diff --git a/deps/evalhyd b/deps/evalhyd
index 54efcfeac2ccba66aa1ad98b72d92b0765f2d14b..44e1bdf16d38a4cf51de7a003864be63d91a31fb 160000
--- a/deps/evalhyd
+++ b/deps/evalhyd
@@ -1 +1 @@
-Subproject commit 54efcfeac2ccba66aa1ad98b72d92b0765f2d14b
+Subproject commit 44e1bdf16d38a4cf51de7a003864be63d91a31fb
diff --git a/src/evalhyd-python.cpp b/src/evalhyd-python.cpp
index 3ebb81ebac55c3fa07beb42f3a25110849190508..8e6cfdcb2e53cd1dc3846d3f8359fbe79d076a8b 100644
--- a/src/evalhyd-python.cpp
+++ b/src/evalhyd-python.cpp
@@ -6,12 +6,43 @@
#define MACRO_STRINGIFY(x) STRINGIFY(x)
#define FORCE_IMPORT_ARRAY
+#include <xtensor/xview.hpp>
#include <xtensor-python/pytensor.hpp>
#include "evalhyd/evald.hpp"
#include "evalhyd/evalp.hpp"
namespace py = pybind11;
+using namespace py::literals;
+
+// reshape 1D tensors to 2D tensors
+auto evald_1d(
+ const xt::xtensor<double, 1>& q_obs,
+ const xt::xtensor<double, 1>& q_prd,
+ const std::vector<std::string>& metrics,
+ const std::string& transform = "none",
+ const double exponent = 1,
+ double epsilon = -9,
+ const xt::xtensor<bool, 2>& t_msk = {},
+ const xt::xtensor<std::array<char, 32>, 1>& m_cdt = {},
+ const std::unordered_map<std::string, int>& bootstrap =
+ {{"n_samples", -9}, {"len_sample", -9}, {"summary", 0}},
+ const std::vector<std::string>& dts = {}
+)
+{
+ return evalhyd::evald(
+ xt::view(q_obs, xt::newaxis(), xt::all()),
+ xt::view(q_prd, xt::newaxis(), xt::all()),
+ metrics,
+ transform,
+ exponent,
+ epsilon,
+ t_msk,
+ m_cdt,
+ bootstrap,
+ dts
+ );
+}
// Python Module and Docstrings
PYBIND11_MODULE(evalhyd, m)
@@ -24,30 +55,30 @@ PYBIND11_MODULE(evalhyd, m)
// deterministic evaluation
m.def(
- "evald", evalhyd::evald<1>,
+ "evald", evald_1d,
R"pbdoc(
Function to evaluate deterministic streamflow predictions.
:Parameters:
q_obs: `numpy.ndarray`
- 1D array of streamflow observations. Time steps with
- missing observations must be assigned `numpy.nan`
- values. Those time steps will be ignored both in
- the observations and in the predictions before the
- *metrics* are computed.
- shape: (time,)
+ 1D array of streamflow observations. Time steps with
+ missing observations must be assigned `numpy.nan`
+ values. Those time steps will be ignored both in
+ the observations and in the predictions before the
+ *metrics* are computed.
+ shape: (time,)
q_prd: `numpy.ndarray`
- 1D array of streamflow predictions. Time steps with
- missing predictions must be assigned `numpy.nan`
- values. Those time steps will be ignored both in
- the observations and in the predictions before the
- *metrics* are computed.
- shape: (time,)
+ 1D array of streamflow predictions. Time steps with
+ missing predictions must be assigned `numpy.nan`
+ values. Those time steps will be ignored both in
+ the observations and in the predictions before the
+ *metrics* are computed.
+ shape: (time,)
metrics: `List[str]`
- The sequence of evaluation metrics to be computed.
+ The sequence of evaluation metrics to be computed.
transform: `str`, optional
The transformation to apply to both streamflow observations
@@ -95,7 +126,7 @@ PYBIND11_MODULE(evalhyd, m)
provided, masks must feature the same number of dimensions as
observations and predictions, and it must broadcastable with
both of them.
