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scatter_regions.py
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"""
scatter_regions.py
This script makes scatter plots of regional means. Can do two
variables, two regions, two seasons. There are figures of just the simulation mean
values with confidence intervals, and figures with all regional averages
in time with regression lines. 10/10/2014
1/26/15: try to make into a function that uses datablobs.
"""
import cccmautils as cutl
import constants as con
import cccmaplots as cplt
import cccmacmaps as ccm
import pandas as pd
import numpy.ma as ma
import matplotlib.font_manager as fm
printtofile=False
plt.close('all')
conv1=1; conv2=1
graveraint= 9.80665 # m/s2 (ERA-Int, different from Canadian models)
plotscatter=True
addobs=True
addle=False
# field1 is x
#field1='st'; ncfield1='ST'
field1='sia'; ncfield1='SICN'
#field1='gz50000'; ncfield1='PHI'; conv1=1/con.get_g()
#field1='pmsl'; ncfield1='PMSL'
#field1='net';
region1='bksmori' #'polcap65'
sea1='DJF' #'ND' #'DJF'
# field2 is y
field2='st'; ncfield2='ST'
#field2='sia'; ncfield2='SICN'
#field2='pmsl'; ncfield2='PMSL'
#field2='gz50000'; ncfield2='PHI'; conv2=1/con.get_g()
region2= 'eurasiamori' #'eurasiamori'
sea2='DJF' #'ND' #'DJF'
sims = ('E1','E2','E3','E4','E5','R1','R2','R3','R4','R5','HAD','NSIDC','ENS','ENSE')
TOT = ('R1','R2','R3','R4','R5')
ANT = ('E1','E2','E3','E4','E5')
siglevel=0.05
flddregdt= {}
flddregdt2 = {}
flddregtsdt= {}
flddregtsdt2 = {}
ciregdt = {}
ciregdt2 = {}
ntime=120
# if one of the scatter vars is winter, need to remove a year from the other one
# if it's not winter too
if (sea1 in ('DJF','NDJ')) or (sea2 in ('DJF','NDJ')):
ntime=ntime-1
#if (sea1 in ('DJF','NDJ') and sea2 not in ('DJF','NDJ')) or (sea2 in ('DJF','NDJ') and sea1 not in ('DJF','NDJ')):
# ntime=ntime-1
nsim=len(TOT)
#if sea2 in ('DJF','NDJ') and sea1 not in ('DJF','NDJ'): # have to shorten other timeseries
# inittime=ntime*nsim-1
#else:
inittime=ntime*nsim
timesel='0002-01-01,0121-12-31'
alltotcr1=np.zeros(inittime)
alltotcr2=np.zeros(inittime)
alltotpr1=np.zeros(inittime)
alltotpr2=np.zeros(inittime)
alltotr1=np.zeros(inittime)
alltotr2=np.zeros(inittime)
allantcr1=np.zeros(inittime)
allantcr2=np.zeros(inittime)
allantpr1=np.zeros(inittime)
allantpr2=np.zeros(inittime)
allantr1=np.zeros(inittime)
allantr2=np.zeros(inittime)
tallii=0 # index to keep track of time in accumulated TOT ensemble
aallii=0 # index to keep track of time in accumulated ANT ensemble
for sim in sims:
if field1 in ('turb','net'):
fielda='hfl'; fieldb='hfs'
fnamec,fnamepa=con.build_filepathpair(sim,fielda)
fnamecb,fnamepb=con.build_filepathpair(sim,fieldb)
fldc = cnc.getNCvar(fnamec,fielda.upper(),timesel=timesel,seas=sea1) +\
cnc.getNCvar(fnamecb,fieldb.upper(),timesel=timesel,seas=sea1)
fldp = cnc.getNCvar(fnamepa,fielda.upper(),timesel=timesel,seas=sea1) +\
cnc.getNCvar(fnamepb,fieldb.upper(),timesel=timesel,seas=sea1)
if field1=='net':
fieldb='flg'
conv=-1
fnamecb,fnamepb=con.build_filepathpair(sim,fieldb)
fldc = fldc + cnc.getNCvar(fnamecb,fieldb.upper(),
timesel=timesel,seas=sea1)*conv
fldp = fldp + cnc.getNCvar(fnamepb,fieldb.upper(),
timesel=timesel,seas=sea1)*conv
conv=1
else:
fnamec,fnamep=con.build_filepathpair(sim,field1)
fldc=cnc.getNCvar(fnamec,ncfield1,timesel=timesel,seas=sea1)*conv1
fldp=cnc.getNCvar(fnamep,ncfield1,timesel=timesel,seas=sea1)*conv1
lat=cnc.getNCvar(fnamec,'lat')
lon=cnc.getNCvar(fnamec,'lon')
if field1=='sia':
fldcreg=cutl.calc_regtotseaicearea(fldc,lat,lon,region1) # isarea=False
fldpreg=cutl.calc_regtotseaicearea(fldp,lat,lon,region1) # isarea=False
else:
# IF FLUX, MASK OUT OPEN-WATER:
#print 'Implement masking out of open water in control for FLUXES @@@'
if field1 in ('hfl','hfs','flg','fsg','turb','net'):
print 'field1, ' + field1 + ' is a flux. Mask out non-ice in control' # @@
# mask out regions that are not ice in the control (as P&M 2014 JClim)
sicfname,junk=con.build_filepathpair(sim,'sicn')
sicnc = cnc.getNCvar(sicfname,'SICN',timesel=timesel,seas=sea1)
fldc = ma.masked_where(sicnc<.10,fldc)
fldp = ma.masked_where(sicnc<.10,fldp)
fldcreg=cutl.calc_regmean(fldc,lat,lon,region1)
fldpreg=cutl.calc_regmean(fldp,lat,lon,region1)
flddregdt[sim] = np.mean(fldpreg-fldcreg,axis=0) # time mean
if sea2 in ('DJF','NDJ') and sea1 not in ('DJF','NDJ'): # have to shorten other timeseries
flddregtsdt[sim] = fldpreg[:-1,...]-np.mean(fldcreg[:-1,...],axis=0) # anomaly timeseries from ctl mean
else:
flddregtsdt[sim] = fldpreg-np.mean(fldcreg,axis=0)
if (field2 != field1) or (sea2 != sea1):
print field2 + ' != ' + field1 + ' or ' + str(sea2) + ' != ' + str(sea1)
if field2 in ('turb','net'):
fielda='hfl'; fieldb='hfs'
fnamec,fnamepa=con.build_filepathpair(sim,fielda)
fnamecb,fnamepb=con.build_filepathpair(sim,fieldb)
fldc2 = cnc.getNCvar(fnamec,fielda.upper(),timesel=timesel,seas=sea2) +\
cnc.getNCvar(fnamecb,fieldb.upper(),timesel=timesel,seas=sea2)
fldp2 = cnc.getNCvar(fnamepa,fielda.upper(),timesel=timesel,seas=sea2) +\
cnc.getNCvar(fnamepb,fieldb.upper(),timesel=timesel,seas=sea2)
if field2=='net':
fieldb='flg'
conv=-1
