def calc_monthly_linpatterncorr(ncdt,lin='one',northof=0,conv=1,vert=False):
""" Computes the pattern correlation b/w the 'SUM' and 'FULL'
for given 'lin'. Input data is climatology of spatial data.
computes the correlation for area north of 'northof' value for e/ month.
returns rval,pval
"""
pico = ncdt['prei2xco2iceb']
pipi = ncdt['preipreiice']
copi = ncdt['2xco2preiice']
coco = ncdt['2xco22xco2ice']
lat = ncdt['lat']
if lin=='one':
ice = (pico - pipi)*conv
co2 = (copi - pipi)*conv
ctl = pipi*conv
suff=''
suff1='cold'; suff2='hi'
elif lin=='two':
ice = (coco - copi)*conv
co2 = (coco - pico)*conv
ctl = coco*conv
suff='2'
suff1='warm'; suff2='lo'
combo = ice+co2
full = (coco - pipi)*conv
#print lat>northof
rvals=np.zeros((12,))
pvals=np.zeros_like(rvals)
for moii in range(0,12):
if vert: # lat in 2nd dim
rvals[moii],pvals[moii] = cutl.pattcorr_pearson(combo[moii,:,lat>northof].flatten(),
full[moii,:,lat>northof].flatten())
else: # lat in 1st dim
rvals[moii],pvals[moii] = cutl.pattcorr_pearson(combo[moii,lat>northof,:].flatten(),
full[moii,lat>northof,:].flatten())
return rvals,pvals
def calc_linpatterncorr(ncdt,lin='one',northof=0,conv=1,vert=False):
""" Computes the pattern correlation b/w the 'SUM' and 'FULL'
for given 'lin'. Input data is seasonal avg timeseries of spatial data.
computes the correlation for area north of 'northof' value.
returns rval,pval
"""
pico = ncdt['prei2xco2iceb']
pipi = ncdt['preipreiice']
copi = ncdt['2xco2preiice']
coco = ncdt['2xco22xco2ice']
lat = ncdt['lat']
if lin=='one':
ice = (np.mean(pico,axis=0) - np.mean(pipi,axis=0))*conv
co2 = (np.mean(copi,axis=0) - np.mean(pipi,axis=0))*conv
ctl = np.mean(pipi,axis=0)*conv
suff=''
suff1='cold'; suff2='hi'
elif lin=='two':
ice = (np.mean(coco,axis=0) - np.mean(copi,axis=0))*conv
co2 = (np.mean(coco,axis=0) - np.mean(pico,axis=0))*conv
ctl = np.mean(coco,axis=0)*conv
suff='2'
suff1='warm'; suff2='lo'
combo = ice+co2
full = (np.mean(coco,axis=0) - np.mean(pipi,axis=0))*conv
if vert: # lat in 2nd dim
rval,pval = cutl.pattcorr_pearson(combo[:,lat>northof].flatten(),
full[:,lat>northof].flatten())
else: # lat in 1st dim
rval,pval = cutl.pattcorr_pearson(combo[lat>northof,:].flatten(),
full[lat>northof,:].flatten())
#print cutl.pattcorr(combo[lat>northof,:].flatten(),full[lat>northof,:].flatten())
return rval,pval
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
colordict = ccm.get_colordict()
# <codecell>
import pandas as pd
pcmeandf = pd.DataFrame(pcmeandict)
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
