diff --git a/experiment/meteo_france_SCM_study/visualization/studies_visualization/main_studies_visualizer.py b/experiment/meteo_france_SCM_study/visualization/studies_visualization/main_studies_visualizer.py
index 86e9ec117b35428263b3d6728e23b7b361c1648d..19d45bc5db026bf820bfb630567722c7b5049be6 100644
--- a/experiment/meteo_france_SCM_study/visualization/studies_visualization/main_studies_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/studies_visualization/main_studies_visualizer.py
@@ -1,5 +1,6 @@
from experiment.trend_analysis.abstract_score import MannKendall, WeigthedScore, MeanScore, MedianScore
-from experiment.trend_analysis.abstract_trend_test import MannKendallTrendTest
+from experiment.trend_analysis.univariate_trend_test.abstract_gev_trend_test import GevLocationTrendTest
+from experiment.trend_analysis.univariate_trend_test.abstract_trend_test import MannKendallTrendTest
from experiment.meteo_france_SCM_study.safran.safran import ExtendedSafranTotalPrecip
from experiment.meteo_france_SCM_study.visualization.studies_visualization.studies import Studies
from experiment.meteo_france_SCM_study.visualization.studies_visualization.studies_visualizer import StudiesVisualizer, \
@@ -42,9 +43,9 @@ def altitude_trends_significant():
# altitudes that have 20 massifs at least
altitudes = ALL_ALTITUDES[3:-6]
# altitudes = ALL_ALTITUDES[3:5]
- # altitudes = ALL_ALTITUDES[:2]
+ # altitudes = ALL_ALTITUDES[2:4]
for study_class in SCM_STUDIES[:1]:
- trend_test_classes = [MannKendallTrendTest][:]
+ trend_test_classes = [MannKendallTrendTest, GevLocationTrendTest][1:]
visualizers = [StudyVisualizer(study, temporal_non_stationarity=True, verbose=False)
for study in study_iterator_global(study_classes=[study_class], only_first_one=only_first_one,
altitudes=altitudes)]
diff --git a/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies_visualizer.py b/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies_visualizer.py
index 27a54572948da80fca09afd7fe152cb4ad7c1c1d..8ca0156c8e34f92edc85185e92b4677ea15f5baa 100644
--- a/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies_visualizer.py
@@ -10,7 +10,7 @@ import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from experiment.meteo_france_SCM_study.abstract_extended_study import AbstractExtendedStudy
-from experiment.trend_analysis.abstract_trend_test import AbstractTrendTest
+from experiment.trend_analysis.univariate_trend_test.abstract_trend_test import AbstractTrendTest
from experiment.meteo_france_SCM_study.visualization.studies_visualization.studies import \
Studies
from experiment.meteo_france_SCM_study.visualization.study_visualization.study_visualizer import StudyVisualizer
diff --git a/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py b/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py
index 402e28f03615a8d98df92bd85e08b15c0dda8aa3..4c2ef1809a4377392b3a71eb94033a0b03cf110a 100644
--- a/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py
@@ -10,7 +10,7 @@ import seaborn as sns
from experiment.trend_analysis.abstract_score import MeanScore, AbstractTrendScore
from experiment.meteo_france_SCM_study.abstract_study import AbstractStudy
-from experiment.trend_analysis.abstract_trend_test import AbstractTrendTest
+from experiment.trend_analysis.univariate_trend_test.abstract_trend_test import AbstractTrendTest
from experiment.trend_analysis.non_stationary_trends import \
