Without *-n* flag, the complete list of inputs for each cell (including
NULLs) is passed to the aggregate function. Individual aggregates can
handle data as they choose. Mostly, they just compute the aggregate
over the non-NULL values, producing a NULL result only if all inputs
are NULL.

The *min_raster* and *max_raster* methods generate a map with the
number of the raster map that holds the minimum/maximum value of the
time-series. The numbering starts at *0* up to *n* for the
first and the last raster listed in *input=*, respectively.

If the *range=* option is given, any values which fall outside
that range will be treated as if they were NULL.
The *range* parameter can be set to *low,high* thresholds:
values outside of this range are treated as NULL (i.e., they will be
ignored by most aggregates, or will cause the result to be NULL if -n is given).
The *low,high* thresholds are floating point, so use *-inf* or
*inf* for a single threshold (e.g., *range=0,inf* to ignore
negative values, or *range=-inf,-200.4* to ignore values above -200.4).

Linear regression (slope, offset, coefficient of determination) assumes equal time intervals. If the data have irregular time intervals, NULL raster maps can be inserted into time series to make time intervals equal (see example).

Number of raster maps to be processed is given by the limit of the
operating system. For example, both the hard and soft limits are
typically 1024. The soft limit can be changed with e.g. `ulimit -n
1500` (UNIX-based operating systems) but not higher than the hard
limit. If it is too low, you can as superuser add an entry in

/etc/security/limits.conf # <domain> <type> <item> <value> your_username hard nofile 1500

r.series input="`g.mlist pattern='insitu_data.*' sep=,`" \ output=insitu_data.stddev method=stddev

Note the *g.mlist* script also supports regular expressions for
selecting map names.

Using *r.series* with NULL raster maps:

r.mapcalc "dummy = null()" r.series in=map2001,map2002,dummy,dummy,map2005,map2006,dummy,map2008 \ out=res_slope,res_offset,res_coeff meth=slope,offset,detcoeff

Example for multiple aggregates to be computed in one run (3 resulting aggregates from two input maps):

r.series in=one,two out=result_avg,res_slope,result_count meth=sum,slope,count

Example for counting the number of days above a certain temperature using daily average maps ('???' as DOY wildcard):

r.series input=`g.mlist rast pat="temp_2003_???_avg" sep=,` \ output=temp_2003_days_over_25deg range=25.0,100.0 method=count

*Last changed: $Date$*