test(rectification): cover upstream acceptance

tests/test_active_rectification_selector.py

@@ -1,7 +1,11 @@
 from __future__ import annotations
 
-from scripts.active_rectification_questions import build_questionnaire
+import hashlib
+import json
+
+from scripts.active_rectification_questions import build_questionnaire, score_answers
 from scripts.active_rectification_selector import select_next_questions
+from scripts.rectification.scoring_service import score_from_matrix
 
 
 def test_selector_asks_one_question_and_ranks_by_separation() -> None:
@@ -117,6 +121,22 @@ def _minute_question(question_id: str, domain: str, layer: str) -> dict[str, obj
     }
 
 
+def _minute_window(*changing_layers: str) -> dict[str, object]:
+    return {
+        "candidate_count": 3,
+        "transitions": [{"between": ["10:19", "10:20"]}],
+        "rows": [
+            {
+                "divisional_ascendants": {
+                    layer: {"sign": sign if layer in changing_layers else "Leo"}
+                    for layer in ("D9", "D10", "D24")
+                }
+            }
+            for sign in ("Aries", "Taurus", "Gemini")
+        ],
+    }
+
+
 def test_selector_changes_with_remaining_candidate_window() -> None:
     questions = [
         _minute_question("relationship_split", "relationship", "D9"),
@@ -154,3 +174,126 @@ def test_selector_changes_with_remaining_candidate_window() -> None:
     assert second["selected_questions"][0]["id"] == "career_split"
     assert first["selected_questions"][0]["minute_relevance"] > 0
     assert second["selected_questions"][0]["minute_relevance"] > 0
+
+
+def test_fictional_adaptive_interview_records_each_selected_question_and_minute_relevance() -> None:
+    questions = [
+        {**_minute_question("career_split", "career", "D10"), "round": 1},
+        {**_minute_question("education_split", "education", "D24"), "round": 2},
+        {**_minute_question("relationship_split", "relationship", "D9"), "round": 3},
+    ]
+    questionnaire = {
+        "question_bank": questions,
+        "candidate_scan": {"candidate_count": 3, "minute_scan": _minute_window("D9", "D10", "D24")},
+    }
+    answers: dict[str, str] = {}
+    transcript = []
+
+    for round_number, answer in enumerate(("A", "B", "C"), start=1):
+        selection = select_next_questions(questionnaire, answers, limit=1)
+        selected = selection["selected_questions"][0]
+        transcript.append({
+            "round": round_number,
+            "question": selected["id"],
+            "minute_relevance": selected["minute_relevance"],
+        })
+        answers[selected["id"]] = answer
+
+    assert transcript == [
+        {"round": 1, "question": "career_split", "minute_relevance": 4.35},
+        {"round": 2, "question": "education_split", "minute_relevance": 4.35},
+        {"round": 3, "question": "relationship_split", "minute_relevance": 4.35},
+    ]
+    assert select_next_questions(questionnaire, answers)["stop"] is True
+
+
+def test_selector_changes_do_not_change_legacy_or_v5_score_bytes() -> None:
+    questions = [
+        {**_minute_question("relationship_split", "relationship", "D9"), "round": 1},
+        {**_minute_question("career_split", "career", "D10"), "round": 2},
+    ]
+    answers = {"relationship_split": "A", "career_split": "C"}
+    questionnaires = [
+        {
+            "questions": questions,
+            "candidate_scan": {"candidate_count": 3, "minute_scan": _minute_window("D9")},
+        },
+        {
+            "questions": questions,
+            "candidate_scan": {"candidate_count": 3, "minute_scan": _minute_window("D10")},
+        },
+    ]
+    selections = [select_next_questions(questionnaire, {}) for questionnaire in questionnaires]
+    assert [selection["selected_questions"][0]["id"] for selection in selections] == [
+        "relationship_split",
+        "career_split",
+    ]
+
+    legacy_bytes = []
+    for questionnaire in questionnaires:
+        scored = score_answers(questionnaire, answers)
+        legacy_contract = {
+            key: value
+            for key, value in scored.items()
+            if key not in {"calculation", "next_round_selection"}
+        }
+        legacy_bytes.append(json.dumps(
+            legacy_contract,
+            ensure_ascii=True,
+            sort_keys=True,
+            separators=(",", ":"),
+        ).encode())
+    assert legacy_bytes[0] == legacy_bytes[1]
+    assert hashlib.sha256(legacy_bytes[0]).hexdigest() == "bb1a300606458357b2b8d94a487272f15d82c6f0b51847c660ba20153773fb63"
+
+    v5_request = {
+        "birth_date": "1997-08-08",
+        "start_time": "05:13",
+        "end_time": "05:14",
+        "lat": 36.419,
+        "lon": 114.213,
+        "tz": 8.0,
+        "events": [
+            {
+                "id": "00000000-0000-4000-8000-000000000001",
+                "domain": "education",
+                "event_kind": "education_start",
+                "date_start": "2016-09-01",
+                "date_end": "2016-09-30",
+                "precision": "month",
+                "summary": "fictional enrollment",
+                "subject": "self",
+            },
+            {
+                "id": "00000000-0000-4000-8000-000000000002",
+                "domain": "career",
+                "event_kind": "career_entry",
+                "date_start": "2020-07-01",
+                "date_end": "2020-07-31",
+                "precision": "month",
+                "summary": "fictional first role",
+                "subject": "self",
+            },
+        ],
+    }
+    built = {
+        "candidate_times": ["05:13", "05:14"],
+        "matrix": {
+            v5_request["events"][0]["id"]: {
+                "05:13": {"points": 4.0, "rule_ids": ["D24:fixture"]},
+                "05:14": {"points": 1.0, "rule_ids": ["D24:fixture"]},
+            },
+            v5_request["events"][1]["id"]: {
+                "05:13": {"points": -1.0, "rule_ids": ["D10:fixture"]},
+                "05:14": {"points": 3.0, "rule_ids": ["D10:fixture"]},
+            },
+        },
+        "missing_layers": [],
+    }
+    v5_bytes = json.dumps(
+        score_from_matrix(v5_request, built),
+        ensure_ascii=True,
+        sort_keys=True,
+        separators=(",", ":"),
+    ).encode()
+    assert hashlib.sha256(v5_bytes).hexdigest() == "23171c47b9746f4b0a440c9b9ac6d8401d622672cd0d6d780789334b732e9d8f"

