Merge branch 'CLIMATE-938' of https://github.com/MichaelArthurAnderson/climate
diff --git a/ocw/dataset_processor.py b/ocw/dataset_processor.py
index cf2e90e..a167acc 100755
--- a/ocw/dataset_processor.py
+++ b/ocw/dataset_processor.py
@@ -15,20 +15,19 @@
# limitations under the License.
#
-from ocw import dataset as ds
-import ocw.utils as utils
-
import datetime
+import logging
+
+import netCDF4
import numpy as np
import numpy.ma as ma
-from scipy.interpolate import griddata
import scipy.ndimage
-from scipy.stats import rankdata
-from scipy.ndimage import map_coordinates
-import netCDF4
from matplotlib.path import Path
+from scipy.interpolate import griddata
+from scipy.ndimage import map_coordinates
-import logging
+import ocw.utils as utils
+from ocw import dataset as ds
logger = logging.getLogger(__name__)
@@ -149,7 +148,7 @@
It is the same as the number of time indicies to be averaged.
length of time dimension in the rebinned dataset) =
(original time dimension length/nt_average)
- :type temporal_resolution: integer
+ :type nt_average: integer
:returns: A new temporally rebinned Dataset
:rtype: :class:`dataset.Dataset`
@@ -505,10 +504,32 @@
:raises: ValueError
'''
+
+ # https://issues.apache.org/jira/browse/CLIMATE-938
+ # netCDF datetimes allow for a variety of calendars while Python has
+ # only one. This would throw an error about a calendar mismatch when
+ # comparing a Python datetime object to a netcdf datetime object.
+ # Cast the date as best we can so the comparison will compare like
+ # data types This will still throw an excdeption if the start / end date are
+ # not valid in given calendar. February 29th in a DatetimeNoLeap calendar for example.
+ slice_start_time = start_time
+ slice_end_time = end_time
+
+ if isinstance(target_dataset.times.item(0), netCDF4.netcdftime._netcdftime.datetime):
+ slice_start_time =\
+ type(target_dataset.times.item(0))(start_time.year, start_time.month, start_time.day,
+ start_time.hour, start_time.minute, start_time.second)
+
+ slice_end_time =\
+ type(target_dataset.times.item(0))(end_time.year, end_time.month, end_time.day,
+ end_time.hour, end_time.minute, end_time.second)
+
start_time_index = np.where(
- target_dataset.times >= start_time)[0][0]
+ target_dataset.times >= slice_start_time)[0][0]
+
end_time_index = np.where(
- target_dataset.times <= end_time)[0][-1]
+ target_dataset.times <= slice_end_time)[0][-1]
+
new_times = target_dataset.times[start_time_index:end_time_index + 1]
new_values = target_dataset.values[start_time_index:end_time_index + 1, :]
