Remove init_soc from problem

examples/notebooks/multi_model_identification.ipynb

@@ -179,7 +179,8 @@
    },
    "outputs": [],
    "source": [
-    "synth_model.build(dataset, init_soc=init_soc)\n",
+    "synth_model.set_init_soc(init_soc)\n",
+    "synth_model.build(dataset)\n",
     "synth_model.signal = [\"Voltage [V]\"]\n",
     "values = synth_model.simulate(t_eval=t_eval, inputs={})"
    ]
@@ -3811,7 +3812,8 @@
     "xs = []\n",
     "for model in models:\n",
     "    print(f\"Running {model.name}\")\n",
-    "    problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)\n",
+    "    model.set_init_soc(init_soc)\n",
+    "    problem = pybop.FittingProblem(model, parameters, dataset)\n",
     "    cost = pybop.SumSquaredError(problem)\n",
     "    optim = pybop.XNES(\n",
     "        cost, verbose=True, max_iterations=60, max_unchanged_iterations=15\n",

examples/notebooks/multi_optimiser_identification.ipynb

@@ -169,8 +169,8 @@
    "outputs": [],
    "source": [
     "t_eval = np.arange(0, 2000, 10)\n",
-    "init_soc = 1.0\n",
-    "values = synth_model.predict(t_eval=t_eval, init_soc=init_soc)"
+    "model.set_init_soc(1.0)\n",
+    "values = synth_model.predict(t_eval=t_eval)"
    ]
   },
   {
@@ -510,7 +510,7 @@
    "source": [
     "optims = []\n",
     "xs = []\n",
-    "problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)\n",
+    "problem = pybop.FittingProblem(model, parameters, dataset)\n",
     "cost = pybop.SumSquaredError(problem)\n",
     "for optimiser in gradient_optimisers:\n",
     "    print(f\"Running {optimiser.__name__}\")\n",

examples/notebooks/optimiser_calibration.ipynb

@@ -133,7 +133,7 @@
     "    }\n",
     ")\n",
     "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)\n",
-    "init_soc = 0.4\n",
+    "model.set_init_soc(0.4)\n",
     "experiment = pybop.Experiment(\n",
     "    [\n",
     "        (\n",
@@ -143,7 +143,7 @@
     "    ]\n",
     "    * 2\n",
     ")\n",
-    "values = model.predict(init_soc=init_soc, experiment=experiment)"
+    "values = model.predict(experiment=experiment)"
    ]
   },
   {
@@ -298,7 +298,7 @@
     }
    ],
    "source": [
-    "problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)\n",
+    "problem = pybop.FittingProblem(model, parameters, dataset)\n",
     "cost = pybop.SumSquaredError(problem)\n",
     "optim = pybop.GradientDescent(cost, sigma0=0.2, max_iterations=100)"
    ]
@@ -457,7 +457,7 @@
     "optims = []\n",
     "for sigma in sigmas:\n",
     "    print(sigma)\n",
-    "    problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)\n",
+    "    problem = pybop.FittingProblem(model, parameters, dataset)\n",
     "    cost = pybop.SumSquaredError(problem)\n",
     "    optim = pybop.GradientDescent(cost, sigma0=sigma, max_iterations=100)\n",
     "    x, final_cost = optim.run()\n",

examples/notebooks/spm_electrode_design.ipynb

@@ -205,7 +205,8 @@
    },
    "outputs": [],
    "source": [
-    "problem = pybop.DesignProblem(model, parameters, experiment, init_soc=1.0)\n",
+    "model.set_init_soc(1.0)\n",
+    "problem = pybop.DesignProblem(model, parameters, experiment)\n",
     "cost = pybop.GravimetricEnergyDensity(problem)"
    ]
   },

examples/scripts/cuckoo.py

@@ -23,7 +23,7 @@
         true_value=0.67,
     ),
 )
-init_soc = 0.7
+model.set_init_soc(0.7)
 experiment = pybop.Experiment(
     [
         (
@@ -32,9 +32,7 @@
         ),
     ]
 )
-values = model.predict(
-    init_soc=init_soc, experiment=experiment, inputs=parameters.as_dict("true")
-)
+values = model.predict(experiment=experiment, inputs=parameters.as_dict("true"))
 
 sigma = 0.002
 corrupt_values = values["Voltage [V]"].data + np.random.normal(
@@ -51,7 +49,7 @@
 )
 
 # Generate problem, cost function, and optimisation class
-problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)
+problem = pybop.FittingProblem(model, parameters, dataset)
 cost = pybop.GaussianLogLikelihood(problem, sigma0=sigma * 4)
 optim = pybop.Optimisation(
     cost,

examples/scripts/spm_AdamW.py

@@ -19,7 +19,7 @@
 )
 
