"""

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 cccmacmaps as ccm

import matplotlib.font_manager as fm

import copy

# 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=1

seasonal=0 # seasonal maps (DJF, MAM, JJA, SON)

addobs=1 # add mean of kemhad* runs to line plots, seasonal maps (so far@@)

latlim = 60 # lat limit for NH plots. Set to None otherwise.

maskland=False # To get ocean-lake-only hist: mutually exclusive

maskocean=False # To get land-only hist: mutually exclusive

seasonal=1 # mutually exclusive

monthly=0 # mutually exclusive

#seasons = 'DJF','MAM','JJA','SON'

seasons = 'SON','DJF','MAM','JJA'

model = 'CanAM4'

threed=0

sia=0 # is the requested field sea ice area

mtype=''

# # # ########### 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'

# # # ######## set Field info ###################

# gz, t, u, v, q (3D !)

# st, sic, sicn (sia), gt, pmsl, pcp, hfl, hfs, turb, flg, fsg, fn, pcpn, zn, su, sv (@@later ufs,vfs)

field = 'pmsl'

print field

# only for threed vars

#level = 30000

#level = 50000 # 500hPa

level = 70000

## # 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'

leglocs = 'upper left', 'upper left', 'upper right', 'upper left'

xlims = -14,14; ylims = 0,0.6; ylims2=0,11

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'

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'

cminn = -1; cmaxn = 1 # for norm by std

leglocs = 'lower left', 'upper left', 'upper center', 'upper left'

xlims = -25,25; ylims = 0,0.2; ylims2=0,6

elif field == 'pcp':

units = 'mm/day' # original: kg m-2 s-1

pct=1; units = '%'

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

#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'

xlims = -4,4; ylims=0,3; ylims2 = 0,20

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! @@"

elif field == 'flg': # net downward LW at the sfc. Positive down?

units = 'W/m2'

conv = 1

cmin = -5

cmax = 5

cminm = -8

cmaxm = 8

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 = -3

cmaxm = 3

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

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

#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

else:

print 'No settings for ' + field

# # # ########## Read NC data ###############

bp=con.get_basepath()

basepath=bp['basepath'] + model + '/'; subdir=bp['subdir']

## plat = platform.system()

## if plat == 'Darwin': # means I'm on my mac

## basepath = '/Volumes/MyPassport1TB/DATA/CanSISE/' + model + '/'

## subdir = '/'

## else: # on linux workstation in Vic

## basepath = '/home/rkm/work/DATA/' + model + '/'

## subdir = '/ts/'

# prob don't need this next chunk if looping down below @@@

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

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

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:

# 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)

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)

lat = cnc.getNCvar(fnamec,'lat')

lon = cnc.getNCvar(fnamec,'lon')

if threed==1:

fieldstr=field+str(level/100)

else:

fieldstr=field

months=con.get_mon()

## darkolivegreen1 = np.array([202, 255, 112])/255 # terrible

## darkolivegreen3 = np.array([162, 205, 90])/255.

## darkseagreen = np.array([143, 188, 143])/255.

## darkseagreen4 = np.array([105, 139, 105])/255.

## dodgerblue = np.array([30, 144, 255])/255.

## orangered4 = np.array([139, 37, 0])/255.

## lightsteelblue3 = np.array([162, 181, 205])/255. # more grey looking

## lightsteelblue4 = np.array([110, 123, 139])/255. # more grey looking

## steelblue3 = np.array([79, 148, 205])/255. # more blue looking

## steelblue4 = np.array([54, 100, 139])/255. # more blue looking

## colordict = {'kemctl1r1': darkseagreen, 'kemctl1r2': darkseagreen4, 'kemctl1r3': lightsteelblue3,

## 'kemctl1r4': lightsteelblue4, 'kemctl1r5': steelblue4, 'kemctl1ens': dodgerblue,

## 'kemctl1': orangered4, 'kemhadctl': darkolivegreen3 }

colordict = {'r1': ccm.get_linecolor('warm1'), #'firebrick'),

'kemhad': ccm.get_linecolor('deepskyblue')}

# Load data into dicts

# order ens simulations in order of most ice loss in melt season to least. Then ens mean, PERT2, observations if requested

