Адрес: {safe_addr}
" if safe_addr else "" + + def _price_cell(val: float | None) -> str: + if val is None: + return "| Комнатность | Доля | Кол-во (от target) | Целевая площадь, м² | +
|---|
Средневзвешенная цена benchmark: {weighted_price}
+Основано на {rec.based_on_obj_count} ЖК / {rec.based_on_total_deals} сделок
+Период данных: + {rec.data_window_start.strftime("%d.%m.%Y")} – {rec.data_window_end.strftime("%d.%m.%Y")} +
+ +| # | Комнаты | Площадь | Продажи/мес | +Ср. площадь, м² | Ср. цена, ₽/м² | Продано (окно) | +
|---|
+ Покрытие: {dq.objects_with_velocity_data} из + {dq.objects_total_in_radius} ЖК с данными velocity + ({dq.velocity_coverage_pct:.1f}%) +
++ Уверенность: + + {dq.confidence.upper()} + +
+ + + +""" + + pdf_bytes = HTML(string=html).write_pdf() + logger.info("Generated layout TZ PDF for cad %s: %d bytes", cad_num, len(pdf_bytes)) + return pdf_bytes diff --git a/backend/app/services/site_finder/weight_profiles.py b/backend/app/services/site_finder/weight_profiles.py index 7f3f0ad2..12259896 100644 --- a/backend/app/services/site_finder/weight_profiles.py +++ b/backend/app/services/site_finder/weight_profiles.py @@ -17,6 +17,7 @@ from __future__ import annotations import json import logging +import math from datetime import datetime from typing import Any @@ -25,7 +26,7 @@ from sqlalchemy import text logger = logging.getLogger(__name__) -# Allowed POI categories — mirrors _POI_WEIGHTS keys in api/v1/parcels.py +# Allowed POI categories — single source of truth; imported by api/v1/parcels.py ALLOWED_CATEGORIES: set[str] = { "school", "kindergarten", @@ -44,7 +45,7 @@ ALLOWED_CATEGORIES: set[str] = { MIN_WEIGHT: float = -2.0 MAX_WEIGHT: float = 3.0 -# System defaults — keep in sync with _POI_WEIGHTS in parcels.py +# System defaults — single source of truth; imported as _POI_WEIGHTS by api/v1/parcels.py _SYSTEM_POI_WEIGHTS: dict[str, float] = { "school": 1.5, "kindergarten": 1.5, @@ -115,7 +116,7 @@ def _validate_weights_dict(v: dict[str, float]) -> dict[str, float]: for k, w in v.items(): if not isinstance(w, int | float): raise ValueError(f"Weight for '{k}' must be number, got {type(w).__name__}") - if w < MIN_WEIGHT or w > MAX_WEIGHT: + if not math.isfinite(w) or w < MIN_WEIGHT or w > MAX_WEIGHT: raise ValueError(f"Weight for '{k}' = {w} out of bounds [{MIN_WEIGHT}, {MAX_WEIGHT}]") return v diff --git a/backend/tests/api/v1/test_analyze_inline_weights.py b/backend/tests/api/v1/test_analyze_inline_weights.py new file mode 100644 index 00000000..80a1a9d6 --- /dev/null +++ b/backend/tests/api/v1/test_analyze_inline_weights.py @@ -0,0 +1,327 @@ +"""Тесты для inline POI-weights в POST /api/v1/parcels/{cad_num}/analyze (#201). + +Покрывает: +1. POST /analyze без body → system defaults (no regression) +2. POST /analyze с inline weights → applied (source = "inline") +3. POST /analyze с невалидной категорией → 422 +4. POST /analyze с весом вне диапазона → 422 +5. POST /analyze с body.weights + profile_id → body.weights wins (priority) + +Стратегия mock: DB патчим через dependency_overrides, тяжёлые service-функции +(weather, velocity, dump и т.