sfpatch breaks the input data into local windows or “patches”, possibly with overlap.

The patching technique is explained by Jon Claerbout in Nonstationarity: patching chapter from Image Estimation by Example.

Suppose you have a 1-D signal with 10 samples:

bash$ sfmath n1=10 output=x1 > data.rsf

You can divide it, for example, into two patches with 5 samples each:

bash$ < data.rsf sfpatch p=2 w=5 > patch.rsf
bash$ sfget n1 n2 < patch.rsf 
n1=5
n2=2

or into 5 overlapping patches with 3 samples each:

bash$ < data.rsf sfpatch p=5 w=3 > patch.rsf
bash$ sfget n1 n2 < patch.rsf 
n1=3
n2=5

If you specify only the patch size (w= parameter), sfpatch tries to select the number of patches to achieve a good overlap:

bash$ < data.rsf sfpatch w=3 > patch.rsf
bash$ sfget n1 n2 < patch.rsf 
n1=3
n2=6

If you put overlapped patches back together, their amplitudes add in the overlapped regions:

bash$ < data.rsf sfdd type=int | sfdisfil 
   0:    0    1    2    3    4    5    6    7    8    9
bash$< patch.rsf sfpatch inv=y | sfdd type=int | sfdisfil
   0:    0    2    6    6    8   10   12   14    8    9

unless you use the weight option:

bash$ < patch.rsf sfpatch inv=y weight=y | sfdd type=int | sfdisfil
   0:    0    1    2    3    4    5    6    7    8    9

bash$ sfpatch easily handles multidimensional data: w= and p= become lists of dimensions, and the number of dimensions in the output effectively doubles. sfpatch is useful in two applications:

1. crude handling of non-stationarity when processing non-stationary signals with locally stationary filters,
2. making non-parallel tasks data-parallel.

A simple example from rsf/rsf/mona illustrates the second use.

Anisotropic diffusion, implemented by sfimpl2, is an effective but relatively slow process. By breaking the input 512×512 image into nine 200×200 overlapping patches and by processing them in parallel on a multi-core computer, we can achieve a significant speed-up without rewriting the original program.

10 previous programs of the month

• sfai2refl
• sftime2depth
• sfwiggle
• sfvscan
• sfnmo
• sfpow
• sfpwd
• sfricker1
• sfhalfint
• sfbandpass

Sometimes it is convenient to have a quick access to all results in your experiment. sfresults, a simple Tkinter script, provides that functionality. Run this script in your project directory. Click on “Cycle” button to run scons view, click on an individual result button to run scons result.view, or select several results and click on “Flip” to flip between the figures on the screen.