Merge branch 'develop', updt __init__.py order, add Parameters type h…

…ints

.github/workflows/periodic_benchmarks.yaml

@@ -10,9 +10,9 @@
 # - Publish website
 name: Benchmarks
 on:
-  # Everyday at 12 pm UTC
+  # Every Monday and Thursday at 12 pm UTC
   schedule:
-    - cron: "0 12 * * *"
+    - cron: "0 12 * * 1,4"
   # Make it possible to trigger the
   # workflow manually
   workflow_dispatch:

.github/workflows/scheduled_tests.yaml

@@ -6,10 +6,9 @@ on:
     branches:
       - main
 
-  # runs every day at 09:00 and 15:00 UTC
+  # runs every day at 09:00 UTC
   schedule:
     - cron: '0 9 * * *'
-    - cron: '0 15 * * *'
 
 # Check noxfile.py for associated environment variables
 env:

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.4.5"
+    rev: "v0.4.7"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

CHANGELOG.md

@@ -21,6 +21,7 @@
 
 ## Bug Fixes
 
+- [#337](https://github.com/pybop-team/PyBOP/issues/337) - Restores benchmarks, relaxes CI schedule for benchmarks and scheduled tests.
 - [#231](https://github.com/pybop-team/PyBOP/issues/231) - Allows passing of keyword arguments to PyBaMM models and disables build on initialisation.
 - [#321](https://github.com/pybop-team/PyBOP/pull/321) - Improves `integration/test_spm_parameterisation.py` stability, adds flakly pytest plugin, and `test_thevenin_parameterisation.py` integration test.
 - [#330](https://github.com/pybop-team/PyBOP/issues/330) - Fixes implementation of default plotting options.

benchmarks/benchmark_parameterisation.py

@@ -82,12 +82,26 @@ def setup(self, model, parameter_set, optimiser):
         # Create cost function
         cost = pybop.SumSquaredError(problem=problem)
 
-        # Create optimization instance
-        self.optim = pybop.Optimisation(cost, optimiser=optimiser)
+        # Create optimization instance and set options for consistent benchmarking
         if optimiser in [pybop.GradientDescent]:
-            self.optim.optimiser.set_learning_rate(
-                0.008
-            )  # Compromise between stability & performance
+            self.optim = pybop.Optimisation(
+                cost,
+                optimiser=optimiser,
+                max_iterations=250,
+                max_unchanged_iterations=25,
+                threshold=1e-5,
+                min_iterations=2,
+                learning_rate=0.008,  # Compromise between stability & performance
+            )
+        else:
+            self.optim = pybop.Optimisation(
+                cost,
+                optimiser=optimiser,
+                max_iterations=250,
+                max_unchanged_iterations=25,
+                threshold=1e-5,
+                min_iterations=2,
+            )
 
     def time_parameterisation(self, model, parameter_set, optimiser):
         """
@@ -99,10 +113,6 @@ def time_parameterisation(self, model, parameter_set, optimiser):
             parameter_set (str): The name of the parameter set being used (unused).
             optimiser (pybop.Optimiser): The optimizer class being used (unused).
         """
-        # Set optimizer options for consistent benchmarking
-        self.optim.set_max_unchanged_iterations(iterations=25, threshold=1e-5)
-        self.optim.set_max_iterations(250)
-        self.optim.set_min_iterations(2)
         self.optim.run()
 
     def time_optimiser_ask(self, model, parameter_set, optimiser):
@@ -115,4 +125,4 @@ def time_optimiser_ask(self, model, parameter_set, optimiser):
             optimiser (pybop.Optimiser): The optimizer class being used.
         """
         if optimiser not in [pybop.SciPyMinimize, pybop.SciPyDifferentialEvolution]:
-            self.optim.optimiser.ask()
+            self.optim.pints_optimiser.ask()

benchmarks/benchmark_track_parameterisation.py

@@ -82,12 +82,26 @@ def setup(self, model, parameter_set, optimiser):
         # Create cost function
         cost = pybop.SumSquaredError(problem=problem)
 
-        # Create optimization instance
-        self.optim = pybop.Optimisation(cost, optimiser=optimiser)
+        # Create optimization instance and set options for consistent benchmarking
         if optimiser in [pybop.GradientDescent]:
-            self.optim.optimiser.set_learning_rate(
-                0.008
-            )  # Compromise between stability & performance
+            self.optim = pybop.Optimisation(
+                cost,
+                optimiser=optimiser,
+                max_iterations=250,
+                max_unchanged_iterations=25,
+                threshold=1e-5,
+                min_iterations=2,
+                learning_rate=0.008,  # Compromise between stability & performance
+            )
+        else:
+            self.optim = pybop.Optimisation(
+                cost,
+                optimiser=optimiser,
+                max_iterations=250,
+                max_unchanged_iterations=25,
+                threshold=1e-5,
+                min_iterations=2,
+            )
 
         # Track output results
         self.x = self.results_tracking(model, parameter_set, optimiser)
@@ -110,10 +124,5 @@ def results_tracking(self, model, parameter_set, optimiser):
             parameter_set (str): The name of the parameter set being used (unused).
             optimiser (pybop.Optimiser): The optimizer class being used (unused).
         """
-
-        # Set optimizer options for consistent benchmarking
-        self.optim.set_max_unchanged_iterations(iterations=25, threshold=1e-5)
-        self.optim.set_max_iterations(250)
-        self.optim.set_min_iterations(2)
         x, _ = self.optim.run()
         return x

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

@@ -1467,7 +1467,7 @@
    "id": "bf63b4f9-de38-4e70-9472-1de4973a0954",
    "metadata": {},
    "source": [
-    "In this example, we are going to try to fit all five parameters at once. To do this, we define a `pybop.parameter` for each fitting parameter as,"
+    "In this example, we are going to try to fit all five parameters at once. To do this, we define a `pybop.Parameter` for each fitting parameter and compile them in pybop.Parameters,"
    ]
   },
   {
@@ -1484,7 +1484,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"R0 [Ohm]\",\n",
     "        prior=pybop.Gaussian(0.005, 0.0001),\n",
@@ -1510,7 +1510,7 @@
     "        prior=pybop.Gaussian(3000, 2500),\n",
     "        bounds=[0.5, 1e4],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/notebooks/equivalent_circuit_identification.ipynb

@@ -239,7 +239,7 @@
    "id": "bf63b4f9-de38-4e70-9472-1de4973a0954",
    "metadata": {},
    "source": [
-    "In this example, we are going to try to fit all five parameters at once. This isn't recommend for real-life application as identifiablity is challenging to guarantee with this large a parameter space. To do this, we define the `pybop.parameters` as,"
+    "In this example, we are going to try to fit all five parameters at once. This isn't recommend for real-life application as identifiablity is challenging to guarantee with this large a parameter space. To do this, we define the `pybop.Parameters` as,"
    ]
   },
   {
@@ -256,7 +256,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"R0 [Ohm]\",\n",
     "        prior=pybop.Gaussian(0.0002, 0.0001),\n",
@@ -278,11 +278,11 @@
     "        bounds=[2500, 5e4],\n",
     "    ),\n",
     "    pybop.Parameter(\n",
-    "        \"C1 [F]\",\n",
+    "        \"C2 [F]\",\n",
     "        prior=pybop.Gaussian(10000, 2500),\n",
     "        bounds=[2500, 5e4],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/notebooks/multi_model_identification.ipynb

@@ -3729,7 +3729,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"Positive electrode thickness [m]\",\n",
     "        prior=pybop.Gaussian(7.56e-05, 0.05e-05),\n",
@@ -3742,7 +3742,7 @@
     "        bounds=[65e-06, 90e-06],\n",
     "        true_value=parameter_set[\"Negative electrode thickness [m]\"],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/notebooks/multi_optimiser_identification.ipynb