д.) патчим через unittest.mock.patch — чтобы не +дублировать все 18 db.execute call'ов в каждом тесте. +""" + +from __future__ import annotations + +from typing import Any +from unittest.mock import MagicMock, patch + +import pytest +from fastapi.testclient import TestClient + +from app.main import app + +# ── Константы ───────────────────────────────────────────────────────────────── + +_CAD = "66:41:0204016:10" +_WKT = "POLYGON((60.6 56.838, 60.61 56.838, 60.61 56.845, 60.6 56.845, 60.6 56.838))" +_GEOJSON = '{"type":"Polygon","coordinates":[[[60.6,56.838],[60.61,56.838]]]}' + + +# ── Mock factories ───────────────────────────────────────────────────────────── + + +def _make_mapping(data: dict[str, Any]) -> MagicMock: + """Создать mock-строку (mapping) с __getitem__ + .get() для dict-like доступа.""" + m = MagicMock() + m.__getitem__ = lambda self, k: data[k] + m.get = lambda k, default=None: data.get(k, default) + return m + + +def _make_db_for_analyze( + geom_found: bool = True, + district_found: bool = True, + poi_rows: list[Any] | None = None, +) -> MagicMock: + """Сконструировать mock DB Session для analyze_parcel. + + Порядок db.execute calls в analyze_parcel: + 0. UNION ALL geom + source → .mappings().first() + 1. WKT query → .mappings().first() + 2. District → .mappings().first() + 3. POI rows → .mappings().all() + 4. Competitor rows → .mappings().all() + 5. Pipeline rows → .mappings().all() + 6. Centroid lat/lon → .mappings().first() + 7. Noise rows → .mappings().all() + 8. Hydrology → .mappings().all() + 9. Utilities → .mappings().all() + 10. Market trend → .mappings().first() + 11. Zoning (begin_nested) → .mappings().first() + 12. Success recommendation (begin_nested) → .mappings().all() + 13. _geotech_risk (industrial count) → .scalar() + 14. _neighbors_summary (neighbor_rows) → .mappings().all() + 15. _neighbors_summary (overlap_row) → .mappings().first() + + begin_nested() — возвращаем context manager чтобы поддержать `with` statement. + """ + db = MagicMock() + + geom_row = ( + _make_mapping({"geom_geojson": _GEOJSON, "geom_wkb": None, "source": "cad_quarter"}) + if geom_found + else None + ) + wkt_row = _make_mapping({"wkt": _WKT}) if geom_found else None + district_row = ( + _make_mapping( + { + "district_name": "Октябрьский", + "median_price_per_m2": 120000, + "dist_to_center": 1500.0, + } + ) + if district_found + else None + ) + centroid_row = _make_mapping({"lat": 56.84, "lon": 60.605}) + + _poi_rows = poi_rows or [] + + # Счётчик вызовов execute — разводим first() / all() / scalar() по очерёдности + call_idx = [0] + # Ответы в порядке вызовов: + responses: list[Any] = [ + ("first", geom_row), # 0: geom UNION ALL + ("first", wkt_row), # 1: WKT + ("first", district_row), # 2: district + ("all", _poi_rows), # 3: POI rows + ("all", []), # 4: competitor rows + ("all", []), # 5: pipeline rows + ("first", centroid_row), # 6: centroid + ("all", []), # 7: noise rows + ("all", []), # 8: hydrology rows + ("all", []), # 9: utilities rows + ("first", None), # 10: market trend + ("first", None), # 11: zoning (inside begin_nested) + ("all", []), # 12: success recommendation (inside begin_nested) + ("scalar", 0), # 13: geotech_risk industrial count + ("all", []), # 14: neighbors + ("first", None), # 15: overlap + ] + + def _execute_side_effect(*args: Any, **kwargs: Any) -> MagicMock: + idx = call_idx[0] + call_idx[0] += 1 + if idx >= len(responses): + # Безопасный fallback для непредусмотренных вызовов + r = MagicMock() + r.mappings.return_value.first.return_value = None + r.mappings.return_value.all.return_value = [] + r.scalar.return_value = 0 + return r + kind, data = responses[idx] + r = MagicMock() + r.mappings.return_value.first.return_value = data + r.mappings.return_value.all.return_value = data if isinstance(data, list) else [] + r.scalar.return_value = data if kind == "scalar" else 0 + return r + + db.execute.side_effect = _execute_side_effect + + # begin_nested() → context manager, остальные execute внутри него проходят + # через тот же side_effect (because db.execute is the same mock). + ctx = MagicMock() + ctx.