diff --git a/gdecore/database_queries.py b/gdecore/database_queries.py
index 874f81db70399d18b23da195fc5c7a115e8b804c..d49e6399a2c27a9461113d4763e48fa27095f225 100644
--- a/gdecore/database_queries.py
+++ b/gdecore/database_queries.py
@@ -631,3 +631,157 @@ class DatabaseQueries:
)
return building_classes_proportions
+
+ @staticmethod
+ def get_data_unit_tiles_of_data_unit(
+ data_unit_id, occupancy_case, aggregated_source_id, db_gde_tiles_config, db_table
+ ):
+ """This function retrieves all the data-unit tiles associated with 'data_unit_id',
+ 'occupancy_case' and 'aggregated_source_id' in 'db_table', in terms of their quadkeys
+ and number of buildings in the data-unit tile as per the aggregated exposure model.
+
+ Args:
+ data_unit_id (str):
+ ID of the data unit for which the data-unit tiles will be retrieved.
+ occupancy_case (str):
+ Name of the occupancy case (e.g. "residential", "commercial", "industrial")
+ for which the data-unit tiles will be retrieved.
+ aggregated_source_id (int):
+ ID of the source of the aggregated exposure model for which the data-unit tiles
+ will be retrieved.
+ db_gde_tiles_config (dict):
+ Dictionary containing the credentials needed to connect to the SQL database in
+ which information on the data-unit tiles is stored. The keys of the dictionary
+ need to be:
+ host (str):
+ SQL database host address.
+ dbname (str):
+ Name of the SQL database.
+ port (int):
+ Port where the SQL database can be found.
+ username (str):
+ User name to connect to the SQL database.
+ password (str):
+ Password associated with self.username.
+ db_table (str):
+ Name of the table of the SQL database where the data-unit tiles are stored. It
+ is assumed that this table contains, at least, the following fields:
+ quadkey (str):
+ String indicating the quadkey of a tile.
+ aggregated_source_id (int):
+ ID of the source of the aggregated exposure model.
+ occupancy_case (enum):
+ SQL enumerated type describing the building occupancy cases.
+ data_unit_id (str):
+ ID of the data unit.
+ aggregated_buildings (float):
+ Number of buildings in the data-unit tile as per the aggregated exposure
+ model with ID 'aggregated_source_id'.
+
+ Returns:
+ quadkeys (array of str):
+ Strings indicating the quadkeys of all data-unit tiles associated with
+ 'data_unit_id', 'occupancy_case' and 'aggregated_source_id'.
+ aggregated_buildings (array of float):
+ Number of buildings in the data-unit tiles as per the aggregated exposure
+ model with ID 'aggregated_source_id'.
+ """
+
+ sql_query = "SELECT quadkey, aggregated_buildings FROM %s"
+ sql_query += " WHERE (data_unit_id='%s' AND occupancy_case='%s'"
+ sql_query += " AND aggregated_source_id='%s');"
+
+ db_gde_tiles = Database(**db_gde_tiles_config)
+ db_gde_tiles.create_connection_and_cursor()
+
+ db_gde_tiles.cursor.execute(
+ sql_query % (db_table, data_unit_id, occupancy_case, aggregated_source_id)
+ )
+ exec_result = db_gde_tiles.cursor.fetchall()
+
+ db_gde_tiles.close_connection()
+
+ if len(exec_result) > 1: # Entries exist --> retrieve
+ quadkeys = numpy.array(
+ [exec_result[i][0] for i in range(len(exec_result))], dtype="str"
+ )
+ aggregated_buildings = numpy.array(
+ [exec_result[i][1] for i in range(len(exec_result))], dtype="float"
+ )
+ else: # No entries found
+ quadkeys = numpy.array([], dtype="str")
+ aggregated_buildings = numpy.array([], dtype="float")
+
+ return quadkeys, aggregated_buildings
+
+ @staticmethod
+ def get_data_unit_tiles_of_data_unit_as_DataFrame(
+ data_unit_id, occupancy_case, aggregated_source_id, db_gde_tiles_config, db_table
+ ):
+ """This function retrieves all the data-unit tiles associated with 'data_unit_id',
+ 'occupancy_case' and 'aggregated_source_id' in 'db_table', in terms of their quadkeys
+ and number of buildings in the data-unit tile as per the aggregated exposure model. The
+ output is gathered together as a Pandas DataFrame.
+
+ Args:
+ data_unit_id (str):
+ ID of the data unit for which the data-unit tiles will be retrieved.
