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Fix tests #145

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merged 2 commits into from
May 14, 2024
Merged

Fix tests #145

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65bf8e3
fix tests
RondeauG May 14, 2024
25f6731
upd changes
RondeauG May 14, 2024
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2 changes: 1 addition & 1 deletion CHANGES.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@ Breaking changes

Internal changes
^^^^^^^^^^^^^^^^
* N/A
* Tests using the `gamma` distribution were changed to the `gumbel_r` to avoid changes in `xclim v0.49.0`. (:pull:`145`).


v0.3.5 (2024-03-20)
Expand Down
10 changes: 7 additions & 3 deletions tests/test_gis.py
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Expand Up @@ -24,7 +24,9 @@ class TestWatershedDelineation:
)
def test_watershed_delineation_from_coords(self, lng_lat, area):
gdf = xh.gis.watershed_delineation(coordinates=lng_lat)
np.testing.assert_almost_equal([gdf.to_crs(32198).area.values[0]], [area])
np.testing.assert_almost_equal(
[gdf.to_crs(32198).area.values[0]], [area], decimal=3
)

@pytest.mark.parametrize("area", [(18891676494.940426)])
def test_watershed_delineation_from_map(self, area):
Expand All @@ -37,7 +39,9 @@ def test_watershed_delineation_from_map(self, area):
}
]
gdf = xh.gis.watershed_delineation(map=self.m)
np.testing.assert_almost_equal([gdf.to_crs(32198).area.values[0]], [area])
np.testing.assert_almost_equal(
[gdf.to_crs(32198).area.values[0]], [area], decimal=3
)

def test_errors(self):
bad_coordinates = (-35.0, 45.0)
Expand Down Expand Up @@ -117,7 +121,7 @@ def test_watershed_properties_xarray(self, watershed_properties_data):
output_dataset["gravelius"].attrs = {"units": "m/m"}
output_dataset["centroid"].attrs = {"units": ("degree_east", "degree_north")}

xr.testing.assert_equal(ds_properties, output_dataset)
xr.testing.assert_allclose(ds_properties, output_dataset)

@pytest.fixture
def land_classification_data_latest(self):
Expand Down
40 changes: 20 additions & 20 deletions tests/test_local.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,11 +16,11 @@ def test_fit(self):
freq="YS",
as_dataset=True,
)
params = xhfa.local.fit(ds, distributions=["gamma", "pearson3"])
params = xhfa.local.fit(ds, distributions=["gumbel_r", "pearson3"])

np.testing.assert_array_equal(params.dparams, ["a", "skew", "loc", "scale"])
np.testing.assert_array_equal(params.scipy_dist, ["gamma", "pearson3"])
assert params.streamflow.attrs["scipy_dist"] == ["gamma", "pearson3"]
np.testing.assert_array_equal(params.dparams, ["skew", "loc", "scale"])
np.testing.assert_array_equal(params.scipy_dist, ["gumbel_r", "pearson3"])
assert params.streamflow.attrs["scipy_dist"] == ["gumbel_r", "pearson3"]
assert params.streamflow.attrs["estimator"] == "Maximum likelihood"
assert params.streamflow.attrs["long_name"] == "Distribution parameters"
assert (
Expand All @@ -31,8 +31,8 @@ def test_fit(self):
params.streamflow,
np.array(
[
[9.95357815e00, np.nan, -3.07846650e01, 1.56498193e01],
[np.nan, -2.25674044e-05, 1.25012261e02, 4.74238877e01],
[np.nan, 1.01397803e02, 4.28189713e01],
[-2.25674044e-05, 1.25012261e02, 4.74238877e01],
]
),
)
Expand Down Expand Up @@ -71,16 +71,16 @@ def test_min_years(self, miny):
freq="YS",
as_dataset=True,
)
params = xhfa.local.fit(ds, distributions=["gamma"], min_years=miny)
params = xhfa.local.fit(ds, distributions=["gumbel_r"], min_years=miny)
if miny == 10:
np.testing.assert_array_almost_equal(
params.streamflow,
[[9.95357815e00, -3.07846650e01, 1.56498193e01]],
[[101.39780283, 42.81897125]],
)
elif miny == 15:
np.testing.assert_array_almost_equal(
params.streamflow,
[[np.nan, np.nan, np.nan]],
[[np.nan, np.nan]],
)


Expand All @@ -93,7 +93,7 @@ def test_quantiles(mode):
freq="YS",
as_dataset=True,
)
params = xhfa.local.fit(ds, distributions=["gamma", "pearson3"])
params = xhfa.local.fit(ds, distributions=["gumbel_r", "pearson3"])

if mode == "foo":
with pytest.raises(ValueError):
Expand All @@ -105,7 +105,7 @@ def test_quantiles(mode):
np.testing.assert_array_equal(
rp.p_quantile, [0.1, 0.05] if mode == "min" else [0.9, 0.95]
)
np.testing.assert_array_equal(rp.scipy_dist, ["gamma", "pearson3"])
np.testing.assert_array_equal(rp.scipy_dist, ["gumbel_r", "pearson3"])
assert rp.streamflow.attrs["long_name"] == "Return period"
assert (
rp.streamflow.attrs["description"]
Expand All @@ -115,9 +115,9 @@ def test_quantiles(mode):
assert rp.streamflow.attrs["mode"] == mode

ans = np.array(
[[190.66041057, 214.08102761], [185.78830382, 203.01731036]]
[[197.75621673, 228.57850782], [185.78830382, 203.01731036]]
if mode == "max"
else [[66.00067153, 53.58658639], [64.23598869, 47.00660287]]
else [[65.68539153, 54.41731141], [64.23598869, 47.00660287]]
)
np.testing.assert_array_almost_equal(rp.streamflow, ans)

Expand All @@ -130,7 +130,7 @@ def test_criteria():
freq="YS",
as_dataset=True,
)
params = xhfa.local.fit(ds, distributions=["gamma", "pearson3"])
params = xhfa.local.fit(ds, distributions=["gumbel_r", "pearson3"])
crit = xhfa.local.criteria(ds, params)
crit_with_otherdim = xhfa.local.criteria(
ds.expand_dims("otherdim"), params.expand_dims("otherdim")
Expand All @@ -139,14 +139,14 @@ def test_criteria():
crit.streamflow, crit_with_otherdim.streamflow.squeeze("otherdim")
)

np.testing.assert_array_equal(crit.scipy_dist, ["gamma", "pearson3"])
np.testing.assert_array_equal(crit.scipy_dist, ["gumbel_r", "pearson3"])
np.testing.assert_array_equal(crit.criterion, ["aic", "bic", "aicc"])
assert crit.streamflow.attrs["long_name"] == "Information criteria"
assert (
crit.streamflow.attrs["description"]
== "Information criteria for the distribution parameters."
)
assert crit.streamflow.attrs["scipy_dist"] == ["gamma", "pearson3"]
assert crit.streamflow.attrs["scipy_dist"] == ["gumbel_r", "pearson3"]
assert all(
attr not in crit.streamflow.attrs
for attr in ["estimator", "method", "min_years"]
Expand All @@ -156,7 +156,7 @@ def test_criteria():
crit.streamflow,
np.array(
[
[118.19066549, 118.58856076, 118.63510993],
[118.78018303, 119.1780783, 119.22462747],
[118.12140939, 118.51930466, 118.56585383],
]
),
Expand Down Expand Up @@ -245,7 +245,7 @@ class Testprepare_plots:
freq="YS",
as_dataset=True,
)
params = xhfa.local.fit(ds, distributions=["gamma", "pearson3"])
params = xhfa.local.fit(ds, distributions=["gumbel_r", "pearson3"])

def test_prepare_plots_default(self):
result = _prepare_plots(self.params)
Expand All @@ -254,7 +254,7 @@ def test_prepare_plots_default(self):
assert result.return_period.max() == 10000
expected = np.array(
[
[-30.78466504, 63.64266447, 78.19229358, 88.62699148, 97.15369501],
[-np.inf, 63.53899244, 76.87079819, 86.6101638, 94.7189071],
[-np.inf, 61.08708903, 79.72126025, 92.05411647, 101.59650405],
]
)
Expand All @@ -265,7 +265,7 @@ def test_prepare_plots_linear(self):

expected = np.array(
[
[-30.78466504, 262.72577254, 281.56238078, 292.27851004, 299.75980757],
[-np.inf, 299.22362135, 328.79813435, 346.12452498, 358.42515317],
[-np.inf, 235.6878466, 247.41104463, 253.8825982, 258.32114457],
]
)
Expand Down