def plot_region(regname,ptype='nh',axis=None,latlim=None,limsdict=None):
""" plot_region(regname,ptype='nh',axis=None,latlim=None,limsdict=None):
Given a region name, plot it for reference.
latlims is unused right now
if passing limsdict (to override regname), set regname='other'
"""
if regname=='other':
reglims=limsdict
else:
reglims = con.get_regionlims(regname)
latlims = reglims['latlims']
lonlims = reglims['lonlims']
dummy = con.get_t63landmask()
lat = con.get_t63lat()
lon = con.get_t63lon()
lons,lats = np.meshgrid(lon,lat)
reglatsbool = np.logical_and(lat>latlims[0],lat<latlims[1])
reglonsbool = np.logical_and(lon>lonlims[0],lon<lonlims[1])
# mask everything but the region of interest
regmask = np.logical_or(
np.logical_or(lats<latlims[0],lats>latlims[1]),
np.logical_or(lons<lonlims[0],lons>lonlims[1]))
dummym = ma.masked_where(regmask,dummy)
plt.figure()
kemmap(dummym,lat,lon,ptype=ptype,axis=axis,latlim=latlim,suppcb=1,
cmin=-11,cmax=2,cmap='blue2blue_w10',drawgrid=True)
def plot_regions(regions, ptype='nh', colors='k', latlim=None, drawgrid=False):
""" plot multiple region boundaries on one map
regions: tuple of region names
default color = black
colors can be a tuple of color specifications.
index will wrap if not enough
"""
dummy = con.get_t63landmask()
lat = con.get_t63lat()
lon = con.get_t63lon()
lons,lats = np.meshgrid(lon,lat)
dummy = dummy*np.nan
plt.figure()
bm,pc = kemmap(dummy,lat,lon,ptype=ptype,latlim=latlim,suppcb=1,
drawgrid=drawgrid, lmask=True)
for ii,reg in enumerate(regions):
if len(colors)==1: clr=colors
else:
if ii==len(colors): ii=0 # wrap index
clr=colors[ii]
add_regionpolym(reg,bm, ec=clr)
def plot_allregions(ptype='nh'):
""" plot_allregions(ptype='nh'): plot all defined regions
"""
regdict = con.get_regiondict()
nreg = len(regdict)
rem = np.mod(nreg,2)
rows = 2
if rem==0:
cols = nreg/rows
else:
cols = nreg/rows + rem # rem will always be 1 if dividing by 2
if cols>8:
rows = 3
cols = nreg/rows + np.mod(nreg,rows)/2
print 'nrows: ' + str(rows) + ' ncols: ' + str(cols) # @@@
lat = con.get_t63lat()
lon = con.get_t63lon()
fig,spax = plt.subplots(rows,cols)
fig.set_size_inches(cols*2,rows*3)
for aii,ax in enumerate(spax.flat):
dummy = con.get_t63landmask() # dummy data
regkey = regdict.keys()[aii]
limsdict = regdict[regkey]
# mask the dummy data
dummym,dmask = cutl.mask_region(dummy,lat,lon,regkey,limsdict=limsdict)
kemmap(dummym,lat,lon,ptype=ptype,axis=ax,suppcb=1,cmin=-11,cmax=2,cmap='blue2blue_w10')
ax.set_title(regkey)
ax.set_xlabel(str(limsdict['latlims']) + ',' +str(limsdict['lonlims']) )
if aii==nreg-1: break # get out of loop if done with regions
seacycylim=None
infodict ={'cmapclimo': 'Spectral_r','leglocs': None,
'seacycylim': None, 'savestr': savestr,
'model': model, 'sigtype': sigtype, 'sigoff': sigoff,
'pct': pct, 'seacyclatlim': seacyclatlim, 'region': region,
'shadeens': shadeens, 'corrlim': corrlim, 'figtrans':figtrans,
'ptype': projtype,'contclr': '0.3', 'contthk': 1, 'contstl': None} # random other info. projtype for maps only
fdict,pparams=ld.loadfldmeta(field,infodict,plottype,ptparams,level=level)
if addcont: # overlay map with another field in contours
# start with just anomaly contours. @@later add option for climo
fdict2,pparams2=ld.loadfldmeta(field2,infodict,plottype,ptparams,level=level2)
coords = {'lev': con.get_t63lev(), 'lat': con.get_t63lat(), 'lon': con.get_t63lon()}
