def setUp(self):
kw = dict(comments='#', skiprows=6, delimiter=',')
station_61 = 'Endeavor_Cruise-88_Station-61.csv'
station_64 = 'Endeavor_Cruise-88_Station-64.csv'
st61 = np.loadtxt(os.path.join(rootpath, 'data', station_61), **kw)
st64 = np.loadtxt(os.path.join(rootpath, 'data', station_64), **kw)
latst = 36. + 40.03 / 60., 37. + 39.93 / 60.
lonst = -(70. + 59.59 / 60.), -71.
Sal = np.c_[st61[:, 2], st64[:, 2]]
Temp = np.c_[st61[:, 1], st64[:, 1]]
Pres = np.c_[st61[:, 0], st61[:, 0]]
Gpan = sw.gpan(Sal, Temp, Pres)
self.values = dict(r=np.array([56.4125, 56.3161, 50.6703, 38.1345,
35.0565, 32.9865]) / c3515,
s=np.array([34.5487, 34.7275, 34.8605, 34.6810,
34.5680, 34.5600]),
t=np.array([28.7856, 28.4329, 22.8103, 10.2600,
6.8863, 4.4036]),
p=np.array([10., 50., 125., 250., 600., 1000.]),
pref=np.array([0.0]),
pt=np.array([28.8099, 28.4392, 22.7862, 10.2262,
6.8272, 4.3236]),
rtx=np.array([0.99353194]),
delt=np.array([13.7856]),
rt=np.array([1.32968079, 1.32094651, 1.18368907,
0.89332541, 0.81977076, 0.76703445]),
units='km',
lon=np.array([-30.] * 6),
lat=np.linspace(-22., -21., 6.),
length=np.array([100.] * 6),
latst=latst, lonst=lonst, Sal=Sal, Temp=Temp,
Pres=Pres, Gpan=Gpan
)
def calcs(self, S, T, P):
self['gpan'] = sw.gpan(S, T, P)
self['pt'] = sw.ptmp(S, T, P)
self['psigma0'] = sw.pden(S, T, P, 0) - 1000
self['psigma1'] = sw.pden(S, T, P, 1000) - 1000
self['psigma2'] = sw.pden(S, T, P, 2000) - 1000
return self
def setUp(self):
# TODO: More tests with station data.
kw = dict(comments='#', skiprows=6, delimiter=',')
station_61 = 'Endeavor_Cruise-88_Station-61.csv'
station_64 = 'Endeavor_Cruise-88_Station-64.csv'
st61 = np.loadtxt(os.path.join(rootpath, station_61), **kw)
st64 = np.loadtxt(os.path.join(rootpath, station_64), **kw)
latst = 36. + 40.03 / 60., 37. + 39.93 / 60.
lonst = -(70. + 59.59 / 60.), -71.
Sal = np.c_[st61[:, 2], st64[:, 2]]
Temp = np.c_[st61[:, 1], st64[:, 1]]
Pres = np.c_[st61[:, 0], st61[:, 0]]
Gpan = sw.gpan(Sal, Temp, Pres)
self.values = dict(
r=np.array([56.4125, 56.3161, 50.6703, 38.1345, 35.0565, 32.9865
]) / c3515,
s=np.array([34.5487, 34.7275, 34.8605, 34.6810, 34.5680, 34.5600]),
t=np.array([28.7856, 28.4329, 22.8103, 10.2600, 6.8863, 4.4036]),
p=np.array([10., 50., 125., 250., 600., 1000.]),
pref=np.array([0.0]),
pt=np.array([28.8099, 28.4392, 22.7862, 10.2262, 6.8272, 4.3236]),
rtx=np.array([0.99353194]),
delt=np.array([13.7856]),
rt=np.array([
1.32968079, 1.32094651, 1.18368907, 0.89332541, 0.81977076,
0.76703445
]),
units='km',
lon=np.array([-30.] * 6),
lat=np.linspace(-22., -21., 6.),
length=np.array([100.] * 6),
latst=latst,
lonst=lonst,
Sal=Sal,
Temp=Temp,
Pres=Pres,
Gpan=Gpan)
def test_3D(self):
steric_height_new = sw.gpan(self.s_mean, self.t_mean,
self.depth[..., None, None])
np.testing.assert_array_almost_equal(self.steric_height,
steric_height_new)
def test_1D(self):
steric_height_new = sw.gpan(self.s_mean[:, 0, 0], self.t_mean[:, 0, 0],
self.depth)
np.testing.assert_array_almost_equal(self.steric_height[:, 0, 0],
steric_height_new)
# Using first dimension from the data-set.
s_mean, pt_mean = s_mean_comp[0], pt_mean_comp[0]
def test_array(arr1, arr2):
try:
np.testing.assert_equal(arr1, arr2)
except AssertionError:
return False
return True
if __name__ == '__main__':
# 1D.
steric_height_new = sw.gpan(s_mean[:, 0, 0], t_mean[:, 0, 0], depth)
if test_array(steric_height[:, 0, 0], steric_height_new):
print("1D sw.gpan test passed.")
else:
print("1D sw.gpan test failed.")
# 2D.
steric_height_new = sw.gpan(s_mean[..., 1], t_mean[..., 1], depth[...,
None])
if test_array(steric_height[..., 1], steric_height_new):
print("2D sw.gpan test passed.")
else:
print("2D sw.gpan test failed.")
