Updated documentation

Author: schilling40

flamingo_tools/segmentation/cochlea_mapping.py

@@ -4,7 +4,6 @@
 import networkx as nx
 import numpy as np
 import pandas as pd
-from networkx.algorithms.approximation import steiner_tree
 from scipy.ndimage import distance_transform_edt, binary_dilation, binary_closing
 from scipy.interpolate import interp1d
 
@@ -148,10 +147,17 @@ def measure_run_length_sgns(centroids: np.ndarray, scale_factor=10):
 
 def measure_run_length_ihcs(centroids, max_edge_distance=50):
     """Measure the run lengths of the IHC segmentation
-    by finding the shortest path between the most distant nodes in a Steiner Tree.
+    by determining the shortest path between the most distant nodes of a graph.
+    The graph is created based on a maximal edge distance between nodes.
+    However, this value may not correspond to the max_edge_distance parameter used to process the IHC segmentation,
+    which may consist of more than one component.
+    That's why a large max_edge_distance is used to connect different components and identify
+    the two most distant nodes and the shortest path between them.
+    The path is then edited using a moving average filter.
 
     Args:
         centroids: Centroids of SGN segmentation.
+        max_edge_distance: Maximal edge distance between graph nodes to create an edge between nodes.
 
     Returns:
         Total distance of the path.
@@ -167,6 +173,9 @@ def measure_run_length_ihcs(centroids, max_edge_distance=50):
     for num, pos in coords.items():
         graph.add_node(num, pos=pos)
 
+    # TODO: Find cleaner option than the creation of a new graph with edges
+    # to identify start / end point and shortest path.
+
     # create edges between points whose distance is less than threshold max_edge_distance
     for num_i, pos_i in coords.items():
         for num_j, pos_j in coords.items():

reproducibility/tonotopic_mapping/2025-07-IHC_fig2.json

@@ -1,22 +1,22 @@
 [
 	{
 		"cochlea": "M_LR_000226_L",
-		"segmentation_channel": "IHC_v3",
+		"segmentation_channel": "IHC_v4b",
 		"type": "ihc"
 	},
 	{
 		"cochlea": "M_LR_000226_R",
-		"segmentation_channel": "IHC_v3",
+		"segmentation_channel": "IHC_v4b",
 		"type": "ihc"
 	},
 	{
 		"cochlea": "M_LR_000227_L",
-		"segmentation_channel": "IHC_v3",
+		"segmentation_channel": "IHC_v4b",
 		"type": "ihc"
 	},
 	{
 		"cochlea": "M_LR_000227_R",
-		"segmentation_channel": "IHC_v3",
+		"segmentation_channel": "IHC_v4b",
 		"type": "ihc"
 	}
 ]

reproducibility/tonotopic_mapping/2025-07-SGN.json

@@ -0,0 +1,47 @@
+[
+	{
+		"cochlea": "M_LR_000143_L",
+		"segmentation_channel": "SGN_v2",
+		"type": "sgn"
+	},
+	{
+		"cochlea": "M_LR_000144_L",
+		"segmentation_channel": "SGN_v2",
+		"type": "sgn"
+	},
+	{
+		"cochlea": "M_LR_000145_L",
+		"segmentation_channel": "SGN_v2",
+		"type": "sgn"
+	},
+	{
+		"cochlea": "M_LR_000189_L",
+		"segmentation_channel": "SGN_v2",
+		"type": "sgn"
+	},
+	{
+		"cochlea": "M_LR_000144_R",
+		"segmentation_channel": "SGN_v2",
+		"type": "sgn"
+	},
+	{
+		"cochlea": "M_LR_000145_R",
+		"segmentation_channel": "SGN_v2",
+		"type": "sgn"
+	},
+	{
+		"cochlea": "M_LR_000153_R",
+		"segmentation_channel": "SGN_v2",
+		"type": "sgn"
+	},
+	{
+		"cochlea": "M_LR_000155_R",
+		"segmentation_channel": "SGN_v2",
+		"type": "sgn"
+	},
+	{
+		"cochlea": "M_LR_000189_R",
+		"segmentation_channel": "SGN_v2",
+		"type": "sgn"
+	}
+]

reproducibility/tonotopic_mapping/2025-07-SGN_ChReef.json

@@ -2,26 +2,21 @@
 	{
 		"cochlea": "M_LR_000226_L",
 		"segmentation_channel": "SGN_v2",
-		"type": "sgn",
-		"filter_factor": 0.75
+		"type": "sgn"
 	},
 	{
 		"cochlea": "M_LR_000226_R",
 		"segmentation_channel": "SGN_v2",
-		"type": "sgn",
-		"filter_factor": 0.75
+		"type": "sgn"
 	},
 	{
 		"cochlea": "M_LR_000227_L",
 		"segmentation_channel": "SGN_v2",
-		"type": "sgn",
-		"max_edge_distance": 70,
-		"filter_factor": 0.75
+		"type": "sgn"
 	},
 	{
 		"cochlea": "M_LR_000227_R",
 		"segmentation_channel": "SGN_v2",
-		"type": "sgn",
-		"filter_factor": 0.75
+		"type": "sgn"
 	}
 ]