edge_colors = ['steelblue', 'indianred'] * len(modoks)
positions.append(pos + 0.5)
positions.append(pos + 1.5)
fig = plt.figure(figsize=(16, 12))
axes = []
for reg in range(4):
ax = fig.add_subplot(2, 2, reg + 1)
axes.append(ax)
ctl.boxplot_on_ax(ax,
allpercs_regs[reg],
modoks_dub,
coloks_dub,
edge_colors=edge_colors,
positions=positions,
versions=versions,
ens_colors=['steelblue', 'indianred'],
ens_names=['hist', 'ssp585'],
plot_mean=False)
ax.axhline(0, color='gray', linewidth=0.5)
ax.set_xticks([])
ax.set_title(reg_names[reg])
ctl.adjust_ax_scale(axes)
ctl.custom_legend(fig,
coloks + ['steelblue', 'indianred'],
modoks + ['hist', 'ssp585'],
ncol=4)
fig.suptitle(
for area in ['NATL', 'NEPAC', 'SPAC']:
xose = dict()
for ru in allru:
gigi = resdict[(ru, 'jli', area, season)]
seasme = gigi.reshape((-1, lensea)).mean(axis = 1)
xose[ru] = seasme
allpercs = dict()
for nu in [10, 25, 50, 75, 90]:
allpercs['p{}'.format(nu)] = [np.percentile(xose[ru], nu) for ru in allru]
allpercs['mean'] = [np.mean(xose[ru]) for ru in allru]
allpercs['min'] = [np.min(xose[ru]) for ru in allru]
allpercs['max'] = [np.max(xose[ru]) for ru in allru]
fig, ax = plt.subplots(figsize = (12,8))
ctl.boxplot_on_ax(ax, allpercs, allru, colors, plot_ensmeans=False)
ax.set_xticklabels(allru)
ax.set_ylabel('{} {} mean jet lat'.format(area, season))
fig.savefig(cart_out + 'seamean_jetlat_{}_{}.pdf'.format(area, season))
xose2 = dict()
for ru in allru:
gigi = resdict[(ru, 'jli', area, season)]
seasstd = gigi.reshape((-1, lensea)).std(axis = 1)
xose2[ru] = seasstd
fig, ax = plt.subplots(figsize = (12,8))
allpercs = dict()
for nu in [10, 25, 50, 75, 90]:
allpercs['p{}'.format(nu)] = [np.percentile(xose2[ru], nu) for ru in allru]
allpercs['mean'] = [np.mean(xose2[ru]) for ru in allru]
# obsperc['p{}'.format(nu)] = np.percentile(enso[ru]['tos'], nu)
# obsperc['mean'] = np.mean(enso[ru]['tos']).values
# obsperc['min'] = np.min(enso[ru]['tos']).values
# obsperc['max'] = np.max(enso[ru]['tos']).values
nams = ['pi'] + [ru+'_'+vers for ru in allru[1:] for vers in ['tr', 'st']]
edgecol = np.append(['black'], np.concatenate([(col, col) for col in colors[1:]]))
positions = [0.]
posticks = [0.]
for i in range(len(allru[1:])):
positions.append(positions[-1]+0.7+0.4)
positions.append(positions[-1]+0.7)
posticks.append(np.mean(positions[-2:]))
ctl.boxplot_on_ax(ax, allpercs, nams, colors_vtr, positions = positions, edge_colors = edgecol, plot_mean = False, plot_minmax = True, plot_ensmeans = False)#, obsperc = obsperc, obs_color = 'black', obs_name = 'pi')
# ax.axhline(0, color = 'gray', linewidth = 0.5)
ax.set_xticks(posticks)
ax.set_xticklabels(allru)
#ax.set_title(tit)
#ctl.custom_legend(fig, colors_vtr, ['pi'] + nams, ncol = 4, add_space_below = 0.1)
ax.set_ylabel('ENSO index (K)')
fig.savefig(cart_out + 'enso_boxplot.pdf')
############# enso plot with tas/net_toa
glomeans, pimean = pickle.load(open(cart_or + 'seasmean/bottino_glomeans.p', 'rb'))
ax = figall.add_subplot(2, 2, reg + 1)
axes.append(ax)
allpercs = dict()
for nu in [10, 25, 50, 75, 90]:
allpercs['p{}'.format(nu)] = [
np.percentile(freqs[(ssp, 'all', cos)][:, reg], nu)
for ssp in allsims
]
ax.axhline(allpercs['p50'][0], color='gray', linewidth=0.5)
ctl.boxplot_on_ax(ax,
allpercs,
allsims,
colsim,
plot_mean=False,
plot_ensmeans=False,
plot_minmax=False)
