cleaned up the reference documentation in the help

Author: robertoostenveld

Contents.m

@@ -19,7 +19,7 @@
 % of the GNU General Public Licence as published by the Free Software Foundation (either
 % version 2, or at your option any later version). See the file COPYING for more
 % details.
-% 
+%
 % The functions in this toolbox are copyrighted by their respective authors:
 %   Robert Oostenveld, DCCN, FCDC, SMI, MBFYS
 %   Jan-Mathijs Schoffelen, CCNi, FCDC
@@ -38,17 +38,17 @@
 %   Lilla Magyari, MPI, DCCN
 %   and many others ...
 %
-% Copyright (C) 2008-2018, Donders Institute, Radboud University, The Netherlands (DCCN, DCC, DCN, DCMN)  
-% Copyright (C) 2014-2018, Karolinska Institute, Stockholm, Sweden (NatMEG)  
-% Copyright (C) 2012-2016, Max Planck Institute for Psycholinguistics, The Netherlands (MPI)  
-% Copyright (C) 2010-2013, Swammerdam Institute for Life Sciences, University of Amsterdam (SILS)  
-% Copyright (C) 2008-2009, Centre for Cognitive Neuroimaging in Glasgow, United Kingdom (CCNi)  
-% Copyright (C) 2009-2009, Netherlands Institute for Neuroscience (NIN)  
-% Copyright (C) 2003-2008, F.C. Donders Centre, Radboud University Nijmegen, The Netherlands (FCDC)  
-% Copyright (C) 2004-2007, Nijmegen Institute for Cognition and Information, The Netherlands (NICI)  
-% Copyright (C) 2004-2005, Universitatsklinikum Hamburg-Eppendorf, Germany (UKE)  
-% Copyright (C) 2003-2004, Center for Sensory Motor Interaction, University Aalborg, Denmark (SMI)  
-% Copyright (C) 1999-2003, Department of Medical Physics, Katholieke Universiteit Nijmegen, The Netherlands (MBFYS)  
+% Copyright (C) 2008-2018, Donders Institute, Radboud University, The Netherlands (DCCN, DCC, DCN, DCMN)
+% Copyright (C) 2014-2018, Karolinska Institute, Stockholm, Sweden (NatMEG)
+% Copyright (C) 2012-2016, Max Planck Institute for Psycholinguistics, The Netherlands (MPI)
+% Copyright (C) 2010-2013, Swammerdam Institute for Life Sciences, University of Amsterdam (SILS)
+% Copyright (C) 2008-2009, Centre for Cognitive Neuroimaging in Glasgow, United Kingdom (CCNi)
+% Copyright (C) 2009-2009, Netherlands Institute for Neuroscience (NIN)
+% Copyright (C) 2003-2008, F.C. Donders Centre, Radboud University Nijmegen, The Netherlands (FCDC)
+% Copyright (C) 2004-2007, Nijmegen Institute for Cognition and Information, The Netherlands (NICI)
+% Copyright (C) 2004-2005, Universitatsklinikum Hamburg-Eppendorf, Germany (UKE)
+% Copyright (C) 2003-2004, Center for Sensory Motor Interaction, University Aalborg, Denmark (SMI)
+% Copyright (C) 1999-2003, Department of Medical Physics, Katholieke Universiteit Nijmegen, The Netherlands (MBFYS)
 %
 % The FieldTrip toolbox depend on functions from other toolboxes to do a large part of
 % the actual work, such as reading data from binary files and forward and inverse
@@ -60,7 +60,7 @@
 % the GPL is not allowed!
 %
 % Below is a non-exhaustive overview of some of the important FieldTrip functions, sorted by category.
-% You can get more details on a function by typing "help functionname" in MATLAB.
+% You can get more details on a function by typing 'help functionname' in MATLAB.
 %
 % Preprocessing, reading and converting data
 %   ft_definetrial

