mod update corresponding examples

comparison.py

@@ -2,6 +2,7 @@ import math
 
 import matplotlib.pyplot as plt
 import numpy as np
+import squidpy as sq
 from graph_tool.all import *
 
 from src import centrality
@@ -9,6 +10,23 @@ from src import plot
 from src import fitting
 
 
+def merfish():
+    """
+    Merfish dataset from `squidpy`.
+    """
+    adata = sq.datasets.merfish()
+    adata = adata[adata.obs.Bregma == -9].copy()
+    return adata
+
+
+def mibitof():
+    """
+    Mibitof dataset from `squidpy`.
+    """
+    adata = sq.datasets.mibitof()
+    return adata
+
+
 def degree(g, weight):
     # VertexPropertyMap
     vp = g.new_vertex_property("double")
@@ -25,6 +43,8 @@ def leverage(g, weight):
         li = 0.0
         neighbours = g.get_all_neighbours(v)
         ki = len(neighbours)
+        if ki == 0:
+            continue
         # sum
         for nv in neighbours:
             other_neighbours = g.get_all_neighbours(nv)
@@ -48,46 +68,6 @@ def random_graph(n=5000, seed=None):
     return rng.random((n, 2)), seed
 
 
-def random_graph_favor_border(n=3000, seed = None):
-    if seed is None:
-        import secrets
-        seed = secrets.randbits(128)
-    rng = np.random.default_rng(seed=seed)
-    vps = np.zeros((n, 2))
-    for i in range(0, n):
-        r_x = rng.random()
-        if rng.random() > 0.5:
-            while (r_x > 0.3 and r_x < 0.7):
-                r_x = rng.random()
-        r_y = rng.random()
-        if rng.random() > 0.5:
-            while (r_y > 0.3 and r_y < 0.7):
-                r_y = rng.random()
-        vps[i][0] = r_x
-        vps[i][1] = r_y
-    return vps, seed
-
-
-def random_graph_favor_center(n=3000, seed = None):
-    if seed is None:
-        import secrets
-        seed = secrets.randbits(128)
-    rng = np.random.default_rng(seed=seed)
-    vps = np.zeros((n, 2))
-    for i in range(0, n):
-        r_x = rng.random()
-        if rng.random() > 0.7:
-            while (r_x < 0.4 or r_x > 0.6):
-                r_x = rng.random()
-        r_y = rng.random()
-        if rng.random() > 0.7:
-            while (r_y < 0.4 or r_y > 0.6):
-                r_y = rng.random()
-        vps[i][0] = r_x
-        vps[i][1] = r_y
-    return vps, seed
-
-
 def spatial_graph(adata):
     """
     Generate the spatial graph using delaunay for the given `adata`.
@@ -102,6 +82,7 @@ def spatial_graph(adata):
         weight[e] = math.sqrt(sum(map(abs, pos[e.source()].a - pos[e.target()].a)))**2
     return g, weight
 
+
 def apply(g, seed, weight, convex_hull, ax, method, method_name):
     # calculate centrality values
     vp = None
@@ -147,8 +128,9 @@ def apply(g, seed, weight, convex_hull, ax, method, method_name):
 #   - apply centrality measure to the next axis
 # - Draw the corresponding resulting models into a grid
 #
-points, seed = random_graph(n=5000)
-g, weight = spatial_graph(points)
+# points, seed = random_graph(n=5000)
+adata = mibitof()
+g, weight = spatial_graph(adata.obsm['spatial'])
 g = GraphView(g)
 # calculate convex hull
 convex_hull = centrality.convex_hull(g)
@@ -157,23 +139,23 @@ convex_hull = centrality.convex_hull(g)
 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"Pointcloud (seed: {seed})")
-fig_graph.savefig("Pointcloud_graph.svg", format='svg')
+plot.graph_plot(fig_graph, ax_graph, g, vp, convex_hull, f"mibitof")
+fig_graph.savefig(f"mibitof_graph.svg", format='svg')
 
 fig = plt.figure(figsize=(15, 12))
 row1, row2 = fig.subplots(2, 4)
 
 ax1, ax2, ax3, ax4 = row1
 # TODO select corresponding centrality measure method
-apply(g, seed, weight, convex_hull, ax1, closeness, "Closeness")
-apply(g, seed, weight, convex_hull, ax2, pagerank, "PageRank")
-apply(g, seed, weight, convex_hull, ax3, betweenness, "Betweeness")
-apply(g, seed, weight, convex_hull, ax4, eigenvector, "Eigenvector")
+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")
 
 ax1, ax2, ax3, ax4 = row2
-apply(g, seed, weight, convex_hull, ax1, katz, "Katz")
-apply(g, seed, weight, convex_hull, ax2, hits, "Hits")
-apply(g, seed, weight, convex_hull, ax3, leverage, "Leverage")
-apply(g, seed, weight, convex_hull, ax4, degree, "Degree")
+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_Pointcloud.svg", format='svg')
+fig.savefig(f"Comparison_node_centralities_mibitof_.svg", format='svg')