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From good...@apache.org
Subject svn commit: r1515640 - /incubator/climate/branches/RefactorInput/ocw/dataset_processor.py
Date Mon, 19 Aug 2013 22:13:24 GMT
Author: goodale
Date: Mon Aug 19 22:13:23 2013
New Revision: 1515640

URL: http://svn.apache.org/r1515640
Log:
GENERAL WORK: Pulled the code that makes new Year, Month, Day values based on the output unit,
to a function that is called now, instead of duplicate blocks of code.

Also changed it so a variable for unique_times is created once, then used in the code instead
of calling np.unique(times) repeatedly.

Modified:
    incubator/climate/branches/RefactorInput/ocw/dataset_processor.py

Modified: incubator/climate/branches/RefactorInput/ocw/dataset_processor.py
URL: http://svn.apache.org/viewvc/incubator/climate/branches/RefactorInput/ocw/dataset_processor.py?rev=1515640&r1=1515639&r2=1515640&view=diff
==============================================================================
--- incubator/climate/branches/RefactorInput/ocw/dataset_processor.py (original)
+++ incubator/climate/branches/RefactorInput/ocw/dataset_processor.py Mon Aug 19 22:13:23
2013
@@ -312,14 +312,17 @@ def _rcmes_calc_average_on_new_time_unit
     # Year list
     if unit=='annual':
         timeunits = np.array([int(d.strftime("%Y")) for d in dates])
+        unique_times = np.unique(timeunits)
          
     # YearMonth format list
     if unit=='monthly':
         timeunits = np.array([int(d.strftime("%Y%m")) for d in dates])
+        unique_times = np.unique(timeunits)
 
     # YearMonthDay format list
     if unit=='daily':
         timeunits = np.array([int(d.strftime("%Y%m%d")) for d in dates])
+        unique_times = np.unique(timeunits)
 
 
     # TODO: add pentad setting using Julian days?
@@ -331,6 +334,7 @@ def _rcmes_calc_average_on_new_time_unit
         for i in np.arange(len(dates)):
             timeunits.append(999)  # i.e. we just want the same value for all times.
         timeunits = np.array(timeunits, dtype=int)
+        unique_times = np.unique(timeunits)
 
     # empty list to store new times
     newTimesList = []
@@ -339,55 +343,35 @@ def _rcmes_calc_average_on_new_time_unit
     #   i.e. if data are already on required time unit then just pass data through and 
     #        calculate and return representative datetimes.
     processing_required = True
-    if len(timeunits)==(len(np.unique(timeunits))):
+    if len(timeunits)==(len(unique_times)):
         processing_required = False
 
     # 1D data arrays, i.e. time series
     if data.ndim==1:
         # Create array to store the resulting data
-        meanstore = np.zeros(len(np.unique(timeunits)))
+        meanstore = np.zeros(len(unique_times))
   
         # Calculate the means across each unique time unit
         i=0
-        for myunit in np.unique(timeunits):
+        for myunit in unique_times:
             if processing_required:
                 datam=ma.masked_array(data,timeunits!=myunit)
                 meanstore[i] = ma.average(datam)
             
             # construct new times list
-            smyunit = str(myunit)
-            if len(smyunit)==4:  # YYYY
-                yyyy = int(myunit[0:4])
-                mm = 1
-                dd = 1
-            if len(smyunit)==6:  # YYYYMM
-                yyyy = int(smyunit[0:4])
-                mm = int(smyunit[4:6])
-                dd = 1
-            if len(smyunit)==8:  # YYYYMMDD
-                yyyy = int(smyunit[0:4])
-                mm = int(smyunit[4:6])
-                dd = int(smyunit[6:8])
-            if len(smyunit)==3:  # Full time range
-                # Need to set an appropriate time representing the mid-point of the entire
time span
-                dt = dates[-1]-dates[0]
-                halfway = dates[0]+(dt/2)
-                yyyy = int(halfway.year)
-                mm = int(halfway.month)
-                dd = int(halfway.day)
-
+            yyyy, mm, dd = _create_new_year_month_day(myunit, dates)
             newTimesList.append(datetime.datetime(yyyy,mm,dd,0,0,0,0))
             i = i+1
 
     # 3D data arrays
     if data.ndim==3:
         # Create array to store the resulting data
-        meanstore = np.zeros([len(np.unique(timeunits)),data.shape[1],data.shape[2]])
+        meanstore = np.zeros([len(unique_times),data.shape[1],data.shape[2]])
   
         # Calculate the means across each unique time unit
         i=0
         datamask_store = []
-        for myunit in np.unique(timeunits):
+        for myunit in unique_times:
             if processing_required:
                 mask = np.zeros_like(data)
                 mask[timeunits!=myunit,:,:] = 1.0
@@ -400,26 +384,7 @@ def _rcmes_calc_average_on_new_time_unit
                 datam = ma.masked_array(data,np.logical_or(mask,datamask_at_this_timeunit))
                 meanstore[i,:,:] = ma.average(datam,axis=0)
             # construct new times list
-            smyunit = str(myunit)
-            if len(smyunit)==4:  # YYYY
-                yyyy = int(smyunit[0:4])
-                mm = 1
-                dd = 1
-            elif len(smyunit)==6:  # YYYYMM
-                yyyy = int(smyunit[0:4])
-                mm = int(smyunit[4:6])
-                dd = 1
-            elif len(smyunit)==8:  # YYYYMMDD
-                yyyy = int(smyunit[0:4])
-                mm = int(smyunit[4:6])
-                dd = int(smyunit[6:8])
-            elif len(smyunit)==3:  # Full time range
-                # Need to set an appropriate time representing the mid-point of the entire
time span
-                dt = dates[-1]-dates[0]
-                halfway = dates[0]+(dt/2)
-                yyyy = int(halfway.year)
-                mm = int(halfway.month)
-                dd = int(halfway.day)
+            yyyy, mm, dd = _create_new_year_month_day(myunit, dates)
             newTimesList.append(datetime.datetime(yyyy,mm,dd))
             i += 1
 
@@ -433,6 +398,30 @@ def _rcmes_calc_average_on_new_time_unit
 
     return meanstorem, newTimesList
 
+def _create_new_year_month_day(time_unit, dates):
+    smyunit = str(time_unit)
+    if len(smyunit)==4:  # YYYY
+        yyyy = int(smyunit[0:4])
+        mm = 1
+        dd = 1
+    elif len(smyunit)==6:  # YYYYMM
+        yyyy = int(smyunit[0:4])
+        mm = int(smyunit[4:6])
+        dd = 1
+    elif len(smyunit)==8:  # YYYYMMDD
+        yyyy = int(smyunit[0:4])
+        mm = int(smyunit[4:6])
+        dd = int(smyunit[6:8])
+    elif len(smyunit)==3:  # Full time range
+        # Need to set an appropriate time representing the mid-point of the entire time span
+        dt = dates[-1]-dates[0]
+        halfway = dates[0]+(dt/2)
+        yyyy = int(halfway.year)
+        mm = int(halfway.month)
+        dd = int(halfway.day)
+    
+    return (yyyy, mm, dd)
+
 def _congrid(a, newdims, method='linear', centre=False, minusone=False):
     '''
     This function is from http://wiki.scipy.org/Cookbook/Rebinning - Example 3



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