diff --git a/alignment.py b/alignment.py
index 227ac67..88c85d3 100644
--- a/alignment.py
+++ b/alignment.py
@@ -77,7 +77,7 @@ kw_2dplot = dict(
   cmap          = cmap
   )
 
-fit_ret = collections.namedtuple("fit_ret",["fit_result","init_pars","final_pars",\
+fit_ret = collections.namedtuple("fit_ret",["init_pars","final_pars",\
   "final_transform1","final_transform2","im1","im2","E","p1","p1_sum","p2","p2_sum","fom","ratio","tneeded"] )
 
 def calcFOM(p1,p2,ratio):
@@ -268,22 +268,21 @@ def loadAlignment(fname):
 def unravel_results(res,nSaveImg='all'):
   final_pars = dict()
   # res[0].fit_result is a list if we are trying to unravel retult from getShots
-  if isinstance(res[0].fit_result,list):
-    parnames = res[0].fit_result[0].parameters
+  if isinstance(res[0].init_pars,list):
+    parnames = res[0].init_pars[0].get_keys()
   else:
-    parnames = res[0].fit_result.parameters
+    parnames = res[0].init_pars.keys()
   final_pars = dict()
   init_pars  = dict()
   for n in parnames:
     final_pars[n] = np.hstack( [r.final_pars[n] for r in res])
     init_pars[n] = np.hstack( [r.init_pars[n] for r in res])
-  im1  = np.asarray( [r.im1 for r in res])
-  im2  = np.asarray( [r.im2 for r in res])
+  im1  = np.asarray( [r.im1 for r in res if r.im1.shape[0] != 0])
+  im2  = np.asarray( [r.im2 for r in res if r.im2.shape[0] != 0])
   if nSaveImg != 'all':
-    im1 = im1[:nSaveImg]
-    im2 = im2[:nSaveImg]
+    im1 = im1[:nSaveImg].copy(); # copy is necessary to free the original memory
+    im2 = im2[:nSaveImg].copy(); # of original array
   return fit_ret(
-    fit_result   = [r.fit_result for r in res],
     init_pars    = init_pars,
     final_pars   = final_pars,
     final_transform1 = [r.final_transform1 for r in res],
@@ -439,7 +438,6 @@ def transformIminuit(im1,im2,init_transform=dict(),show=False,verbose=True,zeroT
   idx = p1>np.nanmax(p1)/10.
   fom = calcFOM(p1,p2,r)
   return fit_ret(
-    fit_result   = imin,
     init_pars    = init_params,
     final_pars    = final_params,
     final_transform1 = t1,
@@ -581,9 +579,9 @@ def doShots(imgs1,imgs2,initpars,nJobs=16,doFit=False,returnBestTransform=False,
     (joblib.delayed(doShot)(imgs1[i],imgs2[i],initpars,doFit=doFit) for i in range(N))
   res = unravel_results(pool,nSaveImg=nSaveImg)
   if returnBestTransform:
-    idx = np.argmin( res.fom );
-    print("FOM for best alignment %.2f"%res.fom[fom])
-    return res,res.final_transform1[idx]
+    idx = np.nanargmin( res.fom );
+    print("FOM for best alignment %.2f"%res.fom[idx])
+    return res,pool[idx].final_pars
   else:
     return res
 #  out = collections.OrderedDict( enumerate(pool) )