diff --git a/extreme_fit/estimator/margin_estimator/utils.py b/extreme_fit/estimator/margin_estimator/utils.py
index 6617a91faa25b420d71c185baffcc3c0bcd221f1..145174be68d32fde906adff400377ea6b3c4a29e 100644
--- a/extreme_fit/estimator/margin_estimator/utils.py
+++ b/extreme_fit/estimator/margin_estimator/utils.py
@@ -4,8 +4,7 @@ import pandas as pd
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 \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel
from spatio_temporal_dataset.coordinates.temporal_coordinates.generated_temporal_coordinates import \
ConsecutiveTemporalCoordinates
@@ -24,7 +23,7 @@ def fitted_linear_margin_estimator(model_class, coordinates, dataset, starting_y
return estimator
-def fitted_stationary_gev(x_gev, fit_method=TemporalMarginFitMethod.is_mev_gev_fit, model_class=StationaryTemporalModel, starting_year=None,
+def fitted_stationary_gev(x_gev, fit_method=MarginFitMethod.is_mev_gev_fit, model_class=StationaryTemporalModel, starting_year=None,
transformation_class=CenteredScaledNormalization) -> GevParams:
coordinates = ConsecutiveTemporalCoordinates.from_nb_temporal_steps(nb_temporal_steps=len(x_gev),
transformation_class=CenteredScaledNormalization)
diff --git a/extreme_fit/estimator/quantile_estimator/quantile_estimator_from_margin.py b/extreme_fit/estimator/quantile_estimator/quantile_estimator_from_margin.py
index 7a672a426b9130050282bc053c1ce2d8ce0eee9a..68e8518cbb5c92c7a461fdc447b56f45b4592b95 100644
--- a/extreme_fit/estimator/quantile_estimator/quantile_estimator_from_margin.py
+++ b/extreme_fit/estimator/quantile_estimator/quantile_estimator_from_margin.py
@@ -5,15 +5,15 @@ from extreme_fit.estimator.quantile_estimator.abstract_quantile_estimator import
from extreme_fit.function.abstract_quantile_function import AbstractQuantileFunction, \
QuantileFunctionFromMarginFunction
from extreme_fit.function.margin_function.abstract_margin_function import AbstractMarginFunction
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
class QuantileEstimatorFromMargin(LinearMarginEstimator, AbstractQuantileEstimator):
def __init__(self, dataset: AbstractDataset, quantile, margin_model_class: type):
- margin_model = margin_model_class(dataset.coordinates, fit_method=TemporalMarginFitMethod.extremes_fevd_mle)
+ margin_model = margin_model_class(dataset.coordinates, fit_method=MarginFitMethod.extremes_fevd_mle)
super().__init__(dataset=dataset, quantile=quantile, margin_model=margin_model)
@cached_property
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 f097aad53416b83cf7e061bfc36e0f2d0c4e9da2..36a7ab012e8a29735c06e8944b74f4530a937c19 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
@@ -6,6 +6,7 @@ import pandas as pd
from extreme_fit.distribution.exp_params import ExpParams
from extreme_fit.distribution.gev.gev_params import GevParams
from extreme_fit.model.margin_model.linear_margin_model.linear_margin_model import LinearMarginModel
+from extreme_fit.model.margin_model.utils import MarginFitMethod, fitmethod_to_str
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.result_from_bayesian_extremes import \
AbstractResultFromExtremes, ResultFromBayesianExtremes
@@ -16,16 +17,6 @@ from extreme_fit.model.utils import safe_run_r_estimator
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
-class TemporalMarginFitMethod(Enum):
- is_mev_gev_fit = 0
- extremes_fevd_bayesian = 1
- extremes_fevd_mle = 2
- extremes_fevd_gmle = 3
- extremes_fevd_l_moments = 4
-
-
-def fitmethod_to_str(fit_method):
- return str(fit_method).split('.')[-1]
class AbstractTemporalLinearMarginModel(LinearMarginModel):
@@ -33,7 +24,7 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
def __init__(self, coordinates: AbstractCoordinates, use_start_value=False, params_start_fit=None,
params_sample=None, starting_point=None,
- fit_method=TemporalMarginFitMethod.is_mev_gev_fit,
+ fit_method=MarginFitMethod.is_mev_gev_fit,
nb_iterations_for_bayesian_fit=5000,
params_start_fit_bayesian=None,
type_for_MLE="GEV",
@@ -43,7 +34,7 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
self.type_for_mle = type_for_MLE
self.params_start_fit_bayesian = params_start_fit_bayesian
self.nb_iterations_for_bayesian_fit = nb_iterations_for_bayesian_fit
- assert isinstance(fit_method, TemporalMarginFitMethod), fit_method
+ assert isinstance(fit_method, MarginFitMethod), fit_method
self.fit_method = fit_method
def fitmargin_from_maxima_gev(self, data: np.ndarray, df_coordinates_spat: pd.DataFrame,
@@ -54,13 +45,13 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
df_coordinates_temp.values))
x = ro.FloatVector(data)
if self.params_class is GevParams:
- if self.fit_method == TemporalMarginFitMethod.is_mev_gev_fit:
+ if self.fit_method == MarginFitMethod.is_mev_gev_fit:
return self.ismev_gev_fit(x, df_coordinates_temp)
- elif self.fit_method == TemporalMarginFitMethod.extremes_fevd_bayesian:
+ elif self.fit_method == MarginFitMethod.extremes_fevd_bayesian:
return self.extremes_fevd_bayesian_fit(x, df_coordinates_temp)
- elif self.fit_method in [TemporalMarginFitMethod.extremes_fevd_mle,
- TemporalMarginFitMethod.extremes_fevd_gmle,
- TemporalMarginFitMethod.extremes_fevd_l_moments,
+ elif self.fit_method in [MarginFitMethod.extremes_fevd_mle,
+ MarginFitMethod.extremes_fevd_gmle,
+ MarginFitMethod.extremes_fevd_l_moments,
]:
return self.extremes_fevd_mle_related_fit(x, df_coordinates_temp)
else:
@@ -82,11 +73,11 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
