Changeset 670 in lmdz_wrf for trunk/tools/nc_var_tools.py

Timestamp:

Jan 15, 2016, 2:46:34 PM (9 years ago)

Author:

lfita

Message:

Adding new functions:

• `varDimension': Function to find the variable with the values of a dimension. It is assumed, that given variable might have a shape with only the dimension, double precision
• `ovar_onc': Function to copy an object variable to a nother netcdf object

File:

• trunk/tools/nc_var_tools.py (modified) (13 diffs)

trunk/tools/nc_var_tools.py

@@ -17164 +17164 @@
     return slicevals
 
+def varDimension(oncf, dname):
+    """ Function to find the variable with the values of a dimension. It is assumed, that given variable
+      might have a shape with only the dimension, double precision
+      oncf: netCDF object
+      dname: name of the dimension
+    """
+    fname = 'varDimension'
+
+    if not oncf.dimensions._has_key(dname):
+        print errormsg
+        print '  ' + fname + ": NetCDF object does not have dimension '" + dname + "' !!"
+        quit(-1)
+
+# 1D variables with the dimension name
+##
+    d1var = []
+    for vn in oncf.variables:
+        ovn = oncf.variables[vn]
+        if len(ovn.shape) == 1 and ovn.dimensions[0] == dname: d1var.append(vn)
+
+    if len(d1var) != 0:
+        print '  ' + fname + ": 1D variables with dimension '" + dname + "':",d1var
+    else:
+        print warnmsg
+        print '  ' + fname ": Any  1D variables with dimension '" + dname + "' !!"
+
+# Dpouble precision
+## 
+    if len(d1var) == 0:
+        dimvarname = None
+    if len(d1var) > 1:
+        d1vardouble = []
+        for vn in d1var
+            ovn = oncf.variables[vn]
+            if ovn.dtype == np.float64(1.): d1vardouble.append(vn)
+        if len(d1vardouble) != 1:
+            print errormsg
+            print '  ' + fname + ": More than 1D double variable with dimension '" + dname + "' !!"
+            quit(-1)
+        else:
+            dimvarname = d1vardouble[0]
+    else:
+        dimvarname = d1var[0]
+
+    return dimvarname
+
+def ovar_onc(ovar, oncf):
+    """ Function to copy an object variable to a nother netcdf object
+
+    """
+    fname = 'ovar_onc'
+
+    varname = ovar.name
+    varadims = ovar.dimensions
+    vartype = ovar.dtype
+ 
+    newvar = oncf.createVariable(varname, vartype, vardims)
+    newvar = ovar[:]
+
+    for ncatr in ovar.ncattrs():
+        ncatrv = ovar.get_ncattribute(ncattr)
+        set_ncatrribute(newvar, ncatr, ncatrv)
+
+    oncf.sync()
+
+    return
+
 def SpatialWeightedMean(values, filen, varn):
     """ Function to compute the spatial mean using weights from a netCDF file
@@ -17285 +17352 @@
                     slicevalues = SliceVar(iovar,dimsloop,[id1])
                     slicewgt = SliceVar(iovarwgt,dimsloop,[id1])
-                    newvals[id1] = np.mean(ivarv[tuple(slicevalues)] /               \
-                      ivarwgtv[tuple(slicewgt)])
+                    TOTsumwgt = np.sum(1./ivarwgtv[tuple(slicewgt)])
+                    newvals[id1] = np.sum(ivarv[tuple(slicevalues)] /                \
+                      ivarwgtv[tuple(slicewgt)]) / TOTsumwgt
             elif len(loopshape) == 2:
                 newvals = np.ones((loopshape[0],loopshape[1]),dtype=np.float)*       \
@@ -17294 +17362 @@
                         slicevalues = SliceVar(iovar,dimsloop,[id1,id2])
                         slicewgt = SliceVar(iovarwgt,dimsloop,[id1,id2])
-                        newvals[id1,id2] = np.mean(ivarv[tuple(slicevalues)] /       \
-                          ivarwgtv[tuple(slicewgt)])
+                        TOTsumwgt = np.sum(1./ivarwgtv[tuple(slicewgt)])
+                        newvals[id1,id2] = np.sum(ivarv[tuple(slicevalues)] /        \
+                          ivarwgtv[tuple(slicewgt)]) / TOTsumwgt
             elif len(loopshape) == 3:
                 newvals = np.ones((loopshape[0],loopshape[1],loopshape[2]),          \
@@ -17304 +17373 @@
                             slicevalues = SliceVar(iovar,dimsloop,[id1,id2,id3])
                             slicewgt = SliceVar(iovarwgt,dimsloop,[id1,id2,id3])
-                            newvals[id1,id2,id3]= np.mean(ivarv[tuple(slicevalues)]/ \
-                              ivarwgtv[tuple(slicewgt)])
+                            TOTsumwgt = np.sum(1./ivarwgtv[tuple(slicewgt)])
+                            newvals[id1,id2,id3]= np.sum(ivarv[tuple(slicevalues)]/  \
+                              ivarwgtv[tuple(slicewgt)]) / TOTsumwgt
             outweightvals = 1./ivarwgtv[tuple(slicewgt)]
         elif oper == 'nothing':
@@ -17314 +17384 @@
                     slicevalues = SliceVar(iovar,dimsloop,[id1])
                     slicewgt = SliceVar(iovarwgt,dimsloop,[id1])
-                    newvals[id1] = np.mean(ivarv[tuple(slicevalues)]*                \
-                      ivarwgtv[tuple(slicewgt)])
+                    TOTsumwgt = np.sum(ivarwgtv[tuple(slicewgt)])
+                    newvals[id1] = np.sum(ivarv[tuple(slicevalues)]*                 \
+                      ivarwgtv[tuple(slicewgt)]) / TOTsumwgt
             elif len(loopshape) == 2:
                 newvals = np.ones((loopshape[0],loopshape[1]), dtype=np.float)*      \
@@ -17323 +17394 @@
                         slicevalues = SliceVar(iovar,dimsloop,[id1,id2])
                         slicewgt = SliceVar(iovarwgt,dimsloop,[id1,id2])
-                        newvals[id1,id2] = np.mean(ivarv[tuple(slicevalues)]*        \
-                          ivarwgtv[tuple(slicewgt)])
+                        TOTsumwgt = np.sum(ivarwgtv[tuple(slicewgt)])
+                        newvals[id1,id2] = np.sum(ivarv[tuple(slicevalues)]*         \
+                          ivarwgtv[tuple(slicewgt)]) / TOTsumwgt
             elif len(loopshape) == 3:
                 newvals = np.ones((loopshape[0],loopshape[1],loopshape[2]),          \
@@ -17333 +17405 @@
                             slicevalues = SliceVar(iovar,dimsloop,[id1,id2.id3])
                             slicewgt = SliceVar(iovarwgt,dimsloop,[id1,id2,id3])
-                            newvals[id1,id2,id3]=np.mean(ivarv[tuple(slicevalues)]*  \
-                              ivarwgtv[tuple(slicewgt)])
+                            TOTsumwgt = np.sum(ivarwgtv[tuple(slicewgt)])
+                            newvals[id1,id2,id3]=np.sum(ivarv[tuple(slicevalues)]*   \
+                              ivarwgtv[tuple(slicewgt)]) / TOTsumwgt
             outweightvals = ivarwgtv[tuple(slicewgt)]
         if oper == 'prodinv':
@@ -17354 +17427 @@
                         slicevalues = SliceVar(iovar,dimsloop,[id1,id2])
                         slicewgt = SliceVar(iovarwgt,dimsloop,[id1,id2])
-                        newvals[id1,id2] = np.mean(ivarv[tuple(slicevalues)] /       \
-                          ivarwgtv[tuple(slicewgt)])
+                        TOTsumwgt = np.sum(1./ivarwgtv[tuple(slicewgt)])
+                        newvals[id1,id2] = np.sum(ivarv[tuple(slicevalues)] /        \
+                          ivarwgtv[tuple(slicewgt)]) / TOTsumwgt
             elif len(loopshape) == 3:
                 newvals = np.ones((loopshape[0],loopshape[1],loopshape[2]),          \
@@ -17364 +17438 @@
                             slicevalues = SliceVar(iovar,dimsloop,[id1,id2,id3])
                             slicewgt = SliceVar(iovarwgt,dimsloop,[id1,id2,id3])
-                            newvals[id1,id2,id3]= np.mean(ivarv[tuple(slicevalues)]/ \
-                              ivarwgtv[tuple(slicewgt)])
+                            TOTsumwgt = np.sum(1./ivarwgtv[tuple(slicewgt)])
+                            newvals[id1,id2,id3]= np.sum(ivarv[tuple(slicevalues)]/  \
+                              ivarwgtv[tuple(slicewgt)]) / TOTsumwgt
             outweightvals = 1./ivarwgtv[tuple(slicewgt)]
     elif weightk == 'reglonlat':
@@ -17388 +17463 @@
 
