# 1d_tool.py¶

- purpose:
- examples (very basic for now):
- Example 1. Select by rows and columns, akin to 1dcat.
- Example 2. Compare with selection by separate options.
- Example 2b. Select or remove columns by label prefixes.
- Example 2c. Select columns by group values, 3 examples.
- Example 2d. Select specific runs from the input.
- Example 3. Transpose a dataset, akin to 1dtranspose.
- Example 4a. Zero-pad a single-run 1D file across many runs.
- Example 4b. Similar to 4a, but specify varying TRs per run.
- Example 5. Display small details about a 1D dataset:
- Example 6a. Show correlation matrix warnings for this matrix.
- Example 6b. Show entire correlation matrix.
- Example 7a. Output temporal derivative of motion regressors.
- Example 7b. Similar to 7a, but let the run lengths vary.
- Example 7c. Use forward differences.
- Example 9a. Given motion.1D, create an Enorm time series.
- Example 9b. Like 9a, but supposing the run lengths vary (still 576 TRs).
- Example 9c. Like 9b, but weight the rotations as 0.9 mm.
- Example 10. Given motion.1D, create censor files to use in 3dDeconvolve.
- Example 11. Demean the data. Use motion parameters as an example.
- Example 12. “Uncensor” the data, zero-padding previously censored TRs.
- Example 13. Show whether the input file is valid as a numeric data file.
- Example 14. Split motion parameters across runs.
- Example 15a. Show the maximum pairwise displacement.
- Example 15b. Like 15a, but do not include displacement from censored TRs.
- Example 16. Randomize a list of numbers, say, those from 1..40.
- Example 17. Display min, mean, max, stdev of 1D file.
- Example 18. Just output censor count for default method.
- Example 19. Compute GCOR from some 1D file.
- Example 20. Display censored or uncensored TRs lists (for use in 3dTcat).
- Example 21. Convert to rank order.
- Example 22. Guess volreg base index from motion parameters.
- Example 23. Convert volreg parameters to those suitable for 3dAllineate.
- Example 24. Show TR counts per run.
- Example 25. Show number of runs.
- Example 26. Convert global index to run and TR index.
- Example 27. Display length of response curve.
- Example 28. Convert slice order to slice times.
- Example 29. Display minimum cluster size from 3dClustSim output.

- command-line options:

1d_tool.py - for manipulating and evaluating 1D files

## purpose:¶

```
This program is meant to read/manipulate/write/diagnose 1D datasets.
Input can be specified using AFNI sub-brick[]/time{} selectors.
```

## examples (very basic for now):¶

### Example 1. Select by rows and columns, akin to 1dcat.¶

```
Note: columns can be X-matrix labels.
1d_tool.py -infile 'data/X.xmat.1D[0..3]{0..5}' -write t1.1D
or using column labels:
1d_tool.py -infile 'data/X.xmat.1D[Run#1Pol#0,,Run#1Pol#3]' \
-write run0_polorts.1D
```

### Example 2. Compare with selection by separate options.¶

```
1d_tool.py -infile data/X.xmat.1D \
-select_cols '0..3' -select_rows '0..5' \
-write t2.1D
diff t1.1D t2.1D
```

### Example 2b. Select or remove columns by label prefixes.¶

```
Keep only bandpass columns:
1d_tool.py -infile X.xmat.1D -write X.bandpass.1D \
-label_prefix_keep bandpass
Remove only bandpass columns (maybe for 3dRFSC):
1d_tool.py -infile X.xmat.1D -write X.no.bandpass.1D \
-label_prefix_drop bandpass
Keep polort columns (start with 'Run') motion shifts ('d') and labels
starting with 'a' and 'b'. But drop 'bandpass' columns:
1d_tool.py -infile X.xmat.1D -write X.weird.1D \
-label_prefix_keep Run d a b \
-label_prefix_drop bandpass
```

### Example 2c. Select columns by group values, 3 examples.¶

```
First be sure of what the group labels represent.
1d_tool.py -infile X.xmat.1D -show_group_labels
i) Select polort (group -1) and other baseline (group 0) terms.
1d_tool.py -infile X.xmat.1D -select_groups -1 0 -write baseline.1D
ii) Select everything but baseline groups (anything positive).
1d_tool.py -infile X.xmat.1D -select_groups POS -write regs.of.int.1D
iii) Reorder to have rests of interest, then motion, then polort.
1d_tool.py -infile X.xmat.1D -select_groups POS 0, -1 -write order.1D
iv) Create stim-only X-matrix file: select non-baseline columns of
X-matrix and write with header comment.
1d_tool.py -infile X.xmat.1D -select_groups POS \
-write_with_header yes -write X.stim.xmat.1D
Or, using a convenience option:
1d_tool.py -infile X.xmat.1D -write_xstim X.stim.xmat.1D
```

### Example 2d. Select specific runs from the input.¶

```
Note that X.xmat.1D may have runs defined automatically, but for an
arbitrary input, they may need to be specified via -set_run_lengths.
i) .... apparently I forgot to do this...
1d_tool.py -infile X.xmat.1D -write X.bandpass.1D \
```

### Example 3. Transpose a dataset, akin to 1dtranspose.¶

```
1d_tool.py -infile t3.1D -transpose -write ttr.1D
```

### Example 4a. Zero-pad a single-run 1D file across many runs.¶

```
Given a file of regressors (for example) across a single run (run 2),
created a new file that is padded with zeros, so that it now spans
many (7) runs. Runs are 1-based here.
1d_tool.py -infile ricor_r02.1D -pad_into_many_runs 2 7 \
-write ricor_r02_all.1D
```

### Example 4b. Similar to 4a, but specify varying TRs per run.¶

```
The number of runs must match the number of run_lengths parameters.
1d_tool.py -infile ricor_r02.1D -pad_into_many_runs 2 7 \
-set_run_lengths 64 61 67 61 67 61 67 \
-write ricor_r02_all.1D
```

### Example 5. Display small details about a 1D dataset:¶

```
a. Display number of rows and columns for a 1D dataset.
Note: to display them "quietly" (only the numbers), add -verb 0.
This is useful for setting a script variable.
1d_tool.py -infile X.xmat.1D -show_rows_cols
1d_tool.py -infile X.xmat.1D -show_rows_cols -verb 0
b. Display indices of regressors of interest.
1d_tool.py -infile X.xmat.1D -show_indices_interest
c. Display labels by group.
1d_tool.py -infile X.xmat.1D -show_group_labels
d. Display "degree of freedom" information:
1d_tool.py -infile X.xmat.1D -show_df_info
```

### Example 6a. Show correlation matrix warnings for this matrix.¶

```
1d_tool.py -infile X.xmat.1D -show_cormat_warnings
```

### Example 6b. Show entire correlation matrix.¶

```
1d_tool.py -infile X.xmat.1D -show_cormat
```

### Example 7a. Output temporal derivative of motion regressors.¶

```
There are 9 runs in dfile_rall.1D, and derivatives are applied per run.
1d_tool.py -infile dfile_rall.1D -set_nruns 9 \
-derivative -write motion.deriv.1D
```

### Example 7b. Similar to 7a, but let the run lengths vary.¶

```
The sum of run lengths should equal the number of time points.
1d_tool.py -infile dfile_rall.1D \
-set_run_lengths 64 64 64 64 64 64 64 64 64 \
-derivative -write motion.deriv.rlens.1D
```

### Example 7c. Use forward differences.¶

```
instead of the default backward differences...
1d_tool.py -infile dfile_rall.1D \
-set_run_lengths 64 64 64 64 64 64 64 64 64 \
-forward_diff -write motion.deriv.rlens.1D
Example 8. Verify whether labels show slice-major ordering.
This is where all slice0 regressors come first, then all slice1
regressors, etc. Either show the labels and verify visually, or
print whether it is true.
1d_tool.py -infile scan_2.slibase.1D'[0..12]' -show_labels
1d_tool.py -infile scan_2.slibase.1D -show_labels
1d_tool.py -infile scan_2.slibase.1D -show_label_ordering
```

### Example 9a. Given motion.1D, create an Enorm time series.¶

```
Take the derivative (ignoring run breaks) and the Euclidean Norm,
and write as e.norm.1D. This might be plotted to show show sudden
motion as a single time series.
1d_tool.py -infile motion.1D -set_nruns 9 \
-derivative -collapse_cols euclidean_norm \
-write e.norm.1D
```

### Example 9b. Like 9a, but supposing the run lengths vary (still 576 TRs).¶

```
1d_tool.py -infile motion.1D \
-set_run_lengths 64 61 67 61 67 61 67 61 67 \
-derivative -collapse_cols euclidean_norm \
-write e.norm.rlens.1D
```

### Example 9c. Like 9b, but weight the rotations as 0.9 mm.¶

```
1d_tool.py -infile motion.1D \
-set_run_lengths 64 61 67 61 67 61 67 61 67 \
-derivative -collapse_cols weighted_enorm \
-weight_vec .9 .9 .9 1 1 1 \
-write e.norm.weighted.1D
```

### Example 10. Given motion.1D, create censor files to use in 3dDeconvolve.¶

```
Here a TR is censored if the derivative values have a Euclidean Norm
above 1.2. It is common to also censor each previous TR, as motion may
span both (previous because "derivative" is actually a backward
difference).
The file created by -write_censor can be used with 3dD's -censor option.
The file created by -write_CENSORTR can be used with -CENSORTR. They
should have the same effect in 3dDeconvolve. The CENSORTR file is more
readable, but the censor file is better for plotting against the data.
```

