diff --git a/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py b/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py
index 12e0169101c0ee91e94e4b1af3d5375c7d219ad5..93f7a7f4fae2dfc97e8ff7f7eb28975b001c0c0c 100644
--- a/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py
+++ b/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py
@@ -75,6 +75,19 @@ class LinearMarginEstimator(AbstractMarginEstimator):
assert not np.isinf(nllh)
return nllh
+ def sorted_empirical_standard_gumbel_quantiles(self, split=Split.all):
+ sorted_empirical_quantiles = []
+ maxima_values = self.dataset.maxima_gev(split=split)
+ coordinate_values = self.dataset.df_coordinates(split=split).values
+ for maximum, coordinate in zip(maxima_values, coordinate_values):
+ gev_param = self.function_from_fit.get_params(
+ coordinate=coordinate,
+ is_transformed=False)
+ maximum_standardized = gev_param.gumbel_standardization(maximum[0])
+ sorted_empirical_quantiles.append(maximum_standardized)
+ sorted_empirical_quantiles = sorted(sorted_empirical_quantiles)
+ return sorted_empirical_quantiles
+
def deviance(self, split=Split.all):
return 2 * self.nllh(split=split)
diff --git a/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py b/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py
index a0573a7d2e26b3ce01eb5a2041c2a90c901194d0..9c47c027a83dfbf7d3a3458d81f7ba69717ca66c 100644
--- a/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py
+++ b/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py
@@ -3,26 +3,19 @@ from typing import List
import matplotlib as mpl
-from projects.altitude_spatial_model.altitudes_fit.plot_histogram_altitude_studies import \
- plot_histogram_all_trends_against_altitudes, plot_histogram_all_models_against_altitudes
+from projects.altitude_spatial_model.altitudes_fit.plots.plot_coherence_curves import plot_coherence_curves
mpl.rcParams['text.usetex'] = True
mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}']
from projects.altitude_spatial_model.altitudes_fit.utils_altitude_studies_visualizer import load_visualizer_list
-from extreme_fit.model.margin_model.polynomial_margin_model.utils import \
- ALTITUDINAL_GEV_MODELS_BASED_ON_POINTWISE_ANALYSIS
from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.altitude_group import altitudes_for_groups
from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.plot_total_aic import plot_individual_aic
from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day, SafranSnowfall3Days, \
- SafranSnowfall5Days, SafranSnowfall7Days, SafranPrecipitation1Day, SafranPrecipitation3Days, \
- SafranPrecipitation5Days, SafranPrecipitation7Days
+ SafranSnowfall5Days, SafranSnowfall7Days
from extreme_data.meteo_france_data.scm_models_data.utils import Season
-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 main():
@@ -59,13 +52,15 @@ def main_loop(altitudes_list, massif_names, seasons, study_classes):
def plot_visualizers(massif_names, visualizer_list):
- plot_histogram_all_trends_against_altitudes(massif_names, visualizer_list)
- plot_histogram_all_models_against_altitudes(massif_names, visualizer_list)
+ # plot_histogram_all_trends_against_altitudes(massif_names, visualizer_list)
+ # plot_histogram_all_models_against_altitudes(massif_names, visualizer_list)
+ # plot_coherence_curves(massif_names, visualizer_list)
+ pass
def plot_visualizer(massif_names, visualizer):
# Plot time series
- visualizer.studies.plot_maxima_time_series(massif_names=massif_names)
+ # visualizer.studies.plot_maxima_time_series(massif_names=massif_names)
# Plot moments against altitude
# for std in [True, False][:]:
# for change in [True, False, None]:
diff --git a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py
index 74ad0ebbe4163ebc33a9821925f7b3e3f38a9e46..f5d7c97ab0bd230f215be7bc71d95fb8b72bfb25 100644
--- a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py
+++ b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py
@@ -1,4 +1,5 @@
from collections import Counter
+from math import ceil, floor
from typing import List, Dict
import matplotlib
@@ -49,11 +50,14 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