## infodict['savestr'] = savestr
## infodict['leglocs'] = leglocs
## infodict['seacycylim'] = seacycylim
## fdict['isflux'] = isflux
## fdict['threed'] = threed
## ###################### end copy to load_fldmeta.py
# do an if elif elif ....
if plottype=='seasonalvert':
infodict['screen']=screen
else:
infodict['screen'] = None
if plottype in ('seasonalmap','seasonalvert'):
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
timesel='2922-01-01,2931-12-31'
Cp=1004 # specific heat at const pressure (J/K/kg)
Lv=2.5e6 # specific heat of condensation (latent heat of vapo) at 0C(?@@) (J/kg)
erad = con.get_earthrad() # m
grav = con.get_g() # m/s2
# 1 cal = 4.186J
# convert to cal / day
# J/s -> cal/day
W2calperday = (60*60*24)/4.186
lat = con.get_t63lat()
lon = con.get_t63lon()
#lev = con.get_t63lev() # 37 levs
fname=basepath + casename + '_u_' + timeper + '_ts.nc' # 22 levs!!
lev = cnc.getNCvar(fname,'plev')
dp = np.diff(lev)
nlon = len(lon)-1 # removing extra lon
# get surface pressure to do vert avg and int here
fname=basepath + casename + '_ps_' + timeper + '_ts.nc'
sfcp = cnc.getNCvar(fname,'PS',timesel=timesel)*100 # into Pa
def clear_belowsfc(fld, sfcp, lev):
#levt=np.tile(lev,((sfcp.shape)+(1,)))
#levt = np.transpose(levt,(3,0,1,2)) # lev x time x lat x lon (for now)
title=ttl2,suppcb=True,
panellab='b',lcol='0.2')
bm.contour(lons,lats,fldsp2onfldr2,levels=conts,
colors='0.5',linewidths=1,latlon=True)
cplt.add_colorbar(fig,pc,orientation='horizontal')
if printtofile:
fig.savefig(fieldsp + '_' + fieldsp2 + seasp + \
'_regresson_' + fieldr+regionr + '_' + fieldr2 + regionr2 + sear + '.pdf')
# ========== AGCM
alat=con.get_t63lat(); alon=con.get_t63lon()
alons, alats = np.meshgrid(alon,alat)
cmlen=15.
incra = (cmaxsp2a-cminsp2a) / (cmlen)
contsa = np.arange(cminsp2a,cmaxsp2a+incra,incra)
fig,axs=plt.subplots(1,2)
fig.set_size_inches(10,5)
fig.subplots_adjust(wspace=0.05)
ax=axs[0]
bm,pc=cplt.kemmap(asponfldr,alat,alon,ptype='nheur',axis=ax,cmin=cminspa,cmax=cmaxspa,
title=ttl1,suppcb=True,
panellab='a',lcol='0.2')
bm.contour(alons,alats,asp2onfldr,levels=contsa,
meta = {'gt': gt, 'sicn': sicn, 'sic': sic}
ylabs = {0: 'OBS', 1: 'E1', 2: 'E2', 3: 'E3', 4: 'E4', 5:'E5'}
convdt = {'gt': 1, 'sicn': 100, 'sic': 1/np.float(deni)}
# === load one year for seasonal cycle:
#flddat,fldcdat,fldpdat,sicnpdat = load_BCs(fields, season=None, timesel=timesel)
# ============== PLOT ==============
printtofile=False
lat = con.get_t63lat() #cnc.getNCvar(fnamec,'lat')
lon = con.get_t63lon() #cnc.getNCvar(fnamep,'lon')
fsz=16
fliptohoriz=True
latlim=57
pparams = {'lcol':'0.9', 'coastres': 'c', 'coastwidth': 0.5,
'area_thresh':70000,'lmask':True}
fields=('sicn','sic','gt')
fields=('sicn',)
tlabs = {'gt': '$\Delta$ SST ($^\circ$C)', 'sicn': '$\Delta$ SIC (%)', 'sic': '$\Delta$ SIT (m)'}
if fliptohoriz:
psuff='_horizor'+str(len(fields))+'b' # horizontal orientation
# rows are fields and cols are sims
if len(fields)==1: