diff --git a/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py b/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py
index 26ecc1d083772229d088701819f1cd558831d9b3..0c140c6e7eab0d04fadf3a188f9336368b15c51e 100644
--- a/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py
+++ b/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py
@@ -55,7 +55,7 @@ class LinearMarginEstimator(AbstractMarginEstimator):
assert len(
maximum) == 1, 'So far, only one observation for each coordinate, but code would be easy to change'
maximum = maximum[0]
- gev_params = self.function_from_fit.get_gev_params(coordinate, is_transformed=False)
+ gev_params = self.function_from_fit.get_params(coordinate, is_transformed=False)
p = gev_params.density(maximum)
nllh -= np.log(p)
assert not np.isinf(nllh)
diff --git a/extreme_fit/estimator/margin_estimator/utils.py b/extreme_fit/estimator/margin_estimator/utils.py
index 23789660a344ad4ec4f06edf35ee24b256aa74f4..75f4315e59a262029a91ea76ccfcd2d2d9fb1232 100644
--- a/extreme_fit/estimator/margin_estimator/utils.py
+++ b/extreme_fit/estimator/margin_estimator/utils.py
@@ -35,7 +35,7 @@ def fitted_stationary_gev(x_gev, fit_method=MarginFitMethod.is_mev_gev_fit, mode
dataset = AbstractDataset(observations=observations, coordinates=coordinates)
estimator = fitted_linear_margin_estimator(model_class, coordinates, dataset, starting_year, fit_method)
first_coordinate = coordinates.coordinates_values()[0]
- gev_param = estimator.function_from_fit.get_gev_params(first_coordinate)
+ gev_param = estimator.function_from_fit.get_params(first_coordinate)
if not -0.5 < gev_param.shape < 0.5:
warnings.warn('fitted shape parameter is outside physical bounds {}'.format(gev_param.shape))
return gev_param
diff --git a/extreme_fit/function/abstract_quantile_function.py b/extreme_fit/function/abstract_quantile_function.py
index e1f4b9f4d7650e7390d40b37fa231c38fcf8514e..687161a8bd5d88a4c3a333ba062cd1865666c1a3 100644
--- a/extreme_fit/function/abstract_quantile_function.py
+++ b/extreme_fit/function/abstract_quantile_function.py
@@ -59,5 +59,5 @@ class QuantileFunctionFromMarginFunction(AbstractQuantileFunction):
self.quantile = quantile
def _get_quantile(self, coordinate: np.ndarray) -> float:
- gev_params = self.margin_function.get_gev_params(coordinate)
+ gev_params = self.margin_function.get_params(coordinate)
return gev_params.quantile(self.quantile)
diff --git a/extreme_fit/function/margin_function/abstract_margin_function.py b/extreme_fit/function/margin_function/abstract_margin_function.py
index 860d25c59238e1b19865116135cf4713f1fa20bd..4a424e1cd08f65d86a75724f19d2da0e21bc30e1 100644
--- a/extreme_fit/function/margin_function/abstract_margin_function.py
+++ b/extreme_fit/function/margin_function/abstract_margin_function.py
@@ -52,14 +52,14 @@ class AbstractMarginFunction(AbstractFunction):
def y(self):
return self.coordinates.y_coordinates
- def get_gev_params(self, coordinate: np.ndarray) -> GevParams:
- """Main method that maps each coordinate to its GEV parameters"""
+ def get_params(self, coordinate: np.ndarray) -> GevParams:
+ """Main method that maps each coordinate to its distribution parameters"""
raise NotImplementedError
@property
def gev_value_name_to_serie(self) -> Dict[str, pd.Series]:
# Load the gev_params
- gev_params = [self.get_gev_params(coordinate) for coordinate in self.coordinates.coordinates_values()]
+ gev_params = [self.get_params(coordinate) for coordinate in self.coordinates.coordinates_values()]
# Load the dictionary of values (distribution parameters + the quantiles)
value_dicts = [gev_param.summary_dict for gev_param in gev_params]
gev_value_name_to_serie = {}
@@ -154,7 +154,7 @@ class AbstractMarginFunction(AbstractFunction):
grid = []
for i, xi in enumerate(linspace):
- gev_param = self.get_gev_params(np.array([xi]))
+ gev_param = self.get_params(np.array([xi]))
assert not gev_param.has_undefined_parameters, 'This case needs to be handled during display,' \
'gev_parameter for xi={} is undefined'.format(xi)
grid.append(gev_param.summary_dict)
@@ -215,7 +215,7 @@ class AbstractMarginFunction(AbstractFunction):
coordinate = [xi, yj]
if temporal_step is not None:
coordinate.append(temporal_step)
- grid.append(self.get_gev_params(np.array(coordinate)).summary_dict)
+ grid.append(self.get_params(np.array(coordinate)).summary_dict)
grid = {value_name: np.array([g[value_name] for g in grid]).reshape(
[self.VISUALIZATION_RESOLUTION, self.VISUALIZATION_RESOLUTION])
for value_name in GevParams.SUMMARY_NAMES}
diff --git a/extreme_fit/function/margin_function/combined_margin_function.py b/extreme_fit/function/margin_function/combined_margin_function.py
index 20b5e474edb1d86efaa3aa4a4744451b78cfdb56..abd1896b5cb78b51e4cc3480809c65826182437d 100644
--- a/extreme_fit/function/margin_function/combined_margin_function.py
+++ b/extreme_fit/function/margin_function/combined_margin_function.py
@@ -15,8 +15,8 @@ class CombinedMarginFunction(AbstractMarginFunction):
super().__init__(coordinates)
self.margin_functions = margin_functions # type: List[AbstractMarginFunction]
- def get_gev_params(self, coordinate: np.ndarray) -> GevParams:
- gev_params_list = [margin_function.get_gev_params(coordinate) for margin_function in self.margin_functions]
+ def get_params(self, coordinate: np.ndarray) -> GevParams:
+ gev_params_list = [margin_function.get_params(coordinate) for margin_function in self.margin_functions]
mean_gev_params = np.mean(np.array([gev_param.to_array() for gev_param in gev_params_list]), axis=0)
gev_param = self.params_class(*mean_gev_params)
return gev_param
diff --git a/extreme_fit/function/margin_function/independent_margin_function.py b/extreme_fit/function/margin_function/independent_margin_function.py
index 43169d713b885f0059ffe394ceae7497e6bc27a6..bdd14bfc30082d499fd7ea6e45d270562f911500 100644
--- a/extreme_fit/function/margin_function/independent_margin_function.py
+++ b/extreme_fit/function/margin_function/independent_margin_function.py
@@ -19,7 +19,7 @@ class IndependentMarginFunction(AbstractMarginFunction):
super().__init__(coordinates, params_class)
self.gev_param_name_to_param_function = None # type: Union[None, Dict[str, AbstractParamFunction]]
- def get_gev_params(self, coordinate: np.ndarray, is_transformed: bool = True) -> GevParams:
+ def get_params(self, coordinate: np.ndarray, is_transformed: bool = True) -> GevParams:
"""Each GEV parameter is computed independently through its corresponding param_function"""
# Since all the coordinates are usually transformed by default
# then we assume that the input coordinate are transformed by default
diff --git a/extreme_fit/function/margin_function/parametric_margin_function.py b/extreme_fit/function/margin_function/parametric_margin_function.py
index 60a1bbb880cbd2ed28127320a5aae1731753255f..f6964588724f213873be9c83c98bea87968f6123 100644
--- a/extreme_fit/function/margin_function/parametric_margin_function.py
+++ b/extreme_fit/function/margin_function/parametric_margin_function.py
@@ -66,7 +66,7 @@ class ParametricMarginFunction(IndependentMarginFunction):
def transformed_starting_point(self):
return self.coordinates.temporal_coordinates.transformation.transform_array(np.array([self.starting_point]))
- def get_gev_params(self, coordinate: np.ndarray, is_transformed: bool = True) -> GevParams:
+ def get_params(self, coordinate: np.ndarray, is_transformed: bool = True) -> GevParams:
if self.starting_point is not None:
starting_point = self.transformed_starting_point if is_transformed else self.starting_point
# Shift temporal coordinate to enable to model temporal trend with starting point
@@ -74,7 +74,7 @@ class ParametricMarginFunction(IndependentMarginFunction):
assert 0 <= self.coordinates.idx_temporal_coordinates < len(coordinate)
if coordinate[self.coordinates.idx_temporal_coordinates] < starting_point:
coordinate[self.coordinates.idx_temporal_coordinates] = starting_point
- return super().get_gev_params(coordinate, is_transformed=is_transformed)
+ return super().get_params(coordinate, is_transformed=is_transformed)
@classmethod
def from_coef_dict(cls, coordinates: AbstractCoordinates, gev_param_name_to_dims: Dict[str, List[int]],
diff --git a/extreme_fit/model/margin_model/abstract_margin_model.py b/extreme_fit/model/margin_model/abstract_margin_model.py
index 9fe18c9d45c5e4d2ca04ee120707ef438ae8b19a..8b6df24d18621c814231fe241afad22eae881123 100644
--- a/extreme_fit/model/margin_model/abstract_margin_model.py
+++ b/extreme_fit/model/margin_model/abstract_margin_model.py
@@ -50,7 +50,7 @@ class AbstractMarginModel(AbstractModel, ABC):
assert len(maxima) == len(coordinates_values)
converted_maxima = []
for x, coordinate in zip(maxima, coordinates_values):
- gev_params = margin_function.get_gev_params(coordinate)
+ gev_params = margin_function.get_params(coordinate)
x_gev = convertion_r_function(x, **gev_params.to_dict())
converted_maxima.append(x_gev)
return np.array(converted_maxima)
@@ -75,7 +75,7 @@ class AbstractMarginModel(AbstractModel, ABC):
sample_r_function='rgev') -> np.ndarray:
maxima_gev = []
for coordinate in coordinates_values:
- gev_params = self.margin_function_sample.get_gev_params(coordinate)
+ gev_params = self.margin_function_sample.get_params(coordinate)
x_gev = r(sample_r_function)(nb_obs, **gev_params.to_dict())
assert not np.isnan(x_gev).any(), 'params={} generated Nan values'.format(gev_params.__str__())
maxima_gev.append(x_gev)
diff --git a/extreme_fit/model/result_from_model_fit/result_from_extremes/abstract_extract_eurocode_return_level.py b/extreme_fit/model/result_from_model_fit/result_from_extremes/abstract_extract_eurocode_return_level.py
index 65dca7ad446c8b61c0211210d97fac3939032ea8..38a173b18cc4861de006f69f8bf19d6d75947cbd 100644
--- a/extreme_fit/model/result_from_model_fit/result_from_extremes/abstract_extract_eurocode_return_level.py
+++ b/extreme_fit/model/result_from_model_fit/result_from_extremes/abstract_extract_eurocode_return_level.py
@@ -79,7 +79,7 @@ class ExtractEurocodeReturnLevelFromMyBayesianExtremes(AbstractExtractEurocodeRe
@property
def gev_params_from_fit_for_temporal_covariate(self) -> List[GevParams]:
- return [margin_function.get_gev_params(coordinate=np.array([self.temporal_covariate]), is_transformed=False)
+ return [margin_function.get_params(coordinate=np.array([self.temporal_covariate]), is_transformed=False)
for margin_function in self.margin_functions_from_fit]
@cached_property
diff --git a/extreme_trend/abstract_gev_trend_test.py b/extreme_trend/abstract_gev_trend_test.py
index dc8f22d89180f1b9c65820d810eeb54556537fe6..d9786929a7c186455d5cc035606d94f1317df753 100644
--- a/extreme_trend/abstract_gev_trend_test.py
+++ b/extreme_trend/abstract_gev_trend_test.py
@@ -113,14 +113,14 @@ class AbstractGevTrendTest(object):
@cached_property
def unconstrained_estimator_gev_params(self) -> GevParams:
# Constant parameters correspond to the gev params in 1958
- return self.unconstrained_estimator.function_from_fit.get_gev_params(coordinate=np.array([1958]),
- is_transformed=False)
+ return self.unconstrained_estimator.function_from_fit.get_params(coordinate=np.array([1958]),
+ is_transformed=False)
@cached_property
def constrained_estimator_gev_params(self) -> GevParams:
# Constant parameters correspond to any gev params
- return self.constrained_estimator.function_from_fit.get_gev_params(coordinate=np.array([1958]),
- is_transformed=False)
+ return self.constrained_estimator.function_from_fit.get_params(coordinate=np.array([1958]),
+ is_transformed=False)
def time_derivative_times_years(self, nb_years):
# Compute the slope strength
@@ -139,7 +139,7 @@ class AbstractGevTrendTest(object):
def relative_change_in_return_level(self, initial_year, final_year):
return_level_values = []
for year in [initial_year, final_year]:
