def plotTimeSerie30YVariability_hiExt(ax=None, plotXLabel=True, plotYLabel=True, warmingLev=2.0):
yrs, rpR8Srs, rpR4Srs, models = loadWlVsScenChange.loadRetPerAllYears(nmodels=nmodels, excludedModels=excludedModels)
rpR8 = getRelChng(yrs, rpR8Srs)
rpR4 = getRelChng(yrs, rpR4Srs)
wlyR8 = getWarmingLevels('rcp85', warmingLev)
r8srs = getNYrsTimeSeriesAroundWl(yrs, rpR8, wlyR8, models)
r8meanWl, r8sigmaWl = getMeanAndSigma(r8srs)
cndPosi = r8meanWl > 0
cndNega = r8meanWl < 0
cnd = cndPosi if positiveChanges else cndNega
cndMtx = np.tile(cnd, [r8srs.shape[0], r8srs.shape[1], 1, 1])
r8srs[~cndMtx] = np.nan
r8mean_ = np.nanmean(r8srs, 0)
r8mean = r8mean_.reshape(r8mean_.shape[0], r8mean_.shape[1]*r8mean_.shape[2])
#r8mean = np.nanmedian(r8mean, 1)
r8mean = np.nanmean(r8mean, 1)
r8spmean = r8srs.reshape(r8srs.shape[0], r8srs.shape[1], r8srs.shape[2]*r8srs.shape[3])
r8spmean = np.nanmean(r8spmean, 2)
#r8spmean = np.nanmean(r8spmean, 2)
r8skew = stats.skew(r8spmean[:, 3])
r8sigma = np.std(r8spmean[:, 3])
r8mean = np.nanmean(r8spmean, 0)
wlyR4 = getWarmingLevels('rcp45', warmingLev)
r4srs = getNYrsTimeSeriesAroundWl(yrs, rpR4, wlyR4, models)
r4meanWl, r4sigmaWl = getMeanAndSigma(r4srs)
cndPosi = r4meanWl > 0
cndNega = r8meanWl < 0
cnd = cndPosi if positiveChanges else cndNega
cndMtx = np.tile(cnd, [r4srs.shape[0], r4srs.shape[1], 1, 1])
r4srs[~cndMtx] = np.nan
r4mean_ = np.nanmean(r4srs, 0)
r4mean = r4mean_.reshape(r4mean_.shape[0], r4mean_.shape[1]*r4mean_.shape[2])
#r4mean = np.nanmedian(r4mean, 1)
r4mean = np.nanmean(r4mean, 1)
r4spmean = r4srs.reshape(r4srs.shape[0], r4srs.shape[1], r4srs.shape[2]*r4srs.shape[3])
r4spmean = np.nanmean(r4spmean, 2)
#r4spmean = np.nanmean(r4spmean, 2)
r4skew = stats.skew(r4spmean[:, 3])
r4sigma = np.std(r4spmean[:, 3])
r4mean = np.nanmean(r4spmean, 0)
ensTot = np.concatenate([r8spmean, r4spmean], 0)
stdDevTot = np.std(ensTot, 0)*100.
meanTot = (r4mean + r8mean)/2.*100.
yr = [-15, -10, -5, 0, 5, 10, 15]
if ax is None:
fg = plt.figure(figsize=[8.46, 4.98])
pstdDev = plt.fill_between(yr, meanTot+stdDevTot, meanTot-stdDevTot, color='gainsboro', label='$\sigma={sgm:1.0f}\%$'.format(sgm=stdDevTot[int(np.floor(len(stdDevTot)/2))]))
#pmdlr8 = plt.plot(yr, r8spmean.transpose()*100, 'sandybrown', label='RCP8.5 models ($\sigma={sgm:1.2f}\%, skw.={s:1.2f}$)'.format(sgm=r8sigma*100, s=r8skew));
#pmdlr4 = plt.plot(yr, r4spmean.transpose()*100, 'skyblue', label='RCP4.5 models ($\sigma={sgm:1.2f}\%, skw.={s:1.2f})$'.format(sgm=r4sigma*100, s=r4skew));
#pmdlr8 = plt.plot(yr, r8spmean.transpose()*100, 'sandybrown', label='RCP8.5 models ($\sigma_{{RCP8.5}}={sgm:1.0f}\%$)'.format(sgm=r8sigma*100, s=r8skew));
#pmdlr4 = plt.plot(yr, r4spmean.transpose()*100, 'skyblue', label='RCP4.5 models ($\sigma_{{RCP4.5}}={sgm:1.0f}\%$)'.format(sgm=r4sigma*100, s=r4skew));
pmdlr8 = plt.plot(yr, r8spmean.transpose()*100, 'sandybrown', label='RCP8.5 models');
pmdlr4 = plt.plot(yr, r4spmean.transpose()*100, 'skyblue', label='RCP4.5 models');
#pmedr8 = plt.plot(yr, r8mean*100, 'firebrick', linewidth=6, label='RCP8.5 mean');
