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Mohammad Sajad Pourajam
2026-05-13 16:45:54 +03:30
parent 948c062b93
commit 46fe62fa04
96 changed files with 3834 additions and 155 deletions
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location_data/test_cluster_recommendation_api.py
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from datetime import date
from unittest.mock import patch
from django.test import TestCase, override_settings
from rest_framework.test import APIClient
from farm_data.models import FarmPlantAssignment, PlantCatalogSnapshot, SensorData
from location_data.models import (
AnalysisGridCell,
AnalysisGridObservation,
BlockSubdivision,
RemoteSensingClusterAssignment,
RemoteSensingClusterBlock,
RemoteSensingRun,
RemoteSensingSubdivisionResult,
SoilLocation,
)
from weather.models import WeatherForecast
@override_settings(ROOT_URLCONF="location_data.urls")
class RemoteSensingClusterRecommendationApiTests(TestCase):
def setUp(self):
self.client = APIClient()
self.boundary = {
"type": "Polygon",
"coordinates": [
[
[51.3890, 35.6890],
[51.3900, 35.6890],
[51.3900, 35.6900],
[51.3890, 35.6900],
[51.3890, 35.6890],
]
],
}
self.location = SoilLocation.objects.create(
latitude="35.689200",
longitude="51.389000",
farm_boundary=self.boundary,
)
self.location.set_input_block_count(1)
self.location.save(update_fields=["input_block_count", "block_layout", "updated_at"])
self.farm = SensorData.objects.create(
farm_uuid="11111111-1111-1111-1111-111111111111",
center_location=self.location,
sensor_payload={},
)
for day_index in range(1, 5):
WeatherForecast.objects.create(
location=self.location,
forecast_date=date(2025, 2, day_index),
temperature_min=12.0,
temperature_max=24.0,
temperature_mean=18.0,
precipitation=1.0,
precipitation_probability=25.0,
humidity_mean=55.0,
wind_speed_max=10.0,
et0=3.0,
weather_code=1,
)
self.subdivision = BlockSubdivision.objects.create(
soil_location=self.location,
block_code="block-1",
source_boundary=self.boundary,
chunk_size_sqm=900,
status="subdivided",
)
self.run = RemoteSensingRun.objects.create(
soil_location=self.location,
block_subdivision=self.subdivision,
block_code="block-1",
chunk_size_sqm=900,
temporal_start=date(2025, 1, 1),
temporal_end=date(2025, 1, 31),
status=RemoteSensingRun.STATUS_SUCCESS,
metadata={"stage": "completed"},
)
self.result = RemoteSensingSubdivisionResult.objects.create(
soil_location=self.location,
run=self.run,
block_subdivision=self.subdivision,
block_code="block-1",
chunk_size_sqm=900,
temporal_start=date(2025, 1, 1),
temporal_end=date(2025, 1, 31),
cluster_count=2,
selected_features=["ndvi", "ndwi", "soil_vv_db"],
metadata={"used_cell_count": 2, "skipped_cell_count": 0},
)
self.cell_1 = AnalysisGridCell.objects.create(
soil_location=self.location,
block_subdivision=self.subdivision,
block_code="block-1",
cell_code="cell-1",
chunk_size_sqm=900,
geometry=self.boundary,
centroid_lat="35.689250",
centroid_lon="51.389250",
)
self.cell_2 = AnalysisGridCell.objects.create(
soil_location=self.location,
block_subdivision=self.subdivision,
block_code="block-1",
cell_code="cell-2",
chunk_size_sqm=900,
geometry=self.boundary,
centroid_lat="35.689750",
centroid_lon="51.389750",
)
AnalysisGridObservation.objects.create(
cell=self.cell_1,
run=self.run,
temporal_start=date(2025, 1, 1),
temporal_end=date(2025, 1, 31),
ndvi=0.51,
ndwi=0.24,
soil_vv=0.13,
soil_vv_db=-10.0,
metadata={"backend_name": "openeo"},
)
AnalysisGridObservation.objects.create(
cell=self.cell_2,
run=self.run,
temporal_start=date(2025, 1, 1),
temporal_end=date(2025, 1, 31),
ndvi=0.71,
ndwi=0.48,
soil_vv=0.19,
soil_vv_db=-7.5,
metadata={"backend_name": "openeo"},
)
RemoteSensingClusterAssignment.objects.create(
result=self.result,
cell=self.cell_1,
cluster_label=0,
raw_feature_values={"ndvi": 0.51, "ndwi": 0.24, "soil_vv_db": -10.0},
scaled_feature_values={"ndvi": -1.0, "ndwi": -1.0, "soil_vv_db": -1.0},
)
RemoteSensingClusterAssignment.objects.create(
result=self.result,
cell=self.cell_2,
cluster_label=1,
raw_feature_values={"ndvi": 0.71, "ndwi": 0.48, "soil_vv_db": -7.5},
