Merge branch 'develop' into 339-updating-capacities

.github/workflows/periodic_benchmarks.yaml

@@ -22,6 +22,11 @@ jobs:
     runs-on: [self-hosted, macOS, ARM64]
     if: github.repository == 'pybop-team/PyBOP'
     steps:
+      - name: Cleanup build folder
+        run: |
+          rm -rf ./* || true
+          rm -rf ./.??* || true
+
       - uses: actions/checkout@v4
 
       - name: Install python & create virtualenv

.github/workflows/scheduled_tests.yaml

@@ -113,6 +113,11 @@ jobs:
       matrix: ${{fromJson(needs.filter_pybamm_matrix.outputs.filtered_pybop_matrix)}}
 
     steps:
+      - name: Cleanup build folder
+        run: |
+          rm -rf ./* || true
+          rm -rf ./.??* || true
+
       - uses: actions/checkout@v4
       - name: Install python & create virtualenv
         shell: bash

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.4.8"
+    rev: "v0.5.0"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 ## Features
 
+- [#379](https://github.com/pybop-team/PyBOP/pull/379) - Adds model.simulateS1 to weekly benchmarks.
 - [#174](https://github.com/pybop-team/PyBOP/issues/174) - Adds new logo and updates Readme for accessibility.
 - [#316](https://github.com/pybop-team/PyBOP/pull/316) - Adds Adam with weight decay (AdamW) optimiser, adds depreciation warning for pints.Adam implementation.
 - [#271](https://github.com/pybop-team/PyBOP/issues/271) - Aligns the output of the optimisers via a generalisation of Result class.
@@ -25,6 +26,9 @@
 ## Bug Fixes
 
 - [#339](https://github.com/pybop-team/PyBOP/issues/339) - Updates the calculation of the cyclable lithium capacity in the spme_max_energy example.
+- [#387](https://github.com/pybop-team/PyBOP/issues/387) - Adds keys to ParameterSet and updates ECM OCV check.
+- [#380](https://github.com/pybop-team/PyBOP/pull/380) - Restore self._boundaries construction for `pybop.PSO`
+- [#372](https://github.com/pybop-team/PyBOP/pull/372) - Converts `np.array` to `np.asarray` for Numpy v2.0 support.
 - [#165](https://github.com/pybop-team/PyBOP/issues/165) - Stores the attempted and best parameter values and the best cost for each iteration in the log attribute of the optimiser and updates the associated plots.
 - [#354](https://github.com/pybop-team/PyBOP/issues/354) - Fixes the calculation of the gradient in the `RootMeanSquaredError` cost.
 - [#347](https://github.com/pybop-team/PyBOP/issues/347) - Resets options between MSMR tests to cope with a bug in PyBaMM v23.9 which is fixed in PyBaMM v24.1.

benchmarks/benchmark_model.py

@@ -81,3 +81,13 @@ def time_model_simulate(self, model, parameter_set):
             parameter_set (str): The name of the parameter set being used.
         """
         self.problem._model.simulate(inputs=self.inputs, t_eval=self.t_eval)
+
+    def time_model_simulateS1(self, model, parameter_set):
+        """
+        Benchmark the simulateS1 method of the model.
+
+        Args:
+            model (pybop.Model): The model class being benchmarked.
+            parameter_set (str): The name of the parameter set being used.
+        """
+        self.problem._model.simulateS1(inputs=self.inputs, t_eval=self.t_eval)

examples/notebooks/multi_model_identification.ipynb

@@ -3958,7 +3958,7 @@
     }
    ],
    "source": [
-    "bounds = np.array([[5.5e-05, 8e-05], [7.5e-05, 9e-05]])\n",
+    "bounds = np.asarray([[5.5e-05, 8e-05], [7.5e-05, 9e-05]])\n",
     "for optim in optims:\n",
     "    pybop.plot2d(optim, bounds=bounds, steps=10, title=optim.cost.problem.model.name)"
    ]

examples/notebooks/multi_optimiser_identification.ipynb

@@ -925,7 +925,7 @@
    ],
    "source": [
     "# Plot the cost landscape with optimisation path and updated bounds\n",
-    "bounds = np.array([[0.5, 0.8], [0.55, 0.8]])\n",
+    "bounds = np.asarray([[0.5, 0.8], [0.55, 0.8]])\n",
     "for optim in optims:\n",
     "    pybop.plot2d(optim, bounds=bounds, steps=10, title=optim.name())"
    ]

