Additions to ruff rules

benchmarks/benchmark_model.py

@@ -1,8 +1,7 @@
 import numpy as np
 
 import pybop
-
-from .benchmark_utils import set_random_seed
+from benchmarks.benchmark_utils import set_random_seed
 
 
 class BenchmarkModel:

benchmarks/benchmark_optim_construction.py

@@ -1,8 +1,7 @@
 import numpy as np
 
 import pybop
-
-from .benchmark_utils import set_random_seed
+from benchmarks.benchmark_utils import set_random_seed
 
 
 class BenchmarkOptimisationConstruction:

benchmarks/benchmark_parameterisation.py

@@ -1,8 +1,7 @@
 import numpy as np
 
 import pybop
-
-from .benchmark_utils import set_random_seed
+from benchmarks.benchmark_utils import set_random_seed
 
 
 class BenchmarkParameterisation:

benchmarks/benchmark_track_parameterisation.py

@@ -1,8 +1,7 @@
 import numpy as np
 
 import pybop
-
-from .benchmark_utils import set_random_seed
+from benchmarks.benchmark_utils import set_random_seed
 
 
 class BenchmarkTrackParameterisation:

docs/_extension/gallery_directive.py

@@ -12,7 +12,7 @@
 """
 
 from pathlib import Path
-from typing import Any, Dict, List
+from typing import Any
 
 from docutils import nodes
 from docutils.parsers.rst import directives
@@ -68,7 +68,7 @@ class GalleryGridDirective(SphinxDirective):
         "class-card": directives.unchanged,
     }
 
-    def run(self) -> List[nodes.Node]:
+    def run(self) -> list[nodes.Node]:
         """Create the gallery grid."""
         if self.arguments:
             # If an argument is given, assume it's a path to a YAML file
@@ -129,7 +129,7 @@ def run(self) -> List[nodes.Node]:
         return [container.children[0]]
 
 
-def setup(app: Sphinx) -> Dict[str, Any]:
+def setup(app: Sphinx) -> dict[str, Any]:
     """Add custom configuration to sphinx app.
 
     Args:

docs/conf.py

@@ -7,11 +7,11 @@
 from pathlib import Path
 
 sys.path.append(str(Path(".").resolve()))
-from pybop._version import __version__  # noqa: E402
+from pybop._version import __version__
 
 # -- Project information -----------------------------------------------------
 project = "PyBOP"
-copyright = "2023, The PyBOP Team"
+copyright = "2023, The PyBOP Team"  # noqa A001
 author = "The PyBOP Team"
 release = f"v{__version__}"
 

examples/standalone/problem.py

@@ -24,7 +24,7 @@ def __init__(
         self._dataset = dataset.data
 
         # Check that the dataset contains time and current
-        for name in ["Time [s]"] + self.signal:
+        for name in ["Time [s]", *self.signal]:
             if name not in self._dataset:
                 raise ValueError(f"expected {name} in list of dataset")
 

pybop/_dataset.py

@@ -1,6 +1,5 @@
 import numpy as np
-from pybamm import Interpolant, solvers
-from pybamm import t as pybamm_t
+from pybamm import solvers
 
 
 class Dataset:
@@ -77,26 +76,7 @@ def __getitem__(self, key):
 
         return self.data[key]
 
-    def Interpolant(self):
-        """
-        Create an interpolation function of the dataset based on the independent variable.
-
-        Currently, only time-based interpolation is supported. This method modifies
-        the instance's Interpolant attribute to be an interpolation function that
-        can be evaluated at different points in time.
-
-        Raises
-        ------
-        NotImplementedError
-            If the independent variable for interpolation is not supported.
-        """
-
-        if self.variable == "time":
-            self.Interpolant = Interpolant(self.x, self.y, pybamm_t)
-        else:
-            NotImplementedError("Only time interpolation is supported")
-
-    def check(self, signal=["Voltage [V]"]):
+    def check(self, signal=None):
         """
         Check the consistency of a PyBOP Dataset against the expected format.
 
