7eb686bd7d
Closes #105 (Phase 3 geocoding — отдельный issue). Phase 4: 'ekburg-permits-monthly' crontab(0 2 1 * *) → tasks.refresh_all Phase 5: /analyze returns engineering.recent_permits_in_quarter + permits_summary (rns/rve counts/area/units, by_developer top-3) Filter: quarter prefix (3-segment cadastral match). Spatial 500m radius — после Phase 3 geocoding. Tests: test_analyze_recent_permits.py (3 mock), comment updates на inline_weights/market_price (shifted execute ordering).
286 lines
8.9 KiB
Python
286 lines
8.9 KiB
Python
import datetime as dt
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from typing import Any, Literal
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from pydantic import BaseModel, Field
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# ── #105 Phase 5: Recent permits schemas ──────────────────────────────────────
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class RecentPermit(BaseModel):
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"""Одно строительное разрешение (РНС или РВЭ) из ekburg_construction_permits."""
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permit_type: str
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permit_number: str
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issue_date: str | None
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developer_name: str | None
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developer_inn: str | None
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object_name: str | None
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object_type: str | None
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construction_address: str | None
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total_area_sqm: float | None
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units_count: int | None
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class PermitsSummary(BaseModel):
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"""Агрегированная сводка по разрешениям в квартале."""
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rns_count: int
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rve_count: int
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rns_total_area_sqm: float
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rve_total_units: int
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by_developer: list[dict[str, Any]]
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# ── Connection points schemas (issue #115) ────────────────────────────────────
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class EngineeringStructure(BaseModel):
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name: str | None
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type: str | None
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cad_num: str | None
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distance_to_boundary_m: float
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geometry_geojson: dict[str, Any]
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readable_address: str | None
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raw_props: dict[str, Any]
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source: str
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class ZouitOverlap(BaseModel):
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reg_numb_border: str | None
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type_zone: str | None
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subcategory: int | None
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intersects_parcel: bool
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geometry_geojson: dict[str, Any]
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raw_props: dict[str, Any]
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source: str
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class ConnectionPointsSummary(BaseModel):
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nearest_structure_distance_m: float | None
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in_protection_zone: bool
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protection_zones_intersecting: int
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total_structures_in_radius: int
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class ConnectionPointsResponse(BaseModel):
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engineering_structures: list[EngineeringStructure]
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zouit_engineering_overlaps: list[ZouitOverlap]
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summary: ConnectionPointsSummary
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dump_available: bool
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dump_fetched_at: str | None
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# ── Parcel search/detail schemas ──────────────────────────────────────────────
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class ParcelFilter(BaseModel):
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vri: list[str] | None = None
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area_min_sqm: float | None = None
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area_max_sqm: float | None = None
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region: str | None = None
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polygon_geojson: dict[str, Any] | None = None
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text_search: str | None = None
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class ScoringWeights(BaseModel):
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distance_to_center: float = 0.4
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poi_density: float = 0.3
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neighborhood_price: float = 0.3
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class ParcelSearchRequest(BaseModel):
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filters: ParcelFilter = Field(default_factory=ParcelFilter)
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weights: ScoringWeights = Field(default_factory=ScoringWeights)
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limit: int = 50
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class ScoreBreakdown(BaseModel):
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total: float
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distance_to_center: float
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poi_density: float
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neighborhood_price: float
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class ParcelSummary(BaseModel):
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id: str
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cadastral_number: str
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area_sqm: float
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vri: str
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address: str | None
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score: ScoreBreakdown
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class ParcelSearchResponse(BaseModel):
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items: list[ParcelSummary]
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total: int
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class ParcelDetail(ParcelSummary):
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geometry_geojson: dict[str, Any]
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enrichment: dict[str, Any]
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# ── Competitors endpoint schemas ──────────────────────────────────────────────
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TimeWindow = Literal["last_month", "last_quarter", "last_year"]
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ObjClassFilter = Literal["economy", "comfort", "business"]
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class CompetitorsRequest(BaseModel):
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radius_km: float = Field(default=1.0, ge=0.1, le=1.5)
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time_window: TimeWindow = "last_quarter"
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obj_class_filter: ObjClassFilter | None = None
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exclude_obj_ids: list[int] = Field(default_factory=list)
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class Competitor(BaseModel):
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obj_id: int
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comm_name: str | None
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dev_name: str | None
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obj_class: str | None
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distance_m: float
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lat: float
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lng: float
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stage: str | None
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flats_total: int | None
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flats_sold: int | None
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sold_pct: float | None
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velocity_per_month: float
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avg_price_per_m2: float | None
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is_active: bool
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class CompetitorsSummary(BaseModel):
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total_competitors: int
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active_count: int
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weighted_avg_velocity: float
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radius_km: float
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time_window: str
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class CompetitorsResponse(BaseModel):
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competitors: list[Competitor]
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summary: CompetitorsSummary
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# ── Layout analysis (Issue #113) ───────────────────────────────────────────
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class LayoutSignature(BaseModel):
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"""Минимальная сигнатура планировки = (room_bucket, area_bin).
