**-n**- Propagate NULLs
**-z**- Do not keep files open
**--overwrite**- Allow output files to overwrite existing files
**--help**- Print usage summary
**--verbose**- Verbose module output
**--quiet**- Quiet module output
**--ui**- Force launching GUI dialog

**input**=*name[,**name*,...]- Name of input raster map(s)
**file**=*name*- Input file with one raster map name and optional one weight per line, field separator between name and weight is |
**output**=*name[,**name*,...]**[required]**- Name for output raster map
**method**=*string[,**string*,...]**[required]**- Aggregate operation
- Options:
*average, count, median, mode, minimum, min_raster, maximum, max_raster, stddev, range, sum, variance, diversity, slope, offset, detcoeff, tvalue, quart1, quart3, perc90, quantile, skewness, kurtosis* **quantile**=*float[,**float*,...]- Quantile to calculate for method=quantile
- Options:
*0.0-1.0* **weights**=*float[,**float*,...]- Weighting factor for each input map, default value is 1.0 for each input map
**range**=*lo,hi*- Ignore values outside this range
**nprocs**=*integer*- Number of threads for parallel computing
- Default:
*1* **memory**=*memory in MB*- Maximum memory to be used (in MB)
- Cache size for raster rows
- Default:
*300*

*Figure: Illustration for an average of three input rasters*

Following methods are available:

- average: average value
- count: count of non-NULL cells
- median: median value
- mode: most frequently occurring value
- minimum: lowest value
- min_raster: raster map number with the minimum time-series value
- maximum: highest value
- max_raster: raster map number with the maximum time-series value
- stddev: standard deviation
- range: range of values (max - min)
- 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
- tvalue: linear regression t-value
- quart1: first quartile
- quart3: third quartile
- perc90: ninetieth percentile
- quantile: arbitrary quantile
- skewness: skewness
- kurtosis: kurtosis

r.series input=map1,...,mapN \ output=map.mean,map.stddev \ method=average,stddev

r.series input=map1,...,mapN \ output=map.p10,map.p50,map.p90 \ method=quantile,quantile,quantile \ quantile=0.1,0.5,0.9

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.

/etc/security/limits.conf # <domain> <type> <item> <value> your_username hard nofile 4096

cat /proc/sys/fs/file-max

For each map a weighting factor can be specified using the
*weights* option. Using weights can be meaningful when computing
the sum or average of maps with different temporal extent. The default
weight is 1.0. The number of weights must be identical to the number
of input maps and must have the same order. Weights can also be
specified in the input file.

Use the **-z** flag to analyze large amounts of raster maps without
hitting open files limit and the *file* option to avoid hitting
the size limit of command line arguments.
Note that the computation using the *file* option is slower
than with the *input* option.
For every single row in the output map(s) all input maps are
opened and closed. The amount of RAM will rise linearly with the number
of specified input maps. The *input* and *file* options are
mutually exclusive: the former is a comma separated list of raster map
names and the latter is a text file with a new line separated list of
raster map names and optional weights. As separator between the map name
and the weight the character "|" must be used.

To reduce the memory requirements to minimum, set option **memory** to zero.
To take advantage of the parallelization, GRASS GIS
needs to compiled with OpenMP enabled.

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

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

Using *r.series* with NULL raster maps (in order to consider a
"complete" time series):

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 to use the file option of r.series:

cat > input.txt << EOF map1 map2 map3 EOF r.series file=input.txt out=result_sum meth=sum

Example to use the file option of r.series including weights. The weight 0.75 should be assigned to map2. As the other maps do not have weights we can leave it out:

cat > input.txt << EOF map1 map2|0.75 map3 EOF r.series file=input.txt out=result_sum meth=sum

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

# Approach for shell based systems r.series input=`g.list rast pattern="temp_2003_???_avg" separator=comma` \ output=temp_2003_days_over_25deg range=25.0,100.0 method=count # Approach in two steps (e.g., for Windows systems) g.list rast pattern="temp_2003_???_avg" output=mapnames.txt r.series file=mapnames.txt \ output=temp_2003_days_over_25deg range=25.0,100.0 method=count

Hints for large raster data processing

Available at: r.series source code (history)

Latest change: Sunday Sep 03 11:34:17 2023 in commit: c33eebe61ad00c75dece78c6e1bb07fd1aba444e

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