position', [105 175+20 80 20], 'string', 'add all >' , 'callback', @label_addall);
uicontrol(dlg, 'style', 'pushbutton', 'position', [105 145+20 80 20], 'string', 'add >' , 'callback', @label_add);
uicontrol(dlg, 'style', 'pushbutton', 'position', [105 115+20 80 20], 'string', '< remove' , 'callback', @label_remove);
uicontrol(dlg, 'style', 'pushbutton', 'position', [105 85+20 80 20], 'string', '< remove all', 'callback', @label_removeall);
uicontrol(dlg, 'style', 'pushbutton', 'position', [ 55 10 80 20], 'string', 'Cancel', 'callback', 'close');
uicontrol(dlg, 'style', 'pushbutton', 'position', [155 10 80 20], 'string', 'OK', 'callback', 'uiresume');
label_redraw(dlg);
% wait untill the dialog is closed or the user presses OK/Cancel
uiwait(dlg);
if ishandle(dlg)
% the user pressed OK, return the selection from the dialog
userdata = get(dlg, 'userdata');
select = userdata.select;
close(dlg);
return
else
% the user pressed Cancel or closed the dialog, return the initial selection
return
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function label_redraw(h);
userdata = get(h, 'userdata');
set(findobj(h, 'tag', 'lbsel' ), 'string', userdata.label(userdata.select));
set(findobj(h, 'tag', 'lbunsel'), 'string', userdata.label(userdata.unselect));
% set the active element in the select listbox, based on the previous active element
tmp = min(get(findobj(h, 'tag', 'lbsel'), 'value'));
tmp = min(tmp, length(get(findobj(h, 'tag', 'lbsel'), 'string')));
if isempty(tmp) | tmp==0
tmp = 1;
end
set(findobj(h, 'tag', 'lbsel' ), 'value', tmp);
% set the active element in the unselect listbox, based on the previous active element
tmp = min(get(findobj(h, 'tag', 'lbunsel'), 'value'));
tmp = min(tmp, length(get(findobj(h, 'tag', 'lbunsel'), 'string')));
if isempty(tmp) | tmp==0
tmp = 1;
end
set(findobj(h, 'tag', 'lbunsel' ), 'value', tmp);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function label_addall(h, eventdata, handles, varargin);
h = get(h, 'parent');
userdata = get(h, 'userdata');
userdata.select = 1:length(userdata.label);
userdata.unselect = [];
set(findobj(h, 'tag', 'lbunsel' ), 'value', 1);
set(h, 'userdata', userdata);
label_redraw(h);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function label_removeall(h, eventdata, handles, varargin);
h = get(h, 'parent');
userdata = get(h, 'userdata');
userdata.unselect = 1:length(userdata.label);
userdata.select = [];
set(findobj(h, 'tag', 'lbsel' ), 'value', 1);
set(h, 'userdata', userdata);
label_redraw(h);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function label_add(h, eventdata, handles, varargin);
h = get(h, 'parent');
userdata = get(h, 'userdata');
if ~isempty(userdata.unselect)
add = userdata.unselect(get(findobj(h, 'tag', 'lbunsel' ), 'value'));
userdata.select = sort([userdata.select add]);
userdata.unselect = sort(setdiff(userdata.unselect, add));
set(h, 'userdata', userdata);
label_redraw(h);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function label_remove(h, eventdata, handles, varargin);
h = get(h, 'parent');
userdata = get(h, 'userdata');
if ~isempty(userdata.select)
remove = userdata.select(get(findobj(h, 'tag', 'lbsel' ), 'value'));
userdata.select = sort(setdiff(userdata.select, remove));
userdata.unselect = sort([userdata.unselect remove]);
set(h, 'userdata', userdata);
label_redraw(h);
end
|
github
|
philippboehmsturm/antx-master
|
ft_headmodel_fns.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/ft_headmodel_fns.m
| 5,731 |
utf_8
|
2a5fde79978f74623faf81f8d9fa4af7
|
function vol = ft_headmodel_fns(seg, varargin)
% FT_HEADMODEL_FNS creates the volume conduction structure to be used
% in the FNS forward solver.
%
% Use as
% vol = ft_headmodel_fns(seg, ...)
%
% Optional input arguments should be specified in key-value pairs and
% can include
% tissuecond = matrix C [9XN tissue types]; where N is the number of
% tissues and a 3x3 tensor conductivity matrix is stored
% in each column.
% tissue = see fns_contable_write
% tissueval = match tissues of segmentation input
% transform = 4x4 transformation matrix (default eye(4))
% units = string (default 'cm')
% sens = sensor information (for which ft_datatype(sens,'sens')==1)
% deepelec = used in the case of deep voxel solution
% tolerance = scalar (default 1e-8)
%
% Standard default values for conductivity matrix C are derived from
% Saleheen HI, Ng KT. New finite difference formulations for general
% inhomogeneous anisotropic bioelectric problems. IEEE Trans Biomed Eng.
% 1997
%
% Additional documentation available at:
% http://hunghienvn.nmsu.edu/wiki/index.php/FNS
%
% See also FT_PREPARE_VOL_SENS, FT_COMPUTE_LEADFIELD
% Copyright (C) 2011, Cristiano Micheli and Hung Dang
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_headmodel_fns.m 7224 2012-12-18 11:09:44Z johzum $
ft_hastoolbox('fns', 1);
% get the optional arguments
tissue = ft_getopt(varargin, 'tissue', []);
tissueval = ft_getopt(varargin, 'tissueval', []);
tissuecond = ft_getopt(varargin, 'tissuecond', []);
transform = ft_getopt(varargin, 'transform', eye(4));
units = ft_getopt(varargin, 'units', 'cm');
sens = ft_getopt(varargin, 'sens', []);
deepelec = ft_getopt(varargin, 'deepelec', []); % used in the case of deep voxel solution
tolerance = ft_getopt(varargin, 'tolerance', 1e-8);
if isempty(sens)
error('A set of sensors is required')
end
if ispc
error('FNS only works on Linux and OSX')
end
% check the consistency between tissue values and the segmentation
vecval = ismember(tissueval,unique(seg(:)));
if any(vecval)==0
warning('Some of the tissue values are not in the segmentation')
end
% create the files to be written
try
tmpfolder = pwd;
cd(tempdir)
[tmp,tname] = fileparts(tempname);
segfile = [tname];
[tmp,tname] = fileparts(tempname);
confile = [tname '.csv'];
[tmp,tname] = fileparts(tempname);
elecfile = [tname '.h5'];
[tmp,tname] = fileparts(tempname);
exefile = [tname '.sh'];
[tmp,tname] = fileparts(tempname);
datafile = [tname '.h5'];
% create a fake mri structure and write the segmentation on disk
disp('writing the segmentation file...')
if ~ft_hastoolbox('fileio')
error('You must have the fileio module to go on')
end
mri = [];
mri.dim = size(seg);
mri.transform = eye(4);
mri.seg = uint8(seg);
cfg = [];
cfg.datatype = 'uint8';
cfg.coordsys = 'ctf';
cfg.parameter = 'seg';
cfg.filename = segfile;
cfg.filetype = 'analyze';
ft_volumewrite(cfg, mri);
% write the cond matrix on disk, load the default cond matrix in case not specified
disp('writing the conductivity file...')
condmatrix = fns_contable_write('tissue',tissue,'tissueval',tissueval,'tissuecond',tissuecond);
csvwrite(confile,condmatrix);
% write the positions of the electrodes on disk
disp('writing the electrodes file...')
pos = warp_apply(inv(transform),sens.chanpos); % in voxel coordinates!
% convert pos into int32 datatype.
hdf5write(elecfile, '/electrodes/gridlocs', int32(pos));
% Exe file
efid = fopen(exefile, 'w');
if ~ispc
fprintf(efid,'#!/usr/bin/env bash\n');
fprintf(efid,['elecsfwd1 -img ' segfile ' -electrodes ./' elecfile ' -data ./', ...
datafile ' -contable ./' confile ' -TOL ' num2str(tolerance) ' \n']);%2>&1 > /dev/null
end
fclose(efid);
% run the shell instructions
dos(sprintf('chmod +x %s', exefile));
dos(['./' exefile]);
% FIXME: find a cleverer way to store the huge transfer matrix (vista?)
[transfer,status] = fns_read_transfer(datafile);
cleaner(segfile,confile,elecfile,exefile,datafile)
catch ME
disp('The transfer matrix was not written')
cleaner(segfile,confile,elecfile,exefile,datafile)
cd(tmpfolder)
rethrow(ME)
end
% start with an empty volume conductor
vol = [];
vol.tissue = tissue;
vol.tissueval = tissueval;
vol.transform = transform;
vol.segdim = size(seg);
vol.units = units;
vol.type = 'fns';
vol.transfer = transfer;
if ~isempty(deepelec)
vol.deepelec = deepelec;
end
function cleaner(segfile,confile,elecfile,exefile,datafile)
delete([segfile '.hdr']);
delete([segfile '.img']);
delete(confile);
delete(elecfile);
delete(exefile);
delete(datafile);
|
github
|
philippboehmsturm/antx-master
|
ft_convert_units.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/ft_convert_units.m
| 7,014 |
utf_8
|
bd2c535cfe17e9623a258e0cabe01226
|
function [obj] = ft_convert_units(obj, target)
% FT_CONVERT_UNITS changes the geometrical dimension to the specified SI unit.
% The units of the input object is determined from the structure field
% object.unit, or is estimated based on the spatial extend of the structure,
% e.g. a volume conduction model of the head should be approximately 20 cm large.
%
% Use as
% [object] = ft_convert_units(object, target)
%
% The following input objects are supported
% simple dipole position
% electrode definition
% gradiometer array definition
% volume conductor definition
% dipole grid definition
% anatomical mri
% segmented mri
%
% Possible target units are 'm', 'dm', 'cm ' or 'mm'. If no target units
% are specified, this function will only determine the native geometrical
% units of the object.
%
% See FT_ESTIMATE_UNITS, FT_READ_VOL, FT_READ_SENS
% Copyright (C) 2005-2012, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_convert_units.m 7336 2013-01-16 15:51:57Z johzum $
% This function consists of three parts:
% 1) determine the input units
% 2) determine the requested scaling factor to obtain the output units
% 3) try to apply the scaling to the known geometrical elements in the input object
if isstruct(obj) && numel(obj)>1
% deal with a structure array
for i=1:numel(obj)
if nargin>1
tmp(i) = ft_convert_units(obj(i), target);
else
tmp(i) = ft_convert_units(obj(i));
end
end
obj = tmp;
return
end
% determine the unit-of-dimension of the input object
if isfield(obj, 'unit') && ~isempty(obj.unit)
% use the units specified in the object
unit = obj.unit;
else
% try to determine the units by looking at the size of the object
if isfield(obj, 'chanpos') && ~isempty(obj.chanpos)
siz = norm(idrange(obj.chanpos));
unit = ft_estimate_units(siz);
elseif isfield(obj, 'pnt') && ~isempty(obj.pnt)
siz = norm(idrange(obj.pnt));
unit = ft_estimate_units(siz);
elseif isfield(obj, 'pos') && ~isempty(obj.pos)
siz = norm(idrange(obj.pos));
unit = ft_estimate_units(siz);
elseif isfield(obj, 'transform') && ~isempty(obj.transform)
% construct the corner points of the volume in voxel and in head coordinates
[pos_voxel, pos_head] = cornerpoints(obj.dim, obj.transform);
siz = norm(idrange(pos_head));
unit = ft_estimate_units(siz);
elseif isfield(obj, 'fid') && isfield(obj.fid, 'pnt') && ~isempty(obj.fid.pnt)
siz = norm(idrange(obj.fid.pnt));
unit = ft_estimate_units(siz);
elseif ft_voltype(obj, 'infinite')
% this is an infinite medium volume conductor, which does not care about units
unit = 'm';
elseif ft_voltype(obj,'singlesphere')
siz = obj.r;
unit = ft_estimate_units(siz);
elseif ft_voltype(obj,'localspheres')
siz = median(obj.r);
unit = ft_estimate_units(siz);
elseif ft_voltype(obj,'concentricspheres')
siz = max(obj.r);
unit = ft_estimate_units(siz);
elseif isfield(obj, 'bnd') && isstruct(obj.bnd) && isfield(obj.bnd(1), 'pnt') && ~isempty(obj.bnd(1).pnt)
siz = norm(idrange(obj.bnd(1).pnt));
unit = ft_estimate_units(siz);
elseif isfield(obj, 'nas') && isfield(obj, 'lpa') && isfield(obj, 'rpa')
pnt = [obj.nas; obj.lpa; obj.rpa];
siz = norm(idrange(pnt));
unit = ft_estimate_units(siz);
else
error('cannot determine geometrical units');
end % recognized type of volume conduction model or sensor array
end % determine input units
if nargin<2 || isempty(target)
% just remember the units in the output and return
obj.unit = unit;
return
elseif strcmp(unit, target)
% no conversion is needed
obj.unit = unit;
return
end
% compue the scaling factor from the input units to the desired ones
scale = scalingfactor(unit, target);
% give some information about the conversion
fprintf('converting units from ''%s'' to ''%s''\n', unit, target)
% volume conductor model
if isfield(obj, 'r'), obj.r = scale * obj.r; end
if isfield(obj, 'o'), obj.o = scale * obj.o; end
if isfield(obj, 'bnd'), for i=1:length(obj.bnd), obj.bnd(i).pnt = scale * obj.bnd(i).pnt; end, end
% gradiometer array
if isfield(obj, 'pnt1'), obj.pnt1 = scale * obj.pnt1; end
if isfield(obj, 'pnt2'), obj.pnt2 = scale * obj.pnt2; end
if isfield(obj, 'prj'), obj.prj = scale * obj.prj; end
% gradiometer array, electrode array, head shape or dipole grid
if isfield(obj, 'pnt'), obj.pnt = scale * obj.pnt; end
if isfield(obj, 'chanpos'), obj.chanpos = scale * obj.chanpos; end
if isfield(obj, 'coilpos'), obj.coilpos = scale * obj.coilpos; end
if isfield(obj, 'elecpos'), obj.elecpos = scale * obj.elecpos; end
% gradiometer array that combines multiple coils in one channel
if isfield(obj, 'tra') && isfield(obj, 'chanunit')
% find the gradiometer channels that are expressed as unit of field strength divided by unit of distance, e.g. T/cm
for i=1:length(obj.chanunit)
tok = tokenize(obj.chanunit{i}, '/');
if length(tok)==1
% assume that it is T or so
elseif length(tok)==2
% assume that it is T/cm or so
obj.tra(i,:) = obj.tra(i,:) / scale;
obj.chanunit{i} = [tok{1} '/' target];
else
error('unexpected units %s', obj.chanunit{i});
end
end % for
end % if
% fiducials
if isfield(obj, 'fid') && isfield(obj.fid, 'pnt'), obj.fid.pnt = scale * obj.fid.pnt; end
% dipole grid
if isfield(obj, 'pos'), obj.pos = scale * obj.pos; end
% anatomical MRI or functional volume
if isfield(obj, 'transform'),
H = diag([scale scale scale 1]);
obj.transform = H * obj.transform;
end
if isfield(obj, 'transformorig'),
H = diag([scale scale scale 1]);
obj.transformorig = H * obj.transformorig;
end
% remember the unit
obj.unit = target;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% IDRANGE interdecile range for more robust range estimation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function r = idrange(x)
keeprow=true(size(x,1),1);
for l=1:size(x,2)
keeprow = keeprow & isfinite(x(:,l));
end
sx = sort(x(keeprow,:), 1);
ii = round(interp1([0, 1], [1, size(x(keeprow,:), 1)], [.1, .9])); % indices for 10 & 90 percentile
r = diff(sx(ii, :));
|
github
|
philippboehmsturm/antx-master
|
ft_apply_montage.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/ft_apply_montage.m
| 13,440 |
utf_8
|
ba256ae86f2bc28cc2d9f26098e25236
|
function [input] = ft_apply_montage(input, montage, varargin)
% FT_APPLY_MONTAGE changes the montage of an electrode or gradiometer array. A
% montage can be used for EEG rereferencing, MEG synthetic gradients, MEG
% planar gradients or unmixing using ICA. This function applies the montage
% to the inputor array. The inputor array can subsequently be used for
% forward computation and source reconstruction of the data.
%
% Use as
% [sens] = ft_apply_montage(sens, montage, ...)
% [data] = ft_apply_montage(data, montage, ...)
% [freq] = ft_apply_montage(freq, montage, ...)
% [montage] = ft_apply_montage(montage1, montage2, ...)
%
% A montage is specified as a structure with the fields
% montage.tra = MxN matrix
% montage.labelnew = Mx1 cell-array
% montage.labelorg = Nx1 cell-array
%
% As an example, a bipolar montage could look like this
% bipolar.labelorg = {'1', '2', '3', '4'}
% bipolar.labelnew = {'1-2', '2-3', '3-4'}
% bipolar.tra = [
% +1 -1 0 0
% 0 +1 -1 0
% 0 0 +1 -1
% ];
%
% Additional options should be specified in key-value pairs and can be
% 'keepunused' string, 'yes' or 'no' (default = 'no')
% 'inverse' string, 'yes' or 'no' (default = 'no')
%
% If the first input is a montage, then the second input montage will be
% applied to the first. In effect, the output montage will first do
% montage1, then montage2.
%
% See also FT_READ_SENS, FT_TRANSFORM_SENS
% Copyright (C) 2008-2012, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_apply_montage.m 7394 2013-01-23 14:33:30Z jorhor $
% get optional input arguments
keepunused = ft_getopt(varargin, 'keepunused', 'no');
inverse = ft_getopt(varargin, 'inverse', 'no');
feedback = ft_getopt(varargin, 'feedback', 'text');
bname = ft_getopt(varargin, 'balancename', '');
if ~isfield(input, 'label') && isfield(input, 'labelnew')
% the input data structure is also a montage
inputlabel = input.labelnew;
else
% the input should describe the channel labels
inputlabel = input.label;
end
% check the consistency of the input inputor array or data
if ~all(isfield(montage, {'tra', 'labelorg', 'labelnew'}))
error('the second input argument does not correspond to a montage');
end
% check the consistency of the montage
if size(montage.tra,1)~=length(montage.labelnew)
error('the number of channels in the montage is inconsistent');
elseif size(montage.tra,2)~=length(montage.labelorg)
error('the number of channels in the montage is inconsistent');
end
if strcmp(inverse, 'yes')
% apply the inverse montage, i.e. undo a previously applied montage
tmp.labelnew = montage.labelorg; % swap around
tmp.labelorg = montage.labelnew; % swap around
tmp.tra = full(montage.tra);
if rank(tmp.tra) < length(tmp.tra)
warning('the linear projection for the montage is not full-rank, the resulting data will have reduced dimensionality');
tmp.tra = pinv(tmp.tra);
else
tmp.tra = inv(tmp.tra);
end
montage = tmp;
end
% use default transfer from sensors to channels if not specified
if isfield(input, 'pnt') && ~isfield(input, 'tra')
nchan = size(input.pnt,1);
input.tra = eye(nchan);
elseif isfield(input, 'chanpos') && ~isfield(input, 'tra')
nchan = size(input.chanpos,1);
input.tra = eye(nchan);
end
% select and keep the columns that are non-empty, i.e. remove the empty columns
selcol = find(~all(montage.tra==0, 1));
montage.tra = montage.tra(:,selcol);
montage.labelorg = montage.labelorg(selcol);
clear selcol
% select and remove the columns corresponding to channels that are not present in the original data
remove = setdiff(montage.labelorg, intersect(montage.labelorg, inputlabel));
selcol = match_str(montage.labelorg, remove);
% we cannot just remove the colums, all rows that depend on it should also be removed
selrow = false(length(montage.labelnew),1);
for i=1:length(selcol)
selrow = selrow & (montage.tra(:,selcol(i))~=0);
end
% convert from indices to logical vector
selcol = indx2logical(selcol, length(montage.labelorg));
% remove rows and columns
montage.labelorg = montage.labelorg(~selcol);
montage.labelnew = montage.labelnew(~selrow);
montage.tra = montage.tra(~selrow, ~selcol);
clear remove selcol selrow i
% add columns for the channels that are present in the data but not involved in the montage, and stick to the original order in the data
[add, ix] = setdiff(inputlabel, montage.labelorg);
add = inputlabel(sort(ix));
m = size(montage.tra,1);
n = size(montage.tra,2);
k = length(add);
if strcmp(keepunused, 'yes')
% add the channels that are not rereferenced to the input and output
montage.tra((m+(1:k)),(n+(1:k))) = eye(k);
montage.labelorg = cat(1, montage.labelorg(:), add(:));
montage.labelnew = cat(1, montage.labelnew(:), add(:));
else
% add the channels that are not rereferenced to the input montage only
montage.tra(:,(n+(1:k))) = zeros(m,k);
montage.labelorg = cat(1, montage.labelorg(:), add(:));
end
clear add m n k
% determine whether all channels are unique
m = size(montage.tra,1);
n = size(montage.tra,2);
if length(unique(montage.labelnew))~=m
error('not all output channels of the montage are unique');
end
if length(unique(montage.labelorg))~=n
error('not all input channels of the montage are unique');
end
% determine whether all channels that have to be rereferenced are available
if length(intersect(inputlabel, montage.labelorg))~=length(montage.labelorg)
error('not all channels that are required in the montage are available in the data');
end
% reorder the columns of the montage matrix
[selinput, selmontage] = match_str(inputlabel, montage.labelorg);
montage.tra = double(montage.tra(:,selmontage));
montage.labelorg = montage.labelorg(selmontage);
% making the tra matrix sparse will speed up subsequent multiplications
% but should not result in a sparse matrix
if size(montage.tra,1)>1
montage.tra = sparse(montage.tra);
end
inputtype = 'unknown';
if isfield(input, 'labelorg') && isfield(input, 'labelnew')
inputtype = 'montage';
elseif isfield(input, 'tra')
inputtype = 'sens';
elseif isfield(input, 'trial')
inputtype = 'raw';
elseif isfield(input, 'fourierspctrm')
inputtype = 'freq';
end
switch inputtype
case 'montage'
% apply the montage on top of the other montage
if isa(input.tra, 'single')
% sparse matrices and single precision do not match
input.tra = full(montage.tra) * input.tra;
else
input.tra = montage.tra * input.tra;
end
input.labelnew = montage.labelnew;
case 'sens'
% apply the montage to an electrode or gradiometer description
sens = input;
clear input
% apply the montage to the inputor array
if isa(sens.tra, 'single')
% sparse matrices and single precision do not match
sens.tra = full(montage.tra) * sens.tra;
else
sens.tra = montage.tra * sens.tra;
end
% The montage operates on the coil weights in sens.tra, but the output
% channels can be different. If possible, we want to keep the original
% channel positions and orientations.
[sel1, sel2] = match_str(montage.labelnew, inputlabel);
keepchans = length(sel1)==length(montage.labelnew);
if isfield(sens, 'chanpos')
if keepchans
sens.chanpos = sens.chanpos(sel2,:);
else
sens.chanpos = nan(numel(montage.labelnew),3);
%input = rmfield(input, 'chanpos');
end
end
if isfield(sens, 'chanori')
if keepchans
sens.chanori = sens.chanori(sel2,:);
else
sens.chanori = nan(numel(montage.labelnew),3);
%input = rmfield(input, 'chanori');
end
end
sens.label = montage.labelnew;
% keep track of the order of the balancing and which one is the current one
if strcmp(inverse, 'yes')
if isfield(sens, 'balance')% && isfield(sens.balance, 'previous')
if isfield(sens.balance, 'previous') && numel(sens.balance.previous)>=1
sens.balance.current = sens.balance.previous{1};
sens.balance.previous = sens.balance.previous(2:end);
elseif isfield(sens.balance, 'previous')
sens.balance.current = 'none';
sens.balance = rmfield(sens.balance, 'previous');
else
sens.balance.current = 'none';
end
end
elseif ~strcmp(inverse, 'yes') && ~isempty(bname)
if isfield(sens, 'balance'),
% check whether a balancing montage with name bname already exist,
% and if so, how many
mnt = fieldnames(sens.balance);
sel = strmatch(bname, mnt);
if numel(sel)==0,
% bname can stay the same
elseif numel(sel)==1
% the original should be renamed to 'bname1' and the new one should
% be 'bname2'
sens.balance.([bname, '1']) = sens.balance.(bname);
sens.balance = rmfield(sens.balance, bname);
if isfield(sens.balance, 'current') && strcmp(sens.balance.current, bname)
sens.balance.current = [bname, '1'];
end
if isfield(sens.balance, 'previous')
sel2 = strmatch(bname, sens.balance.previous);
if ~isempty(sel2)
sens.balance.previous{sel2} = [bname, '1'];
end
end
bname = [bname, '2'];
else
bname = [bname, num2str(length(sel)+1)];
end
end
if isfield(sens, 'balance') && isfield(sens.balance, 'current')
if ~isfield(sens.balance, 'previous')
sens.balance.previous = {};
end
sens.balance.previous = [{sens.balance.current} sens.balance.previous];
sens.balance.current = bname;
sens.balance.(bname) = montage;
end
end
% rename the output variable
input = sens;
clear sens
case 'raw';
% apply the montage to the raw data that was preprocessed using fieldtrip
data = input;
clear input
Ntrials = numel(data.trial);
ft_progress('init', feedback, 'processing trials');
for i=1:Ntrials
ft_progress(i/Ntrials, 'processing trial %d from %d\n', i, Ntrials);
if isa(data.trial{i}, 'single')
% sparse matrices and single precision do not match
data.trial{i} = full(montage.tra) * data.trial{i};
else
data.trial{i} = montage.tra * data.trial{i};
end
end
ft_progress('close');
data.label = montage.labelnew;
% rename the output variable
input = data;
clear data
case 'freq'
% apply the montage to the spectrally decomposed data
freq = input;
clear input
if strcmp(freq.dimord, 'rpttap_chan_freq')
siz = size(freq.fourierspctrm);
nrpt = siz(1);
nchan = siz(2);
nfreq = siz(3);
output = zeros(nrpt, size(montage.tra,1), nfreq);
for foilop=1:nfreq
output(:,:,foilop) = freq.fourierspctrm(:,:,foilop) * montage.tra';
end
elseif strcmp(freq.dimord, 'rpttap_chan_freq_time')
siz = size(freq.fourierspctrm);
nrpt = siz(1);
nchan = siz(2);
nfreq = siz(3);
ntime = siz(4);
output = zeros(nrpt, size(montage.tra,1), nfreq, ntime);
for foilop=1:nfreq
for toilop = 1:ntime
output(:,:,foilop,toilop) = freq.fourierspctrm(:,:,foilop,toilop) * montage.tra';
end
end
else
error('unsupported dimord in frequency data (%s)', freq.dimord);
end
% replace the Fourier spectrum
freq.fourierspctrm = output;
freq.label = montage.labelnew;
% rename the output variable
input = freq;
clear freq
otherwise
error('unrecognized input');
end % switch inputtype
% check whether the input contains chantype and/or chanunit and remove these
% as they may have been invalidated by the transform (e.g. with megplanar)
[sel1, sel2] = match_str(montage.labelnew, inputlabel);
keepchans = (length(sel1)==length(montage.labelnew));
if isfield(input, 'chantype')
if keepchans
% reorder them according to the montage
sens.chantype = input.chantype(sel2,:);
else
input = rmfield(input, 'chantype');
end
end
if isfield(input, 'chanunit')
if keepchans
% reorder them according to the montage
sens.chanunit = input.chanunit(sel2,:);
else
input = rmfield(input, 'chanunit');
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% HELPER FUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function y = indx2logical(x, n)
y = false(1,n);
y(x) = true;
|
github
|
philippboehmsturm/antx-master
|
ft_headmodel_slab.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/ft_headmodel_slab.m
| 3,466 |
utf_8
|
757f6a05af6be4410be4b60f68219358
|
function vol = ft_headmodel_slab(geom1, geom2, Pc, varargin)
% FT_HEADMODEL_SLAB creates an EEG volume conduction model that
% is described with an infinite conductive slab. You can think
% of this as two parallel planes containing a mass of conductive
% material (e.g. water) and externally to them a non-conductive material
% (e.g. air).
%
% Use as
% vol = ft_headmodel_slab(geom1, geom2, Pc, varargin)
% where
% geom1.pnt = Nx3 vector specifying N points through which the 'upper' plane is fitted
% geom2.pnt = Nx3 vector specifying N points through which the 'lower' plane is fitted
% Pc = 1x3 vector specifying the spatial position of a point lying in the conductive slab
% (this determines the plane's normal's direction)
%
% Optional arguments should be specified in key-value pairs and can include
% 'sourcemodel' = 'monopole'
% 'conductivity' = number , conductivity value of the conductive halfspace (default = 1)
%
% See also FT_PREPARE_VOL_SENS, FT_COMPUTE_LEADFIELD
% Copyright (C) 2012, Donders Centre for Cognitive Neuroimaging, Nijmegen, NL
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_headmodel_slab.m 7123 2012-12-06 21:21:38Z roboos $
model = ft_getopt(varargin, 'sourcemodel', 'monopole');
cond = ft_getopt(varargin, 'conductivity');
if isempty(cond)
warning('Conductivity was not specified, using 1');
cond = 1;
end
% the description of this volume conduction model consists of the
% description of the plane, and a point in the void halfspace
if isstruct(geom1) && isfield(geom1,'pnt')
pnt1 = geom1.pnt;
pnt2 = geom2.pnt;
elseif size(geom1,2)==3
pnt1 = geom1;
pnt2 = geom2;
else
error('incorrect specification of the geometry');
end
% fit a plane to the points
[N1,P1] = fit_plane(pnt1);
[N2,P2] = fit_plane(pnt2);
% checks if Pc is in the conductive part. If not, flip
incond = acos(dot(N1,(Pc-P1)./norm(Pc-P1))) > pi/2;
if ~incond
N1 = -N1;
end
incond = acos(dot(N2,(Pc-P2)./norm(Pc-P2))) > pi/2;
if ~incond
N2 = -N2;
end
vol = [];
vol.cond = cond;
vol.pnt1 = P1(:)'; % a point that lies on the plane that separates the conductive tissue from the air
vol.ori1 = N1(:)'; % a unit vector pointing towards the air
vol.ori1 = vol.ori1/norm(vol.ori1);
vol.pnt2 = P2(:)';
vol.ori2 = N2(:)';
vol.ori2 = vol.ori2/norm(vol.ori2);
if strcmpi(model,'monopole')
vol.type = 'slab_monopole';
else
error('unknow method')
end
function [N,P] = fit_plane(X)
% Fits a plane through a number of points in 3D cartesian coordinates
P = mean(X,1); % the plane is spanned by this point and by a normal vector
X = bsxfun(@minus,X,P);
[u, s, v] = svd(X, 0);
N = v(:,3); % orientation of the plane, can be in either direction
|
github
|
philippboehmsturm/antx-master
|
ft_prepare_vol_sens.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/ft_prepare_vol_sens.m
| 23,785 |
utf_8
|
48ad68a40571bf206b4973418d72acab
|
function [vol, sens] = ft_prepare_vol_sens(vol, sens, varargin)
% FT_PREPARE_VOL_SENS does some bookkeeping to ensure that the volume
% conductor model and the sensor array are ready for subsequent forward
% leadfield computations. It takes care of some pre-computations that can
% be done efficiently prior to the leadfield calculations.
%
% Use as
% [vol, sens] = ft_prepare_vol_sens(vol, sens, ...)
% with input arguments
% sens structure with gradiometer or electrode definition
% vol structure with volume conductor definition
%
% The vol structure represents a volume conductor model, its contents
% depend on the type of model. The sens structure represents a sensor
% array, i.e. EEG electrodes or MEG gradiometers.
%
% Additional options should be specified in key-value pairs and can be
% 'channel' cell-array with strings (default = 'all')
% 'order' number, for single shell "Nolte" model (default = 10)
%
% The detailled behaviour of this function depends on whether the input
% consists of EEG or MEG and furthermoree depends on the type of volume
% conductor model:
% - in case of EEG single and concentric sphere models, the electrodes are
% projected onto the skin surface.
% - in case of EEG boundary element models, the electrodes are projected on
% the surface and a blilinear interpoaltion matrix from vertices to
% electrodes is computed.
% - in case of MEG and a localspheres model, a local sphere is determined
% for each coil in the gradiometer definition.
% - in case of MEG with a singleshell Nolte model, the volume conduction
% model is initialized
% In any case channel selection and reordering will be done. The channel
% order returned by this function corresponds to the order in the 'channel'
% option, or if not specified, to the order in the input sensor array.
%
% See also FT_COMPUTE_LEADFIELD, FT_READ_VOL, FT_READ_SENS, FT_TRANSFORM_VOL,
% FT_TRANSFORM_SENS
% Copyright (C) 2004-2012, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_prepare_vol_sens.m 7181 2012-12-13 16:22:50Z roboos $
% get the optional input arguments
% fileformat = ft_getopt(varargin, 'fileformat');
channel = ft_getopt(varargin, 'channel', sens.label); % cell-array with channel labels, default is all
order = ft_getopt(varargin, 'order', 10); % order of expansion for Nolte method; 10 should be enough for real applications; in simulations it makes sense to go higher
% ensure that the sensor description is up-to-date (Aug 2011)
sens = ft_datatype_sens(sens);
% ensure that the volume conduction description is up-to-date (Jul 2012)
vol = ft_datatype_headmodel(vol);
% determine whether the input contains EEG or MEG sensors
iseeg = ft_senstype(sens, 'eeg');
ismeg = ft_senstype(sens, 'meg');
% determine the skin compartment
if ~isfield(vol, 'skin_surface')
if isfield(vol, 'bnd')
vol.skin_surface = find_outermost_boundary(vol.bnd);
elseif isfield(vol, 'r') && length(vol.r)<=4
[dum, vol.skin_surface] = max(vol.r);
end
end
% determine the inner_skull_surface compartment
if ~isfield(vol, 'inner_skull_surface')
if isfield(vol, 'bnd')
vol.inner_skull_surface = find_innermost_boundary(vol.bnd);
elseif isfield(vol, 'r') && length(vol.r)<=4
[dum, vol.inner_skull_surface] = min(vol.r);
end
end
% otherwise the voltype assignment to an empty struct below won't work
if isempty(vol)
vol = [];
end
% this makes them easier to recognise
sens.type = ft_senstype(sens);
vol.type = ft_voltype(vol);
if isfield(vol, 'unit') && isfield(sens, 'unit') && ~strcmp(vol.unit, sens.unit)
error('inconsistency in the units of the volume conductor and the sensor array');
end
switch ft_voltype(vol)
case 'simbio'
ft_hastoolbox('simbio', 1);
case 'openmeeg'
ft_hastoolbox('openmeeg', 1);
end
if ismeg && iseeg
% this is something that could be implemented relatively easily
error('simultaneous EEG and MEG not yet supported');
elseif ~ismeg && ~iseeg
error('the input does not look like EEG, nor like MEG');
elseif ismeg
% keep a copy of the original sensor array, this is needed for the MEG localspheres model
sens_orig = sens;
% always ensure that there is a linear transfer matrix for combining the coils into gradiometers
if ~isfield(sens, 'tra');
Nchans = length(sens.label);
Ncoils = size(sens.coilpos,1);
if Nchans~=Ncoils
error('inconsistent number of channels and coils');
end
sens.tra = eye(Nchans, Ncoils);
end
% select the desired channels from the gradiometer array
% order them according to the users specification
[selchan, selsens] = match_str(channel, sens.label);
% first only modify the linear combination of coils into channels
try, sens.chantype = sens.chantype(selsens,:); end
try, sens.chanunit = sens.chanunit(selsens,:); end
sens.chanpos = sens.chanpos(selsens,:);
sens.chanori = sens.chanori(selsens,:);
sens.label = sens.label(selsens);
sens.tra = sens.tra(selsens,:);
% subsequently remove the coils that do not contribute to any channel output
selcoil = any(sens.tra~=0,1);
sens.coilpos = sens.coilpos(selcoil,:);
sens.coilori = sens.coilori(selcoil,:);
sens.tra = sens.tra(:,selcoil);
switch ft_voltype(vol)
case {'infinite' 'infinite_monopole'}
% nothing to do
case 'singlesphere'
% nothing to do
case 'concentricspheres'
% nothing to do
case 'neuromag'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% if the forward model is computed using the external Neuromag toolbox,
% we have to add a selection of the channels so that the channels
% in the forward model correspond with those in the data.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[selchan, selsens] = match_str(channel, sens.label);
vol.chansel = selsens;
case 'localspheres'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% If the volume conduction model consists of multiple spheres then we
% have to match the channels in the gradiometer array and the volume
% conduction model.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% use the original sensor array instead of the one with a subset of
% channels, because we need the complete mapping of coils to channels
sens = sens_orig;
% remove the coils that do not contribute to any channel output
% since these do not have a corresponding sphere
selcoil = find(sum(sens.tra,1)~=0);
sens.coilpos = sens.coilpos(selcoil,:);
sens.coilori = sens.coilori(selcoil,:);
sens.tra = sens.tra(:,selcoil);
% the initial localspheres volume conductor has a local sphere per
% channel, whereas it should have a local sphere for each coil
if size(vol.r,1)==size(sens.coilpos,1) && ~isfield(vol, 'label')
% it appears that each coil already has a sphere, which suggests
% that the volume conductor already has been prepared to match the
% sensor array
return
elseif size(vol.r,1)==size(sens.coilpos,1) && isfield(vol, 'label')
if ~isequal(vol.label(:), sens.label(:))
% if only the order is different, it would be possible to reorder them
error('the coils in the volume conduction model do not correspond to the sensor array');
else
% the coil-specific spheres in the volume conductor should not have a label
% because the label is already specified for the coils in the
% sensor array
vol = rmfield(vol, 'label');
end
return
end
% the CTF way of representing the headmodel is one-sphere-per-channel
% whereas the FieldTrip way of doing the forward computation is one-sphere-per-coil
Nchans = size(sens.tra,1);
Ncoils = size(sens.tra,2);
Nspheres = size(vol.label);
if isfield(vol, 'orig')
% these are present in a CTF *.hdm file
singlesphere.o(1,1) = vol.orig.MEG_Sphere.ORIGIN_X;
singlesphere.o(1,2) = vol.orig.MEG_Sphere.ORIGIN_Y;
singlesphere.o(1,3) = vol.orig.MEG_Sphere.ORIGIN_Z;
singlesphere.r = vol.orig.MEG_Sphere.RADIUS;
% ensure consistent units
singlesphere = ft_convert_units(singlesphere, vol.unit);
% determine the channels that do not have a corresponding sphere
% and use the globally fitted single sphere for those
missing = setdiff(sens.label, vol.label);
if ~isempty(missing)
warning('using the global fitted single sphere for %d channels that do not have a local sphere', length(missing));
end
for i=1:length(missing)
vol.label(end+1) = missing(i);
vol.r(end+1,:) = singlesphere.r;
vol.o(end+1,:) = singlesphere.o;
end
end
localspheres = [];
% for each coil in the MEG helmet, determine the corresponding channel and from that the corresponding local sphere
for i=1:Ncoils
coilindex = find(sens.tra(:,i)~=0); % to which channel does this coil belong
if length(coilindex)>1
% this indicates that there are multiple channels to which this coil contributes,
% which happens if the sensor array represents a synthetic higher-order gradient.
[dum, coilindex] = max(abs(sens.tra(:,i)));
end
coillabel = sens.label{coilindex}; % what is the label of this channel
chanindex = strmatch(coillabel, vol.label, 'exact'); % what is the index of this channel in the list of local spheres
localspheres.r(i,:) = vol.r(chanindex);
localspheres.o(i,:) = vol.o(chanindex,:);
end
vol = localspheres;
% finally do the selection of channels and coils
% order them according to the users specification
[selchan, selsens] = match_str(channel, sens.label);
% first only modify the linear combination of coils into channels
try, sens.chantype = sens.chantype(selsens,:); end
try, sens.chanunit = sens.chanunit(selsens,:); end
sens.chanpos = sens.chanpos(selsens,:);
sens.chanori = sens.chanori(selsens,:);
sens.label = sens.label(selsens);
sens.tra = sens.tra(selsens,:);
% subsequently remove the coils that do not contribute to any sensor output
selcoil = find(sum(sens.tra,1)~=0);
sens.coilpos = sens.coilpos(selcoil,:);
sens.coilori = sens.coilori(selcoil,:);
sens.tra = sens.tra(:,selcoil);
% make the same selection of coils in the localspheres model
vol.r = vol.r(selcoil);
vol.o = vol.o(selcoil,:);
case 'singleshell'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% if the forward model is computed using the code from Guido Nolte, we
% have to initialize the volume model using the gradiometer coil
% locations
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% compute the surface normals for each vertex point
if ~isfield(vol.bnd, 'nrm')
fprintf('computing surface normals\n');
vol.bnd.nrm = normals(vol.bnd.pnt, vol.bnd.tri);
end
% estimate center and radius
[center,radius] = fitsphere(vol.bnd.pnt);
% initialize the forward calculation (only if gradiometer coils are available)
if size(sens.coilpos,1)>0
vol.forwpar = meg_ini([vol.bnd.pnt vol.bnd.nrm], center', order, [sens.coilpos sens.coilori]);
end
case 'openmeeg'
error('MEG not yet supported with openmeeg');
case 'simbio'
error('MEG not yet supported with simbio');
otherwise
error('unsupported volume conductor model for MEG');
end
elseif iseeg
% select the desired channels from the electrode array
% order them according to the users specification
[selchan, selsens] = match_str(channel, sens.label);
Nchans = length(sens.label);
if isfield(sens, 'tra')
% first only modify the linear combination of electrodes into channels
sens.chanpos = sens.chanpos(selsens,:);
sens.label = sens.label(selsens);
sens.tra = sens.tra(selsens,:);
% subsequently remove the electrodes that do not contribute to any channel output
selelec = any(sens.tra~=0,1);
sens.elecpos = sens.elecpos(selelec,:);
sens.tra = sens.tra(:,selelec);
else
% the electrodes and channels are identical
sens.chanpos = sens.chanpos(selsens,:);
sens.elecpos = sens.elecpos(selsens,:);
sens.label = sens.label(selsens);
end
switch ft_voltype(vol)
case {'infinite' 'infinite_monopole'}
% nothing to do
case {'halfspace', 'halfspace_monopole'}
% electrodes' all-to-all distances
numel = size(sens.elecpos,1);
ref_el = sens.elecpos(1,:);
md = dist( (sens.elecpos-repmat(ref_el,[numel 1]))' );
% take the min distance as reference
md = min(md(1,2:end));
pnt = sens.elecpos;
% scan the electrodes and reposition the ones which are in the
% wrong halfspace (projected on the plane)... if not too far away!
for i=1:size(pnt,1)
P = pnt(i,:);
is_in_empty = acos(dot(vol.ori,(P-vol.pnt)./norm(P-vol.pnt))) < pi/2;
if is_in_empty
dPplane = abs(dot(vol.ori, vol.pnt-P, 2));
if dPplane>md
error('Some electrodes are too distant from the plane: consider repositioning them')
else
% project point on plane
Ppr = pointproj(P,[vol.pnt vol.ori]);
pnt(i,:) = Ppr;
end
end
end
sens.elecpos = pnt;
case {'slab_monopole'}
% electrodes' all-to-all distances
numel = size(sens.elecpos,1);
ref_el = sens.elecpos(1,:);
md = dist( (sens.elecpos-repmat(ref_el,[numel 1]))' );
% choose min distance between electrodes
md = min(md(1,2:end));
pnt = sens.elecpos;
% looks for contacts outside the strip which are not too far away
% and projects them on the nearest plane
for i=1:size(pnt,1)
P = pnt(i,:);
instrip1 = acos(dot(vol.ori1,(P-vol.pnt1)./norm(P-vol.pnt1))) > pi/2;
instrip2 = acos(dot(vol.ori2,(P-vol.pnt2)./norm(P-vol.pnt2))) > pi/2;
is_in_empty = ~(instrip1&instrip2);
if is_in_empty
dPplane1 = abs(dot(vol.ori1, vol.pnt1-P, 2));
dPplane2 = abs(dot(vol.ori2, vol.pnt2-P, 2));
if dPplane1>md && dPplane2>md
error('Some electrodes are too distant from the planes: consider repositioning them')
elseif dPplane2>dPplane1
% project point on nearest plane
Ppr = pointproj(P,[vol.pnt1 vol.ori1]);
pnt(i,:) = Ppr;
else
% project point on nearest plane
Ppr = pointproj(P,[vol.pnt2 vol.ori2]);
pnt(i,:) = Ppr;
end
end
end
sens.elecpos = pnt;
case {'singlesphere', 'concentricspheres'}
% ensure that the electrodes ly on the skin surface
radius = max(vol.r);
pnt = sens.elecpos;
if isfield(vol, 'o')
% shift the the centre of the sphere to the origin
pnt(:,1) = pnt(:,1) - vol.o(1);
pnt(:,2) = pnt(:,2) - vol.o(2);
pnt(:,3) = pnt(:,3) - vol.o(3);
end
distance = sqrt(sum(pnt.^2,2)); % to the center of the sphere
if any((abs(distance-radius)/radius)>0.005)
warning('electrodes do not lie on skin surface -> using radial projection')
end
pnt = pnt * radius ./ [distance distance distance];
if isfield(vol, 'o')
% shift the center back to the original location
pnt(:,1) = pnt(:,1) + vol.o(1);
pnt(:,2) = pnt(:,2) + vol.o(2);
pnt(:,3) = pnt(:,3) + vol.o(3);
end
sens.elecpos = pnt;
case {'bem', 'dipoli', 'asa', 'bemcp', 'openmeeg'}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% do postprocessing of volume and electrodes in case of BEM model
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% project the electrodes on the skin and determine the bilinear interpolation matrix
if ~isfield(vol, 'tra')
% determine boundary corresponding with skin and inner_skull_surface
if ~isfield(vol, 'skin_surface')
vol.skin_surface = find_outermost_boundary(vol.bnd);
fprintf('determining skin compartment (%d)\n', vol.skin_surface);
end
if ~isfield(vol, 'source')
vol.source = find_innermost_boundary(vol.bnd);
fprintf('determining source compartment (%d)\n', vol.source);
end
if size(vol.mat,1)~=size(vol.mat,2) && size(vol.mat,1)==length(sens.elecpos)
fprintf('electrode transfer and system matrix were already combined\n');
else
fprintf('projecting electrodes on skin surface\n');
% compute linear interpolation from triangle vertices towards electrodes
[el, prj] = project_elec(sens.elecpos, vol.bnd(vol.skin_surface).pnt, vol.bnd(vol.skin_surface).tri);
tra = transfer_elec(vol.bnd(vol.skin_surface).pnt, vol.bnd(vol.skin_surface).tri, el);
% replace the original electrode positions by the projected positions
sens.elecpos = prj;
if size(vol.mat,1)==size(vol.bnd(vol.skin_surface).pnt,1)
% construct the transfer from only the skin vertices towards electrodes
interp = tra;
else
% construct the transfer from all vertices (also inner_skull_surface/outer_skull_surface) towards electrodes
interp = [];
for i=1:length(vol.bnd)
if i==vol.skin_surface
interp = [interp, tra];
else
interp = [interp, zeros(size(el,1), size(vol.bnd(i).pnt,1))];
end
end
end
% incorporate the linear interpolation matrix and the system matrix into one matrix
% this speeds up the subsequent repeated leadfield computations
fprintf('combining electrode transfer and system matrix\n');
if strcmp(ft_voltype(vol), 'openmeeg')
nb_points_external_surface = size(vol.bnd(vol.skin_surface).pnt,1);
vol.mat = vol.mat((end-nb_points_external_surface+1):end,:);
vol.mat = interp(:,1:nb_points_external_surface) * vol.mat;
else
% convert to sparse matrix to speed up the subsequent multiplication
interp = sparse(interp);
vol.mat = interp * vol.mat;
% ensure that the model potential will be average referenced
avg = mean(vol.mat, 1);
vol.mat = vol.mat - repmat(avg, size(vol.mat,1), 1);
end
end
end
case 'fns'
if isfield(vol,'bnd')
[el, prj] = project_elec(sens.elecpos, vol.bnd.pnt, vol.bnd.tri);
sens.tra = transfer_elec(vol.bnd.pnt, vol.bnd.tri, el);
% replace the original electrode positions by the projected positions
sens.elecpos = prj;
end
case 'simbio'
if isfield(vol,'bnd')
[el, prj] = project_elec(sens.elecpos, vol.bnd.pnt, vol.bnd.tri);
sens.tra = transfer_elec(vol.bnd.pnt, vol.bnd.tri, el);
% replace the original electrode positions by the projected positions
sens.elecpos = prj;
end
vol.transfer = sb_transfer(vol,sens);
case 'interpolate'
if ~isfield(sens, 'tra') && isequal(sens.chanpos, sens.elecpos)
sens.tra = eye(size(sens.chanpos,1));
end
if ~isfield(vol.sens, 'tra') && isequal(vol.sens.chanpos, vol.sens.elecpos)
vol.sens.tra = eye(size(vol.sens.chanpos,1));
end
% the channel positions can be nan, for example for a bipolar montage
match = isequal(sens.label, vol.sens.label) & ...
isequalwithequalnans(sens.tra, vol.sens.tra) & ...
isequal(sens.elecpos, vol.sens.elecpos) & ...
isequalwithequalnans(sens.chanpos, vol.sens.chanpos);
if match
% the input sensor array matches precisely with the forward model
% no further interpolation is needed
else
% interpolate the channels in the forward model to the desired channels
filename = tempname;
vol = ft_headmodel_interpolate(filename, sens, vol);
% update the sensor array with the one from the volume conductor
sens = vol.sens;
end % if recomputing interpolation
% for the leadfield computations the @nifti object is used to map the image data into memory
ft_hastoolbox('spm8', 1);
for i=1:length(vol.sens.label)
% map each of the leadfield files into memory
vol.chan{i} = nifti(vol.filename{i});
end
otherwise
error('unsupported volume conductor model for EEG');
end
% FIXME this needs careful thought to ensure that the average referencing which is now done here and there, and that the linear interpolation in case of BEM are all dealt with consistently
% % always ensure that there is a linear transfer matrix for
% % rereferencing the EEG potential
% if ~isfield(sens, 'tra');
% sens.tra = eye(length(sens.label));
% end
end % if iseeg or ismeg
if isfield(sens, 'tra')
if issparse(sens.tra) && size(sens.tra, 1)==1
% this multiplication would result in a sparse leadfield, which is not what we want
% the effect can be demonstrated as sparse(1)*rand(1,10), see also http://bugzilla.fcdonders.nl/show_bug.cgi?id=1169#c7
sens.tra = full(sens.tra);
elseif ~issparse(sens.tra) && size(sens.tra, 1)>1
% the multiplication of the "sensor" leadfield (electrode or coil) with the tra matrix to get the "channel" leadfield
% is faster for most cases if the pre-multiplying weighting matrix is made sparse
sens.tra = sparse(sens.tra);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function Ppr = pointproj(P,plane)
% projects a point on a plane
% plane(1:3) is a point on the plane
% plane(4:6) is the ori of the plane
Ppr = [];
ori = plane(4:6);
line = [P ori];
% get indices of line and plane which are parallel
par = abs(dot(plane(4:6), line(:,4:6), 2))<1e-14;
% difference between origins of plane and line
dp = plane(1:3) - line(:, 1:3);
% Divide only for non parallel vectors (DL)
t = dot(ori(~par,:), dp(~par,:), 2)./dot(ori(~par,:), line(~par,4:6), 2);
% compute coord of intersection point
Ppr(~par, :) = line(~par,1:3) + repmat(t,1,3).*line(~par,4:6);
|
github
|
philippboehmsturm/antx-master
|
ft_headmodel_halfspace.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/ft_headmodel_halfspace.m
| 3,240 |
utf_8
|
d78abbad6c8d161643451f71b14c2877
|
function vol = ft_headmodel_halfspace(geom, Pc, varargin)
% FT_HEADMODEL_HALFSPACE creates an EEG volume conduction model that
% is described with an infinite conductive halfspace. You can think
% of this as a plane with on one side a infinite mass of conductive
% material (e.g. water) and on the other side non-conductive material
% (e.g. air).
%
% Use as
% vol = ft_headmodel_halfspace(geom, Pc, ...)
% where
% geom.pnt = Nx3 vector specifying N points through which a plane is fitted
% Pc = 1x3 vector specifying the spatial position of a point lying in the conductive halfspace
% (this determines the plane normal's direction)
%
% Additional optional arguments should be specified as key-value pairs and can include
% 'sourcemodel' = string, 'monopole' or 'dipole' (default = 'dipole')
% 'conductivity' = number, conductivity value of the conductive halfspace (default = 1)
%
% See also FT_PREPARE_VOL_SENS, FT_COMPUTE_LEADFIELD
% Copyright (C) 2012, Donders Centre for Cognitive Neuroimaging, Nijmegen, NL
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_headmodel_halfspace.m 7123 2012-12-06 21:21:38Z roboos $
model = ft_getopt(varargin, 'sourcemodel', 'dipole');
cond = ft_getopt(varargin, 'conductivity');
if isempty(cond)
warning('Conductivity was not specified, using 1');
cond = 1;
end
% the description of this volume conduction model consists of the
% description of the plane, and a point in the void halfspace
if isstruct(geom) && isfield(geom,'pnt')
pnt = geom.pnt;
elseif size(geom,2)==3
pnt = geom;
else
error('incorrect specification of the geometry');
end
% fit a plane to the points
[N,P] = fit_plane(pnt);
% checks if Pc is in the conductive part. If not, flip
incond = acos(dot(N,(Pc-P)./norm(Pc-P))) > pi/2;
if ~incond
N = -N;
end
vol = [];
vol.cond = cond;
vol.pnt = P(:)'; % a point that lies on the plane that separates the conductive tissue from the air
vol.ori = N(:)'; % a unit vector pointing towards the air
vol.ori = vol.ori/norm(vol.ori);
if strcmpi(model,'dipole')
vol.type = 'halfspace';
elseif strcmpi(model,'monopole')
vol.type = 'halfspace_monopole';
else
error('unknow method')
end
function [N,P] = fit_plane(X)
% Fits a plane through a number of points in 3D cartesian coordinates
P = mean(X,1); % the plane is spanned by this point and by a normal vector
X = bsxfun(@minus,X,P);
[u, s, v] = svd(X, 0);
N = v(:,3); % orientation of the plane, can be in either direction
|
github
|
philippboehmsturm/antx-master
|
ft_headmodel_dipoli.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/ft_headmodel_dipoli.m
| 6,816 |
utf_8
|
3465b5ee40c407eb1d5678ae6507e8f3
|
function vol = ft_headmodel_dipoli(geom, varargin)
% FT_HEADMODEL_DIPOLI creates a volume conduction model of the head
% using the boundary element method (BEM) for EEG. This function takes
% as input the triangulated surfaces that describe the boundaries and
% returns as output a volume conduction model which can be used to
% compute leadfields.
%
% This implements
% Oostendorp TF, van Oosterom A. "Source parameter estimation in
% inhomogeneous volume conductors of arbitrary shape." IEEE Trans
% Biomed Eng. 1989 Mar;36(3):382-91.
%
% The implementation of this function uses an external command-line
% executable with the name "dipoli" which is provided by Thom Oostendorp.
%
% Use as
% vol = ft_headmodel_dipoli(geom, ...)
%
% The geom is given as a boundary or a struct-array of boundaries (surfaces)
%
% Optional input arguments should be specified in key-value pairs and can
% include
% isolatedsource = string, 'yes' or 'no'
% hdmfile = string, filename with BEM headmodel
% conductivity = vector, conductivity of each compartment
%
% See also FT_PREPARE_VOL_SENS, FT_COMPUTE_LEADFIELD
% $Id: ft_headmodel_dipoli.m 7310 2013-01-14 15:44:07Z roboos $
ft_hastoolbox('dipoli', 1);
% get the optional arguments
isolatedsource = ft_getopt(varargin, 'isolatedsource');
conductivity = ft_getopt(varargin, 'conductivity');
if isfield(geom,'bnd')
geom = geom.bnd;
end
% start with an empty volume conductor
vol = [];
vol.bnd = geom;
% determine the number of compartments
numboundaries = numel(vol.bnd);
% % The following checks can in principle be performed, but are too
% % time-consuming. Instead the code here relies on the calling function to
% % feed in the correct geometry.
% %
% % if ~all(surface_closed(vol.bnd))
% % error('...');
% % end
% % if any(surface_intersection(vol.bnd))
% % error('...');
% % end
% % if any(surface_selfintersection(vol.bnd))
% % error('...');
% % end
%
% % The following checks should always be done.
% vol.bnd = surface_orientation(vol.bnd, 'outwards'); % might have to be inwards
%
% order = surface_nesting(vol.bnd, 'outsidefirst'); % might have to be insidefirst
% vol.bnd = vol.bnd(order);
% FIXME also the cond
%
if isempty(isolatedsource)
if numboundaries>1
% the isolated source compartment is by default the most inner one
isolatedsource = true;
else
isolatedsource = false;
end
else
% convert into a boolean
isolatedsource = istrue(isolatedsource);
end
% determine the desired nesting of the compartments
order = surface_nesting(vol.bnd, 'outsidefirst');
% rearrange boundaries and conductivities
if numel(vol.bnd)>1
fprintf('reordering the boundaries to: ');
fprintf('%d ', order);
fprintf('\n');
% update the order of the compartments
vol.bnd = vol.bnd(order);
end
if isempty(conductivity)
warning('No conductivity is declared, Assuming standard values\n')
if numboundaries == 1
conductivity = 1;
elseif numboundaries == 3
% skin/skull/brain
conductivity = [1 1/80 1] * 0.33;
elseif numboundaries == 4
%FIXME: check for better default values here
% skin / outer skull / inner skull / brain
conductivity = [1 1/80 1 1] * 0.33;
else
error('Conductivity values are required!')
end
vol.cond = conductivity;
else
if numel(conductivity)~=numboundaries
error('a conductivity value should be specified for each compartment');
end
vol.cond = conductivity(order);
end
vol.skin_surface = 1;
vol.source = numboundaries; % this is now the last one
if isolatedsource
fprintf('using compartment %d for the isolated source approach\n', vol.source);
else
fprintf('not using the isolated source approach\n');
end
% find the location of the dipoli binary
str = which('dipoli.maci');
[p, f, x] = fileparts(str);
dipoli = fullfile(p, f); % without the .m extension
switch mexext
case {'mexmaci' 'mexmaci64'}
% apple computer
dipoli = [dipoli '.maci'];
case {'mexglnx86' 'mexa64'}
% linux computer
dipoli = [dipoli '.glnx86'];
otherwise
error('there is no dipoli executable for your platform');
end
fprintf('using the executable "%s"\n', dipoli);
% write the triangulations to file
bndfile = {};
bnddip = vol.bnd;
for i=1:numboundaries
bndfile{i} = [tempname '.tri'];
% checks if normals are inwards oriented otherwise flips them
ok = checknormals(bnddip(i));
if ~ok
fprintf('flipping normals'' direction\n')
bnddip(i).tri = fliplr(bnddip(i).tri);
end
write_tri(bndfile{i}, bnddip(i).pnt, bnddip(i).tri);
end
% these will hold the shell script and the inverted system matrix
exefile = [tempname '.sh'];
amafile = [tempname '.ama'];
fid = fopen(exefile, 'w');
fprintf(fid, '#!/bin/sh\n');
fprintf(fid, '\n');
fprintf(fid, '%s -i %s << EOF\n', dipoli, amafile);
for i=1:numboundaries
if isolatedsource && vol.source==i
% the isolated potential approach should be applied using this compartment
fprintf(fid, '!%s\n', bndfile{i});
else
fprintf(fid, '%s\n', bndfile{i});
end
fprintf(fid, '%g\n', vol.cond(i));
end
fprintf(fid, '\n');
fprintf(fid, '\n');
fprintf(fid, 'EOF\n');
fclose(fid);
% ensure that the temporary shell script can be executed
dos(sprintf('chmod +x %s', exefile));
try
% execute dipoli and read the resulting file
dos(exefile);
ama = loadama(amafile);
vol = ama2vol(ama);
% This is to maintain the vol.bnd convention (outward oriented), whereas
% in terms of further calculation it shuold not really matter.
% The calculation fo the head model is done with inward normals
% (sometimes flipped from the original input). This assures that the
% outward oriented mesh is saved outward oriiented in the vol structure
for i=1:numel(vol.bnd)
isinw = checknormals(vol.bnd(i));
fprintf('flipping the normals outwards, after head matrix calculation\n')
if isinw
vol.bnd(i).tri = fliplr(vol.bnd(i).tri);
end
end
catch
warning('an error ocurred while running dipoli');
disp(lasterr);
end
% delete the temporary files
for i=1:numboundaries
delete(bndfile{i})
end
delete(amafile);
delete(exefile);
% remember that it is a dipoli model
vol.type = 'dipoli';
function ok = checknormals(bnd)
% checks if the normals are inward oriented
ok = 0;
pnt = bnd.pnt;
tri = bnd.tri;
% translate to the center
org = median(pnt,1);
pnt(:,1) = pnt(:,1) - org(1);
pnt(:,2) = pnt(:,2) - org(2);
pnt(:,3) = pnt(:,3) - org(3);
w = sum(solid_angle(pnt, tri));
if w<0 && (abs(w)-4*pi)<1000*eps
% FIXME: this method is rigorous only for star shaped surfaces
warning('your normals are outwards oriented\n')
ok = 0;
elseif w>0 && (abs(w)-4*pi)<1000*eps
% warning('your normals are inwards oriented\n')
ok = 1;
else
fprintf('attention: your surface probably is irregular!')
ok = 1;
end
|
github
|
philippboehmsturm/antx-master
|
ft_headmodel_openmeeg.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/ft_headmodel_openmeeg.m
| 7,299 |
utf_8
|
471a50b0b7a3a9726773a1f15bcfa4f3
|
function vol = ft_headmodel_openmeeg(geom, varargin)
% FT_HEADMODEL_OPENMEEG creates a volume conduction model of the
% head using the boundary element method (BEM). This function takes
% as input the triangulated surfaces that describe the boundaries and
% returns as output a volume conduction model which can be used to
% compute leadfields.
%
% This function implements
% Gramfort et al. OpenMEEG: opensource software for quasistatic
% bioelectromagnetics. Biomedical engineering online (2010) vol. 9 (1) pp. 45
% http://www.biomedical-engineering-online.com/content/9/1/45
% doi:10.1186/1475-925X-9-45
% and
% Kybic et al. Generalized head models for MEG/EEG: boundary element method
% beyond nested volumes. Phys. Med. Biol. (2006) vol. 51 pp. 1333-1346
% doi:10.1088/0031-9155/51/5/021
%
% The implementation in this function is derived from the the OpenMEEG project
% and uses external command-line executables. See http://gforge.inria.fr/projects/openmeeg
% and http://gforge.inria.fr/frs/?group_id=435.
%
% Use as
% vol = ft_headmodel_openmeeg(geom, ...)
%
% Optional input arguments should be specified in key-value pairs and can
% include
% isolatedsource = string, 'yes' or 'no'
% hdmfile = string, filename with BEM headmodel
% conductivity = vector, conductivity of each compartment
%
% See also FT_PREPARE_VOL_SENS, FT_COMPUTE_LEADFIELD
%$Id: ft_headmodel_openmeeg.m 7310 2013-01-14 15:44:07Z roboos $
ft_hastoolbox('openmeeg', 1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% the first part is largely shared with the dipoli and bemcp implementation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% get the optional arguments
isolatedsource = ft_getopt(varargin, 'isolatedsource');
conductivity = ft_getopt(varargin, 'conductivity');
% copy the boundaries from the geometry into the volume conduction model
if isfield(geom,'bnd')
geom = geom.bnd;
end
% start with an empty volume conductor
vol = [];
vol.bnd = geom;
% determine the number of compartments
numboundaries = length(vol.bnd);
if isempty(isolatedsource)
if numboundaries>1
% the isolated source compartment is by default the most inner one
isolatedsource = true;
else
isolatedsource = false;
end
else
% convert into a boolean
isolatedsource = istrue(isolatedsource);
end
% determine the desired nesting of the compartments
order = surface_nesting(vol.bnd, 'outsidefirst');
% rearrange boundaries and conductivities
if numel(vol.bnd)>1
fprintf('reordering the boundaries to: ');
fprintf('%d ', order);
fprintf('\n');
% update the order of the compartments
vol.bnd = vol.bnd(order);
end
if isempty(conductivity)
warning('No conductivity is declared, Assuming standard values\n')
if numboundaries == 1
conductivity = 1;
elseif numboundaries == 3
% skin/skull/brain
conductivity = [1 1/80 1] * 0.33;
else
error('Conductivity values are required for 2 shells. More than 3 shells not allowed')
end
vol.cond = conductivity;
else
if numel(conductivity)~=numboundaries
error('a conductivity value should be specified for each compartment');
end
vol.cond = conductivity(order);
end
vol.skin_surface = 1;
vol.source = numboundaries;
if isolatedsource
fprintf('using compartment %d for the isolated source approach\n', vol.source);
else
fprintf('not using the isolated source approach\n');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% this uses an implementation that was contributed by INRIA Odyssee Team
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% show the license once
% openmeeg_license
% check that the binaries are ok
om_checkombin;
% store the current path and change folder to the temporary one
tmpfolder = cd;
bndom = vol.bnd;
try
cd(tempdir)
% write the triangulations to file
bndfile = {};
for ii=1:length(bndom)
% check if vertices' normals are inward oriented
ok = checknormals(bndom(ii));
if ~ok
% Flip faces for openmeeg convention (inwards normals)
fprintf('flipping normals'' direction\n')
bndom(ii).tri = fliplr(bndom(ii).tri);
end
end
for ii=1:length(vol.bnd)
[junk,tname] = fileparts(tempname);
bndfile{ii} = [tname '.tri'];
om_save_tri(bndfile{ii}, bndom(ii).pnt, bndom(ii).tri);
end
% these will hold the shell script and the inverted system matrix
[tmp,tname] = fileparts(tempname);
if ~ispc
exefile = [tname '.sh'];
else
exefile = [tname '.bat'];
end
[tmp,tname] = fileparts(tempname);
condfile = [tname '.cond'];
[tmp,tname] = fileparts(tempname);
geomfile = [tname '.geom'];
[tmp,tname] = fileparts(tempname);
hmfile = [tname '.bin'];
[tmp,tname] = fileparts(tempname);
hminvfile = [tname '.bin'];
% write conductivity and geometry files
om_write_geom(geomfile,bndfile);
om_write_cond(condfile,vol.cond);
% Exe file
efid = fopen(exefile, 'w');
omp_num_threads = feature('numCores');
if ~ispc
fprintf(efid,'#!/usr/bin/env bash\n');
fprintf(efid,['export OMP_NUM_THREADS=',num2str(omp_num_threads),'\n']);
fprintf(efid,['om_assemble -HM ./' geomfile ' ./' condfile ' ./' hmfile ' 2>&1 > /dev/null\n']);
fprintf(efid,['om_minverser ./' hmfile ' ./' hminvfile ' 2>&1 > /dev/null\n']);
else
fprintf(efid,['om_assemble -HM ./' geomfile ' ./' condfile ' ./' hmfile '\n']);
fprintf(efid,['om_minverser ./' hmfile ' ./' hminvfile '\n']);
end
fclose(efid);
if ~ispc
dos(sprintf('chmod +x %s', exefile));
end
catch
cd(tmpfolder)
rethrow(lasterror)
end
try
% execute OpenMEEG and read the resulting file
if ispc
dos([exefile]);
else
version = om_getgccversion;
if version>3
dos(['./' exefile]);
else
error('non suitable GCC compiler version (must be superior to gcc3)');
end
end
vol.mat = om_load_sym(hminvfile,'binary');
cleaner(vol,bndfile,condfile,geomfile,hmfile,hminvfile,exefile)
cd(tmpfolder)
catch
warning('an error ocurred while running OpenMEEG');
disp(lasterr);
cleaner(vol,bndfile,condfile,geomfile,hmfile,hminvfile,exefile)
cd(tmpfolder)
end
% remember the type of volume conduction model
vol.type = 'openmeeg';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function cleaner(vol,bndfile,condfile,geomfile,hmfile,hminvfile,exefile)
% delete the temporary files
for i=1:length(vol.bnd)
delete(bndfile{i})
end
delete(condfile);
delete(geomfile);
delete(hmfile);
delete(hminvfile);
delete(exefile);
function ok = checknormals(bnd)
% FIXME: this method is rigorous only for star shaped surfaces
ok = 0;
pnt = bnd.pnt;
tri = bnd.tri;
% translate to the center
org = mean(pnt,1);
pnt(:,1) = pnt(:,1) - org(1);
pnt(:,2) = pnt(:,2) - org(2);
pnt(:,3) = pnt(:,3) - org(3);
w = sum(solid_angle(pnt, tri));
if w<0 && (abs(w)-4*pi)<1000*eps
ok = 0;
warning('your normals are outwards oriented\n')
elseif w>0 && (abs(w)-4*pi)<1000*eps
ok = 1;
% warning('your normals are inwards oriented')
else
error('your surface probably is irregular\n')
ok = 0;
end
|
github
|
philippboehmsturm/antx-master
|
ft_datatype_sens.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/private/ft_datatype_sens.m
| 8,876 |
utf_8
|
9cca02c1384fde3f0c40f1a5a3a0253a
|
function [sens] = ft_datatype_sens(sens, varargin)
% FT_DATATYPE_SENS describes the FieldTrip structure that represents
% an EEG, ECoG, or MEG sensor array. This structure is commonly called
% "elec" for EEG and "grad" for MEG, or more general "sens" for either
% one.
