render_report_docx (python-docx) mirrors report_md/report_pdf section order &
content, reuses report_pdf pure helpers (DRY), graceful on thin/empty report.
Widen /forecast/export format Literal to include docx → Word attachment.
Add python-docx dep + regenerate uv.lock (uv sync --frozen passes). Part of #959.
render_report_telegram_summary (pure, no new deps, DRY-reuses report_pdf str
helpers) + `tg` format on GET /{cad}/forecast/export → inline text/plain snippet
(no attachment, copy-paste-ready). md/json unchanged; no-run 404, bad format 422.
Graceful on thin/empty reports. Part of EPIC #959.
render_report_markdown (pure, no new runtime deps) reuses report_pdf's str
helpers (DRY), + GET /{cad_num}/forecast/export?format=md|json. No forecast run
→ 404; graceful on thin/empty reports; GFM-safe table escaping. PDF/XLSX already
existed; this adds the cheapest no-dep formats. Part of EPIC #959.
#992: attach response_model=AnalyzeResponse to POST /{cad_num}/analyze. Model
uses extra="allow" so any result_payload key not explicitly modeled is preserved
in the 200 response (no silent drops that would break Site Finder), and ALL
fields are Optional so the #93 202 fetch-stub serializes without a 500.
#994: add GET /{cad_num}/runs (light summary list, empty 200 not 404) and
GET /runs/{run_id} (full row incl result, 404 if missing), backed by
list_runs_for/get_run in analysis_runs.repository (psycopg v3 CAST(:x AS type)).
Routes ordered before /{parcel_id} so /runs/{run_id} is not shadowed.
Closes#992. Closes#994. Refs #961.
Add nullable lat/lon (EPSG:4326, 6 dp) to /analyze competitors[] and
pipeline_24mo.top_objects[] so the frontend can plot Leaflet markers. Coords
come from domrf_kn_objects.latitude/longitude (same source as distance_m).
Purely additive: no existing field/shape changed. Frontend map layers follow
in a separate PR. Part of EPIC #958 (958-B4).
POST /analyze 500'd (TypeError: float(None)) for ~964 cad_parcels_geom rows
that have a meta row but geom IS NULL: the resolution query returned the row
(no NULL filter), geom_wkt became NULL → ST_Centroid(NULL) → NULL lat/lon →
float(centroid_row["lat"]) crashed (the `if centroid_row else` guard checked
row existence, not NULL values). A user entering any such "random parcel" hit
a 500.
Fix:
- Add `AND geom IS NOT NULL` to all three geometry-resolution queries (initial
UNION, inline-refetch UNION, geom_row WKT subquery) so a NULL-geometry parcel
is treated as "no usable geometry" → falls into the #93 graceful fallback
(enqueue NSPD on-demand fetch → 202 polling), exactly like a parcel absent
from the DB. The fetch then populates real geometry and analyze succeeds.
- Defensive: centroid_lat/lon now check the VALUE is not None (not just row
existence) before float(), falling back to EKB center.
Verified: badformat→400, not-in-DB→202(fetching), valid parcel→200 unaffected.
39 analyze/by-bbox tests green. Refs #944.
LIVE BUG (from #994, already merged): the analysis_runs persist call-site in
analyze_parcel extracted district via result_payload["district"]["district_name"]
— but a district dict without that key (real data: partial district lookup)
raised KeyError OUTSIDE the best-effort SAVEPOINT (extraction is at the call
site, not inside persist_analysis_run) → 500 on the LIVE /analyze endpoint.
Fix: .get("district_name") → None instead of raising. Caught by reviving the
analyze test suite (below).
Test-infra (the suite was un-runnable, which is why the bug shipped):
- Lazy-import WeasyPrint in layout_tz_pdf.py + trade_in_pdf.py (matches the
existing report_pdf.py / snapshot_pdf.py pattern). The eager top-level imports
made `app.main` (→ parcels/trade_in routers) un-importable on hosts without
WeasyPrint native libs (e.g. macOS dev), breaking pytest COLLECTION of the
whole api/v1 suite. WeasyPrint is still imported when a PDF is actually rendered.
- tests/conftest.py: autouse fixture clears app.dependency_overrides after each
test (anti-leak — a leaked get_db override caused real-DB connection attempts).
