def example_map(sp_sub):
"""
This function should convert sp_sub to the quantity to be mapped,
in this case the inverse-logit of f.
"""
f = sp_sub.copy('F')
p = invlogit(f)
return p
def unexposed_risk(sp_sub):
pr = sp_sub.copy('F')
pr = invlogit(pr)
pr[np.where(pr==0)]=1e-10
pr[np.where(pr==1)]=1-(1e-10)
ur = 1-np.exp(-r*k*((1-pr)**(-1./k)-1)*trip_duration)
ur[np.where(ur==0)]=1e-10
ur[np.where(ur==1)]=1-(1e-10)
return ur
def incidence(sp_sub,
two_ten_facs=two_ten_factors,
p2b = BurdenPredictor('CSE_Asia_and_Americas_scale_0.6_model_exp.hdf5', N_year),
N_year = N_year):
pr = sp_sub.copy('F')
pr = invlogit(pr) * two_ten_facs[np.random.randint(len(two_ten_facs))]
i = np.random.randint(len(p2b.f))
mu = p2b.f[i](pr)
# Uncomment and draw a negative binomial variate to get incidence over a finite time horizon.
r = (p2b.r_int[i] + p2b.r_lin[i] * pr + p2b.r_quad[i] * pr**2)
ar = pm.rgamma(beta=r/mu, alpha=r*N_year)
#out = (1-np.exp(-ar)) # what we originally produced e.g. for Afghanistan
out = ar # effectively what we did for burden paper
out[np.where(out==0)]=1e-10
out[np.where(out==1)]=1-(1e-10)
return out
def male_def(sp_sub):
allele = sp_sub.copy('F')
allele = invlogit(allele)
return allele
def male_def(sp_sub):
allele = sp_sub.copy('F')
allele = invlogit(allele)
return allele
def pr(sp_sub):
pr = sp_sub.copy('F')
return invlogit(pr)
def pr(sp_sub, two_ten_facs=two_ten_factors):
pr = sp_sub.copy('F')
pr = invlogit(pr) * two_ten_facs[np.random.randint(len(two_ten_facs))]
return pr
