bug: compute distance and smoothing (#43)

Co-authored-by: anna-grim <anna.grim@alleninstitute.org>

src/deep_neurographs/feature_extraction.py

@@ -243,7 +243,7 @@ def generate_img_profiles(
     )
     img = utils.normalize_img(img)
     for edge in neurograph.mutable_edges:
-        xyz_i, xyz_j = neurograph.get_edge_attr("xyz", edge)
+        xyz_i, xyz_j = neurograph.get_edge_attr(edge, "xyz")
         xyz_i = utils.world_to_img(neurograph, xyz_i)
         xyz_j = utils.world_to_img(neurograph, xyz_j)
         path = geometry_utils.make_line(xyz_i, xyz_j, N_PROFILE_POINTS)

src/deep_neurographs/geometry_utils.py

@@ -1,10 +1,11 @@
 import heapq
+import math
 from copy import deepcopy
 
 import numpy as np
 from scipy.interpolate import UnivariateSpline
 from scipy.linalg import svd
-
+from scipy.spatial import distance 
 from deep_neurographs import utils
 
 
@@ -427,7 +428,7 @@ def dist(v_1, v_2, metric="l2"):
     if metric == "l1":
         return np.linalg.norm(np.subtract(v_1, v_2), ord=1)
     else:
-        return np.linalg.norm(np.subtract(v_1, v_2), ord=2)
+        return distance.euclidean(v_1, v_2)
 
 
 def make_line(xyz_1, xyz_2, num_steps):

src/deep_neurographs/graph_utils.py

@@ -92,6 +92,11 @@ def get_irreducibles(swc_dict, swc_id=None, prune=True, depth=16, smooth=True):
             nbs = utils.append_dict_value(nbs, root, j)
             nbs = utils.append_dict_value(nbs, j, root)
             root = None
+
+            if all(attrs["xyz"][0] == attrs["xyz"][-1]):
+                print(root, j)
+                print(attrs)
+                stop
 
     # Output
     leafs = set_node_attrs(swc_dict, leafs)
@@ -290,7 +295,7 @@ def upd_endpoint_xyz(edges, key, old_xyz, new_xyz):
     """
     if all(edges[key]["xyz"][0] == old_xyz):
         edges[key]["xyz"][0] = new_xyz
-    else:
+    elif all(edges[key]["xyz"][-1] == old_xyz):
         edges[key]["xyz"][-1] = new_xyz
     return edges
 

src/deep_neurographs/neurograph.py

@@ -352,7 +352,7 @@ def init_targets(self, target_neurograph):
             edge = proposals[idx]
             if self.is_simple(edge):
                 add_bool = self.is_target(
-                    target_neurograph, edge, dist=5, ratio=0.7, exclude=10
+                    target_neurograph, edge, dist=3, ratio=0.7, exclude=5
                 )
                 if add_bool:
                     self.target_edges.add(edge)
@@ -364,7 +364,7 @@ def init_targets(self, target_neurograph):
         for idx in np.argsort(dists):
             edge = remaining_proposals[idx]
             add_bool = self.is_target(
-                target_neurograph, edge, dist=7, ratio=0.4, exclude=15
+                target_neurograph, edge, dist=5, ratio=0.5, exclude=5
             )
             if add_bool:
                 self.target_edges.add(edge)
@@ -517,7 +517,7 @@ def get_immutable_nbs(self, i):
         return nbs
 
     def compute_length(self, edge, metric="l2"):
-        xyz_1, xyz_2 = self.get_edge_attr("xyz", edge)
+        xyz_1, xyz_2 = self.get_edge_attr(edge, "xyz")
         return get_dist(xyz_1, xyz_2, metric=metric)
 
     def path_length(self, metric="l2"):
@@ -555,10 +555,9 @@ def creates_cycle(self, edge):
         self.predicted_graph.remove_edges_from([edge])
         return True
 
-    def get_edge_attr(self, key, edge):
-        i, j = edge
+    def get_edge_attr(self, edge, key):
         xyz_arr = gutils.get_edge_attr(self, edge, key)
-        return xyz_arr[0], xyz_arr[1]
+        return xyz_arr[0], xyz_arr[-1]
 
     def get_complex_proposals(self):
         return set([e for e in self.mutable_edges if not self.is_simple(e)])