def networkx_cooccurrence_graph( nC, v, labels, max_edges = 10 ) :
"""
Returns a Networkx-like undirected graph, to eventually be used for analysis and display
"""
Result = nx.Graph()
nv = v.astype( float32 ) / v.max()
cutoff = cooccur_cutoff( nC, max_edges );
coords = zip(*(nC >= cutoff).nonzero())
# make a dict of all nodes which are mentioned in the coords
nodes = {}
index = 1
# explicitly cast everything so that gexf and other files can convert correctly
for coord in set(chain.from_iterable(coords)) :
if not nodes.has_key( coord ) :
Result.add_node( str(index), label=str(index), coord=str(coord), labelval=str(labels[coord]), index = str(index), width=float(nv[coord]) )
nodes[ coord ] = str(index)
index = index + 1
for coord in coords :
Result.add_edge( nodes[coord[0]], nodes[coord[1]], weight = float(nC[coord]), penwidth = float(nC[coord]) )
return Result
def doi_legend( nC, v, labels, max_edges = 10 ) :
cutoff = cooccur_cutoff( nC, max_edges )
coords = zip(*(nC >= cutoff).nonzero())
dois = [labels[x] for x in set(chain.from_iterable(coords))]
b = Crossref_browser()
result = [legend_line( dois.index(doi) + 1, doi, b ) for doi in dois]
return result
def neato_cooccurrence_graph( nC, v, labels, max_edges = 10, fnam_stem = "test", label_nodes_directly = False, scale=1.0, min_node_size = 0.1 ):
"""
makes a neato-style undirected graph from the given cooccurrence matrix, vector of
total occurrences, and labels. Assume C is normalized as desired and that
all is pruned as desired!
"""
nv = v.astype( float32 ) / v.max()
cutoff = cooccur_cutoff( nC, max_edges );
graph = pydot.Dot( graph_type = 'graph' )
graph.set_overlap("false")
coords = zip(*(nC >= cutoff).nonzero())
# make a dict of all nodes which are mentioned in the coords
nodes = {}
index = 1
for coord in set(chain.from_iterable(coords)) :
if not nodes.has_key( coord ) :
node = pydot.Node( str(coord) )
if v != None :
#print coord
label = labels[coord]
if label_nodes_directly :
node.set_label( label )
else :
node.set_label( str(index) )
#node.set_penwidth( nv[ coord ] )
node.set_fixedsize("true")
node.set_width( max(min_node_size,scale *nv[ coord ]) )
node.set_shape("circle")
nodes[ coord ] = node
graph.add_node( node )
index = index + 1
for coord in coords :
edge = pydot.Edge( nodes[coord[0]], nodes[coord[1]] )
edge.set_weight( nC[coord] )
edge.set_penwidth( nC[coord]*5 )
#edge.set_label( str(int(m[coord]) ))
graph.add_edge(edge)
if not label_nodes_directly :
legend = pydot.Node( "legend" )
nodelist = nodes.items()
nodelist.sort( lambda a,b : cmp(node_index(a[1].get_label()),node_index(b[1].get_label())) )
legend.set_label( "\l".join([x[1].get_label()+":"+labels[x[0]] for x in nodelist])+"\l" )
legend.set_shape("box")
graph.add_node(legend)
#print graph.to_string()
graph.write_dot(fnam_stem+'.dot', prog='neato' )
graph.write_png(fnam_stem+'.png', prog='neato' )
