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canam4sims_analens.py
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canam4sims_analens.py
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"""
canam4sims_analens.py
6/2/2014: taken from canam4sims_stats2.py
This script is specifically for the CanAM4 ensemble runs
(kemctl1r? and kem1pert2r?)
2/20/2014: taken from plot_canam4sims_hists.py:
calculate & plot statistical properties of the runs
"""
import numpy.ma as ma
import scipy.stats
import matplotlib.cm as cm
from subprocess import call # for doing system calls - not really needed
import datetime as datetime
import matplotlib.colors as col
#import platform as platform
import constants as con # my module
import cccmautils as cutl # my module
import matplotlib.font_manager as fm
import copy
import cccmacmaps as ccm
import pandas as pd
# while I'm still creating these modules, have to reload to get changes
cplt = reload(cplt)
con = reload(con)
cutl = reload(cutl)
ccm = reload(ccm)
cnc = reload(cnc)
plt.close("all")
plt.ion()
printtofile=True
plotann=0 # seasonal avg map, comparing ens runs and meanBC
plotallmos=0 # monthly maps (@@ not implemented)
seasonal=0 # seasonal maps (SON, DJF, MAM, JJA)
seasvert=0 # seasonal must =1. seasonal vertical zonal means (SON,DJF,MAM,JJA) instead of maps
screen=True # whether to have screen-style vertical zonal means
plotzonmean=0 # plotzonmean,plotseacyc,pattcorrwithtime are mutually exclusive
plotseacyc=0 # plotzonmean,plotseacyc,pattcorrwithtime are mutually exclusive
seacyclatlim=60 # southern limit for plotting polar mean seasonal cycles (line plot)
withlat=0 # plot the seasonal cycle with latitude dimension too (only for plotseacyc=1)@@for now just std over ens
squatseacyc=0 # plot seacycle figs as shorter than wide
squatterseacyc=1 # even shorter, for paper
pattcorrwithtime=0 # plot pattern correlation with time for each ens member
pattcorryr=0 # if 1, do a yearly anomaly pattern rather than time-integrated
plotregmean=1
region = 'polcap60' # polcap60, polcap65, polcap70, eurasia, ntham, nthatl
testhadisst=0 # check which ens member most similar to hadisst
normbystd=0
halftime=False # get only the first 60yrs. make sure to set the other flag the opp
halftime2=False # get only the last 60yrs. make sure to set the other flag the opp
sensruns=False # sensruns only: addr4ct=1,addsens=1. others=0 no meanBC, r mean, or obs
addobs=1 # add mean of kemhad* & kemnsidc* runs to line plots, seasonal maps.
addr4ct=0 # add kem1pert2r4ct (constant thickness version of ens4)
addsens=0 # add sensitivity runs (kem1pert1b, kem1pert3)
addrcp=1 # add kem1rcp85a simulation (and others if we do more)
simsforpaper=False # meanBC, HAD, NSIDC only. best for maps and zonal mean figs (not line plots)
latlim = None # None #45 # lat limit for NH plots. Set to None otherwise.
levlim= 100 # level limit for vertical ZM plots (in hPa). ignored if screen=True
sigtype = 'cont' # significance: 'cont' or 'hatch' which is default
sigoff=0 # if 1, don't add significance
siglevel=0.05
# # # ######## set Field info ###################
# gz, t, u, v, q (3D !)
# st, sic, sicn (sia), gt, pmsl, pcp, hfl, hfs, turb, net, flg, fsg, fn, pcpn, zn, su, sv (@@later ufs,vfs)
field = 'st'
print field
timeavg = 'DJF'
# only for threed vars
#level = 30000
level = 50000 # 500hPa
#level = 70000
nonstandardlev=False # standards are 700,500,300
seasons = 'SON','DJF','MAM','JJA'
model = 'CanAM4'
threed=0
sia=0 # is the requested field sea ice area
ctstr=''
# # # ########### set Simulations #############
# Control run
bcasename = 'kemctl1'
casename = bcasename + 'ens'
timstr = '001-121'
timesel = '0002-01-01,0121-12-31'
bcasenamep = 'kem1pert2'
casenamep = bcasenamep + 'ens'
timstrp = '001-121'
# ######### set second set of sims (mean BC) ##########
casename2 = 'kemctl1'
casenamep2 = 'kem1pert2'
timstr2='001-121'
if halftime:
timesel= '0002-01-01,0061-12-31'
elif halftime2:
timesel='0062-01-01,0121-12-31'
if sensruns:
addobs=0
addr4ct=1
addsens=1
addrcp=0
# but don't plot the meanBC or mean of Ens members
# HERE THE SECOND SET OF RUNS IS HadISST BC's
if testhadisst:
casename2 = 'kemhadctl'
casenamep2 = 'kemhadpert'
timstr2=timstr
field='sicn'
cmap = 'blue2red_w20' # default cmap
cmapclimo = 'Spectral_r'
