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canesm_LE_general.py
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canesm_LE_general.py
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""" canesm_LE_general.py
This script basically reads in files that are already processed
into area-averages and such.
"""
import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
import pandas as pd
import cccmaNC as cnc
import cccmaplots as cplt
import cccmautils as cutl
import cccmacmaps as ccm
import datetime as datetime
from netCDF4 import num2date
import sys
import loadLE as le
import constants as con
import matplotlib.font_manager as fm
import loadmodeldata as lmd
import matplotlib.patches as mpatches
import matplotlib.lines as mlines
import matplotlib.colors as mcolors
from scipy.stats import norm
import random
import numpy.ma as ma
import scipy.io as sio
import loadCanESM2data as lcd
import corrstats as corrstats
import mpl_toolkits as mpltk
# exception handling works below. add to other clauses @@
cutl=reload(cutl)
local=True
conditional=True # plot scatter conditional on 3rd var
styearsR = [ 8., 7., 2., 8., 8.] # variable SIC styears
styearsE=[ 4., 1., 7., 3., 1.]; # mean SIC styears
styearsN=[1.] # NSIDC sim
# composite when for 2: '14:51:28.762886'
# composite when for ss and ns: 17:01:16.908687
styearPI = 2
# PI anomyrs when: '14:51:28.762886sic'. mean eursat=-0.0026
anomyearsPI =[[74, 8],
[57, 5],
[50, 53],
[51, 42],
[ 7, 81],
[15, 9],
[61, 57],
[48, 21],
[49, 24],
[65, 24],
[33, 66],
[71, 36],
[29, 43],
[48, 68],
[18, 67],
[ 5, 21],
[17, 1],
[40, 16],
[79, 18],
[10, 82],
[ 0, 19],
[70, 36],
[82, 67],
[14, 7],
[57, 13],
[18, 34],
[59, 29],
[20, 6],
[12, 6],
[58, 68],
[82, 49],
[66, 2],
[ 0, 10],
[28, 59],
[63, 73],
[28, 83],
[51, 54],
[19, 2],
[26, 47],
[52, 66],
[27, 10],
[37, 48],
[38, 64],
[33, 36],
[42, 85],
[28, 24],
[79, 51],
[80, 10],
[44, 57],
[32, 41]]
performop1 = True
#op1='div'; region1op='gm' # polar amp: gt60n / gm
#op1='sub'; region1op='deeptrop' # pole-eq temp gradient: gt60n - deeptrop (or trop)
#op1='sub'; region1op='less' # bks sea ice minus less (laptev/east siberian)
#op1='sub'; region1op='eurasiamori' # z500 grad b/w bks and eurasia
#op1='sub'; region1op='eurasiagrad2' # z500 grad b/w bks and eurasia
#op1='sub'; region1op='eurasiamorie' # z500 grad b/w bks and eurasia
op1='sub'; region1op='eurasiasemed' # z500 grad b/w bks and eurasia
performop2 = False
op2='div'; region2op='gm' # polar amp: gt60n / gm
#op2='sub'; region2op='nh'
#op2='sub'; region2op='eurasiamori' # sub z500
timeselc='1979-01-01,1989-12-31'
timeselp='2002-01-01,2012-12-31'
timeselall = '1979-01-01,2012-12-31'
# x field
#field1='turb'; ncfield1='turb'; comp1='Amon'; region1='bksmori'
field1='zg50000.00'; ncfield1='zg'; comp1='Amon'; region1='bksmori'
#field1='sia'; ncfield1='sianh'; comp1='OImon'; region1='nh'
#field1='sic'; ncfield1='sic'; comp1='OImon'; region1='bksmori'; #'gt60n' #region1='bksmori' # @@ a hack. prefer SIA
#field1='tas'; ncfield1='tas'; comp1='Amon'; region1='eurasiamori' #region1='bksmori'
leconv1= 1
sea1='DJF'
# y field
field2='tas'; ncfield2='tas'; comp2='Amon'; region2='eurasiamori' #'eurasiathicke';
#field2='tas'; ncfield2='tas'; comp2='Amon'; region2='gt60n'
#field2='zg50000.00'; ncfield2='zg'; comp2='Amon'; region2='bksmori'
#field2='turb'; ncfield2='turb'; comp2='Amon'; region2='bksmori'
leconv2=1
sea2='DJF'
fieldcnd = 'sic'; ncfieldcnd='sic'; compcnd='OImon'; regioncnd='bksmori'
leconvcnd=1
seacnd=sea1
cmincnd=-12; cmaxcnd=3 # for 5 color colorbar
cmapcnd='blue2red_20'
#fieldcnd = 'tas'; ncfieldcnd='tas'; compcnd='Amon'; regioncnd='gm' #regioncnd='eurasiamori'
#leconvcnd=1
#seacnd=sea1
#if regioncnd=='eurasiamori':
# cmincnd=-2; cmaxcnd=3 # for 5 color colorbar
#elif regioncnd=='gm':
# cmincnd=0.5; cmaxcnd=1
#cmapcnd='blue2red_20'
cisiglevel=0.05
siglevel=0.05
simsR=('R1','R2','R3','R4','R5')
simsE=('E1','E2','E3','E4','E5')
xlab=ylab=None
if field2=='tas' and region2=='eurasiamori' and performop2==True and op2=='sub':
ylab = '$\Delta$ DJF SAT(Eurasia) - SAT(NH) ($^\circ$C)'
elif field2=='tas' and region2=='eurasiamori' and performop2==False:
ylab = '$\Delta$ DJF Eurasian SAT ($^\circ$C)'
if field1=='zg50000.00' and region1=='bksmori' and performop1==False:
xlab = '$\Delta$ DJF Barents-Kara Seas Z500 (m)'
simconv1=simconv2=1
if field1=='tas': simfield1='st'; simncfield1='ST'
elif field1=='zg50000.00': simfield1='gz50000'; simncfield1='PHI'; simconv1=1/con.get_g()