## sims = bcasename+'r1', bcasename+'r4', bcasename+'r3', bcasename+'r5', bcasename+'r2', bcasename+'ens',bcasename

## if addobs:

## sims = sims + ('kemhadctl',)

sims = 'r1', 'r4','r3','r5','r2','ens','' # suffixes to bcasename and bcasenamep

if addobs:

sims = sims + ('kemhad',)

if sia==1:

field = 'sicn' # while getting the data...

flddiffhistdict = dict.fromkeys(sims,{})

binedgedict = dict.fromkeys(sims,{})

#for ridx in range(0,len(sims)): # # of simulations

for ridx,sim in enumerate(sims):

print sim

seadiffhistdict=dict.fromkeys(seasons)

seabinedgedict=dict.fromkeys(seasons)

if sim == 'kemhad':

frootc = basepath + sim + 'ctl' + subdir + sim + 'ctl' + '_' + field + '_'

frootp = basepath + sim + 'pert' + subdir + sim + 'pert' + '_' + field + '_'

else:

frootc = basepath + bcasename + sim + subdir + bcasename + sim + '_' + field + '_'

frootp = basepath + bcasenamep + sim + subdir + bcasenamep + sim + '_' + field + '_'

if threed==0:

fnamec = frootc + timstr + '_ts.nc'

fnamep = frootp + timstrp + '_ts.nc'

else:

fnamec = frootc + '001-061_ts.nc'

fnamec2 = frootc + '062-121_ts.nc'

fnamep = frootp + '001-061_ts.nc'

fnamep2 = frootp + '062-121_ts.nc'

print fnamec

print fnamep

## I think can get rid of all this now, with all runs having

# same timeseries length and organizing simulation names this way

## #if ridx==5: # 2nd to last sim is the ens mean

## if sim == bcasename+'ens':

## frootc = basepath + bcasename + 'ens' + subdir + bcasename +\

## 'ens' + '_' + field + '_'

## frootp = basepath + bcasenamep + 'ens' + subdir + bcasenamep +\

## 'ens' + '_' + field + '_'

## if threed==0:

## fnamec = frootc + timstr + '_ts.nc'

## fnamep = frootp + timstrp + '_ts.nc'

## else:

## fnamec = frootc + '001-061_ts.nc'

## fnamec2 = frootc + '062-121_ts.nc'

## fnamep = frootp + '001-061_ts.nc'

## fnamep2 = frootp + '062-121_ts.nc'

## #sim = bcasename + 'ens'

## #elif ridx==6: # last sim is meanBC

## elif sim = bcasename:

## frootc = basepath + bcasename + subdir + bcasename +\

## '_' + field + '_'

## frootp = basepath + bcasenamep + subdir + bcasenamep +\

## '_' + field + '_'

## if threed==0:

## fnamec = frootc + timstr2 + '_ts.nc'

## fnamep = frootp + timstr2 + '_ts.nc'

## else:

## fnamec = frootc + '001-061_ts.nc'

## fnamec2 = frootc + '062-121_ts.nc'

## fnamep = frootp + '001-061_ts.nc'

## fnamep2 = frootp + '062-121_ts.nc'

## #sim = bcasename

## #elif ridx==7: # observation simulation

## elif sim = 'kemhadctl':

## frootc = basepath + 'kemhadctl' + subdir + 'kemhadctl' + '_' + field + '_'

## frootp = basepath + 'kemhadpert' + subdir + 'kemhadpert' + '_' + field + '_'

## if threed==0:

## fnamec = frootc + timstr + '_ts.nc'

## fnamep = frootp + timstrp + '_ts.nc'

## else:

## fnamec = frootc + '001-061_ts.nc'

## fnamec2 = frootc + '062-121_ts.nc'

## fnamep = frootp + '001-061_ts.nc'

## fnamep2 = frootp + '062-121_ts.nc'

## #sim = 'kemhadctl'

## else:

## frootc = basepath + sim + subdir + sim + '_' + field + '_'

## # have to figure out per casename@@

## frootp = basepath + bcasenamep + 'r' + str(ridx+1) + subdir + bcasenamep +\

## 'r' + str(ridx+1) + '_' + field + '_'

## if threed==0:

## fnamec = frootc + timstr + '_ts.nc'

## fnamep = frootp + timstrp + '_ts.nc'

## else:

## fnamec = frootc + '001-061_ts.nc'

## fnamec2 = frootc + '062-121_ts.nc'

## fnamep = frootp + '001-061_ts.nc'

## fnamep2 = frootp + '062-121_ts.nc'

## #sim = bcasename + 'r' + str(ridx+1)

for sii,sea in enumerate(seasons):

