1NIPY_TSDIFFANA(1) User Commands NIPY_TSDIFFANA(1)
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6 nipy_tsdiffana – Analyze, plot time series difference metrics
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9 nipy_tsdiffana [-h] [--out-file OUT_FILE] [--write-results]
10 [--out-path OUT_PATH] [--out-fname-label OUT_FNAME_LABEL]
11 [--time-axis TIME_AXIS] [--slice-axis SLICE_AXIS] filename
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14 Runs the time series difference algorithm over a 4D image volume, often
15 an FMRI volume.
16 It works in one of three modes:
18 • interactive : the time series difference plot appears on
20 screen. This is the default mode
21 • non-interactive, plot only : write time series difference plot
23 to graphic file. Use the “--out-file=<myfilename>” option to
24 activate this mode
25 • non-interactive, write plot, images and variables : write plot
27 to file, and write generated diagnostic images and variables
28 to files as well. Use the “--write-results” flag to activate
29 this option. The generated filenames come from the results of
30 the “--write-results” “--out-path” and “--out-fname-label” op‐
31 tions (see OPTIONS).
32 WRITE-RESULTS OPTION, GENERATED FILES
34 When doing the time point analysis, we will make a difference volume
35 between each time point and the next time point in the series. If we
36 have T volumes then there will be (T-1) difference volumes. Call the
37 vector of difference volumes DV and the first difference volume DV[0].
38 So DV [0] results from subtraction of the second volume in the 4D input
39 image from the first volume in the 4D input image. The element-wise
40 squared values from DV[0] is DV2[0].
41 The following images will be generated. <ext> is the input filename
43 extension (e.g. ‘.nii’):
44 • “dv2_max_<label><ext>” : 3D image volume, where each slice S
46 is slice from all of DV2 [0] (slice S) through DV2[T-1] (slice
47 S) that has the maximum summed squared values. This volume
48 gives an idea of the worst (highest difference) slices across
49 the whole time series.
50 • “dv2_mean_<label><ext>” : the mean of all DV2 volumes
52 DV2[0] .. DV[T-1] across the volume (time) dimension. Higher
53 voxel values in this volume mean that time-point to time point
54 differences tended to be high in this voxel.
55 We also write the mean signal at each time point, and the mean squared
57 difference between each slice in time, as variables to a ‘npz’ file
58 named “tsdiff_<label>.npz”
59 The filenames for the outputs are of the form <out-path>/<some_pre‐
61 fix><label><file-ext> where <out-path> is the path specified by the
62 --out-path option, or the path of the input filename; <some_prefix> is
63 one of the standard prefixes above, <label> is given by --out-label, or
64 by the filename of the input image (with path and extension removed),
65 and <file-ext> is ‘.png’ for graphics, or the extension of the input
66 filename for volume images. For example, specifying only the input
67 filename /some/path/fname.img will generate filenames of the form
68 /some/path/tsdiff_fname.png, /some/path/dv2_max_fname.img etc.
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71 POSITIONAL ARGUMENTS
72 filename
73 4D image filename
74 OPTIONAL ARGUMENTS
76 -h, --help
77 Show a help message and exit
78 --out-file OUT_FILE
80 Graphics file to write to instead of leaving image on screen
81 --write-results
83 Graphics file to write to instead of leaving image on screen If
84 specified, write diagnostic images and analysis variables, plot
85 to OUT_PATH. Mutually incompatible with OUT_FILE
86 --out-path OUT_PATH
88 Path for output image files (default from filename path)
89 --out-fname-label OUT_FNAME_LABEL
91 Mid part of output image / plot filenames
92 --time-axis TIME_AXIS
94 Image axis for time
95 --slice-axis SLICE_AXIS
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99 nipy_3dto4d(1), nipy_4d_realign(1), nipy_4dto3d(1), nipy_diagnose(1)
100 February 2023 NIPY_TSDIFFANA(1)