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
7       r.series on all or a subset of raster  maps  in  a  space  time  raster
8       dataset.
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KEYWORDS

11       temporal, aggregation, series, raster, time
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SYNOPSIS

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

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

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

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

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

133       Available at: t.rast.series source code (history)
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135       Main index | Temporal index | Topics index | Keywords index | Graphical
136       index | Full index
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138       © 2003-2020 GRASS Development Team, GRASS GIS 7.8.5 Reference Manual
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142GRASS 7.8.5                                                   t.rast.series(1)
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