From f6b0ca93428d3a5cb61d4754f1b6805f3e499e57 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <vsulzer@umich.edu>
Date: Thu, 30 Jan 2020 19:38:50 -0500
Subject: [PATCH 01/11] #709 first go at simplifying on creation

---
 pybamm/expression_tree/operations/simplify.py | 16 ++++-
 pybamm/expression_tree/symbol.py              | 68 +++++++++++++------
 pybamm/spatial_methods/finite_volume.py       |  4 +-
 .../test_discretisation.py                    | 21 +++---
 .../test_binary_operators.py                  | 32 ++++++---
 .../unit/test_expression_tree/test_symbol.py  |  8 +--
 .../test_unary_operators.py                   |  9 +--
 7 files changed, 106 insertions(+), 52 deletions(-)

diff --git a/pybamm/expression_tree/operations/simplify.py b/pybamm/expression_tree/operations/simplify.py
index 2079379991..acfeef195e 100644
--- a/pybamm/expression_tree/operations/simplify.py
+++ b/pybamm/expression_tree/operations/simplify.py
@@ -8,11 +8,17 @@
 from scipy.sparse import issparse
 
 
-def simplify_if_constant(symbol):
+def simplify_if_constant(symbol, keep_domains=False):
     """
     Utility function to simplify an expression tree if it evalutes to a constant
     scalar, vector or matrix
     """
+    if keep_domains is True:
+        domain = symbol.domain
+        auxiliary_domains = symbol.auxiliary_domains
+    else:
+        domain = None
+        auxiliary_domains = None
     if symbol.is_constant():
         result = symbol.evaluate_ignoring_errors()
         if result is not None:
@@ -22,9 +28,13 @@ def simplify_if_constant(symbol):
                 return pybamm.Scalar(result)
             elif isinstance(result, np.ndarray) or issparse(result):
                 if result.ndim == 1 or result.shape[1] == 1:
-                    return pybamm.Vector(result)
+                    return pybamm.Vector(
+                        result, domain=domain, auxiliary_domains=auxiliary_domains
+                    )
                 else:
-                    return pybamm.Matrix(result)
+                    return pybamm.Matrix(
+                        result, domain=domain, auxiliary_domains=auxiliary_domains
+                    )
 
     return symbol
 
diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 49468dcc01..399be2da35 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -360,79 +360,109 @@ def __repr__(self):
 
     def __add__(self, other):
         """return an :class:`Addition` object"""
-        return pybamm.Addition(self, other)
+        return pybamm.simplify_if_constant(
+            pybamm.Addition(self, other), keep_domains=True
+        )
 
     def __radd__(self, other):
         """return an :class:`Addition` object"""
-        return pybamm.Addition(other, self)
+        return pybamm.simplify_if_constant(
+            pybamm.Addition(other, self), keep_domains=True
+        )
 
     def __sub__(self, other):
         """return a :class:`Subtraction` object"""
-        return pybamm.Subtraction(self, other)
+        return pybamm.simplify_if_constant(
+            pybamm.Subtraction(self, other), keep_domains=True
+        )
 
     def __rsub__(self, other):
         """return a :class:`Subtraction` object"""
-        return pybamm.Subtraction(other, self)
+        return pybamm.simplify_if_constant(
+            pybamm.Subtraction(other, self), keep_domains=True
+        )
 
     def __mul__(self, other):
         """return a :class:`Multiplication` object"""
-        return pybamm.Multiplication(self, other)
+        return pybamm.simplify_if_constant(
+            pybamm.Multiplication(self, other), keep_domains=True
+        )
 
     def __rmul__(self, other):
         """return a :class:`Multiplication` object"""
-        return pybamm.Multiplication(other, self)
+        return pybamm.simplify_if_constant(
+            pybamm.Multiplication(other, self), keep_domains=True
+        )
 
     def __matmul__(self, other):
         """return a :class:`MatrixMultiplication` object"""
-        return pybamm.MatrixMultiplication(self, other)
+        return pybamm.simplify_if_constant(
+            pybamm.MatrixMultiplication(self, other), keep_domains=True
+        )
 
     def __rmatmul__(self, other):
         """return a :class:`MatrixMultiplication` object"""
-        return pybamm.MatrixMultiplication(other, self)
+        return pybamm.simplify_if_constant(
+            pybamm.MatrixMultiplication(other, self), keep_domains=True
+        )
 
     def __truediv__(self, other):
         """return a :class:`Division` object"""
-        return pybamm.Division(self, other)
+        return pybamm.simplify_if_constant(
+            pybamm.Division(self, other), keep_domains=True
+        )
 
     def __rtruediv__(self, other):
         """return a :class:`Division` object"""
-        return pybamm.Division(other, self)
+        return pybamm.simplify_if_constant(
+            pybamm.Division(other, self), keep_domains=True
+        )
 
     def __pow__(self, other):
         """return a :class:`Power` object"""
-        return pybamm.Power(self, other)
+        return pybamm.simplify_if_constant(pybamm.Power(self, other), keep_domains=True)
 
     def __rpow__(self, other):
         """return a :class:`Power` object"""
-        return pybamm.Power(other, self)
+        return pybamm.simplify_if_constant(pybamm.Power(other, self), keep_domains=True)
 
     def __lt__(self, other):
         """return a :class:`Heaviside` object"""
-        return pybamm.Heaviside(self, other, equal=False)
+        return pybamm.simplify_if_constant(
+            pybamm.Heaviside(self, other, equal=False), keep_domains=True
+        )
 
     def __le__(self, other):
         """return a :class:`Heaviside` object"""
-        return pybamm.Heaviside(self, other, equal=True)
+        return pybamm.simplify_if_constant(
+            pybamm.Heaviside(self, other, equal=True), keep_domains=True
+        )
 
     def __gt__(self, other):
         """return a :class:`Heaviside` object"""
-        return pybamm.Heaviside(other, self, equal=False)
+        return pybamm.simplify_if_constant(
+            pybamm.Heaviside(other, self, equal=False), keep_domains=True
+        )
 
     def __ge__(self, other):
         """return a :class:`Heaviside` object"""
-        return pybamm.Heaviside(other, self, equal=True)
+        return pybamm.simplify_if_constant(
+            pybamm.Heaviside(other, self, equal=True), keep_domains=True
+        )
 
     def __neg__(self):
         """return a :class:`Negate` object"""
-        return pybamm.Negate(self)
+        return pybamm.simplify_if_constant(pybamm.Negate(self), keep_domains=True)
 
     def __abs__(self):
         """return an :class:`AbsoluteValue` object"""
-        return pybamm.AbsoluteValue(self)
+        return pybamm.simplify_if_constant(
+            pybamm.AbsoluteValue(self), keep_domains=True
+        )
 
     def __getitem__(self, key):
         """return a :class:`Index` object"""
-        return pybamm.Index(self, key)
+        return pybamm.simplify_if_constant(pybamm.Index(self, key), keep_domains=True)
 
     def diff(self, variable):
         """
diff --git a/pybamm/spatial_methods/finite_volume.py b/pybamm/spatial_methods/finite_volume.py
index 3533b1f86e..03ce66a244 100644
--- a/pybamm/spatial_methods/finite_volume.py
+++ b/pybamm/spatial_methods/finite_volume.py
@@ -1025,7 +1025,9 @@ def process_binary_operators(self, bin_op, left, right, disc_left, disc_right):
                 method = "arithmetic"
             disc_left = self.node_to_edge(disc_left, method=method)
         # Return new binary operator with appropriate class
-        out = bin_op.__class__(disc_left, disc_right)
+        out = pybamm.simplify_if_constant(
+            bin_op.__class__(disc_left, disc_right), keep_domains=True
+        )
         return out
 
     def concatenation(self, disc_children):
diff --git a/tests/unit/test_discretisations/test_discretisation.py b/tests/unit/test_discretisations/test_discretisation.py
index e1f2725bc5..4517af196a 100644
--- a/tests/unit/test_discretisations/test_discretisation.py
+++ b/tests/unit/test_discretisations/test_discretisation.py
@@ -251,10 +251,10 @@ def test_process_symbol_base(self):
         self.assertIsInstance(un1_disc, pybamm.Negate)
         self.assertIsInstance(un1_disc.children[0], pybamm.StateVector)
 
-        un2 = abs(scal)
+        un2 = abs(var)
         un2_disc = disc.process_symbol(un2)
         self.assertIsInstance(un2_disc, pybamm.AbsoluteValue)
-        self.assertIsInstance(un2_disc.children[0], pybamm.Scalar)
+        self.assertIsInstance(un2_disc.children[0], pybamm.StateVector)
 
         # function of one variable
         def myfun(x):
@@ -749,7 +749,7 @@ def test_process_empty_model(self):
     def test_broadcast(self):
         whole_cell = ["negative electrode", "separator", "positive electrode"]
 
-        a = pybamm.Scalar(7)
+        a = pybamm.InputParameter("a")
         var = pybamm.Variable("var")
 
         # create discretisation
@@ -761,13 +761,14 @@ def test_broadcast(self):
         # scalar
         broad = disc.process_symbol(pybamm.FullBroadcast(a, whole_cell, {}))
         np.testing.assert_array_equal(
-            broad.evaluate(), 7 * np.ones_like(combined_submesh[0].nodes[:, np.newaxis])
+            broad.evaluate(u={"a": 7}),
+            7 * np.ones_like(combined_submesh[0].nodes[:, np.newaxis]),
         )
         self.assertEqual(broad.domain, whole_cell)
 
         broad_disc = disc.process_symbol(broad)
         self.assertIsInstance(broad_disc, pybamm.Multiplication)
-        self.assertIsInstance(broad_disc.children[0], pybamm.Scalar)
+        self.assertIsInstance(broad_disc.children[0], pybamm.InputParameter)
         self.assertIsInstance(broad_disc.children[1], pybamm.Vector)
 
         # process Broadcast variable
@@ -804,16 +805,14 @@ def test_secondary_broadcast_2D(self):
         # secondary broadcast in 2D --> Matrix multiplication
         disc = get_discretisation_for_testing()
         mesh = disc.mesh
-        var = pybamm.Vector(
-            mesh["negative particle"][0].nodes, domain=["negative particle"]
-        )
+        var = pybamm.Variable("var", domain=["negative particle"])
         broad = pybamm.SecondaryBroadcast(var, "negative electrode")
 
         disc.set_variable_slices([var])
         broad_disc = disc.process_symbol(broad)
         self.assertIsInstance(broad_disc, pybamm.MatrixMultiplication)
         self.assertIsInstance(broad_disc.children[0], pybamm.Matrix)
-        self.assertIsInstance(broad_disc.children[1], pybamm.Vector)
+        self.assertIsInstance(broad_disc.children[1], pybamm.StateVector)
         self.assertEqual(
             broad_disc.shape,
             (mesh["negative particle"][0].npts * mesh["negative electrode"][0].npts, 1),
@@ -905,7 +904,9 @@ def test_exceptions(self):
 
         # check doesn't raise if broadcast
         model.variables = {
-            c_n.name: pybamm.PrimaryBroadcast(pybamm.Scalar(2), ["negative electrode"])
+            c_n.name: pybamm.PrimaryBroadcast(
+                pybamm.InputParameter("a"), ["negative electrode"]
+            )
         }
         disc.process_model(model)
 
diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index 8127b41b22..faabc1f34a 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -45,6 +45,10 @@ def test_addition(self):
         self.assertEqual(summ.children[0].name, a.name)
         self.assertEqual(summ.children[1].name, b.name)
 
+        # test simplifying
+        summ2 = pybamm.Scalar(1) + pybamm.Scalar(3)
+        self.assertEqual(summ2.id, pybamm.Scalar(4).id)
+
     def test_power(self):
         a = pybamm.Symbol("a")
         b = pybamm.Symbol("b")
@@ -61,22 +65,28 @@ def test_power(self):
     def test_known_eval(self):
         # Scalars
         a = pybamm.Scalar(4)
-        b = pybamm.Scalar(2)
+        b = pybamm.StateVector(slice(0, 1))
         expr = (a + b) - (a + b) * (a + b)
-        value = expr.evaluate()
-        self.assertEqual(expr.evaluate(known_evals={})[0], value)
-        self.assertIn((a + b).id, expr.evaluate(known_evals={})[1])
-        self.assertEqual(expr.evaluate(known_evals={})[1][(a + b).id], 6)
+        value = expr.evaluate(y=np.array([2]))
+        self.assertEqual(expr.evaluate(y=np.array([2]), known_evals={})[0], value)
+        self.assertIn((a + b).id, expr.evaluate(y=np.array([2]), known_evals={})[1])
+        self.assertEqual(
+            expr.evaluate(y=np.array([2]), known_evals={})[1][(a + b).id], 6
+        )
 
         # Matrices
         a = pybamm.Matrix(np.random.rand(5, 5))
-        b = pybamm.Matrix(np.random.rand(5, 5))
+        b = pybamm.StateVector(slice(0, 5))
         expr2 = (a @ b) - (a @ b) * (a @ b) + (a @ b)
-        value = expr2.evaluate()
-        np.testing.assert_array_equal(expr2.evaluate(known_evals={})[0], value)
-        self.assertIn((a @ b).id, expr2.evaluate(known_evals={})[1])
+        y_test = np.linspace(0, 1, 5)
+        value = expr2.evaluate(y=y_test)
+        np.testing.assert_array_equal(
+            expr2.evaluate(y=y_test, known_evals={})[0], value
+        )
+        self.assertIn((a @ b).id, expr2.evaluate(y=y_test, known_evals={})[1])
         np.testing.assert_array_equal(
-            expr2.evaluate(known_evals={})[1][(a @ b).id], (a @ b).evaluate()
+            expr2.evaluate(y=y_test, known_evals={})[1][(a @ b).id],
+            (a @ b).evaluate(y=y_test),
         )
 
     def test_diff(self):
@@ -158,7 +168,7 @@ def test_id(self):
     def test_number_overloading(self):
         a = pybamm.Scalar(4)
         prod = a * 3
-        self.assertIsInstance(prod.children[1], pybamm.Scalar)
+        self.assertIsInstance(prod, pybamm.Scalar)
         self.assertEqual(prod.evaluate(), 12)
 
     def test_sparse_multiply(self):
diff --git a/tests/unit/test_expression_tree/test_symbol.py b/tests/unit/test_expression_tree/test_symbol.py
index 9782a57ab9..dbff68dd3a 100644
--- a/tests/unit/test_expression_tree/test_symbol.py
+++ b/tests/unit/test_expression_tree/test_symbol.py
@@ -194,7 +194,7 @@ def test_symbol_evaluates_to_number(self):
         a = pybamm.Parameter("a")
         self.assertFalse(a.evaluates_to_number())
 
-        a = pybamm.Scalar(3) * pybamm.Scalar(2)
+        a = pybamm.Scalar(3) * pybamm.Time()
         self.assertTrue(a.evaluates_to_number())
         # highlight difference between this function and isinstance(a, Scalar)
         self.assertNotIsInstance(a, pybamm.Scalar)
@@ -339,10 +339,10 @@ def test_has_spatial_derivatives(self):
 
     def test_orphans(self):
         a = pybamm.Scalar(1)
-        b = pybamm.Scalar(2)
-        sum = a + b
+        b = pybamm.Parameter("b")
+        summ = a + b
 
-        a_orp, b_orp = sum.orphans
+        a_orp, b_orp = summ.orphans
         self.assertIsNone(a_orp.parent)
         self.assertIsNone(b_orp.parent)
         self.assertEqual(a.id, a_orp.id)
diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py
index 16c6519638..19976e8dca 100644
--- a/tests/unit/test_expression_tree/test_unary_operators.py
+++ b/tests/unit/test_expression_tree/test_unary_operators.py
@@ -124,22 +124,23 @@ def test_integral(self):
             pybamm.Integral(a, y)
 
     def test_index(self):
-        vec = pybamm.Vector(np.array([1, 2, 3, 4, 5]))
+        vec = pybamm.StateVector(slice(0, 5))
+        y_test = np.array([1, 2, 3, 4, 5])
         # with integer
         ind = vec[3]
         self.assertIsInstance(ind, pybamm.Index)
         self.assertEqual(ind.slice, slice(3, 4))
-        self.assertEqual(ind.evaluate(), 4)
+        self.assertEqual(ind.evaluate(y=y_test), 4)
         # with slice
         ind = vec[1:3]
         self.assertIsInstance(ind, pybamm.Index)
         self.assertEqual(ind.slice, slice(1, 3))
-        np.testing.assert_array_equal(ind.evaluate(), np.array([[2], [3]]))
+        np.testing.assert_array_equal(ind.evaluate(y=y_test), np.array([[2], [3]]))
         # with only stop slice
         ind = vec[:3]
         self.assertIsInstance(ind, pybamm.Index)
         self.assertEqual(ind.slice, slice(3))
-        np.testing.assert_array_equal(ind.evaluate(), np.array([[1], [2], [3]]))
+        np.testing.assert_array_equal(ind.evaluate(y=y_test), np.array([[1], [2], [3]]))
 
         # errors
         with self.assertRaisesRegex(TypeError, "index must be integer or slice"):

From 07d6c34c08612c4f9418ed0c140611b020dc5113 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <vsulzer@umich.edu>
Date: Thu, 30 Jan 2020 20:00:13 -0500
Subject: [PATCH 02/11] #709 deprecate param.update_model

---
 .../compare_comsol/compare_comsol_DFN.py      |   3 +-
 .../scripts/compare_comsol/discharge_curve.py |  16 +-
 pybamm/parameters/parameter_values.py         | 102 +++---------
 .../test_models/standard_model_tests.py       |  22 ---
 tests/integration/test_quick_plot.py          |   7 -
 .../test_parameters/test_parameter_values.py  |  31 +++-
 .../test_parameters/test_update_parameters.py | 147 ------------------
 7 files changed, 56 insertions(+), 272 deletions(-)
 delete mode 100644 tests/unit/test_parameters/test_update_parameters.py

diff --git a/examples/scripts/compare_comsol/compare_comsol_DFN.py b/examples/scripts/compare_comsol/compare_comsol_DFN.py
index da6121c4be..f11d26245f 100644
--- a/examples/scripts/compare_comsol/compare_comsol_DFN.py
+++ b/examples/scripts/compare_comsol/compare_comsol_DFN.py
@@ -128,7 +128,8 @@ def myinterp(t):
     "Positive electrode potential [V]": comsol_phi_p,
     "Terminal voltage [V]": comsol_voltage,
 }
-comsol_solution = pybamm.CasadiSolver(mode="fast").solve(pybamm_model, time)
+# Make new solution with same t and y
+comsol_solution = pybamm.Solution(pybamm_solution.t, pybamm_solution.y)
 comsol_solution.model = comsol_model
 # plot
 plot = pybamm.QuickPlot(
diff --git a/examples/scripts/compare_comsol/discharge_curve.py b/examples/scripts/compare_comsol/discharge_curve.py
index 33e3b14c56..110cfc5dbd 100644
--- a/examples/scripts/compare_comsol/discharge_curve.py
+++ b/examples/scripts/compare_comsol/discharge_curve.py
@@ -14,10 +14,16 @@
 model = pybamm.lithium_ion.DFN()
 geometry = model.default_geometry
 
+
 # load parameters and process model and geometry
+def current_function(t):
+    return pybamm.InputParameter("current")
+
+
 param = model.default_parameter_values
 param["Electrode width [m]"] = 1
 param["Electrode height [m]"] = 1
+param["Current function [A]"] = current_function
 param.process_model(model)
 param.process_geometry(geometry)
 
@@ -48,7 +54,7 @@
 plt.ylabel(r"$\vert V - V_{comsol} \vert$", fontsize=20)
 
 for key, C_rate in C_rates.items():
-
+    current = 24 * C_rate
     # load the comsol results
     comsol_variables = pickle.load(
         open("input/comsol_results/comsol_{}C.pickle".format(key), "rb")
@@ -57,8 +63,6 @@
     comsol_voltage = comsol_variables["voltage"]
 
     # update current density
-    param["Current function [A]"] = 24 * C_rate
-    param.update_model(model, disc)
 
     # discharge timescale
     tau = param.process_symbol(
@@ -67,12 +71,14 @@
 
     # solve model at comsol times
     t = comsol_time / tau
-    solution = pybamm.CasadiSolver(mode="fast").solve(model, t)
+    solution = pybamm.CasadiSolver(mode="fast").solve(
+        model, t, inputs={"current": current}
+    )
 
     # discharge capacity
     discharge_capacity = solution["Discharge capacity [A.h]"]
     discharge_capacity_sol = discharge_capacity(solution.t)
-    comsol_discharge_capacity = comsol_time * param["Current function [A]"] / 3600
+    comsol_discharge_capacity = comsol_time * current / 3600
 
     # extract the voltage
     voltage = solution["Terminal voltage [V]"]
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index 57a7f4fb35..ace705420e 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -294,7 +294,7 @@ def check_and_update_parameter_values(self, values):
 
         return values
 
-    def process_model(self, unprocessed_model, processing="process", inplace=True):
+    def process_model(self, unprocessed_model, inplace=True):
         """Assign parameter values to a model.
         Currently inplace, could be changed to return a new model.
 
@@ -302,13 +302,6 @@ def process_model(self, unprocessed_model, processing="process", inplace=True):
         ----------
         unprocessed_model : :class:`pybamm.BaseModel`
             Model to assign parameter values for
-        processing : str, optional
-            Flag to indicate how to process model (default 'process')
-
-            * 'process': Calls :meth:`process_symbol()` (walk through the symbol \
-            and replace any Parameter with a Value)
-            * 'update': Calls :meth:`update_scalars()` for use on already-processed \
-            model (update the value of any Scalars in the expression tree.)
         inplace: bool, optional
             If True, replace the parameters in the model in place. Otherwise, return a
             new model with parameter values set. Default is True.
@@ -335,32 +328,21 @@ def process_model(self, unprocessed_model, processing="process", inplace=True):
         if len(unprocessed_model.rhs) == 0 and len(unprocessed_model.algebraic) == 0:
             raise pybamm.ModelError("Cannot process parameters for empty model")
 
-        if processing == "process":
-            processing_function = self.process_symbol
-        elif processing == "update":
-            processing_function = self.update_scalars
-
         for variable, equation in model.rhs.items():
-            pybamm.logger.debug(
-                "{} parameters for {!r} (rhs)".format(processing.capitalize(), variable)
-            )
-            model.rhs[variable] = processing_function(equation)
+            pybamm.logger.debug("Processing parameters for {!r} (rhs)".format(variable))
+            model.rhs[variable] = self.process_symbol(equation)
 
         for variable, equation in model.algebraic.items():
             pybamm.logger.debug(
-                "{} parameters for {!r} (algebraic)".format(
-                    processing.capitalize(), variable
-                )
+                "Processing parameters for {!r} (algebraic)".format(variable)
             )
-            model.algebraic[variable] = processing_function(equation)
+            model.algebraic[variable] = self.process_symbol(equation)
 
         for variable, equation in model.initial_conditions.items():
             pybamm.logger.debug(
-                "{} parameters for {!r} (initial conditions)".format(
-                    processing.capitalize(), variable
-                )
+                "Processing parameters for {!r} (initial conditions)".format(variable)
             )
-            model.initial_conditions[variable] = processing_function(equation)
+            model.initial_conditions[variable] = self.process_symbol(equation)
 
         # Boundary conditions are dictionaries {"left": left bc, "right": right bc}
         # in general, but may be imposed on the tabs (or *not* on the tab) for a
@@ -369,17 +351,15 @@ def process_model(self, unprocessed_model, processing="process", inplace=True):
         new_boundary_conditions = {}
         sides = ["left", "right", "negative tab", "positive tab", "no tab"]
         for variable, bcs in model.boundary_conditions.items():
-            processed_variable = processing_function(variable)
+            processed_variable = self.process_symbol(variable)
             new_boundary_conditions[processed_variable] = {}
             for side in sides:
                 try:
                     bc, typ = bcs[side]
                     pybamm.logger.debug(
-                        "{} parameters for {!r} ({} bc)".format(
-                            processing.capitalize(), variable, side
-                        )
+                        "Processing parameters for {!r} ({} bc)".format(variable, side)
                     )
-                    processed_bc = (processing_function(bc), typ)
+                    processed_bc = (self.process_symbol(bc), typ)
                     new_boundary_conditions[processed_variable][side] = processed_bc
                 except KeyError as err:
                     # don't raise error if the key error comes from the side not being
@@ -394,42 +374,24 @@ def process_model(self, unprocessed_model, processing="process", inplace=True):
 
         for variable, equation in model.variables.items():
             pybamm.logger.debug(
-                "{} parameters for {!r} (variables)".format(
-                    processing.capitalize(), variable
-                )
+                "Processing parameters for {!r} (variables)".format(variable)
             )
-            model.variables[variable] = processing_function(equation)
+            model.variables[variable] = self.process_symbol(equation)
         for event, equation in model.events.items():
-            pybamm.logger.debug(
-                "{} parameters for event '{}''".format(processing.capitalize(), event)
-            )
-            model.events[event] = processing_function(equation)
+            pybamm.logger.debug("Processing parameters for event '{}''".format(event))
+            model.events[event] = self.process_symbol(equation)
 
         pybamm.logger.info("Finish setting parameters for {}".format(model.name))
 
         return model
 
     def update_model(self, model, disc):
-        """Process a discretised model.
-        Currently inplace, could be changed to return a new model.
-
-        Parameters
-        ----------
-        model : :class:`pybamm.BaseModel`
-            Model to assign parameter values for
-        disc : :class:`pybamm.Discretisation`
-            The class that was used to discretise
-
-        """
-        # process parameter values for the model
-        self.process_model(model, processing="update")
-
-        # update discretised quantities using disc
-        model.concatenated_rhs = disc._concatenate_in_order(model.rhs)
-        model.concatenated_algebraic = disc._concatenate_in_order(model.algebraic)
-        model.concatenated_initial_conditions = disc._concatenate_in_order(
-            model.initial_conditions
-        ).evaluate(0, None)
+        raise NotImplementedError(
+            """
+            update_model functionality has been deprecated. 
+            Use pybamm.InputParameter to quickly change a parameter value instead
+            """
+        )
 
     def process_geometry(self, geometry):
         """
@@ -561,30 +523,6 @@ def _process_symbol(self, symbol):
                     )
                 )
 
-    def update_scalars(self, symbol):
-        """Update the value of any Scalars in the expression tree.
-
-        Parameters
-        ----------
-        symbol : :class:`pybamm.Symbol`
-            Symbol or Expression tree to update
-
-        Returns
-        -------
-        symbol : :class:`pybamm.Symbol`
-            Symbol with Scalars updated
-
-        """
-        for x in symbol.pre_order():
-            if isinstance(x, pybamm.Scalar):
-                # update any Scalar nodes if their name is in the parameter dict
-                if x.name in self._dict_items.keys():
-                    x.value = self._dict_items[x.name]
-                    # update id
-                    x.set_id()
-
-        return symbol
-
     def evaluate(self, symbol):
         """
         Process and evaluate a symbol.
diff --git a/tests/integration/test_models/standard_model_tests.py b/tests/integration/test_models/standard_model_tests.py
index ba827e9cd3..1f1340730d 100644
--- a/tests/integration/test_models/standard_model_tests.py
+++ b/tests/integration/test_models/standard_model_tests.py
@@ -103,28 +103,6 @@ def test_all(
         ):
             self.test_outputs()
 
-    def test_update_parameters(self, param):
-        # check if geometry has changed, throw error if so (need to re-discretise)
-        if any(
-            [
-                length in param.keys()
-                and param[length] != self.parameter_values[length]
-                for length in [
-                    "Negative electrode thickness [m]",
-                    "Separator thickness [m]",
-                    "Positive electrode thickness [m]",
-                ]
-            ]
-        ):
-            raise ValueError(
-                "geometry has changed, the orginal model needs to be re-discretised"
-            )
-        # otherwise update self.param and change the parameters in the discretised model
-        self.param = param
-        param.update_model(self.model, self.disc)
-        # Model should still be well-posed after processing
-        self.model.check_well_posedness(post_discretisation=True)
-
 
 class OptimisationsTest(object):
     """ Test that the optimised models give the same result as the original model. """
diff --git a/tests/integration/test_quick_plot.py b/tests/integration/test_quick_plot.py
index 582503c334..c18e0b3d67 100644
--- a/tests/integration/test_quick_plot.py
+++ b/tests/integration/test_quick_plot.py
@@ -41,13 +41,6 @@ def test_plot_lithium_ion(self):
 
         quick_plot.update(0.01)
 
-        # Update parameters, solve, plot again
-        param.update({"Current function [A]": 0})
-        param.update_model(spm, disc_spm)
-        solution_spm = spm.default_solver.solve(spm, t_eval)
-        quick_plot = pybamm.QuickPlot(solution_spm)
-        quick_plot.plot(0)
-
         # Test with different output variables
         output_vars = [
             "Negative particle surface concentration",
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index 893e5a6d90..5d6d4e9115 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -303,18 +303,18 @@ def test_process_inline_function_parameters(self):
         def D(c):
             return c ** 2
 
-        parameter_values = pybamm.ParameterValues({"a": 3, "Diffusivity": D})
+        parameter_values = pybamm.ParameterValues({"Diffusivity": D})
 
-        a = pybamm.Parameter("a")
+        a = pybamm.InputParameter("a")
         func = pybamm.FunctionParameter("Diffusivity", a)
 
         processed_func = parameter_values.process_symbol(func)
-        self.assertEqual(processed_func.evaluate(), 9)
+        self.assertEqual(processed_func.evaluate(u={"a": 3}), 9)
 
         # process differentiated function parameter
         diff_func = func.diff(a)
         processed_diff_func = parameter_values.process_symbol(diff_func)
-        self.assertEqual(processed_diff_func.evaluate(), 6)
+        self.assertEqual(processed_diff_func.evaluate(u={"a": 3}), 6)
 
     def test_multi_var_function_with_parameters(self):
         def D(a, b):
@@ -362,7 +362,7 @@ def test_process_interpolant(self):
         self.assertEqual(processed_diff_func.evaluate(), 2)
 
     def test_interpolant_against_function(self):
-        parameter_values = pybamm.ParameterValues({"a": 0.6})
+        parameter_values = pybamm.ParameterValues({})
         parameter_values.update(
             {
                 "function": "[function]lico2_ocp_Dualfoil1998",
@@ -379,14 +379,16 @@ def test_interpolant_against_function(self):
             check_already_exists=False,
         )
 
-        a = pybamm.Parameter("a")
+        a = pybamm.InputParameter("a")
         func = pybamm.FunctionParameter("function", a)
         interp = pybamm.FunctionParameter("interpolation", a)
 
         processed_func = parameter_values.process_symbol(func)
         processed_interp = parameter_values.process_symbol(interp)
         np.testing.assert_array_almost_equal(
-            processed_func.evaluate(), processed_interp.evaluate(), decimal=4
+            processed_func.evaluate(u={"a": 0.6}),
+            processed_interp.evaluate(u={"a": 0.6}),
+            decimal=4,
         )
 
         # process differentiated function parameter
@@ -395,7 +397,9 @@ def test_interpolant_against_function(self):
         processed_diff_func = parameter_values.process_symbol(diff_func)
         processed_diff_interp = parameter_values.process_symbol(diff_interp)
         np.testing.assert_array_almost_equal(
-            processed_diff_func.evaluate(), processed_diff_interp.evaluate(), decimal=2
+            processed_diff_func.evaluate(u={"a": 0.6}),
+            processed_diff_interp.evaluate(u={"a": 0.6}),
+            decimal=2,
         )
 
     def test_process_complex_expression(self):
@@ -500,6 +504,17 @@ def test_process_model(self):
         with self.assertRaises(KeyError):
             parameter_values.process_model(model)
 
+    def test_inplace(self):
+        model = pybamm.lithium_ion.SPM()
+        param = model.default_parameter_values
+        new_model = param.process_model(model, inplace=False)
+
+        for val in list(model.rhs.values()):
+            self.assertTrue(val.has_symbol_of_classes(pybamm.Parameter))
+
+        for val in list(new_model.rhs.values()):
+            self.assertFalse(val.has_symbol_of_classes(pybamm.Parameter))
+
     def test_process_empty_model(self):
         model = pybamm.BaseModel()
         parameter_values = pybamm.ParameterValues({"a": 1, "b": 2, "c": 3, "d": 42})
diff --git a/tests/unit/test_parameters/test_update_parameters.py b/tests/unit/test_parameters/test_update_parameters.py
deleted file mode 100644
index 5193e44306..0000000000
--- a/tests/unit/test_parameters/test_update_parameters.py
+++ /dev/null
@@ -1,147 +0,0 @@
-#
-# Tests for updating parameter values
-#
-import pybamm
-
-import unittest
-import numpy as np
-import tests
-
-
-class TestUpdateParameters(unittest.TestCase):
-    def test_update_parameters_eqn(self):
-        a = pybamm.Scalar(1)
-        b = pybamm.Scalar(2, name="test parameter")
-        c = pybamm.Scalar(3)
-        eqn = a + b * c
-        self.assertEqual(eqn.evaluate(), 7)
-
-        parameter_values = pybamm.ParameterValues({"test parameter": 3})
-        eqn_changed = parameter_values.update_scalars(eqn)
-        self.assertEqual(eqn_changed.evaluate(), 10)
-
-    def test_set_and_update_parameters(self):
-        a = pybamm.Scalar(1)
-        b = pybamm.Parameter(name="test parameter")
-        c = pybamm.Scalar(3)
-        eqn = a + b * c
-
-        parameter_values = pybamm.ParameterValues({"test parameter": 2})
-        eqn_processed = parameter_values.process_symbol(eqn)
-        self.assertEqual(eqn_processed.evaluate(), 7)
-
-        parameter_values = pybamm.ParameterValues({"test parameter": 3})
-        eqn_updated = parameter_values.update_scalars(eqn_processed)
-        self.assertEqual(eqn_updated.evaluate(), 10)
-
-    def test_update_model(self):
-        # test on simple lithium-ion model
-        model1 = pybamm.lithium_ion.SPM()
-        modeltest1 = tests.StandardModelTest(model1)
-        t_eval = np.linspace(0, 0.1)
-
-        modeltest1.test_all(t_eval=t_eval, skip_output_tests=True)
-        Y1 = modeltest1.solution.y
-
-        # process and solve the model a first time
-        model2 = pybamm.lithium_ion.SPM()
-        modeltest2 = tests.StandardModelTest(model2)
-        modeltest2.test_all(skip_output_tests=True)
-        self.assertEqual(model2.variables["Current [A]"].evaluate(), 0.680616)
-        # process and solve with updated parameter values
-        parameter_values_update = pybamm.ParameterValues(
-            chemistry=pybamm.parameter_sets.Marquis2019
-        )
-        parameter_values_update.update({"Current function [A]": 1})
-        modeltest2.test_update_parameters(parameter_values_update)
-        self.assertEqual(model2.variables["Current [A]"].evaluate(), 1)
-        modeltest2.test_solving(t_eval=t_eval)
-        Y2 = modeltest2.solution.y
-
-        # results should be different
-        self.assertNotEqual(np.linalg.norm(Y1 - Y2), 0)
-
-        # test with new current function
-        model3 = pybamm.lithium_ion.SPM()
-        modeltest3 = tests.StandardModelTest(model3)
-        modeltest3.test_all(skip_output_tests=True)
-        parameter_values_update = pybamm.ParameterValues(
-            chemistry=pybamm.parameter_sets.Marquis2019
-        )
-        parameter_values_update.update({"Current function [A]": 0})
-        modeltest3.test_update_parameters(parameter_values_update)
-        modeltest3.test_solving(t_eval=t_eval)
-        Y3 = modeltest3.solution.y
-
-        self.assertIsInstance(model3.variables["Current [A]"], pybamm.Multiplication)
-        self.assertIsInstance(model3.variables["Current [A]"].left, pybamm.Scalar)
-        self.assertIsInstance(model3.variables["Current [A]"].right, pybamm.Scalar)
-        self.assertEqual(model3.variables["Current [A]"].evaluate(), 0.0)
-
-        # results should be different
-        self.assertNotEqual(np.linalg.norm(Y1 - Y3), 0)
-
-    def test_inplace(self):
-        model = pybamm.lithium_ion.SPM()
-        param = model.default_parameter_values
-        new_model = param.process_model(model, inplace=False)
-
-        for val in list(model.rhs.values()):
-            self.assertTrue(val.has_symbol_of_classes(pybamm.Parameter))
-
-        for val in list(new_model.rhs.values()):
-            self.assertFalse(val.has_symbol_of_classes(pybamm.Parameter))
-
-    def test_update_geometry(self):
-        # test on simple lead-acid model
-        model1 = pybamm.lead_acid.LOQS()
-        modeltest1 = tests.StandardModelTest(model1)
-        parameter_values = pybamm.ParameterValues(
-            chemistry=pybamm.parameter_sets.Sulzer2019
-        )
-        parameter_values.update({"C-rate": 0.05})
-        t_eval = np.linspace(0, 0.5)
-        modeltest1.test_all(
-            param=parameter_values, t_eval=t_eval, skip_output_tests=True
-        )
-
-        # trying to update the geometry fails
-        parameter_values_update = pybamm.ParameterValues(
-            chemistry=pybamm.parameter_sets.Sulzer2019
-        )
-        parameter_values_update.update(
-            {
-                "C-rate": 0.05,
-                "Negative electrode thickness [m]": 0.0002,
-                "Separator thickness [m]": 0.0003,
-                "Positive electrode thickness [m]": 0.0004,
-            }
-        )
-        with self.assertRaisesRegex(ValueError, "geometry has changed"):
-            modeltest1.test_update_parameters(parameter_values_update)
-
-        # instead we need to make a new model and re-discretise
-        model2 = pybamm.lead_acid.LOQS()
-        # nb: need to be careful make parameters a reasonable size
-        modeltest2 = tests.StandardModelTest(model2)
-        modeltest2.test_all(
-            param=parameter_values_update, t_eval=t_eval, skip_output_tests=True
-        )
-        # results should be different
-        c1 = modeltest1.solution["Electrolyte concentration"].entries
-        c2 = modeltest2.solution["Electrolyte concentration"].entries
-        self.assertNotEqual(np.linalg.norm(c1 - c2), 0)
-        self.assertNotEqual(
-            np.linalg.norm(modeltest1.solution.y - modeltest2.solution.y), 0
-        )
-
-
-if __name__ == "__main__":
-    import sys
-
-    print("Add -v for more debug output")
-
-    if "-v" in sys.argv:
-        debug = True
-    pybamm.settings.debug_mode = True
-    unittest.main()

From ce574edbf8d4a0f2ab058c43e8997c5c25da27a9 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <vsulzer@umich.edu>
Date: Thu, 30 Jan 2020 20:43:41 -0500
Subject: [PATCH 03/11] #709 fix unit tests

---
 pybamm/expression_tree/functions.py           |  24 ++---
 pybamm/expression_tree/operations/simplify.py |   5 +-
 .../test_expression_tree/test_functions.py    | 100 ++++++++++--------
 .../unit/test_expression_tree/test_matrix.py  |   7 --
 .../test_operations/test_simplify.py          |   2 +-
 .../unit/test_expression_tree/test_vector.py  |   7 --
 .../test_geometric_parameters.py              |   1 -
 .../test_parameters/test_parameter_values.py  |  10 +-
 .../test_finite_volume/test_finite_volume.py  |   2 +-
 9 files changed, 78 insertions(+), 80 deletions(-)

diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index 5be8e15a1a..2601034b98 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -260,7 +260,7 @@ def _function_diff(self, children, idx):
 
 def arcsinh(child):
     " Returns arcsinh function of child. "
-    return Arcsinh(child)
+    return pybamm.simplify_if_constant(Arcsinh(child), keep_domains=True)
 
 
 class Cos(SpecificFunction):
@@ -276,7 +276,7 @@ def _function_diff(self, children, idx):
 
 def cos(child):
     " Returns cosine function of child. "
-    return Cos(child)
+    return pybamm.simplify_if_constant(Cos(child), keep_domains=True)
 
 
 class Cosh(SpecificFunction):
@@ -292,7 +292,7 @@ def _function_diff(self, children, idx):
 
 def cosh(child):
     " Returns hyperbolic cosine function of child. "
-    return Cosh(child)
+    return pybamm.simplify_if_constant(Cosh(child), keep_domains=True)
 
 
 class Exponential(SpecificFunction):
@@ -308,7 +308,7 @@ def _function_diff(self, children, idx):
 
 def exp(child):
     " Returns exponential function of child. "
-    return Exponential(child)
+    return pybamm.simplify_if_constant(Exponential(child), keep_domains=True)
 
 
 class Log(SpecificFunction):
@@ -330,7 +330,7 @@ def _function_diff(self, children, idx):
 def log(child, base="e"):
     " Returns logarithmic function of child (any base, default 'e'). "
     if base == "e":
-        return Log(child)
+        return pybamm.simplify_if_constant(Log(child), keep_domains=True)
     else:
         return Log(child) / np.log(base)
 
@@ -342,17 +342,17 @@ def log10(child):
 
 def max(child):
     " Returns max function of child. "
-    return Function(np.max, child)
+    return pybamm.simplify_if_constant(Function(np.max, child), keep_domains=True)
 
 
 def min(child):
     " Returns min function of child. "
-    return Function(np.min, child)
+    return pybamm.simplify_if_constant(Function(np.min, child), keep_domains=True)
 
 
 def sech(child):
     " Returns hyperbolic sec function of child. "
-    return 1 / Cosh(child)
+    return pybamm.simplify_if_constant(1 / Cosh(child), keep_domains=True)
 
 
 class Sin(SpecificFunction):
@@ -368,7 +368,7 @@ def _function_diff(self, children, idx):
 
 def sin(child):
     " Returns sine function of child. "
-    return Sin(child)
+    return pybamm.simplify_if_constant(Sin(child), keep_domains=True)
 
 
 class Sinh(SpecificFunction):
@@ -384,7 +384,7 @@ def _function_diff(self, children, idx):
 
 def sinh(child):
     " Returns hyperbolic sine function of child. "
-    return Sinh(child)
+    return pybamm.simplify_if_constant(Sinh(child), keep_domains=True)
 
 
 class Sqrt(SpecificFunction):
@@ -405,7 +405,7 @@ def _function_diff(self, children, idx):
 
 def sqrt(child):
     " Returns square root function of child. "
-    return Sqrt(child)
+    return pybamm.simplify_if_constant(Sqrt(child), keep_domains=True)
 
 
 class Tanh(SpecificFunction):
@@ -421,4 +421,4 @@ def _function_diff(self, children, idx):
 
 def tanh(child):
     " Returns hyperbolic tan function of child. "
-    return Tanh(child)
+    return pybamm.simplify_if_constant(Tanh(child), keep_domains=True)
diff --git a/pybamm/expression_tree/operations/simplify.py b/pybamm/expression_tree/operations/simplify.py
index acfeef195e..6e090db82d 100644
--- a/pybamm/expression_tree/operations/simplify.py
+++ b/pybamm/expression_tree/operations/simplify.py
@@ -5,7 +5,7 @@
 
 import numpy as np
 import numbers
-from scipy.sparse import issparse
+from scipy.sparse import issparse, csr_matrix
 
 
 def simplify_if_constant(symbol, keep_domains=False):
@@ -32,6 +32,9 @@ def simplify_if_constant(symbol, keep_domains=False):
                         result, domain=domain, auxiliary_domains=auxiliary_domains
                     )
                 else:
+                    # Turn matrix of zeros into sparse matrix
+                    if isinstance(result, np.ndarray) and np.all(result == 0):
+                        result = csr_matrix(result)
                     return pybamm.Matrix(
                         result, domain=domain, auxiliary_domains=auxiliary_domains
                     )
diff --git a/tests/unit/test_expression_tree/test_functions.py b/tests/unit/test_expression_tree/test_functions.py
index 585fdbc7b1..8180723295 100644
--- a/tests/unit/test_expression_tree/test_functions.py
+++ b/tests/unit/test_expression_tree/test_functions.py
@@ -23,7 +23,7 @@ def test_multi_var_function_cube(arg1, arg2):
 class TestFunction(unittest.TestCase):
     def test_number_input(self):
         # with numbers
-        log = pybamm.log(10)
+        log = pybamm.Function(np.log, 10)
         self.assertIsInstance(log.children[0], pybamm.Scalar)
         self.assertEqual(log.evaluate(), np.log(10))
 
@@ -127,27 +127,29 @@ def test_function_unnamed(self):
 
 class TestSpecificFunctions(unittest.TestCase):
     def test_arcsinh(self):
-        a = pybamm.Scalar(3)
+        a = pybamm.InputParameter("a")
         fun = pybamm.arcsinh(a)
         self.assertIsInstance(fun, pybamm.Arcsinh)
-        self.assertEqual(fun.evaluate(), np.arcsinh(3))
+        self.assertEqual(fun.evaluate(u={"a": 3}), np.arcsinh(3))
         h = 0.0000001
         self.assertAlmostEqual(
-            fun.diff(a).evaluate(),
-            (pybamm.arcsinh(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate()) / h,
+            fun.diff(a).evaluate(u={"a": 3}),
+            (pybamm.arcsinh(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate(u={"a": 3}))
+            / h,
             places=5,
         )
 
     def test_cos(self):
-        a = pybamm.Scalar(3)
+        a = pybamm.InputParameter("a")
         fun = pybamm.cos(a)
         self.assertIsInstance(fun, pybamm.Cos)
         self.assertEqual(fun.children[0].id, a.id)
-        self.assertEqual(fun.evaluate(), np.cos(3))
+        self.assertEqual(fun.evaluate(u={"a": 3}), np.cos(3))
         h = 0.0000001
         self.assertAlmostEqual(
-            fun.diff(a).evaluate(),
-            (pybamm.cos(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate()) / h,
+            fun.diff(a).evaluate(u={"a": 3}),
+            (pybamm.cos(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate(u={"a": 3}))
+            / h,
             places=5,
         )
 
@@ -157,110 +159,120 @@ def test_cos(self):
         self.assertEqual(fun.id, fun.simplify().id)
 
     def test_cosh(self):
-        a = pybamm.Scalar(3)
+        a = pybamm.InputParameter("a")
         fun = pybamm.cosh(a)
         self.assertIsInstance(fun, pybamm.Cosh)
         self.assertEqual(fun.children[0].id, a.id)
-        self.assertEqual(fun.evaluate(), np.cosh(3))
+        self.assertEqual(fun.evaluate(u={"a": 3}), np.cosh(3))
         h = 0.0000001
         self.assertAlmostEqual(
-            fun.diff(a).evaluate(),
-            (pybamm.cosh(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate()) / h,
+            fun.diff(a).evaluate(u={"a": 3}),
+            (pybamm.cosh(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate(u={"a": 3}))
+            / h,
             places=5,
         )
 
     def test_exp(self):
-        a = pybamm.Scalar(3)
+        a = pybamm.InputParameter("a")
         fun = pybamm.exp(a)
         self.assertIsInstance(fun, pybamm.Exponential)
         self.assertEqual(fun.children[0].id, a.id)
-        self.assertEqual(fun.evaluate(), np.exp(3))
+        self.assertEqual(fun.evaluate(u={"a": 3}), np.exp(3))
         h = 0.0000001
         self.assertAlmostEqual(
-            fun.diff(a).evaluate(),
-            (pybamm.exp(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate()) / h,
+            fun.diff(a).evaluate(u={"a": 3}),
+            (pybamm.exp(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate(u={"a": 3}))
+            / h,
             places=5,
         )
 
     def test_log(self):
-        a = pybamm.Scalar(3)
+        a = pybamm.InputParameter("a")
         fun = pybamm.log(a)
-        self.assertEqual(fun.evaluate(), np.log(3))
+        self.assertEqual(fun.evaluate(u={"a": 3}), np.log(3))
         h = 0.0000001
         self.assertAlmostEqual(
-            fun.diff(a).evaluate(),
-            (pybamm.log(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate()) / h,
+            fun.diff(a).evaluate(u={"a": 3}),
+            (pybamm.log(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate(u={"a": 3}))
+            / h,
             places=5,
         )
 
         # Base 10
         fun = pybamm.log10(a)
-        self.assertEqual(fun.evaluate(), np.log10(3))
+        self.assertEqual(fun.evaluate(u={"a": 3}), np.log10(3))
         h = 0.0000001
         self.assertAlmostEqual(
-            fun.diff(a).evaluate(),
-            (pybamm.log10(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate()) / h,
+            fun.diff(a).evaluate(u={"a": 3}),
+            (pybamm.log10(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate(u={"a": 3}))
+            / h,
             places=5,
         )
 
     def test_max(self):
-        a = pybamm.Vector(np.array([1, 2, 3]))
+        a = pybamm.StateVector(slice(0, 3))
+        y_test = np.array([1, 2, 3])
         fun = pybamm.max(a)
         self.assertIsInstance(fun, pybamm.Function)
-        self.assertEqual(fun.evaluate(), 3)
+        self.assertEqual(fun.evaluate(y=y_test), 3)
 
     def test_min(self):
-        a = pybamm.Vector(np.array([1, 2, 3]))
+        a = pybamm.StateVector(slice(0, 3))
+        y_test = np.array([1, 2, 3])
         fun = pybamm.min(a)
         self.assertIsInstance(fun, pybamm.Function)
-        self.assertEqual(fun.evaluate(), 1)
+        self.assertEqual(fun.evaluate(y=y_test), 1)
 
     def test_sin(self):
-        a = pybamm.Scalar(3)
+        a = pybamm.InputParameter("a")
         fun = pybamm.sin(a)
         self.assertIsInstance(fun, pybamm.Sin)
         self.assertEqual(fun.children[0].id, a.id)
-        self.assertEqual(fun.evaluate(), np.sin(3))
+        self.assertEqual(fun.evaluate(u={"a": 3}), np.sin(3))
         h = 0.0000001
         self.assertAlmostEqual(
-            fun.diff(a).evaluate(),
-            (pybamm.sin(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate()) / h,
+            fun.diff(a).evaluate(u={"a": 3}),
+            (pybamm.sin(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate(u={"a": 3}))
+            / h,
             places=5,
         )
 
     def test_sinh(self):
-        a = pybamm.Scalar(3)
+        a = pybamm.InputParameter("a")
         fun = pybamm.sinh(a)
         self.assertIsInstance(fun, pybamm.Sinh)
         self.assertEqual(fun.children[0].id, a.id)
-        self.assertEqual(fun.evaluate(), np.sinh(3))
+        self.assertEqual(fun.evaluate(u={"a": 3}), np.sinh(3))
         h = 0.0000001
         self.assertAlmostEqual(
-            fun.diff(a).evaluate(),
-            (pybamm.sinh(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate()) / h,
+            fun.diff(a).evaluate(u={"a": 3}),
+            (pybamm.sinh(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate(u={"a": 3}))
+            / h,
             places=5,
         )
 
     def test_sqrt(self):
-        a = pybamm.Scalar(3)
+        a = pybamm.InputParameter("a")
         fun = pybamm.sqrt(a)
         self.assertIsInstance(fun, pybamm.Sqrt)
-        self.assertEqual(fun.evaluate(), np.sqrt(3))
+        self.assertEqual(fun.evaluate(u={"a": 3}), np.sqrt(3))
         h = 0.0000001
         self.assertAlmostEqual(
-            fun.diff(a).evaluate(),
-            (pybamm.sqrt(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate()) / h,
+            fun.diff(a).evaluate(u={"a": 3}),
+            (pybamm.sqrt(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate(u={"a": 3}))
+            / h,
             places=5,
         )
 
     def test_tanh(self):
-        a = pybamm.Scalar(3)
+        a = pybamm.InputParameter("a")
         fun = pybamm.tanh(a)
-        self.assertEqual(fun.evaluate(), np.tanh(3))
+        self.assertEqual(fun.evaluate(u={"a": 3}), np.tanh(3))
         h = 0.0000001
         self.assertAlmostEqual(
-            fun.diff(a).evaluate(),
-            (pybamm.tanh(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate()) / h,
+            fun.diff(a).evaluate(u={"a": 3}),
+            (pybamm.tanh(pybamm.Scalar(3 + h)).evaluate() - fun.evaluate(u={"a": 3}))
+            / h,
             places=5,
         )
 
diff --git a/tests/unit/test_expression_tree/test_matrix.py b/tests/unit/test_expression_tree/test_matrix.py
index 1575c8d19e..b135bd75cd 100644
--- a/tests/unit/test_expression_tree/test_matrix.py
+++ b/tests/unit/test_expression_tree/test_matrix.py
@@ -29,13 +29,6 @@ def test_matrix_operations(self):
             (self.mat @ self.vect).evaluate(), np.array([[5], [2], [3]])
         )
 
-    def test_matrix_modification(self):
-        exp = self.mat @ self.mat + self.mat
-        self.A[0, 0] = -1
-        self.assertTrue(exp.children[1]._entries[0, 0], -1)
-        self.assertTrue(exp.children[0].children[0]._entries[0, 0], -1)
-        self.assertTrue(exp.children[0].children[1]._entries[0, 0], -1)
-
 
 class TestArray(unittest.TestCase):
     def test_name(self):
diff --git a/tests/unit/test_expression_tree/test_operations/test_simplify.py b/tests/unit/test_expression_tree/test_operations/test_simplify.py
index 9c47da4401..129aef5e98 100644
--- a/tests/unit/test_expression_tree/test_operations/test_simplify.py
+++ b/tests/unit/test_expression_tree/test_operations/test_simplify.py
@@ -518,7 +518,7 @@ def test_matrix_divide_simplify(self):
         expr3 = (m / a).simplify()
         self.assertIsInstance(expr3, pybamm.Matrix)
         self.assertEqual(expr3.shape, m.shape)
-        np.testing.assert_array_equal(expr3.evaluate(), np.zeros((10, 10)))
+        np.testing.assert_array_equal(expr3.evaluate().toarray(), np.zeros((10, 10)))
 
     def test_domain_concatenation_simplify(self):
         # create discretisation
diff --git a/tests/unit/test_expression_tree/test_vector.py b/tests/unit/test_expression_tree/test_vector.py
index 86e54a0526..924876dd83 100644
--- a/tests/unit/test_expression_tree/test_vector.py
+++ b/tests/unit/test_expression_tree/test_vector.py
@@ -31,13 +31,6 @@ def test_vector_operations(self):
             (self.vect * self.vect).evaluate(), np.array([[1], [4], [9]])
         )
 
-    def test_vector_modification(self):
-        exp = self.vect * self.vect + self.vect
-        self.x[0] = -1
-        self.assertTrue(exp.children[1]._entries[0], -1)
-        self.assertTrue(exp.children[0].children[0]._entries[0], -1)
-        self.assertTrue(exp.children[0].children[1]._entries[0], -1)
-
     def test_wrong_size_entries(self):
         with self.assertRaisesRegex(
             ValueError, "Entries must have 1 dimension or be column vector"
diff --git a/tests/unit/test_parameters/test_geometric_parameters.py b/tests/unit/test_parameters/test_geometric_parameters.py
index 64b87695c6..2711c76430 100644
--- a/tests/unit/test_parameters/test_geometric_parameters.py
+++ b/tests/unit/test_parameters/test_geometric_parameters.py
@@ -31,7 +31,6 @@ def test_macroscale_parameters(self):
         self.assertEqual(
             (L_n_eval + L_s_eval + L_p_eval).evaluate(), L_x_eval.evaluate()
         )
-        self.assertEqual((L_n_eval + L_s_eval + L_p_eval).id, L_x_eval.id)
         l_n_eval = parameter_values.process_symbol(l_n)
         l_s_eval = parameter_values.process_symbol(l_s)
         l_p_eval = parameter_values.process_symbol(l_p)
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index 5d6d4e9115..0c8750f2cc 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -279,25 +279,23 @@ def test_process_function_parameter(self):
                 "const": 254,
             }
         )
-        a = pybamm.Parameter("a")
+        a = pybamm.InputParameter("a")
 
         # process function
         func = pybamm.FunctionParameter("func", a)
         processed_func = parameter_values.process_symbol(func)
-        self.assertEqual(processed_func.evaluate(), 369)
+        self.assertEqual(processed_func.evaluate(u={"a": 3}), 369)
 
         # process constant function
         const = pybamm.FunctionParameter("const", a)
         processed_const = parameter_values.process_symbol(const)
-        self.assertIsInstance(processed_const, pybamm.Multiplication)
-        self.assertIsInstance(processed_const.left, pybamm.Scalar)
-        self.assertIsInstance(processed_const.right, pybamm.Scalar)
+        self.assertIsInstance(processed_const, pybamm.Scalar)
         self.assertEqual(processed_const.evaluate(), 254)
 
         # process differentiated function parameter
         diff_func = func.diff(a)
         processed_diff_func = parameter_values.process_symbol(diff_func)
-        self.assertEqual(processed_diff_func.evaluate(), 123)
+        self.assertEqual(processed_diff_func.evaluate(u={"a": 3}), 123)
 
     def test_process_inline_function_parameters(self):
         def D(c):
diff --git a/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py b/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py
index 96de8693ed..af36bdcfc5 100644
--- a/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py
+++ b/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py
@@ -917,7 +917,7 @@ def test_discretise_spatial_variable(self):
 
         r = 3 * pybamm.SpatialVariable("r", ["negative particle"])
         r_disc = disc.process_symbol(r)
-        self.assertIsInstance(r_disc.children[1], pybamm.Vector)
+        self.assertIsInstance(r_disc, pybamm.Vector)
         np.testing.assert_array_equal(
             r_disc.evaluate(),
             3 * disc.mesh["negative particle"][0].nodes[:, np.newaxis],

From 69c2aa2e58d22ebc9f700d3c24b1678c6e8f1239 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <vsulzer@umich.edu>
Date: Thu, 30 Jan 2020 20:45:59 -0500
Subject: [PATCH 04/11] #709 flake8

---
 pybamm/parameters/parameter_values.py               | 2 +-
 tests/unit/test_parameters/test_parameter_values.py | 5 +++++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index ace705420e..39008d593c 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -388,7 +388,7 @@ def process_model(self, unprocessed_model, inplace=True):
     def update_model(self, model, disc):
         raise NotImplementedError(
             """
-            update_model functionality has been deprecated. 
+            update_model functionality has been deprecated.
             Use pybamm.InputParameter to quickly change a parameter value instead
             """
         )
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index 0c8750f2cc..43d9408509 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -502,6 +502,11 @@ def test_process_model(self):
         with self.assertRaises(KeyError):
             parameter_values.process_model(model)
 
+    def test_update_model(self):
+        param = pybamm.ParameterValues({})
+        with self.assertRaises(NotImplementedError):
+            param.update_model(None, None)
+
     def test_inplace(self):
         model = pybamm.lithium_ion.SPM()
         param = model.default_parameter_values

From a19a38926bcedbd3d55d3eefd8f1caa504d899e8 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <vsulzer@umich.edu>
Date: Thu, 30 Jan 2020 21:59:28 -0500
Subject: [PATCH 05/11] #709 some more test fixes

---
 .../compare-comsol-discharge-curve.ipynb      |  32 ++--
 .../models/compare-lithium-ion.ipynb          | 152 ++++++------------
 examples/notebooks/parameter-values.ipynb     | 107 ++++++------
 .../spatial_methods/finite-volumes.ipynb      | 131 +++++----------
 .../unit/test_expression_tree/test_matrix.py  |   4 +-
 .../test_unary_operators.py                   |   1 -
 6 files changed, 152 insertions(+), 275 deletions(-)

