print '\n *** Treating box ' + cbox + ' => ', i1, j1, i2 - 1, j2 - 1
# Filling Convection regions arrays:
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
xsurf = nmp.zeros(ny_b * nx_b)
xsurf.shape = [ny_b, nx_b]
xtmp0 = nmp.zeros(ny_b * nx_b)
xtmp0.shape = [ny_b, nx_b]
xtmp1 = nmp.zeros(ny_b * nx_b)
xtmp1.shape = [ny_b, nx_b]
xtmp2 = nmp.zeros(ny_b * nx_b)
xtmp2.shape = [ny_b, nx_b]
bt.chck4f(cf_in)
print ' Reading SST, SSS and MLD in box ' + cbox + ' in file ' + cf_in
id_in = Dataset(cf_in)
if NN_SST == 'thetao':
xsst = id_in.variables[NN_SST][:, 0, j1:j2, i1:i2]
else:
xsst = id_in.variables[NN_SST][:, j1:j2, i1:i2]
if NN_SSS == 'so':
xsss = id_in.variables[NN_SSS][:, 0, j1:j2, i1:i2]
else:
xsss = id_in.variables[NN_SSS][:, j1:j2, i1:i2]
xmld = id_in.variables[NN_MLD][:, j1:j2, i1:i2]
xtpot = id_in.variables[NN_T][:, :, j1:j2, i1:i2]
xsali = id_in.variables[NN_S][:, :, j1:j2, i1:i2]
id_in.close()
CONFRUN = CONF + "-" + RUN print " CONFRUN = " + CONFRUN print " CPREF = " + CPREF # First will read name and coordinates of rectangular boxes to treat into file FILE_DEF_BOXES ############################################################################################## vboxes, vi1, vj1, vi2, vj2 = bt.read_box_coordinates_in_ascii(FILE_DEF_BOXES) nbb = len(vboxes) print "" # Opening mesh-mask and T-grid file of NEMO: ############################################ bt.chck4f(MM_FILE) id_mm = Dataset(MM_FILE) Xmask = id_mm.variables["tmask"][0, :, :, :] ze1t = id_mm.variables["e1t"][0, :, :] ze2t = id_mm.variables["e2t"][0, :, :] id_mm.close() cf_in = CPREF + cy + "0101_" + cy + "1231_grid_T.nc" bt.chck4f(cf_in) id_in = Dataset(cf_in) Vtime = id_in.variables["time_counter"][:] Vdepth = id_in.variables["deptht"][:] Xlon = id_in.variables["nav_lon"][:, :] Xlat = id_in.variables["nav_lat"][:, :] Xtheta = id_in.variables[NN_T][:, :, :, :]
# First will read name and coordinates of rectangular boxes to treat into file FILE_DEF_BOXES
##############################################################################################
vboxes, vi1, vj1, vi2, vj2 = bt.read_box_coordinates_in_ascii(FILE_DEF_BOXES)
nbb = len(vboxes)
print ''
# Checking presence of NEMO files:
cfroot = CPREF+cy+'0101_'+cy+'1231'
cf_in_T = cfroot+'_grid_T.nc'; bt.chck4f(cf_in_T, script_name=cname_script)
if luv:
cf_in_U = cfroot+'_grid_U.nc'; bt.chck4f(cf_in_U, script_name=cname_script)
cf_in_V = cfroot+'_grid_V.nc'; bt.chck4f(cf_in_V, script_name=cname_script)
# Coordinates, mask and metrics:
bt.chck4f(MM_FILE, script_name=cname_script)
id_mm = Dataset(MM_FILE)
Xmask = id_mm.variables['tmask'] [0,:,:,:]
ze1t = id_mm.variables['e1t'] [0,:,:]
ze2t = id_mm.variables['e2t'] [0,:,:]
ve3t = id_mm.variables['e3t_1d'] [0,:]
zlon = id_mm.variables['nav_lon'] [:,:]
zlat = id_mm.variables['nav_lat'] [:,:]
vzt = id_mm.variables['gdept_1d'][0,:]
os.system("mkdir -p " + path_fig)
# 3D climatology :
# ------------
# Temperature
# bt.chck4f(vdic['F_T_CLIM_3D_12'])
# id_clim = Dataset(vdic['F_T_CLIM_3D_12'])
# Tclim = id_clim.variables[vdic['NN_T_CLIM']][:,0,:,:]; print '(has ',Tclim.shape[0],' time snapshots)\n'
# id_clim.close()
# [ nmn , nk0 , nj0 , ni0 ] = Tclim.shape
# Salinity
if cvar == 'sss':
bt.chck4f(vdic['F_S_CLIM_3D_12'])
id_clim = Dataset(vdic['F_S_CLIM_3D_12'])
SSXclim = id_clim.variables[vdic['NN_S_CLIM']][:, 0, :, :]
print '(has ', SSXclim.shape[0], ' time snapshots)\n'
id_clim.close()
# 2D SST obs :
if cvar == 'sst':
bt.chck4f(vdic['SST_CLIM_12'])
id_clim_sst = Dataset(vdic['SST_CLIM_12'])
SSXclim = id_clim_sst.variables[vdic['NN_SST_CLIM']][:, :, :]
print '(has ', SSXclim.shape[0], ' time snapshots)\n'
id_clim_sst.close()
[nmn, nj0, ni0] = SSXclim.shape
if not ( jy1 >= 1984 and jy2 <= 2006 ):
jy1_clim = 1984 ; jy2_clim = 2006
else:
jy1_clim = jy1 ; jy2_clim = jy2
print ' First and last year to treat:', jy1, jy2
print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n'
# 3D climatology :
# ------------
# Temperature
bt.chck4f(vdic['F_T_OBS_3D_12'])
id_obs = Dataset(vdic['F_T_OBS_3D_12'])
Tobs = id_obs.variables[vdic['NN_T_OBS']][:,:,:,:]; print '(has ',Tobs.shape[0],' time snapshots)\n'
id_obs.close()
[ nmn , nk0 , nj0 , ni0 ] = Tobs.shape
# Salinity
bt.chck4f(vdic['F_S_OBS_3D_12'])
id_obs = Dataset(vdic['F_S_OBS_3D_12'])
Sobs = id_obs.variables[vdic['NN_S_OBS']][:,:,:,:]; print '(has ',Sobs.shape[0],' time snapshots)\n'
id_obs.close()
# 2D SST obs :
print 'We use the following SST climatology:'; print vdic['F_SST_OBS_12']
cv_sst_obs = vdic['NN_SST_OBS']
print ' *** file to read ' + vdic['NN_ICEF'] + ' from: ' + cf_ice + '\n'
if not cvar in ['sst', 'sss', 'ice', 'mld']:
print 'ERROR (prepare_movies.py): variable ' + cvar + ' not supported yet!'
