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Commit 193c7882 authored by Thibault Hallouin's avatar Thibault Hallouin
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add default type in template for temporal mask 

so that specifying the template arguments become optional
parent df97a059
main 11-check-the-computation-of-the-ranks-for-ties add-determinist-to-evalp dev v0.1.2 v0.1.1 v0.1.0
1 merge request!3release v0.1.0
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with 38 additions and 38 deletions
+38 -38
include/evalhyd/evald.hpp
+ 1
− 1
View file @ 193c7882
......@@ -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,
......
include/evalhyd/evalp.hpp
+ 1
− 1
View file @ 193c7882
......@@ -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,
......
tests/test_determinist.cpp
+ 21
− 21
View file @ 193c7882
......@@ -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
......
tests/test_probabilist.cpp
+ 15
− 15
View file @ 193c7882
......@@ -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,
......
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