- generate generic commands
- todo (maybe): 3dttest, GroupAna
This program is to assist in writing group commands. The hardest part (or most tedious) is generally listing datasets and such, particularly including sub-brick selection, and that is the main benefit of using this program.
If used without sufficient options (which might be typical), the generated commands will not be complete (e.g. they might fail). So either provide
sufficient passed options via -options or plan to edit the resulting script.
If -write_script is not given, the command is written to stdout.
- ** NOTE: this program expects one dataset per subject. Single condition
- volumes are accessed using sub-brick selectors via -subs_betas and possbily -subs_tstats.
This program can parse subject IDs from dataset names when the IDs are the varying part of dataset names (e.g. stats_subj1234+tlrc.HEAD), as in:
gen_group_command.py -command 3dttest++
-dsets stats*+tlrc.HEADor when the subject IDs are the varying part of the directory names (while the actual file names are identical), as in:
- gen_group_command.py -command 3dttest++
- -dsets subject_results//.results/stats+tlrc.HEAD
Generic commands do not need to be part of AFNI. Perhaps one just wants an orderly and indented list of file names to be part of a bigger script. consider:
gen_group_command.py -command ls -dsets group_results/OL*Dor perhaps using 3dTcat to collect a sub-brick from each subject:
- gen_group_command.py -command 3dTcat -subs_betas ‘Arel#0_Coef’
- -dsets group_results/OL*D
3dttest++ (not 3dttest)
- Note: these commands apply to the sample group data under
AFNI_data6/group_results.
- Note: The 3dttest++ program defaults to setA minus setB, which is the
opposite of 3dttest and 3dMEMA (though it might be more natural). The direction of the test can be further specified using either
-AminusB or -BminusA, which is always included in the resulting command if there are 2 sets of data.
This program will always supply one of -AminusB or -BminusA, to be clear. If the user does not provide one, -AminusB will be used.
Note also that 3dttest uses sub-brick labels which should make this clear.
The most simple case, providing just the datasets. The subject IDs will be extracted from the dataset names. Since no sub-bricks are provided, the betas will default to sub-brick 0 and the test will be the mean compared with 0.
- gen_group_command.py -command 3dttest++
-dsets REML*.HEAD
Specify the sub-bricks and set labels to compare Vrel vs. Arel. Write the command to the file cmd.tt++.2.
- gen_group_command.py -command 3dttest++
-write_script cmd.tt++.2
-prefix tt++.2_V-A -dsets REML*.HEAD
-set_labels Vrel Arel -subs_betas ‘Vrel#0_Coef’ ‘Arel#0_Coef’
Request a paired t-test and apply a mask.
- gen_group_command.py -command 3dttest++
-write_script cmd.tt++.3 -prefix tt++.3_V-A_paired -dsets REML*.HEAD -set_labels Vrel Arel -subs_betas ‘Vrel#0_Coef’ ‘Arel#0_Coef’ -options
-paired -mask mask+tlrc
Exclude voxels that are identically zero across more than 20% of the input datasets (presumably masked at the single subject level). Convert output directly to z, since the DOF will vary across space.
- gen_group_command.py -command 3dttest++
-write_script cmd.tt++.4 -prefix tt++.4_V-A_zskip -dsets REML*.HEAD -set_labels Vrel Arel -subs_betas ‘Vrel#0_Coef’ ‘Arel#0_Coef’ -options
-zskip 0.8 -toz
Use covariates to account for a sex difference. We might encode females as 0 and males as 1 to get an intercept (main effect) that applies to females (if we do not do any centering). However, we want a main effect for the average between males and females, and therefore have used -1 for males and +1 for females. Add NONE for centering so that 3dttest++ does not do any.
Females have subject indices: 0, 1, 2, 3 and 5. Males have subject indices: 4 and 6 through 9 (the last).
- gen_group_command.py -command 3dttest++
-write_script cmd.tt++.5 -prefix tt++.5_covary -dsets data/OLSQ*.HEAD -subs_betas ‘Vrel#0_Coef’ -options
-covariates sex_encode.txt -center NONE
Use -dset_index0_list to compare female subjects to males. Both subject types are in the same directory (10 subjects total). So the -dsets options will both specify the same list, which will then be paired down via -dset_index0_list to indicate only females and only males.
Females have subject indices: 0, 1, 2, 3 and 5. Males have subject indices: 4 and 6 through 9 (the last).
- gen_group_command.py -command 3dttest++
-write_script cmd.tt++.6 -prefix tt++.6_F-M -dsets data/OLSQ*.HEAD -dset_index0_list ‘0..3,5’ -dsets data/OLSQ*.HEAD -dset_index0_list ‘4,6..$’ -set_labels female male -subs_betas ‘Vrel#0_Coef’
See “3dttest++ -help” for details on its options.
