diff --git a/experiment/eurocode_data/departement_alpes_francaises.py b/experiment/eurocode_data/departement_alpes_francaises.py
index 79d005be2070a455a89e8bf4587d7998acd36edc..e6028a8c646952796000b00283e6a695d74ba69a 100644
--- a/experiment/eurocode_data/departement_alpes_francaises.py
+++ b/experiment/eurocode_data/departement_alpes_francaises.py
@@ -1,12 +1,20 @@
from enum import Enum
from experiment.eurocode_data.eurocode_region import AbstractEurocodeRegion, E, C2, C1
+from root_utils import get_display_name_from_object_type
class AbstractDepartementAlpesFrancaises(object):
- def __init__(self, region: type):
- self.region = region() # type: AbstractEurocodeRegion
+ def __init__(self, eurocode_region: type):
+ self.eurocode_region = eurocode_region() # type: AbstractEurocodeRegion
+
+ def display_limit(self, ax):
+ pass
+
+ def __str__(self):
+ return get_display_name_from_object_type(type(self)) + '( {} Eurocode region)'.format(
+ get_display_name_from_object_type(type(self.eurocode_region)))
class HauteSavoie(AbstractDepartementAlpesFrancaises):
@@ -32,6 +40,7 @@ class Drome(AbstractDepartementAlpesFrancaises):
def __init__(self):
super().__init__(C2)
+
class HautesAlpes(AbstractDepartementAlpesFrancaises):
def __init__(self):
@@ -48,6 +57,3 @@ class AlpesDeHauteProvence(AbstractDepartementAlpesFrancaises):
def __init__(self):
super().__init__(C1)
-
-
-
diff --git a/experiment/eurocode_data/eurocode_drawing.py b/experiment/eurocode_data/eurocode_drawing.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/experiment/eurocode_data/eurocode_region.py b/experiment/eurocode_data/eurocode_region.py
index 2b70fdd60f7cbae714bc54407d5965c00a7670dc..722cc33dbc60ebbfca20f3aaa6f78a451d113a51 100644
--- a/experiment/eurocode_data/eurocode_region.py
+++ b/experiment/eurocode_data/eurocode_region.py
@@ -40,6 +40,10 @@ class AbstractEurocodeRegion(object):
def lois_de_variation_1000_and_2000(self):
return 3.5, -2.45
+ def plot_max_loading(self, ax, altitudes):
+ ax.plot(altitudes, [self.eurocode_max_loading(altitude) for altitude in altitudes],
+ label='Eurocode limit')
+
class C1(AbstractEurocodeRegion):
diff --git a/experiment/eurocode_data/eurocode_return_level_uncertainties.py b/experiment/eurocode_data/eurocode_return_level_uncertainties.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e416a8a78583d35b7887833ea1f96a9b734ed1e
--- /dev/null
+++ b/experiment/eurocode_data/eurocode_return_level_uncertainties.py
@@ -0,0 +1,79 @@
+from typing import List
+
+
+import numpy as np
+from cached_property import cached_property
+
+from experiment.eurocode_data.utils import EUROCODE_QUANTILE
+from experiment.trend_analysis.univariate_test.utils import load_temporal_coordinates_and_dataset, \
+ fitted_linear_margin_estimator
+from extreme_fit.distribution.gev.gev_params import GevParams
+from extreme_fit.estimator.abstract_estimator import AbstractEstimator
+from extreme_fit.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
+from extreme_fit.estimator.utils import load_margin_function
+from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
+ AbstractTemporalLinearMarginModel
+from extreme_fit.model.margin_model.margin_function.linear_margin_function import LinearMarginFunction
+from extreme_fit.model.result_from_model_fit.result_from_extremes import ResultFromExtremes
+
+
+class ExtractEurocodeReturnLevelFromExtremes(object):
+
+ def __init__(self, estimator: LinearMarginEstimator, year_of_interest: int = 2017):
+ self.estimator = estimator
+ self.result_from_fit = self.estimator.result_from_model_fit # type: ResultFromExtremes
+ self.year_of_interest = year_of_interest
+
+ @property
+ def margin_functions_from_fit(self) -> List[LinearMarginFunction]:
+ margin_functions = []
+ for _, s in self.result_from_fit.df_posterior_samples.iterrows():
+ coef_dict = self.result_from_fit.get_coef_dict_from_posterior_sample(s)
+ margin_function = load_margin_function(self.estimator, self.estimator.margin_model, coef_dict=coef_dict)
+ margin_functions.append(margin_function)
+ return margin_functions
+
+ @property
+ def gev_params_from_fit_for_year_of_interest(self) -> List[GevParams]:
+ return [m.get_gev_params(coordinate=np.array([self.year_of_interest]), is_transformed=False)
+ for m in self.margin_functions_from_fit]
+
+ @cached_property
+ def posterior_eurocode_return_level_samples_for_year_of_interest(self) -> np.ndarray:
+ """We divide by 100 to transform the snow water equivalent into snow load"""
+ return np.array([p.quantile(EUROCODE_QUANTILE) for p in self.gev_params_from_fit_for_year_of_interest]) / 100
+
+ @property
+ def posterior_mean_eurocode_return_level_for_the_year_of_interest(self) -> np.ndarray:
+ return np.mean(self.posterior_eurocode_return_level_samples_for_year_of_interest)
+
+ @property
+ def posterior_eurocode_return_level_uncertainty_interval_for_the_year_of_interest(self):
+ bottom_and_upper_quantile = (0.05, 0.95)
+ return [np.quantile(self.posterior_eurocode_return_level_samples_for_year_of_interest, q=q)
+ for q in bottom_and_upper_quantile]
+
+
+class EurocodeLevelUncertaintyFromExtremes(object):
+
+ YEAR_OF_INTEREST = 2017
+
+ def __init__(self, posterior_mean, poster_uncertainty_interval):
+ self.posterior_mean = posterior_mean
+ self.poster_uncertainty_interval = poster_uncertainty_interval
+
+ @classmethod
+ def from_estimator_extremes(cls, estimator_extremes: LinearMarginEstimator):
+ extractor = ExtractEurocodeReturnLevelFromExtremes(estimator_extremes, cls.YEAR_OF_INTEREST)
+ return cls(extractor.posterior_mean_eurocode_return_level_for_the_year_of_interest,
+ extractor.posterior_eurocode_return_level_uncertainty_interval_for_the_year_of_interest)
+
+ @classmethod
+ def from_maxima_years_model_class(cls, maxima, years, model_class):
+ # Load coordinates and dataset
+ coordinates, dataset = load_temporal_coordinates_and_dataset(maxima, years)
+ # Fitted estimator
+ fitted_estimator = fitted_linear_margin_estimator(model_class, coordinates, dataset, starting_year=1958,
+ fit_method=AbstractTemporalLinearMarginModel.EXTREMES_FEVD_BAYESIAN_FIT_METHOD_STR)
+ # Load object from result from extremes
+ return cls.from_estimator_extremes(fitted_estimator)
\ No newline at end of file
diff --git a/experiment/eurocode_data/eurocode_visualizer.py b/experiment/eurocode_data/eurocode_visualizer.py
index 387a8042aa53b71b49f275060a7088cc4272d294..b5231235247479290a877397e1889394760561b6 100644
--- a/experiment/eurocode_data/eurocode_visualizer.py
+++ b/experiment/eurocode_data/eurocode_visualizer.py
@@ -1,31 +1,57 @@
-from collections import OrderedDict
+from typing import Dict, List
+
import matplotlib.pyplot as plt
-from experiment.eurocode_data.massif_name_to_departement import massif_name_to_departement_objects
-from experiment.eurocode_data.eurocode_region import AbstractEurocodeRegion
-from utils import get_display_name_from_object_type
-
-
-def display_region_limit(region_type, altitudes, ordered_massif_name_to_quantiles,
- ordered_massif_name_to_significances=None,
- display=True):
- assert isinstance(ordered_massif_name_to_quantiles, OrderedDict)
- assert ordered_massif_name_to_significances is None or isinstance(ordered_massif_name_to_significances, OrderedDict)
- # First, select massif name correspond to the region
- massif_name_belong_to_the_region = []
- for massif_name in ordered_massif_name_to_quantiles.keys():
- if any([isinstance(dep.region, region_type) for dep in massif_name_to_departement_objects[massif_name]]):
- massif_name_belong_to_the_region.append(massif_name)
- region_object = region_type() # type: AbstractEurocodeRegion
- # Then, display the limit for the region
- fig, ax = plt.subplots(1, 1)
- ax.plot(altitudes, [region_object.eurocode_max_loading(altitude) for altitude in altitudes], label='Eurocode limit')
- # Finally, display the massif curve
- for massif_name in massif_name_belong_to_the_region:
- ax.plot(altitudes, ordered_massif_name_to_quantiles[massif_name], label=massif_name)
- ax.set_title('{} Eurocode region'.format(get_display_name_from_object_type(region_type)))
- ax.set_xlabel('Altitude')
- ax.set_ylabel('0.98 quantile (in N $m^-2$)')
- ax.legend()
- if display:
+from experiment.eurocode_data.eurocode_return_level_uncertainties import EurocodeLevelUncertaintyFromExtremes
+from experiment.eurocode_data.massif_name_to_departement import DEPARTEMENT_TYPES
+from experiment.eurocode_data.utils import EUROCODE_QUANTILE, EUROCODE_ALTITUDES
+from experiment.meteo_france_data.scm_models_data.visualization.utils import create_adjusted_axes
+
+
+def plot_model_name_to_dep_to_ordered_return_level_uncertainties(
+ dep_to_model_name_to_ordered_return_level_uncertainties, show=True):
+ # Create a 9 x 9 plot
+ axes = create_adjusted_axes(3, 3)
+ axes = list(axes.flatten())
+ ax6 = axes[5]
+ ax9 = axes[8]
+
+ axes.remove(ax6)
+ axes.remove(ax9)
+ ax_to_departement = dict(zip(axes, DEPARTEMENT_TYPES[::-1]))
+ for ax, departement in ax_to_departement.items():
+ plot_dep_to_model_name_dep_to_ordered_return_level_uncertainties(ax, departement,
+ dep_to_model_name_to_ordered_return_level_uncertainties[
+ departement]
+ )
+ ax6.remove()
+ ax9.remove()
+
+ plt.suptitle('50-year return levels for all French Alps departements. \n'
+ 'Comparison between the maximum EUROCODE in the departement\n'
+ 'and the maximum return level found for the massif belonging to the departement')
+ if show:
plt.show()
+
+
+def plot_dep_to_model_name_dep_to_ordered_return_level_uncertainties(ax, dep_class,
+ model_name_to_ordered_return_level_uncertainties:
+ Dict[str, List[
+ EurocodeLevelUncertaintyFromExtremes]]):
+ colors = ['red', 'blue', 'green']
+ altitudes = EUROCODE_ALTITUDES
+ alpha = 0.2
+ # Display the EUROCODE return level
+ dep_object = dep_class()
+ dep_object.eurocode_region.plot_max_loading(ax, altitudes=altitudes)
+ # Display the return level from model class
+ for color, (model_name, ordered_return_level_uncertaines) in zip(colors,
+ model_name_to_ordered_return_level_uncertainties.items()):
+ mean = [r.posterior_mean for r in ordered_return_level_uncertaines]
+ ax.plot(altitudes, mean, '-', color=color)
+ lower_bound = [r.poster_uncertainty_interval[0] for r in ordered_return_level_uncertaines]
+ upper_bound = [r.poster_uncertainty_interval[1] for r in ordered_return_level_uncertaines]
+ ax.fill_between(altitudes, lower_bound, upper_bound, color=color, alpha=alpha)
+ ax.set_title(str(dep_object))
+ ax.set_ylabel('Maximum {} quantile (in N $m^-2$)'.format(EUROCODE_QUANTILE))
+ ax.set_xlabel('Altitude')
diff --git a/experiment/eurocode_data/main_eurocode_drawing.py b/experiment/eurocode_data/main_eurocode_drawing.py
new file mode 100644
index 0000000000000000000000000000000000000000..569cb9a843054e468cd286457782844afebb7b98
--- /dev/null
+++ b/experiment/eurocode_data/main_eurocode_drawing.py
@@ -0,0 +1,63 @@
+from collections import OrderedDict
+
+from experiment.eurocode_data.eurocode_visualizer import plot_model_name_to_dep_to_ordered_return_level_uncertainties
+from experiment.eurocode_data.massif_name_to_departement import DEPARTEMENT_TYPES
+from experiment.eurocode_data.utils import EUROCODE_ALTITUDES
+from experiment.meteo_france_data.scm_models_data.crocus.crocus import CrocusSwe3Days
+from experiment.meteo_france_data.scm_models_data.visualization.hypercube_visualization.altitude_hypercube_visualizer import \
+ AltitudeHypercubeVisualizer
+from experiment.meteo_france_data.scm_models_data.visualization.hypercube_visualization.utils_hypercube import \
+ load_altitude_visualizer
+from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryStationModel, \
+ NonStationaryLocationAndScaleModel
+from root_utils import get_display_name_from_object_type
+
+
+# Model class
+
+
+def dep_to_ordered_return_level_uncertainties(model_class, last_year_for_the_data):
+ model_name = get_display_name_from_object_type(type(model_class)) + ' ' + str(last_year_for_the_data)
+ # Load altitude visualizer
+ # todo: add last years attributes that enables to change the years
+ altitude_visualizer = load_altitude_visualizer(AltitudeHypercubeVisualizer, altitudes=EUROCODE_ALTITUDES,
+ last_starting_year=None, nb_data_reduced_for_speed=False,
+ only_first_one=False, save_to_file=False,
+ exact_starting_year=1958,
+ first_starting_year=None,
+ study_classes=[CrocusSwe3Days],
+ trend_test_class=None)
+ # Loop on the data
+ assert isinstance(altitude_visualizer.tuple_to_study_visualizer, OrderedDict)
+ dep_to_ordered_return_level_uncertainty = {dep: [] for dep in DEPARTEMENT_TYPES}
+ for visualizer in altitude_visualizer.tuple_to_study_visualizer.values():
+ dep_to_return_level_uncertainty = visualizer.dep_class_to_eurocode_level_uncertainty(model_class)
+ for dep, return_level_uncertainty in dep_to_return_level_uncertainty.items():
+ dep_to_ordered_return_level_uncertainty[dep].append(return_level_uncertainty)
+
+ return {model_name: dep_to_ordered_return_level_uncertainty}
+
+
+def main_drawing():
+ model_class_and_last_year = [
+ (StationaryStationModel, 1991),
+ (StationaryStationModel, 2017),
+ (NonStationaryLocationAndScaleModel, 2017),
+ ][:1]
+ model_name_to_dep_to_ordered_return_level = {}
+ for model_class, last_year_for_the_data in model_class_and_last_year:
+ model_name_to_dep_to_ordered_return_level.update(
+ dep_to_ordered_return_level_uncertainties(model_class, last_year_for_the_data))
+ # Transform the dictionary into the desired format
+ dep_to_model_name_to_ordered_return_level_uncertainties = {}
+ for dep in DEPARTEMENT_TYPES:
+ d2 = {model_name: model_name_to_dep_to_ordered_return_level[model_name][dep] for model_name in
+ model_name_to_dep_to_ordered_return_level.keys()}
+ dep_to_model_name_to_ordered_return_level_uncertainties[dep] = d2
+ # Plot graph
+ plot_model_name_to_dep_to_ordered_return_level_uncertainties(
+ dep_to_model_name_to_ordered_return_level_uncertainties, show=True)
+
+
+if __name__ == '__main__':
+ main_drawing()
diff --git a/experiment/eurocode_data/massif_name_to_departement.py b/experiment/eurocode_data/massif_name_to_departement.py
index 2e585248588ae06422227f0d533c937862e7b7d2..d059be32ebd84f6566f1d8fb0130215aaa64f1e7 100644
--- a/experiment/eurocode_data/massif_name_to_departement.py
+++ b/experiment/eurocode_data/massif_name_to_departement.py
@@ -3,8 +3,6 @@ from typing import Dict, List
from experiment.eurocode_data.departement_alpes_francaises import HauteSavoie, Savoie, Isere, Drome, HautesAlpes, \
AlpesDeHauteProvence, AlpesMaritimes, AbstractDepartementAlpesFrancaises
-
-
massif_name_to_departement_types = {
'Chablais': [HauteSavoie],
'Aravis': [HauteSavoie, Savoie],
@@ -30,17 +28,15 @@ massif_name_to_departement_types = {
'Haut_Var-Haut_Verdon': [AlpesDeHauteProvence],
'Mercantour': [AlpesMaritimes, AlpesDeHauteProvence]}
-massif_name_to_departement_objects = {m: [d() for d in deps] for m, deps in massif_name_to_departement_types.items()} # type: Dict[str, List[AbstractDepartementAlpesFrancaises]]
+massif_name_to_departement_objects = {m: [d() for d in deps] for m, deps in
+ massif_name_to_departement_types.items()} # type: Dict[str, List[AbstractDepartementAlpesFrancaises]]
DEPARTEMENT_TYPES = [HauteSavoie, Savoie, Isere, Drome, HautesAlpes, AlpesMaritimes, AlpesDeHauteProvence]
-departement_type_to_massif_names = {dep: [k for k, v in massif_name_to_departement_types.items() if dep in v]
- for dep in DEPARTEMENT_TYPES
-}
+dep_class_to_massif_names = {dep: [k for k, v in massif_name_to_departement_types.items() if dep in v]
+ for dep in DEPARTEMENT_TYPES
+ }
if __name__ == '__main__':
- for k, v in departement_type_to_massif_names.items():
+ for k, v in dep_class_to_massif_names.items():
print(k, v)
-
-
-
diff --git a/experiment/eurocode_data/utils.py b/experiment/eurocode_data/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef3b88eb415ca5b23223fce8be45c9a73636e9db
--- /dev/null
+++ b/experiment/eurocode_data/utils.py
@@ -0,0 +1,5 @@
+
+# Eurocode quantile correspond to a 50 year return period
+EUROCODE_QUANTILE = 0.98
+# Altitudes (between low and mid altitudes) < 2000m
+EUROCODE_ALTITUDES = [900, 1200, 1500, 1800][:2]
\ No newline at end of file
diff --git a/experiment/meteo_france_data/scm_models_data/abstract_extended_study.py b/experiment/meteo_france_data/scm_models_data/abstract_extended_study.py
index b832fe110e54aa817fbabdbf8b07149cb23ce3ec..d9c8c9e5cea9d75d825eff14eb43d7fb338ed0b8 100644
--- a/experiment/meteo_france_data/scm_models_data/abstract_extended_study.py
+++ b/experiment/meteo_france_data/scm_models_data/abstract_extended_study.py
@@ -4,7 +4,7 @@ from collections import OrderedDict
from experiment.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
from spatio_temporal_dataset.coordinates.spatial_coordinates.abstract_spatial_coordinates import \
AbstractSpatialCoordinates
-from utils import classproperty
+from root_utils import classproperty
class AbstractExtendedStudy(AbstractStudy):
diff --git a/experiment/meteo_france_data/scm_models_data/abstract_study.py b/experiment/meteo_france_data/scm_models_data/abstract_study.py
index a10ac33e62c6ba0af54be9c6ec0209b3b198050d..b16ead03bcd1eb286b72bab364937aef29fd470d 100644
--- a/experiment/meteo_france_data/scm_models_data/abstract_study.py
+++ b/experiment/meteo_france_data/scm_models_data/abstract_study.py
@@ -30,7 +30,7 @@ from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoo
from spatio_temporal_dataset.coordinates.spatial_coordinates.abstract_spatial_coordinates import \
AbstractSpatialCoordinates
from spatio_temporal_dataset.spatio_temporal_observations.annual_maxima_observations import AnnualMaxima
-from utils import get_full_path, cached_property, NB_CORES, classproperty
+from root_utils import get_full_path, cached_property, NB_CORES, classproperty
f = io.StringIO()
with redirect_stdout(f):
diff --git a/experiment/meteo_france_data/scm_models_data/crocus/taline_data.py b/experiment/meteo_france_data/scm_models_data/crocus/taline_data.py
index 183b0818b3cb72c8b122d5f92fcbce4cc5ce4a21..8de3a72f400c653d9fbf92b2d5847c1c4105fd9e 100644
--- a/experiment/meteo_france_data/scm_models_data/crocus/taline_data.py
+++ b/experiment/meteo_france_data/scm_models_data/crocus/taline_data.py
@@ -5,7 +5,7 @@ import pandas as pd
from experiment.meteo_france_data.scm_models_data.crocus.crocus import CrocusDepth
from experiment.meteo_france_data.scm_models_data.scm_constants import ALTITUDES
-from utils import get_display_name_from_object_type
+from root_utils import get_display_name_from_object_type
massif_name = 'Queyras'
study_class = CrocusDepth
diff --git a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/abstract_hypercube_visualizer.py b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/abstract_hypercube_visualizer.py
index 4cfb5d70450da977f3c4a5bd799f443b0c0f1ae1..30b3417d1ea334048bac50cb326bea55f8f832e3 100644
--- a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/abstract_hypercube_visualizer.py
+++ b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/abstract_hypercube_visualizer.py
@@ -7,7 +7,7 @@ import pandas as pd
from experiment.meteo_france_data.scm_models_data.visualization.study_visualization.study_visualizer import \
