t.rast.aggregate(1grass)

1t.rast.aggregate(1)           Grass User's Manual          t.rast.aggregate(1)
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NAME

6       t.rast.aggregate   -  Aggregates  temporally  the  maps of a space time
7       raster dataset by a user defined granularity.
8

KEYWORDS

10       temporal, aggregation, raster, time
11

SYNOPSIS

13       t.rast.aggregate
14       t.rast.aggregate --help
15       t.rast.aggregate [-n]  input=name  output=name  basename=string   [suf‐
16       fix=string]     granularity=string    method=string    [offset=integer]
17       [nprocs=integer]    [file_limit=integer]     [sampling=name[,name,...]]
18       [where=sql_query]    [--overwrite]   [--help]   [--verbose]   [--quiet]
19       [--ui]
20
21   Flags:
22       -n
23           Register Null maps
24
25       --overwrite
26           Allow output files to overwrite existing files
27
28       --help
29           Print usage summary
30
31       --verbose
32           Verbose module output
33
34       --quiet
35           Quiet module output
36
37       --ui
38           Force launching GUI dialog
39
40   Parameters:
41       input=name [required]
42           Name of the input space time raster dataset
43
44       output=name [required]
45           Name of the output space time raster dataset
46
47       basename=string [required]
48           Basename of the new generated output maps
49           Either a numerical suffix or the start time (s-flag)  separated  by
50           an underscore will be attached to create a unique identifier
51
52       suffix=string
53           Suffix  to  add at basename: set ’gran’ for granularity, ’time’ for
54           the full time format, ’num’ for numerical suffix  with  a  specific
55           number of digits (default %05)
56           Default: gran
57
58       granularity=string [required]
59           Aggregation granularity, format absolute time "x years, x months, x
60           weeks, x days, x hours, x minutes, x seconds" or an  integer  value
61           for relative time
62
63       method=string [required]
64           Aggregate operation to be performed on the raster maps
65           Options:  average,  count, median, mode, minimum, min_raster, maxi‐
66           mum, max_raster, stddev, range, sum,  variance,  diversity,  slope,
67           offset,  detcoeff, quart1, quart3, perc90, quantile, skewness, kur‐
68           tosis
69           Default: average
70
71       offset=integer
72           Offset that is used to create the output map ids, output map id  is
73           generated as: basename_ (count + offset)
74           Default: 0
75
76       nprocs=integer
77           Number of r.series processes to run in parallel
78           Default: 1
79
80       file_limit=integer
81           The maximum number of open files allowed for each r.series process
82           Default: 1000
83
84       sampling=name[,name,...]
85           The method to be used for sampling the input dataset
86           Options:  equal,  overlaps,  overlapped, starts, started, finishes,
87           finished, during, contains
88           Default: contains
89
90       where=sql_query
91           WHERE conditions of SQL statement without ’where’ keyword  used  in
92           the temporal GIS framework
93           Example: start_time > ’2001-01-01 12:30:00’
94

DESCRIPTION

96       t.rast.aggregate  temporally aggregates space time raster datasets by a
97       specific temporal granularity. This module support absolute  and  rela‐
98       tive  time.  The  temporal granularity of absolute time can be seconds,
99       minutes, hours, days, weeks, months or years. Mixing  of  granularities
100       eg.  "1  year,  3  months 5 days" is not supported. In case of relative
101       time the temporal unit of the input space time raster dataset is  used.
102       The granularity must be specified with an integer value.
103
104       This module is sensitive to the current region and mask settings, hence
105       spatial extent and spatial resolution. In case  the  registered  raster
106       maps of the input space time raster dataset have different spatial res‐
107       olutions, the default nearest neighbor resampling method  is  used  for
108       runtime spatial aggregation.
109

