diff --git a/deltaSHAPE.py b/deltaSHAPE.py
index 82eef1a..6a4f157 100644
--- a/deltaSHAPE.py
+++ b/deltaSHAPE.py
@@ -75,8 +75,8 @@ def smooth(data,err,pad):
         # use numpy masked array to calculate average without including no-data (nan) nucleotides.
         new_data.append(np.mean(np.ma.MaskedArray([j for j in data[i-pad:i+pad+1]], np.isnan([j for j in data[i-pad:i+pad+1]]))))
         
-        # use stats.nanmean to calculate average without including no-data (nan) nucleotides. This causes long_scalars runtime warnings.
-        #new_data.append(stats.nanmean([j for j in data[i-pad:i+pad+1] if np.isnan(j) != True]))
+        # use np.nanmean to calculate average without including no-data (nan) nucleotides. This causes long_scalars runtime warnings.
+        #new_data.append(np.nanmean([j for j in data[i-pad:i+pad+1] if np.isnan(j) != True]))
         errs = np.array(err[i-pad:i+pad+1])
         squerrs = np.power([j for j in errs if np.isnan(j) != True], 2)
         total = np.sum(squerrs)
@@ -106,8 +106,8 @@ def z_factor(data1, data2, err1, err2, factor=1.96):
     return z_factors
 
 def calc_zScores(diffs):
-    mean = stats.nanmean(diffs)
-    sigma = stats.nanstd(diffs)
+    mean = np.nanmean(diffs)
+    sigma = np.nanstd(diffs)
     # calc Z-score
     z_scores = (diffs - mean) / sigma
     return np.array(z_scores)