Fix route optimizer and group assignment; add test suite

Route building / optimization (tatami_masterplan.py):
- fast_total_time was permutation-invariant: it applied get_courses to
  group *values* instead of slots and ignored the permutation, so every
  ordering scored identically and the annealing optimized nothing. It now
  maps each rotation slot to its assigned group via the permutation.
- Replaced the broken next_permutation/simulated_annealing (enumerated n!
  orderings per iteration, fed unnormalized Boltzmann weights to
  np.random.choice -> ValueError, and returned the last random sample) with
  a standard neighbor-swap annealer that tracks and returns the best
  solution and handles <2 slots.
- Convert the reduced Timedelta matrix to float seconds before annealing
  (np.exp can't operate on Timedelta).

Group building (tatami_masterplan.py):
- assign_courses set each group's hosts (sorted by course) before all
  courses were assigned, so hosts whose course was still None got
  mis-ordered. Assign all courses first, then wire up hosts.
- get_masterplan no longer mutates the caller's participant list.

classes.py:
- Narrow casts on distance-matrix lookups to satisfy the mypy gate
  (pre-existing failures).

Tests:
- Add pytest suite (74 tests) covering the rotation topology, route cost
  and optimization, the domain model, the masterplan pipeline, and the
  Routes API wrapper (HTTP mocked). The cost cross-check caught the
  assign_courses ordering bug above.

tests/test_masterplan.py

@@ -0,0 +1,141 @@
+"""Integration tests for the masterplan pipeline (group building + assignment)."""
+
+import numpy as np
+import pytest
+
+from tatami.classes import Group
+from tatami.traveltimes import reduce_distance_matrix
+from tatami.tatami_masterplan import (
+    assign_courses,
+    fast_total_time,
+    get_after_party_group,
+    get_courses,
+    get_masterplan,
+    load_csv_to_participants,
+)
+from conftest import make_participants, make_timedelta_matrix
+
+
+def full_matrix(participants, after_party_group, seed=0):
+    """Synthetic uuid-indexed Timedelta matrix over participants + after party."""
+    all_people = participants + [after_party_group.main_member]
+    rng = np.random.default_rng(seed)
+    n = len(all_people)
+    seconds = rng.integers(60, 1200, size=(n, n)).astype(float)
+    np.fill_diagonal(seconds, 0)
+    return make_timedelta_matrix(all_people, seconds)
+
+
+class TestGetAfterPartyGroup:
+    def test_single_member_with_no_penalty(self):
+        group = get_after_party_group("party street 1")
+        assert len(group.members) == 1
+        assert group.main_member.address == "party street 1"
+        assert group.main_member.get_penalty().total_seconds() == 0
+
+
+class TestLoadCsv:
+    def test_parses_tab_separated_file(self, tmp_path):
+        csv = tmp_path / "config.csv"
+        csv.write_text(
+            "name\taddress\tphone\tkitchen_size\tallergies\n"
+            "Alice\tStreet 1\t111\t8\tnone\n"
+            "Bob\tStreet 2\t222\t5\tpeanuts\n"
+        )
+        participants = load_csv_to_participants(str(csv))
+        assert [p.name for p in participants] == ["Alice", "Bob"]
+        assert participants[0].address == "Street 1"
+        assert participants[1].kitchen_size == 5
+
+
+class TestAssignCourses:
+    def test_assigns_cycling_courses_and_hosts(self):
+        groups = [Group(members=[p]) for p in make_participants(6)]
+        courses = ["starter", "main", "dessert"] * 2
+        assign_courses(groups, courses)
+        assert [g.course for g in groups] == courses
+        for i, group in enumerate(groups):
+            expected_host_uuids = {groups[j].uuid for j in get_courses(i, len(groups))}
+            assert {h.uuid for h in group.hosts} == expected_host_uuids
+            assert group.uuid in {h.uuid for h in group.hosts}
+
+
+class TestGetMasterplan:
+    @pytest.mark.parametrize("n", [6, 12, 18])
+    def test_group_count_and_coverage(self, n):
+        participants = make_participants(n, kitchen_sizes=list(np.linspace(0, 10, n)))
+        after_party = get_after_party_group("party street")
+        matrix = full_matrix(participants, after_party)
+
+        group_dicts, participant_dicts = get_masterplan(
+            participants, matrix, after_party
+        )
+
+        expected_groups = 3 * (n // 6)
+        assert len(group_dicts) == expected_groups
+        assert len(participant_dicts) == n
+
+        # Every participant ends up in exactly one group.
+        assigned = [uuid for g in group_dicts for uuid in g["members"]]
+        assert len(assigned) == n
+        assert set(assigned) == {p.uuid for p in participants}
+
+    def test_topology_of_output(self):
+        participants = make_participants(18, kitchen_sizes=list(np.linspace(0, 10, 18)))
+        after_party = get_after_party_group("party street")
+        matrix = full_matrix(participants, after_party)
+
+        group_dicts, _ = get_masterplan(participants, matrix, after_party)
+        by_uuid = {g["uuid"]: g for g in group_dicts}
+
+        # Courses are balanced across the three slots.
+        course_counts = {}
+        for g in group_dicts:
+            course_counts[g["course"]] = course_counts.get(g["course"], 0) + 1
+        assert course_counts == {"starter": 3, "main": 3, "dessert": 3}
+
+        for g in group_dicts:
+            host_courses = sorted(by_uuid[h]["course"] for h in g["hosts"])
+            assert host_courses == ["dessert", "main", "starter"]
+            assert g["uuid"] in g["hosts"]  # a group hosts its own course
+
+    def test_does_not_mutate_input_participant_order(self):
+        participants = make_participants(12)
+        original_order = list(participants)
+        after_party = get_after_party_group("party street")
+        matrix = full_matrix(participants, after_party)
+        get_masterplan(participants, matrix, after_party)
+        assert participants == original_order
+
+
+class TestCostConsistency:
+    def test_fast_total_time_matches_group_get_total_time(self):
+        # The optimizer cost (fast_total_time on the penalty-baked reduced matrix)
+        # must equal the sum of per-group route times computed independently by
+        # Group.get_total_time on the raw matrix.
+        n = 6
+        participants = make_participants(n, kitchen_sizes=[10, 8, 6, 4, 2, 0])
+        after_party = get_after_party_group("party street")
+        groups = [Group(members=[p]) for p in participants]
+        courses = ["starter", "main", "dessert"] * (n // 3)
+        assign_courses(groups, courses)
+
+        full = full_matrix(participants, after_party, seed=5)
+
+        reduced = reduce_distance_matrix(full, [*groups, after_party])
+        reduced_seconds = np.array(
+            [[pd_to_seconds(x) for x in row] for row in reduced.to_numpy()],
+            dtype=float,
+        )
+        optimizer_cost = fast_total_time(reduced_seconds, list(range(n)))
+
+        independent_cost = sum(
+            g.get_total_time(full, after_party).total_seconds() for g in groups
+        )
+        assert optimizer_cost == pytest.approx(independent_cost)
+
+
+def pd_to_seconds(value):
+    import pandas as pd
+
+    return pd.Timedelta(value).total_seconds()