connectivity/ft_connectivity_laggedcoherence.m

@@ -16,7 +16,7 @@
 % the time resolution in freqout.
 %
 % This function must be called separately for each frequency of interest. To analyse
-% multiple frequencies, we advise the use of a for loop like this:
+% multiple frequencies, you can use a for-loop like this:
 %   cfg_F             = [];
 %   cfg_F.method      = 'wavelet';
 %   cfg_F.output      = 'fourier';
@@ -32,8 +32,8 @@
 %       cfg_LC.foi    = foi(counter);
 %       width         = cfg_F.width/cfg_F.foi;
 %       cfg_F.toi     = data.time{1}(1) + ceil(fs*width/2)/fs : ...   %from:
-%         cfg_LC.lag/cfg_F.foi : ...                     %in steps of size:
-%         data.time{1}(end) - ceil(fs*width/2)/fs;                     %to:
+%         cfg_LC.lag/cfg_F.foi : ...                      %in steps of size:
+%         data.time{1}(end) - ceil(fs*width/2)/fs;                      %to:
 %       freqout       = ft_freqanalysis(cfg_F,data);
 %       lcoh(counter) = ft_connectivityanalysis(cfg_LC,freqout);
 %   end
@@ -207,13 +207,13 @@
     hasdata         = false(nrep,cfg.nlags);
     nsmplslaggedcps = zeros(cfg.nlags,nrep);
     lagwidth        = zeros(1,cfg.nlags);
-    
+
     for lagindx=1:cfg.nlags
       % select all pairs of timepoints with relative lag cfg.lag (identified with precision cfg.precision)
       lagwidth(lagindx) = cfg.lag*cyclelength*lagindx;
       lagwidth(lagindx) = dtim(find(abs(dtim-lagwidth(lagindx))==min(abs(dtim(:)-lagwidth(lagindx))),1));
       [t1, t2]          = find(dtim==lagwidth(lagindx));
-      
+
       % calculate laggedcrossproducts and power per channelcmb and trial
       for trialindx=1:nrep
         % get the spectrum for this trial and frequency
@@ -234,7 +234,7 @@
         end
       end
     end
-    
+
     % calculate lagged coherence
     if strcmp('lcoh',cfg.output)
       laggedcoh         = complex(nan(ntrialsets,nchancmb,cfg.nlags));
@@ -283,7 +283,7 @@
           lcoh.dimord      = 'rep_chan_rep';
         end
       end
-      
+
       % calculate lagged crossspectra and corresponding power spectra
     elseif csdflag
       laggedcrsspctrm    = complex(zeros(ntrialsets,nchancmb,cfg.nlags));
@@ -343,24 +343,24 @@
         end
       end
     end
-    
+
   case 'yes'
     % method-specfic checks
     if cfg.nlags>1
       ft_error('when calculating timeresolved lcoh, cfg.nlags must be set to 1');
     end
-    
+
     % select pairs of timepoints with relative lag cfg.lag (identified with precision cfg.precision)
     lagwidth            =  cfg.lag*cyclelength;
     lagwidth            =  dtim(find(abs(dtim-lagwidth)==min(abs(dtim(:)-lagwidth)),1));
     [t1, t2]            =  find(dtim==lagwidth);
-    
+
     % initiate some variables (dependent on nr of timepoints)
     ntoi                =   length(t1);
     laggedcrossproduct  =   complex(zeros(nchancmb,ntoi,nrep));
     power               =   complex(zeros(nchancmb,ntoi,2,nrep));
     hasdata             =   false(nrep,ntoi);
-    
+
     % calculate laggedcrossproducts and power per channel, time of interest, and trial
     for trialindx=1:nrep
       % get the spectrum for this trial and frequency
@@ -379,7 +379,7 @@
         hasdata(trialindx,tcounter)             = true;
       end
     end
-    
+
     % calculate lagged coherence
     if strcmp('lcoh',cfg.output)
       ntrialsets  = length(cfg.trialsets);
@@ -423,7 +423,7 @@
         end
         lcoh.dimord      = 'rep_chan_time';
       end
-      
+
       % calculate lagged crossspectra and corresponding power spectra
     elseif csdflag
       laggedcrsspctrm      = complex(nan(ntrialsets,nchancmb,ntoi));

data2bids.m

@@ -13,9 +13,9 @@
 % or as
 %   data2bids(cfg, data)
 %
-% The first input argument "cfg" is the configuration structure, which contains the
+% The first input argument 'cfg' is the configuration structure, which contains the
 % details for the (meta)data and which specifies the sidecar files you want to write.
-% The optional "data" argument corresponds to preprocessed raw data according to
+% The optional 'data' argument corresponds to preprocessed raw data according to
 % FT_DATAYPE_RAW or an anatomical MRI according to FT_DATAYPE_VOLUME. The optional
 % data argument allows you to write a preprocessed and realigned anatomical MRI to
 % disk, or to write a preprocessed electrophysiological dataset to disk.