elif field1=='sia': simfield1='sicn'; simncfield1='SICN'; print '@@ danger, sia actually sicn average'
elif field1=='sic': simfield1='sicn'; simncfield1='SICN'; simconv1=100
elif field1=='turb': simfield1='turb'; simncfield1='turb'; # the sim var names are placeholders
else: print 'cannot addsims for ' + field1; addsims=False
if field2=='tas': simfield2='st'; simncfield2='ST'
elif field2=='zg50000.00': simfield2='gz50000'; simncfield2='PHI'; simconv2=1/con.get_g()
elif field2=='sia': simfield2='sicn'; simncfield2='SICN'; print '@@ danger, sia actually sicn average'
elif field2=='sic': simfield2='sicn'; simncfield2='SICN'; simconv1=100
elif field2=='turb': simfield2='turb'; simncfield2='turb'; # the sim var names are placeholders
else: print 'cannot addsims for ' + field2; addsims=False
if fieldcnd=='sic': simfieldcnd='sicn'; simncfieldcnd='SICN'; simconvcnd=100
elif fieldcnd=='tas': simfieldcnd='st'; simncfieldcnd='ST'; simconvcnd=1
else: print 'cannot addsims for conditional: ' + fieldcnd; addsims=False
ftype='fullts' # 'fullclimo' or 'climo' or 'fullts'
fdict1 = {'field': field1+region1, 'ncfield': ncfield1, 'comp': comp1}
fdict2 = {'field': field2+region2, 'ncfield': ncfield2, 'comp': comp2}
fdictcnd = {'field': fieldcnd+regioncnd, 'ncfield': ncfieldcnd, 'comp': compcnd}
def subsamp_sims(simsdf,numyrs=11,styears=None,threed=False):
""" select 11 year segments from given simsdf (data should be anoms)
simsdf.keys(): sims
simsdf.index(): time index
if threed = True: the incoming data (simsdf) should be an ndarray
with dims [nsims x ntime x nlat x nlon]. Return data will be
nsamp x nlat x nlon
number of total samples will be determined by length of all sim data
and numyrs (e.g. (ntime / numyrs)*numsims)
returns ndarray of subsample averages, and startyrs
"""
#print simsdf
if threed:
(numsims,ntime,nlat,nlon)=simsdf.shape
initshape=(nlat,nlon)
keys=np.arange(0,numsims)
else:
ntime,numsims=simsdf.values.shape
initshape=()
keys=simsdf.keys()
samp = ntime/numyrs
allsii=0 # keep track of all sims and all subsamps
initshape=(samp*numsims,)+initshape
subsampavg=np.zeros(initshape)
print 'sample each of ' + str(numsims) + ' sims ' + str(samp) + ' times'
savstyears = np.zeros((numsims))
for nii,sim in enumerate(keys):
if threed:
vals = simsdf[nii,...]
else:
print sim
vals=simsdf[sim].values
if styears == None:
# random index to start looping, since we have a remainder when ntime/numyrs
startyr = np.random.randint(np.mod(ntime,numyrs))
else:
# start years were passed in: use them
startyr = styears[nii]
print 'start ' + str(startyr)
savstyears[nii] = startyr
for sii in np.arange(startyr,ntime-numyrs,numyrs):
#print 'sii ' + str(sii) + ', allsii ' + str(allsii)
#subsampavg[allsii] = vals[sii+sii*numyrs:sii+sii*numyrs+numyrs].mean()
subsampavg[allsii,...] = vals[sii:sii+numyrs,...].mean(axis=0)
allsii+=1
return subsampavg,savstyears
def subsamp_anom_pi(pidat, numsamp=50, numyrs=11,styear=None,anomyears=None,verb=False):
""" subsample piControl and produce numsamp anomalies of numyrs-length periods
Make sure the data is seasonalized before passing into function.
returns subsampanom, styear,anomyears
"""
ndim=pidat.ndim
if ndim==1:
ntime = pidat.shape[0]
initshape=()
elif ndim==3:
(ntime,nlat,nlon)=pidat.shape
initshape=(nlat,nlon)
else:
# what is this mysterious shape?
(ntime,nspace)=pidat.shape
samp = ntime/numyrs
allsii=0 # keep track of all subsamps
anomshape = (numsamp,)+initshape
initshape=(samp,)+initshape
subsampavg=np.zeros(initshape)
subsampanom=np.zeros(anomshape)
# chunk up non-overlapping time periods
# then take anomalies
if styear == None:
# random index to start looping, since we have a remainder when ntime/numyrs
startyr = np.random.randint(np.mod(ntime,numyrs))
else:
# start years were passed in: use them
startyr = styear
if verb:
print 'start ' + str(startyr)
for sii in np.arange(startyr,ntime-numyrs,numyrs):
subsampavg[allsii,...] = pidat[sii:sii+numyrs,...].mean(axis=0)
allsii+=1
# now select 2 random time periods to generate anomalies
# make sure they are at least a decade apart
anomyrs=[]
for ii in np.arange(0,numsamp):
keepgoing=True
while keepgoing:
if anomyears == None:
sel = random.sample(np.arange(0,samp),2) # 2 time periods, no dupes
else:
sel = anomyears[ii]
if sel[0] in np.arange(sel[1]-1,sel[1]+2):
# should not happen with anomyrs that are passed in.
if verb:
print 'Bad anom time period indices. Keepgoing. ' + str(sel)
else:
if verb:
print 'anom time period indices: ' + str(sel)
subsampanom[ii,...] = subsampavg[sel[1],...] - subsampavg[sel[0],...]
