import pystan, numpy as np
data = dict(mail = np.array(r["mail"], dtype="int"), num_days=int(r["num_days"]))
model = pystan.StanModel("usps1.stan")
fit = model.sampling(data)

4 chains, each with iter=2000; warmup=1000; thin=1;
post-warmup draws per chain=1000, total post-warmup draws=4000.
mean se_mean sd 2.5% 50% 97.5% n_eff Rhat
avg_mail_per_day 4.96 2.3e-3 0.08 4.8 4.96 5.11 1316 1.0
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).