Fixes to toy model docstrings

pints/toy/_annulus.py

@@ -26,7 +26,7 @@ class AnnulusLogPDF(ToyLogPDF):
     the same radius. In two dimensions, the density looks like a circular
     annulus.
 
-    Extends :class:`pints.LogPDF`.
+    Extends :class:`pints.toy.ToyLogPDF`.
 
     Parameters
     ----------

pints/toy/_cone.py

@@ -23,7 +23,7 @@ class ConeLogPDF(ToyLogPDF):
     mean and variance that are returned relate to expectations on ``|x|`` not
     the multidimensional ``x``.
 
-    Extends :class:`pints.LogPDF`.
+    Extends :class:`pints.toy.ToyLogPDF`.
 
     Parameters
     ----------

pints/toy/_constant_model.py

@@ -8,8 +8,10 @@
 import numpy as np
 import pints
 
+from . import ToyModel
 
-class ConstantModel(pints.ForwardModelS1):
+
+class ConstantModel(ToyModel, pints.ForwardModelS1):
     r"""
     Toy model that's constant over time, linear over the parameters, mostly
     useful for unit testing.
@@ -29,7 +31,7 @@ class ConstantModel(pints.ForwardModelS1):
         \frac{\partial{f_i(t)}}{dp_j} =
             \begin{cases} i, i = j\\0, i \neq j \end{cases}
 
-    Extends :class:`pints.ForwardModelS1`.
+    Extends :class:`pints.ForwardModelS1`, :class:`pints.toy.ToyModel`.
 
     Parameters
     ----------

pints/toy/_eight_schools.py

@@ -15,7 +15,7 @@ class EightSchoolsLogPDF(ToyLogPDF):
     r"""
     The classic Eight Schools example that is discussed in [1]_.
 
-    The aim of this model (implemented as a :class:`pints.ToyLogPDF`) is to
+    The aim of this model (implemented as a :class:`pints.toy.ToyLogPDF`) is to
     determine the effects of coaching on SAT scores in 8 schools (each school
     being denoted by subscript j in the following equations). It it used
     by statisticians to illustrate how hierarchical models can quite easily

pints/toy/_fitzhugh_nagumo_model.py

@@ -77,7 +77,7 @@ def __init__(self, y0=None):
                 raise ValueError('Initial value must have size 2.')
 
     def _dfdp(self, y, t, p):
-        """ See :meth:`pints.ToyODEModel._dfdp()`. """
+        """ See :meth:`pints.toy.ToyODEModel._dfdp()`. """
         V, R = y
         a, b, c = [float(param) for param in p]
         ret = np.empty((2, 3))
@@ -90,7 +90,7 @@ def _dfdp(self, y, t, p):
         return ret
 
     def jacobian(self, y, t, p):
-        """ See :meth:`pints.ToyODEModel.jacobian()`. """
+        """ See :meth:`pints.toy.ToyODEModel.jacobian()`. """
         V, R = y
         a, b, c = [float(param) for param in p]
         ret = np.empty((2, 2))
@@ -109,7 +109,7 @@ def n_parameters(self):
         return 3
 
     def _rhs(self, y, t, p):
-        """ See :meth:`pints.ToyODEModel._rhs()`. """
+        """ See :meth:`pints.toy.ToyODEModel._rhs()`. """
         V, R = y
         a, b, c = [float(x) for x in p]
         dV_dt = (V - V**3 / 3 + R) * c

pints/toy/_german_credit.py

@@ -29,7 +29,7 @@ class GermanCreditLogPDF(ToyLogPDF):
     and :math:`\sigma^2=100`. The dataset here is from [1]_ but the test
     problem is defined in [2]_.
 
-    Extends :class:`pints.LogPDF`.
+    Extends :class:`pints.toy.ToyLogPDF`.
 
     Parameters
     ----------

pints/toy/_german_credit_hierarchical.py

@@ -31,7 +31,7 @@ class GermanCreditHierarchicalLogPDF(ToyLogPDF):
     by creating all interactions between individual variables and themselves
     as defined in [2]_.
 
-    Extends :class:`pints.LogPDF`.
+    Extends :class:`pints.toy.ToyLogPDF`.
 
