t.rast.what(1grass) GRASS GIS User's Manual t.rast.what(1grass)
NAME
t.rast.what - Sample a space time raster dataset at specific vector
point coordinates and write the output to stdout using different lay-
outs
KEYWORDS
temporal, sampling, raster, time
SYNOPSIS
t.rast.what
t.rast.what --help
t.rast.what [-niv] [points=name] [coordinates=east,north]
strds=name [output=name] [where=sql_query] [null_value=string]
[separator=character] [order=string[,string,...]] [layout=string]
[nprocs=integer] [--overwrite] [--help] [--verbose] [--quiet]
[--ui]
Flags:
-n
Output header row
-i
Use stdin as input and ignore coordinates and point option
-v
Show the category for vector points map
--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:
points=name
Name of input vector map
Or data source for direct OGR access
coordinates=east,north
Comma separated list of coordinates
strds=name [required]
Name of the input space time raster dataset
output=name
Name for the output file or "-" in case stdout should be used
Default: -
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’
null_value=string
String representing NULL value
separator=character
Field separator
Special characters: pipe, comma, space, tab, newline
Default: pipe
order=string[,string,...]
Sort the maps by category
Options: id, name, creator, mapset, creation_time, modifica-
tion_time, start_time, end_time, north, south, west, east,
min, max
Default: start_time
layout=string
The layout of the output. One point per row (row), one point per
column (col), all timsteps in one row (timerow)
Options: row, col, timerow
Default: row
nprocs=integer
Number of r.what processes to run in parallel
Default: 1
DESCRIPTION
t.rast.what is designed to sample space time raster datasets at spe-
cific point coordinates using r.what internally. The output of r.what
is transformed to different output layouts. The output layouts can be
specified using the layout option.
Three layouts can be specified:
• row - Row order, one vector sample point value per row
• col - Column order, create a column for each vector sample
point of a single time step/raster layer
• timerow - Time order, create a column for each time step, this
order is the original r.what output, except that the column
names are the timestamps
Please have a look at the example to see the supported layouts.
This module is designed to run several instances of r.what to sample
subsets of a space time raster dataset in parallel. Several intermedi-
ate text files will be created that are merged into a single file at
the end of the processing.
Coordinates can be provided as vector map using the points option or as
comma separated coordinate list with the coordinates option.
An output file can be specified using the output option. Stdout will
be used if no output is specified or if the output option is set to
"-".
EXAMPLES
Data preparation
In the following examples we sample a space time raster dataset that
contains 4 raster map layers. First we create the STRDS that will be
sampled with t.rast.what.
g.region s=0 n=80 w=0 e=120 b=0 t=50 res=10
# Generate data
r.mapcalc expression="a_1 = 1" -s
r.mapcalc expression="a_2 = 2" -s
r.mapcalc expression="a_3 = 3" -s
r.mapcalc expression="a_4 = 4" -s
t.create type=strds output=A title="A test" descr="A test"
t.register -i type=raster input=A maps=a_1,a_2,a_3,a_4 \
start=’1990-01-01’ increment="1 month"
Example 1
The first approach uses text coordinates as input and stdout as output,
the layout is one coordinate(point per column:
t.rast.what strds=A coordinates="115,36,79,45" layout=col -n
start|end|115.0000000000;36.0000000000|79.0000000000;45.0000000000
1990-01-01 00:00:00|1990-02-01 00:00:00|1|1
1990-02-01 00:00:00|1990-03-01 00:00:00|2|2
1990-03-01 00:00:00|1990-04-01 00:00:00|3|3
1990-04-01 00:00:00|1990-05-01 00:00:00|4|4
Example 2
A vector map layer can be used as input to sample the STRDS. All three
available layouts are demonstrated using the vector map for sampling.
# First create the vector map layer based on random points
v.random output=points n=3 seed=1
# Row layout using a text file as output
t.rast.what strds=A points=points output=result.txt layout=row -n
cat result.txt
115.0043586274|36.3593955783|1990-01-01 00:00:00|1990-02-01 00:00:00|1
115.0043586274|36.3593955783|1990-02-01 00:00:00|1990-03-01 00:00:00|2
115.0043586274|36.3593955783|1990-03-01 00:00:00|1990-04-01 00:00:00|3
115.0043586274|36.3593955783|1990-04-01 00:00:00|1990-05-01 00:00:00|4
79.6816763826|45.2391522853|1990-01-01 00:00:00|1990-02-01 00:00:00|1
79.6816763826|45.2391522853|1990-02-01 00:00:00|1990-03-01 00:00:00|2
79.6816763826|45.2391522853|1990-03-01 00:00:00|1990-04-01 00:00:00|3
79.6816763826|45.2391522853|1990-04-01 00:00:00|1990-05-01 00:00:00|4
97.4892579600|79.2347263950|1990-01-01 00:00:00|1990-02-01 00:00:00|1
97.4892579600|79.2347263950|1990-02-01 00:00:00|1990-03-01 00:00:00|2
97.4892579600|79.2347263950|1990-03-01 00:00:00|1990-04-01 00:00:00|3
97.4892579600|79.2347263950|1990-04-01 00:00:00|1990-05-01 00:00:00|4
# Column layout order using stdout as output
t.rast.what strds=A points=points layout=col -n
start|end|115.0043586274;36.3593955783|79.6816763826;45.2391522853|97.4892579600;79.2347263950
1990-01-01 00:00:00|1990-02-01 00:00:00|1|1|1
1990-02-01 00:00:00|1990-03-01 00:00:00|2|2|2
1990-03-01 00:00:00|1990-04-01 00:00:00|3|3|3
1990-04-01 00:00:00|1990-05-01 00:00:00|4|4|4
# Timerow layout, one time series per row
# using the where statement to select a subset of the STRDS
# and stdout as output
t.rast.what strds=A points=points \
where="start_time >= ’1990-03-01’" layout=timerow -n
x|y|1990-03-01 00:00:00;1990-04-01 00:00:00|1990-04-01 00:00:00;1990-05-01 00:00:00
115.004358627375|36.3593955782903|3|4
79.681676382576|45.2391522852909|3|4
97.4892579600048|79.2347263950131|3|4
SEE ALSO
g.region, r.mask r.neighbors, r.what, t.info, t.rast.aggregate.ds,
t.rast.extract, v.what.strds
AUTHOR
Sören Gebbert, Thünen Institute of Climate-Smart Agriculture
SOURCE CODE
Available at: t.rast.what source code (history)
Accessed: unknown
Main index | Temporal index | Topics index | Keywords index | Graphical
index | Full index
© 2003-2022 GRASS Development Team, GRASS GIS 7.8.7 Reference Manual
GRASS 7.8.7 t.rast.what(1grass)
Generated by dwww version 1.14 on Fri Jan 24 09:31:07 CET 2025.