# Gev fit with extRemes package
def extremes_fevd_mle_related_fit(self, x, df_coordinates_temp) -> AbstractResultFromExtremes:
- if self.fit_method == TemporalMarginFitMethod.extremes_fevd_mle:
+ if self.fit_method == MarginFitMethod.extremes_fevd_mle:
method = "MLE"
- elif self.fit_method == TemporalMarginFitMethod.extremes_fevd_gmle:
+ elif self.fit_method == MarginFitMethod.extremes_fevd_gmle:
method = "GMLE"
- elif self.fit_method == TemporalMarginFitMethod.extremes_fevd_l_moments:
+ elif self.fit_method == MarginFitMethod.extremes_fevd_l_moments:
method = "Lmoments"
assert self.margin_function_start_fit.is_a_stationary_model
else:
@@ -156,4 +147,4 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
if __name__ == '__main__':
- print(fitmethod_to_str(TemporalMarginFitMethod.extremes_fevd_l_moments))
+ print(fitmethod_to_str(MarginFitMethod.extremes_fevd_l_moments))
diff --git a/extreme_fit/model/margin_model/linear_margin_model/temporal_linear_margin_models.py b/extreme_fit/model/margin_model/linear_margin_model/temporal_linear_margin_models.py
index 5e69a015a1e7a1dbb0acb9e2993c3e19c4d6f461..4746520c021ceca98dc7eafbbf8e228beda878f1 100644
--- a/extreme_fit/model/margin_model/linear_margin_model/temporal_linear_margin_models.py
+++ b/extreme_fit/model/margin_model/linear_margin_model/temporal_linear_margin_models.py
@@ -1,6 +1,7 @@
from extreme_fit.distribution.exp_params import ExpParams
from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- AbstractTemporalLinearMarginModel, TemporalMarginFitMethod
+ AbstractTemporalLinearMarginModel
+from extreme_fit.model.margin_model.utils import MarginFitMethod
from extreme_fit.model.utils import r
from extreme_fit.distribution.gev.gev_params import GevParams
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
@@ -70,7 +71,7 @@ class NonStationaryLocationAndScaleTemporalModel(AbstractTemporalLinearMarginMod
class GumbelTemporalModel(StationaryTemporalModel):
def __init__(self, coordinates: AbstractCoordinates, use_start_value=False, params_start_fit=None,
- params_sample=None, starting_point=None, fit_method=TemporalMarginFitMethod.is_mev_gev_fit,
+ params_sample=None, starting_point=None, fit_method=MarginFitMethod.is_mev_gev_fit,
nb_iterations_for_bayesian_fit=5000, params_start_fit_bayesian=None):
super().__init__(coordinates, use_start_value, params_start_fit, params_sample, starting_point, fit_method,
nb_iterations_for_bayesian_fit, params_start_fit_bayesian, type_for_MLE="Gumbel")
diff --git a/extreme_fit/model/margin_model/parametric_margin_model.py b/extreme_fit/model/margin_model/parametric_margin_model.py
index 49f1995c99ad612194b1093543ab796c567f20a0..8e83405d81f00e750af37c5c141f8d3357e33377 100644
--- a/extreme_fit/model/margin_model/parametric_margin_model.py
+++ b/extreme_fit/model/margin_model/parametric_margin_model.py
@@ -30,17 +30,17 @@ class ParametricMarginModel(AbstractMarginModel, ABC):
df_coordinates_temp: pd.DataFrame) -> ResultFromSpatialExtreme:
assert data.shape[1] == len(df_coordinates_spat)
+ return self.fit_from_statial_extremes(data, df_coordinates_spat, df_coordinates_temp)
+
+ def fit_from_statial_extremes(self, data, df_coordinates_spat, df_coordinates_temp):
# Margin formula for fitspatgev
fit_params = get_margin_formula_spatial_extreme(self.margin_function_start_fit.form_dict)
-
# Covariables
covariables = get_coord(df_coordinates=df_coordinates_spat)
fit_params['temp.cov'] = get_coord(df_coordinates=df_coordinates_temp)
-
# Start parameters
coef_dict = self.margin_function_start_fit.coef_dict
fit_params['start'] = r.list(**coef_dict)
-
res = safe_run_r_estimator(function=r.fitspatgev, use_start=self.use_start_value, data=data,
covariables=covariables, **fit_params)
return ResultFromSpatialExtreme(res)
diff --git a/extreme_fit/model/margin_model/utils.py b/extreme_fit/model/margin_model/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d511d1ec7437d891090850b11162e74b9d4e0803
--- /dev/null
+++ b/extreme_fit/model/margin_model/utils.py
@@ -0,0 +1,12 @@
+from enum import Enum
+
+
+class MarginFitMethod(Enum):
+ is_mev_gev_fit = 0
+ extremes_fevd_bayesian = 1
+ extremes_fevd_mle = 2
+ extremes_fevd_gmle = 3
+ extremes_fevd_l_moments = 4
+
+def fitmethod_to_str(fit_method):
+ return str(fit_method).split('.')[-1]
diff --git a/extreme_fit/model/result_from_model_fit/result_from_extremes/eurocode_return_level_uncertainties.py b/extreme_fit/model/result_from_model_fit/result_from_extremes/eurocode_return_level_uncertainties.py
index 4f11e4954fa2f679dc27aaa0189c33c2247a2af2..68e6fc82223e100bc8db4159937855e3ea8da980 100644
--- a/extreme_fit/model/result_from_model_fit/result_from_extremes/eurocode_return_level_uncertainties.py
+++ b/extreme_fit/model/result_from_model_fit/result_from_extremes/eurocode_return_level_uncertainties.py
@@ -4,8 +4,8 @@ from extreme_fit.model.result_from_model_fit.result_from_extremes.abstract_extra
ExtractEurocodeReturnLevelFromMyBayesianExtremes, ExtractEurocodeReturnLevelFromCiMethod
from spatio_temporal_dataset.utils import load_temporal_coordinates_and_dataset
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 \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.result_from_model_fit.result_from_extremes.confidence_interval_method import \
ConfidenceIntervalMethodFromExtremes
@@ -46,9 +46,9 @@ class EurocodeConfidenceIntervalFromExtremes(object):
# Select fit method depending on the ci_method
if ci_method in [ConfidenceIntervalMethodFromExtremes.ci_bayes,