%
% The structure for MEG gradiometers and/or magnetometers contains
% sens.label = Mx1 cell-array with channel labels
% sens.chanpos = Mx3 matrix with channel positions
% sens.chanori = Mx3 matrix with channel orientations, used for synthetic planar gradient computation
% sens.tra = MxN matrix to combine coils into channels
% sens.coilpos = Nx3 matrix with coil positions
% sens.coilori = Nx3 matrix with coil orientations
% sens.balance = structure containing info about the balancing, See FT_APPLY_MONTAGE
%
% The structure for EEG or ECoG channels contains
% sens.label = Mx1 cell-array with channel labels
% sens.chanpos = Mx3 matrix with channel positions
% sens.tra = MxN matrix to combine electrodes into channels
% sens.elecpos = Nx3 matrix with electrode positions
% In case sens.tra is not present in the EEG sensor array, the channels
% are assumed to be average referenced.
%
% The following fields are optional
% sens.type = string with the MEG or EEG acquisition system, see FT_SENSTYPE
% sens.chantype = Mx1 cell-array with the type of the channel, see FT_CHANTYPE
% sens.chanunit = Mx1 cell-array with the units of the channel signal, e.g. 'T', 'fT' or 'fT/cm'
% sens.fid = structure with fiducial information
%
% Revision history:
%
% (2011v2/latest) The chantype and chanunit have been added for MEG.
%
% (2011v1) To facilitate determining the position of channels (e.g. for plotting)
% in case of balanced MEG or bipolar EEG, an explicit distinction has been made
% between chanpos+chanori and coilpos+coilori (for MEG) and chanpos and elecpos
% (for EEG). The pnt and ori fields are removed
%
% (2010) Added support for bipolar or otherwise more complex linear combinations
% of EEG electrodes using sens.tra, similar to MEG.
%
% (2009) Noice reduction has been added for MEG systems in the balance field.
%
% (2006) The optional fields sens.type and sens.unit were added.
%
% (2003) The initial version was defined, which looked like this for EEG
% sens.pnt = Mx3 matrix with electrode positions
% sens.label = Mx1 cell-array with channel labels
% and like this for MEG
% sens.pnt = Nx3 matrix with coil positions
% sens.ori = Nx3 matrix with coil orientations
% sens.tra = MxN matrix to combine coils into channels
% sens.label = Mx1 cell-array with channel labels
%
% See also FT_READ_SENS, FT_SENSTYPE, FT_CHANTYPE, FT_APPLY_MONTAGE, CTF2GRAD, FIF2GRAD,
% BTI2GRAD, YOKOGAWA2GRAD, ITAB2GRAD
% Copyright (C) 2011, Robert Oostenveld & Jan-Mathijs Schoffelen
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_datatype_sens.m 7248 2012-12-21 11:37:13Z roboos $
% these are for remembering the type on subsequent calls with the same input arguments
persistent previous_argin previous_argout
current_argin = [{sens} varargin];
if isequal(current_argin, previous_argin)
% don't do the whole cheking again, but return the previous output from cache
sens = previous_argout{1};
end
% get the optional input arguments, which should be specified as key-value pairs
version = ft_getopt(varargin, 'version', 'latest');
if strcmp(version, 'latest')
version = '2011v2';
end
if isempty(sens)
return;
end
switch version
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
case '2011v2'
% This speeds up subsequent calls to ft_senstype and channelposition.
% However, if it is not more precise than MEG or EEG, don't keep it in
% the output (see further down).
if ~isfield(sens, 'type')
sens.type = ft_senstype(sens);
end
% there are many cases which deal with either eeg or meg
ismeg = ft_senstype(sens, 'meg');
if isfield(sens, 'pnt')
if ismeg
% sensor description is a MEG sensor-array, containing oriented coils
sens.coilpos = sens.pnt; sens = rmfield(sens, 'pnt');
sens.coilori = sens.ori; sens = rmfield(sens, 'ori');
else
% sensor description is something else, EEG/ECoG etc
sens.elecpos = sens.pnt; sens = rmfield(sens, 'pnt');
end
end
if ~isfield(sens, 'chanpos')
if ismeg
% sensor description is a MEG sensor-array, containing oriented coils
[chanpos, chanori, lab] = channelposition(sens, 'channel', 'all');
% the channel order can be different in the two representations
[selsens, selpos] = match_str(sens.label, lab);
sens.chanpos = nan(length(sens.label), 3);
sens.chanori = nan(length(sens.label), 3);
% insert the determined position/orientation on the appropriate rows
sens.chanpos(selsens,:) = chanpos(selpos,:);
sens.chanori(selsens,:) = chanori(selpos,:);
if length(selsens)~=length(sens.label)
warning('cannot determine the position and orientation for all channels');
end
else
% sensor description is something else, EEG/ECoG etc
% note that chanori will be all NaNs
[chanpos, chanori, lab] = channelposition(sens, 'channel', 'all');
% the channel order can be different in the two representations
[selsens, selpos] = match_str(sens.label, lab);
sens.chanpos = nan(length(sens.label), 3);
% insert the determined position/orientation on the appropriate rows
sens.chanpos(selsens,:) = chanpos(selpos,:);
if length(selsens)~=length(sens.label)
warning('cannot determine the position and orientation for all channels');
end
end
end
if ~isfield(sens, 'chantype') || all(strcmp(sens.chantype, 'unknown'))
if ismeg
sens.chantype = ft_chantype(sens);
else
% FIXME for EEG we have not yet figured out how to deal with this
end
end
if ~isfield(sens, 'chanunit') || all(strcmp(sens.chanunit, 'unknown'))
if ismeg
sens.chanunit = ft_chanunit(sens);
else
% FIXME for EEG we have not yet figured out how to deal with this
end
end
if ~isfield(sens, 'unit')
sens = ft_convert_units(sens);
end
if any(strcmp(sens.type, {'meg', 'eeg', 'magnetometer', 'electrode', 'unknown'}))
% this is not sufficiently informative, so better remove it
% see also http://bugzilla.fcdonders.nl/show_bug.cgi?id=1806
sens = rmfield(sens, 'type');
end
if size(sens.chanpos,1)~=length(sens.label) || ...
isfield(sens, 'tra') && size(sens.tra,1)~=length(sens.label) || ...
isfield(sens, 'tra') && isfield(sens, 'elecpos') && size(sens.tra,2)~=size(sens.elecpos,1) || ...
isfield(sens, 'tra') && isfield(sens, 'coilpos') && size(sens.tra,2)~=size(sens.coilpos,1) || ...
isfield(sens, 'tra') && isfield(sens, 'coilori') && size(sens.tra,2)~=size(sens.coilori,1) || ...
isfield(sens, 'chanpos') && size(sens.chanpos,1)~=length(sens.label) || ...
isfield(sens, 'chanori') && size(sens.chanori,1)~=length(sens.label)
error('inconsistent number of channels in sensor description');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
otherwise
error('converting to version %s is not supported', version);
end % switch
% this makes the display with the "disp" command look better
sens = sortfieldnames(sens);
% remember the current input and output arguments, so that they can be
% reused on a subsequent call in case the same input argument is given
current_argout = {sens};
previous_argin = current_argin;
previous_argout = current_argout;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function b = sortfieldnames(a)
fn = sort(fieldnames(a));
for i=1:numel(fn)
b.(fn{i}) = a.(fn{i});
end
|
github
|
philippboehmsturm/antx-master
|
normals.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/private/normals.m
| 2,582 |
utf_8
|
c474f14b83010d46459376013fa6e047
|
function [nrm] = normals(pnt, dhk, opt);
% NORMALS compute the surface normals of a triangular mesh
% for each triangle or for each vertex
%
% [nrm] = normals(pnt, dhk, opt)
% where opt is either 'vertex' or 'triangle'
% Copyright (C) 2002-2007, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: normals.m 7123 2012-12-06 21:21:38Z roboos $
if nargin<3
opt='vertex';
elseif (opt(1)=='v' | opt(1)=='V')
opt='vertex';
elseif (opt(1)=='t' | opt(1)=='T')
opt='triangle';
else
error('invalid optional argument');
end
npnt = size(pnt,1);
ndhk = size(dhk,1);
% shift to center
pnt(:,1) = pnt(:,1)-mean(pnt(:,1),1);
pnt(:,2) = pnt(:,2)-mean(pnt(:,2),1);
pnt(:,3) = pnt(:,3)-mean(pnt(:,3),1);
% compute triangle normals
% nrm_dhk = zeros(ndhk, 3);
% for i=1:ndhk
% v2 = pnt(dhk(i,2),:) - pnt(dhk(i,1),:);
% v3 = pnt(dhk(i,3),:) - pnt(dhk(i,1),:);
% nrm_dhk(i,:) = cross(v2, v3);
% end
% vectorized version of the previous part
v2 = pnt(dhk(:,2),:) - pnt(dhk(:,1),:);
v3 = pnt(dhk(:,3),:) - pnt(dhk(:,1),:);
nrm_dhk = cross(v2, v3);
if strcmp(opt, 'vertex')
% compute vertex normals
nrm_pnt = zeros(npnt, 3);
for i=1:ndhk
nrm_pnt(dhk(i,1),:) = nrm_pnt(dhk(i,1),:) + nrm_dhk(i,:);
nrm_pnt(dhk(i,2),:) = nrm_pnt(dhk(i,2),:) + nrm_dhk(i,:);
nrm_pnt(dhk(i,3),:) = nrm_pnt(dhk(i,3),:) + nrm_dhk(i,:);
end
% normalise the direction vectors to have length one
nrm = nrm_pnt ./ (sqrt(sum(nrm_pnt.^2, 2)) * ones(1,3));
else
% normalise the direction vectors to have length one
nrm = nrm_dhk ./ (sqrt(sum(nrm_dhk.^2, 2)) * ones(1,3));
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% fast cross product to replace the Matlab standard version
function [c] = cross(a,b)
c = [a(:,2).*b(:,3)-a(:,3).*b(:,2) a(:,3).*b(:,1)-a(:,1).*b(:,3) a(:,1).*b(:,2)-a(:,2).*b(:,1)];
|
github
|
philippboehmsturm/antx-master
|
meg_ini.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/private/meg_ini.m
| 5,605 |
utf_8
|
ebfaeafe751fd7aea7baf1049e6865b4
|
function forwpar=meg_ini(vc,center,order,sens,refs,gradlocs,weights)
% initializes MEG-forward calculation
% usage: forwpar=meg_ini(vc,center,order,sens,refs,gradlocs,weights);
%
% input:
% vc: Nx6 matrix; N is the number of surface points
% the first three numbers in each row are the location
% and the second three are the orientation of the surface
% normal
% center: 3x1 vector denoting the center of volume the conductor
% order: desired order of spherical spherical harmonics;
% for 'real' realistic volume conductors order=10 is o.k
% sens: Mx6 matrix containing sensor location and orientation,
% format as for vc
% refs: optional argument. If provided, refs contains the location and oriantion
% (format as sens) of additional sensors which are subtracted from the original
% ones. This makes a gradiometer. One can also do this with the
% magnetometer version of this program und do the subtraction outside this program,
% but the gradiometer version is faster.
% gradlocs, weights: optional two arguments (they must come together!).
% gradlocs are the location of additional channels (e.g. to calculate
% a higher order gradiometer) and weights. The i.th row in weights contains
% the weights to correct if the i.th cannel. These extra fields are added!
% (has historical reasons).
%
% output:
% forpwar: structure containing all parameters needed for forward
% calculation
% note: it is assumed that locations are in cm.
% Copyright (C) 2003, Guido Nolte
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: meg_ini.m 7123 2012-12-06 21:21:38Z roboos $
if nargin==4
if order>0;
coeff_sens=getcoeffs(sens,vc,center,order);
forwpar=struct('device_sens',sens,'coeff_sens',coeff_sens,'center',center,'order',order);
else
forwpar=struct('device_sens',sens,'center',center,'order',order);
end
elseif nargin==5
if order>0;
coeff_sens=getcoeffs(sens,vc,center,order);
coeff_refs=getcoeffs(refs,vc,center,order);
forwpar=struct('device_sens',sens,'device_ref',refs,'coeff_sens',coeff_sens,'coeff_ref',coeff_refs,'center',center,'order',order);
else
forwpar=struct('device_sens',sens,'device_ref',refs,'center',center,'order',order);
end
elseif nargin==7;
if order>0;
coeff_sens=getcoeffs(sens,vc,center,order);
coeff_refs=getcoeffs(refs,vc,center,order);
coeff_weights=getcoeffs(gradlocs,vc,center,order);
forwpar=struct('device_sens',sens,'device_ref',refs,'coeff_sens',coeff_sens,...
'coeff_ref',coeff_refs,'center',center,'order',order,...
'device_weights',gradlocs,'coeff_weights',coeff_weights,'weights',weights);
else
forwpar=struct('device_sens',sens,'device_ref',refs,'center',center,'order',order,...
'device_weights',gradlocs,'weights',weights);
end
else
error('you must provide 4,5 or 7 arguments');
end
return
function coeffs=getcoeffs(device,vc,center,order)
[ndip,ndum]=size(vc);
[nchan,ndum]=size(device);
x1=vc(:,1:3)-repmat(center',ndip,1);
n1=vc(:,4:6);
x2=device(:,1:3)-repmat(center',nchan,1);
n2=device(:,4:6);
scale=10;
nbasis=(order+1)^2-1;
[bas,gradbas]=legs(x1,n1,order,scale);
bt=leadsphere_all(x1',x2',n2');
n1rep=reshape(repmat(n1',1,nchan),3,ndip,nchan);
b=dotproduct(n1rep,bt);
ctc=gradbas'*gradbas;
warning('OFF', 'MATLAB:nearlySingularMatrix');
coeffs=inv(ctc)*gradbas'*b;
warning('ON', 'MATLAB:nearlySingularMatrix');
return
function field=getfield(source,device,coeffs,center,order)
[ndip,ndum]=size(source);
[nchan,ndum]=size(device);
x1=source(:,1:3)-repmat(center',ndip,1);
n1=source(:,4:6);
x2=device(:,1:3)-repmat(center',nchan,1);
n2=device(:,4:6);
%spherical
bt=leadsphere_all(x1',x2',n2');
n1rep=reshape(repmat(n1',1,nchan),3,ndip,nchan);
b=dotproduct(n1rep,bt);
field=b';
%correction
if order>0
scale=10;
[bas,gradbas]=legs(x1,n1,order,scale);
nbasis=(order+1)^2-1;
coeffs=coeffs(1:nbasis,:);
fcorr=gradbas*coeffs;
field=field-fcorr';
end
return
function out=crossproduct(x,y)
% usage: out=testprog(x,y)
% testprog calculates the cross-product of vector x and y
[n,m,k]=size(x);
out=zeros(3,m,k);
out(1,:,:)=x(2,:,:).*y(3,:,:)-x(3,:,:).*y(2,:,:);
out(2,:,:)=x(3,:,:).*y(1,:,:)-x(1,:,:).*y(3,:,:);
out(3,:,:)=x(1,:,:).*y(2,:,:)-x(2,:,:).*y(1,:,:);
return;
function out=dotproduct(x,y)
% usage: out=dotproduct(x,y)
% testprog calculates the dotproduct of vector x and y
[n,m,k]=size(x);
outb=x(1,:,:).*y(1,:,:)+x(2,:,:).*y(2,:,:)+x(3,:,:).*y(3,:,:);
out=reshape(outb,m,k);
return;
function result=norms(x)
[n,m,k]=size(x);
resultb=sqrt(x(1,:,:).^2+x(2,:,:).^2+x(3,:,:).^2);
result=reshape(resultb,m,k);
return;
|
github
|
philippboehmsturm/antx-master
|
eeg_halfspace_monopole.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/private/eeg_halfspace_monopole.m
| 3,531 |
utf_8
|
a30cd5fd8a55c538ea729282dd92336a
|
function [lf] = eeg_halfspace_monopole(rd, elc, vol)
% EEG_HALFSPACE_MONOPOLE calculate the halfspace medium leadfield
% on positions pnt for a monopole at position rd and conductivity cond
% The halfspace solution requires a plane dividing a conductive zone of
% conductivity cond, from a non coductive zone (cond = 0)
%
% [lf] = eeg_halfspace_monopole(rd, elc, cond)
%
% Implemented from Malmivuo J, Plonsey R, Bioelectromagnetism (1993)
% http://www.bem.fi/book/index.htm
% Copyright (C) 2011, Cristiano Micheli
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: eeg_halfspace_monopole.m 7123 2012-12-06 21:21:38Z roboos $
siz = size(rd);
if any(siz==1)
% positions are specified as a single vector
Npoles = prod(siz)/3;
rd = rd(:)'; % ensure that it is a row vector
elseif siz(2)==3
% positions are specified as a Nx3 matrix -> reformat to a single vector
Npoles = siz(1);
rd = rd';
rd = rd(:)'; % ensure that it is a row vector
else
error('incorrect specification of dipole locations');
end
Nelc = size(elc,1);
lf = zeros(Nelc,Npoles);
for i=1:Npoles
% this is the position of dipole "i"
pole1 = rd((1:3) + 3*(i-1));
% distances electrodes - corrent poles
r1 = elc - ones(Nelc,1) * pole1;
% Method of mirror charges:
% Defines the position of mirror charge being symmetric to the plane
pole2 = get_mirror_pos(pole1,vol);
% distances electrodes - mirror charge
r2 = elc - ones(Nelc,1) * pole2;
% denominator
R1 = (4*pi*vol.cond) * (sum(r1' .^2 ) )';
% denominator, mirror term
R2 = -(4*pi*vol.cond) * (sum(r2' .^2 ) )';
% condition of poles falling in the non conductive halfspace
invacuum = acos(dot(vol.ori,(pole1-vol.pnt)./norm(pole1-vol.pnt))) < pi/2;
if invacuum
warning('a pole lies on the vacuum side of the plane');
lf(:,i) = NaN(Nelc,1);
elseif any(R1)==0
warning('a pole coincides with one of the electrodes');
lf(:,i) = NaN(Nelc,1);
else
lf(:,i) = (1 ./ R1) + (1 ./ R2);
end
end
function P2 = get_mirror_pos(P1,vol)
% calculates the position of a point symmetric to pnt with respect to a plane
P2 = [];
% define the plane
pnt = vol.pnt;
ori = vol.ori; % already normalized
if abs(dot(P1-pnt,ori))<eps
warning(sprintf ('point %f %f %f lies in the symmetry plane',P1(1),P1(2),P1(3)))
P2 = P1;
else
% define the plane in parametric form
% define a non colinear vector vc with respect to the plane normal
vc = [1 0 0];
if abs(cross(ori, vc, 2))<eps
vc = [0 1 0];
end
% define plane's direction vectors
v1 = cross(ori, vc, 2); v1 = v1/norm(v1);
v2 = cross(pnt, ori, 2); v2 = v2/norm(v2);
plane = [pnt v1 v2];
% distance plane-point P1
d = abs(dot(ori, plane(:,1:3)-P1(:,1:3), 2));
% symmetric point
P2 = P1 + 2*d*ori;
end
|
github
|
philippboehmsturm/antx-master
|
eeg_halfspace_medium_leadfield.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/private/eeg_halfspace_medium_leadfield.m
| 3,539 |
utf_8
|
1e8b5e9c0f452dc034f74f1a4035efd7
|
function [lf] = eeg_halfspace_medium_leadfield(rd, elc, vol)
% HALFSPACE_MEDIUM_LEADFIELD calculate the halfspace medium leadfield
% on positions pnt for a dipole at position rd and conductivity cond
% The halfspace solution requires a plane dividing a conductive zone of
% conductivity cond, from a non coductive zone (cond = 0)
%
% [lf] = halfspace_medium_leadfield(rd, elc, cond)
% Copyright (C) 2011, Cristiano Micheli and Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: eeg_halfspace_medium_leadfield.m 7123 2012-12-06 21:21:38Z roboos $
siz = size(rd);
if any(siz==1)
% positions are specified as a single vector
Ndipoles = prod(siz)/3;
rd = rd(:)'; % ensure that it is a row vector
elseif siz(2)==3
% positions are specified as a Nx3 matrix -> reformat to a single vector
Ndipoles = siz(1);
rd = rd';
rd = rd(:)'; % ensure that it is a row vector
else
error('incorrect specification of dipole locations');
end
Nelc = size(elc,1);
lf = zeros(Nelc,3*Ndipoles);
for i=1:Ndipoles
% this is the position of dipole "i"
dip1 = rd((1:3) + 3*(i-1));
% distances electrodes - dipole
r1 = elc - ones(Nelc,1) * dip1;
% Method of mirror dipoles:
% Defines the position of mirror dipoles being symmetric to the plane
dip2 = get_mirror_pos(dip1,vol);
% distances electrodes - mirror dipole
r2 = elc - ones(Nelc,1) * dip2;
% denominator
R1 = (4*pi*vol.cond) * (sum(r1' .^2 ) .^ 1.5)';
% denominator, mirror term
R2 = -(4*pi*vol.cond) * (sum(r2' .^2 ) .^ 1.5)';
% condition of dipoles falling in the non conductive halfspace
invacuum = acos(dot(vol.ori,(dip1-vol.pnt)./norm(dip1-vol.pnt))) < pi/2;
if invacuum
warning('dipole lies on the vacuum side of the plane');
lf(:,(1:3) + 3*(i-1)) = NaN(Nelc,3);
elseif any(R1)==0
warning('dipole coincides with one of the electrodes');
lf(:,(1:3) + 3*(i-1)) = NaN(Nelc,3);
else
lf(:,(1:3) + 3*(i-1)) = (r1 ./ [R1 R1 R1]) + (r2 ./ [R2 R2 R2]);
end
end
function P2 = get_mirror_pos(P1,vol)
% calculates the position of a point symmetric to pnt with respect to a plane
P2 = [];
% define the plane
pnt = vol.pnt;
ori = vol.ori; % already normalized
if abs(dot(P1-pnt,ori))<eps
warning(sprintf ('point %f %f %f lies in the symmetry plane',P1(1),P1(2),P1(3)))
P2 = P1;
else
% define the plane in parametric form
% define a non colinear vector vc with respect to the plane normal
vc = [1 0 0];
if abs(cross(ori, vc, 2))<eps
vc = [0 1 0];
end
% define plane's direction vectors
v1 = cross(ori, vc, 2); v1 = v1/norm(v1);
v2 = cross(pnt, ori, 2); v2 = v2/norm(v2);
plane = [pnt v1 v2];
% distance plane-point P1
d = abs(dot(ori, plane(:,1:3)-P1(:,1:3), 2));
% symmetric point
P2 = P1 + 2*d*ori;
end
|
github
|
philippboehmsturm/antx-master
|
warning_once.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/private/warning_once.m
| 3,832 |
utf_8
|
07dc728273934663973f4c716e7a3a1c
|
function [ws warned] = warning_once(varargin)
%
% Use as one of the following
% warning_once(string)
% warning_once(string, timeout)
% warning_once(id, string)
% warning_once(id, string, timeout)
% where timeout should be inf if you don't want to see the warning ever
% again. The default timeout value is 60 seconds.
%
% It can be used instead of the MATLAB built-in function WARNING, thus as
% s = warning_once(...)
% or as
% warning_once(s)
% where s is a structure with fields 'identifier' and 'state', storing the
% state information. In other words, warning_once accepts as an input the
% same structure it returns as an output. This returns or restores the
% states of warnings to their previous values.
%
% It can also be used as
% [s w] = warning_once(...)
% where w is a boolean that indicates whether a warning as been thrown or not.
%
% Please note that you can NOT use it like this
% warning_once('the value is %d', 10)
% instead you should do
% warning_once(sprintf('the value is %d', 10))
% Copyright (C) 2012, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: warning_once.m 7123 2012-12-06 21:21:38Z roboos $
persistent stopwatch previous
if nargin < 1
error('You need to specify at least a warning message');
end
warned = false;
if isstruct(varargin{1})
warning(varargin{1});
return;
end
% put the arguments we will pass to warning() in this cell array
warningArgs = {};
if nargin==3
% calling syntax (id, msg, timeout)
warningArgs = varargin(1:2);
timeout = varargin{3};
fname = [warningArgs{1} '_' warningArgs{2}];
elseif nargin==2 && isnumeric(varargin{2})
% calling syntax (msg, timeout)
warningArgs = varargin(1);
timeout = varargin{2};
fname = warningArgs{1};
elseif nargin==2 && ~isnumeric(varargin{2})
% calling syntax (id, msg)
warningArgs = varargin(1:2);
timeout = 60;
fname = [warningArgs{1} '_' warningArgs{2}];
elseif nargin==1
% calling syntax (msg)
warningArgs = varargin(1);
timeout = 60; % default timeout in seconds
fname = [warningArgs{1}];
end
if isempty(timeout)
error('Timeout ill-specified');
end
if isempty(stopwatch)
stopwatch = tic;
end
if isempty(previous)
previous = struct;
end
now = toc(stopwatch); % measure time since first function call
fname = decomma(fixname(fname)); % make a nice string that is allowed as structure fieldname
if length(fname) > 63 % MATLAB max name
fname = fname(1:63);
end
if ~isfield(previous, fname) || ...
(isfield(previous, fname) && now>previous.(fname).timeout)
% warning never given before or timed out
ws = warning(warningArgs{:});
previous.(fname).timeout = now+timeout;
previous.(fname).ws = ws;
warned = true;
else
% the warning has been issued before, but has not timed out yet
ws = previous.(fname).ws;
end
end % function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function name = decomma(name)
name(name==',')=[];
end % function
|
github
|
philippboehmsturm/antx-master
|
leadsphere_all.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/private/leadsphere_all.m
| 2,351 |
utf_8
|
8a0c7658993d13ebbeafaa335b06386a
|
function out=leadsphere_chans(xloc,sensorloc,sensorori)
% usage: out=leadsphere_chans(xloc,sensorloc,sensorori)
% Copyright (C) 2003, Guido Nolte
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: leadsphere_all.m 7123 2012-12-06 21:21:38Z roboos $
[n,nsens]=size(sensorloc); %n=3 m=?
[n,ndip]=size(xloc);
xlocrep=reshape(repmat(xloc,1,nsens),3,ndip,nsens);
sensorlocrep=reshape(repmat(sensorloc,ndip,1),3,ndip,nsens);
sensororirep=reshape(repmat(sensorori,ndip,1),3,ndip,nsens);
r2=norms(sensorlocrep);
veca=sensorlocrep-xlocrep;
a=norms(veca);
adotr2=dotproduct(veca,sensorlocrep);
gradf1=scal2vec(1./r2.*(a.^2)+adotr2./a+2*a+2*r2);
gradf2=scal2vec(a+2*r2+adotr2./a);
gradf=gradf1.*sensorlocrep-gradf2.*xlocrep;
F=a.*(r2.*a+adotr2);
A1=scal2vec(1./F);
A2=A1.^2;
A3=crossproduct(xlocrep,sensororirep);
A4=scal2vec(dotproduct(gradf,sensororirep));
A5=crossproduct(xlocrep,sensorlocrep);
out=1e-7*(A3.*A1-(A4.*A2).*A5); %%GRB change
return;
function out=crossproduct(x,y)
[n,m,k]=size(x);
out=zeros(3,m,k);
out(1,:,:)=x(2,:,:).*y(3,:,:)-x(3,:,:).*y(2,:,:);
out(2,:,:)=x(3,:,:).*y(1,:,:)-x(1,:,:).*y(3,:,:);
out(3,:,:)=x(1,:,:).*y(2,:,:)-x(2,:,:).*y(1,:,:);
return;
function out=dotproduct(x,y)
[n,m,k]=size(x);
outb=x(1,:,:).*y(1,:,:)+x(2,:,:).*y(2,:,:)+x(3,:,:).*y(3,:,:);
out=reshape(outb,m,k);
return;
function result=norms(x)
[n,m,k]=size(x);
resultb=sqrt(x(1,:,:).^2+x(2,:,:).^2+x(3,:,:).^2);
result=reshape(resultb,m,k);
return;
function result=scal2vec(x)
[m,k]=size(x);
% result=zeros(3,m,k);
% for i=1:3
% result(i,:,:)=x;
% end
result=reshape(repmat(x(:)', [3 1]), [3 m k]);
return
|
github
|
philippboehmsturm/antx-master
|
ft_hastoolbox.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/private/ft_hastoolbox.m
| 21,701 |
utf_8
|
7141791b922e3b46334b4d5888532adf
|
function [status] = ft_hastoolbox(toolbox, autoadd, silent)
% FT_HASTOOLBOX tests whether an external toolbox is installed. Optionally
% it will try to determine the path to the toolbox and install it
% automatically.
%
% Use as
% [status] = ft_hastoolbox(toolbox, autoadd, silent)
%
% autoadd = 0 means that it will not be added
% autoadd = 1 means that give an error if it cannot be added
% autoadd = 2 means that give a warning if it cannot be added
% autoadd = 3 means that it remains silent if it cannot be added
%
% silent = 0 means that it will give some feedback about adding the toolbox
% silent = 1 means that it will not give feedback
% Copyright (C) 2005-2012, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_hastoolbox.m 7172 2012-12-13 11:50:49Z roboos $
% this function is called many times in FieldTrip and associated toolboxes
% use efficient handling if the same toolbox has been investigated before
% persistent previous previouspath
%
% if ~isequal(previouspath, path)
% previous = [];
% end
%
% if isempty(previous)
% previous = struct;
% elseif isfield(previous, fixname(toolbox))
% status = previous.(fixname(toolbox));
% return
% end
% this points the user to the website where he/she can download the toolbox
url = {
'AFNI' 'see http://afni.nimh.nih.gov'
'DSS' 'see http://www.cis.hut.fi/projects/dss'
'EEGLAB' 'see http://www.sccn.ucsd.edu/eeglab'
'NWAY' 'see http://www.models.kvl.dk/source/nwaytoolbox'
'SPM99' 'see http://www.fil.ion.ucl.ac.uk/spm'
'SPM2' 'see http://www.fil.ion.ucl.ac.uk/spm'
'SPM5' 'see http://www.fil.ion.ucl.ac.uk/spm'
'SPM8' 'see http://www.fil.ion.ucl.ac.uk/spm'
'MEG-PD' 'see http://www.kolumbus.fi/kuutela/programs/meg-pd'
'MEG-CALC' 'this is a commercial toolbox from Neuromag, see http://www.neuromag.com'
'BIOSIG' 'see http://biosig.sourceforge.net'
'EEG' 'see http://eeg.sourceforge.net'
'EEGSF' 'see http://eeg.sourceforge.net' % alternative name
'MRI' 'see http://eeg.sourceforge.net' % alternative name
'NEUROSHARE' 'see http://www.neuroshare.org'
'BESA' 'see http://www.megis.de, or contact Karsten Hoechstetter'
'EEPROBE' 'see http://www.ant-neuro.com, or contact Maarten van der Velde'
'YOKOGAWA' 'this is deprecated, please use YOKOGAWA_MEG_READER instead'
'YOKOGAWA_MEG_READER' 'see http://www.yokogawa.com/me/me-login-en.htm'
'BEOWULF' 'see http://robertoostenveld.nl, or contact Robert Oostenveld'
'MENTAT' 'see http://robertoostenveld.nl, or contact Robert Oostenveld'
'SON2' 'see http://www.kcl.ac.uk/depsta/biomedical/cfnr/lidierth.html, or contact Malcolm Lidierth'
'4D-VERSION' 'contact Christian Wienbruch'
'SIGNAL' 'see http://www.mathworks.com/products/signal'
'OPTIM' 'see http://www.mathworks.com/products/optim'
'IMAGE' 'see http://www.mathworks.com/products/image' % Mathworks refers to this as IMAGES
'SPLINES' 'see http://www.mathworks.com/products/splines'
'DISTCOMP' 'see http://www.mathworks.nl/products/parallel-computing/'
'COMPILER' 'see http://www.mathworks.com/products/compiler'
'FASTICA' 'see http://www.cis.hut.fi/projects/ica/fastica'
'BRAINSTORM' 'see http://neuroimage.ucs.edu/brainstorm'
'FILEIO' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'PREPROC' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'FORWARD' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'INVERSE' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'SPECEST' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'REALTIME' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'PLOTTING' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'SPIKE' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'CONNECTIVITY' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'PEER' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'PLOTTING' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'DENOISE' 'see http://lumiere.ens.fr/Audition/adc/meg, or contact Alain de Cheveigne'
'BCI2000' 'see http://bci2000.org'
'NLXNETCOM' 'see http://www.neuralynx.com'
'DIPOLI' 'see ftp://ftp.fcdonders.nl/pub/fieldtrip/external'
'MNE' 'see http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php'
'TCP_UDP_IP' 'see http://www.mathworks.com/matlabcentral/fileexchange/345, or contact Peter Rydesaeter'
'BEMCP' 'contact Christophe Phillips'
'OPENMEEG' 'see http://gforge.inria.fr/projects/openmeeg and http://gforge.inria.fr/frs/?group_id=435'
'PRTOOLS' 'see http://www.prtools.org'
'ITAB' 'contact Stefania Della Penna'
'BSMART' 'see http://www.brain-smart.org'
'PEER' 'see http://fieldtrip.fcdonders.nl/development/peer'
'FREESURFER' 'see http://surfer.nmr.mgh.harvard.edu/fswiki'
'SIMBIO' 'see https://www.mrt.uni-jena.de/simbio/index.php/Main_Page'
'VGRID' 'see http://www.rheinahrcampus.de/~medsim/vgrid/manual.html'
'FNS' 'see http://hhvn.nmsu.edu/wiki/index.php/FNS'
'GIFTI' 'see http://www.artefact.tk/software/matlab/gifti'
'XML4MAT' 'see http://www.mathworks.com/matlabcentral/fileexchange/6268-xml4mat-v2-0'
'SQDPROJECT' 'see http://www.isr.umd.edu/Labs/CSSL/simonlab'
'BCT' 'see http://www.brain-connectivity-toolbox.net/'
'CCA' 'see http://www.imt.liu.se/~magnus/cca or contact Magnus Borga'
'EGI_MFF' 'see http://www.egi.com/ or contact either Phan Luu or Colin Davey at EGI'
'TOOLBOX_GRAPH' 'see http://www.mathworks.com/matlabcentral/fileexchange/5355-toolbox-graph or contact Gabriel Peyre'
'NETCDF' 'see http://www.mathworks.com/matlabcentral/fileexchange/15177'
'MYSQL' 'see http://www.mathworks.com/matlabcentral/fileexchange/8663-mysql-database-connector'
'ISO2MESH' 'see http://iso2mesh.sourceforge.net/cgi-bin/index.cgi?Home or contact Qianqian Fang'
'DATAHASH' 'see http://www.mathworks.com/matlabcentral/fileexchange/31272'
'IBTB' 'see http://www.ibtb.org'
'ICASSO' 'see http://www.cis.hut.fi/projects/ica/icasso'
'XUNIT' 'see http://www.mathworks.com/matlabcentral/fileexchange/22846-matlab-xunit-test-framework'
'PLEXON' 'available from http://www.plexon.com/assets/downloads/sdk/ReadingPLXandDDTfilesinMatlab-mexw.zip'
};
if nargin<2
% default is not to add the path automatically
autoadd = 0;
end
if nargin<3
% default is not to be silent
silent = 0;
end
% determine whether the toolbox is installed
toolbox = upper(toolbox);
% set fieldtrip trunk path, used for determining ft-subdirs are on path
fttrunkpath = unixpath(fileparts(which('ft_defaults')));
switch toolbox
case 'AFNI'
status = (exist('BrikLoad') && exist('BrikInfo'));
case 'DSS'
status = exist('denss', 'file') && exist('dss_create_state', 'file');
case 'EEGLAB'
status = exist('runica', 'file');
case 'NWAY'
status = exist('parafac', 'file');
case 'SPM'
status = exist('spm.m'); % any version of SPM is fine
case 'SPM99'
status = exist('spm.m') && strcmp(spm('ver'),'SPM99');
case 'SPM2'
status = exist('spm.m') && strcmp(spm('ver'),'SPM2');
case 'SPM5'
status = exist('spm.m') && strcmp(spm('ver'),'SPM5');
case 'SPM8'
status = exist('spm.m') && strncmp(spm('ver'),'SPM8', 4);
case 'SPM12'
status = exist('spm.m') && strncmp(spm('ver'),'SPM12', 5);
case 'MEG-PD'
status = (exist('rawdata') && exist('channames'));
case 'MEG-CALC'
status = (exist('megmodel') && exist('megfield') && exist('megtrans'));
case 'BIOSIG'
status = (exist('sopen') && exist('sread'));
case 'EEG'
status = (exist('ctf_read_res4') && exist('ctf_read_meg4'));
case 'EEGSF' % alternative name
status = (exist('ctf_read_res4') && exist('ctf_read_meg4'));
case 'MRI' % other functions in the mri section
status = (exist('avw_hdr_read') && exist('avw_img_read'));
case 'NEUROSHARE'
status = (exist('ns_OpenFile') && exist('ns_SetLibrary') && exist('ns_GetAnalogData'));
case 'BESA'
status = (exist('readBESAtfc') && exist('readBESAswf'));
case 'EEPROBE'
status = (exist('read_eep_avr') && exist('read_eep_cnt'));
case 'YOKOGAWA'
status = hasyokogawa('16bitBeta6');
case 'YOKOGAWA12BITBETA3'
status = hasyokogawa('12bitBeta3');
case 'YOKOGAWA16BITBETA3'
status = hasyokogawa('16bitBeta3');
case 'YOKOGAWA16BITBETA6'
status = hasyokogawa('16bitBeta6');
case 'YOKOGAWA_MEG_READER'
status = hasyokogawa('1.4');
case 'BEOWULF'
status = (exist('evalwulf') && exist('evalwulf') && exist('evalwulf'));
case 'MENTAT'
status = (exist('pcompile') && exist('pfor') && exist('peval'));
case 'SON2'
status = (exist('SONFileHeader') && exist('SONChanList') && exist('SONGetChannel'));
case '4D-VERSION'
status = (exist('read4d') && exist('read4dhdr'));
case {'STATS', 'STATISTICS'}
status = license('checkout', 'statistics_toolbox'); % also check the availability of a toolbox license
case {'OPTIM', 'OPTIMIZATION'}
status = license('checkout', 'optimization_toolbox'); % also check the availability of a toolbox license
case {'SPLINES', 'CURVE_FITTING'}
status = license('checkout', 'curve_fitting_toolbox'); % also check the availability of a toolbox license
case 'SIGNAL'
status = license('checkout', 'signal_toolbox'); % also check the availability of a toolbox license
case 'IMAGE'
status = license('checkout', 'image_toolbox'); % also check the availability of a toolbox license
case {'DCT', 'DISTCOMP'}
status = license('checkout', 'distrib_computing_toolbox'); % also check the availability of a toolbox license
case 'COMPILER'
status = license('checkout', 'compiler'); % also check the availability of a toolbox license
case 'FASTICA'
status = exist('fpica', 'file');
case 'BRAINSTORM'
status = exist('bem_xfer');
case 'DENOISE'
status = (exist('tsr', 'file') && exist('sns', 'file'));
case 'CTF'
status = (exist('getCTFBalanceCoefs') && exist('getCTFdata'));
case 'BCI2000'
status = exist('load_bcidat');
case 'NLXNETCOM'
status = (exist('MatlabNetComClient', 'file') && exist('NlxConnectToServer', 'file') && exist('NlxGetNewCSCData', 'file'));
case 'DIPOLI'
status = exist('dipoli.maci', 'file');
case 'MNE'
status = (exist('fiff_read_meas_info', 'file') && exist('fiff_setup_read_raw', 'file'));
case 'TCP_UDP_IP'
status = (exist('pnet', 'file') && exist('pnet_getvar', 'file') && exist('pnet_putvar', 'file'));
case 'BEMCP'
status = (exist('bem_Cij_cog', 'file') && exist('bem_Cij_lin', 'file') && exist('bem_Cij_cst', 'file'));
case 'OPENMEEG'
status = exist('om_save_tri.m', 'file');
case 'PRTOOLS'
status = (exist('prversion', 'file') && exist('dataset', 'file') && exist('svc', 'file'));
case 'ITAB'
status = (exist('lcReadHeader', 'file') && exist('lcReadData', 'file'));
case 'BSMART'
status = exist('bsmart');
case 'FREESURFER'
status = exist('MRIread', 'file') && exist('vox2ras_0to1', 'file');
case 'FNS'
status = exist('elecsfwd', 'file');
case 'SIMBIO'
status = exist('calc_stiff_matrix_val', 'file') && exist('sb_transfer', 'file');
case 'VGRID'
status = exist('vgrid.m', 'file');
case 'GIFTI'
status = exist('gifti', 'file');
case 'XML4MAT'
status = exist('xml2struct.m', 'file') && exist('xml2whos.m', 'file');
case 'SQDPROJECT'
status = exist('sqdread.m', 'file') && exist('sqdwrite.m', 'file');
case 'BCT'
status = exist('macaque71.mat', 'file') && exist('motif4funct_wei.m', 'file');
case 'CCA'
status = exist('ccabss.m', 'file');
case 'EGI_MFF'
status = exist('mff_getObject.m', 'file') && exist('mff_getSummaryInfo.m', 'file');
case 'TOOLBOX_GRAPH'
status = exist('toolbox_graph');
case 'NETCDF'
status = exist('netcdf');
case 'MYSQL'
status = exist(['mysql.' mexext], 'file'); % this only consists of a single mex file
case 'ISO2MESH'
status = exist('vol2surf.m', 'file') && exist('qmeshcut.m', 'file');
case 'QSUB'
status = exist('qsubfeval.m', 'file') && exist('qsubcellfun.m', 'file');
case 'ENGINE'
status = exist('enginefeval.m', 'file') && exist('enginecellfun.m', 'file');
case 'DATAHASH'
status = exist('DataHash.m', 'file');
case 'IBTB'
status = exist('make_ibtb.m', 'file') && exist('binr.m', 'file');
case 'ICASSO'
status = exist('icassoEst.m', 'file');
case 'XUNIT'
status = exist('initTestSuite.m', 'file') && exist('runtests.m', 'file');
case 'PLEXON'
status = exist('plx_adchan_gains.m', 'file') && exist('mexPlex');
% the following are fieldtrip modules/toolboxes
case 'FILEIO'
status = (exist('ft_read_header', 'file') && exist('ft_read_data', 'file') && exist('ft_read_event', 'file') && exist('ft_read_sens', 'file'));
case 'FORWARD'
status = (exist('ft_compute_leadfield', 'file') && exist('ft_prepare_vol_sens', 'file'));
case 'PLOTTING'
status = (exist('ft_plot_topo', 'file') && exist('ft_plot_mesh', 'file') && exist('ft_plot_matrix', 'file'));
case 'PEER'
status = exist('peerslave', 'file') && exist('peermaster', 'file');
case 'CONNECTIVITY'
status = exist('ft_connectivity_corr', 'file') && exist('ft_connectivity_granger', 'file');
case 'SPIKE'
status = exist('ft_spiketriggeredaverage.m', 'file') && exist('ft_spiketriggeredspectrum.m', 'file');
% these were missing, added them using the below style, see bug 1804 - roevdmei
case 'INVERSE'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/inverse'], 'once')); % INVERSE is not added above, consider doing it there -roevdmei
case 'REALTIME'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/realtime'], 'once')); % REALTIME is not added above, consider doing it there -roevdmei
case 'SPECEST'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/specest'], 'once')); % SPECEST is not added above, consider doing it there -roevdmei
case 'PREPROC'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/preproc'], 'once')); % PREPROC is not added above, consider doing it there -roevdmei
% the following are not proper toolboxes, but only subdirectories in the fieldtrip toolbox
% these are added in ft_defaults and are specified with unix-style forward slashes
case 'COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/compat'], 'once'));
case 'STATFUN'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/statfun'], 'once'));
case 'TRIALFUN'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/trialfun'], 'once'));
case 'UTILITIES/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/utilities/compat'], 'once'));
case 'FILEIO/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/fileio/compat'], 'once'));
case 'PREPROC/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/preproc/compat'], 'once'));
case 'FORWARD/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/forward/compat'], 'once'));
case 'PLOTTING/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/plotting/compat'], 'once'));
case 'TEMPLATE/LAYOUT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/layout'], 'once'));
case 'TEMPLATE/ANATOMY'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/anatomy'], 'once'));
case 'TEMPLATE/HEADMODEL'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/headmodel'], 'once'));
case 'TEMPLATE/ELECTRODE'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/electrode'], 'once'));
case 'TEMPLATE/NEIGHBOURS'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/neighbours'], 'once'));
case 'TEMPLATE/SOURCEMODEL'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/sourcemodel'], 'once'));
otherwise
if ~silent, warning('cannot determine whether the %s toolbox is present', toolbox); end
status = 0;
end
% it should be a boolean value
status = (status~=0);
% try to determine the path of the requested toolbox
if autoadd>0 && ~status
% for core fieldtrip modules
prefix = fileparts(which('ft_defaults'));
if ~status
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
end
% for external fieldtrip modules
prefix = fullfile(fileparts(which('ft_defaults')), 'external');
if ~status
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
licensefile = [lower(toolbox) '_license'];
if status && exist(licensefile, 'file')
% this will execute openmeeg_license and mne_license
% which display the license on screen for three seconds
feval(licensefile);
end
end
% for contributed fieldtrip extensions
prefix = fullfile(fileparts(which('ft_defaults')), 'contrib');
if ~status
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
licensefile = [lower(toolbox) '_license'];
if status && exist(licensefile, 'file')
% this will execute openmeeg_license and mne_license
% which display the license on screen for three seconds
feval(licensefile);
end
end
% for linux computers in the Donders Centre for Cognitive Neuroimaging
prefix = '/home/common/matlab';
if ~status && isunix
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
end
% for windows computers in the Donders Centre for Cognitive Neuroimaging
prefix = 'h:\common\matlab';
if ~status && ispc
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
end
% use the matlab subdirectory in your homedirectory, this works on linux and mac
prefix = fullfile(getenv('HOME'), 'matlab');
if ~status && isdir(prefix)
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
end
if ~status
% the toolbox is not on the path and cannot be added
sel = find(strcmp(url(:,1), toolbox));
if ~isempty(sel)
msg = sprintf('the %s toolbox is not installed, %s', toolbox, url{sel, 2});
else
msg = sprintf('the %s toolbox is not installed', toolbox);
end
if autoadd==1
error(msg);
elseif autoadd==2
warning(msg);
else
% fail silently
end
end
end
% this function is called many times in FieldTrip and associated toolboxes
% use efficient handling if the same toolbox has been investigated before
if status
previous.(fixname(toolbox)) = status;
end
% remember the previous path, allows us to determine on the next call
% whether the path has been modified outise of this function
previouspath = path;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function status = myaddpath(toolbox, silent)
if isdeployed
warning('cannot change path settings for %s in a compiled application', toolbox);
elseif exist(toolbox, 'dir')
if ~silent,
ws = warning('backtrace', 'off');
warning('adding %s toolbox to your Matlab path', toolbox);
warning(ws); % return to the previous warning level
end
addpath(toolbox);
status = 1;
else
status = 0;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function path = unixpath(path)
path(path=='\') = '/'; % replace backward slashes with forward slashes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function status = hasfunction(funname, toolbox)
try
% call the function without any input arguments, which probably is inapropriate
feval(funname);
% it might be that the function without any input already works fine
status = true;
catch
% either the function returned an error, or the function is not available
% availability is influenced by the function being present and by having a
% license for the function, i.e. in a concurrent licensing setting it might
% be that all toolbox licenses are in use
m = lasterror;
if strcmp(m.identifier, 'MATLAB:license:checkouterror')
if nargin>1
warning('the %s toolbox is available, but you don''t have a license for it', toolbox);
else
warning('the function ''%s'' is available, but you don''t have a license for it', funname);
end
status = false;
elseif strcmp(m.identifier, 'MATLAB:UndefinedFunction')
status = false;
else
% the function seems to be available and it gave an unknown error,
% which is to be expected with inappropriate input arguments
status = true;
end
end
|
github
|
philippboehmsturm/antx-master
|
eeg_slab_monopole.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/private/eeg_slab_monopole.m
| 4,438 |
utf_8
|
a73da4acab82be2e433ef6b96c5da222
|
function [lf] = eeg_slab_monopole(rd, elc, vol)
% EEG_SLAB_MONOPOLE calculate the strip medium leadfield
% on positions pnt for a monopole at position rd and conductivity cond
% The halfspace solution requires a plane dividing a conductive zone of
% conductivity cond, from a non coductive zone (cond = 0)
%
% [lf] = eeg_slab_monopole(rd, elc, cond)
%
% Implemented from Malmivuo J, Plonsey R, Bioelectromagnetism (1993)
% http://www.bem.fi/book/index.htm
% Copyright (C) 2011, Cristiano Micheli
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: eeg_slab_monopole.m 7123 2012-12-06 21:21:38Z roboos $
siz = size(rd);
if any(siz==1)
% positions are specified as a single vector
Npoles = prod(siz)/3;
rd = rd(:)'; % ensure that it is a row vector
elseif siz(2)==3
% positions are specified as a Nx3 matrix -> reformat to a single vector
Npoles = siz(1);
rd = rd';
rd = rd(:)'; % ensure that it is a row vector
else
error('incorrect specification of pole locations');
end
Nelc = size(elc,1);
lf = zeros(Nelc,Npoles);
for i=1:Npoles
% this is the position of dipole "i"
pole1 = rd((1:3) + 3*(i-1));
% distances electrodes - corrent poles
r1 = elc - ones(Nelc,1) * pole1;
% Method of mirror charges:
% Defines the position of mirror charge being symmetric to the plane
[pole2,pole3,pole4] = get_mirror_pos(pole1,vol);
% distances electrodes - mirror charge
r2 = elc - ones(Nelc,1) * pole2;
r3 = elc - ones(Nelc,1) * pole3;
r4 = elc - ones(Nelc,1) * pole4;
% denominator
R1 = (4*pi*vol.cond) * sqrt(sum(r1' .^2 ) )';
% denominator, mirror term
R2 = -(4*pi*vol.cond) * sqrt(sum(r2' .^2 ) )';
% denominator, mirror term of P1, plane 2
R3 = -(4*pi*vol.cond) * sqrt(sum(r3' .^2 ) )';
% denominator, mirror term of P2, plane 2
R4 = (4*pi*vol.cond) * sqrt(sum(r4' .^2 ) )';
% condition of poles falling in the non conductive halfspace
instrip1 = acos(dot(vol.ori1,(pole1-vol.pnt1)./norm(pole1-vol.pnt1))) > pi/2;
instrip2 = acos(dot(vol.ori2,(pole1-vol.pnt2)./norm(pole1-vol.pnt2))) > pi/2;
invacuum = ~(instrip1&instrip2);
if invacuum
warning('a pole lies on the vacuum side of the plane');
lf(:,i) = NaN(Nelc,1);
elseif any(R1)==0
warning('a pole coincides with one of the electrodes');
lf(:,i) = NaN(Nelc,1);
else
lf(:,i) = (1 ./ R1) + (1 ./ R2) + (1 ./ R3);% + (1 ./ R4);
end
end
function [P2,P3,P4] = get_mirror_pos(P1,vol)
% calculates the position of a point symmetric to the pole, with respect to plane1
% and two points symmetric to the last ones, with respect to plane2
P2 = []; P3 = []; P4 = [];
% define the planes
pnt1 = vol.pnt1;
ori1 = vol.ori1;
pnt2 = vol.pnt2;
ori2 = vol.ori2;
if abs(dot(P1-pnt1,ori1))<eps || abs(dot(P1-pnt2,ori2))<eps
warning(sprintf ('point %f %f %f lies on the plane',P1(1),P1(2),P1(3)))
P2 = P1;
else
% define the planes
plane1 = def_plane(pnt1,ori1);
plane2 = def_plane(pnt2,ori2);
% distance plane1-point P1
d = abs(dot(ori1, plane1(:,1:3)-P1(:,1:3), 2));
% symmetric point
P2 = P1 + 2*d*ori1;
% distance plane2-point P1
d = abs(dot(ori2, plane2(:,1:3)-P1(:,1:3), 2));
% symmetric point
P3 = P1 + 2*d*ori2;
% distance plane2-point P2
d = abs(dot(ori2, plane2(:,1:3)-P2(:,1:3), 2));
% symmetric point
P4 = P2 + 2*d*ori2;
end
function plane = def_plane(pnt,ori)
% define the plane in parametric form
% define a non colinear vector vc with respect to the plane normal
vc = [1 0 0];
if abs(cross(ori, vc, 2))<eps
vc = [0 1 0];
end
% define plane's direction vectors
v1 = cross(ori, vc, 2); v1 = v1/norm(v1);
v2 = cross(pnt, ori, 2); v2 = v2/norm(v2);
plane = [pnt v1 v2];
|
github
|
philippboehmsturm/antx-master
|
eeg_leadfield1.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/private/eeg_leadfield1.m
| 3,851 |
utf_8
|
5b4189b3e0cfc474f626b46f8d47b752
|
function lf = eeg_leadfield1(R, elc, vol)
% EEG_LEADFIELD1 electric leadfield for a dipole in a single sphere
%
% [lf] = eeg_leadfield1(R, elc, vol)
%
% with input arguments
% R position dipole (vector of length 3)
% elc position electrodes
% and vol being a structure with the elements
% vol.r radius of sphere
% vol.c conductivity of sphere
% Copyright (C) 2002, Robert Oostenveld
%
% this implementation is adapted from
% Luetkenhoener, Habilschrift '92
% the original reference is
% R. Kavanagh, T. M. Darccey, D. Lehmann, and D. H. Fender. Evaluation of methods for three-dimensional localization of electric sources in the human brain. IEEE Trans Biomed Eng, 25:421-429, 1978.
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: eeg_leadfield1.m 7123 2012-12-06 21:21:38Z roboos $
Nchans = size(elc, 1);
lf = zeros(Nchans,3);
% always take the outermost sphere, this makes comparison with the 4-sphere computation easier
[vol.r, indx] = max(vol.r);
vol.c = vol.c(indx);
% check whether the electrode ly on the sphere, allowing 0.5% tolerance
dist = sqrt(sum(elc.^2,2));
if any(abs(dist-vol.r)>vol.r*0.005)
warning('electrodes do not ly on sphere surface -> using projection')
end
elc = vol.r * elc ./ [dist dist dist];
% check whether the dipole is inside the brain [disabled for EEGLAB]
% if sqrt(sum(R.^2))>=vol.r
% error('dipole is outside the brain compartment');
% end
c0 = norm(R);
c1 = vol.r;
c2 = 4*pi*c0^2*vol.c;
if c0==0
% the dipole is in the origin, this can and should be handeled as an exception
[phi, el] = cart2sph(elc(:,1), elc(:,2), elc(:,3));
theta = pi/2 - el;
lf(:,1) = sin(theta).*cos(phi);
lf(:,2) = sin(theta).*sin(phi);
lf(:,3) = cos(theta);
% the potential in a homogenous sphere is three times the infinite medium potential
lf = 3/(c1^2*4*pi*vol.c)*lf;
else
for i=1:Nchans
% use another name for the electrode, in accordance with lutkenhoner1992
r = elc(i,:);
c3 = r-R;
c4 = norm(c3);
c5 = c1^2 * c0^2 - dot(r,R)^2; % lutkenhoner A.11
c6 = c0^2*r - dot(r,R)*R; % lutkenhoner, just after A.17
% the original code reads (cf. lutkenhoner1992 equation A.17)
% lf(i,:) = ((dot(R, r/norm(r) - (r-R)/norm(r-R))/(norm(cross(r,R))^2) + 2/(norm(r-R)^3)) * cross(R, cross(r, R)) + ((norm(r)^2-norm(R)^2)/(norm(r-R)^3) - 1/norm(r)) * R) / (4*pi*vol.c(1)*norm(R)^2);
% but more efficient execution of the code is achieved by some precomputations
if c5<1000*eps
% the dipole lies on a single line with the electrode
lf(i,:) = (2/c4^3 * c6 + ((c1^2-c0^2)/c4^3 - 1/c1) * R) / c2;
else
% nothing wrong, do the complete computation
lf(i,:) = ((dot(R, r/c1 - c3/c4)/c5 + 2/c4^3) * c6 + ((c1^2-c0^2)/c4^3 - 1/c1) * R) / c2;
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% fast cross product
function [c] = cross(a,b)
c = [a(2)*b(3)-a(3)*b(2) a(3)*b(1)-a(1)*b(3) a(1)*b(2)-a(2)*b(1)];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% fast dot product
function [c] = dot(a,b)
c = sum(a.*b);
|
github
|
philippboehmsturm/antx-master
|
meg_forward.m
|
.m
|
antx-master/xspm8/external/fieldtrip/forward/private/meg_forward.m
| 4,038 |
utf_8
|
ac153d74863728e2e7c2eb70a19bf210
|
function field=meg_forward(dip_par,forwpar)
% calculates the magnetic field of n dipoles
% in a realistic volume conductor
% usage: field=meg_forward(dip_par,forwpar)
%
% input:
% dip_par nx6 matrix where each row contains location (first 3 numbers)
% and moment (second 3 numbers) of a dipole
% forwpar structure containing all information relevant for this
% calculation; forwpar is calculated with meg_ini
% You have here an option to include linear transformations in
% the forward model by specifying forpwar.lintrafo=A
% where A is an NxM matrix. Then field -> A field
% You can use that, e.g., if you can write the forward model
% with M magnetometer-channels plus a matrix multiplication
% transforming this to a (eventually higher order) gradiometer.
%
% output:
% field mxn matrix where the i.th column is the field in m channels
% of the i.th dipole
%
% note: No assumptions about units made (i.e. no scaling factors)
%
% Copyright (C) 2003, Guido Nolte
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: meg_forward.m 7123 2012-12-06 21:21:38Z roboos $
device_sens=forwpar.device_sens;
field_sens_sphere=getfield_sphere(dip_par,forwpar.device_sens,forwpar.center);
field=field_sens_sphere;clear field_sens_sphere;
if isfield(forwpar,'device_ref')
field=field-getfield_sphere(dip_par,forwpar.device_ref,forwpar.center);
end
if isfield(forwpar,'device_weights')
field=field+forwpar.weights*getfield_sphere(dip_par,forwpar.device_weights,forwpar.center);
end
if forwpar.order>0
coeff=forwpar.coeff_sens;
if isfield(forwpar,'device_ref')
coeff=coeff-forwpar.coeff_ref;
end
if isfield(forwpar,'device_weights')
coeff=coeff+forwpar.coeff_weights*forwpar.weights';
end
field=field+getfield_corr(dip_par,coeff,forwpar.center,forwpar.order);
end
if isfield(forwpar,'lintrafo');
field=forwpar.lintrafo*field;
end
return
function field=getfield_sphere(source,device,center);
[ndip,ndum]=size(source);
[nchan,ndum]=size(device);
x1=source(:,1:3)-repmat(center',ndip,1);
n1=source(:,4:6);
x2=device(:,1:3)-repmat(center',nchan,1);
n2=device(:,4:6);
%spherical
bt=leadsphere_all(x1',x2',n2');
n1rep=reshape(repmat(n1',1,nchan),3,ndip,nchan);
b=dotproduct(n1rep,bt);
field=b';
return
function field=getfield_corr(source,coeffs,center,order);
[ndip,ndum]=size(source);
x1=source(:,1:3)-repmat(center',ndip,1);
n1=source(:,4:6);
%correction
if order>0
scale=10;
[bas,gradbas]=legs(x1,n1,order,scale);
nbasis=(order+1)^2-1;
coeffs=coeffs(1:nbasis,:);
field=-(gradbas*coeffs)';
end
return
function out=crossproduct(x,y)
% usage: out=testprog(x,y)
% testprog calculates the cross-product of vector x and y
[n,m,k]=size(x);
out=zeros(3,m,k);
out(1,:,:)=x(2,:,:).*y(3,:,:)-x(3,:,:).*y(2,:,:);
out(2,:,:)=x(3,:,:).*y(1,:,:)-x(1,:,:).*y(3,:,:);
out(3,:,:)=x(1,:,:).*y(2,:,:)-x(2,:,:).*y(1,:,:);
return;
function out=dotproduct(x,y)
% usage: out=dotproduct(x,y)
% testprog calculates the dotproduct of vector x and y
[n,m,k]=size(x);
outb=x(1,:,:).*y(1,:,:)+x(2,:,:).*y(2,:,:)+x(3,:,:).*y(3,:,:);
out=reshape(outb,m,k);
return;
function result=norms(x);
[n,m,k]=size(x);
resultb=sqrt(x(1,:,:).^2+x(2,:,:).^2+x(3,:,:).^2);
result=reshape(resultb,m,k);
return;
|
github
|
philippboehmsturm/antx-master
|
postpad.m
|
.m
|
antx-master/xspm8/external/fieldtrip/preproc/private/postpad.m
| 2,013 |
utf_8
|
2c9539d77ff0f85c9f89108f4dc811e0
|
% Copyright (C) 1994, 1995, 1996, 1997, 1998, 2000, 2002, 2004, 2005,
% 2006, 2007, 2008, 2009 John W. Eaton
%
% This file is part of Octave.
%
% Octave 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.
%
% Octave 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 Octave; see the file COPYING. If not, see
% <http://www.gnu.org/licenses/>.
% -*- texinfo -*-
% @deftypefn {Function File} {} postpad (@var{x}, @var{l}, @var{c})
% @deftypefnx {Function File} {} postpad (@var{x}, @var{l}, @var{c}, @var{dim})
% @seealso{prepad, resize}
% @end deftypefn
% Author: Tony Richardson <[email protected]>
% Created: June 1994
function y = postpad (x, l, c, dim)
if nargin < 2 || nargin > 4
%print_usage ();
error('wrong number of input arguments, should be between 2 and 4');
end
if nargin < 3 || isempty(c)
c = 0;
else
if ~isscalar(c)
error ('postpad: third argument must be empty or a scalar');
end
end
nd = ndims(x);
sz = size(x);
if nargin < 4
% Find the first non-singleton dimension
dim = 1;
while dim < nd+1 && sz(dim)==1
dim = dim + 1;
end
if dim > nd
dim = 1;
elseif ~(isscalar(dim) && dim == round(dim)) && dim > 0 && dim< nd+1
error('postpad: dim must be an integer and valid dimension');
end
end
if ~isscalar(l) || l<0
error ('second argument must be a positive scalar');
end
if dim > nd
sz(nd+1:dim) = 1;
end
d = sz(dim);
if d >= l
idx = cell(1,nd);
for i = 1:nd
idx{i} = 1:sz(i);
end
idx{dim} = 1:l;
y = x(idx{:});
else
sz(dim) = l-d;
y = cat(dim, x, c * ones(sz));
end
|
github
|
philippboehmsturm/antx-master
|
sftrans.m
|
.m
|
antx-master/xspm8/external/fieldtrip/preproc/private/sftrans.m
| 7,947 |
utf_8
|
f64cb2e7d19bcdc6232b39d8a6d70e7c
|
% Copyright (C) 1999 Paul Kienzle
%
% 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 2 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/>.