anomyrs.append(sel)
keepgoing=False
return subsampanom, startyr, anomyrs
def sliding_subsamp_pi(pidat, overlap=.6, numyrs=33,verb=False):
""" subsample piControl with a sliding numyrs window.
overlap: the amount the sliding samples should overlap, as a fraction of numyrs
Make sure the data is seasonalized before passing into function.
returns subsamp (not anoms)
"""
ndim=pidat.ndim
if ndim==1:
ntime = pidat.shape[0]
initshape=()
elif ndim==3:
(ntime,nlat,nlon)=pidat.shape
initshape=(nlat,nlon)
else:
# what is this mysterious shape?
(ntime,nspace)=pidat.shape
overyr = np.trunc(numyrs*overlap) # number of yrs of overlap
samp = ntime/overyr # number of samples (s/b at least 50)
print samp
allsubsamp = np.zeros((samp,numyrs)+initshape)
allanom = np.zeros((samp,)+initshape
aii=0
for slideii in np.arange(0,len(pidat),overyr):
# select numyrs data
sel = pidat[slideii:slideii+numyrs,...]
print sel.shape
print slideii, slideii+numyrs
selanom = sel[:-10,...].mean(axis=0) - sel[:10,...].mean(axis=0) # difference first and last decades
allsubsamp[aii,...] = sel # [samp x numyrs x lat x lon]
allanom[aii,...] = selanom # [samp x lat x lon]
aii+=1
return allsubsamp, allanom
def sample120yravg(lesea,numsamp,nummems=11,allowreps=True):
""" choose nummems random ens members, and do it numsamp
times.
nummems: number of members to choose. default 11 (for 11mem x 11yr = 122yr)
allowreps: default True. allow repeat ensemble indices to be chosen in different
numsamp chunks (never dupes within each nummems selection)
returns ltavg, ltsigma (average & std of each nummem set. len numsamp)
"""
# sample 11 ensemble members: 11 members x 11 years = ~120 yrs to equal AGCM sims
# (do this numsamp times with diff combos of 11).
savesel=np.zeros((numsamp,nummems))
ltavg=np.zeros((numsamp)) # 'lt' = longterm avg
ltsigma=np.zeros((numsamp))
lelist=list(lesea)
for ii in np.arange(0,numsamp):
#sel = np.random.randint(0,len(lesea),size=nummems) # out of the number of ens members
sel = random.sample(np.arange(0,len(lelist)),nummems) # no duplicates!
print len(lelist)
print sel
savesel[ii]=sel
#c=[ a[i] for i in b] # how to select multiple elements of list w/ indices
tmp = np.array([lelist[ind] for ind in sel]) # select members
if allowreps==False:
# now delete those members
sel.sort(reverse=True) # if remove the indices starting from end, shouldn't fail.
for ind in sel:
print 'deleting index ' + str(ind)
del lelist[ind]
ltavg[ii] = tmp.mean()
ltsigma[ii]= tmp.std()
return ltavg,ltsigma
def plot_shorttermpdf(fig,ax,field,region,xxdat,pdfdat,histdat,meandat,cidat,
cifdat,pointdat,startyearsdat=None,addsims=False,addnat=True,addmisc=True,addpi=False,
combagcm=False,addanno=True,plab=None,pversion=''):
""" startyearsdat is an unnecessary input, but is in 'retdict' from calc_shorttermpdf()
"""
fsz=18
mew=1.5; ms=9
firebrick=ccm.get_linecolor('firebrick')
hcol='0.7'#ccm.get_linecolor('darkolivegreen3')
hcolline='0.7'#ccm.get_linecolor('darkolivegreen3')#'darkseagreen4')
#hcol=ccm.get_linecolor('niceblue')
#hcolline=ccm.get_linecolor('niceblue')
ltcol='0.5'#ccm.get_linecolor('darkseagreen4')
ltcol=ccm.get_linecolor('paperblue') #'niceblue2') #'steelblue4')
natcol=ccm.get_linecolor('steelblue3')
natcolline=ccm.get_linecolor('steelblue3')#4')
miscol=ccm.get_linecolor('orange3') #'deepskyblue')
miscolline=ccm.get_linecolor('orange3') #'deepskyblue')
obscol='r' #'k' #ccm.get_linecolor('midnightblue') #'b'
picol='purple'
sslg=mlines.Line2D([],[],color=ltcol,linewidth=2) # subsamp sims
ss2lg=mlines.Line2D([],[],color='k',linewidth=2) # subsamp sims2 (variable bc)
ss2lg2=mlines.Line2D([],[],color='k',linewidth=2,mew=2,marker='|')
simlg=mlines.Line2D([],[],color='0.3',linestyle='none',marker='o')
nsidclg=mlines.Line2D([],[],color='g',linestyle='none',marker='o')
nsidclg2=mlines.Line2D([],[],color='g',linewidth=2,mew=2,marker='|')
#obslg=mlines.Line2D([],[],color=obscol,linestyle='none',marker='s')
obslg=mlines.Line2D([],[],color=obscol,linewidth=2)
rawlg=mlines.Line2D([],[],color=hcolline,linewidth=2)