     Parameters
     ----------

pints/toy/_goodwin_oscillator_model.py

@@ -49,7 +49,7 @@ def __init__(self):
         self._y0 = [0.0054, 0.053, 1.93]
 
     def _dfdp(self, state, time, parameters):
-        """ See :meth:`pints.ToyODEModel._dfdp()`. """
+        """ See :meth:`pints.toy.ToyODEModel._dfdp()`. """
         x, y, z = state
         k2, k3, m1, m2, m3 = parameters
         ret = np.empty((self.n_outputs(), self.n_parameters()))
@@ -71,7 +71,7 @@ def _dfdp(self, state, time, parameters):
         return ret
 
     def jacobian(self, state, time, parameters):
-        """ See :meth:`pints.ToyODEModel.jacobian()`. """
+        """ See :meth:`pints.toy.ToyODEModel.jacobian()`. """
         x, y, z = state
         k2, k3, m1, m2, m3 = parameters
         ret = np.empty((self.n_outputs(), self.n_outputs()))
@@ -95,7 +95,7 @@ def n_parameters(self):
         return 5
 
     def _rhs(self, state, time, parameters):
-        """ See :meth:`pints.ToyODEModel._rhs()`. """
+        """ See :meth:`pints.toy.ToyODEModel._rhs()`. """
         x, y, z = state
         k2, k3, m1, m2, m3 = parameters
         dxdt = 1 / (1 + z**10) - m1 * x

pints/toy/_hes1_michaelis_menten.py

@@ -30,7 +30,7 @@ class Hes1Model(ToyODEModel, pints.ForwardModelS1):
     the system. The input order of parameters of interest is
     :math:`\\{ P_0, \\nu, k_1, h \\}`.
 
-    Extends :class:`pints.ForwardModel`, :class:`pints.toy.ToyModel`.
+    Extends :class:`pints.ForwardModelS1`, :class:`pints.toy.ToyODEModel`.
 
     Parameters
     ----------
@@ -58,7 +58,7 @@ def __init__(self, m0=None, fixed_parameters=None):
             self.set_m0(m0)
 
     def _dfdp(self, state, time, parameters):
-        """ See :meth:`pints.ToyModel.jacobian()`. """
+        """ See :meth:`pints.toy.ToyModel.jacobian()`. """
         m, p1, p2 = state
         P0, v, k1, h = parameters
         p2_over_p0 = p2 / P0
@@ -96,7 +96,7 @@ def fixed_parameters(self):
         return [self._p0[0], self._p0[1], self._kdeg]
 
     def jacobian(self, state, time, parameters):
-        """ See :meth:`pints.ToyModel.jacobian()`. """
+        """ See :meth:`pints.toy.ToyModel.jacobian()`. """
         m, p1, p2 = state
         P0, v, k1, h = parameters
         k_deg = self._kdeg
@@ -116,7 +116,7 @@ def jacobian(self, state, time, parameters):
         return ret
 
     def n_states(self):
-        """ See :meth:`pints.ToyODEModel.n_states()`. """
+        """ See :meth:`pints.toy.ToyODEModel.n_states()`. """
         return 3
 
     def n_outputs(self):

pints/toy/_logistic_model.py

@@ -29,7 +29,7 @@ class LogisticModel(pints.ForwardModelS1, ToyModel):
     :math:`k`. The initial population size :math:`p_0 = f(0)` is a fixed
     (known) parameter in the model.
 
-    Extends :class:`pints.ForwardModel`, :class:`pints.toy.ToyModel`.
+    Extends :class:`pints.ForwardModelS1`, :class:`pints.toy.ToyModel`.
 
     Parameters
     ----------

pints/toy/_lotka_volterra_model.py

@@ -49,7 +49,7 @@ def __init__(self, y0=None):
             self.set_initial_conditions(y0)
 
     def _dfdp(self, z, t, p):
-        """ See :meth:`pints.ToyModel.jacobian()`. """
+        """ See :meth:`pints.toy.ToyModel.jacobian()`. """
         x, y = z
         a, b, c, d = [float(param) for param in p]
         ret = np.empty((2, 4))
@@ -64,7 +64,7 @@ def _dfdp(self, z, t, p):
         return ret
 
     def jacobian(self, z, t, p):
-        """ See :meth:`pints.ToyModel.jacobian()`. """
+        """ See :meth:`pints.toy.ToyModel.jacobian()`. """
         x, y = z
         a, b, c, d = [float(param) for param in p]
         ret = np.empty((2, 2))

pints/toy/_sho_model.py

@@ -29,7 +29,7 @@ class SimpleHarmonicOscillatorModel(pints.ForwardModelS1, ToyModel):
     ``y(0)``, its initial momentum, ``dy/dt(0)`` and the magnitude of the
     friction force, ``theta``.
 