- gev_params = self.unconstrained_estimator.function_from_fit.get_gev_params(
+ gev_params = self.unconstrained_estimator.function_from_fit.get_params(
coordinate=np.array([year]),
is_transformed=False)
return_level_values.append(gev_params.quantile(self.quantile_level))
@@ -265,7 +265,7 @@ class AbstractGevTrendTest(object):
label = 'Y({})'.format(year) if year is not None else label
if year is None:
year = 2019
- gev_params_year = self.unconstrained_estimator.function_from_fit.get_gev_params(
+ gev_params_year = self.unconstrained_estimator.function_from_fit.get_params(
coordinate=np.array([year]),
is_transformed=False)
extended_maxima = [gev_params_year.gumbel_inverse_standardization(q) for q in extended_quantiles]
@@ -356,7 +356,7 @@ class AbstractGevTrendTest(object):
def compute_empirical_quantiles(self, estimator):
empirical_quantiles = []
for year, maximum in sorted(zip(self.years, self.maxima), key=lambda t: t[1]):
- gev_param = estimator.function_from_fit.get_gev_params(
+ gev_param = estimator.function_from_fit.get_params(
coordinate=np.array([year]),
is_transformed=False)
maximum_standardized = gev_param.gumbel_standardization(maximum)
@@ -402,8 +402,8 @@ class AbstractGevTrendTest(object):
plt.gca().set_ylim(bottom=0)
def get_gev_params_with_big_shape_and_correct_shape(self):
- gev_params = self.unconstrained_estimator.function_from_fit.get_gev_params(coordinate=np.array([YEAR_OF_INTEREST_FOR_RETURN_LEVEL]),
- is_transformed=False) # type: GevParams
+ gev_params = self.unconstrained_estimator.function_from_fit.get_params(coordinate=np.array([YEAR_OF_INTEREST_FOR_RETURN_LEVEL]),
+ is_transformed=False) # type: GevParams
gev_params_with_corrected_shape = GevParams(loc=gev_params.location,
scale=gev_params.scale,
shape=0.5)
diff --git a/projects/quantile_regression_vs_evt/annual_maxima_simulation/abstract_annual_maxima_simulation.py b/projects/quantile_regression_vs_evt/annual_maxima_simulation/abstract_annual_maxima_simulation.py
index f4638c70bda061a14221dc776dedd5687303a087..29bfb2b7dc2c1184764f10c9772b1fe3b4fb88b0 100644
--- a/projects/quantile_regression_vs_evt/annual_maxima_simulation/abstract_annual_maxima_simulation.py
+++ b/projects/quantile_regression_vs_evt/annual_maxima_simulation/abstract_annual_maxima_simulation.py
@@ -47,7 +47,7 @@ class AnnualMaximaSimulation(AbstractSimulation):
last_coordinate = coordinates.coordinates_values()[-1]
# Compute true value
margin_model = self.time_series_lengths_to_margin_model[length]
- true_gev_params = margin_model.margin_function_sample.get_gev_params(last_coordinate)
+ true_gev_params = margin_model.margin_function_sample.get_params(last_coordinate)
true_quantile = true_gev_params.quantile(self.quantile_data)
# Compute estimated values
estimated_quantiles = [estimator.function_from_fit.get_quantile(last_coordinate) for estimator in estimators]
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 c5a2c265f8350953f9ca28878ff1c344b8ef263b..53bac7a22e8b3a1e16515d3f63b33980243f9df8 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
@@ -43,7 +43,7 @@ class TestGevTemporalExtremesBayesian(unittest.TestCase):
fit_method=self.fit_method)
ref = {'loc': 0.34272436381693616, 'scale': 1.3222588712831973, 'shape': 0.30491484962825105}
for year in range(1, 3):
- mle_params_estimated = estimator.function_from_fit.get_gev_params(np.array([year])).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_params(np.array([year])).to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
@@ -55,8 +55,8 @@ class TestGevTemporalExtremesBayesian(unittest.TestCase):
mu1_values = estimator.result_from_model_fit.df_posterior_samples.iloc[:, 1]
self.assertTrue((mu1_values != 0).any())
# Checks that parameters returned are indeed different
- mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(np.array([1])).to_dict()
- mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(np.array([3])).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_params(np.array([3])).to_dict()