sys.exit(0)
path_fig = 'movies'
os.system("mkdir -p " + path_fig)
# 3D climatology :
# ------------
# Salinity
if cvar == 'sss':
bt.chck4f(vdic['F_S_OBS_3D_12'])
id_clim = Dataset(vdic['F_S_OBS_3D_12'])
Vclim = id_clim.variables[vdic['NN_S_OBS']][:, 0, :, :]
print '(has ', Vclim.shape[0], ' time snapshots)\n'
id_clim.close()
# 2D SST obs :
if cvar == 'sst':
cv_sst_obs = vdic['NN_SST_OBS']
bt.chck4f(vdic['F_SST_OBS_12'])
id_clim_sst = Dataset(vdic['F_SST_OBS_12'])
nb_dim = len(id_clim_sst.variables[cv_sst_obs].dimensions)
if nb_dim == 3:
Vclim = id_clim_sst.variables[cv_sst_obs][:, :, :]
print '(has ', Vclim.shape[0], ' time snapshots)\n'
elif nb_dim == 4:
path_fig='./' fig_type='png' narg = len(sys.argv) if narg < 3: print 'Usage: '+sys.argv[0]+' <year1> <year2>'; sys.exit(0) cy1 = sys.argv[1] ; cy2=sys.argv[2]; jy1=int(cy1); jy2=int(cy2) jy1_clim = jy1 ; jy2_clim = jy2 print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n' # Getting coordinates: bt.chck4f(vdic['MM_FILE']) id_mm = Dataset(vdic['MM_FILE']) xlon = id_mm.variables['glamt'][0,:,:] ; xlat = id_mm.variables['gphit'][0,:,:] Xmask = id_mm.variables['tmask'][0,0,:,:] Xe1t = id_mm.variables['e1t'][0,:,:] Xe2t = id_mm.variables['e2t'][0,:,:] id_mm.close() # Getting NEMO mean monthly climatology of SSH coverage: cf_nemo_mnmc = vdic['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[vdic['NN_SSH']][:,:,:] id_nemo.close()
l_force_lim = True
cfig_type = "png"
path_fig = vdic["DIAG_D"] + "/"
cf_dens_sect = vdic["DENSITY_SECTION_FILE"]
print " Using cf_dens_sect = " + cf_dens_sect
list_sections = bo.get_sections_names_from_file(cf_dens_sect)
print "List of sections to treat: ", list_sections
nbsec = len(list_sections)
cf_in = vdic["DIAG_D"] + "/transport_by_sigma_class.nc"
bt.chck4f(cf_in)
id_in = Dataset(cf_in)
vtime = id_in.variables["time"][:]
nbm = len(vtime)
vsigma = id_in.variables["sigma_bins"][:]
nbin = len(vsigma)
print " => " + str(nbin) + " sigma-density bins and " + str(nbm) + " time snapshots..."
if nbm % 12 != 0:
print "nbm is not a multiple of 12!"
sys.exit(0)
elif cdiag == 'seaice':
idfig = 'ice'
cyu = r'10$^6$km$^2$'
else:
print 'ERROR: ' + csn + ' => diagnostic ' + cdiag + ' unknown!'
sys.exit(0)
##########################################
# 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,
if narg < 3: print 'Usage: '+sys.argv[0]+' <year1> <year2>'; sys.exit(0)
cy1 = sys.argv[1] ; cy2=sys.argv[2]; jy1=int(cy1); jy2=int(cy2)
jy1_clim = jy1 ; jy2_clim = jy2
print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n'
store_dir = os.getenv('DIAG_D')
if store_dir == '': print 'The DIAG_D environement variable is no set'; sys.exit(0)
# Getting coordinates:
bt.chck4f(cf_mesh_mask)
id_mm = Dataset(cf_mesh_mask)
xlat = id_mm.variables['gphit'][0,:,:]
xlon = id_mm.variables['glamt'][0,:,:]
Xmask = id_mm.variables['tmask'][0,:,:,:]
#vlev = id_mm.variables['gdept_1d'][0,:]
id_mm.close()
[ nk, nj, ni ] = nmp.shape(Xmask)
# Getting basin mask:
bt.chck4f(cf_basin_mask)
id_bm = Dataset(cf_basin_mask)
Xmask_atl = id_bm.variables['tmaskatl'][:,:]
narg = len(sys.argv)
if narg < 3:
print 'Usage: ' + sys.argv[0] + ' <year1> <year2>'
sys.exit(0)
cy1 = sys.argv[1]
cy2 = sys.argv[2]
jy1 = int(cy1)
jy2 = int(cy2)
jy1_clim = jy1
jy2_clim = jy2
print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n'
# Getting coordinates and mask :
bt.chck4f(vdic['MM_FILE'])
id_mm = Dataset(vdic['MM_FILE'])
xlon = id_mm.variables['nav_lon'][:, :]
xlat = id_mm.variables['nav_lat'][:, :]
xmask = id_mm.variables['tmask'][0, 0, :, :]
id_mm.close()
# Getting obs:
bt.chck4f(vdic['ICE_CLIM_12'])
id_clim = Dataset(vdic['ICE_CLIM_12'])
xclim03 = id_clim.variables[vdic['NN_ICEF_CLIM']][2, :, :]
xclim09 = id_clim.variables[vdic['NN_ICEF_CLIM']][8, :, :]
id_clim.close()
# Getting NEMO mean monthly climatology of sea-ice coverage:
cf_nemo_mnmc = vdic[
narg = len(sys.argv) if narg < 3: print 'Usage: '+sys.argv[0]+' <year1> <year2>'; sys.exit(0) cy1 = sys.argv[1] ; cy2=sys.argv[2]; jy1=int(cy1); jy2=int(cy2) jy1_clim = jy1 ; jy2_clim = jy2 print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n' # Getting coordinates: bt.chck4f(vdic['MM_FILE']) id_mm = Dataset(vdic['MM_FILE']) xlon = id_mm.variables['glamt'][0,:,:] ; xlat = id_mm.variables['gphit'][0,:,:] Xmask = id_mm.variables['tmask'][0,:,:,:] vlev = id_mm.variables['gdept_1d'][0,:] id_mm.close() nk = len(vlev) # Getting NEMO mean monthly climatology of MLD coverage: cf_nemo_mnmc = vdic['DIAG_D']+'/clim/mclim_'+CONFRUN+'_'+cy1+'-'+cy2+'_grid_T.nc4' bt.chck4f(cf_nemo_mnmc) id_nemo = Dataset(cf_nemo_mnmc)
cf_in = sys.argv[1] cv_in = sys.argv[2] cn_file, cn_ext = splitext(cf_in) print cn_ext cn_file = rplc(cn_file, cv_in+'_', '') print cn_file print "\n" bt.chck4f(cf_in) ; f_in = Dataset(cf_in) # Extracting the longitude and 1D array: vlon = f_in.variables['lon'][:] cunt_lon = f_in.variables['lon'].units print 'LONGITUDE: ', cunt_lon # Extracting the longitude 1D array: vlat = f_in.variables['lat'][:] cunt_lat = f_in.variables['lat'].units print 'LATITUDE: ', cunt_lat # Extracting time 1D array: vtime = f_in.variables['time'][:] ; cunt_time = f_in.variables['time'].units print 'TIME: ', cunt_time, '\n'