#### a. general example¶

```
1d_tool.py -infile motion.1D -set_nruns 9 \
-derivative -censor_prev_TR \
-collapse_cols euclidean_norm \
-moderate_mask -1.2 1.2 \
-show_censor_count \
-write_censor subjA_censor.1D \
-write_CENSORTR subjA_CENSORTR.txt
```

#### b. using -censor_motion¶

```
The -censor_motion option is available, which implies '-derivative',
'-collapse_cols euclidean_norm', 'moderate_mask -LIMIT LIMIT', and the
prefix for '-write_censor' and '-write_CENSORTR' output files. This
option will also result in subjA_enorm.1D being written, which is the
euclidean norm of the derivative, before the extreme mask is applied.
1d_tool.py -infile motion.1D -set_nruns 9 \
-show_censor_count \
-censor_motion 1.2 subjA \
-censor_prev_TR
```

#### c. allow the run lengths to vary¶

```
1d_tool.py -infile motion.1D \
-set_run_lengths 64 61 67 61 67 61 67 61 67 \
-show_censor_count \
-censor_motion 1.2 subjA_rlens \
-censor_prev_TR
Consider also '-censor_prev_TR' and '-censor_first_trs'.
```

### Example 11. Demean the data. Use motion parameters as an example.¶

```
The demean operation is done per run (default is 1 when 1d_tool.py
does not otherwise know).
a. across all runs (if runs are not known from input file)
1d_tool.py -infile dfile_rall.1D -demean -write motion.demean.a.1D
b. per run, over 9 runs of equal length
1d_tool.py -infile dfile_rall.1D -set_nruns 9 \
-demean -write motion.demean.b.1D
c. per run, over 9 runs of varying length
1d_tool.py -infile dfile_rall.1D \
-set_run_lengths 64 61 67 61 67 61 67 61 67 \
-demean -write motion.demean.c.1D
```

### Example 12. “Uncensor” the data, zero-padding previously censored TRs.¶

```
Note that an X-matrix output by 3dDeconvolve contains censor
information in GoodList, which is the list of uncensored TRs.
a. if the input dataset has censor information
1d_tool.py -infile X.xmat.1D -censor_fill -write X.uncensored.1D
b. if censor information needs to come from a parent
1d_tool.py -infile sum.ideal.1D -censor_fill_parent X.xmat.1D \
-write sum.ideal.uncensored.1D
c. if censor information needs to come from a simple 1D time series
1d_tool.py -censor_fill_parent motion_FT_censor.1D \
-infile cdata.1D -write cdata.zeropad.1D
```

### Example 13. Show whether the input file is valid as a numeric data file.¶

```
a. as any generic 1D file
1d_tool.py -infile data.txt -looks_like_1D
b. as a 1D stim_file, of 3 runs of 64 TRs (TR is irrelevant)
1d_tool.py -infile data.txt -looks_like_1D \
-set_run_lengths 64 64 64
c. as a stim_times file with local times
1d_tool.py -infile data.txt -looks_like_local_times \
-set_run_lengths 64 64 64 -set_tr 2
d. as a 1D or stim_times file with global times
1d_tool.py -infile data.txt -looks_like_global_times \
-set_run_lengths 64 64 64 -set_tr 2
e. report modulation type (amplitude and/or duration)
1d_tool.py -infile data.txt -looks_like_AM
f. perform all tests, reporting all errors
1d_tool.py -infile data.txt -looks_like_test_all \
-set_run_lengths 64 64 64 -set_tr 2
```

### Example 14. Split motion parameters across runs.¶

```
Split, but keep them at the original length so they apply to the same
multi-run regression. Each file will be the same as the original for
the run it applies to, but zero across all other runs.
Note that -split_into_pad_runs takes the output prefix as a parameter.
1d_tool.py -infile motion.1D \
-set_run_lengths 64 64 64 \
-split_into_pad_runs mot.padded
The output files are:
mot.padded.r01.1D mot.padded.r02.1D mot.padded.r03.1D
If the run lengths are the same -set_nruns is shorter...
1d_tool.py -infile motion.1D \
-set_nruns 3 \
-split_into_pad_runs mot.padded
```

### Example 15a. Show the maximum pairwise displacement.¶

```
Show the max pairwise displacement in the motion parameter file.
So over all TRs pairs, find the biggest displacement.
In one direction it is easy (AP say). If the minimum AP shift is -0.8
and the maximum is 1.5, then the maximum displacement is 2.3 mm. It
is less clear in 6-D space, and instead of trying to find an enveloping
set of "coordinates", distances between all N choose 2 pairs are
evaluated (brute force).
1d_tool.py -infile dfile_rall.1D -show_max_displace
```

### Example 15b. Like 15a, but do not include displacement from censored TRs.¶

```
1d_tool.py -infile dfile_rall.1D -show_max_displace \
-censor_infile motion_censor.1D
```

### Example 16. Randomize a list of numbers, say, those from 1..40.¶

```
The numbers can come from 1deval, with the result piped to
'1d_tool.py -infile stdin -randomize_trs ...'.
1deval -num 40 -expr t+1 | \
1d_tool.py -infile stdin -randomize_trs -write stdout
See also -seed.
```

### Example 17. Display min, mean, max, stdev of 1D file.¶

```
1d_tool.py -show_mmms -infile data.1D
To be more detailed, get stats for each of x, y, and z directional
blur estimates for all subjects. Cat(enate) all of the subject files
and pipe that to 1d_tool.py with infile - (meaning stdin).
cat subject_results/group.*/sub*/*.results/blur.errts.1D \
| 1d_tool.py -show_mmms -infile -
```

### Example 18. Just output censor count for default method.¶

```
This will output nothing but the number of TRs that would be censored,
akin to using -censor_motion and -censor_prev_TR.
1d_tool.py -infile dfile_rall.1D -set_nruns 3 -quick_censor_count 0.3
1d_tool.py -infile dfile_rall.1D -set_run_lengths 100 80 120 \
-quick_censor_count 0.3
```

### Example 19. Compute GCOR from some 1D file.¶

```
* Note, time should be in the vertical direction of the file
(else use -transpose).
1d_tool.py -infile data.1D -show_gcor
Or get some GCOR documentation and many values.
1d_tool.py -infile data.1D -show_gcor_doc
1d_tool.py -infile data.1D -show_gcor_all
```

### Example 20. Display censored or uncensored TRs lists (for use in 3dTcat).¶

```
TRs which were censored:
1d_tool.py -infile X.xmat.1D -show_trs_censored encoded
TRs which were applied in analysis (those NOT censored):
1d_tool.py -infile X.xmat.1D -show_trs_uncensored encoded
Only those applied in run #2 (1-based).
1d_tool.py -infile X.xmat.1D -show_trs_uncensored encoded \
-show_trs_run 2
```

### Example 21. Convert to rank order.¶

```
a. show rank order of slice times from a 1D file
1d_tool.py -infile slice_times.1D -rank -write -
b. show rank order of slice times piped directly from 3dinfo
3dinfo -slice_timing epi+orig | 1d_tool.py -infile - -rank -write -
c. show rank order using 'competition' rank, instead of default 'dense'
3dinfo -slice_timing epi+orig \
| 1d_tool.py -infile - -rank_style competition -write -
```

### Example 22. Guess volreg base index from motion parameters.¶

```
1d_tool.py -infile dfile_rall.1D -collapse_cols enorm -show_argmin
```

### Example 23. Convert volreg parameters to those suitable for 3dAllineate.¶

```
1d_tool.py -infile dfile_rall.1D -volreg2allineate \
-write allin_rall_aff12.1D
```

### Example 24. Show TR counts per run.¶

```
a. list the number of TRs in each run
1d_tool.py -infile X.xmat.1D -show_tr_run_counts trs
b. list the number of TRs censored in each run
1d_tool.py -infile X.xmat.1D -show_tr_run_counts trs_cen
c. list the number of TRs prior to censoring in each run
1d_tool.py -infile X.xmat.1D -show_tr_run_counts trs_no_cen
d. list the fraction of TRs censored per run
1d_tool.py -infile X.xmat.1D -show_tr_run_counts frac_cen
e. list the fraction of TRs censored in run 3
1d_tool.py -infile X.xmat.1D -show_tr_run_counts frac_cen \
-show_trs_run 3
```

### Example 25. Show number of runs.¶

```
1d_tool.py -infile X.xmat.1D -show_num_runs
```

### Example 26. Convert global index to run and TR index.¶

```
Note that run indices are 1-based, while TR indices are 0-based,
as usual. Confusion is key.
a. explicitly, given run lengths
1d_tool.py -set_run_lengths 100 80 120 -index_to_run_tr 217
b. implicitly, given an X-matrix (** be careful about censoring **)
1d_tool.py -infile X.nocensor.xmat.1D -index_to_run_tr 217
```

### Example 27. Display length of response curve.¶

```
1d_tool.py -show_trs_to_zero -infile data.1D
Print out the length of the input (in TRs, say) until the data
values become a constant zero. Zeros that are followed by non-zero
values are irrelevant.
```

