def sound_speed_teos10(lats, lons, z, t, SP):
""" Compute sound speed from temperature, salinity, and depth
for a given latitude and longitude using the Thermodynamic
Equation of Seawater 2010.
https://teos-10.github.io/GSW-Python/
Args:
lats: numpy array
Latitudes (-90 to 90 degrees)
lons: numpy array
Longitudes (-180 to 180 degrees)
z: numpy array
Depths (meters)
t: numpy array
In-situ temperature (Celsius)
SP: numpy array
Practical Salinity (psu)
Returns:
c: numpy array
Sound speed (m/s)
"""
p = gsw.p_from_z(z=-z, lat=lats) # sea pressure (gsw uses negative z below sea surface)
SA = gsw.SA_from_SP(SP, p, lons, lats) # absolute salinity
CT = gsw.CT_from_t(SA, t, p) # conservative temperature
c = gsw.density.sound_speed(SA=SA, CT=CT, p=p)
return c
def get_ht(t, s, d, vh, y, y_loc, cp, rho0):
'''
Compute offshore heat transport, as defined in St-Laurent et al JPO 2012
'''
#cp = 1; rho0 = 1
tmp = np.nonzero(y <= y_loc)[0][-1]
# transport at h point
# mask transport. > 0.
vhh = 0.5 * (vh[:, tmp - 1, :] + vh[:, tmp, :])
vhnew1 = np.ma.masked_where(vhh < 0.0, vhh)
vhnew2 = np.ma.masked_where(vhh > 0.0, vhh)
t_new = t[:, tmp, :] # temp at cavity exit
s_new = s[0, tmp, :] # salinity at cavity exit
d_new = d[0, tmp, :] # depth at cavity exit
p = gsw.p_from_z(d_new, -72.0) * 1.0e4 # in Pascal
t_freeze = eos.tfreeze(s_new, p) + 273.0 # in K
#t_freeze = eos.tfreeze(34.,p) + 273.0 # in K
print 't_freeze min/max', t_freeze.min(), t_freeze.max()
dt = (t_new - t_freeze)
print 'dt max/min', dt.max(), dt.min()
Hout = (vhnew1 * cp * rho0 * dt) # watts
Hin = (vhnew2 * cp * rho0 * dt) # watts
print 'Hout, Hin, difference:', Hout.sum(), Hin.sum(
), Hout.sum() + Hin.sum()
return Hout.sum(), Hin.sum()
def common_thermodynamics(depth, lon, lat, SP, t):
"""Wrapper for various thermodynamic calculations.
Assumes input data is 1D with size N or M, or 2D with size N*M,
where M denotes profiles and N depths.
Parameters
----------
depth : numpy array
Depth (m), size N.
lon : numpy array
Longitude, size M.
lat : numpy array
Latitude, size M.
SP : numpy array
Practical salinity, size N*M.
t : numpy array
Temperature, size N*M.
"""
p = gsw.p_from_z(-depth, np.mean(lat))
SA = gsw.SA_from_SP(SP, p[:, np.newaxis], lon[np.newaxis, :],
lat[np.newaxis, :])
CT = gsw.CT_from_t(SA, t, p[:, np.newaxis])
sig0 = gsw.pot_rho_t_exact(SA, t, p[:, np.newaxis], 0)
N2, p_mid = gsw.Nsquared(SA, CT, p[:, np.newaxis], lat[np.newaxis, :])
return p, SA, CT, sig0, p_mid, N2
def compute_potdens(ds, saltname='SALT', tempname='THETA'):
import gsw
""" compute the potential density
"""
# compute the Conservative Temperature from the model's potential temperature
temp = ds[tempname].transpose(*('time', 'k', 'face', 'j', 'i'))
salt = ds[tempname].transpose(*('time', 'k', 'face', 'j', 'i'))
CT = gsw.CT_from_pt(salt, temp)
z, lat = xr.broadcast(ds['Z'], ds['YC'])
z = z.transpose(*('k', 'face', 'j', 'i'))
lat = lat.transpose(*('k', 'face', 'j', 'i'))
# compute pressure from depth
p = gsw.p_from_z(z, lat)
# compute in-situ temperature
T = gsw.t_from_CT(salt, CT, p)
# compute potential density
rho = gsw.pot_rho_t_exact(salt, T, p, 0.)
# create new dataarray
darho = xr.full_like(temp, 0.)
darho = darho.load().chunk({'time': 1, 'face': 1})
darho.name = 'RHO'
darho.attrs['long_name'] = 'Potential Density ref at 0m'
darho.attrs['standard_name'] = 'RHO'
darho.attrs['units'] = 'kg/m3'
darho.values = rho
# filter special value
darho = darho.where(darho > 1000)
darho = darho.assign_coords(XC=ds['XC'], YC=ds['YC'], Z=ds['Z'])
return darho
def __init__(self, lon, lat, name=None):
self._woa=True
#
self._tfile = 'woa13_decav_t00_01v2.nc'
nc = Dataset(self._tfile,'r')
#
glon = nc.variables['lon'][:]
glat = nc.variables['lat'][:]
ilon = np.argmin(np.abs(lon-glon))
ilat = np.argmin(np.abs(lat-glat))
self.lon = glon[ilon]
self.lat = glat[ilat]
#
self.z = -nc.variables['depth'][:]
self.p = gsw.p_from_z(self.z,self.lat)
#
self.temp = nc.variables['t_an'][0,:,ilat,ilon]
nc.close()
#
self._sfile = 'woa13_decav_s00_01v2.nc'
nc = Dataset(self._sfile,'r')
self.s = nc.variables['s_an'][0,:,ilat,ilon]
nc.close()
# derive absolute salinity and conservative temperature
self.SA = gsw.SA_from_SP(self.s, self.p, self.lon, self.lat)
self.CT = gsw.CT_from_t(self.SA, self.temp, self.p)
# isopycnal displacement
self.eta=0.
#
if name is None:
self.name = 'WOA water profile at lon=%.0f, lat=%.0f'%(self.lon,self.lat)
def raw_to_zref(d, zref):
temp = d['TEMP']
psal = d['PSAL']
pres = d['PRES']
temp_qc = d['TEMP_QC']
psal_qc = d['PSAL_QC']
pres_qc = d['PRES_QC']
lon = d['LONGITUDE']
lat = d['LATITUDE']
klist, ierr = remove_bad_qc(d)
if ierr == 0:
Tis = temp[klist]
SP = psal[klist]
p = pres[klist]
CT, SA, z = insitu_to_absolute(Tis, SP, p, lon, lat, zref)
Ti, Si, dTidz, dSidz = interp_at_zref(CT, SA, z, zref, klist)
pi = gsw.p_from_z(-zref, lat)
#Ri = gsw.rho(Si, Ti, pi)
Ri, alpha, beta = gsw.rho_alpha_beta(Si, Ti, pi)
g = gsw.grav(lat, pi)
BVF2i = g * (beta * dSidz - alpha * dTidz)
flag = True
else:
zero = zref * 0.
Ti, Si, Ri, BVF2i = zero, zero, zero, zero
flag = False
return {'CT': Ti, 'SA': Si, 'RHO': Ri, 'BVF2': BVF2i}, flag
def _load_from_woa(self, lon, lat, name):
self._woa = True
#
self._tfile = 'woa18_A5B7_t00_01.nc'
nc = Dataset(self._tfile, 'r')
#
glon = nc.variables['lon'][:]
glat = nc.variables['lat'][:]
ilon = np.argmin(np.abs(lon - glon))
ilat = np.argmin(np.abs(lat - glat))
self.lon = glon[ilon]
self.lat = glat[ilat]
#
self.z = -nc.variables['depth'][:].data
self.p = gsw.p_from_z(self.z, self.lat)
#
self.temp = nc.variables['t_an'][0, :, ilat, ilon]
nc.close()
#
self._sfile = 'woa18_A5B7_s00_01.nc'
nc = Dataset(self._sfile, 'r')
self.s = nc.variables['s_an'][0, :, ilat, ilon]
nc.close()
#
self._update_eos()
#
if name is None:
self.name = 'WOA water profile at lon=%.0f, lat=%.0f' % (self.lon,
self.lat)
def _in_situ_to_potential_temperature(dsTemp, dsSalin):
z = dsTemp.z.values
lat = dsTemp.lat.values
lon = dsTemp.lon.values
nz = len(z)
ny, nx = lat.shape
dsPotTemp = dsTemp.drop('in_situ_temperature')
pt = numpy.nan * numpy.ones((nz, ny, nx))
for zIndex in range(nz):
pressure = gsw.p_from_z(z[zIndex], lat)
in_situ_temp = dsTemp.in_situ_temperature[zIndex, :, :].values
salin = dsSalin.salinity[zIndex, :, :].values
mask = numpy.isfinite(in_situ_temp)
SA = gsw.SA_from_SP(salin[mask], pressure[mask], lon[mask], lat[mask])
ptSlice = pt[zIndex, :, :]
ptSlice[mask] = gsw.pt_from_t(SA,
in_situ_temp[mask],
pressure[mask],
p_ref=0.)
