2: Simple example
4: Finer Control
5: X-Y Plots
6: Contour Plots
7: Image Plots
9: Gri Commands
12: Emacs Mode
15: Gri Bugs
16: Test Suite
17: Gri in Press
x_new[i] = b * x[i] \ + b * x[i-1] \ + b * x[i-2] \ + ... \ - a * x_new[i-1] \ - a * x_new[i-2] \ - ...
Thus, for example, setting `
a[i]' = 0 results in a simple
backwards-looking moving-average filter applied in two passes. The real
power of this type of filter, however, comes when non-zero `
coefficients are given, thus adding recursion (i.e., `
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
# Filter x column with simple 2-point moving # average. (This slurs into a 3-point 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 rows|columns recursively a a ... b b ...'
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 2-point moving average filter across the columns,
smoothing the grid in the x-direction.
filter image highpass' Remove low-wavenumber components from image (ie, sharpen edges). Do this by subtracting a Laplacian smoothed version of the image.
filter image lowpass' Remove high-wavenumber components from image (ie, smooth shapes). Do this by Laplacian smoothing.
See also see Smooth.