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ADD: improved test coverage
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,85 @@ | ||
| import numpy as np | ||
| import pandas as pd | ||
| import pytest | ||
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| from ilamb3.analysis import bias_analysis | ||
| from ilamb3.regions import Regions | ||
| from ilamb3.tests.test_compare import generate_test_dset | ||
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| def gen_quantile_dbase(): | ||
| df = [] | ||
| for r in Regions().regions: | ||
| for th in [70, 80]: | ||
| df.append( | ||
| { | ||
| "variable": "da", | ||
| "region": r, | ||
| "quantile": th, | ||
| "type": "bias", | ||
| "value": np.random.rand(1)[0] * 1e-10, | ||
| "unit": "kg m-2 s-1", | ||
| } | ||
| ) | ||
| return pd.DataFrame(df) | ||
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| @pytest.mark.parametrize( | ||
| "use_uncertainty,mass_weighting,score", | ||
| [ | ||
| (True, False, 0.5037343625713414), | ||
| (False, False, 0.49809129117395395), | ||
| (True, True, 0.6211524133325482), | ||
| (False, True, 0.6162697692652096), | ||
| ], | ||
| ) | ||
| def test_bias_collier2018(use_uncertainty: bool, mass_weighting: bool, score: float): | ||
| grid = dict(nlat=10, nlon=20) | ||
| ref = generate_test_dset(**grid) | ||
| ref["da_bnds"] = generate_test_dset(seed=2, **grid)["da"] * 1e-2 | ||
| ref["da"].attrs["bounds"] = "da_bnds" | ||
| com = generate_test_dset(seed=3, **grid) | ||
| analysis = bias_analysis("da") | ||
| df, _, _ = analysis( | ||
| ref, | ||
| com, | ||
| method="Collier2018", | ||
| use_uncertainty=use_uncertainty, | ||
| mass_weighting=mass_weighting, | ||
| ) | ||
| df = df[df["type"] == "score"] | ||
| print(df.iloc[0].value) | ||
| assert len(df) == 1 | ||
| assert np.allclose(df.iloc[0].value, score) | ||
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| @pytest.mark.skip("incomplete") | ||
| @pytest.mark.parametrize( | ||
| "use_uncertainty,quantile_threshold,score", | ||
| [ | ||
| (True, 80, 0.5037343625713414), | ||
| (False, 80, 0.49809129117395395), | ||
| (False, 70, 0.49809129117395395), | ||
| ], | ||
| ) | ||
| def test_bias_regionalquantiles( | ||
| use_uncertainty: bool, quantile_threshold: bool, score: float | ||
| ): | ||
| grid = dict(nlat=10, nlon=20) | ||
| ref = generate_test_dset(**grid) | ||
| ref["da_bnds"] = generate_test_dset(seed=2, **grid)["da"] * 1e-2 | ||
| ref["da"].attrs["bounds"] = "da_bnds" | ||
| com = generate_test_dset(seed=3, **grid) | ||
| analysis = bias_analysis("da") | ||
| df, _, _ = analysis( | ||
| ref, | ||
| com, | ||
| method="RegionalQuantiles", | ||
| use_uncertainty=use_uncertainty, | ||
| quantile_dbase=gen_quantile_dbase(), | ||
| quantile_threshold=quantile_threshold, | ||
| ) | ||
| df = df[df["type"] == "score"] | ||
| print(df.iloc[0].value) | ||
| assert len(df) == 1 | ||
| assert np.allclose(df.iloc[0].value, score) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,48 @@ | ||
| import numpy as np | ||
| import pandas as pd | ||
| import xarray as xr | ||
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| import ilamb3.compare as cmp | ||
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| def generate_test_dset(seed: int = 1, ntime=None, nlat=None, nlon=None): | ||
| rs = np.random.RandomState(seed) | ||
| coords = [] | ||
| dims = [] | ||
| if ntime is not None: | ||
| time = pd.date_range(start="2000-01-15", periods=ntime, freq="30D") | ||
| coords.append(time) | ||
| dims.append("time") | ||
| if nlat is not None: | ||
| lat = np.linspace(-90, 90, nlat + 1) | ||
| lat = 0.5 * (lat[1:] + lat[:-1]) | ||
| coords.append(lat) | ||
| dims.append("lat") | ||
| if nlon is not None: | ||
| lon = np.linspace(-180, 180, nlon + 1) | ||
| lon = 0.5 * (lon[1:] + lon[:-1]) | ||
| coords.append(lon) | ||
| dims.append("lon") | ||
| ds = xr.Dataset( | ||
| data_vars={ | ||
| "da": xr.DataArray( | ||
| rs.rand(*[len(c) for c in coords]) * 1e-8, coords=coords, dims=dims | ||
| ), | ||
| } | ||
| ) | ||
| ds["da"].attrs["units"] = "kg m-2 s-1" | ||
| return ds | ||
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| def test_nest_spatial_grids(): | ||
| ds1 = generate_test_dset(nlat=2, nlon=3) | ||
| ds2 = generate_test_dset(nlat=5, nlon=7) | ||
| ds1 = ds1.cf.add_bounds("lat") | ||
| ds1_, ds2_ = cmp.nest_spatial_grids(ds1, ds2) | ||
| assert cmp.is_spatially_aligned(ds1_, ds2_) | ||
| assert np.allclose( | ||
| (ds1_ - ds2_).pint.dequantify().sum()["da"].values, -6.06395917e-08 | ||
| ) | ||
| dsa, dsb = cmp.pick_grid_aligned(ds1, ds2, ds1_, ds2_) | ||
| xr.testing.assert_allclose(dsa, ds1_) | ||
| xr.testing.assert_allclose(dsb, ds2_) |
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