self.massif_name_to_massif_id = {m: i for i, m in enumerate(self.massif_names)}
self.altitude_group = get_altitude_group_from_altitudes(self.studies.altitudes)
# Load one fold fit
+ self.massif_name_to_massif_altitudes = {}
self._massif_name_to_one_fold_fit = {}
for massif_name in self.massif_names:
# Load valid massif altitudes
massif_altitudes = self.get_massif_altitudes(massif_name)
if self.load_condition(massif_altitudes):
+ # Save the massif altitudes only for those who pass the condition
+ self.massif_name_to_massif_altitudes[massif_name] = massif_altitudes
# Load dataset
dataset = studies.spatio_temporal_dataset(massif_name=massif_name, massif_altitudes=massif_altitudes)
old_fold_fit = OneFoldFit(massif_name, dataset, model_classes, self.fit_method,
@@ -79,8 +83,8 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
percentage_of_non_zeros = 100 * np.count_nonzero(annual_maxima) / len(annual_maxima)
if percentage_of_non_zeros > 90:
massif_altitudes.append(altitude)
- else:
- print(massif_name, altitude, percentage_of_non_zeros)
+ # else:
+ # print(massif_name, altitude, percentage_of_non_zeros)
return massif_altitudes
def load_condition(self, massif_altitudes):
@@ -435,3 +439,34 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
def model_names(self):
massif_name = list(self.massif_name_to_one_fold_fit.keys())[0]
return self.massif_name_to_one_fold_fit[massif_name].model_names
+
+ def plot_qqplots(self):
+ for massif_name, one_fold_fit in self.massif_name_to_one_fold_fit.items():
+ ax = plt.gca()
+ standard_gumbel_quantiles = one_fold_fit.standard_gumbel_quantiles()
+ unconstrained_empirical_quantiles = one_fold_fit.best_estimator.sorted_empirical_standard_gumbel_quantiles()
+ all_quantiles = standard_gumbel_quantiles + unconstrained_empirical_quantiles
+ epsilon = 0.1
+ ax_lim = [min(all_quantiles) - epsilon, max(all_quantiles) + epsilon]
+
+ model_name = self.massif_name_to_best_name[massif_name]
+ altitudes = self.massif_name_to_massif_altitudes[massif_name]
+ massif_name_corrected = massif_name.replace('_', ' ')
+ label = '{} for altitudes {}'.format(massif_name_corrected, ' & '.join([str(a) + 'm' for a in altitudes]))
+ ax.plot(standard_gumbel_quantiles, unconstrained_empirical_quantiles, linestyle='None',
+ label=label + '\n(selected model is ${}$)'.format(model_name), marker='o')
+
+ size_label = 20
+ ax.set_xlabel("Theoretical quantile", fontsize=size_label)
+ ax.set_ylabel("Empirical quantile", fontsize=size_label)
+ ax.set_xlim(ax_lim)
+ ax.set_ylim(ax_lim)
+
+ ax.plot(ax_lim, ax_lim, color='k')
+ ticks = [i for i in range(ceil(ax_lim[0]), floor(ax_lim[1]) + 1)]
+ ax.set_xticks(ticks)
+ ax.set_yticks(ticks)
+ ax.tick_params(labelsize=15)
+ plot_name = 'qqplot/{}'.format(massif_name_corrected)
+ self.studies.show_or_save_to_file(plot_name=plot_name, show=self.show)
+ plt.close()
diff --git a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py
index 59daa9b9963daed6939fae01f549eaa6a2dc2f0a..a84e1258f30e17a2fca302ff2e76e8ee3d598bd4 100644
--- a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py
+++ b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py
@@ -4,6 +4,7 @@ import rpy2
from cached_property import cached_property
from scipy.stats import chi2
+from extreme_fit.distribution.gev.gev_params import GevParams
from extreme_fit.distribution.gumbel.gumbel_gof import goodness_of_fit_anderson
from extreme_fit.estimator.margin_estimator.abstract_margin_estimator import AbstractMarginEstimator, \
LinearMarginEstimator
@@ -128,7 +129,9 @@ class OneFoldFit(object):
def sorted_estimators_with_stationary(self):
if self.only_models_that_pass_anderson_test:
return [e for e in self.sorted_estimators
- if self.goodness_of_fit_test(e) and self.sensitivity_of_fit_test(e)]
+ if self.goodness_of_fit_test(e)
+ and self.sensitivity_of_fit_test_top_maxima(e)
+ and self.sensitivity_of_fit_test_last_years(e)]
else:
return self._sorted_estimators_without_stationary
@@ -256,7 +259,7 @@ class OneFoldFit(object):
# self._folder_to_goodness[self.folder_for_plots] = goodness_of_fit_anderson_test
# return goodness_of_fit_anderson_test
- def sensitivity_of_fit_test(self, estimator: LinearMarginEstimator):
