t.rast.whatcsv - GRASS GIS manual

Note: This document is for an older version of GRASS GIS that will be discontinued soon. You should upgrade, and read the current manual page.

NAME

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

KEYWORDS

temporal, raster, sampling, time

SYNOPSIS

t.rast.whatcsv

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]

Flags:

-n: Output header row
--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:

csv=name [required]: Name for the output input csv file
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
skip=integer [required]: Number of header lines to skip in the csv file

DESCRIPTION
EXAMPLES
SEE ALSO
AUTHOR

DESCRIPTION

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:

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 time stamps

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 "-".

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

AUTHOR

Sören Gebbert, Thünen Institute of Climate-Smart Agriculture

SOURCE CODE

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

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