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

6       t.rast.series   -  Performs different aggregation algorithms from r.se‐
7       ries on all or a subset of raster maps in a space time raster dataset.
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KEYWORDS

10       temporal, aggregation, series, raster, time
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SYNOPSIS

13       t.rast.series
14       t.rast.series --help
15       t.rast.series  [-tn]  input=name   method=string[,string,...]    [quan‐
16       tile=float[,float,...]]                     [order=string[,string,...]]
17       [where=sql_query]   output=name[,name,...]    [--overwrite]    [--help]
18       [--verbose]  [--quiet]  [--ui]
19
20   Flags:
21       -t
22           Do  not  assign the space time raster dataset start and end time to
23           the output map
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25       -n
26           Propagate NULLs
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28       --overwrite
29           Allow output files to overwrite existing files
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31       --help
32           Print usage summary
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34       --verbose
35           Verbose module output
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37       --quiet
38           Quiet module output
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40       --ui
41           Force launching GUI dialog
42
43   Parameters:
44       input=name [required]
45           Name of the input space time raster dataset
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47       method=string[,string,...] [required]
48           Aggregate operation to be performed on the raster maps
49           Options: average, count, median, mode, minimum,  min_raster,  maxi‐
50           mum,  max_raster,  stddev,  range, sum, variance, diversity, slope,
51           offset, detcoeff, quart1, quart3, perc90, quantile, skewness,  kur‐
52           tosis
53           Default: average
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55       quantile=float[,float,...]
56           Quantile to calculate for method=quantile
57           Options: 0.0-1.0
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59       order=string[,string,...]
60           Sort the maps by category
61           Options:  id,   name,  creator,  mapset,  creation_time,  modifica‐
62           tion_time,  start_time,  end_time,  north,   south,   west,   east,
63           min,  max
64           Default: start_time
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66       where=sql_query
67           WHERE  conditions  of SQL statement without ’where’ keyword used in
68           the temporal GIS framework
69           Example: start_time > ’2001-01-01 12:30:00’
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71       output=name[,name,...] [required]
72           Name for output raster map(s)
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DESCRIPTION

75       The input of this module is a single space  time  raster  dataset,  the
76       output  is  a single raster map layer. A subset of the input space time
77       raster dataset can be selected using the where option. The  sorting  of
78       the  raster  map layer can be set using the order option. Be aware that
79       the order of the maps can significantly influence the result of the ag‐
80       gregation (e.g.: slope). By default the maps are ordered by start_time.
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82       t.rast.series  is  a  simple wrapper for the raster module r.series. It
83       supports a subset of the aggregation methods of r.series.
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EXAMPLES

86   Estimate the average temperature for the whole time series
87       Here the entire stack of input maps is considered:
88       t.rast.series input=tempmean_monthly output=tempmean_average method=average
89
90   Estimate the average temperature for a subset of the time series
91       Here the stack of input maps is limited to a certain period of time:
92       t.rast.series input=tempmean_daily output=tempmean_season method=average \
93         where="start_time >= ’2012-06’ and start_time <= ’2012-08’"
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95   Climatology: single month in a multi-annual time series
96       By considering only a single month in a multi-annual  time  series  the
97       so-called  climatology  can  be computed.  Estimate average temperature
98       for all January maps in the time series:
99       t.rast.series input=tempmean_monthly \
100           method=average output=tempmean_january \
101           where="strftime(’%m’, start_time)=’01’"
102       # equivalently, we can use
103       t.rast.series input=tempmean_monthly \
104           output=tempmean_january method=average \
105           where="start_time = datetime(start_time, ’start of year’, ’0 month’)"
106       # if we want also February and March averages
107       t.rast.series input=tempmean_monthly \
108           output=tempmean_february method=average \
109           where="start_time = datetime(start_time, ’start of year’, ’1 month’)"
110       t.rast.series input=tempmean_monthly \
111           output=tempmean_march method=average \
112           where="start_time = datetime(start_time, ’start of year’, ’2 month’)"
113       Generalizing a bit, we  can  estimate  monthly  climatologies  for  all
114       months by means of different methods
115       for i in `seq -w 1 12` ; do
116         for m in average stddev minimum maximum ; do
117           t.rast.series input=tempmean_monthly method=${m} output=tempmean_${m}_${i} \
118           where="strftime(’%m’, start_time)=’${i}’"
119         done
120       done
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SEE ALSO

123        r.series, t.create, t.info
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125       Temporal data processing Wiki
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AUTHOR

128       Sören Gebbert, Thünen Institute of Climate-Smart Agriculture
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SOURCE CODE

131       Available at: t.rast.series source code (history)
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133       Accessed: Saturday Jan 21 21:17:05 2023
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135       Main index | Temporal index | Topics index | Keywords index | Graphical
136       index | Full index
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138       © 2003-2023 GRASS Development Team, GRASS GIS 8.2.1 Reference Manual
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142GRASS 8.2.1                                                   t.rast.series(1)
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