if scen != "RCP85":
print rd(contrib, 50, printyr), "(", rd(contrib, 5, printyr), "to", rd(contrib, 95, printyr), ")",
else:
print rd(contrib, 50, printyr), "(", rd(contrib, 5, printyr), "to", rd(contrib, 95, printyr), ")"
#### total slr; sum up contributions first ####
def rdd(numb):
# in mm
return '%s' % float('%.4g' % numb)
print "total", ":",
for k, scen in enumerate(["RCP3PD", "RCP45", "RCP85", ]):
total_slr = da.zeros_like(projection_data[scen]["thermexp"])
for i, name in enumerate(contrib_ids):
# sum up all contributions
single_contrib = projection_data[scen][name]
# if nans occur, clear these contributions
# single_contrib[np.isnan(single_contrib)] = 0.
total_slr += single_contrib
di_total_slr = da.DimArray(total_slr, dims=["time", "runnumber"])
di_total_slr -= di_total_slr[1986:2005, :].mean(axis="time")
if isinstance(printyr, int):
mn = di_total_slr[printyr, :] * 1.e3
else:
mn = di_total_slr[printyr, :].mean(axis="time") * 1.e3
# mn = di_total_slr[2081:2100,:].mean(axis="time")
low = np.percentile(mn, 5)
def fig4(projection_data):
plot_period = np.arange(2000, 2101, 1)
plt.subplots_adjust(left=0.1, bottom=0.1, right=1.0, top=0.97,
wspace=0.1, hspace=None)
ax6 = plt.subplot(111)
# ipcc ranges
divider = make_axes_locatable(ax6)
axy = divider.append_axes("right", size=0.8, pad=0.0, sharey=ax6)
axy.axis("off")
axy.axvspan(0, 10, facecolor='0.5', alpha=0.2, lw=0)
total_contribs = {}
xloc = 10
oloc = 4
for k, scen in enumerate(["RCP85", "RCP45", "RCP3PD"]):
total_slr = da.zeros_like(projection_data[scen]["thermexp"])
for i, name in enumerate(contrib_ids):
# sum up all contributions
single_contrib = projection_data[scen][name]
total_slr += single_contrib
contrib = total_slr - \
total_slr[1986:2005, :].mean(axis=0)
upper_perc = da.DimArray(np.percentile(contrib, 95, axis=1),
axes=contrib.time, dims="time")
lower_perc = da.DimArray(np.percentile(contrib, 5, axis=1),
axes=contrib.time, dims="time")
median = da.DimArray(np.percentile(contrib, 50, axis=1),
axes=contrib.time, dims="time")
h = ax6.fill_between(
plot_period,
lower_perc[plot_period] * 1e3,
upper_perc[plot_period] * 1e3,
color=rcpcoldict[scen],
alpha=.4,
lw=0.5)
ax6.plot(plot_period, median[plot_period] * 1e3, lw=3, color=rcpcoldict[scen],
alpha=1., label=rcpnamedict[scen])
total_contribs[scen] = np.array(
[median[2100], lower_perc[2100], upper_perc[2100]])
oloc -= 1.
xloc -= 1.
low = lower_perc[2081:2100].mean() * 1.e3
med = median[2081:2100].mean() * 1.e3
upp = upper_perc[2081:2100].mean() * 1.e3
axy.plot([oloc - 1, oloc + 1], [med, med],
lw=3, color=rcpcoldict[scen])
axy.fill_between([oloc - 1, oloc + 1], [low, low],
[upp, upp], color=rcpcoldict[scen], alpha=.4, lw=0.)
low, med, upp = ipcc.get_ipcc_range(scen, "mean_slr_2081_2100")
axy.fill_between([xloc - 1, xloc + 1], [low, low],
[upp, upp], color=rcpcoldict[scen], alpha=.4, lw=0.)
axy.plot([xloc - 1, xloc + 1], [med, med],
color=rcpcoldict[scen], lw=3, alpha=1.)
axy.set_xlim(-1, 11)
axy.text(1.5, 1200, "M16", rotation="vertical", horizontalalignment='center',
verticalalignment='center')
axy.text(7.5, 1200, "IPCC", rotation="vertical", horizontalalignment='center',
verticalalignment='center')
ax6.set_xlim(plot_period[0], plot_period[-1])
ax6.set_xlabel("Time in years")
ax6.set_ylabel("Sea level in mm")
l1 = ax6.legend(ncol=1, loc="center left")
l1.draw_frame(0)
for l in l1.get_lines():
l.set_alpha(1)
