def heatcap(depth, latitude, temperature, salinity) -> np.ndarray:
"""
Calculates the heat capacity of sea water.
Parameters:
depth: The depth(s) in meters
latitude: The latitude(s) in degrees North
temperature: The temperatures(s) in Celsius
salinity: The salinity (unitless)
"""
press = __calc_pressure(depth, latitude)
heatcap = seawater.cp(salinity, temperature, press)
return np.array(heatcap)
#
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, :, :]
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 makeHeatContent(salt,temp,destMask,thetao,pressure):
"""
The makeHeatContent() function takes 3D (not temporal) arguments and creates
heat content which is then mapped to a destination grid and written to a
specified variable
Author: Paul J. Durack : [email protected] : @durack1.
Created on Tue Nov 24 15:34:30 2015.
Inputs:
------
- salt(lev,lat,lon) - 3D array.
- temp(lev,lat,lon) - 3D array either in-situ or potential temperature.
- destGridArray(str) - 2D array with valid grid and mask.
- thetao(bool) - boolean value specifying either in-situ or potential temperature arrays provided.
- pressure(bool) - boolean value specifying whether lev-coordinate is pressure (dbar) or depth (m).
Usage:
------
>>> from oceanLib import makeHeatContent
>>> makeHeatContent(salt,temp,destGridArray,thetao=True,pressure=False)
Notes:
-----
- PJD 24 Nov 2015 - Migrated into new oceanLib from heatContentLib
- TODO: Better deal with insitu vs thetao variables
- TODO:
"""
# Remap variables to short names
#print salt.getAxisIds()
s = salt(squeeze=1) ; # Trim off singleton time dimension
#print s.getAxisIds()
t = temp(squeeze=1)
mask = destMask
#print mask.getAxisIds()
del(salt,temp,destMask) ; gc.collect()
depthInd = 0 ; # Set depth coordinate index
#print 's: ',s.min(),s.max()
#print 't: ',t.min(),t.max()
# Fix out of bounds values
t = mv.where(t<-2.6,-2.6,t) ; # Fix for NaN values
# Calculate pressure - inputs depth & lat
# Create z-coordinate from salinity input
if not pressure:
zCoord = s.getAxis(depthInd) ; # Assume time,depth,latitude,longitude grid
yCoord = s.getAxis(depthInd+1)
yCoord = tile(yCoord,(s.shape[depthInd+2],1)).transpose()
depthLevels = tile(zCoord.getValue(),(s.shape[depthInd+2],s.shape[depthInd+1],1)).transpose()
pressureLevels = sw.pres(np.array(depthLevels),np.array(yCoord))
del(zCoord,yCoord,depthLevels) ; gc.collect()
else:
pressureLevels = s.getAxis(depthInd)
#print pressureLevels.getValue()
pressureLevels = transpose(tile(pressureLevels,(s.shape[depthInd+2],s.shape[depthInd+1],1)))
pressureLevels = cdm.createVariable(pressureLevels,id='pressureLevels')
pressureLevels.setAxis(0,s.getAxis(depthInd))
pressureLevels.setAxis(1,s.getAxis(depthInd+1))
pressureLevels.setAxis(2,s.getAxis(depthInd+2))
pressureLevels.units_long = 'decibar (pressure)'
pressureLevels.positive = 'down'
pressureLevels.long_name = 'sea_water_pressure'
pressureLevels.standard_name = 'sea_water_pressure'
pressureLevels.units = 'decibar'
pressureLevels.axis = 'Z'
#print 'pres: ',pressureLevels.min(),pressureLevels.max()
#print pressureLevels.shape
#print s.shape
#print t.shape
#print mask.shape
# Calculate temp,rho,cp - inputs temp,salt,pressure
if thetao:
# Process potential temperature to in-situ
temp = sw.temp(np.array(s),np.array(t),np.array(pressureLevels)); # units degrees C
rho = sw.dens(np.array(s),np.array(temp),np.array(pressureLevels)) ; # units kg m-3
cp = sw.cp(np.array(s),np.array(temp),np.array(pressureLevels)) ; # units J kg-1 C-1
# Correct instances of NaN values and fix masks - applied before cdms variables are created otherwise names/ids/attributes are reset
temp = scrubNaNAndMask(temp,s)
rho = scrubNaNAndMask(rho,s)
cp = scrubNaNAndMask(cp,s)
#print 'temp: ',temp.min(),temp.max()
#print 'rho: ',rho.min(),rho.max()
#print 'cp: ',cp.min(),cp.max()
# Calculate heatContent - inputs temp,rho,cp
heatContent = np.array(temp)*np.array(rho)*np.array(cp) ; # units J
# Correct instances of NaN values and fix masks - applied before cdms variables are created otherwise names/ids/attributes are reset
heatContent = scrubNaNAndMask(heatContent,s)
#print 'hc: ',heatContent.min(),heatContent.max()
# Interpolate to standard levels - inputs heatContent,levels
newDepth = np.array([5,10,20,30,40,50,75,100,125,150,200,300,500,700,1000,1500,1800,2000]).astype('f');
newDepth_bounds = np.array([[0,5],[5,10],[10,20],[20,30],[30,40],[40,50],[50,75],[75,100],[100,125],[125,150],
[150,200],[200,300],[300,500],[500,700],[700,1000],[1000,1500],[1500,1800],[1800,2000]]).astype('f')
# Interpolate to standard levels
#print heatContent.shape
#print heatContent.getAxisIds()
#print pressureLevels.shape
#print pressureLevels.getAxisIds()
# Reset variable axes
heatContent.setAxis(0,s.getAxis(0))
#heatContent.setAxis(1,s.getAxis(1))
#heatContent.setAxis(2,s.getAxis(2))
pdb.set_trace()
heatContent.setGrid(s.getGrid())
#print heatContent.shape
#print heatContent.getAxisIds()
#print pressureLevels.shape
#print pressureLevels.getAxisIds()
heatContent_depthInterp = cdu.linearInterpolation(heatContent,pressureLevels,levels=newDepth)
# Fix bounds
newDepth = heatContent_depthInterp.getAxis(0)
newDepth.setBounds(newDepth_bounds)
del(newDepth_bounds)
newDepth.id = 'depth2'
newDepth.units_long = 'decibar (pressure)'
newDepth.positive = 'down'
newDepth.long_name = 'sea_water_pressure'
newDepth.standard_name = 'sea_water_pressure'
newDepth.units = 'decibar'
newDepth.axis = 'Z'
#print 'hc_interp:',heatContent_depthInterp.min(),heatContent_depthInterp.max()
# Integrate to 700 dbar - inputs heatContent
heatContent_depthInteg = cdu.averager(heatContent_depthInterp[0:14,...],axis=0,weights='weighted',action='sum')(squeeze=1) # Calculate depth-weighted-integrated thetao
# Assign all axis info
#print heatContent_depthInteg.shape
pdb.set_trace()
# Interpolate in x,y - inputs heatContent
#tmp1 = heatContent_depthInteg.regrid(mask.getGrid(),regridTool='esmf',regridMethod='linear') ; # Use defaults - ,coordSys='deg',diag = {},periodicity=1)
tmp1 = heatContent_depthInteg.regrid(mask,regridTool='esmf',regridMethod='linear') ; # Use defaults - ,coordSys='deg',diag = {},periodicity=1)
#print tmp1.shape
tmp1 = mv.where(tmp1<0,0,tmp1) ; # Fix for negative values
# Infill - inputs heatContent
# Create inputs for interpolation
points = np.zeros([(mask.shape[0]*mask.shape[1]),2]) ; # Create 25380 vectors of lon/lat
latcounter = 0 ; loncounter = 0
for count,data in enumerate(points):
if not np.mod(count,180) and not count == 0:
latcounter = latcounter + 1
loncounter = 0
points[count,0] = mask.getLatitude().getValue()[latcounter]
points[count,1] = mask.getLongitude().getValue()[loncounter]
loncounter = loncounter + 1
del(count,data,latcounter,loncounter); gc.collect()
valid = np.logical_not(tmp1.mask) ; # Get inverted-logic boolean mask from variable
if valid.size == 1:
print '** No valid mask found, skipping **'
return
valid = valid.flatten() ; # Flatten 2D to 1D
#maskFilled = mask(tmp,points,valid)
interpolant = interpolate.LinearNDInterpolator(points[valid,:],np.array(tmp1.flatten())[valid]) ; # Create interpolant
maskFill = interpolant(points[:,0].squeeze(),points[:,1].squeeze()) ; # Use interpolant to create filled matrix
maskFill = np.reshape(maskFill,mask.shape) ; # Resize to original dimensions
# Fix issues with interpolant
tmp2 = mv.where(np.isnan(maskFill),1e+20,maskFill) ; # Fix for NaN values
tmp2 = mv.where(tmp2>tmp1.max(),0,tmp2) ; # Fix for max values
tmp2 = mv.where(tmp2<tmp1.min(),0,tmp2) ; # Fix for min values
tmp = mv.masked_where(mask.mask,tmp2)
#print tmp.shape
# Redress variable
heatContent = cdm.createVariable([tmp],id='heatContent')
depthInt = cdm.createAxis([350],id='depth')
depthInt.setBounds(np.array([0,700]))
depthInt.units_long = 'decibar (pressure)'
depthInt.positive = 'down'
depthInt.long_name = 'sea_water_pressure'
depthInt.standard_name = 'sea_water_pressure'
depthInt.units = 'decibar'
depthInt.axis = 'Z'
heatContent.setAxis(0,depthInt)
heatContent.setAxis(1,mask.getAxis(0))
heatContent.setAxis(2,mask.getAxis(1))
heatContent.units_long = 'Joules'
heatContent.long_name = 'sea_water_heat_content'
heatContent.standard_name = 'sea_water_heat_content'
heatContent.units = 'J'
return heatContent ; #,tmp1 ; # 0-700 dbar standard masked variable
def makeSteric(salinity,salinityChg,temp,tempChg,outFileName,thetao,pressure):
"""
The makeSteric() function takes 3D (not temporal) arguments and creates
heat content and steric fields which are written to a specified outfile
Author: Paul J. Durack : [email protected] : @durack1.
