def spice0(salt_PSU, temp_C, pres_db, lat, lon):
"""
Calculate spiciness from glider measurements of salinity and temperature.
Parameters
----------
salt_PSU : array, dtype=float, shape=[n, ]
practical salinty
temp_C : array, dtype=float, shape=[n, ]
temperature in deg C
pres_db : array, dtype=float, shape=[n, ]
pressure in decibar
lat : array, dtype=float, shape=[n, ]
latitude in degrees north
lon : array, dtype=float, shape=[n, ]
longitude in degrees east
Returns
-------
potential_density : array, dtype=float, shape=[n, ]
Note
----
Using seawater.dens does not yield the same results as this function. We
get very close results to what the SeaGlider Basestation returns with this
function. The difference of this function with the basestation is on
average ~ 0.003 kg/m3
"""
import gsw
salt_abs = gsw.SA_from_SP(salt_PSU, pres_db, lon, lat)
cons_temp = gsw.CT_from_t(salt_abs, temp_C, pres_db)
spice0 = gsw.spiciness0(salt_abs, cons_temp)
spice0 = transfer_nc_attrs(
getframe(),
temp_C,
spice0,
"spiciness0",
units=" ",
comment="",
standard_name="spiciness0",
)
return spice0
# Load data
tic1 = time.time()
SpiceFile = '/Users/Ellen/Documents/GitHub/BGCArgo_BCGyre/CSVFiles/Spice.csv'
SpiceData = pd.read_csv(SpiceFile)
var_list = SpiceData.columns.to_list()
check = 0
for i in var_list:
if i == 'Spice':
check = 1
if check == 0:
SA = gsw.SA_from_SP(SpiceData.loc[:, 'Sal'], SpiceData.loc[:, 'Pres'],
SpiceData.loc[:, 'Lon'], SpiceData.loc[:, 'Lat'])
CT = gsw.CT_from_t(SA, SpiceData.loc[:, 'Temp'], SpiceData.loc[:, 'Pres'])
Spicy = gsw.spiciness0(SA, CT)
SpiceData['Spice'] = Spicy
SpiceData.to_csv(SpiceFile)
# General plots
for i in np.arange(len(dense_range) - 1):
tic2 = time.time()
lower_bound = np.round(dense_range[i], 3)
upper_bound = np.round(dense_range[i + 1], 3)
print('\nDense range: ', lower_bound, '-', upper_bound)
# Load data
SpiceData = pd.read_csv(
'/Users/Ellen/Documents/GitHub/BGCArgo_BCGyre/CSVFiles/Spice.csv')
'z': ('height', 'f8', (
'profile',
'plevel',
), z_from_p(p, lat)),
'pt': ('sea_water_potential_temperature', 'f8', (
'profile',
'plevel',
), pt),
'sigma0': ('sea_water_sigmat', 'f8', (
'profile',
'plevel',
), sigma0(SA, CT)),
'spiciness0': ('sea_water_spiciness', 'f8', (
'profile',
'plevel',
), spiciness0(SA, CT)),
'deltaD': ('dynamic_height_anomaly', 'f8', (
'profile',
'plevel',
), deltaD),
'lonv': ('longitude', 'f8', ('profile_vel', ), lonv),
'latv': ('latitude', 'f8', ('profile_vel', ), latv),
'Vg': ('geostrophic_sea_water_velocity', 'f8', (
'profile_vel',
'plevel',
), Vg)
}
# save data in netcdf file using OceanPy's createNetCDF class
nc = createNetCDF(output_file)
nc.add_dims(dim)
Parray = np.tile(dict_vars['P'], (dict_vars['S'].shape[1], 1)).T
elif filename[4:] == 'stations':
dict_stations['P'] = P
# calculate TEOS-10 variables and store in dict
p_ref = 1500
if filename[4:] == 'vars':
dict_vars['SA'] = SA_from_SP(dict_vars['S'], Parray,
dict_vars['lon'], dict_vars['lat'])
dict_vars['CT'] = CT_from_t(dict_vars['SA'], dict_vars['T'],
Parray)
dict_vars['depth'] = abs(z_from_p(Parray, dict_vars['lat']))
dict_vars['pt'] = pt_from_t(dict_vars['SA'], dict_vars['T'],
Parray, p_ref)
dict_vars['sigma0'] = sigma0(dict_vars['SA'], dict_vars['CT'])
dict_vars['spiciness0'] = spiciness0(dict_vars['SA'],
dict_vars['CT'])
# save dictionary to file
write_dict(dict_vars, path, filename)
print('data stored in file')
elif filename[4:] == 'stations':
for station in dict_ctd['station'][0, :]:
dict_stations[station]['SA'] = SA_from_SP(
dict_stations[station]['S'], dict_stations['P'],
dict_stations[station]['lon'],
dict_stations[station]['lat'])
dict_stations[station]['CT'] = CT_from_t(
dict_stations[station]['SA'], dict_stations[station]['T'],
dict_stations['P'])
dict_stations[station]['g'] = grav(