From 17d97bcb42ad0517e8aac450ef632788f1e87034 Mon Sep 17 00:00:00 2001
From: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
Date: Mon, 8 Apr 2024 16:30:37 +0100
Subject: [PATCH 1/5] #279 refactor problem into multiple files

---
 pybop/__init__.py                 |   4 +-
 pybop/_problem.py                 | 347 ------------------------------
 pybop/problems/__init__.py        |   0
 pybop/problems/base_problem.py    | 142 ++++++++++++
 pybop/problems/design_problem.py  |  82 +++++++
 pybop/problems/fitting_problem.py | 127 +++++++++++
 6 files changed, 354 insertions(+), 348 deletions(-)
 delete mode 100644 pybop/_problem.py
 create mode 100644 pybop/problems/__init__.py
 create mode 100644 pybop/problems/base_problem.py
 create mode 100644 pybop/problems/design_problem.py
 create mode 100644 pybop/problems/fitting_problem.py

diff --git a/pybop/__init__.py b/pybop/__init__.py
index 236c15012..be0ada532 100644
--- a/pybop/__init__.py
+++ b/pybop/__init__.py
@@ -26,7 +26,9 @@
 #
 # Problem class
 #
-from ._problem import BaseProblem, FittingProblem, DesignProblem
+from .problems.base_problem import BaseProblem
+from .problems.fitting_problem import FittingProblem
+from .problems.design_problem import DesignProblem
 
