From 1078ecbaef7ddfd2a00dc76f90fa825d7508bc76 Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Tue, 11 Feb 2020 15:18:09 +0100
Subject: [PATCH] [paper 1] put by default the fact of discarding shape
parameters that are not in the shape support. improve selection curve plot
---
.../plot_selection_curves.py | 31 ++++++++++++-------
...dy_visualizer_for_non_stationary_trends.py | 2 +-
2 files changed, 21 insertions(+), 12 deletions(-)
diff --git a/papers/exceeding_snow_loads/result_trends_and_return_levels/plot_selection_curves.py b/papers/exceeding_snow_loads/result_trends_and_return_levels/plot_selection_curves.py
index 0e2e0bbd..910cdb99 100644
--- a/papers/exceeding_snow_loads/result_trends_and_return_levels/plot_selection_curves.py
+++ b/papers/exceeding_snow_loads/result_trends_and_return_levels/plot_selection_curves.py
@@ -24,30 +24,39 @@ def plot_selection_curves(altitude_to_visualizer: Dict[int, StudyVisualizerForNo
selected_counter = merge_counter([v.selected_trend_test_class_counter for v in altitude_to_visualizer.values()])
selected_and_significative_counter = merge_counter([v.selected_and_significative_trend_test_class_counter for v in altitude_to_visualizer.values()])
total_of_selected_models = sum(selected_counter.values())
- l = sorted(enumerate(visualizer.non_stationary_trend_test), key=lambda e:selected_counter[e[1]])
+ l = sorted(enumerate(visualizer.non_stationary_trend_test), key=lambda e: selected_counter[e[1]])
permutation = [i for i, v in l][::-1]
+
select_list = get_ordered_list_from_counter(selected_counter, total_of_selected_models, visualizer, permutation)
selected_and_signifcative_list = get_ordered_list_from_counter(selected_and_significative_counter, total_of_selected_models, visualizer, permutation)
labels = permute(['${}$'.format(t.label) for t in visualizer.non_stationary_trend_test], permutation)
+ # print(l)
+ # print(select_list)
+ # print(selected_and_signifcative_list)
+ # [(5, <class 'experiment.trend_analysis.univariate_test.extreme_trend_test.trend_test_two_parameters.gev_trend_test_two_parameters.GevLocationAgainstGumbel'> ), (6, < class 'experiment.trend_analysis.univariate_test.extreme_trend_test.trend_test_two_parameters.gev_trend_test_two_parameters.GevScaleAgainstGumbel' > ), (2, < class 'experiment.trend_analysis.univariate_test.extreme_trend_test.trend_test_one_parameter.gumbel_trend_test_one_parameter.GumbelScaleTrendTest' > ), (1, < class 'experiment.trend_analysis.univariate_test.extreme_trend_test.trend_test_one_parameter.gumbel_trend_test_one_parameter.GumbelLocationTrendTest' > ), (7, < class 'experiment.trend_analysis.univariate_test.extreme_trend_test.trend_test_three_parameters.gev_trend_test_three_parameters.GevLocationAndScaleTrendTestAgainstGumbel' > ), (3, < class 'experiment.trend_analysis.univariate_test.extreme_trend_test.trend_test_two_parameters.gumbel_test_two_parameters.GumbelLocationAndScaleTrendTest' > ), (4, < class 'experiment.trend_analysis.univariate_test.extreme_trend_test.trend_test_one_parameter.gumbel_trend_test_one_parameter.GevStationaryVersusGumbel' > ), (0, < class 'experiment.trend_analysis.univariate_test.extreme_trend_test.trend_test_one_parameter.gumbel_trend_test_one_parameter.GumbelVersusGumbel' > )]
+ # [32.520325203252035, 25.609756097560975, 12.195121951219512, 9.34959349593496, 9.34959349593496, 4.471544715447155, 3.252032520325203, 3.252032520325203]
+ # [0, 13.821138211382113, 7.317073170731708, 8.536585365853659, 5.691056910569106, 1.6260162601626016, 1.2195121951219512, 2.4390243902439024]
+
# parameters
width = 5
- size = 20
- legend_fontsize = 20
- color = 'white'
- labelsize = 15
+ size = 30
+ legend_fontsize = 30
+ labelsize = 25
linewidth = 3
x = [10 * (i+1) for i in range(len(select_list))]
- ax.bar(x, select_list, width=width, color=color, edgecolor='blue', label='selected model',
+ ax.bar(x, select_list, width=width, color='grey', edgecolor='grey', label='Non significative model',
linewidth=linewidth)
- ax.bar(x, selected_and_signifcative_list, width=width, color=color, edgecolor='black',
- label='significative selected model',
+ ax.bar(x, selected_and_signifcative_list, width=width, color='black', edgecolor='black',
+ label='Significative model',
linewidth=linewidth)
- ax.legend(loc='upper left', prop={'size': size})
- ax.set_ylabel('Selected model (\%)', fontsize=legend_fontsize)
+ ax.legend(loc='upper right', prop={'size': size})
+ ax.set_ylabel('Time series where the model is selected\n'
+ 'i.e. where it minimizes the AIC (\%)', fontsize=legend_fontsize)
ax.set_xlabel('Models', fontsize=legend_fontsize)
-
+ ax.tick_params(axis='both', which='major', labelsize=labelsize)
ax.set_xticks(x)
+ ax.yaxis.grid()
ax.set_xticklabels(labels)
# for ax_horizontal in [ax, ax_twiny]:
diff --git a/papers/exceeding_snow_loads/study_visualizer_for_non_stationary_trends.py b/papers/exceeding_snow_loads/study_visualizer_for_non_stationary_trends.py
index df7703e9..7a81fbe2 100644
--- a/papers/exceeding_snow_loads/study_visualizer_for_non_stationary_trends.py
+++ b/papers/exceeding_snow_loads/study_visualizer_for_non_stationary_trends.py
@@ -51,7 +51,7 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
relative_change_trend_plot=True,
non_stationary_trend_test_to_marker=None,
fit_method=TemporalMarginFitMethod.extremes_fevd_mle,
- select_only_acceptable_shape_parameter=False):
+ select_only_acceptable_shape_parameter=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,
--
GitLab