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Le Roux Erwan authored146310db
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import matplotlib as mpl
import numpy as np
from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.one_fold_fit import OneFoldFit
mpl.rcParams['text.usetex'] = True
mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}']
from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day, SafranSnowfall3Days, \
SafranSnowfall5Days, SafranSnowfall7Days, SafranPrecipitation1Day, SafranPrecipitation3Days, \
SafranPrecipitation5Days, SafranPrecipitation7Days
from extreme_data.meteo_france_data.scm_models_data.utils import Season
from extreme_fit.model.margin_model.polynomial_margin_model.utils import ALTITUDINAL_MODELS
from projects.altitude_spatial_model.altitudes_fit.altitudes_studies import AltitudesStudies
from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.altitudes_studies_visualizer_for_non_stationary_models import \
AltitudesStudiesVisualizerForNonStationaryModels
def plot_altitudinal_fit(studies, massif_names=None):
model_classes = ALTITUDINAL_MODELS
visualizer = AltitudesStudiesVisualizerForNonStationaryModels(studies=studies,
model_classes=model_classes,
massif_names=massif_names,
show=False)
# Plot the results for the model that minimizes the individual aic
visualizer.plot_moments()
visualizer.plot_shape_map()
# Compute the mean AIC for each model_class
OneFoldFit.best_estimator_minimizes_mean_aic = True
model_class_to_aic_scores = {model_class: [] for model_class in model_classes}
for one_fold_fit in visualizer.massif_name_to_one_fold_fit.values():
for model_class, estimator in one_fold_fit.model_class_to_estimator_with_finite_aic.items():
print(estimator.n())
aic_score_normalized = estimator.aic() / estimator.n()
model_class_to_aic_scores[model_class].append(aic_score_normalized)
model_class_to_mean_aic_score = {model_class: np.array(aic_scores).mean()
for model_class, aic_scores in model_class_to_aic_scores.items()}
sorted_model_class = sorted(model_classes, key=lambda m: model_class_to_mean_aic_score[m])
best_model_class_for_mean_aic = sorted_model_class[0]
for one_fold_fit in visualizer.massif_name_to_one_fold_fit.values():
one_fold_fit.best_estimator_class_for_mean_aic = best_model_class_for_mean_aic
# Plot the results for the model that minimizes the mean aic
visualizer.plot_moments()
visualizer.plot_shape_map()
def plot_time_series(studies, massif_names=None):
studies.plot_maxima_time_series(massif_names=massif_names)
def plot_moments(studies, massif_names=None):
for std in [True, False][:]:
for change in [True, False, None]:
studies.plot_mean_maxima_against_altitude(massif_names=massif_names, std=std, change=change)
def main():
altitudes = [900, 1200, 1500, 1800, 2100, 2400, 2700, 3000][4:7]
altitudes = [900, 1200, 1500, 1800, 2100, 2400, 2700, 3000][:]
altitudes = [900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600, 3900]
study_classes = [SafranSnowfall1Day, SafranSnowfall3Days, SafranSnowfall5Days, SafranSnowfall7Days][:2]
study_classes = [SafranPrecipitation1Day, SafranPrecipitation3Days, SafranPrecipitation5Days,
SafranPrecipitation7Days][:]
study_classes = [SafranSnowfall1Day, SafranPrecipitation1Day, SafranSnowfall3Days, SafranPrecipitation3Days][:1]
massif_names = None
# massif_names = ['Aravis']
# massif_names = ['Chartreuse', 'Belledonne']
for study_class in study_classes:
studies = AltitudesStudies(study_class, altitudes, season=Season.winter_extended)
# plot_time_series(studies, massif_names)
# plot_moments(studies, massif_names)
plot_altitudinal_fit(studies, massif_names)
if __name__ == '__main__':
main()