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)
if idfig == 'fwf':
id_temp.close()
id_sali = Dataset(cf_sali)
XS = id_sali.variables[cname_sali][:,:]
id_sali.close()
if jo == 0:
vyears = nmp.trunc(vyears) + 0.5 ; # in case 1990 and not 1990.5 !!!
yr1=float(int(min(vyears)))
yr2=float(int(max(vyears)))
[nby, nz] = XT.shape
ixtics = bt.iaxe_tick(nby)
# Number of NaN vertical points:
visnan = nmp.isnan(XT[0,:])
nz_nan = nmp.sum(visnan)
nz = nz - nz_nan
XTe = nmp.zeros((nz,nby))
XTe[:,:] = nmp.flipud(nmp.rot90(XT[:,:nz]))
XSe = nmp.zeros((nz,nby))
XSe[:,:] = nmp.flipud(nmp.rot90(XS[:,:nz]))
# Removing value for first year to all years:
nb_years = y2 - y1 + 1
clist_runs = vdic['LIST_RUNS'].split()
clist_confruns = []
for crun in clist_runs:
clist_confruns.append(vdic['CONF'] + '-' + crun)
print sys.argv[0] + ': will compare following runs: '
print clist_confruns
print ' ... saved into ' + cd_diag + '\n'
nbrun = len(clist_confruns)
ittic = bt.iaxe_tick(nb_years)
vtime = nmp.zeros(nb_years)
Xf = nmp.zeros((nbrun, nb_years))
def test_nb_mnth_rec(nbmn, nbyr, cnd):
print ' *** nb. mnth. records =', nbmn
if nbmn % 12 != 0:
print 'ERROR: compare_time_series.py => number of monthly records is not a multile of 12 in the netcdf file! diag = ' + cnd
sys.exit(0)
if nbmn / 12 > nbyr:
print 'ERROR: compare_time_series.py => too many monthly records in netcdf file! diag = ' + cnd
print ' number of expected monthy records =', nbyr * 12
print ' number found =', nbmn
sys.exit(0)
fig_type = 'png'
cf_in = DIAG_D + '/merid_transport_T_S_' + CONFRUN + '.nc'
if not os.path.exists(cf_in):
print ' ERROR: plot_hovm_merid_trsp.py => old ascii file system not supported anymore!'
print ' => need file ' + cf_in + ' !!!'
sys.exit(0)
nbasins = len(bo.voce2treat)
id_in = Dataset(cf_in)
vyear = id_in.variables['time'][:]
Nby = len(vyear)
ittic = bt.iaxe_tick(Nby)
vyear = vyear - 0.5
vlat = id_in.variables['lat'][:]
Nlat = len(vlat)
for jb in range(nbasins):
cbasin = bo.voce2treat[jb]
# long name of basin
cbas = cbasin[:3]
# name as in cf_in ...
if jb == 0:
Xheat = nmp.zeros(nbasins * Nby * Nlat)
Xheat.shape = [nbasins, Nby, Nlat]
Xsalt = nmp.zeros(nbasins * Nby * Nlat)
clist_runs = vdic['LIST_RUNS'].split()
clist_confruns = []
for crun in clist_runs:
clist_confruns.append(vdic['CONF']+'-'+crun)
print sys.argv[0]+': will compare following runs: '; print clist_confruns
print ' ... saved into '+cd_diag+'\n'
nbrun = len(clist_confruns)
ittic = bt.iaxe_tick(nb_years)
vtime = nmp.zeros(nb_years)
Xf = nmp.zeros((nbrun, nb_years))
def test_nb_mnth_rec(nbmn, nbyr, cnd):
print ' *** nb. mnth. records =', nbmn
if nbmn%12 != 0:
print 'ERROR: compare_time_series.py => number of monthly records is not a multile of 12 in the netcdf file! diag = '+cnd
sys.exit(0)
if nbmn/12 > nbyr:
print 'ERROR: compare_time_series.py => too many monthly records in netcdf file! diag = '+cnd
print ' number of expected monthy records =', nbyr*12
print ' number found =', nbmn
sys.exit(0)
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)')
if not os.path.exists(cf_in):
print ' ERROR: plot_hovm_merid_trsp.py => old ascii file system not supported anymore!'
print ' => need file '+cf_in+' !!!' ; sys.exit(0)
#list_basin_names, list_basin_lgnms = bo.get_basin_info(vdic['BM_FILE'])
# As in cdfmhst.F90:
list_basin_names = [ 'GLO','atl','pac','ind' ]
list_basin_lgnms = [ 'Global Ocean','Atlantic Ocean','Pacific Ocean','Indian Ocean' ]
nbasins = len(list_basin_names)
id_in = Dataset(cf_in)
vyear = id_in.variables['time'][:] ; Nby = len(vyear) ; ittic = bt.iaxe_tick(Nby)
vyear = vyear - 0.5
vlat = id_in.variables['lat'][:] ; Nlat = len(vlat)
for joce in range(nbasins):
cbas = list_basin_names[joce] ; # name as in cf_in ...
