From 172ced69d8049dc5bb5ca9acadbd888c9d31335f Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Wed, 18 Dec 2019 11:57:16 +0100
Subject: [PATCH] [Confidence Interval] improve uncertainty plot
---
.../main_result_trends_and_return_levels.py | 12 ++++----
.../plot_uncertainty_curves.py | 29 ++++++++++++-------
2 files changed, 25 insertions(+), 16 deletions(-)
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 858f8b33..77eb695b 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
@@ -91,12 +91,12 @@ def major_result():
if __name__ == '__main__':
- major_result()
- # intermediate_result(altitudes=[900, 1200], massif_names=['Belledonne'],
- # uncertainty_methods=[ConfidenceIntervalMethodFromExtremes.my_bayes,
- # ConfidenceIntervalMethodFromExtremes.ci_mle][1:],
- # non_stationary_uncertainty=[False, True][1:],
- # multiprocessing=True)
+ # major_result()
+ intermediate_result(altitudes=[900, 1200], massif_names=['Vercors'],
+ uncertainty_methods=[ConfidenceIntervalMethodFromExtremes.my_bayes,
+ ConfidenceIntervalMethodFromExtremes.ci_mle][1:],
+ non_stationary_uncertainty=[False, True][1:],
+ multiprocessing=True)
# intermediate_result(altitudes=[900, 1200], massif_names=['Maurienne'],
# uncertainty_methods=[ConfidenceIntervalMethodFromExtremes.my_bayes,
# ConfidenceIntervalMethodFromExtremes.ci_mle][1:],
diff --git a/experiment/paper_past_snow_loads/result_trends_and_return_levels/plot_uncertainty_curves.py b/experiment/paper_past_snow_loads/result_trends_and_return_levels/plot_uncertainty_curves.py
index 41406af0..9fbb0793 100644
--- a/experiment/paper_past_snow_loads/result_trends_and_return_levels/plot_uncertainty_curves.py
+++ b/experiment/paper_past_snow_loads/result_trends_and_return_levels/plot_uncertainty_curves.py
@@ -14,7 +14,8 @@ from extreme_fit.model.result_from_model_fit.result_from_extremes.abstract_extra
AbstractExtractEurocodeReturnLevel
from experiment.eurocode_data.massif_name_to_departement import massif_name_to_eurocode_region
from experiment.meteo_france_data.scm_models_data.visualization.utils import create_adjusted_axes
-from extreme_fit.model.result_from_model_fit.result_from_extremes.confidence_interval_method import ci_method_to_color
+from extreme_fit.model.result_from_model_fit.result_from_extremes.confidence_interval_method import ci_method_to_color, \
+ ConfidenceIntervalMethodFromExtremes
from root_utils import get_display_name_from_object_type
@@ -66,14 +67,17 @@ def plot_single_uncertainty_massif(altitude_to_visualizer: Dict[int, StudyVisual
plt.close()
-def get_label_name(non_stationary_context, ci_method_name):
+def get_label_name(non_stationary_context, ci_method_name, add_method_suffix):
model_symbol = 'N' if non_stationary_context else '0'
parameter = ', 2017' if non_stationary_context else ''
- model_name = ' $ \widehat{z_p}(\\boldsymbol{\\theta_{\mathcal{M}'
+ model_name = ' $ z_p(\\boldsymbol{\\widehat{\\theta}_{\\mathcal{M}'
# model_name += '_' + model_symbol
model_name += '}}'
model_name += parameter
- model_name += ')_{ \\textrm{' + ci_method_name.upper().split(' ')[1] + '}} $ '
+ model_name += ')'
+ if add_method_suffix:
+ model_name += '_{ \\textrm{' + ci_method_name.upper().split(' ')[1] + '}} '
+ model_name += '$'
return model_name
@@ -90,6 +94,7 @@ def plot_single_uncertainty_massif_and_non_stationary_context(ax, massif_name, n
eurocode_region = massif_name_to_eurocode_region[massif_name]()
# Display the return level from model class
+ nb_uncertainty_methods = len(visualizer.uncertainty_methods)
for j, uncertainty_method in enumerate(visualizer.uncertainty_methods):
if j == 0:
# Plot eurocode norm
@@ -99,7 +104,8 @@ def plot_single_uncertainty_massif_and_non_stationary_context(ax, massif_name, n
# Plot uncertainties
color = ci_method_to_color[uncertainty_method]
valid_altitudes = plot_valid_return_level_uncertainties(alpha, altitude_to_visualizer, altitudes, ax, color,
- massif_name, non_stationary_context, uncertainty_method)
+ massif_name, non_stationary_context, uncertainty_method,
+ nb_uncertainty_methods)
# Plot some data for the non valid altitudes
# Plot bars of TDRL only in the non stationary case
@@ -142,12 +148,14 @@ def plot_tdrl_bars(altitude_to_visualizer, ax, massif_name, valid_altitudes, leg
# colors = [v.massif_name_to_color[massif_name] for v in visualizers]
# From snow on, we set a black color for the bars
colors = ['white' for v in visualizers]
- ax.bar(valid_altitudes, tdrl_values, width=150, color=colors, label=visualizers[0].label_tdrl_bar,
- edgecolor='black', hatch='//')
+ non_null_tdrl_index = [i for i, t in enumerate(tdrl_values) if t != 0.0]
+ if len(non_null_tdrl_index) > 0:
+ ax.bar(np.array(valid_altitudes)[non_null_tdrl_index], np.array(tdrl_values)[non_null_tdrl_index],
+ width=150, color=colors, label=visualizers[0].label_tdrl_bar, edgecolor='black', hatch='//')
# Plot markers
markers_kwargs = [v.massif_name_to_marker_style[massif_name] for v in visualizers]
for k in markers_kwargs:
- k['markersize'] = 7
+ k['markersize'] = 10
for altitude, marker_kwargs, value in zip(valid_altitudes, markers_kwargs, tdrl_values):
# ax.plot([altitude], [value / 2], **marker_kwargs)
# Better to plot all the markers on the same line
@@ -166,7 +174,7 @@ def plot_tdrl_bars(altitude_to_visualizer, ax, massif_name, valid_altitudes, leg
def plot_valid_return_level_uncertainties(alpha, altitude_to_visualizer, altitudes, ax, color, massif_name,
- non_stationary_context, uncertainty_method):
+ non_stationary_context, uncertainty_method, nb_uncertainty_methods):
# Compute ordered_return_level_uncertaines for a given massif_name, uncertainty methods, and non stationary context
ordered_return_level_uncertainties = []
for visualizer in altitude_to_visualizer.values():
@@ -180,7 +188,8 @@ def plot_valid_return_level_uncertainties(alpha, altitude_to_visualizer, altitud
valid_altitudes = list(np.array(altitudes)[not_nan_index])
ordered_return_level_uncertainties = list(np.array(ordered_return_level_uncertainties)[not_nan_index])
ci_method_name = str(uncertainty_method).split('.')[1].replace('_', ' ')
- label_name = get_label_name(non_stationary_context, ci_method_name)
+ add_method_suffix = nb_uncertainty_methods > 1 or 'mle' not in ci_method_name
+ label_name = get_label_name(non_stationary_context, ci_method_name, add_method_suffix=add_method_suffix)
ax.plot(valid_altitudes, mean, linestyle='--', marker='o', color=color,
label=label_name)
lower_bound = [r.confidence_interval[0] for r in ordered_return_level_uncertainties]
--
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