fixes for multipolygon export to geojson

fixes for multipolygon export to geojson

brdr/aligner.py

@@ -456,23 +456,14 @@ def process_dict_thematic(
             dict_result_diff_min[key] = result_diff_min
             dict_relevant_intersection[key] = relevant_intersection
             dict_relevant_diff[key] = relevant_diff
-        self.dict_result = merge_geometries_by_theme_id(dict_result)
-        self.dict_result_diff = merge_geometries_by_theme_id(dict_result_diff)
-        self.dict_result_diff_plus = merge_geometries_by_theme_id(dict_result_diff_plus)
-        self.dict_result_diff_min = merge_geometries_by_theme_id(dict_result_diff_min)
-        self.dict_relevant_intersection = merge_geometries_by_theme_id(
-            dict_relevant_intersection
-        )
-        self.dict_relevant_difference = merge_geometries_by_theme_id(dict_relevant_diff)
+        self.dict_result = dict_result
+        self.dict_result_diff = dict_result_diff
+        self.dict_result_diff_plus = dict_result_diff_plus
+        self.dict_result_diff_min = dict_result_diff_min
+        self.dict_relevant_intersection = dict_relevant_intersection
+        self.dict_relevant_difference = dict_relevant_diff
         self.feedback_info("thematic dictionary processed")
-        return (
-            self.dict_result,
-            self.dict_result_diff,
-            self.dict_result_diff_plus,
-            self.dict_result_diff_min,
-            self.dict_relevant_intersection,
-            self.dict_relevant_difference,
-        )
+        return self.get_results_as_dict(merged=False)
 
     def predictor(
         self,
@@ -687,31 +678,44 @@ def get_last_version_date(self, geometry, grb_type=GRBType.ADP):
         update_dates = sorted(update_dates, reverse=True)
         return update_dates[0]
 
-    def get_results_as_dict(self):
+    def get_results_as_dict(self, merged=True):
         """
         get a dict-tuple of the results
         """
-        return (
-            self.dict_result,
-            self.dict_result_diff,
-            self.dict_result_diff_plus,
-            self.dict_result_diff_min,
-            self.dict_relevant_intersection,
-            self.dict_relevant_difference,
-        )
+        if merged:
+            return (
+                merge_geometries_by_theme_id(self.dict_result),
+                merge_geometries_by_theme_id(self.dict_result_diff),
+                merge_geometries_by_theme_id(self.dict_result_diff_plus),
+                merge_geometries_by_theme_id(self.dict_result_diff_min),
+                merge_geometries_by_theme_id(self.dict_relevant_intersection),
+                merge_geometries_by_theme_id(self.dict_relevant_difference),
+                merge_geometries_by_theme_id(self.dict_result),
+            )
+        else:
+            return (
+                self.dict_result,
+                self.dict_result_diff,
+                self.dict_result_diff_plus,
+                self.dict_result_diff_min,
+                self.dict_relevant_intersection,
+                self.dict_relevant_difference,
+            )
 
-    def get_results_as_geojson(self, formula=False):
+    def get_results_as_geojson(self, formula=False, merged=True):
         """
         get a geojson-tuple of the results
         """
         prop_dictionary = {}
         p = {}
-        for key in self.dict_result.keys():
-            formula = self.get_formula(self.dict_result[key])
-            p[key] = {"formula": formula}
+        tuple_results = self.get_results_as_dict(merged=merged)
+        dict_results = tuple_results[0]
+        for key in dict_results.keys():
+            formula = self.get_formula(dict_results[key])
+            p[key] = {"formula": json.dumps(formula)}
         prop_dictionary[self.relevant_distance] = p
         return geojson_tuple_from_series(
-            {self.relevant_distance: self.get_results_as_dict()},
+            {self.relevant_distance: tuple_results},
             self.CRS,
             self.name_thematic_id,
             prop_dict=prop_dictionary,
@@ -733,7 +737,7 @@ def get_predictions_as_geojson(self, formula=False):
                 formula = self.get_formula(self.dict_predicted[key][rel_dist][0][key])
             p = None
             if formula:
-                p = {key: {"formula": formula}}
+                p = {key: {"formula": json.dumps(formula)}}
             prop_dictionary[key] = dict.fromkeys(self.dict_predicted[key].keys(), p)
         return geojson_tuple_from_dict_theme(
             self.dict_predicted,

