if lfig0:
if CC == 'CLIM':
ctt = CONFRUN + ': Mean Annual Zonal Anomaly of SST / Reynolds, (' + cy1 + '-' + cy2 + ')'
else:
ctt = CONFRUN + ': Mean Annual Zonal Anomaly of SST / ' + CC + ', (' + cy1 + '-' + cy2 + ')'
vzc[:] = bt.mk_zonal(SSTnemo_annual[:, :] - SSTclim_annual[:, :],
imask[0, :, :])
# Only at the end of all the runs we do 2d plotting:
bp.plot("zonal")(vlat,
vzc,
cfignm=path_fig + '1d_zonal_temp_anom_vs_' + CC,
zmin=-5.,
zmax=5.,
dz=1.,
xmin=-75.,
xmax=65.,
czunit=r'$^{\circ}$C',
cfig_type=fig_type,
ctitle=ctt)
if CC == 'CLIM':
ctt = CONFRUN + ': Mean Annual Zonal Anomaly of SSS / WOA2009, (' + cy1 + '-' + cy2 + ')'
else:
ctt = CONFRUN + ': Mean Annual Zonal Anomaly of SSS / ' + CC + ', (' + cy1 + '-' + cy2 + ')'
vzc[:] = bt.mk_zonal(Snemo_annual[0, :, :] - Sclim_annual[0, :, :],
imask[0, :, :])
# Only at the end of all the runs we do 2d plotting:
bp.plot("zonal")(vlat,
[ nt, nj, ni ] = rnf.shape ; print ' Shape of Runoffs :', nt, nj, ni, '\n'
rnf_plot = nmp.zeros(nj*ni) ; rnf_plot.shape = [ nj , ni ]
rnf_plot[:,:] = nmp.mean(rnf[:,:,:],axis=0)
#rnf_plot[:,:] = nmp.log(rnf_plot[:,:]+1.0)
# With lat-lon axis:
#bp.plot_2d(xlon[0,:], xlat[:,ji_lat0], rnf_plot[:,:], Xmask[0,:,:], 0., zmax_rnf_atl, dz_rnf,
# corca=ORCA, lkcont=False, cpal='sst',
# cfignm=path_fig+'runoffs_mean_'+CONFRUN, cbunit=r'10$^{-3}$mm/day',
# ctitle='Mean Runoffs, '+CONFRUN+' ('+cy1+'-'+cy2+')', lforce_lim=True, i_sub_samp=2,
# cfig_type=fig_type, lat_min=-79., lat_max=85., lpix=True)
# Without:
bp.plot("2d")([0], [0], rnf_plot[:,:], Xmask[0,:,:], 0., zmax_rnf_atl, dz_rnf,
corca=ORCA, lkcont=False, cpal='sst',
cfignm=path_fig+'runoffs_mean_'+CONFRUN, cbunit=r'10$^{-3}$mm/day',
ctitle='Mean Runoffs, '+CONFRUN+' ('+cy1+'-'+cy2+')', lforce_lim=True, i_sub_samp=2,
cfig_type=fig_type, lpix=True)
print '\n Bye!'
xclim090[js_ext:nje,:] = xclim09[:,:]
ratio = 1.
#if COMP2D == 'CLIM': ratio = 100.
if xclim03.max()>90.:
ratio = 100.
#DEBUG:
if False:
bp.plot("nproj")('spstere', 0., 1., 0.1, xlon0, xlat0, xclim090[:,:]/ratio,
cfignm=path_fig+'sea-ice_SP_sept_obs', cpal='ice', cbunit='frac.',
ctitle='Sea-Ice, Sept., obs.',
lkcont=True, cfig_type=fig_type,
lforce_lim=True)
bp.plot("nproj")('npol2', 0., 1., 0.1, xlon, xlat, xclim03[:,:]/ratio,
cfignm=path_fig+'sea-ice_NP_march_obs', cpal='ice', cbunit='frac.',
ctitle='Sea-Ice, March, obs.',
lkcont=True, cfig_type=fig_type,
lforce_lim=True)
sys.exit(0)
#DEBUG.
# September
# FIGURE 1
###########
ittic = bt.iaxe_tick(nbm/12)
# We want rmax to be a multile of 0.2:
rmax = nmp.amax(nmp.abs(Xst_ann))
r1 = round(rmax+0.05,1)*100; rmax = (r1 + r1%20)/100. ; rmin = -rmax
dc = (int(round(100*(rmax+0.6)/20.,2))/5*5)/100.
bp.plot("trsp_sig_class")(vtime_ann, vsigma_bounds, Xst_ann, rmin, rmax, dc, dsigma,
lkcont=True, cpal='bbr2_r', dt_year=ittic,
cfignm='transport_sigma_class_'+csec+'_'+CONFRUN,
cfig_type='png', ctitle=r'Transport by $\sigma_0$ class, '+csec+', '+CONFRUN,
lforce_lim=False, vcont_spec1 = [], i_colorbar_jump=2)
# Volume transport for density > rsigma_dense
# ====================================
if jsec == 0:
v278 = nmp.zeros(nbm/12*nbsec) ; v278.shape = [ nbsec, nbm/12 ]
j278 = 0 ; nsig = len(vsigma)
while vsigma[j278] < rsigdense0: j278 = j278 + 1
for jt in range(nbm/12): v278[jsec,jt] = nmp.sum(Xst_ann[j278:,jt])
cv_t='time',
l_return_time=True)
nbm = len(vt0)
test_nb_mnth_rec(nbm, nb_years, cdiag)
VY, FY = bt.monthly_2_annual(vt0, vd0)
vtime[:nbm / 12] = VY[:]
Xf[jrun, :nbm / 12] = FY[:]
Xf[jrun, nbm / 12:] = -999.
jrun = jrun + 1
bp.plot("1d_multi")(vtime[:],
Xf[:, :],
clist_runs,
cfig_type=cffig,
cfignm=cdiag + '_comparison_' + cocean,
dt_year=ittic,
loc_legend=DEFAULT_LEGEND_LOC,
cyunit=vunit[jvar],
ctitle=vname[jvar] + ', ' + cocean,
ymin=0,
ymax=0)
jvar = jvar + 1
if imld == 1:
cvar = vdic['NN_MLD']
cdiag = 'mean_' + cvar
vname = [r'Mixed layer depth']
lplot = True
cm = "%02d" % (jt + 1)
cdate = cy + cm
if cvar == 'sst':
# SST:
bp.plot("2d")(vlon,
vlat,
SSXnemo[jt, :, :] - SSXclim[jt, :, :],
imask[0, :, :],
tmin,
tmax,
dtemp,
corca=vdic['ORCA'],
lkcont=False,
cpal='RdBu_r',
cfignm=path_fig + '/' + cv_dsst + '_' + cdate,
cbunit='K',
cfig_type=fig_type,
lat_min=-65.,
lat_max=75.,
ctitle='SST (NEMO - obs) ' + CONFRUN + ' (' + cdate +
')',
lforce_lim=True,
i_cb_subsamp=2)
if cvar == 'sss':
# SSS:
bp.plot("2d")(vlon,
vlat,
SSXnemo[jt, :, :] - SSXclim[jt, :, :],
bt.chck4f(cf_nemo_moc)
id_nemo = Dataset(cf_nemo_moc)
vz = id_nemo.variables['depthw'][:]
amoc = id_nemo.variables[cv_moc][0, :, :]
id_nemo.close()
[nk, nj] = amoc.shape
print ' Shape of AMOC :', nk, nj, '\n'
# Building a latitude vector:
vlat = nmp.zeros(nj)
ji_lat_mid_atlantic = bt.find_index_from_value(-28., xlon[0, :])
vlat[:] = xlat[:, ji_lat_mid_atlantic]
# Building the vertical mask:
msk_vert = nmp.zeros((nk, nj))
msk_vert[:, :] = nmp.sum(Xmask[:, :, :], axis=2)
idxm = nmp.where(msk_vert[:, :] > 0.)
msk_vert[idxm] = 1.
bp.plot("amoc_lat_depth")(vlat[:], -vz[:], amoc[:,:], msk_vert[:,:], -3., 20., 1., \
cfig_type=fig_type, lkcont=True, cpal='amoc', ymin=0., ymax=70.,
cfignm='AMOC_annual_'+CONFRUN, cbunit='Sv',
cxunit=r'Latitude ($^{\circ}$N)', zmin = 5000., zmax = 0., l_zlog=False,
czunit='Depth (m)', ctitle='AMOC, '+CONFRUN+' ('+cy1+'-'+cy2+')',
lforce_lim=True, i_cb_subsamp=1)
print '\n Bye!'
#ztot = nmp.sum(psi_plot*Xmask*Xe1t*Xe2t)/nmp.sum(Xmask*Xe1t*Xe2t)
#print 'ztot =', ztot
#
#psi_plot = psi_plot - ztot
#cztot = str(round(ztot,2))
# the Jean-Marc Molines method:
ji_lat0 = nmp.argmax(xlat[nj - 1, :])
bp.plot("2d")(xlon[0, :],
xlat[:, ji_lat0],
psi_plot[:, :],
Xmask,
-100.,
100.,
5.,
corca=vdic['ORCA'],
lkcont=True,
cpal='BrBG_r',
cfignm=path_fig + 'psi_mean_' + CONFEXP,
cbunit=r'$(10^{6} m^3/s)$',
ctitle='Mean barotropic stream function , ' + CONFEXP + ' (' +
cy1 + '-' + cy2 + ')',
lforce_lim=True,
i_cb_subsamp=2,
cfig_type=fig_type,
lat_min=-70.,
lat_max=68.,
lpix=False,
vcont_spec=[0.])
a = ( sumx*sumy - nt*sumxy ) / ( sumx*sumx - nt*sumxx )
b = ( sumy - a*sumx ) / nt
print 'a, b =', a, b
# least-square linear trend:
xnino[:,2] = a*vtime[:] + b
print 'mean value for least-square linear trend = ', nmp.sum(xnino[:,2])/nt
# anomaly
xnino[:,3] = xnino[:,1] - xnino[:,2] ; # anomaly for 5-month running mean
print 'mean value for anomaly = ', nmp.sum(xnino[:,3])/nt
# save serie into a file:
#cf_out = cname+'_anomaly.nc'
#f = open(cf_out, 'w')
#f.write('# created with '+sys.argv[0]+' from file '+cf_in+'\n')
#f.write('# time, mean SST, 5-month RM, linear trend, anomaly \n')
#for jt in range(nt):
# f.write(str(round(vtime[jt],3))+' '+str(round(xnino[jt,0],5))+' '\
# +str(round(xnino[jt,1],5))+' '+str(round(xnino[jt,2],5))+' '+str(round(xnino[jt,3],5))+'\n')
#print '\n'
#print ' ascii file '+cf_out+' created!\n'
bp.plot("enso")( vtime, xnino[:,0], cfignm=cname, dt_year=ittic )
if idfig == 'simple':
cf_in = 'mean_'+cvar+'_'+CONFRUN+'_global.nc' ; bt.chck4f(cf_in, script_name=csn)
id_in = Dataset(cf_in)
vtime = id_in.variables['time'][:] ; nbm = len(vtime)
vvar = id_in.variables[cvar][:]
id_in.close()
nby = __test_nb_years__(nbm, cdiag)
# Annual data
VY, FY = bt.monthly_2_annual(vtime[:], vvar[:])
ittic = bt.iaxe_tick(nbm/12)
# Time to plot
bp.plot("1d_mon_ann")(vtime, VY, vvar, FY, cfignm=cdiag+'_'+CONFRUN, dt_year=ittic,
cyunit=cyu, ctitle = CONFRUN+': '+clnm, ymin=ym, ymax=yp, cfig_type=ff)
if idfig == 'htf':
l_qsr = False
cf_in = cdiag+'_'+CONFRUN+'_global.nc' ; bt.chck4f(cf_in, script_name=csn)
id_in = Dataset(cf_in)
list_var = id_in.variables.keys()
vtime = id_in.variables['time'][:] ; nbm = len(vtime)
vqnt = id_in.variables[cvar][:]
if cvr2 in list_var[:]:
l_qsr = True
vqsr = id_in.variables[cvr2][:]
for jt in range(Nt):
jy = jt/12 + 1
if jt % 12 == 0: jm = 0
jm = jm + 1
#print 'jt, jm, jy =', jt, jm, jy
ct = str(jt).zfill(4)
cm = str(jm).zfill(2)
cy = str(jy).zfill(4)
cfig = cv_in+'_'+cfig_suff+'_'+ct
print ' *** will create fig '+cfig+' (year = '+cy+', month = '+cm+')'
bp.plot("nproj")('spstere', rmin, rmax, dr, xlon, xlat, XF2D[jt,:,:],
cfignm=cfig, cpal=colmap, cbunit=cunit,
ctitle='Sea-Ice, year = '+cy+', month = '+cm,
lkcont=True, cfig_type=FIF,
lforce_lim=True)
cfig_out = cv_in+'_'+cfig_suff+'.gif'
os.system("convert -delay 100 -loop 0 "+cv_in+"*.png "+cfig_out+" > out_conv.out")
for jt in range(nt):
cm = "%02d" % (jt + 1)
cdate = cy + cm
if cvar == 'sst':
bp.plot("2d")(vlon,
vlat,
Vnemo[jt, :, :] - Vclim[jt, :, :],
imask[:, :],
tmin,
tmax,
dtemp,
corca=vdic['ORCA'],
lkcont=False,
cpal='RdBu_r',
cfignm=path_fig + '/' + cv + '_' + cdate,
cbunit='K',
cfig_type=fig_type,
lat_min=-65.,
lat_max=75.,
ctitle='SST (NEMO - obs) ' + CONFRUN + ' (' + cdate +
')',
lforce_lim=True,
i_cb_subsamp=2)
if cvar == 'sss':
bp.plot("2d")(vlon,
vlat,
Vnemo[jt, :, :] - Vclim[jt, :, :],
imask[:, :],
for cocean in bo.voce2treat:
Xf[:,:] = 0. ; jrun = 0
for confrun in clist_confruns:
cf_in = DIAG_DIR+'/'+confrun+'/'+cdiag+'_'+confrun+'_'+cocean+'.nc'
vt0, vd0 = bn.read_1d_series(cf_in, cvar, cv_t='time', l_return_time=True)
nbm = len(vt0) ; print ' *** nmb =', nbm
if nbm%12 != 0: print 'ERROR: compare_time_series.py => PROBLEM#1. diag ='+cdiag; sys.exit(0)
if nbm/12 > nb_years: print 'ERROR: compare_time_series.py => PROBLEM#2. diag ='+cdiag, nbm/12, nb_years ; sys.exit(0)
VY, FY = bt.monthly_2_annual(vt0, vd0)
vtime[:nbm/12] = VY[:]
Xf[jrun,:nbm/12] = FY[:] ; Xf[jrun,nbm/12:] = -999.
jrun = jrun + 1
bp.plot("1d_multi")(vtime[:], Xf[:,:], clist_confruns, cfig_type=FIG_FORMAT,
cfignm=cdiag+'_comparison_'+cocean, dt_year=ittic, loc_legend=DEFAULT_LEGEND_LOC,
cyunit=vunit[jvar], ctitle = vname[jvar]+', '+cocean, ymin=0, ymax=0)
jvar = jvar+1
if imld == 1:
zz = vlev[jk]
Sigma0_nemo[jk, :, :] = Sigma0_nemo[jk, :, :] * mmask[:, :]
Sigma0_nemo[jk + 1, :, :] = Sigma0_nemo[jk + 1, :, :] * mmask[:, :]
ijloc = nmp.where(
Sigma0_nemo[jk + 1, :, :] - 0.01 > Sigma0_nemo[jk, :, :])
Xmld_obs[ijloc] = zz
mmask[ijloc] = 0.
# these points won't be checked again only first occurence of the criterion matters!
bp.plot("nproj")('nseas',
200.,
zmax_mld_atl,
dz_mld,
xlon,
xlat,
Xmld_obs[:, :],
cfignm=path_fig + 'mld_NEMO_001_NSeas_march_' +
CONFRUN + '_vs_' + vdic['COMP2D'],
cpal='sst0',
cbunit='m',
ctitle='MLD NEMO, March, ' + CONFRUN + ' (' +
cy1 + '-' + cy2 + ')',
lkcont=True,
cfig_type=fig_type,
lforce_lim=True)
# FIGURES MARCH #
#################
# the Jean-Marc Molines method:
ji_lat0 = nmp.argmax(xlat[nj - 1])
bp.plot("nproj")('nseas',
# We want rmax to be a multile of 0.2:
rmax = nmp.amax(nmp.abs(Xst_ann))
r1 = round(rmax + 0.05, 1) * 100
rmax = (r1 + r1 % 20) / 100.0
rmin = -rmax
dc = (int(round(100 * (rmax + 0.6) / 20.0, 2)) / 5 * 5) / 100.0
bp.plot("trsp_sig_class")(
vtime_ann,
vsigma_bounds,
Xst_ann,
rmin,
rmax,
dc,
dsigma,
lkcont=True,
cpal="bbr2_r",
dt_year=ittic,
cfignm="transport_sigma_class_" + csec + "_" + CONFRUN,
cfig_type="png",
ctitle=r"Transport by $\sigma_0$ class, " + csec + ", " + CONFRUN,
lforce_lim=False,
vcont_spec1=[],
i_cb_subsamp=2,
)
# bp.plot("vert_section")(vtime_ann, vsigma_bounds, Xst_ann, Xst_ann*0.+1., rmin, rmax, dc,
# lkcont=True, cpal='bbr2_r', xmin=nmp.min(vtime_ann), xmax=nmp.max(vtime_ann), dx=ittic,
# cfignm='transport_sigma_class_'+csec+'_'+CONFRUN,
# cfig_type='png', czunit=r'$\sigma_0$',
# ctitle=r'Transport by $\sigma_0$ class, '+csec+', '+CONFRUN, i_cb_subsamp=1 )
rnf_plot[:, :] = nmp.mean(rnf[:, :, :], axis=0)
#rnf_plot[:,:] = nmp.log(rnf_plot[:,:]+1.0)
# With lat-lon axis:
#bp.plot_2d(xlon[0,:], xlat[:,ji_lat0], rnf_plot[:,:], Xmask[0,:,:], 0., zmax_rnf_atl, dz_rnf,
# corca=vdic['ORCA'], lkcont=False, cpal='sst',
# cfignm=path_fig+'runoffs_mean_'+CONFRUN, cbunit=r'10$^{-3}$mm/day',
# ctitle='Mean Runoffs, '+CONFRUN+' ('+cy1+'-'+cy2+')', lforce_lim=True, i_cb_subsamp=2,
# cfig_type=fig_type, lat_min=-79., lat_max=85., lpix=True)
# Without:
bp.plot("2d")([0], [0],
rnf_plot[:, :],
Xmask[0, :, :],
0.,
zmax_rnf_atl,
dz_rnf,
corca=vdic['ORCA'],
lkcont=False,
cpal='sst',
cfignm=path_fig + 'runoffs_mean_' + CONFRUN,
cbunit=r'10$^{-3}$mm/day',
ctitle='Mean Runoffs, ' + CONFRUN + ' (' + cy1 + '-' + cy2 + ')',
lforce_lim=True,
i_cb_subsamp=2,
cfig_type=fig_type,
lpix=True)
print '\n Bye!'
[rmin, rmax, rdf] = bt.get_min_max_df(Xheat[jb, jt_ini:, :], 40)
bp.plot("vert_section")(
vyear[:],
vlat[:],
nmp.flipud(nmp.rot90(Xheat[jb, :, :])),
nmp.flipud(nmp.rot90(imask[:, :])),
rmin,
rmax,
rdf,
cpal="RdBu",
xmin=yr1,
xmax=yr2 + 1.0,
dx=ittic,
lkcont=False,
zmin=vlat[0],
zmax=vlat[Nlat - 1],
l_zlog=False,
cfignm=path_fig + "MHT_" + CONFRUN + "_" + cbasin,
cbunit="PW",
cxunit="",
czunit=r"Latitude ($^{\circ}$N)",
ctitle=CONFRUN + ": Northward advective meridional heat transport, " + cbasin,
cfig_type=fig_type,
lforce_lim=False,
i_cb_subsamp=2,
l_z_increase=True,
)
# Salt transport
vtmp = nmp.zeros(Nt)
vtime = nmp.zeros(Nt - 5)
xplot = nmp.zeros((Nt - 5, 4))
# Nt-5 because 5-month-running mean
vtime[:] = vt[2:-3]
vtmp = bs.running_mean_5(vsst)
# 5-month running mean
xplot[:, 0] = vsst[2:-3]
xplot[:, 1] = vtmp[2:-3]
(za, zb) = bs.least_sqr_line(vtime[:], xplot[:, 1])
# Least-square linear trend
xplot[:, 2] = za * vtime[:] + zb
xplot[:, 3] = xplot[:, 1] - xplot[:, 2]
# anomaly for 5-month running mean
ittic = bt.iaxe_tick(Nt / 12)
bp.plot("oscillation_index")(vtime,
xplot[:, 3],
ymax=2.1,
dy=0.5,
yplusminus=0.4,
dt=ittic,
cfignm=cname,
cfig_type=fig_form,
cyunit=r'SST anomaly ($^{\circ}$C)',
ctitle='ENSO (over Nino box 3.4)')
# FIGURE 1
###########
rmax = nmp.amax(nmp.abs(Xst_ann))
rmin = -rmax
rmin, rmax, dc = bp.__suitable_axis_dx__(rmin, rmax, nb_val=25, lsym0=True)
i_cbssmp = 2
if dc in [0.5, 1., 2., 5.]: i_cbssmp = 1
bp.plot("trsp_sig_class")(
vtime_ann,
vsigma_bounds,
Xst_ann,
rmin,
rmax,
dc,
lkcont=False,
cpal='ncview_jaisnb',
dt=ittic,
cfignm='transport_sigma_class_' + csec + '_' + CONFEXP,
cfig_type='png',
ctitle=r'Transport by $\sigma_0$ class, ' + csec + ', ' + CONFEXP,
vcont_spec1=[],
i_cb_subsamp=i_cbssmp)
# Volume transport for density > rsigma_dense
# ====================================
if jsec == 0:
v278 = nmp.zeros(nby * nbsec)
v278.shape = [nbsec, nby]
j278 = 0
[rmin, rmax, rdf] = bt.get_min_max_df(Xheat[jb, jt_ini:, :], 40)
bp.plot("vert_section")(
vyear[:],
vlat[:],
nmp.flipud(nmp.rot90(Xheat[jb, :, :])),
nmp.flipud(nmp.rot90(imask[:, :])),
rmin,
rmax,
rdf,
cpal='RdBu',
xmin=yr1,
xmax=yr2 + 1.,
dx=ittic,
lkcont=False,
zmin=vlat[0],
zmax=vlat[Nlat - 1],
l_zlog=False,
cfignm=path_fig + 'MHT_' + CONFRUN + '_' + cbasin,
cbunit='PW',
cxunit='',
czunit=r'Latitude ($^{\circ}$N)',
ctitle=CONFRUN + ': Northward advective meridional heat transport, ' +
cbasin,
cfig_type=fig_type,
lforce_lim=False,
i_cb_subsamp=2,
l_z_increase=True)
# Salt transport
if n_run_mean == 11:
vtmp = bs.running_mean_11(vsst)
# 11-month running mean
if n_run_mean == 21:
vtmp = bs.running_mean_21(vsst)
# 21-month running mean
xplot[:, 0] = vsst[n1:n2]
xplot[:, 1] = vtmp[n1:n2]
(za, zb) = bs.least_sqr_line(vtime[:], xplot[:, 1])
# Least-square linear trend
xplot[:, 2] = za * vtime[:] + zb
xplot[:, 3] = xplot[:, 1] - xplot[:, 2]
# anomaly for 11-month running mean
ittic = bt.iaxe_tick(Nm / 12)
bp.plot("oscillation_index")(vtime,
xplot[:, 3],
ymax=0.3,
dy=0.05,
tmin=vt_m[0],
tmax=vt_m[-1],
dt=ittic,
cfignm=cname,
cfig_type=fig_form,
cyunit=r'SST anomaly ($^{\circ}$C)',
ctitle='Atlantic Multidecadal Oscillation (' +
str(n_run_mean) + '-year running mean)')
# Time for figures:
# -----------------
if lfig0:
ctt = CONFEXP+': Mean Annual Zonal Anomaly of SST / "'+cname_obs+'", ('+cy1+'-'+cy2+')'
vzc[:] = bt.mk_zonal(SSTnemo_annual[:,:] - SSTobs_annual[:,:], XMSK=imask[0,:,:])
# Only at the end of all the experiments we do 2d plotting:
bp.plot("zonal")(vlat, vzc, cfignm=path_fig+'1d_zonal_temp_anom_vs_'+CC, zmin=-5., zmax=5., dz=1.,
xmin=-75., xmax=65., czunit=r'$^{\circ}$C', cfig_type=fig_type,
ctitle=ctt)
ctt = CONFEXP+': Mean Annual Zonal Anomaly of SSS / "'+cname_obs+'", ('+cy1+'-'+cy2+')'
vzc[:] = bt.mk_zonal(SSSnemo_annual[:,:] - Sobs_annual[0,:,:], XMSK=imask[0,:,:])
# Only at the end of all the experiments we do 2d plotting:
bp.plot("zonal")(vlat, vzc, cfignm=path_fig+'1d_zonal_sali_anom_vs_'+CC , zmin=-2.5, zmax=2.5, dz=0.5,
xmin=-75., xmax=65., czunit='PSU', cfig_type=fig_type,
ctitle=ctt)
if lfig1:
# SST / Reynolds
xlon = id_mm.variables['glamt'][0,:,:] ; xlat = id_mm.variables['gphit'][0,:,:]
Xmask = id_mm.variables['tmask'][0,:,:,:]
id_mm.close()
# Getting NEMO mean monthly climatology of SSH coverage:
cf_nemo_mnmc = DIAG_D+'/clim/mclim_'+CONFRUN+'_'+cy1+'-'+cy2+'_grid_T.nc4'
bt.chck4f(cf_nemo_mnmc)
id_nemo = Dataset(cf_nemo_mnmc)
ssh = id_nemo.variables[NN_SSH][:,:,:]
id_nemo.close()
[ nt, nj, ni ] = ssh.shape ; print ' Shape of SSH :', nt, nj, ni, '\n'
ssh_plot = nmp.zeros(nj*ni) ; ssh_plot.shape = [ nj , ni ]
ssh_plot[:,:] = nmp.mean(ssh[:,:,:],axis=0)
bp.plot("2d")(xlon[0,:], xlat[:,ji_lat0], ssh_plot[:,:], Xmask[0,:,:], -2.6, 1.6, 0.1,
corca=ORCA, lkcont=True, cpal='jet',
cfignm=path_fig+'ssh_mean_'+CONFRUN, cbunit='m',
ctitle='Mean SSH, '+CONFRUN+' ('+cy1+'-'+cy2+')', lforce_lim=True, i_sub_samp=2,
cfig_type=fig_type, lat_min=-77., lat_max=75., lpix=False, vcont_spec = [ 0. ])
if nt != 1:
print 'ERROR: AMOC clim supposed to have only 1 time record'
sys.exit(0)
# Building the vertical mask:
msk_vert = nmp.zeros(nk * nj)
msk_vert.shape = [nk, nj]
msk_vert[:, :] = nmp.sum(Xmask_atl[:, :, :], axis=2)
idxm = nmp.where(msk_vert[:, :] > 0.)
msk_vert[idxm] = 1.
bp.plot("amoc_lat_depth")(vlat[:], -vz[:], Amoc1[0,:,:], msk_vert[:,:], -3.5, 22., 0.5, \
cfig_type=fig_type, lkcont=True, cpal='amoc', ymin=0., ymax=70.,
cfignm='AMOC_'+ORCA+'-'+crun1, cbunit='Sv',
cxunit=r'Latitude ($^{\circ}$N)', zmin = 5000., zmax = 0., l_zlog=False,
czunit='Depth (m)', ctitle='AMOC, ('+cy1_1+'-'+cy2_1+'), '+ORCA+'-'+crun1, lforce_lim=True)
bp.plot("amoc_lat_depth")(vlat[:], -vz[:], Amoc2[0,:,:], msk_vert[:,:], -3.5, 22., 0.5, \
cfig_type=fig_type, lkcont=True, cpal='amoc', ymin=0., ymax=70.,
cfignm='AMOC_'+ORCA+'-'+crun2, cbunit='Sv',
cxunit=r'Latitude ($^{\circ}$N)', zmin = 5000., zmax = 0., l_zlog=False,
czunit='Depth (m)', ctitle='AMOC, ('+cy1_2+'-'+cy2_2+'), '+ORCA+'-'+crun1, lforce_lim=True)
# Difference of AMOC:
bp.plot("amoc_lat_depth")(vlat[:], -vz[:], Amoc2[0,:,:]-Amoc1[0,:,:], msk_vert[:,:], -0.36, 0.36, 0.02, \
cfig_type=fig_type, lkcont=True, cpal='bbr2', ymin=0., ymax=70.,
cfignm='AMOC_'+ORCA+'_'+crun2+'-'+crun1, cbunit='Sv',
cxunit=r'Latitude ($^{\circ}$N)', zmin = 5000., zmax = 0., l_zlog=False,
czunit='Depth (m)', ctitle='AMOC, ('+cy1_1+'-'+cy2_1+'), '+ORCA+' '+crun2+'-'+crun1, lforce_lim=True)
xclim090[js_ext:nje,:] = xclim09[:,:]
ratio = 1.
#if vdic['COMP2D'] == 'OBS': ratio = 100.
if xclim03.max()>90.:
ratio = 100.
#DEBUG:
if False:
bp.plot("nproj")('spstere', 0., 1., 0.1, xlon0, xlat0, xclim090[:,:]/ratio,
cfignm=path_fig+'sea-ice_SP_sept_obs', cpal='ice', cbunit='(frac.)',
ctitle='Ice fraction, Sept., "'+cn_obs+'"',
lkcont=True, cfig_type=fig_type,
lforce_lim=True)
bp.plot("nproj")('npol2', 0., 1., 0.1, xlon, xlat, xclim03[:,:]/ratio,
cfignm=path_fig+'sea-ice_NP_march_obs', cpal='ice', cbunit='(frac.)',
ctitle='Ice fraction, March, "'+cn_obs+'"',
lkcont=True, cfig_type=fig_type,
lforce_lim=True)
sys.exit(0)
#DEBUG.