examples/notebooks/optimiser_calibration.ipynb

@@ -677,7 +677,7 @@
    ],
    "source": [
     "# Plot the cost landscape with optimisation path and updated bounds\n",
-    "bounds = np.array([[0.6, 0.9], [0.5, 0.8]])\n",
+    "bounds = np.asarray([[0.6, 0.9], [0.5, 0.8]])\n",
     "for optim, sigma in zip(optims, sigmas):\n",
     "    pybop.plot2d(optim, bounds=bounds, steps=10, title=f\"Sigma: {sigma}\")"
    ]

examples/notebooks/spm_AdamW.ipynb

@@ -530,7 +530,7 @@
     "# Plot the cost landscape\n",
     "pybop.plot2d(cost, steps=15)\n",
     "# Plot the cost landscape with optimisation path and updated bounds\n",
-    "bounds = np.array([[0.6, 0.9], [0.5, 0.8]])\n",
+    "bounds = np.asarray([[0.6, 0.9], [0.5, 0.8]])\n",
     "pybop.plot2d(optim, bounds=bounds, steps=15);"
    ]
   },

examples/scripts/ecm_CMAES.py

@@ -101,5 +101,5 @@
 pybop.plot2d(cost, steps=15)
 
 # Plot the cost landscape with optimisation path and updated bounds
-bounds = np.array([[1e-4, 1e-2], [1e-5, 1e-2]])
+bounds = np.asarray([[1e-4, 1e-2], [1e-5, 1e-2]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

examples/scripts/exp_UKF.py

@@ -1,11 +1,10 @@
 import numpy as np
-import pybamm
 
 import pybop
 from examples.standalone.model import ExponentialDecay
 
 # Parameter set and model definition
-parameter_set = pybamm.ParameterValues({"k": "[input]", "y0": "[input]"})
+parameter_set = {"k": "[input]", "y0": "[input]"}
 model = ExponentialDecay(parameter_set=parameter_set, n_states=1)
 
 # Fitting parameters

examples/scripts/spm_AdamW.py

@@ -77,5 +77,5 @@ def noise(sigma):
 pybop.plot_parameters(optim)
 
 # Plot the cost landscape with optimisation path
-bounds = np.array([[0.5, 0.8], [0.4, 0.7]])
+bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

examples/scripts/spm_IRPropMin.py

@@ -51,5 +51,5 @@
 pybop.plot_parameters(optim)
 
 # Plot the cost landscape with optimisation path
-bounds = np.array([[0.5, 0.8], [0.4, 0.7]])
+bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

examples/scripts/spm_MAP.py

@@ -69,5 +69,5 @@
 pybop.plot2d(cost, steps=15)
 
 # Plot the cost landscape with optimisation path
-bounds = np.array([[0.55, 0.77], [0.48, 0.68]])
+bounds = np.asarray([[0.55, 0.77], [0.48, 0.68]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

examples/scripts/spm_MLE.py

@@ -69,5 +69,5 @@
 pybop.plot2d(likelihood, steps=15)
 
 # Plot the cost landscape with optimisation path
-bounds = np.array([[0.55, 0.77], [0.48, 0.68]])
+bounds = np.asarray([[0.55, 0.77], [0.48, 0.68]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

examples/scripts/spm_NelderMead.py

@@ -77,5 +77,5 @@ def noise(sigma):
 pybop.plot_parameters(optim)
 
 # Plot the cost landscape with optimisation path
-bounds = np.array([[0.5, 0.8], [0.4, 0.7]])
+bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

examples/scripts/spm_descent.py

@@ -57,5 +57,5 @@
 pybop.plot_parameters(optim)
 
 # Plot the cost landscape with optimisation path
-bounds = np.array([[0.5, 0.8], [0.4, 0.7]])
+bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

examples/standalone/model.py

@@ -18,7 +18,8 @@ def __init__(
         parameter_set: pybamm.ParameterValues = None,
         n_states: int = 1,
     ):
-        super().__init__()
+        super().__init__(name=name, parameter_set=parameter_set)
+
         self.n_states = n_states
         if n_states < 1:
             raise ValueError("The number of states (n_states) must be greater than 0")
@@ -38,10 +39,11 @@ def __init__(
         )
 
         self._unprocessed_model = self.pybamm_model
-        self.name = name
 
         self.default_parameter_values = (
-            default_parameter_values if parameter_set is None else parameter_set
+            default_parameter_values
+            if self._parameter_set is None
+            else self._parameter_set
         )
         self._parameter_set = self.default_parameter_values
         self._unprocessed_parameter_set = self._parameter_set

pybop/costs/_likelihoods.py

@@ -47,7 +47,7 @@ def set_sigma(self, sigma):
             )
 
         if not isinstance(sigma, np.ndarray):
-            sigma = np.array(sigma)
+            sigma = np.asarray(sigma)
 
         if not np.issubdtype(sigma.dtype, np.number):
             raise ValueError("Sigma must contain only numeric values")
@@ -74,7 +74,7 @@ def _evaluate(self, x, grad=None):
             if len(y.get(key, [])) != len(self._target.get(key, [])):
                 return -np.float64(np.inf)  # prediction doesn't match target
 