pct = 0 # if 1, do calculation as a percent
# # # ###########################################
# Shouldn't have to mod below....
""" plev = 100, 200, 300, 500, 700, 1000, 2000, 3000, 5000, 7000, 10000, 12500,
15000, 17500, 20000, 22500, 25000, 30000, 35000, 40000, 45000, 50000,
55000, 60000, 65000, 70000, 75000, 77500, 80000, 82500, 85000, 87500,
90000, 92500, 95000, 97500, 100000 ;
"""
if field == 'st':
units = 'K'
conv = 1 # no conversion
cmin = -2; cmax = 2 # for anomaly plots
cminp=-.5; cmaxp=.5 # for when pert is 'ctl'
cminm = -3; cmaxm = 3 # monthly
## print 'small clim!'
## cmin = -1; cmax = 1 for anomaly plots
## cminm = -1.5; cmaxm = 1.5 monthly
cminmp = -1; cmaxmp = 1 # for when pert is 'ctl'
cminn = -5; cmaxn = 5 # for norm by std
cmap = 'blue2red_w20'
if plotseacyc==1 and withlat==1:
cminm=-.5; cmaxm=.5 # @@ will have to update this when add subplots
leglocs = 'upper left', 'upper left', 'upper right', 'upper left'
seacycylim=(-.5,4) # >70N
elif field == 'sic':
units='m'
conv=1/913.
cmin=-.5
cmax=.5
cminm=-.5
cmaxm=.5
cmap = 'red2blue_w20'
leglocs = 'lower left', 'lower left', 'upper left', 'upper left'
elif field == 'sicn' or field == 'sia':
units = 'frac'
conv=1
cmin=-.15; cmax=.15
cminm=-.15; cmaxm=.15
cmap = 'red2blue_w20'
leglocs = 'lower left', 'lower left', 'upper left', 'upper left'
seacycylim=(-2e12,0) # for sia
elif field == 'gt':
units='K'
conv=1
cmin=-2; cmax=2
cminm=-3; cmaxm=3
cminp=-.5; cmaxp=.5 # for when pert is 'ctl'
cminmp = -1; cmaxmp = 1 # for when pert is 'ctl'
cmap = 'blue2red_w20'
elif field == 'pmsl':
units = 'hPa' # pretty sure hpa @@double check
conv = 1
cmin = -1; cmax = 1 # for anomaly plots
cminm=-2; cmaxm=2 # for monthly maps
cminp=cmin; cmaxp=cmax # for when pert is 'ctl'
cminmp=cminm; cmaxmp=cmaxm
cmap = 'blue2red_20'
## print 'new cmap and small clim! @@ '
## cmap = 'blue2red_w20'
## cminm=-1; cmaxm=1 for monthly maps, small clim
cminn = -1; cmaxn = 1 # for norm by std
if plotseacyc==1 and withlat==1:
cminm=-1; cmaxm=1 # @@ will have to update this when add subplots
leglocs = 'lower left', 'lower left', 'upper center', 'lower left'
seacycylim=(-2,1.5) # >70N
elif field == 'pcp':
units = 'mm/day' # original: kg m-2 s-1
#pct=1; units = '%'; print 'PCT'
conv = 86400 # convert from kg m-2 s-1 to mm/day
cmin = -.2; cmax = .2 # for anomaly plots
cminp=-.15; cmaxp=.15
cminm = -.2; cmaxm = .2
## print '@@ big clim!'
## cmin = -.75; cmax = .75
## cminm = -.75; cmaxm = .75
## print '@@ medium clim!'
## cmin = -.5; cmax = .5
## cminm = -.5; cmaxm = .5
#cmap = 'PuOr'
cmap = 'brown2blue_16w'
cminpct=-12; cmaxpct=12
cminmpct=-20; cmaxmpct=20
cminmp =-.25; cmaxmp=.25
cminpctp=-8; cmaxpctp=8
cminpctmp=-12; cmaxpctmp=12
leglocs = 'upper left', 'upper left', 'upper left', 'upper left'
elif field == 'hfl': # sfc upward LH flux
units = 'W/m2'
conv = 1
cmin = -5
cmax = 5
cminm = -8
cmaxm = 8
elif field == 'hfs': # sfc upward SH flux
units = 'W/m2'
conv = 1
cmin = -5
cmax = 5
cminm = -8
cmaxm = 8
elif field == 'turb': # combine hfl and hfs
units = 'W/m2'
conv=1
cmin=-10
cmax=10
cminm = -20
cmaxm = 20
cmap='blue2red_20'
elif field == 'net': # net of all sfc fluxes
#print " 'net ' not yet implemented! @@"
units = 'W/m2'
conv=1
cmin=-10
cmax=10
cminm = -20
cmaxm = 20
cmap='blue2red_20'
leglocs = 'upper left', 'upper left', 'upper left', 'upper left'
seacycylim=(-5,25) # always >40N (where there is ice in CTL)
elif field == 'flg': # net downward LW at the sfc.
