mod: node vs edge centrality based euklidian distance calculation

distance_types.py

@@ -38,18 +38,18 @@ def spatial_graph(adata):
 
 def apply(g, seed, weight, convex_hull, ax, ax2, method):
     # calculate centrality values
-    vp, ep = method(g, weight=weight)
-    ep.a = np.nan_to_num(ep.a) # correct floating point values
-    min_val, max_val = ep.a.min(), ep.a.max()
-    ep.a = (ep.a - min_val) / (max_val - min_val)
+    vp = method(g, weight=weight)
+    vp.a = np.nan_to_num(vp.a) # correct floating point values
+    min_val, max_val = vp.a.min(), vp.a.max()
+    vp.a = (vp.a - min_val) / (max_val - min_val)
 
     # euklidian distance
-    quantification = plot.quantification_data(g, ep, convex_hull)
+    quantification = plot.quantification_data(g, vp, convex_hull)
     plot.quantification_plot(ax, quantification, None, None, "Euklidian Distance", None)
 
     # generate model based on convex hull and associated centrality values
-    # path distance
-    quantification = plot.quantification_data_path_distance(g, weight, ep, convex_hull)
+    # path distance (node based centrality)
+    quantification = plot.quantification_data_node_path_distance(g, weight, vp, convex_hull)
     plot.quantification_plot(ax2, quantification, None, None, "Shortest Path Distance", None)
 
 
@@ -63,13 +63,13 @@ fig = plt.figure(figsize=(21, 5))
 ax1, ax2, ax3 = fig.subplots(1, 3)
 
 # plot graph with convex_hull
-vp, ep = betweenness(g, weight=weight)
-ep.a = np.nan_to_num(ep.a) # correct floating point values
-min_val, max_val = ep.a.min(), ep.a.max()
-ep.a = (ep.a - min_val) / (max_val - min_val)
+vp = closeness(g, weight=weight)
+vp.a = np.nan_to_num(vp.a) # correct floating point values
+min_val, max_val = vp.a.min(), vp.a.max()
+vp.a = (vp.a - min_val) / (max_val - min_val)
 
-plot.graph_plot(fig, ax1, g, ep, convex_hull, f"Pointcloud (seed: {seed})", True)
+plot.graph_plot(fig, ax1, g, vp, convex_hull, f"Pointcloud (seed: {seed})", False)
 
-apply(g, seed, weight, convex_hull, ax2, ax3, betweenness)
+apply(g, seed, weight, convex_hull, ax2, ax3, closeness)
 
-fig.savefig(f"Distance_5000_betweenness_edge_euklidian.svg", format='svg')
+fig.savefig(f"Distance_5000_node_closeness.svg", format='svg')