Changeset 517 in lmdz_wrf for trunk/tools

Timestamp:

Jun 19, 2015, 3:21:28 PM (10 years ago)

Author:

lfita

Message:

Getting there for the means on `between' times

File:

• trunk/tools/validation_sim.py (modified) (3 diffs)

trunk/tools/validation_sim.py

@@ -1120 +1120 @@
     return
 
+class Quantiles(object):
+    """ Class to provide quantiles from a given arrayof values
+    """
+
+    def __init__(self, values, Nquants):
+        import numpy.ma as ma
+
+        if values is None:
+            self.quantilesv = None
+
+        else:
+            self.quantilesv = []
+
+            vals0 = values.flatten()
+            Nvalues = len(vals0)
+            vals = ma.masked_equal(vals0, None)
+            Nvals=len(vals.compressed())
+
+            sortedvals = sorted(vals.compressed())
+            for iq in range(Nquants):
+                self.quantilesv.append(sortedvals[int((Nvals-1)*iq/Nquants)])
+
+            self.quantilesv.append(sortedvals[Nvals-1])
+
+
 def getting_ValidationValues(okind, dimt, ds, trjpos, ovs, ovo, tvalues, oFill, Ng, kvals):
     """ Function to get the values to validate accroding to the type of observation
@@ -1157 +1182 @@
             if it == 0:
                 postf[uniqt[it]] = [0,0]
+            elif it == 1:
+                posprev = postf[uniqt[it-1]][1]
+                posit = list(tvalues[:,3]).index(uniqt[it])
+                postf[uniqt[it]] = [posprev, posit+1]
             else:
                 posprev = postf[uniqt[it-1]][1]
-                posit = tvalues[:,3].index(uniqt[it])
-                postf[uniqt[it]] = [posprev, posit]
-        print postf
-        quit()
+                posit = list(tvalues[:,3]).index(uniqt[it])
+                postf[uniqt[it]] = [posprev+1, posit+1]
     elif kvals == 'tbackwardOmean':
         print '  ' + fname + ':',kvals,'!!'
@@ -1346 +1373 @@
             None
 
-        for it in range(dimtf):
-            tv = uniqt(it)
-            
+        for it in range(1,dimtf):
+            tv = uniqt[it]
+            intv = postf[tv]
+
+# Temporal statistics
+            print sovalues[intv[0]:intv[1]]
+            sovs = np.array(sovalues[intv[0]:intv[1]])[:,0]
+            minv = np.min(sovs)
+            maxv = np.max(sovs)
+            meanv = np.mean(sovs)
+            stdv = np.std(sovs)
+
+            fsovalues.append([meanv, np.array(sovalues[intv[0]:intv[1]])[0,1], minv, \
+              maxv, stdv])
+            print 'fsovalues:',fsovalues
+            if ds.has_key('Z'):
+                ftrjs[it,
+                for iz in range(2*Ng+1):
+                    for iy in rangedd(2*Ng+1):
+                        for ix in rangedd(2*Ng+1):
+                            fsoSvalues[it,iz,iy,ix] = np.meanv(soSvalues[intv[0]:    \
+                              intv[1],iz,iy,ix])
 
     elif kvals == 'tbackwardOmean':