Renaming variables throughout for clarity

model/docs/example_with_datasets.qmd

@@ -127,16 +127,18 @@ from pyrenew import latent, deterministic, metaclass
 import jax.numpy as jnp
 import numpyro.distributions as dist
 
-inf_hosp_int = deterministic.DeterministicPMF(inf_hosp_int, name="inf_hosp_int")
+inf_hosp_int = deterministic.DeterministicPMF(
+    inf_hosp_int, name="inf_hosp_int"
+)
 
 hosp_rate = metaclass.DistributionalRV(
     dist=dist.LogNormal(jnp.log(0.05), 0.1),
     name="IHR",
 )
 
 latent_hosp = latent.HospitalAdmissions(
-    infection_to_admission_interval=inf_hosp_int,
-    infect_hosp_rate_dist=hosp_rate,
+    infection_to_admission_interval_rv=inf_hosp_int,
+    infect_hosp_rate_rv=hosp_rate,
 )
 ```
 
@@ -173,12 +175,12 @@ Notice all the components are `RandomVariable` instances. We can now build the m
 ```{python}
 # | label: init-model
 hosp_model = model.HospitalAdmissionsModel(
-    latent_infections=latent_inf,
-    latent_admissions=latent_hosp,
-    I0=I0,
-    gen_int=gen_int,
-    Rt_process=rtproc,
-    observation_process=obs,
+    latent_infections_rv=latent_inf,
+    latent_hosp_admissions_rv=latent_hosp,
+    I0_rv=I0,
+    gen_int_rv=gen_int,
+    Rt_process_rv=rtproc,
+    hosp_admission_obs_process_rv=obs,
 )
 ```
 
@@ -208,7 +210,7 @@ axs[0].plot(sim_data.Rt)
 axs[0].set_ylabel("Rt")
 
 # Infections plot
-axs[1].plot(sim_data.sampled_admissions)
+axs[1].plot(sim_data.sampled_observed_hosp_admissions)
 axs[1].set_ylabel("Infections")
 axs[1].set_yscale("log")
 
@@ -230,7 +232,7 @@ import jax
 hosp_model.run(
     num_samples=2000,
     num_warmup=2000,
-    observed_admissions=dat["daily_hosp_admits"].to_numpy(),
+    observed_hosp_admissions=dat["daily_hosp_admits"].to_numpy(),
     rng_key=jax.random.PRNGKey(54),
     mcmc_args=dict(progress_bar=False),
 )
@@ -244,7 +246,7 @@ We can use the `plot_posterior` method to visualize the results[^capture]:
 # | label: fig-output-hospital-admissions
 # | fig-cap: Hospital Admissions posterior distribution
 out = hosp_model.plot_posterior(
-    var="predicted_admissions",
+    var="observed_hosp_admissions",
     ylab="Hospital Admissions",
     obs_signal=dat["daily_hosp_admits"].to_numpy(),
 )
@@ -268,7 +270,7 @@ dat_w_padding = np.hstack((np.repeat(np.nan, days_to_impute), dat_w_padding))
 hosp_model.run(
     num_samples=2000,
     num_warmup=2000,
-    observed_admissions=dat_w_padding,
+    observed_hosp_admissions=dat_w_padding,
     rng_key=jax.random.PRNGKey(54),
     mcmc_args=dict(progress_bar=False),
     padding=days_to_impute,  # Padding the model
@@ -281,7 +283,7 @@ And plotting the results:
 # | label: fig-output-admissions-with-padding
 # | fig-cap: Hospital Admissions posterior distribution
 out = hosp_model.plot_posterior(
-    var="predicted_admissions",
+    var="observed_hosp_admissions",
     ylab="Hospital Admissions",
     obs_signal=dat_w_padding,
 )
@@ -343,18 +345,18 @@ Notice that the instance's `nweeks` and `len` members are passed during construc
 ```{python}
 # | label: latent-hosp-weekday
 latent_hosp_wday_effect = latent.HospitalAdmissions(
-    infection_to_admission_interval=inf_hosp_int,
-    infect_hosp_rate_dist=hosp_rate,
-    weekday_effect_dist=weekday_effect,
+    infection_to_admission_interval_rv=inf_hosp_int,
+    infect_hosp_rate_rv=hosp_rate,
+    weekday_effect_rv=weekday_effect,
 )
 
