## DESCRIPTION

r.mask - Facilitates creation of a raster "MASK" map to control raster operations.

The MASK is applied when reading an existing GRASS raster map, for example when used as an input map in a module. The MASK will block out certain areas of a raster map from analysis and/or display, by "hiding" them from sight of other GRASS modules. Data falling within the boundaries of the MASK can be modified and operated upon by other GRASS raster modules; data falling outside the MASK is treated as if it were NULL.

By default, r.mask converts any non-NULL value in the input map, including zero, to 1. All these areas will be part of the MASK (see the notes for more details). To only convert specific values (or range of values) to 1 and the rest to NULL, use the maskcats parameter.

Because the MASK created with r.mask is actually only a reclass map named "MASK", it can be copied, renamed, removed, and used in analyses, just like other GRASS raster map layers.

The user should be aware that a MASK remains in place until a user renames it to something other than "MASK", or removes it. To remove a mask and restore raster operations to normal (i.e., all cells of the current region), remove the MASK by setting the -r remove MASK flag (`r.mask -r`). Alternatively, a mask can be removed using g.remove or by renaming it to any other name with g.rename.

## NOTES

The above method for specifying a "mask" may seem counterintuitive. Areas inside the MASK are not hidden; areas outside the MASK will be ignored until the MASK file is removed.

r.mask uses r.reclass to create a reclassification of an existing raster map and name it `MASK`. A reclass map takes up less space, but is affected by any changes to the underlying map from which it was created. The user can select category values from the input raster to use in the MASK with the maskcats parameter; if r.mask is run from the command line, the category values listed in maskcats must be quoted (see example below). Note that the maskcats can only be used if the input map is an integer map.

### Different ways to create a MASK

The r.mask function creates a MASK with values 1 and NULL. But note that a MASK can also be created using other functions that have a raster as output, by naming the output raster 'MASK'. Such layers could have other values than 1 and NULL. The user should therefore be aware that grid cells in the MASK map containing `NULL` or `0` will replace data with NULL, while cells containing other values will allow data to pass through unaltered. This means that:

If a binary map with [0,1] values is used as input in r.mask, all raster cells with 0 and 1 will be part of the MASK. This is because r.mask converts all non-NULL cells to 1.

    r.mapcalc -s "map1 = round(rand(0,1))"
    r.mask raster=map1

On the other hand, if a binary map is used as an input in g.copy to create a MASK, only the raster cells with value 1 will be part of the MASK.

    r.mapcalc -s "map2 = round(rand(0,1))"
    g.copy raster=map2,MASK

### Handling of floating-point maps

r.mask treats floating-point maps the same as integer maps (except that floating maps are not allowed in combination with the maskcats parameter); all non-NULL values of the input raster map are converted to 1 and are thus part of the MASK. In the example below, all raster cells are part of the MASK, i.e., nothing is blocked out from analysis and/or display.

    r.mapcalc -s "map3 = rand(0.0,1.0)"
    r.mask raster=map3

However, when using another method than r.mask to create a mask, the user should be aware that the MASK is read as an integer map. If MASK is a floating-point map, the values will be converted to integers using the map's quantisation rules (this defaults to round-to-nearest, but can be changed with r.quant).

    r.mapcalc -s "map4 = rand(0.0,1.0)"
    g.copy raster=map4,MASK

In the example above, raster cells with a rounded value of 1 are part of the MASK, while raster cells with a rounded value of 0 are converted to NULL and consequently blocked out from analysis and/or display.

## EXAMPLES

The examples are based on the North Carolina sample dataset.

Create a raster mask, for contraining the calculation of univariate statistics of the elevation values for "lakes":

    # set computation region to lakes raster map
    g.region raster=lakes -p
    # use lakes as MASK
    r.mask raster=lakes
    # get statistics for elevation pixels of lakes:
    r.univar elevation

Remove the raster mask ("MASK" map) with the -r flag:

    r.mask -r

Creating a mask from selected categories in the North Carolina 'geology_30m' raster map:

    g.region raster=geology_30m -p
    r.category geology_30m
    d.mon wx0
    d.rast geology_30m
    r.mask raster=geology_30m maskcats="217 thru 720"
    d.mon wx0
    d.rast geology_30m

## SEE ALSO

g.region, r.mapcalc, r.reclass, g.remove, g.rename r.quant

## AUTHOR

Michael Barton, Arizona State University