units = 'W/m2'
conv = -1 # so positive heats atmos
cmin = -5
cmax = 5
cminm = -8
cmaxm = 8
leglocs = 'upper left', 'lower left', 'upper left', 'upper left'
elif field == 'fsg': # net (absorbed) solar downard at sfc
units = 'W/m2'
conv = 1
cmin = -5
cmax = 5
cminm = -8
cmaxm = 8
elif field == 'fn': # snow fraction
units = '%'
conv=100
cmin = -5
cmax = 5
cminm = -5
cmaxm = 5
cmap = 'red2blue_w20'
elif field == 'pcpn': # snowfall rate (water equivalent, kg/m2/s)
#pct = 1; units='%'
units = 'mm/day'
conv = 86400 # convert from kg m-2 s-1 to mm/day (I think it's same as pcp) @@
cmap = 'brown2blue_16w'
cmin = -.1 # for anomaly plots
cmax = .1 # for anomaly plots
cminm = -.15
cmaxm = .15
cminpct=-12
cmaxpct=12
cminmpct=-25
cmaxmpct=25
leglocs = 'upper left', 'upper left', 'upper center', 'upper left'
elif field == 'zn': # snow depth (m)
# pct=1; units='%'
units = 'cm'
conv = 100; # convert to cm
cmap = 'brown2blue_16w'
cmin = -2
cmax = 2
cminm = -2.5; cmaxm = 2.5
cminpct=-10
cmaxpct=10
cminmpct=-10
cmaxmpct=10
leglocs = 'upper right', 'lower left', 'upper right', 'lower right'
elif field == 'su':
units = 'm/s'
conv = 1;
cmap = 'blue2red_20'
cmin = -1; cmax = 1
cminm = -1; cmaxm = 1
cminp = -.5; cmaxp=.5
cminmp = -.5; cmaxmp=.5
leglocs = 'upper left', 'lower left', 'upper left', 'upper left'
elif field == 'sv':
units = 'm/s'
conv = 1;
cmap = 'blue2red_20'
cmin = -.5
cmax = .5
cminm = -.5
cmaxm = .5
elif field == 't':
threed = 1
conv=1
ncfield = 'TEMP'
units = 'K' # @@
## if level == 30000:
## cminc = 215; cmaxc = 245
## elif level == 70000:
## cminc = 245; cmaxc = 285
if level == 30000:
cmin = -.3; cmax = .3
cminm = -.5; cmaxm = .5 # for monthly
#cminsea = -.5; cmaxsea = .5
elif level == 70000:
cmin = -.3; cmax = .3
cminm = -.5; cmaxm = .5 # for monthly
#cminsea = -.5; cmaxsea = .5
if seasvert:
if screen:
cmin=-2.5; cmax=2.5
cminm=-2.5; cmaxm=2.5
else:
cmin = -.5; cmax = .5
cminm = -.8; cmaxm = .8
elif field == 'u':
threed = 1
conv=1
ncfield = 'U'
units = 'm/s' #@@
## if level==50000:
## cminc=-25; cmaxc=25
## elif level==70000:
## cminc=-15; cmaxc=15
## elif level == 30000:
## cminc=-40; cmaxc=40
if level == 30000:
cmin = -2; cmax = 2
cminm = -3; cmaxm = 3
#cminsea = -3; cmaxsea = 3
else:
cmin = -1; cmax = 1
cminm = -1; cmaxm = 1
#cminsea = -1; cmaxsea = 1
if seasvert:
cmin=-.5; cmax=.5
cminm=-1; cmaxm=1
#cmapclimo='blue2red_20'
elif field == 'gz':
threed=1
ncfield = 'PHI'
units = 'm' # @@
conv = 1/con.get_g()
## if level==50000:
## cminc = 5200; cmaxc = 5900 # climo 500hPa
## elif level==70000:
## cminc=2800; cmaxc = 3200
## elif level==30000:
## cminc=8600; cmaxc = 9800
## elif level==100000: # use this for thickness calc 1000-700
## cminc=2650; cmaxc = 3050
cmin = -8 # annual mean
cmax = 8 # annual mean
if level==30000:
cmin = -15; cmax = 15
#cminsea=-20; cmaxsea = 20
cminm = -20; cmaxm = 20 # for monthly
else:
#cminsea = -15; cmaxsea = 15
cminm = -15; cmaxm = 15 # for monthly
seacycylim=(-16,20) # >70N, 500hPa
if seasvert:
if screen:
cmin=-10; cmax=10
cminm=-25; cmaxm=25
else:
cmin = -10; cmax = 10
cminm = -15; cmaxm = 15
else:
print 'No settings for ' + field
fluxes = 'hfl','hfs','flg' # LH, SH, LWdown
# # # ########## Read NC data ###############
bp=con.get_basepath()
