diff --git a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files_after_conf/main_starting_years.py b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files_after_conf/main_choice_starting_years.py
similarity index 88%
rename from experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files_after_conf/main_starting_years.py
rename to experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files_after_conf/main_choice_starting_years.py
index 4ac6423cce980ed8e5b376d8888c2eac37d58ab3..51e242af71620301b6f91812cfaaf30dfdb82a96 100644
--- a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files_after_conf/main_starting_years.py
+++ b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files_after_conf/main_choice_starting_years.py
@@ -18,7 +18,8 @@ from experiment.meteo_france_data.scm_models_data.visualization.hypercube_visual
load_altitude_visualizer, load_quantity_visualizer
from experiment.meteo_france_data.scm_models_data.visualization.study_visualization.main_study_visualizer import \
ALL_ALTITUDES, SCM_STUDIES
-from experiment.trend_analysis.univariate_test.abstract_gev_change_point_test import GevLocationChangePointTest
+from experiment.trend_analysis.univariate_test.abstract_gev_change_point_test import GevLocationChangePointTest, \
+ GevScaleChangePointTest
def get_fast_altitude_visualizer(altitude_hypercube_class):
@@ -41,6 +42,7 @@ def get_full_altitude_visualizer(altitude_hypercube_class, exact_starting_year=N
if exact_starting_year is not None:
first_starting_year, last_starting_year = None, None
study_classes = [CrocusRecentSwe]
+ trend_test_class = GevScaleChangePointTest
visualizer = load_altitude_visualizer(altitude_hypercube_class, altitudes, last_starting_year,
nb_data_reduced_for_speed, only_first_one, save_to_file, study_classes,
trend_test_class, first_starting_year=first_starting_year,
@@ -48,21 +50,23 @@ def get_full_altitude_visualizer(altitude_hypercube_class, exact_starting_year=N
return visualizer
-FULL_ALTITUDES = [900, 1500, 2100, 2700]
+FULL_ALTITUDES = [900, 1200, 1500, 1800, 2100, 2400, 2700, 3000]
+HALF_ALTITUDES = [900, 1200, 1500, 1800, 2100, 2400, 2700, 3000][::2]
def main_fast_spatial_repartition():
for altitude in FULL_ALTITUDES[-1:]:
vizualiser = get_full_altitude_visualizer(Altitude_Hypercube_Year_Visualizer, altitude=altitude,
exact_starting_year=1958)
+ vizualiser.save_to_file = False
vizualiser.visualize_massif_trend_test_one_altitude()
def main_full_spatial_repartition():
for altitude in FULL_ALTITUDES[:]:
# Compute for the most likely starting year
- vizualiser = get_full_altitude_visualizer(Altitude_Hypercube_Year_Visualizer, altitude=altitude)
- vizualiser.visualize_massif_trend_test_one_altitude()
+ # vizualiser = get_full_altitude_visualizer(Altitude_Hypercube_Year_Visualizer, altitude=altitude)
+ # vizualiser.visualize_massif_trend_test_one_altitude()
# Compute the trend for a linear trend
vizualiser = get_full_altitude_visualizer(Altitude_Hypercube_Year_Visualizer, altitude=altitude,
exact_starting_year=1958)
@@ -70,8 +74,8 @@ def main_full_spatial_repartition():
def main_run():
- # main_full_spatial_repartition()
- main_fast_spatial_repartition()
+ main_full_spatial_repartition()
+ # main_fast_spatial_repartition()
if __name__ == '__main__':
diff --git a/experiment/meteo_france_data/scm_models_data/visualization/study_visualization/main_study_visualizer.py b/experiment/meteo_france_data/scm_models_data/visualization/study_visualization/main_study_visualizer.py
index 7d22db85b060aed072bb2e4f33a80ffcdc4d6ff9..8b6e82bd277378138917ee6b458ca23612660838 100644
--- a/experiment/meteo_france_data/scm_models_data/visualization/study_visualization/main_study_visualizer.py
+++ b/experiment/meteo_france_data/scm_models_data/visualization/study_visualization/main_study_visualizer.py
