✏️ Minor docs notation for posterioplot function(s)

docs/source/api/plots.rst

@@ -10,8 +10,7 @@ library, a general purpose library for
 "exploratory analysis of Bayesian models."
 Refer to its documentation to use the plotting functions directly.
 
-.. warning::
-    The `plot_posterior_predictive_glm` function will removed in a future PyMC3 release.
-    Keep up to date with `ArviZ <https://arviz-devs.github.io/arviz/>`_ for a future equivalent of `plot_posterior_predictive_glm`.
+.. currentmodule:: pymc3.plots.posteriorplot
 
-- :func:`pymc3.plot_posterior_predictive_glm <arviz:arviz.plot_trace>`
+.. automodule:: pymc3.plots.posteriorplot
+   :members:

pymc3/plots/__init__.py

@@ -1,4 +1,4 @@
-#   Copyright 2021 The PyMC Developers
+#   Copyright 2020 The PyMC Developers
 #
 #   Licensed under the Apache License, Version 2.0 (the "License");
 #   you may not use this file except in compliance with the License.
@@ -15,9 +15,9 @@
 """PyMC3 Plotting.
 
 Plots are delegated to the `ArviZ <https://arviz-devs.github.io/arviz/>`_ library, a general purpose library for
-"exploratory analysis of Bayesian models." For more details, see https://arviz-devs.github.io/arviz/
+exploratory analysis of Bayesian models. For more details, see https://arviz-devs.github.io/arviz/.
 
-Only the `plot_posterior_predictive_glm` is kept in the PyMC code base for now,  but it'll will move to ArviZ once the latter adds features for regression plot.
+Only `plot_posterior_predictive_glm` is kept in the PyMC code base for now, but it will move to ArviZ once the latter adds features for regression plots.
 """
 import functools
 import sys

pymc3/plots/posteriorplot.py

@@ -35,19 +35,25 @@ def plot_posterior_predictive_glm(
     **kwargs: Any
 ) -> None:
     """Plot posterior predictive of a linear model.
-    :Arguments:
-        trace: InferenceData or MultiTrace
-            Output of pm.sample()
-        eval: <array>
-            Array over which to evaluate lm
-        lm: function <default: linear function>
-            Function mapping parameters at different points
-            to their respective outputs.
-            input: point, sample
-            output: estimated value
-        samples: int <default=30>
-            How many posterior samples to draw.
-    Additional keyword arguments are passed to pylab.plot().
+    Parameters
+    ----------
+    trace: InferenceData or MultiTrace
+        Output of pm.sample()
+    eval: <array>
+        Array over which to evaluate lm
+    lm: function <default: linear function>
+        Function mapping parameters at different points
+        to their respective outputs.
+        input: point, sample
+        output: estimated value
+    samples: int <default=30>
+        How many posterior samples to draw.
+    kwargs : mapping, optional
+        Additional keyword arguments are passed to ``matplotlib.pyplot.plot()``.
+
+    Warnings
+    --------
+    The `plot_posterior_predictive_glm` function will removed in a future PyMC3 release.
     """
     warnings.warn(
         "The `plot_posterior_predictive_glm` function will migrate to Arviz in a future release. "