nt = fldsel.shape[0]
nlon=fldsel.shape[2]
nlat=fldsel.shape[1]
fldre = fldsel.reshape((nt,nlon*nlat))
xx=np.arange(0,nt)
#ensseldt[eii] = fldsel
if field=='sic':
ensnhdt[eii],ensshdt[eii] = cutl.calc_totseaicearea(fld/100.,lat,lon,isarea=False)
fldsel=cutl.calc_seaicearea(fldsel,lat,lon)
fldre=fldsel.reshape((nt,nlon*nlat))
else:
ensgmdt[eii] = cutl.global_mean_areawgted3d(fld,lat,lon)
ensrmdt[eii] = cutl.calc_regmean(fld,lat,lon,region)
#slope[eii], intercept, r_value, p_value, std_err = sp.stats.linregress(xx,dat) # not good for 3d data?
# this is just the second timesel (ie 2002-2012)
slope,intercept = np.polyfit(xx,fldre,1) # supposedly can do w/ higher dims?
enstrnddt[eii] = slope #.reshape((nlat,nlon)) # reshape later @@
# also save all trends into one dictionary (don't differentiate by seed/base run)
alltrnddt[superii] = slope
superii+=1
if field=='sic':
simnhdt[sim] = pd.DataFrame(ensnhdt,index=years)
if field1=='sia':
fldcreg=cutl.calc_regtotseaicearea(fldc,lat,lon,region1) # isarea=False
fldpreg=cutl.calc_regtotseaicearea(fldp,lat,lon,region1) # isarea=False
else:
# IF FLUX, MASK OUT OPEN-WATER:
#print 'Implement masking out of open water in control for FLUXES @@@'
if field1 in ('hfl','hfs','flg','fsg','turb','net'):
print 'field1, ' + field1 + ' is a flux. Mask out non-ice in control' # @@
# mask out regions that are not ice in the control (as P&M 2014 JClim)
sicfname,junk=con.build_filepathpair(sim,'sicn')
sicnc = cnc.getNCvar(sicfname,'SICN',timesel=timesel,seas=sea1)
fldc = ma.masked_where(sicnc<.10,fldc)
fldp = ma.masked_where(sicnc<.10,fldp)
fldcreg=cutl.calc_regmean(fldc,lat,lon,region1)
fldpreg=cutl.calc_regmean(fldp,lat,lon,region1)
flddregdt[sim] = np.mean(fldpreg-fldcreg,axis=0) # time mean
if sea2 in ('DJF','NDJ') and sea1 not in ('DJF','NDJ'): # have to shorten other timeseries
flddregtsdt[sim] = fldpreg[:-1,...]-np.mean(fldcreg[:-1,...],axis=0) # anomaly timeseries from ctl mean
else:
flddregtsdt[sim] = fldpreg-np.mean(fldcreg,axis=0)
if (field2 != field1) or (sea2 != sea1):
print field2 + ' != ' + field1 + ' or ' + str(sea2) + ' != ' + str(sea1)
if field2 in ('turb','net'):
fielda='hfl'; fieldb='hfs'
fnamec,fnamepa=con.build_filepathpair(sim,fielda)
def loaddata(fields, simulations, ncfields=None,model='CanAM4',timeper='001-121',timefreq=None,
levsel=None,meantype=None,filetype='diff',region=None,alsomask=None,rettype='dict'):
""" loaddata(fields, simulations,ncfields=None,model='CanAM4',timeper='001-121',timefreq=None,
levsel=None, meantype=None,filetype='diff',region=None)
fields: tuple of field names [@@update. only one field now but still need tuple]
simulations: tuple of simulation names (diff names='E1'...'ENS','NSIDC' etc)
ncfields: tuple of ncfield names (var name in file itself). Default to upper case of field
model: for now only 'CanAM4' is implemented
timeper: time period of the data (used for filename). default '001-121'
timefreq: time frequency TO RETURN. default all data
'monthly'|'seasonal'|'climo'|'ANN'|'DJF'|'JJA'|'NDJ'|'MAM'|'SON'|
1,2,3,4,5,6,7,8,9,10,11,12
levsel: select level in Pa (e.g. 50000 for 500hPa). default all levels
meantype: 'time','zonal' @@for now. default None, but recommended to choose
one if loading multiple variables and multiple simulations at once.
