[refactor] remove default_param_start_fit and default_param_sample by a single default_param

extreme_fit/model/abstract_model.py

 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):

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)

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

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})

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)