def write_table_group_value(dm, key, item, ws, x, y, group_sizes):
"""Write estimated values into table for all age_groups
Parameters
----------
dm : str or DiseaseJson object
the json string or a thin python wrapper around this data that
is to be plotted
key : key
item :
ws : work sheet
x : horizontal shift
y : vertical shift
group_sizes : list of group sizes in order
"""
c = dismod3.utils.KEY_DELIM_CHAR
type, region, year, sex = key.split(c)
start = 0
end = 0
if type == 'with-condition-death':
key_prevalence = 'prevalence' + c + region + c + year + c + sex
key_mortality = 'mortality' + c + region + c + year + c + sex
if len(dm.get_mcmc(item, key_prevalence)) == dismod3.MAX_AGE and \
len(dm.get_mcmc(item, key_mortality)) == dismod3.MAX_AGE and \
len(population_by_region_year_sex(region, year, sex)) == dismod3.MAX_AGE:
for j, gs in enumerate(group_sizes):
start = end
end = start + gs
raw_rate = dm.get_mcmc(item, key_prevalence)[start:end] * \
dm.get_mcmc(item, key_mortality)[start:end] * \
population_by_region_year_sex(region, year, sex)[start:end]
age_indices = []
for i in range(len(raw_rate)):
age_indices.append(i)
age_weights = dm.get_population(region)[start:end]
ws.write(x + j, y, rate_for_range(raw_rate, age_indices, age_weights) / gs)
else:
if(len(dm.get_mcmc(item, key)) == dismod3.MAX_AGE):
for j, gs in enumerate(group_sizes):
start = end
end = start + gs
raw_rate = dm.get_mcmc(item, key)[start:end]
age_indices = []
for i in range(len(raw_rate)):
age_indices.append(i)
age_weights = dm.get_population(region)[start:end]
ws.write(x + j, y, rate_for_range(raw_rate, age_indices, age_weights) / gs)
def obs(f=vars['rate_stoch'],
age_indices=age_indices,
age_weights=age_weights,
value=np.log(dm.value_per_1(d)),
tau=se**-2, data=d):
f_i = rate_for_range(f, age_indices, age_weights)
return mc.normal_like(value, np.log(f_i), tau)
def obs(f=rate_stoch,
age_indices=age_indices,
age_weights=age_weights,
value=d_val,
tau=1./(d_se)**2):
f_i = rate_for_range(f, age_indices, age_weights)
return mc.normal_like(value, f_i, tau)
def obs(value=logit_val, logit_se=logit_se,
X=covariates(d),
alpha=alpha, beta=beta, gamma=gamma, sigma=sigma,
age_indices=age_indices,
age_weights=age_weights):
# calculate study-specific rate function
mu = predict_logit_rate(X, alpha, beta, gamma)
mu_i = rate_for_range(mu, age_indices, age_weights)
tau_i = 1. / (sigma**2 + logit_se**2)
logp = mc.normal_like(x=value, mu=mu_i, tau=tau_i)
return logp
def b_i(beta=beta, age_indices=age_indices, age_weights=age_weights):
return rate_for_range(beta, age_indices, age_weights)
def a_i(alpha=alpha, age_indices=age_indices, age_weights=age_weights):
return rate_for_range(alpha, age_indices, age_weights)