It is assumed that time is the first dimension.
filetype: 'diff','ctl','pert','pval'
Default is diff where both ctl and pert are read in and differenced.
region: any of the regions in constants -> region dict. default None.
alsomask: if specified as 'land' or 'ocean', then calc_regmean() will mask before
computing regional avg (ie. will NOT include it in average).
Only used if region!=None. Default None.
returns: nested dictionary@@
FIELDS->SIMULATIONS->TIMEFREQ
Load requested fields from requested CanAM4 simulations
into dictionaries (dataframes?). Function automatically
skips first year of simulation and gets a timeseries (or climo
if requested) of the rest (assumed through year 121).
3D data and 'turb' not yet implemented! @@
"""
if model!='CanAM4':
print 'model not supported!'
return -1
print '@@ probably should invert the order such that it is field, season, sim?'
#bp=con.get_basepath()
#basepath=bp['basepath'] + model + '/'; subdir=bp['subdir']
timesel='0002-01-01,0121-12-31'
seabool=False # set to True if requested time freq is one season or climo
monbool=False # set to True if requested time freq is one month
if timefreq=='monthly':
# get all months
tf = con.get_mon()
elif timefreq=='seasonal':
# get all 4 seasons
tf = 'DJF','MAM','JJA','SON'
elif timefreq in range(1,13):
# choose an individual month
tf=timefreq
monbool=True
elif timefreq in ('climo','ANN','DJF','JJA','NDJ','MAM','SON','ND','JF','SO'):
tf=timefreq
seabool=True
print tf # @@
# @@@@@ add handling of sia!
#datadict = dict.fromkeys(fields,{})
#for fii,field in enumerate(fields):
if 1: # GET RID OF FIELD dim too 5/8/2015
fii=0; field=fields[0]
if ncfields==None:
ncfield=field.upper()
else:
ncfield=ncfields[fii]
print field,ncfield #@@
# assume simulations entered are of the form: E1, R3, ENSE etc. Then
# filetype input arg tells which simulation to get (or both)
simdict = dict.fromkeys(simulations,{})
for sim in simulations:
print sim # @@
#timdict = dict.fromkeys(tf)
# construct filename here @@
fnamec,fnamep = con.build_filepathpair(sim,field)
#fname = basepath+sim+subdir+sim+'_'+field+'_'+timeper+'_ts.nc'
print fnamec
tfkey = tf
#for tfkey in tf:
# print tfkey # @@
#print 'too many levels in the dict...get rid of seasonal keys and just do one@@@ 5/1/2015'
# @@ get the data with cnc.getNCvar() here
ncparams = {}
if monbool:
ncparams = {'monsel': tfkey}
elif seabool:
ncparams = {'seas': tfkey}
if levsel!=None:
ncparams['levsel'] = levsel
if meantype=='zonal':
ncparams['calc'] = 'zm'
if filetype=='diff' or filetype=='pval':
if field in ('turb','net'):
fnamec,fnamep = con.build_filepathpair(sim,'hfl')
fnamecb,fnamepb = con.build_filepathpair(sim,'hfs')
ctl = cnc.getNCvar(fnamec,'HFL',timesel=timesel,**ncparams) +\
cnc.getNCvar(fnamecb,'HFS',timesel=timesel, **ncparams)
pert = cnc.getNCvar(fnamep,'HFL',timesel=timesel,**ncparams) +\
cnc.getNCvar(fnamepb,'HFS',timesel=timesel,**ncparams)
if field=='net':
fnamecc,fnamepc = con.build_filepathpair(sim,'flg')
ctl = ctl - cnc.getNCvar(fnamecc,'FLG',timesel=timesel,**ncparams)
pert = pert - cnc.getNCvar(fnamepc,'FLG',timesel=timesel,**ncparams)
else:
pert = cnc.getNCvar(fnamep,ncfield,timesel=timesel,**ncparams)
