### r.series (1)

#### NAME

r.series - Makes each output cell value a function of the values assigned to the corresponding cells in the input raster map layers.

#### KEYWORDS

raster, aggregation, series

#### SYNOPSIS

r.series

r.series --help

r.series [-nz] [input=name[,name,...]] [file=name] output=name[,name,...] method=string[,string,...] [quantile=float[,float,...]] [weights=float[,float,...]] [range=lo,hi] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

#### Flags:

-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

#### Parameters:

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

#### DESCRIPTION

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:

o average: average value

o count: count of non-NULL cells

o median: median value

o mode: most frequently occurring value

o minimum: lowest value

o maximum: highest value

o range: range of values (max - min)

o stddev: standard deviation

o sum: sum of values

o variance: statistical variance

o diversity: number of different values

o slope: linear regression slope

o offset: linear regression offset

o detcoeff: linear regression coefficient of determination

o tvalue: linear regression t-value

o min_raster: raster map number with the minimum time-series value

o max_raster: raster map number with the maximum time-series value

Note that most parameters accept multiple answers, allowing multiple aggregates to be computed in a single run, e.g.:

r.series input=map1,...,mapN \

output=map.mean,map.stddev \

method=average,stddev

or:

r.series input=map1,...,mapN \

output=map.p10,map.p50,map.p90 \

method=quantile,quantile,quantile \

quantile=0.1,0.5,0.9

The same number of values must be provided for all options.

#### NOTES

#### No-data (NULL) handling

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.

#### Minimum and maximum analysis

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.

#### Range analysis

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

Linear regression (slope, offset, coefficient of determination, t-value) 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).

#### Quantiles

r.series can calculate arbitrary quantiles.

#### Memory consumption

Memory usage is not an issue, as r.series only needs to hold one row from each map at a time.

#### Management of open file limits

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

#

your_username hard nofile 1500

This would raise the hard limit to 1500 file. Be warned that more files open need more RAM. See also the Wiki page Hints for large raster data processing.

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

Use the file option to analyze large amount of raster maps without hitting open files limit and the size limit of command line arguments. The computation is slower than the input option method. For every sinlge row in the output map(s) all input maps are opened and closed. The amount of RAM will rise linear with the number of specified input maps. The input and file options are mutually exclusive. Input 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.

#### EXAMPLES

Using r.series with wildcards:

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" sep=,` \

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

#### SEE ALSO

g.list, g.region, r.quantile, r.series.accumulate, r.series.interp, r.univar

Hints for large raster data processing

#### AUTHOR

Glynn Clements

Last changed: $Date: 2016-01-29 10:29:57 +0100 (Fri, 29 Jan 2016) $

#### SOURCE CODE

Available at: r.series source code (history)

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