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Fix mislabeled tutorial plots #269

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Jul 16, 2024
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Fix mislabeled tutorial plots #269

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22 changes: 11 additions & 11 deletions docs/source/tutorials/hospital_admissions_model.qmd
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Original file line number Diff line number Diff line change
Expand Up @@ -209,7 +209,7 @@ with npro.handlers.seed(rng_seed=np.random.randint(1, timeframe)):

```{python}
# | label: fig-basic
# | fig-cap: Rt and Infections
# | fig-cap: Rt and Admissions
import matplotlib.pyplot as plt

fig, axs = plt.subplots(1, 2)
Expand All @@ -218,9 +218,9 @@ fig, axs = plt.subplots(1, 2)
axs[0].plot(sim_data.Rt)
axs[0].set_ylabel("Rt")

# Infections plot
# Admissions plot
axs[1].plot(sim_data.observed_hosp_admissions)
axs[1].set_ylabel("Infections")
axs[1].set_ylabel("Admissions")
axs[1].set_yscale("log")

fig.suptitle("Basic renewal model")
Expand Down Expand Up @@ -249,7 +249,7 @@ hosp_model.run(
)
```

We can use the `plot_posterior` method to visualize the results[^capture]:
We can use the `plot_posterior` method to visualize the results, plotting the observed values against the inferred latent values[^capture]:

[^capture]: The output is captured to avoid `quarto` from displaying the output twice.

Expand All @@ -267,7 +267,7 @@ out = hosp_model.plot_posterior(
)
```

Test `posterior_predictive` and `prior_predictive` methods to generate posterior and prior predictive samples.
Test `posterior_predictive` and `prior_predictive` methods to generate posterior and prior predictive samples for observed admissions.

```{python}
# | label: demonstrate-use-of-predictive-methods
Expand Down Expand Up @@ -572,7 +572,7 @@ Below we plot the prior predictive distributions using equal tailed bayesian cre

```{python}
# | label: fig-output-prior-predictive
# | fig-cap: Prior Predictive Infections
# | fig-cap: Prior Predictive Admissions
def compute_eti(dataset, eti_prob):
eti_bdry = dataset.quantile(
((1 - eti_prob) / 2, 1 / 2 + eti_prob / 2), dim=("chain", "draw")
Expand Down Expand Up @@ -611,17 +611,17 @@ plt.scatter(
color="black",
)

axes.set_title("Prior Predictive Infections", fontsize=10)
axes.set_title("Prior Predictive Admissions", fontsize=10)
axes.set_xlabel("Time", fontsize=10)
axes.set_ylabel("Observed Infections", fontsize=10)
axes.set_ylabel("Observed Admissions", fontsize=10)
plt.yscale("log")
plt.show()
```

And now we plot the posterior predictive distributions with a `{python} n_forecast_points`-day-ahead forecast:
```{python}
# | label: fig-output-posterior-predictive
# | fig-cap: Posterior Predictive Infections
# | fig-cap: Posterior Predictive Admissions
fig, axes = plt.subplots(figsize=(6, 5))
az.plot_hdi(
idata_weekday.posterior_predictive["negbinom_rv_dim_0"]
Expand Down Expand Up @@ -670,8 +670,8 @@ plt.scatter(
color="black",
)
axes.legend()
axes.set_title("Posterior Predictive Infections", fontsize=10)
axes.set_title("Posterior Predictive Admissions", fontsize=10)
axes.set_xlabel("Time", fontsize=10)
axes.set_ylabel("Observed Infections", fontsize=10)
axes.set_ylabel("Observed Admissions", fontsize=10)
plt.show()
```