diff --git a/backend/app/services/forecasting/report_assembler.py b/backend/app/services/forecasting/report_assembler.py index 9dd27051..44ed966e 100644 --- a/backend/app/services/forecasting/report_assembler.py +++ b/backend/app/services/forecasting/report_assembler.py @@ -189,10 +189,10 @@ def _domrf_coverage(analyze: dict[str, Any], supply_layers: dict[str, Any] | Non """ if supply_layers is not None: coverage = supply_layers.get("domrf_coverage") - if isinstance(coverage, (int, float)) and not isinstance(coverage, bool): + if isinstance(coverage, int | float) and not isinstance(coverage, bool): return _clamp_fraction(float(coverage)) pct = analyze.get("market_data_coverage_pct") - if isinstance(pct, (int, float)) and not isinstance(pct, bool): + if isinstance(pct, int | float) and not isinstance(pct, bool): return _clamp_fraction(float(pct) / 100.0) return None @@ -223,6 +223,19 @@ def _history_months( return None +def _deal_count_months(market_metrics: dict[str, Any] | None) -> int | None: + """Окно наблюдения для deal_count (мес) — для deal_count_months #990. PURE. + + Читает тот же `market_metrics.window_months` (§9.2), что и `_history_months` — + именно за это окно считается n_sold. Нет → None (#990 пропускает суффикс «за N мес»). + """ + if market_metrics is not None: + window = market_metrics.get("window_months") + if isinstance(window, int) and window > 0: + return window + return None + + def _confounded(forecasts: Sequence[dict[str, Any]]) -> bool: """Пересекает ли окно прогноза шок-период — для confounded #990. PURE. @@ -293,10 +306,10 @@ def _primary_deficit_index(forecasts: Sequence[dict[str, Any]]) -> float | None: ) if primary is not None and primary.get("deficit_index") is not None: di = primary["deficit_index"] - return float(di) if isinstance(di, (int, float)) and not isinstance(di, bool) else None + return float(di) if isinstance(di, int | float) and not isinstance(di, bool) else None for f in forecasts: di = f.get("deficit_index") - if isinstance(di, (int, float)) and not isinstance(di, bool): + if isinstance(di, int | float) and not isinstance(di, bool): return float(di) return None @@ -314,10 +327,10 @@ def _primary_months_of_inventory(forecasts: Sequence[dict[str, Any]]) -> float | ) if primary is not None and primary.get("months_of_inventory") is not None: moi = primary["months_of_inventory"] - return float(moi) if isinstance(moi, (int, float)) and not isinstance(moi, bool) else None + return float(moi) if isinstance(moi, int | float) and not isinstance(moi, bool) else None for f in forecasts: moi = f.get("months_of_inventory") - if isinstance(moi, (int, float)) and not isinstance(moi, bool): + if isinstance(moi, int | float) and not isinstance(moi, bool): return float(moi) return None @@ -342,7 +355,7 @@ def _overall_score(product_scores: dict[str, Any] | None) -> float | None: if not isinstance(product_scores, dict): return None overall = product_scores.get("overall") - if isinstance(overall, (int, float)) and not isinstance(overall, bool): + if isinstance(overall, int | float) and not isinstance(overall, bool): return float(overall) return None @@ -388,10 +401,10 @@ def _market_now_summary( parts: list[str] = [] if market_metrics is not None: velocity = market_metrics.get("unit_velocity") - if isinstance(velocity, (int, float)) and not isinstance(velocity, bool): + if isinstance(velocity, int | float) and not isinstance(velocity, bool): parts.append(f"абсорбция ~{round(float(velocity), 1)} ед./мес") avg_price = analyze.get("market_avg_price_per_m2") - if isinstance(avg_price, (int, float)) and not isinstance(avg_price, bool): + if isinstance(avg_price, int | float) and not isinstance(avg_price, bool): parts.append(f"средняя цена ~{round(float(avg_price)):,} ₽/м²".replace(",", " ")) # #1634: НЕ через _analog_count — он отдаёт market_metrics.obj_count (число ЖК во # всей district-wide/микрорайонной выборке §9.2), что НЕ равно «конкурентов рядом». @@ -618,6 +631,7 @@ def _build_confidence( market_metrics, future_supply, forecasts, product_scores, special_indices ), deal_count=_deal_count(analyze, market_metrics), + deal_count_months=_deal_count_months(market_metrics), analog_count=_analog_count(analyze, market_metrics), domrf_coverage=_domrf_coverage(analyze, supply_layers), history_months=_history_months(market_metrics, forecasts),