rawnlg=mlines.Line2D([],[],color=natcolline,linewidth=2)
rawmlg=mlines.Line2D([],[],color=miscolline,linewidth=2)
pilg=mlines.Line2D([],[],color=picol,linewidth=2)
prstr =''
#legh = (sslg,rawlg)
#legstr = ('AGCM Ice','CGCM All')
# actually add AGCM to end
legh = (rawlg,)
legstr = ('CGCM',)
skey='obs'
obsreg= np.squeeze(pointdat[skey])
skey='orig'
or5sea= np.squeeze(pointdat[skey])
skey='AGCM120'
simflddf={}
simflddf['NSIDC']= np.squeeze(pointdat[skey])
if not combagcm:
skey='AGCM2'
else:
skey='BOTHAGCM'
ss2xx = np.squeeze(xxdat[skey]); ss2pdf_fitted=np.squeeze(pdfdat[skey]);
ss2mean= np.squeeze(meandat[skey]); ss2ci= np.squeeze(cidat[skey]); ss2cif= np.squeeze(cifdat[skey])
ss2hist=histdat[skey]
if not combagcm:
skey='AGCM'
ssxx = np.squeeze(xxdat[skey]); sspdf_fitted= np.squeeze(pdfdat[skey]);
ssmean= np.squeeze(meandat[skey]); ssci= np.squeeze(cidat[skey]); sscif= np.squeeze(cifdat[skey])
skey='histle'
lesea= histdat[skey]; rawxx=xxdat[skey]; rawpdf_fitted=pdfdat[skey];
rawmean=meandat[skey]; rawci=cidat[skey]; rawcif=cifdat[skey]
if addnat:
skey='histnat'
lensea= histdat[skey]; rawnxx=xxdat[skey]; rawnpdf_fitted=pdfdat[skey];
rawnmean=meandat[skey]; rawnci=cidat[skey]; rawncif=cifdat[skey]
if addmisc:
skey='histmisc'
lemsea= histdat[skey]; rawmxx=xxdat[skey]; rawmpdf_fitted=pdfdat[skey];
rawmmean=meandat[skey]; rawmci=cidat[skey]; rawmcif=cifdat[skey]
if addpi:
skey='pi'
pisea= histdat[skey]; pixx=xxdat[skey]; pipdf_fitted=pdfdat[skey];
pimean=meandat[skey]; pici=cidat[skey]; picif=cifdat[skey]
if field not in ('sia','sic') and pversion != 'c':
ax.hist(ss2hist,normed=True,color=ltcol,alpha=0.4,histtype='stepfilled')#@@ maybe?
ax.plot(ss2xx,ss2pdf_fitted,color=ltcol,linewidth=3)
#ax.plot(ss2xx,ss2pdf_fitted,color='k',linewidth=2) # variable boundary forcings
#ax.hist(osubsamp,normed=True,color='g',alpha=0.3)
#ax.plot(ossxx,osspdf_fitted,color='g',linewidth=2)
#prstr=prstr+'addRsimsnsidcsim_'
#legstr=legstr+('AGCM NSIDC','AGCM var ICE')
#legh=legh+(nsidclg2,ss2lg)
#prstr=prstr+'addRsims_'
#legstr=legstr+('AGCM var ICE',)
#legh=legh+(ss2lg,)
if addsims:
prstr=prstr+'120yrsims_'
simsy=-0.02
for es in simsE:
ax.plot(simflddf[es],simsy,color=ltcol,
marker='o',linestyle='',fillstyle='none',mew=mew,markersize=ms)
ax.plot(simfldm[es],simsy,color=ltcol,alpha=0.6,
marker='o',linestyle='',fillstyle='full')
for rs in simsR:
ax.plot(simflddf[rs],simsy,color='k',
marker='o',linestyle='',fillstyle='none',mew=mew,markersize=ms)
ax.plot(simfldm[rs],simsy,color='k',alpha=0.6,
marker='o',linestyle='',fillstyle='full')
if subnh: # HACK @@
ax.plot(simflddf['NSIDC'],simsy,color='g',
marker='o',linestyle='',fillstyle='none',mew=mew,markersize=ms,mec='g')
else:
ax.plot(simflddf['NSIDC'],simsy,color='g',
marker='o',linestyle='',fillstyle='none',mew=mew,markersize=ms,mec='g')
ax.plot(simfldm['NSIDC'],simsy,color='g',alpha=0.6,
marker='o',linestyle='',fillstyle='full')
# add RAW LE
#print 'plotting LE histogram.............' + str(lesea) #@@
# @@@ NOTES about np.histogram:
# counts,bins=np.histogram(agcm2,density=True)
# The counts only add up to one if bin width is 1.
# So np.sum(counts*np.diff(bins)) = 1.0
# If want better y-axis ticks for "density" (now not plotted at all)
# can do counts*np.diff(bins) as y-ticklabels (I'm guessing) Aug 20 2015 @@
# ALSO if want better binedges (for AGCM eursat anyway) where no bins
# cross over zero (encompass zero): (this example assumes 10 bins)
# binedges=np.arange(-1*max(np.abs(agcm2)), max(np.abs(agcm2))+2*max(np.abs(agcm2))/10., 2*max(np.abs(agcm2))/10.)
if pversion not in ('d',):
ax.hist(lesea,normed=True,color=hcol,alpha=0.3,histtype='stepfilled')
ax.plot(rawxx,rawpdf_fitted,color=hcolline,linewidth=3)
fs='full'
for eii in range(0,5): # orig five
# search for difforig[eii] nearest to axx,
# then plot apdf_fitted[axx] as marker, filled or not based on sig.
plotx = or5sea[eii]
idx = cutl.find_nearest(rawxx,plotx)
ploty = rawpdf_fitted[idx]
ax.plot(plotx,ploty,marker='o',color=hcolline,mec='k',fillstyle=fs,mew=1,markersize=ms)