print sea

if seasonal:# @@ fix

ncparams = {'seas': sea}

elif monthly: # @@fix

ncparams = {'monsel': sii+1}

# Now get the data

if field=='turb':

print 'not implemented @@'

field='hfl'; fieldb='hfs'

fldc = cnc.getNCvar(fnamec,field.upper(),timesel=timesel,

**ncparams)*conv + cnc.getNCvar(fnamecb,fieldb.upper(),

timesel=timesel,**ncparams)*conv

fldp = cnc.getNCvar(fnamep,field.upper(),timesel=timesel,

**ncparams)*conv + cnc.getNCvar(fnamepb,fieldb.upper(),

timesel=timesel,**ncparams)*conv

field='turb'

else:

if threed==0:

fldc = cnc.getNCvar(fnamec,field.upper(),timesel=timesel,

**ncparams)*conv

fldp = cnc.getNCvar(fnamep,field.upper(),timesel=timesel,

**ncparams)*conv

else:

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)

if sia==1:

fldc = cutl.calc_seaicearea(fldc,lat,lon)

fldp = cutl.calc_seaicearea(fldp,lat,lon)

# Prepare the data here: @@

# @@ from hists script:

areas = cutl.calc_cellareas(lat,lon,repeat=fldc.shape)

areas = areas[:,lat>latlim,:]

fldcorig=copy.copy(fldc)

fldporig=copy.copy(fldp)

fldc = fldc[:,lat>latlim,:]

fldp = fldp[:,lat>latlim,:]

if maskland:

lmask = con.get_t63landmask(repeat=fldc.shape)

areas = ma.masked_where(lmask[:,lat>latlim,:]==-1,areas)

fldc = ma.masked_where(lmask[:,lat>latlim,:]==-1,fldc)

fldp = ma.masked_where(lmask[:,lat>latlim,:]==-1,fldp)

mtype='ocn'

elif maskocean:

lmask = con.get_t63landmask(repeat=fldc.shape)

areas = ma.masked_where(lmask[:,lat>latlim,:]!=-1,areas)

fldc = ma.masked_where(lmask[:,lat>latlim,:]!=-1,fldc)

fldp = ma.masked_where(lmask[:,lat>latlim,:]!=-1,fldp)

mtype='lnd'

totarea = np.sum(np.sum(areas,axis=2),axis=1)

totarea = np.tile(totarea,(fldc.shape[1],fldc.shape[2],1))

totarea = np.transpose(totarea,(2,0,1))

weights = areas/totarea

#weights = np.tile(weights,(len(lat),len(lon),fldc.shape[0]))

#weights = np.transpose(weights,(2,0,1))

histfld = fldp-fldc

print 'max val: ' + str(np.max(np.max(histfld)))

print 'min val: ' + str(np.min(np.min(histfld)))

if maskocean or maskland: # pass only the non-masked data to histogram

(seadiffhistdict[sea],seabinedgedict[sea]) = np.histogram(histfld.compressed(),

bins=100,weights=weights.compressed(),

density=True)

else:

(seadiffhistdict[sea],seabinedgedict[sea]) = np.histogram(histfld.flatten(),

bins=100,weights=weights.flatten(),

density=True)

#widths = np.diff(binedges)

## seafldcstddict[sea] = np.std(fldc,axis=0)

## seafldpstddict[sea] = np.std(fldpaxis=0)

## ttmp,pvtmp = sp.stats.ttest_ind(fldp,fldc,axis=0)

## seatstatdict[sea] = ttmp

## seapvaldict[sea] = pvtmp

## seafldcdict[sea] = np.mean(fldc,axis=0)

## 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

flddiffhistdict[sim] = seadiffhistdict

binedgedict[sim] = seabinedgedict

## fldcstddict[sim] = seafldcstddict

## fldpstddict[sim] = seafldpstddict

## tstatdict[sim] = seatstatdict

## pvaldict[sim] = seapvaldict

## fldcdict[sim] = seafldcdict

## fldpdict[sim] = seafldpdict

## flddiffdict[sim] = seadiffdict

## flddmaskdict[sim] = seadmaskdict

# end loop through simulations

if sia==1:

field = 'sia' # put back after getting the data

# @@ plot HISTOGRAMS HERE

widths=0.15 # np.diff(binedges)

sea='DJF'

fig = plt.figure()

for sim in sims:

binedges = binedgedict[sim][sea]

plt.bar(binedges[0:-1],flddiffhistdict[sim][sea],

width=widths,color=colordict[sim],alpha=0.5)

