WIP

point_cloud_example.py

@@ -3,6 +3,7 @@ import math
 import matplotlib.pyplot as plt
 from matplotlib.collections import LineCollection
 import numpy as np
+import squidpy as sq
 from graph_tool.all import *
 
 
@@ -19,6 +20,14 @@ def random_graph(n=5000, seed=None):
     return rng.random((n, 2)), seed
 
 
+def mibitof():
+    """
+    Mibitof dataset from `squidpy`.
+    """
+    adata = sq.datasets.mibitof()
+    return adata
+
+
 def spatial_graph(adata):
     """
     Generate the spatial graph using delaunay for the given `adata`.
@@ -55,6 +64,11 @@ def draw_graph(G, ax, name):
         ax.add_collection(LineCollection([np.column_stack([ex, ey])], colors=['k'], linewidths=0.1))
 
     ax.scatter(x, y, s=1) # map closeness values as color mapping on the verticies
+    # for v in G.vertices():
+    #     neighbours = g.get_all_neighbours(v)
+    #     if len(neighbours) == 0:
+    #         ax.scatter(pos[v][0], pos[v][1], s=1, color=['r'])
+
     ax.set_title(name)
 
 
@@ -66,11 +80,24 @@ def draw_graph(G, ax, name):
 #   - apply centrality measure to the next axis
 # - Draw the corresponding resulting models into a grid
 #
-points, seed = random_graph(n=3000)
-g, weight = spatial_graph(points)
+# points, seed = random_graph(n=3000)
+# g, weight = spatial_graph(points)
+adata = mibitof()
+g, weight = spatial_graph(adata.obsm['spatial'])
 g = GraphView(g)
 
+for v in g.vertices():
+    neighbours = g.get_all_neighbours(v)
+    if len(neighbours) == 0:
+        g.remove_vertex(v)
+        break
+
+pos = g.vp["pos"]
+weight = g.new_edge_property("double")
+for e in g.edges():
+    weight[e] = math.sqrt(sum(map(abs, pos[e.source()].a - pos[e.target()].a)))**2
+
 # plot graph with convex_hull
 fig_graph, ax_graph = plt.subplots(figsize=(15, 12))
-draw_graph(g, ax_graph, f"Pointcould (seed: {seed} | n: 500)")
-fig_graph.savefig("point_cloud_example.svg", format='svg')
+draw_graph(g, ax_graph, f"mibitof")
+fig_graph.savefig("mibitof_graph.svg", format='svg')