def map_allmonths(fld, lat, lon,title='',units='',cmap='blue2red_w20',ptype='sq',
cmin='',cmax='',axis=None, suppcb=0,lmask=0,climo=0,flipmask=0,
pvals = None,sigtype='hatch',conts=None,latlim=None):
months = con.get_mon()
midx=0
fig, spax = plt.subplots(2,6)
fig.set_size_inches(12,4.5) # changed from 12,6 on 4/24/14. see how it saves
if latlim!=None:
fig.subplots_adjust(hspace=0.05,wspace=0.05)
else:
fig.subplots_adjust(hspace=0,wspace=0)
for ax in spax.flat:
if climo == 0:
monfld = fld[midx::12,...]
plotfld = np.mean(monfld,axis=0)
else:
plotfld = fld[midx,...]
#tstat,pval = sp.stats.ttest_ind(monfldp,monfldc,axis=0)
#sigs[midx,:,:] = ma.masked_where(pval>0.05,pval)
bm,pc = kemmap(plotfld,lat,lon,cmin=cmin,cmax=cmax,cmap=cmap,ptype=ptype,\
axis=ax,suppcb=1,lmask=lmask,flipmask=flipmask,units=units,latlim=latlim)
ax.set_title(months[midx])
if pvals != None:
addtsigm(bm,pvals[midx,...],lat,lon,sigtype=sigtype)
if conts != None:
# add specified contour(s)
lons, lats = np.meshgrid(lon,lat)
bm.contour(lons,lats,plotfld,[conts],colors='k',linewidths=2,latlon=True)
midx = midx+1
cbar_ax = fig.add_axes([.91,.25, .02,.5])
fig.colorbar(pc,cax=cbar_ax) # or do bm.colorbar....
plt.suptitle(title)
return fig
fig.savefig(
prfield + "diffsig" + sigtype + "_" + casenamep + "_v_" + casename + "_timexlat_seas_nh_v2." + suff
)
else:
fig.savefig(
prfield + "diffsig" + sigtype + "_" + casenamep + "_v_" + casename + "_timexlat_seas_nh." + suff
)
if seacycle: # want month x lat (or height)
cmlen = float(plt.cm.get_cmap(cmap).N) # or: from __future__ import division
incr = (cmaxm - cminm) / (cmlen)
conts = np.arange(cminm, cmaxm + incr, incr)
months = con.get_mon()
if threed:
print "fix 3D vars to use premade files @@"
fldc = np.append(
cnc.getNCvar(fnamec, ncfield, timesel=timesel, levsel=level) * conv,
cnc.getNCvar(fnamec2, ncfield, levsel=level) * conv,
axis=0,
)
fldp = np.append(
cnc.getNCvar(fnamep, ncfield, timesel=timesel, levsel=level) * conv,
cnc.getNCvar(fnamep2, ncfield, levsel=level) * conv,
axis=0,
)
else:
if field == "turb":
def plot_pattcorrs(pcdf, pcdf2=None, rmin=None, axis=None, tftype='seasonal'):
""" plot_pattcorrs(pcdf, axis):
this figure plots range of pattern correlations
as bars, with mean patt corr marker, plus % common
variation annotated.
pcdf: DataFrame of pattern correlations
pcdf2: second set of data to plot
rmin: minimum r value (pattern correlation) that is significant for the data
axis: plot axis
tftype: time frequency type: 'seasonal' or 'monthly'
"""
import math as math
if tftype=='seasonal':
numt=4
seasons=('SON','DJF','MAM','JJA')
else:
print 'ONLY SEASONAL IMPLEMENTED @@ 11/25/14'
numt=12 # monthly
seasons=con.get_mon()
