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StockwellDs.py
StockwellDs.py
computes time-frequency representations of MEG data using the Stockwell transform.
Download StockwellDs-1.6.tar.gz. Dated 4/29/08.
StockwellDs.py [options] $ds
The default behavior is to average all trials and compute the Stockwell transform of the average. Each channel is averaged separately and the resulting Stockwells are averaged together.
Following a 3dWilcoxon
command like
3dWilcoxon -out ${cond1}V${cond2} $dsetlist1 $dsetlist2
the out brik has 2 subbriks. You need to do two things; threshold it, and copy the tfdim information.
3dmerge −1thresh 1.96 -datum float -prefix ${cond1}V${cond2}_z ${cond1}V${cond2}+orig
This 3dmerge
command will create one brik thresholded at p < .05. The 1.96 is the .025 cutoff for the normal distribution. The 3dWilcoxon
program creates zscores. If you want p < .01, use 2.57.
To copy the tfdim header, do this: start with a Stockwell brik that has the same dimensions (one of your input briks), here $subj$cond1
h=`3dNotes ${subj}${cond1}+orig | grep tfdim | sed 's/.*\(tfdim: .*\)/\1/'` 3dNotes -h "$h" ${cond1}V${cond2}_z+orig
The variable h contains the stuff that disptfbrik.py
needs to display the axes correctly.
3dWilcoxon
produces images of dset2 minus dset1.