def plotGrossEnsembles():
outPng = 'wlRelChngGrossEnsembles.png'
f = plt.figure(figsize=(9,8))
gs = gridspec.GridSpec(2, 3, width_ratios=[1,1,.05])
mp = None
warmingLev = 1.5
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(warmingLev=warmingLev)
rc_mega = (rc_r8 + rc_r4)/2.
ax0 = plt.subplot(gs[0,0])
pcl, mp = plotRelChngDiff(ax0, rc_mega, mp, 'a: rcp all, rel. chng. at $' + str(warmingLev) +'^\circ$', vmax=30)
sigma_im = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
sigmaT = getTimeSigmaGrossEnsemble(warmingLev)
sigma = np.sqrt(sigma_im**2. + sigmaT**2.)
sigma_ratio = sigma/rc_mega
ax1 = plt.subplot(gs[1,0])
pcl, mp = plotSigma(ax1, sigma_ratio, None, mp, 'c: $\sigma$, % of rel. chng. at $' + str(warmingLev) +'^\circ$', sigmamax=2,
prcTxtTmpl = '% of pixel where ${thr}\|\Delta d_{{100-wl}}\| > \sigma$: {p:2.2f}%')
warmingLev = 2.0
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(warmingLev=warmingLev)
rc_mega = (rc_r8 + rc_r4)/2.
ax2 = plt.subplot(gs[0,1])
pclChng, mp = plotRelChngDiff(ax2, rc_mega, mp, 'b: rcp all, rel. chng. at $' + str(warmingLev) +'^\circ$', vmax=30)
sigma_im = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
sigmaT = getTimeSigmaGrossEnsemble(warmingLev)
sigma = np.sqrt(sigma_im**2. + sigmaT**2.)
sigma_ratio = sigma/rc_mega
ax3 = plt.subplot(gs[1,1])
pclSigma, mp = plotSigma(ax3, sigma_ratio, None, mp, 'd: $\sigma$, % of rel. chng. at $' + str(warmingLev) +'^\circ$', sigmamax=2,
prcTxtTmpl = '% of pixel where ${thr}\|\Delta d_{{100-wl}}\| > \sigma$: {p:2.2f}%')
cax1 = plt.subplot(gs[0,2])
cb = plt.colorbar(pclChng, ax=ax2, cax=cax1)
cb.set_label('$\Delta$ 100-y discharge (%)')
cax2 = plt.subplot(gs[1,2])
cb = plt.colorbar(pclSigma, ax=ax3, cax=cax2)
cb.set_label('$\sigma$ (fraction of relative change)')
ax0.set_aspect('auto')
ax1.set_aspect('auto')
ax2.set_aspect('auto')
ax3.set_aspect('auto')
cax1.set_aspect('auto')
cax2.set_aspect('auto')
plt.tight_layout()
f.savefig(outPng, dpi=300)
def plotFigureWlVsScenChange():
outPng = 'wlRelChngScenVsScen.png'
f = plt.figure(figsize=(8.5, 12))
gs = gridspec.GridSpec(3, 3, width_ratios=[1,1,.5/8.5])
mp = None
ax0 = plt.subplot(gs[0,0])
relChngDiff, _ = ldEnsmbl.loadWlVsScenChange(warmingLev=1.5)
pcl, mp = plotRelChngDiff(ax0, relChngDiff, mp, 'a: $\Delta RCP85 - \Delta RCP45$, w.l. $1.5^\circ$')
ax = plt.subplot(gs[0,1])
relChngDiff, _ = ldEnsmbl.loadWlVsScenChange(warmingLev=2)
pcl, mp = plotRelChngDiff(ax, relChngDiff, mp, 'b: $\Delta RCP85 - \Delta RCP45$, w.l. $2.0^\circ$')
cax = plt.subplot(gs[0,2])
cb = plt.colorbar(pcl, ax=ax, cax=cax)
cb.set_label('$\Delta RCP85 - \Delta RCP45$ (%)')
ax0.set_aspect('auto')
ax.set_aspect('auto')
cax.set_aspect('auto')
ax0 = plt.subplot(gs[1,0])
pValue, agrMdlCnt15, std15 = estimateChngSignificanceAndRobustness.computeRlChngPValueAtWarmingLevBtwScen(warmingLev=1.5)
pcl, mp = plotPvalue(ax0, pValue, relChngDiff, mp, 'c: p-value, w.l. $1.5^\circ$')
ax = plt.subplot(gs[1,1])
pValue, agrMdlCnt20, std20 = estimateChngSignificanceAndRobustness.computeRlChngPValueAtWarmingLevBtwScen(warmingLev=2)
pcl, mp = plotPvalue(ax, pValue, relChngDiff, mp, 'd: p-value. w.l. $2.0^\circ$')
cax = plt.subplot(gs[1,2])
cb = plt.colorbar(pcl, ax=ax, cax=cax)
cb.set_label('p-value')
ax0.set_aspect('auto')
ax.set_aspect('auto')
cax.set_aspect('auto')
ax0 = plt.subplot(gs[2,0])
pcl, mp = plotAgreeingMdlCnt(ax0, agrMdlCnt15, mp, 'e: agr. mdl. count $1.5^\circ$')
ax = plt.subplot(gs[2,1])
pcl, mp = plotAgreeingMdlCnt(ax, agrMdlCnt20, mp, 'f: agr. mdl.c ount $2.0^\circ$')
cax = plt.subplot(gs[2,2])
cb = plt.colorbar(pcl, ax=ax, cax=cax)
cb.set_label('agreeing mdl. count')
ax0.set_aspect('auto')
ax.set_aspect('auto')
cax.set_aspect('auto')
plt.tight_layout()
f.savefig(outPng, dpi=300)
def plotScenVsScen(warmingLev=2.0):
outPng = 'wlRelChngScenVsScen_wl' + str(warmingLev) + '.png'
f = plt.figure(figsize=(13, 8))
gs = gridspec.GridSpec(2, 4, width_ratios=[1,1,1,1./12.])