Created on Thu Jul 18 13:03:37 2013.
Inputs:
------
- salinity(lev,lat,lon) - 3D array for the climatological period.
- salinityChg(lev,lat,lon) - 3D array for the temporal change period.
- temp(lev,lat,lon) - 3D array for the climatological period either in-situ or potential temperature.
- tempChg(lev,lat,lon) - 3D array for the temporal change period as with temp, either in-situ or potential temperature.
- outFileName(str) - output filename with full path specified.
- thetao(bool) - boolean value specifying either in-situ or potential temperature arrays provided.
- pressure(bool) - boolean value specifying whether lev-coordinate is pressure (dbar) or depth (m).
Usage:
------
>>> from makeStericLib import makeSteric
>>> makeSteric(salinity,salinityChg,thetao,thetaoChg,'outfile.nc',True,False)
Notes:
-----
- PJD 18 Jul 2013 - Validated Ishii v6.13 data against WOA94 - checks out ok. Units: dyn decimeter compared to http://www.nodc.noaa.gov/OC5/WOA94/dyn.html uses cm (not decimeter; x 10)
- PJD 18 Jul 2013 - Added attribute scrub to incoming variables (so,so_chg,temp,temp_chg) to maintain output consistency
- PJD 22 Jul 2013 - Added name attributes to so and temp variables, added units to so_chg
- PJD 22 Jul 2013 - removed duplicated code by converting repetition to function scrubNaNAndMask
- PJD 23 Jul 2013 - Further cleaned up so,so_chg,temp,temp_chg outputs specifying id/name attributes
- PJD 5 Aug 2013 - Updated python-seawater library to version 3.3.1 from github repo, git clone http://github.com/ocefpaf/python-seawater, python setup.py install --user
- PJD 7 Aug 2013 - FIXED: thetao rather than in-situ temperature propagating throughout calculations
- PJD 7 Aug 2013 - Replaced looping with 3D gpan
- PJD 7 Aug 2013 - Further code duplication cleanup
- PJD 8 Aug 2013 - FIXED: scrubNanAndMask function type/mask/grid issue - encase sw arguments in np.array() (attempt to strip cdms fluff)
- PJD 8 Aug 2013 - FIXED: removed depth variable unit edits - not all inputs are depth (m)
- PJD 15 Aug 2013 - Increased interpolated field resolution [200,300,500,700,1000,1500,1800,2000] - [5,10,20,30,40,50,75,100,125,150,200, ...]
- PJD 18 Aug 2013 - AR5 hard coded rho=1020,cp=4187 == 4.3e6 vs Ishii 1970 rho.mean=1024,cp.mean=3922 == 4.1e6 ~5% too high
- PJD 13 Jan 2014 - Corrected steric_height_anom and steric_height_thermo_anom to true anomaly fields, needed to remove climatology
- PJD 3 May 2014 - Turned off thetao conversion, although convert to numpy array rather than cdms2 transient variable
- PJD 13 Oct 2014 - Added seawater_library_version as a global attribute
- PJD 13 Oct 2014 - FIXED: bug with calculation of rho_halo variable was calculating gpan
- PJD 13 Oct 2014 - Added alternate calculation of halosteric anomaly (direct salinity anomaly calculation, rather than total-thermosteric)
- PJD 13 Oct 2014 - Added makeSteric_version as a global attribute
- TODO: Better deal with insitu vs thetao variables
- TODO: Query Charles on why *.name attributes are propagating
- TODO: validate outputs and compare to matlab versions - 10e-7 errors.
"""
# Remap all variables to short names
so = salinity
so_chg = salinityChg
temp = temp
temp_chg = tempChg
del(salinity,salinityChg,tempChg) ; gc.collect()
# Strip attributes to maintain consistency between datasets
for count,x in enumerate(so.attributes.keys()):
delattr(so,x)
#print so.listattributes() ; # Print remaining attributes
for count,x in enumerate(so_chg.attributes.keys()):
delattr(so_chg,x)
for count,x in enumerate(temp.attributes.keys()):
delattr(temp,x)
for count,x in enumerate(temp_chg.attributes.keys()):
delattr(temp_chg,x)
del(count,x)
# Create z-coordinate from salinity input
if not pressure:
z_coord = so.getAxis(0)
y_coord = so.getAxis(1)
y_coord = tile(y_coord,(so.shape[2],1)).transpose()
depth_levels = tile(z_coord.getValue(),(so.shape[2],so.shape[1],1)).transpose()
pressure_levels = sw.pres(np.array(depth_levels),np.array(y_coord))
del(z_coord,y_coord,depth_levels) ; gc.collect()
else:
pressure_levels = so.getAxis(0)
pressure_levels = transpose(tile(pressure_levels,(so.shape[2],so.shape[1],1)))
pressure_levels = cdm.createVariable(pressure_levels,id='pressure_levels')
pressure_levels.setAxis(0,so.getAxis(0))
pressure_levels.setAxis(1,so.getAxis(1))
pressure_levels.setAxis(2,so.getAxis(2))
pressure_levels.id = 'pressure_levels'
pressure_levels.units_long = 'decibar (pressure)'
pressure_levels.positive = 'down'
pressure_levels.long_name = 'sea_water_pressure'
pressure_levels.standard_name = 'sea_water_pressure'
pressure_levels.units = 'decibar'
pressure_levels.axis = 'Z'
# Cleanup depth axis attributes
depth = so.getAxis(0)
depth.id = 'depth'
depth.name = 'depth'
depth.long_name = 'depth'
depth.standard_name = 'depth'
depth.axis = 'Z'
so.setAxis(0,depth)
so_chg.setAxis(0,depth)
temp.setAxis(0,depth)
temp_chg.setAxis(0,depth)
del(depth)
# Convert using python-seawater library (v3.3.1 - 130807)
if thetao:
# Process potential temperature to in-situ - default conversion sets reference pressure to 0 (surface)
#temp_chg = sw.temp(np.array(so),np.array(temp_chg),np.array(pressure_levels)); # units degrees C
#temp = sw.temp(np.array(so),np.array(temp),np.array(pressure_levels)); # units degrees C
#temp_chg = sw.ptmp(np.array(so),np.array(temp_chg),np.array(pressure_levels),np.array(pressure_levels)); # units degrees C
#temp = sw.ptmp(np.array(so),np.array(temp),np.array(pressure_levels),np.array(pressure_levels)); # units degrees C
temp_chg = np.array(temp_chg); # units degrees C
temp = np.array(temp); # units degrees C
# Climatologies - rho,cp,steric_height
rho = sw.dens(np.array(so),np.array(temp),np.array(pressure_levels)) ; # units kg m-3
cp = sw.cp(np.array(so),np.array(temp),np.array(pressure_levels)) ; # units J kg-1 C-1
steric_height = sw.gpan(np.array(so),np.array(temp),np.array(pressure_levels)) ; # units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
# Halosteric - rho,cp
ss = map(array,(so+so_chg))
rho_halo = sw.dens(np.array(ss),np.array(temp),np.array(pressure_levels)) ; # units kg m-3
cp_halo = sw.cp(np.array(ss),np.array(temp),np.array(pressure_levels)) ; # units J kg-1 C-1
tmp = sw.gpan(np.array(ss),np.array(temp),np.array(pressure_levels)) ; # units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
steric_height_halo_anom2 = tmp-steric_height ; # units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
# Full steric - steric_height
tt = map(array,(temp+temp_chg))
tmp = sw.gpan(np.array(ss),np.array(tt),np.array(pressure_levels)) ; # units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
steric_height_anom = tmp-steric_height ; # units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
del(ss,tmp) ; gc.collect()
# Thermosteric - rho,cp,steric_height
rho_thermo = sw.dens(np.array(so),np.array(tt),np.array(pressure_levels)) ; # units kg m-3
cp_thermo = sw.cp(np.array(so),np.array(tt),np.array(pressure_levels)) ; # units J kg-1 C-1
tmp = sw.gpan(np.array(so),np.array(tt),np.array(pressure_levels)) ; # units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
steric_height_thermo_anom = tmp-steric_height ; # units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
del(tt,tmp) ; gc.collect()
# Halosteric - steric_height
steric_height_halo_anom = steric_height_anom-steric_height_thermo_anom ; # units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
# Create heat content
heat_content = np.array(temp)*np.array(rho)*np.array(cp) ; # units J
heat_content_sanom = np.array(temp)*np.array(rho_halo)*np.array(cp_halo) ; # units J
heat_content_tanom = np.array(temp_chg)*np.array(rho)*np.array(cp) ; # units J
#heat_content_tanom = np.array(temp_chg)*np.array(1020)*np.array(4187) ; # units J - try hard-coded - AR5 numbers
heat_content_tsanom = np.array(temp_chg)*np.array(rho_halo)*np.array(cp_halo) ; # units J
# Correct all instances of NaN values and fix masks - applied before cdms variables are created otherwise names/ids/attributes are reset
temp = scrubNaNAndMask(temp,so)
temp_chg = scrubNaNAndMask(temp_chg,so)
rho = scrubNaNAndMask(rho,so)
cp = scrubNaNAndMask(cp,so)