pt[zIndex, :, :] = ptSlice
dsPotTemp['temperature'] = (('z', 'y', 'x'), pt)
dsPotTemp['temperature'].attrs = dsTemp.in_situ_temperature.attrs
return dsPotTemp
def compute_B0_MO_lenght(temp, salt, PRCmE, depth, t, y, args):
'''
Compute net surface buoyancy flux and Monin-Obukhov lenght scale
'''
tmp1 = np.nonzero(y <= args.ISL)[0][-1]
tmp2 = np.nonzero(y <= args.cshelf_lenght)[0][-1]
# constants
rho_0 = 1028.0
vonKar = 0.41
g = 9.8
Cp = 3974.0
# get EoS coeffs
p = gsw.p_from_z(depth, -70.0) * 1.0e4 # in Pascal
beta = eos.beta_wright_eos(temp, salt, p) / rho_0
alpha = eos.alpha_wright_eos(temp, salt, p) / rho_0
#print 'depth, beta, alpha',depth.min(), depth.max(), beta.min(), beta.max(), alpha.min(), alpha.max()
# load local data
ustar = mask_bad_values(Dataset(args.sfc_file).variables['ustar'][t, :])
sensible = mask_bad_values(
Dataset(args.sfc_file).variables['sensible'][t, :])
latent = mask_bad_values(Dataset(args.sfc_file).variables['latent'][t, :])
shelf_area = Dataset(args.ice_shelf_file).variables['shelf_area'][0, :]
# buoyancy flux
B0 = -g * (alpha * (-(sensible + latent) / (rho_0 * Cp)) - beta *
(PRCmE * salt / rho_0))
B0_shelf = B0[tmp1:tmp2, :].mean()
B0_IS = B0[0:tmp1, :].mean()
# Monin-Obukhov Length
l = ustar**3 / (vonKar * B0)
# mask values outside cavity
l[l == 0.0] = -1e+34
# l = np.ma.masked_where(depth == depth.max(), l)
# print 'Monin-Obukhov Length min/max',l.min(), l.max()
return l, B0, B0.mean(), B0_shelf, B0_IS
def comp_rhostar(Si, Ti, lat):
pi = gsw.p_from_z(-zref, lat)
cs = gsw.sound_speed(Si, Ti, pi)
Ri = gsw.rho(Si, Ti, pi)
g = gsw.grav(lat, pi[0])
E = np.zeros((len(zref), ))
#plt.plot(Ri, -zref)
f = interpolate.interp1d(zref, cs)
def e(x):
return -g / f(x)**2
if True:
for k, z in enumerate(zref):
if k == 0:
r, E[k] = 0., 1.
else:
#r1,p = integrate.quad(e,zref[k-1],z,epsrel=1e-1)
x = np.linspace(zref[k - 1], z, 10)
dx = x[1] - x[0]
r1 = integrate.trapz(e(x), dx=dx)
r += r1
E[k] = np.exp(r)
return Ri * E, E
def get_ht(t, s, d, vh, y, direction, y_loc):
'''
Compute heat transport, onshore (direction=1) or offshore (direction=2), as defined in St-Laurent et al JPO 2012
'''
cp = 3974.0 # heat capacity
rho0 = 1028.0
tmp = np.nonzero(y <= y_loc)[0][-1]
if direction == 1:
# mask transport. > 0.
vhnew = np.ma.masked_where(vh[:, tmp, :] > 0.0, vh[:, tmp, :])
t_new = np.ma.masked_where(vh[:, tmp, :] > 0.0, t[:, tmp, :])
s_new = np.ma.masked_where(vh[:, tmp, :] > 0.0, s[:, tmp, :])
d_new = np.ma.masked_where(vh[:, tmp, :] > 0.0, d[:, tmp, :])
else:
# mask transport. < 0.
vhnew = np.ma.masked_where(vh[:, tmp, :] < 0.0, vh[:, tmp, :])
t_new = np.ma.masked_where(vh[:, tmp, :] < 0.0, t[:, tmp, :])
s_new = np.ma.masked_where(vh[:, tmp, :] < 0.0, s[:, tmp, :])
d_new = np.ma.masked_where(vh[:, tmp, :] < 0.0, d[:, tmp, :])
p = gsw.p_from_z(d_new, -70.0) * 1.0e4 # in Pascal
t_freeze = eos.tfreeze(s_new, p)
dt = (t_new - t_freeze)
#print 't-tf min/max',dt.min(),dt.max()
if direction == 1:
oht = -(vhnew * cp * rho0 * dt).sum() # watts
else:
oht = (vhnew * cp * rho0 * dt).sum()
return oht
def ctd_bincast(data, dz, zmin, zmax):
"""
Depth-bin CTD time series.
Parameters
----------
data : xr.Dataset
CTD time series
dz : float
Bin size [m]
zmin : float
Minimum bin depth center [m]
zmax : float
Maximum bin depth center [m]
Returns
-------
data : xr.Dataset
Depth-binned CTD profile
"""
dz2 = dz / 2
zbin = np.arange(zmin - dz2, zmax + dz + dz2, dz)
zbinlabel = np.arange(zmin, zmax + dz, dz)
# prepare dataset
tmp = data.swap_dims({"time": "depth"})
tmp = tmp.reset_coords()
# need to bin time separately, not sure why
btime = tmp.time.groupby_bins("depth",
bins=zbin,
labels=zbinlabel,
right=True,
include_lowest=True).mean()
# bin all variables
out = tmp.groupby_bins("depth",
bins=zbin,
labels=zbinlabel,
right=True,
include_lowest=True).mean()
# organize
out.coords["time"] = btime
out = out.set_coords(["lon", "lat"])
out = out.rename_dims({"depth_bins": "z"})
out = out.rename({"depth_bins": "depth"})
# copy attributes
# get data variable names
varnames = [k for k, v in data.data_vars.items()]
for vari in varnames:
out[vari].attrs = data[vari].attrs
out["depth"].attrs = {"long_name": "depth", "units": "m"}
out.attrs = data.attrs
# recalculate pressure from depth bins
out["p"] = (["z"], gsw.p_from_z(-1 * out.depth, out.lat))
out.p.attrs = {"long_name": "pressure", "units": "dbar"}
return out
def compute_pot_density(prefix, inGridName, inDir):
config = MpasAnalysisConfigParser()
config.read('mpas_analysis/config.default')
outDescriptor = get_comparison_descriptor(config, 'antarctic')
outGridName = outDescriptor.meshName
description = 'Monthly potential density climatologies from ' \
'2005-2010 average of the Southern Ocean State ' \
'Estimate (SOSE)'
botDescription = 'Monthly potential density climatologies at sea ' \
'floor from 2005-2010 average from SOSE'
for gridName in [inGridName, outGridName]:
outFileName = '{}_pot_den_{}.nc'.format(prefix, gridName)
TFileName = '{}_pot_temp_{}.nc'.format(prefix, gridName)
SFileName = '{}_salinity_{}.nc'.format(prefix, gridName)
if not os.path.exists(outFileName):
with xarray.open_dataset(TFileName) as dsT:
with xarray.open_dataset(SFileName) as dsS:
dsPotDensity = dsT.drop(['theta', 'botTheta'])
lat, lon, z = xarray.broadcast(dsS.lat, dsS.lon, dsS.z)
pressure = gsw.p_from_z(z.values, lat.values)
SA = gsw.SA_from_SP(dsS.salinity.values, pressure,
lon.values, lat.values)
CT = gsw.CT_from_pt(SA, dsT.theta.values)
dsPotDensity['potentialDensity'] = (dsS.salinity.dims,
gsw.rho(SA, CT, 0.))
