diff --git a/src/nmreval/io/fcbatchreader.py b/src/nmreval/io/fcbatchreader.py
index f67a661..ea319b4 100644
--- a/src/nmreval/io/fcbatchreader.py
+++ b/src/nmreval/io/fcbatchreader.py
@@ -133,7 +133,7 @@ class FCReader:
                     no_bevo = True
                     break
 
-                if str(k.parent) != 'ACC_ABS_FID_sig (group)':
+                if str(k.parent) != 'ABS_ACC_FID_sig (group)':
                     continue
 
                 tevo = k.parameter['tevo']
@@ -144,14 +144,14 @@ class FCReader:
                 if bevo not in _temp:
                     _temp[bevo] = []
 
-                _temp[bevo].append((tevo, *[pp[1] for pp in pts]))
+                _temp[bevo].append((tevo, *[np.real(pp[1]) for pp in pts]))
 
             if no_bevo:
                 break
 
         if no_bevo:
             # hopefully only for old scripts
-            sig = reader.get_selected('/data/B=*/ACC_ABS_FID*', dtype='signal')
+            sig = reader.get_selected('/data/B=*/ABS_ACC_FID*', dtype='signal')
             _temp = {}
             for s in sig:
                 pts = s.points([region])
@@ -159,7 +159,7 @@ class FCReader:
                 if b not in _temp:
                     _temp[b] = []
 
-                _temp[b].append([s.value, *[pp[1] for pp in pts]])
+                _temp[b].append([s.value, *[np.real(pp[1]) for pp in pts]])
 
         for b, m in sorted(_temp.items()):
             m = np.array(m)
@@ -192,15 +192,16 @@ class FCReader:
             freqs = []
 
             for k, v in sorted(self.data[temperature].items()):
-                freqs.append(k)
-
                 # fit
                 p0 = [v.y[0], v.y[-1]-v.y[0], v.x[int(0.5*len(v.x) - 0.5)], 1]
                 try:
                     p0, pcov = curve_fit(FCReader.kww, v.x, v.y, p0=p0, bounds=bounds, max_nfev=500*len(v))
-                except RuntimeError:
+                except Exception as e:
+                    logger.error(f'Fit of field {k} failed with exception', exc_info=e)
                     continue
 
+                freqs.append(k)
+
                 perr = np.sqrt(np.diag(pcov))
                 params.append(p0)
                 errors.append(perr)