Include pymc in the documentation.

doc/api.rst

@@ -24,6 +24,7 @@ API reference
    pypesto.profile.profile_next_guess
    pypesto.result
    pypesto.sample
+   pypesto.sample.pymc
    pypesto.select
    pypesto.select.postprocessors
    pypesto.startpoint

pypesto/result/sample.py

@@ -6,35 +6,7 @@
 
 
 class McmcPtResult(dict):
-    """
-    The result of a sampler run using Markov-chain Monte Carlo.
-
-    Currently result object of all supported samplers. Can be used like a dict.
-
-    Parameters
-    ----------
-    trace_x: [n_chain, n_iter, n_par]
-        Parameters.
-    trace_neglogpost: [n_chain, n_iter]
-        Negative log posterior values.
-    trace_neglogprior: [n_chain, n_iter]
-        Negative log prior values.
-    betas: [n_chain]
-        The associated inverse temperatures.
-    burn_in: [n_chain]
-        The burn in index.
-    time: [n_chain]
-        The computation time.
-    auto_correlation: [n_chain]
-        The estimated chain autcorrelation.
-    effective_sample_size: [n_chain]
-        The estimated effective sample size.
-    message: str
-        Textual comment on the profile result.
-
-    Here, `n_chain` denotes the number of chains, `n_iter` the number of
-    iterations (i.e., the chain length), and `n_par` the number of parameters.
-    """
+    """The result of a sampler run using Markov-chain Monte Carlo."""
 
     def __init__(
         self,
@@ -48,6 +20,34 @@ def __init__(
         effective_sample_size: float = None,
         message: str = None,
     ):
+        """
+        Initialize the McmcPtResult.
+
+        Here, `n_chain` denotes the number of chains, `n_iter` the number of
+        iterations (i.e., the chain length), and `n_par` the number of
+        parameters.
+
+        Parameters
+        ----------
+        trace_x: [n_chain, n_iter, n_par]
+            Parameters.
+        trace_neglogpost: [n_chain, n_iter]
+            Negative log posterior values.
+        trace_neglogprior: [n_chain, n_iter]
+            Negative log prior values.
+        betas: [n_chain]
+            The associated inverse temperatures.
+        burn_in: [n_chain]
+            The burn in index.
+        time: [n_chain]
+            The computation time.
+        auto_correlation: [n_chain]
+            The estimated chain autcorrelation.
+        effective_sample_size: [n_chain]
+            The estimated effective sample size.
+        message: str
+            Textual comment on the profile result.
+        """
         super().__init__()
 
         self.trace_x = trace_x

pypesto/sample/pymc.py

@@ -18,6 +18,14 @@
 
 logger = logging.getLogger(__name__)
 
+# __all__ definition for Sphinx
+__all__ = [
+    'PymcSampler',
+    'PymcObjectiveWithGradientOp',
+    'PymcObjectiveOp',
+    'PymcGradientOp',
+]
+
 # implementation based on:
 # https://www.pymc.io/projects/examples/en/latest/case_studies/blackbox_external_likelihood_numpy.html
 

setup.cfg

@@ -150,6 +150,7 @@ doc =
     %(petab)s
     %(aesara)s
     %(jax)s
+    %(pymc)s
 example =
     %(julia)s
     %(pymc)s