 #
 # Cost function class
diff --git a/pybop/_problem.py b/pybop/_problem.py
deleted file mode 100644
index 5665d2365..000000000
--- a/pybop/_problem.py
+++ /dev/null
@@ -1,347 +0,0 @@
-import numpy as np
-
-
-class BaseProblem:
-    """
-    Base class for defining a problem within the PyBOP framework, compatible with PINTS.
-
-    Parameters
-    ----------
-    parameters : list
-        List of parameters for the problem.
-    model : object, optional
-        The model to be used for the problem (default: None).
-    check_model : bool, optional
-        Flag to indicate if the model should be checked (default: True).
-    signal: List[str]
-      The signal to observe.
-    init_soc : float, optional
-        Initial state of charge (default: None).
-    x0 : np.ndarray, optional
-        Initial parameter values (default: None).
-    """
-
-    def __init__(
-        self,
-        parameters,
-        model=None,
-        check_model=True,
-        signal=["Voltage [V]"],
-        init_soc=None,
-        x0=None,
-    ):
-        self.parameters = parameters
-        self._model = model
-        self.check_model = check_model
-        if isinstance(signal, str):
-            signal = [signal]
-        elif not all(isinstance(item, str) for item in signal):
-            raise ValueError("Signal should be either a string or list of strings.")
-        self.signal = signal
-        self.init_soc = init_soc
-        self.x0 = x0
-        self.n_parameters = len(self.parameters)
-        self.n_outputs = len(self.signal)
-        self._time_data = None
-        self._target = None
-
-        # Set bounds (for all or no parameters)
-        all_unbounded = True  # assumption
-        self.bounds = {"lower": [], "upper": []}
-        for param in self.parameters:
-            if param.bounds is not None:
-                self.bounds["lower"].append(param.bounds[0])
-                self.bounds["upper"].append(param.bounds[1])
-                all_unbounded = False
-            else:
-                self.bounds["lower"].append(-np.inf)
-                self.bounds["upper"].append(np.inf)
-        if all_unbounded:
-            self.bounds = None
-
-        # Set initial standard deviation (for all or no parameters)
-        all_have_sigma = True  # assumption
-        self.sigma0 = []
-        for param in self.parameters:
-            if hasattr(param.prior, "sigma"):
-                self.sigma0.append(param.prior.sigma)
-            else:
-                all_have_sigma = False
-        if not all_have_sigma:
-            self.sigma0 = None
-
-        # Sample from prior for x0
-        if x0 is None:
-            self.x0 = np.zeros(self.n_parameters)
-            for i, param in enumerate(self.parameters):
-                self.x0[i] = param.rvs(1)
-        elif len(x0) != self.n_parameters:
-            raise ValueError("x0 dimensions do not match number of parameters")
-
-        # Add the initial values to the parameter definitions
-        for i, param in enumerate(self.parameters):
-            param.update(initial_value=self.x0[i])
-
-    def evaluate(self, x):
-        """
-        Evaluate the model with the given parameters and return the signal.
-
-        Parameters
-        ----------
-        x : np.ndarray
-            Parameter values to evaluate the model at.
-
-        Raises
-        ------
-        NotImplementedError
-            This method must be implemented by subclasses.
-        """
-        raise NotImplementedError
-
-    def evaluateS1(self, x):
-        """
-        Evaluate the model with the given parameters and return the signal and
-        its derivatives.
-
-        Parameters
-        ----------
-        x : np.ndarray
-            Parameter values to evaluate the model at.
-
-        Raises
-        ------
-        NotImplementedError
-            This method must be implemented by subclasses.
-        """
-        raise NotImplementedError
-
-    def time_data(self):
-        """
-        Returns the time data.
-
-        Returns
-        -------
-        np.ndarray
-            The time array.
-        """
-        return self._time_data
-
-    def target(self):
-        """
-        Return the target dataset.
-
-        Returns
-        -------
-        np.ndarray
-            The target dataset array.
-        """
-        return self._target
-
-    @property
-    def model(self):
-        return self._model
-
-
-class FittingProblem(BaseProblem):
-    """
-    Problem class for fitting (parameter estimation) problems.
-
-    Extends `BaseProblem` with specifics for fitting a model to a dataset.
-
-    Parameters
-    ----------
-    model : object
-        The model to fit.
-    parameters : list
-        List of parameters for the problem.
-    dataset : Dataset
-        Dataset object containing the data to fit the model to.
-    signal : str, optional
-        The signal to fit (default: "Voltage [V]").
-    """
-
-    def __init__(
-        self,
-        model,
-        parameters,
-        dataset,
-        check_model=True,
-        signal=["Voltage [V]"],
-        init_soc=None,
-        x0=None,
-    ):
-        super().__init__(parameters, model, check_model, signal, init_soc, x0)
-        self._dataset = dataset.data
-        self.x = self.x0
-
-        # Check that the dataset contains time and current
-        for name in ["Time [s]", "Current function [A]"] + self.signal:
-            if name not in self._dataset:
-                raise ValueError(f"Expected {name} in list of dataset")
-
-        self._time_data = self._dataset["Time [s]"]
