import numpy as np, matplotlib.pyplot as plt, xray, gsw
#1
woa = xray.open_dataset(
'http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/'
'.Grid-1x1/.Annual/dods')
p0 = -10.1325 #surface pressure (dbars)
pr = 0
S,T,Lat,Lon,dep = xray.broadcast_arrays\
(woa['salinity.s_an'][0],woa['temperature.t_an'][0],woa.lat*1,woa.lon*1,woa.depth*1)
p = -1 * gsw.p_from_z(dep, Lat)
SA = gsw.SA_from_SP(S, p, Lon, Lat)
TC = gsw.CT_from_t(SA, T, p)
rho = gsw.rho_CT_exact(SA, TC, pr)
rhoArr = xray.DataArray(rho,
dims=['depth', 'lat', 'lon'],
coords=[woa.depth, woa.lat, woa.lon])
rhoArr1 = rhoArr.sel(lat=slice(30, 50), lon=slice(170.5, 170.5))
rhoArr2 = rhoArr.sel(lat=slice(-72, -55), lon=slice(170.5, 170.5))
plt.figure(figsize=(7, 7))
plt.contourf(rhoArr1.lat,
rhoArr1.depth,
rhoArr1.squeeze(dim=None),
cmap='ocean')
plt.title('Potential Density of Kuroshio Extension')
plt.xlabel('lat')
plt.ylabel('depth(m)')
def derive(self,property):
"""
Derives seawater properties as salinity, buoyancy frequency squared etc.
Args:
ST:
N2:
"""
try:
gsw
except:
logger.warning('GSW toolbox missing, derive will not work, doing nothing')
return
if(property == 'ST'):
# Poor mans check if the variables exist
sensor_pair0 = False
sensor_pair1 = False
try:
tmp1 = self.data['cond0']
tmp1 = self.data['temp0']
tmp1 = self.data['p']
sensor_pair0 = True
except:
sensor_pair0 = False
try:
tmp1 = self.data['cond1']
tmp1 = self.data['temp1']
tmp1 = self.data['p']
sensor_pair1 = True
except:
sensor_pair1 = False
if(sensor_pair0):
logger.debug('Calculating PSU0/SA11/CT11/rho11')
SP = gsw.SP_from_C(self.data['cond0'],self.data['temp0'],self.data['p'])
self.derived['SP00'] = SP
SA = gsw.SA_from_SP(SP,self.data['p'],self.header['lon'],self.header['lat'])
self.derived['SA00'] = SA
CT = gsw.CT_from_t(SA,self.data['temp0'],self.data['p'])
self.derived['CT00'] = CT
rho = gsw.rho_CT_exact(SA,CT,self.data['p'])
self.derived['rho00'] = rho
if(sensor_pair1):
logger.debug('Calculating PSU1/SA11/CT11/rho11')
SP = gsw.SP_from_C(self.data['cond1'],self.data['temp1'],self.data['p'])
self.derived['SP11'] = SP
SA = gsw.SA_from_SP(SP,self.data['p'],self.header['lon'],self.header['lat'])
self.derived['SA11'] = SA
CT = gsw.CT_from_t(SA,self.data['temp1'],self.data['p'])
self.derived['CT11'] = CT
rho = gsw.rho_CT_exact(SA,CT,self.data['p'])
self.derived['rho11'] = rho
if(property == 'N2'):
# Poor mans check if the variables exist
sensor_pair0 = False
sensor_pair1 = False
try:
tmp1 = self.derived['SA00']
tmp1 = self.derived['CT00']
tmp1 = self.data['p']
sensor_pair0 = True
except:
logger.info('Did not find absolute salinities and temperature, do first a .derive("ST")')
sensor_pair0 = False
try:
tmp1 = self.derived['SA11']
tmp1 = self.derived['CT11']
tmp1 = self.data['p']
sensor_pair1 = True
except:
logger.info('Did not find absolute salinities and temperature, do first a .derive("ST")')
sensor_pair1 = False
if(sensor_pair0):
logger.debug('Calculating Nsquared00')
[N2,p_mid] = gsw.Nsquared(self.derived['SA00'],self.derived['CT00'],self.data['p'])
self.derived['Nsquared00'] = interp(self.data['p'],p_mid,N2)
if(sensor_pair1):
logger.debug('Calculating Nsquared11')
[N2,p_mid] = gsw.Nsquared(self.derived['SA11'],self.derived['CT11'],self.data['p'])
self.derived['Nsquared11'] = interp(self.data['p'],p_mid,N2)
if(property == 'alphabeta'):
# Poor mans check if the variables exist
sensor_pair0 = False
sensor_pair1 = False
try:
tmp1 = self.derived['SA00']
tmp1 = self.derived['CT00']
tmp1 = self.data['p']
sensor_pair0 = True
except:
logger.info('Did not find absolute salinities and temperature, do first a .derive("ST")')
sensor_pair0 = False
try:
tmp1 = self.derived['SA11']
tmp1 = self.derived['CT11']
tmp1 = self.data['p']
sensor_pair1 = True
except:
logger.info('Did not find absolute salinities and temperature, do first a .derive("ST")')
sensor_pair1 = False
if(sensor_pair0):
logger.debug('Calculating Nsquared00')
alpha = gsw.alpha(self.derived['SA00'],self.derived['CT00'],self.data['p'])
beta = gsw.beta(self.derived['SA00'],self.derived['CT00'],self.data['p'])
self.derived['alpha00'] = alpha
self.derived['beta00'] = beta
if(sensor_pair1):
logger.debug('Calculating Nsquared11')
alpha = gsw.alpha(self.derived['SA11'],self.derived['CT11'],self.data['p'])
beta = gsw.beta(self.derived['SA11'],self.derived['CT11'],self.data['p'])
self.derived['alpha11'] = alpha
self.derived['beta11'] = beta
# P (Pressure, dbar))
p1 = 0
p2 = 20
pr = 0 #reference pressure at the surface
Sa1=sw.SA_from_SP(S1,p1,lon,lat)
Sa2=sw.SA_from_SP(S2,p2,lon,lat)
Tc1 = sw.CT_from_t(Sa1,T1,p1)
Tc2 = sw.CT_from_t(Sa2,T2,p2)
