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simulation_funcs.py
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simulation_funcs.py
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""" simulation_funcs.py
9/9/2014: Goal is to use this to house functions to
prepare simulation data, and then plot data (at a higher
level than cccmaplots.py)
e.g. simulations and figures more specific to
sea ice paper.
In future, this need not be limited to canam4, so
I have left the filename general, and will update
the innards to be able to handle other model data.
(right now model='CanAM4' etc is hard-coded. 9/9/14)
"""
import numpy as np
import numpy.ma as ma
import scipy as sp
import scipy.stats
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
import cccmaplots as cplt
import constants as con
import cccmautils as cutl
import cccmacmaps as ccm
import cccmaNC as cnc
import pandas as pd
cplt = reload(cplt)
con = reload(con)
cutl = reload(cutl)
ccm = reload(ccm)
cnc = reload(cnc)
def calc_plot_seasonal_maps(fielddict,coords,sims,pparams,vert=False,loctimesel=None,
info=None,printtofile=False,seas=None,addflds=None,addpparams=None):
""" calc_plot_seasonal_maps(fielddict,coords,sims,pparams,vert=False,loctimesel=None,info=None,printtofile=False,seas=None, addflds=None,addpparams=None):
info should be a dict of catch-all keywords/info
addflds,addpparams are tuples of fdicts and pparams for field contours to be overlain
returns figure handle
"""
addcont=False # overlay with contours. Start with one extra field.
field=fielddict['field']
ncfield=fielddict['ncfield']
fieldstr=fielddict['fieldstr']
conv=fielddict['conv']
addsie=False
if addflds:
addcont=True
fdictadd = addflds[0]
fieldadd = fdictadd['field']
fieldaddstr = fdictadd['fieldstr']
ncfieldadd = fdictadd['ncfield']
print 'add: ' + fieldadd
if seas != None:
seasons=seas
else: # standard seasons
seasons=('SON','DJF','MAM','JJA')
model=info['model'] #'CanAM4' # @@ move out of function
sigtype=info['sigtype'] # specify sig marking type
sigoff=info['sigoff'] # turn off significance marking?
pct=info['pct'] # percentage change?
nonstandardlev=fielddict['nonstandardlev']
savestr=info['savestr']
screen = info['screen']
figtrans = info['figtrans'] # boolean specifying whether to switch rows/cols
pltype= info['ptype'] # plot projection type for kemmap
bp=con.get_basepath()
basepath=bp['basepath'] + model + '/'; subdir=bp['subdir'] # @@ move out of function?
lat=coords['lat']
nlat=len(lat)
if vert==True:
lev=coords['lev']
nlev=len(lev)
theshape=(len(seasons),nlev,nlat)
else:
lon=coords['lon']
nlon=len(lon)
theshape=(len(seasons),nlat,nlon)
tstat = np.zeros(theshape)
pval = np.zeros(theshape)
fldcallseas = np.zeros(theshape)
fldpallseas = np.zeros(theshape)
fldcallseasadd = np.zeros(theshape)
fldpallseasadd = np.zeros(theshape)
latlim=pparams['latlim']
cmap=pparams['cmap']
cmin=pparams['cmin']; cmax=pparams['cmax']
cmlen=float( plt.cm.get_cmap(cmap).N)
incr = (cmax-cmin) / (cmlen)
conts = np.arange(cmin,cmax+incr,incr)
if addcont:
if fieldadd=='sie':
addsie=True
contsadd=[0.15, 0.15] # @@
else:
cminadd=addpparams[0]['cmin']; cmaxadd=addpparams[0]['cmax']
cmlen=float( plt.cm.get_cmap(cmap).N)
incradd = (cmaxadd-cminadd) / (cmlen)
contsadd = np.arange(cminadd,cmaxadd+incradd,incradd)
contsadd = contsadd[::2]
print 'added contours: ' + str(contsadd)
contclradd=info['contclr'] # contour color
contthkadd=info['contthk'] # linewidths
contstladd=info['contstl'] # linestyles
print contclradd # @@@
if figtrans:
fig6,ax6 = plt.subplots(len(sims),len(seasons)) # 1 row for e/ simulation, 1 col per season
fig6.set_size_inches(7,13)
#lastcol=len(seasons)-1
#lastrow=len(sims)-1
#print 'lastrow ' + str(lastrow) # @@@
print 'figtrans ' + str(figtrans)
if vert:
fig6.subplots_adjust(hspace=.03,wspace=.03)
else:
fig6.subplots_adjust(hspace=.02,wspace=.02)
print '@@@@@@@@@ ax6.shape ' + str(ax6.shape)
print seasons
print sims
print '@@@@@@@@@'
else:
fig6,ax6 = plt.subplots(len(seasons),len(sims)) # 1 col for e/ simulation, 1 row per season
fig6.set_size_inches(12,8)
#lastcol=len(sims)-1
#lastrow=len(seasons)-1
fig6.subplots_adjust(hspace=.15,wspace=.05)
print '@@@@@@@@@ ax6.shape ' + str(ax6.shape)
print seasons
print sims
print '@@@@@@@@@'
lastcol=len(sims)-1
lastrow=len(seasons)-1
for colidx,sim in enumerate(sims): # traverse cols
rowidx=0
simpair = con.get_simpair(sim)
simctl = simpair['ctl']['fullname']
timstr = simpair['ctl']['timestr']
simpt = simpair['pert']['fullname']
timstrp = simpair['pert']['timestr']
frootc = basepath + simctl + subdir + simctl + '_' + field + '_'
frootp = basepath + simpt + subdir + simpt + '_' + field + '_'
rowl=sim
if loctimesel !=None: # e.g. for when want to look at half of a run...
timesel = loctimesel
else:
timesel = simpair['ctl']['timesel']
fnamec = frootc + timstr + '_ts.nc'
fnamep = frootp + timstrp + '_ts.nc'
if addcont:
if addsie:
fieldadd='sicn'; ncfieldadd='SICN'
fnamecadd,fnamepadd = con.build_filepathpair(sim,fieldadd)
print fnamecadd
print fnamepadd
# @@@ I *think* I don't need this anymore since processing the 3D files more
# @@ although getting a nonstandard lev is not supported
if nonstandardlev:
print 'not yet supported for seasonal'
## if threed==0:
## fnamec = frootc + timstr + '_ts.nc'
## fnamep = frootp + timstrp + '_ts.nc'
## else:
## print '@@ fix to use the level NC files'
## fnamec = frootc + '001-061_ts.nc'
## fnamec2 = frootc + '062-121_ts.nc'
## fnamep = frootp + '001-061_ts.nc'
## fnamep2 = frootp + '062-121_ts.nc'
for sea in seasons: # traverse rows (or use map_allseas() ?? @@)
#ax = ax6[ridx][cidx]
if figtrans:
if len(sims)==1:
ax=ax6[colidx]
elif len(seasons)==1:
ax=ax6[colidx]
else:
ax=ax6[colidx][rowidx]
else:
if len(sims)==1:
ax=ax6[rowidx]
elif len(seasons)==1:
#ax=ax6[rowidx] # @@should this be colidx?? YES. prob means above is broken too....
ax=ax6[colidx]
print 'rowidx ' + str(rowidx) + ', colidx ' + str(colidx) + ' ax6.shape ' + str(ax6.shape) # @@@@@
else:
ax = ax6[rowidx][colidx]
ncparams={'timesel':timesel, 'seas': sea}
if field=='turb':
field='hfl'; fieldb='hfs'
fldcsea = cnc.getNCvar(fnamec,field.upper(),**ncparams)*conv + cnc.getNCvar(fnamecb,fieldb.upper(),
**ncparams)*conv
fldpsea = cnc.getNCvar(fnamep,field.upper(),**ncparams)*conv + cnc.getNCvar(fnamepb,fieldb.upper(),
**ncparams)*conv
field='turb'
elif field=='net':
print '@@ not implemented for seasonal maps'
else:
print fnamec # @@@@
#print ncparams
fldcsea = cnc.getNCvar(fnamec,ncfield,**ncparams)*conv
fldpsea = cnc.getNCvar(fnamep,ncfield,**ncparams)*conv
## if threed==0:
## fldcsea = cnc.getNCvar(fnamec,field.upper(),timesel=timesel,
## seas=sea)*conv
## fldpsea = cnc.getNCvar(fnamep,field.upper(),timesel=timesel,
## seas=sea)*conv
## else:
## fldcsea = np.append(cnc.getNCvar(fnamec,ncfield,timesel='0002-01-01,061-12-31',levsel=level,
## seas=sea)*conv,
## cnc.getNCvar(fnamec2,ncfield,levsel=level,seas=sea)*conv,axis=0)
## fldpsea = np.append(cnc.getNCvar(fnamep,ncfield,timesel='0002-01-01,061-12-31',levsel=level,
## seas=sea)*conv,
## cnc.getNCvar(fnamep2,ncfield,levsel=level,seas=sea)*conv,axis=0)
if addcont:
fldcseaadd=cnc.getNCvar(fnamecadd,ncfieldadd,**ncparams)*fdictadd['conv']
fldpseaadd = cnc.getNCvar(fnamepadd,ncfieldadd,**ncparams)*fdictadd['conv']
fldcallseasadd[rowidx,:,:] = np.mean(fldcseaadd,axis=0)
fldpallseasadd[rowidx,:,:] = np.mean(fldpseaadd,axis=0)
if pct:
plotfldadd = (fldpallseasadd[rowidx,:,:]-fldcallseasadd[rowidx,:,:]) / fldcallseasadd[rowidx,:,:] *100
else:
plotfldadd = fldpallseasadd[rowidx,:,:] - fldcallseasadd[rowidx,:,:]
if addsie:
fieldadd='sie';
plotfldadd = fldcallseasadd[rowidx,:,:]
plotfldaddp = fldpallseasadd[rowidx,:,:]
tstat[rowidx,:,:],pval[rowidx,:,:] = sp.stats.ttest_ind(fldpsea,fldcsea,axis=0)
fldcallseas[rowidx,:,:] = np.mean(fldcsea,axis=0)
fldpallseas[rowidx,:,:] = np.mean(fldpsea,axis=0)
if pct:
plotfld = (fldpallseas[rowidx,:,:]-fldcallseas[rowidx,:,:]) / fldcallseas[rowidx,:,:] *100
else:
plotfld = fldpallseas[rowidx,:,:] - fldcallseas[rowidx,:,:]
pparams['axis']=ax
if vert: # zonal mean with height
pparams['suppcb'] = True
#pparams['levlim'] = levlim
pparams['screen'] = screen
pparams['addcontlines'] = True
if colidx!=0: # if not the first column, suppress y labels
if not figtrans:
pparams['suppylab'] = True
if rowidx!=0:
if figtrans: # if not first column, suppress y labels
pparams['suppylab'] = True
else:
if figtrans:
pparams['suppylab'] = False
if rowidx==lastrow:
if figtrans: # if last col
ax.set_ylabel(rowl,fontsize=18)
ax.yaxis.set_label_position("right")
if colidx==lastcol:
if figtrans: # if last row, don't suppress xlabels
pparams['suppxlab'] = False
else:
if figtrans: # otherwise, do suppress xlabels
pparams['suppxlab'] = True
pc = cplt.vert_plot(plotfld,lev,lat,**pparams)
if sigoff==False: # add sig
cplt.addtsig(ax,pval[rowidx,...],lat,lev/100.,sigtype=sigtype) # @@ dims?
