make usage of landuse. FIRST: you need to fetch osm data whrer landus…

…e is not null!

ding0/config/landuse_table_and_sql_request.py

@@ -0,0 +1,26 @@
+# landuse has to be fetched and stored in DB as selct * from osm data where osmdata.landuse is not null;
+
+class Landuse(Base):
+    """Landuse Model"""
+
+    __tablename__ = "landuse"
+
+    osm_id = Column(Integer, primary_key=True)
+    landuse = Column(String(50))
+    natural = Column(String(50))
+    geometry = Column(Geometry('POLYGON'))
+    area = Column(Float)
+
+
+
+def get_osm_landuse(geo_area, session_osm):
+    """ load ways from db for given polygon as geo_area_wkt """
+
+    landuse = session_osm.query(Landuse).filter(
+        func.st_intersects(func.ST_GeomFromText(geo_area, srid), Landuse.geometry))
+
+    landuse_sql_df = pd.read_sql(
+                landuse.statement,
+                con=session_osm.bind)
+# con=engine_osm both ways are working. select the easier/ more appropriate one
+    return landuse_sql_df

ding0/core/__init__.py

@@ -839,11 +839,17 @@ def import_lv_load_areas_and_build_new_lv_districts(
 
             # Get n_clusters based on capacity of the load area.
             n_cluster = get_cluster_numbers(buildings_w_loads_df, simp_graph)
+
+            # fetch landuse like oam ways
+            # todo: check if wokring. landuse has to be stored in DB before !
+            landuse_sql_df = db_io.get_osm_landuse(self.orm, session, buffer_poly_list[-1].wkt)
+            landuse_gdf['geometry'] = landuse_gdf.geometry.apply(to_shape)
+            landuse_gdf = gpd.GeoDataFrame(landuse_gdf, geometry="geometry", crs=3035)
 
             # Cluster graph and locate lv stations based on load center per cluster.
             clustering_successfully, cluster_graph, mvlv_subst_list, nodes_w_labels = \
                 distance_restricted_clustering(
-                    simp_graph, n_cluster, street_loads_df, mv_grid_district, id_db
+                    simp_graph, n_cluster, street_loads_df, mv_grid_district, id_db, landuse_sql_df
                 )
             if not clustering_successfully:
                 return f"Clustering not successful for " \
@@ -920,6 +926,7 @@ def import_lv_load_areas_and_build_new_lv_districts(
 
                     # Get convex hull for ding0 objects.
                     points = get_points_in_load_area(cluster_geo_list)
+
                     if create_lvgd_geo_method == 'convex_hull':
                         polygon = get_convex_hull_from_points(points)
                     elif create_lvgd_geo_method == 'bounding_box':

ding0/grid/lv_grid/clustering.py

@@ -65,7 +65,7 @@ def apply_AgglomerativeClustering(G, k, round_decimals=True):
     else: return np.array([0])
 
 
-def distance_restricted_clustering(simp_graph, n_cluster, street_loads_df, mv_grid_district, id_db):
+def distance_restricted_clustering(simp_graph, n_cluster, street_loads_df, mv_grid_district, id_db, landuse):
     """
     Apply ward hierarchical AgglomerativeClustering with connectivity constraints for underlying graph
     https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering
@@ -101,7 +101,7 @@ def distance_restricted_clustering(simp_graph, n_cluster, street_loads_df, mv_gr
         # locate stations for districts
         mvlv_subst_list, valid_cluster_distance = get_mvlv_subst_loc_list(cluster_graph, nodes_w_labels, \
                                                                           street_loads_df, labels, n_cluster, \
-                                                                          check_distance_criterion)
+                                                                          check_distance_criterion, landuse)
 
         if valid_cluster_distance:
 

ding0/grid/lv_grid/routing.py

@@ -120,7 +120,25 @@ def loads_in_ons_dist_threshold(dm_cluster, cluster_nodes, osmid):
         return True
 
 
-def get_mvlv_subst_loc_list(cluster_graph, nodes, street_loads_df, labels, n_cluster, check_distance_criterion=True):
+def _get_landuse_in_area(landuse_gdf, area_geo):
+    landuse_gdf.loc[:, "intersecting_cluster"] = landuse_gdf.intersects(area_geo)
+    landuse_gdf = landuse_gdf.loc[landuse_gdf.intersecting_cluster]
+    landuse_intersecting_dict = {}
+    for landuse, grp in landuse_gdf.groupby("landuse"):
+        areasin = grp.intersection(area_geo.buffer(0))
+        areasqm = 0
+        if len(areasin) > 1:
+            for ain in areasin:
+                areasqm += ain.area
+        else:
+            areasqm = areasin.area.item()
+        landuse_intersecting_dict[landuse] = areasqm
+
+    return landuse_intersecting_dict
+
+
+
+def get_mvlv_subst_loc_list(cluster_graph, nodes, street_loads_df, labels, n_cluster, check_distance_criterion=True, landuse_gdf=None):
     """
     identify position of station at street load center
     get list of location of mvlv substations for load areal
@@ -154,9 +172,27 @@ def get_mvlv_subst_loc_list(cluster_graph, nodes, street_loads_df, labels, n_clu
                 # due to distance threshold to ons is trespassed
                 valid_cluster_distance = False
                 return mvlv_subst_list, valid_cluster_distance
+
+        area_geo = df_cluster.geometry.to_list()  # looks like duple somewhere in further coding, maybe just keep from here instead same processing later
+        if len(area_geo) == 1:
+            area_geo = area_geo[0]
+            area_geo_area = 100
+        elif len(area_geo) == 2:
+            area_geo = LineString(area_geo)
+            area_geo_area = area_geo.length
+        else:
+            area_geo = Polygon(area_geo)
+            area_geo_area = area_geo.area
+
+        if landuse_gdf is not None:
+            landuse_gdf.loc[:, "intersecting_cluster"] = False  # init False and set True where intersecting
+            landuse_dict = _get_landuse_in_area(landuse_gdf, area_geo)
+        else:
+            landuse_dict = {}
 
         mvlv_subst_loc = cluster_graph.nodes[osmid]
         mvlv_subst_loc['osmid'] = osmid
+        mvlv_subst_loc['landuse'] = landuse_dict  # todo: reagarding this dict: how to choose if village, rural, city or whatever ?
         mvlv_subst_loc['graph_district'] = cluster_subgraph
         mvlv_subst_list.append(mvlv_subst_loc)