Merge branch 'CLIMATE-651'
Conflicts:
ocw/utils.py
Project: http://git-wip-us.apache.org/repos/asf/climate/repo
Commit: http://git-wip-us.apache.org/repos/asf/climate/commit/496f8bb0
Tree: http://git-wip-us.apache.org/repos/asf/climate/tree/496f8bb0
Diff: http://git-wip-us.apache.org/repos/asf/climate/diff/496f8bb0
Branch: refs/heads/master
Commit: 496f8bb055420adcf439a76c8fa0b03b35a3d920
Parents: 7cfd7e9 77227b1
Author: huikyole <[email protected]>
Authored: Sun Aug 2 20:29:22 2015 -0700
Committer: huikyole <[email protected]>
Committed: Sun Aug 2 20:29:22 2015 -0700
----------------------------------------------------------------------
ocw/utils.py | 36 ++++++++++++++++++++++++++++++++++++
1 file changed, 36 insertions(+)
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http://git-wip-us.apache.org/repos/asf/climate/blob/496f8bb0/ocw/utils.py
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diff --cc ocw/utils.py
index 7c7776f,d6a5708..0b2f9da
--- a/ocw/utils.py
+++ b/ocw/utils.py
@@@ -344,15 -344,37 +344,51 @@@ def calc_time_series(dataset)
return t_series
+def get_temporal_overlap(dataset_array):
+ ''' Find the maximum temporal overlap across the observation and model
datasets
+
+ :param dataset_array: an array of OCW datasets
+ '''
+ start_time =[]
+ end_time =[]
+ for dataset in dataset_array:
+ start_time.append(dataset.time_range()[0])
+ end_time.append(dataset.time_range()[1])
+
+ return np.max(start_time), np.min(end_time)
++
+ def calc_subregion_area_mean_and_std(dataset_array, subregions):
+ ''' Calculate area mean and standard deviation values for a given
subregions using datasets on common grid points
+ :param dataset_array: An array of OCW Dataset Objects
+ :type list:
+ :param subregions: list of subregions
+ :type subregions: :class:`numpy.ma.array`
+ :returns: area averaged time series for the dataset of shape (ntime,
nsubregion)
+ '''
+
+ ndata = len(dataset_array)
+ dataset0 = dataset_array[0]
+ if dataset0.lons.ndim == 1:
+ lons, lats = np.meshgrid(dataset0.lons, dataset0.lats)
+ else:
+ lons = dataset0.lons
+ lats = dataset0.lats
+ subregion_array = np.zeros(lons.shape)
+ mask_array = dataset_array[0].values[0,:].mask
+ # dataset0.values.shsape[0]: length of the time dimension
+ # spatial average
+ t_series =ma.zeros([ndata, dataset0.values.shape[0], len(subregions)])
+ # spatial standard deviation
+ spatial_std =ma.zeros([ndata, dataset0.values.shape[0], len(subregions)])
+
+ for iregion, subregion in enumerate(subregions):
+ lat_min, lat_max, lon_min, lon_max = subregion[1]
+ y_index,x_index = np.where((lats >= lat_min) & (lats <= lat_max) &
(lons >= lon_min) & (lons <= lon_max))
+ subregion_array[y_index,x_index] = iregion+1
+ for idata in np.arange(ndata):
+ t_series[idata, :, iregion] =
ma.mean(dataset_array[idata].values[:,y_index, x_index], axis=1)
+ spatial_std[idata, :, iregion] =
ma.std(dataset_array[idata].values[:,y_index, x_index], axis=1)
+ subregion_array = ma.array(subregion_array, mask=mask_array)
+ return t_series, spatial_std, subregion_array
+
++>>>>>>> CLIMATE-651
