def getO2(ctd,vvar):
if 'sal' not in ctd:
ctd['sal'] = sw.salt(ctd['c'] * 10. / seawater.constants.c3515,
ctd['t'], ctd['p'])
ctd['O2sat'] = np.real( seawater.extras.satO2(ctd['sal'], ctd['t']))
# from April 2008 calibration
Voff = -0.4877
Soc = 0.4401
Boc = 0.0000
Tcor = 0.0005
Pcor = 1.35e-4
#below numbers are true for CTD casts prior to April 2008
#Voff = -0.4893;
#Soc = 0.4227;
#Boc = 0.0000;
#Tcor = 0.001;
#Pcor = 1.35e-4;
ctd['O2'] = (Soc * (ctd[vvar] + Voff) * np.exp(Tcor * ctd['t']) *
ctd['O2sat'] * np.exp(ctd['p'] * Pcor))
if 'den' not in ctd:
ctd['pden']=sw.dens(ctd['sal'], ctd['t'], ctd['p'])
ctd['O2'] = 1.e6 * ctd['O2']/(ctd['pden'] * 22.3916);
return ctd
def calc_density(temp, salt, levels):
assert len(temp.shape) == 3
assert len(salt.shape) == 3
assert len(levels.shape) == 1
num_levs = levels.shape[0]
lats = salt.shape[1]
lons = salt.shape[2]
depth = np.vstack(([levels] * lats * lons)).T.reshape(num_levs, lats, lons)
# Pressure in dbar
pressure = depth * 0.1
density = eos80.dens(salt, temp, pressure)
return density
def calc_density(S, T, p):
args = S, T, p
prho = sw.pden(*args)
rho = sw.dens(*args)
return prho, rho
def loadMVP_raw(file,depthbins):
import numpy as np
from matplotlib.dates import date2num
import datetime
import seawater.eos80 as sw
import numpy.lib.recfunctions as rec
#import time
import scipy.signal as signal
#import bindata
#from binMVPdata import binMVPdata
import binMVPdata
import calendar
# try:
if 1:
f = open(file, 'r')
# print ("Processing " + file)
header = f.read(7000) #read in 7000 bytes to get the header
profile_xyt = {}
i = header.find('LAT ( ddmm.mmmmmmm,N):')
profile_xyt['lat_DDMMmm'] = header[i+23:i+36]
profile_xyt['lat_hemisphere'] = header[i+37:i+38]
profile_xyt['latitude'] = (float(profile_xyt['lat_DDMMmm'][1:3])) +(np.divide((float(profile_xyt['lat_DDMMmm'][3:])),60))
if (profile_xyt['lat_hemisphere']=='S'):
profile_xyt['latitude']=-profile_xyt['latitude']
i = header.find('LON (dddmm.mmmmmmm,E):')
profile_xyt['lon_DDMMmm'] = header[i+23:i+36]
profile_xyt['lon_hemisphere'] = header[i+37:i+38]
profile_xyt['longitude'] = (float(profile_xyt['lon_DDMMmm'][1:3])) +(np.divide((float(profile_xyt['lon_DDMMmm'][3:])),60))
if profile_xyt['lon_hemisphere']=='W':
profile_xyt['longitude'] =-profile_xyt['longitude']
i = header.find('Time (hh|mm|ss.s):')
profile_xyt['time'] = header[i+19:i+29]
i = header.find('Date (dd/mm/yyyy):')
profile_xyt['date'] = header[i+19:i+29]
datetimestring = profile_xyt['date']+' '+profile_xyt['time']
datetimefmt = ('%d/%m/%Y %H:%M:%S.%f')
#print datetimestring[:-5]
try:
profile_xyt['datetime'] = datetime.datetime.strptime(datetimestring, datetimefmt)
except:
datetimefmt = ('%d/%m/%Y %H:%M')
profile_xyt['datetime'] = datetime.datetime.strptime(datetimestring[:-5], datetimefmt)
profile_xyt['unixtime'] = calendar.timegm(profile_xyt['datetime'].utctimetuple())
profile_xyt['mtime'] = date2num(profile_xyt['datetime'])
profile_xyt['matlabtime'] = date2num(profile_xyt['datetime'])+366.
