Changeset 312 in lmdz_wrf

Timestamp:

Feb 26, 2015, 11:56:22 AM (10 years ago)

Author:

lfita

Message:

Fixing a last issue on the slice when var has 4dims

File:

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

trunk/tools/nc_var_tools.py

@@ -10829 +10829 @@
 # box values
                     slicev.append(slice(0,dimz))
-                    slicev.append(slice(yrangeslice[it0][0],yrangeslice[it][1]))
-                    slicev.append(slice(xrangeslice[it0][0],xrangeslice[it][1]))
+                    slicev.append(slice(yrangeslice[it][0],yrangeslice[it][1]))
+                    slicev.append(slice(xrangeslice[it][0],xrangeslice[it][1]))
 
                     slicevnoT.append(slice(0,dimz))
@@ -10914 +10914 @@
                     rvarvalst[tuple(cslice2D)] = varobj[tuple(cslicev)]
                     for iz in range(dimz):
-                        tslice = [slice(it)]+cslice2Dhor
-                        rvarvalst[iz,:,:] = np.where(circdist[tuple(tslice)] >       \
-                          np.float(Nrad), fillValue, rvarvalst[iz,:,:]) 
+                        tslice = [slice(it,it)]+cslice2Dhor
+                        zslice = [slice(iz,iz)]+cslice2Dhor
+                        rvarvalst[tuple(zslice)] = np.where(circdist[tuple(tslice)] > \
+                          np.float(Nrad), fillValue, rvarvalst[tuple(zslice)]) 
 
                     rvarvals[it,:,:,:] = rvarvalst
@@ -10924 +10925 @@
                     maskedvals2 = maskedvals*maskedvals
                     for iz in range(dimz):
-                        rtatvarvals[it,iz,0] = varvalst[iz,box2,box2]
-                        rtatvarvals[it,iz,1] = np.min(varvalst[iz,:,:])
-                        rtatvarvals[it,iz,2] = np.max(varvalst[iz,:,:])
-                        rtatvarvals[it,iz,3] = np.mean(varvalst[iz,:,:])
-                        rtatvarvals[it,iz,4] = maskedvals2[iz,:,:].mean()
-                        rtatvarvals[it,iz,5] = np.sqrt(rstatvarvals[it,iz,4] -       \
-                          rstatvarvals[it,iz,3]*rstatvarvals[it,iz,3])
+                        rvarvals[it,iz,0] = varvalst[iz,box2,box2]
+                        rvarvals[it,iz,1] = np.min(varvalst[iz,:,:])
+                        rvarvals[it,iz,2] = np.max(varvalst[iz,:,:])
+                        rvarvals[it,iz,3] = np.mean(varvalst[iz,:,:])
+                        rvarvals[it,iz,4] = maskedvals2[iz,:,:].mean()
+                        rvarvals[it,iz,5] = np.sqrt(rvarvals[it,iz,4] -              \
+                          rvarvals[it,iz,3]*rvarvals[it,iz,3])
 
                 else: