def getNCvar(filename,field,timesel=None,levsel=None,monsel=None,seas=None,calc=None,remlon=1,sqz=True,att=False):
""" gets a variable from netcdf file.
Time is assumed to be the 1st dimension, Lon is assumed to be the last.
If any calculations are requested to be performed on the data, the user
needs to make sure that the requested operations can be performed (b/c
some of the other functions only handle certain # of dims, etc. My bad.)
filename: full path to file
field: NC variable to read in
timesel: comma-delim string of date range in 'YYYY-MM-DD' fmt to select (a la CDO)
levsel: select level in Pa (e.g. 50000 for 500hPa)
monsel: select month from timeseries
seas: seasonally (annually) average or return climatology {climo|ANN|DJF|JJA|NDJ|MAM|SON}
calc: zm (zonal mean),
remlon: removes extra wrap-around longitude for zonal mean.
default is 1, remove it
sqz: squeeze the data if 'getting all data'. Default True.
Trying to avoid situation where need singular dims and squeeze them out
(e.g. MOC variable in CCSM4)
att: if True, include the full netcdf variable (including attributes). Most useful for time.
returns fld
"""
# IF ON MAC: CDO bindings don't work yet, use old function 3/25/2014 #########
plat = platform.system()
if False: #@@@@TESTING 9/20/2016 plat == 'Darwin': # means I'm on my mac
# Call old func
if calc != None:
print '@@ not sure calc will work on mac. calc=' + calc
if levsel!=None:
plev= np.array([100, 200, 300, 500, 700, 1000, 2000, 3000, 5000, 7000, 10000, 12500,
15000, 17500, 20000, 22500, 25000, 30000, 35000, 40000, 45000, 50000,
55000, 60000, 65000, 70000, 75000, 77500, 80000, 82500, 85000, 87500,
90000, 92500, 95000, 97500, 100000])
level=cutl.find_nearest(plev,levsel)
else:
level=None
if timesel == '0002-01-01,0061-12-31':
print 'hard-coded skipping of first year of 61-yr chunk @@'
fld = getNCvar_old(filename,field,seas=seas, monsel=monsel,timechunk=(12,),level=level,calc=calc,sqz=sqz)
else:
# if timesel=='0002-01-01,0121-12-31' then just don't set timechunk because
# files on the mac are already selected to skip first year, and they reside
# in the 'timsel' subdirectory. Check for that?
if timesel=='0002-01-01,0121-12-31':
if 'timsel/' not in filename:
print 'On mac, use files in timsel/ subdirectory! @@ NEEDS TESTING'
fld = getNCvar_old(filename,field,seas=seas,monsel=monsel,level=level,calc=calc,sqz=sqz,timesel=timesel) # doesn't work with all arguments yet @@
return fld
else: # on linux workstation in Vic
ncfile = openNC(filename)
ndims = len(ncfile.dimensions)
ncvar = ncfile.variables[field]
#print ncvar # @@@@@
#### READ VARIABLE FROM NC FILE ########
if timesel == None and calc == None:
if levsel !=None:
if monsel != None:
fld = np.squeeze(cdo.sellevel(levsel,input = cdo.selmon(monsel,input = filename),returnMaArray = field))
else:
fld = np.squeeze(cdo.sellevel(levsel,input = filename, returnMaArray = field))
os.system('rm -rf /tmp/cdoPy*')
else:
if monsel != None:
#print 'timesel==None and calc==None and monsel !=None'
fld = np.squeeze(cdo.selmon(monsel,input = filename, returnMaArray = field))
#print fld.shape
os.system('rm -rf /tmp/cdoPy*')
else: # get everything
if sqz:
#print field + ': squeezing data upon read all' # @@@
# for most situations, this is what we want. @@@@
fld=np.squeeze(ncfile.variables[field][...])
else:
fld = ncfile.variables[field][...]
elif timesel != None and calc == 'zm':
# have to remove the lon before zonal mean, which means have to separate the
# select dates and zm. thus can't use CDO for zm (unless can pass it data instead of a file?)
#fld = np.squeeze(cdo.zonmean( input = cdo.seldate(timesel,input = filename), returnMaArray = field))
print 'assuming T42(63) 64x128 resolution for zonal mean'
if levsel != None:
if monsel != None:
fld = np.squeeze(cdo.seldate(timesel,input = cdo.zonmean( input =
cdo.selindexbox(1,128,1,64,input =
cdo.sellevel(levsel,input =
cdo.selmon(monsel, input = filename)))),
returnMaArray = field))# @@@@
else:
fld = np.squeeze(cdo.seldate(timesel,input = cdo.zonmean( input =
cdo.selindexbox(1,128,1,64,input =
cdo.sellevel(levsel,input = filename))),
returnMaArray = field))
else:
if monsel != None:
fld = np.squeeze(cdo.seldate(timesel,input =
cdo.zonmean( input =
cdo.selindexbox(1,128,1,64,input =
cdo.selmon(monsel,input = filename))),
returnMaArray = field))
else:
fld = np.squeeze(cdo.seldate(timesel,input =
cdo.zonmean(input =
cdo.selindexbox(1,128,1,64,input = filename)),
returnMaArray = field))
os.system('rm -rf /tmp/cdoPy*')
## if remlon:
## # remove extra lon
## fld = np.squeeze(fld[...,0:-1])
## lastdimidx = ndims-1
## fld = np.mean(fld,lastdimidx)
elif timesel != None and calc != None:
if levsel != None and monsel == None:
fld = np.squeeze(cdo.seldate(timesel,input =
cdo.sellevel(levsel,input = filename),
returnMaArray = field))
elif levsel != None and monsel != None:
fld = np.squeeze(
cdo.seldate(timesel,input =
cdo.sellevel(levsel,input =
cdo.selmon(monsel,input = filename)),
returnMaArray = field))
elif levsel == None and monsel != None:
fld = np.squeeze(cdo.seldate(timesel,input =
cdo.selmon(monsel,input = filename),
returnMaArray = field))
else: # levsel and monsel are both None
fld = np.squeeze(cdo.seldate(timesel,input = filename, returnMaArray = field))
os.system('rm -rf /tmp/cdoPy*')
print "only calc='zm' is implemented now. Returning only selected date range/level/month."
elif timesel != None:
if levsel != None and monsel == None:
fld = np.squeeze(cdo.seldate(timesel,input = cdo.sellevel(levsel,input = filename),returnMaArray = field))
elif levsel != None and monsel != None:
fld = np.squeeze(
cdo.seldate(timesel,input =
cdo.sellevel(levsel,input =
cdo.selmon(monsel,input = filename)),
returnMaArray = field))
elif levsel == None and monsel != None:
fld = np.squeeze(cdo.seldate(timesel,input =
cdo.selmon(monsel,input = filename),
returnMaArray = field))
else: # levsel and monsel are both None
fld = np.squeeze(cdo.seldate(timesel,input = filename, returnMaArray = field))
os.system('rm -rf /tmp/cdoPy*')
elif calc == 'zm': # and timesel must be None
print 'assuming T42(63) 64x128 resolution for zonal mean'
if levsel != None and monsel == None:
fld = np.squeeze(cdo.sellevel(levsel,input =
cdo.zonmean(input =
cdo.selindexbox(1,128,1,64,input =
filename)),
returnMaArray = field))
elif levsel != None and monsel != None:
fld = np.squeeze(
cdo.sellevel(levsel,input =
cdo.zonmean(input =
cdo.selindexbox(1,128,1,64,input =
cdo.selmon(monsel,input =
filename))),
returnMaArray = field))
elif levsel == None and monsel != None:
fld = np.squeeze(cdo.zonmean(input =
cdo.selindexbox(1,128,1,64,input =
cdo.selmon(monsel,input =
filename)),
returnMaArray = field))
else: # get all data
fld = np.squeeze(cdo.zonmean(input =
cdo.selindexbox(1,128,1,64,input =
filename),
returnMaArray = field))
#print '@@ getting memory errors here...try using CDO to select appropriate lons for the zm calc'
#fld = ncfile.variables[field][...] # have to get field before removing lon
os.system('rm -rf /tmp/cdoPy*')
