diff --git a/include/evalhyd/evald.hpp b/include/evalhyd/evald.hpp
index 2efb64745614fe03fe6c7e0e68cd108b8e842955..8b0041b6a9667f8d89532dc1ff4eb38d3006ae8a 100644
--- a/include/evalhyd/evald.hpp
+++ b/include/evalhyd/evald.hpp
@@ -154,7 +154,7 @@ namespace evalhyd
/// evalhyd::evald(obs, prd, {"NSE"}, "none", 1, -9, msk);
///
/// \endrst
- template <class XD2, class XB2>
+ template <class XD2, class XB2 = xt::xtensor<bool, 2>>
std::vector<xt::xarray<double>> evald(
const xt::xexpression<XD2>& q_obs,
const xt::xexpression<XD2>& q_prd,
diff --git a/include/evalhyd/evalp.hpp b/include/evalhyd/evalp.hpp
index 89088ade3fbc78e57f9a43976785efeebcbbb21e..a425a6692e23cfc9f1790511a7d51cd9a2730aee 100644
--- a/include/evalhyd/evalp.hpp
+++ b/include/evalhyd/evalp.hpp
@@ -136,7 +136,7 @@ namespace evalhyd
/// evalhyd::evalp(obs, prd, {"CRPS"});
///
/// \endrst
- template <class XD2, class XD4, class XB4>
+ template <class XD2, class XD4, class XB4 = xt::xtensor<bool, 4>>
std::vector<xt::xarray<double>> evalp(
const xt::xexpression<XD2>& q_obs,
const xt::xexpression<XD4>& q_prd,
diff --git a/tests/test_determinist.cpp b/tests/test_determinist.cpp
index d5f725e328445df06a1619fa3b5db6247a00b926..a64f56c570a28a0d92f667434b53a696b80f4aa9 100644
--- a/tests/test_determinist.cpp
+++ b/tests/test_determinist.cpp
@@ -43,7 +43,7 @@ TEST(DeterministTests, TestMetrics)
// compute scores (with 2D tensors)
std::vector<xt::xarray<double>> metrics =
- evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(
+ evalhyd::evald(
observed, predicted, {"RMSE", "NSE", "KGE", "KGEPRIME"}
);
@@ -90,7 +90,7 @@ TEST(DeterministTests, TestTransform)
// compute and check results on square-rooted streamflow series
std::vector<xt::xarray<double>> metrics =
- evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(observed, predicted, {"NSE"}, "sqrt");
+ evalhyd::evald(observed, predicted, {"NSE"}, "sqrt");
xt::xtensor<double, 3> nse_sqrt =
{{{0.882817}},
@@ -101,7 +101,7 @@ TEST(DeterministTests, TestTransform)
EXPECT_TRUE(xt::allclose(metrics[0], nse_sqrt));
// compute and check results on inverted streamflow series
- metrics = evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(observed, predicted, {"NSE"}, "inv");
+ metrics = evalhyd::evald(observed, predicted, {"NSE"}, "inv");
xt::xtensor<double, 3> nse_inv =
{{{0.737323}},
@@ -112,7 +112,7 @@ TEST(DeterministTests, TestTransform)
EXPECT_TRUE(xt::allclose(metrics[0], nse_inv));
// compute and check results on square-rooted streamflow series
- metrics = evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(observed, predicted, {"NSE"}, "log");
+ metrics = evalhyd::evald(observed, predicted, {"NSE"}, "log");
xt::xtensor<double, 3> nse_log =
{{{0.893344}},
@@ -123,7 +123,7 @@ TEST(DeterministTests, TestTransform)
EXPECT_TRUE(xt::allclose(metrics[0], nse_log));
// compute and check results on power-transformed streamflow series
- metrics = evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(observed, predicted, {"NSE"}, "pow", 0.2);
+ metrics = evalhyd::evald(observed, predicted, {"NSE"}, "pow", 0.2);
xt::xtensor<double, 3> nse_pow =
{{{0.899207}},
@@ -152,14 +152,14 @@ TEST(DeterministTests, TestMasks)
{"RMSE", "NSE", "KGE", "KGEPRIME"};
std::vector<xt::xarray<double>> metrics_masked =
- evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(observed, predicted, metrics, "none", 1, -9, masks);
+ evalhyd::evald(observed, predicted, metrics, {}, {}, {}, masks);
// compute scores on pre-computed subset of whole record
xt::xtensor<double, 2> obs = xt::view(observed, xt::all(), xt::range(20, _));
xt::xtensor<double, 2> prd = xt::view(predicted, xt::all(), xt::range(20, _));
std::vector<xt::xarray<double>> metrics_subset =
- evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(obs, prd, metrics);
+ evalhyd::evald(obs, prd, metrics);
// check results are identical
for (std::size_t m = 0; m < metrics.size(); m++)
@@ -192,12 +192,12 @@ TEST(DeterministTests, TestMaskingConditions)
std::vector<xt::xarray<double>> metrics_q_conditioned =
evalhyd::evald(
observed, predicted, metrics,
- "none", 1, -9, masks, q_conditions
+ {}, {}, {}, masks, q_conditions
);