__enter__ = MagicMock(return_value=ctx) + ctx.__exit__ = MagicMock(return_value=False) + db.begin_nested.return_value = ctx + + return db + + +def _override_db(db: MagicMock): + def _get_db_override(): + yield db + + return _get_db_override + + +# Патчим тяжёлые внешние вызовы (weather / velocity / nspd-dump), +# чтобы тесты не зависели от сети и не требовали полного mock DB. +_PATCHES = [ + patch("app.api.v1.parcels._fetch_air_quality_sync", return_value=None), + patch("app.api.v1.parcels._fetch_weather_sync", return_value=None), + patch("app.api.v1.parcels._fetch_seasonal_weather_sync", return_value=None), + patch( + "app.api.v1.parcels.get_quarter_dump_data", + return_value={ + "nspd_zoning": None, + "nspd_zouit_overlaps": [], + "nspd_engineering_nearby": [], + "nspd_dump": {"available": False, "stale": False, "harvest_triggered": False}, + }, + ), + patch("app.api.v1.parcels.compute_velocity", return_value=None), + patch("app.api.v1.parcels.compute_gate_verdict", return_value={"verdict": "unknown"}), +] + + +def _start_patches() -> list[Any]: + started = [p.start() for p in _PATCHES] + return started + + +def _stop_patches() -> None: + for p in _PATCHES: + p.stop() + + +# ── Тесты ───────────────────────────────────────────────────────────────────── + + +def test_analyze_no_body_uses_system_defaults() -> None: + """POST /analyze без body → source = 'system', нет регрессии.""" + from app.core.db import get_db + + db = _make_db_for_analyze() + app.dependency_overrides[get_db] = _override_db(db) + _start_patches() + try: + client = TestClient(app) + resp = client.post(f"/api/v1/parcels/{_CAD}/analyze") + assert resp.status_code == 200, resp.text + body = resp.json() + assert "weights_profile" in body + assert body["weights_profile"]["source"] == "system" + assert body["weights_profile"]["inline_weights"] is None + finally: + app.dependency_overrides.clear() + _stop_patches() + + +def test_analyze_inline_weights_applied() -> None: + """POST /analyze с body.weights → source = 'inline', веса применены.""" + from app.core.db import get_db + + db = _make_db_for_analyze() + app.dependency_overrides[get_db] = _override_db(db) + _start_patches() + try: + client = TestClient(app) + resp = client.post( + f"/api/v1/parcels/{_CAD}/analyze", + json={"weights": {"kindergarten": 2.5}}, + ) + assert resp.status_code == 200, resp.text + body = resp.json() + wp = body["weights_profile"] + assert wp["source"] == "inline" + assert wp["inline_weights"] == {"kindergarten": 2.5} + # applied weights содержат inline override поверх defaults + assert wp["weights_applied"]["kindergarten"] == pytest.approx(2.5) + finally: + app.dependency_overrides.clear() + _stop_patches() + + +def test_analyze_invalid_category_returns_422() -> None: + """POST /analyze с невалидной POI-категорией → 422.""" + from app.core.db import get_db + + db = _make_db_for_analyze() + app.dependency_overrides[get_db] = _override_db(db) + _start_patches() + try: + client = TestClient(app) + resp = client.post( + f"/api/v1/parcels/{_CAD}/analyze", + json={"weights": {"nonexistent_category": 1.0}}, + ) + assert resp.status_code == 422, resp.text + detail = resp.json()["detail"] + assert "nonexistent_category" in detail + finally: + app.dependency_overrides.clear() + _stop_patches() + + +def test_analyze_weight_out_of_range_returns_422() -> None: + """POST /analyze с весом вне [-2, 3] → 422.""" + from app.core.db import get_db + + db = _make_db_for_analyze() + app.dependency_overrides[get_db] = _override_db(db) + _start_patches() + try: + client = TestClient(app) + # Слишком большой вес + resp = client.post( + f"/api/v1/parcels/{_CAD}/analyze", + json={"weights": {"school": 99.9}}, + ) + assert resp.status_code == 422, resp.text + detail = resp.json()["detail"] + assert "school" in detail + + # Слишком маленький вес + resp2 = client.post( + f"/api/v1/parcels/{_CAD}/analyze", + json={"weights": {"park": -5.0}}, + ) + assert resp2.status_code == 422, resp2.text + assert "park" in resp2.json()["detail"] + finally: + app.dependency_overrides.clear() + _stop_patches() + + +def test_inline_weights_rejects_nan() -> None: + """NaN weight должен вернуть 422, а не propagate в score.""" + from app.core.db import get_db + + db = _make_db_for_analyze() + app.dependency_overrides[get_db] = _override_db(db) + _start_patches() + try: + client = TestClient(app) + # Отправляем raw JSON с NaN — httpx.Client не умеет encode float('nan'), + # поэтому используем content= с явным bytes-телом. + raw_body = b'{"weights": {"school": NaN}}' + resp = client.post( + f"/api/v1/parcels/{_CAD}/analyze", + content=raw_body, + headers={"Content-Type": "application/json"}, + ) + assert ( + resp.status_code == 422 + ), f"Ожидали 422 для NaN-weight, получили {resp.status_code}: {resp.text}" + finally: + app.dependency_overrides.clear() + _stop_patches() + + +def test_analyze_inline_weights_beats_profile_id() -> None: + """body.weights + profile_id → body.weights имеет приоритет (source = 'inline').""" + from app.core.db import get_db + + db = _make_db_for_analyze() + app.dependency_overrides[get_db] = _override_db(db) + _start_patches() + try: + client = TestClient(app) + # Передаём и profile_id=1, и inline weights — inline должен победить + resp = client.post( + f"/api/v1/parcels/{_CAD}/analyze?profile_id=1", + json={"weights": {"metro_stop": 2.0}}, + ) + assert resp.status_code == 200, resp.text + wp = resp.json()["weights_profile"] + assert wp["source"] == "inline", f"Ожидали source='inline', получили '{wp['source']}'" + assert wp["weights_applied"]["metro_stop"] == pytest.approx(2.0) + # profile_id всё ещё присутствует в ответе для трассировки + assert wp["profile_id"] == 1 + finally: + app.dependency_overrides.clear() + _stop_patches() diff --git a/backend/tests/test_layout_tz_pdf.py b/backend/tests/test_layout_tz_pdf.py new file mode 100644 index 00000000..1fbe2dd2 --- /dev/null +++ b/backend/tests/test_layout_tz_pdf.py @@ -0,0 +1,136 @@ +"""Tests для layout_tz_pdf renderer (Issue #113 PR D). + +WeasyPrint requires native GTK/Pango/GObject shared libraries. These are present +in the Docker container (Linux) but absent on Windows dev machines. All tests in +this module are skipped automatically when the native libs are unavailable. +""" + +import datetime as dt + +import pytest + +# Attempt to