         ivarwgtv = np.abs(np.cos(latv*np.pi/180.))/(lonv.shape[0]*lonv.shape[1])
+        TOTsumwgt = np.sum(ivarwgt)
         if len(loopshape) == 1:
             newvals = np.ones((loopshape[0]), dtype=np.float)*fillValueF
             for id1 in range(loopshape[0]):
                 slicevalues = SliceVar(iovar,dimsloop,[id1])
-                newvals[id1] = np.mean(ivarv[tuple(slicevalues)] * ivarwgtv)
+                newvals[id1] = np.sum(ivarv[tuple(slicevalues)]*ivarwgtv)/TOTsumwgt
         elif len(loopshape) == 2:
             newvals = np.ones((loopshape[0],loopshape[1]), dtype=np.float)*fillValueF
@@ -17398 +17474 @@
                 for id2 in range(loopshape[1]):
                     slicevalues = SliceVar(iovar,dimsloop,[id1,id2])
-                    newvals[id1,id2] = np.mean(ivarv[tuple(slicevalues)] * ivarwgtv)
+                    newvals[id1,id2] = np.sum(ivarv[tuple(slicevalues)]*ivarwgtv) /  \
+                      TOTsumwgt
         elif len(loopshape) == 3:
             newvals = np.ones((loopshape[0],loopshape[1],loopshape[2]),              \
@@ -17406 +17483 @@
                     for id3 in range(loopshape[2]):
                         slicevalues = SliceVar(iovar,dimsloop,[id1,id2,id3])
-                        newvals[id1,id2,id3] = np.mean(ivarv[tuple(slicevalues)] *   \
-                          ivarwgtv)
+                        newvals[id1,id2,id3] = np.sum(ivarv[tuple(slicevalues)] *    \
+                          ivarwgtv) / TOTsumwgt
         outweightvals = ivarwgtv
 
@@ -17418 +17495 @@
     for dim in dimsloop:
         newdim = onewnc.createDimension(dim, len(onc.dimensions[dim]))
+        vardim = varDimension(onc, dim)
+        if vardim is not None:
+            ovar_nc(onc.variables[vardim], onewnc)
+            
 
 # Output variable