t.rast.aggregate.ds(1)

📦
version 8.4.2 · 8 versions
, in fedora 44

✨ t.rast.aggregate.ds ✨

okay bestie, welcome to t.rast.aggregate.ds(1), straight outta the GRASS GIS User's Manual 💖 this one is a whole vibe and she is iconic, so grab a snack and let's get into it.

🎀 NAME 🎀

t.rast.aggregate.ds — aggregates data of an existing space time raster dataset using the time intervals of a second space time dataset. it's giving "let that other dataset decide your schedule" fr fr 🦋

🔥 KEYWORDS 🔥

temporal, aggregation, raster, time

🌸 SYNOPSIS 🌸

t.rast.aggregate.ds
t.rast.aggregate.ds --help
t.rast.aggregate.ds [-n] input=name sample=name  [type=name]
output=name basename=string  [suffix=string]  method=string
[offset=integer]   [nprocs=integer]   [sampling=name[,name,...]]
[where=sql_query]   [--overwrite]  [--help]  [--verbose]  [--quiet]
[--ui]

✨ Flags ✨

  • -n — register Null maps. yes she's literally registering the void, no cap 💀
  • --overwrite — allow output files to overwrite existing files (main character energy: she does NOT ask permission 👑)
  • --help — print usage summary
  • --verbose — verbose module output
  • --quiet — quiet module output (she's not in the mood to chat 🙈)
  • --ui — force launching GUI dialog

💅 Parameters 💅

  • input=name [required] — name of the input space time raster dataset. this is your starting material bestie
  • sample=name [required] — time intervals from this space time dataset (raster, vector or raster3d) are used for aggregation computation. she's the one setting the schedule ✨
  • type=name — type of the space time dataset from which aggregation will be copied
    • Options: strds, stvds, str3ds
    • Default: strds
  • output=name [required] — name of the output space time raster dataset
  • basename=string [required] — basename of the new generated output maps
    • a numerical suffix 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
  • method=string [required] — aggregate operation to be performed on the raster maps
    • Options: average, count, median, mode, minimum, min_raster, maximum, max_raster, stddev, range, sum, variance, diversity, slope, offset, detcoeff, quart1, quart3, perc90, quantile, skewness, kurtosis
    • 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.mapcalc processes to run in parallel
    • Default: 1
  • 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.ds works like t.rast.aggregate but instead of defining a fixed granularity for temporal aggregation the time intervals of all maps registered in a second space time dataset (can be STRDS, STR3DS or STVDS) are used to aggregate the maps of the input space time raster dataset.

so basically: instead of you picking the chunks, the other dataset picks the chunks and your girl just goes along with it 🌈 periodt.

💜 NOTES 💜

The sampling method must be specified from the sampler dataset point of view. It defines the temporal relationships between intervals of the sampling dataset and the input space time raster dataset.

read that twice bestie — from the sampler dataset point of view. it's the sampler's world and we're all just living in it 🧚

⭐ EXAMPLES ⭐

🌧️ Precipitation aggregation 🌧️

In this example we create 7 raster maps that will be registered in a single space time raster dataset named precipitation_daily using a daily temporal granularity. The names of the raster maps are stored in a text file that is used for raster map registration.

A space time vector dataset is created out of two vector maps with different temporal resolution. The maps are created using v.random. The first map has a granule of 3 days the second a granule of 4 days.

more coming, bestie — don't go anywhere 💖

okay so picture this, bestie: we're taking precipitation_daily — our space time raster dataset with daily temporal granularity — and we're aggregating it using a space time vector dataset, and the glow-up result lands in the output space time raster dataset precipitation_agg. 💖

The aggregation method is set to sum, because we wanna accumulate the precipitation values of all the intervals in the space time vector dataset. And the sampling option? That's the ✨vibe check✨ that makes sure only raster maps that are temporally during the time intervals of the space time vector dataset get considered for computation. So obvi we set it to contains — meaning time stamped vector map layers temporally contain the raster map layers. 🫶

MAPS="map_1 map_2 map_3 map_4 map_5 map_6 map_7"
for map in ${MAPS} ; do
    r.mapcalc expression="${map} = 1"
    echo ${map} >> map_list.txt
done
t.create type=strds temporaltype=absolute \
         output=precipitation_daily \
         title="Daily precipitation" \
         description="Test dataset with daily precipitation"
t.register -i type=raster input=precipitation_daily \
           file=map_list.txt start="2012-08-20" increment="1 days"
t.info type=strds input=precipitation_daily
 +-------------------- Space Time Raster Dataset -----------------------------+
 |                                                                            |
 +-------------------- Basic information -------------------------------------+
 | Id: ........................ precipitation_daily@PERMANENT
 | Name: ...................... precipitation_daily
 | Mapset: .................... PERMANENT
 | Creator: ................... soeren
 | Temporal type: ............. absolute
 | Creation time: ............. 2014-11-23 16:48:17.686979
 | Modification time:.......... 2014-11-23 16:48:18.302978
 | Semantic type:.............. mean
 +-------------------- Absolute time -----------------------------------------+
 | Start time:................. 2012-09-10 00:00:00
 | End time:................... 2012-09-17 00:00:00
 | Granularity:................ 1 day
 | Temporal type of maps:...... interval
 +-------------------- Spatial extent ----------------------------------------+
 | North:...................... 80.0
 | South:...................... 0.0
 | East:.. .................... 120.0
 | West:....................... 0.0
 | Top:........................ 0.0
 | Bottom:..................... 0.0
 +-------------------- Metadata information ----------------------------------+
 | Raster register table:...... raster_map_register_3225725979b14b5db343a00835b882c7
 | North-South resolution min:. 10.0
 | North-South resolution max:. 10.0
 | East-west resolution min:... 10.0
 | East-west resolution max:... 10.0
 | Minimum value min:.......... 1.0
 | Minimum value max:.......... 1.0
 | Maximum value min:.......... 1.0
 | Maximum value max:.......... 1.0
 | Aggregation type:........... None
 | Number of registered maps:.. 7
 |
 | Title:
 | Daily precipitation
 | Description:
 | Test dataset with daily precipitation
 | Command history:
 | # 2014-11-23 16:48:17
 | t.create type="strds" temporaltype="absolute"
 |     output="precipitation_daily" title="Daily precipitation"
 |     description="Test dataset with daily precipitation"
 | # 2014-11-23 16:48:18
 | t.register -i type="rast" input="precipitation_daily"
 |     file="map_list.txt" start="2012-08-20" increment="1 days"
 |
 +----------------------------------------------------------------------------+

next up, we gotta make ourselves some points to sample with. Two v.random runs, a fresh stvds, and boom — two vector maps registered on their own little schedules. 🎀

v.random output=points_1 n=20
v.random output=points_2 n=20
t.create type=stvds temporaltype=absolute \
         output=points \
         title="Points" \
         description="Points for aggregation"
t.register -i type=vector input=points \
           map=points_1 start="2012-08-20" increment="3 days"
t.register -i type=vector input=points \
           map=points_2 start="2012-08-23" increment="4 days"
t.info type=stvds input=points
 +-------------------- Space Time Vector Dataset -----------------------------+
 |                                                                            |
 +-------------------- Basic information -------------------------------------+
 | Id: ........................ points@PERMANENT
 | Name: ...................... points
 | Mapset: .................... PERMANENT
 | Creator: ................... soeren
 | Temporal type: ............. absolute
 | Creation time: ............. 2014-11-23 16:48:49.193903
 | Modification time:.......... 2014-11-23 16:48:50.185671
 | Semantic type:.............. mean
 +-------------------- Absolute time -----------------------------------------+
 | Start time:................. 2012-08-20 00:00:00
 | End time:................... 2012-08-27 00:00:00
 | Granularity:................ 1 day
 | Temporal type of maps:...... interval
 +-------------------- Spatial extent ----------------------------------------+
 | North:...................... 79.283411
 | South:...................... 5.724954
 | East:.. .................... 118.881168
 | West:....................... 0.016755
 | Top:........................ 0.0
 | Bottom:..................... 0.0
 +-------------------- Metadata information ----------------------------------+
 | Vector register table:...... vector_map_register_6f02d33e0ee243d1a521aaaca39ecb31
 | Number of points ........... 40
 | Number of lines ............ 0
 | Number of boundaries ....... 0
 | Number of centroids ........ 0
 | Number of faces ............ 0
 | Number of kernels .......... 0
 | Number of primitives ....... 40
 | Number of nodes ............ 0
 | Number of areas ............ 0
 | Number of islands .......... 0
 | Number of holes ............ 0
 | Number of volumes .......... 0
 | Number of registered maps:.. 2
 |
 | Title:
 | Points
 | Description:
 | Points for aggregation
 | Command history:
 | # 2014-11-23 16:48:49
 | t.create type="stvds" temporaltype="absolute"
 |     output="points" title="Points" description="Points for aggregation"
 | # 2014-11-23 16:48:49
 | t.register -i type="vect" input="points"
 |     map="points_1" start="2012-08-20" increment="3 days"
 | # 2014-11-23 16:48:50
 | t.register -i type="vect" input="points"
 |     map="points_2" start="2012-08-23" increment="4 days"
 |
 +----------------------------------------------------------------------------+