### Example 28. Convert slice order to slice times.¶

```
A slice order might be the sequence in which slices were acquired.
For example, with 33 slices, perhaps the order is:
set slice_order = ( 0 6 12 18 24 30 1 7 13 19 25 31 2 8 14 20 \
26 32 3 9 15 21 27 4 10 16 22 28 5 11 17 23 29 )
Put this in a file:
echo $slice_order > slice_order.1D
1d_tool.py -set_tr 2 -slice_order_to_times \
-infile slice_order.1D -write slice_times.1D
Or as a filter:
echo $slice_order | 1d_tool.py -set_tr 2 -slice_order_to_times \
-infile - -write -
```

### Example 29. Display minimum cluster size from 3dClustSim output.¶

```
Given a text file output by 3dClustSim, e.g. ClustSim.ACF.NN1_1sided.1D,
and given both an uncorrected (pthr) and a corrected (alpha) p-value,
look up the entry that specifies the minimum cluster size needed for
corrected p-value significance.
If requested in afni_proc.py, they are under files_ClustSim.
a. with modestly verbose output (default is -verb 1)
1d_tool.py -infile ClustSim.ACF.NN1_1sided.1D -csim_show_clustsize
b. quiet, to see just the output value
1d_tool.py -infile ClustSim.ACF.NN1_1sided.1D -csim_show_clustsize \
-verb 0
c. quiet, and capture the output value (tcsh syntax)
set clustsize = `1d_tool.py -infile ClustSim.ACF.NN1_1sided.1D \
-csim_show_clustsize -verb 0`
```

## command-line options:¶

### basic informational options:¶

```
-help : show this help
-hist : show the module history
-show_valid_opts : show all valid options
-ver : show the version number
```