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Phase 2.1: layout_type/balcony_count в БД нет, ждут B2B Объектив (#52).
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"""
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room_bucket: Literal["studio", "1", "2", "3", "4+"]
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area_bin: Literal["<25", "25-40", "40-60", "60-80", "80-100", "100+"]
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class BestLayoutsRequest(BaseModel):
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"""Параметры запроса top-планировок в радиусе вокруг участка."""
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radius_km: float = Field(default=1.0, ge=0.1, le=1.5)
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time_window: Literal["last_month", "last_quarter", "last_year"] = "last_quarter"
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filter_competitor_obj_ids: list[int] | None = None
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exclude_competitor_obj_ids: list[int] = Field(default_factory=list)
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min_velocity_per_month: float = Field(default=0.5, ge=0.0, le=100.0)
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obj_class_filter: Literal["economy", "comfort", "business"] | None = None
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target_total_flats: int | None = Field(default=None, ge=1, le=10000)
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class TopLayoutRow(BaseModel):
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"""Одна строка top-planirovok ranking'а."""
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rank: int
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room_bucket: str
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area_bin: str
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signature: str
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competitor_obj_ids: list[int]
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competitor_count: int
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total_sold_in_window: int
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velocity_per_month: float
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avg_price_per_m2_rub: float | None # NULL если objective не покрывает obj
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avg_area_m2: float
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supply_units_in_radius: int
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sold_pct_of_supply: float | None # NULL если supply=0
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class LayoutTzMixRow(BaseModel):
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"""Строка рекомендации unit-mix для ТЗ."""
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room_bucket: str
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pct: int # 0..100, sum total = 100
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abs_units: int | None # NULL если target_total_flats не задан
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avg_target_area_m2: float | None
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class LayoutTzRecommendation(BaseModel):
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"""Рекомендация для ТЗ на проектирование."""
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rationale_text: str
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mix: list[LayoutTzMixRow]
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weighted_avg_price_per_m2_rub: float | None
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based_on_obj_count: int
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based_on_total_deals: int
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data_window_start: dt.date
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data_window_end: dt.date
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class LayoutDataQuality(BaseModel):
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"""Метаданные качества данных (coverage)."""
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objects_with_velocity_data: int
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objects_total_in_radius: int
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velocity_coverage_pct: float
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confidence: Literal["high", "medium", "low"]
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class BestLayoutsResponse(BaseModel):
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"""Ответ /best-layouts endpoint'а."""
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top_layouts: list[TopLayoutRow]
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recommendation_for_tz: LayoutTzRecommendation
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data_quality: LayoutDataQuality
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# ── Analyze endpoint market price (#33) ──────────────────────────────────────
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class MarketPrice(BaseModel):
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"""Ценовая статистика квартала из mv_quarter_price_per_m2 (Issue #33).
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Источник: rosreestr_deals, фильтр realestate_type_code='002001003000' (ДДУ),
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скользящее окно 24 мес., 30K–800K руб/м², HAVING >= 3 сделок.
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"""
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p25: float | None = None
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median: float | None = None
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p75: float | None = None
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mean: float | None = None
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deals_count: int = 0
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median_6m: float | None = None
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median_12m: float | None = None
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median_24m: float | None = None
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last_deal_date: str | None = None # ISO date
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source: Literal["quarter_mv", "no_data"] = "no_data"
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# ── Analyze endpoint inline weights (#201) ────────────────────────────────────
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class AnalyzeRequest(BaseModel):
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"""Опциональное тело запроса POST /analyze.
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Позволяет передать inline POI-веса напрямую в запросе без сохранения
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профиля. Если задан weights — применяется с наивысшим приоритетом
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(выше profile_id и user default).
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"""
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weights: dict[str, float] | None = Field(
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default=None,
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description=(
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"Inline POI weights override (категория → weight). "
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"Если задан — применяется к scoring, без обязательного profile save. "
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"Validated против ALLOWED_CATEGORIES + MIN_WEIGHT/MAX_WEIGHT."
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),
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)
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