and now the main character moment 💅 — we run t.rast.aggregate.ds with input=precipitation_daily, output=precipitation_agg, sample=points, type=stvds, basename=prec_agg, method=sum and sampling=contains:

t.rast.aggregate.ds input=precipitation_daily \
                    output=precipitation_agg \
                    sample=points type=stvds \
                    basename=prec_agg \
                    method=sum sampling=contains
t.support input=precipitation_agg \
          title="Aggregated precipitation" \
          description="Aggregated precipitation dataset"
t.info type=strds input=precipitation_agg
 +-------------------- Space Time Raster Dataset -----------------------------+
 |                                                                            |
 +-------------------- Basic information -------------------------------------+
 | Id: ........................ precipitation_agg@PERMANENT
 | Name: ...................... precipitation_agg
 | Mapset: .................... PERMANENT
 | Creator: ................... soeren
 | Temporal type: ............. absolute
 | Creation time: ............. 2014-11-23 16:53:23.488799
 | Modification time:.......... 2014-11-23 16:53:28.714886
 | Semantic type:.............. mean
 +-------------------- Absolute time -----------------------------------------+
 | Start time:................. 2012-08-20 00:00:00
 | End time:................... 2012-08-27 00:00:00
 | Granularity:................ 1 day
 | Temporal type of maps:...... interval
 +-------------------- Spatial extent ----------------------------------------+
 | North:...................... 80.0
 | South:...................... 0.0
 | East:.. .................... 120.0
 | West:....................... 0.0
 | Top:........................ 0.0
 | Bottom:..................... 0.0
 +-------------------- Metadata information ----------------------------------+
 | Raster register table:...... raster_map_register_7b025eb7431747c98c5c1ad971e8c282
 | North-South resolution min:. 10.0
 | North-South resolution max:. 10.0
 | East-west resolution min:... 10.0
 | East-west resolution max:... 10.0
 | Minimum value min:.......... 3.0
 | Minimum value max:.......... 4.0
 | Maximum value min:.......... 3.0
 | Maximum value max:.......... 4.0
 | Aggregation type:........... sum
 | Number of registered maps:.. 2
 |
 | Title:
 | Aggregated precipitation
 | Description:
 | Aggregated precipitation dataset
 | Command history:
 | # 2014-11-23 16:53:23
 | t.rast.aggregate.ds input="precipitation_daily"
 |     output="precipitation_agg" sample="points" type="stvds" basename="prec_agg"
 |     method="sum" sampling="contains"
 | # 2014-11-23 16:53:28
 | t.support input="precipitation_agg"
 |     title="Aggregated precipitation"
 |     description="Aggregated precipitation dataset"
 |
 +----------------------------------------------------------------------------+

🛰️ MODIS satellite sensor daily data aggregation to 8 days

Okay bestie, this one is the classic — rolling daily data up into eight-day chunks, the way some MODIS satellite sensor products do it. This "eight-day week" is literally a thing in those products, and yes, the calendar math is a little cursed, but we're built different. 💅