self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
def test_gev_temporal_margin_fit_non_stationary_location_and_scale(self):
@@ -67,8 +67,8 @@ class TestGevTemporalExtremesBayesian(unittest.TestCase):
mu1_values = estimator.result_from_model_fit.df_posterior_samples.iloc[:, 1]
self.assertTrue((mu1_values != 0).any())
# Checks that parameters returned are indeed different
- mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(np.array([1])).to_dict()
- mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(np.array([3])).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_params(np.array([3])).to_dict()
self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
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 9fb0bd03112f478dabe46e516ff7dadd9e1496b1..b790d0ab55249b6eebc9185faf56eb6a679c0a7e 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
@@ -42,7 +42,7 @@ class TestGevTemporalExtremesGumbel(unittest.TestCase):
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()
+ mle_params_estimated = estimator.function_from_fit.get_params(np.array([year])).to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
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 6fda38ebd29acd637584555729f57e5fe6d15109..78a933aff01a5f2e3596e21659093f8c25331500 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
@@ -45,7 +45,7 @@ class TestGevTemporalExtremesLMoments(unittest.TestCase):
fit_method=self.fit_method)
ref = {'loc': 0.0813843045950251, 'scale': 1.1791830110181365, 'shape': 0.6610403806908737}
for year in range(1, 3):
- mle_params_estimated = estimator.function_from_fit.get_gev_params(np.array([year])).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_params(np.array([year])).to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
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 5f4b227e14bd5e48cbd815f69750318a84b38f2d..425d41105e98a3bc4e7a961a2c927c2d554a327c 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
@@ -43,7 +43,7 @@ class TestGevTemporalExtremesMle(unittest.TestCase):
fit_method=self.fit_method)
ref = {'loc': 0.02191974259369493, 'scale': 1.0347946062900268, 'shape': 0.829052520147379}
for year in range(1, 3):
- mle_params_estimated = estimator.function_from_fit.get_gev_params(np.array([year])).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_params(np.array([year])).to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
self.assertAlmostEqual(estimator.result_from_model_fit.nllh, estimator.nllh())
@@ -54,8 +54,8 @@ class TestGevTemporalExtremesMle(unittest.TestCase):
starting_year=0,
fit_method=self.fit_method)
# Checks that parameters returned are indeed different
- mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(np.array([1])).to_dict()
- mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(np.array([3])).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_params(np.array([3])).to_dict()
self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
self.assertAlmostEqual(estimator.result_from_model_fit.nllh, estimator.nllh())
@@ -66,8 +66,8 @@ class TestGevTemporalExtremesMle(unittest.TestCase):
starting_year=0,
fit_method=self.fit_method)
# Checks that parameters returned are indeed different
- mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(np.array([1])).to_dict()
- mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(np.array([3])).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_params(np.array([3])).to_dict()
self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
self.assertAlmostEqual(estimator.result_from_model_fit.nllh, estimator.nllh())
# self.assertAlmostEqual(estimator.result_from_model_fit.aic, estimator.aic())
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 bab1dca61e4127f257b9f27d7aa3af6a52ca475a..4431b8ee9b1110ef671d77df581c53df75696f48 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
@@ -45,7 +45,7 @@ class TestGevTemporal(unittest.TestCase):
fit_method=self.fit_method)
ref = {'loc': 0.04309190816463247, 'scale': 2.0688696961628437, 'shape': 0.8291528207825063}
for year in range(1, 3):
- mle_params_estimated = estimator.function_from_fit.get_gev_params(np.array([year])).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_params(np.array([year])).to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
@@ -56,8 +56,8 @@ class TestGevTemporal(unittest.TestCase):
estimator = LinearMarginEstimator(self.dataset, margin_model)
estimator.fit()
# Checks that parameters returned are indeed different
- mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(np.array([1])).to_dict()
- mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(np.array([3])).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_params(np.array([3])).to_dict()
self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
def test_gev_temporal_margin_fit_nonstationary_with_start_point(self):
@@ -67,10 +67,10 @@ class TestGevTemporal(unittest.TestCase):
# Checks starting point parameter are well passed
self.assertEqual(3, estimator.function_from_fit.starting_point)
# Checks that parameters returned are indeed different
- mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(np.array([1])).to_dict()
- mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(np.array([3])).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_params(np.array([3])).to_dict()
self.assertEqual(mle_params_estimated_year1, mle_params_estimated_year3)
- mle_params_estimated_year5 = estimator.function_from_fit.get_gev_params(np.array([5])).to_dict()
+ mle_params_estimated_year5 = estimator.function_from_fit.get_params(np.array([5])).to_dict()
self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year3)
def fit_non_stationary_estimator(self, starting_point):
diff --git a/test/test_extreme_fit/test_function/test_margin_function.py b/test/test_extreme_fit/test_function/test_margin_function.py
index 7f474fb9d183b928be9419de2f8c289a9a2205d8..fb586df77565cbe3a5fb8f5ee72397c09323542b 100644
--- a/test/test_extreme_fit/test_function/test_margin_function.py
+++ b/test/test_extreme_fit/test_function/test_margin_function.py
@@ -32,7 +32,7 @@ class MarginFunction(unittest.TestCase):
margin_function = self.margin_function_class.from_coef_dict(coordinates,
margin_model.margin_function_sample.gev_param_name_to_dims,
coef_dict)
- gev_param = margin_function.get_gev_params(coordinate=np.array([0.5, 1.0]), is_transformed=False)
+ gev_param = margin_function.get_params(coordinate=np.array([0.5, 1.0]), is_transformed=False)
self.assertEqual({'loc': 2, 'scale': 2, 'shape': 2}, gev_param.to_dict())
def test_coef_dict_spatial_coordinates(self):
@@ -46,7 +46,7 @@ class MarginFunction(unittest.TestCase):
margin_function = self.margin_function_class.from_coef_dict(coordinates,
margin_model.margin_function_sample.gev_param_name_to_dims,
coef_dict)
- gev_param = margin_function.get_gev_params(coordinate=np.array([1]), is_transformed=False)
+ gev_param = margin_function.get_params(coordinate=np.array([1]), is_transformed=False)
self.assertEqual({'loc': 3, 'scale': 1, 'shape': 1}, gev_param.to_dict())
if __name__ == '__main__':
diff --git a/test/test_extreme_fit/test_model/test_margin_temporal.py b/test/test_extreme_fit/test_model/test_margin_temporal.py
index 31b87e87d18d56e4b143e59a1802eb183bd8eaab..59b0ee359b25db16c86ba8f46888764ae7328db0 100644
--- a/test/test_extreme_fit/test_model/test_margin_temporal.py
+++ b/test/test_extreme_fit/test_model/test_margin_temporal.py
@@ -33,7 +33,7 @@ class TestMarginTemporal(unittest.TestCase):
ref = {'loc': 1.3456595684773085, 'scale': 1.090369430386199, 'shape': 0.6845422250749476}
for year in range(1, 3):
coordinate = np.array([0.0, 0.0, year])
- mle_params_estimated = estimator.function_from_fit.get_gev_params(coordinate).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_params(coordinate).to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
