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
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.
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.
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.
Here the entire stack of input maps is considered:
t.rast.series input=tempmean_monthly output=tempmean_average method=average
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'"
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
r.series,
t.create,
t.info
Temporal data processing Wiki
Sören Gebbert, Thünen Institute of Climate-Smart Agriculture
SOURCE CODE
Available at:
t.rast.series source code
(history)
Latest change: Wednesday Nov 27 22:53:26 2024 in commit: 3b5486c0463a7103ab2109e28bf860fe34539868
Main index |
Temporal index |
Topics index |
Keywords index |
Graphical index |
Full index
© 2003-2024
GRASS Development Team,
GRASS GIS 8.5.0dev Reference Manual