ax.set_xticks([])
ax.set_title(reg_names[reg])
if reg == 0: ax.set_ylabel('Regime frequency')
ax.scatter(0,
results_ref['freq_clus'][reg],
color='black',
marker='*',
s=5)
ctl.custom_legend(figall, colsim, allsims, ncol=4)
figall.savefig(cart_out + 'WRfreq_cmip5_{}.pdf'.format(area))
axes = []
for reg in range(4):
ax = figall.add_subplot(2, 2, reg + 1)
axes.append(ax)
allpercs = dict()
for nu in [10, 25, 50, 75, 90]:
allpercs['p{}'.format(nu)] = [
np.percentile(freqs[(ssp, tip)][:, reg], nu) - histme[reg]
for tip in alltips
]
ctl.boxplot_on_ax(ax,
allpercs,
alltips,
colorz,
plot_mean=False,
plot_ensmeans=False,
plot_minmax=False)
ax.axhline(0, color='gray', linewidth=0.5)
ax.set_xticks([])
ax.set_title(reg_names[reg])
if reg == 0 or reg == 2: ax.set_ylabel('Regime frequency anomaly')
ctl.adjust_ax_scale(axes)
#ctl.custom_legend(figall, colorz, alltips, ncol = 2)
ctl.custom_legend(figall,
colorz, ['MPI-ESM1-2-LR', 'UKESM1-0-LL'],
ncol=2,
allpercs = dict()
for nu in [10, 25, 50, 75, 90]:
allpercs['p{}'.format(nu)] = [
np.percentile(freqs[(ssp, 'all', cos)][:, reg], nu)
for ssp in allsims
]
ax.axhline(allpercs['p50'][0], color='gray', linewidth=0.5)
positions = list(np.arange(len(allsims) - 1) * 0.7)
positions.append(positions[-1] + 0.3 + 0.7)
ctl.boxplot_on_ax(ax,
allpercs,
allsims,
colsim,
plot_mean=False,
plot_ensmeans=False,
plot_minmax=False,
positions=positions)
ax.set_xticks([])
ax.set_title(reg_names[reg])
if reg == 0: ax.set_ylabel('Regime frequency')
ax.scatter(0,
results_ref['freq_clus'][reg],
color='black',
marker='*',
s=5)
ax.axvline(np.mean([positions[-1], positions[-2]]),
color='lightslategray',
linewidth=0.2,
fig = plt.figure(figsize = (16,12))
axes = []
for reg in range(4):
ax = fig.add_subplot(2, 2, reg + 1)
axes.append(ax)
allpercs = dict()
allpercs['mean'] = [np.mean(trend_ssp[(ssp, 'all', pio, cos, reg)]) for ssp in allsims[1:]]
for nu in [10, 25, 50, 75, 90]:
allpercs['p{}'.format(nu)] = [np.percentile(trend_ssp[(ssp, 'all', pio, cos, reg)], nu) for ssp in allsims[1:]]
allpercs['ens_min'] = [np.min(trend_ssp[(ssp, 'all', pio, cos, reg)]) for ssp in allsims[1:]]
allpercs['ens_max'] = [np.max(trend_ssp[(ssp, 'all', pio, cos, reg)]) for ssp in allsims[1:]]
ctl.boxplot_on_ax(ax, allpercs, allsims[1:], colsim[1:], plot_mean = False, plot_ensmeans = False, plot_minmax = True)
ax.axhline(0, color = 'gray', linewidth = 0.5)
ax.set_xticks([])
ax.set_title(reg_names[reg])
ctl.adjust_ax_scale(axes)
ctl.custom_legend(fig, colsim[1:], allsims[1:], ncol = 4)
#fig.suptitle('30yr bsp of WR freq. in 2050-2100 wrt 1964-2014')
fig.savefig(cart_out + '{}_WRfreq_allssp_{}_{}.pdf'.format(pio, area, cos))
fig = plt.figure(figsize = (16,12))
axes = []
for reg in range(4):
obsperc = dict()
for nu in [10, 25, 50, 75, 90]:
obsperc['p{}'.format(nu)] = np.percentile(cose[area]['obs'], nu)
obsperc['mean'] = np.mean(cose[area]['obs'])
#nomi = [mod + '_' + vers for mod in mod_names for vers in versions]
# allvers = [vers for mod in mod_names for vers in versions]
nomi = mod_list
ctl.boxplot_on_ax(ax,
allpercs,
nomi,
colors,
versions=allvers,
plot_mean=True,
plot_ensmeans=True,
ens_colors=['steelblue', 'indianred'],
ens_names=['LR', 'HR'],
obsperc=obsperc,
obs_color='black',
obs_name='ERA5',
plot_minmax=False,
positions=positions)
# ax.axhline(0, color = 'gray', linewidth = 0.5)
ax.set_xticks(posticks)