# @@@ check significance?
if field!='sia':
nsx= simflddf['NSIDC']
idx=cutl.find_nearest(ss2xx,nsx)
nsy=ss2pdf_fitted[idx]
#ax.plot(nsx,nsy,marker='o',color='g',mec='g',fillstyle=fs,mew=mew,markersize=ms)
#if addanno:
# ax.annotate('Obs AGCM',xy=(nsx,nsy),xytext=(10,20),
# textcoords='offset points',fontsize=fsz,
# arrowprops=dict(arrowstyle='-',connectionstyle='arc3',
# facecolor='k', edgecolor='k')) # label the marker on the pdf curve
ylims=ax.get_ylim()
print '@@@ YLIMS: ' + str(ylims) # @@@@
if field=='sia':
yytop = 1.9e-12
boxywi= 0.002
yincr=boxywi+0.001
ax.set_ylim(0,2e-12)
obsanny=1.8e-12
offsetpts=(15,0)
elif field=='tas':
yytop = 0.95
boxywi = 0.01
yincr=boxywi+0.02
#ax.set_ylim(-0.05,ylims[1])
obsanny=0.8
offsetpts=(-50,0)
elif field == 'sic':
yytop = 0.19
boxywi = 0.002
yincr=boxywi+0.001
obsanny=0.14
offsetpts=(15,0)
if pversion=='c' and field=='tas':
pass
else:
ax.set_ylabel('Density',fontsize=fsz)
ax.set_yticklabels('')
ax.axhline(y=0,color='k')
#ax.axvline(x=0,color='k',linestyle='--')
if pversion in ('c','d'):
# add uncertainty boxes
ax.plot((rawmean,rawmean),(yytop-boxywi,yytop+boxywi),linewidth=2,color=hcolline)
ax.add_patch(mpatches.Rectangle((rawcif[0],yytop-boxywi),
rawcif[1]-rawcif[0],boxywi*2,ec=hcol,fc='white',linewidth=2))
ax.add_patch(mpatches.Rectangle((rawci[0],yytop-boxywi),
rawci[1]-rawci[0],boxywi*2,ec=hcol,fc=hcol,linewidth=2,alpha=0.5))
yytop-=yincr
if pversion in ('d',) and field in ('tas',):
ax.plot((ss2mean,ss2mean),(yytop-boxywi,yytop+boxywi),linewidth=2,color=ltcol)
ax.add_patch(mpatches.Rectangle((ss2cif[0],yytop-boxywi),
ss2cif[1]-ss2cif[0],boxywi*2,ec=ltcol,fc='white',linewidth=2))
ax.add_patch(mpatches.Rectangle((ss2ci[0],yytop-boxywi),
ss2ci[1]-ss2ci[0],boxywi*2,ec=ltcol,fc=ltcol,linewidth=2,alpha=0.5))
if not combagcm:
xlab = '$\Delta$ SAT ($^\circ$C); AGCM= $%.2f$'%(ssmean)+ ' CGCM= $%.2f$'%(rawmean)
else:
xlab = '$\Delta$ SAT ($^\circ$C); BOTHAGCM= $%.2f$'%(ss2mean)+ ' CGCM= $%.2f$'%(rawmean)
#obslg=ax.axvline(obsreg,color=obscol,linewidth=2) # vert line for obs
#if field=='sia' and region=='nh':
if pversion in ('d',) and field=='tas':
pass
else:
#plotx=obsreg
#idx=cutl.find_nearest(rawxx,plotx)
#ploty=rawpdf_fitted[idx]
#ax.plot(plotx,ploty,marker='o',color=obscol,mec=obscol,fillstyle=fs,mew=mew,markersize=ms)
#ax.annotate('Obs',xy=(plotx,ploty),xytext=(-28,2),
# textcoords='offset points') # label the marker on the pdf curve
ax.axvline(x=obsreg,color=obscol,linewidth=2)
if addanno:
ax.annotate('Obs',xy=(obsreg,obsanny),xytext=offsetpts,
textcoords='offset points',
arrowprops=dict(arrowstyle='-',connectionstyle='arc3',
facecolor='k', edgecolor='k'),fontsize=fsz) # label vertical obs line
al =0.9; histtype='step'
#al =0.3; histtype='stepfilled'
if addnat:
#ax.hist(lensea,normed=True,color=natcol,alpha=al,histtype=histtype)
ax.plot(rawnxx,rawnpdf_fitted,color=natcolline,linewidth=2,alpha=al)
legh = legh + (rawnlg,)
legstr = legstr + ('CGCM Nat',)
prstr = prstr+'nat'
xlab = xlab + ' Nat=$%.2f$'%(rawnmean)
if addmisc:
#ax.hist(lemsea,normed=True,color=miscol,alpha=al,histtype=histtype)
ax.plot(rawmxx,rawmpdf_fitted,color=miscolline,linewidth=2,alpha=al)
legh = legh + (rawmlg,)
legstr = legstr + ('CGCM Aero',)
prstr = prstr+'misc'
xlab = xlab + ' Aero=$%.2f$'%(rawmmean)
if addpi:
if field=='sia':
# for the paper: add PI hist too, to sia
ax.hist(pisea,normed=True,color=picol,alpha=0.5,histtype='stepfilled')
ax.plot(pixx,pipdf_fitted,color=picol,linewidth=2,alpha=al)
legh = legh + (pilg,)
legstr = legstr + ('PreIndustrial',)
prstr = prstr+'PI'
xlab = xlab + ' PI=$%.2f$'%(pimean)
xlab = xlab + ' Obs=$%.2f$'%(obsreg)
#legh=legh+(obslg,)
#if field=='sia':
# legstr=legstr+('NSIDC Obs',)
#else:
# legstr=legstr+('Obs',)
if field=='sia':
ax.set_xlim((-2.5e12,1.6e12))
units='millions of km$^2$'
xlab = 'Change in sea ice area, $\Delta$SIC (' + units + ')' # for paper.
xtlabs = ax.get_xticks()/1e12
ax.set_xticklabels(xtlabs)
axesloc=[.71, .69, .18, .2]
elif field=='tas':
if pversion in ('d',):
ax.set_xlim((-2.5,4))
else:
ax.set_xlim((-2.5,5))
units = '$^\circ$C'
xlab = 'Change in Eurasian surface air temperature, $\Delta$SAT ('+units +')' # for paper.
axesloc=[.71, .69, .18, .2]
elif field=='sic':
units='%'
xlab = 'Change in Barents/Kara sea ice concentration, $\Delta$SIC (' + units + ')'
axesloc=[.71, .69, .18, .2]
#axesloc=[.69, .69, .29, .3] # for inset
axesloc=[.7, .79, .29, .2] # for inset. smaller version
""" http://matplotlib.org/examples/pylab_examples/axes_demo.html
a = axes([.65, .6, .2, .2], axisbg='y')
"""
if pversion not in ('c','d'):
# # INSET!
# http://stackoverflow.com/questions/17458580/embedding-small-plots-inside-subplots-in-matplotlib
pos=ax.get_position()
wid=pos.width
height=pos.height
inax_pos = ax.transAxes.transform(axesloc[0:2])
transFigure = fig.transFigure.inverted()
infig_pos = transFigure.transform(inax_pos)
xpos=infig_pos[0]
ypos=infig_pos[1]
wid*=axesloc[2] # not sure about this?
height*=axesloc[3] # and this?
print 'add_axes: ' + str([xpos,ypos,wid,height])
inax = fig.add_axes([xpos,ypos,wid,height])
#inax = fig.add_axes(axesloc)
top=2.5
yy=top
if field!='sia':
yy=top=3.5
boxwi=0.25
else:
boxwi=0.1
#inax.plot(rawmean, yy, linestyle='none',marker='s',mec=hcolline,color=hcolline)
inax.plot((rawmean,rawmean),(yy-boxwi,yy+boxwi),linewidth=2,color=hcolline)
# from matplotlib.patches import Rectangle
# someX, someY = 2, 3
# currentAxis = plt.gca()
# currentAxis.add_patch(Rectangle((someX - .5, someY - .5), 1, 1, facecolor="grey"))
inax.add_patch(mpatches.Rectangle((rawcif[0],yy-boxwi),
rawcif[1]-rawcif[0],boxwi*2,ec=hcolline,fc='white',linewidth=2))
inax.add_patch(mpatches.Rectangle((rawci[0],yy-boxwi),
rawci[1]-rawci[0],boxwi*2,ec=hcolline,fc=hcolline,linewidth=2,alpha=0.5))
#inax.plot(rawci, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec=hcolline,color=hcolline)
#inax.plot(rawcif, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec=hcolline,color=hcolline)
yy=yy-1
if addnat:
inax.plot(rawnmean, yy, linestyle='none',marker='s',mec=natcolline,color=natcolline)
inax.plot(rawnci, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec=natcolline,color=natcolline)
inax.plot(rawncif, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec=natcolline,color=natcolline)
yy=yy-1
if addmisc:
inax.plot(rawmmean, yy, linestyle='none',marker='s',mec=miscolline,color=miscolline)
inax.plot(rawmci, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec=miscolline,color=miscolline)
inax.plot(rawmcif, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec=miscolline,color=miscolline)
yy=yy-1
if addpi:
inax.plot((pimean,pimean),(yy-boxwi,yy+boxwi),linewidth=2,color=picol)
inax.add_patch(mpatches.Rectangle((picif[0],yy-boxwi),
picif[1]-picif[0],boxwi*2,ec=picol,fc='white',linewidth=2))
inax.add_patch(mpatches.Rectangle((pici[0],yy-boxwi),
pici[1]-pici[0],boxwi*2,ec=picol,fc=picol,linewidth=2,alpha=0.5))
#inax.plot(pimean, yy, linestyle='none',marker='s',mec=picol,color=picol)
#inax.plot(pici, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec=picol,color=picol)
#inax.plot(picif, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec=picol,color=picol)
yy=yy-1
if field not in ('sia','sic'):
inax.plot((ss2mean,ss2mean),(yy-boxwi,yy+boxwi),linewidth=2,color=ltcol)
inax.add_patch(mpatches.Rectangle((ss2cif[0],yy-boxwi),
ss2cif[1]-ss2cif[0],boxwi*2,ec=ltcol,fc='white',linewidth=2))
inax.add_patch(mpatches.Rectangle((ss2ci[0],yy-boxwi),
ss2ci[1]-ss2ci[0],boxwi*2,ec=ltcol,fc=ltcol,linewidth=2,alpha=0.5))
#inax.plot(ss2mean, yy, linestyle='none',marker='s',mec=ltcol,color=ltcol)
#inax.plot(ss2ci, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec=ltcol,color=ltcol)
#inax.plot(ss2cif, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec=ltcol,color=ltcol)
yy=yy-1
##if field!='sia':
## inax.plot(ss2mean, yy, linestyle='none',marker='s',mec='k',color='k')
## inax.plot(ss2ci, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec='k',color='k')
## inax.plot(ss2cif, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec='k',color='k')
## yy=yy-1
## inax.plot(ossmean, yy, linestyle='none',marker='s',mec='g',color='g')
## inax.plot(ossci, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec='g',color='g')
## inax.plot(osscif, (yy,yy), linewidth=2,marker='|',markersize=6,mew=2,mec='g',color='g')
#inax.axvline(x=0,linestyle='--',color='k')
inax.axvline(x=obsreg,color=obscol,linewidth=2,alpha=0.7)
inax.set_yticklabels('')
inax.set_yticks([])
inax.set_ylim(yy+0.5,top+.5)
if field=='tas' and region=='eurasiamori':
inax.set_xlim(-1.4,1.8) # taseurasiamori limits
xticks=inax.get_xticks()
inax.set_xticklabels(('','-1','','0','','1','','2'))
#legh=legh+(ss2lg2,nsidclg2)
#legstr=legstr+('AGCM var ICE','AGCM NSIDC ICE')
elif field=='sia' and region=='nh':
inax.set_xlim(-1.4e12,0.1e12)
inax.set_xticklabels(('',-1.2,'',-0.8,'',-0.4,'',0))
elif field=='sic' and region=='bksmori':
inax.set_xlim(-12,4)
inax.set_xticklabels((-12,'',-8,'',-4,'',0,'',4))
inax.set_xlabel('Mean change\n ('+units+')',fontsize=fsz-3)
# add 'Obs' annotation
if field=='sia':
#tlabx=0.5;tlabx2=0.49; tlaby=tlaby2=0.7
#labx=0.41; laby=0.56
laby=1.8 # data coord
offsetpts=(20,15)
elif field=='tas':