fnamecb,fnamepb=con.build_filepathpair(sim,fieldb)
fldc2 = fldc2 + cnc.getNCvar(fnamecb,fieldb.upper(),
timesel=timesel,seas=sea2)*conv
fldp2 = fldp2 + cnc.getNCvar(fnamepb,fieldb.upper(),
timesel=timesel,seas=sea2)*conv
conv=1
else:
fnamec,fnamep=con.build_filepathpair(sim,field2)
fldc2=cnc.getNCvar(fnamec,ncfield2,timesel=timesel,seas=sea2)*conv2
fldp2=cnc.getNCvar(fnamep,ncfield2,timesel=timesel,seas=sea2)*conv2
## elif sea2 != sea1:
## fldc2=cnc.getNCvar(fnamec,ncfield2,timesel=timesel,seas=sea2)*conv2
## fldp2=cnc.getNCvar(fnamep,ncfield2,timesel=timesel,seas=sea2)*conv2
else:
fldc2=fldc
fldp2=fldp
if field2=='sia':
fldcreg2=cutl.calc_regtotseaicearea(fldc,lat,lon,region2) # isarea=False
fldpreg2=cutl.calc_regtotseaicearea(fldp,lat,lon,region2) # isarea=False
else:
if field2 in ('hfl','hfs','flg','fsg','turb','net'):
# mask out regions that are not ice in the control (as P&M 2014 JClim)
print 'field2, ' + field2 + ' is a flux. Mask out non-ice in control' # @@
sicfname,junk=con.build_filepathpair(sim,'sicn')
sicnc2 = cnc.getNCvar(sicfname,'SICN',timesel=timesel,seas=sea2)
fldc2 = ma.masked_where(sicnc2<.10,fldc2)
fldp2 = ma.masked_where(sicnc2<.10,fldp2)
fldcreg2=cutl.calc_regmean(fldc2,lat,lon,region2)
fldpreg2=cutl.calc_regmean(fldp2,lat,lon,region2)
flddregdt2[sim] = np.mean(fldpreg2-fldcreg2,axis=0)
# if sea2 in ('DJF','NDJ') and sea1 not in ('DJF','NDJ'): # have to shorten other timeseries
# flddregtsdt2[sim] = fldpreg2[:-1,...]-np.mean(fldcreg2[:-1,...],axis=0)
# else:
flddregtsdt2[sim] = fldpreg2-np.mean(fldcreg2,axis=0) # anomaly timeseries from ctl mean
# @@@ there are files with concated ensembles now: e.g. sim name kemctl1enscat, diffname ENSCAT
if sim in TOT:
#print 'concat tot ens members'
alltotcr1[tallii:tallii+ntime] = fldcreg[:ntime] # control reg1
alltotcr2[tallii:tallii+ntime] = fldcreg2[:ntime] # control reg2
alltotpr1[tallii:tallii+ntime] = fldpreg[:ntime] # pert reg1
alltotpr2[tallii:tallii+ntime] = fldpreg2[:ntime] # pert reg2
alltotr1[tallii:tallii+ntime] = flddregtsdt[sim] # diff reg1
alltotr2[tallii:tallii+ntime] = flddregtsdt2[sim] # diff reg2
tallii+=ntime
#totser.append(pd.Series(flddregtsdt))
#totser2.append(pd.Series(flddregtsdt2))
elif sim in ANT:
#print 'concat ant ens members'
allantcr1[aallii:aallii+ntime] = fldcreg[:ntime] # to deal w/ SON vs DJF for example
allantcr2[aallii:aallii+ntime] = fldcreg2[:ntime]
allantpr1[aallii:aallii+ntime] = fldpreg[:ntime]
allantpr2[aallii:aallii+ntime] = fldpreg2[:ntime]
allantr1[aallii:aallii+ntime] = flddregtsdt[sim]
allantr2[aallii:aallii+ntime] = flddregtsdt2[sim]
aallii+=ntime
# calculate confidence interval
# double-check the scale setting
ciregdt[sim] = sp.stats.t.interval(1-siglevel,len(fldpreg)-1,loc=np.mean(fldpreg,axis=0)-np.mean(fldcreg,axis=0),
scale=np.std(fldpreg-fldcreg,axis=0)/np.sqrt(len(fldpreg)))
ciregdt2[sim] = sp.stats.t.interval(1-siglevel,len(fldpreg2)-1,loc=np.mean(fldpreg2,axis=0)-np.mean(fldcreg2,axis=0),
scale=np.std(fldpreg2-fldcreg2,axis=0)/np.sqrt(len(fldpreg2)))
# @@@ add confidence int for allant and alltot (means have to concat control and pert separately as well)
# calculate confidence interval
# double-check the scale setting
ciregalltotr1 = sp.stats.t.interval(1-siglevel,len(alltotpr1)-1,loc=np.mean(alltotpr1,axis=0)-np.mean(alltotcr1,axis=0),
scale=np.std(alltotpr1-alltotcr1,axis=0)/np.sqrt(len(alltotpr1)))
ciregalltotr2 = sp.stats.t.interval(1-siglevel,len(alltotpr2)-1,loc=np.mean(alltotpr2,axis=0)-np.mean(alltotcr2,axis=0),
scale=np.std(alltotpr2-alltotcr2,axis=0)/np.sqrt(len(alltotpr2)))
ciregallantr1 = sp.stats.t.interval(1-siglevel,len(allantpr1)-1,loc=np.mean(allantpr1,axis=0)-np.mean(allantcr1,axis=0),
scale=np.std(allantpr1-allantcr1,axis=0)/np.sqrt(len(allantpr1)))
ciregallantr2 = sp.stats.t.interval(1-siglevel,len(allantpr2)-1,loc=np.mean(allantpr2,axis=0)-np.mean(allantcr2,axis=0),
scale=np.std(allantpr2-allantcr2,axis=0)/np.sqrt(len(allantpr2)))
if plotscatter:
# SCATTER SUPER ENSEMBLES IN TIME
# super ensembles
fig,ax= plt.subplots(1,1) #plt.figure();
plt.scatter(allantr1,allantr2,color='r')
plt.scatter(alltotr1,alltotr2,color='k')
antr1std=allantr1.std()
antr2std=allantr2.std()
totr1std=alltotr1.std()
totr2std=alltotr2.std()
axylims = ax.get_ylim()
if axylims[0]<=0 and axylims[1]>=0:
ax.axhline(y=0,color='k',linewidth=.5)
axxlims = ax.get_xlim()
if axxlims[0]<=0 and axxlims[1]>=0:
ax.axvline(x=0,color='k',linewidth=.5)
# SCATTER SUPER ENS CTL and PERT separately
#pltc1=allantcr1; pltc2=allantcr2
#pltp1=allantpr1; pltp2=allantpr2
# or as anomaly
pltc1=allantcr1-allantcr1.mean(); pltc2=allantcr2-allantcr2.mean()
pltp1=allantpr1-allantcr1.mean(); pltp2=allantpr2-allantcr2.mean()
fig,axs= plt.subplots(1,2,sharex=True,sharey=True) #plt.figure();
fig.set_size_inches(9,5)
ax=axs[0]
ax.scatter(pltc1,pltc2,color='k')
ax.scatter(pltp1,pltp2,color='b')
ax.set_title('ANT')
ax.legend(('CTL','PERT'),loc='best',fancybox=True,framealpha=0.5)
ax.set_xlabel(field1 + region1 + str(sea1))
ax.set_ylabel(field2 + region2 + str(sea2))
axylims = ax.get_ylim()
if axylims[0]<=0 and axylims[1]>=0:
ax.axhline(y=0,color='k',linewidth=.5)