brdr/utils.py

@@ -42,8 +42,13 @@ def geojson_tuple_from_series(
         my_tuple = dict_series[rel_dist]
         prop_rel_dist = {"relevant_distance": rel_dist}
         prop_dictionary = dict.fromkeys(my_tuple[0].keys(), prop_rel_dist)
-        if prop_dict is not None and rel_dist in prop_dict:
-            prop_dictionary = prop_dictionary | prop_dict[rel_dist]
+        if (
+            prop_dict is not None
+            and rel_dist in prop_dict
+            and prop_dict[rel_dist] is not None
+        ):
+            for key in prop_dictionary.keys():
+                prop_dictionary[key] = prop_dictionary[key] | prop_dict[rel_dist][key]
         fcs = geojson_tuple_from_tuple(
             my_tuple,
             crs,
@@ -505,17 +510,20 @@ def diffs_from_dict_series(dict_series, dict_thematic):
         results_diff = dict_series[rel_dist][1]
         for key in keys:
             if key not in results.keys() and key not in results_diff.keys():
-                raise KeyError("No results calculated for theme_id " + str(key))
-
-            # calculate the diffs you want to have
-            # diff = results_diff[key].area * 100 / results[key].area #percentage of change
-            diff = (
-                results[key].area - dict_thematic[key].area
-            )  # difference (m²) between area of resulting geometry and original geometry
-            diff = round(diff, 1)  # round, so the detected changes are within 10cm²
-            # diff = abs(results[key].area - dict_thematic[key].area) #absolute difference (m²) between area of resulting geometry and original geometry
-            # diff = abs(results[key].area - dict_thematic[key].area)*100/dict_thematic[key].area #absolute difference (%) between area of resulting geometry and original geometry
-            # TODO: determine a good diff-value for determination
+                logging.info(
+                    "No diff calculated for theme_id " + str(key) + ": diff set to zero"
+                )
+                diff = 0
+            else:
+                # calculate the diffs you want to have
+                # diff = results_diff[key].area * 100 / results[key].area #percentage of change
+                diff = (
+                    results[key].area - dict_thematic[key].area
+                )  # difference (m²) between area of resulting geometry and original geometry
+                diff = round(diff, 1)  # round, so the detected changes are within 10cm²
+                # diff = abs(results[key].area - dict_thematic[key].area) #absolute difference (m²) between area of resulting geometry and original geometry
+                # diff = abs(results[key].area - dict_thematic[key].area)*100/dict_thematic[key].area #absolute difference (%) between area of resulting geometry and original geometry
+                # TODO: determine a good diff-value for determination
             diffs[key][rel_dist] = diff
     return diffs
 

examples/example_multipolygon.py

@@ -0,0 +1,39 @@
+# Initiate brdr
+import tests
+from brdr.aligner import Aligner
+from brdr.enums import OpenbaarDomeinStrategy
+from brdr.utils import multipolygons_to_singles, write_geojson
+from examples import show_map
+
+aligner0 = Aligner()
+
+# Load thematic data
+
+aligner0.load_thematic_data_file(
+    "../tests/testdata/multipolygon.geojson", "theme_identifier"
+)
+aligner0.dict_thematic = multipolygons_to_singles(aligner0.dict_thematic)
+aligner0.load_thematic_data_dict(
+    aligner0.dict_thematic,
+)
+# gebruik de actuele adp-percelen adp= administratieve percelen
+aligner = Aligner()
+aligner.load_thematic_data_dict(
+    aligner0.dict_thematic,
+)
+aligner.load_reference_data_grb_actual(grb_type="adp", partition=1000)
+
+# Example how to use the Aligner
+# rel_dist = 2
+# dict_results_by_distance = {}
+# dict_results_by_distance[aligner.relevant_distance] = aligner.process_dict_thematic(
+#     relevant_distance=rel_dist,
+#     od_strategy=OpenbaarDomeinStrategy.SNAP_FULL_AREA_ALL_SIDE,
+# )
+# aligner.export_results("output/")
+# show_map(dict_results_by_distance, aligner.dict_thematic, aligner.dict_reference)
+dict_predicted, diffs = aligner.predictor()
+fcs = aligner.get_predictions_as_geojson(formula=True)
+aligner.export_results("output/")
+write_geojson("output/predicted.geojson", fcs[0])
+write_geojson("output/predicted_diff.geojson", fcs[1])

examples/examples_predictor.py

@@ -22,7 +22,7 @@
 
     series = np.arange(0, 300, 10, dtype=int) / 100
     # predict which relevant distances are interesting to propose as resulting geometry
-    dict_predicted = aligner.predictor(
+    dict_predicted, diffs = aligner.predictor(
         relevant_distances=series, od_strategy=4, treshold_overlap_percentage=50
     )
     for key in dict_predicted.keys():

tests/testdata/multipolygon.geojson

@@ -0,0 +1,8 @@
+{
+"type": "FeatureCollection",
+"name": "themelayer",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::31370" } },
+"features": [
+{ "type": "Feature", "properties": { "id": 600, "theme_identifier": "600" }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ 172907.478557254769839, 171306.329770992975682 ], [ 172907.423273425694788, 171307.187050310545601 ], [ 172907.258293797436636, 171308.030809821182629 ], [ 172906.986220197344664, 171308.847742933343397 ], [ 172906.611343388562091, 171309.62496612383984 ], [ 172906.139575402339688, 171310.350222118664533 ], [ 172905.578356301615713, 171311.012073197518475 ], [ 172904.936536846886156, 171311.600081573560601 ], [ 172904.224238914379384, 171312.104974003392272 ], [ 172903.452695867919829, 171312.518788031826261 ], [ 172902.634075402340386, 171312.834997564437799 ], [ 172901.781287651334424, 171313.048615787964081 ], [ 172900.907781587186037, 171313.156273815431632 ], [ 172900.027332922356436, 171313.156273815431632 ], [ 172899.153826858208049, 171313.048615787964081 ], [ 172898.301039107202087, 171312.834997564437799 ], [ 172897.482418641622644, 171312.518788031826261 ], [ 172896.710875595163088, 171312.104974003392272 ], [ 172895.998577662656317, 171311.600081573560601 ], [ 172895.356758207926759, 171311.012073197518475 ], [ 172894.795539107202785, 171310.350222118664533 ], [ 172894.323771120980382, 171309.62496612383984 ], [ 172893.948894312197808, 171308.847742933343397 ], [ 172893.676820712105837, 171308.030809821182629 ], [ 172893.511841083847685, 171307.187050310545601 ], [ 172893.456557254772633, 171306.329770992975682 ], [ 172893.511841083847685, 171305.472491675405763 ], [ 172893.676820712105837, 171304.628732164768735 ], [ 172893.948894312197808, 171303.811799052607967 ], [ 172894.323771120980382, 171303.034575862111524 ], [ 172894.795539107202785, 171302.309319867286831 ], [ 172895.356758207926759, 171301.647468788432889 ], [ 172895.998577662656317, 171301.059460412390763 ], [ 172896.710875595163088, 171300.554567982559092 ], [ 172897.482418641622644, 171300.140753954125103 ], [ 172898.301039107202087, 171299.824544421513565 ], [ 172899.153826858208049, 171299.610926197987283 ], [ 172900.027332922356436, 171299.503268170519732 ], [ 172900.907781587186037, 171299.503268170519732 ], [ 172901.781287651334424, 171299.610926197987283 ], [ 172902.634075402340386, 171299.824544421513565 ], [ 172903.452695867919829, 171300.140753954125103 ], [ 172904.224238914379384, 171300.554567982559092 ], [ 172904.936536846886156, 171301.059460412390763 ], [ 172905.578356301615713, 171301.647468788432889 ], [ 172906.139575402339688, 171302.309319867286831 ], [ 172906.611343388562091, 171303.034575862111524 ], [ 172906.986220197344664, 171303.811799052607967 ], [ 172907.258293797436636, 171304.628732164768735 ], [ 172907.423273425694788, 171305.472491675405763 ], [ 172907.478557254769839, 171306.329770992975682 ] ] ], [ [ [ 172859.258396949415328, 171379.685496183810756 ], [ 172893.114885499031516, 171367.032061069301562 ], [ 172879.777480918884976, 171334.201526718155947 ], [ 172847.630916033376707, 171347.025954199081752 ], [ 172859.258396949415328, 171379.685496183810756 ] ] ], [ [ [ 172908.846183208952425, 171363.954198473889846 ], [ 172929.536259544838686, 171356.259541985346004 ], [ 172926.116412216593744, 171336.766412214346929 ], [ 172902.177480918879155, 171346.341984733415302 ], [ 172908.846183208952425, 171363.954198473889846 ] ] ], [ [ [ 172911.411068705143407, 171403.96641221435857 ], [ 172908.846183208952425, 171402.598473283054773 ], [ 172908.333206109731691, 171398.323664122755872 ], [ 172910.214122140256222, 171395.75877862656489 ], [ 172915.172900766221574, 171395.929770992981503 ], [ 172918.592748094466515, 171399.007633588393219 ], [ 172917.908778628800064, 171401.914503817417426 ], [ 172915.685877865442308, 171404.308396947191795 ], [ 172912.608015270030592, 171404.992366412829142 ], [ 172911.411068705143407, 171403.96641221435857 ] ] ] ] } }
+]
+}