Use isinstance to check for types

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

src/pylife/materiallaws/notch_approximation_law.py

@@ -140,7 +140,7 @@ def _neuber_strain(self, stress, load):
 
         # bad conditioned problem for stress approximately 0 (divide by 0), use factor 1 instead
         # convert data from int to float
-        if type(load) is not float:
+        if not isinstance(load, float):
             load = load.astype(float)
         # factor = load / stress, avoid division by 0
         factor = np.divide(load, stress, out=np.ones_like(load), where=stress!=0)
@@ -195,7 +195,7 @@ def _neuber_strain_secondary(self, delta_stress, delta_load):
 
         # bad conditioned problem for delta_stress approximately 0 (divide by 0), use factor 1 instead
         # convert data from int to float
-        if type(delta_load) is not float:
+        if not isinstance(delta_load, float):
             delta_load = delta_load.astype(float)
         # factor = load / stress, avoid division by 0
         factor = np.divide(delta_load, delta_stress, out=np.ones_like(delta_load), where=delta_stress!=0)
@@ -277,6 +277,7 @@ def stress(self, load, *, rtol=1e-4, tol=1e-4):
         elastic-plastic stress (load), from a FE computation.
         This is done by solving for the root of f(sigma) in eq. 2.5-45 of FKM nonlinear.
 
+
         Parameters
         ----------
         load : array-like float
@@ -463,7 +464,7 @@ def _create_bins(self):
 
 
         # for multiple assessment points at once use a DataFrame with MultiIndex
-        if type(self._maximum_absolute_load) == pd.core.frame.DataFrame:
+        if isinstance(self._maximum_absolute_load, pd.DataFrame):
             assert self._maximum_absolute_load.index.name == "node_id"
 
             self._create_bins_multiple_assessment_points()
@@ -560,12 +561,12 @@ def _create_bins_multiple_assessment_points(self):
 
     @property
     def ramberg_osgood_relation(self):
-        '''Get the ramberg osgood relation object
+        '''The ramberg osgood relation object
         '''
         return self._notch_approximation_law.ramberg_osgood_relation
 
     def stress(self, load, rtol=1e-5, tol=1e-6):
-        '''Get the stress of the primary path in the stress-strain diagram at a given load
+        '''The stress of the primary path in the stress-strain diagram at a given load
         by using the value of the look-up table.
         
         .. note::
@@ -592,7 +593,7 @@ def stress(self, load, rtol=1e-5, tol=1e-6):
         sign = np.sign(load)
 
         # if the assessment is performed for multiple points at once, i.e. load is a DataFrame with values for every node
-        if type(load) == pd.core.frame.DataFrame and isinstance(self._lut_primary_branch.index, pd.MultiIndex):
+        if isinstance(load, pd.DataFrame) and isinstance(self._lut_primary_branch.index, pd.MultiIndex):
 
             # the lut is a DataFrame with MultiIndex with levels class_index and node_id
 
@@ -665,7 +666,7 @@ def strain(self, stress, load):
         sign = np.sign(load)
 
         # if the assessment is performed for multiple points at once, i.e. load is a DataFrame with values for every node
-        if type(load) == pd.core.frame.DataFrame and isinstance(self._lut_primary_branch.index, pd.MultiIndex):
+        if isinstance(load, pd.DataFrame) and isinstance(self._lut_primary_branch.index, pd.MultiIndex):
 
             # the lut is a DataFrame with MultiIndex with levels class_index and node_id
 
@@ -745,7 +746,7 @@ def stress_secondary_branch(self, delta_load, rtol=1e-5, tol=1e-6):
         sign = np.sign(delta_load)
 
         # if the assessment is performed for multiple points at once, i.e. load is a DataFrame with values for every node
-        if type(delta_load) == pd.core.frame.DataFrame and isinstance(self._lut_primary_branch.index, pd.MultiIndex):
+        if isinstance(delta_load, pd.DataFrame) and isinstance(self._lut_primary_branch.index, pd.MultiIndex):
 
             # the lut is a DataFrame with MultiIndex with levels class_index and node_id
 
@@ -821,7 +822,7 @@ def strain_secondary_branch(self, delta_stress, delta_load):
         sign = np.sign(delta_load)
 
