grdhisteq - Histogram equalization for grd files


       grdhisteq  in_grdfile [ -Gout_grdfile ] [ -Cn_cells ] [ -D ] [ -N[norm]
       ] [ -Q ] [ -V ]


       grdhisteq allows the user to find the data values which divide a  given
       grdfile into patches of equal area. One common use of grdhisteq is in a
       kind of histogram equalization of an image. In  this  application,  the
       user  might  have  a  grdfile of flat topography with a mountain in the
       middle.  Ordinary gray shading of this  file  (using  grdimage/grdview)
       with  a  linear mapping from topography to graytone will result in most
       of the image being very dark  gray,  with  the  mountain  being  almost
       white.  One  could  use  grdhisteq  to write to stdout an ASCII list of
       those data values which divide the range of the data into n_cells  seg-
       ments,  each  of  which  has  an  equal area in the image. Using awk or
       makecpt one can take this output and build a cpt file; using  the  cpt-
       file  with  grdimage  will  result  in an image with all levels of gray
       occurring equally. Alternatively, see grd2cpt.
               The second common use of grdhisteq is in writing a grdfile with
       statistics  based  on some kind of cumulative distribution function. In
       this application, the output has relative highs and lows  in  the  same
       (x,y)  locations  as  the  input  file,  but  the values are changed to
       reflect their place in some cumulative distribution. One example  would
       be  to  find  the lowest 10% of the data: Take a grdfile, run grdhisteq
       and make a grdfile using n_cells = 10, and then contour the  result  to
       trace  the  1  contour.   This will enclose the lowest 10% of the data,
       regardless of their original values. Another example is  in  equalizing
       the output of grdgradient.  For shading purposes it is desired that the
       data have a smooth distribution, such as a gaussian. If you run grdhis-
       teq on output from grdgradient and make a grdfile output with the Gaus-
       sian option, you will have  a  grdfile  whose  values  are  distributed
       according  to a gaussian distribution with zero mean and unit variance.
       The locations of these values will correspond to the locations  of  the
       input;  that  is,  the  most negative output value will be in the (x,y)
       location of the most negative input value, and so on.
               No space between the option flag and the associated  arguments.
       Use upper case for the option flags and lower case for modifiers.

              2-D binary grd file to be equalized.


       -C     Sets how many cells (or divisions) of data range to make.

       -D     Dump level information to standard output.

       -G     Name of output 2-D grd file. Used with -N only.

       -N     Gaussian  output.  Use  with  -G  to make an output grdfile with
              standard normal scores.  Append norm to force the scores to fall
              in the <-1,+1> range [Default is standard normal scores].

       -Q     Use quadratic intensity scaling. [Default is linear].

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


       To find the height intervals that divide the file heights.grd  into  16
       divisions of equal area:

       grdhisteq heights.grd -C16 -D > levels.d

       To  make  the poorly distributed intensities in the file raw_intens.grd
       suitable for use with grdimage or grdview, run

       grdhisteq raw_intens.grd -Gsmooth_intens.grd -N -V


       If you use grdhisteq to make a gaussian output for gradient shading  in
       grdimage  or  grdview, you should be aware of the following: the output
       will be in the range [-x, x], where x is based on the number of data in
       the  input  grdfile  (nx * ny) and the cumulative gaussian distribution
       function F(x).  That is, let N = nx * ny. Then x will  be  adjusted  so
       that F(x) = (N - 1 + 0.5)/N. Since about 68% of the values from a stan-
       dard normal distribution fall within +/- 1, this will be  true  of  the
       output  grdfile.   But  if  N is very large, it is possible for x to be
       greater than 4. Therefore, with the grdimage program clipping gradients
       to the range [-1, 1], you will get correct shading of 68% of your data,
       while 16% of them will be clipped to -1 and 16% of them clipped to  +1.
       If  this  makes too much of the image too light or too dark, you should
       take the output of grdhisteq and rescale it using grdmath and multiply-
       ing by something less than 1.0, to shrink the range of the values, thus
       bringing more than 68% of the image into the range  [-1,  1].  Alterna-
       tively, supply a normalization factor with -N.


       gmtdefaults(l),  gmt(l),  grd2cpt(l), grdgradient(l), grdimage(l), grd-
       math(l), grdview(l), makecpt(l)

GMT4.0                            1 Oct 2004                      GRDHISTEQ(l)

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