+ def sensitivity_of_fit_test_top_maxima(self, estimator: LinearMarginEstimator):
# Build the dataset without the maxima for each altitude
new_dataset = AbstractDataset.remove_top_maxima(self.dataset.observations,
self.dataset.coordinates)
@@ -266,10 +269,27 @@ class OneFoldFit(object):
fit_method=self.fit_method,
temporal_covariate_for_fit=self.temporal_covariate_for_fit)
# Compare sign of change
- print(self.massif_name, self.sign_of_change(estimator), self.sign_of_change(new_estimator))
has_not_opposite_sign = self.sign_of_change(estimator) * self.sign_of_change(new_estimator) >= 0
return has_not_opposite_sign
+
+ def sensitivity_of_fit_test_last_years(self, estimator: LinearMarginEstimator):
+ # Build the dataset without the maxima for each altitude
+ new_dataset = AbstractDataset.remove_last_maxima(self.dataset.observations,
+ self.dataset.coordinates,
+ nb_years=10)
+ # Fit the new estimator
+ model_class = type(estimator.margin_model)
+ new_estimator = fitted_linear_margin_estimator_short(model_class=model_class,
+ dataset=new_dataset,
+ fit_method=self.fit_method,
+ temporal_covariate_for_fit=self.temporal_covariate_for_fit)
+ # Compare sign of change
+ has_not_opposite_sign = self.sign_of_change(estimator) * self.sign_of_change(new_estimator) >= 0
+ # if not has_not_opposite_sign:
+ # print('Last years', self.massif_name, model_class, self.sign_of_change(estimator), self.sign_of_change(new_estimator))
+ return has_not_opposite_sign
+
def sign_of_change(self, estimator):
return_levels = []
for year in [2019 - 50, 2019]:
@@ -281,25 +301,19 @@ class OneFoldFit(object):
return 100 * (return_levels[1] - return_levels[0]) / return_levels[0]
def goodness_of_fit_test(self, estimator):
- quantiles = self.compute_empirical_quantiles(estimator=estimator)
+ quantiles = estimator.sorted_empirical_standard_gumbel_quantiles()
goodness_of_fit_anderson_test = goodness_of_fit_anderson(quantiles, self.SIGNIFICANCE_LEVEL)
return goodness_of_fit_anderson_test
- def compute_empirical_quantiles(self, estimator):
- empirical_quantiles = []
- df_maxima_gev = self.dataset.observations.df_maxima_gev
- df_coordinates = self.dataset.coordinates.df_coordinates()
- for coordinate, maximum in zip(df_coordinates.values.copy(), df_maxima_gev.values.copy()):
- gev_param = estimator.function_from_fit.get_params(
- coordinate=coordinate,
- is_transformed=False)
- assert len(maximum) == 1
- maximum_standardized = gev_param.gumbel_standardization(maximum[0])
- empirical_quantiles.append(maximum_standardized)
- empirical_quantiles = sorted(empirical_quantiles)
- return empirical_quantiles
+ def standard_gumbel_quantiles(self):
+ standard_gumbel_distribution = GevParams(loc=0, scale=1, shape=0)
+ n = len(self.dataset.coordinates)
+ standard_gumbel_quantiles = [standard_gumbel_distribution.quantile(i / (n + 1)) for i in range(1, n + 1)]
+ return standard_gumbel_quantiles
+
# def best_confidence_interval(self):
# EurocodeConfidenceIntervalFromExtremes.from_estimator_extremes(self.best_estimator,
# ci_method=ConfidenceIntervalMethodFromExtremes.ci_mle,
# temporal_covariate=np.array([2019, self.altitude_plot]),)
+
diff --git a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/plot_total_aic.py b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/plot_total_aic.py
index af5bdaefef9cb5f7365520e228bcef27fcbc37e6..92cc046bce1130292765090e655f8b1a61d251d3 100644
--- a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/plot_total_aic.py
+++ b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/plot_total_aic.py
@@ -12,9 +12,12 @@ from projects.exceeding_snow_loads.utils import dpi_paper1_figure
def plots(visualizer):
visualizer.plot_shape_map()
+ # visualizer.plot_moments()
+ visualizer.plot_qqplots()
+
+
# for plot_mean in [True, False]:
# visualizer.plot_year_for_the_peak(plot_mean=plot_mean)
- visualizer.plot_moments()
