48 simplify notch approximation lawbinned

src/pylife/materiallaws/notch_approximation_law.py

@@ -583,10 +583,11 @@ def strain(self, stress, load):
         '''Get the strain of the primary path in the stress-strain diagram at a given stress and load
         by using the value of the look-up table.
 
+        This method performs the task for for multiple points at once,
+        i.e. delta_load is a DataFrame with values for every node.
+
         Parameters
         ----------
-        stress : array-like float
-            The stress
         load : array-like float
             The load
 
@@ -655,7 +656,10 @@ def strain(self, stress, load):
 
     def stress_secondary_branch(self, delta_load, *, rtol=1e-5, tol=1e-6):
         '''Get the stress on secondary branches in the stress-strain diagram at a given load
-        by using the value of the look-up table.
+        by using the value of the look-up table (lut).
+
+        This method performs the task for for multiple points at once,
+        i.e. delta_load is a DataFrame with values for every node.
 
         Parameters
         ----------
@@ -734,12 +738,14 @@ def stress_secondary_branch(self, delta_load, *, rtol=1e-5, tol=1e-6):
 
     def strain_secondary_branch(self, delta_stress, delta_load):
         '''Get the strain on secondary branches in the stress-strain diagram at a given stress and load
-        by using the value of the look-up table.
+        by using the value of the look-up table (lut).
+        The lut is a DataFrame with MultiIndex with levels class_index and node_id.
+
+        This method performs the task for for multiple points at once,
+        i.e. delta_load is a DataFrame with values for every node.
 
         Parameters
         ----------
-        delta_stress : array-like float
-            The stress increment
         delta_load : array-like float
             The load increment
 

src/pylife/strength/damage_parameter.py

@@ -87,7 +87,7 @@ def _compute_values(self):
         self._M_sigma = self._constants.a_M * 1e-3 * R_m + self._constants.b_M
 
         # compute k according to eq. (2.6-83)
-        self._collective["k"] = 0
+        self._collective["k"] = 0.0
         self._collective.loc[self._collective["S_m"]>=0, "k"] = self._M_sigma * (self._M_sigma + 2)
         self._collective.loc[self._collective["S_m"]<0,  "k"] = self._M_sigma/3 * (self._M_sigma/3 + 2)
 
@@ -98,7 +98,7 @@ def _compute_values(self):
         self._collective["P_RAM"] = np.where(self._collective["discriminant"] >= 0,
                                              np.sqrt(self._collective["discriminant"]
                                                      * self._collective["epsilon_a"]
-                                                     * self._assessment_parameters.E), 0)
+                                                     * self._assessment_parameters.E), 0.0)
 
         # delete temporary columns
         self._collective.drop(columns = ["discriminant", "k"], inplace=True)
@@ -295,37 +295,39 @@ def _compute_crack_opening_loop(self):
         """compute crack opening strain with history (chapter 2.8.9.3, chapter 2.9.8.1 is better)"""
 
         # initialize new columns in collective DataFrame
-        self._collective["epsilon_open_alt"] = 0
-        self._collective["epsilon_open"] = 0
-        self._collective["P_RAJ"] = 0
-        self._collective["D"] = 0
-        self._collective["S_close"] = 0
+        self._collective["epsilon_open_alt"] = 0.0
+        self._collective["epsilon_open"] = 0.0
+        self._collective["P_RAJ"] = 0.0
+        self._collective["D"] = 0.0
+        self._collective["S_close"] = 0.0
         self._collective["case_name"] = ""
 
-        self._collective["epsilon_min_alt_SP"] = 0
-        self._collective["epsilon_max_alt_SP"] = 0
+        self._collective["epsilon_min_alt_SP"] = 0.0
+        self._collective["epsilon_max_alt_SP"] = 0.0
 
         assessment_point_index = self._collective[self._collective.index.get_level_values("hysteresis_index")==0]\
             .index.get_level_values("assessment_point_index")
 