- shape: (time,)
+ shape: (subsets, time)
m_cdt: `numpy.ndarray`, optional
1D array of masking condition(s) to use to generate
@@ -106,46 +137,69 @@ PYBIND11_MODULE(evalhyd, m)
If not provided and neither is *t_msk*, no subset is
performed. If provided, only one condition per time series
of observations can be provided.
- shape: (1,)
+ shape: (subsets,)
+
+ bootstrap: `dict`, optional
+ Parameters for the bootstrapping method used to estimate the
+ sampling uncertainty in the evaluation of the predictions.
+ Two parameters are mandatory ('n_samples' the number of
+ random samples, 'len_sample' the length of one sample in
+ number of years, and 'summary' the statistics to return to
+ characterise the sampling distribution), and one parameter
+ is optional ('seed'). If not provided, no bootstrapping is
+ performed. If provided, *dts* must also be provided.
+
+ dts: `List[str]`, optional
+ Datetimes. The corresponding date and time for the temporal
+ dimension of the streamflow observations and predictions.
+ The date and time must be specified in a string following
+ the ISO 8601-1:2019 standard, i.e. "YYYY-MM-DD hh:mm:ss"
+ (e.g. the 21st of May 2007 at 4 in the afternoon is
+ "2007-05-21 16:00:00"). If provided, it is only used if
+ *bootstrap* is also provided.
:Returns:
`List[numpy.ndarray]`
The sequence of evaluation metrics computed
in the same order as given in *metrics*.
- shape: [(components,)+]
+ shape: [(1, subsets, samples), ...]
)pbdoc",
py::arg("q_obs"), py::arg("q_prd"), py::arg("metrics"),
- py::arg("transform") = "none", py::arg("exponent") = 1,
+ py::arg("transform") = "none",
+ py::arg("exponent") = 1,
py::arg("epsilon") = -9,
- py::arg("t_msk") = xt::pytensor<bool, 1>({}),
- py::arg("m_cdt") = xt::pytensor<std::array<char, 32>, 1>({})
+ py::arg("t_msk") = xt::pytensor<bool, 2>({0}),
+ py::arg("m_cdt") = xt::pytensor<std::array<char, 32>, 1>({}),
+ py::arg("bootstrap") =
+ py::dict("n_samples"_a=-9, "len_sample"_a=-9, "summary"_a=0),
+ py::arg("dts") = py::list()
);
m.def(
- "evald", evalhyd::evald<2>,
+ "evald", evalhyd::evald,
R"pbdoc(
Function to evaluate deterministic streamflow predictions.
:Parameters:
q_obs: `numpy.ndarray`
- 2D array of streamflow observations. Time steps with
- missing observations must be assigned `numpy.nan`
- values. Those time steps will be ignored both in
- the observations and in the predictions before the
- *metrics* are computed.
- shape: (1+, time)
+ 2D array of streamflow observations. Time steps with
+ missing observations must be assigned `numpy.nan`
+ values. Those time steps will be ignored both in
+ the observations and in the predictions before the
+ *metrics* are computed.
+ shape: (1, time)
q_prd: `numpy.ndarray`
- 2D array of streamflow predictions. Time steps with
- missing predictions must be assigned `numpy.nan`
- values. Those time steps will be ignored both in
- the observations and in the predictions before the
- *metrics* are computed.
- shape: (1+, time)
+ 2D array of streamflow predictions. Time steps with
+ missing predictions must be assigned `numpy.nan`
+ values. Those time steps will be ignored both in
+ the observations and in the predictions before the
+ *metrics* are computed.
+ shape: (series, time)
metrics: `List[str]`
- The sequence of evaluation metrics to be computed.
+ The sequence of evaluation metrics to be computed.
transform: `str`, optional
The transformation to apply to both streamflow observations
@@ -193,10 +247,10 @@ PYBIND11_MODULE(evalhyd, m)
provided, masks must feature the same number of dimensions as
observations and predictions, and it must broadcastable with
both of them.