#plt.xlim(xlims)

plt.grid()

fig = plt.figure()

for sim in sims:

binedges = binedgedict[sim][sea]

plotfld = flddiffhistdict[sim][sea]

plotfld = plotfld / np.sum(plotfld)*100

plt.bar(binedges[0:-1],plotfld,

width=widths,color=colordict[sim],alpha=0.5)

#plt.xlim(xlims)

plt.grid()

plt.ylabel('% of total')

plt.title(sea + ' ' + fieldstr + '>' + str(latlim) + 'N')

if printtofile:

fig.savefig(fieldstr + 'diffpcthist_' + str(latlim) + 'N' + mtype + '_' + sea + '.pdf')

#xlims = -15,15; ylims = 0,0.6; ylims2=0,11 # ST DJF @@

fig2,axs = plt.subplots(2,4)

for sii,ax in enumerate(axs.flat):

sim = sims[sii]

plotfld=flddiffhistdict[sim][sea]

binedges = binedgedict[sim][sea]

ax.bar(binedges[0:-1],plotfld,

width=widths,color=colordict[sim],alpha=0.7,

linewidth=0,edgecolor='none')

ax.set_xlim(xlims)

ax.set_ylim(ylims)

ax.set_title(sim)

ax.grid()

if printtofile:

fig2.savefig(fieldstr + 'diffhist_' + str(latlim) + 'N' + mtype + '_' + sea + '_subplt.pdf')

fig2,axs = plt.subplots(2,4)

for sii,ax in enumerate(axs.flat):

sim = sims[sii]

plotfld=flddiffhistdict[sim][sea]

plotfld=plotfld / np.sum(plotfld)*100

binedges = binedgedict[sim][sea]

ax.bar(binedges[0:-1],plotfld,

width=widths,color=colordict[sim],alpha=0.7,

linewidth=0,edgecolor='none')

ax.set_xlim(xlims)

ax.set_ylim(ylims2)

ax.set_title(sim)

if sii==0 or sii==4:

ax.set_ylabel('% of total')

ax.grid()

if printtofile:

fig2.savefig(fieldstr + 'diffpcthist_' + str(latlim) + 'N' + mtype + '_' + sea + '_subplt.pdf')

# # Cumulative PDF

# @@ consider using 'ax.fill_between' for the r set of runs

# @@ also see example of cum here:

# http://matplotlib.org/examples/pylab_examples/histogram_demo_extended.html

fig = plt.figure()

for sim in sims:

binedges = binedgedict[sim][sea]

plotfld = flddiffhistdict[sim][sea]

dcum = np.zeros(plotfld.shape)

for el in np.arange(1,len(dcum)+1):

dcum[el-1] = np.sum(plotfld[0:el])

plotfld = dcum / np.sum(plotfld)

plt.plot(binedges[0:-1],plotfld,

color=colordict[sim],linewidth=2)

plt.xlim(xlims)

plt.grid()

plt.ylabel('Fraction of total')

plt.title(sea + ' ' + fieldstr + '>' + str(latlim) + 'N')

if printtofile:

fig.savefig(fieldstr + 'difffracCDD_' + str(latlim) + 'N' + mtype + '_' + sea + '.pdf')

"""

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

tstat = np.zeros((len(seasons),nlev,nlat))

pval = np.zeros((len(seasons),nlev,nlat))

fldcallseas = np.zeros((len(seasons),nlev,nlat)) # @@ dont' need to do this anymore, not saving all

fldpallseas = np.zeros((len(seasons),nlev,nlat))

incr = (cmaxm-cminm) / (cmlen)

conts = np.arange(cminm,cmaxm+incr,incr)

if addobs:

sims = bcasename + 'r1', bcasename+'r2',bcasename+'r3',bcasename+'r4', bcasename+'r5',bcasename+'ens',bcasename,'kemhadctl'

else:

sims = bcasename + 'r1', bcasename+'r2',bcasename+'r3',bcasename+'r4', bcasename+'r5',bcasename+'ens',bcasename

