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From huiky...@apache.org
Subject [09/12] climate git commit: Request for the time parser only
Date Wed, 15 Jul 2015 05:08:22 GMT
Request for the time parser only


Project: http://git-wip-us.apache.org/repos/asf/climate/repo
Commit: http://git-wip-us.apache.org/repos/asf/climate/commit/1340036f
Tree: http://git-wip-us.apache.org/repos/asf/climate/tree/1340036f
Diff: http://git-wip-us.apache.org/repos/asf/climate/diff/1340036f

Branch: refs/heads/master
Commit: 1340036ff488decfcefd6cd46c0c17bf2dfe13a8
Parents: b377af7
Author: huikyole <huikyole@argo.jpl.nasa.gov>
Authored: Fri May 1 15:02:12 2015 -0700
Committer: huikyole <huikyole@argo.jpl.nasa.gov>
Committed: Fri May 1 15:02:12 2015 -0700

----------------------------------------------------------------------
 ocw/dataset_processor.py | 70 -------------------------------------------
 1 file changed, 70 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/climate/blob/1340036f/ocw/dataset_processor.py
----------------------------------------------------------------------
diff --git a/ocw/dataset_processor.py b/ocw/dataset_processor.py
index 859c162..eb93104 100755
--- a/ocw/dataset_processor.py
+++ b/ocw/dataset_processor.py
@@ -29,45 +29,6 @@ import logging
 
 logger = logging.getLogger(__name__)
 
-def temporal_subset(target_dataset, month_index):
-    """ Temporally subset data between month_begin and month_end in each year.
-
-    For example, to extract DJF time series, monthBegin = 12, monthEnd =2
-    This can handle monthBegin=monthEnd i.e. for time series of a specific month
-
-    :param target_dataset: Dataset object that needs temporal subsetting 
-    :type target_dataset: Open Climate Workbench Dataset Object
-    :param month_index: an integer array of subset months (December ~ February: [12,1,2])
-    :type temporal_resolution: interger array
-
-    :returns: A temporal subset OCW Dataset
-    :rtype: Open Climate Workbench Dataset Object
-    """
-
-    dates = target_dataset.times
-    months = np.array([d.month for d in dates])
-    time_index = []
-    for m_value in month_index:
-        time_index = np.append(time_index, np.where(months == m_value)[0]) 
-        if m_value == month_index[0]:
-            time_index_first = np.min(np.where(months == m_value)[0])
-        if m_value == month_index[-1]:
-            time_index_last = np.max(np.where(months == m_value)[0])
-
-    time_index = np.sort(time_index)
-  
-    time_index = time_index[np.where((time_index >= time_index_first) & (time_index
<= time_index_last))]
-
-    time_index = list(time_index)
-    
-    new_dataset = ds.Dataset(target_dataset.lats,
-                             target_dataset.lons,
-                             target_dataset.times[time_index],
-                             target_dataset.values[time_index,:],
-                             target_dataset.variable,
-                             target_dataset.name) 
-    return new_dataset
-    
 def temporal_rebin(target_dataset, temporal_resolution):
     """ Rebin a Dataset to a new temporal resolution
     
@@ -107,37 +68,6 @@ def temporal_rebin(target_dataset, temporal_resolution):
     
     return new_dataset
 
-def spatial_aggregation(target_dataset, lon_min, lon_max, lat_min, lat_max):
-    """ Spatially subset a dataset within the given longitude and latitude boundaryd_lon-grid_space,
grid_lon+grid_space
-
-    :param target_dataset: Dataset object that needs spatial subsetting
-    :type target_dataset: Open Climate Workbench Dataset Object
-    :param lon_min: minimum longitude (western boundary)
-    :type lon_min: float
-    :param lon_max: maximum longitude (eastern boundary)
-    :type lon_max: float
-    :param lat_min: minimum latitude (southern boundary) 
-    :type lat_min: float
-    :param lat_max: maximum latitude (northern boundary) 
-    :type lat_max: float
-
-    :returns: A new spatially subset Dataset
-    :rtype: Open Climate Workbench Dataset Object
-    """
-
-    new_lon, new_lat = np.meshgrid(target_dataset.lons, target_dataset.lats)
-    y_index, x_index = np.where((new_lon >= lon_min) & (new_lon <= lon_max) &
(new_lat >= lat_min) & (new_lat <= lat_max))[0:2]
-
-    new_dataset = ds.Dataset(target_dataset.lats[y_index.min():y_index.max()+1],
-                             target_dataset.lons[x_index.min():x_index.max()+1],
-                             target_dataset.times,
-                             target_dataset.values[:,y_index.min():y_index.max()+1,x_index.min():x_index.max()+1],
-                             target_dataset.variable,
-                             target_dataset.name) 
-    return new_dataset 
-     
-
-
 def spatial_regrid(target_dataset, new_latitudes, new_longitudes):
     """ Regrid a Dataset using the new latitudes and longitudes
 


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