- shape: (1+, time)
+ shape: (subsets, time)
m_cdt: `numpy.ndarray`, optional
- 2D array of masking condition(s) to use to generate
+ 1D array of masking condition(s) to use to generate
temporal subsets. Each condition consists in a string and
can be specified on observed streamflow values/statistics
(mean, median, quantile), or on time indices. If provided
@@ -204,20 +258,43 @@ PYBIND11_MODULE(evalhyd, m)
If not provided and neither is *t_msk*, no subset is
performed. If provided, only one condition per time series
of observations can be provided.
- shape: (1+, 1)
+ shape: (subsets,)
+
+ bootstrap: `dict`, optional
+ Parameters for the bootstrapping method used to estimate the
+ sampling uncertainty in the evaluation of the predictions.
+ Two parameters are mandatory ('n_samples' the number of
+ random samples, 'len_sample' the length of one sample in
+ number of years, and 'summary' the statistics to return to
+ characterise the sampling distribution), and one parameter
+ is optional ('seed'). If not provided, no bootstrapping is
+ performed. If provided, *dts* must also be provided.
+
+ dts: `List[str]`, optional
+ Datetimes. The corresponding date and time for the temporal
+ dimension of the streamflow observations and predictions.
+ The date and time must be specified in a string following
+ the ISO 8601-1:2019 standard, i.e. "YYYY-MM-DD hh:mm:ss"
+ (e.g. the 21st of May 2007 at 4 in the afternoon is
+ "2007-05-21 16:00:00"). If provided, it is only used if
+ *bootstrap* is also provided.
:Returns:
`List[numpy.ndarray]`
- The sequence of evaluation metrics computed
- in the same order as given in *metrics*.
- shape: [(1+, components), ...]
+ The sequence of evaluation metrics computed
+ in the same order as given in *metrics*.
+ shape: [(series, subsets, samples), ...]
)pbdoc",
py::arg("q_obs"), py::arg("q_prd"), py::arg("metrics"),
- py::arg("transform") = "none", py::arg("exponent") = 1,
+ py::arg("transform") = "none",
+ py::arg("exponent") = 1,
py::arg("epsilon") = -9,
py::arg("t_msk") = xt::pytensor<bool, 2>({0}),
- py::arg("m_cdt") = xt::pytensor<std::array<char, 32>, 2>({0})
+ py::arg("m_cdt") = xt::pytensor<std::array<char, 32>, 1>({}),
+ py::arg("bootstrap") =
+ py::dict("n_samples"_a=-9, "len_sample"_a=-9, "summary"_a=0),
+ py::arg("dts") = py::list()
);
// probabilistic evaluation
@@ -229,63 +306,85 @@ PYBIND11_MODULE(evalhyd, m)
:Parameters:
q_obs: `numpy.ndarray`
- 2D array of streamflow observations. Time steps with
- missing observations must be assigned `numpy.nan`
- values. Those time steps will be ignored both in
- the observations and in the predictions before the
- *metrics* are computed.
- shape: (sites, time)
+ 2D array of streamflow observations. Time steps with
+ missing observations must be assigned `numpy.nan`
+ values. Those time steps will be ignored both in
+ the observations and in the predictions before the
+ *metrics* are computed.
+ shape: (sites, time)
q_prd: `numpy.ndarray`
- 4D array of streamflow predictions. Time steps with
- missing predictions must be assigned `numpy.nan`
- values. Those time steps will be ignored both in
- the observations and in the predictions before the
- *metrics* are computed.
- shape: (sites, lead times, members, time)
+ 4D array of streamflow predictions. Time steps with
+ missing predictions must be assigned `numpy.nan`
+ values. Those time steps will be ignored both in
+ the observations and in the predictions before the
+ *metrics* are computed.
