def load_agcmfield(field,sims,seas,conv=1,region=None,subsampyrs=11,styears=None):
""" TAKEN FROM load_agcmfield() in canesm_LE_composite.py
loads subsampled agcm data from specified simulations
number of total samples will be determined by length of all sim data
and numyrs (e.g. (ntime / numyrs)*numsims)
returns subsamp
styears
"""
threed=False
simconv=conv
if field=='tas': simfield='st'; simncfield='ST'
elif field=='zg50000.00': simfield='gz50000'; simncfield='PHI'; simconv=1/con.get_g()
elif field=='sia': simfield='sicn'; simncfield='SICN'; print '@@ danger, sia actually sicn average'
elif field=='sic': simfield='sicn'; simncfield='SICN'
elif field=='turb': simfield='turb'; simncfield='turb'; # the sim var names are placeholders
else: print 'cannot addsims for ' + field
if region!=None:
simflddf = pd.DataFrame(lmd.loaddata((simfield,),sims,ncfields=(simncfield,), timefreq=seas,
region=region))*simconv
else:
# assume threed b/c no region given.
threed=True
simflddf = lmd.loaddata((simfield,),sims,ncfields=(simncfield,), timefreq=seas,
region=region,rettype='ndarray')*simconv
subsamp,styearsss = leg.subsamp_sims(simflddf,numyrs=subsampyrs,styears=styears,threed=threed)
return subsamp,styearsss
cmaxmp = 0.5
elif field == "sv":
units = "m/s"
conv = 1
cmap = "blue2red_20"
cmin = -0.5
cmax = 0.5
cminm = -0.5
cmaxm = 0.5
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
cmin = -8 # annual mean
cmax = 8 # annual mean
if level == 30000:
cmin = -15
def loadfldmeta(field,infodict,plottype,ptparams,level=None):
""" NOTE not all Fields are implemented into fdict and infodict, etc!
11/20/2014
"""
# check for nonstandardlev
# set plottype
# ptparams has specific settings for plottype
print ptparams
smclim=ptparams['smclim']
latlim=ptparams['latlim']
round=ptparams['round']
levlim=ptparams['levlim']
region = ptparams['region']
savestr=infodict['savestr'] # string for plot filenames
seacycylim=infodict['seacycylim']
threed=False # is the field three dimensional
sia=False # is the requested field sea ice area
conv=1
isflux=False
leglocs='best','best','best','best'
if level!=None:
nonstandardlev = con.is_standardlev(level)
else:
nonstandardlev=False
# initialize flags
seasonalmap = seasonalvert = plotzonmean = plotseacyc = pattcorrwithtime = plotregmean = calregmeanwithtime = timetosig = timetosigsuper = False
if plottype=='seasonalmap':
seasonalmap=True
elif plottype=='seasonalvert':
seasonalvert=True
screen=ptparams['screen']
elif plottype=='plotzonmean':
plotzonmean=True
elif plottype=='plotseacyc':
plotseacyc=True
seacyclatlim=ptparams['seacyclatlim']
withlat = ptparams['withlat']
elif plottype=='pattcorrwithtime':
pattcorrwithtime=True
pattcorryr = ptparams['pattcorryr']
elif plottype=='plotregmean':
plotregmean=True
elif plottype=='calcregmeanwithtime':
calcregmeanwithtime=True
elif plottype=='timetosig':
timetosig=True
elif plottype=='timetosigsuper':
timetosigsuper=True
else:
print 'Plottype not recognized!'
#return
############################ start copy to load_fldmeta.py
# # # ######## 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)
print field
## if halftime:
## timesel= '0002-01-01,0061-12-31'
## elif halftime2:
## timesel='0062-01-01,0121-12-31'
# # # ###########################################
# 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 ;
"""
fdict = {'field': field, 'ncfield': None, 'fieldstr': None,
'units': None, 'conv': conv,
'nonstandardlev': nonstandardlev,
'threed': threed} # fielddict
# reserved for expansion into the plotfunction call
pparams = {'cmin': None, 'cmax': None, 'cmap': 'blue2red_20',
'ptype':'nh', 'latlim': latlim, 'lmask': 0} # plotparams
if seasonalmap:
pparams['round'] = round
## seacycylim=None
## infodict ={'cmapclimo': 'Spectral_r','leglocs': None,
## 'seacycylim': None, 'savestr': None,
## 'model': model, 'sigtype': sigtype, 'sigoff': sigoff,
## 'pct': pct, 'seacyclatlim': seacyclatlim, 'region': region,
## 'shadeens': shadeens, 'corrlim': corrlim} # random other info
if field == 'st':
fdict['units'] = 'K'
fdict['ncfield'] = field.upper()
fdict['fieldstr'] = field
pparams['cmin'] = -3; pparams['cmax'] = 3 # seasonal/monthly
pparams['cmap'] = 'blue2red_w20'
if smclim:
pparams['cmin'] = -1; pparams['cmax'] = 1 # seasonal/monthly
pparams['cmap'] = 'blue2red_20'
savestr= savestr + '_smclim'
#cminmp = -1; cmaxmp = 1 # for when pert is 'ctl'
#cminn = -5; cmaxn = 5 # for norm by std
if plotseacyc and withlat:
pparams['cmin']=-.5;
pparams['cmax']=.5 # @@ will have to update this when add subplots
leglocs = 'upper left', 'upper left', 'upper right', 'upper left'
if region not in ('eurasia','ntham','nthatl'):
seacycylim=(-.5,4) # >70N
elif field == 'sic':
fdict['units'] = 'm'
fdict['ncfield'] = field.upper()
fdict['fieldstr'] = field
pparams['cmin'] = -.5; pparams['cmax'] = .5 # seasonal/monthly
fdict['conv']=1/913.
