Provided by: gmt_4.5.11-1build1_amd64

**NAME**

dimfilter - Directional filtering of 2-D gridded files in the space (or time) domain

**SYNOPSIS**

dimfilterinput_file.grd-Ddistance_flag-F<filtertype><width>[mode]-Goutput_file.grd-N<filtertype><n_sectors>-Qcols[-Ixinc[unit][=|+][/yinc[unit][=|+]] ] [-Rwest/east/south/north[r] ] [-T] [-V]

**DESCRIPTION**

dimfilterwill filter a.grdfile in the space (or time) domain by dividing the given filter circle inton_sectors, applying one of the selected primary convolution or non- convolution filters to each sector, and choosing the final outcome according to the selected secondary filter. It computes distances using Cartesian or Spherical geometries. The output.grdfile 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.-Qis for the error analysis mode and only requires the total number of columns in the input file, which contains the filtered depths. Finally, one should know thatdimfilterwill not produce a smooth output as other spatial filters do because it returns a minimum median out ofNmedians ofNsectors. The output can be edgy unless the input data is noise-free. Thus, an additional filtering (e.g., Gaussian) to the DiM-filtered data is generally recommended.input_file.grdThe file of points to be filtered.-DDistanceflagtells how grid (x,y) relates to filterwidthas follows:flag= 0: grid (x,y) same units aswidth, Cartesian distances.flag= 1: grid (x,y) in degrees,widthin kilometers, Cartesian distances.flag= 2: grid (x,y) in degrees,widthin 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 three options are slower because they recompute weights for each latitude.flag= 3: grid (x,y) in degrees,widthin km, dx scaled by cosine(y), Cartesian distance calculation.flag= 4: grid (x,y) in degrees,widthin km, Spherical distance calculation.-FSets the primary filter type. Choose among convolution and non-convolution filters. Append the filter code followed by the full diameterwidth. 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.-NSets the secondary filter type and the number of bow-tie sectors.n_sectorsmust be integer and larger than 0. Whenn_sectorsis 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.-Goutput_file.grdis the output of the filter.

**OPTIONS**

-Ix_inc[and optionallyy_inc] is the output Increment. Appendmto indicate minutes, orcto indicate seconds. If the newx_inc,y_incare NOT integer multiples of the old ones (in the input data), filtering will be considerably slower. [Default: Same as input.]-Rwest,east,south,andnorthdefines the Region of the output points. [Default: Same as input.]-TToggle 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].-Qcolsis 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.-VSelects verbose mode, which will send progress reports to stderr [Default runs "silently"].

**GRID** **FILE** **FORMATS**

By defaultGMTwrites out grid as single precision floats in a COARDS-complaint netCDF file format. However,GMTis 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 4-byte integers. To specify the precision, scale and offset, the user should add the suffix=id[/scale/offset[/nan]], whereidis a two-letter identifier of the grid type and precision, andscaleandoffsetare optional scale factor and offset to be applied to all grid values, andnanis 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. Seegrdreformat(1) and Section 4.17 of the GMT Technical Reference and Cookbook for more information. When reading a netCDF file that contains multiple grids,GMTwill read, by default, the first 2-dimensional grid that can find in that file. To coaxGMTinto reading another multi-dimensional variable in the grid file, append?varnameto the file name, wherevarnameis 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?varnamesuffix can also be used for output grids to specify a variable name different from the default: "z". Seegrdreformat(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 5-dimensional grids.

**GEOGRAPHICAL** **AND** **TIME** **COORDINATES**

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-for-Roptions. For example, both-f0x-f1tand-R90w/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 byTIME_UNITandTIME_EPOCHin the .gmtdefaults file or on the command line. In addition, theunitattribute of the time variable will indicate both this unit and epoch.

**EXAMPLES**

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:dimfilternorth_pacific_dbdb5.grd-Gfiltered_pacific.grd-Fm600-D4-Nl6-R150/250/10/40-I0.5-VSuppose 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 small-length-scale 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:dimfiltercape_verde.grd-Gt.grd-Fm220-Nl8-D2-R-27.5/-20.5/12.5/19.5-I2m-Vgrdfiltert.grd-Gcape_swell.grd-Fg50-D2-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.grdf110.grdf120.grdf130.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:grd2xyzf100.grd-Z> f100.dgrd2xyzf110.grd-Z> f110.dgrd2xyzf120.grd-Z> f120.dgrd2xyzf130.grd-Z> f130.dpastef100.d f110.d f120.d f130.d > depths.ddimfilterdepths.d-Q4 > output.z

**LIMITATIONS**

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 (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 dimfilter on the resulting Cartesian grid.

**SCRIPT** **TEMPLATE**

The dim.template.sh is a skeleton shell script that can be used to set up a complete DiM analysis, including the MAD analysis.

**REFERENCE**

Kim, S.-S., and Wessel, P. (2008), Directional Median Filtering for Regional-Residual Separation of Bathymetry,Geochem.Geophys.Geosyst.,9(Q03005), doi:10.1029/2007GC001850.

**SEE** **ALSO**

GMT(1),grdfilter(1)