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- function [ S, f, Serr ]= mtspectrumc_unequal_length_trials( data, movingwin, params, sMarkers )
- % This routine computes the multi-taper spectrum for a given set of unequal length segments. It is
- % based on modifications to the Chronux routines. The segments are continuously structured in the
- % data matrix, with the segment boundaries given by markers. Below,
- % movingwin is used in a non-overlaping way to partition each segment into
- % various windows. Th spectrum is evaluated for each window, and then the
- % window spectrum estimates averaged. Further averaging is conducted by
- % repeating the process for each segment.
- %
- % Inputs:
- %
- % data = data( samples, channels )- here segments must be stacked
- % as explained in the email
- % movingwin = [window winstep] i.e length of moving
- % window and step size. Note that units here have
- % to be consistent with units of Fs. If Fs=1 (ie normalized)
- % then [window winstep]should be in samples, or else if Fs is
- % unnormalized then they should be in time (secs).
- % sMarkers = N x 2 array of segment start & stop marks. sMarkers(n, 1) = start
- % sample index; sMarkers(n,2) = stop sample index for the nth segment
- % params = see Chronux help on mtspecgramc
- %
- % Output:
- %
- % S frequency x channels
- % f frequencies x 1
- % Serr (error bars) only for err(1)>=1
- %
- %
- iwAvg = 1; % 0=no weighted average, 1=weighted average
- debug = 0; % will display intermediate calcs.
- if nargin < 2; error('Unequal length trials:: Need data and window parameters'); end;
- if nargin < 3; params=[]; end;
- if isempty( sMarkers ), error( 'Unequal length trials:: Need Markers...' ); end
- [ tapers, pad, Fs, fpass, err, trialave, params ] = getparams( params );
- if nargout > 2 && err(1)==0;
- % Cannot compute error bars with err(1)=0. change params and run again.
- error('Unequal length trials:: When Serr is desired, err(1) has to be non-zero.');
- end;
- % Set moving window parameters to no-overlapping
- if abs(movingwin(2) - movingwin(1)) >= 1e-6, disp( 'avgSpectrum:: Warming: Window parameters for averaging should be non-overlapping. Set movingwin(2) = movingwin(1).' ); end
- wLength = round( Fs * movingwin(1) ); % number of samples in window
- wStep = round( movingwin(2) * Fs ); % number of samples to step through
- % Check whether window lengths satify segment length > NW/2
- if ( wLength < 2*tapers(1) ), error( 'avgSpectrum:: movingwin(1) > 2*tapers(1)' ); end
- % Left align segment markers for easier coding
- sM = ones( size( sMarkers, 1 ), 2 );
- sM( :, 2 ) = sMarkers( :, 2 ) - sMarkers( :, 1 ) + 1;
- % min-max segments
- Nmax = max( sM(:,2) ); Nmin = min( sM(:,2) );
- if ( Nmin < 2*tapers(1) ), error( 'avgSpectrum:: Smallest segment length > 2*tapers(1). Change taper settings' ); end
- % max time-sample length will be the window length.
- nfft = 2^( nextpow2( wLength ) + pad );
- [ f, findx ] = getfgrid( Fs, nfft, fpass);
- % Precompute all the tapers
- sTapers = tapers;
- sTapers = dpsschk( sTapers, wLength, Fs ); % compute tapers for window length
- nChannels = size( data, 2 );
- nSegments = size( sMarkers, 1 );
- if debug
- disp( ['Window Length = ' num2str(wLength)] );
- disp( ['Window Step = ' num2str(wStep)] );
- disp( ' ' );
- end
- s = zeros( length(f), nChannels );
- serr = zeros( 2, length(f), nChannels );
- S = zeros( length(f), nChannels );
- Serr = zeros( 2, length(f), nChannels );
- nWins = 0;
- for sg = 1 : nSegments
- % Window lengths & steps fixed above
- % For the given segment, compute the positions & number of windows
- N = sM(sg,2);
- wStartPos = 1 : wStep : ( N - wLength + 1 );
- nWindows = length( wStartPos );
- if nWindows
- nWins = nWins + nWindows; % for averaging purposes
- w=zeros(nWindows,2);
- for n = 1 : nWindows
- w(n,:) = [ wStartPos(n), (wStartPos(n) + wLength - 1) ]; % nWindows x 2. just like segment end points
- end
- % Shift window limits back to original sample-stamps
- w(:, 1) = w(:,1) + (sMarkers( sg, 1 ) - 1);
- w(:, 2) = w(:,2) + (sMarkers( sg, 1 ) - 1);
- if debug
- disp( ['Segment Start/Stop = ' num2str( w(1,1) ) ' ' num2str( w(end,2) ) ] );
- disp( ['Min / Max Window Positions = ' num2str( min(w(:,1)) ) ' ' num2str( max(w(:,1)) ) ] );
- disp( ['Total Number of Windows = ' num2str(nWindows) ]);
- disp( ' ' );
- end
- % Pile up window segments similar to segment pileup
- wData = zeros( wLength, nChannels, nWindows ); %initialize to avoid fragmentation
- for n = 1:nWindows
- %wData( :, :, n ) = detrend( data( w(n,1):w(n,2), : ), 'constant' );
- wData( :, :, n ) = detrend( data( w(n,1):w(n,2), : ) );
- end
- % J1 = frequency x taper x nWindows
- % J2 = frequency x taper x nWindows x nChannels
- J2 = zeros( length(f), tapers(2), nWindows, nChannels ); J2 = complex( J2, J2 );
- for c = 1 : nChannels
- J1 = mtfftc( squeeze(wData( :, c, : )), sTapers, nfft, Fs ); % FFT for the tapered data
- J2( :, :, :, c ) = J1(findx,:,:);
- end
- % J2 = frequency x taper x nWindows x nChannels
- % Inner mean = Average over tapers => frequency x nWindows x nChannels
- % Outer mean = Average over windows => frequency x nChannels
- dim1 = [length(f), nWindows, nChannels];
- dim2 = [length(f), nChannels];
- % s = frequency x nChannels
- s = reshape( squeeze( mean( reshape( squeeze( mean( conj(J2).*J2, 2 ) ), dim1), 2 ) ), dim2 );
- % Now treat the various "windowed data" as "trials"
- % serr = 2 x frequency x channels. Output from specerr = 2 x frequency x 1
- for c = 1 : nChannels
- serr( :, :, c ) = specerr( squeeze( s(:, c ) ), squeeze( J2(:,:,:, c ) ), err, 1 );
- end
-
- if iwAvg
- % Segment Weighted error estimates.
- S = S + nWindows*s;
- Serr = Serr + nWindows*serr;
- else
- S = S + s;
- Serr = Serr + serr;
- end
- else
- if debug, disp(['avgSpectrum:: Zero windows for segment: ' num2str(sg) ]); end
- end
- end
- % Segment Weighted error estimates.
- % Only over those that had non-zero windows
- if nWins && iwAvg
- S=S/nWins; Serr=Serr/nWins;
- end
- if ~nWins
- if debug, disp(['avgCoherence:: No segment long enough with movingwin parameters found. Reduce movingwin.' ]); end
- end
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