@@ -110,11 +90,13 @@ def check(self, signal=["Voltage [V]"]):
         ValueError
             If the time series and the data series are not consistent.
         """
+        if signal is None:
+            signal = ["Voltage [V]"]
         if isinstance(signal, str):
             signal = [signal]
 
         # Check that the dataset contains time and chosen signal
-        for name in ["Time [s]"] + signal:
+        for name in ["Time [s]", *signal]:
             if name not in self.names:
                 raise ValueError(f"expected {name} in list of dataset")
 

pybop/costs/_likelihoods.py

@@ -1,4 +1,4 @@
-from typing import List, Tuple, Union
+from typing import Union
 
 import numpy as np
 
@@ -14,7 +14,7 @@ class BaseLikelihood(BaseCost):
     """
 
     def __init__(self, problem: BaseProblem):
-        super(BaseLikelihood, self).__init__(problem)
+        super().__init__(problem)
         self.n_time_data = problem.n_time_data
 
 
@@ -32,8 +32,8 @@ class GaussianLogLikelihoodKnownSigma(BaseLikelihood):
         per dimension.
     """
 
-    def __init__(self, problem: BaseProblem, sigma0: Union[List[float], float]):
-        super(GaussianLogLikelihoodKnownSigma, self).__init__(problem)
+    def __init__(self, problem: BaseProblem, sigma0: Union[list[float], float]):
+        super().__init__(problem)
         sigma0 = self.check_sigma0(sigma0)
         self.sigma2 = sigma0**2.0
         self._offset = -0.5 * self.n_time_data * np.log(2 * np.pi * self.sigma2)
@@ -62,7 +62,7 @@ def _evaluate(self, inputs: Inputs, grad: Union[None, np.ndarray] = None) -> flo
 
         return e if self.n_outputs != 1 else e.item()
 
-    def _evaluateS1(self, inputs: Inputs) -> Tuple[float, np.ndarray]:
+    def _evaluateS1(self, inputs: Inputs) -> tuple[float, np.ndarray]:
         """
         Calls the problem.evaluateS1 method and calculates the log-likelihood and gradient.
         """
@@ -90,7 +90,7 @@ def check_sigma0(self, sigma0: Union[np.ndarray, float]):
         if np.shape(sigma0) not in [(), (1,), (self.n_outputs,)]:
             raise ValueError(
                 "sigma0 must be either a scalar value (one standard deviation for "
-                + "all coordinates) or an array with one entry per dimension."
+                "all coordinates) or an array with one entry per dimension."
             )
         return sigma0
 
@@ -115,10 +115,10 @@ class GaussianLogLikelihood(BaseLikelihood):
     def __init__(
         self,
         problem: BaseProblem,
-        sigma0: Union[float, List[float], List[Parameter]] = 0.002,
+        sigma0: Union[float, list[float], list[Parameter]] = 0.002,
         dsigma_scale: float = 1.0,
     ):
-        super(GaussianLogLikelihood, self).__init__(problem)
+        super().__init__(problem)
         self._dsigma_scale = dsigma_scale
         self._logpi = -0.5 * self.n_time_data * np.log(2 * np.pi)
 
@@ -128,7 +128,7 @@ def __init__(
         self._dl = np.ones(self.n_parameters)
 
     def _add_sigma_parameters(self, sigma0):
-        sigma0 = [sigma0] if not isinstance(sigma0, List) else sigma0
+        sigma0 = [sigma0] if not isinstance(sigma0, list) else sigma0
         sigma0 = self._pad_sigma0(sigma0)
 
         for i, value in enumerate(sigma0):
@@ -214,7 +214,7 @@ def _evaluate(self, inputs: Inputs, grad: Union[None, np.ndarray] = None) -> flo
 
         return e if self.n_outputs != 1 else e.item()
 
-    def _evaluateS1(self, inputs: Inputs) -> Tuple[float, np.ndarray]:
+    def _evaluateS1(self, inputs: Inputs) -> tuple[float, np.ndarray]:
         """
         Calls the problem.evaluateS1 method and calculates the log-likelihood.
 
