[projection snowfall] refactor the formula for the effects. add...

[projection snowfall] refactor the formula for the effects. add param_name_to_ordered_climate_effects for the spline_margin_function.py and for the polynomial_margin_function.py. add some assertion

extreme_fit/function/margin_function/parametric_margin_function.py

@@ -103,6 +103,14 @@ class ParametricMarginFunction(IndependentMarginFunction):
                                                  coordinates=coordinates)
             param_name_to_coef[param_name] = coef
         # Load param_name_to_ordered_climate_effects
+        param_name_to_ordered_climate_effects = cls.load_param_name_to_ordered_climate_effects(coef_dict,
+                                                                                               name_of_the_climatic_effects)
+        return cls(coordinates, param_name_to_dims, param_name_to_coef,
+                   starting_point=starting_point, log_scale=log_scale,
+                   param_name_to_ordered_climate_effects=param_name_to_ordered_climate_effects)
+
+    @classmethod
+    def load_param_name_to_ordered_climate_effects(cls, coef_dict, name_of_the_climatic_effects):
         if name_of_the_climatic_effects is None:
             param_name_to_ordered_climate_effects = None
         else:
@@ -110,9 +118,7 @@ class ParametricMarginFunction(IndependentMarginFunction):
             for param_name in GevParams.PARAM_NAMES:
                 ordered_climate_effects = [coef_dict[param_name + name] for name in name_of_the_climatic_effects]
                 param_name_to_ordered_climate_effects[param_name] = ordered_climate_effects
-        return cls(coordinates, param_name_to_dims, param_name_to_coef,
-                   starting_point=starting_point, log_scale=log_scale,
-                   param_name_to_ordered_climate_effects=param_name_to_ordered_climate_effects)
+        return param_name_to_ordered_climate_effects
 
     @property
     def form_dict(self) -> Dict[str, str]:

extreme_fit/function/margin_function/polynomial_margin_function.py

@@ -14,13 +14,15 @@ class PolynomialMarginFunction(LinearMarginFunction):
 
     def __init__(self, coordinates: AbstractCoordinates, param_name_to_dim_and_max_degree: Dict[str, List[Tuple[int, int]]],
                  param_name_to_coef: Dict[str, PolynomialAllCoef], starting_point: Union[None, int] = None,
-                 params_class: type = GevParams, log_scale=None):
+                 params_class: type = GevParams, log_scale=None,
+                 param_name_to_ordered_climate_effects=None):
         param_name_to_dims = {}
         for param_name in param_name_to_dim_and_max_degree.keys():
             dims = [c[0] for c in param_name_to_dim_and_max_degree[param_name]]
             param_name_to_dims[param_name] = dims
         self.param_name_to_dim_and_max_degree = param_name_to_dim_and_max_degree
-        super().__init__(coordinates, param_name_to_dims, param_name_to_coef, starting_point, params_class, log_scale)
+        super().__init__(coordinates, param_name_to_dims, param_name_to_coef, starting_point, params_class, log_scale,
+                         param_name_to_ordered_climate_effects)
 
     COEF_CLASS = PolynomialAllCoef
 
@@ -48,5 +50,9 @@ class PolynomialMarginFunction(LinearMarginFunction):
                                                  dims=dims,
                                                  coordinates=coordinates)
             param_name_to_coef[param_name] = coef
-        return cls(coordinates, param_name_to_dim_and_max_degree, param_name_to_coef, starting_point, log_scale=log_scale)
+
+        param_name_to_ordered_climate_effects = cls.load_param_name_to_ordered_climate_effects(coef_dict, name_of_the_climatic_effects)
+
+        return cls(coordinates, param_name_to_dim_and_max_degree, param_name_to_coef, starting_point, log_scale=log_scale,
+                   param_name_to_ordered_climate_effects=param_name_to_ordered_climate_effects)
 

