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"""
Comprehensive regression tests for map_raster covering all 4 cases:
1. No antimeridian crossing, no NaN
2. No antimeridian crossing, with NaN
3. With antimeridian crossing, no NaN
4. With antimeridian crossing, with NaN
Uses fake_dataset and fake_ecmwf_0100_1h from tools_test (no I/O).
"""
import numpy as np
from tools_test import build_footprint, fake_dataset, fake_ecmwf_0100_1h
from mapraster.main import map_raster
def test_no_antimeridian_no_nan():
"""
Case 1: No antimeridian crossing, no NaN in ECMWF.
"""
# Create datasets
sar_dataset = fake_dataset(cross_antimeridian=False)
raster = fake_ecmwf_0100_1h(to180=True, with_nan=False)
footprint = build_footprint(sar_dataset)
# Run map_raster
result = map_raster(
raster_ds=raster,
originalDataset=sar_dataset,
footprint=footprint,
cross_antimeridian=False,
)
# Basic checks
assert set(result.data_vars) == {"U10", "V10"}, "Missing variables"
assert result["U10"].shape == sar_dataset["longitude"].shape, "Shape mismatch"
assert result["V10"].shape == sar_dataset["longitude"].shape, "Shape mismatch"
# Check no full NaN
assert not np.all(np.isnan(result["U10"].values)), "U10 is all NaN"
assert not np.all(np.isnan(result["V10"].values)), "V10 is all NaN"
# Reference values (from first run)
reference_values = {
"U10": {
"mean": 6.778062602480738,
"std": 0.0141060230914798,
},
"V10": {
"mean": -0.8602113795325333,
"std": 0.1465258384034195,
},
}
# Compare
u10_mean = float(np.mean(result["U10"].values))
u10_std = float(np.std(result["U10"].values))
v10_mean = float(np.mean(result["V10"].values))
v10_std = float(np.std(result["V10"].values))
tolerance = 1e-6
assert abs(u10_mean - reference_values["U10"]["mean"]) < tolerance
assert abs(u10_std - reference_values["U10"]["std"]) < tolerance
assert abs(v10_mean - reference_values["V10"]["mean"]) < tolerance
assert abs(v10_std - reference_values["V10"]["std"]) < tolerance
print("✓ Case 1 passed: No antimeridian, no NaN")
def test_no_antimeridian_with_nan():
"""
Case 2: No antimeridian crossing, with NaN in ECMWF.
"""
# Create datasets
sar_dataset = fake_dataset(cross_antimeridian=False)
raster = fake_ecmwf_0100_1h(to180=True, with_nan=True)
footprint = build_footprint(sar_dataset)
# Run map_raster
result = map_raster(
raster_ds=raster,
originalDataset=sar_dataset,
footprint=footprint,
cross_antimeridian=False,
)
# Basic checks
assert set(result.data_vars) == {"U10", "V10"}, "Missing variables"
assert result["U10"].shape == sar_dataset["longitude"].shape, "Shape mismatch"
# In this case, the SAR region doesn't overlap with NaN zone
# So we might not have NaN, which is fine
# Just check not all NaN
assert not np.all(np.isnan(result["U10"].values)), "U10 is all NaN"
# Reference values (from first run, using nanmean/nanstd)
reference_values = {
"U10": {
"nanmean": 6.778062602480738,
"nanstd": 0.0141060230914798,
"nan_ratio": 0.0, # No NaN in this region
},
"V10": {
"nanmean": -0.8602113795325333,
"nanstd": 0.1465258384034195,
},
}
u10_mean = float(np.nanmean(result["U10"].values))
u10_std = float(np.nanstd(result["U10"].values))
v10_mean = float(np.nanmean(result["V10"].values))
v10_std = float(np.nanstd(result["V10"].values))
# More relaxed tolerance for NaN case (interpolation differences)
tolerance = 1e-5
assert abs(u10_mean - reference_values["U10"]["nanmean"]) < tolerance or np.isnan(
u10_mean
)
assert abs(v10_mean - reference_values["V10"]["nanmean"]) < tolerance or np.isnan(
v10_mean
)
assert abs(u10_std - reference_values["U10"]["nanstd"]) < tolerance
assert abs(v10_std - reference_values["V10"]["nanstd"]) < tolerance
print("✓ Case 2 passed: No antimeridian, with NaN")
def test_with_antimeridian_no_nan():
"""
Case 3: With antimeridian crossing, no NaN in ECMWF.