diff --git a/examples/notebooks/compare-comsol-discharge-curve.ipynb b/examples/notebooks/compare-comsol-discharge-curve.ipynb
index 6c9e882bb0..c7fe967b4a 100644
--- a/examples/notebooks/compare-comsol-discharge-curve.ipynb
+++ b/examples/notebooks/compare-comsol-discharge-curve.ipynb
@@ -68,10 +68,14 @@
     "model = pybamm.lithium_ion.DFN()\n",
     "geometry = model.default_geometry\n",
     "\n",
+    "def current_function(t):\n",
+    "    return pybamm.InputParameter(\"current\")\n",
+    "\n",
     "# load parameters and process model and geometry\n",
     "param = model.default_parameter_values\n",
     "param[\"Electrode width [m]\"] = 1\n",
     "param[\"Electrode height [m]\"] = 1\n",
+    "param[\"Current function [A]\"] = current_function\n",
     "param.process_model(model)\n",
     "param.process_geometry(geometry)\n",
     "\n",
@@ -97,26 +101,9 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'Discharge capacity [A.h]'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/solution.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;31m# return it if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    182\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'Discharge capacity [A.h]'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-4-3d17bdbc0066>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0;31m# discharge capacity\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m     \u001b[0mdischarge_capacity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolution\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Discharge capacity [A.h]\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m     \u001b[0mdischarge_capacity_sol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdischarge_capacity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0mcomsol_discharge_capacity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomsol_time\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Current function [A]\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m3600\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/solution.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    182\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             \u001b[0;31m# otherwise create it, save it and then return it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/solution.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, variables)\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0mpybamm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Post-processing {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m             var = pybamm.ProcessedVariable(\n\u001b[0;32m--> 151\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mknown_evals\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m             )\n\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'Discharge capacity [A.h]'"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x576 with 2 Axes>"
       ]
@@ -154,8 +141,7 @@
     "    comsol_voltage = comsol_variables[\"voltage\"]\n",
     "\n",
     "    # update current density\n",
-    "    param[\"Current function [A]\"] = 24 * C_rate\n",
-    "    param.update_model(model, disc)\n",
+    "    current = 24 * C_rate\n",
     "\n",
     "    # discharge timescale\n",
     "    tau = param.process_symbol(\n",
@@ -165,12 +151,12 @@
     "    # solve model at comsol times\n",
     "    solver = pybamm.CasadiSolver(mode=\"fast\")\n",
     "    t = comsol_time / tau\n",
-    "    solution = solver.solve(model, t)\n",
+    "    solution = solver.solve(model, t, inputs={\"current\": current})\n",
     "\n",
     "    # discharge capacity\n",
     "    discharge_capacity = solution[\"Discharge capacity [A.h]\"]\n",
     "    discharge_capacity_sol = discharge_capacity(solution.t)\n",
-    "    comsol_discharge_capacity = comsol_time * param[\"Current function [A]\"] / 3600\n",
+    "    comsol_discharge_capacity = comsol_time * current / 3600\n",
     "\n",
     "    # extract the voltage\n",
     "    voltage = solution[\"Terminal voltage [V]\"]\n",
@@ -227,7 +213,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebooks/models/compare-lithium-ion.ipynb b/examples/notebooks/models/compare-lithium-ion.ipynb
index 34f506f792..8dc595a4c5 100644
--- a/examples/notebooks/models/compare-lithium-ion.ipynb
+++ b/examples/notebooks/models/compare-lithium-ion.ipynb
@@ -104,7 +104,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x135636e80>"
+       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x7fc494f416d8>"
       ]
      },
      "execution_count": 4,
@@ -143,7 +143,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For simplicity, we use the default parameters values associated with the DFN model:"
+    "For simplicity, we use the default parameters values associated with the DFN model, but change the current function to be an input so that we can quickly solve the model with different currents"
    ]
   },
   {
@@ -152,7 +152,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "param = dfn.default_parameter_values"
+    "param = dfn.default_parameter_values\n",
+    "\n",
+    "def current_function(t):\n",
+    "    return pybamm.InputParameter(\"current\")\n",
+    "\n",
+    "param[\"Current function [A]\"] = current_function"
    ]
   },
   {
@@ -250,25 +255,42 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solved the Doyle-Fuller-Newman model in 2.032 seconds\n",
-      "Solved the Single Particle Model in 0.121 seconds\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/vsulzer/Documents/Energy_storage/PyBaMM/PyBaMM-env/lib/python3.7/site-packages/scipy/integrate/_ivp/ivp.py:146: RuntimeWarning: invalid value encountered in greater_equal\n",
-      "  up = (g <= 0) & (g_new >= 0)\n",
-      "/Users/vsulzer/Documents/Energy_storage/PyBaMM/PyBaMM-env/lib/python3.7/site-packages/scipy/integrate/_ivp/ivp.py:147: RuntimeWarning: invalid value encountered in less_equal\n",
-      "  down = (g >= 0) & (g_new <= 0)\n"
+      "Solved the Doyle-Fuller-Newman model in 3.515 seconds\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Solved the Single Particle Model with electrolyte in 0.243 seconds\n"
+     "ename": "KeyError",
+     "evalue": "\"Input parameter 'current' not found\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/input_parameter.py\u001b[0m in \u001b[0;36m_base_evaluate\u001b[0;34m(self, t, y, u)\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;31m# raise more informative error if can't find name in dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'current'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-e47831a38128>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0msolution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_solver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_eval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"current\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Solved the {} in {:.3f} seconds\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/solvers/base_solver.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self, model, t_eval, external_variables, inputs)\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;31m# Solve\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         solution, solve_time, termination = self.compute_solution(\n\u001b[0;32m---> 96\u001b[0;31m             \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_eval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m         )\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/solvers/ode_solver.py\u001b[0m in \u001b[0;36mcompute_solution\u001b[0;34m(self, model, t_eval, inputs)\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0;31m# Identify the event that caused termination\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0mtermination\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_termination_reason\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msolution\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolve_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtermination\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/solvers/base_solver.py\u001b[0m in \u001b[0;36mget_termination_reason\u001b[0;34m(self, solution, events)\u001b[0m\n\u001b[1;32m    304\u001b[0m                 \u001b[0my_event\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_external\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_event\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_pad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_ext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m                 final_event_values[name] = abs(\n\u001b[0;32m--> 306\u001b[0;31m                     \u001b[0mevent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt_event\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_event\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    307\u001b[0m                 )\n\u001b[1;32m    308\u001b[0m             \u001b[0mtermination_event\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_event_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfinal_event_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/functions.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m             \u001b[0mevaluated_children\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluated_children\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/functions.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m             \u001b[0mevaluated_children\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluated_children\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/unary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0mchild\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/symbol.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    556\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 558\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_base_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_for_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/input_parameter.py\u001b[0m in \u001b[0;36m_base_evaluate\u001b[0;34m(self, t, y, u)\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;31m# raise more informative error if can't find name in dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Input parameter '{}' not found\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: \"Input parameter 'current' not found\""
      ]
     }
    ],
@@ -278,7 +300,7 @@
     "t_eval = np.linspace(0, 0.5, 100)\n",
     "for model_name, model in models.items():\n",
     "    start = timer.time()\n",
-    "    solution = model.default_solver.solve(model, t_eval)\n",
+    "    solution = model.default_solver.solve(model, t_eval, inputs={\"current\": 1})\n",
     "    end = timer.time()\n",
     "    print(\"Solved the {} in {:.3f} seconds\".format(model.name, end-start))\n",
     "    solutions[model_name] = solution"
@@ -300,22 +322,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "for model_name, model in models.items():\n",
     "    t = solutions[model_name].t\n",
@@ -337,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,24 +364,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0b39d0725e264a34898185182a4c4865",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.077935920470844, step=0.05), Output()), _d…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "quick_plot = pybamm.QuickPlot(list_of_solutions)\n",
     "import ipywidgets as widgets\n",
@@ -390,62 +384,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The typical current on the previous run of the model can be found from:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.680616"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "param[\"Typical current [A]\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "However, it is easy to change parameters (e.g. the current) and then perform the calculations again:"
+    "Since we have made current an input, it is easy to change it and then perform the calculations again:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8eed9b3c754c4f8f89721c96c8d5844c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=0.7120821863752079, step=0.05), Output()), _…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# update parameter values and solve again\n",
     "param.update({\"Typical current [A]\": 3})\n",
     "for model_name, model in models.items():\n",
     "    param.update_model(model, discs[model_name])\n",
-    "    solutions[model_name] = model.default_solver.solve(model, t_eval)\n",
+    "    solutions[model_name] = model.default_solver.solve(model, t_eval, inputs={\"current\": 10})\n",
     "\n",
     "# Plot\n",
     "list_of_models = list(models.values())\n",
@@ -479,7 +431,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebooks/parameter-values.ipynb b/examples/notebooks/parameter-values.ipynb
index d4a12a04c0..90d7f9a6dd 100644
--- a/examples/notebooks/parameter-values.ipynb
+++ b/examples/notebooks/parameter-values.ipynb
@@ -47,7 +47,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x13dd5e0f0>\n"
+      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x7f1f70335080>\n"
      ]
     }
    ],
@@ -73,7 +73,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x13dd5e860>\n"
+      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x7f1f70335518>\n"
      ]
     }
    ],
@@ -138,7 +138,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x13dd5e860>\n"
+      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x7f1f70335518>\n"
      ]
     }
    ],
@@ -165,7 +165,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x13dd5e860>\n"
+      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x7f1f70335518>\n"
      ]
     }
    ],
@@ -261,7 +261,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "a ** 2.0 = 16.0\n"
+      "16.0 = 16.0\n"
      ]
     }
    ],
@@ -274,7 +274,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also update an expression using a new set of parameter values (e.g. for parameter fitting). Note that any parameter values not included in `new_parameter_values` remain unchanged."
+    "If a parameter needs to be changed often (for example, for convergence studies or parameter estimation), the `InputParameter` class should be used. This is not fixed by parameter values, and its value can be set on evaluation (or on solve):"
    ]
   },
   {
@@ -286,19 +286,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "a + b * c = 32.0\n",
-      "a ** 2.0 = 4.0\n"
+      "with d = 3, 2.0 + d = 5.0\n",
+      "with d = 5, 2.0 + d = 7.0\n"
      ]
     }
    ],
    "source": [
-    "new_parameter_values = pybamm.ParameterValues({\"a\": 2})\n",
-    "\n",
-    "expr_eval_update = new_parameter_values.update_scalars(expr_eval)\n",
-    "print(\"{} = {}\".format(expr_eval_update, expr_eval_update.evaluate()))\n",
-    "\n",
-    "func_eval_update = new_parameter_values.update_scalars(func_eval)\n",
-    "print(\"{} = {}\".format(func_eval_update, func_eval_update.evaluate()))"
+    "d = pybamm.InputParameter(\"d\")\n",
+    "expr = 2 + d\n",
+    "expr_eval = parameter_values.process_symbol(expr)\n",
+    "print(\"with d = {}, {} = {}\".format(3, expr_eval, expr_eval.evaluate(u={\"d\": 3})))\n",
+    "print(\"with d = {}, {} = {}\".format(5, expr_eval, expr_eval.evaluate(u={\"d\": 5})))"
    ]
   },
   {
@@ -330,16 +328,25 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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feNTMGgTqXQQ8EIa4jy/wD0nhhw9B1lZ+iGtK/SEP6/5PEakSHnp3Fau3hXegSZcW9Rg/tGup6n7wwQd88sknjBkzhkaNGvF///d/3HnnjxOg5+XlcccddzBz5kyaNGnCG2+8we9+9zumTJnC1KlTueqqq3jqqaeYO3cuX3zx4x0biYmJrFy5kpdffpm77rqL2bNn8/nnn9OrV6+QsRQUFNCrVy/Wr1/PqFGj6NOn+EjVbdu2MX78eG688UbatWvHqFGj+Nvf/nZk/9lnn81ll13GkCFDuOqqq46U9+zZk88//5wrr7yyVH+TMPsS6GBm7fAnmtcA1x5VZxYwElgIXAV8VN77P73+XgE88sgjXHDBBUyZMoW9e/fSu3dvLrzwQu68807OP/983n77bR555BGef/55atWqBcCKFStYtGgRBw4c4PTTT+fSSy8lNzeXBg0aEBcXF/Q4M2fOpGXLlnTv3j1kLL///e+5+OKLiY6O5plnnuGmm24qVn/SpEk88cQTxYb4pqSkMGnSJCWglUUZh7i6FdNg1phi9wnmvXMH81ams6DWBew9mMfuA4fYc/DwI49votKCJoW1s7fzf/PXUScumrrxhx8xNKwdS5tGtWm2NnRi+O+b+pAQG0XtuChqxUSTEBtFrdgo7NlWIRO8V24+M8i7Ad+FToYuTQ6RRIVIUqIuHE+duOOkBifSwVPd2pxou8Pfy7IMya7MbcqpND2gfYHrgZVmtjxQ9lugNYBz7jlgDnAJsB44CPwisG+3mT2M/2QLMPHwhEQVLnk4vuTh3DT1S77Zvo/PTusfdHiziIgUd+GFFzJw4EAmTJjAzTffzNF5z9q1a0lNTWXgwIGAP0ls3tz/Q6dr165cf/31DBkyhIULFxIbG3uk3YgRI448Hx7Sm56eTufOnUPGEhUVxfLly9m7dy9XXHEFqampdOvW7cj+Fi1a8Pe//52pU6dy7rnn8rOf/axUn/Gkk05i27ZtpaobboF7OkcD7wNRwBTn3Cozmwgscc7Nwn/h919mth7YjT9JrfLmzZvHrFmzeOKJJwD/fb6bN2+mc+fOTJ06leTkZH75y1/St2/fI22GDRtGQkICCQkJ9O/fn8WLF3PSSSfRpEnwXqSDBw/y6KOPMm/evOPG8vDDD2NmfPXVV0fuAS2Jl98bOUqIIa6rtmbxdYOL2Lkvl4x9uWTsyyFjXy479+XyVs6xw1VjCnPo/u1kHozuQoNaMTSoFUurhrVITkqkQe1YDixvTt3c9GOPn9iSDXddQtTRvYSHHafn75wOIe4vjFQSdaIJR2VOiCKZeJWnXVmTusrcphxKMwvuZwS/NbVoHQeMCrFvCjDlhKILg8GnNWf+Nzv5Oi2LHqG6uEVEKpGy9ChVhMPDXCdMmFDs9WHOObp27crChQuDtl+5ciX169dn587i9wsVfZ/D2wkJCUfWT9yyZQtDhw4F4LbbbuO22247Ur9+/fr079+fuXPnFktAD7vhhhvK8An9iU9CQkKZ2oSTc24O/ou3RcvGFdnOAa4O5zG9+F4988wz/P3vfwdgzpw5OOeYPn36McNdAdatW0edOnWOSfCO/v6ZWbHvDcAvfvELvvrqK1q0aMFjjz3Gd999d6Q3My0tjZ49e7J48eJiQ65L+p4H4/X3plo7Tm9mYaFjx74cvs88yLa92Wzbm811C4+dEMeXn02DhZP4/aEWmEGj2rE0qRtPk7pxdGhalxarQvRKWibLHhwYPK6WE4MmeL4Lx/tvkgylMveSHW53IglHZU6IIpl4RThhq27KNAtuVTSwc1OifcZ7K9OVgIqIhMGpp55KRkYGCxcu5KyzziIvL49vv/2Wrl27MmPGDHbv3s2nn37KkCFDWLx4MfXr+//tfeONN7j//vt54403OOusswDo3Lkz69evB6BVq1YsX778yHEyMjKIiYmhfv36ZGdn88EHHwS9H7UkdevWZd++fcXKvv32W66+Oqz5nQQxatQoRo368fr0oEGDeOqpp3jqqaeO9D6efvrpZGVlMWbMGD799FNGjx7NW2+9dWTI9MyZM3nggQc4cOAAn3zyCZMmTSIxMZFNmzYded9//vOfxY5b9OJH27ZtWbJkyTGz4JYk1Pcm2AUQKacV03DvjsGK9GYeeucOXv7fRt48dDbf7z5ATl5hsSa3xwefEKelL5MvfjuARrVjiY7yFd+ZFrpXMqTq2ksm4qFqn4Am1oqh7ymNmZOazv2DO2mxaBGRcoqNjeWtt95izJgxZGVlkZ+fz1133UXTpk25//77mT9/Pq1atWL06NHceeedvPTSSwDs2bOH5ORk4uLieO01/3LQgwcP5vrrrw96nPT0dEaOHElBQQGFhYUMHz6cIUOGlDnea665hltuuYXJkyfz1ltv0bp1a9avX09KSsqJ/xHkhDz44IPcddddJCcnU1hYSLt27Zg9ezZjx45l1KhRdOzYkX/84x/079+ffv36AZCcnEz//v3ZtWsXDz74IC1atACgffv2rF+/nlNOCTEpSTklJycTFRVF9+7dueGGGxg7diwff/wxl156aYUcr1o5Tm+mc46te7NZt2M/3+7Yx9od+7j/m2OX6ogtzGHorhdZ1PZC+nVsTJtGtWnTqBYt6yfQon4CvqdDJ5NN68UHj+tE7/lTL5lIWFllXNM6JSXFLVkSfE2cEzHtyy3cO30Fs+84h24tE8P2viIi4bJmzZrj3gtZ1R2vF+qKK67g8ccfp0OHDhGJ5e2332bZsmU8/PDDx+wL9t/BzJY65yp9thrs3FnVv1cTJkygTp063H333cfse/vtt1m6dOlxZ8INp9zcXM477zw+++yzoLM2V/W/ddgcfW8mkO+L560W9/B67lms27GPA4cKjuxrWi+ORYeuwgj2e9Rgwt5SH4eYBBg6ueTlUSI42YpITRbq/Fnte0ABBnZpStTbxn9WpisBFRGpZCZNmkR6enrEEtD8/Hx+85vfRORYUnGuuOIKMjMjtzLN5s2bmTRpUtDkUyA3v4BV236g45xx1Dnq3szowhz6pf2NmS3O4apeSXRsVpeOTevS8aS6JNaKCTlhz3HXIYz0vY8iEjY14l/RBrVjObt9I95bmc69g07VMFwRkQgrer/e0U499dSgk9JUFN37WXUcniAolJtvvjkygQAdOnSI2EWSSuM4vYWZ+3NZ+v2eI48VW7M4lF/Ixrj0oFNXNieT124NsZRIpIfGioinakQCCnDJac15YMZKVqf/QNcW6gUVERERCSnIMif5M+9g2uLNvLg3hY27DgAQE2V0a5nIyLPa0KtNAwrfT8K3L+2Yt6uQiX5EpEqqMQnoRV2a8vt3Upm9Il0JqIiIiMhxFH74EL6jh9IW5HB+2t/4qN3rXJ3Sil5tGpCclEh8TFSRhuPVmykix1VjEtBGdeI455TGzFq+TcNwRURERI6yIWM/89fs4MM1O3k9Ky3kUNoXR54R+k3UmykiJagxCSjAsB4t+PW0r1m2eQ+92jT0OhwRERGRyAhyP2d+16tY8v0e5q/Zwfw1O48Mq+3UrC774pqReGj7MW9z3KG0h6k3U0SOw1dylerjoq7NiIv2MXP5Nq9DERGpMt588026du2Kz+cjnEtkSc12zz330KlTJ5KTk7niiivYuzfEchtSfofv58zaAjjI2sKht0cz7g/jueaFRUxdsImWDRJ46LKufHZff+be1Y/EIQ/7h84WVZqhtCIiJahRCWiduGgu7NyUOSvTyS8o9DocEZETt2IaPNkNJtT3P6+YVmGH6tatGzNmzKBfv34VdgypJCL4vRo4cCCpqamsWLGCjh078sc//rHCjlXTufkTi9+TCcS6XH4T9QbPXteTZQ8O5F839WHk2W1JalDLXyF5uH9NzcRWgPmfS1pjU0SkFGrUEFyAod1b8J+V6SzYkEm/jk28DkdEpOyCzE7Ju2P82+X4cbhp0yaGDBlCamoqAE888QT79+8vcSkMqSY8/F6deeaZvPXWWyd8DAkuPSub6UvTuD0rLdjtnDTK38klpzUP/QYaSisiFaBG9YACnH9qE+rGR2sYrohUXUF6M8jL9peLnCgPv1dTpkxh8ODBFX6cmiA3v4D/rEhn5JTF9J30EU/M+5bMqBAX3EtzP6eISJjVuB7Q+JgoLu7ajPdSt/NIXrfiU4eLiFQFWceusXfccpHS8Oh79cgjjxAdHc11111XocepVoJMKLS9zWW88sX3vPrFZjIPHKJ5Yjyj+p/CVb2SaLL1kRNbGkVEpALUuAQUYFiPlry5NI2Pv9nJ4OMNPRERqYwSkwKTiQQpL4fo6GgKC3+8Pz4nJ6dc7ydVjAffq6lTpzJ79mzmz5+v5dFKK8hQ6dy3R/NY3nLeKejLgE5N+dmZrTm3QxOifIG/aSMtjSIilUeNG4ILcFb7RjSuE8esrzUMV0SqoAHjKmR2yqZNm7Jz504yMzPJzc1l9uzZ5Xo/qWIi/L2aO3cujz/+OLNmzaJWrVrlOkaNEmSodJzLZXyt6fz37v68ODKF80896cfk87Dk4TA2FSbs9T8r+RQRj5SYgJrZFDPbaWapIfbfY2bLA49UMysws4aBfZvMbGVgX6WZuz/KZwxJbs78b3byQ06e1+GIiJRNBc1OGRMTw7hx4+jduzcDBw6kU6dOALz99tskJSWxcOFCLr30UgYNGhSGDyGVToS/V6NHj2bfvn0MHDiQHj16cNttt4XhQ1Rvh/ILcSGGRNc/tIPWjZTIi0jlZ86541cw6wfsB152znUroe5QYKxz7oLA601AinNuV1mCSklJcRW91txXm/dwxbMLeOzK0/jpGa0r9FgiIiVZs2YNnTt39jqMGi/YfwczW+qcS/EopFILdu7U9ypyKvJvnZNXwBtfbuH5/25gWvatJPmC/KxKbOXv2RQRqSRCnT9L7AF1zn0K7C7lcUYAr5UxNk/0aFWfk5vUZvrSrV6HIiIiInKM/IJC3vhyMxc88QnjZ62iRf0Esvo+gKuAodIiIpEStkmIzKwWcDEwukixA+aZmQOed869cJz2twK3ArRuXfE9kmbGlT2T+NP7a9mceVDDVkRERKRSKCx0vJe6nT9/sJaNGQfo3qo+f7q6O31PaQycDc3raUIhEamywjkL7lDgc+dc0d7Sc5xzW83sJOADM/sm0KN6jEBy+gL4hxGFMa6QftKzJU/MW8v0ZWmMHdgxEocUEQnJOaeZQD1U0i0pVZW+VxWvXN+do5ZU2dj9N4xd3YGv07LocFIdnr++Fxd1aVr8v2HycCWcIlJlhXMW3Gs4avitc25r4Hkn8DbQO4zHK7fmiQn0bd+YGV+lUVhYPX94iEjVEB8fT2ZmZrVNgio75xyZmZnEx8d7HUpY6XtV8cr13Tm8pErWFsBB1haa/fdekvfM44mruzP3rn4M6tpMFxBEpFoJSw+omSUC5wE/K1JWG/A55/YFti8CJobjeOF0Za+WjH3ja77ctJs+JzfyOhwRqaGSkpJIS0sjIyPD61BqrPj4eJKSyrfmZWWj71VknPB3J8iSKrXsEA/Vno6vV6X7ySQiEhYlJqBm9hpwPtDYzNKA8UAMgHPuuUC1K4B5zrkDRZo2Bd4OXLWLBl51zs0NX+jhMahrM2rHpjJ9WZoSUBHxTExMDO3atfM6DKlm9L2q3FxWGsH6Nn0/aIJEEam+SkxAnXMjSlFnKjD1qLKNQPcTDSxSasVGc8lpzZmzcjsPXdaNhNgor0MSERGRamxfTh6Pzf2G2wobhVhSpXr1xIuIFBXOe0CrrCt7JbE/N5/3V233OhQRERGpxj5cvYOBf/mUV7/YzOL2o3HRWlJFRGoWJaBA77YNSWqQwPRlaV6HIiIiItXQ/tx87nnza25+eQn1a8Uw4/a+/GTkWOyyyZDYCjD/89DJmuFWRKq1cC7DUmX5fMZPeibx1EfrSNtzkKQGWhNUREREwmPp97sZ+8bXpO05yKj+7blzQEdiowN9AFpSRURqGPWABgxP8d9vMW2JekFFRESk/PIKCvnzvLVc/dxCHI5pvzyLewZ1+jH5FBGpgdQDGpDUoBb3Nl/B5Z/fift8F5aY5L8HQ1clRUREpDRWTPMvrZKVRkHdljxrI3hq5+lc1SuJ8UO7UDc+xs0tdeQAACAASURBVOsIRUQ8pwT0sBXTuCXrr0ST43+dtcW/ODQoCRUREZHjWzHN/7shsK5n1L40bnX/R9++fyBl6KUeByciUnloDMhh8ycSXZBTvCwv238lU0REJAzMrKGZfWBm6wLPDYLU6WFmC81slZmtMLOfehGrlNH8iUeSz8MS7BAp65/yKCARkcpJCehhWSHu/QxVLiIiUnb3A/Odcx2A+YHXRzsI/Nw51xW4GPirmdWPYIxyApx+R4iIlIoS0MNCLfqsxaBFRCR8hgEvBbZfAi4/uoJz7lvn3LrA9jZgJ9AkYhFKmW3dm81OX+PgO/U7QkSkGCWghw0Y51/8uQinxaBFRCS8mjrn0gPb24Gmx6tsZr2BWGBDiP23mtkSM1uSkZER3kilVJZt3sOwpz/nLwXXUBAVX3ynfkeIiBxDCehhycP9iz8ntsJhpBU25tvej2gCIhERKRMz+9DMUoM8hhWt55xzgDvO+zQH/gX8wjlXGKyOc+4F51yKcy6lSRN1kkbau19v45oXFlErNoqbb7+PqGFPQWIrwPzPQyfrd4SIyFE0C25RgcWgc/MKuPTR+Zyb0ZinvY5JRESqFOfchaH2mdkOM2vunEsPJJg7Q9SrB/wH+J1zblEFhSrl8M/Pv+Ohd1dzRtsGPH99Cg1rx0LT4Uo4RURKoB7QIOJjovhJz5a8v2o7GftyvQ5HRESqj1nAyMD2SGDm0RXMLBZ4G3jZOfdWBGOTUnDO8af3v+Ghd1dzUZem/OumPv7kU0RESkUJaAjXn9mGvALH64s3ex2KiIhUH5OAgWa2Drgw8BozSzGzFwN1hgP9gBvMbHng0cObcKWo/IJC7p++kmc+3sA1Z7Ti2et6Eh8T5XVYIiJViobghnBykzqc26Exry7ezK/Ob090lHJ1EREpH+dcJjAgSPkS4ObA9r+Bf0c4NClBXkEhd77+FXNWbmd0/1P4zUUdMTOvwxIRqXKUVR3H9We2IT0rhw/X7PA6FBEREfHIofxCRr+6jDkrt/O7Szpz96BTlXyKiJygEhNQM5tiZjvNLDXE/vPNLKvIMKFxRfZdbGZrzWy9mQVbbLtSG9C5KS3rJ/Dywu+9DkVEREQ8kJtfwO2vLOP9VTsYP7QLt/Q72euQRESqtNL0gE4FLi6hzv+ccz0Cj4kAZhYFPAMMBroAI8ysS3mCjbQon3Ftn9Ys2JDJuh37vA5HREREImHFNHiyG25Cffb9sRO11k5n4rCu/KJvO68jExGp8kpMQJ1znwK7T+C9ewPrnXMbnXOHgNeBYSW0qXSuOaMVsVE+/rVIvaAiIiLV3opp8O4YyNqC4WhcsJO/xE/h57UXex2ZiEi1EK57QM8ys6/N7D0z6xooawlsKVInLVAWlJndamZLzGxJRkZGmMIqv0Z14hiS3JwZy7ayPzff63BERESkIs2fCHnZxYqiC3P85SIiUm7hSECXAW2cc92Bp4B3TuRNnHMvOOdSnHMpTZo0CUNY4XP9WW3Yn5vP28vSvA5FREREKpDLCnGuD1UuIiJlUu4E1Dn3g3Nuf2B7DhBjZo2BrUCrIlWTAmVVTo9W9UlOSuSfCzZRWOi8DkdEREQqyL64psF3JCZFNhARkWqq3AmomTWzwFzkZtY78J6ZwJdABzNrZ2axwDXArPIezwtmxk3ntGNjxgE++Xan1+GIiIhIBfjXou/5/b6fcMjiiu+ISYAB44I3EhGRMinNMiyvAQuBU80szcxuMrPbzOy2QJWrgFQz+xqYDFzj/PKB0cD7wBpgmnNuVcV8jIp3yWnNaVYvnn989p3XoYiIiEiYffzNTsbPTOVAx58QNewpSGwFmP956GRIHu51iCIi1UJ0SRWccyNK2P808HSIfXOAOScWWuUSE+Vj5NlteWzuN6ze9gNdWtTzOiQREREJg1Xbshj96jI6N6/H5BGnExV3BvT4qddhiYhUS+GaBbdGuLZ3axJiotQLKiIiUk2kZ2Vz49QvqZcQw5QbzqB2XInX5kVEpByUgJZBYq0YhqckMevrrez8IcfrcERERKQcDh7K58apSziQW8CUG86gab14r0MSEan2lICW0S/6tiO/0PGvRd97HYqIiIicIOcc97y1grXbf+Dpa0+nc3PdWiMiEglKQMuobePaDOzclH8v+p7sQwVehyMiIiIn4IVPN/KfFencM6gT5596ktfhiIjUGEpAT8DN557MnoN5TF+mRalFRESqmv+ty+Cxud9w6WnNue28k70OR0SkRlECegLOaNuA7q3q88KnG8kvKPQ6HBERESmlLbsPcsdrX9HhpLo8flUygaXMRUQkQpSAngAz41fntWfz7oPMSd3udTgiIiJSCjl5BfzyX0spLHS88PNemvFWRMQDSkBP0EVdmtK+SW3+9skGnHNehyMiIiLBrJgGT3aDCfXJebwzp+yYw1+v6UGbRrW9jkxEpEZSAnqCfD7jtvPasyb9Bz5Zm+F1OCIiInK0FdPg3TGQtQVw1M/bwRPxU7jg0H+9jkxEpMZSAloOw3q0ZGTtxXSb1hcm1PdfYV0xzeuwREREBGD+RMjLLlYUW5jjLxcREU/o5odyiF39Fg+654guzPEXZG3xX2kFSB7uXWAiIiICWSFmqw9VLiIiFU49oOUxf+KPyedhedm6sioiIlIZJCaVrVxERCqcEtDy0JVVERGRSmtj919z0MUWL4xJgAHjvAlIRESUgJaLrqyKiIhUSgcP5XPj0nY8HnM7hfWSAIPEVjB0sm6TERHxkO4BLY8B4/z3fBaZ4KAwOgGfrqyKiIh46rH3vmFT5kEuuuUOfO0f8jocEREJUA9oeSQP919JTWyFw9jmGjO14VhdWRUREfHQ5+t38dLC7/lF37ac3b6x1+GIiEgRJfaAmtkUYAiw0znXLcj+64D7AAP2Ab9yzn0d2LcpUFYA5DvnUsIXeiWRPBySh2PA6/PWMvmj9Zy57Qe6tKjndWQiIiI1zr6cPO5582tOblybewd18jocERE5Sml6QKcCFx9n/3fAec6504CHgReO2t/fOdejWiafR7npnJOpGxfN5PnrvA5FRESkRvrT+2tJ/yGHJ4Z3JyE2yutwRETkKCUmoM65T4Hdx9m/wDm3J/ByEVBjZ+BJrBXDL85px9xV21m97QevwxEREalRln6/h38t+p6RZ7WlZ+sGXocjIiJBhPse0JuA94q8dsA8M1tqZrcer6GZ3WpmS8xsSUZGRpjDipyb+rZTL6iIiEiEHcov5IEZK2heL567B53qdTgiIhJC2BJQM+uPPwG9r0jxOc65nsBgYJSZ9QvV3jn3gnMuxTmX0qRJk3CFFXFFe0FTt2Z5HY6IiEiN8Px/N/Dtjv384Ypu1InTJP8iIpVVWBJQM0sGXgSGOecyD5c757YGnncCbwO9w3G8yu7mc9tRv1YMj7+/1utQRESkEjGzhmb2gZmtCzyHHCdqZvXMLM3Mno5kjFXRhoz9PPXRei5Nbs4FnZp6HY6IiBxHuRNQM2sNzACud859W6S8tpnVPbwNXASklvd4VUG9+BhuP789n36bwYINu7wOR0REKo/7gfnOuQ7A/MDrUB4GPo1IVFWYc44H30klPsbH+KFdvA5HRERKUGICamavAQuBUwNXYm8ys9vM7LZAlXFAI+BZM1tuZksC5U2Bz8zsa2Ax8B/n3NwK+AyV0s/PakvzxHgem7sW55zX4YiISOUwDHgpsP0ScHmwSmbWC/95dF6E4qqy3kvdzoINmdw96FROqhvvdTgiIlKCEm+ScM6NKGH/zcDNQco3At1PPLSqLT4mirsu7MB901fy/qrtXNytudchiYiI95o659ID29vxJ5nFmJkP+DPwM+DC471ZYIK/WwFat24d3kirgOxDBfxh9mo6N6/Htb1r3ucXEamKwj0LrhRxZc8k2jepzZ/eX0t+QaHX4YiISASY2YdmlhrkMaxoPecfHhNsiMztwBznXFpJx6ouE/idqGc/Wc+2rBweuqwr0VH6SSMiUhVomrgKFB3l455Bnbjt30uZviyNn56hq7MiItWdcy5kr6WZ7TCz5s65dDNrDuwMUu0s4Fwzux2oA8Sa2X7n3PHuF61xNmce5PlPNzKsRwt6t2vodTgiIlJKulxYwQZ1bUqPVvX564fryMkr8DocERHx1ixgZGB7JDDz6ArOueucc62dc22Bu4GXlXwea+Ls1UT7jAcGd/Y6FBERKQMloBXMzLjv4k6kZ+Xwz883eR2OiIh4axIw0MzW4b+/cxKAmaWY2YueRlaFfL5+Fx+u2cHoC06hWaImHhIRqUo0BDcCzmrfiAs7n8QzH6/nql5JNKkb53VIIiLigcBa2QOClC8h+IR+U4GpFR5YVbFiGm7+RM7KSmNRfGMaJv4BOMXrqEREpAzUAxohv72kMzl5Bfzlg7VehyIiIlL1rJgG747Bsrbgw9GMDGLn3OUvFxGRKkMJaISc3KQOI89uy+tfbmHVtiyvwxEREala5k+EvOziZXnZ/nIREakylIBG0JgLOlA/IYaHZ6/GP/u+iIiIlEpWiFVpQpWLiEilpAQ0ghJrxfDrgR1ZtHE376/a4XU4IiIiVUZBvZbBdyQmRTYQEREpFyWgETaid2turb+EHtPPwU2oD0920/0rIiIiJZjV6GYOutjihTEJMGCcNwGJiMgJUQIaYdGr3uK+vGdp5jIwHGRtgXfHKAkVEREJYecPOdy/rhMzWt4Lia0A8z8PnQzJw70OT0REykDLsETa/IlEFeQULzs8iYJOoiIiIsd49pMN5Bc6zr3ydmh0j9fhiIhIOagHNNI0iYKIiEipbd2bzatfbObqXkm0aVTb63BERKSclIBGWqjJEjSJgoiIyDGe/mg9DsfoC07xOhQREQkDJaCRNmCcf9KEInKI49D5v/coIBERkcppc+ZB3lyyhWvOaE1Sg1pehyMiImGgBDTSkof7J00ITKKQU7sF9x66iWcze3odmYiISKUy+aN1+HzGqP7q/RQRqS5KlYCa2RQz22lmqSH2m5lNNrP1ZrbCzHoW2TfSzNYFHiPDFXiVljwcxqbChL3E37MGd9pwnv1kA5t2HfA6MhERkUphc+ZB3v5qK9f1aU2zxHivwxERkTApbQ/oVODi4+wfDHQIPG4F/gZgZg2B8UAfoDcw3swanGiw1dXvL+1MbJSPB2em4pzzOhwRERHPPf/pBqLM+GW/9l6HIiIiYVSqBNQ59ymw+zhVhgEvO79FQH0zaw4MAj5wzu12zu0BPuD4iWyN1LRePPcMOpX/rdvFjGVbvQ5HRETEUzt/yOHNJWlc2StJvZ8iItVMuO4BbQlsKfI6LVAWqvwYZnarmS0xsyUZGRlhCqvquP7MNqS0acDE2avJ2JfrdTgiIiKeefGz78gvLOS28072OhQREQmzSjMJkXPuBedcinMupUmTJl6HE3E+nzHpymSy8woYPyvorbYiIiLV3t6Dh3hl0fcM7d5C636KiFRD4UpAtwKtirxOCpSFKpcgTjmpDncO6MCclduZm5rudTgiIiIR99KC7zlwqIBfna97P0VEqqNwJaCzgJ8HZsM9E8hyzqUD7wMXmVmDwORDFwXKJIRb+51Ml+b1eHDmKrIO5nkdjoiISMQcyM3nnwu+48LOTenUrJ7X4YiISAUo7TIsrwELgVPNLM3MbjKz28zstkCVOcBGYD3wd+B2AOfcbuBh4MvAY2KgTEKIifLx+FXJ7D5wiIf/s9rrcERERCLm9S+3sPdgHrf3V++niEh1FV2aSs65ESXsd8CoEPumAFPKHlrN1a1lIr/sdzLPfrKBwd2aMaBzU69DEhERqVAFhY5/fv4dZ7RtQM/WWrFNRKS6qjSTEElxd17YgU7N6nLf9BVk7tesuCIiUr3NW7WdtD3Z3HROO69DERGRCqQEtJKKi47ir9f04IfsfB6YsRJ/J7OIiEj19I/PvqNVwwQGdmnmdSgiIlKBlIBWYp2a1eOeQacyb/UO3lya5nU4IiIiFWL5lr0s+X4Pvzi7HVE+8zocERGpQEpAK7mbzmnHmSc3ZOK7q9my+6DX4YiIiITdPz77jrpx0Qw/o1XJlUVEpEpTAlrJ+XzGE1d3x4AZU5/EPdkNJtSHJ7vBimlehyciIlIuW/dmM2dlOj89oxV14ko1N6KIiFRh+pe+CkhqUIsXe37Hacv+itkhf2HWFnh3jH87ebh3wYmIiJTDyws24Zzjhr5tvQ5FREQiQD2gVUTvjU9T63DyeVheNsyf6E1AIiIi5bFiGoV/6cp9i8/iy9p3kbRlttcRiYhIBKgHtIqwrBCTEIUqFxERqaxWTIN3x+DLywagUf5OjeoREakh1ANaVSQmla1cRESkspo/0T+KpyiN6hERqRGUgFYVA8ZBTEKxojxfvL9cRESkKtGoHhGRGksJaFWRPByGTobEVjiMzOiTuPfQTSxNHOh1ZCIiImWjUT0iIjWWEtCqJHk4jE3FJuwl+jerWVpvILe/spSd+3K8jkxERErBzBqa2Qdmti7w3CBEvdZmNs/M1pjZajNrG9lIK9aBc39LtostXhiToFE9IiI1gBLQKioxIYbnftaLrOw8Rr/yFXkFhV6HJCIiJbsfmO+c6wDMD7wO5mXgT865zkBvYGeE4ouI17LP5L68mzlUpyVgkNjKP8pHExCJiFR7SkCrsC4t6vHYlcks3rSbR/6zxutwRESkZMOAlwLbLwGXH13BzLoA0c65DwCcc/udcwcjF2LFKix0vPLFZtKShhB792qYsBfGpir5FBGpIZSAVnHDerTkpnPaMXXBJmYs0+QNIiKVXFPnXHpgezvQNEidjsBeM5thZl+Z2Z/MLCrYm5nZrWa2xMyWZGRkVFTMYbVwYybf7TrA9We18ToUERHxgBLQauCBwZ048+SGPDBjJalbs7wOR0SkRjOzD80sNchjWNF6zjkHuCBvEQ2cC9wNnAGcDNwQ7FjOuReccynOuZQmTZqE94NUkDe+3EK9+GgGd2vudSgiIuKBUiWgZnaxma01s/Vmdsz9Kmb2pJktDzy+NbO9RfYVFNk3K5zBi190lI+nr+1Jo9qx3PryEnb+oEmJRES84py70DnXLchjJrDDzJoDBJ6D3duZBix3zm10zuUD7wA9I/cJKk7WwTzmrtrO5ae3JD4maKeuiIhUcyUmoIFhP88Ag4EuwIjA/SlHOOfGOud6OOd6AE8BM4rszj68zzl3WRhjlyIa14njhZ+nsOdgHje/vITsQwVehyQiIseaBYwMbI8EZgap8yVQ38wOd2leAKyOQGwVbtbXWzmUX8jwlFZehyIiIh4pTQ9ob2B94ErsIeB1/JMohDICeC0cwUnZdGuZyOQRp7Nyaxa/nracwsJgI7tERMRDk4CBZrYOuDDwGjNLMbMXAZxzBfiH3843s5WAAX/3KN6wmrYkjS7N69GtZaLXoYiIiEdKk4C2BLYUeZ0WKDuGmbUB2gEfFSmOD0yQsMjMjpntr0jbKjeRQmU0sEtTfju4M++lbueJeWu9DkdERIpwzmU65wY45zoEhuruDpQvcc7dXKTeB865ZOfcac65GwIXgKu01dt+YOXWLIanJHkdioiIeCg6zO93DfBW4OrtYW2cc1vN7GTgIzNb6ZzbcHRD59wLwAsAKSkp6rorh5vPbcfGXft59pMN9Mv5mDO/eway0iAxyb/It6a6FxGRCHtz6RZio3wM6xH0GraIiNQQpUlAtwJFb9ZICpQFcw0wqmiBc25r4HmjmX0CnA4ck4BK+JgZE4d1I2nLbJK/ehIscOE8awu8O8a/rSRUREQiJDe/gHe+2srArk1pUDvW63BERMRDpRmC+yXQwczamVks/iTzmNlszawT0ABYWKSsgZnFBbYbA32pJhMpVHYxUT5+VfAKteyoUVt52TB/ojdBiYhIjfTh6p3sOZinyYdERKTkHlDnXL6ZjQbeB6KAKc65VWY2EVjinDucjF4DvB5Y1+ywzsDzZlaIP9md5JxTAhohvh9CdFRnpUU2EBERqdHeXLqF5onxnHNKY69DERERj5XqHlDn3BxgzlFl4456PSFIuwXAaeWIT8ojMck/7DZYuYiISARk7Mvlf+t28ct+JxPlM6/DERERj5VmCK5UVQPGQUxCsaIc4jh47u88CkhERGqa2Su2UVDouPx0TT4kIiJKQKu35OEwdDIktgKMnFot+G3+LYz4ojX7cvK8jk5ERGqAd5Zvo0vzenRsWtfrUEREpBJQAlrdJQ+HsakwYS/x965h8LVjWLU1i1/880sO5OZ7HZ2IiFRj3+06wNdb9nL56S28DkVERCoJJaA1zMAuTZk84nSWbd7DzS8tISevoORGIiIiJ+Cdr7ZiBpd11/BbERHxUwJaA11yWnP+MrwHi77L5NZ/LVUSKiIiYeec453lWznr5EY0S4z3OhwREakklIDWUJef3pLHfpLMp99m8Kt/KwkVEZHwWr5lL99nHuTyHur9FBGRHykBrcGGn9GKR684jY/XZnDzS0s4eEj3hIqISHjMXL6N2GgfF5/WzOtQRESkElECWsNd26c1f766Ows27GLklMWaHVdERMotr6CQd7/exoWdT6JefIzX4YiISCWiBFS4slcST43oyVeb93Ldi1+w9+Ahr0MSEZEqbMGGTDIPHGKYht+KiMhRor0OQCqHS5ObExft4/ZXlvHcU5O4J/oNovZthcQkGDDOv5yLiIhIKcxZkU6duGjO69jE61BERKSSUQIqR1zYpSmzzttGm8+fIsoCvaBZW+DdMf5tJaEiIlKCvIJC3l+9nQs7n0R8TJTX4YiISCWjIbhSTKdVT5JgRw3BzcuG+RO9CUhERKqUhRsy2Xswj0tOa+51KCIiUgkpAZXistLKVi4iIlLEnJX+4bf9NPxWRESCUAIqxSUmBS0+kKBp9EVE5PjyCgp5f9V2Bmj4rYiIhKAEVIobMA5iEooV5VocD2RdwaNz1lBQ6DwKTEREKrtFGzPZczCPwd00/FZERIJTAirFJQ+HoZMhsRVgkNiKqGFPkdj7Ol74dCO3vrxEa4WKiEhQc1amUzs2ivNP1fBbEREJrlQJqJldbGZrzWy9md0fZP8NZpZhZssDj5uL7BtpZusCj5HhDF4qSPJwGJsKE/bC2FSie/yUhy/vxsPDuvLJtxlc+bcFbM486HWUIiJSieQXFPL+qh1c0Lmpht+KiEhIJSagZhYFPAMMBroAI8ysS5CqbzjnegQeLwbaNgTGA32A3sB4M2sQtugloq4/qy0v39ibHT/kMuyZz1iwYZfXIYmISCWxaONudh84xKWnac4AEREJrTQ9oL2B9c65jc65Q8DrwLBSvv8g4APn3G7n3B7gA+DiEwtVKoO+pzTmnVF9aVg7lp+9+AV/+2QDzum+UBGRGmvFNHiyG31fOYXP48ZwQd5/vY5IREQqsdIkoC2BLUVepwXKjnalma0ws7fMrFUZ22Jmt5rZEjNbkpGRUYqwxCvtGtdm5uhzGHxacx6b+w23vLyUg0tegye7wYT6/ucV07wOU0REKtqKafDuGMjaguFoabuInXOXzgEiIhJSuCYhehdo65xLxt/L+VJZ38A594JzLsU5l9KkiSYvqOzqxEXz9IjTGTekC3XXzcA3+07I2gI4//O7Y/QDRESkups/EfKyi5flZfvLRUREgihNAroVaFXkdVKg7AjnXKZzLjfw8kWgV2nbStVlZtx4TjsmJb5DPLnFd+oHiIhI9ZeVVrZyERGp8UqTgH4JdDCzdmYWC1wDzCpawcyKLvh1GbAmsP0+cJGZNQhMPnRRoEyqkbgD24Lv0A8QEZHqLTGpbOUiIlLjlZiAOufygdH4E8c1wDTn3Cozm2hmlwWqjTGzVWb2NTAGuCHQdjfwMP4k9ktgYqBMqpMQPzRyamkhchGRam3AOAqjE4qXxSTAgHHexCMiIpVeqe4Bdc7Ncc51dM61d849Eigb55ybFdh+wDnX1TnX3TnX3zn3TZG2U5xzpwQe/6yYjyGeGjDO/4OjiBziuHfv5Tw6Zw25+QUeBSYiIhUqeTgfd/wdaYWNcRgktoKhk/3rSYuIiAQR7XUAUg0c/qExf6J/2G1iEr7zfk+9zV154dON/HdtBn+6OpnkpPrexikiImH33O5e7G80lffuPNfrUEREpApQAirhkTy82BXvWOAPPWFAp6Y8MGMllz/zObf2a89dF3YgPibKuzhFRDxkZg2BN4C2wCZgeGCd7KPrPQ5cin+k0gfAna4SLrq8a38uS77fwx0XdPA6FBERqSLCtQyLSFD9O53EvF/34+perXjuvxu4ZPL/WPq9bgMWkRrrfmC+c64DMD/wuhgzOxvoCyQD3YAzgPMiGWRpfbRmJ87BRV2aeh2KiIhUEUpApcLVi4/hsauSefnG3uTmFXLVcwsZPzOVg0tegye7wYT6/metGyoi1d8wflwr+yXg8iB1HBCPfzBJHBAD7IhIdGU0b/UOWiTG07VFPa9DERGRKkJDcCVi+nVswvtj+/H43G/Yu/gVLOYfcHj90Kwt8O4Y/7YmrxCR6qupcy49sL0dOKbr0Dm30Mw+BtIB4//bu/Mwqeo73+Pvb3VXL2zNvjaLKCAuKNpxixlBIG4h6PUOYTLzXB31Es3kJuq9N2PUoOFJJj6TZ+KSzEx0HO/VmUyMGnMliYwLLgkiKEZlUdGmRdII0jTQ0HTTW33vH3W6KZoqurqprlPVfF7PU09XnXN+Vd/6caq+fOuc3+/AT939/c7bAZjZYmAxwIQJE3on4hQam9tYWVnDworxmFlWX1tERPKXjoBKVg0oLmTpgtP40ZBnKG0vPtu1NMYnMhIRyWNm9qKZbUhyW5C4XTCm84hxnWZ2EjAdKAfGARebWdIZftz9IXevcPeKESNG9MK7Se31ql0cbIkxZ7pOvxURkfTpCKiEoqj+06TLva4a/Y4uIvnM3eemWmdmn5nZGHffbmZjgJ1JrWSySQAAGK5JREFUNrsKWO3u9UGb5cD5wB96JeAeemVTDaXRAs49YWjYoYiISB7REVAJR1l50sXbGcZjr2+hpS2W3XhERLJjGXBNcP8a4Jkk22wFLjKzQjOLEp+AKOkpuGFxd17etJMLThymmc1FRKRbVIBKOOYsgWjpYYtihaX8avB1LHlmI5fc93tefO8zcvCqAyIix+IeYJ6ZfQTMDR5jZhVm9nCwzVPAZmA98C7wrrv/JoxgU6nadYA/7W5k1skjww5FRETyjE7BlXC0TzS0YinUVUNZOZE5S/jG6X/Oye/v5IfPvs8Nj63l/MnD+Pal05g5YUi48YqIZIC71wJzkixfC9wQ3G8Dvpbl0Lrl5Q/iZw7PmprdcaciIpL/VIBKeGYsPGLGWwPmnTKKWdNG8B9rtnL/io+46p9WMefkkdwybyqnjSuLX64loXBlzhLNnCsikkWvfljDSSMHMH5ov7BDERGRPKMCVHJStCDCNRdM4uqzy3l01RYefHUzX/rJSr47YQN/vfteIq2N8Q11+RYRkaw60NTKmqrd/LfzJ4YdioiI5CGNAZWcNqC4kL+ZfRIrb7uYb82ZwmWfPXSo+Gyny7eIiGTN65traW6LMVvjP0VEpAdUgEpeGFQS5ZZ5UxljtUnXe111liMSETk+vbxpJ/2LCqiYpLH5IiLSfSpAJa9Yisu31ERG8PzGHcRimjVXRKS3uDuvbKrhgpOGU1yoy6+IiEj3qQCV/JLk8i2tkRL+qeCrLP63t5h776s8umoL9U2tIQUoItJ3Ve6sZ9veRmZP0+m3IiLSM2kVoGZ2qZltMrNKM7styfpbzew9M1tnZivMbGLCujYzeye4Lctk8HIcmrEQ5j8AZeMBg7LxFF75E+687S7uX3QmA0ui3LVsI+f93QruXraRqpr6eLt1T8C9p8Hdg+N/1z0R6tsQEclHr2yqAWDWNF1+RUREeqbLWXDNrAD4R2AeUA28aWbL3P29hM3eBircvcHMbgL+HvhKsK7R3c/McNxyPEty+ZZCYMGZ41hw5jje3rqHR1dt4edrPuH/rtrC345dx+K6+yhoOxjfWDPnioj0yO8/qmHKyAGMHVza9cYiIiJJpHME9Byg0t2r3L0ZeBxYkLiBu7/s7g3Bw9VA8oF6Ilkwc8IQ7ls0k9duu5hb5k7lyt0PHyo+22nmXBGRbjnY0sYbH+/mwinDww5FRETyWDoF6DjgTwmPq4NlqVwPLE94XGJma81stZldmaqRmS0OtltbU1OTRlgiRzdyYAnfmjuF0aSeOfdgS1uWoxIRyU9vfbKHptYYX1ABKiIixyCjkxCZ2V8BFcCPEhZPdPcK4KvAfWZ2YrK27v6Qu1e4e8WIERpbIpmTaubcbbFhfO4HL/Ltp95l5Ue7aNMMuiIiKa2s3EVhxDjnhGFhhyIiInmsyzGgwDZgfMLj8mDZYcxsLnAHcJG7N7Uvd/dtwd8qM3sFmAlsPoaYRbpnzpL4mM+Wxo5FHi2l4ZzbmbdnFM+u38ETa6sZMbCYL80Yw4Izx3FGeRm2/sn4abp11VBWHn8ejRkVkePUyo92cdaEIQwoTue/DiIiIsmlk0XeBKaY2QnEC89FxI9mdjCzmcCDwKXuvjNh+RCgwd2bzGw48HniExSJZE970ZhQTNqcJUydsZAfEx/X9NIHO3nmnW38fPVW/s9rW7hu0Jt8p/WficY0cZGIyJ4DzWz4tI6b50wNOxQREclzXRag7t5qZt8AngMKgEfcfaOZLQXWuvsy4qfcDgCeNDOAre7+ZWA68KCZxYif7ntPp9lzRbIjycy57UqiBVx++hguP30MdY0tPLdxB7OX33yo+GzX0oiv+B6mAlREjjOrNtfijiYgEhGRY5bWeTTu/izwbKdlSxLuz03RbhVw+rEEKJJNZaVRFlaMh98mnwjL67bxP3/5DhdPH8kXThpBWb9oliMUEcm+lZW7GFBcyBnlZWGHIiIieU4DOUSSKSuPn3bbyd7Ckaz4YCdPv72NiMUv+TJr6ggumjaC08aWEdmgcaMi0vesrKzhvMnDKCzI6NyFIiJyHFIBKpJMkomLiJYydP73eevUubxbvZdXN9Xwyoc1/MMLH/IPL3zIX5au5i4epKh9Di6NGxWRPmBrbQN/2t3IDRdODjsUERHpA1SAiiSTZOKi9qOZhcDZE4dy9sSh3PrFaeyqb+IPH9Vw0e9uoai16fDnaWmkYfkS6ibOZ0xZadbfhojIsfpDZXxIwudP0vhPERE5dipARVI5ysRFiYYPKOaqmeXwzM6k60sadnDKD19i0rB+nDd5GGdPHMJZE4cweXh/zAzWPaHTdkUkZ71WuYsxZSWcOKJ/2KGIiEgfoAJUJFNSjBttHTiWO+dOZ3XVbp5dv53H34xvM7hflJuGvMV1e+7T5V5EJCfFYs6qzbXMnT6KYJZ7ERGRY6ICVCRTUowbLfri3dwwYzI3fGEysZhTWVPP21v38MdP9nLlxn8l6kde7qX+2SVsKpvHKWMGUVpUkPz1dORURHrZ+zv2sbehhc+fNCzsUEREpI9QASqSKUcZN9ouEjGmjhrI1FED+crnJsCGXUmfql/jDq7+51VEDE4cMYDTx5Vx6rgyThs7iOljBzHow18fXuzqyKmI9ILVVbsBOPcEFaAiIpIZKkBFMinNcaMdUpy2Gxs0jn+5rIIN2+rYsK2O1zbv4um3t3Wsf73kdsbQeHijlsZ48Xu019dRUxHphtVVtUwc1o+xgzWJmoiIZIYKUJEwpThtt3DeXcw7ZRTzThnVsXjn/oNs/HQf72/fx+hXkh85jdVVs/jRtUwZNYDJw/tzQnAb2r8IW/+kjpqKSNpiMeeNj3dzyamjut5YREQkTSpARcKUxmm77UYOLGHktBJmTxsJbyc/crqncCRbdx/glU07aY15x/KBJYWsiNzOyJiOmopIej7YsZ+6xhbOm6zTb0VEJHNUgIqErbun7ULKI6fD5n+f52dcRGtbjOo9jXy860DHbcQ7qY+aXnH/HygfUhrc+nXcP+HTZ+n33C3dP2qqolUk762uqgXgXBWgIiKSQSpARfJRF0dOCwsiTBren0nD+zO7vc3HyY+a7ouOZExZCZ/UHuC1yl00NLd1rFtZdCf9IkceNT2wfAlrS2YzalAxoweVUFYaPXSJhnVP9PxUXxWuIjljzce1TBjaj3Ea/ykiIhmkAlQkX3X3yGmKo6aD53+fR2Z8DgB3Z09DC9v2NFK9p4Fxv6pN+lSlDTu45pE3Oh4XF0YYXVbCqEEl/KzmToa2Hlm0tr7wPdpOuZriwqNcVqYnhauKVpGMi8WcNR/vZt50jf8UEZHMUgEqcrxIY7ypmTG0fxFD+xdxenkZvJh6lt4nrz6fHXUH+Wxf/LZjXxOf1R1kcOvOpC8f2beNk+78TwaVFDJ8QDHDBhQxrH8xwwfG//73t5YwoOXIwtVXLMVSFZQqWkV6xabP9rO3QeM/RUQk81SAihxPMnTUtHDeXXxu0tDkbe5NXrQ2lo7m1j+bSm19E7sONFNb38Tmmnre2NLMnoZmvlW0HezIp/O91Zy19HkG9yuirDTK4H5RBpdGGdyviFs3fpdBSYrWlufvZvekLzOguJB+RQWHTg+G7BatKnQlT63pGP+Z4nMuIiLSQypARSS1bszS2yFF0dr/8qV8c8aUpE1a22L4feWwv/qIdfUlo/jS9LHsbWxhb0Mzuw80U1VzgL0NzSzxHUmL1oL9n3Lu360AIGIwoLiQgSVRBpYU8ti+O5LOBrz/d0t4uv4cSosK6BfcSqPxAnbklmWMevXbRFq7UbRmeyxsLreRvLO6ajfjh8YnJRMREckkc/euNzK7FLgfKAAedvd7Oq0vBh4DzgZqga+4+5Zg3XeA64E24Jvu/lxXr1dRUeFr167t3jsRkdzR08ImSeHK/AdStvV7T8OSHG1tKB3Dr2c9R/3BVvYfbKW+qZV9B1uoP9jKz6rmEuHI772YG5Obfp70dVYWfZPyyJGzCG9nOIv6P0xptIDiaAHFhRGKCyOURAu4Z+tXGdb62RFt9heP4ckvLKc4GqG4sKBj+/a2oz75DRNeu+1QsQt4YSmNl96LzVhItMAoiNjRj+qm0XdZa9PeridFa44UyGb2lrtXHNOTHHquPwfuBqYD57h70mTXVd5NJiO5c90T+IqleF01e6MjGTr/+/qBQUREeiRV/uyyADWzAuBDYB5QDbwJ/IW7v5ewzdeBGe5+o5ktAq5y96+Y2SnAL4BzgLHAi8BUd2/r/DqJVICKHKe6Wzz0pCC697QU41rL2bP4jzQ0t9HY0kZDcxsNza00Nrdx8S+nYUmKVse45eSXaWhuo6k1RlNr8Lclxm/3zO92oQupi93q2HAubH4AADOIRiJEC4xoYYTlsZsYw5FtdkZG8s3RjxEtiAQ3I1oQoaggwncrFzKk5cgCua5oNP9asYyCSITCAiNiRmEkXvQuXHkZAw5uP6JNQ+lYfn/Fy/HtCoLtLd5mxJZnmPTa7UTaDv0bxQpLqZn99zROuzoopqEgEn+t+A1KPvgV/f7zViyxEI+WErvifiJnLDy8AG/X0wK5CxkuQKcDMeBB4H8lK0DTybvJHHPu7KX+ExGR41Oq/JnOKbjnAJXuXhU80ePAAiAxES4g/osuwFPATy3+v4MFwOPu3gR8bGaVwfO93tM3IiJ9WHfHqGbwFOHI3LsYNqCYpFOulCUf12pl5dy3aGby10kxFpaycbx74xc7itWDLQnFa0uMcf+efObhcZFa/vbSk2lti9HSFqO5zWlpi9HaFmP028nbDI/VEHOob2oNtnWag/ZlLcknixrY9BkPvFSZdN21xcnH6ZY0bOfGf38raZuVRT8g0ulSPpHWRlqe+x6zfpN6gpuVRd+lf6d21tLI9qdv58LHBwDtRSsdheuKyHcYa0eeXs2KpTlTQLn7+0DyAvqQdPJu5q1YevjnAnKu/0REJP+lU4COAxL/F1UNnJtqG3dvNbM6YFiwfHWntuOSvYiZLQYWA0yYMCGd2EVEQi1ambOk220ic++irF8UiCZvd5Ri96ZZJyZvU5W8TaSsnCe+dn7yNikK5MjgcrbccgWxmNMac9piTmssRiwG/rNy2HfkON22gWP53dcuJBaD1lgsaOPEYn7UgvrHC88g5vFLfsTcaXPveDzu+dTtbp47JWhD0Cb+WmPeTN6GuiNjznHp5F0gw7kzVT/lX/+JiEgOy5lJiNz9IeAhiJ9GFHI4ItKXZaNo7UkbyGixeyxtIhGjKNJ+lC64duvcu5K2iX7xbk4dW5b8dY5SUP+Xs8pTx7cmdbub505N3ubDVEedj/I6vcDMXgRGJ1l1h7s/k8nXymjuTPFvle3+ExGRvi2dAnQbMD7hcXmwLNk21WZWCJQRn4wonbYiIrmvu0XrsbSB3i92s9WmJ8VxT9v19LUyzN3nHuNThJM7c6T/RESkb0unAH0TmGJmJxBPgIuAr3baZhlwDfGxnf8VeMnd3cyWAf9hZj8mPgnRFOCNTAUvItInZbPY7e02PT0SnM2jzrknnbybeX2n/0REJIelexmWy4H7iJ+H9Yi7/8DMlgJr3X2ZmZUA/wbMBHYDixImT7gDuA5oBW529+VdvZ5mwRURkVyR4VlwrwJ+AowA9gLvuPslZjaW+OVWLg+2OyLvdvXcyp0iIpJLenwZljAoiYqISK7IZAHam5Q7RUQkl6TKn5EwghEREREREZHjjwpQERERERERyQoVoCIiIiIiIpIVKkBFREREREQkK3JyEiIzqwE+ydDTDQd2Zei5wqD4w5PPsYPiD5viD1cm45/o7iMy9Fy9RrnzMIo/XIo/XIo/XPkcf6ZjT5o/c7IAzSQzW5sPsxemovjDk8+xg+IPm+IPV77HH7Z87z/FHy7FHy7FH658jj9bsesUXBEREREREckKFaAiIiIiIiKSFcdDAfpQ2AEcI8UfnnyOHRR/2BR/uPI9/rDle/8p/nAp/nAp/nDlc/xZib3PjwEVERERERGR3HA8HAEVERERERGRHKACVERERERERLIibwtQM7vUzDaZWaWZ3ZZkfbGZ/TJYv8bMJiWs+06wfJOZXZLNuBNi6Cr+W83sPTNbZ2YrzGxiwro2M3snuC3LbuQdMXQV/7VmVpMQ5w0J664xs4+C2zXZjbwjhq7ivzch9g/NbG/CulD738weMbOdZrYhxXozsweC97bOzM5KWJcLfd9V/H8ZxL3ezFaZ2RkJ67YEy98xs7XZi/qw+LqKf5aZ1SXsI0sS1h11v8uGNOL/3wmxbwj296HBulzo//Fm9nLw/bjRzL6VZJuc/gyETflT+bOn8jl3BjEofyp/9ohyZ4b3f3fPuxtQAGwGJgNFwLvAKZ22+Trws+D+IuCXwf1Tgu2LgROC5ynIwfhnA/2C+ze1xx88rs+D/r8W+GmStkOBquDvkOD+kFyLv9P2/wN4JIf6/8+As4ANKdZfDiwHDDgPWJMrfZ9m/Be0xwVc1h5/8HgLMDzH+38W8Ntj3e/Cir/TtvOBl3Ks/8cAZwX3BwIfJvn+yenPQMj9p/yZ+/1/LTmYP7v7HUaO5c4gBuXP3O7/WeRo/uwq9k7bKnd2ccvXI6DnAJXuXuXuzcDjwIJO2ywAHg3uPwXMMTMLlj/u7k3u/jFQGTxfNnUZv7u/7O4NwcPVQHmWYzyadPo/lUuAF9x9t7vvAV4ALu2lOFPpbvx/AfwiK5Glwd1/D+w+yiYLgMc8bjUw2MzGkBt932X87r4qiA9yb99Pp/9TOZbPTcZ0M/6c2vcB3H27u/8xuL8feB8Y12mznP4MhEz5M1z5nD/zOneC8mfY8jl/Kndmdv/P1wJ0HPCnhMfVHNmJHdu4eytQBwxLs21v624M1xP/RaJdiZmtNbPVZnZlbwTYhXTjvzo4hP+UmY3vZtvelHYMwalbJwAvJSwOu/+7kur95ULfd1fnfd+B583sLTNbHFJM6TjfzN41s+VmdmqwLK/638z6EU8wv0pYnFP9b/FTQ2cCazqt6kufgUxT/lT+7Km+njuhb313KH+GQLkzPYXH0lh6n5n9FVABXJSweKK7bzOzycBLZrbe3TeHE2FKvwF+4e5NZvY14r+mXxxyTD2xCHjK3dsSluVD/+c9M5tNPIFemLD4wqDvRwIvmNkHwa+SueSPxPeRejO7HPh/wJSQY+qJ+cBr7p74i2/O9L+ZDSCe4G92931hxCC5TfkzVMqdIVL+DJVyZxry9QjoNmB8wuPyYFnSbcysECgDatNs29vSisHM5gJ3AF9296b25e6+LfhbBbxC/FeMbOoyfnevTYj5YeDsdNtmQXdiWESn0yhyoP+7kur95ULfp8XMZhDfbxa4e2378oS+3wn8muyf/tcld9/n7vXB/WeBqJkNJ4/6P3C0fT/U/jezKPEE+nN3fzrJJnn/GehFyp8of/ZQX8+d0Ae+O5Q/Q6fcmQ4PcUBsT2/Ej9xWET+9o30w8qmdtvkbDp9E4Yng/qkcPolCFdmfRCGd+GcSH3A9pdPyIUBxcH848BHZH4idTvxjEu5fBawO7g8FPg7ex5Dg/tBciz/Y7mTiA8ctl/o/eO1JpB7EfwWHDyJ/I1f6Ps34JxAfW3ZBp+X9gYEJ91cBl+Zg/KPb9xniSWZr8G+R1n4XdvzB+jLiY13651r/B335GHDfUbbJ+c9AWLc0v7+VP8ONPyfzZ7rfYeRw7gxe/2jf3zn/3dFF/MqfIcUerFfuTDeeMHa+DHXk5cRncNoM3BEsW0r8106AEuDJ4IP4BjA5oe0dQbtNwGU5Gv+LwGfAO8FtWbD8AmB98OFbD1yfo/H/ENgYxPkycHJC2+uCf5dK4K9zMf7g8d3APZ3ahd7/xH9Z2w60ED8P/3rgRuDGYL0B/xi8t/VARY71fVfxPwzsSdj31wbLJwf9/m6wb92Ro/F/I2HfX03CfwSS7Xe5Fn+wzbXEJ5tJbJcr/X8h8fE06xL2kcvz6TMQ9q2r7z+UP8OOP2fzZ1exB4/vJgdzZxCH8qfyZ6/EHmxzLcqdad3af2UQERERERER6VX5OgZURERERERE8owKUBEREREREckKFaAiIiIiIiKSFSpARUREREREJCtUgIqIiIiIiEhWqAAV6YPMbLCZfT3sOERERPKFcqdIdqgAFembBgNKoiIiIulT7hTJAhWgIn3TPcCJZvaOmf0o7GBERETygHKnSBaYu4cdg4hkmJlNAn7r7qeFHIqIiEheUO4UyQ4dARUREREREZGsUAEqIiIiIiIiWaECVKRv2g8MDDsIERGRPKLcKZIFKkBF+iB3rwVeM7MNmkhBRESka8qdItmhSYhEREREREQkK3QEVERERERERLJCBaiIiIiIiIhkhQpQERERERERyQoVoCIiIiIiIpIVKkBFREREREQkK1SAioiIiIiISFaoABUREREREZGs+P/MkaVa0rMAdQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 936x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "ename": "KeyError",
+     "evalue": "\"Input parameter 'b' not found\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/input_parameter.py\u001b[0m in \u001b[0;36m_base_evaluate\u001b[0;34m(self, t, y, u)\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;31m# raise more informative error if can't find name in dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'b'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-11-1f3adb996155>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# Discretise using default discretisation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mdisc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpybamm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDiscretisation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mdisc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# Solve\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/discretisations/discretisation.py\u001b[0m in \u001b[0;36mprocess_model\u001b[0;34m(self, model, inplace, check_model)\u001b[0m\n\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m         \u001b[0mpybamm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Discretise initial conditions for {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m         \u001b[0mics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconcat_ics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_initial_conditions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m         \u001b[0mmodel_disc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitial_conditions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0mmodel_disc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenated_initial_conditions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconcat_ics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/discretisations/discretisation.py\u001b[0m in \u001b[0;36mprocess_initial_conditions\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m    382\u001b[0m         processed_concatenated_initial_conditions = self._concatenate_in_order(\n\u001b[1;32m    383\u001b[0m             \u001b[0mprocessed_initial_conditions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_complete\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m         ).evaluate(0, None)\n\u001b[0m\u001b[1;32m    385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mprocessed_initial_conditions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocessed_concatenated_initial_conditions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/concatenations.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0mchildren_eval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m                 \u001b[0mchildren_eval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_concatenation_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchildren_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/symbol.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    556\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 558\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_base_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_for_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/input_parameter.py\u001b[0m in \u001b[0;36m_base_evaluate\u001b[0;34m(self, t, y, u)\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;31m# raise more informative error if can't find name in dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Input parameter '{}' not found\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: \"Input parameter 'b' not found\""
+     ]
     }
    ],
    "source": [
@@ -352,8 +359,8 @@
     "model.initial_conditions = {u: b}\n",
     "model.variables = {\"u\": u}\n",
     "\n",
-    "# Set parameters ############################################\n",
-    "parameter_values = pybamm.ParameterValues({\"a\": 3, \"b\": 2})\n",
+    "# Set parameters, with b as an input ########################\n",
+    "parameter_values = pybamm.ParameterValues({\"a\": 3, \"b\": \"[input]\"})\n",
     "parameter_values.process_model(model)\n",
     "#############################################################\n",
     "\n",
@@ -364,16 +371,14 @@
     "# Solve\n",
     "t_eval = np.linspace(0, 2, 30)\n",
     "ode_solver = pybamm.ScipySolver()\n",
-    "solution = ode_solver.solve(model, t_eval)\n",
+    "solution = ode_solver.solve(model, t_eval, inputs={\"b\": 2})\n",
     "\n",
     "# Post-process, so that u1 can be called at any time t (using interpolation)\n",
     "t_sol1 = solution.t\n",
     "u1 = solution[\"u\"]\n",
     "\n",
-    "# Update parameters and solve again ###############################\n",
-    "new_parameter_values = pybamm.ParameterValues({\"a\": 4, \"b\": -1})\n",
-    "new_parameter_values.update_model(model, disc) # no need to re-discretise\n",
-    "solution = ode_solver.solve(model, t_eval)\n",
+    "# Solve again with different inputs ###############################\n",
+    "solution = ode_solver.solve(model, t_eval, inputs={\"b\": -1})\n",
     "t_sol2 = solution.t\n",
     "u2 = solution[\"u\"]\n",
     "###################################################################\n",
@@ -387,10 +392,10 @@
     "ax1.legend([\" * exp(-3 * t)\", \"u1\"], loc=\"best\")\n",
     "ax1.set_title(\"a = 3, b = 2\")\n",
     "\n",
-    "ax2.plot(t_fine, - np.exp(-4 * t_fine), t_sol2, u2(t_sol2), \"o\")\n",
+    "ax2.plot(t_fine, - np.exp(-3 * t_fine), t_sol2, u2(t_sol2), \"o\")\n",
     "ax2.set_xlabel(\"t\")\n",
-    "ax2.legend([\"-exp(-4 * t)\", \"u2\"], loc=\"best\")\n",
-    "ax2.set_title(\"a = 4, b = -1\")\n",
+    "ax2.legend([\"-exp(-3 * t)\", \"u2\"], loc=\"best\")\n",
+    "ax2.set_title(\"a = 3, b = -1\")\n",
     "\n",
     "\n",
     "plt.tight_layout()\n",
@@ -399,20 +404,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{Variable(0x1b4aef6f668c631, u, children=[], domain=[], auxiliary_domains={}): Multiplication(-0x36caade7fdf02dac, *, children=['-a', 'y[0:1]'], domain=[], auxiliary_domains={})}"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.rhs"
    ]
@@ -430,20 +424,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a: 3.0\n",
-      "b: 2.0\n",
-      "a + b: 5.0\n",
-      "a * b: 6.0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "parameters = {\"a\": a, \"b\": b, \"a + b\": a + b, \"a * b\": a * b}\n",
     "param_eval = pybamm.print_parameters(parameters, parameter_values)\n",
@@ -482,7 +465,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebooks/spatial_methods/finite-volumes.ipynb b/examples/notebooks/spatial_methods/finite-volumes.ipynb
index 819a778f4d..45d8f40bb0 100644
--- a/examples/notebooks/spatial_methods/finite-volumes.ipynb
+++ b/examples/notebooks/spatial_methods/finite-volumes.ipynb
@@ -615,19 +615,7 @@
       "    ├── @\n",
       "    │   ├── Sparse Matrix (42, 40)\n",
       "    │   └── y[0:40]\n",
-      "    └── numpy concatenation\n",
-      "        ├── Column vector of length 0\n",
-      "        ├── *\n",
-      "        │   ├── *\n",
-      "        │   │   ├── 2.0\n",
-      "        │   │   └── 1.0\n",
-      "        │   └── Column vector of length 1\n",
-      "        ├── Column vector of length 40\n",
-      "        └── *\n",
-      "            ├── *\n",
-      "            │   ├── 2.0\n",
-      "            │   └── 2.0\n",
-      "            └── Column vector of length 1\n",
+      "    └── Column vector of length 42\n",
       "The value of u on the left-hand boundary is [1.]\n",
       "The value of u on the right-hand boundary is [2.]\n"
      ]
@@ -663,25 +651,13 @@
      "output_type": "stream",
      "text": [
       "The gradient object is:\n",
-      "@\n",
-      "├── Sparse Matrix (41, 42)\n",
-      "└── +\n",
-      "    ├── @\n",
-      "    │   ├── Sparse Matrix (42, 40)\n",
-      "    │   └── y[0:40]\n",
-      "    └── numpy concatenation\n",
-      "        ├── Column vector of length 0\n",
-      "        ├── *\n",
-      "        │   ├── *\n",
-      "        │   │   ├── -0.024999999999999998\n",
-      "        │   │   └── 3.0\n",
-      "        │   └── Column vector of length 1\n",
-      "        ├── Column vector of length 40\n",
-      "        └── *\n",
-      "            ├── *\n",
-      "            │   ├── 0.02499999999999991\n",
-      "            │   └── 4.0\n",
-      "            └── Column vector of length 1\n",
+      "+\n",
+      "├── @\n",
+      "│   ├── Sparse Matrix (41, 39)\n",
+      "│   └── @\n",
+      "│       ├── Sparse Matrix (39, 40)\n",
+      "│       └── y[0:40]\n",
+      "└── Column vector of length 41\n",
       "The gradient on the left-hand boundary is [3.]\n",
       "The gradient of u on the right-hand boundary is [4.]\n"
      ]
@@ -714,26 +690,18 @@
      "output_type": "stream",
      "text": [
       "The gradient object is:\n",
-      "@\n",
-      "├── Sparse Matrix (41, 42)\n",
-      "└── +\n",
-      "    ├── @\n",
-      "    │   ├── Sparse Matrix (42, 40)\n",
-      "    │   └── y[0:40]\n",
-      "    └── numpy concatenation\n",
-      "        ├── Column vector of length 0\n",
-      "        ├── *\n",
-      "        │   ├── *\n",
-      "        │   │   ├── 2.0\n",
-      "        │   │   └── 5.0\n",
-      "        │   └── Column vector of length 1\n",
-      "        ├── Column vector of length 40\n",
-      "        └── *\n",
-      "            ├── *\n",
-      "            │   ├── 0.02499999999999991\n",
-      "            │   └── 6.0\n",
-      "            └── Column vector of length 1\n",
-      "The value of u on the left-hand boundary is [5.]\n",
+      "+\n",
+      "├── @\n",
+      "│   ├── Sparse Matrix (41, 40)\n",
+      "│   └── @\n",
+      "│       ├── Sparse Matrix (40, 41)\n",
+      "│       └── +\n",
+      "│           ├── @\n",
+      "│           │   ├── Sparse Matrix (41, 40)\n",
+      "│           │   └── y[0:40]\n",
+      "│           └── Column vector of length 41\n",
+      "└── Column vector of length 41\n",
+      "The value of u on the left-hand boundary is [0.]\n",
       "The gradient on the right-hand boundary is [6.]\n"
      ]
     }
@@ -797,25 +765,13 @@
      "text": [
       "@\n",
       "├── Sparse Matrix (40, 41)\n",
-      "└── @\n",
-      "    ├── Sparse Matrix (41, 42)\n",
-      "    └── +\n",
-      "        ├── @\n",
-      "        │   ├── Sparse Matrix (42, 40)\n",
-      "        │   └── y[0:40]\n",
-      "        └── numpy concatenation\n",
-      "            ├── Column vector of length 0\n",
-      "            ├── *\n",
-      "            │   ├── *\n",
-      "            │   │   ├── -0.024999999999999998\n",
-      "            │   │   └── -1.0\n",
-      "            │   └── Column vector of length 1\n",
-      "            ├── Column vector of length 40\n",
-      "            └── *\n",
-      "                ├── *\n",
-      "                │   ├── 0.02499999999999991\n",
-      "                │   └── 1.0\n",
-      "                └── Column vector of length 1\n"
+      "└── +\n",
+      "    ├── @\n",
+      "    │   ├── Sparse Matrix (41, 39)\n",
+      "    │   └── @\n",
+      "    │       ├── Sparse Matrix (39, 40)\n",
+      "    │       └── y[0:40]\n",
+      "    └── Column vector of length 41\n"
      ]
     }
    ],
@@ -878,11 +834,11 @@
      "output_type": "stream",
      "text": [
       "@\n",
-      "├── Sparse Matrix (40, 42)\n",
+      "├── Sparse Matrix (40, 41)\n",
       "└── +\n",
-      "    ├── Column vector of length 42\n",
+      "    ├── Column vector of length 41\n",
       "    └── @\n",
-      "        ├── Sparse Matrix (42, 40)\n",
+      "        ├── Sparse Matrix (41, 40)\n",
       "        └── y[0:40]\n"
      ]
     }
@@ -904,13 +860,13 @@
       "laplacian matrix is:\n",
       "\n",
       "1/dx^2 *\n",
-      "[[ 1. -2.  1. ...  0.  0.  0.]\n",
-      " [ 0.  1. -2. ...  0.  0.  0.]\n",
-      " [ 0.  0.  1. ...  0.  0.  0.]\n",
+      "[[-0.025  0.025  0.    ...  0.     0.     0.   ]\n",
+      " [ 0.    -0.025  0.025 ...  0.     0.     0.   ]\n",
+      " [ 0.     0.    -0.025 ...  0.     0.     0.   ]\n",
       " ...\n",
-      " [ 0.  0.  0. ...  1.  0.  0.]\n",
-      " [ 0.  0.  0. ... -2.  1.  0.]\n",
-      " [ 0.  0.  0. ...  1. -2.  1.]]\n"
+      " [ 0.     0.     0.    ...  0.025  0.     0.   ]\n",
+      " [ 0.     0.     0.    ... -0.025  0.025  0.   ]\n",
+      " [ 0.     0.     0.    ...  0.    -0.025  0.025]]\n"
      ]
     }
    ],
@@ -936,7 +892,7 @@
     {
      "data": {
       "text/plain": [
-       "0.5950588420091663"
+       "0.4124502999475226"
       ]
      },
      "execution_count": 25,
@@ -957,7 +913,7 @@
     {
      "data": {
       "text/plain": [
-       "0.3470027389994357"
+       "0.2925413999473676"
       ]
      },
      "execution_count": 26,
@@ -1035,9 +991,7 @@
       "int(v):\n",
       "\n",
       "@\n",
-      "├── *\n",
-      "│   ├── 39.47841760435743\n",
-      "│   └── Sparse Matrix (1, 10)\n",
+      "├── Sparse Matrix (1, 10)\n",
       "└── *\n",
       "    ├── /\n",
       "    │   ├── y[40:50]\n",
@@ -1083,7 +1037,8 @@
     {
      "data": {
       "text/plain": [
-       "matrix([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])"
+       "matrix([[39.4784176, 39.4784176, 39.4784176, 39.4784176, 39.4784176,\n",
+       "         39.4784176, 39.4784176, 39.4784176, 39.4784176, 39.4784176]])"
       ]
      },
      "execution_count": 30,
@@ -1092,7 +1047,7 @@
     }
    ],
    "source": [
-    "int_v_over_r_disc.children[0].children[1].evaluate() / micro_mesh.d_edges"
+    "int_v_over_r_disc.children[0].evaluate() / micro_mesh.d_edges"
    ]
   },
   {
@@ -1282,7 +1237,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/tests/unit/test_expression_tree/test_matrix.py b/tests/unit/test_expression_tree/test_matrix.py
index b135bd75cd..fa3aa26bb1 100644
--- a/tests/unit/test_expression_tree/test_matrix.py
+++ b/tests/unit/test_expression_tree/test_matrix.py
@@ -24,7 +24,9 @@ def test_matrix_evaluate(self):
 
