def values(S, T, P):
"""Compute a row in the table"""
return (
P / 10,
S,
T,
dens(S, T, P) - 1000,
drhods(S, T, P),
10 ** 7 * alpha(S, T, P),
heatcap(S, T, P),
1000 * temppot0(S, T, P),
soundvel(S, T, P),
)
def add_den_usv(ds):
import seawater as sw
import numpy as np
import xarray as xr
ds['wspd']=np.sqrt(ds.UWND_MEAN**2+ds.VWND_MEAN**2)
tem=sw.dens0(ds.SAL_CTD_MEAN,ds.TEMP_CTD_MEAN)
ds['density_mean']=xr.DataArray(tem,dims=('time'),coords={'time':ds.time})
tem=sw.alpha(ds.SAL_CTD_MEAN,ds.TEMP_CTD_MEAN,ds.BARO_PRES_MEAN*0) #pressure =0 at surface
ds['alpha_ME']=xr.DataArray(tem,dims=('time'),coords={'time':ds.time})
tem=sw.beta(ds.SAL_CTD_MEAN,ds.TEMP_CTD_MEAN,ds.BARO_PRES_MEAN*0) #pressure =0 at surface
ds['beta_MEAN']=xr.DataArray(tem,dims=('time'),coords={'time':ds.time})
xlat=ds.lat
xlon=ds.lon
dkm2 = abs(np.abs((((xlon[1:].data-xlon[0:-1].data)**2+(xlat[1:].data-xlat[0:-1].data)**2)**.5)*110.567*np.cos(np.pi*xlat[1:].data/180)))
dkm2=np.append(dkm2,dkm2[len(dkm2)-1]) #add on last point
dkm3 = dkm2.cumsum()
ds['dist_total']=xr.DataArray(dkm3,dims=('time'),coords={'time':ds.time})
ds['dist_between']=xr.DataArray(dkm2,dims=('time'),coords={'time':ds.time})
return ds
def buoyancyFlux(SSS, SST, Q, EP, dz):
"""
Calculate the surface buoyancy flux
Inputs:
SSS - sea surface salinity (psu)
SST - sea surface temperater (celsius)
Q - net heat flux (W m-2)
EP - evaporation minus precipitation (m s-1)
dz - grid cell height [m]
Returns:
B_heat - heat buoyancy flux [W m-2]
B_salt - salt buoyancy flux [W m-2]
Ref:
Gill, 1982
"""
try:
import seawater # CSIRO seawater toolbox
except:
raise Exception(' need to install CSIRO seawater toolbox')
# Constants
Cpinv = 1. / 4200.0 # Specific heat capacity [J kg-1 K-1]
g = 9.81
RHO0 = 1000.0
# Convert EP from [m s-1] -> [kg m-2 s-1]
EP = EP * RHO0
# Calculate thermal expansion and saline contraction coefficient
alpha = seawater.alpha(SSS, SST, 0 * SST)
beta = seawater.beta(SSS, SST, 0 * SST)
# Buoyancy flux equation (see Gill eq 2.7.1, p36)
# Note that units are [kg m s-3] or [W m-1]
B_heat = Cpinv * g * alpha * Q
B_salt = g * beta * EP * SSS
# returns the fluxes in units [W m-2]
return B_heat / dz, B_salt / dz
def buoyancyFlux(SSS,SST,Q,EP,dz):
"""
Calculate the surface buoyancy flux
Inputs:
SSS - sea surface salinity (psu)
SST - sea surface temperater (celsius)
Q - net heat flux (W m-2)
EP - evaporation minus precipitation (m s-1)
dz - grid cell height [m]
Returns:
B_heat - heat buoyancy flux [W m-2]
B_salt - salt buoyancy flux [W m-2]
Ref:
Gill, 1982
"""
try:
import seawater # CSIRO seawater toolbox
except:
raise Exception, ' need to install CSIRO seawater toolbox'
# Constants
Cpinv = 1./4200.0 # Specific heat capacity [J kg-1 K-1]
g = 9.81
RHO0 = 1000.0
# Convert EP from [m s-1] -> [kg m-2 s-1]
EP = EP*RHO0
# Calculate thermal expansion and saline contraction coefficient
alpha = seawater.alpha(SSS,SST,0*SST)
beta = seawater.beta(SSS,SST,0*SST)
# Buoyancy flux equation (see Gill eq 2.7.1, p36)
# Note that units are [kg m s-3] or [W m-1]
B_heat = Cpinv * g * alpha * Q
B_salt = g*beta*EP*SSS