rho_halo = scrubNaNAndMask(rho_halo,so)
cp_halo = scrubNaNAndMask(cp_halo,so)
rho_thermo = scrubNaNAndMask(rho_thermo,so)
cp_thermo = scrubNaNAndMask(cp_thermo,so)
steric_height = scrubNaNAndMask(steric_height,so)
steric_height_anom = scrubNaNAndMask(steric_height_anom,so)
steric_height_thermo_anom = scrubNaNAndMask(steric_height_thermo_anom,so)
steric_height_halo_anom = scrubNaNAndMask(steric_height_halo_anom,so)
steric_height_halo_anom2 = scrubNaNAndMask(steric_height_halo_anom2,so)
heat_content = scrubNaNAndMask(heat_content,so)
heat_content_sanom = scrubNaNAndMask(heat_content_sanom,so)
heat_content_tanom = scrubNaNAndMask(heat_content_tanom,so)
heat_content_tsanom = scrubNaNAndMask(heat_content_tsanom,so)
# Recreate and redress variables
so.id = 'so_mean'
so.units = '1e-3'
so_chg.id = 'so_chg'
so_chg.units = '1e-3'
temp = cdm.createVariable(temp,id='temp_mean')
temp.setAxis(0,so.getAxis(0))
temp.setAxis(1,so.getAxis(1))
temp.setAxis(2,so.getAxis(2))
temp.units = 'degrees_C'
temp_chg = cdm.createVariable(temp_chg,id='temp_chg')
temp_chg.setAxis(0,so.getAxis(0))
temp_chg.setAxis(1,so.getAxis(1))
temp_chg.setAxis(2,so.getAxis(2))
temp_chg.units = 'degrees_C'
rho = cdm.createVariable(rho,id='rho')
rho.setAxis(0,so.getAxis(0))
rho.setAxis(1,so.getAxis(1))
rho.setAxis(2,so.getAxis(2))
rho.name = 'density_mean'
rho.units = 'kg m^-3'
cp = cdm.createVariable(cp,id='cp')
cp.setAxis(0,so.getAxis(0))
cp.setAxis(1,so.getAxis(1))
cp.setAxis(2,so.getAxis(2))
cp.name = 'heat_capacity_mean'
cp.units = 'J kg^-1 C^-1'
rho_halo = cdm.createVariable(rho_halo,id='rho_halo')
rho_halo.setAxis(0,so.getAxis(0))
rho_halo.setAxis(1,so.getAxis(1))
rho_halo.setAxis(2,so.getAxis(2))
rho_halo.name = 'density_mean_halo'
rho_halo.units = 'kg m^-3'
cp_halo = cdm.createVariable(cp_halo,id='cp_halo')
cp_halo.setAxis(0,so.getAxis(0))
cp_halo.setAxis(1,so.getAxis(1))
cp_halo.setAxis(2,so.getAxis(2))
cp_halo.name = 'heat_capacity_mean_halo'
cp_halo.units = 'J kg^-1 C^-1'
rho_thermo = cdm.createVariable(rho_thermo,id='rho_thermo')
rho_thermo.setAxis(0,so.getAxis(0))
rho_thermo.setAxis(1,so.getAxis(1))
rho_thermo.setAxis(2,so.getAxis(2))
rho_thermo.name = 'density_mean_thermo'
rho_thermo.units = 'kg m^-3'
cp_thermo = cdm.createVariable(cp_thermo,id='cp_thermo')
cp_thermo.setAxis(0,so.getAxis(0))
cp_thermo.setAxis(1,so.getAxis(1))
cp_thermo.setAxis(2,so.getAxis(2))
cp_thermo.name = 'heat_capacity_mean_thermo'
cp_thermo.units = 'J kg^-1 C^-1'
steric_height = cdm.createVariable(steric_height,id='steric_height')
steric_height.setAxis(0,so.getAxis(0))
steric_height.setAxis(1,so.getAxis(1))
steric_height.setAxis(2,so.getAxis(2))
steric_height.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_anom = cdm.createVariable(steric_height_anom,id='steric_height_anom')
steric_height_anom.setAxis(0,so.getAxis(0))
steric_height_anom.setAxis(1,so.getAxis(1))
steric_height_anom.setAxis(2,so.getAxis(2))
steric_height_anom.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_thermo_anom = cdm.createVariable(steric_height_thermo_anom,id='steric_height_thermo_anom')
steric_height_thermo_anom.setAxis(0,so.getAxis(0))
steric_height_thermo_anom.setAxis(1,so.getAxis(1))
steric_height_thermo_anom.setAxis(2,so.getAxis(2))
steric_height_thermo_anom.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_halo_anom = cdm.createVariable(steric_height_halo_anom,id='steric_height_halo_anom')
steric_height_halo_anom.setAxis(0,so.getAxis(0))
steric_height_halo_anom.setAxis(1,so.getAxis(1))
steric_height_halo_anom.setAxis(2,so.getAxis(2))
steric_height_halo_anom.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_halo_anom2 = cdm.createVariable(steric_height_halo_anom2,id='steric_height_halo_anom2')
steric_height_halo_anom2.setAxis(0,so.getAxis(0))
steric_height_halo_anom2.setAxis(1,so.getAxis(1))
steric_height_halo_anom2.setAxis(2,so.getAxis(2))
steric_height_halo_anom2.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
heat_content = cdm.createVariable(heat_content,id='heat_content')
heat_content.setAxis(0,so.getAxis(0))
heat_content.setAxis(1,so.getAxis(1))
heat_content.setAxis(2,so.getAxis(2))
heat_content.units = 'J'
heat_content_sanom = cdm.createVariable(heat_content_sanom,id='heat_content_sanom')
heat_content_sanom.setAxis(0,so.getAxis(0))
heat_content_sanom.setAxis(1,so.getAxis(1))
heat_content_sanom.setAxis(2,so.getAxis(2))
heat_content_sanom.units = 'J'
heat_content_tanom = cdm.createVariable(heat_content_tanom,id='heat_content_tanom')
heat_content_tanom.setAxis(0,so.getAxis(0))
heat_content_tanom.setAxis(1,so.getAxis(1))
heat_content_tanom.setAxis(2,so.getAxis(2))
heat_content_tanom.units = 'J'
heat_content_tsanom = cdm.createVariable(heat_content_tsanom,id='heat_content_tsanom')
heat_content_tsanom.setAxis(0,so.getAxis(0))
heat_content_tsanom.setAxis(1,so.getAxis(1))
heat_content_tsanom.setAxis(2,so.getAxis(2))
heat_content_tsanom.units = 'J'
# Create model-based depth index for subset target levels
newdepth = np.array([5,10,20,30,40,50,75,100,125,150,200,300,500,700,1000,1500,1800,2000]).astype('f');
newdepth_bounds = np.array([[0,5],[5,10],[10,20],[20,30],[30,40],[40,50],[50,75],[75,100],[100,125],[125,150],
[150,200],[200,300],[300,500],[500,700],[700,1000],[1000,1500],[1500,1800],[1800,2000]]).astype('f')
#newdepth = np.array([200,300,500,700,1000,1500,1800,2000]).astype('f');
#newdepth_bounds = np.array([[0,200],[200,300],[300,500],[500,700],[700,1000],[1000,1500],[1500,1800],[1800,2000]]).astype('f')
# Interpolate to depths
so_depthInterp = cdu.linearInterpolation(so,pressure_levels,levels=newdepth)
temp_depthInterp = cdu.linearInterpolation(temp,pressure_levels,levels=newdepth)
steric_height_depthInterp = cdu.linearInterpolation(steric_height,pressure_levels,levels=newdepth)
steric_height_anom_depthInterp = cdu.linearInterpolation(steric_height_anom,pressure_levels,levels=newdepth)
steric_height_thermo_anom_depthInterp = cdu.linearInterpolation(steric_height_thermo_anom,pressure_levels,levels=newdepth)
steric_height_halo_anom_depthInterp = cdu.linearInterpolation(steric_height_halo_anom,pressure_levels,levels=newdepth)
steric_height_halo_anom2_depthInterp = cdu.linearInterpolation(steric_height_halo_anom2,pressure_levels,levels=newdepth)
heat_content_sanom_depthInterp = cdu.linearInterpolation(heat_content_sanom,pressure_levels,levels=newdepth)
heat_content_tanom_depthInterp = cdu.linearInterpolation(heat_content_tanom,pressure_levels,levels=newdepth)
heat_content_tsanom_depthInterp = cdu.linearInterpolation(heat_content_tanom,pressure_levels,levels=newdepth)
# Fix masks - applied before cdms variables are created otherwise names/ids/attributes are reset
temp_depthInterp = scrubNaNAndMask(temp_depthInterp,so_depthInterp)
steric_height_depthInterp = scrubNaNAndMask(steric_height_depthInterp,so_depthInterp)
steric_height_anom_depthInterp = scrubNaNAndMask(steric_height_anom_depthInterp,so_depthInterp)
steric_height_thermo_anom_depthInterp = scrubNaNAndMask(steric_height_thermo_anom_depthInterp,so_depthInterp)
steric_height_halo_anom_depthInterp = scrubNaNAndMask(steric_height_halo_anom_depthInterp,so_depthInterp)
steric_height_halo_anom2_depthInterp = scrubNaNAndMask(steric_height_halo_anom2_depthInterp,so_depthInterp)
heat_content_sanom_depthInterp = scrubNaNAndMask(heat_content_sanom_depthInterp,so_depthInterp)
heat_content_tanom_depthInterp = scrubNaNAndMask(heat_content_tanom_depthInterp,so_depthInterp)
heat_content_tsanom_depthInterp = scrubNaNAndMask(heat_content_tsanom_depthInterp,so_depthInterp)
# Fix bounds
newdepth = so_depthInterp.getAxis(0)
newdepth.setBounds(newdepth_bounds)
del(newdepth_bounds)
newdepth.id = 'depth2'
newdepth.units_long = 'decibar (pressure)'
newdepth.positive = 'down'
newdepth.long_name = 'sea_water_pressure'
newdepth.standard_name = 'sea_water_pressure'
newdepth.units = 'decibar'
newdepth.axis = 'Z'
# Assign corrected bounds
so_depthInterp.setAxis(0,newdepth)
temp_depthInterp.setAxis(0,newdepth)
steric_height_depthInterp.setAxis(0,newdepth)
steric_height_anom_depthInterp.setAxis(0,newdepth)
steric_height_thermo_anom_depthInterp.setAxis(0,newdepth)