% usage: [Sz, Sp, Sg] = sftrans(Sz, Sp, Sg, W, stop)
%
% Transform band edges of a generic lowpass filter (cutoff at W=1)
% represented in splane zero-pole-gain form. W is the edge of the
% target filter (or edges if band pass or band stop). Stop is true for
% high pass and band stop filters or false for low pass and band pass
% filters. Filter edges are specified in radians, from 0 to pi (the
% nyquist frequency).
%
% Theory: Given a low pass filter represented by poles and zeros in the
% splane, you can convert it to a low pass, high pass, band pass or
% band stop by transforming each of the poles and zeros individually.
% The following table summarizes the transformation:
%
% Transform Zero at x Pole at x
% ---------------- ------------------------- ------------------------
% Low Pass zero: Fc x/C pole: Fc x/C
% S -> C S/Fc gain: C/Fc gain: Fc/C
% ---------------- ------------------------- ------------------------
% High Pass zero: Fc C/x pole: Fc C/x
% S -> C Fc/S pole: 0 zero: 0
% gain: -x gain: -1/x
% ---------------- ------------------------- ------------------------
% Band Pass zero: b ? sqrt(b^2-FhFl) pole: b ? sqrt(b^2-FhFl)
% S^2+FhFl pole: 0 zero: 0
% S -> C -------- gain: C/(Fh-Fl) gain: (Fh-Fl)/C
% S(Fh-Fl) b=x/C (Fh-Fl)/2 b=x/C (Fh-Fl)/2
% ---------------- ------------------------- ------------------------
% Band Stop zero: b ? sqrt(b^2-FhFl) pole: b ? sqrt(b^2-FhFl)
% S(Fh-Fl) pole: ?sqrt(-FhFl) zero: ?sqrt(-FhFl)
% S -> C -------- gain: -x gain: -1/x
% S^2+FhFl b=C/x (Fh-Fl)/2 b=C/x (Fh-Fl)/2
% ---------------- ------------------------- ------------------------
% Bilinear zero: (2+xT)/(2-xT) pole: (2+xT)/(2-xT)
% 2 z-1 pole: -1 zero: -1
% S -> - --- gain: (2-xT)/T gain: (2-xT)/T
% T z+1
% ---------------- ------------------------- ------------------------
%
% where C is the cutoff frequency of the initial lowpass filter, Fc is
% the edge of the target low/high pass filter and [Fl,Fh] are the edges
% of the target band pass/stop filter. With abundant tedious algebra,
% you can derive the above formulae yourself by substituting the
% transform for S into H(S)=S-x for a zero at x or H(S)=1/(S-x) for a
% pole at x, and converting the result into the form:
%
% H(S)=g prod(S-Xi)/prod(S-Xj)
%
% The transforms are from the references. The actual pole-zero-gain
% changes I derived myself.
%
% Please note that a pole and a zero at the same place exactly cancel.
% This is significant for High Pass, Band Pass and Band Stop filters
% which create numerous extra poles and zeros, most of which cancel.
% Those which do not cancel have a 'fill-in' effect, extending the
% shorter of the sets to have the same number of as the longer of the
% sets of poles and zeros (or at least split the difference in the case
% of the band pass filter). There may be other opportunistic
% cancellations but I will not check for them.
%
% Also note that any pole on the unit circle or beyond will result in
% an unstable filter. Because of cancellation, this will only happen
% if the number of poles is smaller than the number of zeros and the
% filter is high pass or band pass. The analytic design methods all
% yield more poles than zeros, so this will not be a problem.
%
% References:
%
% Proakis & Manolakis (1992). Digital Signal Processing. New York:
% Macmillan Publishing Company.
% Author: Paul Kienzle <[email protected]>
% 2000-03-01 [email protected]
% leave transformed Sg as a complex value since cheby2 blows up
% otherwise (but only for odd-order low-pass filters). bilinear
% will return Zg as real, so there is no visible change to the
% user of the IIR filter design functions.
% 2001-03-09 [email protected]
% return real Sg; don't know what to do for imaginary filters
function [Sz, Sp, Sg] = sftrans(Sz, Sp, Sg, W, stop)
if (nargin ~= 5)
usage('[Sz, Sp, Sg] = sftrans(Sz, Sp, Sg, W, stop)');
end;
C = 1;
p = length(Sp);
z = length(Sz);
if z > p || p == 0
error('sftrans: must have at least as many poles as zeros in s-plane');
end
if length(W)==2
Fl = W(1);
Fh = W(2);
if stop
% ---------------- ------------------------- ------------------------
% Band Stop zero: b ? sqrt(b^2-FhFl) pole: b ? sqrt(b^2-FhFl)
% S(Fh-Fl) pole: ?sqrt(-FhFl) zero: ?sqrt(-FhFl)
% S -> C -------- gain: -x gain: -1/x
% S^2+FhFl b=C/x (Fh-Fl)/2 b=C/x (Fh-Fl)/2
% ---------------- ------------------------- ------------------------
if (isempty(Sz))
Sg = Sg * real (1./ prod(-Sp));
elseif (isempty(Sp))
Sg = Sg * real(prod(-Sz));
else
Sg = Sg * real(prod(-Sz)/prod(-Sp));
end
b = (C*(Fh-Fl)/2)./Sp;
Sp = [b+sqrt(b.^2-Fh*Fl), b-sqrt(b.^2-Fh*Fl)];
extend = [sqrt(-Fh*Fl), -sqrt(-Fh*Fl)];
if isempty(Sz)
Sz = [extend(1+rem([1:2*p],2))];
else
b = (C*(Fh-Fl)/2)./Sz;
Sz = [b+sqrt(b.^2-Fh*Fl), b-sqrt(b.^2-Fh*Fl)];
if (p > z)
Sz = [Sz, extend(1+rem([1:2*(p-z)],2))];
end
end
else
% ---------------- ------------------------- ------------------------
% Band Pass zero: b ? sqrt(b^2-FhFl) pole: b ? sqrt(b^2-FhFl)
% S^2+FhFl pole: 0 zero: 0
% S -> C -------- gain: C/(Fh-Fl) gain: (Fh-Fl)/C
% S(Fh-Fl) b=x/C (Fh-Fl)/2 b=x/C (Fh-Fl)/2
% ---------------- ------------------------- ------------------------
Sg = Sg * (C/(Fh-Fl))^(z-p);
b = Sp*((Fh-Fl)/(2*C));
Sp = [b+sqrt(b.^2-Fh*Fl), b-sqrt(b.^2-Fh*Fl)];
if isempty(Sz)
Sz = zeros(1,p);
else
b = Sz*((Fh-Fl)/(2*C));
Sz = [b+sqrt(b.^2-Fh*Fl), b-sqrt(b.^2-Fh*Fl)];
if (p>z)
Sz = [Sz, zeros(1, (p-z))];
end
end
end
else
Fc = W;
if stop
% ---------------- ------------------------- ------------------------
% High Pass zero: Fc C/x pole: Fc C/x
% S -> C Fc/S pole: 0 zero: 0
% gain: -x gain: -1/x
% ---------------- ------------------------- ------------------------
if (isempty(Sz))
Sg = Sg * real (1./ prod(-Sp));
elseif (isempty(Sp))
Sg = Sg * real(prod(-Sz));
else
Sg = Sg * real(prod(-Sz)/prod(-Sp));
end
Sp = C * Fc ./ Sp;
if isempty(Sz)
Sz = zeros(1,p);
else
Sz = [C * Fc ./ Sz];
if (p > z)
Sz = [Sz, zeros(1,p-z)];
end
end
else
% ---------------- ------------------------- ------------------------
% Low Pass zero: Fc x/C pole: Fc x/C
% S -> C S/Fc gain: C/Fc gain: Fc/C
% ---------------- ------------------------- ------------------------
Sg = Sg * (C/Fc)^(z-p);
Sp = Fc * Sp / C;
Sz = Fc * Sz / C;
end
end
|
github
|
philippboehmsturm/antx-master
|
filtfilt.m
|
.m
|
antx-master/xspm8/external/fieldtrip/preproc/private/filtfilt.m
| 3,297 |
iso_8859_1
|
d01a26a827bc3379f05bbc57f46ac0a9
|
% Copyright (C) 1999 Paul Kienzle
% Copyright (C) 2007 Francesco Potortì
% Copyright (C) 2008 Luca Citi
%
% 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 2 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/>.
% usage: y = filtfilt(b, a, x)
%
% Forward and reverse filter the signal. This corrects for phase
% distortion introduced by a one-pass filter, though it does square the
% magnitude response in the process. That's the theory at least. In
% practice the phase correction is not perfect, and magnitude response
% is distorted, particularly in the stop band.
%%
% Example
% [b, a]=butter(3, 0.1); % 10 Hz low-pass filter
% t = 0:0.01:1.0; % 1 second sample
% x=sin(2*pi*t*2.3)+0.25*randn(size(t)); % 2.3 Hz sinusoid+noise
% y = filtfilt(b,a,x); z = filter(b,a,x); % apply filter
% plot(t,x,';data;',t,y,';filtfilt;',t,z,';filter;')
% Changelog:
% 2000 02 [email protected]
% - pad with zeros to load up the state vector on filter reverse.
% - add example
% 2007 12 [email protected]
% - use filtic to compute initial and final states
% - work for multiple columns as well
% 2008 12 [email protected]
% - fixed instability issues with IIR filters and noisy inputs
% - initial states computed according to Likhterov & Kopeika, 2003
% - use of a "reflection method" to reduce end effects
% - added some basic tests
% TODO: (pkienzle) My version seems to have similar quality to matlab,
% but both are pretty bad. They do remove gross lag errors, though.
function y = filtfilt(b, a, x)
if (nargin ~= 3)
usage('y=filtfilt(b,a,x)');
end
rotate = (size(x, 1)==1);
if rotate % a row vector
x = x(:); % make it a column vector
end
lx = size(x,1);
a = a(:).';
b = b(:).';
lb = length(b);
la = length(a);
n = max(lb, la);
lrefl = 3 * (n - 1);
if la < n, a(n) = 0; end
if lb < n, b(n) = 0; end
% Compute a the initial state taking inspiration from
% Likhterov & Kopeika, 2003. "Hardware-efficient technique for
% minimizing startup transients in Direct Form II digital filters"
kdc = sum(b) / sum(a);
if (abs(kdc) < inf) % neither NaN nor +/- Inf
si = fliplr(cumsum(fliplr(b - kdc * a)));
else
si = zeros(size(a)); % fall back to zero initialization
end
si(1) = [];
y = zeros(size(x));
for c = 1:size(x, 2) % filter all columns, one by one
v = [2*x(1,c)-x((lrefl+1):-1:2,c); x(:,c);
2*x(end,c)-x((end-1):-1:end-lrefl,c)]; % a column vector
% Do forward and reverse filtering
v = filter(b,a,v,si*v(1)); % forward filter
v = flipud(filter(b,a,flipud(v),si*v(end))); % reverse filter
y(:,c) = v((lrefl+1):(lx+lrefl));
end
if (rotate) % x was a row vector
y = rot90(y); % rotate it back
end
|
github
|
philippboehmsturm/antx-master
|
bilinear.m
|
.m
|
antx-master/xspm8/external/fieldtrip/preproc/private/bilinear.m
| 4,339 |
utf_8
|
17250db27826cad87fa3384823e1242f
|
% Copyright (C) 1999 Paul Kienzle
%
% 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 2 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/>.
% usage: [Zz, Zp, Zg] = bilinear(Sz, Sp, Sg, T)
% [Zb, Za] = bilinear(Sb, Sa, T)
%
% Transform a s-plane filter specification into a z-plane
% specification. Filters can be specified in either zero-pole-gain or
% transfer function form. The input form does not have to match the
% output form. 1/T is the sampling frequency represented in the z plane.
%
% Note: this differs from the bilinear function in the signal processing
% toolbox, which uses 1/T rather than T.
%
% Theory: Given a piecewise flat filter design, you can transform it
% from the s-plane to the z-plane while maintaining the band edges by
% means of the bilinear transform. This maps the left hand side of the
% s-plane into the interior of the unit circle. The mapping is highly
% non-linear, so you must design your filter with band edges in the
% s-plane positioned at 2/T tan(w*T/2) so that they will be positioned
% at w after the bilinear transform is complete.
%
% The following table summarizes the transformation:
%
% +---------------+-----------------------+----------------------+
% | Transform | Zero at x | Pole at x |
% | H(S) | H(S) = S-x | H(S)=1/(S-x) |
% +---------------+-----------------------+----------------------+
% | 2 z-1 | zero: (2+xT)/(2-xT) | zero: -1 |
% | S -> - --- | pole: -1 | pole: (2+xT)/(2-xT) |
% | T z+1 | gain: (2-xT)/T | gain: (2-xT)/T |
% +---------------+-----------------------+----------------------+
%
% With tedious algebra, you can derive the above formulae yourself by
% substituting the transform for S into H(S)=S-x for a zero at x or
% H(S)=1/(S-x) for a pole at x, and converting the result into the
% form:
%
% H(Z)=g prod(Z-Xi)/prod(Z-Xj)
%
% Please note that a pole and a zero at the same place exactly cancel.
% This is significant since the bilinear transform creates numerous
% extra poles and zeros, most of which cancel. Those which do not
% cancel have a 'fill-in' effect, extending the shorter of the sets to
% have the same number of as the longer of the sets of poles and zeros
% (or at least split the difference in the case of the band pass
% filter). There may be other opportunistic cancellations but I will
% not check for them.
%
% Also note that any pole on the unit circle or beyond will result in
% an unstable filter. Because of cancellation, this will only happen
% if the number of poles is smaller than the number of zeros. The
% analytic design methods all yield more poles than zeros, so this will
% not be a problem.
%
% References:
%
% Proakis & Manolakis (1992). Digital Signal Processing. New York:
% Macmillan Publishing Company.
% Author: Paul Kienzle <[email protected]>
function [Zz, Zp, Zg] = bilinear(Sz, Sp, Sg, T)
if nargin==3
T = Sg;
[Sz, Sp, Sg] = tf2zp(Sz, Sp);
elseif nargin~=4
usage('[Zz, Zp, Zg]=bilinear(Sz,Sp,Sg,T) or [Zb, Za]=blinear(Sb,Sa,T)');
end;
p = length(Sp);
z = length(Sz);
if z > p || p==0
error('bilinear: must have at least as many poles as zeros in s-plane');
end
% ---------------- ------------------------- ------------------------
% Bilinear zero: (2+xT)/(2-xT) pole: (2+xT)/(2-xT)
% 2 z-1 pole: -1 zero: -1
% S -> - --- gain: (2-xT)/T gain: (2-xT)/T
% T z+1
% ---------------- ------------------------- ------------------------
Zg = real(Sg * prod((2-Sz*T)/T) / prod((2-Sp*T)/T));
Zp = (2+Sp*T)./(2-Sp*T);
if isempty(Sz)
Zz = -ones(size(Zp));
else
Zz = [(2+Sz*T)./(2-Sz*T)];
Zz = postpad(Zz, p, -1);
end
if nargout==2, [Zz, Zp] = zp2tf(Zz, Zp, Zg); end
|
github
|
philippboehmsturm/antx-master
|
butter.m
|
.m
|
antx-master/xspm8/external/fieldtrip/preproc/private/butter.m
| 3,559 |
utf_8
|
ad82b4c04911a5ea11fd6bd2cc5fd590
|
% Copyright (C) 1999 Paul Kienzle
%
% 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 2 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/>.
% Generate a butterworth filter.
% Default is a discrete space (Z) filter.
%
% [b,a] = butter(n, Wc)
% low pass filter with cutoff pi*Wc radians
%
% [b,a] = butter(n, Wc, 'high')
% high pass filter with cutoff pi*Wc radians
%
% [b,a] = butter(n, [Wl, Wh])
% band pass filter with edges pi*Wl and pi*Wh radians
%
% [b,a] = butter(n, [Wl, Wh], 'stop')
% band reject filter with edges pi*Wl and pi*Wh radians
%
% [z,p,g] = butter(...)
% return filter as zero-pole-gain rather than coefficients of the
% numerator and denominator polynomials.
%
% [...] = butter(...,'s')
% return a Laplace space filter, W can be larger than 1.
%
% [a,b,c,d] = butter(...)
% return state-space matrices
%
% References:
%
% Proakis & Manolakis (1992). Digital Signal Processing. New York:
% Macmillan Publishing Company.
% Author: Paul Kienzle <[email protected]>
% Modified by: Doug Stewart <[email protected]> Feb, 2003
function [a, b, c, d] = butter (n, W, varargin)
if (nargin>4 || nargin<2) || (nargout>4 || nargout<2)
usage ('[b, a] or [z, p, g] or [a,b,c,d] = butter (n, W [, "ftype"][,"s"])');
end
% interpret the input parameters
if (~(length(n)==1 && n == round(n) && n > 0))
error ('butter: filter order n must be a positive integer');
end
stop = 0;
digital = 1;
for i=1:length(varargin)
switch varargin{i}
case 's', digital = 0;
case 'z', digital = 1;
case { 'high', 'stop' }, stop = 1;
case { 'low', 'pass' }, stop = 0;
otherwise, error ('butter: expected [high|stop] or [s|z]');
end
end
[r, c]=size(W);
if (~(length(W)<=2 && (r==1 || c==1)))
error ('butter: frequency must be given as w0 or [w0, w1]');
elseif (~(length(W)==1 || length(W) == 2))
error ('butter: only one filter band allowed');
elseif (length(W)==2 && ~(W(1) < W(2)))
error ('butter: first band edge must be smaller than second');
end
if ( digital && ~all(W >= 0 & W <= 1))
error ('butter: critical frequencies must be in (0 1)');
elseif ( ~digital && ~all(W >= 0 ))
error ('butter: critical frequencies must be in (0 inf)');
end
% Prewarp to the band edges to s plane
if digital
T = 2; % sampling frequency of 2 Hz
W = 2/T*tan(pi*W/T);
end
% Generate splane poles for the prototype butterworth filter
% source: Kuc
C = 1; % default cutoff frequency
pole = C*exp(1i*pi*(2*[1:n] + n - 1)/(2*n));
if mod(n,2) == 1, pole((n+1)/2) = -1; end % pure real value at exp(i*pi)
zero = [];
gain = C^n;
% splane frequency transform
[zero, pole, gain] = sftrans(zero, pole, gain, W, stop);
% Use bilinear transform to convert poles to the z plane
if digital
[zero, pole, gain] = bilinear(zero, pole, gain, T);
end
% convert to the correct output form
if nargout==2,
a = real(gain*poly(zero));
b = real(poly(pole));
elseif nargout==3,
a = zero;
b = pole;
c = gain;
else
% output ss results
[a, b, c, d] = zp2ss (zero, pole, gain);
end
|
github
|
philippboehmsturm/antx-master
|
ft_statfun_roc.m
|
.m
|
antx-master/xspm8/external/fieldtrip/statfun/ft_statfun_roc.m
| 5,372 |
utf_8
|
ec4b890f9fa0c3a897be1b3cc74e9910
|
function [s, cfg] = statfun_roc(cfg, dat, design)
% STATFUN_roc computes the area under the curve (AUC) of the
% Receiver Operator Characteristic (ROC). This is a measure of the
% separability of the data divided over two conditions. The AUC can
% be used to test statistical significance of being able to predict
% on a single observation basis to which condition the observation
% belongs.
%
% Use this function by calling one of the high-level statistics
% functions as
% [stat] = ft_timelockstatistics(cfg, timelock1, timelock2, ...)
% [stat] = ft_freqstatistics(cfg, freq1, freq2, ...)
% [stat] = ft_sourcestatistics(cfg, source1, source2, ...)
% with the following configuration option:
% cfg.statistic = 'roc'
%
% Configuration options that are relevant for this function are
% cfg.ivar = number, index into the design matrix with the independent variable
% cfg.logtransform = 'yes' or 'no' (default = 'no')
%
% Note that this statfun performs a one sided test in which condition "1"
% is assumed to be larger than condition "2".
% A low-level example for this function is
% a = randn(1,1000) + 1;
% b = randn(1,1000);
% design = [1*ones(1,1000) 2*ones(1,1000)];
% auc = statfun_roc([], [a b], design);
% Copyright (C) 2008, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_statfun_roc.m 7123 2012-12-06 21:21:38Z roboos $
if ~isfield(cfg, 'ivar'), cfg.ivar = 1; end
if ~isfield(cfg, 'logtransform'), cfg.logtransform = 'no'; end
if strcmp(cfg.logtransform, 'yes'),
dat = log10(dat);
end
if isfield(cfg, 'numbins')
% this function was completely reimplemented on 21 July 2008 by Robert Oostenveld
% the old function had a positive bias in the AUC (i.e. the expected value was not 0.5)
error('the option cfg.numbins is not supported any more');
end
% start with a quick test to see whether there appear to be NaNs
if any(isnan(dat(1,:)))
% exclude trials that contain NaNs for all observed data points
sel = all(isnan(dat),1);
dat = dat(:,~sel);
design = design(:,~sel);
end
% logical indexing is faster than using find(...)
selA = (design(cfg.ivar,:)==1);
selB = (design(cfg.ivar,:)==2);
% select the data in the two classes
datA = dat(:, selA);
datB = dat(:, selB);
nobs = size(dat,1);
na = size(datA,2);
nb = size(datB,2);
auc = zeros(nobs, 1);
for k = 1:nobs
% compute the area under the curve for each channel/time/frequency
a = datA(k,:);
b = datB(k,:);
% to speed up the AUC, the critical value is determined by the actual
% values in class B, which also ensures a regular sampling of the False Alarms
b = sort(b);
ca = zeros(nb+1,1);
ib = zeros(nb+1,1);
% cb = zeros(nb+1,1);
% ia = zeros(nb+1,1);
for i=1:nb
% for the first approach below, the critval could also be choosen based on e.g. linspace(min,max,n)
critval = b(i);
% for each of the two distributions, determine the number of correct and incorrect assignments given the critical value
% ca(i) = sum(a>=critval);
% ib(i) = sum(b>=critval);
% cb(i) = sum(b<critval);
% ia(i) = sum(a<critval);
% this is a much faster approach, which works due to using the sorted values in b as the critical values
ca(i) = sum(a>=critval); % correct assignments to class A
ib(i) = nb-i+1; % incorrect assignments to class B
end
% add the end point
ca(end) = 0;
ib(end) = 0;
% cb(end) = nb;
% ia(end) = na;
hits = ca/na;
fa = ib/nb;
% the numerical integration is faster if the points are sorted
hits = fliplr(hits);
fa = fliplr(fa);
if false
% this part is optional and should only be used when exploring the data
figure
plot(fa, hits, '.-')
xlabel('false positive');
ylabel('true positive');
title('ROC-curve');
end
% compute the area under the curve using numerical integration
auc(k) = numint(fa, hits);
end
s.stat = auc;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% NUMINT computes a numerical integral of a set of sampled points using
% linear interpolation. Alugh the algorithm works for irregularly sampled points
% along the x-axis, it will perform best for regularly sampled points
%
% Use as
% z = numint(x, y)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function z = numint(x, y)
if ~all(diff(x)>=0)
% ensure that the points are sorted along the x-axis
[x, i] = sort(x);
y = y(i);
end
n = length(x);
z = 0;
for i=1:(n-1)
x0 = x(i);
y0 = y(i);
dx = x(i+1)-x(i);
dy = y(i+1)-y(i);
z = z + (y0 * dx) + (dy*dx/2);
end
|
github
|
philippboehmsturm/antx-master
|
nansum.m
|
.m
|
antx-master/xspm8/external/fieldtrip/statfun/private/nansum.m
| 185 |
utf_8
|
4859ec062780c478011dd7a8d94684a0
|
% NANSUM provides a replacement for MATLAB's nanmean.
%
% For usage see SUM.
function y = nansum(x, dim)
if nargin == 1
dim = 1;
end
idx = isnan(x);
x(idx) = 0;
y = sum(x, dim);
end
|
github
|
philippboehmsturm/antx-master
|
nanstd.m
|
.m
|
antx-master/xspm8/external/fieldtrip/statfun/private/nanstd.m
| 231 |
utf_8
|
0ff62b1c345b5ad76a9af59cf07c2983
|
% NANSTD provides a replacement for MATLAB's nanstd that is almost
% compatible.
%
% For usage see STD. Note that the three-argument call with FLAG is not
% supported.
function Y = nanstd(varargin)
Y = sqrt(nanvar(varargin{:}));
|
github
|
philippboehmsturm/antx-master
|
warning_once.m
|
.m
|
antx-master/xspm8/external/fieldtrip/statfun/private/warning_once.m
| 3,832 |
utf_8
|
07dc728273934663973f4c716e7a3a1c
|
function [ws warned] = warning_once(varargin)
%
% Use as one of the following
% warning_once(string)
% warning_once(string, timeout)
% warning_once(id, string)
% warning_once(id, string, timeout)
% where timeout should be inf if you don't want to see the warning ever
% again. The default timeout value is 60 seconds.
%
% It can be used instead of the MATLAB built-in function WARNING, thus as
% s = warning_once(...)
% or as
% warning_once(s)
% where s is a structure with fields 'identifier' and 'state', storing the
% state information. In other words, warning_once accepts as an input the
% same structure it returns as an output. This returns or restores the
% states of warnings to their previous values.
%
% It can also be used as
% [s w] = warning_once(...)
% where w is a boolean that indicates whether a warning as been thrown or not.
%
% Please note that you can NOT use it like this
% warning_once('the value is %d', 10)
% instead you should do
% warning_once(sprintf('the value is %d', 10))
% Copyright (C) 2012, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: warning_once.m 7123 2012-12-06 21:21:38Z roboos $
persistent stopwatch previous
if nargin < 1
error('You need to specify at least a warning message');
end
warned = false;
if isstruct(varargin{1})
warning(varargin{1});
return;
end
% put the arguments we will pass to warning() in this cell array
warningArgs = {};
if nargin==3
% calling syntax (id, msg, timeout)
warningArgs = varargin(1:2);
timeout = varargin{3};
fname = [warningArgs{1} '_' warningArgs{2}];
elseif nargin==2 && isnumeric(varargin{2})
% calling syntax (msg, timeout)
warningArgs = varargin(1);
timeout = varargin{2};
fname = warningArgs{1};
elseif nargin==2 && ~isnumeric(varargin{2})
% calling syntax (id, msg)
warningArgs = varargin(1:2);
timeout = 60;
fname = [warningArgs{1} '_' warningArgs{2}];
elseif nargin==1
% calling syntax (msg)
warningArgs = varargin(1);
timeout = 60; % default timeout in seconds
fname = [warningArgs{1}];
end
if isempty(timeout)
error('Timeout ill-specified');
end
if isempty(stopwatch)
stopwatch = tic;
end
if isempty(previous)
previous = struct;
end
now = toc(stopwatch); % measure time since first function call
fname = decomma(fixname(fname)); % make a nice string that is allowed as structure fieldname
if length(fname) > 63 % MATLAB max name
fname = fname(1:63);
end
if ~isfield(previous, fname) || ...
(isfield(previous, fname) && now>previous.(fname).timeout)
% warning never given before or timed out
ws = warning(warningArgs{:});
previous.(fname).timeout = now+timeout;
previous.(fname).ws = ws;
warned = true;
else
% the warning has been issued before, but has not timed out yet
ws = previous.(fname).ws;
end
end % function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function name = decomma(name)
name(name==',')=[];
end % function
|
github
|
philippboehmsturm/antx-master
|
nanmean.m
|
.m
|
antx-master/xspm8/external/fieldtrip/statfun/private/nanmean.m
| 165 |
utf_8
|
e6c473a49d8be6e12960af55ced45e54
|
% NANMEAN provides a replacement for MATLAB's nanmean.
%
% For usage see MEAN.
function y = nanmean(x, dim)
N = sum(~isnan(x), dim);
y = nansum(x, dim) ./ N;
end
|
github
|
philippboehmsturm/antx-master
|
nanvar.m
|
.m
|
antx-master/xspm8/external/fieldtrip/statfun/private/nanvar.m
| 1,093 |
utf_8
|
d9641af3bba1e2c6e3512199221e686c
|
% NANVAR provides a replacement for MATLAB's nanvar that is almost
% compatible.
%
% For usage see VAR. Note that the weight-vector is not supported. If you
% need it, please file a ticket at our bugtracker.
function Y = nanvar(X, w, dim)
switch nargin
case 1
% VAR(x)
% Normalize by n-1 when no dim is given.
Y = nanvar_base(X);
n = nannumel(X);
w = 0;
case 2
% VAR(x, 1)
% VAR(x, w)
% In this case, the default of normalizing by n is expected.
Y = nanvar_base(X);
n = nannumel(X);
case 3
% VAR(x, w, dim)
% if w=0 normalize by n-1, if w=1 normalize by n.
Y = nanvar_base(X, dim);
n = nannumel(X, dim);
otherwise
error ('Too many input arguments!')
end
% Handle different forms of normalization:
if numel(w) == 0
% empty weights vector defaults to 0
w = 0;
end
if numel(w) ~= 1
error('Weighting vector w is not implemented! Please file a bug.');
end
if ~isreal(X)
Y = real(Y) + imag(Y);
n = real(n);
end
if w == 1
Y = Y ./ n;
end
if w == 0
Y = Y ./ max(1, (n - 1)); % don't divide by zero!
end
|
github
|
philippboehmsturm/antx-master
|
tinv.m
|
.m
|
antx-master/xspm8/external/fieldtrip/statfun/private/tinv.m
| 7,634 |
utf_8
|
8fe66ec125f91e1a7ac5f8d3cb2ac51a
|
function x = tinv(p,v);
% TINV Inverse of Student's T cumulative distribution function (cdf).
% X=TINV(P,V) returns the inverse of Student's T cdf with V degrees
% of freedom, at the values in P.
%
% The size of X is the common size of P and V. A scalar input
% functions as a constant matrix of the same size as the other input.
%
% This is an open source function that was assembled by Eric Maris using
% open source subfunctions found on the web.
% Subversion does not use the Log keyword, use 'svn log <filename>' or 'svn -v log | less' to get detailled information
if nargin < 2,
error('Requires two input arguments.');
end
[errorcode p v] = distchck(2,p,v);
if errorcode > 0
error('Requires non-scalar arguments to match in size.');
end
% Initialize X to zero.
x=zeros(size(p));
k = find(v < 0 | v ~= round(v));
if any(k)
tmp = NaN;
x(k) = tmp(ones(size(k)));
end
k = find(v == 1);
if any(k)
x(k) = tan(pi * (p(k) - 0.5));
end
% The inverse cdf of 0 is -Inf, and the inverse cdf of 1 is Inf.
k0 = find(p == 0);
if any(k0)
tmp = Inf;
x(k0) = -tmp(ones(size(k0)));
end
k1 = find(p ==1);
if any(k1)
tmp = Inf;
x(k1) = tmp(ones(size(k1)));
end
k = find(p >= 0.5 & p < 1);
if any(k)
z = betainv(2*(1-p(k)),v(k)/2,0.5);
x(k) = sqrt(v(k) ./ z - v(k));
end
k = find(p < 0.5 & p > 0);
if any(k)
z = betainv(2*(p(k)),v(k)/2,0.5);
x(k) = -sqrt(v(k) ./ z - v(k));
end
%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION distchck
%%%%%%%%%%%%%%%%%%%%%%%%%
function [errorcode,varargout] = distchck(nparms,varargin)
%DISTCHCK Checks the argument list for the probability functions.
errorcode = 0;
varargout = varargin;
if nparms == 1
return;
end
% Get size of each input, check for scalars, copy to output
isscalar = (cellfun('prodofsize',varargin) == 1);
% Done if all inputs are scalars. Otherwise fetch their common size.
if (all(isscalar)), return; end
n = nparms;
for j=1:n
sz{j} = size(varargin{j});
end
t = sz(~isscalar);
size1 = t{1};
% Scalars receive this size. Other arrays must have the proper size.
for j=1:n
sizej = sz{j};
if (isscalar(j))
t = zeros(size1);
t(:) = varargin{j};
varargout{j} = t;
elseif (~isequal(sizej,size1))
errorcode = 1;
return;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION betainv
%%%%%%%%%%%%%%%%%%%%%%%%%%%
function x = betainv(p,a,b);
%BETAINV Inverse of the beta cumulative distribution function (cdf).
% X = BETAINV(P,A,B) returns the inverse of the beta cdf with
% parameters A and B at the values in P.
%
% The size of X is the common size of the input arguments. A scalar input
% functions as a constant matrix of the same size as the other inputs.
%
% BETAINV uses Newton's method to converge to the solution.
% Reference:
% [1] M. Abramowitz and I. A. Stegun, "Handbook of Mathematical
% Functions", Government Printing Office, 1964.
% B.A. Jones 1-12-93
if nargin < 3,
error('Requires three input arguments.');
end
[errorcode p a b] = distchck(3,p,a,b);
if errorcode > 0
error('Requires non-scalar arguments to match in size.');
end
% Initialize x to zero.
x = zeros(size(p));
% Return NaN if the arguments are outside their respective limits.
k = find(p < 0 | p > 1 | a <= 0 | b <= 0);
if any(k),
tmp = NaN;
x(k) = tmp(ones(size(k)));
end
% The inverse cdf of 0 is 0, and the inverse cdf of 1 is 1.
k0 = find(p == 0 & a > 0 & b > 0);
if any(k0),
x(k0) = zeros(size(k0));
end
k1 = find(p==1);
if any(k1),
x(k1) = ones(size(k1));
end
% Newton's Method.
% Permit no more than count_limit interations.
count_limit = 100;
count = 0;
k = find(p > 0 & p < 1 & a > 0 & b > 0);
pk = p(k);
% Use the mean as a starting guess.
xk = a(k) ./ (a(k) + b(k));
% Move starting values away from the boundaries.
if xk == 0,
xk = sqrt(eps);
end
if xk == 1,
xk = 1 - sqrt(eps);
end
h = ones(size(pk));
crit = sqrt(eps);
% Break out of the iteration loop for the following:
% 1) The last update is very small (compared to x).
% 2) The last update is very small (compared to 100*eps).
% 3) There are more than 100 iterations. This should NEVER happen.
while(any(abs(h) > crit * abs(xk)) & max(abs(h)) > crit ...
& count < count_limit),
count = count+1;
h = (betacdf(xk,a(k),b(k)) - pk) ./ betapdf(xk,a(k),b(k));
xnew = xk - h;
% Make sure that the values stay inside the bounds.
% Initially, Newton's Method may take big steps.
ksmall = find(xnew < 0);
klarge = find(xnew > 1);
if any(ksmall) | any(klarge)
xnew(ksmall) = xk(ksmall) /10;
xnew(klarge) = 1 - (1 - xk(klarge))/10;
end
xk = xnew;
end
% Return the converged value(s).
x(k) = xk;
if count==count_limit,
fprintf('\nWarning: BETAINV did not converge.\n');
str = 'The last step was: ';
outstr = sprintf([str,'%13.8f'],h);
fprintf(outstr);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION betapdf
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function y = betapdf(x,a,b)
%BETAPDF Beta probability density function.
% Y = BETAPDF(X,A,B) returns the beta probability density
% function with parameters A and B at the values in X.
%
% The size of Y is the common size of the input arguments. A scalar input
% functions as a constant matrix of the same size as the other inputs.
% References:
% [1] M. Abramowitz and I. A. Stegun, "Handbook of Mathematical
% Functions", Government Printing Office, 1964, 26.1.33.
if nargin < 3,
error('Requires three input arguments.');
end
[errorcode x a b] = distchck(3,x,a,b);
if errorcode > 0
error('Requires non-scalar arguments to match in size.');
end
% Initialize Y to zero.
y = zeros(size(x));
% Return NaN for parameter values outside their respective limits.
k1 = find(a <= 0 | b <= 0 | x < 0 | x > 1);
if any(k1)
tmp = NaN;
y(k1) = tmp(ones(size(k1)));
end
% Return Inf for x = 0 and a < 1 or x = 1 and b < 1.
% Required for non-IEEE machines.
k2 = find((x == 0 & a < 1) | (x == 1 & b < 1));
if any(k2)
tmp = Inf;
y(k2) = tmp(ones(size(k2)));
end
% Return the beta density function for valid parameters.
k = find(~(a <= 0 | b <= 0 | x <= 0 | x >= 1));
if any(k)
y(k) = x(k) .^ (a(k) - 1) .* (1 - x(k)) .^ (b(k) - 1) ./ beta(a(k),b(k));
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION betacdf
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function p = betacdf(x,a,b);
%BETACDF Beta cumulative distribution function.
% P = BETACDF(X,A,B) returns the beta cumulative distribution
% function with parameters A and B at the values in X.
%
% The size of P is the common size of the input arguments. A scalar input
% functions as a constant matrix of the same size as the other inputs.
%
% BETAINC does the computational work.
% Reference:
% [1] M. Abramowitz and I. A. Stegun, "Handbook of Mathematical
% Functions", Government Printing Office, 1964, 26.5.
if nargin < 3,
error('Requires three input arguments.');
end
[errorcode x a b] = distchck(3,x,a,b);
if errorcode > 0
error('Requires non-scalar arguments to match in size.');
end
% Initialize P to 0.
p = zeros(size(x));
k1 = find(a<=0 | b<=0);
if any(k1)
tmp = NaN;
p(k1) = tmp(ones(size(k1)));
end
% If is X >= 1 the cdf of X is 1.
k2 = find(x >= 1);
if any(k2)
p(k2) = ones(size(k2));
end
k = find(x > 0 & x < 1 & a > 0 & b > 0);
if any(k)
p(k) = betainc(x(k),a(k),b(k));
end
% Make sure that round-off errors never make P greater than 1.
k = find(p > 1);
p(k) = ones(size(k));
|
github
|
philippboehmsturm/antx-master
|
nansum.m
|
.m
|
antx-master/xspm8/external/fieldtrip/src/nansum.m
| 185 |
utf_8
|
4859ec062780c478011dd7a8d94684a0
|
% NANSUM provides a replacement for MATLAB's nanmean.
%
% For usage see SUM.
function y = nansum(x, dim)
if nargin == 1
dim = 1;
end
idx = isnan(x);
x(idx) = 0;
y = sum(x, dim);
end
|
github
|
philippboehmsturm/antx-master
|
nanstd.m
|
.m
|
antx-master/xspm8/external/fieldtrip/src/nanstd.m
| 231 |
utf_8
|
0ff62b1c345b5ad76a9af59cf07c2983
|
% NANSTD provides a replacement for MATLAB's nanstd that is almost
% compatible.
%
% For usage see STD. Note that the three-argument call with FLAG is not
% supported.
function Y = nanstd(varargin)
Y = sqrt(nanvar(varargin{:}));
|
github
|
philippboehmsturm/antx-master
|
nanmean.m
|
.m
|
antx-master/xspm8/external/fieldtrip/src/nanmean.m
| 165 |
utf_8
|
e6c473a49d8be6e12960af55ced45e54
|
% NANMEAN provides a replacement for MATLAB's nanmean.
%
% For usage see MEAN.
function y = nanmean(x, dim)
N = sum(~isnan(x), dim);
y = nansum(x, dim) ./ N;
end
|
github
|
philippboehmsturm/antx-master
|
nanvar.m
|
.m
|
antx-master/xspm8/external/fieldtrip/src/nanvar.m
| 1,093 |
utf_8
|
d9641af3bba1e2c6e3512199221e686c
|
% NANVAR provides a replacement for MATLAB's nanvar that is almost
% compatible.
%
% For usage see VAR. Note that the weight-vector is not supported. If you
% need it, please file a ticket at our bugtracker.
function Y = nanvar(X, w, dim)
switch nargin
case 1
% VAR(x)
% Normalize by n-1 when no dim is given.
Y = nanvar_base(X);
n = nannumel(X);
w = 0;
case 2
% VAR(x, 1)
% VAR(x, w)
% In this case, the default of normalizing by n is expected.
Y = nanvar_base(X);
n = nannumel(X);
case 3
% VAR(x, w, dim)
% if w=0 normalize by n-1, if w=1 normalize by n.
Y = nanvar_base(X, dim);
n = nannumel(X, dim);
otherwise
error ('Too many input arguments!')
end
% Handle different forms of normalization:
if numel(w) == 0
% empty weights vector defaults to 0
w = 0;
end
if numel(w) ~= 1
error('Weighting vector w is not implemented! Please file a bug.');
end
if ~isreal(X)
Y = real(Y) + imag(Y);
n = real(n);
end
if w == 1
Y = Y ./ n;
end
if w == 0
Y = Y ./ max(1, (n - 1)); % don't divide by zero!
end
|
github
|
philippboehmsturm/antx-master
|
ft_connectivity_corr.m
|
.m
|
antx-master/xspm8/external/fieldtrip/connectivity/ft_connectivity_corr.m
| 8,643 |
utf_8
|
ff666c57a9e6619f5cef4afbccd42832
|
function [c, v, outcnt] = ft_connectivity_corr(input, varargin)
% FT_CONNECTIVITY_CORR computes correlation or coherence (or a related
% quantity) from a data-matrix containing a covariance or cross-spectral
% density. It implements the methods described in:
% Coherence: Rosenberg et al, The Fourier approach to the identification of
% functional coupling between neuronal spike trains. Prog Biophys Molec
% Biol 1989; 53; 1-31
% Partial coherence: Rosenberg et al, Identification of patterns of
% neuronal connectivity - partial spectra, partial coherence, and neuronal
% interactions. J. Neurosci. Methods, 1998; 83; 57-72
% Phase locking value: Lachaux et al, Measuring phase sychrony in brain
% signals. Human Brain Mapping, 1999; 8; 194-208
% Imaginary part of coherency: Nolte et al, Identifying true brain
% interaction from EEG data using the imaginary part of coherence. Clinical
% Neurophysiology, 2004; 115; 2292-2307
%
% Use as
% [c, v, n] = ft_connectivity_corr(input, varargin)
%
% The input data input should be organized as:
%
% Repetitions x Channel x Channel (x Frequency) (x Time)
%
% or
%
% Repetitions x Channelcombination (x Frequency) (x Time)
%
% The first dimension should be singleton if the input already contains an
% average. Furthermore, the input data can be complex-valued cross spectral
% densities, or real-valued covariance estimates. If the former is the
% case, the output will be coherence (or a derived metric), if the latter
% is the case, the output will be the correlation coefficient.
%
% Additional input arguments come as key-value pairs:
%
% hasjack 0 or 1 specifying whether the Repetitions represent
% leave-one-out samples
% complex 'abs', 'angle', 'real', 'imag', 'complex', 'logabs' for
% post-processing of coherency
% feedback 'none', 'text', 'textbar' type of feedback showing progress of
% computation
% dimord specifying how the input matrix should be interpreted
% powindx required if the input data contain linearly indexed
% channel pairs. should be an Nx2 matrix indexing on each
% row for the respective channel pair the indices of the
% corresponding auto-spectra
% pownorm flag that specifies whether normalisation with the
% product of the power should be performed (thus should
% be true when correlation/coherence is requested, and
% false when covariance or cross-spectral density is
% requested).
%
% Partialisation can be performed when the input data is (chan x chan). The
% following options need to be specified:
%
% pchanindx index-vector to the channels that need to be
% partialised
% allchanindx index-vector to all channels that are used
% (including the "to-be-partialised" ones).
%
% The output c contains the correlation/coherence, v is a variance estimate
% which only can be computed if the data contains leave-one-out samples,
% and n is the number of repetitions in the input data.
%
% See also FT_CONNECTIVITYANALYSIS
% Copyright (C) 2009-2010 Donders Institute, Jan-Mathijs Schoffelen
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_connectivity_corr.m 7123 2012-12-06 21:21:38Z roboos $
% FiXME: If output is angle, then jack-knifing should be done
% differently since it is a circular variable
hasjack = ft_getopt(varargin, 'hasjack', 0);
cmplx = ft_getopt(varargin, 'complex', 'abs');
feedback = ft_getopt(varargin, 'feedback', 'none');
dimord = ft_getopt(varargin, 'dimord');
powindx = ft_getopt(varargin, 'powindx');
pownorm = ft_getopt(varargin, 'pownorm', 0);
pchanindx = ft_getopt(varargin, 'pchanindx');
allchanindx = ft_getopt(varargin, 'allchanindx');
if isempty(dimord)
error('input parameters should contain a dimord');
end
siz = [size(input) 1];
% do partialisation if necessary
if ~isempty(pchanindx),
% partial spectra are computed as in Rosenberg JR et al (1998) J.Neuroscience Methods, equation 38
chan = allchanindx;
nchan = numel(chan);
pchan = pchanindx;
npchan = numel(pchan);
newsiz = siz;
newsiz(2:3) = numel(chan); % size of partialised csd
A = zeros(newsiz);
% FIXME this only works for data without time dimension
if numel(siz)==5 && siz(5)>1, error('this only works for data without time'); end
for j = 1:siz(1) %rpt loop
AA = reshape(input(j, chan, chan, : ), [nchan nchan siz(4:end)]);
AB = reshape(input(j, chan, pchan,: ), [nchan npchan siz(4:end)]);
BA = reshape(input(j, pchan, chan, : ), [npchan nchan siz(4:end)]);
BB = reshape(input(j, pchan, pchan, :), [npchan npchan siz(4:end)]);
for k = 1:siz(4) %freq loop
%A(j,:,:,k) = AA(:,:,k) - AB(:,:,k)*pinv(BB(:,:,k))*BA(:,:,k);
A(j,:,:,k) = AA(:,:,k) - AB(:,:,k)/(BB(:,:,k))*BA(:,:,k);
end
end
input = A;
siz = size(input);
else
% do nothing
end
% compute the metric
if (length(strfind(dimord, 'chan'))~=2 || length(strfind(dimord, 'pos'))>0) && ~isempty(powindx),
% crossterms are not described with chan_chan_therest, but are linearly indexed
outsum = zeros(siz(2:end));
outssq = zeros(siz(2:end));
outcnt = zeros(siz(2:end));
ft_progress('init', feedback, 'computing metric...');
for j = 1:siz(1)
ft_progress(j/siz(1), 'computing metric for replicate %d from %d\n', j, siz(1));
if pownorm
p1 = reshape(input(j,powindx(:,1),:,:,:), siz(2:end));
p2 = reshape(input(j,powindx(:,2),:,:,:), siz(2:end));
denom = sqrt(p1.*p2); clear p1 p2
else
denom = 1;
end
tmp = complexeval(reshape(input(j,:,:,:,:), siz(2:end))./denom, cmplx);
outsum = outsum + tmp;
outssq = outssq + tmp.^2;
outcnt = outcnt + double(~isnan(tmp));
end
ft_progress('close');
elseif length(strfind(dimord, 'chan'))==2 || length(strfind(dimord, 'pos'))==2,
% crossterms are described by chan_chan_therest
outsum = zeros(siz(2:end));
outssq = zeros(siz(2:end));
outcnt = zeros(siz(2:end));
ft_progress('init', feedback, 'computing metric...');
for j = 1:siz(1)
ft_progress(j/siz(1), 'computing metric for replicate %d from %d\n', j, siz(1));
if pownorm
p1 = zeros([siz(2) 1 siz(4:end)]);
p2 = zeros([1 siz(3) siz(4:end)]);
for k = 1:siz(2)
p1(k,1,:,:,:,:) = input(j,k,k,:,:,:,:);
p2(1,k,:,:,:,:) = input(j,k,k,:,:,:,:);
end
p1 = p1(:,ones(1,siz(3)),:,:,:,:);
p2 = p2(ones(1,siz(2)),:,:,:,:,:);
denom = sqrt(p1.*p2); clear p1 p2;
else
denom = 1;
end
tmp = complexeval(reshape(input(j,:,:,:,:,:,:), siz(2:end))./denom, cmplx); % added this for nan support marvin
%tmp(isnan(tmp)) = 0; % added for nan support
outsum = outsum + tmp;
outssq = outssq + tmp.^2;
outcnt = outcnt + double(~isnan(tmp));
end
ft_progress('close');
end
n = siz(1);
if all(outcnt(:)==n)
outcnt = n;
end
%n1 = shiftdim(sum(~isnan(input),1),1);
%c = outsum./n1; % added this for nan support marvin
c = outsum./outcnt;
% correct the variance estimate for the under-estimation introduced by the jackknifing
if n>1,
if hasjack
%bias = (n1-1).^2; % added this for nan support marvin
bias = (outcnt-1).^2;
else
bias = 1;
end
%v = bias.*(outssq - (outsum.^2)./n1)./(n1 - 1); % added this for nan support marvin
v = bias.*(outssq - (outsum.^2)./outcnt)./(outcnt-1);
else
v = [];
end
function [c] = complexeval(c, str)
switch str
case 'complex'
%do nothing
case 'abs'
c = abs(c);
case 'angle'
c = angle(c); % negative angle means first row leads second row
case 'imag'
c = imag(c);
case 'real'
c = real(c);
case '-logabs'
c = -log(1 - abs(c).^2);
otherwise
error('complex = ''%s'' not supported', str);
end
|
github
|
philippboehmsturm/antx-master
|
ft_connectivity_psi.m
|
.m
|
antx-master/xspm8/external/fieldtrip/connectivity/ft_connectivity_psi.m
| 5,704 |
utf_8
|
f3a2ec7e8d4d69aca6e1b1f88eb342cf
|
function [p, v, n] = ft_connectivity_psi(input, varargin)
% FT_CONNECTIVITY_PSI computes the phase slope index from a data-matrix
% containing the cross-spectral density. It implements the method described
% in: Nolte et al., Robustly estimating the flow direction of information
% in complex physical systems. Physical Review Letters, 2008; 100; 234101.
%
% Use as
% [c, v, n] = ft_connectivity_psi(input, varargin)
%
% The input data input should be organized as:
%
% Repetitions x Channel x Channel (x Frequency) (x Time)
%
% or
%
% Repetitions x Channelcombination (x Frequency) (x Time)
%
% The first dimension should be singleton if the input already contains an
% average
%
% Additional input arguments come as key-value pairs:
%
% hasjack 0 or 1 specifying whether the Repetitions represent
% leave-one-out samples (allowing for a variance
% estimate)
% feedback 'none', 'text', 'textbar' type of feedback showing progress of
% computation
% dimord specifying how the input matrix should be interpreted
% powindx normalize nbin the number of frequency bins across
% which to integrate
%
% The output p contains the phase slope index, v is a variance estimate
% which only can be computed if the data contains leave-one-out samples,
% and n is the number of repetitions in the input data. If the phase slope
% index is positive, then the first chan (1st dim) becomes more lagged (or
% less leading) with higher frequency, indicating that it is causally
% driven by the second channel (2nd dim)
%
% See also FT_CONNECTIVITYANALYSIS
% Copyright (C) 2009-2010 Donders Institute, Jan-Mathijs Schoffelen
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_connectivity_psi.m 7123 2012-12-06 21:21:38Z roboos $
% FIXME: interpretation of the slope
hasjack = ft_getopt(varargin, 'hasjack', 0);
feedback = ft_getopt(varargin, 'feedback', 'none');
dimord = ft_getopt(varargin, 'dimord');
powindx = ft_getopt(varargin, 'powindx');
normalize = ft_getopt(varargin, 'normalize', 'no');
nbin = ft_getopt(varargin, 'nbin');
if isempty(dimord)
error('input parameters should contain a dimord');
end
if (length(strfind(dimord, 'chan'))~=2 || ~isempty(strfind(dimord, 'pos'))>0) && ~isempty(powindx),
%crossterms are not described with chan_chan_therest, but are linearly indexed
siz = size(input);
outsum = zeros(siz(2:end));
outssq = zeros(siz(2:end));
pvec = [2 setdiff(1:numel(siz),2)];
ft_progress('init', feedback, 'computing metric...');
%first compute coherency and then phaseslopeindex
for j = 1:siz(1)
ft_progress(j/siz(1), 'computing metric for replicate %d from %d\n', j, siz(1));
c = reshape(input(j,:,:,:,:), siz(2:end));
p1 = abs(reshape(input(j,powindx(:,1),:,:,:), siz(2:end)));
p2 = abs(reshape(input(j,powindx(:,2),:,:,:), siz(2:end)));
p = ipermute(phaseslope(permute(c./sqrt(p1.*p2), pvec), nbin, normalize), pvec);
outsum = outsum + p;
outssq = outssq + p.^2;
end
ft_progress('close');
elseif length(strfind(dimord, 'chan'))==2 || length(strfind(dimord, 'pos'))==2,
%crossterms are described by chan_chan_therest
siz = size(input);
outsum = zeros(siz(2:end));
outssq = zeros(siz(2:end));
pvec = [3 setdiff(1:numel(siz),3)];
ft_progress('init', feedback, 'computing metric...');
for j = 1:siz(1)
ft_progress(j/siz(1), 'computing metric for replicate %d from %d\n', j, siz(1));
p1 = zeros([siz(2) 1 siz(4:end)]);
p2 = zeros([1 siz(3) siz(4:end)]);
for k = 1:siz(2)
p1(k,1,:,:,:,:) = input(j,k,k,:,:,:,:);
p2(1,k,:,:,:,:) = input(j,k,k,:,:,:,:);
end
c = reshape(input(j,:,:,:,:,:,:), siz(2:end));
p1 = p1(:,ones(1,siz(3)),:,:,:,:);
p2 = p2(ones(1,siz(2)),:,:,:,:,:);
p = ipermute(phaseslope(permute(c./sqrt(p1.*p2), pvec), nbin, normalize), pvec);
p(isnan(p)) = 0;
outsum = outsum + p;
outssq = outssq + p.^2;
end
ft_progress('close');
end
n = siz(1);
c = outsum./n;
if n>1,
n = shiftdim(sum(~isnan(input),1),1);
if hasjack
bias = (n-1).^2;
else
bias = 1;
end
v = bias.*(outssq - (outsum.^2)./n)./(n - 1);
else
v = [];
end
%---------------------------------------
function [y] = phaseslope(x, n, norm)
m = size(x, 1); %total number of frequency bins
y = zeros(size(x));
x(1:end-1,:,:,:,:) = conj(x(1:end-1,:,:,:,:)).*x(2:end,:,:,:,:);
if strcmp(norm, 'yes')
coh = zeros(size(x));
coh(1:end-1,:,:,:,:) = (abs(x(1:end-1,:,:,:,:)) .* abs(x(2:end,:,:,:,:))) + 1;
%FIXME why the +1? get the coherence
for k = 1:m
begindx = max(1,k-n);
endindx = min(m,k+n);
y(k,:,:,:,:) = imag(nansum(x(begindx:endindx,:,:,:,:)./coh(begindx:endindx,:,:,:,:),1));
end
else
for k = 1:m
begindx = max(1,k-n);
endindx = min(m,k+n);
y(k,:,:,:,:) = imag(nansum(x(begindx:endindx,:,:,:,:),1));
end
end
|
github
|
philippboehmsturm/antx-master
|
nansum.m
|
.m
|
antx-master/xspm8/external/fieldtrip/connectivity/private/nansum.m
| 185 |
utf_8
|
4859ec062780c478011dd7a8d94684a0
|
% NANSUM provides a replacement for MATLAB's nanmean.
%
% For usage see SUM.
function y = nansum(x, dim)
if nargin == 1
dim = 1;
end
idx = isnan(x);
x(idx) = 0;
y = sum(x, dim);
end
|
github
|
philippboehmsturm/antx-master
|
sfactorization_wilson2x2.m
|
.m
|
antx-master/xspm8/external/fieldtrip/connectivity/private/sfactorization_wilson2x2.m
| 4,756 |
utf_8
|
9d8be7f686ab1325b21f3b94de613d12
|
function [H, Z, S, psi] = sfactorization_wilson2x2(S,freq,Niterations,tol,cmbindx,fb,init)
% Usage : [H, Z, psi] = sfactorization_wilson(S,fs,freq);
% Inputs : S (1-sided, 3D-spectral matrix in the form of Channel x Channel x frequency)
% : fs (sampling frequency in Hz)
% : freq (a vector of frequencies) at which S is given
% Outputs: H (transfer function)
% : Z (noise covariance)
% : S (cross-spectral density 1-sided)
% : psi (left spectral factor)
% This function is an implemention of Wilson's algorithm (Eq. 3.1)
% for spectral matrix factorization
% Ref: G.T. Wilson,"The Factorization of Matricial Spectral Densities,"
% SIAM J. Appl. Math.23,420-426(1972).
% Written by M. Dhamala & G. Rangarajan, UF, Aug 3-4, 2006.
% Email addresses: [email protected], [email protected]
m = size(cmbindx,1);
N = length(freq)-1;
N2 = 2*N;
% preallocate memory for efficiency
Sarr = zeros(2,2,m,N2) + 1i.*zeros(2,2,m,N2);
I = repmat(eye(2),[1 1 m N2]); % Defining 2 x 2 identity matrix
%Step 1: Forming 2-sided spectral densities for ifft routine in matlab
for c = 1:m
% f_ind = 0;
Stmp = S(cmbindx(c,:),cmbindx(c,:),:);
for f_ind = 1:(N+1)
% for f = freq
% f_ind = f_ind+1;
Sarr(:,:,c,f_ind) = Stmp(:,:,f_ind);
if(f_ind>1)
Sarr(:,:,c,2*N+2-f_ind) = Stmp(:,:,f_ind).';
end
end
end
%Step 2: Computing covariance matrices
gam = real(reshape(ifft(reshape(Sarr, [4*m N2]), [], 2),[2 2 m N2]));
%Step 3: Initializing for iterations
gam0 = gam(:,:,:,1);
h = complex(zeros(size(gam0)));
for k = 1:m
switch init
case 'chol'
[tmp, dum] = chol(gam0(:,:,k));
if dum
warning('initialization with ''chol'' for iterations did not work well, using arbitrary starting condition');
tmp = rand(2,2); %arbitrary initial condition
tmp = triu(tmp);
end
case 'rand'
tmp = rand(2,2); %arbitrary initial condition
tmp = triu(tmp);
otherwise
error('initialization method should be eithe ''chol'' or ''rand''');
end
h(:,:,k) = tmp;
%h(:,:,k) = chol(gam0(:,:,k));
end
psi = repmat(h, [1 1 1 N2]);
%Step 4: Iterating to get spectral factors
ft_progress('init', fb, 'computing spectral factorization');
for iter = 1:Niterations
ft_progress(iter./Niterations, 'computing iteration %d/%d\n', iter, Niterations);
invpsi = inv2x2(psi);
g = sandwich2x2(invpsi, Sarr) + I;
gp = PlusOperator2x2(g,m,N+1); %gp constitutes positive and half of zero lags
psi_old = psi;
psi = mtimes2x2(psi, gp);
%psierr = sum(sum(abs(psi-psi_old)));
psierr = abs(psi-psi_old)./abs(psi);
if 0
plot(squeeze(psierr(2,1,1,:))); hold on
plot(squeeze(psierr(1,1,1,:)),'r');drawnow
end
psierrf = mean(psierr(:));
if(psierrf<tol),
fprintf('reaching convergence at iteration %d\n',iter);
break;
end; % checking convergence
end
ft_progress('close');
%Step 5: Getting covariance matrix from spectral factors
gamtmp = reshape(real(ifft(transpose(reshape(psi, [4*m N2]))))', [2 2 m N2]);
%Step 6: Getting noise covariance & transfer function (see Example pp. 424)
A0 = gamtmp(:,:,:,1);
A0inv = inv2x2(A0);
Z = zeros(2,2,m);
for k = 1:m
%Z = A0*A0.'*fs; %Noise covariance matrix
Z(:,:,k) = A0(:,:,k)*A0(:,:,k).'; %Noise covariance matrix not multiplied by sampling frequency
%FIXME check this; at least not multiplying it removes the need to correct later on
%this also makes it more equivalent to the noisecov estimated by biosig's mvar-function
end
H = complex(zeros(2,2,m,N+1));
S = complex(zeros(2,2,m,N+1));
for k = 1:(N+1)
for kk = 1:m
H(:,:,kk,k) = psi(:,:,kk,k)*A0inv(:,:,kk); % Transfer function
S(:,:,kk,k) = psi(:,:,kk,k)*psi(:,:,kk,k)'; % Cross-spectral density
end
end
siz = [size(H) 1 1];
H = reshape(H, [4*siz(3) siz(4:end)]);
siz = [size(S) 1 1];
S = reshape(S, [4*siz(3) siz(4:end)]);
siz = [size(Z) 1 1];
Z = reshape(Z, [4*siz(3) siz(4:end)]);
siz = [size(psi) 1 1];
psi = reshape(psi, [4*siz(3) siz(4:end)]);
%---------------------------------------------------------------------
function gp = PlusOperator2x2(g,ncmb,nfreq)
% This function is for [ ]+operation:
% to take the positive lags & half of the zero lag and reconstitute
% M. Dhamala, UF, August 2006
g = transpose(reshape(g, [4*ncmb 2*(nfreq-1)]));
gam = ifft(g);
% taking only the positive lags and half of the zero lag
gamp = gam;
beta0 = 0.5*gam(1,:);
%for k = 1:ncmb
% gamp(1,(k-1)*4+1:k*4) = reshape(triu(reshape(beta0(1,(k-1)*4+1:k*4),[2 2])),[1 4]);
%end
beta0(2:4:4*ncmb) = 0;
gamp(1,:) = beta0;
gamp(nfreq+1:end,:) = 0;
% reconstituting
gp = fft(gamp);
gp = reshape(transpose(gp), [2 2 ncmb 2*(nfreq-1)]);
|
github
|
philippboehmsturm/antx-master
|
nanstd.m
|
.m
|
antx-master/xspm8/external/fieldtrip/connectivity/private/nanstd.m
| 231 |
utf_8
|
0ff62b1c345b5ad76a9af59cf07c2983
|
% NANSTD provides a replacement for MATLAB's nanstd that is almost
% compatible.
%
% For usage see STD. Note that the three-argument call with FLAG is not
% supported.
function Y = nanstd(varargin)
Y = sqrt(nanvar(varargin{:}));
|
github
|
philippboehmsturm/antx-master
|
warning_once.m
|
.m
|
antx-master/xspm8/external/fieldtrip/connectivity/private/warning_once.m
| 3,832 |
utf_8
|
07dc728273934663973f4c716e7a3a1c
|
function [ws warned] = warning_once(varargin)
%
% Use as one of the following
% warning_once(string)
% warning_once(string, timeout)
% warning_once(id, string)
% warning_once(id, string, timeout)
% where timeout should be inf if you don't want to see the warning ever
% again. The default timeout value is 60 seconds.
%
% It can be used instead of the MATLAB built-in function WARNING, thus as
% s = warning_once(...)
% or as
% warning_once(s)
% where s is a structure with fields 'identifier' and 'state', storing the
% state information. In other words, warning_once accepts as an input the
% same structure it returns as an output. This returns or restores the
% states of warnings to their previous values.
%
% It can also be used as
% [s w] = warning_once(...)
% where w is a boolean that indicates whether a warning as been thrown or not.
%
% Please note that you can NOT use it like this
% warning_once('the value is %d', 10)
% instead you should do
% warning_once(sprintf('the value is %d', 10))
% Copyright (C) 2012, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: warning_once.m 7123 2012-12-06 21:21:38Z roboos $
persistent stopwatch previous
if nargin < 1
error('You need to specify at least a warning message');
end
warned = false;
if isstruct(varargin{1})
warning(varargin{1});
return;
end
% put the arguments we will pass to warning() in this cell array
warningArgs = {};
if nargin==3
% calling syntax (id, msg, timeout)
warningArgs = varargin(1:2);
timeout = varargin{3};
fname = [warningArgs{1} '_' warningArgs{2}];
elseif nargin==2 && isnumeric(varargin{2})
% calling syntax (msg, timeout)
warningArgs = varargin(1);
timeout = varargin{2};
fname = warningArgs{1};
elseif nargin==2 && ~isnumeric(varargin{2})
% calling syntax (id, msg)
warningArgs = varargin(1:2);
timeout = 60;
fname = [warningArgs{1} '_' warningArgs{2}];
elseif nargin==1
% calling syntax (msg)
warningArgs = varargin(1);
timeout = 60; % default timeout in seconds
fname = [warningArgs{1}];
end
if isempty(timeout)
error('Timeout ill-specified');
end
if isempty(stopwatch)
stopwatch = tic;
end
if isempty(previous)
previous = struct;
end
now = toc(stopwatch); % measure time since first function call
fname = decomma(fixname(fname)); % make a nice string that is allowed as structure fieldname
if length(fname) > 63 % MATLAB max name
fname = fname(1:63);
end
if ~isfield(previous, fname) || ...
(isfield(previous, fname) && now>previous.(fname).timeout)
% warning never given before or timed out
ws = warning(warningArgs{:});
previous.(fname).timeout = now+timeout;
previous.(fname).ws = ws;
warned = true;
else
% the warning has been issued before, but has not timed out yet
ws = previous.(fname).ws;
end
end % function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function name = decomma(name)
name(name==',')=[];
end % function
|
github
|
philippboehmsturm/antx-master
|
nanmean.m
|
.m
|
antx-master/xspm8/external/fieldtrip/connectivity/private/nanmean.m
| 165 |
utf_8
|
e6c473a49d8be6e12960af55ced45e54
|
% NANMEAN provides a replacement for MATLAB's nanmean.
%
% For usage see MEAN.
function y = nanmean(x, dim)
N = sum(~isnan(x), dim);
y = nansum(x, dim) ./ N;
end
|
github
|
philippboehmsturm/antx-master
|
sfactorization_wilson.m
|
.m
|
antx-master/xspm8/external/fieldtrip/connectivity/private/sfactorization_wilson.m
| 4,706 |
utf_8
|
ebcbea0ae06debde297adbb94bfb0e5e
|
function [H, Z, S, psi] = sfactorization_wilson(S,freq,Niterations,tol,fb,init)
% Usage : [H, Z, S, psi] = sfactorization_wilson(S,fs,freq);
% Inputs : S (1-sided, 3D-spectral matrix in the form of Channel x Channel x frequency)
% : fs (sampling frequency in Hz)
% : freq (a vector of frequencies) at which S is given
% Outputs: H (transfer function)
% : Z (noise covariance)
% : psi (left spectral factor)
% This function is an implemention of Wilson's algorithm (Eq. 3.1)
% for spectral matrix factorization
% Ref: G.T. Wilson,"The Factorization of Matricial Spectral Densities,"
% SIAM J. Appl. Math.23,420-426(1972).
% Written by M. Dhamala & G. Rangarajan, UF, Aug 3-4, 2006.
