Changeset 520 in lmdz_wrf for trunk/tools

Timestamp:

Jun 19, 2015, 4:16:37 PM (10 years ago)

Author:

lfita

Message:

Removing `simobsTvalues'

File:

• trunk/tools/validation_sim.py (modified) (10 diffs)

trunk/tools/validation_sim.py

@@ -1163 +1163 @@
       sovalues= simulated values at the observation point and time
       soSvalues= values Ngrid points around the simulated point
-      soTvalues= values around observed times (for `single-station')
       soTtvalues= inital/ending period between two consecutive obsevations (for `single-station')
       trjs= trajectory on the simulation space
@@ -1215 +1214 @@
 
     if okind == 'single-station':
-        soTvalues = {}
         soTtvalues = np.zeros((dimt,2), dtype=np.float)
     else:
@@ -1270 +1268 @@
                 itod = int( (int(tvalues[it+1,1]) - ito) / 2 )
                 itof = ito + itod
-
-            slicev = ds['T'][1] + ':' + str(itoi) + '@' + str(itof + 1)
-
-            slicevar, dimslice = slice_variable(ovo, slicev)
-            soTvalues[str(it)] = slicevar
 
             soTtvalues[it,0] = valdimobs['T'][itoi]
@@ -1350 +1343 @@
 ##
     if kvals == 'instantaneous':
-        return sovalues, soSvalues, soTvalues, soTtvalues, trjs
+        return sovalues, soSvalues, soTtvalues, trjs
 
     elif kvals == 'tbackwardSmean':
@@ -1368 +1361 @@
 
         if okind == 'single-station':
-            fsoTvalues = {}
             fsoTtvalues = np.zeros((dimtf,2), dtype=np.float)
         else:
@@ -1407 +1399 @@
                           iy,ix])
             print fsoSvalues[it,]
-            print 'soTvalues________________',type(soTvalues)
-            for itt in range(intv[0],intv[1]):
-                print soTvalues[str(itt)]
             fsoTtvalues[it,0] = soTtvalues[intv[0],0]
             fsoTtvalues[it,1] = soTtvalues[intv[1],0]
             print fsoTtvalues[it,:]
-            quit()
-
+
+        quit()
+        return sovalues, soSvalues, soTtvalues, trjs
 
     elif kvals == 'tbackwardOmean':
@@ -1423 +1413 @@
 
 
-    return sovalues, soSvalues, soTvalues, soTtvalues, trjs
+    return 
 
 
@@ -1939 +1929 @@
 
 # Observed values temporally exact times
-    Esimobsvalues, EsimobsSvalues, EsimobsTvalues, EsimobsTtvalues, trjsim =         \
+    Esimobsvalues, EsimobsSvalues, EsimobsTtvalues, trjsim =                         \
         getting_ValidationValues(obskind, Nexactt, dims, trajpos, ovsim, ovobs,      \
         exacttvalues, oFillValue, Ngrid, 'instantaneous')
 
 # Observed values temporally around coincident times
-    simobsvalues, simobsSvalues, simobsTvalues, simobsTtvalues, trjsim =             \
+    simobsvalues, simobsSvalues, simobsTtvalues, trjsim =                            \
         getting_ValidationValues(obskind, Ncoindt, dims, trajpos, ovsim, ovobs,      \
         coindtvalues, oFillValue, Ngrid, 'tbackwardSmean')
@@ -2109 +2099 @@
           Earoundstats[2,it])
 
-# Statistics around obs values exact
-    Earoundostats = np.zeros((6,Nexactt), dtype=np.float)
-
-    for it in range(Nexactt):
-        obsmask = ma.masked_equal(EsimobsTvalues[str(it)], fillValueF)
-        obsmask2 = obsmask*obsmask
-
-        Earoundostats[0,it] = len(obsmask.flatten())
-        Earoundostats[1,it] = obsmask.min()
-        Earoundostats[2,it] = obsmask.max()
-        Earoundostats[3,it] = obsmask.mean()
-        Earoundostats[4,it] = obsmask2.mean()
-        Earoundostats[5,it] = np.sqrt(Earoundostats[4,it] - Earoundostats[3,it]*     \
-          Earoundostats[3,it])
-
 # Statistics around sim values between
     aroundstats = np.zeros((5,Ncoindt), dtype=np.float)
@@ -2133 +2108 @@
         aroundstats[4,it] = np.sqrt(aroundstats[3,it] - aroundstats[2,it]*           \
           aroundstats[2,it])
-
-# Statistics around obs values between
-    aroundostats = np.zeros((6,Ncoindt), dtype=np.float)
-
-    for it in range(Ncoindt):
-        obsmask = ma.masked_equal(simobsTvalues[str(it)], fillValueF)
-        obsmask2 = obsmask*obsmask
-
-        aroundostats[0,it] = len(obsmask.flatten())
-        aroundostats[1,it] = obsmask.min()
-        aroundostats[2,it] = obsmask.max()
-        aroundostats[3,it] = obsmask.mean()
-        aroundostats[4,it] = obsmask2.mean()
-        aroundostats[5,it] = np.sqrt(aroundostats[4,it] - aroundostats[3,it]*        \
-          aroundostats[3,it])
 
 # exact sim Values to netCDF