Increase test coverage

Author: Joseph

markov_builder/MarkovChain.py

@@ -283,8 +283,7 @@ def eval_transition_matrix(self, rates_dict: dict) -> Tuple[List[str], sp.Matrix
         """
 
         if rates_dict is None:
-            assert len(rates_dict) > 0
-            rates_dict = self.rate_expressions
+            Exception('rate dictionary not provided')
 
         l, Q = self.get_transition_matrix(use_parameters=True)
         Q_evaled = np.array(Q.evalf(subs=rates_dict)).astype(np.float64)
@@ -428,7 +427,9 @@ def sample_trajectories(self, no_trajectories: int, time_range: list = [0, 1],
                 if s_i == 0:
                     waiting_times[state_index] = np.inf
                 else:
-                    waiting_times[state_index] = self.rng.exponential(mean_waiting_times[state_index] / (s_i))
+                    waiting_times[state_index] =\
+                        self.rng.exponential(mean_waiting_times[state_index] /
+                                             (s_i))
 
             if t + min(waiting_times) > time_range[1] - time_range[0]:
                 break
@@ -463,7 +464,15 @@ def get_equilibrium_distribution(self, param_dict: dict = None) -> Tuple[List[st
         A, B = self.eliminate_state_from_transition_matrix(use_parameters=True)
 
         labels = self.graph.nodes()
-        ss = -np.array(A.LUsolve(B).evalf(subs=param_dict)).astype(np.float64)
+        try:
+            ss = -np.array(A.LUsolve(B).evalf(subs=param_dict)).astype(np.float64)
+
+        except TypeError as exc:
+            logging.warning("Couldn't evaluate equilibrium distribution as float."
+                            "Is every parameter defined?"
+                            "%s" % str(exc))
+            raise exc
+
         logging.debug("ss is %s", ss)
         ss = np.append(ss, 1 - ss.sum())
         return labels, ss
@@ -781,6 +790,9 @@ def define_auxiliary_expression(self, expression: sp.Expr, label: str = None, de
         self.default_values = {**self.default_values, **default_values}
         self.auxiliary_expression = expression
 
+    def get_states(self):
+        return list(self.graph)
+
     def as_latex(self, state_to_remove: str = None, include_auxiliary_expression: bool = False,
                  column_vector=True, label_order: list = None) -> str:
         """Creates a LaTeX expression describing the Markov chain, its parameters and

tests/test_MarkovChain.py

@@ -11,6 +11,8 @@
 
 import markov_builder.example_models as example_models
 
+from markov_builder.rate_expressions import negative_rate_expr, positive_rate_expr
+
 
 class TestMarkovChain(unittest.TestCase):
 
@@ -55,10 +57,13 @@ def test_transition_matrix(self):
         nx.drawing.nx_agraph.write_dot(mc.graph, "Beattie_dotfile.dot")
 
         # Draw graph using pyvis
+        mc.draw_graph(os.path.join(self.output_dir, "BeattieModel.html"), show_options=True)
         mc.draw_graph(os.path.join(self.output_dir, "BeattieModel.html"))
         logging.debug(mc.graph)
 
+        labels, Q = mc.get_transition_matrix(use_parameters=True)
         labels, Q = mc.get_transition_matrix()
+
         logging.debug("Q^T matrix is {}, labels are {}".format(Q.T, labels))
 
         system = mc.eliminate_state_from_transition_matrix(['C', 'O', 'I'])
@@ -84,7 +89,23 @@ def test_construct_examples(self):
             nx.drawing.nx_agraph.write_dot(mc.graph, "%s_dotfile.dot" % name)
             nx.drawing.nx_agraph.write_dot(mc.graph, "%s_dotfile.dot" % name)
 
-    def test_parameterise_rates(self):
+    def test_parameterise_rates_no_default(self):
+        """Test parameterise rates using a dictionary with no default parameter values.
+
+        Using the Beattie model
+        """
+
+        mc = example_models.construct_four_state_chain()
+
+        rate_dictionary = {'k_1': positive_rate_expr,
+                           'k_2': negative_rate_expr,
+                           'k_3': positive_rate_expr,
+                           'k_4': negative_rate_expr,
+                           }
+
+        mc.parameterise_rates(rate_dictionary, shared_variables=('V',))
+
+    def test_myokit_output(self):
         """
         Test the MarkovChain.parameterise_rates function.
         """
@@ -109,7 +130,11 @@ def test_parameterise_rates(self):
             self.assertEqual(param_list.count('V'), 1)
 
             # Generate myokit code
+            myokit_model = mc.generate_myokit_model(eliminate_state='O')
             myokit_model = mc.generate_myokit_model()
+            myokit_model = mc.generate_myokit_model(drug_binding=True)
+            myokit_model = mc.generate_myokit_model(drug_binding=True, eliminate_state='O')
+
             myokit.save(os.path.join(self.output_dir, 'beattie_model.mmt'), myokit_model)
 
             # Eliminate last node
@@ -214,6 +239,7 @@ def test_latex_printing(self):
         for mc in models:
             logging.debug(f"Printing latex for {mc.name}")
             logging.debug(mc.as_latex())
+            logging.debug(mc.as_latex(label_order=mc.get_states()))
             logging.debug(mc.as_latex(state_to_remove='O'))
             logging.debug(mc.as_latex(include_auxiliary_expression=True))
             logging.debug(mc.as_latex('O', True))
@@ -232,9 +258,13 @@ def test_sample_trajectories(self):
         param_dict = mc.default_values
         param_dict['V'] = 0
 
+        self.assertRaises(TypeError, mc.get_equilibrium_distribution)
+
         labels, eqm_dist = mc.get_equilibrium_distribution(param_dict=param_dict)
         starting_distribution = [int(val) for val in n_samples * eqm_dist]
 
+        df = mc.sample_trajectories(n_samples, (0, 10))
+
         df = mc.sample_trajectories(n_samples, (0, 250), param_dict=param_dict,
                                     starting_distribution=starting_distribution)
         df = df.set_index('time')