From 2b7601f155a4a1941ed7e2121439f0a99984a0a7 Mon Sep 17 00:00:00 2001
From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com>
Date: Thu, 27 Mar 2025 20:08:10 +0000
Subject: [PATCH 1/2] Adjust domain-based weighting

---
 pybop/costs/fitting_costs.py | 18 +++++++-----------
 tests/unit/test_cost.py      |  3 +++
 2 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/pybop/costs/fitting_costs.py b/pybop/costs/fitting_costs.py
index 906c38b20..ca2572090 100644
--- a/pybop/costs/fitting_costs.py
+++ b/pybop/costs/fitting_costs.py
@@ -32,19 +32,15 @@ def __init__(self, problem, weighting: Union[str, np.ndarray] = None):
             # Normalise the residuals by the domain spacing (for a uniform domain,
             # this is the same as a uniform weighting)
             domain_data = self.problem.domain_data
-            n_domain_data = len(domain_data)
             domain_spacing = domain_data[1:] - domain_data[:-1]
-            self.weighting = (
-                np.concatenate(
-                    (
-                        [domain_spacing[0]],
-                        (domain_spacing[1:] + domain_spacing[:-1]) / 2,
-                        [domain_spacing[-1]],
-                    )
+            mean_spacing = np.mean(domain_spacing)
+            self.weighting = np.concatenate(
+                (
+                    [(mean_spacing + domain_spacing[0]) / 2],
+                    (domain_spacing[1:] + domain_spacing[:-1]) / 2,
+                    [(domain_spacing[-1] + mean_spacing) / 2],
                 )
-                * (n_domain_data - 1)
-                / (domain_data[-1] - domain_data[0])
-            )
+            ) * ((len(domain_data) - 1) / (domain_data[-1] - domain_data[0]))
         else:
             self.weighting = np.asarray(weighting)
 
diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py
index c57c1e642..532b9b375 100644
--- a/tests/unit/test_cost.py
+++ b/tests/unit/test_cost.py
@@ -251,6 +251,9 @@ def test_error_weighting(
         np.testing.assert_allclose(e, eR, rtol=1e-2, atol=1e-9)
         np.testing.assert_allclose(de, deR, rtol=1e-2, atol=1e-9)
 
+        # Check that the sum (and therefore mean) are the same as an even weighting
+        np.testing.assert_allclose(np.sum(costR.weighting), len(costR.weighting))
+
         # Check gradient calculation using finite difference
         delta = 1e-6 * x[0]
         cost_right = costR(x[0] + delta / 2)

From f6bb9d0da3bde89b801089d841dc3725b583c22b Mon Sep 17 00:00:00 2001
From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com>
Date: Fri, 28 Mar 2025 11:14:50 +0000
Subject: [PATCH 2/2] Fix typo

---
 tests/unit/test_cost.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py
index 532b9b375..17a2772d3 100644
--- a/tests/unit/test_cost.py
+++ b/tests/unit/test_cost.py
@@ -238,7 +238,7 @@ def test_error_weighting(
         np.testing.assert_allclose(e, eE)
         np.testing.assert_allclose(de, deE)
 
-        # Test that domain-basd weighting also matches for evenly spaced data
+        # Test that domain-based weighting also matches for evenly spaced data
         costD = cost_class(problem, weighting="domain")
         eD, deD = costD(x, calculate_grad=True)
         np.testing.assert_allclose(e, eD)