NAME

       nipy_tsdiffana - Analyze, plot time series difference metrics

DESCRIPTION

       usage: nipy_tsdiffana [-h] [--out-file OUT_FILE] [--write-results]

       [--out-path OUT_PATH]
              [--out-fname-label OUT_FNAME_LABEL] [--time-axis TIME_AXIS] [--slice-axis SLICE_AXIS] filename

              Analyze, plot time series difference metrics

   positional arguments:
       filename
              4D image filename

   optional arguments:
       -h, --help
              show this help message and exit

       --out-file OUT_FILE
              graphics file to write to instead of leaving image on screen

       --write-results
              if  specified,  write  diagnostic  images  and  analysis  variables,  plot  to  OUT_PATH. Mutually
              incompatible with OUT_FILE

       --out-path OUT_PATH
              path for output image files (default from FILENAME path

       --out-fname-label OUT_FNAME_LABEL
              mid part of output image / plot filenames

       --time-axis TIME_AXIS
              Image axis for time

       --slice-axis SLICE_AXIS
              Image axis for slice

       nipy_tsdiffana runs the time series diference algorithm over a 4D image volume, often and FMRI volume.

       It works in one of three modes:

       * interactive : the time series difference plot appears on screen.  This is the

              default mode

       * non-interactive, plot only : write time series difference plot to graphic

              file. Use the "--out-file=<myfilename>" option to activate this mode

       * non-interactive, write plot, images and variables : write plot to file, and

              write generated diagnostic images and variables to files as well. Use the  "--write-results"  flag
              to  activate  this  option.  The generated filenames come from the results of the "--out-path" and
              "--out-fname-label" options (see help).

       Write-results option, generated files -------------------------------------

       When doing the time point analysis, we will make a difference volume between each time point and the next
       time point in the series.  If we have T volumes then there will be (T-1)  difference  volumes.  Call  the
       vector of difference volumes DV and the first difference volume DV[0].  So DV[0] results from subtraction
       of the second volume in the 4D input image from the first volume in the 4D input image.  The element-wise
       squared values from DV[0] is *DV2[0]*.

       The following images will be generated. <ext> is the input filename extension (e.g. '.nii'):

       * "dv2_max_<label><ext>" : 3D image volume, where each slice S is slice from

              all  of  DV2[0]  (slice  S) throudh DV2[T-1] (slice S) that has the maximum summed squared values.
              This volume gives an idea of the worst (highest difference) slices across the whole time series.

       * "dv2_mean_<label><ext>" : the mean of all DV2 volumes DV2[0] .. DV[T-1]

       across the volume (time) dimension.
              Higher voxel values in this volume mean

              that time-point to time point differences tended to be high in this voxel.

       We also write the mean signal at each time point, and the mean squared difference between each  slice  in
       time, as variables to a 'npz' file named "tsdiff_<label>.npz"

       The  filenames for the outputs are of the form <out-path>/<some_prefix><label><file-ext> where <out-path>
       is the path specified by the --out-path option, or the path of the input filename; <some_prefix>  is  one
       of  the  standard  prefixes above, <label> is given by --out-label, or by the filename of the input image
       (with path and extension removed), and <file-ext> is '.png' for graphics, or the extension of  the  input
       filename  for  volume  images.   For example, specifying only the input filename ``/some/path/fname.img``
       will generate filenames of the form ``/some/path/tsdiff_fname.png, /some/path/dv2_max_fname.img`` etc.

nipy_tsdiffana 0.4.2                              February 2020                                NIPY_TSDIFFANA(1)