- test_parcel_by_bbox.py: rewrite get_db mocking from patch("...get_db") (a no-op
— FastAPI Depends holds the original ref) to app.dependency_overrides[get_db],
add explicit X-Authenticated-User header (RBAC gate), patch latest_run_dates,
+ a new test asserting last_analysis_date from a latest run (#994). 5/5 green.
NOTE: reviving collectability exposes PRE-EXISTING rot in other api/v1 suites
(analyze/admin/best_layouts: RBAC-header + stale-assertion/mock drift) — those
are NOT regressions from this PR (they were uncollectable before) and are
tracked separately for a deliberate suite-rehab + CI test-gate effort.
Refs #994.
Persist each successful POST /analyze into analysis_runs (migration 127):
best-effort SAVEPOINT-wrapped INSERT in a thin analysis_runs repository, then
explicit db.commit() (get_db has no commit-on-success; SAVEPOINT RELEASE alone
does not persist — without the commit the row rolls back on request teardown,
a silent no-op caught in code review). A persist failure never breaks the
response or poisons the session. Result serialized via jsonable_encoder→
json.dumps (handles embedded Pydantic models/dates); confidence/status
normalized to satisfy the 127 CHECK constraints (unknown→NULL/'complete').
Populate by-bbox last_analysis_date from v_analysis_runs_latest via a single
batch query (no N+1), replacing the #307 placeholder. The read is best-effort
wrapped too — a view-missing deploy window or future drift must not 500 the
map (falls back to last_analysis_date=None).
Additive only — analyze response shape unchanged. Tests: by-bbox suite patches
latest_run_dates (mock-db returns same rows for any query) + new test asserts
ISO last_analysis_date from a latest run. Analyze mock-suite unaffected
(graceful side_effect overflow + best-effort persist absorb the extra INSERT).
Incidental: ruff-format fixed one pre-existing f-string spacing (line ~2391)
the format hook flags on touch.
Closes#994. Refs #961.
When Objective mapping coverage falls below 50% of competitors in the radius,
fall back to rosreestr_deals JOIN on the parcel's cadastral quarter. Audit shows
237/237 EKB quarters (100%) have rosreestr data for the last 12 months, compared
to <20% Objective coverage before bulk mapping.
- velocity.py: add _compute_rosreestr_fallback(), _OBJECTIVE_COVERAGE_MIN_RATIO
constant, velocity_source field on VelocityResult (objective/rosreestr_fallback/none)
- parcels.py: extract cad_quarter from cad_num, pass to compute_velocity
- site-finder.ts: add velocity_source field to Velocity interface
- VelocityBlock.tsx: badge "Источник: квартальные сделки" when rosreestr_fallback
Epic #271 item #17
- v_bucket_success_score HAVING >= 30 → >= 15 so sparse districts
(Кировский etc.) are no longer silently excluded from the block
- Backend: SUCCESS_REC_MIN_DEALS=15, SUCCESS_REC_STRONG_DEALS=30;
data_confidence='weak' (15-29 deals) or 'strong' (≥30) in response
- TS type ParcelSuccessRecommendation: add data_confidence literal
- SuccessRecommendationBlock: amber badge for weak, all hex → CSS tokens,
emoji star removed (ui-ux.md), empty-state text min 30→15
ekb_districts.median_price_per_m2 was populated from a 24-month window.
LEFT JOIN mv_quarter_price_per_m2 on the cad quarter and COALESCE(median_12m,
fallback) so the API returns the 12-month median where available.
Frontend OverviewTab adds a "(12 мес)" hint next to the value.
Closes#271 item 7
TASK A (#29 G2): add parcel_meta to analyze response
- New ParcelMeta Pydantic schema in app/schemas/parcel.py
- SELECT from cad_parcels WHERE cad_num=:c in analyze_parcel() (step 9f)
- Returns permitted_use_established_by_document, land_record_category_type,
land_record_subtype, cost_value; None when row absent
- Tests: test_analyze_parcel_meta.py (found + not-found cases)
TASK B (#232 G3): cad_zouit fallback in _get_zouit_overlaps
- When nspd_quarter_dumps has zouit_count==0, fall back to ST_Intersects
query on cad_zouit (3483 rows, GIST indexed)
- Overlaps tagged with source='cad_zouit'; format compatible with NSPD path
- gate_verdict.py: BLOCKER_TYPE_ZONE_KEYWORDS tuple for keyword-based
classification (охранная зона / трубопровод / электр / газ -> blocker;
СЗЗ -> warning); NSPD subcategory path preserved backward-compat
- Tests: 6 new test cases in test_gate_verdict.py covering cad_zouit path
and backward-compat for NSPD subcategory path
Updated db.execute call sequence in test_analyze_*.py (index shift +1 at pos 10).
* fix(parcels): _parse_floors handle int (post-migration #169 schema change)
After PR #169 cad_buildings schema migration, `floors` column is INT
(was TEXT in legacy schema). Existing `_parse_floors(r.get("floors"))`
call in analyze_parcel → _neighbors_summary crashes with:
AttributeError: 'int' object has no attribute 'strip'
Fix: type union str | int | None. If int → return directly (no strip).
Preserve TEXT range parsing ("5-7" → 7) for backwards-compat with
any legacy data still in cad_buildings_old_apr26.
* test(smoke): production smoke tests for post-deploy regressions (#168)
Add tests/smoke/test_prod_smoke.py covering known regression surfaces.
Marks: prod_smoke + slow. Env: PROD_SMOKE_BASE_URL, PROD_SMOKE_ADMIN_TOKEN.
Run manually: cd backend && uv run pytest tests/smoke/ -m prod_smoke -v
* fix(cadastre): exact-match headers to nspd_lite to bypass NSPD WAF (#168)
Pilot v2 (job_id=2) failed 50/50 with HTTP 403 WAF block. Comparing
nspd_bulk_client.DEFAULT_HEADERS vs legacy nspd_lite.HEADERS (which works
on VPS IP since April 2026):
PascalCase keys → lowercase keys
Chrome/148 UA → Chrome/144 UA
No cache-control / pragma → "no-cache" both
accept-language ru first → en first
No origin → "https://nspd.gov.ru"
referer "/map" → "/map?thematic=PKK"
NSPD WAF (BotShield-class) likely fingerprints на header order + values
combined with TLS fingerprint. Matching legacy exactly minimizes deltas.
Test plan: retry pilot job after deploy, expect 0 WAF blocks for first
5 quarters.
* fix(test): exclude prod_smoke tests by default (#168)
CI ran tests/smoke/test_prod_smoke.py and they hit production
https://gendsgn.ru/api/v1/parcels/.../analyze which currently returns 500
(parse_floors regression — exactly what this PR fixes). Catch-22: PR can't
merge because smoke tests fail against pre-merge prod.
Fix: add `addopts = ["-m", "not prod_smoke"]` so default pytest excludes
them. Run manually post-deploy with: pytest -m prod_smoke -v
---------
Co-authored-by: lekss361 <claudestars@proton.me>
Production POST /parcels/{cad}/analyze падает с 500:
sqlalchemy.exc.InternalError: (psycopg.errors.InFailedSqlTransaction)
current transaction is aborted, commands ignored until end of transaction block
## Root cause
`compute_velocity()` SQL fails (probably на cad's без conkurrentов / sparse
sale_graph data) → exception caught в try/except → НО db.rollback() отсутствует
→ transaction остаётся в aborted state.
Следующая query (_geotech_risk на line 828) пытается выполниться → крашится
с InFailedSqlTransaction.
## Fix
Wrap velocity в `with db.begin_nested()` — SAVEPOINT pattern (consistent
with PR #124 pzz_loader fix). Failure внутри savepoint:
- Rollbacks ТОЛЬКО savepoint
- Outer transaction остаётся clean
- Subsequent queries (_geotech_risk и пр.) работают
Pattern matches feedback_subagent_delegation audit recommendation для
in-loop / per-section exception handling.
## Impact
POST /parcels/{cad}/analyze больше не 500 при velocity failure. Возвращает
`velocity: null` + остальные fields normal.
Refs: user report 2026-05-15 InFailedSqlTransaction
Co-authored-by: lekss361 <claudestars@proton.me>
* feat(site-finder): auto-fetch cadastre geometry on-demand (#93)
Когда пользователь вводит cad-номер которого нет в БД (cad_quarters_geom /
cad_buildings / cad_parcels_geom), вместо 404 «Загрузи через NSPD geo» (dead-
end для non-admin) — теперь backend автоматически инициирует NSPD fetch,
frontend показывает loading state с polling /fetch-status каждые 2с.