### required input:¶

```
-infile DATASET.1D : specify input 1D file
```

### general options:¶

```
-add_cols NEW_DSET.1D : extend dset to include these columns
-backward_diff : take derivative as first backward difference
Take the backward differences at each time point. For each index > 0,
value[index] = value[index] - value[index-1], and value[0] = 0.
This option is identical to -derivative.
See also -forward_diff, -derivative, -set_nruns, -set_run_lens.
-collapse_cols METHOD : collapse multiple columns into one, where
METHOD is one of: min, max, minabs, maxabs, euclidean_norm,
weighted_enorm.
Consideration of the euclidean_norm method:
For censoring, the euclidean_norm method is used (sqrt(sum squares)).
This combines rotations (in degrees) with shifts (in mm) as if they
had the same weight.
Note that assuming rotations are about the center of mass (which
should produce a minimum average distance), then the average arc
length (averaged over the brain mask) of a voxel rotated by 1 degree
(about the CM) is the following (for the given datasets):
TT_N27+tlrc: 0.967 mm (average radius = 55.43 mm)
MNIa_caez_N27+tlrc: 1.042 mm (average radius = 59.69 mm)
MNI_avg152T1+tlrc: 1.088 mm (average radius = 62.32 mm)
The point of these numbers is to suggest that equating degrees and
mm should be fine. The average distance caused by a 1 degree
rotation is very close to 1 mm (in an adult human).
* 'enorm' is short for 'euclidean_norm'.
* Use of weighted_enorm requires the -weight_vec option.
e.g. -collapse_cols weighted_enorm -weight_vec .9 .9 .9 1 1 1
-censor_motion LIMIT PREFIX : create censor files
This option implies '-derivative', '-collapse_cols euclidean_norm',
'moderate_mask -LIMIT LIMIT' and applies PREFIX for '-write_censor'
and '-write_CENSORTR' output files. It also outputs the derivative
of the euclidean norm, before the limit it applied.
The temporal derivative is taken with run breaks applied (derivative
of the first run of a TR is 0), then the columns are collapsed into
one via each TR's vector length (Euclidean Norm: sqrt(sum of squares)).
After that, a mask time series is made from TRs with values outside
(-LIMIT,LIMIT), i.e. if >= LIMIT or <= LIMIT, result is 1.
This binary time series is then written out in -CENSORTR format, with
the moderate TRs written in -censor format (either can be applied in
3dDeconvolve). The output files will be named PREFIX_censor.1D,
PREFIX_CENSORTR.txt and PREFIX_enorm.1D (e.g. subj123_censor.1D,
subj123_CENSORTR.txt and subj123_enorm.1D).
Besides an input motion file (-infile), the number of runs is needed
(-set_nruns or -set_run_lengths).
Consider also '-censor_prev_TR' and '-censor_first_trs'.
See example 10.
-censor_fill : expand data, filling censored TRs with zeros
-censor_fill_parent PARENT : similar, but get censor info from a parent
The output of these operation is a longer dataset. Each TR that had
previously been censored is re-inserted as a zero.
The purpose of this is to make 1D time series data properly align
with the all_runs dataset, for example. Otherwise, the ideal 1D data
might have missing TRs, and so will align worse with responses over
the duration of all runs (it might start aligned, but drift earlier
and earlier as more TRs are censored).
See example 12.
-censor_infile CENSOR_FILE : apply censoring to -infile dataset
This removes TRs from the -infile dataset where the CENSOR_FILE is 0.
The censor file is akin to what is used with "3dDeconvolve -censor",
where TRs with 1 are kept and those with 0 are excluded from analysis.
See example 15b.
-censor_first_trs N : when censoring motion, also censor the first
N TRs of each run
-censor_next_TR : for each censored TR, also censor next one
(probably for use with -forward_diff)
-censor_prev_TR : for each censored TR, also censor previous
-cormat_cutoff CUTOFF : set cutoff for cormat warnings (in [0,1])
-csim_show_clustsize : for 3dClustSim input, show min clust size
Given a 3dClustSim table output (e.g. ClustSim.ACF.NN1_1sided.1D),
along with uncorrected (pthr) and corrected (alpha) p-values, show the
minimum cluster size to achieve significance.
The pthr and alpha values can be controlled via the options -csim_pthr
and -csim_alpha (with defaults of 0.001 and 0.05, respectively).
The -verb option can be used to provide additional or no details
about the clustering method.
See Example 29, along with options -csim_pthr, -csim_alpha and -verb.
-csim_pthr THRESH : specify uncorrected threshold for csim output
e.g. -csim_pthr 0.0001
This option implies -csim_show_clustsize, and is used to specify the
uncorrected p-value of the 3dClustSim output.
See also -csim_show_clustsize.
-csim_alpha THRESH : specify corrected threshold for csim output
e.g. -csim_alpha 0.01
This option implies -csim_show_clustsize, and is used to specify the
corrected, cluster-wise p-value of the 3dClustSim output.
See also -csim_show_clustsize.
-demean : demean each run (new mean of each run = 0.0)
-derivative : take the temporal derivative of each vector
(done as first backward difference)
Take the backward differences at each time point. For each index > 0,
value[index] = value[index] - value[index-1], and value[0] = 0.
This option is identical to -backward_diff.
See also -backward_diff, -forward_diff, -set_nruns, -set_run_lens.
-extreme_mask MIN MAX : make mask of extreme values
Convert to a 0/1 mask, where 1 means the given value is extreme
(outside the (MIN, MAX) range), and 0 means otherwise. This is the
opposite of -moderate_mask (not exactly, both are inclusive).
Note: values = MIN or MAX will be in both extreme and moderate masks.
Note: this was originally described incorrectly in the help.
-forward_diff : take first forward difference of each vector
Take the first forward differences at each time point. For index<last,
value[index] = value[index+1] - value[index], and value[last] = 0.
The difference between -forward_diff and -backward_diff is a time shift