# returns the fluxes in units [W m-2]
return B_heat/dz, B_salt/dz
# computing averages and perturbations ####################################
ubar, vbar, tbar = u.mean(axis=0), v.mean(axis=0), t.mean(axis=0)
unot, vnot, tnot = u * 0, v * 0, t * 0
for l in range(lm):
unot[l, ...] = u[l, ...] - ubar
vnot[l, ...] = v[l, ...] - vbar
tnot[l, ...] = t[l, ...] - tbar
# baroclinic energy conversions ##########################################
bec = []
g = 9.8
theta_z = tbar.mean()
alpha = sw.alpha(35, theta_z, zlev)
gradT = np.gradient(tbar)
dTdx = gradT[1] / (0.05 * 111000)
dTdy = gradT[0] / (0.05 * 111000)
count = 0
for l in range(lm):
count += 1
bc = ((g * alpha) / theta_z) * (unot[l] * tnot[l] * dTdx +
vnot[l] * tnot[l] * dTdy)
bec.append(bc.mean())
bec = np.array(bec)
bec = smooth(bec, window_len=10, window='hanning')
hefti = heft * 1.
#
empta = empt * 1.
emptp = empt * 1.
empti = empt * 1.
#
# Compute density
rhon[t, ...] = eosNeutral(tost.data, sost.data) - 1000.
rhon[t, ...].mask = maski
rhon[t, ...] = maskVal(rhon[t, ...], valmask)
rhonl = rhon.data[t, ...]
# Compute buoyancy/density flux as mass fluxes in kg/m2/s (SI units)
# convwf : kg/m2/s = mm/s -> m/s
convwf = 1.e-3
pres = tost.data * 0.
denflxh[t, ...] = (-sw.alpha(sost.data, tost.data, pres) /
sw.cp(sost.data, tost.data, pres)) * heft.data
if empsw == 0:
denflxw[t, ...] = (rhonl + 1000.) * sw.beta(
sost.data, tost.data, pres) * sost.data * empt.data * convwf
else:
denflxw[t, ...] = (rhonl + 1000.) * sw.beta(
sost.data, tost.data, pres) * empt.data * convwf
denflx[t, ...] = denflxh[t, ...] + denflxw[t, ...]
denflx[t, ...].mask = maski
denflxh[t, ...].mask = maski
denflxw[t, ...].mask = maski
denflx[t, ...] = maskVal(denflx[t, ...], valmask)
denflxh[t, ...] = maskVal(denflxh[t, ...], valmask)
denflxw[t, ...] = maskVal(denflxw[t, ...], valmask)
dflxh = denflxh.data[t, :, :]
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 model_timestep(T,
S,
U,
V,
z,
I,
L,
E,
P,
tau_x,
tau_y,
dt,
nabla_b=None,
Ekman_Q_flux=None,
use_static_stability=True,
use_mixed_layer_stability=True,
use_shear_stability=True,
use_Ekman_flux=False,
use_MLI=False,
tracer=None,
vert_diffusivity=None,
verbose=False,
I1=0.62,
I2=None,
lambda1=.6,
lambda2=20,
T0=0,
S0=34,
rho0=None,
alpha=None,
beta=None,
f=sw.f(40),
return_MLD=False,
advection=False,
l_poly=1e4,
phi=5e-2,
T_cdw=1,
S_cdw=34.5,
shelf_thick=350,
debug=False,
T_out=-1,
S_out=33.8,
return_dTdS=False,
return_TSrelax=False,
return_vel=False):
# define initial variables
c = 4218 # heat capacity (J kg^-1 K^-1)
if I2 is None:
I2 = 1 - I1
if I1 + I2 != 1:
raise Exception('Shortwave insolation amplitudes need to sum to unity')
if rho0 is None:
rho0 = sw.dens(S0, T0, 0)
if alpha is None:
alpha = -sw.alpha(
S0, T0, 0
) * rho0 # multiply by rho to fit into nice equation of state (see get_rho function)
if beta is None:
beta = sw.beta(
S0, T0, 0
) * rho0 # # multiply by rho to fit into nice equation of state (see get_rho function)
dz = z[1] - z[0]
if use_Ekman_flux and Ekman_Q_flux is None:
raise Exception(
'Using Ekman-induced buoyacy flux but no buoyancy gradients were given.'
