Merge pull request #24 from mb3152/master

Update nodal_roles.py

Author: whitergh

brainx/nodal_roles.py

@@ -1,4 +1,5 @@
 #Author: Maxwell Bertolero, [email protected], [email protected]
+#Author: Maxwell Bertolero, [email protected], [email protected]
 
 import numpy as np
 from random import choice
@@ -46,7 +47,7 @@ def within_community_degree(weighted_partition, nan = 0.0, catch_edgeless_node=T
             if std == 0.0: #so we don't divide by 0
                 wc_dict[node] = (within_community_degree - mean) #z_score
                 continue
-            wc_dict[node] = (within_community_degree - mean / std) #z_score
+            wc_dict[node] = ((within_community_degree - mean) / std) #z_score
     return wc_dict
 
 def participation_coefficient(weighted_partition, catch_edgeless_node=True):
@@ -71,20 +72,19 @@ def participation_coefficient(weighted_partition, catch_edgeless_node=True):
         Dictionary of the participation coefficient of each node.
     '''
     pc_dict = {}
-    graph = weighted_partition.graph
-    for node in graph:
+    for node in weighted_partition.graph:
         node_degree = weighted_partition.node_degree(node)
         if node_degree == 0.0: 
             if catch_edgeless_node:
                 raise ValueError("Node {} is edgeless".format(node))
             pc_dict[node] = 0.0
             continue    
-        deg_per_comm = weighted_partition.node_degree_by_community(node)
-        deg_per_comm.pop(weighted_partition.get_node_community(node))
-        bc_degree = sum(deg_per_comm) #between community degree
-        if bc_degree == 0.0:
-            pc_dict[node] = 0.0
-            continue
-        pc = 1 - ((float(bc_degree) / float(node_degree))**2)
+        pc = 0.0
+        for comm_degree in weighted_partition.node_degree_by_community(node):
+            try:
+                pc = pc + ((comm_degree/node_degree)**2)
+            except:
+                continue
+        pc = 1-pc
         pc_dict[node] = pc
-    return pc_dict
+    return pc_dict

build/lib/brainx/__init__.py

@@ -0,0 +1,35 @@
+"""Top-level init file for brainx package.
+"""
+
+def patch_nx():
+    """Temporary fix for NX's watts_strogatz routine, which has a bug in versions 1.1-1.3
+    """
+
+    import networkx as nx
+
+    # Quick test to see if we get the broken version
+    g = nx.watts_strogatz_graph(2, 0, 0)
+
+    if g.number_of_nodes() != 2:
+        # Buggy version detected.  Create a patched version and apply it to nx
+        
+        nx._watts_strogatz_graph_ori = nx.watts_strogatz_graph        
+
+        def patched_ws(n, k, p, seed=None):
+            if k<2:
+                g = nx.Graph()
+                g.add_nodes_from(range(n))
+                return g
+            else:
+                return nx._watts_strogatz_graph_ori(n, k, p, seed)
+
+        patched_ws.__doc__ = nx._watts_strogatz_graph_ori.__doc__
+
+        # Applying monkeypatch now
+        import warnings
+        warnings.warn("Monkeypatching NetworkX's Watts-Strogatz routine")
+
+        nx.watts_strogatz_graph = patched_ws
+        
+
+patch_nx()

build/lib/brainx/detect_modules.py

@@ -0,0 +1,7 @@
+"""DEPRECATED - use the modularity module instead."""
+
+import warnings
+warnings.warn(__doc__, DeprecationWarning)
+
+# Backwards compatibility
+from modularity import *