From 2901ed6697b602cd13d13cbfd003b49c34a9f385 Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Thu, 7 Jan 2021 15:44:58 +0100
Subject: [PATCH] [contrasting] decompose the coherence plot into 4 subplots.
---
.../altitudes_fit/main_altitudes_studies.py | 10 +-
...es_visualizer_for_non_stationary_models.py | 2 +-
.../one_fold_analysis/one_fold_fit.py | 7 +-
.../plots/plot_coherence_curves.py | 130 +++++++++---------
4 files changed, 77 insertions(+), 72 deletions(-)
diff --git a/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py b/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py
index 0532fa44..86732094 100644
--- a/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py
+++ b/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py
@@ -26,7 +26,7 @@ def main():
study_classes = [SafranSnowfall1Day, SafranSnowfall3Days, SafranSnowfall5Days, SafranSnowfall7Days][:1]
seasons = [Season.annual, Season.winter, Season.spring, Season.automn][:1]
- fast = False
+ fast = True
if fast is None:
massif_names = None
altitudes_list = altitudes_for_groups[1:2]
@@ -55,9 +55,9 @@ def main_loop(altitudes_list, massif_names, seasons, study_classes):
def plot_visualizers(massif_names, visualizer_list):
- plot_histogram_all_trends_against_altitudes(massif_names, visualizer_list)
- for relative in [True, False]:
- plot_shoe_plot_changes_against_altitude(massif_names, visualizer_list, relative=relative)
+ # plot_histogram_all_trends_against_altitudes(massif_names, visualizer_list)
+ # for relative in [True, False]:
+ # plot_shoe_plot_changes_against_altitude(massif_names, visualizer_list, relative=relative)
# plot_shoe_plot_changes_against_altitude_for_maxima_and_total(massif_names, visualizer_list, relative=relative)
# plot_coherence_curves(massif_names, visualizer_list)
plot_coherence_curves(['Vanoise'], visualizer_list)
@@ -69,7 +69,7 @@ def plot_visualizer(massif_names, visualizer):
# visualizer.studies.plot_maxima_time_series(['Vanoise'])
# Plot the results for the model that minimizes the individual aic
- plot_individual_aic(visualizer)
+ # plot_individual_aic(visualizer)
# Plot the results for the model that minimizes the total aic
diff --git a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py
index dc2a2beb..8b7f57f6 100644
--- a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py
+++ b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py
@@ -148,7 +148,7 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
massif_name_to_text = self.massif_name_to_best_name
if 'change' in method_name:
plot_name = plot_name.replace(str_for_last_year, '')
- plot_name += ' between {} and {}'.format(2019 - 50, 2019)
+ plot_name += ' between {} and {}'.format(2019 - OneFoldFit.nb_years, 2019)
if 'relative' not in method_name:
# Put the relative score as text on the plot for the change.
massif_name_to_text = {m: ('+' if v > 0 else '') + str(int(v)) + '\%' for m, v in
diff --git a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py
index 5f5880dd..35c892f1 100644
--- a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py
+++ b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py
@@ -33,6 +33,7 @@ class OneFoldFit(object):
best_estimator_minimizes_total_aic = False
return_period = 100
quantile_level = 1 - (1 / return_period)
+ nb_years = 60
def __init__(self, massif_name: str, dataset: AbstractDataset, models_classes,
fit_method=MarginFitMethod.extremes_fevd_mle, temporal_covariate_for_fit=None,
@@ -100,7 +101,7 @@ class OneFoldFit(object):
def relative_change_in_return_level_for_reference_altitude(self) -> float:
return self.relative_changes_of_moment(altitudes=[self.altitude_plot], order=None)[0]
- def changes_of_moment(self, altitudes, year=2019, nb_years=60, order=1):
+ def changes_of_moment(self, altitudes, year=2019, nb_years=nb_years, order=1):
changes = []
for altitude in altitudes:
mean_after = self.get_moment(altitude, year, order)
@@ -109,7 +110,7 @@ class OneFoldFit(object):
changes.append(change)
return changes
- def relative_changes_of_moment(self, altitudes, year=2019, nb_years=60, order=1):
+ def relative_changes_of_moment(self, altitudes, year=2019, nb_years=nb_years, order=1):
relative_changes = []
for altitude in altitudes:
mean_after = self.get_moment(altitude, year, order)
@@ -293,7 +294,7 @@ class OneFoldFit(object):
def sign_of_change(self, estimator):
return_levels = []
- for year in [2019 - 60, 2019]:
+ for year in [2019 - self.nb_years, 2019]:
coordinate = np.array([self.altitude_plot, year])
return_level = estimator.function_from_fit.get_params(
coordinate=coordinate,
diff --git a/projects/altitude_spatial_model/altitudes_fit/plots/plot_coherence_curves.py b/projects/altitude_spatial_model/altitudes_fit/plots/plot_coherence_curves.py
index 87cdf9ae..91135f7a 100644
--- a/projects/altitude_spatial_model/altitudes_fit/plots/plot_coherence_curves.py
+++ b/projects/altitude_spatial_model/altitudes_fit/plots/plot_coherence_curves.py
@@ -9,86 +9,90 @@ from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.one_fold_fi
def plot_coherence_curves(massif_names, visualizer_list: List[AltitudesStudiesVisualizerForNonStationaryModels]):
folder = 'Coherence'
+ elevation_as_xaxis = True
visualizer = visualizer_list[0]
all_valid_names = set.union(*[v.get_valid_names(massif_names) for v in visualizer_list])
for massif_name in all_valid_names:
# For plotting the legend
legend = False
- if legend:
- ax = plt.gca()
- axes = [ax for _ in range(4)]
- else:
- _, axes = plt.subplots(2, 2)
- axes = axes.flatten()
- for i, ax in enumerate(axes):
- if i % 2 == 1:
- ax.set_yticks([])
- axes = [ax if i % 2 == 0 else ax.twinx() for i, ax in enumerate(axes)]
colors = ['blue', 'red', 'green']
elevational_str = 'Piecewise elevational-temporal models in'
labels = ['{} 1959'.format(elevational_str), '{} 2019'.format(elevational_str), 'Pointwise distributions']
altitudinal_model = [True, True, False]
years = [1959, 2019, None]
- for color, global_label, boolean, year in list(zip(colors, labels, altitudinal_model, years))[:]:
- plot_coherence_curve(axes, massif_name, visualizer_list, boolean, color, global_label, year, legend)
- visualizer.plot_name = '{}/{}'.format(folder, massif_name.replace('_', '-'))
- visualizer.show_or_save_to_file(add_classic_title=False, no_title=True, dpi=200)
- plt.close()
+ for i in range(4):
+ # Load ax
+ ax = plt.gca()
+ if i % 2 == 0:
+ ax.set_yticks([])
+ ax = ax.twinx() if i % 2 == 0 else ax
+
+ for color, global_label, is_altitudinal, year in list(zip(colors, labels, altitudinal_model, years))[:]:
+ x_all_list, values_all_list, labels, all_bound_list = load_all_list(massif_name, visualizer_list,
+ is_altitudinal,
+ year)
+ label = labels[i]
+ plot_coherence_curve(ax, i, x_all_list, values_all_list, label, all_bound_list ,
+ is_altitudinal, color, global_label, year, legend,
+ elevation_as_xaxis)
+ visualizer.plot_name = '{}/{}_{}'.format(folder, massif_name.replace('_', '-'), label)
+ visualizer.show_or_save_to_file(add_classic_title=False, no_title=True, dpi=200)
+ plt.close()
-def plot_coherence_curve(axes, massif_name, visualizer_list: List[AltitudesStudiesVisualizerForNonStationaryModels],
- is_altitudinal, color, global_label, year, legend):
- x_all_list, values_all_list, labels, all_bound_list = load_all_list(massif_name, visualizer_list, is_altitudinal,
- year)
+
+def plot_coherence_curve(ax, i, x_all_list, values_all_list, label, all_bound_list,
+ is_altitudinal, color, global_label, year, legend, elevation_as_xaxis):
legend_line = True
- for i, label in enumerate(labels):
- if legend and i != 3:
- continue
- ax = axes[i]
- # Plot with complete line
- for j, (x_list, value_list) in enumerate(list(zip(x_all_list, values_all_list))):