StudyVisualizer
-from utils import cached_property, VERSION_TIME, get_display_name_from_object_type
+from root_utils import cached_property, VERSION_TIME, get_display_name_from_object_type
class AbstractHypercubeVisualizer(object):
diff --git a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/altitude_hypercube_visualizer.py b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/altitude_hypercube_visualizer.py
index dc3fd7973aef1d0f3ecde8aa610bcf5e28757130..d918abe93629d8ca3a5cf7700d34e2b9fafdc953 100644
--- a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/altitude_hypercube_visualizer.py
+++ b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/altitude_hypercube_visualizer.py
@@ -12,7 +12,7 @@ from experiment.meteo_france_data.scm_models_data.visualization.study_visualizat
StudyVisualizer
from experiment.trend_analysis.univariate_test.abstract_gev_trend_test import AbstractGevTrendTest
from experiment.trend_analysis.univariate_test.abstract_univariate_test import AbstractUnivariateTest
-from utils import get_display_name_from_object_type
+from root_utils import get_display_name_from_object_type
ALTITUDES_XLABEL = 'altitudes'
diff --git a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files/main_fast_hypercube_several_altitudes.py b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files/main_fast_hypercube_several_altitudes.py
index 822bd604e5a2a7fc82b907a0f0fcaff8aa4602ea..086e668bbcbe22053e9937e232abc6c72c37f854 100644
--- a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files/main_fast_hypercube_several_altitudes.py
+++ b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files/main_fast_hypercube_several_altitudes.py
@@ -1,12 +1,7 @@
import time
-from experiment.meteo_france_data.scm_models_data.visualization.hypercube_visualization.altitude_hypercube_visualizer import \
- AltitudeHypercubeVisualizer
from experiment.meteo_france_data.scm_models_data.visualization.hypercube_visualization.altitude_hypercube_visualizer_extended import \
- AltitudeHypercubeVisualizerBisExtended, QuantityHypercubeWithoutTrendExtended, \
- AltitudeHypercubeVisualizerWithoutTrendExtended, QuantityHypercubeWithoutTrend
-from experiment.meteo_france_data.scm_models_data.visualization.hypercube_visualization.quantity_altitude_visualizer import \
- QuantityAltitudeHypercubeVisualizer
+ AltitudeHypercubeVisualizerBisExtended, QuantityHypercubeWithoutTrend
from experiment.meteo_france_data.scm_models_data.visualization.hypercube_visualization.utils_hypercube import \
load_altitude_visualizer, load_quantity_visualizer
from experiment.meteo_france_data.scm_models_data.visualization.study_visualization.main_study_visualizer import \
diff --git a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files/main_full_hypercube.py b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files/main_full_hypercube.py
index 4ede274dabcd304b7874349a98cb4d73219f15f9..21311fc226b259ed6063df1202555e875a49bbd5 100644
--- a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files/main_full_hypercube.py
+++ b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files/main_full_hypercube.py
@@ -1,12 +1,7 @@
import time
-from experiment.meteo_france_data.scm_models_data.visualization.hypercube_visualization.altitude_hypercube_visualizer import \
- AltitudeHypercubeVisualizer
from experiment.meteo_france_data.scm_models_data.visualization.hypercube_visualization.altitude_hypercube_visualizer_extended import \
- AltitudeHypercubeVisualizerBisExtended, QuantityHypercubeWithoutTrendExtended, \
- AltitudeHypercubeVisualizerWithoutTrendExtended, QuantityHypercubeWithoutTrend
-from experiment.meteo_france_data.scm_models_data.visualization.hypercube_visualization.quantity_altitude_visualizer import \
- QuantityAltitudeHypercubeVisualizer
+ AltitudeHypercubeVisualizerBisExtended, QuantityHypercubeWithoutTrend
from experiment.meteo_france_data.scm_models_data.visualization.hypercube_visualization.utils_hypercube import \
load_altitude_visualizer, load_quantity_visualizer
from experiment.meteo_france_data.scm_models_data.visualization.study_visualization.main_study_visualizer import \
diff --git a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files/main_poster_IMSC2019.py b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files/main_poster_IMSC2019.py
index e11a6d31f22729975efaf1ca7349a796086903f3..011477f7efff572b3f60859ccaf1f8158e33d272 100644
--- a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files/main_poster_IMSC2019.py
+++ b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/main_files/main_poster_IMSC2019.py
@@ -9,7 +9,7 @@ from experiment.meteo_france_data.scm_models_data.visualization.hypercube_visual
load_altitude_visualizer
from experiment.meteo_france_data.scm_models_data.visualization.study_visualization.main_study_visualizer import \
SCM_STUDIES, altitude_massif_name_and_study_class_for_poster, SCM_STUDIES_NAMES, SCM_STUDY_NAME_TO_SCM_STUDY
-from utils import get_display_name_from_object_type
+from root_utils import get_display_name_from_object_type
POSTER_ALTITUDES = [900, 1800, 2700]
diff --git a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/utils_hypercube.py b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/utils_hypercube.py
index 608b484227778fa1fe7ef788409107bf6d6f296e..5b7fbcb94e2986f316f81c40743ee36570f67099 100644
--- a/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/utils_hypercube.py
+++ b/experiment/meteo_france_data/scm_models_data/visualization/hypercube_visualization/utils_hypercube.py
@@ -9,7 +9,7 @@ from experiment.meteo_france_data.scm_models_data.visualization.study_visualizat
study_iterator_global
from experiment.meteo_france_data.scm_models_data.visualization.study_visualization.study_visualizer import \
StudyVisualizer
-from utils import get_display_name_from_object_type
+from root_utils import get_display_name_from_object_type
def load_quantity_visualizer(quantity_hypercube_class, altitudes, last_starting_year, nb_data_reduced_for_speed,
diff --git a/experiment/meteo_france_data/scm_models_data/visualization/studies_visualization/studies_visualizer.py b/experiment/meteo_france_data/scm_models_data/visualization/studies_visualization/studies_visualizer.py
index 92da9208e0af6d725a96d763f03e3d49158ba86a..f553d1ac624b5194fe5b6424b1acf76e3cb47510 100644
--- a/experiment/meteo_france_data/scm_models_data/visualization/studies_visualization/studies_visualizer.py
+++ b/experiment/meteo_france_data/scm_models_data/visualization/studies_visualization/studies_visualizer.py
@@ -15,7 +15,7 @@ from experiment.meteo_france_data.scm_models_data.visualization.study_visualizat
StudyVisualizer
from experiment.trend_analysis.univariate_test.abstract_univariate_test import AbstractUnivariateTest
from experiment.meteo_france_data.scm_models_data.visualization.utils import plot_df
-from utils import cached_property, get_display_name_from_object_type, VERSION_TIME
+from root_utils import cached_property, get_display_name_from_object_type, VERSION_TIME
class StudiesVisualizer(object):
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 6dcb3303a4be9b2967dba446fa57c02c186f5ff4..aeb8f63e479ade6850960b6850c7e7a571696c14 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
@@ -16,7 +16,7 @@ from collections import OrderedDict
from experiment.trend_analysis.univariate_test.gev_trend_test_one_parameter import GevLocationTrendTest
from spatio_temporal_dataset.coordinates.transformed_coordinates.transformation.uniform_normalization import \
BetweenZeroAndOneNormalization, BetweenMinusOneAndOneNormalization
-from utils import get_display_name_from_object_type
+from root_utils import get_display_name_from_object_type
SCM_STUDIES = [SafranSnowfall, CrocusSweTotal, CrocusDepth, CrocusSwe3Days]
SCM_STUDIES_NAMES = [get_display_name_from_object_type(k) for k in SCM_STUDIES]
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 7263aa3da2f39006d2e1e0873847107c936d712d..4b361e437ea578391d96d43081d745564cf06d6a 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
@@ -10,6 +10,9 @@ import numpy as np
import pandas as pd
import seaborn as sns
+from experiment.eurocode_data.eurocode_return_level_uncertainties import ExtractEurocodeReturnLevelFromExtremes, \
+ EurocodeLevelUncertaintyFromExtremes
+from experiment.eurocode_data.massif_name_to_departement import dep_class_to_massif_names
from experiment.meteo_france_data.scm_models_data.abstract_extended_study import AbstractExtendedStudy
from experiment.trend_analysis.abstract_score import MeanScore, AbstractTrendScore
from experiment.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
@@ -17,13 +20,14 @@ from experiment.trend_analysis.univariate_test.abstract_univariate_test import A
from experiment.trend_analysis.non_stationary_trends import \
ConditionalIndedendenceLocationTrendTest, MaxStableLocationTrendTest, IndependenceLocationTrendTest
from experiment.meteo_france_data.scm_models_data.visualization.utils import create_adjusted_axes
-from experiment.trend_analysis.univariate_test.utils import compute_gev_change_point_test_results
+from experiment.trend_analysis.univariate_test.univariate_test_results import compute_gev_change_point_test_results
from experiment.utils import average_smoothing_with_sliding_window
from extreme_fit.distribution.abstract_params import AbstractParams
from extreme_fit.estimator.full_estimator.abstract_full_estimator import \
FullEstimatorInASingleStepWithSmoothMargin
from extreme_fit.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