-    Extends :class:`pints.ForwardModel`, :class:`pints.toy.ToyModel`.
+    Extends :class:`pints.ForwardModelS1`, :class:`pints.toy.ToyModel`.
 
     References
     ----------

pints/toy/_toy_classes.py

@@ -37,12 +37,12 @@ def suggested_bounds(self):
         raise NotImplementedError
 
 
-class ToyModel(object):
+class ToyModel():
     """
     Defines an interface for toy problems.
 
     Note that toy models should extend both ``ToyModel`` and one of the forward
-    model classes, e.g. :class:`pints.ForwardModel`.
+    model classes, i.e. :class:`pints.ForwardModel` or :class:`ForwardModelS1`.
     """
     def suggested_parameters(self):
         """
@@ -68,15 +68,17 @@ class ToyODEModel(ToyModel):
     ordinary differential equation (ODE) that describes some time-series
     generating model.
 
-    Note that toy ODE models should extend both :class:`pints.ToyODEModel` and
-    one of the forward model classes, e.g. :class:`pints.ForwardModel` or
+    Note that toy ODE models should extend both :class:`pints.toy.ToyODEModel`
+    and one of the forward model classes, e.g. :class:`pints.ForwardModel` or
     :class:`pints.ForwardModelS1`.
 
     To use this class as the basis for a :class:`pints.ForwardModel`, the
     method :meth:`_rhs()` should be reimplemented.
 
     Models implementing :meth:`_rhs()`, :meth:`jacobian()` and :meth:`_dfdp()`
     can be used to create a :class:`pints.ForwardModelS1`.
+
+    Extends: :class:`pints.toy.ToyODEModel`.
     """
     def _dfdp(self, y, t, p):
         """

pints/toy/stochastic/_degradation_model.py

@@ -19,7 +19,7 @@ class DegradationModel(MarkovJumpModel):
     .. math::
         A \xrightarrow{k} 0
 
-    Extends :class:`pints.MarkovJumpModel`.
+    Extends: :class:`pints.toy.stochastic.MarkovJumpModel`.
 
     Parameters
     ----------

pints/toy/stochastic/_logistic_model.py

@@ -24,7 +24,7 @@ class LogisticModel(MarkovJumpModel):
     The model is simulated using the Gillespie stochastic simulation algorithm
     [2]_, [3]_.
 
-    *Extends:* :class:`pints.ForwardModel`, :class:`pints.toy.ToyModel`.
+    Extends: :class:`pints.toy.stochastic.MarkovJumpModel`.
 
     Parameters
     ----------

pints/toy/stochastic/_michaelis_menten_model.py

@@ -19,6 +19,8 @@ class MichaelisMentenModel(MarkovJumpModel):
         - X3 -> X1+X2 with rate k2
         - X3 -> X2+X4 with rate k3
 
+    Extends: :class:`pints.toy.stochastic.MarkovJumpModel`.
+
     Parameters
     ----------
     initial_molecule_count : Array of size 3 of integers

pints/toy/stochastic/_production_degradation_model.py

@@ -20,7 +20,7 @@ class ProductionDegradationModel(MarkovJumpModel):
     .. math::
         A \xrightarrow{k1} 0, 0 \xrightarrow{k2} A
 
-    Extends :class:`pints.ForwardModel`, :class:`pints.toy.ToyModel`.
+    Extends: :class:`pints.toy.stochastic.MarkovJumpModel`.
 
     Parameters
     ----------

pints/toy/stochastic/_schlogl_model.py

@@ -13,17 +13,19 @@
 class SchloglModel(MarkovJumpModel):
     r"""
     Schlogl's system of chemical reactions has a single type of molecules and
-    starts with an initial count :math:`A(0)`. The evolution of the molecule
-    count is defined through the rates :math:`k_1`, :math:`k_2`, :math:`k_3`
-    and :math:`k_4` and the following equations:
+    starts with an initial count :math:`A(0)`.
+
+    The evolution of the molecule count is defined through the rates
+    :math:`k_1`, :math:`k_2`, :math:`k_3` and :math:`k_4` and the following
+    equations:
 
     ..math::
         2A \xrightarrow{k_1} 3A
         3A \xrightarrow{k_2} 2A
         0 \xrightarrow{k_3} A
         A \xrightarrow{k_4} 0
 
-    Extends :class:`pints.ForwardModel`, :class:`pints.toy.ToyModel`.
+    Extends: :class:`pints.toy.stochastic.MarkovJumpModel`.
 
     Parameters
     ----------