Fixes for upcoming PyBaMM v24.5

.gitignore

@@ -314,3 +314,6 @@ $RECYCLE.BIN/
 # Airspeed Velocity
 *.asv/
 results/
+
+# Pycharm
+*.idea/

CHANGELOG.md

@@ -5,6 +5,7 @@
 - [#327](https://github.com/pybop-team/PyBOP/issues/327) - Adds the `WeightedCost` subclass, defines when to evaluate a problem and adds the `spm_weighted_cost` example script.
 - [#393](https://github.com/pybop-team/PyBOP/pull/383) - Adds Minkowski and SumofPower cost classes, with an example and corresponding tests.
 - [#403](https://github.com/pybop-team/PyBOP/pull/403/) - Adds lychee link checking action.
+- [#313](https://github.com/pybop-team/PyBOP/pull/313/) - Fixes for PyBaMM v24.5, drops support for PyBaMM v23.9, v24.1
 
 ## Bug Fixes
 

examples/scripts/gitt.py

@@ -36,10 +36,10 @@
 model = pybop.lithium_ion.WeppnerHuggins(parameter_set=parameter_set)
 
 parameters = pybop.Parameter(
-    "Positive electrode diffusivity [m2.s-1]",
+    "Positive particle diffusivity [m2.s-1]",
     prior=pybop.Gaussian(5e-14, 1e-13),
     bounds=[1e-16, 1e-11],
-    true_value=parameter_set["Positive electrode diffusivity [m2.s-1]"],
+    true_value=parameter_set["Positive particle diffusivity [m2.s-1]"],
 )
 
 problem = pybop.FittingProblem(

examples/scripts/parameters/example_BPX.json

@@ -1,7 +1,7 @@
 {
     "Header": {
-          "BPX": 0.1,
-          "Title": "Parameterisation example of an LFP|graphite 2 Ah cylindrical 18650 cell.",
+          "BPX": 0.4,
+          "Title": "Parameterisation example of an LFP|graphite 2 Ah cylindrical 18650 cell. File downloaded on 19/3/24 from https://github.com/FaradayInstitution/BPX/blob/main/examples/lfp_18650_cell_BPX.json",
           "Description": "LFP|graphite 2 Ah cylindrical 18650 cell. Parameterisation by About:Energy Limited (aboutenergy.io), December 2022, based on cell cycling data, and electrode data gathered after cell teardown. Electrolyte properties from Nyman et al. 2008 (doi:10.1016/j.electacta.2008.04.023). Negative electrode entropic coefficient data are from O'Regan et al. 2022 (doi:10.1016/j.electacta.2022.140700). Positive electrode entropic coefficient data are from Gerver and Meyers 2011 (doi:10.1149/1.3591799). Other thermal properties are estimated.",
           "Model": "DFN"
     },
@@ -70,9 +70,6 @@
                 "Thickness [m]": 2e-05,
                 "Porosity": 0.47,
                 "Transport efficiency": 0.3222
-          },
-          "User-defined": {
-                "Source:": "An example BPX json file downloaded on 19/3/24 from https://github.com/FaradayInstitution/BPX/blob/main/examples/lfp_18650_cell_BPX.json"
           }
     }
 }

examples/scripts/spm_UKF.py

@@ -22,7 +22,7 @@
 
 # Make a prediction with measurement noise
 sigma = 0.001
-t_eval = np.arange(0, 300, 2)
+t_eval = np.arange(0, 900, 0.5)
 values = model.predict(t_eval=t_eval)
 corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval))
 