NOTES

111       The  raster  module  r.series  is  used internally. Hence all aggregate
112       methods of r.series are supported. See the  r.series  manual  page  for
113       details.
114
115       This  module  will  shift  the  start date for each aggregation process
116       depending on the provided temporal granularity.  The  following  shifts
117       will performed:
118
119           ·   granularity  years:  will  start at the first of January, hence
120               14-08-2012 00:01:30 will be shifted to 01-01-2012 00:00:00
121
122           ·   granularity months: will start at the first  day  of  a  month,
123               hence 14-08-2012 will be shifted to 01-08-2012 00:00:00
124
125           ·   granularity  weeks: will start at the first day of a week (Mon‐
126               day), hence 14-08-2012 01:30:30 will be shifted  to  13-08-2012
127               01:00:00
128
129           ·   granularity  days: will start at the first hour of a day, hence
130               14-08-2012 00:01:30 will be shifted to 14-08-2012 00:00:00
131
132           ·   granularity hours: will start at the first minute  of  a  hour,
133               hence   14-08-2012  01:30:30  will  be  shifted  to  14-08-2012
134               01:00:00
135
136           ·   granularity minutes: will  start  at  the  first  second  of  a
137               minute, hence 14-08-2012 01:30:30 will be shifted to 14-08-2012
138               01:30:00
139
140       The specification of the  temporal  relation  between  the  aggregation
141       intervals  and  the  raster  map  layers  is always formulated from the
142       aggregation interval viewpoint. Hence, the relation contains has to  be
143       specified  to  aggregate  map  layer  that are temporally located in an
144       aggregation interval.
145
146       Parallel processing is supported in case that more than one interval is
147       available for aggregation computation. Internally several r.series mod‐
148       ules will be started, depending on the  number  of  specified  parallel
149       processes (nprocs) and the number of intervals to aggregate.
150

