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t.rast.whatcsv - Sample a space time raster dataset at specific space-time point coordinates from a csv file and write the output to stdout


temporal, raster, sampling, time


t.rast.whatcsv --help
t.rast.whatcsv [-n] csv=name strds=name [output=name] [where=sql_query] [null_value=string] [separator=character] skip=integer [--overwrite] [--help] [--verbose] [--quiet] [--ui]


Output header row
Allow output files to overwrite existing files
Print usage summary
Verbose module output
Quiet module output
Force launching GUI dialog


csv=name [required]
Name for the output input csv file
strds=name [required]
Name of the input space time raster dataset
Name for the output file or "-" in case stdout should be used
Default: -
WHERE conditions of SQL statement without 'where' keyword used in the temporal GIS framework
Example: start_time > '2001-01-01 12:30:00'
String representing NULL value
Field separator
Special characters: pipe, comma, space, tab, newline
Default: pipe
skip=integer [required]
Number of header lines to skip in the csv file

Table of contents


t.rast.what is designed to sample space time raster datasets at specific 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:

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 intermediate 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 "-".


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

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

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


r.what , r.neighbors, t.rast.aggregate.ds, t.rast.extract,, g.region, r.mask


Sören Gebbert, Thünen Institute of Climate-Smart Agriculture


Available at: t.rast.whatcsv source code (history)

Latest change: Monday Jan 30 19:52:26 2023 in commit: cac8d9d848299297977d1315b7e90cc3f7698730

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