ConditionalIndedendenceLocationTrendTest, MaxStableLocationTrendTest, IndependenceLocationTrendTest
from experiment.meteo_france_SCM_study.visualization.utils import create_adjusted_axes
@@ -382,14 +382,14 @@ class StudyVisualizer(object):
massif_name_to_trend_test_count = self.massif_name_to_trend_test_count(trend_test_class, starting_year_to_weight)
df = pd.concat(list(massif_name_to_trend_test_count.values()), axis=1, sort=False)
df.fillna(0.0, inplace=True)
- df = df.mean(axis=1)
+ s = df.mean(axis=1)
assert np.allclose(df.sum(), 100)
# Add the significant trend into the count of normal trend
- if AbstractTrendTest.SIGNIFICATIVE_POSITIVE_TREND in df.columns:
- df[AbstractTrendTest.POSITIVE_TREND] += df[AbstractTrendTest.SIGNIFICATIVE_POSITIVE_TREND]
- if AbstractTrendTest.SIGNIFICATIVE_NEGATIVE_TREND in df.columns:
- df[AbstractTrendTest.NEGATIVE_TREND] += df[AbstractTrendTest.SIGNIFICATIVE_NEGATIVE_TREND]
- return df
+ if AbstractTrendTest.SIGNIFICATIVE_POSITIVE_TREND in s.index:
+ s[AbstractTrendTest.POSITIVE_TREND] += s[AbstractTrendTest.SIGNIFICATIVE_POSITIVE_TREND]
+ if AbstractTrendTest.SIGNIFICATIVE_NEGATIVE_TREND in s.index:
+ s[AbstractTrendTest.NEGATIVE_TREND] += s[AbstractTrendTest.SIGNIFICATIVE_NEGATIVE_TREND]
+ return s
@cached_property
def massif_name_to_scores(self):
diff --git a/experiment/trend_analysis/non_stationary_trends.py b/experiment/trend_analysis/non_stationary_trends.py
index ae8de281e0ceea2a5068569886e6183e1a52373c..66913e07dd9fbc9b514cfd8e4569bab837438748 100644
--- a/experiment/trend_analysis/non_stationary_trends.py
+++ b/experiment/trend_analysis/non_stationary_trends.py
@@ -16,7 +16,7 @@ from extreme_estimator.extreme_models.margin_model.linear_margin_model import \
LinearStationaryMarginModel, LinearNonStationaryLocationMarginModel
from extreme_estimator.extreme_models.margin_model.margin_function.linear_margin_function import LinearMarginFunction
from extreme_estimator.extreme_models.margin_model.temporal_linear_margin_model import StationaryStationModel, \
- NonStationaryStationModel
+ NonStationaryLocationStationModel
from extreme_estimator.extreme_models.utils import OptimizationConstants
from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
from utils import get_display_name_from_object_type
@@ -183,7 +183,7 @@ class IndependenceLocationTrendTest(AbstractNonStationaryTrendTest):
super().__init__(*args, **kwargs,
estimator_class=LinearMarginEstimator,
stationary_margin_model_class=StationaryStationModel,
- non_stationary_margin_model_class=NonStationaryStationModel)
+ non_stationary_margin_model_class=NonStationaryLocationStationModel)
self.station_name = station_name
@property
diff --git a/experiment/trend_analysis/univariate_trend_test/__init__.py b/experiment/trend_analysis/univariate_trend_test/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/experiment/trend_analysis/univariate_trend_test/abstract_gev_trend_test.py b/experiment/trend_analysis/univariate_trend_test/abstract_gev_trend_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..77248e32fba18652260fee0496faeb43398c271a
--- /dev/null
+++ b/experiment/trend_analysis/univariate_trend_test/abstract_gev_trend_test.py
@@ -0,0 +1,58 @@
+import pandas as pd
+from scipy.stats import chi2
+import numpy as np
+from sklearn.preprocessing import normalize
+from experiment.trend_analysis.univariate_trend_test.abstract_trend_test import AbstractTrendTest