steric_height_halo_anom_depthInterp.setAxis(0,newdepth)
steric_height_halo_anom2_depthInterp.setAxis(0,newdepth)
heat_content_sanom_depthInterp.setAxis(0,newdepth)
heat_content_tanom_depthInterp.setAxis(0,newdepth)
heat_content_tsanom_depthInterp.setAxis(0,newdepth)
# Average/integrate to surface - configure bounds
# Preallocate arrays
so_depthAve = np.ma.zeros([len(newdepth),shape(so)[1],shape(so)[2]])
temp_depthAve = so_depthAve.copy()
heat_content_sanom_depthInteg = so_depthAve.copy()
heat_content_tanom_depthInteg = so_depthAve.copy()
heat_content_tsanom_depthInteg = so_depthAve.copy()
for count,depth in enumerate(newdepth):
tmp = cdu.averager(so_depthInterp[0:(count+1),...],axis=0,weights='weighted',action='average')
so_depthAve[count,] = tmp;
tmp = cdu.averager(temp_depthInterp[0:(count+1),...],axis=0,weights='weighted',action='average')
temp_depthAve[count,] = tmp;
tmp = cdu.averager(heat_content_sanom_depthInterp[0:(count+1),...],axis=0,weights='weighted',action='sum')
heat_content_sanom_depthInteg[count,] = tmp
tmp = cdu.averager(heat_content_tanom_depthInterp[0:(count+1),...],axis=0,weights='weighted',action='sum')
heat_content_tanom_depthInteg[count,] = tmp
tmp = cdu.averager(heat_content_tsanom_depthInterp[0:(count+1),...],axis=0,weights='weighted',action='sum')
heat_content_tsanom_depthInteg[count,] = tmp
del(heat_content_tanom_depthInterp,heat_content_tsanom_depthInterp); gc.collect()
# Fix masks - applied before cdms variables are created otherwise names/ids/attributes are reset
so_depthAve = scrubNaNAndMask(so_depthAve,so_depthInterp)
temp_depthAve = scrubNaNAndMask(temp_depthAve,so_depthInterp)
heat_content_sanom_depthInteg = scrubNaNAndMask(heat_content_sanom_depthInteg,so_depthInterp)
heat_content_tanom_depthInteg = scrubNaNAndMask(heat_content_tanom_depthInteg,so_depthInterp)
heat_content_tsanom_depthInteg = scrubNaNAndMask(heat_content_tsanom_depthInteg,so_depthInterp)
del(so_depthInterp)
# Convert numpy arrays to cdms objects
heat_content_sanom_depthInteg = cdm.createVariable(heat_content_sanom_depthInteg,id='heat_content_sanom_depthInteg')
heat_content_sanom_depthInteg.id = 'heat_content_sanom_depthInteg'
heat_content_sanom_depthInteg.setAxis(0,newdepth)
heat_content_sanom_depthInteg.setAxis(1,so.getAxis(1))
heat_content_sanom_depthInteg.setAxis(2,so.getAxis(2))
heat_content_sanom_depthInteg.units = 'J'
heat_content_tanom_depthInteg = cdm.createVariable(heat_content_tanom_depthInteg,id='heat_content_tanom_depthInteg')
heat_content_tanom_depthInteg.id = 'heat_content_tanom_depthInteg'
heat_content_tanom_depthInteg.setAxis(0,newdepth)
heat_content_tanom_depthInteg.setAxis(1,so.getAxis(1))
heat_content_tanom_depthInteg.setAxis(2,so.getAxis(2))
heat_content_tanom_depthInteg.units = 'J'
heat_content_tsanom_depthInteg = cdm.createVariable(heat_content_tsanom_depthInteg,id='heat_content_tsanom_depthInteg')
heat_content_tsanom_depthInteg.id = 'heat_content_tsanom_depthInteg'
heat_content_tsanom_depthInteg.setAxis(0,newdepth)
heat_content_tsanom_depthInteg.setAxis(1,so.getAxis(1))
heat_content_tsanom_depthInteg.setAxis(2,so.getAxis(2))
heat_content_tsanom_depthInteg.units = 'J'
so_depthAve = cdm.createVariable(so_depthAve,id='so_depthAve')
so_depthAve.id = 'so_depthAve'
so_depthAve.setAxis(0,newdepth)
so_depthAve.setAxis(1,so.getAxis(1))
so_depthAve.setAxis(2,so.getAxis(2))
so_depthAve.units = '1e-3'
temp_depthAve = cdm.createVariable(temp_depthAve,id='temp_depthAve')
temp_depthAve.id = 'temp_depthAve'
temp_depthAve.setAxis(0,newdepth)
temp_depthAve.setAxis(1,so.getAxis(1))
temp_depthAve.setAxis(2,so.getAxis(2))
temp_depthAve.units = 'degrees_C'
steric_height_depthInterp = cdm.createVariable(steric_height_depthInterp,id='steric_height_depthInterp')
steric_height_depthInterp.setAxis(0,newdepth)
steric_height_depthInterp.setAxis(1,so.getAxis(1))
steric_height_depthInterp.setAxis(2,so.getAxis(2))
steric_height_depthInterp.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_anom_depthInterp = cdm.createVariable(steric_height_anom_depthInterp,id='steric_height_anom_depthInterp')
steric_height_anom_depthInterp.setAxis(0,newdepth)
steric_height_anom_depthInterp.setAxis(1,so.getAxis(1))
steric_height_anom_depthInterp.setAxis(2,rho.getAxis(2))
steric_height_anom_depthInterp.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_thermo_anom_depthInterp = cdm.createVariable(steric_height_thermo_anom_depthInterp,id='steric_height_thermo_anom_depthInterp')
steric_height_thermo_anom_depthInterp.setAxis(0,newdepth)
steric_height_thermo_anom_depthInterp.setAxis(1,so.getAxis(1))
steric_height_thermo_anom_depthInterp.setAxis(2,so.getAxis(2))
steric_height_thermo_anom_depthInterp.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_halo_anom_depthInterp = cdm.createVariable(steric_height_halo_anom_depthInterp,id='steric_height_halo_anom_depthInterp')
steric_height_halo_anom_depthInterp.setAxis(0,newdepth)
steric_height_halo_anom_depthInterp.setAxis(1,so.getAxis(1))
steric_height_halo_anom_depthInterp.setAxis(2,so.getAxis(2))
steric_height_halo_anom_depthInterp.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_halo_anom2_depthInterp = cdm.createVariable(steric_height_halo_anom2_depthInterp,id='steric_height_halo_anom2_depthInterp')
steric_height_halo_anom2_depthInterp.setAxis(0,newdepth)
steric_height_halo_anom2_depthInterp.setAxis(1,so.getAxis(1))
steric_height_halo_anom2_depthInterp.setAxis(2,so.getAxis(2))
steric_height_halo_anom2_depthInterp.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
# Cleanup workspace
del(newdepth) ; gc.collect()
# Write variables to file
if os.path.isfile(outFileName):
os.remove(outFileName)
filehandle = cdm.open(outFileName,'w')
# Global attributes
globalAttWrite(filehandle,options=None) ; # Use function to write standard global atts
# Write seawater version
filehandle.seawater_library_version = sw.__version__
# Write makeSteric version
makeStericPath = str(makeSteric.__code__).split(' ')[6]
makeStericPath = replace(replace(makeStericPath,'"',''),',','') ; # Clean scraped path
filehandle.makeSteric_version = ' '.join(getGitInfo(makeStericPath)[0:3])
# Master variables
filehandle.write(so.astype('float32'))
filehandle.write(so_chg.astype('float32'))
filehandle.write(so_depthAve.astype('float32'))
filehandle.write(temp.astype('float32'))
filehandle.write(temp_chg.astype('float32'))
filehandle.write(temp_depthAve.astype('float32'))
# Derived variables
filehandle.write(cp.astype('float32'))
filehandle.write(cp_halo.astype('float32'))
filehandle.write(cp_thermo.astype('float32'))
filehandle.write(rho.astype('float32'))
filehandle.write(rho_halo.astype('float32'))
filehandle.write(rho_thermo.astype('float32'))
filehandle.write(heat_content.astype('float32'))
filehandle.write(heat_content_sanom.astype('float32'))
filehandle.write(heat_content_sanom_depthInteg.astype('float32'))
filehandle.write(heat_content_tanom.astype('float32'))
filehandle.write(heat_content_tanom_depthInteg.astype('float32'))
filehandle.write(heat_content_tsanom.astype('float32'))
filehandle.write(heat_content_tsanom_depthInteg.astype('float32'))
filehandle.write(steric_height.astype('float32'))
filehandle.write(steric_height_depthInterp.astype('float32'))
filehandle.write(steric_height_anom.astype('float32'))
filehandle.write(steric_height_anom_depthInterp.astype('float32'))
filehandle.write(steric_height_halo_anom.astype('float32'))
filehandle.write(steric_height_halo_anom2.astype('float32'))
filehandle.write(steric_height_halo_anom_depthInterp.astype('float32'))
filehandle.write(steric_height_halo_anom2_depthInterp.astype('float32'))
filehandle.write(steric_height_thermo_anom.astype('float32'))
filehandle.write(steric_height_thermo_anom_depthInterp.astype('float32'))
filehandle.close()
# Cleanup workspace
del(outFileName) ; gc.collect()
def makeHeatContent(salt, temp, destMask, thetao, pressure):
"""
The makeHeatContent() function takes 3D (not temporal) arguments and creates
heat content which is then mapped to a destination grid and written to a
specified variable
Author: Paul J. Durack : [email protected] : @durack1.