@@ -45,9 +45,9 @@ class TestMarginTemporal(unittest.TestCase):
self.assertNotEqual(estimator.function_from_fit.mu1_temporal_trend, 0.0)
# Checks that parameters returned are indeed different
coordinate1 = np.array([0.0, 0.0, 1])
- mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(coordinate1).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(coordinate1).to_dict()
coordinate3 = np.array([0.0, 0.0, 3])
- mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(coordinate3).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_params(coordinate3).to_dict()
self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
def test_margin_fit_nonstationary_with_start_point(self):
@@ -62,12 +62,12 @@ class TestMarginTemporal(unittest.TestCase):
self.assertEqual(2, estimator.function_from_fit.starting_point)
# Checks that parameters returned are indeed different
coordinate1 = np.array([0.0, 0.0, 1])
- mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(coordinate1).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(coordinate1).to_dict()
coordinate2 = np.array([0.0, 0.0, 2])
- mle_params_estimated_year2 = estimator.function_from_fit.get_gev_params(coordinate2).to_dict()
+ mle_params_estimated_year2 = estimator.function_from_fit.get_params(coordinate2).to_dict()
self.assertEqual(mle_params_estimated_year1, mle_params_estimated_year2)
coordinate5 = np.array([0.0, 0.0, 5])
- mle_params_estimated_year5 = estimator.function_from_fit.get_gev_params(coordinate5).to_dict()
+ mle_params_estimated_year5 = estimator.function_from_fit.get_params(coordinate5).to_dict()
self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year2)
def fit_non_stationary_estimator(self, starting_point):
diff --git a/test/test_extreme_fit/test_model/test_margin_temporal_transformed.py b/test/test_extreme_fit/test_model/test_margin_temporal_transformed.py
index 66392b462da6dc3c498fabf3006c85163e720576..c03c419338ce7a0fb0e6eb11db420314ebbe737e 100644
--- a/test/test_extreme_fit/test_model/test_margin_temporal_transformed.py
+++ b/test/test_extreme_fit/test_model/test_margin_temporal_transformed.py
@@ -41,8 +41,8 @@ class TestMarginTemporalTransformed(unittest.TestCase):
'shape': 0.7289248773961512}
for year in range(1, 3):
coordinate = np.array([0.0, 0.0, year])
- mle_params_estimated = estimator.function_from_fit.get_gev_params(coordinate,
- is_transformed=False).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_params(coordinate,
+ is_transformed=False).to_dict()
self.assertEqual(mle_params_estimated, ref)
def test_margin_fit_nonstationary(self):
@@ -53,11 +53,11 @@ class TestMarginTemporalTransformed(unittest.TestCase):
self.assertNotEqual(estimator.function_from_fit.mu1_temporal_trend, 0.0)
# Checks that parameters returned are indeed different
coordinate1 = np.array([0.0, 0.0, 1])
- mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(coordinate1,
- is_transformed=False).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(coordinate1,
+ is_transformed=False).to_dict()
coordinate3 = np.array([0.0, 0.0, 3])
- mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(coordinate3,
- is_transformed=False).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_params(coordinate3,
+ is_transformed=False).to_dict()
self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
def test_margin_fit_nonstationary_with_start_point(self):
@@ -67,15 +67,15 @@ class TestMarginTemporalTransformed(unittest.TestCase):
self.assertNotEqual(estimator.function_from_fit.mu1_temporal_trend, 0.0)
# Checks that parameters returned are indeed different
coordinate1 = np.array([0.0, 0.0, 1])
- mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(coordinate1,
- is_transformed=False).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(coordinate1,
+ is_transformed=False).to_dict()
coordinate2 = np.array([0.0, 0.0, 2])
- mle_params_estimated_year2 = estimator.function_from_fit.get_gev_params(coordinate2,