if addcont:
lats,levs = np.meshgrid(lat,lev/100.)
ax.contour(lats,levs,plotfldadd,levels=contsadd,colors=contclradd,linewidths=contthkadd,linestyles=contstladd)
if colidx==lastcol:
print 'lastcol ' + str(lastcol) # @@@
if figtrans: # last row?
pass
else:
# put season label on right side.
ax.yaxis.set_label_position("right")
ax.set_ylabel(sea)
if colidx==0:
if figtrans:
# if first row
ax.set_title(sea,fontsize=18)
else: # maps
if pltype !=None:
pparams['ptype']=pltype
pparams['suppcb'] = 1
bm,pc = cplt.kemmap(plotfld,lat,lon,**pparams)#@@
if sigoff==False:
cplt.addtsigm(bm,pval[rowidx,:,:],lat,lon,sigtype=sigtype)
if addcont:
lons, lats = np.meshgrid(lon,lat)
if addsie:
bm.contour(lons,lats,plotfldadd,levels=contsadd,
colors=contclradd,linewidths=contthkadd,latlon=True)
bm.contour(lons,lats,plotfldaddp,levels=contsadd,
colors=contclradd,linewidths=contthkadd,latlon=True,linestyles='--')
else:
bm.contour(lons,lats,plotfldadd,levels=contsadd,
colors=contclradd,linewidths=contthkadd,latlon=True,linestyles=contstladd)
# @@@@ eventually add more contours?
if colidx==0: # when col index is 0, set season
if figtrans: # if first row
ax.set_title(sea,fontsize=18)
else:
ax.set_ylabel(sea,fontsize=18)
if rowidx==0: # when row index is 0, set simulation
if figtrans:# first column
ax.set_ylabel(rowl,fontsize=18)
else:
ax.set_title(rowl,fontsize=18)
rowidx = rowidx+1
if figtrans:
if vert:
cbar_ax = fig6.add_axes([.25,0.04, 0.5, .02])
else:
cbar_ax = fig6.add_axes([.25,0.07, 0.5, .02])
cbor='horizontal'
else:
cbar_ax = fig6.add_axes([.91,.25, .02,.5])
cbor='vertical'
cbar_ax.tick_params(labelsize=15)
fig6.colorbar(pc,cax=cbar_ax, orientation=cbor,format='%.1f')
plt.suptitle(fieldstr)
if printtofile:
if sigoff==False:
sigstr='sig' + sigtype
else:
sigstr=''
#sigstr='sigcont' #@@ hard code
if sigoff==False and sigtype=='hatch':
suff='png'
else:
suff='pdf'
if latlim!= None:
latstr=str(latlim)
else:
latstr=''
if addcont:
savestr=savestr+'_' + fieldaddstr + 'cont3'
if vert:
if screen:
style = 'screen'
else:
style = str(latlim) + 'N' + str(levlim) + 'hPa'
if pct:
fig6.savefig(fieldstr + 'pctdiff' + sigstr + '_enssubplot' + savestr +
'_seas_' + style + '2.' + suff)
else:
fig6.savefig(fieldstr + 'diff' + sigstr + '_enssubplot' + savestr +
'_seas_' + style + '2.' + suff)
else: # maps
if pct: # version 2 has new season order, new filename/key org
fig6.savefig(fieldstr + 'pctdiff' + sigstr + '_enssubplot' + savestr +
'_seas_nh' + latstr + '2.' + suff)
else:
fig6.savefig(fieldstr + 'diff' + sigstr + '_enssubplot' + savestr + '_seas_' + pltype
+ latstr + '2.' + suff)
return fig6
def calc_seasons(fielddict,coords,sims,loctimesel=None,info=None,siglevel=0.05,
calctype=None,seas=None,effdof=False):
""" calc_seasons(fielddict,coords,sims,withlat=False,loctimesel=None,info=None,siglevel=0.05)
info: a dict which specifies lots of things, but in particular, which region to average
calctype: 'zonmean', 'regmean', 'pattcorrwithtime', 'pattcorrwithtimeyr', 'timetosig', 'timetosigsuper',None
seasons default to 'SON','DJF','MAM','JJA'
@@@@ should add a regular anomaly calc here. make it default?
returns a data blob:
blob['ctl'] = fldcdict
blob['pert'] = fldpdict
blob['tstat'] = tstatdict
blob['pval'] = pvaldict
blob['ci'] = cidict
blob['ctlstd'] = fldcstddict
blob['pertstd'] = fldpstddict
blob['diff'] = flddiffdict
blob['mask'] = flddmaskdict
blob['pcorr'] = fldpcorrdict # pattern corr with time only
"""
if seas==None:
seasons='SON','DJF','MAM','JJA'
else:
seasons=seas
sia = False; sie=False
field=fielddict['field']
ncfield=fielddict['ncfield']
fieldstr=fielddict['fieldstr']
conv=fielddict['conv']
threed=fielddict['threed']
isflux=fielddict['isflux']
nonstandardlev=fielddict['nonstandardlev']
model=info['model']
pct=info['pct'] # percentage change?
corrlim=info['corrlim'] # southern limit for pattern correlation
bp=con.get_basepath()
basepath=bp['basepath'] + model + '/'; subdir=bp['subdir'] # @@ move out of function?
lat=coords['lat']
nlat=len(lat)
lon=coords['lon']
nlon=len(lon)
plotzonmean = plotregmean = pattcorrwithtime = pattcorryr = timetosig = timetosigsuper = calcregmeanwithtime = False
if calctype==None:
print 'calctype is None. Just doing regular diff of means for full grid'
#return -1
elif calctype=='zonmean':
plotzonmean=True
elif calctype=='regmean':
plotregmean=True
region=info['region']
elif calctype=='regmeanwithtime':
calcregmeanwithtime=True
region=info['region']
elif calctype=='pattcorrwithtime':
# note that this is pattern correlating a run's
# pattern in time (cumulative avg) w/ its final pattern
pattcorrwithtime=True
elif calctype=='pattcorrwithtimeyr':
# note that this is pattern correlating a run's
# pattern each year in time w/ its final pattern, then sorted
pattcorrwithtime=True
pattcorryr=True
elif calctype=='timetosig':
timetosig=True
elif calctype=='timetosigsuper':
timetosig=True
timetosigsuper=True
else:
print 'calctype not recognized! Just doing regular diff of means'
#return -1
# note that pattern corr with time will be a 1D processed field -->
# for each season, fld.shape = ntime
# zonal mean will be a 1D processed field -->
# for each season, fld.shape = nlat
# regional mean will be a scalar processed field -->
# for each season, fld.shape = 1 (regional mean)
# time to significance is a map for each season
# for each season, fld.shape = nlat x nlon
if field=='sia':
sia=True
field = 'sicn' # while getting the data...
elif field=='sie':
sie=True
field = 'sicn'
tstatdict = dict.fromkeys(sims,{}); pvaldict = dict.fromkeys(sims,{})
fldcdict = dict.fromkeys(sims,{}); fldpdict = dict.fromkeys(sims,{})
fldcstddict = dict.fromkeys(sims,{}); fldpstddict = dict.fromkeys(sims,{})
flddiffdict = dict.fromkeys(sims,{}); flddmaskdict = dict.fromkeys(sims,{})
fldpcorrdict = dict.fromkeys(sims,{}); fldtimetosigdict = dict.fromkeys(sims,{})
cidict = dict.fromkeys(sims,{})
for ridx,sim in enumerate(sims):
seatstatdict=dict.fromkeys(seasons); seapvaldict=dict.fromkeys(seasons)
seafldcdict=dict.fromkeys(seasons); seafldpdict=dict.fromkeys(seasons)
seafldcstddict=dict.fromkeys(seasons); seafldpstddict=dict.fromkeys(seasons)
seadiffdict=dict.fromkeys(seasons); seadmaskdict=dict.fromkeys(seasons)
seapcorrdict=dict.fromkeys(seasons)
seacidict=dict.fromkeys(seasons)
seatimetosigdict=dict.fromkeys(seasons)
simpair = con.get_simpair(sim)
simctl = simpair['ctl']['fullname']
timstr = simpair['ctl']['timestr']
simpt = simpair['pert']['fullname']
timstrp = simpair['pert']['timestr']
frootc = basepath + simctl + subdir + simctl + '_'
frootp = basepath + simpt + subdir + simpt + '_'
if loctimesel !=None: # e.g. for when want to look at half of a run...
timesel = loctimesel
else:
timesel = simpair['ctl']['timesel']
if timesel!=None:
print 'calc_seasons(): selecting time ' + timesel # @@@@@@
if threed and nonstandardlev:
fnamec = frootc + field + '_' + '001-061_ts.nc'
fnamec2 = frootc + field + '_' + '062-121_ts.nc'
fnamep = frootp + field + '_' + '001-061_ts.nc'
fnamep2 = frootp + field + '_' + '062-121_ts.nc'
else:
fnamec = frootc + field + '_' + timstr + '_ts.nc'
fnamep = frootp + field + '_' + timstrp + '_ts.nc'
for sii,sea in enumerate(seasons):
ncparams = {'seas': sea}
# Now get the data
if field in ('turb','net'):
#print 'not implemented @@'
#print 'field is ' + field + '. getting hfl, hfs'
fielda='hfl'; fieldb='hfs'
fnamec = frootc + fielda + '_' + timstr + '_ts.nc'
fnamep = frootp + fielda + '_' + timstrp + '_ts.nc'
fnamecb = frootc + fieldb + '_' + timstr + '_ts.nc'
fnamepb = frootp + fieldb + '_' + timstrp + '_ts.nc'
fldc = cnc.getNCvar(fnamec,fielda.upper(),timesel=timesel,
**ncparams)*conv + cnc.getNCvar(fnamecb,fieldb.upper(),
timesel=timesel,**ncparams)*conv
fldp = cnc.getNCvar(fnamep,fielda.upper(),timesel=timesel,
**ncparams)*conv + cnc.getNCvar(fnamepb,fieldb.upper(),
timesel=timesel,**ncparams)*conv
if field=='net':
#print 'getting flg for net'
fieldb='flg'
conv=-1
fnamecb = frootc + fieldb + '_' + timstr + '_ts.nc'
fnamepb = frootp + fieldb + '_' + timstrp + '_ts.nc'
fldc = fldc + cnc.getNCvar(fnamecb,fieldb.upper(),
timesel=timesel,**ncparams)*conv
fldp = fldp + cnc.getNCvar(fnamepb,fieldb.upper(),
timesel=timesel,**ncparams)*conv
field='net'
conv=1
else:
field='turb'
else:
if threed and nonstandardlev:
ncparams['levsel'] = level
fldc = np.append(cnc.getNCvar(fnamec,ncfield,timesel='0002-01-01,061-12-31',**ncparams)*conv,
cnc.getNCvar(fnamec2,ncfield,**ncparams)*conv,
axis=0)
fldp = np.append(cnc.getNCvar(fnamep,ncfield,timesel='0002-01-01,061-12-31',**ncparams)*conv,
cnc.getNCvar(fnamep2,ncfield,**ncparams)*conv,
axis=0)
else:
print fnamec
print 'ncparams: ' + str(ncparams)
fldc = cnc.getNCvar(fnamec,ncfield,timesel=timesel,
**ncparams)*conv
fldp = cnc.getNCvar(fnamep,ncfield,timesel=timesel,
**ncparams)*conv
if sia:
fldc = cutl.calc_seaicearea(fldc,lat,lon)
fldp = cutl.calc_seaicearea(fldp,lat,lon)
elif sie:
fldc = ma.masked_where(fldc<.15,fldc)#really just mask out <.15?
fldp = ma.masked_where(fldp<.15,fldp)#really just maske out <.15
if isflux: #field in (fluxes,'fsg','turb','net'):
# mask out regions that are not ice in the control (as P&M 2014 JClim)
sicnc = cnc.getNCvar(frootc + 'sicn_' + timstr + '_ts.nc','SICN',timesel=timesel,**ncparams)
fldc = ma.masked_where(sicnc<.10,fldc)
fldp = ma.masked_where(sicnc<.10,fldp)
if plotzonmean:
fldc = np.mean(fldc[...,:-1],axis=2)# take zonal mean, removing extra lon
fldp = np.mean(fldp[...,:-1],axis=2)
elif pattcorrwithtime:
# loop through each year
# calc pattern corr either yearly or integrated
years=np.arange(0,fldc.shape[0])
fldctm = np.mean(fldc[:,lat>corrlim,...],axis=0)
pcorr = np.zeros(len(years))
for yr in years:
areas = cutl.calc_cellareas(lat,lon)
areas = areas[lat>corrlim,:]
weights = areas / np.sum(np.sum(areas,axis=1),axis=0)
if pattcorryr:
# yearly anomaly pattern corr w/ the time mean pattern
tmp = fldp[yr,lat>corrlim,...]-fldctm
else:
tmp = np.mean(fldp[:yr,lat>corrlim,...],axis=0)-fldctm # integrated anomaly pattern
tmpmean = np.mean(fldp[:,lat>corrlim,...],axis=0) - fldctm # end pattern to compare against
pcorr[yr] = cutl.pattcorr(tmp.flatten()*weights.flatten(),tmpmean.flatten()*weights.flatten())
seapcorrdict[sea] = pcorr
elif timetosig: # @@@@@ time to become significant
# use the control climo as the baseline (rather than 1 year), and give
# the full control timeseries to the ttest every time
nyr,nlat,nlon = fldc.shape
years = np.arange(0,nyr)
plotd = np.zeros(fldc.shape)
tstat = np.zeros(fldc.shape)
pval = np.zeros(fldc.shape)
fldctm = np.mean(fldc,axis=0) # time mean of control
for yr in years:
plotd[yr,...] = np.mean(fldp[0:yr,...]-fldctm,axis=0)
if yr>5:
tstat[yr,...],pval[yr,...] = sp.stats.ttest_ind(fldp[0:yr,...],fldc,axis=0)
else:
pval[yr,...] = np.ones((1,nlat,nlon))
# this should be a map of first year of significance.
firstsigyr = np.argmax(pval<=siglevel,axis=0) # returns index of first occurrence (True)
#firstsigyr = ma.masked_where(firstsigyr==0,firstsigyr) # somehow the mask doesn't get carried on?
firstsigyr = firstsigyr.astype(float) # in order to set nans below, need to convert to floats.
