diff --git a/include/evalhyd/evald.hpp b/include/evalhyd/evald.hpp
index 0bebd065b0f38b469463c7675b605ec174fed19c..cb21e5ff2062ed3e84a4d6db555f7d3b96788636 100644
--- a/include/evalhyd/evald.hpp
+++ b/include/evalhyd/evald.hpp
@@ -46,22 +46,31 @@ namespace evalhyd
/// ========================= =================================
/// transformations details
/// ========================= =================================
- /// ``"inv"`` The reciprocal function
- /// **f(Q) = 1/Q** is applied.
/// ``"sqrt"`` The square root function
/// **f(Q) = √Q** is applied.
+ /// ``"pow"`` The power function
+ /// **f(Q) = Q^n** is applied (where
+ /// **n** can be set through the
+ /// *exponent* parameter).
+ /// ``"inv"`` The reciprocal function
+ /// **f(Q) = 1/Q** is applied.
/// ``"log"`` The natural logarithm function
/// **f(Q) = ln(Q)** is applied.
/// ========================= =================================
///
+ /// exponent: ``xt::xscalar<double>``, optional
+ /// The value of the exponent n to use when the *transform* is the
+ /// power function. If not provided (or set to default value 1),
+ /// the streamflow observations and predictions remain untransformed.
+ ///
/// epsilon: ``xt::xscalar<double>``, optional
/// The value of the small constant ε to add to both the streamflow
/// observations and predictions prior to the calculation of the
/// *metrics* when the *transform* is the reciprocal function or
- /// the natural logarithm since neither are defined for 0. If not
- /// provided, set to default value equal to one hundredth of the
- /// mean of the streamflow observations, as recommended by
- /// `Pushpalatha et al. (2012)
+ /// the natural logarithm (since neither are defined for 0). If not
+ /// provided (or set to default value -9), one hundredth of the mean
+ /// of the streamflow observations is used as value for epsilon, as
+ /// recommended by `Pushpalatha et al. (2012)
/// <https://doi.org/10.1016/j.jhydrol.2011.11.055>`_.
///
/// :Returns:
@@ -90,7 +99,11 @@ namespace evalhyd
///
/// .. code-block:: c++
///
- /// evalhyd::evald(obs, prd, {"NSE"}, "log", 0.05);
+ /// evalhyd::evald(obs, prd, {"NSE"}, "pow", 0.2);
+ ///
+ /// .. code-block:: c++
+ ///
+ /// evalhyd::evald(obs, prd, {"NSE"}, "log", 1, 0.05);
///
/// \endrst
template <class A>
@@ -99,6 +112,7 @@ namespace evalhyd
const xt::xexpression<A>& q_prd,
const std::vector<std::string>& metrics,
const std::string& transform="none",
+ const xt::xscalar<double> exponent=1,
xt::xscalar<double> epsilon=-9
)
{
@@ -106,10 +120,11 @@ namespace evalhyd
A prd;
// apply streamflow transformation if requested
- if ( transform == "none" )
- {
- obs = std::move(q_obs.derived_cast());
- prd = std::move(q_prd.derived_cast());
+ if ( transform == "none" ) {
+ obs = std::move(
+ q_obs.derived_cast());
+ prd = std::move(
+ q_prd.derived_cast());
}
else if ( transform == "sqrt" )
{
@@ -138,6 +153,14 @@ namespace evalhyd
obs = xt::log(q_obs.derived_cast() + epsilon);
prd = xt::log(q_prd.derived_cast() + epsilon);
}
+ else if ( transform == "pow" )
+ {
+ if ( exponent != 1 )
+ {
+ obs = xt::pow(q_obs.derived_cast(), exponent);
+ prd = xt::pow(q_prd.derived_cast(), exponent);
+ }
+ }
else
{
throw std::runtime_error(
diff --git a/tests/test_determinist.cpp b/tests/test_determinist.cpp
index 088f3b09aead2e1ee6f96d92ef5e75ac29451f8d..dc7689ed2a40840c132123449b8aa823a837bad6 100644
--- a/tests/test_determinist.cpp
+++ b/tests/test_determinist.cpp
@@ -9,58 +9,56 @@
#include "evalhyd/evald.hpp"
TEST(DeterministTests, TestMetrics) {
-// read in data
-std::ifstream ifs;
-ifs.open("./data/q_obs.csv");
-xt::xtensor<double, 2> observed =xt::load_csv<int>(ifs);
-ifs.close();
-
-ifs.open("./data/q_prd.csv");
-xt::xtensor<double, 2> predicted_2d = xt::view(
- xt::load_csv<double>(ifs), xt::range(0, 5), xt::all()