cbasin = list_basin_lgnms[joce] ; # long name of basin
if joce == 0:
Xheat = nmp.zeros(nbasins*Nby*Nlat) ; Xheat.shape = [ nbasins, Nby, Nlat ]
Xsalt = nmp.zeros(nbasins*Nby*Nlat) ; Xsalt.shape = [ nbasins, Nby, Nlat ]
rmiss_val = id_in.variables['zomht_'+cbas].getncattr('_FillValue')
Xheat[joce,:,:] = id_in.variables['zomht_'+cbas][:,:]
Xsalt[joce,:,:] = id_in.variables['zomst_'+cbas][:,:]
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,
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)
cf_in = DIAG_D+'/merid_transport_T_S_'+CONFRUN+'.nc'
if not os.path.exists(cf_in):
print ' ERROR: plot_hovm_merid_trsp.py => old ascii file system not supported anymore!'
print ' => need file '+cf_in+' !!!' ; sys.exit(0)
nbasins = len(bo.voce2treat)
id_in = Dataset(cf_in)
vyear = id_in.variables['time'][:] ; Nby = len(vyear) ; ittic = bt.iaxe_tick(Nby)
vyear = vyear - 0.5
vlat = id_in.variables['lat'][:] ; Nlat = len(vlat)
for jb in range(nbasins):
cbasin = bo.voce2treat[jb] ; # long name of basin
cbas = cbasin[:3] ; # name as in cf_in ...
if jb == 0:
Xheat = nmp.zeros(nbasins*Nby*Nlat) ; Xheat.shape = [ nbasins, Nby, Nlat ]
Xsalt = nmp.zeros(nbasins*Nby*Nlat) ; Xsalt.shape = [ nbasins, Nby, Nlat ]
Xheat[jb,:,:] = id_in.variables['zomht_'+cbas][:,:]
Xsalt[jb,:,:] = id_in.variables['zomst_'+cbas][:,:]
print ' *** zomht_'+cbas+' and zomst_'+cbas+' sucessfully read into '+cf_in
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)')
cf_in = DIAG_D + "/merid_transport_T_S_" + CONFRUN + ".nc"
if not os.path.exists(cf_in):
print " ERROR: plot_hovm_merid_trsp.py => old ascii file system not supported anymore!"
print " => need file " + cf_in + " !!!"
sys.exit(0)
nbasins = len(bo.voce2treat)
id_in = Dataset(cf_in)
vyear = id_in.variables["time"][:]
Nby = len(vyear)
ittic = bt.iaxe_tick(Nby)
vyear = vyear - 0.5
vlat = id_in.variables["lat"][:]
Nlat = len(vlat)
for jb in range(nbasins):
cbasin = bo.voce2treat[jb]
# long name of basin
cbas = cbasin[:3]
# name as in cf_in ...
if jb == 0:
Xheat = nmp.zeros(nbasins * Nby * Nlat)
Xheat.shape = [nbasins, Nby, Nlat]
Xsalt = nmp.zeros(nbasins * Nby * Nlat)
fig_type='png'
bt.chck4f(cf_in)
id_in = Dataset(cf_in)
vtime = id_in.variables['time'][:] ; nbm = len(vtime)
vsst = id_in.variables[NN_SST][:]
id_in.close()
nt = len(vsst)
print ' => '+str(nt)+' months => '+str(nt/12)+' years\n'
ittic = bt.iaxe_tick(nt/12)
# Array to contain nino series:
xnino = nmp.zeros(nt*4) ; xnino.shape = [ nt, 4 ]
for jt in nmp.arange(nt): xnino[jt,0] = vsst[jt]
# 5-month running mean:
for jt in nmp.arange(2,nt-2):
xnino[jt,1] = (xnino[jt-2,0] + xnino[jt-1,0] + xnino[jt,0] + xnino[jt+1,0] + xnino[jt+2,0]) / 5.
xnino[0:2,1] = xnino[2,1] ; xnino[nt-2:nt,1] = xnino[nt-3,1]
print "\n"
fig_type = 'png'
bt.chck4f(cf_in)
id_in = Dataset(cf_in)
vtime = id_in.variables['time'][:]
nbm = len(vtime)
vsst = id_in.variables[NN_SST][:]
id_in.close()
nt = len(vsst)
print ' => ' + str(nt) + ' months => ' + str(nt / 12) + ' years\n'
ittic = bt.iaxe_tick(nt / 12)
# Array to contain nino series:
xnino = nmp.zeros(nt * 4)
xnino.shape = [nt, 4]
for jt in nmp.arange(nt):
xnino[jt, 0] = vsst[jt]
# 5-month running mean:
for jt in nmp.arange(2, nt - 2):
xnino[jt, 1] = (xnino[jt - 2, 0] + xnino[jt - 1, 0] + xnino[jt, 0] +
xnino[jt + 1, 0] + xnino[jt + 2, 0]) / 5.
xnino[0:2, 1] = xnino[2, 1]