diff --git a/include/evalhyd/determinist.hpp b/include/evalhyd/determinist.hpp
index 19dce0afb95ecb5a12fb2c403dab2f7e44b42bd9..06ad762ac4e786d0a27b36b42e170ca369ece6f2 100644
--- a/include/evalhyd/determinist.hpp
+++ b/include/evalhyd/determinist.hpp
@@ -4,6 +4,7 @@
#include <unordered_map>
#include <vector>
#include <xtensor/xexpression.hpp>
+#include <xtensor/xarray.hpp>
#include "utils.hpp"
#include "determinist/evaluator.hpp"
@@ -26,7 +27,7 @@ namespace evalhyd
/// \return
/// Vector of 1D array of metrics for each time series.
template <class A>
- std::vector<A> evald(
+ std::vector<xt::xarray<double>> evald(
const xt::xexpression<A>& q_obs,
const xt::xexpression<A>& q_prd,
const std::vector<std::string>& metrics
@@ -73,7 +74,7 @@ namespace evalhyd
}
// retrieve or compute requested metrics
- std::vector<A> r;
+ std::vector<xt::xarray<double>> r;
for ( const auto& metric : metrics )
{
@@ -82,7 +83,7 @@ namespace evalhyd
if (std::find(req_dep.begin(), req_dep.end(), metric)
== req_dep.end())
evaluator.calc_NSE();
- r.emplace_back(evaluator.NSE);
+ r.emplace_back(xt::squeeze(evaluator.NSE));
}
}
diff --git a/include/evalhyd/probabilist.hpp b/include/evalhyd/probabilist.hpp
index 9dbfe7d013268dd457fd96d82dbec43bbd0a7e8c..8c401695e383a54925857f89eb0b845bb0c9d6fd 100644
--- a/include/evalhyd/probabilist.hpp
+++ b/include/evalhyd/probabilist.hpp
@@ -5,6 +5,7 @@
#include <unordered_map>
#include <vector>
#include <xtensor/xtensor.hpp>
+#include <xtensor/xarray.hpp>
#include <xtensor/xview.hpp>
#include "utils.hpp"
@@ -39,7 +40,7 @@ namespace evalhyd
/// shape: (1+, time)
/// \return
/// Vector of 2D array of metrics for each threshold.
- std::vector<xt::xtensor<double, 3>> evalp(
+ std::vector<xt::xarray<double>> evalp(
const xt::xtensor<double, 2>& q_obs,
const xt::xtensor<double, 2>& q_prd,
const std::vector<std::string>& metrics,
@@ -107,7 +108,7 @@ namespace evalhyd
}
// retrieve or compute requested metrics
- std::vector<xt::xtensor<double, 3>> r;
+ std::vector<xt::xarray<double>> r;
for (const auto& metric : metrics)
{
@@ -116,42 +117,42 @@ namespace evalhyd
if (std::find(req_dep.begin(), req_dep.end(), metric)
== req_dep.end())
evaluator.calc_BS();
- r.emplace_back(evaluator.BS);
+ r.emplace_back(xt::squeeze(evaluator.BS));
}
else if ( metric == "BSS" )
{
if (std::find(req_dep.begin(), req_dep.end(), metric)
== req_dep.end())
evaluator.calc_BSS();
- r.emplace_back(evaluator.BSS);
+ r.emplace_back(xt::squeeze(evaluator.BSS));
}
else if ( metric == "BS_CRD" )
{
if (std::find(req_dep.begin(), req_dep.end(), metric)
== req_dep.end())
evaluator.calc_BS_CRD();
- r.emplace_back(evaluator.BS_CRD);
+ r.emplace_back(xt::squeeze(evaluator.BS_CRD));
}
else if ( metric == "BS_LBD" )
{
if (std::find(req_dep.begin(), req_dep.end(), metric)
== req_dep.end())
evaluator.calc_BS_LBD();
- r.emplace_back(evaluator.BS_LBD);
+ r.emplace_back(xt::squeeze(evaluator.BS_LBD));
}
else if ( metric == "QS" )
{
if (std::find(req_dep.begin(), req_dep.end(), metric)
== req_dep.end())
evaluator.calc_QS();
- r.emplace_back(evaluator.QS);
+ r.emplace_back(xt::squeeze(evaluator.QS));
}
else if ( metric == "CRPS" )
{
if (std::find(req_dep.begin(), req_dep.end(), metric)
== req_dep.end())
evaluator.calc_CRPS();
- r.emplace_back(evaluator.CRPS);
+ r.emplace_back(xt::squeeze(evaluator.CRPS));
}
}
diff --git a/include/evalhyd/probabilist/evaluator.h b/include/evalhyd/probabilist/evaluator.h
index be6908904adda60bf189084bcfd2d5184bea5ebe..26f0223430efc75759aa13eaa3461a4044e86fa8 100644