ConfidenceIntervalMethodFromExtremes.my_bayes]:
- fit_method = TemporalMarginFitMethod.extremes_fevd_bayesian
+ fit_method = MarginFitMethod.extremes_fevd_bayesian
else:
- fit_method = TemporalMarginFitMethod.extremes_fevd_mle
+ fit_method = MarginFitMethod.extremes_fevd_mle
# Fitted estimator
fitted_estimator = fitted_linear_margin_estimator(model_class, coordinates, dataset, starting_year=None,
fit_method=fit_method, nb_iterations_for_bayesian_fit=20000)
diff --git a/extreme_trend/abstract_gev_trend_test.py b/extreme_trend/abstract_gev_trend_test.py
index 48173331786c40f3bb874184ae2c2ce7c8d07d21..b31820f38db712a527f8d71c047b7f30413b1388 100644
--- a/extreme_trend/abstract_gev_trend_test.py
+++ b/extreme_trend/abstract_gev_trend_test.py
@@ -9,8 +9,8 @@ from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE, YEAR_OF_INTEREST
from extreme_data.meteo_france_data.scm_models_data.crocus.crocus_variables import AbstractSnowLoadVariable
from extreme_fit.estimator.margin_estimator.utils import fitted_linear_margin_estimator
from extreme_fit.distribution.gev.gev_params import GevParams
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import \
StationaryTemporalModel
from extreme_fit.model.utils import SafeRunException
@@ -27,7 +27,7 @@ class AbstractGevTrendTest(object):
def __init__(self, years, maxima, starting_year, unconstrained_model_class,
constrained_model_class=StationaryTemporalModel,
quantile_level=EUROCODE_QUANTILE,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ fit_method=MarginFitMethod.extremes_fevd_mle):
self.years = years
self.maxima = maxima
self.starting_year = starting_year
diff --git a/extreme_trend/trend_test_one_parameter/abstract_comparison_non_stationary_model.py b/extreme_trend/trend_test_one_parameter/abstract_comparison_non_stationary_model.py
index 97e5e53a091eb404ff216bcabcde36b80cb13b1c..56a6c6ce2d0a00d747649e92207c459b4b9eeb88 100644
--- a/extreme_trend/trend_test_one_parameter/abstract_comparison_non_stationary_model.py
+++ b/extreme_trend/trend_test_one_parameter/abstract_comparison_non_stationary_model.py
@@ -2,8 +2,8 @@ from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE
from extreme_trend.trend_test_one_parameter.gev_trend_test_one_parameter import \
GevTrendTestOneParameter
from extreme_trend.trend_test_two_parameters.gev_trend_test_two_parameters import GevLocationAndScaleTrendTest
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import \
NonStationaryLocationTemporalModel, NonStationaryScaleTemporalModel
import numpy as np
@@ -20,13 +20,13 @@ class AbstractComparisonNonStationaryModelOneParameter(GevTrendTestOneParameter)
class ComparisonAgainstMu(AbstractComparisonNonStationaryModelOneParameter, GevLocationAndScaleTrendTest):
- def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year, constrained_model_class=NonStationaryLocationTemporalModel,
quantile_level=quantile_level, fit_method=fit_method)
class ComparisonAgainstSigma(AbstractComparisonNonStationaryModelOneParameter, GevLocationAndScaleTrendTest):
- def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year, constrained_model_class=NonStationaryScaleTemporalModel,
quantile_level=quantile_level, fit_method=fit_method)
diff --git a/extreme_trend/trend_test_one_parameter/gev_trend_test_one_parameter.py b/extreme_trend/trend_test_one_parameter/gev_trend_test_one_parameter.py
index fa1a4c3c405ccbe2287f41ca68e735366874177d..c2371186de3b9d037d4c27523dd5323e409b3c2b 100644
--- a/extreme_trend/trend_test_one_parameter/gev_trend_test_one_parameter.py
+++ b/extreme_trend/trend_test_one_parameter/gev_trend_test_one_parameter.py
@@ -1,7 +1,7 @@
from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE
from extreme_trend.abstract_gev_trend_test import AbstractGevTrendTest
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import \
NonStationaryLocationTemporalModel, NonStationaryScaleTemporalModel, NonStationaryShapeTemporalModel, \
StationaryTemporalModel
@@ -20,7 +20,7 @@ class GevTrendTestOneParameterAgainstStationary(GevTrendTestOneParameter):
def __init__(self, years, maxima, starting_year, unconstrained_model_class, gev_param_name,
quantile_level=EUROCODE_QUANTILE,
constrained_model_class=StationaryTemporalModel,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year,
unconstrained_model_class=unconstrained_model_class,
quantile_level=quantile_level,
@@ -36,7 +36,7 @@ class GevTrendTestOneParameterAgainstStationary(GevTrendTestOneParameter):
class GevLocationTrendTest(GevTrendTestOneParameterAgainstStationary):
def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, constrained_model_class=StationaryTemporalModel,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year,
unconstrained_model_class=NonStationaryLocationTemporalModel,
constrained_model_class=constrained_model_class,
@@ -61,7 +61,7 @@ class GevLocationTrendTest(GevTrendTestOneParameterAgainstStationary):
class GevScaleTrendTest(GevTrendTestOneParameterAgainstStationary):
def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, constrained_model_class=StationaryTemporalModel,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year,
unconstrained_model_class=NonStationaryScaleTemporalModel,
constrained_model_class=constrained_model_class,
@@ -88,7 +88,7 @@ class GevScaleTrendTest(GevTrendTestOneParameterAgainstStationary):
class GevShapeTrendTest(GevTrendTestOneParameterAgainstStationary):
