mod update corresponding examples

comparison_edge_scores.py

@@ -85,23 +85,18 @@ def spatial_graph(adata):
 
 def apply(g, seed, weight, convex_hull, ax, method, method_name):
     # calculate centrality values
-    vp = None
+    ep = None
     if method_name == "Betweeness":
         vp, ep = method(g, weight=weight)
-    elif method_name == "Eigenvector":
-        ep, vp = method(g, weight=weight)
-    elif method_name == "Hits":
-        ep, vp, hub_centrality = method(g, weight=weight)
     else:
-        vp = method(g, weight=weight)
-    vp.a = np.nan_to_num(vp.a) # correct floating point values
+        ep = method(g, weight=weight)
+    ep.a = np.nan_to_num(ep.a) # correct floating point values
 
     # normalization
-    min_val, max_val = vp.a.min(), vp.a.max()
-    vp.a = (vp.a - min_val) / (max_val - min_val)
+    min_val, max_val = ep.a.min(), ep.a.max()
+    ep.a = (ep.a - min_val) / (max_val - min_val)
 
-    # generate model based on convex hull and associated centrality values
-    quantification = plot.quantification_data(g, vp, convex_hull)
+    quantification = plot.quantification_data_edges(g, ep, convex_hull)
 
     # optimize model's piece-wise linear function
     d = quantification[:, 0]
@@ -129,16 +124,16 @@ def apply(g, seed, weight, convex_hull, ax, method, method_name):
 # - Draw the corresponding resulting models into a grid
 #
 points, seed = random_graph(n=5000)
-g, weight = spatial_graph(adata.obsm['spatial'])
+g, weight = spatial_graph(points)
 g = GraphView(g)
 # calculate convex hull
 convex_hull = centrality.convex_hull(g)
 
 # plot graph with convex_hull
 fig_graph, ax_graph = plt.subplots(figsize=(15, 12))
-# draw without any centrality measure `vp`
-vp = g.new_vertex_property("double")
-plot.graph_plot(fig_graph, ax_graph, g, vp, convex_hull, f"Pointcould (seed: {seed})")
+# draw without any centrality measure `ep`
+ep = g.new_edge_property("double")
+plot.graph_plot(fig_graph, ax_graph, g, ep, convex_hull, f"Pointcould (seed: {seed})", True) # draw edges
 fig_graph.savefig(f"comparison_edge_scores_artificial_graph.svg", format='svg')
 
 fig = plt.figure(figsize=(15, 12))
@@ -148,15 +143,12 @@ row1, row2 = fig.subplots(2, 4)
 # - some share similarities to the node based counter parts
 
 ax1, ax2, ax3, ax4 = row1
-apply(g, None, weight, convex_hull, ax1, closeness, "Closeness")
-apply(g, None, weight, convex_hull, ax2, pagerank, "PageRank")
-apply(g, None, weight, convex_hull, ax3, betweenness, "Betweeness")
-apply(g, None, weight, convex_hull, ax4, eigenvector, "Eigenvector")
+apply(g, None, weight, convex_hull, ax1, betweenness, "Betweeness")
 
-ax1, ax2, ax3, ax4 = row2
-apply(g, None, weight, convex_hull, ax1, katz, "Katz")
-apply(g, None, weight, convex_hull, ax2, hits, "Hits")
-apply(g, None, weight, convex_hull, ax3, leverage, "Leverage")
-apply(g, None, weight, convex_hull, ax4, degree, "Degree")
+# ax1, ax2, ax3, ax4 = row2
+# apply(g, None, weight, convex_hull, ax1, katz, "Katz")
+# apply(g, None, weight, convex_hull, ax2, hits, "Hits")
+# apply(g, None, weight, convex_hull, ax3, leverage, "Leverage")
+# apply(g, None, weight, convex_hull, ax4, degree, "Degree")
 
 fig.savefig(f"Comparison_edge_centralities_artificial_.svg", format='svg')