# within ensemble pattern correlation combinations
# number of combinations: n choose k (here k=2)
# n! / (k! *(n-k)!)
ncomb = math.factorial(len(pcdf)) / (2 * math.factorial(len(pcdf)-2) )
# first, create matrix of data to take mean, max, min, etc
pcstack = np.zeros((ncomb,numt)) # ncomb combos x 4 seasons (or 12 mon)
stackii=0
keys=pcdf.keys()
for eii,skey1 in enumerate(keys):
for ineii,skey2 in enumerate(keys[eii+1:len(keys)]):
# stack the pc's so I can take max/min, etc
pcstack[stackii] = pcdf[skey1][skey2]
stackii+=1
mxsea = np.max(pcstack,axis=0)
mnsea = np.min(pcstack,axis=0)
avgsea = np.mean(pcstack,axis=0)
tmpstack=copy.copy(pcstack)
tmpstack[pcstack<0] = 0 # get rid of negative correlations. they are zero for all intents & purposes in this case
pcstacksq = np.power(tmpstack,2) # square the patt corr (the ones that are positive)
avgseasq = np.mean(pcstacksq,axis=0) # average squared patt corr (coef of determination)
if not pcdf2.empty:
ncomb2 = math.factorial(len(pcdf2)) / (2 * math.factorial(len(pcdf2)-2) )
pcstack2 = np.zeros((ncomb2,numt)) # ncomb combos x 4 seasons
stackii=0
keys=pcdf2.keys()
for eii,skey1 in enumerate(keys):
for ineii,skey2 in enumerate(keys[eii+1:len(keys)]):
# stack the pc's so I can take max/min, etc
pcstack2[stackii] = pcdf2[skey1][skey2]
stackii+=1
mxsea2 = np.max(pcstack2,axis=0)
mnsea2 = np.min(pcstack2,axis=0)
avgsea2 = np.mean(pcstack2,axis=0)
tmpstack2=copy.copy(pcstack2)
tmpstack2[pcstack2<0] = 0 # get rid of negative correlations. they are zero for all intents & purposes in this case
pcstacksq2 = np.power(tmpstack2,2) # square the patt corr (the ones that are positive)
avgseasq2 = np.mean(pcstacksq2,axis=0) # average squared patt corr (coef of determination)
# add annual mean
annwgts = np.array((91,90,92,92))/365.
print annwgts
#fig,axs = plt.subplots(1,1)
#fig.set_size_inches(6,4) # more squat
ax = axis #[0]
legtpl=()
wi=0.1 # width of bar
incr=0.3 # how much to shift in b/w the 2 sets of data
xxsea2=np.arange(1,6)
xboxmin=xxsea2-.2
if rmin!=None:
ax.axhspan(-1*rmin,rmin,color='orange',alpha=.3) # shade where corr is NOT significant
fillcol='0.7'
fillcol2=ccm.get_linecolor('dodgerblue')
for boxii in range(0,4): # loop through seasons
# shaded bars/boxes
ax.fill_between((xboxmin[boxii],xboxmin[boxii]+wi),mnsea[boxii],mxsea[boxii],color=fillcol, alpha=.5)
if not pcdf2.empty:
ax.fill_between((xboxmin[boxii]+incr,xboxmin[boxii]+incr+wi),mnsea2[boxii], mxsea2[boxii],color=fillcol2, alpha=.5)
# markers
ax.plot(xboxmin[boxii]+wi/2.,avgsea[boxii],color='k',marker='_',linestyle='none',markersize=15)#,alpha=.7)
if not pcdf2.empty:
ax.plot(xboxmin[boxii]+wi/2.+incr,avgsea2[boxii],color='b',marker='_',linestyle='none',markersize=15)#,alpha=.7)
# mean values (text)
val = '$%.0f$'%(avgseasq[boxii]*100) # @@ square the corrs before taking mean
ax.annotate(val+'%', xy=(xboxmin[boxii]+wi/2. -.09, .95), xycoords='data')
if not pcdf2.empty:
val = '$%.0f$'%(avgseasq2[boxii]*100) # @@ square the corrs before taking mean
ax.annotate(val+'%', xy=(xboxmin[boxii]+wi/2.+incr -.06, .95), xycoords='data')
boxii=boxii+1
# add annual mean -----------
annwgtst = np.tile(annwgts,(len(pcdf),1)) # tile
# each ens w/ each other
# pcstacksq is ncomb x numt
annwgtst = np.tile(annwgts,(ncomb,1)) # tile
ann = np.average(pcstack,weights=annwgts,axis=1) # ann mean per patt corr
annmax = np.max(ann)
annmin = np.min(ann)
avgann = np.mean(ann)