tests/test_candidate_time_sensitivity_scan.py

@@ -30,3 +30,28 @@ def test_scanner_reports_real_divisional_transitions(monkeypatch):
     assert report["input_contract"]["settings"]["node_mode"] == "mean"
     assert report["rows"][0]["input_fingerprint"] != report["rows"][1]["input_fingerprint"]
     assert report["stability_contract"]["minute_confirmation_allowed"] is False
+
+
+def test_scanner_keeps_raman_default_and_applies_explicit_lahiri() -> None:
+    birth = {
+        "year": 2000,
+        "month": 1,
+        "day": 1,
+        "hour": 12,
+        "minute": 1,
+        "lat": 25.04,
+        "lon": 121.56,
+        "tz": 8,
+    }
+
+    default_report = scanner.scan_candidate_times(birth, uncertainty_minutes=1)
+    lahiri_report = scanner.scan_candidate_times(
+        {**birth, "ayanamsa": "lahiri"},
+        uncertainty_minutes=1,
+    )
+    default_center = next(row for row in default_report["rows"] if row["offset_minutes"] == 0)
+    lahiri_center = next(row for row in lahiri_report["rows"] if row["offset_minutes"] == 0)
+
+    assert default_report["input_contract"]["settings"]["ayanamsa"] == "raman"
+    assert lahiri_report["input_contract"]["settings"]["ayanamsa"] == "lahiri"
+    assert abs(default_center["d1_longitude"] - lahiri_center["d1_longitude"]) > 1