+ shape: (sites, lead times, members, time)
metrics: `List[str]`
- The sequence of evaluation metrics to be computed.
+ The sequence of evaluation metrics to be computed.
q_thr: `List[float]`, optional
- The streamflow threshold(s) to consider for the *metrics*
- assessing the prediction of exceedance events. If not
- provided, set to default value as an empty `list`.
- shape: (thresholds,)
+ The streamflow threshold(s) to consider for the *metrics*
+ assessing the prediction of exceedance events. If not
+ provided, set to default value as an empty `list`.
+ shape: (thresholds,)
t_msk: `numpy.ndarray`, optional
- 4D array of masks to generate temporal subsets of the whole
- streamflow time series (where True/False is used for the
- time steps to include/discard in a given subset). If not
- provided, no subset is performed and only one set of metrics
- is returned corresponding to the whole time series. If
- provided, as many sets of metrics are returned as they are
- masks provided.
- shape: (sites, lead times, subsets, time)
+ 4D array of masks to generate temporal subsets of the whole
+ streamflow time series (where True/False is used for the
+ time steps to include/discard in a given subset). If not
+ provided, no subset is performed and only one set of metrics
+ is returned corresponding to the whole time series. If
+ provided, as many sets of metrics are returned as they are
+ masks provided.
+ shape: (sites, lead times, subsets, time)
m_cdt: `numpy.ndarray`, optional
- 2D array of conditions to generate temporal subsets. Each
- condition consists in a string and can be specified on
- observed/predicted streamflow values/statistics (mean,
- median, quantile), or on time indices. If provided in
- combination with t_msk, the latter takes precedence. If not
- provided and neither is t_msk, no subset is performed and
- only one set of metrics is returned corresponding to the
- whole time series. If provided, as many sets of metrics are
- returned as they are conditions provided.
- shape: (sites, subsets)
+ 2D array of conditions to generate temporal subsets. Each
+ condition consists in a string and can be specified on
+ observed/predicted streamflow values/statistics (mean,
+ median, quantile), or on time indices. If provided in
+ combination with t_msk, the latter takes precedence. If not
+ provided and neither is t_msk, no subset is performed and
+ only one set of metrics is returned corresponding to the
+ whole time series. If provided, as many sets of metrics are
+ returned as they are conditions provided.
+ shape: (sites, subsets)
+
+ bootstrap: `dict`, optional
+ Parameters for the bootstrapping method used to estimate the
+ sampling uncertainty in the evaluation of the predictions.
+ Two parameters are mandatory ('n_samples' the number of
+ random samples, 'len_sample' the length of one sample in
+ number of years, and 'summary' the statistics to return to
+ characterise the sampling distribution), and one parameter
+ is optional ('seed'). If not provided, no bootstrapping is
+ performed. If provided, *dts* must also be provided.
+
+ dts: `List[str]`, optional
+ Datetimes. The corresponding date and time for the temporal
+ dimension of the streamflow observations and predictions.
+ The date and time must be specified in a string following
+ the ISO 8601-1:2019 standard, i.e. "YYYY-MM-DD hh:mm:ss"
+ (e.g. the 21st of May 2007 at 4 in the afternoon is
+ "2007-05-21 16:00:00"). If provided, it is only used if
+ *bootstrap* is also provided.
:Returns:
`List[numpy.ndarray]`
- The sequence of evaluation metrics computed
- in the same order as given in *metrics*.
- shape: [(sites, lead times, subsets, {quantiles,} {thresholds,} {components}), ...]
+ The sequence of evaluation metrics computed
+ in the same order as given in *metrics*.
+ shape: [(sites, lead times, subsets, samples, {quantiles,} {thresholds,} {components}), ...]
