GRDFILTER

grdfilter − Filter a .grd file in the Time domain

grdfilter input_file.grd −Dflag −F<type><width>[mode] −Goutput_file.grd [ −Ix_inc[m|c][/y_inc[m|c]] ] [ −Rwest/east/south/north[r] ] [ −T ] [ −V ]

grdfilter will filter a .grd file in the time domain using one of the selected convolution or non-convolution filters and compute distances using Cartesian or Spherical geometries. The output .grd file can optionally be generated as a sub−Region of the input and/or with a new −Increment. In this way, one may have "extra space" in the input data so that the edges will not be used and the output can be within one-half- width of the input edges. If the filter is low-pass, then the output may be less frequently sampled than the input.

input_file.grd

The file of points to be filtered.

−D

Distance flag tells how grid (x,y) relates to filter width as follows:

flag = 0: grid (x,y) same units as width, Cartesian distances.

flag = 1: grid (x,y) in degrees, width in kilometers, Cartesian distances.
flag = 2: grid (x,y) in degrees, width in km, dx scaled by cos(middle y), Cartesian distances.

The above options are fastest because they allow weight matrix to be computed only once. The next two options are slower because they recompute weights for each latitude.

flag = 3: grid (x,y) in degrees, width in km, dx scaled by cosine(y), Cartesian distance calculation.
flag = 4: grid (x,y) in degrees, width in km, Spherical distance calculation.

−F

Sets the filter type. Choose among convolution and non-convolution filters. Append the filter code followed by the full diameter width. Available convolution filters are:

(b) Boxcar: All weights are equal.
(c) Cosine Arch: Weights follow a cosine arch curve.
(g) Gaussian: Weights are given by the Gaussian function.
Non-convolution filters are:
(m) Median: Returns median value.
(p) Maximum likelihood probability (a mode estimator): Return modal value. If more than one mode is found we return their average value. Append - or + to the filter width if you rather want to return the smallest or largest of the modal values.
(l) Lower: Return the minimum of all values.
(L) Lower: Return minimum of all positive values only.
(u) Upper: Return maximum of all values.
(U) Upper: Return maximum or all negative values only.
In the case of L|U it is possible that no data passes the initial sign test; in that case the filter will return 0.0.

−G

output_file.grd is the output of the filter.

−I

x_inc [and optionally y_inc] is the output Increment. Append m to indicate minutes, or c to indicate seconds. If the new x_inc, y_inc are NOT integer multiples of the old ones (in the input data), filtering will be considerably slower. [Default: Same as input.]

−R

west, east, south, and north defines the Region of the output points. [Default: Same as input.]

−T

Toggle the node registration for the output grid so as to become the opposite of the input grid [Default gives the same registration as the input grid].

−V

Selects verbose mode, which will send progress reports to stderr [Default runs "silently"].

Suppose that north_pacific_dbdb5.grd is a file of 5 minute bathymetry from 140E to 260E and 0N to 50N, and you want to find the medians of values within a 300km radius (600km full width) of the output points, which you choose to be from 150E to 250E and 10N to 40N, and you want the output values every 0.5 degree. Using spherical distance calculations, you need:

grdfilter north_pacific_dbdb5.grd −Gfiltered_pacific.grd −Fm600 −D4 −R150/250/10/40 −I0.5 −V

When working with geographic (lat, lon) grids, all three convolution filters (boxcar, cosine arch, and gaussian) will properly normalize the filter weights for the variation in gridbox size with latitude, and correctly determine which nodes are needed for the convolution when the fiter "circle" crosses a periodic (0-360) boundary or contains a geographic pole. However, the spatial filters, such as median and mode filters, do not use weights and thus should only be used on Cartesian grids (or at very low latitudes) only. If you want to apply such spatial filters you should project your data to an equal-area projection and run grdfilter on the resulting Cartesian grid.

GMT(l), grdfft(l)