@@ -42,8 +42,8 @@
 signal = ["Voltage [V]"]
 n_states = model.n_states
 n_signals = len(signal)
-covariance = np.diag([0] * 19 + [sigma**2] + [0] * 19 + [sigma**2])
-process_noise = np.diag([0] * 19 + [1e-6] + [0] * 19 + [1e-6])
+covariance = np.diag([0] * 20 + [sigma**2] + [0] * 20 + [sigma**2])
+process_noise = np.diag([0] * 20 + [1e-6] + [0] * 20 + [1e-6])
 measurement_noise = np.diag([sigma**2])
 observer = pybop.UnscentedKalmanFilterObserver(
     parameters,

pybop/_experiment.py

@@ -49,6 +49,4 @@ def __init__(
             period,
             temperature,
             termination,
-            drive_cycles,
-            cccv_handling,
         )

pybop/models/base_model.py

@@ -119,9 +119,13 @@ def build(
             self.set_params()
 
             self._mesh = pybamm.Mesh(self.geometry, self.submesh_types, self.var_pts)
-            self._disc = pybamm.Discretisation(self.mesh, self.spatial_methods)
+            self._disc = pybamm.Discretisation(
+                mesh=self.mesh,
+                spatial_methods=self.spatial_methods,
+                check_model=check_model,
+            )
             self._built_model = self._disc.process_model(
-                self._model_with_set_params, inplace=False, check_model=check_model
+                self._model_with_set_params, inplace=False
             )
 
             # Clear solver and setup model
@@ -230,9 +234,13 @@ def rebuild(
 
         self.set_params(rebuild=True)
         self._mesh = pybamm.Mesh(self.geometry, self.submesh_types, self.var_pts)
-        self._disc = pybamm.Discretisation(self.mesh, self.spatial_methods)
+        self._disc = pybamm.Discretisation(
+            mesh=self.mesh,
+            spatial_methods=self.spatial_methods,
+            check_model=check_model,
+        )
         self._built_model = self._disc.process_model(
-            self._model_with_set_params, inplace=False, check_model=check_model
+            self._model_with_set_params, inplace=False
         )
 
         # Clear solver and setup model

pybop/models/lithium_ion/weppner_huggins.py

@@ -65,7 +65,7 @@ def __init__(self, name="Weppner & Huggins model", **model_kwargs):
         # Parameters
         ######################
 
-        d_s = Parameter("Positive electrode diffusivity [m2.s-1]")
+        d_s = Parameter("Positive particle diffusivity [m2.s-1]")
 
         c_s_max = Parameter("Maximum concentration in positive electrode [mol.m-3]")
 

pyproject.toml

@@ -26,7 +26,7 @@ classifiers = [
 ]
 requires-python = ">=3.9, <3.13"
 dependencies = [
-  "pybamm>=23.9",
+   "pybamm>=24.5rc2",
   "numpy>=1.16, <2.0",
   "scipy>=1.3",
   "pints>=0.5",

scripts/ci/build_matrix.sh

@@ -11,8 +11,11 @@
 
 python_version=("3.9" "3.10" "3.11" "3.12")
 os=("ubuntu-latest" "windows-latest" "macos-13" "macos-14")
-# This command fetches the last two PyBaMM versions excluding release candidates from PyPI
-pybamm_version=($(curl -s https://pypi.org/pypi/pybamm/json | jq -r '.releases | keys[]' | grep -v rc | tail -n 2 | paste -sd " " -))
+# This command fetches the last PyBaMM version excluding release candidates from PyPI
+#pybamm_version=($(curl -s https://pypi.org/pypi/pybamm/json | jq -r '.releases | keys[]' | grep -v rc | tail -n 1 | paste -sd " " -))
+
+# This command fetches the last PyBaMM versions including release candidates from PyPI
+pybamm_version=($(curl -s https://pypi.org/pypi/pybamm/json | jq -r '.releases | keys[]' | tail -n 1 | paste -sd " " -))
 
 # open dict
 json='{
@@ -40,7 +43,7 @@ json+='
   ]
 }'
 
-# Filter out incompatible combinations
-json=$(echo "$json" | jq -c 'del(.include[] | select(.pybamm_version == "23.9" and .python_version == "3.12"))')
+# Example for filtering out incompatible combinations
+#json=$(echo "$json" | jq -c 'del(.include[] | select(.pybamm_version == "23.9" and .python_version == "3.12"))')
 
 echo "$json" | jq -c .