## if remlon:
## # remove extra lon
## if ndims==4:
## fld = np.squeeze(fld[:,:,:,0:-1])
## elif ndims==3:
## fld = np.squeeze(fld[:,:,0:-1])
## else: # shouldn't really get here, not expecting 2D (time x lon?)
## fld = np.squeeze(fld[:,0:-1])
## lastdimidx = ndims-1
## fld = np.mean(fld,lastdimidx)
else:
print "huh? timesel and calc combo doesn't make sense"
####### TIME AVERAGE the VARIABLE ##########
# fld has to be 3d by the time it is passed to func
# (time,lev,lat) or (time,lat,lon)
if seas != None:
#print 'getNCvar(): seas!=None: fld.shape: ' + str(fld.shape) # @@@
## if fld.ndim != 3:
## ## if 1 in fld.shape:
## ## fld=fld.squeeze() # attempting to deal with spurious dims of 1 @@@
## ## if fld.ndim != 3:
## ## print 'data must be 3 dimensional to seasonalize()'
## ## return
## ## else:
## print 'data must be 3 dimensional to seasonalize()'
## return
if monsel != None:
print "Can't do seasonal average when monsel != None"
return
elif seas == 'climo':
fld,stddev = cutl.climatologize(fld)
elif type(seas) == int: # @@ does this work?
#elif seas not in ('ANN','DJF','JJA','MAM','SON','NDJ'):
# means seas is an int value for a month
#fld = cutl.seasonalize_monthlyts(fld,mo=seas)
fld = cutl.seasonalize(fld,mo=seas)
else:
#print 'seasonalizing'
#fld = cutl.seasonalize_monthlyts(fld,season=seas)
fld = cutl.seasonalize(fld,season=seas)
#print fld.shape
# Apply any scaling and offsetting needed:
try:
var_offset = ncvar.add_offset
except:
var_offset = 0
try:
var_scale = ncvar.scale_factor
print 'var_scale ' + str(var_scale)
except:
var_scale = 1
fld = fld*var_scale + var_offset
ncfile.close()
return fld
def load_ncfield(field, ncfield, zonal=True,conv=1,mo=0,season=None,last='last100',includeyr1=False,verb=False):
"""
If zonal=True, compute zonal mean.
zonal can also be a region, defined in regiondict in constants.py
returns dictionaries: full field w/ time, zonal mean w/ time
"""
timepers,timesels = get_timeinfo(last,includeyr1)
seasonalizedt = {'mo':mo, 'season': season}
ncflddt={}; ncfldtmdt={}; ncfldzmdt={}
for casename in casenames:
fname= basedir + casename +'/ts/' + casename + '_' + field + '_' + timepers[casename] + '_ts.nc'
timesel=timesels[casename]
if verb:
print fname
try:
fld=cnc.getNCvar(fname,ncfield,timesel=timesel)*conv # remlon not operational??? why commented out??
if verb:
print fname + ', fld.shape ' + str(fld.shape) # @@@
except IOError as rte: # previously this was a RuntimeError. Not sure what changed. 10/14/2016
print rte
if 'No such file or directory' in rte.args:
# Here the problem is most likely that pico2ipulse filename fails b/c
# there is no NH version of it (for when last200=True). Remove the NH and try again.
# @@ Of course this could be a problem too b/c now we have full globe for just this run.
if field[-2:] != 'NH':
print 'not an NH versus global problem...File really not found'
raise
fname= basedir + casename +'/ts/' + casename + '_' + field[:-2] + '_' + timepers[casename] + '_ts.nc'
print 'Try again: ' + fname
fld=cnc.getNCvar(fname,ncfield,timesel=timesel)*conv
tmplat=cnc.getNCvar(fname,'lat')
if last=='last200': # how to tell which spot lat is?
if zonal: # then it is likely time x lat x lon
#print 'zonal is true. fld dims? ' + str(fld.shape)
fld=fld[...,tmplat>0,:]
else: # likely time x lev x lat
fld=fld[...,tmplat>0]
else:
raise
# just get the full timeseries, and zonal mean if asked.
if 1: #timeseries: # do not time average (actually save time average anyway)
if (season==None and mo==0):
pass
else:
fld = cutl.seasonalize(fld,**seasonalizedt)
#fldtm = fld
# zonal average
if zonal==True:
fldzm = fld[...,:-1].mean(axis=2) # remove extra lon
elif zonal in con.get_regiondict().keys():
print 'lat,lon shapes ' + str(lat.shape),str(lon.shape)
tempo, _ = cutl.mask_region(fld,lat,lon,region=zonal)
fldzm = tempo.mean(axis=2)
else:
fldzm = None
#fldtm = fldtmzm.mean(axis=0)
# else:
# # seasonal & time mean
# fldtm = np.mean(cutl.seasonalize(fld,**seasonalizedt),axis=0)
# # zonal average
# if zonal:
# fldtmzm = fldtm[...,:-1].mean(axis=1)
# elif zonal in con.get_regiondict().keys():
# tempo, _ = cutl.mask_region(fldtm,lat,lon,region=zonal)
# fldtmzm = tempo.mean(axis=1)
# else:
# fldtmzm = fldtm
print 'fld.shape: ' + str(fld.shape)
if fldzm!=None:
print 'fldzm.shape: ' + str(fldzm.shape)
ncflddt[casename] = fld # keep all dims
#ncfldtmdt[casename] = fldtm # time mean
ncfldzmdt[casename] = fldzm # zonal mean (w/ time dim)
return ncflddt, ncfldzmdt
def getNCvar_old(filename,field,timechunk=None,monsel=None,level=None,seas=None,calc=None,remlon=1,sqz=True,timesel=None):
""" gets a variable from netcdf file.
Time is assumed to be the 1st dimension, Lon is assumed to be the last.
If any calculations are requested to be performed on the data, the user
needs to make sure that the requested operations can be performed (b/c
some of the other functions only handle certain # of dims, etc. My bad.)
filename: full path to file
field: NC variable to read in
timechunk: tuple of start,stop
timesel: same format as CDO. will be parsed into startyear, startmon, startday and endyr,enmon,enday
monsel: index of month to choose (1=all Jans, 2=all Febs, etc. really meant for CDO bindings)
level: index of plev to select
seas: seasonally (annually) average {ANN|DJF|JJA|NDJ|MAM|SON}
calc: zm (zonal mean),
remlon: removes extra wrap-around longitude for zonal mean.
default is 1, remove it
sqz: squeeze the data if 'getting all data'. Default True.
Trying to avoid situation where need singular dims and squeeze them out
(e.g. MOC variable in CCSM4)
returns fld
"""
# chunk = None
ncfile = openNC(filename)
ndims = len(ncfile.dimensions)
#print 'getNCvar_old()'
#### READ VARIABLE FROM NC FILE ########
if (timechunk == None) and (timesel == None):
if level != None:
fld = ncfile.variables[field][:,level,...]
else:
fld = ncfile.variables[field][...]
# @@@shit, caused problems with MOC.
if sqz:
fld=fld.squeeze() # remove spurious dimensions of 1
else:
if timesel == None:
print 'timesel is None' # @@@
else:
#print 'timesel ' + timesel
# parse timesel:
# year-month-day
(timselst,timselen)=timesel.split(',')
(styear,stmon,stday) = timselst.split('-')
(enyear,enmon,enday) = timselen.split('-')
#print styear + ' to ' + enyear # @@@@@@
# ############################
# from Joe's util.py (originally Phil Austin cookbook)
#
# first convert to netcdftime datetime objects
#
time_nc=ncfile.variables['time']
the_times=time_nc[...]
#try:
the_dates=num2date(the_times,time_nc.units,get_NCtimecal(filename))
#print the_dates # @@@@@@
#
# netCDF4 bug(?) means that netcdftime objects can't be compared/sorted
# so convert to python datetime objects
#
py_dates=[datetime.date(*item.timetuple()[:3]) for item in the_dates] # This only deals in Year/Month/Day.
py_dates=np.array(py_dates)
# Convert start and stop dates to python datetimes
# Presently assume monthly data!
date1 = datetime.date( int(styear), int(stmon), 1 ) #Starts Jan 1
date2 = datetime.date( int(enyear), int(enmon), 31 ) #Ends Dec 31
#print str(date1) + ' and ' + str(date2) # @@@@@@
#
#
if styear is None:
start_index=0
else:
start_index=find_index(py_dates,date1)[0]
if enyear is not None:
stop_index=find_index(py_dates,date2)[0]
if stop_index == 0:
# The stop index is 0 since you asked for a date beyond that in the
# file. So print a warning and set it to the last date in the file
print 'You are asking for a date beyond that in the file.'
print 'I will take the last time index in the file and move on.'
stop_index = len(the_times)
else:
stop_index=None
time_slice=slice(start_index,stop_index+1) # b/c it's monthly data, want inclusize last month@@
the_times=py_dates[time_slice] # may not need this...@@
#print 'the_times: ' + str(the_times) # @@@@@@@@@@@@@
# @@@@ end testing new time functionality
# ############################
#if (timesel!= None):# and (len(timechunk)==1): # start time until end
# firsttime = ncfile.variables['time'][timechunk[0],...] #@@@
# firstdate = datetime.date(1850, 1, 1) + datetime.timedelta(int(firsttime))
# print firsttime # @@@
# print firstdate # @@@
# if level != None:
# fld = ncfile.variables[field][time_slice,level,...]
# else:
# fld = ncfile.variables[field][time_slice,...]
# else:
if level != None:
fld = ncfile.variables[field][time_slice,level,...]
else:
fld = ncfile.variables[field][time_slice,...]
