Add and configure intersphinx

docs/source/conf.py

@@ -26,6 +26,7 @@
     "sphinx.ext.autodoc",
     "sphinx.ext.autosectionlabel",
     "sphinx.ext.autosummary",
+    "sphinx.ext.intersphinx",
     "sphinx.ext.doctest",
     "sphinx.ext.napoleon",  # numpydoc
     "sphinx.ext.duration",
@@ -87,3 +88,22 @@
 
 myst_fence_as_directive = ["mermaid"]
 myst_enable_extensions = ["amsmath", "dollarmath"]
+
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3/", None),
+    "matplotlib": ("https://matplotlib.org/stable/", None),
+    "numpy": ("https://numpy.org/doc/stable/", None),
+    "numpyro": ("https://num.pyro.ai/en/latest/", None),
+    "jax": ("https://jax.readthedocs.io/en/latest/", None),
+    "polars": ("https://docs.pola.rs/api/python/stable/", None),
+}
+
+napoleon_preprocess_types = True
+autodoc_typehints = "description"
+autodoc_typehints_format = "short"
+autodoc_type_aliases = {
+    "ArrayLike": ":obj:`ArrayLike <jax.typing.ArrayLike>`",
+    "RandomVariable": ":class:`RandomVariable <pyrenew.metaclass.52RandomVariable>`",
+    "Any": ":obj:`Any <typing.Any>`",
+}
+napoleon_type_aliases = autodoc_type_aliases

pyrenew/convolve.py

@@ -8,8 +8,9 @@
 :py:func:`jax.lax.scan`.
 Factories generate functions
 that can be passed to
-:py:func:`jax.lax.scan` with an
-appropriate array to scan along.
+:func:`jax.lax.scan` or
+:func:`numpyro.contrib.control_flow.scan`
+with an appropriate array to scan along.
 """
 
 from __future__ import annotations
@@ -26,7 +27,8 @@ def new_convolve_scanner(
 ) -> Callable:
     r"""
     Factory function to create a "scanner" function
-    that can be used with :py:func:`jax.lax.scan` to
+    that can be used with :func:`jax.lax.scan` or
+    :func:`numpyro.contrib.control_flow.scan` to
     construct an array via backward-looking iterative
     convolution.
 
@@ -44,7 +46,9 @@ def new_convolve_scanner(
     -------
     Callable
         A scanner function that can be used with
-        :py:func:`jax.lax.scan` for convolution.
+        :func:`jax.lax.scan` or
+        :func:`numpyro.contrib.control_flow.scan`
+        for convolution.
         This function takes a history subset array and
         a scalar, computes the dot product of
         the supplied convolution array with the history

pyrenew/latent/hospitaladmissions.py

@@ -16,7 +16,8 @@
 
 class HospitalAdmissionsSample(NamedTuple):
     """
-    A container to hold the output of `latent.HospAdmissions()`.
+    A container to hold the output of
+    :meth:`HospitalAdmissions.sample`.
 
     Attributes
     ----------
@@ -192,10 +193,12 @@ def sample(
         Parameters
         ----------
         latent_infections : ArrayLike
-            Latent infections. Possibly the output of the `latent.Infections()`.
+            Latent infections.
         **kwargs : dict, optional
-            Additional keyword arguments passed through to internal `sample()`
-            calls, should there be any.
+            Additional keyword arguments passed through to
+            internal :meth:`sample()
+            <pyrenew.metaclass.RandomVariable.sample>` calls,
+            should there be any.
 
         Returns
         -------
@@ -217,7 +220,8 @@ def sample(
         # Applying the day of the week effect. For this we need to:
         # 1. Get the day of the week effect
         # 2. Identify the offset of the latent_infections
-        # 3. Apply the day of the week effect to the latent_hospital_admissions
+        # 3. Apply the day of the week effect to the
+        # latent_hospital_admissions
         dow_effect_sampled = self.day_of_week_effect_rv(**kwargs)
 
         if dow_effect_sampled.size != 7:

pyrenew/latent/infection_functions.py

@@ -14,11 +14,11 @@ def compute_infections_from_rt(
     I0: ArrayLike,
     Rt: ArrayLike,
     reversed_generation_interval_pmf: ArrayLike,
-) -> ArrayLike:
+) -> jnp.ndarray:
     """
     Generate infections according to a
     renewal process with a time-varying
-    reproduction number R(t)
+    reproduction number :math:`\\mathcal{R}(t)`
 
     Parameters
     ----------
@@ -27,7 +27,7 @@ def compute_infections_from_rt(
         same length as the generation interval
         pmf vector.
     Rt : ArrayLike
-        Timeseries of R(t) values
+        Timeseries of :math:`\\mathcal{R}(t)` values
     reversed_generation_interval_pmf : ArrayLike
         discrete probability mass vector
         representing the generation interval
@@ -38,8 +38,8 @@ def compute_infections_from_rt(
 
     Returns
     -------
-    ArrayLike
-        The timeseries of infections, as a JAX array
+    jnp.ndarray
+        The timeseries of infections.
     """
     incidence_func = new_convolve_scanner(
         reversed_generation_interval_pmf, IdentityTransform()
@@ -58,8 +58,9 @@ def logistic_susceptibility_adjustment(
     """
     Apply the logistic susceptibility
     adjustment to a potential new
-    incidence I_unadjusted proposed in
-    equation 6 of Bhatt et al 2023 [1]_
+    incidence ``I_raw_t`` proposed in
+    equation 6 of `Bhatt et al 2023
+    <https://doi.org/10.1093/jrsssa/qnad030>`_.
 