// compute scores using "NaN-ed" time indices where conditions on streamflow met
std::vector<xt::xarray<double>> metrics_q_preconditioned =
- evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(
+ evalhyd::evald(
xt::eval(xt::where((observed < 2000) | (observed > 3000), observed, NAN)),
predicted,
metrics
@@ -225,12 +225,12 @@ TEST(DeterministTests, TestMaskingConditions)
std::vector<xt::xarray<double>> metrics_q_conditioned_ =
evalhyd::evald(
observed, predicted, metrics,
- "none", 1, -9, masks, q_conditions_
+ {}, {}, {}, masks, q_conditions_
);
// compute scores using "NaN-ed" time indices where conditions on streamflow met
std::vector<xt::xarray<double>> metrics_q_preconditioned_ =
- evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(
+ evalhyd::evald(
xt::eval(xt::where(observed >= mean, observed, NAN)),
predicted,
metrics
@@ -241,7 +241,7 @@ TEST(DeterministTests, TestMaskingConditions)
{
EXPECT_TRUE(
xt::allclose(
- metrics_q_conditioned[m], metrics_q_preconditioned[m]
+ metrics_q_conditioned_[m], metrics_q_preconditioned_[m]
)
) << "Failure for (" << metrics[m] << ")";
}
@@ -256,12 +256,12 @@ TEST(DeterministTests, TestMaskingConditions)
std::vector<xt::xarray<double>> metrics_t_conditioned =
evalhyd::evald(
observed, predicted, metrics,
- "none", 1, -9, masks, t_conditions
+ {}, {}, {}, masks, t_conditions
);
// compute scores on already subset time series
std::vector<xt::xarray<double>> metrics_t_subset =
- evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(
+ evalhyd::evald(
xt::eval(xt::view(observed, xt::all(), xt::range(0, 100))),
xt::eval(xt::view(predicted, xt::all(), xt::range(0, 100))),
metrics
@@ -299,7 +299,7 @@ TEST(DeterministTests, TestMissingData)
// compute metrics with observations containing NaN values
std::vector<xt::xarray<double>> metrics_nan =
- evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(observed, predicted, metrics);
+ evalhyd::evald(observed, predicted, metrics);
for (std::size_t m = 0; m < metrics.size(); m++)
{
@@ -313,7 +313,7 @@ TEST(DeterministTests, TestMissingData)
xt::view(predicted, p, xt::range(20+(3*(p+1)), _));
std::vector<xt::xarray<double>> metrics_sbs =
- evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(
+ evalhyd::evald(
xt::eval(xt::view(obs, xt::newaxis(), xt::all())),
xt::eval(xt::view(prd, xt::newaxis(), xt::all())),
{metrics[m]}
@@ -356,13 +356,13 @@ TEST(DeterministTests, TestBootstrap)
{{"n_samples", 10}, {"len_sample", 3}, {"summary", 0}};
std::vector<xt::xarray<double>> metrics_bts =
- evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(
+ evalhyd::evald(
xt::eval(xt::view(observed, xt::newaxis(), xt::all())),
predicted,
metrics,
- "none", // transform
- 1, // exponent
- -9, // epsilon
+ {}, // transform
+ {}, // exponent
+ {}, // epsilon
xt::xtensor<bool, 2>({}), // t_msk
{}, // m_cdt
bootstrap,
@@ -381,7 +381,7 @@ TEST(DeterministTests, TestBootstrap)
xt::concatenate(xt::xtuple(predicted, predicted, predicted), 1);
std::vector<xt::xarray<double>> metrics_rep =
- evalhyd::evald<xt::xtensor<double, 2>, xt::xtensor<bool, 2>>(
+ evalhyd::evald(
xt::eval(xt::view(observed_x3, xt::newaxis(), xt::all())),
predicted_x3,
metrics
diff --git a/tests/test_probabilist.cpp b/tests/test_probabilist.cpp
index 342cbf04ddc608692c50adc5da23dcb61f721521..2c657dff86df2d29a69780a5aa3c08969d60f722 100644
--- a/tests/test_probabilist.cpp
+++ b/tests/test_probabilist.cpp
@@ -43,7 +43,7 @@ TEST(ProbabilistTests, TestBrier)
xt::xtensor<double, 2> thresholds = {{690, 534, 445, NAN}};
std::vector<xt::xarray<double>> metrics =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
// shape: (sites [1], time [t])
xt::eval(xt::view(observed, xt::newaxis(), xt::all())),
// shape: (sites [1], lead times [1], members [m], time [t])
@@ -101,7 +101,7 @@ TEST(ProbabilistTests, TestQuantiles)
// compute scores
std::vector<xt::xarray<double>> metrics =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
// shape: (sites [1], time [t])
xt::eval(xt::view(observed, xt::newaxis(), xt::all())),
// shape: (sites [1], lead times [1], members [m], time [t])
@@ -150,7 +150,7 @@ TEST(ProbabilistTests, TestMasks)
{"BS", "BSS", "BS_CRD", "BS_LBD", "QS", "CRPS"};