scaled_feature_values={"ndvi": 1.0, "ndwi": 1.0, "soil_vv_db": 1.0},
)
self.cluster_0 = RemoteSensingClusterBlock.objects.create(
result=self.result,
soil_location=self.location,
block_subdivision=self.subdivision,
block_code="block-1",
sub_block_code="cluster-0",
cluster_label=0,
chunk_size_sqm=900,
centroid_lat="35.689250",
centroid_lon="51.389250",
center_cell_code="cell-1",
cell_count=1,
cell_codes=["cell-1"],
geometry=self.boundary,
)
self.cluster_1 = RemoteSensingClusterBlock.objects.create(
result=self.result,
soil_location=self.location,
block_subdivision=self.subdivision,
block_code="block-1",
sub_block_code="cluster-1",
cluster_label=1,
chunk_size_sqm=900,
centroid_lat="35.689750",
centroid_lon="51.389750",
center_cell_code="cell-2",
cell_count=1,
cell_codes=["cell-2"],
geometry=self.boundary,
)
self.location.block_layout = {
"input_block_count": 1,
"default_full_farm": True,
"algorithm_status": "completed",
"blocks": [
{
"block_code": "block-1",
"order": 1,
"source": "input",
"boundary": self.boundary,
"needs_subdivision": True,
"sub_blocks": [
{
"sub_block_code": "cluster-0",
"cluster_label": 0,
"cluster_uuid": str(self.cluster_0.uuid),
},
{
"sub_block_code": "cluster-1",
"cluster_label": 1,
"cluster_uuid": str(self.cluster_1.uuid),
},
],
}
],
}
self.location.save(update_fields=["block_layout", "updated_at"])
self.tomato = PlantCatalogSnapshot.objects.create(
backend_plant_id=101,
name="Tomato",
growth_profile={"simulation": {"crop_parameters": {"crop_name": "Tomato", "MAX_BIOMASS": 14000.0}}},
)
self.wheat = PlantCatalogSnapshot.objects.create(
backend_plant_id=102,
name="Wheat",
growth_profile={"simulation": {"crop_parameters": {"crop_name": "Wheat", "MAX_BIOMASS": 11000.0}}},
)
FarmPlantAssignment.objects.create(farm=self.farm, plant=self.tomato, position=0, stage="vegetative")
FarmPlantAssignment.objects.create(farm=self.farm, plant=self.wheat, position=1, stage="vegetative")
@patch("location_data.cluster_recommendation._simulate_candidate")
def test_cluster_recommendations_return_ranked_plants_for_each_cluster(self, simulate_mock):
def fake_simulation(*, base_payload, soil_parameters, site_parameters):
plant_name = base_payload["crop_parameters"]["crop_name"]
smfcf = float(soil_parameters["SMFCF"])
if plant_name == "Tomato":
yield_estimate = 150.0 if smfcf >= 0.4 else 80.0
else:
yield_estimate = 110.0 if smfcf >= 0.4 else 120.0
return (
{
"engine": "pcse",
"model_name": "Wofost81_NWLP_CWB_CNB",
"metrics": {
"yield_estimate": yield_estimate,
"biomass": yield_estimate * 2,
"max_lai": 4.2,
},
},
None,
)
simulate_mock.side_effect = fake_simulation
response = self.client.get(
"/remote-sensing/cluster-recommendations/",
data={"farm_uuid": str(self.farm.farm_uuid)},
)
self.assertEqual(response.status_code, 200)
payload = response.json()["data"]
self.assertEqual(payload["cluster_count"], 2)
self.assertEqual(payload["evaluated_plant_count"], 2)
self.assertEqual(len(payload["registered_plants"]), 2)
clusters = {item["cluster_label"]: item for item in payload["clusters"]}
self.assertEqual(clusters[0]["resolved_metrics"]["ndvi"], 0.51)
self.assertEqual(clusters[0]["resolved_metrics"]["ndwi"], 0.24)
self.assertEqual(clusters[0]["resolved_metrics"]["soil_vv"], 0.13)
self.assertEqual(clusters[1]["resolved_metrics"]["ndwi"], 0.48)
self.assertEqual(clusters[0]["suggested_plant"]["plant_name"], "Wheat")
self.assertEqual(clusters[1]["suggested_plant"]["plant_name"], "Tomato")
self.assertEqual(clusters[0]["candidate_plants"][0]["score"], 120.0)
self.assertEqual(clusters[1]["candidate_plants"][0]["score"], 150.0)
self.assertEqual(clusters[0]["cluster_block"]["uuid"], str(self.cluster_0.uuid))
self.assertEqual(clusters[1]["cluster_block"]["uuid"], str(self.cluster_1.uuid))
def test_cluster_recommendations_return_400_when_no_plants_registered(self):
FarmPlantAssignment.objects.all().delete()
response = self.client.get(
"/remote-sensing/cluster-recommendations/",
data={"farm_uuid": str(self.farm.farm_uuid)},
)
self.assertEqual(response.status_code, 400)
self.assertEqual(
response.json()["msg"],
"برای این مزرعه هنوز هیچ گیاهی در farm_data ثبت نشده است.",
)