)pbdoc",
py::arg("q_obs"), py::arg("q_prd"), py::arg("metrics"),
py::arg("q_thr") = xt::pytensor<double, 2>({0}),
py::arg("t_msk") = xt::pytensor<bool, 4>({0}),
- py::arg("m_cdt") = xt::pytensor<std::array<char, 32>, 2>({0})
+ py::arg("m_cdt") = xt::pytensor<std::array<char, 32>, 2>({0}),
+ py::arg("bootstrap") =
+ py::dict("n_samples"_a=-9, "len_sample"_a=-9, "summary"_a=0),
+ py::arg("dts") = py::list()
);
#ifdef VERSION_INFO
diff --git a/tests/data/q_obs_1yr.csv b/tests/data/q_obs_1yr.csv
new file mode 100644
index 0000000000000000000000000000000000000000..97a759eae9f6c32e6d2acf3dcef0828550e21dfe
--- /dev/null
+++ b/tests/data/q_obs_1yr.csv
@@ -0,0 +1,2 @@
+1963-10-01 00:00:00,1963-10-02 00:00:00,1963-10-03 00:00:00,1963-10-04 00:00:00,1963-10-05 00:00:00,1963-10-06 00:00:00,1963-10-07 00:00:00,1963-10-08 00:00:00,1963-10-09 00:00:00,1963-10-10 00:00:00,1963-10-11 00:00:00,1963-10-12 00:00:00,1963-10-13 00:00:00,1963-10-14 00:00:00,1963-10-15 00:00:00,1963-10-16 00:00:00,1963-10-17 00:00:00,1963-10-18 00:00:00,1963-10-19 00:00:00,1963-10-20 00:00:00,1963-10-21 00:00:00,1963-10-22 00:00:00,1963-10-23 00:00:00,1963-10-24 00:00:00,1963-10-25 00:00:00,1963-10-26 00:00:00,1963-10-27 00:00:00,1963-10-28 00:00:00,1963-10-29 00:00:00,1963-10-30 00:00:00,1963-10-31 00:00:00,1963-11-01 00:00:00,1963-11-02 00:00:00,1963-11-03 00:00:00,1963-11-04 00:00:00,1963-11-05 00:00:00,1963-11-06 00:00:00,1963-11-07 00:00:00,1963-11-08 00:00:00,1963-11-09 00:00:00,1963-11-10 00:00:00,1963-11-11 00:00:00,1963-11-12 00:00:00,1963-11-13 00:00:00,1963-11-14 00:00:00,1963-11-15 00:00:00,1963-11-16 00:00:00,1963-11-17 00:00:00,1963-11-18 00:00:00,1963-11-19 00:00:00,1963-11-20 00:00:00,1963-11-21 00:00:00,1963-11-22 00:00:00,1963-11-23 00:00:00,1963-11-24 00:00:00,1963-11-25 00:00:00,1963-11-26 00:00:00,1963-11-27 00:00:00,1963-11-28 00:00:00,1963-11-29 00:00:00,1963-11-30 00:00:00,1963-12-01 00:00:00,1963-12-02 00:00:00,1963-12-03 00:00:00,1963-12-04 00:00:00,1963-12-05 00:00:00,1963-12-06 00:00:00,1963-12-07 00:00:00,1963-12-08 00:00:00,1963-12-09 00:00:00,1963-12-10 00:00:00,1963-12-11 00:00:00,1963-12-12 00:00:00,1963-12-13 00:00:00,1963-12-14 00:00:00,1963-12-15 00:00:00,1963-12-16 00:00:00,1963-12-17 00:00:00,1963-12-18 00:00:00,1963-12-19 00:00:00,1963-12-20 00:00:00,1963-12-21 00:00:00,1963-12-22 00:00:00,1963-12-23 00:00:00,1963-12-24 00:00:00,1963-12-25 00:00:00,1963-12-26 00:00:00,1963-12-27 00:00:00,1963-12-28 00:00:00,1963-12-29 00:00:00,1963-12-30 00:00:00,1963-12-31 00:00:00,1964-01-01 00:00:00,1964-01-02 00:00:00,1964-01-03 00:00:00,1964-01-04 00:00:00,1964-01-05 00:00:00,1964-01-06 00:00:00,1964-01-07 00:00:00,1964-01-08 