diff --git a/extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py b/extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py
index 1058570d81a22f31692057e3b571241ff9c83dcb..15ff9be2d7022cc6f0b13f1650bd7d24169ea7e5 100644
--- a/extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py
+++ b/extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py
@@ -172,8 +172,6 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
return massif_name_to_trend_test_that_minimized_aic
-
-
def get_trend_trend_test(self, massif_name, trend_test_classes):
x, y = self.massif_name_to_years_and_maxima_for_model_fitting[massif_name]
quantile_level = self.massif_name_to_eurocode_quantile_level_in_practice[massif_name]
@@ -226,7 +224,7 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
ax.get_xaxis().set_visible(True)
ax.set_xticks([])
ax.set_xlabel('Altitude = {}m'.format(self.study.altitude), fontsize=15)
- middle_word = 'o' if (not add_colorbar and self.study.altitude == 2700) else ''
+ middle_word = 'o' if (not add_colorbar and self.study.altitude in [1800, 2700]) else ''
self.plot_name = 'tdlr_trends_w' + middle_word + '_colorbar'
self.show_or_save_to_file(add_classic_title=False, tight_layout=True, no_title=True,
dpi=500)
diff --git a/projects/exceeding_snow_loads/section_data/main_example_swe_total_plot.py b/projects/exceeding_snow_loads/section_data/main_example_swe_total_plot.py
index 9b6a86ed4f3e4b5af803764a21caf99cc851b2a5..4f9760fc82fe25270a4ffe2b00786a2f1d9881ff 100644
--- a/projects/exceeding_snow_loads/section_data/main_example_swe_total_plot.py
+++ b/projects/exceeding_snow_loads/section_data/main_example_swe_total_plot.py
@@ -19,7 +19,7 @@ def tuples_for_examples_paper1(examples_for_the_paper=True):
else:
marker_altitude_massif_name_for_paper1 = [
('magenta', 600, 'Parpaillon'),
- ('darkmagenta', 300, 'Devoluy'),
+ # ('darkmagenta', 300, 'Devoluy'),
('mediumpurple', 300, 'Aravis'),
]
return marker_altitude_massif_name_for_paper1
@@ -34,7 +34,7 @@ def max_graph_annual_maxima_poster():
save_to_file = True
study_class = CrocusSnowLoadTotal
- examples_for_the_paper = True
+ examples_for_the_paper = False
ax = plt.gca()
if examples_for_the_paper:
diff --git a/projects/exceeding_snow_loads/section_results/main_result_trends_and_return_levels.py b/projects/exceeding_snow_loads/section_results/main_result_trends_and_return_levels.py
index 6aba0ed6d1f3ed11a0f7bfe2cceaaade09571d14..9053d8ce360c89bdfcdeeaa5db42239bc3e31514 100644
--- a/projects/exceeding_snow_loads/section_results/main_result_trends_and_return_levels.py
+++ b/projects/exceeding_snow_loads/section_results/main_result_trends_and_return_levels.py
@@ -38,11 +38,11 @@ def compute_minimized_aic(visualizer):
_ = visualizer.massif_name_to_trend_test_that_minimized_aic
return True
-
def intermediate_result(altitudes, massif_names=None,
model_subsets_for_uncertainty=None, uncertainty_methods=None,
study_class=CrocusSnowLoadTotal,
- multiprocessing=False):
+ multiprocessing=False,
+ only_histogram=False):
"""
Plot all the trends for all altitudes
And enable to plot uncertainty plot for some specific massif_names, uncertainty methods to be fast
@@ -56,29 +56,32 @@ def intermediate_result(altitudes, massif_names=None,
# Load altitude to visualizer
altitude_to_visualizer = load_altitude_to_visualizer(altitudes, massif_names, model_subsets_for_uncertainty,
study_class, uncertainty_methods)
- # Load variable object efficiently
- for v in altitude_to_visualizer.values():
- _ = v.study.year_to_variable_object
- # Compute minimized value efficiently
- # visualizers = list()
- if multiprocessing:
- with Pool(NB_CORES) as p:
- _ = p.imap(compute_minimized_aic, altitude_to_visualizer.values())
+ if only_histogram:
+ plot_uncertainty_histogram(altitude_to_visualizer)
else:
- for visualizer in altitude_to_visualizer.values():
- _ = compute_minimized_aic(visualizer)
+ # Load variable object efficiently
+ for v in altitude_to_visualizer.values():
+ _ = v.study.year_to_variable_object
+ # Compute minimized value efficiently
+ # visualizers = list()
+ if multiprocessing:
+ with Pool(NB_CORES) as p:
+ _ = p.imap(compute_minimized_aic, altitude_to_visualizer.values())
+ else:
+ for visualizer in altitude_to_visualizer.values():
+ _ = compute_minimized_aic(visualizer)
- # Plots
- plot_trend_map(altitude_to_visualizer)
- plot_trend_curves(altitude_to_visualizer={a: v for a, v in altitude_to_visualizer.items() if a >= 900})
- plot_uncertainty_massifs(altitude_to_visualizer)
- plot_uncertainty_histogram(altitude_to_visualizer)
- plot_selection_curves(altitude_to_visualizer)
- plot_intensity_against_gumbel_quantile_for_3_examples(altitude_to_visualizer)
+ # Plots
+ plot_trend_map(altitude_to_visualizer)
+ plot_trend_curves(altitude_to_visualizer={a: v for a, v in altitude_to_visualizer.items() if a >= 900})
+ plot_uncertainty_massifs(altitude_to_visualizer)
+ plot_uncertainty_histogram(altitude_to_visualizer)
+ plot_selection_curves(altitude_to_visualizer)
+ plot_intensity_against_gumbel_quantile_for_3_examples(altitude_to_visualizer)
- # Additional plots
- # uncertainty_interval_size(altitude_to_visualizer)
- # plot_full_diagnostic(altitude_to_visualizer)
+ # Additional plots
+ # uncertainty_interval_size(altitude_to_visualizer)
+ # plot_full_diagnostic(altitude_to_visualizer)
def major_result():
@@ -86,20 +89,24 @@ def major_result():
ConfidenceIntervalMethodFromExtremes.ci_mle][1:]
# massif_names = ['Beaufortain', 'Vercors']
massif_names = None
- study_classes = paper_study_classes[:1]
+ study_classes = paper_study_classes[:]
# study_classes = [CrocusSnowLoad3Days, CrocusSnowLoad5Days, CrocusSnowLoad7Days][::-1]
altitudes = [300, 600, 900, 1800, 2700][:2]
altitudes = [300, 600, 900, 1200, 1500, 1800]
altitudes = paper_altitudes
# altitudes = [900, 1800, 270{{0][:1]
for study_class in study_classes:
- if study_class == CrocusSnowLoadEurocode:
+ print('new stuy class', study_class)
+ if study_class is CrocusSnowLoadEurocode:
model_subsets_for_uncertainty = [ModelSubsetForUncertainty.stationary_gumbel]
+ only_histogram = True
else:
model_subsets_for_uncertainty = None
+ only_histogram = False
intermediate_result(altitudes, massif_names, model_subsets_for_uncertainty,
uncertainty_methods, study_class,
- multiprocessing=True)
+ multiprocessing=True,
+ only_histogram=only_histogram)
if __name__ == '__main__':
diff --git a/projects/exceeding_snow_loads/section_results/plot_trend_curves.py b/projects/exceeding_snow_loads/section_results/plot_trend_curves.py
index a2516bda714c34ee2d0f869184afbafcad1854d4..01ec6441c0118d596b3c5c182bc992cc773dd3dd 100644
--- a/projects/exceeding_snow_loads/section_results/plot_trend_curves.py
+++ b/projects/exceeding_snow_loads/section_results/plot_trend_curves.py
@@ -22,6 +22,8 @@ def plot_trend_map(altitude_to_visualizer):
# Plot 2700 also with a colorbar
if altitude == 2700:
visualizer.plot_trends(max_abs_tdrl_above_900, add_colorbar=True)
+ if altitude == 1800:
+ visualizer.plot_trends(max_abs_tdrl_above_900, add_colorbar=False)
else:
max_abs_tdrl_below_900 = max(altitude_to_visualizer[300].max_abs_change,
altitude_to_visualizer[600].max_abs_change)
diff --git a/projects/exceeding_snow_loads/section_results/plot_uncertainty_curves.py b/projects/exceeding_snow_loads/section_results/plot_uncertainty_curves.py
index 78a3e7bafb8b5af6e09752977573164426c58e67..cebdc81c014259ebbf1d1a551aad492fa14880a4 100644
--- a/projects/exceeding_snow_loads/section_results/plot_uncertainty_curves.py
+++ b/projects/exceeding_snow_loads/section_results/plot_uncertainty_curves.py
@@ -30,11 +30,11 @@ def plot_uncertainty_massifs(altitude_to_visualizer: Dict[int, StudyVisualizerFo
visualizer = list(altitude_to_visualizer.values())[-1]
# Subdivide massif names in group of 3
m = 1
- uncertainty_massif_names = visualizer.uncertainty_massif_names
- n = (len(uncertainty_massif_names) // m)
+ massif_names_fitted = visualizer.intersection_of_massif_names_fitted
+ n = (len(massif_names_fitted) // m)
print('total nb of massif', n)
for i in list(range(n))[:]:
- massif_names = uncertainty_massif_names[m * i: m * (i + 1)]
+ massif_names = massif_names_fitted[m * i: m * (i + 1)]
print(massif_names)
plot_subgroup_uncertainty_massifs(altitude_to_visualizer, massif_names)