     def test_matrix_operations(self):
         np.testing.assert_array_equal((self.mat + self.mat).evaluate(), 2 * self.A)
-        np.testing.assert_array_equal((self.mat - self.mat).evaluate(), 0 * self.A)
+        np.testing.assert_array_equal(
+            (self.mat - self.mat).evaluate().toarray(), 0 * self.A
+        )
         np.testing.assert_array_equal(
             (self.mat @ self.vect).evaluate(), np.array([[5], [2], [3]])
         )
diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py
index 19976e8dca..4ae1eb7740 100644
--- a/tests/unit/test_expression_tree/test_unary_operators.py
+++ b/tests/unit/test_expression_tree/test_unary_operators.py
@@ -16,7 +16,6 @@ def test_unary_operator(self):
 
         # with number
         log = pybamm.log(10)
-        self.assertIsInstance(log.children[0], pybamm.Scalar)
         self.assertEqual(log.evaluate(), np.log(10))
 
     def test_negation(self):

From c2e1c1a5de99410a2d8b7d83dd702bf7221df9a3 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Fri, 31 Jan 2020 23:35:48 -0500
Subject: [PATCH 06/11] #709 fixing examples

---
 .../models/compare-lithium-ion.ipynb          | 54 +++++------
 examples/notebooks/models/lead-acid.ipynb     | 97 ++++++++++---------
 2 files changed, 77 insertions(+), 74 deletions(-)

diff --git a/examples/notebooks/models/compare-lithium-ion.ipynb b/examples/notebooks/models/compare-lithium-ion.ipynb
index 8dc595a4c5..8971dea6c2 100644
--- a/examples/notebooks/models/compare-lithium-ion.ipynb
+++ b/examples/notebooks/models/compare-lithium-ion.ipynb
@@ -104,7 +104,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x7fc494f416d8>"
+       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x10291c630>"
       ]
      },
      "execution_count": 4,
@@ -248,14 +248,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solved the Doyle-Fuller-Newman model in 3.515 seconds\n"
+      "Solved the Doyle-Fuller-Newman model in 0.426 seconds\n"
      ]
     },
     {
@@ -265,31 +265,30 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/input_parameter.py\u001b[0m in \u001b[0;36m_base_evaluate\u001b[0;34m(self, t, y, u)\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;31m# raise more informative error if can't find name in dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/input_parameter.py\u001b[0m in \u001b[0;36m_base_evaluate\u001b[0;34m(self, t, y, u)\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;31m# raise more informative error if can't find name in dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mKeyError\u001b[0m: 'current'",
       "\nDuring handling of the above exception, another exception occurred:\n",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-e47831a38128>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0msolution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_solver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_eval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"current\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Solved the {} in {:.3f} seconds\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/solvers/base_solver.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self, model, t_eval, external_variables, inputs)\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;31m# Solve\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         solution, solve_time, termination = self.compute_solution(\n\u001b[0;32m---> 96\u001b[0;31m             \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_eval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m         )\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/solvers/ode_solver.py\u001b[0m in \u001b[0;36mcompute_solution\u001b[0;34m(self, model, t_eval, inputs)\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0;31m# Identify the event that caused termination\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0mtermination\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_termination_reason\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msolution\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolve_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtermination\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/solvers/base_solver.py\u001b[0m in \u001b[0;36mget_termination_reason\u001b[0;34m(self, solution, events)\u001b[0m\n\u001b[1;32m    304\u001b[0m                 \u001b[0my_event\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_external\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_event\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_pad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_ext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m                 final_event_values[name] = abs(\n\u001b[0;32m--> 306\u001b[0;31m                     \u001b[0mevent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt_event\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_event\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    307\u001b[0m                 )\n\u001b[1;32m    308\u001b[0m             \u001b[0mtermination_event\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_event_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfinal_event_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/functions.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m             \u001b[0mevaluated_children\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluated_children\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/functions.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m             \u001b[0mevaluated_children\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluated_children\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/unary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0mchild\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/symbol.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    556\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 558\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_base_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_for_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/input_parameter.py\u001b[0m in \u001b[0;36m_base_evaluate\u001b[0;34m(self, t, y, u)\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;31m# raise more informative error if can't find name in dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Input parameter '{}' not found\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-11-c3cb7a07a49f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0msolution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_eval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"current\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Solved the {} in {:.3f} seconds\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/base_solver.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self, model, t_eval, external_variables, inputs)\u001b[0m\n\u001b[1;32m    426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m         \u001b[0;31m# Identify the event that caused termination\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mtermination\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_termination_reason\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m         \u001b[0mpybamm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Finish solving {} ({})\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtermination\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/base_solver.py\u001b[0m in \u001b[0;36mget_termination_reason\u001b[0;34m(self, solution, events)\u001b[0m\n\u001b[1;32m    563\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m                 final_event_values[name] = abs(\n\u001b[0;32m--> 565\u001b[0;31m                     \u001b[0mevent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt_event\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_event\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    566\u001b[0m                 )\n\u001b[1;32m    567\u001b[0m             \u001b[0mtermination_event\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_event_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfinal_event_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/functions.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m             \u001b[0mevaluated_children\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluated_children\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/functions.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m             \u001b[0mevaluated_children\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluated_children\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/unary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0mchild\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/symbol.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    556\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 558\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_base_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_for_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/input_parameter.py\u001b[0m in \u001b[0;36m_base_evaluate\u001b[0;34m(self, t, y, u)\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;31m# raise more informative error if can't find name in dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Input parameter '{}' not found\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m: \"Input parameter 'current' not found\""
      ]
     }
@@ -298,9 +297,10 @@
     "timer = pybamm.Timer()\n",
     "solutions = {}\n",
     "t_eval = np.linspace(0, 0.5, 100)\n",
+    "solver = pybamm.CasadiSolver()\n",
     "for model_name, model in models.items():\n",
     "    start = timer.time()\n",
-    "    solution = model.default_solver.solve(model, t_eval, inputs={\"current\": 1})\n",
+    "    solution = solver.solve(model, t_eval, inputs={\"current\": 1})\n",
     "    end = timer.time()\n",
     "    print(\"Solved the {} in {:.3f} seconds\".format(model.name, end-start))\n",
     "    solutions[model_name] = solution"
@@ -431,7 +431,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebooks/models/lead-acid.ipynb b/examples/notebooks/models/lead-acid.ipynb
index 09e42c6b13..42e073f627 100644
--- a/examples/notebooks/models/lead-acid.ipynb
+++ b/examples/notebooks/models/lead-acid.ipynb
@@ -206,7 +206,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We load process parameters for each model, using the same set of (default) parameters for all"
+    "We load process parameters for each model, using the same set of (default) parameters for all. In anticipation of changing the current later, we make current an input parameter"
    ]
   },
   {
@@ -219,7 +219,10 @@
     "models = [loqs, composite, full]\n",
     "\n",
     "# process parameters\n",
+    "def current_function(t):\n",
+    "    return pybamm.InputParameter(\"current\")\n",
     "param = models[0].default_parameter_values\n",
+    "param[\"Current function [A]\"] = current_function\n",
     "for model in models:\n",
     "    param.process_model(model)"
    ]
@@ -237,7 +240,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "discs = {}\n",
     "for model in models:\n",
     "    # load and process default geometry\n",
     "    geometry = model.default_geometry\n",
@@ -246,15 +248,14 @@
     "    # discretise using default settings\n",
     "    mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts)\n",
     "    disc = pybamm.Discretisation(mesh, model.default_spatial_methods)\n",
-    "    disc.process_model(model)\n",
-    "    discs[model] = disc"
+    "    disc.process_model(model)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we solve each model using its own individual solver"
+    "Finally, we solve each model using CasADi's solver and a current of 1A"
    ]
   },
   {
@@ -262,23 +263,13 @@
    "execution_count": 7,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/vsulzer/Documents/Energy_storage/PyBaMM/PyBaMM-env/lib/python3.7/site-packages/scipy/integrate/_ivp/ivp.py:146: RuntimeWarning: invalid value encountered in greater_equal\n",
-      "  up = (g <= 0) & (g_new >= 0)\n",
-      "/Users/vsulzer/Documents/Energy_storage/PyBaMM/PyBaMM-env/lib/python3.7/site-packages/scipy/integrate/_ivp/ivp.py:147: RuntimeWarning: invalid value encountered in less_equal\n",
-      "  down = (g >= 0) & (g_new <= 0)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solved the LOQS model in 0.038 seconds\n",
-      "Solved the Composite model in 0.273 seconds\n",
-      "Solved the Full model in 1.584 seconds\n"
+      "Solved the LOQS model in 0.133 seconds\n",
+      "Solved the Composite model in 1.034 seconds\n",
+      "Solved the Full model in 1.187 seconds\n"
      ]
     }
    ],
@@ -286,9 +277,10 @@
     "timer = pybamm.Timer()\n",
     "solutions = {}\n",
     "t_eval = np.linspace(0, 0.5, 100)\n",
+    "solver = pybamm.CasadiSolver()\n",
     "for model in models:\n",
     "    start = timer.time()\n",
-    "    solution = model.default_solver.solve(model, t_eval)\n",
+    "    solution = solver.solve(model, t_eval, inputs={\"current\": 1})\n",
     "    end = timer.time()\n",
     "    print(\"Solved the {} in {:.3f} seconds\".format(model.name, end-start))\n",
     "    solutions[model] = solution"
@@ -313,26 +305,9 @@
    "execution_count": 8,
    "metadata": {},
    "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'Time [h]'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/solution.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;31m# return it if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    182\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'Time [h]'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-8-26bb05dbf5bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolutions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolutions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Time [h]\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mvoltage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolutions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Terminal voltage [V]\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvoltage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/solution.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    182\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             \u001b[0;31m# otherwise create it, save it and then return it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/solution.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, variables)\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0mpybamm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Post-processing {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m             var = pybamm.ProcessedVariable(\n\u001b[0;32m--> 151\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mknown_evals\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m             )\n\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'Time [h]'"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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WVtSrIn2eX0X7Q8sZt3EQqw23OOz1D2Xco0iMr0P7yc1oHurD+00fwc9DN0hqmpY37jYifGourifAbkxrYmh5wK98M8YGNWXnljGM3D+D/bYKSm8k2GMbvx9uS4OxZ+hWJ5DXI8pR1NHu3hfUNE3LgXtVT9UFnMzcigLZL8mlWY5ShD/xDnNe2MpQzyfwSl386WrAHEJKDOf7jRt5avQ65mzXiz9pmmZZd0sa24DzInLLnA24kXqOXiziATE4FKH1M1/xU6sl9LD3w94oHHK9hEO58ZRx/pwPFu/gmc83suWf89YOVbOShQsXUr9+fdzd3XFwcKBChQrpcy4VNlFRUXh5/bfI2cGDB4mKiuLSpYI7vunatWsopZgxY0aOznvppZcID8+bxU2zTRoiUktE9pt7IRExpp5z0DKhaeYyLf60nKVPjqGx0ZFbBsUhzyP4BUXBje957uvN9PxuJ/+ev27tULUHqG/fvnTo0IGyZcvy3XffsWrVKv73v//x22+/0atXL2uHZ3Hdu3dn5cqV6a8PHjzIkCFDCnTSyI/u1hDeG5grprUrtALAJ6gxo8s14vlt4/l033T+soULpTYTXGw7e4+2oeHYeF6uHUiv+kG46vaOQm3ZsmWMHTuWadOm8fLL//VpqVevHq+88gqrVq2yYnR5w9fXN302Wy3v3K16ajwQq5RarZTqqpRye1BBafdBKao+/j9md97KMK/aFE8xctwxmSv+8wkrNZxZmzfx1KhoZm3T7R2F2bhx46hatWqGhJHGxsYmw0JC586d48UXX8TT0xNnZ2ciIiK4c5LPgIAA3nnnHT755BNKlSqFm5sbffv2RURYvnw5wcHBFC1alNatW3Px4sX086Kjo1FKsWrVKp555hlcXFwoU6YMkydPzhTXvHnzCA0NxcHBAT8/v0xrfVy6dInu3btTunRpHB0dKVOmDD169Eg/fnv1VHR0NC1atAAgMDAQpRQBAQHpZY8fP07Hjh3x8PDA2dmZxo0bc+DAgbt+pjNmzEApRUxMDBERETg7O/Poo48SExPD9evX6dq1K25ubpQtW5bZs2dnOn/ixImUL18eBwcHgoKCsly9cOHChVSoUAEnJyfq1q2b7YJVU6dOJTg4GAcHB/z9/Rk5cuRdY7eo7Ka/BXyBd4AdgBFTm8WPQAfAKbvz8tuWX6dGf1CuXzgiE2Y3k2rTK0vIjBCpOj1Y2oxpIYHvL5DG49bJpkNnrR1ivlPQp0ZPTEwUBwcH+eCDD8wqX7t2bSlRooRMnz5dli5dKk8++aQUKVJEDh06lF7G399ffHx8pE2bNrJixQoZNmyYAPL2229L1apVZeHChfL999+Lu7u79OzZM/28tKnOfX19pX///vLLL79Iz549BZBly5all1u5cqUA0qVLF1mxYoV8+umnYm9vn+FaXbt2lYoVK8qcOXMkOjpavvvuO+nRo0f68dunKr98+bKMHj1aAFm0aJFs2bJFYmJiRETk/Pnz4ufnJ48++qjMnTtXli1bJrVr1xZfX19JSEjI9nP65ptvBJCQkBCZPHmyLF++XMLCwiQwMFAiIyPlgw8+kFWrVknHjh3F1tZWTpw4kX7ulClTBJA+ffrIypUr5f333xellIwYMSK9zK5du8TGxkbatWsny5cvl5EjR0pgYKAA8s0336SXGzlypNja2qbfb8SIEWJvb59h6vgXX3xR7vXdl9up0c364gXKAR8Ce1ITyFXgB6A5YGvONay1PexJI03s4VXSb3r19PU76k4NkcYjeor/e0ulx8wdcvRs1usnPIyy/GMa7GrdLQfi4uIEkMmTJ9+z7IoVKwSQ6Ojo9H3Xrl0TLy8veeWVV9L3+fv7S7ly5SQ5OTl9X/Xq1cXGxkaOHDmSvq9fv37i7e2d/jotadz+5S4i0rBhQ6lZs2b665o1a0pERESGMp9++qkYDIb0L9/g4GCZMGFCtu/lzvUtli1bJoAcPXo0Q7kPP/xQPDw85Pz58+n7Lly4IK6urjJx4sRsr5+WNGbMmJG+7+effxZAunbtmr7v0qVLYmtrK1988YWIiKSkpEjp0qXlpZdeynC91157TVxdXeXGDdPaOe3bt5dKlSqJ0WhML5OWnNOSxuXLl8XFxUWioqIyXGvgwIFSokSJ9P8/eZk0zBoRLiL/iMgwEQkFQjFVXYUDS4EzSqmvcvukoz0Ypcs9zciXtvFtxe4EJ8MFW4gttYngwEHsPbqCp8etY8Ty/Vy5mWTtUDULSVt69W62b9+Ot7c39erVS9/n4uLCM888k2n98IiICGxs/lvbJSgoiICAAAIDAzPsO3v2LImJiRnObdOmTYbXzz77LLt27SIlJYWUlBRiYmJo3759hjKRkZEYjUa2bNkCwKOPPsqoUaP44osvOHgw9/1tfv31V55++mlcXV1JTk4mOTmZokWLUq1atUzVcllp0KBB+s9BQUEA1K9fP32fm5sbxYsXJzY2FoCTJ09y6tSpLN/flStX2LNnD2D6f9GyZcsM/9+effbZDOds2bKF69ev0759+/TYk5OTqV+/PmfOnOHkyZM5/DRyLsfLvYrIPmCgUmo08DHwGtAdyJs1HDXLUYrHHn+LWVV7sGx1Hz47s4HjjskQMI/Qq6v4YfOLLNh1kr6NKhJZ3Q8bgx52ky7qsrUjMJunpycODg4cP378nmXj4uLw9vbOtL9EiRJcuJBxjK67u3uG1/b29lnuExESExOxt7dP33/nPby9vUlOTubcuXMAJCUlZVo7PO11WhwTJ05k0KBBfPTRR/Tq1YugoCCGDh1Kx44d7/k+b3fu3Dm2bt3K3LlzMx27PSFk5/b3nPYes/ocbt68CZg+49vfT5o739/p06ez/JzujB0gODg4y9hOnDiBv7//Pd/D/chR0lBKOQMtgY5AY8Ae2ABkbvXR8i2DvTOtmk/m6YtHmbqyFzNvHOdQ0Us4uozH72IQH/7Yme+2/sugZypTq5yntcPVcsjOzo7atWuzcuVKhg0bdteypUqVIj4+cwfJM2fO4OHhYbGY7rxHfHw8tra26Q3XdnZ2mcqcOXMGID0Od3d3JkyYwIQJE/jzzz8ZOXIknTp1IiwsjMqVK5sdi4eHBy1btmTgwIGZjhUtWjRH78scpUqVAjJ/Bne+v5IlS2b5Od0urexPP/2UKQkBVKxY0TJB38U9q6eUUvZKqTZKqblAPDAL8AEGAGVEJEJEdPVUAeRcLJA3O5rGdzRKHd9x2PMf/IOiMF6fzXNfb+HV73Zx/Lxeka2gefvtt9m5cyczZ87MdMxoNPLLL78AULNmTeLj41m/fn368YSEBH7++Wfq1KljsXgWL16c6XW1atWwsbHBxsaGatWqMX/+/Axl5s2bh8FgoFatWpmuFxYWxqhRozAajdn2MEp7Ckj7F3+aBg0asG/fPoKDgwkPD8+w5cWXrq+vL6VLl87y/bm6uhIaGgpA9erVWbp0aYZVOhctWpThnFq1auHk5MSpU6cyxR4eHp4nSe9Odxun0RTTE0UrwBX4G/gUmC0ih/M8Mu2B8QlqzJhyjdi5ZSwj939jms/KZwOhnlvZfaQ9DcfG0+3JQHo9FUQRhxzXaGpW0KJFC/r06UO3bt3YtGkTrVq1okiRIvz9999MnjyZgIAAmjRpQuPGjXniiSeIjIzkk08+wdPTk9GjR3Pjxg369etnsXhWrFjBgAEDqFevHosWLWL16tUsWbIk/fiQIUNo3LgxXbt2pWPHjuzZs4eBAwfSo0eP9LEXderUoU2bNoSEhKCU4uuvv8bFxYUaNWpkec+0BPDVV1/RsWNHnJ2dCQ0NpU+fPnz//ffUr1+f3r174+Pjw5kzZ1i3bh116tThueees9j7BjAYDERFRdGzZ088PT15+umnWbduHV9++SXDhw/H0dERgPfee4+aNWvSoUMHunXrxt69e5k2bVqGa7m7uxMVFcVbb73Fv//+S926dTEajRw8eJC1a9dmSs55IrsWcky9pI4BnwBVsiuX3zfdeypnkm9ckYXLukvdaZXTe1o9O+FJqTxguoQPWy1zdxyXlBTjvS9UgBX0Lre3W7BggURERIirq6vY2dlJ+fLlpW/fvhIXF5deJj4+Xjp37izu7u7i6OgodevWle3bt2e4jr+/v/Tt2zfDvqx66KT1MLp69aqI/Nd76pdffpEmTZqIk5OT+Pj4yKRJkzLFOmfOHAkJCRE7Ozvx8fGRDz74QJKSktKPv/POOxISEiJFihQRNzc3iYiIkPXr16cfv7P3lIjI6NGjpUyZMmJjYyP+/v7p+2NjY+Wll14Sb29vsbe3F39/f+nUqZPs3bs328/yzvcmInL06NFM3Yez+7wmTJgg5cqVEzs7OwkMDJSxY8dmuse8efOkXLly4uDgILVr15bt27dn6nIrIvLdd99J1apVxdHRUdzd3aVGjRoyZsyY9ON52XtKiWQ9wEsp9YSIbM77tJW3wsPDxZweEVpG184dZMrq3nx/K5YkpXAyCj7nK7L7fCeCSxdncItgqgdYrs47P9m/fz+VKlWydhiFQnR0NE899RR79uwhJCTE2uFot7nb77lSapeIZDl51d3aNHL8TauUsr93Ka0gKOJVgT7PrWRJrU+pb7TPsF75rasLaD95M71mxXDyom7v0LSHyd2Sxg2lVNaVhVlQStmknlP1/sPS8gu/is357MUdTC3XifLJwlk7OO2zhrCAKLYfWEODMesYu+oACYl6vXJNexjcrVVTAeFKqSJmXsuAXk+jcDIYqFnnfeZVe5WFq95i4vmdHHW6hSFwBiGXvflq3cvM23mS95s+QqtHS5s1qEx7OERERJBdFbhWMN2rK8zEHF5P/3YUYrZO7kS2mkmTM3uZvPpN5iTHc9D9LMVcP6HEuVDenhvJt1tM7R1V/NzvfUFN0wqcuyWN3LYEHsvleVoB4VYihPdeWEP7ffMZuW04m2ySOeK9lwru+7h0pimtJl2ibVVf3mtSEW9XR2uHq2maBWWbNETk7vMEaw+9ssHt+fKRNmxYP4SRRxbxr70B/Jbz2PVo1u59gV/2xtGrfhAv1w7E0c7m3hfUNC3fM2vCQk3LjrKxpe5TQ1kcGc07RSpTxGjksEsCxrJf8Uix8YxbtYtG49azct9pXbetaYWAThqaRdgVKc6LbefyU6MZtFVuGIGDHrGUCBpKUfmWnt/t4IVp2zhw+qq1Q9U07T7opKFZlKdPdaK6bGRulT5UTVZctlEcL7mTymUH8u+pX2j62XoGLdnLxeuJ976Ypmn5jk4aWp6o9NjLzOiyg1ElG1Ay2cgJhxQu+y/kMZ8RLN6xlafGRPPtlmMkpxitHaqmaTmQq6ShlPJQSumEo92VsnOgSePxLG27glcd/XEwGjlY9DIO5cZT1uVLopbG0GzCBjYdPmftUAudqKgolFKZtoYNG+b4OmnTl8N/a37v3bvX0iHfNy8vL6KionJ0zp3vT7s3s6csVUo1AAYB1QBHoAYQo5SaBKwXkcwrmmga4ORehl6RP9Hmn18Zs74/qww3OeR1mAD3wXDmKTpNvUrj4JIMaFaZMp7O1g630HBzc0ufAv32fZp2P8xKGkqp54DvgQVAX+DL2w4fB14BdNLQ7qp0uYaMKduAHVvG8On+GRywVeCzlioem4k5/DwNx52lx5OBvB4RhIuegv2+2dra8vjjj1s7DK2QMbeKaRAwTkQigal3HNsLZL32oKbdSSmqP/EOczttZmCxarinGDnidIvEwOmEFR/D1PW7qD8mmsW/n9RddPPQsWPHUErx008/Zdj/0ksvER6e5eSmZkurwvrtt99o1aoVLi4ulC9fnlWrVpGSkkK/fv3w8vLCx8eHsWPHZjp/3rx5hIaG4uDggJ+fHwMGDCA5OePcZuvXr6dKlSo4OjpSrVo1Nm/OekLuJUuWEB4ejqOjIyVLluTdd98lKSnpvt7fw87cpBEILM/mWAJg1jOvUspPKbVWKfWXUmqfUuqtLMo8opTaopS6pZR6545jTZRSB5RSh5VS75sZu5YP2Ti60qHlDH5qPofnbbxQwAH3M3iWG0EJ25n8b+4u2n65mT9PXrJ2qAVacnJyhu1BJuKePXtSp04dFi9ejL+/P+3ateONN97g6tWrzJo1i+JxVwoAACAASURBVHbt2tG3b1+2bduWfs6qVauIjIykatWqLFmyhN69ezN69GjeeOON9DKnTp2iadOmeHh4sGDBAnr27EmnTp1ISMg44/K8efN49tlnqVGjBkuXLmXw4MFMmTKF/v37P7DPoDAytw4gFggD1mRxrCpwxMzrJAN9RSRGKVUU2KWUWi0if91W5gLwJtD69hNTZ9GdBDwNnAR2KKWW3nGuVsC4lQil/wtrabdnFp9u/5RttkaulthNpWJ/cu50S1pNukSHan70a1IRryIOVoszdGao1e4NsOfFPTk+5/z589jZ2WXYt3r16hw3hudW586d01f/8/X1JTg4mAMHDrBmjelrpGHDhsydO5dFixZRs2ZNAAYNGkRERET6MrVNmjQBoH///nz44Yf4+voyfvx4HB0d+fnnn3F2NrWBubi48MILL6TfW0To168fXbp04Ysvvkjf7+DgQK9evejfvz+enp55/yEUQuY+acwAopRS7YC030JRStUG3gOmZXfi7UQkTkRiUn++CuzHtN747WXiRWQHcOczZA3gsIgcEZFEYA6mpWi1QqB86PN8/eJOxvk0xSfZyEl7IxfL/EhVn+Es372Vp0ZFM3XDEZJ0F12zubm5sWPHjgxb2pfzg9CgQYP0n4OCggCoX79++j6DwUDZsmWJjY0FICUlhZiYGNq3b5/hOpGRkRiNRrZs2QLA9u3befrpp9MTBkCbNm0ynHPw4EGOHz9Ohw4dMjxp1a9fn5s3b+bL3l8FhblPGh8DAcA8IG2V9o2YelHNEJHMFZP3oJQKAB4Dtt29ZDof4MRtr08CWf4FKKVewdQ4T5kyZXIammYlytaOhg1HUufK28z45XWmXT/EwaJXcHCZQJkLQQxf3oXZ248zuEUwdSsUf6Cx5eZf+tZma2t73+0T98Pd/b+Zju3t7TPtS9t/86bpK+XcuXMkJSVRokSJDGXSXl+4cAGA06dPExYWlqGMs7MzRYr8t4rDuXOmbtzNmjXLMrYTJ05kuV+7N7OShogYgW5KqbFAA8ALUzXSGhH5M6c3TV2jYyHwtohcyen59yIiU4ApYFru1dLX1/KWo2tpXu3wI62OrGHsuvf5xXCDQ17/EOA+GMOZ+nSZfo2GlUoy8JlK+Hu6WDvcAsnR0TT7cGJixpH5Fy9etEY4gGmchZ2dHfHx8Rn2nzlzBgAPD9PywiVLlsxUJiEhgWvXrqW/Tis7ZcoUHnvssUz3CgwMtGjsD5Mc9WsUkX3Avvu5oVLKDlPC+EFEFuXg1FjA77bXvqn7tEKqVNn6jArcRoetY/nkr284aKvAZw1VPDaz+5/neXrsWbo/GUivp3QX3Zzy9vbGzs6O/fv3p++7du0amzdvxt/f3yox2djYUK1aNebPn89rr72Wvn/evHkYDAZq1aoFQPXq1Zk+fToJCQnpVVSLFy/OcK2KFSvi4+PDsWPH6NGjx4N7Ew8Bc8dp3G3ZVyNwBfhHRFLucR2Fqf1jfy6qtHYA5ZVSgZiSRUfg+RxeQytolKJ6rb7Mfaw7C1e+yefnd3LE6SY2ZacRdqk0U9d3ZWHMST5oVomWVfSqgeYyGAy0atWKcePG4e/vj7u7O2PGjMHJycmqcQ0ZMoTGjRvTtWtXOnbsyJ49exg4cCA9evTA19cXgLfffptJkybxzDPP0KdPH06dOsWIESMyxG4wGBgzZgydO3fmypUrNG3aFHt7e44cOcKPP/7IggULMrSJaOYz959nW7n3qnzXlFKTgf6p1VlZqQ10BvYopXan7vsAKAMgIpOVUiWBnYArYFRKvQ1UFpErSqk3gJWADTA99clHewjYOroR2WomjU//wcTVbzE/5RwHisXh7fox7mer89acZ/l+679EtQwmuLQe9WyOiRMn8sorr/D6669TrFgxBgwYwObNm63aSNyoUSPmzJnDsGHD+OGHH/D29qZv374MGTIkvYyPjw/Lly/nzTffpG3btlSqVInvv/+eVq0y9ouJjIzE1dWV4cOHM336dGxsbChbtizPPPNMehuLlnPKnH7bSqnGwFfAb8BS4CxQHFPvpQaYelCFAO8An4hIVB7Fm2Ph4eGyc+dOa4ehWdiBP75lxM7R7LI1/f6WuWXLpbi2xN18jOdqlOGdRhUp5pK7L4b9+/dTqVJuF67UtILhbr/nSqldIpJlLwpzu9x2A74XkW4iskRENqf+92VM04tEisiHwGjgxVzEr2k5UrFKF77psoORJRvgnWLkuEMyVwLmUrX0SBbv3EXE6Gi+26Jn0dU0SzM3aTQForM5Fo1pwB3AWqD0/YWkaeZRdg40bTyeZW1+poeDH3YiHHC9QNGg0QQ6T2Pgkj945vONbDty3tqhalqhYW7SuIwpcWSlKZA214MjpkZxTXtgnIsF8GbH5Sx5YiQRKfYkGBT/eP9FhaCB3Lz6C5FTttJ79u/EXb5h7VA1rcAztyF8LDBaKeUHLCNjm8azmGa+BagH7LJ0kJpmDr8Kzfg8qAkbNnzEp4fn86+dAcoso9q1aKL/epFf/zrDG/WD6P5kIA62NtYOV9MKJLOeNFK7x3YCHgFmYpq8cCZQAegkIuNSi45Hd4PVrMlg4Ml6USyKXMtbLhVxMho5WOQq9mU/p5L7ZMas+pNG49bz2/4zd72MnmFXK8zu5/fb7NX3RGS2iIQBzphmvXUWkTARmX1bmVMiciHX0WiahdgX8aZ7uwUsrf8lTcWZRIPikNc/BAYNxj5xGd1m7uDlGTs4du56pnPt7Oy4cUNXZWmF140bNzJNZmmuHC/ZKiI3ReRfEbl579KaZl0l/esy8sWtTK/wEuWThXhbOO27msf8P2L3ka00GreeUSv/JiHxv/UavL29iY2NJSEhQT9xaIWKiJCQkEBsbCze3t65uoZZ4zQAlFI+wHOYqqQcswimS64iyGN6nIaWJvnGJeaufINJF3/nqsGArQhlL5Zhd3xXvF09GNC8Es1DS6GU4sqVK8THx+sFe7RCx87ODm9vb1xdXbMtc7dxGuYO7qsCbADOAf7A30AxoCQQB/wrIk/kPPy8p5OGdqfzsTv57Le3WJw6V6ZnMjieqcvfV5pSq6wXUS2DqViyqJWj1DTrscTgvtGYek1VABTQWURKAw2BFGCgJQLVtAfB0yecjzpvZFbwGwQnw3lbiPVZT5WAKA7HbqbZhA0M/ekvrtzUTxmadidzk8ZjwHeYJieE1OopEVkDDAVGWT40TctDShEa3pNZnbcS5VEzfa3ypMDpVPUez7eb91B/9DoW7DqJ0ajbNTQtjblJwwDcTJ2I8CwZpyg/ClS0dGCa9iAY7F1o22IqPzWfS0eDJwL8XSyOUkFD8badxzvzd9P+qy3sjb1s7VA1LV8wN2nsB8qm/rwNeEsp5aeUKgH8DziWB7Fp2gPjViKEAZ2jmftoPx5NVly0UZwotYWwwMGcOL2ZlhM3MvDHvVxKSLz3xTStEDM3aUwjdfpyYACmpV+PAaeACOBdC8elaVbxyKMvMrPLNoYVr4NHipGjjoncCpxBtZLjmbt9D/XHrGPO9uO6ykp7aJnd5TbDSUq5A08CTsAmEcm3K+jp3lNabl05+zdfrHyd2cnxGJXCPUVwjX+cfZdaUcW3GB+1CqGKn/u9L6RpBYwlutx2AFaLSKYFhFMTSCMRmXffkeYBnTS0+3Xgz+8ZvmMkMalrdwTctOPsqUjOJIbQsXoZ3m2c+7U7NC0/skTSSAFqicj2LI5VA7aLSL6cAU4nDc0SJOkWP/3alzFxazlvY8AgQoXLpfjjzMs4O3rSr3FFOlYvg41BLzerFXyWGKdxt7+EYsDVHEelaQWIsnOgRdOJLGu5mBdsvVHA3+6n8Q76GB/7eQxY/CdtvtjE7hOX7nktTSvIsn3SUEo1B5qnvnwVWATE31HMEXgKOCIiDfIqyPuhnzS0vHBgzw8M3/5pepVV4E17zpzqSHxiZTpW96Nf40fw0FVWWgGVq+oppdRrwOupL4OBI8CdU38mYppSJEpEDlkmXMvSSUPLK6Yqqz6MiYv+r8rqUml2x3fDxbEY7zZ+hMjqfrrKSitwLNGmsQXoJiJ/WTq4vKaThpbX7uxlVSxFcImvzf5LLaji687Q1iGE+epeVlrBcd9JoyDTSUN7UA788R3Ddo5id2qVVdmbDpw69TznEivyfI0y9GtcEXdnXWWl5X+5rZ56OSc3EZHpuYgtz+mkoT1IxqQbLFn9P8ad3sBFGwM2IpS/5MfvZ7ri6lyM95s8Qrtqvhh0lZWWj+U2aRizPJA10V1uNe0/l+P38fnKXsxLOYcohWcyOJyJ4MCVxlQtU4yhrUMILu1m7TA1LUu5TRoOObmJiNzKRWx5TicNzZr2xnzNsN8nsM/W9Lr8DSeOxXbhUnIgXWoF0KdRBVwdc7fspqblFd2moZOGZkUpt66xcGVvxp/fzlWDATsRyl4IJOZsVzyLuDKgWSVaPVoapXSVlZY/WCRpKKWKAi8DdQAP4AKm1fymi8g1C8VqcTppaPnF+VO7GLe6N0tSx8KWSFZIXGP+uRbB42U9GNY6hCBvvWKgZn2W6HIbAKwFfIEdwBmgBFAdOAE8JSL/Wihei9JJQ8tXRIjZNoGh+77msK3pyaLi9aIcONWVG0Yfuj9ZljcbBOFsb2vlQLWHmSWSxiKgMtBURI7etj8Q+BnYLyJtLRSvRemkoeVHSTcuMmvFq3xxeS8JBgNORsHnfGV+P9cJH/eiDGpRmUaVS+gqK80qLJE0LgMvi8jCLI61B6aKSL7sCqKThpafnf53PSPX9mW1ugmAb6KBa3FtOJFQnfqPeDOkZTB+Hs5WjlJ72FhqwsLssouRu09oqGlaNkr612Vsl218GdgB32QjJ+2NXPJfSA3fUWw+9DdPj1vHpLWHuZWcYu1QNQ0w/0ljGabV+hqLyKnb9pcCVgLHRKRlXgV5P/SThlZQ3Lwax7TlrzDtxlGSlMI1RSh2tjp7Lz5LueJFGdo6hCfKeVk7TO0hYInqqSBMDeHFga2YGsK9gVqYZr59SkT+sVjEFqSThlbQHDuwlGGbBrHNxvR0UfaWHXGxzxN/qxKtHy3NgOaVKV40R8OoNC1HLNXl1hHTFOnVgVJAHLAN+FpE7pz9Nt/QSUMriCQ5iRW/9WNU7GrO3TYdScyZbjg7FOXdJo/wfA296JOWN/TgPp00tALq6rmDTPjlFeYmm6YjKZ4MtqcbcvBqQ6r4ufNx6xBCfPJlHxStALvvhnCl1CqlVNfU9cA1TXtAinpVYMAL0cwKe4tKyXDWFuJ8fyXcfxhH4vbRcuJGhizbx9WbSdYOVXtImNt76hbwJXBaKbVMKfW8UqpIHsaladptQqr2YFanTbzvVgUXo5EDztdwKjeBKp7f8s2mwzQcu47le+Io7DUHmvWZlTREpAWmEeCvAbbADOCMUmqBUqp9anuHpml5yNbRlU6tv2dJgyk8LY7cMCgOF/+L4HKDsU3ayus/xPDyjB2cuJBg7VC1QszcJw1E5LKIfCMiTTE1hP8PcAd+wNSb6p6UUn5KqbVKqb+UUvuUUm9lUUYppSYopQ4rpf5USlW97ViKUmp36rbU3Ng1rTApUaY2Y7tsY5J/W3ySjRy3T+Gq/xxq+oxj06F/eHrcOr6IPkxSSk5WN9A08+S6IVwpVQ3oCHQGipuznkbquI5SIhKTOgHiLqD17cvIKqWaAb2BZkBN4DMRqZl67JqI5KhaTDeEa4XZjcsnmby8B9/eOkGyUnikCE6nI/j7ShMqlCjK8DahhAd4WDtMrYCxxIjwtAuFKaU+VkodArYDrYCvgTBzzheROBGJSf35KrAf8LmjWCvgWzHZCrinJhtN0+7g5ObL/55bwbzwD3k0WXHBRhHrs47wgKGcubCfdpO30H/Rn1xKSLR2qFohYW7vqSFKqf3A78DzwCKguohUEJGBIrIvpzdOnTn3MUxjPW7ng2nm3DQn+S+xOCqldiqltiqlWt/l2q+kltt59uzZnIamaQVO+ZCOzOyyjcEeNSlqNHLAKQG7shOpVnwGs7cfpeHYdSzZHasbyrX7Zu6TRndM04XUFpFAEXkv7YkhN1J7Xi0E3haRKzk41T/1kel5YLxSqlxWhURkioiEi0h48eLFcxumphUoBjsn2rWYytJGM2kmztwyKA56/U1w0GAcUrbx1pzddJ62nWPnrls7VK0AMzdp+IrI26nVRfdFKWWHKWH8ICKLsigSC/jdfu/UfYhI2n+PANGYnlQ0TbuNl084n764lcmBkfgmGzluZ2oof9xnPNuOHKPR+PVMXHOIxGTdUK7lnLldbi3yTKtMiwNMw7T+xthsii0FuqT2onocuCwicUqpYmnrliulvIDawF/ZXEPTHm5KUbvuhyxqt4pu9r7YAPtcT1M6aBgBTisYveoAzSdsYMexC9aOVCtgHug0IkqpOpiWiN2DaUp1gA+AMgAiMjk1sUwEmgAJQFcR2amUegL4KvU8AzBeRKbd656695SmwcG9sxmybQR/2pr+3h+5UYS/T77M1eTSPFejDO83eQQ3ZzsrR6nlF3ruKZ00NI2UW9eZ/0svxl/YwXWDAWejUPrco/x+vgNeRZyJalmZ5qGl9GqBmuW63GqaVnDZOLjQsdUMljSYQkOjIwkGxWHvP6hSLgpD0m7emPU73Wbu5ORFPaJcy55OGpr2kClRpjbjXtzGeL8WeKcYOWKfRFLgDGqW+pLoAydoNG490zYeJcVYuGshtNzJtnpKKVU/JxcSkTUWicjCdPWUpmXv2vnDfLaie/rU66WSFYmnWnDs+hOE+box4tlQgkvrqdcfNrlq01BKGTGtC25OBaeYM42INeikoWn3tnv7JIbs+ZLDtqY/95BrXsTEdieZYnSvE8jbDSvgZJ8v/8S1PJDbpFExJzcRkQO5iC3P6aShaeZJun6eacu7MeX6YZKUolgKuJypy/7LTSnj4cLwNqHUKa/XKH8Y6N5TOmlomtmO7P+RIZsHE2Nr6hVf6UYR9p/sxtXkUrSt6suHzStRzMXeylFqecliSSN1DEUpINP6GamjtPMdnTQ0LeeMSTdY+MsbjD23lWvp3XOr8vv59ni6ODKoRWVaVimtu+cWUvedNJRStsAo4GUgy6nJdZuGphU+8cc38/Fvb7LGcAuA8rcc+PdkZ84nBvFUxeIMaxOKj7uTlaPULM0S4zQ+ACKBtzE1jPcBXgc2AceAtvcfpqZp+Y13mSf47MXtjC3dFK8UI4ccbkHZr6lRYjprD8TRaOw6Zm4+hlF3z31omPuk8TcwFtO8UUmYpkXflXpsFnBRRHrlZaC5pZ80NM0yLsf/xZhfXmGxXAbAP8mG8ycjibsZRjX/YnzaNpQg76JWjlKzBEs8aZTBNMlgCnAL0zKvaWYCHe4vRE3T8js378p81HkDXwe9gG+ykX/tUkgI+IFapb/g9+OnafbZRj7/Tc+eW9iZmzROA2kjfI5hmmE2jX8OrqNpWkGmFI/Xfo+F7X6hi21JAPa6Had8UBSlHLYwZvVBWk7cyJ8nL1k3Ti3PmPtlv57/EsV04EOl1HSl1JfAGOCnvAhO07T8ydnNj36dVvNdSG+CkoVTtsIF/8U84Teef+LjaD1pEyOW7+dGYoq1Q9UszNw2DV/AW0RiUrvdvg+0A5yA1cCHqWt+5zu6TUPT8lbS9XN8/VNXvr5xlGSlKJ4ChlNNOXytHgGeznzaNoyaZT2tHaaWA3pwn04ampbnDu6dzaBtw9lna3odluDFzhM9uGV044XHy/Bek0co6qjX7CgI9NTomqbluQohz/F9p438r0gl7I3Cn87nKBk0gkpuv/L91uM0Hree6APx1g5Tu0/mVk/ZAK8Bz2JaszurEeFlLB6dBegnDU178I7+vZTBmz7k99SVAkNvFCPmRA9upnjQtqovA5+phLuznookv7LEiPDxQG9gFaZ1uRPvLCMi/e8zzjyhk4amWUfKrevMWdGTzy7+zg2DAfcUwTX+KfZdakLxog583DqERsElrR2mlgVLJI3TwDgR+dTSweU1nTQ0zbpOHF7J4PXvscPG1JMq9KYbu4/3ICHFi5ZVShPVMhgPPQFivmKJNg1bYJflQtI07WHhF9SYqZ238mGxcJyNRvY4XsYjaBShxZaz9I9YGo1bx4o9cdYOUzOTuUljOtA+LwPRNK3wMtg5EtnyGxbVm8DjKbZcNiiOlVzPE4GfkHDjFK/9EEOvH2I4d+2WtUPV7sHc6qmeQH9gP6ZxGZmGe4rIdItHZwG6ekrT8hdJusWCX15n9LmtJBgMuBkFj/gI/rzYFA8Xe4a2CqF5WClrh/lQs0Sbxr0mk9HLvWqaliOnjq4ham1fttgkAxB2052Y4z24keJJ89BSfNQqGM8iDlaO8uFkiTYNp3tszhaIU9O0h0jpwPp81WUbgzxq4Gw08qfjJbyCRhJabBU/74mj0bj1uq0jHzIraYjIrXtteR2opmmFj7K1p32LaSyq9xk1U2y4ZFAcK7mGJwNHcu1mPK/9EEPv2b9z8XqmXv6alWRbPaWUKgucEJGk1J/vSi/3qmna/TAm3WTe8p6MvbCTGwYDningcqYR+y7Xx6uIA8Pb6HEdD0qu2jRS2zEeF5HtqT9n1/ih0G0amqZZyIlDK/hww/vE2JiaUh+7UZytx18h0ViUZx/zYXCLYNyc9RxWeSm3SaMxsFlErqb+fFcisvL+wswbOmloWsFjTEzg+5+789mlP0k0KLxTwOZ0Sw5eeYISrg580jaMpyp6WzvMQkvPcquThqYVSEf2L2bA5kHsTZ05t2qCDxuPdydFnHiuRhkGNK9EEQdb6wZZCFl8llullOHO7f5C1DRNy6xspTZ812kjbzgHYStCjHMs5csPJcDld2ZvP06T8evZeuS8tcN8qJj1Za+UKqKUGquUOqqUugUkZbFpmqZZnK2jGz3bL2bWY+8SlCzE2hi56DeHen5fE3vxMs99vZVhP/3FzSS9SuCDYO7gvvlAI2AGcJisZ7n9ytLBWYKuntK0wiPxWjwTl3ZmRmIsohRlk205c6ILp29WIMi7COM6PEqor5u1wyzwLDEi/DLQS0S+t3RweU0nDU0rfHZt+4wBe6cQa2vAXoTKVx9lQ2wktgYb3mpQntciymFro2vNc8sSbRqxwGXLhaRpmpZ71Wq+xcI2P9FWuZOoFLtd/6Bm0FCcDCcZs/og7SZv4cjZa9YOs1AyN2n0BwYopfQsYpqm5QsuHoFEdV7PhDKt8Egx8pfdDYqUm0AN7xXsPnGJ5hM28v3WfynsPUQfNLO73CqlRgNvAAfJepbbupYNzTJ09ZSmFX7nT+0iamVPog2mGY2qJnqw9WhPbhndiKhYnJFtw/B2zbRKtZaN+66eUkoNB/oAB4BDmKqr7tw0TdOswrN0NSZ03kKUZy2cjEZi7C/gGzSCyu7biD5wlsbj1/PL3tPWDrNQMLch/CIwXkSG3NfNlPIDvgVKYJqWZIqIfHZHGQV8BjQDEoCXRCQm9diLwIepRYeJyMx73VM/aWjaw+X4oeX0X/8+f9oKSoTqtwJZc+xlROxpX82XwS2D9YDAe7BEQ/gtYLMFYkkG+opIZeBxoJdSqvIdZZoC5VO3V4AvAZRSHsBgoCZQAxislCpmgZg0TStEypRvxsxOG3jdqSwGYLvjMcLKf0Rp58PM33WSZp9tYNe/F60dZoFlbtKYCLx8vzcTkbi0pwYRuYppJUCfO4q1Ar4Vk62Ae2oDfGNgtYhcEJGLmFYQbHK/MWmaVvjYOrrxWoclzAx5A99kI0dskkks8zX1fBdy/MJ1Ony1hXGrD5Kccq/15bQ7mfuM5gjUVkrtAdaSuSFcRGRwTm6slAoAHgO23XHIBzhx2+uTqfuy269pmpalKuGvsqBcE0Ys68QSrhBTdAdPlv+bXUdf5bPfhPWHzvJZ5GOU8dTryJnL3CeNboAd4AW0B3pksZlNKVUEWAi8LSJXcnKumdd/RSm1Uym18+zZs5a+vKZpBYhLsQCGdd7I6NJNKGo0stv2Kt7lRvGY5xZ+P36JZhM2sCjmpO6aayZzV+4rdY+ttLk3VErZYUoYP4jIoiyKxAJ+t7325b8eWlntzyreKSISLiLhxYsXNzc0TdMKK6Vo/PQoFj31JeEpBs4b4LD3EhqVncz1xAT6zPuDN+fs5vINPY3evdwzaSilHJVSS5VS9z0OI7Vn1DRgv4iMzabYUqCLMnkcuCwiccBKoJFSqlhqA3ij1H2apmlmKRlQl6mdNvGmcwVsRdjicIxHyw/Fx+Uoy/44ldpIfsHaYeZr90waInITqIv57R93UxvoDNRXSu1O3ZoppV5VSr2aWmY5cATTxIhfA6+nxnEBGArsSN0+St2naZpmNhuHIvRov5CZoW/ik2zksE0Sib6TifD9idhLCXT4aiuf/XqIFKOursqKueM0fgDOiEifvA/JsvQ4DU3TsnPtwj8MW9aJn7kOQI1kLzb805NEY1FqBHrwWcdHKeXmZOUoHzxLzHLbHhgHRGN6EjjDHWuGi8ia+440D+ikoWnaXRmNLF35BsNOr+OGwUBpo8Lm7PP8dSEUd2c7Pm0bRuPgktaO8oGyRNK4V2dmERGb3ASX13TS0DTNHMcO/sS7G/qz3xZsRah56zF+OdoBMPBiLX/6N6uEo12+/JqzOEskjYr3KiMiB3IRW57TSUPTNHMlXj/LuB878n1yPACPpbjy+7FXuZroQaVSrnz+3GMEeRexcpR5776TRkGmk4amaTkiQnT0ID48uojLNga8jOB5sT0746vhZGfD0NYhtKvma+0o85Ql5p5CKWWrlOqqlJqU2gW3XOr+Nkqp8pYKVtM0zaqUIuKpoSyo/yVVUwycM8Ahj3k0D/qeG0mJvDP/D/rM3c31W8nWjtQqzJ0avSymeaImAFWA5kDaQrxPAx/kSXSapmlWUjLg/+3deZQU5bnH8e/TM8MSZF8E15ueOQAAD8RJREFUAUVEUERB5CIoIahBRQO4IovGHT3XxHhObpRo4nYSI3r1miiKqAi4AMELbohBQET2sA6bVxZR9kVwWAaUmX7uH1XjaYeeoYGZXuD3OafOVNfS/TtFnX6ot6rftzOv9f2cOys2xoFpOUvp1OIJalXawdiFG+j+wnRWbCrzDi3SXqJXGv8AvgVOBboAFrNuKsHvOEREjinZlapxb++PGHxaX2oVRlkc2Uu1U56iQ4OFrNm2l6sGzWDk3G+Oqy5IEi0aXQjGr9hOsUdtgc2AhoEVkWPWBT9/kDFdX/uxC5IV1UfR4/S3+b6ggD+OXcLvRi1iz3HSXJVo0ThA0GFhPA2A4+8aTUSOK/Uad+CVsLkqasan2bl0afEkNSvl8f7ijfR4YTpfbD72vwoTLRqTgAFmViVmmZtZNnAP8HGZJxMRSTNFzVUvndqLGoVR5kd2UfuUJ2lff9mPzVVj5q079BtlsESLxh8IephdSdAflAMDgEVAU+ChckknIpKGOnX+M2MufpFzCowtEWdljRH0bP4u+w8U8od3cnngnVz2HyhMdcxykWjX6GsJnpp6C2hD0CV5C4JeZs9z97hdlIuIHKvqN/kFw/pM5cbsehSYMSVrNpef+SxVcvYxet46rn5xJmu37011zDJX4o/7wq7QF7j7nuRGKlv6cZ+IlCt3/jXpfh5e/xH5kQhNolnw7V0s2X4yVStm89+9Wmdc31VH+uO+T4GW5RNJROQYEQ7wNKrj32hW4KyNFLK19iC6N/uc3d8XcNcb83lywhfHzHjkpRUNK2WdiIjEOPWMHrx13QS6UYX8iDE1ZzzXtnyNrEghgz9bzU2vzWX7nu9THfOoJdyNiIiIlO5n1RszsN/nDKjemmx3JvpKLmz+BA2r7mLWmm/p/vx0Fn6zM9Uxj0pp9zSiwOMEo+gdkruPKMNcZUb3NEQkFRbMfZ7fLx3M9qwI9aNGg/zbmbauGRWyIjzSoyV9259MMAJ2+jmiXm4TGEMjlsbTEBEpZuu62fz+k/4synJy3Oka6cro5b8EoFe7Rjzes1VajtFxNL3cXgRUTWCqVmZpRUSOEfUad2DoDZ9yQ6Q2B8z4yCdx7VkvUyknyj/nreeGl2exKW9fqmMelkMVjX3uvjeRKSlpRUQyTE6V2vyp3xQeq9ORHHcmRr+i3Wl/oWnNPSxen0f356czZ823qY6ZMN0IFxEpb5EI11w5hNfPvpd6hVEWWz5Z9f7KJad+zfY9P9Dv1Tm8MWttRvSWq6IhIpIkrc/rz6hLX6d1obE54uRWfJG+raZTEHX+/N4y/jh2Cd8XpHf3IyUWDXePuPvcZIYRETnW1W3UnqE3TOZqq85+Mz4o/JC+57xJxWxn1L/X0feVOWzbnb6/59CVhohIklWoUpfH+k1lQPXWZLnzwYGlXNTiaRpVP8D8r3fS84XpLN2Ql+qYcaloiIikgGVl0++qN3mp2U1UjUaZEd1BnQaP0aHxTjbm7ee6wTMZn7sp1TEPoqIhIpJCHTs9wMgLn6JJgbPaCtj4s4Fc33I1+w9EueftBTw36cu0ukGuoiEikmKnNL+St3qO5YLCbHZGYHJ0CHe0/ZyIwXOTVvLbkQvTZnwOFQ0RkTRQrU5zBvX9jD5ZdThgxuh947mpzducUDHCh7mb6PXyLLbu2p/qmCoaIiLpIrtSNR7sO5kHa5xLljtj9+dycfNnaFwDctfncdWgGSzfmNpxyFU0RETSSSRCn54jGNS0D1WiUT4t2ELjBo9y/skH2Ji3n+sHz2Tyii2pi5eyTxYRkRJd2Pkh3mj/CA0KoyxhP99VfpirWu5k7w+F3DliHsNmfJWSXCoaIiJp6vSzevH2ZcNpVQAbI87cwifp/x9fEXV49IPlPPbBMgqjyX2ySkVDRCSN1WnYjqHXfcxF0Ursjhhjdg/m3g5zyMkyXp+xlrvemE/+DwVJy6OiISKS5ipXb8j/9J1Kv+x6FJjxet447jjvPapVzmbSii30eWVO0oaSVdEQEckAWRWrMKDPJ9xfrRXmzpu7Z9LzzJdpWKMCi9d9xzUvzmTNtj3lnkNFQ0QkU0Qi3HT1SJ5u2I0cd97dt5JzGz/B2SdV4Jsd+Vz70kwWlPMY5CoaIiIZ5rKuTzPkzDuoGo3yWcF2atb4Exefns3O/AP0fWV2uT6Sq6IhIpKB2p1/HyPOf5wTC6Ms9Hx2ZA3g+jZR9h+IcueIeYya+025fG5Si4aZDTWzrWa2tIT1Nc1snJnlmtlcM2sVs26tmS0xs0VmNi95qUVE0lOzltfy5sUv0TTs7HDRvoe4u0M+UYcBY5cwcdnmMv/MZF9pDAMuL2X9g8Aidz8H+DXw92LrL3L3Nu7erpzyiYhklPpNOjOi+z9pUxhhc8QZv/Nx7v/FDi4+ox4XnVGvzD8vqUXD3acBO0rZpCUwJdz2C6CJmZ2YjGwiIpmqer2WDLluAj+PVuC7CAzfPJA7z/uSnKyy/4pPt3sai4FrAMysPXAK0Chc58BEM5tvZv1LexMz629m88xs3rZt28o1sIhIOqhc7ST+3nsyV3IC+RHjnvl/Y9biYWX+OelWNJ4EapjZIuC3wEKgqBP5Tu7eFugG3GNmnUt6E3cf4u7t3L1d3bp1yz20iEg6yKlcgyf6TqFvVl2aFBotG3Ys88/ILvN3PAruvgu4FcDMDPgKWBOu2xD+3Wpm44D2wLQURRURSUuRnMoM6DOR/O++pkrt08r+/cv8HY+CmdUwswrhyzuAae6+y8yqmFnVcJsqwKVA3CewRESOd5aVXS4FA5J8pWFmI4EuQB0zWw88AuQAuPtg4ExguJk5sAy4Pdz1RGBccPFBNvC2u3+czOwiIpLkouHufQ6xfhbQPM7yNUDr8solIiKJSavmKRERSW8qGiIikjAVDRERSZiKhoiIJExFQ0REEmbuyR2UPNnMbBvw9RHuXgfYXoZxkiVTc0PmZs/U3JC52ZW7/Jzi7nG70zjmi8bRMLN5mdijbqbmhszNnqm5IXOzK3dqqHlKREQSpqIhIiIJU9Eo3ZBUBzhCmZobMjd7puaGzM2u3CmgexoiIpIwXWmIiEjCVDRERCRhKhqAmV1uZv9nZqvMbECc9RXNbHS4fo6ZNUl+yoMyNTazT81suZktM7Pfxdmmi5nlmdmicHo4FVnjMbO1ZrYkzDUvznozs3+ExzzXzNqmImexTC1ijuUiM9tlZvcV2yZtjrmZDTWzrWa2NGZZLTP7xMxWhn9rlrDvzeE2K83s5uSlLjH302b2RXgujDOzGiXsW+p5VZ5KyP2omW2IOR+uKGHfUr+D0oq7H9cTkAWsBpoCFQjGKW9ZbJv/BAaH872B0WmQuwHQNpyvCnwZJ3cX4MNUZy0h/1qgTinrrwAmAAZ0AOakOnOc82YzwY+g0vKYA52BtsDSmGVPAQPC+QHAwDj71SIYMbMWUDOcr5ni3JcC2eH8wHi5EzmvUpD7UeC/EjiXSv0OSqdJVxrBsLGr3H2Nu/8AjAJ6FtumJzA8nH8HuCQcjjZl3H2Tuy8I53cDK4CGqcxUxnoCIzwwm2Ds+AapDhXjEmC1ux9pbwPlzt2nATuKLY49l4cDV8XZ9TLgE3ff4e47gU+Ay8staDHxcrv7RHcvCF/OBholK0+iSjjeiUjkOyhtqGgEX7TrYl6v5+Av3x+3CU/cPKB2UtIlIGwuOxeYE2d1RzNbbGYTzOyspAYrnQMTzWy+mfWPsz6Rf5dU6g2MLGFduh5zgBPdfVM4v5lgVMzi0v3Y30ZwFRrPoc6rVPhN2Kw2tITmwHQ/3j+hopHhzOwE4H+B+9x9V7HVCwiaT1oDzwPvJjtfKTq5e1ugG3CPmXVOdaBEhePY9wDGxFmdzsf8JzxoG8moZ+7N7CGgAHirhE3S7bx6CTgNaANsAp5JbZyjp6IBG4DGMa8bhcvibmNm2UB14NukpCuFmeUQFIy33H1s8fXuvsvd94TzHwE5ZlYnyTHjcvcN4d+twDiCS/RYify7pEo3YIG7bym+Ip2PeWhLUTNf+HdrnG3S8tib2S3Ar4B+YcE7SALnVVK5+xZ3L3T3KPBKCXnS8niXREUD/g2cbmanhv+D7A28X2yb94GiJ0iuA6aUdNImS3hP5TVghbs/W8I29YvuvZhZe4J/73QodlXMrGrRPMFNzqXFNnsf+HX4FFUHIC+mWSXV+lBC01S6HvMYsefyzcB7cbb5F3CpmdUMm1MuDZeljJldDtwP9HD3/BK2SeS8Sqpi9+GuJn6eRL6D0keq78Snw0TwpM6XBE8wPBQue5zgBAWoRNAUsQqYCzRNg8ydCJoWcoFF4XQFcDdwd7jNb4BlBE9jzAYuSHXuMFfTMNPiMF/RMY/NbsCg8N9kCdAu1bnDXFUIikD1mGVpecwJCtsm4ABBO/ntBPfiJgMrgUlArXDbdsCrMfveFp7vq4Bb0yD3KoJ2/6JzvehpxpOAj0o7r1Kc+43w/M0lKAQNiucOXx/0HZSuk7oRERGRhKl5SkREEqaiISIiCVPREBGRhKloiIhIwlQ0REQkYSoactwyM09g6mJmt4TzJ6Qw67CYTM8VW37I3lzD3l+L9v9V+aaVY1l2qgOIpFDHmPnKwBTgL8D4mOXLCZ757wjE/VFZEn0B3ErwW4DDdTXQBDio5wCRw6GiIcctD3rPBX7swwuCnmtnx9l8W3JSlWpvCdkOyd0XmtnOsg4kxx81T4kcQvHmKTNrEr7ubWavWzAY03ozuzFcf7+ZbTSzbWY20Mwixd6vlZmNN7Pd4TTGzOofZcauYU+qe81sehr2rivHCBUNkSM3kKCp6Frgc2C4mT1D0CndbcBzBP0l9SrawcyaATMIuqa5EbgFOAv44CjGaDkZeBr4K0G/WPWA0ake80WOTWqeEjlyU9z9QQAzm0PQmWUP4Ax3LwQ+NrOeBPcTRoX7PEIwjkU3DwbcwcxyCe5XXMFP76ckqhZwobuvDN8vQtDDa4vwfUXKjK40RI7c5KIZD8Yy2QZ8FhaMIqv46YA6vyT4Qo+aWXbY1f5XBMOUtjvCHGuLCkZoefg37Ua3k8ynoiFy5L4r9vqHEpZVinldB3iAoCfU2KkpPx1T4WhzUOxzRcqEmqdEkmsHwZXGq3HWbU9yFpHDpqIhklyTCW58z3eNSyAZSEVDJLkeJRjIa7yZDSW4umgIdAWGufvU1EUTOTTd0xBJInf/EuhA8OvyIcAE4DHge4Kb5iJpTSP3iWQAMxsGtCIoOFF3jx7m/lkE3YisArq7+4dlnVGOD7rSEMkc5xE8afXsEey7Gl3JSBnQlYZIBjCzJgSP6wJscfd1h7n/2UDF8OVKd88ru3RyPFHREBGRhKl5SkREEqaiISIiCVPREBGRhKloiIhIwlQ0REQkYf8PkVBz5AsukNoAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -364,11 +339,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6145d7740fa04796aa4dae9bb09c125e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=16.38242960161448, step=0.05), Output()), _d…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "mesh = list(discs.values())[0].mesh\n",
     "solution_values = [solutions[model] for model in models]\n",
     "quick_plot = pybamm.QuickPlot(solution_values)\n",
     "import ipywidgets as widgets\n",
@@ -384,15 +373,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3578ee915f79432c931ffecf4b543c1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=16.38242960161448, step=0.05), Output()), _d…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# update parameter values and solve again\n",
-    "param.update({\"Typical current [A]\": 20})\n",
     "for model in models:\n",
-    "    param.update_model(model, discs[model])\n",
-    "    solutions[model] = model.default_solver.solve(model, t_eval)\n",
+    "    solutions[model] = solver.solve(model, t_eval, inputs={\"current\": 20})\n",
     "\n",
     "# Plot\n",
     "solution_values = [solutions[model] for model in models]\n",

From 5a707afb34d2b22b5ca76687bccbb51593dd5401 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Fri, 31 Jan 2020 23:56:20 -0500
Subject: [PATCH 07/11] #709 fix some more examples

---
 examples/notebooks/change-input-current.ipynb |  56 ++++-----
 .../models/compare-lithium-ion.ipynb          | 109 ++++++++++--------
 examples/notebooks/parameter-values.ipynb     |  90 +++++++++------
 pybamm/solvers/base_solver.py                 |   6 +-
 4 files changed, 136 insertions(+), 125 deletions(-)

diff --git a/examples/notebooks/change-input-current.ipynb b/examples/notebooks/change-input-current.ipynb
index a9c17e3443..4f158ab34c 100644
--- a/examples/notebooks/change-input-current.ipynb
+++ b/examples/notebooks/change-input-current.ipynb
@@ -22,7 +22,7 @@
     "\n",
     "In this notebook we will use the SPM as the example model, and change the input current from the default option. If you are not familiar with running a model in PyBaMM, please see [this](./models/SPM.ipynb) notebook for more details.\n",
     "\n",
-    "In PyBaMM, the current function is set using the parameter \"Current function [A]\". Below we load the SPM with the default parameters, and then change the the current function to 16A."
+    "In PyBaMM, the current function is set using the parameter \"Current function [A]\". Below we load the SPM with the default parameters, and then change the the current function to be an input parameter, so that we can change it easily later."
    ]
   },
   {
@@ -45,15 +45,18 @@
     "# set the default model parameters\n",
     "param = model.default_parameter_values\n",
     "\n",
-    "# change the current function\n",
-    "param[\"Current function [A]\"] = 16"
+    "# change the current function to be an input parameter\n",
+    "def current_function(t):\n",
+    "    return pybamm.InputParameter(\"current\")\n",
+    "\n",
+    "param[\"Current function [A]\"] = current_function"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can now solve the model in the ususal way"
+    "We can now solve the model in the ususal way, with a 16A current"
    ]
   },
   {
@@ -66,12 +69,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "000da8e81b0b4e91984553ba15ba179c",
+       "model_id": "839ef921550749ecaae748a2068ee9cc",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=0.0036363636363636364, step=0.005), Output()…"
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=0.003612040133779264, step=0.005), Output())…"
       ]
      },
      "metadata": {},
@@ -92,9 +95,9 @@
     "\n",
     "# Solve the model at the given time points\n",
     "solver = model.default_solver\n",
-    "n = 100\n",
+    "n = 300\n",
     "t_eval = np.linspace(0, 0.02, n)\n",
-    "solution = solver.solve(model, t_eval)\n",
+    "solution = solver.solve(model, t_eval, inputs={\"current\": 16})\n",
     "\n",
     "# plot\n",
     "quick_plot = pybamm.QuickPlot(solution)\n",
@@ -114,27 +117,11 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "param[\"Current function [A]\"] = 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model may then be updated and solved again in the usual way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cc4fd2e7c9ea4129a7acf0e519fea268",
+       "model_id": "cfb285a7877c4853ae7144891b38e4cd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -147,11 +134,8 @@
     }
    ],
    "source": [
-    "# set the parameters for the model and the geometry\n",
-    "param.update_model(model, disc)\n",
-    "\n",
     "# Solve the model at the given time points\n",
-    "solution = solver.solve(model, t_eval)\n",
+    "solution = solver.solve(model, t_eval, inputs={\"current\": 0})\n",
     "\n",
     "# plot\n",
     "quick_plot = pybamm.QuickPlot(solution)\n",
@@ -171,13 +155,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "84f6cac9b14f43a1907322e650f3c9f8",
+       "model_id": "d31307d03a2b4082a5ad7b4d91f85823",
        "version_major": 2,
        "version_minor": 0
       },
@@ -242,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -295,13 +279,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "09ced8a9e10b4e769a9b4cc8573b7fa2",
+       "model_id": "bb1a66bff842443cb81abb44a4b4a6a1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -366,4 +350,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/examples/notebooks/models/compare-lithium-ion.ipynb b/examples/notebooks/models/compare-lithium-ion.ipynb
index 8971dea6c2..bb9418e5bf 100644
--- a/examples/notebooks/models/compare-lithium-ion.ipynb
+++ b/examples/notebooks/models/compare-lithium-ion.ipynb
@@ -104,7 +104,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x10291c630>"
+       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x10597e518>"
       ]
      },
      "execution_count": 4,
@@ -225,11 +225,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "discs = {}\n",
     "for model_name, model in models.items():\n",
     "    disc = pybamm.Discretisation(mesh[model_name], model.default_spatial_methods)\n",
-    "    disc.process_model(model)\n",
-    "    discs[model_name] = disc"
+    "    disc.process_model(model)"
    ]
   },
   {
@@ -248,55 +246,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solved the Doyle-Fuller-Newman model in 0.426 seconds\n"
-     ]
-    },
-    {
-     "ename": "KeyError",
-     "evalue": "\"Input parameter 'current' not found\"",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/input_parameter.py\u001b[0m in \u001b[0;36m_base_evaluate\u001b[0;34m(self, t, y, u)\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;31m# raise more informative error if can't find name in dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'current'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-c3cb7a07a49f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0msolution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_eval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"current\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Solved the {} in {:.3f} seconds\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/base_solver.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self, model, t_eval, external_variables, inputs)\u001b[0m\n\u001b[1;32m    426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m         \u001b[0;31m# Identify the event that caused termination\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mtermination\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_termination_reason\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m         \u001b[0mpybamm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Finish solving {} ({})\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtermination\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/base_solver.py\u001b[0m in \u001b[0;36mget_termination_reason\u001b[0;34m(self, solution, events)\u001b[0m\n\u001b[1;32m    563\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m                 final_event_values[name] = abs(\n\u001b[0;32m--> 565\u001b[0;31m                     \u001b[0mevent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt_event\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_event\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    566\u001b[0m                 )\n\u001b[1;32m    567\u001b[0m             \u001b[0mtermination_event\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_event_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfinal_event_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/functions.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m             \u001b[0mevaluated_children\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluated_children\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/functions.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m             \u001b[0mevaluated_children\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluated_children\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/unary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0mchild\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/symbol.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    556\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 558\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_base_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_for_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/expression_tree/input_parameter.py\u001b[0m in \u001b[0;36m_base_evaluate\u001b[0;34m(self, t, y, u)\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;31m# raise more informative error if can't find name in dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Input parameter '{}' not found\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m: \"Input parameter 'current' not found\""
+      "Solved the Doyle-Fuller-Newman model in 2.111 seconds\n",
+      "Solved the Single Particle Model in 0.386 seconds\n",
+      "Solved the Single Particle Model with electrolyte in 0.399 seconds\n"
      ]
     }
    ],
    "source": [
     "timer = pybamm.Timer()\n",
     "solutions = {}\n",
-    "t_eval = np.linspace(0, 0.5, 100)\n",
+    "t_eval = np.linspace(0, 0.15, 300)\n",
     "solver = pybamm.CasadiSolver()\n",
     "for model_name, model in models.items():\n",
     "    start = timer.time()\n",
@@ -322,9 +288,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "for model_name, model in models.items():\n",
     "    t = solutions[model_name].t\n",
@@ -346,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,9 +343,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af31d03838e4456aac24954eb1b5d0df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=0.673927318840113, step=0.05), Output()), _d…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "quick_plot = pybamm.QuickPlot(list_of_solutions)\n",
     "import ipywidgets as widgets\n",
@@ -389,15 +383,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7226178875b24a0eacb8c38ab1c4fafc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=0.11652014391160832, step=0.05), Output()), …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# update parameter values and solve again\n",
-    "param.update({\"Typical current [A]\": 3})\n",
     "for model_name, model in models.items():\n",
-    "    param.update_model(model, discs[model_name])\n",
-    "    solutions[model_name] = model.default_solver.solve(model, t_eval, inputs={\"current\": 10})\n",
+    "    solutions[model_name] = model.default_solver.solve(model, t_eval, inputs={\"current\": 5})\n",
     "\n",
     "# Plot\n",
     "list_of_models = list(models.values())\n",
diff --git a/examples/notebooks/parameter-values.ipynb b/examples/notebooks/parameter-values.ipynb
index 90d7f9a6dd..1f3fd77ba1 100644
--- a/examples/notebooks/parameter-values.ipynb
+++ b/examples/notebooks/parameter-values.ipynb
@@ -47,7 +47,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x7f1f70335080>\n"
+      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x1033b8860>\n"
      ]
     }
    ],
@@ -73,7 +73,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x7f1f70335518>\n"
+      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x12fb02fd0>\n"
      ]
     }
    ],
@@ -138,7 +138,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x7f1f70335518>\n"
+      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x12fb02fd0>\n"
      ]
     }
    ],
@@ -165,7 +165,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x7f1f70335518>\n"
+      "parameter values are <pybamm.parameters.parameter_values.ParameterValues object at 0x12fb02fd0>\n"
      ]
     }
    ],
@@ -328,25 +328,16 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "\"Input parameter 'b' not found\"",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/input_parameter.py\u001b[0m in \u001b[0;36m_base_evaluate\u001b[0;34m(self, t, y, u)\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;31m# raise more informative error if can't find name in dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'b'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-1f3adb996155>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# Discretise using default discretisation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mdisc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpybamm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDiscretisation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mdisc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# Solve\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/discretisations/discretisation.py\u001b[0m in \u001b[0;36mprocess_model\u001b[0;34m(self, model, inplace, check_model)\u001b[0m\n\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m         \u001b[0mpybamm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Discretise initial conditions for {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m         \u001b[0mics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconcat_ics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_initial_conditions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m         \u001b[0mmodel_disc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitial_conditions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0mmodel_disc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenated_initial_conditions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconcat_ics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/discretisations/discretisation.py\u001b[0m in \u001b[0;36mprocess_initial_conditions\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m    382\u001b[0m         processed_concatenated_initial_conditions = self._concatenate_in_order(\n\u001b[1;32m    383\u001b[0m             \u001b[0mprocessed_initial_conditions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_complete\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m         ).evaluate(0, None)\n\u001b[0m\u001b[1;32m    385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mprocessed_initial_conditions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocessed_concatenated_initial_conditions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/concatenations.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0mchildren_eval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m                 \u001b[0mchildren_eval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_concatenation_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchildren_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/binary_operators.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binary_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/symbol.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, t, y, u, known_evals)\u001b[0m\n\u001b[1;32m    556\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknown_evals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 558\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_base_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_for_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/c/Users/vsulzer/Documents/Energy_Storage/PyBaMM/pybamm/expression_tree/input_parameter.py\u001b[0m in \u001b[0;36m_base_evaluate\u001b[0;34m(self, t, y, u)\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;31m# raise more informative error if can't find name in dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Input parameter '{}' not found\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m: \"Input parameter 'b' not found\""
-     ]
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -357,10 +348,10 @@
     "b = pybamm.Parameter(\"b\")\n",
     "model.rhs = {u: -a * u}\n",
     "model.initial_conditions = {u: b}\n",
-    "model.variables = {\"u\": u}\n",
+    "model.variables = {\"u\": u, \"a\": a, \"b\": b}\n",
     "\n",
-    "# Set parameters, with b as an input ########################\n",
-    "parameter_values = pybamm.ParameterValues({\"a\": 3, \"b\": \"[input]\"})\n",
+    "# Set parameters, with a as an input ########################\n",
+    "parameter_values = pybamm.ParameterValues({\"a\": \"[input]\", \"b\": 2})\n",
     "parameter_values.process_model(model)\n",
     "#############################################################\n",
     "\n",
@@ -371,14 +362,14 @@
     "# Solve\n",
     "t_eval = np.linspace(0, 2, 30)\n",
     "ode_solver = pybamm.ScipySolver()\n",
-    "solution = ode_solver.solve(model, t_eval, inputs={\"b\": 2})\n",
+    "solution = ode_solver.solve(model, t_eval, inputs={\"a\": 3})\n",
     "\n",
     "# Post-process, so that u1 can be called at any time t (using interpolation)\n",
     "t_sol1 = solution.t\n",
     "u1 = solution[\"u\"]\n",
     "\n",
     "# Solve again with different inputs ###############################\n",
-    "solution = ode_solver.solve(model, t_eval, inputs={\"b\": -1})\n",
+    "solution = ode_solver.solve(model, t_eval, inputs={\"a\": -1})\n",
     "t_sol2 = solution.t\n",
     "u2 = solution[\"u\"]\n",
     "###################################################################\n",
@@ -389,13 +380,13 @@
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n",
     "ax1.plot(t_fine, 2 * np.exp(-3 * t_fine), t_sol1, u1(t_sol1), \"o\")\n",
     "ax1.set_xlabel(\"t\")\n",
-    "ax1.legend([\" * exp(-3 * t)\", \"u1\"], loc=\"best\")\n",
+    "ax1.legend([\"2 * exp(-3 * t)\", \"u1\"], loc=\"best\")\n",
     "ax1.set_title(\"a = 3, b = 2\")\n",
     "\n",
-    "ax2.plot(t_fine, - np.exp(-3 * t_fine), t_sol2, u2(t_sol2), \"o\")\n",
+    "ax2.plot(t_fine, 2 * np.exp(t_fine), t_sol2, u2(t_sol2), \"o\")\n",
     "ax2.set_xlabel(\"t\")\n",
-    "ax2.legend([\"-exp(-3 * t)\", \"u2\"], loc=\"best\")\n",
-    "ax2.set_title(\"a = 3, b = -1\")\n",
+    "ax2.legend([\"2 * exp(t)\", \"u2\"], loc=\"best\")\n",
+    "ax2.set_title(\"a = -1, b = 2\")\n",
     "\n",
     "\n",
     "plt.tight_layout()\n",
@@ -404,9 +395,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{Variable(0x2e8252a3f8de591b, u, children=[], domain=[], auxiliary_domains={}): Multiplication(0x20992ec716be4474, *, children=['-a', 'y[0:1]'], domain=[], auxiliary_domains={})}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.rhs"
    ]
@@ -424,10 +426,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a: 4.0\n",
+      "b: 3.0\n",
+      "a + b: 7.0\n",
+      "a * b: 12.0\n"
+     ]
+    }
+   ],
    "source": [
+    "a = pybamm.Parameter(\"a\")\n",
+    "b = pybamm.Parameter(\"b\")\n",
+    "parameter_values = pybamm.ParameterValues({\"a\": 4, \"b\": 3})\n",
     "parameters = {\"a\": a, \"b\": b, \"a + b\": a + b, \"a * b\": a * b}\n",
     "param_eval = pybamm.print_parameters(parameters, parameter_values)\n",
     "for name, (value,C_dependence) in param_eval.items():\n",
@@ -465,7 +481,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py
index 07d0819eb1..a17ed8b3b9 100644
--- a/pybamm/solvers/base_solver.py
+++ b/pybamm/solvers/base_solver.py
@@ -562,7 +562,11 @@ def get_termination_reason(self, solution, events):
             final_event_values = {}
             for name, event in events.items():
                 final_event_values[name] = abs(
-                    event.evaluate(solution.t_event, solution.y_event, solution.inputs)
+                    event.evaluate(
+                        solution.t_event,
+                        solution.y_event,
+                        {k: v[-1] for k, v in solution.inputs.items()},
+                    )
                 )
             termination_event = min(final_event_values, key=final_event_values.get)
             # Add the event to the solution object

From 71b68ee6c0406b6ffd1993d9d84bb46f2a1e0226 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Sun, 2 Feb 2020 12:39:45 -0500
Subject: [PATCH 08/11] #709 allow current to be input parameter

---
 examples/notebooks/change-input-current.ipynb |  17 ++-
 examples/notebooks/change-settings.ipynb      | 106 ++++++++----------
 .../compare-comsol-discharge-curve.ipynb      |  11 +-
 .../models/compare-lithium-ion.ipynb          |  24 ++--
 examples/notebooks/models/lead-acid.ipynb     |  18 ++-
 .../scripts/compare_comsol/discharge_curve.py |   8 +-
 pybamm/parameters/parameter_values.py         |  29 ++++-
 .../test_asymptotics_convergence.py           |  30 ++---
 .../test_parameters/test_parameter_values.py  |  22 ++++
 tests/unit/test_simulation.py                 |  17 +--
 10 files changed, 142 insertions(+), 140 deletions(-)

diff --git a/examples/notebooks/change-input-current.ipynb b/examples/notebooks/change-input-current.ipynb
index 4f158ab34c..80a3d55318 100644
--- a/examples/notebooks/change-input-current.ipynb
+++ b/examples/notebooks/change-input-current.ipynb
@@ -46,10 +46,7 @@
     "param = model.default_parameter_values\n",
     "\n",
     "# change the current function to be an input parameter\n",
-    "def current_function(t):\n",
-    "    return pybamm.InputParameter(\"current\")\n",
-    "\n",
-    "param[\"Current function [A]\"] = current_function"
+    "param[\"Current function [A]\"] = \"[input]\""
    ]
   },
   {
@@ -69,7 +66,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "839ef921550749ecaae748a2068ee9cc",
+       "model_id": "770ba7f159054c5bad11038ac7be3f72",
        "version_major": 2,
        "version_minor": 0
       },
@@ -97,7 +94,7 @@
     "solver = model.default_solver\n",
     "n = 300\n",
     "t_eval = np.linspace(0, 0.02, n)\n",
-    "solution = solver.solve(model, t_eval, inputs={\"current\": 16})\n",
+    "solution = solver.solve(model, t_eval, inputs={\"Current function [A]\": 16})\n",
     "\n",
     "# plot\n",
     "quick_plot = pybamm.QuickPlot(solution)\n",
@@ -121,7 +118,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cfb285a7877c4853ae7144891b38e4cd",
+       "model_id": "4625a91d401b456d8e43aae201f4a380",
        "version_major": 2,
        "version_minor": 0
       },
@@ -135,7 +132,7 @@
    ],
    "source": [
     "# Solve the model at the given time points\n",
-    "solution = solver.solve(model, t_eval, inputs={\"current\": 0})\n",
+    "solution = solver.solve(model, t_eval, inputs={\"Current function [A]\": 0})\n",
     "\n",
     "# plot\n",
     "quick_plot = pybamm.QuickPlot(solution)\n",
@@ -161,7 +158,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d31307d03a2b4082a5ad7b4d91f85823",
+       "model_id": "88c9c957b666420e9cf6f615ff43817c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -285,7 +282,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bb1a66bff842443cb81abb44a4b4a6a1",
+       "model_id": "a8b90dfe860644c68af15c7c07e3adeb",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/examples/notebooks/change-settings.ipynb b/examples/notebooks/change-settings.ipynb
index 5ff3129cc7..075a0e3188 100644
--- a/examples/notebooks/change-settings.ipynb
+++ b/examples/notebooks/change-settings.ipynb
@@ -127,7 +127,7 @@
     {
      "data": {
       "text/plain": [
-       "<bound method ParameterValues.items of <pybamm.parameters.parameter_values.ParameterValues object at 0x1113519e8>>"
+       "<bound method ParameterValues.items of <pybamm.parameters.parameter_values.ParameterValues object at 0x111ec89b0>>"
       ]
      },
      "execution_count": 3,
@@ -175,8 +175,8 @@
       "Typical current [A]                                                                      0.680616\n",
       "Negative electrode conductivity [S.m-1]                                                     100.0\n",
       "Maximum concentration in negative electrode [mol.m-3]                            24983.2619938437\n",
-      "Negative electrode diffusivity [m2.s-1]                                      <function graphite_mcmb2528_diffusivity_Dualfoil1998 at 0x13dad4400>\n",
-      "Negative electrode OCP [V]                                                   <function graphite_mcmb2528_ocp_Dualfoil1998 at 0x13dad4598>\n",
+      "Negative electrode diffusivity [m2.s-1]                                      <function graphite_mcmb2528_diffusivity_Dualfoil1998 at 0x13e62b6a8>\n",
+      "Negative electrode OCP [V]                                                   <function graphite_mcmb2528_ocp_Dualfoil1998 at 0x13e62b730>\n",
       "Negative electrode porosity                                                                   0.3\n",
       "Negative electrode active material volume fraction                                            0.7\n",
       "Negative particle radius [m]                                                                1e-05\n",
@@ -193,15 +193,15 @@
       "Negative electrode density [kg.m-3]                                                        1657.0\n",
       "Negative electrode specific heat capacity [J.kg-1.K-1]                                      700.0\n",
       "Negative electrode thermal conductivity [W.m-1.K-1]                                           1.7\n",
-      "Negative electrode OCP entropic change [V.K-1]                               <function graphite_entropic_change_Moura2016 at 0x13dad4488>\n",
+      "Negative electrode OCP entropic change [V.K-1]                               <function graphite_entropic_change_Moura2016 at 0x13e62b620>\n",
       "Reference temperature [K]                                                                  298.15\n",
-      "Negative electrode reaction rate                                             <function graphite_electrolyte_reaction_rate_Dualfoil1998 at 0x13dad4620>\n",
+      "Negative electrode reaction rate                                             <function graphite_electrolyte_reaction_rate_Dualfoil1998 at 0x13e62b7b8>\n",
       "Negative reaction rate activation energy [J.mol-1]                                        37480.0\n",
       "Negative solid diffusion activation energy [J.mol-1]                                      42770.0\n",
       "Positive electrode conductivity [S.m-1]                                                      10.0\n",
       "Maximum concentration in positive electrode [mol.m-3]                            51217.9257309275\n",
-      "Positive electrode diffusivity [m2.s-1]                                      <function lico2_diffusivity_Dualfoil1998 at 0x13dad4510>\n",
-      "Positive electrode OCP [V]                                                   <function lico2_ocp_Dualfoil1998 at 0x13dad46a8>\n",
+      "Positive electrode diffusivity [m2.s-1]                                      <function lico2_diffusivity_Dualfoil1998 at 0x13e62b8c8>\n",
+      "Positive electrode OCP [V]                                                   <function lico2_ocp_Dualfoil1998 at 0x13e62b840>\n",
       "Positive electrode porosity                                                                   0.3\n",
       "Positive electrode active material volume fraction                                            0.7\n",
       "Positive particle radius [m]                                                                1e-05\n",
@@ -218,8 +218,8 @@
       "Positive electrode density [kg.m-3]                                                        3262.0\n",
       "Positive electrode specific heat capacity [J.kg-1.K-1]                                      700.0\n",
       "Positive electrode thermal conductivity [W.m-1.K-1]                                           2.1\n",
-      "Positive electrode OCP entropic change [V.K-1]                               <function lico2_entropic_change_Moura2016 at 0x13dad47b8>\n",
-      "Positive electrode reaction rate                                             <function lico2_electrolyte_reaction_rate_Dualfoil1998 at 0x13dad4840>\n",
+      "Positive electrode OCP entropic change [V.K-1]                               <function lico2_entropic_change_Moura2016 at 0x13e62b950>\n",
+      "Positive electrode reaction rate                                             <function lico2_electrolyte_reaction_rate_Dualfoil1998 at 0x13e62b9d8>\n",
       "Positive reaction rate activation energy [J.mol-1]                                        39570.0\n",
       "Positive solid diffusion activation energy [J.mol-1]                                      18550.0\n",
       "Separator porosity                                                                            1.0\n",
@@ -230,8 +230,8 @@
       "Separator thermal conductivity [W.m-1.K-1]                                                   0.16\n",
       "Typical electrolyte concentration [mol.m-3]                                                1000.0\n",
       "Cation transference number                                                                    0.4\n",
-      "Electrolyte diffusivity [m2.s-1]                                             <function electrolyte_diffusivity_Capiglia1999 at 0x13dad49d8>\n",
-      "Electrolyte conductivity [S.m-1]                                             <function electrolyte_conductivity_Capiglia1999 at 0x13dad4730>\n",
+      "Electrolyte diffusivity [m2.s-1]                                             <function electrolyte_diffusivity_Capiglia1999 at 0x13e62ba60>\n",
+      "Electrolyte conductivity [S.m-1]                                             <function electrolyte_conductivity_Capiglia1999 at 0x13e62bb70>\n",
       "Electrolyte diffusion activation energy [J.mol-1]                                         37040.0\n",
       "Electrolyte conductivity activation energy [J.mol-1]                                      34700.0\n",
       "Heat transfer coefficient [W.m-2.K-1]                                                        10.0\n",
@@ -295,51 +295,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once this parameter has been changed, the most foolproof way of re-running the simulation is to go through the entire process given above of (1) generating the model, (2) setting the parameters of the model, (3) meshing the domain, (4) discretising the equations, and finally (5), solving the equations. However, this is only neccessary in a small number of cases, if for example you change one of the geometry parameters, such as the *Negative electrode thickness*. In this case you would need to remesh the geometry as the relative length of the electrodes would have altered. This would in turn require you to re-discretise the model equations. \n",
-    "\n",
-    "In most use-cases, however, fully regenerating the discretisated model is not neccessary, and you can simply use the `update_model` function for [`pybamm.ParameterValues`](https://pybamm.readthedocs.io/en/latest/source/parameters/parameter_values.html)"
+    "In order to compare solutions with different parameter values, parameters must be changed to `InputParameter` objects, whose value can then be specified when solving. For example, we can compare the Terminal voltage calculated using both the old and new current values."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "param.update_model(model, disc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can simply call the solver again to solve the new model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "solution = solver.solve(model, t_eval)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compare the Terminal voltage calculated using both the old and new parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -351,16 +317,37 @@
     }
    ],
    "source": [
-    "new_voltage = solution[\"Terminal voltage [V]\"].entries\n",
-    "new_time = solution[\"Time [h]\"].entries\n",
-    "plt.plot(time, voltage, lw=2, label=\"Current = {}\".format(old_value))\n",
-    "plt.plot(new_time, new_voltage, lw=2, label=\"Current = {}\".format(new_value))\n",
+    "# Set up\n",
+    "model = pybamm.lithium_ion.SPM()\n",
+    "param[\"Current function [A]\"] = \"[input]\"\n",
+    "param.process_model(model)\n",
+    "disc.process_model(model)\n",
+    "\n",
+    "# Solution with current = 0.68\n",
+    "old_solution = solver.solve(model, t_eval, inputs={\"Current function [A]\": 0.68})\n",
+    "old_time = old_solution[\"Time [h]\"].entries\n",
+    "old_voltage = old_solution[\"Terminal voltage [V]\"].entries\n",
+    "\n",
+    "# Solution with current = 1.4\n",
+    "new_solution = solver.solve(model, t_eval, inputs={\"Current function [A]\": 1.4})\n",
+    "new_time = new_solution[\"Time [h]\"].entries\n",
+    "new_voltage = new_solution[\"Terminal voltage [V]\"].entries\n",
+    "\n",
+    "plt.plot(old_time, old_voltage, lw=2, label=\"Current = 0.68\")\n",
+    "plt.plot(new_time, new_voltage, lw=2, label=\"Current = 1.4\")\n",
     "plt.xlabel(\"Time [h]\", fontsize=15)\n",
     "plt.ylabel(\"Terminal voltage [V]\", fontsize=15)\n",
     "plt.legend(fontsize=15)\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that there is a small number of parameters for which this is not possible, since the discretisation depends on them (for example, *Negative electrode thickness*). In this case you would need to remesh the geometry as the relative length of the electrodes would have altered. This would in turn require you to re-discretise the model equations."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -372,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -404,7 +391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -428,12 +415,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1080x576 with 8 Axes>"
       ]
@@ -445,8 +432,9 @@
     }
    ],
    "source": [
-    "# re-generate the model and set parameters\n",
+    "# re-generate the model and set parameters (with fixed current function this time)\n",
     "model = pybamm.lithium_ion.SPM()\n",
+    "param[\"Current function [A]\"] = 0.68\n",
     "param.process_model(model)\n",
     "\n",
     "# re-discretise the model ...\n",
@@ -473,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -497,12 +485,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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mqla1I7SGR+YKTHO58hlVtb5dnw+cTzOP2Gya+QnfmebS7oer6sNtu5uAn6+qf00zD9mRwN9V1e90xHcoHefzJFcA51XVlzravAd4flWNjOp/uKpGLtfUNGBSrMuSLKG51HI3msmPf31kctQpjuNQ7KBJ0oSSnElzqeIvVZWTGkvaYfa5JGlm2JnzuUmx6cekmKSBlmQl8PPAPWN94bVzBHyMZqLmR2guuft6R/3eNJfSXFxVp09N1JIkSZK6Kckimku/753saq/2UvfPAXtV1VjTVKhPOaeYpEG3Clg6Qf1raW6rvoRmfpOPj6p/H0+9jbwkSZKkaa6qNlXVou2Z/qiq7mgn9jchNs2YFJM00KrqKuC+CZqcDFxQjWuAfZMcBJDkJTS3cv5y9yOVJEmSJO1KO3QL0n4xf/78OvTQQ3sdhqQeufbaa79bVUNTdLgFwKaO9c3AgiTfobkL7VuAn5loB+1ddZYDPOtZz3rJ4Ycf3qVQJfW7KT5/7VL2v6TBNp3PX3PmzKmf/Mmf7HUYknpkovPXtEyKHXrooaxfv77XYUjqkSTf7HUMwK8Da6tqczPt2PiqagXNLdkZHh4uz1/S4OqT89dOsf8lDbbpfP6aN2+e5y9pgE10/pqWSTFJmkJbgEUd6wvbspcDr0zy68CewNz2bjNn9iBGSZIkSdIOMikmSRNbA5yeZDXwUuDBqrob+KWRBklOA4ZNiEmSJEnS9GFSTNJAS3IhcDwwP8lm4GxgDkBVfQJYC5wEbAQeAd7em0glSZK0M/bff/9ehyCpT5kUkzTQqmrZJPUFvGuSNquAVbsuKkmSJO0qQ0PT8v4AkqbAbr0OQJIkSZKkbnniiSd6HYKkPmVSTJIkSZI0Y91+++29DkFSnzIpJkmSJEmSpIHT9aRYkqVJNiTZmORpd2ZLcnCSK5Jcl+TGJCd1OyZJkiRJkiQNtq4mxZLMAs4HXgscASxLcsSoZr8PfLaqXgScCvxZN2OSJEmSJEmSun33yWOAjVV1J0CS1cDJwC0dbQrYu13eB/j2pHu99lpIdm2kkiRJkiRJGhjdvnxyAbCpY31zW9bpHOAtSTYDa4F3j7WjJMuTrE+yvhuBSpIkSZJmnvnz5/c6BEl9qtsjxbbHMmBVVX0kycuBTyV5QVU95b65VbUCWAEwPDxcrDc3Jg0sR4pKkiRpOx1wwAG9DkFSn+r2SLEtwKKO9YVtWad3AJ8FqKqrgXmAqXxJkiRJ0jO2bdu2XocgqU91Oym2DliSZHGSuTQT6a8Z1eZbwE8DJHk+TVJsa5fjkiRJkiQNgDvuuKPXIUjqU11NilXVNuB04BLgVpq7TN6c5Nwkr2ub/Tbwq0luAC4ETquq6mZckiRJkiRJGmxdn1OsqtbSTKDfWXZWx/ItwLHdjkOSJEmSJEka0e3LJyVJkiRJkqS+Y1JMkiRJkiRJA8ekmCRJkiRpxnr2s5/d6xAk9SmTYpIkSdNUkqVJNiTZmOTMMeoPSXJZkhuTXJlkYUfd40mubx+j7w4uSTPGfvvt1+sQJPWprk+0L0mSpF0vySzgfOA1wGZgXZI17U2MRnwYuKCq/jLJCcAHgF9u675XVUdNadCS1AOPPvpor0OQ1KccKSZJkjQ9HQNsrKo7q+pRYDVw8qg2RwCXt8tXjFEvSTPeXXfd1esQJPUpk2KSJEnT0wJgU8f65ras0w3AG9rlU4C9khzQrs9Lsj7JNUleP9YBkixv26zfunXrroxdkiSp50yKSZIkzVxnAMcluQ44DtgCPN7WHVJVw8AvAh9NctjojatqRVUNV9Xw0NDQlAUtSZI0FZxTTJIkaXraAizqWF/Ylj2pqr5NO1IsyZ7AG6vqgbZuS/vvnUmuBF4E3NH9sCVJkvqDI8UkSZKmp3XAkiSLk8wFTgWechfJJPOTjPT33gusbMv3S7L7SBvgWKBzgn5JkqQZz6SYJEnSNFRV24DTgUuAW4HPVtXNSc5N8rq22fHAhiS3AQcC72/Lnw+sT3IDzQT8Hxx110pJmjGe85zn9DoESX3KyyclDbQkK4GfB+6pqheMUR/gY8BJwCPAaVX19SSHAJ+n+XFhDvAnVfWJqYtckqCq1gJrR5Wd1bF8EXDRGNt9FXhh1wOUpD6wzz779DoESX3KkWKSBt0qYOkE9a8FlrSP5cDH2/K7gZdX1VHAS4Ezk/xoF+OUJEnSTvj+97/f6xAk9SmTYpIGWlVdBdw3QZOTgQuqcQ2wb5KDqurRqvpB22Z3PJ9KkiT1pW9+85u9DkFSn/KPOEma2AJgU8f65raMJIuS3NjWn9fe5e1pkixPsj7J+q1bt3Y9YEmSJEnS5EyKSdJOqqpNVXUk8FzgbUkOHKfdiqoarqrhoaGhqQ1SkiSpzyRZmeSeJDdN0u7oJNuSvGlU+d5JNif50+5GKmmmMykmSRPbAizqWF/Ylj2pHSF2E/DKKYxLkiRpulrFxHO6kmQWcB7w5TGq3wdctevDkjRoTIpJ0sTWAG9N42XAg1V1d5KFSX4EIMl+wCuADb0MVJIkaTrYjjldAd4NfA64p7MwyUuAAxk7WSZJO2R2rwOQpF5KciFwPDA/yWbgbGAOQFV9AlgLnARsBB4B3t5u+nzgI0kKCPDhqvrG1EYvSZI08yRZAJwCvBo4uqN8N+AjwFuAn5lkH8tp7hzOs5/97K7FKml6MykmaaBV1bJJ6gt41xjllwJHdisuSZKkAfZR4Her6okkneW/Dqytqs2jyp+mqlYAKwCGh4erW4FKmt5MikmSJEmS+skwsLpNfM0HTkqyDXg58Mokvw7sCcxN8nBVnTnRzh555JFuxytpmjIpJkmSJEnqG1W1eGQ5ySrgC1V1MXBxR/lpwPBkCTGATZs2dSFKSTOBSTFJkiRJ0pTZjjldJWlKmBSTJEmSJE2ZyeZ0HdX2tHHKVwGrdk1EkgbVbr0OQJIkSZIkSZpqJsUkSZIkSZI0cEyKSZIkSZJmrAULFvQ6BEl9yqSYJEmSJGnG2nPPPXsdgqQ+1fWkWJKlSTYk2ZjkabfLTfJHSa5vH7cleaDbMUmSJEmSBsPDDz/c6xAk9amu3n0yySzgfOA1wGZgXZI1VXXLSJuq+s2O9u8GXtTNmCRJkiRJg2PLli29DkFSn+r2SLFjgI1VdWdVPQqsBk6eoP0y4MIuxyRJkiRJkqQB1+2k2AJgU8f65rbsaZIcAiwGLh+nfnmS9UnWb926dZcHKkmSJEmSpMHRTxPtnwpcVFWPj1VZVSuqariqhoeGhqY4NEmSJEmSJM0k3U6KbQEWdawvbMvGcipeOilJkiRJkqQp0O2k2DpgSZLFSebSJL7WjG6U5HBgP+DqLscjSZI0Y2zHXb4PSXJZkhuTXJlkYUfd25Lc3j7eNrWRS9LUWbRo0eSNJA2kribFqmobcDpwCXAr8NmqujnJuUle19H0VGB1VVU345EkSZopOu7y/VrgCGBZkiNGNfswcEFVHQmcC3yg3XZ/4GzgpTQ3Rjo7yX5TFbskTaU99tij1yFI6lOzu32AqloLrB1Vdtao9XO6HYckSdIM8+RdvgGSjNzl+5aONkcAv9UuXwFc3C7/LHBpVd3XbnspsBSnspA0Az300EO9DkFSn+qnifYlSZK0/bbnLt83AG9ol08B9kpywHZu692/Jc0Id999d69DkNSnTIpJkiTNXGcAxyW5DjiO5oZHY97peyze/VuSJM1kXb98UpIkSV0x6V2+q+rbtCPFkuwJvLGqHkiyBTh+1LZXdjNYSZKkfuNIMUmSpOlp0rt8J5mfZKS/915gZbt8CXBikv3aCfZPbMskSZIGhkkxSZKkaWg77/J9PLAhyW3AgcD7223vA95Hk1hbB5w7Mum+JEnSoPDySUkDLclK4OeBe6rqBWPUB/gYcBLwCHBaVX09yVHAx4G9aebneX9VfWbqIpekye/yXVUXAReNs+1KfjhyTJJmrEMOOaTXIUjqU44UkzToVgFLJ6h/LbCkfSynSYRBkyB7a1X9RLv9R5Ps28U4JUmStBPmzZvX6xAk9SlHikkaaFV1VZJDJ2hyMnBBVRVwTZJ9kxxUVbd17OPbSe4BhoAHuhqwJEmSdsiDDz7Y6xAk9SlHiknSxBYAmzrWN7dlT0pyDDAXuGOsHSRZnmR9kvVbt27tWqCSJEl6un/7t3/rdQiS+pRJMUl6BpIcBHwKeHtVPTFWm6paUVXDVTU8NDQ0tQFKkiRJksZkUkySJrYFWNSxvrAtI8newBeB36uqa3oQmyRJkiRpJ5kUk6SJrQHemsbLgAer6u4kc4HP08w3Nuad3SRJkiRJ/cuJ9iUNtCQXAscD85NsBs4G5gBU1SeAtcBJwEaaO06+vd30F4BXAQckOa0tO62qrp+y4CVJkiRJO82kmKSBVlXLJqkv4F1jlH8a+HS34pIkSdKusXjx4l6HIKlPefmkJEmSJGnGmjt3bq9DkNSnTIpJkiRJkmas+++/v9chSOpTJsUkSZIkSTPWPffc0+sQJPUpk2KSJEmSJEkaOCbFJEmSJEmSNHBMikmSJEmSJGngmBSTJEmSJEnSwDEpJkmSJEmasQ477LBehyCpT5kUkyRJkiTNWLNnz+51CJL6lEkxSZIkSdKUSbIyyT1Jbpqk3dFJtiV5U7t+VJKrk9yc5MYkb96e49177727ImxJM5BJMUmSJEnSVFoFLJ2oQZJZwHnAlzuKHwHeWlU/0W7/0ST7Tnaw7373uzsfqaQZzaSYJEmSJGnKVNVVwH2TNHs38Dngno7tbquq29vlb7d1Q92KU9LMZ1JMkiRJktQ3kiwATgE+PkGbY4C5wB3j1C9Psj7J+scee6w7gUqa9kyKSZIkTVNJlibZkGRjkjPHqD84yRVJrmvn3zmpLT80yfeSXN8+PjH10UvSuD4K/G5VPTFWZZKDgE8Bbx+vTVWtqKrhqhqeM2dOF0OVNJ11/TYcSZYCHwNmAZ+sqg+O0eYXgHOAAm6oql/sdlySJEnTWTvfzvnAa4DNwLoka6rqlo5mvw98tqo+nuQIYC1waFt3R1UdNZUxS9J2GgZWJwGYD5yUZFtVXZxkb+CLwO9V1TW9DFLS9NfVpNj2dNaSLAHeCxxbVfcneXY3Y5IkSZohjgE2VtWdAElWAycDnUmxAvZul/cBvj2lEUrSTqiqxSPLSVYBX2gTYnOBzwMXVNVF27u/JUuW7PogJc0I3b588snOWlU9Cox01jr9KnB+Vd0PUFX3IEmSpMksADZ1rG9uyzqdA7wlyWaaUWLv7qhb3F5W+Q9JXjnWATrn5Nm6desuDF3SIEtyIXA18ONJNid5R5J3JnnnJJv+AvAq4LSOy78nHfG6227OGiRpbN2+fHKsztpLR7V5HkCSf6K5xPKcqvpSl+OSJEkaBMuAVVX1kSQvBz6V5AXA3cDBVXVvkpcAFyf5iap6qHPjqloBrAAYHh6uqQ5e0sxUVct2oO1pHcufBj69o8czqS9pPP2QMp8NLAGOp+m4/UWSfUc38pdKSZKkp9gCLOpYX9iWdXoH8FmAqroamAfMr6ofVNW9bfm1NHdve17XI5akHrjvvvt6HYKkPtXtpNj2dNY2A2uq6rGqugu4jSZJ9hSddw8ZGhrqWsCSJEnTxDpgSZLF7Tw7pwJrRrX5FvDTAEmeT5MU25pkqJ37lSQ/RtP3unPKIpckSeoD3U6KbU9n7WKaUWIkmU/zK6WdMkmSpAlU1TbgdOAS4Faau0zenOTcJK9rm/028KtJbgAuBE6rqqKZk+fGJNcDFwHvrCqHUkiSpIHS1TnFqmpbkpHO2ixg5UhnDVhfVWvauhOT3AI8DvzOyHB+SZIkja+q1tJMoN9ZdlbH8i3AsWNs9zngc10PUJIkqY91e6L97emsFfBb7UOSplSSlcDPA/dU1QvGqA/wMeAk4BGaURZfb+u+BLwM+Meq+vmpi1qSJEmS9Ez1w0T7ktRLq4ClE9S/lmaunSXAcuDjHXUfAn65a5FJkiTpGfvxH//xXocgqU+ZFJM00KrqKmCieXROBi6oxjXAvkkOare9DPj3KQhTkiRJkrSLmRSTpIktADZ1rG9uy7ZbkuVJ1idZv3Xr1l0anCRJkib2ne98p9chSOpTJsUkqcuqakVVDVfV8D01H38AACAASURBVNDQUK/DkSRJGigPPPBAr0OQ1KdMiknSxLYAizrWF7ZlkiRJkqRpzKSYJE1sDfDWNF4GPFhVd/c6KEmSJEnSMzO71wFIUi8luRA4HpifZDNwNjAHoKo+AawFTgI2Ao8Ab+/Y9ivA4cCe7bbvqKpLpvQJSJIkSZJ2ikkxSQOtqpZNUl/Au8ape2VXgpIkSdIus9tuXiAlaWyeHSRJkiRJM9aSJUt6HYKkPmVSTJIkSZIkSQPHpJgkSZIkaca6+27vkSRpbCbFJEmSJEkz1kMPPdTrECT1KZNikiRJkiRJGjgmxSRJkiRJkjRwTIpJkiRJkiRp4JgUkyRJkiTNWLNnz+51CJL6lEkxSZIkSdKMddhhh/U6BEl9yqSYJEmSJEmSBo5JMUmSJEnSjLVly5ZehyCpT5kUkyRJkiTNWA8//HCvQ5DUp0yKSZIkTVNJlibZkGRjkjPHqD84yRVJrktyY5KTOure2263IcnPTm3kkiRJvedtOCRJkqahJLOA84HXAJuBdUnWVNUtHc1+H/hsVX08yRHAWuDQdvlU4CeAHwX+PsnzqurxqX0WkiRJveNIMUmSpOnpGGBjVd1ZVY8Cq4GTR7UpYO92eR/g2+3yycDqqvpBVd0FbGz3J0mSNDBMikmSJE1PC4BNHeub27JO5wBvSbKZZpTYu3dgW5IsT7I+yfqtW7fuqrglaUrNnTu31yFI6lMmxSRJkmauZcCqqloInAR8Ksl29/+qakVVDVfV8NDQUNeClKRuWrx4ca9DkNSnnFNMkiRpetoCLOpYX9iWdXoHsBSgqq5OMg+Yv53bSpIkzWiOFJMkSZqe1gFLkixOMpdm4vw1o9p8C/hpgCTPB+YBW9t2pybZPcliYAnwtSmLXJKm0KZNmyZvJGkgOVJMkiRpGqqqbUlOBy4BZgErq+rmJOcC66tqDfDbwF8k+U2aSfdPq6oCbk7yWeAWYBvwLu88KWmmeuSRR3odgqQ+ZVJMkiRpmqqqtTQT6HeWndWxfAtw7Djbvh94f1cDlCRJ6mNdv3wyydIkG5JsTHLmGPWnJdma5Pr28V+6HZMkSZIkSZIGW1dHiiWZBZwPvIbmVt/rkqxpf7Xs9JmqOr2bsUiSJEmSJEkjuj1S7BhgY1XdWVWPAquBk7t8TEnabklWJrknyU3j1CfJH7ejXW9M8uKOurclub19vG3qopYkSZq+Jut/dbQ7Osm2JG/qKNvh/te8efOeaciSZqhuJ8UWAJ23+tjclo32xvaPzYuSLBqjniTLk6xPsn7r1q3diFXSYFoFLJ2g/rU0d2VbAiwHPg6QZH/gbOClND8AnJ1kv65GKkmSNDOsYuL+18hVR+cBX+4o26n+1yGHHPJMYpU0g/XDRPt/C1xYVT9I8mvAXwInjG5UVSuAFQDDw8M1tSFKmqmq6qokh07Q5GTggvZubdck2TfJQcDxwKVVdR9AkktpOncXTnjAa6+FZBdELkmSND1tR/8L4N3A54CjO8p+Fvtfknahbo8U2wJ0jvxa2JY9qaruraoftKufBF7S5ZgkaUeMN+J1e0fCPmWka9eilCRJmiGSLABOoR2h32Gn+l/f7E6YkmaAbo8UWwcsSbKYJhl2KvCLnQ2SHFRVd7errwNu7XJMkjSlnjbSdb25MWlgOVJBkrbHR4HfraonspPnzc7+11577VX8+7/vwvAkTSsTnEe6mhSrqm1JTgcuAWYBK6vq5iTnAuurag3wniSvA7YB9wGndTMmSdpB44143UJzCWVn+ZVTFpUkSdLMNQysbhNi84GTkmzD/pekXazrc4pV1Vpg7aiyszqW3wu8t9txSNJOWgOcnmQ1zaSuD1bV3UkuAf5Xx+SuJ+K5TJIk6RmrqsUjy0lWAV+oqovbifbtf0naZfphon1J6pkkF9L84jg/yWaaOxrNAaiqT9Ak9U8CNgKPAG9v6+5L8j6ay8QBzh2Z9FWSJEnj247+15jsf0na1UyKSRpoVbVskvoC3jVO3UpgZTfikiRJmqkm63+NanvaqPUd7n/tscceO9Jc0gDp9t0nJUmSJEnqmUWLFk3eSNJAMikmSZIkSZKkgWNSTJIkSZI0Y9111129DkFSnzIpJkmSJEmasR599NFehyCpT5kUkyRJkiRJ0sAxKSZJkiRJkqSBY1JMkiRJkiRJA8ekmCRJkiRpxtpzzz17HYKkPmVSTJIkSZI0Yy1YsKDXIUjqUybFJEmSJEmSNHBMikmSJE1TSZYm2ZBkY5Izx6j/oyTXt4/bkjzQUfd4R92aqY1ckqbOHXfc0esQJPWp2b0OQJIkSTsuySzgfOA1wGZgXZI1VXXLSJuq+s2O9u8GXtSxi+9V1VFTFa8k9cq2bdt6HYKkPuVIMUmSpOnpGGBjVd1ZVY8Cq4GTJ2i/DLhwSiKTJEmaBkyKSZIkTU8LgE0d65vbsqdJcgiwGLi8o3hekvVJrkny+u6FKUmS1J+8fFKSJGnmOxW4qKoe7yg7pKq2JPkx4PIk36iqp0y8k2Q5sBzg4IMPnrpoJUmSpoAjxSRJkqanLcCijvWFbdlYTmXUpZNVtaX9907gSp4639hImxVVNVxVw0NDQ7siZkmacnvvvXevQ5DUp0yKSZIkTU/rgCVJFieZS5P4etpdJJMcDuwHXN1Rtl+S3dvl+cCxwC2jt5WkmeCggw7qdQiS+pSXT0qSJE1DVbUtyenAJcAsYGVV3ZzkXGB9VY0kyE4FVldVdWz+fODPkzxB8yPpBzvvWilJkjQITIpJkiRNU1W1Flg7quysUevnjLHdV4EXdjU4SeoTt99+e69DkNSnvHxSkiRJkjRjPfHEE70OQVKfMikmSZIkSZKkgWNSTJIkSZIkSQPHpJikgZZkaZINSTYmOXOM+kOSXJbkxiRXJlnYUXdekpvax5unNnJJkiRJ0jNhUkzSwEoyCzgfeC1wBLAsyRGjmn0YuKCqjgTOBT7QbvtzwIuBo4CXAmck2XuqYpckSdL22XfffXsdgqQ+ZVJM0iA7BthYVXdW1aPAauDkUW2OAC5vl6/oqD8CuKqqtlXVfwA3AkunIGZJkiTtgAMPPLDXIUjqUybFJA2yBcCmjvXNbVmnG4A3tMunAHslOaAtX5pkjyTzgVcDi8Y6SJLlSdYnWb9169Zd+gQkSZIkSTun60mxyebr6Wj3xiSVZLjbMUnSDjgDOC7JdcBxwBbg8ar6MrAW+CpwIXA18PhYO6iqFVU1XFXDQ0NDUxS2JEmSADZs2NDrECT1qa4mxbZzvh6S7AX8BvDP3YxHkkbZwlNHdy1sy55UVd+uqjdU1YuA32vLHmj/fX9VHVVVrwEC3DY1YUuSJEmSnqlujxTbnvl6AN4HnAd8v8vxSFKndcCSJIuTzAVOBdZ0NkgyP8nIufK9wMq2fFZ7GSVJjgSOBL48ZZFLkiRJkp6RbifFJp2vJ8mLgUVV9cWJduScPJJ2taraBpwOXALcCny2qm5Ocm6S17XNjgc2JLkNOBB4f1s+B/hKkluAFcBb2v1JkiRJkqaB2b08eDv64g+B0yZrW1UraP7wZHh4uLobmaRBUVVraeYG6yw7q2P5IuCiMbb7Ps1l4ZIkSZKkaajbI8Umm69nL+AFwJVJ/hV4GbDGyfYlSZIkSbvC/vvv3+sQJPWpbifFJpyvp6oerKr5VXVoVR0KXAO8rqrWdzkuSZIkSdIA8O7fksbT1aTYds7XI0mSJElSVzzxxBO9DkFSn+r6nGKTzdczqvz4bscjSZIkSRoct99+e69DkNSnun35pCRJkiRJT0qyMsk9SW4ap/7kJDcmuT7J+iSv6Kj7gyQ3J7k1yR8nydRFLmmmMSkmSZIkSZpKq4ClE9RfBvxkVR0F/ArwSYAkPwUcCxxJc8O2o4HjuhqppBnNpJgkSZIkacpU1VXAfRPUP1xV1a4+CxhZLmAeMBfYHZgDfKeLoUqa4UyKSZIkSZL6SpJTkvwL8EWa0WJU1dXAFcDd7eOSqrp1nO2Xt5dern/sscemKmxJ04xJMUmSJElSX6mqz1fV4cDrgfcBJHku8HxgIbAAOCHJK8fZfkVVDVfV8EEHHTRVYUuaZkyKSZIkSZL6Unup5Y8lmQ+cAlzTXl75MPB3wMsn28cBBxzQ5SglTVcmxSRJkqapJEuTbEiyMcmZY9T/UXv3tuuT3JbkgY66tyW5vX28bWojl6TxJXnuyF0lk7yYZv6we4FvAcclmZ1kDs0k+2NePtlp27Zt3QxX0jQ2u9cBSJIkacclmQWcD7wG2AysS7Kmqm4ZaVNVv9nR/t3Ai9rl/YGzgWGaiauvbbe9fwqfgqQBleRC4HhgfpLNNOejOQBV9QngjcBbkzwGfA94c1VVkouAE4Bv0Jy7vlRVfzvZ8e64446uPA9J059JMUmSpOnpGGBjVd0JkGQ1cDJwyzjtl9H84Qnws8ClVXVfu+2lwFLgwq5GLElAVS2bpP484Lwxyh8Hfq1bcUkaPF4+KUmSND0tADZ1rG9uy54mySHAYuDyHdm28+5tW7du3SVBS5Ik9QuTYpIkSTPfqcBF7SiL7dZ597ahoaEuhSZJktQbJsUkSZKmpy3Aoo71hW3ZWE7lqZdG7si2kiRJM5JJMUmSpOlpHbAkyeIkc2kSX2tGN0pyOLAfcHVH8SXAiUn2S7IfcGJbJkkzzrOf/exehyCpTznRviRJ0jRUVduSnE6TzJoFrKyqm5OcC6yvqpEE2anA6qqqjm3vS/I+msQawLkjk+5L0kyz33779ToESX3KpJgkSdI0VVVrgbWjys4atX7OONuuBFZ2LThJ6hOPPvpor0OQ1Ke8fFKSJEmSNGPdddddvQ5BUp8yKSZJkiRJkqSBY1JMkiRJkiRJA8ekmKSBlmRpkg1JNiY5c4z6Q5JcluTGJFcmWdhR9wdJbk5ya5I/TpKpjV6SJEmStLNMikkaWElmAecDrwWOAJYlOWJUsw8DF1TVkcC5wAfabX8KOBY4EngBcDRw3BSFLkmSJEl6hkyKSRpkxwAbq+rOqnoUWA2cPKrNEcDl7fIVHfUFzAPmArsDc4DvdD1iSZIk7ZDnPOc5vQ5BUp8yKSZpkC0ANnWsb27LOt0AvKFdPgXYK8kBVXU1TZLs7vZxSVXdOtZBkixPsj7J+q1bt+7SJyBJkqSJ7bPPPr0OQVKfMikmSRM7AzguyXU0l0duAR5P8lzg+cBCmkTaCUleOdYOqmpFVQ1X1fDQ0NBUxS1JkiTg+9//fq9DkNSnZvc6AEnqoS3Aoo71hW3Zk6rq27QjxZLsCbyxqh5I8qvANVX1cFv3d8DLga9MReCSJEnaPt/85jd7HYKkPuVIMUmDbB2wJMniJHOBU4E1nQ2SzE8ycq58L7CyXf4WzQiy2Unm0IwiG/PySUmSJElS/zEpJmlgVdU24HTgEpqE1mer6uYk5yZ5XdvseGBDktuAA4H3t+UXAXcA36CZd+yGqvrbqYxfkiRJkrTzvHxS0kCrqrXA2lFlZ3UsX0STABu93ePAr3U9QEmSJElSVzhSTJIkSZIkSQOn60mxJEuTbEiyMcmZY9S/M8k3klyf5B+THNHtmCRJkiRJg+Gggw7qdQiS+lRXk2JJZgHnA68FjgCWjZH0+j9V9cKqOgr4A+APuxmTJEmSJGlw7L333r0OQVKf6vZIsWOAjVV1Z1U9CqwGTu5sUFUPdaw+C6guxyRJkiRJGhCPPPJIr0OQ1Ke6PdH+AmBTx/pm4KWjGyV5F/BbwFzghLF2lGQ5sBzg4IMP3uWBSpIkSZJmnk2bNk3eSNJA6ouJ9qvq/Ko6DPhd4PfHabOiqoaranhoaGhqA5QkSZIkSdKM0u2k2BZgUcf6wrZsPKuB13c1IkmSJEmSJA28bifF1gFLkixOMhc4FVjT2SDJko7VnwNu73JMkiRJkiRJGnBdnVOsqrYlOR24BJgFrKyqm5OcC6yvqjXA6Ul+BngMuB94WzdjkiRJkiRJkro90T5VtRZYO6rsrI7l3+h2DJIkSTNRkqXAx2h+fPxkVX1wjDa/AJxDc4fvG6rqF9vyx4FvtM2+VVWvm5KgJWmKLViwoNchSOpTXU+KSZIkaddLMgs4H3gNzR2+1yVZU1W3dLRZArwXOLaq7k/y7I5dfK+qjprSoCWpB/bcc89ehyCpT/XF3SclSZK0w44BNlbVnVX1KM0Ni04e1eZXgfOr6n6AqrpnimOUpJ57+OGHex2CpD5lUkySJGl6WgBs6ljf3JZ1eh7wvCT/lOSa9nLLEfOSrG/Lx7z7d5LlbZv1W7du3bXRS9IU2bJlS69DkNSnvHxSkiRp5poNLAGOBxYCVyV5YVU9ABxSVVuS/BhweZJvVNUdnRtX1QpgBcDw8HBNbeiSJEnd5UgxSZKk6WkLsKhjfWFb1mkzsKaqHququ4DbaJJkVNWW9t87gSuBF3U7YEmSpH5iUkySJGl6WgcsSbI4yVzgVGDNqDYX04wSI8l8mssp70yyX5LdO8qPBW5BkiRpgHj5pCRJ0jRUVduSnA5cAswCVlbVzUnOBdZX1Zq27sQktwCPA79TVfcm+Sngz5M8QfMj6Qc771opSZI0CEyKSZIkTVNVtRZYO6rsrI7lAn6rfXS2+SrwwqmIUZJ6bdGiRZM3kjSQvHxSkiRJkjRlkqxMck+Sm8apPznJjUmub++A+4qOuoOTfDnJrUluSXLoZMfbY489dl3wkmYUk2KSJEmSpKm0Clg6Qf1lwE9W1VHArwCf7Ki7APhQVT0fOAa4Z7KDPfTQQzsfqaQZzaSYJEmSJGnKVNVVwH0T1D/cXv4N8CygAJIcAcyuqks72j0y2fHuvvvuZx60pBnJpJgkSZIkqa8kOSXJvwBfpBktBs0ddB9I8jdJrkvyoSSzxtl+eXvp5frHHntsqsKWNM2YFJMkSZIk9ZWq+nxVHQ68HnhfWzwbeCVwBnA08GPAaeNsv6KqhqtqeM6cOVMQsaTpyKSYpIGWZGmSDUk2JjlzjPpDklzWTvZ6ZZKFbfmr28lfRx7fT/L6qX8GkiRJM1d7qeWPJZkPbAaur6o7q2obcDHw4p4GKGlaMykmaWC1w+3PB14LHAEsa+eq6PRh4IKqOhI4F/gAQFVdUVVHtRPAngA8Anx5yoKXJEmaoZI8N0na5RcDuwP3AuuAfZMMtU1PAG7pTZSSZoLZvQ5AknroGGBjVd0JkGQ1cDJP7VwdAfxWu3wFzS+So70J+LvtmehVkiRp0CW5EDgemJ9kM3A2MAegqj4BvBF4a5LHgO8Bb24n3n88yRnAZW3S7FrgLyY73iGHHNKV5yFp+jMpJmmQLQA2daxvBl46qs0NwBuAjwGnAHslOaCq7u1ocyrwh+MdJMlyYDnAwQcfvAvCliRJmr6qatkk9ecB541Tdylw5I4cb968eTvSXNIA8fJJSZrYGcBxSa4DjgO2AI+PVCY5CHghcMl4O+ic6HVoaGi8ZpIkSeqCBx98sNchSOpTjhSTNMi2AIs61he2ZU+qqm/TjBQjyZ7AG/8fe3ceb1Vd73/89WYSRwRBRVBBBRXnRLTUcg7Nq94yhzK1HMoyU2/dq7dfZt66mTbYoLfMLNNSzDLRnEhBLQcGRRRwIBwAURAlU1QUPr8/vt/t2Wz3ORw4ezpnv5+Px3rsNXzXWp+911nfs/Znf9d3RcTioiJHATdGhJ/1bWZmZtaAXnzxxXqHYGYNyi3FzKyZTQKGSRoqqRfpNsixxQUk9ZdUqCvPBa4s2caxwLVVj9TMzMzMzMwqykkxM2ta+VHep5NufZwJXB8R0yVdIOmwXGwf4ElJTwEbAd8prC9pCKml2T01DNvMzMzMzMwqwLdPmllTi4hbgVtL5p1XNH4DcEMr6z5L6qzfzMzMzMzMOhm3FDMzMzMzMzMzs6bjpJiZmZmZmZl1WUOHDq13CGbWoJwUMzMzMzMzsy6rV69e9Q7BzBqUk2JmZmZmZmbWZb366qv1DsHMGpSTYmZmZmZmZtZlLViwoN4hmFmDclLMzMzMzMzMzMyaTtWTYpJGS3pS0ixJ55RZfrakGZKmSbpL0ubVjsnMzMzMzMzMzJpbVZNikroDlwIHAyOAYyWNKCn2CDAyInYEbgAuqmZMZmZmZl3Fyn58zGWOyj9ATpf0+6L5J0h6Og8n1C5qMzMzs8bQo8rbHwXMiojZAJKuAw4HZhQKRMT4ovIPAsdVOSYzMzOzTq/ox8cDgbnAJEljI2JGUZlhwLnAnhHxqqQN8/x+wDeBkUAAU/K67o3azMzMmka1k2KDgDlF03OB3dsofxJwW7kFkk4FTgXYbLPNKhWfmZmZWWe10h8fgVOASwvJrogo9Db9UWBcRLyS1x0HjAaubXVvU6aAVOn3YGZWdVtuuWW9QzCzBtUwHe1LOo70a+XF5ZZHxOURMTIiRg4YMKC2wZmZmZk1nnI/Pg4qKTMcGC7p75IelDR6FdZF0qmSJkuaXMG4zcxqqkeParcFMbPOqtq1wzxg06LpwXneCiQdAHwd+EhEvF3lmMzMzMyaRQ9gGLAP6TrsXkk7tHfliLgcuBxg5MiRwWTnxsyaViduKbpo0aJ6h2BmDaraLcUmAcMkDZXUCzgGGFtcQNIuwC+Aw4qa9JuZmZlZ29rz4+NcYGxEvBMRzwBPkZJk7frh0sysK3j55ZfrHYKZNaiqJsUi4l3gdOAOYCZwfURMl3SBpMNysYuBdYA/SJoqaWwrmzMzMzOzFiv98RH4M6mVGJL6k26nnE26NjtIUl9JfYGD8jwzMzOzplH1m6sj4lbg1pJ55xWNH1DtGMzMzMy6moh4V1Lhx8fuwJWFHx+ByRExlpbk1wxgGfC1iFgEIOl/SIk1gAsKne6bmZmZNQv3OGhmZmbWSbXjx8cAzs5D6bpXAldWO0YzMzOzRtUwT580MzMzMzMzMzOrFSfFzMzMzMzMrMsaNmxYvUMwswblpJiZmZmZmZl1Wd26+WuvmZXn2sHMzMzMzMy6rIULF9Y7BDNrUE6KmZmZmZmZWZf1yit+uK6ZleekmJmZmZmZmZmZNR0nxcysqUkaLelJSbMknVNm+eaS7pI0TdIESYOLlm0m6U5JMyXNkDSklrGbmZmZmZnZ6nNSzMyalqTuwKXAwcAI4FhJI0qKfR/4bUTsCFwAfLdo2W+BiyNiW2AUsKD6UZuZmZmZmVklOClmZs1sFDArImZHxFLgOuDwkjIjgLvz+PjC8pw86xER4wAi4vWIWFKbsM3MzMzMzKyjetQ7gNUxZcqU1yU9We84rKz+wMv1DsLep6sdl80rtJ1BwJyi6bnA7iVlHgU+DvwY+HdgXUkbAMOBxZL+BAwF/gqcExHLSnci6VTg1Dz5tqTHKxS/ta2r/d03On/e7VOp+qvmpkyZ8rKk5+odRxPwudS4mv3YdNr66/XXX/f3x86p2c+5zqhRj1mr9VenTIoBT0bEyHoHYe8nabKPTePxcemQrwI/k3QicC8wD1hGqj/3BnYBngfGACcCvyrdQERcDlwOPha15M+6tvx5d30RMaDeMTQDn0uNy8emU/P3x07I51zn0xmPmW+fNLNmNg/YtGh6cJ73noh4ISI+HhG7AF/P8xaTWpVNzbdevgv8GfhAbcI2MzMzMzOzjnJSzMya2SRgmKShknoBxwBjiwtI6i+pUFeeC1xZtO76kgotJ/YDZtQgZjMzMzMzM6uAzpoUu7zeAVirfGwak49LGbmF1+nAHcBM4PqImC7pAkmH5WL7AE9KegrYCPhOXncZ6dbKuyQ9Bgj4ZTt262NRO/6sa8uft1ll+FxqXD42nZePXefk49b5dLpjpoiodwxmZmZmZmZmZmY11VlbipmZmZmZmZmZma02J8XMzMzMzMzMzKzp1CwpJmm0pCclzZJ0Tpnla0gak5c/JGlI0bJz8/wnJX10ZdvMnWY/lOePyR1ot7mPZtYIx6Zo+SckhaRO9RjXammEYyNpM0njJT0iaZqkQ6r7rhtPgxyHpqm/GuTzboq/+xp/1qfneSGpf8l+9pE0VdJ0SfdU592aVY/PpcZV42Pzuzz/cUlXSuqZ50vST3L5aZL8tOoq6cjxtvpoxzE7W9KMfO7cJWnzesRpK1rZcSsq1/jf7yOi6gPQHfgHsAXQC3gUGFFS5ovAz/P4McCYPD4il18DGJq3072tbQLXA8fk8Z8Dp7W1j2YeGuXY5Ol1gXuBB4GR9f5s6j00yrEhdZZ4WtF2n633Z9Okx6Ep6q8G+ry7/N99HT7rXYAhwLNA/6J9rE96cutmeXrDen82HjysyuBzqXGHOhybQ0gP3hFwbdH/kUOA2/L8PYCH6v3ZdMWhI8fbQ0Mfs32BtfL4aT5m9R/ac9xyuU7x/b5WLcVGAbMiYnZELAWuAw4vKXM4cFUevwHYX5Ly/Osi4u2IeAaYlbdXdpt5nf3yNsjbPGIl+2hmjXJsAP4H+B7wVqXfZCfVKMcmgPXyeB/ghQq/z0bXKMehWeqvRvm8m+HvvmafNUBEPBIRz5aJ41PAnyLi+VxuQSXfpFkN+FxqXLU+NrdGBkwEBhft47d50YPA+pIGVutNN7GOHG+rj5Ues4gYHxFL8uSDtJxXVj/tOdegk3y/r1VSbBAwp2h6bp5XtkxEvAv8E9igjXVbm78BsDhvo3Rfre2jmTXEscnNyDeNiL90/C11GQ1xbIDzgeMkzQVuBb7ckTfVCTXKcWiW+qtRPu/z6fp/97X8rNsyHOgraYKkKZKOX8X3YVZvPpcaV12OTb5t8jPA7asQh3VcR4631ceqnhsnkVpdWn21px7sNN/ve9Q7ADNJ3YAfAifWORQr71jgNxHxA0kfBK6WtH1ELK93YGZV5L/72ukB7ArsD6wJPCDpwYh4qr5hmXU6Ppcax2XAvRFxX70DMesqJB0HjAQ+Uu9YrG2d7ft9rVqKzQM2LZoenOeVLSOpB+l2lUVtrNva/EWkJsk9Sua3tY9m1gjHZl1ge2CCpGdJBKlnwQAAIABJREFUfS2MbejO+GqjEY4NpF9krgeIiAeA3sAKHft2cY1yHJql/mqUz7sZ/u5r+Vm3ZS5wR0S8EREvk/qe2GmV3olZfflcalw1PzaSvgkMAM5exTis4zpyvK0+2nVuSDoA+DpwWES8XaPYrHUrO26d6/t9JTsoa20g/XI1m9RJZaEjtu1KynyJFTs9vD6Pb8eKnVzOJnXs1uo2gT+wYsfJX2xrH808NMqxKdnfBBq4I75mOzakJson5vFtSX0rqd6fTxMeh6aovxro8+7yf/e1/qyLtvksK3YOvi1wV153LeBxYPt6fz4ePLR38LnUuEMd/qecDNwPrFmyj4+xYkf7E+v92XTFoSPH20NDH7NdSJ26D6t3vB7af9xKyk+ggb/f1/KDOwR4Kv9Bfz3Pu4CU7YX0K/wfSJ1YTgS2KFr363m9J4GD29pmnr9F3sasvM01VraPZh4a4diUxNPQJ02zHRvS05f+niu7qcBB9f5cmvQ4NE391SCfd1P83df4sz6D1JLlXVKS8YqiZV8jPTXvceDMen8uHjys6uBzqXGHGh+bd/O8qXk4L88XcGle9hi+zm3I4+2hYY/ZX4GXis6rsfWO2cPKj1tJ2QmNXO8pB2lmZmZmZmZmZtY0atWnmJmZmZmZmZmZWcNwUszMzMzMzMzMzJqOk2J1Iikk/aBo+quSzq/Cfv67ZPr+Su+jZPvPSlrlJ7RJ2kfSh+q1/1XY/hGSRnRg/SGSPlU0PVLST1ayzj6Sbikz/0RJCyVdIWktSYskrVdS5s+Sjs7DrHLbMWsEkpZJmlo0nJPnT1idJ9VI2lnSIauxXtnzraRMh+qBdsaxQl2xmts4U9JaRdO3Slp/JeuUrUOLjs8mkn4t6fMly4+QdJukNXO5pdWsi80qoejv+nFJfyg+X1ZhG1cU6gNfc1WWr7nM6kvSBkXXZS9Kmlc03auC+9lU0pgOrP83STtXII65ktaX1E/SFzq6vXbs79v5Mz1P0laSnpOkkjKPS9pV0tckPS/pkmrH1YycFKuft4GP1+BLwwoXaBHR4YugKtkHKBub0uOSG8URpA64V9cQ4L0LtIiYHBFndGB7YyLi5IhYAtwB/HthgaQ+wF7AzRExhvREJLNG9WZE7Fw0XNjB7e1M6gD0fSpQp3S0HmiPIRTVFavpTNIT6ACIiEMiYvFqbqtwfF4AriU9savYMcC1EfFmROxM6vzbrNEV/q63B5YCq/wlKP8PnpEnfc1VWb7mMqujiFhUuC4jPaH7R0XXaUtXtr6k7u3cz5yIOLqj8VZQP1bj/8FqujgiLoiIWaSHCbxXN0vaHugVEVMi4mJSJ/ZWBU6K1c+7wOXAWaULJA2Q9EdJk/KwZ9H8cZKm51+qnisk1fKvU1PyslPzvAuBwq/2v8vzXs+v10n6WNE+fyPpSEndJV2c9zuttDVAUfnjJE3M2/5FuUqvtTKSRkt6WNKjku6SNIRU8ZyVy+6d4/m5pIeAi3LG/s85pgcl7Zi3tYGkOwufCenpPqsS47OSLpL0WC67VZ4/RNLdeX93SdpM6VfVw4CL8za3zMPt+bO/T9I2RZ/nTyTdL2m2pCPzLi8E9s7rn6WiXyQljZL0gKRH8npbl/3LaV3pF9V/B+7IF29mnZ6kg/I58rBSq4518vzd8jnzaD6P+5AuHI7O59rRks6XdLWkvwNXS+qt1OLpsXzO7Vuyr26SnpY0oGh6lqSP0M56oGR7hf0/kLd7Sp6vXOc+nmMpXBSW1hVl6+Zch0yQdIOkJyT9Lm/zDGATYLyk8bnse606VOZ/xiq4C9hG0sC8rbWBA4A/r+J2zBrJfUDhGuDsfE4+LunMPG9tSX/J9czjhXM1n38j5WsuX3P5msuaiKQTis75y5Suk3pIWizpEknTgFFKra/+N9dBkyR9INcj/1DLtdBWkqbm8ZPzNc0dStdL3y3a5+WSJuc66LyVxHeopGuLpg+Q9Oc8flyuhx6X9L9lVr8Q2Dq/twslrZfrqIdzPXVo0Xa/JenJXCeNUcv/jGH5PUyRdK+k4e34WEvrlWPyPKu2ej/+slkH4HVgPeBZoA/wVeD8vOz3wF55fDNgZh7/GXBuHh8NBNA/T/fLr2uSHsO9QWE/pfvNr/8OXJXHewFz8rqnAv8vz18DmAwMLdnGtsDNQM88fRlwfB5/FujfWhlgQN7X0JK4zwe+WrSP3wC3AN3z9E+Bb+bx/YCpefwntDzu+mOFz6StGEvey7O0PEL2eOCWPH4zcEIe/xzw56K4jixa/y5gWB7fHbi7qNwfSInnEcCsPH+fwj5Kp0l/Dz3y+AHAH8utU7TuicDPiqZ7kX5hKBz724FDy+3Lg4dGG4BltDxqeypwdJ4/ARiZz+t7gbXz/P8Czst/97OB3fL89YAeZc6P84EpwJp5+j+AK/P4NsDzpMe0F5+T3wTOzOMHFZ2T7aoHSt7f+cCjpHq2P6ke3AT4BDAO6A5slOMYWKauKFs353L/BAbn+uYBWv5/PEv+H1E6Tev/M1ZYp2jd0v8lPwO+ksePAW4oWV52Ox48NNJAyzVRD+Am4DRgV+AxYG1gHWA6sEs+V39ZtG6f/DqB/Jj5MueJr7lWfC/P4msuDx465VBcbwDbk34IK5xDl5NaZfbI9cLHi9abC5ySx38KPEKqXzcCXszztyqqZ04Gns7n6Jq5DtskLyvUYT1IP2SMyNN/A3Yuife9ujZP/5J0vTKYlrqzJ3BP4dzNsa5fHE+e3xNYL49vCDydx/cgXVuukeOdTct143hgyzy+J3Bnmc/024XyeXoTYB7QLU8/DWxTtPxk4JJ6/y10xaGRmkg3nYh4TdJvgTOAN4sWHQCMUMstxesptYjYi9xUOyJul/Rq0TpnSCo0494UGAYsamP3twE/lrQGKcF2b0S8KekgYMeiX9n65G09U7Tu/qSLxkk5xjWBBSXbb63MHnlfz+T38UobMf4hIpblXzW/BDwBfCsi7s6/Vq4HfBj4eN7WX4o+k/1JFdDrkt4CXiwTY8G1Ra8/yuMfLGwXuBq4qHSlfEw+BPyh6FitUVTkzxGxHJghaaM23mdBH+AqScNI/1B6tmOd90TEUkljgSMl/ZF0EX/HqmzDrI4Kt921Zg/Sl52/5/OtFykBtDUwPyImQapXAbRilwwFYyOiUNfuRbo4IyKekPQcUPor3pWkL8qXkL6o/bp0gyurB3L9NZOUuLo87/9NpdZbo3Ic10bEMuAlSfcAu5Hqz30l3RIRh5KScuXq5qXAxIiYm/c3lXTL0N/KfQBFVvV/Rqlrge8DPyZdZF69CuuaNYo1C60TSF+wfkVKjN0YEW8ASPoTsDcp6fEDSd8jJTvuW4X9dJprrjy+F/CJXH/9Bejejmuu4v1vCBwj6ahcfxXzNZdZ53cA6VplclGdMycvWwrcWFJ+bH59jJRIewN4Q9LyfG6X+ivpnLyXlKR6LH+fC0n9SOfsm6Trwhll1i+co+OAj0m6iVT3nplf746IlwEk/R74sFJL+oGk5P8FQD9JP4uI00mtYi+UtBewHNhCqSXwFaS6523g7aKWqOuT6t8/FtVXK827RMQLkp4iXf/9k/TDyhMrW886zkmx+rsEeJgVv2x1A/aIiLeKC7byJQ9J+5Aqpw9GxBJJE0gtHloVEW/lch8FjgauK2wO+HJEtPWPXaRfPM9d1TKS/q2tuEq8UTT+NlB6YdUWAb8gXaB8tcxFWbFoZXxlugGL2/gi/3ZJPCvzP8D4iPj3fCE6YRViKbgW+Ebe300R8c5qbMOsEQkYFxHHrjBT2mEVtvHGyou0iIg5kl6StB8pgfXpMsVWVg8A/AO4gffXA23VN+NJibCCsnVzrv+L65plrOR/++r8zyjjfmCgpJ1IX1RL+xgz6wzel4xv7VorIp6S9AFSX4XflnRXRLSrf5dOeM1V7B+kBFJb3rf/XM98tUxZX3OZdX4itbb/xgozU5+Eb0ZE6bldOD+Xs+K5upzy1yxvR8QiYGdJt5P6KV1ASp5vHxGLJV3Dyq9driO1rloCPBARb7RWx2dvkhqrlPb5fTypHvxARLwraS4tScByBLy8kmvD1hRuofwnvnWyZtynWJ3lX+2uB04qmn0n8OXChFqepvF34Kg87yCgb57fB3g1f7nZhpSZLnhHUmu/fo0BPkvLL6CQkkinFdaRNFypv5hid5F+Gdswl+knafN2lnmQlI0fmufvr3TP+ZukjPx0pU4FSy0hfyGVdAzploCfAJsDNyrdJz49fya7FPZP+nWhtRgLji56fSCP30/Ll7xPk35BBvgXsC681yLlGUmfzPtQ/oLYlvfWL6MPqckspGb6q2MC6VfmL+GK1LqWB4E91dIHzdpK/TM8SUrO7Jbnr5svyto61yCd04U6ZTjpVvUny5S7AriGFVtR/AtYN+/zb8CzSv1TTJe0fRv1wOGSts6/An4c+CGpdcVpkv4uaTYpWTUx72PNonXbUzeXau0zaOt/RrvkC94xwFXAbaU/4ph1YvcBRyg9YXBtUgv9+yRtAiyJiGuAi4EPlFm30a+5+uXy/yL1lzNNUm/Sl9IfFV1/vVc/km5zejlf8zwCPKTUh9dc0jXXh/P7+g9JHy3aT2uttXzNZdb5/RU4Si39lG4gabMq73MN0jn9mqQ3SD8ybKvUwn5b4Bal/r8+rdTX2WOk2yR3J7UA3lTSJFIie3SOuQep7rmnZF//IiXcNslJue8Cw3NC7EBgUC73KHCYpDUkrUt+wFNEvArMV26Rr9Tf2srqq4IbgH8DPknLDyhWZU6KNYYfsGJG+gxgZL5YmUHL0y++BRwk6XHSifIi6aS9HeghaSapY8AHi7Z1OTBNudPXEncCHwH+Gi1PELmC1Az14byfX1CSwY/0lKX/B9yZE1rjSM1NV1omIhaS+tD4k6RHgXNITWqH5/l9aUn2FVsA7Jq39V85ph+QLkaGkvqSuB9YCJxVtP/vky7Y3hdjkb55u1+h5cEHXwY+m+d/Ji+DVDl9Talj1i1JF28n5fcyHTi8lX0UTAOWKXU2WfqQhYuA70p6hNVsxZlvHbgB2ID3V/BmjazQQXVhWOHpk7nuOBG4Np+XD5D6WVhK+nL103wejiNdyIwn3YY+VS2d1xe7DOiWL5rGACfm5u+lxpL6FSpuzXsd8DVS/Xov6XaA75CeVnQ9rdcD00hfnIaR6qZhpC+cvUlf0LoBcyPixVx2OekL7Vm0o24u43LgduWO9ou09T9jVVwL7IS/DFoXEhEPk/qomgg8BFwREY8AOwATlW63/CapL5hSjX7NNSavcjPpi+KGpLptV9Jtlo/n5efnebeRklsn5Pk/Jl1f7JWXLSX1tbZrXnZ90f43KPMZgK+5zDq9iHiM9L30r/m8vZPWE+GVMo9UXz5BSpD9Pc/fCZhF+gHjM6Tk1ShS/fpFUl01mtSf4W6kB3gEKak9FXgwIv5SvKOIeImUUDuU1AXGB0i3uj9BSqI9nYs+Rrqmegy4Nb/+My87BvhCUX3VrjuecoOZycCciHi+PetYx+n9rRutUSn1RbEsZ6k/CPzfajbLbCiSegGTgLeADxW1xigsH0Lqv2P7oulxETEsT/+W9MSf30naAvhT4XNRbr7f2u2Tkp4ldZD7csXfWJVJOpEU++ntLL8PK7+V1MyKSBpJegT53q0sb1f9Rfri9Hp+rUj9VUuSXo+Icv1+tFb+WTpp3WrWLGp9/dWZ6wVfc5nVj6TzSf1rfT9Pvx4R6+Tz7OsRcWCefy/poXR/V+r64oyIOELSAtItmAUDgK0j4vWifZxI0Tmep/eMiMITMm8DvhMRf8vTE0i3iD8REa8rtfL9G+mhIdPa+b6+TWqJe0k7y59Mun30zPaUt/ZzS7HOZTNSJ6aPkm4dPKXO8VTKBqSWGOvS/n5tSu9HL75XvVn6ynsTOFjpsehtyi1lLgNeXVlZM0sknQP8EWirL59mqb9ey63uNmmrkKRCx+U9Se/HzBpXs9RfleBrLrPG1J46qdBf9855GFScEGvntlvrs/VX+bpnCunBSe1KiGX/Ar4o6byVFZT0NdJdCq+twvatnSryz0vSlaQmgQsKvyaVLBepWfUhpL6hTszN05F0AqnJN8C3I+KqSsTUFUXE06T+srqaX5Du7x4KfA9o169wlRARQ2q1r0qLiDG03ApRsbJmlkTEhaTbC9vSrvorIs6H91padDoR0WYyrKjcm0Cnb8Fs1iRqev3lay4zq5NCf90XQ+qvOyKmtr1K+0REuS462rvu90h1b3vKXkyO3yqvUi3FfkO6V7c1B5P6ThlG6tvg/+C9jji/SerXYBTwTUnl+pOyLkrS8cA7EfF70pfP3XJzVzOzhub6y8w6K9dfZtZEWuuv2wyoYJ9ipf0OlCz7BTAhIq7N008C+xSGiPh8uXJm0PbfVjvW3Qf36WBmdeL6y8w6K9dfZtYsVqPfwAmkOm5yNeOy2qhVUuwW4MKijunuIj1BcB+gd0R8O8//BvBmoRO9km2cSmplxtprr73rNttsU5G4zazzmTJlyssRMaCj28mtVccAQ0hPmTkqP0a5tNztwB7A30o6Dd6f1JS5G6kT9RMjYlZb++zfv38MGTKko6GbWSdVqfqrHlx/mTW3zlx/9ezZM3baaad6h2FmddJW/dVpOsSMiMtJj7pm5MiRMXmyk7JmzUrScxXa1DnAXRFxYe5U/RxSwr7UxcBawOdL5v8fcHhEzJT0RVL/iCe2tcMhQ4bg+suseVWw/qo5119mza0z11+9e/d2/WXWxNqqv2r19Ml5wKZF04PzvNbmm5nVwuFA4eEeVwFHlCsUEXeRnhDzvkXAenm8Dys+7tnMzMzMzMwaWK2SYmOB45XsAfwzIuYDdwAHSeqbO9g/KM8zM6uFjXJdBPAisNEqrn8ycKukucBnaOVJhZJOlTRZ0uSFCxeufrRmZmZmZmZWMRW5fVLStaT+wfrnL4ffBHoCRMTPgVuBQ4BZwBLgs3nZK5L+B5iUN3VBRLxSiZjMzAAk/RXYuMyirxdPRERIWtVOFs8CDomIhyR9DfghKVG2gtLbv1dxH2ZmZmbWAf369at3CGbWoCqSFIuIY1eyPIAvtbLsSuDKSsRhZlYqIg5obZmklyQNjIj5kgYCC9q7XUkDgJ0i4qE8awxwe8eiNTMzM7NKGzCgUz4fwMxqoFa3T5qZNaKxwAl5/ATgplVY91Wgj6ThefpAYGYFYzMzMzOzCli+fHm9QzCzBtVpnj5pZlYFFwLXSzoJeA44CkDSSOALEXFynr4P2AZYJ98iflJE3CHpFOCPkpaTkmSfq8ebMDMzM7PWPf300/UOwcwalJNiZta0ImIRsH+Z+ZMp6hssIvZuZf0bgRurFqCZmZmZmZlVjW+fNDMzMzMzMzOzpuOkmJmZmZmZmZmZNR0nxczMzMzMzMzMrOk4KWZmZmZmZmZdVv/+/esdgpk1KCfFzMzMzMzMrMvaYIMN6h2CmTUoJ8XMzMzMzMysy3r33XfrHYKZNSgnxczMzMzMzKzL+sc//lHvEMysQfWodwBmZk1lyhSQ6h2FmZmZmZlZ03NLMTMzMzMzMzMzazpuKWZmVku77gqTJ9c7CjOrF7cUNTMzM2sYbilmZmZmZmZmZmZNx0kxMzMzMzMz67I23HDDeodgZg3KSTEzMzMzMzPrsvr27VvvEMysQTkpZmZmZmZmZl3W0qVL6x2CmTUoJ8XMzMzMzMysy3rmmWfqHYKZNSgnxczMzMzMzMzMrOk4KWZmZmZmZmZmZk3HSTEzMzMzMzMzM2s6ToqZmZmZdSGSekuaKOlRSdMlfatMmc0kjZf0iKRpkg6pR6xmZsVcf5lZrVUkKSZptKQnJc2SdE6Z5T+SNDUPT0laXLRsWdGysZWIx8zMzKyJvQ3sFxE7ATsDoyXtUVLm/wHXR8QuwDHAZTWO0cysnKrUXxtvvHHFAzWzrqFHRzcgqTtwKXAgMBeYJGlsRMwolImIs4rKfxnYpWgTb0bEzh2Nw8zMzMwgIgJ4PU/2zEOUFgPWy+N9gBdqE52ZWeuqVX/16dOnUiGaWRdTiZZio4BZETE7IpYC1wGHt1H+WODaCuzXzMzMzMqQ1F3SVGABMC4iHiopcj5wnKS5wK3Al1vZzqmSJkuavHDhwqrGbGYG1am/5s+fX9WYzazzqkRSbBAwp2h6bp73PpI2B4YCdxfN7p0rqwclHdHaTnxRZmZmZtY+EbEst8QfDIyStH1JkWOB30TEYOAQ4GpJ77sujIjLI2JkRIwcMGBA9QM3s6ZXjfrrtddeq37gZtYp1bqj/WOAGyJiWdG8zSNiJPAp4BJJW5Zb0RdlZmZmZqsmIhYD44HRJYtOAq7PZR4AegP9axudmVnrXH+ZWS1UIik2D9i0aHpwnlfOMZTcOhkR8/LrbGACK/Y3ZmZmZmarQNIASevn8TVJ/b4+UVLseWD/XGZb0pdKN8U3s7py/WVmtVaJpNgkYJikoZJ6kRJf73uKpKRtgL7AA0Xz+kpaI4/3B/YEZpSua2ZmZmbtNhAYL2ka6TptXETcIukCSYflMv8BnCLpUdIPlifmDq7NzOrJ9ZeZ1VSHnz4ZEe9KOh24A+gOXBkR0yVdAEyOiEKC7BjgupIKa1vgF5KWkxJ0FxY/tdLMzMzMVk1ETKNMy/uIOK9ofAbpx0gzs4bh+svMaq3DSTGAiLiV9OSP4nnnlUyfX2a9+4EdKhGDmZmZmZmZWamBAwfWOwQza1C17mjfzMzMzMzMrGbWW2+9eodgZg3KSTEzMzMzMzPrspYsWVLvEMysQTkpZmZmZmZmZl3WnDlz6h2CmTUoJ8XMzMzMzMzMzKzpOClmZmZmZmZmZmZNx0kxMzMzMzMzMzNrOk6KmZmZmZmZmZlZ03FSzMzMzMzMzLqsQYMG1TsEM2tQToqZmZmZmZlZl7XOOuvUOwQza1BOiplZ05LUT9I4SU/n175lyuws6QFJ0yVNk3R00bKhkh6SNEvSGEm9avsOzMzMzGxlXn/99XqHYGYNykkxM2tm5wB3RcQw4K48XWoJcHxEbAeMBi6RtH5e9j3gRxGxFfAqcFINYjYzMzOzVTBv3rx6h2BmDcpJMTNrZocDV+Xxq4AjSgtExFMR8XQefwFYAAyQJGA/4Ia21jczMzOzOlu+vN4RmFmD6lHvAMzM6mijiJifx18ENmqrsKRRQC/gH8AGwOKIeDcvnguU7cVV0qnAqQCbbbZZBcI2M6uDKVNAqncUZmarbskS+NvfYK+96h2JmTUYtxQzsy5N0l8lPV5mOLy4XEQEEG1sZyBwNfDZiFilnxsj4vKIGBkRIwcMGLBa78PMzMzMOuCww2DmzHpHYWYNxi3FzKxLi4gDWlsm6SVJAyNifk56LWil3HrAX4CvR8SDefYiYH1JPXJrscGAO6wws65r111h8uR6R2Fm9dKZW4p27w6vvgoHHwwPPAADB9Y7IjNrEG4pZmbNbCxwQh4/AbiptEB+ouSNwG8jotB/WKFl2XjgyLbWNzMzM7P62nTYMNh9d3juOTjkEPjXv+odkpk1CCfFzKyZXQgcKOlp4IA8jaSRkq7IZY4CPgycKGlqHnbOy/4LOFvSLFIfY7+qbfhmZmZmtjJrrb023HwzbLUVTJ0KRx4J77xT77DMrAH49kkza1oRsQjYv8z8ycDJefwa4JpW1p8NjKpmjGZmZmbWMa+99hoMGAC33w4f/CDceSeccgr8+ted+7ZQM+swtxQzMzMzMzOzLmv+/Pyw8S23hL/8BdZaC666Cs47r76BmVndOSlmZmZmZmZmzWG33WDMGOjWDb79bfjFL+odkZnVkZNiZmZmZmZm1jwOPRR+/vM0ftpp8KUvweLF9Y3JzOrCSTEzMzMzMzNrLqecAt/7XmoxdtllsPXWcPXVEFHvyMyshpwUMzMzMzMzs+bzn/8JjzwCe+0FCxbA8cfDvvvCjBn1jszMaqQiSTFJoyU9KWmWpHPKLD9R0kJJU/NwctGyEyQ9nYcTKhGPmZmZWbOS1FvSREmPSpou6Vtlyvyo6LrsKUm+b8jM6q5a9dfmm2/e+sIddoB774Xf/Ab694d77oGddoJzzoE33ujQ+zGzxtfhpJik7sClwMHACOBYSSPKFB0TETvn4Yq8bj/gm8DuwCjgm5L6djQmMzMzsyb2NrBfROwE7AyMlrRHcYGIOKtwXQb8FPhTHeI0MytVlfqrd+/ebReQ4IQT4Mkn4fOfh2XL0q2V22yTOuJfunS135CZNbZKtBQbBcyKiNkRsRS4Dji8net+FBgXEa9ExKvAOGB0BWIyMzMza0qRvJ4ne+ahrU5yjgWurXpgZmYrUa3665///Gf7AujXL3XA/8AD8IEPwNy58IUvwPDh8KtfwTvvtG87ZtZpVCIpNgiYUzQ9N88r9QlJ0yTdIGnTVVwXSadKmixp8sKFCysQtpmZmVnXJKm7pKnAAtIPkA+1Um5zYChwdy3jMzNrTTXqrxdffHHVgth9d5g0CcaMgW23heeeg5NPTi3HrroK3n131bZnZg2rVh3t3wwMiYgdSa3BrlrVDUTE5RExMiJGDhgwoOIBmpmZmXUVEbEs31o0GBglaftWih4D3BARy8ot9I+SZlZr1ai/3lmdFl7dusFRR8Fjj8Hvf59ai82eDSeeCCNGwG9/65ZjZl1AJZJi84BNi6YH53nviYhFEfF2nrwC2LW965qZmZnZ6omIxcB4Wu+e4hjauPXIP0qaWb1Usv7q2bPn6gfSvTsceyxMn54SYVttBU8/nfogGzoULroIFvtZJWadVSWSYpOAYZKGSupFqpzGFheQNLBo8jBgZh6/AzhIUt/cwf5BeZ6ZmZmZrQZJAyStn8fXBA4EnihTbhugL/BAbSM0MyuvoeuvHj3gM5+BmTPh179OrcXmzYP/+i8YPBi+8hV45pmahWNmldHhpFhEvAucTkpmzQSuj4jpki5LfYa+AAAgAElEQVSQdFgudkZ+pO6jwBnAiXndV4D/ISXWJgEX5HlmZmZmtnoGAuMlTSNdX42LiFtKrs0g/ZB5XUS01Ym1mVktNX791aNHuoXy8cfh1lvhgAPgjTfgJz9Jrcg++Um4/35w1WrWKagzXgeNHDkyJk+eXO8wzKxOJE2JiJH1jmN1uP4ya26uv8yss+rM9deOO+4Y06ZNq94OHn0UfvhDuPbaln7GdtgBPv95OO446NOnevs2s5Vqq/6qVUf7ZmZmZmZmZjXXq1ev6u5gp53SUymffRbOPRcGDEgd9J9+OmyySXpypX9UMGtIToqZmZmZmZlZl/Xqq6/WZkebbAL/+78wdy5cdx3suy8sWQK/+hXsthvsuiv8/OdQq3jMbKWcFDMzMzMzM7Mua8GCBbXdYa9ecPTRcPfd8MQTcPbZ0K8fPPwwnHYabLwxHHkkjB0LS5fWNjYzW4GTYmZmZmZmZmbVsPXW8IMfpCdVXnMNHHhg6nfsj3+Eww9Prcu+/GWYNMmd85vVgZNiZmZmZmZmZtXUuzd8+tNw550wZw5cdBFstx0sWgQ/+xmMGpUSaP/93/DII06QmdWIk2JmZmZmZmZmtTJoEHzta6kz/ocfhrPOgo02gqefhu9+Fz7wARg+PHXa//DDTpCZVZGTYmZmZmZmZma1JsEuu8APf5g657/rrtTn2IYbwqxZcOGFqXP+rbaCr34V7rkH3n233lGbdSlOipmZmZmZmVmXteWWW9Y7hJXr0QP22w8uuwxeeAHGj4cvfjG1IJs9O/VLts8+KWH26U+np1suXlzvqM06PSfFzMzMzMzMrMvq0aNHvUNYNd27pwTYpZemDvrvuQf+4z/SLZWvvgq//z0ceyz07w/77gsXXwyPPurbLM1Wg5NiZmZmZmZm1mUtWrSo3iGsvu7d4cMfhu9/H558Mg2FVmMAEybAf/4n7LwzDBwIn/kMXH01zJ9fz6jNOg0nxczMzMzMzKzLevnll+sdQuUMHw5nn51ur3z5Zbj2WvjsZ1Pn/S+9BNdcA8cfD5tsAjvumDrxv+kmeOWVekdu1pA6WTtSMzMzMzMzM2P99eGYY9IQATNnwrhxcOedqQXZY4+l4ZJLUqf+O+2UWpjtsw/svTf061fnN2BWf06KmZmZmZmZmXVmEowYkYavfAXefhvuvz8lxyZMgAcfhKlT01BIkm2/Pey1V8uw2Wb1fhdmNeekmJmZmZmZmVlXssYaqRP+ffdN02++mRJjxUmyQkuy//u/VGazzVZMkm23HXRzj0vWtTkpZmZmZmZmZtaVrbnmikmyt96CSZPgb39Lw9//Ds8/n55s+fvfpzJ9+sCHPtSSJNttt7Qdsy7ESTEzMzMzMzPrsoYNG1bvEBpP796pX7G9907Ty5fD9OkpQXbffS1JsttuSwNAz54wcmTqk2y//WDPPZ0ks07PSTEzMzMzMzPrsrr5FsCV69YNdtghDaedluY9/3xKjhVakz32GDzwQBq++13o1Su1JNtvP9h//9SSrGfP+r4Ps1XkpJiZmZmZmZl1WQsXLqx3CJ3TZpul4dhj0/TixSlJNn483H136rS/0EfZeefBOuvAwQfDkUfCIYekabMG56SYmZmZmZmZdVmvvPJKvUPoGtZfHz72sTQALFqUEmJ3352GJ56AP/whDb17w0c/Cp/4BPzbv6V1zRqQ25GamZmZmZmZ2arZYIOU9Lr0Upg5E555Bn7wg3RL5VtvwU03wfHHw4YbpkTajTfCu+/WO2qzFTgpZmZmZmZmZmYdM2QInH12usVy7lz46U9Tp/zLlsGtt8LHPw5bbJH6I/MtrdYgnBQzMzMzMzMzs8oZNAhOPz31PzZ/PvzoRzBsGMyZA//93zB4MJxwAkyaVO9IrclVJCkmabSkJyXNknROmeVnS5ohaZqkuyRtXrRsmaSpeRhbiXjMzMzMzMzMrAFsuCGceWbqc+z22+HQQ+Gdd+C3v4VRo2D33eGGG1KLMrMa63BSTFJ34FLgYGAEcKykESXFHgFGRsSOwA3ARUXL3oyInfNwWEfjMTNrD0n9JI2T9HR+7VumzM6SHpA0PSf1jy5a9rv8Y8Djkq6U5OdPm5mZmTWgrbfeut4hGEC3bqnz/Ztvhlmz4Ktfhb59YeJE+OQnYcQIuPJKWLq03pFaE6lES7FRwKyImB0RS4HrgMOLC0TE+IhYkicfBAZXYL9mZh1xDnBXRAwD7srTpZYAx0fEdsBo4BJJhUfn/A7YBtgBWBM4ufohm5mZmZl1AVtsARdfnPoeu/TS1B/ZU0/BSSfBllvCJZfAG2/UO0prApVIig0C5hRNz83zWnMScFvRdG9JkyU9KOmI1laSdGouN3mhO+Uzs447HLgqj18FvK/+iYinIuLpPP4CsAAYkKdvjQyYiJP9ZtYgJPWWNFHSo7ml67daKXdU7t5iuqTf1zpOM7NS1aq/XnrppcoHa5Wx1lrwxS+mhNjVV8N226VE2VlnwWabwbe+BS++WO8orQuraUf7ko4DRgIXF83ePCJGAp8itcLYsty6EXF5RIyMiJEDBgyoQbRm1sVtFBHz8/iLwEZtFZY0CugF/KNkfk/gM8DtbazrpL6Z1dLbwH4RsROwMzBa0h7FBSQNA84F9sytYc+sfZhmZu9Tlfpr8eLF1YjVKqlnTzjuOJg2DW66CfbYA155Bc4/HzbdFI4+GiZMgIh6R2pdTCWSYvOATYumB+d5K5B0APB14LCIeLswPyLm5dfZwARglwrEZGaGpL/mPr9Kh9JbvANo9T+spIHA1cBnI2J5yeLLgHsj4r7W1ndS38xqKTdifT1P9sxDaR13CnBpRLya11lQwxDNzMpy/WV06waHHQb335+SYEccAcuXw/XXw777pn7HfvxjcKLTKqQSSbFJwDBJQyX1Ao4BVniKpKRdgF+QEmILiub3lbRGHu8P7AnMqEBMZmZExAERsX2Z4SbgpZzsKiS9yl5QSVoP+Avw9Yh4sGTZN0m3U55d3XdiZrZqJHWXNJVUt42LiIdKigwHhkv6e+7CYnQr23FLVzOrqWrUX++88061w7ZKk+AjH4Ebb4TnnoPzzoOBA9MTLM88EzbZBD77WRg71n2PWYd0OCkWEe8CpwN3ADOB6yNiuqQLJBWeJnkxsA7wB0lTJRWSZtsCkyU9CowHLowIJ8XMrBbGAifk8ROAm0oL5ET/jcBvI+KGkmUnAx8Fji3TeszMrK4iYllE7ExqwT9K0vYlRXoAw4B9gGOBXxY9SKR4O27pamY1VY36q2dPPyS8Uxs8OPUt9txzcMMNsP/+8Oab8JvfwOGHwwYbwCGHwGWXpTJmq6BHJTYSEbcCt5bMO69o/IBW1ruf9OQ2M7NauxC4XtJJwHPAUQCSRgJfiIiT87wPAxtIOjGvd2JETAV+ntd7QBLAnyLigtq+BTOztkXEYknjSU/Qfbxo0VzgoYh4B3hG0lOkL5mT6hCmmdn7VLL+6tatpl1pW7X07Amf+EQannoKxoyBW26BiRPhttvS8KUvwQ47wMEHwwc/CKNGpVZlZq2oSFLMzKyziYhFwP5l5k8GTs7j1wDXtLK+608za0iSBgDv5C+UawIHAt8rKfZnUguLX+cuLIYDs2sbqZnZiqpVfw0bNqwa4Vo9DR8O3/hGGl58MSXEbrkF7rwTHnssDQWbbJKSY4Vh111h/fc1LrQm5S91ZmZmZl3LQOAqSd1JXWVcHxG3SLoAmBwRY0ndXhwkaQawDPha/rHAzKyeXH/Zqtt449S/2Gc/C2+/DffeC/fck1qQTZwIL7wAf/5zGgo22QS22y513F889OtXv/dhdeGkmJmZmVkXEhHTKPM075KuLYL0kBA/KMTMGka16q/58+dXJD7rBNZYAw48MA2Qnlw5a1ZLguyhh2DatJQoe+EFGDduxfUHDIBhw9Kw1VYrvq63Xu3fj1Wdk2JmZmZmZmbWZb322mv1DsHqpVu3dKvl8OFw3HFp3rJlqUP+GTPSMH16ep05ExYuTMP9979/W/37wxZbpGHo0BVfN90Ueji90hn5qJmZmZmZmZlZc+jevSW5deihLfOXL0+tx55+OrUuK319+eU0TJxYfpuDBsHmm6dhyJCW8c03T0/QXGutmr1Faz8nxczMzMzMzMysuXXrlpJXgwfDvvuuuGz58tSh/zPPwOzZaSgef+EFeP75NNx3X/ntb7BBalFWPAwenJJpgwalfs7WWaf679NW4KSYmZmZmZmZmVlrunVLSatNNoE993z/8rffhjlz0m2Zzz0Hzz7b8jpnDsydC4sWpWHq1Nb3s956LUmygQPTQwTKDX37glStd9tUnBQzMzMzMzOzLquH+3qyaltjjdQh/1ZblV++fDksWJBaks2Z0zLMm5damc2bl4bXXkvDzJlt769nT9hoo/LDxhu3JNQGDoR113UCrQ2uHczMzMzMzKzL2nLLLesdgjW7bt1aWnmNGlW+TAS8+mpLguzFF1cc5s9vef3Xv1Lrs7lzV77vtdZqSZBtuumKfZ0Vhia+bdNJMTMzMzMzMzOzepKgX7807LBD22XffDO1PHvppfcPhcRZYViypKXvs9b07w/bbgvbb5+GHXZIr337VvY9NiAnxczMzMzMzKzLmjdvXr1DMKusNddsaeW1Mv/6V0qOFR4GUOj3rDA8/3x6quZ9973/IQGDBqUE2V57pYcP7LZbunWzC3FSzMzMzMzMzLqs119/vd4hmNXPuuumYfjw8suXL09Js+nT4fHH0/DYY2m6cCvn7bensmuv3ZIg228/2GUX6OR99nXu6M3MzMzMzMzMbPV069byxMuDDmqZv2wZPPMMPPwwTJgA48fDE0/AHXekAaBPH/jEJ+CEE2DvvTtlh/5OipmZmZmZmZmZWYvu3VueqHnUUWne/PkpOVYY/vEPuPLKNGyxBRx/fBqGDq1v7KugW70DMDMzMzMzMzOzBjdwIHzqU/DLX8KsWTBjBpxzTmplNns2nH9+So595CPw61/D0qX1jnilnBQzMzMzMzOzLqtXr171DsGsa9p2W/jud1OH/XfeCZ/+dHoIwL33wuc+l5Zffz1E1DvSVjkpZmZmZmZmZl3W0E50K5dZp9S9Oxx4IFxzDbz4IlxxRUqIzZ4NRx8Ne+yREmUNyEkxMzMzMzMzMzPruPXWg5NOgmnT4PLLYeONYeLEdEvlYYelWy4biJNiZmZmZmZm1mXNmTOn3iGYNZ8ePeCUU1LfYxdcAOusAzffDDvsAKeeCq+9Vu8IASfFzMzMzMzMrAtbsmRJvUMwa15rrw3f+EZKjn3xiyCljvr33hsaIGHtpJiZmZmZmZmZmVXPRhvBpZfCY4/B1lun2yt33x0efriuYTkpZmZmZmZmZmZm1bfttnD//amPsfnzU4uxm2+uWzgVSYpJGi3pSUmzJJ1TZvkaksbk5Q9JGlK07Nw8/0lJH61EPGZmZmZmZmZm1oD69YM774Tjj4clS+CII+AnP6lLKB1OiknqDlwKHAyMAI6VNKKk2EnAqxGxFfAj4Ht53RHAMcB2wGjgsrw9MzMzMzMzsw7r3bt3vUMws1K9esFvfpM64V++HL7yFTjjDFi2rKZh9KjANkYBsyJiNoCk64DDgeLnbB4OnJ/HbwB+Jkl5/nUR8TbwjKRZeXsPtLnHKVNS52xmZmZmZmZmbdh8883rHYKZlSOlTvi32AI+9zn46U/hmWfghhtgjTVqEkIlbp8cBBQ/MmBunle2TES8C/wT2KCd6wIg6VRJkyVNrkDMZmZmZmZmZmZWb5/+NPz1r+m2yltuSa3HaqQSLcVqIiIuBy4HGDlyZDDZuTGzpuWWomZmZmbWTs8991y9QzCzlSl0uL/XXvC978GRR8Iuu1R9t5VoKTYP2LRoenCeV7aMpB5AH2BRO9c1MzMzs3aS1FvSREmPSpou6VtlypwoaaGkqXk4uR6xmpkVq1b99dZbb1UnYDOrrA99qKVfsc99Dt55p+q7rERSbBIwTNJQSb1IHeePLSkzFjghjx8J3B0Rkecfk59OORQYBkysQExmZmZmzeptYL+I2AnYGRgtaY8y5cZExM55uKK2IZqZleX6y6zZfec7MHQoTJ0KF11U9d11OCmW+wg7HbgDmAlcHxHTJV0g6bBc7FfABrkj/bOBc/K604HrSZ3y3w58KSJq+6gBMzMzsy4kktfzZM88RB1DMjNrF9dfZsbaa8Mvf5nGL7gAZsxou3wHVaKlGBFxa0QMj4gtI+I7ed55ETE2j78VEZ+MiK0iYlThSZV52XfyeltHxG2ViMfMzMysmUnqLmkqsAAYFxEPlSn2CUnTJN0gadMyy83Mas71l5mx//5wyimwdGm6jXJZ9dpOVSQpZmZmZmaNIyKWRcTOpP5aR0navqTIzcCQiNgRGAdcVW47xU//XrhwYXWDNjOjOvWX/JAms87n4oth0CB46CH4yU+qthsnxczMzMy6qIhYDIwHRpfMXxQRb+fJK4BdW1n/8ogYGREjBwwYUN1gzcyKVLL+Gj58eHWDNbPK69MHfv7zNP71r8OsWVXZjZNiZmZmZl2IpAGS1s/jawIHAk+UlBlYNHkYqV9YM7O6cv1lZis49FD41KfgzTfT7ZTLl1d8F06KmZmZmXUtA4HxkqaRnhI+LiJuKXkI0hmSpkt6FDgDOLFOsZqZFatK/fXMM89ULWAzq7If/xgGDIAJE1o64K+gHhXfopmZmZnVTURMA3YpM/+8ovFzgXNrGZeZ2cpUq/5aunRpx4Oz/9/enYdZVZ35Hv/9imKehxIqoqICMqYwVEN8OsageCVpo6iNptt0oDvEDG20Y5Kr3eZ6E+1+rraaeDUmkcc24hAlTTTSaEICQZIYh+BlLBQKDEYRpCTIIAgW9d4/alf6UNZwilNnqDrfz/Psp/aw1trv2eucpedl73WA/BgyRPrud6XLLpO+/nXpoouk445rt+a5UwwAAAAAAACFadYsafp0ad8+6bHH2rVpkmIAAAAAAAAoTHb93GISSTE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\n",
       "text/plain": [
        "<Figure size 1080x576 with 8 Axes>"
       ]
diff --git a/examples/notebooks/compare-comsol-discharge-curve.ipynb b/examples/notebooks/compare-comsol-discharge-curve.ipynb
index c7fe967b4a..3395742a43 100644
--- a/examples/notebooks/compare-comsol-discharge-curve.ipynb
+++ b/examples/notebooks/compare-comsol-discharge-curve.ipynb
@@ -68,14 +68,11 @@
     "model = pybamm.lithium_ion.DFN()\n",
     "geometry = model.default_geometry\n",
     "\n",
-    "def current_function(t):\n",
-    "    return pybamm.InputParameter(\"current\")\n",
-    "\n",
     "# load parameters and process model and geometry\n",
     "param = model.default_parameter_values\n",
     "param[\"Electrode width [m]\"] = 1\n",
     "param[\"Electrode height [m]\"] = 1\n",
-    "param[\"Current function [A]\"] = current_function\n",
+    "param[\"Current function [A]\"] = \"[input]\"\n",
     "param.process_model(model)\n",
     "param.process_geometry(geometry)\n",
     "\n",
@@ -103,7 +100,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x576 with 2 Axes>"
       ]
@@ -151,7 +148,7 @@
     "    # solve model at comsol times\n",
     "    solver = pybamm.CasadiSolver(mode=\"fast\")\n",
     "    t = comsol_time / tau\n",
-    "    solution = solver.solve(model, t, inputs={\"current\": current})\n",
+    "    solution = solver.solve(model, t, inputs={\"Current function [A]\": current})\n",
     "\n",
     "    # discharge capacity\n",
     "    discharge_capacity = solution[\"Discharge capacity [A.h]\"]\n",
@@ -213,7 +210,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebooks/models/compare-lithium-ion.ipynb b/examples/notebooks/models/compare-lithium-ion.ipynb
index bb9418e5bf..138f7eac80 100644
--- a/examples/notebooks/models/compare-lithium-ion.ipynb
+++ b/examples/notebooks/models/compare-lithium-ion.ipynb
@@ -104,7 +104,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x10597e518>"
+       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x109dcd710>"
       ]
      },
      "execution_count": 4,
@@ -153,11 +153,7 @@
    "outputs": [],
    "source": [
     "param = dfn.default_parameter_values\n",
-    "\n",
-    "def current_function(t):\n",
-    "    return pybamm.InputParameter(\"current\")\n",
-    "\n",
-    "param[\"Current function [A]\"] = current_function"
+    "param[\"Current function [A]\"] = \"[input]\""
    ]
   },
   {
@@ -253,9 +249,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solved the Doyle-Fuller-Newman model in 2.111 seconds\n",
-      "Solved the Single Particle Model in 0.386 seconds\n",
-      "Solved the Single Particle Model with electrolyte in 0.399 seconds\n"
+      "Solved the Doyle-Fuller-Newman model in 2.609 seconds\n",
+      "Solved the Single Particle Model in 0.462 seconds\n",
+      "Solved the Single Particle Model with electrolyte in 0.491 seconds\n"
      ]
     }
    ],
@@ -266,7 +262,7 @@
     "solver = pybamm.CasadiSolver()\n",
     "for model_name, model in models.items():\n",
     "    start = timer.time()\n",
-    "    solution = solver.solve(model, t_eval, inputs={\"current\": 1})\n",
+    "    solution = solver.solve(model, t_eval, inputs={\"Current function [A]\": 1})\n",
     "    end = timer.time()\n",
     "    print(\"Solved the {} in {:.3f} seconds\".format(model.name, end-start))\n",
     "    solutions[model_name] = solution"
@@ -349,7 +345,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "af31d03838e4456aac24954eb1b5d0df",
+       "model_id": "36cafdf1324c44b19fa3bf54a8e579cb",
        "version_major": 2,
        "version_minor": 0
       },
@@ -371,7 +367,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# A note on changing parameters:"
+    "# Changing parameters"
    ]
   },
   {
@@ -389,7 +385,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7226178875b24a0eacb8c38ab1c4fafc",
+       "model_id": "fc6e5fae66414fdda8e177864e49cb02",
        "version_major": 2,
        "version_minor": 0
       },
@@ -404,7 +400,7 @@
    "source": [
     "# update parameter values and solve again\n",
     "for model_name, model in models.items():\n",
-    "    solutions[model_name] = model.default_solver.solve(model, t_eval, inputs={\"current\": 5})\n",
+    "    solutions[model_name] = model.default_solver.solve(model, t_eval, inputs={\"Current function [A]\": 5})\n",
     "\n",
     "# Plot\n",
     "list_of_models = list(models.values())\n",
diff --git a/examples/notebooks/models/lead-acid.ipynb b/examples/notebooks/models/lead-acid.ipynb
index 42e073f627..e021d96854 100644
--- a/examples/notebooks/models/lead-acid.ipynb
+++ b/examples/notebooks/models/lead-acid.ipynb
@@ -219,10 +219,8 @@
     "models = [loqs, composite, full]\n",
     "\n",
     "# process parameters\n",
-    "def current_function(t):\n",
-    "    return pybamm.InputParameter(\"current\")\n",
     "param = models[0].default_parameter_values\n",
-    "param[\"Current function [A]\"] = current_function\n",
+    "param[\"Current function [A]\"] = \"[input]\"\n",
     "for model in models:\n",
     "    param.process_model(model)"
    ]
@@ -267,9 +265,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solved the LOQS model in 0.133 seconds\n",
-      "Solved the Composite model in 1.034 seconds\n",
-      "Solved the Full model in 1.187 seconds\n"
+      "Solved the LOQS model in 0.127 seconds\n",
+      "Solved the Composite model in 1.075 seconds\n",
+      "Solved the Full model in 1.143 seconds\n"
      ]
     }
    ],
@@ -280,7 +278,7 @@
     "solver = pybamm.CasadiSolver()\n",
     "for model in models:\n",
     "    start = timer.time()\n",
-    "    solution = solver.solve(model, t_eval, inputs={\"current\": 1})\n",
+    "    solution = solver.solve(model, t_eval, inputs={\"Current function [A]\": 1})\n",
     "    end = timer.time()\n",
     "    print(\"Solved the {} in {:.3f} seconds\".format(model.name, end-start))\n",
     "    solutions[model] = solution"
@@ -345,7 +343,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6145d7740fa04796aa4dae9bb09c125e",
+       "model_id": "a08ab3900db7448f81089148ae3ee749",
        "version_major": 2,
        "version_minor": 0
       },
@@ -381,7 +379,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3578ee915f79432c931ffecf4b543c1b",
+       "model_id": "71e4761492ee47d6836cb51fd0882812",
        "version_major": 2,
        "version_minor": 0
       },
@@ -395,7 +393,7 @@
    ],
    "source": [
     "for model in models:\n",
-    "    solutions[model] = solver.solve(model, t_eval, inputs={\"current\": 20})\n",
+    "    solutions[model] = solver.solve(model, t_eval, inputs={\"Current function [A]\": 20})\n",
     "\n",
     "# Plot\n",
     "solution_values = [solutions[model] for model in models]\n",
diff --git a/examples/scripts/compare_comsol/discharge_curve.py b/examples/scripts/compare_comsol/discharge_curve.py
index 110cfc5dbd..7205f4ac85 100644
--- a/examples/scripts/compare_comsol/discharge_curve.py
+++ b/examples/scripts/compare_comsol/discharge_curve.py
@@ -16,14 +16,10 @@
 
 
 # load parameters and process model and geometry
-def current_function(t):
-    return pybamm.InputParameter("current")
-
-
 param = model.default_parameter_values
 param["Electrode width [m]"] = 1
 param["Electrode height [m]"] = 1
-param["Current function [A]"] = current_function
+param["Current function [A]"] = "[input]"
 param.process_model(model)
 param.process_geometry(geometry)
 
@@ -72,7 +68,7 @@ def current_function(t):
     # solve model at comsol times
     t = comsol_time / tau
     solution = pybamm.CasadiSolver(mode="fast").solve(
-        model, t, inputs={"current": current}
+        model, t, inputs={"Current function [A]": current}
     )
 
     # discharge capacity
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index 39008d593c..0a8b972e8d 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -278,6 +278,8 @@ def check_and_update_parameter_values(self, values):
                     data = values["C-rate"][1]
                     data[:, 1] = data[:, 1] * capacity
                     value = (values["C-rate"][0] + "_to_Crate", data)
+                elif values["C-rate"] == "[input]":
+                    value = CrateToCurrent(values["C-rate"], capacity, typ="input")
                 else:
                     value = values["C-rate"] * capacity
                 self._dict_items["Current function [A]"] = value
@@ -288,6 +290,10 @@ def check_and_update_parameter_values(self, values):
                     data = values["Current function [A]"][1]
                     data[:, 1] = data[:, 1] / capacity
                     value = (values["Current function [A]"][0] + "_to_current", data)
+                elif values["Current function [A]"] == "[input]":
+                    value = CurrentToCrate(
+                        values["Current function [A]"], capacity, typ="input"
+                    )
                 else:
                     value = values["Current function [A]"] / capacity
                 self._dict_items["C-rate"] = value
@@ -472,6 +478,9 @@ def _process_symbol(self, symbol):
                 function = pybamm.Scalar(
                     function_name, name=symbol.name
                 ) * pybamm.ones_like(*new_children)
+            elif isinstance(function_name, pybamm.InputParameter):
+                # Replace the function with an input parameter
+                function = function_name
             else:
                 # otherwise evaluate the function to create a new PyBaMM object
                 function = function_name(*new_children)
@@ -547,20 +556,28 @@ def evaluate(self, symbol):
 class CurrentToCrate:
     "Convert a current function to a C-rate function"
 
-    def __init__(self, function, capacity):
-        self.function = function
+    def __init__(self, current, capacity, typ="function"):
+        self.current = current
         self.capacity = capacity
+        self.type = typ
 
     def __call__(self, t):
-        return self.function(t) / self.capacity
+        if self.type == "function":
+            return self.current(t) / self.capacity
+        elif self.type == "input":
+            return pybamm.InputParameter("Current function [A]") / self.capacity
 
 
 class CrateToCurrent:
     "Convert a C-rate function to a current function"
 
-    def __init__(self, function, capacity):
-        self.function = function
+    def __init__(self, Crate, capacity, typ="function"):
+        self.Crate = Crate
         self.capacity = capacity
+        self.type = typ
 
     def __call__(self, t):
-        return self.function(t) * self.capacity
+        if self.type == "function":
+            return self.Crate(t) * self.capacity
+        elif self.type == "input":
+            return pybamm.InputParameter("C-rate") * self.capacity
diff --git a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py b/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py
index 8b4e16f037..ddc43724b1 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py
@@ -18,12 +18,9 @@ def test_leading_order_convergence(self):
         composite_model = pybamm.lead_acid.Composite()
         full_model = pybamm.lead_acid.Full()
 
-        def current_function(t):
-            return pybamm.InputParameter("Current")
-
         # Same parameters, same geometry
         parameter_values = full_model.default_parameter_values
-        parameter_values["Current function [A]"] = current_function
+        parameter_values["Current function [A]"] = "[input]"
         parameter_values.process_model(leading_order_model)
         parameter_values.process_model(composite_model)
         parameter_values.process_model(full_model)
@@ -51,23 +48,17 @@ def get_max_error(current):
             pybamm.logger.info("current = {}".format(current))
             # Solve, make sure times are the same and use tight tolerances
             t_eval = np.linspace(0, 0.6)
-            solver_loqs = leading_order_model.default_solver
-            solver_loqs.rtol = 1e-8
-            solver_loqs.atol = 1e-8
-            solution_loqs = solver_loqs.solve(
-                leading_order_model, t_eval, inputs={"Current": current}
+            solver = pybamm.CasadiSolver()
+            solver.rtol = 1e-8
+            solver.atol = 1e-8
+            solution_loqs = solver.solve(
+                leading_order_model, t_eval, inputs={"Current function [A]": current}
             )
-            solver_comp = composite_model.default_solver
-            solver_comp.rtol = 1e-8
-            solver_comp.atol = 1e-8
-            solution_comp = solver_comp.solve(
-                composite_model, t_eval, inputs={"Current": current}
+            solution_comp = solver.solve(
+                composite_model, t_eval, inputs={"Current function [A]": current}
             )
-            solver_full = full_model.default_solver
-            solver_full.rtol = 1e-8
-            solver_full.atol = 1e-8
-            solution_full = solver_full.solve(
-                full_model, t_eval, inputs={"Current": current}
+            solution_full = solver.solve(
+                full_model, t_eval, inputs={"Current function [A]": current}
             )
 
             # Post-process variables
@@ -105,5 +96,4 @@ def get_max_error(current):
 
     if "-v" in sys.argv:
         debug = True
-    pybamm.set_logging_level("DEBUG")
     unittest.main()
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index 43d9408509..a5426a7942 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -115,6 +115,18 @@ def test_check_and_update_parameter_values(self):
             param["C-rate"][1], np.hstack([x, 0.2 * x])
         )
 
+        # With input parameters
+        # if only C-rate and capacity provided, update current
+        values = {"C-rate": "[input]", "Cell capacity [A.h]": 10}
+        param = pybamm.ParameterValues(values)
+        self.assertEqual(param["Current function [A]"](2).evaluate(u={"C-rate": 1}), 10)
+        # if only current and capacity provided, update C-rate
+        values = {"Current function [A]": "[input]", "Cell capacity [A.h]": 10}
+        param = pybamm.ParameterValues(values)
+        self.assertEqual(
+            param["C-rate"](5).evaluate(u={"Current function [A]": 5}), 0.5
+        )
+
     def test_process_symbol(self):
         parameter_values = pybamm.ParameterValues({"a": 1, "b": 2, "c": 3})
         # process parameter
@@ -297,6 +309,16 @@ def test_process_function_parameter(self):
         processed_diff_func = parameter_values.process_symbol(diff_func)
         self.assertEqual(processed_diff_func.evaluate(u={"a": 3}), 123)
 
+        # function itself as input (different to the variable being an input)
+        parameter_values = pybamm.ParameterValues({"func": "[input]"})
+        a = pybamm.Scalar(3)
+        func = pybamm.FunctionParameter("func", a)
+        processed_func = parameter_values.process_symbol(func)
+        self.assertEqual(processed_func.evaluate(u={"func": 13}), 13)
+
+        
+
+
     def test_process_inline_function_parameters(self):
         def D(c):
             return c ** 2
diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py
index fb0c7c141b..5e3a090a40 100644
--- a/tests/unit/test_simulation.py
+++ b/tests/unit/test_simulation.py
@@ -224,28 +224,29 @@ def test_step(self):
         self.assertEqual(sim.solution.t[1], 3 * dt)
 
     def test_step_with_inputs(self):
-        def current_function(t):
-            return pybamm.InputParameter("Current")
-
         dt = 0.001
         model = pybamm.lithium_ion.SPM()
         param = model.default_parameter_values
-        param.update({"Current function [A]": current_function})
+        param.update({"Current function [A]": "[input]"})
         sim = pybamm.Simulation(model, parameter_values=param)
-        sim.step(dt, inputs={"Current": 1})  # 1 step stores first two points
+        sim.step(
+            dt, inputs={"Current function [A]": 1}
+        )  # 1 step stores first two points
         self.assertEqual(sim.solution.t.size, 2)
         self.assertEqual(sim.solution.y[0, :].size, 2)
         self.assertEqual(sim.solution.t[0], 0)
         self.assertEqual(sim.solution.t[1], dt)
-        np.testing.assert_array_equal(sim.solution.inputs["Current"], 1)
-        sim.step(dt, inputs={"Current": 2})  # automatically append the next step
+        np.testing.assert_array_equal(sim.solution.inputs["Current function [A]"], 1)
+        sim.step(
+            dt, inputs={"Current function [A]": 2}
+        )  # automatically append the next step
         self.assertEqual(sim.solution.t.size, 3)
         self.assertEqual(sim.solution.y[0, :].size, 3)
         self.assertEqual(sim.solution.t[0], 0)
         self.assertEqual(sim.solution.t[1], dt)
         self.assertEqual(sim.solution.t[2], 2 * dt)
         np.testing.assert_array_equal(
-            sim.solution.inputs["Current"], np.array([1, 1, 2])
+            sim.solution.inputs["Current function [A]"], np.array([1, 1, 2])
         )
 
     def test_save_load(self):

From f4fcf5f5ba7d3d2205e214701ef650cd65829c13 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Sun, 2 Feb 2020 20:07:48 -0500
Subject: [PATCH 09/11] #709 flake8 and changelog

---
 CHANGELOG.md                                        | 1 +
 tests/unit/test_parameters/test_parameter_values.py | 3 ---
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index a499529b04..4ab9fea930 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -36,6 +36,7 @@
 
 ## Optimizations
 
+-   Now simplifying objects that are constant as soon as they are created ([#801](https://github.com/pybamm-team/PyBaMM/pull/801))
 -   Simplified solver interface ([#800](https://github.com/pybamm-team/PyBaMM/pull/800))
 -   Added caching for shape evaluation, used during discretisation ([#780](https://github.com/pybamm-team/PyBaMM/pull/780))
 -   Added an option to skip model checks during discretisation, which could be slow for large models ([#739](https://github.com/pybamm-team/PyBaMM/pull/739))
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index a5426a7942..ca84859432 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -316,9 +316,6 @@ def test_process_function_parameter(self):
         processed_func = parameter_values.process_symbol(func)
         self.assertEqual(processed_func.evaluate(u={"func": 13}), 13)
 
-        
-
-
     def test_process_inline_function_parameters(self):
         def D(c):
             return c ** 2

From 58218df75b8f62702eda5d6c610c7e5bff5ae727 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <vsulzer@umich.edu>
Date: Mon, 3 Feb 2020 16:46:20 -0500
Subject: [PATCH 10/11] #709 fix test for windows

---
 pybamm/expression_tree/symbol.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 399be2da35..cd3ec77acf 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -618,6 +618,8 @@ def evaluate_ignoring_errors(self):
                 return None
             else:
                 raise error
+        except ValueError as e:
+            raise pybamm.ShapeError("Cannot find shape (original error: {})".format(e))
         return result
 
     def evaluates_to_number(self):

From 27c118da6a5c71bc3b0c5913a352deb3ea10ba3b Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <vsulzer@umich.edu>
Date: Mon, 3 Feb 2020 18:46:17 -0500
Subject: [PATCH 11/11] #709 coverage

---
 tests/unit/test_expression_tree/test_binary_operators.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index faabc1f34a..a35a4df290 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -207,6 +207,8 @@ def test_sparse_multiply(self):
             (pybammS1 * pybammS2).test_shape()
         with self.assertRaisesRegex(pybamm.ShapeError, "inconsistent shapes"):
             (pybammS2 * pybammS1).test_shape()
+        with self.assertRaisesRegex(pybamm.ShapeError, "inconsistent shapes"):
+            (pybammS2 * pybammS1).evaluate_ignoring_errors()
 
         # Matrix multiplication is normal matrix multiplication
         np.testing.assert_array_equal(