solved bug: singular matrices in best fitting ellipsoid

brick/processing/best_fit_ellipsoid.py

@@ -56,7 +56,7 @@ def ls_ellipsoid(xx: array_t, yy: array_t, zz: array_t) -> array_t:  # finds the
         return (ellipsoid_coef)
 
     except:
-        print("Singular matrix, cannot find the best fitting ellipsoid.")
+        print("WARNING: Singular matrix, cannot find the best fitting ellipsoid. The soma will probably be deleted in the resulting table. \n")
         return (np_.zeros(9))
 
 
@@ -179,7 +179,7 @@ def FindBestFittingEllipsoid3D(soma: soma_t) -> tuple:
     # # get ellipsoid polynomial coefficients
     ellipsoid_coef = ls_ellipsoid(convex_hull[0], convex_hull[1], convex_hull[2])
 
-    if ellipsoid_coef == (np_.zeros(9)):
+    if ellipsoid_coef.all() == 0:
         return ellipsoid_coef, "NaN", "NaN", "NaN", "NaN", "NaN", "NaN"
 
     else:

brick/processing/graph_feat_extraction.py

@@ -293,22 +293,30 @@ def ExtractFeaturesInDF(name_file, somas, size_voxel_in_micron: list, bins_lengt
         _, _, soma.axes_ellipsoid, _, spherical_coor,_, volume_convex_hull = bf_.FindBestFittingEllipsoid3D(soma)
 
         # This ratios give info about the shape of the soma. ex: rather flat, rather patatoide, rather spherical...
-        Coef_axes_ellips_b__a = soma.axes_ellipsoid[0] / soma.axes_ellipsoid[2]
-        Coef_axes_ellips_c__a = soma.axes_ellipsoid[1] / soma.axes_ellipsoid[2]
-
-        # Spherical angles give the orientation of the somas in the 3D volume
-        spherical_angles_eva = (spherical_coor[0][1], spherical_coor[0][2])
-        spherical_angles_evb = (spherical_coor[1][1], spherical_coor[1][2])
-
-        # Volume of the  in micron**3
-        soma.volume_soma_micron = in_.ToMicron(len(soma.sites[0]), size_voxel_in_micron, dimension=(0, 1, 2), decimals=2)
-        # Calculates volume of soma's convex hull in voxel volume
-        # Take into account anisotropy of the 3D space ( volume = x * y * z with z > x=y)
-        volume_convex_hull = volume_convex_hull * size_voxel_in_micron[2] / size_voxel_in_micron[0]
-
-        # Volume of the soma / Volume of its convex hull gives the info about the convexity of the soma
-        # If close to 0, the soma has a lot of invaginations, if close to 1, it is smooth and convex
-        Coef_V_soma__V_convex_hull = len(soma.sites[0]) / round(volume_convex_hull + 0.5)
+        if type(soma.axes_ellipsoid[0]) is str:
+            Coef_axes_ellips_b__a = None
+            Coef_axes_ellips_c__a = None
+            spherical_angles_eva = None
+            spherical_angles_evb = None
+            soma.volume_soma_micron = None
+            Coef_V_soma__V_convex_hull = None
+        else:
+            Coef_axes_ellips_b__a = soma.axes_ellipsoid[0] / soma.axes_ellipsoid[2]
+            Coef_axes_ellips_c__a = soma.axes_ellipsoid[1] / soma.axes_ellipsoid[2]
+
+            # Spherical angles give the orientation of the somas in the 3D volume
+            spherical_angles_eva = (spherical_coor[0][1], spherical_coor[0][2])
+            spherical_angles_evb = (spherical_coor[1][1], spherical_coor[1][2])
+
+            # Volume of the  in micron**3
+            soma.volume_soma_micron = in_.ToMicron(len(soma.sites[0]), size_voxel_in_micron, dimension=(0, 1, 2), decimals=2)
+            # Calculates volume of soma's convex hull in voxel volume
+            # Take into account anisotropy of the 3D space ( volume = x * y * z with z > x=y)
+            volume_convex_hull = volume_convex_hull * size_voxel_in_micron[2] / size_voxel_in_micron[0]
+
+            # Volume of the soma / Volume of its convex hull gives the info about the convexity of the soma
+            # If close to 0, the soma has a lot of invaginations, if close to 1, it is smooth and convex
+            Coef_V_soma__V_convex_hull = len(soma.sites[0]) / round(volume_convex_hull + 0.5)
 
         # -- Extension features
         # Graph features