diff --git a/aosstower/level_00/influxdb.py b/aosstower/level_00/influxdb.py
index d29faa477f3c5663c363a948baedc055e34d8712..4dee9a4fd4b5fb50389517b0538a4862fc97922d 100644
--- a/aosstower/level_00/influxdb.py
+++ b/aosstower/level_00/influxdb.py
@@ -78,7 +78,13 @@ class Updater(object):
             if self.data.get(key) is None:
                 self.data[key] = np.array([])
             self.data[key] = np.append(self.data[key][time_mask], record[key])
-        # Gets the seconds from the start of the current year. This makes any interval chronologically consistent.
+        # Gets the seconds from the start of the current year. This makes any interval equal to or smaller than a
+        # year an absolute interval (ie if the submit interval was 5 minutes, then data is submitted at 5, 10, 15,
+        # etc). For example: if "reference" used the start of the current minute and we wanted to submit anything in
+        # longer intervals than a minute, then the times that data is submitted would not be consistent. i.e. if the
+        # current minute was 13 and the submit interval was 5 minutes, then data would be submitted at 18, 23, 28,
+        # etc. If data collection went down and the next recorded current minute was 14, then data would be submitted
+        # at 19, 24, 29, etc (inconsistent).
         reference = pd.datetime(record['timestamp'].year, 1, 1)
         progress = (record['timestamp'] - reference).total_seconds() % self.submit_interval
         # If data hits or will pass over a submit_interval interval, return data.
@@ -86,8 +92,8 @@ class Updater(object):
             return self._calculate_averages()
 
     def _calculate_averages(self):
-        frame = pd.DataFrame(self.data)
-        frame.set_index('timestamp', inplace=True)
+        index = self.data.pop('timestamp')
+        frame = pd.DataFrame(self.data, index=index)
         frame.mask(frame == -99999., inplace=True)
         # Add wind direction components so we can average wind direction properly.
         frame['wind_east'], frame['wind_north'], _ = calc.wind_vector_components(frame['wind_speed'],