mp = None
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(warmingLev=warmingLev)
ax0 = plt.subplot(gs[0,0])
cmap = 'bwr_r'
pcl, mp = plotRelChngDiff(ax0, rc_r8, mp, 'a: RCP85 - hist., w.l. $' + str(warmingLev) +'^\circ$', vmax=30, cmap=cmap)
ax1 = plt.subplot(gs[0,1])
pcl, mp = plotRelChngDiff(ax1, rc_r4, mp, 'b: RCP45 - hist., w.l. $' + str(warmingLev) +'^\circ$', vmax=30, cmap=cmap)
ax2 = plt.subplot(gs[0,2])
pcl, mp = plotRelChngDiff(ax2, relChngDiff, mp, 'c: $\Delta RCP85 - \Delta RCP45$', vmax=30, cmap=cmap)
cax = plt.subplot(gs[0,3])
cb = plt.colorbar(pcl, ax=ax2, cax=cax)
cb.set_label('$\Delta$ 100-y discharge (%)')
ax0.set_aspect('auto')
ax1.set_aspect('auto')
ax2.set_aspect('auto')
cax.set_aspect('auto')
ax0 = plt.subplot(gs[1,0])
pValue, _, sigma_im = estimateChngSignificanceAndRobustness.computeRlChngPValueAtWarmingLev(scen='rcp85', warmingLev=warmingLev)
std = sigma_im/np.abs(rc_r8)
#pcl, mp = plotPvalue(ax0, pValue, None, mp, 'd: p-value, $\Delta rcp85$')
pcl, mp = plotSigma(ax0, std, None, mp, 'd: $\sigma_{im}$, ratio of $\Delta RCP85$', sigmamax=2.)
ax1 = plt.subplot(gs[1,1])
pValue, _, sigma_im = estimateChngSignificanceAndRobustness.computeRlChngPValueAtWarmingLev(scen='rcp45', warmingLev=warmingLev)
std = sigma_im/np.abs(rc_r4)
#pcl, mp = plotPvalue(ax1, pValue, None, mp, 'e: p-value, $\Delta rcp45$')
pcl, mp = plotSigma(ax1, std, None, mp, 'e: $\sigma_{im}$, ratio of $\Delta RCP45$', sigmamax=2)
ax2 = plt.subplot(gs[1,2])
#pValue, _, _ = estimateChngSignificanceAndRobustness.computeRlChngPValueAtWarmingLevBtwScen(warmingLev=warmingLev)
#pcl, mp = plotPvalue(ax2, pValue, relChngDiff, mp, 'f: p-value, $\Delta RCP85 - \Delta RCP45$')
std = np.std(rc_r8all-rc_r4all, 0)
std = std/np.abs(rc_r8)
pcl, mp = plotSigma(ax2, std, relChngDiff, mp, 'f: $\sigma_{im}$, $\Delta RCP85 - \Delta RCP45$', sigmamax=2, printSignTxt=False)
cax = plt.subplot(gs[1,3])
cb = plt.colorbar(pcl, ax=ax2, cax=cax)
#cb.set_label('p-value')
cb.set_label('$\sigma_{im}$ (fraction of relative change)')
ax0.set_aspect('auto')
ax1.set_aspect('auto')
ax2.set_aspect('auto')
cax.set_aspect('auto')
plt.tight_layout()
f.savefig(outPng, dpi=300)
def plotEnsembleStaticVsDynamicDiffMin():
import pdb; pdb.set_trace()
cacheFlPth = 'staticVsDynamicDiffMin.pkl'
figFlNamePattern = 'staticVsDynamicDifMin_{wl}.png'
if os.path.isfile(cacheFlPth):
fl = open(cacheFlPth)
relChngDiffChng15, rlR8Chng15, rlR4Chng15, rlR8AllChng15, rlR4AllChng15,\
relChngDiffCnst15, rlR8Cnst15, rlR4Cnst15, rlR8AllCnst15, rlR4AllCnst15,\
relChngDiffChng20, rlR8Chng20, rlR4Chng20, rlR8AllChng20, rlR4AllChng20,\
relChngDiffCnst20, rlR8Cnst20, rlR4Cnst20, rlR8AllCnst20, rlR4AllCnst20\
= pickle.load(fl)
fl.close()
else:
relChngDiffChng15, rlR8Chng15, rlR4Chng15, rlR8AllChng15, rlR4AllChng15 = ldEnsmbl.loadWlVsScenChange(warmingLev=1.5, retPer=15,
threshold=1., rlVarName='rl_min', flpattern='projection_dis_{scen}_{mdl}_wuChang_statistics.nc')
relChngDiffCnst15, rlR8Cnst15, rlR4Cnst15, rlR8AllCnst15, rlR4AllCnst15 = ldEnsmbl.loadWlVsScenChange(warmingLev=1.5, retPer=15,
threshold=1., rlVarName='rl_min', flpattern='projection_dis_{scen}_{mdl}_wuConst_statistics.nc')
relChngDiffChng20, rlR8Chng20, rlR4Chng20, rlR8AllChng20, rlR4AllChng20 = ldEnsmbl.loadWlVsScenChange(warmingLev=2.0, retPer=15,
threshold=1., rlVarName='rl_min', flpattern='projection_dis_{scen}_{mdl}_wuChang_statistics.nc')
relChngDiffCnst20, rlR8Cnst20, rlR4Cnst20, rlR8AllCnst20, rlR4AllCnst20 = ldEnsmbl.loadWlVsScenChange(warmingLev=2.0, retPer=15,
threshold=1., rlVarName='rl_min', flpattern='projection_dis_{scen}_{mdl}_wuConst_statistics.nc')
cacheLst = [
relChngDiffChng15, rlR8Chng15, rlR4Chng15, rlR8AllChng15, rlR4AllChng15,
relChngDiffCnst15, rlR8Cnst15, rlR4Cnst15, rlR8AllCnst15, rlR4AllCnst15,
relChngDiffChng20, rlR8Chng20, rlR4Chng20, rlR8AllChng20, rlR4AllChng20,
relChngDiffCnst20, rlR8Cnst20, rlR4Cnst20, rlR8AllCnst20, rlR4AllCnst20]
fl = open(cacheFlPth, 'w')
pickle.dump(cacheLst, fl)
fl.close()
mp = None
fig15, mp = plotFigureDiff(rlR8Cnst15, rlR8Chng15, rlR4Cnst15, rlR4Chng15, 'Dynamic vs static w.u., $1.5^\circ$. % pts', mp=mp)
fig20, mp = plotFigureDiff(rlR8Cnst20, rlR8Chng20, rlR4Cnst20, rlR4Chng20, 'Dynamic vs static w.u., $2.0^\circ$. % pts', mp=mp)
fig15.savefig(figFlNamePattern.format(wl='15'), dpi=300)
fig20.savefig(figFlNamePattern.format(wl='20'), dpi=300)
plt.show()
def printStatsByGrossEnsemble(warmingLev=2.0):
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(warmingLev=warmingLev)
rc_mega = (rc_r8 + rc_r4)/2.
