fedstat ИПЦ is reCAPTCHA-blocked; CBR publishes inflation openly. Add
fetch_inflation + parse_inflation_xlsx (CBR UniDbQuery DownloadExcel/132934,
monthly % г/г, region=rf, source=cbr) to cbr_macro.py; upsert
indicator_type=inflation_yoy via the existing cbr_macro_sync task (per-series
guard, SAVEPOINT-per-row, CAST not ::, ON CONFLICT on the PK).
Surface inflation_yoy in MonthlyMacro (frozen, carry-forward) and ACTIVATE the
reserved §9.5 inflation channel (macro_coefficient f_inflation: level-vs-4%-target
nudge, non-positive to avoid double-counting f_rate, excluded from
_RATE_DRIVEN_FACTORS). Channel was DEGRADED (no data) -> now BACKED + consumed;
_CONF_HIGH_MIN_BACKED 4->5. Deterministic (§16/§26); renorm claims the reserved
0.08 slice as designed. Live-verified (2026-04 5.58%); 194 macro + 902 forecasting
tests green. No migration, no new deps.
Refs #946.
Cold §22 forecast measured ~215-233s on prod: §9.x layers re-execute the same
horizon/segment-invariant DB loads with identical args hundreds of times per
report (profiled: get_competitors x69, market_metrics x124, get_monthly_macro
x290). Add a per-report ContextVar cache (forecast_cache(), opened once in the
orchestrator) + @cached(key_builder) on the expensive §9.x loaders so each
unique load runs ONCE and reuses the same frozen, read-only instance.
Output is byte-identical (memoized producers are frozen dataclasses / read-only
Pydantic, callers never mutate; cache is per-report, discarded on exit; no-op
outside the report build). No concurrency, no signature changes.
- forecast_request_cache.py: ContextVar cache + cached() decorator (no-op
outside context, reentrant, _MISS sentinel for cached None)
- @cached on competitors/future_supply/market_metrics/macro_series/
sales_series/macro_coefficient/demand_normalization/regression loaders
- orchestrator: wrap build_site_finder_report in forecast_cache()
- 58 tests: key discrimination (call-counting regression guard), no-op-outside,
per-report isolation, reentrancy, frozen-producer canary, amplification proof
(real get_monthly_macro xN->1)
code-reviewer APPROVE (keys correct, mutation-safe, output identical). 1265
forecast/cache tests green. No new deps. Refs #1129.
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.
_http_get_json/_http_post_json → verify=False (const _VERIFY_TLS): геопортал ЕКБ отдаёт цепочку с росс-гос-CA, которого нет в trust-store контейнера → verify=True падал CERTIFICATE_VERIFY_FAILED, весь geoportal-слой (C8b/D9b/#1085) не работал бы с прода. Данные публичные open-data, секреты не передаются. Прецедент: nspd_lite/ekburg_permits/pzz_loader. Прод-probe подтвердил. +регрессия-тест.
Refs #1067.
Co-authored-by: lekss361 <lekss361@gendsgn.local>
Co-committed-by: lekss361 <lekss361@gendsgn.local>
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.
NSPDClient.list_layers() парсил _walk_layer_tree по id/children, но НСПД layers-theme-tree отдаёт слои в плоском layers[] с ключом layerId → list_layers() молча возвращал [] для любой темы. Fix: сначала плоский data["layers"], иначе fallback на _walk_layer_tree; defensive None-guard на missing id. +тест на flat-форму, backward-compat сохранён.
Refs #1067.
Co-authored-by: lekss361 <lekss361@gendsgn.local>
Co-committed-by: lekss361 <lekss361@gendsgn.local>
build_site_finder_report (§22) takes ~30-180s → runs in a background Celery task,
not inline on the sync /analyze endpoint.
- repository: latest_run_for gains keyword-only schema_version (default None keeps
v_analysis_runs_latest behavior, backward-compat); when given, reads base
analysis_runs filtered by schema_version ORDER BY created_at DESC LIMIT 1 — fetches
the latest analyze-1.0 site-analysis run even when newer 1.0 (§22) rows exist on top
(index-served via 127's (cad_num, created_at DESC)).
- new workers/tasks/forecast.py::forecast_site_finder_report: reads latest analyze-1.0,
runs the §22 orchestrator, persists SiteFinderReport.as_dict() as a 1.0 run via
persist_analysis_run. Graceful: no base run / compute error → logger + return None
(worker not crashed). time_limit=900/soft=840 (no global limit). Registered in include.
Prod-confirmed: analyze-1.0 result carries the full analyze dict (competitors+district)
→ orchestrator input valid. Endpoint trigger (3b-ii) + §9.x untouched. 943 tests pass;
code-review APPROVE (contracts verified vs real as_dict(); status done→complete normalized,
no IntegrityError). Refs #994#961.
deficit_index pins to -1.0 for every ЕКБ segment (12mo demand flow vs multi-year
supply stock → log-ratio clamps) → zero discriminating power, though the oversupply
is partly real. Add MOI (gross competing supply / demand_per_mo), the real-estate
absorption standard, as an additive non-saturating companion that DISCRIMINATES
(Уралмаш 42mo … Чермет 109mo) where deficit cannot. deficit_index math kept exactly
as-is (honest absolute: -1 = genuinely oversupplied); docstrings clarify -1 is common
and MOI is the discriminating companion (no recalibration). _gross_supply extract-method
(single source of truth; _project_supply behavior byte-identical, code-review-verified).
Surface MOI in §22 future_market (passthrough) + exec_summary key_numbers/verdict.
Guards: no demand → None, no supply → 0. Prod: MOI varies 42→109mo, deficit stays -1.
Discrimination test pins MOI separating two segments both at deficit -1. Refs #952.
/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.
_KEY_RATE_MARKET_SPREAD_PP was a 0.0 placeholder, so §7.9 affordability payments
used the bare CBR key_rate (~14.5%), understating borrower cost and OVERSTATING
affordability. Calibrate to 4.5pp from the prod anchor (macro_indicator
mortgage_rate_primary_domrf 19.125% @ 2026-04-19 vs key_rate ~14.5% -> observed
~4.6pp, rounded conservatively; inside the typical RF 3-5pp band), so
rate_used = key_rate + 4.5 ~= 19% matches the directly-observed market primary
mortgage rate. Makes affordability LESS optimistic / more accurate. Docs + tests
updated to the symbolic spread (new regression anchor pins spread==4.5 and
key_rate 14.5 -> ~19.0); rate_kind/graceful-fallback semantics unchanged.
Forecasting suite 841 passed; ruff clean.
Backtest (OOS directional hit-rate): single-best-lag compute_rate_sensitivity
is directionally noise (0.148 Source B EKB-wide, lag-unstable); the Almon
distributed-lag estimator (compute_district_rate_regression) is strictly less
noisy on every tier (0.407 Source B / 0.60 survivorship-free Source A,
lag-stable). Add a thin adapter compute_rate_regime_sensitivity mapping
DistributedLagFit onto the existing RateSensitivity contract (beta=long-run
sum-beta, confidence regression->medium / fallback->low, district=None->low and
no call) and repoint the three consumers (demand_normalization, product_scoring,
demand_supply_forecast). Magnitude bounded by the existing [0.5,1.2] clamp.
Reversible; compute_rate_sensitivity kept for the backtest. Consumer tests
repointed to the real Almon path (mutation-verified genuine) + adapter unit
tests + end-to-end fallback degradation. Forecasting suite 840 passed; ruff clean.
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.
