def dynamic_sp_comm_view(self, type_viz='consumption', random_state=None):
"""
Same as :py:meth:`dynamic_view` but the species nodes are grouped
by the communities they belong to. Communities are obtained using the
Louvain algorithm.
Parameters
----------
type_viz: str
Type of visualization. It can be `consumption` to see how species are being consumed
or `production` to see how the species are being produced.
random_state: int
Seed used by the random generator in community detection
Returns
-------
dict
A Dictionary Object with all nodes and edges information that
can be converted into Cytoscape.js JSON to be visualized
"""
self.type_viz = type_viz
self.sp_graph = PysbStaticViz(self.model).species_graph()
hf.add_louvain_communities(self.sp_graph,
all_levels=False,
random_state=random_state)
self.sp_graph.graph['nsims'] = self.nsims
self.sp_graph.graph['tspan'] = self.tspan.tolist()
self._add_edge_node_dynamics()
data = from_networkx(self.sp_graph)
return data
def sp_comm_louvain_hierarchy_view(self, random_state=None):
"""
Use the Louvain algorithm https://en.wikipedia.org/wiki/Louvain_Modularity
for community detection to find groups of nodes that are densely connected.
It generates the data of all the intermediate clusters obtained during the Louvain
algorithm generate to create a network with compound nodes that hold the communities.
Parameters
==========
random_state : int, optional
Random state seed use by the community detection algorithm, by default None
Returns
-------
dict
A Dictionary object that can be converted into Cytoscape.js JSON. This dictionary
contains all the information (nodes,edges, parent nodes, positions) to generate
a cytoscapejs network.
"""
graph = self.species_graph()
hf.add_louvain_communities(graph,
all_levels=True,
random_state=random_state)
data = from_networkx(graph)
return data