176 lines
6.2 KiB
Python
176 lines
6.2 KiB
Python
from datetime import date
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from django.test import TestCase
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from location_data.data_driven_subdivision import (
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EmptyObservationDatasetError,
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build_clustering_dataset,
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sync_block_subdivision_with_result,
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)
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from location_data.models import (
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AnalysisGridCell,
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AnalysisGridObservation,
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BlockSubdivision,
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RemoteSensingRun,
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RemoteSensingSubdivisionResult,
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SoilLocation,
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)
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class DataDrivenSubdivisionSyncTests(TestCase):
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def setUp(self):
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self.boundary = {
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"type": "Polygon",
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"coordinates": [
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[
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[51.3890, 35.6890],
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[51.3900, 35.6890],
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[51.3900, 35.6900],
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[51.3890, 35.6900],
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[51.3890, 35.6890],
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]
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],
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}
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self.location = SoilLocation.objects.create(
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latitude="35.689200",
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longitude="51.389000",
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farm_boundary=self.boundary,
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)
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self.subdivision = BlockSubdivision.objects.create(
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soil_location=self.location,
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block_code="block-1",
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source_boundary=self.boundary,
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chunk_size_sqm=900,
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status="defined",
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)
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self.run = RemoteSensingRun.objects.create(
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soil_location=self.location,
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block_subdivision=self.subdivision,
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block_code="block-1",
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chunk_size_sqm=900,
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temporal_start=date(2025, 1, 1),
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temporal_end=date(2025, 1, 31),
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status=RemoteSensingRun.STATUS_SUCCESS,
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)
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def test_sync_block_subdivision_with_result_updates_saved_sub_blocks(self):
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cell_1 = AnalysisGridCell.objects.create(
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soil_location=self.location,
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block_subdivision=self.subdivision,
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block_code="block-1",
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cell_code="cell-1",
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chunk_size_sqm=900,
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geometry=self.boundary,
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centroid_lat="35.689200",
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centroid_lon="51.389200",
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)
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cell_2 = AnalysisGridCell.objects.create(
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soil_location=self.location,
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block_subdivision=self.subdivision,
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block_code="block-1",
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cell_code="cell-2",
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chunk_size_sqm=900,
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geometry=self.boundary,
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centroid_lat="35.689700",
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centroid_lon="51.389700",
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)
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observation_1 = AnalysisGridObservation.objects.create(
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cell=cell_1,
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run=self.run,
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temporal_start=date(2025, 1, 1),
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temporal_end=date(2025, 1, 31),
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ndvi=0.5,
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)
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observation_2 = AnalysisGridObservation.objects.create(
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cell=cell_2,
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run=self.run,
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temporal_start=date(2025, 1, 1),
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temporal_end=date(2025, 1, 31),
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ndvi=0.7,
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)
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result = RemoteSensingSubdivisionResult.objects.create(
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soil_location=self.location,
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run=self.run,
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block_subdivision=self.subdivision,
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block_code="block-1",
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chunk_size_sqm=900,
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temporal_start=date(2025, 1, 1),
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temporal_end=date(2025, 1, 31),
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cluster_count=2,
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selected_features=["ndvi"],
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metadata={
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"used_cell_count": 2,
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"skipped_cell_count": 0,
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"inertia_curve": [{"k": 1, "sse": 1.0}, {"k": 2, "sse": 0.1}],
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},
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)
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sync_block_subdivision_with_result(
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block_subdivision=self.subdivision,
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result=result,
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observations=[observation_1, observation_2],
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cluster_summaries=[
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{
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"cluster_label": 0,
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"centroid_lat": 35.6892,
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"centroid_lon": 51.3892,
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"cell_count": 1,
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"cell_codes": ["cell-1"],
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},
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{
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"cluster_label": 1,
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"centroid_lat": 35.6897,
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"centroid_lon": 51.3897,
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"cell_count": 1,
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"cell_codes": ["cell-2"],
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},
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],
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)
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self.subdivision.refresh_from_db()
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self.assertEqual(self.subdivision.status, "subdivided")
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self.assertEqual(self.subdivision.grid_point_count, 2)
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self.assertEqual(self.subdivision.centroid_count, 2)
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self.assertEqual(self.subdivision.grid_points[0]["cell_code"], "cell-1")
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self.assertEqual(self.subdivision.centroid_points[0]["sub_block_code"], "cluster-0")
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self.assertEqual(
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self.subdivision.metadata["data_driven_subdivision"]["cluster_count"],
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2,
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)
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def test_build_clustering_dataset_raises_clear_error_when_all_selected_features_are_null(self):
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cell = AnalysisGridCell.objects.create(
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soil_location=self.location,
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block_subdivision=self.subdivision,
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block_code="block-1",
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cell_code="cell-null",
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chunk_size_sqm=900,
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geometry=self.boundary,
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centroid_lat="35.689200",
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centroid_lon="51.389200",
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)
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observation = AnalysisGridObservation.objects.create(
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cell=cell,
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run=self.run,
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temporal_start=date(2025, 1, 1),
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temporal_end=date(2025, 1, 31),
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metadata={"job_refs": {"ndvi": "job-1"}},
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)
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with self.assertLogs("location_data.data_driven_subdivision", level="ERROR") as captured:
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with self.assertRaisesRegex(
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EmptyObservationDatasetError,
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"Upstream processing completed but no usable feature values were persisted.",
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):
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build_clustering_dataset(
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observations=[observation],
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selected_features=["ndvi", "ndwi", "lst_c", "soil_vv_db"],
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run=self.run,
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location=self.location,
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)
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joined = "\n".join(captured.output)
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self.assertIn("No usable observations available for clustering", joined)
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self.assertIn('"run_id": {}'.format(self.run.id), joined)
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self.assertIn('"region_id": {}'.format(self.location.id), joined)
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