Note: A new GRASS GIS stable version has been released: GRASS GIS 7.4, available here.
Updated manual page: here
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
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.
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,
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).
r.series can calculate arbitrary quantiles.
Memory usage is not an issue, as
r.series only needs to hold
one row from each map at a time.
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.
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
g.list,
g.region,
r.quantile,
r.series.accumulate,
r.series.interp,
r.univar
Hints for large raster data processing
Glynn Clements
Last changed: $Date: 2016-01-29 01:56:07 -0800 (Fri, 29 Jan 2016) $
SOURCE CODE
Available at: r.series source code (history)
Note: A new GRASS GIS stable version has been released: GRASS GIS 7.4, available here.
Updated manual page: here
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GRASS Development Team,
GRASS GIS 7.0.7svn Reference Manual