     Parameters
     ----------
@@ -76,16 +77,7 @@ def logistic_susceptibility_adjustment(
     Returns
     -------
     float
-        The adjusted value of I(t)
-
-    References
-    ----------
-    .. [1] Bhatt, Samir, et al.
-    "Semi-mechanistic Bayesian modelling of
-    COVID-19 with renewal processes."
-    Journal of the Royal Statistical Society
-    Series A: Statistics in Society 186.4 (2023): 601-615.
-    https://doi.org/10.1093/jrsssa/qnad030
+        The adjusted value of :math:`I(t)`.
     """
     approx_frac_infected = 1 - jnp.exp(-I_raw_t / n_population)
     return n_population * frac_susceptible * approx_frac_infected
@@ -101,8 +93,8 @@ def compute_infections_from_rt_with_feedback(
     r"""
     Generate infections according to
     a renewal process with infection
-    feedback (generalizing Asher 2018:
-    https://doi.org/10.1016/j.epidem.2017.02.009)
+    feedback (generalizing `Asher 2018
+    <https://doi.org/10.1016/j.epidem.2017.02.009>`_).
 
     Parameters
     ----------
@@ -111,7 +103,7 @@ def compute_infections_from_rt_with_feedback(
         same length as the generation interval
         pmf vector.
     Rt_raw : ArrayLike
-        Timeseries of raw R(t) values not
+        Timeseries of raw :math:`\mathcal{R}(t)` values not
         adjusted by infection feedback
     infection_feedback_strength : ArrayLike
         Strength of the infection feedback.
@@ -139,21 +131,21 @@ def compute_infections_from_rt_with_feedback(
     Returns
     -------
     tuple
-        A tuple `(infections, Rt_adjusted)`,
-        where `Rt_adjusted` is the infection-feedback-adjusted
-        timeseries of the reproduction number R(t) and
-        infections is the incident infection timeseries.
+        A tuple ``(infections, Rt_adjusted)``,
+        where ``Rt_adjusted`` is the infection-feedback-adjusted
+        timeseries of the reproduction number :math:`\mathcal{R}(t)`
+        and ``infections`` is the incident infection timeseries.
 
     Notes
     -----
     This function implements the following renewal process:
 
     .. math::
-
-        I(t) & = \mathcal{R}(t)\sum_{\tau=1}^{T_g}I(t - \tau)g(\tau)
-
+        \begin{aligned}
+        I(t) & = \mathcal{R}(t)\sum_{\tau=1}^{T_g}I(t - \tau)g(\tau) \\
         \mathcal{R}(t) & = \mathcal{R}^u(t)\exp\left(\gamma(t)\
             \sum_{\tau=1}^{T_f}I(t - \tau)f(\tau)\right)
+        \end{aligned}
 
     where :math:`\mathcal{R}(t)` is the reproductive number,
     :math:`\gamma(t)` is the infection feedback strength,
@@ -166,7 +158,7 @@ def compute_infections_from_rt_with_feedback(
     that recent incident infections reduce :math:`\mathcal{R}(t)`
     below its raw value in the absence of feedback, while
     positive :math:`\gamma` implies that recent incident infections
-    _increase_ :math:`\mathcal{R}(t)` above its raw value, and
+    *increase* :math:`\mathcal{R}(t)` above its raw value, and
     :math:`\gamma(t)=0` implies no feedback.
 
     In general, negative :math:`\gamma` is the more common modeling

pyrenew/latent/infection_initialization_method.py

@@ -11,12 +11,14 @@ class InfectionInitializationMethod(metaclass=ABCMeta):
     """Method for initializing infections in a renewal process."""
 
     def __init__(self, n_timepoints: int):
-        """Default constructor for the ``InfectionInitializationMethod`` class.
+        """Default constructor for
+        :class:`InfectionInitializationMethod`.
 
         Parameters
         ----------
         n_timepoints : int
-            the number of time points to generate initial infections for
+            the number of time points for which to
+            generate initial infections
 
         Returns
         -------
@@ -27,7 +29,10 @@ def __init__(self, n_timepoints: int):
 
     @staticmethod
     def validate(n_timepoints: int) -> None:
-        """Validate inputs for the ``InfectionInitializationMethod`` class constructor
+        """
+        Validate inputs to the
+        :class:`InfectionInitializationMethod`
+        constructor.
 