if not ( jy1 >= 1984 and jy2 <= 2006 ):
jy1_clim = 1984 ; jy2_clim = 2006
else:
jy1_clim = jy1 ; jy2_clim = jy2
print ' First and last year to treat:', jy1, jy2
print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n'
# 3D climatology :
# ------------
# Temperature
bt.chck4f(vdic['F_T_CLIM_3D_12'])
id_clim = Dataset(vdic['F_T_CLIM_3D_12'])
Tclim = id_clim.variables[vdic['NN_T_CLIM']][:,:,:,:]; print '(has ',Tclim.shape[0],' time snapshots)\n'
id_clim.close()
[ nmn , nk0 , nj0 , ni0 ] = Tclim.shape
# Salinity
bt.chck4f(vdic['F_S_CLIM_3D_12'])
id_clim = Dataset(vdic['F_S_CLIM_3D_12'])
Sclim = id_clim.variables[vdic['NN_S_CLIM']][:,:,:,:]; print '(has ',Sclim.shape[0],' time snapshots)\n'
id_clim.close()
# 2D SST obs :
if narg < 3: print 'Usage: '+sys.argv[0]+' <year1> <year2>'; sys.exit(0)
cy1 = sys.argv[1] ; cy2=sys.argv[2]; jy1=int(cy1); jy2=int(cy2)
jy1_clim = jy1 ; jy2_clim = jy2
print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n'
store_dir = os.getenv('DIAG_D')
if store_dir == '': print 'The DIAG_D environement variable is no set'; sys.exit(0)
# Getting coordinates:
bt.chck4f(cf_mesh_mask)
id_mm = Dataset(cf_mesh_mask)
xlon = id_mm.variables['glamt'][0,:,:] ; xlat = id_mm.variables['gphit'][0,:,:]
Xmask = id_mm.variables['tmask'][0,:,:,:]
vlev = id_mm.variables['gdept_1d'][0,:]
id_mm.close()
nk = len(vlev)
# Getting NEMO mean monthly climatology of RNF coverage:
cf_nemo_mnmc = DIAG_D+'/clim/mclim_'+CONFRUN+'_'+cy1+'-'+cy2+'_SBC.nc4'
bt.chck4f(cf_nemo_mnmc)
id_nemo = Dataset(cf_nemo_mnmc)
cn_file, cn_ext = splitext(cf_in)
#cn_file = replace(cn_file, cv_in+'_', '')
cpath = '.'
if dirname(cf_in) != '': cpath=dirname(cf_in)
cf_out = cpath+'/zonal_'+basename(cn_file)+'.nc'
#print ' *[mk_zonal_average.py]* file to write: ', cf_out ; sys.exit(0)
l_msk_from_val = False
if cf_msk in [ 'value', 'val', 'Value' ]:
l_msk_from_val = True
rmv = float(cv_msk)
print ' *[mk_zonal_average.py]* mask is where '+cv_in+' == '+str(rmv)
else:
bt.chck4f(cf_msk)
f_msk = Dataset(cf_msk)
Ndim = len(f_msk.variables[cv_msk].dimensions)
if Ndim == 4:
xmsk = f_msk.variables[cv_msk][0,0,:,:]
elif Ndim == 3:
xmsk = f_msk.variables[cv_msk][0,:,:]
elif Ndim == 2:
xmsk = f_msk.variables[cv_msk][:,:]
else:
print ' ERROR (mk_zonal_average.py) => weird shape for your mask array!'
sys.exit(0)
f_msk.close()
if cv_in == 'sosstsst':
cfield = 'SST'
tmin=-2. ; tmax=32. ; dtemp = 1.
cpal_fld = 'ncview_nrl'
cunit = r'$^{\circ}C$'
cb_jump = 2
elif cv_in == 'somxl010':
cfield == 'MLD'
tmin=50. ; tmax=1500. ; dtemp = 50.
cpal_fld = 'viridis_r'
#bt.chck4f(cf_ice)
bt.chck4f(cf_in)
id_fld = Dataset(cf_in)
vtime = id_fld.variables['time_counter'][:]
id_fld.close()
Nt = len(vtime)
if jtN > Nt: print 'ERROR: your jtN > Nt !!!'; sys.exit(0)
if jtN == 0: jtN = Nt
bt.chck4f(cf_lsm)
id_lsm = Dataset(cf_lsm)
#LOLO for curl => F !!!
if lforce_mask: XMSK = id_lsm.variables['fmask'][0,0,:,:]
XLON = id_lsm.variables['glamf'][0,:,:]
XLAT = id_lsm.variables['gphif'][0,:,:]
path_fig = vdic['DIAG_D'] + '/'
# Image type? eps, png, jpg...
#FIG_FORM = 'pdf'
FIG_FORM = 'png'
# First will read name and coordinates of rectangular boxes to treat into file FILE_DEF_BOXES
##############################################################################################
vboxes, vi1, vj1, vi2, vj2 = bt.read_coor(vdic['FILE_DEF_BOXES'])
nbb = len(vboxes)
print ''
# Checking presence of NEMO files:
cfroot = vdic['CPREF'] + cy + '0101_' + cy + '1231'
cf_in_T = cfroot + '_grid_T.nc'
bt.chck4f(cf_in_T, script_name=cname_script)
if luv:
cf_in_U = cfroot + '_grid_U.nc'
bt.chck4f(cf_in_U, script_name=cname_script)
cf_in_V = cfroot + '_grid_V.nc'
bt.chck4f(cf_in_V, script_name=cname_script)
# Coordinates, mask and metrics:
bt.chck4f(vdic['MM_FILE'], script_name=cname_script)
id_mm = Dataset(vdic['MM_FILE'])
Xmask = id_mm.variables['tmask'][0, :, :, :]
ze1t = id_mm.variables['e1t'][0, :, :]
ze2t = id_mm.variables['e2t'][0, :, :]
ve3t = id_mm.variables['e3t_1d'][0, :]
zlon = id_mm.variables['nav_lon'][:, :]
zlat = id_mm.variables['nav_lat'][:, :]
list_sections = bo.get_sections_names_from_file(vdic['TRANSPORT_SECTION_FILE'])
print 'List of sections to treat: ', list_sections
nbsect = len(list_sections)
vstuff = [ 'volume', 'heat' , 'salt' ]
vunit = [ 'Sv' , 'PW' , 'kt/s' ]
jrun = 0
for confrun in clist_confruns:
jsect=0
for csect in list_sections:
print '\n Treating transports through '+csect
cf_in = cd_diag+'/'+confrun+'/transport_sect_'+csect+'.nc' ; bt.chck4f(cf_in, script_name='compare_time_series.py')
id_in = Dataset(cf_in)
if jsect == 0:
if jrun == 0:
vtime = nmp.zeros(nb_years*12)
vyear = nmp.zeros(nb_years)
Xtrsp = nmp.zeros((nbrun,nbsect,3,nb_years))
vtime_t = id_in.variables['time'][:] ; nbm = len(vtime_t) ; nby = nbm/12
test_nb_mnth_rec(nbm, nb_years, cdiag)
if nby == nb_years: vtime[:] = vtime_t[:]
Xtrsp[jrun,jsect,:,:] = -999.