ctl = cnc.getNCvar(fnamec,ncfield,timesel=timesel,**ncparams)
fld = pert - ctl
elif filetype=='ctl':
if field in ('turb','net'):
fnamec,fnamep = con.build_filepathpair(sim,'hfl')
fnamecb,fnamepb = con.build_filepathpair(sim,'hfs')
fld = cnc.getNCvar(fnamec,'HFL',timesel=timesel,**ncparams) +\
cnc.getNCvar(fnamecb,'HFS',timesel=timesel, **ncparams)
if field=='net':
fnamecc,fnamepc = con.build_filepathpair(sim,'flg')
fld = fld - cnc.getNCvar(fnamecc,'FLG',timesel=timesel,**ncparams)
else:
fld = cnc.getNCvar(fnamec,ncfield,timesel=timesel,**ncparams)
elif filetype=='pert':
if field in ('turb','net'):
fnamec,fnamep = con.build_filepathpair(sim,'hfl')
fnamecb,fnamepb = con.build_filepathpair(sim,'hfs')
fld = cnc.getNCvar(fnamep,'HFL',timesel=timesel,**ncparams) +\
cnc.getNCvar(fnamepb,'HFS',timesel=timesel,**ncparams)
if field=='net':
fnamecc,fnamepc = con.build_filepathpair(sim,'flg')
fld = fld - cnc.getNCvar(fnamepc,'FLG',timesel=timesel,**ncparams)
else:
fld = cnc.getNCvar(fnamep,ncfield,timesel=timesel,**ncparams)
else:
print "filetype not supported! ['diff'|'ctl'|'pert'|'pval']"
return -1
if region != None:
lat=cnc.getNCvar(fnamec,'lat'); lon=cnc.getNCvar(fnamec,'lon')
if filetype=='pval':
pert = cutl.calc_regmean(pert,lat,lon,region,alsomask=alsomask)
ctl = cutl.calc_regmean(ctl,lat,lon,region,alsomask=alsomask)
(tstat,pval) = cutl.ttest_ind(pert,ctl)
fld=pval
else:
fld = cutl.calc_regmean(fld,lat,lon,region,alsomask=alsomask)
if meantype=='time':
if filetype=='pval':
# this is an error. filetype supercedes meantype so time avg won't be done
print 'filetype=pval and meantype-time. Ignore meantype and return pvals @@'
timdict=fld
else:
#fldstd = np.std(fld,axis=0)
fld = np.mean(fld,axis=0)
#timdict[tfkey] = fld #,fldstd
timdict=fld
else:
#timdict[tfkey] = fld
timdict=fld
simdict[sim] = timdict
#datadict[field]=simdict
# can I set attributes to a dictionary? @@ like for nfields, nsims, ntimes?
#return datadict
if rettype=='ndarray':
# convert the dict to an array:
# get the last sim data to initialize ndarray
initshape=simdict[sim].shape
initshape=(len(simulations),) + initshape
tmp=np.zeros(initshape)
for sii,skey in enumerate(simulations):
tmp[sii,:] = simdict[skey]
return tmp
else:
return simdict
fldc = flddt["iga"]
fldcclim, std = cutl.climatologize(fldc) # climo mean (iga)
fldclimdt["iga"] = fldcclim
for skey in sims[2:10]: # Group I
fld = flddt[skey]
# timeseries
flddiffdt[skey] = fldc2x - fld
# climos
fldclimdt[skey], std = cutl.climatologize(fld)
flddiffclimdt[skey] = fldc2xclim - fldclimdt[skey]
rmse = np.sqrt(np.square(flddiffdt[skey]))
rmseclim = np.sqrt(np.square(flddiffclimdt[skey]))
rmsedt[skey] = cutl.calc_regmean(rmse, lat, lon, region)
rmseclimdt[skey] = cutl.calc_regmean(rmseclim, lat, lon, region)
annrmseclimdt[skey] = cutl.annualize_monthlyts(rmseclimdt[skey])
climrmsedt[skey], rmsestd = cutl.climatologize(rmsedt[skey])
for skey in sims[10:]: # Group II
fld = flddt[skey]
# timeseries
flddiffdt[skey] = fldc - fld
# climos
fldclimdt[skey], std = cutl.climatologize(fld)
flddiffclimdt[skey] = fldcclim - fldclimdt[skey]
rmse = np.sqrt(np.square(flddiffdt[skey]))
