def correct_ts(datadir, filename, corr_coeff_filename):
"""
Corrects TB Temp and salinity according to correlation coefficients and adds corrected
var to ncfile
Also converts practical salinity to absolute salinity and conservative temperature to
potential temperature
"""
coeffs = pd.read_csv(corr_coeff_filename, delim_whitespace=True)
ls = os.listdir(datadir)
ls.sort()
for filename in ls:
if filename[:11] == 'TB_20181211':
# print(filename)
data = xr.open_dataset(os.path.join(datadir, filename))
varkey = [i for i in data.data_vars.keys()]
if 'lon' in varkey:
print(filename)
data['t_corrected']=('DEPTH', \
(coeffs.a_temp.values*data.TEMP+(coeffs.b_temp.values)))
data['s_corrected']=('DEPTH', \
(coeffs.a_sal.values*data.PSAL+(coeffs.b_sal.values)))
data['ab_sal']=('DEPTH', \
gsw.SA_from_SP(data.s_corrected,data.DEPTH,data.lon.values,data.lat.values))
data['ptemp']=('DEPTH', \
gsw.pt_from_CT(data.ab_sal,data.TEMP))
data['ab_sal_bal']=('DEPTH', \
gsw.SA_from_SP_Baltic(data.s_corrected,data.lon.values,data.lat.values))
data['ptemp_bal']=('DEPTH', \
gsw.pt_from_CT(data.ab_sal_bal,data.TEMP))
data.to_netcdf(
os.path.join('Data/ctd_files/gridded_calibrated_updated',
filename))
def gsw_rho(C, T, P, lon, lat):
SP = gsw.SP_from_C(C, T, P)
# This particular data set was collected in the Baltic.
SA = gsw.SA_from_SP_Baltic(SP, lon, lat)
# in-situ density
rho = gsw.density.rho_t_exact(SA, T, P)
return rho
def _compute_data(self,data, units, names, p_ref = 0, baltic = False, lon=0, lat=0, isen = '0'):
""" Computes convservative temperature, absolute salinity and potential density from input data, expects a recarray with the following entries data['C']: conductivity in mS/cm, data['T']: in Situ temperature in degree Celsius (ITS-90), data['p']: in situ sea pressure in dbar
Arguments:
p_ref: Reference pressure for potential density
baltic: if True use the Baltic Sea density equation instead of open ocean
lon: Longitude of ctd cast default=0
lat: Latitude of ctd cast default=0
Returns:
list [cdata,cunits,cnames] with cdata: recarray with entries 'SP', 'SA', 'pot_rho', etc., cunits: dictionary with units, cnames: dictionary with names
"""
sen = isen + isen
# Check for units and convert them if neccessary
if(units['C' + isen] == 'S/m'):
logger.info('Converting conductivity units from S/m to mS/cm')
Cfac = 10
if(('68' in units['T' + isen]) or ('68' in names['T' + isen]) ):
logger.info('Converting IPTS-68 to T90')
T = gsw.t90_from_t68(data['T' + isen])
else:
T = data['T' + isen]
SP = gsw.SP_from_C(data['C' + isen], T, data['p'])
SA = gsw.SA_from_SP(SP,data['p'],lon = lon, lat = lat)
if(baltic == True):
SA = gsw.SA_from_SP_Baltic(SA,lon = lon, lat = lat)
PT = gsw.pt0_from_t(SA, T, data['p'])
CT = gsw.CT_from_t(SA, T, data['p'])
pot_rho = gsw.pot_rho_t_exact(SA, T, data['p'], p_ref)
names = ['SP' + sen,'SA' + sen,'pot_rho' + sen,'pt0' + sen,'CT' + sen]
formats = ['float','float','float','float','float']
cdata = {}
cdata['SP' + sen] = SP
cdata['SA' + sen] = SA
cdata['pot_rho' + sen] = pot_rho
cdata['pt' + sen] = PT