# NOT WORKING?
#tlabx=0.02; tlaby=0.85 # text position
#labx=0.03; laby=0.84; # start of arrow
#tlabx2=0.25; tlaby2=0.69 # head of arrow
laby=2.65 # data coord
offsetpts=(-40,20)
elif field=='sic':
laby=1.8
offsetpts=(20,15)
if addanno:
inax.annotate('Obs',xy=(obsreg,laby),xytext=offsetpts,
textcoords='offset points',
arrowprops=dict(arrowstyle='-',connectionstyle='arc3',
facecolor='k', edgecolor='k'),fontsize=fsz-2) # label vertical obs line
inax.xaxis.set_ticks_position('bottom')
if addsims:
legstr=legstr+('120-yr AGCM',)
legh=legh+(simlg,)
fontP = fm.FontProperties()
fontP.set_size(fsz-2)
# add AGCM Ice to legend
if field=='tas':
legloc=(0.71,0.47)
if pversion in ('c','d'): # no inset
legloc=(0.65,0.8) #0.71,0.8)
if pversion not in ('c',):
legstr=legstr+('AGCM',)# variable',)
legh=legh+(sslg,)
if pversion not in ('d',):
legstr=legstr+('GIStemp',)
legh=legh+(obslg,)
ax.legend(legh,legstr, loc=legloc,frameon=False,prop=fontP)
elif field in ('sia','sic'):
legstr=legstr+('NSIDC',)
legh=legh+(obslg,)
legloc=(0.69,0.5)
if pversion=='c':
legloc=(0.69,0.8)
ax.legend(legh,legstr, loc=legloc,frameon=False,prop=fontP)
ax.set_xlabel(xlab,fontsize=fsz)
ax.xaxis.set_ticks_position('bottom')
ax.xaxis.set_tick_params(direction='out')
# panel labels
if plab != None:
ax.annotate(plab,xy=(0.01,1.01),xycoords='axes fraction',fontsize=fsz,fontweight='bold')
return ax,prstr
# end plot_shorttermpdf() function -------------------------------------
def plot_shorttermpdf_paper(fig,ax,field,region,xxdat,pdfdat,histdat,meandat,cidat,
cifdat,pointdat,startyearsdat=None,addsims=False,addnat=True,addmisc=True,addpi=False,
combagcm=False,addanno=True,plab=None,pversion='',simctlpert=False):
""" startyearsdat is an unnecessary input, but is in 'retdict' from calc_shorttermpdf()
Try to simplify the func for paper fig
"""
extra=False
fsz=22
mew=1.5; ms=9
firebrick=ccm.get_linecolor('firebrick')
hcol='0.7'#ccm.get_linecolor('darkolivegreen3')
hcolline='0.3' #'k' #'0.7'#ccm.get_linecolor('darkolivegreen3')#'darkseagreen4')
#hcol=ccm.get_linecolor('niceblue')
#hcolline=ccm.get_linecolor('niceblue')
ltcol='0.5'#ccm.get_linecolor('darkseagreen4')
ltcol=ccm.get_linecolor('paperblue') #'niceblue2') #'steelblue4')
natcol=ccm.get_linecolor('steelblue3')
natcolline=ccm.get_linecolor('steelblue3')#4')
miscol=ccm.get_linecolor('orange3') #'deepskyblue')
miscolline=ccm.get_linecolor('orange3') #'deepskyblue')
obscol='r' #'k' #ccm.get_linecolor('midnightblue') #'b'
picol='purple'
sslg=mlines.Line2D([],[],color=ltcol,linewidth=2) # subsamp sims
sslgp=mlines.Line2D([],[],color=ltcol,linewidth=2,linestyle='--') # subsamp sims, PERT
ss2lg=mlines.Line2D([],[],color='k',linewidth=2) # subsamp sims2 (variable bc)
ss2lg2=mlines.Line2D([],[],color='k',linewidth=2,mew=2,marker='|')
simlg=mlines.Line2D([],[],color='0.3',linestyle='none',marker='o')
nsidclg=mlines.Line2D([],[],color='g',linestyle='none',marker='o')
nsidclg2=mlines.Line2D([],[],color='g',linewidth=2,mew=2,marker='|')
#obslg=mlines.Line2D([],[],color=obscol,linestyle='none',marker='s')
obslg=mlines.Line2D([],[],color=obscol,linewidth=2)
rawlg=mlines.Line2D([],[],color=hcolline,linewidth=2)
rawnlg=mlines.Line2D([],[],color=natcolline,linewidth=2)
rawmlg=mlines.Line2D([],[],color=miscolline,linewidth=2)
