area, reg, seas)
ctl.plot_map_contour(pval_map,
lat,
lon,
filename=fnam,
cbar_range=(0., 0.1),
plot_anomalies=False,
extend_opt='neither',
draw_grid=True,
cb_label='P-value',
title='area: {}, regime: {}, seas: {}'.format(
area, reg_names_area[area][reg], seas))
cmape = [corrmaps[('corr', area, reg)] for reg in range(4)]
pvlep = [corrmaps[('pval', area, reg)] <= 0.05 for reg in range(4)]
fnam = cart_out_wcmip5 + 'tas_corrmap_{}_allregs_{}.pdf'.format(
area, seas)
ctl.plot_multimap_contour(cmape,
lat,
lon,
filename=fnam,
add_hatching=pvlep,
cbar_range=(-1, 1),
cb_label='Correlation',
title='area: {}, seas: {}'.format(
area, seas),
subtitles=reg_names_area[area],
draw_grid=True,
figsize=(18, 12))
titlefont = 24
plt.rcParams['figure.titlesize'] = titlefont
plt.rcParams['axes.titlesize'] = 18
plt.rcParams['axes.labelsize'] = 18
#############################################################################
cart_out = '/home/fedef/Research/lavori/CMIP6/Clusters_of_regime_hist/'
ctl.mkdir(cart_out)
for area in ['EAT', 'PNA']:
filox = '/home/fedef/Research/lavori/CMIP6/cmip6_hist/out_cmip6_hist_NDJFM_{}_4clus_4pcs_1964-2014_refEOF_dtr.p'.format(
area)
coso, coso_ref = ctl.load_wrtool(filox)
lat = coso['BCC-CSM2-MR_r1i1p1f1']['lat']
lon = coso['BCC-CSM2-MR_r1i1p1f1']['lon']
for reg in range(4):
cluspatterns = [coso[mo]['cluspattern'][reg] for mo in coso.keys()]
centroids, labels, patts, repres, distances = cd.EnsClus_light(
cluspatterns, lat, numclus=4, numpcs=4, flag_perc=False, perc=None)
filename = cart_out + 'clu2_{}_{}.pdf'.format(area, reg)
ctl.plot_multimap_contour(patts,
lat,
lon,
filename,
plot_margins=area,
title='{} - reg {}'.format(area, reg))
allmaps[('ERA',
'full')], lato, lono = ctl.sel_area(lat, lon, ERA_map_full, 'EAT')
ERA_allmaps = []
for reg in range(4):
okind = wri == reg
okblo_map = np.mean(blok[okind, ...], axis=0) - ERA_map_full
ERA_allmaps.append(okblo_map)
allmaps[('ERA',
reg)], lato, lono = ctl.sel_area(lat, lon, okblo_map, 'EAT')
allsums[('ERA', reg)] = np.sum(allmaps[('ERA', reg)])
ERA_allmaps = np.stack(ERA_allmaps)
ctl.plot_multimap_contour(ERA_allmaps,
lat,
lon,
filename=cart_out_maps + 'map_regs_ERA.pdf',
visualization='Nstereo')
for mod in model_names:
filok = [fi for fi in all_fils_bloc if mod == fi.split('.')[5]]
all_mems = [fi.split('.')[7] for fi in filok]
print(filok)
for filo, mem in zip(filok, all_mems):
if 'EC-Earth' in mod and mem == 'r1i1p1f1':
continue
if mod == 'HadGEM3-GC31-LL' and mem == 'r1i1p2f1':
continue
blocked_days, datacoords, aux_info = ctl.readxDncfield(cart_bloc +
filo)
#### Figurez
# only stab
fig, ax = ctl.get_cartopy_fig_ax(coast_lw=0.1)
proj = ccrs.PlateCarree()
for ru, col in zip(allru, colors):
coso = monreg[(ru, 'stab')]
ctl.plot_mapc_on_ax(ax,
coso.values,
coso.lat.values,
coso.lon.values,
proj,
None, (0, 1),