Backend:
- Новый модуль app/services/site_finder/cadastre_fetch.py с helpers:
find_or_enqueue_fetch (atomic check + enqueue с дедупликацией по cad),
fetch_status (smart polling endpoint — отличает not_in_nspd от failed),
detect_thematic_id (3-сегм quarter / 4-сегм parcel / 5-сегм building),
validate_cad_format.
- Reuse: enqueue_geo_job + process_nspd_geo_job (workers/tasks/nspd_geo).
source_kind='auto_on_demand' отличает от bulk; rate_ms=200, priority=9.
- POST /parcels/{cad}/analyze graceful fallback: inline await до 25s
(fast path), затем 202 + job_id + eta_seconds для polling, либо
400/404/503 в зависимости от статуса (с Retry-After 60s на 503).
- GET /parcels/{cad}/fetch-status новый endpoint для polling.
Frontend:
- useSiteAnalysis расширен: fetchingState + cancel(). POST analyze + при 202
polling каждые 2с (max 60 итераций = 2 мин cap). status=ready → re-trigger
analyze; not_in_nspd/failed/invalid_format → typed errors.
- apiFetchWithStatus<T> + HTTPError в lib/api.ts — status-aware variant
для 200 vs 202.
- FetchingState.tsx: animated spinner, progress bar (linear до etaSeconds,
asymptote после), elapsed counter, cancel button. Светло-голубая
scheme отличается от обычного pending skeleton.
- site-finder/page.tsx: FetchingState когда fetchingState активен; обычный
pending skeleton — только при первичном analyze без 202.
Edge cases (per #93 acceptance):
- cad валидный в НСПД, fetch <25s → inline 200 OK
- cad валидный, fetch >25s → 202 + frontend polls → ready → analyze
- cad валидный, не в НСПД → 404 с понятным сообщением + формат hint
- cad invalid format → 400 + формат hint
- NSPD rate-limited / failed → 503 + Retry-After 60s
- Параллельные запросы на тот же cad → один job, оба клиента poll'ят (дедуп
через find_active_on_demand_job).
Closes#93.
* fix(site-finder): address PR #95 auto-review minor feedback
Backend (cadastre_fetch.py):
1. (race condition) Advisory lock `pg_try_advisory_xact_lock(hashtext(cad_num))`
обёрнут вокруг шагов "check active job → enqueue" в find_or_enqueue_fetch.
Lock transaction-scoped, released at COMMIT. Параллельные запросы на тот
же cad: первый получает lock и enqueue; второй lock=false → re-check
active job (уже виден после первого COMMIT) → возвращает тот же job_id.
Docstring обновлён, упоминание SELECT FOR UPDATE удалено.
Backend (parcels.py):
3. (threadpool exhaustion) _INLINE_FETCH_WAIT_S снижен 25 → 15s с подробным
комментарием: tradeoff про default Starlette anyio threadpool (40 slots)
и concurrent burst saturation. 15s баланс: НСПД avg 5-15s для quarter,
~70% fast path; остальные 30% получают 202 + polling без блока.
Data (87_on_demand_indexes.sql):
2. (missing index) New migration:
- `nspd_geo_targets_cad_num_idx ON nspd_geo_targets(cad_num)` — для
find_active/recent_completed_job (existing UNIQUE composite не покрывает
WHERE cad_num=:c).
- `nspd_geo_jobs_source_status_idx ON nspd_geo_jobs(source_kind, status)`
composite для filter auto_on_demand + queued/running.
Idempotent (CREATE INDEX IF NOT EXISTS), не блокер при текущем размере,
критично при росте on-demand traffic.
Frontend (useSiteAnalysis.ts):
4. (UI flicker) setFetchingState(null) перемещён ПОСЛЕ `await second
apiFetch`. Иначе между clear и resolve есть момент когда mutation
isPending=true + fetchingState=null → пустой экран ~1 RTT.
NOT addressed (rebuttal):
5. (Tailwind convention) — проверил: в проекте нет ни globals.css, ни
@tailwind directives. ВСЕ существующие site-finder components используют
inline styles (ConfidenceBadge, GeotechRiskBlock, ScoreBreakdownPanel etc).
Tailwind в deps но не wired up. Keep inline styles для consistency.
6. (animate-spin) — требует Tailwind globals (см. #5). `<style jsx>`
keyframes — built-in Next.js, работает.
