

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.
The above options are fastest because they allow weight matrix to be computed only once. The next three 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.

F 
Sets the primary filter type. Choose among convolution and nonconvolution 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. Nonconvolution 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. 
N 
Sets the secondary filter type and the number of bowtie sectors. n_sectors must be integer and larger than 0.
When n_sectors is set to 1, the secondary filter is not effective. Available secondary filters are:
(l) Lower: Return the minimum of all filtered values. (u) Upper: Return the maximum of all filtered values. (a) Average: Return the mean of all filtered values. (m) Median: Return the median of all filtered values. (p) Mode: Return the mode of all filtered values. 
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]. Q cols is the total number of columns in the input file. For this mode, it expects to read depths consisted of several columns. Each column represents a filtered grid with a filter width, which can be obtained by ’grd2xyz Z’. The outcome will be median, MAD, and mean. So, the column with the medians is used to generate the regional component and the column with the MADs to conduct the error analysis. V Selects verbose mode, which will send progress reports to stderr [Default runs "silently"].
By default GMT writes out grid as single precision floats in a COARDScomplaint netCDF file format. However, GMT is able to produce grid files in many other commonly used grid file formats and also facilitates so called "packing" of grids, writing out floating point data as 2 or 4byte integers. To specify the precision, scale and offset, the user should add the suffix =id[/scale/offset[/nan]], where id is a twoletter identifier of the grid type and precision, and scale and offset are optional scale factor and offset to be applied to all grid values, and nan is the value used to indicate missing data. When reading grids, the format is generally automatically recognized. If not, the same suffix can be added to input grid file names. See grdreformat(1) and Section 4.17 of the GMT Technical Reference and Cookbook for more information.When reading a netCDF file that contains multiple grids, GMT will read, by default, the first 2dimensional grid that can find in that file. To coax GMT into reading another multidimensional variable in the grid file, append ?varname to the file name, where varname is the name of the variable. Note that you may need to escape the special meaning of ? in your shell program by putting a backslash in front of it, or by placing the filename and suffix between quotes or double quotes. The ?varname suffix can also be used for output grids to specify a variable name different from the default: "z". See grdreformat(1) and Section 4.18 of the GMT Technical Reference and Cookbook for more information, particularly on how to read splices of 3, 4, or 5dimensional grids.
When the output grid type is netCDF, the coordinates will be labeled "longitude", "latitude", or "time" based on the attributes of the input data or grid (if any) or on the f or R options. For example, both f0x f1t and R 90w/90e/0t/3t will result in a longitude/time grid. When the x, y, or z coordinate is time, it will be stored in the grid as relative time since epoch as specified by TIME_UNIT and TIME_EPOCH in the .gmtdefaults file or on the command line. In addition, the unit attribute of the time variable will indicate both this unit and epoch.
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. To prevent the medians from being biased by the sloping plane, you want to divide the filter circle into 6 sectors and to choose the lowest value among 6 medians. Using spherical distance calculations, you need:dimfilter north_pacific_dbdb5.grd G filtered_pacific.grd Fm600 D 4 N l6 R150/250/10/40 I 0.5 V
Suppose that cape_verde.grd is a file of 0.5 minute bathymetry from 32W to 15W and 8N to 25N, and you want to remove smalllengthscale features in order to define a swell in an area extending from 27.5W to 20.5W and 12.5N to 19.5N, and you want the output value every 2 minute. Using cartesian distance calculations, you need:
dimfilter cape_verde.grd G t.grd Fm220 Nl8 D 2 R27.5/20.5/12.5/19.5 I 2m V
grdfilter t.grd G cape_swell.grd Fg50 D 2 VSuppose that you found a range of filter widths for a given area, and you filtered the given bathymetric data using the range of filter widths (e.g., f100.grd f110.grd f120.grd f130.grd), and you want to define a regional trend using the range of filter widths, and you want to obtain median absolute deviation (MAD) estimates at each data point, you need:
grd2xyz f100.grd Z > f100.d
grd2xyz f110.grd Z > f110.d
grd2xyz f120.grd Z > f120.d
grd2xyz f130.grd Z > f130.d
paste f100.d f110.d f120.d f130.d > depths.d
dimfilter depths.d Q4 > output.z
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 filter "circle" crosses a periodic (0360) 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 equalarea projection and run dimfilter on the resulting Cartesian grid.
The dim.template.sh is a skeleton shell script that can be used to set up a complete DiM analysis, including the MAD analysis.
Kim, S.S., and Wessel, P. (2008), Directional Median Filtering for RegionalResidual Separation of Bathymetry, Geochem. Geophys. Geosyst., 9(Q03005), doi:10.1029/2007GC001850.
GMT(1), grdfilter(1)
GMT 4.5.14  DIMFILTER (1)  1 Nov 2015 
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