00:00:00,1964-01-09 00:00:00,1964-01-10 00:00:00,1964-01-11 00:00:00,1964-01-12 00:00:00,1964-01-13 00:00:00,1964-01-14 00:00:00,1964-01-15 00:00:00,1964-01-16 00:00:00,1964-01-17 00:00:00,1964-01-18 00:00:00,1964-01-19 00:00:00,1964-01-20 00:00:00,1964-01-21 00:00:00,1964-01-22 00:00:00,1964-01-23 00:00:00,1964-01-24 00:00:00,1964-01-25 00:00:00,1964-01-26 00:00:00,1964-01-27 00:00:00,1964-01-28 00:00:00,1964-01-29 00:00:00,1964-01-30 00:00:00,1964-01-31 00:00:00,1964-02-01 00:00:00,1964-02-02 00:00:00,1964-02-03 00:00:00,1964-02-04 00:00:00,1964-02-05 00:00:00,1964-02-06 00:00:00,1964-02-07 00:00:00,1964-02-08 00:00:00,1964-02-09 00:00:00,1964-02-10 00:00:00,1964-02-11 00:00:00,1964-02-12 00:00:00,1964-02-13 00:00:00,1964-02-14 00:00:00,1964-02-15 00:00:00,1964-02-16 00:00:00,1964-02-17 00:00:00,1964-02-18 00:00:00,1964-02-19 00:00:00,1964-02-20 00:00:00,1964-02-21 00:00:00,1964-02-22 00:00:00,1964-02-23 00:00:00,1964-02-24 00:00:00,1964-02-25 00:00:00,1964-02-26 00:00:00,1964-02-27 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diff --git a/tests/data/q_prd_1yr.csv b/tests/data/q_prd_1yr.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2f88626c355216c64911dc5104e95b7f898911bb
--- /dev/null
+++ b/tests/data/q_prd_1yr.csv
@@ -0,0 +1,51 @@
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diff --git a/tests/test_determinist.py b/tests/test_determinist.py
index fea8727c36bbe9f202a152de186f35171658e731..4669b526c0088d83f1d40bc2bb8dd7314591b502 100644
--- a/tests/test_determinist.py
+++ b/tests/test_determinist.py
@@ -13,29 +13,29 @@ class TestMetrics(unittest.TestCase):
expected = {
'RMSE':
- [[777.03427238],
- [776.87847854],
- [777.80021654],
- [778.15108180],
- [778.61486998]],
+ [[[777.03427238]],
+ [[776.87847854]],
+ [[777.80021654]],
+ [[778.15108180]],
+ [[778.61486998]]],
'NSE':
- [[0.71891219],
- [0.71902490],
- [0.71835777],
- [0.71810361],
- [0.71776748]],
+ [[[0.71891219]],
+ [[0.71902490]],
+ [[0.71835777]],
+ [[0.71810361]],
+ [[0.71776748]]],
'KGE':
- [[0.74808767],
- [0.74610620],
- [0.74411103],
- [0.74301085],
- [0.74176777]],
+ [[[0.74808767]],
+ [[0.74610620]],
+ [[0.74411103]],
+ [[0.74301085]],
+ [[0.74176777]]],
'KGEPRIME':
- [[0.81314075],
- [0.81277485],
- [0.81203242],
- [0.81178671],
- [0.81138658]]
+ [[[0.81314075]],
+ [[0.81277485]],
+ [[0.81203242]],
+ [[0.81178671]],
+ [[0.81138658]]]
}
def test_metrics_2d(self):
@@ -51,7 +51,7 @@ class TestMetrics(unittest.TestCase):
with self.subTest(metric=metric):
numpy.testing.assert_almost_equal(
evalhyd.evald(_obs[0], _prd[0], [metric])[0],
- self.expected[metric][0]
+ [self.expected[metric][0]]
)
@@ -97,7 +97,7 @@ class TestMasking(unittest.TestCase):