% Email addresses: [email protected], [email protected]
% number of channels
m = size(S,1);
N = length(freq)-1;
N2 = 2*N;
% preallocate memory for efficiency
Sarr = zeros(m,m,N2) + 1i.*zeros(m,m,N2);
gam = zeros(m,m,N2);
gamtmp = zeros(m,m,N2);
psi = zeros(m,m,N2);
I = eye(m); % Defining m x m identity matrix
%Step 1: Forming 2-sided spectral densities for ifft routine in matlab
f_ind = 0;
for f = freq
f_ind = f_ind+1;
Sarr(:,:,f_ind) = S(:,:,f_ind);
if(f_ind>1)
Sarr(:,:,2*N+2-f_ind) = S(:,:,f_ind).';
end
end
%Step 2: Computing covariance matrices
for k1 = 1:m
for k2 = 1:m
%gam(k1,k2,:) = real(ifft(squeeze(Sarr(k1,k2,:)))*fs); %FIXME think about this
gam(k1,k2,:) = real(ifft(squeeze(Sarr(k1,k2,:))));
end
end
%Step 3: Initializing for iterations
gam0 = gam(:,:,1);
switch init
case 'chol'
[tmp, dum] = chol(gam0);
if dum
warning('initialization with ''chol'' for iterations did not work well, using arbitrary starting condition');
tmp = rand(m,m); %arbitrary initial condition
tmp = triu(tmp);
end
case 'rand'
tmp = rand(m,m); %arbitrary initial condition
tmp = triu(tmp);
otherwise
error('initialization method should be eithe ''chol'' or ''rand''');
end
h = tmp;
for ind = 1:N2
psi(:,:,ind) = h;
end
%Step 4: Iterating to get spectral factors
ft_progress('init', fb, 'computing spectral factorization');
for iter = 1:Niterations
ft_progress(iter./Niterations, 'computing iteration %d/%d\n', iter, Niterations);
for ind = 1:N2
invpsi = inv(psi(:,:,ind));% + I*eps(psi(:,:,ind)));
g(:,:,ind) = invpsi*Sarr(:,:,ind)*invpsi'+I;%Eq 3.1
end
gp = PlusOperator(g,m,N+1); %gp constitutes positive and half of zero lags
psi_old = psi;
for k = 1:N2
psi(:,:,k) = psi(:,:,k)*gp(:,:,k);
psierr(k) = norm(psi(:,:,k)-psi_old(:,:,k),1);
end
psierrf = mean(psierr);
if(psierrf<tol),
fprintf('reaching convergence at iteration %d\n',iter);
break;
end; % checking convergence
end
ft_progress('close');
%Step 5: Getting covariance matrix from spectral factors
for k1 = 1:m
for k2 = 1:m
gamtmp(k1,k2,:) = real(ifft(squeeze(psi(k1,k2,:))));
end
end
%Step 6: Getting noise covariance & transfer function (see Example pp. 424)
A0 = gamtmp(:,:,1);
A0inv = inv(A0);
%Z = A0*A0.'*fs; %Noise covariance matrix
Z = A0*A0.'; %Noise covariance matrix not multiplied by sampling frequency
%FIXME check this; at least not multiplying it removes the need to correct later on
%this also makes it more equivalent to the noisecov estimated by biosig's mvar-function
H = zeros(m,m,N+1) + 1i*zeros(m,m,N+1);
for k = 1:N+1
H(:,:,k) = psi(:,:,k)*A0inv; %Transfer function
S(:,:,k) = psi(:,:,k)*psi(:,:,k)'; %Updated cross-spectral density
end
%---------------------------------------------------------------------
function gp = PlusOperator(g,nchan,nfreq)
% This function is for [ ]+operation:
% to take the positive lags & half of the zero lag and reconstitute
% M. Dhamala, UF, August 2006
g = transpose(reshape(g, [nchan^2 2*(nfreq-1)]));
gam = ifft(g);
% taking only the positive lags and half of the zero lag
gamp = gam;
beta0 = 0.5*gam(1,:);
gamp(1, :) = reshape(triu(reshape(beta0, [nchan nchan])),[1 nchan^2]);
gamp(nfreq+1:end,:) = 0;
% reconstituting
gp = fft(gamp);
gp = reshape(transpose(gp), [nchan nchan 2*(nfreq-1)]);
%------------------------------------------------------
%this is the original code; above is vectorized version
%which is assumed to be faster with many channels present
%for k1 = 1:nchan
% for k2 = 1:nchan
% gam(k1,k2,:) = ifft(squeeze(g(k1,k2,:)));
% end
%end
%
%% taking only the positive lags and half of the zero lag
%gamp = gam;
%beta0 = 0.5*gam(:,:,1);
%gamp(:,:,1) = triu(beta0); %this is Stau
%gamp(:,:,nfreq+1:end) = 0;
%
%% reconstituting
%for k1 = 1:nchan
% for k2 = 1:nchan
% gp(k1,k2,:) = fft(squeeze(gamp(k1,k2,:)));
% end
%end
|
github
|
philippboehmsturm/antx-master
|
nanvar.m
|
.m
|
antx-master/xspm8/external/fieldtrip/connectivity/private/nanvar.m
| 1,093 |
utf_8
|
d9641af3bba1e2c6e3512199221e686c
|
% NANVAR provides a replacement for MATLAB's nanvar that is almost
% compatible.
%
% For usage see VAR. Note that the weight-vector is not supported. If you
% need it, please file a ticket at our bugtracker.
function Y = nanvar(X, w, dim)
switch nargin
case 1
% VAR(x)
% Normalize by n-1 when no dim is given.
Y = nanvar_base(X);
n = nannumel(X);
w = 0;
case 2
% VAR(x, 1)
% VAR(x, w)
% In this case, the default of normalizing by n is expected.
Y = nanvar_base(X);
n = nannumel(X);
case 3
% VAR(x, w, dim)
% if w=0 normalize by n-1, if w=1 normalize by n.
Y = nanvar_base(X, dim);
n = nannumel(X, dim);
otherwise
error ('Too many input arguments!')
end
% Handle different forms of normalization:
if numel(w) == 0
% empty weights vector defaults to 0
w = 0;
end
if numel(w) ~= 1
error('Weighting vector w is not implemented! Please file a bug.');
end
if ~isreal(X)
Y = real(Y) + imag(Y);
n = real(n);
end
if w == 1
Y = Y ./ n;
end
if w == 0
Y = Y ./ max(1, (n - 1)); % don't divide by zero!
end
|
github
|
philippboehmsturm/antx-master
|
beamformer_pcc.m
|
.m
|
antx-master/xspm8/external/fieldtrip/inverse/beamformer_pcc.m
| 11,650 |
utf_8
|
9448fde03bcc26701dc45d4191e63e50
|
function [dipout] = beamformer_pcc(dip, grad, vol, dat, Cf, varargin)
% BEAMFORMER_PCC implements an experimental beamformer based on partial canonical
% correlations or coherences.
% Copyright (C) 2005-2008, Robert Oostenveld & Jan-Mathijs Schoffelen
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: beamformer_pcc.m 7123 2012-12-06 21:21:38Z roboos $
if mod(nargin-5,2)
% the first 5 arguments are fixed, the other arguments should come in pairs
error('invalid number of optional arguments');
end
% these optional settings do not have defaults
refchan = keyval('refchan', varargin);
refdip = keyval('refdip', varargin);
supchan = keyval('supchan', varargin);
supdip = keyval('supdip', varargin);
% these settings pertain to the forward model, the defaults are set in compute_leadfield
reducerank = keyval('reducerank', varargin);
normalize = keyval('normalize', varargin);
normalizeparam = keyval('normalizeparam', varargin);
% these optional settings have defaults
feedback = keyval('feedback', varargin); if isempty(feedback), feedback = 'text'; end
keepcsd = keyval('keepcsd', varargin); if isempty(keepcsd), keepcsd = 'no'; end
keepfilter = keyval('keepfilter', varargin); if isempty(keepfilter), keepfilter = 'no'; end
keepleadfield = keyval('keepleadfield', varargin); if isempty(keepleadfield), keepleadfield = 'no'; end
keepmom = keyval('keepmom', varargin); if isempty(keepmom), keepmom = 'yes'; end
lambda = keyval('lambda', varargin); if isempty(lambda ), lambda = 0; end
projectnoise = keyval('projectnoise', varargin); if isempty(projectnoise), projectnoise = 'yes'; end
realfilter = keyval('realfilter', varargin); if isempty(realfilter), realfilter = 'yes'; end
fixedori = ft_getopt(varargin,'fixedori','no');
fixedori = strcmp(fixedori, 'yes');
% convert the yes/no arguments to the corresponding logical values
keepcsd = strcmp(keepcsd, 'yes'); % see below
keepfilter = strcmp(keepfilter, 'yes');
keepleadfield = strcmp(keepleadfield, 'yes');
keepmom = strcmp(keepmom, 'yes');
projectnoise = strcmp(projectnoise, 'yes');
realfilter = strcmp(realfilter, 'yes');
% the postprocessing of the pcc beamformer always requires the csd matrix
keepcsd = 1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% find the dipole positions that are inside/outside the brain
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~isfield(dip, 'inside') & ~isfield(dip, 'outside');
insideLogical = ft_inside_vol(dip.pos, vol);
dip.inside = find(insideLogical);
dip.outside = find(~dip.inside);
elseif isfield(dip, 'inside') & ~isfield(dip, 'outside');
dip.outside = setdiff(1:size(dip.pos,1), dip.inside);
elseif ~isfield(dip, 'inside') & isfield(dip, 'outside');
dip.inside = setdiff(1:size(dip.pos,1), dip.outside);
end
% select only the dipole positions inside the brain for scanning
dip.origpos = dip.pos;
dip.originside = dip.inside;
dip.origoutside = dip.outside;
if isfield(dip, 'mom')
dip.mom = dip.mom(:, dip.inside);
end
if isfield(dip, 'leadfield')
fprintf('using precomputed leadfields\n');
dip.leadfield = dip.leadfield(dip.inside);
end
if isfield(dip, 'filter')
fprintf('using precomputed filters\n');
dip.filter = dip.filter(dip.inside);
end
dip.pos = dip.pos(dip.inside, :);
dip.inside = 1:size(dip.pos,1);
dip.outside = [];
if ~isempty(refdip)
rf = ft_compute_leadfield(refdip, grad, vol, 'reducerank', reducerank, 'normalize', normalize);
else
rf = [];
end
if ~isempty(supdip)
sf = ft_compute_leadfield(supdip, grad, vol, 'reducerank', reducerank, 'normalize', normalize);
else
sf = [];
end
refchan = refchan; % these can be passed as optional inputs
supchan = supchan; % these can be passed as optional inputs
megchan = setdiff(1:size(Cf,1), [refchan supchan]);
Nrefchan = length(refchan);
Nsupchan = length(supchan);
Nmegchan = length(megchan);
Nchan = size(Cf,1); % should equal Nmegchan + Nrefchan + Nsupchan
Cmeg = Cf(megchan,megchan); % the filter uses the csd between all MEG channels
isrankdeficient = (rank(Cmeg)<size(Cmeg,1));
% it is difficult to give a quantitative estimate of lambda, therefore also
% support relative (percentage) measure that can be specified as string (e.g. '10%')
if ~isempty(lambda) && ischar(lambda) && lambda(end)=='%'
ratio = sscanf(lambda, '%f%%');
ratio = ratio/100;
lambda = ratio * trace(Cmeg)/size(Cmeg,1);
end
if projectnoise
% estimate the noise power, which is further assumed to be equal and uncorrelated over channels
if isrankdeficient
% estimated noise floor is equal to or higher than lambda
noise = lambda;
else
% estimate the noise level in the covariance matrix by the smallest singular value
noise = svd(Cmeg);
noise = noise(end);
% estimated noise floor is equal to or higher than lambda
noise = max(noise, lambda);
end
end
if realfilter
% construct the filter only on the real part of the CSD matrix, i.e. filter is real
invCmeg = pinv(real(Cmeg) + lambda*eye(Nmegchan));
else
% construct the filter on the complex CSD matrix, i.e. filter contains imaginary component as well
% this results in a phase rotation of the channel data if the filter is applied to the data
invCmeg = pinv(Cmeg + lambda*eye(Nmegchan));
end
% start the scanning with the proper metric
ft_progress('init', feedback, 'beaming sources\n');
for i=1:size(dip.pos,1)
if isfield(dip, 'leadfield') && isfield(dip, 'mom') && size(dip.mom, 1)==size(dip.leadfield{i}, 2)
% reuse the leadfield that was previously computed and project
lf = dip.leadfield{i} * dip.mom(:,i);
elseif isfield(dip, 'leadfield') && isfield(dip, 'mom')
% reuse the leadfield that was previously computed but don't project
lf = dip.leadfield{i};
elseif isfield(dip, 'leadfield') && ~isfield(dip, 'mom'),
% reuse the leadfield that was previously computed
lf = dip.leadfield{i};
elseif ~isfield(dip, 'leadfield') && isfield(dip, 'mom')
% compute the leadfield for a fixed dipole orientation
lf = ft_compute_leadfield(dip.pos(i,:), grad, vol, 'reducerank', reducerank, 'normalize', normalize, 'normalizeparam', normalizeparam) * dip.mom(:,i);
else
% compute the leadfield
lf = ft_compute_leadfield(dip.pos(i,:), grad, vol, 'reducerank', reducerank, 'normalize', normalize, 'normalizeparam', normalizeparam);
end
% concatenate scandip, refdip and supdip
lfa = [lf rf sf];
if fixedori
if isempty(refdip) && isempty(supdip) && isempty(refchan) && isempty(supchan) && (size(lf,2)==3)
% compute the leadfield for the optimal dipole orientation
% subsequently the leadfield for only that dipole orientation will
% be used for the final filter computation
if isfield(dip, 'filter') && size(dip.filter{i},1)~=1
filt = dip.filter{i};
else
filt = pinv(lfa' * invCmeg * lfa) * lfa' * invCmeg;
end
[u, s, v] = svd(real(filt * Cmeg * ctranspose(filt)));
maxpowori = u(:,1);
eta = s(1,1)./s(2,2);
lfa = lfa * maxpowori;
dipout.ori{i} = maxpowori;
dipout.eta{i} = eta;
else
warning_once('Ignoring ''fixedori''. The fixedori option is supported only if there is ONE dipole for location.')
end
end
if isfield(dip, 'filter')
% use the provided filter
filt = dip.filter{i};
else
% construct the spatial filter
filt = pinv(lfa' * invCmeg * lfa) * lfa' * invCmeg; % use PINV/SVD to cover rank deficient leadfield
end
% concatenate the source filters with the channel filters
Ndip = size(lfa, 2);
filtn = zeros(Ndip+Nrefchan+Nsupchan, Nmegchan+Nrefchan+Nsupchan);
% this part of the filter relates to the sources
filtn(1:Ndip,megchan) = filt;
% this part of the filter relates to the channels
filtn((Ndip+1):end,setdiff(1:(Nmegchan+Nrefchan+Nsupchan), megchan)) = eye(Nrefchan+Nsupchan);
filt = filtn;
clear filtn
if keepcsd
dipout.csd{i} = filt * Cf * ctranspose(filt);
end
if projectnoise
dipout.noisecsd{i} = noise * (filt * ctranspose(filt));
end
if keepmom && ~isempty(dat)
dipout.mom{i} = filt * dat;
end
if keepfilter
dipout.filter{i} = filt;
end
if keepleadfield
dipout.leadfield{i} = lf;
end
ft_progress(i/size(dip.pos,1), 'beaming source %d from %d\n', i, size(dip.pos,1));
end % for all dipoles
ft_progress('close');
dipout.inside = dip.originside;
dipout.outside = dip.origoutside;
dipout.pos = dip.origpos;
% remember how all components in the output csd should be interpreted
scandiplabel = repmat({'scandip'}, 1, size(lf, 2)); % based on last leadfield
refdiplabel = repmat({'refdip'}, 1, size(rf, 2));
supdiplabel = repmat({'supdip'}, 1, size(sf, 2));
refchanlabel = repmat({'refchan'}, 1, Nrefchan);
supchanlabel = repmat({'supchan'}, 1, Nsupchan);
% concatenate all the labels
dipout.csdlabel = [scandiplabel refdiplabel supdiplabel refchanlabel supchanlabel];
% reassign the scan values over the inside and outside grid positions
if isfield(dipout, 'leadfield')
dipout.leadfield(dipout.inside) = dipout.leadfield;
dipout.leadfield(dipout.outside) = {[]};
end
if isfield(dipout, 'filter')
dipout.filter(dipout.inside) = dipout.filter;
dipout.filter(dipout.outside) = {[]};
end
if isfield(dipout, 'mom')
dipout.mom(dipout.inside) = dipout.mom;
dipout.mom(dipout.outside) = {[]};
end
if isfield(dipout, 'csd')
dipout.csd(dipout.inside) = dipout.csd;
dipout.csd(dipout.outside) = {[]};
end
if isfield(dipout, 'noisecsd')
dipout.noisecsd(dipout.inside) = dipout.noisecsd;
dipout.noisecsd(dipout.outside) = {[]};
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function to compute the pseudo inverse. This is the same as the
% standard Matlab function, except that the default tolerance is twice as
% high.
% Copyright 1984-2004 The MathWorks, Inc.
% $Revision: 7123 $ $Date: 2009/01/07 13:12:03 $
% default tolerance increased by factor 2 (Robert Oostenveld, 7 Feb 2004)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function X = pinv(A,varargin)
[m,n] = size(A);
if n > m
X = pinv(A',varargin{:})';
else
[U,S,V] = svd(A,0);
if m > 1, s = diag(S);
elseif m == 1, s = S(1);
else s = 0;
end
if nargin == 2
tol = varargin{1};
else
tol = 10 * max(m,n) * max(s) * eps;
end
r = sum(s > tol);
if (r == 0)
X = zeros(size(A'),class(A));
else
s = diag(ones(r,1)./s(1:r));
X = V(:,1:r)*s*U(:,1:r)';
end
end
|
github
|
philippboehmsturm/antx-master
|
beamformer_dics.m
|
.m
|
antx-master/xspm8/external/fieldtrip/inverse/beamformer_dics.m
| 24,638 |
utf_8
|
9201784dbd8090bd17ebbb75c3c3d1e5
|
function [dipout] = beamformer_dics(dip, grad, vol, dat, Cf, varargin)
% BEAMFORMER_DICS scans on pre-defined dipole locations with a single dipole
% and returns the beamformer spatial filter output for a dipole on every
% location. Dipole locations that are outside the head will return a
% NaN value.
%
% Use as
% [dipout] = beamformer_dics(dipin, grad, vol, dat, cov, varargin)
% where
% dipin is the input dipole model
% grad is the gradiometer definition
% vol is the volume conductor definition
% dat is the data matrix with the ERP or ERF
% cov is the data covariance or cross-spectral density matrix
% and
% dipout is the resulting dipole model with all details
%
% The input dipole model consists of
% dipin.pos positions for dipole, e.g. regular grid, Npositions x 3
% dipin.mom dipole orientation (optional), 3 x Npositions
%
% Additional options should be specified in key-value pairs and can be
% 'Pr' = power of the external reference channel
% 'Cr' = cross spectral density between all data channels and the external reference channel
% 'refdip' = location of dipole with which coherence is computed
% 'lambda' = regularisation parameter
% 'powmethod' = can be 'trace' or 'lambda1'
% 'feedback' = give ft_progress indication, can be 'text', 'gui' or 'none'
% 'fixedori' = use fixed or free orientation, can be 'yes' or 'no'
% 'projectnoise' = project noise estimate through filter, can be 'yes' or 'no'
% 'realfilter' = construct a real-valued filter, can be 'yes' or 'no'
% 'keepfilter' = remember the beamformer filter, can be 'yes' or 'no'
% 'keepleadfield' = remember the forward computation, can be 'yes' or 'no'
% 'keepcsd' = remember the estimated cross-spectral density, can be 'yes' or 'no'
%
% These options influence the forward computation of the leadfield
% 'reducerank' = reduce the leadfield rank, can be 'no' or a number (e.g. 2)
% 'normalize' = normalize the leadfield
% 'normalizeparam' = parameter for depth normalization (default = 0.5)
%
% If the dipole definition only specifies the dipole location, a rotating
% dipole (regional source) is assumed on each location. If a dipole moment
% is specified, its orientation will be used and only the strength will
% be fitted to the data.
% Copyright (C) 2003-2008, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: beamformer_dics.m 7123 2012-12-06 21:21:38Z roboos $
if mod(nargin-5,2)
% the first 5 arguments are fixed, the other arguments should come in pairs
error('invalid number of optional arguments');
end
% these optional settings do not have defaults
Pr = keyval('Pr', varargin);
Cr = keyval('Cr', varargin);
refdip = keyval('refdip', varargin);
powmethod = keyval('powmethod', varargin); % the default for this is set below
realfilter = keyval('realfilter', varargin); % the default for this is set below
% these settings pertain to the forward model, the defaults are set in compute_leadfield
reducerank = keyval('reducerank', varargin);
normalize = keyval('normalize', varargin);
normalizeparam = keyval('normalizeparam', varargin);
% these optional settings have defaults
feedback = keyval('feedback', varargin); if isempty(feedback), feedback = 'text'; end
keepcsd = keyval('keepcsd', varargin); if isempty(keepcsd), keepcsd = 'no'; end
keepfilter = keyval('keepfilter', varargin); if isempty(keepfilter), keepfilter = 'no'; end
keepleadfield = keyval('keepleadfield', varargin); if isempty(keepleadfield), keepleadfield = 'no'; end
lambda = keyval('lambda', varargin); if isempty(lambda ), lambda = 0; end
projectnoise = keyval('projectnoise', varargin); if isempty(projectnoise), projectnoise = 'yes'; end
fixedori = keyval('fixedori', varargin); if isempty(fixedori), fixedori = 'no'; end
subspace = keyval('subspace', varargin);
% convert the yes/no arguments to the corresponding logical values
keepcsd = strcmp(keepcsd, 'yes');
keepfilter = strcmp(keepfilter, 'yes');
keepleadfield = strcmp(keepleadfield, 'yes');
projectnoise = strcmp(projectnoise, 'yes');
fixedori = strcmp(fixedori, 'yes');
% FIXME besides regular/complex lambda1, also implement a real version
% default is to use the largest singular value of the csd matrix, see Gross 2001
if isempty(powmethod)
powmethod = 'lambda1';
end
% default is to be consistent with the original description of DICS in Gross 2001
if isempty(realfilter)
realfilter = 'no';
end
% use these two logical flags instead of doing the string comparisons each time again
powtrace = strcmp(powmethod, 'trace');
powlambda1 = strcmp(powmethod, 'lambda1');
if ~isempty(Cr)
% ensure that the cross-spectral density with the reference signal is a column matrix
Cr = Cr(:);
end
if isfield(dip, 'mom') && fixedori
error('you cannot specify a dipole orientation and fixedmom simultaneously');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% find the dipole positions that are inside/outside the brain
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~isfield(dip, 'inside') && ~isfield(dip, 'outside');
insideLogical = ft_inside_vol(dip.pos, vol);
dip.inside = find(insideLogical);
dip.outside = find(~dip.inside);
elseif isfield(dip, 'inside') && ~isfield(dip, 'outside');
dip.outside = setdiff(1:size(dip.pos,1), dip.inside);
elseif ~isfield(dip, 'inside') && isfield(dip, 'outside');
dip.inside = setdiff(1:size(dip.pos,1), dip.outside);
end
% select only the dipole positions inside the brain for scanning
dip.origpos = dip.pos;
dip.originside = dip.inside;
dip.origoutside = dip.outside;
if isfield(dip, 'mom')
dip.mom = dip.mom(:,dip.inside);
end
if isfield(dip, 'leadfield')
fprintf('using precomputed leadfields\n');
dip.leadfield = dip.leadfield(dip.inside);
end
if isfield(dip, 'filter')
fprintf('using precomputed filters\n');
dip.filter = dip.filter(dip.inside);
end
if isfield(dip, 'subspace')
fprintf('using subspace projection\n');
dip.subspace = dip.subspace(dip.inside);
end
dip.pos = dip.pos(dip.inside, :);
dip.inside = 1:size(dip.pos,1);
dip.outside = [];
% dics has the following sub-methods, which depend on the function input arguments
% power only, cortico-muscular coherence and cortico-cortical coherence
if ~isempty(Cr) && ~isempty(Pr) && isempty(refdip)
% compute cortico-muscular coherence, using reference cross spectral density
submethod = 'dics_refchan';
elseif isempty(Cr) && isempty(Pr) && ~isempty(refdip)
% compute cortico-cortical coherence with a dipole at the reference position
submethod = 'dics_refdip';
elseif isempty(Cr) && isempty(Pr) && isempty(refdip)
% only compute power of a dipole at the grid positions
submethod = 'dics_power';
else
error('invalid combination of input arguments for dics');
end
isrankdeficient = (rank(Cf)<size(Cf,1));
% it is difficult to give a quantitative estimate of lambda, therefore also
% support relative (percentage) measure that can be specified as string (e.g. '10%')
if ~isempty(lambda) && ischar(lambda) && lambda(end)=='%'
ratio = sscanf(lambda, '%f%%');
ratio = ratio/100;
lambda = ratio * trace(Cf)/size(Cf,1);
end
if projectnoise
% estimate the noise power, which is further assumed to be equal and uncorrelated over channels
if isrankdeficient
% estimated noise floor is equal to or higher than lambda
noise = lambda;
else
% estimate the noise level in the covariance matrix by the smallest singular value
noise = svd(Cf);
noise = noise(end);
% estimated noise floor is equal to or higher than lambda
noise = max(noise, lambda);
end
end
% the inverse only has to be computed once for all dipoles
if strcmp(realfilter, 'yes')
% the filter is computed using only the leadfield and the inverse covariance or CSD matrix
% therefore using the real-valued part of the CSD matrix here ensures a real-valued filter
invCf = pinv(real(Cf) + lambda * eye(size(Cf)));
else
invCf = pinv(Cf + lambda * eye(size(Cf)));
end
if isfield(dip, 'subspace')
fprintf('using source-specific subspace projection\n');
% remember the original data prior to the voxel dependent subspace projection
dat_pre_subspace = dat;
Cf_pre_subspace = Cf;
if strcmp(submethod, 'dics_refchan')
Cr_pre_subspace = Cr;
Pr_pre_subspace = Pr;
end
elseif ~isempty(subspace)
fprintf('using data-specific subspace projection\n');
% TODO implement an "eigenspace beamformer" as described in Sekihara et al. 2002 in HBM
if numel(subspace)==1,
% interpret this as a truncation of the eigenvalue-spectrum
% if <1 it is a fraction of the largest eigenvalue
% if >=1 it is the number of largest eigenvalues
dat_pre_subspace = dat;
Cf_pre_subspace = Cf;
[u, s, v] = svd(real(Cf));
if subspace<1,
sel = find(diag(s)./s(1,1) > subspace);
subspace = max(sel);
end
Cf = s(1:subspace,1:subspace);
% this is equivalent to subspace*Cf*subspace' but behaves well numerically
% by construction.
invCf = diag(1./diag(Cf));
subspace = u(:,1:subspace)';
dat = subspace*dat;
if strcmp(submethod, 'dics_refchan')
Cr = subspace*Cr;
end
else
Cf_pre_subspace = Cf;
Cf = subspace*Cf*subspace'; % here the subspace can be different from
% the singular vectors of Cy, so we have to do the sandwiching as opposed
% to line 216
if strcmp(realfilter, 'yes')
invCf = pinv(real(Cf));
else
invCf = pinv(Cf);
end
if strcmp(submethod, 'dics_refchan')
Cr = subspace*Cr;
end
end
end
% start the scanning with the proper metric
ft_progress('init', feedback, 'scanning grid');
switch submethod
case 'dics_power'
% only compute power of a dipole at the grid positions
for i=1:size(dip.pos,1)
if isfield(dip, 'leadfield') && isfield(dip, 'mom') && size(dip.mom, 1)==size(dip.leadfield{i}, 2)
% reuse the leadfield that was previously computed and project
lf = dip.leadfield{i} * dip.mom(:,i);
elseif isfield(dip, 'leadfield') && isfield(dip, 'mom')
% reuse the leadfield that was previously computed but don't project
lf = dip.leadfield{i};
elseif isfield(dip, 'leadfield') && ~isfield(dip, 'mom')
% reuse the leadfield that was previously computed
lf = dip.leadfield{i};
elseif ~isfield(dip, 'leadfield') && isfield(dip, 'mom')
% compute the leadfield for a fixed dipole orientation
lf = ft_compute_leadfield(dip.pos(i,:), grad, vol, 'reducerank', reducerank, 'normalize', normalize, 'normalizeparam', normalizeparam) * dip.mom(:,i);
else
% compute the leadfield
lf = ft_compute_leadfield(dip.pos(i,:), grad, vol, 'reducerank', reducerank, 'normalize', normalize, 'normalizeparam', normalizeparam);
end
if isfield(dip, 'subspace')
% do subspace projection of the forward model
lf = dip.subspace{i} * lf;
% the cross-spectral density becomes voxel dependent due to the projection
Cf = dip.subspace{i} * Cf_pre_subspace * dip.subspace{i}';
if strcmp(realfilter, 'yes')
invCf = pinv(dip.subspace{i} * (real(Cf_pre_subspace) + lambda * eye(size(Cf_pre_subspace))) * dip.subspace{i}');
else
invCf = pinv(dip.subspace{i} * (Cf_pre_subspace + lambda * eye(size(Cf_pre_subspace))) * dip.subspace{i}');
end
elseif ~isempty(subspace)
% do subspace projection of the forward model only
lforig = lf;
lf = subspace * lf;
% according to Kensuke's paper, the eigenspace bf boils down to projecting
% the 'traditional' filter onto the subspace
% spanned by the first k eigenvectors [u,s,v] = svd(Cy); filt = ESES*filt;
% ESES = u(:,1:k)*u(:,1:k)';
% however, even though it seems that the shape of the filter is identical to
% the shape it is obtained with the following code, the w*lf=I does not
% hold.
end
if fixedori
% compute the leadfield for the optimal dipole orientation
% subsequently the leadfield for only that dipole orientation will
% be used for the final filter computation
if isfield(dip, 'filter') && size(dip.filter{i},1)~=1
filt = dip.filter{i};
else
filt = pinv(lf' * invCf * lf) * lf' * invCf;
end
[u, s, v] = svd(real(filt * Cf * ctranspose(filt)));
maxpowori = u(:,1);
eta = s(1,1)./s(2,2);
lf = lf * maxpowori;
dipout.ori{i} = maxpowori;
dipout.eta{i} = eta;
if ~isempty(subspace), lforig = lforig * maxpowori; end
end
if isfield(dip, 'filter')
% use the provided filter
filt = dip.filter{i};
else
% construct the spatial filter
filt = pinv(lf' * invCf * lf) * lf' * invCf; % Gross eqn. 3, use PINV/SVD to cover rank deficient leadfield
end
csd = filt * Cf * ctranspose(filt); % Gross eqn. 4 and 5
if powlambda1
dipout.pow(i) = lambda1(csd); % compute the power at the dipole location, Gross eqn. 8
elseif powtrace
dipout.pow(i) = real(trace(csd)); % compute the power at the dipole location
end
if keepcsd
dipout.csd{i} = csd;
end
if projectnoise
if powlambda1
dipout.noise(i) = noise * lambda1(filt * ctranspose(filt));
elseif powtrace
dipout.noise(i) = noise * real(trace(filt * ctranspose(filt)));
end
if keepcsd
dipout.noisecsd{i} = noise * filt * ctranspose(filt);
end
end
if keepfilter
if ~isempty(subspace)
dipout.filter{i} = filt*subspace; %FIXME should this be subspace, or pinv(subspace)?
else
dipout.filter{i} = filt;
end
end
if keepleadfield
if ~isempty(subspace)
dipout.leadfield{i} = lforig;
else
dipout.leadfield{i} = lf;
end
end
ft_progress(i/size(dip.pos,1), 'scanning grid %d/%d\n', i, size(dip.pos,1));
end
case 'dics_refchan'
% compute cortico-muscular coherence, using reference cross spectral density
for i=1:size(dip.pos,1)
if isfield(dip, 'leadfield')
% reuse the leadfield that was previously computed
lf = dip.leadfield{i};
elseif isfield(dip, 'mom')
% compute the leadfield for a fixed dipole orientation
lf = ft_compute_leadfield(dip.pos(i,:), grad, vol, 'reducerank', reducerank, 'normalize', normalize) .* dip.mom(i,:)';
else
% compute the leadfield
lf = ft_compute_leadfield(dip.pos(i,:), grad, vol, 'reducerank', reducerank, 'normalize', normalize);
end
if isfield(dip, 'subspace')
% do subspace projection of the forward model
lforig = lf;
lf = dip.subspace{i} * lf;
% the cross-spectral density becomes voxel dependent due to the projection
Cf = dip.subspace{i} * Cf_pre_subspace * dip.subspace{i}';
invCf = pinv(dip.subspace{i} * (Cf_pre_subspace + lambda * eye(size(Cf))) * dip.subspace{i}');
elseif ~isempty(subspace)
% do subspace projection of the forward model only
lforig = lf;
lf = subspace * lf;
% according to Kensuke's paper, the eigenspace bf boils down to projecting
% the 'traditional' filter onto the subspace
% spanned by the first k eigenvectors [u,s,v] = svd(Cy); filt = ESES*filt;
% ESES = u(:,1:k)*u(:,1:k)';
% however, even though it seems that the shape of the filter is identical to
% the shape it is obtained with the following code, the w*lf=I does not
% hold.
end
if fixedori
% compute the leadfield for the optimal dipole orientation
% subsequently the leadfield for only that dipole orientation will be used for the final filter computation
filt = pinv(lf' * invCf * lf) * lf' * invCf;
[u, s, v] = svd(real(filt * Cf * ctranspose(filt)));
maxpowori = u(:,1);
lf = lf * maxpowori;
dipout.ori{i} = maxpowori;
end
if isfield(dip, 'filter')
% use the provided filter
filt = dip.filter{i};
else
% construct the spatial filter
filt = pinv(lf' * invCf * lf) * lf' * invCf; % use PINV/SVD to cover rank deficient leadfield
end
if powlambda1
[pow, ori] = lambda1(filt * Cf * ctranspose(filt)); % compute the power and orientation at the dipole location, Gross eqn. 4, 5 and 8
elseif powtrace
pow = real(trace(filt * Cf * ctranspose(filt))); % compute the power at the dipole location
end
csd = filt*Cr; % Gross eqn. 6
if powlambda1
% FIXME this should use the dipole orientation with maximum power
coh = lambda1(csd)^2 / (pow * Pr); % Gross eqn. 9
elseif powtrace
coh = norm(csd)^2 / (pow * Pr);
end
dipout.pow(i) = pow;
dipout.coh(i) = coh;
if keepcsd
dipout.csd{i} = csd;
end
if projectnoise
if powlambda1
dipout.noise(i) = noise * lambda1(filt * ctranspose(filt));
elseif powtrace
dipout.noise(i) = noise * real(trace(filt * ctranspose(filt)));
end
if keepcsd
dipout.noisecsd{i} = noise * filt * ctranspose(filt);
end
end
if keepfilter
dipout.filter{i} = filt;
end
if keepleadfield
if ~isempty(subspace)
dipout.leadfield{i} = lforig;
else
dipout.leadfield{i} = lf;
end
end
ft_progress(i/size(dip.pos,1), 'scanning grid %d/%d\n', i, size(dip.pos,1));
end
case 'dics_refdip'
if isfield(dip, 'subspace') || ~isempty(subspace)
error('subspace projections are not supported for beaming cortico-cortical coherence');
end
if fixedori
error('fixed orientations are not supported for beaming cortico-cortical coherence');
end
% compute cortio-cortical coherence with a dipole at the reference position
lf1 = ft_compute_leadfield(refdip, grad, vol, 'reducerank', reducerank, 'normalize', normalize);
% construct the spatial filter for the first (reference) dipole location
filt1 = pinv(lf1' * invCf * lf1) * lf1' * invCf; % use PINV/SVD to cover rank deficient leadfield
if powlambda1
Pref = lambda1(filt1 * Cf * ctranspose(filt1)); % compute the power at the first dipole location, Gross eqn. 8
elseif powtrace
Pref = real(trace(filt1 * Cf * ctranspose(filt1))); % compute the power at the first dipole location
end
for i=1:size(dip.pos,1)
if isfield(dip, 'leadfield')
% reuse the leadfield that was previously computed
lf2 = dip.leadfield{i};
elseif isfield(dip, 'mom')
% compute the leadfield for a fixed dipole orientation
lf2 = ft_compute_leadfield(dip.pos(i,:), grad, vol, 'reducerank', reducerank, 'normalize', normalize) .* dip.mom(i,:)';
else
% compute the leadfield
lf2 = ft_compute_leadfield(dip.pos(i,:), grad, vol, 'reducerank', reducerank, 'normalize', normalize);
end
if isfield(dip, 'filter')
% use the provided filter
filt2 = dip.filter{i};
else
% construct the spatial filter for the second dipole location
filt2 = pinv(lf2' * invCf * lf2) * lf2' * invCf; % use PINV/SVD to cover rank deficient leadfield
end
csd = filt1 * Cf * ctranspose(filt2); % compute the cross spectral density between the two dipoles, Gross eqn. 4
if powlambda1
pow = lambda1(filt2 * Cf * ctranspose(filt2)); % compute the power at the second dipole location, Gross eqn. 8
elseif powtrace
pow = real(trace(filt2 * Cf * ctranspose(filt2))); % compute the power at the second dipole location
end
if powlambda1
coh = lambda1(csd)^2 / (pow * Pref); % compute the coherence between the first and second dipole
elseif powtrace
coh = real(trace((csd)))^2 / (pow * Pref); % compute the coherence between the first and second dipole
end
dipout.pow(i) = pow;
dipout.coh(i) = coh;
if keepcsd
dipout.csd{i} = csd;
end
if projectnoise
if powlambda1
dipout.noise(i) = noise * lambda1(filt2 * ctranspose(filt2));
elseif powtrace
dipout.noise(i) = noise * real(trace(filt2 * ctranspose(filt2)));
end
if keepcsd
dipout.noisecsd{i} = noise * filt2 * ctranspose(filt2);
end
end
if keepleadfield
dipout.leadfield{i} = lf2;
end
ft_progress(i/size(dip.pos,1), 'scanning grid %d/%d\n', i, size(dip.pos,1));
end
end % switch submethod
ft_progress('close');
dipout.inside = dip.originside;
dipout.outside = dip.origoutside;
dipout.pos = dip.origpos;
% reassign the scan values over the inside and outside grid positions
if isfield(dipout, 'leadfield')
dipout.leadfield(dipout.inside) = dipout.leadfield;
dipout.leadfield(dipout.outside) = {[]};
end
if isfield(dipout, 'filter')
dipout.filter(dipout.inside) = dipout.filter;
dipout.filter(dipout.outside) = {[]};
end
if isfield(dipout, 'ori')
dipout.ori(dipout.inside) = dipout.ori;
dipout.ori(dipout.outside) = {[]};
end
if isfield(dipout, 'pow')
dipout.pow(dipout.inside) = dipout.pow;
dipout.pow(dipout.outside) = nan;
end
if isfield(dipout, 'noise')
dipout.noise(dipout.inside) = dipout.noise;
dipout.noise(dipout.outside) = nan;
end
if isfield(dipout, 'coh')
dipout.coh(dipout.inside) = dipout.coh;
dipout.coh(dipout.outside) = nan;
end
if isfield(dipout, 'csd')
dipout.csd(dipout.inside) = dipout.csd;
dipout.csd(dipout.outside) = {[]};
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function to obtain the largest singular value
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [s, ori] = lambda1(x)
% determine the largest singular value, which corresponds to the power along the dominant direction
[u, s, v] = svd(x);
s = s(1);
ori = u(:,1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function to compute the pseudo inverse. This is the same as the
% standard Matlab function, except that the default tolerance is twice as
% high.
% Copyright 1984-2004 The MathWorks, Inc.
% $Revision: 7123 $ $Date: 2009/06/17 13:40:37 $
% default tolerance increased by factor 2 (Robert Oostenveld, 7 Feb 2004)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function X = pinv(A,varargin)
[m,n] = size(A);
if n > m
X = pinv(A',varargin{:})';
else
[U,S,V] = svd(A,0);
if m > 1, s = diag(S);
elseif m == 1, s = S(1);
else s = 0;
end
if nargin == 2
tol = varargin{1};
else
tol = 10 * max(m,n) * max(s) * eps;
end
r = sum(s > tol);
if (r == 0)
X = zeros(size(A'),class(A));
else
s = diag(ones(r,1)./s(1:r));
X = V(:,1:r)*s*U(:,1:r)';
end
end
|
github
|
philippboehmsturm/antx-master
|
beamformer_lcmv.m
|
.m
|
antx-master/xspm8/external/fieldtrip/inverse/beamformer_lcmv.m
| 16,360 |
utf_8
|
24543219357b20596a7a2d8f35070a41
|
function [dipout] = beamformer_lcmv(dip, grad, vol, dat, Cy, varargin)
% BEAMFORMER_LCMV scans on pre-defined dipole locations with a single dipole
% and returns the beamformer spatial filter output for a dipole on every
% location. Dipole locations that are outside the head will return a
% NaN value.
%
% Use as
% [dipout] = beamformer_lcmv(dipin, grad, vol, dat, cov, varargin)
% where
% dipin is the input dipole model
% grad is the gradiometer definition
% vol is the volume conductor definition
% dat is the data matrix with the ERP or ERF
% cov is the data covariance or cross-spectral density matrix
% and
% dipout is the resulting dipole model with all details
%
% The input dipole model consists of
% dipin.pos positions for dipole, e.g. regular grid, Npositions x 3
% dipin.mom dipole orientation (optional), 3 x Npositions
%
% Additional options should be specified in key-value pairs and can be
% 'lambda' = regularisation parameter
% 'powmethod' = can be 'trace' or 'lambda1'
% 'feedback' = give ft_progress indication, can be 'text', 'gui' or 'none' (default)
% 'fixedori' = use fixed or free orientation, can be 'yes' or 'no'
% 'projectnoise' = project noise estimate through filter, can be 'yes' or 'no'
% 'projectmom' = project the dipole moment timecourse on the direction of maximal power, can be 'yes' or 'no'
% 'keepfilter' = remember the beamformer filter, can be 'yes' or 'no'
% 'keepleadfield' = remember the forward computation, can be 'yes' or 'no'
% 'keepmom' = remember the estimated dipole moment, can be 'yes' or 'no'
% 'keepcov' = remember the estimated dipole covariance, can be 'yes' or 'no'
%
% These options influence the forward computation of the leadfield
% 'reducerank' = reduce the leadfield rank, can be 'no' or a number (e.g. 2)
% 'normalize' = normalize the leadfield
% 'normalizeparam' = parameter for depth normalization (default = 0.5)
%
% If the dipole definition only specifies the dipole location, a rotating
% dipole (regional source) is assumed on each location. If a dipole moment
% is specified, its orientation will be used and only the strength will
% be fitted to the data.
% Copyright (C) 2003-2008, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: beamformer_lcmv.m 7123 2012-12-06 21:21:38Z roboos $
if mod(nargin-5,2)
% the first 5 arguments are fixed, the other arguments should come in pairs
error('invalid number of optional arguments');
end
% these optional settings do not have defaults
powmethod = keyval('powmethod', varargin); % the default for this is set below
subspace = keyval('subspace', varargin); % used to implement an "eigenspace beamformer" as described in Sekihara et al. 2002 in HBM
% these settings pertain to the forward model, the defaults are set in compute_leadfield
reducerank = keyval('reducerank', varargin);
normalize = keyval('normalize', varargin);
normalizeparam = keyval('normalizeparam', varargin);
% these optional settings have defaults
feedback = keyval('feedback', varargin); if isempty(feedback), feedback = 'text'; end
keepfilter = keyval('keepfilter', varargin); if isempty(keepfilter), keepfilter = 'no'; end
keepleadfield = keyval('keepleadfield', varargin); if isempty(keepleadfield), keepleadfield = 'no'; end
keepcov = keyval('keepcov', varargin); if isempty(keepcov), keepcov = 'no'; end
keepmom = keyval('keepmom', varargin); if isempty(keepmom), keepmom = 'yes'; end
lambda = keyval('lambda', varargin); if isempty(lambda ), lambda = 0; end
projectnoise = keyval('projectnoise', varargin); if isempty(projectnoise), projectnoise = 'yes'; end
projectmom = keyval('projectmom', varargin); if isempty(projectmom), projectmom = 'no'; end
fixedori = keyval('fixedori', varargin); if isempty(fixedori), fixedori = 'no'; end
% convert the yes/no arguments to the corresponding logical values
keepfilter = strcmp(keepfilter, 'yes');
keepleadfield = strcmp(keepleadfield, 'yes');
keepcov = strcmp(keepcov, 'yes');
keepmom = strcmp(keepmom, 'yes');
projectnoise = strcmp(projectnoise, 'yes');
projectmom = strcmp(projectmom, 'yes');
fixedori = strcmp(fixedori, 'yes');
% default is to use the trace of the covariance matrix, see Van Veen 1997
if isempty(powmethod)
powmethod = 'trace';
end
% use these two logical flags instead of doing the string comparisons each time again
powtrace = strcmp(powmethod, 'trace');
powlambda1 = strcmp(powmethod, 'lambda1');
if isfield(dip, 'mom') && fixedori
error('you cannot specify a dipole orientation and fixedmom simultaneously');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% find the dipole positions that are inside/outside the brain
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~isfield(dip, 'inside') && ~isfield(dip, 'outside');
insideLogical = ft_inside_vol(dip.pos, vol);
dip.inside = find(insideLogical);
dip.outside = find(~dip.inside);
elseif isfield(dip, 'inside') && ~isfield(dip, 'outside');
dip.outside = setdiff(1:size(dip.pos,1), dip.inside);
elseif ~isfield(dip, 'inside') && isfield(dip, 'outside');
dip.inside = setdiff(1:size(dip.pos,1), dip.outside);
end
% select only the dipole positions inside the brain for scanning
dip.origpos = dip.pos;
dip.originside = dip.inside;
dip.origoutside = dip.outside;
if isfield(dip, 'mom')
dip.mom = dip.mom(:, dip.inside);
end
if isfield(dip, 'leadfield')
fprintf('using precomputed leadfields\n');
dip.leadfield = dip.leadfield(dip.inside);
end
if isfield(dip, 'filter')
fprintf('using precomputed filters\n');
dip.filter = dip.filter(dip.inside);
end
if isfield(dip, 'subspace')
fprintf('using subspace projection\n');
dip.subspace = dip.subspace(dip.inside);
end
dip.pos = dip.pos(dip.inside, :);
dip.inside = 1:size(dip.pos,1);
dip.outside = [];
isrankdeficient = (rank(Cy)<size(Cy,1));
% it is difficult to give a quantitative estimate of lambda, therefore also
% support relative (percentage) measure that can be specified as string (e.g. '10%')
if ~isempty(lambda) && ischar(lambda) && lambda(end)=='%'
ratio = sscanf(lambda, '%f%%');
ratio = ratio/100;
lambda = ratio * trace(Cy)/size(Cy,1);
end
if projectnoise
% estimate the noise power, which is further assumed to be equal and uncorrelated over channels
if isrankdeficient
% estimated noise floor is equal to or higher than lambda
noise = lambda;
else
% estimate the noise level in the covariance matrix by the smallest singular value
noise = svd(Cy);
noise = noise(end);
% estimated noise floor is equal to or higher than lambda
noise = max(noise, lambda);
end
end
% the inverse only has to be computed once for all dipoles
invCy = pinv(Cy + lambda * eye(size(Cy)));
if isfield(dip, 'subspace')
fprintf('using source-specific subspace projection\n');
% remember the original data prior to the voxel dependent subspace projection
dat_pre_subspace = dat;
Cy_pre_subspace = Cy;
elseif ~isempty(subspace)
fprintf('using data-specific subspace projection\n');
% TODO implement an "eigenspace beamformer" as described in Sekihara et al. 2002 in HBM
if numel(subspace)==1,
% interpret this as a truncation of the eigenvalue-spectrum
% if <1 it is a fraction of the largest eigenvalue
% if >=1 it is the number of largest eigenvalues
dat_pre_subspace = dat;
Cy_pre_subspace = Cy;
[u, s, v] = svd(real(Cy));
if subspace<1,
sel = find(diag(s)./s(1,1) > subspace);
subspace = max(sel);
else
Cy = s(1:subspace,1:subspace);
% this is equivalent to subspace*Cy*subspace' but behaves well numerically
% by construction.
invCy = diag(1./diag(Cy));
subspace = u(:,1:subspace)';
dat = subspace*dat;
end
else
dat_pre_subspace = dat;
Cy_pre_subspace = Cy;
Cy = subspace*Cy*subspace'; % here the subspace can be different from
% the singular vectors of Cy, so we have to do the sandwiching as opposed
% to line 216
invCy = pinv(Cy);
dat = subspace*dat;
end
end
% start the scanning with the proper metric
ft_progress('init', feedback, 'scanning grid');
for i=1:size(dip.pos,1)
if isfield(dip, 'leadfield') && isfield(dip, 'mom') && size(dip.mom, 1)==size(dip.leadfield{i}, 2)
% reuse the leadfield that was previously computed and project
lf = dip.leadfield{i} * dip.mom(:,i);
elseif isfield(dip, 'leadfield') && isfield(dip, 'mom')
% reuse the leadfield that was previously computed but don't project
lf = dip.leadfield{i};
elseif isfield(dip, 'leadfield') && ~isfield(dip, 'mom')
% reuse the leadfield that was previously computed
lf = dip.leadfield{i};
elseif ~isfield(dip, 'leadfield') && isfield(dip, 'mom')
% compute the leadfield for a fixed dipole orientation
lf = ft_compute_leadfield(dip.pos(i,:), grad, vol, 'reducerank', reducerank, 'normalize', normalize, 'normalizeparam', normalizeparam) * dip.mom(:,i);
else
% compute the leadfield
lf = ft_compute_leadfield(dip.pos(i,:), grad, vol, 'reducerank', reducerank, 'normalize', normalize, 'normalizeparam', normalizeparam);
end
if isfield(dip, 'subspace')
% do subspace projection of the forward model
lf = dip.subspace{i} * lf;
% the data and the covariance become voxel dependent due to the projection
dat = dip.subspace{i} * dat_pre_subspace;
Cy = dip.subspace{i} * (Cy_pre_subspace + lambda * eye(size(Cy_pre_subspace))) * dip.subspace{i}';
invCy = pinv(dip.subspace{i} * (Cy_pre_subspace + lambda * eye(size(Cy_pre_subspace))) * dip.subspace{i}');
elseif ~isempty(subspace)
% do subspace projection of the forward model only
lforig = lf;
lf = subspace * lf;
% according to Kensuke's paper, the eigenspace bf boils down to projecting
% the 'traditional' filter onto the subspace
% spanned by the first k eigenvectors [u,s,v] = svd(Cy); filt = ESES*filt;
% ESES = u(:,1:k)*u(:,1:k)';
% however, even though it seems that the shape of the filter is identical to
% the shape it is obtained with the following code, the w*lf=I does not hold.
end
if fixedori
% compute the leadfield for the optimal dipole orientation
% subsequently the leadfield for only that dipole orientation will be used for the final filter computation
% filt = pinv(lf' * invCy * lf) * lf' * invCy;
% [u, s, v] = svd(real(filt * Cy * ctranspose(filt)));
% in this step the filter computation is not necessary, use the quick way to compute the voxel level covariance (cf. van Veen 1997)
[u, s, v] = svd(real(pinv(lf' * invCy *lf)));
eta = u(:,1);
lf = lf * eta;
if ~isempty(subspace), lforig = lforig * eta; end
dipout.ori{i} = eta;
end
if isfield(dip, 'filter')
% use the provided filter
filt = dip.filter{i};
else
% construct the spatial filter
filt = pinv(lf' * invCy * lf) * lf' * invCy; % van Veen eqn. 23, use PINV/SVD to cover rank deficient leadfield
end
if projectmom
[u, s, v] = svd(filt * Cy * ctranspose(filt));
mom = u(:,1);
filt = (mom') * filt;
end
if powlambda1
% dipout.pow(i) = lambda1(pinv(lf' * invCy * lf)); % this is more efficient if the filters are not present
dipout.pow(i) = lambda1(filt * Cy * ctranspose(filt)); % this is more efficient if the filters are present
elseif powtrace
% dipout.pow(i) = trace(pinv(lf' * invCy * lf)); % this is more efficient if the filters are not present, van Veen eqn. 24
dipout.pow(i) = trace(filt * Cy * ctranspose(filt)); % this is more efficient if the filters are present
end
if keepcov
% compute the source covariance matrix
dipout.cov{i} = filt * Cy * ctranspose(filt);
end
if keepmom && ~isempty(dat)
% estimate the instantaneous dipole moment at the current position
dipout.mom{i} = filt * dat;
end
if projectnoise
% estimate the power of the noise that is projected through the filter
if powlambda1
dipout.noise(i) = noise * lambda1(filt * ctranspose(filt));
elseif powtrace
dipout.noise(i) = noise * trace(filt * ctranspose(filt));
end
if keepcov
dipout.noisecov{i} = noise * filt * ctranspose(filt);
end
end
if keepfilter
if ~isempty(subspace)
dipout.filter{i} = filt*subspace;
%dipout.filter{i} = filt*pinv(subspace);
else
dipout.filter{i} = filt;
end
end
if keepleadfield
if ~isempty(subspace)
dipout.leadfield{i} = lforig;
else
dipout.leadfield{i} = lf;
end
end
ft_progress(i/size(dip.pos,1), 'scanning grid %d/%d\n', i, size(dip.pos,1));
end
ft_progress('close');
dipout.inside = dip.originside;
dipout.outside = dip.origoutside;
dipout.pos = dip.origpos;
% reassign the scan values over the inside and outside grid positions
if isfield(dipout, 'leadfield')
dipout.leadfield(dipout.inside) = dipout.leadfield;
dipout.leadfield(dipout.outside) = {[]};
end
if isfield(dipout, 'filter')
dipout.filter(dipout.inside) = dipout.filter;
dipout.filter(dipout.outside) = {[]};
end
if isfield(dipout, 'mom')
dipout.mom(dipout.inside) = dipout.mom;
dipout.mom(dipout.outside) = {[]};
end
if isfield(dipout, 'ori')
dipout.ori(dipout.inside) = dipout.ori;
dipout.ori(dipout.outside) = {[]};
end
if isfield(dipout, 'cov')
dipout.cov(dipout.inside) = dipout.cov;
dipout.cov(dipout.outside) = {[]};
end
if isfield(dipout, 'noisecov')
dipout.noisecov(dipout.inside) = dipout.noisecov;
dipout.noisecov(dipout.outside) = {[]};
end
if isfield(dipout, 'pow')
dipout.pow(dipout.inside) = dipout.pow;
dipout.pow(dipout.outside) = nan;
end
if isfield(dipout, 'noise')
dipout.noise(dipout.inside) = dipout.noise;
dipout.noise(dipout.outside) = nan;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function to obtain the largest singular value
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function s = lambda1(x)
% determine the largest singular value, which corresponds to the power along the dominant direction
s = svd(x);
s = s(1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function to compute the pseudo inverse. This is the same as the
% standard Matlab function, except that the default tolerance is twice as
% high.
% Copyright 1984-2004 The MathWorks, Inc.
% $Revision: 7123 $ $Date: 2009/03/23 21:14:42 $
% default tolerance increased by factor 2 (Robert Oostenveld, 7 Feb 2004)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function X = pinv(A,varargin)
[m,n] = size(A);
if n > m
X = pinv(A',varargin{:})';
else
[U,S,V] = svd(A,0);
if m > 1, s = diag(S);
elseif m == 1, s = S(1);
else s = 0;
end
if nargin == 2
tol = varargin{1};
else
tol = 10 * max(m,n) * max(s) * eps;
end
r = sum(s > tol);
if (r == 0)
X = zeros(size(A'),class(A));
else
s = diag(ones(r,1)./s(1:r));
X = V(:,1:r)*s*U(:,1:r)';
end
end
|
github
|
philippboehmsturm/antx-master
|
dipole_fit.m
|
.m
|
antx-master/xspm8/external/fieldtrip/inverse/dipole_fit.m
| 11,095 |
utf_8
|
3ed8bcfd404bb481eb98539a1e371239
|
function [dipout] = dipole_fit(dip, sens, vol, dat, varargin)
% DIPOLE_FIT performs an equivalent current dipole fit with a single
% or a small number of dipoles to explain an EEG or MEG scalp topography.
%
% Use as
% [dipout] = dipole_fit(dip, sens, vol, dat, ...)
%
% Additional input arguments should be specified as key-value pairs and can include
% 'constr' = Structure with constraints
% 'display' = Level of display [ off | iter | notify | final ]
% 'optimfun' = Function to use [fminsearch | fminunc ]
% 'maxiter' = Maximum number of function evaluations allowed [ positive integer ]
% 'metric' = Error measure to be minimised [ rv | var | abs ]
% 'checkinside' = Boolean flag to check whether dipole is inside source compartment [ 0 | 1 ]
% 'weight' = weight matrix for maximum likelihood estimation, e.g. inverse noise covariance
%
% The following optional input arguments relate to the computation of the leadfields
% 'reducerank' = 'no' or number
% 'normalize' = 'no', 'yes' or 'column'
% 'normalizeparam' = parameter for depth normalization (default = 0.5)
%
% The maximum likelihood estimation implements
% Lutkenhoner B. "Dipole source localization by means of maximum
% likelihood estimation I. Theory and simulations" Electroencephalogr Clin
% Neurophysiol. 1998 Apr;106(4):314-21.
% Copyright (C) 2003-2008, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: dipole_fit.m 7123 2012-12-06 21:21:38Z roboos $
% It is neccessary to provide backward compatibility support for the old function call
% in case people want to use it in conjunction with EEGLAB and the dipfit1 plugin.
% old style: function [dipout] = dipole_fit(dip, dat, sens, vol, constr), where constr is optional
% new style: function [dipout] = dipole_fit(dip, sens, vol, dat, varargin), where varargin is in key-value pairs
if nargin==4 && ~isstruct(sens) && isstruct(dat)
% looks like old style, the order of the input arguments has to be changed
warning('converting from old style input\n');
olddat = sens;
oldsens = vol;
oldvol = dat;
dat = olddat;
sens = oldsens;
vol = oldvol;
elseif nargin==5 && ~isstruct(sens) && isstruct(dat)
% looks like old style, the order of the input arguments has to be changed
% furthermore the additional constraint has to be fixed
warning('converting from old style input\n');
olddat = sens;
oldsens = vol;
oldvol = dat;
dat = olddat;
sens = oldsens;
vol = oldvol;
varargin = {'constr', varargin{1}}; % convert into a key-value pair
else
% looks like new style, i.e. with optional key-value arguments
% this is dealt with below
end
constr = keyval('constr', varargin); % default is not to have constraints
metric = keyval('metric', varargin); if isempty(metric), metric = 'rv'; end
checkinside = keyval('checkinside', varargin); if isempty(checkinside), checkinside = 0; end
display = keyval('display', varargin); if isempty(display), display = 'iter'; end
optimfun = keyval('optimfun', varargin); if isa(optimfun, 'char'), optimfun = str2fun(optimfun); end
maxiter = keyval('maxiter', varargin);
reducerank = keyval('reducerank', varargin); % for leadfield computation
normalize = keyval('normalize' , varargin); % for leadfield computation
normalizeparam = keyval('normalizeparam', varargin); % for leadfield computation
weight = keyval('weight', varargin); % for maximum likelihood estimation
if isempty(optimfun)
% determine whether the Matlab Optimization toolbox is available and can be used
if ft_hastoolbox('optim')
optimfun = @fminunc;
else
optimfun = @fminsearch;
end
end
if isempty(maxiter)
% set a default for the maximum number of iterations, depends on the optimization function
if isequal(optimfun, @fminunc)
maxiter = 100;
else
maxiter = 500;
end
end
% determine whether it is EEG or MEG
iseeg = ft_senstype(sens, 'eeg');
ismeg = ft_senstype(sens, 'meg');
if ismeg && iseeg
% this is something that I might implement in the future
error('simultaneous EEG and MEG not supported');
elseif iseeg
% ensure that the potential data is average referenced, just like the model potential
dat = avgref(dat);
end
% ensure correct dipole position and moment specification
dip = fixdipole(dip);
% reformat the position parameters in case of multiple dipoles, this
% should result in the matrix changing from [x1 y1 z1; x2 y2 z2] to
% [x1 y1 z1 x2 y2 z2] for the constraints to work
numdip = size(dip.pos, 1);
param = dip.pos';
param = param(:)';
% add the orientation to the nonlinear parameters
if isfield(constr, 'fixedori') && constr.fixedori
for i=1:numdip
% add the orientation to the list of parameters
[th, phi, r] = cart2sph(dip.mom(1,i), dip.mom(2,i), dip.mom(3,i));
param = [param th phi];
end
end
% reduce the number of parameters to be fitted according to the constraints
if isfield(constr, 'mirror')
param = param(constr.reduce);
end
% set the parameters for the optimization function
if isequal(optimfun, @fminunc)
options = optimset(...
'TolFun',1e-9,...
'TypicalX',ones(size(param)),...
'LargeScale','off',...
'HessUpdate','bfgs',...
'MaxIter',maxiter,...
'MaxFunEvals',2*maxiter*length(param),...
'Display',display);
elseif isequal(optimfun, @fminsearch)
options = optimset(...
'MaxIter',maxiter,...
'MaxFunEvals',2*maxiter*length(param),...
'Display',display);
else
warning('unknown optimization function "%s", using default parameters', func2str(optimfun));
end
% perform the optimization with either the fminsearch or fminunc function
[param, fval, exitflag, output] = optimfun(@dipfit_error, param, options, dat, sens, vol, constr, metric, checkinside, reducerank, normalize, normalizeparam, weight);
if exitflag==0
error('Maximum number of iterations exceeded before reaching the minimum, please try with another initial guess.')
end
% do linear optimization of dipole moment parameters
[err, mom] = dipfit_error(param, dat, sens, vol, constr, metric, checkinside, reducerank, normalize, normalizeparam, weight);
% expand the number of parameters according to the constraints
if isfield(constr, 'mirror')
param = constr.mirror .* param(constr.expand);
end
% get the dipole position and orientation
if isfield(constr, 'fixedori') && constr.fixedori
numdip = numel(param)/5;
ori = zeros(3,numdip);
for i=1:numdip
th = param(end-(2*i)+1);
phi = param(end-(2*i)+2);
[ori(1,i), ori(2,i), ori(3,i)] = sph2cart(th, phi, 1);
end
pos = reshape(param(1:(numdip*3)), 3, numdip)';
else
numdip = numel(param)/3;
pos = reshape(param, 3, numdip)';
end
% return the optimal dipole parameters
dipout.pos = pos;
if isfield(constr, 'fixedori') && constr.fixedori
dipout.mom = ori; % dipole orientation as vector
dipout.ampl = mom; % dipole strength
else
dipout.mom = mom; % dipole moment as vector or matrix, which represents both the orientation and strength as vector
end
% ensure correct dipole position and moment specification
dipout = fixdipole(dipout);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% DIPFIT_ERROR computes the error between measured and model data
% and can be used for non-linear fitting of dipole position
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [err, mom] = dipfit_error(param, dat, sens, vol, constr, metric, checkinside, reducerank, normalize, normalizeparam, weight)
% flush pending graphics events, ensure that fitting is interruptible
drawnow;
if ~isempty(get(0, 'currentfigure')) && strcmp(get(gcf, 'tag'), 'stop')
% interrupt the fitting
close;
error('USER ABORT');
end;
% expand the number of parameters according to the constraints
if isfield(constr, 'mirror')
param = constr.mirror .* param(constr.expand);
end
% get the dipole positions and optionally also the orientation
if isfield(constr, 'fixedori') && constr.fixedori
numdip = numel(param)/5;
ori = zeros(3,numdip);
for i=1:numdip
th = param(end-(2*i)+1);
phi = param(end-(2*i)+2);
[ori(1,i), ori(2,i), ori(3,i)] = sph2cart(th, phi, 1);
end
pos = reshape(param(1:(numdip*3)), 3, numdip)';
else
numdip = numel(param)/3;
pos = reshape(param, 3, numdip)';
end
% check whether the dipole is inside the source compartment
if checkinside
inside = ft_inside_vol(pos, vol);
if ~all(inside)
error('Dipole is outside the source compartment');
end
end
% construct the leadfield matrix for all dipoles
lf = ft_compute_leadfield(pos, sens, vol, 'reducerank', reducerank, 'normalize', normalize, 'normalizeparam', normalizeparam);
if isfield(constr, 'fixedori') && constr.fixedori
lf = lf * ori;
end
% compute the optimal dipole moment and the model error
if ~isempty(weight)
% maximum likelihood estimation using the weigth matrix
mom = pinv(lf'*weight*lf)*lf'*weight*dat; % Lutkenhoner equation 5
dif = dat - lf*mom;
% compute the generalized goodness-of-fit measure
switch metric
case 'rv' % relative residual variance
num = dif' * weight * dif;
denom = dat' * weight * dat;
err = sum(num(:)) ./ sum(denom(:)); % Lutkenhonner equation 7, except for the gof=1-rv
case 'var' % residual variance
num = dif' * weight * dif';
err = sum(num(:));
otherwise
error('Unsupported error metric for maximum likelihood dipole fitting');
end
else
% ordinary least squares, this is the same as MLE with weight=eye(nchans,nchans)
mom = pinv(lf)*dat;
dif = dat - lf*mom;
% compute the ordinary goodness-of-fit measures
switch metric
case 'rv' % relative residual variance
err = sum(dif(:).^2) / sum(dat(:).^2);
case 'var' % residual variance
err = sum(dif(:).^2);
case 'abs' % absolute difference
err = sum(abs(dif));
otherwise
error('Unsupported error metric for dipole fitting');
end
end
if ~isreal(err)
% this happens for complex valued data, i.e. when fitting a dipole to spectrally decomposed data
% the error function should return a positive valued real number, otherwise fminunc fails
err = abs(err);
end
|
github
|
philippboehmsturm/antx-master
|
ft_hastoolbox.m
|
.m
|
antx-master/xspm8/external/fieldtrip/inverse/private/ft_hastoolbox.m
| 21,701 |
utf_8
|
7141791b922e3b46334b4d5888532adf
|
function [status] = ft_hastoolbox(toolbox, autoadd, silent)
% FT_HASTOOLBOX tests whether an external toolbox is installed. Optionally
% it will try to determine the path to the toolbox and install it
% automatically.
%
% Use as
% [status] = ft_hastoolbox(toolbox, autoadd, silent)
%
% autoadd = 0 means that it will not be added
% autoadd = 1 means that give an error if it cannot be added
% autoadd = 2 means that give a warning if it cannot be added
% autoadd = 3 means that it remains silent if it cannot be added
%
% silent = 0 means that it will give some feedback about adding the toolbox
% silent = 1 means that it will not give feedback
% Copyright (C) 2005-2012, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip 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.
%
% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_hastoolbox.m 7172 2012-12-13 11:50:49Z roboos $
% this function is called many times in FieldTrip and associated toolboxes
% use efficient handling if the same toolbox has been investigated before
% persistent previous previouspath
%
% if ~isequal(previouspath, path)
% previous = [];
% end
%
% if isempty(previous)
% previous = struct;
% elseif isfield(previous, fixname(toolbox))
% status = previous.(fixname(toolbox));
% return
% end
% this points the user to the website where he/she can download the toolbox
url = {
'AFNI' 'see http://afni.nimh.nih.gov'
'DSS' 'see http://www.cis.hut.fi/projects/dss'
'EEGLAB' 'see http://www.sccn.ucsd.edu/eeglab'
'NWAY' 'see http://www.models.kvl.dk/source/nwaytoolbox'
'SPM99' 'see http://www.fil.ion.ucl.ac.uk/spm'
'SPM2' 'see http://www.fil.ion.ucl.ac.uk/spm'
'SPM5' 'see http://www.fil.ion.ucl.ac.uk/spm'
'SPM8' 'see http://www.fil.ion.ucl.ac.uk/spm'
'MEG-PD' 'see http://www.kolumbus.fi/kuutela/programs/meg-pd'
'MEG-CALC' 'this is a commercial toolbox from Neuromag, see http://www.neuromag.com'
'BIOSIG' 'see http://biosig.sourceforge.net'
'EEG' 'see http://eeg.sourceforge.net'
'EEGSF' 'see http://eeg.sourceforge.net' % alternative name
'MRI' 'see http://eeg.sourceforge.net' % alternative name
'NEUROSHARE' 'see http://www.neuroshare.org'
'BESA' 'see http://www.megis.de, or contact Karsten Hoechstetter'
'EEPROBE' 'see http://www.ant-neuro.com, or contact Maarten van der Velde'
'YOKOGAWA' 'this is deprecated, please use YOKOGAWA_MEG_READER instead'
'YOKOGAWA_MEG_READER' 'see http://www.yokogawa.com/me/me-login-en.htm'
'BEOWULF' 'see http://robertoostenveld.nl, or contact Robert Oostenveld'
'MENTAT' 'see http://robertoostenveld.nl, or contact Robert Oostenveld'
'SON2' 'see http://www.kcl.ac.uk/depsta/biomedical/cfnr/lidierth.html, or contact Malcolm Lidierth'
'4D-VERSION' 'contact Christian Wienbruch'
'SIGNAL' 'see http://www.mathworks.com/products/signal'
'OPTIM' 'see http://www.mathworks.com/products/optim'
'IMAGE' 'see http://www.mathworks.com/products/image' % Mathworks refers to this as IMAGES
'SPLINES' 'see http://www.mathworks.com/products/splines'
'DISTCOMP' 'see http://www.mathworks.nl/products/parallel-computing/'
'COMPILER' 'see http://www.mathworks.com/products/compiler'
'FASTICA' 'see http://www.cis.hut.fi/projects/ica/fastica'
'BRAINSTORM' 'see http://neuroimage.ucs.edu/brainstorm'
'FILEIO' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'PREPROC' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'FORWARD' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'INVERSE' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'SPECEST' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'REALTIME' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'PLOTTING' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'SPIKE' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'CONNECTIVITY' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'PEER' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'PLOTTING' 'see http://www.ru.nl/neuroimaging/fieldtrip'
'DENOISE' 'see http://lumiere.ens.fr/Audition/adc/meg, or contact Alain de Cheveigne'
'BCI2000' 'see http://bci2000.org'
'NLXNETCOM' 'see http://www.neuralynx.com'
'DIPOLI' 'see ftp://ftp.fcdonders.nl/pub/fieldtrip/external'
'MNE' 'see http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php'
'TCP_UDP_IP' 'see http://www.mathworks.com/matlabcentral/fileexchange/345, or contact Peter Rydesaeter'
'BEMCP' 'contact Christophe Phillips'
'OPENMEEG' 'see http://gforge.inria.fr/projects/openmeeg and http://gforge.inria.fr/frs/?group_id=435'
'PRTOOLS' 'see http://www.prtools.org'
'ITAB' 'contact Stefania Della Penna'
'BSMART' 'see http://www.brain-smart.org'
'PEER' 'see http://fieldtrip.fcdonders.nl/development/peer'
'FREESURFER' 'see http://surfer.nmr.mgh.harvard.edu/fswiki'
'SIMBIO' 'see https://www.mrt.uni-jena.de/simbio/index.php/Main_Page'
'VGRID' 'see http://www.rheinahrcampus.de/~medsim/vgrid/manual.html'
'FNS' 'see http://hhvn.nmsu.edu/wiki/index.php/FNS'
'GIFTI' 'see http://www.artefact.tk/software/matlab/gifti'
'XML4MAT' 'see http://www.mathworks.com/matlabcentral/fileexchange/6268-xml4mat-v2-0'
'SQDPROJECT' 'see http://www.isr.umd.edu/Labs/CSSL/simonlab'
'BCT' 'see http://www.brain-connectivity-toolbox.net/'
'CCA' 'see http://www.imt.liu.se/~magnus/cca or contact Magnus Borga'
'EGI_MFF' 'see http://www.egi.com/ or contact either Phan Luu or Colin Davey at EGI'
'TOOLBOX_GRAPH' 'see http://www.mathworks.com/matlabcentral/fileexchange/5355-toolbox-graph or contact Gabriel Peyre'
'NETCDF' 'see http://www.mathworks.com/matlabcentral/fileexchange/15177'
'MYSQL' 'see http://www.mathworks.com/matlabcentral/fileexchange/8663-mysql-database-connector'
'ISO2MESH' 'see http://iso2mesh.sourceforge.net/cgi-bin/index.cgi?Home or contact Qianqian Fang'
'DATAHASH' 'see http://www.mathworks.com/matlabcentral/fileexchange/31272'
'IBTB' 'see http://www.ibtb.org'
'ICASSO' 'see http://www.cis.hut.fi/projects/ica/icasso'
'XUNIT' 'see http://www.mathworks.com/matlabcentral/fileexchange/22846-matlab-xunit-test-framework'
'PLEXON' 'available from http://www.plexon.com/assets/downloads/sdk/ReadingPLXandDDTfilesinMatlab-mexw.zip'
};
if nargin<2
% default is not to add the path automatically
autoadd = 0;
end
if nargin<3
% default is not to be silent
silent = 0;
end
% determine whether the toolbox is installed
toolbox = upper(toolbox);
% set fieldtrip trunk path, used for determining ft-subdirs are on path
fttrunkpath = unixpath(fileparts(which('ft_defaults')));
switch toolbox
case 'AFNI'
status = (exist('BrikLoad') && exist('BrikInfo'));
case 'DSS'
status = exist('denss', 'file') && exist('dss_create_state', 'file');
case 'EEGLAB'
status = exist('runica', 'file');
case 'NWAY'
status = exist('parafac', 'file');
case 'SPM'
status = exist('spm.m'); % any version of SPM is fine
case 'SPM99'
status = exist('spm.m') && strcmp(spm('ver'),'SPM99');
case 'SPM2'
status = exist('spm.m') && strcmp(spm('ver'),'SPM2');
case 'SPM5'
status = exist('spm.m') && strcmp(spm('ver'),'SPM5');
case 'SPM8'
status = exist('spm.m') && strncmp(spm('ver'),'SPM8', 4);
case 'SPM12'
status = exist('spm.m') && strncmp(spm('ver'),'SPM12', 5);
case 'MEG-PD'
status = (exist('rawdata') && exist('channames'));
case 'MEG-CALC'
status = (exist('megmodel') && exist('megfield') && exist('megtrans'));
case 'BIOSIG'
status = (exist('sopen') && exist('sread'));
case 'EEG'
status = (exist('ctf_read_res4') && exist('ctf_read_meg4'));
case 'EEGSF' % alternative name
status = (exist('ctf_read_res4') && exist('ctf_read_meg4'));
case 'MRI' % other functions in the mri section
status = (exist('avw_hdr_read') && exist('avw_img_read'));
case 'NEUROSHARE'
status = (exist('ns_OpenFile') && exist('ns_SetLibrary') && exist('ns_GetAnalogData'));
case 'BESA'
status = (exist('readBESAtfc') && exist('readBESAswf'));
case 'EEPROBE'
status = (exist('read_eep_avr') && exist('read_eep_cnt'));
case 'YOKOGAWA'
status = hasyokogawa('16bitBeta6');
case 'YOKOGAWA12BITBETA3'
status = hasyokogawa('12bitBeta3');
case 'YOKOGAWA16BITBETA3'
status = hasyokogawa('16bitBeta3');
case 'YOKOGAWA16BITBETA6'
status = hasyokogawa('16bitBeta6');
case 'YOKOGAWA_MEG_READER'
status = hasyokogawa('1.4');
case 'BEOWULF'
status = (exist('evalwulf') && exist('evalwulf') && exist('evalwulf'));
case 'MENTAT'
status = (exist('pcompile') && exist('pfor') && exist('peval'));
case 'SON2'
status = (exist('SONFileHeader') && exist('SONChanList') && exist('SONGetChannel'));
case '4D-VERSION'
status = (exist('read4d') && exist('read4dhdr'));
case {'STATS', 'STATISTICS'}
status = license('checkout', 'statistics_toolbox'); % also check the availability of a toolbox license
case {'OPTIM', 'OPTIMIZATION'}
status = license('checkout', 'optimization_toolbox'); % also check the availability of a toolbox license
case {'SPLINES', 'CURVE_FITTING'}
status = license('checkout', 'curve_fitting_toolbox'); % also check the availability of a toolbox license
case 'SIGNAL'
status = license('checkout', 'signal_toolbox'); % also check the availability of a toolbox license
case 'IMAGE'
status = license('checkout', 'image_toolbox'); % also check the availability of a toolbox license
case {'DCT', 'DISTCOMP'}
status = license('checkout', 'distrib_computing_toolbox'); % also check the availability of a toolbox license
case 'COMPILER'
status = license('checkout', 'compiler'); % also check the availability of a toolbox license
case 'FASTICA'
status = exist('fpica', 'file');
case 'BRAINSTORM'
status = exist('bem_xfer');
case 'DENOISE'
status = (exist('tsr', 'file') && exist('sns', 'file'));
case 'CTF'
status = (exist('getCTFBalanceCoefs') && exist('getCTFdata'));
case 'BCI2000'
status = exist('load_bcidat');
case 'NLXNETCOM'
status = (exist('MatlabNetComClient', 'file') && exist('NlxConnectToServer', 'file') && exist('NlxGetNewCSCData', 'file'));
case 'DIPOLI'
status = exist('dipoli.maci', 'file');
case 'MNE'
status = (exist('fiff_read_meas_info', 'file') && exist('fiff_setup_read_raw', 'file'));
case 'TCP_UDP_IP'
status = (exist('pnet', 'file') && exist('pnet_getvar', 'file') && exist('pnet_putvar', 'file'));
case 'BEMCP'
status = (exist('bem_Cij_cog', 'file') && exist('bem_Cij_lin', 'file') && exist('bem_Cij_cst', 'file'));
case 'OPENMEEG'
status = exist('om_save_tri.m', 'file');
case 'PRTOOLS'
status = (exist('prversion', 'file') && exist('dataset', 'file') && exist('svc', 'file'));
case 'ITAB'
status = (exist('lcReadHeader', 'file') && exist('lcReadData', 'file'));
case 'BSMART'
status = exist('bsmart');
case 'FREESURFER'
status = exist('MRIread', 'file') && exist('vox2ras_0to1', 'file');
case 'FNS'
status = exist('elecsfwd', 'file');
case 'SIMBIO'
status = exist('calc_stiff_matrix_val', 'file') && exist('sb_transfer', 'file');
case 'VGRID'
status = exist('vgrid.m', 'file');
case 'GIFTI'
status = exist('gifti', 'file');
case 'XML4MAT'
status = exist('xml2struct.m', 'file') && exist('xml2whos.m', 'file');
case 'SQDPROJECT'
status = exist('sqdread.m', 'file') && exist('sqdwrite.m', 'file');
case 'BCT'
status = exist('macaque71.mat', 'file') && exist('motif4funct_wei.m', 'file');
case 'CCA'
status = exist('ccabss.m', 'file');
case 'EGI_MFF'
status = exist('mff_getObject.m', 'file') && exist('mff_getSummaryInfo.m', 'file');
case 'TOOLBOX_GRAPH'
status = exist('toolbox_graph');
case 'NETCDF'
status = exist('netcdf');
case 'MYSQL'
status = exist(['mysql.' mexext], 'file'); % this only consists of a single mex file
case 'ISO2MESH'
status = exist('vol2surf.m', 'file') && exist('qmeshcut.m', 'file');
case 'QSUB'
status = exist('qsubfeval.m', 'file') && exist('qsubcellfun.m', 'file');
case 'ENGINE'
status = exist('enginefeval.m', 'file') && exist('enginecellfun.m', 'file');
case 'DATAHASH'
status = exist('DataHash.m', 'file');
case 'IBTB'
status = exist('make_ibtb.m', 'file') && exist('binr.m', 'file');
case 'ICASSO'
status = exist('icassoEst.m', 'file');
case 'XUNIT'
status = exist('initTestSuite.m', 'file') && exist('runtests.m', 'file');
case 'PLEXON'
status = exist('plx_adchan_gains.m', 'file') && exist('mexPlex');
% the following are fieldtrip modules/toolboxes
case 'FILEIO'
status = (exist('ft_read_header', 'file') && exist('ft_read_data', 'file') && exist('ft_read_event', 'file') && exist('ft_read_sens', 'file'));
case 'FORWARD'
status = (exist('ft_compute_leadfield', 'file') && exist('ft_prepare_vol_sens', 'file'));
case 'PLOTTING'
status = (exist('ft_plot_topo', 'file') && exist('ft_plot_mesh', 'file') && exist('ft_plot_matrix', 'file'));
case 'PEER'
status = exist('peerslave', 'file') && exist('peermaster', 'file');
case 'CONNECTIVITY'
status = exist('ft_connectivity_corr', 'file') && exist('ft_connectivity_granger', 'file');
case 'SPIKE'
status = exist('ft_spiketriggeredaverage.m', 'file') && exist('ft_spiketriggeredspectrum.m', 'file');
% these were missing, added them using the below style, see bug 1804 - roevdmei
case 'INVERSE'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/inverse'], 'once')); % INVERSE is not added above, consider doing it there -roevdmei
case 'REALTIME'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/realtime'], 'once')); % REALTIME is not added above, consider doing it there -roevdmei
case 'SPECEST'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/specest'], 'once')); % SPECEST is not added above, consider doing it there -roevdmei
case 'PREPROC'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/preproc'], 'once')); % PREPROC is not added above, consider doing it there -roevdmei
% the following are not proper toolboxes, but only subdirectories in the fieldtrip toolbox
% these are added in ft_defaults and are specified with unix-style forward slashes
case 'COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/compat'], 'once'));
case 'STATFUN'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/statfun'], 'once'));
case 'TRIALFUN'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/trialfun'], 'once'));
case 'UTILITIES/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/utilities/compat'], 'once'));
case 'FILEIO/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/fileio/compat'], 'once'));
case 'PREPROC/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/preproc/compat'], 'once'));
case 'FORWARD/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/forward/compat'], 'once'));
case 'PLOTTING/COMPAT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/plotting/compat'], 'once'));
case 'TEMPLATE/LAYOUT'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/layout'], 'once'));
case 'TEMPLATE/ANATOMY'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/anatomy'], 'once'));
case 'TEMPLATE/HEADMODEL'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/headmodel'], 'once'));
case 'TEMPLATE/ELECTRODE'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/electrode'], 'once'));
case 'TEMPLATE/NEIGHBOURS'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/neighbours'], 'once'));
case 'TEMPLATE/SOURCEMODEL'
status = ~isempty(regexp(unixpath(path), [fttrunkpath '/template/sourcemodel'], 'once'));
otherwise
if ~silent, warning('cannot determine whether the %s toolbox is present', toolbox); end
status = 0;
end
% it should be a boolean value
status = (status~=0);
% try to determine the path of the requested toolbox
if autoadd>0 && ~status
% for core fieldtrip modules
prefix = fileparts(which('ft_defaults'));
if ~status
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
end
% for external fieldtrip modules
prefix = fullfile(fileparts(which('ft_defaults')), 'external');
if ~status
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
licensefile = [lower(toolbox) '_license'];
if status && exist(licensefile, 'file')
% this will execute openmeeg_license and mne_license
% which display the license on screen for three seconds
feval(licensefile);
end
end
% for contributed fieldtrip extensions
prefix = fullfile(fileparts(which('ft_defaults')), 'contrib');
if ~status
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
licensefile = [lower(toolbox) '_license'];
if status && exist(licensefile, 'file')
% this will execute openmeeg_license and mne_license
% which display the license on screen for three seconds
feval(licensefile);
end
end
% for linux computers in the Donders Centre for Cognitive Neuroimaging
prefix = '/home/common/matlab';
if ~status && isunix
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
end
% for windows computers in the Donders Centre for Cognitive Neuroimaging
prefix = 'h:\common\matlab';
if ~status && ispc
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
end
% use the matlab subdirectory in your homedirectory, this works on linux and mac
prefix = fullfile(getenv('HOME'), 'matlab');
if ~status && isdir(prefix)
status = myaddpath(fullfile(prefix, lower(toolbox)), silent);
end
if ~status
% the toolbox is not on the path and cannot be added
sel = find(strcmp(url(:,1), toolbox));
if ~isempty(sel)
msg = sprintf('the %s toolbox is not installed, %s', toolbox, url{sel, 2});
else
msg = sprintf('the %s toolbox is not installed', toolbox);
end
if autoadd==1
error(msg);
elseif autoadd==2
warning(msg);
else
% fail silently
end
end
end
% this function is called many times in FieldTrip and associated toolboxes
% use efficient handling if the same toolbox has been investigated before
if status
previous.(fixname(toolbox)) = status;
end
% remember the previous path, allows us to determine on the next call
% whether the path has been modified outise of this function
previouspath = path;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function status = myaddpath(toolbox, silent)
if isdeployed
warning('cannot change path settings for %s in a compiled application', toolbox);
elseif exist(toolbox, 'dir')
if ~silent,
ws = warning('backtrace', 'off');
warning('adding %s toolbox to your Matlab path', toolbox);
warning(ws); % return to the previous warning level
end
addpath(toolbox);
status = 1;
else
status = 0;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function path = unixpath(path)
path(path=='\') = '/'; % replace backward slashes with forward slashes
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% helper function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function status = hasfunction(funname, toolbox)
try
% call the function without any input arguments, which probably is inapropriate
feval(funname);
% it might be that the function without any input already works fine
status = true;
catch
% either the function returned an error, or the function is not available
% availability is influenced by the function being present and by having a
% license for the function, i.e. in a concurrent licensing setting it might
% be that all toolbox licenses are in use
m = lasterror;
if strcmp(m.identifier, 'MATLAB:license:checkouterror')
if nargin>1
warning('the %s toolbox is available, but you don''t have a license for it', toolbox);
else
warning('the function ''%s'' is available, but you don''t have a license for it', funname);
end
status = false;
elseif strcmp(m.identifier, 'MATLAB:UndefinedFunction')
status = false;
else
% the function seems to be available and it gave an unknown error,
% which is to be expected with inappropriate input arguments
status = true;
end
end
|
github
|
philippboehmsturm/antx-master
|
subsasgn.m
|
.m
|
antx-master/xspm8/@nifti/subsasgn.m
| 14,541 |
utf_8
|
ffdb84b7956d93c2f9a0a980ccbe8a22
|
function obj = subsasgn(obj,subs,varargin)
% Subscript assignment
% See subsref for meaning of fields.
% _______________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
%
% $Id: subsasgn.m 4136 2010-12-09 22:22:28Z guillaume $
switch subs(1).type,
case {'.'},
if numel(obj)~=nargin-2,
error('The number of outputs should match the number of inputs.');
end;
objs = struct(obj);
for i=1:length(varargin),
val = varargin{i};
obji = nifti(objs(i));
obji = fun(obji,subs,val);
objs(i) = struct(obji);
end;
obj = nifti(objs);
case {'()'},
objs = struct(obj);
if length(subs)>1,
t = subsref(objs,subs(1));
% A lot of this stuff is a little flakey, and may cause Matlab to bomb.
%
%if numel(t) ~= nargin-2,
% error('The number of outputs should match the number of inputs.');
%end;
for i=1:numel(t),
val = varargin{1};
obji = nifti(t(i));
obji = subsasgn(obji,subs(2:end),val);
t(i) = struct(obji);
end;
objs = subsasgn(objs,subs(1),t);
else
if numel(varargin)>1,
error('Illegal right hand side in assignment. Too many elements.');
end;
val = varargin{1};
if isa(val,'nifti'),
objs = subsasgn(objs,subs,struct(val));
elseif isempty(val),
objs = subsasgn(objs,subs,[]);
else
error('Assignment between unlike types is not allowed.');
end;
end;
obj = nifti(objs);
otherwise
error('Cell contents reference from a non-cell array object.');
end;
return;
%=======================================================================
%=======================================================================
function obj = fun(obj,subs,val)
% Subscript referencing
switch subs(1).type,
case {'.'},
objs = struct(obj);
for ii=1:numel(objs)
obj = objs(ii);
if any(strcmpi(subs(1).subs,{'dat'})),
if length(subs)>1,
val = subsasgn(obj.dat,subs(2:end),val);
end;
obj = assigndat(obj,val);
objs(ii) = obj;
continue;
end;
if isempty(obj.hdr), obj.hdr = empty_hdr; end;
if ~isfield(obj.hdr,'magic'), error('Not a NIFTI-1 header'); end;
if length(subs)>1, % && ~strcmpi(subs(1).subs,{'raw','dat'}),
val0 = subsref(nifti(obj),subs(1));
val1 = subsasgn(val0,subs(2:end),val);
else
val1 = val;
end;
switch(subs(1).subs)
case {'extras'}
if length(subs)>1,
obj.extras = subsasgn(obj.extras,subs(2:end),val);
else
obj.extras = val;
end;
case {'mat0'}
if ~isnumeric(val1) || ndims(val1)~=2 || any(size(val1)~=[4 4]) || sum((val1(4,:)-[0 0 0 1]).^2)>1e-8,
error('"mat0" should be a 4x4 matrix, with a last row of 0,0,0,1.');
end;
if obj.hdr.qform_code==0, obj.hdr.qform_code=2; end;
s = double(bitand(obj.hdr.xyzt_units,7));
if s
d = findindict(s,'units');
val1 = diag([[1 1 1]/d.rescale 1])*val1;
end;
obj.hdr = encode_qform0(double(val1), obj.hdr);
case {'mat0_intent'}
if isempty(val1),
obj.hdr.qform_code = 0;
else
if ~ischar(val1) && ~(isnumeric(val1) && numel(val1)==1),
error('"mat0_intent" should be a string or a scalar.');
end;
d = findindict(val1,'xform');
if ~isempty(d)
obj.hdr.qform_code = d.code;
end;
end;
case {'mat'}
if ~isnumeric(val1) || ndims(val1)~=2 || any(size(val1)~=[4 4]) || sum((val1(4,:)-[0 0 0 1]).^2)>1e-8
error('"mat" should be a 4x4 matrix, with a last row of 0,0,0,1.');
end;
if obj.hdr.sform_code==0, obj.hdr.sform_code=2; end;
s = double(bitand(obj.hdr.xyzt_units,7));
if s
d = findindict(s,'units');
val1 = diag([[1 1 1]/d.rescale 1])*val1;
end;
val1 = val1 * [eye(4,3) [1 1 1 1]'];
obj.hdr.srow_x = val1(1,:);
obj.hdr.srow_y = val1(2,:);
obj.hdr.srow_z = val1(3,:);
case {'mat_intent'}
if isempty(val1),
obj.hdr.sform_code = 0;
else
if ~ischar(val1) && ~(isnumeric(val1) && numel(val1)==1),
error('"mat_intent" should be a string or a scalar.');
end;
d = findindict(val1,'xform');
if ~isempty(d),
obj.hdr.sform_code = d.code;
end;
end;
case {'intent'}
if ~valid_fields(val1,{'code','param','name'})
obj.hdr.intent_code = 0;
obj.hdr.intent_p1 = 0;
obj.hdr.intent_p2 = 0;
obj.hdr.intent_p3 = 0;
obj.hdr.intent_name = '';
else
if ~isfield(val1,'code'),
val1.code = obj.hdr.intent_code;
end;
d = findindict(val1.code,'intent');
if ~isempty(d),
obj.hdr.intent_code = d.code;
if isfield(val1,'param'),
prm = [double(val1.param(:)) ; 0 ; 0; 0];
prm = [prm(1:length(d.param)) ; 0 ; 0; 0];
obj.hdr.intent_p1 = prm(1);
obj.hdr.intent_p2 = prm(2);
obj.hdr.intent_p3 = prm(3);
end;
if isfield(val1,'name'),
obj.hdr.intent_name = val1.name;
end;
end;
end;
case {'diminfo'}
if ~valid_fields(val1,{'frequency','phase','slice','slice_time'})
tmp = obj.hdr.dim_info;
for bit=1:6,
tmp = bitset(tmp,bit,0);
end;
obj.hdr.dim_info = tmp;
obj.hdr.slice_start = 0;
obj.hdr.slice_end = 0;
obj.hdr.slice_duration = 0;
obj.hdr.slice_code = 0;
else
if isfield(val1,'frequency'),
tmp = val1.frequency;
if ~isnumeric(tmp) || numel(tmp)~=1 || tmp<0 || tmp>3,
error('Invalid frequency direction');
end;
obj.hdr.dim_info = bitset(obj.hdr.dim_info,1,bitget(tmp,1));
obj.hdr.dim_info = bitset(obj.hdr.dim_info,2,bitget(tmp,2));
end;
if isfield(val1,'phase'),
tmp = val1.phase;
if ~isnumeric(tmp) || numel(tmp)~=1 || tmp<0 || tmp>3,
error('Invalid phase direction');
end;
obj.hdr.dim_info = bitset(obj.hdr.dim_info,3,bitget(tmp,1));
obj.hdr.dim_info = bitset(obj.hdr.dim_info,4,bitget(tmp,2));
end;
if isfield(val1,'slice'),
tmp = val1.slice;
if ~isnumeric(tmp) || numel(tmp)~=1 || tmp<0 || tmp>3,
error('Invalid slice direction');
end;
obj.hdr.dim_info = bitset(obj.hdr.dim_info,5,bitget(tmp,1));
obj.hdr.dim_info = bitset(obj.hdr.dim_info,6,bitget(tmp,2));
end;
if isfield(val1,'slice_time')
tim = val1.slice_time;
if ~valid_fields(tim,{'start','end','duration','code'}),
obj.hdr.slice_code = 0;
obj.hdr.slice_start = 0;
obj.hdr.end_slice = 0;
obj.hdr.slice_duration = 0;
else
% sld = double(bitget(obj.hdr.dim_info,5)) + 2*double(bitget(obj.hdr.dim_info,6));
if isfield(tim,'start'),
ss = double(tim.start);
if isnumeric(ss) && numel(ss)==1 && ~rem(ss,1), % && ss>=1 && ss<=obj.hdr.dim(sld+1)
obj.hdr.slice_start = ss-1;
else
error('Inappropriate "slice_time.start".');
end;
end;
if isfield(tim,'end'),
ss = double(tim.end);
if isnumeric(ss) && numel(ss)==1 && ~rem(ss,1), % && ss>=1 && ss<=obj.hdr.dim(sld+1)
obj.hdr.slice_end = ss-1;
else
error('Inappropriate "slice_time.end".');
end;
end;
if isfield(tim,'duration')
sd = double(tim.duration);
if isnumeric(sd) && numel(sd)==1,
s = double(bitand(obj.hdr.xyzt_units,24));
d = findindict(s,'units');
if ~isempty(d) && d.rescale, sd = sd/d.rescale; end;
obj.hdr.slice_duration = sd;
else
error('Inappropriate "slice_time.duration".');
end;
end;
if isfield(tim,'code'),
d = findindict(tim.code,'sliceorder');
if ~isempty(d),
obj.hdr.slice_code = d.code;
end;
end;
end;
end;
end;
case {'timing'}
if ~valid_fields(val1,{'toffset','tspace'}),
obj.hdr.pixdim(5) = 0;
obj.hdr.toffset = 0;
else
s = double(bitand(obj.hdr.xyzt_units,24));
d = findindict(s,'units');
if isfield(val1,'toffset'),
if isnumeric(val1.toffset) && numel(val1.toffset)==1,
if d.rescale,
val1.toffset = val1.toffset/d.rescale;
end;
obj.hdr.toffset = val1.toffset;
else
error('"timing.toffset" needs to be numeric with 1 element');
end;
end;
if isfield(val1,'tspace'),
if isnumeric(val1.tspace) && numel(val1.tspace)==1,
if d.rescale,
val1.tspace = val1.tspace/d.rescale;
end;
obj.hdr.pixdim(5) = val1.tspace;
else
error('"timing.tspace" needs to be numeric with 1 element');
end;
end;
end;
case {'descrip'}
if isempty(val1), val1 = char(val1); end;
if ischar(val1),
obj.hdr.descrip = val1;
else
error('"descrip" must be a string.');
end;
case {'cal'}
if isempty(val1),
obj.hdr.cal_min = 0;
obj.hdr.cal_max = 0;
else
if isnumeric(val1) && numel(val1)==2,
obj.hdr.cal_min = val1(1);
obj.hdr.cal_max = val1(2);
else
error('"cal" should contain two elements.');
end;
end;
case {'aux_file'}
if isempty(val1), val1 = char(val1); end;
if ischar(val1),
obj.hdr.aux_file = val1;
else
error('"aux_file" must be a string.');
end;
case {'hdr'}
error('hdr is a read-only field.');
obj.hdr = val1;
otherwise
error(['Reference to non-existent field ''' subs(1).subs '''.']);
end;
objs(ii) = obj;
end
obj = nifti(objs);
otherwise
error('This should not happen.');
end;
return;
%=======================================================================
%=======================================================================
function obj = assigndat(obj,val)
if isa(val,'file_array'),
sz = size(val);
if numel(sz)>7,
error('Too many dimensions in data.');
end;
sz = [sz 1 1 1 1 1 1 1];
sz = sz(1:7);
sval = struct(val);
d = findindict(sval.dtype,'dtype');
if isempty(d)
error(['Unknown datatype (' num2str(double(sval.datatype)) ').']);
end;
[pth,nam,suf] = fileparts(sval.fname);
if any(strcmp(suf,{'.img','.IMG'}))
val.offset = max(sval.offset,0);
obj.hdr.magic = ['ni1' char(0)];
elseif any(strcmp(suf,{'.nii','.NII'}))
val.offset = max(sval.offset,352);
obj.hdr.magic = ['n+1' char(0)];
else
error(['Unknown filename extension (' suf ').']);
end;
val.offset = (ceil(val.offset/16))*16;
obj.hdr.vox_offset = val.offset;
obj.hdr.dim(2:(numel(sz)+1)) = sz;
nd = max(find(obj.hdr.dim(2:end)>1));
if isempty(nd), nd = 3; end;
obj.hdr.dim(1) = nd;
obj.hdr.datatype = sval.dtype;
obj.hdr.bitpix = d.size*8;
if ~isempty(sval.scl_slope), obj.hdr.scl_slope = sval.scl_slope; end;
if ~isempty(sval.scl_inter), obj.hdr.scl_inter = sval.scl_inter; end;
obj.dat = val;
else
error('"raw" must be of class "file_array"');
end;
return;
function ok = valid_fields(val,allowed)
if isempty(val), ok = false; return; end;
if ~isstruct(val),
error(['Expecting a structure, not a ' class(val) '.']);
end;
fn = fieldnames(val);
for ii=1:length(fn),
if ~any(strcmpi(fn{ii},allowed)),
fprintf('Allowed fieldnames are:\n');
for i=1:length(allowed), fprintf(' %s\n', allowed{i}); end;
error(['"' fn{ii} '" is not a valid fieldname.']);
end
end
ok = true;
return;
|
github
|
philippboehmsturm/antx-master
|
subsref.m
|
.m
|
antx-master/xspm8/@nifti/subsref.m
| 8,741 |
utf_8
|
2d67123e4dc7e0b20a7ee19962324fee
|
function varargout = subsref(opt,subs)
% Subscript referencing
%
% Fields are:
% dat - a file-array representing the image data
% mat0 - a 9-parameter affine transform (from qform0)
% Note that the mapping is from voxels (where the first
% is considered to be at [1,1,1], to millimetres. See
% mat0_interp for the meaning of the transform.
% mat - a 12-parameter affine transform (from sform0)
% Note that the mapping is from voxels (where the first
% is considered to be at [1,1,1], to millimetres. See
% mat1_interp for the meaning of the transform.
% mat_intent - intention of mat. This field may be missing/empty.
% mat0_intent - intention of mat0. This field may be missing/empty.
% intent - interpretation of image. When present, this structure
% contains the fields
% code - name of interpretation
% params - parameters needed to interpret the image
% diminfo - MR encoding of different dimensions. This structure may
% contain some or all of the following fields
% frequency - a value of 1-3 indicating frequency direction
% phase - a value of 1-3 indicating phase direction
% slice - a value of 1-3 indicating slice direction
% slice_time - only present when "slice" field is present.
% Contains the following fields
% code - ascending/descending etc
% start - starting slice number
% end - ending slice number
% duration - duration of each slice acquisition
% Setting frequency, phase or slice to 0 will remove it.
% timing - timing information. When present, contains the fields
% toffset - acquisition time of first volume (seconds)
% tspace - time between sucessive volumes (seconds)
% descrip - a brief description of the image
% cal - a two-element vector containing cal_min and cal_max
% aux_file - name of an auxiliary file
% _______________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
%
% $Id: subsref.m 4136 2010-12-09 22:22:28Z guillaume $
varargout = rec(opt,subs);
return;
function c = rec(opt,subs)
switch subs(1).type,
case {'.'},
c = {};
opts = struct(opt);
for ii=1:numel(opts)
opt = nifti(opts(ii));
%if ~isstruct(opt)
% error('Attempt to reference field of non-structure array.');
%end;
h = opt.hdr;
if isempty(h),
%error('No header.');
h = empty_hdr;
end;
% NIFTI-1 FORMAT
switch(subs(1).subs)
case 'extras',
t = opt.extras;
case 'raw', % A hidden field
if isa(opt.dat,'file_array'),
tmp = struct(opt.dat);
tmp.scl_slope = [];
tmp.scl_inter = [];
t = file_array(tmp);
else
t = opt.dat;
end;
case 'dat',
t = opt.dat;
case 'mat0',
t = decode_qform0(h);
s = double(bitand(h.xyzt_units,7));
if s
d = findindict(s,'units');
if ~isempty(d)
t = diag([d.rescale*[1 1 1] 1])*t;
end;
end;
case 'mat0_intent',
d = findindict(h.qform_code,'xform');
if isempty(d) || d.code==0,
t = '';
else
t = d.label;
end;
case 'mat',
if h.sform_code > 0
t = double([h.srow_x ; h.srow_y ; h.srow_z ; 0 0 0 1]);
t = t * [eye(4,3) [-1 -1 -1 1]'];
else
t = decode_qform0(h);
end
s = double(bitand(h.xyzt_units,7));
if s
d = findindict(s,'units');
t = diag([d.rescale*[1 1 1] 1])*t;
end;
case 'mat_intent',
if h.sform_code>0,
t = h.sform_code;
else
t = h.qform_code;
end;
d = findindict(t,'xform');
if isempty(d) || d.code==0,
t = '';
else
t = d.label;
end;
case 'intent',
d = findindict(h.intent_code,'intent');
if isempty(d) || d.code == 0,
%t = struct('code','UNKNOWN','param',[]);
t = [];
else
t = struct('code',d.label,'param',...
double([h.intent_p1 h.intent_p2 h.intent_p3]), 'name',deblank(h.intent_name));
t.param = t.param(1:length(d.param));
end
case 'diminfo',
t = [];
tmp = bitand( h.dim_info ,3); if tmp, t.frequency = double(tmp); end;
tmp = bitand(bitshift(h.dim_info,-2),3); if tmp, t.phase = double(tmp); end;
tmp = bitand(bitshift(h.dim_info,-4),3); if tmp, t.slice = double(tmp); end;
% t = struct('frequency',bitand( h.dim_info ,3),...
% 'phase',bitand(bitshift(h.dim_info,-2),3),...
% 'slice',bitand(bitshift(h.dim_info,-4),3))
if isfield(t,'slice')
sc = double(h.slice_code);
ss = double(h.slice_start)+1;
se = double(h.slice_end)+1;
ss = max(ss,1);
se = min(se,double(h.dim(t.slice+1)));
sd = double(h.slice_duration);
s = double(bitand(h.xyzt_units,24));
d = findindict(s,'units');
if d.rescale, sd = sd*d.rescale; end;
ns = (se-ss+1);
d = findindict(sc,'sliceorder');
if isempty(d)
label = 'UNKNOWN';
else
label = d.label;
end;
t.slice_time = struct('code',label,'start',ss,'end',se,'duration',sd);
if 0, % Never
t.times = zeros(1,double(h.dim(t.slice+1)))+NaN;
switch sc,
case 0, % Unknown
t.times(ss:se) = zeros(1,ns);
case 1, % sequential increasing
t.times(ss:se) = (0:(ns-1))*sd;
case 2, % sequential decreasing
t.times(ss:se) = ((ns-1):-1:0)*sd;
case 3, % alternating increasing
t.times(ss:2:se) = (0:floor((ns+1)/2-1))*sd;
t.times((ss+1):2:se) = (floor((ns+1)/2):(ns-1))*sd;
case 4, % alternating decreasing
t.times(se:-2:ss) = (0:floor((ns+1)/2-1))*sd;
t.times(se:-2:(ss+1)) = (floor((ns+1)/2):(ns-1))*sd;
end;
end;
end;
case 'timing',
to = double(h.toffset);
dt = double(h.pixdim(5));
if to==0 && dt==0,
t = [];
else
s = double(bitand(h.xyzt_units,24));
d = findindict(s,'units');
if d.rescale,
to = to*d.rescale;
dt = dt*d.rescale;
end;
t = struct('toffset',to,'tspace',dt);
end;
case 'descrip',
t = deblank(h.descrip);
msk = find(t==0);
if any(msk), t=t(1:(msk(1)-1)); end;
case 'cal',
t = [double(h.cal_min) double(h.cal_max)];
if all(t==0), t = []; end;
case 'aux_file',
t = deblank(h.aux_file);
case 'hdr', % Hidden field
t = h;
otherwise
error(['Reference to non-existent field ''' subs(1).subs '''.']);
end;
if numel(subs)>1,
t = subsref(t,subs(2:end));
end;
c{ii} = t;
end;
case {'{}'},
error('Cell contents reference from a non-cell array object.');
case {'()'},
opt = struct(opt);
t = subsref(opt,subs(1));
if length(subs)>1
c = {};
for i=1:numel(t),
ti = nifti(t(i));
ti = rec(ti,subs(2:end));
c = {c{:}, ti{:}};
end;
else
c = {nifti(t)};
end;
otherwise
error('This should not happen.');
end;
|
github
|
philippboehmsturm/antx-master
|
create.m
|
.m
|
antx-master/xspm8/@nifti/create.m
| 1,963 |
utf_8
|
3c70cc73a693e9e93f4a3f3b60c91d1c
|
function create(obj,wrt)
% Create a NIFTI-1 file
% FORMAT create(obj)
% This writes out the header information for the nifti object
%
% create(obj,wrt)
% This also writes out an empty image volume if wrt==1
% _______________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
%
% $Id: create.m 1143 2008-02-07 19:33:33Z spm $
for i=1:numel(obj)
create_each(obj(i));
end;
function create_each(obj)
if ~isa(obj.dat,'file_array'),
error('Data must be a file-array');
end;
fname = obj.dat.fname;
if isempty(fname),
error('No filename to write to.');
end;
dt = obj.dat.dtype;
ok = write_hdr_raw(fname,obj.hdr,dt(end-1)=='B');
if ~ok,
error(['Unable to write header for "' fname '".']);
end;
write_extras(fname,obj.extras);
if nargin>2 && any(wrt==1),
% Create an empty image file if necessary
d = findindict(obj.hdr.datatype, 'dtype');
dim = double(obj.hdr.dim(2:end));
dim((double(obj.hdr.dim(1))+1):end) = 1;
nbytes = ceil(d.size*d.nelem*prod(dim(1:2)))*prod(dim(3:end))+double(obj.hdr.vox_offset);
[pth,nam,ext] = fileparts(obj.dat.fname);
if any(strcmp(deblank(obj.hdr.magic),{'n+1','nx1'})),
ext = '.nii';
else
ext = '.img';
end;
iname = fullfile(pth,[nam ext]);
fp = fopen(iname,'a+');
if fp==-1,
error(['Unable to create image for "' fname '".']);
end;
fseek(fp,0,'eof');
pos = ftell(fp);
if pos<nbytes,
bs = 2048; % Buffer-size
nbytes = nbytes - pos;
buf = uint8(0);
buf(bs) = 0;
while(nbytes>0)
if nbytes<bs, buf = buf(1:nbytes); end;
nw = fwrite(fp,buf,'uint8');
if nw<min(bs,nbytes),
fclose(fp);
error(['Problem while creating image for "' fname '".']);
end;
nbytes = nbytes - nw;
end;
end;
fclose(fp);
end;
return;
|
github
|
philippboehmsturm/antx-master
|
getdict.m
|
.m
|
antx-master/xspm8/@nifti/private/getdict.m
| 5,226 |
utf_8
|
94716c88e3d44be3c8207f14a928510a
|
function d = getdict
% Dictionary of NIFTI stuff
% _______________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
%
% $Id: getdict.m 1143 2008-02-07 19:33:33Z spm $
persistent dict;
if ~isempty(dict),
d = dict;
return;
end;
% Datatype
t = true;
f = false;
table = {...
0 ,'UNKNOWN' ,'uint8' ,@uint8 ,1,1 ,t,t,f
1 ,'BINARY' ,'uint1' ,@logical,1,1/8,t,t,f
256 ,'INT8' ,'int8' ,@int8 ,1,1 ,t,f,t
2 ,'UINT8' ,'uint8' ,@uint8 ,1,1 ,t,t,t
4 ,'INT16' ,'int16' ,@int16 ,1,2 ,t,f,t
512 ,'UINT16' ,'uint16' ,@uint16 ,1,2 ,t,t,t
8 ,'INT32' ,'int32' ,@int32 ,1,4 ,t,f,t
768 ,'UINT32' ,'uint32' ,@uint32 ,1,4 ,t,t,t
1024,'INT64' ,'int64' ,@int64 ,1,8 ,t,f,f
1280,'UINT64' ,'uint64' ,@uint64 ,1,8 ,t,t,f
16 ,'FLOAT32' ,'float32' ,@single ,1,4 ,f,f,t
64 ,'FLOAT64' ,'double' ,@double ,1,8 ,f,f,t
1536,'FLOAT128' ,'float128',@crash ,1,16 ,f,f,f
32 ,'COMPLEX64' ,'float32' ,@single ,2,4 ,f,f,f
1792,'COMPLEX128','double' ,@double ,2,8 ,f,f,f
2048,'COMPLEX256','float128',@crash ,2,16 ,f,f,f
128 ,'RGB24' ,'uint8' ,@uint8 ,3,1 ,t,t,f};
dtype = struct(...
'code' ,table(:,1),...
'label' ,table(:,2),...
'prec' ,table(:,3),...
'conv' ,table(:,4),...
'nelem' ,table(:,5),...
'size' ,table(:,6),...
'isint' ,table(:,7),...
'unsigned' ,table(:,8),...
'min',-Inf,'max',Inf',...
'supported',table(:,9));
for i=1:length(dtype),
if dtype(i).isint
if dtype(i).unsigned
dtype(i).min = 0;
dtype(i).max = 2^(8*dtype(i).size)-1;
else
dtype(i).min = -2^(8*dtype(i).size-1);
dtype(i).max = 2^(8*dtype(i).size-1)-1;
end;
end;
end;
% Intent
table = {...
0 ,'NONE' ,'None',{}
2 ,'CORREL' ,'Correlation statistic',{'DOF'}
3 ,'TTEST' ,'T-statistic',{'DOF'}
4 ,'FTEST' ,'F-statistic',{'numerator DOF','denominator DOF'}
5 ,'ZSCORE' ,'Z-score',{}
6 ,'CHISQ' ,'Chi-squared distribution',{'DOF'}
7 ,'BETA' ,'Beta distribution',{'a','b'}
8 ,'BINOM' ,'Binomial distribution',...
{'number of trials','probability per trial'}
9 ,'GAMMA' ,'Gamma distribution',{'shape','scale'}
10 ,'POISSON' ,'Poisson distribution',{'mean'}
11 ,'NORMAL' ,'Normal distribution',{'mean','standard deviation'}
12 ,'FTEST_NONC' ,'F-statistic noncentral',...
{'numerator DOF','denominator DOF','numerator noncentrality parameter'}
13 ,'CHISQ_NONC' ,'Chi-squared noncentral',{'DOF','noncentrality parameter'}
14 ,'LOGISTIC' ,'Logistic distribution',{'location','scale'}
15 ,'LAPLACE' ,'Laplace distribution',{'location','scale'}
16 ,'UNIFORM' ,'Uniform distribition',{'lower end','upper end'}
17 ,'TTEST_NONC' ,'T-statistic noncentral',{'DOF','noncentrality parameter'}
18 ,'WEIBULL' ,'Weibull distribution',{'location','scale','power'}
19 ,'CHI' ,'Chi distribution',{'DOF'}
20 ,'INVGAUSS' ,'Inverse Gaussian distribution',{'mu','lambda'}
21 ,'EXTVAL' ,'Extreme Value distribution',{'location','scale'}
22 ,'PVAL' ,'P-value',{}
23 ,'LOGPVAL' ,'Log P-value',{}
24 ,'LOG10PVAL' ,'Log_10 P-value',{}
1001,'ESTIMATE' ,'Estimate',{}
1002,'LABEL' ,'Label index',{}
1003,'NEURONAMES' ,'NeuroNames index',{}
1004,'MATRIX' ,'General matrix',{'M','N'}
1005,'MATRIX_SYM' ,'Symmetric matrix',{}
1006,'DISPLACEMENT' ,'Displacement vector',{}
1007,'VECTOR' ,'Vector',{}
1008,'POINTS' ,'Pointset',{}
1009,'TRIANGLE' ,'Triangle',{}
1010,'QUATERNION' ,'Quaternion',{}
1011,'DIMLESS' ,'Dimensionless',{}
};
intent = struct('code',table(:,1),'label',table(:,2),...
'fullname',table(:,3),'param',table(:,4));
% Units
table = {...
0, 1,'UNKNOWN'
1,1000,'m'
2, 1,'mm'
3,1e-3,'um'
8, 1,'s'
16,1e-3,'ms'
24,1e-6,'us'
32, 1,'Hz'
40, 1,'ppm'
48, 1,'rads'};
units = struct('code',table(:,1),'label',table(:,3),'rescale',table(:,2));
% Reference space
% code = {0,1,2,3,4};
table = {...
0,'UNKNOWN'
1,'Scanner Anat'
2,'Aligned Anat'
3,'Talairach'
4,'MNI_152'};
anat = struct('code',table(:,1),'label',table(:,2));
% Slice Ordering
table = {...
0,'UNKNOWN'
1,'sequential_increasing'
2,'sequential_decreasing'
3,'alternating_increasing'
4,'alternating_decreasing'};
sliceorder = struct('code',table(:,1),'label',table(:,2));
% Q/S Form Interpretation
table = {...
0,'UNKNOWN'
1,'Scanner'
2,'Aligned'
3,'Talairach'
4,'MNI152'};
xform = struct('code',table(:,1),'label',table(:,2));
dict = struct('dtype',dtype,'intent',intent,'units',units,...
'space',anat,'sliceorder',sliceorder,'xform',xform);
d = dict;
return;
function varargout = crash(varargin)
error('There is a NIFTI-1 data format problem (an invalid datatype).');
|
github
|
philippboehmsturm/antx-master
|
write_extras.m
|
.m
|
antx-master/xspm8/@nifti/private/write_extras.m
| 876 |
utf_8
|
f3686c5d5d6e88449972819a302e8c5c
|
function extras = write_extras(fname,extras)
% Write extra bits of information
%_______________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
%
% $Id: write_extras.m 1143 2008-02-07 19:33:33Z spm $
[pth,nam,ext] = fileparts(fname);
switch ext
case {'.hdr','.img','.nii'}
mname = fullfile(pth,[nam '.mat']);
case {'.HDR','.IMG','.NII'}
mname = fullfile(pth,[nam '.MAT']);
otherwise
mname = fullfile(pth,[nam '.mat']);
end
if isstruct(extras) && ~isempty(fieldnames(extras)),
savefields(mname,extras);
end;
function savefields(fnam,p)
if length(p)>1, error('Can''t save fields.'); end;
fn = fieldnames(p);
for i_=1:length(fn),
eval([fn{i_} '= p.' fn{i_} ';']);
end;
if str2num(version('-release'))>=14,
fn = {'-V6',fn{:}};
end;
if numel(fn)>0,
save(fnam,fn{:});
end;
return;
|
github
|
philippboehmsturm/antx-master
|
setpath.m
|
.m
|
antx-master/freiburgLight/setpath.m
| 1,503 |
utf_8
|
61605b0bdc54dfec7f0f06543e7c6327
|
%% SET PATHS For freiburg tools
% setpath or setpath(1) : SETS PATHS For freiburg tools
% setpath(0) :REMOVES PATHS For freiburg tools
function setpath(arg)
warning off;
if exist('arg')==0; arg=1; end
if arg==1
addpath(genpath(fullfile(pwd,'matlab', 'matlab_new')))
addpath(genpath(fullfile(pwd,'matlab', 'diffusion')))
addpath(genpath(fullfile(pwd,'matlab', 'spm8')))
addpath( fullfile(pwd, 'allen'))
addpath( fullfile(pwd, 'fiberViewer3D'))
addpath( fullfile(pwd, 'AMA'))
% disp('PATHS of freiburg-tools added to pathlist');
disp('..PATHS: [matlabToolsFreiburg] added to matlab-path >> useful GUIS: AMA_gui2 , atlasbrowser , fiberViewer3D , Startgui_fv3d');
else
rmpath(genpath(fullfile(pwd,'matlab', 'matlab_new')))
rmpath(genpath(fullfile(pwd,'matlab', 'diffusion')))
rmpath(genpath(fullfile(pwd,'matlab', 'spm8')))
rmpath( fullfile(pwd, 'allen'))
rmpath( fullfile(pwd, 'fiberViewer3D'))
rmpath( fullfile(pwd, 'AMA'))
disp('PATHS of freiburg tools removed from pathlist');
end
warning on;
%% gui main function
%atlas: atlasbrowser(allen): BrAt_StartGui
% ama: automaticMouseAnalyzer : AMA_gui2
% visualization: fiberViewer3D
% Startgui_fv3d: fmri+restingState netzwerke
% k=dir(pwd);
% k(1:2)=[];
% k([k(:).isdir]==0)=[];
%
% for i=1:length(k)
% addpath(genpath(pwd,k(i).name));
% end
|
github
|
philippboehmsturm/antx-master
|
buildtable.m
|
.m
|
antx-master/freiburgLight/allen/buildtable.m
| 1,038 |
utf_8
|
533e7574bda6931cad1b44fc010347b9
|
function [table idxLUT] = buildtable(tree)
table = recur(tree);
cnt = 1;
for k = 1:length(table)
for j = 1:length(table{k})
str = table{k}(j).allinfo;
str.includes = [table{k}(1:j-1).id];
str.children = [table{k}(j).children];
str.nameinlist = [repmat('-',[1 j]) str.name];
idxLUT(cnt) = str;
cnt = cnt + 1;
end
end
[dummy ids] = unique([idxLUT.id],'first');
ids = sort(ids,'ascend');
idxLUT = idxLUT(ids);
function list = recur(tree)
list = [];
children = [];
for k=1:length(tree.children)
clist = recur(tree.children{k});
children = [children ; cellfun(@(x) x(end).id,clist)];
list = [list ; clist];
end
item.id = tree.id;
item.acro = tree.acronym;
item.atlas_id =tree.atlas_id;
item.children = children;
item.allinfo = rmfield(tree,'children');
if isfield(tree,'name')
item.name = tree.name;
else
item.name = '<>';
end
if not(isempty(list))
for k=1:length(list)
list{k} = [item list{k}];
end
else
list = {item};
end
|
github
|
philippboehmsturm/antx-master
|
spm_uitab.m
|
.m
|
antx-master/freiburgLight/matlab/spm8/spm_uitab.m
| 7,695 |
utf_8
|
e80b69279b276644a59f0e838bc07817
|
function [handles] = spm_uitab(hparent,labels,callbacks,...
tag,active,height,tab_height)
% Create tabs in the SPM Graphics window
% FORMAT [handles] = spm_uitab(hfig,labels,callbacks,...
% tag,active,height,tab_height)
% This functiuon creates tabs in the SPM graphics window.
% These tabs may be associated with different sets of axes and uicontrol,
% through the use of callback functions linked to the tabs.
% IN:
% - hparent: the handle of the parent of the tabs (can be the SPM graphics
% windows, or the handle of the uipanel of a former spm_uitab...)
% - labels: a cell array of string containing the labels of the tabs
% - callbacks: a cell array of strings which will be evaluated using the
% 'eval' function when clicking on a tab
% - tag: a string which is the tags associated with the tabs (useful for
% finding them in a window...)
% - active: the index of the active tab when creating the uitabs (default
% = 1, ie the first tab is active)
% - height: the relative height of the tab panels within its parent
% spatial extent (default = 1)
% - tab_height: the relative height of the tabs within its parent spatial
% extent (default = 1)
% OUT:
% - handles: a structure of handles for the differents tab objects.
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% Jean Daunizeau
% $Id: spm_uitab.m 3062 2009-04-17 14:07:40Z jean $
Ntabs = length(labels);
if ~exist('callbacks','var') || isempty(callbacks)
for i=1:Ntabs
callbacks{i} = [];
end
end
if ~exist('tag','var') || isempty(tag)
tag = '';
end
if ~exist('active','var') || isempty(active)
active = 1;
end
if ~exist('height','var') || isempty(height)
height = 1;
end
if ~exist('tab_height','var') || isempty(tab_height)
tab_height = 0.025;
end
if ~isequal(get(hparent,'type'),'figure')
set(hparent,'units','normalized')
POS = get(hparent,'position');
pos1 = [POS(1)+0.02,POS(2)+0.01,POS(3)-0.04,POS(4)-(tab_height+0.035)];
dx = 0.1*(POS(3)-0.04)./0.98;
dx2 = [0.04,0.93]*(POS(3)-0.04)./0.98;
else
pos1 = [0.01 0.005 0.98 1-(tab_height+0.01)];
dx = 0.1;
dx2 = [0.04,0.93];
end
pos1(4) = pos1(4).*height;
COLOR = 0.95*[1 1 1];
handles.hp = uipanel(...
'parent',hparent,...
'position',pos1,...
'BorderType','beveledout',...
'BackgroundColor',COLOR,...
'tag',tag);
set(handles.hp,'units','normalized');
xl = pos1(1);
yu = pos1(2) +pos1(4);
ddx = 0.0025;
ddy = 0.005;
dy = tab_height;
if Ntabs > 9
handles.hs(1) = uicontrol(...
'parent',hparent,...'enable','off',...
'style','pushbutton',...
'units','normalized','position',[xl yu dx2(1) dy],...
'SelectionHighlight','off',...
'BackgroundColor',COLOR,...
'callback',@doScroll,...
'value',0,'min',0,'max',Ntabs-9,...
'string','<',...
'tag',tag,...
'BusyAction','cancel',...
'Interruptible','off');
handles.hs(2) = uicontrol(...
'parent',hparent,...
'style','pushbutton',...
'units','normalized','position',[xl+dx2(2) yu 0.05 dy],...
'SelectionHighlight','off',...
'BackgroundColor',COLOR,...
'callback',@doScroll,...
'value',1,'min',1,'max',Ntabs-9,...
'string','>',...
'tag',tag,...
'BusyAction','cancel',...
'Interruptible','off');
set(handles.hs,'units','normalized')
xl = xl + dx2(1);
end
for i =1:min([Ntabs,9])
pos = [xl+dx*(i-1) yu dx dy];
handles.htab(i) = uicontrol(...
'parent',hparent,...
'style','pushbutton',...
'units','normalized','position',pos,...
'SelectionHighlight','off',...
'string',labels{i},...
'BackgroundColor',COLOR,...
'tag',tag);
set(handles.htab(i),'units','normalized')
pos = [xl+dx*(i-1)+ddx yu-ddy dx-2*ddx 2*ddy];
handles.hh(i) = uicontrol(...
'parent',hparent,...
'style','text',...
'units','normalized','position',pos,...
'BackgroundColor',COLOR,...
'tag',tag);
set(handles.hh(i),'units','normalized')
end
try
set(handles.hh(active),'visible','on')
catch
active = 1;
set(handles.hh(active),'visible','on')
end
others = setdiff(1:min([Ntabs,9]),active);
set(handles.htab(active),...
'FontWeight','bold');
set(handles.hh(others),'visible','off');
set(handles.htab(others),...
'ForegroundColor',0.25*[1 1 1]);
ud.handles = handles;
ud.Ntabs = Ntabs;
for i =1:min([Ntabs,9])
ud.ind = i;
ud.callback = callbacks{i};
set(handles.htab(i),'callback',@doChoose,'userdata',ud,...
'BusyAction','cancel',...
'Interruptible','off');
if i > 9
set(handles.htab(i),'visible','off');
end
end
if Ntabs > 9
UD.in = [1:9];
UD.Ntabs = Ntabs;
UD.h = handles;
UD.active = active;
UD.who = -1;
UD.callbacks = callbacks;
UD.labels = labels;
set(handles.hs(1),'userdata',UD,'enable','off');
UD.who = 1;
set(handles.hs(2),'userdata',UD);
end
%==========================================================================
% doChoose
%==========================================================================
function doChoose(o1,o2)
ud = get(o1,'userdata');
% Do nothing if called tab is current (active) tab
if ~strcmp(get(ud.handles.htab(ud.ind),'FontWeight'),'bold')
spm('pointer','watch');
set(ud.handles.hh(ud.ind),'visible','on');
set(ud.handles.htab(ud.ind),...
'ForegroundColor',0*[1 1 1],...
'FontWeight','bold');
others = setdiff(1:length(ud.handles.hh),ud.ind);
set(ud.handles.hh(others),'visible','off');
set(ud.handles.htab(others),...
'ForegroundColor',0.25*[1 1 1],...
'FontWeight','normal');
if ud.Ntabs >9
UD = get(ud.handles.hs(1),'userdata');
UD.active = UD.in(ud.ind);
UD.who = -1;
set(ud.handles.hs(1),'userdata',UD);
UD.who = 1;
set(ud.handles.hs(2),'userdata',UD);
end
drawnow
if ~isempty(ud.callback)
if isa(ud.callback, 'function_handle')
feval(ud.callback);
else
eval(ud.callback);
end
end
drawnow
spm('pointer','arrow');
end
%==========================================================================
% doScroll
%==========================================================================
function doScroll(o1,o2)
ud = get(o1,'userdata');
% active = ud.in(ud.active);
ud.in = ud.in + ud.who;
if min(ud.in) ==1
set(ud.h.hs(1),'enable','off');
set(ud.h.hs(2),'enable','on');
elseif max(ud.in) ==ud.Ntabs
set(ud.h.hs(1),'enable','on');
set(ud.h.hs(2),'enable','off');
else
set(ud.h.hs,'enable','on');
end
UD.handles = ud.h;
UD.Ntabs = ud.Ntabs;
for i = 1:length(ud.in)
UD.ind = i;
UD.callback = ud.callbacks{ud.in(i)};
set(ud.h.htab(i),'userdata',UD,...
'string',ud.labels{ud.in(i)});
if ismember(ud.active,ud.in)
ind = find(ud.in==ud.active);
set(ud.h.hh(ind),'visible','on');
set(ud.h.htab(ind),...
'ForegroundColor',0*[1 1 1],...
'FontWeight','bold');
others = setdiff(1:9,ind);
set(ud.h.hh(others),'visible','off');
set(ud.h.htab(others),...
'ForegroundColor',0.25*[1 1 1],...
'FontWeight','normal');
else
others = 1:9;
set(ud.h.hh(others),'visible','off');
set(ud.h.htab(others),...
'ForegroundColor',0.25*[1 1 1],...
'FontWeight','normal');
end
end
ud.who = -1;
set(ud.h.hs(1),'userdata',ud)
ud.who = 1;
set(ud.h.hs(2),'userdata',ud)
|
github
|
philippboehmsturm/antx-master
|
spm_vb_ppm_anova.m
|
.m
|
antx-master/freiburgLight/matlab/spm8/spm_vb_ppm_anova.m
| 3,873 |
utf_8
|
5360b2f4d1d7fe1f61c455b53a468fd5
|
function spm_vb_ppm_anova(SPM)
% Bayesian ANOVA using model comparison
% FORMAT spm_vb_ppm_anova(SPM)
%
% SPM - Data structure corresponding to a full model (ie. one
% containing all experimental conditions).
%
% This function creates images of differences in log evidence
% which characterise the average effect, main effects and interactions
% in a factorial design.
%
% The factorial design is specified in SPM.factor. For a one-way ANOVA
% the images
%
% avg_effect.img
% main_effect.img
%
% are produced. For a two-way ANOVA the following images are produced
%
% avg_effect.img
% main_effect_'factor1'.img
% main_effect_'factor2'.img
% interaction.img
%
% These images can then be thresholded. For example a threshold of 4.6
% corresponds to a posterior effect probability of [exp(4.6)] = 0.999.
% See paper VB4 for more details.
%_______________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% Will Penny
% $Id: spm_vb_ppm_anova.m 1143 2008-02-07 19:33:33Z spm $
disp('Warning: spm_vb_ppm_anova only works for single session data.');
model = spm_vb_models(SPM,SPM.factor);
analysis_dir = pwd;
for m=1:length(model)-1,
model_subdir = ['model_',int2str(m)];
mkdir(analysis_dir,model_subdir);
SPM.swd = fullfile(analysis_dir,model_subdir);
SPM.Sess(1).U = model(m).U;
SPM.Sess(1).U = spm_get_ons(SPM,1);
SPM = spm_fMRI_design(SPM,0); % 0 = don't save SPM.mat
SPM.PPM.update_F = 1; % Compute evidence for each model
SPM.PPM.compute_det_D = 1;
spm_spm_vb(SPM);
end
% Compute differences in contributions to log-evidence images
% to assess main effects and interactions
nf = length(SPM.factor);
if nf==1
% For a single factor
% Average effect
image1 = fullfile(analysis_dir, 'model_1','LogEv.img');
image2 = fullfile(analysis_dir, 'model_2','LogEv.img');
imout = fullfile(analysis_dir, 'avg_effect.img');
img_subtract(image1,image2,imout);
% Main effect of factor
image1 = fullfile(analysis_dir, 'model_2','LogEv.img');
image2 = fullfile(analysis_dir, 'LogEv.img');
imout = fullfile(analysis_dir, 'main_effect.img');
img_subtract(image1,image2,imout);
elseif nf==2
% For two factors
% Average effect
image1 = fullfile(analysis_dir, 'model_1','LogEv.img');
image2 = fullfile(analysis_dir, 'model_2','LogEv.img');
imout = fullfile(analysis_dir, 'avg_effect.img');
img_subtract(image1,image2,imout);
% Main effect of factor 1
image1 = fullfile(analysis_dir, 'model_2','LogEv.img');
image2 = fullfile(analysis_dir, 'model_3','LogEv.img');
imout = fullfile(analysis_dir, ['main_effect_',SPM.factor(1).name,'.img']);
img_subtract(image1,image2,imout);
% Main effect of factor 2
image1 = fullfile(analysis_dir, 'model_2','LogEv.img');
image2 = fullfile(analysis_dir, 'model_4','LogEv.img');
imout = fullfile(analysis_dir, ['main_effect_',SPM.factor(2).name,'.img']);
img_subtract(image1,image2,imout);
% Interaction
image1 = fullfile(analysis_dir, 'model_5','LogEv.img');
image2 = fullfile(analysis_dir, 'LogEv.img');
imout = fullfile(analysis_dir, 'interaction.img');
img_subtract(image1,image2,imout);
end
%-----------------------------------------------------------------------
function img_subtract(image1,image2,image_out)
% Subtract image 1 from image 2 and write to image out
% Note: parameters are names of files
Vi = spm_vol(strvcat(image1,image2));
Vo = struct(...
'fname', image_out,...
'dim', [Vi(1).dim(1:3)],...
'dt', [spm_type('float32') spm_platform('bigend')],...
'mat', Vi(1).mat,...
'descrip', 'Difference in Log Evidence');
f = 'i2-i1';
flags = {0,0,1};
Vo = spm_imcalc(Vi,Vo,f,flags);
|
github
|
philippboehmsturm/antx-master
|
spm_coreg_paul.m
|
.m
|
antx-master/freiburgLight/matlab/spm8/spm_coreg_paul.m
| 15,181 |
utf_8
|
9d4ef60b26a7fd206c6d5484ab26092e
|
function [x ffmin] = spm_coreg_paul(varargin)
% Between modality coregistration using information theory
% FORMAT x = spm_coreg(VG,VF,flags)
% VG - handle for reference image (see spm_vol).
% VF - handle for source (moved) image.