-from extreme_fit.model.margin_model.linear_margin_model.linear_margin_model import LinearNonStationaryLocationMarginModel, \
+from extreme_fit.model.margin_model.linear_margin_model.linear_margin_model import \
+ LinearNonStationaryLocationMarginModel, \
LinearStationaryMarginModel
from extreme_fit.model.margin_model.margin_function.abstract_margin_function import \
AbstractMarginFunction
@@ -42,7 +46,7 @@ from spatio_temporal_dataset.coordinates.temporal_coordinates.generated_temporal
from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
from spatio_temporal_dataset.spatio_temporal_observations.annual_maxima_observations import AnnualMaxima
from test.test_utils import load_test_max_stable_models
-from utils import get_display_name_from_object_type, VERSION_TIME, float_to_str_with_only_some_significant_digits, \
+from root_utils import get_display_name_from_object_type, VERSION_TIME, float_to_str_with_only_some_significant_digits, \
cached_property
BLOCK_MAXIMA_DISPLAY_NAME = 'block maxima '
@@ -350,6 +354,36 @@ class StudyVisualizer(VisualizationParameters):
start_year, stop_year = self.study.start_year_and_stop_year
return list(range(start_year, stop_year))
+ def massif_name_to_eurocode_level_uncertainty(self, model_class) -> Dict[str, EurocodeLevelUncertaintyFromExtremes]:
+ # todo: add multiprocessing
+ massif_name_to_eurocode_return_level_uncertainty = {}
+ for massif_id, massif_name in enumerate(self.study.study_massif_names):
+ print(massif_name)
+ years, smooth_maxima = self.smooth_maxima_x_y(massif_id)
+ res = EurocodeLevelUncertaintyFromExtremes.from_maxima_years_model_class(smooth_maxima, years, model_class)
+ massif_name_to_eurocode_return_level_uncertainty[massif_name] = res
+ return massif_name_to_eurocode_return_level_uncertainty
+
+ def dep_class_to_eurocode_level_uncertainty(self, model_class):
+ """ Take the max with respect to the posterior mean for each departement """
+ massif_name_to_eurocode_level_uncertainty = self.massif_name_to_eurocode_level_uncertainty(model_class)
+ print(massif_name_to_eurocode_level_uncertainty)
+ dep_class_to_eurocode_level_uncertainty = {}
+ for dep_class, massif_names in dep_class_to_massif_names.items():
+ # Filter the couple of interest
+ couples = [(k, v) for k, v in massif_name_to_eurocode_level_uncertainty.items() if k in massif_names]
+ assert len(couples) > 0
+ # Find the massif name with the maximum posterior mean
+ for c in couples:
+ print(c)
+ print(c[1].posterior_mean)
+ massif_name, eurocode_level_uncertainty = max(couples, key=lambda c: c[1].posterior_mean)
+ dep_class_to_eurocode_level_uncertainty[dep_class] = eurocode_level_uncertainty
+ return dep_class_to_eurocode_level_uncertainty
+
+
+
+
@cached_property
def massif_name_to_detailed_scores(self):
"""
@@ -430,7 +464,8 @@ class StudyVisualizer(VisualizationParameters):
massif_names = [AbstractExtendedStudy.region_name_to_massif_names[r][0]
for r in AbstractExtendedStudy.real_region_names]
massif_names_for_sampling = list(set(self.study.study_massif_names) - set(massif_names))
- nb_massif_for_sampling = self.nb_massif_for_change_point_test - len(AbstractExtendedStudy.real_region_names)
+ nb_massif_for_sampling = self.nb_massif_for_change_point_test - len(
+ AbstractExtendedStudy.real_region_names)
massif_names += sample(massif_names_for_sampling, k=nb_massif_for_sampling)
else:
massif_names = sample(self.study.study_massif_names, k=self.nb_massif_for_change_point_test)
diff --git a/experiment/meteo_france_data/stations_data/comparison_analysis.py b/experiment/meteo_france_data/stations_data/comparison_analysis.py
index c85b1cdc7cdb9de087cab91507d8744f65df9eb9..ee5160279ff0ab29cadfe7a968ae0ca9bb98dbdc 100644
--- a/experiment/meteo_france_data/stations_data/comparison_analysis.py
+++ b/experiment/meteo_france_data/stations_data/comparison_analysis.py
@@ -20,7 +20,7 @@ from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
AbstractSpatioTemporalObservations
from test.test_utils import load_test_max_stable_models
-from utils import get_display_name_from_object_type
+from root_utils import get_display_name_from_object_type
REANALYSE_STR = 'reanalyse'
ALTITUDE_COLUMN_NAME = 'ALTITUDE'
diff --git a/experiment/meteo_france_data/stations_data/main_spatial_comparison.py b/experiment/meteo_france_data/stations_data/main_spatial_comparison.py
index 80374664c00543d036e6a1a7815fb2b9faf64a22..b2bdf90a1b60fd875c0e79637e181f6ff2dd3e78 100644
--- a/experiment/meteo_france_data/stations_data/main_spatial_comparison.py
+++ b/experiment/meteo_france_data/stations_data/main_spatial_comparison.py
@@ -2,7 +2,7 @@ from experiment.meteo_france_data.stations_data.comparison_analysis import Compa
from extreme_fit.model.margin_model.linear_margin_model.linear_margin_model import LinearAllParametersAllDimsMarginModel
from spatio_temporal_dataset.coordinates.transformed_coordinates.transformation.uniform_normalization import \
BetweenZeroAndOneNormalization
-from utils import get_display_name_from_object_type
+from root_utils import get_display_name_from_object_type
def choice_of_altitude_and_nb_border_data_to_remove_to_get_data_without_nan():
diff --git a/experiment/meteo_france_data/stations_data/visualization/comparisons_visualization/comparisons_visualization.py b/experiment/meteo_france_data/stations_data/visualization/comparisons_visualization/comparisons_visualization.py
index af0fd9b579eedf432b5b5826a7e3e16618f7ddeb..7be7297adac50bdefe23884753541c8b43e7faf5 100644
--- a/experiment/meteo_france_data/stations_data/visualization/comparisons_visualization/comparisons_visualization.py
+++ b/experiment/meteo_france_data/stations_data/visualization/comparisons_visualization/comparisons_visualization.py
@@ -15,7 +15,7 @@ from experiment.meteo_france_data.scm_models_data.visualization.study_visualizat
from experiment.meteo_france_data.stations_data.comparison_analysis import ComparisonAnalysis, MASSIF_COLUMN_NAME, \
REANALYSE_STR, ALTITUDE_COLUMN_NAME, STATION_COLUMN_NAME
from experiment.trend_analysis.univariate_test.abstract_univariate_test import AbstractUnivariateTest
-from experiment.trend_analysis.univariate_test.utils import compute_gev_change_point_test_results
+from experiment.trend_analysis.univariate_test.univariate_test_results import compute_gev_change_point_test_results
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
path = r'/home/erwan/Documents/projects/spatiotemporalextremes/experiment/meteo_france_data/stations_data/csv'
diff --git a/experiment/robustness_plot/single_plot.py b/experiment/robustness_plot/single_plot.py
index 70f68923dbcedc3c8869538e704309167113334b..e2fe71331960c1ee1fdb0e761887f49bf47eb645 100644
--- a/experiment/robustness_plot/single_plot.py
+++ b/experiment/robustness_plot/single_plot.py
@@ -7,7 +7,7 @@ from itertools import product
from extreme_fit.estimator.abstract_estimator import AbstractEstimator
from experiment.robustness_plot.display_item import DisplayItem
-from utils import get_full_path
+from root_utils import get_full_path
plt.style.use('seaborn-white')
diff --git a/experiment/simulation/abstract_simulation.py b/experiment/simulation/abstract_simulation.py
index 9e7370c561f02032c08e4f9b85cccd18fa86063e..bc4e14e3b3c15b7ac6f2ffdf271e32b892fe0bda 100644
--- a/experiment/simulation/abstract_simulation.py
+++ b/experiment/simulation/abstract_simulation.py
@@ -23,7 +23,7 @@ from extreme_fit.distribution.gev.gev_params import GevParams
from spatio_temporal_dataset.dataset.abstract_dataset import get_subset_dataset
from spatio_temporal_dataset.dataset.simulation_dataset import SimulatedDataset
from spatio_temporal_dataset.slicer.split import split_to_display_kwargs
-from utils import get_full_path, get_display_name_from_object_type
+from root_utils import get_full_path, get_display_name_from_object_type
SIMULATION_RELATIVE_PATH = op.join('local', 'simulation')
diff --git a/experiment/trend_analysis/non_stationary_trends.py b/experiment/trend_analysis/non_stationary_trends.py
index 3b6a362e62c9b3b9db57812b21d87d3d47634246..ab4debc4f54722d021ee1e58805fcdc15ef7ccaa 100644
--- a/experiment/trend_analysis/non_stationary_trends.py
+++ b/experiment/trend_analysis/non_stationary_trends.py
@@ -17,7 +17,7 @@ from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_m
from extreme_fit.model.margin_model.margin_function.linear_margin_function import LinearMarginFunction
from extreme_fit.model.utils import OptimizationConstants
from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
-from utils import get_display_name_from_object_type
+from root_utils import get_display_name_from_object_type
class AbstractNonStationaryTrendTest(object):
diff --git a/experiment/trend_analysis/univariate_test/abstract_gev_trend_test.py b/experiment/trend_analysis/univariate_test/abstract_gev_trend_test.py
index af9ae8994711e5bf5dd7d6b2d6bb1c584f4c8b13..cbea66c2a8f10d8edaccc9a42652d5bab2c3f399 100644
--- a/experiment/trend_analysis/univariate_test/abstract_gev_trend_test.py
+++ b/experiment/trend_analysis/univariate_test/abstract_gev_trend_test.py
@@ -3,49 +3,38 @@ import pandas as pd
from cached_property import cached_property
from scipy.stats import chi2
+from experiment.eurocode_data.utils import EUROCODE_QUANTILE
from experiment.trend_analysis.univariate_test.abstract_univariate_test import AbstractUnivariateTest
+from experiment.trend_analysis.univariate_test.utils import load_temporal_coordinates_and_dataset, \
+ fitted_linear_margin_estimator
from extreme_fit.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import AbstractTemporalLinearMarginModel
