AFNI program: 1d_tool.py
Output of -help
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1d_tool.py - for manipulating and evaluating 1D files
This program is meant to read/manipulate/write/diagnose 1D datasets.
Input can be specified using AFNI sub-brick[]/time{} selectors.
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examples (very basic for now):
1. Select by rows and columns, akin to 1dcat.
1d_tool.py -infile 'data/X.xmat.1D[0..3]{0..5}' -write t1.1D
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
3. Transpose a dataset, akin to 1dtranspose.
1d_tool.py -infile t3.1D -transpose -write ttr.1D
4a. Pad a file of regressors for a single run (#2) with zeros, so
that it becomes run 2 of 7 (runs are 1-based).
1d_tool.py -infile ricor_r02.1D -pad_into_many_runs 2 7 \
-write ricor_r02_all.1D
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
5. Display rows and columns for a 1D dataset.
1d_tool.py -infile ricor_r02.1D -show_rows_cols
6. Show correlation matrix warnings for this matrix.
1d_tool.py -infile X.xmat.1D -show_cormat_warnings
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
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
8. Verify whether labels show slice-major ordering (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
9a. Given motion.1D, 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
9b. Similar to 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
10. Given motion.1D, create censor files to use in 3dDeconvolve, where a
TR is censored if the derivative values have a Euclidean Norm above 1.2.
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 -set_tr 3.0 \
-derivative -collapse_cols euclidean_norm \
-extreme_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', 'extreme_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 -set_tr 3.0 \
-show_censor_count \
-censor_motion 1.2 subjA
c. allow the run lengths to vary
1d_tool.py -infile motion.1D -set_tr 3.0 \
-set_run_lengths 64 61 67 61 67 61 67 61 67 \
-show_censor_count \
-censor_motion 1.2 subjA_rlens
Consider also '-censor_prev_TR'.
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
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
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basic informational options:
-help : show this help
-hist : show the module history
-show_valid_opts : show all valid options
-ver : show the version number
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required input:
-infile DATASET.1D : specify input 1D file
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general options:
-add_cols NEW_DSET.1D : extend dset to include these columns
-collapse_cols METHOD : collapse multiple columns into one, where
METHOD is one of: min, max, minabs, maxabs, euclidean_norm.
-censor_motion LIMIT PREFIX : create censor files
This option implies '-derivative', '-collapse_cols euclidean_norm',
'extreme_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).
The other information necessary besides an input motion file (-infile)
is the number of runs (-set_nruns or -set_run_lengths) and the TR
(-set_tr).
Consider also '-censor_prev_TR'.
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_prev_TR : for each censored TR, also censor previous
-cormat_cutoff CUTOFF : set cutoff for cormat warnings (in [0,1])
-demean : demean each run (new mean of each run = 0.0)
-derivative : take the temporal derivative of each vector
-extreme_mask MIN MAX : mask extreme values
Convert to a 0/1 mask, where 1 means the given value is in [MIN,MAX],
and 0 means otherwise. This is useful for censoring motion outliers.
-overwrite : allow overwriting of any output dataset
-reverse : reverse data over time
-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)}'
-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.
-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 and 10c.
-set_tr TR : set the TR (in seconds) for the data
-show_censor_count : display the total number of censored TRs
-show_cormat_warnings : display correlation matrix warnings
-show_label_ordering : display the labels
-show_labels : display the labels
-show_rows_cols : display the number of rows and columns
-sort : sort data over time (smallest to largest)
- sorts EVERY vector
- consider the -reverse option
-transpose : transpose the matrix (rows for columns)
-write FILE : write the current 1D data to FILE
-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
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R Reynolds March 2009
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This page auto-generated on
Thu Nov 5 23:06:14 EST 2009