         # initialize variables
         epsilon_open_alt = -np.inf   # initialized according to 2.9.7 point 2
-        epsilon_open_alt = pd.Series(0, index=assessment_point_index)
+        epsilon_open_alt = pd.Series(0.0, index=assessment_point_index)
 
         epsilon_min_alt_SP = pd.Series(np.infty, index=assessment_point_index)
         epsilon_max_alt_SP = pd.Series(-np.infty, index=assessment_point_index)
 
-        epsilon_min_alt_SP = pd.Series(0, index=assessment_point_index)
-        epsilon_max_alt_SP = pd.Series(0, index=assessment_point_index)
+        epsilon_min_alt_SP = pd.Series(0.0, index=assessment_point_index)
+        epsilon_max_alt_SP = pd.Series(0.0, index=assessment_point_index)
 
         # cumulative damage value
-        D_akt = pd.Series(0, index=assessment_point_index)
+        D_akt = pd.Series(0.0, index=assessment_point_index)
 
         # initialize current fatigue limit
         P_RAJ_D = pd.Series(self._P_RAJ_D_0, index=assessment_point_index)
 
+        P_RAJ_D = pd.Series(self._P_RAJ_D_0, index=assessment_point_index)
+
         # initialize helper variables
-        epsilon_open = pd.Series(0, index=assessment_point_index)
+        epsilon_open = pd.Series(0.0, index=assessment_point_index)
 
         # Find the last hysteresis of the first run of the HCM algorithm. After this hysteresis, we need to initialize some variables for the second HCM run.
         last_index_of_first_run = self._collective[(self._collective["run_index"]==1) & (self._collective["run_index"].shift(-1)==2)].index
@@ -505,7 +507,7 @@ def optimize(row):
             # compute value of P_RAJ, eq. (2.9-110)
             P_RAJ = np.where(is_damage_in_current_hysteresis,
                              self._calculate_P_RAJ(delta_S_eff, delta_epsilon_eff),
-                             0)
+                             0.0)
 
             # store value in collective DataFrame for current hysteresis
             self._collective.loc[self._collective.index.get_level_values("hysteresis_index")==index,"P_RAJ"] = P_RAJ
@@ -524,7 +526,7 @@ def optimize(row):
 
             # compute damage contribution
             D = np.where(group.is_closed_hysteresis,
-                         1/N,
+                         1.0/N,
                          0.5/N)
 
             # store damage in collective

tests/mesh/test_gradient.py

@@ -47,7 +47,7 @@ def test_grad_constant():
 
     grad = df.gradient.gradient_of('fct')
 
-    pd.testing.assert_frame_equal(grad, expected)
+    pd.testing.assert_frame_equal(grad, expected, rtol=1e-12)
 
 
 def test_grad_dx():
@@ -68,7 +68,7 @@ def test_grad_dx():
 
     grad = df.gradient.gradient_of('fct')
 
-    pd.testing.assert_frame_equal(grad, expected)
+    pd.testing.assert_frame_equal(grad, expected, rtol=1e-12)
 
 
 def test_grad_dx_flipped_index_levels():
@@ -88,7 +88,7 @@ def test_grad_dx_flipped_index_levels():
 
     grad = df.gradient.gradient_of('fct')
 
-    pd.testing.assert_frame_equal(grad, expected)
+    pd.testing.assert_frame_equal(grad, expected, rtol=1e-12)
 
 
 def test_grad_dx_shuffle():
@@ -110,7 +110,7 @@ def test_grad_dx_shuffle():
 
     grad = df.gradient.gradient_of('fct')
 
-    pd.testing.assert_frame_equal(grad, expected)
+    pd.testing.assert_frame_equal(grad, expected, rtol=1e-12)
 
 
 def test_grad_dy():
@@ -132,7 +132,7 @@ def test_grad_dy():
 
     grad = df.gradient.gradient_of('fct')
 
-    pd.testing.assert_frame_equal(grad, expected)
+    pd.testing.assert_frame_equal(grad, expected, rtol=1e-12)
 
 
 def test_grad_dy_shuffle():
@@ -153,7 +153,7 @@ def test_grad_dy_shuffle():
 
     grad = df.gradient.gradient_of('fct')
 
-    pd.testing.assert_frame_equal(grad, expected)
+    pd.testing.assert_frame_equal(grad, expected, rtol=1e-12)
 
 
 def test_grad_dxy_simple():
@@ -174,7 +174,7 @@ def test_grad_dxy_simple():
 
     grad = df.gradient.gradient_of('fct')
 
-    pd.testing.assert_frame_equal(grad, expected)
+    pd.testing.assert_frame_equal(grad, expected, rtol=1e-12)
 
 
 def test_grad_dxy_complex():
@@ -195,7 +195,7 @@ def test_grad_dxy_complex():
 
     grad = df.gradient.gradient_of('fct')
 
-    pd.testing.assert_frame_equal(grad, expected)
+    pd.testing.assert_frame_equal(grad, expected, rtol=1e-12)
 
 
 def test_grad_dxy_simple_shuffle():
@@ -217,7 +217,7 @@ def test_grad_dxy_simple_shuffle():
 
     grad = df.gradient.gradient_of('fct')
 
-    pd.testing.assert_frame_equal(grad, expected)
+    pd.testing.assert_frame_equal(grad, expected, rtol=1e-12)
 
 
 def test_grad_dxy_complex_shuffle():
@@ -239,7 +239,7 @@ def test_grad_dxy_complex_shuffle():
 
     grad = df.gradient.gradient_of('fct')
 
-    pd.testing.assert_frame_equal(grad, expected)
+    pd.testing.assert_frame_equal(grad, expected, rtol=1e-12)
 
 
 # ---- gradient_3D
@@ -260,7 +260,7 @@ def test_gradient_3D_constant():
 
     grad = df.gradient_3D.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False)
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False, rtol=1e-12)
 
 def test_gradient_3D_is_not_3D():
     fkt = [5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1]
@@ -279,7 +279,7 @@ def test_gradient_3D_is_not_3D():
 
     grad = df.gradient_3D.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False)
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False, rtol=1e-12)
 
 
 def test_gradient_3D_dx():
@@ -316,7 +316,7 @@ def test_gradient_3D_dx():
 
     grad = df.gradient_3D.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False)
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False, rtol=1e-12)
 
 
 def test_gradient_3D_dx_flipped_index_levels():
@@ -338,7 +338,7 @@ def test_gradient_3D_dx_flipped_index_levels():
 
     grad = df.gradient_3D.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False)
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False, rtol=1e-12)
 
 
 def test_gradient_3D_dy():
@@ -361,7 +361,7 @@ def test_gradient_3D_dy():
 
     grad = df.gradient_3D.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False)
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False, rtol=1e-12)
 
 def test_gradient_3D_dy_flipped_index_levels():
 
@@ -383,7 +383,7 @@ def test_gradient_3D_dy_flipped_index_levels():
 
     grad = df.gradient_3D.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False)
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False, rtol=1e-12)
 
 
 def test_gradient_3D_dxy():
@@ -406,7 +406,7 @@ def test_gradient_3D_dxy():
 
     grad = df.gradient_3D.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False)
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False, rtol=1e-12)
 
 def test_gradient_3D_dxy_flipped_index_levels():
     fkt_x = np.array([1, 4, 4, 1, 1, 4, 4, 1, 4, 7, 7, 4, 4, 7, 7, 4, 1, 4, 4, 1, 1, 4, 4, 1, 4, 7, 7, 4, 4, 7, 7, 4]) # x: 1 4 7, y: 1 5 9
@@ -428,7 +428,7 @@ def test_gradient_3D_dxy_flipped_index_levels():
 
     grad = df.gradient_3D.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False)
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), check_dtype=False, rtol=1e-12)
 
 
 def test_gradient_3D_dxyz_8nodes():
@@ -460,7 +460,7 @@ def test_gradient_3D_dxyz_8nodes():
 
     grad = df.gradient_3D.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index())
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), rtol=1e-12)
 
 def test_gradient_3D_dxyz_8_nodes_flipped_index_levels():
 