#pmedr4 = plt.plot(yr, r4mean*100, 'royalblue', linewidth=6, label='RCP4.5 mean');
pmn = plt.plot(yr, (r4mean+r8mean)/2.*100, 'k', linewidth=6, label='ens. mean');
plt.grid('on')
lgndLoc = 2 if positiveChanges else 3
#plt.legend(handles=[pmdlr8[0], pmedr8[0], pmdlr4[0], pmedr4[0], pstdDev], fontsize=12, loc=lgndLoc)
plt.legend(handles=[pmdlr8[0], pmdlr4[0], pmn[0], pstdDev], fontsize=12, loc=lgndLoc)
if plotXLabel:
plt.xlabel('years to $' + str(warmingLev) + '^\circ$C w.l.', fontsize=18)
else:
ax.set_xticklabels([])
if plotYLabel:
plt.ylabel('$Q_{H100}$ % change', fontsize=17)
else:
ax.set_yticklabels([])
ax.tick_params(axis="x", labelsize=axFontSize)
ax.tick_params(axis="y", labelsize=axFontSize)
ylm = ax.get_ylim()
dlt = max(ylm) - min(ylm)
pzr = plt.plot([0, 0], [min(ylm)-dlt*4, max(ylm)+dlt*4], 'k', linewidth=2)
pzr[0].set_zorder(1)
ax.set_ylim(ylm)
plt.tight_layout()
if ax is None:
fg.savefig('./ensemblesVariabilityAround2deg.png', dpi=400)
def plotTimeSerieVariabilityAtWl_lowExt(ax=None, axmap=None, plotXLabel=True, plotYLabel=True, warmingLev=2.0, bsmp=None):
yrs, rpR8Srs, rpR4Srs, models = loadWlVsScenChange.loadRetPerAllYears(rlVarName='rl_min', nmodels=nmodels, excludedModels=excludedModels)
rpR8 = getRelChng(yrs, rpR8Srs)
rpR4 = getRelChng(yrs, rpR4Srs)
wlyR8 = getWarmingLevels('rcp85', warmingLev)
r8srs = getNYrsTimeSeriesAroundWl(yrs, rpR8, wlyR8, models)
r8srs[r8srs == np.inf] = np.nan
wlyR4 = getWarmingLevels('rcp45', warmingLev)
r4srs = getNYrsTimeSeriesAroundWl(yrs, rpR4, wlyR4, models)
r4srs[r4srs == np.inf] = np.nan
srstot = np.concatenate([r8srs, r4srs], 0)
rallmeanWl, rallsigmaWl = getMeanAndSigma(r8srs)
cndPosi = rallmeanWl > 0
cndNega = rallmeanWl < 0
cnd = cndPosi if positiveChanges else cndNega
r8meanWl, r8sigmaWl = getMeanAndSigma(r8srs)
cndMtx = np.tile(cnd, [r8srs.shape[0], r8srs.shape[1], 1, 1])
r8srs[~cndMtx] = np.nan
r8mean_ = np.nanmean(r8srs, 0)
r8mean = r8mean_.reshape(r8mean_.shape[0], r8mean_.shape[1]*r8mean_.shape[2])
r8mean = np.nanmedian(r8mean, 1)
#r8mean = np.nanmean(r8mean, 1)
r8spmean = r8srs.reshape(r8srs.shape[0], r8srs.shape[1], r8srs.shape[2]*r8srs.shape[3])
r8spmean = np.nanmedian(r8spmean, 2)
#r8spmean = np.nanmean(r8spmean, 2)
r8skew = stats.skew(r8spmean[:, 0])
r8sigma = np.std(r8spmean[:, 0])
r8mean = np.nanmean(r8spmean, 0)
r4meanWl, r4sigmaWl = getMeanAndSigma(r4srs)
cndMtx = np.tile(cnd, [r4srs.shape[0], r4srs.shape[1], 1, 1])
r4srs[~cndMtx] = np.nan
r4mean_ = np.nanmean(r4srs, 0)
r4mean = r4mean_.reshape(r4mean_.shape[0], r4mean_.shape[1]*r4mean_.shape[2])
r4mean = np.nanmedian(r4mean, 1)
#r4mean = np.nanmean(r4mean, 1)
r4spmean = r4srs.reshape(r4srs.shape[0], r4srs.shape[1], r4srs.shape[2]*r4srs.shape[3])
r4spmean = np.nanmedian(r4spmean, 2)
#r4spmean = np.nanmean(r4spmean, 2)
r4skew = stats.skew(r4spmean[:, 0])
r4sigma = np.std(r4spmean[:, 0])
r4mean = np.nanmean(r4spmean, 0)
ensTot = np.concatenate([r8spmean, r4spmean], 0)
stdDevTot = np.std(ensTot, 0)*100.
meanTot = (r4mean + r8mean)/2.*100.