dsPotDensity.potentialDensity.attrs['units'] = \
'kg m$^{-3}$'
dsPotDensity.potentialDensity.attrs['description'] = \
description
lat, lon, z = xarray.broadcast(dsS.lat, dsS.lon, dsS.zBot)
pressure = gsw.p_from_z(z.values, lat.values)
SA = gsw.SA_from_SP(dsS.botSalinity.values, pressure,
lon.values, lat.values)
CT = gsw.CT_from_pt(SA, dsT.botTheta.values)
dsPotDensity['botPotentialDensity'] = \
(dsS.botSalinity.dims, gsw.rho(SA, CT, 0.))
dsPotDensity.botPotentialDensity.attrs['units'] = \
'kg m$^{-3}$'
dsPotDensity.botPotentialDensity.attrs['description'] = \
botDescription
write_netcdf(dsPotDensity, outFileName)
def potential_to_in_situ_temperature(dsPotTemp, dsSalin):
z = dsPotTemp.z.values
lat = numpy.maximum(dsPotTemp.lat.values, -80.)
lon = dsPotTemp.lon.values
if len(lat.shape) == 1:
lon, lat = numpy.meshgrid(lon, lat)
nz = len(z)
ny, nx = lat.shape
if 'time' in dsPotTemp.dims:
nt = dsPotTemp.sizes['time']
T = numpy.nan * numpy.ones((nt, nz, ny, nx))
for zIndex in range(nz):
pressure = gsw.p_from_z(z[zIndex], lat)
for tIndex in range(nt):
pt = dsPotTemp.temperature[tIndex, zIndex, :, :].values
salin = dsSalin.salinity[tIndex, zIndex, :, :].values
mask = numpy.logical_and(numpy.isfinite(pt),
numpy.isfinite(salin))
SA = gsw.SA_from_SP(salin[mask], pressure[mask], lon[mask],
lat[mask])
TSlice = T[tIndex, zIndex, :, :]
CT = gsw.CT_from_pt(SA, pt[mask])
TSlice[mask] = gsw.t_from_CT(SA, CT, pressure[mask])
T[tIndex, zIndex, :, :] = TSlice
else:
T = numpy.nan * numpy.ones((nz, ny, nx))
for zIndex in range(nz):
pressure = gsw.p_from_z(z[zIndex], lat)
pt = dsPotTemp.temperature[zIndex, :, :].values
salin = dsSalin.salinity[zIndex, :, :].values
mask = numpy.logical_and(numpy.isfinite(pt), numpy.isfinite(salin))
SA = gsw.SA_from_SP(salin[mask], pressure[mask], lon[mask],
lat[mask])
TSlice = T[zIndex, :, :]
CT = gsw.CT_from_pt(SA, pt[mask])
TSlice[mask] = gsw.t_from_CT(SA, CT, pressure[mask])
T[zIndex, :, :] = TSlice
dsTemp = dsPotTemp.drop('temperature')
dsTemp['temperature'] = (dsPotTemp.temperature.dims, T)
dsTemp['temperature'].attrs = dsPotTemp.temperature.attrs
return dsTemp
def get_soundc(t, s, z, lon, lat):
''' compute sound velocity
'''
import gsw
p = gsw.p_from_z(z, lat.mean())
SA = gsw.SA_from_SP(s, p, lon, lat)
CT = gsw.CT_from_pt(SA, t)
c = gsw.sound_speed(s, t, p)
# inputs are: SA (absolute salinity) and CT (conservative temperature)
return c
def test_pz_roundtrip():
"""
The p_z conversion functions have Matlab-based checks that use
only the first two arguments.
Here we verify that the functions are also inverses when the optional
arguments are used.
"""
z = np.array([-10, -100, -1000, -5000], dtype=float)
p = gsw.p_from_z(z, 30, 0.5, 0.25)
zz = gsw.z_from_p(p, 30, 0.5, 0.25)
assert_almost_equal(z, zz)
def _main(args):
"""Run the command line program."""
temperature_cube, temperature_history = gio.combine_files(args.temperature_file, args.temperature_var, checks=True)
salinity_cube, salinity_history = gio.combine_files(args.salinity_file, 'sea_water_salinity', checks=True)
assert 'c' in str(temperature_cube.units).lower(), "Input temperature units must be in celsius"
# if not 'C' in str(bigthetao_cube.units):
# bigthetao_cube.data = bigthetao_cube.data - 273.15
# data_median = np.ma.median(bigthetao_cube.data)
# assert data_median < 100
# assert data_median > -10
# bigthetao_cube.units = 'C'
target_shape = temperature_cube.shape[1:]
depth = temperature_cube.coord('depth').points * -1
broadcast_depth = uconv.broadcast_array(depth, 0, target_shape)
broadcast_longitude = uconv.broadcast_array(temperature_cube.coord('longitude').points, [1, 2], target_shape)
broadcast_latitude = uconv.broadcast_array(temperature_cube.coord('latitude').points, [1, 2], target_shape)
pressure = gsw.p_from_z(broadcast_depth, broadcast_latitude)
absolute_salinity = gsw.SA_from_SP(salinity_cube.data, pressure, broadcast_longitude, broadcast_latitude)
if args.temperature_var == 'sea_water_conservative_temperature':
conservative_temperature = temperature_cube.data
elif args.temperature_var == 'sea_water_potential_temperature':
conservative_temperature = gsw.CT_from_pt(absolute_salinity, temperature_cube.data)
else:
raise ValueError('Invalid temperature variable')
if args.coefficient == 'alpha':
coefficient_data = gsw.alpha(absolute_salinity, conservative_temperature, pressure)
var_name = 'alpha'
standard_name = 'thermal_expansion_coefficient'
long_name = 'thermal expansion coefficient'
units = '1/K'
elif args.coefficient == 'beta':
coefficient_data = gsw.beta(absolute_salinity, conservative_temperature, pressure)
var_name = 'beta'
standard_name = 'saline_contraction_coefficient'
long_name = 'saline contraction coefficient'
units = 'kg/g'
else:
raise ValueError('Coefficient must be alpha or beta')
iris.std_names.STD_NAMES[standard_name] = {'canonical_units': units}
coefficient_cube = temperature_cube.copy()
coefficient_cube.data = coefficient_data
coefficient_cube.standard_name = standard_name
coefficient_cube.long_name = long_name
coefficient_cube.var_name = var_name
coefficient_cube.units = units
coefficient_cube.attributes['history'] = cmdprov.new_log(git_repo=repo_dir)
iris.save(coefficient_cube, args.outfile)
def depth_to_pressure(z, lat):
"""Converts depths to pressures.
z: scalar or numpy array of depth (m).
lat: scalar or numpy array of latitude (deg)."""
assert np.array(lat).size > 0 and np.array(z).size > 0, "No value provided for z or lat"
p = gsw.p_from_z(-z, lat)
return p
def depth_to_pressure(z, lat):
"""Converts depths to pressures.
z: scalar or numpy array of depth (m).
lat: scalar or numpy array of latitude (deg)."""
assert np.array(lat).size > 0 and np.array(
z).size > 0, 'No value provided for z or lat'
p = gsw.p_from_z(-z, lat)
return p
def return_density(
pt_values: np.ndarray,
practical_salt_values: np.ndarray,
lon_values: np.ndarray,
lat_values: np.ndrray,
z_values: np.ndarray,
) -> Tuple[np.array, np.ndarray, np.ndarray]:
"""
Wrapper around the gsw to make it work.
Args:
pt_values (np.array): Potential temperature.
practical_salt_values (np.array): Salt values.
lon_values (np.array): Longitude values.
lat_values (np.array): Latitude values.
z_values (np.array): Height values.