Created on Tue Nov 24 15:34:30 2015.
Inputs:
------
- salt(lev,lat,lon) - 3D array.
- temp(lev,lat,lon) - 3D array either in-situ or potential temperature.
- destGridArray(str) - 2D array with valid grid and mask.
- thetao(bool) - boolean value specifying either in-situ or potential temperature arrays provided.
- pressure(bool) - boolean value specifying whether lev-coordinate is pressure (dbar) or depth (m).
Usage:
------
>>> from oceanLib import makeHeatContent
>>> makeHeatContent(salt,temp,destGridArray,thetao=True,pressure=False)
Notes:
-----
- PJD 24 Nov 2015 - Migrated into new oceanLib from heatContentLib
- TODO: Better deal with insitu vs thetao variables
- TODO:
"""
# Remap variables to short names
#print salt.getAxisIds()
s = salt(squeeze=1)
# Trim off singleton time dimension
#print s.getAxisIds()
t = temp(squeeze=1)
mask = destMask
#print mask.getAxisIds()
del (salt, temp, destMask)
gc.collect()
depthInd = 0
# Set depth coordinate index
#print 's: ',s.min(),s.max()
#print 't: ',t.min(),t.max()
# Fix out of bounds values
t = mv.where(t < -2.6, -2.6, t)
# Fix for NaN values
# Calculate pressure - inputs depth & lat
# Create z-coordinate from salinity input
if not pressure:
zCoord = s.getAxis(depthInd)
# Assume time,depth,latitude,longitude grid
yCoord = s.getAxis(depthInd + 1)
yCoord = tile(yCoord, (s.shape[depthInd + 2], 1)).transpose()
depthLevels = tile(
zCoord.getValue(),
(s.shape[depthInd + 2], s.shape[depthInd + 1], 1)).transpose()
pressureLevels = sw.pres(np.array(depthLevels), np.array(yCoord))
del (zCoord, yCoord, depthLevels)
gc.collect()
else:
pressureLevels = s.getAxis(depthInd)
#print pressureLevels.getValue()
pressureLevels = transpose(
tile(pressureLevels,
(s.shape[depthInd + 2], s.shape[depthInd + 1], 1)))
pressureLevels = cdm.createVariable(pressureLevels, id='pressureLevels')
pressureLevels.setAxis(0, s.getAxis(depthInd))
pressureLevels.setAxis(1, s.getAxis(depthInd + 1))
pressureLevels.setAxis(2, s.getAxis(depthInd + 2))
pressureLevels.units_long = 'decibar (pressure)'
pressureLevels.positive = 'down'
pressureLevels.long_name = 'sea_water_pressure'
pressureLevels.standard_name = 'sea_water_pressure'
pressureLevels.units = 'decibar'
pressureLevels.axis = 'Z'
#print 'pres: ',pressureLevels.min(),pressureLevels.max()
#print pressureLevels.shape
#print s.shape
#print t.shape
#print mask.shape
# Calculate temp,rho,cp - inputs temp,salt,pressure
if thetao:
# Process potential temperature to in-situ
temp = sw.temp(np.array(s), np.array(t), np.array(pressureLevels))
# units degrees C
rho = sw.dens(np.array(s), np.array(temp), np.array(pressureLevels))
# units kg m-3
cp = sw.cp(np.array(s), np.array(temp), np.array(pressureLevels))
# units J kg-1 C-1
# Correct instances of NaN values and fix masks - applied before cdms variables are created otherwise names/ids/attributes are reset
temp = scrubNaNAndMask(temp, s)
rho = scrubNaNAndMask(rho, s)
cp = scrubNaNAndMask(cp, s)
#print 'temp: ',temp.min(),temp.max()
#print 'rho: ',rho.min(),rho.max()
#print 'cp: ',cp.min(),cp.max()
# Calculate heatContent - inputs temp,rho,cp
heatContent = np.array(temp) * np.array(rho) * np.array(cp)
# units J
# Correct instances of NaN values and fix masks - applied before cdms variables are created otherwise names/ids/attributes are reset
heatContent = scrubNaNAndMask(heatContent, s)
#print 'hc: ',heatContent.min(),heatContent.max()
# Interpolate to standard levels - inputs heatContent,levels
newDepth = np.array([
5, 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 300, 500, 700, 1000,
1500, 1800, 2000
]).astype('f')
newDepth_bounds = np.array([[0, 5], [5, 10], [10, 20], [20, 30], [30, 40],
[40, 50], [50, 75], [75, 100], [100, 125],
[125, 150], [150, 200], [200, 300], [300, 500],
[500, 700], [700, 1000], [1000, 1500],
[1500, 1800], [1800, 2000]]).astype('f')
# Interpolate to standard levels
#print heatContent.shape
#print heatContent.getAxisIds()
#print pressureLevels.shape
#print pressureLevels.getAxisIds()
# Reset variable axes
heatContent.setAxis(0, s.getAxis(0))
#heatContent.setAxis(1,s.getAxis(1))
#heatContent.setAxis(2,s.getAxis(2))
pdb.set_trace()
heatContent.setGrid(s.getGrid())
#print heatContent.shape
#print heatContent.getAxisIds()
#print pressureLevels.shape
#print pressureLevels.getAxisIds()
heatContent_depthInterp = cdu.linearInterpolation(heatContent,
pressureLevels,
levels=newDepth)
# Fix bounds
newDepth = heatContent_depthInterp.getAxis(0)
newDepth.setBounds(newDepth_bounds)
del (newDepth_bounds)
newDepth.id = 'depth2'
newDepth.units_long = 'decibar (pressure)'
newDepth.positive = 'down'
newDepth.long_name = 'sea_water_pressure'
newDepth.standard_name = 'sea_water_pressure'
newDepth.units = 'decibar'
newDepth.axis = 'Z'
#print 'hc_interp:',heatContent_depthInterp.min(),heatContent_depthInterp.max()
# Integrate to 700 dbar - inputs heatContent
heatContent_depthInteg = cdu.averager(
heatContent_depthInterp[0:14, ...],
axis=0,
weights='weighted',
action='sum')(squeeze=1) # Calculate depth-weighted-integrated thetao
# Assign all axis info
#print heatContent_depthInteg.shape
pdb.set_trace()
# Interpolate in x,y - inputs heatContent
#tmp1 = heatContent_depthInteg.regrid(mask.getGrid(),regridTool='esmf',regridMethod='linear') ; # Use defaults - ,coordSys='deg',diag = {},periodicity=1)
tmp1 = heatContent_depthInteg.regrid(mask,
regridTool='esmf',
regridMethod='linear')
# Use defaults - ,coordSys='deg',diag = {},periodicity=1)
#print tmp1.shape
tmp1 = mv.where(tmp1 < 0, 0, tmp1)
# Fix for negative values
# Infill - inputs heatContent
# Create inputs for interpolation
points = np.zeros([(mask.shape[0] * mask.shape[1]), 2])
# Create 25380 vectors of lon/lat
latcounter = 0
loncounter = 0
for count, data in enumerate(points):
if not np.mod(count, 180) and not count == 0:
latcounter = latcounter + 1
loncounter = 0
points[count, 0] = mask.getLatitude().getValue()[latcounter]
points[count, 1] = mask.getLongitude().getValue()[loncounter]
loncounter = loncounter + 1
del (count, data, latcounter, loncounter)
gc.collect()
valid = np.logical_not(tmp1.mask)
# Get inverted-logic boolean mask from variable
if valid.size == 1:
print '** No valid mask found, skipping **'
return
valid = valid.flatten()
# Flatten 2D to 1D
#maskFilled = mask(tmp,points,valid)
interpolant = interpolate.LinearNDInterpolator(
points[valid, :],
np.array(tmp1.flatten())[valid])
# Create interpolant
maskFill = interpolant(points[:, 0].squeeze(), points[:, 1].squeeze())
# Use interpolant to create filled matrix
maskFill = np.reshape(maskFill, mask.shape)
# Resize to original dimensions
# Fix issues with interpolant
tmp2 = mv.where(np.isnan(maskFill), 1e+20, maskFill)
# Fix for NaN values
tmp2 = mv.where(tmp2 > tmp1.max(), 0, tmp2)
# Fix for max values
tmp2 = mv.where(tmp2 < tmp1.min(), 0, tmp2)
# Fix for min values
tmp = mv.masked_where(mask.mask, tmp2)
#print tmp.shape
# Redress variable
heatContent = cdm.createVariable([tmp], id='heatContent')
depthInt = cdm.createAxis([350], id='depth')
depthInt.setBounds(np.array([0, 700]))
depthInt.units_long = 'decibar (pressure)'
depthInt.positive = 'down'
depthInt.long_name = 'sea_water_pressure'
depthInt.standard_name = 'sea_water_pressure'
depthInt.units = 'decibar'
depthInt.axis = 'Z'
heatContent.setAxis(0, depthInt)
heatContent.setAxis(1, mask.getAxis(0))
heatContent.setAxis(2, mask.getAxis(1))
heatContent.units_long = 'Joules'
heatContent.long_name = 'sea_water_heat_content'
heatContent.standard_name = 'sea_water_heat_content'
heatContent.units = 'J'
return heatContent
def makeSteric(salinity, salinityChg, temp, tempChg, outFileName, thetao,
pressure):
"""
The makeSteric() function takes 3D (not temporal) arguments and creates
heat content and steric fields which are written to a specified outfile
Author: Paul J. Durack : [email protected] : @durack1.