Heads up: the example is written in shell language, so grab your terminal and let's go.
# NOTE: the example is written in shell language
# create maps every 8 days as seed maps
for year in `seq 2000 2001` ; do
   for doy in `seq -w 1 8 365` ; do
      r.mapcalc -s expression="8day_${year}_${doy} = rand(0.0,40.0)"
   done
done
# From de name of each map, we take year and doy, and convert it
# to a YYYY-MM-DD date for start and end, and create a file with
# mapnames, start date and end date
g.list type=raster pattern=8day_20??_* > names_list
for NAME in `cat names_list` ; do
   # Parse
   YEAR=`echo $NAME | cut -d’_’ -f2`
   DOY=`echo $NAME | cut -d’_’ -f3`
   # convert YYYY_DOY to YYYY-MM-DD
   DOY=`echo "$DOY" | sed ’s/^0*//’`
   doy_end=0
   if [ $DOY -le "353" ] ; then
      doy_end=$(( $DOY + 8 ))
   elif [ $DOY -eq "361" ] ; then
      if [ $[$YEAR % 4] -eq 0 ] && [ $[$YEAR % 100] -ne 0 ] || [ $[$YEAR % 400] -eq 0 ] ; then
         doy_end=$(( $DOY + 6 ))
      else
            doy_end=$(( $DOY + 5 ))
      fi
   fi
   DATE_START=`date -d "${YEAR}-01-01 +$(( ${DOY} - 1 ))days" +%Y-%m-%d`
   DATE_END=`date -d "${YEAR}-01-01 +$(( ${doy_end} -1 ))days" +%Y-%m-%d`
   # text file with mapnames, start date and end date
   echo "$NAME|$DATE_START|$DATE_END" >> list_map_start_end_time.txt
done
# check the list created.
cat list_map_start_end_time.txt
8day_2000_001|2000-01-01|2000-01-09
8day_2000_009|2000-01-09|2000-01-17
...
8day_2000_353|2000-12-18|2000-12-26
8day_2000_361|2000-12-26|2001-01-01
8day_2001_001|2001-01-01|2001-01-09
8day_2001_009|2001-01-09|2001-01-17
...
8day_2001_345|2001-12-11|2001-12-19
8day_2001_353|2001-12-19|2001-12-27
8day_2001_361|2001-12-27|2002-01-01
# all maps except for the last map in each year represent 8-days
# intervals. But the aggregation starts all over again every
# January 1st.
# create 8-day MODIS-like strds
t.create type=strds temporaltype=absolute \
   output=8day_ts title="8 day time series" \
   description="STRDS with MODIS like 8 day aggregation"
# register maps
t.register type=raster input=8day_ts \
   file=list_map_start_end_time.txt
# check
t.info input=8day_ts
t.rast.list input=8day_ts
# finally, copy the aggregation to a daily time series
t.rast.aggregate.ds input=daily_ts sample=8day_ts \
   output=8day_agg basename=8day_agg method=average \
   sampling=contains suffix=gran
# add metadata
t.support input=8day_agg \
   title="8 day aggregated ts" \
   description="8 day MODIS-like aggregated dataset"
# check map list in newly created aggregated strds
t.rast.list input=8day_agg
name|mapset|start_time|end_time
8day_agg_2000_01_01|modis|2000-01-01 00:00:00|2000-01-09 00:00:00
8day_agg_2000_01_09|modis|2000-01-09 00:00:00|2000-01-17 00:00:00
8day_agg_2000_01_17|modis|2000-01-17 00:00:00|2000-01-25 00:00:00
...
8day_agg_2000_12_18|modis|2000-12-18 00:00:00|2000-12-26 00:00:00
8day_agg_2000_12_26|modis|2000-12-26 00:00:00|2001-01-01 00:00:00
8day_agg_2001_01_01|modis|2001-01-01 00:00:00|2001-01-09 00:00:00
...
8day_agg_2001_12_11|modis|2001-12-11 00:00:00|2001-12-19 00:00:00
8day_agg_2001_12_19|modis|2001-12-19 00:00:00|2001-12-27 00:00:00
8day_agg_2001_12_27|modis|2001-12-27 00:00:00|2002-01-01 00:00:00

See how it all lines up? Daily in, eight-day out, boundaries matching exactly because sampling=contains said so. That's the whole assignment, and it ate. 🔥

👀 SEE ALSO

  • t.rast.aggregate
  • t.create
  • t.info

✍️ AUTHOR

Sören Gebbert, Thünen Institute of Climate-Smart Agriculture — an absolute legend, no cap. 👑

🔎 SOURCE CODE

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

Accessed: Saturday Jan 17 05:41:53 2026

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© 2003-2025 GRASS Development Team, GRASS GIS 8.4.2 Reference Manual

And that's a wrap on t.rast.aggregate.ds(1), bestie! 💖 The footer's chilling down there telling you this is GRASS 8.4.2 and the page landed on Saturday Jan 17 05:41:53 2026. Go forth and aggregate, you gorgeous temporal genius. 🦋✨

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