import the module under test; skip entire module if native libs missing. +try: + from app.services.exporters.layout_tz_pdf import render_layout_tz_pdf +except (OSError, ImportError) as _e: # GTK libs missing on Windows, or weasyprint not installed + pytest.skip(f"WeasyPrint deps missing: {_e}", allow_module_level=True) + +from app.schemas.parcel import ( + BestLayoutsResponse, + LayoutDataQuality, + LayoutTzMixRow, + LayoutTzRecommendation, + TopLayoutRow, +) + + +def _sample_response() -> BestLayoutsResponse: + return BestLayoutsResponse( + top_layouts=[ + TopLayoutRow( + rank=1, + room_bucket="1", + area_bin="25-40", + signature="1__25-40", + competitor_obj_ids=[1234, 5678], + competitor_count=2, + total_sold_in_window=67, + velocity_per_month=8.4, + avg_price_per_m2_rub=148000.0, + avg_area_m2=38.5, + supply_units_in_radius=312, + sold_pct_of_supply=21.5, + ), + TopLayoutRow( + rank=2, + room_bucket="studio", + area_bin="<25", + signature="studio__<25", + competitor_obj_ids=[1234], + competitor_count=1, + total_sold_in_window=40, + velocity_per_month=5.0, + avg_price_per_m2_rub=160000.0, + avg_area_m2=22.0, + supply_units_in_radius=100, + sold_pct_of_supply=40.0, + ), + ], + recommendation_for_tz=LayoutTzRecommendation( + rationale_text="Test rationale текст с кириллицей", + mix=[ + LayoutTzMixRow(room_bucket="studio", pct=10, abs_units=30, avg_target_area_m2=22.0), + LayoutTzMixRow(room_bucket="1", pct=60, abs_units=180, avg_target_area_m2=38.5), + LayoutTzMixRow(room_bucket="2", pct=30, abs_units=90, avg_target_area_m2=55.0), + ], + weighted_avg_price_per_m2_rub=152000.0, + based_on_obj_count=5, + based_on_total_deals=107, + data_window_start=dt.date(2026, 2, 1), + data_window_end=dt.date(2026, 5, 1), + ), + data_quality=LayoutDataQuality( + objects_with_velocity_data=5, + objects_total_in_radius=8, + velocity_coverage_pct=62.5, + confidence="medium", + ), + ) + + +def test_pdf_renders_non_empty_bytes() -> None: + pdf = render_layout_tz_pdf( + _sample_response(), + cad_num="66:41:0204016:10", + radius_km=1.0, + time_window="last_quarter", + ) + assert len(pdf) > 1000 # PDF минимум ~1KB + + +def test_pdf_starts_with_pdf_magic() -> None: + pdf = render_layout_tz_pdf( + _sample_response(), + cad_num="66:41:0204016:10", + radius_km=1.0, + time_window="last_quarter", + ) + assert pdf[:4] == b"%PDF" + + +def test_pdf_renders_cyrillic_correctly() -> None: + """Smoke — WeasyPrint должен handle кириллический rationale_text без UnicodeEncodeError.""" + response = _sample_response() + pdf = render_layout_tz_pdf( + response, + cad_num="66:41:0303161:42", + radius_km=1.5, + time_window="last_year", + ) + # Embedded text может быть compressed, но без exception = OK + assert len(pdf) > 1000 + + +def test_pdf_handles_empty_top_layouts() -> None: + response = _sample_response() + response.top_layouts = [] + pdf = render_layout_tz_pdf( + response, + cad_num="66:41:0204016:10", + radius_km=1.0, + time_window="last_quarter", + ) + assert pdf[:4] == b"%PDF" + + +def test_pdf_handles_null_avg_price() -> None: + """avg_price_per_m2_rub=None (ЖК не покрыт Objective) → должно рендериться как '—'.""" + response = _sample_response() + response.top_layouts[0].avg_price_per_m2_rub = None + pdf = render_layout_tz_pdf( + response, + cad_num="66:41:0204016:10", + radius_km=1.0, + time_window="last_quarter", + ) + assert pdf[:4] == b"%PDF" diff --git