+from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
+from extreme_estimator.extreme_models.margin_model.temporal_linear_margin_model import StationaryStationModel, \
+ NonStationaryLocationStationModel
+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.coordinates.transformed_coordinates.transformation.uniform_normalization import \
+ BetweenZeroAndOneNormalization
+from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
+from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
+ AbstractSpatioTemporalObservations
+
+
+class AbstractGevTrendTest(AbstractTrendTest):
+
+ def __init__(self, years_after_change_point, maxima_after_change_point, non_stationary_model_class):
+ super().__init__(years_after_change_point, maxima_after_change_point)
+ df = pd.DataFrame({AbstractCoordinates.COORDINATE_T: years_after_change_point})
+ df_maxima_gev = pd.DataFrame(maxima_after_change_point, index=df.index)
+ observations = AbstractSpatioTemporalObservations(df_maxima_gev=df_maxima_gev)
+ self.coordinates = AbstractTemporalCoordinates.from_df(df, transformation_class=BetweenZeroAndOneNormalization)
+ # self.coordinates = AbstractTemporalCoordinates.from_df(df, transformation_class=CenteredScaledNormalization)
+ self.dataset = AbstractDataset(observations=observations, coordinates=self.coordinates)
+
+ # Fit stationary model
+ self.stationary_estimator = LinearMarginEstimator(self.dataset, StationaryStationModel(self.coordinates))
+ self.stationary_estimator.fit()
+
+ # Fit non stationary model
+ self.non_stationary_estimator = LinearMarginEstimator(self.dataset,
+ non_stationary_model_class(self.coordinates))
+ self.non_stationary_estimator.fit()
+
+ @property
+ def likelihood_ratio(self):
+ return 2 * (self.non_stationary_estimator.result_from_fit.deviance -
+ self.stationary_estimator.result_from_fit.deviance)
+
+ @property
+ def is_significant(self) -> bool:
+ return self.likelihood_ratio > chi2.ppf(q=1 - self.SIGNIFICANCE_LEVEL, df=1)
+
+
+class GevLocationTrendTest(AbstractGevTrendTest):
+
+ def __init__(self, years_after_change_point, maxima_after_change_point):
+ super().__init__(years_after_change_point, maxima_after_change_point, NonStationaryLocationStationModel)
+
+ @property
+ def test_sign(self) -> int:
+ return np.sign(self.non_stationary_estimator.margin_function_fitted.mu1_temporal_trend)
diff --git a/experiment/trend_analysis/abstract_trend_test.py b/experiment/trend_analysis/univariate_trend_test/abstract_trend_test.py
similarity index 97%
rename from experiment/trend_analysis/abstract_trend_test.py
rename to experiment/trend_analysis/univariate_trend_test/abstract_trend_test.py
index 7a43a7da513220d6b4ad5a2281c83d03c370023e..a87598b3136d3f66bfc5fdf1bd9cef9b79d5cbd4 100644
--- a/experiment/trend_analysis/abstract_trend_test.py
+++ b/experiment/trend_analysis/univariate_trend_test/abstract_trend_test.py
@@ -99,7 +99,3 @@ class MannKendallTrendTest(AbstractTrendTest):
class SpearmanRhoTrendTest(AbstractTrendTest):
pass
-
-
-class AbstractNonStationaryGevTrendTest(AbstractTrendTest):
- pass
diff --git a/extreme_estimator/extreme_models/margin_model/temporal_linear_margin_model.py b/extreme_estimator/extreme_models/margin_model/temporal_linear_margin_model.py
index 425743336a33ab39dab220a5adcf7e2429fee32f..aee5481c372ce97f46d8f58b38e2294b9d9c5370 100644
--- a/extreme_estimator/extreme_models/margin_model/temporal_linear_margin_model.py
