adding first tutorial edited w/ arviz, still need to get proper plot_ppc

docs/source/tutorials/day_of_the_week.qmd

@@ -197,13 +197,42 @@ hosp_model.run(
     rng_key=jax.random.key(54),
     mcmc_args=dict(progress_bar=False),
 )
+```
 
-# Plotting the posterior
-out = hosp_model.plot_posterior(
-    var="latent_hospital_admissions",
-    ylab="Hospital Admissions",
-    obs_signal=daily_hosp_admits.astype(float),
+```{python}
+import arviz as az
+import matplotlib.pyplot as plt
+
+ppc_samples = hosp_model.posterior_predictive(
+    n_datapoints=daily_hosp_admits.size
+)
+idata = az.from_numpyro(
+    posterior=hosp_model.mcmc,
+    posterior_predictive=ppc_samples,
+    constant_data={"daily_hosp_admits": daily_hosp_admits},
+    coords={"time": np.arange(daily_hosp_admits.size)},
+    dims={
+        "daily_hosp_admits": ["time"],
+        "latent_hospital_admissions": ["time"],
+    },
 )
+print(idata.observed_data)
+fig, ax = plt.subplots(figsize=(8, 6))
+az.plot_ppc(data=idata, kind="kde", ax=ax, num_pp_samples=100)
+# ax.plot(np.arange(daily_hosp_admits.size), daily_hosp_admits.astype(float), color="black", label="Observed signal")
+
+ax.legend()
+plt.xlabel("Time")
+plt.ylabel("Hospital Admissions")
+plt.show()
+
+
+# # Plotting the posterior
+# out = hosp_model.plot_posterior(
+#     var="latent_hospital_admissions",
+#     ylab="Hospital Admissions",
+#     obs_signal=daily_hosp_admits.astype(float),
+# )
 ```