a/frontend/src/app/site-finder/page.tsx b/frontend/src/app/site-finder/page.tsx index faeddd61..5f914ca2 100644 --- a/frontend/src/app/site-finder/page.tsx +++ b/frontend/src/app/site-finder/page.tsx @@ -147,14 +147,18 @@ function SiteFinderContent() { function handleAnalyze(cadNum: string) { setIsochrones(undefined); setTab("overview"); + // Priority: named profile → user default profile → inline draft weights. + // When activeProfileId is set, backend uses that profile (ignores inline). + // When no profile is selected, pass currentWeights as inline so draft + // slider values are always respected even without a saved profile (#201). mutate({ cad: cadNum, options: activeProfileId != null ? { profileId: activeProfileId } : profileUserId - ? { profileUserId } - : undefined, + ? { profileUserId, weights: currentWeights } + : { weights: currentWeights }, }); } @@ -163,10 +167,20 @@ function SiteFinderContent() { profileId: number | null, ) { setCurrentWeights(weights); - // Store the active profile id so we can pass it to the analyze call. - // If user edited weights without saving a named profile, profileId=null - // and backend will use system defaults (sub-PR 5 would enable inline weights). setActiveProfileId(profileId); + // Re-analyze with the new weights if a parcel is already loaded (#201). + if (data?.cad_num) { + setIsochrones(undefined); + mutate({ + cad: data.cad_num, + options: + profileId != null + ? { profileId } + : profileUserId + ? { profileUserId, weights } + : { weights }, + }); + } } // Derive KPI values from data diff --git a/frontend/src/components/site-finder/BestLayoutsBlock.tsx b/frontend/src/components/site-finder/BestLayoutsBlock.tsx new file mode 100644 index 00000000..63224696 --- /dev/null +++ b/frontend/src/components/site-finder/BestLayoutsBlock.tsx @@ -0,0 +1,761 @@ +"use client"; + +import { useState } from "react"; +import { useBestLayouts } from "@/hooks/useBestLayouts"; +import { API_BASE_URL } from "@/lib/api"; +import type { + BestLayoutsRequest, + BestLayoutsResponse, + Confidence, + LayoutTzMixRow, + TimeWindow, + TopLayoutRow, +} from "@/types/best-layouts"; + +// ── Constants ───────────────────────────────────────────────────────────────── + +const CONFIDENCE_STYLES: Record< + Confidence, + { bg: string; fg: string; label: string } +> = { + high: { bg: "#dcfce7", fg: "#166534", label: "Высокое" }, + medium: { bg: "#fef3c7", fg: "#854d0e", label: "Среднее" }, + low: { bg: "#fee2e2", fg: "#991b1b", label: "Низкое" }, +}; + +const TIME_WINDOW_LABELS: Record| + {h} + | + ))} +||||||
|---|---|---|---|---|---|---|
| + {row.rank} + | ++ {ROOM_BUCKET_LABELS[row.room_bucket] ?? row.room_bucket} + | ++ {row.area_bin} м² + | ++ {row.velocity_per_month.toFixed(2)} + | ++ {row.avg_area_m2.toFixed(1)} + | ++ {row.avg_price_per_m2_rub != null + ? Math.round(row.avg_price_per_m2_rub).toLocaleString( + "ru-RU", + ) + : "—"} + | ++ {row.sold_pct_of_supply != null + ? `${(row.sold_pct_of_supply ?? 0).toFixed(0)}%` + : "—"} + | +
| + {h} + | + ))} +|||
|---|---|---|---|
| + {ROOM_BUCKET_LABELS[row.room_bucket] ?? row.room_bucket} + | ++ {row.pct}% + | ++ {row.abs_units != null + ? row.abs_units.toLocaleString("ru-RU") + : "—"} + | ++ {row.avg_target_area_m2 != null + ? row.avg_target_area_m2.toFixed(1) + : "—"} + | +
+ {rec.rationale_text} +
+ + {/* Unit-mix bar chart */} + {rec.mix.length > 0 && ( +