         Parameters
         ----------
@@ -99,7 +104,7 @@ def initialize_infections(self, I_pre_init: ArrayLike):
 class InitializeInfectionsFromVec(InfectionInitializationMethod):
     """Create initial infections from a vector of infections."""
 
-    def initialize_infections(self, I_pre_init: ArrayLike):
+    def initialize_infections(self, I_pre_init: ArrayLike) -> ArrayLike:
         """Create initial infections from a vector of infections.
 
         Parameters
@@ -112,7 +117,7 @@ def initialize_infections(self, I_pre_init: ArrayLike):
         -------
         ArrayLike
             An array of length ``n_timepoints`` with the number of
-        initialized infections at each time point.
+            initialized infections at each time point.
         """
         I_pre_init = jnp.array(I_pre_init)
         if I_pre_init.size != self.n_timepoints:

pyrenew/latent/infection_initialization_process.py

@@ -1,4 +1,6 @@
 # numpydoc ignore=GL08
+from __future__ import annotations
+
 from jax.typing import ArrayLike
 
 from pyrenew.latent.infection_initialization_method import (

pyrenew/latent/infections.py

@@ -13,24 +13,23 @@
 
 class InfectionsSample(NamedTuple):
     """
-    A container for holding the output from `latent.Infections()`.
+    A container for holding the output from
+    :meth:`Infections.sample()
+    <pyrenew.latent.infections.Infections.sample>`.
 
     Attributes
     ----------
-    post_initialization_infections : SampledValue | None, optional
-        The estimated latent infections. Defaults to None.
+    post_initialization_infections:
+        The estimated latent infections. Default :obj:`None`.
     """
 
     post_initialization_infections: ArrayLike | None = None
 
-    def __repr__(self):
-        return f"InfectionsSample(post_initialization_infections={self.post_initialization_infections})"
-
 
 class Infections(RandomVariable):
     r"""Latent infections
 
-    This class samples infections given Rt,
+    This class samples infections given :math:`\mathcal{R}(t)`,
     initial infections, and generation interval.
 
     Notes
@@ -57,9 +56,10 @@ def sample(
         gen_int: ArrayLike,
         **kwargs,
     ) -> InfectionsSample:
-        """
-        Samples infections given Rt, initial infections, and generation
-        interval.
+        r"""
+        Sample infections given
+        :math:`\mathcal{R}(t)`, initial infections,
+        and generation interval.
 
         Parameters
         ----------
@@ -78,7 +78,8 @@ def sample(
         Returns
         -------
         InfectionsSample
-            Named tuple with "infections".
+            A named tuple with a
+            ``post_initialization_infections`` field.
         """
         if I0.shape[0] < gen_int.size:
             raise ValueError(
@@ -90,7 +91,8 @@ def sample(
 
         if I0.shape[1:] != Rt.shape[1:]:
             raise ValueError(
-                "Initial infections and Rt must have the same batch shapes. "
+                "Initial infections and Rt must have the "
+                "same batch shapes. "
                 f"Got initial infections of batch shape {I0.shape[1:]} "
                 f"and Rt of batch shape {Rt.shape[1:]}."
             )

pyrenew/math.py

@@ -19,7 +19,8 @@ def get_leslie_matrix(
     Create the Leslie matrix
     corresponding to a basic
     renewal process with the
-    given R value and discrete
+    given :math:`\\mathcal{R}`
+    value and discrete
     generation interval pmf
     vector.
 
@@ -79,7 +80,8 @@ def get_asymptotic_growth_rate_and_age_dist(
     Raises
     ------
     ValueError
-        If an age distribution vector with non-zero imaginary part is produced.
+        If an age distribution vector with non-zero
+        imaginary part is produced.
     """
     L = get_leslie_matrix(R, generation_interval_pmf)
     eigenvals, eigenvecs = jnp.linalg.eig(L)
@@ -148,8 +150,8 @@ def get_asymptotic_growth_rate(
     """
     Get the asymptotic per timestep growth rate
     for a renewal process with a given value of
-    R and a given discrete generation interval
-    probability mass vector.
+    :math:`\\mathcal{R}` and a given discrete
+    generation interval probability mass vector.
 
     This function computes that growth rate
     finding the dominant eigenvalue of the

pyrenew/mcmcutils.py

@@ -24,16 +24,18 @@ def spread_draws(
     Parameters
     ----------
     posteriors: dict
-        A dictionary of posteriors with variable names as keys and numpy
-        ndarrays as values (with the first axis corresponding to the posterior
-        draw number.
+        A dictionary of posteriors with variable names
+        as keys and numpy ndarrays as values (with the
+        first axis corresponding to the posterior
+        draw number).
     variables_names: list[str] | list[tuple]
-        list of strings or of tuples identifying which variables to retrieve.
+        list of strings or of tuples identifying which
+        variables to retrieve.
 
     Returns
     -------
     pl.DataFrame
-        A dataframe of draw-indexed
+        A polars dataframe of draw-indexed posterior samples.
     """
 
     for i_var, v in enumerate(variables_names):