def density(eosType,
salt,
temp,
press,
rhoConst=None,
Tref=None,
Sref=None,
tAlpha=None,
sBeta=None):
# Check if pressure is a constant value
if isinstance(press, float) or isinstance(press, int):
# Make it an array
press = np.zeros(temp.shape) + press
if eosType == 'MDJWF':
from MITgcmutils.mdjwf import densmdjwf
return densmdjwf(salt, temp, press)
elif eosType == 'JMD95':
from MITgcmutils.jmd95 import densjmd95
return densjmd95(salt, temp, press)
elif eosType == 'LINEAR':
if None in [rhoConst, Tref, Sref, tAlpha, sBeta]:
print 'Error (density): for eosType LINEAR, you must set rhoConst, Tref, Sref, tAlpha, sBeta'
sys.exit()
return dens_linear(salt, temp, rhoConst, Tref, Sref, tAlpha, sBeta)
else:
print 'Error (density): invalid eosType ' + eosType
sys.exit()
def compare_keith_ctd (ctd_dir='/work/n02/n02/kaight/raw_input_data/ctd_data/', output_dir='./', grid_dir='../grid/', pload_file='/work/n02/n02/shared/baspog/MITgcm/WS/WSK/pload_WSK', fig_dir='./', rhoConst=1035., prec=64):
# Site names
sites = ['S1', 'S2', 'S3', 'S4', 'S5', 'F1', 'F2', 'F3', 'F4']
num_sites = len(sites)
ctd_dir = real_dir(ctd_dir)
output_dir = real_dir(output_dir)
grid_dir = real_dir(grid_dir)
fig_dir = real_dir(fig_dir)
# Build Grid object
grid = Grid(grid_dir)
# Read pressure load anomaly
pload_anom_2d = read_binary(pload_file, [grid.nx, grid.ny], 'xy', prec=prec)
# Make one figure for each site
for i in range(num_sites):
# Construct the filename for this site
site_file = ctd_dir + sites[i] + '.nc'
# Read arrays
ctd_press = read_netcdf(site_file, 'pressure')
ctd_temp = read_netcdf(site_file, 'theta')
ctd_salt = read_netcdf(site_file, 'salt')
# Read global attributes
id = nc.Dataset(site_file, 'r')
ctd_lat = id.lat
ctd_lon = id.lon
ctd_year = int(id.year)
ctd_month = int(id.month)
id.close()
# Get a list of all the output files from MITgcm
output_files = build_file_list(output_dir)
# Find the right file and time index corresponding to the given year and month
file0 = None
for file in output_files:
time = netcdf_time(file)
for t in range(time.size):
if time[t].year == ctd_year and time[t].month == ctd_month:
file0 = file
t0 = t
date_string = parse_date(time[t])
break
if file0 is not None:
break
if file0 is None:
print(("Error (compare_keith_ctd): couldn't find " + str(ctd_month) + '/' + str(ctd_year) + ' in model output'))
sys.exit()
# Read variables
mit_temp_3d = read_netcdf(file0, 'THETA', time_index=t0)
mit_salt_3d = read_netcdf(file0, 'SALT', time_index=t0)
# Interpolate to given point
# Temperature and land mask
mit_temp, hfac = interp_bilinear(mit_temp_3d, ctd_lon, ctd_lat, grid, return_hfac=True)
# Salinity
mit_salt = interp_bilinear(mit_salt_3d, ctd_lon, ctd_lat, grid)
# Pressure loading anomaly
pload_anom = interp_bilinear(pload_anom_2d, ctd_lon, ctd_lat, grid)
# Ice shelf draft
draft = interp_bilinear(grid.draft, ctd_lon, ctd_lat, grid)
# Calculate density, assuming pressure in dbar equals depth in m (this term is small)
rho = densmdjwf(mit_salt, mit_temp, abs(grid.dz))
# Now calculate pressure in dbar
mit_press = (pload_anom + rhoConst*gravity*abs(draft) + np.cumsum(rho*gravity*grid.dz*hfac))*1e-4
# Mask all arrays with hfac
mit_temp = np.ma.masked_where(hfac==0, mit_temp)
mit_salt = np.ma.masked_where(hfac==0, mit_salt)
mit_press = np.ma.masked_where(hfac==0, mit_press)
# Find the bounds on pressure, adding 5% for white space in the plot
press_min = 0.95*min(np.amin(ctd_press), np.amin(mit_press))
press_max = 1.05*max(np.amax(ctd_press), np.amax(mit_press))
# Set up the plot
fig, gs_1, gs_2 = set_panels('CTD')
# Plot temperature and salinity
# First wrap some T/S parameters up in arrays so we can iterate
ctd_data = [ctd_temp, ctd_salt]
mit_data = [mit_temp, mit_salt]
var_name = ['Temperature ('+deg_string+'C)', 'Salinity (psu)']
for j in range(2):
ax = plt.subplot(gs_1[0,j])
ax.plot(ctd_data[j], ctd_press, color='blue', label='CTD')
ax.plot(mit_data[j], mit_press, color='red', label='Model')
ax.set_ylim([press_max, press_min])
ax.grid(True)
plt.title(var_name[j], fontsize=16)
if j==0:
plt.ylabel('Pressure (dbar)', fontsize=14)
if j==1:
# No need for pressure labels
ax.set_yticklabels([])
# Legend in bottom right, below the map
ax.legend(loc=(1.2, 0), ncol=2)
# Plot map
ax = plt.subplot(gs_2[0,0])
plot_empty(grid, ax=ax, zoom_fris=True)
ax.plot(ctd_lon, ctd_lat, '*', color='red', markersize=15)
# Main title with month and year
plt.suptitle(sites[i] + ', ' + date_string, fontsize=22)
finished_plot(fig, fig_name=fig_dir+sites[i]+'.png')