)
if use_MLI and MLI_flux is None:
raise Exception('Using MLI but no horizontal buoyancy gradient given.')
T = T.copy()
S = S.copy()
U = U.copy()
V = V.copy()
# so don't overwrite data
if tracer is not None:
tracer = tracer.copy()
# make initial heat profile
I_profile = -I / dz * (
I1 * np.exp(-z / lambda1) *
(np.exp(-dz / 2 / lambda1) - np.exp(dz / 2 / lambda1)) +
I2 * np.exp(-z / lambda2) *
(np.exp(-dz / 2 / lambda2) - np.exp(dz / 2 / lambda2)))
L_profile = np.zeros(len(z))
L_profile[0] = L / dz
Q_profile = I_profile + L_profile
if use_Ekman_flux:
A = 0.1 # eddy viscosity m^2 s^-1
z_Ek = np.sqrt(A / np.abs(f))
if verbose:
print('Using Ekman depth of %d m' % z_Ek)
z_Ek_ind = np.where(z > z_Ek)[0][0]
Q_Ek_profile = np.zeros(len(z))
Q_Ek_profile[0:z_Ek_ind] = Ekman_Q_flux / z_Ek * dz
Q_profile += Q_Ek_profile
if advection == True:
Tf = sw.fp(S, z)
#freezing temperature for the column using salinity and pressure
Tf_mean = np.mean(T[51::] - Tf[51::])
#find temperature of no motion
v_profile = ((T - Tf) - Tf_mean) * phi
#create the velocity profile
v_profile[0:50] = 0
#there is no mean advection in the top 25 m
h_interface = (np.abs(v_profile[51::] - 0)).argmin() + 50
#find the depth of the point of no motion
inv_time_length = np.zeros(shape=len(z))
inv_time_length = np.absolute(v_profile) / l_poly
#create 1/tau
#Create the relaxation profiles for S and T
T_relax = np.zeros(shape=len(z))
T_relax = np.tanh((z - z[h_interface]) /
100) * (T_cdw - T_out) / 2 + (T_cdw + T_out) / 2
T_relax[h_interface:len(z)] = T_relax[h_interface:len(z)] + 0.6 * (
(z[h_interface:len(z)] - z[h_interface]) / 1000)
S_relax = np.zeros(shape=len(z))
S_relax = np.tanh((z - z[h_interface]) /
100) * (S_cdw - S_out) / 2 + (S_cdw + S_out) / 2
S_relax[h_interface:len(z)] = S_relax[h_interface:len(z)] + 0.25 * (
(z[h_interface:len(z)] - z[h_interface]) / 1000)
# update temperature
if advection == False:
dTdt = Q_profile / (c * rho0)
else:
dTdt = Q_profile / (c * rho0) + inv_time_length * (T_relax - T)
if debug == True:
print('inv_time_length*(T_relax-T): ',
inv_time_length[0:100] * (T_relax[0:100] - T[0:100]))
if use_MLI:
mld_ind = get_mld_ind(T, S, U, V, z)
mld = z[mld_ind]
C_e = 0.06
g = 9.81
# gravitational acceleration (m s^-2)
c = 4218
# heat capacity (J kg^-1 degC^-1
MLI_dTdt = -C_e * nabla_b**2 * mld**2 * rho0 / (np.abs(f) * alpha * g)
vert_profile = 4 / mld * (1 - 2 * z / mld) * (
16 + 10 *
(1 - 2 * z / mld)**2) / 21 # this is vertical derivative of mu(z)
vert_profile[mld_ind::] = 0
vert_profile[0:mld_ind] -= np.mean(
vert_profile[0:mld_ind]
) # just to ensure that no heat added to system
dTdt += MLI_dTdt * vert_profile
T += dTdt * dt
# update salinity
dSdt_0 = S[0] * (E - P) / dz / 1000
S[0] += dSdt_0 * dt
if advection == True:
dSdt = inv_time_length * (S_relax - S)
S += dSdt * dt
if use_MLI:
rho = get_rho(T, S, T0, S0, rho0, alpha, beta)
half_mld_ind = int(mld_ind / 2)
if np.any(np.diff(rho[half_mld_ind::]) < 0):
if verbose:
print(
'Need to homogenize discontinuity at base of previous mixed layer'
)
# get rid of discontinuity at base of previous mixed layer
# homogenize the region of water from mld/2 to z*
# z* is the shallowest value (> mld/2) such that the homogenized rho <= rho(z*)
zstar_ind = mld_ind.copy()
while np.mean(rho[half_mld_ind:zstar_ind]) >= rho[zstar_ind]:
if verbose:
print('Deepening z*...')
zstar_ind += 1
T[half_mld_ind:zstar_ind] = np.mean(T[half_mld_ind:zstar_ind])
S[half_mld_ind:zstar_ind] = np.mean(S[half_mld_ind:zstar_ind])
if tracer is not None:
tracer[:, half_mld_ind:zstar_ind] = np.atleast_2d(
np.mean(tracer[:, half_mld_ind:zstar_ind], axis=1)).T
elif verbose:
print('No need to homogenize base of previous mixed layer')
# update momentum
# first rotate momentum halfway
angle = -f * dt / 2 # currently assuming this is in rad
U, V = rotate(angle, U, V)
# then add wind stress
mld_ind = get_mld_ind(T, S, U, V, z)
mld = z[mld_ind]
U[0:mld_ind] += tau_x / mld / rho0 * dz * dt
V[0:mld_ind] += tau_y / mld / rho0 * dz * dt
# then rotate second half
U, V = rotate(angle, U, V)
if use_static_stability:
T, S, U, V = static_stability(T, S, U, V, z, T0, S0, rho0, alpha, beta)
if get_mld_ind(T, S, U, V, z) == (T.size - 1):
use_mixed_layer_stability = False
use_shear_stability = False
if use_mixed_layer_stability:
T, S, U, V = mixed_layer_stability(T,
S,
U,
V,
z,
T0,
S0,
rho0,
alpha,
beta,
verbose=verbose)
if use_shear_stability:
T, S, U, V = shear_stability(T,
S,
U,
V,
z,
T0,
S0,
rho0,
alpha,
beta,
verbose=verbose)
if vert_diffusivity is not None:
dTdt_vd = np.zeros(len(T))
dTdt_vd[1:-1] = np.diff(np.diff(T)) / dz**2
T += vert_diffusivity * dTdt_vd * dt
dSdt_vd = np.zeros(len(S))
dSdt_vd[1:-1] = np.diff(np.diff(S)) / dz**2
S += vert_diffusivity * dSdt_vd * dt
dUdt = np.zeros(len(U))
dUdt[1:-1] = np.diff(np.diff(U)) / dz**2
U += vert_diffusivity * dUdt * dt
dVdt = np.zeros(len(V))
dVdt[1:-1] = np.diff(np.diff(V)) / dz**2
V += vert_diffusivity * dVdt * dt
if tracer is not None:
dtdt = np.zeros(shape=tracer.shape)
dtdt[:, 1:-1] = np.diff(np.diff(tracer, axis=1), axis=1) / dz**2
tracer += vert_diffusivity * dtdt * dt
return_variables = (T, S, U, V)
if tracer is not None:
return_variables += (tracer, )
if return_MLD:
return_variables += (get_mld(T, S, U, V, z), )
if return_dTdS:
return_variables += (
dTdt,
dSdt,
)
if return_TSrelax:
return_variables += (
T_relax,
S_relax,
)
if return_vel:
return_variables += (v_profile, )
return return_variables
def model_timestep(T,
S,
U,
V,
z,
I,
L,
E,
P,
tau_x,
tau_y,
dt,
nabla_b=None,
Ekman_Q_flux=None,
use_static_stability=True,
use_mixed_layer_stability=True,
use_shear_stability=True,
use_Ekman_flux=False,
use_MLI=False,
tracer=None,