- def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year,
unconstrained_model_class=NonStationaryShapeTemporalModel,
gev_param_name=GevParams.SHAPE,
diff --git a/extreme_trend/trend_test_one_parameter/gumbel_trend_test_one_parameter.py b/extreme_trend/trend_test_one_parameter/gumbel_trend_test_one_parameter.py
index 0c7c000816d081ce45182618ca1a6f747d3a767f..e3028a00dda0791e045179f9ddde93f81353c6a4 100644
--- a/extreme_trend/trend_test_one_parameter/gumbel_trend_test_one_parameter.py
+++ b/extreme_trend/trend_test_one_parameter/gumbel_trend_test_one_parameter.py
@@ -3,8 +3,8 @@ from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE
from extreme_trend.trend_test_one_parameter.gev_trend_test_one_parameter import \
GevTrendTestOneParameter, GevTrendTestOneParameterAgainstStationary
from extreme_fit.distribution.gev.gev_params import GevParams
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel, \
GumbelTemporalModel, NonStationaryLocationGumbelModel, NonStationaryScaleGumbelModel
from root_utils import classproperty
@@ -12,7 +12,7 @@ from root_utils import classproperty
class GumbelVersusGumbel(GevTrendTestOneParameter):
- def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year,
unconstrained_model_class=GumbelTemporalModel,
constrained_model_class=GumbelTemporalModel,
@@ -42,7 +42,7 @@ class GumbelVersusGumbel(GevTrendTestOneParameter):
class GevStationaryVersusGumbel(GevTrendTestOneParameter):
def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year,
unconstrained_model_class=StationaryTemporalModel,
constrained_model_class=GumbelTemporalModel,
@@ -67,7 +67,7 @@ class GevStationaryVersusGumbel(GevTrendTestOneParameter):
class GumbelLocationTrendTest(GevTrendTestOneParameterAgainstStationary):
- def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year,
unconstrained_model_class=NonStationaryLocationGumbelModel,
gev_param_name=GevParams.LOC,
@@ -94,7 +94,7 @@ class GumbelLocationTrendTest(GevTrendTestOneParameterAgainstStationary):
class GumbelScaleTrendTest(GevTrendTestOneParameterAgainstStationary):
def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year,
unconstrained_model_class=NonStationaryScaleGumbelModel,
gev_param_name=GevParams.SCALE,
diff --git a/extreme_trend/trend_test_three_parameters/gev_trend_test_three_parameters.py b/extreme_trend/trend_test_three_parameters/gev_trend_test_three_parameters.py
index baa69e62fa91dfffac34299414d0d9a063f2f525..793c7ea3bf3f6c37ee9d54a6b9dd1a6818c854ea 100644
--- a/extreme_trend/trend_test_three_parameters/gev_trend_test_three_parameters.py
+++ b/extreme_trend/trend_test_three_parameters/gev_trend_test_three_parameters.py
@@ -1,5 +1,5 @@
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE
from extreme_trend.abstract_gev_trend_test import AbstractGevTrendTest
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import \
@@ -17,7 +17,7 @@ class GevTrendTestThreeParameters(AbstractGevTrendTest):
class GevLocationAndScaleTrendTestAgainstGumbel(GevTrendTestThreeParameters):
- def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year,
unconstrained_model_class=NonStationaryLocationAndScaleTemporalModel,
constrained_model_class=GumbelTemporalModel,
diff --git a/extreme_trend/trend_test_two_parameters/gev_trend_test_two_parameters.py b/extreme_trend/trend_test_two_parameters/gev_trend_test_two_parameters.py
index eb4acd0cb6cf04ffbe816ff23dc12d991d8d7bec..21d596e8b090739c3559b67281a999d51479049e 100644
--- a/extreme_trend/trend_test_two_parameters/gev_trend_test_two_parameters.py
+++ b/extreme_trend/trend_test_two_parameters/gev_trend_test_two_parameters.py
@@ -2,8 +2,8 @@ from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE
from extreme_trend.abstract_gev_trend_test import AbstractGevTrendTest
from extreme_trend.trend_test_one_parameter.gev_trend_test_one_parameter import \
GevLocationTrendTest, GevScaleTrendTest
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import \
NonStationaryLocationAndScaleTemporalModel, StationaryTemporalModel, GumbelTemporalModel
from extreme_fit.distribution.gev.gev_params import GevParams
@@ -20,7 +20,7 @@ class GevTrendTestTwoParameters(AbstractGevTrendTest):
class GevLocationAndScaleTrendTest(GevTrendTestTwoParameters):
def __init__(self, years, maxima, starting_year, constrained_model_class=StationaryTemporalModel,
- quantile_level=EUROCODE_QUANTILE, fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ quantile_level=EUROCODE_QUANTILE, fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year,
unconstrained_model_class=NonStationaryLocationAndScaleTemporalModel,
constrained_model_class=constrained_model_class,
@@ -53,7 +53,7 @@ class GevLocationAndScaleTrendTest(GevTrendTestTwoParameters):
class GevLocationAgainstGumbel(GevTrendTestTwoParameters, GevLocationTrendTest):
- def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year, quantile_level, GumbelTemporalModel, fit_method=fit_method)
@classproperty
@@ -71,7 +71,7 @@ class GevLocationAgainstGumbel(GevTrendTestTwoParameters, GevLocationTrendTest):
class GevScaleAgainstGumbel(GevTrendTestTwoParameters, GevScaleTrendTest):
- def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year, quantile_level, GumbelTemporalModel, fit_method=fit_method)
@classproperty
diff --git a/extreme_trend/trend_test_two_parameters/gumbel_test_two_parameters.py b/extreme_trend/trend_test_two_parameters/gumbel_test_two_parameters.py
index 8d9f2d577eb3d02a1f0c6a469b5b078e497e0a03..812f259f984454c50e3b53035ac73494f8994f58 100644