plot_type='binary_contour',
lw_contour=2.,
line_color=col)
ctl.custom_legend(fig, colors, allru, ncol=4)
fig.savefig(cart_out + 'monsoon_stab.pdf')
cosi = [mpi[(ru, 'stab')] for ru in allru]
for co in cosi:
co.values[co < 0.5] = np.nan
ctl.plot_multimap_contour(cosi,
filename=cart_out + 'monsoon_all.pdf',
cbar_range=(0.5, 1.),
subtitles=allru,
cmap='viridis')
c5 = np.percentile(figtrend, 5)
c95 = np.percentile(figtrend, 95)
if var == 'pr_rel':
c5 = -0.1
c95 = 0.3
divnorm = colors.TwoSlopeNorm(vmin=c5, vcenter=vcen, vmax=c95)
ctl.plot_multimap_contour(figtrend,
coso.lat,
coso.lon,
filename=cart_out + var + '_trendz.pdf',
fix_subplots_shape=(3, 3),
figsize=(16, 9),
cbar_range=(c5, c95),
color_norm=divnorm,
subtitles=t**s,
add_hatching=fighatch,
cmap=cmap,
n_color_levels=17,
hatch_styles=['////', '', ''])
okfi = [fi for fi, tit in zip(figtrend, t**s) if '1st' in tit
] + [fi for fi, tit in zip(figtrend, t**s) if '5th' in tit]
oktit = [tit for tit in t**s if '1st' in tit
] + [tit for tit in t**s if '5th' in tit]
okha = [fi for fi, tit in zip(fighatch, t**s) if '1st' in tit
] + [fi for fi, tit in zip(fighatch, t**s) if '5th' in tit]
ctl.plot_multimap_contour(okfi,
cmaps['pr'] = 'BrBG'
cbar_range = dict()
cbar_range['tas'] = (-5, 5)
cbar_range['pr'] = (-3, 3)
lat = results_ref['lat']
lon = results_ref['lon']
cblab = dict()
cblab['tas'] = 'Temperature (K)'
cblab['pr'] = 'Daily prec (mm)'
margs = [-30,70, 20,80]
for varnam in ['tas', 'pr']:
fields = composites[('present', 'ref', varnam, 'mean')]
filnam = cart_out + 'refcomp_{}.pdf'.format(varnam)
ctl.plot_multimap_contour(fields, lat, lon, filnam, visualization = 'standard', central_lat_lon = (70, -20), plot_margins = margs, cmap = cmaps[varnam], title = '', subtitles = regnames, cb_label = cblab[varnam], bounding_lat = 0., draw_grid = True, n_color_levels = 10, draw_contour_lines = False, lw_contour = 0.7, cbar_range = cbar_range[varnam])#, plot_type = 'pcolormesh')
for cos in ['present', 'future']:
for resol in ['LR', 'HR']:
for varnam in ['tas', 'pr']:
fields = compcomp[(cos, varnam, resol)]
filnam = cart_out + 'modcomp_{}_{}_{}.pdf'.format(varnam, cos, resol)
ctl.plot_multimap_contour(fields, lat, lon, filnam, visualization = 'standard', central_lat_lon = (70, -20), plot_margins = margs, cmap = cmaps[varnam], title = '', subtitles = regnames, cb_label = cblab[varnam], bounding_lat = 0., draw_grid = True, n_color_levels = 10, draw_contour_lines = False, lw_contour = 0.7, cbar_range = cbar_range[varnam])#, plot_type = 'pcolormesh')
for varnam in ['tas', 'pr']:
fields = composites[('present', 'ref', varnam, 'mean')]
filnam = cart_out + 'refcomp_{}.pdf'.format(varnam)
