Update deprecated pandas time intervals

metobscommon/util/nc.py

@@ -223,7 +223,7 @@ def calculate_wind_gust(wind_speed_5s, wind_speed_2m):
 
     """
     # 1 minute rolling peaks
-    wind_peak_1m = wind_speed_5s.rolling(window='1T', center=False).max()
+    wind_peak_1m = wind_speed_5s.rolling(window='1min', center=False).max()
     # criteria for a fast wind to be considered a wind gust
     gust_mask = (wind_speed_2m >= KNOTS_9) & \
                 (wind_peak_1m >= wind_speed_2m + KNOTS_5)
@@ -231,9 +231,9 @@ def calculate_wind_gust(wind_speed_5s, wind_speed_2m):
 
     # determine highest gust in the last 10 minutes
     # 5 seconds * 120 = 10 minutes
-    max_10m_gusts = gusts.rolling(window='10T', center=False).max()
+    max_10m_gusts = gusts.rolling(window='10min', center=False).max()
     # Minimum 5-second average in the past 10 minutes
-    min_10m_5avg = wind_speed_5s.rolling(window='10T', center=False).min()
+    min_10m_5avg = wind_speed_5s.rolling(window='10min', center=False).min()
     # criteria for a wind gust to be reportable
     reportable_mask = (max_10m_gusts >= wind_speed_2m + KNOTS_3) & \
                       (wind_speed_2m > KNOTS_2) & \
@@ -253,13 +253,13 @@ def minute_averages(frame):
     frame['wind_east'], frame['wind_north'], _ = calc.wind_vector_components(frame['wind_speed'], frame['wind_dir'])
     # round up each 1 minute group so data at time T is the average of data
     # from T - 1 (exclusive) to T (inclusive).
-    new_frame = frame.resample('1T', closed='right').mean()
-    new_frame.index = new_frame.index + to_offset("1T")
+    new_frame = frame.resample('1min', closed='right').mean()
+    new_frame.index = new_frame.index + to_offset("1min")
 
     # 2 minute rolling average of 5 second data (5 seconds * 24 = 120 seconds = 2 minutes)
     winds_frame_5s = frame[['wind_speed', 'wind_east', 'wind_north']]
-    winds_frame_5s = winds_frame_5s.resample('5S', closed='right').mean()
-    winds_frame_5s.index = winds_frame_5s.index + to_offset('5S')
+    winds_frame_5s = winds_frame_5s.resample('5s', closed='right').mean()
+    winds_frame_5s.index = winds_frame_5s.index + to_offset('5s')
     winds_frame_2m = winds_frame_5s.rolling(24, win_type='boxcar').mean()
 
     # rolling average is used for 1 minute output
@@ -270,8 +270,8 @@ def minute_averages(frame):
 
     gust = calculate_wind_gust(winds_frame_5s['wind_speed'], winds_frame_2m['wind_speed'])
     # "average" the gusts to minute resolution to match the rest of the data
-    new_gust = gust.resample('1T', closed='right').max()
-    new_gust.index = new_gust.index + to_offset('1T')
+    new_gust = gust.resample('1min', closed='right').max()
+    new_gust.index = new_gust.index + to_offset('1min')
     new_frame['gust'] = new_gust
 
     return new_frame.fillna(np.nan)