Fatigue hinzugefügt, DSV angefangen

2023-06-30 14:11:59 +02:00
parent 109df5bef1
commit 3b6eda29d5
5 changed files with 583 additions and 1 deletions
--- a/src/paveit/labtest/citt_fatigue.py
+++ b/src/paveit/labtest/citt_fatigue.py
@@ -0,0 +1,91 @@
+import numpy as np
+import pandas as pd
+
+from paveit.labtest.citt import calc_E
+
+
+class CittAnalyseFatigue():
+    
+    def _fit_split_data(self):
+        
+        data_exp = []
+        
+        N = self.data['N'].unique()
+        N = np.array(N)
+        gaps = N[1:][np.diff(N)>1]
+
+        for i,gap in enumerate(gaps):
+            print(i, gap)
+            if i == 0:
+                f = self.data['N']<gap
+            elif i == len(gaps):
+                f = self.data['N']>=gap
+            else:
+                f = (self.data['N']>=gaps[i-1]) & (self.data['N']<gap)
+        
+            # filter data by geps
+            d = self.data[f]
+            
+            # get 5 cycles
+            if i == 0:
+                f = (d['N']>=98) & (d['N']<=102)
+            else:
+                Nsel = d['N'].unique()
+                f = (d['N']>=Nsel[-5]) & (d['N']<=Nsel[-1])
+            
+            d = d[f]
+            
+            
+            
+            data_exp.append(d)
+        
+        
+        self.data = data_exp
+        
+    
+    def _fit_select_data(self):
+        
+        ''' analyse data
+
+        '''
+        pass 
+        
+    def _calc(self):
+        print('calc fatigue')
+        
+        print(self.metadata)
+        
+        fit = []
+        
+        # Je Aufzeichnungsintervall
+        
+        
+        for i, d in enumerate(self.data):
+            
+            try:
+                res = calc_E(d, metadata=self.metadata, columns_analyse=['F', 's_hor_sum'])
+            
+                res['idx'] = i
+                
+                res['energy_ratio'] = res['stiffness']*np.round(res['N_from'] + (res['N_to'] - res['N_from'])/2, 0)
+            
+                fit.append(res)
+            except:
+                raise
+            
+        self.fit_single_results = pd.DataFrame.from_records(fit)
+        
+        EN_max = self.fit_single_results['energy_ratio'].max()
+        
+        sel_f = self.fit_single_results[(self.fit_single_results['energy_ratio']>=0.8*EN_max) & (self.fit_single_results['energy_ratio']<=1.2*EN_max)]
+        par = np.polyfit(sel_f['N_from'], sel_f['energy_ratio'], 4)
+        
+        x = np.arange(sel_f['N_from'].min(),sel_f['N_from'].max(), 1)
+        y = np.polyval(par, x)
+
+        Nmakro = x[y.argmax()]
+
+        self.fit = {'Nmakro': Nmakro, 
+                        'energy_ratio_max': y.max(),
+                        'par_fit': par,
+                        'epislon_elast_98': self.fit_single_results.iloc[0]['el_strains']}