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Return NaN from nse and r2 for pandas input with NaN model values #721

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Return NaN from nse and r2 for pandas input with NaN model values #721

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6 changes: 6 additions & 0 deletions src/modelskill/metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -251,6 +251,9 @@ def nash_sutcliffe_efficiency(obs: ArrayLike, model: ArrayLike) -> Any:

Nash, J. E.; Sutcliffe, J. V. (1970). "River flow forecasting through conceptual models part I — A discussion of principles". Journal of Hydrology. 10 (3): 282–290. <https://doi.org/10.1016/0022-1694(70)90255-6>
"""
# np.sum on a pandas Series skips NaN, so convert to get NaN like the other metrics
obs = np.asarray(obs)
model = np.asarray(model)
assert obs.size == model.size

if len(obs) == 0:
Expand Down Expand Up @@ -331,6 +334,9 @@ def r2(obs: ArrayLike, model: ArrayLike) -> Any:
>>> r2(obs,model)
np.float64(0.6379999999999998)
"""
# np.sum on a pandas Series skips NaN, so convert to get NaN like the other metrics
obs = np.asarray(obs)
model = np.asarray(model)
assert obs.size == model.size
if len(obs) == 0:
return np.nan
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13 changes: 13 additions & 0 deletions tests/test_metrics.py
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Expand Up @@ -74,6 +74,19 @@ def test_kge_climatology_model():
assert mtr.kling_gupta_efficiency(obs, mod) == pytest.approx(-0.41, abs=1e-2)


@pytest.mark.parametrize("metric", [mtr.nse, mtr.r2])
@pytest.mark.parametrize(
"model", [[1.0, 2.0, np.nan, np.nan], [np.nan, np.nan, np.nan, np.nan]]
)
def test_nse_r2_nan_in_model_series(metric: Callable, model: list) -> None:
# pandas Series must give the same result as NumPy arrays (NaN), not 1.0
obs = pd.Series([1.0, 2.0, 3.0, 4.0])
mod = pd.Series(model)

assert np.isnan(metric(obs.to_numpy(), mod.to_numpy()))
assert np.isnan(metric(obs, mod))


def test_nse_suboptimal():
obs = np.array([1.0, 0.5, 0])
mod = np.array([1.0, 0.0, 0.5])
Expand Down