ctl.plot_multimap_contour(fields, lat, lon, filnam, visualization = 'standard', central_lat_lon = (70, -20), plot_margins = margs, cmap = cmaps[varnam], title = '', subtitles = regnames, cb_label = cblab[varnam], bounding_lat = 0., draw_grid = True, n_color_levels = 10, draw_contour_lines = False, lw_contour = 0.7, cbar_range = cbar_range[varnam])#, plot_type = 'pcolormesh')
cbar_range['tas'] = (-2, 2)
cbar_range['pr'] = (-2, 2)
ax.plot(coso, lata, color=cols[2], linewidth=2, label='EAT')
ax.axvline(0., color='lightslategray', linewidth=0.2)
ax.set_xlabel('Zonal trend anomaly (m/yr)')
ax.set_ylabel('Latitude')
plt.legend()
fig.savefig(filename)
filename = cart_out_orig + 'trend_anom_ssp585.pdf'
ctl.plot_multimap_contour(trendsanom,
lat,
lon,
filename,
plot_anomalies=True,
plot_margins=(-180, 180, 20, 90),
cbar_range=(-2, 2),
add_hatching=hatchs,
fix_subplots_shape=(7, 2),
figsize=(15, 20),
subtitles=allmods_MM,
cb_label='m/year',
verbose=True,
draw_grid=True)
filename = cart_out_orig + 'trend_anom_ssp585_EAT.pdf'
ctl.plot_multimap_contour(trendsanom,
lat,
lon,
filename,
plot_anomalies=True,
visualization='nearside',
central_lat_lon=(65, -30),
print(i, LA.norm(el), LA.norm(elpr))
allclus_ml.append(cluster_ml)
allclus_sk.append(cluster_sk)
allclus_ml = np.stack(allclus_ml)
allclus_sk = np.stack(allclus_sk)
mean_ml = np.mean(allclus_ml, axis=0)
mean_sk = np.mean(allclus_sk, axis=0)
std_ml = np.std(allclus_ml, axis=0)
std_sk = np.std(allclus_sk, axis=0)
print('Standard deviation of the different clusters:\n')
for i, (el, elpr) in enumerate(zip(std_ml, std_sk)):
print(i, LA.norm(el), LA.norm(elpr))
print('Mean of the different clusters:\n')
for i, (el, elpr) in enumerate(zip(mean_ml, mean_sk)):
print(i, LA.norm(el - elpr))
#plt.ion()
ctl.plot_multimap_contour(cluspatt_ml,
lat_area,
lon_area,
filename=cart + 'clusmap_ml.pdf')
ctl.plot_multimap_contour(cluspatt_sk,
lat_area,
lon_area,
filename=cart + 'clusmap_sk.pdf')
]
nams += subtits
patts += patt_ref
filename = cart_out + 'Allclus_OBSERVED_{}.pdf'.format(area)
figs = ctl.plot_multimap_contour(
patt_ref,
lat,
lon,
filename,
visualization=proj,
central_lat_lon=clatlo[area],
cmap=cmappa,
title='',
subtitles=subtits,
cb_label='Geopotential height anomaly (m)',
color_percentiles=(0.5, 99.5),
number_subplots=False,
bounding_lat=blat,
draw_grid=True,
n_color_levels=10,
n_lines=10,
draw_contour_lines=True,
clevels=clevels,
lw_contour=0.7)
filename = cart_out + 'Allclus_OBSERVED_{}_line.pdf'.format(area)
figs = ctl.plot_multimap_contour(
patt_ref,
lat,
lon,
cartmap = cart + 'Maps/'
area = 'EAT'
for num, nam in enumerate(patnames[area]):
patts = [results_fullp[(ens, area, (1850,2100))]['cluspattern'][num] for ens in ensmem]
#patts = [hist_runs[(ens,area)]['cluspattern'][num] for ens in ensmem]
filename = cartmap + 'Clusfullp_{}{}_all_new.pdf'.format('EAT',num)
print(filename)
for pa in patts:
print(ens, pa.min())
if area == 'EAT':
clatlo = (70.,0.)
else:
clatlo = (70.,-90.)
ctl.plot_multimap_contour(patts, lat, lon, filename, visualization = 'polar', central_lat_lon = clatlo, cmap = 'RdBu_r', title = nam, subtitles = ensmem, cb_label = 'Geopotential height anomaly (m)', color_percentiles = (0.5,99.5), fix_subplots_shape = (2,3), number_subplots = False, figsize = (15,15), draw_contour_lines = False)
sys.exit()
cartfr = cart+'freq_histo/'
for area in ['EAT', 'PNA']:
for ran in [histran, futran]:
fig = plt.figure()
lab1 = 'base'
lab2 = 'stoc'
wi = 0.3
for clu, clunam in enumerate(patnames[area]):
if clu > 0:
lab1 = None
lab2 = None
subtitles.append(ru + ' - eq')
cbr0 = ctl.get_cbar_range([ma.values for ma in mappe[:3]], True, (2, 98))
cbr1 = ctl.get_cbar_range([ma.values for ma in mappe[3:]], True, (2, 98))
cbar_range = 3 * [cbr0] + 3 * [cbr1]
# subtitles = ['{} - {}'.format(ru, seasok) for seasok in ['DJF', 'MAM', 'JJA', 'SON'] for ru in allru]
if var in var_map:
fig = ctl.plot_multimap_contour(
mappe,
figsize=(16, 8),
title=var,
plot_anomalies=True,
add_contour_field=6 * [pimean],
add_contour_plot_anomalies=False,
add_contour_same_levels=False,
fix_subplots_shape=(2, 3),
subtitles=subtitles,
use_different_cbars=True,
cbar_range=cbar_range
) #, cmap = cmaps, cbar_range = cbar_range, use_different_cbars = True, use_different_cmaps = True)
figs_map.append(fig[0])
else:
# 3D vars
fig, axs = plt.subplots(2, 3, figsize=(12, 8))
for ma, ax, subt in zip(mappe, axs.flatten(), subtitles):
if 'tr' in subt:
vma = 10
else:
vma = 5
cba = None
cma = 'viridis'
if var == 'siconc':
cba = (0, 1)
cma = 'Blues_r'
#for pol in ['Npolar', 'Spolar']:
for clat in [90, -90]:
print(var, copl, clat)
fig = ctl.plot_multimap_contour(mappeseas,
figsize=(24, 12),
plot_anomalies=False,
subtitles=subtitles,
title=var + ' - ' + copl,
add_contour_field=mapcont,
add_contour_plot_anomalies=False,
visualization='nearside',
cmap=cma,
cbar_range=cba,
central_lat_lon=(clat, 0),
bounding_lat=40 * np.sign(clat),
fix_subplots_shape=(2, 4))
figs_map.append(fig)
figs_map = np.concatenate(figs_map)
fignames = [
var + '_' + copl + '_' + pole for var in var_map_200 for copl in allcopls
for pole in ['N', 'S']
]
ctl.plot_pdfpages(cart_out + 'bottino_seaicemap.pdf', figs_map, True, fignames)
print('adesso {}\n'.format(tag))
patt = results['cluspattern'][tag]
if np.any(np.isnan(patt)):
print('There are {} NaNs in this patt.. replacing with zeros\n'.format(
np.sum(np.isnan(patt))))
patt[np.isnan(patt)] = 0.0
cartout = cart + 'Model_{}/'.format(tag)
if not os.path.exists(cartout): os.mkdir(cartout)
filename = cartout + 'Allclus_' + tag + '.pdf'
ctl.plot_multimap_contour(
patt,
lat,
lon,
filename,
visualization='polar',
central_lat_lon=(50., 0.),
cmap='RdBu_r',
title='North-Atlantic weather regimes - {}'.format(tag),
subtitles=patnames,
cb_label='Geopotential height anomaly (m)',
color_percentiles=(0.5, 99.5),
fix_subplots_shape=(2, 2),
number_subplots=False)
for patuno, patuno_ref, pp, pps in zip(patt, patt_ref, patnames,
patnames_short):
nunam = cartout + 'clus_' + pps + '_' + tag + '.pdf'
ctl.plot_double_sidebyside(patuno,
patuno_ref,
lat,
lon,
filename=nunam,
visualization='polar',
'DJFM')] = ctl.seasonal_climatology(
varok, dateok, 'DJFM')
resdict[(ru, varnam, 'mean200',
'JJAS')], resdict[(ru, varnam, 'std200',
'JJAS')] = ctl.seasonal_climatology(
varok, dateok, 'JJAS')
pickle.dump(glomean, open(cart_out + 'mean_climate.p', 'wb'))
for varnam in ['tas', 'pr', 'uas']:
fiout = cart_out + '{}_mean200.pdf'.format(varnam)
varall = [resdict[(ru, varnam, 'mean200')] for ru in allru
