.TH r.series 1grass "" "GRASS 8.3.2" "GRASS GIS User's Manual"
.SH NAME
\fI\fBr.series\fR\fR  \- Makes each output cell value a function of the values assigned to the corresponding cells in the input raster map layers.
.SH KEYWORDS
raster, aggregation, series, parallel
.SH SYNOPSIS
\fBr.series\fR
.br
\fBr.series \-\-help\fR
.br
\fBr.series\fR [\-\fBnz\fR]  [\fBinput\fR=\fIname\fR[,\fIname\fR,...]]   [\fBfile\fR=\fIname\fR]  \fBoutput\fR=\fIname\fR[,\fIname\fR,...] \fBmethod\fR=\fIstring\fR[,\fIstring\fR,...]  [\fBquantile\fR=\fIfloat\fR[,\fIfloat\fR,...]]   [\fBweights\fR=\fIfloat\fR[,\fIfloat\fR,...]]   [\fBrange\fR=\fIlo,hi\fR]   [\fBnprocs\fR=\fIinteger\fR]   [\fBmemory\fR=\fImemory in MB\fR]   [\-\-\fBoverwrite\fR]  [\-\-\fBhelp\fR]  [\-\-\fBverbose\fR]  [\-\-\fBquiet\fR]  [\-\-\fBui\fR]
.SS Flags:
.IP "\fB\-n\fR" 4m
.br
Propagate NULLs
.IP "\fB\-z\fR" 4m
.br
Do not keep files open
.IP "\fB\-\-overwrite\fR" 4m
.br
Allow output files to overwrite existing files
.IP "\fB\-\-help\fR" 4m
.br
Print usage summary
.IP "\fB\-\-verbose\fR" 4m
.br
Verbose module output
.IP "\fB\-\-quiet\fR" 4m
.br
Quiet module output
.IP "\fB\-\-ui\fR" 4m
.br
Force launching GUI dialog
.SS Parameters:
.IP "\fBinput\fR=\fIname[,\fIname\fR,...]\fR" 4m
.br
Name of input raster map(s)
.IP "\fBfile\fR=\fIname\fR" 4m
.br
Input file with one raster map name and optional one weight per line, field separator between name and weight is |
.IP "\fBoutput\fR=\fIname[,\fIname\fR,...]\fR \fB[required]\fR" 4m
.br
Name for output raster map
.IP "\fBmethod\fR=\fIstring[,\fIstring\fR,...]\fR \fB[required]\fR" 4m
.br
Aggregate operation
.br
Options: \fIaverage, count, median, mode, minimum, min_raster, maximum, max_raster, stddev, range, sum, variance, diversity, slope, offset, detcoeff, tvalue, quart1, quart3, perc90, quantile, skewness, kurtosis\fR
.IP "\fBquantile\fR=\fIfloat[,\fIfloat\fR,...]\fR" 4m
.br
Quantile to calculate for method=quantile
.br
Options: \fI0.0\-1.0\fR
.IP "\fBweights\fR=\fIfloat[,\fIfloat\fR,...]\fR" 4m
.br
Weighting factor for each input map, default value is 1.0 for each input map
.IP "\fBrange\fR=\fIlo,hi\fR" 4m
.br
Ignore values outside this range
.IP "\fBnprocs\fR=\fIinteger\fR" 4m
.br
Number of threads for parallel computing
.br
Default: \fI1\fR
.IP "\fBmemory\fR=\fImemory in MB\fR" 4m
.br
Maximum memory to be used (in MB)
.br
Cache size for raster rows
.br
Default: \fI300\fR
.SH DESCRIPTION
\fIr.series\fR makes each output cell value a function of the values
assigned to the corresponding cells in the input raster map layers.
.PP
\fIFigure: Illustration for an average of three input rasters\fR
.PP
Following methods are available:
.RS 4n
.IP \(bu 4n
average: average value
.IP \(bu 4n
count: count of non\-NULL cells
.IP \(bu 4n
median: median value
.IP \(bu 4n
mode: most frequently occurring value
.IP \(bu 4n
minimum: lowest value
.IP \(bu 4n
min_raster: raster map number with the minimum time\-series value
.IP \(bu 4n
maximum: highest value
.IP \(bu 4n
max_raster: raster map number with the maximum time\-series value
.IP \(bu 4n
stddev: standard deviation
.IP \(bu 4n
range: range of values (max \- min)
.IP \(bu 4n
sum: sum of values
.IP \(bu 4n
variance: statistical variance
.IP \(bu 4n
diversity: number of different values
.IP \(bu 4n
slope: linear regression slope
.IP \(bu 4n
offset: linear regression offset
.IP \(bu 4n
detcoeff: linear regression coefficient of determination
.IP \(bu 4n
tvalue: linear regression t\-value
.IP \(bu 4n
quart1: first quartile
.IP \(bu 4n
quart3: third quartile
.IP \(bu 4n
perc90: ninetieth percentile
.IP \(bu 4n
quantile: arbitrary quantile
.IP \(bu 4n
skewness: skewness
.IP \(bu 4n
kurtosis: kurtosis
.RE
Note that most parameters accept multiple answers, allowing multiple
aggregates to be computed in a single run, e.g.:
.PP
.br
.nf
\fC
r.series input=map1,...,mapN \(rs
         output=map.mean,map.stddev \(rs
	 method=average,stddev
\fR
.fi
or:
.PP
.br
.nf
\fC
r.series input=map1,...,mapN \(rs
         output=map.p10,map.p50,map.p90 \(rs
         method=quantile,quantile,quantile \(rs
         quantile=0.1,0.5,0.9
\fR
.fi
The same number of values must be provided for all options.