@@ -216,10 +216,10 @@ def trend_analysis():
def maxima_analysis():
save_to_file = False
only_first_one = True
- durand_altitude = [1800]
+ durand_altitude = [2700]
altitudes = durand_altitude
normalization_class = BetweenZeroAndOneNormalization
- study_classes = [SafranSnowfall][:]
+ study_classes = [CrocusRecentSwe][:]
for study in study_iterator_global(study_classes, only_first_one=only_first_one, altitudes=altitudes):
study_visualizer = StudyVisualizer(study, save_to_file=save_to_file,
transformation_class=normalization_class,
@@ -228,9 +228,11 @@ def maxima_analysis():
multiprocessing=True,
complete_non_stationary_trend_analysis=True,
score_class=MannKendall)
- study_visualizer.visualize_all_score_wrt_starting_year()
+ # study_visualizer.visualize_all_score_wrt_starting_year()
# study_visualizer.visualize_all_independent_temporal_trend()
- study_visualizer.visualize_all_mean_and_max_graphs()
+ # study_visualizer.visualize_independent_margin_fits()
+ # study_visualizer.visualize_all_mean_and_max_graphs()
+ study_visualizer.visualize_summary_of_annual_values_and_stationary_gev_fit()
def max_graph_annual_maxima_poster():
@@ -258,7 +260,8 @@ def main_run():
# trend_analysis()
# altitude_analysis()
- max_graph_annual_maxima_poster()
+ # max_graph_annual_maxima_poster()
+ maxima_analysis()
# case_study()
# all_scores_vizu()
# maxima_analysis()
diff --git a/experiment/meteo_france_data/scm_models_data/visualization/study_visualization/study_visualizer.py b/experiment/meteo_france_data/scm_models_data/visualization/study_visualization/study_visualizer.py
index 0c60d348b5b350c031ef14c7afa5156858022ba2..059c6afc25755b7dd257dd1f9e95db23689f33b2 100644
--- a/experiment/meteo_france_data/scm_models_data/visualization/study_visualization/study_visualizer.py
+++ b/experiment/meteo_france_data/scm_models_data/visualization/study_visualization/study_visualizer.py
@@ -3,6 +3,7 @@ import os.path as op
from collections import OrderedDict
from multiprocessing.pool import Pool
from random import sample
+from typing import Dict
import math
import matplotlib.pyplot as plt
@@ -620,7 +621,8 @@ class StudyVisualizer(VisualizationParameters):
x_gev = np.linspace(0.0, 1.5 * max(y), num=1000)
y_gev = [gev_param.density(x) for x in x_gev]
ax.plot(x_gev, y_gev, color=color, linewidth=5)
- ax.set_xlabel('y = annual maxima of {} (in {})'.format(snow_abbreviation, self.study.variable_unit), color=color, fontsize=15)
+ ax.set_xlabel('y = annual maxima of {} (in {})'.format(snow_abbreviation, self.study.variable_unit),
+ color=color, fontsize=15)
ax.set_ylabel('$f_{GEV}' + '(y|\mu={},\sigma={},\zeta={})$'.format(*gev_param.to_array()), fontsize=15)
ax.tick_params(axis='both', which='major', labelsize=13)
@@ -631,7 +633,6 @@ class StudyVisualizer(VisualizationParameters):
self.show_or_save_to_file(add_classic_title=False, no_title=True)
ax.clear()
-
def visualize_mean_and_max_graph(self, ax, massif_id):
# Display the graph of the max on top
color_maxima = 'r'
@@ -815,6 +816,44 @@ class StudyVisualizer(VisualizationParameters):
if show:
plt.show()
+ def visualize_summary_of_annual_values_and_stationary_gev_fit(self):
+ fig, axes = plt.subplots(3, 4)
+ fig.subplots_adjust(hspace=self.subplot_space, wspace=self.subplot_space)
+
+ # 1) First row, some experimental indicator
+ axes_first_row = axes[0]
+ df_maxima_gev = self.df_maxima_gev
+ name_to_serie = OrderedDict()
+ name_to_serie['mean'] = df_maxima_gev.mean(axis=1)
+ name_to_serie['std'] = df_maxima_gev.std(axis=1)
+ name_to_serie['quantile 0.9'] = df_maxima_gev.quantile(q=0.9, axis=1)
+ name_to_serie['quantile 0.99'] = df_maxima_gev.quantile(q=0.99, axis=1)
+ for (name, serie), ax in zip(name_to_serie.items(), axes_first_row):
+ self.study.visualize_study(ax=ax,
+ massif_name_to_value=serie.to_dict(),
+ show=False,
+ label='empirical ' + name)
+
+ # 2) Second row, gev parameters fitted independently (and a qqplot)
+ axes_second_row = axes[1]
+ for ax, gev_param_name in zip(axes_second_row, GevParams.PARAM_NAMES):
+ self.study.visualize_study(ax=ax,
+ massif_name_to_value=self.df_gev_parameters.loc[gev_param_name, :].to_dict(),
+ show=False,
+ replace_blue_by_white=gev_param_name != GevParams.SHAPE,
+ label=gev_param_name)
+ # self.clean_axes_write_title_on_the_left(axes, title='Independent fits')
+
+ # 3) Third row, gev indicator
+ axes_third_row = axes[2]
+ for ax, indicator_name in zip(axes_third_row, GevParams.indicator_names()):
+ self.study.visualize_study(ax=ax,
+ massif_name_to_value=self.df_gev_indicators.loc[indicator_name, :].to_dict(),
+ show=False,
+ label='gev ' + indicator_name)
+
+ plt.show()
+
def visualize_annual_mean_values(self, ax=None, take_mean_value=True):
if ax is None:
_, ax = plt.subplots(1, 1, figsize=self.figsize)
@@ -837,11 +876,21 @@ class StudyVisualizer(VisualizationParameters):
def df_maxima_gev(self) -> pd.DataFrame:
return self.study.observations_annual_maxima.df_maxima_gev
- @property
- def df_gev_mle(self) -> pd.DataFrame:
- # Fit a margin_fits on each massif
- massif_to_gev_mle = {massif_name: IsmevGevFit(self.df_maxima_gev.loc[massif_name]).gev_params.summary_serie
- for massif_name in self.study.study_massif_names}
+ @cached_property
+ def massif_name_to_gev_mle_fitted(self) -> Dict[str, GevParams]:
+ return {massif_name: IsmevGevFit(self.df_maxima_gev.loc[massif_name]).gev_params
+ for massif_name in self.study.study_massif_names}
+
+ @cached_property
+ def df_gev_parameters(self) -> pd.DataFrame:
+ massif_to_gev_mle = {massif_name: gev_params.to_dict()
+ for massif_name, gev_params in self.massif_name_to_gev_mle_fitted.items()}
+ return pd.DataFrame(massif_to_gev_mle, columns=self.study.study_massif_names)
+
+ @cached_property
+ def df_gev_indicators(self) -> pd.DataFrame:
+ massif_to_gev_mle = {massif_name: gev_params.indicator_name_to_value
+ for massif_name, gev_params in self.massif_name_to_gev_mle_fitted.items()}
return pd.DataFrame(massif_to_gev_mle, columns=self.study.study_massif_names)
@property
diff --git a/extreme_estimator/margin_fits/abstract_params.py b/extreme_estimator/margin_fits/abstract_params.py
index 338b4b8d05a430246f1ee96b837866b82ff62a29..5858afb324ece5a371f060dd1e57f745fe58370f 100644
--- a/extreme_estimator/margin_fits/abstract_params.py
+++ b/extreme_estimator/margin_fits/abstract_params.py
@@ -60,3 +60,5 @@ class AbstractParams(object):
def summary_serie(self) -> pd.Series:
return pd.Series(self.summary_dict, index=self.SUMMARY_NAMES)
+ # Estimators
+
diff --git a/extreme_estimator/margin_fits/gev/gev_fit.py b/extreme_estimator/margin_fits/gev/gev_fit.py
index 16865750bccb480bfa748eddbb6b50340e295cba..5b3600da6b36fa178832521bcb6f240c44d6cc19 100644
--- a/extreme_estimator/margin_fits/gev/gev_fit.py
+++ b/extreme_estimator/margin_fits/gev/gev_fit.py
@@ -12,5 +12,5 @@ class GevFit(object):
self.x_gev = x_gev
@property
- def gev_params(self):
+ def gev_params(self) -> GevParams:
raise NotImplementedError
\ No newline at end of file
diff --git a/extreme_estimator/margin_fits/gev/gev_params.py b/extreme_estimator/margin_fits/gev/gev_params.py
index 355b3e0abf6ed5414d1c0c15f69e5c135a8fccc1..2094bbeb31a41996be8729f774e0106e6e433237 100644
--- a/extreme_estimator/margin_fits/gev/gev_params.py
+++ b/extreme_estimator/margin_fits/gev/gev_params.py
@@ -1,10 +1,14 @@
+from collections import OrderedDict
+
+from cached_property import cached_property
+
from extreme_estimator.extreme_models.utils import r
from extreme_estimator.margin_fits.extreme_params import ExtremeParams
import numpy as np
