def sspeed(depth: Union[np.ndarray, xr.Variable],
latitude: np.ndarray,
temperature: np.ndarray,
salinity: np.ndarray) -> np.ndarray:
"""
Calculates the speed of sound.
Required Arguments:
* depth: The depth(s) in meters
* latitude: The latitude(s) in degrees North
* temperature: The temperatures(s) in Celsius
* salinity: The salinity (unitless)
"""
depth, latitude, temperature, salinity = __validate_depth_lat_temp_sal(
depth, latitude, temperature, salinity)
press = __calc_pressure(depth, latitude)
if salinity.shape != press.shape:
# pad array shape to match otherwise seawater freaks out
press = press[..., np.newaxis]
speed = seawater.svel(salinity, temperature, press)
return np.squeeze(speed)
def build_data(ping_xyt_template, ds_strong, xyt0, rx_xy):
matrix = ds_strong.matrix.values
temps = ds_strong.temp.values
tx_beacon = np.zeros(matrix.shape[0], np.int32)
for p, tag in enumerate(ds_strong.tag.values):
tx_beacon[p] = 1 + np.nonzero(ds_strong.rx_beacon.values == tag)[0][0]
data = dict(
Np=matrix.shape[0],
Nb=matrix.shape[1],
rx_t=(matrix - xyt0[2]) * time_scale,
rx_c=seawater.svel(0, ds_strong['temp'], 0) / time_scale,
rx_x=rx_xy[:, 0] - xyt0[0],
rx_y=rx_xy[:, 1] - xyt0[1],
tx_x=ping_xyt_template[:, 0] - xyt0[0],
tx_y=ping_xyt_template[:, 1] - xyt0[1],
# tx_t=ping_xyt_template[:,2]-xyt0[2],
tx_beacon=tx_beacon,
sigma_t=0.0005 * time_scale, # 0.5ms timing precision
)
# explicitly pass in the indexing for matrix to linear
Nb = data['Nb']
dist_k = np.zeros((Nb, Nb), np.int32)
for ai in range(Nb):
for bi in range(ai + 1, Nb):
a = ai + 1
b = bi + 1
k = (Nb * (Nb - 1) / 2) - (Nb - a + 1) * (Nb - a) / 2 + b - a
dist_k[ai, bi] = dist_k[bi, ai] = k
data['dist_k'] = dist_k
data['Ndist'] = (Nb * (Nb - 1)) // 2
return data
def load_data(self):
super(SoundSpeedPlotter, self).load_data()
self.pressure = [
seawater.pres(self.temperature_depths[idx], ll[0])
for idx, ll in enumerate(self.points)
]
ureg = pint.UnitRegistry()
try:
u = ureg.parse_units(self.variable_units[0].lower())
except:
u = ureg.dimensionless
if u == ureg.boltzmann_constant:
u = ureg.kelvin
if u == ureg.kelvin:
unit = "Celsius"
temperature_c = ureg.Quantity(self.temperature,
u).to(ureg.celsius).magnitude
else:
temperature_c = self.temperature
self.sspeed = seawater.svel(self.salinity, temperature_c,
self.pressure)
def sspeed(
depth: Union[np.ndarray, xr.Variable],
latitude: np.ndarray,
temperature: np.ndarray,
salinity: np.ndarray,
) -> np.ndarray:
"""
Calculates the speed of sound.
Required Arguments:
* depth: The depth(s) in meters
* latitude: The latitude(s) in degrees North
* temperature: The temperatures(s) in Celsius
* salinity: The salinity (unitless)
"""
depth, latitude, temperature, salinity = __validate_depth_lat_temp_sal(
depth, latitude, temperature, salinity)
press = __calc_pressure(depth, latitude)
if salinity.shape != press.shape:
# Need to pad press so it can broadcast against temperature and salinity.
# eg. if using GIOPS and salinity has shape (3, 50, 3, 12) then press has
# shape (50, 3). This logic pads press to give shape (1, 50, 3, 1).
for ax, val in enumerate(salinity.shape):
if ax > press.ndim - 1 or press.shape[ax] != val:
press = np.expand_dims(press, axis=ax)
speed = seawater.svel(salinity, temperature, press)
return np.squeeze(speed)
def load_data(self):
super(SoundSpeedPlotter, self).load_data()
self.pressure = [seawater.pres(self.temperature_depths[idx], ll[0])
for idx, ll in enumerate(self.points)]
self.sspeed = seawater.svel(
self.salinity, self.temperature, self.pressure
)
def load_data(self):
super(SoundSpeedPlotter, self).load_data()
self.pressure = [
seawater.pres(self.temperature_depths[idx], ll[0])
for idx, ll in enumerate(self.points)
]
self.sspeed = seawater.svel(self.salinity, self.temperature,
self.pressure)
def sspeed(depth, latitude, temperature, salinity):
"""
Calculates the speed of sound.