edf2fieldtrip.m

@@ -6,7 +6,7 @@
 % output of FT_PREPROCESSING.
 %
 % Use as
-%   data = edf2fieldtrip(filename);
+%   data = edf2fieldtrip(filename)
 %
 % For reading EDF files in which all channels have the same sampling rate, you can
 % use the standard procedure with FT_DEFINETRIAL and FT_PREPROCESSING.
@@ -41,14 +41,14 @@
 for i=1:numel(samplerate)
   chanindx = find(hdr.orig.SampleRate==samplerate(i));
   fprintf('reading %d channels with %g Hz sampling rate\n', numel(chanindx), samplerate(i));
-  
+
   % read the header and data for the selected channels
   hdr = ft_read_header(filename, 'chanindx', chanindx);
   dat = ft_read_data(filename, 'header', hdr);
-  
+
   % construct a time axis, starting at 0 seconds
   time = ((1:(hdr.nTrials*hdr.nSamples)) - 1)./hdr.Fs;
-  
+
   % make a raw data structure
   data{i}.hdr   = hdr;
   data{i}.label = hdr.label;
@@ -64,7 +64,7 @@
     continue
   end
   fprintf('upsampling %d channels from %g to %g Hz\n', numel(data{i}.label), samplerate(i), maxrate);
-  
+
   cfg = [];
   cfg.time = data{maxindex}.time;
   data{i} = ft_resampledata(cfg, data{i});
@@ -88,5 +88,3 @@
   % remove this, as otherwise it might be very confusing with the subselections
   data = rmfield(data, 'hdr');
 end
-
-

external/artinis/ft_nirs_prepare_ODtransformation.m

@@ -5,7 +5,7 @@
 % oxygenated hemoglobin.
 %
 % Use as
-%   [montage] = ft_prepare_ODtransformation(cfg, data);
+%   [montage] = ft_prepare_ODtransformation(cfg, data)
 %
 % It is neccessary to input the data on which you want to perform the
 % inverse computations, since that data generally contain the optode

external/artinis/ft_nirs_transform_ODs.m

@@ -5,10 +5,10 @@
 % other way around.
 %
 % Use as either
-%   [data]      = ft_nirs_transform_ODs(cfg, data);
-%   [freq]      = ft_nirs_transform_ODs(cfg, freq);
-%   [timelock]  = ft_nirs_transform_ODs(cfg, timelock);
-%   [component] = ft_nirs_transform_ODs(cfg, component);
+%   [data]      = ft_nirs_transform_ODs(cfg, data)
+%   [freq]      = ft_nirs_transform_ODs(cfg, freq)
+%   [timelock]  = ft_nirs_transform_ODs(cfg, timelock)
+%   [component] = ft_nirs_transform_ODs(cfg, component)
 %
 %  The configuration "cfg" is a structure containing information about
 %  target of the transformation. The configuration should contain

fieldtrip2besa.m

@@ -6,13 +6,13 @@ function fieldtrip2besa(filename, data, varargin)
 %
 % Use as
 %   fieldtrip2besa(filename, elec)
-% to export single trial data as a set of ascii-vectorized files (.avr)
+% to export single trial data as a set of .avr files.
 %
 % Use as
 %   fieldtrip2besa(filename, elec)
 % or
 %   fieldtrip2besa(filename, grad)
-% to export channel positions (.elp).
+% to export channel positions  to an .elp file.
 %
 % Additional key-value pairs can be specified according to
 %   channel   = cell-array, can be used to make subset and to reorder the channels
@@ -53,7 +53,7 @@ function fieldtrip2besa(filename, data, varargin)
 
     if isfield(data, 'trial') && strcmp(getdimord(data, 'trial'), 'rpt_chan_time')
       [NumTrials, NumChans, NumSamp] = size(data.trial);
-      
+
       % Multiply by 1000 to get the time in milliseconds.
       time_samples = data.time.*1000;
       channel_labels = data.label;

ft_annotate.m

@@ -3,15 +3,14 @@
 % FT_ANNOTATE returns the same output data as the user has provided as input, but allows
 % to add comments to that data structure. These comments are stored along with the other
 % provenance information and can be displayed with FT_ANALYSISPIPELINE. Adding comments
-% is especially useful if you have manually (i.e. in plain MATLAB) modified ythe data
+% is especially useful if you have manually (i.e. in plain MATLAB) modified the data
 % structure, whereby some provenance information is missing.
 %
 % Use as
 %   outdata = ft_examplefunction(cfg, indata)
-% where the input data structure can be any of the FieldTrip data structures and where
-% cfg is a configuratioun structure that should contain
-%
-%  cfg.comment    = string
+% where the input data structure can be any of the FieldTrip data structures and
+% the configuration structure should contain
+%   cfg.comment    = string
 %
 % To facilitate data-handling and distributed computing you can use
 %   cfg.inputfile   =  ...

