diff --git a/experiment/paper_past_snow_loads/check_mle_convergence_for_trends/study_visualizer_for_shape_repartition.py b/experiment/paper_past_snow_loads/check_mle_convergence_for_trends/study_visualizer_for_shape_repartition.py
index 729f7c538ce56f0622d9e3be3ae69d5823376e2c..0a25e17896e9a627e57846f3185fe0e7fa8d18f1 100644
--- a/experiment/paper_past_snow_loads/check_mle_convergence_for_trends/study_visualizer_for_shape_repartition.py
+++ b/experiment/paper_past_snow_loads/check_mle_convergence_for_trends/study_visualizer_for_shape_repartition.py
@@ -10,18 +10,8 @@ from experiment.trend_analysis.abstract_score import MeanScore
class StudyVisualizerForShape(StudyVisualizerForNonStationaryTrends):
- def __init__(self, study: AbstractStudy, show=True, save_to_file=False, only_one_graph=False, only_first_row=False,
- vertical_kde_plot=False, year_for_kde_plot=None, plot_block_maxima_quantiles=False,
- temporal_non_stationarity=False, transformation_class=None, verbose=False, multiprocessing=False,
- complete_non_stationary_trend_analysis=False, normalization_under_one_observations=True,
- score_class=MeanScore, uncertainty_methods=None, non_stationary_contexts=None,
- uncertainty_massif_names=None, effective_temporal_covariate=2017, relative_change_trend_plot=True):
- super().__init__(study, show, save_to_file, only_one_graph, only_first_row, vertical_kde_plot,
- year_for_kde_plot, plot_block_maxima_quantiles, temporal_non_stationarity,
- transformation_class, verbose, multiprocessing, complete_non_stationary_trend_analysis,
- normalization_under_one_observations, score_class, uncertainty_methods,
- non_stationary_contexts, uncertainty_massif_names, effective_temporal_covariate,
- relative_change_trend_plot)
+ def __init__(self, study: AbstractStudy, **kwargs):
+ super().__init__(study, **kwargs)
@cached_property
def massif_name_to_unconstrained_shape_parameter(self):
diff --git a/experiment/paper_past_snow_loads/paper_main_utils.py b/experiment/paper_past_snow_loads/paper_main_utils.py
index 510dcdec4cef196ea82c6718fecc8f755cec563e..f41fb8181f7abf03323e8b5ab7d6bd3f483153f3 100644
--- a/experiment/paper_past_snow_loads/paper_main_utils.py
+++ b/experiment/paper_past_snow_loads/paper_main_utils.py
@@ -2,15 +2,18 @@ from collections import OrderedDict
from experiment.paper_past_snow_loads.study_visualizer_for_non_stationary_trends import \
StudyVisualizerForNonStationaryTrends
+from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
+ TemporalMarginFitMethod
def load_altitude_to_visualizer(altitudes, massif_names, non_stationary_uncertainty, study_class, uncertainty_methods,
study_visualizer_class=StudyVisualizerForNonStationaryTrends,
save_to_file=True):
+ fit_method = TemporalMarginFitMethod.extremes_fevd_gmle
altitude_to_visualizer = OrderedDict()
for altitude in altitudes:
altitude_to_visualizer[altitude] = study_visualizer_class(
study=study_class(altitude=altitude), multiprocessing=True, save_to_file=save_to_file,
uncertainty_massif_names=massif_names, uncertainty_methods=uncertainty_methods,
- non_stationary_contexts=non_stationary_uncertainty)
+ non_stationary_contexts=non_stationary_uncertainty, fit_method=fit_method)
return altitude_to_visualizer
diff --git a/experiment/paper_past_snow_loads/result_trends_and_return_levels/main_result_trends_and_return_levels.py b/experiment/paper_past_snow_loads/result_trends_and_return_levels/main_result_trends_and_return_levels.py
index 57022684050fafd87a002514aec4d721bad38f03..5987b7a627c913707b585524afa548c71254d596 100644
--- a/experiment/paper_past_snow_loads/result_trends_and_return_levels/main_result_trends_and_return_levels.py
+++ b/experiment/paper_past_snow_loads/result_trends_and_return_levels/main_result_trends_and_return_levels.py
@@ -83,8 +83,8 @@ def major_result():
uncertainty_methods = [ConfidenceIntervalMethodFromExtremes.my_bayes,
ConfidenceIntervalMethodFromExtremes.ci_mle][1:]
massif_names = None
- study_classes = paper_study_classes[:2]
- study_classes = [CrocusSnowLoad3Days, CrocusSnowLoad5Days, CrocusSnowLoad7Days][::-1]
+ study_classes = paper_study_classes[:1]
+ # study_classes = [CrocusSnowLoad3Days, CrocusSnowLoad5Days, CrocusSnowLoad7Days][::-1]
for study_class in study_classes:
if study_class == CrocusSnowLoadEurocode:
non_stationary_uncertainty = [False]
diff --git a/experiment/paper_past_snow_loads/study_visualizer_for_non_stationary_trends.py b/experiment/paper_past_snow_loads/study_visualizer_for_non_stationary_trends.py
index 998a8126cc6fdb2889859bf816460236921ee2f5..f6bae071d5135512782a769af2e20a60c5fa29fe 100644
--- a/experiment/paper_past_snow_loads/study_visualizer_for_non_stationary_trends.py
+++ b/experiment/paper_past_snow_loads/study_visualizer_for_non_stationary_trends.py
@@ -2,7 +2,7 @@ from collections import OrderedDict
import matplotlib.pyplot as plt
from multiprocessing.pool import Pool
-from typing import Dict
+from typing import Dict, List
import numpy as np
from cached_property import cached_property
@@ -48,12 +48,14 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
uncertainty_massif_names=None,
effective_temporal_covariate=2017,
relative_change_trend_plot=True,
- non_stationary_trend_test_to_marker=None):
+ non_stationary_trend_test_to_marker=None,
+ fit_method=None):
super().__init__(study, show, save_to_file, only_one_graph, only_first_row, vertical_kde_plot,
year_for_kde_plot, plot_block_maxima_quantiles, temporal_non_stationarity,
transformation_class, verbose, multiprocessing, complete_non_stationary_trend_analysis,
normalization_under_one_observations, score_class)
# Add some attributes
+ self.fit_method = fit_method
self.non_stationary_trend_test_to_marker = non_stationary_trend_test_to_marker
self.relative_change_trend_plot = relative_change_trend_plot
self.effective_temporal_covariate = effective_temporal_covariate
@@ -118,7 +120,12 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
quantile_level = self.massif_name_to_eurocode_quantile_level_in_practice[massif_name]
non_stationary_trend_test = [
t(years=x, maxima=y, starting_year=starting_year, quantile_level=quantile_level)
- for t in self.non_stationary_trend_test]
+ for t in self.non_stationary_trend_test] # type: List[AbstractGevTrendTest]
+ # Set appropriate fit method for all objects (#todo: set this parameter directly in the init function)
+ if self.fit_method is not None:
+ for t in non_stationary_trend_test:
+ t.fit_method = self.fit_method
+ # Extract
trend_test_that_minimized_aic = sorted(non_stationary_trend_test, key=lambda t: t.aic)[0]
massif_name_to_trend_test_that_minimized_aic[massif_name] = trend_test_that_minimized_aic
return massif_name_to_trend_test_that_minimized_aic
@@ -131,7 +138,10 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
@cached_property
def _max_abs_change(self):
- return self.global_max_abs_change if self.global_max_abs_change is not None else self.max_abs_change
+ max_abs_change = self.global_max_abs_change if self.global_max_abs_change is not None else self.max_abs_change
+ if max_abs_change == 0:
+ max_abs_change = 1e-10
+ return max_abs_change
def plot_trends(self, max_abs_tdrl=None, add_colorbar=True):
if max_abs_tdrl is not None: