diff --git a/scripts/checkPulseData.py b/scripts/checkPulseData.py
index d826906..8f7eb70 100644
--- a/scripts/checkPulseData.py
+++ b/scripts/checkPulseData.py
@@ -61,6 +61,10 @@
     return (np.all(dx <= 0) or np.all(dx >= 0))
 
 
+def max_adc(x):
+    return np.max(x)
+
+
 def prune(x):
     return [element for element in x if element is not None]
 
@@ -190,5 +194,23 @@
     # Select the instances where pulse data are monotonic
     monotonics = data_frame[data_frame['MPVs'].apply(is_monotonic)]
 
-    import pickle
-    pickle.dump(monotonics, open('monotonics.pkl', 'wb'))
+    if not os.path.isfile('monotonics.pkl'):
+        import pickle
+        pickle.dump(monotonics, open('monotonics.pkl', 'wb'))
+
+    # Combine masks with the '&' operator
+    # (intersection in numpy), e.g.:
+    # data_frame[mask_highVbias(125) & mask_monotonics]
+    def mask_highVbias(V):
+        return data_frame['V bias'] > V
+
+    def mask_lowVbias(V):
+        return data_frame['V bias'] < V
+
+    def mask_highsig(ADC):
+        return data_frame['MPVs'].apply(max_adc) > ADC
+
+    def mask_lowsig(ADC):
+        return data_frame['MPVs'].apply(max_adc) < ADC
+
+    mask_monotonics = data_frame['MPVs'].apply(is_monotonic)