r.series makes each output cell value a function of the values assigned to the corresponding cells in the input raster map layers. Following methods are available:
  • average: average value
  • count: count of non-NULL cells
  • median: median value
  • mode: most frequently occuring value
  • minimum: lowest value
  • maximum: highest value
  • range: range of values (max - min)
  • stddev: standard deviation
  • sum: sum of values
  • variance: statistical variance
  • diversity: number of different values
  • slope: linear regression slope
  • offset: linear regression offset
  • detcoeff: linear regression coefficient of determination
  • min_raster: raster map number with the minimum time-series value
  • max_raster: raster map number with the maximum time-series value


    With -n flag, any cell for which any of the corresponding input cells are NULL is automatically set to NULL (NULL propagation). The aggregate function is not called, so all methods behave this way with respect to the -n flag.

    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

    # <domain>      <type>  <item>         <value>
    your_username  hard    nofile          1500
    This would raise the hard limit to 1500 file. Be warned that more files open need more RAM.


    Using r.series with wildcards:
    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


    g.mlist, g.region


    Glynn Clements

    Last changed: $Date$