# if first sig year is 0 that mean there was never a True. set to a year past sim length
firstsigyr[firstsigyr==0] = np.nan #nyr+1 @@@
#print firstsigyr.shape # @@
seatimetosigdict[sea] = firstsigyr
elif plotregmean or calcregmeanwithtime:
#limsdict = con.get_regionlims(region)
if sia:
# calc total SIA in the requested region
#print 'should isarea=True here? @@@ 10/28/2014'
fldc = cutl.calc_regtotseaicearea(fldc,lat,lon,region,isarea=True)
fldp = cutl.calc_regtotseaicearea(fldp,lat,lon,region,isarea=True)
else:
if sie:
print 'sie does not make sense for regmean'
return -1
fldc = cutl.calc_regmean(fldc,lat,lon,region)#limsdict)
fldp = cutl.calc_regmean(fldp,lat,lon,region)#limsdict)
else: # just calculate a polar mean
print '@@@ no calctype flags are True. '
print '@@@ Previous behavior calculated '
print '@@@ polar mean (seacyclatlim) or SIA'
print '@@@ New behavior is to do nothing and '
print '@@@ simply return the full grid '
print '@@@ difference, std, pvals, etc'
#if sia:
# print 'should isarea=True here? @@@ 10/28/2014' # I think so...
# fldc,sh = cutl.calc_totseaicearea(fldc,lat,lon,isarea=True)
# fldp,sh = cutl.calc_totseaicearea(fldp,lat,lon,isarea=True)
#else:
# fldc = cutl.polar_mean_areawgted3d(fldc,lat,lon,latlim=seacyclatlim)
# fldp = cutl.polar_mean_areawgted3d(fldp,lat,lon,latlim=seacyclatlim)
seafldcstddict[sea] = np.std(fldc,axis=0)
seafldpstddict[sea] = np.std(fldp,axis=0)
#@@ttmp,pvtmp = sp.stats.ttest_ind(fldp,fldc,axis=0)
#print 'calculating ttest with effective DOF @@'
ttmp,pvtmp = cutl.ttest_ind(fldp,fldc,axis=0,effdof=effdof) # @@
# calculate confidence interval
# double-check the scale setting
# interval(alpha, df, loc=0, scale=1): Endpoints of the range that contains
# alpha percent of the distribution
meananom = np.mean(fldp,axis=0)-np.mean(fldc,axis=0)
df = len(fldp)-1 # degrees of freedom @@@
if effdof:
print 'calculating conf int with effective DOF @@'
df = cutl.calc_effectiveDOF(fldp-fldc)
#stder = np.std(fldp,axis=0)/np.sqrt(df+1) # standard error: sigma/sqrt(n)
stder = np.std(fldp-fldc,axis=0)/np.sqrt(df+1) #@@@
#stder = np.std(fldp-np.mean(fldc,axis=0),axis=0)/np.sqrt(df+1)
ci = sp.stats.t.interval(1-siglevel, df, loc= meananom, scale=stder)
seacidict[sea] = ci
seatstatdict[sea] = ttmp
seapvaldict[sea] = pvtmp
if calcregmeanwithtime:
seafldcdict[sea] = fldc # keep time dim
seafldpdict[sea] = fldp
seadiffdict[sea] = fldp-np.mean(fldc,axis=0)
else:
seafldcdict[sea] = np.mean(fldc,axis=0) # time mean
seafldpdict[sea] = np.mean(fldp,axis=0)
seadiffdict[sea] = np.mean(fldp,axis=0)- np.mean(fldc,axis=0)
seadmaskdict[sea] = ma.masked_where(pvtmp>siglevel,seadiffdict[sea])
# end loop through seasons
fldcstddict[sim] = seafldcstddict
fldpstddict[sim] = seafldpstddict
cidict[sim] = seacidict
tstatdict[sim] = seatstatdict
pvaldict[sim] = seapvaldict
fldcdict[sim] = seafldcdict
fldpdict[sim] = seafldpdict
flddiffdict[sim] = seadiffdict
flddmaskdict[sim] = seadmaskdict
if pattcorrwithtime==1:
fldpcorrdict[sim] = seapcorrdict
if timetosig:
fldtimetosigdict[sim] = seatimetosigdict
# end loop through simulations
if sia:
field = 'sia' # put back after getting the data. prob not important in the function context
elif sie:
field = 'sie'
blob = {}
blob['ctl'] = fldcdict
blob['pert'] = fldpdict
blob['tstat'] = tstatdict
blob['pval'] = pvaldict
blob['ci'] = cidict
blob['ctlstd'] = fldcstddict
blob['pertstd'] = fldpstddict
blob['diff'] = flddiffdict
blob['mask'] = flddmaskdict
if pattcorrwithtime:
blob['pcorr'] = fldpcorrdict
if timetosig:
blob['timetosig'] = fldtimetosigdict
return blob # datablob
def calc_seasonal_cycle(fielddict,coords,sims,withlat=False,loctimesel=None,info=None,siglevel=0.05):
""" calc_seasonal_cycle(fielddict,coords,sims,withlat=False,loctimesel=None,info=None,siglevel=0.05)
info: a dict which specifies lots of things, but in particular, which region to average
returns a data blob:
blob['ctl'] = fldcdict
blob['pert'] = fldpdict
blob['tstat'] = tstatdict
blob['pval'] = pvaldict
blob['ci'] = cidict
blob['ctlstd'] = fldcstddict
blob['pertstd'] = fldpstddict
blob['diff'] = flddiffdict
blob['mask'] = flddmaskdict
blob['sigarea'] # @@@
"""
print 'calc_seasonal_cycle()'
