sim = 'ENSE'
sea = 'SON' #1 #'DJF'
if threed:
uncfield='U'
vncfield='V'
ufield='u' + str(level)
vfield='v' + str(level)
level=str(level/100) # for printing figs
else:
level=''
fuc,fup=con.build_filepathpair(sim,ufield)
lat=cnc.getNCvar(fuc,'lat')
lon=cnc.getNCvar(fuc,'lon')
uc=cnc.getNCvar(fuc,uncfield,timesel='0002-01-01,0121-12-31',seas=sea)
up=cnc.getNCvar(fup,uncfield,timesel='0002-01-01,0121-12-31',seas=sea)
ucm=np.mean(uc,axis=0) # time mean
upm=np.mean(up,axis=0)
ud=upm-ucm
fvc,fvp=con.build_filepathpair(sim,vfield)
vc=cnc.getNCvar(fvc,vncfield,timesel='0002-01-01,0121-12-31',seas=sea)
vp=cnc.getNCvar(fvp,vncfield,timesel='0002-01-01,0121-12-31',seas=sea)
type = "nh" # plot type
latlims = [20, 90]
lonlims = [0, 180]
# lonlims=[0,360]
limsdict = {"latlims": latlims, "lonlims": lonlims}
if docorr: # show EOF as correlation. otherwise, covariance
cmin = -1
cmax = 1
else:
cmin = ""
cmax = ""
fnamec, fnamep = con.build_filepathpair(sim, field)
fldc = cnc.getNCvar(fnamec, ncfield, seas=sea)
fldp = cnc.getNCvar(fnamep, ncfield, seas=sea)
lon = cnc.getNCvar(fnamec, "lon")
lat = cnc.getNCvar(fnamec, "lat")
fldca = np.squeeze(fldc - np.mean(fldc, axis=0)) # remove time mean
fldpa = np.squeeze(fldp - np.mean(fldp, axis=0))
if subset:
msk1 = con.get_t63regionmask("other", limsdict=limsdict)
fldca, msk = cutl.mask_region(fldca, lat, lon, "other", limsdict=limsdict)
fldpa, msk = cutl.mask_region(fldpa, lat, lon, "other", limsdict=limsdict)
def pattcorr_ensemble(ename, field, latlim=60):
# @@@@@@@@@@@@ is this fully implemented? Don't think so. 12/2/14
if ename=='ANT':
ename='HistIC'
elif ename=='TOT':
ename='HistBC'
enssims = con.build_ensemblesims(ename)
ensnum=len(enssims)
# ======= copied from Canam4_BCpatterncorr-Copy0.py ===========
#ensnum=5
diffdict = {}
pcmeandict = {} # fldp-fldc pattern corr compared to mean BC
pchaddict = {} # fldp-fldc pattern corr compared to hadisst
seadiffdict = {} # seasonal mean
pcseameandict = {}
pcsea2meandict = {} # to test the other pattern corr calc
pcsea2pvalmeandict = {} # to test the other pattern corr calc
# generate weights for the pattern corr
lat = con.get_t63lat()
lon = con.get_t63lon()
areas = cutl.calc_cellareas(lat,lon)
areas = areas[lat>latlim,:]
weights = areas / np.sum(np.sum(areas,axis=1),axis=0)
#for eii in range(1,ensnum+1):
for skey in enssims:
#skey = etype + str(eii)
#casenamec = bcasenamec + skey
#casenamep = bcasenamep + skey
#fnamec = basepath + casenamec+ subdir + casenamec + '_' + field + '_001-121_ts.nc'
#fnamep = basepath + casenamep+ subdir + casenamep + '_' + field + '_001-121_ts.nc'
fnamec,fnamep = con.build_filepathpair(skey,field)
# monthly calc
fldc = cnc.getNCvar(fnamec,ncfield,timesel=timesel)*conv
fldp = cnc.getNCvar(fnamep,ncfield,timesel=timesel)*conv
fldd = fldp-fldc
# take the pattern correlation
flddclimo,flddstd = cutl.climatologize(fldd) # climo first (don't need to do for BCs technically)
flddcclimo,flddcstd = cutl.climatologize(flddc) # climo first. baseline diff data
diffdict[skey] = flddclimo
# for each month, compute pattern corr
pc = np.zeros((12))
for mii,mon in enumerate(con.get_mon()):
tmp = np.squeeze(flddclimo[mii,lat>latlim,...])