-        self.n_time_data = len(self._time_data)
-        if np.any(self._time_data < 0):
-            raise ValueError("Times can not be negative.")
-        if np.any(self._time_data[:-1] >= self._time_data[1:]):
-            raise ValueError("Times must be increasing.")
-
-        for signal in self.signal:
-            if len(self._dataset[signal]) != self.n_time_data:
-                raise ValueError(
-                    f"Time data and {signal} data must be the same length."
-                )
-        target = [self._dataset[signal] for signal in self.signal]
-        self._target = np.vstack(target).T
-
-        # Add useful parameters to model
-        if model is not None:
-            self._model.signal = self.signal
-            self._model.n_outputs = self.n_outputs
-            self._model.n_time_data = self.n_time_data
-
-            # Build the model from scratch
-            if self._model._built_model is not None:
-                self._model._model_with_set_params = None
-                self._model._built_model = None
-                self._model._built_initial_soc = None
-                self._model._mesh = None
-                self._model._disc = None
-            self._model.build(
-                dataset=self._dataset,
-                parameters=self.parameters,
-                check_model=self.check_model,
-                init_soc=self.init_soc,
-            )
-
-    def evaluate(self, x):
-        """
-        Evaluate the model with the given parameters and return the signal.
-
-        Parameters
-        ----------
-        x : np.ndarray
-            Parameter values to evaluate the model at.
-
-        Returns
-        -------
-        y : np.ndarray
-            The model output y(t) simulated with inputs x.
-        """
-        if (x != self.x).any() and self._model.matched_parameters:
-            for i, param in enumerate(self.parameters):
-                param.update(value=x[i])
-
-            self._model.rebuild(parameters=self.parameters)
-            self.x = x
-
-        y = np.asarray(self._model.simulate(inputs=x, t_eval=self._time_data))
-
-        return y
-
-    def evaluateS1(self, x):
-        """
-        Evaluate the model with the given parameters and return the signal and its derivatives.
-
-        Parameters
-        ----------
-        x : np.ndarray
-            Parameter values to evaluate the model at.
-
-        Returns
-        -------
-        tuple
-            A tuple containing the simulation result y(t) and the sensitivities dy/dx(t) evaluated
-            with given inputs x.
-        """
-        if self._model.matched_parameters:
-            raise RuntimeError(
-                "Gradient not available when using geometric parameters."
-            )
-
-        y, dy = self._model.simulateS1(
-            inputs=x,
-            t_eval=self._time_data,
-        )
-
-        return (np.asarray(y), np.asarray(dy))
-
-
-class DesignProblem(BaseProblem):
-    """
-    Problem class for design optimization problems.
-
-    Extends `BaseProblem` with specifics for applying a model to an experimental design.
-
-    Parameters
-    ----------
-    model : object
-        The model to apply the design to.
-    parameters : list
-        List of parameters for the problem.
-    experiment : object
-        The experimental setup to apply the model to.
-    """
-
-    def __init__(
-        self,
-        model,
-        parameters,
-        experiment,
-        check_model=True,
-        signal=["Voltage [V]"],
-        init_soc=None,
-        x0=None,
-    ):
-        super().__init__(parameters, model, check_model, signal, init_soc, x0)
-        self.experiment = experiment
-
-        # Build the model if required
-        if experiment is not None:
-            # Leave the build until later to apply the experiment
-            self._model.parameters = self.parameters
-            if self.parameters is not None:
-                self._model.fit_keys = [param.name for param in self.parameters]
-
-        elif self._model._built_model is None:
-            self._model.build(
-                experiment=self.experiment,
-                parameters=self.parameters,
-                check_model=self.check_model,
-                init_soc=self.init_soc,
-            )
-
-        # Add an example dataset for plotting comparison
-        sol = self.evaluate(self.x0)
-        self._time_data = sol[:, -1]
-        self._target = sol[:, 0:-1]
-        self._dataset = None
-
-    def evaluate(self, x):
-        """
-        Evaluate the model with the given parameters and return the signal.
-
-        Parameters
-        ----------
-        x : np.ndarray
-            Parameter values to evaluate the model at.
-
-        Returns
-        -------
-        y : np.ndarray
-            The model output y(t) simulated with inputs x.
-        """
-
-        sol = self._model.predict(
-            inputs=x,
-            experiment=self.experiment,
-            init_soc=self.init_soc,
-        )
-
-        if sol == [np.inf]:
-            return sol
-
-        else:
-            predictions = [sol[signal].data for signal in self.signal + ["Time [s]"]]
-
-            return np.vstack(predictions).T
diff --git a/pybop/problems/__init__.py b/pybop/problems/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/pybop/problems/base_problem.py b/pybop/problems/base_problem.py
new file mode 100644
index 000000000..7450ff878
--- /dev/null
+++ b/pybop/problems/base_problem.py
@@ -0,0 +1,142 @@
+import numpy as np
+
+
