fix: updates scipy bounds with keep_feasible, clipping groundtruth fo…

…r integration tests, small fixes on examples

examples/scripts/ecm_tau_constraints.py

@@ -134,7 +134,7 @@ def check_params(
 )
 
 # Fitting parameters
-parameters = [
+parameters = pybop.Parameters(
     pybop.Parameter(
         "R0 [Ohm]",
         prior=pybop.Gaussian(0.0002, 0.0001),
@@ -150,7 +150,7 @@ def check_params(
         prior=pybop.Gaussian(10000, 2500),
         bounds=[2500, 5e4],
     ),
-]
+)
 
 sigma = 0.001
 t_eval = np.arange(0, 900, 3)
@@ -172,6 +172,7 @@ def check_params(
 optim = pybop.XNES(
     cost,
     allow_infeasible_solutions=False,
+    max_iterations=100,
 )
 
 x, final_cost = optim.run()

pybop/optimisers/scipy_optimisers.py

@@ -2,7 +2,7 @@
 from typing import Union
 
 import numpy as np
-from scipy.optimize import OptimizeResult, differential_evolution, minimize
+from scipy.optimize import Bounds, OptimizeResult, differential_evolution, minimize
 
 from pybop import BaseOptimiser, Result
 
@@ -55,12 +55,18 @@ def _sanitise_inputs(self):
 
         # Convert bounds to SciPy format
         if isinstance(self.bounds, dict):
-            self._scipy_bounds = [
-                (lower, upper)
-                for lower, upper in zip(self.bounds["lower"], self.bounds["upper"])
-            ]
-        else:
+            self._scipy_bounds = Bounds(
+                self.bounds["lower"], self.bounds["upper"], True
+            )
+        elif isinstance(self.bounds, list):
+            lb, ub = zip(*self.bounds)
+            self._scipy_bounds = Bounds(lb, ub, True)
+        elif isinstance(self.bounds, Bounds) or self.bounds is None:
             self._scipy_bounds = self.bounds
+        else:
+            raise TypeError(
+                "Bounds provided must be either type dict, list or SciPy.optimize.bounds object."
+            )
 
     def _run(self):
         """
@@ -286,9 +292,8 @@ def _set_up_optimiser(self):
         if self._scipy_bounds is None:
             raise ValueError("Bounds must be specified for differential_evolution.")
         else:
-            if not all(
-                np.isfinite(value) for pair in self._scipy_bounds for value in pair
-            ):
+            bnds = self._scipy_bounds
+            if not (np.isfinite(bnds.lb).all() and np.isfinite(bnds.ub).all()):
                 raise ValueError("Bounds must be specified for differential_evolution.")
 
         # Apply default maxiter and tolerance

tests/integration/test_thevenin_parameterisation.py

@@ -11,8 +11,10 @@ class TestTheveninParameterisation:
 
     @pytest.fixture(autouse=True)
     def setup(self):
-        self.ground_truth = np.asarray([0.05, 0.05]) + np.random.normal(
-            loc=0.0, scale=0.01, size=2
+        self.ground_truth = np.clip(
+            np.asarray([0.05, 0.05]) + np.random.normal(loc=0.0, scale=0.01, size=2),
+            a_min=0.0,
+            a_max=0.1,
         )
 
     @pytest.fixture

tests/integration/test_transformation.py

@@ -11,8 +11,10 @@ class TestTransformation:
 
     @pytest.fixture(autouse=True)
     def setup(self):
-        self.ground_truth = np.array([0.5, 0.2]) + np.random.normal(
-            loc=0.0, scale=0.05, size=2
+        self.ground_truth = np.clip(
+            np.array([0.5, 0.1]) + np.random.normal(loc=0.0, scale=0.05, size=2),
+            a_min=[0.375, 0.02],
+            a_max=[0.7, 0.5],
         )
 
     @pytest.fixture
@@ -39,7 +41,7 @@ def parameters(self):
             pybop.Parameter(
                 "Positive electrode conductivity [S.m-1]",
                 prior=pybop.Uniform(0.05, 0.45),
-                bounds=[0.04, 0.5],
+                bounds=[0.02, 0.5],
                 transformation=pybop.LogTransformation(),
             ),
         )
@@ -78,10 +80,12 @@ def cost(self, model, parameters, init_soc):
     @pytest.mark.integration
     def test_spm_optimisers(self, optimiser, cost):
         x0 = cost.parameters.initial_value()
+        sigma0 = None if optimiser is pybop.AdamW else 0.2
         optim = optimiser(
             cost=cost,
+            sigma0=sigma0,
             max_unchanged_iterations=35,
-            max_iterations=125,
+            max_iterations=150,
         )
 
         initial_cost = optim.cost(x0)

tests/integration/test_weighted_cost.py

@@ -12,8 +12,10 @@ class TestWeightedCost:
     @pytest.fixture(autouse=True)
     def setup(self):
         self.sigma0 = 0.002
-        self.ground_truth = np.asarray([0.55, 0.55]) + np.random.normal(
-            loc=0.0, scale=0.05, size=2
+        self.ground_truth = np.clip(
+            np.asarray([0.55, 0.55]) + np.random.normal(loc=0.0, scale=0.05, size=2),
+            a_min=0.4,
+            a_max=0.75,
         )
 
     @pytest.fixture