.SH NOTES
.SS No\-data (NULL) handling
With \fI\-n\fR 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 \fI\-n\fR flag.
.PP
Without \fI\-n\fR 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.
.SS Minimum and maximum analysis
The \fImin_raster\fR and \fImax_raster\fR 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 \fI0\fR up to \fIn\fR
for the first and the last raster listed in \fIinput=\fR,
respectively.
.SS Range analysis
If the \fIrange=\fR option is given, any values which fall outside
that range will be treated as if they were NULL. The \fIrange\fR
parameter can be set to \fIlow,high\fR 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
\fIlow,high\fR thresholds are floating point, so use \fI\-inf\fR
or \fIinf\fR for a single threshold (e.g., \fIrange=0,inf\fR to
ignore negative values, or \fIrange=\-inf,\-200.4\fR to ignore values
above \-200.4).
.SS 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).
.SS Quantiles
\fIr.series\fR can calculate arbitrary quantiles.
.SS Memory consumption
Memory usage is not an issue, as \fIr.series\fR only needs to hold
one row from each map at a time.
.SS Management of open file limits
The maximum number of raster maps that can be processed is given by the
user\-specific limit of the operating system. For example, the soft limits
for users are typically 1024 files. The soft limit can be changed with e.g.
ulimit \-n 4096 (UNIX\-based operating systems) but it cannot be
higher than the hard limit. If the latter is too low, you can as superuser
add an entry in:
.br
.nf
\fC
/etc/security/limits.conf
# <domain>      <type>  <item>         <value>
your_username  hard    nofile          4096
\fR
.fi
This will raise the hard limit to 4096 files. Also have a look at the
overall limit of the operating system
.br
.nf
\fC
cat /proc/sys/fs/file\-max
\fR
.fi
which on modern Linux systems is several 100,000 files.
.PP
For each map a weighting factor can be specified using the
\fIweights\fR 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.
.PP
Use the \fB\-z\fR flag to analyze large amounts of raster maps without
hitting open files limit and the \fIfile\fR option to avoid hitting
the size limit of command line arguments.
Note that the computation using the \fIfile\fR option is slower
than with the \fIinput\fR 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 \fIinput\fR and \fIfile\fR 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 \(dq|\(dq must be used.
.SS Performance
To enable parallel processing, the user can specify the number of threads to be
used with the \fBnprocs\fR parameter (default 1). The \fBmemory\fR parameter
(default 300 MB) can also be provided to determine the size of the buffer in MB for
computation.
.br
\fIFigure: Benchmark on the left shows execution time for different
number of cells, benchmark on the right shows execution time
for different memory size for 10000x10000 raster. See benchmark scripts in source code.
(Intel Core i9\-10940X CPU @ 3.30GHz x 28) \fR
.PP
To reduce the memory requirements to minimum, set option \fBmemory\fR to zero.
To take advantage of the parallelization, GRASS GIS
needs to compiled with OpenMP enabled.
.SH EXAMPLES
Using \fIr.series\fR with wildcards:
.br
.br
.nf
\fC
r.series input=\(dq\(gag.list pattern=\(cqinsitu_data.*\(cq sep=,\(ga\(dq \(rs
         output=insitu_data.stddev method=stddev
\fR
.fi
.PP
Note the \fIg.list\fR script also supports regular expressions for
selecting map names.
.PP
Using \fIr.series\fR with NULL raster maps (in order to consider a
\(dqcomplete\(dq time series):
.br
.br
.nf
\fC
r.mapcalc \(dqdummy = null()\(dq
r.series in=map2001,map2002,dummy,dummy,map2005,map2006,dummy,map2008 \(rs
         out=res_slope,res_offset,res_coeff meth=slope,offset,detcoeff
\fR
.fi
.PP
Example for multiple aggregates to be computed in one run (3 resulting aggregates
from two input maps):
.br
.nf
\fC
r.series in=one,two out=result_avg,res_slope,result_count meth=sum,slope,count
\fR
.fi
.PP
Example to use the file option of r.series:
.br
.nf
\fC
cat > input.txt << EOF
map1
map2
map3
EOF
r.series file=input.txt out=result_sum meth=sum
\fR
.fi
.PP
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:
.br
.nf
\fC
cat > input.txt << EOF
map1
map2|0.75
map3
EOF
r.series file=input.txt out=result_sum meth=sum
\fR
.fi
.PP
Example for counting the number of days above a certain temperature using
daily average maps (\(cq???\(cq as DOY wildcard):
.br
.nf
\fC
# Approach for shell based systems
r.series input=\(gag.list rast pattern=\(dqtemp_2003_???_avg\(dq separator=comma\(ga \(rs
         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=\(dqtemp_2003_???_avg\(dq output=mapnames.txt
r.series file=mapnames.txt \(rs
         output=temp_2003_days_over_25deg range=25.0,100.0 method=count
\fR
.fi
.SH SEE ALSO
\fI
g.list,
g.region,
r.quantile,
r.series.accumulate,
r.series.interp,
r.univar
\fR
.PP
Hints for large raster data processing
.SH AUTHOR
Glynn Clements
.SH SOURCE CODE
.PP
Available at:
r.series source code
(history)
.PP
Accessed: Friday Mar 08 07:34:41 2024
.PP
Main index |
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Full index
.PP
© 2003\-2024
GRASS Development Team,
GRASS GIS 8.3.2 Reference Manual