by one index.
See also -backward_diff, -derivative, -set_nruns, -set_run_lens.
-index_to_run_tr INDEX : convert global INDEX to run and TR indices
Given a list of run lengths, convert INDEX to a run and TR index pair.
This option requires -set_run_lens or maybe an Xmat.
See also -set_run_lens example 26.
-moderate_mask MIN MAX : make mask of moderate values
Convert to a 0/1 mask, where 1 means the given value is moderate
(within [MIN, MAX]), and 0 means otherwise. This is useful for
censoring motion (in the -censor case, not -CENSORTR), where the
-censor file should be a time series of TRs to apply.
See also -extreme_mask.
-label_prefix_drop prefix1 prefix2 ... : remove labels matching prefix list
e.g. to remove motion shift (starting with 'd') and bandpass labels:
-label_prefix_drop d bandpass
This is a type of column selection.
Use this option to remove columns from a matrix that have labels
starting with any from the given prefix list.
This option can be applied along with -label_prefix_keep.
See also -label_prefix_keep and example 2b.
-label_prefix_keep prefix1 prefix2 ... : keep labels matching prefix list
e.g. to keep only motion shift (starting with 'd') and bandpass labels:
-label_prefix_keep d bandpass
This is a type of column selection.
Use this option to keep columns from a matrix that have labels starting
with any from the given prefix list.
This option can be applied along with -label_prefix_drop.
See also -label_prefix_drop and example 2b.
"Looks like" options:
These are terminal options that check whether the input file seems to
be of type 1D, local stim_times or global stim_times formats. The only
associated options are currently -infile, -set_run_lens, -set_tr and
-verb.
They are terminal in that no other 1D-style actions are performed.
See 'timing_tool.py -help' for details on stim_times operations.
-looks_like_1D : is the file in 1D format
Does the input data file seem to be in 1D format?
- must be rectangular (same number of columns per row)
- duration must match number of rows (if run lengths are given)
-looks_like_AM : does the file have modulators?
Does the file seem to be in local or global times format, and
do the times have modulators?
- amplitude modulators should use '*' format (e.g. 127.3*5.1)
- duration modulators should use trailing ':' format (12*5.1:3.4)
- number of amplitude modulators should be constant
-looks_like_local_times : is the file in local stim_times format
Does the input data file seem to be in the -stim_times format used by
3dDeconvolve (and timing_tool.py)? More specifically, is it the local
format, with one scanning run per row.
- number of rows must match number of runs
- times cannot be negative
- times must be unique per run (per row)
- times cannot exceed the current run time
-looks_like_global_times : is the file in global stim_times format
Does the input data file seem to be in the -stim_times format used by
3dDeconvolve (and timing_tool.py)? More specifically, is it the global
format, either as one long row or one long line?
- must be one dimensional (either a single row or column)
- times cannot be negative
- times must be unique
- times cannot exceed total duration of all runs
-looks_like_test_all : run all -looks_like tests
Applies all "looks like" test options: -looks_like_1D, -looks_like_AM,
-looks_like_local_times and -looks_like_global_times.
-overwrite : allow overwriting of any output dataset
-pad_into_many_runs RUN NRUNS : pad as current run of num_runs
e.g. -pad_into_many_runs 2 7
This option is used to create a longer time series dataset where the
input is consider one particular run out of many. The output is
padded with zero for all run TRs before and after this run.
Given the example, there would be 1 run of zeros, then the input would
be treated as run 2, and there would be 5 more runs of zeros.
-quick_censor_count LIMIT : output # TRs that would be censored
e.g. -quick_censor_count 0.3
This is akin to -censor_motion, but it only outputs the number of TRs
that would be censored. It does not actually create a censor file.
This option essentially replaces these:
-derivative -demean -collapse_cols euclidean_norm
-censor_prev_TR -verb 0 -show_censor_count
-moderate_mask 0 LIMIT
-rank : convert data to rank order
0-based index order of small to large values
The default rank STYLE is 'dense'.
See also -rank_style.
-rank_style STYLE : convert to rank using the given style
The STYLE refers to what to do in the case of repeated values.
Assuming inputs 4 5 5 9...
dense - repeats get same rank, no gaps in rank
- same a "3dmerge -1rank"
- result: 0 1 1 2
competition - repeats get same rank, leading to gaps in rank
- same a "3dmerge -1rank"
- result: 0 1 1 3
(case '2' is counted, though no such rank occurs)
Option '-rank' uses style 'dense'.
See also -rank.
-reverse_rank : convert data to reverse rank order
(large values come first)
-reverse : reverse data over time
-randomize_trs : randomize the data over time
-seed SEED : set random number seed (integer)
-select_groups g0 g1 ... : select columns by group numbers
e.g. -select groups 0
e.g. -select groups POS 0
An X-matrix dataset (e.g. X.xmat.1D) often has columns partitioned by
groups, such as:
-1 : polort regressors
0 : motion regressors and other (non-polort) baseline terms
N>0: regressors of interest
This option can be used to select columns by integer groups, with
special cases of POS (regs of interest), NEG (probably polort).
Note that NONNEG is unneeded as it is the pair POS 0.
See also -show_group_labels.
-select_cols SELECTOR : apply AFNI column selectors, [] is optional
e.g. '[5,0,7..21(2)]'
-select_rows SELECTOR : apply AFNI row selectors, {} is optional
e.g. '{5,0,7..21(2)}'
-select_runs r1 r2 ... : extract the given runs from the dataset
(these are 1-based run indices)
e.g. 2
e.g. 2 3 1 1 1 1 1 4
-set_nruns NRUNS : treat the input data as if it has nruns
(e.g. applies to -derivative and -demean)