Returns:
Tuple[np.array, np.array, np.array]: rho_values, ct_values, pressure_values
"""
lat_mesh, z_mesh = np.meshgrid(lat_values, z_values)
# pylint: disable=no-value-for-parameter
pressure_mesh = gsw.p_from_z(z_mesh, lat_mesh)
pressure_values = np.zeros(np.shape(pt_values))
lat_grid = np.zeros(np.shape(pt_values))
lon_grid = np.zeros(np.shape(pt_values))
# TODO these two loops could be vectorized
for i in range(np.shape(pt_values)[2]):
pressure_values[:, :, i] = pressure_mesh[:, :]
lat_grid[:, :, i] = lat_mesh[:, :]
for i in range(np.shape(pt_values)[0]):
for j in range(np.shape(pt_values)[1]):
lon_grid[i, j, :] = lon_values[:]
absolute_salinity = gsw.SA_from_SP(
practical_salt_values, pressure_values, lon_grid, lat_grid
)
ct_values = gsw.conversions.CT_from_pt(absolute_salinity, pt_values)
rho_values = gsw.density.rho(absolute_salinity, ct_values, pressure_values)
# print(np.shape(rho_values))
return rho_values, ct_values, pressure_values
def add_SA_CT_PT(xray):
if 'PRES' in list(xray.data_vars):
PRES_out = xray['PRES']
else:
PRES_out = -gsw.p_from_z(xray['DEPTH'])
SA_out = gsw.SA_from_SP(xray['PSAL'], PRES_out, xray.LONGITUDE,
xray.LATITUDE)
if 'PTMP' in list(xray.data_vars):
PT_out = xray['PTMP']
else:
PT_out = gsw.pt0_from_t(SA_out, xray['TEMP'], PRES_out)
CT_out = gsw.CT_from_pt(SA_out, PT_out)
PD_out = gsw.sigma0(SA_out, CT_out)
xray['ASAL'] = (('TIME', 'DEPTH'), SA_out)
xray['PTMP'] = (('TIME', 'DEPTH'), PT_out)
xray['CTMP'] = (('TIME', 'DEPTH'), CT_out)
xray['PDEN'] = (('TIME', 'DEPTH'), PD_out)
def teos10_insitu_dens(t, s, z, lat, lon):
"""
Computes the insitu density from potential temperature and salinity using the
Thermodynamic Equation of Seawater 2010 (TEOS-10; IOC, SCOR and IAPSO, 2010).
http://www.teos-10.org/pubs/TEOS-10_Manual.pdf
"""
depth = np.ones_like(t) * z[None, :, None]
lat = np.ones_like(t) * lat[None, None, :]
lon = np.ones_like(t) * lon[None, None, :]
p = gsw.p_from_z(-depth, lat)
SA = gsw.SA_from_SP(s, p, lon, lat)
CT = gsw.CT_from_pt(SA, t)
rho = gsw.rho(SA, CT, p)
return rho
def pd_sa_ct_rho_sigmatheta(dataframe,
temperature,
salinity,
latitude=50.0,
longitude=-65.0,
z=None,
pressure=None):
"""
Make common thermodynamic conversions of T-S data in dataframes.
Parameters
----------
dataframe: pandas.DataFrame
In which to add columns.
temperature, salinity: str
Names of the in situ T and practical S columns.
latitude: float or str
User specified lat or column name.
longitude: float or str
User specified lon or column name.
z: float or str
User specified depth or column name.
pressure: float or str
User specified sea pressure or column name.
Returns
-------
pandas.Dataframe:
Input dataframe with added SA, CT, rho and ST columns.
"""
# Insure z, pressure specified as strings are in dataframe
if isinstance(pressure, str) and pressure not in dataframe.keys():
raise KeyError(
'pressure must be a key of dataframe when specified as str.')
if isinstance(z, str) and z not in dataframe.keys():
raise KeyError('z must be a key of dataframe when specified as str.')
# Insure depth information is specified
if z is None and pressure is None:
raise TypeError('One of z or pressure must be float or str, not: .', z,
pressure)
# Determine pressure from depth column
elif pressure is None and isinstance(z, str):
pressure = gsw.p_from_z(-dataframe[z], latitude)
# Determine pressure from depth value
elif pressure is None and isinstance(z, (int, float)):
pressure = gsw.p_from_z(-z, latitude)
# Use pressure column
else:
pressure = dataframe[pressure]
# Use dataframe column or value as geographical coordinate
if isinstance(latitude, str):
lat = dataframe[latitude]
else:
lat = latitude
if isinstance(longitude, str):
lon = dataframe[longitude]
else:
lon = longitude
# Call to TEOS10
with warnings.catch_warnings():
warnings.simplefilter('ignore', RuntimeWarning)
SA, CT, rho, sigma_theta = sa_ct_rho_sigmatheta(dataframe[temperature],
dataframe[salinity],
pressure,
latitude=lat,
longitude=lon)
# Add columns to dataframe
dataframe.loc[:, 'SA'] = SA
dataframe.loc[:, 'CT'] = CT
dataframe.loc[:, 'rho'] = rho
dataframe.loc[:, 'ST'] = sigma_theta
return dataframe
def text_to_netcdf(inDir, outDir):
inFileName = '{}/Antarctic_shelf_data.txt'.format(inDir)
outFileName = '{}/Schmidtko_et_al_2014_bottom_PT_S_PD_' \
'SouthernOcean_0.25x0.125degree.nc'.format(outDir)
if os.path.exists(outFileName):
return
# 1/4 x 1/8 degree grid cells
cellsPerLon = 4
cellsPerLat = 8
obsFile = pandas.read_csv(inFileName, delim_whitespace=True)
inLon = numpy.array(obsFile.iloc[:, 0])
inLat = numpy.array(obsFile.iloc[:, 1])
inZ = numpy.array(obsFile.iloc[:, 2])
inCT = numpy.array(obsFile.iloc[:, 3])
inCT_std = numpy.array(obsFile.iloc[:, 4])
inSA = numpy.array(obsFile.iloc[:, 5])
inSA_std = numpy.array(obsFile.iloc[:, 6])
pressure = gsw.p_from_z(inZ, inLat)
inS = gsw.SP_from_SA(inSA, pressure, inLon, inLat)
inPT = gsw.pt_from_CT(inSA, inCT)
inPD = gsw.rho(inSA, inCT, 0.)
minLat = int(numpy.amin(inLat) * cellsPerLat) / cellsPerLat
maxLat = int(numpy.amax(inLat) * cellsPerLat) / cellsPerLat
deltaLat = 1. / cellsPerLat
outLat = numpy.arange(minLat - deltaLat, maxLat + 2 * deltaLat, deltaLat)
deltaLon = 1. / cellsPerLon
outLon = numpy.arange(0., 360., deltaLon)
xIndices = numpy.array(cellsPerLon * inLon + 0.5, int)
yIndices = numpy.array(cellsPerLat * (inLat - outLat[0]) + 0.5, int)
Lon, Lat = numpy.meshgrid(outLon, outLat)
ds = xarray.Dataset()
ds['lon'] = (('lon', ), outLon)
ds.lon.attrs['units'] = 'degrees'
ds.lon.attrs['description'] = 'longitutude'
ds['lat'] = (('lat', ), outLat)
ds.lat.attrs['units'] = 'degrees'
ds.lat.attrs['description'] = 'latitutude'
z = numpy.ma.masked_all(Lon.shape)
z[yIndices, xIndices] = inZ
ds['z'] = (('lat', 'lon'), z)
ds.z.attrs['units'] = 'meters'
ds.z.attrs['description'] = 'depth of the seafloor (positive up)'
PT = numpy.ma.masked_all(Lon.shape)
PT[yIndices, xIndices] = inPT
ds['botTheta'] = (('lat', 'lon'), PT)
ds.botTheta.attrs['units'] = '$\degree$C'
ds.botTheta.attrs['description'] = \
'potential temperature at sea floor'
PT_std = numpy.ma.masked_all(Lon.shape)
# neglect difference between std of PT and CT
PT_std[yIndices, xIndices] = inCT_std
ds['botThetaStd'] = (('lat', 'lon'), PT_std)
ds.botThetaStd.attrs['units'] = '$\degree$C'
ds.botThetaStd.attrs['description'] = \
'standard deviation in potential temperature at sea floor'
S = numpy.ma.masked_all(Lon.shape)
S[yIndices, xIndices] = inS
ds['botSalinity'] = (('lat', 'lon'), S)
ds.botSalinity.attrs['units'] = 'PSU'
ds.botSalinity.attrs['description'] = \
'salinity at sea floor'
S_std = numpy.ma.masked_all(Lon.shape)
# neglect difference between std of S and SA
S_std[yIndices, xIndices] = inSA_std
ds['botSalinityStd'] = (('lat', 'lon'), S_std)
ds.botSalinityStd.attrs['units'] = 'PSU'
ds.botSalinityStd.attrs['description'] = \
'standard deviation in salinity at sea floor'
PD = numpy.ma.masked_all(Lon.shape)
PD[yIndices, xIndices] = inPD
ds['botPotentialDensity'] = (('lat', 'lon'), PD)
ds.botPotentialDensity.attrs['units'] = 'kg m$^{-3}$'
ds.botPotentialDensity.attrs['description'] = \
'potential desnity at sea floor'
write_netcdf(ds, outFileName)