Parameters:
depth: The depth(s) in meters
latitude: The latitude(s) in degrees North
temperature: The temperatures(s) in Celsius
salinity: The salinity (unitless)
"""
try:
press = [seawater.pres(d, latitude) for d in depth]
except TypeError:
press = seawater.pres(depth, latitude)
speed = seawater.svel(salinity, temperature, press)
return np.array(speed)
def gen_dim(self, dim_name):
"""
Desc:
Auto-generates and stores other dimensions if they
can be calculated from other stored dimensions
ie:// density(S,T,P)
"""
if dim_name is 'pressure':
z = self.get_dim('z')
p = sw.eos80.pres(z, self.lat)
self.add_dim('pressure', p)
elif dim_name is 'density':
s = self.get_dim('salinity')
t = self.get_dim('temperature')
p = self.get_dim('pressure')
d = sw.eos80.dens(s, t, p)
self.add_dim('density', d)
elif dim_name is 'bfrq':
s = self.get_dim('salinity')
t = self.get_dim('temperature')
p = self.get_dim('pressure')
strat = sw.geostrophic.bfrq(s, t, p, lat=self.lat)
self.add_dim('bfrq',
np.hstack(strat[0])) # Brunt Vaisala Frequency
self.add_dim('ppv',
np.hstack(strat[1])) # Planetary Potential Vorticity
self.add_dim('pmp', np.hstack(strat[2])) # Midpoint pressure
elif dim_name is 'svel':
s = self.get_dim('salinity')
t = self.get_dim('temperature')
p = self.get_dim('pressure')
svel = sw.svel(s, t, p)
self.add_dim('svel', svel)
else:
print("Cannot be generated from stored information")
return None
#%%
var_name = 'Temperature'
depthg_gridded, tempg_gridded, timegg = \
grid_glider_data_thredd(timeg,latg,long,depthg,tempg,var_name,inst_id)
#%%
var_name = 'Salinity'
depthg_gridded, saltg_gridded, timegg = \
grid_glider_data_thredd(timeg,latg,long,depthg,saltg,var_name,inst_id)
#%% Calculate sound velocity glider
svelg = sw.svel(saltg,tempg,pressg)
var_name = 'Sound Velocity'
depthg_gridded, svelg_gridded, timegg = \
grid_glider_data_thredd(timeg,latg,long,depthg,svelg,var_name,inst_id,\
contour_plot='no')
#%% Calculate sound velocity GOFS 3.1
svel31 = sw.svel(salt31,temp31,np.tile(depth31,(len(timestamp31),1)).T)
#%% Fig temperatuer glider
var_name = 'Temperature'
fig, ax=plt.subplots(figsize=(10, 3), facecolor='w', edgecolor='w')
#Mapping Sound Profile onto IW Field
try:
T = mprof['T']
S = mprof['S']
except KeyError:
print("No scalars to map")
exit(0)
#Compute interpolation and potential derivative of T&S
tprof = interp.UnivariateSpline(mprof['depth'], mprof['T'])
adtg = interp.UnivariateSpline(mprof['depth'],
sw.adtg(mprof['S'], mprof['T'], mprof['depth']))
sprof = interp.UnivariateSpline(mprof['depth'], mprof['S'])
sg = sprof.derivative()
cprof = interp.UnivariateSpline(
mprof['depth'], sw.svel(mprof['S'], mprof['T'], mprof['depth']))
#Maps displacment to anomaly
mat = functools.partial(map_anom, mean=tprof, grad=adtg)
mas = functools.partial(map_anom, mean=sprof, grad=sg)
mac = functools.partial(map_svel, func=sw.svel)
maca = functools.partial(map_svel_anom, func=cprof)
map_scalars(p['path'], 'T', mat)
map_scalars(p['path'], 'S', mas)
map_scalars(p['path'], 'c', mac)
map_scalars(p['path'], 'ca', maca)
def test(fileout='python-test.txt'):
r"""Copy of the Matlab test.
Modifications: Phil Morgan
03-12-12. Lindsay Pender, Converted to ITS-90.
"""
f = open(fileout, 'w')
asterisks = '*' * 76
f.write(asterisks)
f.write('\n TEST REPORT ')
f.write('\n')
f.write('\n SEA WATER LIBRARY %s' % sw.__version__)
f.write('\n')
# Show some info about this Python.
f.write('\npython version: %s' % sys.version)
f.write('\n on %s computer %s' % (uname()[0], uname()[-1]))
f.write('\n')
f.write('\n')
f.write(asctime(localtime()))
f.write('\n')
f.write('\n')
# Test main module ptmp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: ptmp')
f.write('\n** and SUB-MODULE: adtg')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
# Test 1 - data from Unesco 1983 p45.
T = np.array([[0, 0, 0, 0, 0, 0], [10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20], [30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]])
T = T / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
Pr = np.array([0, 0, 0, 0, 0, 0])
UN_ptmp = np.array([[0, -0.3061, -0.9667, 0, -0.3856, -1.0974],
[10, 9.3531, 8.4684, 10, 9.2906, 8.3643],
[20, 19.0438, 17.9426, 20, 18.9985, 17.8654],
[30, 28.7512, 27.4353, 30, 28.7231, 27.3851],
[40, 38.4607, 36.9254, 40, 38.4498, 36.9023]])
PT = sw.ptmp(S, T, P, Pr) * 1.00024
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p45)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape # TODO: so many loops there must be a better way.
for icol in range(0, n):
f.write('\n Sal Temp Press PTMP ptmp')
f.write('\n (psu) (C) (db) (C) (C)\n')
result = np.vstack(
(S[:, icol], T[:, icol], P[:, icol], UN_ptmp[:, icol], PT[:,
icol]))
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.4f %11.5f\n" %
tuple(result[:, iline]))
# Test main module svan.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: svan')
f.write('\n** and SUB-MODULE: dens dens0 smow seck pden ptmp')
f.write('\n%s' % asterisks)
# Test data FROM: Unesco Tech. Paper in Marine Sci. No. 44, p22.
s = np.array([0, 0, 0, 0, 35, 35, 35, 35])
p = np.array([0, 10000, 0, 10000, 0, 10000, 0, 10000])
t = np.array([0, 0, 30, 30, 0, 0, 30, 30]) / 1.00024
UN_svan = np.array(
[2749.54, 2288.61, 3170.58, 3147.85, 0.0, 0.00, 607.14, 916.34])
SVAN = sw.svan(s, t, p)
# DISPLAY RESULTS
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\nComparison of accepted values from UNESCO 1983')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p22)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n Sal Temp Press SVAN svan')
f.write('\n (psu) (C) (db) (1e-8*m3/kg) (1e-8*m3/kg)\n')
result = np.vstack([s, t, p, UN_svan, 1e+8 * SVAN])
for iline in range(0, len(SVAN)):
f.write(" %4.0f %4.0f %5.0f %11.2f %11.3f\n" %
tuple(result[:, iline]))
# Test main module salt.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: salt')
f.write('\n** and SUB-MODULE: salrt salrp sals')
f.write('\n%s' % asterisks)
f.write('\n')
# Test 1 - data from Unesco 1983 p9.
R = np.array([1, 1.2, 0.65]) # cndr = R.
T = np.array([15, 20, 5]) / 1.00024
P = np.array([0, 2000, 1500])
#Rt = np.array([ 1, 1.0568875, 0.81705885])
UN_S = np.array([35, 37.245628, 27.995347])
S = sw.salt(R, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n(Unesco Tech. Paper in Marine Sci. No. 44, p9)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n Temp Press R S salt')
f.write('\n (C) (db) (no units) (psu) (psu)\n')
table = np.vstack([T, P, R, UN_S, S])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %4.0f %8.2f %11.6f %14.7f\n" %
tuple(table[:, iline]))
# Test main module cndr.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: cndr')
f.write('\n** and SUB-MODULE: salds')
f.write('\n%s' % asterisks)
# Test 1 - data from Unesco 1983 p9.
T = np.array([0, 10, 0, 10, 10, 30]) / 1.00024
P = np.array([0, 0, 1000, 1000, 0, 0])
S = np.array([25, 25, 25, 25, 40, 40])
UN_R = np.array(
[0.498088, 0.654990, 0.506244, 0.662975, 1.000073, 1.529967])
R = sw.cndr(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p14)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
f.write('\n Temp Press S cndr cndr')
f.write('\n (C) (db) (psu) (no units) (no units)\n')
table = np.vstack([T, P, S, UN_R, R])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %4.0f %8.6f %11.6f %14.8f\n" %
tuple(table[:, iline]))
# Test main module depth.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: depth')
f.write('\n%s' % asterisks)
# Test data - matrix "pressure", vector "lat" Unesco 1983 data p30.
lat = np.array([0, 30, 45, 90])
P = np.array([[500, 500, 500, 500], [5000, 5000, 5000, 5000],
[10000, 10000, 10000, 10000]])
UN_dpth = np.array([[496.65, 496.00, 495.34, 494.03],
[4915.04, 4908.56, 4902.08, 4889.13],
[9725.47, 9712.65, 9699.84, 9674.23]])
dpth = sw.dpth(P, lat)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from Unesco 1983 ')
f.write('\n(Unesco Tech. Paper in Marine Sci. No. 44, p28)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
for irow in range(0, 3):
f.write('\n Lat Press DPTH dpth')
f.write('\n (degree) (db) (meter) (meter)\n')
table = np.vstack([lat, P[irow, :], UN_dpth[irow, :], dpth[irow, :]])
m, n = table.shape
for iline in range(0, n):
f.write(" %6.3f %6.0f %8.2f %8.3f\n" %
tuple(table[:, iline]))
# Test main module fp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: fp')
f.write('\n%s' % asterisks)
