[paper 1] add upper and lower bound into the gev param object. Add script to...

[paper 1] add upper and lower bound into the gev param object. Add script to run fit wihout maximum, and then compare the upper bound with the maximum values that was removed. The test is ok for all altitudes, the upper bound found was always higher than the maximum values

experiment/paper_past_snow_loads/check_mle_convergence_for_trends/without_maximum/main_fit_without_maximum.py

+from typing import Dict
+
+from experiment.meteo_france_data.scm_models_data.crocus.crocus import CrocusSnowLoadTotal
+from experiment.meteo_france_data.scm_models_data.visualization.study_visualization.main_study_visualizer import \
+    ALL_ALTITUDES_WITHOUT_NAN
+from experiment.paper_past_snow_loads.check_mle_convergence_for_trends.without_maximum.study_visualizer_for_fit_witout_maximum import \
+    StudyVisualizerForFitWithoutMaximum
+
+
+def fit_without_maximum_value(altitude_to_visualizer: Dict[int, StudyVisualizerForFitWithoutMaximum]):
+    for v in altitude_to_visualizer.values():
+        v.maximum_value_test()
+
+
+if __name__ == '__main__':
+    altitudes = ALL_ALTITUDES_WITHOUT_NAN[:]
+    altitude_to_visualizer = {altitude: StudyVisualizerForFitWithoutMaximum(CrocusSnowLoadTotal(altitude=altitude),
+                                                                            multiprocessing=True)
+                              for altitude in altitudes}
+    fit_without_maximum_value(altitude_to_visualizer)

experiment/paper_past_snow_loads/check_mle_convergence_for_trends/without_maximum/study_visualizer_for_fit_witout_maximum.py

+from typing import Dict, Tuple
+
+import matplotlib
+import numpy as np
+from cached_property import cached_property
+
+from experiment.meteo_france_data.plot.create_shifted_cmap import get_shifted_map
+from experiment.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
+from experiment.paper_past_snow_loads.study_visualizer_for_non_stationary_trends import \
+    StudyVisualizerForNonStationaryTrends
+from experiment.trend_analysis.abstract_score import MeanScore
+
+
+class StudyVisualizerForFitWithoutMaximum(StudyVisualizerForNonStationaryTrends):
+
+    def __init__(self, study: AbstractStudy, **kwargs):
+        super().__init__(study, **kwargs)
+
+    @cached_property
+    def massif_name_to_maximum_index_for_non_null_values(self) -> Tuple[Dict, Dict]:
+        d = super().massif_name_to_non_null_years_and_maxima
+        d_without_maximum = {}
+        d_maximum = {}
+        for m, (years, maxima) in d.items():
+            idx = np.argmax(maxima)
+            maximum = maxima[idx]
+            maxima = np.delete(maxima, idx)
+            years = np.delete(years, idx)
+            d_without_maximum[m] = (years, maxima)
+            d_maximum[m] = maximum
+        return d_without_maximum, d_maximum
+
+    @property
+    def massif_name_to_maximum(self) -> Dict:
+        return self.massif_name_to_maximum_index_for_non_null_values[1]
+
+    @cached_property
+    def massif_name_to_non_null_years_and_maxima(self):
+        return self.massif_name_to_maximum_index_for_non_null_values[0]
+
+    def maximum_value_test(self):
+        diff = []
+        for massif_name, maximum in self.massif_name_to_maximum.items():
+            t = self.massif_name_to_trend_test_that_minimized_aic[massif_name]
+            msg = '{} {}m'.format(massif_name, self.study.altitude)
+            upper_bound = t.unconstrained_estimator_gev_params.density_upper_bound
+            if not np.isinf(upper_bound):
+                diff.append(upper_bound - maximum)
+            assert maximum <= upper_bound, msg
+        if len(diff) > 1:
+            print('{} mean difference={}'.format(self.study.altitude, min(diff)))
+
+
+
+
+
+
+
+
+
+

extreme_fit/distribution/gev/gev_params.py

@@ -130,4 +130,22 @@ class GevParams(AbstractParams):
         if self.shape == 0:
             return x
         else:
-            return np.log(1 + self.shape * x) / self.shape
+            return np.log(1 + self.shape * x) / self.shape
\ No newline at end of file
+
+    @property
+    def bound(self):
+        return self.location - (self.scale / self.shape)
+
+    @property
+    def density_upper_bound(self):
+        if self.shape >= 0:
+            return np.inf
+        else:
+            return self.bound
+
+    @property
+    def density_lower_bound(self):
+        if self.shape <= 0:
+            return np.inf
+        else:
+            return self.bound
\ No newline at end of file