Chapters: 1: Introduction 2: Simple example 3: Invocation 4: Finer Control 5: XY Plots 6: Contour Plots 7: Image Plots 8: Examples 9: Gri Commands 10: Programming 11: Environment 12: Emacs Mode 13: History 14: Installation 15: Gri Bugs 16: Test Suite 17: Gri in Press 18: Acknowledgments 19: License Indices: Concepts Commands Variables 
9.3.11: `

x_new[i] = b[0] * x[i] \ + b[1] * x[i1] \ + b[2] * x[i2] \ + ... \  a[1] * x_new[i1] \  a[2] * x_new[i2] \  ... 
Thus, for example, setting `a[i]
' = 0 results in a simple
backwardslooking movingaverage filter applied in two passes. The real
power of this type of filter, however, comes when nonzero `a[i]
'
coefficients are given, thus adding recursion (i.e., `x_new[i]
'
depends on `x_new[i...]
'). See any standard reference on digital
filters for an explanation. You might find that the Matlab command
`butter
' an easy way to design filter coefficients. Here are some
examples:
# Filter x column with simple 2point moving # average. (This slurs into a 3point moving # average, in effect, since the filter is run # forwards and then backwards.) filter column x recursively 0 0 0.5 0.5 
filter grid rowscolumns recursively a[0] a[1] ... b[0] b[1] ...
'
Apply recursive filter (see `filter column ... recursively
' for
meaning of this filter operation) to the individual rows or columns of
the grid data. For example, the command
`filter grid columns recursively 0 0 0.5 0.5
'
applies a 2point moving average filter across the columns,
smoothing the grid in the xdirection.
filter image highpass
'
Remove lowwavenumber components from image (ie, sharpen edges). Do
this by subtracting a Laplacian smoothed version of the image.
filter image lowpass
'
Remove highwavenumber components from image (ie, smooth shapes). Do
this by Laplacian smoothing.
See also see Smooth.