diff --git a/tests/expected/evald/CONT_TBL.csv b/tests/expected/evald/CONT_TBL.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2a6370d276de47340b13af813dc3d952d6139c34
--- /dev/null
+++ b/tests/expected/evald/CONT_TBL.csv
@@ -0,0 +1,204 @@
+157.,19.,15.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+157.,19.,15.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+157.,19.,15.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+157.,19.,15.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,19.,14.,120.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,21.,14.,118.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,21.,14.,118.
+200.,15.,8.,88.
+220.,21.,6.,64.
+nan,nan,nan,nan
+158.,21.,14.,118.
+200.,16.,8.,87.
+221.,21.,5.,64.
+nan,nan,nan,nan
diff --git a/tests/test_determinist.py b/tests/test_determinist.py
index 980493a4c076b5a1336819af136966fe6c5f494f..7757d64602e11f6fa189033107695102b9de9041 100644
--- a/tests/test_determinist.py
+++ b/tests/test_determinist.py
@@ -8,12 +8,19 @@ import evalhyd
_prd = numpy.genfromtxt("./data/q_prd.csv", delimiter=',')[:, :]
_obs = numpy.genfromtxt("./data/q_obs.csv", delimiter=',')[numpy.newaxis, :]
+_thr = numpy.repeat(
+ numpy.array([[690, 534, 445, numpy.nan]]), repeats=_prd.shape[0], axis=0
+)
+_events = "high"
+
# list all available deterministic metrics
_all_metrics = (
# errors-based
'MAE', 'MARE', 'MSE', 'RMSE',
# efficiencies-based
'NSE', 'KGE', 'KGE_D', 'KGEPRIME', 'KGEPRIME_D',
+ # contingency table-based
+ 'CONT_TBL'
)
# list all available deterministic diagnostics
@@ -30,12 +37,18 @@ class TestMetrics(unittest.TestCase):
[:, numpy.newaxis, numpy.newaxis]
) for metric in _all_metrics
}
+ # /!\ stacked-up thresholds in CSV file for CONT_TBL
+ # because 5D metric so need to reshape array
+ expected['CONT_TBL'] = (
+ expected['CONT_TBL'].reshape(expected['NSE'].shape + (4, 4))
+ )
def test_metrics_2d(self):
for metric in self.expected.keys():
with self.subTest(metric=metric):
numpy.testing.assert_almost_equal(
- evalhyd.evald(_obs, _prd, [metric])[0],
+ evalhyd.evald(_obs, _prd, [metric],
+ q_thr=_thr, events=_events)[0],
self.expected[metric]
)
@@ -43,7 +56,8 @@ class TestMetrics(unittest.TestCase):
for metric in self.expected.keys():
with self.subTest(metric=metric):
numpy.testing.assert_almost_equal(
- evalhyd.evald(_obs[0], _prd[0], [metric])[0],
+ evalhyd.evald(_obs[0], _prd[0], [metric],
+ q_thr=_thr[0], events=_events)[0],
[self.expected[metric][0]]
)
@@ -51,31 +65,48 @@ class TestMetrics(unittest.TestCase):
class TestTransform(unittest.TestCase):
def test_transform_sqrt(self):
- numpy.testing.assert_almost_equal(
- evalhyd.evald(_obs, _prd, ["NSE"], transform="sqrt")[0],
- evalhyd.evald(_obs ** 0.5, _prd ** 0.5, ["NSE"])[0]
- )
+ for metric in _all_metrics:
+ with self.subTest(metric=metric):
+ numpy.testing.assert_almost_equal(
+ evalhyd.evald(_obs, _prd, ["NSE"], transform="sqrt",
+ q_thr=_thr, events=_events)[0],
+ evalhyd.evald(_obs ** 0.5, _prd ** 0.5, ["NSE"],
+ q_thr=_thr ** 0.5, events=_events)[0]
+ )
def test_transform_inv(self):
eps = 0.01 * numpy.mean(_obs)
- numpy.testing.assert_almost_equal(
- evalhyd.evald(_obs, _prd, ["NSE"], transform="inv")[0],
- evalhyd.evald(1 / (_obs + eps), 1 / (_prd + eps), ["NSE"])[0]
- )
+ for metric in _all_metrics:
+ with self.subTest(metric=metric):
+ numpy.testing.assert_almost_equal(
+ evalhyd.evald(_obs, _prd, ["NSE"], transform="inv",
+ q_thr=_thr, events=_events)[0],
+ evalhyd.evald(1 / (_obs + eps), 1 / (_prd + eps), ["NSE"],
+ q_thr=1 / (_thr + eps), events=_events)[0]
+ )
def test_transform_log(self):
eps = 0.01 * numpy.mean(_obs)
- numpy.testing.assert_almost_equal(
- evalhyd.evald(_obs, _prd, ["NSE"], transform="log")[0],
- evalhyd.evald(numpy.log(_obs + eps), numpy.log(_prd + eps),
- ["NSE"])[0]
- )
+ for metric in _all_metrics:
+ with self.subTest(metric=metric):
+ numpy.testing.assert_almost_equal(
+ evalhyd.evald(_obs, _prd, ["NSE"], transform="log",
+ q_thr=_thr, events=_events)[0],