basepath=bp['basepath'] + model + '/'; subdir=bp['subdir']
# set filename for getting lat,lon
if threed==1: # either map of a level, or a zonal mean
if seasonal==1 and seasvert==1:
fieldstr=field+'ZM'
field=field+'ZM'
#fnamec = basepath + casename + subdir + casename + '_' + field + '_' + timstr + '_ts.nc' # for lat,lon,plev
else:
fieldstr=field+str(level/100) # figure names. want integer for string
field=field+str(level) # read in files
#fnamec = basepath + casename + subdir + casename + '_' + field + '_001-061_ts.nc' # for lat,lon
fnamec = basepath + casename + subdir + casename + '_' + field + '_' + timstr + '_ts.nc' # for lat,lon
else:
fieldstr=field
if field == 'sia':
sia=1
field='sicn' # only really sia for zonal mean and seasonal cycle
fnamec = basepath + casename + subdir + casename + '_' + field + '_' + timstr + '_ts.nc'
if sia==1:
field='sia'
if field in ('turb','net'): # just for lat/lon
fnamec = basepath + casename + subdir + casename + '_flg_' + timstr + '_ts.nc'
lat = cnc.getNCvar(fnamec,'lat')
lon = cnc.getNCvar(fnamec,'lon')
if threed:
lev=cnc.getNCvar(fnamec,'plev')
nlev=len(lev)
nlat = len(lat)
nlon = len(lon)
if sigtype=='cont' or sigoff==1:
suff='pdf'
else:
suff='png'
if simsforpaper:
sims = 'kemhad','kemnsidc',''
ctstr = '_forpap'
addobs=0
addr4ct=0
addsens=0
addrcp=0
seasons = ('SON','DJF')
else:
# order ens simulations in order of most ice loss in melt season to least.
# Then ens mean, PERT2, observations if requested
sims = 'r1','r4','r3','r5','r2','ens','' # suffixes to bcasename and bcasenamep
if addobs:
sims = sims + ('kemhad','kemnsidc')
if sensruns: # add sensitivity runs. don't plot meanBC, mean of ens
sims = sims[0:5] + ('r4ct','kem1pert1b','kem1pert3')
ctstr = '_sensruns'
else:
if addr4ct:
sims = sims + ('r4ct',)
ctstr = '_r4ct' # for figure filenames
if addsens:
sims = sims + ('kem1pert1b','kem1pert3') # control is kemctl1 (or '' key)
ctstr = ctstr + 'sens'
if addrcp:
sims = sims + ('kem1rcp85a',) # control is kemctl1
ctstr = ctstr + 'rcpa'
if halftime:
ctstr = ctstr + '_60yrs' # @@
elif halftime2:
ctstr = ctstr + '_60yrs2' # @@
print sims
#ensmems=np.arange(0,5)
if plotann:
if field=='turb':
print 'not fully implemented! @@, no second set of sims'
field='hfl'; fieldb='hfs'
fnamec = basepath + casename + subdir + casename + '_' + field + '_' + timstr + '_ts.nc'
fnamep = basepath + casenamep + subdir + casenamep + '_' + field + '_' + timstrp + '_ts.nc'
fnamecb = basepath + casename + subdir + casename + '_' + fieldb + '_' + timstr + '_ts.nc'
fnamepb = basepath + casenamep + subdir + casenamep + '_' + fieldb + '_' + timstrp + '_ts.nc'
fldc = cnc.getNCvar(fnamec,field.upper(),timesel=timesel)*conv + \
cnc.getNCvar(fnamecb,fieldb.upper(),timesel=timesel)*conv
fldp = cnc.getNCvar(fnamep,field.upper(),timesel=timesel)*conv+ \
cnc.getNCvar(fnamepb,fieldb.upper(),timesel=timesel)*conv
field='turb'
else:
if threed==0:
if field == 'sia':
sia=1
field='sicn' # only really sia for zonal mean and seasonal cycle
# get ensemble mean
fnamec = basepath + casename + subdir + casename + '_' + field + '_' + timstr + '_ts.nc'
fnamep = basepath + casenamep + subdir + casenamep + '_' + field + '_' + timstrp + '_ts.nc'
fldc = cnc.getNCvar(fnamec,field.upper(),timesel=timesel)*conv