tmpcmp = np.squeeze(flddcclimo[mii,lat>latlim,...])
pc[mii] = cutl.pattcorr(tmp.flatten()*weights.flatten(),tmpcmp.flatten()*weights.flatten())
pcmeandict[skey] = pc # monthly
# seasonal calc
fldcsea = np.zeros((4,len(lat),len(lon)))
fldpsea = np.zeros((4,len(lat),len(lon)))
flddsea = np.zeros((4,len(lat),len(lon)))
pcsea = np.zeros((4))
pcsea2 = np.zeros((4)) # test pattcorr_pearson() @@
pcsea2pval = np.zeros((4)) # test pattcorr_pearson()
for seaii,sea in enumerate(seasons):
fldcsea[seaii,...] = np.mean(cnc.getNCvar(fnamec,ncfield,timesel=timesel,seas=sea)*conv,axis=0)
fldpsea[seaii,...] = np.mean(cnc.getNCvar(fnamep,ncfield,timesel=timesel,seas=sea)*conv,axis=0)
flddsea[seaii,...] = fldpsea[seaii,...]-fldcsea[seaii,...]
tmp = np.squeeze(flddsea[seaii,lat>latlim,...])
tmpcmp = np.squeeze(flddcsea[seaii,lat>latlim,...])
pcsea[seaii] = cutl.pattcorr(tmp.flatten()*weights.flatten(),
tmpcmp.flatten()*weights.flatten())
pcsea2[seaii],pcsea2pval[seaii] = cutl.pattcorr_pearson(tmp.flatten()*weights.flatten(),
tmpcmp.flatten()*weights.flatten())
seadiffdict[skey] = flddsea
pcseameandict[skey] = pcsea
pcsea2meandict[skey] = pcsea2
pcsea2pvalmeandict[skey] = pcsea2pval
def pattcorr_withinensemble(ename,fdict,latlim=60,timesel='0002-01-01,0121-12-31'):
""" pattcorr_withinensemble(ename,field,latlim=60)
pattern corr each member of ensemble with each other one
return pctable, pctablesea (DataFrames)
"""
# @@ need diffdict
field=fdict['field']
ncfield=fdict['ncfield']
conv=fdict['conv']
seasons=('SON','DJF','MAM','JJA')
if ename=='ANT':
ename='histIC'
elif ename=='TOT':
ename='histBC'
enssims = con.build_ensemblesims(ename)
ensnum=len(enssims)
print 'ENSSIMS: ' # @@@
print enssims # @@
# generate weights for the pattern corr
lat = con.get_t63lat()
lon = con.get_t63lon()
areas = cutl.calc_cellareas(lat,lon)
areas = areas[lat>latlim,:]
weights = areas / np.sum(np.sum(areas,axis=1),axis=0)
# ========= create diffdict first =====
diffdict={}
seadiffdict={}
for skey in enssims:
fnamec,fnamep = con.build_filepathpair(skey,field)
# monthly calc
fldc = cnc.getNCvar(fnamec,ncfield,timesel=timesel)*conv
fldp = cnc.getNCvar(fnamep,ncfield,timesel=timesel)*conv
fldd = fldp-fldc
# Monthly
flddclimo,flddstd = cutl.climatologize(fldd) # climo first (don't need to do for BCs technically)
#flddcclimo,flddcstd = cutl.climatologize(flddc) # climo first. baseline diff data
diffdict[skey] = flddclimo
print skey + ' ' + str(flddclimo.shape) # @@@
# Seasonal
flddsea = np.zeros((4,len(lat),len(lon)))
for seaii,sea in enumerate(seasons):
fldcsea = np.mean(cnc.getNCvar(fnamec,ncfield,timesel=timesel,seas=sea)*conv,axis=0)
fldpsea = np.mean(cnc.getNCvar(fnamep,ncfield,timesel=timesel,seas=sea)*conv,axis=0)
flddsea[seaii,...] = fldpsea-fldcsea
seadiffdict[skey] = flddsea
# ======= Now do pattern corrs within ensemble ====
# ======= copied from Canam4_BCpatterncorr-Copy0.py ===========
outterdict= dict.fromkeys(enssims)
for skey1 in enssims:
outfld = diffdict[skey1]
innerdict = dict.fromkeys(enssims)
for skey2 in enssims:
#print skey1 + ' compared to ' + skey2
infld = diffdict[skey2]
# for each month, compute pattern corr
pc = np.zeros((12))
for mii,mon in enumerate(con.get_mon()):
tmp = np.squeeze(infld[mii,lat>latlim,...])
