diff --git a/experiment/meteo_france_SCM_study/main_visualize.py b/experiment/meteo_france_SCM_study/main_visualize.py
index fea9ed0de0194920b16209fddf0dfc5228faaa33..b522100ddc8d18374b2f383b0036d6c98ce0d1da 100644
--- a/experiment/meteo_france_SCM_study/main_visualize.py
+++ b/experiment/meteo_france_SCM_study/main_visualize.py
@@ -37,7 +37,7 @@ def extended_visualization():
for study in study_iterator(study_class, only_first_one=True):
study_visualizer = StudyVisualizer(study)
# study_visualizer.visualize_all_kde_graphs()
- study_visualizer.visualize_experimental_law()
+ study_visualizer.visualize_all_experimental_law()
def normal_visualization():
@@ -56,7 +56,7 @@ def complete_analysis(only_first_one=False):
print('Extended study')
for extended_study in study_iterator(extended_study_class, only_first_one=only_first_one):
study_visualizer = StudyVisualizer(extended_study, save_to_file=True)
- study_visualizer.visualize_all_kde_graphs()
+ study_visualizer.visualize_all_mean_and_max_graphs()
print('Study normal')
for study in study_iterator(study_class, only_first_one=only_first_one):
study_visualizer = StudyVisualizer(study, save_to_file=True)
@@ -65,5 +65,5 @@ def complete_analysis(only_first_one=False):
if __name__ == '__main__':
# normal_visualization()
- # extended_visualization()
- complete_analysis()
+ extended_visualization()
+ # complete_analysis()
diff --git a/experiment/meteo_france_SCM_study/safran/safran_visualizer.py b/experiment/meteo_france_SCM_study/safran/safran_visualizer.py
index ae25628670693baa2c2a1ffc7ba88e52e7e25538..65b17f7f93e91592baa89f04109d23875515b1b2 100644
--- a/experiment/meteo_france_SCM_study/safran/safran_visualizer.py
+++ b/experiment/meteo_france_SCM_study/safran/safran_visualizer.py
@@ -1,19 +1,21 @@
import math
import os
import os.path as op
-import numpy as np
import matplotlib.pyplot as plt
+import numpy as np
import pandas as pd
+import seaborn as sns
from experiment.meteo_france_SCM_study.abstract_study import AbstractStudy
+from experiment.utils import average_smoothing_with_sliding_window
from extreme_estimator.estimator.full_estimator.abstract_full_estimator import \
FullEstimatorInASingleStepWithSmoothMargin
from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import SmoothMarginEstimator
from extreme_estimator.extreme_models.margin_model.smooth_margin_model import LinearAllParametersAllDimsMarginModel
from extreme_estimator.extreme_models.max_stable_model.abstract_max_stable_model import CovarianceFunction, \
AbstractMaxStableModelWithCovarianceFunction
-from extreme_estimator.extreme_models.max_stable_model.max_stable_models import Smith
+from extreme_estimator.margin_fits.abstract_params import AbstractParams
from extreme_estimator.margin_fits.gev.gev_params import GevParams
from extreme_estimator.margin_fits.gev.gevmle_fit import GevMleFit
from extreme_estimator.margin_fits.gpd.gpd_params import GpdParams
@@ -21,7 +23,7 @@ from extreme_estimator.margin_fits.gpd.gpdmle_fit import GpdMleFit
from extreme_estimator.margin_fits.plot.create_shifted_cmap import get_color_rbga_shifted
from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
from test.test_utils import load_test_max_stable_models
-from utils import get_display_name_from_object_type, VERSION, VERSION_TIME
+from utils import get_display_name_from_object_type, VERSION_TIME
class StudyVisualizer(object):
@@ -31,7 +33,7 @@ class StudyVisualizer(object):
self.study = study
self.show = False if self.save_to_file else show