"""
# Create datasets
sar_dataset = fake_dataset(cross_antimeridian=True)
# Use 0-360 for antimeridian
raster = fake_ecmwf_0100_1h(to180=False, with_nan=False)
footprint = build_footprint(sar_dataset)
# Run map_raster
result = map_raster(
raster_ds=raster,
originalDataset=sar_dataset,
footprint=footprint,
cross_antimeridian=True,
)
# Basic checks
assert set(result.data_vars) == {"U10", "V10"}, "Missing variables"
assert result["U10"].shape == sar_dataset["longitude"].shape, "Shape mismatch"
# Check no full NaN
assert not np.all(np.isnan(result["U10"].values)), "U10 is all NaN"
assert not np.all(np.isnan(result["V10"].values)), "V10 is all NaN"
# Reference values (from first run)
reference_values = {
"U10": {
"mean": 6.778062602480738,
"std": 0.01410602309147989,
},
"V10": {
"mean": 0.1933075747771534,
"std": 0.16163073381182927,
},
}
u10_mean = float(np.mean(result["U10"].values))
u10_std = float(np.std(result["U10"].values))
v10_mean = float(np.mean(result["V10"].values))
v10_std = float(np.std(result["V10"].values))
tolerance = 1e-6
assert abs(u10_mean - reference_values["U10"]["mean"]) < tolerance
assert abs(u10_std - reference_values["U10"]["std"]) < tolerance
assert abs(v10_mean - reference_values["V10"]["mean"]) < tolerance
assert abs(v10_std - reference_values["V10"]["std"]) < tolerance
print("✓ Case 3 passed: With antimeridian, no NaN")
def test_with_antimeridian_with_nan():
"""
Case 4: With antimeridian crossing, with NaN in ECMWF.
"""
# Create datasets
sar_dataset = fake_dataset(cross_antimeridian=True)
# Use 0-360 for antimeridian
raster = fake_ecmwf_0100_1h(to180=False, with_nan=True)
footprint = build_footprint(sar_dataset)
# Run map_raster
result = map_raster(
raster_ds=raster,
originalDataset=sar_dataset,
footprint=footprint,
cross_antimeridian=True,
)
# Basic checks
assert set(result.data_vars) == {"U10", "V10"}, "Missing variables"
assert result["U10"].shape == sar_dataset["longitude"].shape, "Shape mismatch"
# Should have some NaN but not all
assert not np.all(np.isnan(result["U10"].values)), "U10 is all NaN"
# Reference values (from first run)
reference_values = {
"U10": {
"nanmean": 6.780413853544618,
"nanstd": 0.01312769010318517,
"nan_ratio": 0.6206666666666667, # ~62% NaN due to overlap with NaN zone
},
"V10": {
"nanmean": 0.2644554730704113,
"nanstd": 0.18536406900329389,
},
}
u10_mean = float(np.nanmean(result["U10"].values))
u10_std = float(np.nanstd(result["U10"].values))
v10_mean = float(np.nanmean(result["V10"].values))
v10_std = float(np.nanstd(result["V10"].values))
nan_ratio = float(np.isnan(result["U10"].values).mean())
tolerance = 1e-8
assert abs(u10_mean - reference_values["U10"]["nanmean"]) < tolerance
assert abs(v10_mean - reference_values["V10"]["nanmean"]) < tolerance
assert abs(u10_std - reference_values["U10"]["nanstd"]) < tolerance
assert abs(v10_std - reference_values["V10"]["nanstd"]) < tolerance
# Check NaN ratio is reasonable (within 5% tolerance)
assert abs(nan_ratio - reference_values["U10"]["nan_ratio"]) < 0.05
print("✓ Case 4 passed: With antimeridian, with NaN")
def test_all_cases():
"""
Run all 4 test cases and display summary.
"""
print("\n" + "=" * 50)
print("Running all 4 map_raster regression test cases")
print("=" * 50)
print("\n[1/4] No antimeridian, no NaN...")
test_no_antimeridian_no_nan()
print("\n[2/4] No antimeridian, with NaN...")
test_no_antimeridian_with_nan()
print("\n[3/4] With antimeridian, no NaN...")
test_with_antimeridian_no_nan()
print("\n[4/4] With antimeridian, with NaN...")
test_with_antimeridian_with_nan()
print("\n" + "=" * 50)
print("✓ All 4 test cases passed successfully!")
print("=" * 50 + "\n")
if __name__ == "__main__":
test_all_cases()
|