vert_diffusivity=None,
verbose=False,
I1=0.62,
I2=None,
lambda1=.6,
lambda2=20,
T0=17,
S0=36,
rho0=None,
alpha=None,
beta=None,
f=sw.f(40),
return_MLD=False):
# define initial variables
c = 4218 # heat capacity (J kg^-1 K^-1)
if I2 is None:
I2 = 1 - I1
if I1 + I2 != 1:
raise Exception('Shortwave insolation amplitudes need to sum to unity')
if rho0 is None:
rho0 = sw.dens(S0, T0, 0)
if alpha is None:
alpha = -sw.alpha(
S0, T0, 0
) * rho0 # multiply by rho to fit into nice equation of state (see get_rho function)
if beta is None:
beta = sw.beta(
S0, T0, 0
) * rho0 # # multiply by rho to fit into nice equation of state (see get_rho function)
dz = z[1] - z[0]
if use_Ekman_flux and Ekman_Q_flux is None:
raise Exception(
'Using Ekman-induced buoyacy flux but no buoyancy gradients were given.'
)
if use_MLI and nabla_b is None:
raise Exception('Using MLI but no horizontal buoyancy gradient given.')
T = T.copy()
S = S.copy()
U = U.copy()
V = V.copy()
# so don't overwrite data
if tracer is not None:
tracer = tracer.copy()
# make initial heat profile
I_profile = -I / dz * (
I1 * np.exp(-z / lambda1) *
(np.exp(-dz / 2 / lambda1) - np.exp(dz / 2 / lambda1)) +
I2 * np.exp(-z / lambda2) *
(np.exp(-dz / 2 / lambda2) - np.exp(dz / 2 / lambda2)))
L_profile = np.zeros(len(z))
L_profile[0] = L / dz
Q_profile = I_profile + L_profile
if use_Ekman_flux:
A = 0.1 # eddy viscosity m^2 s^-1
z_Ek = np.sqrt(A / np.abs(f))
if verbose:
print('Using Ekman depth of %d m' % z_Ek)
z_Ek_ind = np.where(z > z_Ek)[0][0]
Q_Ek_profile = np.zeros(len(z))
Q_Ek_profile[0:z_Ek_ind] = Ekman_Q_flux / z_Ek * dz
Q_profile += Q_Ek_profile
# update temperature
dTdt = Q_profile / (c * rho0)
if use_MLI:
mld_ind = get_mld_ind(T, S, U, V, z)
mld = z[mld_ind]
C_e = 0.06
g = 9.81
# gravitational acceleration (m s^-2)
c = 4218
# heat capacity (J kg^-1 degC^-1
MLI_dTdt = -C_e * nabla_b**2 * mld**2 * rho0 / (np.abs(f) * alpha * g)
vert_profile = 4 / mld * (1 - 2 * z / mld) * (
16 + 10 *
(1 - 2 * z / mld)**2) / 21 # this is vertical derivative of mu(z)
vert_profile[mld_ind::] = 0
vert_profile[0:mld_ind] -= np.mean(
vert_profile[0:mld_ind]
) # just to ensure that no heat added to system
dTdt += MLI_dTdt * vert_profile
T += dTdt * dt
# update salinity
dSdt = S[0] * (E - P) / dz / 1000
S[0] += dSdt * dt
if use_MLI:
rho = get_rho(T, S, T0, S0, rho0, alpha, beta)
half_mld_ind = int(mld_ind / 2)
if np.any(np.diff(rho[half_mld_ind::]) < 0):
if verbose:
print(
'Need to homogenize discontinuity at base of previous mixed layer'
)
# get rid of discontinuity at base of previous mixed layer
# homogenize the region of water from mld/2 to z*
# z* is the shallowest value (> mld/2) such that the homogenized rho <= rho(z*)
zstar_ind = mld_ind.copy()
while np.mean(rho[half_mld_ind:zstar_ind]) >= rho[zstar_ind]:
if verbose:
print('Deepening z*...')
