Changeset 382 for trunk/UTIL/PYTHON/myplot.py

Timestamp:

Nov 14, 2011, 4:14:44 PM (13 years ago)

Author:

acolaitis

Message:

PYTHON.

Added new option: --yint, to switch the way vertical coordinates are treated when --vert z1,z2 is specified.

Without specifying --yint, a simple average of the field is done along the vertical coordinate:
mean(vert[z1,z2])

When specifying --yint, a trapezoidal integration of the field is performed along the vertical coordinate.
It is worth mentionning that this operation greatly depends on the "quality" of the z-axis data. By default, z-axis values
stored in the netcdf file for GCM is an approximation. We recommend using zrecast on the file to get trusty values of altitude.
This can be simply done by specifying -i 4 in the plot command (for gcm).

File:

• trunk/UTIL/PYTHON/myplot.py (modified) (5 diffs)

trunk/UTIL/PYTHON/myplot.py

@@ -56 +56 @@
     return field
 
-## Author: AS + TN
-def reducefield (input,d4=None,d3=None,d2=None,d1=None):
+## Author: AS + TN + AC
+def reducefield (input,d4=None,d3=None,d2=None,d1=None,yint=False,alt=None):
     ### we do it the reverse way to be compliant with netcdf "t z y x" or "t y x" or "y x"
     ### it would be actually better to name d4 d3 d2 d1 as t z y x
@@ -97 +97 @@
         elif max(d2) >= shape[2]: error = True
         elif max(d1) >= shape[3]: error = True
-        elif d4 is not None and d3 is not None and d2 is not None and d1 is not None:  
-            output = mean(input[d4,:,:,:],axis=0); output = mean(output[d3,:,:],axis=0); output = mean(output[d2,:],axis=0); output = mean(output[d1],axis=0)
+        elif d4 is not None and d3 is not None and d2 is not None and d1 is not None:
+             output = mean(input[d4,:,:,:],axis=0); output = reduce_zaxis(output[d3,:,:],ax=0,yint=yint,vert=alt[d3]); output = mean(output[d2,:],axis=0); output = mean(output[d1],axis=0)
         elif d4 is not None and d3 is not None and d2 is not None: 
-            output = mean(input[d4,:,:,:],axis=0); output = mean(output[d3,:,:],axis=0); output = mean(output[d2,:],axis=0)
+            output = mean(input[d4,:,:,:],axis=0); output = reduce_zaxis(output[d3,:,:],ax=0,yint=yint,vert=alt[d3]); output = mean(output[d2,:],axis=0)
         elif d4 is not None and d3 is not None and d1 is not None: 
-            output = mean(input[d4,:,:,:],axis=0); output = mean(output[d3,:,:],axis=0); output = mean(output[:,d1],axis=1)
+            output = mean(input[d4,:,:,:],axis=0); output = reduce_zaxis(output[d3,:,:],ax=0,yint=yint,vert=alt[d3]); output = mean(output[:,d1],axis=1)
         elif d4 is not None and d2 is not None and d1 is not None: 
             output = mean(input[d4,:,:,:],axis=0); output = mean(output[:,d2,:],axis=1); output = mean(output[:,d1],axis=1)
         elif d3 is not None and d2 is not None and d1 is not None: 
-            output = mean(input[:,d3,:,:],axis=1); output = mean(output[:,d2,:],axis=1); output = mean(output[:,d1],axis=1) 
-        elif d4 is not None and d3 is not None:  output = mean(input[d4,:,:,:],axis=0); output = mean(output[d3,:,:],axis=0)
+            output = reduce_zaxis(input[:,d3,:,:],ax=1,yint=yint,vert=alt[d3]); output = mean(output[:,d2,:],axis=1); output = mean(output[:,d1],axis=1) 
+        elif d4 is not None and d3 is not None:  output = mean(input[d4,:,:,:],axis=0); output = reduce_zaxis(output[d3,:,:],ax=0,yint=yint,vert=alt[d3])
         elif d4 is not None and d2 is not None:  output = mean(input[d4,:,:,:],axis=0); output = mean(output[:,d2,:],axis=1)
         elif d4 is not None and d1 is not None:  output = mean(input[d4,:,:,:],axis=0); output = mean(output[:,:,d1],axis=2)
-        elif d3 is not None and d2 is not None:  output = mean(input[:,d3,:,:],axis=1); output = mean(output[:,d2,:],axis=1)
-        elif d3 is not None and d1 is not None:  output = mean(input[:,d3,:,:],axis=1); output = mean(output[:,:,d1],axis=0)
+        elif d3 is not None and d2 is not None:  output = reduce_zaxis(input[:,d3,:,:],ax=1,yint=yint,vert=alt[d3]); output = mean(output[:,d2,:],axis=1)
+        elif d3 is not None and d1 is not None:  output = reduce_zaxis(input[:,d3,:,:],ax=1,yint=yint,vert=alt[d3]); output = mean(output[:,:,d1],axis=0)
         elif d2 is not None and d1 is not None:  output = mean(input[:,:,d2,:],axis=2); output = mean(output[:,:,d1],axis=2)
         elif d1 is not None:                     output = mean(input[:,:,:,d1],axis=3)
         elif d2 is not None:                     output = mean(input[:,:,d2,:],axis=2)
-        elif d3 is not None:                     output = mean(input[:,d3,:,:],axis=1)
+        elif d3 is not None:                     output = reduce_zaxis(input[:,d3,:,:],ax=0,yint=yint,vert=alt[d3])
         elif d4 is not None:                     output = mean(input[d4,:,:,:],axis=0)
     dimension = np.array(output).ndim
@@ -121 +121 @@
     print 'dim,shape: ',dimension,shape
     return output, error
+
+## Author: AC
+
+def reduce_zaxis (input,ax=None,yint=False,vert=None):
+    from mymath import max,mean
+    from scipy import integrate
+    if yint:
+       output = integrate.trapz(input,x=vert,axis=ax)
+    else:
+       output = mean(input,axis=ax)
+    return output
 
 ## Author: AS + TN
@@ -578 +589 @@
              "ALBBARE":      "%.2f",\
              "TAU":          "%.1f",\
+             "CO2":          "%.2f",\
              #### T.N.
              "TEMP":         "%.0f",\
@@ -617 +629 @@
              "ALBBARE":      "spectral",\
              "TAU":          "YlOrBr_r",\
+             "CO2":          "YlOrBr_r",\
              #### T.N.
              "MTOT":         "Jet",\