# man grdhisteq () - Histogram equalization for grd files

## NAME

grdhisteq - Histogram equalization for grd files

## SYNOPSIS

**grdhisteq** *in_grdfile* [ **-G***out_grdfile* ]
[ **-C***n_cells* ] [ **-D** ] [ **-N**[*norm*] ] [ **-Q** ] [ **-V** ]

## DESCRIPTION

**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* segments, 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 cptfile 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 **grdhisteq** on output from grdgradient
and make a grdfile output with the Gaussian 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.

*in_grdfile*- 2-D binary grd file to be equalized.

## OPTIONS

**-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"].

## EXAMPLES

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

grdhisteq heights.grd **-C**16 **-D** > levels.d

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

grdhisteq raw_intens.grd **-G**smooth_intens.grd **-N -V**

## RESTRICTIONS

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 standard 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 multiplying 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]. Alternatively, supply a normalization factor with **-N**.