Created on Thu Jul 18 13:03:37 2013.
Inputs:
------
- salinity(lev,lat,lon) - 3D array for the climatological period.
- salinityChg(lev,lat,lon) - 3D array for the temporal change period.
- temp(lev,lat,lon) - 3D array for the climatological period either in-situ or potential temperature.
- tempChg(lev,lat,lon) - 3D array for the temporal change period as with temp, either in-situ or potential temperature.
- outFileName(str) - output filename with full path specified.
- thetao(bool) - boolean value specifying either in-situ or potential temperature arrays provided.
- pressure(bool) - boolean value specifying whether lev-coordinate is pressure (dbar) or depth (m).
Usage:
------
>>> from makeStericLib import makeSteric
>>> makeSteric(salinity,salinityChg,thetao,thetaoChg,'outfile.nc',True,False)
Notes:
-----
- PJD 18 Jul 2013 - Validated Ishii v6.13 data against WOA94 - checks out ok. Units: dyn decimeter compared to http://www.nodc.noaa.gov/OC5/WOA94/dyn.html uses cm (not decimeter; x 10)
- PJD 18 Jul 2013 - Added attribute scrub to incoming variables (so,so_chg,temp,temp_chg) to maintain output consistency
- PJD 22 Jul 2013 - Added name attributes to so and temp variables, added units to so_chg
- PJD 22 Jul 2013 - removed duplicated code by converting repetition to function scrubNaNAndMask
- PJD 23 Jul 2013 - Further cleaned up so,so_chg,temp,temp_chg outputs specifying id/name attributes
- PJD 5 Aug 2013 - Updated python-seawater library to version 3.3.1 from github repo, git clone http://github.com/ocefpaf/python-seawater, python setup.py install --user
- PJD 7 Aug 2013 - FIXED: thetao rather than in-situ temperature propagating throughout calculations
- PJD 7 Aug 2013 - Replaced looping with 3D gpan
- PJD 7 Aug 2013 - Further code duplication cleanup
- PJD 8 Aug 2013 - FIXED: scrubNanAndMask function type/mask/grid issue - encase sw arguments in np.array() (attempt to strip cdms fluff)
- PJD 8 Aug 2013 - FIXED: removed depth variable unit edits - not all inputs are depth (m)
- PJD 15 Aug 2013 - Increased interpolated field resolution [200,300,500,700,1000,1500,1800,2000] - [5,10,20,30,40,50,75,100,125,150,200, ...]
- PJD 18 Aug 2013 - AR5 hard coded rho=1020,cp=4187 == 4.3e6 vs Ishii 1970 rho.mean=1024,cp.mean=3922 == 4.1e6 ~5% too high
- PJD 13 Jan 2014 - Corrected steric_height_anom and steric_height_thermo_anom to true anomaly fields, needed to remove climatology
- PJD 3 May 2014 - Turned off thetao conversion, although convert to numpy array rather than cdms2 transient variable
- PJD 13 Oct 2014 - Added seawater_library_version as a global attribute
- PJD 13 Oct 2014 - FIXED: bug with calculation of rho_halo variable was calculating gpan
- PJD 13 Oct 2014 - Added alternate calculation of halosteric anomaly (direct salinity anomaly calculation, rather than total-thermosteric)
- PJD 13 Oct 2014 - Added makeSteric_version as a global attribute
- TODO: Better deal with insitu vs thetao variables
- TODO: Query Charles on why *.name attributes are propagating
- TODO: validate outputs and compare to matlab versions - 10e-7 errors.
"""
# Remap all variables to short names
so = salinity
so_chg = salinityChg
temp = temp
temp_chg = tempChg
del (salinity, salinityChg, tempChg)
gc.collect()
# Strip attributes to maintain consistency between datasets
for count, x in enumerate(so.attributes.keys()):
delattr(so, x)
#print so.listattributes() ; # Print remaining attributes
for count, x in enumerate(so_chg.attributes.keys()):
delattr(so_chg, x)
for count, x in enumerate(temp.attributes.keys()):
delattr(temp, x)
for count, x in enumerate(temp_chg.attributes.keys()):
delattr(temp_chg, x)
del (count, x)
# Create z-coordinate from salinity input
if not pressure:
z_coord = so.getAxis(0)
y_coord = so.getAxis(1)
y_coord = tile(y_coord, (so.shape[2], 1)).transpose()
depth_levels = tile(z_coord.getValue(),
(so.shape[2], so.shape[1], 1)).transpose()
pressure_levels = sw.pres(np.array(depth_levels), np.array(y_coord))
del (z_coord, y_coord, depth_levels)
gc.collect()
else:
pressure_levels = so.getAxis(0)
pressure_levels = transpose(
tile(pressure_levels, (so.shape[2], so.shape[1], 1)))
pressure_levels = cdm.createVariable(pressure_levels, id='pressure_levels')
pressure_levels.setAxis(0, so.getAxis(0))
pressure_levels.setAxis(1, so.getAxis(1))
pressure_levels.setAxis(2, so.getAxis(2))
pressure_levels.id = 'pressure_levels'
pressure_levels.units_long = 'decibar (pressure)'
pressure_levels.positive = 'down'
pressure_levels.long_name = 'sea_water_pressure'
pressure_levels.standard_name = 'sea_water_pressure'
pressure_levels.units = 'decibar'
pressure_levels.axis = 'Z'
# Cleanup depth axis attributes
depth = so.getAxis(0)
depth.id = 'depth'
depth.name = 'depth'
depth.long_name = 'depth'
depth.standard_name = 'depth'
depth.axis = 'Z'
so.setAxis(0, depth)
so_chg.setAxis(0, depth)
temp.setAxis(0, depth)
temp_chg.setAxis(0, depth)
del (depth)
# Convert using python-seawater library (v3.3.1 - 130807)
if thetao:
# Process potential temperature to in-situ - default conversion sets reference pressure to 0 (surface)
#temp_chg = sw.temp(np.array(so),np.array(temp_chg),np.array(pressure_levels)); # units degrees C
#temp = sw.temp(np.array(so),np.array(temp),np.array(pressure_levels)); # units degrees C
#temp_chg = sw.ptmp(np.array(so),np.array(temp_chg),np.array(pressure_levels),np.array(pressure_levels)); # units degrees C
#temp = sw.ptmp(np.array(so),np.array(temp),np.array(pressure_levels),np.array(pressure_levels)); # units degrees C
temp_chg = np.array(temp_chg)
# units degrees C
temp = np.array(temp)
# units degrees C
# Climatologies - rho,cp,steric_height
rho = sw.dens(np.array(so), np.array(temp), np.array(pressure_levels))
# units kg m-3
cp = sw.cp(np.array(so), np.array(temp), np.array(pressure_levels))
# units J kg-1 C-1
steric_height = sw.gpan(np.array(so), np.array(temp),
np.array(pressure_levels))
# units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
# Halosteric - rho,cp
ss = map(array, (so + so_chg))
rho_halo = sw.dens(np.array(ss), np.array(temp), np.array(pressure_levels))
# units kg m-3
cp_halo = sw.cp(np.array(ss), np.array(temp), np.array(pressure_levels))
# units J kg-1 C-1
tmp = sw.gpan(np.array(ss), np.array(temp), np.array(pressure_levels))
# units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
steric_height_halo_anom2 = tmp - steric_height
# units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
# Full steric - steric_height
tt = map(array, (temp + temp_chg))
tmp = sw.gpan(np.array(ss), np.array(tt), np.array(pressure_levels))
# units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
steric_height_anom = tmp - steric_height
# units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
del (ss, tmp)
gc.collect()
# Thermosteric - rho,cp,steric_height
rho_thermo = sw.dens(np.array(so), np.array(tt), np.array(pressure_levels))
# units kg m-3
cp_thermo = sw.cp(np.array(so), np.array(tt), np.array(pressure_levels))
# units J kg-1 C-1
tmp = sw.gpan(np.array(so), np.array(tt), np.array(pressure_levels))
# units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
steric_height_thermo_anom = tmp - steric_height
# units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
del (tt, tmp)
gc.collect()
# Halosteric - steric_height
steric_height_halo_anom = steric_height_anom - steric_height_thermo_anom
# units m3 kg-1 Pa == m2 s-2 == J kg-1 (dynamic decimeter)
# Create heat content
heat_content = np.array(temp) * np.array(rho) * np.array(cp)
# units J
heat_content_sanom = np.array(temp) * np.array(rho_halo) * np.array(
cp_halo)
# units J
heat_content_tanom = np.array(temp_chg) * np.array(rho) * np.array(cp)
# units J
#heat_content_tanom = np.array(temp_chg)*np.array(1020)*np.array(4187) ; # units J - try hard-coded - AR5 numbers
heat_content_tsanom = np.array(temp_chg) * np.array(rho_halo) * np.array(
cp_halo)
# units J
# Correct all instances of NaN values and fix masks - applied before cdms variables are created otherwise names/ids/attributes are reset
temp = scrubNaNAndMask(temp, so)
temp_chg = scrubNaNAndMask(temp_chg, so)
rho = scrubNaNAndMask(rho, so)
