diff --git a/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py b/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
index eb8ba9cf1786a152e928a19c3a37c6bc88bc2ca1..467f89068bdd07ee3beee618f4e8a12411de1a57 100644
--- a/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
+++ b/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
@@ -21,6 +21,7 @@ class TemporalMarginFitMethod(Enum):
extremes_fevd_bayesian = 1
extremes_fevd_mle = 2
extremes_fevd_gmle = 3
+ extremes_fevd_l_moments = 4
class AbstractTemporalLinearMarginModel(LinearMarginModel):
@@ -53,7 +54,9 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
elif self.fit_method == TemporalMarginFitMethod.extremes_fevd_bayesian:
return self.extremes_fevd_bayesian_fit(x, df_coordinates_temp)
elif self.fit_method in [TemporalMarginFitMethod.extremes_fevd_mle,
- TemporalMarginFitMethod.extremes_fevd_gmle]:
+ TemporalMarginFitMethod.extremes_fevd_gmle,
+ TemporalMarginFitMethod.extremes_fevd_l_moments,
+ ]:
return self.extremes_fevd_mle_related_fit(x, df_coordinates_temp)
else:
raise NotImplementedError
@@ -78,6 +81,9 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
method = "MLE"
elif self.fit_method == TemporalMarginFitMethod.extremes_fevd_gmle:
method = "GMLE"
+ elif self.fit_method == TemporalMarginFitMethod.extremes_fevd_l_moments:
+ method = "Lmoments"
+ assert self.margin_function_start_fit.is_a_stationary_model
else:
raise ValueError('wrong method')
return self.run_fevd_fixed(df_coordinates_temp, method, x)
diff --git a/extreme_fit/model/result_from_model_fit/result_from_extremes/result_from_mle_extremes.py b/extreme_fit/model/result_from_model_fit/result_from_extremes/result_from_mle_extremes.py
index 3cf68a4673ec6eb6081b125dd02223bc1626a81e..9333ff44fc5a64efca60f61c603ba0f192dcc596 100644
--- a/extreme_fit/model/result_from_model_fit/result_from_extremes/result_from_mle_extremes.py
+++ b/extreme_fit/model/result_from_model_fit/result_from_extremes/result_from_mle_extremes.py
@@ -21,7 +21,10 @@ class ResultFromMleExtremes(AbstractResultFromExtremes):
def margin_coef_ordered_dict(self):
values = self.name_to_value['results']
d = self.get_python_dictionary(values)
- values = {i: param for i, param in enumerate(np.array(d['par']))}
+ if 'par' in d:
+ values = {i: param for i, param in enumerate(np.array(d['par']))}
+ else:
+ values = {i: np.array(v)[0] for i, v in enumerate(d.values())}
return get_margin_coef_ordered_dict(self.gev_param_name_to_dim, values, self.type_for_mle)
def _confidence_interval_method(self, common_kwargs, ci_method, return_period):
diff --git a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_l_moments.py b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_l_moments.py
new file mode 100644
index 0000000000000000000000000000000000000000..c94c33ad467d7906a46c4bb6c00f30d55837b0e6
--- /dev/null
+++ b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_l_moments.py
@@ -0,0 +1,62 @@
+import unittest
+
+import numpy as np
+import pandas as pd
+
+from extreme_trend.abstract_gev_trend_test import fitted_linear_margin_estimator
+from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
+ TemporalMarginFitMethod
+from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel, \
+ NonStationaryLocationTemporalModel, NonStationaryLocationAndScaleTemporalModel
+from extreme_fit.model.utils import r, set_seed_r
+from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
+from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_coordinates import \
+ AbstractTemporalCoordinates
+from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
+from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
+ AbstractSpatioTemporalObservations
+
+
+class TestGevTemporalExtremesLMoments(unittest.TestCase):
+
+ def setUp(self) -> None:
+ set_seed_r()
+ r("""
+ N <- 50
+ loc = 0; scale = 1; shape <- 1
+ x_gev <- rgev(N, loc = loc, scale = scale, shape = shape)
+ start_loc = 0; start_scale = 1; start_shape = 1
+ """)
+ # Compute the stationary temporal margin with isMev
+ self.start_year = 0
+ df = pd.DataFrame({AbstractCoordinates.COORDINATE_T: range(self.start_year, self.start_year + 50)})
+ self.coordinates = AbstractTemporalCoordinates.from_df(df)
+ df2 = pd.DataFrame(data=np.array(r['x_gev']), index=df.index)
+ observations = AbstractSpatioTemporalObservations(df_maxima_gev=df2)
+ self.dataset = AbstractDataset(observations=observations, coordinates=self.coordinates)
+ self.fit_method = TemporalMarginFitMethod.extremes_fevd_l_moments
+
+ ref = {'loc': 0.0813843045950251, 'scale': 1.1791830110181365, 'shape': 0.6610403806908737}
+
+ def test_gev_temporal_margin_fit_stationary(self):
+ # Create estimator
+ estimator = fitted_linear_margin_estimator(StationaryTemporalModel, self.coordinates, self.dataset,
+ starting_year=0,
+ fit_method=self.fit_method)
+ ref = {'loc': 0.0813843045950251, 'scale': 1.1791830110181365, 'shape': 0.6610403806908737}
+ for year in range(1, 3):
+ mle_params_estimated = estimator.function_from_fit.get_gev_params(np.array([year])).to_dict()
+ for key in ref.keys():
+ self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
+
+ def test_gev_temporal_margin_fit_non_stationary_location(self):
+ # Create estimator
+ with self.assertRaises(AssertionError):
+ fitted_linear_margin_estimator(NonStationaryLocationTemporalModel, self.coordinates,
+ self.dataset,
+ starting_year=0,
+ fit_method=self.fit_method)
+
+
+if __name__ == '__main__':
+ unittest.main()