# cidx=0 # traverse cols (seasons)

# ridx=0 # traverse rows

# fig6,ax6 = plt.subplots(7,4) # 1 row for e/ of 5 ens members, plus mean, plus meanBC

# fig6.set_size_inches(8,12)

fig6,ax6 = plt.subplots(4,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)

for ridx in range(0,len(sims)): # traverse rows

cidx=0

if ridx==5: # 2nd to last row is for the ens mean

frootc = basepath + bcasename + 'ens' + subdir + bcasename +\

'ens' + '_' + field + '_'

frootp = basepath + bcasenamep + 'ens' + subdir + bcasenamep +\

'ens' + '_' + field + '_'

if threed==0:

fnamec = frootc + timstr + '_ts.nc'

fnamep = frootp + timstrp + '_ts.nc'

else:

fnamec = frootc + '001-061_ts.nc'

fnamec2 = frootc + '062-121_ts.nc'

fnamep = frootp + '001-061_ts.nc'

fnamep2 = frootp + '062-121_ts.nc'

rowl = 'r mean' # row label

elif ridx==6: # last row is for meanBC

frootc = basepath + bcasename + subdir + bcasename +\

'_' + field + '_'

frootp = basepath + bcasenamep + subdir + bcasenamep +\

'_' + field + '_'

if threed==0:

fnamec = frootc + timstr2 + '_ts.nc'

fnamep = frootp + timstr2 + '_ts.nc'

else:

fnamec = frootc + '001-061_ts.nc'

fnamec2 = frootc + '062-121_ts.nc'

fnamep = frootp + '001-061_ts.nc'

fnamep2 = frootp + '062-121_ts.nc'

rowl = 'meanBC'

elif ridx==7: # observation simulation

frootc = basepath + 'kemhadctl' + subdir + 'kemhadctl' + '_' + field + '_'

frootp = basepath + 'kemhadpert' + subdir + 'kemhadpert' + '_' + field + '_'

if threed==0:

fnamec = frootc + timstr + '_ts.nc'

fnamep = frootp + timstrp + '_ts.nc'

else:

fnamec = frootc + '001-061_ts.nc'

fnamec2 = frootc + '062-121_ts.nc'

fnamep = frootp + '001-061_ts.nc'

fnamep2 = frootp + '062-121_ts.nc'

rowl = 'had'

else:

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 + '_'

if threed==0:

fnamec = frootc + timstr + '_ts.nc'

fnamep = frootp + timstrp + '_ts.nc'

else:

fnamec = frootc + '001-061_ts.nc'

fnamec2 = frootc + '062-121_ts.nc'

fnamep = frootp + '001-061_ts.nc'

fnamep2 = frootp + '062-121_ts.nc'

rowl = 'r' + str(ridx+1)

for sea in seasons: # traverse cols

#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'

else:

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,:,:]

bm,pc = cplt.kemmap(plotfld,lat,lon,cmin=cminm,cmax=cmaxm,cmap=cmap,type='nh',\

axis=ax,suppcb=1,latlim=latlim)#@@

if cidx==0: # when row index is 0, set simulation

ax.set_title(rowl)

if sigoff==0:

cplt.addtsigm(bm,pval[cidx,:,:],lat,lon,type=sigtype)

if ridx==0: # when col index is 0, set season

ax.set_ylabel(sea)

cidx = cidx+1

cbar_ax = fig6.add_axes([.91,.25, .02,.5])

fig6.colorbar(pc,cax=cbar_ax) # or do bm.colorbar....

plt.suptitle(fieldstr)

if printtofile:

if sigoff==0:

sigstr='sig' + sigtype

else:

sigstr=''

if addobs:

obsstr = 'had'

else:

obsstr = ''

if latlim!= None:

latstr=str(latlim)

else:

latstr=''

if pct:

fig6.savefig(fieldstr + 'pctdiff' + sigstr + '_enssubplot' + obsstr +

'_seas_nh' + latstr + '.' + suff)

else:

fig6.savefig(fieldstr + 'diff' + sigstr + '_enssubplot' + obsstr + '_seas_nh'

+ latstr + '.' + suff)

"""