 hosp_model_weekday = model.HospitalAdmissionsModel(
-    latent_infections=latent_inf,
-    latent_admissions=latent_hosp_wday_effect,
-    I0=I0,
-    gen_int=gen_int,
-    Rt_process=rtproc,
-    observation_process=obs,
+    latent_infections_rv=latent_inf,
+    latent_hosp_admissions_rv=latent_hosp_wday_effect,
+    I0_rv=I0,
+    gen_int_rv=gen_int,
+    Rt_process_rv=rtproc,
+    hosp_admission_obs_process_rv=obs,
 )
 ```
 
@@ -365,7 +367,7 @@ Running the model (with the same padding as before):
 hosp_model_weekday.run(
     num_samples=2000,
     num_warmup=2000,
-    observed_admissions=dat_w_padding,
+    observed_hosp_admissions=dat_w_padding,
     rng_key=jax.random.PRNGKey(54),
     mcmc_args=dict(progress_bar=False),
     padding=days_to_impute,
@@ -378,7 +380,7 @@ And plotting the results:
 # | label: fig-output-admissions-padding-and-weekday
 # | fig-cap: Hospital Admissions posterior distribution
 out = hosp_model_weekday.plot_posterior(
-    var="predicted_admissions",
+    var="observed_hosp_admissions",
     ylab="Hospital Admissions",
     obs_signal=dat_w_padding,
 )

model/docs/extending_pyrenew.qmd

@@ -63,11 +63,11 @@ With all the components defined, we can build the model:
 ```{python}
 # | label: build1
 model0 = RtInfectionsRenewalModel(
-    gen_int=gen_int,
-    I0=I0,
-    latent_infections=latent_infections,
-    Rt_process=rt,
-    observation_process=None,
+    gen_int_rv=gen_int,
+    I0_rv=I0,
+    latent_infections_rv=latent_infections,
+    Rt_process_rv=rt,
+    infection_obs_process_rv=None,
 )
 ```
 
@@ -239,11 +239,11 @@ latent_infections2 = InfFeedback(
 )
 
 model1 = RtInfectionsRenewalModel(
-    gen_int=gen_int,
-    I0=I0,
-    latent_infections=latent_infections2,
-    Rt_process=rt,
-    observation_process=None,
+    gen_int_rv=gen_int,
+    I0_rv=I0,
+    latent_infections_rv=latent_infections2,
+    Rt_process_rv=rt,
+    infection_obs_process_rv=None,
 )
 
 # Sampling and fitting model 0 (with no obs for infections)

model/docs/getting_started.qmd

@@ -113,11 +113,11 @@ With these five pieces, we can build the basic renewal model as an instance of t
 ```{python}
 # | label: model-creation
 model1 = RtInfectionsRenewalModel(
-    gen_int=gen_int,
-    I0=I0,
-    Rt_process=rt_proc,
-    latent_infections=latent_infections,
-    observation_process=observation_process,
+    gen_int_rv=gen_int,
+    I0_rv=I0,
+    Rt_process_rv=rt_proc,
+    latent_infections_rv=latent_infections,
+    infection_obs_process_rv=observation_process,
 )
 ```
 
@@ -167,7 +167,7 @@ axs[0].plot(sim_data.Rt)
 axs[0].set_ylabel("Rt")
 
 # Infections plot
-axs[1].plot(sim_data.sampled_infections)
+axs[1].plot(sim_data.sampled_observed_infections)
 axs[1].set_ylabel("Infections")
 
 fig.suptitle("Basic renewal model")
@@ -185,7 +185,7 @@ import jax
 model1.run(
     num_warmup=2000,
     num_samples=1000,
-    observed_infections=sim_data.sampled_infections,
+    observed_infections=sim_data.sampled_observed_infections,
     rng_key=jax.random.PRNGKey(54),
     mcmc_args=dict(progress_bar=False),
 )

model/docs/pyrenew_demo.qmd

@@ -37,15 +37,15 @@ import numpyro.distributions as dist
 from pyrenew.process import SimpleRandomWalkProcess
 ```
 
-To understand the simple random walk process underlying the sampling within the renewal process model, we first examine a single random walk path. Using the `sample` method from an instance of the `SimpleRandomWalkProcess` class, we first create an instance of the `SimpleRandomWalkProcess` class with a normal distribution of mean = 0 and standard deviation = 0.0001 as its input. Next, the `with` statement sets the seed for the random number generator for the duration of the block that follows. Inside the `with` block, the `q_samp = q.sample(duration=100)` generates the sample instance over a duration of 100 time units. Finally, this single random walk process is visualized using `matplot.pyplot` to plot the exponential of the sample instance.
+To understand the simple random walk process underlying the sampling within the renewal process model, we first examine a single random walk path. Using the `sample` method from an instance of the `SimpleRandomWalkProcess` class, we first create an instance of the `SimpleRandomWalkProcess` class with a normal distribution of mean = 0 and standard deviation = 0.0001 as its input. Next, the `with` statement sets the seed for the random number generator for the n_timepoints of the block that follows. Inside the `with` block, the `q_samp = q.sample(n_timepoints=100)` generates the sample instance over a n_timepoints of 100 time units. Finally, this single random walk process is visualized using `matplot.pyplot` to plot the exponential of the sample instance.
 
 ```{python}
 # | label: fig-randwalk
 # | fig-cap: Random walk example
 np.random.seed(3312)
 q = SimpleRandomWalkProcess(dist.Normal(0, 0.001))
 with seed(rng_seed=np.random.randint(0, 1000)):
-    q_samp = q.sample(duration=100)
+    q_samp = q.sample(n_timepoints=100)
 
 plt.plot(np.exp(q_samp[0]))
 ```
@@ -112,8 +112,8 @@ inf_hosp_int = DeterministicPMF(
 )
 