+class BaseProblem:
+    """
+    Base class for defining a problem within the PyBOP framework, compatible with PINTS.
+
+    Parameters
+    ----------
+    parameters : list
+        List of parameters for the problem.
+    model : object, optional
+        The model to be used for the problem (default: None).
+    check_model : bool, optional
+        Flag to indicate if the model should be checked (default: True).
+    signal: List[str]
+      The signal to observe.
+    init_soc : float, optional
+        Initial state of charge (default: None).
+    x0 : np.ndarray, optional
+        Initial parameter values (default: None).
+    """
+
+    def __init__(
+        self,
+        parameters,
+        model=None,
+        check_model=True,
+        signal=["Voltage [V]"],
+        init_soc=None,
+        x0=None,
+    ):
+        self.parameters = parameters
+        self._model = model
+        self.check_model = check_model
+        if isinstance(signal, str):
+            signal = [signal]
+        elif not all(isinstance(item, str) for item in signal):
+            raise ValueError("Signal should be either a string or list of strings.")
+        self.signal = signal
+        self.init_soc = init_soc
+        self.x0 = x0
+        self.n_parameters = len(self.parameters)
+        self.n_outputs = len(self.signal)
+        self._time_data = None
+        self._target = None
+
+        # Set bounds (for all or no parameters)
+        all_unbounded = True  # assumption
+        self.bounds = {"lower": [], "upper": []}
+        for param in self.parameters:
+            if param.bounds is not None:
+                self.bounds["lower"].append(param.bounds[0])
+                self.bounds["upper"].append(param.bounds[1])
+                all_unbounded = False
+            else:
+                self.bounds["lower"].append(-np.inf)
+                self.bounds["upper"].append(np.inf)
+        if all_unbounded:
+            self.bounds = None
+
+        # Set initial standard deviation (for all or no parameters)
+        all_have_sigma = True  # assumption
+        self.sigma0 = []
+        for param in self.parameters:
+            if hasattr(param.prior, "sigma"):
+                self.sigma0.append(param.prior.sigma)
+            else:
+                all_have_sigma = False
+        if not all_have_sigma:
+            self.sigma0 = None
+
+        # Sample from prior for x0
+        if x0 is None:
+            self.x0 = np.zeros(self.n_parameters)
+            for i, param in enumerate(self.parameters):
+                self.x0[i] = param.rvs(1)
+        elif len(x0) != self.n_parameters:
+            raise ValueError("x0 dimensions do not match number of parameters")
+
+        # Add the initial values to the parameter definitions
+        for i, param in enumerate(self.parameters):
+            param.update(initial_value=self.x0[i])
+
+    def evaluate(self, x):
+        """
+        Evaluate the model with the given parameters and return the signal.
+
+        Parameters
+        ----------
+        x : np.ndarray
+            Parameter values to evaluate the model at.
+
+        Raises
+        ------
+        NotImplementedError
+            This method must be implemented by subclasses.
+        """
+        raise NotImplementedError
+
+    def evaluateS1(self, x):
+        """
+        Evaluate the model with the given parameters and return the signal and
+        its derivatives.
+
+        Parameters
+        ----------
+        x : np.ndarray
+            Parameter values to evaluate the model at.
+
+        Raises
+        ------
+        NotImplementedError
+            This method must be implemented by subclasses.
+        """
+        raise NotImplementedError
+
+    def time_data(self):
+        """
+        Returns the time data.
+
+        Returns
+        -------
+        np.ndarray
+            The time array.
+        """
+        return self._time_data
+
+    def target(self):
+        """
+        Return the target dataset.
+
+        Returns
+        -------
+        np.ndarray
+            The target dataset array.
+        """
+        return self._target
+
+    @property
+    def model(self):
+        return self._model
diff --git a/pybop/problems/design_problem.py b/pybop/problems/design_problem.py
new file mode 100644
index 000000000..2ad294305
--- /dev/null
+++ b/pybop/problems/design_problem.py
@@ -0,0 +1,82 @@
+import numpy as np
+
+from pybop import BaseProblem
+
+class DesignProblem(BaseProblem):
+    """
+    Problem class for design optimization problems.
+
+    Extends `BaseProblem` with specifics for applying a model to an experimental design.
+
+    Parameters
+    ----------
+    model : object
+        The model to apply the design to.
+    parameters : list
+        List of parameters for the problem.
+    experiment : object
+        The experimental setup to apply the model to.
+    """
+
+    def __init__(
+        self,
+        model,
+        parameters,
+        experiment,
+        check_model=True,
+        signal=["Voltage [V]"],
+        init_soc=None,
+        x0=None,
+    ):
+        super().__init__(parameters, model, check_model, signal, init_soc, x0)
+        self.experiment = experiment
+
+        # Build the model if required
+        if experiment is not None:
+            # Leave the build until later to apply the experiment
+            self._model.parameters = self.parameters
+            if self.parameters is not None:
+                self._model.fit_keys = [param.name for param in self.parameters]
+