sia = False
# make standard function that will be called by "seasonal_cycle", "regional mean", etc...
# this can be adapted for lots of processing rather than have multiple copies of code. @@
field=fielddict['field']
ncfield=fielddict['ncfield']
fieldstr=fielddict['fieldstr']
conv=fielddict['conv']
threed=fielddict['threed']
isflux=fielddict['isflux']
model=info['model']
pct=info['pct'] # percentage change?
nonstandardlev=fielddict['nonstandardlev']
bp=con.get_basepath()
basepath=bp['basepath'] + model + '/'; subdir=bp['subdir'] # @@ move out of function?
lat=coords['lat']
nlat=len(lat)
lon=coords['lon']
nlon=len(lon)
# ====== from canam4sims_analens......
""" # get data for either zonal mean or sea cycle figures
if plotzonmean==1 or pattcorrwithtime==1 or plotregmean==1:
# seasons is defined above
corrlim = 45
elif plotseacyc==1:
if field in (fluxes,'fsg','turb','net'):
seacyclatlim=40
# else leave seacyclatlim as set at top of script"""
seacyc = con.get_mon()
seacyclatlim=info['seacyclatlim'] # should be 40N for (fluxes,'fsg','turb','net')
region = info['region'] # if region is set, it supercedes seacyclatlim! @@
if field=='sia':
sia=True
field = 'sicn' # while getting the data...
tstatdict = dict.fromkeys(sims,{}); pvaldict = dict.fromkeys(sims,{})
fldcdict = dict.fromkeys(sims,{}); fldpdict = dict.fromkeys(sims,{})
fldcstddict = dict.fromkeys(sims,{}); fldpstddict = dict.fromkeys(sims,{})
flddiffdict = dict.fromkeys(sims,{}); flddmaskdict = dict.fromkeys(sims,{})
fldpcorrdict = dict.fromkeys(sims,{});
cidict = dict.fromkeys(sims,{}); sigardict = dict.fromkeys(sims,{})
for ridx,sim in enumerate(sims):
seatstat = np.zeros((12)); seapval = np.zeros((12))
seafldc = np.zeros((12)); seafldp = np.zeros((12))
seafldcstd = np.zeros((12)); seafldpstd = np.zeros((12))
seadiff= np.zeros((12)); seadmask=np.zeros((12))
seaci = np.zeros((12,2));
seasigar = np.zeros((12));
## seatstatdict=dict.fromkeys(seacyc); seapvaldict=dict.fromkeys(seacyc)
## seafldcdict=dict.fromkeys(seacyc); seafldpdict=dict.fromkeys(seacyc)
## seafldcstddict=dict.fromkeys(seacyc); seafldpstddict=dict.fromkeys(seacyc)
## seadiffdict=dict.fromkeys(seacyc); seadmaskdict=dict.fromkeys(seacyc)
## seapcorrdict=dict.fromkeys(seacyc)
## seacidict=dict.fromkeys(seacyc)
# switch these to just an array? 0:12. @@
simpair = con.get_simpair(sim)
simctl = simpair['ctl']['fullname']
timstr = simpair['ctl']['timestr']
simpt = simpair['pert']['fullname']
timstrp = simpair['pert']['timestr']
frootc = basepath + simctl + subdir + simctl + '_'
frootp = basepath + simpt + subdir + simpt + '_'
if loctimesel !=None: # e.g. for when want to look at half of a run...
timesel = loctimesel
else:
timesel = simpair['ctl']['timesel']
"""if sim=='kemhad' or sim=='kemnsidc':
frootc = basepath + sim + 'ctl' + subdir + sim + 'ctl' + '_'
frootp = basepath + sim + 'pert' + subdir + sim + 'pert' + '_'
elif sim in ('kem1pert1b','kem1pert3','kem1rcp85a'):
frootc = basepath + 'kemctl1' + subdir + 'kemctl1' + '_'
frootp = basepath + sim + subdir + sim + '_'
else:
frootc = basepath + bcasename + sim + subdir + bcasename + sim + '_'
frootp = basepath + bcasenamep + sim + subdir + bcasenamep + sim + '_' """
if threed and nonstandardlev:
fnamec = frootc + field + '_' + '001-061_ts.nc'
fnamec2 = frootc + field + '_' + '062-121_ts.nc'
fnamep = frootp + field + '_' + '001-061_ts.nc'
fnamep2 = frootp + field + '_' + '062-121_ts.nc'
else:
fnamec = frootc + field + '_' + timstr + '_ts.nc'
fnamep = frootp + field + '_' + timstrp + '_ts.nc'
#for sii,sea in enumerate(seacyc):
if 1: # @@ try getting rid of loop through months.