3dMEMA
- Note: these commands apply to the sample group data under
AFNI_data6/group_results.
- Note: As with 3dttest, group comparisons are done as the second set minus
the first set.
The most simple case, providing just the datasets. The subject IDs will be extracted from the dataset names. Since no sub-bricks are provided, the betas will be 0 and t-stats will be 1.
- gen_group_command.py -command 3dMEMA
-dsets REML*.HEAD
This does not quite apply to AFNI_data6. Assuming there are 2 group directories, write a 2-sample command.
- gen_group_command.py -command 3dMEMA
-write_script cmd.mema.2 -dsets groupA/REML*.HEAD -dsets groupB/REML*.HEAD
Specify the sub-bricks, to compare Vrel vs. Arel.
- gen_group_command.py -command 3dMEMA
-write_script cmd.mema.3 -dsets REML*.HEAD -set_labels Arel Vrel -subs_betas 2 0 -subs_tstats 3 1
Similar to 3, but complete. This basically generates the sample command AFNI_data6/group_results/s4.3dMEMA.V-A.
Specify sub-bricks using the labels, request a paired test, and add some extra 3dMEMA options.
- gen_group_command.py -command 3dMEMA
-write_script cmd.mema.4 -prefix mema_V-A_paired
-type paired -dsets REML*.HEAD -set_labels Arel Vrel -subs_betas ‘Arel#0_Coef’ ‘Vrel#0_Coef’
-subs_tstats ‘Arel#0_Tstat’ ‘Vrel#0_Tstat’ -options
-mask mask+tlrc -max_zeros 0.25 -model_outliers -HKtest -jobs 2
Make a paired test across 2 groups, but restrict the subjects to partial lists of all within a group. This applies -dset_index0_list (or the sister -dset_index1_list).
# assume these 9 subjects represent all under the ‘data’ dir set subjects = ( AA BB CC DD EE FF GG HH II )
- Do a simple test on subjects AA, HH, II and FF. Indices are:
0-based: 0, 7, 8, 5 (AA=0, ..., II=8) 1-based: 1, 8, 9, 6 (AA=1, ..., II=9)
- gen_group_command.py -command 3dMEMA
-write_script cmd.mema.5a -dsets data/REML*.HEAD -dset_index0_list ‘0,7,8,5’
Do a paired test on those same subjects between betas with labels Arel and Vrel (and corresponding t-stats).
- gen_group_command.py -command 3dMEMA
-write_script cmd.mema.5b -dsets data/REML*.HEAD -dset_index0_list ‘0,7,8,5’ -dsets data/REML*.HEAD -dset_index0_list ‘0,7,8,5’ -subs_betas ‘Arel#0_Coef’ ‘Vrel#0_Coef’ -subs_tstats ‘Arel#0_Tstat’ ‘Vrel#0_Tstat’ -type paired
See “3dMEMA -help” for details on the extra options.
3dANOVA2
- Note: these commands apply to the sample group data under
AFNI_data6/group_results.
- Note: it seems better to create the script without any contrasts, and
add them afterwards (so the user can format well). However, if no contrasts are given, the program will add 1 trivial one.
The most simple case, providing just the datasets and a list of sub-bricks.
- gen_group_command.py -command 3dANOVA2
-dsets OLSQ*.HEAD -subs_betas 0 1
- Get more useful:
- apply with a directory
- specify a script name
- specify a dataset prefix for the 3dANOVA2 command
- use labels for sub-brick indices
- specify a simple contrast
- gen_group_command.py -command 3dANOVA2
-write_script cmd.A2.2 -prefix outset.A2.2 -dsets AFNI_data6/group_results/REML*.HEAD -subs_betas ‘Vrel#0_Coef’ ‘Arel#0_Coef’ -options
-adiff 1 2 VvsA
3dANOVA3
- Note: these commands apply to the sample group data under
AFNI_data6/group_results.
- Note: it seems better to create the script without any contrasts, and
add them afterwards (so the user can format well). However, if no contrasts are given, the program will add 2 trivial ones, just for a starting point.
- Note: this applies either -type 4 or -type 5 from 3dANOVA3.
See “3dANOVA3 -help” for details on the types.
The user does not specify type 4 or 5.
type 4: there should be one -dsets option and a -factors option type 5: there should be two -dsets options and no -factor
3dANOVA3 -type 4
This is a simple example of a 2-way factorial ANOVA (color by image type), across many subjects. The colors are pink and blue, while the images are of houses, faces and donuts. So there are 6 stimulus types in this 2 x 3 design:
pink house pink face pink donut blue house blue face blue donut
Since those were the labels given to 3dDeconvolve, the beta weights will have #0_Coef appended, as in pink_house#0_Coef. Note that in a script, the ‘#’ character will need to be quoted.
There is only one set of -dsets given, as there are no groups.