-        e = np.array(
+        e = np.asarray(
             [
                 np.sum(
                     self._offset
@@ -102,7 +102,7 @@ def _evaluateS1(self, x, grad=None):
                 dl = self._dl * np.ones(self.n_parameters)
                 return -likelihood, -dl
 
-        r = np.array([self._target[signal] - y[signal] for signal in self.signal])
+        r = np.asarray([self._target[signal] - y[signal] for signal in self.signal])
         likelihood = self._evaluate(x)
         dl = np.sum((self.sigma2 * np.sum((r * dy.T), axis=2)), axis=1)
         return likelihood, dl
@@ -148,7 +148,7 @@ def _evaluate(self, x, grad=None):
             if len(y.get(key, [])) != len(self._target.get(key, [])):
                 return -np.float64(np.inf)  # prediction doesn't match target
 
-        e = np.array(
+        e = np.asarray(
             [
                 np.sum(
                     self._logpi
@@ -181,7 +181,7 @@ def _evaluateS1(self, x, grad=None):
                 dl = self._dl * np.ones(self.n_parameters)
                 return -likelihood, -dl
 
-        r = np.array([self._target[signal] - y[signal] for signal in self.signal])
+        r = np.asarray([self._target[signal] - y[signal] for signal in self.signal])
         likelihood = self._evaluate(x)
         dl = sigma ** (-2.0) * np.sum((r * dy.T), axis=2)
         dsigma = -self.n_time_data / sigma + sigma**-(3.0) * np.sum(r**2, axis=1)

pybop/costs/fitting_costs.py

@@ -47,7 +47,7 @@ def _evaluate(self, x, grad=None):
             if len(prediction.get(key, [])) != len(self._target.get(key, [])):
                 return np.float64(np.inf)  # prediction doesn't match target
 
-        e = np.array(
+        e = np.asarray(
             [
                 np.sqrt(np.mean((prediction[signal] - self._target[signal]) ** 2))
                 for signal in self.signal
@@ -87,7 +87,7 @@ def _evaluateS1(self, x):
                 de = self._de * np.ones(self.n_parameters)
                 return e, de
 
-        r = np.array([y[signal] - self._target[signal] for signal in self.signal])
+        r = np.asarray([y[signal] - self._target[signal] for signal in self.signal])
         e = np.sqrt(np.mean(r**2, axis=1))
         de = np.mean((r * dy.T), axis=2) / (e + np.finfo(float).eps)
 
@@ -159,7 +159,7 @@ def _evaluate(self, x, grad=None):
             if len(prediction.get(key, [])) != len(self._target.get(key, [])):
                 return np.float64(np.inf)  # prediction doesn't match target
 
-        e = np.array(
+        e = np.asarray(
             [
                 np.sum(((prediction[signal] - self._target[signal]) ** 2))
                 for signal in self.signal
@@ -197,7 +197,7 @@ def _evaluateS1(self, x):
                 de = self._de * np.ones(self.n_parameters)
                 return e, de
 
-        r = np.array([y[signal] - self._target[signal] for signal in self.signal])
+        r = np.asarray([y[signal] - self._target[signal] for signal in self.signal])
         e = np.sum(np.sum(r**2, axis=0), axis=0)
         de = 2 * np.sum(np.sum((r * dy.T), axis=2), axis=1)
 

pybop/models/base_model.py

@@ -292,7 +292,7 @@ def reinit(
         if x is None:
             x = self._built_model.y0
 
-        sol = pybamm.Solution([np.array([t])], [x], self._built_model, inputs)
+        sol = pybamm.Solution([np.asarray([t])], [x], self._built_model, inputs)
 
         return TimeSeriesState(sol=sol, inputs=inputs, t=t)
 
@@ -303,7 +303,7 @@ def get_state(self, inputs: Inputs, t: float, x: np.ndarray) -> TimeSeriesState:
         if self._built_model is None:
             raise ValueError("Model must be built before calling get_state")
 