vyear[:nby], Xtrsp[jrun,jsect,0,:nby] = bt.monthly_2_annual(vtime_t, id_in.variables['trsp_volu'][:nbm])
vyear[:nby], Xtrsp[jrun,jsect,1,:nby] = bt.monthly_2_annual(vtime_t, id_in.variables['trsp_heat'][:nbm])
sys.exit(0)
cf_u = sys.argv[1]
cf_v = string.replace(cf_u, cv_u, cv_v)
cf_wm = basename(string.replace(cf_u, cv_u, cv_wm))
if len(sys.argv) == 3:
cvy = sys.argv[2]
iyear = int(cvy)
#print iyear ; sys.exit(0)
# First need time length:
bt.chck4f(cf_u) ; f_u_in = Dataset(cf_u)
vlon = f_u_in.variables['lon'][:]
cunt_lon = f_u_in.variables['lon'].units
print 'LONGITUDE: ', cunt_lon
# Extracting the longitude 1D array:
vlat = f_u_in.variables['lat'][:]
cunt_lat = f_u_in.variables['lat'].units
print 'LATITUDE: ', cunt_lat
# Extracting time 1D array:
vtime = f_u_in.variables['time'][:] ; cunt_time = f_u_in.variables['time'].units
print 'TIME: ', cunt_time, '\n'
f_u_in.close()
idfig = 'ice'
cyu = r'10$^6$km$^2$'
else:
print 'ERROR: '+csn+' => diagnostic '+cdiag+' unknown!'; sys.exit(0)
#############################################################
# Time series of 2D-averaged 2D fields such as SST, SSS, SSH
#############################################################
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'
if len(sys.argv) < 2 and len(sys.argv) > 3:
print 'Usage: ' + sys.argv[0] + ' <IN_FILE_U.nc> (Year)'
sys.exit(0)
cf_u = sys.argv[1]
cf_v = string.replace(cf_u, cv_u, cv_v)
cf_wm = basename(string.replace(cf_u, cv_u, cv_wm))
if len(sys.argv) == 3:
cvy = sys.argv[2]
iyear = int(cvy)
#print iyear ; sys.exit(0)
# First need time length:
bt.chck4f(cf_u)
f_u_in = Dataset(cf_u)
vlon = f_u_in.variables['lon'][:]
cunt_lon = f_u_in.variables['lon'].units
print 'LONGITUDE: ', cunt_lon
# Extracting the longitude 1D array:
vlat = f_u_in.variables['lat'][:]
cunt_lat = f_u_in.variables['lat'].units
print 'LATITUDE: ', cunt_lat
# Extracting time 1D array:
vtime = f_u_in.variables['time'][:]
cunt_time = f_u_in.variables['time'].units
print 'TIME: ', cunt_time, '\n'
f_u_in.close()
elif cdiag == 'seaice':
idfig = 'ice'
cyu = r'10$^6$km$^2$'
else:
print 'ERROR: plot_time_series.py => diagnostic ' + cdiag + ' unknown!'
sys.exit(0)
##########################################
# 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 + ', 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)
cn_obs = vdic['NM_IC_OBS'] narg = len(sys.argv) if narg < 3: print 'Usage: '+sys.argv[0]+' <year1> <year2>'; sys.exit(0) cy1 = sys.argv[1] ; cy2=sys.argv[2]; jy1=int(cy1); jy2=int(cy2) jy1_clim = jy1 ; jy2_clim = jy2 print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n' # Getting coordinates and mask : bt.chck4f(vdic['MM_FILE']) id_mm = Dataset(vdic['MM_FILE']) xlon = id_mm.variables['nav_lon'][:,:] xlat = id_mm.variables['nav_lat'][:,:] xmask = id_mm.variables['tmask'][0,0,:,:] id_mm.close() # Getting obs: bt.chck4f(vdic['F_ICE_OBS_12']) id_clim = Dataset(vdic['F_ICE_OBS_12']) xclim03 = id_clim.variables[vdic['NN_ICEF_OBS']][2,:,:] xclim09 = id_clim.variables[vdic['NN_ICEF_OBS']][8,:,:] id_clim.close()
nx_b = i2 - i1
ny_b = j2 - j1
print '\n *** Treating box '+cbox+' => ', i1, j1, i2-1, j2-1
# Filling Convection regions arrays:
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
xsurf = nmp.zeros(ny_b*nx_b) ; xsurf.shape = [ ny_b, nx_b ]
xtmp0 = nmp.zeros(ny_b*nx_b) ; xtmp0.shape = [ ny_b, nx_b ]
xtmp1 = nmp.zeros(ny_b*nx_b) ; xtmp1.shape = [ ny_b, nx_b ]
xtmp2 = nmp.zeros(ny_b*nx_b) ; xtmp2.shape = [ ny_b, nx_b ]
bt.chck4f(cf_in)
print ' Reading SST, SSS and MLD in box '+cbox+' in file '+cf_in
id_in = Dataset(cf_in)
if NN_SST == 'thetao':
xsst = id_in.variables[NN_SST][:,0,j1:j2,i1:i2]
else:
xsst = id_in.variables[NN_SST][:,j1:j2,i1:i2]
if NN_SSS == 'so':
xsss = id_in.variables[NN_SSS][:,0,j1:j2,i1:i2]
else:
xsss = id_in.variables[NN_SSS][:,j1:j2,i1:i2]
xmld = id_in.variables[NN_MLD][:,j1:j2,i1:i2]
xtpot = id_in.variables[NN_T][:,:,j1:j2,i1:i2]
xsali = id_in.variables[NN_S][:,:,j1:j2,i1:i2]
id_in.close()
# 3D climatology :
# ------------
# Temperature
# bt.chck4f(vdic['F_T_CLIM_3D_12'])
# id_clim = Dataset(vdic['F_T_CLIM_3D_12'])
# Tclim = id_clim.variables[vdic['NN_T_CLIM']][:,0,:,:]; print '(has ',Tclim.shape[0],' time snapshots)\n'
# id_clim.close()
# [ nmn , nk0 , nj0 , ni0 ] = Tclim.shape
# Salinity
if cvar == 'sss':
bt.chck4f(vdic['F_S_CLIM_3D_12'])
id_clim = Dataset(vdic['F_S_CLIM_3D_12'])
Vclim = id_clim.variables[vdic['NN_S_CLIM']][:,0,:,:]; print '(has ',Vclim.shape[0],' time snapshots)\n'
id_clim.close()
# 2D SST obs :
if cvar == 'sst':
bt.chck4f(vdic['SST_CLIM_12'])
id_clim_sst = Dataset(vdic['SST_CLIM_12'])
Vclim = id_clim_sst.variables[vdic['NN_SST_CLIM']][:,:,:]; print '(has ',Vclim.shape[0],' time snapshots)\n'
id_clim_sst.close()
