[GRASS-dev] adding lib_arraystats

[Glynn Clements]

Roger Bivand wrote:

> > > What would be particularly useful is if it's possible for GRASS to use
> > > R functions on small amounts of data. E.g. r.series and r.resamp.stats
> > > both compute aggregates over relatively small amounts of data.
> > 
> > This is essentially what I had in mind when I first posted the suggestion-- 
> > using R code on simple arrays of data.
> 
> This is essentially what classInterval() needs:
> 
> initiate the R backend and workspace;
> put the vector of doubles into the workspace;
> load the classInt package into the workspace;
> run the classInterval function with the argument values;
> collect the break values vector from the output object;
> optionally continue to collect a vector of strings giving the RGB values;
> optionally generate an empirical cumulative distribution function plot
>   of the data showing the class intervals and chosen colours as PNG;
> terminate the R backend;

What r.series and r.resamp.stats need is:

	Initialise R
	For each cell:
		push a vector of doubles into R
		call the R function on the vector
		pull the result (a single double) from R
	Shut down R

> > > If it was practical to have e.g. "r.series method=R expression=...",
> > > that would be much more useful than having to start R and load
> > > potentially hundreds of rasters into memory.
> > 
> > Right-- the "not-in-memory" features of GRASS are extremely valuble.
> 
> Since R operations are vectorised, it might be possible to pass a block of
> values (say k rows, p columns, where p is the number of input rasters) and do
> apply() on it to get k rows back, but the blocking would be on the GRASS side.
> But this would be for specialist things, I expect.

This how any GRASS modules would want to use R. If you're transferring
entire maps, you would be better off just coding the whole thing in R.

GRASS modules inherently operate on chunks of data, either rows, or a
sliding window of several rows, or a region of cells, etc. 

Processes which need to operate upon an entire map don't really need
to "integrate" with R; they can just run an R program as a
self-contained operation.

-- 
Glynn Clements <glynn at gclements.plus.com>