remove print

src/paveit/postprocessing/citt.py

@@ -54,17 +54,12 @@ def citt(task_id: str):
     line_mod = lm.models.LinearModel()
     out = line_mod.fit(data.stiffness, x=data.phase)
     
-    print(out.best_values)
-    
     Emax = line_mod.eval(out.params, x=0.0)
     
     Emin = 0
     
     assert Emin < Emax
     
-    print(data.head())
-    
-    
     # Fit data        
     mod = lm.models.Model(stiffness_tp26, independent_vars=['f','T'])
     
@@ -105,24 +100,31 @@ def citt(task_id: str):
     results = []
     r2 = []
     
-    methods = ['leastsq', 'powell']
     
-    for method in methods:
-        result = mod.fit(data.stiffness, T=data.T_set, f=data.f_set, method=method, verbose=False)
+    try:
+        methods = ['leastsq', 'powell']
 
-        r2temp =  1.0 - result.redchi / np.var(data.stiffness.values, ddof=2)
-        r2.append(r2temp)
+        for method in methods:
+            result = mod.fit(data.stiffness, T=data.T_set, f=data.f_set, method=method, verbose=False)
             
-        results.append(result)
+            r2temp =  1.0 - result.redchi / np.var(data.stiffness.values, ddof=2)
+            r2.append(r2temp)
 
-    best = np.nanargmax(r2)
+            results.append(result)
 
-    res = results[best].best_values
+        best = np.nanargmax(r2)
+        
+        res = results[best].best_values
+        res['stat_r2'] = r2[best]
+        
+    except:
+        print('error regression, send default values')
+        
+        res = mod.valuesdict()
+        
+    #add metadata
     res['nsamples'] = len(data)
     res['task_id'] = task_id        
-    
-    res['stat_r2'] = r2[best]
-    
     res['date'] = datetime.datetime.now()
 
     print(res)