# September
[ nt0, nk0, nj0 ] = Amoc2.shape
if [ nt0, nk0, nj0 ] != [ nt, nk, nj ]: print 'ERROR: AMOC clim and mesh_mask do no agree in shape! #2'; sys.exit(0)
if nt != 1: print 'ERROR: AMOC clim supposed to have only 1 time record'; sys.exit(0)
# Building the vertical mask:
msk_vert = nmp.zeros(nk*nj) ; msk_vert.shape = [ nk, nj ]
msk_vert[:,:] = nmp.sum(Xmask_atl[:,:,:],axis=2)
idxm = nmp.where(msk_vert[:,:] > 0.);
msk_vert[idxm] = 1.
bp.plot("amoc_lat_depth")(vlat[:], -vz[:], Amoc1[0,:,:], msk_vert[:,:], -3.5, 22., 0.5, \
cfig_type=fig_type, lkcont=True, cpal='amoc', ymin=0., ymax=70.,
cfignm='AMOC_'+ORCA+'-'+crun1, cbunit='Sv',
cxunit=r'Latitude ($^{\circ}$N)', zmin = 5000., zmax = 0., l_zlog=False,
czunit='Depth (m)', ctitle='AMOC, ('+cy1_1+'-'+cy2_1+'), '+ORCA+'-'+crun1, lforce_lim=True)
bp.plot("amoc_lat_depth")(vlat[:], -vz[:], Amoc2[0,:,:], msk_vert[:,:], -3.5, 22., 0.5, \
cfig_type=fig_type, lkcont=True, cpal='amoc', ymin=0., ymax=70.,
cfignm='AMOC_'+ORCA+'-'+crun2, cbunit='Sv',
cxunit=r'Latitude ($^{\circ}$N)', zmin = 5000., zmax = 0., l_zlog=False,
czunit='Depth (m)', ctitle='AMOC, ('+cy1_2+'-'+cy2_2+'), '+ORCA+'-'+crun1, lforce_lim=True)
# Difference of AMOC:
bp.plot("amoc_lat_depth")(vlat[:], -vz[:], Amoc2[0,:,:]-Amoc1[0,:,:], msk_vert[:,:], -0.36, 0.36, 0.02, \
cfig_type=fig_type, lkcont=True, cpal='bbr2', ymin=0., ymax=70.,
cfignm='AMOC_'+ORCA+'_'+crun2+'-'+crun1, cbunit='Sv',
cxunit=r'Latitude ($^{\circ}$N)', zmin = 5000., zmax = 0., l_zlog=False,
#print 'jt, jm, jy =', jt, jm, jy
ct = str(jt).zfill(4)
cm = str(jm).zfill(2)
cy = str(jy).zfill(4)
cfig = cv_in + '_' + cfig_suff + '_' + ct
print ' *** will create fig ' + cfig + ' (year = ' + cy + ', month = ' + cm + ')'
bp.plot("nproj")('spstere',
rmin,
rmax,
dr,
xlon,
xlat,
XF2D[jt, :, :],
cfignm=cfig,
cpal=colmap,
cbunit=cunit,
ctitle='Sea-Ice, year = ' + cy + ', month = ' + cm,
lkcont=True,
cfig_type=FIF,
lforce_lim=True)
cfig_out = cv_in + '_' + cfig_suff + '.gif'
os.system("convert -delay 100 -loop 0 " + cv_in + "*.png " + cfig_out +
" > out_conv.out")
amoc = id_nemo.variables[cv_moc][0,:,:]
id_nemo.close()
[ nk, nj ] = amoc.shape ; print ' Shape of AMOC :', nk, nj, '\n'
# Building a latitude vector:
vlat = nmp.zeros(nj)
ji_lat_mid_atlantic = bt.find_index_from_value( -28., xlon[0,:] )
vlat[:] = xlat[:,ji_lat_mid_atlantic]
# Building the vertical mask:
msk_vert = nmp.zeros((nk,nj))
msk_vert[:,:] = nmp.sum(Xmask[:,:,:],axis=2)
idxm = nmp.where(msk_vert[:,:] > 0.);
msk_vert[idxm] = 1.
bp.plot("amoc_lat_depth")(vlat[:], -vz[:], amoc[:,:], msk_vert[:,:], -3., 20., 1., \
cfig_type=fig_type, lkcont=True, cpal='amoc', ymin=0., ymax=70.,
cfignm='AMOC_annual_'+CONFRUN, cbunit='Sv',
cxunit=r'Latitude ($^{\circ}$N)', zmin = 5000., zmax = 0., l_zlog=False,
czunit='Depth (m)', ctitle='AMOC, '+CONFRUN+' ('+cy1+'-'+cy2+')',
lforce_lim=True, i_cb_subsamp=1)
print '\n Bye!'
if cvar == 'ice':
# Extraoplating sea values on continents:
bt.drown(Vnemo[:,:,:], imask, k_ew=2, nb_max_inc=10, nb_smooth=10)
for jt in range(nt):
cm = "%02d" % (jt+1)
cdate = cy+cm
if cvar == 'sst':
bp.plot("2d")(vlon, vlat, Vnemo[jt,:,:] - Vclim[jt,:,:],
imask[:,:], tmin, tmax, dtemp,
corca=vdic['ORCA'], lkcont=False, cpal='RdBu_r',
cfignm=path_fig+'/'+cv+'_'+cdate,
cbunit='K', cfig_type=fig_type, lat_min=-65., lat_max=75.,
ctitle='SST (NEMO - obs) '+CONFRUN+' ('+cdate+')',
lforce_lim=True, i_cb_subsamp=2)
if cvar == 'sss':
bp.plot("2d")(vlon, vlat, Vnemo[jt,:,:] - Vclim[jt,:,:],
imask[:,:], smin, smax, dsali,
corca=vdic['ORCA'], lkcont=False, cpal='PiYG_r',
cfignm=path_fig+'/'+cv+'_'+cdate,
cbunit='PSU', cfig_type=fig_type, lat_min=-65., lat_max=75.,
ctitle='SSS (NEMO - obs) '+CONFRUN+' ('+cdate+')',
lforce_lim=True, i_cb_subsamp=2)
if cvar == 'ice':
# We want rmax to be a multile of 0.2:
rmax = nmp.amax(nmp.abs(Xst_ann))
r1 = round(rmax + 0.05, 1) * 100
rmax = (r1 + r1 % 20) / 100.
rmin = -rmax
dc = (int(round(100 * (rmax + 0.6) / 20., 2)) / 5 * 5) / 100.
bp.plot("trsp_sig_class")(
vtime_ann,
vsigma_bounds,
Xst_ann,
rmin,
rmax,
dc,
dsigma,
lkcont=True,
cpal='bbr2_r',
dt_year=ittic,
cfignm='transport_sigma_class_' + csec + '_' + CONFRUN,
cfig_type='png',
ctitle=r'Transport by $\sigma_0$ class, ' + csec + ', ' + CONFRUN,
lforce_lim=False,
vcont_spec1=[],
i_cb_subsamp=2)
#bp.plot("vert_section")(vtime_ann, vsigma_bounds, Xst_ann, Xst_ann*0.+1., rmin, rmax, dc,
# lkcont=True, cpal='bbr2_r', xmin=nmp.min(vtime_ann), xmax=nmp.max(vtime_ann), dx=ittic,
# cfignm='transport_sigma_class_'+csec+'_'+CONFRUN,
# cfig_type='png', czunit=r'$\sigma_0$',
# ctitle=r'Transport by $\sigma_0$ class, '+csec+', '+CONFRUN, i_cb_subsamp=1 )
mmask[ijloc] = 0. ; # these points won't be checked again only first occurence of the criterion matters!
if ldebug:
# Testing my MLD method built of T and S from NEMO (to check the obs. MLD I build the same way...)
Xmld_obs[:,:] = 0.
mmask[:,:] = 1.
for jk in range(nk-1):
zz = vlev[jk]
Sigma0_nemo[jk,:,:] = Sigma0_nemo[jk,:,:]*mmask[:,:]
Sigma0_nemo[jk+1,:,:] = Sigma0_nemo[jk+1,:,:]*mmask[:,:]
ijloc = nmp.where( Sigma0_nemo[jk+1,:,:] - 0.01 > Sigma0_nemo[jk,:,:] )
Xmld_obs[ijloc] = zz
mmask[ijloc] = 0. ; # these points won't be checked again only first occurence of the criterion matters!
bp.plot("nproj")('nseas', 200., zmax_mld_atl, dz_mld, xlon, xlat, Xmld_obs[:,:],
cfignm=path_fig+'mld_NEMO_001_NSeas_march_'+CONFRUN+'_vs_'+COMP2D, cpal='sst0', cbunit='m',
ctitle='MLD NEMO (0.01 crit.), March, '+CONFRUN+' ('+cy1+'-'+cy2+')',
lkcont=True, cfig_type=fig_type, lforce_lim=True)
# FIGURES MARCH #
#################
bp.plot("nproj")('nseas', 200., zmax_mld_atl, dz_mld, xlon, xlat, mldr10[imnth,:,:],
cfignm=path_fig+'mld_NSeas_march_'+CONFRUN, cpal='sst0', cbunit='m',
ctitle='MLD, March, '+CONFRUN+' ('+cy1+'-'+cy2+')',
lkcont=True, cfig_type=fig_type,
lforce_lim=True)
bp.plot("nproj")('spstere', 50., 200., 10., xlon, xlat, mldr10[imnth,:,:],
cm = "%02d" % (jt + 1)
cdate = cy + cm
cdatet = cy + '/' + cm
if cvar == 'sst':
bp.plot("2d")(vlon,
vlat,
Vnemo[jt, :, :] - Vclim[jt, :, :],
imask[:, :],
tmin,
tmax,
dtemp,
corca=vdic['ORCA'],
lkcont=False,
cpal='RdBu_r',
cfignm=path_fig + '/' + cv + '_' + cdate,
cbunit='K',
cfig_type=fig_type,
lat_min=-77.,
lat_max=75.,
ctitle='SST (NEMO - "' + cn_obs_ts + '"), ' + CONFEXP +
' (' + cdatet + ')',
lforce_lim=True,
i_cb_subsamp=2,
lpix=lpix)
if cvar == 'sss':
bp.plot("2d")(vlon,
vlat,
Vnemo[jt, :, :] - Vclim[jt, :, :],
imask[:, :],
# Time for figures:
# -----------------
if lfig0:
if COMP2D == 'CLIM':
ctt = CONFRUN+': Mean Annual Zonal Anomaly of SST / Reynolds, ('+cy1+'-'+cy2+')'
else:
ctt = CONFRUN+': Mean Annual Zonal Anomaly of SST / '+COMP2D+', ('+cy1+'-'+cy2+')'
vzc[:] = bt.mk_zonal(SSTnemo_annual[:,:] - SSTclim_annual[:,:], imask[0,:,:])
# Only at the end of all the runs we do 2d plotting:
bp.plot("zonal")(vlat, vzc, cfignm=path_fig+'1d_zonal_temp_anom_vs_'+COMP2D, zmin=-5., zmax=5., dz=1.,
xmin=-75., xmax=65., cyunit=r'$^{\circ}$C', cfig_type=fig_type,
ctitle=ctt)
if COMP2D == 'CLIM':
ctt = CONFRUN+': Mean Annual Zonal Anomaly of SSS / WOA2009, ('+cy1+'-'+cy2+')'
else:
ctt = CONFRUN+': Mean Annual Zonal Anomaly of SSS / '+COMP2D+', ('+cy1+'-'+cy2+')'
vzc[:] = bt.mk_zonal(Snemo_annual[0,:,:] - Sclim_annual[0,:,:], imask[0,:,:])
# Only at the end of all the runs we do 2d plotting:
bp.plot("zonal")(vlat, vzc, cfignm=path_fig+'1d_zonal_sali_anom_vs_'+COMP2D , zmin=-2.5, zmax=2.5, dz=0.5,
xmin=-75., xmax=65., cyunit='PSU', cfig_type=fig_type,
ctitle=ctt)
for cocean in bo.voce2treat:
Xf[:,:] = 0. ; jrun = 0
for confrun in clist_confruns:
cf_in = cd_diag+'/'+confrun+'/'+cdiag+'_'+confrun+'_'+cocean+'.nc'
vt0, vd0 = bn.read_1d_series(cf_in, cvar, cv_t='time', l_return_time=True)
nbm = len(vt0)
test_nb_mnth_rec(nbm, nb_years, cdiag)
VY, FY = bt.monthly_2_annual(vt0, vd0)
vtime[:nbm/12] = VY[:]
Xf[jrun,:nbm/12] = FY[:] ; Xf[jrun,nbm/12:] = -999.
jrun = jrun + 1
bp.plot("1d_multi")(vtime[:], Xf[:,:], clist_runs, cfig_type=cffig,
cfignm=cdiag+'_comparison_'+cocean, dt_year=ittic, loc_legend=DEFAULT_LEGEND_LOC,
cyunit=vunit[jvar], ctitle = vname[jvar]+', '+cocean, ymin=0, ymax=0)
jvar = jvar+1
if imld == 1:
for jb in range(nbasins):
cbasin = bo.voce2treat[jb]; print '\n *** Basin: '+cbasin
imask[:,:] = 0
Lfinite = nmp.isfinite(Xheat[jb,:,:]) ; idx_good = nmp.where(Lfinite)
imask[idx_good] = 1
[ rmin, rmax, rdf ] = bt.get_min_max_df(Xheat[jb,5:,:],40)
#print ' After get_min_max_df => rmin, rmax, rdf = ', rmin, rmax, rdf
bp.plot("vert_section")(vyear[:], vlat[:], nmp.flipud(nmp.rot90(Xheat[jb,:,:])), nmp.flipud(nmp.rot90(imask[:,:])),
rmin, rmax, rdf,
cpal='jet', xmin=vyear[0], xmax=vyear[Nby-1], dx=ittic, lkcont=False,
zmin = vlat[0], zmax = vlat[Nlat-1], l_zlog=False,
cfignm=path_fig+'MHT_'+CONFRUN+'_'+cbasin, cbunit='PW', cxunit='',
czunit=r'Latitude ($^{\circ}$N)',
ctitle=CONFRUN+': Northward advective meridional heat transport, '+cbasin,
cfig_type=fig_type, lforce_lim=False, i_sub_samp=2, l_z_increase=True)
# Salt transport
#imask[:,:] = 0
#Lfinite = nmp.isfinite(Xsalt[jb,:,:]) ; idx_good = nmp.where(Lfinite)
#imask[idx_good] = 1
[ rmin, rmax, rdf ] = bt.get_min_max_df(Xsalt[jb,5:,:],40)
#print ' After get_min_max_df => rmin, rmax, rdf = ', rmin, rmax, rdf
bp.plot("vert_section")(vyear[:], vlat[:], nmp.flipud(nmp.rot90(Xsalt[jb,:,:])), nmp.flipud(nmp.rot90(imask[:,:])),
[rmin, rmax, rdf] = bt.get_min_max_df(Xheat[joce, jt_ini:, js + 1:je - 1],
40)
bp.plot("hovmoeller")(vyear[:],
vlat[:],
nmp.flipud(nmp.rot90(Xheat[joce, :, :])),
mask,
rmin,
rmax,
rdf,
c_y_is='latitude',
cpal='RdBu_r',
tmin=yr1,
tmax=yr2 + 1.,
dt=ittic,
lkcont=True,
ymin=ymin,
ymax=ymax,
dy=dy,
cfignm=path_fig + 'MHT_' + CONFEXP + '_' + cbas,
cbunit='PW',
ctunit='',
cyunit=r'Latitude ($^{\circ}$N)',
ctitle=CONFEXP +
': Northward advective meridional heat transport, ' +
cbasin,
cfig_type=fig_type,
i_cb_subsamp=2,
l_y_increase=True)
# Salt transport
xclim090[js_ext:nje,:] = xclim09[:,:]
ratio = 1.
#if vdic['COMP2D'] == 'CLIM': ratio = 100.
if xclim03.max()>90.:
ratio = 100.
#DEBUG:
if False:
bp.plot("nproj")('spstere', 0., 1., 0.1, xlon0, xlat0, xclim090[:,:]/ratio,
cfignm=path_fig+'sea-ice_SP_sept_obs', cpal='ice', cbunit='(frac.)',
ctitle='Ice fraction, Sept., obs.',
lkcont=True, cfig_type=fig_type,
lforce_lim=True)
bp.plot("nproj")('npol2', 0., 1., 0.1, xlon, xlat, xclim03[:,:]/ratio,
cfignm=path_fig+'sea-ice_NP_march_obs', cpal='ice', cbunit='(frac.)',
ctitle='Ice fraction, March, obs.',
lkcont=True, cfig_type=fig_type,
lforce_lim=True)
sys.exit(0)
#DEBUG.
# September
bt.chck4f(cf_nemo_mnmc)
id_nemo = Dataset(cf_nemo_mnmc)
ssh = id_nemo.variables[vdic['NN_SSH']][:,:,:]
id_nemo.close()
[ nt, nj, ni ] = ssh.shape ; print ' Shape of SSH :', nt, nj, ni, '\n'
ssh_plot = nmp.zeros((nj,ni))
ssh_plot[:,:] = nmp.mean(ssh[:,:,:],axis=0)
ztot = nmp.sum(ssh_plot*Xmask*Xe1t*Xe2t)/nmp.sum(Xmask*Xe1t*Xe2t)
print 'ztot =', ztot
ssh_plot = ssh_plot - ztot
cztot = str(round(ztot,2))
# the Jean-Marc Molines method:
ji_lat0 = nmp.argmax(xlat[nj-1]) ;
bp.plot("2d")(xlon[0,:], xlat[:,ji_lat0], ssh_plot[:,:], Xmask, -2., 2., 0.1,
corca=vdic['ORCA'], lkcont=True, cpal='BrBG_r',
cfignm=path_fig+'ssh_mean_'+CONFRUN, cbunit=r'$(m)$',
ctitle='Mean SSH (corrected about z=0, removed '+cztot+'m), '+CONFRUN+' ('+cy1+'-'+cy2+')',
lforce_lim=True, i_cb_subsamp=2,
cfig_type=fig_type, lat_min=-77., lat_max=75., lpix=False, vcont_spec = [ 0. ])
cf_nemo_moc = vdic[
'DIAG_D'] + '/clim/aclim_' + CONFEXP + '_' + cy1 + '-' + cy2 + '_MOC.nc4'
bt.chck4f(cf_nemo_moc)
id_nemo = Dataset(cf_nemo_moc)
vz = id_nemo.variables['depthw'][:]
amoc = id_nemo.variables[cv_moc][0, :, :]
id_nemo.close()
[nk, nj] = amoc.shape
print ' Shape of AMOC :', nk, nj, '\n'
# Building a latitude vector:
vlat = nmp.zeros(nj)
ji_lat_mid_atlantic = bt.find_index_from_value(-28., xlon[0, :])
vlat[:] = xlat[:, ji_lat_mid_atlantic]
# Building the vertical mask:
msk_vert = nmp.zeros((nk, nj))
msk_vert[:, :] = nmp.sum(Xmask[:, :, :], axis=2)
idxm = nmp.where(msk_vert[:, :] > 0.)
msk_vert[idxm] = 1.
bp.plot("vert_section")( vlat[:], -vz[:], amoc[:,:], msk_vert[:,:], -4., 20., 1., \
lkcont=True, cpal='ncview_nrl', lzonal=False, xmin=10., xmax=70., dx=5.,
cfignm='AMOC_annual_'+CONFEXP, cbunit='Sv',
zmin=0., zmax=5000., l_zlog=False, cfig_type=fig_type,
czunit='Depth (m)', ctitle='AMOC, '+CONFEXP+' ('+cy1+'-'+cy2+')' )
print '\n Bye!'
#############################################################
if idfig == 'simple':
cf_in = 'mean_'+cvar+'_'+CONFEXP+'_GLO.nc' ; bt.chck4f(cf_in, script_name=csn)
id_in = Dataset(cf_in)
vtime = id_in.variables['time'][:]
vvar = id_in.variables[cvar][:]
id_in.close()
(nby, nbm, nbr, ittic) = bt.test_nb_years(vtime, cdiag)
# Annual data
VY, FY = bt.monthly_2_annual(vtime[:], vvar[:])
# Time to plot
bp.plot("1d_mon_ann")(vtime, VY, vvar, FY, cfignm=cdiag+'_'+CONFEXP, dt=ittic,
cyunit=cyu, ctitle = CONFEXP+': '+clnm, ymin=ym, ymax=yp, cfig_type=ff)
if cvar == vdic['NN_SSH']:
clnm = 'Global freshwater imbalance based on annual SSH drift'
Fimb = nmp.zeros(nby)
for jy in range(1,nby):
Fimb[jy] = (FY[jy] - FY[jy-1])*Socean/Lt_1y
Fimb[0] = nmp.nan
bp.plot("1d_mon_ann")(VY, VY, Fimb, Fimb, cfignm=cdiag+'-imb_'+CONFEXP, dt=ittic,
cyunit='Sv', ctitle = CONFEXP+': '+clnm,
ymin=-0.8, ymax=0.8, dy=0.1, cfig_type=ff, y_cst_to_add=0.)
############################################################
# Time series of 3D-averaged 3D fields such as SST, SSS, SSH
cf_in = 'mean_'+cvar+'_'+CONFRUN+'_global.nc' ; bt.chck4f(cf_in, script_name='plot_time_series.py')
id_in = Dataset(cf_in)
vtime = id_in.variables['time'][:] ; nbm = len(vtime)
vvar = id_in.variables[cvar][:]
id_in.close()
if nbm%12 != 0: print 'ERROR: plot_time_series.py => '+cvar+', numberof records not a multiple of 12!', sys.exit(0)
# Annual data
VY, FY = bt.monthly_2_annual(vtime[:], vvar[:])
ittic = bt.iaxe_tick(nbm/12)
# Time to plot
bp.plot("1d_mon_ann")(vtime, VY, vvar, FY, cfignm=cdiag+'_'+CONFRUN, dt_year=ittic,
cyunit=cyu, ctitle = CONFRUN+': '+clnm, ymin=ym, ymax=yp)
if idfig == 'ts3d':
nb_oce = len(bo.voce2treat)
vzrange = [ '0-bottom', '0-100' , '100-1000', '1000-bottom' ] ; nbzrange = len(vzrange)
vlab = [ 'AllDepth', '0m-100m', '100m-1000m', '1000m-bottom' ]
joce = 0
for coce in bo.voce2treat[:]:
cf_in = '3d_'+cvar+'_'+CONFRUN+'_'+coce+'.nc' ; bt.chck4f(cf_in, script_name='plot_time_series.py')
ztot = nmp.sum(ssh_plot * Xmask * Xe1t * Xe2t) / nmp.sum(Xmask * Xe1t * Xe2t)
print 'ztot =', ztot
ssh_plot = ssh_plot - ztot
cztot = str(round(ztot, 2))
# the Jean-Marc Molines method:
ji_lat0 = nmp.argmax(xlat[nj - 1, :])
bp.plot("2d")(xlon[0, :],
xlat[:, ji_lat0],
ssh_plot[:, :],
Xmask,
-2.,
2.,
0.1,
corca=vdic['ORCA'],
lkcont=True,
cpal='BrBG_r',
cfignm=path_fig + 'ssh_mean_' + CONFEXP,
cbunit=r'$(m)$',
ctitle='Mean SSH (corrected about z=0, removed ' + cztot +
'm), ' + CONFEXP + ' (' + cy1 + '-' + cy2 + ')',
lforce_lim=True,
i_cb_subsamp=2,
cfig_type=fig_type,
lat_min=-77.,
lat_max=75.,
lpix=False,
vcont_spec=[0.])
# Summer
# the Jean-Marc Molines method:
ji_lat0 = nmp.argmax(xlat[nj - 1, :])
# Annual Curl:
bp.plot("2d")(xlon[0, :],
xlat[:, ji_lat0],
Xcurl_plot[0, :, :],
Xmask,
-Rmax,
Rmax,
dR,
corca=vdic['ORCA'],
lkcont=False,
cpal=CPAL_CURL,
cfignm=path_fig + 'tau_curl_annual_' + CONFEXP,
cbunit=r'$(10^{-6}s^{-1})$',
ctitle='Wind stress curl, ' + CONFEXP + ' (' + cy1 + '-' + cy2 +
')' + cextra_crl,
lforce_lim=False,
i_cb_subsamp=1,
cfig_type=fig_type,
lat_min=-77.,
lat_max=75.,
lpix=True)
if not l_tau_is_annual:
# JFM Curl:
bp.plot("2d")(xlon[0, :],
xlat[:, ji_lat0],
Xcurl_plot[1, :, :],
if nbm % 12 != 0:
print 'ERROR: plot_time_series.py => ' + cvar + ', number of records not a multiple of 12!'
sys.exit(0)
# Annual data
VY, FY = bt.monthly_2_annual(vtime[:], vvar[:])
ittic = bt.iaxe_tick(nbm / 12)
# Time to plot
bp.plot("1d_mon_ann")(vtime,
VY,
vvar,
FY,
cfignm=cdiag + '_' + CONFRUN,
dt_year=ittic,
cyunit=cyu,
ctitle=CONFRUN + ': ' + clnm,
ymin=ym,
ymax=yp,
cfig_type=ff)
if idfig == 'fwf':
l_rnf = False
l_emp = False
l_prc = False
l_clv = False
l_evp = False
l_evb = False
vlat[:] = xlat[:, ji_lat0]
vz_nemo = nmp.zeros((Nt, nj))
# a zonal profile...
vz_nemo[:, :] = bt.mk_zonal(Xqsol_nemo, XMSK=Xmask)
ctt = 'Zonnaly-averaged annual Net Solar heat flux, ' + CONFEXP + ' (' + cy1 + '-' + cy2 + ') vs OBS.'
bp.plot("zonal")(vlat,
nmp.mean(vz_nemo, axis=0),
VY1=vlat_obs,
VZ1=nmp.mean(vz_obs, axis=0),
cfignm=path_fig + 'zonal_Qsol_vs_obs_annual_' + CONFEXP,
zmin=50.,
zmax=250.,
dz=10.,
xmin=-70.,
xmax=65.,
dx=10.,
czunit=r'$(W/m^2)$',
cfig_type=fig_type,
ctitle=ctt,
lab='NEMO (' + cv_qsol + ')',
lab1=vdic['NM_QSOL_OBS'],
loc_legend='center')
if Nt == 12:
ctt = 'Zonnaly-averaged JFM Net Solar heat flux, ' + CONFEXP + ' (' + cy1 + '-' + cy2 + ') vs OBS.'
bp.plot("zonal")(vlat,
nmp.mean(vz_nemo[:3, :], axis=0),
VY1=vlat_obs,
# Removing value for first year to all years:
vy1 = nmp.zeros(nz) ; vy1[:] = XTe[:,0]
for jy in range(nby): XTe[:,jy] = XTe[:,jy] - vy1[:]
vy1 = nmp.zeros(nz) ; vy1[:] = XSe[:,0]
for jy in range(nby): XSe[:,jy] = XSe[:,jy] - vy1[:]
z0 = vdepth[0]
zK = max(vdepth)
[ rmin, rmax, rdf ] = bt.get_min_max_df(XTe,40)
bp.plot("vert_section")(vyears[:], vdepth[:nz], XTe[:,:], XTe[:,:]*0.+1., rmin, rmax, rdf,
cpal='bbr2', xmin=yr1, xmax=yr2+1., dx=ixtics, lkcont=False,
zmin = z0, zmax = zK, l_zlog=True,
cfignm=path_fig+'hov_temperature_'+CONFRUN+'_'+coce, cbunit=r'$^{\circ}$C', cxunit='',
czunit='Depth (m)',
ctitle=CONFRUN+': Spatially-averaged temperature evolution, '+voceans_u[jo]+', ('+str(int(yr1))+'-'+str(int(yr2))+')',
cfig_type=fig_type, lforce_lim=True, i_cb_subsamp=2)
XSe = 1000.*XSe
[ rmin, rmax, rdf ] = bt.get_min_max_df(XSe,40)
bp.plot("vert_section")(vyears[:], vdepth[:nz], XSe[:,:], XSe[:,:]*0.+1., rmin, rmax, rdf,
cpal='bbr2', xmin=yr1, xmax=yr2+1., dx=ixtics, lkcont=False,
zmin = z0, zmax = zK, l_zlog=True,
cfignm=path_fig+'hov_salinity_'+CONFRUN+'_'+coce, cbunit=r'10$^{-3}$PSU', cxunit='',
czunit='Depth (m)',
ctitle=CONFRUN+': Spatially-averaged salinity evolution, '+voceans_u[jo]+', ('+str(int(yr1))+'-'+str(int(yr2))+')',
cfig_type=fig_type, lforce_lim=True, i_cb_subsamp=2)
# least-square curve for 5-month running mean:
sumx = nmp.sum(vtime[:])
sumy = nmp.sum(xnino[:, 1])
sumxx = nmp.sum(vtime[:] * vtime[:])
sumxy = nmp.sum(vtime[:] * xnino[:, 1])
a = (sumx * sumy - nt * sumxy) / (sumx * sumx - nt * sumxx)
b = (sumy - a * sumx) / nt
print 'a, b =', a, b
# least-square linear trend:
xnino[:, 2] = a * vtime[:] + b
print 'mean value for least-square linear trend = ', nmp.sum(xnino[:, 2]) / nt
# anomaly
xnino[:, 3] = xnino[:, 1] - xnino[:, 2]
# anomaly for 5-month running mean
print 'mean value for anomaly = ', nmp.sum(xnino[:, 3]) / nt
# save serie into a file:
#cf_out = cname+'_anomaly.nc'
#f = open(cf_out, 'w')
#f.write('# created with '+sys.argv[0]+' from file '+cf_in+'\n')
#f.write('# time, mean SST, 5-month RM, linear trend, anomaly \n')
#for jt in range(nt):
# f.write(str(round(vtime[jt],3))+' '+str(round(xnino[jt,0],5))+' '\
# +str(round(xnino[jt,1],5))+' '+str(round(xnino[jt,2],5))+' '+str(round(xnino[jt,3],5))+'\n')
#print '\n'
#print ' ascii file '+cf_out+' created!\n'
bp.plot("enso")(vtime, xnino[:, 0], cfignm=cname, dt_year=ittic)
