Merge branch 'develop' into 441-functional-parameters

.github/workflows/test_on_push.yaml → .github/workflows/test_on_pull_request.yaml

@@ -105,6 +105,37 @@ jobs:
         run: |
           nox -s examples
 
+  # Quick benchmarks on macos-14
+  benchmarks:
+    needs: style
+    runs-on: macos-14
+    strategy:
+      fail-fast: false
+    name: Benchmarks
+
+    steps:
+      - name: Check out PyBOP repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 2
+
+      - name: Set up Python 3.12
+        id: setup-python
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.12
+
+      - name: Install dependencies
+        shell: bash
+        run: |
+          python -m pip install --upgrade pip asv[virtualenv]
+
+      - name: Run quick benchmarks
+        shell: bash
+        run: |
+          asv machine --machine "GitHubRunner"
+          asv run --machine "GitHubRunner" --quick --show-stderr
+
   # Runs only on macos-14 with Python 3.12
   check_coverage:
     needs: style

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.5.6"
+    rev: "v0.5.7"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

CHANGELOG.md

@@ -3,6 +3,10 @@
 ## Features
 
 - [#441](https://github.com/pybop-team/PyBOP/issues/441) - Adds an example for estimating constants within a `pybamm.FunctionalParameter`.
+- [#460](https://github.com/pybop-team/PyBOP/pull/460) - Notebook example files added for ECM and folder structure updated.
+- [#450](https://github.com/pybop-team/PyBOP/pull/450) - Adds support for IDAKLU with output variables, and corresponding examples, tests.
+- [#364](https://github.com/pybop-team/PyBOP/pull/364) - Adds the MultiFittingProblem class and the multi_fitting example script.
+- [#444](https://github.com/pybop-team/PyBOP/issues/444) - Merge `BaseModel` `build()` and `rebuild()` functionality.
 - [#435](https://github.com/pybop-team/PyBOP/pull/435) - Adds SLF001 linting for private members.
 - [#418](https://github.com/pybop-team/PyBOP/issues/418) - Wraps the `get_parameter_info` method from PyBaMM to get a dictionary of parameter names and types.
 - [#413](https://github.com/pybop-team/PyBOP/pull/413) - Adds `DesignCost` functionality to `WeightedCost` class with additional tests.
@@ -14,6 +18,7 @@
 
 ## Bug Fixes
 
+- [#454](https://github.com/pybop-team/PyBOP/issue/454) - Fixes benchmarking suite.
 - [#421](https://github.com/pybop-team/PyBOP/issues/421) - Adds a default value for the initial SOC for design problems.
 
 ## Breaking Changes
@@ -28,6 +33,7 @@
 
 ## Bug Fixes
 
+
 ## Breaking Changes
 
 # [v24.6](https://github.com/pybop-team/PyBOP/tree/v24.6) - 2024-07-08

asv.conf.json

@@ -8,7 +8,7 @@
         "python -m build --wheel -o {build_cache_dir} {build_dir}"
     ],
     "default_benchmark_timeout": 180,
-    "branches": ["develop"],
+    "branches": ["HEAD"],
     "environment_type": "virtualenv",
     "matrix": {
         "req":{

benchmarks/benchmark_model.py

@@ -26,19 +26,20 @@ def setup(self, model, parameter_set):
         self.model = model(parameter_set=pybop.ParameterSet.pybamm(parameter_set))
 
         # Define fitting parameters
-        parameters = [
+        parameters = pybop.Parameters(
             pybop.Parameter(
                 "Current function [A]",
                 prior=pybop.Gaussian(0.4, 0.02),
                 bounds=[0.2, 0.7],
                 initial_value=0.4,
             )
-        ]
+        )
 
         # Generate synthetic data
         sigma = 0.001
         self.t_eval = np.arange(0, 900, 2)
-        values = self.model.predict(t_eval=self.t_eval)
+        self.init_state = {"Initial SoC": 0.5}
+        values = self.model.predict(t_eval=self.t_eval, initial_state=self.init_state)
         corrupt_values = values["Voltage [V]"].data + np.random.normal(
             0, sigma, len(self.t_eval)
         )
@@ -58,7 +59,7 @@ def setup(self, model, parameter_set):
 
         # Create fitting problem
         self.problem = pybop.FittingProblem(
-            model=self.model, dataset=dataset, parameters=parameters, init_soc=0.5
+            model=self.model, dataset=dataset, parameters=parameters
         )
 
     def time_model_predict(self, model, parameter_set):
@@ -69,7 +70,9 @@ def time_model_predict(self, model, parameter_set):
             model (pybop.Model): The model class being benchmarked.
             parameter_set (str): The name of the parameter set being used.
         """
-        self.model.predict(inputs=self.inputs, t_eval=self.t_eval)
+        self.model.predict(
+            inputs=self.inputs, t_eval=self.t_eval, initial_state=self.init_state
+        )
 
     def time_model_simulate(self, model, parameter_set):
         """

benchmarks/benchmark_optim_construction.py

@@ -28,7 +28,7 @@ def setup(self, model, parameter_set, optimiser):
         model_instance = model(parameter_set=pybop.ParameterSet.pybamm(parameter_set))
 
