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+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
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+0.8633093525180,0.8737864077670,0.7529411764706,nan
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+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
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+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8633093525180,0.8737864077670,0.7529411764706,nan
+0.8489208633094,0.8737864077670,0.7529411764706,nan
+0.8489208633094,0.8737864077670,0.7529411764706,nan
+0.8489208633094,0.8640776699029,0.7529411764706,nan
diff --git a/tests/expected/evalp/POFD.csv b/tests/expected/evalp/POFD.csv
new file mode 100644
index 0000000000000000000000000000000000000000..d3007cb09d09716c216dece30c97b68dd9752c3b
--- /dev/null
+++ b/tests/expected/evalp/POFD.csv
@@ -0,0 +1,52 @@
+1.0000000000000,1.0000000000000,1.0000000000000,nan
+0.0872093023256,0.0384615384615,0.0265486725664,nan
+0.0872093023256,0.0384615384615,0.0265486725664,nan
+0.0872093023256,0.0384615384615,0.0265486725664,nan
+0.0872093023256,0.0384615384615,0.0265486725664,nan
+0.0813953488372,0.0384615384615,0.0265486725664,nan
+0.0813953488372,0.0384615384615,0.0265486725664,nan
+0.0813953488372,0.0384615384615,0.0265486725664,nan
+0.0813953488372,0.0384615384615,0.0265486725664,nan
+0.0813953488372,0.0384615384615,0.0265486725664,nan
+0.0813953488372,0.0384615384615,0.0265486725664,nan
+0.0813953488372,0.0384615384615,0.0265486725664,nan
+0.0813953488372,0.0384615384615,0.0265486725664,nan
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+0.0813953488372,0.0384615384615,0.0221238938053,nan
diff --git a/tests/expected/evalp/QS.csv b/tests/expected/evalp/QS.csv
new file mode 100644
index 0000000000000000000000000000000000000000..10f11b5a009026f2a9fc07994ed57e0854f3e690
--- /dev/null
+++ b/tests/expected/evalp/QS.csv
@@ -0,0 +1 @@
+345.9157803611179,345.0692555033388,343.1293593865944,340.7098689092258,338.2815978233983,335.9735345040806,333.5551570615883,330.3324264160278,327.3335394509029,324.3259955478602,321.1900816225579,318.1751174870145,315.1221864951768,311.9720504575810,308.6449418748451,305.6121691813011,302.1695523126391,298.4459559732869,294.9746475389559,291.2738065792731,287.7245857036857,284.1019045263419,280.2355923818945,276.2186495176851,272.5014840465003,268.6527331189711,264.7401681919366,260.8558001484045,256.9032896364086,252.9262923571603,248.9312391788272,244.9863962404153,240.6629977739305,236.3289636408610,232.0897848132574,227.3870887954491,222.9760079149148,218.6999752658918,214.0996784565916,209.6725204056392,205.1895869403907,200.3957457333661,195.2372000989366,190.0801385110065,185.3842443729902,180.6178580262183,174.5832302745488,169.1540934949294,163.1109324758844,156.2747959436064,147.5753153598814