# Test 1 - UNESCO data p.30.
S = np.array([[5, 10, 15, 20, 25, 30, 35, 40],
[5, 10, 15, 20, 25, 30, 35, 40]])
P = np.array([[0, 0, 0, 0, 0, 0, 0, 0],
[500, 500, 500, 500, 500, 500, 500, 500]])
UN_fp = np.array(
[[-0.274, -0.542, -0.812, -1.083, -1.358, -1.638, -1.922, -2.212],
[-0.650, -0.919, -1.188, -1.460, -1.735, -2.014, -2.299, -2.589]])
FP = sw.fp(S, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p30)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
for irow in range(0, 2):
f.write('\n Sal Press fp fp')
f.write('\n (psu) (db) (C) (C)\n')
table = np.vstack(
[S[irow, :], P[irow, :], UN_fp[irow, :], FP[irow, :]])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %5.0f %8.3f %11.4f\n" %
tuple(table[:, iline]))
# Test main module cp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: cp')
f.write('\n%s' % asterisks)
# Test 1.
# Data from Pond and Pickard Intro. Dynamical Oceanography 2nd ed. 1986
T = np.array([[0, 0, 0, 0, 0, 0], [10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20], [30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]]) / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
UN_cp = np.array([[4048.4, 3896.3, 3807.7, 3986.5, 3849.3, 3769.1],
[4041.8, 3919.6, 3842.3, 3986.3, 3874.7, 3804.4],
[4044.8, 3938.6, 3866.7, 3993.9, 3895.0, 3828.3],
[4049.1, 3952.0, 3883.0, 4000.7, 3909.2, 3844.3],
[4051.2, 3966.1, 3905.9, 4003.5, 3923.9, 3868.3]])
CP = sw.cp(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p37)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp Press Cp cp')
f.write('\n (psu) (C) (db) (J/kg.C) (J/kg.C)\n')
result = np.vstack(
[S[:, icol], T[:, icol], P[:, icol], UN_cp[:, icol], CP[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.1f %11.2f\n" %
tuple(result[:, iline]))
# Test main module svel.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: svel')
f.write('\n%s' % asterisks)
# Test 1.
# Data from Pond and Pickard Intro. Dynamical Oceanography 2nd ed. 1986
T = np.array([[0, 0, 0, 0, 0, 0], [10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20], [30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]]) / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
UN_svel = np.array([[1435.8, 1520.4, 1610.4, 1449.1, 1534.0, 1623.2],
[1477.7, 1561.3, 1647.4, 1489.8, 1573.4, 1659.0],
[1510.3, 1593.6, 1676.8, 1521.5, 1604.5, 1687.2],
[1535.2, 1619.0, 1700.6, 1545.6, 1629.0, 1710.1],
[1553.4, 1638.0, 1719.2, 1563.2, 1647.3, 1727.8]])
SVEL = sw.svel(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p50)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = SVEL.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp Press SVEL svel')
f.write('\n (psu) (C) (db) (m/s) (m/s)\n')
result = np.vstack([
S[:, icol], T[:, icol], P[:, icol], UN_svel[:, icol], SVEL[:, icol]
])
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.1f %11.3f\n" %
tuple(result[:, iline]))
# Test submodules alpha beta aonb.
f.write('\n%s' % asterisks)
f.write('\n** and SUB-MODULE: alpha beta aonb')
f.write('\n%s' % asterisks)
# Data from McDouogall 1987.
s = 40
PT = 10
p = 4000
beta_lit = 0.72088e-03
aonb_lit = 0.34763
alpha_lit = aonb_lit * beta_lit
BETA = sw.beta(s, PT, p, pt=True)
ALPHA = sw.alpha(s, PT, p, pt=True)
AONB = sw.aonb(s, PT, p, pt=True)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from MCDOUGALL 1987 ')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
f.write('\n Sal Temp Press BETA beta')
f.write('\n (psu) (C) (db) (psu^-1) (psu^-1)\n')
table = np.hstack([s, PT, p, beta_lit, BETA])
f.write(" %4.0f %4.0f %5.0f %11.4e %11.5e\n" % tuple(table))
f.write('\n Sal Temp Press AONB aonb')
f.write('\n (psu) (C) (db) (psu C^-1) (psu C^-1)\n')
table = np.hstack([s, PT, p, aonb_lit, AONB])
f.write(" %4.0f %4.0f %5.0f %8.5f %11.6f\n" % tuple(table))
f.write('\n Sal Temp Press ALPHA alpha')
f.write('\n (psu) (C) (db) (psu^-1) (psu^-1)\n')
table = np.hstack([s, PT, p, alpha_lit, ALPHA])
f.write(" %4.0f %4.0f %5.0f %11.4e %11.4e\n" % tuple(table))
# Test main moduleS satO2 satN2 satAr.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: satO2 satN2 satAr')
f.write('\n%s' % asterisks)
f.write('\n')