- value_list_i = value_list[i]
- label_plot = global_label if j == 0 else None
- if is_altitudinal:
- if legend and legend_line:
- ax.plot(x_list, value_list_i, linestyle='solid', color=color, label=label_plot, linewidth=5)
- else:
- ax.plot(x_list, value_list_i, linestyle='solid', color=color)
+ if legend and i != 3:
+ return
+ # Plot with complete line
+ for j, (x_list, value_list) in enumerate(list(zip(x_all_list, values_all_list))):
+ value_list_i = value_list[i]
+ label_plot = global_label if j == 0 else None
+ args = [x_list, value_list_i] if elevation_as_xaxis else [value_list_i, x_list]
+
+ if is_altitudinal:
+ if legend and legend_line:
+ ax.plot(*args, linestyle='solid', color=color, label=label_plot, linewidth=5)
else:
- if legend and legend_line:
- ax.plot(x_list, value_list_i, linestyle='None', color=color, label=label_plot, marker='o', markersize=10)
+ ax.plot(*args, linestyle='solid', color=color)
+ else:
+ if legend and legend_line:
+ ax.plot(*args, linestyle='None', color=color, label=label_plot, marker='o', markersize=10)
+ else:
+ ax.plot(*args, linestyle='None', color=color, marker='o')
+ ax.plot(*args, linestyle='dotted', color=color)
+
+ # Plot with dotted line
+ for x_list_before, value_list_before, x_list_after, value_list_after in zip(x_all_list, values_all_list,
+ x_all_list[1:],
+ values_all_list[1:]):
+ x_list = [x_list_before[-1], x_list_after[0]]
+ value_list_dotted = [value_list_before[i][-1], value_list_after[i][0]]
+ args = [x_list, value_list_dotted] if elevation_as_xaxis else [value_list_dotted, x_list]
+ ax.plot(*args, linestyle='dotted', color=color)
+
+ # Plot confidence interval
+ if i == 3 and year in [None, 2019]:
+ for j, (x_list, bounds) in enumerate(list(zip(x_all_list, all_bound_list))):
+ if len(bounds) > 0:
+ lower_bound, upper_bound = bounds
+ if legend and not legend_line:
+ model_name = 'piecewise elevational-temporal models in 2019' if is_altitudinal else 'pointwise distributions'
+ fill_label = "95\% confidence interval for the {}".format(model_name) if j == 0 else None
+ ax.fill_between(x_list, lower_bound, upper_bound, color=color, alpha=0.2, label=fill_label)
else:
- ax.plot(x_list, value_list_i, linestyle='None', color=color, marker='o')
- ax.plot(x_list, value_list_i, linestyle='dotted', color=color)
-
- # Plot with dotted line
- for x_list_before, value_list_before, x_list_after, value_list_after in zip(x_all_list, values_all_list,
- x_all_list[1:],
- values_all_list[1:]):
- x_list = [x_list_before[-1], x_list_after[0]]
- value_list_dotted = [value_list_before[i][-1], value_list_after[i][0]]
- ax.plot(x_list, value_list_dotted, linestyle='dotted', color=color)
-
- # Plot confidence interval
- if i == 3 and year in [None, 2019]:
- for j, (x_list, bounds) in enumerate(list(zip(x_all_list, all_bound_list))):
- if len(bounds) > 0:
- lower_bound, upper_bound = bounds
- if legend and not legend_line:
- model_name = 'piecewise elevational-temporal models in 2019' if is_altitudinal else 'pointwise distributions'
- fill_label = "95\% confidence interval for the {}".format(model_name) if j == 0 else None
- ax.fill_between(x_list, lower_bound, upper_bound, color=color, alpha=0.2, label=fill_label)
- else:
- ax.fill_between(x_list, lower_bound, upper_bound, color=color, alpha=0.2)
-
- if legend:
- min, max = ax.get_ylim()
- ax.set_ylim([min, 2 * max])
- size = 15 if legend_line else 11
- ax.legend(prop={'size': size})
- ax.set_ylabel(label)
+ ax.fill_between(x_list, lower_bound, upper_bound, color=color, alpha=0.2)
+
+ if legend:
+ min, max = ax.get_ylim()
+ ax.set_ylim([min, 2 * max])
+ size = 15 if legend_line else 11
+ ax.legend(prop={'size': size})
+ ax.set_ylabel(label)
def load_all_list(massif_name, visualizer_list, altitudinal_model=True, year=2019):
--
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