cdata['CT' + sen] = CT
cnames = {'SA' + sen:'Absolute salinity','SP' + sen: 'Practical Salinity on the PSS-78 scale',
'pot_rho' + sen: 'Potential density',
'pt' + sen:'potential temperature with reference sea pressure (p_ref) = 0 dbar',
'CT' + sen:'Conservative Temperature (ITS-90)'}
cunits = {'SA' + sen:'g/kg','SP' + sen:'PSU','pot_rho' + sen:'kg/m^3' ,'CT' + sen:'deg C','pt' + sen:'deg C'}
return [cdata,cunits,cnames]
def calc_teos10_columns(self, lat, lng):
# Practical Salinity
SP = gsw.SP_from_C(self.data['Cond'], self.data['Temp'], self.data['Pres'])
# Absolute Salinity
SA = gsw.SA_from_SP_Baltic(SP, lng, lat)
# Conservative Temperature
CT = gsw.CT_from_t(SA, self.data['Temp'], self.data['Pres'])
# Sigma(density) with reference pressure of 0 dbar
sigma = gsw.sigma0(SA, CT)
# Depth
depth = list(map(abs, gsw.z_from_p(self.data['Pres'], lat)))
return {'PracticalSalinity' : SP,
'AbsoluteSalinity' : SA,
'ConservativeTemperature' : CT,
'Sigma(density)' : sigma,
'Depth' : depth}
def convert_ts(datadir, filename):
"""
Function to convert Practical salinity to Absolute salinity
and Conservative Temperature to potential temperature
"""
ls = os.listdir(datadir)
ls.sort()
for filename in ls:
if filename[:11] == 'SK_20181210':
# print(filename)
data = xr.open_dataset(os.path.join(datadir, filename))
print(filename)
data['ab_sal']=('DEPTH', \
gsw.SA_from_SP(data.PSAL,data.DEPTH,data.lon.values,data.lat.values))
data['ptemp']=('DEPTH', \
gsw.pt_from_CT(data.ab_sal,data.TEMP))
data['ab_sal_bal']=('DEPTH', \
gsw.SA_from_SP_Baltic(data.PSAL,data.lon.values,data.lat.values))
data['ptemp_bal']=('DEPTH', \
gsw.pt_from_CT(data.ab_sal_bal,data.TEMP))
data.to_netcdf(
os.path.join('Data/ctd_files/gridded_calibrated_updated',
filename))
def SA_from_C(C, t, p, lon, lat):
SP = gsw.conversions.SP_from_C(C, t, p)
return gsw.SA_from_SP_Baltic(SP, lon, lat)
#### ---------- MSS cast --------- ####
# CTD casts before storm (pd is powerful!))
lat = 55
lon = 16
Pbin = np.arange(0.5, 85, 1)
MSS_S1_1_dict = loadmat('./data/MSS_DATA/S1_1.mat',
squeeze_me=True,
struct_as_record=False)
Zmss = MSS_S1_1_dict['CTD'][2].P
Tmss = MSS_S1_1_dict['CTD'][2].T
Smss = MSS_S1_1_dict['CTD'][2].S
digitized = np.digitize(Zmss, Pbin) #<- this is awesome!
TTbin = np.array([Tmss[digitized == i].mean() for i in range(0, len(Pbin))])
SSbin = np.array([Smss[digitized == i].mean() for i in range(0, len(Pbin))])
SA_MSS_01 = gsw.SA_from_SP_Baltic(SSbin, lon, lat)
CT_MSS_01 = gsw.CT_from_t(SA_MSS_01, TTbin, Pbin)
SIG0_MSS_01 = gsw.sigma0(SA_MSS_01, CT_MSS_01)
N2_MSS_01 = gsw.Nsquared(SA_MSS_01, CT_MSS_01, Pbin, lat)
import SW_extras as swe
N2_01 = N2_MSS_01[0]
zN2_01 = N2_MSS_01[1]
N2_period_01 = 60.0 / swe.cph(N2_01)
MSS_S1_2_dict = loadmat('./data/MSS_DATA/S1_2.mat',
squeeze_me=True,
struct_as_record=False)
Zmss = MSS_S1_2_dict['CTD'][2].P
Tmss = MSS_S1_2_dict['CTD'][2].T
Smss = MSS_S1_2_dict['CTD'][2].S
digitized = np.digitize(Zmss, Pbin) #<- this is awesome!
def abs_suolaisuus(salt_p, lon, lat):
a_salt = gsw.SA_from_SP_Baltic(salt_p, lon, lat)
return np.asarray(a_salt)