# Sea-ice concentration :
# => no clim used!
if cvar == 'sss' or cvar == 'sst': [ nmn , nj0 , ni0 ] = Vclim.shape
if DIAG_DIR == None: print 'The DIAG_DIR environement variable is no set'; sys.exit(0) print '\n '+sys.argv[0]+':' narg = len(sys.argv) if narg < 3: print 'Usage: '+sys.argv[0]+' <RUN1> <RUN2>'; sys.exit(0) crun1 = sys.argv[1] ; crun2=sys.argv[2] CONFRUN1 = ORCA+'-'+crun1 ; print CONFRUN1 CONFRUN2 = ORCA+'-'+crun2 ; print CONFRUN2 # Getting coordinates and mask cf_mask = MM_FILE ; print 'Reading coordinates into '+cf_mask bt.chck4f(cf_mask) id_mask = Dataset(cf_mask) mask = id_mask.variables['tmask'][0,:,:,:] xlon = id_mask.variables['nav_lon'][:,:] ; xlat = id_mask.variables['nav_lat'][:,:] id_mask.close() [ nk, nj, ni ] = nmp.shape(mask) # Getting basin mask: cf_basin_mask = BM_FILE bt.chck4f(cf_basin_mask) id_bm = Dataset(cf_basin_mask) Xmask_atl = id_bm.variables['tmaskatl'][:,:,:] id_bm.close()
if narg < 3: print 'Usage: '+sys.argv[0]+' <year1> <year2>'; sys.exit(0)
cy1 = sys.argv[1] ; cy2=sys.argv[2]; jy1=int(cy1); jy2=int(cy2)
jy1_clim = jy1 ; jy2_clim = jy2
print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n'
store_dir = os.getenv('DIAG_D')
if store_dir == '': print 'The DIAG_D environement variable is no set'; sys.exit(0)
# Getting coordinates:
bt.chck4f(cf_mesh_mask)
id_mm = Dataset(cf_mesh_mask)
xlon = id_mm.variables['glamt'][0,:,:] ; xlat = id_mm.variables['gphit'][0,:,:]
Xmask = id_mm.variables['tmask'][0,:,:,:]
vlev = id_mm.variables['gdept_1d'][0,:]
id_mm.close()
nk = len(vlev)
# Getting NEMO mean monthly climatology of MLD 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)
cpal_fld = 'on2'
tmin = -1.2
tmax = 1.2
df = 0.05
l_apply_lap = True
cunit = r'SSH (m)'
cb_jump = 1
elif cv_in == 'somxl010':
cfield == 'MLD'
tmin = 50.
tmax = 1500.
df = 50.
cpal_fld = 'viridis_r'
if l_do_ice: bt.chck4f(cf_ice)
bt.chck4f(cf_in)
id_fld = Dataset(cf_in)
vtime = id_fld.variables['time_counter'][:]
id_fld.close()
Nt = len(vtime)
if l_show_lsm or l_apply_lap:
bt.chck4f(cf_lsm)
id_lsm = Dataset(cf_lsm)
nb_dim = len(id_lsm.variables['tmask'].dimensions)
if l_show_lsm:
if nb_dim == 4: XMSK = id_lsm.variables['tmask'][0, 0, j1:j2, i1:i2]
if nb_dim == 3: XMSK = id_lsm.variables['tmask'][0, j1:j2, i1:i2]
if nb_dim == 2: XMSK = id_lsm.variables['tmask'][j1:j2, i1:i2]
if not ( jy1 >= 1984 and jy2 <= 2006 ):
jy1_clim = 1984 ; jy2_clim = 2006
else:
jy1_clim = jy1 ; jy2_clim = jy2
print ' First and last year to treat:', jy1, jy2
print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n'
# 3D climatology :
# ------------
# Temperature
bt.chck4f(F_T_CLIM_3D_12) ; id_clim = Dataset(F_T_CLIM_3D_12)
Tclim = id_clim.variables[NN_T_CLIM][:,:,:,:]; print '(has ',Tclim.shape[0],' time snapshots)\n'
id_clim.close()
[ nmn , nk0 , nj0 , ni0 ] = Tclim.shape
# Salinity
bt.chck4f(F_S_CLIM_3D_12) ; id_clim = Dataset(F_S_CLIM_3D_12)
Sclim = id_clim.variables[NN_S_CLIM][:,:,:,:]; print '(has ',Sclim.shape[0],' time snapshots)\n'
id_clim.close()
# 2D SST obs :
# ------------
print 'We use the following SST climatology:'; print SST_CLIM_12
path_fig='./' fig_type='png' narg = len(sys.argv) if narg < 3: print 'Usage: '+sys.argv[0]+' <year1> <year2>'; sys.exit(0) cy1 = sys.argv[1] ; cy2=sys.argv[2]; jy1=int(cy1); jy2=int(cy2) jy1_clim = jy1 ; jy2_clim = jy2 print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n' # Getting coordinates: bt.chck4f(vdic['MM_FILE']) id_mm = Dataset(vdic['MM_FILE']) xlat = id_mm.variables['gphit'][0,:,:] xlon = id_mm.variables['glamt'][0,:,:] Xmask = id_mm.variables['tmask'][0,:,:,:] id_mm.close() [ nk, nj, ni ] = nmp.shape(Xmask) # Getting basin mask: bt.chck4f(vdic['BM_FILE']) id_bm = Dataset(vdic['BM_FILE']) Xmask_atl = id_bm.variables['tmaskatl'][:,:] id_bm.close()
fig_type='png' narg = len(sys.argv) if narg < 3: print 'Usage: '+sys.argv[0]+' <year1> <year2>'; sys.exit(0) cy1 = sys.argv[1] ; cy2=sys.argv[2]; jy1=int(cy1); jy2=int(cy2) jy1_clim = jy1 ; jy2_clim = jy2 print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n' # Getting coordinates and mask : bt.chck4f(vdic['MM_FILE']) id_mm = Dataset(vdic['MM_FILE']) xlon = id_mm.variables['nav_lon'][:,:] xlat = id_mm.variables['nav_lat'][:,:] xmask = id_mm.variables['tmask'][0,0,:,:] id_mm.close() # Getting obs: bt.chck4f(vdic['ICE_CLIM_12']) id_clim = Dataset(vdic['ICE_CLIM_12']) xclim03 = id_clim.variables[vdic['NN_ICEF_CLIM']][2,:,:] xclim09 = id_clim.variables[vdic['NN_ICEF_CLIM']][8,:,:] id_clim.close()
vMLD_crit = []
vv = vdic['MLD_CRIT'].split(',')
for cv in vv: vMLD_crit.append(float(cv))
print "\n All the z_crit to use:", vMLD_crit[:]
# First will read name and coordinates of rectangular boxes to treat into file FILE_DMV_BOXES
##############################################################################################
vboxes, vi1, vj1, vi2, vj2 = bt.read_coor(vdic['FILE_DMV_BOXES'])
nbb = len(vboxes)
print ''
bt.chck4f(vdic['MM_FILE'])
id_mm = Dataset(vdic['MM_FILE'])
zmask_orca = id_mm.variables['tmask'][0,0,:,:]
ze1t_orca = id_mm.variables['e1t'] [0,:,:]
ze2t_orca = id_mm.variables['e2t'] [0,:,:]
id_mm.close()
bt.chck4f(cf_in)
id_in = Dataset(cf_in)
Xmld_orca = id_in.variables[vdic['NN_MLD']][:,:,:]
print '(has ',Xmld_orca.shape[0],' time snapshots)\n'
xlat = id_in.variables['nav_lat'][:,:]
id_in.close()
narg = len(sys.argv)
if narg < 3:
print 'Usage: ' + sys.argv[0] + ' <year1> <year2>'
sys.exit(0)
cy1 = sys.argv[1]
cy2 = sys.argv[2]
jy1 = int(cy1)
jy2 = int(cy2)
jy1_clim = jy1
jy2_clim = jy2
print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n'
# Getting coordinates:
bt.chck4f(vdic['MM_FILE'])
id_mm = Dataset(vdic['MM_FILE'])
xlat = id_mm.variables['gphit'][0, :, :]
xlon = id_mm.variables['glamt'][0, :, :]
Xmask = id_mm.variables['tmask'][0, :, :, :]
id_mm.close()
[nk, nj, ni] = nmp.shape(Xmask)
# Getting basin mask:
bt.chck4f(vdic['BM_FILE'])
id_bm = Dataset(vdic['BM_FILE'])
Xmask_atl = id_bm.variables['tmaskatl'][:, :]
id_bm.close()
for jk in range(nk):
narg = len(sys.argv)
if narg < 3:
print 'Usage: ' + sys.argv[0] + ' <year1> <year2>'
sys.exit(0)
cy1 = sys.argv[1]
cy2 = sys.argv[2]
jy1 = int(cy1)
jy2 = int(cy2)
jy1_clim = jy1
jy2_clim = jy2
print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n'
# Getting coordinates:
bt.chck4f(vdic['MM_FILE'])
id_mm = Dataset(vdic['MM_FILE'])
xlon = id_mm.variables['glamt'][0, :, :]
xlat = id_mm.variables['gphit'][0, :, :]
Xmask = id_mm.variables['tmask'][0, :, :, :]
vlev = id_mm.variables['gdept_1d'][0, :]
id_mm.close()
nk = len(vlev)
# Getting NEMO mean monthly climatology of MLD coverage:
cf_nemo_mnmc = vdic[
'DIAG_D'] + '/clim/mclim_' + CONFRUN + '_' + cy1 + '-' + cy2 + '_grid_T.nc4'
bt.chck4f(cf_nemo_mnmc)
id_nemo = Dataset(cf_nemo_mnmc)
print " cf_crl = ", cf_crl
print "\n"
rmult = 1.
if cv_Vx == 'EWSS':
rmult = 1000.
CUNIT_CRL = '???'
if cv_Vx == 'U10M':
rmult = 1.
CUNIT_CRL = 's^-1'
# TAUY
# ~~~~
bt.chck4f(cf_Vy)
f_Vy_in = Dataset(cf_Vy)
# Extracting the longitude and 1D array:
vlon = f_Vy_in.variables['lon'][:]
clnm_lon = f_Vy_in.variables['lon'].long_name
cunt_lon = f_Vy_in.variables['lon'].units
csnm_lon = f_Vy_in.variables['lon'].standard_name
print 'LONGITUDE: ', clnm_lon, cunt_lon, csnm_lon
# Extracting the longitude 1D array:
vlat = f_Vy_in.variables['lat'][:]
clnm_lat = f_Vy_in.variables['lat'].long_name
cunt_lat = f_Vy_in.variables['lat'].units
csnm_lat = f_Vy_in.variables['lat'].standard_name
print ' CONF = ' + CONF print ' RUN = ' + RUN CONFRUN = CONF + '-' + RUN print ' CONFRUN = ' + CONFRUN print ' CPREF = ' + CPREF # First will read name and coordinates of rectangular boxes to treat into file FILE_DEF_BOXES ############################################################################################## vboxes, vi1, vj1, vi2, vj2 = bt.read_box_coordinates_in_ascii(FILE_DEF_BOXES) nbb = len(vboxes) print '' # Opening mesh-mask and T-grid file of NEMO: ############################################ bt.chck4f(MM_FILE) id_mm = Dataset(MM_FILE) Xmask = id_mm.variables['tmask'][0, :, :, :] ze1t = id_mm.variables['e1t'][0, :, :] ze2t = id_mm.variables['e2t'][0, :, :] id_mm.close() cf_in = CPREF + cy + '0101_' + cy + '1231_grid_T.nc' bt.chck4f(cf_in) id_in = Dataset(cf_in) Vtime = id_in.variables['time_counter'][:] Vdepth = id_in.variables['deptht'][:] Xlon = id_in.variables['nav_lon'][:, :] Xlat = id_in.variables['nav_lat'][:, :] Xtheta = id_in.variables[NN_T][:, :, :, :] Xsali = id_in.variables[NN_S][:, :, :, :]
##########################################
# 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 narg != 2: print 'Usage: '+sys.argv[0]+' <diag>'; sys.exit(0)
#cdiag = sys.argv[1]
#print '\n plot_time_series.py: diag => "'+cdiag+'"'
v_var_names = [
'msl', 'tas', 'totp', 'NetTOA', 'NetSFC', 'PminE', 'e', 'tcc', 'PminE',
'tsr', 'ssr'
]
nbvar = len(v_var_names)
v_var_units = nmp.zeros(nbvar, dtype=nmp.dtype('a8'))
v_var_lngnm = nmp.zeros(nbvar, dtype=nmp.dtype('a64'))
print '\n *** plot_atmo_time_series.py => USING time series in ' + SUPA_FILE
bt.chck4f(SUPA_FILE)
# Reading all var in netcdf file:
id_clim = Dataset(SUPA_FILE)
vtime = id_clim.variables['time'][:]
nbr = len(vtime)
XX = nmp.zeros(nbvar * nbr)
XX.shape = [nbvar, nbr]
for jv in range(nbvar):
print ' **** reading ' + v_var_names[jv]
XX[jv, :] = id_clim.variables[v_var_names[jv]][:]
narg = len(sys.argv)
if narg < 3:
print 'Usage: ' + sys.argv[0] + ' <year1> <year2>'
sys.exit(0)
cy1 = sys.argv[1]
cy2 = sys.argv[2]
jy1 = int(cy1)
jy2 = int(cy2)
jy1_clim = jy1
jy2_clim = jy2
print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n'
ctag = CONFEXP + '_' + cy1 + '-' + cy2
# Getting coordinates:
bt.chck4f(vdic['MM_FILE'])
id_mm = Dataset(vdic['MM_FILE'])
xlon = id_mm.variables['glamt'][0, :, :]
xlat = id_mm.variables['gphit'][0, :, :]
Xmask = id_mm.variables['tmask'][0, 0, :, :]
id_mm.close()
l_tau_is_annual = False
cf_nemo_curl = cd_clim + '/mclim_' + ctag + '_TCURL.nc4'
if not path.exists(cf_nemo_curl):
cf_nemo_curl = cd_clim + '/aclim_' + ctag + '_TCURL.nc4'
if path.exists(cf_nemo_curl):
l_tau_is_annual = True
print '\n *** wind.py : wind stress is annual...'
else:
vdic['TRANSPORT_SECTION_FILE'])
print 'List of sections to treat: ', list_sections
nbsect = len(list_sections)
vstuff = ['volume', 'heat', 'salt']
vunit = ['Sv', 'PW', 'kt/s']
jrun = 0
for confrun in clist_confruns:
jsect = 0
for csect in list_sections:
print '\n Treating transports through ' + csect
cf_in = cd_diag + '/' + confrun + '/transport_sect_' + csect + '.nc'
bt.chck4f(cf_in, script_name='compare_time_series.py')
id_in = Dataset(cf_in)
if jsect == 0:
if jrun == 0:
vtime = nmp.zeros(nb_years * 12)
vyear = nmp.zeros(nb_years)
Xtrsp = nmp.zeros((nbrun, nbsect, 3, nb_years))
vtime_t = id_in.variables['time'][:]
nbm = len(vtime_t)
nby = nbm / 12
test_nb_mnth_rec(nbm, nb_years, cdiag)
if nby == nb_years: vtime[:] = vtime_t[:]
Xtrsp[jrun, jsect, :, :] = -999.
sys.exit(0)
cy1 = sys.argv[1]
cy2 = sys.argv[2]
jy1 = int(cy1)
jy2 = int(cy2)
jy1_clim = jy1
jy2_clim = jy2
print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n'
ctag = CONFEXP + '_' + cy1 + '-' + cy2
ct_nemo = vdic['FILE_FLX_SUFFIX']
# (SBC or grid_T, ect.)
# Getting coordinates and mask:
bt.chck4f(vdic['MM_FILE'])
id_mm = Dataset(vdic['MM_FILE'])
xlon = id_mm.variables['glamt'][0, :, :]
xlat = id_mm.variables['gphit'][0, :, :]
Xmask = id_mm.variables['tmask'][0, 0, :, :]
id_mm.close()
# Getting NEMO mean monthly climatology surface flux:
cextra_qsol = ''
cv_qsol = vdic['NN_QSOL']
if cv_qsol != 'X':
cf_nemo_qsol = cd_clim + '/mclim_' + ctag + '_' + ct_nemo + '.nc4'
else:
print '\n *** WARNING: sfluxes.py : if no "NN_QSOL" ! No sfluxes.py! Aborting !!!\n'
sys.exit(0)
narg = len(sys.argv)
if narg < 3:
print 'Usage: ' + sys.argv[0] + ' <year1> <year2>'
sys.exit(0)
cy1 = sys.argv[1]
cy2 = sys.argv[2]
jy1 = int(cy1)
jy2 = int(cy2)
jy1_clim = jy1
jy2_clim = jy2
print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n'
# Getting coordinates:
bt.chck4f(vdic['MM_FILE'])
id_mm = Dataset(vdic['MM_FILE'])
xlon = id_mm.variables['glamt'][0, :, :]
xlat = id_mm.variables['gphit'][0, :, :]
Xmask = id_mm.variables['tmask'][0, 0, :, :]
Xe1t = id_mm.variables['e1t'][0, :, :]
Xe2t = id_mm.variables['e2t'][0, :, :]
id_mm.close()
# Getting NEMO mean monthly climatology of SSH coverage:
cf_nemo_mnmc = vdic[
'DIAG_D'] + '/clim/mclim_' + CONFEXP + '_' + cy1 + '-' + cy2 + '_grid_T.nc4'
bt.chck4f(cf_nemo_mnmc)
id_nemo = Dataset(cf_nemo_mnmc)
ssh = id_nemo.variables[vdic['NN_SSH']][:, :, :]
narg = len(sys.argv)
if narg < 3:
print 'Usage: ' + sys.argv[0] + ' <RUN1> <RUN2>'
sys.exit(0)
crun1 = sys.argv[1]
crun2 = sys.argv[2]
CONFRUN1 = ORCA + '-' + crun1
print CONFRUN1
CONFRUN2 = ORCA + '-' + crun2
print CONFRUN2
# Getting coordinates and mask
cf_mask = MM_FILE
print 'Reading coordinates into ' + cf_mask
bt.chck4f(cf_mask)
id_mask = Dataset(cf_mask)
mask = id_mask.variables['tmask'][0, :, :, :]
xlon = id_mask.variables['nav_lon'][:, :]
xlat = id_mask.variables['nav_lat'][:, :]
id_mask.close()
[nk, nj, ni] = nmp.shape(mask)
# Getting basin mask:
cf_basin_mask = BM_FILE
bt.chck4f(cf_basin_mask)
id_bm = Dataset(cf_basin_mask)
Xmask_atl = id_bm.variables['tmaskatl'][:, :, :]
id_bm.close()
cf_in = sys.argv[1]
cyear = sys.argv[2] ; jyear = int(cyear); cyear = '%4.4i'%jyear
cv_sst = vdic['NN_SST']
cv_sss = vdic['NN_SSS']
if na == 5:
cv_sst = sys.argv[3]
cv_sss = sys.argv[4]
print 'Current year is '+cyear+' !\n'
bt.chck4f(vdic['MM_FILE'])
id_mm = Dataset(vdic['MM_FILE'])
rmsk = id_mm.variables['tmask'][0,0,:,:]
rlon = id_mm.variables['glamt'][0,:,:]
rlat = id_mm.variables['gphit'][0,:,:]
Xe1t = id_mm.variables['e1t'][0,:,:]
Xe2t = id_mm.variables['e2t'][0,:,:]
id_mm.close()
[ nj, ni ] = rmsk.shape
bt.chck4f(cf_in)
cv_d2 = 'D2M'
cv_p0 = 'MSL'
cv_q2 = 'Q2M'
# OUTPUT !
if len(sys.argv) != 2:
print 'Usage: ' + sys.argv[0] + ' <IN_FILE_D2.nc>'
sys.exit(0)
cf_d2 = sys.argv[1]
cf_p0 = string.replace(cf_d2, cv_d2, cv_p0)
cf_q2 = basename(string.replace(cf_d2, cv_d2, cv_q2))
# First need time length:
bt.chck4f(cf_d2)
f_d2_in = Dataset(cf_d2)
vlon = f_d2_in.variables['lon'][:]
cunt_lon = f_d2_in.variables['lon'].units
print 'LONGITUDE: ', cunt_lon
# Extracting the longitude 1D array:
vlat = f_d2_in.variables['lat'][:]
cunt_lat = f_d2_in.variables['lat'].units
print 'LATITUDE: ', cunt_lat
# Extracting time 1D array:
vtime = f_d2_in.variables['time'][:]
cunt_time = f_d2_in.variables['time'].units
print 'TIME: ', cunt_time, '\n'
f_d2_in.close()
cv_q2='Q2M' ; # OUTPUT !