# Data from Weiss 1970.
T = np.array([[-1, -1], [10, 10], [20, 20], [40, 40]]) / 1.00024
S = np.array([[20, 40], [20, 40], [20, 40], [20, 40]])
lit_O2 = np.array([[9.162, 7.984], [6.950, 6.121], [5.644, 5.015],
[4.050, 3.656]])
lit_N2 = np.array([[16.28, 14.01], [12.64, 11.01], [10.47, 9.21],
[7.78, 6.95]])
lit_Ar = np.array([[0.4456, 0.3877], [0.3397, 0.2989], [0.2766, 0.2457],
[0.1986, 0.1794]])
O2 = sw.satO2(S, T)
N2 = sw.satN2(S, T)
Ar = sw.satAr(S, T)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from Weiss, R.F. 1979 ')
f.write('\n"The solubility of nitrogen, oxygen and argon in water')
f.write('\n and seawater." Deep-Sea Research., 1970, Vol 17, pp721-735.')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp O2 satO2')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack(
[S[:, icol], T[:, icol], lit_O2[:, icol], O2[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.2f %9.3f\n" %
tuple(result[:, iline]))
for icol in range(0, n):
f.write('\n Sal Temp N2 satN2')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack(
[S[:, icol], T[:, icol], lit_N2[:, icol], N2[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.2f %9.3f\n" %
tuple(result[:, iline]))
for icol in range(0, n):
f.write('\n Sal Temp Ar satAr')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack(
[S[:, icol], T[:, icol], lit_Ar[:, icol], Ar[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.4f %9.4f\n" %
tuple(result[:, iline]))
def effective_clock_offset(rxs, ds_tot):
ds = ds_tot.sel(rx=rxs)
# weird. sometimes adds an extra array(..,dtype=object) layer
ds['rx_beacon'] = ('rx', ), [
ds_tot.rx_beacon.sel(rx=rx).item() for rx in rxs
]
beacon_to_xy = dict([(ds.rx_beacon.values[i], (ds.rx_x.values[i],
ds.rx_y.values[i]))
for i in range(ds.dims['rx'])])
# both beacons heard it
is_multirx = (np.isfinite(ds.matrix).sum(axis=1) > 1).values
# it came from one of them.
is_selfaware = np.zeros_like(is_multirx)
for i, tag in enumerate(ds.tag.values):
if tag not in beacon_to_xy:
continue
else:
# And did the rx see itself?
local_rx = np.nonzero(ds.rx_beacon.values == tag)[0]
if local_rx.size and np.isfinite(ds.matrix.values[i, local_rx[0]]):
is_selfaware[i] = True
sel_pings = is_multirx & is_selfaware
ds_strong = ds.isel(index=sel_pings)
matrix = ds_strong.matrix.values
rx_xy = np.c_[ds_strong.rx_x.values, ds_strong.rx_y.values]
temps = ds_strong.temp.values
tx_beacon = np.zeros(matrix.shape[0], np.int32)
for p, tag in enumerate(ds_strong.tag.values):
tx_beacon[p] = np.nonzero(ds_strong.rx_beacon.values == tag)[0][0]
rx_c = seawater.svel(0, ds_strong['temp'], 0) / time_scale
# occasional missing data...
rx_c = utils.fill_invalid(rx_c, axis=1)
# do the calculation manually for two rxs:
ab_mask = (tx_beacon == 0)
ba_mask = (tx_beacon == 1)
