[contrasting] add test and refactor

extreme_fit/distribution/gumbel/gumbel_gof.py

-import io
-from contextlib import redirect_stdout
-
 import numpy as np
 
 from extreme_fit.model.result_from_model_fit.abstract_result_from_model_fit import AbstractResultFromModelFit

extreme_trend/abstract_gev_trend_test.py

@@ -3,21 +3,19 @@ from math import ceil, floor
 import matplotlib.pyplot as plt
 import numpy as np
 from cached_property import cached_property
-from scipy.stats import chi2, kstest, anderson
-from scipy.stats.morestats import AndersonResult
+from scipy.stats import chi2, kstest
 from scipy.stats.stats import KstestResult
 
 from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE, YEAR_OF_INTEREST_FOR_RETURN_LEVEL
 from extreme_data.meteo_france_data.scm_models_data.crocus.crocus_variables import AbstractSnowLoadVariable
+from extreme_fit.distribution.gev.gev_params import GevParams
 from extreme_fit.distribution.gumbel.gumbel_gof import \
     cramer_von_mises_and_anderson_darling_tests_pvalues_for_gumbel_distribution
 from extreme_fit.estimator.margin_estimator.utils import fitted_linear_margin_estimator
-from extreme_fit.distribution.gev.gev_params import GevParams
-from extreme_fit.model.margin_model.utils import \
-    MarginFitMethod
 from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import \
     StationaryTemporalModel, GumbelTemporalModel
-from extreme_fit.model.utils import SafeRunException
+from extreme_fit.model.margin_model.utils import \
+    MarginFitMethod
 from root_utils import classproperty
 from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
 from spatio_temporal_dataset.utils import load_temporal_coordinates_and_dataset

projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/main_snowfall_article.py

@@ -66,7 +66,7 @@ def intermediate_result(altitudes, massif_names=None,
     # Compute minimized value efficiently
     visualizers = list(altitude_to_visualizer.values())
     if multiprocessing:
-        with Pool(NB_CORES) as p:
+        with Pool(4) as p:
             _ = p.map(compute_minimized_aic, visualizers)
     else:
         for visualizer in visualizers:
@@ -95,7 +95,7 @@ def major_result():
 
     for study_class in study_classes:
         intermediate_result(altitudes, massif_names, model_subsets_for_uncertainty,
-                            uncertainty_methods, study_class, multiprocessing=True)
+                            uncertainty_methods, study_class, multiprocessing=False)
 
 
 if __name__ == '__main__':

test/test_extreme_trend/test_extreme_trend.py

 import unittest
 
+from extreme_fit.distribution.gev.gev_params import GevParams
+from extreme_trend.trend_test_one_parameter.gumbel_trend_test_one_parameter import GumbelVersusGumbel
 from projects.exceeding_snow_loads.utils import NON_STATIONARY_TREND_TEST_PAPER_1, NON_STATIONARY_TREND_TEST_PAPER_2
 
 
@@ -19,6 +21,13 @@ class TestTrendAnalysis(unittest.TestCase):
         for trend_test_class, nb in zip(trend_test_classes, nb_expected):
             self.assertEqual(trend_test_class.total_number_of_parameters_for_unconstrained_model, nb)
 
+    def test_anderson_goodness_of_fit(self):
+        nb_data = 50
+        years = list(range(nb_data))
+        maxima = GevParams(5, 1, 0).sample(nb_data)
+        trend_test = GumbelVersusGumbel(years, maxima, None)
+        self.assertTrue(trend_test.goodness_of_fit_anderson_test)
+
 
 if __name__ == '__main__':
     unittest.main()