cp = scrubNaNAndMask(cp, so)
rho_halo = scrubNaNAndMask(rho_halo, so)
cp_halo = scrubNaNAndMask(cp_halo, so)
rho_thermo = scrubNaNAndMask(rho_thermo, so)
cp_thermo = scrubNaNAndMask(cp_thermo, so)
steric_height = scrubNaNAndMask(steric_height, so)
steric_height_anom = scrubNaNAndMask(steric_height_anom, so)
steric_height_thermo_anom = scrubNaNAndMask(steric_height_thermo_anom, so)
steric_height_halo_anom = scrubNaNAndMask(steric_height_halo_anom, so)
steric_height_halo_anom2 = scrubNaNAndMask(steric_height_halo_anom2, so)
heat_content = scrubNaNAndMask(heat_content, so)
heat_content_sanom = scrubNaNAndMask(heat_content_sanom, so)
heat_content_tanom = scrubNaNAndMask(heat_content_tanom, so)
heat_content_tsanom = scrubNaNAndMask(heat_content_tsanom, so)
# Recreate and redress variables
so.id = 'so_mean'
so.units = '1e-3'
so_chg.id = 'so_chg'
so_chg.units = '1e-3'
temp = cdm.createVariable(temp, id='temp_mean')
temp.setAxis(0, so.getAxis(0))
temp.setAxis(1, so.getAxis(1))
temp.setAxis(2, so.getAxis(2))
temp.units = 'degrees_C'
temp_chg = cdm.createVariable(temp_chg, id='temp_chg')
temp_chg.setAxis(0, so.getAxis(0))
temp_chg.setAxis(1, so.getAxis(1))
temp_chg.setAxis(2, so.getAxis(2))
temp_chg.units = 'degrees_C'
rho = cdm.createVariable(rho, id='rho')
rho.setAxis(0, so.getAxis(0))
rho.setAxis(1, so.getAxis(1))
rho.setAxis(2, so.getAxis(2))
rho.name = 'density_mean'
rho.units = 'kg m^-3'
cp = cdm.createVariable(cp, id='cp')
cp.setAxis(0, so.getAxis(0))
cp.setAxis(1, so.getAxis(1))
cp.setAxis(2, so.getAxis(2))
cp.name = 'heat_capacity_mean'
cp.units = 'J kg^-1 C^-1'
rho_halo = cdm.createVariable(rho_halo, id='rho_halo')
rho_halo.setAxis(0, so.getAxis(0))
rho_halo.setAxis(1, so.getAxis(1))
rho_halo.setAxis(2, so.getAxis(2))
rho_halo.name = 'density_mean_halo'
rho_halo.units = 'kg m^-3'
cp_halo = cdm.createVariable(cp_halo, id='cp_halo')
cp_halo.setAxis(0, so.getAxis(0))
cp_halo.setAxis(1, so.getAxis(1))
cp_halo.setAxis(2, so.getAxis(2))
cp_halo.name = 'heat_capacity_mean_halo'
cp_halo.units = 'J kg^-1 C^-1'
rho_thermo = cdm.createVariable(rho_thermo, id='rho_thermo')
rho_thermo.setAxis(0, so.getAxis(0))
rho_thermo.setAxis(1, so.getAxis(1))
rho_thermo.setAxis(2, so.getAxis(2))
rho_thermo.name = 'density_mean_thermo'
rho_thermo.units = 'kg m^-3'
cp_thermo = cdm.createVariable(cp_thermo, id='cp_thermo')
cp_thermo.setAxis(0, so.getAxis(0))
cp_thermo.setAxis(1, so.getAxis(1))
cp_thermo.setAxis(2, so.getAxis(2))
cp_thermo.name = 'heat_capacity_mean_thermo'
cp_thermo.units = 'J kg^-1 C^-1'
steric_height = cdm.createVariable(steric_height, id='steric_height')
steric_height.setAxis(0, so.getAxis(0))
steric_height.setAxis(1, so.getAxis(1))
steric_height.setAxis(2, so.getAxis(2))
steric_height.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_anom = cdm.createVariable(steric_height_anom,
id='steric_height_anom')
steric_height_anom.setAxis(0, so.getAxis(0))
steric_height_anom.setAxis(1, so.getAxis(1))
steric_height_anom.setAxis(2, so.getAxis(2))
steric_height_anom.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_thermo_anom = cdm.createVariable(
steric_height_thermo_anom, id='steric_height_thermo_anom')
steric_height_thermo_anom.setAxis(0, so.getAxis(0))
steric_height_thermo_anom.setAxis(1, so.getAxis(1))
steric_height_thermo_anom.setAxis(2, so.getAxis(2))
steric_height_thermo_anom.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_halo_anom = cdm.createVariable(steric_height_halo_anom,
id='steric_height_halo_anom')
steric_height_halo_anom.setAxis(0, so.getAxis(0))
steric_height_halo_anom.setAxis(1, so.getAxis(1))
steric_height_halo_anom.setAxis(2, so.getAxis(2))
steric_height_halo_anom.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_halo_anom2 = cdm.createVariable(
steric_height_halo_anom2, id='steric_height_halo_anom2')
steric_height_halo_anom2.setAxis(0, so.getAxis(0))
steric_height_halo_anom2.setAxis(1, so.getAxis(1))
steric_height_halo_anom2.setAxis(2, so.getAxis(2))
steric_height_halo_anom2.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
heat_content = cdm.createVariable(heat_content, id='heat_content')
heat_content.setAxis(0, so.getAxis(0))
heat_content.setAxis(1, so.getAxis(1))
heat_content.setAxis(2, so.getAxis(2))
heat_content.units = 'J'
heat_content_sanom = cdm.createVariable(heat_content_sanom,
id='heat_content_sanom')
heat_content_sanom.setAxis(0, so.getAxis(0))
heat_content_sanom.setAxis(1, so.getAxis(1))
heat_content_sanom.setAxis(2, so.getAxis(2))
heat_content_sanom.units = 'J'
heat_content_tanom = cdm.createVariable(heat_content_tanom,
id='heat_content_tanom')
heat_content_tanom.setAxis(0, so.getAxis(0))
heat_content_tanom.setAxis(1, so.getAxis(1))
heat_content_tanom.setAxis(2, so.getAxis(2))
heat_content_tanom.units = 'J'
heat_content_tsanom = cdm.createVariable(heat_content_tsanom,
id='heat_content_tsanom')
heat_content_tsanom.setAxis(0, so.getAxis(0))
heat_content_tsanom.setAxis(1, so.getAxis(1))
heat_content_tsanom.setAxis(2, so.getAxis(2))
heat_content_tsanom.units = 'J'
# Create model-based depth index for subset target levels
newdepth = np.array([
5, 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 300, 500, 700, 1000,
1500, 1800, 2000
]).astype('f')
newdepth_bounds = np.array([[0, 5], [5, 10], [10, 20], [20, 30], [30, 40],
[40, 50], [50, 75], [75, 100], [100, 125],
[125, 150], [150, 200], [200, 300], [300, 500],
[500, 700], [700, 1000], [1000, 1500],
[1500, 1800], [1800, 2000]]).astype('f')
#newdepth = np.array([200,300,500,700,1000,1500,1800,2000]).astype('f');
#newdepth_bounds = np.array([[0,200],[200,300],[300,500],[500,700],[700,1000],[1000,1500],[1500,1800],[1800,2000]]).astype('f')
# Interpolate to depths
so_depthInterp = cdu.linearInterpolation(so,
pressure_levels,
levels=newdepth)
temp_depthInterp = cdu.linearInterpolation(temp,
pressure_levels,
levels=newdepth)
steric_height_depthInterp = cdu.linearInterpolation(steric_height,
pressure_levels,
levels=newdepth)
steric_height_anom_depthInterp = cdu.linearInterpolation(
steric_height_anom, pressure_levels, levels=newdepth)
steric_height_thermo_anom_depthInterp = cdu.linearInterpolation(
steric_height_thermo_anom, pressure_levels, levels=newdepth)
steric_height_halo_anom_depthInterp = cdu.linearInterpolation(
steric_height_halo_anom, pressure_levels, levels=newdepth)
steric_height_halo_anom2_depthInterp = cdu.linearInterpolation(
steric_height_halo_anom2, pressure_levels, levels=newdepth)
heat_content_sanom_depthInterp = cdu.linearInterpolation(
heat_content_sanom, pressure_levels, levels=newdepth)
heat_content_tanom_depthInterp = cdu.linearInterpolation(
heat_content_tanom, pressure_levels, levels=newdepth)
heat_content_tsanom_depthInterp = cdu.linearInterpolation(
heat_content_tanom, pressure_levels, levels=newdepth)
# Fix masks - applied before cdms variables are created otherwise names/ids/attributes are reset
temp_depthInterp = scrubNaNAndMask(temp_depthInterp, so_depthInterp)
steric_height_depthInterp = scrubNaNAndMask(steric_height_depthInterp,
so_depthInterp)
steric_height_anom_depthInterp = scrubNaNAndMask(
steric_height_anom_depthInterp, so_depthInterp)
steric_height_thermo_anom_depthInterp = scrubNaNAndMask(
steric_height_thermo_anom_depthInterp, so_depthInterp)
steric_height_halo_anom_depthInterp = scrubNaNAndMask(
steric_height_halo_anom_depthInterp, so_depthInterp)
steric_height_halo_anom2_depthInterp = scrubNaNAndMask(
steric_height_halo_anom2_depthInterp, so_depthInterp)
heat_content_sanom_depthInterp = scrubNaNAndMask(
heat_content_sanom_depthInterp, so_depthInterp)
heat_content_tanom_depthInterp = scrubNaNAndMask(
heat_content_tanom_depthInterp, so_depthInterp)
heat_content_tsanom_depthInterp = scrubNaNAndMask(
heat_content_tsanom_depthInterp, so_depthInterp)
# Fix bounds
newdepth = so_depthInterp.getAxis(0)
newdepth.setBounds(newdepth_bounds)
del (newdepth_bounds)
newdepth.id = 'depth2'
newdepth.units_long = 'decibar (pressure)'
newdepth.positive = 'down'
newdepth.long_name = 'sea_water_pressure'
newdepth.standard_name = 'sea_water_pressure'
newdepth.units = 'decibar'
newdepth.axis = 'Z'
# Assign corrected bounds
so_depthInterp.setAxis(0, newdepth)
temp_depthInterp.setAxis(0, newdepth)
