Add random seed, updt branch target, increment python for publish job…

… in workflow

.github/workflows/periodic_benchmarks.yaml

@@ -10,7 +10,6 @@
 # - Publish website
 name: Benchmarks
 on:
-  push:
   # Everyday at 12 pm UTC
   schedule:
     - cron: "0 12 * * *"
@@ -62,10 +61,10 @@ jobs:
     runs-on: ubuntu-latest
     if: github.repository == 'pybop-team/PyBOP'
     steps:
-      - name: Set up Python 3.11
+      - name: Set up Python 3.12
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
 
       - name: Install asv
         run: pip install asv

asv.conf.json

@@ -8,7 +8,7 @@
         "python -m build --wheel -o {build_cache_dir} {build_dir}"
     ],
     "default_benchmark_timeout": 180,
-    "branches": ["179-add-airspeed-velocity-for-automated-benchmarking"],
+    "branches": ["develop"],
     "environment_type": "virtualenv",
     "matrix": {
         "req":{

benchmarks/README.md

@@ -48,7 +48,7 @@ With `asv` installed and your benchmarks set up, you can now run benchmarks usin
 To run all benchmarks in your python env:
 
 ```bash
-asv run --python=same
+asv run
 ```
 
 This will test the current state of your codebase by default. You can specify a range of commits to run benchmarks against by appending a commit range to the command, like so:
@@ -57,6 +57,12 @@ This will test the current state of your codebase by default. You can specify a
 asv run <commit-hash-1>..<commit-hash-2>
 ```
 
+For quick benchmarking, pass the `--quick` argument to `asv run`. This runs each benchmark once and returns the singular value.
+
+```bash
+asv run --quick
+```
+
 ### Running Specific Benchmarks
 
 To run a specific benchmark, use:

benchmarks/benchmark_model.py

@@ -1,5 +1,6 @@
 import pybop
 import numpy as np
+from .benchmark_utils import set_random_seed
 
 
 class BenchmarkModel:
@@ -17,6 +18,9 @@ def setup(self, model, parameter_set):
             model (pybop.Model): The model class to be benchmarked.
             parameter_set (str): The name of the parameter set to be used.
         """
+        # Set random seed
+        set_random_seed()
+
         # Create model instance
         self.model = model(parameter_set=pybop.ParameterSet.pybamm(parameter_set))
 

benchmarks/benchmark_optim_construction.py

@@ -1,5 +1,6 @@
 import pybop
 import numpy as np
+from .benchmark_utils import set_random_seed
 
 
 class BenchmarkOptimisationConstruction:
@@ -19,6 +20,9 @@ def setup(self, model, parameter_set, optimiser):
             parameter_set (str): The name of the parameter set to be used.
             optimiser (pybop.Optimiser): The optimizer class to be used.
         """
+        # Set random seed
+        set_random_seed()
+
         # Create model instance
         model_instance = model(parameter_set=pybop.ParameterSet.pybamm(parameter_set))
 
@@ -74,7 +78,7 @@ def time_optimisation_construction(self, model, parameter_set, optimiser):
         """
         self.optim = pybop.Optimisation(self.cost, optimiser=optimiser)
 
-    def time_cost(self, model, parameter_set, optimiser):
+    def time_cost_evaluate(self, model, parameter_set, optimiser):
         """
         Benchmark the cost function evaluation.
 

benchmarks/benchmark_utils.py

@@ -0,0 +1,5 @@
+import numpy as np
+
+
+def set_random_seed(seed_value=8):
+    np.random.seed(seed_value)

benchmarks/parameterisation_benchmark.py

@@ -1,8 +1,9 @@
 import pybop
 import numpy as np
+from .benchmark_utils import set_random_seed
 
 
-class ParameterisationBenchmark:
+class BenchmarkParameterisation:
     param_names = ["model", "parameter_set", "optimiser"]
     params = [
         [pybop.lithium_ion.SPM, pybop.lithium_ion.SPMe],
@@ -29,6 +30,9 @@ def setup(self, model, parameter_set, optimiser):
             parameter_set (str): The name of the parameter set to be used.
             optimiser (pybop.Optimiser): The optimizer class to be used.
         """
+        # Set random seed
+        set_random_seed()
+
         # Create model instance
         model_instance = model(parameter_set=pybop.ParameterSet.pybamm(parameter_set))