 # Generate data
-init_soc = 0.5
+model.set_init_soc(0.5)
 sigma = 0.003
 experiment = pybop.Experiment(
     [
@@ -30,7 +30,7 @@
     ]
     * 2
 )
-values = model.predict(init_soc=init_soc, experiment=experiment)
+values = model.predict(experiment=experiment)
 
 
 def noise(sigma):
@@ -50,9 +50,7 @@ def noise(sigma):
 
 signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"]
 # Generate problem, cost function, and optimisation class
-problem = pybop.FittingProblem(
-    model, parameters, dataset, signal=signal, init_soc=init_soc
-)
+problem = pybop.FittingProblem(model, parameters, dataset, signal=signal)
 cost = pybop.RootMeanSquaredError(problem)
 optim = pybop.AdamW(
     cost,

examples/scripts/spm_NelderMead.py

@@ -19,7 +19,7 @@
 )
 
 # Generate data
-init_soc = 0.5
+model.set_init_soc(0.5)
 sigma = 0.003
 experiment = pybop.Experiment(
     [
@@ -30,7 +30,7 @@
     ]
     * 2
 )
-values = model.predict(init_soc=init_soc, experiment=experiment)
+values = model.predict(experiment=experiment)
 
 
 def noise(sigma):
@@ -50,9 +50,7 @@ def noise(sigma):
 
 signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"]
 # Generate problem, cost function, and optimisation class
-problem = pybop.FittingProblem(
-    model, parameters, dataset, signal=signal, init_soc=init_soc
-)
+problem = pybop.FittingProblem(model, parameters, dataset, signal=signal)
 cost = pybop.RootMeanSquaredError(problem)
 optim = pybop.NelderMead(
     cost,

examples/scripts/spm_scipymin.py

@@ -34,7 +34,8 @@
 
 # Define the cost to optimise
 signal = ["Voltage [V]"]
-problem = pybop.FittingProblem(model, parameters, dataset, signal=signal, init_soc=0.98)
+model.set_init_soc(0.98)
+problem = pybop.FittingProblem(model, parameters, dataset, signal=signal)
 cost = pybop.RootMeanSquaredError(problem)
 
 # Build the optimisation problem

examples/scripts/spme_max_energy.py

@@ -37,13 +37,11 @@
 experiment = pybop.Experiment(
     ["Discharge at 1C until 2.5 V (5 seconds period)"],
 )
-init_soc = 1  # start from full charge
+model.set_init_soc(1.0)  # start from full charge
 signal = ["Voltage [V]", "Current [A]"]
 
 # Generate problem
-problem = pybop.DesignProblem(
-    model, parameters, experiment, signal=signal, init_soc=init_soc
-)
+problem = pybop.DesignProblem(model, parameters, experiment, signal=signal)
 
 # Generate cost function and optimisation class:
 cost = pybop.GravimetricEnergyDensity(problem)

examples/standalone/problem.py

@@ -16,11 +16,8 @@ def __init__(
         check_model=True,
         signal=None,
         additional_variables=None,
-        init_soc=None,
     ):
-        super().__init__(
-            parameters, model, check_model, signal, additional_variables, init_soc
-        )
+        super().__init__(parameters, model, check_model, signal, additional_variables)
         self._dataset = dataset.data
 
         # Check that the dataset contains time and current

pybop/observers/observer.py

@@ -26,8 +26,6 @@ class Observer(BaseProblem):
       The signal to observe.
     additional_variables : list[str], optional
         Additional variables to observe and store in the solution (default: []).
-    init_soc : float, optional
-        Initial state of charge (default: None).
     """
 
     # define a subtype for covariance matrices for use by derived classes
@@ -40,11 +38,8 @@ def __init__(
         check_model: bool = True,
         signal: Optional[list[str]] = None,
         additional_variables: Optional[list[str]] = None,
-        init_soc: Optional[float] = None,
     ) -> None:
-        super().__init__(
-            parameters, model, check_model, signal, additional_variables, init_soc
-        )
+        super().__init__(parameters, model, check_model, signal, additional_variables)
         if model._built_model is None:
             raise ValueError("Only built models can be used in Observers")
 

pybop/observers/unscented_kalman.py

@@ -34,8 +34,6 @@ class UnscentedKalmanFilterObserver(Observer):
         Flag to indicate if the model should be checked (default: True).
     signal: str
         The signal to observe.
-    init_soc : float, optional
-        Initial state of charge (default: None).
     """
 
