add first unit tests to check score values

tests/data/q_obs.csv

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tests/test_determinist.cpp

+#include <istream>
+#include <fstream>
+#include <vector>
+#include <gtest/gtest.h>
+#include <xtensor/xtensor.hpp>
+#include <xtensor/xview.hpp>
+#include <xtensor/xmanipulation.hpp>
+#include <xtensor/xcsv.hpp>
+
+#include "evalhyd/determinist.hpp"
+
+TEST(EvalHydDeterministTests, TestNSE) {
+    // read in data
+    std::ifstream ifs;
+    ifs.open("./evalhyd/tests/data/q_obs.csv");
+    xt::xtensor<double, 2> observed_2d = xt::transpose(xt::load_csv<int>(ifs));
+    ifs.close();
+
+    xt::xtensor<double, 1> observed_1d = xt::squeeze(observed_2d);
+
+    ifs.open("./evalhyd/tests/data/q_frc.csv");
+    xt::xtensor<double, 2> forecast_2d = xt::view(
+            xt::transpose(xt::load_csv<double>(ifs)), xt::range(0, 5), xt::all()
+    );
+    ifs.close();
+
+    xt::xtensor<double, 1> forecast_1d = xt::row(forecast_2d, 0);
+
+    // compute scores (both with 2D and 1D tensors)
+    std::vector<xt::xtensor<double, 2>> metrics_2d =
+            evalhyd::determinist::evaluate<xt::xtensor<double, 2>>(
+                    {"nse"}, observed_2d, forecast_2d
+            );
+
+    std::vector<xt::xtensor<double, 1>> metrics_1d =
+            evalhyd::determinist::evaluate<xt::xtensor<double, 1>>(
+                    {"nse"}, observed_1d, forecast_1d
+            );
+
+    // check results (both with 2D and 1D tensors)
+    xt::xtensor<double, 2> 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};
+    EXPECT_TRUE(xt::allclose(metrics_1d[0], nse_1d));
+
+}

tests/test_probabilist.cpp

+#include <istream>
+#include <fstream>
+#include <vector>
+#include <gtest/gtest.h>
+#include <xtensor/xtensor.hpp>
+#include <xtensor/xmanipulation.hpp>
+#include <xtensor/xcsv.hpp>
+
+#include "evalhyd/probabilist.hpp"
+
+TEST(EvalHydProbabilistTests, TestBrier) {
+    // read in data
+    std::ifstream ifs;
+    ifs.open("./evalhyd/tests/data/q_obs.csv");
+    xt::xtensor<double, 2> observed = xt::load_csv<int>(ifs);
+    ifs.close();
+
+    ifs.open("./evalhyd/tests/data/q_frc.csv");
+    xt::xtensor<double, 2> forecast = xt::load_csv<double>(ifs);
+    ifs.close();
+
+    // compute scores
+    xt::xtensor<double, 1> thresholds = {690, 534, 445};
+
+    std::vector<xt::xtensor<double, 2>> metrics =
+            evalhyd::probabilist::evaluate(
+                    {"bs", "bss", "bs_crd", "bs_lbd"},
+                    xt::transpose(observed), xt::transpose(forecast),
+                    thresholds
+            );
+
+    // check results
+    // Brier scores
+    xt::xtensor<double, 2> bs =
+            {{0.10615136},
+             {0.07395622},
+             {0.08669186}};
+    EXPECT_TRUE(xt::allclose(metrics[0], bs));
+
+    // Brier skill scores
+    xt::xtensor<double, 2> bss =
+            {{0.5705594},
+             {0.6661165},
+             {0.5635126}};
+    EXPECT_TRUE(xt::allclose(metrics[1], bss));
+
+    // Brier calibration-refinement decompositions
+    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-based decompositions
+    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));
+}