/analyze passes the official ЕКБ admin district (ekb_districts polygon, e.g.
'Кировский'), but objective_lots/corpus_room_month store informal micro-districts
('Втузгородок','ЖБИ') -> admin name matched 0 rows -> silent empty forecast.
Add resolve_objective_districts() (site_finder/district_resolver.py) mapping an
admin name to its clean micros via ekb_district_alias (note IS NULL), with
None -> EKB-wide fallback and raw-micro pass-through. Wire into the objective_lots
district filters of market_metrics (§9.2 stock+sales), supply_layers L1 (§9.3),
and sales_series Sources A+B (crm shares the micro vocab, prod-verified),
switching the scalar filter to psycopg3-safe = ANY(CAST(:districts AS text[])).
supply_layers L2/L3 keep the admin name (domrf_kn_objects.district_name is admin vocab).
Prod: Кировский/Ленинский/Орджоникидзевский obj_count 0 -> 32/64/31.
Tests mutation-verified non-vacuous. 192 module tests pass; ruff clean. Refs #969#949.
weighted_avg_velocity was a naive mean despite the name — a 500-flat ЖК weighed
the same as a 20-flat one. Now count-weighted by flats_total (sql.md AVG
principle): Σ(velocity*flats_total)/Σ(flats_total). Competitors with unknown
flats_total are excluded from weights; if sizes are unknown for ALL, graceful
fallback to the simple mean (den>0 guard). Field name + API contract UNCHANGED
(zero consumer ripple — traced: only CompetitorsSummary, no frontend ref).
Tests: equal sizes → weighted==naive (existing 6.0 stays); NEW test with
500-flat@40 + 20-flat@2 → 38.54 (not naive 21.0), proving the weighting.
FOLLOW-UP (Leha: not a bug, follow-up).
get_macro_series / get_latest_macro defaulted region='rf', but CBR mortgage_*
series live under 'sverdl' in macro_indicator (key_rate under 'rf'). Only caller
passes region explicitly so no active leak, but a future caller omitting region
would silently get None — latent footgun. region now str|None; when None →
_canonical_region (mortgage set sourced from _CBR_SERIES_MAP, single source of
truth). Explicit region always wins → existing callers unchanged. Region-binding
tests added. 159 tests pass.
Documented (NOT code-fixed — upstream data-quality): mortgage_debt /
mortgage_overdue stay empty in macro_indicator because cbr_mortgage_series.period
holds value-like garbage (e.g. '10054588.0') for the Debt series — corruption
from the upstream CBR-XLSX scraper that built domrf.db (imported verbatim by
44_import_anton_db.py), NOT an ingest bug. 123_macro_indicator.sql correctly
skips unparseable periods. Needs a separate scraper re-ingest (idempotent
backfill once period is valid). Refs #945
REOPENED. _SALES_WINDOW_SQL derived "sales in window" from objective_lots_history
snapshots, but history is only ~17 days deep — every currently-sold lot had a
sold-snapshot in the window, so window-sales collapsed into the entire cumulative
sold stock (Автовокзал 6mo: 33,245 vs real ~2,308). Inflated absorption_rate
(~235%/mo with confidence=high), months_of_supply, unit_velocity, liquidity,
demand_concentration → contaminated forecast #950/#952.
Count window sales directly from objective_lots by contract_date in the window
(the real sale date — present on 100% of sold lots: 41,091/41,091). Return
contract of _query_sales_window unchanged (units/area/by-room ROLLUP); downstream
formulas untouched. Removed the now-dead objective_lots_history JOIN/CTE.
Regression test: lots sold outside window (contract_date out of range) not counted
(41,091 cumulative vs 2,308 window → absorption 2.35→0.04). 288 tests green.
Verification = prod compute_market_metrics(Автовокзал) post-deploy. Refs #949
REOPENED. L3 future-supply rows are computed per (district_name, dev_group_name)
but dev_group_name was never a key column — only embedded in method text. With
complex_id/obj_class NULL for L3, every dev_group of a district collapsed to one
upsert key → ~95.6% loss. Ground-truth (Академический, prod): should be 13,808
units / 15 dev_groups / 54 objects; only 1 row / 607 units survived.
