Make time-series scripts use timeseries_yr1 & 2

This merge updates the 3 time-series analysis scripts to make use
of the time series start and end dates, computed via timeseries_yr1
and timeseries_yr2, allowing analysis of a subset of the output
data.

Author: xylar

mpas_analysis/ocean/ohc_timeseries.py

@@ -11,12 +11,14 @@
 
 from ..shared.io import NameList, StreamsFile
 
+from ..shared.timekeeping.Date import Date
+
 def ohc_timeseries(config):
     """
     Performs analysis of ocean heat content (OHC) from time-series output.
 
     Author: Xylar Asay-Davis, Milena Veneziani
-    Last Modified: 11/25/2016
+    Last Modified: 11/28/2016
     """
 
     # read parameters from config file
@@ -50,8 +52,6 @@ def ohc_timeseries(config):
     plots_dir = config.get('paths','plots_dir')
 
     yr_offset = config.getint('time','yr_offset')
-    timeseries_yr1 = yr_offset + config.getint('time', 'timeseries_yr1')
-    timeseries_yr2 = yr_offset + config.getint('time', 'timeseries_yr2')
 
     N_movavg = config.getint('ohc_timeseries','N_movavg')
 
@@ -90,14 +90,17 @@ def ohc_timeseries(config):
 
     ds = remove_repeated_time_index(ds)
 
+    # convert the start and end dates to datetime objects using
+    # the Date class, which ensures the results are within the
+    # supported range
+    time_start = Date(startDate).to_datetime(yr_offset)
+    time_end = Date(endDate).to_datetime(yr_offset)
     # select only the data in the specified range of years
-    # time_start = datetime.datetime(timeseries_yr1, 1, 1)
-    # time_end = datetime.datetime(timeseries_yr2, 12, 31)
-    # ds = ds.sel(Time=slice(time_start, time_end))
+    ds = ds.sel(Time=slice(time_start, time_end))
 
     # Select year-1 data and average it (for later computing anomalies)
-    time_start = datetime.datetime(timeseries_yr1, 1, 1)
-    time_end = datetime.datetime(timeseries_yr1, 12, 31)
+    time_start = datetime.datetime(time_start.year, 1, 1)
+    time_end = datetime.datetime(time_start.year, 12, 31)
     ds_yr1 = ds.sel(Time=slice(time_start,time_end))
     mean_yr1 = ds_yr1.mean('Time')
 

mpas_analysis/ocean/sst_timeseries.py

@@ -9,13 +9,15 @@
 
 from ..shared.io import StreamsFile
 
+from ..shared.timekeeping.Date import Date
+
 def sst_timeseries(config):
     """
     Performs analysis of the time-series output of sea-surface temperature
     (SST).
 
     Author: Xylar Asay-Davis, Milena Veneziani
-    Last Modified: 10/27/2016
+    Last Modified: 11/28/2016
     """
     # Define/read in general variables
     print "  Load SST data..."
@@ -55,6 +57,14 @@ def sst_timeseries(config):
                                            onlyvars=['time_avg_avgValueWithinOceanRegion_avgSurfaceTemperature']))
     ds = remove_repeated_time_index(ds)
 
+    # convert the start and end dates to datetime objects using
+    # the Date class, which ensures the results are within the
+    # supported range
+    time_start = Date(startDate).to_datetime(yr_offset)
+    time_end = Date(endDate).to_datetime(yr_offset)
+    # select only the data in the specified range of years
+    ds = ds.sel(Time=slice(time_start, time_end))
+
     SSTregions = ds.time_avg_avgValueWithinOceanRegion_avgSurfaceTemperature
 
     year_start = (pd.to_datetime(ds.Time.min().values)).year

mpas_analysis/sea_ice/timeseries.py

@@ -11,12 +11,14 @@
 from ..shared.io import StreamsFile
 from ..shared.io.utility import paths
 
+from ..shared.timekeeping.Date import Date
+
 def seaice_timeseries(config):
     """
     Performs analysis of time series of sea-ice properties.
 
     Author: Xylar Asay-Davis, Milena Veneziani
-    Last Modified: 10/27/2016
+    Last Modified: 11/28/2016
     """
 
     # read parameters from config file
@@ -79,6 +81,14 @@ def seaice_timeseries(config):
                                                      'timeSeriesStatsMonthly_avg_iceVolumeCell_1']))
     ds = remove_repeated_time_index(ds)
 
+    # convert the start and end dates to datetime objects using
+    # the Date class, which ensures the results are within the
+    # supported range
+    time_start = Date(startDate).to_datetime(yr_offset)
+    time_end = Date(endDate).to_datetime(yr_offset)
+    # select only the data in the specified range of years
+    ds = ds.sel(Time=slice(time_start, time_end))
+
     ds = ds.merge(dsmesh)
 
     year_start = (pd.to_datetime(ds.Time.min().values)).year