""" old functionality...@@
if (timechunk!= None) and (len(timechunk)==1): # start time until end
print timechunk # @@
firsttime = ncfile.variables['time'][timechunk[0],...] #@@@
firstdate = datetime.date(1850, 1, 1) + datetime.timedelta(int(firsttime))
print firsttime # @@@
print firstdate # @@@
if level != None:
fld = ncfile.variables[field][timechunk[0]:,level,...]
else:
fld = ncfile.variables[field][timechunk[0]:,...]
else:
if level != None:
fld = ncfile.variables[field][timechunk[0]:timechunk[1],level,...]
else:
fld = ncfile.variables[field][timechunk[0]:timechunk[1],...]
"""
# @@@shit, caused problems with MOC.
if sqz:
fld=fld.squeeze() # remove spurious dimensions of 1
## #print 'chunking time. ndims= ' + str(ndims) + ' styr,enyr: ' + str(timechunk[0]) + ',' + str(timechunk[1])
## if ndims==4: # must be a better way!@@
## if len(timechunk)==1: # start time until end
## fld = ncfile.variables[field][timechunk[0]:,:,:,:]
## else:
## fld = ncfile.variables[field][timechunk[0]:timechunk[1],:,:,:]
## elif ndims==3:
## if len(timechunk)==1: # start time until end
## fld = ncfile.variables[field][timechunk[0]:,:,:]
## else:
## fld = ncfile.variables[field][timechunk[0]:timechunk[1],:,:]
## else: # shouldn't get here
## if len(timechunk)==1: # start time until end
## fld = ncfile.variables[field][timechunk[0]:,:]
## else:
## fld = ncfile.variables[field][timechunk[0]:timechunk[1],:]
#print fld.shape
ncfile.close()
###### CALCULATIONS on the VARIABLE ########
if calc == 'zm':
#print 'zonal mean'
# lon is assumed to be last dimension
if remlon:
# remove extra lon
fld = np.squeeze(fld[...,0:-1]) # @@
## if ndims==4:
## fld = np.squeeze(fld[:,:,:,0:-1])
## elif ndims==3:
## fld = np.squeeze(fld[:,:,0:-1])
## else: # shouldn't really get here
## fld = np.squeeze(fld[:,0:-1])
print 'removed lon. ' + str(fld.shape)
lastdimidx = ndims-1
fld = np.mean(fld,lastdimidx)
#print fld.shape
elif calc == None:
pass
else:
print 'only zm is implemented so far'
return
####### TIME AVERAGE the VARIABLE ##########
# fld has to be 3d by the time it is passed to func
# (time,lev,lat) or (time,lat,lon)
if seas != None:
if fld.ndim != 3:
## if 1 in fld.shape:
## fld=fld.squeeze() # attempting to deal with spurious dims of 1 @@@
## if fld.ndim != 3:
## print 'data must be 3 dimensional to seasonalize()'
## return
## else:
print 'data must be 3 dimensional to seasonalize()'
return
if monsel != None:
print "Can't do seasonal average when monsel != None"
return
else:
#print 'seasonalizing ' + seas # @@@
#fld = cutl.seasonalize_monthlyts(fld,seas)
fld = cutl.seasonalize(fld,seas)
#print fld.shape
if monsel != None:
# send back only selected month
# monsel is 1-based! ie 1=Jan, 2=Feb. But the index is zero-based
# so shift by 1:
monsel = monsel-1
fld = fld[monsel::12,...]
return fld
def load_ncfield(field, ncfield, zonal=True,conv=1,mo=0,season=None,last='last100',
includeyr1=False,verb=False,local=False,seacyc=False,pulse=False):
"""
If zonal=True, compute zonal mean.