profile_xyt['tzone'] = 'Z'
i = header.find('Index: ')
profile_xyt['cast_number'] = int(header[i+7:i+11])
# profile_xyt['datetime']=date2num(profile_xyt['datetime'])
#
i = header.find('Bottom Depth (m):')
profile_xyt['bottom']=float(header[i+17:i+22])
# print profile_xyt['bottom']
newline1 = header.find('\n',6000,len(header))
newline2 = header.find('\n',newline1+1,len(header))
rawfields = ["pressure","cond","temp","analog"]
if newline2 - newline1 > 26:
data = np.genfromtxt(file, usecols = (0,1,2,3), skip_header = 61,
names = ["pressure","cond","temp","analog"])
else:
data = np.genfromtxt(file, usecols = (0,1,2), skip_header = 61,
names = ["pressure","cond","temp"] )
temparray = np.empty((data['pressure'].shape))
temparray.fill(np.NaN)
data = rec.append_fields(data,'analog',temparray,dtypes = float)
# lag conductivity to match w. temp cell
N = np.shape(data['cond'])[0]
#print N
data = rec.append_fields(data,'cond0',data['cond'],dtypes = float)
tt=np.arange(0,N,1.)
data['cond']=np.interp(tt-2.25,tt,data['cond0'])
#this loop here will find the indices for when the fish is going down
pressurederivative = np.diff(data['pressure']) # take the derivative of the pressure profile
B, A = signal.butter(2, 0.01, output='ba') # constants for the filter
smooth_pressurederivative = signal.filtfilt(B,A, pressurederivative) #filter the pressure derivative, to smooth out the curve
smooth_pressurederivative = np.append(smooth_pressurederivative,[0]) # make the arrays the same size
down_inds = np.where(smooth_pressurederivative>0.05) # find the indices where the descent rate is more than 0.1
#depth_bins = np.arange(0,300,1)
profile_grid = {}
for k in rawfields:
profile_grid[k] = binMVPdata.binMVPdata(depthbins,
data['pressure'][down_inds],data[k][down_inds])
profile_grid['salinity'] = sw.salt(profile_grid['cond']/42.9140,
profile_grid['temp'], profile_grid['pressure'])
profile_grid['density'] = sw.dens(profile_grid['salinity'],
profile_grid['temp'],profile_grid['pressure'])
profile_grid['pden'] = sw.pden(profile_grid['salinity'],
profile_grid['temp'],profile_grid['pressure'],0)
f.close()
return profile_grid, profile_xyt, data
timest = l[11:28]
ctd['time'] = datetime.strptime(timest, '%d %b %Y %H:%M')
ctd['matlabtime'] = ctdtrans.datetime2matlab(ctd['time'])
except:
logging.info(basename)
pass
#
# get salt and pden...
ctd['c0'] = ctd['c']
N = len(ctd['c'])
ctd['coffset'] = coffset
ctd['c'] = np.interp(np.arange(N)-coffset, np.arange(N), ctd['c0'])
ctd['sal'] = sw.salt(ctd['c'] * 10. / seawater.constants.c3515,
ctd['t'], ctd['p'])
ctd['pden']=sw.dens(ctd['sal'], ctd['t'], ctd['p'])
ctd = ctdtrans.getO2(ctd,'v1');
ctd = ctdtrans.getFlu(ctd,'v2');
ctd = ctdtrans.getPar(ctd,'v3');
alongx, acrossx = getInletX.getInletX(ctd['lon'], ctd['lat'])
ctd['alongx'] = alongx
ctd['acrossx'] = acrossx
ds = xr.Dataset(
{
'cond': (['scan'], ctd['c'], {'units':'S/m', 'offset': '{} scans'.format(coffset)}),
'cond0': (['scan'], ctd['c0'], {'units':'S/m'}),
'temp': (['scan'],ctd['t'], {'units':'deg C'}),
def convert_to_mll(o2, s, t, p):
'''Convert dissolved oxygen concentration from um/kg to ml/l.
'''
return sw.dens(s, t, p) * o2 / 44.66 / 1000.0
def convert_to_mll(o2, s, t, p):
'''Convert dissolved oxygen concentration from um/kg to ml/l.
'''
return sw.dens(s, t, p) * o2 / 44.66 / 1000.0
