5da66bbc93
* feat(cadastre): bulk_harvest_quarter Celery task + grid-walker + saga state (#168 PR3/5) Add bulk cadastre harvest pipeline: - services/cadastre/bulk_harvest.py: async harvest_quarter() orchestrator (4 phases) + 7 upsert helpers (parcels/buildings/constructions/oncs/enks/zouit/quarter_stats) using CAST(:x AS jsonb) pattern, begin_nested() SAVEPOINT per grid-walk upsert - services/cadastre/grid_geometry.py: quarter_bbox_3857 (PostGIS ST_Extent) + generate_grid_click_points (15x15 = 225 sub-bbox grid) - workers/tasks/scrape_cadastre.py: bulk_harvest_quarter_task (acks_late=True, dont_autoretry_for NspdBulkWafError), enqueue_cadastre_harvest, cleanup_zombies - api/v1/admin_cadastre.py: 5 endpoints behind AdminTokenAuth — create/list/get/cancel/resume - Tests: 13 service unit + 8 API tests * fixup(cadastre): drop importorskip (PR2 merged) — imports now top-level (#168 PR3) * fixup(cadastre): tile_size unified param + register slow marker (#168 PR3) Blocker #1: rename tile_width/tile_height → tile_size in generate_grid_click_points. Callers in bulk_harvest.py and test_cadastre_bulk.py used tile_size; def had tile_width+tile_height → TypeError at runtime. Blocker #2 (10 failures in test_admin_cadastre.py) was side-effect of #1: TypeError at import chain (app.main → admin_cadastre → bulk_harvest) broke FastAPI app load → dependency_overrides AttributeError. Now resolved. Also register `slow` pytest marker in pyproject.toml to suppress PytestUnknownMarkWarning. * fixup(cadastre): use importlib.util.find_spec instead of import (#168 PR3) Root cause Blocker #2: `import app.workers.tasks.scrape_cadastre` at module top-level REBOUND `app` from FastAPI instance (line 14) to the Python package. All subsequent `app.dependency_overrides[get_db] = ...` failed with AttributeError because `app` was now the namespace module. Fix: probe module availability with importlib.util.find_spec — does not import or rebind names. FastAPI `app` instance stays intact. All 28 cadastre tests pass locally. --------- Co-authored-by: lekss361 <claudestars@proton.me>
98 lines
3.4 KiB
Python
98 lines
3.4 KiB
Python
"""Геометрические утилиты для grid-walk по кадастровому кварталу.
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Используется в bulk_harvest.py для вычисления bbox квартала в EPSG:3857
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и генерации сетки точек для WMS GetFeatureInfo.
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"""
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from __future__ import annotations
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import logging
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from sqlalchemy import text
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from sqlalchemy.orm import Session
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logger = logging.getLogger(__name__)
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def quarter_bbox_3857(db: Session, quarter: str) -> tuple[float, float, float, float] | None:
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"""Получить bbox квартала из cad_quarters_geom в EPSG:3857.
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Args:
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db: SQLAlchemy session.
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quarter: кадастровый номер квартала, например '66:41:0303161'.
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Returns:
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(xmin, ymin, xmax, ymax) в метрах EPSG:3857, или None если квартал не найден.
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"""
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row = (
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db.execute(
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text("""
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SELECT
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ST_XMin(b) AS xmin,
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ST_YMin(b) AS ymin,
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ST_XMax(b) AS xmax,
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ST_YMax(b) AS ymax
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FROM (
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SELECT ST_Extent(ST_Transform(geom, 3857)) AS b
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FROM cad_quarters_geom
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WHERE cad_number = :q
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) sub
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"""),
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{"q": quarter},
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)
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.mappings()
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.first()
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)
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if not row or row["xmin"] is None:
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logger.warning("quarter_bbox_3857: квартал %s не найден в cad_quarters_geom", quarter)
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return None
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return (
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float(row["xmin"]),
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float(row["ymin"]),
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float(row["xmax"]),
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float(row["ymax"]),
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)
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def generate_grid_click_points(
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bbox: tuple[float, float, float, float],
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grid_size: int = 15,
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tile_size: int = 512,
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) -> list[tuple[tuple[float, float, float, float], tuple[int, int]]]:
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"""Генерировать grid_size × grid_size ячеек для bbox квартала.
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Каждая ячейка — свой sub-bbox + центральный пиксель (click_xy) для
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WMS GetFeatureInfo. 15×15 = 225 запросов обеспечивают хорошее покрытие
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при шаге ~50м для типичного квартала ЕКБ (~750м × ~750м).
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Args:
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bbox: (xmin, ymin, xmax, ymax) в EPSG:3857.
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grid_size: количество ячеек по каждой оси (15 → 225 запросов).
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tile_size: размер виртуального WMS тайла в пикселях (квадратный).
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Returns:
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Список (sub_bbox, click_xy) — sub_bbox в EPSG:3857, click_xy в пикселях.
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"""
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xmin, ymin, xmax, ymax = bbox
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x_step = (xmax - xmin) / grid_size
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y_step = (ymax - ymin) / grid_size
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result: list[tuple[tuple[float, float, float, float], tuple[int, int]]] = []
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for i in range(grid_size):
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for j in range(grid_size):
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cell_xmin = xmin + i * x_step
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cell_ymin = ymin + j * y_step
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cell_xmax = cell_xmin + x_step
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cell_ymax = cell_ymin + y_step
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# Клик в центр ячейки
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click_x = tile_size // 2
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click_y = tile_size // 2
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result.append(
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(
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(cell_xmin, cell_ymin, cell_xmax, cell_ymax),
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(click_x, click_y),
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)
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)
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return result
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