tmpcmp = np.squeeze(outfld[mii,lat>latlim,...])
pc[mii] = cutl.pattcorr(tmp.flatten()*weights.flatten(),
tmpcmp.flatten()*weights.flatten())
innerdict[skey2] = pc
outterdict[skey1] = innerdict
pctable = pd.DataFrame(outterdict) # 5x5
# seasonal
outterdictsea= dict.fromkeys(enssims)
for skey1 in enssims:
outfld = seadiffdict[skey1]
innerdictsea = dict.fromkeys(enssims)
for skey2 in enssims:
#print skey1 + ' compared to ' + skey2
infld = seadiffdict[skey2]
# for each season, compute pattern corr
pcsea = np.zeros((4))
for seaii,sea in enumerate(seasons):
tmp = np.squeeze(infld[seaii,lat>latlim,...])
tmpcmp = np.squeeze(outfld[seaii,lat>latlim,...])
pcsea[seaii] = cutl.pattcorr(tmp.flatten()*weights.flatten(),
tmpcmp.flatten()*weights.flatten())
innerdictsea[skey2] = pcsea
outterdictsea[skey1] = innerdictsea
pctablesea = pd.DataFrame(outterdictsea) # 5x5
return pctable, pctablesea
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
allantcr1=np.zeros(inittime)
allantcr2=np.zeros(inittime)
allantpr1=np.zeros(inittime)
allantpr2=np.zeros(inittime)
allantr1=np.zeros(inittime)
allantr2=np.zeros(inittime)
tallii=0 # index to keep track of time in accumulated TOT ensemble
aallii=0 # index to keep track of time in accumulated ANT ensemble
for sim in sims:
if field1 in ('turb','net'):
fielda='hfl'; fieldb='hfs'
fnamec,fnamepa=con.build_filepathpair(sim,fielda)
fnamecb,fnamepb=con.build_filepathpair(sim,fieldb)
fldc = cnc.getNCvar(fnamec,fielda.upper(),timesel=timesel,seas=sea1) +\
cnc.getNCvar(fnamecb,fieldb.upper(),timesel=timesel,seas=sea1)
fldp = cnc.getNCvar(fnamepa,fielda.upper(),timesel=timesel,seas=sea1) +\
cnc.getNCvar(fnamepb,fieldb.upper(),timesel=timesel,seas=sea1)
if field1=='net':
fieldb='flg'
conv=-1
fnamecb,fnamepb=con.build_filepathpair(sim,fieldb)
fldc = fldc + cnc.getNCvar(fnamecb,fieldb.upper(),
timesel=timesel,seas=sea1)*conv
fldp = fldp + cnc.getNCvar(fnamepb,fieldb.upper(),
timesel=timesel,seas=sea1)*conv