ft_anonymizedata.m

@@ -19,11 +19,11 @@
 % value of each provenance element, and whether it should be kept or
 % removed. Furthermore, it has a number of buttons:
 %   - sort        specify which column is used for sorting
-%   - apply       apply the current selection of "keep" and "remove" and hide the corresponding rows
-%   - keep all    toggle all visibe rows to "keep"
-%   - remove all  toggle all visibe rows to "keep"
-%   - clear all   clear all visibe rows, i.e. neither "keep" nor "remove"
-%   - quit        apply the current selection of "keep" and "remove" and exit
+%   - apply       apply the current selection of 'keep' and 'remove' and hide the corresponding rows
+%   - keep all    toggle all visibe rows to 'keep'
+%   - remove all  toggle all visibe rows to 'keep'
+%   - clear all   clear all visibe rows, i.e. neither 'keep' nor 'remove'
+%   - quit        apply the current selection of 'keep' and 'remove' and exit
 %
 % To facilitate data-handling and distributed computing you can use
 %   cfg.inputfile  = ...
@@ -218,15 +218,15 @@
 resize_cb(h);
 
 while ~info.cleanup
-  
+
   uiwait(h); % we only get part this point with abort or cleanup
-  
+
   if ~ishandle(h)
     ft_error('aborted by user');
   end
-  
+
   info = getappdata(h, 'info');
-  
+
   if info.cleanup
     if ~all(xor(info.keep, info.remove))
       ft_warning('not all fields have been marked as "keep" or "remove"');

ft_appendsens.m

@@ -7,9 +7,9 @@
 %   combined = ft_appendsens(cfg, sens1, sens2, ...)
 %
 % A call to FT_APPENDSENS results in the label, pos and ori fields to be
-% concatenated, and the tra matrix to be merged. Any duplicates will be removed.
-% The labelold and chanposold fields are kept under the condition that they
-% are identical across the inputs.
+% concatenated, and the tra matrix to be merged. Any duplicate electrodes
+% will be removed. The labelold and chanposold fields are kept under the
+% condition that they are identical across the inputs.
 %
 % See also FT_ELECTRODEPLACEMENT, FT_ELECTRODEREALIGN, FT_DATAYPE_SENS,
 % FT_APPENDDATA, FT_APPENDTIMELOCK, FT_APPENDFREQ, FT_APPENDSOURCE
@@ -108,7 +108,7 @@
   if isfield(varargin{i}, 'chanpos')
     chanpos{i} = varargin{i}.chanpos;
   end
-  
+
   % some the following fields are likely present in a sens structure
   if isfield(varargin{i}, 'elecpos') % EEG
     elecpos{i} = varargin{i}.elecpos;
@@ -134,7 +134,7 @@
     tra{i} = varargin{i}.tra;
     hastra = 1;
   end
-  
+
   % the following fields might be present in a sens structure
   if isfield(varargin{i}, 'labelold')
     labelold{i} = varargin{i}.labelold(:); % ensure column orientation

ft_appendsource.m

@@ -3,6 +3,9 @@
 % FT_APPENDSOURCE concatenates multiple volumetric source reconstruction data
 % structures that have been processed separately.
 %
+% Use as
+%   combined = ft_appendsource(cfg, source1, source2, ...)
+%
 % If the source reconstructions were computed for different ROIs or different slabs
 % of a regular 3D grid (as indicated by the source positions), the data will be
 % concatenated along the spatial dimension.
@@ -12,9 +15,6 @@
 % decomposed data, the data will be concatenared along the frequency and/or time
 % dimension.
 %
-% Use as
-%   combined = ft_appendsource(cfg, source1, source2, ...)
-%
 % See also FT_SOURCEANALYSIS, FT_DATATYPE_SOURCE, FT_APPENDDATA, FT_APPENDTIMELOCK,
 % FT_APPENDFREQ
 

ft_appendtimelock.m

@@ -9,9 +9,7 @@
 % Use as
 %   combined = ft_appendtimelock(cfg, timelock1, timelock2, ...)
 %
-% The following configuration options are supported:
-%
-% The configuration can optionally contain
+% The configuration can contain
 %   cfg.appenddim       = string, the dimension to concatenate over which to append,
 %                         this can be 'chan' and 'rpt' (default is automatic)
 %   cfg.tolerance       = scalar, tolerance to determine how different the time axes
@@ -113,7 +111,7 @@
 if any(strcmp(cfg.parameter, 'avg')) && any(strcmp(cfg.parameter, 'trial'))
   ft_warning('appending the individual trials, not the averages');
   % also prevent var and dof from being appended
-  cfg.parameter = setdiff(cfg.parameter, {'avg', 'var', 'dof'}); 
+  cfg.parameter = setdiff(cfg.parameter, {'avg', 'var', 'dof'});
 end
 
 % use a low-level function that is shared with the other ft_appendxxx functions

ft_artifact_nan.m

@@ -97,4 +97,3 @@
 ft_postamble trackconfig
 ft_postamble previous   data
 ft_postamble savevar
-