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PROGRAM:

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:

· average: average value

· count: count of non-NULL cells

· median: median value

· mode: most frequently occurring 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

· tvalue: linear regression t-value

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

· 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
# <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. 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

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