#-*- coding: utf-8 -*- """ Mixing matrices for node attributes and degree. """ import networkx as nx from networkx.utils import dict_to_numpy_array from networkx.algorithms.assortativity.pairs import node_degree_xy, \ node_attribute_xy __author__ = ' '.join(['Aric Hagberg ']) __all__ = ['attribute_mixing_matrix', 'attribute_mixing_dict', 'degree_mixing_matrix', 'degree_mixing_dict', 'numeric_mixing_matrix', 'mixing_dict'] def attribute_mixing_dict(G,attribute,nodes=None,normalized=False): """Return dictionary representation of mixing matrix for attribute. Parameters ---------- G : graph NetworkX graph object. attribute : string Node attribute key. nodes: list or iterable (optional) Unse nodes in container to build the dict. The default is all nodes. normalized : bool (default=False) Return counts if False or probabilities if True. Examples -------- >>> G=nx.Graph() >>> G.add_nodes_from([0,1],color='red') >>> G.add_nodes_from([2,3],color='blue') >>> G.add_edge(1,3) >>> d=nx.attribute_mixing_dict(G,'color') >>> print(d['red']['blue']) 1 >>> print(d['blue']['red']) # d symmetric for undirected graphs 1 Returns ------- d : dictionary Counts or joint probability of occurrence of attribute pairs. """ xy_iter=node_attribute_xy(G,attribute,nodes) return mixing_dict(xy_iter,normalized=normalized) def attribute_mixing_matrix(G,attribute,nodes=None,mapping=None, normalized=True): """Return mixing matrix for attribute. Parameters ---------- G : graph NetworkX graph object. attribute : string Node attribute key. nodes: list or iterable (optional) Use only nodes in container to build the matrix. The default is all nodes. mapping : dictionary, optional Mapping from node attribute to integer index in matrix. If not specified, an arbitrary ordering will be used. normalized : bool (default=False) Return counts if False or probabilities if True. Returns ------- m: numpy array Counts or joint probability of occurrence of attribute pairs. """ d=attribute_mixing_dict(G,attribute,nodes) a=dict_to_numpy_array(d,mapping=mapping) if normalized: a=a/a.sum() return a def degree_mixing_dict(G, x='out', y='in', weight=None, nodes=None, normalized=False): """Return dictionary representation of mixing matrix for degree. Parameters ---------- G : graph NetworkX graph object. x: string ('in','out') The degree type for source node (directed graphs only). y: string ('in','out') The degree type for target node (directed graphs only). weight: string or None, optional (default=None) The edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1. The degree is the sum of the edge weights adjacent to the node. normalized : bool (default=False) Return counts if False or probabilities if True. Returns ------- d: dictionary Counts or joint probability of occurrence of degree pairs. """ xy_iter=node_degree_xy(G, x=x, y=y, nodes=nodes, weight=weight) return mixing_dict(xy_iter,normalized=normalized) def degree_mixing_matrix(G, x='out', y='in', weight=None, nodes=None, normalized=True): """Return mixing matrix for attribute. Parameters ---------- G : graph NetworkX graph object. x: string ('in','out') The degree type for source node (directed graphs only). y: string ('in','out') The degree type for target node (directed graphs only). nodes: list or iterable (optional) Build the matrix using only nodes in container. The default is all nodes. weight: string or None, optional (default=None) The edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1. The degree is the sum of the edge weights adjacent to the node. normalized : bool (default=False) Return counts if False or probabilities if True. Returns ------- m: numpy array Counts, or joint probability, of occurrence of node degree. """ d=degree_mixing_dict(G, x=x, y=y, nodes=nodes, weight=weight) s=set(d.keys()) for k,v in d.items(): s.update(v.keys()) m=max(s) mapping=dict(zip(range(m+1),range(m+1))) a=dict_to_numpy_array(d,mapping=mapping) if normalized: a=a/a.sum() return a def numeric_mixing_matrix(G,attribute,nodes=None,normalized=True): """Return numeric mixing matrix for attribute. The attribute must be an integer. Parameters ---------- G : graph NetworkX graph object. attribute : string Node attribute key. The corresponding attribute must be an integer. nodes: list or iterable (optional) Build the matrix only with nodes in container. The default is all nodes. normalized : bool (default=False) Return counts if False or probabilities if True. Returns ------- m: numpy array Counts, or joint, probability of occurrence of node attribute pairs. """ d=attribute_mixing_dict(G,attribute,nodes) s=set(d.keys()) for k,v in d.items(): s.update(v.keys()) m=max(s) mapping=dict(zip(range(m+1),range(m+1))) a=dict_to_numpy_array(d,mapping=mapping) if normalized: a=a/a.sum() return a def mixing_dict(xy,normalized=False): """Return a dictionary representation of mixing matrix. Parameters ---------- xy : list or container of two-tuples Pairs of (x,y) items. attribute : string Node attribute key normalized : bool (default=False) Return counts if False or probabilities if True. Returns ------- d: dictionary Counts or Joint probability of occurrence of values in xy. """ d={} psum=0.0 for x,y in xy: if x not in d: d[x]={} if y not in d: d[y]={} v = d[x].get(y,0) d[x][y] = v+1 psum+=1 if normalized: for k,jdict in d.items(): for j in jdict: jdict[j]/=psum return d # fixture for nose tests def setup_module(module): from nose import SkipTest try: import numpy except: raise SkipTest("NumPy not available") try: import scipy except: raise SkipTest("SciPy not available")