np.array adapted

np.array adapted

brdr/aligner.py

@@ -458,7 +458,7 @@ def process_dict_thematic(
 
     def predictor(
             self,
-            relevant_distances=np.arange(0.1, 5.05, 0.1, dtype=float),
+            relevant_distances=np.arange(0, 300, 10, dtype=int)/100,
             od_strategy=OpenbaarDomeinStrategy.SNAP_SINGLE_SIDE,
             treshold_overlap_percentage=50,
     ):
@@ -516,7 +516,7 @@ def predictor(
                 for zs in zero_streaks:
                     dict_predicted[key][zs[0]] = dict_series[zs[0]]
                 dict_predicted[key] = filter_resulting_series_by_key(dict_predicted[key],key)
-        return dict_predicted
+        return dict_predicted, diffs
 
     def process_series(
         self,

examples/example_eo.py

@@ -1,21 +1,44 @@
+import numpy as np
+
 from brdr.aligner import Aligner
 from brdr.utils import get_oe_dict_by_ids
-from examples import show_map
+from examples import show_map, plot_series
 
 if __name__ == "__main__":
     # EXAMPLE to test the algorithm for erfgoedobject with relevant distance 0.2m and od_strategy SNAP_ALL_SIDE
 
     # Initiate brdr
     aligner = Aligner()
     # Load thematic data & reference data
-    dict_theme = get_oe_dict_by_ids([206363], oetype='erfgoedobjecten')
+    #dict_theme = get_oe_dict_by_ids([206363], oetype='erfgoedobjecten')
+
+    erfgoedobjecten =[206407,
+# 206403,
+# 206372,
+# 206369,
+# 206377,
+# 206371,
+# 206370,
+# 206368,
+206786
+]
+    dict_theme = get_oe_dict_by_ids(erfgoedobjecten, oetype='erfgoedobjecten')
     aligner.load_thematic_data_dict(dict_theme)
     aligner.load_reference_data_grb_actual(grb_type="adp", partition=1000)
 
     #RESULTS
-    rel_dist = 0.2
-    dict_results_by_distance = {}
-    #put resulting tuple in a dictionary
-    dict_results_by_distance[rel_dist] = aligner.process_dict_thematic(rel_dist,2)
-    aligner.export_results("output/")
-    show_map(dict_results_by_distance, aligner.dict_thematic, aligner.dict_reference)
+    # rel_dist = 0.2
+    # dict_results_by_distance = {}
+    # #put resulting tuple in a dictionary
+    # dict_results_by_distance[rel_dist] = aligner.process_dict_thematic(rel_dist,2)
+    # aligner.export_results("output/")
+    # show_map(dict_results_by_distance, aligner.dict_thematic, aligner.dict_reference)
+
+    series = np.arange(0, 200, 20, dtype=int)/100
+    #predict which relevant distances are interesting to propose as resulting geometry
+    dict_predicted, diffs = aligner.predictor(relevant_distances=series, od_strategy=2,treshold_overlap_percentage=50)
+    for key in dict_predicted.keys():
+        diff ={}
+        diff[key]= diffs[key]
+        plot_series(series,diff)
+        show_map(dict_predicted[key], {key:aligner.dict_thematic[key]}, aligner.dict_reference)

examples/example_parcel_vs_building.py

@@ -26,7 +26,7 @@
     )  # gebruik de actuele adp-percelen adp= administratieve percelen
 
     # Example how to use a series (for histogram)
-    series = np.arange(0.1, 5.05, 0.1, dtype=float)
+    series = np.arange(0, 300, 10, dtype=int)/100
     x_dict_series = aligner_x.process_series(series, 4, 50)
     x_resulting_areas = diffs_from_dict_series(x_dict_series, aligner_x.dict_thematic)
     y_dict_series = aligner_y.process_series(series, 4, 50)

examples/example_wanted_changes.py

@@ -35,7 +35,7 @@
         print(aligner.get_formula(results[key]))
 
     #Example how to use a series (for histogram)
-    series = np.arange(0.1, 10.05, 0.1, dtype=float)
+    series = np.arange(0, 500, 10, dtype=int)/100
     dict_series = aligner.process_series(series,4,50)
     resulting_areas = diffs_from_dict_series(dict_series, aligner.dict_thematic)
     plot_series(series, resulting_areas)

examples/examples_predictor.py

@@ -16,7 +16,7 @@
     aligner.load_thematic_data_file("../tests/testdata/test_wanted_changes.geojson", 'theme_id')
     aligner.load_reference_data_grb_actual(grb_type='adp', partition=1000) #gebruik de actuele adp-percelen adp= administratieve percelen
 
-    series = np.arange(0.1, 5.00, 0.2, dtype=float)
+    series = np.arange(0, 300, 10, dtype=int)/100
     #predict which relevant distances are interesting to propose as resulting geometry
     dict_predicted = aligner.predictor(relevant_distances=series, od_strategy=4,treshold_overlap_percentage=50)
     for key in dict_predicted.keys():

examples/stats_snapping_distance_creation.py

@@ -12,7 +12,7 @@
 array_treshold_overlap_percentage = [50]
 array_od = [1]
 # array_relevant_distance = [0.2, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 8, 10]
-array_relevant_distance = np.arange(0.1, 10.05, 0.1, dtype=float)
+array_relevant_distance = np.arange(0, 500, 10, dtype=int)/100
 print(array_relevant_distance)
 
 x = Aligner()

tests/test_aligner.py

@@ -125,9 +125,9 @@ def test_predictor(self):
         self.sample_aligner.load_thematic_data_dict(thematic_dict)
         # LOAD REFERENCE DICTIONARY
         self.sample_aligner.load_reference_data_dict(reference_dict)
-        series = np.arange(0.1, 5.00, 0.2, dtype=float)
+        series = np.arange(0, 300, 10, dtype=int)/100
         # predict which relevant distances are interesting to propose as resulting geometry
-        dict_predicted = self.sample_aligner.predictor(relevant_distances=series, od_strategy=4,
+        dict_predicted, diffs = self.sample_aligner.predictor(relevant_distances=series, od_strategy=4,
                                                        treshold_overlap_percentage=50)
         self.assertEqual(len(dict_predicted), len(thematic_dict))
 

tests/test_examples.py

@@ -81,7 +81,7 @@ def test_example_multi_to_single(self):
     #     )  # gebruik de actuele adp-percelen adp= administratieve percelen
     #
     #     # Example how to use a series (for histogram)
-    #     series = np.arange(0.1, 5.05, 0.1, dtype=float)
+    #     series = np.arange(0, 300, 10, dtype=int)/100
     #     x_dict_series = aligner_x.process_series(series, 4, 50)
     #     x_resulting_areas = diffs_from_dict_series(x_dict_series, aligner_x.dict_thematic)
     #     y_dict_series = aligner_y.process_series(series, 4, 50)
@@ -106,7 +106,7 @@ def test_example_wanted_changes(self):
         dict_results_by_distance[rel_dist] = aligner.process_dict_thematic(rel_dist, 4)
 
         # Example how to use a series (for histogram)
-        series = np.arange(0.1, 10.05, 0.1, dtype=float)
+        series = np.arange(0, 300, 10, dtype=int)/100
         dict_series = aligner.process_series(series, 4, 50)
         resulting_areas = diffs_from_dict_series(dict_series, aligner.dict_thematic)
         for key in resulting_areas:
@@ -130,8 +130,8 @@ def test_example_predictor(self):
         aligner.load_reference_data_grb_actual(grb_type='adp',
                                                partition=1000)  # gebruik de actuele adp-percelen adp= administratieve percelen
 
-        series = np.arange(0.1, 5.00, 0.2, dtype=float)
+        series = np.arange(0, 300, 10, dtype=int)/100
         # predict which relevant distances are interesting to propose as resulting geometry
-        dict_predicted = aligner.predictor(relevant_distances=series, od_strategy=4, treshold_overlap_percentage=50)
+        dict_predicted,diffs = aligner.predictor(relevant_distances=series, od_strategy=4, treshold_overlap_percentage=50)
         for key in dict_predicted.keys():
             continue