** 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** superseded by the afni_proc.py script.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.]

 -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 across cluster nodes).
* For some implementation and compilation details, please see
* 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 = Sep  1 2021 {AFNI_21.2.06:linux_ubuntu_16_64}