def test_conditions(self):
with self.subTest(condtions="observed streamflow values"):
- cdt = numpy.array([["q_obs{<2000,>3000}"]], dtype='|S32')
+ cdt = numpy.array(["q_obs{<2000,>3000}"], dtype='|S32')
msk = (_obs[0] < 2000) | (_obs[0] > 3000)
@@ -110,7 +110,7 @@ class TestMasking(unittest.TestCase):
)
with self.subTest(condtions="observed streamflow statistics"):
- cdt = numpy.array([["q_obs{>=median}"]], dtype='|S32')
+ cdt = numpy.array(["q_obs{>=median}"], dtype='|S32')
msk = _obs[0] >= numpy.median(_obs)
@@ -126,27 +126,65 @@ class TestMasking(unittest.TestCase):
class TestMissingData(unittest.TestCase):
def test_nan(self):
+ for metric in ('RMSE', 'NSE', 'KGE', 'KGEPRIME'):
+ obs = numpy.array(
+ [[4.7, numpy.nan, 5.5, 2.7, 4.1]]
+ )
+ prd = numpy.array(
+ [[5.3, 4.2, 5.7, 2.3, numpy.nan],
+ [numpy.nan, 4.2, 4.7, 4.3, 3.3],
+ [5.3, 5.2, 5.7, numpy.nan, 3.9]]
+ )
+
+ with self.subTest(metric=metric):
+ res = evalhyd.evald(obs, prd, [metric])[0]
+
+ for i in range(prd.shape[0]):
+ msk = ~numpy.isnan(obs[0]) & ~numpy.isnan(prd[i])
+
+ numpy.testing.assert_almost_equal(
+ # missing data flagged as NaN
+ res[[i]],
+ # missing data pairwise deleted from series
+ evalhyd.evald(
+ obs[:, msk],
+ prd[i, msk][numpy.newaxis],
+ [metric]
+ )[0]
+ )
+
+
+class TestUncertainty(unittest.TestCase):
+
+ def test_bootstrap(self):
+ prd_1yr = numpy.genfromtxt(
+ "./data/q_prd_1yr.csv", delimiter=',', skip_header=1
+ )
+ obs_1yr = numpy.genfromtxt(
+ "./data/q_obs_1yr.csv", delimiter=',', skip_header=1
+ )[numpy.newaxis]
+ dts_1yr = numpy.genfromtxt(
+ "./data/q_obs_1yr.csv", delimiter=',', dtype=str, skip_footer=1
+ )
+
+ obs_3yrs = numpy.hstack((obs_1yr,) * 3)
+ prd_3yrs = numpy.hstack((prd_1yr,) * 3)
+
for metric in ('RMSE', 'NSE', 'KGE', 'KGEPRIME'):
with self.subTest(metric=metric):
numpy.testing.assert_almost_equal(
- # missing data flagged as NaN
- evalhyd.evald(
- [[4.7, numpy.nan, 5.5, 2.7, 4.1]],
- [[5.3, 4.2, 5.7, 2.3, numpy.nan],
- [numpy.nan, 4.2, 4.7, 4.3, 3.3],
- [5.3, 5.2, 5.7, numpy.nan, 3.9]],
- [metric]
- )[0],
- # missing data pairwise deleted from series
+ # bootstrap with only one year of data
+ # (compare last sample only to have matching dimensions)
evalhyd.evald(
- [[4.7, 5.5, 2.7],
- [5.5, 2.7, 4.1],
- [4.7, 5.5, 4.1]],
- [[5.3, 5.7, 2.3],
- [4.7, 4.3, 3.3],
- [5.3, 5.7, 3.9]],
- [metric]
- )[0]
+ obs_1yr, prd_1yr, [metric],
+ bootstrap={
+ "n_samples": 10, "len_sample": 3, "summary": 0
+ },
+ dts=dts_1yr
+ )[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]
)
@@ -166,6 +204,9 @@ if __name__ == '__main__':
test_suite.addTests(
test_loader.loadTestsFromTestCase(TestMissingData)
)
+ test_suite.addTests(
