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+#!/usr/bin/env python
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+# -*- coding: utf-8 -*-
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+############################################################################
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+#
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+# MODULE: tr.mapcalc
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+# AUTHOR(S): Soeren Gebbert
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+#
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+# PURPOSE: Perform r.mapcalc expressions on maps of sampled space time raster datasets
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+# COPYRIGHT: (C) 2012 by the GRASS Development Team
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+#
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+# This program is free software under the GNU General Public
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+# License (version 2). Read the file COPYING that comes with GRASS
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+# for details.
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+#
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+#############################################################################
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+
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+#%module
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+#% description: Perform r.mapcalc expressions on maps of sampled space time raster datasets
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+#% keywords: temporal
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+#% keywords: mapcalc
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+#%end
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+
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+#%option G_OPT_STRDS_INPUTS
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+#%end
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+
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+#%option
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+#% key: expression
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+#% type: string
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+#% description: The r.mapcalc expression applied to each time step of the sampled data
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+#% required: yes
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+#% multiple: no
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+#%end
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+
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+#%option G_OPT_T_SAMPLE
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+#% key: method
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+#% answer: during,overlap,contain,equal
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+#%end
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+
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+#%option G_OPT_STRDS_OUTPUT
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+#%end
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+
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+#%option G_OPT_R_BASE
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+#% required: yes
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+#%end
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+
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+#%option
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+#% key: nprocs
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+#% type: integer
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+#% description: The number of r.mapcalc processes to run in parallel
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+#% required: no
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+#% multiple: no
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+#% answer: 1
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+#%end
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+
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+#%flag
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+#% key: n
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+#% description: Register Null maps
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+#%end
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+
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+#%flag
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+#% key: s
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+#% description: Check spatial overlap
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+#%end
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+
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+from multiprocessing import Process
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+import copy
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+import grass.script as grass
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+import grass.temporal as tgis
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+
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+############################################################################
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+
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+def main():
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+
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+ # Get the options
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+ inputs = options["inputs"]
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+ output = options["output"]
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+ expression = options["expression"]
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+ base = options["base"]
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+ method = options["method"]
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+ nprocs = int(options["nprocs"])
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+ register_null = flags["n"]
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+ spatial = flags["s"]
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+
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+ # Create the method list
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+ method = method.split(",")
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+
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+ # Make sure the temporal database exists
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+ tgis.create_temporal_database()
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+
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+ # We need a database interface for fast computation
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+ dbif = tgis.sql_database_interface()
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+ dbif.connect()
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+
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+ mapset = grass.gisenv()["MAPSET"]
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+
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+ input_name_list = inputs.split(",")
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+
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+ # Process the first input
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+ if input_name_list[0].find("@") >= 0:
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+ id = input_name_list[0]
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+ else:
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+ id = input_name_list[0] + "@" + mapset
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+
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+ first_input = tgis.space_time_raster_dataset(id)
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+
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+ if first_input.is_in_db(dbif) == False:
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+ dbif.close()
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+ grass.fatal(_("Space time raster dataset <%s> not found in temporal database") % (id))
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+
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+ # Fill the object with data from the temporal database
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+ first_input.select(dbif)
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+
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+ # All additional inputs in reverse sorted order to avoid wrong name substitution
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+ input_name_list = input_name_list[1:]
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+ input_name_list.sort()
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+ input_name_list.reverse()
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+ input_list = []
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+
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+ for input in input_name_list:
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+
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+ if input.find("@") >= 0:
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+ id = input
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+ else:
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+ id = input + "@" + mapset
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+
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+ sp = tgis.space_time_raster_dataset(id)
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+
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+ if sp.is_in_db(dbif) == False:
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+ dbif.close()
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+ grass.fatal(_("Space time raster dataset <%s> not found in temporal database") % (id))
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+
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+ sp.select(dbif)
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+
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+ input_list.append(copy.copy(sp))
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+
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+ # Create the new space time raster dataset
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+ if output.find("@") >= 0:
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+ out_id = output
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+ else:
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+ out_id = output + "@" + mapset
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+
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+ new_sp = tgis.space_time_raster_dataset(out_id)
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+
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+ # Check if in database
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+ if new_sp.is_in_db(dbif):
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+ if grass.overwrite() == False:
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+ dbif.close()
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+ grass.fatal(_("Space time raster dataset <%s> is already in database, use overwrite flag to overwrite") % out_id)
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+
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+ # Sample all inputs by the first input and create a sample matrix
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+ if spatial:
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+ grass.message(_("Start spatio-temporal sampling"))
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+ else:
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+ grass.message(_("Start temporal sampling"))
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+ map_matrix = []
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+ id_list = []
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+ sample_map_list = []
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+ # First entry is the first dataset id
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+ id_list.append(first_input.get_name())
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+
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+ if len(input_list) > 0:
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+ has_samples = False
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+ for dataset in input_list:
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+ list = dataset.sample_by_dataset(stds=first_input, method=method, spatial=spatial, dbif=dbif)
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+
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+ # In case samples are not found
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+ if not list and len(list) == 0:
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+ dbif.close()
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+ grass.message(_("No samples found for map calculation"))
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+ return 0
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+
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+ # The fist entries are the samples
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+ map_name_list = []
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+ if has_samples == False:
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+ for entry in list:
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+ granule = entry["granule"]
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+ # Do not consider gaps
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+ if granule.get_id() == None:
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+ continue
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+ sample_map_list.append(granule)
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+ map_name_list.append(granule.get_name())
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+ # Attach the map names
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+ map_matrix.append(copy.copy(map_name_list))
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+ has_samples = True
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+
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+ map_name_list = []
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+ for entry in list:
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+ maplist = entry["samples"]
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+ granule = entry["granule"]
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+
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+ # Do not consider gaps in the sampler
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+ if granule.get_id() == None:
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+ continue
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+
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+ if len(maplist) > 1:
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+ grass.warning(_("Found more than a single map in a sample granule. "\
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+ "Only the first map is used for computation. "\
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+ "Use tr.aggregate.ds to create synchronous datasets."))
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+
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+ # Store all maps! This includes non existent maps, identified by id == None
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+ map_name_list.append(maplist[0].get_name())
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+
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+ # Attach the map names
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+ map_matrix.append(copy.copy(map_name_list))
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+
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+ id_list.append(dataset.get_name())
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+ else:
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+ list = first_input.get_registered_maps_as_objects(dbif=dbif)
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+
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+ if list == None:
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+ dbif.close()
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+ grass.message(_("No maps in input dataset"))
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+ return 0
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+
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+ map_name_list = []
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+ for map in list:
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+ map_name_list.append(map.get_name())
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+ sample_map_list.append(map)
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+
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+ # Attach the map names
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+ map_matrix.append(copy.copy(map_name_list))
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+
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+
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+ # Needed for map registration
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+ map_list = []
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+
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+ if len(map_matrix) > 0:
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+
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+ grass.message(_("Start r.mapcalc computation"))
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+
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+ count = 0
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+ # Get the number of samples
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+ num = len(map_matrix[0])
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+
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+ # Parallel processing
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+ proc_list = []
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+ proc_count = 0
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+
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+ # For all samples
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+ for i in range(num):
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+
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+ count += 1
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+ grass.percent(count, num, 1)
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+
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+ # Create the r.mapcalc statement for the current time step
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+ map_name = "%s_%i" % (base, count)
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+ expr = "%s = %s" % (map_name, expression)
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+
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+ # Check that all maps are in the sample
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+ valid_maps = True
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+ # Replace all dataset names with their map names of the current time step
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+ for j in range(len(map_matrix)):
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+ if map_matrix[j][i] == None:
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+ valid_maps = False
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+ break
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+ # Substitute the dataset name with the map name
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+ expr = expr.replace(id_list[j], map_matrix[j][i])
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+
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+ # Proceed with the next sample
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+ if valid_maps == False:
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+ continue
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+
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+ # Create the new map id and check if the map is already in the database
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+ map_id = map_name + "@" + mapset
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+
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+ new_map = first_input.get_new_map_instance(map_id)
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+
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+ # Check if new map is in the temporal database
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+ if new_map.is_in_db(dbif):
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+ if grass.overwrite() == True:
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+ # Remove the existing temporal database entry
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+ new_map.delete(dbif)
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+ new_map = first_input.get_new_map_instance(map_id)
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+ else:
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+ grass.error(_("Raster map <%s> is already in temporal database, use overwrite flag to overwrite"))
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+ continue
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+
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+ # Set the time stamp
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+ if sample_map_list[i].is_time_absolute():
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+ start, end, tz = sample_map_list[i].get_absolute_time()
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+ new_map.set_absolute_time(start, end, tz)
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+ else:
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+ start, end = sample_map_list[i].get_relative_time()
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+ new_map.set_relative_time(start, end)
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+
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+ map_list.append(new_map)
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+
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+ # Start the parallel r.mapcalc computation
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+ grass.verbose(_("Apply r.mapcalc expression: \"%s\"") % expr)
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+
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+ proc_list.append(Process(target=run_mapcalc, args=(expr,)))
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+ proc_list[proc_count].start()
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+ proc_count += 1
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+
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+ if proc_count == nprocs:
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+ proc_count = 0
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+ exitcodes = 0
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+ for proc in proc_list:
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+ proc.join()
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+ exitcodes += proc.exitcode
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+
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+ if exitcodes != 0:
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+ dbif.close()
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+ grass.fatal(_("Error while r.mapcalc computation"))
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+
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+ # Empty process list
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+ proc_list = []
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+
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+ # Register the new maps in the output space time dataset
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+ grass.message(_("Start map registration in temporal database"))
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+
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+ # Overwrite an existing dataset if requested
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+ if new_sp.is_in_db(dbif):
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+ if grass.overwrite() == True:
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+ new_sp.delete(dbif)
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+ new_sp = tgis.space_time_raster_dataset(out_id)
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+
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+ # Copy the ids from the first input
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+ temporal_type, semantic_type, title, description = first_input.get_initial_values()
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+ new_sp.set_initial_values(temporal_type, semantic_type, title, description)
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+ # Insert the dataset in the temporal database
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+ new_sp.insert(dbif)
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+
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+ count = 0
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+
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+ for new_map in map_list:
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+
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+ count += 1
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+ grass.percent(count, num, 1)
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+
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+ # Read the raster map data
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+ new_map.load()
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+
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+ # In case of a null map continue, do not register null maps
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+ if new_map.metadata.get_min() == None and new_map.metadata.get_max() == None:
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+ if not register_null:
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+ continue
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+
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+ # Insert map in temporal database
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+ new_map.insert(dbif)
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+
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+ new_sp.register_map(new_map, dbif)
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+
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+ # Update the spatio-temporal extent and the raster metadata table entries
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+ new_sp.update_from_registered_maps(dbif)
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+
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+ grass.percent(1, 1, 1)
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+
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+ dbif.close()
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+
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+###############################################################################
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+
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+def run_mapcalc(expr):
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+ """Helper function to run r.mapcalc in parallel"""
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+ return grass.run_command("r.mapcalc", expression=expr, overwrite=grass.overwrite(), quiet=True)
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+
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+###############################################################################
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+
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+if __name__ == "__main__":
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+ options, flags = grass.parser()
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+ main()
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+
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Soeren Gebbert