+        elif self._model._built_model is None:
+            self._model.build(
+                experiment=self.experiment,
+                parameters=self.parameters,
+                check_model=self.check_model,
+                init_soc=self.init_soc,
+            )
+
+        # Add an example dataset for plotting comparison
+        sol = self.evaluate(self.x0)
+        self._time_data = sol[:, -1]
+        self._target = sol[:, 0:-1]
+        self._dataset = None
+
+    def evaluate(self, x):
+        """
+        Evaluate the model with the given parameters and return the signal.
+
+        Parameters
+        ----------
+        x : np.ndarray
+            Parameter values to evaluate the model at.
+
+        Returns
+        -------
+        y : np.ndarray
+            The model output y(t) simulated with inputs x.
+        """
+
+        sol = self._model.predict(
+            inputs=x,
+            experiment=self.experiment,
+            init_soc=self.init_soc,
+        )
+
+        if sol == [np.inf]:
+            return sol
+
+        else:
+            predictions = [sol[signal].data for signal in self.signal + ["Time [s]"]]
+
+            return np.vstack(predictions).T
diff --git a/pybop/problems/fitting_problem.py b/pybop/problems/fitting_problem.py
new file mode 100644
index 000000000..3f0da42f7
--- /dev/null
+++ b/pybop/problems/fitting_problem.py
@@ -0,0 +1,127 @@
+import numpy as np
+
+from pybop import BaseProblem
+
+class FittingProblem(BaseProblem):
+    """
+    Problem class for fitting (parameter estimation) problems.
+
+    Extends `BaseProblem` with specifics for fitting a model to a dataset.
+
+    Parameters
+    ----------
+    model : object
+        The model to fit.
+    parameters : list
+        List of parameters for the problem.
+    dataset : Dataset
+        Dataset object containing the data to fit the model to.
+    signal : str, optional
+        The signal to fit (default: "Voltage [V]").
+    """
+
+    def __init__(
+        self,
+        model,
+        parameters,
+        dataset,
+        check_model=True,
+        signal=["Voltage [V]"],
+        init_soc=None,
+        x0=None,
+    ):
+        super().__init__(parameters, model, check_model, signal, init_soc, x0)
+        self._dataset = dataset.data
+        self.x = self.x0
+
+        # Check that the dataset contains time and current
+        for name in ["Time [s]", "Current function [A]"] + self.signal:
+            if name not in self._dataset:
+                raise ValueError(f"Expected {name} in list of dataset")
+
+        self._time_data = self._dataset["Time [s]"]
+        self.n_time_data = len(self._time_data)
+        if np.any(self._time_data < 0):
+            raise ValueError("Times can not be negative.")
+        if np.any(self._time_data[:-1] >= self._time_data[1:]):
+            raise ValueError("Times must be increasing.")
+
+        for signal in self.signal:
+            if len(self._dataset[signal]) != self.n_time_data:
+                raise ValueError(
+                    f"Time data and {signal} data must be the same length."
+                )
+        target = [self._dataset[signal] for signal in self.signal]
+        self._target = np.vstack(target).T
+
+        # Add useful parameters to model
+        if model is not None:
+            self._model.signal = self.signal
+            self._model.n_outputs = self.n_outputs
+            self._model.n_time_data = self.n_time_data
+
+            # Build the model from scratch
+            if self._model._built_model is not None:
+                self._model._model_with_set_params = None
+                self._model._built_model = None
+                self._model._built_initial_soc = None
+                self._model._mesh = None
+                self._model._disc = None
+            self._model.build(
+                dataset=self._dataset,
+                parameters=self.parameters,
+                check_model=self.check_model,
+                init_soc=self.init_soc,
+            )
+
+    def evaluate(self, x):
+        """
+        Evaluate the model with the given parameters and return the signal.
+
+        Parameters
+        ----------
+        x : np.ndarray
+            Parameter values to evaluate the model at.
+
+        Returns
+        -------
+        y : np.ndarray
+            The model output y(t) simulated with inputs x.
+        """
+        if (x != self.x).any() and self._model.matched_parameters:
+            for i, param in enumerate(self.parameters):
+                param.update(value=x[i])
+
+            self._model.rebuild(parameters=self.parameters)
+            self.x = x
+
+        y = np.asarray(self._model.simulate(inputs=x, t_eval=self._time_data))
+
+        return y
+
+    def evaluateS1(self, x):
+        """
+        Evaluate the model with the given parameters and return the signal and its derivatives.
+
+        Parameters
+        ----------
+        x : np.ndarray
+            Parameter values to evaluate the model at.
+
+        Returns
+        -------
+        tuple
+            A tuple containing the simulation result y(t) and the sensitivities dy/dx(t) evaluated
+            with given inputs x.
+        """
+        if self._model.matched_parameters:
+            raise RuntimeError(
+                "Gradient not available when using geometric parameters."
+            )
+
+        y, dy = self._model.simulateS1(
+            inputs=x,
+            t_eval=self._time_data,
+        )
+
+        return (np.asarray(y), np.asarray(dy))
\ No newline at end of file