+++ b/extreme_estimator/extreme_models/margin_model/temporal_linear_margin_model.py
@@ -21,6 +21,7 @@ class TemporalLinearMarginModel(LinearMarginModel):
# Modify df_coordinates_temp
df_coordinates_temp = self.add_starting_temporal_point(df_coordinates_temp)
# Gev Fit
+ assert data.shape[1] == len(df_coordinates_temp.values)
res = safe_run_r_estimator(function=r('gev.fit'), use_start=self.use_start_value,
xdat=ro.FloatVector(data[0]), y=df_coordinates_temp.values, mul=self.mul)
return ResultFromIsmev(res, self.margin_function_start_fit.gev_param_name_to_dims)
@@ -40,7 +41,7 @@ class StationaryStationModel(TemporalLinearMarginModel):
return get_null()
-class NonStationaryStationModel(TemporalLinearMarginModel):
+class NonStationaryLocationStationModel(TemporalLinearMarginModel):
def load_margin_functions(self, gev_param_name_to_dims=None):
super().load_margin_functions({GevParams.LOC: [self.coordinates.idx_temporal_coordinates]})
diff --git a/extreme_estimator/extreme_models/result_from_fit.py b/extreme_estimator/extreme_models/result_from_fit.py
index d20187c11455e88b92d875614d5c7fb6c94e0c66..afd325d27c3e238f24cb6aabc1cad7e22f1024dd 100644
--- a/extreme_estimator/extreme_models/result_from_fit.py
+++ b/extreme_estimator/extreme_models/result_from_fit.py
@@ -46,12 +46,13 @@ class ResultFromFit(object):
class ResultFromIsmev(ResultFromFit):
- def __init__(self, result_from_fit: robjects.ListVector, gev_param_name_to_dim) -> None:
+ def __init__(self, result_from_fit: robjects.ListVector, gev_param_name_to_dim=None) -> None:
super().__init__(result_from_fit)
self.gev_param_name_to_dim = gev_param_name_to_dim
@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
diff --git a/extreme_estimator/margin_fits/gev/ismev_gev_fit.py b/extreme_estimator/margin_fits/gev/ismev_gev_fit.py
index 42eba04a42b5301c0303a91f34c740ea54ced72e..778fc1bbee768128604d95431171bb6f7d812d57 100644
--- a/extreme_estimator/margin_fits/gev/ismev_gev_fit.py
+++ b/extreme_estimator/margin_fits/gev/ismev_gev_fit.py
@@ -7,6 +7,7 @@ from extreme_estimator.margin_fits.margin_fits_utils import spatial_extreme_gevm
class IsmevGevFit(GevFit):
# todo: this could be modeled with a margin_function depending only on time
+ # todo: I should remove the call to this object, in order to centralize all the calls
def __init__(self, x_gev: np.ndarray, y=None, mul=None):
super().__init__(x_gev)
diff --git a/extreme_estimator/margin_fits/margin_fits_utils.py b/extreme_estimator/margin_fits/margin_fits_utils.py
index c22a9ca3a3b97a1372ecbe5b606f60bb237a0157..31de2d174bcea68bf6dac5dd5e3ad48821fe933e 100644
--- a/extreme_estimator/margin_fits/margin_fits_utils.py
+++ b/extreme_estimator/margin_fits/margin_fits_utils.py
@@ -1,7 +1,8 @@
import numpy as np
import rpy2.robjects as ro
-from extreme_estimator.extreme_models.utils import r, get_null
+from extreme_estimator.extreme_models.result_from_fit import ResultFromIsmev
+from extreme_estimator.extreme_models.utils import r, get_null, safe_run_r_estimator
"""
These two functions are “extremely light†functions to fit the GEV/GPD. These functions are mainlyuseful
@@ -29,6 +30,13 @@ IsMev fit functions
def ismev_gev_fit(x_gev, y, mul):
+ """
+ For non-stationary fitting it is recommended that the covariates within the generalized linear models are
+ (at least approximately) centered and scaled (i.e.the columns of ydat should be approximately centered and scaled).
