r.series(1grass) GRASS GIS User's Manual r.series(1grass)
NAME
r.series - Makes each output cell value a function of the values as-
signed 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] out-
put=name[,name,...] method=string[,string,...] [quan-
tile=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, maxi-
mum, max_raster, stddev, range, sum, variance, diversity, slope,
offset, detcoeff, tvalue, quart1, quart3, perc90, quantile, skew-
ness, 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
• 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
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 aggre-
gate function is not called, so all methods behave this way with re-
spect 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 re-
sult 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
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 lim-
its 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 su-
peruser add an entry in:
/etc/security/limits.conf
# <domain> <type> <item> <value>
your_username hard nofile 4096
This will raise the hard limit to 4096 files. Also have a look at the
overall limit of the operating system
cat /proc/sys/fs/file-max
which on modern Linux systems is several 100,000 files.
For each map a weighting factor can be specified using the weights op-
tion. 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 op-
tion 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 sepa-
rated 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 sepa-
rator 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 us-
ing 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
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
SOURCE CODE
Available at: r.series source code (history)
Accessed: unknown
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GRASS 7.8.7 r.series(1grass)
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