pparams['cmap'] = 'red2blue_w20'
#pparams['leglocs'] = 'lower left', 'lower left', 'upper left', 'upper left'
elif field == 'sicn' or field == 'sia':
fdict['units'] = 'frac'
fdict['ncfield'] = field.upper()
fdict['fieldstr'] = field
pparams['cmin'] = -.15; pparams['cmax'] = .15 # seasonal/monthly
pparams['lmask'] = 1
pparams['cmap'] = 'red2blue_w20'
leglocs = 'lower left', 'lower left', 'upper left', 'upper left'
if field=='sia':
fdict['ncfield'] = 'SICN'
seacycylim=(-2e12,0) # for sia
sia=True
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':
fdict['units'] = 'hPa'
fdict['ncfield'] = field.upper()
fdict['fieldstr'] = field
#pparams['cmin'] = -2; pparams['cmax'] = 2 # seasonal/monthly
pparams['cmin'] = -1.8; pparams['cmax'] = 1.8 # seasonal/monthly
if smclim:
pparams['cmin'] = -1; pparams['cmax'] = 1 # seasonal/monthly
savestr= savestr + '_smclim'
pparams['cmap'] = 'blue2red_20'
if plotseacyc and withlat:
pparams['cmin']=-1;
pparams['cmax']=1 # @@ will have to update this when add subplots
leglocs = 'lower left', 'lower left', 'upper center', 'lower left'
if region not in ('eurasia','ntham','nthatl'):
seacycylim=(-2,1.5) # >70N
## 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
elif field == 'pcp':
fdict['fieldstr'] = field
fdict['ncfield'] = field.upper()
fdict['units'] = 'mm/day' # original: kg m-2 s-1
fdict['conv'] = 86400 # convert from kg m-2 s-1 to mm/day
pparams['cmap'] = 'brown2blue_16w'
if pct:
pparams['cmin'] = -20; pparams['cmax'] = 20
fdict['units'] = '%'
else:
pparams['cmin'] = -0.2; pparams['cmax'] = 0.2
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
isflux=True
elif field == 'hfs': # sfc upward SH flux
units = 'W/m2'
conv = 1
cmin = -5
cmax = 5
cminm = -8
cmaxm = 8
isflux=True
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)
isflux=True
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'
isflux=True
elif field == 'fsg': # net (absorbed) solar downard at sfc
units = 'W/m2'
conv = 1
cmin = -5
cmax = 5
cminm = -8
cmaxm = 8
isflux=True
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':
fdict['units'] = 'm/s'
fdict['ncfield'] = 'SU'
fdict['conv'] = 1;
pparams['cmap'] = 'blue2red_20'
pparams['cmin'] = -1; pparams['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':
fdict['units'] = 'K'
fdict['ncfield'] = 'TEMP'
threed = True
fdict['conv'] = 1
pparams['cmap'] = 'blue2red_w20'
if level == 30000:
pparams['cmin'] = -.5; pparams['cmax'] = .5
#cminm = -.5; cmaxm = .5 # for monthly
#cminsea = -.5; cmaxsea = .5
elif level == 70000:
#cmin = -.3; cmax = .3
pparams['cmin'] = -.5; pparams['cmax'] = .5 # for monthly
#cminsea = -.5; cmaxsea = .5
if seasonalvert:
if screen:
pparams['levlim']=300
pparams['cmin']=-2.5; pparams['cmax']=2.5
#cminm=-2.5; cmaxm=2.5
if smclim:
pparams['cmin']=-1.5; pparams['cmax']=1.5
savestr= savestr + '_smclim'
else:
cmin = -.5; cmax = .5
pparams['cmin'] = -.8; pparams['cmax'] = .8
elif field == 'u':
threed = True
fdict['units'] = 'm/s'
fdict['ncfield'] = 'U'
fdict['conv'] = 1
if level == 30000:
#cmin = -2; cmax = 2
pparams['cmin'] = -2; pparams['cmax'] = 2
#cminsea = -3; cmaxsea = 3
else:
#cmin = -1; cmax = 1
pparams['cmin'] = -1; pparams['cmax'] = 1
#cminsea = -1; cmaxsea = 1
if seasonalvert:
#cmin=-.5; cmax=.5
pparams['cmin'] = -1; pparams['cmax'] = 1
if screen:
pparams['levlim']=300
pparams['cmap']='blue2red_20'
elif field == 'gz':
fdict['units'] = 'm'
fdict['ncfield'] = 'PHI'
threed = True
fdict['conv'] = 1/con.get_g()
if level==30000:
pparams['cmin'] = -20; pparams['cmax'] = 20 # seasonal/monthly
else:
pparams['cmin'] = -15; pparams['cmax'] = 15 # seasonal/monthly
if region not in ('eurasia','ntham','nthatl'):
seacycylim=(-16,20) # >70N, 500hPa
if seasonalvert:
if screen:
#cmin=-10; cmax=10
#cminm=-25; cmaxm=25
pparams['cmin'] = -25; pparams['cmax'] = 25 # seasonal/monthly
pparams['levlim'] = 300
if smclim:
pparams['cmin'] = -12; pparams['cmax'] = 12
savestr= savestr + '_smclim2'
else:
#cmin = -10; cmax = 10
#cminm = -15; cmaxm = 15
pparams['cmin'] = -15; pparams['cmax'] = 15 # seasonal/monthly
pparams['cmap'] = 'blue2red_w20'
leglocs = 'upper left', 'upper left', 'upper right', 'upper left'
else:
print 'No settings for ' + field
if threed: # either map of a level, or a zonal mean
if seasonalvert:
fieldstr=field+'ZM'
field=field+'ZM'
else:
fieldstr=field+str(level/100) # figure names. want integer for string
field=field+str(level) # read in files
fdict['field']=field
fdict['fieldstr']=fieldstr
else:
fdict['fieldstr']=field
fluxes = 'hfl','hfs','flg' # LH, SH, LWdown
infodict['savestr'] = savestr
infodict['leglocs'] = leglocs
infodict['seacycylim'] = seacycylim
fdict['isflux'] = isflux
fdict['threed'] = threed
###################### end copy to load_fldmeta.py
return fdict,pparams
timesel='3002-01-01,3041-12-31'
fldsuff=''
"""
casename='preitest'
basepath=basedir+casename+'/ts/'
timeper = '2922-2931'
ncfield='MLT'
timesel='2922-01-01,2931-12-31'
Cp=1004 # specific heat at const pressure (J/K/kg)
Lv=2.5e6 # specific heat of condensation (latent heat of vapo) at 0C(?@@) (J/kg)
erad = con.get_earthrad() # m
grav = con.get_g() # m/s2
# 1 cal = 4.186J
# convert to cal / day
# J/s -> cal/day
W2calperday = (60*60*24)/4.186
lat = con.get_t63lat()
lon = con.get_t63lon()
#lev = con.get_t63lev() # 37 levs
fname=basepath + casename + '_u_' + timeper + '_ts.nc' # 22 levs!!
lev = cnc.getNCvar(fname,'plev')
dp = np.diff(lev)
nlon = len(lon)-1 # removing extra lon
# get surface pressure to do vert avg and int here
#convr=1 #aconvr=1 # for agcm sims #ylev=0.3 # for mean dots #fieldr='tas'; ncfieldr='tas'; compr='Amon'; #regionr='eurasiamori'; rstr='Eur SAT'; rstrlong='Eurasian SAT'; runits='$^\circ$C'; rkey='eursat' #regionr='eurasiathicke'; rstr='EEur SAT'; rstrlong='East Eurasian SAT'; runits='$^\circ$C'; rkey='eeursat' #convr=1 #aconvr=1 # for agcm sims #ylev=0.3 # for mean dots fieldr='zg50000.00'; ncfieldr='zg'; compr='Amon'; regionr='gt60n'; rstr='POL Z500'; rstrlong='Polar Z500'; runits='m'; rkey='gt60nz500' #regionr='bksmori'; rstr='BKS Z500'; rstrlong='Barents/Kara Z500'; runits='m'; rkey='bksz500' convr=1 aconvr=1/con.get_g() # for agcm sims ylev=0.005 # for mean dots in pdf fig #fieldr2='zg50000.00'; ncfieldr2='zg'; compr2='Amon'; #regionr2='bksmori'; rstr2='BKS Z500'; rstr2long='Barents/Kara Z500'; runits2='m'; rkey2='bksz500' #convr2=1 #aconvr2=1/con.get_g() # for agcm sims #ylev=0.005 # for mean dots in pdf fig fieldr2='tas'; ncfieldr2='tas'; compr2='Amon'; regionr2='eurasiamori'; rstr2='Eur SAT'; rstr2long='Eurasian SAT'; runits2='$^\circ$C'; rkey2='eursat' convr2=1 aconvr2=1 # for agcm sims sear2='DJF'