+ test_loader.loadTestsFromTestCase(TestUncertainty)
+ )
runner = unittest.TextTestRunner(verbosity=2)
result = runner.run(test_suite)
diff --git a/tests/test_probabilist.py b/tests/test_probabilist.py
index 6a86805a409d291fa0b71942e7db0859a3b54b15..3d458ef6a9eceff66c3b35918d62b6224087e8db 100644
--- a/tests/test_probabilist.py
+++ b/tests/test_probabilist.py
@@ -16,27 +16,27 @@ class TestMetrics(unittest.TestCase):
expected_thr = {
'BS':
- [[[[0.1081672, 0.073954980, 0.08681672, numpy.nan]]]],
+ [[[[[0.1081672, 0.073954980, 0.08681672, numpy.nan]]]]],
'BSS':
- [[[[0.56240422, 0.66612211, 0.56288391, numpy.nan]]]],
+ [[[[[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]]]]],
+ [[[[[[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]]]]]
+ [[[[[[0.01244569, 0.14933386, 0.24505537],
+ [0.00801337, 0.14745568, 0.21339730],
+ [0.01719462, 0.10479711, 0.17441921],
+ [numpy.nan, numpy.nan, numpy.nan]]]]]]
}
expected_qtl = {
'QS':
- [[[[321.1607717, 294.3494105, 265.70418006,
- 236.15648446, 206.03965702]]]],
+ [[[[[321.1607717, 294.3494105, 265.70418006,
+ 236.15648446, 206.03965702]]]]],
'CRPS':
- [[[176.63504823]]]
+ [[[[176.63504823]]]]
}
def test_threshold_metrics(self):
@@ -143,6 +143,50 @@ class TestMissingData(unittest.TestCase):
)
+class TestUncertainty(unittest.TestCase):
+
+ def test_bootstrap(self):
+ thr = numpy.array([[690, 534, 445, numpy.nan]])
+
+ prd_1yr = numpy.genfromtxt(
+ "./data/q_prd_1yr.csv", delimiter=',', skip_header=1
+ )
+ obs_1yr = numpy.genfromtxt(
+ "./data/q_obs_1yr.csv", delimiter=',', skip_header=1
+ )
+ dts_1yr = numpy.genfromtxt(
+ "./data/q_obs_1yr.csv", delimiter=',', dtype=str, skip_footer=1
+ )
+
+ obs_3yrs = numpy.hstack((obs_1yr,) * 3)
+ prd_3yrs = numpy.hstack((prd_1yr,) * 3)
+
+ for metric in ("BS", "BSS", "BS_CRD", "BS_LBD", "QS", "CRPS"):
+ 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.evalp(
+ obs_1yr[numpy.newaxis],
+ prd_1yr[numpy.newaxis, numpy.newaxis],
+ [metric],
+ q_thr=thr,
+ bootstrap={
+ "n_samples": 10, "len_sample": 3, "summary": 0
+ },
+ dts=dts_1yr
+ )[0][:, :, :, [0]],
+ # repeat year of data three times to correspond to a
+ # bootstrap sample of length 3
+ evalhyd.evalp(
+ obs_3yrs[numpy.newaxis],
+ prd_3yrs[numpy.newaxis, numpy.newaxis],
+ [metric],
+ q_thr=thr
+ )[0]
+ )
+
+
if __name__ == '__main__':
test_loader = unittest.TestLoader()
test_suite = unittest.TestSuite()
@@ -159,6 +203,9 @@ if __name__ == '__main__':
test_suite.addTests(
test_loader.loadTestsFromTestCase(TestMissingData)
)
+ test_suite.addTests(
+ test_loader.loadTestsFromTestCase(TestUncertainty)
+ )
runner = unittest.TextTestRunner(verbosity=2)
result = runner.run(test_suite)