diff --git a/extreme_data/meteo_france_data/scm_models_data/visualization/create_shifted_cmap.py b/extreme_data/meteo_france_data/scm_models_data/visualization/create_shifted_cmap.py
index e3ad6d16e256c9b5bcf01ed81b96fece868d4f8f..662212c08cc1a0cf52a8506ebeefb02d05c274fd 100644
--- a/extreme_data/meteo_france_data/scm_models_data/visualization/create_shifted_cmap.py
+++ b/extreme_data/meteo_france_data/scm_models_data/visualization/create_shifted_cmap.py
@@ -4,6 +4,7 @@ import matplotlib.cm as cm
import matplotlib.colorbar as cbar
import matplotlib.pyplot as plt
import numpy as np
+from matplotlib.colors import LinearSegmentedColormap
from mpl_toolkits.axes_grid1 import make_axes_locatable
from extreme_fit.distribution.abstract_params import AbstractParams
@@ -24,6 +25,13 @@ def get_shifted_map(vmin, vmax, cmap=plt.cm.bwr):
return shifted_cmap
+def get_half_colormap(cmap):
+ colors = cmap(np.linspace(0.5, 1, cmap.N // 2))
+ # Create a new colormap from those colors
+ cmap2 = LinearSegmentedColormap.from_list('Upper Half', colors)
+ return cmap2
+
+
def create_colorbase_axis(ax, label, cmap, norm, ticks_values_and_labels=None, fontsize=15):
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.0)
@@ -78,6 +86,15 @@ def ticks_values_and_labels_for_percentages(graduation, max_abs_change):
ticks_values = [((t / max_abs_change) + 1) / 2 for t in all_ticks_labels]
return ticks_values, all_ticks_labels
+def ticks_values_and_labels_for_positive_value(graduation, max_abs_change):
+ positive_ticks = []
+ tick = 0
+ while tick < max_abs_change:
+ positive_ticks.append(round(tick, 1))
+ tick += graduation
+ ticks_values = [(t / max_abs_change) for t in positive_ticks]
+ return ticks_values, positive_ticks
+
def ticks_and_labels_centered_on_one(max_ratio, min_ratio):
"""When we compute some ratio of two values.
diff --git a/extreme_data/meteo_france_data/scm_models_data/visualization/plot_utils.py b/extreme_data/meteo_france_data/scm_models_data/visualization/plot_utils.py
index 70e8fa68e568b46714a3cac2d774a8376b2256e6..1779e184db7a02ec61781c01e8b4783da0566616 100644
--- a/extreme_data/meteo_france_data/scm_models_data/visualization/plot_utils.py
+++ b/extreme_data/meteo_france_data/scm_models_data/visualization/plot_utils.py
@@ -3,7 +3,8 @@ import matplotlib.colors as mcolors
from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
from extreme_data.meteo_france_data.scm_models_data.visualization.create_shifted_cmap import \
- ticks_values_and_labels_for_percentages, get_shifted_map, get_colors
+ ticks_values_and_labels_for_percentages, get_shifted_map, get_colors, ticks_values_and_labels_for_positive_value, \
+ get_half_colormap
def plot_against_altitude(altitudes, ax, massif_id, massif_name, values):
@@ -21,12 +22,20 @@ def plot_against_altitude(altitudes, ax, massif_id, massif_name, values):
ax.plot(altitudes, values, color=color, linewidth=2, label=massif_name_str, linestyle=linestyle)
-def load_plot(cmap, graduation, label, massif_name_to_value, altitude, fit_method, add_x_label=True):
+def load_plot(cmap, graduation, label, massif_name_to_value, altitude, fit_method, add_x_label=True,
+ negative_and_positive_values=True):
max_abs_change = max([abs(e) for e in massif_name_to_value.values()])
- ticks, labels = ticks_values_and_labels_for_percentages(graduation=graduation, max_abs_change=max_abs_change)
- min_ratio = -max_abs_change
- max_ratio = max_abs_change
- cmap = get_shifted_map(min_ratio, max_ratio, cmap)
+ if negative_and_positive_values:
+ ticks, labels = ticks_values_and_labels_for_percentages(graduation=graduation, max_abs_change=max_abs_change)
+ min_ratio = -max_abs_change
+ max_ratio = max_abs_change
+ cmap = get_shifted_map(min_ratio, max_ratio, cmap)
+ else:
+ ticks, labels = ticks_values_and_labels_for_positive_value(graduation=graduation, max_abs_change=max_abs_change)
+ cmap = get_half_colormap(cmap)
+ min_ratio = 0
+ max_ratio = max_abs_change
+
massif_name_to_color = {m: get_colors([v], cmap, min_ratio, max_ratio)[0]
for m, v in massif_name_to_value.items()}
ticks_values_and_labels = ticks, labels
diff --git a/extreme_data/meteo_france_data/scm_models_data/visualization/study_visualizer.py b/extreme_data/meteo_france_data/scm_models_data/visualization/study_visualizer.py
index 34174e08676015ea7f00839da2d87e32fba4b7b8..3288a89df130052b9f362c8eb2ffdcdea80d9bef 100644
--- a/extreme_data/meteo_france_data/scm_models_data/visualization/study_visualizer.py
+++ b/extreme_data/meteo_france_data/scm_models_data/visualization/study_visualizer.py
@@ -708,19 +708,21 @@ class StudyVisualizer(VisualizationParameters):
# PLot functions that should be common
- def plot_map(self, cmap, fit_method, graduation, label, massif_name_to_value, plot_name, add_x_label=True):
+ def plot_map(self, cmap, fit_method, graduation, label, massif_name_to_value, plot_name, add_x_label=True,
+ negative_and_positive_values=True):
load_plot(cmap, graduation, label, massif_name_to_value, self.study.altitude, fitmethod_to_str(fit_method),
- add_x_label=add_x_label)
+ add_x_label=add_x_label, negative_and_positive_values=negative_and_positive_values)
self.plot_name = plot_name
# self.show_or_save_to_file(add_classic_title=False, tight_layout=True, no_title=True, dpi=500)
self.show_or_save_to_file(add_classic_title=False, no_title=True, dpi=500)
plt.close()
- def plot_abstract(self, massif_name_to_value, label, plot_name, fit_method='', graduation=10.0, cmap=plt.cm.bwr, add_x_label=True):
+ def plot_abstract(self, massif_name_to_value, label, plot_name, fit_method='', graduation=10.0, cmap=plt.cm.bwr,
+ add_x_label=True, negative_and_positive_values=True):
# Regroup the plot by altitudes
plot_name1 = '{}/{}'.format(self.study.altitude, plot_name)
# Regroup the plot by type of plot also
plot_name2 = '{}/{}'.format(plot_name.split()[0], plot_name)
for plot_name in [plot_name1, plot_name2]:
- self.plot_map(cmap, fit_method, graduation, label, massif_name_to_value, plot_name, add_x_label)
+ self.plot_map(cmap, fit_method, graduation, label, massif_name_to_value, plot_name, add_x_label, negative_and_positive_values)
diff --git a/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/main_snowfall_article.py b/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/main_snowfall_article.py
index 3a6a9feccf6cc6f2999aa0f1eea41b4b2924c21e..8c4090873a7868389ac45ed921aef81a5f42c020 100644
--- a/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/main_snowfall_article.py
+++ b/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/main_snowfall_article.py
@@ -72,8 +72,8 @@ def intermediate_result(altitudes, massif_names=None,
_ = compute_minimized_aic(visualizer)
# Plots
- # validation_plot(altitude_to_visualizer)
- # plot_snowfall_mean(altitude_to_visualizer)
+ validation_plot(altitude_to_visualizer)
+ plot_snowfall_mean(altitude_to_visualizer)
plot_snowfall_time_derivative_mean(altitude_to_visualizer)
@@ -84,7 +84,7 @@ def major_result():
study_classes = [SafranSnowfall1Day]
model_subsets_for_uncertainty = None
altitudes = paper_altitudes
- altitudes = [900, 1200, 1500, 1800, 2100, 2400, 2700, 3000]
+ altitudes = [300, 600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600, 3900]
# altitudes = [900, 1200]
for study_class in study_classes:
diff --git a/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/snowfall_plot.py b/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/snowfall_plot.py
index a51a470135b80e19ccc934a41dd72f384908c3ce..2565d6c1d413f7be167cb68437879e43e1be8c32 100644
--- a/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/snowfall_plot.py
+++ b/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/snowfall_plot.py
@@ -22,43 +22,47 @@ def fit_linear_regression(x, y):
def plot_snowfall_time_derivative_mean(altitude_to_visualizer: Dict[int, StudyVisualizerForMeanValues]):
visualizer = list(altitude_to_visualizer.values())[0]
+ study = visualizer.study
# Plot the curve for the evolution of the mean
massif_name_to_a, massif_name_to_b, massif_name_to_r2_score = plot_mean(altitude_to_visualizer, derivative=True)
# Augmentation every km
massif_name_to_augmentation_every_km = {m: a * 1000 for m, a in massif_name_to_a.items()}
visualizer.plot_abstract_fast(massif_name_to_augmentation_every_km,
- label='Augmentation of time derivative of mean annual maxima for every km of elevation ()',
+ label='Augmentation of time derivative of mean annual maxima of {}\n for every km of elevation ({})'.format(SCM_STUDY_CLASS_TO_ABBREVIATION[type(study)], study.variable_unit),
add_x_label=False)
# Value at 2000 m
massif_name_to_mean_at_2000 = {m: a * 2000 + massif_name_to_b[m] for m, a in massif_name_to_a.items()}
- visualizer.plot_abstract_fast(massif_name_to_mean_at_2000, label='Time derivative of mean annual maxima of at 2000 m ()',
+ visualizer.plot_abstract_fast(massif_name_to_mean_at_2000, label='Time derivative of mean annual maxima \nof {} at 2000 m ({})'.format(SCM_STUDY_CLASS_TO_ABBREVIATION[type(study)], study.variable_unit),
add_x_label=False)
# Altitude for the change of dynamic
massif_name_to_altitude_change_dynamic = {m: - massif_name_to_b[m] / a for m, a in massif_name_to_a.items()}
# Keep only those that are in a reasonable range
massif_name_to_altitude_change_dynamic = {m: d for m, d in massif_name_to_altitude_change_dynamic.items()
if 0 < d < 3000}
- visualizer.plot_abstract_fast(massif_name_to_altitude_change_dynamic, label='Altitude for the change of dynamic ()',
+ visualizer.plot_abstract_fast(massif_name_to_altitude_change_dynamic, label='Altitude for the change of dynamic (m)',
add_x_label=False, graduation=500)
# R2 score
- visualizer.plot_abstract_fast(massif_name_to_r2_score, label='r2', graduation=0.1, add_x_label=False)
+ visualizer.plot_abstract_fast(massif_name_to_r2_score, label='r2 time derivative of the mean', graduation=0.1, add_x_label=False,
+ negative_and_positive_values=False)
def plot_snowfall_mean(altitude_to_visualizer: Dict[int, StudyVisualizerForMeanValues]):
visualizer = list(altitude_to_visualizer.values())[0]
+ study = visualizer.study
# Plot the curve for the evolution of the mean
massif_name_to_a, massif_name_to_b, massif_name_to_r2_score = plot_mean(altitude_to_visualizer, derivative=False)
# Augmentation every km
massif_name_to_augmentation_every_km = {m: a * 1000 for m, a in massif_name_to_a.items()}
visualizer.plot_abstract_fast(massif_name_to_augmentation_every_km,
- label='Augmentation of mean annual maxima for every km of elevation ()',
- add_x_label=False)
+ label='Augmentation of mean annual maxima of {} \nfor every km of elevation ({})'.format(SCM_STUDY_CLASS_TO_ABBREVIATION[type(study)], study.variable_unit),
+ add_x_label=False, negative_and_positive_values=False)
# Value at 2000 m
massif_name_to_mean_at_2000 = {m: a * 2000 + massif_name_to_b[m] for m, a in massif_name_to_a.items()}
- visualizer.plot_abstract_fast(massif_name_to_mean_at_2000, label='Mean annual maxima of at 2000 m ()',
- add_x_label=False)
+ visualizer.plot_abstract_fast(massif_name_to_mean_at_2000, label='Mean annual maxima of {} at 2000 m ()'.format(SCM_STUDY_CLASS_TO_ABBREVIATION[type(study)], study.variable_unit),
+ add_x_label=False, negative_and_positive_values=False)
# R2 score
- visualizer.plot_abstract_fast(massif_name_to_r2_score, label='r2', graduation=0.1, add_x_label=False)
+ visualizer.plot_abstract_fast(massif_name_to_r2_score, label='r2 mean', graduation=0.1,
+ add_x_label=False, negative_and_positive_values=False)
def plot_mean(altitude_to_visualizer: Dict[int, StudyVisualizerForMeanValues], derivative=False):
diff --git a/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/study_visualizer_for_mean_values.py b/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/study_visualizer_for_mean_values.py
index 70e5d768e149f0762c0c2fe1458291cccae86048..9fd53430349a839b029fc0afa3529d6e34c88350 100644
--- a/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/study_visualizer_for_mean_values.py
+++ b/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/study_visualizer_for_mean_values.py