+from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
+ AbstractTemporalLinearMarginModel
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import \
StationaryStationModel
from extreme_fit.model.utils import SafeRunException
from extreme_fit.distribution.gev.gev_params import GevParams
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
-from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_coordinates import \
- AbstractTemporalCoordinates
-from spatio_temporal_dataset.coordinates.transformed_coordinates.transformation.abstract_transformation import \
- CenteredScaledNormalization
-from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
-from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
- AbstractSpatioTemporalObservations
class AbstractGevTrendTest(AbstractUnivariateTest):
RRunTimeError_TREND = 'R RunTimeError trend'
# I should use the quantile from the Eurocode for the buildings
- quantile_for_strength = 0.98
+ quantile_for_strength = EUROCODE_QUANTILE
nb_years_for_quantile_evolution = 10
- def __init__(self, years, maxima, starting_year, unconstrained_model_class, constrained_model_class=StationaryStationModel,
+ def __init__(self, years, maxima, starting_year, unconstrained_model_class,
+ constrained_model_class=StationaryStationModel,
fit_method=AbstractTemporalLinearMarginModel.ISMEV_GEV_FIT_METHOD_STR):
super().__init__(years, maxima, starting_year)
self.fit_method = fit_method
# Load observations, coordinates and datasets
- df = pd.DataFrame({AbstractCoordinates.COORDINATE_T: years})
- df_maxima_gev = pd.DataFrame(maxima, index=df.index)
- observations = AbstractSpatioTemporalObservations(df_maxima_gev=df_maxima_gev)
- self.coordinates = AbstractTemporalCoordinates.from_df(df, transformation_class=CenteredScaledNormalization)
- self.dataset = AbstractDataset(observations=observations, coordinates=self.coordinates)
-
+ self.coordinates, self.dataset = load_temporal_coordinates_and_dataset(maxima, years)
try:
# Fit constrained model
- constrained_model = constrained_model_class(self.coordinates, starting_point=self.starting_year, fit_method=self.fit_method)
- self.constrained_estimator = LinearMarginEstimator(self.dataset, constrained_model)
- self.constrained_estimator.fit()
+ self.constrained_estimator = fitted_linear_margin_estimator(constrained_model_class, self.coordinates, self.dataset, self.starting_year, self.fit_method)
# Fit unconstrained model
- unconstrained_model = unconstrained_model_class(self.coordinates, starting_point=self.starting_year, fit_method=self.fit_method)
- self.unconstrained_estimator = LinearMarginEstimator(self.dataset, unconstrained_model)
- self.unconstrained_estimator.fit()
+ self.unconstrained_estimator = fitted_linear_margin_estimator(unconstrained_model_class, self.coordinates, self.dataset, self.starting_year, self.fit_method)
self.crashed = False
except SafeRunException:
self.crashed = True
@@ -127,7 +116,6 @@ class AbstractGevTrendTest(AbstractUnivariateTest):
return self.constrained_estimator.margin_function_from_fit.get_gev_params(coordinate=np.array([1958]),
is_transformed=False)
-
@property
def test_trend_slope_strength(self):
if self.crashed:
@@ -136,7 +124,8 @@ class AbstractGevTrendTest(AbstractUnivariateTest):
# Compute the slope strength
slope = self._slope_strength()
# Delta T must in the same unit as were the parameter of slope mu1 and sigma1
- slope *= self.nb_years_for_quantile_evolution * self.coordinates.transformed_distance_between_two_successive_years[0]
+ slope *= self.nb_years_for_quantile_evolution * \
+ self.coordinates.transformed_distance_between_two_successive_years[0]
return slope
def _slope_strength(self):
@@ -163,6 +152,3 @@ class AbstractGevTrendTest(AbstractUnivariateTest):
return 0.0
else:
return self.non_stationary_constant_gev_params.quantile(p=self.quantile_for_strength)
-
-
-
diff --git a/experiment/trend_analysis/univariate_test/univariate_test_results.py b/experiment/trend_analysis/univariate_test/univariate_test_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..aeea3f63c4eb0a6edfd3fcb4a8d7dbe0e82855e0
--- /dev/null
+++ b/experiment/trend_analysis/univariate_test/univariate_test_results.py
@@ -0,0 +1,44 @@
+from multiprocessing.pool import Pool
+
+import numpy as np
+
+from experiment.trend_analysis.univariate_test.abstract_gev_trend_test import AbstractGevTrendTest
+from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
+ AbstractTemporalLinearMarginModel
+from root_utils import NB_CORES
+
+
+def compute_gev_change_point_test_result(smooth_maxima, starting_year, trend_test_class, years, fit_method=AbstractTemporalLinearMarginModel.ISMEV_GEV_FIT_METHOD_STR):
+ trend_test = trend_test_class(years, smooth_maxima, starting_year) # type: AbstractGevTrendTest
+ assert isinstance(trend_test, AbstractGevTrendTest)
+ return trend_test.test_trend_type, \
+ trend_test.test_trend_slope_strength, \
+ trend_test.unconstained_nllh, \
+ trend_test.test_trend_constant_quantile, \
+ trend_test.mean_difference_same_sign_as_slope_strenght, \
+ trend_test.variance_difference_same_sign_as_slope_strenght, \
+ trend_test.unconstrained_model_deviance, \
+ trend_test.constrained_model_deviance
+
+
+def compute_gev_change_point_test_results(multiprocessing, maxima, starting_years, trend_test_class,
+ years):
+ if multiprocessing:
+ list_args = [(maxima, starting_year, trend_test_class, years) for starting_year in
+ starting_years]
+ with Pool(NB_CORES) as p:
+ trend_test_res = p.starmap(compute_gev_change_point_test_result, list_args)
+ else:
+ trend_test_res = [
+ compute_gev_change_point_test_result(maxima, starting_year, trend_test_class, years)
+ for starting_year in starting_years]
+ # Keep only the most likely starting year
+ # (i.e. the starting year that minimizes its negative log likelihood)
+ # (set all the other data to np.nan so that they will not be taken into account in mean function)
+ best_idx = list(np.argmin(trend_test_res, axis=0))[2]
+ # print(best_idx, trend_test_res)
+ best_idxs = [best_idx]
+ # todo: by doing a sorting on the deviance, I could get the nb_top_likelihood_values values
+ # best_idxs = list(np.argmax(trend_test_res, axis=0))[-nb_top_likelihood_values:]
+
+ return trend_test_res, best_idxs
diff --git a/experiment/trend_analysis/univariate_test/utils.py b/experiment/trend_analysis/univariate_test/utils.py
index 8c208ec24e67af7567ac874061d30d0f9fa8704c..75e4f7ff9c43157a0fa3f6cfde768bebd5c38367 100644
--- a/experiment/trend_analysis/univariate_test/utils.py
+++ b/experiment/trend_analysis/univariate_test/utils.py
@@ -1,44 +1,27 @@
-from multiprocessing.pool import Pool
+import pandas as pd
-import numpy as np
+from extreme_fit.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
+from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
+from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_coordinates import \
+ AbstractTemporalCoordinates
+from spatio_temporal_dataset.coordinates.transformed_coordinates.transformation.abstract_transformation import \
+ CenteredScaledNormalization
+from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
+from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
+ AbstractSpatioTemporalObservations
-from experiment.trend_analysis.univariate_test.abstract_gev_trend_test import AbstractGevTrendTest
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- AbstractTemporalLinearMarginModel
-from utils import NB_CORES
+def load_temporal_coordinates_and_dataset(maxima, years):
+ df = pd.DataFrame({AbstractCoordinates.COORDINATE_T: years})
+ df_maxima_gev = pd.DataFrame(maxima, index=df.index)
+ observations = AbstractSpatioTemporalObservations(df_maxima_gev=df_maxima_gev)
+ coordinates = AbstractTemporalCoordinates.from_df(df, transformation_class=CenteredScaledNormalization)
+ dataset = AbstractDataset(observations=observations, coordinates=coordinates)
+ return coordinates, dataset
-def compute_gev_change_point_test_result(smooth_maxima, starting_year, trend_test_class, years, fit_method=AbstractTemporalLinearMarginModel.ISMEV_GEV_FIT_METHOD_STR):
- trend_test = trend_test_class(years, smooth_maxima, starting_year) # type: AbstractGevTrendTest
- assert isinstance(trend_test, AbstractGevTrendTest)
- return trend_test.test_trend_type, \
- trend_test.test_trend_slope_strength, \
- trend_test.unconstained_nllh, \
- trend_test.test_trend_constant_quantile, \
- trend_test.mean_difference_same_sign_as_slope_strenght, \
- trend_test.variance_difference_same_sign_as_slope_strenght, \
- trend_test.unconstrained_model_deviance, \
- trend_test.constrained_model_deviance
-
-def compute_gev_change_point_test_results(multiprocessing, maxima, starting_years, trend_test_class,
- years):
- if multiprocessing:
- list_args = [(maxima, starting_year, trend_test_class, years) for starting_year in
- starting_years]
- with Pool(NB_CORES) as p:
- trend_test_res = p.starmap(compute_gev_change_point_test_result, list_args)
- else:
- trend_test_res = [
- compute_gev_change_point_test_result(maxima, starting_year, trend_test_class, years)
- for starting_year in starting_years]
- # Keep only the most likely starting year
- # (i.e. the starting year that minimizes its negative log likelihood)
- # (set all the other data to np.nan so that they will not be taken into account in mean function)
- best_idx = list(np.argmin(trend_test_res, axis=0))[2]
- # print(best_idx, trend_test_res)
- best_idxs = [best_idx]
- # todo: by doing a sorting on the deviance, I could get the nb_top_likelihood_values values