ax.set_xticklabels(mod_names + ['ERA5', 'LR', 'HR'])
#ax.set_title(tit)
#ctl.custom_legend(fig, colors, nomi, ncol = 2, add_space_below = 0.1)
ax.set_ylabel('LWA (m/s)')
#plt.title('cmip6 vs cmip6 var_fr '+tip)
allpercs['min'] = [np.min(enso_abs50[ru]) for ru in allru]
allpercs['max'] = [np.max(enso_abs50[ru]) for ru in allru]
nams = ['pi'] + allru[1:]
edgecol = colors
positions = 0.7 * np.arange(len(allru))
posticks = 0.7 * np.arange(len(allru))
ax.axhline(0., color='grey', lw=0.1)
ctl.boxplot_on_ax(
ax,
allpercs,
nams,
colors,
positions=positions,
edge_colors=edgecol,
plot_mean=False,
plot_minmax=False,
plot_ensmeans=False
) #, obsperc = obsperc, obs_color = 'black', obs_name = 'pi')
# for ru, col, pos in zip(allru[1:], colors[1:], positions[1:]):
# ax.scatter(pos, enso_abs50[ru][0], color = col, marker = 'D', s = 100)
# ax.axhline(0, color = 'gray', linewidth = 0.5)
ax.set_xticks(posticks)
ax.set_xticklabels(allru)
#ax.set_title(tit)
#ctl.custom_legend(fig, colors_vtr, ['pi'] + nams, ncol = 4, add_space_below = 0.1)
allpercs['p{}'.format(nu)] = [
np.percentile(plocos[cose, cos + tip, area], nu)
for area in ['EAT', 'PNA'] for cos in ['cmip5', 'cmip6']
]
colorzi = [
colormip[(cos, area)] for area in ['EAT', 'PNA']
for cos in ['cmip5', 'cmip6']
]
nomi = [
cos + ' ' + aa for aa in ['EAT', 'PAC']
for cos in ['cmip5', 'cmip6']
]
ctl.boxplot_on_ax(ax,
allpercs,
nomi,
colorzi,
plot_mean=False,
plot_ensmeans=False,
plot_minmax=False,
positions=positions)
# ax.axhline(0, color = 'gray', linewidth = 0.5)
ax.set_xticks([])
#ax.set_title(tit)
#ax.axvline(np.mean([positions[-1], positions[-2]]), color = 'lightslategray', linewidth = 0.2, linestyle = '--')
ax.axvline(1.25, color='lightslategray', linewidth=0.2, linestyle='--')
ax.text(0.25,
1.05,
'EAT',
horizontalalignment='center',
verticalalignment='center',
rotation='horizontal',
]
allpercs['mean'] = [np.mean(cose) for cose in dist_cose]
# obsperc = dict()
# for nu in [10, 25, 50, 75, 90]:
# obsperc['p{}'.format(nu)] = np.percentile(dist_obs, nu)
# obsperc['mean'] = np.mean(dist_obs)
positions = [0., 0.7, 1.4, 2.1] #, 3.2]
#ctl.boxplot_on_ax(ax, allpercs, keall, colorz, plot_mean = True, plot_ensmeans = False, obsperc = obsperc, obs_color = 'black', obs_name = 'ERA', plot_minmax = False, positions = positions)
ctl.boxplot_on_ax(ax,
allpercs,
keall,
colorz,
plot_mean=True,
plot_ensmeans=False,
obsperc=None,
obs_color='black',
obs_name=None,
plot_minmax=False,
positions=positions,
wi=0.4)
for pos, tte1, tte5 in zip(positions, ttests01, ttests05):
if tte1:
ax.scatter(pos, 0, color='black', marker='*', s=80)
elif tte5:
ax.scatter(pos,
0,
color='black',
marker='*',
s=80,
nu) for ssp in allsims[1:]
]
allpercs['ens_min'] = [
np.min(trend_ssp[(ssp, 'all', pio, cos, reg)])
for ssp in allsims[1:]
]
allpercs['ens_max'] = [
np.max(trend_ssp[(ssp, 'all', pio, cos, reg)])
for ssp in allsims[1:]
]
ctl.boxplot_on_ax(ax,
allpercs,
allsims[1:],
colsim[1:],
plot_mean=False,
plot_ensmeans=False,
plot_minmax=True)
ax.axhline(0, color='gray', linewidth=0.5)
ax.set_xticks([])
ax.set_title(reg_names[reg])
ctl.adjust_ax_scale(axes)
ctl.custom_legend(fig, colsim[1:], allsims[1:], ncol=3)
#fig.suptitle('30yr bsp of WR freq. in 2050-2100 wrt 1964-2014')
fig.savefig(cart_out +
'{}_WRfreq_allssp_{}_{}.pdf'.format(pio, area, cos))