fix(rectification): restore classic eight methods and observe KP Placidus cusps

Co-authored-by: Cursor <cursoragent@cursor.com>

scripts/active_rectification_event_engine.py

@@ -44,6 +44,7 @@ import saham_daynight  # noqa: E402
 import special_lagnas  # noqa: E402
 import varga  # noqa: E402
 from scripts.rectification.refinement_packet import NAKSHATRA_SPAN  # noqa: E402
+from scripts.rectification.kp_cusp_observation import observe_kp_cusps  # noqa: E402
 
 AYANAMSA: Final = "lahiri"
 NODE_MODE: Final = "mean"
@@ -61,9 +62,12 @@ DOMAIN_CONFIG: Final[dict[EventDomain, DomainConfig]] = {
     "family": (("D12", "D7", "D3"), (3, 4, 5, 9)),
     # Dated appearance/marks: D1 lagna / 1st house only. Not a primary formula.
     "appearance": ((), (1,)),
+    # Occupation notes: D10 + D1 10th house, auxiliary, no type labels.
+    "occupation": (("D10",), (10,)),
 }
-AUXILIARY_DOMAINS: Final[frozenset[str]] = frozenset({"appearance"})
+AUXILIARY_DOMAINS: Final[frozenset[str]] = frozenset({"appearance", "occupation"})
 AUXILIARY_SCORE_FACTOR: Final = 0.4
+OBSERVATION_ONLY_LAYERS: Final[frozenset[str]] = frozenset({"KP_cusps"})
 
 
 class RectificationEventCalculationError(RuntimeError):
@@ -234,7 +238,11 @@ def _score_event(
         if sign_index is not None and _relative_house(sign_index, ascendant_index) in target_houses:
             rules.append(f"{label}_domain_house")
             points += weight
-    arudha_keys = ("A7", "UL") if event["domain"] == "relationship" else ("A10",) if event["domain"] == "career" else ()
+    arudha_keys = (
+        ("A7", "UL") if event["domain"] == "relationship"
+        else ("A10",) if event["domain"] in {"career", "occupation"}
+        else ()
+    )
     arudha_signs = {
         value.get("sign_idx") for key in arudha_keys
         if isinstance((value := arudha_padas.get(key)), dict) and isinstance(value.get("sign_idx"), int)
@@ -249,7 +257,11 @@ def _score_event(
     event_kind = event.get("event_kind", event["domain"])
     if event["domain"] in AUXILIARY_DOMAINS:
         points *= AUXILIARY_SCORE_FACTOR
-        rules.append("appearance_auxiliary_not_primary")
+        rules.append(
+            "occupation_auxiliary_not_primary"
+            if event["domain"] == "occupation"
+            else "appearance_auxiliary_not_primary"
+        )
     if not rules:
         rules.append("no_domain_activation")
     rules.append(f"event_kind:{event_kind}")
@@ -435,7 +447,7 @@ def build_candidate_static_context(
         for prefix in ("D2", "D3", "D4", "D5", "D7", "D9", "D10", "D11", "D12", "D24", "D30")
     }
     available_layers = ["D1"]
-    blocked_layers = ["KP_cusps"]
+    blocked_layers: list[str] = []
     varga_ascendants: dict[str, int] = {}
     for prefix, value in varga_charts.items():
         ascendant = (value or {}).get("Ascendant") or {}
@@ -471,6 +483,17 @@ def build_candidate_static_context(
     except (KeyError, TypeError, ValueError):
         blocked_layers.append("Shadbala")
 
+    birth_info = chart.get("birth_info") if isinstance(chart.get("birth_info"), dict) else {}
+    kp_snapshot = observe_kp_cusps(
+        birth_info.get("julian_day"),
+        float(request["lat"]),
+        float(request["lon"]),
+    )
+    if kp_snapshot.get("status") == "executed":
+        available_layers.append("KP_cusps")
+    else:
+        blocked_layers.append("KP_cusps")
+
     feature_payload = {
         "time": candidate_at.strftime("%H:%M"),
         "ascendant_degree": ascendant_longitude,
@@ -484,6 +507,8 @@ def build_candidate_static_context(
             sun_degree=planet_longitudes.get("Sun") if isinstance(planet_longitudes.get("Sun"), (int, float)) else None,
             moon_degree=planet_longitudes.get("Moon") if isinstance(planet_longitudes.get("Moon"), (int, float)) else None,
         ),
+        **(kp_snapshot.get("indices") or {}),
+        "kp_cusps": kp_snapshot,
         "varga_ascendants": varga_ascendants,
         "arudha_signs": arudha_signs,
         "available_layers": sorted(set(available_layers)),
@@ -585,7 +610,10 @@ def _candidate_row(
         "time": candidate_at.strftime("%H:%M"),
         "score": round(sum(item["points"] for item in evidence), 4),
         "evidence": evidence,
-        "missing_layers": sorted(set(missing_layers + context["feature"]["blocked_layers"])),
+        "missing_layers": sorted(set(
+            missing_layers
+            + [layer for layer in context["feature"]["blocked_layers"] if layer not in OBSERVATION_ONLY_LAYERS]
+        )),
     }