print '@@ will have to remove ncparams'
#@@ncparams = {'monsel': sii+1}
# Now get the data
if field in ('turb','net'):
#print 'not implemented @@'
#print 'field is ' + field + '. getting hfl, hfs'
fielda='hfl'; fieldb='hfs'
fnamec = frootc + fielda + '_' + timstr + '_ts.nc'
fnamep = frootp + fielda + '_' + timstrp + '_ts.nc'
fnamecb = frootc + fieldb + '_' + timstr + '_ts.nc'
fnamepb = frootp + fieldb + '_' + timstrp + '_ts.nc'
fldc = cnc.getNCvar(fnamec,fielda.upper(),timesel=timesel,
**ncparams)*conv + cnc.getNCvar(fnamecb,fieldb.upper(),
timesel=timesel,**ncparams)*conv
fldp = cnc.getNCvar(fnamep,fielda.upper(),timesel=timesel,
**ncparams)*conv + cnc.getNCvar(fnamepb,fieldb.upper(),
timesel=timesel,**ncparams)*conv
if field=='net':
#print 'getting flg for net'
fieldb='flg'
conv=-1
fnamecb = frootc + fieldb + '_' + timstr + '_ts.nc'
fnamepb = frootp + fieldb + '_' + timstrp + '_ts.nc'
fldc = fldc + cnc.getNCvar(fnamecb,fieldb.upper(),
timesel=timesel,**ncparams)*conv
fldp = fldp + cnc.getNCvar(fnamepb,fieldb.upper(),
timesel=timesel,**ncparams)*conv
field='net' # return field name to what it should be
conv=1
else:
field='turb' # return field name to what it should be
# end if turb or net
else:
"""if threed==0:
fldczm = cnc.getNCvar(fnamec,field.upper(),timesel=timesel,
**ncparams)*conv
fldpzm = cnc.getNCvar(fnamep,field.upper(),timesel=timesel,
**ncparams)*conv
else:
#print '@@ fix to use level NC files'"""
if threed and nonstandardlev:
ncparams['levsel'] = level
fldc = np.append(cnc.getNCvar(fnamec,ncfield,timesel='0002-01-01,061-12-31',**ncparams)*conv,
cnc.getNCvar(fnamec2,ncfield,**ncparams)*conv,
axis=0)
fldp = np.append(cnc.getNCvar(fnamep,ncfield,timesel='0002-01-01,061-12-31',**ncparams)*conv,
cnc.getNCvar(fnamep2,ncfield,**ncparams)*conv,
axis=0)
else:
# @@ really have only tested this one...
fldc = cnc.getNCvar(fnamec,ncfield,timesel=timesel)*conv#,
#**ncparams)*conv
fldp = cnc.getNCvar(fnamep,ncfield,timesel=timesel)*conv#,
#**ncparams)*conv
if sia==True:
fldc = cutl.calc_seaicearea(fldc,lat,lon)
fldp = cutl.calc_seaicearea(fldp,lat,lon)
# end getting data initially
# now do processing (regional means, tot SIA, etc)
if isflux: #field in (fluxes,'fsg','turb','net'):
# mask out regions that are not ice in the control (as P&M 2014 JClim)
sicnc = cnc.getNCvar(frootc + 'sicn_' + timstr + '_ts.nc','SICN',timesel=timesel,**ncparams)
fldc = ma.masked_where(sicnc<.10,fldc)
fldp = ma.masked_where(sicnc<.10,fldp)
#elif plotseacyc==1:
if withlat:
# leave the latitude dimension intact
# dims are time x lat x lon to start
# take zonal mean
fldc = np.mean(fldc,axis=2) # take zonal mean
fldp = np.mean(fldp,axis=2)
else:
if sia:
#calc total area instead of average
fldc,sh = cutl.calc_totseaicearea(fldc,lat,lon,isarea=True) #np.sum(np.sum(fldczm[:,lat>0,:],axis=2),axis=1)
fldp,sh = cutl.calc_totseaicearea(fldp,lat,lon,isarea=True) #np.sum(np.sum(fldpzm[:,lat>0,:],axis=2),axis=1)
else:
# consider masking out land for sfc fluxes...?
# @@@ switch to regional mean? which includes polar means....
if region!=None:
fldc = cutl.calc_regmean(fldc,lat,lon,region)
fldp = cutl.calc_regmean(fldp,lat,lon,region)
else:
fldc = cutl.polar_mean_areawgted3d(fldc,lat,lon,latlim=seacyclatlim)
fldp = cutl.polar_mean_areawgted3d(fldp,lat,lon,latlim=seacyclatlim)
# now save the processed fields
# @@ got rid of loop through months so have to deal with that here:
seafldcstd = cutl.calc_monthlystd(fldc)
seafldpstd = cutl.calc_monthlystd(fldp)
seatstat,seapval = cutl.calc_monthlytstat(fldp,fldc)
#seafldcstddict[sea] = np.std(fldc,axis=0)
#seafldpstddict[sea] = np.std(fldp,axis=0)
#ttmp,pvtmp = sp.stats.ttest_ind(fldp,fldc,axis=0)