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NAME

t.remove.py - Removes space time datasets from temporal database.

KEYWORDS

temporal, map management, remove

SYNOPSIS

t.remove.py
t.remove.py --help
t.remove.py [-rf] [inputs=name[,name,...]] [type=string] [file=name] [--help] [--verbose] [--quiet] [--ui]

Flags:

-r
Remove all registered maps from the temporal and spatial database
-f
Force recursive removing
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--ui
Force launching GUI dialog

Parameters:

inputs=name[,name,...]
Name of the input space time datasets
type=string
Type of the space time dataset, default is strds
Options: strds, str3ds, stvds
Default: strds
file=name
Input file with dataset names, one per line

Table of contents

DESCRIPTION

The module t.remove removes space time datasets (STRDS, STR3DS, STVDS) from the temporal database. In other words, it deletes the relevant database entries and not the maps.

Optionally, also the raster, 3D raster and vector maps of the space time datasets can be removed using the -r (recursive) and -f (force) flags. Recursive removal works only if both flags are checked (use -rf).

EXAMPLE

In this example a space time raster dataset (STRDS) named precip_months_sum will be created using a subset of the monthly precipitation raster maps from the North Carolina climate sample data set. In order to be able to show case recursive removal without deleting original sample data, new data is generated by means of computing yearly precipitation sums. Finally, all newly produced data (STRDS and raster maps) is removed again.
#Create new and empty STRDS
t.create output=precip_months_sum semantictype=mean \
  title="Monthly sum of precipitation" \
  description="Monthly sum of precipitation for the \
  North Carolina sample data location"

#Register maps from sample dataset (selecting a subset with g.list)
t.register -i type=raster input=precip_months_sum \
  maps=$(g.list type=raster pattern=201*_precip separator=comma) \
  start="2010-01-01" increment="1 months"

#Create some new data by aggregating with 1 years granularity
t.rast.aggregate input=precip_months_sum \
  output=precip_years_sum basename=precip_years_sum granularity="1 \
  years" method=sum

#Remove all newly produced data:
# a) the aggregated STRDS with 1 years granularity together with its raster maps
t.remove -rf type=strds input=precip_years_sum

# b) the STRDS with 1 months granularity, but not the original sample data
t.remove type=strds input=precip_months_sum

SEE ALSO

t.create, t.info, t.register

AUTHOR

Sören Gebbert, Thünen Institute of Climate-Smart Agriculture

Last changed: $Date: 2014-12-27 00:50:11 +0100 (so, 27 12 2014) $


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