- gen_group_command.py -command 3dANOVA3
-dsets OLSQ*.HEAD -subs_betas
“pink_house#0_Coef” “pink_face#0_Coef” “pink_donut#0_Coef” “blue_house#0_Coef” “blue_face#0_Coef” “blue_donut#0_Coef”
-factors 2 3
3dANOVA3 -type 4
- Get more useful:
- apply with an input data directory
- specify a script name
- specify a dataset prefix for the 3dANOVA3 command
- specify simple contrasts
- gen_group_command.py -command 3dANOVA3
-write_script cmd.A3.2 -prefix outset.A3.2 -dsets AFNI_data6/group_results/OLSQ*.HEAD -subs_betas
“pink_house#0_Coef” “pink_face#0_Coef” “pink_donut#0_Coef” “blue_house#0_Coef” “blue_face#0_Coef” “blue_donut#0_Coef”
-factors 2 3 -options
-adiff 1 2 pink_vs_blue -bcontr -0.5 -0.5 1.0 donut_vs_house_face
3dANOVA3 -type 5
Here is a simple case, providing just 2 groups of datasets and a list of sub-bricks.
- gen_group_command.py -command 3dANOVA3
-dsets OLSQ*.HEAD -dsets REML*.HEAD -subs_betas 0 1
3dANOVA3 -type 5
- Get more useful:
- apply with an input data directory
- specify a script name
- specify a dataset prefix for the 3dANOVA3 command
- use labels for sub-brick indices
- specify simple contrasts
- gen_group_command.py -command 3dANOVA3
-write_script cmd.A3.4 -prefix outset.A3.2 -dsets AFNI_data6/group_results/OLSQ*.HEAD -dsets AFNI_data6/group_results/REML*.HEAD -subs_betas ‘Vrel#0_Coef’ ‘Arel#0_Coef’ -options
-adiff 1 2 OvsR -bdiff 1 2 VvsA
generic/other programs
These commands apply to basically any program, as specified. Options may be provided, along with 1 or 2 sets of data. If provided, the -subs_betas selectors will be applied.
This might be useful for simply making part of a longer script, where the dataset names are explicit.
- perhaps a fairly useless example with ‘ls’, just for demonstration
gen_group_command.py -command ls -dsets group_results/OL*D
- using 3dTcat to collect a sub-brick from each subject
- gen_group_command.py -command 3dTcat -subs_betas ‘Arel#0_Coef’
-dsets group_results/OL*D
- including 2 sets of subjects, with a different sub-brick per set
- gen_group_command.py -command 3dTcat -subs_betas 0 1
-dsets group_results/OLSQ*D -dsets group_results/REML*D
2 sets of subjects (in different directories, and with different sub-brick selectors), along with:
- a script name (to write the script to a text file)
- a -prefix
- options for the command (just 1 in this case)
- common sub-brick selectors for dataset lists
- gen_group_command.py -command 3dMean
-write_script cmd.3dmean.txt -prefix aud_vid_stdev -options -stdev -subs_betas ‘Arel#0_Coef’ -dsets group_results/OLSQ*D -dsets group_results/REML*D
-help : show this help
-hist : show module history
-show_valid_opts : list valid options
-ver : show current version
required parameters:
-command COMMAND_NAME : resulting command, such as 3dttest++
The current list of group commands is: 3dttest++, 3dMEMA, 3dANOVA2, 3dANOVA3.
3dANOVA2: applied as -type 3 only (factor x subjects) 3dANOVA3: -type 4: condition x condition x subject
(see -factors option)-type 5: group x condition x subject
-dsets datasets ... : list of datasets
Each use of this option essentially describes one group of subjects. All volumes for a given subject should be in a single dataset.
This option can be used multiple times, once per group.
other options:
-dset_index0_list values... : restrict -dsets datasets to this 0-based list
-dset_index1_list values... : restrict -dsets datasets to this 1-based list
In some cases it is easy to use a wildcard to specify datasets via -dsets, but there may be a grouping of subjects within that list. For example, if both males and females are in the list of datasets provided by -dsets, and if one wants a comparison between those 2 groups, then a pair of -dset_index0_list could be specified (1 for each -dset) option to list which are the females and males.
Consider this example:
-dsets all/stats.*.HEAD -dset_index0_list ‘0..5,10..15’ -dsets all/stats.*.HEAD -dset_index0_list ‘6..9,16..$’Note that -dsets is used twice, with IDENTICAL lists of datasets. The respective -dset_index0_list options then restrict those lists to 0-based index lists, one for females, the other for males.
One must be careful to get the indices correct, so check the output command script to be sure the correct subjects are in each group.
The difference between -dset_index0_list and -dset_index1_list is just that the former is a 0-based list (such as is used by AFNI programs), while the latter is 1-based (such as is used by tcsh). A 0-based list begins counting at 0 (as in offsets), while a list 1-based starts at 1. Since use of either makes sense, both are provided.