zonal can also be a region, defined in regiondict in constants.py
If local, use local data (which is limited)
If seacycle, return the 12 month climo average, which only exists
1. locally (as of Mar 24 2017) and 2. is only avail for the last200
returns dictionaries: full field w/ time, zonal mean w/ time
"""
timepers,timesels = get_timeinfo(last,includeyr1)
if local:
#bd='/Users/kelly/DropboxWork/UWashCanSISE/DATA/relaxationruns/' #limited fields here
bd='/Users/kelly/DATA/DataDisk/' # limited but not shared so there are more data
subdir='/'
else:
bd=basedir
subdir='/ts/'
seasonalizedt = {'mo':mo, 'season': season}
ncflddt={}; ncfldtmdt={}; ncfldzmdt={}
for casename in casenames:
if pulse==False and ('pulse' in casename): continue
if seacyc:
if last!='last200':
raise Exception('load_ncfield(): if seacyc=True, last must = last200')
if local!=True:
raise Exception('load_ncfield(): if seacyc=True, local must be True (as of Mar 24 2017)')
fname = bd + casename+subdir+casename + '_' + field + '_' + timepers[casename] + '_seacycle.nc'
else:
fname= bd + casename +subdir + casename + '_' + field + '_' + timepers[casename] + '_ts.nc'
timesel=timesels[casename]
if verb:
print fname
try:
fld=cnc.getNCvar(fname,ncfield,timesel=timesel)*conv # remlon not operational??? why commented out??
if verb:
print fname + ', fld.shape ' + str(fld.shape) # @@@
except IOError as rte: # previously this was a RuntimeError. Not sure what changed. 10/14/2016
print rte
if 'No such file or directory' in rte.args:
# Here the problem is most likely that pico2ipulse filename fails b/c
# there is no NH version of it (for when last200=True). Remove the NH and try again.
# @@ Of course this could be a problem too b/c now we have full globe for just this run.
if field[-2:] != 'NH':
print 'not an NH versus global problem...File really not found'
raise
fname= basedir + casename +'/ts/' + casename + '_' + field[:-2] + '_' + timepers[casename] + '_ts.nc'
print 'Try again: ' + fname
fld=cnc.getNCvar(fname,ncfield,timesel=timesel)*conv
tmplat=cnc.getNCvar(fname,'lat')
if last=='last200': # how to tell which spot lat is?
if zonal: # then it is likely time x lat x lon
#print 'zonal is true. fld dims? ' + str(fld.shape)
fld=fld[...,tmplat>0,:]
else: # likely time x lev x lat
fld=fld[...,tmplat>0]
else:
raise
# just get the full timeseries, and zonal mean if asked.
if 1: #timeseries: # do not time average (actually save time average anyway)
if (season==None and mo==0):
pass
else:
fld = cutl.seasonalize(fld,**seasonalizedt)
#fldtm = fld
# zonal average
if zonal==True:
fldzm = fld[...,:-1].mean(axis=2) # remove extra lon
elif zonal in con.get_regiondict().keys():
print 'lat,lon shapes ' + str(lat.shape),str(lon.shape)
tempo, _ = cutl.mask_region(fld,lat,lon,region=zonal)
fldzm = tempo.mean(axis=2)
else:
fldzm = None
#fldtm = fldtmzm.mean(axis=0)
# else:
# # seasonal & time mean
# fldtm = np.mean(cutl.seasonalize(fld,**seasonalizedt),axis=0)
# # zonal average
# if zonal:
# fldtmzm = fldtm[...,:-1].mean(axis=1)
# elif zonal in con.get_regiondict().keys():
# tempo, _ = cutl.mask_region(fldtm,lat,lon,region=zonal)
# fldtmzm = tempo.mean(axis=1)
# else:
# fldtmzm = fldtm
print 'fld.shape: ' + str(fld.shape)
if fldzm!=None:
print 'fldzm.shape: ' + str(fldzm.shape)
ncflddt[casename] = fld # keep all dims
#ncfldtmdt[casename] = fldtm # time mean
ncfldzmdt[casename] = fldzm # zonal mean (w/ time dim)
# try this: add coords # @@@@@@@@@@
lat,lon = load_nclatlon(field,last=last,includeyr1=includeyr1,verb=verb,local=local)
ncflddt['lat'] = lat; ncflddt['lon'] = lon;
try:
lev = load_nclev(field,last=last,includeyr1=includeyr1,verb=verb)
ncflddt['lev'] = lev
except:
print "No lev coord. Leave it"
ncfldzmdt['lat'] = lat
return ncflddt, ncfldzmdt