def mld(S,thetao,depth_cube,latitude_deg): """Compute the mixed layer depth. Parameters ---------- SA : array_like Absolute Salinity [g/kg] CT : array_like Conservative Temperature [:math:`^\circ` C (ITS-90)] p : array_like sea pressure [dbar] criterion : str, optional MLD Criteria Mixed layer depth criteria are: 'temperature' : Computed based on constant temperature difference criterion, CT(0) - T[mld] = 0.5 degree C. 'density' : computed based on the constant potential density difference criterion, pd[0] - pd[mld] = 0.125 in sigma units. `pdvar` : computed based on variable potential density criterion pd[0] - pd[mld] = var(T[0], S[0]), where var is a variable potential density difference which corresponds to constant temperature difference of 0.5 degree C. Returns ------- MLD : array_like Mixed layer depth idx_mld : bool array Boolean array in the shape of p with MLD index. Examples -------- >>> import os >>> import gsw >>> import matplotlib.pyplot as plt >>> from oceans import mld >>> from gsw.utilities import Bunch >>> # Read data file with check value profiles >>> datadir = os.path.join(os.path.dirname(gsw.utilities.__file__), 'data') >>> cv = Bunch(np.load(os.path.join(datadir, 'gsw_cv_v3_0.npz'))) >>> SA, CT, p = (cv.SA_chck_cast[:, 0], cv.CT_chck_cast[:, 0], ... cv.p_chck_cast[:, 0]) >>> fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, sharey=True) >>> l0 = ax0.plot(CT, -p, 'b.-') >>> MDL, idx = mld(SA, CT, p, criterion='temperature') >>> l1 = ax0.plot(CT[idx], -p[idx], 'ro') >>> l2 = ax1.plot(CT, -p, 'b.-') >>> MDL, idx = mld(SA, CT, p, criterion='density') >>> l3 = ax1.plot(CT[idx], -p[idx], 'ro') >>> l4 = ax2.plot(CT, -p, 'b.-') >>> MDL, idx = mld(SA, CT, p, criterion='pdvar') >>> l5 = ax2.plot(CT[idx], -p[idx], 'ro') >>> _ = ax2.set_ylim(-500, 0) References ---------- .. [1] Monterey, G., and S. Levitus, 1997: Seasonal variability of mixed layer depth for the World Ocean. NOAA Atlas, NESDIS 14, 100 pp. Washington, D.C. """ #depth_cube.data = np.ma.masked_array(np.swapaxes(np.tile(depths,[360,180,1]),0,2)) MLD_out = S.extract(iris.Constraint(depth = np.min(depth_cube.data))) MLD_out_data = MLD_out.data for i in range(np.shape(MLD_out)[0]): print'calculating mixed layer for year: ',i thetao_tmp = thetao[i] S_tmp = S[i] depth_cube.data = np.abs(depth_cube.data) depth_cube = depth_cube * (-1.0) p = gsw.p_from_z(depth_cube.data,latitude_deg.data) # dbar SA = S_tmp.data*1.004715 CT = gsw.CT_from_pt(SA,thetao_tmp.data - 273.15) SA, CT, p = map(np.asanyarray, (SA, CT, p)) SA, CT, p = np.broadcast_arrays(SA, CT, p) SA, CT, p = map(ma.masked_invalid, (SA, CT, p)) p_min, idx = p.min(axis = 0), p.argmin(axis = 0) sigma = SA.copy() to_mask = np.where(sigma == S.data.fill_value) sigma = gsw.rho(SA, CT, p_min) - 1000. sigma[to_mask] = np.NAN sig_diff = sigma[0,:,:].copy() sig_diff += 0.125 # Levitus (1982) density criteria sig_diff = np.tile(sig_diff,[np.shape(sigma)[0],1,1]) idx_mld = sigma <= sig_diff #NEED TO SORT THS PIT - COMPARE WWITH OTHER AND FIX!!!!!!!!!! MLD = ma.masked_all_like(S_tmp.data) MLD[idx_mld] = depth_cube.data[idx_mld] * -1 MLD_out_data[i,:,:] = np.ma.max(MLD,axis=0) return MLD_out_data
def standardize(self, gps_prefix=None):
df = self.data.copy()
# Convert NMEA coordinates to decimal degrees
for col in df.columns:
# Ignore if the m_gps_lat and/or m_gps_lon value is the default masterdata value
if col.endswith('_lat'):
df[col] = df[col].map(lambda x: get_decimal_degrees(x)
if x <= 9000 else np.nan)
elif col.endswith('_lon'):
df[col] = df[col].map(lambda x: get_decimal_degrees(x)
if x < 18000 else np.nan)
# Standardize 'time' to the 't' column
for t in self.TIMESTAMP_SENSORS:
if t in df.columns:
df['t'] = pd.to_datetime(df[t], unit='s')
break
# Interpolate GPS coordinates
if 'm_gps_lat' in df.columns and 'm_gps_lon' in df.columns:
df['drv_m_gps_lat'] = df.m_gps_lat.copy()
df['drv_m_gps_lon'] = df.m_gps_lon.copy()
# Fill in data will nulls where value is the default masterdata value
masterdatas = (df.drv_m_gps_lon >= 18000) | (df.drv_m_gps_lat >
9000)
df.loc[masterdatas, 'drv_m_gps_lat'] = np.nan
df.loc[masterdatas, 'drv_m_gps_lon'] = np.nan
try:
# Interpolate the filled in 'x' and 'y'
y_interp, x_interp = interpolate_gps(masked_epoch(df.t),
df.drv_m_gps_lat,
df.drv_m_gps_lon)
except (ValueError, IndexError):
L.warning("Raw GPS values not found!")
y_interp = np.empty(df.drv_m_gps_lat.size) * np.nan
x_interp = np.empty(df.drv_m_gps_lon.size) * np.nan
df['y'] = y_interp
df['x'] = x_interp
"""
---- Option 1: Always calculate Z from pressure ----
It's really a matter of data provider preference and varies from one provider to another.
That being said, typically the sci_water_pressure or m_water_pressure variables, if present
in the raw data files, will typically have more non-NaN values than m_depth. For example,
all MARACOOS gliders typically have both m_depth and sci_water_pressure contained in them.
However, m_depth is typically heavily decimated while sci_water_pressure contains a more
complete pressure record. So, while we transmit both m_depth and sci_water_pressure, I
calculate depth from pressure & (interpolated) latitude and use that as my NetCDF depth
variable. - Kerfoot
"""
# Search for a 'pressure' column
for p in self.PRESSURE_SENSORS:
if p in df.columns:
# Convert bar to dbar here
df['pressure'] = df[p].copy() * 10
# Calculate depth from pressure and latitude
# Negate the results so that increasing values note increasing depths
df['z'] = -z_from_p(df.pressure, df.y)
break
if 'z' not in df and 'pressure' not in df:
# Search for a 'z' column
for p in self.DEPTH_SENSORS:
if p in df.columns:
df['z'] = df[p].copy()
# Calculate pressure from depth and latitude
# Negate the results so that increasing values note increasing depth
df['pressure'] = -p_from_z(df.z, df.y)
break
# End Option 1
"""
---- Option 2: Use raw pressure/depth data that was sent across ----
# Standardize to the 'pressure' column
for p in self.PRESSURE_SENSORS:
if p in df.columns:
# Convert bar to dbar here
df['pressure'] = df[p].copy() * 10
break
# Standardize to the 'z' column
for p in self.DEPTH_SENSORS:
if p in df.columns:
df['z'] = df[p].copy()
break
# Don't calculate Z from pressure if a metered depth column exists already
if 'pressure' in df and 'z' not in df:
# Calculate depth from pressure and latitude
# Negate the results so that increasing values note increasing depths
df['z'] = -z_from_p(df.pressure, df.y)
if 'z' in df and 'pressure' not in df:
# Calculate pressure from depth and latitude
# Negate the results so that increasing values note increasing depth
df['pressure'] = -p_from_z(df.z, df.y)
# End Option 2
"""
rename_columns = {
'm_water_vx': 'u_orig',
'm_water_vy': 'v_orig',
}
# These need to be standardize so we can compute salinity and density!
for vname in self.TEMPERATURE_SENSORS:
if vname in df.columns:
rename_columns[vname] = 'temperature'
break
for vname in self.CONDUCTIVITY_SENSORS:
if vname in df.columns:
rename_columns[vname] = 'conductivity'
break
# Standardize columns
df = df.rename(columns=rename_columns)
# Compute additional columns
df = self.compute(df)
return df
def main(args):
"""Parse one or more Slocum glider dba files and write time-series based NetCDF file(s)."""
status = 0
log_level = args.loglevel
log_level = getattr(logging, log_level.upper())
log_format = '%(asctime)s:%(module)s:%(levelname)s:%(message)s [line %(lineno)d]'
logging.basicConfig(format=log_format, level=log_level)
debug = args.debug
config_path = args.config_path
nc_files = args.nc_files
drop_missing = args.drop
profiles = args.profiles
ngdac = args.ngdac
nc_dest = args.output_path or os.path.realpath(os.curdir)
clobber = args.clobber
nc_format = args.nc_format
if not os.path.isdir(config_path):
logging.error('Invalid configuration path: {:}'.format(config_path))
return 1
if not nc_files:
logging.error('No NAVOCEANO NetCDF files specified')
return 1
if not os.path.isdir(nc_dest):
logging.error(
'Invalid NetCDF destination specified: {:}'.format(nc_dest))
return 1
logging.info('Configuration path: {:}'.format(config_path))
logging.info('NetCDF destination: {:}'.format(nc_dest))
logging.info('Processing {:} dba files'.format(len(nc_files)))
if profiles:
logging.info('Writing profile-based NetCDFs')
else:
logging.info('Writing time-series NetCDFs')
gdm = GliderDataModel(cfg_dir=config_path)
logging.debug('{:}'.format(gdm))
netcdf_count = 0
for nc_file in nc_files:
if nc_file.endswith('optics.nc'):
logging.info('Skipping optics file: {:}'.format(nc_file))
continue
logging.info('Processing {:}'.format(nc_file))
nc_path, nc_name = os.path.split(nc_file)
fname, ext = os.path.splitext(nc_name)
nc_df, pro_meta, nc_ds = load_navo_nc(nc_file)
# NAVOCEANO NetCDF files do not contain pressure, so we need to calculate that from depth and latitude
nc_df['pressure'] = p_from_z(-nc_df.depth, nc_df.latitude.mean())
# NAVOCEANO NetCDF files do not contain density, so we need to calculate it
nc_df['density'] = calculate_density(nc_df.temp, nc_df.pressure,
nc_df.salinity, nc_df.latitude,
nc_df.longitude)
# Convert nc_df.scitime from a timedelta to a datetime64
nc_df['scitime'] = pd.to_datetime(pd.Series(
[td.total_seconds() for td in nc_df.scitime]),
unit='s').values
if nc_df.empty:
continue
gdm.data = nc_df
gdm.profiles = pro_meta
if debug:
logging.info('{:}'.format(gdm))
logging.info('debug switch set so no NetCDF creation')
continue
# dba_meta = build_dbas_data_frame(nc_file)
# if dba_meta.empty:
# continue
if not profiles:
logging.info('Writing time-series...')
ds = gdm.to_timeseries_dataset(drop_missing=drop_missing)
# Update history attribute
if 'history' not in ds.attrs:
ds.attrs['history'] = ''
new_history = '{:}: {:} {:}'.format(
datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ'),
sys.argv[0], nc_file)
if ds.attrs['history'].strip():
ds.attrs['history'] = '{:}\n{:}'.format(
ds.attrs['history'], new_history)
else:
ds.attrs['history'] = '{:}'.format(ds.attrs['history'],
new_history)
# Update the source global attribute
ds.attrs['source'] = nc_file
# Update the source global attribute
ds.attrs['source'] = nc_file
# Add the source_file variable
source_file_attrs = nc_ds.attrs.copy()
source_file_attrs['bytes'] = '{:}'.format(os.path.getsize(nc_file))
ds['source_file'] = DataArray(source_file_attrs['filename_label'],
attrs=source_file_attrs)
netcdf_path = os.path.join(nc_dest, '{:}.nc'.format(fname))
logging.info('Writing: {:}'.format(netcdf_path))
ds.to_netcdf(netcdf_path)
netcdf_count += 1
else:
logging.info('Writing profiles...')
glider = os.path.basename(nc_file).split('_')[0]
dbd_type = 'rt'
for profile_time, profile_ds in gdm.iter_profiles(
drop_missing=drop_missing):
netcdf_path = os.path.join(
nc_dest, '{:}_{:}_{:}.nc'.format(
glider, profile_time.strftime('%Y%m%dT%H%M%SZ'),
dbd_type))
# Rename latitude and longitude to lat and lon
profile_ds = profile_ds.rename({
'latitude': 'lat',
'longitude': 'lon'
})
# Set profile_lat and profile_lon
profile_ds.profile_lat.values = profile_ds.lat.mean()
profile_ds.profile_lon.values = profile_ds.lon.mean()
if os.path.isfile(netcdf_path):
if not clobber:
logging.info('Ignoring existing NetCDF: {:}'.format(
netcdf_path))
continue
else:
logging.info('Clobbering existing NetCDF: {:}'.format(
netcdf_path))
# Update history attribute
if 'history' not in profile_ds.attrs:
profile_ds.attrs['history'] = ''
new_history = '{:}: {:} --profiles {:}'.format(
datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ'),
sys.argv[0], nc_file)
if profile_ds.attrs['history'].strip():
profile_ds.attrs['history'] = '{:}\n{:}'.format(
profile_ds.attrs['history'], new_history)
else:
profile_ds.attrs['history'] = '{:}'.format(new_history)
# Update the source global attribute
profile_ds.attrs['source'] = nc_file
# Add the source_file variable
source_file_attrs = nc_ds.attrs.copy()
source_file_attrs['bytes'] = '{:}'.format(
os.path.getsize(nc_file))
profile_ds['source_file'] = DataArray(nc_name,
attrs=source_file_attrs)
logging.info('Writing: {:}'.format(netcdf_path))
profile_ds.to_netcdf(netcdf_path)
netcdf_count += 1
logging.info('{:} NetCDF files written'.format(netcdf_count))
return status
if not isinstance(fdirs, list):
fdirs = [fdirs]
fnames = []
for fdir in fdirs:
ystr = int(fdir[-4:])
if np.logical_or(ystr < START_YEAR, ystr > END_YEAR):
continue
fnamesi = glob(fdir + fdir_tail)
fnamesi.sort()
for f in fnamesi:
fnames.append(f)
nt = len(fnames) # Total number of files.
Tfmins = []
p = p_from_z(z, latu[msk].mean()) # lats closest to the isobath.
t = []
#
UQxm_circ_all = []
UQxe_circ_all = []
UQxm_100m_all = []
UQxe_100m_all = []
UQxm_100m_700m_all = []
UQxe_100m_700m_all = []
UQxm_700m_1000m_all = []
UQxe_700m_1000m_all = []
#
Ux_all = []
#
UQx_all = []
UQxm_all = []
#
# Load data
data = sp.io.loadmat('~/erics_python_plotting/data/temp_depth.mat')
depth = data['depth']
T_CTRL = data['T_CTRL']
T_A1B = data['T_A1B']
S_CTRL = data['S_CTRL']
S_A1B = data['S_A1B']
# Location
lon = 160
lat = -40
# Convert depth to pressure
p = gsw.p_from_z(-depth, lat)
# Convert practical salinity to absolute salinity
SA_CTRL = gsw.SA_from_SP(S_CTRL, p, lon, lat)
SA_A1B = gsw.SA_from_SP(S_A1B, p, lon, lat)
# Convert in-situ temperature to conservative temperature
TC_CTRL = gsw.CT_from_t(SA_CTRL, T_CTRL, p)
TC_A1B = gsw.CT_from_t(SA_A1B, T_A1B, p)
# Calculate density on a T-S grid
T_grid, S_grid = np.meshgrid(np.arange(0,35,0.05), np.arange(33,37,0.001))
rho = gsw.rho(S_grid, T_grid, 0) # SHOULD calc pot. dens. NOT in-situ density assuming no depth
# Plot
plt.figure()
#trim to the desired length
utc = full_utc[start_index:stop_index]
print("full time", full_utc[0], full_utc[-1])
print("measurement time", start_time, stop_time)
#print("\n\n\n\n\n")
#print(full_utc)
#print("\n\n\n\n\n")
#unit conversion because of different standards for the conductivity
if np.nanmean(conductivity) < 3:
conductivity *= 10
constant_pressure = gsw.p_from_z(-float(sensor_depth),
54.32) #latitude of the transect
#print("pressure",constant_pressure)
computed_salinity = SA_from_C(conductivity, temperature,
constant_pressure, 20.6, 54.32)
#print("comparison:",np.nanmean(salinity),np.nanmean(computed_salinity))
#print("check:",np.nanmean(conductivity))
#TODO Start- und Stopzeit so waehlen, dass hier was rauskommt
print("TEST:", np.arange(0, full_utc.size)[full_utc == start_time])
print("TEST:", np.arange(0, full_utc.size)[full_utc == stop_time])
temperature = full_temperature[start_index:stop_index]
utc = full_utc[start_index:stop_index]
print("start", utc[0], start_time, utc[0] == start_time)
print("stop", utc[-1], stop_time, utc[-1] == stop_time)
def BV2(S,T,depth,lon,lat):
p1=gsw.p_from_z(depth,np.array(lat))
sa=gsw.SA_from_SP(S,p1,lon,lat)
ct=gsw.CT_from_pt(sa,T)
N2,pOut=gsw.Nsquared(sa,ct,p1,lat)
return N2,pOut
def wod_cast_n(rag_arr,
n,
var_names=['Temperature', 'Salinity'],
anc_names=None,
do_qc=True,
do_teos10=True):
"""
Get an individual cast from WOD ragged array.