--- a/extreme_trend/trend_test_two_parameters/gumbel_test_two_parameters.py
+++ b/extreme_trend/trend_test_two_parameters/gumbel_test_two_parameters.py
@@ -2,8 +2,8 @@ from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE
from extreme_trend.trend_test_two_parameters.gev_trend_test_two_parameters import \
GevTrendTestTwoParameters
from extreme_fit.distribution.gev.gev_params import GevParams
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import \
NonStationaryLocationAndScaleGumbelModel, GumbelTemporalModel
from root_utils import classproperty
@@ -11,7 +11,7 @@ from root_utils import classproperty
class GumbelLocationAndScaleTrendTest(GevTrendTestTwoParameters):
- def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE, fit_method=MarginFitMethod.extremes_fevd_mle):
super().__init__(years, maxima, starting_year,
unconstrained_model_class=NonStationaryLocationAndScaleGumbelModel,
constrained_model_class=GumbelTemporalModel,
diff --git a/extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py b/extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py
index e55b047cdc036a6a4ae7413019895674b6e68a89..75188fd51f9772547325653e3575bc20da90ac41 100644
--- a/extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py
+++ b/extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py
@@ -23,8 +23,8 @@ from extreme_trend.trend_test_one_parameter.gumbel_trend_test_one_parameter impo
GumbelLocationTrendTest, GevStationaryVersusGumbel, GumbelScaleTrendTest, GumbelVersusGumbel
from extreme_trend.trend_test_two_parameters.gumbel_test_two_parameters import \
GumbelLocationAndScaleTrendTest
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import GumbelTemporalModel, \
StationaryTemporalModel
from extreme_fit.model.result_from_model_fit.result_from_extremes.confidence_interval_method import \
@@ -54,7 +54,7 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
effective_temporal_covariate=YEAR_OF_INTEREST_FOR_RETURN_LEVEL,
relative_change_trend_plot=True,
non_stationary_trend_test_to_marker=None,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle,
+ fit_method=MarginFitMethod.extremes_fevd_mle,
select_only_acceptable_shape_parameter=True,
fit_gev_only_on_non_null_maxima=False,
fit_only_time_series_with_ninety_percent_of_non_null_values=True,
diff --git a/extreme_trend/visualizers/utils.py b/extreme_trend/visualizers/utils.py
index 78a4f4ad08838c5c6100ef35c89ea32a4c552f61..ab8b1b17d51084b1825da16c1893bab94e39548e 100644
--- a/extreme_trend/visualizers/utils.py
+++ b/extreme_trend/visualizers/utils.py
@@ -1,8 +1,8 @@
from collections import OrderedDict
from extreme_data.meteo_france_data.scm_models_data.utils import SeasonForTheMaxima
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_trend.visualizers.study_visualizer_for_non_stationary_trends import \
StudyVisualizerForNonStationaryTrends
@@ -13,7 +13,7 @@ def load_altitude_to_visualizer(altitudes, massif_names, model_subsets_for_uncer
save_to_file=True,
multiprocessing=True,
season=SeasonForTheMaxima.annual):
- fit_method = TemporalMarginFitMethod.extremes_fevd_mle
+ fit_method = MarginFitMethod.extremes_fevd_mle
altitude_to_visualizer = OrderedDict()
for altitude in altitudes:
study = study_class(altitude=altitude, multiprocessing=multiprocessing, season=season)
diff --git a/projects/contrasting_trends_in_snow_loads/check_mcmc_convergence_for_return_levels/gelman_convergence_test.py b/projects/contrasting_trends_in_snow_loads/check_mcmc_convergence_for_return_levels/gelman_convergence_test.py
index 0495452c64118be6a2d524402d7c37dcdb160bb7..5d8b829d47aead3e8c186bdc42161de777baf197 100644
--- a/projects/contrasting_trends_in_snow_loads/check_mcmc_convergence_for_return_levels/gelman_convergence_test.py
+++ b/projects/contrasting_trends_in_snow_loads/check_mcmc_convergence_for_return_levels/gelman_convergence_test.py
@@ -1,8 +1,8 @@
import numpy as np
import pandas as pd
from extreme_fit.estimator.margin_estimator.utils import fitted_linear_margin_estimator
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.result_from_model_fit.result_from_extremes.result_from_bayesian_extremes import \
ResultFromBayesianExtremes
from extreme_fit.model.utils import r
@@ -57,7 +57,7 @@ def compute_mean_and_variance(mcmc_iterations, model_class, non_null_years_and_m
maxima, years = non_null_years_and_maxima
coordinates, dataset = load_temporal_coordinates_and_dataset(maxima, years)
fitted_estimator = fitted_linear_margin_estimator(model_class, coordinates, dataset, starting_year=None,
- fit_method=TemporalMarginFitMethod.extremes_fevd_bayesian,
+ fit_method=MarginFitMethod.extremes_fevd_bayesian,
nb_iterations_for_bayesian_fit=mcmc_iterations,
params_start_fit_bayesian=params_start_fit)
res = fitted_estimator.result_from_model_fit # type: ResultFromBayesianExtremes
diff --git a/projects/contrasting_trends_in_snow_loads/check_mcmc_convergence_for_return_levels/main_bayesian_mcmc.py b/projects/contrasting_trends_in_snow_loads/check_mcmc_convergence_for_return_levels/main_bayesian_mcmc.py
index 0de594536a9d4071a13a3da0d17d7855940d69b4..969b8bae98b7373ec35f0c8fbc982964247a153a 100644
--- a/projects/contrasting_trends_in_snow_loads/check_mcmc_convergence_for_return_levels/main_bayesian_mcmc.py
+++ b/projects/contrasting_trends_in_snow_loads/check_mcmc_convergence_for_return_levels/main_bayesian_mcmc.py
@@ -8,8 +8,8 @@ from extreme_data.exceeding_snow_loads.check_mcmc_convergence_for_return_levels.
