feat: compute erl (#35)

Co-authored-by: anna-grim <[email protected]>

Author: anna-grim

src/segmentation_skeleton_metrics/graph_utils.py

@@ -6,8 +6,10 @@
 @email: [email protected]
 
 """
+from scipy.spatial.distance import euclidean as dist
 from random import sample
 
+import math
 import networkx as nx
 
 
@@ -162,44 +164,50 @@ def count_splits(graph):
 
 
 def compute_run_lengths(graph):
-    pass
+    run_lengths = []
+    for nodes in nx.connected_components(graph):
+        subgraph = graph.subgraph(nodes)
+        run_lengths.append(compute_path_length(subgraph))
+    return run_lengths
 
 
-# -- miscellaneous --
-def sample_leaf(graph):
+def compute_path_length(graph):
     """
-    Samples leaf node from "graph".
+    Computes path length of graph.
 
     Parameters
     ----------
     graph : networkx.Graph
-        Graph to be sampled from.
+        Graph to be parsed.
 
     Returns
     -------
-    int
-        Leaf node of "graph"
+    path_length : float
+        Path length of graph.
 
     """
-    leafs = [i for i in graph.nodes if graph.degree[i] == 1]
-    return sample(leafs, 1)[0]
-
+    path_length = 0
+    for i, j in nx.dfs_edges(graph):
+        xyz_1 = graph.nodes[i]["xyz"]
+        xyz_2 = graph.nodes[j]["xyz"]
+        path_length += dist(xyz_1, xyz_2)
+    return path_length
 
-def empty_copy(graph):
+# -- miscellaneous --
+def sample_leaf(graph):
     """
-    Creates a copy of "graph" that does not contain the node level attributes.
+    Samples leaf node from "graph".
 
     Parameters
     ----------
     graph : networkx.Graph
-        Graph to be copied.
+        Graph to be sampled from.
 
     Returns
     -------
-    graph : netowrkx.Graph
-        Copy of "graph" that does not contain its node level attributes.
+    int
+        Leaf node of "graph"
+
     """
-    graph_copy = nx.Graph(graph, pred_ids=set())
-    for i in graph_copy.nodes():
-        graph_copy.nodes[i].clear()
-    return graph_copy
+    leafs = [i for i in graph.nodes if graph.degree[i] == 1]
+    return sample(leafs, 1)[0]

src/segmentation_skeleton_metrics/skeleton_metric.py

@@ -126,12 +126,12 @@ def label_graph(self, target_graph):
             Updated graph with node-level attributes called "pred_id".
 
         """
-        pred_graph = gutils.empty_copy(target_graph)
+        pred_graph = nx.Graph(target_graph, pred_ids=set())
         with ThreadPoolExecutor() as executor:
             # Assign threads
             threads = []
             for i in pred_graph.nodes:
-                img_coord = gutils.get_coord(target_graph, i)
+                img_coord = gutils.get_coord(pred_graph, i)
                 threads.append(executor.submit(self.get_label, img_coord, i))
             # Store results
             for thread in as_completed(threads):
@@ -431,7 +431,9 @@ def compute_edge_accuracy(self):
     def compute_erl(self):
         self.erl = dict()
         for swc_id in self.target_graphs.keys():
-            None
+            graph = self.pred_graphs[swc_id]
+            path_lengths = gutils.compute_run_lengths(graph)
+            self.erl[swc_id] = np.mean(path_lengths)
 
 
 # -- utils --

src/segmentation_skeleton_metrics/utils.py

@@ -202,3 +202,9 @@ def time_writer(t, unit="seconds"):
         unit = upd_unit[unit]
         t, unit = time_writer(t, unit=unit)
     return t, unit
+
+
+def progress_bar(current, total, bar_length=50):
+    progress = int(current / total * bar_length)
+    bar = f"[{'=' * progress}{' ' * (bar_length - progress)}] {current}/{total}"
+    print(f"\r{bar}", end="", flush=True)