+from scipy.special import gamma
class GevParams(ExtremeParams):
-
# Parameters
PARAM_NAMES = [ExtremeParams.LOC, ExtremeParams.SCALE, ExtremeParams.SHAPE]
# Summary
@@ -39,4 +43,41 @@ class GevParams(ExtremeParams):
return [self.location, self.scale, self.shape]
def __str__(self):
- return self.to_dict().__str__()
\ No newline at end of file
+ return self.to_dict().__str__()
+
+ def g(self, k):
+ # Compute the g_k parameters as defined in wikipedia
+ # https://fr.wikipedia.org/wiki/Loi_d%27extremum_g%C3%A9n%C3%A9ralis%C3%A9e
+ return gamma(1 - k * self.shape)
+
+ @property
+ def mean(self):
+ if self.shape >= 1:
+ return np.inf
+ else:
+ return self.location + self.scale * (self.g(k=1) - 1) / self.shape
+
+ @property
+ def variance(self):
+ if self.shape >= 0.5:
+ return np.inf
+ else:
+ return ((self.scale / self.shape) ** 2) * (self.g(k=2) - self.g(k=1) ** 2)
+
+ @property
+ def std(self):
+ return np.sqrt(self.variance)
+
+ @classmethod
+ def indicator_names(cls):
+ return ['mean', 'std'] + cls.QUANTILE_NAMES[:2]
+
+ @cached_property
+ def indicator_name_to_value(self) -> OrderedDict:
+ indicator_name_to_value = OrderedDict()
+ indicator_name_to_value['mean'] = self.mean
+ indicator_name_to_value['std'] = self.std
+ for quantile_name, quantile_value in zip(self.QUANTILE_NAMES[:2], self.QUANTILE_P_VALUES):
+ indicator_name_to_value[quantile_name] = self.quantile(quantile_value)
+ assert all([a == b for a, b in zip(self.indicator_names(), indicator_name_to_value.keys())])
+ return indicator_name_to_value
diff --git a/extreme_estimator/margin_fits/gev/gevmle_fit.py b/extreme_estimator/margin_fits/gev/gevmle_fit.py
index fff6dad1f4d0c2d2088069d9dc13f88b0163bdf6..81513e2cdb040792723420cfd5a4ceaeb4daec61 100644
--- a/extreme_estimator/margin_fits/gev/gevmle_fit.py
+++ b/extreme_estimator/margin_fits/gev/gevmle_fit.py
@@ -13,7 +13,7 @@ class GevMleFit(GevFit):
self.gev_params_object = GevParams.from_dict({**self._gev_params, 'block_size': block_size})
@property
- def gev_params(self):
- return self._gev_params
+ def gev_params(self) -> GevParams:
+ return self.gev_params_object
diff --git a/extreme_estimator/margin_fits/gev/ismev_gev_fit.py b/extreme_estimator/margin_fits/gev/ismev_gev_fit.py
index 778fc1bbee768128604d95431171bb6f7d812d57..b214cc23e6dfb4acf57284491e6ff6656e660435 100644
--- a/extreme_estimator/margin_fits/gev/ismev_gev_fit.py
+++ b/extreme_estimator/margin_fits/gev/ismev_gev_fit.py
@@ -16,9 +16,10 @@ class IsmevGevFit(GevFit):
self.res = ismev_gev_fit(x_gev, y, mul)
@property
- def gev_params(self):
+ def gev_params(self) -> GevParams:
assert self.y is None
- return dict(zip(GevParams.PARAM_NAMES, self.res['mle']))
+ gev_params_dict = dict(zip(GevParams.PARAM_NAMES, self.res['mle']))
+ return GevParams.from_dict(gev_params_dict)
diff --git a/test/test_extreme_estimator/test_margin_fits/test_gev/test_gevmle_fit.py b/test/test_extreme_estimator/test_margin_fits/test_gev/test_gevmle_fit.py
index 27b972710ed94e4413e9558003cdde49ad7234d9..82616977e72575c9c43f5e859cdbc0de8207ea57 100644
--- a/test/test_extreme_estimator/test_margin_fits/test_gev/test_gevmle_fit.py
+++ b/test/test_extreme_estimator/test_margin_fits/test_gev/test_gevmle_fit.py
@@ -3,6 +3,7 @@ import unittest
import numpy as np
from extreme_estimator.extreme_models.utils import r, set_seed_r
+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.gev.ismev_gev_fit import IsmevGevFit
@@ -31,6 +32,8 @@ class TestGevMleFit(unittest.TestCase):
def fit_estimator(self, estimator, ref):
# Compare the MLE estimated parameters to the reference
mle_params_estimated = estimator.gev_params
+ self.assertIsInstance(mle_params_estimated, GevParams)
+ mle_params_estimated = mle_params_estimated.to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)