def plotTimeDist():
outpng = 'timeDist.png'
fig = plt.figure(figsize=[10, 8])
gc = GridSpec(2, 2)
ax00 = plt.subplot(gc[0, :])
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([])
ax10 = plt.subplot(gc[1, :])
pdftot, pdfr8, pdfr4 = gwl.getWarmingLevelMixDistributions(2.0)
plotPdfs(ax10,
pdfr8,
pdfr4,
pdftot,
'c: time pdf, warming level $2.0^\circ$',
showLegend=False)
ax00.set_aspect('auto')
ax10.set_aspect('auto')
plt.tight_layout()
fig.savefig(outpng, dpi=300)
def plotTimeUncertainty():
outPng = 'timeSigma_grossEnsemble.png'
fig = plt.figure(figsize=[15,8])
gc = GridSpec(2, 3, width_ratios=[2,1,.05])
ax00 = plt.subplot(gc[0, 0])
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, 1])
mp = None
pcl, mp = plotUncertaintyMap(ax01, pdfTot, mp, 'b: $\sigma$ of relative change, w.l. $1.5^\circ$')
ax10 = plt.subplot(gc[1, 0])
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, 1])
pcl, mp = plotUncertaintyMap(ax11, pdfTot, mp, 'd: $\sigma$ of relative change, w.l. $2.0^\circ$')
cax = plt.subplot(gc[:, 2])
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)
ax00.set_aspect('auto')
ax01.set_aspect('auto')
ax10.set_aspect('auto')
ax11.set_aspect('auto')
cax.set_aspect('auto')
plt.tight_layout()
fig.savefig(outPng, dpi=300)
def plotTimeUncertaintyOfWl(warmingLev=2.0):
outPng = 'timeSigma_grossEnsemble_' + str(warmingLev) + 'deg.png'
fig = plt.figure(figsize=[15,8])
gc = GridSpec(2, 4, width_ratios=[1,1,1,.05])
ax00 = plt.subplot(gc[0, :])
pdfTot, pdfR8, pdfR4 = gwl.getWarmingLevelMixDistributions(warmingLev)
plotPdfs(ax00, pdfR8, pdfR4, pdfTot, 'a: time pdf, warming level $' + str(warmingLev) + '^\circ$', showLegend=True)
ax00.set_xticklabels([])
ax10 = plt.subplot(gc[1, 0])
mp = None
pcl, mp = plotUncertaintyMap(ax10, pdfR8, mp, 'b: $\sigma$ of relative change, rcp85, w.l. $' + str(warmingLev) + '^\circ$')
ax11 = plt.subplot(gc[1, 1])
pcl, mp = plotUncertaintyMap(ax11, pdfR4, mp, 'c: $\sigma$ of relative change, rcp45, w.l. $' + str(warmingLev) + '^\circ$')
ax12 = plt.subplot(gc[1, 2])
pcl, mp = plotUncertaintyMap(ax12, pdfTot, mp, 'd: $\sigma$ of relative change, gross ensemble, $' + str(warmingLev) + '^\circ$')
cax = plt.subplot(gc[1, 3])
cb = plt.colorbar(pcl, ax=ax11, cax=cax)
cb.set_label('$\sigma$ of % change', fontsize=14)
cax.tick_params(labelsize=13, rotation=90)
ax00.set_aspect('auto')
ax10.set_aspect('auto')
ax11.set_aspect('auto')
ax12.set_aspect('auto')
cax.set_aspect('auto')
plt.tight_layout()
plt.show()
fig.savefig(outPng, dpi=300)
def getTimeSigmaGrossEnsemble(warmingLev):
pdfTot, pdfR8, pdfR4 = gwl.getWarmingLevelMixDistributions(warmingLev)
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)
return sigmaT
def getTimeSigmaByScen(warmingLev, scen):
_, pdfR8, pdfR4 = gwl.getWarmingLevelMixDistributions(warmingLev)
pdf = None
if scen == 'rcp85':
pdf = pdfR8
elif scen == 'rcp45':
pdf = pdfR4
stdev = pdf.std()
mn = pdf.mean()
sdvmin = int(np.round(mn - stdev))
sdvmax = int(np.round(mn + stdev))
rlChngInf = ldEnsmbl.getRcpEnsembleAtYear(sdvmin, scen=scen)
rlChngSup = ldEnsmbl.getRcpEnsembleAtYear(sdvmax, scen=scen)
sigmaT = np.abs(rlChngSup - rlChngInf)
return sigmaT
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)