Changeset 2392 in lmdz_wrf for trunk

Timestamp:

Mar 11, 2019, 4:20:14 PM (6 years ago)

Author:

lfita

Message:

Starting to fix `temporal_stats' for ' agg'
Adding date fixing in 'CFtimes_plot'

Location:

trunk/tools

Files:

• drawing_tools.py (modified) (1 diff)
• nc_var_tools.py (modified) (2 diffs)

trunk/tools/drawing_tools.py

@@ -2393 +2393 @@
         Srefdate = txtunits[Ntxtunits - 2]
 
+    # Looking for too short date
+    if len(Srefdate) == 8:
+        # Assuming Y-m[1 digit]-d[1 digit]
+        if Srefdate.count('-') == 2:
+            vals=Srefdate.split('-')
+            Srefdate=''
+            Srefdate = vals[0] + '-' + vals[1].zfill(2) + '-' + vals[2].zfill(2) 
+        elif Srefdate.count('/') == 2:
+            vals=Srefdate.split('/')
+            Srefdate=''
+            Srefdate = vals[0] + '/' + vals[1].zfill(2) + '/' + vals[2].zfill(2) 
+        else:
+            print errormsg
+            print '  ' + fname + ": I do not know how to deal with this '"+ Srefdate+\
+              "' date !!"
+            quit(-1)
+
     if not trefT == -1:
 #        print '  ' + fname + ': refdate with time!'

trunk/tools/nc_var_tools.py

@@ -26115 +26115 @@
 
     Lperiodn = {'year': 'yearly', 'month': 'monthly', 'day': 'daily',                \
-      'LTday': 'local-time daily', 'hour': 'hourly', 'minute': 'minutely'}
+      'LTday': 'local-time daily', 'hour': 'hourly', 'minute': 'minutely',           \
+      'aggseason': 'seasonal aggregation'}
     Lstatn = {'min': 'minimum', 'max': 'maximum', 'mean': 'mean',                    \
       'mean2': 'quadratic mean', 'std': 'standard deviation',                        \
@@ -26453 +26454 @@
                     tvals = ovar[tuple(timeslc)]
                 else:
-                    newshape = list(vals.shape)
-                    aggslc = tslc[islc]
+                    newshape = list(ovar.shape)
+                    aggslc = tslcs[islc]
                     NNtslc = len(aggslc)
                     newshape[itdim] = NNtslc
-                    tvals = np.zeros(tuple(newshape), dtype=vals.dtype)
-                    origshape = list(vals.shape)
-                    iishape = list(vals.shape)
+                    tvals = np.zeros(tuple(newshape), dtype=ovar.dtype)
+                    origshape = list(ovar.shape)
+                    iishape = list(ovar.shape)
                     for iislc in range(NNtslc):
                         origshape[itdim] = aggslc[iislc]
                         iishape[itdim] = iislc
-                        tvals[tuple(iishape)] = vals[tuple(origshape)]
+                        tvals[tuple(iishape)] = ovar[tuple(origshape)]
 
                 if statn == 'min':