 latent_admissions = HospitalAdmissions(
-    infection_to_admission_interval=inf_hosp_int,
-    infect_hosp_rate_dist=DistributionalRV(
+    infection_to_admission_interval_rv=inf_hosp_int,
+    infect_hosp_rate_rv=DistributionalRV(
         dist=dist.LogNormal(jnp.log(0.05), 0.05), name="IHR"
     ),
 )
@@ -131,12 +131,12 @@ The `HospitalAdmissionsModel` is then initialized using the initial conditions j
 ```{python}
 # Initializing the model
 hospmodel = HospitalAdmissionsModel(
-    gen_int=gen_int,
-    I0=I0,
-    latent_admissions=latent_admissions,
-    observation_process=admissions_process,
-    latent_infections=latent_infections,
-    Rt_process=Rt_process,
+    gen_int_rv=gen_int,
+    I0_rv=I0,
+    latent_hosp_admissions_rv=latent_admissions,
+    hosp_admission_obs_process_rv=admissions_process,
+    latent_infections_rv=latent_infections,
+    Rt_process_rv=Rt_process,
 )
 ```
 
@@ -151,13 +151,13 @@ x
 Visualizations of the single model output show (top) infections over the 30 time steps, (middle) hospital admissions over the 30 time steps, and (bottom)
 
 ```{python}
-#| label: fig-hosp
-#| fig-cap: Infections
+# | label: fig-hosp
+# | fig-cap: Infections
 fig, ax = plt.subplots(nrows=3, sharex=True)
 ax[0].plot(x.latent_infections)
-ax[0].set_ylim([1/5, 5])
-ax[1].plot(x.latent_admissions)
-ax[2].plot(x.sampled_admissions, 'o')
+ax[0].set_ylim([1 / 5, 5])
+ax[1].plot(x.latent_hosp_admissions)
+ax[2].plot(x.sampled_observed_hosp_admissions, "o")
 for axis in ax[:-1]:
     axis.set_yscale("log")
 ```
@@ -169,7 +169,7 @@ To fit the `hospmodel` to the simulated data, we call `hospmodel.run()`, an MCMC
 hospmodel.run(
     num_warmup=1000,
     num_samples=1000,
-    observed_admissions=x.sampled_admissions,
+    observed_hosp_admissions=x.sampled_observed_hosp_admissions,
     rng_key=jax.random.PRNGKey(54),
     mcmc_args=dict(progress_bar=False),
 )

model/pyproject.toml

@@ -32,6 +32,28 @@ pytest-cov = "^5.0.0"
 pytest-mpl = "^0.17.0"
 numpydoc = "^1.7.0"
 
+[tool.numpydoc_validation]
+checks = [
+    "GL03",
+    "GL08",
+    "SS01",
+    "PR03",
+    "PR04",
+    "PR07",
+    "RT01"
+]
+ignore = [
+    "ES01",
+    "SA01",
+    "EX01",
+    "SS06",
+    "RT05"
+]
+exclude = [  # don't report on objects that match any of these regex
+    '\.undocumented_method$',
+    '\.__repr__$',
+    '\.__call__$'
+]
 
 [build-system]
 requires = ["poetry-core"]

model/src/pyrenew/deterministic/nullrv.py

@@ -129,7 +129,7 @@ def validate() -> None:
 
     def sample(
         self,
-        predicted: ArrayLike,
+        mu: ArrayLike,
         obs: ArrayLike | None = None,
         name: str | None = None,
         **kwargs,
@@ -139,8 +139,8 @@ def sample(
 
         Parameters
         ----------
-        predicted : ArrayLike
-            Rate parameter of the Poisson distribution.
+        mu : ArrayLike
+            Unused parameter, represents mean of non-null distributions
         obs : ArrayLike, optional
             Observed data. Defaults to None.
         name : str, optional