Implemented and added an example of size-aware layout for Euler diagr…

…ams.

hypernetx/drawing/util.py

@@ -193,3 +193,41 @@ def get_set_layering(H, collapse=True):
         levels[v] = max(parent_levels) + 1 if len(parent_levels) else 0
 
     return levels
+
+def layout_with_radius(B, node_and_edge_radius=1, **kwargs):
+    """
+    Convenience function allowing the user to specify ideal radii for nodes and edges in the drawing
+
+    The Kamada-Kawai (networkx.kamada_kawai_layout) algorithm is used. The algorithm is passed
+    a all pairs shorteset path matrix that is calculated from the bipartite graph input. The
+    shortest path is determined
+
+
+    Parameters
+    ----------
+    B: nx.Graph
+        a bipartite graph representing the hypergraph
+    node_and_edge_radius: float, int, dict, list, or function
+        encoding of the radii of nodes in B, which are hyper-edges or hyper-nodes (0 by default)
+    kwargs: dict
+        Keyword arguments are passed through to the networkx.kamada_kawai_layout function
+
+    Returns
+    -------
+    dict
+        mapping of node and edge positions to R^2
+    """
+
+    # get radii encodings and convert to dictionary
+    radius_dict = dict(zip(B, inflate(B, node_and_edge_radius)))
+
+    # edges weights are the sum of the radii of the edge endpoints
+    for u, v, d in B.edges(data=True):
+        d['weight'] = radius_dict.get(u, 0) + radius_dict.get(v, 0)
+
+    # compute all pairs shortest path (APSP)
+    dist = dict(nx.all_pairs_dijkstra_path_length(B))
+
+    # compute and return layout using above APSP; pass through arguments
+    return nx.kamada_kawai_layout(B, dist=dist, **kwargs)
+