From dd5cbd48bff6cf25ec5f9c0d6a7a395adc163504 Mon Sep 17 00:00:00 2001
From: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
Date: Mon, 8 Apr 2024 16:35:45 +0100
Subject: [PATCH 2/5] #279 fix tests

---
 examples/standalone/problem.py | 2 +-
 pybop/observers/observer.py    | 2 +-
 tests/unit/test_problem.py     | 8 ++++----
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/examples/standalone/problem.py b/examples/standalone/problem.py
index 5a29138e8..55b8a6c72 100644
--- a/examples/standalone/problem.py
+++ b/examples/standalone/problem.py
@@ -1,5 +1,5 @@
 import numpy as np
-from pybop._problem import BaseProblem
+from pybop import BaseProblem
 
 
 class StandaloneProblem(BaseProblem):
diff --git a/pybop/observers/observer.py b/pybop/observers/observer.py
index 0a93fe8a4..0b8bdc3f3 100644
--- a/pybop/observers/observer.py
+++ b/pybop/observers/observer.py
@@ -1,6 +1,6 @@
 from typing import List, Optional
 import numpy as np
-from pybop._problem import BaseProblem
+from pybop import BaseProblem
 from pybop.models.base_model import BaseModel, Inputs, TimeSeriesState
 from pybop.parameters.parameter import Parameter
 