# visualizer.plot_best_coef_maps()
# visualizer.plot_peak_year_against_altitude()
# visualizer.plot_altitude_switch_against_peak_year()
diff --git a/projects/altitude_spatial_model/altitudes_fit/plots/__init__.py b/projects/altitude_spatial_model/altitudes_fit/plots/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/projects/altitude_spatial_model/altitudes_fit/plots/plot_coherence_curves.py b/projects/altitude_spatial_model/altitudes_fit/plots/plot_coherence_curves.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ac1eb919340c87e3ff747bfd8c5469ad90a968b
--- /dev/null
+++ b/projects/altitude_spatial_model/altitudes_fit/plots/plot_coherence_curves.py
@@ -0,0 +1,47 @@
+from typing import List
+import matplotlib.pyplot as plt
+
+from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.altitudes_studies_visualizer_for_non_stationary_models import \
+ AltitudesStudiesVisualizerForNonStationaryModels
+from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.one_fold_fit import OneFoldFit
+
+
+def plot_coherence_curves(massif_names, visualizer_list: List[
+ AltitudesStudiesVisualizerForNonStationaryModels]):
+ folder = 'Coherence'
+ visualizer = visualizer_list[0]
+ names = visualizer.get_valid_names(massif_names)
+ all_valid_names = set.union(*[v.get_valid_names(massif_names) for v in visualizer_list])
+ for massif_name in all_valid_names:
+ plot_coherence_curve(massif_name, visualizer_list)
+ visualizer.plot_name = '{}/{}'.format(folder, massif_name.replace('_', '-'))
+ visualizer.show_or_save_to_file(add_classic_title=False, no_title=True)
+ plt.close()
+
+
+def plot_coherence_curve(massif_name, visualizer_list: List[AltitudesStudiesVisualizerForNonStationaryModels]):
+ x, gev_params_list = load_all_list(massif_name, visualizer_list)
+ values = [(gev_params.location, gev_params.scale, gev_params.shape, gev_params.return_level(return_period=OneFoldFit.return_period))
+ for gev_params in gev_params_list]
+ values = list(zip(*values))
+ labels = ['loc', 'scale', 'shape', 'return level']
+ _, axes = plt.subplots(2, 2)
+ for label, value, ax in zip(labels, values, axes.flatten()):
+ ax.plot(x, value)
+ ax.set_ylabel(label)
+ ax.set_xlabel('Altitude')
+
+def load_all_list(massif_name, visualizer_list):
+ all_altitudes_list = []
+ all_gev_params_list = []
+ for visualizer in visualizer_list:
+ if massif_name in visualizer.massif_name_to_one_fold_fit:
+ min_altitude, *_, max_altitude = visualizer.massif_name_to_massif_altitudes[massif_name]
+ altitudes_list = list(range(min_altitude, max_altitude, 10))
+ gev_params_list = []
+ for altitude in altitudes_list:
+ gev_params = visualizer.massif_name_to_one_fold_fit[massif_name].get_gev_params(altitude, 2019)
+ gev_params_list.append(gev_params)
+ all_gev_params_list.extend(gev_params_list)
+ all_altitudes_list.extend(altitudes_list)
+ return all_altitudes_list, all_gev_params_list
diff --git a/projects/altitude_spatial_model/altitudes_fit/plot_histogram_altitude_studies.py b/projects/altitude_spatial_model/altitudes_fit/plots/plot_histogram_altitude_studies.py
similarity index 100%
rename from projects/altitude_spatial_model/altitudes_fit/plot_histogram_altitude_studies.py
rename to projects/altitude_spatial_model/altitudes_fit/plots/plot_histogram_altitude_studies.py
diff --git a/spatio_temporal_dataset/dataset/abstract_dataset.py b/spatio_temporal_dataset/dataset/abstract_dataset.py
index e4a213423de21eedf7821aa01a6c8433a1764163..8c319931bf1dad3da623a1365e32a80022eeac2b 100644
--- a/spatio_temporal_dataset/dataset/abstract_dataset.py
+++ b/spatio_temporal_dataset/dataset/abstract_dataset.py
@@ -17,8 +17,9 @@ from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_tempor
class AbstractDataset(object):
- def __init__(self, observations: AbstractSpatioTemporalObservations, coordinates: AbstractCoordinates,):
- assert pd.Index.equals(observations.index, coordinates.index), '\n{}\n{}'.format(observations.index, coordinates.index)
+ def __init__(self, observations: AbstractSpatioTemporalObservations, coordinates: AbstractCoordinates, ):
+ assert pd.Index.equals(observations.index, coordinates.index), '\n{}\n{}'.format(observations.index,
+ coordinates.index)
self.observations = observations # type: AbstractSpatioTemporalObservations
self.coordinates = coordinates # type: AbstractCoordinates