EXAMPLES

152   Aggregation of monthly data into yearly data
153       In this example the user is going to aggregate monthly data into yearly
154       data, running:
155       t.rast.aggregate input=tempmean_monthly output=tempmean_yearly \
156                        basename=tempmean_year \
157                        granularity="1 years" method=average
158       t.support input=tempmean_yearly \
159                 title="Yearly precipitation" \
160                 description="Aggregated precipitation dataset with yearly resolution"
161       t.info tempmean_yearly
162        +-------------------- Space Time Raster Dataset -----------------------------+
163        |                                                                            |
164        +-------------------- Basic information -------------------------------------+
165        | Id: ........................ tempmean_yearly@climate_2000_2012
166        | Name: ...................... tempmean_yearly
167        | Mapset: .................... climate_2000_2012
168        | Creator: ................... lucadelu
169        | Temporal type: ............. absolute
170        | Creation time: ............. 2014-11-27 10:25:21.243319
171        | Modification time:.......... 2014-11-27 10:25:21.862136
172        | Semantic type:.............. mean
173        +-------------------- Absolute time -----------------------------------------+
174        | Start time:................. 2009-01-01 00:00:00
175        | End time:................... 2013-01-01 00:00:00
176        | Granularity:................ 1 year
177        | Temporal type of maps:...... interval
178        +-------------------- Spatial extent ----------------------------------------+
179        | North:...................... 320000.0
180        | South:...................... 10000.0
181        | East:.. .................... 935000.0
182        | West:....................... 120000.0
183        | Top:........................ 0.0
184        | Bottom:..................... 0.0
185        +-------------------- Metadata information ----------------------------------+
186        | Raster register table:...... raster_map_register_514082e62e864522a13c8123d1949dea
187        | North-South resolution min:. 500.0
188        | North-South resolution max:. 500.0
189        | East-west resolution min:... 500.0
190        | East-west resolution max:... 500.0
191        | Minimum value min:.......... 7.370747
192        | Minimum value max:.......... 8.81603
193        | Maximum value min:.......... 17.111387
194        | Maximum value max:.......... 17.915511
195        | Aggregation type:........... average
196        | Number of registered maps:.. 4
197        |
198        | Title: Yearly precipitation
199        | Monthly precipitation
200        | Description: Aggregated precipitation dataset with yearly resolution
201        | Dataset with monthly precipitation
202        | Command history:
203        | # 2014-11-27 10:25:21
204        | t.rast.aggregate input="tempmean_monthly"
205        |     output="tempmean_yearly" basename="tempmean_year" granularity="1 years"
206        |     method="average"
207        |
208        | # 2014-11-27 10:26:21
209        | t.support input=tempmean_yearly \
210        |        title="Yearly precipitation" \
211        |        description="Aggregated precipitation dataset with yearly resolution"
212        +----------------------------------------------------------------------------+
213
214   Different aggregations and map name suffix variants
215       Examples of resulting naming schemes for  different  aggregations  when
216       using the suffix option:
217
218   Weekly aggregation
219       t.rast.aggregate input=daily_temp output=weekly_avg_temp \
220         basename=weekly_avg_temp method=average granularity="1 weeks"
221       t.rast.list weekly_avg_temp
222       name|mapset|start_time|end_time
223       weekly_avg_temp_2003_01|climate|2003-01-03 00:00:00|2003-01-10 00:00:00
224       weekly_avg_temp_2003_02|climate|2003-01-10 00:00:00|2003-01-17 00:00:00
225       weekly_avg_temp_2003_03|climate|2003-01-17 00:00:00|2003-01-24 00:00:00
226       weekly_avg_temp_2003_04|climate|2003-01-24 00:00:00|2003-01-31 00:00:00
227       weekly_avg_temp_2003_05|climate|2003-01-31 00:00:00|2003-02-07 00:00:00
228       weekly_avg_temp_2003_06|climate|2003-02-07 00:00:00|2003-02-14 00:00:00
229       weekly_avg_temp_2003_07|climate|2003-02-14 00:00:00|2003-02-21 00:00:00
230       Variant with suffix set to granularity:
231       t.rast.aggregate input=daily_temp output=weekly_avg_temp \
232         basename=weekly_avg_temp suffix=gran method=average \
233         granularity="1 weeks"
234       t.rast.list weekly_avg_temp
235       name|mapset|start_time|end_time
236       weekly_avg_temp_2003_01_03|climate|2003-01-03 00:00:00|2003-01-10 00:00:00
237       weekly_avg_temp_2003_01_10|climate|2003-01-10 00:00:00|2003-01-17 00:00:00
238       weekly_avg_temp_2003_01_17|climate|2003-01-17 00:00:00|2003-01-24 00:00:00
239       weekly_avg_temp_2003_01_24|climate|2003-01-24 00:00:00|2003-01-31 00:00:00
240       weekly_avg_temp_2003_01_31|climate|2003-01-31 00:00:00|2003-02-07 00:00:00
241       weekly_avg_temp_2003_02_07|climate|2003-02-07 00:00:00|2003-02-14 00:00:00
242       weekly_avg_temp_2003_02_14|climate|2003-02-14 00:00:00|2003-02-21 00:00:00
243
244   Monthly aggregation
245       t.rast.aggregate input=daily_temp output=monthly_avg_temp \
246         basename=monthly_avg_temp suffix=gran method=average \
247         granularity="1 months"
248       t.rast.list monthly_avg_temp
249       name|mapset|start_time|end_time
250       monthly_avg_temp_2003_01|climate|2003-01-01 00:00:00|2003-02-01 00:00:00
251       monthly_avg_temp_2003_02|climate|2003-02-01 00:00:00|2003-03-01 00:00:00
252       monthly_avg_temp_2003_03|climate|2003-03-01 00:00:00|2003-04-01 00:00:00
253       monthly_avg_temp_2003_04|climate|2003-04-01 00:00:00|2003-05-01 00:00:00
254       monthly_avg_temp_2003_05|climate|2003-05-01 00:00:00|2003-06-01 00:00:00
255       monthly_avg_temp_2003_06|climate|2003-06-01 00:00:00|2003-07-01 00:00:00
256
257   Yearly aggregation
258       t.rast.aggregate input=daily_temp output=yearly_avg_temp \
259         basename=yearly_avg_temp suffix=gran method=average \
260         granularity="1 years"
261       t.rast.list yearly_avg_temp
262       name|mapset|start_time|end_time
263       yearly_avg_temp_2003|climate|2003-01-01 00:00:00|2004-01-01 00:00:00
264       yearly_avg_temp_2004|climate|2004-01-01 00:00:00|2005-01-01 00:00:00
265

AUTHOR

273       Sören Gebbert, Thünen Institute of Climate-Smart Agriculture
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275       Last changed: $Date: 2016-09-19 12:29:41 +0200 (Mon, 19 Sep 2016) $
276

SOURCE CODE

278       Available at: t.rast.aggregate source code (history)
279
280       Main index | Temporal index | Topics index | Keywords index | Graphical
281       index | Full index
282
283       © 2003-2019 GRASS Development Team, GRASS GIS 7.6.0 Reference Manual
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287GRASS 7.6.0                                                t.rast.aggregate(1)