152 lines
4.4 KiB
Python
152 lines
4.4 KiB
Python
"""Regression coverage for bounded, stable segmentation result listings."""
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from __future__ import annotations
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from datetime import UTC, datetime
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from types import SimpleNamespace
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from uuid import UUID
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import pytest
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from fastapi import FastAPI
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from fastapi.testclient import TestClient
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from app.api.routes import segmentation as segmentation_routes
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from app.db.session import get_db
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from app.schemas.segmentation import SegmentationListResponse
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from app.services.segmentation_service import SegmentationService
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RUN_ID = UUID("00000000-0000-0000-0000-000000000101")
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DATASET_ID = UUID("00000000-0000-0000-0000-000000000102")
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PROJECT_ID = UUID("00000000-0000-0000-0000-000000000103")
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def _segmentation(index: int) -> SimpleNamespace:
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return SimpleNamespace(
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id=UUID(int=index + 1),
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project_id=PROJECT_ID,
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dataset_id=DATASET_ID,
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analysis_run_id=RUN_ID,
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job_id=None,
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model_name="segmentation-test-model",
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model_version="1",
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class_name="building",
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confidence=0.99 - index / 100,
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bbox_json=None,
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area_m2=float(index + 1),
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mask_path=None,
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source_tile_path=None,
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tile_index=index,
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properties_json={},
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provenance_json={},
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created_at=datetime(2026, 8, 23, tzinfo=UTC),
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)
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class _Session:
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def get(self, _model, identifier):
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if identifier == RUN_ID:
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return SimpleNamespace(analysis_type="segmentation")
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return None
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def test_service_returns_one_stable_page_with_complete_metadata(monkeypatch) -> None:
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rows = [_segmentation(index) for index in range(5)]
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monkeypatch.setattr(
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SegmentationService,
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"_query_segmentation_rows",
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staticmethod(lambda _db, **_filters: rows),
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)
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result = SegmentationService.list_segmentations(
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_Session(),
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analysis_run_id=RUN_ID,
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dataset_id=DATASET_ID,
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limit=2,
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offset=1,
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)
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assert [item.id for item in result.items] == [rows[1].id, rows[2].id]
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assert result.total == 5
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assert result.limit == 2
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assert result.offset == 1
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assert result.truncated is True
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def test_service_pages_cover_the_stably_ordered_population_once(monkeypatch) -> None:
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rows = [_segmentation(index) for index in range(5)]
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monkeypatch.setattr(
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SegmentationService,
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"_query_segmentation_rows",
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staticmethod(lambda _db, **_filters: rows),
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)
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seen = []
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for offset in (0, 2, 4):
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result = SegmentationService.list_segmentations(
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_Session(),
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dataset_id=DATASET_ID,
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limit=2,
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offset=offset,
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)
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seen.extend(item.id for item in result.items)
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assert result.total == len(rows)
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assert result.offset == offset
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assert seen == [row.id for row in rows]
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@pytest.mark.parametrize(
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("path", "expected_run_id", "expected_dataset_id"),
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[
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(f"/api/v1/segmentation/runs/{RUN_ID}/segmentations", RUN_ID, None),
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(f"/api/v1/segmentation/datasets/{DATASET_ID}/segmentations", None, DATASET_ID),
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],
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)
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def test_both_listing_routes_forward_the_page_window_and_return_it(
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monkeypatch,
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path: str,
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expected_run_id: UUID | None,
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expected_dataset_id: UUID | None,
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) -> None:
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calls: list[dict] = []
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def _list(_db, analysis_run_id=None, **parameters):
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calls.append({"analysis_run_id": analysis_run_id, **parameters})
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return SegmentationListResponse(
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items=[],
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total=9,
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limit=2,
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offset=4,
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truncated=True,
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)
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monkeypatch.setattr(SegmentationService, "list_segmentations", staticmethod(_list))
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app = FastAPI()
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app.include_router(segmentation_routes.router, prefix="/api/v1")
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app.dependency_overrides[get_db] = lambda: object()
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response = TestClient(app).get(
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path,
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params={"limit": 2, "offset": 4, "class_name": "building", "min_confidence": 0.5},
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)
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assert response.status_code == 200
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assert response.json()["data"] == {
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"items": [],
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"total": 9,
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"limit": 2,
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"offset": 4,
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"truncated": True,
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}
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assert calls == [
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{
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"analysis_run_id": expected_run_id,
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"limit": 2,
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"offset": 4,
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"dataset_id": expected_dataset_id,
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"class_name": "building",
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"min_confidence": 0.5,
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}
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]
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