def si_likelihood(pop, event_db, alpha, beta):
"""Determine the likelihood of the data from an SI simulation given alpha
and beta
"""
daily_likelihood=[0]*(np.max(event_db.time)-1)
t=1
infectious=find_infectious(event_db, t)
susceptible=find_susceptible(pop, event_db, t)
for t in range(1, np.max(event_db.time)):
new_infectious=find_infectious(event_db, t+1)
new_susceptible=find_susceptible(pop, event_db, t+1)
infection_probs=infect_prob(pop, alpha, beta, infectious, susceptible)
def new_infections_func(x):
return any(susceptible.ind_ID[x] == new_infectious.ind_ID)
new_infections = map(new_infections_func, susceptible.index)
daily_likelihood[t-1]=np.prod(np.subtract(1, infection_probs[np.where(np.invert(new_infections))]))*np.prod(infection_probs[np.where(new_infections)])
infectious=new_infectious
susceptible=new_susceptible
return np.prod(daily_likelihood)
def plot_si(pop, event_db, time):
"""This will create a simple scatterplot of susceptible and infectious individuals at
a given time
"""
i=find_infectious(event_db, time)
s=find_susceptible(pop, event_db, time)
status=pd.DataFrame({"status":np.append(np.repeat("i", i.shape[0]), np.repeat("s", s.shape[0])),
"colour":np.append(np.repeat("k", i.shape[0]), np.repeat("b", s.shape[0])),
"ind_ID":np.append(i.ind_ID, s.ind_ID),
"x":pop.iloc[np.append(i.ind_ID, s.ind_ID)].x,
"y":pop.iloc[np.append(i.ind_ID, s.ind_ID)].y})
plt.scatter(status.x, status.y, c=status.colour, s=60, edgecolors='none')