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t.rast.aggregate(1)	    GRASS GIS User's Manual	   t.rast.aggregate(1)

NAME
       t.rast.aggregate	  -  Aggregates	 temporally  the  maps of a space time
       raster dataset by a user	defined	granularity.

KEYWORDS
       temporal, aggregation, raster, time

SYNOPSIS
       t.rast.aggregate
       t.rast.aggregate	--help
       t.rast.aggregate	[-n]  input=name  output=name  basename=string	 [suf-
       fix=string]     granularity=string    method=string    [offset=integer]
       [nprocs=integer]	   [file_limit=integer]	    [sampling=name[,name,...]]
       [where=sql_query]    [--overwrite]   [--help]   [--verbose]   [--quiet]
       [--ui]

   Flags:
       -n
	   Register Null maps

       --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=nameA [required]
	   Name	of the input space time	raster dataset

       output=nameA [required]
	   Name	of the output space time raster	dataset

       basename=stringA	[required]
	   Basename of the new generated output	maps
	   Either a numerical suffix or	the start time (s-flag)	 separated  by
	   an underscore will be attached to create a unique identifier

       suffix=string
	   Suffix  to  add at basename:	set 'gran' for granularity, 'time' for
	   the full time format, 'num' for numerical suffix  with  a  specific
	   number of digits (default %05)
	   Default: gran

       granularity=stringA [required]
	   Aggregation granularity, format absolute time "x years, x months, x
	   weeks, x days, x hours, x minutes, x	seconds" or an	integer	 value
	   for relative	time

       method=stringA [required]
	   Aggregate operation to be performed on the raster maps
	   Options:  average,  count, median, mode, minimum, min_raster, maxi-
	   mum,	max_raster, stddev, range, sum,	 variance,  diversity,	slope,
	   offset,  detcoeff, quart1, quart3, perc90, quantile,	skewness, kur-
	   tosis
	   Default: average

       offset=integer
	   Offset that is used to create the output map	ids, output map	id  is
	   generated as: basename_ (count + offset)
	   Default: 0

       nprocs=integer
	   Number of r.series processes	to run in parallel
	   Default: 1

       file_limit=integer
	   The maximum number of open files allowed for	each r.series process
	   Default: 1000

       sampling=name[,name,...]
	   The method to be used for sampling the input	dataset
	   Options:  equal,  overlaps,	overlapped, starts, started, finishes,
	   finished, during, contains
	   Default: contains

       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'

DESCRIPTION
       t.rast.aggregate	 temporally aggregates space time raster datasets by a
       specific	temporal granularity. This module support absolute  and	 rela-
       tive  time.  The	 temporal granularity of absolute time can be seconds,
       minutes,	hours, days, weeks, months or years. Mixing  of	 granularities
       eg.  "1	year,  3  months 5 days" is not	supported. In case of relative
       time the	temporal unit of the input space time raster dataset is	 used.
       The granularity must be specified with an integer value.

       This module is sensitive	to the current region and mask settings, hence
       spatial extent and spatial resolution. In case  the  registered	raster
       maps of the input space time raster dataset have	different spatial res-
       olutions, the default nearest neighbor resampling method	 is  used  for
       runtime spatial aggregation.

NOTES
       The  raster  module  r.series  is  used internally. Hence all aggregate
       methods of r.series are supported. See the r.series manual page for de-
       tails.

       This  module will shift the start date for each aggregation process de-
       pending on the provided temporal	granularity. The following shifts will
       performed:

	   o   granularity  years:  will  start	at the first of	January, hence
	       14-08-2012 00:01:30 will	be shifted to 01-01-2012 00:00:00

	   o   granularity months: will	start at the first  day	 of  a	month,
	       hence 14-08-2012	will be	shifted	to 01-08-2012 00:00:00

	   o   granularity  weeks: will	start at the first day of a week (Mon-
	       day), hence 14-08-2012 01:30:30 will be shifted	to  13-08-2012
	       01:00:00

	   o   granularity  days: will start at	the first hour of a day, hence
	       14-08-2012 00:01:30 will	be shifted to 14-08-2012 00:00:00

	   o   granularity hours: will start at	the first minute  of  a	 hour,
	       hence   14-08-2012  01:30:30  will  be  shifted	to  14-08-2012
	       01:00:00

	   o   granularity minutes: will  start	 at  the  first	 second	 of  a
	       minute, hence 14-08-2012	01:30:30 will be shifted to 14-08-2012
	       01:30:00