+ occupancy_case (str):
+ Name of the occupancy case (e.g. "residential", "commercial", "industrial")
+ for which the data-unit tiles will be retrieved.
+ aggregated_source_id (int):
+ ID of the source of the aggregated exposure model for which the data-unit tiles
+ will be retrieved.
+ db_gde_tiles_config (dict):
+ Dictionary containing the credentials needed to connect to the SQL database in
+ which information on the data-unit tiles is stored. The keys of the dictionary
+ need to be:
+ host (str):
+ SQL database host address.
+ dbname (str):
+ Name of the SQL database.
+ port (int):
+ Port where the SQL database can be found.
+ username (str):
+ User name to connect to the SQL database.
+ password (str):
+ Password associated with self.username.
+ db_table (str):
+ Name of the table of the SQL database where the data-unit tiles are stored. It
+ is assumed that this table contains, at least, the following fields:
+ quadkey (str):
+ String indicating the quadkey of a tile.
+ aggregated_source_id (int):
+ ID of the source of the aggregated exposure model.
+ occupancy_case (enum):
+ SQL enumerated type describing the building occupancy cases.
+ data_unit_id (str):
+ ID of the data unit.
+ aggregated_buildings (float):
+ Number of buildings in the data-unit tile as per the aggregated exposure
+ model with ID 'aggregated_source_id'.
+
+ Returns:
+ data_unit_tiles (Pandas DataFrame):
+ Pandas DataFrame with all data-unit tiles associated with 'data_unit_id',
+ 'occupancy_case' and 'aggregated_source_id' in 'db_table'. It contains the
+ following columns:
+ quadkey (str):
+ String indicating the quadkey of a tile.
+ aggregated_buildings (float):
+ Number of buildings in the data-unit tile as per the aggregated exposure
+ model with ID 'aggregated_source_id'.
+ """
+
+ quadkeys, aggregated_buildings = DatabaseQueries.get_data_unit_tiles_of_data_unit(
+ data_unit_id, occupancy_case, aggregated_source_id, db_gde_tiles_config, db_table
+ )
+
+ data_unit_tiles = pandas.DataFrame(
+ {
+ "quadkey": quadkeys,
+ "aggregated_buildings": aggregated_buildings,
+ }
+ )
+
+ return data_unit_tiles
diff --git a/gdecore/gdecore.py b/gdecore/gdecore.py
index 579207ffdce2204da0aeecbe61c5d714106e7442..b8a4a6f5fac5ad55b591dd2c8a1101bc8ea120b6 100644
--- a/gdecore/gdecore.py
+++ b/gdecore/gdecore.py
@@ -159,6 +159,19 @@ def main():
% (aux_log_string, str(len(obm_buildings_building_classes.keys())))
)
+ # Retrieve data-unit tiles (quadkey, aggregated_buildings) as a Pandas DataFrame
+ data_unit_tiles = DatabaseQueries.get_data_unit_tiles_of_data_unit_as_DataFrame(
+ data_unit_id,
+ occupancy_case,
+ aggregated_source_id,
+ config.database_gde_tiles,
+ "data_unit_tiles",
+ )
+ logger.info(
+ "%s: %s data-unit tiles retrieved"
+ % (aux_log_string, str(data_unit_tiles.shape[0]))
+ )
+
# Leave the program
logger.info("gde-core has finished")
sys.exit()
diff --git a/tests/data/test_database_set_up.sql b/tests/data/test_database_set_up.sql
index ac93a2f8c77ca70295dcc91e0d9db0a4a46ce42e..be3a9dadb93166177897cf2dff68d0afcd641f65 100644
--- a/tests/data/test_database_set_up.sql
+++ b/tests/data/test_database_set_up.sql
@@ -2,6 +2,7 @@ DROP TABLE IF EXISTS aggregated_sources;
DROP TABLE IF EXISTS data_units;
DROP TABLE IF EXISTS obm_buildings;
DROP TABLE IF EXISTS data_units_buildings;
+DROP TABLE IF EXISTS data_unit_tiles;
DROP TYPE IF EXISTS occupancycase;
DROP TYPE IF EXISTS settlement;
DROP EXTENSION IF EXISTS postgis;
@@ -164,3 +165,35 @@ VALUES ('A1/HBET:1-3', 'urban', 'all', 2, 'ABC', 'residential', 'ABC_10269', 0.2