zstar_ind += 1
T[half_mld_ind:zstar_ind] = np.mean(T[half_mld_ind:zstar_ind])
S[half_mld_ind:zstar_ind] = np.mean(S[half_mld_ind:zstar_ind])
if tracer is not None:
tracer[:, half_mld_ind:zstar_ind] = np.atleast_2d(
np.mean(tracer[:, half_mld_ind:zstar_ind], axis=1)).T
elif verbose:
print('No need to homogenize base of previous mixed layer')
# update momentum
# first rotate momentum halfway
angle = -f * dt / 2 # currently assuming this is in rad
U, V = rotate(angle, U, V)
# then add wind stress
mld_ind = get_mld_ind(T, S, U, V, z)
mld = z[mld_ind]
U[0:mld_ind] += tau_x / mld / rho0 * dz * dt
V[0:mld_ind] += tau_y / mld / rho0 * dz * dt
# then rotate second half
U, V = rotate(angle, U, V)
if use_static_stability:
T, S, U, V, tracer = static_stability(T,
S,
U,
V,
z,
T0,
S0,
rho0,
alpha,
beta,
tracer=tracer,
verbose=verbose)
if use_mixed_layer_stability:
T, S, U, V, tracer = mixed_layer_stability(T,
S,
U,
V,
z,
T0,
S0,
rho0,
alpha,
beta,
tracer=tracer,
verbose=verbose)
if use_shear_stability:
T, S, U, V, tracer = shear_stability(T,
S,
U,
V,
z,
T0,
S0,
rho0,
alpha,
beta,
tracer=tracer,
verbose=verbose)
if vert_diffusivity is not None:
dTdt = np.zeros(len(T))
dTdt[1:-1] = np.diff(np.diff(T)) / dz**2
T += vert_diffusivity * dTdt * dt
dSdt = np.zeros(len(S))
dSdt[1:-1] = np.diff(np.diff(S)) / dz**2
S += vert_diffusivity * dSdt * dt
dUdt = np.zeros(len(U))
dUdt[1:-1] = np.diff(np.diff(U)) / dz**2
U += vert_diffusivity * dUdt * dt
dVdt = np.zeros(len(V))
dVdt[1:-1] = np.diff(np.diff(V)) / dz**2
V += vert_diffusivity * dSdt * dt
if tracer is not None:
dtdt = np.zeros(shape=tracer.shape)
dtdt[:, 1:-1] = np.diff(np.diff(tracer, axis=1), axis=1) / dz**2
tracer += vert_diffusivity * dtdt * dt
return_variables = (
T,
S,
U,
V,
)
if tracer is not None:
return_variables += (tracer, )
if return_MLD:
return_variables += (get_mld(T, S, U, V, z), )
return return_variables
import seawater as gsw import ctd import practical_salinity as pracsal import numpy as np alpha = np.vectorize(lambda sa, ct, p : gsw.alpha(sa,ct,p)) alpha_wrt_t_exact = np.vectorize(lambda sa,t,p : gsw.alpha_wrt_t_exact(sa,t,p)) beta_const_t_exact = np.vectorize(lambda sa,t,p : gsw.beta_const_t_exact(sa,t,p)) beta = np.vectorize(lambda sa,ct,p : gsw.beta(sa,ct,p)) cp_t_exact = np.vectorize(lambda sa,t,p : gsw.cp_t_exact(sa,t,p)) ct_freezing = np.vectorize(lambda sa,p,saturation_fraction : gsw.ct_freezing(sa,p,saturation_fraction)) ct_from_pt = np.vectorize(lambda sa,pt : gsw.ct_from_pt(sa,pt)) ct_from_t = np.vectorize(lambda sa,t,p : gsw.ct_from_t(sa,t,p)) deltasa_from_sp = np.vectorize(lambda sp,p,lon,lat: gsw.deltasa_from_sp(sp,p,lon,lat)) delta_sa_ref = np.vectorize(lambda p,lon,lat : gsw.delta_sa_ref(p,lon,lat)) dynamic_enthalpy = np.vectorize(lambda sa,ct,p : gsw.dynamic_enthalpy(sa,ct,p)) enthalpy = np.vectorize(lambda sa,ct,p : gsw.enthalpy(sa,ct,p)) enthalpy_t_exact = np.vectorize(lambda sa,t,p : gsw.enthalpy_t_exact(sa,t,p))
hefti = heft*1.