--- a/include/evalhyd/probabilist/evaluator.h
+++ b/include/evalhyd/probabilist/evaluator.h
@@ -55,12 +55,12 @@ namespace evalhyd
xt::xtensor<double, 2> crps;
// members for evaluation metrics
- xt::xtensor<double, 3> BS;
+ xt::xtensor<double, 2> BS;
xt::xtensor<double, 3> BS_CRD;
xt::xtensor<double, 3> BS_LBD;
- xt::xtensor<double, 3> BSS;
- xt::xtensor<double, 3> QS;
- xt::xtensor<double, 3> CRPS;
+ xt::xtensor<double, 2> BSS;
+ xt::xtensor<double, 2> QS;
+ xt::xtensor<double, 1> CRPS;
// methods to compute derived data
void calc_q_qnt();
diff --git a/include/evalhyd/probabilist/evaluator_brier.cpp b/include/evalhyd/probabilist/evaluator_brier.cpp
index 10c2e338bab8c308790a6376b5a6c1ae80f6c98a..dcc8b7e17a6d63d20f9e4a7e8c47cde04b7db6cb 100644
--- a/include/evalhyd/probabilist/evaluator_brier.cpp
+++ b/include/evalhyd/probabilist/evaluator_brier.cpp
@@ -50,7 +50,7 @@ namespace evalhyd
{
// initialise output variable
// shape: (subsets, thresholds, 1)
- BS = xt::zeros<double>({n_msk, n_thr, std::size_t {1}});
+ BS = xt::zeros<double>({n_msk, n_thr});
// compute variable one mask at a time to minimise memory imprint
for (int m = 0; m < n_msk; m++)
@@ -61,8 +61,8 @@ namespace evalhyd
// calculate the mean over the time steps
// $BS = \frac{1}{n} \sum_{k=1}^{n} (o_k - y_k)^2$
- xt::view(BS, m, xt::all(), xt::all()) =
- xt::nanmean(bs_masked, -1, xt::keep_dims);
+ xt::view(BS, m, xt::all()) =
+ xt::nanmean(bs_masked, -1);
}
}
@@ -306,7 +306,7 @@ namespace evalhyd
{
// declare and initialise output variable
// shape: (subsets, thresholds, components)
- BSS = xt::zeros<double>({n_msk, n_thr, std::size_t {1}});
+ BSS = xt::zeros<double>({n_msk, n_thr});
// compute variable one mask at a time to minimise memory imprint
for (int m = 0; m < n_msk; m++)
@@ -332,8 +332,8 @@ namespace evalhyd
// compute Brier skill score(s)
// $BSS = \frac{1}{n} \sum_{k=1}^{n} 1 - \frac{bs}{bs_{ref}}
- xt::view(BSS, m, xt::all(), xt::all()) =
- xt::nanmean(1 - (bs_masked / bs_ref), -1, xt::keep_dims);
+ xt::view(BSS, m, xt::all()) =
+ xt::nanmean(1 - (bs_masked / bs_ref), -1);
}
}
}
diff --git a/include/evalhyd/probabilist/evaluator_quantiles.cpp b/include/evalhyd/probabilist/evaluator_quantiles.cpp
index 42bb1b76fb1a533f49867b27bee7e41c3e784879..726d14d8f0a0a8ac0bcd2f36c485bb302e1146af 100644
--- a/include/evalhyd/probabilist/evaluator_quantiles.cpp
+++ b/include/evalhyd/probabilist/evaluator_quantiles.cpp
@@ -55,7 +55,7 @@ namespace evalhyd
{
// initialise output variable
// shape: (subsets, quantiles, 1)
- QS = xt::zeros<double>({n_msk, n_mbr, std::size_t {1}});
+ QS = xt::zeros<double>({n_msk, n_mbr});
// compute variable one mask at a time to minimise memory imprint
for (int m = 0; m < n_msk; m++)
@@ -66,8 +66,8 @@ namespace evalhyd
// calculate the mean over the time steps
// $QS = \frac{1}{n} \sum_{k=1}^{n} qs$
- xt::view(QS, m, xt::all(), xt::all()) =
- xt::nanmean(qs_masked, -1, xt::keep_dims);
+ xt::view(QS, m, xt::all()) =
+ xt::nanmean(qs_masked, -1);
}
}
@@ -111,7 +111,7 @@ namespace evalhyd
{
// initialise output variable
// shape: (subsets, thresholds, 1)
- CRPS = xt::zeros<double>({n_msk, std::size_t {1}, std::size_t {1}});
+ CRPS = xt::zeros<double>({n_msk});
// compute variable one mask at a time to minimise memory imprint
for (int m = 0; m < n_msk; m++)
@@ -122,8 +122,8 @@ namespace evalhyd
// calculate the mean over the time steps
// $CRPS = \frac{1}{n} \sum_{k=1}^{n} crps$
- xt::view(CRPS, m, xt::all(), xt::all()) =
- xt::nanmean(crps_masked, -1, xt::keep_dims);
+ xt::view(CRPS, m) =
+ xt::nanmean(crps_masked, -1);
}
}
}
diff --git a/tests/test_determinist.cpp b/tests/test_determinist.cpp
index c89553e57cff3a67463e1d684bc644562c4abf79..076d2072faa7f48f7056e779e4ed60d8209ab1ed 100644
--- a/tests/test_determinist.cpp
+++ b/tests/test_determinist.cpp
@@ -3,6 +3,7 @@
#include <vector>
#include <gtest/gtest.h>
#include <xtensor/xtensor.hpp>
+#include <xtensor/xarray.hpp>
#include <xtensor/xview.hpp>
#include <xtensor/xmanipulation.hpp>
#include <xtensor/xcsv.hpp>
@@ -27,19 +28,19 @@ TEST(DeterministTests, TestNSE) {
xt::xtensor<double, 1> predicted_1d = xt::row(predicted_2d, 0);
// compute scores (both with 2D and 1D tensors)
- std::vector<xt::xtensor<double, 2>> metrics_2d =
+ std::vector<xt::xarray<double>> metrics_2d =
evalhyd::evald<xt::xtensor<double, 2>>(
observed_2d, predicted_2d, {"NSE"}
);
- std::vector<xt::xtensor<double, 1>> metrics_1d =
+ std::vector<xt::xarray<double>> metrics_1d =
evalhyd::evald<xt::xtensor<double, 1>>(
observed_1d, predicted_1d, {"NSE"}
);
// check results (both with 2D and 1D tensors)
- xt::xtensor<double, 2> nse_2d =
- {{0.71891219}, {0.7190249} , {0.71835777}, {0.71810361}, {0.71776748}};
+ xt::xtensor<double, 1> nse_2d =
+ {0.71891219, 0.7190249, 0.71835777, 0.71810361, 0.71776748};
EXPECT_TRUE(xt::allclose(metrics_2d[0], nse_2d));
xt::xtensor<double, 1> nse_1d = {0.71891219};
diff --git a/tests/test_probabilist.cpp b/tests/test_probabilist.cpp
index 7bc1734953d9aba500179fdc6610e4e5229d4498..33a4009a767a149b6195995e32d3d08f3a1558bd 100644
--- a/tests/test_probabilist.cpp
+++ b/tests/test_probabilist.cpp
@@ -3,6 +3,7 @@
#include <vector>
#include <gtest/gtest.h>
#include <xtensor/xtensor.hpp>
+#include <xtensor/xarray.hpp>
#include <xtensor/xmanipulation.hpp>
#include <xtensor/xcsv.hpp>
@@ -22,7 +23,7 @@ TEST(ProbabilistTests, TestBrier) {
// compute scores
xt::xtensor<double, 1> thresholds = {690, 534, 445};
- std::vector<xt::xtensor<double, 3>> metrics =
+ std::vector<xt::xarray<double>> metrics =
evalhyd::evalp(
observed, predicted, {"BS", "BSS", "BS_CRD", "BS_LBD"},
thresholds
@@ -30,30 +31,26 @@ TEST(ProbabilistTests, TestBrier) {
// check results
// Brier scores
- xt::xtensor<double, 3> bs =
- {{{0.10615136},
- {0.07395622},
- {0.08669186}}};
+ xt::xtensor<double, 1> bs =
+ {0.10615136, 0.07395622, 0.08669186};
EXPECT_TRUE(xt::allclose(metrics[0], bs));
// Brier skill scores
- xt::xtensor<double, 3> bss =
- {{{0.5705594},
- {0.6661165},
- {0.5635126}}};
+ xt::xtensor<double, 1> bss =
+ {0.5705594, 0.6661165, 0.5635126};
EXPECT_TRUE(xt::allclose(metrics[1], bss));
// Brier calibration-refinement decompositions
- xt::xtensor<double, 3> bs_crd =
- {{{0.011411758, 0.1524456, 0.2471852},
- {0.005532413, 0.1530793, 0.2215031},
- {0.010139431, 0.1220601, 0.1986125}}};
+ xt::xtensor<double, 2> bs_crd =
+ {{0.011411758, 0.1524456, 0.2471852},
+ {0.005532413, 0.1530793, 0.2215031},
+ {0.010139431, 0.1220601, 0.1986125}};
EXPECT_TRUE(xt::allclose(metrics[2], bs_crd));
// Brier likelihood-base rate decompositions
- xt::xtensor<double, 3> bs_lbd =
- {{{0.012159881, 0.1506234, 0.2446149},
- {0.008031746, 0.1473869, 0.2133114},
- {0.017191279, 0.1048221, 0.1743227}}};
+ xt::xtensor<double, 2> bs_lbd =
+ {{0.012159881, 0.1506234, 0.2446149},
+ {0.008031746, 0.1473869, 0.2133114},
+ {0.017191279, 0.1048221, 0.1743227}};
EXPECT_TRUE(xt::allclose(metrics[3], bs_lbd));
}