compute_gelman_score
from extreme_fit.estimator.margin_estimator.utils import fitted_linear_margin_estimator
from extreme_fit.distribution.gev.gev_params import GevParams
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel
from extreme_fit.model.result_from_model_fit.result_from_extremes.abstract_extract_eurocode_return_level import \
ExtractEurocodeReturnLevelFromMyBayesianExtremes
@@ -103,7 +103,7 @@ def get_return_level_bayesian_example(nb_iterations_for_bayesian_fit):
model_class = StationaryTemporalModel
coordinates, dataset = load_temporal_coordinates_and_dataset(maxima, years)
fitted_estimator = fitted_linear_margin_estimator(model_class, coordinates, dataset, starting_year=1959,
- fit_method=TemporalMarginFitMethod.extremes_fevd_bayesian,
+ fit_method=MarginFitMethod.extremes_fevd_bayesian,
nb_iterations_for_bayesian_fit=nb_iterations_for_bayesian_fit)
return_level_bayesian = ExtractEurocodeReturnLevelFromMyBayesianExtremes(estimator=fitted_estimator,
ci_method=ConfidenceIntervalMethodFromExtremes.my_bayes,
diff --git a/projects/contrasting_trends_in_snow_loads/gorman_figures/figure1/figure1_mean_ratio_return_level_ratio.py b/projects/contrasting_trends_in_snow_loads/gorman_figures/figure1/figure1_mean_ratio_return_level_ratio.py
index af9cbc34c79eb1ce18c34ae63af3681acae24b93..7bfecbeb663dd0213d1ca0522f4ba7a3319a40ca 100644
--- a/projects/contrasting_trends_in_snow_loads/gorman_figures/figure1/figure1_mean_ratio_return_level_ratio.py
+++ b/projects/contrasting_trends_in_snow_loads/gorman_figures/figure1/figure1_mean_ratio_return_level_ratio.py
@@ -5,8 +5,8 @@ from extreme_data.meteo_france_data.scm_models_data.crocus.crocus import CrocusS
from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day, SafranSnowfall3Days, \
SafranSnowfall7Days, SafranSnowfall5Days
from extreme_data.meteo_france_data.scm_models_data.visualization.main_study_visualizer import ALL_ALTITUDES_WITHOUT_NAN
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from projects.contrasting_trends_in_snow_loads.gorman_figures.figure1.comparative_curve_wrt_altitude import \
ComparativeCurveWrtAltitude
@@ -23,7 +23,7 @@ def load_altitude_to_study_visualizer(study_class, save_to_file=True) -> Ordered
altitude=altitude,
return_period=return_period,
save_to_file=save_to_file,
- fit_method=TemporalMarginFitMethod.extremes_fevd_l_moments)
+ fit_method=MarginFitMethod.extremes_fevd_l_moments)
altitude_to_study_visualizer[altitude] = study_visualizer
return altitude_to_study_visualizer
diff --git a/projects/contrasting_trends_in_snow_loads/gorman_figures/figure1/study_visualizer_for_double_stationary_fit.py b/projects/contrasting_trends_in_snow_loads/gorman_figures/figure1/study_visualizer_for_double_stationary_fit.py
index 144a35021e9c2f0b6c358ed05c388db6a25fd77c..71ecc857fdd783c4549294d98038b17b401629fe 100644
--- a/projects/contrasting_trends_in_snow_loads/gorman_figures/figure1/study_visualizer_for_double_stationary_fit.py
+++ b/projects/contrasting_trends_in_snow_loads/gorman_figures/figure1/study_visualizer_for_double_stationary_fit.py
@@ -8,8 +8,8 @@ from extreme_data.meteo_france_data.scm_models_data.visualization.create_shifted
get_colors, ticks_values_and_labels_for_percentages
from extreme_data.meteo_france_data.scm_models_data.visualization.main_study_visualizer import ALL_ALTITUDES_WITHOUT_NAN
from extreme_data.meteo_france_data.scm_models_data.visualization.study_visualizer import StudyVisualizer
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod, fitmethod_to_str
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod, fitmethod_to_str
import matplotlib.pyplot as plt
@@ -21,7 +21,7 @@ class StudyVisualizerForReturnLevelChange(StudyVisualizer):
def __init__(self, study_class, altitude, return_period=30, year_min=1959, year_middle=1989, year_max=2019,
save_to_file=False,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle):
+ fit_method=MarginFitMethod.extremes_fevd_mle):
self.return_period = return_period
self.fit_method = fit_method
diff --git a/projects/exceeding_snow_loads/checks/qqplot/plot_qqplot.py b/projects/exceeding_snow_loads/checks/qqplot/plot_qqplot.py
index f392813ad7e097e3084363939b2ad8f0cf15bd25..b0e9901fa30c9f6cac5b3d3ec17a5e3b94cb8fb8 100644
--- a/projects/exceeding_snow_loads/checks/qqplot/plot_qqplot.py
+++ b/projects/exceeding_snow_loads/checks/qqplot/plot_qqplot.py
@@ -7,8 +7,8 @@ import pandas as pd
from matplotlib.ticker import PercentFormatter
from extreme_data.meteo_france_data.scm_models_data.crocus.crocus import CrocusSnowLoadTotal
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.result_from_model_fit.result_from_extremes.abstract_extract_eurocode_return_level import \
AbstractExtractEurocodeReturnLevel
from projects.exceeding_snow_loads.data.main_example_swe_total_plot import tuples_for_examples_paper1
@@ -188,7 +188,7 @@ if __name__ == '__main__':
# altitudes = [900, 1800, 2700]
altitude_to_visualizer = {altitude: StudyVisualizerForNonStationaryTrends(CrocusSnowLoadTotal(altitude=altitude),
select_only_acceptable_shape_parameter=True,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle,
+ fit_method=MarginFitMethod.extremes_fevd_mle,
multiprocessing=True,
fit_gev_only_on_non_null_maxima=True,
fit_only_time_series_with_ninety_percent_of_non_null_values=True,
diff --git a/projects/quantile_regression_vs_evt/annual_maxima_simulation/daily_exp_simulation.py b/projects/quantile_regression_vs_evt/annual_maxima_simulation/daily_exp_simulation.py
index f33e4f62a8d008574216608edb263820798bc3a0..3f02d4fedbb15c68feebcec91c920c1ef4a48628 100644
--- a/projects/quantile_regression_vs_evt/annual_maxima_simulation/daily_exp_simulation.py
+++ b/projects/quantile_regression_vs_evt/annual_maxima_simulation/daily_exp_simulation.py
@@ -3,8 +3,8 @@ from abc import ABC
from extreme_fit.distribution.abstract_params import AbstractParams
from extreme_fit.distribution.exp_params import ExpParams
from extreme_fit.model.daily_data_model import AbstractModelOnDailyData
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_exp_models import \
NonStationaryRateTemporalModel