tests/integration/test_model_experiment_changes.py

@@ -22,7 +22,7 @@ class TestModelAndExperimentChanges:
             ),
             pybop.Parameters(
                 pybop.Parameter(  # non-geometric parameter
-                    "Positive electrode diffusivity [m2.s-1]",
+                    "Positive particle diffusivity [m2.s-1]",
                     prior=pybop.Gaussian(3.43e-15, 1e-15),
                     bounds=[1e-15, 5e-15],
                     true_value=4e-15,

tests/unit/test_cost.py

@@ -296,7 +296,7 @@ def test_design_costs(self, cost_class, design_problem):
 
             # Compute after updating nominal capacity
             cost = cost_class(design_problem, update_capacity=True)
-            cost([0.4])
+            assert np.isfinite(cost([0.4]))
 
     @pytest.mark.unit
     def test_weighted_fitting_cost(self, problem):

tests/unit/test_experiment.py

@@ -18,9 +18,9 @@ def test_experiment(self):
         pybop_experiment = pybop.Experiment(protocol)
         pybamm_experiment = pybamm.Experiment(protocol)
 
-        assert [
-            step.to_dict() for step in pybop_experiment.operating_conditions_steps
-        ] == [step.to_dict() for step in pybamm_experiment.operating_conditions_steps]
+        assert [step.to_dict() for step in pybop_experiment.steps] == [
+            step.to_dict() for step in pybamm_experiment.steps
+        ]
 
         assert pybop_experiment.cycle_lengths == pybamm_experiment.cycle_lengths
 

tests/unit/test_models.py

@@ -358,11 +358,13 @@ def test_non_converged_solution(self):
                 "Voltage [V]": np.zeros(100),
             }
         )
-
         problem = pybop.FittingProblem(model, parameters=parameters, dataset=dataset)
-        res = problem.evaluate([-0.2, -0.2])
-        _, res_grad = problem.evaluateS1([-0.2, -0.2])
+
+        # Simulate the DFN with active material values of 0.
+        # The solution elements will not change as the solver will not converge.
+        output = problem.evaluate([0, 0])
+        output_S1, _ = problem.evaluateS1([0, 0])
 
         for key in problem.signal:
-            assert np.isinf(res.get(key, [])).any()
-        assert np.isinf(res_grad).any()
+            assert np.allclose(output.get(key, [])[0], output.get(key, []))
+            assert np.allclose(output_S1.get(key, [])[0], output_S1.get(key, []))

tests/unit/test_observer_unscented_kalman.py

@@ -93,6 +93,15 @@ def test_cholupdate(self):
         SquareRootUKF.cholupdate(R1_, u.copy(), 1.0)
         np.testing.assert_array_almost_equal(R1, R1_)
 
+        # Test hypot
+        f = 10.0
+        j = 20.0
+        out_1 = f * np.sqrt(1 + 1.0 * f**2 / j**2)
+        np.testing.assert_allclose(SquareRootUKF.hypot(f, j, 1.0), out_1)
+
+        j = 10.0
+        np.testing.assert_allclose(SquareRootUKF.hypot(f, j, 1.0), float(0))
+
     @pytest.mark.unit
     def test_unscented_kalman_filter(self, dataset, observer):
         t_eval = dataset["Time [s]"]
@@ -116,6 +125,11 @@ def test_unscented_kalman_filter(self, dataset, observer):
                 decimal=4,
             )
 
+        # Test get covariance
+        cov = observer.get_current_measure()
+        assert cov.shape == (1, 1)
+        assert float(0) <= cov[0]
+
     @pytest.mark.unit
     def test_observe_no_measurement(self, observer):
         with pytest.raises(ValueError):