assert np.all(ab_mask | ba_mask) # somebody has to hear it
# how much 'later' b saw it than a
t = matrix[:, 0] # take rx 0 as the reference
# without rx_c, transit time varies from 0.051970 to 0.051945
# for a variation of 0.48ppt
# with rx_c, transit time distance varies 0.07721 to 0.07710
# for a variation of 1.4ppt, and it introduces some step changes.
# so for this application it's better to have the smaller variation
# that also changes smoothly, rather than correct back to a precise
# distance
deltas = (matrix[:, 1] - matrix[:, 0]) # * rx_c.mean(axis=1)
# partial time series
dt_ab = deltas[ab_mask]
dt_ba = deltas[ba_mask]
dt_ab_dense = np.interp(t, t[ab_mask], deltas[ab_mask])
dt_ba_dense = np.interp(t, t[ba_mask], deltas[ba_mask])
# proxy for the uncertainty. Not scaled!
dt_ab_dense_std = np.abs(t - utils.nearest_val(t[ab_mask], t))
dt_ba_dense_std = np.abs(t - utils.nearest_val(t[ba_mask], t))
dt_offset = 0.5 * (dt_ab_dense + dt_ba_dense
) # sum of clock offset and travel asymmetry
dt_transit = 0.5 * (dt_ab_dense - dt_ba_dense) # transit time
dt_std = dt_ab_dense_std + dt_ba_dense_std
ds = xr.Dataset()
ds['time'] = ('time', ), t
ds['offset'] = ('time', ), dt_offset
ds['transit'] = ('time', ), dt_transit
ds['error'] = ('time', ), dt_std
ds['c'] = ('time', ), rx_c.mean(axis=1)
ds['rx'] = ('rx', ), rxs
return ds
def test(fileout='python-test.txt'):
r"""Copy of the Matlab test.
Modifications: Phil Morgan
03-12-12. Lindsay Pender, Converted to ITS-90.
"""
f = open(fileout, 'w')
asterisks = '*' * 76
f.write(asterisks)
f.write('\n TEST REPORT ')
f.write('\n')
f.write('\n SEA WATER LIBRARY %s' % sw.__version__)
f.write('\n')
# Show some info about this Python.
f.write('\npython version: %s' % sys.version)
f.write('\n on %s computer %s' % (uname()[0], uname()[-1]))
f.write('\n')
f.write('\n')
f.write(asctime(localtime()))
f.write('\n')
f.write('\n')
# Test main module ptmp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: ptmp')
f.write('\n** and SUB-MODULE: adtg')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
# Test 1 - data from Unesco 1983 p45.
T = np.array([[0, 0, 0, 0, 0, 0],
[10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20],
[30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]])
T = T / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
Pr = np.array([0, 0, 0, 0, 0, 0])
UN_ptmp = np.array([[0, -0.3061, -0.9667, 0, -0.3856, -1.0974],
[10, 9.3531, 8.4684, 10, 9.2906, 8.3643],
[20, 19.0438, 17.9426, 20, 18.9985, 17.8654],
[30, 28.7512, 27.4353, 30, 28.7231, 27.3851],
[40, 38.4607, 36.9254, 40, 38.4498, 36.9023]])
PT = sw.ptmp(S, T, P, Pr) * 1.00024
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p45)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape # TODO: so many loops there must be a better way.
for icol in range(0, n):
f.write('\n Sal Temp Press PTMP ptmp')
f.write('\n (psu) (C) (db) (C) (C)\n')
result = np.vstack((S[:, icol], T[:, icol], P[:, icol],
UN_ptmp[:, icol], PT[:, icol]))
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.4f %11.5f\n" %
tuple(result[:, iline]))
# Test main module svan.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: svan')
f.write('\n** and SUB-MODULE: dens dens0 smow seck pden ptmp')
f.write('\n%s' % asterisks)
# Test data FROM: Unesco Tech. Paper in Marine Sci. No. 44, p22.
s = np.array([0, 0, 0, 0, 35, 35, 35, 35])
p = np.array([0, 10000, 0, 10000, 0, 10000, 0, 10000])
t = np.array([0, 0, 30, 30, 0, 0, 30, 30]) / 1.00024
UN_svan = np.array([2749.54, 2288.61, 3170.58, 3147.85,
0.0, 0.00, 607.14, 916.34])
SVAN = sw.svan(s, t, p)
# DISPLAY RESULTS
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\nComparison of accepted values from UNESCO 1983')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p22)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n Sal Temp Press SVAN svan')
f.write('\n (psu) (C) (db) (1e-8*m3/kg) (1e-8*m3/kg)\n')
result = np.vstack([s, t, p, UN_svan, 1e+8 * SVAN])
for iline in range(0, len(SVAN)):
f.write(" %4.0f %4.0f %5.0f %11.2f %11.3f\n" %
tuple(result[:, iline]))
# Test main module salt.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: salt')
f.write('\n** and SUB-MODULE: salrt salrp sals')
f.write('\n%s' % asterisks)
f.write('\n')
# Test 1 - data from Unesco 1983 p9.
R = np.array([1, 1.2, 0.65]) # cndr = R.
T = np.array([15, 20, 5]) / 1.00024
P = np.array([0, 2000, 1500])
#Rt = np.array([ 1, 1.0568875, 0.81705885])
UN_S = np.array([35, 37.245628, 27.995347])
S = sw.salt(R, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n(Unesco Tech. Paper in Marine Sci. No. 44, p9)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n Temp Press R S salt')
f.write('\n (C) (db) (no units) (psu) (psu)\n')
table = np.vstack([T, P, R, UN_S, S])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %4.0f %8.2f %11.6f %14.7f\n" %
tuple(table[:, iline]))
# Test main module cndr.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: cndr')
f.write('\n** and SUB-MODULE: salds')
f.write('\n%s' % asterisks)
# Test 1 - data from Unesco 1983 p9.
T = np.array([0, 10, 0, 10, 10, 30]) / 1.00024
P = np.array([0, 0, 1000, 1000, 0, 0])
S = np.array([25, 25, 25, 25, 40, 40])
UN_R = np.array([0.498088, 0.654990, 0.506244, 0.662975, 1.000073,
1.529967])
R = sw.cndr(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p14)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
f.write('\n Temp Press S cndr cndr')
f.write('\n (C) (db) (psu) (no units) (no units)\n')
table = np.vstack([T, P, S, UN_R, R])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %4.0f %8.6f %11.6f %14.8f\n" %
tuple(table[:, iline]))
# Test main module depth.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: depth')
f.write('\n%s' % asterisks)
# Test data - matrix "pressure", vector "lat" Unesco 1983 data p30.
lat = np.array([0, 30, 45, 90])
P = np.array([[500, 500, 500, 500],
[5000, 5000, 5000, 5000],
[10000, 10000, 10000, 10000]])
UN_dpth = np.array([[496.65, 496.00, 495.34, 494.03],
[4915.04, 4908.56, 4902.08, 4889.13],
[9725.47, 9712.65, 9699.84, 9674.23]])
dpth = sw.dpth(P, lat)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from Unesco 1983 ')
f.write('\n(Unesco Tech. Paper in Marine Sci. No. 44, p28)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
for irow in range(0, 3):
f.write('\n Lat Press DPTH dpth')
f.write('\n (degree) (db) (meter) (meter)\n')
table = np.vstack([lat, P[irow, :], UN_dpth[irow, :], dpth[irow, :]])
m, n = table.shape
for iline in range(0, n):
f.write(" %6.3f %6.0f %8.2f %8.3f\n" %
tuple(table[:, iline]))
# Test main module fp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: fp')
f.write('\n%s' % asterisks)
# Test 1 - UNESCO data p.30.
S = np.array([[5, 10, 15, 20, 25, 30, 35, 40],
[5, 10, 15, 20, 25, 30, 35, 40]])
P = np.array([[0, 0, 0, 0, 0, 0, 0, 0],
[500, 500, 500, 500, 500, 500, 500, 500]])
UN_fp = np.array([[-0.274, -0.542, -0.812, -1.083, -1.358, -1.638, -1.922,
-2.212], [-0.650, -0.919, -1.188, -1.460, -1.735,
-2.014, -2.299, -2.589]])
FP = sw.fp(S, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p30)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
for irow in range(0, 2):
f.write('\n Sal Press fp fp')
f.write('\n (psu) (db) (C) (C)\n')
table = np.vstack([S[irow, :], P[irow, :], UN_fp[irow, :],
FP[irow, :]])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %5.0f %8.3f %11.4f\n" %
tuple(table[:, iline]))
# Test main module cp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: cp')
f.write('\n%s' % asterisks)
# Test 1.
# Data from Pond and Pickard Intro. Dynamical Oceanography 2nd ed. 1986
T = np.array([[0, 0, 0, 0, 0, 0],
[10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20],
[30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]]) / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
UN_cp = np.array([[4048.4, 3896.3, 3807.7, 3986.5, 3849.3, 3769.1],
[4041.8, 3919.6, 3842.3, 3986.3, 3874.7, 3804.4],
[4044.8, 3938.6, 3866.7, 3993.9, 3895.0, 3828.3],
[4049.1, 3952.0, 3883.0, 4000.7, 3909.2, 3844.3],
[4051.2, 3966.1, 3905.9, 4003.5, 3923.9, 3868.3]])
CP = sw.cp(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p37)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp Press Cp cp')
f.write('\n (psu) (C) (db) (J/kg.C) (J/kg.C)\n')
result = np.vstack([S[:, icol], T[:, icol], P[:, icol],
UN_cp[:, icol], CP[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.1f %11.2f\n" %
tuple(result[:, iline]))
# Test main module svel.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: svel')
f.write('\n%s' % asterisks)
