refactor: output directory

src/deep_neurographs/inference.py

@@ -133,7 +133,7 @@ def __init__(
 
         # Set output directory
         date = datetime.today().strftime("%Y-%m-%d")
-        self.output_dir = f"{output_dir}/{segmentation_id}-{date}"
+        self.output_dir = output_dir
         util.mkdir(self.output_dir, delete=True)
         if self.log_runtimes:
             log_path = os.path.join(self.output_dir, "runtimes.txt")
@@ -221,17 +221,7 @@ def build_graph(self, fragments_pointer):
             prune_depth=self.graph_config.prune_depth,
         )
         self.graph = graph_builder.run(fragments_pointer)
-
-        # Filter fragments
-        n_curvy = fragment_filtering.remove_curvy(self.graph, 200)
-        n_curvy = util.reformat_number(n_curvy)
-        if self.graph_config.remove_doubles_bool:
-            n_doubles = fragment_filtering.remove_doubles(
-                self.graph, 200, self.graph_config.node_spacing
-            )
-            n_doubles = util.reformat_number(n_doubles)
-            self.report(f"# Double Fragments Deleted: {n_doubles}")
-        self.report(f"# Curvy Fragments Deleted: {n_curvy}")
+        self.filter_fragments()
 
         # Save valid labels and current graph
         swcs_path = os.path.join(self.output_dir, "processed-swcs.zip")
@@ -248,6 +238,20 @@ def build_graph(self, fragments_pointer):
         self.report("\nInitial Graph...")
         self.report_graph()
 
+    def filter_fragments(self):
+        # Filter curvy fragments
+        n_curvy = fragment_filtering.remove_curvy(self.graph, 200)
+        n_curvy = util.reformat_number(n_curvy)
+
+        # Filter doubles
+        if self.graph_config.remove_doubles_bool:
+            n_doubles = fragment_filtering.remove_doubles(
+                self.graph, 200, self.graph_config.node_spacing
+            )
+            n_doubles = util.reformat_number(n_doubles)
+            self.report(f"# Double Fragments Deleted: {n_doubles}")
+        self.report(f"# Curvy Fragments Deleted: {n_curvy}")
+
     def generate_proposals(self, radius=None):
         """
         Generates proposals for the fragment graph based on the specified

src/deep_neurographs/visualization.py

@@ -16,7 +16,7 @@
 
 
 def visualize_connected_components(
-    graph, line_width=5, return_data=False, title=""
+    graph, line_width=3, return_data=False, title=""
 ):
     """
     Visualizes the connected components in "graph".
@@ -202,11 +202,15 @@ def plot_nodes(graph):
     )
 
 
-def plot_proposals(graph, proposals, color=None, line_width=3.5):
+def plot_proposals(graph, proposals, color=None, line_width=5):
+    # Set preferences
+    if color is None:
+        line = dict(width=line_width)
+    else:
+        line = dict(color=color, width=line_width)
+
+    # Add traces
     traces = []
-    line = (
-        dict(width=5) if color is None else dict(color=color, width=line_width)
-    )
     for p in proposals:
         xyz = graph.proposal_xyz(p)
         trace = go.Scatter3d(
@@ -221,7 +225,7 @@ def plot_proposals(graph, proposals, color=None, line_width=3.5):
     return traces
 
 
-def plot_edges(graph, edges, color=None, line_width=3.5):
+def plot_edges(graph, edges, color=None, line_width=3):
     traces = []
     line = (
         dict(width=5) if color is None else dict(color=color, width=line_width)