- # best_idxs = list(np.argmax(trend_test_res, axis=0))[-nb_top_likelihood_values:]
-
- return trend_test_res, best_idxs
+def fitted_linear_margin_estimator(model_class, coordinates, dataset, starting_year, fit_method):
+ model = model_class(coordinates, starting_point=starting_year, fit_method=fit_method)
+ estimator = LinearMarginEstimator(dataset, model)
+ estimator.fit()
+ return estimator
diff --git a/extreme_fit/distribution/gev/main_fevd_fixed.R b/extreme_fit/distribution/gev/main_fevd_fixed.R
index 6ecf87bb3661fa5aa6bb4d871120553c4b9446e1..6c9dca5b8f69b070845383df4ceb2f1d2834e80f 100644
--- a/extreme_fit/distribution/gev/main_fevd_fixed.R
+++ b/extreme_fit/distribution/gev/main_fevd_fixed.R
@@ -50,6 +50,7 @@ m = res$results
print(dim(m))
print(m[1,])
print(m[1,1])
+print(res$chain.info[1,])
diff --git a/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py b/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py
index da993d39bbbe70c13df924c2e37ba0ee3bf9110d..cc8ae95541b8a74df94b6598a08fffc5845c6a95 100644
--- a/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py
+++ b/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py
@@ -31,8 +31,8 @@ class LinearMarginEstimator(AbstractMarginEstimator):
df_coordinates_temp = self.dataset.coordinates.df_temporal_coordinates_for_fit(split=self.train_split,
starting_point=self.margin_model.starting_point)
return self.margin_model.fitmargin_from_maxima_gev(data=maxima_gev_specialized,
- df_coordinates_spat=df_coordinates_spat,
- df_coordinates_temp=df_coordinates_temp)
+ df_coordinates_spat=df_coordinates_spat,
+ df_coordinates_temp=df_coordinates_temp)
@cached_property
def margin_function_from_fit(self) -> LinearMarginFunction:
diff --git a/extreme_fit/estimator/utils.py b/extreme_fit/estimator/utils.py
index 4f27e94943f62d1b3ae7b47396d8b28de109582b..dec6231828cbc6e60f2aaff9ca4fee0db0ae2f4c 100644
--- a/extreme_fit/estimator/utils.py
+++ b/extreme_fit/estimator/utils.py
@@ -3,8 +3,10 @@ from extreme_fit.model.margin_model.linear_margin_model.linear_margin_model impo
from extreme_fit.model.margin_model.margin_function.linear_margin_function import LinearMarginFunction
-def load_margin_function(estimator: AbstractEstimator, margin_model: LinearMarginModel, margin_function_class=LinearMarginFunction):
+def load_margin_function(estimator: AbstractEstimator, margin_model: LinearMarginModel, margin_function_class=LinearMarginFunction, coef_dict=None):
+ if coef_dict is None:
+ coef_dict = estimator.result_from_model_fit.margin_coef_dict
return margin_function_class.from_coef_dict(coordinates=estimator.dataset.coordinates,
gev_param_name_to_dims=margin_model.margin_function_start_fit.gev_param_name_to_dims,
- coef_dict=estimator.result_from_model_fit.margin_coef_dict,
+ coef_dict=coef_dict,
starting_point=margin_model.starting_point)
diff --git a/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py b/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
index f67a5b85834efd182be0ff62c469a447c4eebfe6..1b96be1dfcd07848f22a24dba38e316ea8c99f7c 100644
--- a/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
+++ b/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
@@ -5,7 +5,7 @@ from extreme_fit.model.margin_model.linear_margin_model.linear_margin_model impo
from extreme_fit.model.result_from_model_fit.abstract_result_from_model_fit import AbstractResultFromModelFit
from extreme_fit.model.result_from_model_fit.result_from_extremes import ResultFromExtremes
from extreme_fit.model.result_from_model_fit.result_from_ismev import ResultFromIsmev
-from extreme_fit.model.utils import r, ro, get_null
+from extreme_fit.model.utils import r, ro, get_null, get_margin_formula_extremes
from extreme_fit.model.utils import safe_run_r_estimator
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
@@ -46,6 +46,7 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
# 9)), iter = 5000, verbose = TRUE, use.phi = FALSE)
r_type_argument_kwargs = {'use.phi': False}
+ r_type_argument_kwargs.update(get_margin_formula_extremes(self.margin_function_start_fit.form_dict))
# location.fun = ~1,
# scale.fun = ~1, shape.fun = ~1
# , priorParams = list(q=c(6), p=c(9))
diff --git a/extreme_fit/model/margin_model/margin_function/abstract_margin_function.py b/extreme_fit/model/margin_model/margin_function/abstract_margin_function.py
index 9dd671bc0be22c3c22b07586e1f68084b5df450c..fb10aa4786ed0abbde175bf83ac9b9fbbedfb066 100644
--- a/extreme_fit/model/margin_model/margin_function/abstract_margin_function.py
+++ b/extreme_fit/model/margin_model/margin_function/abstract_margin_function.py
@@ -9,7 +9,7 @@ from extreme_fit.distribution.gev.gev_params import GevParams
from experiment.meteo_france_data.plot.create_shifted_cmap import imshow_shifted
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
from spatio_temporal_dataset.slicer.split import Split
-from utils import cached_property
+from root_utils import cached_property
class AbstractMarginFunction(object):
diff --git a/extreme_fit/model/margin_model/parametric_margin_model.py b/extreme_fit/model/margin_model/parametric_margin_model.py
index 9a762b1440ab16f554046231b19fbc4023c7d483..35ae8fa5e94bc417e5e05db4eb5abec43882b338 100644
--- a/extreme_fit/model/margin_model/parametric_margin_model.py
+++ b/extreme_fit/model/margin_model/parametric_margin_model.py
@@ -8,7 +8,7 @@ from extreme_fit.model.margin_model.margin_function.parametric_margin_function i
from extreme_fit.model.result_from_model_fit.result_from_spatial_extreme import ResultFromSpatialExtreme
from extreme_fit.model.margin_model.abstract_margin_model import AbstractMarginModel
from extreme_fit.model.utils import safe_run_r_estimator, r, get_coord, \
- get_margin_formula
+ get_margin_formula_spatial_extreme
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
@@ -29,7 +29,7 @@ class ParametricMarginModel(AbstractMarginModel, ABC):
assert data.shape[1] == len(df_coordinates_spat)
# Margin formula for fitspatgev
- fit_params = get_margin_formula(self.margin_function_start_fit.form_dict)
+ fit_params = get_margin_formula_spatial_extreme(self.margin_function_start_fit.form_dict)
# Covariables
covariables = get_coord(df_coordinates=df_coordinates_spat)
diff --git a/extreme_fit/model/max_stable_model/abstract_max_stable_model.py b/extreme_fit/model/max_stable_model/abstract_max_stable_model.py
index b2daca59a6ef060719935c27e78091c2b41b9038..3d6a4f61412a6cd5f6c2e234cec1880a723f16f0 100644
--- a/extreme_fit/model/max_stable_model/abstract_max_stable_model.py
+++ b/extreme_fit/model/max_stable_model/abstract_max_stable_model.py
@@ -8,7 +8,7 @@ from rpy2.rinterface._rinterface import RRuntimeError
from extreme_fit.model.abstract_model import AbstractModel
from extreme_fit.model.result_from_model_fit.result_from_spatial_extreme import ResultFromSpatialExtreme
from extreme_fit.model.utils import r, safe_run_r_estimator, get_coord, \
- get_margin_formula, SafeRunException
+ get_margin_formula_spatial_extreme, SafeRunException
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
@@ -54,7 +54,7 @@ class AbstractMaxStableModel(AbstractModel):
start_dict = self.remove_unused_parameters(start_dict, fitmaxstab_with_one_dimensional_data)
if fit_marge:
start_dict.update(margin_start_dict)
- margin_formulas = get_margin_formula(fit_marge_form_dict)
+ margin_formulas = get_margin_formula_spatial_extreme(fit_marge_form_dict)
fit_params.update(margin_formulas)
if fitmaxstab_with_one_dimensional_data:
fit_params['iso'] = True
diff --git a/extreme_fit/model/result_from_model_fit/result_from_extremes.py b/extreme_fit/model/result_from_model_fit/result_from_extremes.py
index 7bb02b5781e0a68b40be1be188d512ca130cd5ef..162fe62861cd2c31f2c1f36f8c372b3514601089 100644
--- a/extreme_fit/model/result_from_model_fit/result_from_extremes.py
+++ b/extreme_fit/model/result_from_model_fit/result_from_extremes.py
@@ -1,17 +1,54 @@
+import numpy as np
+import pandas as pd
from rpy2 import robjects
+from extreme_fit.distribution.gev.gev_params import GevParams
from extreme_fit.model.result_from_model_fit.abstract_result_from_model_fit import \
AbstractResultFromModelFit
+from extreme_fit.model.result_from_model_fit.utils import get_margin_coef_dict
class ResultFromExtremes(AbstractResultFromModelFit):
- def __init__(self, result_from_fit: robjects.ListVector, gev_param_name_to_dim=None) -> None:
+ def __init__(self, result_from_fit: robjects.ListVector, gev_param_name_to_dim=None,
+ burn_in_percentage=0.1) -> None:
super().__init__(result_from_fit)
+ self.burn_in_percentage = burn_in_percentage
self.gev_param_name_to_dim = gev_param_name_to_dim
- print(list(self.name_to_value.keys()))
- # @property
+ @property
+ def results(self):
+ return np.array(self.name_to_value['results'])
+
+ @property
+ def chain_info(self):
+ return np.array(self.name_to_value['chain.info'])
+
+ @property
+ def df_posterior_samples(self) -> pd.DataFrame:
+ d = dict(zip(GevParams.PARAM_NAMES, self.results.transpose()))
+ d['loglik'] = self.chain_info[:, -2]
+ d['prior'] = self.chain_info[:, -1]
+ df_all_samples = pd.DataFrame(d)
+ # Remove the burn in period
+ burn_in_last_index = int(self.burn_in_percentage * len(df_all_samples))
+ df_posterior_samples = df_all_samples.iloc[burn_in_last_index:, :]
+ return df_posterior_samples
+
+ def get_coef_dict_from_posterior_sample(self, s: pd.Series):
+ assert len(s) >= 3
+ values = {i: v for i, v in enumerate(s)}
+ return get_margin_coef_dict(self.gev_param_name_to_dim, values)
+
+ @property
+ def margin_coef_dict(self):
+ """ It is the coef for the margin function corresponding to the mean posterior parameters """
+ mean_posterior_parameters = self.df_posterior_samples.iloc[:, :-2].mean(axis=0)
+ return self.get_coef_dict_from_posterior_sample(mean_posterior_parameters)
+
+
+
+# @property
# def
# @property
@@ -49,4 +86,3 @@ class ResultFromExtremes(AbstractResultFromModelFit):
# @property
# def convergence(self) -> str:
# return convertFloatVector_to_float(self.name_to_value['conv']) == 0
-
diff --git a/extreme_fit/model/result_from_model_fit/result_from_ismev.py b/extreme_fit/model/result_from_model_fit/result_from_ismev.py
index e3691fc26ac6b0965be02ab4d64b7e63aca06c92..d83dacecbc9b1610fd35e04092feecc3ceade29e 100644
--- a/extreme_fit/model/result_from_model_fit/result_from_ismev.py
+++ b/extreme_fit/model/result_from_model_fit/result_from_ismev.py
@@ -1,14 +1,13 @@
-from typing import Dict
+from rpy2 import robjects
-import numpy as np
+from extreme_fit.model.result_from_model_fit.abstract_result_from_model_fit import \
+ AbstractResultFromModelFit
+from extreme_fit.model.result_from_model_fit.utils import convertFloatVector_to_float, get_margin_coef_dict
from rpy2 import robjects
-from extreme_fit.model.margin_model.param_function.linear_coef import LinearCoef
from extreme_fit.model.result_from_model_fit.abstract_result_from_model_fit import \
AbstractResultFromModelFit
from extreme_fit.model.result_from_model_fit.utils import convertFloatVector_to_float
-from extreme_fit.distribution.gev.gev_params import GevParams
-from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
class ResultFromIsmev(AbstractResultFromModelFit):
@@ -19,23 +18,7 @@ class ResultFromIsmev(AbstractResultFromModelFit):
@property
def margin_coef_dict(self):
- assert self.gev_param_name_to_dim is not None
- # Build the Coeff dict from gev_param_name_to_dim
- coef_dict = {}
- i = 0
- mle_values = self.name_to_value['mle']
- for gev_param_name in GevParams.PARAM_NAMES:
- # Add intercept
- intercept_coef_name = LinearCoef.coef_template_str(gev_param_name, LinearCoef.INTERCEPT_NAME).format(1)
- coef_dict[intercept_coef_name] = mle_values[i]
- i += 1
- # Add a potential linear temporal trend
- if gev_param_name in self.gev_param_name_to_dim:
- temporal_coef_name = LinearCoef.coef_template_str(gev_param_name,
- AbstractCoordinates.COORDINATE_T).format(1)
- coef_dict[temporal_coef_name] = mle_values[i]
- i += 1
- return coef_dict
+ return get_margin_coef_dict(self.gev_param_name_to_dim, self.name_to_value['mle'])
@property
def all_parameters(self):
diff --git a/extreme_fit/model/result_from_model_fit/utils.py b/extreme_fit/model/result_from_model_fit/utils.py
index ebd20e933d72386e9f91564710cffbe02f379a78..6d559b4f79abc69925565f3f5cb54a2a02dd4941 100644
--- a/extreme_fit/model/result_from_model_fit/utils.py
+++ b/extreme_fit/model/result_from_model_fit/utils.py
@@ -1,5 +1,28 @@
import numpy as np
+from extreme_fit.distribution.gev.gev_params import GevParams
+from extreme_fit.model.margin_model.param_function.linear_coef import LinearCoef
+from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
+
def convertFloatVector_to_float(f):
return np.array(f)[0]
+
+
+def get_margin_coef_dict(gev_param_name_to_dim, mle_values):
+ assert gev_param_name_to_dim is not None
+ # Build the Coeff dict from gev_param_name_to_dim
+ coef_dict = {}
+ i = 0
+ for gev_param_name in GevParams.PARAM_NAMES:
+ # Add intercept
+ intercept_coef_name = LinearCoef.coef_template_str(gev_param_name, LinearCoef.INTERCEPT_NAME).format(1)
+ coef_dict[intercept_coef_name] = mle_values[i]
+ i += 1
+ # Add a potential linear temporal trend
+ if gev_param_name in gev_param_name_to_dim:
+ temporal_coef_name = LinearCoef.coef_template_str(gev_param_name,
+ AbstractCoordinates.COORDINATE_T).format(1)
+ coef_dict[temporal_coef_name] = mle_values[i]
+ i += 1
+ return coef_dict
diff --git a/extreme_fit/model/utils.py b/extreme_fit/model/utils.py
index f7ffaf044abb6f61203661362cf300037ea1c9ca..aec823bf7131b2462c4c7bac8ca4e41e87b4e5e3 100644
--- a/extreme_fit/model/utils.py
+++ b/extreme_fit/model/utils.py
@@ -1,26 +1,23 @@
import io
import os.path as op
+import random
import warnings
from contextlib import redirect_stdout
+from typing import Dict
import numpy as np
-import random
-import sys
-from types import TracebackType
-from typing import Dict, Optional
-
import pandas as pd
import rpy2.robjects as ro
from rpy2 import robjects
from rpy2.rinterface import RRuntimeWarning
from rpy2.rinterface._rinterface import RRuntimeError
-
from rpy2.robjects import numpy2ri
from rpy2.robjects import pandas2ri
-from utils import get_root_path
# Load R variables
+from root_utils import get_root_path
+
r = ro.R()
numpy2ri.activate()
pandas2ri.activate()
@@ -128,10 +125,27 @@ def get_null():
return as_null(1.0)
-def get_margin_formula(fit_marge_form_dict) -> Dict:
+# todo: move that to the result class maybe
+def get_margin_formula_spatial_extreme(fit_marge_form_dict) -> Dict:
margin_formula = {k: robjects.Formula(v) if v != 'NULL' else get_null() for k, v in fit_marge_form_dict.items()}
return margin_formula
+
+def old_coef_name_to_new_coef_name():
+ return {
+ 'temp.form.loc': 'location.fun',
+ 'temp.form.scale': 'scale.fun',
+ 'temp.form.shape': 'shape.fun',
+ }
+
+
+def get_margin_formula_extremes(fit_marge_form_dict) -> Dict:
+ d = old_coef_name_to_new_coef_name()
+ form_dict = {d[k]: v if v != 'NULL' else ' ~1' for k, v in fit_marge_form_dict.items()}
+ return {k: robjects.Formula(v) for k, v in form_dict.items()}
+
+
+
# def conversion_to_FloatVector(data):
# """Convert DataFrame or numpy array into FloatVector for r"""
# if isinstance(data, pd.DataFrame):
diff --git a/utils.py b/root_utils.py
similarity index 100%
rename from utils.py
rename to root_utils.py
diff --git a/spatio_temporal_dataset/coordinates/abstract_coordinates.py b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
index 290bbe31941aedfbd1e79da6fffe6ed81db89ab3..16cc04ad889777c65f04e166f8554f5bc621c66c 100644
--- a/spatio_temporal_dataset/coordinates/abstract_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
@@ -251,7 +251,9 @@ class AbstractCoordinates(object):
# Compute the indices to modify
ind_to_modify = df_temporal_coordinates.iloc[:, 0] <= starting_point # type: pd.Series
# Assert that some coordinates are selected but not all
- assert 0 < sum(ind_to_modify) < len(ind_to_modify), sum(ind_to_modify)
+ msg = '{} First year={} Last_year={}'.format(sum(ind_to_modify), df_temporal_coordinates.iloc[0, 0],
+ df_temporal_coordinates.iloc[-1, 0])
+ assert 0 < sum(ind_to_modify) < len(ind_to_modify), msg
# Modify the temporal coordinates to enforce the stationarity
df_temporal_coordinates.loc[ind_to_modify] = starting_point
# Load the temporal transformation object
diff --git a/spatio_temporal_dataset/coordinates/spatial_coordinates/alps_station_3D_coordinates.py b/spatio_temporal_dataset/coordinates/spatial_coordinates/alps_station_3D_coordinates.py
index 0f1df96dcf5f88878616f2b30a1c2f9258a953d3..8007bfd98a283851e3f062a3dad7f464175ff8d0 100644
--- a/spatio_temporal_dataset/coordinates/spatial_coordinates/alps_station_3D_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/spatial_coordinates/alps_station_3D_coordinates.py
@@ -7,7 +7,7 @@ from spatio_temporal_dataset.coordinates.spatial_coordinates.abstract_spatial_co
from spatio_temporal_dataset.coordinates.transformed_coordinates.transformation.tranformation_3D import \
AnisotropyTransformation
from spatio_temporal_dataset.coordinates.transformed_coordinates.transformed_coordinates import TransformedCoordinates
-from utils import get_full_path
+from root_utils import get_full_path
class AlpsStation3DCoordinates(AbstractSpatialCoordinates):
diff --git a/spatio_temporal_dataset/spatio_temporal_observations/alps_precipitation_observations.py b/spatio_temporal_dataset/spatio_temporal_observations/alps_precipitation_observations.py
index f69ed0b4ab998ac28038eaba9646e3f9e7a4011b..83ead9544aba119a5d350bf4af807c8bfc350aa4 100644
--- a/spatio_temporal_dataset/spatio_temporal_observations/alps_precipitation_observations.py
+++ b/spatio_temporal_dataset/spatio_temporal_observations/alps_precipitation_observations.py
@@ -2,7 +2,7 @@ import os.path as op
from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
AbstractSpatioTemporalObservations
-from utils import get_full_path
+from root_utils import get_full_path
class AlpsPrecipitationObservations(AbstractSpatioTemporalObservations):
diff --git a/test/test_experiment/test_SCM_study.py b/test/test_experiment/test_SCM_study.py
index 8c0e1e675d4ddc71c575405f027948bcd9af022d..7a0aa485325cb3420c07c24b6ccdc57bdbadc31f 100644
--- a/test/test_experiment/test_SCM_study.py
+++ b/test/test_experiment/test_SCM_study.py
@@ -13,7 +13,7 @@ from experiment.meteo_france_data.scm_models_data.visualization.study_visualizat
from experiment.meteo_france_data.scm_models_data.visualization.study_visualization.study_visualizer import \
StudyVisualizer
from experiment.trend_analysis.univariate_test.gev_trend_test_one_parameter import GevLocationTrendTest
-from utils import get_display_name_from_object_type
+from root_utils import get_display_name_from_object_type
class TestSCMAllStudy(unittest.TestCase):
diff --git a/test/test_experiment/test_coordinate_sensitivity.py b/test/test_experiment/test_coordinate_sensitivity.py
index 081d5ce00fc46ec9a49152c2ce79237f5b9b1452..e709029abfec4075542c843475e1eb6a15a32b42 100644
--- a/test/test_experiment/test_coordinate_sensitivity.py
+++ b/test/test_experiment/test_coordinate_sensitivity.py