('C4/HBET:2-3', 'urban', 'Trade', 2, 'ABC', 'commercial', 'ABC_10269', 0.10, 0.0, 0.0, 2, 3),
('C5/HBET:1-2', 'urban', 'Offices', 2, 'ABC', 'commercial', 'ABC_10269', 0.20, 0.0, 0.0, 1, 2),
('C6/HBET:3-5', 'urban', 'Offices', 2, 'ABC', 'commercial', 'ABC_10269', 0.30, 0.0, 0.0, 3, 5);
+
+CREATE TABLE data_unit_tiles
+(
+ quadkey char(18),
+ aggregated_source_id SMALLINT,
+ occupancy_case occupancycase,
+ exposure_entity char(3),
+ data_unit_id varchar,
+ size_data_unit_tile_area FLOAT,
+ size_data_unit_tile_built_up_area FLOAT,
+ fraction_data_unit_area FLOAT,
+ fraction_data_unit_built_up_area FLOAT,
+ aggregated_buildings FLOAT,
+
+ PRIMARY KEY (quadkey, aggregated_source_id, occupancy_case, data_unit_id)
+);
+INSERT INTO data_unit_tiles(quadkey,
+ aggregated_source_id,
+ occupancy_case,
+ exposure_entity,
+ data_unit_id,
+ size_data_unit_tile_area,
+ size_data_unit_tile_built_up_area,
+ fraction_data_unit_area,
+ fraction_data_unit_built_up_area,
+ aggregated_buildings)
+VALUES ('122010321033023130', 2, 'residential', 'ABC', 'ABC_10269', 0.0, 0.0, 0.0, 0.0, 15.7),
+('122010321033023130', 2, 'commercial', 'ABC', 'ABC_10269', 0.0, 0.0, 0.0, 0.0, 23.4),
+('122010321033023120', 2, 'residential', 'ABC', 'ABC_10269', 0.0, 0.0, 0.0, 0.0, 39.1),
+('122010321033023120', 2, 'commercial', 'ABC', 'ABC_10269', 0.0, 0.0, 0.0, 0.0, 17.6),
+('122010321033023132', 2, 'residential', 'ABC', 'ABC_10269', 0.0, 0.0, 0.0, 0.0, 34.4),
+('122010321033023132', 2, 'commercial', 'ABC', 'ABC_10269', 0.0, 0.0, 0.0, 0.0, 11.5);
diff --git a/tests/test_database_queries.py b/tests/test_database_queries.py
index 4daa33a5e5a1a5913b8c22da959969e42eeae31a..e53406497f5e35e4fdaf38fc8c93128c3ed3b7ec 100644
--- a/tests/test_database_queries.py
+++ b/tests/test_database_queries.py
@@ -339,3 +339,43 @@ def test_get_building_classes_of_data_unit(test_db):
for col_name in expected_columns:
assert col_name in returned_building_classes.columns
assert round(returned_building_classes["proportions"].sum(), 5) == 0.0
+
+
+def test_get_data_unit_tiles_of_data_unit_as_DataFrame(test_db):
+ # Database connection (the Configuration class will define the credentials based on whether
+ # the code is running in the CI or locally)
+ config = Configuration(
+ os.path.join(os.path.dirname(__file__), "data", "config_for_testing_good.yml")
+ )
+
+ expected_quadkeys = ["122010321033023130", "122010321033023120", "122010321033023132"]
+
+ expected_aggregated_buildings = {}
+ expected_aggregated_buildings["residential"] = [15.7, 39.1, 34.4]
+ expected_aggregated_buildings["commercial"] = [23.4, 17.6, 11.5]
+
+ for occupancy in expected_aggregated_buildings:
+ returned_data_unit_tiles = (
+ DatabaseQueries.get_data_unit_tiles_of_data_unit_as_DataFrame(
+ "ABC_10269", occupancy, 2, config.database_gde_tiles, "data_unit_tiles"
+ )
+ )
+
+ assert returned_data_unit_tiles.shape[0] == len(expected_quadkeys)
+
+ for i, quadkey in enumerate(expected_quadkeys):
+ assert (
+ returned_data_unit_tiles[returned_data_unit_tiles.quadkey == quadkey][
+ "aggregated_buildings"
+ ].to_numpy()[0]
+ == expected_aggregated_buildings[occupancy][i]
+ )
+
+ # Test case in which there are no entries to retrieve
+ returned_data_unit_tiles = DatabaseQueries.get_data_unit_tiles_of_data_unit_as_DataFrame(
+ "ABC_10269", "industrial", 2, config.database_gde_tiles, "data_unit_tiles"
+ )
+
+ assert returned_data_unit_tiles.shape[0] == 0
+ assert "quadkey" in returned_data_unit_tiles.columns
+ assert "aggregated_buildings" in returned_data_unit_tiles.columns