self.window_size_for_smoothing = 21
- self.figsize=(16.0, 10.0)
+ self.figsize = (16.0, 10.0)
@property
def observations(self):
@@ -45,58 +47,77 @@ class StudyVisualizer(object):
def dataset(self):
return AbstractDataset(self.observations, self.coordinates)
- def visualize_experimental_law(self):
- plot_name = ' experimental law'
- self.show_or_save_to_file(plot_name)
+ # Graph for each massif / or groups of massifs
- def visualize_all_kde_graphs(self):
- massif_names = self.study.safran_massif_names
+ def visualize_massif_graphs(self, visualize_function):
nb_columns = 5
- nb_rows = math.ceil(len(massif_names) / nb_columns)
+ nb_rows = math.ceil(len(self.study.safran_massif_names) / nb_columns)
fig, axes = plt.subplots(nb_rows, nb_columns, figsize=self.figsize)
fig.subplots_adjust(hspace=1.0, wspace=1.0)
- for i, massif_name in enumerate(massif_names):
- row_id, column_id = i // nb_columns, i % nb_columns
+ for massif_id, massif_name in enumerate(self.study.safran_massif_names):
+ row_id, column_id = massif_id // nb_columns, massif_id % nb_columns
ax = axes[row_id, column_id]
- self.visualize_kde_graph(ax, i, massif_name)
- plot_name = ' mean with sliding window of size {}'.format(self.window_size_for_smoothing)
+ visualize_function(ax, massif_id)
+
+ def visualize_all_experimental_law(self):
+ self.visualize_massif_graphs(self.visualize_experimental_law)
+ plot_name = ' Experimental law with all the data'
self.show_or_save_to_file(plot_name)
- def visualize_kde_graph(self, ax, i, massif_name):
- self.maxima_plot(ax, i)
- self.mean_plot(ax, i)
- ax.set_xlabel('year')
- ax.set_title(massif_name)
+ def visualize_experimental_law(self, ax, massif_id):
+ # Display the experimental law for a given massif
+ all_massif_data = np.concatenate([data[:, massif_id] for data in self.study.year_to_daily_time_serie.values()])
+ all_massif_data = np.sort(all_massif_data)
+
+ # Kde plot, and retrieve the data forming the line
+ color_kde = 'b'
+ sns.kdeplot(all_massif_data, bw=1, ax=ax, color=color_kde).set(xlim=0)
+ data_x, data_y = ax.lines[0].get_data()
+
+ # Plot the mean point in green
+ x_level_to_color = {
+ np.mean(all_massif_data): 'g',
+ }
+ # Plot some specific quantiles in red
+ for p in AbstractParams.QUANTILE_P_VALUES:
+ x_level = all_massif_data[int(p * len(all_massif_data))]
+ x_level_to_color[x_level] = 'r'
+
+ for xi, color in x_level_to_color.items():
+ yi = np.interp(xi, data_x, data_y)
+ ax.plot([xi], [yi], color=color, marker="o")
+
+ ax.set_ylabel('Density', color=color_kde)
+ ax.set_xlabel(self.study.title)
+ extraticks = [round(x) for x in sorted(list(x_level_to_color.keys()))]
+ ax.set_xticks(extraticks)
+ ax.set_title(self.study.safran_massif_names[massif_id])
+
+ def visualize_all_mean_and_max_graphs(self):
+ self.visualize_massif_graphs(self.visualize_mean_and_max_graph)
+ plot_name = ' mean with sliding window of size {}'.format(self.window_size_for_smoothing)
+ self.show_or_save_to_file(plot_name)
- def mean_plot(self, ax, i):
+ def visualize_mean_and_max_graph(self, ax, massif_id):
+ # Display the graph of the max on top
+ color_maxima = 'r'
+ tuples_x_y = [(year, annual_maxima[massif_id]) for year, annual_maxima in
+ self.study.year_to_annual_maxima.items()]
+ x, y = list(zip(*tuples_x_y))
+ ax2 = ax.twinx()
+ ax2.plot(x, y, color=color_maxima)
+ ax2.set_ylabel('maxima', color=color_maxima)
# Display the mean graph
# Counting the sum of 3-consecutive days of snowfall does not have any physical meaning,
# as we are counting twice some days
color_mean = 'g'
- tuples_x_y = [(year, np.mean(data[:, i])) for year, data in self.study.year_to_daily_time_serie.items()]
+ tuples_x_y = [(year, np.mean(data[:, massif_id])) for year, data in self.study.year_to_daily_time_serie.items()]
x, y = list(zip(*tuples_x_y))
- x, y = self.smooth(x, y)
+ x, y = average_smoothing_with_sliding_window(x, y, window_size_for_smoothing=self.window_size_for_smoothing)
ax.plot(x, y, color=color_mean)
ax.set_ylabel('mean', color=color_mean)
-
- def maxima_plot(self, ax, i):
- # Display the graph of the max on top
- color_maxima = 'r'
- tuples_x_y = [(year, annual_maxima[i]) for year, annual_maxima in self.study.year_to_annual_maxima.items()]
- x, y = list(zip(*tuples_x_y))
- ax2 = ax.twinx()
- ax2.plot(x, y, color=color_maxima)
- ax2.set_ylabel('maxima', color=color_maxima)
-
- def smooth(self, x, y):
- # Average on windows of size 2*M+1 (M elements on each side)
- filter = np.ones(self.window_size_for_smoothing) / self.window_size_for_smoothing
- y = np.convolve(y, filter, mode='valid')
- assert self.window_size_for_smoothing % 2 == 1
- nb_to_delete = int(self.window_size_for_smoothing // 2)
- x = np.array(x)[nb_to_delete:-nb_to_delete]
- assert len(x) == len(y)
- return x, y
+ ax.set_xlabel('year')
+ ax.set_title(self.study.safran_massif_names[massif_id])
def visualize_linear_margin_fit(self, only_first_max_stable=False):
plot_name = 'Full Likelihood with Linear marginals and max stable dependency structure'
diff --git a/experiment/utils.py b/experiment/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b378946153da54fe801df190371dc99057e66c3
--- /dev/null
+++ b/experiment/utils.py
@@ -0,0 +1,12 @@
+import numpy as np
+
+
+def average_smoothing_with_sliding_window(x, y, window_size_for_smoothing):
+ # Average on windows of size 2*M+1 (M elements on each side)
+ kernel = np.ones(window_size_for_smoothing) / window_size_for_smoothing
+ y = np.convolve(y, kernel, mode='valid')
+ assert window_size_for_smoothing % 2 == 1
+ nb_to_delete = int(window_size_for_smoothing // 2)
+ x = np.array(x)[nb_to_delete:-nb_to_delete]
+ assert len(x) == len(y)
+ return x, y
diff --git a/extreme_estimator/margin_fits/abstract_params.py b/extreme_estimator/margin_fits/abstract_params.py
index fc5b80e3530c433fa7aaeab5865279eeea2050e8..659aa06d706bc223815e6e5748f4f62da782bf27 100644
--- a/extreme_estimator/margin_fits/abstract_params.py
+++ b/extreme_estimator/margin_fits/abstract_params.py
@@ -12,7 +12,7 @@ class AbstractParams(object):
QUANTILE_100 = 'quantile 100'
QUANTILE_1000 = 'quantile 1000'
QUANTILE_NAMES = [QUANTILE_10, QUANTILE_100, QUANTILE_1000][:-1]
- QUANTILE_P_VALUES = [0.9, 0.99, 0.999]
+ QUANTILE_P_VALUES = [0.9, 0.99, 0.999][:-1]
# Summary
SUMMARY_NAMES = PARAM_NAMES + QUANTILE_NAMES
diff --git a/test/test_meteo_france_SCM_study/test_SCM_study.py b/test/test_meteo_france_SCM_study/test_SCM_study.py
index ec4765c956e62566aa65b1faea02da862495ad1f..2f670d3dd7af27b32e529373c329f1413b7fb0c3 100644
--- a/test/test_meteo_france_SCM_study/test_SCM_study.py
+++ b/test/test_meteo_france_SCM_study/test_SCM_study.py
@@ -24,7 +24,7 @@ class TestSCMStudy(unittest.TestCase):
for study_class in [ExtendedSafran, ExtendedCrocusSwe]:
for study in study_iterator(study_class, only_first_one=True, both_altitude=True, verbose=False):
study_visualizer = StudyVisualizer(study, show=False, save_to_file=False)
- study_visualizer.visualize_all_kde_graphs()
+ study_visualizer.visualize_all_mean_and_max_graphs()
self.assertTrue(True)