Migration 128: ADD COLUMN supply_layers.dev_group_name TEXT + rebuild
uq_supply_layers_logical to (layer, district_name, complex_id, obj_class,
dev_group_name, source, snapshot_date) NULLS NOT DISTINCT (L1/L2 dev_group_name
NULL stays transparent → their dedup unchanged; L3 distinct groups no longer
collapse). Dry-run-verified vs prod catalog (applies clean, ROLLBACK clean).
Worker: SupplyLayerRow gains dev_group_name (L1/L2=None, L3=group); _UPSERT_SQL
adds it to INSERT/VALUES (CAST(:dev_group_name AS text)) + ON CONFLICT (key col,
not in DO UPDATE SET). Service+worker regression tests assert same-district/
different-dev_group → distinct keys (no collapse). 234 supply tests pass.
Deploy applies migration before container restart; collapsed data self-heals on
next supply_layers_refresh. Verification = prod re-measure post-deploy.
Refs #970
Complements the NULL-geom 500 fix. cad_exists_in_db (docstring: "is there
GEOMETRY") checked only row existence, not geom IS NOT NULL — so for the ~964
meta-but-NULL-geom parcels it returned True. Consequence after the 500 fix:
such a parcel fell into the analyze fallback, find_or_enqueue_fetch step 2 saw
cad_exists_in_db=True → returned ("ready", None) → NO NSPD fetch enqueued →
analyze looped to a 202 with job_id=null and the parcel was stuck "fetching"
forever (never pulled real geometry, never resolved).
Fix: add `AND geom IS NOT NULL` to all three EXISTS branches (aligns the
function with its docstring). Now a NULL-geom parcel → cad_exists_in_db=False →
a real NSPD fetch is enqueued (202 + real job_id) → geometry populates →
re-poll → analyze succeeds (or 404 not_in_nspd if NSPD lacks it). No more
stuck-202. Valid-geom parcels unaffected. All 3 callers want geometry-presence
semantics. 37 analyze/fetch/by-bbox tests green. Refs #944.
Replace unweighted AVG(deals_total_avg_area_m2) and AVG(deals_total_avg_price_thousand_rub_per_m2)
with SUM(x * count) / NULLIF(SUM(count), 0) pattern in _INLINE_VELOCITY_SQL.
Months with zero deals no longer dilute the weighted mean 2-4x.
P0 follow-up to PR #290 (mv fix, issue #21).
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
Replace INNER JOIN on objective_complex_mapping with LEFT JOIN approach:
competitors without mapping now return velocity=0 with
velocity_data_available=False instead of being silently dropped.
UI: VelocityBlock shows 'нет данных velocity' badge when flag is False
and hides the gauge (meaningless zero). TS type updated (additive optional
field, backward compat).
Audit (EKB, region_cd=66): mapped=129, unmapped=1387, total=1516.
Closes#271 item #10
Round 1 (commit bcd7dc8) был broken: на 2-bucket входах surplus уходил в free
полностью без учёта capacity → free превышал cap → следующая итерация
clamp'ировала его и наоборот. Infinite oscillation в FastAPI handler.
Round 2 fix per review BLOCK (#282 comment):
- Surplus распределяется пропорционально available capacity (cap - v),
не текущему v. Free никогда не вылетит выше cap.
- free = строго < cap (не <=) — иначе деление на 0 capacity.
- Hard guard `for _ in range(N+1)` — гарантированно завершается.
- Pathological (surplus > total_capacity): возвращаем оригинальный pct_map
+ cap_skipped=True (sum=100 invariant сохранён).
- Hamilton round вынесен в _hamilton_round() helper.
Tests:
- 2-bucket cases (90/10, 70/30, 99/1) expected cap_skipped=True
- test_cap_iteration_count_bounded — все pathological завершаются < 100ms
- All 13 cases verified standalone (3 fast-path + 7 reproduced + 3 pathological)
_build_recommendation зеркалило рынок через чистый Hamilton apportionment.
