Move private functions at the end for every class

Signed-off-by: Benjamin Maier <[email protected]>

src/pylife/strength/damage_parameter.py

@@ -51,6 +51,10 @@ def __init__(self, collective, assessment_parameters):
 
         self._compute_values()
 
+    @property
+    def collective(self):
+        return self._collective
+
     def _compute_values(self):
         """Compute the P_RAM damage parameter according to FKM nonlinear.
         """
@@ -78,11 +82,7 @@ def _compute_values(self):
 
         # delete temporary columns
         self._collective.drop(columns = ["discriminant", "k"], inplace=True)
-
-    @property
-    def collective(self):
-        return self._collective
-
+
 class P_RAJ:
 
     def __init__(self, collective, assessment_parameters, component_woehler_curve_P_RAJ):
@@ -160,7 +160,10 @@ def __init__(self, collective, assessment_parameters, component_woehler_curve_P_
             self._P_RAJ_D_0.reset_index(drop=True, inplace=True)
 
         self._compute_values()
-
+
+    @property
+    def collective(self):
+
     def _compute_values(self):
         """Compute the P_RAJ damage parameter according to FKM nonlinear.
         """
@@ -262,8 +265,7 @@ def _calculate_fatigue_limit_variables(self, D_akt):
         #P_RAJ_D = pd.Series(self._P_RAJ_D_0)
 
         return a_0, delta_J_eff_th, P_RAJ_D
-
-
+
     def _compute_crack_opening_loop(self):
         """compute crack opening strain with history (chapter 2.8.9.3, chapter 2.9.8.1 is better)"""
 
@@ -553,7 +555,5 @@ def optimize(row):
                 self._assessment_parameters.P_RAJ_klass_max = self._calculate_P_RAJ(delta_stress, delta_strain)
 
 
-
-    @property
-    def collective(self):
+
         return self._collective

src/pylife/strength/fkm_load_distribution.py

@@ -94,66 +94,6 @@ class FKMLoadSequence(PylifeSignal):
         mesh.fkm_load_sequence.scaled_by_constant(2)
     '''
 
-    def _validate(self):
-        if len(self._obj) == 0:
-            raise AttributeError("Load series is empty.")
-
-    def _validate_parameters(self, input_parameters, required_parameters):
-
-        for required_parameter in required_parameters:
-            if required_parameter not in input_parameters:
-                raise ValueError(f"Given parameters have to include \"{required_parameter}\".")
-
-    def _get_beta(self, input_parameters):
-        """
-        Compute a scaling factor for assessing a load sequence for a given failure probability,
-        for details, refer to the FKM nonlinear document.
-
-        The beta factors are also described in "A. Fischer. Bestimmung modifizierter Teilsicherheitsbeiwerte zur semiprobabilistischen
-        Bemessung von Stahlbetonkonstruktionen im Bestand. TU Kaiserslautern, 2010"
-
-        Parameters
-        ----------
-        input_parameters : pd.Series
-            The set of assessment parameters, has to contain the assessment failure probability ``input_parameters.P_A``.
-            This variable has to be one of {1e-7, 1e-6, 1e-5, 7.2e-5, 1e-3, 2.3e-1, 0.5}.
-
-        Raises
-        ------
-        ValueError
-            If the given failure probability has an invalid value.
-
-        Returns
-        -------
-        float
-            The value of the beta parameter.
-
-        """
-
-        if np.isclose(input_parameters.P_A, 1e-7):
-            return 5.20
-
-        elif np.isclose(input_parameters.P_A, 1e-6):
-            return 4.75
-
-        elif np.isclose(input_parameters.P_A, 1e-5):
-            return 4.27
-
-        elif np.isclose(input_parameters.P_A, 7.2e-5):
-            return 3.8
-
-        elif np.isclose(input_parameters.P_A, 1e-3):
-            return 3.09
-
-        elif np.isclose(input_parameters.P_A, 2.3e-1):
-            return 0.739
-
-        elif np.isclose(input_parameters.P_A, 0.5):
-            return 0
-
-        else:
-            raise ValueError(f"P_A={input_parameters.P_A} has to be one of "+"{1e-7, 1e-6, 1e-5, 7.2e-5, 1e-3, 2.3e-1, 0.5}.")
-
     def scaled_by_constant(self, gamma_L):
         """
         Scales the load sequence by the given constant gamma_L. This method basically computes
@@ -243,6 +183,66 @@ def maximum_absolute_load(self, max_load_independently_for_nodes=False):
 
         return float(L_max)
 
+    def _validate(self):
+        if len(self._obj) == 0:
+            raise AttributeError("Load series is empty.")
+
+    def _validate_parameters(self, input_parameters, required_parameters):
+
+        for required_parameter in required_parameters:
+            if required_parameter not in input_parameters:
+                raise ValueError(f"Given parameters have to include \"{required_parameter}\".")
+
+    def _get_beta(self, input_parameters):
+        """
+        Compute a scaling factor for assessing a load sequence for a given failure probability,
+        for details, refer to the FKM nonlinear document.
+
+        The beta factors are also described in "A. Fischer. Bestimmung modifizierter Teilsicherheitsbeiwerte zur semiprobabilistischen
+        Bemessung von Stahlbetonkonstruktionen im Bestand. TU Kaiserslautern, 2010"
+
+        Parameters
+        ----------
+        input_parameters : pd.Series
+            The set of assessment parameters, has to contain the assessment failure probability ``input_parameters.P_A``.
+            This variable has to be one of {1e-7, 1e-6, 1e-5, 7.2e-5, 1e-3, 2.3e-1, 0.5}.
+
+        Raises
+        ------
+        ValueError
+            If the given failure probability has an invalid value.
+
+        Returns
+        -------
+        float
+            The value of the beta parameter.
+
+        """
+
+        if np.isclose(input_parameters.P_A, 1e-7):
+            return 5.20
+
+        elif np.isclose(input_parameters.P_A, 1e-6):
+            return 4.75
+
+        elif np.isclose(input_parameters.P_A, 1e-5):
+            return 4.27
+
+        elif np.isclose(input_parameters.P_A, 7.2e-5):
+            return 3.8
+
+        elif np.isclose(input_parameters.P_A, 1e-3):
+            return 3.09
+
+        elif np.isclose(input_parameters.P_A, 2.3e-1):
+            return 0.739
+
+        elif np.isclose(input_parameters.P_A, 0.5):
+            return 0
+
+        else:
+            raise ValueError(f"P_A={input_parameters.P_A} has to be one of "+"{1e-7, 1e-6, 1e-5, 7.2e-5, 1e-3, 2.3e-1, 0.5}.")
+
 
 @pd.api.extensions.register_dataframe_accessor("fkm_safety_normal_from_stddev")
 @pd.api.extensions.register_series_accessor("fkm_safety_normal_from_stddev")