fldp = cnc.getNCvar(fnamep,field.upper(),timesel=timesel)*conv
# get original runs (mean BC)
fnamec2 = basepath + casename2 + subdir + casename2 + '_' + field + '_' + timstr2 + '_ts.nc'
fnamep2 = basepath + casenamep2 + subdir + casenamep2 + '_' + field + '_' + timstr2 + '_ts.nc'
fldc2 = cnc.getNCvar(fnamec2,field.upper(),timesel=timesel)*conv
fldp2 = cnc.getNCvar(fnamep2,field.upper(),timesel=timesel)*conv
if sia==1:
field='sia'
else: # is threed
# get ensemble mean
if nonstandardlev: # leave this in in case want a different level, not tested w/ 3D zonal mean
# read 3D variables at specified level, 2 timeseries files
frootc = basepath + casename + subdir + casename + '_' + field + '_'
frootp = basepath + casenamep + subdir + casenamep + '_' + field + '_'
fnamec = frootc + '001-061_ts.nc'
fldc = np.append(cnc.getNCvar(fnamec,ncfield,
timesel='0002-01-01,061-12-31',levsel=level)*conv,
cnc.getNCvar(frootc+'062-121_ts.nc',ncfield,levsel=level)*conv,
axis=0)
fldp = np.append(cnc.getNCvar(frootp+'001-061_ts.nc',ncfield,
timesel='0002-01-01,061-12-31',levsel=level)*conv,
cnc.getNCvar(frootp+'062-121_ts.nc',ncfield,levsel=level)*conv,
axis=0)
# get original runs (mean BC)
frootc2 = basepath + casename2 + subdir + casename2 + '_' + field + '_'
frootp2 = basepath + casenamep2 + subdir + casenamep2 + '_' + field + '_'
fldc2 = np.append(cnc.getNCvar(frootc2+'001-061_ts.nc',ncfield,
timesel='0002-01-01,061-12-31',levsel=level)*conv,
cnc.getNCvar(frootc2+'062-121_ts.nc',ncfield,levsel=level)*conv,
axis=0)
fldp2 = np.append(cnc.getNCvar(frootp2+'001-061_ts.nc',ncfield,
timesel='0002-01-01,061-12-31',levsel=level)*conv,
cnc.getNCvar(frootp2+'062-121_ts.nc',ncfield,levsel=level)*conv,
axis=0)
else: # read in a standard level from processed file (default) @@reorg since same as above almost
fnamec = basepath + casename + subdir + casename + '_' + field + '_' + timstr + '_ts.nc'
fnamep = basepath + casenamep + subdir + casenamep + '_' + field + '_' + timstrp + '_ts.nc'
fldc = cnc.getNCvar(fnamec,ncfield,timesel=timesel)*conv
fldp = cnc.getNCvar(fnamep,ncfield,timesel=timesel)*conv
# get original runs (mean BC)
fnamec2 = basepath + casename2 + subdir + casename2 + '_' + field + '_' + timstr2 + '_ts.nc'
fnamep2 = basepath + casenamep2 + subdir + casenamep2 + '_' + field + '_' + timstr2 + '_ts.nc'
fldc2 = cnc.getNCvar(fnamec2,ncfield,timesel=timesel)*conv
fldp2 = cnc.getNCvar(fnamep2,ncfield,timesel=timesel)*conv
# annual time-series (3d)
seastsc = cutl.seasonalize_monthlyts(fldc,timeavg)
seastsp = cutl.seasonalize_monthlyts(fldp,timeavg)
nt,nlev,nlat = seastsc.shape # @@the var names are "wrong" but work fine in the script as written
tstat,pval = sp.stats.ttest_ind(seastsp,seastsc,axis=0)
seastdc = np.std(seastsc,axis=0)
seastdp = np.std(seastsp,axis=0)
seastsc2 = cutl.seasonalize_monthlyts(fldc2,timeavg)
seastsp2 = cutl.seasonalize_monthlyts(fldp2,timeavg)
nt2,nlev,nlat = seastsc.shape # @@the var names are "wrong" but work fine in the script as written
tstat2,pval2 = sp.stats.ttest_ind(seastsp2,seastsc2,axis=0)
seastdc2 = np.std(seastsc2,axis=0)
seastdp2 = np.std(seastsp2,axis=0)
#tstatb,pvalb = sp.stats.ttest_ind(anntsp,anntsc,axis=0,equal_var=False) # basically the same as above