+ evalhyd.evald(numpy.log(_obs + eps), numpy.log(_prd + eps),
+ ["NSE"],
+ q_thr=numpy.log(_thr + eps), events=_events)[0]
+ )
def test_transform_pow(self):
- numpy.testing.assert_almost_equal(
- evalhyd.evald(_obs, _prd, ["NSE"], transform="pow", exponent=0.3)[0],
- evalhyd.evald(_obs ** 0.3, _prd ** 0.3, ["NSE"])[0]
- )
+ for metric in _all_metrics:
+ with self.subTest(metric=metric):
+ numpy.testing.assert_almost_equal(
+ evalhyd.evald(_obs, _prd, ["NSE"],
+ q_thr=_thr, events=_events,
+ transform="pow", exponent=0.3)[0],
+ evalhyd.evald(_obs ** 0.3, _prd ** 0.3, ["NSE"],
+ q_thr=_thr ** 0.3, events=_events)[0]
+ )
class TestMasking(unittest.TestCase):
@@ -90,8 +121,10 @@ class TestMasking(unittest.TestCase):
prd = _prd[..., 99:].copy()
numpy.testing.assert_almost_equal(
- evalhyd.evald(_obs, _prd, ["NSE"], t_msk=msk)[0],
- evalhyd.evald(obs, prd, ["NSE"])[0]
+ evalhyd.evald(_obs, _prd, ["NSE"],
+ q_thr=_thr, events=_events, t_msk=msk)[0],
+ evalhyd.evald(obs, prd, ["NSE"],
+ q_thr=_thr, events=_events)[0]
)
def test_conditions(self):
@@ -106,8 +139,10 @@ class TestMasking(unittest.TestCase):
prd = _prd[..., msk].copy()
numpy.testing.assert_almost_equal(
- evalhyd.evald(_obs, _prd, ["NSE"], m_cdt=cdt)[0],
- evalhyd.evald(obs, prd, ["NSE"])[0]
+ evalhyd.evald(_obs, _prd, ["NSE"],
+ q_thr=_thr, events=_events, m_cdt=cdt)[0],
+ evalhyd.evald(obs, prd, ["NSE"],
+ q_thr=_thr, events=_events)[0]
)
with self.subTest(conditions="observed streamflow statistics"):
@@ -121,8 +156,10 @@ class TestMasking(unittest.TestCase):
prd = _prd[..., msk].copy()
numpy.testing.assert_almost_equal(
- evalhyd.evald(_obs, _prd, ["NSE"], m_cdt=cdt)[0],
- evalhyd.evald(obs, prd, ["NSE"])[0]
+ evalhyd.evald(_obs, _prd, ["NSE"],
+ q_thr=_thr, events=_events, m_cdt=cdt)[0],
+ evalhyd.evald(obs, prd, ["NSE"],
+ q_thr=_thr, events=_events)[0]
)
with self.subTest(conditions="time indices"):
@@ -138,15 +175,17 @@ class TestMasking(unittest.TestCase):
prd = _prd[..., 20:].copy()
numpy.testing.assert_almost_equal(
- evalhyd.evald(_obs, _prd, ["NSE"], m_cdt=cdt)[0],
- evalhyd.evald(obs, prd, ["NSE"])[0]
+ evalhyd.evald(_obs, _prd, ["NSE"],
+ q_thr=_thr, events=_events, m_cdt=cdt)[0],
+ evalhyd.evald(obs, prd, ["NSE"],
+ q_thr=_thr, events=_events)[0]
)
class TestMissingData(unittest.TestCase):
def test_nan(self):
- for metric in ('RMSE', 'NSE', 'KGE', 'KGEPRIME'):
+ for metric in _all_metrics:
obs = numpy.array(
[[4.7, numpy.nan, 5.5, 2.7, 4.1]]
)
@@ -155,9 +194,16 @@ class TestMissingData(unittest.TestCase):
[numpy.nan, 4.2, 4.7, 4.3, 3.3],
[5.3, 5.2, 5.7, numpy.nan, 3.9]]
)
+ thr = numpy.array(
+ [[4., 5.],
+ [4., 5.],
+ [4., 5.]]
+ )
+ events = "low"
with self.subTest(metric=metric):
- res = evalhyd.evald(obs, prd, [metric])[0]
+ res = evalhyd.evald(obs, prd, [metric],
+ q_thr=thr, events=events)[0]
for i in range(prd.shape[0]):
msk = ~numpy.isnan(obs[0]) & ~numpy.isnan(prd[i])
@@ -169,7 +215,8 @@ class TestMissingData(unittest.TestCase):
evalhyd.evald(
obs[:, msk],
prd[i, msk][numpy.newaxis],
- [metric]
+ [metric],
+ q_thr=thr[i, :][numpy.newaxis], events=events
)[0]
)
@@ -190,21 +237,30 @@ class TestUncertainty(unittest.TestCase):
obs_3yrs = numpy.hstack((obs_1yr,) * 3)
prd_3yrs = numpy.hstack((prd_1yr,) * 3)
- for metric in ('RMSE', 'NSE', 'KGE', 'KGEPRIME'):
+ thr = numpy.repeat(
+ numpy.array([[690, 534, 445, numpy.nan]]),
+ repeats=prd_1yr.shape[0], axis=0
+ )
+ events = "low"
+
+ for metric in _all_metrics:
with self.subTest(metric=metric):
numpy.testing.assert_almost_equal(
# bootstrap with only one year of data
# (compare last sample only to have matching dimensions)
evalhyd.evald(
obs_1yr, prd_1yr, [metric],
+ q_thr=thr,
+ events=events,
bootstrap={
"n_samples": 10, "len_sample": 3, "summary": 0
},
dts=dts_1yr
- )[0][..., [0]],
+ )[0][:, :, [0]],
# repeat year of data three times to correspond to a
# bootstrap sample of length 3
- evalhyd.evald(obs_3yrs, prd_3yrs, [metric])[0]
+ evalhyd.evald(obs_3yrs, prd_3yrs, [metric],
+ q_thr=thr, events=events)[0]
)