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel
@@ -45,7 +45,7 @@ class StationaryExpSimulation(AbstractDailyExpSimulation):
AbstractParams.RATE: [10],
}
return StationaryTemporalModel.from_coef_list(coordinates, gev_param_name_to_coef_list,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle,
+ fit_method=MarginFitMethod.extremes_fevd_mle,
params_class=ExpParams)
@@ -56,5 +56,5 @@ class NonStationaryExpSimulation(AbstractDailyExpSimulation):
AbstractParams.RATE: [0.1, 0.01],
}
return NonStationaryRateTemporalModel.from_coef_list(coordinates, gev_param_name_to_coef_list,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle,
+ fit_method=MarginFitMethod.extremes_fevd_mle,
params_class=ExpParams)
diff --git a/projects/quantile_regression_vs_evt/annual_maxima_simulation/gev_simulation.py b/projects/quantile_regression_vs_evt/annual_maxima_simulation/gev_simulation.py
index decc48180c287cefc131069b5032a7f50acf3a5d..07a79aa9109b7a83357afe0a96ef49833f878a5f 100644
--- a/projects/quantile_regression_vs_evt/annual_maxima_simulation/gev_simulation.py
+++ b/projects/quantile_regression_vs_evt/annual_maxima_simulation/gev_simulation.py
@@ -7,8 +7,8 @@ from cached_property import cached_property
from extreme_fit.distribution.gev.gev_params import GevParams
from extreme_fit.estimator.quantile_estimator.abstract_quantile_estimator import AbstractQuantileEstimator
from extreme_fit.model.margin_model.abstract_margin_model import AbstractMarginModel
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel, \
NonStationaryLocationTemporalModel
from projects.quantile_regression_vs_evt.AbstractSimulation import AbstractSimulation
@@ -36,7 +36,7 @@ class StationarySimulation(GevSimulation):
GevParams.SCALE: [1],
}
return StationaryTemporalModel.from_coef_list(coordinates, gev_param_name_to_coef_list,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle)
+ fit_method=MarginFitMethod.extremes_fevd_mle)
class NonStationaryLocationGumbelSimulation(GevSimulation):
@@ -48,7 +48,7 @@ class NonStationaryLocationGumbelSimulation(GevSimulation):
GevParams.SCALE: [1],
}
return NonStationaryLocationTemporalModel.from_coef_list(coordinates, gev_param_name_to_coef_list,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle)
+ fit_method=MarginFitMethod.extremes_fevd_mle)
class NonStationaryLocationGevSimulation(GevSimulation):
@@ -60,4 +60,4 @@ class NonStationaryLocationGevSimulation(GevSimulation):
GevParams.SCALE: [1],
}
return NonStationaryLocationTemporalModel.from_coef_list(coordinates, gev_param_name_to_coef_list,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle)
+ fit_method=MarginFitMethod.extremes_fevd_mle)
diff --git a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_bayesian.py b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_bayesian.py
index fd3c4e6ed183b7353f2f2564ff30281688184c88..c5a2c265f8350953f9ca28878ff1c344b8ef263b 100644
--- a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_bayesian.py
+++ b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_bayesian.py
@@ -4,8 +4,8 @@ import numpy as np
import pandas as pd
from extreme_trend.abstract_gev_trend_test import fitted_linear_margin_estimator
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel, \
NonStationaryLocationTemporalModel, NonStationaryLocationAndScaleTemporalModel
from extreme_fit.model.utils import r, set_seed_r
@@ -34,7 +34,7 @@ class TestGevTemporalExtremesBayesian(unittest.TestCase):
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 = TemporalMarginFitMethod.extremes_fevd_bayesian
+ self.fit_method = MarginFitMethod.extremes_fevd_bayesian
def test_gev_temporal_margin_fit_stationary(self):
# Create estimator
diff --git a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_gumbel.py b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_gumbel.py
index 63113950ab0d5ead6a36debe75c6881f748a0833..9fb0bd03112f478dabe46e516ff7dadd9e1496b1 100644
--- a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_gumbel.py
+++ b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_gumbel.py
@@ -4,8 +4,8 @@ import numpy as np
import pandas as pd
from extreme_trend.abstract_gev_trend_test import fitted_linear_margin_estimator
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import GumbelTemporalModel
from extreme_fit.model.utils import r, set_seed_r
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
@@ -39,7 +39,7 @@ class TestGevTemporalExtremesGumbel(unittest.TestCase):
estimator = fitted_linear_margin_estimator(GumbelTemporalModel,
self.coordinates, self.dataset,
starting_year=0,
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle)
+ fit_method=MarginFitMethod.extremes_fevd_mle)
ref = {'loc': -0.0862185692806497, 'scale': 1.0818465357627252, 'shape': 0}
for year in range(1, 3):
mle_params_estimated = estimator.function_from_fit.get_gev_params(np.array([year])).to_dict()
diff --git a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_l_moments.py b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_l_moments.py
index c94c33ad467d7906a46c4bb6c00f30d55837b0e6..6fda38ebd29acd637584555729f57e5fe6d15109 100644
--- a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_l_moments.py
+++ b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_l_moments.py
@@ -4,8 +4,8 @@ import numpy as np
import pandas as pd
from extreme_trend.abstract_gev_trend_test import fitted_linear_margin_estimator
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel, \
NonStationaryLocationTemporalModel, NonStationaryLocationAndScaleTemporalModel
from extreme_fit.model.utils import r, set_seed_r
@@ -34,7 +34,7 @@ class TestGevTemporalExtremesLMoments(unittest.TestCase):
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 = TemporalMarginFitMethod.extremes_fevd_l_moments
+ self.fit_method = MarginFitMethod.extremes_fevd_l_moments
ref = {'loc': 0.0813843045950251, 'scale': 1.1791830110181365, 'shape': 0.6610403806908737}
diff --git a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_mle.py b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_mle.py
index a91b6ffba75acd7d7d271cdb3ad2b0f6e6d4a907..e237f091120c7077a3d446afe53bc05a68c31736 100644