# Note that NaN is returned for zero variance (I think..from googling..)
# If that is the case, pcolormesh() needs a masked_array rather than ndarray (??)
# : http://stackoverflow.com/questions/7778343/pcolormesh-with-missing-values
#if plotann: @@ moving this might require defining some things again...test...6/17
print timeavg
if field=='sia':
print 'Plotting maps of SICN instead of sia'
field='sicn'
if pct:
seastsctm = np.mean(seastsc,0)
seastsctm = ma.masked_where(seastsctm<=0.01,seastsctm)
plotfld = np.mean(seastsp-seastsc,0) / seastsctm * 100
seastsctm2 = np.mean(seastsc2,0)
seastsctm2 = ma.masked_where(seastsctm2<=0.01,seastsctm2)
plotfld2 = np.mean(seastsp2-seastsc2,0) / seastsctm2 * 100
cmin=cminpct
cmax=cmaxpct
else:
if normbystd:
plotfld = (np.mean(seastsp,0)-np.mean(seastsc,0))/seastdc
plotfld2 = (np.mean(seastsp2,0)-np.mean(seastsc2,0))/seastdc2
cmin=cminn; cmax=cmaxn
sigoff=1
else:
plotfld = np.mean(seastsp,0)-np.mean(seastsc,0)
plotfld2 = np.mean(seastsp2,0)-np.mean(seastsc2,0)
# Plot mean of ens runs, the meanBC run, and their difference
fig1,ax1 = plt.subplots(1,3)
ax = ax1[0]
bm,pc = cplt.kemmap(plotfld,lat,lon,cmin=cmin,cmax=cmax,cmap=cmap,type='nh',\
title='ens',units=units,axis=ax,suppcb=1)
if sigoff==0:
cplt.addtsigm(bm,pval,lat,lon,type=sigtype) # add significance info (hatching. for contour, type='contour')
ax = ax1[1]
bm,pc = cplt.kemmap(plotfld2,lat,lon,cmin=cmin,cmax=cmax,cmap=cmap,type='nh',\
title=casenamep2 + '-' + casename2,units=units,axis=ax,suppcb=1)
if sigoff==0:
cplt.addtsigm(bm,pval2,lat,lon,type=sigtype)
ax = ax1[2]
bm,pc = cplt.kemmap(plotfld-plotfld2,lat,lon,cmin=cmin,cmax=cmax,cmap=cmap,type='nh',\
title='ens-meanBC',units=units,axis=ax,suppcb=1)
if sigoff==0:
tstattmp,pvaltmp = sp.stats.ttest_ind(seastsp-seastsc,seastsp2-seastsc2,axis=0)
cplt.addtsigm(bm,pvaltmp,lat,lon,type=sigtype)
cbar_ax = fig1.add_axes([.91,.25, .02,.5])
fig1.colorbar(pc,cax=cbar_ax) # or do bm.colorbar....
plt.suptitle(timeavg + ': ' + fieldstr)
if printtofile:
if sigoff==0:
sigstr='sig' + sigtype
else:
if normbystd:
sigstr='norm'
else:
sigstr=''
if pct:
fig1.savefig(fieldstr + 'pctdiff' + sigstr + '_ens_v_meanBC_' + timeavg + '_nh.' + suff )
else:
fig1.savefig(fieldstr + 'diff' + sigstr + '_ens_v_meanBC_' + timeavg + '_nh.' + suff)
# ==================================================
# do a subplot with each ens member, plus the mean
ridx=0
fig,spax = plt.subplots(2,3)
for ax in spax.flat:
if ridx==len(spax.flat)-1: # last spot is for the ens mean
if threed==0:
fnamec = basepath + bcasename + 'ens' + subdir + bcasename +\
'ens_' + field + '_' + timstr + '_ts.nc'
fnamep = basepath + bcasenamep + 'ens' + subdir + bcasenamep +\
'ens_' + field + '_' + timstrp + '_ts.nc'
seasfldc = cnc.getNCvar(fnamec,field.upper(),timesel=timesel,seas=timeavg)*conv
seasfldp = cnc.getNCvar(fnamep,field.upper(),timesel=timesel,seas=timeavg)*conv
else:
# read 3D variables at specified level, 2 timeseries files
if nonstandardlev:
frootc = basepath + bcasename + 'ens' + subdir + bcasename + 'ens_' + field + '_'
frootp = basepath + bcasenamep + 'ens' + subdir + bcasenamep + 'ens_' + field + '_'
fnamec = frootc + '001-061_ts.nc'
seasfldc = np.append(cnc.getNCvar(fnamec,ncfield,
timesel='0002-01-01,061-12-31',levsel=level,seas=timeavg)*conv,
cnc.getNCvar(frootc+'062-121_ts.nc',ncfield,levsel=level,seas=timeavg)*conv,
axis=0)
seasfldp = np.append(cnc.getNCvar(frootp+'001-061_ts.nc',ncfield,
timesel='0002-01-01,061-12-31',levsel=level,seas=timeavg)*conv,
cnc.getNCvar(frootp+'062-121_ts.nc',ncfield,levsel=level,seas=timeavg)*conv,
axis=0)
else: # read from already processed file (default). @@ should reorg b/c this is almost same as above