+ """
+ # print('Mean x={}, variance x={}'.format(np.mean(x_gev), np.var(x_gev)))
+ # print('Mean y={}, variance y={}'.format(np.mean(y), np.var(y)))
+
xdat = ro.FloatVector(x_gev)
gev_fit = r('gev.fit')
y = y if y is not None else get_null()
@@ -45,4 +53,4 @@ def ismev_gev_fit(x_gev, y, mul):
# a = np.array([2, 2])
# v = ro.vectors.FloatVector((1, 2, 3, 4, 5))
# ro.globalenv['a'] = a
-# print(r('class(a)'))
\ No newline at end of file
+# print(r('class(a)'))
diff --git a/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/abstract_transformation.py b/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/abstract_transformation.py
index e4092e0fc3c17d11cccdd34586bc2dbc4af4958c..aaa82a541d4ee86a9dbc8af924f4b55dfe5bb411 100644
--- a/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/abstract_transformation.py
+++ b/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/abstract_transformation.py
@@ -1,5 +1,6 @@
import pandas as pd
import numpy as np
+from sklearn.preprocessing import Normalizer
class AbstractTransformation(object):
@@ -29,3 +30,16 @@ class IdentityTransformation(AbstractTransformation):
def transform_array(self, coordinate: np.ndarray):
super().transform_array(coordinate)
return coordinate
+
+
+class CenteredScaledNormalization(AbstractTransformation):
+
+ def __init__(self, df_coordinates):
+ super().__init__(df_coordinates)
+ assert self.nb_dimensions == 1
+ self.transformer = Normalizer().fit(df_coordinates.transpose().values.reshape(-1, 1))
+
+ def transform_array(self, coordinate: np.ndarray):
+ return self.transformer.transform(np.array([coordinate]))[0]
+
+
diff --git a/test/test_extreme_estimator/test_extreme_models/test_margin_temporal.py b/test/test_extreme_estimator/test_extreme_models/test_margin_temporal.py
index 2388fcb35c781bf7cd1a1151f5fc2ee727604798..6ba798ee4bf4714fd6c3307e251a0cd334d22f72 100644
--- a/test/test_extreme_estimator/test_extreme_models/test_margin_temporal.py
+++ b/test/test_extreme_estimator/test_extreme_models/test_margin_temporal.py
@@ -1,25 +1,13 @@
-import random
import unittest
import numpy as np
-import pandas as pd
from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
from extreme_estimator.extreme_models.margin_model.linear_margin_model import LinearNonStationaryLocationMarginModel, \
LinearStationaryMarginModel
-from extreme_estimator.extreme_models.margin_model.temporal_linear_margin_model import StationaryStationModel, \
- NonStationaryStationModel
-from extreme_estimator.extreme_models.utils import r, set_seed_r, set_seed_for_test
-from extreme_estimator.margin_fits.gev.gevmle_fit import GevMleFit
-from extreme_estimator.margin_fits.gev.ismev_gev_fit import IsmevGevFit
-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.dataset.abstract_dataset import AbstractDataset
+from extreme_estimator.extreme_models.utils import set_seed_for_test
from spatio_temporal_dataset.dataset.simulation_dataset import MarginDataset
-from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
- AbstractSpatioTemporalObservations
-from test.test_utils import load_test_spatiotemporal_coordinates, load_smooth_margin_models
+from test.test_utils import load_test_spatiotemporal_coordinates
class TestMarginTemporal(unittest.TestCase):
diff --git a/test/test_extreme_estimator/test_extreme_models/test_margin_temporal_transformed.py b/test/test_extreme_estimator/test_extreme_models/test_margin_temporal_transformed.py
index 8f4f688f7e804fa676d0957f0d91f7cc8b22a77b..bb6f266272efa083d44fe390c89718532ce40b8a 100644
--- a/test/test_extreme_estimator/test_extreme_models/test_margin_temporal_transformed.py
+++ b/test/test_extreme_estimator/test_extreme_models/test_margin_temporal_transformed.py
@@ -1,29 +1,17 @@
-import random
import unittest
import numpy as np
-import pandas as pd
from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
from extreme_estimator.extreme_models.margin_model.linear_margin_model import LinearNonStationaryLocationMarginModel, \
LinearStationaryMarginModel
-from extreme_estimator.extreme_models.margin_model.temporal_linear_margin_model import StationaryStationModel, \
- NonStationaryStationModel
-from extreme_estimator.extreme_models.utils import r, set_seed_r, set_seed_for_test
-from extreme_estimator.margin_fits.gev.gevmle_fit import GevMleFit
-from extreme_estimator.margin_fits.gev.ismev_gev_fit import IsmevGevFit
-from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
+from extreme_estimator.extreme_models.utils import set_seed_for_test
from spatio_temporal_dataset.coordinates.spatio_temporal_coordinates.abstract_spatio_temporal_coordinates import \
