How to visualize Bayesian goodness of fit for logistic regression

gistfile1.py

figsize(12.5, 5)

simulations = mcmc.trace("bernoulli_sim")[:]

ck = ['darkgoldenrod', 'tomato', 'lime', 'darkblue',
      'chocolate', 'crimson','green', 'cornflowerblue']
plt.title("Simulated dataset using posterior parameters")
figsize(12.5, 6)
plt.scatter(temperature, D, marker='v', color="k", s=50, alpha=1., label='Observed')
for i in range(8):
    plt.scatter(temperature, simulations[1000*i, :]+(i*.05), color=ck[i],
                s=50, alpha=0.5, label='simulation {}'.format(1000*i))

plt.xlim(xmax=92)
plt.ylim(ymax=1.8,ymin=-0.1)
plt.yticks([0,1],('OK','Fail'))
plt.xlabel("Temperature")
_ = plt.legend()