Добавлен _cap_and_redistribute: bucket >35% clamp к 35%, surplus
пропорционально перераспределяется в остальные bucket'ы (Hamilton финальный
pass гарантирует sum=100). Pathological case (все bucket'ы >35%) — warning
в лог, cap пропускается. Frontend: warning banner при maxPct>60 в
RecommendationCard (Tailwind amber utilities, без inline hex).
DOM.РФ API classifies euro-format flats (26-50м²) as rooms=2.
_SUPPLY_BATCH_SQL now maps:
rooms=2 + area<35 → 'euro-1'
rooms=2 + area<50 → 'euro-2'
before the generic rooms IN (1,2,3) branch.
Audit: 4 140 units rooms=2 + area<35 in domrf_kn_flats were inflating
the supply count for genuine 2-room apartments (median 57м²).
euro buckets have no velocity counterpart in objective_corpus_room_month
so they are filtered out of top_layouts by min_velocity.
Also extends RoomBucket Literal and room_bucket_from_flat() in
layout_signature.py with the new euro-1/euro-2 values.
Closes#271 item 8
Resolve conflict in best_layouts.py: keep SF-01 inline velocity
(deals_window numerator) + add SF-05 sold_pct clamp at 100% and
is_oversold flag from main.
Раньше _VELOCITY_DIVISORS делил агрегаты mv_layout_velocity (24 мес)
на 4/12 для quarter/year, не меняя реальное окно данных. Теперь
inline SQL из objective_corpus_room_month с CAST(:window_interval AS interval).
velocity_per_month = deals_window / months_in_window (1.0/3.0/12.0).
Разные time_window → разные строки из БД → разный mix/velocity/jk_count.
Closes (epic part) #271 item 1
Symptom: sold_pct_of_supply = sum_deals_24mo / supply_count_snapshot * 100
yields >100% (e.g. 199%) for fast-selling small formats due to incompatible
time windows (24-month deals vs point-in-time supply snapshot).
Option A (hotfix): clamp to 100.0, expose is_oversold=True for UI badge.
- best_layouts.py: compute sold_pct_raw, clamp with min(..., 100.0),
set is_oversold = raw > 100%
- parcel.py TopLayoutRow: add is_oversold: bool field
- best-layouts.ts TopLayoutRow: add is_oversold: boolean
- BestLayoutsBlock.tsx: show warn badge ">100%" when is_oversold=True
- tests: two new cases — raw 199% clamps + is_oversold=True;
raw 50% passes through + is_oversold=False
Closes (epic part) #271 item 5
Backend (quarter_dump_lookup.py):
- _acquire_harvest_lock: Redis SETNX TTL=120s на quarter, защищает от burst
N concurrent analyze, ставящих N одинаковых harvest task в очередь
- _trigger_harvest: использует lock перед apply_async, возвращает False если
lock уже взят (другой запрос триггернул раньше)
- make_empty_result/EMPTY_DUMP_RESULT: новое поле harvest_eta_seconds в
nspd_dump dict, типичный harvest_quarter = 60с
- /analyze: пробрасывает поле через nspd_dump dict (нет typed schema —
response_model=None для /analyze endpoint, dict уходит как есть)
Frontend (NspdFreshnessBadge, NspdZoningBlock):
- Countdown «НСПД: загрузка ~Nс» вместо бесконечного спиннера
- После остановки countdown (remaining=0) NspdZoningBlock показывает
«загрузка дольше обычного» + ссылку на ПКК вместо infinite skeleton
Tests: 5 новых unit + 2 для empty_result schema (всего +7, pass)
Closes#234 (UX-side; data-side resolves когда Sub-PR B + D merged).
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).
Add SQL migration 100_user_weight_profiles_default_seed.sql with system
presets Эконом/Комфорт/Бизнес (user_id='__system__'). Migration is
idempotent via ON CONFLICT DO UPDATE.
Backend:
- weight_profiles.py: add SYSTEM_USER_ID constant + list_profiles_with_system()
- admin_weight_profiles.py: add include_system query param to GET list endpoint
Tests: 3 new tests covering include_system flag and service sentinel behaviour.