AbstractSpatioTemporalCoordinates
-from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_coordinates import \
- AbstractTemporalCoordinates
from spatio_temporal_dataset.coordinates.transformed_coordinates.transformation.uniform_normalization import \
BetweenZeroAndOneNormalization
-from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
from spatio_temporal_dataset.dataset.simulation_dataset import MarginDataset
-from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
- AbstractSpatioTemporalObservations
-from test.test_utils import load_test_spatiotemporal_coordinates, load_smooth_margin_models
+from test.test_utils import load_test_spatiotemporal_coordinates
class TestMarginTemporal(unittest.TestCase):
diff --git a/test/test_extreme_estimator/test_extreme_models/test_max_stable_temporal.py b/test/test_extreme_estimator/test_extreme_models/test_max_stable_temporal.py
index 035c21c2d099f5bbb2f9b7059f5cc3697ced5863..6a359c9f3b478e9102013c8fd583c4ca55452039 100644
--- a/test/test_extreme_estimator/test_extreme_models/test_max_stable_temporal.py
+++ b/test/test_extreme_estimator/test_extreme_models/test_max_stable_temporal.py
@@ -1,27 +1,14 @@
-import random
import unittest
import numpy as np
-import pandas as pd
from extreme_estimator.estimator.full_estimator.abstract_full_estimator import \
FullEstimatorInASingleStepWithSmoothMargin
-from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
from extreme_estimator.extreme_models.margin_model.linear_margin_model import LinearNonStationaryLocationMarginModel, \
LinearStationaryMarginModel
-from extreme_estimator.extreme_models.margin_model.temporal_linear_margin_model import StationaryStationModel, \
- NonStationaryStationModel
-from extreme_estimator.extreme_models.utils import r, set_seed_r, set_seed_for_test
-from extreme_estimator.margin_fits.gev.gevmle_fit import GevMleFit
-from extreme_estimator.margin_fits.gev.ismev_gev_fit import IsmevGevFit
-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.dataset.abstract_dataset import AbstractDataset
-from spatio_temporal_dataset.dataset.simulation_dataset import MarginDataset, FullSimulatedDataset
-from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
- AbstractSpatioTemporalObservations
-from test.test_utils import load_test_spatiotemporal_coordinates, load_smooth_margin_models, load_test_max_stable_models
+from extreme_estimator.extreme_models.utils import set_seed_for_test
+from spatio_temporal_dataset.dataset.simulation_dataset import FullSimulatedDataset
+from test.test_utils import load_test_spatiotemporal_coordinates, load_test_max_stable_models
class TestMaxStableTemporal(unittest.TestCase):
diff --git a/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal.py b/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal.py
index 249b5167c6a70b4838307f354998739495ae10e7..38636360b2a5d375a273a8211d66be3bc400bf4d 100644
--- a/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal.py
+++ b/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal.py
@@ -5,10 +5,8 @@ import pandas as pd
from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
from extreme_estimator.extreme_models.margin_model.temporal_linear_margin_model import StationaryStationModel, \
- NonStationaryStationModel
+ NonStationaryLocationStationModel
from extreme_estimator.extreme_models.utils import r, set_seed_r
-from extreme_estimator.margin_fits.gev.gevmle_fit import GevMleFit
-from extreme_estimator.margin_fits.gev.ismev_gev_fit import IsmevGevFit
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_coordinates import \
AbstractTemporalCoordinates
@@ -48,7 +46,7 @@ class TestGevTemporal(unittest.TestCase):
def test_gev_temporal_margin_fit_nonstationary(self):
# Create estimator
- margin_model = NonStationaryStationModel(self.coordinates)
+ margin_model = NonStationaryLocationStationModel(self.coordinates)
estimator = LinearMarginEstimator(self.dataset, margin_model)
estimator.fit()
self.assertNotEqual(estimator.margin_function_fitted.mu1_temporal_trend, 0.0)
@@ -71,7 +69,7 @@ class TestGevTemporal(unittest.TestCase):
self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year3)
def fit_non_stationary_estimator(self, starting_point):
- margin_model = NonStationaryStationModel(self.coordinates, starting_point=starting_point + self.start_year)
+ margin_model = NonStationaryLocationStationModel(self.coordinates, starting_point=starting_point + self.start_year)
estimator = LinearMarginEstimator(self.dataset, margin_model)
estimator.fit()
return estimator