@@ -35,7 +35,7 @@ class StudyVisualizerForMeanValues(StudyVisualizerForNonStationaryTrends):
return massif_name_to_empirical_value
@cached_property
- def massif_name_to_parametric_mean(self):
+ def massif_name_to_model_mean(self):
massif_name_to_parameter_value = {}
for massif_name, trend_test in self.massif_name_to_trend_test_that_minimized_aic.items():
parameter_value = trend_test.unconstrained_average_mean_value
@@ -47,11 +47,12 @@ class StudyVisualizerForMeanValues(StudyVisualizerForNonStationaryTrends):
massif_name_to_relative_difference = {}
for massif_name in self.massif_name_to_trend_test_that_minimized_aic.keys():
e = self.massif_name_to_empirical_mean[massif_name]
- p = self.massif_name_to_parametric_mean[massif_name]
+ p = self.massif_name_to_model_mean[massif_name]
relative_diference = 100 * (p-e) / e
massif_name_to_relative_difference[massif_name] = relative_diference
return massif_name_to_relative_difference
- def plot_abstract_fast(self, massif_name_to_value, label, graduation=10.0, cmap=plt.cm.coolwarm, add_x_label=True):
- super().plot_abstract(massif_name_to_value, label, label, self.fit_method, graduation, cmap, add_x_label)
+ def plot_abstract_fast(self, massif_name_to_value, label, graduation=10.0, cmap=plt.cm.coolwarm, add_x_label=True,
+ negative_and_positive_values=True):
+ super().plot_abstract(massif_name_to_value, label, label, self.fit_method, graduation, cmap, add_x_label, negative_and_positive_values)
diff --git a/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/validation_plot.py b/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/validation_plot.py
index 5fa8ee8bb790eb7846073a95ed0af46c72e40458..2da8bda5d8de1dce3d4e161fcfa38e1a738dedfe 100644
--- a/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/validation_plot.py
+++ b/projects/contrasting_trends_in_snow_loads/snowfall_versus_time_and_altitude/validation_plot.py
@@ -2,6 +2,8 @@ from typing import Dict
import matplotlib.pyplot as plt
+from extreme_data.meteo_france_data.scm_models_data.visualization.main_study_visualizer import \
+ SCM_STUDY_CLASS_TO_ABBREVIATION
from projects.contrasting_trends_in_snow_loads.snowfall_versus_time_and_altitude.study_visualizer_for_mean_values import \
StudyVisualizerForMeanValues
from projects.ogorman.gorman_figures.figure1.study_visualizer_for_double_stationary_fit import \
@@ -28,7 +30,7 @@ def plot_shoe_relative_differences_distribution(altitude_to_relative_differences
width = 150
ax.boxplot([altitude_to_relative_differences[a] for a in altitudes], positions=altitudes, widths=width)
ax.set_xlim([min(altitudes) - width, max(altitudes) + width])
- ylabel = 'Relative difference between empirical mean and parametric mean (\%)'
+ ylabel = 'Relative difference of the model mean w.r.t. the empirical mean (\%)'
ax.set_ylabel(ylabel)
ax.set_xlabel('Altitude (m)')
ax.legend()
@@ -36,12 +38,13 @@ def plot_shoe_relative_differences_distribution(altitude_to_relative_differences
def plot_relative_difference_map(visualizer: StudyVisualizerForMeanValues):
- label = ' mean annual maxima of {} ({})'.format('', '')
+ study = visualizer.study
+ label = ' mean annual maxima of {} ({})'.format(SCM_STUDY_CLASS_TO_ABBREVIATION[type(study)], study.variable_unit)
visualizer.plot_abstract_fast(massif_name_to_value=visualizer.massif_name_to_empirical_mean,
- label='Empirical' + label)
-
- visualizer.plot_abstract_fast(massif_name_to_value=visualizer.massif_name_to_parametric_mean,
- label='Model' + label)
+ label='Empirical' + label, negative_and_positive_values=False)
+ visualizer.plot_abstract_fast(massif_name_to_value=visualizer.massif_name_to_model_mean,
+ label='Model' + label, negative_and_positive_values=False)
visualizer.plot_abstract_fast(massif_name_to_value=visualizer.massif_name_to_relative_difference_for_mean,
- label='Relative difference of' + label, graduation=1)
+ label='Relative difference of the model mean w.r.t. the empirical mean \n'
+ 'for the ' + label, graduation=1)
return list(visualizer.massif_name_to_relative_difference_for_mean.values())