diff --git a/h5_helpers/selfcal_quality.py b/h5_helpers/selfcal_quality.py
index 4b951ef3..f84a1e3e 100644
--- a/h5_helpers/selfcal_quality.py
+++ b/h5_helpers/selfcal_quality.py
@@ -39,8 +39,11 @@ def __init__(self, folder: str = None, remote_only: bool = False, international_
 
         # selfcal folder
         self.folder = folder
+
         # merged selfcal h5parms
-        self.h5s = glob(f"{self.folder}/merged_selfcalcyle*.h5")
+        self.h5s = [h5 for h5 in glob(f"{self.folder}/merged_selfcalcyle*.h5") if 'linearfulljones' not in h5]
+        if len(self.h5s)==0:
+            self.h5s = glob(f"{self.folder}/merged_selfcalcyle*.h5")
         assert len(self.h5s) != 0, "No h5 files found"
 
         # select all sources
@@ -50,13 +53,14 @@ def __init__(self, folder: str = None, remote_only: bool = False, international_
 
         # select all fits images
         fitsfiles = sorted(glob(self.folder + "/*MFS-I-image.fits"))
-        if len(fitsfiles) == 0:
+        if len(fitsfiles) == 0 or '000' not in fitsfiles[0]:
             fitsfiles = sorted(glob(self.folder + "/*MFS-image.fits"))
         self.fitsfiles = [f for f in fitsfiles if 'arcsectaper' not in f]
+        assert len(self.fitsfiles) != 0, "No fits files found"
 
         # select all fits model images
         modelfiles = sorted(glob(self.folder + "/*MFS-I-model.fits"))
-        if len(modelfiles) == 0:
+        if len(modelfiles) == 0 or '000' not in modelfiles[0]:
             modelfiles = sorted(glob(self.folder + "/*MFS-model.fits"))
         self.modelfiles = [f for f in modelfiles if 'arcsectaper' not in f]
 
@@ -65,7 +69,7 @@ def __init__(self, folder: str = None, remote_only: bool = False, international_
         self.international_only = international_only
         self.dutch_only = dutch_only
 
-        self.textfile = open('selfcal_performance.csv', 'w')
+        self.textfile = open(f'selfcal_performance_{self.sourcename}.csv', 'w')
         self.writer = csv.writer(self.textfile)
         self.writer.writerow(['solutions', 'dirty'] + [str(i) for i in range(len(self.fitsfiles))])
 
@@ -258,14 +262,12 @@ def get_solution_scores(self, h5_1: str = None, h5_2: str = None):
 
         # take std from ratio of previous and current selfcal cycle
         if h5_2 is not None:
-
             if len(pols1) != len(pols2):
                 if min(len(pols1), len(pols2)) == 1:
                     vals1 = np.take(vals1, [0], axis=axes.index('pol'))
                     vals2 = np.take(vals2, [0], axis=axes.index('pol'))
                     weights1 = np.take(weights1, [0], axis=axes.index('pol'))
                     weights2 = np.take(weights2, [0], axis=axes.index('pol'))
-
                 elif min(len(pols1), len(pols2)) == 2:
                     vals1 = np.take(vals1, [0, -1], axis=axes.index('pol'))
                     vals2 = np.take(vals2, [0, -1], axis=axes.index('pol'))
@@ -296,7 +298,6 @@ def solution_stability(self):
 
         # loop over sources to get scores
         for k, source in enumerate(self.sources):
-            print(source)
             sub_h5s = sorted([h5 for h5 in self.h5s if source in h5])
             phase_scores = []
             amp_scores = []
@@ -475,7 +476,7 @@ def main():
     bestcycle_solutions, accept_solutions = sq.solution_stability()
     bestcycle_image, accept_image = sq.image_stability()
     sq.textfile.close()
-    df = pd.read_csv('selfcal_performance.csv').set_index('solutions').T
+    df = pd.read_csv(f'selfcal_performance_{sq.sourcename}.csv').set_index('solutions').T
     print(df)
     df.to_csv(f'selfcal_performance_{sq.sourcename}.csv', index=False)