#
empta = empt*1.
emptp = empt*1.
empti = empt*1.
#
# Compute density
rhon[t,...] = eosNeutral(tost.data, sost.data) - 1000.
rhon[t,...].mask = maski
rhon[t,...] = maskVal(rhon[t,...], valmask)
rhonl = rhon.data[t,...]
# Compute buoyancy/density flux as mass fluxes in kg/m2/s (SI units)
# convwf : kg/m2/s = mm/s -> m/s
convwf = 1.e-3
pres = tost.data*0.
denflxh[t,...] = (-sw.alpha(sost.data,tost.data,pres)/sw.cp(sost.data,tost.data,pres))*heft.data
if empsw == 0:
denflxw[t,...] = (rhonl+1000.)*sw.beta(sost.data,tost.data,pres)*sost.data*empt.data*convwf
else:
denflxw[t,...] = (rhonl+1000.)*sw.beta(sost.data,tost.data,pres)*empt.data*convwf
denflx [t,...] = denflxh[t,...] + denflxw[t,...]
denflx [t,...].mask = maski
denflxh[t,...].mask = maski
denflxw[t,...].mask = maski
denflx [t,...] = maskVal(denflx [t,...], valmask)
denflxh[t,...] = maskVal(denflxh[t,...], valmask)
denflxw[t,...] = maskVal(denflxw[t,...], valmask)
dflxh = denflxh.data[t,:,:]
dflxw = denflxw.data[t,:,:]
#
# Transformation (integral of density flux on density outcrops)
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]))
import numpy as np, matplotlib.pyplot as plt, seawater as sw, mPWP as PWP, time
from datetime import datetime
# set up initial parameters (these are also set as defaults in the PWP code, so don't need to put them in)
T0 = 0
# reference temperature (degC)
S0 = 34
# reference salinity (parts per thousand; ppt)
rho0 = 1025
# reference density (kg m^-3)
alpha = -sw.alpha(
S0, T0, 0) * rho0 # thermal expansion coefficient (kg m^-3 degC^-1)
beta = sw.beta(S0, T0,
0) * rho0 # haline contraction coefficient (kg m^-3 ppt^-1 )
latitude = -75
# degrees north
f = sw.f(latitude) # planetary vorticity
fwflux1 = np.loadtxt('/home/erobo/ThompsonResearch/Data/fwflux1_oneyear.csv',
delimiter=',')
qnet1 = np.loadtxt('/home/erobo/ThompsonResearch/Data/qnet1_oneyear.csv',
delimiter=',')
taux1 = np.loadtxt('/home/erobo/ThompsonResearch/Data/taux1_oneyear.csv',
delimiter=',')
tauy1 = np.loadtxt('/home/erobo/ThompsonResearch/Data/tauy1_oneyear.csv',
delimiter=',')
temp_profile = np.loadtxt(
'/home/erobo/ThompsonResearch/Data/temp_profile_snapshot.txt')
salt_profile = np.loadtxt(
'/home/erobo/ThompsonResearch/Data/salt_profile_snapshot.txt')
depth_profile = np.loadtxt(