If do_qc is true, data are filtered keeping only those with
quality flags of 0 or 1. Refused data are returned as NaN. Some
profiles do not have quality flags. There are three possible cases
and here are the meaning of the quality flags they produce.
Profile quality flag missing
-> Profile flag = -1
-> value flags = -1
Profile quality flag exists but is not accepted
-> Profile flag = passed from original file
-> Value flags = -2
Profile quality flag exists and is accepted, but value flags are missing
-> Profile flag = passed from original file
-> Value flags = -3
Parameters
----------
rag_arr: xarray.Dataset or straight
Path to a WOD netCDF file containing a CTD ragged array or named
it is read into.
n: int
Cast number to return as xarray Dataset.
var_names: list of str
Names of the variables to extract. Defaults to ['Temperature', 'Salinity'].
anc_names: list of str
Names of the ancillary data variables to extract. Defaults to None.
do_qc: bool
If True keep only data with WOD quality flags 0 or 1. Defaults to True.
This also passes the WOD quality flags to the child cast.
do_teos10: bool
If True calculate CT, SA and sigma0 using the gsw package, implementing
TEOS10. Defaults to True.
Returns
-------
xarray.Dataset
The isolated nth cast of the ragged array.
"""
# Read netcdf or pass xarray
if isinstance(rag_arr, str):
dset = xr.open_dataset(rag_arr)
elif isinstance(rag_arr, xr.Dataset):
dset = rag_arr
else:
raise TypeError('rag_arr is not string or xarray Dataset')
# Replace VAR_row_size NaN values with 0
for variable in var_names + ['z']:
field = '%s_row_size' % variable
dset[field] = dset[field].where(isfinite(dset[field]), 0)
# Get cast depth information
depth_name = 'z'
time_name = 'time' # For ease if ever necessary
lon_name = 'lon' # to make more flexible
lat_name = 'lat'
c_strt = int(dset['%s_row_size' % depth_name][:n].values.sum())
c_stop = c_strt + int(dset['%s_row_size' % depth_name][n].values)
depth = dset[depth_name][c_strt:c_stop].values
# Add requested variables
cast = xr.Dataset(coords={depth_name: depth}, attrs=dset.attrs)
# Default of TEOS10 switches
has_temp = False
has_sal = False
# Loop over requested variables
for variable in var_names:
# Check this variable is not empty for this cast
if dset['%s_row_size' % variable][n] > 0:
# Get variable index values
c_strt = int(dset['%s_row_size' % variable][:n].values.sum())
c_stop = c_strt + int(dset['%s_row_size' % variable][n].values)
# Assign
cast[variable] = (depth_name, dset[variable][c_strt:c_stop])
# Switches for TEOS10
if 'Temperature' == variable:
has_temp = True
if 'Salinity' == variable:
has_sal = True
# Do quality control (keeps flags 0 and 1)
if do_qc:
# Logic switches
pf_exists = '%s_WODprofileflag' % variable in dset.data_vars.keys(
)
vl_exists = '%s_WODflag' % variable in dset.data_vars.keys()
# Check quality flag exists for cast
if pf_exists:
# Pass value of cast quality flag
cast.attrs['%s_WODprofileflag' %
variable] = dset['%s_WODprofileflag' %
variable].values[n]
# Check quality flag is accepted for cast
if cast.attrs['%s_WODprofileflag' % variable] in [0, 1]:
# Check existence of observation flags
if vl_exists:
cast['%s_WODflag' %
variable] = (depth_name,
dset['%s_WODflag' %
variable][c_strt:c_stop])
condition = ((cast['%s_WODflag' % variable] == 0) |
(cast['%s_WODflag' % variable] == 1))
cast[variable] = cast[variable].where(condition)
# Value flags do not exist
else:
print('Warning: No flags for variable %s' %
variable)
cast[variable] *= nan
cast['%s_WODflag' %
variable] = (depth_name,
-3 * ones_like(depth, dtype=int))
# Profile quality flag is not accepted
else:
# Pass cast quality flag
cast[variable] *= nan
cast['%s_WODflag' %
variable] = (depth_name,
-2 * ones_like(depth, dtype=int))
# Profile quality flag does not exist
else:
cast.attrs['%s_WODprofileflag' % variable] = -1
cast[variable] *= nan
cast['%s_WODflag' %
variable] = (depth_name,
-1 * ones_like(depth, dtype=int))
# Variable exists but profile is empty
else:
print('Warning: No data for variable %s' % variable)
# Convert other coordinates to attributes
for coord in [time_name, lon_name, lat_name]:
cast.attrs[coord] = dset[coord].values[n]
# Gather ancillary data if requested
if anc_names is not None:
for anc in anc_names:
cast.attrs[anc] = dset[anc].values[n]
# Use TEOS10 to get CT, SA and sigma0 if requested
if has_temp and has_sal:
has_rfields = (
all(cast.attrs['%s_WODprofileflag' %
field] in [0, 1] # Has the required fields
for field in ['Temperature', 'Salinity']))
else:
has_rfields = False
has_rattrs = (
all(attr in cast.attrs # Has the required attributes
for attr in ['lon', 'lat']))
if do_teos10 and has_rattrs and has_rfields:
# Remove NaN values
cast = cast.where(
(isfinite(cast.Temperature)) & (isfinite(cast.Salinity)),
drop=True)
try:
cast['Sea_Pres'] = (depth_name,
gsw.p_from_z(-cast.z.values, cast.lat))
cast['SA'] = (depth_name,
gsw.SA_from_SP(cast.Salinity.values,
cast.Sea_Pres.values, cast.lon,
cast.lat))
cast['CT'] = (depth_name,
gsw.CT_from_t(cast.SA.values,
cast.Temperature.values,
cast.Sea_Pres.values))
cast['RHO'] = (depth_name,
gsw.rho(cast.SA.values, cast.CT.values,
cast.Sea_Pres.values))
cast['SIGMA_THETA'] = (depth_name,
gsw.density.sigma0(cast.SA.values,
cast.CT.values))
except:
print('Could not do TEOS 10 conversions')
# Gather ancillary data if requested
if anc_names is not None:
for anc in anc_names:
cast.attrs[anc] = dset[anc].values[n]
# Output result
return cast
def driver(args):
path_out = args.path_out
file_out_unfilled = args.file_out_unfilled
file_out_filled = args.file_out_filled
# URL for NODC THREDSS server
ncei_thredds_url = 'https://data.nodc.noaa.gov/thredds/dodsC/ncei/woa/'
# Setup which WOA data set to download
# 1 deg. WOA
if args.resolution == '01':
fext = '01'
res = '1.00'
elif args.resolution == '04':
# 0.25 deg WOA
fext = '04'
res = '0.25'
else:
raise ValueError(
'The resolution provided, {}, is not supported. Please use 01 or 04.'