] + [resdict[(ru, varnam, 'std200')] for ru in allru]
ctl.plot_multimap_contour(varall,
lat,
lon,
fiout,
plot_anomalies=False,
fix_subplots_shape=(2, 3))
fiout = cart_out + '{}_mean200_rsp025.pdf'.format(varnam)
varall = [
resdict[(ru, varnam, 'mean200')] - resdict[('b025', varnam, 'mean200')]
for ru in allru[1:]
] + [resdict[(ru, varnam, 'std200')] for ru in allru[1:]]
ctl.plot_multimap_contour(varall,
lat,
lon,
fiout,
plot_anomalies=False,
fix_subplots_shape=(2, 2))
all_figures = []
for ex in exps_all:
patt = results[ex]['cluspattern_area']
if ex != 'ERA': patt = patt / gg
patt_ref = results_ref['cluspattern_area']
lat_ref = results[ex]['lat_area']
lon_ref = results[ex]['lon_area']
figs = ctl.plot_multimap_contour(patt,
lat_ref,
lon_ref,
None,
visualization='nearside',
central_lat_lon=(70, -20),
cmap='RdBu_r',
title=ex,
subtitles=patnames,
cb_label='Geop. height anomaly (m)',
cbar_range=(-150, 150),
number_subplots=False,
bounding_lat=0,
plot_margins=None,
draw_grid=True,
plot_type='pcolormesh')
all_figures += figs
figs = ctl.plot_multimap_contour(patt_ref,
lat_ref,
lon_ref,
None,
visualization='nearside',
central_lat_lon=(70, -20),
gi11tos_dtr.append(ginko)
gi11tos = np.concatenate(gi11tos, axis = 0)
gi11tos_dtr = np.concatenate(gi11tos_dtr, axis = 0)
pickle.dump([pi11tos, pi11tos_dtr, gi11tos, gi11tos_dtr], open(cart_out + 'tos_data.p', 'wb'))
pickle.dump([obs_states, mod_states], open(cart_out + 'tos_data_xr.p', 'wb'))
pi11tos, pi11tos_dtr, gi11tos, gi11tos_dtr = pickle.load(open(cart_out + 'tos_data.nc', 'rb'))
obs_states, mod_states = pickle.load(open(cart_out + 'tos_data_xr.p', 'rb'))
############################################################
solver = ctl.eof_computation(pi11tos, latitude=pino.lat.values)
filout = cart_out + 'tos_eofs_obs_withtrend.pdf'
ctl.plot_multimap_contour(solver.eofs(eofscaling=2)[:n_ref], lat, lon, filout, plot_anomalies=True, cbar_range=(-0.6,0.6), subtitles= ['eof {}'.format(i) for i in range(n_ref)], cb_label='T (K)')
### detrended
solver_dtr = ctl.eof_computation(pi11tos_dtr, latitude=pino.lat.values)
filout2 = cart_out + 'tos_eofs_obs_detrended.pdf'
ctl.plot_multimap_contour(solver_dtr.eofs(eofscaling=2)[:n_ref], lat, lon, filout2, plot_anomalies=True, cbar_range=(-0.6,0.6), subtitles= ['eof {}'.format(i) for i in range(n_ref)], cb_label='T (K)')
# match
#eof2 = ctl.match_pc_sets(solver.eofs(eofscaling=2)[:n_ref], solver_dtr.eofs(eofscaling=2)[:n_ref], latitude = lat)
#filout3 = cart_out + 'tos_eofs_opa0_diff_dtr-wtr.pdf'
#ctl.plot_multimap_contour(eof2-solver.eofs(eofscaling=2)[:n_ref], pino.lat.values, pino.lon.values, filout3, plot_anomalies=True, cbar_range=(-0.2,0.2), subtitles= ['eof {}'.format(i) for i in range(n_ref)], cb_label='T (K)')
#######################################
obseofs = solver.eofs(eofscaling=2)[:n_ref]
if varnam == 'pr_rel':
transient[thres] = np.nan
equi[thres] = np.nan
mappe = [(transient - pimap) / pimap, (equi - transient) / pimap]
elif varnam == 'tas':
mappe = [transient - pimap, equi - transient]
#mappe = [ma - ctl.global_mean(ma, coso.lat) for ma in mappe]
else:
mappe = [transient - pimap, equi - transient]
ctl.plot_multimap_contour(mappe,
coso.lat,
coso.lon,
filename=cart_out +
'trans_vs_stab_{}_{}.pdf'.format(varnam, ru),
subtitles=['transient', 'stabilization'],
cmap=cmappa,
plot_anomalies=True,
cbar_range=ext,
figsize=(16, 6),
cb_label=cblab)
fig_patt_trans.append(mappe[0])
fig_patt_stab.append(mappe[1])
mappe += [mappe[0] + mappe[1]]
ctl.plot_multimap_contour(
mappe,
coso.lat,
coso.lon,
filename=cart_out +