fnamec = basepath + bcasename + 'ens' + subdir + bcasename +\
'ens_' + field + '_' + timstr + '_ts.nc'
fnamep = basepath + bcasenamep + 'ens' + subdir + bcasenamep +\
'ens_' + field + '_' + timstrp + '_ts.nc'
seasfldc = cnc.getNCvar(fnamec,ncfield,timesel=timesel,seas=timeavg)*conv
seasfldp = cnc.getNCvar(fnamep,ncfield,timesel=timesel,seas=timeavg)*conv
ttl = 'ens'
else:
if threed==0:
fnamec = basepath + bcasename + 'r' + str(ridx+1) + subdir + bcasename +\
'r' + str(ridx+1) + '_' + field + '_' + timstr + '_ts.nc'
fnamep = basepath + bcasenamep + 'r' + str(ridx+1) + subdir + bcasenamep +\
'r' + str(ridx+1) + '_' + field + '_' + timstrp + '_ts.nc'
seasfldc = cnc.getNCvar(fnamec,field.upper(),timesel=timesel,seas=timeavg)*conv
seasfldp = cnc.getNCvar(fnamep,field.upper(),timesel=timesel,seas=timeavg)*conv
else:
if nonstandardlev:
frootc = basepath + bcasename + 'r' + str(ridx+1) + subdir + bcasename +\
'r' + str(ridx+1) + '_' + field + '_'
frootp = basepath + bcasenamep + 'r' + str(ridx+1) + subdir + bcasenamep +\
'r' + str(ridx+1) + '_' + field + '_'
fnamec = frootc + '001-061_ts.nc'
seasfldc = np.append(cnc.getNCvar(fnamec,ncfield,
timesel='0002-01-01,061-12-31',levsel=level,seas=timeavg)*conv,
cnc.getNCvar(frootc+'062-121_ts.nc',ncfield,levsel=level,seas=timeavg)*conv,
axis=0)
seasfldp = np.append(cnc.getNCvar(frootp+'001-061_ts.nc',ncfield,
timesel='0002-01-01,061-12-31',levsel=level,seas=timeavg)*conv,
cnc.getNCvar(frootp+'062-121_ts.nc',ncfield,levsel=level,seas=timeavg)*conv,
axis=0)
else:
fnamec = basepath + bcasename + 'r' + str(ridx+1) + subdir + bcasename +\
'r' + str(ridx+1) + '_' + field + '_' + timstr + '_ts.nc'
fnamep = basepath + bcasenamep + 'r' + str(ridx+1) + subdir + bcasenamep +\
'r' + str(ridx+1) + '_' + field + '_' + timstrp + '_ts.nc'
seasfldc = cnc.getNCvar(fnamec,ncfield,timesel=timesel,seas=timeavg)*conv
seasfldp = cnc.getNCvar(fnamep,ncfield,timesel=timesel,seas=timeavg)*conv
ttl = 'r' + str(ridx+1)
seastdc = np.std(seasfldc,axis=0)
seastdp = np.std(seasfldp,axis=0)
tstat,pval = sp.stats.ttest_ind(seasfldp,seasfldc,axis=0)
plotfld = np.mean(seasfldp,axis=0)-np.mean(seasfldc,axis=0)
if normbystd:
plotfld = plotfld/seastdc
cmin=cminn; cmax=cmaxn
sigoff=1
bm,pc = cplt.kemmap(plotfld,lat,lon,cmin=cmin,cmax=cmax,cmap=cmap,type='nh',\
title=ttl,units=units,axis=ax,suppcb=1)
if sigoff==0:
cplt.addtsigm(bm,pval,lat,lon,type=sigtype)
ridx = ridx+1
cbar_ax = fig.add_axes([.91,.25, .02,.5])
fig.colorbar(pc,cax=cbar_ax) # or do bm.colorbar....
plt.suptitle(timeavg + ': ' + fieldstr)
if printtofile:
if sigoff==0:
sigstr='sig' + sigtype
else:
if normbystd:
sigstr='norm'
else:
sigstr=''
if pct:
fig.savefig(fieldstr + 'pctdiff' + sigstr + '_enssubplot_' + timeavg + '_nh.' + suff )
else:
fig.savefig(fieldstr + 'diff' + sigstr + '_enssubplot_' + timeavg + '_nh.' + suff)
# end plotann
#sigs = np.ones((12,fldc.shape[1],fldc.shape[2])) unused
if addobs:
obsstr = 'obs' #'had' # for figure file name
else:
obsstr = ''
months=con.get_mon()
if plotallmos:
print 'plotallmos not implemented'
## title = field + ": " + casenamep + "-" + casename
## midx=0
## fig, spax = plt.subplots(2,6)
## #fig.set_size_inches(12,6)
## fig.set_size_inches(12,4.5)
## fig.subplots_adjust(hspace=0,wspace=0)
## for ax in spax.flat:
## monfldc = fldc[midx::12,:,:]
## monfldp = fldp[midx::12,:,:]
## tstat,pval = sp.stats.ttest_ind(monfldp,monfldc,axis=0)
## sigs[midx,:,:] = ma.masked_where(pval>0.05,pval)
## if pct:
## monfldctm = np.mean(monfldc,0)
## monfldctm = ma.masked_where(monfldctm<=0.01,monfldctm)
## plotfld = np.mean(monfldp-monfldc,0) / monfldctm *100
## cminm=cminmpct
## cmaxm=cmaxmpct
## else:
## plotfld = np.mean(monfldp,0)-np.mean(monfldc,0)
## bm,pc = cplt.kemmap(plotfld,lat,lon,cmin=cminm,cmax=cmaxm,cmap=cmap,type='nh',\
## title=months[midx],axis=ax,suppcb=1)
## ax.set_title(months[midx])
## cplt.addtsigm(bm,pval,lat,lon,type=sigtype)
## 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)
## if printtofile:
## if pct:
## fig.savefig(field + 'pctdiffsig' + sigtype + '_' + casenamep +\
## '_v_' + casename + '_allmos_nh.' + suff)
## else:
## fig.savefig(field + 'diffsig' + sigtype + '_' + casenamep +\
## '_v_' + casename + '_allmos_nh.' + suff)
# done with if plotallmos
if seasonal:
if field=='sia':
print 'Plotting maps of SICN instead of sia'
field='sicn'
cmlen=float( plt.cm.get_cmap(cmap).N) # or: from __future__ import division
if seasvert:
tstat = np.zeros((len(seasons),nlev,nlat))
pval = np.zeros((len(seasons),nlev,nlat))
fldcallseas = np.zeros((len(seasons),nlev,nlat))
fldpallseas = np.zeros((len(seasons),nlev,nlat))
else:
tstat = np.zeros((len(seasons),nlat,nlon))
pval = np.zeros((len(seasons),nlat,nlon))
fldcallseas = np.zeros((len(seasons),nlat,nlon))
fldpallseas = np.zeros((len(seasons),nlat,nlon))
incr = (cmaxm-cminm) / (cmlen)
conts = np.arange(cminm,cmaxm+incr,incr)
fig6,ax6 = plt.subplots(len(seasons),len(sims)) # 1 row for e/ of 5 ens members, plus mean, plus meanBC
fig6.set_size_inches(12,8)
fig6.subplots_adjust(hspace=.15,wspace=.05)
lastcol=len(sims)-1
for ridx,sim in enumerate(sims): # traverse cols
cidx=0
if sim in ('kemhad','kemnsidc'):
frootc = basepath + sim + 'ctl' + subdir + sim + 'ctl' + '_' + field + '_'
frootp = basepath + sim + 'pert' + subdir + sim + 'pert' + '_' + field + '_'
if sim=='kemhad':
rowl='had'
else:
rowl='nsidc'
elif sim in ('kem1pert1b','kem1pert3','kemrcp85'):
frootc = basepath + 'kemctl1' + subdir + 'kemctl1' + '_' + field + '_'
frootp = basepath + sim + subdir + sim + '_' + field + '_'
if sim=='kem1pert1b':
rowl='nosst'
elif sim=='kem1pert3':
rowl='nosit'
else:
rowl='rcp85a'
else:
frootc = basepath + bcasename + sim + subdir + bcasename + sim + '_' + field + '_'
frootp = basepath + bcasenamep + sim + subdir + bcasenamep + sim + '_' + field + '_'
rowl=sim
fnamec = frootc + timstr + '_ts.nc'
fnamep = frootp + timstrp + '_ts.nc'
# @@@ 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]
ax = ax6[cidx][ridx] # swapped row and col index positions in subplot
if field=='turb':
field='hfl'; fieldb='hfs'
fldcsea = cnc.getNCvar(fnamec,field.upper(),timesel=timesel,
seas=sea)*conv + cnc.getNCvar(fnamecb,fieldb.upper(),
timesel=timesel,seas=sea)*conv
fldpsea = cnc.getNCvar(fnamep,field.upper(),timesel=timesel,
seas=seas)*conv + cnc.getNCvar(fnamepb,fieldb.upper(),
timesel=timesel,seas=sea)*conv
field='turb'
elif field=='net':
print '@@ not implemented for seasonal maps'
else:
if threed:
getfld=ncfield
else:
getfld=field.upper()
fldcsea = cnc.getNCvar(fnamec,getfld,timesel=timesel,
seas=sea)*conv
fldpsea = cnc.getNCvar(fnamep,getfld,timesel=timesel,
seas=sea)*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)
tstat[cidx,:,:],pval[cidx,:,:] = sp.stats.ttest_ind(fldpsea,fldcsea,axis=0)
fldcallseas[cidx,:,:] = np.mean(fldcsea,axis=0)
fldpallseas[cidx,:,:] = np.mean(fldpsea,axis=0)
if pct:
plotfld = (fldpallseas[cidx,:,:]-fldcallseas[cidx,:,:]) / fldcallseas[cidx,:,:] *100
cminm=cminmpct
cmaxm=cmaxmpct
else:
plotfld = fldpallseas[cidx,:,:] - fldcallseas[cidx,:,:]
pparams = dict(cmin=cminm,cmax=cmaxm,cmap=cmap,type='nh',axis=ax,latlim=latlim)
if seasvert: # zonal mean with height
pparams['suppcb'] = True
pparams['levlim'] = levlim
pparams['screen'] = screen
pparams['addcontlines'] = True
if ridx!=0: # if not the first column, suppress y labels
pparams['suppylab'] = True
pc = cplt.vert_plot(plotfld,lev,lat,**pparams)
if sigoff==0:
cplt.addtsig(ax,pval[cidx,...],lat,lev/100.,type=sigtype) # @@ dims?