errors correction - graph features extraction completed

brick/processing/graph_feat_extraction.py

@@ -366,7 +366,10 @@ def ExtractFeaturesInDF(somas, size_voxel_in_micron: list, number_of_bins: int,
             mean_torsion = np_.mean(torsions)
             median_torsion = np_.median(torsions)
             std_torsion = np_.std(torsions)
-            entropy_torsion = st_.entropy(torsions)
+            if any(torsions > 0):
+                entropy_torsion = st_.entropy(torsions)
+            else:
+                entropy_torsion = 0
             #
 
             # print(
@@ -427,7 +430,10 @@ def ExtractFeaturesInDF(somas, size_voxel_in_micron: list, number_of_bins: int,
             mean_torsion_P = np_.mean(torsions_P)
             median_torsion_P = np_.median(torsions_P)
             std_torsion_P = np_.std(torsions_P)
-            entropy_torsion_P = st_.entropy(torsions_P)
+            if any(torsions_P > 0):
+                entropy_torsion_P = st_.entropy(torsions_P)
+            else:
+                entropy_torsion_P = 0
             #
 
             # print(
@@ -494,7 +500,10 @@ def ExtractFeaturesInDF(somas, size_voxel_in_micron: list, number_of_bins: int,
                 mean_torsion_S = np_.mean(torsions_S)
                 median_torsion_S = np_.median(torsions_S)
                 std_torsion_S = np_.std(torsions_S)
-                entropy_torsion_S = st_.entropy(torsions_S)
+                if any(torsions_S > 0):
+                    entropy_torsion_S = st_.entropy(torsions_S)
+                else:
+                    entropy_torsion_S = 0
                 #
 
                 # print(

nutrimorph.py

@@ -408,7 +408,7 @@ number_of_bins = int(number_of_bins)
 max_range = hist_step * number_of_bins - hist_min_length
 # DF creation
 features_df = ge_.ExtractFeaturesInDF(somas, size_voxel_in_micron, number_of_bins, max_range, hist_min_length, ext_scales)
-features_df.to_csv(".../features.csv")
+# features_df.to_csv("...\features.csv")
 
 #
 elapsed_time = tm_.gmtime(tm_.time() - start_time)

parameters.ini

@@ -70,7 +70,7 @@ soma_min_area_c            :    (0.24050024 ** 2) * 1000
 [Extensions]
 ; for hysteresis
 ext_low_c                  :    1e-20
-ext_high_c                 :    0.15
+ext_high_c                 :    0.2
 ; for morphological cleaning
 ext_selem_micron_c         :    0.24050024 * 1
 ext_min_area_c             :    (0.24050024 ** 2) * 100
@@ -99,12 +99,11 @@ method                     :    c
 hist_min_length            :    0
 ; in micron
 ; by default, 0
-hist_step                  :    5
+hist_step                  :    6
 ; in micron
-; by default, 5
 number_of_bins             :    5
 ; by default, 5
-; extensions generally do not exceed 25 microns
+; extensions generally do not exceed 30 microns
 
 [Program Running]
 with_plot                  :    False

sklgraph/brick/edge.py

@@ -95,7 +95,7 @@ class edge_t:
 
         self.widths = widths[self.sites]
 
-    def SetLengths(self, size_voxel: array_t, widths: array_t = None, check_validity: bool = False) -> None:
+    def SetLengths(self, size_voxel: array_t, widths: array_t = None, check_validity: bool = True) -> None:
         #
         if self.node_uids.__len__() != 2:
             raise ValueError("Edge: Missing sites from adjacent nodes")
@@ -103,9 +103,10 @@ class edge_t:
         sites_as_array = np_.array(self.sites)
         segments = np_.diff(sites_as_array, axis=1)
         # segmentsT = segments.transpose()
-        # sq_lengths = ((segmentsT.dot(np_.diag(size_voxel)).dot(segments))**2).sum(axis=0)
+        # sq_lengths = (segmentsT.dot(np_.diag(size_voxel)).dot(segments))
+        # sq_lengths = np_.diag(sq_lengths)
         # sq_lengths = (segments ** 2).sum(axis=0)
-        sq_lengths = ((np_.diag(size_voxel).dot(segments)) ** 2).sum(axis=0)
+        sq_lengths = (np_.diag(size_voxel).dot(segments ** 2)).sum(axis=0)
         lengths = np_.sqrt(sq_lengths)
         length = lengths.sum().item()
 
@@ -126,6 +127,7 @@ class edge_t:
         if check_validity:
             # A global condition: self.sites[0].size - 1 <= length
             if cast(array_t, sq_lengths == 0).any():
+                np_.save("D:\\MorganeNadal\\site_as_array_in_SetLength.npy", sites_as_array)
                 raise ValueError("Edge: Repeated sites")
             if cast(array_t, sq_lengths > self.sites.__len__()).any():
                 raise ValueError("Edge: Site gaps")
@@ -139,9 +141,9 @@ class edge_t:
             if self.lengths is None:
                 self.SetLengths(size_voxel=size_voxel)
             edge_lengths = self.lengths.sq_lengths.tolist()
-            if 0.0 in edge_lengths:
-                index0 = edge_lengths.index(0.0)
-                edge_lengths[index0] = 1e-10
+            # if 0.0 in edge_lengths:
+            #     index0 = edge_lengths.index(0.0)
+            #     edge_lengths[index0] = 1e-10
             arc_lengths = tuple(it_.accumulate((0, *edge_lengths)))
             self.as_curve = tuple(
                 in_.PchipInterpolator(arc_lengths, self.sites[idx_])
@@ -192,7 +194,7 @@ class edge_t:
                 c = derivates["x2"][idx][1] * derivates["x1"][idx][0] - derivates["x1"][idx][1] * derivates["x2"][idx][0]
                 d = (derivates["x1"][idx][0]**2 + derivates["x2"][idx][0]**2 + derivates["x3"][idx][0]**2)**(3/2)
                 if d != 0:
-                    k = np_.abs((np_.sqrt(a**2 + b**2 + c**2)) / d)
+                    k = (np_.sqrt(a**2 + b**2 + c**2)) / d
                 else:
                     k = 0