- is_transformed=False).to_dict()
+ mle_params_estimated_year2 = estimator.function_from_fit.get_params(coordinate2,
+ is_transformed=False).to_dict()
self.assertEqual(mle_params_estimated_year1, mle_params_estimated_year2)
coordinate5 = np.array([0.0, 0.0, 5])
- mle_params_estimated_year5 = estimator.function_from_fit.get_gev_params(coordinate5,
- is_transformed=False).to_dict()
+ mle_params_estimated_year5 = estimator.function_from_fit.get_params(coordinate5,
+ is_transformed=False).to_dict()
self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year2)
def fit_non_stationary_estimator(self, starting_point):
diff --git a/test/test_extreme_fit/test_model/test_max_stable_temporal.py b/test/test_extreme_fit/test_model/test_max_stable_temporal.py
index cd111c342fcf92aee847d4aaa2b34cabe7340ceb..dc4d8934ee39581a76d82f5a73c3cd0cd86d5f17 100644
--- a/test/test_extreme_fit/test_model/test_max_stable_temporal.py
+++ b/test/test_extreme_fit/test_model/test_max_stable_temporal.py
@@ -37,7 +37,7 @@ class TestMaxStableTemporal(unittest.TestCase):
ref = {'loc': 1.2091156634312243, 'scale': 1.1210085591373455, 'shape': 0.9831957705294134}
for year in range(1, 3):
coordinate = np.array([0.0, 0.0, year])
- mle_params_estimated = estimator.function_from_fit.get_gev_params(coordinate).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_params(coordinate).to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
@@ -50,9 +50,9 @@ class TestMaxStableTemporal(unittest.TestCase):
self.assertNotEqual(estimator.function_from_fit.mu1_temporal_trend, 0.0)
# Checks that parameters returned are indeed different
coordinate1 = np.array([0.0, 0.0, 1])
- mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(coordinate1).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(coordinate1).to_dict()
coordinate3 = np.array([0.0, 0.0, 3])
- mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(coordinate3).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_params(coordinate3).to_dict()
self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
def test_margin_fit_nonstationary_with_start_point(self):
@@ -67,12 +67,12 @@ class TestMaxStableTemporal(unittest.TestCase):
self.assertEqual(2, estimator.function_from_fit.starting_point)
# Checks that parameters returned are indeed different
coordinate1 = np.array([0.0, 0.0, 1])
- mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(coordinate1).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_params(coordinate1).to_dict()
coordinate2 = np.array([0.0, 0.0, 2])
- mle_params_estimated_year2 = estimator.function_from_fit.get_gev_params(coordinate2).to_dict()
+ mle_params_estimated_year2 = estimator.function_from_fit.get_params(coordinate2).to_dict()
self.assertEqual(mle_params_estimated_year1, mle_params_estimated_year2)
coordinate5 = np.array([0.0, 0.0, 5])
- mle_params_estimated_year5 = estimator.function_from_fit.get_gev_params(coordinate5).to_dict()
+ mle_params_estimated_year5 = estimator.function_from_fit.get_params(coordinate5).to_dict()
self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year2)
def fit_non_stationary_estimator(self, starting_point):
diff --git a/test/test_spatio_temporal_dataset/test_spatio_temporal_observations.py b/test/test_spatio_temporal_dataset/test_spatio_temporal_observations.py
index de24b0d6ba9bf8fafe4b9dfb724cb99bcb78ec9f..e3285a308fd83261a0df8ce2db6230652ddf31e4 100644
--- a/test/test_spatio_temporal_dataset/test_spatio_temporal_observations.py
+++ b/test/test_spatio_temporal_dataset/test_spatio_temporal_observations.py
@@ -63,7 +63,7 @@ class TestDailyObservations(unittest.TestCase):
def test_instance_exp_params(self):
last_coordinate = self.coordinates.coordinates_values()[-1]
- params = self.margin_model.margin_function_sample.get_gev_params(last_coordinate)
+ params = self.margin_model.margin_function_sample.get_params(last_coordinate)
self.assertIsInstance(params, ExpParams)
def test_exponential_observations(self):