For example, these options are equivalent:
-dset_index0_list 0,5..8 -dset_index1_list 1,6..9
The format for these index lists is the same as for AFNI sub-brick selection.
-factors NF1 NF2 ... : list of factor levels, per condition
example: -factors 2 3This option is currently only for ‘3dANOVA3 -type 4’, which is a condition x condition x subject test. It is meant to parse the -subs_betas option, which lists all sub-bricks input to the ANOVA.
Assuming condition A has nA levels, and B has nB (2 and 3 in the above example), then this option (applied ‘-factors nA nB’, and -subs_betas) would take nA * nB parameters (for the cross product of factor A and factor B levels). The betas should be specified in A major order, as in:
-subs_betas A1B1_name A1B2_name ... A1BnB A2B1 A2B2 ... AnABnB_nameor as in the 2 x 3 case:
-subs_betas A1B1 A1B2 A1B3 A2B1 A2B2 A2B3 -factors 2 3
- e.g. for pink/blue x house/face/donut, output be 3dDeconvolve
(i.e. each betas probably has #0_Coef attached)
-subs_betas “pink_house#0_Coef” “pink_face#0_Coef” “pink_donut#0_Coef” “blue_house#0_Coef” “blue_face#0_Coef” “blue_donut#0_Coef” -factors 2 3
Again, these factor combination names should be either sub-brick labels or indices (labels are suggested, to avoid confusion).
See the example with ‘3dANOVA3 -type 4’ as part of example D, above. See also -subs_betas.
-keep_dirent_pre : keep directory entry prefix
Akin to -subj_prefix, this flag expands the subject prefix list to include everything up to the beginning of the directory names (at the level that varies across input datasets).
- Example 1:
- datasets:
- subj.FP/betas+tlrc subj.FR/betas+tlrc subj.FT/betas+tlrc subj.FV/betas+tlrc subj.FW/betas+tlrc subj.FX/betas+tlrc subj.FY/betas+tlrc subj.FZ/betas+tlrc
- The default subject IDs would be:
- P R T V W X Y Z
- When using -keep_dirent_pre, subject IDs would be:
- subj.FP subj.FR subj.FT subj.FV subj.FW subj.FX subj.FY subj.FZ
Note that these IDs come at the directory level, since the dataset names do not vary.
- Example 2:
- datasets:
- subj.FP/OLSQ.FP.betas+tlrc subj.FR/OLSQ.FR.betas+tlrc subj.FT/OLSQ.FT.betas+tlrc subj.FV/OLSQ.FV.betas+tlrc subj.FW/OLSQ.FW.betas+tlrc subj.FX/OLSQ.FX.betas+tlrc subj.FY/OLSQ.FY.betas+tlrc subj.FZ/OLSQ.FZ.betas+tlrc
- The default subject IDs would be:
- P R T V W X Y Z
- When using -keep_dirent_pre, subject IDs would be:
- OLSQ.FP OLSQ.FR OLSQ.FT OLSQ.FV OLSQ.FW OLSQ.FX OLSQ.FY OLSQ.FZ
Note that these IDs come at the dataset level, since the dataset names vary.
-options OPT1 OPT2 ... : list of options to pass along to result
The given options will be passed directly to the resulting command. If the -command is 3dMEMA, say, these should be 3dMEMA options. This program will not evaluate or inspect the options, but will put them at the end of the command.-prefix PREFIX : apply as COMMAND -prefix -set_labels LAB1 LAB2 ... : labels corresponding to -dsets entries
-subj_prefix PREFIX : prefix for subject names (3dMEMA)
-subj_suffix SUFFIX : suffix for subject names (3dMEMA) -subs_betas B0 B1 : sub-bricks for beta weights (or similar)
If this option is not given, sub-brick 0 will be used. The entries can be either numbers or labels (which should match what is seen in the afni GUI, for example).
If there are 2 -set_labels, there should be 2 betas (or no option).
-subs_tstats T0 T1 : sub-bricks for t-stats (3dMEMA)
If this option is not given, sub-brick 1 will be used. The entries can be either numbers or labels (which should match what is seen in the afni GUI, for example).
This option applies only to 3dMEMA currently, and in that case, its use should match that of -subs_betas.
See also -subs_betas.
-type TEST_TYPE : specify the type of test to perform
The test type may depend on the given command, but generally implies there are multiple sets of values to compare. Currently valid tests are (for the given program):
3dMEMA: paired, unpairedIf this option is not applied, a useful default will be chosen.
-verb LEVEL : set the verbosity level
-write_script FILE_NAME : write command script to FILE_NAME
If this option is given, the command will be written to the specified file name. Otherwise, it will be written to the terminal window.