.format(args.resolution))
decade = 'decav'
date_mon = 1 # This is the month of the output IC file
date_seas = range(13, 17) # all four seasons
# Get the data from NODC
# Get monthly temperature & salinity
base_url_t = ncei_thredds_url + '/temperature/' + decade + '/' + res + '/'
file_root_t = 'woa18_' + decade + '_t'
files_t = '{0:s}{1:s}{2:02d}_{3:s}.nc'.format(base_url_t, file_root_t,
date_mon, fext)
print('Monthly temperature files:', files_t)
dst_mon = xr.open_dataset(
files_t,
decode_times=False) #,data_vars='minimal',combine='by_coords')
base_url_s = ncei_thredds_url + '/salinity/' + decade + '/' + res + '/'
file_root_s = 'woa18_' + decade + '_s'
files_s = '{0:s}{1:s}{2:02d}_{3:s}.nc'.format(base_url_s, file_root_s,
date_mon, fext)
print('Monthly salinity files:', files_s)
dss_mon = xr.open_dataset(
files_s,
decode_times=False) #,data_vars='minimal',combine='by_coords')
# Merge monthly input data sets
ds_mon = dst_mon.merge(dss_mon).squeeze()
# Get seasonal temperature & salinity
files_t = [
'{0:s}{1:s}{2:02d}_{3:s}.nc'.format(base_url_t, file_root_t, date_str,
fext) for date_str in date_seas
]
print('Seasonal temeperature files:', files_t)
dst_seas = xr.open_mfdataset(files_t,
decode_times=False,
data_vars='minimal',
combine='by_coords')
files_s = [
'{0:s}{1:s}{2:02d}_{3:s}.nc'.format(base_url_s, file_root_s, date_str,
fext) for date_str in date_seas
]
print('Seasonal salinity files:', files_s)
dss_seas = xr.open_mfdataset(files_s,
decode_times=False,
data_vars='minimal',
combine='by_coords')
# Merge seasonal data sets
# force download to overcome limitation of interpolation on chunked dims
ds_seas = dst_seas.merge(dss_seas).compute()
###########################################################################
# Extend Monthly data to deepest levels by interpolating from seasonal
nseas = np.shape(ds_seas['time'])[0]
nmon = np.shape(ds_mon['time'])
print('length seasonal file=', nseas)
print('length monthly file=', nmon)
time_target = ds_mon['time'].values
print('target time of ic file=', time_target)
nlon = np.shape(ds_mon['lon'])[0]
nlat = np.shape(ds_mon['lat'])[0]
print(' nlon=', nlon, ' nlat=', nlat)
ndep_upper = np.shape(ds_mon['depth'])[0]
ndep_full = np.shape(ds_seas['depth'])[0]
print('# levels seasonal file=', ndep_full)
print('# levels monthly file=', ndep_upper)
# Check that the right depths are being extracted and concatinated
#tmp_dep = xr.concat([ds_mon['depth'],ds_seas['depth'][ndep_upper:]],dim='depth')
#print('# of levels in extended file =',np.shape(tmp_dep)[0])
#print('concatinated full depth')
#for k in range(0,ndep_full) :
# print(' k=',k,' z=',tmp_dep.values[k])
# Pull out seasons -1 and N for periodic b.c on time interpolation.
ds_seas_bef = ds_seas.isel(time=nseas - 1)
ds_seas_aft = ds_seas.isel(time=0)
# Adjust times to be monotonic
ds_seas_bef['time'] = ds_seas_bef['time'] - 12.
ds_seas_aft['time'] = ds_seas_aft['time'] + 12.
print('time before=', ds_seas_bef['time'].values, ' time after =',
ds_seas_aft['time'].values)
###########################################################################
# Create a data set with monthly data in upper ocean,
# interpolated seasonal data in deeper ocean
ds_deep = xr.Dataset()
for var in ('t_an', 's_an'):
print('Starting ', var, ' ...')
tmp_extend = xr.concat([
ds_seas_bef[var][ndep_upper:, :, :],
ds_seas[var][:, ndep_upper:, :, :],
ds_seas_aft[var][ndep_upper:, :, :]
],
dim='time').compute()
print(' ... extended seasonal data in time. shape=',
np.shape(tmp_extend))
print(' ... extended seasonal data times=', tmp_extend['time'].values)
print(' ... interpolating to time ', time_target)
tmp_interp = tmp_extend.interp(time=time_target)
print('... finished inteprolation to months. shape=',
np.shape(tmp_interp))
print(' ... shape of monthly upper ocean array=',
np.shape(ds_mon[var]))
ds_deep[var] = xr.concat([ds_mon[var], tmp_interp], dim='depth')
print(
'... finished inserting deep values into monthly arrays. shape = ',
np.shape(ds_deep[var]))
###########################################################################
# Compute pressure, reference salinity and potential temperature using TEOS-10
z1d = -ds_deep['depth']
lat1d = ds_deep['lat']
lon1d = ds_deep['lon']
z3d, lat3d, lon3d = xr.broadcast(z1d, lat1d, lon1d)
p3d = gsw.p_from_z(z3d, lat3d)
SR = gsw.SR_from_SP(ds_deep['s_an'].data)
PT0 = gsw.conversions.pt0_from_t(SR, ds_deep['t_an'], p3d)
ds_deep['theta0'] = xr.DataArray(PT0, dims=('depth', 'lat', 'lon'))
for var in ('depth', 'lat', 'lon'):
ds_deep['theta0'].assign_coords({var: ds_seas[var]})
ds_deep['theta0'].attrs = ds_seas['t_an'].attrs
ds_deep['theta0'].attrs[
'long_name'] = 'Potential temperature from objectively analyzed in situ temperature and salinty'
ds_deep['theta0'].attrs[
'standard_name'] = 'sea_water_potential_temperature'
ds_deep['theta0'].encoding = ds_seas['t_an'].encoding
ds_deep.to_netcdf(path_out + file_out_unfilled)
###########################################################################
# Fill all land with values interpolated from nearest ocean
mask_all_points = xr.DataArray(np.ones(
(ndep_full, nlat, nlon)).astype('bool'),
dims=('depth', 'lat', 'lon'),
coords=(ds_seas['depth'], ds_seas['lat'],
ds_seas['lon']))
ds_fill = xr.Dataset()
for var in ('s_an', 'theta0'):
print('Starting ', var, ' ...')
ds_fill[var] = fill.lateral_fill(ds_deep[var],
mask_all_points,
ltripole=False,
tol=5.0e-3,
use_sor=True,
rc=1.88,
max_iter=1000)
ds_fill['depth'].attrs = ds_seas['depth'].attrs
ds_fill['lat'].attrs = ds_seas['lat'].attrs
ds_fill['lon'].attrs = ds_seas['lon'].attrs
# Global attrs
ds_fill.attrs['title'] = 'T and S from WOA filled over continents'
ds_fill.attrs[
'WOA_resolution'] = args.resolution + ', 01 (1 deg), 04 (0.25 deg)'
ds_fill.attrs['author'] = args.author
ds_fill.attrs['date'] = datetime.now().isoformat()
ds_fill.attrs['created_using'] = os.path.basename(__file__) + ' -path_out ' + path_out + ' -author ' + \
args.author + ' -resolution ' + args.resolution + ' -file_out_unfilled ' + file_out_unfilled + \
' -file_out_filled ' + file_out_filled
ds_fill.attrs['url'] = os.path.basename(
__file__) + ' can be found at https://github.com/NCAR/WOA_MOM6'
ds_fill.attrs['git_hash'] = str(
subprocess.check_output(["git", "describe", "--always"]).strip())
# save
ds_fill.to_netcdf(path_out + file_out_filled)
return
import numpy as np, matplotlib.pyplot as plt, xray, gsw
#1
woa = xray.open_dataset('http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/'
'.Grid-1x1/.Annual/dods')
p0 = -10.1325 #surface pressure (dbars)
pr = 0
S,T,Lat,Lon,dep = xray.broadcast_arrays\
(woa['salinity.s_an'][0],woa['temperature.t_an'][0],woa.lat*1,woa.lon*1,woa.depth*1)
p = -1*gsw.p_from_z(dep,Lat)
SA = gsw.SA_from_SP(S,p,Lon,Lat)
TC = gsw.CT_from_t(SA,T,p)
rho= gsw.rho_CT_exact(SA,TC,pr)
rhoArr = xray.DataArray(rho,dims=['depth', 'lat','lon'],coords=[woa.depth,woa.lat,woa.lon])
rhoArr1 = rhoArr.sel(lat=slice(30, 50),lon=slice(170.5,170.5))
rhoArr2 = rhoArr.sel(lat=slice(-72, -55),lon=slice(170.5,170.5))
plt.figure(figsize=(7,7))
plt.contourf(rhoArr1.lat,rhoArr1.depth,rhoArr1.squeeze(dim=None),cmap='ocean')
plt.title('Potential Density of Kuroshio Extension')
plt.xlabel('lat')
plt.ylabel('depth(m)')
plt.ylim(0,3000)
cbar = plt.colorbar(orientation='vertical',fraction=0.046, pad=0.04)
cbar.ax.set_xlabel('rho kg m^-3')
plt.show()