--- a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_mle.py
+++ b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_mle.py
@@ -4,8 +4,8 @@ import numpy as np
import pandas as pd
from extreme_trend.abstract_gev_trend_test import fitted_linear_margin_estimator
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel, \
NonStationaryLocationTemporalModel, NonStationaryLocationAndScaleTemporalModel
from extreme_fit.model.utils import r, set_seed_r
@@ -34,7 +34,7 @@ class TestGevTemporalExtremesMle(unittest.TestCase):
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 = TemporalMarginFitMethod.extremes_fevd_mle
+ self.fit_method = MarginFitMethod.extremes_fevd_mle
def test_gev_temporal_margin_fit_stationary(self):
# Create estimator
diff --git a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_is_mev.py b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_is_mev.py
index 69196d438a97be42dfacf30151507b48e5bb58c5..bab1dca61e4127f257b9f27d7aa3af6a52ca475a 100644
--- a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_is_mev.py
+++ b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_is_mev.py
@@ -5,8 +5,8 @@ import pandas as pd
from extreme_fit.estimator.margin_estimator.utils import 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 \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel, \
NonStationaryLocationTemporalModel
from extreme_fit.model.utils import r, set_seed_r
@@ -36,7 +36,7 @@ class TestGevTemporal(unittest.TestCase):
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 = TemporalMarginFitMethod.is_mev_gev_fit
+ self.fit_method = MarginFitMethod.is_mev_gev_fit
def test_gev_temporal_margin_fit_stationary(self):
# Create estimator
diff --git a/test/test_extreme_fit/test_distribution/test_gev/test_stationary_gev_fit.py b/test/test_extreme_fit/test_distribution/test_gev/test_stationary_gev_fit.py
index 737bab8080b830e9904ac517525ee5f3e859000a..e8a6e3a613a2e6acbedb44b1dff004b3fd1d5ffa 100644
--- a/test/test_extreme_fit/test_distribution/test_gev/test_stationary_gev_fit.py
+++ b/test/test_extreme_fit/test_distribution/test_gev/test_stationary_gev_fit.py
@@ -3,8 +3,8 @@ import unittest
import numpy as np
from extreme_fit.estimator.margin_estimator.utils import fitted_stationary_gev
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.utils import r, set_seed_r
from extreme_fit.distribution.gev.gev_params import GevParams
@@ -22,19 +22,19 @@ class TestStationaryGevFit(unittest.TestCase):
def test_stationary_gev_fit_with_ismev(self):
params_estimated = fitted_stationary_gev(x_gev=np.array(r['x_gev']),
- fit_method=TemporalMarginFitMethod.is_mev_gev_fit)
+ fit_method=MarginFitMethod.is_mev_gev_fit)
ref = {'loc': 0.0219, 'scale': 1.0347, 'shape': 0.8295}
self.fit_estimator(params_estimated, ref)
def test_stationary_gev_fit_with_mle(self):
params_estimated = fitted_stationary_gev(x_gev=np.array(r['x_gev']),
- fit_method=TemporalMarginFitMethod.extremes_fevd_mle)
+ fit_method=MarginFitMethod.extremes_fevd_mle)
ref = {'loc': 0.02191974259369493, 'scale': 1.0347946062900268, 'shape': 0.829052520147379}
self.fit_estimator(params_estimated, ref)
def test_stationary_gev_fit_with_l_moments(self):
params_estimated = fitted_stationary_gev(x_gev=np.array(r['x_gev']),
- fit_method=TemporalMarginFitMethod.extremes_fevd_l_moments)
+ fit_method=MarginFitMethod.extremes_fevd_l_moments)
ref = {'loc': 0.0813843045950251, 'scale': 1.1791830110181365, 'shape': 0.6610403806908737}
self.fit_estimator(params_estimated, ref)
diff --git a/test/test_extreme_fit/test_model/test_confidence_interval.py b/test/test_extreme_fit/test_model/test_confidence_interval.py
index 00f2475d513425dc6aef4c7f5469fde80248991b..40abf8d7fd519c6e5d35406e808137f6bcaa32ed 100644
--- a/test/test_extreme_fit/test_model/test_confidence_interval.py
+++ b/test/test_extreme_fit/test_model/test_confidence_interval.py
@@ -4,8 +4,8 @@ import numpy as np
import pandas as pd
from extreme_fit.estimator.margin_estimator.utils import fitted_linear_margin_estimator
-from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
- TemporalMarginFitMethod
+from extreme_fit.model.margin_model.utils import \
+ MarginFitMethod
from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel, \
NonStationaryLocationTemporalModel, NonStationaryLocationAndScaleTemporalModel, \
NonStationaryLocationAndScaleGumbelModel, NonStationaryLocationGumbelModel
@@ -65,17 +65,17 @@ class TestConfidenceInterval(unittest.TestCase):
}
def test_my_bayes(self):
- self.fit_method = TemporalMarginFitMethod.extremes_fevd_bayesian
+ self.fit_method = MarginFitMethod.extremes_fevd_bayesian
self.ci_method = ConfidenceIntervalMethodFromExtremes.my_bayes
self.model_class_to_triplet = self.bayesian_ci
def test_ci_bayes(self):
- self.fit_method = TemporalMarginFitMethod.extremes_fevd_bayesian
+ self.fit_method = MarginFitMethod.extremes_fevd_bayesian
self.ci_method = ConfidenceIntervalMethodFromExtremes.ci_bayes
self.model_class_to_triplet = self.bayesian_ci
def test_ci_normal_mle(self):
- self.fit_method = TemporalMarginFitMethod.extremes_fevd_mle
+ self.fit_method = MarginFitMethod.extremes_fevd_mle
self.ci_method = ConfidenceIntervalMethodFromExtremes.ci_mle
self.model_class_to_triplet = {
StationaryTemporalModel: (-4.703945484843988, 30.482318639674023, 65.66858276419204),
@@ -86,7 +86,7 @@ class TestConfidenceInterval(unittest.TestCase):
}
def test_ci_normal_gmle(self):
- self.fit_method = TemporalMarginFitMethod.extremes_fevd_gmle
+ self.fit_method = MarginFitMethod.extremes_fevd_gmle
self.ci_method = ConfidenceIntervalMethodFromExtremes.ci_mle
self.model_class_to_triplet = {
# Test only for the GEV cases (for the Gumbel cases results are just the same, since there is no shape parameter)
@@ -97,7 +97,7 @@ class TestConfidenceInterval(unittest.TestCase):
def test_ci_boot(self):
- self.fit_method = TemporalMarginFitMethod.extremes_fevd_mle
+ self.fit_method = MarginFitMethod.extremes_fevd_mle
self.ci_method = ConfidenceIntervalMethodFromExtremes.ci_boot
self.model_class_to_triplet= {
# I think the boostrapping works only in the stationary context