Changeset 2192 in lmdz_wrf

Timestamp:

Oct 17, 2018, 10:20:41 PM (6 years ago)

Author:

lfita

Message:

Working in merging masks

Location:

trunk/tools

Files:

• generic_tools.py (modified) (2 diffs)
• nc_var_tools.py (modified) (1 diff)

trunk/tools/generic_tools.py

@@ -9650 +9650 @@
     return diffs, Bdiff
 
-
 def coincident_CFtimes(tvalB, tunitA, tunitB):
     """ Function to make coincident times for two different sets of CFtimes
@@ -14562 +14561 @@
     return rightCFtimeu
 
+def masks_mult(dimns1, mask1, dimns2, mask2, dimsuse, shapeuse, nonusemaskdims):
+    """ Function to multiply two sets of masks on coincident diensions
+      dimns1= list of dimensions names for mask 1
+      mask1= mask 1 
+      dimns2= list of dimensions names for mask 2
+      mask2= mask 2
+      dimnuse= list of dimensions to use
+      shapeuse: shape of being used
+      nonusedmaskdims: dictionary with values of the given dimennsios of the masks
+        not to be used
+    """
+    fname = 'masks_mult'
+
+    coincdims = list(set(dimns1) & set(dimns2))
+    NOTcoincdims = list(set(dimns1) - set(dimns2))
+    print coincdims
+    print NOTcoincdims
+    
+    NOTsliceuse = []
+    idim = 0
+    for dimn in dimsuse:
+        if not searchInlist(dimns1, dimn):
+            NOTsliceuse.append(dimn)
+            continue
+        if searchInlist(dimns2, dimn):
+            NOTsliceuse.append(dimn)
+            continue
+
+    mask = np.zeros(shapeuse, dtype=bool)
+    slices = []
+    idim = 0
+    for dimn in dimsuse:   
+        if searchInlist(NOTsliceuse, dimn):
+            slices.append(-9)
+        else:
+            slices.append(slice(0,shapeuse[idim])) 
+        idim = idim+1
+
+    finalslc = []
+    idim = 0
+    for slc in slices:
+        if type(slc) == type(slice(0,1)):
+            finalslc.append(slc)
+        else:
+            finalslc.append(range(shapeuse[idim]))
+        idim = idim + 1
+
+    print 'finalslc:', finalslc   
+
+        
+
+
+    return 
+
+mask1 = np.zeros((3,3,3), dtype=bool).reshape(3,3,3)
+mask1[1,:,:] = True
+
+mask2 = np.zeros((3,3,3), dtype=bool).reshape(3,3,3)
+mask2[1,1,1] = True
+
+masks_mult(['dt', 'dx', 'dy'], mask1, ['dt', 'dx', 'dy'], mask2, ['dz', 'dy', 'dx'], (2, 3, 3), {'dt': 0})
+
 #quit()
 

trunk/tools/nc_var_tools.py

@@ -26131 +26131 @@
     return filledstrings
 
+def compute_slice2Dstats(values, ncfile, variable):
+    """ Function to compute stats of variables of a file following slices along 2 variables
+      values=dimn1,varn1,minvar1,maxvar1,slcevar1,dimn2,varn2,minvar2,maxvar2,slcevar2
+        dimn[1/2]: dimension from which produce the slices of variable 1/2 ('all' 
+          for non-specific dimension to cut along)
+        varn[1/2]: name of the variable 1/2
+        minvar[1/2]: minimum value to start the slices for variable 1/2
+        maxvar[1/2]: maximum value to end the slices for variable 1/2
+        slcevar[1/2]: length of slices in variable 1/2
+      ncfile= netCDF file to use
+      variable: ',' list of variables ('all' for all variables)
+    """
+    fname = 'compute_slice2Dstats'
+
+    if values == 'h':
+        print fname + '_____________________________________________________________'
+        print compute_slice2Dstats.__doc__
+        quit()
+
+    expectargs = 'dimn1,varn1,minvar1,maxvar1,slcevar1,dimn2,varn2,minvar2,' +       \
+     'maxvar2,slcevar2'
+    gen.check_arguments(fname, values, expectargs, ',')
+
+    dimn1 = values.split(',')[0]
+    varn1 = values.split(',')[1]
+    minvar1 = np.float(values.split(',')[2])
+    maxvar1 = np.float(values.split(',')[3])
+    slicevar1 = np.float(values.split(',')[4])
+    dimn2 = values.split(',')[5]
+    varn2 = values.split(',')[6]
+    minvar2 = np.float(values.split(',')[7])
+    maxvar2 = np.float(values.split(',')[8])
+    slicevar2 = np.float(values.split(',')[9])
+    
+    onc = NetCDFFile(ncfile, 'r')
+
+    if dimn1 != 'all' and not gen.searchInlist(onc.dimensions, dimn1):
+        print errormsg
+        print '  ' + fname + ": file '" + ncfile + "' does not have dimension 1: '" +\
+          dimn1 + "' !!"
+        dimns = list(onc.dimensions)
+        dimns.sort()
+        print '    available ones:', dimns
+        quit(-1)
+
+    if dimn2 != 'all' and not gen.searchInlist(onc.dimensions, dimn2):
+        print errormsg
+        print '  ' + fname + ": file '" + ncfile + "' does not have dimension 2: '" +\
+          dimn2 + "' !!"
+        dimns = list(onc.dimensions)
+        dimns.sort()
+        print '    available ones:', dimns
+        quit(-1)
+
+    if not onc.variables.has_key(varn1):
+        print errormsg
+        print '  ' + fname + ": file '" + ncfile + "' does not have variable 1: '" + \
+          varn1 + "' !!"
+        varns = onc.variables.keys()
+        varns.sort()
+        print '    available ones:', varns
+        quit(-1)
+
+    if not onc.variables.has_key(varn2):
+        print errormsg
+        print '  ' + fname + ": file '" + ncfile + "' does not have variable 2: '" + \
+          varn2 + "' !!"
+        varns = onc.variables.keys()
+        varns.sort()
+        print '    available ones:', varns
+        quit(-1)
+
+    ovar1 = onc.variables[varn1]
+    ovar2 = onc.variables[varn2]
+
+    dvar1 = (maxvar1 - minvar1+slicevar1)/slicevar1
+    slices1 = np.arange(minvar1, maxvar1+slicevar1, slicevar1)
+    Nslices1 = slices1.shape[0]
+
+    dvar2 = (maxvar2 - minvar2)/slicevar2
+    slices2 = np.arange(minvar2, maxvar2+slicevar2, slicevar2)
+    Nslices2 = slices2.shape[0]
+
+    print '    ' + fname + ': slices ____'
+    print '      var1:', varn1, ':', Nslices1, '(', minvar1, ',', maxvar1+slicevar1, \
+      ',', slicevar1, ')'
+    print '      var2:', varn2, ':', Nslices2, '(', minvar2, ',', maxvar2+slicevar2, \
+      ',', slicevar2, ')'
+
+    # Slices var1
+    sliceshape1 = [Nslices1] + list(ovar1.shape)
+    slcvar1 = np.zeros(tuple(sliceshape1), dtype=bool)
+    var1v = ovar1[:]
+    for islc in range(Nslices1-1):
+        slcvar1[islc,] = ma.masked_inside(var1v, slices1[islc], slices1[islc+1]).mask
+
+    # Slices var2
+    sliceshape2 = [Nslices2] + list(ovar2.shape)
+    slcvar2 = np.zeros(tuple(sliceshape2), dtype=bool)
+    var2v = ovar2[:]
+    for islc in range(Nslices2-1):
+        slcvar2[islc,] = ma.masked_inside(var2v, slices2[islc], slices2[islc+1]).mask
+
+    # Looking for coincident dimensions
+    for dn1 in ovar1.diensions:
+        for dn2 in ovar1.diensions:
+            print 'Hola'
+
+    return
+
+#values='south_north,XLAT,-63.,19.,2.,all,HGT,250.,7000.,500.'
+
+#compute_slice2Dstats(values, '/home/lluis/PY/wrfout_d01_1995-01-01_00:00:00', 'T2')
+
 #quit() 
+