if len(sys.argv) != 2:
print 'Usage: '+sys.argv[0]+' <IN_FILE_D2.nc>'
sys.exit(0)
cf_d2 = sys.argv[1]
cf_p0 = string.replace(cf_d2, cv_d2, cv_p0)
cf_q2 = basename(string.replace(cf_d2, cv_d2, cv_q2))
# First need time length:
bt.chck4f(cf_d2) ; f_d2_in = Dataset(cf_d2)
vlon = f_d2_in.variables['lon'][:]
cunt_lon = f_d2_in.variables['lon'].units
print 'LONGITUDE: ', cunt_lon
# Extracting the longitude 1D array:
vlat = f_d2_in.variables['lat'][:]
cunt_lat = f_d2_in.variables['lat'].units
print 'LATITUDE: ', cunt_lat
# Extracting time 1D array:
vtime = f_d2_in.variables['time'][:] ; cunt_time = f_d2_in.variables['time'].units
print 'TIME: ', cunt_time, '\n'
f_d2_in.close()
tmin = -0.3
tmax = 0.3
df = 0.1
cpal_fld = 'ncview_jaisnc'
cunit = r'SSH (m)'
cb_jump = 1
elif cv_in == 'somxl010':
cfield == 'MLD'
tmin = 50.
tmax = 1500.
df = 50.
cpal_fld = 'viridis_r'
# Time record stuff...
bt.chck4f(cf_fld)
id_fld = Dataset(cf_fld)
list_var = id_fld.variables.keys()
if 'time_counter' in list_var:
vtime = id_fld.variables['time_counter'][:]
Nt = len(vtime)
print '\n There is a "time_counter" in file ' + cf_fld + ' !'
print ' => ' + str(Nt) + ' snapshots!'
if jt <= 0 or jt > Nt:
print ' PROBLEM: your time record does not exist!', jt
sys.exit(0)
else:
print '\n There is NO "time_counter" in file ' + cf_fld + ' !'
Nt = 0
id_fld.close()
#cy1 = sys.argv[1] ; cy2 = sys.argv[2] ; jy1=int(cy1); jy2=int(cy2)
path_fig=vdic['DIAG_D']+'/'
fig_type='png'
voceans_u = [cc.upper() for cc in bo.voce2treat] ; # same list but in uppercase
jo = 0
for coce in bo.voce2treat:
cf_temp = cname_temp+'_mean_Vprofile_'+CONFRUN+'_'+coce+'.nc' ; bt.chck4f(cf_temp)
cf_sali = cname_sali+'_mean_Vprofile_'+CONFRUN+'_'+coce+'.nc' ; bt.chck4f(cf_sali)
id_temp = Dataset(cf_temp)
if jo == 0:
vyears = id_temp.variables['time'][:]
vdepth = id_temp.variables['deptht'][:]
XT = id_temp.variables[cname_temp][:,:]
id_temp.close()
id_sali = Dataset(cf_sali)
XS = id_sali.variables[cname_sali][:,:]
id_sali.close()
if jo == 0:
l_force_lim = True cfig_type = 'png' path_fig = DIAG_D+'/' CONFRUN = CONF+'-'+RUN print ' Using DENSITY_SECTION_FILE = '+DENSITY_SECTION_FILE list_sections = bo.get_sections_names_from_file(DENSITY_SECTION_FILE) print 'List of sections to treat: ', list_sections nbsec = len(list_sections) cf_in = DIAG_D+'/transport_by_sigma_class.nc' ; bt.chck4f(cf_in) id_in = Dataset(cf_in) vtime = id_in.variables['time'][:] ; nbm = len(vtime) vsigma = id_in.variables['sigma_bins'][:] ; nbin = len(vsigma) print ' => '+str(nbin)+' sigma-density bins and '+str(nbm)+' time snapshots...' if nbm % 12 != 0: print 'nbm is not a multiple of 12!'; sys.exit(0) # Reconstructing bounds of bins: vsigma_bounds = nmp.zeros(nbin+1)
elif cv_in == 'sosstsst':
cfield = 'SST'
log_ctrl=0 ; # 0 if you want no log involved in the colorscale...
tmin=-2. ; tmax=32. ; dtemp = 2.
#cpal_fld = 'ncview_hotres'
#cpal_fld = 'hot_r'
cunit = r'deg.C'
cb_jump = 2
j1=ny_exclude_north
bt.chck4f(cf_in)
bt.chck4f(cf_mm)
id_mm = Dataset(cf_mm)
XLONo = id_mm.variables['glamt'][0,:,:]
(njo,nio) = nmp.shape(XLONo)
j1=ny_exclude_south
j2=njo-ny_exclude_north
del XLONo
XLONo = id_mm.variables['glamt'][0,j1:j2,:]
XLATo = id_mm.variables['gphit'][0,j1:j2,:]
if l_force_mask: XMSKo = id_mm.variables['tmask'][0,0,j1:j2,:]
id_mm.close()
if narg < 3: print 'Usage: '+sys.argv[0]+' <year1> <year2>'; sys.exit(0)
cy1 = sys.argv[1] ; cy2=sys.argv[2]; jy1=int(cy1); jy2=int(cy2)
jy1_clim = jy1 ; jy2_clim = jy2
print ' => mean on the clim : ', jy1_clim, jy2_clim, '\n'
store_dir = os.getenv('DIAG_D')
if store_dir == '': print 'The DIAG_D environement variable is no set'; sys.exit(0)
# Getting coordinates:
bt.chck4f(cf_mesh_mask) ; id_coor = Dataset(cf_mesh_mask)
xlon = id_coor.variables['nav_lon'][:,:] ; xlat = id_coor.variables['nav_lat'][:,:]
id_coor.close()
# Getting obs:
bt.chck4f(ICE_CLIM_12) ; id_clim = Dataset(ICE_CLIM_12)
xclim03 = id_clim.variables[NN_ICEF_CLIM][2,:,:]
xclim09 = id_clim.variables[NN_ICEF_CLIM][8,:,:]
id_clim.close()
# Getting land-sea mask:
#-----------------------
xmask = 0