         # if the assessment is performed for multiple points at once, i.e. load is a DataFrame with values for every node
-        if type(delta_load) == pd.core.frame.DataFrame and isinstance(self._lut_primary_branch.index, pd.MultiIndex):
+        if isinstance(delta_load, pd.DataFrame) and isinstance(self._lut_primary_branch.index, pd.MultiIndex):
 
             # the lut is a DataFrame with MultiIndex with levels class_index and node_id
 

src/pylife/materiallaws/notch_approximation_law_seegerbeste.py

@@ -86,7 +86,7 @@ def _neuber_strain(self, stress, load):
 
         # bad conditioned problem for stress approximately 0 (divide by 0), use factor 1 instead
         # convert data from int to float
-        if type(load) is not float:
+        if not isinstance(load, float):
             load = load.astype(float)
         # factor = load / stress, avoid division by 0
         factor = np.divide(load, stress, out=np.ones_like(load), where=stress!=0)
@@ -100,7 +100,7 @@ def _u_term(self, stress, load):
         (pi/2*(L/Sigma-1/k_p-1))
 
         '''
-        if type(load) is not float:
+        if not isinstance(load, float):
             load = load.astype(float)
         factor = np.divide(load, stress, out=np.ones_like(load), where=stress!=0)
         return (np.pi/2)*((factor-1)/(self._K_p-1))
@@ -120,7 +120,7 @@ def _middle_term(self, stress, load):
 
         '''
         # convert stress value to float
-        if type(stress) is not float:
+        if not isinstance(stress, float):
             stress = stress.astype(float)
         factor = np.divide(stress, load, out=np.ones_like(stress), where=load!=0)
 
@@ -153,7 +153,7 @@ def _neuber_strain_secondary(self, delta_stress, delta_load):
 
         # bad conditioned problem for delta_stress approximately 0 (divide by 0), use factor 1 instead
         # convert data from int to float
-        if type(delta_load) is not float:
+        if not isinstance(delta_load, float):
             delta_load = delta_load.astype(float)
         # factor = load / stress, avoid division by 0
         factor = np.divide(delta_load, delta_stress, out=np.ones_like(delta_load), where=delta_stress!=0)
@@ -167,7 +167,7 @@ def _u_term_secondary(self, delta_stress, delta_load):
         (pi/2*(delta_L/delta_Sigma-1/k_p-1))
 
         '''
-        if type(delta_load) is not float:
+        if not isinstance(delta_load, float):
             delta_load = delta_load.astype(float)
         factor = np.divide(delta_load, delta_stress, out=np.ones_like(delta_load), where=delta_stress!=0)
         return (np.pi/2)*((factor-1)/(self._K_p-1))
@@ -186,7 +186,7 @@ def _middle_term_secondary(self, delta_stress, delta_load):
         <=>  1/(delta_L/delta_Sigma - 2) < k_p <= 1/(delta_L/delta_Sigma - 1)
 
         '''
-        if type(delta_stress) is not float:
+        if not isinstance(delta_stress, float):
             delta_stress = delta_stress.astype(float)
         factor = np.divide(delta_stress, delta_load, out=np.ones_like(delta_stress), where=delta_load!=0)
 
@@ -317,7 +317,7 @@ def strain(self, stress, load):
             The resulting strain
         '''
 
-        if type(stress) is not float:
+        if not isinstance(stress, float):
             stress = stress.astype(float)
 
         return self._ramberg_osgood_relation.strain(stress)
@@ -430,7 +430,7 @@ def strain_secondary_branch(self, delta_stress, delta_load):
             The resulting strain
         '''
 
-        if type(delta_stress) is not float:
+        if not isinstance(delta_stress, float):
             delta_stress = delta_stress.astype(float)
 
         return self._ramberg_osgood_relation.delta_strain(delta_stress)

src/pylife/strength/fkm_load_distribution.py

@@ -213,7 +213,7 @@ def maximum_absolute_load(self, max_load_independently_for_nodes=False):
         """
 
         # if the load sequence is a pd.Series
-        if type(self._obj) == pd.core.series.Series:
+        if isinstance(self._obj, pd.Series):
             L_max = max(abs(self._obj))
 
         # if the load sequence is a pd.DataFrame

src/pylife/stress/rainflow/fkm_nonlinear.py

@@ -25,14 +25,14 @@ def load(self):
 
         @property
         def load_representative(self):
-            if type(self._load) == pd.core.frame.DataFrame:
+            if isinstance(self._load, pd.DataFrame):
                 return self._load.iloc[0].values[0]
             else:
                 return self._load
 
         @property
         def strain_representative(self):
-            if type(self._strain) == pd.core.frame.DataFrame:
+            if isinstance(self._strain, pd.DataFrame):
                 return self._strain.iloc[0].values[0]
             else:
                 return self._strain
@@ -46,7 +46,7 @@ def strain(self):
             return self._strain
 
         def __str__(self):
-            if type(self._load) == pd.core.series.Series or type(self._load) == pd.core.frame.DataFrame:
+            if isinstance(self._load, pd.Series) or isinstance(self._load, pd.DataFrame):
                 if self._stress is None:
                     if self._load is None:
                         return "()"
@@ -624,7 +624,7 @@ def _get_scalar_current_load(self, current_load):
         or the node from the first assessment point if multiple points are 
         considered at once."""
 
-        if type(current_load) == pd.core.frame.DataFrame:
+        if isinstance(current_load, pd.DataFrame):
             current_load_representative = current_load.iloc[0].values[0]
         else:
             current_load_representative = current_load
@@ -637,7 +637,7 @@ def _initialize_epsilon_min_for_hcm_run(self, samples, load_turning_points):
         if self._is_load_sequence_start:
             self._is_load_sequence_start = False
 
-            if type(load_turning_points) != np.ndarray:
+            if not isinstance(load_turning_points, np.ndarray):
                 # properly initialize self._epsilon_min_LF and self._epsilon_max_LF
                 first_sample = samples[samples.index.get_level_values("load_step") == 0].reset_index(drop=True)
 

src/pylife/stress/rainflow/general.py

@@ -328,7 +328,7 @@ def _new_turns(self, samples, flush=False, preserve_start=False):
             preserve_start = False
 
         _is_multiple_assessment_points = False
-        if type(samples) == pd.core.frame.DataFrame:
+        if isinstance(samples, pd.DataFrame):
             _is_multiple_assessment_points = True
 
             # convert to list 
@@ -438,7 +438,7 @@ def _new_turns_multiple_assessment_points(self, samples, flush=False, preserve_s
             The values of the turning points as data frames.
         """
 
-        assert type(samples[0]) == pd.core.frame.DataFrame
+        assert isinstance(samples[0], pd.DataFrame)
         assert samples[0].index.names == ["load_step", "node_id"]
 
         # extract the representative samples for the first node

src/pylife/stress/rainflow/recorders.py

@@ -236,7 +236,7 @@ def _get_for_every_node(self, boolean_array):
     def is_zero_mean_stress_and_strain(self):
 
         # if the assessment is performed for multiple points at once
-        if len(self._S_min) > 0 and type(self._S_min[0]) == pd.core.series.Series:
+        if len(self._S_min) > 0 and isinstance(self._S_min[0], pd.Series):
             return self._get_for_every_node(self._is_zero_mean_stress_and_strain)
         else:
             return self._is_zero_mean_stress_and_strain
@@ -258,7 +258,7 @@ def is_closed_hysteresis(self):
         was recorded as a memory 3 hysteresis, which counts only half the damage in the FKM nonlinear procedure."""
 
         # if the assessment is performed for multiple points at once
-        if len(self._S_min) > 0 and type(self._S_min[0]) == pd.core.series.Series:
+        if len(self._S_min) > 0 and isinstance(self._S_min[0], pd.Series):
             return self._get_for_every_node(self._is_closed_hysteresis)
         else:
             return self._is_closed_hysteresis
@@ -288,7 +288,7 @@ def collective(self):
         """
 
         # if the assessment is performed for multiple points at once
-        if len(self._S_min) > 0 and type(self._S_min[0]) == pd.core.series.Series:
+        if len(self._S_min) > 0 and isinstance(self._S_min[0], pd.Series):
 
             n_hystereses = self.R.shape[0]
             n_nodes = self.R.shape[1]