Per auto-review on 2252236.
---------
Co-authored-by: lekss361 <claudestars@proton.me>
* feat(site-finder): D4 pipeline 24mo — future competition (#36)
Backend (parcels.py):
- Запрос к domrf_kn_objects в радиусе 5км с ready_dt BETWEEN NOW() AND NOW()+24mo.
- _aggregate_pipeline() — сводка: objects_count, flats_total, by_class (эконом/
комфорт/бизнес/...), by_quarter (хронологически, для UI bar), severity
(low <500 / medium <3000 / high) per spec, top_objects (десятка по flat_count desc).
- Поле analyze.pipeline_24mo. Backward-compat — optional.
Frontend:
- Pipeline24moBlock.tsx — severity badge + 3 summary numbers (объектов, квартир,
горизонт/радиус), by-class chips, гистограмма bar по кварталам сдачи
(нормирована на max), разворачиваемый top-N список с классом + датой сдачи.
- Добавлен в MarketTab выше "Market trend".
- TS типы: Pipeline24mo, PipelineObject, PipelineQuarterSlot.
Closes#36. Relates to #19 (Конкурентный 360 — закрыт ранее в #36 scope).
* fix(site-finder): address PR #90 auto-review feedback
Must-fix (3):
1. distance_m falsy guard: `if obj.get("distance_m") is not None` вместо
`if obj.get("distance_m")` — centroid-on-building даёт 0.0 (falsy float),
raw Decimal иначе упал бы в JSON serialization.
2. SQL plan note добавлен про seq scan ~3000 строк OK; при росте — нужен
GIST/index на (latitude, longitude) — отдельный issue для database-expert
(будет создан separately).
3. obj_class NULL bug помечен в docstring _aggregate_pipeline с reference на
fixes/Bug_Kn_API_Obj_Class_Always_Null_OPEN. D6/#38 — fix плановый.
Cleanup (3 из 5):
4. CLASS_LABEL.null dead key убран — JSON null приходит as absent key, не
"null" string.
6. Magic numbers вынесены: PIPELINE_RADIUS_M=5000, PIPELINE_HORIZON_MONTHS=24,
PIPELINE_SEVERITY_MEDIUM_THRESHOLD=500, PIPELINE_SEVERITY_HIGH_THRESHOLD=3000,
PIPELINE_TOP_OBJECTS_LIMIT=10. SQL query теперь через f-string подставляет
их (защищённое от injection — это int литералы).
8. obj.ready_dt formatting через fmtMonth() с new Date + toLocaleDateString —
robust к datetime suffix vs date-only, fallback к substring(0,7) при NaN.
Не сделано (defer):
5. Async 3 HTTP calls (pre-existing pattern, нужен ThreadPoolExecutor refactor
отдельным PR — затрагивает weather/air_quality fetch architecture).
7. ST_GeomFromText дважды — CSE справляется на этом масштабе.
Per auto-review on ade511b.
* fix(site-finder): address PR #90 auto-review minor feedback
1. TS PipelineObject.distance_m — `number | null` для отражения defensive
Python guard (`if obj.get("distance_m") is not None`). Comment объясняет
почему.
2. Pipeline SQL: `text(f"...")` → `text("...")` + parameters. radius_m и
horizon_months через `:param` placeholders + `cast(:horizon_months || ' months'
AS interval)`. Consistency с остальными SQL в файле, plus защита от
accidental injection при будущих изменениях.
3. top_objects: explicit field selection вместо `dict(r) for r in rows`.
Раньше leak'ило все колонки из CTE `SELECT *` (latitude/longitude/
snapshot_date/region_cd/dev_id) в API response. Теперь только nominated
fields: obj_id, comm_name, dev_name, obj_class, flat_count, ready_dt,
distance_m. Schema clean.
Per auto-review on 4e431bf.
---------
Co-authored-by: lekss361 <claudestars@proton.me>
Backend (parcels.py):
- POI scoring loop teper строит score_breakdown_detailed: per-factor list с
verbal explain (через _verbal_for_poi helper) и группировкой
(_POI_GROUP: Социалка / Торговля / Парки / Транспорт / Шум/трамвай / Локация).
- center_bonus добавлен как synthetic factor группы "Локация" с weight=1.0
(decay не применяется — bonus IS the value).
- factor key включает enumerate idx — prevents React key collision когда
два POI одной категории совпадают по округлённому расстоянию.
- Skip факторов с |contribution| < 0.01 (POI > 1км) — UI шуму не нужен.
- abs_total fallback на 1.0 — защита от division-by-zero для empty factors.
- Top-3 positives/negatives: explicit ascending sort для негативов
("most-negative first" очевидно из кода).
- score_by_group: stacked-bar данные с count + contribution_pct.
- group_totals type: dict[str, dict[str, float | int]] (count это int).
Frontend:
- Новый ScoreBreakdownPanel.tsx: stacked-bar по группам с tooltip + legend,
топ-3 плюса (▲ зелёный) / топ-3 минуса (▼ красный) с verbal, отдельная
строка "Снижают балл — Шум/трамвай: ..." для negative groups, разворачиваемая
таблица всех факторов (sticky thead, scrollable).
- Интегрирован в OverviewTab под секцией "Балл".
- TS типы: FactorContribution, ScoreGroupTotal.
Closes#47.
NB: branch создан заново из-за rebase mess (см. PR #87 comments). Логически
эквивалентно но history clean.
Co-authored-by: lekss361 <claudestars@proton.me>
* feat(site-finder): P1 geometry suitability score (#45)
Backend (parcels.py):
- _polygon_suitability() — Shapely metrics on parcel WGS84 polygon:
area_ha/m2 (projected via cos(lat) к UTM-like meters), perimeter_m,
aspect_ratio (long/short side of MABR), convex_hull_ratio (area / hull area),
min_inscribed_rect_dim_m.
- Composite suitability score 0..1:
base = area_subscore (0 при <0.2 ha, linear → 1 при ≥0.5 ha)
−0.3 если aspect_ratio > 5 (вытянутый)
−0.3 если convex_hull_ratio < 0.65 (изрезанный)
−0.5 если min_inscribed_rect_dim_m < 30 (узкий)
- Label: микро / подходящий / сложная форма / слабо подходит
- Recommendation: "для МКД 16+ нужно >0.3 ha + минимум 40м"
- В analyze response → geometry_suitability.
Frontend:
- Новый GeometrySuitabilityBlock.tsx с color-coded badge + метрики grid +
penalties + recommendation
- Добавлен в LandTab (выше "Зонирование (ПЗЗ)")
- TS типы расширены: GeometrySuitability
Closes#45.
* fix(site-finder): address PR #89 auto-review minor feedback
Backend (parcels.py):
1. Фактическая ошибка фикс: recommendation теперь различает "строительный
минимум 30м" (physical penalty trigger) и "комфорт МКД 12-16эт 40м"
(recommendation level). Пользователю чётко видны 2 порога.
2. Lazy imports → module-level: `from shapely import wkt as _shp_wkt`,
`from shapely.geometry import Polygon`.
3. MABR inner exception теперь логирует: `logger.debug("MABR computation
failed, falling back to sqrt(area): %s", mabr_err)`.
4. Magic numbers вынесены в константы: _GEOM_MIN_AREA_HA, _GEOM_AREA_SCORE_FULL_HA,
_GEOM_ASPECT_PENALTY_THRESHOLD, _GEOM_CONVEX_PENALTY_THRESHOLD,
_GEOM_MIN_WIDTH_PHYSICAL_M, _GEOM_MIN_WIDTH_COMFORT_M, _GEOM_LABEL_MICRO_HA,
_GEOM_LABEL_GOOD, _GEOM_LABEL_MEDIUM, плюс penalty константы.
5. Label "микро" теперь комбинируется с penalties: "микро, узкий" — UI видит
обе проблемы. Pure "микро" остаётся когда нет penalty.
Frontend (GeometrySuitabilityBlock.tsx + types):
6. Заменил emoji ⚠ (U+26A0) на текстовый <strong>"Проблемы формы:"</strong> —
стабильнее в WeasyPrint PDF-экспорте и cross-platform.
- TS типы: GeometrySuitabilityBaseLabel экспортирован, label расширен до
`string` для допуска combo-labels; helper colorForLabel() парсит base часть.
Per auto-review on f4e7491.
* fix(site-finder): P1 #45 — extract 0.3 ha to _GEOM_AREA_COMFORT_HA
Auto-review нашёл: recommendation упоминала "от 0.3 га" но в constants block
не было 0.3 — magic literal в строке. Тот же класс проблемы что 30/40м inconsistency
из прошлого review, только в другом поле.
Fix: новая константа `_GEOM_AREA_COMFORT_HA = 0.3` с комментарием
"рекомендуемая комфортная площадь МКД (recommendation)". Размещена между
_GEOM_MIN_AREA_HA (физический минимум) и _GEOM_AREA_SCORE_FULL_HA (premium) —
третий semantic threshold. Recommendation теперь f-string использует константу.
Per auto-review on ae5e8de.
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Co-authored-by: lekss361 <claudestars@proton.me>
* feat(site-finder): P2 cad_buildings соседи + overlap check (#46)
Backend (parcels.py):
- _parse_floors() helper для TEXT column (cad_buildings.floors хранится как
строка, могут быть диапазоны "5-7"). Возвращает верхнюю границу.
- _neighbors_summary(db, geom_wkt, our_cad) — query соседей в 100м (GIST):
cad_num, building_name, floors, year_built, cost_value, area, address, distance.
Aggregate: avg_floors_100m, max_floors_100m, median_cost_per_m2_100m,
count_buildings_100m. Outliers cost/m² фильтруются (1k < x < 500k).
- Overlap check: ST_Intersects + ST_Area(ST_Intersection) > 50 m² (transformed
to UTM 32641 для метров). Если есть → has_existing_buildings: true +
overlap_buildings list.
- В response → neighbors_summary.
Frontend:
- Новый NeighborsBlock.tsx: hard red warn block для overlap (с building names +
overlap_m2 + "Инвестиции невозможны без сноса"); summary metrics (avg/max
floors, median price); toggle "Показать N ближайших" → таблица.
- Border меняется на красный при has_existing_buildings — visual cue.
- Добавлен в LandTab выше "Зонирование (ПЗЗ)".
- TS типы: NeighborBuilding, OverlapBuilding, NeighborsSummary.
Closes#46. Closes#21 (cad_buildings в Site Finder фильтрах).
* fix(site-finder): address PR #91 auto-review minor feedback
Backend (parcels.py):
1. (medium) Aggregation loop _neighbors_summary теперь обёрнут в try/except
(ValueError, TypeError) с fallback к data_available:False + log warning.
Защищает от non-numeric cost_value/area придёт в строке (e.g. "N/A") —
ранее весь endpoint падал 500.
2. Magic numbers вынесены: _COST_PER_M2_MIN=1000, _COST_PER_M2_MAX=500_000.
3. _parse_floors docstring + inline note про malformed parts ("5а-7" filter,
multi-range "1-2-3" max acceptable degradation).
Frontend (NeighborsBlock.tsx):
5. Русский plural fix: pluralBuildings(n) helper — 1 здание, 2-4 здания,
5+/11-14 зданий. Раньше "3 зданий" — теперь "3 здания".
Не сделано (defer):
4. ST_Area для overlap query — практически 0-5 buildings в ЕКБ, GIST scan OK.
6. Discriminated union для NeighborsSummary — refactor а не bug.
7. Vault entry для P2 — добавится batch'ем после merge всех текущих PR.
Per auto-review on 60d53bb.
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Co-authored-by: lekss361 <claudestars@proton.me>
Backend (parcels.py):
- _compute_confidence() composite score 0..1 from 7 subscores: poi_freshness,
geom_source (parcel vs quarter), district, market_trend (rosreestr_deals depth),
competitors, environment (noise/air/weather availability), zoning (placeholder
до G1).
- confidence_label: high (>0.75) / medium (0.4-0.75) / low (<0.4)
- confidence_caveats: list of конкретных проблем для UI
- confidence_breakdown: per-subscore 0..1 для прозрачности
Это stub-версия (полная — после G1/G2/D1/D2). Использует только текущие сигналы.
Frontend:
- Новый ConfidenceBadge.tsx — color-coded (green/yellow/red) badge с %
- Caveats для low — показываются сразу; для medium/high — под toggle
- Toggle "Подробнее" → breakdown per-subscore + полный список caveats
- Размещён в начале OverviewTab (выше "Район")
- TS типы расширены: confidence, confidence_label, confidence_breakdown, confidence_caveats
Closes#48.
Co-authored-by: lekss361 <claudestars@proton.me>
