#1477 add some tests and remove uncovered lines not neccessary

Author: martinjrobins

pybamm/expression_tree/operations/evaluate_python.py

@@ -685,11 +685,7 @@ def evaluate(self, t=None, y=None, y_dot=None, inputs=None, known_evals=None):
         result = self._jac_evaluate(*self._constants, t, y, y_dot, inputs, known_evals)
         result = result.reshape(result.shape[0], -1)
 
-        # don't need known_evals, but need to reproduce Symbol.evaluate signature
-        if known_evals is not None:
-            return result, known_evals
-        else:
-            return result
+        return result
 
 
 class EvaluatorJaxSensitivities:
@@ -708,8 +704,4 @@ def evaluate(self, t=None, y=None, y_dot=None, inputs=None, known_evals=None):
         # execute code
         result = self._jac_evaluate(*self._constants, t, y, y_dot, inputs, known_evals)
 
-        # don't need known_evals, but need to reproduce Symbol.evaluate signature
-        if known_evals is not None:
-            return result, known_evals
-        else:
-            return result
+        return result

pybamm/solvers/base_solver.py

@@ -220,13 +220,6 @@ def set_up(self, model, inputs=None, t_eval=None, ics_only=False):
         if model.calculate_sensitivities and not isinstance(self, pybamm.IDAKLUSolver):
             calculate_sensitivities_explicit = True
 
-        if calculate_sensitivities_explicit and model.convert_to_format != 'casadi':
-            raise NotImplementedError(
-                "Sensitivities only supported for:\n"
-                "  - model.convert_to_format = 'casadi'\n"
-                "  - IDAKLUSolver (any convert_to_format)"
-            )
-
         # if we are calculating sensitivities explicitly then the number of
         # equations will change
         if calculate_sensitivities_explicit:
@@ -284,12 +277,7 @@ def report(string):
                 report(f"Converting {name} to jax")
                 func = pybamm.EvaluatorJax(func)
                 jacp = None
-                if calculate_sensitivities_explicit:
-                    raise NotImplementedError(
-                        "explicit sensitivity equations not supported for "
-                        "convert_to_format='jax'"
-                    )
-                elif model.calculate_sensitivities:
+                if model.calculate_sensitivities:
                     report((
                         f"Calculating sensitivities for {name} with respect "
                         f"to parameters {model.calculate_sensitivities} using jax"
@@ -308,12 +296,7 @@ def report(string):
             elif model.convert_to_format != "casadi":
                 # Process with pybamm functions, optionally converting
                 # to python evaluator
-                if calculate_sensitivities_explicit:
-                    raise NotImplementedError(
-                        "explicit sensitivity equations not supported for "
-                        "convert_to_format='{}'".format(model.convert_to_format)
-                    )
-                elif model.calculate_sensitivities:
+                if model.calculate_sensitivities:
                     report((
                         f"Calculating sensitivities for {name} with respect "
                         f"to parameters {model.calculate_sensitivities}"

pybamm/solvers/solution.py

@@ -97,7 +97,7 @@ def __init__(
         elif isinstance(sensitivities, dict):
             self._sensitivities = sensitivities
         else:
-            raise RuntimeError('sensitivities arg needs to be a bool or dict')
+            raise TypeError('sensitivities arg needs to be a bool or dict')
 
         self._t_event = t_event
         self._y_event = y_event
@@ -304,10 +304,6 @@ def all_ts(self):
     def all_ys(self):
         return self._all_ys
 
-    @property
-    def all_ys_and_sens(self):
-        return self._all_ys_and_sens
-
     @property
     def all_models(self):
         """Model(s) used for solution"""

tests/unit/test_solvers/test_processed_variable.py

@@ -58,6 +58,48 @@ def test_processed_variable_0D(self):
         )
         np.testing.assert_array_equal(processed_var.entries, y_sol[0])
 
+        # check empty sensitivity works
+
+    def test_processed_variable_0D_no_sensitivity(self):
+        # without space
+        t = pybamm.t
+        y = pybamm.StateVector(slice(0, 1))
+        var = t * y
+        var.mesh = None
+        t_sol = np.linspace(0, 1)
+        y_sol = np.array([np.linspace(0, 5)])
+        var_casadi = to_casadi(var, y_sol)
+        processed_var = pybamm.ProcessedVariable(
+            [var],
+            [var_casadi],
+            pybamm.Solution(t_sol, y_sol, pybamm.BaseModel(), {}),
+            warn=False,
+        )
+
+        # test no inputs (i.e. no sensitivity)
+        self.assertDictEqual(processed_var.sensitivities, {})
+
+        # with parameter
+        t = pybamm.t
+        y = pybamm.StateVector(slice(0, 1))
+        a = pybamm.InputParameter('a')
+        var = t * y * a
+        var.mesh = None
+        t_sol = np.linspace(0, 1)
+        y_sol = np.array([np.linspace(0, 5)])
+        inputs = {'a': np.array([1.0])}
+        var_casadi = to_casadi(var, y_sol, inputs=inputs)
+        processed_var = pybamm.ProcessedVariable(
+            [var],
+            [var_casadi],
+            pybamm.Solution(t_sol, y_sol, pybamm.BaseModel(), inputs),
+            warn=False,
+        )
+
+        # test no sensitivity raises error
+        with self.assertRaisesRegex(ValueError, 'Cannot compute sensitivities'):
+            print(processed_var.sensitivities)
+
     def test_processed_variable_1D(self):
         t = pybamm.t
         var = pybamm.Variable("var", domain=["negative electrode", "separator"])

tests/unit/test_solvers/test_solution.py

@@ -22,6 +22,12 @@ def test_init(self):
         self.assertEqual(sol.all_inputs, [{}])
         self.assertIsInstance(sol.all_models[0], pybamm.BaseModel)
 
+    def test_sensitivities(self):
+        t = np.linspace(0, 1)
+        y = np.tile(t, (20, 1))
+        with self.assertRaises(TypeError):
+            pybamm.Solution(t, y, pybamm.BaseModel(), {}, sensitivities=1.0)
+
     def test_errors(self):
         bad_ts = [np.array([1, 2, 3]), np.array([3, 4, 5])]
         sol = pybamm.Solution(