annsq = np.average(pcstacksq,weights=annwgts,axis=1) # ann mean per patt corr
avgannsq = np.mean(annsq)
if not pcdf2.empty:
# add annual mean
annwgtst = np.tile(annwgts,(len(pcdf2),1)) # tile
# each ens w/ each other
# pcstacksq is ncomb x numt
annwgtst = np.tile(annwgts,(ncomb2,1)) # tile
ann = np.average(pcstack2,weights=annwgts,axis=1) # ann mean per patt corr
annmax2 = np.max(ann)
annmin2 = np.min(ann)
avgann2 = np.mean(ann)
annsq2 = np.average(pcstacksq2,weights=annwgts,axis=1) # ann mean per patt corr
avgannsq2 = np.mean(annsq2)
# plot annual mean markers
ax.fill_between((xboxmin[boxii],xboxmin[boxii]+wi),annmin,annmax,color=fillcol,alpha=.5)
if not pcdf2.empty:
ax.fill_between((xboxmin[boxii]+incr,xboxmin[boxii]+incr+wi),annmin2,annmax2,color=fillcol2,alpha=.5)
ax.plot(xboxmin[boxii]+wi/2.,avgann,color='k',marker='_',linestyle='none',markersize=15)#,alpha=.9)
if not pcdf2.empty:
ax.plot(xboxmin[boxii]+wi/2.+incr,avgann2,color='b',marker='_',linestyle='none',markersize=15)#,alpha=.9)
val = '$%.0f$'%(avgannsq*100) # @@ square the corrs before taking mean
ax.annotate(val+'%', xy=(xboxmin[boxii]+wi/2. -.09, .95), xycoords='data')
if not pcdf2.empty:
val = '$%.0f$'%(avgannsq2*100) # @@ square the corrs before taking mean
ax.annotate(val+'%', xy=(xboxmin[boxii]+wi/2.+incr -.06, .95), xycoords='data')
ax.set_ylabel('Pattern Correlation')
ax.set_xlim((.5,5.5))
ax.set_xticks(xxsea2)
ax.set_xticklabels((seasons)+('ANN',))
ax.set_ylim((0,1))
ax.grid(True)
fnamep = basepath + casenamep+ subdir + casenamep + '_' + field + '_001-121_ts.nc'
# 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):
plt.figure()
bm,cf = cplt.kemmap(slpd,lat,lon,cmin=-2,cmax=2,type='nh',cmap='blue2red_20')
# transform vectors to projection grid.
uproj,vproj,xx,yy = bm.transform_vector(ugrid,vgrid,newlons,lat,46,54,returnxy=True,masked=True)
qu=bm.quiver(xx,yy,uproj,vproj)#,scale=700)
#qu=bm.quiver(splons,splats,spud,spvd,latlon=True)
plt.quiverkey(qu,0.1,0.1,1,'1 m/s',labelpos='W')
if printtofile:
plt.savefig('pmsl_windvecs' + level + '_' + str(sea) + '_' + sim + '.png')
fuc,fup=con.build_filepathpair(sim,ufield)
fvc,fvp=con.build_filepathpair(sim,vfield)
monstr=con.get_mon()
if sea=='DJF':
months=[12,1,2,3]
prstr='DJFM'
elif sea=='SON':
months=[9,10,11,12]
prstr='SOND'
fig,axs=plt.subplots(2,2) # D, J, F, M
fig.set_size_inches(10,8)
for aii,ax in enumerate(axs.flat):
sea=months[aii]
#print sea
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
printtofile=False
df = pd.DataFrame.from_csv('/raid/ra40/data/ncs/for_edhawkins_sic/rcp45/cmip5_rcp45_sie.csv')
df_CanESM2 = df.filter(regex="CanESM2")
models = ('ACCESS1-0','ACCESS1-3','BNU-ESM','CCSM4','CESM1-BGC','CESM1-CAM5','CMCC-CMS',
'CMCC-CM','CNRM-CM5','CSIRO-Mk3-6-0','CanESM2','EC-EARTH','FGOALS-g2','GFDL-CM3',
'GFDL-ESM2M','GISS-E2-H','GISS-E2-R','HadGEM2-CC','HadGEM2-ES','IPSL-CM5A-LR',
'IPSL-CM5A-MR','IPSL-CM5B-LR','MIROC-ESM-CHEM','MIROC-ESM','MIROC5','MPI-ESM-LR',
'MPI-ESM-MR','MRI-CGCM3','NorESM1-ME')
styr='1979'
endyr='2012' # inclusive
mons = con.get_mon()
montrenddt = coll.OrderedDict() #dict.fromkeys(mons)
monmndt = coll.OrderedDict()
monmxdt = coll.OrderedDict()
monavgdt = coll.OrderedDict()
hmontrenddt = coll.OrderedDict()
nmontrenddt = coll.OrderedDict()
#mondf = pd.DataFrame() # index will be np.arange
totens=len(df.keys()) # total # of ens members
## # trend calc
## mm, bb = np.polyfit(xx, dat, 1)
## ax.plot(onex,mm*onex + bb, color='k')#,linestyle='--')
