def calculate_bottle_oxygen(ssscc_list, ssscc_col, titr_vol, titr_temp,
flask_nums):
"""
Wrapper function for collecting parameters and calculating oxygen values from
Winkler titrations.
Parameters
----------
ssscc_list : list of str
List of stations to process
ssscc_col : array-like
Station/cast for each sample taken
titr_vol : array-like
Titration volume [mL]
titr_temp : array-like
Temperature of titration [degC]
flask_nums : array-like
Oxygen flask used for each sample
Returns
-------
oxy_mL_L : array-like
Oxygen concentration [mL/L]
Notes
-----
Titration equation comes from WHP Operations and Methods, Culberson (1991):
https://cchdo.github.io/hdo-assets/documentation/manuals/pdf/91_1/culber2.pdf
"""
params = pd.DataFrame()
for ssscc in ssscc_list:
df = gather_oxy_params(cfg.directory["oxy"] + ssscc)
df["SSSCC"] = ssscc
params = pd.concat([params, df])
# get flask volumes and merge with titration parameters
flask_df = load_flasks() # TODO: volume correction from thermal expansion?
volumes = pd.merge(flask_nums, flask_df, how="left")["FLASK_VOL"].values
params = pd.merge(ssscc_col, params, how="left")
# find 20degC equivalents
rho_20C = gsw.rho_t_exact(0, 20, 0)
rho_T_KIO3 = gsw.rho_t_exact(0, params["T_KIO3"], 0)
N_KIO3_20C = params["N_KIO3"] * (rho_T_KIO3 / rho_20C)
# TODO: does KIO3 volume get corrected? what is the recorded value?
# V_KIO3_20C = correct_flask_vol(params["V_KIO3"], t=params["T_KIO3"])
# calculate O2 concentration (in mL/L)
E = 5598 # stoichiometric relationship between thio_n and DO
DO_reg = 0.0017 # correction for oxygen added by reagents
V_reg = 2.0 # volume of reagents (mL)
oxy_mL_L = ((((titr_vol.values - params["V_blank"]) * params["V_KIO3"] *
N_KIO3_20C * E) / (params["V_std"] - params["V_blank"]) -
1000 * DO_reg)) / (volumes - V_reg)
return oxy_mL_L.values
def bfrq(T, S, z, lat, lon):
"""
Calculate Bouyancy frequency from practical salinity and temperature.
Converts to potential temperature and absolute salinity to calculate density
"""
SA = gsw.conversions.SA_from_SP(S, z, lon, lat)
g = gsw.grav(lat, z)
pdens = gsw.rho_t_exact(SA, T, z)
dpdens = np.gradient(gsw.rho_t_exact(SA, T, z), axis=0)
dz = np.gradient(z, axis=0)
N2 = (g / pdens) * (dpdens / dz)
return N2
def read_dbds(self, fns, settings):
if settings['buoyancy_engine']=='shallow':
buoyancy_variable='m_ballast_pumped'
elif settings['buoyancy_engine']=='deep':
buoyancy_variable='m_de_oil_vol'
else:
raise ValueError('Unknown buoyancy engine. Accepted values: (shallow|deep)')
dbd = dbdreader.MultiDBD(filenames=fns, include_paired=True)
tmp = dbd.get_sync("sci_ctd41cp_timestamp",["sci_water_pressure",
"sci_water_cond",
"sci_water_temp",
"m_pitch",
"m_battpos",
buoyancy_variable])
_, tctd, P, C, T, pitch, battpos, buoyancy_change = tmp.compress(tmp[3]>0.01, axis=1)
SP = gsw.SP_from_C(C*10, T, P*10)
SA = gsw.SA_from_SP(SP, P*10, settings['longitude'], settings['latitude'])
density = gsw.rho_t_exact(SA, T, P*10)
#density = fast_gsw.rho(C*10, T, P*10, settings['longitude'], settings['latitude'])
data = dict(time=tctd, pressure=P, pitch=pitch,
buoyancy_change=buoyancy_change, battpos=battpos, density=density)
self.model.input_data = data
mask=np.zeros(tctd.shape,'int').astype(bool) # use all data by default
self.model.set_mask(mask)
self.model.OR(P*10<settings['minlimitdepth'])
self.model.OR(P*10>settings['maxlimitdepth'])
def compute_buoyancy(data, temperature_var='SST', salinity_var='SSS'):
"""
Compute buoyancy from a dataset containing salinity and temperature
variables
Parameters
----------
data : xarray.Dataset
A Dataset containing salinity and temperature variables
temperature_var : str, optional
The name of the temperature variable. Default is "SST".
salinity_var : str, optional
The name of the salinity variable Default is "SSS"
Returns
-------
res : xarray.Dataset
A Dataset similar to the input but with a new variable
"buoyancy"
"""
p = 0 * data['lon']
SA = gsw.SA_from_SP(data[salinity_var], p, data['lon'], data['lat'])
rho = gsw.rho_t_exact(SA, data[temperature_var], p)
b = 9.81 * (1 - rho / 1025.)
return data.assign(buoyancy=xr.DataArray(b, dims='time'))
def read_dbds(self, fns, settings):
if settings['buoyancy_engine'] == 'shallow':
buoyancy_variable = 'm_ballast_pumped'
elif settings['buoyancy_engine'] == 'deep':
buoyancy_variable = 'm_de_oil_vol'
else:
raise ValueError(
'Unknown buoyancy engine. Accepted values: (shallow|deep)')
dbd = dbdreader.MultiDBD(filenames=fns, complement_files=True)
try:
tmp = dbd.get_CTD_sync("m_pitch", "m_battpos", buoyancy_variable)
except dbdreader.DbdError:
tmp = dbd.get_sync("sci_water_cond", "sci_water_temp",
"sci_water_pressure", "m_pitch", "m_battpos",
buoyancy_variable)
try:
(_, lat), (_, lon) = dbd.get("m_gps_lat", "m_gps_lon")
except dbdreader.DbdError:
latitude = float(input("Provide decimal latitude:"))
longitude = float(input("Provide decimal longitude:"))
else:
latitude = np.median(lat)
longitude = np.median(lon)
condition = np.logical_and(tmp[2] > 0.01,
np.isfinite(np.sum(tmp, axis=0)))
tctd, C, T, P, pitch, battpos, buoyancy_change = np.compress(condition,
tmp,
axis=1)
SP = gsw.SP_from_C(C * 10, T, P * 10)
SA = gsw.SA_from_SP(SP, P * 10, longitude, latitude)
density = gsw.rho_t_exact(SA, T, P * 10)
#density = fast_gsw.rho(C*10, T, P*10, longitude, latitude)
data = dict(time=tctd,
pressure=P,
pitch=pitch,
buoyancy_change=buoyancy_change,
battpos=battpos,
density=density)
self.model.input_data = data
mask = np.zeros(tctd.shape,
'int').astype(bool) # use all data by default
self.model.set_mask(mask)
self.model.OR(P * 10 < settings['minlimitdepth'])
self.model.OR(P * 10 > settings['maxlimitdepth'])