extreme_fit/function/margin_function/spline_margin_function.py

@@ -18,13 +18,15 @@ class SplineMarginFunction(LinearMarginFunction):
     def __init__(self, coordinates: AbstractCoordinates,
                  param_name_to_dim_and_max_degree: Dict[str, List[Tuple[int, int]]],
                  param_name_to_coef: Dict[str, SplineAllCoef], starting_point: Union[None, int] = None,
-                 params_class: type = GevParams, log_scale=None):
+                 params_class: type = GevParams, log_scale=None,
+                 param_name_to_ordered_climate_effects=None):
         param_name_to_dims = {}
         for param_name in param_name_to_dim_and_max_degree.keys():
             dims = [c[0] for c in param_name_to_dim_and_max_degree[param_name]]
             param_name_to_dims[param_name] = dims
         self.param_name_to_dim_and_max_degree = param_name_to_dim_and_max_degree
-        super().__init__(coordinates, param_name_to_dims, param_name_to_coef, starting_point, params_class, log_scale)
+        super().__init__(coordinates, param_name_to_dims, param_name_to_coef, starting_point, params_class, log_scale,
+                         param_name_to_ordered_climate_effects)
 
     COEF_CLASS = SplineAllCoef
 
@@ -50,7 +52,9 @@ class SplineMarginFunction(LinearMarginFunction):
 
         coef_dict, spline_param_name_to_dim_to_knots_and_coefficient = coef_dict
         # Load polynomial coefficient
-        polynomial_margin_function = PolynomialMarginFunction.from_coef_dict(coordinates, param_name_to_dims, coef_dict, starting_point, log_scale)
+        polynomial_margin_function = PolynomialMarginFunction.from_coef_dict(coordinates, param_name_to_dims, coef_dict,
+                                                                             starting_point, log_scale,
+                                                                             name_of_the_climatic_effects)
         param_name_to_coef = polynomial_margin_function.param_name_to_coef
         param_name_to_dim_and_max_degree = param_name_to_dims
         # Load the remaining spline coefficient
@@ -60,4 +64,6 @@ class SplineMarginFunction(LinearMarginFunction):
             for dim, (knots, coefficients) in dim_to_knots_and_coefficients.items():
                 dim_to_spline_coef[dim] = SplineCoef(param_name, coefficients, knots)
             param_name_to_coef[param_name] = SplineAllCoef(param_name, dim_to_spline_coef)
-        return cls(coordinates, param_name_to_dim_and_max_degree, param_name_to_coef, starting_point, log_scale=log_scale)
+
+        return cls(coordinates, param_name_to_dim_and_max_degree, param_name_to_coef, starting_point, log_scale=log_scale,
+                   param_name_to_ordered_climate_effects=polynomial_margin_function.param_name_to_ordered_climate_effects)

extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py

@@ -78,10 +78,13 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
         # Add potential climate effects
         name_of_the_climatic_effects = [c for c in df_coordinates_temp.columns if
                                         c not in AbstractCoordinates.COORDINATES_NAMES]
-        name_of_the_climatic_effects_for_formula = [" + {}".format(c) for c in name_of_the_climatic_effects]
-        formula_effect_str = ' '.join(name_of_the_climatic_effects_for_formula)
+        name_of_the_climatic_effects_for_formula = ["{}".format(c) for c in name_of_the_climatic_effects]
+        formula_effect_str = ' + '.join(name_of_the_climatic_effects_for_formula)
         # We apply the effect on all the parameters
-        formula_list = [f + ' ' + formula_effect_str for f in formula_list]
+        if len(name_of_the_climatic_effects) > 0:
+            formula_list = [f.replace(' 1', '') for f in formula_list]
+            formula_list = [f + ' + ' if f[-2:] != '~ ' else f for f in formula_list]
+            formula_list = [f + formula_effect_str for f in formula_list]
         formula = r.list(*[robjects.Formula(f) for f in formula_list])
         df = pd.DataFrame({maxima_column_name: np.array(x)})
         df = pd.concat([df, df_coordinates_spat, df_coordinates_temp], axis=1)

extreme_fit/model/result_from_model_fit/utils.py

@@ -48,4 +48,9 @@ def get_margin_coef_ordered_dict(param_name_to_dims, mle_values, type_for_mle="G
                 coef_name = param_name + name
                 coef_dict[coef_name] = mle_values[i]
                 i += 1
+
+    if type_for_mle == "Gumbel":
+        assert len(coef_dict) == len(mle_values) + 1
+    else:
+        assert len(coef_dict) == len(mle_values)
     return coef_dict