cdiag = 'mean_' + cvar
print '\n Treating ' + cdiag
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,
if idfig == 'simple':
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 + ', 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)
jvar=0
for cvar in vvar:
cdiag = 'mean_'+cvar
print '\n Treating '+cdiag
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
v_var_lngnm[jv] = id_clim.variables[v_var_names[jv]].long_name
except AttributeError:
v_var_lngnm[jv] = v_var_names[jv]
id_clim.close()
for jv in range(nbvar):
cv = v_var_names[jv]
cln = v_var_lngnm[jv]
cfn = cv + '_' + CRUN
print ' Creating figure ' + cfn
# Annual data
VY, FY = bt.monthly_2_annual(vtime[:], XX[jv, :])
ittic = bt.iaxe_tick(nbr / 12)
# Time to plot
bp.plot_1d_mon_ann(vtime[:],
VY,
XX[jv, :],
FY,
cfignm=cfn,
dt_year=ittic,
cyunit=v_var_units[jv],
ctitle=CRUN + ': ' + cln,
cfig_type='svg',
l_tranparent_bg=False)
# Loop along sections:
######################
jsec = 0
for csec in list_sections:
Xst = nmp.flipud(nmp.rot90(id_in.variables['sigtrsp_'+csec][:,:]))
print ' Shape of "sigtrsp_'+csec+'" => ', nmp.shape(Xst)
# Annual array:
vtime_ann, Xst_ann = bt.monthly_2_annual(vtime, Xst)
# 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,
dsigma = vsigma[1] - vsigma[0]
vsigma_bounds[:nbin] = vsigma[:] - 0.5 * dsigma
vsigma_bounds[nbin] = vsigma[nbin - 1] + 0.5 * dsigma
# Loop along sections:
######################
jsec = 0
for csec in list_sections:
Xst = nmp.flipud(nmp.rot90(id_in.variables['sigtrsp_' + csec][:, :]))
print ' Shape of "sigtrsp_' + csec + '" => ', nmp.shape(Xst)
# Annual array:
vtime_ann, Xst_ann = bt.monthly_2_annual(vtime, Xst)
# 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,
bp.plot_1d_multi(vt_y, vice_c, vlabels=['March', 'September'], cfignm=cfn,
dt_year=ittic, cyunit=list_units[jv], ctitle = CRUN+': Sea-Ice extent ('+cv[13:]+')',
cfig_type='svg', l_tranparent_bg=False)
elif cv == 'votemper' or cv == 'vosaline':
if not l3d:
print ' ERROR: '+sys.argv[0]+' => variable '+cv+' are here but '+cv_depth+' was not there!'
sys.exit(0)
VY, ZY = bt.monthly_2_annual(vtime, nmp.flipud(nmp.rot90(X2d[jv2d,:,:])))
y1 = int(VY[0]) ; y2 = int(VY[nbr/12-1])
ittic = bt.iaxe_tick(nbr/12)
# Anomaly with regards to first year:
vini = nmp.zeros(nblev) ; vini[:] = ZY[:,0]
for jy in range(nbr/12): ZY[:,jy] = ZY[:,jy] - vini[:]
[ rmin, rmax, rdf ] = bt.get_min_max_df(ZY,40)
bp.plot_vert_section(VY-0.5, vdepth[:], ZY, ZY*0.+1., rmin, rmax, rdf,
cpal='bbr2', xmin=y1, xmax=y2, dx=ittic, lkcont=True,
zmin = vdepth[0], zmax = max(vdepth), l_zlog=True,
cfignm=cfn, cbunit=r''+list_units[jv],
czunit='Depth (m)',
##########################################
# Basic temp., sali. and SSH time series #
##########################################
if idfig == 'simple':
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)
cf_in = sys.argv[1]
cv_in = sys.argv[2]
bt.chck4f(cf_in)
cname = replace(os.path.basename(cf_in), '.nc', '')
id_in = Dataset(cf_in)
vt_m = id_in.variables['time'][:]
vsst_m = id_in.variables[cv_in][:]
id_in.close()
Nm = len(vsst_m)
# Annual averaging first:
vt, vsst = bt.monthly_2_annual(vt_m, vsst_m)
Nt = len(vt)
n1 = (n_run_mean - 1) / 2
n2 = -n1 - 1
vtmp = nmp.zeros(Nt)
vtime = nmp.zeros(Nt - n_run_mean)
xplot = nmp.zeros((Nt - n_run_mean, 4))
# Nt-n_run_mean because X month-running mean
vtime[:] = vt[n1:n2]
if n_run_mean == 11:
vtmp = bs.running_mean_11(vsst)
# 11-month running mean
##########################################
# Basic temp., sali. and SSH time series #
##########################################
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)
print '\n Treating ' + cdiag
joce = 0
for cocean in list_basin_names:
Xf[:, :] = 0.
jexp = 0
for confexp in clist_confexps:
cf_in = cd_diag + '/' + confexp + '/' + cdiag + '_' + confexp + '_' + 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)
Vt[:nbm / 12] = VY[:]
Xf[jexp, :nbm / 12] = FY[:]
Xf[jexp, nbm / 12:] = -999.
jexp = jexp + 1
bp.plot("1d_multi")(Vt[:],
Xf[:, :],
clist_exps,
cfig_type=cffig,
cfignm=cdiag + '_comparison_' + cocean,
dt=ittic,
loc_legend=DEFAULT_LEGEND_LOC,
cyunit=vunit[jvar],
ctitle=vname[jvar] + ', ' +
list_basin_lgnms[joce],