@@ -41,7 +42,7 @@ class AbstractDataset(object):
@classmethod
def remove_top_maxima(cls, observations: AbstractSpatioTemporalObservations,
- coordinates: AbstractSpatioTemporalCoordinates):
+ coordinates: AbstractSpatioTemporalCoordinates):
""" We remove the top maxima w.r.t. each spatial coordinates"""
assert isinstance(coordinates, AbstractSpatioTemporalCoordinates)
idxs_top = []
@@ -52,6 +53,16 @@ class AbstractDataset(object):
ind = ~coordinates.index.isin(idxs_top)
return cls.create_new_dataset(coordinates, ind, observations)
+ @classmethod
+ def remove_last_maxima(cls, observations: AbstractSpatioTemporalObservations,
+ coordinates: AbstractSpatioTemporalCoordinates,
+ nb_years=1):
+ """ We remove the top maxima w.r.t. each spatial coordinates"""
+ assert isinstance(coordinates, AbstractSpatioTemporalCoordinates)
+ years = list(coordinates.temporal_coordinates.coordinates_values()[:, 0])
+ last_years = sorted(years)[-nb_years:]
+ ind = ~coordinates.df_all_coordinates[coordinates.COORDINATE_T].isin(last_years)
+ return cls.create_new_dataset(coordinates, ind, observations)
@property
def df_dataset(self) -> pd.DataFrame:
@@ -111,4 +122,3 @@ class AbstractDataset(object):
def __str__(self) -> str:
return 'coordinates:\n{}\nobservations:\n{}'.format(self.coordinates.__str__(), self.observations.__str__())
-
diff --git a/test/test_spatio_temporal_dataset/test_dataset.py b/test/test_spatio_temporal_dataset/test_dataset.py
index abcfd9145d3b39fc23f94c072ba6596bf1a2cd01..e480e29442d44611c9104f33399218c9af665178 100644
--- a/test/test_spatio_temporal_dataset/test_dataset.py
+++ b/test/test_spatio_temporal_dataset/test_dataset.py
@@ -3,7 +3,8 @@ from itertools import product
import numpy as np
-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
from extreme_fit.model.utils import set_seed_for_test, SafeRunException
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
from spatio_temporal_dataset.coordinates.spatial_coordinates.abstract_spatial_coordinates import \
@@ -36,10 +37,21 @@ class TestDataset(unittest.TestCase):
def test_remove_top_maxima_from_dataset(self):
coordinates, dataset_initial, observations = self.build_initial_dataset()
dataset_without_top = AbstractDataset.remove_top_maxima(observations,
- coordinates)
+ coordinates)
self.assertEqual(4, len(dataset_initial.coordinates), 4)
self.assertEqual(2, len(dataset_without_top.coordinates))
- self.assertEqual(set(dataset_without_top.coordinates.index.values), {0, 3})
+ maxima = list(dataset_without_top.observations.df_maxima_gev.values[:, 0])
+ self.assertEqual(set(maxima), {0})
+
+ def test_remove_last_maxima_from_dataset(self):
+ coordinates, dataset_initial, observations = self.build_initial_dataset()
+ dataset_without_last = AbstractDataset.remove_last_maxima(observations,
+ coordinates,
+ nb_years=1)
+ self.assertEqual(4, len(dataset_initial.coordinates), 4)
+ self.assertEqual(2, len(dataset_without_last.coordinates))
+ maxima = list(dataset_without_last.observations.df_maxima_gev.values[:, 0])
+ self.assertEqual(set(maxima), {0, 2})
def build_initial_dataset(self):
temporal_coordinates = ConsecutiveTemporalCoordinates.from_nb_temporal_steps(nb_temporal_steps=2)
@@ -56,7 +68,6 @@ class TestDataset(unittest.TestCase):
dataset_initial = AbstractDataset(observations, coordinates)
return coordinates, dataset_initial, observations
-
def test_max_stable_dataset_R1_and_R2(self):
max_stable_models = load_test_max_stable_models()[:]
coordinates = load_test_1D_and_2D_spatial_coordinates(self.nb_points)
@@ -79,8 +90,9 @@ class TestDataset(unittest.TestCase):
def test_max_stable_dataset_R3_with_R2_spatial_structure(self):
"""Test to create a dataset in R3, but where the spatial structure is only with respect to the 2 fisrt coordinates"""
coordinates = \
- load_test_3D_spatial_coordinates(nb_points=self.nb_points, transformation_class=BetweenZeroAndOneNormalization)[
- 0]
+ load_test_3D_spatial_coordinates(nb_points=self.nb_points,
+ transformation_class=BetweenZeroAndOneNormalization)[
+ 0]
smith_process = load_test_max_stable_models()[0]
MaxStableDataset.from_sampling(nb_obs=self.nb_obs,
max_stable_model=smith_process,