def __init__(self, N = 10000, m_0 = 3):
"""
:Purpose:
This is the base class used to generate the social network
and neighborhoods. The class inherits from the PopulationClass.
:Input:
N : int
Number of agents. Default: 10000
m_0: int
Number of nodes each node is connected to in preferential
attachment step
"""
if type(N) is not int:
raise ValueError(('Population size must be integer,\
n = %s, not %s')%(string(N), type(N)))
else: pass
if m_0 not in range(10):
raise ValueError('m_0 must be integer smaller than 10')
else: self.m_0 = m_0
PopulationClass.__init__(self, n = N) # Create population
self.repeated_nodes = {'All':[], 'IDU':[], 'NIDU':[], 'ND':[], 'HIV':[]}
self.G = nx.Graph()
self._set_assortative_graph() # create graph
def __init__(self, N = 10000, m_0 = 3):
"""
:Purpose:
This is the base class used to generate the social network
for the other agents, i.e. . The class inherits from the PopulationClass.
:Input:
N : int
Number of agents. Default: 10000
m_0: int
Number of nodes each node is connected to in preferential
attachment step
"""
if type(N) is not int:
raise ValueError(('Population size must be integer,\
n = %s, not %s')%(string(N), type(N)))
else: pass
if m_0 not in range(10):
raise ValueError('m_0 must be integer smaller than 10')
else: self.m_0 = m_0
PopulationClass.__init__(self, n = N) # Create population
SpecialAgents = set(self.IDU_agents).union(set(self.MSM_agents))
SpecialAgents = SpecialAgents.union(set(self.NIDU_agents))
NormalAgents = set(range(self.PopulationSize)).difference(SpecialAgents)
NormalAgents = list(NormalAgents)
self.NetworkSize = len(NormalAgents)
# scale free Albert Barabsai Graph
self.G = nx.barabasi_albert_graph(self.NetworkSize,m_0)