From 4a87de78ad921d15224c5069934accc3795dd45a Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Thu, 1 Apr 2021 17:45:48 +0200
Subject: [PATCH] [projection snowfall] add other test for
tet_gev_temporal_evgam
---
.../test_gev_temporal_evgam.py | 76 +++++++++----------
1 file changed, 38 insertions(+), 38 deletions(-)
diff --git a/test/test_extreme_fit/test_estimator/test_temporal_estimator/test_gev_temporal_evgam.py b/test/test_extreme_fit/test_estimator/test_temporal_estimator/test_gev_temporal_evgam.py
index 71a69072..9952646a 100644
--- a/test/test_extreme_fit/test_estimator/test_temporal_estimator/test_gev_temporal_evgam.py
+++ b/test/test_extreme_fit/test_estimator/test_temporal_estimator/test_gev_temporal_evgam.py
@@ -49,44 +49,44 @@ class TestGevTemporalEvGam(unittest.TestCase):
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
- # def test_gev_temporal_margin_fit_nonstationary(self):
- # # Create estimator
- # margin_models = load_non_stationary_temporal_margin_models(self.coordinates)
- # for margin_model in margin_models:
- # estimator = LinearMarginEstimator(self.dataset, margin_model)
- # estimator.fit()
- # # Checks that parameters returned are indeed different
- # mle_params_estimated_year1 = estimator.function_from_fit.get_params(np.array([1])).to_dict()
- # mle_params_estimated_year3 = estimator.function_from_fit.get_params(np.array([3])).to_dict()
- # self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
- #
- # def test_gev_temporal_margin_fit_nonstationary_with_start_point(self):
- # # Create estimator
- # estimator = self.fit_non_stationary_estimator(starting_point=3)
- # self.assertNotEqual(estimator.function_from_fit.mu1_temporal_trend, 0.0)
- # # Checks starting point parameter are well passed
- # self.assertEqual(3, estimator.function_from_fit.starting_point)
- # # Checks that parameters returned are indeed different
- # mle_params_estimated_year1 = estimator.function_from_fit.get_params(np.array([1])).to_dict()
- # mle_params_estimated_year3 = estimator.function_from_fit.get_params(np.array([3])).to_dict()
- # self.assertEqual(mle_params_estimated_year1, mle_params_estimated_year3)
- # mle_params_estimated_year5 = estimator.function_from_fit.get_params(np.array([5])).to_dict()
- # self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year3)
- #
- # def fit_non_stationary_estimator(self, starting_point):
- # margin_model = NonStationaryLocationTemporalModel(self.coordinates,
- # starting_point=starting_point + self.start_year)
- # estimator = LinearMarginEstimator(self.dataset, margin_model)
- # estimator.fit()
- # return estimator
- #
- # def test_two_different_starting_points(self):
- # # Create two different estimators
- # estimator1 = self.fit_non_stationary_estimator(starting_point=3)
- # estimator2 = self.fit_non_stationary_estimator(starting_point=28)
- # mu1_estimator1 = estimator1.function_from_fit.mu1_temporal_trend
- # mu1_estimator2 = estimator2.function_from_fit.mu1_temporal_trend
- # self.assertNotEqual(mu1_estimator1, mu1_estimator2)
+ def test_gev_temporal_margin_fit_nonstationary(self):
+ # Create estimator
+ margin_models = load_non_stationary_temporal_margin_models(self.coordinates)
+ for margin_model in margin_models:
+ estimator = LinearMarginEstimator(self.dataset, margin_model)
+ estimator.fit()
+ # Checks that parameters returned are indeed different
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_params(np.array([3])).to_dict()
+ self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
+
+ def test_gev_temporal_margin_fit_nonstationary_with_start_point(self):
+ # Create estimator
+ estimator = self.fit_non_stationary_estimator(starting_point=3)
+ self.assertNotEqual(estimator.function_from_fit.mu1_temporal_trend, 0.0)
+ # Checks starting point parameter are well passed
+ self.assertEqual(3, estimator.function_from_fit.starting_point)
+ # Checks that parameters returned are indeed different
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_params(np.array([3])).to_dict()
+ self.assertEqual(mle_params_estimated_year1, mle_params_estimated_year3)
+ mle_params_estimated_year5 = estimator.function_from_fit.get_params(np.array([5])).to_dict()
+ self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year3)
+
+ def fit_non_stationary_estimator(self, starting_point):
+ margin_model = NonStationaryLocationTemporalModel(self.coordinates,
+ starting_point=starting_point + self.start_year)
+ estimator = LinearMarginEstimator(self.dataset, margin_model)
+ estimator.fit()
+ return estimator
+
+ def test_two_different_starting_points(self):
+ # Create two different estimators
+ estimator1 = self.fit_non_stationary_estimator(starting_point=3)
+ estimator2 = self.fit_non_stationary_estimator(starting_point=28)
+ mu1_estimator1 = estimator1.function_from_fit.mu1_temporal_trend
+ mu1_estimator2 = estimator2.function_from_fit.mu1_temporal_trend
+ self.assertNotEqual(mu1_estimator1, mu1_estimator2)
if __name__ == '__main__':
--
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