# 3D.
steric_height_new = sw.gpan(s_mean, t_mean, depth[..., None, None])
depth = mat.get('depths').astype(float).squeeze()
# Using first dimension from the data-set.
s_mean, pt_mean = s_mean_comp[0], pt_mean_comp[0]
def test_array(arr1, arr2):
try:
np.testing.assert_equal(arr1, arr2)
except AssertionError:
return False
return True
if __name__ == '__main__':
# 1D.
steric_height_new = sw.gpan(s_mean[:, 0, 0], t_mean[:, 0, 0], depth)
if test_array(steric_height[:, 0, 0], steric_height_new):
print("1D sw.gpan test passed.")
else:
print("1D sw.gpan test failed.")
# 2D.
steric_height_new = sw.gpan(s_mean[..., 1], t_mean[..., 1],
depth[..., None])
if test_array(steric_height[..., 1], steric_height_new):
print("2D sw.gpan test passed.")
else:
print("2D sw.gpan test failed.")
# 3D.
steric_height_new = sw.gpan(s_mean, t_mean, depth[..., None, None])
def ctdproc(lista,
temp_name='t068C',
lathint='Latitude =',
lonhint='Longitude =',
cond_name='c0S/m',
press_name='prDM',
down_cast=True,
looped=True,
hann_f=False,
hann_block=20,
hann_times=2,
latline=[],
lonline=[]):
'''
This function do the basic proccess to all .cnv CTD data from
given list.
'''
for fname in lista:
lon, lat, data = ctdread(fname,
press_name=press_name,
down_cast=down_cast,
lathint=lathint,
lonhint=lonhint,
lonline=lonline,
latline=latline)
if looped:
data = loopedit(data)
dataname = basename(fname)[1]
if (data.shape[0] < 101) & (
data.shape[0] >
10): # se o tamanho do perfil for com menos de 101 medidas
if (data.shape[0] /
2) % 2 == 0: # caso a metade dos dados seja par
blk = (data.shape[0] / 2) + 1 # bloco = a metade +1
else:
blk = data.shape[0] / 2 # se for impar o bloco e a metade
# remove spikes dos perfis de temperatura e condutividade
data = despike(data, propname=temp_name, block=blk, wgth=2)
data = despike(data, propname=cond_name, block=blk, wgth=2)
elif data.shape[0] >= 101:
# para perfis com mais de 101 medidas, utiliza-se blocos de 101
data = despike(data, propname=temp_name, block=101, wgth=2)
data = despike(data, propname=cond_name, block=101, wgth=2)
else:
print('radial muito rasa')
# realiza média em caixa de 1 metro
data = binning(data, delta=1.)
if temp_name == 't068C':
data['t090C'] = gsw.t90_from_t68(data['t068C'])
data['sp'] = gsw.SP_from_C(data[cond_name] * 10, data['t090C'],
data.index.values)
if hann_f:
times = 0
while times < hann_times:
data = hann_filter(data, 't090C', hann_block)
data = hann_filter(data, 'sp', hann_block)
times += 1
data['pt'] = sw.ptmp(data['sp'], data['t090C'], data.index.values)
#data['ct'] = gsw.CT_from_pt(data['sa'],data['pt'])
data['psigma0'] = sw.pden(
data['sp'], data['t090C'], data.index.values, pr=0) - 1000
data['psigma1'] = sw.pden(
data['sp'], data['t090C'], data.index.values, pr=1000) - 1000
data['psigma2'] = sw.pden(
data['sp'], data['t090C'], data.index.values, pr=2000) - 1000
data['gpan'] = sw.gpan(data['sp'], data['t090C'], data.index.values)
data['lat'] = lat
data['lon'] = lon
data.to_pickle(
os.path.split(fname)[0] + '/' +
os.path.splitext(os.path.split(fname)[1])[0])
print(dataname)
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 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()
if not os.path.exists(path):
raise ValueError("matlab seawater path %s not found" % path)
_ = octave.addpath(octave.genpath(path))
kw = dict(comment='#', header=5, index_col=0)
st61 = read_csv('Endeavor_Cruise-88_Station-61.csv', **kw)
st64 = read_csv('Endeavor_Cruise-88_Station-64.csv', **kw)
latst = 36. + 40.03 / 60., 37. + 39.93 / 60.
lonst = -(70. + 59.59 / 60.), -71.
Sal=np.c_[st61['S'].values, st64['S'].values]
Temp=np.c_[st61['t'].values, st64['t'].values]
Pres=np.c_[st61.index.values.astype(float), st64.index.values.astype(float)]
Gpan = sw.gpan(Sal, Temp, Pres)
def compare_results(name, function, args):
args = [values.get(arg) for arg in args]
try: # Python.
res = function(*args)
except:
print('%s: python runtime error' % name)
raise
return 'no_python'
# FIXME: Testing only the first output when multiple outputs are present.
nout = 1
if isinstance(res, tuple):
nout = len(res)
lasty = Y[i,-1]; yaux = np.linspace(lasty, lasty, rpt)
YAUX[i,:] = yaux
# coordinates:
X = np.hstack((X, XAUX))
Y = np.hstack((Y, YAUX))
# velocity:
# computing geostrophic velocity to EASTERN boundary, to check SEC structure
temp = np.squeeze(TEMP[...,-1])
salt = np.squeeze(SALT[...,-1])
y = Y[:,-1]
y, z = np.meshgrid(y,Z); z = -z
gp = sw.gpan(salt, temp, z)
gp = (gp - gp[-1,:]) * -1 # to reference in the bottom
dgp = np.array(np.gradient(gp))
dgp = np.squeeze(dgp[1,:,:])
dy = np.array(np.gradient(y))
dy = np.squeeze(dy[1,:,:]) * 111000
dgpdy = dgp / dy
usec = -dgpdy / f0
# getting the right transport
usec = usec*0.4
f = np.where(usec > 0); usec[f] = 0