@@ -259,7 +259,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"Negative electrode active material volume fraction\",\n",
     "        prior=pybop.Gaussian(0.6, 0.02),\n",
@@ -270,7 +270,7 @@
     "        prior=pybop.Gaussian(0.48, 0.02),\n",
     "        bounds=[0.4, 0.7],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/notebooks/optimiser_calibration.ipynb

@@ -232,7 +232,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"Negative electrode active material volume fraction\",\n",
     "        prior=pybop.Gaussian(0.7, 0.025),\n",
@@ -243,7 +243,7 @@
     "        prior=pybop.Gaussian(0.6, 0.025),\n",
     "        bounds=[0.5, 0.8],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/notebooks/optimiser_interface.ipynb

@@ -100,13 +100,11 @@
     ")\n",
     "\n",
     "# Define the parameters\n",
-    "parameters = [\n",
-    "    pybop.Parameter(\n",
-    "        \"R0 [Ohm]\",\n",
-    "        prior=pybop.Gaussian(0.0002, 0.0001),\n",
-    "        bounds=[1e-4, 1e-2],\n",
-    "    )\n",
-    "]\n",
+    "parameters = pybop.Parameter(\n",
+    "    \"R0 [Ohm]\",\n",
+    "    prior=pybop.Gaussian(0.0002, 0.0001),\n",
+    "    bounds=[1e-4, 1e-2],\n",
+    ")\n",
     "\n",
     "# Generate synthetic data\n",
     "t_eval = np.arange(0, 900, 2)\n",

examples/notebooks/pouch_cell_identification.ipynb

@@ -323,7 +323,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"Negative electrode active material volume fraction\",\n",
     "        prior=pybop.Gaussian(0.7, 0.05),\n",
@@ -334,7 +334,7 @@
     "        prior=pybop.Gaussian(0.58, 0.05),\n",
     "        bounds=[0.5, 0.8],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/notebooks/spm_electrode_design.ipynb

@@ -143,7 +143,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"Positive electrode thickness [m]\",\n",
     "        prior=pybop.Gaussian(7.56e-05, 0.05e-05),\n",
@@ -154,7 +154,7 @@
     "        prior=pybop.Gaussian(5.22e-06, 0.05e-06),\n",
     "        bounds=[2e-06, 9e-06],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/scripts/BPX_spm.py

@@ -10,7 +10,7 @@
 model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
 # Fitting parameters
-parameters = [
+parameters = pybop.Parameters(
     pybop.Parameter(
         "Negative particle radius [m]",
         prior=pybop.Gaussian(6e-06, 0.1e-6),
@@ -23,7 +23,7 @@
         bounds=[1e-7, 9e-7],
         true_value=parameter_set["Positive particle radius [m]"],
     ),
-]
+)
 
 # Generate data
 sigma = 0.001
@@ -47,13 +47,7 @@
 
 # Run the optimisation
 x, final_cost = optim.run()
-print(
-    "True parameters:",
-    [
-        parameters[0].true_value,
-        parameters[1].true_value,
-    ],
-)
+print("True parameters:", parameters.true_value())
 print("Estimated parameters:", x)
 
 # Plot the timeseries output

examples/scripts/ecm_CMAES.py

@@ -45,7 +45,7 @@
 )
 
 # Fitting parameters
-parameters = [
+parameters = pybop.Parameters(
     pybop.Parameter(
         "R0 [Ohm]",
         prior=pybop.Gaussian(0.0002, 0.0001),
@@ -56,7 +56,7 @@
         prior=pybop.Gaussian(0.0001, 0.0001),
         bounds=[1e-5, 1e-2],
     ),
-]
+)
 
 sigma = 0.001
 t_eval = np.arange(0, 900, 3)