@@ -9,7 +9,7 @@ from experiment.trend_analysis.non_stationary_trends import \
ConditionalIndedendenceLocationTrendTest
from spatio_temporal_dataset.coordinates.transformed_coordinates.transformation.uniform_normalization import \
BetweenZeroAndOneNormalization, BetweenZeroAndOneNormalizationMinEpsilon, BetweenZeroAndOneNormalizationMaxEpsilon
-from utils import get_display_name_from_object_type
+from root_utils import get_display_name_from_object_type
class TestCoordinateSensitivity(unittest.TestCase):
diff --git a/test/test_experiment/test_region_eurocode.py b/test/test_experiment/test_region_eurocode.py
index 97e7d3df8bce498c23dc33a8ca8b7ddc0cd9b61e..4aa64fb10b1e94ffa731f5ffe1d30e7b300bfe34 100644
--- a/test/test_experiment/test_region_eurocode.py
+++ b/test/test_experiment/test_region_eurocode.py
@@ -1,21 +1,24 @@
import unittest
from collections import OrderedDict
-from experiment.eurocode_data.eurocode_visualizer import display_region_limit
-from experiment.eurocode_data.eurocode_region import C1, E, C2
+from experiment.eurocode_data.eurocode_return_level_uncertainties import EurocodeLevelUncertaintyFromExtremes
+from experiment.eurocode_data.eurocode_visualizer import plot_model_name_to_dep_to_ordered_return_level_uncertainties
+from experiment.eurocode_data.massif_name_to_departement import DEPARTEMENT_TYPES
+from experiment.eurocode_data.utils import EUROCODE_ALTITUDES
class TestCoordinateSensitivity(unittest.TestCase):
DISPLAY = False
- def test_region_eurocode(self):
- altitudes = [900, 1200, 1500, 1800]
- ordered_massif_name_to_quantiles = OrderedDict()
- ordered_massif_name_to_quantiles['Vanoise'] = [1.2, 1.5, 1.7, 2.1]
- ordered_massif_name_to_quantiles['Vercors'] = [0.7, 0.8, 1.1, 1.5]
- display_region_limit(C1, altitudes, ordered_massif_name_to_quantiles, display=self.DISPLAY)
- display_region_limit(C2, altitudes, ordered_massif_name_to_quantiles, display=self.DISPLAY)
- display_region_limit(E, altitudes, ordered_massif_name_to_quantiles, display=self.DISPLAY)
+ def test_departement_eurocode_plot(self):
+ # Create an example
+ example = EurocodeLevelUncertaintyFromExtremes(posterior_mean=1.0,
+ poster_uncertainty_interval=(0.5, 1.25))
+ dep_to_model_name_toreturn_level_uncertainty = {
+ dep: {"example": [example for _ in EUROCODE_ALTITUDES]} for dep in DEPARTEMENT_TYPES
+ }
+ plot_model_name_to_dep_to_ordered_return_level_uncertainties(dep_to_model_name_toreturn_level_uncertainty,
+ show=self.DISPLAY)
if __name__ == '__main__':
diff --git a/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal_bayesian.py b/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal_bayesian.py
index 5f5ba22c95cc359aa037976d793eca6c4da5e802..2b9aad9aef6871dc19c5de00fc6746293c3427ca 100644
--- a/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal_bayesian.py
+++ b/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal_bayesian.py
@@ -3,11 +3,14 @@ import unittest
import numpy as np
import pandas as pd
+from experiment.trend_analysis.univariate_test.abstract_gev_trend_test import fitted_linear_margin_estimator
+from extreme_fit.distribution.gev.gev_params import GevParams
from extreme_fit.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
AbstractTemporalLinearMarginModel
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryStationModel, \
NonStationaryLocationStationModel
+from extreme_fit.model.result_from_model_fit.result_from_extremes import ResultFromExtremes
from extreme_fit.model.utils import r, set_seed_r
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_coordinates import \
@@ -18,74 +21,50 @@ from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_tempor
from test.test_utils import load_non_stationary_temporal_margin_models
-# class TestGevTemporalBayesian(unittest.TestCase):
-#
-# def setUp(self) -> None:
-# set_seed_r()
-# r("""
-# N <- 50
-# loc = 0; scale = 1; shape <- 1
-# x_gev <- rgev(N, loc = loc, scale = scale, shape = shape)
-# start_loc = 0; start_scale = 1; start_shape = 1
-# """)
-# # Compute the stationary temporal margin with isMev
-# self.start_year = 0
-# df = pd.DataFrame({AbstractCoordinates.COORDINATE_T: range(self.start_year, self.start_year + 50)})
-# self.coordinates = AbstractTemporalCoordinates.from_df(df)
-# df2 = pd.DataFrame(data=np.array(r['x_gev']), index=df.index)
-# observations = AbstractSpatioTemporalObservations(df_maxima_gev=df2)
-# self.dataset = AbstractDataset(observations=observations, coordinates=self.coordinates)
-# self.fit_method = AbstractTemporalLinearMarginModel.EXTREMES_FEVD_BAYESIAN_FIT_METHOD_STR
-#
-# def test_gev_temporal_margin_fit_stationary(self):
-# # Create estimator
-# margin_model = StationaryStationModel(self.coordinates, fit_method=self.fit_method)
-# estimator = LinearMarginEstimator(self.dataset, margin_model)
-# estimator.fit()
-# ref = {'loc': 0.0219, 'scale': 1.0347, 'shape': 0.8295}
-# for year in range(1, 3):
-# mle_params_estimated = estimator.margin_function_fitted.get_gev_params(np.array([year])).to_dict()
-# for key in ref.keys():
-# self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
+class TestGevTemporalBayesian(unittest.TestCase):
- # def test_gev_temporal_margin_fit_nonstationary(self):
- # # Create estimator
- # margin_models = load_non_stationary_temporal_margin_models(self.coordinates)
- # for margin_model in margin_models:
- # # margin_model = NonStationaryLocationStationModel(self.coordinates)
- # estimator = LinearMarginEstimator(self.dataset, margin_model)
- # estimator.fit()
- # # Checks that parameters returned are indeed different
- # mle_params_estimated_year1 = estimator.margin_function_fitted.get_gev_params(np.array([1])).to_dict()
- # mle_params_estimated_year3 = estimator.margin_function_fitted.get_gev_params(np.array([3])).to_dict()
- # self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
- #
- # def test_gev_temporal_margin_fit_nonstationary_with_start_point(self):
+ def setUp(self) -> None:
+ set_seed_r()
+ r("""
+ N <- 50
+ loc = 0; scale = 1; shape <- 1
+ x_gev <- rgev(N, loc = loc, scale = scale, shape = shape)
+ start_loc = 0; start_scale = 1; start_shape = 1
+ """)
+ # Compute the stationary temporal margin with isMev
+ self.start_year = 0
+ df = pd.DataFrame({AbstractCoordinates.COORDINATE_T: range(self.start_year, self.start_year + 50)})
+ self.coordinates = AbstractTemporalCoordinates.from_df(df)
+ df2 = pd.DataFrame(data=np.array(r['x_gev']), index=df.index)
+ observations = AbstractSpatioTemporalObservations(df_maxima_gev=df2)
+ self.dataset = AbstractDataset(observations=observations, coordinates=self.coordinates)
+ self.fit_method = AbstractTemporalLinearMarginModel.EXTREMES_FEVD_BAYESIAN_FIT_METHOD_STR
+
+ def test_gev_temporal_margin_fit_stationary(self):
+ # Create estimator
+ estimator_fitted = fitted_linear_margin_estimator(StationaryStationModel, self.coordinates, self.dataset,
+ starting_year=0,
+ fit_method=AbstractTemporalLinearMarginModel.EXTREMES_FEVD_BAYESIAN_FIT_METHOD_STR)
+ ref = {'loc': 0.082261, 'scale': 1.183703, 'shape': 0.882750}
+ for year in range(1, 3):
+ mle_params_estimated = estimator_fitted.margin_function_from_fit.get_gev_params(np.array([year])).to_dict()
+ for key in ref.keys():
+ self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
+
+ # def test_gev_temporal_margin_fit_non_stationary(self):
# # Create estimator
- # estimator = self.fit_non_stationary_estimator(starting_point=3)
- # self.assertNotEqual(estimator.margin_function_fitted.mu1_temporal_trend, 0.0)
- # # Checks starting point parameter are well passed
- # self.assertEqual(3, estimator.margin_function_fitted.starting_point)
- # # Checks that parameters returned are indeed different
- # mle_params_estimated_year1 = estimator.margin_function_fitted.get_gev_params(np.array([1])).to_dict()
- # mle_params_estimated_year3 = estimator.margin_function_fitted.get_gev_params(np.array([3])).to_dict()
- # self.assertEqual(mle_params_estimated_year1, mle_params_estimated_year3)
- # mle_params_estimated_year5 = estimator.margin_function_fitted.get_gev_params(np.array([5])).to_dict()
- # self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year3)
- #
- # def fit_non_stationary_estimator(self, starting_point):
- # margin_model = NonStationaryLocationStationModel(self.coordinates, starting_point=starting_point + self.start_year)
- # estimator = LinearMarginEstimator(self.dataset, margin_model)
- # estimator.fit()
- # return estimator
- #
- # def test_two_different_starting_points(self):
- # # Create two different estimators
- # estimator1 = self.fit_non_stationary_estimator(starting_point=3)
- # estimator2 = self.fit_non_stationary_estimator(starting_point=28)
- # mu1_estimator1 = estimator1.margin_function_fitted.mu1_temporal_trend
- # mu1_estimator2 = estimator2.margin_function_fitted.mu1_temporal_trend
- # self.assertNotEqual(mu1_estimator1, mu1_estimator2)
+ # estimator_fitted = fitted_linear_margin_estimator(NonStationaryLocationStationModel, self.coordinates, self.dataset,
+ # starting_year=0,
+ # fit_method=AbstractTemporalLinearMarginModel.EXTREMES_FEVD_BAYESIAN_FIT_METHOD_STR)
+ # result_from_model_fit = estimator_fitted.result_from_model_fit # type: ResultFromExtremes
+ # print(result_from_model_fit.posterior_samples)
+
+ # print(estimator.result_from_model_fit.r)
+ # ref = {'loc': 0.0219, 'scale': 1.0347, 'shape': 0.8295}
+ # for year in range(1, 3):
+ # mle_params_estimated = estimator.margin_function_from_fit.get_gev_params(np.array([year])).to_dict()
+ # for key in ref.keys():
+ # self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
if __name__ == '__main__':