# Test 1.
# Data from Pond and Pickard Intro. Dynamical Oceanography 2nd ed. 1986
T = np.array([[0, 0, 0, 0, 0, 0],
[10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20],
[30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]]) / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
UN_svel = np.array([[1435.8, 1520.4, 1610.4, 1449.1, 1534.0, 1623.2],
[1477.7, 1561.3, 1647.4, 1489.8, 1573.4, 1659.0],
[1510.3, 1593.6, 1676.8, 1521.5, 1604.5, 1687.2],
[1535.2, 1619.0, 1700.6, 1545.6, 1629.0, 1710.1],
[1553.4, 1638.0, 1719.2, 1563.2, 1647.3, 1727.8]])
SVEL = sw.svel(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p50)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = SVEL.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp Press SVEL svel')
f.write('\n (psu) (C) (db) (m/s) (m/s)\n')
result = np.vstack([S[:, icol], T[:, icol], P[:, icol],
UN_svel[:, icol], SVEL[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.1f %11.3f\n" %
tuple(result[:, iline]))
# Test submodules alpha beta aonb.
f.write('\n%s' % asterisks)
f.write('\n** and SUB-MODULE: alpha beta aonb')
f.write('\n%s' % asterisks)
# Data from McDouogall 1987.
s = 40
PT = 10
p = 4000
beta_lit = 0.72088e-03
aonb_lit = 0.34763
alpha_lit = aonb_lit * beta_lit
BETA = sw.beta(s, PT, p, pt=True)
ALPHA = sw.alpha(s, PT, p, pt=True)
AONB = sw.aonb(s, PT, p, pt=True)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from MCDOUGALL 1987 ')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
f.write('\n Sal Temp Press BETA beta')
f.write('\n (psu) (C) (db) (psu^-1) (psu^-1)\n')
table = np.hstack([s, PT, p, beta_lit, BETA])
f.write(" %4.0f %4.0f %5.0f %11.4e %11.5e\n" %
tuple(table))
f.write('\n Sal Temp Press AONB aonb')
f.write('\n (psu) (C) (db) (psu C^-1) (psu C^-1)\n')
table = np.hstack([s, PT, p, aonb_lit, AONB])
f.write(" %4.0f %4.0f %5.0f %8.5f %11.6f\n" %
tuple(table))
f.write('\n Sal Temp Press ALPHA alpha')
f.write('\n (psu) (C) (db) (psu^-1) (psu^-1)\n')
table = np.hstack([s, PT, p, alpha_lit, ALPHA])
f.write(" %4.0f %4.0f %5.0f %11.4e %11.4e\n" %
tuple(table))
# Test main moduleS satO2 satN2 satAr.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: satO2 satN2 satAr')
f.write('\n%s' % asterisks)
f.write('\n')
# Data from Weiss 1970.
T = np.array([[-1, -1],
[10, 10],
[20, 20],
[40, 40]]) / 1.00024
S = np.array([[20, 40],
[20, 40],
[20, 40],
[20, 40]])
lit_O2 = np.array([[9.162, 7.984],
[6.950, 6.121],
[5.644, 5.015],
[4.050, 3.656]])
lit_N2 = np.array([[16.28, 14.01],
[12.64, 11.01],
[10.47, 9.21],
[7.78, 6.95]])
lit_Ar = np.array([[0.4456, 0.3877],
[0.3397, 0.2989],
[0.2766, 0.2457],
[0.1986, 0.1794]])
O2 = sw.satO2(S, T)
N2 = sw.satN2(S, T)
Ar = sw.satAr(S, T)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from Weiss, R.F. 1979 ')
f.write('\n"The solubility of nitrogen, oxygen and argon in water')
f.write('\n and seawater." Deep-Sea Research., 1970, Vol 17, pp721-735.')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp O2 satO2')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack([S[:, icol], T[:, icol],
lit_O2[:, icol], O2[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.2f %9.3f\n" %
tuple(result[:, iline]))
for icol in range(0, n):
f.write('\n Sal Temp N2 satN2')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack([S[:, icol], T[:, icol],
lit_N2[:, icol], N2[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.2f %9.3f\n" %
tuple(result[:, iline]))
for icol in range(0, n):
f.write('\n Sal Temp Ar satAr')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack([S[:, icol], T[:, icol],
lit_Ar[:, icol], Ar[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.4f %9.4f\n" %
tuple(result[:, iline]))
tx_beacon[i]=-1
else:
tx_beacon[i]=np.nonzero(ds.rx_beacon.values==tag)[0][0]
# And did the rx see itself?
is_selfaware[i]=np.isfinite(ds.matrix.values[i,tx_beacon[i]])
ds['tx_beacon']=('index',),tx_beacon
is_beacon=tx_beacon>=0
temps=ds.temp.values
# WHOA -- comparisons to the beacon-beacon transits suggest
# that this has some significant error. The inferred and
# calculated speeds of sound are close-ish if temperature is
# offset 4.5 degC.
rx_c=seawater.svel(0,ds['temp']-4.5,0)
# occasional missing data...
rx_c=utils.fill_invalid(rx_c,axis=1)
ds['c']=('index','rx'),rx_c
##
sel_pings=is_multirx&is_selfaware
ds_strong=ds.isel(index=sel_pings)
##
# Calculate time deltas for each ordered pair of receivers
Nping=ds_strong.dims['index']
Nrx=ds_strong.dims['rx']