     Covariance = np.ndarray
@@ -51,16 +49,30 @@ def __init__(
         check_model: bool = True,
         signal: Optional[list[str]] = None,
         additional_variables: Optional[list[str]] = None,
-        init_soc: Optional[float] = None,
     ) -> None:
-        super().__init__(
-            parameters, model, check_model, signal, additional_variables, init_soc
-        )
         if dataset is not None:
-            self._dataset = dataset.data
+            # Check that the dataset contains necessary variables
+            dataset.check([*signal, "Current function [A]"])
+            dataset = dataset.data
+
+        if model is not None:
+            # Clear any existing built model and its properties
+            if model._built_model is not None:
+                model._model_with_set_params = None
+                model._built_model = None
+                model._mesh = None
+                model._disc = None
+
+            # Build the model from scratch
+            model.build(
+                dataset=dataset,
+                parameters=parameters,
+                check_model=check_model,
+            )
 
-            # Check that the dataset contains time and current
-            dataset.check([*self.signal, "Current function [A]"])
+        super().__init__(parameters, model, check_model, signal, additional_variables)
+        if dataset is not None:
+            self._dataset = dataset
 
             self._time_data = self._dataset["Time [s]"]
             self.n_time_data = len(self._time_data)

pybop/problems/base_problem.py

@@ -20,8 +20,6 @@ class BaseProblem:
       The signal to observe.
     additional_variables : list[str], optional
         Additional variables to observe and store in the solution (default: []).
-    init_soc : float, optional
-        Initial state of charge (default: None).
 
     Attributes
     ----------
@@ -36,7 +34,6 @@ def __init__(
         check_model: bool = True,
         signal: Optional[list[str]] = None,
         additional_variables: Optional[list[str]] = None,
-        init_soc: Optional[float] = None,
     ):
         if signal is None:
             signal = ["Voltage [V]"]
@@ -71,7 +68,6 @@ def __init__(
         if additional_variables is not None:
             self.variables.extend(additional_variables)
             self.variables = list(set(self.variables))
-        self.init_soc = init_soc
         self.n_outputs = len(self.signal)
         self._dataset = None
         self._time_data = None
@@ -180,7 +176,7 @@ def set_target(self, dataset: Dataset):
 
         Parameters
         ----------
-        target : np.ndarray
+        target : Dataset
             The target dataset array.
         """
         if self.signal is None:

pybop/problems/design_problem.py

@@ -26,8 +26,6 @@ class DesignProblem(BaseProblem):
         The signal to fit (default: "Voltage [V]").
     additional_variables : list[str], optional
         Additional variables to observe and store in the solution (default additions are: ["Time [s]", "Current [A]"]).
-    init_soc : float, optional
-        Initial state of charge (default: None).
     """
 
     def __init__(
@@ -38,11 +36,8 @@ def __init__(
         check_model: bool = True,
         signal: Optional[list[str]] = None,
         additional_variables: Optional[list[str]] = None,
-        init_soc: Optional[float] = None,
     ):
-        super().__init__(
-            parameters, model, check_model, signal, additional_variables, init_soc
-        )
+        super().__init__(parameters, model, check_model, signal, additional_variables)
 
         # Add time and current as additional variables and remove duplicates
         self.variables.extend(["Time [s]", "Current [A]"])
@@ -60,7 +55,6 @@ def __init__(
                 experiment=self.experiment,
                 parameters=self.parameters,
                 check_model=self.check_model,
-                init_soc=self.init_soc,
             )
 
         # Add an example dataset for plotting comparison
@@ -86,7 +80,6 @@ def _evaluate(self, inputs: Inputs):
         sol = self._model.predict(
             inputs=inputs,
             experiment=self.experiment,
-            init_soc=self.init_soc,
         )
 
         if sol == [np.inf]:

pybop/problems/fitting_problem.py

@@ -27,8 +27,6 @@ class FittingProblem(BaseProblem):
         The variable used for fitting (default: "Voltage [V]").
     additional_variables : list[str], optional
         Additional variables to observe and store in the solution (default: []).
-    init_soc : float, optional
-        Initial state of charge (default: None).
 
     Additional Attributes
     ---------------------
@@ -50,11 +48,8 @@ def __init__(
         check_model: bool = True,
         signal: Optional[list[str]] = None,
         additional_variables: Optional[list[str]] = None,
-        init_soc: Optional[float] = None,
     ):
-        super().__init__(
-            parameters, model, check_model, signal, additional_variables, init_soc
-        )
+        super().__init__(parameters, model, check_model, signal, additional_variables)
         self._dataset = dataset.data
         self.parameters.initial_value()
 
@@ -79,7 +74,6 @@ def __init__(
                 dataset=self._dataset,
                 parameters=self.parameters,
                 check_model=self.check_model,
-                init_soc=self.init_soc,
             )
 
     def _evaluate(self, inputs: Inputs):