@@ -265,7 +265,7 @@ class MAP(BaseLikelihood):
     """
 
     def __init__(self, problem, likelihood, sigma0=None, gradient_step=1e-3):
-        super(MAP, self).__init__(problem)
+        super().__init__(problem)
         self.sigma0 = sigma0
         self.gradient_step = gradient_step
         if self.sigma0 is None:
@@ -278,7 +278,7 @@ def __init__(self, problem, likelihood, sigma0=None, gradient_step=1e-3):
         except Exception as e:
             raise ValueError(
                 f"An error occurred when constructing the Likelihood class: {e}"
-            )
+            ) from e
 
         if hasattr(self, "likelihood") and not isinstance(
             self.likelihood, BaseLikelihood
@@ -310,7 +310,7 @@ def _evaluate(self, inputs: Inputs, grad=None) -> float:
         posterior = log_likelihood + log_prior
         return posterior
 
-    def _evaluateS1(self, inputs: Inputs) -> Tuple[float, np.ndarray]:
+    def _evaluateS1(self, inputs: Inputs) -> tuple[float, np.ndarray]:
         """
         Compute the maximum a posteriori with respect to the parameters.
         The method passes the likelihood gradient to the optimiser without modification.

pybop/costs/base_cost.py

@@ -72,7 +72,7 @@ def evaluate(self, x, grad=None):
             raise e
 
         except Exception as e:
-            raise ValueError(f"Error in cost calculation: {e}")
+            raise ValueError(f"Error in cost calculation: {e}") from e
 
     def _evaluate(self, inputs: Inputs, grad=None):
         """
@@ -129,7 +129,7 @@ def evaluateS1(self, x):
             raise e
 
         except Exception as e:
-            raise ValueError(f"Error in cost calculation: {e}")
+            raise ValueError(f"Error in cost calculation: {e}") from e
 
     def _evaluateS1(self, inputs: Inputs):
         """

pybop/costs/design_costs.py

@@ -31,7 +31,7 @@ def __init__(self, problem, update_capacity=False):
         problem : object
             The problem instance containing the model and data.
         """
-        super(DesignCost, self).__init__(problem)
+        super().__init__(problem)
         self.problem = problem
         if update_capacity is True:
             nominal_capacity_warning = (
@@ -41,7 +41,7 @@ def __init__(self, problem, update_capacity=False):
             nominal_capacity_warning = (
                 "The nominal capacity is fixed at the initial model value."
             )
-        warnings.warn(nominal_capacity_warning, UserWarning)
+        warnings.warn(nominal_capacity_warning, UserWarning, stacklevel=2)
         self.update_capacity = update_capacity
         self.parameter_set = problem.model.parameter_set
         self.update_simulation_data(self.parameters.as_dict("initial"))
@@ -97,7 +97,7 @@ class GravimetricEnergyDensity(DesignCost):
     """
 
     def __init__(self, problem, update_capacity=False):
-        super(GravimetricEnergyDensity, self).__init__(problem, update_capacity)
+        super().__init__(problem, update_capacity)
 
     def _evaluate(self, inputs: Inputs, grad=None):
         """
@@ -153,7 +153,7 @@ class VolumetricEnergyDensity(DesignCost):
     """
 
     def __init__(self, problem, update_capacity=False):
-        super(VolumetricEnergyDensity, self).__init__(problem, update_capacity)
+        super().__init__(problem, update_capacity)
 
     def _evaluate(self, inputs: Inputs, grad=None):
         """

pybop/costs/fitting_costs.py

@@ -18,7 +18,7 @@ class RootMeanSquaredError(BaseCost):
     """
 
     def __init__(self, problem):
-        super(RootMeanSquaredError, self).__init__(problem)
+        super().__init__(problem)
 
         # Default fail gradient
         self._de = 1.0
@@ -131,7 +131,7 @@ class SumSquaredError(BaseCost):
     """
 
     def __init__(self, problem):
-        super(SumSquaredError, self).__init__(problem)
+        super().__init__(problem)
 
         # Default fail gradient
         self._de = 1.0
@@ -161,7 +161,7 @@ def _evaluate(self, inputs: Inputs, grad=None):
 
         e = np.asarray(
             [
-                np.sum(((prediction[signal] - self._target[signal]) ** 2))
+                np.sum((prediction[signal] - self._target[signal]) ** 2)
                 for signal in self.signal
             ]
         )