-);
-ifs.close();
-
-xt::xtensor<double, 1> predicted_1d = xt::row(predicted_2d, 0);
-
-// compute scores (both with 2D and 1D tensors)
-std::vector<xt::xarray<double>> metrics =
- evalhyd::evald<xt::xtensor<double, 2>>(
- observed, predicted_2d, {"RMSE", "NSE", "KGE", "KGEPRIME"}
- );
-
-// check results on all metrics
-xt::xtensor<double, 2> rmse =
- {{777.034272},
- {776.878479},
- {777.800217},
- {778.151082},
- {778.61487}};
-EXPECT_TRUE(xt::allclose(metrics[0], rmse));
-
-xt::xtensor<double, 2> nse =
- {{0.718912},
- {0.719025},
- {0.718358},
- {0.718104},
- {0.717767}};
-EXPECT_TRUE(xt::allclose(metrics[1], nse));
-
-xt::xtensor<double, 2> kge =
- {{0.748088},
- {0.746106},
- {0.744111},
- {0.743011},
- {0.741768}};
-EXPECT_TRUE(xt::allclose(metrics[2], kge));
-
-xt::xtensor<double, 2> kgeprime =
- {{0.813141},
- {0.812775},
- {0.812032},
- {0.811787},
- {0.811387}};
-EXPECT_TRUE(xt::allclose(metrics[3], kgeprime));
+ // read in data
+ std::ifstream ifs;
+ ifs.open("./data/q_obs.csv");
+ xt::xtensor<double, 2> observed =xt::load_csv<int>(ifs);
+ ifs.close();
+
+ ifs.open("./data/q_prd.csv");
+ xt::xtensor<double, 2> predicted = xt::view(
+ xt::load_csv<double>(ifs), xt::range(0, 5), xt::all()
+ );
+ ifs.close();
+
+ // compute scores (both with 2D and 1D tensors)
+ std::vector<xt::xarray<double>> metrics =
+ evalhyd::evald<xt::xtensor<double, 2>>(
+ observed, predicted, {"RMSE", "NSE", "KGE", "KGEPRIME"}
+ );
+
+ // check results on all metrics
+ xt::xtensor<double, 2> rmse =
+ {{777.034272},
+ {776.878479},
+ {777.800217},
+ {778.151082},
+ {778.61487}};
+ EXPECT_TRUE(xt::allclose(metrics[0], rmse));
+
+ xt::xtensor<double, 2> nse =
+ {{0.718912},
+ {0.719025},
+ {0.718358},
+ {0.718104},
+ {0.717767}};
+ EXPECT_TRUE(xt::allclose(metrics[1], nse));
+
+ xt::xtensor<double, 2> kge =
+ {{0.748088},
+ {0.746106},
+ {0.744111},
+ {0.743011},
+ {0.741768}};
+ EXPECT_TRUE(xt::allclose(metrics[2], kge));
+
+ xt::xtensor<double, 2> kgeprime =
+ {{0.813141},
+ {0.812775},
+ {0.812032},
+ {0.811787},
+ {0.811387}};
+ EXPECT_TRUE(xt::allclose(metrics[3], kgeprime));
}
TEST(DeterministTests, TestNSE_1d2d) {
@@ -112,17 +110,15 @@ TEST(DeterministTests, TestNSE_transform) {
ifs.close();
ifs.open("./data/q_prd.csv");
- xt::xtensor<double, 2> predicted_2d = xt::view(
+ xt::xtensor<double, 2> predicted = xt::view(
xt::load_csv<double>(ifs), xt::range(0, 5), xt::all()
);
ifs.close();
- xt::xtensor<double, 1> predicted_1d = xt::row(predicted_2d, 0);
-
// compute and check results on square-rooted streamflow series
std::vector<xt::xarray<double>> metrics =
evalhyd::evald<xt::xtensor<double, 2>>(
- observed, predicted_2d, {"NSE"}, "sqrt"
+ observed, predicted, {"NSE"}, "sqrt"
);
xt::xtensor<double, 2> nse_sqrt =
@@ -136,7 +132,7 @@ TEST(DeterministTests, TestNSE_transform) {
// compute and check results on inverted streamflow series
metrics =
evalhyd::evald<xt::xtensor<double, 2>>(
- observed, predicted_2d, {"NSE"}, "inv"
+ observed, predicted, {"NSE"}, "inv"
);
xt::xtensor<double, 2> nse_inv =
@@ -150,7 +146,7 @@ TEST(DeterministTests, TestNSE_transform) {
// compute and check results on square-rooted streamflow series
metrics =
evalhyd::evald<xt::xtensor<double, 2>>(
- observed, predicted_2d, {"NSE"}, "log"
+ observed, predicted, {"NSE"}, "log"
);
xt::xtensor<double, 2> nse_log =
@@ -161,4 +157,18 @@ TEST(DeterministTests, TestNSE_transform) {
{0.893793}};
EXPECT_TRUE(xt::allclose(metrics[0], nse_log));
+ // compute and check results on power-transformed streamflow series
+ metrics =
+ evalhyd::evald<xt::xtensor<double, 2>>(
+ observed, predicted, {"NSE"}, "pow", 0.2
+ );
+
+ xt::xtensor<double, 2> nse_pow =
+ {{0.899207},
+ {0.899395},
+ {0.899451},
+ {0.899578},
+ {0.899588}};
+ EXPECT_TRUE(xt::allclose(metrics[0], nse_pow));
+
}