yr = [-15, -10, -5, 0, 5, 10, 15]
if ax is None:
fg = plt.figure(figsize=[8.46, 4.98])
ax = fg.gca()
doPlotChanges(ax, warmingLev, meanTot, stdDevTot, r8spmean, r4spmean, r8mean, r4mean, plotXLabel, plotYLabel)
if not axmap is None:
bsmp = doPlotAreaMap(axmap, cnd, bsmp)
plt.tight_layout()
if ax is None:
fg.savefig('./ensemblesVariabilityAround2deg_lowExt.png', dpi=400)
return bsmp
def plotTimeSerie30YVariability_hiExt(ax=None,
plotXLabel=True,
warmingLev=2.0):
yrs, rpR8Srs, rpR4Srs, models = loadWlVsScenChange.loadRetPerAllYears(
nmodels=nmodels)
rpR8 = getRelChng(yrs, rpR8Srs)
rpR4 = getRelChng(yrs, rpR4Srs)
wlyR8 = getWarmingLevels('rcp85', warmingLev)
r8srs = getNYrsTimeSeriesAroundWl(yrs, rpR8, wlyR8, models)
r8medianWl, r8sigmaWl = getMeanAndSigma(r8srs)
cndSignR8 = np.abs(r8medianWl) > r8sigmaWl
cndPosi = r8medianWl > 0
#cnd = np.logical_and(cndSignR8, cndPosi)
cnd = cndPosi
cndMtx = np.tile(cnd, [r8srs.shape[0], r8srs.shape[1], 1, 1])
r8srs[~cndMtx] = np.nan
r8median_ = np.median(r8srs, 0)
r8median = r8median_.reshape(r8median_.shape[0],
r8median_.shape[1] * r8median_.shape[2])
r8median = np.nanmedian(r8median, 1)
#r8median = np.nanmean(r8median, 1)
r8spmean = r8srs.reshape(r8srs.shape[0], r8srs.shape[1],
r8srs.shape[2] * r8srs.shape[3])
r8spmean = np.nanmedian(r8spmean, 2)
#r8spmean = np.nanmean(r8spmean, 2)
r8skew = stats.skew(r8spmean[:, 3])
r8sigma = np.std(r8spmean[:, 3])
r8median = np.nanmedian(r8spmean, 0)
wlyR4 = getWarmingLevels('rcp45', 2.0)
r4srs = getNYrsTimeSeriesAroundWl(yrs, rpR4, wlyR4, models)
r4medianWl, r4sigmaWl = getMeanAndSigma(r4srs)
cndSignR4 = np.abs(r4medianWl) > r4sigmaWl
cndPosi = r4medianWl > 0
cnd = cndPosi
cndMtx = np.tile(cnd, [r4srs.shape[0], r4srs.shape[1], 1, 1])
r4srs[~cndMtx] = np.nan
r4median_ = np.median(r4srs, 0)
r4median = r4median_.reshape(r4median_.shape[0],
r4median_.shape[1] * r4median_.shape[2])
r4median = np.nanmedian(r4median, 1)
#r4median = np.nanmean(r4median, 1)
r4spmean = r4srs.reshape(r4srs.shape[0], r4srs.shape[1],
r4srs.shape[2] * r4srs.shape[3])
r4spmean = np.nanmedian(r4spmean, 2)
#r4spmean = np.nanmean(r4spmean, 2)
r4skew = stats.skew(r4spmean[:, 3])
r4sigma = np.std(r4spmean[:, 3])
r4median = np.nanmedian(r4spmean, 0)
yr = [-15, -10, -5, 0, 5, 10, 15]
if ax is None:
fg = plt.figure(figsize=[8.46, 4.98])
#pmdlr8 = plt.plot(yr, r8spmean.transpose()*100, 'peachpuff', label='RCP8.5 models ($\sigma={sgm:1.2f}\%, skw.={s:1.2f}$)'.format(sgm=r8sigma*100, s=r8skew));
#pmdlr4 = plt.plot(yr, r4spmean.transpose()*100, 'skyblue', label='RCP4.5 models ($\sigma={sgm:1.2f}\%, skw.={s:1.2f})$'.format(sgm=r4sigma*100, s=r4skew));
pmdlr8 = plt.plot(yr,
r8spmean.transpose() * 100,
'peachpuff',
label='RCP8.5 models ($\sigma={sgm:1.2f}\%$'.format(
sgm=r8sigma * 100, s=r8skew))
pmdlr4 = plt.plot(yr,
r4spmean.transpose() * 100,
'skyblue',
label='RCP4.5 models ($\sigma={sgm:1.2f}\%$'.format(
sgm=r4sigma * 100, s=r4skew))
pmedr8 = plt.plot(yr,
r8median * 100,
'firebrick',
linewidth=6,
label='RCP8.5 mean')
pmedr4 = plt.plot(yr,
r4median * 100,
'royalblue',
linewidth=6,
label='RCP4.5 mean')
plt.grid('on')
plt.legend(handles=[pmdlr8[0], pmedr8[0], pmdlr4[0], pmedr4[0]])
if plotXLabel:
plt.xlabel('years to $2^\circ C w.l.$', fontsize=14)
plt.ylabel('$Q_{H100}$ % change', fontsize=14)
plt.tight_layout()
if ax is None:
fg.savefig('./ensemblesVariabilityAround2deg.png', dpi=400)