pilg=mlines.Line2D([],[],color=picol,linewidth=2)
prstr =''
#legh = (sslg,rawlg)
#legstr = ('AGCM Ice','CGCM All')
# actually add AGCM to end
legh = (rawlg,)
legstr = ('AOGCM',)
skey='obs'
obsreg= np.squeeze(pointdat[skey])
skey='orig'
or5sea= np.squeeze(pointdat[skey])
skey='AGCM120'
simflddf={}
simflddf['NSIDC']= np.squeeze(pointdat[skey])
if not combagcm:
skey='AGCM2'
else:
skey='BOTHAGCM'
ss2xx = np.squeeze(xxdat[skey]); ss2pdf_fitted=np.squeeze(pdfdat[skey]);
ss2mean= np.squeeze(meandat[skey]); ss2ci= np.squeeze(cidat[skey]); ss2cif= np.squeeze(cifdat[skey])
ss2hist=histdat[skey]
if not combagcm:
skey='AGCM'
ssxx = np.squeeze(xxdat[skey]); sspdf_fitted= np.squeeze(pdfdat[skey]);
ssmean= np.squeeze(meandat[skey]); ssci= np.squeeze(cidat[skey]); sscif= np.squeeze(cifdat[skey])
if simctlpert:
if not combagcm:
skey='AGCM2p'
else:
skey='BOTHAGCMp'
ss2pxx = np.squeeze(xxdat[skey]); ss2ppdf_fitted=np.squeeze(pdfdat[skey]);
ss2pmean= np.squeeze(meandat[skey]); ss2pci= np.squeeze(cidat[skey]); ss2pcif= np.squeeze(cifdat[skey])
ss2phist=histdat[skey]
if not combagcm:
skey='AGCMp'
sspxx = np.squeeze(xxdat[skey]); ssppdf_fitted= np.squeeze(pdfdat[skey]);
sspmean= np.squeeze(meandat[skey]); sspci= np.squeeze(cidat[skey]); sspcif= np.squeeze(cifdat[skey])
skey='histle'
lesea= histdat[skey]; rawxx=xxdat[skey]; rawpdf_fitted=pdfdat[skey];
rawmean=meandat[skey]; rawci=cidat[skey]; rawcif=cifdat[skey]
if extra:
# TESTING kernel shape
kernel=sp.stats.gaussian_kde(lesea)
kernel2=sp.stats.gaussian_kde(lesea,bw_method='silverman')
kernelf1=sp.stats.gaussian_kde(lesea,bw_method=1)
kernelfp7=sp.stats.gaussian_kde(lesea,bw_method=.7)
kernelfp35=sp.stats.gaussian_kde(lesea,bw_method=.35)
kernelfp25=sp.stats.gaussian_kde(lesea,bw_method=.25)
plt.figure()
plt.plot(rawxx,kernel(rawxx),'b--')
plt.plot(rawxx,kernel2(rawxx),'r--')
plt.plot(rawxx,kernelf1(rawxx),'g--')
plt.plot(rawxx,kernelfp7(rawxx),'c--')
plt.plot(rawxx,kernelfp35(rawxx),'m--')
plt.plot(rawxx,kernelfp25(rawxx),color='orange',linestyle='--')
plt.hist(lesea,normed=True,color=hcol,alpha=0.3,histtype='stepfilled')
plt.legend(('scott (scale=0.457)','silverman (scale=0.484)',
'scale=1','scale=0.7','scale=0.35','scale=0.25','orig'),
loc='best')
if addnat:
skey='histnat'
lensea= histdat[skey]; rawnxx=xxdat[skey]; rawnpdf_fitted=pdfdat[skey];
rawnmean=meandat[skey]; rawnci=cidat[skey]; rawncif=cifdat[skey]
if addmisc:
skey='histmisc'
lemsea= histdat[skey]; rawmxx=xxdat[skey]; rawmpdf_fitted=pdfdat[skey];
rawmmean=meandat[skey]; rawmci=cidat[skey]; rawmcif=cifdat[skey]
if addpi:# or (pversion=='d' and field=='tas'):
skey='pi'
pisea= histdat[skey]; pixx=xxdat[skey]; pipdf_fitted=pdfdat[skey];
pimean=meandat[skey]; pici=cidat[skey]; picif=cifdat[skey]
if field not in ('sia','sic') and pversion != 'c': # probably paper fig, type='d'
if simctlpert:
# control
ax.hist(ss2hist,normed=True,color=ltcol,alpha=0.4)#,histtype='stepfilled')#@@ maybe?
ax.plot(ss2xx,ss2pdf_fitted,color=ltcol,linewidth=3)
# pert
ax.hist(ss2phist,normed=True,color=ltcol,alpha=0.2)#,histtype='stepfilled')#@@ maybe?
ax.plot(ss2pxx,ss2pdf_fitted,color=ltcol,linewidth=3, linestyle='--')
else:
ax.hist(ss2hist,normed=True,color=ltcol,alpha=0.4)#,histtype='stepfilled')#@@ maybe?