sigma_im = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
sigmaT = getTimeSigmaGrossEnsemble(warmingLev)
sigma_rcpdiff = np.abs(relChngDiff)/2.
sigma_im0 = np.sqrt(sigma_im**2. - sigma_rcpdiff**2.)
sigma2 = sigma_im**2. + sigmaT**2.
sigma_im0_ratio = sigma_im0**2./sigma2
sigmaT_ratio = sigmaT**2./sigma2
sigma_rcpdiff_ratio = sigma_rcpdiff**2./sigma2
print('% sigma_im0**2: ' + str(np.nanmean(sigma_im0_ratio)*100))
print('% sigma_t**2: ' + str(np.nanmean(sigmaT_ratio)*100))
print('% sigma_rcpdiff**2: ' + str(np.nanmean(sigma_rcpdiff_ratio)*100))
def plotTimeUncertaintyAndSigmaDiffRatio():
def plotTimeSigmaVsDifference(ax, pdf, mp, relChngDiff, text):
stdev = pdfTot.std()
mn = pdfTot.mean()
sdvmin = int(np.round(mn - stdev))
sdvmax = int(np.round(mn + stdev))
rlChngInf = ldEnsmbl.getGrossEnsembleAtYear(sdvmin)
rlChngSup = ldEnsmbl.getGrossEnsembleAtYear(sdvmax)
sigmaT = np.abs(rlChngSup - rlChngInf) / 2.
ratio = sigmaT / np.abs(relChngDiff)
sgm_ = ratio[~np.isnan(ratio)]
print('mean time sigma: ' + str(np.mean(sgm_)))
print('p60 time sigma: ' + str(np.percentile(sgm_, 60)))
print('p70 time sigma: ' + str(np.percentile(sgm_, 70)))
print('p80 time sigma: ' + str(np.percentile(sgm_, 80)))
print('p90 time sigma: ' + str(np.percentile(sgm_, 90)))
print('p99 time sigma: ' + str(np.percentile(sgm_, 99)))
print('p99.1 time sigma: ' + str(np.percentile(sgm_, 99.1)))
print('p99.2 time sigma: ' + str(np.percentile(sgm_, 99.2)))
print('p99.3 time sigma: ' + str(np.percentile(sgm_, 99.3)))
print('p99.4 time sigma: ' + str(np.percentile(sgm_, 99.4)))
print('p99.5 time sigma: ' + str(np.percentile(sgm_, 99.5)))
print('p99.6 time sigma: ' + str(np.percentile(sgm_, 99.6)))
print('p99.7 time sigma: ' + str(np.percentile(sgm_, 99.7)))
print('p99.8 time sigma: ' + str(np.percentile(sgm_, 99.8)))
print('p99.9 time sigma: ' + str(np.percentile(sgm_, 99.9)))
print('p1 time sigma: ' + str(np.percentile(sgm_, 1)))
return plotRelChngDiff(ax,
ratio / 100.,
mp,
text,
cmap='RdBu_r',
vmin=0,
vmax=2)
outPng = 'timeSigma_grossEnsemble_sigmaDiffRatio.png'
fig = plt.figure(figsize=[15, 12])
gc = GridSpec(3, 7, width_ratios=[1, 1, 1, 1, 1, 1, .1])
# plotting the time distribution + maps of time-related sigma
ax00 = plt.subplot(gc[0, :4])
pdfTot, pdfR8, pdfR4 = gwl.getWarmingLevelMixDistributions(1.5)
plotPdfs(ax00,
pdfR8,
pdfR4,
pdfTot,
'a: time pdf, warming level $1.5^\circ$',
showLegend=True)
ax00.set_xticklabels([])
ax01 = plt.subplot(gc[0, 4:6])
mp = None
pcl, mp = plotUncertaintyMap(
ax01, pdfTot, mp, 'b: $\sigma$ of relative change, w.l. $1.5^\circ$')
pdf15 = pdfTot
ax10 = plt.subplot(gc[1, :4])
pdfTot, pdfR8, pdfR4 = gwl.getWarmingLevelMixDistributions(2.0)
plotPdfs(ax10,
pdfR8,
pdfR4,
pdfTot,
'c: time pdf, warming level $2.0^\circ$',
showLegend=False)
ax11 = plt.subplot(gc[1, 4:6])
pcl, mp = plotUncertaintyMap(
ax11, pdfTot, mp, 'd: $\sigma$ of relative change, w.l. $2.0^\circ$')
pdf20 = pdfTot
cax = plt.subplot(gc[:2, 6])
cb = plt.colorbar(pcl, ax=ax11, cax=cax)
cb.set_label('$\sigma_{ywl}$ (ratio of % change)', fontsize=14)
cax.tick_params(labelsize=14, rotation=90)
# plotting the ratio time-sigma/scenarios difference
ax20 = plt.subplot(gc[2, 2:4])
relChngDiff = ldEnsmbl.loadWlVsScenChange(warmingLev=1.5)[0]
pcl, mp = plotTimeSigmaVsDifference(
ax20, pdf15, mp, relChngDiff,
'e: $\sigma_{ywl}/(\Delta rcp85- \Delta rcp45)$ at $1.5^\circ$')
ax21 = plt.subplot(gc[2, 4:6])
relChngDiff = ldEnsmbl.loadWlVsScenChange(warmingLev=2.0)[0]
pcl, mp = plotTimeSigmaVsDifference(
ax21, pdf15, mp, relChngDiff,
'f: $\sigma_{ywl}/(\Delta rcp85 -\Delta rcp45)$ at $2.0^\circ$')
cax2 = plt.subplot(gc[2, 6])
cb = plt.colorbar(pcl, ax=ax21, cax=cax2, ticks=[0., .5, 1., 1.5, 2.])
cb.set_label('$\sigma_{ywl}/(\Delta rcp85 - \Delta rcp45)$', fontsize=13)
cax2.tick_params(labelsize=11, rotation=90)
ax00.set_aspect('auto')
ax01.set_aspect('auto')
ax10.set_aspect('auto')
ax11.set_aspect('auto')
ax20.set_aspect('auto')
ax21.set_aspect('auto')
cax.set_aspect('auto')
cax2.set_aspect('auto')
plt.tight_layout()
fig.savefig(outPng, dpi=300)
def plotEnglandPoints():
outPng = 'englandPoints_ANOVA.png'
warmingLev = 2.0
relChngDiffMean, rc_r8_mean, rc_r4_mean, rc_r8all_mean, rc_r4all_mean = ldEnsmbl.loadMeanChangesAtWl(
ncDir=ncDir, warmingLev=warmingLev, nmodels=nmodels)
_, _, _, pValueMean, _, _ = anovaAnalysis(relChngDiffMean, rc_r8_mean,
rc_r4_mean, rc_r8all_mean,
rc_r4all_mean)
relChngDiffExt, rc_r8_ext, rc_r4_ext, rc_r8all_ext, rc_r4all_ext = ldEnsmbl.loadWlVsScenChange(
ncDir=ncDir,
warmingLev=warmingLev,
nmodels=nmodels,
rlVarName='rl',
retPer=100)
_, _, _, pValueExt, _, _ = anovaAnalysis(relChngDiffMean, rc_r8_mean,
rc_r4_mean, rc_r8all_mean,
rc_r4all_mean)
dslonlat = netCDF4.Dataset('lonlat.nc')
lon = dslonlat.variables['lon'][:].transpose()
lat = dslonlat.variables['lat'][:].transpose()
dslonlat.close()
cndlonlat = np.logical_and(lon < 2, lat > 50)
cndlonlatMtx = np.tile(cndlonlat, [rc_r8all_mean.shape[0], 1, 1])
cndpval = pValueMean <= .05
cndpvalMtx = np.tile(pvalcnd, [rc_r8all_mean.shape[0], 1, 1])
cnd = np.logical_and(cndpvalMtx, cndlonlatMtx)
rc_r8all_mean[~cnd] = np.nan
rc_r4all_mean[~cnd] = np.nan
shp = rc_r4all_mean.shape
rc_r8_ = rc_r8all_mean.reshape([shp[0], shp[1] * shp[2]])
rc_r4_ = rc_r4all_mean.reshape([shp[0], shp[1] * shp[2]])
mn_r8_mean = np.nanmean(rc_r8_, 1) * 100
mn_r4_mean = np.nanmean(rc_r4_, 1) * 100
rc_r8all_ext[~cnd] = np.nan
rc_r4all_ext[~cnd] = np.nan
shp = rc_r4all_ext.shape
rc_r8_ = rc_r8all_ext.reshape([shp[0], shp[1] * shp[2]])
rc_r4_ = rc_r4all_ext.reshape([shp[0], shp[1] * shp[2]])
mn_r8_ext = np.nanmean(rc_r8_, 1) * 100
mn_r4_ext = np.nanmean(rc_r4_, 1) * 100
f = plt.figure(figsize=(7, 3))
gs = gridspec.GridSpec(1, 2)
vv = np.concatenate([mn_r4_mean, mn_r8_mean, mn_r4_ext, mn_r8_ext], axis=0)
ylm = [np.min(vv) - 1, np.max(vv) + 1]
ax0 = plt.subplot(gs[0])
ones = np.ones(mn_r8_mean.shape)
plt.plot(ones / 3., mn_r4_mean, 'o', color='green', label='RCP4.5')
plt.plot(ones * 2. / 3., mn_r8_mean, 'o', color='navy', label='RCP8.5')
plt.xlim([0, 1])
plt.ylim(ylm)
plt.ylabel('projected change at $2^\circ C$ (%)')
plt.xticks([])
plt.legend(loc='lower right')
plt.text(.05, 17, 'a: mean dis.', fontsize=11)
plt.grid('on')
ax1 = plt.subplot(gs[1])
ones = np.ones(mn_r8_ext.shape)
plt.plot(ones / 3., mn_r4_ext, 'o', color='green', label='RCP4.5')
plt.plot(ones * 2. / 3., mn_r8_ext, 'o', color='navy', label='RCP8.5')
plt.xlim([0, 1])
plt.ylim(ylm)
ax1.set_yticklabels('')
plt.xticks([])
plt.text(.05, 17, 'b: extreme high dis.', fontsize=11)
plt.grid('on')
plt.tight_layout()
f.savefig(outPng, dpi=300)
def plotFigureANOVA(varType='extHigh', ncDir='/ClimateRun4/multi-hazard/eva'):
#varType can be extHigh, extLow, mean
outPng = 'anova_' + varType + '.png'
f = plt.figure(figsize=(9, 12))
gs = gridspec.GridSpec(3, 3, width_ratios=[1, 1, .05])
mp = None
#### at 1.5 ####
warmingLev = 1.5
if varType == 'mean':
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadMeanChangesAtWl(
ncDir=ncDir, warmingLev=warmingLev, nmodels=nmodels)
descrTxt = 'Means'
sigmaMax = 25
elif varType == 'extHigh':
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(
ncDir=ncDir,
warmingLev=warmingLev,
nmodels=nmodels,
rlVarName='rl',
retPer=100,
shapeParamNcVarName='shape_fit')
descrTxt = 'High extremes'
sigmaMax = 30
elif varType == 'extLow':
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(
ncDir=ncDir,
warmingLev=warmingLev,
nmodels=nmodels,
rlVarName='rl_min',
retPer=15,
threshold=.1,
shapeParamNcVarName='')
descrTxt = 'Low extremes'
sigmaMax = 50
sigmaTot, sigmaWithin, sigmaBetween, pValue, effectSize, pValueTtest = anovaAnalysis(
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all)
printAnovaStats(sigmaTot, sigmaWithin, sigmaBetween, pValue, pValueTtest)
ax00 = plt.subplot(gs[0, 0])
plt00, mp = plotSigma(ax00,
sigmaWithin * 100,
mp,
'a: $\sigma_{within}, 1.5^\circ C$',
txt2=descrTxt,
sigmamax=sigmaMax)
ax10 = plt.subplot(gs[1, 0])
plt10, mp = plotSigma(ax10,
sigmaBetween * 100,
mp,
'c: $\sigma_{between}, 1.5^\circ C$',
sigmamax=sigmaMax)
ax20 = plt.subplot(gs[2, 0])
_, _, mp = plotPvalue(ax20, pValue, mp, 'e: p-val of interpathway diff.')
#### at 2.0 ####
warmingLev = 2.0
if varType == 'mean':
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadMeanChangesAtWl(
ncDir=ncDir, warmingLev=warmingLev, nmodels=nmodels)
elif varType == 'extHigh':
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(
ncDir=ncDir,
warmingLev=warmingLev,
nmodels=nmodels,
rlVarName='rl',
retPer=100)
elif varType == 'extLow':
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(
ncDir=ncDir,
warmingLev=warmingLev,
nmodels=nmodels,
rlVarName='rl_min',
retPer=15,
threshold=.1)
sigmaTot, sigmaWithin, sigmaBetween, pValue, effectSize, pValueTtest = anovaAnalysis(
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all)
printAnovaStats(sigmaTot, sigmaWithin, sigmaBetween, pValue, pValueTtest)
ax01 = plt.subplot(gs[0, 1])
plt01, mp = plotSigma(ax01,
sigmaWithin * 100,
mp,
'b: $\sigma_{within}, 2.0^\circ C$',
sigmamax=sigmaMax)
ax11 = plt.subplot(gs[1, 1])
plt11, mp = plotSigma(ax11,
sigmaBetween * 100,
mp,
'd: $\sigma_{between}, 2.0^\circ C$',
sigmamax=sigmaMax)
ax21 = plt.subplot(gs[2, 1])
_, _, mp = plotPvalue(ax21, pValue, mp, 'f: p-val of interpathway diff.')
cax = plt.subplot(gs[:2, 2])
cb = plt.colorbar(plt01, ax=ax01, cax=cax)
cb.set_label('$\sigma$ (%)')
ax00.set_aspect('auto')
ax10.set_aspect('auto')
ax20.set_aspect('auto')
ax01.set_aspect('auto')
ax11.set_aspect('auto')
ax21.set_aspect('auto')
cax.set_aspect('auto')
plt.tight_layout()
f.savefig(outPng, dpi=300)
def plotScenVsScenAll():
outPng = 'wlRelChngScenVsScen_wlAll.png'
f = plt.figure(figsize=(13, 16))
gs = gridspec.GridSpec(5, 4, width_ratios=[1,1,1,1./12.], height_ratios=[1,1,1./30.,1.,1.])
mp = None
warmingLev = 1.5
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(warmingLev=warmingLev)
ax0 = plt.subplot(gs[0,0])
pcl, mp = plotRelChngDiff(ax0, rc_r8, mp, 'a: rel. chng. RCP85 at $' + str(warmingLev) +'^\circ$', vmax=30)
ax1 = plt.subplot(gs[0,1])
pcl, mp = plotRelChngDiff(ax1, rc_r4, mp, 'b: rel. chng. RCP45 at $' + str(warmingLev) +'^\circ$', vmax=30)
ax2 = plt.subplot(gs[0,2])
pcl, mp = plotRelChngDiff(ax2, relChngDiff, mp, 'c: difference RCP85-RCP45 at $' + str(warmingLev) +'^\circ$', vmax=30)
cax = plt.subplot(gs[0,3])
cb = plt.colorbar(pcl, ax=ax2, cax=cax)
cb.set_label('$\Delta$ 100-y discharge (%)')
ax0.set_aspect('auto')
ax1.set_aspect('auto')
ax2.set_aspect('auto')
cax.set_aspect('auto')
ax0 = plt.subplot(gs[1,0])
pValue, _, std = estimateChngSignificanceAndRobustness.computeRlChngPValueAtWarmingLev(scen='rcp85', warmingLev=warmingLev)
std = std/rc_r8
#pcl, mp = plotPvalue(ax0, pValue, None, mp, 'd: p-value, $\Delta rcp85$')
pcl, mp = plotSigma(ax0, std, None, mp, 'd: $\sigma_{im}$, ratio of $\Delta$ RCP85', sigmamax=2)
ax1 = plt.subplot(gs[1,1])
pValue, _, std = estimateChngSignificanceAndRobustness.computeRlChngPValueAtWarmingLev(scen='rcp45', warmingLev=warmingLev)
std = std/rc_r4
#pcl, mp = plotPvalue(ax1, pValue, None, mp, 'e: p-value, $\Delta RCP45$')
pcl, mp = plotSigma(ax1, std, None, mp, 'e: $\sigma_{im}$, ratio of $\Delta$ RCP45', sigmamax=2)
ax2 = plt.subplot(gs[1,2])
#pValue, _, _ = estimateChngSignificanceAndRobustness.computeRlChngPValueAtWarmingLevBtwScen(warmingLev=warmingLev)
#pcl, mp = plotPvalue(ax2, pValue, relChngDiff, mp, 'f: p-value, $\Delta RCP85 - \Delta RCP45$')
std = np.std(rc_r8all-rc_r4all, 0)
std = std/rc_r8
pcl, mp = plotSigma(ax2, std, relChngDiff, mp, 'f: $\sigma$, difference RCP85-RCP45', sigmamax=2, printSignTxt=False)
cax = plt.subplot(gs[1,3])
cb = plt.colorbar(pcl, ax=ax2, cax=cax)
#cb.set_label('p-value')
cb.set_label('$\sigma$ (fraction of relative change)')
ax0.set_aspect('auto')
ax1.set_aspect('auto')
ax2.set_aspect('auto')
cax.set_aspect('auto')
warmingLev = 2.0
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(warmingLev=warmingLev)
ax0 = plt.subplot(gs[3,0])
pcl, mp = plotRelChngDiff(ax0, rc_r8, mp, 'g: rel. chng. RCP85 $' + str(warmingLev) +'^\circ$', vmax=30)
ax1 = plt.subplot(gs[3,1])
pcl, mp = plotRelChngDiff(ax1, rc_r4, mp, 'h: rel. chng. RCP45 $' + str(warmingLev) +'^\circ$', vmax=30)
ax2 = plt.subplot(gs[3,2])
pcl, mp = plotRelChngDiff(ax2, relChngDiff, mp, 'i: difference RCP85-RCP45 at $' + str(warmingLev) +'^\circ$', vmax=30)
cax = plt.subplot(gs[3,3])
cb = plt.colorbar(pcl, ax=ax2, cax=cax)
cb.set_label('$\Delta$ 100-y discharge (%)')
ax0.set_aspect('auto')
ax1.set_aspect('auto')
ax2.set_aspect('auto')
cax.set_aspect('auto')
ax0 = plt.subplot(gs[4,0])
pValue, _, std = estimateChngSignificanceAndRobustness.computeRlChngPValueAtWarmingLev(scen='rcp85', warmingLev=warmingLev)
std = std/np.abs(rc_r8)
#pcl, mp = plotPvalue(ax0, pValue, None, mp, 'd: p-value, $\Delta RCP85$')
pcl, mp = plotSigma(ax0, std, None, mp, 'j: $\sigma_{im}$, ratio of $\Delta$ RCP85', sigmamax=2)
ax1 = plt.subplot(gs[4,1])
pValue, _, std = estimateChngSignificanceAndRobustness.computeRlChngPValueAtWarmingLev(scen='rcp45', warmingLev=warmingLev)
std = std/np.abs(rc_r4)
#pcl, mp = plotPvalue(ax1, pValue, None, mp, 'e: p-value, $\Delta RCP45$')
pcl, mp = plotSigma(ax1, std, None, mp, 'k: $\sigma_{im}$, ratio of $\Delta$ RCP45', sigmamax=2)
ax2 = plt.subplot(gs[4,2])
#pValue, _, _ = estimateChngSignificanceAndRobustness.computeRlChngPValueAtWarmingLevBtwScen(warmingLev=warmingLev)
#pcl, mp = plotPvalue(ax2, pValue, relChngDiff, mp, 'f: p-value, $\Delta RCP85 - \Delta RCP45$')
std = np.std(rc_r8all-rc_r4all, 0)
std = std/rc_r8
pcl, mp = plotSigma(ax2, std, relChngDiff, mp, 'l: $\sigma$, difference RCP85-RCP45', sigmamax=2, printSignTxt=False)
cax = plt.subplot(gs[4,3])
cb = plt.colorbar(pcl, ax=ax2, cax=cax)
#cb.set_label('p-value')
cb.set_label('$\sigma$ (fraction of relative change)')
ax0.set_aspect('auto')
ax1.set_aspect('auto')
ax2.set_aspect('auto')
cax.set_aspect('auto')
plt.tight_layout()
f.savefig(outPng, dpi=300)
def plotGrossEnsembleChange(ncDir='/ClimateRun4/multi-hazard/eva'):
outPng = 'wlRelChngGrossEnsembles.png'
f = plt.figure(figsize=(9, 12))
gs = gridspec.GridSpec(3, 3, width_ratios=[1, 1, .05])
mp = None
def writeSigmaRatioTxt(ax, sigma, relChng, varname):
sigma_ratio = sigma / np.abs(relChng)
percSign = float(np.nansum(sigma_ratio <= 1)) / np.nansum(
np.logical_not(np.isnan(sigma_ratio)))
prcTxtTmpl = '% of pixel where $\|{varname}\| > \sigma$: {p:2.0f}%'
prcTxt = prcTxtTmpl.format(varname=varname, p=percSign * 100)
#prcTxt = '% of pixel where $\|\Delta ' + varname + '\| > \sigma$: {p:2.0f}%'.format(p=percSign*100)
plt.axes(ax)
txtpos = mp(-24.3, 70.3)
bb = {'boxstyle': 'square,pad=0', 'ec': 'none', 'fc': 'w'}
plt.annotate(prcTxt,
xy=txtpos,
xycoords='data',
xytext=txtpos,
textcoords='data',
fontsize=10,
bbox=bb)
warmingLev = 1.5
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(
ncDir=ncDir, warmingLev=warmingLev, nmodels=nmodels)
rc_mega = (rc_r8 + rc_r4) / 2.
ax00 = plt.subplot(gs[0, 0])
pcl, mp = plotRelChngDiff(ax00,
rc_mega,
mp,
'a: $\Delta Q_{H100}$ at $' + str(warmingLev) +
'^\circ$',
vmax=50)
sigma = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
writeSigmaRatioTxt(ax00, sigma, rc_mega, '\Delta Q_{H100}')
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadMeanChangesAtWl(
ncDir=ncDir, warmingLev=warmingLev, nmodels=nmodels)
rc_mega = (rc_r8 + rc_r4) / 2.
ax10 = plt.subplot(gs[1, 0])
pcl, mp = plotRelChngDiff(ax10,
rc_mega,
mp,
'b: $\Delta Q_M$ at $' + str(warmingLev) +
'^\circ$',
vmax=50)
sigma = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
writeSigmaRatioTxt(ax10, sigma, rc_mega, '\Delta Q_M')
retPer = 15
rlVarName = 'rl_min'
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(
ncDir=ncDir,
warmingLev=warmingLev,
rlVarName=rlVarName,
retPer=retPer,
nmodels=nmodels,
threshold=.1)
rc_mega = (rc_r8 + rc_r4) / 2.
ax20 = plt.subplot(gs[2, 0])
pcl, mp = plotRelChngDiff(ax20,
rc_mega,
mp,
'c: $\Delta Q_{L15}$ at $' + str(warmingLev) +
'^\circ$',
vmax=50)
sigma = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
writeSigmaRatioTxt(ax20, sigma, rc_mega, '\Delta Q_{L15}')
warmingLev = 2.0
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(
ncDir=ncDir, warmingLev=warmingLev, nmodels=nmodels)
rc_mega = (rc_r8 + rc_r4) / 2.
ax01 = plt.subplot(gs[0, 1])
pcl, mp = plotRelChngDiff(ax01,
rc_mega,
mp,
'd: $\Delta Q_{H100}$ at $' + str(warmingLev) +
'^\circ$',
vmax=50)
sigma = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
writeSigmaRatioTxt(ax01, sigma, rc_mega, '\Delta Q_{H100}')
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadMeanChangesAtWl(
ncDir=ncDir, warmingLev=warmingLev, nmodels=nmodels)
rc_mega = (rc_r8 + rc_r4) / 2.
ax11 = plt.subplot(gs[1, 1])
pcl, mp = plotRelChngDiff(ax11,
rc_mega,
mp,
'e: $\Delta Q_M$ at $' + str(warmingLev) +
'^\circ$',
vmax=50)
sigma = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
writeSigmaRatioTxt(ax11, sigma, rc_mega, '\Delta Q_{H100}')
retPer = 15
rlVarName = 'rl_min'
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(
ncDir=ncDir,
warmingLev=warmingLev,
rlVarName=rlVarName,
retPer=retPer,
nmodels=nmodels,
threshold=.1)
rc_mega = (rc_r8 + rc_r4) / 2.
ax21 = plt.subplot(gs[2, 1])
pcl, mp = plotRelChngDiff(ax21,
rc_mega,
mp,
'f: $\Delta Q_{L15}$ at $' + str(warmingLev) +
'^\circ$',
vmax=50)
sigma = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
writeSigmaRatioTxt(ax21, sigma, rc_mega, '\Delta Q_M')
cax00 = plt.subplot(gs[:, 2])
cb = plt.colorbar(pcl, ax=ax21, cax=cax00)
cb.set_label('%', fontsize=13)
cb.ax.tick_params(labelsize=11)
sigma = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
writeSigmaRatioTxt(ax21, sigma, rc_mega, '\Delta Q_{L15}')
ax00.set_aspect('auto')
ax10.set_aspect('auto')
ax20.set_aspect('auto')
ax01.set_aspect('auto')
ax11.set_aspect('auto')
ax21.set_aspect('auto')
cax00.set_aspect('auto')
plt.tight_layout()
f.savefig(outPng, dpi=300)
def plotErrorDecomposition(ncDir='/ClimateRun4/multi-hazard/eva'):
outPng = 'errorDecomposition.png'
f = plt.figure(figsize=(9, 11))
gs = gridspec.GridSpec(3,
3,
width_ratios=[1, 1, 1. / 20.],
height_ratios=[1, 1, 1])
#gs.update(hspace=0.05, wspace=0.14)
mp = None
warmingLev = 1.5
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(
ncDir=ncDir, warmingLev=warmingLev)
ax00 = plt.subplot(gs[0, 0])
sigma_im = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
pcl, mp = plotSigma(ax00,
sigma_im * 100, (rc_r8 + rc_r4) / 2. * 100.,
mp,
'a: $\sigma_{\Delta Q0}$ (%) at $' + str(warmingLev) +
'^\circ$',
sigmamax=30,
printSignTxt=False)
#cax00 = plt.subplot(gs[0,1])
#cb = plt.colorbar(pcl, ax=ax00, cax=cax00)
ax10 = plt.subplot(gs[1, 0])
sigma_r8r4 = relChngDiff / 2.
pcl, mp = plotSigma(ax10,
sigma_r8r4 * 100, (rc_r8 + rc_r4) / 2. * 100.,
mp,
'c: $\sigma_{r8-r4}$ (%) at $' + str(warmingLev) +
'^\circ$',
sigmamax=30,
printSignTxt=False)
#cax10 = plt.subplot(gs[1,1])
#cb = plt.colorbar(pcl, ax=ax10, cax=cax10)
ax20 = plt.subplot(gs[2, 0])
sigmaT = getTimeSigmaGrossEnsemble(warmingLev, ncDir=ncDir)
pcl, mp = plotSigma(ax20,
sigmaT * 100, (rc_r8 + rc_r4) / 2. * 100.,
mp,
'e: $\sigma_{t}$ (%) at $' + str(warmingLev) +
'^\circ$',
sigmamax=30,
printSignTxt=False)
#cax20 = plt.subplot(gs[2,1])
#cb = plt.colorbar(pcl, ax=ax20, cax=cax20)
warmingLev = 2.0
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(
ncDir=ncDir, warmingLev=warmingLev)
ax01 = plt.subplot(gs[0, 1])
sigma_im = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
pcl, mp = plotSigma(ax01,
sigma_im * 100, (rc_r8 + rc_r4) / 2. * 100.,
mp,
'b: $\sigma_{\Delta Q0}$ (%) at $' + str(warmingLev) +
'^\circ$',
sigmamax=30,
printSignTxt=False)
cax00 = plt.subplot(gs[0, 2])
cb = plt.colorbar(pcl, ax=ax00, cax=cax00)
cb.set_label('%', fontsize=13)
cb.ax.tick_params(labelsize=11)
ax11 = plt.subplot(gs[1, 1])
sigma_r8r4 = relChngDiff / 2.
pcl, mp = plotSigma(ax11,
sigma_r8r4 * 100, (rc_r8 + rc_r4) / 2. * 100.,
mp,
'd: $\sigma_{r8-r4}$ (%) at $' + str(warmingLev) +
'^\circ$',
sigmamax=30,
printSignTxt=False)
#cax10 = plt.subplot(gs[1,1])
#cb = plt.colorbar(pcl, ax=ax10, cax=cax10)
ax21 = plt.subplot(gs[2, 1])
sigmaT = getTimeSigmaGrossEnsemble(warmingLev, ncDir=ncDir)
pcl, mp = plotSigma(ax21,
sigmaT * 100, (rc_r8 + rc_r4) / 2. * 100.,
mp,
'f: $\sigma_{t}$ (%) at $' + str(warmingLev) +
'^\circ$',
sigmamax=30,
printSignTxt=False)
#cax20 = plt.subplot(gs[2,1])
#cb = plt.colorbar(pcl, ax=ax20, cax=cax20)
ax00.set_aspect('auto')
ax10.set_aspect('auto')
ax20.set_aspect('auto')
ax01.set_aspect('auto')
ax11.set_aspect('auto')
ax21.set_aspect('auto')
cax00.set_aspect('auto')
plt.tight_layout()
f.savefig(outPng, dpi=300)
def plotGrossEnsembles_lowExt(ncDir='/ClimateRun4/multi-hazard/eva'):
outPng = 'wlRelChngGrossEnsembles_extLow.png'
f = plt.figure(figsize=(9, 8))
gs = gridspec.GridSpec(2, 3, width_ratios=[1, 1, .05])
mp = None
warmingLev = 1.5
retPer = 15
rlVarName = 'rl_min'
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(
ncDir=ncDir,
warmingLev=warmingLev,
rlVarName=rlVarName,
retPer=retPer,
nmodels=nmodels,
threshold=.1)
rc_mega = (rc_r8 + rc_r4) / 2.
ax0 = plt.subplot(gs[0, 0])
pcl, mp = plotRelChngDiff(ax0,
rc_mega,
mp,
'a: $\Delta Q_{L15}$ at $' + str(warmingLev) +
'^\circ$',
vmax=50)
#sigma_im = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
#sigmaT = getTimeSigmaGrossEnsemble(warmingLev, ncDir=ncDir)
#sigma = np.sqrt(sigma_im**2. + sigmaT**2.)
sigma = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
ax1 = plt.subplot(gs[1, 0])
pcl, mp = plotSigma(
ax1,
sigma * 100,
rc_mega * 100,
mp,
'c: $\sigma_{L15}$ (%) at $' + str(warmingLev) + '^\circ$',
prcTxtTmpl=
'% of pixel where ${thr}\|\Delta Q_{{L15}}\| > \sigma_{{L15}}$: {p:2.0f}%',
sigmamax=50)
warmingLev = 2.0
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(
ncDir=ncDir,
warmingLev=warmingLev,
rlVarName=rlVarName,
retPer=retPer,
nmodels=nmodels,
threshold=.1)
rc_mega = (rc_r8 + rc_r4) / 2.
ax2 = plt.subplot(gs[0, 1])
pclChng, mp = plotRelChngDiff(ax2,
rc_mega,
mp,
'b: $\Delta Q_{{L15}}$ at $' +
str(warmingLev) + '^\circ$',
vmax=50)
#sigma_im = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
#sigmaT = getTimeSigmaGrossEnsemble(warmingLev, ncDir=ncDir)
#sigma = np.sqrt(sigma_im**2. + sigmaT**2.)
sigma = np.nanstd(np.concatenate([rc_r8all, rc_r4all], 0), 0)
ax3 = plt.subplot(gs[1, 1])
pclSigma, mp = plotSigma(
ax3,
sigma * 100,
rc_mega * 100,
mp,
'd: $\sigma_{L15}$ (%) at $' + str(warmingLev) + '^\circ$',
prcTxtTmpl=
'% of pixel where ${thr}\|\Delta Q_{{L15}}\| > \sigma_{{L15}}$: {p:2.0f}%',
sigmamax=50)
cax1 = plt.subplot(gs[0, 2])
cb = plt.colorbar(pclChng, ax=ax2, cax=cax1)
cb.set_label('$\Delta Q_{L15}$ (%)')
cax2 = plt.subplot(gs[1, 2])
cb = plt.colorbar(pclSigma, ax=ax3, cax=cax2)
cb.set_label('$\sigma_{L15}$ (%)')
ax0.set_aspect('auto')
ax1.set_aspect('auto')
ax2.set_aspect('auto')
ax3.set_aspect('auto')
cax1.set_aspect('auto')
cax2.set_aspect('auto')
plt.tight_layout()
f.savefig(outPng, dpi=300)
def plotGrossEnsembles_lowExt(ncDir='/ClimateRun4/multi-hazard/eva'):
outPng = 'wlRelChngGrossEnsembles_extLow.png'
f = plt.figure(figsize=(9,12))
gs = gridspec.GridSpec(3, 3, width_ratios=[1,1,.05])
mp = None
def writeSigmaRatioTxt(ax, sigma, relChng, varname):
sigma_ratio = sigma/np.abs(relChng)
percSign = float(np.nansum(sigma_ratio <= 1))/np.nansum(np.logical_not(np.isnan(sigma_ratio)))
prcTxtTmpl = '% of pixel where $\|{varname}\| > \sigma$: {p:2.0f}%'
prcTxt = prcTxtTmpl.format(varname=varname, p=percSign*100)
#prcTxt = '% of pixel where $\|\Delta ' + varname + '\| > \sigma$: {p:2.0f}%'.format(p=percSign*100)
plt.axes(ax)
txtpos = mp(-24.3, 70.3)
bb = {'boxstyle': 'square,pad=0', 'ec': 'none', 'fc': 'w'}
plt.annotate(prcTxt, xy=txtpos, xycoords='data', xytext=txtpos, textcoords='data', fontsize=10, bbox=bb)
warmingLev = 1.5
vmax = 100
retPer = 15
rlVarName = 'rl_min'
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(ncDir=ncDir,
warmingLev=warmingLev, rlVarName=rlVarName, retPer=retPer,
nmodels=nmodels, threshold=.1)
rc_mega = (rc_r8 + rc_r4)/2.
sigmaTot, sigmaWithin, sigmaBetween, pValue, effectSize = anovaAnalysis(relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all)
ax0 = plt.subplot(gs[0,0])
pcl, mp = plotRelChngDiff(ax0, rc_mega, sigmaTot, mp, 'a: $\Delta Q_{L15}$ at $' + str(warmingLev) +'^\circ$', vmax=vmax)
printPosNegChanges(warmingLev, rc_mega)
writeSigmaRatioTxt(ax0, sigmaTot, rc_mega, '\Delta Q_{L15}')
ax1 = plt.subplot(gs[1,0])
pcl, mp = plotSigma(ax1, sigmaWithin*100, rc_mega*100, mp, 'c: $\sigma_{within}$ (%) at $' + str(warmingLev) +'^\circ$',
prcTxtTmpl = '', sigmamax=vmax)
ax2 = plt.subplot(gs[2,0])
pcl, mp = plotSigma(ax2, sigmaBetween*100, rc_mega*100, mp, 'e: $\sigma_{between}$ (%) at $' + str(warmingLev) +'^\circ$',
prcTxtTmpl = '', sigmamax=vmax)
warmingLev = 2.0
relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all = ldEnsmbl.loadWlVsScenChange(ncDir=ncDir,
warmingLev=warmingLev, rlVarName=rlVarName, retPer=retPer,
nmodels=nmodels, threshold=.1)
rc_mega = (rc_r8 + rc_r4)/2.
sigmaTot, sigmaWithin, sigmaBetween, pValue, effectSize = anovaAnalysis(relChngDiff, rc_r8, rc_r4, rc_r8all, rc_r4all)
ax3 = plt.subplot(gs[0,1])
pclChng, mp = plotRelChngDiff(ax3, rc_mega, sigmaTot, mp, 'b: $\Delta Q_{{L15}}$ at $' + str(warmingLev) +'^\circ$', vmax=vmax)
printPosNegChanges(warmingLev, rc_mega)
writeSigmaRatioTxt(ax3, sigmaTot, rc_mega, '\Delta Q_{L15}')
ax4 = plt.subplot(gs[1,1])
pclSigma, mp = plotSigma(ax4, sigmaWithin*100, rc_mega*100, mp, 'd: $\sigma_{within}$ (%) at $' + str(warmingLev) +'^\circ$',
prcTxtTmpl = '', sigmamax=vmax)
ax5 = plt.subplot(gs[2,1])
pclSigma, mp = plotSigma(ax5, sigmaBetween*100, rc_mega*100, mp, 'f: $\sigma_{between}$ (%) at $' + str(warmingLev) +'^\circ$',
prcTxtTmpl = '', sigmamax=vmax)
cax1 = plt.subplot(gs[0,2])
cb = plt.colorbar(pclChng, ax=ax2, cax=cax1)
cb.set_label('$\Delta Q_{L15}$ (%)')
cax2 = plt.subplot(gs[1:,2])
cb = plt.colorbar(pclSigma, ax=ax3, cax=cax2)
cb.set_label('$\sigma_{L15}$ (%)')
ax0.set_aspect('auto')
ax1.set_aspect('auto')
ax2.set_aspect('auto')
ax3.set_aspect('auto')
ax4.set_aspect('auto')
ax5.set_aspect('auto')
cax1.set_aspect('auto')
cax2.set_aspect('auto')
plt.tight_layout()
f.savefig(outPng, dpi=300)