std::vector<xt::xarray<double>> metrics_masked =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
// shape: (sites [1], time [t])
xt::eval(xt::view(observed, xt::newaxis(), xt::all())),
// shape: (sites [1], lead times [1], members [m], time [t])
@@ -163,7 +163,7 @@ TEST(ProbabilistTests, TestMasks)
// compute scores on pre-computed subset of whole record
std::vector<xt::xarray<double>> metrics_subset =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
// shape: (sites [1], time [t-20])
xt::eval(xt::view(observed, xt::newaxis(), xt::range(20, _))),
// shape: (sites [1], lead times [1], members [m], time [t-20])
@@ -205,7 +205,7 @@ TEST(ProbabilistTests, TestMaskingConditions)
};
std::vector<xt::xarray<double>> metrics_q_conditioned =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
xt::eval(observed),
xt::eval(xt::view(predicted, xt::newaxis(), xt::newaxis(), xt::all(), xt::all())),
metrics,
@@ -216,7 +216,7 @@ TEST(ProbabilistTests, TestMaskingConditions)
// compute scores using "NaN-ed" time indices where conditions on streamflow met
std::vector<xt::xarray<double>> metrics_q_preconditioned =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
xt::eval(xt::where((observed < 2000) | (observed > 3000), observed, NAN)),
xt::eval(xt::view(predicted, xt::newaxis(), xt::newaxis(), xt::all(), xt::all())),
metrics,
@@ -244,7 +244,7 @@ TEST(ProbabilistTests, TestMaskingConditions)
double median = xt::median(q_prd_mean);
std::vector<xt::xarray<double>> metrics_q_conditioned_ =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
xt::eval(observed),
xt::eval(xt::view(predicted, xt::newaxis(), xt::newaxis(), xt::all(), xt::all())),
metrics,
@@ -255,7 +255,7 @@ TEST(ProbabilistTests, TestMaskingConditions)
// compute scores using "NaN-ed" time indices where conditions on streamflow met
std::vector<xt::xarray<double>> metrics_q_preconditioned_ =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
xt::eval(xt::where(q_prd_mean >= median, observed, NAN)),
xt::eval(xt::view(predicted, xt::newaxis(), xt::newaxis(), xt::all(), xt::all())),
metrics,
@@ -280,7 +280,7 @@ TEST(ProbabilistTests, TestMaskingConditions)
};
std::vector<xt::xarray<double>> metrics_t_conditioned =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
xt::eval(observed),
xt::eval(xt::view(predicted, xt::newaxis(), xt::newaxis(), xt::all(), xt::all())),
metrics,
@@ -291,7 +291,7 @@ TEST(ProbabilistTests, TestMaskingConditions)
// compute scores on already subset time series
std::vector<xt::xarray<double>> metrics_t_subset =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
xt::eval(xt::view(observed_, xt::newaxis(), xt::range(0, 100))),
xt::eval(xt::view(predicted, xt::newaxis(), xt::newaxis(), xt::all(), xt::range(0, 100))),
metrics,
@@ -332,7 +332,7 @@ TEST(ProbabilistTests, TestMissingData)
{{ 4.7, 4.3, NAN, 2.7, 4.1 }};
std::vector<xt::xarray<double>> metrics_nan =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
observed_nan,
forecast_nan,
metrics,
@@ -351,7 +351,7 @@ TEST(ProbabilistTests, TestMissingData)
{{ 4.7, 4.3, 2.7 }};
std::vector<xt::xarray<double>> metrics_pp1 =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
observed_pp1,
forecast_pp1,
metrics,
@@ -369,7 +369,7 @@ TEST(ProbabilistTests, TestMissingData)
{{ 4.3, 2.7, 4.1 }};
std::vector<xt::xarray<double>> metrics_pp2 =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
observed_pp2,
forecast_pp2,
metrics,
@@ -425,7 +425,7 @@ TEST(ProbabilistTests, TestBootstrap)
{{"n_samples", 10}, {"len_sample", 3}, {"summary", 0}};
std::vector<xt::xarray<double>> metrics_bts =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
xt::eval(xt::view(observed, xt::newaxis(), xt::all())),
xt::eval(xt::view(predicted, xt::newaxis(), xt::newaxis(), xt::all(), xt::all())),
metrics,
@@ -448,7 +448,7 @@ TEST(ProbabilistTests, TestBootstrap)
xt::concatenate(xt::xtuple(predicted, predicted, predicted), 1);
std::vector<xt::xarray<double>> metrics_rep =
- evalhyd::evalp<xt::xtensor<double, 2>, xt::xtensor<double, 4>, xt::xtensor<bool, 4>>(
+ evalhyd::evalp(
xt::eval(xt::view(observed_x3, xt::newaxis(), xt::all())),
xt::eval(xt::view(predicted_x3, xt::newaxis(), xt::newaxis(), xt::all(), xt::all())),
metrics,