def calculate_density(temperature, pressure, salinity, latitude, longitude):
"""Calculates density given glider practical salinity, pressure, latitude,
and longitude using Gibbs gsw SA_from_SP and rho functions.
Parameters:
temperature (C), pressure (dbar), salinity (psu PSS-78),
latitude (decimal degrees), longitude (decimal degrees)
Returns:
density (kg/m**3),
"""
correct_sizes = (temperature.size == pressure.size == salinity.size ==
latitude.size == longitude.size)
if correct_sizes is False:
raise ValueError('Arguments must all be the same length')
with warnings.catch_warnings():
warnings.simplefilter("ignore")
absolute_salinity = SA_from_SP(salinity, pressure, longitude, latitude)
conservative_temperature = CT_from_t(absolute_salinity, temperature,
pressure)
density = rho(absolute_salinity, conservative_temperature, pressure)
return density
def calculate_sound_speed(temperature, pressure, salinity, latitude,
longitude):
"""Calculates sound speed given glider practical in-situ temperature, pressure and salinity using Gibbs gsw
SA_from_SP and rho functions.
Parameters:
temperature (C), pressure (dbar), salinity (psu PSS-78), latitude, longitude
Returns:
sound speed (m s-1)
"""
absolute_salinity = SA_from_SP(salinity, pressure, longitude, latitude)
conservative_temperature = CT_from_t(absolute_salinity, temperature,
pressure)
speed = sound_speed(absolute_salinity, conservative_temperature, pressure)
return speed
def calculate_density(temperature, pressure, salinity, latitude, longitude):
"""Calculates density given glider practical salinity, pressure, latitude,
and longitude using Gibbs gsw SA_from_SP and rho functions.
Parameters:
timestamps (UNIX epoch),
temperature (C), pressure (dbar), salinity (psu PSS-78),
latitude (decimal degrees), longitude (decimal degrees)
Returns:
density (kg/m**3),
"""
# dBar_pressure = pressure * 10
absolute_salinity = SA_from_SP(salinity, pressure, longitude, latitude)
conservative_temperature = CT_from_t(absolute_salinity, temperature,
pressure)
density = rho(absolute_salinity, conservative_temperature, pressure)
return density
# for istat, (xs, ys) in enumerate(zip(ctd['lon'][0], ctd['lat'][0])):
# ax.text(xs, ys, str(ctd['profilid'][0, istat])[-3:], transform=ccrs.PlateCarree())
# ax.scatter(ctd['lon'][0, transects[nts]], ctd['lat'][0, transects[nts]], transform=ccrs.PlateCarree(), zorder=4, facecolors='yellow')
# if savefig:
# fig.savefig(os.path.join(pathsave, 'map_transect%s.png' %nts), transparent=True)
# DYNAMIC HEIGHT and depth
nanarray = np.empty((ctd['P'].shape[0], len(transects[nts])))
nanarray[:] = np.nan
SA, CT, g, depth, pt, nprof = nanarray.copy(), nanarray.copy(), nanarray.copy(), nanarray.copy(), nanarray.copy(), nanarray.copy()
for i, profile in enumerate(transects[nts]):
nprof[:, i] = profile
SA[:, i] = SA_from_SP(ctd['S'][:, profile], P, ctd['lon'][0, profile], ctd['lat'][0, profile])
CT[:, i] = CT_from_t(SA[:, i], ctd['T'][:, profile], P)
g[:, i] = grav(ctd['lat'][0, profile], P)
depth[:, i] = abs(z_from_p(P, ctd['lat'][0, profile]))
pt[:, i] = pt_from_t(SA[:, i], ctd['T'][:, profile], P, p_ref)
sig0 = sigma0(SA, CT)
# for i
deltaD = geo_strf_dyn_height(SA, CT, P, p_ref=p_ref) / g
deltaD500 = np.tile(deltaD[i500], (deltaD.shape[0],1))
# g_p = grav(ctd['lat'][0, profile], P)
# SA = SA_from_SP(ctd['S'][:, profile], P, ctd['lon'][0, profile], ctd['lat'][0, profile])
# CT = CT_from_t(SA, ctd['T'][:, profile], P)
# pt = pt_from_t(SA, ctd['T'][:, profile], P, 0)
# depth = abs(z_from_p(P, ctd['lat'][0, profile]))
nc.close()
print('Output file %s already exists, variable %s added.' %
(os.path.split(output_file)[-1], gsw_vars[-1]))
else:
# change one dimension variables to two dimensions
p, lon, lat = nc['p'][:], nc['lon'][:, 0][:, np.newaxis], nc[
'lat'][:, 0][:, np.newaxis]
# convert in-situ variables to gsw variables
p_ref = 1494 # shallowest profile (4) except (34) which goes until 1004
SA = SA_from_SP(nc['SP'][:], p, lon, lat)
CT = CT_from_t(SA, nc['t'][:], p)
pt = pt_from_t(SA, nc['t'][:], p, p_ref)
deltaD = np.ma.masked_invalid(
geo_strf_dyn_height(SA.data,
CT.data,
p,
p_ref=p_ref,
axis=1))
# transect stations
transects = {
1: list(reversed(range(2, 10))),
2: list(range(10, 18)),
3: list(reversed(range(18, 27))),
4: list(range(26, 34)),
P = np.zeros(dict_ctd['P'].shape[0])
P[:] = np.nan
for ip, p in enumerate(dict_ctd['P']):
P[ip] = p
if filename[4:] == 'vars':
dict_vars['P'] = P
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'],