#251 increment pybamm>23.5, fix examples incorrect model, remove non-…

…integration tests in parameterisation tests

examples/scripts/spm_adam.py

@@ -4,7 +4,7 @@
 
 # Parameter set and model definition
 parameter_set = pybop.ParameterSet.pybamm("Chen2020")
-model = pybop.lithium_ion.SPMe(parameter_set=parameter_set)
+model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
 # Fitting parameters
 parameters = [

examples/scripts/spm_descent.py

@@ -4,7 +4,7 @@
 
 # Parameter set and model definition
 parameter_set = pybop.ParameterSet.pybamm("Chen2020")
-model = pybop.lithium_ion.SPMe(parameter_set=parameter_set)
+model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
 # Fitting parameters
 parameters = [

pyproject.toml

@@ -27,7 +27,7 @@ classifiers = [
 ]
 requires-python = ">=3.8, <3.13"
 dependencies = [
-  "pybamm>=23.5",
+  "pybamm>23.5",
   "numpy>=1.16",
   "scipy>=1.3",
   "pints>=0.5",

scripts/ci/build_matrix.sh

@@ -12,7 +12,7 @@
 python_version=("3.8" "3.9" "3.10" "3.11" "3.12")
 os=("ubuntu-latest" "windows-latest" "macos-latest")
 # This command fetches the last three PyBaMM versions excluding release candidates from PyPI
-pybamm_version=($(curl -s https://pypi.org/pypi/pybamm/json | jq -r '.releases | keys[]' | grep -v rc | tail -n 3 | paste -sd " " -))
+pybamm_version=($(curl -s https://pypi.org/pypi/pybamm/json | jq -r '.releases | keys[]' | grep -v rc | tail -n 2 | paste -sd " " -))
 
 # open dict
 json='{

tests/integration/test_parameterisations.py

@@ -94,7 +94,9 @@ def spm_costs(self, model, parameters, cost_class, init_soc):
     def test_spm_optimisers(self, optimiser, spm_costs):
         # Some optimisers require a complete set of bounds
         if optimiser in [pybop.SciPyDifferentialEvolution, pybop.PSO]:
-            spm_costs.problem.parameters[1].set_bounds([0.375, 0.75])
+            spm_costs.problem.parameters[1].set_bounds(
+                [0.3, 0.8]
+            )  # Large range to ensure IC within bounds
             bounds = {"lower": [], "upper": []}
             for param in spm_costs.problem.parameters:
                 bounds["lower"].append(param.bounds[0])
@@ -106,38 +108,16 @@ def test_spm_optimisers(self, optimiser, spm_costs):
         parameterisation = pybop.Optimisation(
             cost=spm_costs, optimiser=optimiser, sigma0=0.05
         )
-        parameterisation.set_max_unchanged_iterations(iterations=35, threshold=5e-4)
+        parameterisation.set_max_unchanged_iterations(iterations=35, threshold=1e-5)
         parameterisation.set_max_iterations(125)
 
         initial_cost = parameterisation.cost(spm_costs.x0)
 
-        if optimiser in [pybop.CMAES]:
-            parameterisation.set_f_guessed_tracking(True)
-            parameterisation.cost.problem.model.allow_infeasible_solutions = False
-            assert parameterisation._use_f_guessed is True
-            parameterisation.set_max_iterations(1)
-            x, final_cost = parameterisation.run()
-
-            parameterisation.set_f_guessed_tracking(False)
-            parameterisation.set_max_iterations(125)
-
-            x, final_cost = parameterisation.run()
-            assert parameterisation._max_iterations == 125
-
-        elif optimiser in [pybop.GradientDescent]:
+        if optimiser in [pybop.GradientDescent]:
             if isinstance(spm_costs, pybop.GaussianLogLikelihoodKnownSigma):
                 parameterisation.optimiser.set_learning_rate(1.8e-5)
-                parameterisation.set_min_iterations(150)
             else:
                 parameterisation.optimiser.set_learning_rate(0.02)
-            parameterisation.set_max_iterations(150)
-            x, final_cost = parameterisation.run()
-
-        elif optimiser in [pybop.SciPyDifferentialEvolution]:
-            with pytest.raises(ValueError):
-                parameterisation.optimiser.set_population_size(-5)
-
-            parameterisation.optimiser.set_population_size(5)
             x, final_cost = parameterisation.run()
 
         elif optimiser in [pybop.SciPyMinimize]:
@@ -192,7 +172,9 @@ def spm_two_signal_cost(self, parameters, model, cost_class):
     def test_multiple_signals(self, multi_optimiser, spm_two_signal_cost):
         # Some optimisers require a complete set of bounds
         if multi_optimiser in [pybop.SciPyDifferentialEvolution]:
-            spm_two_signal_cost.problem.parameters[1].set_bounds([0.375, 0.75])
+            spm_two_signal_cost.problem.parameters[1].set_bounds(
+                [0.3, 0.8]
+            )  # Large range to ensure IC within bounds
             bounds = {"lower": [], "upper": []}
             for param in spm_two_signal_cost.problem.parameters:
                 bounds["lower"].append(param.bounds[0])
@@ -208,10 +190,6 @@ def test_multiple_signals(self, multi_optimiser, spm_two_signal_cost):
         parameterisation.set_max_iterations(125)
 
         initial_cost = parameterisation.cost(spm_two_signal_cost.x0)
-
-        if multi_optimiser in [pybop.SciPyDifferentialEvolution]:
-            parameterisation.optimiser.set_population_size(5)
-
         x, final_cost = parameterisation.run()
 
         # Assertions
@@ -224,7 +202,7 @@ def test_model_misparameterisation(self, parameters, model, init_soc):
         # Define two different models with different parameter sets
         # The optimisation should fail as the models are not the same
         second_parameter_set = pybop.ParameterSet.pybamm("Ecker2015")
-        second_model = pybop.lithium_ion.SPM(parameter_set=second_parameter_set)
+        second_model = pybop.lithium_ion.SPMe(parameter_set=second_parameter_set)
 
         # Form dataset
         solution = self.getdata(second_model, self.ground_truth, init_soc)
@@ -237,10 +215,7 @@ def test_model_misparameterisation(self, parameters, model, init_soc):
         )
 
         # Define the cost to optimise
-        signal = ["Voltage [V]"]
-        problem = pybop.FittingProblem(
-            model, parameters, dataset, signal=signal, init_soc=init_soc
-        )
+        problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)
         cost = pybop.RootMeanSquaredError(problem)
 
         # Select optimiser