diff --git a/tests/unit/test_problem.py b/tests/unit/test_problem.py
index 94f1ff1a0..ac2117ee9 100644
--- a/tests/unit/test_problem.py
+++ b/tests/unit/test_problem.py
@@ -68,10 +68,10 @@ def signal(self):
     def test_base_problem(self, parameters, model):
         # Test incorrect number of initial parameter values
         with pytest.raises(ValueError):
-            pybop._problem.BaseProblem(parameters, model=model, x0=np.array([]))
+            pybop.BaseProblem(parameters, model=model, x0=np.array([]))
 
         # Construct Problem
-        problem = pybop._problem.BaseProblem(parameters, model=model)
+        problem = pybop.BaseProblem(parameters, model=model)
 
         assert problem._model == model
 
@@ -81,12 +81,12 @@ def test_base_problem(self, parameters, model):
             problem.evaluateS1([1e-5, 1e-5])
 
         with pytest.raises(ValueError):
-            pybop._problem.BaseProblem(parameters, model=model, signal=[1e-5, 1e-5])
+            pybop.BaseProblem(parameters, model=model, signal=[1e-5, 1e-5])
 
         # Test without bounds
         for param in parameters:
             param.bounds = None
-        problem = pybop._problem.BaseProblem(parameters, model=model)
+        problem = pybop.BaseProblem(parameters, model=model)
         assert problem.bounds is None
 
     @pytest.mark.unit

From b108747edc86337f13e33472b94f9f8e6066fcd0 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 8 Apr 2024 15:39:57 +0000
Subject: [PATCH 3/5] style: pre-commit fixes

---
 examples/standalone/problem.py    | 1 +
 pybop/observers/observer.py       | 1 +
 pybop/problems/base_problem.py    | 2 +-
 pybop/problems/design_problem.py  | 1 +
 pybop/problems/fitting_problem.py | 3 ++-
 5 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/examples/standalone/problem.py b/examples/standalone/problem.py
index 7fb916e84..6fdcf97f5 100644
--- a/examples/standalone/problem.py
+++ b/examples/standalone/problem.py
@@ -1,4 +1,5 @@
 import numpy as np
+
 from pybop import BaseProblem
 
 
diff --git a/pybop/observers/observer.py b/pybop/observers/observer.py
index 35cb5578a..38c7ef863 100644
--- a/pybop/observers/observer.py
+++ b/pybop/observers/observer.py
@@ -1,6 +1,7 @@
 from typing import List, Optional
 
 import numpy as np
+
 from pybop import BaseProblem
 from pybop.models.base_model import BaseModel, Inputs, TimeSeriesState
 from pybop.parameters.parameter import Parameter
diff --git a/pybop/problems/base_problem.py b/pybop/problems/base_problem.py
index 7aced2686..ab4a070eb 100644
--- a/pybop/problems/base_problem.py
+++ b/pybop/problems/base_problem.py
@@ -163,4 +163,4 @@ def set_target(self, dataset):
 
     @property
     def model(self):
-        return self._model
\ No newline at end of file
+        return self._model
diff --git a/pybop/problems/design_problem.py b/pybop/problems/design_problem.py
index 0c9efd012..29f58ba95 100644
--- a/pybop/problems/design_problem.py
+++ b/pybop/problems/design_problem.py
@@ -2,6 +2,7 @@
 
 from pybop import BaseProblem
 
+
 class DesignProblem(BaseProblem):
     """
     Problem class for design optimization problems.
diff --git a/pybop/problems/fitting_problem.py b/pybop/problems/fitting_problem.py
index ad4918ed6..6bfa38149 100644
--- a/pybop/problems/fitting_problem.py
+++ b/pybop/problems/fitting_problem.py
@@ -2,6 +2,7 @@
 
 from pybop import BaseProblem
 
+
 class FittingProblem(BaseProblem):
     """
     Problem class for fitting (parameter estimation) problems.
@@ -127,4 +128,4 @@ def evaluateS1(self, x):
             t_eval=self._time_data,
         )
 
-        return (y, np.asarray(dy))
\ No newline at end of file
+        return (y, np.asarray(dy))

From 3654cd2811058b3be696b6fd9eadce4dd634b192 Mon Sep 17 00:00:00 2001
From: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
Date: Mon, 8 Apr 2024 16:41:02 +0100
Subject: [PATCH 4/5] ruff

---
 pybop/problems/base_problem.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pybop/problems/base_problem.py b/pybop/problems/base_problem.py
index 7aced2686..b3deed64e 100644
--- a/pybop/problems/base_problem.py
+++ b/pybop/problems/base_problem.py
@@ -1,3 +1,4 @@
+import pybop
 import numpy as np
 
 

From c2147bafb0bf79db0121d7f00124eca8855573be Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 8 Apr 2024 15:41:37 +0000
Subject: [PATCH 5/5] style: pre-commit fixes

---
 pybop/problems/base_problem.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pybop/problems/base_problem.py b/pybop/problems/base_problem.py
index f3621b4e6..37a6af253 100644
--- a/pybop/problems/base_problem.py
+++ b/pybop/problems/base_problem.py
@@ -1,6 +1,7 @@
-import pybop
 import numpy as np
 
+import pybop
+
 
 class BaseProblem:
     """