% flags - a structure containing the following elements:
% sep - optimisation sampling steps (mm)
% default: [4 2]
% params - starting estimates (6 elements)
% default: [0 0 0 0 0 0]
% cost_fun - cost function string:
% 'mi' - Mutual Information
% 'nmi' - Normalised Mutual Information
% 'ecc' - Entropy Correlation Coefficient
% 'ncc' - Normalised Cross Correlation
% default: 'nmi'
% tol - tolerences for accuracy of each param
% default: [0.02 0.02 0.02 0.001 0.001 0.001]
% fwhm - smoothing to apply to 256x256 joint histogram
% default: [7 7]
% graphics - display coregistration outputs
% default: ~spm('CmdLine')
%
% x - the parameters describing the rigid body rotation, such that a
% mapping from voxels in G to voxels in F is attained by:
% VF.mat\spm_matrix(x(:)')*VG.mat
%
% At the end, the voxel-to-voxel affine transformation matrix is
% displayed, along with the histograms for the images in the original
% orientations, and the final orientations. The registered images are
% displayed at the bottom.
%__________________________________________________________________________
%
% The registration method used here is based on the work described in:
% A Collignon, F Maes, D Delaere, D Vandermeulen, P Suetens & G Marchal
% (1995) "Automated Multi-modality Image Registration Based On
% Information Theory". In the proceedings of Information Processing in
% Medical Imaging (1995). Y. Bizais et al. (eds.). Kluwer Academic
% Publishers.
%
% The original interpolation method described in this paper has been
% changed in order to give a smoother cost function. The images are
% also smoothed slightly, as is the histogram. This is all in order to
% make the cost function as smooth as possible, to give faster convergence
% and less chance of local minima.
%__________________________________________________________________________
% Copyright (C) 1994-2011 Wellcome Trust Centre for Neuroimaging
% John Ashburner
% $Id: spm_coreg.m 4156 2011-01-11 19:03:31Z guillaume $
%==========================================================================
% References
%==========================================================================
%
% Mutual Information
% -------------------------------------------------------------------------
% Collignon, Maes, Delaere, Vandermeulen, Suetens & Marchal (1995).
% "Automated multi-modality image registration based on information theory".
% In Bizais, Barillot & Di Paola, editors, Proc. Information Processing
% in Medical Imaging, pages 263--274, Dordrecht, The Netherlands, 1995.
% Kluwer Academic Publishers.
%
% Wells III, Viola, Atsumi, Nakajima & Kikinis (1996).
% "Multi-modal volume registration by maximisation of mutual information".
% Medical Image Analysis, 1(1):35-51, 1996.
%
% Entropy Correlation Coefficient
% -------------------------------------------------------------------------
% Maes, Collignon, Vandermeulen, Marchal & Suetens (1997).
% "Multimodality image registration by maximisation of mutual
% information". IEEE Transactions on Medical Imaging 16(2):187-198
%
% Normalised Mutual Information
% -------------------------------------------------------------------------
% Studholme, Hill & Hawkes (1998).
% "A normalized entropy measure of 3-D medical image alignment".
% in Proc. Medical Imaging 1998, vol. 3338, San Diego, CA, pp. 132-143.
%
% Optimisation
% -------------------------------------------------------------------------
% Press, Teukolsky, Vetterling & Flannery (1992).
% "Numerical Recipes in C (Second Edition)".
% Published by Cambridge.
%==========================================================================
if nargin >= 4
x = optfun(varargin{:});
return;
end
def_flags = spm_get_defaults('coreg.estimate');
def_flags.params = [0 0 0 0 0 0];
def_flags.graphics = ~spm('CmdLine');
if nargin < 3
flags = def_flags;
else
flags = varargin{3};
fnms = fieldnames(def_flags);
for i=1:length(fnms)
if ~isfield(flags,fnms{i})
flags.(fnms{i}) = def_flags.(fnms{i});
end
end
end
if nargin < 1
VG = spm_vol(spm_select(1,'image','Select reference image'));
else
VG = varargin{1};
if ischar(VG), VG = spm_vol(VG); end
end
if nargin < 2
VF = spm_vol(spm_select(Inf,'image','Select moved image(s)'));
else
VF = varargin{2};
if ischar(VF) || iscellstr(VF), VF = spm_vol(char(VF)); end;
end
if ~isfield(VG, 'uint8')
VG.uint8 = loaduint8(VG);
vxg = sqrt(sum(VG.mat(1:3,1:3).^2));
fwhmg = sqrt(max([1 1 1]*flags.sep(end)^2 - vxg.^2, [0 0 0]))./vxg;
VG = smooth_uint8(VG,fwhmg); % Note side effects
end
sc = flags.tol(:)'; % Required accuracy
sc = sc(1:length(flags.params));
xi = diag(sc*20);
x = zeros(numel(VF),numel(flags.params));
for k=1:numel(VF)
VFk = VF(k);
if ~isfield(VFk, 'uint8')
VFk.uint8 = loaduint8(VFk);
vxf = sqrt(sum(VFk.mat(1:3,1:3).^2));
fwhmf = sqrt(max([1 1 1]*flags.sep(end)^2 - vxf.^2, [0 0 0]))./vxf;
VFk = smooth_uint8(VFk,fwhmf); % Note side effects
end
xk = flags.params(:);
for samp=flags.sep(:)'
[xk ffmin ] = spm_powell(xk(:), xi,sc,mfilename,VG,VFk,samp,flags.cost_fun,flags.fwhm);
x(k,:) = xk(:)';
ffmin(k,:) = ffmin(:)';
end
if flags.graphics
display_results(VG(1),VFk(1),xk(:)',flags);
end
end
%==========================================================================
% function o = optfun(x,VG,VF,s,cf,fwhm)
%==========================================================================
function o = optfun(x,VG,VF,s,cf,fwhm)
% The function that is minimised.
if nargin<6, fwhm = [7 7]; end
if nargin<5, cf = 'mi'; end
if nargin<4, s = [1 1 1]; end
% Voxel sizes
vxg = sqrt(sum(VG.mat(1:3,1:3).^2));sg = s./vxg;
% Create the joint histogram
H = spm_hist2(VG.uint8,VF.uint8, VF.mat\spm_matrix(x(:)')*VG.mat ,sg);
% Smooth the histogram
lim = ceil(2*fwhm);
krn1 = smoothing_kernel(fwhm(1),-lim(1):lim(1)) ; krn1 = krn1/sum(krn1); H = conv2(H,krn1);
krn2 = smoothing_kernel(fwhm(2),-lim(2):lim(2))'; krn2 = krn2/sum(krn2); H = conv2(H,krn2);
% Compute cost function from histogram
H = H+eps;
sh = sum(H(:));
H = H/sh;
s1 = sum(H,1);
s2 = sum(H,2);
switch lower(cf)
case 'mi'
% Mutual Information:
H = H.*log2(H./(s2*s1));
mi = sum(H(:));
o = -mi;
case 'ecc'
% Entropy Correlation Coefficient of:
% Maes, Collignon, Vandermeulen, Marchal & Suetens (1997).
% "Multimodality image registration by maximisation of mutual
% information". IEEE Transactions on Medical Imaging 16(2):187-198
H = H.*log2(H./(s2*s1));
mi = sum(H(:));
ecc = -2*mi/(sum(s1.*log2(s1))+sum(s2.*log2(s2)));
o = -ecc;
case 'nmi'
% Normalised Mutual Information of:
% Studholme, Hill & Hawkes (1998).
% "A normalized entropy measure of 3-D medical image alignment".
% in Proc. Medical Imaging 1998, vol. 3338, San Diego, CA, pp. 132-143.
nmi = (sum(s1.*log2(s1))+sum(s2.*log2(s2)))/sum(sum(H.*log2(H)));
o = -nmi;
case 'ncc'
% Normalised Cross Correlation
i = 1:size(H,1);
j = 1:size(H,2);
m1 = sum(s2.*i');
m2 = sum(s1.*j);
sig1 = sqrt(sum(s2.*(i'-m1).^2));
sig2 = sqrt(sum(s1.*(j -m2).^2));
[i,j] = ndgrid(i-m1,j-m2);
ncc = sum(sum(H.*i.*j))/(sig1*sig2);
o = -ncc;
otherwise
error('Invalid cost function specified');
end
%==========================================================================
% function udat = loaduint8(V)
%==========================================================================
function udat = loaduint8(V)
% Load data from file indicated by V into an array of unsigned bytes.
if size(V.pinfo,2)==1 && V.pinfo(1) == 2
mx = 255*V.pinfo(1) + V.pinfo(2);
mn = V.pinfo(2);
else
spm_progress_bar('Init',V.dim(3),...
['Computing max/min of ' spm_str_manip(V.fname,'t')],...
'Planes complete');
mx = -Inf; mn = Inf;
for p=1:V.dim(3)
img = spm_slice_vol(V,spm_matrix([0 0 p]),V.dim(1:2),1);
mx = max([max(img(:))+paccuracy(V,p) mx]);
mn = min([min(img(:)) mn]);
spm_progress_bar('Set',p);
end
end
% Another pass to find a maximum that allows a few hot-spots in the data.
spm_progress_bar('Init',V.dim(3),...
['2nd pass max/min of ' spm_str_manip(V.fname,'t')],...
'Planes complete');
nh = 2048;
h = zeros(nh,1);
for p=1:V.dim(3)
img = spm_slice_vol(V,spm_matrix([0 0 p]),V.dim(1:2),1);
img = img(isfinite(img));
img = round((img+((mx-mn)/(nh-1)-mn))*((nh-1)/(mx-mn)));
h = h + accumarray(img,1,[nh 1]);
spm_progress_bar('Set',p);
end
tmp = [find(cumsum(h)/sum(h)>0.9999); nh];
mx = (mn*nh-mx+tmp(1)*(mx-mn))/(nh-1);
% Load data from file indicated by V into an array of unsigned bytes.
spm_progress_bar('Init',V.dim(3),...
['Loading ' spm_str_manip(V.fname,'t')],...
'Planes loaded');
udat = zeros(V.dim,'uint8');
st = rand('state'); % st = rng;
rand('state',100); % rng(100,'v5uniform'); % rng('defaults');
for p=1:V.dim(3)
img = spm_slice_vol(V,spm_matrix([0 0 p]),V.dim(1:2),1);
acc = paccuracy(V,p);
if acc==0
udat(:,:,p) = uint8(max(min(round((img-mn)*(255/(mx-mn))),255),0));
else
% Add random numbers before rounding to reduce aliasing artifact
r = rand(size(img))*acc;
udat(:,:,p) = uint8(max(min(round((img+r-mn)*(255/(mx-mn))),255),0));
end
spm_progress_bar('Set',p);
end
spm_progress_bar('Clear');
rand('state',st); % rng(st);
%==========================================================================
% function acc = paccuracy(V,p)
%==========================================================================
function acc = paccuracy(V,p)
if ~spm_type(V.dt(1),'intt')
acc = 0;
else
if size(V.pinfo,2)==1
acc = abs(V.pinfo(1,1));
else
acc = abs(V.pinfo(1,p));
end
end
%==========================================================================
% function V = smooth_uint8(V,fwhm)
%==========================================================================
function V = smooth_uint8(V,fwhm)
% Convolve the volume in memory (fwhm in voxels).
lim = ceil(2*fwhm);
x = -lim(1):lim(1); x = smoothing_kernel(fwhm(1),x); x = x/sum(x);
y = -lim(2):lim(2); y = smoothing_kernel(fwhm(2),y); y = y/sum(y);
z = -lim(3):lim(3); z = smoothing_kernel(fwhm(3),z); z = z/sum(z);
i = (length(x) - 1)/2;
j = (length(y) - 1)/2;
k = (length(z) - 1)/2;
spm_conv_vol(V.uint8,V.uint8,x,y,z,-[i j k]);
%==========================================================================
% function krn = smoothing_kernel(fwhm,x)
%==========================================================================
function krn = smoothing_kernel(fwhm,x)
% Variance from FWHM
s = (fwhm/sqrt(8*log(2)))^2+eps;
% The simple way to do it. Not good for small FWHM
% krn = (1/sqrt(2*pi*s))*exp(-(x.^2)/(2*s));
% For smoothing images, one should really convolve a Gaussian
% with a sinc function. For smoothing histograms, the
% kernel should be a Gaussian convolved with the histogram
% basis function used. This function returns a Gaussian
% convolved with a triangular (1st degree B-spline) basis
% function.
% Gaussian convolved with 0th degree B-spline
% int(exp(-((x+t))^2/(2*s))/sqrt(2*pi*s),t= -0.5..0.5)
% w1 = 1/sqrt(2*s);
% krn = 0.5*(erf(w1*(x+0.5))-erf(w1*(x-0.5)));
% Gaussian convolved with 1st degree B-spline
% int((1-t)*exp(-((x+t))^2/(2*s))/sqrt(2*pi*s),t= 0..1)
% +int((t+1)*exp(-((x+t))^2/(2*s))/sqrt(2*pi*s),t=-1..0)
w1 = 0.5*sqrt(2/s);
w2 = -0.5/s;
w3 = sqrt(s/2/pi);
krn = 0.5*(erf(w1*(x+1)).*(x+1) + erf(w1*(x-1)).*(x-1) - 2*erf(w1*x ).* x)...
+w3*(exp(w2*(x+1).^2) + exp(w2*(x-1).^2) - 2*exp(w2*x.^2));
krn(krn<0) = 0;
%==========================================================================
% function display_results(VG,VF,x,flags)
%==========================================================================
function display_results(VG,VF,x,flags)
fig = spm_figure('FindWin','Graphics');
if isempty(fig), return; end;
set(0,'CurrentFigure',fig);
spm_figure('Clear','Graphics');
%txt = 'Information Theoretic Coregistration';
switch lower(flags.cost_fun)
case 'mi', txt = 'Mutual Information Coregistration';
case 'ecc', txt = 'Entropy Correlation Coefficient Registration';
case 'nmi', txt = 'Normalised Mutual Information Coregistration';
case 'ncc', txt = 'Normalised Cross Correlation';
otherwise, error('Invalid cost function specified');
end
% Display text
%--------------------------------------------------------------------------
ax = axes('Position',[0.1 0.8 0.8 0.15],'Visible','off','Parent',fig);
text(0.5,0.7, txt,'FontSize',16,...
'FontWeight','Bold','HorizontalAlignment','center','Parent',ax);
Q = inv(VF.mat\spm_matrix(x(:)')*VG.mat);
text(0,0.5, sprintf('X1 = %0.3f*X %+0.3f*Y %+0.3f*Z %+0.3f',Q(1,:)),'Parent',ax);
text(0,0.3, sprintf('Y1 = %0.3f*X %+0.3f*Y %+0.3f*Z %+0.3f',Q(2,:)),'Parent',ax);
text(0,0.1, sprintf('Z1 = %0.3f*X %+0.3f*Y %+0.3f*Z %+0.3f',Q(3,:)),'Parent',ax);
% Display joint histograms
%--------------------------------------------------------------------------
ax = axes('Position',[0.1 0.5 0.35 0.3],'Visible','off','Parent',fig);
H = spm_hist2(VG.uint8,VF.uint8,VF.mat\VG.mat,[1 1 1]);
tmp = log(H+1);
image(tmp*(64/max(tmp(:))),'Parent',ax');
set(ax,'DataAspectRatio',[1 1 1],...
'PlotBoxAspectRatioMode','auto','XDir','normal','YDir','normal',...
'XTick',[],'YTick',[]);
title('Original Joint Histogram','Parent',ax);
xlabel(spm_str_manip(VG.fname,'k22'),'Parent',ax);
ylabel(spm_str_manip(VF.fname,'k22'),'Parent',ax);
H = spm_hist2(VG.uint8,VF.uint8,VF.mat\spm_matrix(x(:)')*VG.mat,[1 1 1]);
ax = axes('Position',[0.6 0.5 0.35 0.3],'Visible','off','Parent',fig);
tmp = log(H+1);
image(tmp*(64/max(tmp(:))),'Parent',ax');
set(ax,'DataAspectRatio',[1 1 1],...
'PlotBoxAspectRatioMode','auto','XDir','normal','YDir','normal',...
'XTick',[],'YTick',[]);
title('Final Joint Histogram','Parent',ax);
xlabel(spm_str_manip(VG.fname,'k22'),'Parent',ax);
ylabel(spm_str_manip(VF.fname,'k22'),'Parent',ax);
% Display ortho-views
%--------------------------------------------------------------------------
spm_orthviews('Reset');
spm_orthviews('Image',VG,[0.01 0.01 .48 .49]);
h2 = spm_orthviews('Image',VF,[.51 0.01 .48 .49]);
global st
st.vols{h2}.premul = inv(spm_matrix(x(:)'));
spm_orthviews('Space');
spm_print;
|
github
|
philippboehmsturm/antx-master
|
spm_fmri_spm_ui.m
|
.m
|
antx-master/freiburgLight/matlab/spm8/spm_fmri_spm_ui.m
| 19,710 |
utf_8
|
9d0ac73f1907933c2ee6bdad95b8210b
|
function [SPM] = spm_fmri_spm_ui(SPM)
% Setting up the general linear model for fMRI time-series
% FORMAT [SPM] = spm_fmri_spm_ui(SPM)
%
% creates SPM with the following fields
%
% xY: [1x1 struct] - data structure
% nscan: [double] - vector of scans per session
% xBF: [1x1 struct] - Basis function structure (see spm_fMRI_design)
% Sess: [1x1 struct] - Session structure (see spm_fMRI_design)
% xX: [1x1 struct] - Design matrix structure (see spm_fMRI_design)
% xGX: [1x1 struct] - Global variate structure
% xVi: [1x1 struct] - Non-sphericity structure
% xM: [1x1 struct] - Masking structure
% xsDes: [1x1 struct] - Design description structure
%
%
% SPM.xY
% P: [n x ? char] - filenames
% VY: [n x 1 struct] - filehandles
% RT: Repeat time
%
% SPM.xGX
%
% iGXcalc: {'none'|'Scaling'} - Global normalization option
% sGXcalc: 'mean voxel value' - Calculation method
% sGMsca: 'session specific' - Grand mean scaling
% rg: [n x 1 double] - Global estimate
% GM: 100 - Grand mean
% gSF: [n x 1 double] - Global scaling factor
%
% SPM.xVi
% Vi: {[n x n sparse]..} - covariance components
% form: {'none'|'AR(1)'} - form of non-sphericity
%
% SPM.xM
% T: [n x 1 double] - Masking index
% TH: [n x 1 double] - Threshold
% I: 0
% VM: - Mask filehandles
% xs: [1x1 struct] - cellstr description
%
% (see also spm_spm_ui)
%
%__________________________________________________________________________
%
% spm_fmri_spm_ui configures the design matrix, data specification and
% filtering that specify the ensuing statistical analysis. These
% arguments are passed to spm_spm that then performs the actual parameter
% estimation.
%
% The design matrix defines the experimental design and the nature of
% hypothesis testing to be implemented. The design matrix has one row
% for each scan and one column for each effect or explanatory variable.
% (e.g. regressor or stimulus function). The parameters are estimated in
% a least squares sense using the general linear model. Specific profiles
% within these parameters are tested using a linear compound or contrast
% with the T or F statistic. The resulting statistical map constitutes
% an SPM. The SPM{T}/{F} is then characterized in terms of focal or regional
% differences by assuming that (under the null hypothesis) the components of
% the SPM (i.e. residual fields) behave as smooth stationary Gaussian fields.
%
% spm_fmri_spm_ui allows you to (i) specify a statistical model in terms
% of a design matrix, (ii) associate some data with a pre-specified design
% [or (iii) specify both the data and design] and then proceed to estimate
% the parameters of the model.
% Inferences can be made about the ensuing parameter estimates (at a first
% or fixed-effect level) in the results section, or they can be re-entered
% into a second (random-effect) level analysis by treating the session or
% subject-specific [contrasts of] parameter estimates as new summary data.
% Inferences at any level obtain by specifying appropriate T or F contrasts
% in the results section to produce SPMs and tables of p values and statistics.
%
% spm_fmri_spm calls spm_fMRI_design which allows you to configure a
% design matrix in terms of events or epochs.
%
% spm_fMRI_design allows you to build design matrices with separable
% session-specific partitions. Each partition may be the same (in which
% case it is only necessary to specify it once) or different. Responses
% can be either event- or epoch related, The only distinction is the duration
% of the underlying input or stimulus function. Mathematically they are both
% modelled by convolving a series of delta (stick) or box functions (u),
% indicating the onset of an event or epoch with a set of basis
% functions. These basis functions model the hemodynamic convolution,
% applied by the brain, to the inputs. This convolution can be first-order
% or a generalized convolution modelled to second order (if you specify the
% Volterra option). [The same inputs are used by the hemodynamic model or
% or dynamic causal models which model the convolution explicitly in terms of
% hidden state variables (see spm_hdm_ui and spm_dcm_ui).]
% Basis functions can be used to plot estimated responses to single events
% once the parameters (i.e. basis function coefficients) have
% been estimated. The importance of basis functions is that they provide
% a graceful transition between simple fixed response models (like the
% box-car) and finite impulse response (FIR) models, where there is one
% basis function for each scan following an event or epoch onset. The
% nice thing about basis functions, compared to FIR models, is that data
% sampling and stimulus presentation does not have to be synchronized
% thereby allowing a uniform and unbiased sampling of peri-stimulus time.
%
% Event-related designs may be stochastic or deterministic. Stochastic
% designs involve one of a number of trial-types occurring with a
% specified probably at successive intervals in time. These
% probabilities can be fixed (stationary designs) or time-dependent
% (modulated or non-stationary designs). The most efficient designs
% obtain when the probabilities of every trial type are equal.
% A critical issue in stochastic designs is whether to include null events
% If you wish to estimate the evoke response to a specific event
% type (as opposed to differential responses) then a null event must be
% included (even if it is not modelled explicitly).
%
% The choice of basis functions depends upon the nature of the inference
% sought. One important consideration is whether you want to make
% inferences about compounds of parameters (i.e. contrasts). This is
% the case if (i) you wish to use a SPM{T} to look separately at
% activations and deactivations or (ii) you with to proceed to a second
% (random-effect) level of analysis. If this is the case then (for
% event-related studies) use a canonical hemodynamic response function
% (HRF) and derivatives with respect to latency (and dispersion). Unlike
% other bases, contrasts of these effects have a physical interpretation
% and represent a parsimonious way of characterising event-related
% responses. Bases such as a Fourier set require the SPM{F} for
% inference.
%
% See spm_fMRI_design for more details about how designs are specified.
%
% Serial correlations in fast fMRI time-series are dealt with as
% described in spm_spm. At this stage you need to specify the filtering
% that will be applied to the data (and design matrix) to give a
% generalized least squares (GLS) estimate of the parameters required.
% This filtering is important to ensure that the GLS estimate is
% efficient and that the error variance is estimated in an unbiased way.
%
% The serial correlations will be estimated with a ReML (restricted
% maximum likelihood) algorithm using an autoregressive AR(1) model
% during parameter estimation. This estimate assumes the same
% correlation structure for each voxel, within each session. The ReML
% estimates are then used to correct for non-sphericity during inference
% by adjusting the statistics and degrees of freedom appropriately. The
% discrepancy between estimated and actual intrinsic (i.e. prior to
% filtering) correlations are greatest at low frequencies. Therefore
% specification of the high-pass filter is particularly important.
%
% High-pass filtering is implemented at the level of the
% filtering matrix K (as opposed to entering as confounds in the design
% matrix). The default cut-off period is 128 seconds. Use 'explore design'
% to ensure this cut-off is not removing too much experimental variance.
% Note that high-pass filtering uses a residual forming matrix (i.e.
% it is not a convolution) and is simply to a way to remove confounds
% without estimating their parameters explicitly. The constant term
% is also incorporated into this filter matrix.
%
%--------------------------------------------------------------------------
% Refs:
%
% Friston KJ, Holmes A, Poline J-B, Grasby PJ, Williams SCR, Frackowiak
% RSJ & Turner R (1995) Analysis of fMRI time-series revisited. NeuroImage
% 2:45-53
%
% Worsley KJ and Friston KJ (1995) Analysis of fMRI time-series revisited -
% again. NeuroImage 2:178-181
%
% Friston KJ, Frith CD, Frackowiak RSJ, & Turner R (1995) Characterising
% dynamic brain responses with fMRI: A multivariate approach NeuroImage -
% 2:166-172
%
% Frith CD, Turner R & Frackowiak RSJ (1995) Characterising evoked
% hemodynamics with fMRI Friston KJ, NeuroImage 2:157-165
%
% Josephs O, Turner R and Friston KJ (1997) Event-related fMRI, Hum. Brain
% Map. 0:00-00
%
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% Karl Friston, Jean-Baptiste Poline & Christian Buchel
% $Id: spm_fmri_spm_ui.m 669 2012-02-27 14:10:24Z vglauche $
SVNid = '$Rev: 669 $';
%-GUI setup
%--------------------------------------------------------------------------
[Finter,Fgraph,CmdLine] = spm('FnUIsetup','fMRI stats model setup',0);
% get design matrix and/or data
%==========================================================================
if ~nargin
str = 'specify design or data';
if spm_input(str,1,'b',{'design','data'},[1 0]);
% specify a design
%------------------------------------------------------------------
if sf_abort, spm_clf(Finter), return, end
SPM = spm_fMRI_design;
spm_fMRI_design_show(SPM);
return
else
% get design
%------------------------------------------------------------------
load(spm_select(1,'^SPM\.mat$','Select SPM.mat'));
end
else
% get design matrix
%----------------------------------------------------------------------
SPM = spm_fMRI_design(SPM);
end
% get Repeat time
%--------------------------------------------------------------------------
try
SPM.xY.RT;
catch
SPM.xY.RT = spm_input('Interscan interval {secs}','+1');
end
% session and scan number
%--------------------------------------------------------------------------
nscan = SPM.nscan;
nsess = length(nscan);
% check data are specified
%--------------------------------------------------------------------------
try
SPM.xY.P;
catch
% get filenames
%----------------------------------------------------------------------
P = [];
for i = 1:nsess
str = sprintf('select scans for session %0.0f',i);
q = spm_select(nscan(i),'image',str);
P = strvcat(P,q);
end
% place in data field
%----------------------------------------------------------------------
SPM.xY.P = P;
end
% Assemble remaining design parameters
%==========================================================================
SPM.SPMid = spm('FnBanner',mfilename,SVNid);
% Global normalization
%--------------------------------------------------------------------------
try
SPM.xGX.iGXcalc;
catch
spm_input('Global intensity normalisation...',1,'d',mfilename)
str = 'remove Global effects';
SPM.xGX.iGXcalc = spm_input(str,'+1','scale|none',{'Scaling' 'None'});
end
SPM.xGX.sGXcalc = 'mean voxel value';
SPM.xGX.sGMsca = 'session specific';
% High-pass filtering and serial correlations
%==========================================================================
% low frequency confounds
%--------------------------------------------------------------------------
try
myLastWarn = 0;
HParam = [SPM.xX.K(:).HParam];
if length(HParam) == 1
HParam = HParam*ones(1,nsess);
elseif length(HParam) ~= nsess
myLastWarn = 1;
error('Continue with manual HPF specification in the catch block');
end
catch
% specify low frequency confounds
%----------------------------------------------------------------------
spm_input('Temporal autocorrelation options','+1','d',mfilename)
switch spm_input('High-pass filter?','+1','b','none|specify');
case 'specify' % default in seconds
%--------------------------------------------------------------
HParam = spm_get_defaults('stats.fmri.hpf')*ones(1,nsess);
str = 'cutoff period (secs)';
HParam = spm_input(str,'+1','e',HParam,[1 nsess]);
case 'none' % Inf seconds (i.e. constant term only)
%--------------------------------------------------------------
HParam = Inf(1,nsess);
end
if myLastWarn
warning('SPM:InvalidHighPassFilterSpec',...
['Different number of High-pass filter values and sessions.\n',...
'HPF filter configured manually. Design setup will proceed.']);
clear myLastWarn
end
end
% create and set filter struct
%--------------------------------------------------------------------------
for i = 1:nsess
K(i) = struct('HParam', HParam(i),...
'row', SPM.Sess(i).row,...
'RT', SPM.xY.RT);
end
SPM.xX.K = spm_filter(K);
% intrinsic autocorrelations (Vi)
%--------------------------------------------------------------------------
try
cVi = SPM.xVi.form;
catch
% Construct Vi structure for non-sphericity ReML estimation
%----------------------------------------------------------------------
str = 'Correct for serial correlations?';
cVi = {'none','AR(1)'};
cVi = spm_input(str,'+1','b',cVi);
end
% create Vi struct
%--------------------------------------------------------------------------
if isnumeric(cVi) % AR coefficient specified
%----------------------------------------------------------------------
SPM.xVi.Vi = spm_Ce(nscan,cVi(1));
cVi = ['AR( ' sprintf('%0.1f ',cVi) ')'];
elseif iscell(cVi) % ARFIT selection - 1st element is label string, 2nd
% AR order
SPM.xVi.arfit.exp = 32; % ACF expansion time
if cVi{2} > 0
% fix order (2 passes)
[SPM.xVi.arfit.Sess{1:nsess}] = deal(arfit_init(cVi{2}));
SPM.xVi.arfit.firstpass=true;
else
% estimate order up to -cVi{2}
[SPM.xVi.arfit.Sess{1:nsess}] = deal(arfit_init(-cVi{2}));
end
cVi = cVi{1};
else
switch lower(cVi)
case 'none' % xVi.V is i.i.d
%--------------------------------------------------------------
SPM.xVi.V = speye(sum(nscan));
cVi = 'i.i.d';
otherwise % otherwise assume AR(0.2) in xVi.Vi
%--------------------------------------------------------------
SPM.xVi.Vi = spm_Ce(nscan,0.2);
cVi = 'AR(0.2)';
end
end
SPM.xVi.form = cVi;
%==========================================================================
% - C O N F I G U R E D E S I G N
%==========================================================================
spm_clf(Finter);
spm('FigName','Configuring, please wait...',Finter,CmdLine);
spm('Pointer','Watch');
% get file identifiers
%==========================================================================
%-Map files
%--------------------------------------------------------------------------
fprintf('%-40s: ','Mapping files') %-#
VY = spm_vol(SPM.xY.P);
fprintf('%30s\n','...done') %-#
%-check internal consistency of images
%--------------------------------------------------------------------------
spm_check_orientations(VY);
%-place in xY
%--------------------------------------------------------------------------
SPM.xY.VY = VY;
%-Compute Global variate
%==========================================================================
GM = 100;
q = length(VY);
g = zeros(q,1);
fprintf('%-40s: %30s','Calculating globals',' ') %-#
for i = 1:q
fprintf('%s%30s',repmat(sprintf('\b'),1,30),sprintf('%4d/%-4d',i,q))%-#
g(i) = spm_global(VY(i));
end
fprintf('%s%30s\n',repmat(sprintf('\b'),1,30),'...done') %-#
% scale if specified (otherwise session specific grand mean scaling)
%--------------------------------------------------------------------------
gSF = GM./g;
if strcmpi(SPM.xGX.iGXcalc,'none')
for i = 1:nsess
gSF(SPM.Sess(i).row) = GM./mean(g(SPM.Sess(i).row));
end
end
%-Apply gSF to memory-mapped scalefactors to implement scaling
%--------------------------------------------------------------------------
for i = 1:q
SPM.xY.VY(i).pinfo(1:2,:) = SPM.xY.VY(i).pinfo(1:2,:)*gSF(i);
end
%-place global variates in global structure
%--------------------------------------------------------------------------
SPM.xGX.rg = g;
SPM.xGX.GM = GM;
SPM.xGX.gSF = gSF;
%-Masking structure automatically set to 80% of mean
%==========================================================================
try
TH = g.*gSF*spm_get_defaults('mask.thresh');
catch
TH = g.*gSF*0.8;
end
SPM.xM = struct('T', ones(q,1),...
'TH', TH,...
'I', 0,...
'VM', {[]},...
'xs', struct('Masking','analysis threshold'));
%-Design description - for saving and display
%==========================================================================
for i = 1:nsess, ntr(i) = length(SPM.Sess(i).U); end
Fstr = sprintf('[min] Cutoff: %d {s}',min([SPM.xX.K(:).HParam]));
SPM.xsDes = struct(...
'Basis_functions', SPM.xBF.name,...
'Number_of_sessions', sprintf('%d',nsess),...
'Trials_per_session', sprintf('%-3d',ntr),...
'Interscan_interval', sprintf('%0.2f {s}',SPM.xY.RT),...
'High_pass_Filter', sprintf('Cutoff: %d {s}',SPM.xX.K(1).HParam),...
'Global_calculation', SPM.xGX.sGXcalc,...
'Grand_mean_scaling', SPM.xGX.sGMsca,...
'Global_normalisation', SPM.xGX.iGXcalc);
%-Save SPM.mat
%==========================================================================
fprintf('%-40s: ','Saving SPM configuration') %-#
if spm_check_version('matlab','7') >=0
save('SPM.mat', 'SPM', '-V6');
else
save('SPM.mat', 'SPM');
end
fprintf('%30s\n','...SPM.mat saved') %-#
%-Display Design report
%==========================================================================
if ~CmdLine
fprintf('%-40s: ','Design reporting') %-#
fname = cat(1,{SPM.xY.VY.fname}');
spm_DesRep('DesMtx',SPM.xX,fname,SPM.xsDes)
fprintf('%30s\n','...done') %-#
end
%-End: Cleanup GUI
%==========================================================================
spm_clf(Finter)
spm('FigName','Stats: configured',Finter,CmdLine);
spm('Pointer','Arrow')
%==========================================================================
%- S U B - F U N C T I O N S
%==========================================================================
function abort = sf_abort
%==========================================================================
if exist(fullfile(pwd,'SPM.mat'),'file')
str = { 'Current directory contains existing SPM file:',...
'Continuing will overwrite existing file!'};
abort = spm_input(str,1,'bd','stop|continue',[1,0],1,mfilename);
if abort, fprintf('%-40s: %30s\n\n',...
'Abort... (existing SPM files)',spm('time')), end
else
abort = 0;
end
|
github
|
philippboehmsturm/antx-master
|
spm_input.m
|
.m
|
antx-master/freiburgLight/matlab/spm8/spm_input.m
| 89,728 |
utf_8
|
d9477978de23772976e2d790d78bf1f4
|
function varargout = spm_input(varargin)
% Comprehensive graphical and command line input function
% FORMATs (given in Programmers Help)
%_______________________________________________________________________
%
% spm_input handles most forms of interactive user input for SPM.
% (File selection is handled by spm_select.m)
%
% There are five types of input: String, Evaluated, Conditions, Buttons
% and Menus: These prompt for string input; string input which is
% evaluated to give a numerical result; selection of one item from a
% set of buttons; selection of an item from a menu.
%
% - STRING, EVALUATED & CONDITION input -
% For STRING, EVALUATED and CONDITION input types, a prompt is
% displayed adjacent to an editable text entry widget (with a lilac
% background!). Clicking in the entry widget allows editing, pressing
% <RETURN> or <ENTER> enters the result. You must enter something,
% empty answers are not accepted. A default response may be pre-specified
% in the entry widget, which will then be outlined. Clicking the border
% accepts the default value.
%
% Basic editing of the entry widget is supported *without* clicking in
% the widget, provided no other graphics widget has the focus. (If a
% widget has the focus, it is shown highlighted with a thin coloured
% line. Clicking on the window background returns the focus to the
% window, enabling keyboard accelerators.). This enables you to type
% responses to a sequence of questions without having to repeatedly
% click the mouse in the text widgets. Supported are BackSpace and
% Delete, line kill (^U). Other standard ASCII characters are appended
% to the text in the entry widget. Press <RETURN> or <ENTER> to submit
% your response.
%
% A ContextMenu is provided (in the figure background) giving access to
% relevant utilities including the facility to load input from a file
% (see spm_load.m and examples given below): Click the right button on
% the figure background.
%
% For EVALUATED input, the string submitted is evaluated in the base
% MatLab workspace (see MatLab's `eval` command) to give a numerical
% value. This permits the entry of numerics, matrices, expressions,
% functions or workspace variables. I.e.:
% i) - a number, vector or matrix e.g. "[1 2 3 4]"
% "[1:4]"
% "1:4"
% ii) - an expression e.g. "pi^2"
% "exp(-[1:36]/5.321)"
% iii) - a function (that will be invoked) e.g. "spm_load('tmp.dat')"
% (function must be on MATLABPATH) "input_cov(36,5.321)"
% iv) - a variable from the base workspace
% e.g. "tmp"
%
% The last three options provide a great deal of power: spm_load will
% load a matrix from an ASCII data file and return the results. When
% called without an argument, spm_load will pop up a file selection
% dialog. Alternatively, this facility can be gained from the
% ContextMenu. The second example assummes a custom funcion called
% input_cov has been written which expects two arguments, for example
% the following file saved as input_cov.m somewhere on the MATLABPATH
% (~/matlab, the matlab subdirectory of your home area, and the current
% directory, are on the MATLABPATH by default):
%
% function [x] = input_cov(n,decay)
% % data input routine - mono-exponential covariate
% % FORMAT [x] = input_cov(n,decay)
% % n - number of time points
% % decay - decay constant
% x = exp(-[1:n]/decay);
%
% Although this example is trivial, specifying large vectors of
% empirical data (e.g. reaction times for 72 scans) is efficient and
% reliable using this device. In the last option, a variable called tmp
% is picked up from the base workspace. To use this method, set the
% variables in the MatLab base workspace before starting an SPM
% procedure (but after starting the SPM interface). E.g.
% >> tmp=exp(-[1:36]/5.321)
%
% Occasionally a vector of a specific length will be required: This
% will be indicated in the prompt, which will start with "[#]", where
% # is the length of vector(s) required. (If a matrix is entered then
% at least one dimension should equal #.)
%
% Occasionally a specific type of number will be required. This should
% be obvious from the context. If you enter a number of the wrong type,
% you'll be alerted and asked to re-specify. The types are i) Real
% numbers; ii) Integers; iii) Whole numbers [0,1,2,3,...] & iv) Natural
% numbers [1,2,3,...]
%
% CONDITIONS type input is for getting indicator vectors. The features
% of evaluated input described above are complimented as follows:
% v) - a compressed list of digits 0-9 e.g. "12121212"
% ii) - a list of indicator characters e.g. "abababab"
% a-z mapped to 1-26 in alphabetical order, *except* r ("rest")
% which is mapped to zero (case insensitive, [A:Z,a:z] only)
% ...in addition the response is checked to ensure integer condition indices.
% Occasionally a specific number of conditions will be required: This
% will be indicated in the prompt, which will end with (#), where # is
% the number of conditions required.
%
% CONTRAST type input is for getting contrast weight vectors. Enter
% contrasts as row-vectors. Contrast weight vectors will be padded with
% zeros to the correct length, and checked for validity. (Valid
% contrasts are estimable, which are those whose weights vector is in
% the row-space of the design matrix.)
%
% Errors in string evaluation for EVALUATED & CONDITION types are
% handled gracefully, the user notified, and prompted to re-enter.
%
% - BUTTON input -
% For Button input, the prompt is displayed adjacent to a small row of
% buttons. Press the approprate button. The default button (if
% available) has a dark outline. Keyboard accelerators are available
% (provided no graphics widget has the focus): <RETURN> or <ENTER>
% selects the default button (if available). Typing the first character
% of the button label (case insensitive) "presses" that button. (If
% these Keys are not unique, then the integer keys 1,2,... "press" the
% appropriate button.)
%
% The CommandLine variant presents a simple menu of buttons and prompts
% for a selection. Any default response is indicated, and accepted if
% an empty line is input.
%
%
% - MENU input -
% For Menu input, the prompt is displayed in a pull down menu widget.
% Using the mouse, a selection is made by pulling down the widget and
% releasing the mouse on the appropriate response. The default response
% (if set) is marked with an asterisk. Keyboard accelerators are
% available (provided no graphic widget has the focus) as follows: 'f',
% 'n' or 'd' move forward to next response down; 'b', 'p' or 'u' move
% backwards to the previous response up the list; the number keys jump
% to the appropriate response number; <RETURN> or <ENTER> slelects the
% currently displayed response. If a default is available, then
% pressing <RETURN> or <ENTER> when the prompt is displayed jumps to
% the default response.
%
% The CommandLine variant presents a simple menu and prompts for a selection.
% Any default response is indicated, and accepted if an empty line is
% input.
%
%
% - Combination BUTTON/EDIT input -
% In this usage, you will be presented with a set of buttons and an
% editable text widget. Click one of the buttons to choose that option,
% or type your response in the edit widget. Any default response will
% be shown in the edit widget. The edit widget behaves in the same way
% as with the STRING/EVALUATED input, and expects a single number.
% Keypresses edit the text widget (rather than "press" the buttons)
% (provided no other graphics widget has the focus). A default response
% can be selected with the mouse by clicking the thick border of the
% edit widget.
%
%
% - Command line -
% If YPos is 0 or global CMDLINE is true, then the command line is used.
% Negative YPos overrides CMDLINE, ensuring the GUI is used, at
% YPos=abs(YPos). Similarly relative YPos beginning with '!'
% (E.g.YPos='!+1') ensures the GUI is used.
%
% spm_input uses the SPM 'Interactive' window, which is 'Tag'ged
% 'Interactive'. If there is no such window, then the current figure is
% used, or an 'Interactive' window created if no windows are open.
%
%-----------------------------------------------------------------------
% Programers help is contained in the main body of spm_input.m
%-----------------------------------------------------------------------
% See : input.m (MatLab Reference Guide)
% See also : spm_select.m (SPM file selector dialog)
% : spm_input.m (Input wrapper function - handles batch mode)
%_______________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% Andrew Holmes
% $Id: spm_input.m 4143 2010-12-22 11:55:43Z guillaume $
%=======================================================================
% - FORMAT specifications for programers
%=======================================================================
% generic - [p,YPos] = spm_input(Prompt,YPos,Type,...)
% string - [p,YPos] = spm_input(Prompt,YPos,'s',DefStr)
% string+ - [p,YPos] = spm_input(Prompt,YPos,'s+',DefStr)
% evaluated - [p,YPos] = spm_input(Prompt,YPos,'e',DefStr,n)
% - natural - [p,YPos] = spm_input(Prompt,YPos,'n',DefStr,n,mx)
% - whole - [p,YPos] = spm_input(Prompt,YPos,'w',DefStr,n,mx)
% - integer - [p,YPos] = spm_input(Prompt,YPos,'i',DefStr,n)
% - real - [p,YPos] = spm_input(Prompt,YPos,'r',DefStr,n,mm)
% condition - [p,YPos] = spm_input(Prompt,YPos,'c',DefStr,n,m)
% contrast - [p,YPos] = spm_input(Prompt,YPos,'x',DefStr,n,X)
% permutation- [p,YPos] = spm_input(Prompt,YPos,'p',DefStr,P,n)
% button - [p,YPos] = spm_input(Prompt,YPos,'b',Labels,Values,DefItem)
% button/edit combo's (edit for string or typed scalar evaluated input)
% [p,YPos] = spm_input(Prompt,YPos,'b?1',Labels,Values,DefStr,mx)
% ...where ? in b?1 specifies edit widget type as with string & eval'd input
% - [p,YPos] = spm_input(Prompt,YPos,'n1',DefStr,mx)
% - [p,YPos] = spm_input(Prompt,YPos,'w1',DefStr,mx)
% button dialog
% - [p,YPos] = spm_input(Prompt,YPos,'bd',...
% Labels,Values,DefItem,Title)
% menu - [p,YPos] = spm_input(Prompt,YPos,'m',Labels,Values,DefItem)
% display - spm_input(Message,YPos,'d',Label)
% display - (GUI only) spm_input(Alert,YPos,'d!',Label)
%
% yes/no - [p,YPos] = spm_input(Prompt,YPos,'y/n',Values,DefItem)
% buttons (shortcut) where Labels is a bar delimited string
% - [p,YPos] = spm_input(Prompt,YPos,Labels,Values,DefItem)
%
% NB: Natural numbers are [1:Inf), Whole numbers are [0:Inf)
%
% -- Parameters (input) --
%
% Prompt - prompt string
% - Defaults (missing or empty) to 'Enter an expression'
%
% YPos - (numeric) vertical position {1 - 12}
% - overriden by global CMDLINE
% - 0 for command line
% - negative to force GUI
% - (string) relative vertical position E.g. '+1'
% - relative to last position used
% - overriden by global CMDLINE
% - YPos(1)=='!' forces GUI E.g. '!+1'
% - '_' is a shortcut for the lowest GUI position
% - Defaults (missing or empty) to '+1'
%
% Type - type of interrogation
% - 's'tring
% - 's+' multi-line string
% - p returned as cellstr (nx1 cell array of strings)
% - DefStr can be a cellstr or string matrix
% - 'e'valuated string
% - 'n'atural numbers
% - 'w'hole numbers
% - 'i'ntegers
% - 'r'eals
% - 'c'ondition indicator vector
% - 'x' - contrast entry
% - If n(2) or design matrix X is specified, then
% contrast matrices are padded with zeros to have
% correct length.
% - if design matrix X is specified, then contrasts are
% checked for validity (i.e. in the row-space of X)
% (checking handled by spm_SpUtil)
% - 'b'uttons
% - 'bd' - button dialog: Uses MatLab's questdlg
% - For up to three buttons
% - Prompt can be a cellstr with a long multiline message
% - CmdLine support as with 'b' type
% - button/edit combo's: 'be1','bn1','bw1','bi1','br1'
% - second letter of b?1 specifies type for edit widget
% - 'n1' - single natural number (buttons 1,2,... & edit)
% - 'w1' - single whole number (buttons 0,1,... & edit)
% - 'm'enu pulldown
% - 'y/n' : Yes or No buttons
% (See shortcuts below)
% - bar delimited string : buttons with these labels
% (See shortcuts below)
% - Defaults (missing or empty) to 'e'
%
% DefStr - Default string to be placed in entry widget for string and
% evaluated types
% - Defaults to ''
%
% n ('e', 'c' & 'p' types)
% - Size of matrix requred
% - NaN for 'e' type implies no checking - returns input as evaluated
% - length of n(:) specifies dimension - elements specify size
% - Inf implies no restriction
% - Scalar n expanded to [n,1] (i.e. a column vector)
% (except 'x' contrast type when it's [n,np] for np
% - E.g: [n,1] & [1,n] (scalar n) prompt for an n-vector,
% returned as column or row vector respectively
% [1,Inf] & [Inf,1] prompt for a single vector,
% returned as column or row vector respectively
% [n,Inf] & [Inf,n] prompts for any number of n-vectors,
% returned with row/column dimension n respectively.
% [a,b] prompts for an 2D matrix with row dimension a and
% column dimension b
% [a,Inf,b] prompt for a 3D matrix with row dimension a,
% page dimension b, and any column dimension.
% - 'c' type can only deal with single vectors
% - NaN for 'c' type treated as Inf
% - Defaults (missing or empty) to NaN
%
% n ('x'type)
% - Number of contrasts required by 'x' type (n(1))
% ( n(2) can be used specify length of contrast vectors if )
% ( a design matrix isn't passed )
% - Defaults (missing or empty) to 1 - vector contrast
%
% mx ('n', 'w', 'n1', 'w1', 'bn1' & 'bw1' types)
% - Maximum value (inclusive)
%
% mm ('r' type)
% - Maximum and minimum values (inclusive)
%
% m - Number of unique conditions required by 'c' type
% - Inf implies no restriction
% - Defaults (missing or empty) to Inf - no restriction
%
% P - set (vector) of numbers of which a permutation is required
%
% X - Design matrix for contrast checking in 'x' type
% - Can be either a straight matrix or a space structure (see spm_sp)
% - Column dimension of design matrix specifies length of contrast
% vectors (overriding n(2) is specified).
%
% Title - Title for questdlg in 'bd' type
%
% Labels - Labels for button and menu types.
% - string matrix, one label per row
% - bar delimited string
% E.g. 'AnCova|Scaling|None'
%
% Values - Return values corresponding to Labels for button and menu types
% - j-th row is returned if button / menu item j is selected
% (row vectors are transposed)
% - Defaults (missing or empty) to - (button) Labels
% - ( menu ) menu item numbers
%
% DefItem - Default item number, for button and menu types.
%
% -- Parameters (output) --
% p - results
% YPos - Optional second output argument returns GUI position just used
%
%-----------------------------------------------------------------------
% WINDOWS:
%
% spm_input uses the SPM 'Interactive' 'Tag'ged window. If this isn't
% available and no figures are open, an 'Interactive' SPM window is
% created (`spm('CreateIntWin')`). If figures are available, then the
% current figure is used *unless* it is 'Tag'ged.
%
%-----------------------------------------------------------------------
% SHORTCUTS:
%
% Buttons SHORTCUT - If the Type parameter is a bar delimited string, then
% the Type is taken as 'b' with the specified labels, and the next parameter
% (if specified) is taken for the Values.
%
% Yes/No question shortcut - p = spm_input(Prompt,YPos,'y/n') expands
% to p = spm_input(Prompt,YPos,'b','yes|no',...), enabling easy use of
% spm_input for yes/no dialogue. Values defaults to 'yn', so 'y' or 'n'
% is returned as appropriate.
%
%-----------------------------------------------------------------------
% EXAMPLES:
% ( Specified YPos is overriden if global CMDLINE is )
% ( true, when the command line versions are used. )
%
% p = spm_input
% Command line input of an evaluated string, default prompt.
% p = spm_input('Enter a value',1)
% Evaluated string input, prompted by 'Enter a value', in
% position 1 of the dialog figure.
% p = spm_input(str,'+1','e',0.001)
% Evaluated string input, prompted by contents of string str,
% in next position of the dialog figure.
% Default value of 0.001 offered.
% p = spm_input(str,2,'e',[],5)
% Evaluated string input, prompted by contents of string str,
% in second position of the dialog figure.
% Vector of length 5 required - returned as column vector
% p = spm_input(str,2,'e',[],[Inf,5])
% ...as above, but can enter multiple 5-vectors in a matrix,
% returned with 5-vectors in rows
% p = spm_input(str,0,'c','ababab')
% Condition string input, prompted by contents of string str
% Uses command line interface.
% Default string of 'ababab' offered.
% p = spm_input(str,0,'c','010101')
% As above, but default string of '010101' offered.
% [p,YPos] = spm_input(str,'0','s','Image')
% String input, same position as last used, prompted by str,
% default of 'Image' offered. YPos returns GUI position used.
% p = spm_input(str,'-1','y/n')
% Yes/No buttons for question with prompt str, in position one
% before the last used Returns 'y' or 'n'.
% p = spm_input(str,'-1','y/n',[1,0],2)
% As above, but returns 1 for yes response, 0 for no,
% with 'no' as the default response
% p = spm_input(str,4,'AnCova|Scaling')
% Presents two buttons labelled 'AnCova' & 'Scaling', with
% prompt str, in position 4 of the dialog figure. Returns the
% string on the depresed button, where buttons can be pressed
% with the mouse or by the respective keyboard accelerators
% 'a' & 's' (or 'A' & 'S').
% p = spm_input(str,-4,'b','AnCova|Scaling',[],2)
% As above, but makes "Scaling" the default response, and
% overrides global CMDLINE
% p = spm_input(str,0,'b','AnCova|Scaling|None',[1,2,3])
% Prompts for [A]ncova / [S]caling / [N]one in MatLab command
% window, returns 1, 2, or 3 according to the first character
% of the entered string as one of 'a', 's', or 'n' (case
% insensitive).
% p = spm_input(str,1,'b','AnCova',1)
% Since there's only one button, this just displays the response
% in GUI position 1 (or on the command line if global CMDLINE
% is true), and returns 1.
% p = spm_input(str,'+0','br1','None|Mask',[-Inf,NaN],0.8)
% Presents two buttons labelled "None" & "Mask" (which return
% -Inf & NaN if clicked), together with an editable text widget
% for entry of a single real number. The default of 0.8 is
% initially presented in the edit window, and can be selected by
% pressing return.
% Uses the previous GUI position, unless global CMDLINE is true,
% in which case a command-line equivalent is used.
% p = spm_input(str,'+0','w1')
% Prompts for a single whole number using a combination of
% buttons and edit widget, using the previous GUI position,
% or the command line if global CMDLINE is true.
% p = spm_input(str,'!0','m','Single Subject|Multi Subject|Multi Study')
% Prints the prompt str in a pull down menu containing items
% 'Single Subject', 'Multi Subject' & 'Multi Study'. When OK is
% clicked p is returned as the index of the choice, 1,2, or 3
% respectively. Uses last used position in GUI, irrespective of
% global CMDLINE
% p = spm_input(str,5,'m',...
% 'Single Subject|Multi Subject|Multi Study',...
% ['SS';'MS';'SP'],2)
% As above, but returns strings 'SS', 'MS', or 'SP' according to
% the respective choice, with 'MS; as the default response.
% p = spm_input(str,0,'m',...
% 'Single Subject|Multi Subject|Multi Study',...
% ['SS';'MS';'SP'],2)
% As above, but the menu is presented in the command window
% as a numbered list.
% spm_input('AnCova, GrandMean scaling',0,'d')
% Displays message in a box in the MatLab command window
% [null,YPos]=spm_input('Session 1','+1','d!','fMRI')
% Displays 'fMRI: Session 1' in next GUI position of the
% 'Interactive' window. If CMDLINE is 1, then nothing is done.
% Position used is returned in YPos.
%
%-----------------------------------------------------------------------
% FORMAT h = spm_input(Prompt,YPos,'m!',Labels,cb,UD,XCB);
% GUI PullDown menu utility - creates a pulldown menu in the Interactive window
% FORMAT H = spm_input(Prompt,YPos,'b!',Labels,cb,UD,XCB);
% GUI Buttons utility - creates GUI buttons in the Interactive window
%
% Prompt, YPos, Labels - as with 'm'enu/'b'utton types
% cb - CallBack string
% UD - UserData
% XCB - Extended CallBack handling - allows different CallBack for each item,
% and use of UD in CallBack strings. [Defaults to 1 for PullDown type
% when multiple CallBacks specified, 0 o/w.]
% H - Handle of 'PullDown' uicontrol / 'Button's
%
% In "normal" mode (when XCB is false), this is essentially a utility
% to create a PullDown menu widget or set of buttons in the SPM
% 'Interactive' figure, using positioning and Label definition
% conveniences of the spm_input 'm'enu & 'b'utton types. If Prompt is
% not empty, then the PullDown/Buttons appears on the right, with the
% Prompt on the left, otherwise the PullDown/Buttons use the whole
% width of the Interactive figure. The PopUp's CallBack string is
% specified in cb, and [optional] UserData may be passed as UD.
%
% For buttons, a separate callback can be specified for each button, by
% passing the callbacks corresponding to the Labels as rows of a
% cellstr or string matrix.
%
% This "different CallBacks" facility can also be extended to the
% PullDown type, using the "extended callback" mode (when XCB is
% true). % In addition, in "extended callback", you can use UD to
% refer to the UserData argument in the CallBack strings. (What happens
% is this: The cb & UD are stored as fields in the PopUp's UserData
% structure, and the PopUp's callback is set to spm_input('!m_cb'),
% which reads UD into the functions workspace and eval's the
% appropriate CallBack string. Note that this means that base
% workspace variables are inaccessible (put what you need in UD), and
% that any return arguments from CallBack functions are not passed back
% to the base workspace).
%
%
%-----------------------------------------------------------------------
% UTILITY FUNCTIONS:
%
% FORMAT colour = spm_input('!Colour')
% Returns colour for input widgets, as specified in COLOUR parameter at
% start of code.
% colour - [r,g,b] colour triple
%
% FORMAT [iCond,msg] = spm_input('!iCond',str,n,m)
% Parser for special 'c'ondition type: Handles digit strings and
% strings of indicator chars.
% str - input string
% n - length of condition vector required [defaut Inf - no restriction]
% m - number of conditions required [default Inf - no restrictions]
% iCond - Integer condition indicator vector
% msg - status message
%
% FORMAT hM = spm_input('!InptConMen',Finter,H)
% Sets a basic Input ContextMenu for the figure
% Finter - figure to set menu in
% H - handles of objects to delete on "crash out" option
% hM - handle of UIContextMenu
%
% FORMAT [CmdLine,YPos] = spm_input('!CmdLine',YPos)
% Sorts out whether to use CmdLine or not & canonicalises YPos
% CmdLine - Binary flag
% YPos - Position index
%
% FORMAT Finter = spm_input('!GetWin',F)
% Locates (or creates) figure to work in
% F - Interactive Figure, defaults to 'Interactive'
% Finter - Handle of figure to use
%
% FORMAT [PLoc,cF] = spm_input('!PointerJump',RRec,F,XDisp)
% Raise window & jump pointer over question
% RRec - Response rectangle of current question
% F - Interactive Figure, Defaults to 'Interactive'
% XDisp - X-displacement of cursor relative to RRec
% PLoc - Pointer location before jumping
% cF - Current figure before making F current.
%
% FORMAT [PLoc,cF] = spm_input('!PointerJumpBack',PLoc,cF)
% Replace pointer and reset CurrentFigure back
% PLoc - Pointer location before jumping
% cF - Previous current figure
%
% FORMAT spm_input('!PrntPrmpt',Prompt,TipStr,Title)
% Print prompt for CmdLine questioning
% Prompt - prompt string, callstr, or string matrix
% TipStr - tip string
% Title - title string
%
% FORMAT [Frec,QRec,PRec,RRec] = spm_input('!InputRects',YPos,rec,F)
% Returns rectangles (pixels) used in GUI
% YPos - Position index
% rec - Rectangle specifier: String, one of 'Frec','QRec','PRec','RRec'
% Defaults to '', which returns them all.
% F - Interactive Figure, Defaults to spm_figure('FindWin','Interactive')
% FRec - Position of interactive window
% QRec - Position of entire question
% PRec - Position of prompt
% RRec - Position of response
%
% FORMAT spm_input('!DeleteInputObj',F)
% Deltes input objects (only) from figure F
% F - Interactive Figure, Defaults to spm_figure('FindWin','Interactive')
%
% FORMAT [CPos,hCPos] = spm_input('!CurrentPos',F)
% Returns currently used GUI question positions & their handles
% F - Interactive Figure, Defaults to spm_figure('FindWin','Interactive')
% CPos - Vector of position indices
% hCPos - (n x CPos) matrix of object handles
%
% FORMAT h = spm_input('!FindInputObj',F)
% Returns handles of input GUI objects in figure F
% F - Interactive Figure, Defaults to spm_figure('FindWin','Interactive')
% h - vector of object handles
%
% FORMAT [NPos,CPos,hCPos] = spm_input('!NextPos',YPos,F,CmdLine)
% Returns next position index, specified by YPos
% YPos - Absolute (integer) or relative (string) position index
% Defaults to '+1'
% F - Interactive Figure, defaults to spm_figure('FindWin','Interactive')
% CmdLine - Command line? Defaults to spm_input('!CmdLine',YPos)
% NPos - Next position index
% CPos & hCPos - as for !CurrentPos
%
% FORMAT NPos = spm_input('!SetNextPos',YPos,F,CmdLine)
% Sets up for input at next position index, specified by YPos. This utility
% function can be used stand-alone to implicitly set the next position
% by clearing positions NPos and greater.
% YPos - Absolute (integer) or relative (string) position index
% Defaults to '+1'
% F - Interactive Figure, defaults to spm_figure('FindWin','Interactive')
% CmdLine - Command line? Defaults to spm_input('!CmdLine',YPos)
% NPos - Next position index
%
% FORMAT MPos = spm_input('!MaxPos',F,FRec3)
% Returns maximum position index for figure F
% F - Interactive Figure, Defaults to spm_figure('FindWin','Interactive')
% Not required if FRec3 is specified
% FRec3 - Length of interactive figure in pixels
%
% FORMAT spm_input('!EditableKeyPressFcn',h,ch)
% KeyPress callback for GUI string / eval input
%
% FORMAT spm_input('!ButtonKeyPressFcn',h,Keys,DefItem,ch)
% KeyPress callback for GUI buttons
%
% FORMAT spm_input('!PullDownKeyPressFcn',h,ch,DefItem)
% KeyPress callback for GUI pulldown menus
%
% FORMAT spm_input('!m_cb')
% Extended CallBack handler for 'p' PullDown utility type
%
% FORMAT spm_input('!dScroll',h,str)
% Scroll text string in object h
% h - handle of text object
% Prompt - Text to scroll (Defaults to 'UserData' of h)
%
%-----------------------------------------------------------------------
% SUBFUNCTIONS:
%
% FORMAT [Keys,Labs] = sf_labkeys(Labels)
% Make unique character keys for the Labels, ignoring case.
% Used with 'b'utton types.
%
% FORMAT [p,msg] = sf_eEval(str,Type,n,m)
% Common code for evaluating various input types.
%
% FORMAT str = sf_SzStr(n,l)
% Common code to construct prompt strings for pre-specified vector/matrix sizes
%
% FORMAT [p,msg] = sf_SzChk(p,n,msg)
% Common code to check (& canonicalise) sizes of input vectors/matrices
%
%_______________________________________________________________________
% @(#)spm_input.m 2.8 Andrew Holmes 03/03/04
%-Parameters
%=======================================================================
PJump = 1; %-Jumping of pointer to question?
TTips = 1; %-Use ToolTipStrings? (which can be annoying!)
ConCrash = 1; %-Add "crash out" option to 'Interactive'fig.ContextMenu
%-Condition arguments
%=======================================================================
if nargin<1||isempty(varargin{1}), Prompt=''; else Prompt=varargin{1}; end
if ~isempty(Prompt) && ischar(Prompt) && Prompt(1)=='!'
%-Utility functions have Prompt string starting with '!'
Type = Prompt;
else %-Should be an input request: get Type & YPos
if nargin<3||isempty(varargin{3}), Type='e'; else Type=varargin{3}; end
if any(Type=='|'), Type='b|'; end
if nargin<2||isempty(varargin{2}), YPos='+1'; else YPos=varargin{2}; end
[CmdLine,YPos] = spm_input('!CmdLine',YPos);
if ~CmdLine %-Setup for GUI use
%-Locate (or create) figure to work in
Finter = spm_input('!GetWin');
COLOUR = get(Finter,'Color');
%-Find out which Y-position to use, setup for use
YPos = spm_input('!SetNextPos',YPos,Finter,CmdLine);
%-Determine position of objects
[FRec,QRec,PRec,RRec]=spm_input('!InputRects',YPos,'',Finter);
end
end
switch lower(Type)
case {'s','s+','e','n','w','i','r','c','x','p'} %-String and evaluated input
%=======================================================================
%-Condition arguments
if nargin<6||isempty(varargin{6}), m=[]; else m=varargin{6}; end
if nargin<5||isempty(varargin{5}), n=[]; else n=varargin{5}; end
if nargin<4, DefStr=''; else DefStr=varargin{4}; end
if strcmpi(Type,'s+')
%-DefStr should be a cellstr for 's+' type.
if isempty(DefStr), DefStr = {};
else DefStr = cellstr(DefStr); end
DefStr = DefStr(:);
else
%-DefStr needs to be a string
if ~ischar(DefStr), DefStr=num2str(DefStr); end
DefStr = DefStr(:)';
end
strM='';
switch lower(Type) %-Type specific defaults/setup
case 's', TTstr='enter string';
case 's+',TTstr='enter string - multi-line';
case 'e', TTstr='enter expression to evaluate';
case 'n', TTstr='enter expression - natural number(s)';
if ~isempty(m), strM=sprintf(' (in [1,%d])',m); TTstr=[TTstr,strM]; end
case 'w', TTstr='enter expression - whole number(s)';
if ~isempty(m), strM=sprintf(' (in [0,%d])',m); TTstr=[TTstr,strM]; end
case 'i', TTstr='enter expression - integer(s)';
case 'r', TTstr='enter expression - real number(s)';
if ~isempty(m), TTstr=[TTstr,sprintf(' in [%g,%g]',min(m),max(m))]; end
case 'c', TTstr='enter indicator vector e.g. 0101... or abab...';
if ~isempty(m) && isfinite(m), strM=sprintf(' (%d)',m); end
case 'x', TTstr='enter contrast matrix';
case 'p',
if isempty(n), error('permutation of what?'), else P=n(:)'; end
if isempty(m), n = [1,length(P)]; end
m = P;
if isempty(setxor(m,[1:max(m)]))
TTstr=['enter permutation of [1:',num2str(max(m)),']'];
else
TTstr=['enter permutation of [',num2str(m),']'];
end
otherwise
TTstr='enter expression';
end
strN = sf_SzStr(n);
if CmdLine %-Use CmdLine to get answer
%-----------------------------------------------------------------------
spm_input('!PrntPrmpt',[Prompt,strN,strM],TTstr)
%-Do Eval Types in Base workspace, catch errors
switch lower(Type), case 's'
if ~isempty(DefStr)
Prompt=[Prompt,' (Default: ',DefStr,' )'];
end
str = input([Prompt,' : '],'s');
if isempty(str), str=DefStr; end
while isempty(str)
spm('Beep')
fprintf('! %s : enter something!\n',mfilename)
str = input([Prompt,' : '],'s');
if isempty(str), str=DefStr; end
end
p = str; msg = '';
case 's+'
fprintf(['Multi-line input: Type ''.'' on a line',...
' of its own to terminate input.\n'])
if ~isempty(DefStr)
fprintf('Default : (press return to accept)\n')
fprintf(' : %s\n',DefStr{:})
end
fprintf('\n')
str = input('l001 : ','s');
while (isempty(str) || strcmp(str,'.')) && isempty(DefStr)
spm('Beep')
fprintf('! %s : enter something!\n',mfilename)
str = input('l001 : ','s');
end
if isempty(str)
%-Accept default
p = DefStr;
else
%-Got some input, allow entry of additional lines
p = {str};
str = input(sprintf('l%03u : ',length(p)+1),'s');
while ~strcmp(str,'.')
p = [p;{str}];
str = input(sprintf('l%03u : ',length(p)+1),'s');
end
end
msg = '';
otherwise
if ~isempty(DefStr)
Prompt=[Prompt,' (Default: ',DefStr,' )'];
end
str = input([Prompt,' : '],'s');
if isempty(str), str=DefStr; end
[p,msg] = sf_eEval(str,Type,n,m);
while ischar(p)
spm('Beep'), fprintf('! %s : %s\n',mfilename,msg)
str = input([Prompt,' : '],'s');
if isempty(str), str=DefStr; end
[p,msg] = sf_eEval(str,Type,n,m);
end
end
if ~isempty(msg), fprintf('\t%s\n',msg), end
else %-Use GUI to get answer
%-----------------------------------------------------------------------
%-Create text and edit control objects
%---------------------------------------------------------------
hPrmpt = uicontrol(Finter,'Style','Text',...
