[TREND ANALYSIS] few refactoring of the code

experiment/trend_analysis/univariate_test/abstract_trend_test.py

-import random
 import warnings
 
-import matplotlib.pyplot as plt
-from collections import OrderedDict
-
 import numpy as np
-from cached_property import cached_property
 
-from experiment.trend_analysis.mann_kendall_test import mann_kendall_test
+from experiment.trend_analysis.univariate_test.mann_kendall_test import mann_kendall_test
 from experiment.trend_analysis.abstract_score import MannKendall
 from experiment.trend_analysis.univariate_test.abstract_univariate_test import AbstractUnivariateTest
 

experiment/trend_analysis/univariate_test/abstract_univariate_test.py

 import random
-import warnings
-
-import matplotlib.pyplot as plt
 from collections import OrderedDict
 
-import numpy as np
+import matplotlib.pyplot as plt
 from cached_property import cached_property
 from matplotlib import colors
 
-from experiment.trend_analysis.mann_kendall_test import mann_kendall_test
-from experiment.trend_analysis.abstract_score import MannKendall
-
 
 class AbstractUnivariateTest(object):
     SIGNIFICATIVE = 'significative'

extreme_estimator/estimator/abstract_estimator.py

@@ -58,11 +58,6 @@ class AbstractEstimator(object):
                                                    coef_dict=self.result_from_fit.margin_coef_dict,
                                                    starting_point=margin_model.starting_point)
 
-    # @property
-    # def max_stable_fitted(self) -> AbstractMarginFunction:
-    #     assert self._margin_function_fitted is not None, 'Error: estimator has not been fitted'
-    #     return self._margin_function_fitted
-
     @property
     def train_split(self):
         return self.dataset.train_split

extreme_estimator/estimator/margin_estimator/abstract_margin_estimator.py

@@ -39,7 +39,9 @@ class LinearMarginEstimator(AbstractMarginEstimator):
 
     @property
     def margin_function_fitted(self) -> LinearMarginFunction:
-        return super().margin_function_fitted
+        margin_function_fitted = super().margin_function_fitted
+        assert isinstance(margin_function_fitted, LinearMarginFunction)
+        return margin_function_fitted
 
     def extract_function_fitted(self) -> LinearMarginFunction:
         return self.extract_function_fitted_from_the_model_shape(self.margin_model)