diff --git a/extreme_fit/model/abstract_model.py b/extreme_fit/model/abstract_model.py
index 28e19653d23939caf41b5dd849e67c946752ff75..9aed76a7714e6d07122cd95b6f7c9dc7db1b50a5 100644
--- a/extreme_fit/model/abstract_model.py
+++ b/extreme_fit/model/abstract_model.py
@@ -1,19 +1,18 @@
class AbstractModel(object):
def __init__(self, use_start_value=False, params_start_fit=None, params_sample=None):
- self.default_params_start_fit = None
- self.default_params_sample = None
+ self.default_params = None
self.use_start_value = use_start_value
self.user_params_start_fit = params_start_fit
self.user_params_sample = params_sample
@property
def params_start_fit(self) -> dict:
- return self.merge_params(default_params=self.default_params_start_fit, input_params=self.user_params_start_fit)
+ return self.merge_params(default_params=self.default_params, input_params=self.user_params_start_fit)
@property
def params_sample(self) -> dict:
- return self.merge_params(default_params=self.default_params_sample, input_params=self.user_params_sample)
+ return self.merge_params(default_params=self.default_params, input_params=self.user_params_sample)
@staticmethod
def merge_params(default_params, input_params):
diff --git a/extreme_fit/model/margin_model/linear_margin_model/linear_margin_model.py b/extreme_fit/model/margin_model/linear_margin_model/linear_margin_model.py
index 68166e993ea5ad4eb655e583f5691afdd740968c..bce0d46a162fa075ed85d223ff4bf3febf2bf894 100644
--- a/extreme_fit/model/margin_model/linear_margin_model/linear_margin_model.py
+++ b/extreme_fit/model/margin_model/linear_margin_model/linear_margin_model.py
@@ -19,8 +19,7 @@ class LinearMarginModel(ParametricMarginModel):
'load_margin_functions needs to be implemented in child class'
# Load default params (with a dictionary format to enable quick replacement)
# IMPORTANT: Using a dictionary format enable using the default/user params methodology
- self.default_params_sample = self.default_param_name_and_dim_to_coef
- self.default_params_start_fit = self.default_param_name_and_dim_to_coef
+ self.default_params = self.default_param_name_and_dim_to_coef
# Load sample coef
coef_sample = self.param_name_to_linear_coef(param_name_and_dim_to_coef=self.params_sample)
diff --git a/extreme_fit/model/max_stable_model/abstract_max_stable_model.py b/extreme_fit/model/max_stable_model/abstract_max_stable_model.py
index 3d6a4f61412a6cd5f6c2e234cec1880a723f16f0..d1942525fc617270ca395e20f33ef2b36b9f06ad 100644
--- a/extreme_fit/model/max_stable_model/abstract_max_stable_model.py
+++ b/extreme_fit/model/max_stable_model/abstract_max_stable_model.py
@@ -110,7 +110,7 @@ class AbstractMaxStableModelWithCovarianceFunction(AbstractMaxStableModel):
super().__init__(use_start_value, params_start_fit, params_sample)
assert covariance_function is not None
self.covariance_function = covariance_function
- self.default_params_sample = {
+ self.default_params = {
'range': 3,
'smooth': 0.5,
'nugget': 0.5
diff --git a/extreme_fit/model/max_stable_model/max_stable_models.py b/extreme_fit/model/max_stable_model/max_stable_models.py
index 410f9d3f24e50aa59dedd2383d758586758fea38..e0a973cc47c624e26c8498d3d517f8109dd801eb 100644
--- a/extreme_fit/model/max_stable_model/max_stable_models.py
+++ b/extreme_fit/model/max_stable_model/max_stable_models.py
@@ -9,13 +9,12 @@ class Smith(AbstractMaxStableModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.cov_mod = 'gauss'
- self.default_params_start_fit = {
+ self.default_params = {
'var': 1,
'cov11': 1,
'cov12': 0,
'cov22': 1
}
- self.default_params_sample = self.default_params_start_fit.copy()
def remove_unused_parameters(self, start_dict, fitmaxstab_with_one_dimensional_data):
if fitmaxstab_with_one_dimensional_data:
@@ -30,23 +29,17 @@ class BrownResnick(AbstractMaxStableModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.cov_mod = 'brown'
- self.default_params_start_fit = {
+ self.default_params = {
'range': 3,
'smooth': 0.5,
}
- self.default_params_sample = {
- 'range': 3,
- 'smooth': 0.5,
- }
-
class Schlather(AbstractMaxStableModelWithCovarianceFunction):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.cov_mod = self.covariance_function.name
- self.default_params_sample.update({})
- self.default_params_start_fit = self.default_params_sample.copy()
+ self.default_params.update({})
class Geometric(AbstractMaxStableModelWithCovarianceFunction):
@@ -54,8 +47,7 @@ class Geometric(AbstractMaxStableModelWithCovarianceFunction):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.cov_mod = 'g' + self.covariance_function.name
- self.default_params_sample.update({'sigma2': 0.5})
- self.default_params_start_fit = self.default_params_sample.copy()
+ self.default_params.update({'sigma2': 0.5})
class ExtremalT(AbstractMaxStableModelWithCovarianceFunction):
@@ -63,8 +55,7 @@ class ExtremalT(AbstractMaxStableModelWithCovarianceFunction):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.cov_mod = 't' + self.covariance_function.name
- self.default_params_sample.update({'DoF': 2})
- self.default_params_start_fit = self.default_params_sample.copy()
+ self.default_params.update({'DoF': 2})
class ISchlather(AbstractMaxStableModelWithCovarianceFunction):
@@ -72,5 +63,4 @@ class ISchlather(AbstractMaxStableModelWithCovarianceFunction):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.cov_mod = 'i' + self.covariance_function.name
- self.default_params_sample.update({'alpha': 0.5})
- self.default_params_start_fit = self.default_params_sample.copy()
+ self.default_params.update({'alpha': 0.5})
diff --git a/test/test_unitary_r_packages/test_spatial_extreme/test_fitmaxstab/test_fitmaxstab_with_margin.py b/test/test_unitary_r_packages/test_spatial_extreme/test_fitmaxstab/test_fitmaxstab_with_margin.py
index a435d09f09863cb2a33e349546d3a4eb9bfcf299..5bde5dfa36f2fc8b662a32049a6c503d892d7e60 100644
--- a/test/test_unitary_r_packages/test_spatial_extreme/test_fitmaxstab/test_fitmaxstab_with_margin.py
+++ b/test/test_unitary_r_packages/test_spatial_extreme/test_fitmaxstab/test_fitmaxstab_with_margin.py
@@ -87,7 +87,7 @@ class TestMaxStableFitWithLinearMargin(TestUnitaryAbstract):
# @property
# def python_output(self):
# dataset = TestRMaxStabWithMarginConstant.python_code()
-# max_stable_model = Schlather(covariance_function=CovarianceFunction.whitmat, use_start_value=False)
+# max_stable_model = Schlather(covariance_function=CovarianceFunction.whitmat)
# margin_model = LinearMarginModelExample(dataset.coordinates)
# full_estimator = FullEstimatorInASingleStepWithSmoothMargin(dataset, margin_model,
# max_stable_model)