-        sol = pybamm.Solution([np.array([t])], [x], self._built_model, inputs)
+        sol = pybamm.Solution([np.asarray([t])], [x], self._built_model, inputs)
 
         return TimeSeriesState(sol=sol, inputs=inputs, t=t)
 

pybop/models/empirical/base_ecm.py

@@ -52,14 +52,14 @@ def __init__(
         # Correct OCP if set to default
         if (
             parameter_set is not None
-            and "Open-circuit voltage [V]" in parameter_set.params
+            and "Open-circuit voltage [V]" in parameter_set.keys()
         ):
             default_ocp = self.pybamm_model.default_parameter_values[
                 "Open-circuit voltage [V]"
             ]
-            if parameter_set.params["Open-circuit voltage [V]"] == "default":
+            if parameter_set["Open-circuit voltage [V]"] == "default":
                 print("Setting open-circuit voltage to default function")
-                parameter_set.params["Open-circuit voltage [V]"] = default_ocp
+                parameter_set["Open-circuit voltage [V]"] = default_ocp
 
         super().__init__(name=name, parameter_set=parameter_set)
 

pybop/models/lithium_ion/base_echem.py

@@ -47,10 +47,7 @@ def __init__(
         solver=None,
         **model_kwargs,
     ):
-        super().__init__(
-            name=name,
-            parameter_set=parameter_set,
-        )
+        super().__init__(name=name, parameter_set=parameter_set)
 
         model_options = dict(build=False)
         for key, value in model_kwargs.items():

pybop/observers/observer.py

@@ -134,7 +134,7 @@ def get_current_covariance(self) -> Covariance:
 
     def get_measure(self, x: TimeSeriesState) -> np.ndarray:
         measures = [x.sol[s].data[-1] for s in self._signal]
-        return np.array([[m] for m in measures])
+        return np.asarray([[m] for m in measures])
 
     def get_current_time(self) -> float:
         """

pybop/observers/unscented_kalman.py

@@ -118,7 +118,7 @@ def observe(self, time: float, value: np.ndarray) -> float:
         if value is None:
             raise ValueError("Measurement must be provided.")
         elif isinstance(value, np.floating):
-            value = np.array([value])
+            value = np.asarray([value])
 
         dt = time - self.get_current_time()
         if dt < 0:
@@ -201,7 +201,7 @@ def __init__(
         zero_cols = np.logical_and(np.all(P0 == 0, axis=1), np.all(Rp == 0, axis=1))
         zeros = np.logical_and(zero_rows, zero_cols)
         ones = np.logical_not(zeros)
-        states = np.array(range(len(x0)))[ones]
+        states = np.asarray(range(len(x0)))[ones]
         bool_mask = np.ix_(ones, ones)
 
         S_filtered = linalg.cholesky(P0[ones, :][:, ones])
@@ -276,11 +276,11 @@ def gen_sigma_points(
 
         # Define the weights of the sigma points
         w_m0 = sigma / (L + sigma)
-        w_m = np.array([w_m0] + [1 / (2 * (L + sigma))] * (2 * L))
+        w_m = np.asarray([w_m0] + [1 / (2 * (L + sigma))] * (2 * L))
 
         # Define the weights of the covariance of the sigma points
         w_c0 = w_m0 + (1 - alpha**2 + beta)
-        w_c = np.array([w_c0] + [1 / (2 * (L + sigma))] * (2 * L))
+        w_c = np.asarray([w_c0] + [1 / (2 * (L + sigma))] * (2 * L))
 
         return (points, w_m, w_c)
 

pybop/optimisers/base_pints_optimiser.py

@@ -134,22 +134,20 @@ def _sanitise_inputs(self):
 
         # Convert bounds to PINTS boundaries
         if self.bounds is not None:
-            if issubclass(
-                self.pints_optimiser,
-                (PintsGradientDescent, PintsAdam, PintsNelderMead),
-            ):
+            ignored_optimisers = (PintsGradientDescent, PintsAdam, PintsNelderMead)
+            if issubclass(self.pints_optimiser, ignored_optimisers):
                 print(f"NOTE: Boundaries ignored by {self.pints_optimiser}")
                 self.bounds = None
-            elif issubclass(self.pints_optimiser, PintsPSO):
-                if not all(
-                    np.isfinite(value)
-                    for sublist in self.bounds.values()
-                    for value in sublist
-                ):
-                    raise ValueError(
-                        "Either all bounds or no bounds must be set for Pints PSO."
-                    )
             else:
+                if issubclass(self.pints_optimiser, PintsPSO):
+                    if not all(
+                        np.isfinite(value)
+                        for sublist in self.bounds.values()
+                        for value in sublist
+                    ):
+                        raise ValueError(
+                            f"Either all bounds or no bounds must be set for {self.pints_optimiser.__name__}."
+                        )
                 self._boundaries = PintsRectangularBoundaries(
                     self.bounds["lower"], self.bounds["upper"]
                 )