# Assess the stability of the water column by comparing the densities of the two
# water masses referenced to the same pressue (i.e. use potential density).
rho1 = sw.rho_CT_exact(Sa1,Tc1,pr)
rho2 = sw.rho_CT_exact(Sa2,Tc2,pr)
# Is the water column stably stratified in this region?
print"\n\nThe water mass from the first measurement at the surface has a density of",round(rho1,SF),\
"which is",round(rho1-rho2,SF)*(-1),"less dense than water mass below ("+str(round(rho2,SF))+")...\nCreating stable stratification."
#Now imagine that ocean circulation transports the same two water masses to pressures
#of 4990 dbar and 5010 dbar respectively. (One is still approx 20 m deeper than the other.)
p3 = 4990
p4 = 5010
pr2 = 5000
Sa1=sw.SA_from_SP(S1,p3,lon,lat)
Sa1=sw.SA_from_SP(S2,p4,lon,lat)
Tc1 = sw.CT_from_t(Sa1,T1,p3)
import numpy as np, matplotlib.pyplot as plt, xray, gsw
#1
woa = xray.open_dataset('http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/'
'.Grid-1x1/.Annual/dods')
p0 = -10.1325 #surface pressure (dbars)
pr = 0
S,T,Lat,Lon,dep = xray.broadcast_arrays\
(woa['salinity.s_an'][0],woa['temperature.t_an'][0],woa.lat*1,woa.lon*1,woa.depth*1)
p = -1*gsw.p_from_z(dep,Lat)
SA = gsw.SA_from_SP(S,p,Lon,Lat)
TC = gsw.CT_from_t(SA,T,p)
rho= gsw.rho_CT_exact(SA,TC,pr)
rhoArr = xray.DataArray(rho,dims=['depth', 'lat','lon'],coords=[woa.depth,woa.lat,woa.lon])
rhoArr1 = rhoArr.sel(lat=slice(30, 50),lon=slice(170.5,170.5))
rhoArr2 = rhoArr.sel(lat=slice(-72, -55),lon=slice(170.5,170.5))
plt.figure(figsize=(7,7))
plt.contourf(rhoArr1.lat,rhoArr1.depth,rhoArr1.squeeze(dim=None),cmap='ocean')
plt.title('Potential Density of Kuroshio Extension')
plt.xlabel('lat')
plt.ylabel('depth(m)')
plt.ylim(0,3000)
cbar = plt.colorbar(orientation='vertical',fraction=0.046, pad=0.04)
cbar.ax.set_xlabel('rho kg m^-3')
plt.show()
def GetData(PathHav, station):
"""
GetData
aquire ocean data for each station
GetData enters each station file, gets relevant data,
calculates density from the GSW package and returns
depth, pressure, temperature, salinity, fluorescence, oxygen and density
The data is aguired from two files, since the oxygen data is in a separate file.
The relevant data is picked (num and qual are ignored).
Input: PathHav, station
Output: Data
"""
# prepare variables and create paths
StNum, lon, lat = station.name, station.Lon, station.Lat
StPath1 = PathHav + '{}.raw'.format(StNum)
StPath2 = PathHav + 'Ox{}.raw'.format(StNum)
#GET DATA
# get complicated data from txt with genfromtxt and make DataFrame
dfDat = pd.DataFrame(
np.genfromtxt(StPath1,
skip_header=9,
delimiter=(5, 8, 8, 8, 8, 6, 5),
missing_values=' ',
names=('depth', 'pres', 'temp', 'sal', 'flu', 'num',
'qual')))
# get ox data
dfOx = pd.DataFrame(
np.genfromtxt(StPath2,
skip_header=9,
delimiter=(5, 8, 8, 6, 4),
missing_values=' ',
names=('depth', 'pres', 'ox', 'num', 'qual')))
# pick out relevant data
D = pd.concat([dfDat.loc[:, 'depth':'flu'], dfOx['ox']], axis=1)
D = D[pd.notnull(D.depth)] #dropping faulty rows where depth = NaN
#CALCULATE DENSITY from gibbs sea water
SA = gsw.SA_from_SP(D.sal, D.pres, lon,
lat) # gsw.SA_from_SP(SP,p,long,lat)
CT = gsw.CT_from_t(SA, D.temp, D.pres) # gsw.CT_from_t(SA,t,p)
p_ref = 0
dens = gsw.rho_CT_exact(
SA, CT, p_ref) - 1000 # sigma0 = rho_CT_exact(SA, CT, 0) - 1000
# SP : salinity (PSS-78) [unitless],
# p : pressure [dbar]
# lon : decimal degrees east [0..+360] or [-180..+180]
# lat : decimal degrees (+ve N, -ve S) [-90..+90]
# SA : Absolute salinity [g/kg ]
# t : in situ temperature degC
GSW = pd.DataFrame({'CT': CT, 'SA': SA, 'dens': dens})
# Create Data frame
Data = pd.concat([D, GSW], axis=1)
Data = Data.set_index('depth', drop=False)
return Data