       The specification of the	temporal relation between the aggregation  in-
       tervals	and the	raster map layers is always formulated from the	aggre-
       gation interval viewpoint. Hence, the relation contains has to be spec-
       ified to	aggregate map layer that are temporally	located	in an aggrega-
       tion interval.

       Parallel	processing is supported	in case	that more than one interval is
       available for aggregation computation. Internally several r.series mod-
       ules will be started, depending on the  number  of  specified  parallel
       processes (nprocs) and the number of intervals to aggregate.

EXAMPLES
   Aggregation of monthly data into yearly data
       In this example the user	is going to aggregate monthly data into	yearly
       data, running:
       t.rast.aggregate	input=tempmean_monthly output=tempmean_yearly \
			basename=tempmean_year \
			granularity="1 years" method=average
       t.support input=tempmean_yearly \
		 title="Yearly precipitation" \
		 description="Aggregated precipitation dataset with yearly resolution"
       t.info tempmean_yearly
	+-------------------- Space Time Raster	Dataset	-----------------------------+
	|									     |
	+-------------------- Basic information	-------------------------------------+
	| Id: ........................ tempmean_yearly@climate_2000_2012
	| Name:	...................... tempmean_yearly
	| Mapset: .................... climate_2000_2012
	| Creator: ................... lucadelu
	| Temporal type: ............. absolute
	| Creation time: ............. 2014-11-27 10:25:21.243319
	| Modification time:.......... 2014-11-27 10:25:21.862136
	| Semantic type:.............. mean
	+-------------------- Absolute time -----------------------------------------+
	| Start	time:................. 2009-01-01 00:00:00
	| End time:................... 2013-01-01 00:00:00
	| Granularity:................ 1 year
	| Temporal type	of maps:...... interval
	+-------------------- Spatial extent ----------------------------------------+
	| North:...................... 320000.0
	| South:...................... 10000.0
	| East:.. .................... 935000.0
	| West:....................... 120000.0
	| Top:........................ 0.0
	| Bottom:..................... 0.0
	+-------------------- Metadata information ----------------------------------+
	| Raster register table:...... raster_map_register_514082e62e864522a13c8123d1949dea
	| North-South resolution min:. 500.0
	| North-South resolution max:. 500.0
	| East-west resolution min:... 500.0
	| East-west resolution max:... 500.0
	| Minimum value	min:.......... 7.370747
	| Minimum value	max:.......... 8.81603
	| Maximum value	min:.......... 17.111387
	| Maximum value	max:.......... 17.915511
	| Aggregation type:........... average
	| Number of registered maps:.. 4
	|
	| Title: Yearly	precipitation
	| Monthly precipitation
	| Description: Aggregated precipitation	dataset	with yearly resolution
	| Dataset with monthly precipitation
	| Command history:
	| # 2014-11-27 10:25:21
	| t.rast.aggregate input="tempmean_monthly"
	|     output="tempmean_yearly" basename="tempmean_year"	granularity="1 years"
	|     method="average"
	|
	| # 2014-11-27 10:26:21
	| t.support input=tempmean_yearly \
	|	 title="Yearly precipitation" \
	|	 description="Aggregated precipitation dataset with yearly resolution"
	+----------------------------------------------------------------------------+

   Different aggregations and map name suffix variants
       Examples	of resulting naming schemes for	 different  aggregations  when
       using the suffix	option:

   Weekly aggregation
       t.rast.aggregate	input=daily_temp output=weekly_avg_temp	\
	 basename=weekly_avg_temp method=average granularity="1	weeks"
       t.rast.list weekly_avg_temp
       name|mapset|start_time|end_time
       weekly_avg_temp_2003_01|climate|2003-01-03 00:00:00|2003-01-10 00:00:00
       weekly_avg_temp_2003_02|climate|2003-01-10 00:00:00|2003-01-17 00:00:00
       weekly_avg_temp_2003_03|climate|2003-01-17 00:00:00|2003-01-24 00:00:00
       weekly_avg_temp_2003_04|climate|2003-01-24 00:00:00|2003-01-31 00:00:00
       weekly_avg_temp_2003_05|climate|2003-01-31 00:00:00|2003-02-07 00:00:00
       weekly_avg_temp_2003_06|climate|2003-02-07 00:00:00|2003-02-14 00:00:00
       weekly_avg_temp_2003_07|climate|2003-02-14 00:00:00|2003-02-21 00:00:00
       Variant with suffix set to granularity:
       t.rast.aggregate	input=daily_temp output=weekly_avg_temp	\
	 basename=weekly_avg_temp suffix=gran method=average \
	 granularity="1	weeks"
       t.rast.list weekly_avg_temp
       name|mapset|start_time|end_time
       weekly_avg_temp_2003_01_03|climate|2003-01-03 00:00:00|2003-01-10 00:00:00
       weekly_avg_temp_2003_01_10|climate|2003-01-10 00:00:00|2003-01-17 00:00:00
       weekly_avg_temp_2003_01_17|climate|2003-01-17 00:00:00|2003-01-24 00:00:00
       weekly_avg_temp_2003_01_24|climate|2003-01-24 00:00:00|2003-01-31 00:00:00
       weekly_avg_temp_2003_01_31|climate|2003-01-31 00:00:00|2003-02-07 00:00:00
       weekly_avg_temp_2003_02_07|climate|2003-02-07 00:00:00|2003-02-14 00:00:00
       weekly_avg_temp_2003_02_14|climate|2003-02-14 00:00:00|2003-02-21 00:00:00

   Monthly aggregation
       t.rast.aggregate	input=daily_temp output=monthly_avg_temp \
	 basename=monthly_avg_temp suffix=gran method=average \
	 granularity="1	months"
       t.rast.list monthly_avg_temp
       name|mapset|start_time|end_time
       monthly_avg_temp_2003_01|climate|2003-01-01 00:00:00|2003-02-01 00:00:00
       monthly_avg_temp_2003_02|climate|2003-02-01 00:00:00|2003-03-01 00:00:00
       monthly_avg_temp_2003_03|climate|2003-03-01 00:00:00|2003-04-01 00:00:00
       monthly_avg_temp_2003_04|climate|2003-04-01 00:00:00|2003-05-01 00:00:00
       monthly_avg_temp_2003_05|climate|2003-05-01 00:00:00|2003-06-01 00:00:00
       monthly_avg_temp_2003_06|climate|2003-06-01 00:00:00|2003-07-01 00:00:00

   Yearly aggregation
       t.rast.aggregate	input=daily_temp output=yearly_avg_temp	\
	 basename=yearly_avg_temp suffix=gran method=average \
	 granularity="1	years"
       t.rast.list yearly_avg_temp
       name|mapset|start_time|end_time
       yearly_avg_temp_2003|climate|2003-01-01 00:00:00|2004-01-01 00:00:00
       yearly_avg_temp_2004|climate|2004-01-01 00:00:00|2005-01-01 00:00:00

SEE ALSO
	  t.rast.aggregate.ds,	t.rast.extract,	 t.info,  r.series,  g.region,
       r.mask

       Temporal	data processing	Wiki

AUTHOR
       SA<paragraph>ren	Gebbert, ThA1/4nen Institute of	Climate-Smart Agricul-
       ture

SOURCE CODE
       Available at: t.rast.aggregate source code (history)

       Main index | Temporal index | Topics index | Keywords index | Graphical
       index | Full index

       A(C) 2003-2020 GRASS Development	Team, GRASS GIS	7.8.3 Reference	Manual

GRASS 7.8.3						   t.rast.aggregate(1)

NAME | KEYWORDS | SYNOPSIS | DESCRIPTION | NOTES | EXAMPLES | SEE ALSO | AUTHOR | SOURCE CODE

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