docstrings, toc, and release notes

Author: Dpananos

RELEASE-NOTES.md

@@ -3,7 +3,7 @@
 ## PyMC3 3.8 (on deck)
 
 ### New features
-- Add capabilities to do inference on parameters in a differential equation with `DifferentialEquation`.
+- Add capabilities to do inference on parameters in a differential equation with `DifferentialEquation`. See [#3590](https://github.com/pymc-devs/pymc3/pull/3590).
 - Distinguish between `Data` and `Deterministic` variables when graphing models with graphviz. PR [#3491](https://github.com/pymc-devs/pymc3/pull/3491).
 - Sequential Monte Carlo - Approximate Bayesian Computation step method is now available. The implementation is in an experimental stage and will be further improved.
 - Added `Matern12` covariance function for Gaussian processes. This is the Matern kernel with nu=1/2.

docs/source/notebooks/table_of_contents_examples.js

@@ -53,5 +53,6 @@ Gallery.contents = {
     "normalizing_flows_overview": "Variational Inference",
     "gaussian-mixture-model-advi": "Variational Inference",
     "GLM-hierarchical-advi-minibatch": "Variational Inference",
-    "ODE_API_parameter_estimation": "Inference in ODE models"
+    "ODE_parameter_estimation": "Inference in ODE models",
+    "ODE_API_parameter_estimation": "Inference in ODE models with DifferentialEquation"
 }

pymc3/ode/ode.py

@@ -70,7 +70,8 @@ def __init__(self, func, times, n_states, n_odeparams, t0=0):
         self._grad_op = ODEGradop(self._numpy_vsp)
 
     def _make_sens_ic(self):
-        """The sensitivity matrix will always have consistent form.
+        """
+        The sensitivity matrix will always have consistent form.
         If the first n_odeparams entries of the parameters vector in the simulate call
         correspond to ode paramaters, then the first n_odeparams columns in
         the sensitivity matrix will be 0 
@@ -94,14 +95,8 @@ def _make_sens_ic(self):
         return dydp
 
     def _system(self, Y, t, p):
-        """This is the function that will be passed to odeint.
-        Solves both ODE and sensitivities
-        Args:
-            Y (vector): current state and current gradient state
-            t (scalar): current time
-            p (vector): parameters
-        Returns:
-            derivatives (vector): derivatives of state and gradient
+        """This is the function that will be passed to odeint. Solves both ODE and sensitivities
+
         """
 
         dydt, ddt_dydp = self._augmented_func(Y[: self._n], t, p, Y[self._n :])

pymc3/ode/utils.py

@@ -4,7 +4,8 @@
 
 
 def augment_system(ode_func, n, m):
-    """Function to create augmented system.
+    """
+    Function to create augmented system.
 
     Take a function which specifies a set of differential equations and return
     a compiled function which allows for computation of gradients of the