def _animation_update(self, t, drawn_nodes, drawn_I, time_markers):
status = EoN.get_statuses(self.G, self.node_history, t)
Inodelist = [node for node in self.nodelist if status[node] == 'I']
drawn_nodes.set_color([self.colordict[status[node]] for node in self.nodelist])
drawn_I[0].remove()
drawn_I[0] = nx.draw_networkx_nodes(self.G, pos=self.pos, nodelist=Inodelist, color = self.colordict['I'], ax = self.network_axes, **self.kwargs)
for tm in time_markers:
tm.remove()
self._highlight_time(t, time_markers)
def _initialize(self):
initial_status = EoN.get_statuses(self.G, self.node_history, self.frame_times[0])
colorlist = [self.colordict[initial_status[node]] for node in self.nodelist]
nodeset = {node for node in self.nodelist}
edgelist = [edge for edge in self.G.edges() if edge[0] in nodeset and edge[1] in nodeset]
nx.draw_networkx_edges(self.G, pos=self.pos, edgelist=edgelist, ax = self.network_axes)
drawn_nodes = nx.draw_networkx_nodes(self.G, pos=self.pos, ax = self.network_axes, nodelist = self.nodelist, color=colorlist, **self.kwargs)
Inodelist = [node for node in self.nodelist if initial_status[node] == 'I']
drawn_I = [nx.draw_networkx_nodes(self.G, pos=self.pos, nodelist=Inodelist, color = self.colordict['I'], ax = self.network_axes, **self.kwargs)]
self.network_axes.set_xticks([])
self.network_axes.set_yticks([])
time_markers = [None for ax in self.timeseries_axi]
self._highlight_time(self.frame_times[0], time_markers)
return drawn_nodes, drawn_I, time_markers
def visualize(G, node_history, time, colordict={'S':'#009a80','I':'#ff2020', 'R':'gray'}, t=None, S=None, I=None, R=None, pos = None, SIR = True, figname=None):
r'''
'''
if SIR:
if t is None:
visualization = EoN_Plot(timeseries=[])
else:
visualization = EoN_plot()
else:
if t is None:
visualization = EoN_Plot(timeseries = [], SIR = SIR)
else:
visualization = EoN_Plot(timeseries = ['S', 'I'], SIR = SIR)
status = EoN.get_statuses(G, node_history, time)
visualization.plot_network(G, status, pos=pos, colordict=colordict)
if t is not None:
visualization.plot_timeseries(t, S, I, R, label = 'simulation')
visualization.legend()
if figname is not None:
visualization.save(figname)
return visualization