'String',[strN,Prompt,strM],...
'Tag',['GUIinput_',int2str(YPos)],...
'UserData','',...
'BackgroundColor',COLOUR,...
'HorizontalAlignment','Right',...
'Position',PRec);
if TTips, set(hPrmpt,'ToolTipString',[strN,Prompt,strM]); end
%-Default button surrounding edit widget (if a DefStr given)
%-Callback sets hPrmpt UserData, and EditWidget string, to DefStr
% (Buttons UserData holds handles [hPrmpt,hEditWidget], set later)
cb = ['set(get(gcbo,''UserData'')*[1;0],''UserData'',',...
'get(gcbo,''String'')),',...
'set(get(gcbo,''UserData'')*[0;1],''String'',',...
'get(gcbo,''String''))'];
if ~isempty(DefStr)
if iscellstr(DefStr), str=[DefStr{1},'...'];
else str=DefStr; end
hDef = uicontrol(Finter,'Style','PushButton',...
'String',DefStr,...
'ToolTipString',...
['Click on border to accept default: ' str],...
'Tag',['GUIinput_',int2str(YPos)],...
'UserData',[],...
'BackgroundColor',COLOUR,...
'CallBack',cb,...
'Position',RRec+[-2,-2,+4,+4]);
else
hDef = [];
end
%-Edit widget: Callback puts string into hPrompts UserData
cb = 'set(get(gcbo,''UserData''),''UserData'',get(gcbo,''String''))';
h = uicontrol(Finter,'Style','Edit',...
'String',DefStr,...
'Max',strcmpi(Type,'s+')+1,...
'Tag',['GUIinput_',int2str(YPos)],...
'UserData',hPrmpt,...
'CallBack',cb,...
'Horizontalalignment','Left',...
'BackgroundColor','w',...
'Position',RRec);
set(hDef,'UserData',[hPrmpt,h])
uifocus(h);
if TTips, set(h,'ToolTipString',TTstr), end
%-Figure ContextMenu for shortcuts
hM = spm_input('!InptConMen',Finter,[hPrmpt,hDef,h]);
cb = [ 'set(get(gcbo,''UserData''),''String'',',...
'[''spm_load('''''',spm_select(1),'''''')'']), ',...
'set(get(get(gcbo,''UserData''),''UserData''),''UserData'',',...
'get(get(gcbo,''UserData''),''String''))'];
uimenu(hM,'Label','load from text file','Separator','on',...
'CallBack',cb,'UserData',h)
%-Bring window to fore & jump pointer to edit widget
[PLoc,cF] = spm_input('!PointerJump',RRec,Finter);
%-Setup FigureKeyPressFcn for editing of entry widget without clicking
set(Finter,'KeyPressFcn',[...
'spm_input(''!EditableKeyPressFcn'',',...
'findobj(gcf,''Tag'',''GUIinput_',int2str(YPos),''',',...
'''Style'',''edit''),',...
'get(gcbf,''CurrentCharacter''))'])
%-Wait for edit, do eval Types in Base workspace, catch errors
%---------------------------------------------------------------
waitfor(hPrmpt,'UserData')
if ~ishandle(hPrmpt), error(['Input window cleared whilst waiting ',...
'for response: Bailing out!']), end
str = get(hPrmpt,'UserData');
switch lower(Type), case 's'
p = str; msg = '';
case 's+'
p = cellstr(str); msg = '';
otherwise
[p,msg] = sf_eEval(str,Type,n,m);
while ischar(p)
set(h,'Style','Text',...
'String',msg,'HorizontalAlignment','Center',...
'ForegroundColor','r')
spm('Beep'), pause(2)
set(h,'Style','Edit',...
'String',str,...
'HorizontalAlignment','Left',...
'ForegroundColor','k')
%set(hPrmpt,'UserData','');
waitfor(hPrmpt,'UserData')
if ~ishandle(hPrmpt), error(['Input window cleared ',...
'whilst waiting for response: Bailing out!']),end
str = get(hPrmpt,'UserData');
[p,msg] = sf_eEval(str,Type,n,m);
end
end
%-Fix edit window, clean up, reposition pointer, set CurrentFig back
delete([hM,hDef]), set(Finter,'KeyPressFcn','')
set(h,'Style','Text','HorizontalAlignment','Center',...
'ToolTipString',msg,...
'BackgroundColor',COLOUR)
spm_input('!PointerJumpBack',PLoc,cF)
drawnow
end % (if CmdLine)
%-Return response
%-----------------------------------------------------------------------
varargout = {p,YPos};
case {'b','bd','b|','y/n','be1','bn1','bw1','bi1','br1',...
'-n1','n1','-w1','w1','m'} %-'b'utton & 'm'enu Types
%=======================================================================
%-Condition arguments
switch lower(Type), case {'b','be1','bi1','br1','m'}
m = []; Title = '';
if nargin<6, DefItem=[]; else DefItem=varargin{6}; end
if nargin<5, Values=[]; else Values =varargin{5}; end
if nargin<4, Labels=''; else Labels =varargin{4}; end
case 'bd'
if nargin<7, Title=''; else Title =varargin{7}; end
if nargin<6, DefItem=[]; else DefItem=varargin{6}; end
if nargin<5, Values=[]; else Values =varargin{5}; end
if nargin<4, Labels=''; else Labels =varargin{4}; end
case 'y/n'
Title = '';
if nargin<5, DefItem=[]; else DefItem=varargin{5}; end
if nargin<4, Values=[]; else Values =varargin{4}; end
if isempty(Values), Values='yn'; end
Labels = {'yes','no'};
case 'b|'
Title = '';
if nargin<5, DefItem=[]; else DefItem=varargin{5}; end
if nargin<4, Values=[]; else Values =varargin{4}; end
Labels = varargin{3};
case 'bn1'
if nargin<7, m=[]; else m=varargin{7}; end
if nargin<6, DefItem=[]; else DefItem=varargin{6}; end
if nargin<5, Values=[]; else Values =varargin{5}; end
if nargin<4, Labels=[1:5]'; Values=[1:5]; Type='-n1';
else Labels=varargin{4}; end
case 'bw1'
if nargin<7, m=[]; else m=varargin{7}; end
if nargin<6, DefItem=[]; else DefItem=varargin{6}; end
if nargin<5, Values=[]; else Values =varargin{5}; end
if nargin<4, Labels=[0:4]'; Values=[0:4]; Type='-w1';
else Labels=varargin{4}; end
case {'-n1','n1','-w1','w1'}
if nargin<5, m=[]; else m=varargin{5}; end
if nargin<4, DefItem=[]; else DefItem=varargin{4}; end
switch lower(Type)
case {'n1','-n1'}, Labels=[1:min([5,m])]'; Values=Labels'; Type='-n1';
case {'w1','-w1'}, Labels=[0:min([4,m])]'; Values=Labels'; Type='-w1';
end
end
%-Check some labels were specified
if isempty(Labels), error('No Labels specified'), end
if iscellstr(Labels), Labels=char(Labels); end
%-Convert Labels "option" string to string matrix if required
if ischar(Labels) && any(Labels(:)=='|')
OptStr=Labels;
BarPos=find([OptStr=='|',1]);
Labels=OptStr(1:BarPos(1)-1);
for Bar = 2:sum(OptStr=='|')+1
Labels=strvcat(Labels,OptStr(BarPos(Bar-1)+1:BarPos(Bar)-1));
end
end
%-Set default Values for the Labels
if isempty(Values)
if strcmpi(Type,'m')
Values=[1:size(Labels,1)]';
else
Values=Labels;
end
else
%-Make sure Values are in rows
if size(Labels,1)>1 && size(Values,1)==1, Values = Values'; end
%-Check numbers of Labels and Values match
if (size(Labels,1)~=size(Values,1))
error('Labels & Values incompatible sizes'), end
end
%-Numeric Labels to strings
if isnumeric(Labels)
tmp = Labels; Labels = cell(size(tmp,1),1);
for i=1:numel(tmp), Labels{i}=num2str(tmp(i,:)); end
Labels=char(Labels);
end
switch lower(Type), case {'b','bd','b|','y/n'} %-Process button types
%=======================================================================
%-Make unique character keys for the Labels, sort DefItem
%---------------------------------------------------------------
nLabels = size(Labels,1);
[Keys,Labs] = sf_labkeys(Labels);
if ~isempty(DefItem) && any(DefItem==[1:nLabels])
DefKey = Keys(DefItem);
else
DefItem = 0;
DefKey = '';
end
if CmdLine
%-Display question prompt
spm_input('!PrntPrmpt',Prompt,'',Title)
%-Build prompt
%-------------------------------------------------------
if ~isempty(Labs)
Prmpt = ['[',Keys(1),']',deblank(Labs(1,:)),' '];
for i = 2:nLabels
Prmpt=[Prmpt,'/ [',Keys(i),']',deblank(Labs(i,:)),' '];
end
else
Prmpt = ['[',Keys(1),'] '];
for i = 2:nLabels, Prmpt=[Prmpt,'/ [',Keys(i),'] ']; end
end
if DefItem
Prmpt = [Prmpt,...
' (Default: ',deblank(Labels(DefItem,:)),')'];
end
%-Ask for user response
%-------------------------------------------------------
if nLabels==1
%-Only one choice - auto-pick & display
k = 1; fprintf('%s: %s\t(only option)',Prmpt,Labels)
else
str = input([Prmpt,'? '],'s');
if isempty(str), str=DefKey; end
while isempty(str) || ~any(lower(Keys)==lower(str(1)))
if ~isempty(str),fprintf('%c\t!Out of range\n',7),end
str = input([Prmpt,'? '],'s');
if isempty(str), str=DefKey; end
end
k = find(lower(Keys)==lower(str(1)));
end
fprintf('\n')
p = Values(k,:); if ischar(p), p=deblank(p); end
elseif strcmpi(Type,'bd')
if nLabels>3, error('at most 3 labels for GUI ''bd'' type'), end
tmp = cellstr(Labels);
if DefItem
tmp = [tmp; tmp(DefItem)];
Prompt = cellstr(Prompt); Prompt=Prompt(:);
Prompt = [Prompt;{' '};...
{['[default: ',tmp{DefItem},']']}];
else
tmp = [tmp; tmp(1)];
end
k = min(find(strcmp(tmp,...
questdlg(Prompt,sprintf('%s%s: %s...',spm('ver'),...
spm('GetUser',' (%s)'),Title),tmp{:}))));
p = Values(k,:); if ischar(p), p=deblank(p); end
else
Tag = ['GUIinput_',int2str(YPos)]; %-Tag for widgets
%-Create text and edit control objects
%-'UserData' of prompt contains answer
%-------------------------------------------------------
hPrmpt = uicontrol(Finter,'Style','Text',...
'String',Prompt,...
'Tag',Tag,...
'UserData',[],...
'BackgroundColor',COLOUR,...
'HorizontalAlignment','Right',...
'Position',PRec);
if TTips, set(hPrmpt,'ToolTipString',Prompt); end
if nLabels==1
%-Only one choice - auto-pick
k = 1;
else
%-Draw buttons and process response
dX = RRec(3)/nLabels;
if TTips, str = ['select with mouse or use kbd: ',...
sprintf('%c/',Keys(1:end-1)),Keys(end)];
else str=''; end
%-Store button # in buttons 'UserData' property
%-Store handle of prompt string in buttons 'Max' property
%-Button callback sets UserData of prompt string to
% number of pressed button
cb = ['set(get(gcbo,''Max''),''UserData'',',...
'get(gcbo,''UserData''))'];
H = [];
XDisp = [];
for i=1:nLabels
if i==DefItem
%-Default button, outline it
h = uicontrol(Finter,'Style','Frame',...
'BackGroundColor','k',...
'ForeGroundColor','k',...
'Tag',Tag,...
'Position',...
[RRec(1)+(i-1)*dX ...
RRec(2)-1 dX RRec(4)+2]);
XDisp = (i-1/3)*dX;
H = [H,h];
end
h = uicontrol(Finter,'Style','Pushbutton',...
'String',deblank(Labels(i,:)),...
'ToolTipString',sprintf('%s\n%s',deblank(Labels(i,:)),str),...
'Tag',Tag,...
'Max',hPrmpt,...
'UserData',i,...
'BackgroundColor',COLOUR,...
'Callback',cb,...
'Position',[RRec(1)+(i-1)*dX+1 ...
RRec(2) dX-2 RRec(4)]);
if i == DefItem, uifocus(h); end
H = [H,h];
end
%-Figure ContextMenu for shortcuts
hM = spm_input('!InptConMen',Finter,[hPrmpt,H]);
%-Bring window to fore & jump pointer to default button
[PLoc,cF]=spm_input('!PointerJump',RRec,Finter,XDisp);
%-Callback for KeyPress, to store valid button # in
% UserData of Prompt, DefItem if (DefItem~=0)
% & return (ASCII-13) is pressed
set(Finter,'KeyPressFcn',...
['spm_input(''!ButtonKeyPressFcn'',',...
'findobj(gcf,''Tag'',''',Tag,''',',...
'''Style'',''text''),',...
'''',lower(Keys),''',',num2str(DefItem),',',...
'get(gcbf,''CurrentCharacter''))'])
%-Wait for button press, process results
%-----------------------------------------------
waitfor(hPrmpt,'UserData')
if ~ishandle(hPrmpt)
error(['Input objects cleared whilst ',...
'waiting for response: Bailing out!'])
end
k = get(hPrmpt,'UserData');
%-Clean up
delete([H,hM]), set(Finter,'KeyPressFcn','')
spm_input('!PointerJumpBack',PLoc,cF)
end
%-Display answer
uicontrol(Finter,'Style','Text',...
'String',deblank(Labels(k,:)),...
'Tag',Tag,...
'Horizontalalignment','Center',...
'BackgroundColor',COLOUR,...
'Position',RRec);
drawnow
p = Values(k,:); if ischar(p), p=deblank(p); end
end
case {'be1','bn1','bw1','bi1','br1','-n1','-w1'}
%-Process button/entry combo types
%=======================================================================
if ischar(DefItem), DefStr=DefItem; else DefStr=num2str(DefItem); end
if isempty(m), strM=''; else strM=sprintf(' (<=%d)',m); end
if CmdLine
%-Process default item
%---------------------------------------------------------------
if ~isempty(DefItem)
[DefVal,msg] = sf_eEval(DefStr,Type(2),1);
if ischar(DefVal), error(['Invalid DefItem: ',msg]), end
Labels = strvcat(Labels,DefStr);
Values = [Values;DefVal];
DefItem = size(Labels,1);
end
%-Add option to specify...
Labels = strvcat(Labels,'specify...');
%-Process options
nLabels = size(Labels,1);
[Keys,Labs] = sf_labkeys(Labels);
if ~isempty(DefItem), DefKey = Keys(DefItem); else DefKey = ''; end
%-Print banner prompt
%---------------------------------------------------------------
spm_input('!PrntPrmpt',Prompt) %-Display question prompt
if Type(1)=='-' %-No special buttons - go straight to input
k = size(Labels,1);
else %-Offer buttons, default or "specify..."
%-Build prompt
%-------------------------------------------------------
if ~isempty(Labs)
Prmpt = ['[',Keys(1),']',deblank(Labs(1,:)),' '];
for i = 2:nLabels
Prmpt=[Prmpt,'/ [',Keys(i),']',deblank(Labs(i,:)),' '];
end
else
Prmpt = ['[',Keys(1),'] '];
for i = 2:nLabels, Prmpt=[Prmpt,'/ [',Keys(i),'] ']; end
end
if DefItem, Prmpt = [Prmpt,...
' (Default: ',deblank(Labels(DefItem,:)),')']; end
%-Ask for user response
%-------------------------------------------------------
if nLabels==1
%-Only one choice - auto-pick & display
k = 1; fprintf('%s: %s\t(only option)',Prmpt,Labels)
else
str = input([Prmpt,'? '],'s');
if isempty(str), str=DefKey; end
while isempty(str) || ~any(lower(Keys)==lower(str(1)))
if ~isempty(str),fprintf('%c\t!Invalid response\n',7),end
str = input([Prmpt,'? '],'s');
if isempty(str), str=DefKey; end
end
k = find(lower(Keys)==lower(str(1)));
end
fprintf('\n')
end
%-Process response: prompt for value if "specify..." option chosen
%===============================================================
if k<size(Labels,1)
p = Values(k,:); if ischar(p), p=deblank(p); end
else
%-"specify option chosen: ask user to specify
%-------------------------------------------------------
switch lower(Type(2))
case 's', tstr=' string';
case 'e', tstr='n expression';
case 'n', tstr=' natural number';
case 'w', tstr=' whole number';
case 'i', tstr='n integer';
case 'r', tstr=' real number';
otherwise, tstr='';
end
Prompt = sprintf('%s (a%s%s)',Prompt,tstr,strM);
if ~isempty(DefStr)
Prompt=sprintf('%s\b, default %s)',Prompt,DefStr); end
str = input([Prompt,' : '],'s');
if isempty(str), str=DefStr; end
%-Eval in Base workspace, catch errors
[p,msg] = sf_eEval(str,Type(2),1,m);
while ischar(p)
spm('Beep'), fprintf('! %s : %s\n',mfilename,msg)
str = input([Prompt,' : '],'s');
if isempty(str), str=DefStr; end
[p,msg] = sf_eEval(str,Type(2),1,m);
end
end
else
Tag = ['GUIinput_',int2str(YPos)]; %-Tag for widgets
nLabels = size(Labels,1); %-#buttons
%-Create text and edit control objects
%-'UserData' of prompt contains answer
%---------------------------------------------------------------
hPrmpt = uicontrol(Finter,'Style','Text',...
'String',[Prompt,strM],...
'Tag',Tag,...
'UserData',[],...
'BackgroundColor',COLOUR,...
'HorizontalAlignment','Right',...
'Position',PRec);
if TTips, set(hPrmpt,'ToolTipString',[Prompt,strM]); end
%-Draw buttons & entry widget, & process response
dX = RRec(3)*(2/3)/nLabels;
%-Store button # in buttons 'UserData'
%-Store handle of prompt string in buttons 'Max' property
%-Callback sets UserData of prompt string to button number.
cb = ['set(get(gcbo,''Max''),''UserData'',get(gcbo,''UserData''))'];
if TTips, str=sprintf('select by mouse or enter value in text widget');
else str=''; end
H = [];
for i=1:nLabels
h = uicontrol(Finter,'Style','Pushbutton',...
'String',deblank(Labels(i,:)),...
'Max',hPrmpt,...
'ToolTipString',sprintf('%s\n%s',deblank(Labels(i,:)),str),...
'Tag',Tag,...
'UserData',i,...
'BackgroundColor',COLOUR,...
'Callback',cb,...
'Position',[RRec(1)+(i-1)*dX+1 RRec(2) dX-2 RRec(4)]);
H = [H,h];
end
%-Default button surrounding edit widget (if a DefStr given)
%-Callback sets hPrmpt UserData, and EditWidget string, to DefStr
% (Buttons UserData holds handles [hPrmpt,hEditWidget], set later)
cb = ['set(get(gcbo,''UserData'')*[1;0],''UserData'',',...
'get(gcbo,''String'')),',...
'set(get(gcbo,''UserData'')*[0;1],''String'',',...
'get(gcbo,''String''))'];
if ~isempty(DefStr)
hDef = uicontrol(Finter,'Style','PushButton',...
'String',DefStr,...
'ToolTipString',['Click on border to accept ',...
'default: ' DefStr],...
'Tag',Tag,...
'UserData',[],...
'CallBack',cb,...
'BackgroundColor',COLOUR,...
'Position',...
[RRec(1)+RRec(3)*(2/3) RRec(2)-2 RRec(3)/3+2 RRec(4)+4]);
H = [H,hDef];
else
hDef = [];
end
%-Edit widget: Callback puts string into hPrompts UserData
cb = ['set(get(gcbo,''UserData''),''UserData'',get(gcbo,''String''))'];
h = uicontrol(Finter,'Style','Edit',...
'String',DefStr,...
'ToolTipString',str,...
'Tag',Tag,...
'UserData',hPrmpt,...
'CallBack',cb,...
'Horizontalalignment','Center',...
'BackgroundColor','w',...
'Position',...
[RRec(1)+RRec(3)*(2/3)+2 RRec(2) RRec(3)/3-2 RRec(4)]);
set(hDef,'UserData',[hPrmpt,h])
uifocus(h);
H = [H,h];
%-Figure ContextMenu for shortcuts
hM = spm_input('!InptConMen',Finter,[hPrmpt,H]);
%-Bring window to fore & jump pointer to default button
[PLoc,cF] = spm_input('!PointerJump',RRec,Finter,RRec(3)*0.95);
%-Setup FigureKeyPressFcn for editing of entry widget without clicking
set(Finter,'KeyPressFcn',[...
'spm_input(''!EditableKeyPressFcn'',',...
'findobj(gcf,''Tag'',''GUIinput_',int2str(YPos),''',',...
'''Style'',''edit''),',...
'get(gcbf,''CurrentCharacter''))'])
%-Wait for button press, process results
%---------------------------------------------------------------
waitfor(hPrmpt,'UserData')
if ~ishandle(hPrmpt), error(['Input objects cleared whilst waiting ',...
'for response: Bailing out!']), end
p = get(hPrmpt,'UserData');
if ~ischar(p)
k = p;
p = Values(k,:); if ischar(p), p=deblank(p); end
else
Labels = strvcat(Labels,'specify...');
k = size(Labels,1);
[p,msg] = sf_eEval(p,Type(2),1,m);
while ischar(p)
set(H,'Visible','off')
h = uicontrol('Style','Text','String',msg,...
'Horizontalalignment','Center',...
'ForegroundColor','r',...
'BackgroundColor',COLOUR,...
'Tag',Tag,'Position',RRec);
spm('Beep')
pause(2), delete(h), set(H,'Visible','on')
set(hPrmpt,'UserData','')
waitfor(hPrmpt,'UserData')
if ~ishandle(hPrmpt), error(['Input objects cleared ',...
'whilst waiting for response: Bailing out!']),end
p = get(hPrmpt,'UserData');
if ischar(p), [p,msg] = sf_eEval(p,Type(2),1,m); end
end
end
%-Clean up
delete([H,hM]), set(Finter,'KeyPressFcn','')
spm_input('!PointerJumpBack',PLoc,cF)
%-Display answer
uicontrol(Finter,'Style','Text',...
'String',num2str(p),...
'Tag',Tag,...
'Horizontalalignment','Center',...
'BackgroundColor',COLOUR,...
'Position',RRec);
drawnow
end % (if CmdLine)
case 'm' %-Process menu type
%=======================================================================
nLabels = size(Labels,1);
if ~isempty(DefItem) && ~any(DefItem==[1:nLabels]), DefItem=[]; end
%-Process pull down menu type
if CmdLine
spm_input('!PrntPrmpt',Prompt)
nLabels = size(Labels,1);
for i = 1:nLabels, fprintf('\t%2d : %s\n',i,Labels(i,:)), end
Prmpt = ['Menu choice (1-',int2str(nLabels),')'];
if DefItem
Prmpt=[Prmpt,' (Default: ',num2str(DefItem),')'];
end
%-Ask for user response
%-------------------------------------------------------
if nLabels==1
%-Only one choice - auto-pick & display
k = 1;
fprintf('Menu choice: 1 - %s\t(only option)',Labels)
else
k = input([Prmpt,' ? ']);
if DefItem && isempty(k), k=DefItem; end
while isempty(k) || ~any([1:nLabels]==k)
if ~isempty(k),fprintf('%c\t!Out of range\n',7),end
k = input([Prmpt,' ? ']);
if DefItem && isempty(k), k=DefItem; end
end
end
fprintf('\n')
else
Tag = ['GUIinput_',int2str(YPos)]; %-Tag for widgets
if nLabels==1
%-Only one choice - auto-pick
k = 1;
else
Labs=[repmat(' ',nLabels,2),Labels];
if DefItem
Labs(DefItem,1)='*';
H = uicontrol(Finter,'Style','Frame',...
'BackGroundColor','k',...
'ForeGroundColor','k',...
'Position',QRec+[-1,-1,+2,+2]);
else
H = [];
end
cb = ['if (get(gcbo,''Value'')>1),',...
'set(gcbo,''UserData'',''Selected''), end'];
hPopUp = uicontrol(Finter,'Style','PopUp',...
'HorizontalAlignment','Left',...
'ForegroundColor','k',...
'BackgroundColor',COLOUR,...
'String',strvcat([Prompt,'...'],Labs),...
'Tag',Tag,...
'UserData',DefItem,...
'CallBack',cb,...
'Position',QRec);
if TTips
cLabs = cellstr(Labels);
cInd = num2cell(1:nLabels);
scLabs = [cInd; cLabs'];
scLabs = sprintf('%d: %s\n',scLabs{:});
set(hPopUp,'ToolTipString',sprintf(['select with ',...
'mouse or type option number (1-',...
num2str(nLabels),') & press return\n%s'],scLabs));
end
%-Figure ContextMenu for shortcuts
hM = spm_input('!InptConMen',Finter,[hPopUp,H]);
%-Bring window to fore & jump pointer to menu widget
[PLoc,cF] = spm_input('!PointerJump',RRec,Finter);
%-Callback for KeyPresses
cb=['spm_input(''!PullDownKeyPressFcn'',',...
'findobj(gcf,''Tag'',''',Tag,'''),',...
'get(gcf,''CurrentCharacter''))'];
set(Finter,'KeyPressFcn',cb)
%-Wait for menu selection
%-----------------------------------------------
waitfor(hPopUp,'UserData')
if ~ishandle(hPopUp), error(['Input object cleared ',...
'whilst waiting for response: Bailing out!']),end
k = get(hPopUp,'Value')-1;
%-Clean up
delete([H,hM]), set(Finter,'KeyPressFcn','')
set(hPopUp,'Style','Text',...
'Horizontalalignment','Center',...
'String',deblank(Labels(k,:)),...
'BackgroundColor',COLOUR)
spm_input('!PointerJumpBack',PLoc,cF)
end
%-Display answer
uicontrol(Finter,'Style','Text',...
'String',deblank(Labels(k,:)),...
'Tag',Tag,...
'Horizontalalignment','Center',...
'BackgroundColor',COLOUR,...
'Position',QRec);
drawnow
end
p = Values(k,:); if ischar(p), p=deblank(p); end
otherwise, error('unrecognised type')
end % (switch lower(Type) within case {'b','b|','y/n'})
%-Return response
%-----------------------------------------------------------------------
varargout = {p,YPos};
case {'m!','b!'} %-GUI PullDown/Buttons utility
%=======================================================================
% H = spm_input(Prompt,YPos,'p',Labels,cb,UD,XCB)
%-Condition arguments
if nargin<7, XCB = 0; else XCB = varargin{7}; end
if nargin<6, UD = []; else UD = varargin{6}; end
if nargin<5, cb = ''; else cb = varargin{5}; end
if nargin<4, Labels = []; else Labels = varargin{4}; end
if CmdLine, error('Can''t do CmdLine GUI utilities!'), end
if isempty(cb), cb = 'disp(''(CallBack not set)'')'; end
if ischar(cb), cb = cellstr(cb); end
if length(cb)>1 && strcmpi(Type,'m!'), XCB=1; end
if iscellstr(Labels), Labels=char(Labels); end
%-Convert Labels "option" string to string matrix if required
if any(Labels=='|')
OptStr=Labels;
BarPos=find([OptStr=='|',1]);
Labels=OptStr(1:BarPos(1)-1);
for Bar = 2:sum(OptStr=='|')+1
Labels=strvcat(Labels,OptStr(BarPos(Bar-1)+1:BarPos(Bar)-1));
end
end
%-Check #CallBacks
if ~( length(cb)==1 || (length(cb)==size(Labels,1)) )
error('Labels & Callbacks size mismatch'), end
%-Draw Prompt
%-----------------------------------------------------------------------
Tag = ['GUIinput_',int2str(YPos)]; %-Tag for widgets
if ~isempty(Prompt)
uicontrol(Finter,'Style','Text',...
'String',Prompt,...
'Tag',Tag,...
'HorizontalAlignment','Right',...
'BackgroundColor',COLOUR,...
'Position',PRec)
Rec = RRec;
else
Rec = QRec;
end
%-Sort out UserData for extended callbacks (handled by spm_input('!m_cb')
%-----------------------------------------------------------------------
if XCB, if iscell(UD), UD={UD}; end, UD = struct('UD',UD,'cb',{cb}); end
%-Draw PullDown or Buttons
%-----------------------------------------------------------------------
switch lower(Type), case 'm!'
if XCB, UD.cb=cb; cb = {'spm_input(''!m_cb'')'}; end
H = uicontrol(Finter,'Style','PopUp',...
'HorizontalAlignment','Left',...
'ForegroundColor','k',...
'BackgroundColor',COLOUR,...
'String',Labels,...
'Tag',Tag,...
'UserData',UD,...
'CallBack',char(cb),...
'Position',Rec);
case 'b!'
nLabels = size(Labels,1);
dX = Rec(3)/nLabels;
H = [];
for i=1:nLabels
if length(cb)>1, tcb=cb(i); else tcb=cb; end
if XCB, UD.cb=tcb; tcb = {'spm_input(''!m_cb'')'}; end
h = uicontrol(Finter,'Style','Pushbutton',...
'String',deblank(Labels(i,:)),...
'ToolTipString','',...
'Tag',Tag,...
'UserData',UD,...
'BackgroundColor',COLOUR,...
'Callback',char(tcb),...
'Position',[Rec(1)+(i-1)*dX+1 ...
Rec(2) dX-2 Rec(4)]);
H = [H,h];
end
end
%-Bring window to fore & jump pointer to menu widget
[PLoc,cF] = spm_input('!PointerJump',RRec,Finter);
varargout = {H};
case {'d','d!'} %-Display message
%=======================================================================
%-Condition arguments
if nargin<4, Label=''; else Label=varargin{4}; end
if CmdLine && strcmpi(Type,'d')
fprintf('\n +-%s%s+',Label,repmat('-',1,57-length(Label)))
Prompt = [Prompt,' '];
while ~isempty(Prompt)
tmp = length(Prompt);
if tmp>56, tmp=min([max(find(Prompt(1:56)==' ')),56]); end
fprintf('\n | %s%s |',Prompt(1:tmp),repmat(' ',1,56-tmp))
Prompt(1:tmp)=[];
end
fprintf('\n +-%s+\n',repmat('-',1,57))
elseif ~CmdLine
if ~isempty(Label), Prompt = [Label,': ',Prompt]; end
figure(Finter)
%-Create text axes and edit control objects
%---------------------------------------------------------------
h = uicontrol(Finter,'Style','Text',...
'String',Prompt(1:min(length(Prompt),56)),...
'FontWeight','bold',...
'Tag',['GUIinput_',int2str(YPos)],...
'HorizontalAlignment','Left',...
'ForegroundColor','k',...
'BackgroundColor',COLOUR,...
'UserData',Prompt,...
'Position',QRec);
if length(Prompt)>56
pause(1)
set(h,'ToolTipString',Prompt)
spm_input('!dScroll',h)
uicontrol(Finter,'Style','PushButton','String','>',...
'ToolTipString','press to scroll message',...
'Tag',['GUIinput_',int2str(YPos)],...
'UserData',h,...
'CallBack',[...
'set(gcbo,''Visible'',''off''),',...
'spm_input(''!dScroll'',get(gcbo,''UserData'')),',...
'set(gcbo,''Visible'',''on'')'],...
'BackgroundColor',COLOUR,...
'Position',[QRec(1)+QRec(3)-10,QRec(2),15,QRec(4)]);
end
end
if nargout>0, varargout={[],YPos}; end
%=======================================================================
% U T I L I T Y F U N C T I O N S
%=======================================================================
case '!colour'
%=======================================================================
% colour = spm_input('!Colour')
varargout = {COLOUR};
case '!icond'
%=======================================================================
% [iCond,msg] = spm_input('!iCond',str,n,m)
% Parse condition indicator spec strings:
% '2 3 2 3', '0 1 0 1', '2323', '0101', 'abab', 'R A R A'
if nargin<4, m=Inf; else m=varargin{4}; end
if nargin<3, n=NaN; else n=varargin{3}; end
if any(isnan(n(:)))
n=Inf;
elseif (length(n(:))==2 && ~any(n==1)) || length(n(:))>2
error('condition input can only do vectors')
end
if nargin<2, i=''; else i=varargin{2}; end
if isempty(i), varargout={[],'empty input'}; return, end
msg = ''; i=i(:)';
if ischar(i)
if i(1)=='0' && all(ismember(unique(i(:)),char(abs('0'):abs('9'))))
%-Leading zeros in a digit list
msg = sprintf('%s expanded',i);
z = min(find([diff(i=='0'),1]));
i = [zeros(1,z), spm_input('!iCond',i(z+1:end))'];
else
%-Try an eval, for functions & string #s
i = evalin('base',['[',i,']'],'i');
end
end
if ischar(i)
%-Evaluation error from above: see if it's an 'abab' or 'a b a b' type:
[c,null,i] = unique(lower(i(~isspace(i))));
if all(ismember(c,char(abs('a'):abs('z'))))
%-Map characters a-z to 1-26, but let 'r' be zero (rest)
tmp = c-'a'+1; tmp(tmp=='r'-'a'+1)=0;
i = tmp(i);
msg = [sprintf('[%s] mapped to [',c),...
sprintf('%d,',tmp(1:end-1)),...
sprintf('%d',tmp(end)),']'];
else
i = '!'; msg = 'evaluation error';
end
elseif ~all(floor(i(:))==i(:))
i = '!'; msg = 'must be integers';
elseif length(i)==1 && prod(n)>1
msg = sprintf('%d expanded',i);
i = floor(i./10.^[floor(log10(i)+eps):-1:0]);
i = i-[0,10*i(1:end-1)];
end
%-Check size of i & #conditions
if ~ischar(i), [i,msg] = sf_SzChk(i,n,msg); end
if ~ischar(i) && isfinite(m) && length(unique(i))~=m
i = '!'; msg = sprintf('%d conditions required',m);
end
varargout = {i,msg};
case '!inptconmen'
%=======================================================================
% hM = spm_input('!InptConMen',Finter,H)
if nargin<3, H=[]; else H=varargin{3}; end
if nargin<2, varargout={[]}; else Finter=varargin{2}; end
hM = uicontextmenu('Parent',Finter);
uimenu(hM,'Label','help on spm_input',...
'CallBack','spm_help(''spm_input.m'')')
if ConCrash
uimenu(hM,'Label','crash out','Separator','on',...
'CallBack','delete(get(gcbo,''UserData''))',...
'UserData',[hM,H])
end
set(Finter,'UIContextMenu',hM)
varargout={hM};
case '!cmdline'
%=======================================================================
% [CmdLine,YPos] = spm_input('!CmdLine',YPos)
%-Sorts out whether to use CmdLine or not & canonicalises YPos
if nargin<2, YPos=''; else YPos=varargin{2}; end
if isempty(YPos), YPos='+1'; end
CmdLine = [];
%-Special YPos specifications
if ischar(YPos)
if(YPos(1)=='!'), CmdLine=0; YPos(1)=[]; end
elseif YPos==0
CmdLine=1;
elseif YPos<0
CmdLine=0;
YPos=-YPos;
end
CmdLine = spm('CmdLine',CmdLine);
if CmdLine, YPos=0; end
varargout = {CmdLine,YPos};
case '!getwin'
%=======================================================================
% Finter = spm_input('!GetWin',F)
%-Locate (or create) figure to work in (Don't use 'Tag'ged figs)
if nargin<2, F='Interactive'; else F=varargin{2}; end
Finter = spm_figure('FindWin',F);
if isempty(Finter)
if any(get(0,'Children'))
if isempty(get(gcf,'Tag')), Finter = gcf;
else Finter = spm('CreateIntWin'); end
else Finter = spm('CreateIntWin'); end
end
varargout = {Finter};
case '!pointerjump'
%=======================================================================
% [PLoc,cF] = spm_input('!PointerJump',RRec,F,XDisp)
%-Raise window & jump pointer over question
if nargin<4, XDisp=[]; else XDisp=varargin{4}; end
if nargin<3, F='Interactive'; else F=varargin{3}; end
if nargin<2, error('Insufficient arguments'), else RRec=varargin{2}; end
F = spm_figure('FindWin',F);
PLoc = get(0,'PointerLocation');
cF = get(0,'CurrentFigure');
if ~isempty(F)
figure(F)
FRec = get(F,'Position');
if isempty(XDisp), XDisp=RRec(3)*4/5; end
if PJump, set(0,'PointerLocation',...
floor([(FRec(1)+RRec(1)+XDisp), (FRec(2)+RRec(2)+RRec(4)/3)]));
end
end
varargout = {PLoc,cF};
case '!pointerjumpback'
%=======================================================================
% spm_input('!PointerJumpBack',PLoc,cF)
%-Replace pointer and reset CurrentFigure back
if nargin<4, cF=[]; else F=varargin{3}; end
if nargin<2, error('Insufficient arguments'), else PLoc=varargin{2}; end
if PJump, set(0,'PointerLocation',PLoc), end
cF = spm_figure('FindWin',cF);
if ~isempty(cF), set(0,'CurrentFigure',cF); end
case '!prntprmpt'
%=======================================================================
% spm_input('!PrntPrmpt',Prompt,TipStr,Title)
%-Print prompt for CmdLine questioning
if nargin<4, Title = ''; else Title = varargin{4}; end
if nargin<3, TipStr = ''; else TipStr = varargin{3}; end
if nargin<2, Prompt = ''; else Prompt = varargin{2}; end
if isempty(Prompt), Prompt='Enter an expression'; end
Prompt = cellstr(Prompt);
if ~isempty(TipStr)
tmp = 8 + length(Prompt{end}) + length(TipStr);
if tmp < 62
TipStr = sprintf('%s(%s)',repmat(' ',1,70-tmp),TipStr);
else
TipStr = sprintf('\n%s(%s)',repmat(' ',1,max(0,70-length(TipStr))),TipStr);
end
end
if isempty(Title)
fprintf('\n%s\n',repmat('~',1,72))
else
fprintf('\n= %s %s\n',Title,repmat('~',1,72-length(Title)-3))
end
fprintf('\t%s',Prompt{1})
for i=2:numel(Prompt), fprintf('\n\t%s',Prompt{i}), end
fprintf('%s\n%s\n',TipStr,repmat('~',1,72))
case '!inputrects'
%=======================================================================
% [Frec,QRec,PRec,RRec,Sz,Se] = spm_input('!InputRects',YPos,rec,F)
if nargin<4, F='Interactive'; else F=varargin{4}; end
if nargin<3, rec=''; else rec=varargin{3}; end
if nargin<2, YPos=1; else YPos=varargin{2}; end
F = spm_figure('FindWin',F);
if isempty(F), error('Figure not found'), end
Units = get(F,'Units');
set(F,'Units','pixels')
FRec = get(F,'Position');
set(F,'Units',Units);
Xdim = FRec(3); Ydim = FRec(4);
WS = spm('WinScale');
Sz = round(22*min(WS)); %-Height
Pd = Sz/2; %-Pad
Se = 2*round(25*min(WS)/2); %-Seperation
Yo = round(2*min(WS)); %-Y offset for responses
a = 5.5/10;
y = Ydim - Se*YPos;
QRec = [Pd y Xdim-2*Pd Sz]; %-Question
PRec = [Pd y floor(a*Xdim)-2*Pd Sz]; %-Prompt
RRec = [ceil(a*Xdim) y+Yo floor((1-a)*Xdim)-Pd Sz]; %-Response
% MRec = [010 y Xdim-50 Sz]; %-Menu PullDown
% BRec = MRec + [Xdim-50+1, 0+1, 50-Xdim+30, 0]; %-Menu PullDown OK butt
if ~isempty(rec)
varargout = {eval(rec)};
else
varargout = {FRec,QRec,PRec,RRec,Sz,Se};
end
case '!deleteinputobj'
%=======================================================================
% spm_input('!DeleteInputObj',F)
if nargin<2, F='Interactive'; else F=varargin{2}; end
h = spm_input('!FindInputObj',F);
delete(h(h>0))
case {'!currentpos','!findinputobj'}
%=======================================================================
% [CPos,hCPos] = spm_input('!CurrentPos',F)
% h = spm_input('!FindInputObj',F)
% hPos contains handles: Columns contain handles corresponding to Pos
if nargin<2, F='Interactive'; else F=varargin{2}; end
F = spm_figure('FindWin',F);
%-Find tags and YPos positions of 'GUIinput_' 'Tag'ged objects
H = [];
YPos = [];
for h = get(F,'Children')'
tmp = get(h,'Tag');
if ~isempty(tmp)
if strcmp(tmp(1:min(length(tmp),9)),'GUIinput_')
H = [H, h];
YPos = [YPos, eval(tmp(10:end))];
end
end
end
switch lower(Type), case '!findinputobj'
varargout = {H};
case '!currentpos'
if nargout<2
varargout = {max(YPos),[]};
elseif isempty(H)
varargout = {[],[]};
else
%-Sort out
tmp = sort(YPos);
CPos = tmp(find([1,diff(tmp)]));
nPos = length(CPos);
nPerPos = diff(find([1,diff(tmp),1]));
hCPos = zeros(max(nPerPos),nPos);
for i = 1:nPos
hCPos(1:nPerPos(i),i) = H(YPos==CPos(i))';
end
varargout = {CPos,hCPos};
end
end
case '!nextpos'
%=======================================================================
% [NPos,CPos,hCPos] = spm_input('!NextPos',YPos,F,CmdLine)
%-Return next position to use
if nargin<3, F='Interactive'; else F=varargin{3}; end
if nargin<2, YPos='+1'; else YPos=varargin{2}; end
if nargin<4, [CmdLine,YPos]=spm_input('!CmdLine',YPos);
else CmdLine=varargin{4}; end
F = spm_figure('FindWin',F);
%-Get current positions
if nargout<3
CPos = spm_input('!CurrentPos',F);
hCPos = [];
else
[CPos,hCPos] = spm_input('!CurrentPos',F);
end
if CmdLine
NPos = 0;
else
MPos = spm_input('!MaxPos',F);
if ischar(YPos)
%-Relative YPos
%-Strip any '!' prefix from YPos
if(YPos(1)=='!'), YPos(1)=[]; end
if strncmp(YPos,'_',1)
%-YPos='_' means bottom
YPos=eval(['MPos+',YPos(2:end)],'MPos');
else
YPos = max([0,CPos])+eval(YPos);
end
else
%-Absolute YPos
YPos=abs(YPos);
end
NPos = min(max(1,YPos),MPos);
end
varargout = {NPos,CPos,hCPos};
case '!setnextpos'
%=======================================================================
% NPos = spm_input('!SetNextPos',YPos,F,CmdLine)
%-Set next position to use
if nargin<3, F='Interactive'; else F=varargin{3}; end
if nargin<2, YPos='+1'; else YPos=varargin{2}; end
if nargin<4, [CmdLine,YPos]=spm_input('!CmdLine',YPos);
else CmdLine=varargin{4}; end
%-Find out which Y-position to use
[NPos,CPos,hCPos] = spm_input('!NextPos',YPos,F,CmdLine);
%-Delete any previous inputs using positions NPos and after
if any(CPos>=NPos), h=hCPos(:,CPos>=NPos); delete(h(h>0)), end
varargout = {NPos};
case '!maxpos'
%=======================================================================
% MPos = spm_input('!MaxPos',F,FRec3)
%
if nargin<3
if nargin<2, F='Interactive'; else F=varargin{2}; end
F = spm_figure('FindWin',F);
if isempty(F)
FRec3=spm('WinSize','Interactive')*[0;0;0;1];
else
%-Get figure size
Units = get(F,'Units');
set(F,'Units','pixels')
FRec3 = get(F,'Position')*[0;0;0;1];
set(F,'Units',Units);
end
end
Se = round(25*min(spm('WinScale')));
MPos = floor((FRec3-5)/Se);
varargout = {MPos};
case '!editablekeypressfcn'
%=======================================================================
% spm_input('!EditableKeyPressFcn',h,ch,hPrmpt)
if nargin<2, error('Insufficient arguments'), else h=varargin{2}; end
if isempty(h), set(gcbf,'KeyPressFcn','','UserData',[]), return, end
if nargin<3, ch=get(get(h,'Parent'),'CurrentCharacter'); else ch=varargin{3};end
if nargin<4, hPrmpt=get(h,'UserData'); else hPrmpt=varargin{4}; end
tmp = get(h,'String');
if isempty(tmp), tmp=''; end
if iscellstr(tmp) && length(tmp)==1; tmp=tmp{:}; end
if isempty(ch) %- shift / control / &c. pressed
return
elseif any(abs(ch)==[32:126]) %-Character
if iscellstr(tmp), return, end
tmp = [tmp, ch];
elseif abs(ch)==21 %- ^U - kill
tmp = '';
elseif any(abs(ch)==[8,127]) %-BackSpace or Delete
if iscellstr(tmp), return, end
if ~isempty(tmp), tmp(length(tmp))=''; end
elseif abs(ch)==13 %-Return pressed
if ~isempty(tmp)
set(hPrmpt,'UserData',get(h,'String'))
end
return
else
%-Illegal character
return
end
set(h,'String',tmp)
case '!buttonkeypressfcn'
%=======================================================================
% spm_input('!ButtonKeyPressFcn',h,Keys,DefItem,ch)
%-Callback for KeyPress, to store valid button # in UserData of Prompt,
% DefItem if (DefItem~=0) & return (ASCII-13) is pressed
%-Condition arguments
if nargin<2, error('Insufficient arguments'), else h=varargin{2}; end
if isempty(h), set(gcf,'KeyPressFcn','','UserData',[]), return, end
if nargin<3, error('Insufficient arguments'); else Keys=varargin{3}; end
if nargin<4, DefItem=0; else DefItem=varargin{4}; end
if nargin<5, ch=get(gcf,'CurrentCharacter'); else ch=varargin{5}; end
if isempty(ch)
%- shift / control / &c. pressed
return
elseif (DefItem && ch==13)
But = DefItem;
else
But = find(lower(ch)==lower(Keys));
end
if ~isempty(But), set(h,'UserData',But), end
case '!pulldownkeypressfcn'
%=======================================================================
% spm_input('!PullDownKeyPressFcn',h,ch,DefItem)
if nargin<2, error('Insufficient arguments'), else h=varargin{2}; end
if isempty(h), set(gcf,'KeyPressFcn',''), return, end
if nargin<3, ch=get(get(h,'Parent'),'CurrentCharacter'); else ch=varargin{3};end
if nargin<4, DefItem=get(h,'UserData'); else ch=varargin{4}; end
Pmax = get(h,'Max');
Pval = get(h,'Value');
if Pmax==1, return, end
if isempty(ch)
%- shift / control / &c. pressed
return
elseif abs(ch)==13
if Pval==1
if DefItem, set(h,'Value',max(2,min(DefItem+1,Pmax))), end
else
set(h,'UserData','Selected')
end
elseif any(ch=='bpu')
%-Move "b"ack "u"p to "p"revious entry
set(h,'Value',max(2,Pval-1))
elseif any(ch=='fnd')
%-Move "f"orward "d"own to "n"ext entry
set(h,'Value',min(Pval+1,Pmax))
elseif any(ch=='123456789')
%-Move to entry n
set(h,'Value',max(2,min(eval(ch)+1,Pmax)))
else
%-Illegal character
end
case '!m_cb' %-CallBack handler for extended CallBack 'p'ullDown type
%=======================================================================
% spm_input('!m_cb')
%-Get PopUp handle and value
h = gcbo;
n = get(h,'Value');
%-Get PopUp's UserData, check cb and UD fields exist, extract cb & UD
tmp = get(h,'UserData');
if ~(isfield(tmp,'cb') && isfield(tmp,'UD'))
error('Invalid UserData structure for spm_input extended callback')
end
cb = tmp.cb;
UD = tmp.UD;
%-Evaluate appropriate CallBack string (ignoring any return arguments)
% NB: Using varargout={eval(cb{n})}; gives an error if the CallBack
% has no return arguments!
if length(cb)==1, eval(char(cb)); else eval(cb{n}); end
case '!dscroll'
%=======================================================================
% spm_input('!dScroll',h,Prompt)
%-Scroll text in object h
if nargin<2, return, else h=varargin{2}; end
if nargin<3, Prompt = get(h,'UserData'); else Prompt=varargin{3}; end
tmp = Prompt;
if length(Prompt)>56
while length(tmp)>56
tic, while(toc<0.1), pause(0.05), end
tmp(1)=[];
set(h,'String',tmp(1:min(length(tmp),56)))
end
pause(1)
set(h,'String',Prompt(1:min(length(Prompt),56)))
end
otherwise
%=======================================================================
error(['Invalid type/action: ',Type])
%=======================================================================
end % (case lower(Type))
%=======================================================================
%- S U B - F U N C T I O N S
%=======================================================================
function [Keys,Labs] = sf_labkeys(Labels)
%=======================================================================
%-Make unique character keys for the Labels, ignoring case
if nargin<1, error('insufficient arguments'), end
if iscellstr(Labels), Labels = char(Labels); end
if isempty(Labels), Keys=''; Labs=''; return, end
Keys=Labels(:,1)';
nLabels = size(Labels,1);
if any(~diff(abs(sort(lower(Keys)))))
if nLabels<10
Keys = sprintf('%d',[1:nLabels]);
elseif nLabels<=26
Keys = sprintf('%c',abs('a')+[0:nLabels-1]);
else
error('Too many buttons!')
end
Labs = Labels;
else
Labs = Labels(:,2:end);
end
function [p,msg] = sf_eEval(str,Type,n,m)
%=======================================================================
%-Evaluation and error trapping of typed input
if nargin<4, m=[]; end
if nargin<3, n=[]; end
if nargin<2, Type='e'; end
if nargin<1, str=''; end
if isempty(str), p='!'; msg='empty input'; return, end
switch lower(Type)
case 's'
p = str; msg = '';
case 'e'
p = evalin('base',['[',str,']'],'''!''');
if ischar(p)
msg = 'evaluation error';
else
[p,msg] = sf_SzChk(p,n);
end
case 'n'
p = evalin('base',['[',str,']'],'''!''');
if ischar(p)
msg = 'evaluation error';
elseif any(floor(p(:))~=p(:)|p(:)<1)||~isreal(p)
p='!'; msg='natural number(s) required';
elseif ~isempty(m) && any(p(:)>m)
p='!'; msg=['max value is ',num2str(m)];
else
[p,msg] = sf_SzChk(p,n);
end
case 'w'
p = evalin('base',['[',str,']'],'''!''');
if ischar(p)
msg = 'evaluation error';
elseif any(floor(p(:))~=p(:)|p(:)<0)||~isreal(p)
p='!'; msg='whole number(s) required';
elseif ~isempty(m) && any(p(:)>m)
p='!'; msg=['max value is ',num2str(m)];
else
[p,msg] = sf_SzChk(p,n);
end
case 'i'
p = evalin('base',['[',str,']'],'''!''');
if ischar(p)
msg = 'evaluation error';
elseif any(floor(p(:))~=p(:))||~isreal(p)
p='!'; msg='integer(s) required';
else
[p,msg] = sf_SzChk(p,n);
end
case 'p'
p = evalin('base',['[',str,']'],'''!''');
if ischar(p)
msg = 'evaluation error';
elseif ~isempty(setxor(p(:)',m))
p='!'; msg='invalid permutation';
else
[p,msg] = sf_SzChk(p,n);
end
case 'r'
p = evalin('base',['[',str,']'],'''!''');
if ischar(p)
msg = 'evaluation error';
elseif ~isreal(p)
p='!'; msg='real number(s) required';
elseif ~isempty(m) && ( max(p)>max(m) || min(p)<min(m) )
p='!'; msg=sprintf('real(s) in [%g,%g] required',min(m),max(m));
else
[p,msg] = sf_SzChk(p,n);
end
case 'c'
if isempty(m), m=Inf; end
[p,msg] = spm_input('!iCond',str,n,m);
case 'x'
X = m; %-Design matrix/space-structure
if isempty(n), n=1; end
%-Sort out contrast matrix dimensions (contrast vectors in rows)
if length(n)==1, n=[n,Inf]; else n=reshape(n(1:2),1,2); end
if ~isempty(X) % - override n(2) w/ design column dimension
n(2) = spm_SpUtil('size',X,2);
end
p = evalin('base',['[',str,']'],'''!''');
if ischar(p)
msg = 'evaluation error';
else
if isfinite(n(2)) && size(p,2)<n(2)
tmp = n(2) -size(p,2);
p = [p, zeros(size(p,1),tmp)];
if size(p,1)>1, str=' columns'; else str='s'; end
msg = sprintf('right padded with %d zero%s',tmp,str);
else
msg = '';
end
if size(p,2)>n(2)
p='!'; msg=sprintf('too long - only %d prams',n(2));
elseif isfinite(n(1)) && size(p,1)~=n(1)
p='!';
if n(1)==1, msg='vector required';
else msg=sprintf('%d contrasts required',n(1)); end
elseif ~isempty(X) && ~spm_SpUtil('allCon',X,p')
p='!'; msg='invalid contrast';
end
end
otherwise
error('unrecognised type');
end
function str = sf_SzStr(n,l)
%=======================================================================
%-Size info string construction
if nargin<2, l=0; else l=1; end
if nargin<1, error('insufficient arguments'), end
if isempty(n), n=NaN; end
n=n(:); if length(n)==1, n=[n,1]; end, dn=length(n);
if any(isnan(n)) || (prod(n)==1 && dn<=2) || (dn==2 && min(n)==1 && isinf(max(n)))
str = ''; lstr = '';
elseif dn==2 && min(n)==1
str = sprintf('[%d]',max(n)); lstr = [str,'-vector'];
elseif dn==2 && sum(isinf(n))==1
str = sprintf('[%d]',min(n)); lstr = [str,'-vector(s)'];
else
str='';
for i = 1:dn
if isfinite(n(i)), str = sprintf('%s,%d',str,n(i));
else str = sprintf('%s,*',str); end
end
str = ['[',str(2:end),']']; lstr = [str,'-matrix'];
end
if l, str=sprintf('\t%s',lstr); else str=[str,' ']; end
function [p,msg] = sf_SzChk(p,n,msg)
%=======================================================================
%-Size checking
if nargin<3, msg=''; end
if nargin<2, n=[]; end, if isempty(n), n=NaN; else n=n(:)'; end
if nargin<1, error('insufficient arguments'), end
if ischar(p) || any(isnan(n(:))), return, end
if length(n)==1, n=[n,1]; end
dn = length(n);
sp = size(p);
dp = ndims(p);
if dn==2 && min(n)==1
%-[1,1], [1,n], [n,1], [1,Inf], [Inf,1] - vector - allow transpose
%---------------------------------------------------------------
i = min(find(n==max(n)));
if n(i)==1 && max(sp)>1
p='!'; msg='scalar required';
elseif ndims(p)~=2 || ~any(sp==1) || ( isfinite(n(i)) && max(sp)~=n(i) )
%-error: Not2D | not vector | not right length
if isfinite(n(i)), str=sprintf('%d-',n(i)); else str=''; end
p='!'; msg=[str,'vector required'];
elseif sp(i)==1 && n(i)~=1
p=p'; msg=[msg,' (input transposed)'];
end
elseif dn==2 && sum(isinf(n))==1
%-[n,Inf], [Inf,n] - n vector(s) required - allow transposing
%---------------------------------------------------------------
i = find(isfinite(n));
if ndims(p)~=2 || ~any(sp==n(i))
p='!'; msg=sprintf('%d-vector(s) required',min(n));
elseif sp(i)~=n
p=p'; msg=[msg,' (input transposed)'];
end
else
%-multi-dimensional matrix required - check dimensions
%---------------------------------------------------------------
if ndims(p)~=dn || ~all( size(p)==n | isinf(n) )
p = '!'; msg='';
for i = 1:dn
if isfinite(n(i)), msg = sprintf('%s,%d',msg,n(i));
else msg = sprintf('%s,*',msg); end
end
msg = ['[',msg(2:end),']-matrix required'];
end
end
%==========================================================================
function uifocus(h)
try
if strcmpi(get(h, 'Style'), 'PushButton') == 1
uicontrol(gcbo);
else
uicontrol(h);
end
end
|
github
|
philippboehmsturm/antx-master
|
spm_realign.m
|
.m
|
antx-master/freiburgLight/matlab/spm8/spm_realign.m
| 18,205 |
utf_8
|
71e4880ae3886c6cff4d532cbaa0810a
|
function P = spm_realign(P,flags)
% Estimation of within modality rigid body movement parameters
% FORMAT P = spm_realign(P,flags)
%
% P - matrix of filenames {one string per row}
% All operations are performed relative to the first image.
% ie. Coregistration is to the first image, and resampling
% of images is into the space of the first image.
% For multiple sessions, P should be a cell array, where each
% cell should be a matrix of filenames.
%
% flags - a structure containing various options. The fields are:
% quality - Quality versus speed trade-off. Highest quality
% (1) gives most precise results, whereas lower
% qualities gives faster realignment.
% The idea is that some voxels contribute little to
% the estimation of the realignment parameters.
% This parameter is involved in selecting the number
% of voxels that are used.
%
% fwhm - The FWHM of the Gaussian smoothing kernel (mm)
% applied to the images before estimating the
% realignment parameters.
%
% sep - the default separation (mm) to sample the images.
%
% rtm - Register to mean. If field exists then a two pass
% procedure is to be used in order to register the
% images to the mean of the images after the first
% realignment.
%
% PW - a filename of a weighting image (reciprocal of
% standard deviation). If field does not exist, then
% no weighting is done.
%
% interp - B-spline degree used for interpolation
%
%__________________________________________________________________________
%
% Inputs
% A series of *.img conforming to SPM data format (see 'Data Format').
%
% Outputs
% If no output argument, then an updated voxel to world matrix is written
% to the headers of the images (a .mat file is created for 4D images).
% The details of the transformation are displayed in the
% results window as plots of translation and rotation.
% A set of realignment parameters are saved for each session, named:
% rp_*.txt.
%__________________________________________________________________________
%
% The voxel to world mappings.
%
% These are simply 4x4 affine transformation matrices represented in the
% NIFTI headers (see http://nifti.nimh.nih.gov/nifti-1 ).
% These are normally modified by the `realignment' and `coregistration'
% modules. What these matrixes represent is a mapping from
% the voxel coordinates (x0,y0,z0) (where the first voxel is at coordinate
% (1,1,1)), to coordinates in millimeters (x1,y1,z1).
%
% x1 = M(1,1)*x0 + M(1,2)*y0 + M(1,3)*z0 + M(1,4)
% y1 = M(2,1)*x0 + M(2,2)*y0 + M(2,3)*z0 + M(2,4)
% z1 = M(3,1)*x0 + M(3,2)*y0 + M(3,3)*z0 + M(3,4)
%
% Assuming that image1 has a transformation matrix M1, and image2 has a
% transformation matrix M2, the mapping from image1 to image2 is: M2\M1
% (ie. from the coordinate system of image1 into millimeters, followed
% by a mapping from millimeters into the space of image2).
%
% These matrices allow several realignment or coregistration steps to be
% combined into a single operation (without the necessity of resampling the
% images several times). The `.mat' files are also used by the spatial
% normalisation module.
%__________________________________________________________________________
% Ref:
% Friston KJ, Ashburner J, Frith CD, Poline J-B, Heather JD & Frackowiak
% RSJ (1995) Spatial registration and normalization of images Hum. Brain
% Map. 2:165-189
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% John Ashburner
% $Id: spm_realign.m 4152 2011-01-11 14:13:35Z volkmar $
if nargin==0, return; end;
def_flags = spm_get_defaults('realign.estimate');
def_flags.PW = '';
def_flags.graphics = 1;
def_flags.lkp = 1:6;
if nargin < 2,
flags = def_flags;
else
fnms = fieldnames(def_flags);
for i=1:length(fnms),
if ~isfield(flags,fnms{i}),
flags.(fnms{i}) = def_flags.(fnms{i});
end;
end;
end;
if ~iscell(P), tmp = cell(1); tmp{1} = P; P = tmp; end;
for i=1:length(P), if ischar(P{i}), P{i} = spm_vol(P{i}); end; end;
if ~isempty(flags.PW) && ischar(flags.PW), flags.PW = spm_vol(flags.PW); end;
% Remove empty cells
PN = {};
j = 1;
for i=1:length(P),
if ~isempty(P{i}), PN{j} = P{i}; j = j+1; end;
end;
P = PN;
if isempty(P), warning('Nothing to do'); return; end;
if length(P)==1,
P{1} = realign_series(P{1},flags);
if nargout==0, save_parameters(P{1}); end;
else
Ptmp = P{1}(1);
for s=2:numel(P),
Ptmp = [Ptmp ; P{s}(1)];
end;
Ptmp = realign_series(Ptmp,flags);
for s=1:numel(P),
M = Ptmp(s).mat*inv(P{s}(1).mat);
for i=1:numel(P{s}),
P{s}(i).mat = M*P{s}(i).mat;
end;
end;
for s=1:numel(P),
P{s} = realign_series(P{s},flags);
if nargout==0, save_parameters(P{s}); end;
end;
end;
if nargout==0,
% Save Realignment Parameters
%---------------------------------------------------------------------------
for s=1:numel(P),
for i=1:numel(P{s}),
spm_get_space([P{s}(i).fname ',' num2str(P{s}(i).n)], P{s}(i).mat);
end;
end;
end;
if flags.graphics, plot_parameters(P); end;
if length(P)==1, P=P{1}; end;
return;
%_______________________________________________________________________
%_______________________________________________________________________
function P = realign_series(P,flags)
% Realign a time series of 3D images to the first of the series.
% FORMAT P = realign_series(P,flags)
% P - a vector of volumes (see spm_vol)
%-----------------------------------------------------------------------
% P(i).mat is modified to reflect the modified position of the image i.
% The scaling (and offset) parameters are also set to contain the
% optimum scaling required to match the images.
%_______________________________________________________________________
if numel(P)<2, return; end;
skip = sqrt(sum(P(1).mat(1:3,1:3).^2)).^(-1)*flags.sep;
d = P(1).dim(1:3);
lkp = flags.lkp;
rand('state',0); % want the results to be consistant.
if d(3) < 3,
lkp = [1 2 6];
[x1,x2,x3] = ndgrid(1:skip(1):d(1)-.5, 1:skip(2):d(2)-.5, 1:skip(3):d(3));
x1 = x1 + rand(size(x1))*0.5;
x2 = x2 + rand(size(x2))*0.5;
else
[x1,x2,x3]=ndgrid(1:skip(1):d(1)-.5, 1:skip(2):d(2)-.5, 1:skip(3):d(3)-.5);
x1 = x1 + rand(size(x1))*0.5;
x2 = x2 + rand(size(x2))*0.5;
x3 = x3 + rand(size(x3))*0.5;
end;
x1 = x1(:);
x2 = x2(:);
x3 = x3(:);
% Possibly mask an area of the sample volume.
%-----------------------------------------------------------------------
if ~isempty(flags.PW),
[y1,y2,y3]=coords([0 0 0 0 0 0],P(1).mat,flags.PW.mat,x1,x2,x3);
wt = spm_sample_vol(flags.PW,y1,y2,y3,1);
msk = find(wt>0.01);
x1 = x1(msk);
x2 = x2(msk);
x3 = x3(msk);
wt = wt(msk);
else
wt = [];
end;
% Compute rate of change of chi2 w.r.t changes in parameters (matrix A)
%-----------------------------------------------------------------------
V = smooth_vol(P(1),flags.interp,flags.wrap,flags.fwhm);
deg = [flags.interp*[1 1 1]' flags.wrap(:)];
[G,dG1,dG2,dG3] = spm_bsplins(V,x1,x2,x3,deg);
clear V
A0 = make_A(P(1).mat,x1,x2,x3,dG1,dG2,dG3,wt,lkp);
b = G;
if ~isempty(wt), b = b.*wt; end;
%-----------------------------------------------------------------------
if numel(P) > 2,
% Remove voxels that contribute very little to the final estimate.
% Simulated annealing or something similar could be used to
% eliminate a better choice of voxels - but this way will do for
% now. It basically involves removing the voxels that contribute
% least to the determinant of the inverse covariance matrix.
spm_plot_convergence('Init','Eliminating Unimportant Voxels',...
