Add tie-break coverage for tolerance-based tuple alias matching

Agent-Logs-Url: https://github.com/DHI/modelskill/sessions/1128e01b-f2da-4282-97c2-453c5bff0482

Co-authored-by: jpalm3r <28826351+jpalm3r@users.noreply.github.com>

Author: Copilot

src/modelskill/model/network.py

@@ -151,7 +151,7 @@ def __init__(
     def __repr__(self) -> str:
         return f"<{self.__class__.__name__}>: {self.name}"
 
-    _CHAINAGE_TOLERANCE = 1e-3  # Tolerance in the same units as source-network edge/breakpoint distances.
+    _CHAINAGE_TOLERANCE = 1e-3  # Tolerance in source-network distance units (e.g., meters if chainage is in meters).
 
     @property
     def time(self) -> pd.DatetimeIndex:
@@ -239,7 +239,8 @@ def _resolve_alias(self, alias: str | tuple[str, float]) -> int | None:
         Breakpoint tuple aliases are matched first by exact key lookup and then
         by edge ID and distance within ``_CHAINAGE_TOLERANCE``. If multiple
         candidates are within tolerance, the closest distance is selected; ties
-        are broken by choosing the smallest node ID.
+        are broken by choosing the smallest node ID. Distance units are the
+        same as the network chainage units.
         """
         if alias in self._alias_map:
             return self._alias_map[alias]

tests/test_network.py

@@ -710,6 +710,23 @@ def test_extract_with_tuple_breakpoint_uses_closest_within_tolerance(
         extracted = nmr.extract(obs)
         assert extracted.node == node_b
 
+    def test_extract_with_tuple_breakpoint_tie_uses_smallest_node_id(
+        self, sample_network, sample_node_data
+    ):
+        nmr = NetworkModelResult(sample_network)
+        base_distance = 10.0
+        tol = NetworkModelResult._CHAINAGE_TOLERANCE
+        node_a = int(sample_network.find(node="123"))
+        node_b = int(sample_network.find(node="456"))
+        nmr._alias_map[("reach_test", base_distance + 4e-4)] = node_a
+        nmr._alias_map[("reach_test", base_distance + 8e-4)] = node_b
+
+        obs = NodeObservation(
+            sample_node_data, at=("reach_test", base_distance + tol * 0.6)
+        )
+        extracted = nmr.extract(obs)
+        assert extracted.node == min(node_a, node_b)
+
     def test_extract_tuple_alias_wrong_key_raises(self, sample_network, sample_node_data):
         nmr = NetworkModelResult(sample_network)
         obs = NodeObservation(sample_node_data, at=("nonexistent_edge", 0.0))