Add M1 GitHub runner and update scheduled tests

.github/workflows/scheduled_tests.yaml

@@ -41,7 +41,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
       - uses: actions/checkout@v4

.github/workflows/test_on_push.yaml

@@ -29,12 +29,22 @@ jobs:
 
 
   build:
+    needs: style
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
         python-version: ["3.8", "3.9", "3.10", "3.11"]
+        exclude: # We run the coverage tests on Ubuntu with Python 3.11
+          - os: ubuntu-latest
+            python-version: "3.11"
+        # Include MacOS M-series Runners
+        include:
+          - os: macos-14
+            python-version: "3.10"
+          - os: macos-14
+            python-version: "3.11"
 
     steps:
       - uses: actions/checkout@v4

CHANGELOG.md

@@ -2,13 +2,15 @@
 
 ## Features
 
+- [#187](https://github.com/pybop-team/PyBOP/issues/187) - Adds M1 Github runner to `test_on_push` workflow, updt. self-hosted supported python versions in scheduled tests.
 - [#118](https://github.com/pybop-team/PyBOP/issues/118) - Adds example jupyter notebooks.
 - [#151](https://github.com/pybop-team/PyBOP/issues/151) - Adds a standalone version of the Problem class.
 - [#12](https://github.com/pybop-team/PyBOP/issues/12) - Adds initial implementation of an Observer class and an unscented Kalman filter.
 - [#190](https://github.com/pybop-team/PyBOP/issues/190) - Adds a second example design cost, namely the VolumetricEnergyDensity.
 
 ## Bug Fixes
 
+- [#196](https://github.com/pybop-team/PyBOP/issues/196) - Fixes failing observer cost tests.
 - [#63](https://github.com/pybop-team/PyBOP/issues/63) - Removes NLOpt Optimiser from future releases. This is to support deployment to the Apple M-Series platform.
 - [#164](https://github.com/pybop-team/PyBOP/issues/164) - Fixes convergence issues with gradient-based optimisers, changes default `model.check_params()` to allow infeasible solutions during optimisation iterations. Adds a feasibility check on the optimal parameters.
 

tests/unit/test_cost.py

@@ -27,7 +27,7 @@ def parameters(self):
     def experiment(self):
         return pybop.Experiment(
             [
-                ("Discharge at 1C for 5 minutes (5 second period)"),
+                ("Discharge at 1C for 10 minutes (20 second period)"),
             ]
         )
 
@@ -60,7 +60,9 @@ def signal(self):
     def problem(self, model, parameters, dataset, signal, x0, request):
         cut_off = request.param
         model.parameter_set.update({"Lower voltage cut-off [V]": cut_off})
-        problem = pybop.FittingProblem(model, parameters, dataset, signal=signal, x0=x0)
+        problem = pybop.FittingProblem(
+            model, parameters, dataset, signal=signal, x0=x0, init_soc=1.0
+        )
         return problem
 
     @pytest.fixture(
@@ -112,7 +114,7 @@ def test_design_base(self, problem):
 
     @pytest.mark.unit
     def test_costs(self, cost):
-        higher_cost = cost([0.5])
+        higher_cost = cost([0.55])
         lower_cost = cost([0.52])
         assert higher_cost > lower_cost or (
             higher_cost == lower_cost and higher_cost == np.inf