axxlims = ax.get_xlim()
if axxlims[0]<=0 and axxlims[1]>=0:
ax.axvline(x=0,color='k',linewidth=.5)
onex=np.linspace(axxlims[0],axxlims[1])
mm, bb, rvalc1, pval, std_err = sp.stats.linregress(pltc1,pltc2)
ax.plot(onex,mm*onex + bb, color='k',linewidth=2)
mm, bb, rvalp1, pval, std_err = sp.stats.linregress(pltp1,pltp2)
ax.plot(onex,mm*onex + bb, color='b',linewidth=2)
## axylims = ax.get_ylim()
## axxlims = ax.get_xlim()
## ax.annotate('$Rctl$= ' + '$%.2f$'%(rvalc) + ',$Rctl$='+'$%.2f$'%(rvalp),
## xy=(axxlims[0]+.1*axxlims[1], axylims[1]-.1*axylims[1]),
## xycoords='data')
ax=axs[1]
#pltc1=alltotcr1; pltc2=alltotcr2
#pltp1=alltotpr1; pltp2=alltotpr2
# or as anomaly
pltc1=alltotcr1-alltotcr1.mean(); pltc2=alltotcr2-alltotcr2.mean()
pltp1=alltotpr1-alltotcr1.mean(); pltp2=alltotpr2-alltotcr2.mean()
ax.scatter(pltc1,pltc2,color='k')
ax.scatter(pltp1,pltp2,color='b')
ax.set_title('TOT')
ax.legend(('CTL','PERT'),loc='best',fancybox=True,framealpha=0.5)
ax.set_xlabel(field1 + region1 + str(sea1))
#ax.set_ylabel(field2 + region2 + str(sea2))
axylims = ax.get_ylim()
if axylims[0]<=0 and axylims[1]>=0:
ax.axhline(y=0,color='k',linewidth=.5)
axxlims = ax.get_xlim()
if axxlims[0]<=0 and axxlims[1]>=0:
ax.axvline(x=0,color='k',linewidth=.5)
onex=np.linspace(axxlims[0],axxlims[1])
mm, bb, rvalc, pval, std_err = sp.stats.linregress(pltc1,pltc2)
ax.plot(onex,mm*onex + bb, color='k',linewidth=2)
mm, bb, rvalp, pval, std_err = sp.stats.linregress(pltp1,pltp2)
ax.plot(onex,mm*onex + bb, color='b',linewidth=2)
axylims = ax.get_ylim()
axxlims = ax.get_xlim()
ax.annotate('$Rctl$= ' + '$%.2f$'%(rvalc) + ',$Rpt$='+ '$%.2f$'%(rvalp),
xy=(axxlims[0]+.1*axxlims[1], axylims[1]-.1*axylims[1]),
xycoords='data')
ax=axs[0] # go back and annotate first panel
axylims = ax.get_ylim()
axxlims = ax.get_xlim()
ax.annotate('$Rctl$= ' + '$%.2f$'%(rvalc1) + ',$Rpt$='+ '$%.2f$'%(rvalp1),
xy=(axxlims[0]+.1*axxlims[1], axylims[1]-.1*axylims[1]),
xycoords='data')
if printtofile:
fig.savefig('scatterregress_' + field1 + region1 + str(sea1) + '_v_' + field2 + region2 + str(sea2) + '_ANTTOTsuperens_ctlpertsbplt.pdf')
#SCATTER SUPER ENSEMBLE MEANS with conf interval
fig,ax= plt.subplots(1,1) #plt.figure();
ant=plt.scatter(allantr1.mean(),allantr2.mean(),color='r',marker='s',s=8**2)
tot=plt.scatter(alltotr1.mean(),alltotr2.mean(),color='k',marker='s',s=8**2)
plt.plot(ciregalltotr1,(alltotr2.mean(),alltotr2.mean()),
color='k',linewidth=2,marker='_',markersize=6)
plt.plot((alltotr1.mean(),alltotr1.mean()),ciregalltotr2,
color='k',linewidth=2,marker='_',markersize=6)
plt.plot(ciregallantr1,(allantr2.mean(),allantr2.mean()),
color='r',linewidth=2,marker='_',markersize=6)
plt.plot((allantr1.mean(),allantr1.mean()),ciregallantr2,
color='r',linewidth=2,marker='_',markersize=6)
plt.xlabel(str(sea1) + ' ' + field1 + ' ' + region1)
plt.ylabel(str(sea2) + ' ' + field2 + ' ' + region2)
plt.legend((ant,tot),('ANT','TOT'),loc='best',fancybox=True,framealpha=0.5)
axylims = ax.get_ylim()
if axylims[0]<=0 and axylims[1]>=0:
ax.axhline(y=0,color='k',linewidth=.5)
axxlims = ax.get_xlim()
if axxlims[0]<=0 and axxlims[1]>=0:
ax.axvline(x=0,color='k',linewidth=.5)
if printtofile:
plt.savefig('scatterCI_' + field1 + region1 + str(sea1) + '_v_' + field2 + region2 + str(sea2) + '_ANTTOTsuperens.pdf')
ser1 = pd.Series(flddregdt)
ser2 = pd.Series(flddregdt2)
serts1=pd.Series(flddregtsdt)
serts2=pd.Series(flddregtsdt2)
serci1 = pd.Series(ciregdt)
serci2 = pd.Series(ciregdt2)
df = pd.DataFrame([ser1,ser2],index=(region1,region2))
dfts = pd.DataFrame([serts1,serts2],index=(region1,region2))
cidf = pd.DataFrame([serci1,serci2],index=(region1,region2))
print df
cd = ccm.get_colordict()
# SCATTER PLOT OF SIM MEANS
fig,ax = plt.subplots(1) #plt.figure()
plt.scatter(df.filter(regex='R').values[0],df.filter(regex='R').values[1],color='k',marker='s',s=8**2)
plt.scatter(df.filter(regex='E').values[0],df.filter(regex='E').values[1],color='0.5',marker='o',s=8**2)
plt.scatter(df['HAD'].values[0],df['HAD'].values[1],color=cd['HAD'],marker='s',s=8**2)
plt.scatter(df['NSIDC'].values[0],df['NSIDC'].values[1],color=cd['NSIDC'],marker='s',s=8**2)
plt.scatter(df['ENS'].values[0],df['ENS'].values[1],color=cd['ENS'],marker='s',s=10**2)
plt.scatter(df['ENSE'].values[0],df['ENSE'].values[1],color=cd['ENSE'],marker='o',s=10**2)
plt.legend(('TOT','ANT','HAD','NSIDC','$\overline{TOT}$','$\overline{ANT}$'),loc='best',fancybox=True,framealpha=0.5)
plt.xlabel(str(sea1) + ' ' + field1 + ' ' + region1)
plt.ylabel(str(sea2) + ' ' + field2 + ' ' + region2)
axylims = ax.get_ylim()
if axylims[0]<=0 and axylims[1]>=0:
ax.axhline(y=0,color='k',linewidth=.5)
axxlims = ax.get_xlim()
if axxlims[0]<=0 and axxlims[1]>=0:
ax.axvline(x=0,color='k',linewidth=.5)
if printtofile:
fig.savefig('scatter_' + field1 + region1 + str(sea1) + '_v_' + field2 + region2 + str(sea2) + '.pdf')
# ################# PAPER ###############
fontP = fm.FontProperties()
fontP.set_size('small')
# SCATTER PLOT OF SIM MEANS
# prepare data for regression line
#mm, bb, rval, pval, std_err = sp.stats.linregress(dfts[sim].values[0],dfts[sim].values[1])
# a way to subset: df.ix[:,'R1':'R5'] -- but it can't get both R and E data.
#rdf=df.ix[:,'R1':'R5']
#edf=df.ix[:,'E1':'E5']
#regressdf = rdf.append(edf) # didn't append like I expected. inserted NaNs in places
# this will work:
allens=['R1','R2','R3','R4','R5','E1','E2','E3','E4','E5'] # has to be an array!
rens=['R1','R2','R3','R4','R5']
eens=['E1','E2','E3','E4','E5']
sub = df.loc[:,allens]
subx=sub.ix[0,:]
suby=sub.ix[1,:]
mm, bb, rval, pval, std_err = sp.stats.linregress(subx,suby)
#sube = df.loc[0,['E1','E2','E3','E4','E5']]
firebrick=ccm.get_linecolor('firebrick')
printtofile=True
fig,ax = plt.subplots(1)
fig.set_size_inches(6,5)
rr = plt.scatter(df.filter(regex='R').values[0],df.filter(regex='R').values[1],
color='0.3',marker='o',s=8**2,alpha=0.7)
ee = plt.scatter(sub.ix[0,5:],sub.ix[1,5:],
color=firebrick,marker='o',s=8**2,alpha=0.7)
#plt.scatter(df['HAD'].values[0],df['HAD'].values[1],color=cd['HAD'],marker='s',s=8**2)
#ns=plt.scatter(df['NSIDC'].values[0],df['NSIDC'].values[1],color=cd['NSIDC'],marker='o',s=8**2,alpha=0.7) # @@@@ add back?
ns=plt.scatter(df['NSIDC'].values[0],df['NSIDC'].values[1],color='green',marker='o',s=8**2,alpha=0.7)
#plt.scatter(df['ENS'].values[0],df['ENS'].values[1],color=cd['ENS'],marker='s',s=10**2)
#plt.scatter(df['ENSE'].values[0],df['ENSE'].values[1],color=cd['ENSE'],marker='o',s=10**2)
axylims = ax.get_ylim()
axxlims = ax.get_xlim()
onex=np.linspace(axxlims[0],axxlims[1])
plt.plot(onex,mm*onex + bb, color='0.5',linewidth=1)#,linestyle='--')
rsq = rval**2
print 'PAPER --- mean sims regr info: SLOPE: ' + str(mm) + ', RVAL: ' + str(rval) + ', PVAL: ' + str(pval) + ', R2: ' + str(rsq)
if sea1 in ('ND','DJF') and sea2 in ('ND','DJF') and region1=='bksmori' and region2 in ('eurasia','eurasiathin','eurasiamori') and field1=='gz50000' and field2=='st':
# PAPER FIG!
if sea1 == sea2 == 'DJF':
monlab = 'Dec-Jan-Feb'
else:
monlab = 'Nov-Dec'
xlab = '$\Delta$ ' + monlab + ' Barents-Kara Seas'
if field1=='pmsl':
xlab = xlab + ' SLP (hPa)'
else:
xlab = xlab + ' Z500 (m)'
ylab = '$\Delta$ ' + monlab + ' Eurasian SAT ($^\circ$C)'
# ### ADD ACTUAL OBS HERE:
# GISS (SAT) and ERA-INT (Z500)
erafile = '/HOME/rkm/work/DATA/ERAINT/td_era_int_197901_201412_gp_128_64_phi500_1979011612-2014121612.nc'
gisfile = '/HOME/rkm/work/DATA/GISS/gistemp1200_ERSST.nc'
eraz500c= cnc.getNCvar(erafile,ncfield1,timesel='1979-01-01,1989-12-31',seas=sea1)/graveraint
eraz500p= cnc.getNCvar(erafile,ncfield1,timesel='2002-01-01,2012-12-31',seas=sea1)/graveraint
erareg = cutl.calc_regmean(eraz500p-eraz500c,lat,lon,region1)
gissatc= cnc.getNCvar(gisfile,'tempanomaly',timesel='1979-01-01,1989-12-31',seas=sea2) # @@ need to deal w/ scale factor? OH IT HAPPENS IN MY FUNCTION
gissatp= cnc.getNCvar(gisfile,'tempanomaly',timesel='2002-01-01,2012-12-31',seas=sea2)
latgis=cnc.getNCvar(gisfile,'lat')
longis=cnc.getNCvar(gisfile,'lon')
gisreg = cutl.calc_regmean(gissatp-gissatc,latgis,longis,region2)
if addobs:
obs=plt.scatter(erareg.mean(),gisreg.mean(),color='blue',marker='s',s=8**2)
else:
addobs=False
print 'Cannot add obs for this field combo' #@@
xlab = str(sea1) + ' ' + field1 + ' ' + region1
ylab = str(sea2) + ' ' + field2 + ' ' + region2
if addobs:
probs='obs' # print obs string
plt.legend((rr,ee,ns,obs),('Individual SIC forcings',
'Average SIC forcing','NSIDC SIC forcing','Observations'),
loc='best',fancybox=True,framealpha=0.5,prop=fontP)#,frameon=False)
else:
probs=''
plt.legend((rr,ee,ns),('Individual SIC forcings',
'Average SIC forcing','NSIDC SIC forcing'),
loc='best',fancybox=True,framealpha=0.5,prop=fontP)#,frameon=False)
plt.xlabel(xlab)
plt.ylabel(ylab)
axylims = ax.get_ylim()
if axylims[0]<=0 and axylims[1]>=0:
ax.axhline(y=0,color='k',linewidth=.5,linestyle='--')
axxlims = ax.get_xlim()
if axxlims[0]<=0 and axxlims[1]>=0:
ax.axvline(x=0,color='k',linewidth=.5,linestyle='--')
if printtofile:
fig.savefig('scatterregress_' + field1 + region1 + str(sea1) + '_v_' +
field2 + region2 + str(sea2) + 'wacepap' + probs + '.pdf')
fig.savefig('scatterregress_' + field1 + region1 + str(sea1) + '_v_' +
field2 + region2 + str(sea2) + 'wacepap' + probs + '.eps')
# ### ADD LE DATA #####
if addle:
printtofile=True
import loadLE as le
timeselc='1979-01-01,1989-12-31'
timeselp='2002-01-01,2012-12-31'
lefield1='zg50000.00'; lencfield1='zg'; comp1='Amon'; leconv1=1 #region1='bksmori'
lefield2='tas'; lencfield2='tas'; comp2='Amon'; #region2='eurasiamori'
fdict1 = {'field': lefield1+region1, 'ncfield': lencfield1, 'comp': comp1}
fdict2 = {'field': lefield2+region2, 'ncfield': lencfield2, 'comp': comp2}
ftype='fullts' # 'fullclimo' or 'climo' or 'fullts'
lefdict1 = {'field': lefield1+region1, 'ncfield': lencfield1, 'comp': comp1}
lefdict2 = {'field': lefield2+region2, 'ncfield': lencfield2, 'comp': comp2}
# historical
lecdat1 = le.load_LEdata(fdict1,'historical',timesel=timeselc, rettype='ndarray',conv=leconv1,ftype=ftype)
(numens1,ntime1) = lecdat1.shape
lepdat1=le.load_LEdata(fdict1,'historical',timesel=timeselp, rettype='ndarray',conv=leconv1,ftype=ftype)
lecsea1 = cutl.seasonalize_monthlyts(lecdat1.T,season=sea1).T
lepsea1 = cutl.seasonalize_monthlyts(lepdat1.T,season=sea1).T
lefld1=lepsea1.mean(axis=1)-lecsea1.mean(axis=1)
lecdat2 = le.load_LEdata(fdict2,'historical',timesel=timeselc, rettype='ndarray',conv=conv2,ftype=ftype)
(numens2,ntime2) = lecdat1.shape
lepdat2=le.load_LEdata(fdict2,'historical',timesel=timeselp, rettype='ndarray',conv=conv2,ftype=ftype)
lecsea2 = cutl.seasonalize_monthlyts(lecdat2.T,season=sea2).T
lepsea2 = cutl.seasonalize_monthlyts(lepdat2.T,season=sea2).T
lefld2=lepsea2.mean(axis=1)-lecsea2.mean(axis=1)
obs=plt.scatter(erareg.mean(),gisreg.mean(),color='blue',marker='s',s=8**2)
ledat=plt.scatter(lefld1,lefld2,color=ccm.get_linecolor('darkolivegreen3'),marker='*',s=5**2,alpha=0.5)
lemm, lebb, lerval, lepval, lestd_err = sp.stats.linregress(lefld1,lefld2)
axylims = ax.get_ylim()
axxlims = ax.get_xlim()
onex=np.linspace(axxlims[0],axxlims[1])
ax.plot(onex,lemm*onex + lebb, color=ccm.get_linecolor('darkolivegreen3'),linewidth=1)
# historicalNat
lecdat1n = le.load_LEdata(fdict1,'historicalNat',timesel=timeselc, rettype='ndarray',conv=leconv1,ftype=ftype)
(numens1,ntime1) = lecdat1n.shape
lepdat1n=le.load_LEdata(fdict1,'historicalNat',timesel=timeselp, rettype='ndarray',conv=leconv1,ftype=ftype)
lecsea1n = cutl.seasonalize_monthlyts(lecdat1n.T,season=sea1).T
lepsea1n = cutl.seasonalize_monthlyts(lepdat1n.T,season=sea1).T
lefld1n=lepsea1n.mean(axis=1)-lecsea1n.mean(axis=1)
lecdat2n = le.load_LEdata(fdict2,'historicalNat',timesel=timeselc, rettype='ndarray',conv=conv2,ftype=ftype)
(numens2,ntime2) = lecdat1n.shape
lepdat2n=le.load_LEdata(fdict2,'historicalNat',timesel=timeselp, rettype='ndarray',conv=conv2,ftype=ftype)
lecsea2n = cutl.seasonalize_monthlyts(lecdat2n.T,season=sea2).T
lepsea2n = cutl.seasonalize_monthlyts(lepdat2n.T,season=sea2).T
lefld2n=lepsea2n.mean(axis=1)-lecsea2n.mean(axis=1)
ledatn=plt.scatter(lefld1n,lefld2n,color=ccm.get_linecolor('steelblue3'),marker='*',s=5**2,alpha=0.5)
lemmn, lebbn, lervaln, lepvaln, lestd_errn = sp.stats.linregress(lefld1n,lefld2n)
axylims = ax.get_ylim()
axxlims = ax.get_xlim()
onex=np.linspace(axxlims[0],axxlims[1])
ax.plot(onex,lemmn*onex + lebbn, color=ccm.get_linecolor('steelblue3'),linewidth=1)
plt.legend((rr,ee,ns,obs,ledat,ledatn),('Individual SIC forcings','Average SIC forcing',
'NSIDC SIC forcing','Observations',
'historical LE', 'historicalNat LE'),
loc='best',fancybox=True,framealpha=0.5,prop=fontP)#,frameon=False)
if printtofile:
fig.savefig('scatterregress_' + field1 + region1 + str(sea1) + '_v_' +
field2 + region2 + str(sea2) + 'wacepapobs_LE.pdf')
#fig.savefig('scatterregress_' + field1 + region1 + str(sea1) + '_v_' +
# field2 + region2 + str(sea2) + 'wacepapobs_LE.eps')
# ##### JUST SIMS ONE COLOR #########
fig,ax = plt.subplots(1)
fig.set_size_inches(6,5)
rr = plt.scatter(df.filter(regex='R').values[0],df.filter(regex='R').values[1],
color='0.3',marker='o',s=8**2,alpha=0.7)
ee = plt.scatter(sub.ix[0,5:],sub.ix[1,5:],
color='0.3',marker='o',s=8**2,alpha=0.7)
#plt.scatter(df['HAD'].values[0],df['HAD'].values[1],color=cd['HAD'],marker='s',s=8**2)
#ns=plt.scatter(df['NSIDC'].values[0],df['NSIDC'].values[1],color=cd['NSIDC'],marker='o',s=8**2,alpha=0.7) # @@@@ add back?
ns=plt.scatter(df['NSIDC'].values[0],df['NSIDC'].values[1],color='green',marker='o',s=8**2,alpha=0.7)
#plt.scatter(df['ENS'].values[0],df['ENS'].values[1],color=cd['ENS'],marker='s',s=10**2)
#plt.scatter(df['ENSE'].values[0],df['ENSE'].values[1],color=cd['ENSE'],marker='o',s=10**2)
axylims = ax.get_ylim()
axxlims = ax.get_xlim()
onex=np.linspace(axxlims[0],axxlims[1])
plt.plot(onex,mm*onex + bb, color='0.5',linewidth=1)#,linestyle='--')
if addobs:
obs=plt.scatter(erareg.mean(),gisreg.mean(),color='blue',marker='s',s=8**2)
if addobs:
probs='obs' # print obs string
plt.legend((ee,ns,obs),('Modelled SIC forcings',
'NSIDC SIC forcing','Observations'),
loc='best',fancybox=True,framealpha=0.5,prop=fontP)#,frameon=False)
else:
probs=''
plt.legend((ee,ns),('Modelled SIC forcings','NSIDC SIC forcing'),
loc='best',fancybox=True,framealpha=0.5,prop=fontP)#,frameon=False)
plt.xlabel(xlab)
plt.ylabel(ylab)
axylims = ax.get_ylim()
if axylims[0]<=0 and axylims[1]>=0:
ax.axhline(y=0,color='k',linewidth=.5,linestyle='--')
axxlims = ax.get_xlim()
if axxlims[0]<=0 and axxlims[1]>=0:
ax.axvline(x=0,color='k',linewidth=.5,linestyle='--')
if printtofile:
fig.savefig('scatterregress_' + field1 + region1 + str(sea1) + '_v_' +
field2 + region2 + str(sea2) + 'wacepap' + probs + '_onecol.pdf')
fig.savefig('scatterregress_' + field1 + region1 + str(sea1) + '_v_' +
field2 + region2 + str(sea2) + 'wacepap' + probs + '_onecol.eps')
if addle:
# ### JUST LE DATA #####
fig,ax = plt.subplots(1)
fig.set_size_inches(6,5)
obs=ax.scatter(erareg.mean(),gisreg.mean(),color='blue',marker='s',s=8**2)
ledat=ax.scatter(lefld1,lefld2,color=ccm.get_linecolor('darkolivegreen3'),marker='*',s=5**2,alpha=0.5)
lemm, lebb, lerval, lepval, lestd_err = sp.stats.linregress(lefld1,lefld2)
axylims = ax.get_ylim()
axxlims = ax.get_xlim()
onex=np.linspace(axxlims[0],axxlims[1])
ax.plot(onex,lemm*onex + lebb, color=ccm.get_linecolor('darkolivegreen3'),linewidth=1)
ledatn=ax.scatter(lefld1n,lefld2n,color=ccm.get_linecolor('steelblue3'),marker='*',s=5**2,alpha=0.5)
lemmn, lebbn, lervaln, lepvaln, lestd_errn = sp.stats.linregress(lefld1n,lefld2n)
axylims = ax.get_ylim()
axxlims = ax.get_xlim()
onex=np.linspace(axxlims[0],axxlims[1])
ax.plot(onex,lemmn*onex + lebbn, color=ccm.get_linecolor('steelblue3'),linewidth=1)
plt.legend((obs,ledat,ledatn),('Observations',
'historical LE', 'historicalNat LE'),
loc='best',fancybox=True,framealpha=0.5,prop=fontP)#,frameon=False)
plt.xlabel(xlab)
plt.ylabel(ylab)
axylims = ax.get_ylim()
if axylims[0]<=0 and axylims[1]>=0:
ax.axhline(y=0,color='k',linewidth=.5,linestyle='--')
axxlims = ax.get_xlim()
if axxlims[0]<=0 and axxlims[1]>=0:
ax.axvline(x=0,color='k',linewidth=.5,linestyle='--')
if printtofile:
fig.savefig('scatterregress_' + field1 + region1 + str(sea1) + '_v_' +
field2 + region2 + str(sea2) + '_obs_LE.pdf')
# ### JUST LE HISTORICAL DATA #####
fig,ax = plt.subplots(1)
fig.set_size_inches(6,5)
obs=plt.scatter(erareg.mean(),gisreg.mean(),color='blue',marker='s',s=8**2)
ledat=plt.scatter(lefld1,lefld2,color=ccm.get_linecolor('darkolivegreen3'),marker='*',s=5**2,alpha=0.5)
lemm, lebb, lerval, lepval, lestd_err = sp.stats.linregress(lefld1,lefld2)
axylims = ax.get_ylim()
axxlims = ax.get_xlim()
onex=np.linspace(axxlims[0],axxlims[1])
ax.plot(onex,lemm*onex + lebb, color=ccm.get_linecolor('darkolivegreen3'),linewidth=1)
plt.legend((obs,ledat),('Observations','historical LE'),
loc='best',fancybox=True,framealpha=0.5,prop=fontP)#,frameon=False)
plt.xlabel(xlab)
plt.ylabel(ylab)
axylims = ax.get_ylim()
if axylims[0]<=0 and axylims[1]>=0:
ax.axhline(y=0,color='k',linewidth=.5,linestyle='--')
axxlims = ax.get_xlim()
if axxlims[0]<=0 and axxlims[1]>=0:
ax.axvline(x=0,color='k',linewidth=.5,linestyle='--')
if printtofile:
fig.savefig('scatterregress_' + field1 + region1 + str(sea1) + '_v_' +
field2 + region2 + str(sea2) + '_obs_historicalLE.pdf')
#printtofile=False
# #######################################
# SCATTER PLOT OF SIM MEANS v2 (with conf intervals)
fig,ax = plt.subplots(1) #plt.figure()
for sim in ('R1','R2','R3','R4','R5'): #,'ENS','HAD','NSIDC'):
rs = ax.scatter(df[sim].values[0],df[sim].values[1],color='k',marker='s',s=8**2,alpha=0.5)
ax.plot(cidf[sim].values[0],
(df[sim].values[1],df[sim].values[1]),
color='k',linewidth=2,marker='_',markersize=6)
ax.plot((df[sim].values[0],df[sim].values[0]),
cidf[sim].values[1],
color='k',linewidth=2,marker='_',markersize=6)
for sim in ('E1','E2','E3','E4','E5'):#,'ENSE','HAD','NSIDC'):
es = ax.scatter(df[sim].values[0],df[sim].values[1],color='0.5',marker='o',s=8**2,alpha=0.5)
ax.plot(cidf[sim].values[0],
(df[sim].values[1],df[sim].values[1]),
color='0.5',linewidth=2,marker='_',markersize=6)
ax.plot((df[sim].values[0],df[sim].values[0]),
cidf[sim].values[1],
color='0.5',linewidth=2,marker='_',markersize=6)
for sim in ('HAD',):
had = ax.scatter(df[sim].values[0],df[sim].values[1],color=cd[sim],marker='s',s=8**2,alpha=0.5)
ax.plot(cidf[sim].values[0],
(df[sim].values[1],df[sim].values[1]),
color=cd[sim],linewidth=2,marker='_',markersize=6)
ax.plot((df[sim].values[0],df[sim].values[0]),
cidf[sim].values[1],
color=cd[sim],linewidth=2,marker='_',markersize=6)
for sim in ('NSIDC',):
ns = ax.scatter(df[sim].values[0],df[sim].values[1],color=cd[sim],marker='s',s=8**2,alpha=0.5)
ax.plot(cidf[sim].values[0],
(df[sim].values[1],df[sim].values[1]),
color=cd[sim],linewidth=2,marker='_',markersize=6)
ax.plot((df[sim].values[0],df[sim].values[0]),
cidf[sim].values[1],
color=cd[sim],linewidth=2,marker='_',markersize=6)
for sim in ('ENSE',): # different marker
em = ax.scatter(df[sim].values[0],df[sim].values[1],color=cd[sim],marker='o',s=8**2,alpha=0.5)
ax.plot(cidf[sim].values[0],
(df[sim].values[1],df[sim].values[1]),
color=cd[sim],linewidth=2,marker='_',markersize=6)
ax.plot((df[sim].values[0],df[sim].values[0]),
cidf[sim].values[1],
color=cd[sim],linewidth=2,marker='_',markersize=6)
for sim in ('ENS',):
rm = ax.scatter(df[sim].values[0],df[sim].values[1],color=cd[sim],marker='s',s=8**2,alpha=0.5)
ax.plot(cidf[sim].values[0],
(df[sim].values[1],df[sim].values[1]),
color=cd[sim],linewidth=2,marker='_',markersize=6)
ax.plot((df[sim].values[0],df[sim].values[0]),
cidf[sim].values[1],
color=cd[sim],linewidth=2,marker='_',markersize=6)
plt.legend((rs,es,had,ns,rm,em),
('TOT','ANT','HAD','NSIDC','$\overline{TOT}$','$\overline{ANT}$'),
loc='best',fancybox=True,framealpha=0.5)
plt.xlabel(str(sea1) + ' ' + field1 + ' ' + region1)
plt.ylabel(str(sea2) + ' ' + field2 + ' ' + region2)
axylims = ax.get_ylim()
if axylims[0]<=0 and axylims[1]>=0:
ax.axhline(y=0,color='k',linewidth=.5)
axxlims = ax.get_xlim()
if axxlims[0]<=0 and axxlims[1]>=0:
ax.axvline(x=0,color='k',linewidth=.5)
if printtofile:
fig.savefig('scatterCI_' + field1 + region1 + str(sea1) + '_v_' + field2 + region2 + str(sea2) + '.pdf')
## fig,ax = plt.subplots(1) #plt.figure()
## plt.scatter(dfts['R4'].values[0],dfts['R4'].values[1],color=cd['R4'],marker='s',s=3**2,alpha=0.5)
## plt.scatter(dfts['R3'].values[0],dfts['R3'].values[1],color=cd['R3'],marker='s',s=3**2,alpha=0.5)
## plt.scatter(dfts['ENS'].values[0],dfts['ENS'].values[1],color=cd['ENS'],marker='s',s=3**2,alpha=0.5)
## plt.scatter(dfts['ENSE'].values[0],dfts['ENSE'].values[1],color=cd['ENSE'],marker='s',s=3**2,alpha=0.5)
## plt.scatter(df['R4'].values[0],df['R4'].values[1],color=cd['R4'],marker='s',s=8**2,edgecolor='k',alpha=0.3)
## plt.scatter(df['R3'].values[0],df['R3'].values[1],color=cd['R3'],marker='s',s=8**2,edgecolor='k',alpha=0.3)
## plt.scatter(df['ENS'].values[0],df['ENS'].values[1],color=cd['ENS'],marker='s',s=8**2,edgecolor='k',alpha=0.3)
## plt.scatter(df['ENSE'].values[0],df['ENSE'].values[1],color=cd['ENSE'],marker='s',s=8**2,edgecolor='k',alpha=0.3)
## plt.xlabel(sea1 + ' ' + field1 + ' ' + region1)
## plt.ylabel(sea2 + ' ' + field2 + ' ' + region2)
## axylims = ax.get_ylim()
## if axylims[0]<=0 and axylims[1]>=0:
## ax.axhline(y=0,color='k',linewidth=.5)
## axxlims = ax.get_xlim()
## if axxlims[0]<=0 and axxlims[1]>=0:
## ax.axvline(x=0,color='k',linewidth=.5)
# SCATTER PLOT IN TIME AND SIM
fig,axs = plt.subplots(1,2,sharey=True,sharex=True) #plt.figure()
fig.set_size_inches(9,5)
ax=axs[0]
for sim in ('R1','R2','R3','R4','R5','ENS'):#,'HAD','NSIDC'):
ax.scatter(dfts[sim].values[0],dfts[sim].values[1],color=cd[sim],marker='s',s=3**2,alpha=0.5)
for sim in ('R1','R2','R3','R4','R5','ENS'): # means
ax.scatter(df[sim].values[0],df[sim].values[1],color=cd[sim],marker='s',s=8**2,edgecolor='k',alpha=0.7)
ax.set_xlabel(str(sea1) + ' ' + field1 + ' ' + region1)
ax.set_ylabel(str(sea2) + ' ' + field2 + ' ' + region2)
if axylims[0]<=0 and axylims[1]>=0:
ax.axhline(y=0,color='k',linewidth=.5)
if axxlims[0]<=0 and axxlims[1]>=0:
ax.axvline(x=0,color='k',linewidth=.5)
ax.set_title('TOT')
ax=axs[1]
for sim in ('E1','E2','E3','E4','E5','ENSE'):#,'HAD','NSIDC'):
ax.scatter(dfts[sim].values[0],dfts[sim].values[1],color=cd[sim],marker='s',s=3**2,alpha=0.5)
for sim in ('E1','E2','E3','E4','E5','ENSE'):
ax.scatter(df[sim].values[0],df[sim].values[1],color=cd[sim],marker='s',s=8**2,edgecolor='k',alpha=0.7)
axylims = ax.get_ylim()
axxlims = ax.get_xlim()
onex=np.linspace(axxlims[0],axxlims[1])
ax=axs[0]
for sim in ('R1','R2','R3','R4','R5','ENS'):#,'HAD','NSIDC'):
mm, bb, rval, pval, std_err = sp.stats.linregress(dfts[sim].values[0],dfts[sim].values[1])
#mm, bb = np.polyfit(dfts[sim].values[0],dfts[sim].values[1], 1)
ax.plot(onex,mm*onex + bb, color=cd[sim],linewidth=2)#,linestyle='--')
#rr = np.corrcoef(dfts[sim].values[0],dfts[sim].values[1] )[0,1]
rsq = rval**2
print 'TOT: R: ' + str(rval) + ', R squared: ' + str(rsq) + ', SLOPE: ' + str(mm) + ', PVAL: ' + str(pval)
## val = '$%.2f$'%(rsq)
## ax.annotate('$R^2$= ' + val, xy=(axxlims[0]+.1*axxlims[1], axylims[1]-.2*axylims[1]),
## xycoords='data') # upper left?
ax=axs[1]
for sim in ('E1','E2','E3','E4','E5','ENSE'):#,'HAD','NSIDC'):
mm, bb, rval, pval, std_err = sp.stats.linregress(dfts[sim].values[0],dfts[sim].values[1])
#mm, bb = np.polyfit(dfts[sim].values[0],dfts[sim].values[1], 1)
ax.plot(onex,mm*onex + bb, color=cd[sim],linewidth=2)#,linestyle='--')
#rr = np.corrcoef(dfts[sim].values[0],dfts[sim].values[1] )[0,1]
rsq = rval**2
print 'ANT: R: ' + str(rval) + ', R squared: ' + str(rsq) + ', SLOPE: ' + str(mm) + ', PVAL: ' + str(pval)
ax.set_xlabel(str(sea1) + ' ' + field1 + ' ' + region1)
ax.set_ylabel(str(sea2) + ' ' + field2 + ' ' + region2)
axylims = ax.get_ylim()
if axylims[0]<=0 and axylims[1]>=0:
ax.axhline(y=0,color='k',linewidth=.5)
axxlims = ax.get_xlim()
if axxlims[0]<=0 and axxlims[1]>=0:
ax.axvline(x=0,color='k',linewidth=.5)
ax.set_title('ANT')
if printtofile:
fig.savefig('scatterregress_' + field1 + region1 + str(sea1) +\
'_v_' + field2 + region2 + str(sea2) + '_ANTTOTobssbplt.pdf')
#flddregdf = pd.DataFrame(flddregdt,i)
## plt.figure()
## cplt.kemscatter(flddreg,flddreg2)
## plt.xlabel(region1)
## plt.ylabel(region2)
## plt.title(sea)
# ###### HISTOGRAMS #########
print 'HISTOGRAMS' # @@@
print allantr2.shape
## fig,ax=plt.subplots(1,1)
## nna,binsa,patchesa = ax.hist(allantr2,color='.5',alpha=0.5,normed=True,histtype='stepfilled')#,orientation='horizontal')#,cumulative=True)
## nnt, binst, patchest = ax.hist(alltotr2,color='b',alpha=0.5,normed=True,histtype='stepfilled')#,orientation='horizontal')#,cumulative=True)
from scipy.stats import norm
## anormed=norm.fit(allantr2)
## amean=anormed[0]
## asd=anormed[1]
## #Generate X points
## axlims = [-6*asd+amean, 6*asd+amean] # large limits
## axx = np.linspace(axlims[0],axlims[1],500)
## #Get Y points via Normal PDF with fitted parameters
## apdf_fitted = norm.pdf(axx,loc=amean,scale=asd)
## tnormed=norm.fit(alltotr2)
## tmean=tnormed[0]
## tsd=tnormed[1]
## #Generate X points
## txlims = [-6*tsd+tmean, 6*tsd+tmean] # large limits
## txx = np.linspace(txlims[0],txlims[1],500)
## #Get Y points via Normal PDF with fitted parameters
## tpdf_fitted = norm.pdf(txx,loc=tmean,scale=tsd)
## ant=ax.plot(axx,apdf_fitted,'k')
## tot=ax.plot(txx,tpdf_fitted,'b')
## ax.set_xlabel((field2 + region2 + '_' + str(sea2)))
## ax.set_title('Anomalies')
## ax.legend(('ANT','TOT'))
## if printtofile:
## fig.savefig('histpdf_' + (field2 + region2 + '_' + str(sea2)) + '_ANTTOTanom.pdf')
## print 'ANT: ' + str(anormed)
## print 'TOT: ' + str(tnormed)
def plot_anttotanom_histpdf(antdata,totdata,printtofile=False,label=''):
fig,ax=plt.subplots(1,1)
nna,binsa,patchesa = ax.hist(antdata,color='.5',alpha=0.5,normed=True,histtype='stepfilled')#,orientation='horizontal')#,cumulative=True)
nnt, binst, patchest = ax.hist(totdata,color='b',alpha=0.5,normed=True,histtype='stepfilled')#,orientation='horizontal')#,cumulative=True)
anormed=norm.fit(antdata)
amean=anormed[0]
asd=anormed[1]
#Generate X points
axlims = [-4*asd+amean, 4*asd+amean] # large limits
axx = np.linspace(axlims[0],axlims[1],500)
#Get Y points via Normal PDF with fitted parameters
apdf_fitted = norm.pdf(axx,loc=amean,scale=asd)
tnormed=norm.fit(totdata)
tmean=tnormed[0]
tsd=tnormed[1]
#Generate X points
txlims = [-4*tsd+tmean, 4*tsd+tmean] # large limits
txx = np.linspace(txlims[0],txlims[1],500)
#Get Y points via Normal PDF with fitted parameters
tpdf_fitted = norm.pdf(txx,loc=tmean,scale=tsd)
ax.plot(axx,apdf_fitted,'k')
ax.plot(txx,tpdf_fitted,'b')
ax.set_xlabel(label)
ax.set_title('Anomalies')
ax.legend(('ANT','TOT'))
if printtofile:
fig.savefig('histpdf_' + label + '_ANTTOTanom.pdf')
print '\n'
print label
print 'ANT (mean anom, anom std): ' + str(anormed)
print 'TOT (mean anom, anom std): ' + str(tnormed)
def plot_anttotsbplt_histpdf(antdata,totdata,printtofile=False,label=''):
""" antdata and totdata must be tuples of CTL, PERT !!
"""
allantc=antdata[0]
allantp=antdata[1]
alltotc=totdata[0]
alltotp=totdata[1]
antcfld = allantc-np.mean(allantc)
antpfld = allantp-np.mean(allantc) # subtract its own mean or control mean??
print '\n'
print label
print 'antcfld mean ' + str(antcfld.mean())
print 'antpfld mean ' + str(antpfld.mean())
totcfld = alltotc-np.mean(alltotc)
totpfld = alltotp-np.mean(alltotc)
fig,axs=plt.subplots(1,2) ########### SUBPLOT
fig.set_size_inches(10,5)
ax=axs[0]
nnac,binsac,patchesac = ax.hist(antcfld,color='.5',alpha=0.5,normed=True,histtype='stepfilled')#,orientation='horizontal')#,cumulative=True)
nnap, binsap, patchesap = ax.hist(antpfld,color='r',alpha=0.5,normed=True,histtype='stepfilled')#,orientation='horizontal')#,cumulative=True)
acnormed=norm.fit(antcfld)
amean=acnormed[0]
asd=acnormed[1]
#Generate X points
axlims = [-4*asd+amean, 4*asd+amean] # large limits
axx = np.linspace(axlims[0],axlims[1],500)
#Get Y points via Normal PDF with fitted parameters
acpdf_fitted = norm.pdf(axx,loc=amean,scale=asd)
apnormed=norm.fit(antpfld)
apmean=apnormed[0]
apsd=apnormed[1]
#Generate X points
apxlims = [-4*apsd+apmean, 4*apsd+apmean] # large limits
apxx = np.linspace(apxlims[0],apxlims[1],500)
#Get Y points via Normal PDF with fitted parameters
appdf_fitted = norm.pdf(apxx,loc=apmean,scale=apsd)
ax.plot(axx,acpdf_fitted,'k')
ax.plot(apxx,appdf_fitted,'r')
ax.set_xlabel(label)
ax.set_title('ANT')
ax.legend(('CTL','PERT'))
print label
print 'ANT CTL (mean,std): ' + str(acnormed)
print 'ANT PERT (mean,std): ' + str(apnormed)
ax=axs[1]
nntc, binstc, patchestc = ax.hist(totcfld,color='0.5',alpha=0.5,normed=True,histtype='stepfilled')
nntp, binstp, patchestp = ax.hist(totpfld,color='r',alpha=0.5,normed=True,histtype='stepfilled')
tcnormed=norm.fit(totcfld)