steric_height_depthInterp.setAxis(0, newdepth)
steric_height_anom_depthInterp.setAxis(0, newdepth)
steric_height_thermo_anom_depthInterp.setAxis(0, newdepth)
steric_height_halo_anom_depthInterp.setAxis(0, newdepth)
steric_height_halo_anom2_depthInterp.setAxis(0, newdepth)
heat_content_sanom_depthInterp.setAxis(0, newdepth)
heat_content_tanom_depthInterp.setAxis(0, newdepth)
heat_content_tsanom_depthInterp.setAxis(0, newdepth)
# Average/integrate to surface - configure bounds
# Preallocate arrays
so_depthAve = np.ma.zeros([len(newdepth), shape(so)[1], shape(so)[2]])
temp_depthAve = so_depthAve.copy()
heat_content_sanom_depthInteg = so_depthAve.copy()
heat_content_tanom_depthInteg = so_depthAve.copy()
heat_content_tsanom_depthInteg = so_depthAve.copy()
for count, depth in enumerate(newdepth):
tmp = cdu.averager(so_depthInterp[0:(count + 1), ...],
axis=0,
weights='weighted',
action='average')
so_depthAve[count, ] = tmp
tmp = cdu.averager(temp_depthInterp[0:(count + 1), ...],
axis=0,
weights='weighted',
action='average')
temp_depthAve[count, ] = tmp
tmp = cdu.averager(heat_content_sanom_depthInterp[0:(count + 1), ...],
axis=0,
weights='weighted',
action='sum')
heat_content_sanom_depthInteg[count, ] = tmp
tmp = cdu.averager(heat_content_tanom_depthInterp[0:(count + 1), ...],
axis=0,
weights='weighted',
action='sum')
heat_content_tanom_depthInteg[count, ] = tmp
tmp = cdu.averager(heat_content_tsanom_depthInterp[0:(count + 1), ...],
axis=0,
weights='weighted',
action='sum')
heat_content_tsanom_depthInteg[count, ] = tmp
del (heat_content_tanom_depthInterp, heat_content_tsanom_depthInterp)
gc.collect()
# Fix masks - applied before cdms variables are created otherwise names/ids/attributes are reset
so_depthAve = scrubNaNAndMask(so_depthAve, so_depthInterp)
temp_depthAve = scrubNaNAndMask(temp_depthAve, so_depthInterp)
heat_content_sanom_depthInteg = scrubNaNAndMask(
heat_content_sanom_depthInteg, so_depthInterp)
heat_content_tanom_depthInteg = scrubNaNAndMask(
heat_content_tanom_depthInteg, so_depthInterp)
heat_content_tsanom_depthInteg = scrubNaNAndMask(
heat_content_tsanom_depthInteg, so_depthInterp)
del (so_depthInterp)
# Convert numpy arrays to cdms objects
heat_content_sanom_depthInteg = cdm.createVariable(
heat_content_sanom_depthInteg, id='heat_content_sanom_depthInteg')
heat_content_sanom_depthInteg.id = 'heat_content_sanom_depthInteg'
heat_content_sanom_depthInteg.setAxis(0, newdepth)
heat_content_sanom_depthInteg.setAxis(1, so.getAxis(1))
heat_content_sanom_depthInteg.setAxis(2, so.getAxis(2))
heat_content_sanom_depthInteg.units = 'J'
heat_content_tanom_depthInteg = cdm.createVariable(
heat_content_tanom_depthInteg, id='heat_content_tanom_depthInteg')
heat_content_tanom_depthInteg.id = 'heat_content_tanom_depthInteg'
heat_content_tanom_depthInteg.setAxis(0, newdepth)
heat_content_tanom_depthInteg.setAxis(1, so.getAxis(1))
heat_content_tanom_depthInteg.setAxis(2, so.getAxis(2))
heat_content_tanom_depthInteg.units = 'J'
heat_content_tsanom_depthInteg = cdm.createVariable(
heat_content_tsanom_depthInteg, id='heat_content_tsanom_depthInteg')
heat_content_tsanom_depthInteg.id = 'heat_content_tsanom_depthInteg'
heat_content_tsanom_depthInteg.setAxis(0, newdepth)
heat_content_tsanom_depthInteg.setAxis(1, so.getAxis(1))
heat_content_tsanom_depthInteg.setAxis(2, so.getAxis(2))
heat_content_tsanom_depthInteg.units = 'J'
so_depthAve = cdm.createVariable(so_depthAve, id='so_depthAve')
so_depthAve.id = 'so_depthAve'
so_depthAve.setAxis(0, newdepth)
so_depthAve.setAxis(1, so.getAxis(1))
so_depthAve.setAxis(2, so.getAxis(2))
so_depthAve.units = '1e-3'
temp_depthAve = cdm.createVariable(temp_depthAve, id='temp_depthAve')
temp_depthAve.id = 'temp_depthAve'
temp_depthAve.setAxis(0, newdepth)
temp_depthAve.setAxis(1, so.getAxis(1))
temp_depthAve.setAxis(2, so.getAxis(2))
temp_depthAve.units = 'degrees_C'
steric_height_depthInterp = cdm.createVariable(
steric_height_depthInterp, id='steric_height_depthInterp')
steric_height_depthInterp.setAxis(0, newdepth)
steric_height_depthInterp.setAxis(1, so.getAxis(1))
steric_height_depthInterp.setAxis(2, so.getAxis(2))
steric_height_depthInterp.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_anom_depthInterp = cdm.createVariable(
steric_height_anom_depthInterp, id='steric_height_anom_depthInterp')
steric_height_anom_depthInterp.setAxis(0, newdepth)
steric_height_anom_depthInterp.setAxis(1, so.getAxis(1))
steric_height_anom_depthInterp.setAxis(2, rho.getAxis(2))
steric_height_anom_depthInterp.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_thermo_anom_depthInterp = cdm.createVariable(
steric_height_thermo_anom_depthInterp,
id='steric_height_thermo_anom_depthInterp')
steric_height_thermo_anom_depthInterp.setAxis(0, newdepth)
steric_height_thermo_anom_depthInterp.setAxis(1, so.getAxis(1))
steric_height_thermo_anom_depthInterp.setAxis(2, so.getAxis(2))
steric_height_thermo_anom_depthInterp.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_halo_anom_depthInterp = cdm.createVariable(
steric_height_halo_anom_depthInterp,
id='steric_height_halo_anom_depthInterp')
steric_height_halo_anom_depthInterp.setAxis(0, newdepth)
steric_height_halo_anom_depthInterp.setAxis(1, so.getAxis(1))
steric_height_halo_anom_depthInterp.setAxis(2, so.getAxis(2))
steric_height_halo_anom_depthInterp.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
steric_height_halo_anom2_depthInterp = cdm.createVariable(
steric_height_halo_anom2_depthInterp,
id='steric_height_halo_anom2_depthInterp')
steric_height_halo_anom2_depthInterp.setAxis(0, newdepth)
steric_height_halo_anom2_depthInterp.setAxis(1, so.getAxis(1))
steric_height_halo_anom2_depthInterp.setAxis(2, so.getAxis(2))
steric_height_halo_anom2_depthInterp.units = 'm^3 kg^-1 Pa (dynamic decimeter)'
# Cleanup workspace
del (newdepth)
gc.collect()
# Write variables to file
if os.path.isfile(outFileName):
os.remove(outFileName)
filehandle = cdm.open(outFileName, 'w')
# Global attributes
globalAttWrite(filehandle, options=None)
# Use function to write standard global atts
# Write seawater version
filehandle.seawater_library_version = sw.__version__
# Write makeSteric version
makeStericPath = str(makeSteric.__code__).split(' ')[6]
makeStericPath = replace(replace(makeStericPath, '"', ''), ',', '')
# Clean scraped path
filehandle.makeSteric_version = ' '.join(getGitInfo(makeStericPath)[0:3])
# Master variables
filehandle.write(so.astype('float32'))
filehandle.write(so_chg.astype('float32'))
filehandle.write(so_depthAve.astype('float32'))
filehandle.write(temp.astype('float32'))
filehandle.write(temp_chg.astype('float32'))
filehandle.write(temp_depthAve.astype('float32'))
# Derived variables
filehandle.write(cp.astype('float32'))
filehandle.write(cp_halo.astype('float32'))
filehandle.write(cp_thermo.astype('float32'))
filehandle.write(rho.astype('float32'))
filehandle.write(rho_halo.astype('float32'))
filehandle.write(rho_thermo.astype('float32'))
filehandle.write(heat_content.astype('float32'))
filehandle.write(heat_content_sanom.astype('float32'))
filehandle.write(heat_content_sanom_depthInteg.astype('float32'))
filehandle.write(heat_content_tanom.astype('float32'))
filehandle.write(heat_content_tanom_depthInteg.astype('float32'))
filehandle.write(heat_content_tsanom.astype('float32'))
filehandle.write(heat_content_tsanom_depthInteg.astype('float32'))
filehandle.write(steric_height.astype('float32'))
filehandle.write(steric_height_depthInterp.astype('float32'))
filehandle.write(steric_height_anom.astype('float32'))
filehandle.write(steric_height_anom_depthInterp.astype('float32'))
filehandle.write(steric_height_halo_anom.astype('float32'))
filehandle.write(steric_height_halo_anom2.astype('float32'))
filehandle.write(steric_height_halo_anom_depthInterp.astype('float32'))
filehandle.write(steric_height_halo_anom2_depthInterp.astype('float32'))
filehandle.write(steric_height_thermo_anom.astype('float32'))
filehandle.write(steric_height_thermo_anom_depthInterp.astype('float32'))
filehandle.close()
# Cleanup workspace
del (outFileName)
gc.collect()
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]))