@@ -492,7 +492,7 @@ def test_gradient_3D_dxyz_8_nodes_flipped_index_levels():
 
     grad = df.gradient_3D.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index())
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), rtol=1e-12)
 
 def test_gradient_3D_dxyz_16nodes():
 
@@ -523,7 +523,7 @@ def test_gradient_3D_dxyz_16nodes():
 
     grad = df.gradient_3D.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index())
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), rtol=1e-12)
 
 
 def test_gradient_3D_dxyz_20nodes():
@@ -555,7 +555,7 @@ def test_gradient_3D_dxyz_20nodes():
 
     grad = df.gradient_3D.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index())
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), rtol=1e-12)
 
 
 def test_gradient_3D_tetrahedron():
@@ -577,7 +577,7 @@ def test_gradient_3D_tetrahedron():
 
     grad = df.gradient_3D.gradient_of('f').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index())
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), rtol=1e-12)
 
 
 def test_gradient_3D_tetrahedron_10_nodes():
@@ -599,7 +599,7 @@ def test_gradient_3D_tetrahedron_10_nodes():
 
     grad = df.gradient_3D.gradient_of('f').sort_index()
 
-    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index())
+    pd.testing.assert_frame_equal(grad.reset_index(), expected.reset_index(), rtol=1e-12)
 
 
 def test_gradient_3D_tetrahedron_compare():
@@ -624,7 +624,7 @@ def test_gradient_3D_tetrahedron_compare():
 
     # the following fails, which means that the Gradient class is less accurate for tet elements
     #pd.testing.assert_frame_equal(grad_3D.reset_index(), grad.reset_index(), check_dtype=False)
-    pd.testing.assert_frame_equal(grad_3D.reset_index(), expected.reset_index(), check_dtype=False)
+    pd.testing.assert_frame_equal(grad_3D.reset_index(), expected.reset_index(), check_dtype=False, rtol=1e-12)
 
 
 def test_gradient_3D_hex_compare_1():
@@ -658,8 +658,8 @@ def test_gradient_3D_hex_compare_1():
     grad_3D = df.gradient_3D.gradient_of('fct').sort_index()
     grad = df.gradient.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad_3D.reset_index(), grad.reset_index(), check_dtype=False)
-    pd.testing.assert_frame_equal(grad_3D.reset_index(), expected.reset_index(), check_dtype=False)
+    pd.testing.assert_frame_equal(grad_3D.reset_index(), grad.reset_index(), check_dtype=False, rtol=1e-12)
+    pd.testing.assert_frame_equal(grad_3D.reset_index(), expected.reset_index(), check_dtype=False, rtol=1e-12)
 
 
 def test_gradient_3D_hex_compare_2():
@@ -693,5 +693,5 @@ def test_gradient_3D_hex_compare_2():
     grad_3D = df.gradient_3D.gradient_of('fct').sort_index()
     grad = df.gradient.gradient_of('fct').sort_index()
 
-    pd.testing.assert_frame_equal(grad_3D.reset_index(), grad.reset_index(), check_dtype=False)
-    pd.testing.assert_frame_equal(grad_3D.reset_index(), expected.reset_index(), check_dtype=False)
+    pd.testing.assert_frame_equal(grad_3D.reset_index(), grad.reset_index(), check_dtype=False, rtol=1e-12)
+    pd.testing.assert_frame_equal(grad_3D.reset_index(), expected.reset_index(), check_dtype=False, rtol=1e-12)