         # Define fitting parameters
-        parameters = [
+        parameters = pybop.Parameters(
             pybop.Parameter(
                 "Negative electrode active material volume fraction",
                 prior=pybop.Gaussian(0.6, 0.02),
@@ -41,7 +41,7 @@ def setup(self, model, parameter_set, optimiser):
                 bounds=[0.375, 0.625],
                 initial_value=0.51,
             ),
-        ]
+        )
 
         # Generate synthetic data
         sigma = 0.001
@@ -75,7 +75,7 @@ def time_optimisation_construction(self, model, parameter_set, optimiser):
         Args:
             model (pybop.Model): The model class being benchmarked.
             parameter_set (str): The name of the parameter set being used.
-            optimiser (pybop.Optimiser): The optimizer class being used.
+            optimiser (pybop.Optimiser): The optimiser class being used.
         """
         self.optim = pybop.Optimisation(self.cost, optimiser=optimiser)
 
@@ -86,6 +86,6 @@ def time_cost_evaluate(self, model, parameter_set, optimiser):
         Args:
             model (pybop.Model): The model class being benchmarked.
             parameter_set (str): The name of the parameter set being used.
-            optimiser (pybop.Optimiser): The optimizer class being used.
+            optimiser (pybop.Optimiser): The optimiser class being used.
         """
         self.cost([0.63, 0.51])

benchmarks/benchmark_parameterisation.py

@@ -29,7 +29,7 @@ def setup(self, model, parameter_set, optimiser):
         Args:
             model (pybop.Model): The model class to be benchmarked.
             parameter_set (str): The name of the parameter set to be used.
-            optimiser (pybop.Optimiser): The optimizer class to be used.
+            optimiser (pybop.Optimiser): The optimiser class to be used.
         """
         # Set random seed
         set_random_seed()
@@ -45,7 +45,7 @@ def setup(self, model, parameter_set, optimiser):
         model_instance = model(parameter_set=params)
 
         # Define fitting parameters
-        parameters = [
+        parameters = pybop.Parameters(
             pybop.Parameter(
                 "Negative electrode active material volume fraction",
                 prior=pybop.Gaussian(0.55, 0.03),
@@ -56,7 +56,7 @@ def setup(self, model, parameter_set, optimiser):
                 prior=pybop.Gaussian(0.55, 0.03),
                 bounds=[0.375, 0.7],
             ),
-        ]
+        )
 
         # Generate synthetic data
         sigma = 0.003
@@ -110,7 +110,7 @@ def time_parameterisation(self, model, parameter_set, optimiser):
         Args:
             model (pybop.Model): The model class being benchmarked (unused).
             parameter_set (str): The name of the parameter set being used (unused).
-            optimiser (pybop.Optimiser): The optimizer class being used (unused).
+            optimiser (pybop.Optimiser): The optimiser class being used (unused).
         """
         self.optim.run()
 

benchmarks/benchmark_track_parameterisation.py

@@ -45,7 +45,7 @@ def setup(self, model, parameter_set, optimiser):
         model_instance = model(parameter_set=params)
 
         # Define fitting parameters
-        parameters = [
+        parameters = pybop.Parameters(
             pybop.Parameter(
                 "Negative electrode active material volume fraction",
                 prior=pybop.Gaussian(0.55, 0.03),
@@ -56,7 +56,7 @@ def setup(self, model, parameter_set, optimiser):
                 prior=pybop.Gaussian(0.55, 0.03),
                 bounds=[0.375, 0.7],
             ),
-        ]
+        )
 
         # Generate synthetic data
         sigma = 0.003

examples/notebooks/LG_M50_ECM/1-single-pulse-circuit-model.ipynb

@@ -1639,8 +1639,7 @@
    "outputs": [],
    "source": [
     "problem.set_target(dataset_two_pulse)\n",
-    "model.parameter_set[\"Initial SoC\"] = 0.8 - 0.0075\n",
-    "model.rebuild(dataset_two_pulse)"
+    "model.build(dataset=dataset_two_pulse, initial_state={\"Initial SoC\": 0.8 - 0.0075})"
    ]
   },
   {

examples/notebooks/equivalent_circuit_identification.ipynb

@@ -11,7 +11,7 @@
     "\n",
     "### Setting up the Environment\n",
     "\n",
-    "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:"
+    "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:"
    ]
   },
   {

examples/notebooks/multi_model_identification.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "### Setting up the Environment\n",
     "\n",
-    "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:"
+    "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:"
    ]
   },
   {
@@ -147,7 +147,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "synth_model.build(dataset, initial_state=initial_state)\n",
+    "synth_model.build(dataset=dataset, initial_state=initial_state)\n",
     "synth_model.signal = [\"Voltage [V]\"]\n",
     "values = synth_model.simulate(t_eval=t_eval, inputs={})"
    ]

examples/notebooks/multi_optimiser_identification.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "### Setting up the Environment\n",
     "\n",
-    "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:"
+    "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:"
    ]
   },
   {

examples/notebooks/optimiser_calibration.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "### Setting up the Environment\n",
     "\n",
-    "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:"
+    "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:"
    ]
   },
   {

examples/notebooks/pouch_cell_identification.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "### Setting up the Environment\n",
     "\n",
-    "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:"
+    "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:"
    ]
   },
   {