See examples 7a, 10a and b, and 14.
-set_run_lengths N1 N2 ... : treat as if data has run lengths N1, N2, etc.
(applies to -derivative, for example)
Notes: o option -set_nruns is not allowed with -set_run_lengths
o the sum of run lengths must equal NT
See examples 7b, 10c and 14.
-set_tr TR : set the TR (in seconds) for the data
-show_argmin : display the index of min arg (of first column)
-show_censor_count : display the total number of censored TRs
Note : if input is a valid xmat.1D dataset, then the
count will come from the header. Otherwise
the input is assumed to be a binary censor
file, and zeros are simply counted.
-show_cormat : display correlation matrix
-show_cormat_warnings : display correlation matrix warnings
-show_df_info : display info about degrees of freedom in xmat.1D file
-show_df_protect yes/no : protection flag (def=yes)
-show_gcor : display GCOR: the average correlation
-show_gcor_all : display many ways of computing (a) GCOR
-show_gcor_doc : display descriptions of those ways
-show_group_labels : display group and label, per column
-show_indices_baseline : display column indices for baseline
-show_indices_motion : display column indices for motion regressors
-show_indices_interest : display column indices for regs of interest
-show_label_ordering : display the labels
-show_labels : display the labels
-show_max_displace : display max displacement (from motion params)
- the maximum pairwise distance (enorm)
-show_mmms : display min, mean, max, stdev of columns
-show_num_runs : display number of runs found
-show_rows_cols : display the number of rows and columns
-show_tr_run_counts STYLE : display TR counts per run, according to STYLE
STYLE can be one of:
trs : TR counts
trs_cen : censored TR counts
trs_no_cen : TR counts, as if no censoring
frac_cen : fractions of TRs censored
See example 24.
-show_trs_censored STYLE : display a list of TRs which were censored
-show_trs_uncensored STYLE : display a list of TRs which were not censored
STYLE can be one of:
comma : comma delimited
space : space delimited
encoded : succinct selector list
verbose : chatty
See example 20.
-show_trs_run RUN : restrict -show_trs_[un]censored to the given
1-based run
-show_trs_to_zero : display number of TRs before final zero value
(e.g. length of response curve)
-slice_order_to_times : convert a list of slice indices to times
Programs like to3d, 3drefit, 3dTcat and 3dTshift expect slice timing
to be a list of slice times over the sequential slices. But in some
cases, people have an ordered list of slices. So the sorting needs
to change.
If TR=2 and the slice order is: 0 2 4 6 8 1 3 5 7 9
Then the slices/times ordered by time (as input) are:
slices: 0 2 4 6 8 1 3 5 7 9
times: 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8
And the slices/times ordered instead by slice index are:
slices: 0 1 2 3 4 5 6 7 8 9
times: 0.0 1.0 0.2 1.2 0.4 1.4 0.6 1.6 0.8 1.8
It is this final list of times that is output.
See example 28.
-sort : sort data over time (smallest to largest)
- sorts EVERY vector
- consider the -reverse option
-split_into_pad_runs PREFIX : split input into one padded file per run
e.g. -split_into_pad_runs motion.pad
This option is used for breaking a set of regressors up by run. The
output would be one file per run, where each file is the same as the
input for the run it corresponds to, and is padded with 0 across all
other runs.
Assuming the 300 row input dataset spans 3 100-TR runs, then there
would be 3 output datasets created, each still be 300 rows:
motion.pad.r01.1D : 100 rows as input, 200 rows of 0
motion.pad.r02.1D : 100 rows of 0, 100 rows as input, 100 of 0
motion.pad.r03.1D : 200 rows of 0, 100 rows as input
This option requires either -set_nruns or -set_run_lengths.
See example 14.
-transpose : transpose the input matrix (rows for columns)
-transpose_write : transpose the output matrix before writing
-volreg2allineate : convert 3dvolreg parameters to 3dAllineate
This option should be used when the -infile file is a 6 column file
of motion parameters (roll, pitch, yaw, dS, dL, dP). The output would
be converted to a 12 parameter file, suitable for input to 3dAllineate
via the -1Dparam_apply option.
volreg: roll, pitch, yaw, dS, dL, dP
3dAllinate: -dL, -dP, -dS, roll, pitch, yaw, 0,0,0, 0,0,0
These parameters would be to correct the motion, akin to what 3dvolreg
did (i.e. they are the negative estimates of how the subject moved).
See example 23.
-write FILE : write the current 1D data to FILE
-weight_vec v1 v2 ... : supply weighting vector
e.g. -weight_vec 0.9 0.9 0.9 1 1 1
This vector currently works only with the weighted_enorm method for
the -collapse_cols option. If supplied (as with the example), it will
weight the angles at 0.9 times the weights of the shifts in the motion
parameters output by 3dvolreg.
See also -collapse_cols.
-write_censor FILE : write as boolean censor.1D
e.g. -write_censor subjA_censor.1D
This file can be given to 3dDeconvolve to censor TRs with excessive
motion, applied with the -censor option.
e.g. 3dDeconvolve -censor subjA_censor.1D
This file works well for plotting against the data, where the 0 entries
are removed from the regression of 3dDeconvolve. Alternatively, the
file created with -write_CENSORTR is probably more human readable.
-write_CENSORTR FILE : write censor times as CENSORTR string
e.g. -write_CENSORTR subjA_CENSORTR.txt
This file can be given to 3dDeconvolve to censor TRs with excessive
motion, applied with the -CENSORTR option.
e.g. 3dDeconvolve -CENSORTR `cat subjA_CENSORTR.txt`
Which might expand to:
3dDeconvolve -CENSORTR '1:16..19,44 3:28 4:19,37..39'
Note that the -CENSORTR option requires the text on the command line.
This file is in the easily readable format applied with -CENSORTR.
It has the same effect on 3dDeconvolve as the sister file from
-write_censor, above.
-verb LEVEL : set the verbosity level
R Reynolds March 2009
```