Provided by: grass-doc_8.3.0-1_all 
NAME
t.rast.series - Performs different aggregation algorithms from r.series on all or a
subset of raster maps in a space time raster dataset.
KEYWORDS
temporal, aggregation, series, raster, time
SYNOPSIS
t.rast.series
t.rast.series --help
t.rast.series [-tn] input=name method=string[,string,...] [quantile=float[,float,...]]
[order=string[,string,...]] [nprocs=integer] [memory=memory in MB] [where=sql_query]
output=name[,name,...] [file_limit=integer] [--overwrite] [--help] [--verbose]
[--quiet] [--ui]
Flags:
-t
Do not assign the space time raster dataset start and end time to the output map
-n
Propagate NULLs
--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 [required]
Name of the input space time raster dataset
method=string[,string,...] [required]
Aggregate operation to be performed on the raster maps
Options: average, count, median, mode, minimum, min_raster, maximum, max_raster,
stddev, range, sum, variance, diversity, slope, offset, detcoeff, quart1, quart3,
perc90, quantile, skewness, kurtosis
Default: average
quantile=float[,float,...]
Quantile to calculate for method=quantile
Options: 0.0-1.0
order=string[,string,...]
Sort the maps by category
Options: id, name, creator, mapset, creation_time, modification_time,
start_time, end_time, north, south, west, east, min, max
Default: start_time
nprocs=integer
Number of threads for parallel computing
Default: 1
memory=memory in MB
Maximum memory to be used (in MB)
Cache size for raster rows
Default: 300
where=sql_query
WHERE conditions of SQL statement without ’where’ keyword used in the temporal GIS
framework
Example: start_time > ’2001-01-01 12:30:00’
output=name[,name,...] [required]
Name for output raster map(s)
file_limit=integer
The maximum number of open files allowed for each r.series process
Default: 1000
DESCRIPTION
The input of this module is a single space time raster dataset, the output is a single
raster map layer. A subset of the input space time raster dataset can be selected using
the where option. The sorting of the raster map layer can be set using the order option.
Be aware that the order of the maps can significantly influence the result of the
aggregation (e.g.: slope). By default the maps are ordered by start_time.
t.rast.series is a simple wrapper for the raster module r.series. It supports a subset of
the aggregation methods of r.series.
NOTES
To avoid problems with too many open files, by default, the maximum number of open files
is set to 1000. If the number of input raster files exceeds this number, the -z flag will
be invoked. Because this will slow down processing, the user can set a higher limit with
the file_limit parameter. Note that file_limit limit should not exceed the user-specific
limit on open files set by your operating system. See the Wiki for more information.
Performance
To enable parallel processing, the user can specify the number of threads to be used with
the nprocs parameter (default 1). The memory parameter (default 300 MB) can also be
provided to determine the size of the buffer in MB for computation. Both parameters are
passed to r.series. To take advantage of the parallelization, GRASS GIS needs to be
compiled with OpenMP enabled.
EXAMPLES
Estimate the average temperature for the whole time series
Here the entire stack of input maps is considered:
t.rast.series input=tempmean_monthly output=tempmean_average method=average
Estimate the average temperature for a subset of the time series
Here the stack of input maps is limited to a certain period of time:
t.rast.series input=tempmean_daily output=tempmean_season method=average \
where="start_time >= ’2012-06’ and start_time <= ’2012-08’"
Climatology: single month in a multi-annual time series
By considering only a single month in a multi-annual time series the so-called climatology
can be computed. Estimate average temperature for all January maps in the time series:
t.rast.series input=tempmean_monthly \
method=average output=tempmean_january \
where="strftime(’%m’, start_time)=’01’"
# equivalently, we can use
t.rast.series input=tempmean_monthly \
output=tempmean_january method=average \
where="start_time = datetime(start_time, ’start of year’, ’0 month’)"
# if we want also February and March averages
t.rast.series input=tempmean_monthly \
output=tempmean_february method=average \
where="start_time = datetime(start_time, ’start of year’, ’1 month’)"
t.rast.series input=tempmean_monthly \
output=tempmean_march method=average \
where="start_time = datetime(start_time, ’start of year’, ’2 month’)"
Generalizing a bit, we can estimate monthly climatologies for all months by means of
different methods
for i in `seq -w 1 12` ; do
for m in average stddev minimum maximum ; do
t.rast.series input=tempmean_monthly method=${m} output=tempmean_${m}_${i} \
where="strftime(’%m’, start_time)=’${i}’"
done
done
SEE ALSO
r.series, t.create, t.info
Temporal data processing Wiki
AUTHOR
Sören Gebbert, Thünen Institute of Climate-Smart Agriculture
SOURCE CODE
Available at: t.rast.series source code (history)
Accessed: Tuesday Jun 27 11:14:36 2023
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