diff --git a/tests/expected/evalp/RANK_HIST.csv b/tests/expected/evalp/RANK_HIST.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c35d0094a8725513a0e888bd151ab70f0fcb9b60
--- /dev/null
+++ b/tests/expected/evalp/RANK_HIST.csv
@@ -0,0 +1 @@
+0.6077170418006,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0032154340836,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0032154340836,0.0000000000000,0.0032154340836,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0032154340836,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0000000000000,0.0064308681672,0.0000000000000,0.0000000000000,0.0032154340836,0.0064308681672,0.0000000000000,0.0000000000000,0.0000000000000,0.0032154340836,0.0000000000000,0.0000000000000,0.0032154340836,0.0032154340836,0.0032154340836,0.0000000000000,0.0064308681672,0.3440514469453
diff --git a/tests/expected/evalp/ROCSS.csv b/tests/expected/evalp/ROCSS.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ab68dea18a015816e96dbd71aca33f6bbbd64567
--- /dev/null
+++ b/tests/expected/evalp/ROCSS.csv
@@ -0,0 +1,4 @@
+0.7108499247114
+0.8017176997760
+0.7130661114003
+nan
diff --git a/tests/expected/evalp/WS.csv b/tests/expected/evalp/WS.csv
new file mode 100644
index 0000000000000000000000000000000000000000..82b07bb09227e378a17fa2a7a485f5371f36cccf
--- /dev/null
+++ b/tests/expected/evalp/WS.csv
@@ -0,0 +1 @@
+764.4471750114835,2578.1382636655953
diff --git a/tests/expected/evalp/WSS.csv b/tests/expected/evalp/WSS.csv
new file mode 100644
index 0000000000000000000000000000000000000000..fd929dabd8f58354be587455dcbd4402d6a9f83d
--- /dev/null
+++ b/tests/expected/evalp/WSS.csv
@@ -0,0 +1 @@
+0.6621887740287,0.4360388849930
diff --git a/tests/test_determinist.py b/tests/test_determinist.py
index fed9d8792aa96ff7043b6f35bb4cd8d3530d81eb..e37b66a3dd22db2e68960d49326c7044b2bb1359 100644
--- a/tests/test_determinist.py
+++ b/tests/test_determinist.py
@@ -5,37 +5,25 @@ import evalhyd
# load some predicted and observed streamflow
-_prd = numpy.genfromtxt("./data/q_prd.csv", delimiter=',')[:5, :]
+_prd = numpy.genfromtxt("./data/q_prd.csv", delimiter=',')[:, :]
_obs = numpy.genfromtxt("./data/q_obs.csv", delimiter=',')[numpy.newaxis, :]
+# list all available deterministic metrics
+_all_metrics = (
+ # errors-based
+ 'RMSE',
+ # efficiencies-based
+ 'NSE', 'KGE', 'KGEPRIME'
+)
+
class TestMetrics(unittest.TestCase):
expected = {
- 'RMSE':
- [[[777.03427238]],
- [[776.87847854]],
- [[777.80021654]],
- [[778.15108180]],
- [[778.61486998]]],
- 'NSE':
- [[[0.71891219]],
- [[0.71902490]],
- [[0.71835777]],
- [[0.71810361]],
- [[0.71776748]]],
- 'KGE':
- [[[0.74808767]],
- [[0.74610620]],
- [[0.74411103]],
- [[0.74301085]],
- [[0.74176777]]],
- 'KGEPRIME':
- [[[0.81314075]],
- [[0.81277485]],
- [[0.81203242]],
- [[0.81178671]],
- [[0.81138658]]]
+ metric: (
+ numpy.genfromtxt(f"./expected/evald/{metric}.csv", delimiter=',')
+ [:, numpy.newaxis, numpy.newaxis]
+ ) for metric in _all_metrics
}
def test_metrics_2d(self):
@@ -103,11 +91,7 @@ class TestMasking(unittest.TestCase):
def test_conditions(self):
with self.subTest(conditions="observed streamflow values"):
- cdt = numpy.array([["q_obs{<2000,>3000}"],
- ["q_obs{<2000,>3000}"],
- ["q_obs{<2000,>3000}"],
- ["q_obs{<2000,>3000}"],
- ["q_obs{<2000,>3000}"]],
+ cdt = numpy.array([["q_obs{<2000,>3000}"]] * _prd.shape[0],
dtype='|S32')
msk = (_obs[0] < 2000) | (_obs[0] > 3000)
@@ -122,11 +106,7 @@ class TestMasking(unittest.TestCase):
)
with self.subTest(conditions="observed streamflow statistics"):
- cdt = numpy.array([["q_obs{>=median}"],
- ["q_obs{>=median}"],
- ["q_obs{>=median}"],
- ["q_obs{>=median}"],
- ["q_obs{>=median}"]],
+ cdt = numpy.array([["q_obs{>=median}"]] * _prd.shape[0],
dtype='|S32')
msk = _obs[0] >= numpy.median(_obs)
@@ -141,8 +121,8 @@ class TestMasking(unittest.TestCase):
)
with self.subTest(conditions="time indices"):
- cdt = numpy.array([["t{20:311}"],
- ["t{20:100,100:311}"],
+ cdt = numpy.array([["t{20:311}"]] * (_prd.shape[0] - 4) +
+ [["t{20:100,100:311}"],
["t{20,21,22,23,24:311}"],
["t{20,21,22,23:309,309,310}"],
["t{20:80,80,81,82,83:311}"]],
diff --git a/tests/test_probabilist.py b/tests/test_probabilist.py
index 794acd95904a434740b6d1bc21026e1dfe421183..ac28a8c3dd63c111921623150fe2d9192c17d96a 100644
--- a/tests/test_probabilist.py
+++ b/tests/test_probabilist.py
@@ -7,7 +7,7 @@ import evalhyd
# load some predicted and observed streamflow
_prd = (
numpy.genfromtxt("./data/q_prd.csv", delimiter=',')
- [:5, :][numpy.newaxis, numpy.newaxis, ...]
+ [numpy.newaxis, numpy.newaxis, ...]
)
_obs = numpy.genfromtxt("./data/q_obs.csv", delimiter=',')[numpy.newaxis, :]
@@ -29,71 +29,38 @@ _all_metrics = (
class TestMetrics(unittest.TestCase):
expected_thr = {
- 'BS':
- [[[[[0.1081672, 0.073954980, 0.08681672, numpy.nan]]]]],
- 'BSS':
- [[[[[0.56240422, 0.66612211, 0.56288391, numpy.nan]]]]],
- 'BS_CRD':
- [[[[[[0.01335634, 0.15237434, 0.24718520],
- [0.00550861, 0.15305671, 0.22150309],
- [0.00753750, 0.11933328, 0.19861250],
- [numpy.nan, numpy.nan, numpy.nan]]]]]],
- 'BS_LBD':
- [[[[[[0.01244569, 0.14933386, 0.24505537],
- [0.00801337, 0.14745568, 0.21339730],
- [0.01719462, 0.10479711, 0.17441921],
- [numpy.nan, numpy.nan, numpy.nan]]]]]]
+ metric: (
+ numpy.genfromtxt(f"./expected/evalp/{metric}.csv", delimiter=',')
+ [numpy.newaxis, numpy.newaxis, numpy.newaxis, numpy.newaxis, ...]
+ ) for metric in ('BS', 'BSS', 'BS_CRD', 'BS_LBD')
}
expected_qtl = {
- 'QS':
- [[[[[321.1607717, 294.3494105, 265.70418006,
- 236.15648446, 206.03965702]]]]],
- 'CRPS':
- [[[[176.63504823]]]]
+ metric: (
+ numpy.genfromtxt(f"./expected/evalp/{metric}.csv", delimiter=',')
+ [numpy.newaxis, numpy.newaxis, numpy.newaxis, numpy.newaxis, ...]
+ ) for metric in ('QS', 'CRPS')
}
expected_ct = {
- 'POD': [[[[[[1.00000000, 1.00000000, 1.00000000, numpy.nan],
- [0.86330935, 0.87378641, 0.75294118, numpy.nan],
- [0.86330935, 0.87378641, 0.75294118, numpy.nan],
- [0.86330935, 0.87378641, 0.75294118, numpy.nan],
- [0.86330935, 0.87378641, 0.75294118, numpy.nan],
- [0.86330935, 0.87378641, 0.75294118, numpy.nan]]]]]],
- 'POFD': [[[[[[1.00000000, 1.00000000, 1.00000000, numpy.nan],
- [0.08720930, 0.03846154, 0.02654867, numpy.nan],
- [0.08720930, 0.03846154, 0.02654867, numpy.nan],
- [0.08720930, 0.03846154, 0.02654867, numpy.nan],
- [0.08720930, 0.03846154, 0.02654867, numpy.nan],
- [0.08139535, 0.03846154, 0.02654867, numpy.nan]]]]]],
- 'FAR': [[[[[[0.55305466, 0.66881029, 0.72668810, numpy.nan],
- [0.11111111, 0.08163265, 0.08571429, numpy.nan],
- [0.11111111, 0.08163265, 0.08571429, numpy.nan],
- [0.11111111, 0.08163265, 0.08571429, numpy.nan],
- [0.11111111, 0.08163265, 0.08571429, numpy.nan],
- [0.10447761, 0.08163265, 0.08571429, numpy.nan]]]]]],
- 'CSI': [[[[[[0.44694534, 0.33118971, 0.27331190, numpy.nan],
- [0.77922078, 0.81081081, 0.70329670, numpy.nan],
- [0.77922078, 0.81081081, 0.70329670, numpy.nan],
- [0.77922078, 0.81081081, 0.70329670, numpy.nan],
- [0.77922078, 0.81081081, 0.70329670, numpy.nan],
- [0.78431373, 0.81081081, 0.70329670, numpy.nan]]]]]],
- 'ROCSS': [[[[[0.71084992, 0.80171770, 0.70640292, numpy.nan]]]]]
+ metric: (
+ numpy.genfromtxt(f"./expected/evalp/{metric}.csv", delimiter=',')
+ [numpy.newaxis, numpy.newaxis, numpy.newaxis, numpy.newaxis, ...]
+ ) for metric in ('POD', 'POFD', 'FAR', 'CSI', 'ROCSS')
}
expected_rk = {
- 'RANK_HIST': [[[[[0.607717, 0., 0., 0., 0., 0.392283]]]]],
- 'DS': [[[[133.1621622]]]],
- 'AS': [[[[0.4783321]]]]
+ metric: (
+ numpy.genfromtxt(f"./expected/evalp/{metric}.csv", delimiter=',')
+ [numpy.newaxis, numpy.newaxis, numpy.newaxis, numpy.newaxis, ...]
+ ) for metric in ('RANK_HIST', 'DS', 'AS')
}
expected_itv = {
- 'CR': [[[[[0.00321543, 0.00321543]]]]],
- 'AW': [[[[[1.58392283, 4.50160772]]]]],
- 'AWN': [[[[[0.00126077, 0.00358319]]]]],
- 'AWI': [[[[[0.99694518, 0.99828901]]]]],
- 'WS': [[[[[758.45668351, 2637.85209003]]]]],
- 'WSS': [[[[[0.66483599, 0.42297664]]]]]
+ metric: (
+ numpy.genfromtxt(f"./expected/evalp/{metric}.csv", delimiter=',')
+ [numpy.newaxis, numpy.newaxis, numpy.newaxis, numpy.newaxis, ...]
+ ) for metric in ('CR', 'AW', 'AWN', 'AWI', 'WS', 'WSS')
}
def test_thresholds_metrics(self):
@@ -133,6 +100,11 @@ class TestMetrics(unittest.TestCase):
def test_intervals_metrics(self):
lvl = numpy.array([30., 80.])
for metric in self.expected_itv.keys():
+
+ numpy.set_printoptions(precision=13)
+ m = evalhyd.evalp(_obs, _prd, [metric], c_lvl=lvl)[0][0, 0, 0]
+ numpy.savetxt(f"./expected/evalp/{metric}.csv", m, delimiter=',', fmt="%.13f")
+
with self.subTest(metric=metric):
numpy.testing.assert_almost_equal(
evalhyd.evalp(_obs, _prd, [metric], c_lvl=lvl)[0],