'Relative quality','Iteration');
Alpha = [A0 b];
Alpha = Alpha'*Alpha;
det0 = det(Alpha);
det1 = det0;
spm_plot_convergence('Set',det1/det0);
while det1/det0 > flags.quality,
dets = zeros(size(A0,1),1);
for i=1:size(A0,1),
tmp = [A0(i,:) b(i)];
dets(i) = det(Alpha - tmp'*tmp);
end;
clear tmp
[junk,msk] = sort(det1-dets);
msk = msk(1:round(length(dets)/10));
A0(msk,:) = []; b(msk,:) = []; G(msk,:) = [];
x1(msk,:) = []; x2(msk,:) = []; x3(msk,:) = [];
dG1(msk,:) = []; dG2(msk,:) = []; dG3(msk,:) = [];
if ~isempty(wt), wt(msk,:) = []; end;
Alpha = [A0 b];
Alpha = Alpha'*Alpha;
det1 = det(Alpha);
spm_plot_convergence('Set',single(det1/det0));
end;
spm_plot_convergence('Clear');
end;
%-----------------------------------------------------------------------
if flags.rtm,
count = ones(size(b));
ave = G;
grad1 = dG1;
grad2 = dG2;
grad3 = dG3;
end;
spm_progress_bar('Init',length(P)-1,'Registering Images');
% Loop over images
%-----------------------------------------------------------------------
for i=2:length(P),
V = smooth_vol(P(i),flags.interp,flags.wrap,flags.fwhm);
d = [size(V) 1 1];
d = d(1:3);
ss = Inf;
countdown = -1;
for iter=1:64,
[y1,y2,y3] = coords([0 0 0 0 0 0],P(1).mat,P(i).mat,x1,x2,x3);
msk = find((y1>=1 & y1<=d(1) & y2>=1 & y2<=d(2) & y3>=1 & y3<=d(3)));
if length(msk)<32, error_message(P(i)); end;
F = spm_bsplins(V, y1(msk),y2(msk),y3(msk),deg);
if ~isempty(wt), F = F.*wt(msk); end;
A = A0(msk,:);
b1 = b(msk);
sc = sum(b1)/sum(F);
b1 = b1-F*sc;
soln = (A'*A)\(A'*b1);
p = [0 0 0 0 0 0 1 1 1 0 0 0];
p(lkp) = p(lkp) + soln';
P(i).mat = inv(spm_matrix(p))*P(i).mat;
pss = ss;
ss = sum(b1.^2)/length(b1);
if (pss-ss)/pss < 1e-8 && countdown == -1, % Stopped converging.
countdown = 2;
end;
if countdown ~= -1,
if countdown==0, break; end;
countdown = countdown -1;
end;
end;
if flags.rtm,
% Generate mean and derivatives of mean
tiny = 5e-2; % From spm_vol_utils.c
msk = find((y1>=(1-tiny) & y1<=(d(1)+tiny) &...
y2>=(1-tiny) & y2<=(d(2)+tiny) &...
y3>=(1-tiny) & y3<=(d(3)+tiny)));
count(msk) = count(msk) + 1;
[G,dG1,dG2,dG3] = spm_bsplins(V,y1(msk),y2(msk),y3(msk),deg);
ave(msk) = ave(msk) + G*sc;
grad1(msk) = grad1(msk) + dG1*sc;
grad2(msk) = grad2(msk) + dG2*sc;
grad3(msk) = grad3(msk) + dG3*sc;
end;
spm_progress_bar('Set',i-1);
end;
spm_progress_bar('Clear');
if ~flags.rtm, return; end;
%_______________________________________________________________________
M=P(1).mat;
A0 = make_A(M,x1,x2,x3,grad1./count,grad2./count,grad3./count,wt,lkp);
if ~isempty(wt), b = (ave./count).*wt;
else b = (ave./count); end
clear ave grad1 grad2 grad3
% Loop over images
%-----------------------------------------------------------------------
spm_progress_bar('Init',length(P),'Registering Images to Mean');
for i=1:length(P),
V = smooth_vol(P(i),flags.interp,flags.wrap,flags.fwhm);
d = [size(V) 1 1 1];
ss = Inf;
countdown = -1;
for iter=1:64,
[y1,y2,y3] = coords([0 0 0 0 0 0],M,P(i).mat,x1,x2,x3);
msk = find((y1>=1 & y1<=d(1) & y2>=1 & y2<=d(2) & y3>=1 & y3<=d(3)));
if length(msk)<32, error_message(P(i)); end;
F = spm_bsplins(V, y1(msk),y2(msk),y3(msk),deg);
if ~isempty(wt), F = F.*wt(msk); end;
A = A0(msk,:);
b1 = b(msk);
sc = sum(b1)/sum(F);
b1 = b1-F*sc;
soln = (A'*A)\(A'*b1);
p = [0 0 0 0 0 0 1 1 1 0 0 0];
p(lkp) = p(lkp) + soln';
P(i).mat = inv(spm_matrix(p))*P(i).mat;
pss = ss;
ss = sum(b1.^2)/length(b1);
if (pss-ss)/pss < 1e-8 && countdown == -1 % Stopped converging.
% Do three final iterations to finish off with
countdown = 2;
end;
if countdown ~= -1
if countdown==0, break; end;
countdown = countdown -1;
end;
end;
spm_progress_bar('Set',i);
end;
spm_progress_bar('Clear');
% Since we are supposed to be aligning everything to the first
% image, then we had better do so
%-----------------------------------------------------------------------
M = M/P(1).mat;
for i=1:length(P)
P(i).mat = M*P(i).mat;
end
return;
%_______________________________________________________________________
%_______________________________________________________________________
function [y1,y2,y3]=coords(p,M1,M2,x1,x2,x3)
% Rigid body transformation of a set of coordinates.
M = (inv(M2)*inv(spm_matrix(p))*M1);
y1 = M(1,1)*x1 + M(1,2)*x2 + M(1,3)*x3 + M(1,4);
y2 = M(2,1)*x1 + M(2,2)*x2 + M(2,3)*x3 + M(2,4);
y3 = M(3,1)*x1 + M(3,2)*x2 + M(3,3)*x3 + M(3,4);
return;
%_______________________________________________________________________
%_______________________________________________________________________
function V = smooth_vol(P,hld,wrp,fwhm)
% Convolve the volume in memory.
s = sqrt(sum(P.mat(1:3,1:3).^2)).^(-1)*(fwhm/sqrt(8*log(2)));
x = round(6*s(1)); x = -x:x;
y = round(6*s(2)); y = -y:y;
z = round(6*s(3)); z = -z:z;
x = exp(-(x).^2/(2*(s(1)).^2));
y = exp(-(y).^2/(2*(s(2)).^2));
z = exp(-(z).^2/(2*(s(3)).^2));
x = x/sum(x);
y = y/sum(y);
z = z/sum(z);
i = (length(x) - 1)/2;
j = (length(y) - 1)/2;
k = (length(z) - 1)/2;
d = [hld*[1 1 1]' wrp(:)];
V = spm_bsplinc(P,d);
spm_conv_vol(V,V,x,y,z,-[i j k]);
return;
%_______________________________________________________________________
%_______________________________________________________________________
function A = make_A(M,x1,x2,x3,dG1,dG2,dG3,wt,lkp)
% Matrix of rate of change of weighted difference w.r.t. parameter changes
p0 = [0 0 0 0 0 0 1 1 1 0 0 0];
A = zeros(numel(x1),length(lkp));
for i=1:length(lkp)
pt = p0;
pt(lkp(i)) = pt(i)+1e-6;
[y1,y2,y3] = coords(pt,M,M,x1,x2,x3);
tmp = sum([y1-x1 y2-x2 y3-x3].*[dG1 dG2 dG3],2)/(-1e-6);
if ~isempty(wt), A(:,i) = tmp.*wt;
else A(:,i) = tmp; end
end
return;
%_______________________________________________________________________
%_______________________________________________________________________
function error_message(P)
str = { 'There is not enough overlap in the images',...
'to obtain a solution.',...
' ',...
'Offending image:',...
P.fname,...
' ',...
'Please check that your header information is OK.',...
'The Check Reg utility will show you the initial',...
'alignment between the images, which must be',...
'within about 4cm and about 15 degrees in order',...
'for SPM to find the optimal solution.'};
spm('alert*',str,mfilename,sqrt(-1));
error('insufficient image overlap')
%_______________________________________________________________________
%_______________________________________________________________________
function plot_parameters(P)
fg=spm_figure('FindWin','Graphics');
if ~isempty(fg),
P = cat(1,P{:});
if length(P)<2, return; end;
Params = zeros(numel(P),12);
for i=1:numel(P),
Params(i,:) = spm_imatrix(P(i).mat/P(1).mat);
end
% display results
% translation and rotation over time series
%-------------------------------------------------------------------
spm_figure('Clear','Graphics');
ax=axes('Position',[0.1 0.65 0.8 0.2],'Parent',fg,'Visible','off');
set(get(ax,'Title'),'String','Image realignment','FontSize',16,'FontWeight','Bold','Visible','on');
x = 0.1;
y = 0.9;
for i = 1:min([numel(P) 12])
text(x,y,[sprintf('%-4.0f',i) P(i).fname],'FontSize',10,'Interpreter','none','Parent',ax);
y = y - 0.08;
end
if numel(P) > 12
text(x,y,'................ etc','FontSize',10,'Parent',ax); end
ax=axes('Position',[0.1 0.35 0.8 0.2],'Parent',fg,'XGrid','on','YGrid','on');
plot(Params(:,1:3),'Parent',ax)
s = ['x translation';'y translation';'z translation'];
%text([2 2 2], Params(2, 1:3), s, 'Fontsize',10,'Parent',ax)
legend(ax, s, 0)
set(get(ax,'Title'),'String','translation','FontSize',16,'FontWeight','Bold');
set(get(ax,'Xlabel'),'String','image');
set(get(ax,'Ylabel'),'String','mm');
ax=axes('Position',[0.1 0.05 0.8 0.2],'Parent',fg,'XGrid','on','YGrid','on');
plot(Params(:,4:6)*180/pi,'Parent',ax)
s = ['pitch';'roll ';'yaw '];
%text([2 2 2], Params(2, 4:6)*180/pi, s, 'Fontsize',10,'Parent',ax)
legend(ax, s, 0)
set(get(ax,'Title'),'String','rotation','FontSize',16,'FontWeight','Bold');
set(get(ax,'Xlabel'),'String','image');
set(get(ax,'Ylabel'),'String','degrees');
% print realigment parameters
spm_print
end
return;
%_______________________________________________________________________
%_______________________________________________________________________
function save_parameters(V)
fname = [spm_str_manip(prepend(V(1).fname,'rp_'),'s') '.txt'];
n = length(V);
Q = zeros(n,6);
for j=1:n,
qq = spm_imatrix(V(j).mat/V(1).mat);
Q(j,:) = qq(1:6);
end;
save(fname,'Q','-ascii');
return;
%_______________________________________________________________________
%_______________________________________________________________________
function PO = prepend(PI,pre)
[pth,nm,xt,vr] = spm_fileparts(deblank(PI));
PO = fullfile(pth,[pre nm xt vr]);
return;
%_______________________________________________________________________
|
github
|
philippboehmsturm/antx-master
|
spm_surf.m
|
.m
|
antx-master/freiburgLight/matlab/spm8/spm_surf.m
| 9,740 |
utf_8
|
aa92ddde8b875463a0fefee45e0a1d79
|
function varargout = spm_surf(P,mode,thresh)
% Surface extraction
% FORMAT spm_surf(P,mode,thresh)
%
% P - char array of filenames
% Usually, this will be c1xxx.img & c2xxx.img - grey and white
% matter segments created using the segmentation routine.
% mode - operation mode [1: rendering, 2: surface, 3: both]
% thresh - vector or threshold values for extraction [default: 0.5]
% This is only relevant for extracting surfaces, not rendering.
%
% Generated files (depending on 'mode'):
% A "render_xxx.mat" file can be produced that can be used for
% rendering activations on to, see spm_render.
%
% A "xxx.surf.gii" file can also be written, which is created using
% Matlab's isosurface function.
% This extracted brain surface can be viewed using code something like:
% FV = gifti(spm_select(1,'mesh','Select surface data'));
% FV = export(FV,'patch');
% fg = spm_figure('GetWin','Graphics');
% ax = axes('Parent',fg);
% p = patch(FV, 'Parent',ax,...
% 'FaceColor', [0.8 0.7 0.7], 'FaceVertexCData', [],...
% 'EdgeColor', 'none',...
% 'FaceLighting', 'phong',...
% 'SpecularStrength' ,0.7, 'AmbientStrength', 0.1,...
% 'DiffuseStrength', 0.7, 'SpecularExponent', 10);
% set(0,'CurrentFigure',fg);
% set(fg,'CurrentAxes',ax);
% l = camlight(-40, 20);
% axis image;
% rotate3d on;
%
% FORMAT out = spm_surf(job)
%
% Input
% A job structure with fields
% .data - cell array of filenames
% .mode - operation mode
% .thresh - thresholds for extraction
% Output
% A struct with fields (depending on operation mode)
% .rendfile - cellstring containing render filename
% .surffile - cellstring containing surface filename(s)
%__________________________________________________________________________
%
% This surface extraction is not particularly sophisticated. It simply
% smooths the data slightly and extracts the surface at a threshold of
% 0.5. The input segmentation images can be manually cleaned up first using
% e.g., MRIcron.
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% John Ashburner
% $Id: spm_surf.m 4341 2011-06-03 11:24:02Z john $
SVNrev = '$Rev: 4341 $';
spm('FnBanner',mfilename,SVNrev);
spm('FigName','Surface');
%-Get input: filenames 'P'
%--------------------------------------------------------------------------
try
if isstruct(P)
job = P;
P = strvcat(job.data);
mode = job.mode;
thresh = job.thresh;
end
catch
[P, sts] = spm_select([1 Inf],'image','Select images');
if ~sts, varargout = {}; return; end
end
%-Get input: operation mode 'mode'
%--------------------------------------------------------------------------
try
mode;
catch
mode = spm_input('Save','+1','m',...
['Save Rendering|'...
'Save Extracted Surface|'...
'Save Rendering and Surface'],[1 2 3],3);
end
%-Get input: threshold for extraction 'thresh'
%--------------------------------------------------------------------------
try
thresh;
catch
thresh = 0.5;
end
%-Surface extraction
%--------------------------------------------------------------------------
spm('FigName','Surface: working');
spm('Pointer','Watch');
out = do_it(P,mode,thresh);
spm('Pointer','Arrow');
spm('FigName','Surface: done');
if nargout > 0
varargout{1} = out;
end
return;
%==========================================================================
function out = do_it(P,mode,thresh)
V = spm_vol(P);
br = zeros(V(1).dim(1:3));
for i=1:V(1).dim(3),
B = spm_matrix([0 0 i]);
tmp = spm_slice_vol(V(1),B,V(1).dim(1:2),1);
for j=2:length(V),
M = V(j).mat\V(1).mat*B;
tmp = tmp + spm_slice_vol(V(j),M,V(1).dim(1:2),1);
end
br(:,:,i) = tmp;
end
% Build a 3x3x3 seperable smoothing kernel and smooth
%--------------------------------------------------------------------------
kx=[0.75 1 0.75];
ky=[0.75 1 0.75];
kz=[0.75 1 0.75];
sm=sum(kron(kron(kz,ky),kx))^(1/3);
kx=kx/sm; ky=ky/sm; kz=kz/sm;
spm_conv_vol(br,br,kx,ky,kz,-[1 1 1]);
[pth,nam,ext] = fileparts(V(1).fname);
if any(mode==[1 3])
% Produce rendering
%----------------------------------------------------------------------
out.rendfile{1} = fullfile(pth,['render_' nam '.mat']);
tmp = struct('dat',br,'dim',size(br),'mat',V(1).mat);
renviews(tmp,out.rendfile{1});
end
if any(mode==[2 3])
% Produce extracted surface
%----------------------------------------------------------------------
for k=1:numel(thresh)
[faces,vertices] = isosurface(br,thresh(k));
% Swap around x and y because isosurface does for some
% wierd and wonderful reason.
Mat = V(1).mat(1:3,:)*[0 1 0 0;1 0 0 0;0 0 1 0; 0 0 0 1];
vertices = (Mat*[vertices' ; ones(1,size(vertices,1))])';
if numel(thresh)==1
nam1 = nam;
else
nam1 = sprintf('%s-%d',nam,k);
end
out.surffile{k} = fullfile(pth,[nam1 '.surf.gii']);
save(gifti(struct('faces',faces,'vertices',vertices)),out.surffile{k});
end
end
return;
%==========================================================================
function renviews(V,oname)
% Produce images for rendering activations to
%
% FORMAT renviews(V,oname)
% V - mapped image to render, or alternatively
% a structure of:
% V.dat - 3D array
% V.dim - size of 3D array
% V.mat - affine mapping from voxels to millimeters
% oname - the name of the render.mat file.
%__________________________________________________________________________
%
% Produces a matrix file "render_xxx.mat" which contains everything that
% "spm_render" is likely to need.
%
% Ideally, the input image should contain values in the range of zero
% and one, and be smoothed slightly. A threshold of 0.5 is used to
% distinguish brain from non-brain.
%__________________________________________________________________________
linfun = inline('fprintf([''%-30s%s''],x,[repmat(sprintf(''\b''),1,30)])','x');
linfun('Rendering: ');
linfun('Rendering: Transverse 1..'); rend{1} = make_struct(V,[pi 0 pi/2]);
linfun('Rendering: Transverse 2..'); rend{2} = make_struct(V,[0 0 pi/2]);
linfun('Rendering: Sagittal 1..'); rend{3} = make_struct(V,[0 pi/2 pi]);
linfun('Rendering: Sagittal 2..'); rend{4} = make_struct(V,[0 pi/2 0]);
linfun('Rendering: Coronal 1..'); rend{5} = make_struct(V,[pi/2 pi/2 0]);
linfun('Rendering: Coronal 2..'); rend{6} = make_struct(V,[pi/2 pi/2 pi]);
linfun('Rendering: Save..');
if spm_check_version('matlab','7') >= 0
save(oname,'-V6','rend');
else
save(oname,'rend');
end
linfun(' ');
if ~spm('CmdLine')
disp_renderings(rend);
spm_print;
end
return;
%==========================================================================
function str = make_struct(V,thetas)
[D,M] = matdim(V.dim(1:3),V.mat,thetas);
[ren,dep] = make_pic(V,M*V.mat,D);
str = struct('M',M,'ren',ren,'dep',dep);
return;
%==========================================================================
function [ren,zbuf] = make_pic(V,M,D)
% A bit of a hack to try and make spm_render_vol produce some slightly
% prettier output. It kind of works...
if isfield(V,'dat'), vv = V.dat; else vv = V; end;
[REN, zbuf, X, Y, Z] = spm_render_vol(vv, M, D, [0.5 1]);
fw = max(sqrt(sum(M(1:3,1:3).^2)));
msk = find(zbuf==1024);
brn = ones(size(X));
brn(msk) = 0;
brn = spm_conv(brn,fw);
X(msk) = 0;
Y(msk) = 0;
Z(msk) = 0;
msk = find(brn<0.5);
tmp = brn;
tmp(msk) = 100000;
sX = spm_conv(X,fw)./tmp;
sY = spm_conv(Y,fw)./tmp;
sZ = spm_conv(Z,fw)./tmp;
zbuf = spm_conv(zbuf,fw)./tmp;
zbuf(msk) = 1024;
vec = [-1 1 3]; % The direction of the lighting.
vec = vec/norm(vec);
[t,dx,dy,dz] = spm_sample_vol(vv,sX,sY,sZ,3);
IM = inv(diag([0.5 0.5 1])*M(1:3,1:3))';
ren = IM(1:3,1:3)*[dx(:)' ; dy(:)' ; dz(:)'];
len = sqrt(sum(ren.^2,1))+eps;
ren = [ren(1,:)./len ; ren(2,:)./len ; ren(3,:)./len];
ren = reshape(vec*ren,[size(dx) 1]);
ren(ren<0) = 0;
ren(msk) = ren(msk)-0.2;
ren = ren*0.8+0.2;
mx = max(ren(:));
ren = ren/mx;
return;
%==========================================================================
function disp_renderings(rend)
Fgraph = spm_figure('GetWin','Graphics');
spm_results_ui('Clear',Fgraph);
hght = 0.95;
nrow = ceil(length(rend)/2);
ax=axes('Parent',Fgraph,'units','normalized','Position',[0, 0, 1, hght],'Visible','off');
image(0,'Parent',ax);
set(ax,'YTick',[],'XTick',[]);
for i=1:length(rend),
ren = rend{i}.ren;
ax=axes('Parent',Fgraph,'units','normalized',...
'Position',[rem(i-1,2)*0.5, floor((i-1)/2)*hght/nrow, 0.5, hght/nrow],...
'Visible','off');
image(ren*64,'Parent',ax);
set(ax,'DataAspectRatio',[1 1 1], ...
'PlotBoxAspectRatioMode','auto',...
'YTick',[],'XTick',[],'XDir','normal','YDir','normal');
end
drawnow;
return;
%==========================================================================
function [d,M] = matdim(dim,mat,thetas)
R = spm_matrix([0 0 0 thetas]);
bb = [[1 1 1];dim(1:3)];
c = [ bb(1,1) bb(1,2) bb(1,3) 1
bb(1,1) bb(1,2) bb(2,3) 1
bb(1,1) bb(2,2) bb(1,3) 1
bb(1,1) bb(2,2) bb(2,3) 1
bb(2,1) bb(1,2) bb(1,3) 1
bb(2,1) bb(1,2) bb(2,3) 1
bb(2,1) bb(2,2) bb(1,3) 1
bb(2,1) bb(2,2) bb(2,3) 1]';
tc = diag([2 2 1 1])*R*mat*c;
tc = tc(1:3,:)';
mx = max(tc);
mn = min(tc);
M = spm_matrix(-mn(1:2))*diag([2 2 1 1])*R;
d = ceil(abs(mx(1:2)-mn(1:2)))+1;
return;
|
github
|
philippboehmsturm/antx-master
|
spm_eeg_plotScalpData.m
|
.m
|
antx-master/freiburgLight/matlab/spm8/spm_eeg_plotScalpData.m
| 11,761 |
utf_8
|
47767f883d6ed147946a3f14900db01d
|
function [ZI,f] = spm_eeg_plotScalpData(Z,pos,ChanLabel,in)
% Display interpolated sensor data on the scalp in a new figure
% FORMAT [ZI,f] = spm_eeg_plotScalpData(Z,pos,ChanLabel,in)
%
% INPUT:
% Z - the data matrix at the sensors
% pos - the positions of the sensors
% ChanLabel - the names of the sensors
% in - a structure containing some informations related to the
% main PRESELECTDATA window. This entry is not necessary
% OUTPUT
% ZI - an image of interpolated data onto the scalp
% f - the handle of the figure which displays the interpolated
% data
%__________________________________________________________________________
%
% This function creates a figure whose purpose is to display an
% interpolation of the sensor data on the scalp (an image)
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% Jean Daunizeau
% $Id: spm_eeg_plotScalpData.m 4375 2011-06-23 10:00:06Z vladimir $
ParentAxes = [];
f = [];
clim = [min(Z(:))-( max(Z(:))-min(Z(:)) )/63 , max(Z(:))];
figName = 'Image Scalp data';
noButtons = 0;
if nargin < 4 || isempty(in)
in = [];
else
if isfield(in,'min') && ...
isfield(in,'max') && ...
isfield(in,'type')
clim = [in.min, in.max];
dc = abs(diff(clim))./63;
clim(1) = clim(1) - dc;
figName = ['Image Scalp data: ',in.type,' sensors'];
if isfield(in,'trN')
figName = [figName ', trial #',num2str(in.trN),'.'];
end
end
if isfield(in,'ParentAxes')
ParentAxes = in.ParentAxes;
end
if isfield(in,'f')
f = in.f;
end
if isfield(in,'noButtons')
noButtons = ~~in.noButtons;
end
end
if ~isfield(in,'cbar')
in.cbar = 1;
end
if ~isfield(in,'plotpos')
in.plotpos = 1;
end
if size(pos,2) ~= length(ChanLabel)
pos = pos';
end
nD = size(pos,1);
if nD ~= 2
% get 2D positions from 3D positions
xyz = pos;
[pos] = get2Dfrom3D(xyz);
pos = pos';
end
% exclude channels ?
goodChannels = find(~isnan(pos(1,:)));
pos = pos(:,goodChannels);
Z = Z(goodChannels,:);
ChanLabel = ChanLabel(goodChannels);
if ~isempty(in) && isfield(in,'type') && strcmp(in.type, 'MEGPLANAR')
[cZ, cpos, cChanLabel] = combineplanar(Z, pos, ChanLabel);
else
cZ = Z;
cpos = pos;
cChanLabel = ChanLabel;
end
xmin = min(cpos(1,:));
xmax = max(cpos(1,:));
dx = (xmax-xmin)./100;
ymin = min(cpos(2,:));
ymax = max(cpos(2,:));
dy = (ymax-ymin)./100;
x = xmin:dx:xmax;
y = ymin:dy:ymax;
[XI,YI] = meshgrid(x,y);
ZI = griddata(cpos(1,:)',cpos(2,:)',full(double(cZ')),XI,YI);
try
figure(f)
catch
f=figure(...
'name',figName,...
'color',[1 1 1],...
'deleteFcn',@dFcn);
ParentAxes = axes('parent',f);
end
COLOR = get(f,'color');
d.hi = image(flipud(ZI),...
'CDataMapping','scaled',...
'Parent',ParentAxes);
set(ParentAxes,'nextPlot','add',...
'tag','spm_eeg_plotScalpData')
try
if length(unique(ZI)) ~= 1
[C,d.hc] = contour(ParentAxes,flipud(ZI),...
'linecolor',0.5.*ones(3,1));
end
end
caxis(ParentAxes,clim);
col = jet;
col(1,:) = COLOR;
colormap(ParentAxes,col)
if in.cbar
d.cbar = colorbar('peer',ParentAxes);
end
axis(ParentAxes,'off')
axis(ParentAxes,'equal')
axis(ParentAxes,'tight')
fpos = cpos;
fpos(1,:) = fpos(1,:) - xmin;
fpos(2,:) = fpos(2,:) - ymin;
fpos(1,:) = fpos(1,:)./(dx);
fpos(2,:) = fpos(2,:)./(dy);
fpos(2,:) = 100-fpos(2,:); % for display purposes (flipud imagesc)
figure(f);
if in.plotpos
d.hp = plot(ParentAxes,...
fpos(1,:),fpos(2,:),...
'ko');
end
d.ht = text(fpos(1,:),fpos(2,:),cChanLabel,...
'Parent',ParentAxes,...
'visible','off');
axis(ParentAxes,'image')
d.interp.XI = XI;
d.interp.YI = YI;
d.interp.pos = cpos;
d.f = f;
d.pos = fpos;
d.goodChannels = goodChannels;
d.ChanLabel = cChanLabel;
d.origChanLabel = ChanLabel;
d.origpos = pos;
d.ParentAxes = ParentAxes;
d.in = in;
if ~noButtons
d.hsp = uicontrol(f,...
'style','pushbutton',...
'callback',{@dosp},...
'BusyAction','cancel',...
'Interruptible','off',...
'position',[10 50 80 20],...
'string','channel pos');
d.hsn = uicontrol(f,...
'style','pushbutton',...
'callback',{@dosn},...
'BusyAction','cancel',...
'Interruptible','off',...
'position',[10 80 80 20],...
'string','channel names');
end
if ~isempty(in) && isfield(in,'handles')
ud = get(in.handles.hfig,'userdata');
nT = ud.Nsamples;
d.hti = uicontrol(f,...
'style','text',...
'BackgroundColor',COLOR,...
'string',[num2str(in.gridTime(in.x)),' (',in.unit,')'],...
'position',[10 10 120 20]);
d.hts = uicontrol(f,...
'style','slider',...
'Position',[130 10 250 20],...
'min',1,'max',nT,...
'value',in.x,'sliderstep',[1./(nT-1) 1./(nT-1)],...
'callback',{@doChangeTime},...
'BusyAction','cancel',...
'Interruptible','off');
set(d.hti,'userdata',d);
set(d.hts,'userdata',d);
end
if ~noButtons
set(d.hsp,'userdata',d);
set(d.hsn,'userdata',d);
end
set(d.ParentAxes,'userdata',d);
%==========================================================================
% dFcn
%==========================================================================
function dFcn(btn,evd)
hf = findobj('tag','Graphics');
D = get(hf,'userdata');
try delete(D.PSD.handles.hli); end
%==========================================================================
% dosp
%==========================================================================
function dosp(btn,evd)
d = get(btn,'userdata');
switch get(d.hp,'visible');
case 'on'
set(d.hp,'visible','off');
case 'off'
set(d.hp,'visible','on');
end
%==========================================================================
% dosn
%==========================================================================
function dosn(btn,evd)
d = get(btn,'userdata');
switch get(d.ht(1),'visible')
case 'on'
set(d.ht,'visible','off');
case 'off'
set(d.ht,'visible','on');
end
%==========================================================================
%
%==========================================================================
function doChangeTime(btn,evd)
d = get(btn,'userdata');
v = get(btn,'value');
% get data
if ishandle(d.in.handles.hfig)
D = get(d.in.handles.hfig,'userdata');
if ~isfield(d.in,'trN')
trN = 1;
else
trN = d.in.trN;
end
if isfield(D,'data')
Z = D.data.y(d.in.ind,v,trN);
Z = Z(d.goodChannels);
if strcmp(d.in.type, 'MEGPLANAR')
Z = combineplanar(Z, d.origpos, d.origChanLabel);
end
clear ud;
% interpolate data
ZI = griddata(d.interp.pos(1,:),d.interp.pos(2,:),full(double(Z)),d.interp.XI,d.interp.YI);
% update data display
set(d.hi,'Cdata',flipud(ZI));
% update time index display
v = round(v);
set(d.hti,'string',[num2str(d.in.gridTime(v)), ' (', d.in.unit, ')']);
% update display marker position
try;set(d.in.hl,'xdata',[v;v]);end
set(d.ParentAxes,'nextPlot','add')
try
% delete current contour plot
delete(findobj(d.ParentAxes,'type','hggroup'));
% create new one
[C,hc] = contour(d.ParentAxes,flipud(ZI),...
'linecolor',[0.5.*ones(3,1)]);
end
axis(d.ParentAxes,'image')
drawnow
else
error('Did not find the data!')
end
else
error('SPM Graphics Figure has been deleted!')
end
%==========================================================================
% get2Dfrom3D
%==========================================================================
function [xy] = get2Dfrom3D(xyz)
% function [xy] = get2Dfrom3D(xyz)
% This function is used to flatten 3D sensor positions onto the 2D plane
% using a modified spherical projection operation.
% It is used to visualize channel data.
% IN:
% - xyz: the carthesian sensor position in 3D space
% OUT:
% - xy: the (x,y) carthesian coordinates of the sensors after projection
% onto the best-fitting sphere
if size(xyz,2) ~= 3
xyz = xyz';
end
% exclude channels ?
badChannels = find(isnan(xyz(:,1)));
goodChannels = find(isnan(xyz(:,1))~=1);
xyz = xyz(goodChannels,:);
% Fit sphere to 3d sensors and center frame
[C,R,out] = fitSphere(xyz(:,1),xyz(:,2),xyz(:,3));
xyz = xyz - repmat(C,size(xyz,1),1);
% apply transformation using spherical coordinates
[TH,PHI,RAD] = cart2sph(xyz(:,1),xyz(:,2),xyz(:,3));
TH = TH - mean(TH);
[X,Y,Z] = sph2cart(TH,zeros(size(TH)),RAD.*(cos(PHI+pi./2)+1));
xy = [X(:),Y(:)];
%==========================================================================
% combineplanar
%==========================================================================
function [Z, pos, ChanLabel] = combineplanar(Z, pos, ChanLabel)
chanind = zeros(1, numel(ChanLabel));
for i = 1:numel(ChanLabel)
chanind(i) = sscanf(ChanLabel{i}, 'MEG%d');
end
pairs = [];
unpaired = [];
paired = zeros(length(chanind));
for i = 1:length(chanind)
if ~paired(i)
cpair = find(abs(chanind - chanind(i))<2);
if length(cpair) == 1
unpaired = [unpaired cpair];
else
pairs = [pairs; cpair(:)'];
end
paired(cpair) = 1;
end
end
if ~isempty(unpaired)
warning(['Could not pair all channels. Ignoring ' num2str(length(unpaired)) ' unpaired channels.']);
end
Z = sqrt(Z(pairs(:, 1)).^2 + Z(pairs(:, 2)).^2);
pos = (pos(:, pairs(:, 1)) + pos(:, pairs(:, 2)))./2;
ChanLabel = {};
for i = 1:size(pairs,1)
ChanLabel{i} = ['MEG' num2str(min(pairs(i,:))) '+' num2str(max(pairs(i,:)))];
end
%==========================================================================
% fitSphere
%==========================================================================
function [C,R,out] = fitSphere(x,y,z)
% fitSphere Fit sphere.
% A = fitSphere(x,y,z) returns the parameters of the best-fit
% [C,R,out] = fitSphere(x,y,z) returns the center and radius
% sphere to data points in vectors (x,y,z) using Taubin's method.
% IN:
% - x/y/z: 3D carthesian ccordinates
% OUT:
% - C: the center of sphere coordinates
% - R: the radius of the sphere
% - out: an output structure devoted to graphical display of the best fit
% sphere
% Make sugary one and zero vectors
l = ones(length(x),1);
O = zeros(length(x),1);
% Make design mx
D = [(x.*x + y.*y + z.*z) x y z l];
Dx = [2*x l O O O];
Dy = [2*y O l O O];
Dz = [2*z O O l O];
% Create scatter matrices
M = D'*D;
N = Dx'*Dx + Dy'*Dy + Dz'*Dz;
% Extract eigensystem
[v, evalues] = eig(M);
evalues = diag(evalues);
Mrank = sum(evalues > eps*5*norm(M));
if (Mrank == 5)
% Full rank -- min ev corresponds to solution
Minverse = v'*diag(1./evalues)*v;
[v,evalues] = eig(inv(M)*N);
[dmin,dminindex] = max(diag(evalues));
pvec = v(:,dminindex(1))';
else
% Rank deficient -- just extract nullspace of M
pvec = null(M)';
[m,n] = size(pvec);
if m > 1
pvec = pvec(1,:)
end
end
% Convert to (R,C)
if nargout == 1,
if pvec(1) < 0
pvec = -pvec;
end
C = pvec;
else
C = -0.5*pvec(2:4) / pvec(1);
R = sqrt(sum(C*C') - pvec(5)/pvec(1));
end
[X,Y,Z] = sphere;
[TH,PHI,R0] = cart2sph(X,Y,Z);
[X,Y,Z] = sph2cart(TH,PHI,R);
X = X + C(1);
Y = Y + C(2);
Z = Z + C(3);
out.X = X;
out.Y = Y;
out.Z = Z;
|
github
|
philippboehmsturm/antx-master
|
spm_eeg_render.m
|
.m
|
antx-master/freiburgLight/matlab/spm8/spm_eeg_render.m
| 10,872 |
utf_8
|
52b7ba99932d4227bf2efefcc8766540
|
function [out] = spm_eeg_render(m,options)
% Visualisation routine for the cortical surface
% FORMAT [out] = spm_eeg_render(m,options)
%
% INPUT:
% - m = MATLAB mesh (containing the fields .faces et .vertices) or GIFTI
% format file.
% - options = structure variable:
% .texture = texture to be projected onto the mesh
% .clusters = cortical parcelling (cell variable containing the
% vertex indices of each cluster)
% .clustersName = name of the clusters
% .figname = name to be given to the window
% .ParentAxes = handle of the axes within which the mesh should be
% displayed
% .hfig = handle of existing figure. If this option is provided, then
% visu_maillage_surf adds the (textured) mesh to the figure hfig, and
% a control for its transparancy.
%
% OUTPUT:
% - out: a structure containing the fields:
% .hfra: frame structure for movie building
% .handles: a structure containing the handles of the created
% uicontrols and mesh objects.
% .m: the structure used to create the mesh
%__________________________________________________________________________
%
% This function is a visualization routine, mainly for texture and
% clustering on the cortical surface.
% NB: The texture and the clusters can not be visualized at the same time.
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% Jean Daunizeau
% $Id: spm_eeg_render.m 3051 2009-04-06 14:47:09Z jean $
%----------------------------------------------------------------------%
%------------- Common features for any visualization ------------------%
%----------------------------------------------------------------------%
% Check mesh format
try
if ischar(m) && exist(m,'file')==2
try m = gifti(m);end
end
m0.faces = m.faces;
m0.vertices = m.vertices;
m = m0;
clear m0;
catch
disp('spm_eeg_render: unknown mesh format!')
return
end
% Default options
handles.fi = figure(...
'visible','off',...
'color',ones(1,3),...
'NumberTitle','Off',...
'Name','Mesh visualization',...
'tag','visu_maillage_surf');
ns = 0;
texture = 'none';
clusters = 'none';
subplotBIN = 0;
addMesh = 0;
tag = '';
visible = 'on';
ParentAxes = axes('parent',handles.fi);
try, options; catch options = [];end
% Now get options
if ~isempty(options)
% get texture if provided
try texture = options.texture;end
% get ParentAxes
try ParentAxes = options.ParentAxes;end
% get tag
try tag = options.tag;end
% get flag for visibility: useful for displaying all objects at once
try visible = options.visible;end
% get custers if provided
try
clusters = options.clusters;
IND = zeros(1,length(m.vertices));
K = length(clusters);
for k = 1:K
IND(clusters{k}) = k+1./K;
end
texture = IND';
end
% get figname if provided
try
set(handles.fi,'NumberTitle','Off','Name',options.figname);
end
% get figure handle (should be parent of ParentAxes)
try
figure(options.hfig)
if isempty(ParentAxes)
ParentAxes = axes('parent',options.hfig,...
'nextplot','add');
end
close(handles.fi);
handles.fi = options.hfig;
addMesh = 1;
try % get number of transparency sliders in current figure...
hh=get(handles.fi,'children');
ns=length(findobj(hh,'userdata','tag_UIC_transparency'));
catch
ns=1;
end
end
end
handles.ParentAxes = ParentAxes;
oldRenderer = get(handles.fi,'renderer');
try
if ismac
set(handles.fi,'renderer','zbuffer');
else
set(handles.fi,'renderer','OpenGL');
end
catch
set(handles.fi,'renderer','OpenGL');
end
% Plot mesh and texture/clusters
if isequal(texture,'none')
figure(handles.fi)
handles.p = patch(m,...
'facecolor', [.5 .5 .5], 'EdgeColor', 'none',...
'FaceLighting','gouraud',...
'parent',ParentAxes,...
'userdata',oldRenderer,...
'visible',visible,...
'tag',tag);
else
texture = texture(:);
figure(handles.fi)
if isequal(length(texture),length(m.vertices))
handles.p = patch(m,...
'facevertexcdata',texture,...
'facecolor','interp',...
'EdgeColor', 'none',...
'FaceLighting','gouraud',...
'parent',ParentAxes,...
'userdata',oldRenderer,...
'visible',visible,...
'tag',tag,...
'deleteFcn',@doDelMesh);
col = colormap(ParentAxes,jet(256));
udd.tex = texture;
udd.cax = caxis(ParentAxes);
else
texture = 'none';
disp('Warning: size of texture does not match number of vertices!')
handles.p = patch(m,'facecolor', [.5 .5 .5], 'EdgeColor', 'none',...
'parent',ParentAxes,...
'userdata',oldRenderer,...
'visible',visible,...
'tag',tag,...
'deleteFcn',@doDelMesh);
end
end
daspect(ParentAxes,[1 1 1]);
axis(ParentAxes,'tight');
axis(ParentAxes,'off')
camva(ParentAxes,'auto');
set(ParentAxes,'view',[25,45]);
% build internal userdata structure
udd.p = handles.p;
%----------------------------------------------------------------------%
%---------------------- GUI tools and buttons -------------------------%
%----------------------------------------------------------------------%
% Transparancy sliders
pos = [20 100 20 245];
pos(1) = pos(1) + ns.*25;
handles.transp = uicontrol(handles.fi,...
'style','slider',...
'position',pos,...
'min',0,...
'max',1,...
'value',1,...
'sliderstep',[0.01 0.05],...
'userdata',handles.p,...
'tooltipstring',['mesh #',num2str(ns+1),' transparency control'],...
'callback',{@doTransp},...
'BusyAction','cancel',...
'Interruptible','off',...
'visible',visible,...
'tag',tag);
set(handles.transp,'units','normalized')
handles.tag = uicontrol(handles.fi,...
'style','text',...
'visible','off',...
'tag',tag,...
'userdata','tag_UIC_transparency');
udd.transp = handles.transp;
% Clustering buttons and popup menu
if ~isequal(clusters,'none')
if subplotBIN
subplot(2,1,1)
end
% set(p,'FaceColor','flat');
col=lines;
nc = floor(256./K);
col = [repmat([0.8157 0.6666 0.5762],nc/2,1);...
kron(col(1:K,:),ones(nc,1))];
if K > 1
col(end-nc/2:end,:) = [];
end
colormap(ParentAxes,col);
tex = zeros(length(m.vertices),length(clusters)+1);
tex(:,1) = texture;
string = cell(length(clusters)+1,1);
string{1} = 'all clusters';
for i = 1:length(clusters)
if ~isfield(options,'clustersName')
string{i+1} = ['cluster ',num2str(i)];
else
string{i+1} = options.clustersName{i};
end
tex(clusters{i},i+1) = 1;
end
udd.tex = tex;
udd.tex0 = tex;
udd.p = handles.p;
udd.col = col;
udd.nc = length(clusters);
handles.pop = uicontrol(handles.fi,...
'style','popupmenu',...
'position',[20 20 100 40],...
'string',string,...
'callback',{@doSelectCluster},...
'BusyAction','cancel',...
'Interruptible','off',...
'visible',visible,'tag',tag);
set(handles.pop,'units','normalized')
handles.sli = uicontrol(handles.fi,...
'style','slider',...
'position',[50 10 30 20],'max',udd.nc,...
'sliderstep',[1./(udd.nc+0) 1./(udd.nc+0)],...
'callback',{@doSwitch2nextCluster},...
'BusyAction','cancel',...
'Interruptible','off',...
'visible',visible,'tag',tag);
set(handles.sli,'units','normalized')
udd.pop = handles.pop;
udd.sli = handles.sli;
set(handles.pop,'userdata',udd);
set(handles.sli,'userdata',udd);
end
% Texture thresholding sliders
if ~isequal(texture,'none') && isequal(clusters,'none')
if subplotBIN
subplot(2,1,1)
end
udd.tex0 = texture;
udd.col = col;
handles.hc = colorbar('peer',ParentAxes);
set(handles.hc,'visible',visible)
increment = 0.01;
% right slider
handles.s1 = uicontrol(handles.fi,...
'style','slider',...
'position',[440 28 20 380],...
'min',0,'max',length(udd.col),'value',0,...
'sliderstep',[increment increment],...
'tooltipstring','texture thresholding control',...
'callback',{@doThresh},...
'BusyAction','cancel',...
'Interruptible','off',...
'visible',visible,'tag',tag);
set(handles.s1,'units','normalized')
udd.s1 = handles.s1;
% left slider
handles.s2 = uicontrol(handles.fi,...
'style','slider',...
'position',[420 28 20 380],...
'min',1,'max',length(udd.col),...
'value',length(udd.col),...
'sliderstep',[increment increment],...
'tooltipstring','texture thresholding control',...
'callback',{@doThresh},...
'BusyAction','cancel',...
'Interruptible','off',...
'visible',visible,'tag',tag);
set(handles.s2,'units','normalized')
udd.s2 = handles.s2;
set(handles.s1,'userdata',udd);
set(handles.s2,'userdata',udd);
end
set(handles.fi,'visible','on');
drawnow
% if ~addMesh
camlight
% end
cameratoolbar(handles.fi,'setmode','orbit')
out.hfra = getframe(gcf);
out.handles = handles;
out.m = m;
%--------- subfunctions : BUTTONS CALLBACKS ------------%
function doDelMesh(btn,evd)
renderer=get(btn,'userdata');
set(gcf,'renderer',renderer);
function doTransp(btn,evd)
v00=get(btn,'value');
p00=get(btn,'userdata');
set(p00,'facealpha',v00);
function doThresh(btn,evd)
udd00 = get(btn,'userdata');
ind00 = round(get(udd00.s1,'value'));
ind200 = round(get(udd00.s2,'value'));
if(ind200>ind00)
udd00.col(1:ind00,:)=0.5*ones(ind00,3);
udd00.col(ind200+1:end,:)=0.5*ones(size(udd00.col(ind200+1:end,:)));
else
udd00.col(ind200:ind00,:)=0.5*ones(size(udd00.col(ind200:ind00,:)));
end
colormap(udd00.col);
udd00.cax = caxis;
function doSelectCluster(btn,evd)
udd00 = get(btn,'userdata');
ind00=get(gcbo,'value');
set(udd00.sli,'value',ind00-1);
set(udd00.p,'facevertexcdata',udd00.tex(:,ind00));
if ind00 == 1
colormap(udd00.col);
else
col00 = colormap(jet);
col00(1:end/2,:)=0.5*ones(size(col00(1:end/2,:)));
colormap(col00);
end
udd00.cax = caxis;
function doSwitch2nextCluster(btn,evd)
v00=get(btn,'value')+1;
udd00=get(gcbo,'userdata');
ind00=min([v00 udd00.nc+1]);
set(udd00.pop,'value',ind00);
set(udd00.p,'facevertexcdata',udd00.tex(:,ind00));
if ind00 == 1
colormap(udd00.col);
else
col00 = colormap(jet);
col00(1:end/2,:)=0.5;
colormap(col00);
end
udd00.cax = caxis;
|
github
|
philippboehmsturm/antx-master
|
spm_write_sn.m
|
.m
|
antx-master/freiburgLight/matlab/spm8/spm_write_sn.m
| 19,859 |
utf_8
|
0ea1c7ae2ba1644c71deaf8ae1518452
|
function VO = spm_write_sn(V,prm,flags,extras)
% Write out warped images
% FORMAT VO = spm_write_sn(V,prm,flags,msk)
% V - Images to transform (filenames or volume structure).
% prm - Transformation information (filename or structure).
% flags - flags structure, with fields...
% interp - interpolation method (0-7)
% wrap - wrap edges (e.g., [1 1 0] for 2D MRI sequences)
% vox - voxel sizes (3 element vector - in mm)
% Non-finite values mean use template vox.
% bb - bounding box (2x3 matrix - in mm)
% Non-finite values mean use template bb.
% preserve - either 0 or 1. A value of 1 will "modulate"
% the spatially normalised images so that total
% units are preserved, rather than just
% concentrations.
% prefix - Prefix for normalised images. Defaults to 'w'.
% msk - An optional cell array for masking the spatially
% normalised images (see below).
%
% Warped images are written prefixed by "w".
%
% Non-finite vox or bounding box suggests that values should be derived
% from the template image.
%
% Don't use interpolation methods greater than one for data containing
% NaNs.
%__________________________________________________________________________
%
% FORMAT msk = spm_write_sn(V,prm,flags,'mask')
% V - Images to transform (filenames or volume structure).
% prm - Transformation information (filename or structure).
% flags - flags structure, with fields...
% wrap - wrap edges (e.g., [1 1 0] for 2D MRI sequences)
% vox - voxel sizes (3 element vector - in mm)
% Non-finite values mean use template vox.
% bb - bounding box (2x3 matrix - in mm)
% Non-finite values mean use template bb.
% msk - a cell array for masking a series of spatially normalised
% images.
%
%
%_________________________________________________________________________
%
% FORMAT VO = spm_write_sn(V,prm,'modulate')
% V - Spatially normalised images to modulate (filenames or
% volume structure).
% prm - Transformation information (filename or structure).
%
% After nonlinear spatial normalization, the relative volumes of some
% brain structures will have decreased, whereas others will increase.
% The resampling of the images preserves the concentration of pixel
% units in the images, so the total counts from structures that have
% reduced volumes after spatial normalization will be reduced by an
% amount proportional to the volume reduction.
%
% This routine rescales images after spatial normalization, so that
% the total counts from any structure are preserved. It was written
% as an optional step in performing voxel based morphometry.
%
%__________________________________________________________________________
% Copyright (C) 1996-2011 Wellcome Trust Centre for Neuroimaging
% John Ashburner
% $Id: spm_write_sn.m 4201 2011-02-15 10:52:00Z ged $
if isempty(V), return; end;
if ischar(prm), prm = load(prm); end;
if ischar(V), V = spm_vol(V); end;
if nargin==3 && ischar(flags) && strcmpi(flags,'modulate'),
if nargout==0,
modulate(V,prm);
else
VO = modulate(V,prm);
end;
return;
end;
def_flags = spm_get_defaults('normalise.write');
def_flags.prefix = 'w';
if nargin < 3,
flags = def_flags;
else
fnms = fieldnames(def_flags);
for i=1:length(fnms),
if ~isfield(flags,fnms{i}),
flags.(fnms{i}) = def_flags.(fnms{i});
end;
end;
end;
[x,y,z,mat] = get_xyzmat(prm,flags.bb,flags.vox);
if nargin==4,
if ischar(extras) && strcmpi(extras,'mask'),
VO = get_snmask(V,prm,x,y,z,flags.wrap);
return;
end;
if iscell(extras),
msk = extras;
end;
end;
if nargout>0 && length(V)>8,
error('Too many images to save in memory');
end;
if ~exist('msk','var')
msk = get_snmask(V,prm,x,y,z,flags.wrap);
end;
if nargout==0,
if isempty(prm.Tr),
affine_transform(V,prm,x,y,z,mat,flags,msk);
else
nonlin_transform(V,prm,x,y,z,mat,flags,msk);
end;
else
if isempty(prm.Tr),
VO = affine_transform(V,prm,x,y,z,mat,flags,msk);
else
VO = nonlin_transform(V,prm,x,y,z,mat,flags,msk);
end;
end;
return;
%==========================================================================
%==========================================================================
function VO = affine_transform(V,prm,x,y,z,mat,flags,msk)
[X,Y] = ndgrid(x,y);
d = [flags.interp*[1 1 1]' flags.wrap(:)];
spm_progress_bar('Init',numel(V),'Resampling','volumes/slices completed');
for i=1:numel(V),
VO = make_hdr_struct(V(i),x,y,z,mat, flags.prefix);
if flags.preserve
VO.fname = prepend(VO.fname,'m');
end
detAff = det(prm.VF(1).mat*prm.Affine/prm.VG(1).mat);
if flags.preserve, VO.pinfo(1:2,:) = VO.pinfo(1:2,:)/detAff; end;
%Dat= zeros(VO.dim(1:3));
Dat = single(0);
Dat(VO.dim(1),VO.dim(2),VO.dim(3)) = 0;
C = spm_bsplinc(V(i),d);
for j=1:length(z), % Cycle over planes
[X2,Y2,Z2] = mmult(X,Y,z(j),V(i).mat\prm.VF(1).mat*prm.Affine);
dat = spm_bsplins(C,X2,Y2,Z2,d);
if flags.preserve, dat = dat*detAff; end;
dat(msk{j}) = NaN;
Dat(:,:,j) = single(dat);
if numel(V)<5, spm_progress_bar('Set',i-1+j/length(z)); end;
end;
if nargout~=0,
VO.pinfo = [1 0]';
VO.dt = [spm_type('float32') spm_platform('bigend')];
VO.dat = Dat;
else
spm_write_vol(VO, Dat);
end;
spm_progress_bar('Set',i);
end;
spm_progress_bar('Clear');
return;
%==========================================================================
%==========================================================================
function VO = nonlin_transform(V,prm,x,y,z,mat,flags,msk)
[X,Y] = ndgrid(x,y);
Tr = prm.Tr;
BX = spm_dctmtx(prm.VG(1).dim(1),size(Tr,1),x-1);
BY = spm_dctmtx(prm.VG(1).dim(2),size(Tr,2),y-1);
BZ = spm_dctmtx(prm.VG(1).dim(3),size(Tr,3),z-1);
if flags.preserve,
DX = spm_dctmtx(prm.VG(1).dim(1),size(Tr,1),x-1,'diff');
DY = spm_dctmtx(prm.VG(1).dim(2),size(Tr,2),y-1,'diff');
DZ = spm_dctmtx(prm.VG(1).dim(3),size(Tr,3),z-1,'diff');
end;
d = [flags.interp*[1 1 1]' flags.wrap(:)];
spm_progress_bar('Init',numel(V),'Resampling','volumes completed');
for i=1:numel(V),
VO = make_hdr_struct(V(i),x,y,z,mat, flags.prefix);
if flags.preserve
VO.fname = prepend(VO.fname,'m');
end
detAff = det(prm.VF(1).mat*prm.Affine/prm.VG(1).mat);
% Accumulate data
%Dat= zeros(VO.dim(1:3));
Dat = single(0);
Dat(VO.dim(1),VO.dim(2),VO.dim(3)) = 0;
C = spm_bsplinc(V(i),d);
for j=1:length(z), % Cycle over planes
% Nonlinear deformations
%------------------------------------------------------------------
tx = get_2Dtrans(Tr(:,:,:,1),BZ,j);
ty = get_2Dtrans(Tr(:,:,:,2),BZ,j);
tz = get_2Dtrans(Tr(:,:,:,3),BZ,j);
X1 = X + BX*tx*BY';
Y1 = Y + BX*ty*BY';
Z1 = z(j) + BX*tz*BY';
[X2,Y2,Z2] = mmult(X1,Y1,Z1,V(i).mat\prm.VF(1).mat*prm.Affine);
dat = spm_bsplins(C,X2,Y2,Z2,d);
dat(msk{j}) = NaN;
if ~flags.preserve,
Dat(:,:,j) = single(dat);
else
j11 = DX*tx*BY' + 1; j12 = BX*tx*DY'; j13 = BX*get_2Dtrans(Tr(:,:,:,1),DZ,j)*BY';
j21 = DX*ty*BY'; j22 = BX*ty*DY' + 1; j23 = BX*get_2Dtrans(Tr(:,:,:,2),DZ,j)*BY';
j31 = DX*tz*BY'; j32 = BX*tz*DY'; j33 = BX*get_2Dtrans(Tr(:,:,:,3),DZ,j)*BY' + 1;
% The determinant of the Jacobian reflects relative volume changes
%-----------------------------------------------------------------
dat = dat .* (j11.*(j22.*j33-j23.*j32) - j21.*(j12.*j33-j13.*j32) + j31.*(j12.*j23-j13.*j22)) * detAff;
Dat(:,:,j) = single(dat);
end;
if numel(V)<5, spm_progress_bar('Set',i-1+j/length(z)); end;
end;
if nargout==0,
if flags.preserve, VO = rmfield(VO,'pinfo'); end
VO = spm_write_vol(VO,Dat);
else
VO.pinfo = [1 0]';
VO.dt = [spm_type('float32') spm_platform('bigend')];
VO.dat = Dat;
end;
spm_progress_bar('Set',i);
end;
spm_progress_bar('Clear');
return;
%==========================================================================
%==========================================================================
function VO = modulate(V,prm)
spm_progress_bar('Init',numel(V),'Modulating','volumes completed');
for i=1:numel(V),
VO = V(i);
VO = rmfield(VO,'pinfo');
VO.fname = prepend(VO.fname,'m');
detAff = det(prm.VF(1).mat*prm.Affine/prm.VG(1).mat);
%Dat = zeros(VO.dim(1:3));
Dat = single(0);
Dat(VO.dim(1),VO.dim(2),VO.dim(3)) = 0;
[x,y,z,mat] = get_xyzmat(prm,NaN,NaN,VO);
if sum((mat(:)-VO.mat(:)).^2)>1e-7, error('Orientations not compatible'); end;
Tr = prm.Tr;
if isempty(Tr),
for j=1:length(z), % Cycle over planes
dat = spm_slice_vol(V(i),spm_matrix([0 0 j]),V(i).dim(1:2),0);
Dat(:,:,j) = single(dat);
if numel(V)<5, spm_progress_bar('Set',i-1+j/length(z)); end;
end;
else
BX = spm_dctmtx(prm.VG(1).dim(1),size(Tr,1),x-1);
BY = spm_dctmtx(prm.VG(1).dim(2),size(Tr,2),y-1);
BZ = spm_dctmtx(prm.VG(1).dim(3),size(Tr,3),z-1);
DX = spm_dctmtx(prm.VG(1).dim(1),size(Tr,1),x-1,'diff');
DY = spm_dctmtx(prm.VG(1).dim(2),size(Tr,2),y-1,'diff');
DZ = spm_dctmtx(prm.VG(1).dim(3),size(Tr,3),z-1,'diff');
for j=1:length(z), % Cycle over planes
tx = get_2Dtrans(Tr(:,:,:,1),BZ,j);
ty = get_2Dtrans(Tr(:,:,:,2),BZ,j);
tz = get_2Dtrans(Tr(:,:,:,3),BZ,j);
j11 = DX*tx*BY' + 1; j12 = BX*tx*DY'; j13 = BX*get_2Dtrans(Tr(:,:,:,1),DZ,j)*BY';
j21 = DX*ty*BY'; j22 = BX*ty*DY' + 1; j23 = BX*get_2Dtrans(Tr(:,:,:,2),DZ,j)*BY';
j31 = DX*tz*BY'; j32 = BX*tz*DY'; j33 = BX*get_2Dtrans(Tr(:,:,:,3),DZ,j)*BY' + 1;
% The determinant of the Jacobian reflects relative volume changes
%-----------------------------------------------------------------
dat = spm_slice_vol(V(i),spm_matrix([0 0 j]),V(i).dim(1:2),0);
dat = dat .* (j11.*(j22.*j33-j23.*j32) - j21.*(j12.*j33-j13.*j32) + j31.*(j12.*j23-j13.*j22)) * detAff;
Dat(:,:,j) = single(dat);
if numel(V)<5, spm_progress_bar('Set',i-1+j/length(z)); end;
end;
end;
if nargout==0,
VO = spm_write_vol(VO,Dat);
else
VO.pinfo = [1 0]';
VO.dt = [spm_type('float32') spm_platform('bigend')];
VO.dat = Dat;
end;
spm_progress_bar('Set',i);
end;
spm_progress_bar('Clear');
return;
%==========================================================================
%==========================================================================
function VO = make_hdr_struct(V,x,y,z,mat,prefix)
VO = V;
VO.fname = prepend(V.fname,prefix);
VO.mat = mat;
VO.dim(1:3) = [length(x) length(y) length(z)];
VO.pinfo = V.pinfo;
VO.descrip = 'spm - 3D normalized';
return;
%==========================================================================
%==========================================================================
function T2 = get_2Dtrans(T3,B,j)
d = [size(T3) 1 1 1];
tmp = reshape(T3,d(1)*d(2),d(3));
T2 = reshape(tmp*B(j,:)',d(1),d(2));
return;
%==========================================================================
%_______________________________________________________________________
function PO = prepend(PI,pre)
[pth,nm,xt,vr] = spm_fileparts(deblank(PI));
PO = fullfile(pth,[pre nm xt vr]);
return;
%==========================================================================
%==========================================================================
function Mask = getmask(X,Y,Z,dim,wrp)
% Find range of slice
tiny = 5e-2;
Mask = true(size(X));
if ~wrp(1), Mask = Mask & (X >= (1-tiny) & X <= (dim(1)+tiny)); end;
if ~wrp(2), Mask = Mask & (Y >= (1-tiny) & Y <= (dim(2)+tiny)); end;
if ~wrp(3), Mask = Mask & (Z >= (1-tiny) & Z <= (dim(3)+tiny)); end;
return;
%==========================================================================
%==========================================================================
function [X2,Y2,Z2] = mmult(X1,Y1,Z1,Mult)
if length(Z1) == 1,
X2= Mult(1,1)*X1 + Mult(1,2)*Y1 + (Mult(1,3)*Z1 + Mult(1,4));
Y2= Mult(2,1)*X1 + Mult(2,2)*Y1 + (Mult(2,3)*Z1 + Mult(2,4));
Z2= Mult(3,1)*X1 + Mult(3,2)*Y1 + (Mult(3,3)*Z1 + Mult(3,4));
else
X2= Mult(1,1)*X1 + Mult(1,2)*Y1 + Mult(1,3)*Z1 + Mult(1,4);
Y2= Mult(2,1)*X1 + Mult(2,2)*Y1 + Mult(2,3)*Z1 + Mult(2,4);
Z2= Mult(3,1)*X1 + Mult(3,2)*Y1 + Mult(3,3)*Z1 + Mult(3,4);
end;
return;
%==========================================================================
%==========================================================================
function msk = get_snmask(V,prm,x,y,z,wrap)
% Generate a mask for where there is data for all images
%--------------------------------------------------------------------------
msk = cell(length(z),1);
t1 = cat(3,V.mat);
t2 = cat(1,V.dim);
t = [reshape(t1,[16 length(V)])' t2(:,1:3)];
Tr = prm.Tr;
[X,Y] = ndgrid(x,y);
BX = spm_dctmtx(prm.VG(1).dim(1),size(Tr,1),x-1);
BY = spm_dctmtx(prm.VG(1).dim(2),size(Tr,2),y-1);
BZ = spm_dctmtx(prm.VG(1).dim(3),size(Tr,3),z-1);
if numel(V)>1 && any(any(diff(t,1,1))),
spm_progress_bar('Init',length(z),'Computing available voxels','planes completed');
for j=1:length(z), % Cycle over planes
Count = zeros(length(x),length(y));
if isempty(Tr),
% Generate a mask for where there is data for all images
%--------------------------------------------------------------
for i=1:numel(V),
[X2,Y2,Z2] = mmult(X,Y,z(j),V(i).mat\prm.VF(1).mat*prm.Affine);
Count = Count + getmask(X2,Y2,Z2,V(i).dim(1:3),wrap);
end;
else
% Nonlinear deformations
%--------------------------------------------------------------
X1 = X + BX*get_2Dtrans(Tr(:,:,:,1),BZ,j)*BY';
Y1 = Y + BX*get_2Dtrans(Tr(:,:,:,2),BZ,j)*BY';
Z1 = z(j) + BX*get_2Dtrans(Tr(:,:,:,3),BZ,j)*BY';
% Generate a mask for where there is data for all images
%--------------------------------------------------------------
for i=1:numel(V),
[X2,Y2,Z2] = mmult(X1,Y1,Z1,V(i).mat\prm.VF(1).mat*prm.Affine);
Count = Count + getmask(X2,Y2,Z2,V(i).dim(1:3),wrap);
end;
end;
msk{j} = uint32(find(Count ~= numel(V)));
spm_progress_bar('Set',j);
end;
spm_progress_bar('Clear');
else
for j=1:length(z), msk{j} = uint32([]); end;
end;
return;
%==========================================================================
%==========================================================================
function [x,y,z,mat] = get_xyzmat(prm,bb,vox,VG)
% The old voxel size and origin notation is used here.
% This requires that the position and orientation
% of the template is transverse. It would not be
% straitforward to account for templates that are
% in different orientations because the basis functions
% would no longer be seperable. The seperable basis
% functions mean that computing the deformation field
% from the parameters is much faster.
% bb = sort(bb);
% vox = abs(vox);
if nargin<4,
VG = prm.VG(1);
if all(~isfinite(bb(:))) && all(~isfinite(vox(:))),
x = 1:VG.dim(1);
y = 1:VG.dim(2);
z = 1:VG.dim(3);
mat = VG.mat;
return;
end
end
[bb0 vox0] = spm_get_bbox(VG, 'old');
if ~all(isfinite(vox(:))), vox = vox0; end;
if ~all(isfinite(bb(:))), bb = bb0; end;
msk = find(vox<0);
bb = sort(bb);
bb(:,msk) = flipud(bb(:,msk));
% Adjust bounding box slightly - so it rounds to closest voxel.
% Comment out if not needed.
%bb(:,1) = round(bb(:,1)/vox(1))*vox(1);
%bb(:,2) = round(bb(:,2)/vox(2))*vox(2);
%bb(:,3) = round(bb(:,3)/vox(3))*vox(3);
M = prm.VG(1).mat;
vxg = sqrt(sum(M(1:3,1:3).^2));
if det(M(1:3,1:3))<0, vxg(1) = -vxg(1); end;
ogn = M\[0 0 0 1]';
ogn = ogn(1:3)';
% Convert range into range of voxels within template image
x = (bb(1,1):vox(1):bb(2,1))/vxg(1) + ogn(1);
y = (bb(1,2):vox(2):bb(2,2))/vxg(2) + ogn(2);
z = (bb(1,3):vox(3):bb(2,3))/vxg(3) + ogn(3);
og = -vxg.*ogn;
% Again, chose whether to round to closest voxel.
%of = -vox.*(round(-bb(1,:)./vox)+1);
of = bb(1,:)-vox;
M1 = [vxg(1) 0 0 og(1) ; 0 vxg(2) 0 og(2) ; 0 0 vxg(3) og(3) ; 0 0 0 1];
M2 = [vox(1) 0 0 of(1) ; 0 vox(2) 0 of(2) ; 0 0 vox(3) of(3) ; 0 0 0 1];
mat = prm.VG(1).mat*inv(M1)*M2;
LEFTHANDED = true;
if (LEFTHANDED && det(mat(1:3,1:3))>0) || (~LEFTHANDED && det(mat(1:3,1:3))<0),
Flp = [-1 0 0 (length(x)+1); 0 1 0 0; 0 0 1 0; 0 0 0 1];
mat = mat*Flp;
x = flipud(x(:))';
end;
return;
%==========================================================================
%==========================================================================
function VO = write_dets(P,bb,vox)
if nargin==1,
job = P;
P = job.P;
bb = job.bb;
vox = job.vox;
end;
spm_progress_bar('Init',numel(P),'Writing','volumes completed');
for i=1:numel(V),
prm = load(deblank(P{i}));
[x,y,z,mat] = get_xyzmat(prm,bb,vox);
Tr = prm.Tr;
BX = spm_dctmtx(prm.VG(1).dim(1),size(Tr,1),x-1);
BY = spm_dctmtx(prm.VG(1).dim(2),size(Tr,2),y-1);
BZ = spm_dctmtx(prm.VG(1).dim(3),size(Tr,3),z-1);
DX = spm_dctmtx(prm.VG(1).dim(1),size(Tr,1),x-1,'diff');
DY = spm_dctmtx(prm.VG(1).dim(2),size(Tr,2),y-1,'diff');
DZ = spm_dctmtx(prm.VG(1).dim(3),size(Tr,3),z-1,'diff');
[pth,nam,ext,nm] = spm_fileparts(P{i});
VO = struct('fname',fullfile(pth,['jy_' nam ext nm]),...
'dim',[numel(x),numel(y),numel(z)],...
'dt',[spm_type('float32') spm_platform('bigend')],...
'pinfo',[1 0 0]',...
'mat',mat,...
'n',1,...
'descrip','Jacobian determinants');
VO = spm_create_vol(VO);
detAff = det(prm.VF(1).mat*prm.Affine/prm.VG(1).mat);
Dat = single(0);
Dat(VO.dim(1),VO.dim(2),VO.dim(3)) = 0;
for j=1:length(z), % Cycle over planes
% Nonlinear deformations
tx = get_2Dtrans(Tr(:,:,:,1),BZ,j);
ty = get_2Dtrans(Tr(:,:,:,2),BZ,j);
tz = get_2Dtrans(Tr(:,:,:,3),BZ,j);
%------------------------------------------------------------------
j11 = DX*tx*BY' + 1; j12 = BX*tx*DY'; j13 = BX*get_2Dtrans(Tr(:,:,:,1),DZ,j)*BY';
j21 = DX*ty*BY'; j22 = BX*ty*DY' + 1; j23 = BX*get_2Dtrans(Tr(:,:,:,2),DZ,j)*BY';
j31 = DX*tz*BY'; j32 = BX*tz*DY'; j33 = BX*get_2Dtrans(Tr(:,:,:,3),DZ,j)*BY' + 1;
% The determinant of the Jacobian reflects relative volume changes.
%------------------------------------------------------------------
dat = (j11.*(j22.*j33-j23.*j32) - j21.*(j12.*j33-j13.*j32) + j31.*(j12.*j23-j13.*j22)) * detAff;
Dat(:,:,j) = single(dat);
if numel(P)<5, spm_progress_bar('Set',i-1+j/length(z)); end;
end;
VO = spm_write_vol(VO,Dat);
spm_progress_bar('Set',i);
end;
spm_progress_bar('Clear');
return;
%==========================================================================
|
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