# 3dBandpass¶

```
--------------------------------------------------------------------------
** NOTA BENE: For the purpose of preparing resting-state FMRI datasets **
** for analysis (e.g., with 3dGroupInCorr), this program is now mostly **
** superseded by the afni_proc.py script. See the 'afni_proc.py -help' **
** section 'Resting state analysis (modern)' to get our current rs-FMRI **
** pre-processing recommended sequence of steps. -- RW Cox, et alii. **
--------------------------------------------------------------------------
** If you insist on doing your own bandpassing, I now recommend using **
** program 3dTproject instead of this program. 3dTproject also can do **
** censoring and other nuisance regression at the same time -- RW Cox. **
--------------------------------------------------------------------------
Usage: 3dBandpass [options] fbot ftop dataset
* One function of this program is to prepare datasets for input
to 3dSetupGroupInCorr. Other uses are left to your imagination.
* 'dataset' is a 3D+time sequence of volumes
++ This must be a single imaging run -- that is, no discontinuities
in time from 3dTcat-ing multiple datasets together.
* fbot = lowest frequency in the passband, in Hz
++ fbot can be 0 if you want to do a lowpass filter only;
HOWEVER, the mean and Nyquist freq are always removed.
* ftop = highest frequency in the passband (must be > fbot)
++ if ftop > Nyquist freq, then it's a highpass filter only.
* Set fbot=0 and ftop=99999 to do an 'allpass' filter.
++ Except for removal of the 0 and Nyquist frequencies, that is.
* You cannot construct a 'notch' filter with this program!
++ You could use 3dBandpass followed by 3dcalc to get the same effect.
++ If you are understand what you are doing, that is.
++ Of course, that is the AFNI way -- if you don't want to
understand what you are doing, use Some other PrograM, and
you can still get Fine StatisticaL maps.
* 3dBandpass will fail if fbot and ftop are too close for comfort.
++ Which means closer than one frequency grid step df,
where df = 1 / (nfft * dt) [of course]
* The actual FFT length used will be printed, and may be larger
than the input time series length for the sake of efficiency.
++ The program will use a power-of-2, possibly multiplied by
a power of 3 and/or 5 (up to and including the 3rd power of
each of these: 3, 9, 27, and 5, 25, 125).
* Note that the results of combining 3dDetrend and 3dBandpass will
depend on the order in which you run these programs. That's why
3dBandpass has the '-ort' and '-dsort' options, so that the
time series filtering can be done properly, in one place.
* The output dataset is stored in float format.
* The order of processing steps is the following (most are optional):
(0) Check time series for initial transients [does not alter data]
(1) Despiking of each time series
(2) Removal of a constant+linear+quadratic trend in each time series
(3) Bandpass of data time series
(4) Bandpass of -ort time series, then detrending of data
with respect to the -ort time series
(5) Bandpass and de-orting of the -dsort dataset,
then detrending of the data with respect to -dsort
(6) Blurring inside the mask [might be slow]
(7) Local PV calculation [WILL be slow!]
(8) L2 normalization [will be fast.]
--------
OPTIONS:
--------
-despike = Despike each time series before other processing.
++ Hopefully, you don't actually need to do this,
which is why it is optional.
-ort f.1D = Also orthogonalize input to columns in f.1D
++ Multiple '-ort' options are allowed.
-dsort fset = Orthogonalize each voxel to the corresponding
voxel time series in dataset 'fset', which must
have the same spatial and temporal grid structure
as the main input dataset.
++ At present, only one '-dsort' option is allowed.
-nodetrend = Skip the quadratic detrending of the input that
occurs before the FFT-based bandpassing.
++ You would only want to do this if the dataset
had been detrended already in some other program.
-dt dd = set time step to 'dd' sec [default=from dataset header]
-nfft N = set the FFT length to 'N' [must be a legal value]
-norm = Make all output time series have L2 norm = 1
++ i.e., sum of squares = 1
-mask mset = Mask dataset
-automask = Create a mask from the input dataset
-blur fff = Blur (inside the mask only) with a filter
width (FWHM) of 'fff' millimeters.
-localPV rrr = Replace each vector by the local Principal Vector
(AKA first singular vector) from a neighborhood
of radius 'rrr' millimiters.
++ Note that the PV time series is L2 normalized.
++ This option is mostly for Bob Cox to have fun with.
-input dataset = Alternative way to specify input dataset.
-band fbot ftop = Alternative way to specify passband frequencies.
-prefix ppp = Set prefix name of output dataset.
-quiet = Turn off the fun and informative messages. (Why?)
-notrans = Don't check for initial positive transients in the data:
*OR* ++ The test is a little slow, so skipping it is OK,
-nosat if you KNOW the data time series are transient-free.
++ Or set AFNI_SKIP_SATCHECK to YES.
++ Initial transients won't be handled well by the
bandpassing algorithm, and in addition may seriously
contaminate any further processing, such as inter-voxel
correlations via InstaCorr.
++ No other tests are made [yet] for non-stationary behavior
in the time series data.
=========================================================================
* This binary version of 3dBandpass is compiled using OpenMP, a semi-
automatic parallelizer software toolkit, which splits the work across
multiple CPUs/cores on the same shared memory computer.
* OpenMP is NOT like MPI -- it does not work with CPUs connected only
by a network (e.g., OpenMP doesn't work with 'cluster' setups).
* For implementation and compilation details, please see
https://afni.nimh.nih.gov/pub/dist/doc/misc/OpenMP.html
* The number of CPU threads used will default to the maximum number on
your system. You can control this value by setting environment variable
OMP_NUM_THREADS to some smaller value (including 1).
* Un-setting OMP_NUM_THREADS resets OpenMP back to its default state of
using all CPUs available.
++ However, on some systems, it seems to be necessary to set variable
OMP_NUM_THREADS explicitly, or you only get one CPU.
++ On other systems with many CPUS, you probably want to limit the CPU
count, since using more than (say) 16 threads is probably useless.
* You must set OMP_NUM_THREADS in the shell BEFORE running the program,
since OpenMP queries this variable BEFORE the program actually starts.
++ You can't usefully set this variable in your ~/.afnirc file or on the
command line with the '-D' option.
* How many threads are useful? That varies with the program, and how well
it was coded. You'll have to experiment on your own systems!
* The number of CPUs on this particular computer system is ...... 2.
* The maximum number of CPUs that will be used is now set to .... 2.
* At present, the only part of 3dBandpass that is parallelized is the
'-blur' option, which processes each sub-brick independently.
=========================================================================
++ Compile date = Nov 12 2020 {AFNI_20.3.02:linux_ubuntu_16_64}
```