def pw13spice(sal,temp,pden):
beta = 7.7e-4*1000
alpha = 2.0e-4*1000
mt=reshape(mt,shape(temp))
tau=2*(temp-mt)*0.2
Sline=np.array([33.2,33.8])
Tline=np.array([7.75,6.6])
Sarray = np.arange(30,35,0.01)
m = (np.diff(Tline)/np.diff(Sline))
Tarray = m*(Sarray-Sline[0])+Tline[0]
Pdarray=sw.pden(Sarray,Tarray,100.+0.*Tarray,0.)
T0 = np.interp(pden,sp['pdengrid'][:-1],sp['meanT'][0])
tau = 2*alpha*(temp-T0)
return tau
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
def recalc_S_and_sigma(C, T, p):
S = salt(C/c3515, T, p)
sigma = pden(S, T, p) - 1000
return S, sigma
def den_1file(filename_tem, filename_sal, savename, temname,
salname, depname, denname, latname, lonname,
pressure_lvl, mlat, timname):
"""
Computes the density profile from a given file.
Check the documentation of den_main for more information.
"""
#############
# LOAD DATA #
#############
if filename_tem == filename_sal:
# Launch reading routine for 1 file
[tem, sal], lats, lons, tim, deps = load_main(filename_tem,
[temname, salname],
latname, lonname,
depname, timname)
else:
# Launch reading routine for 2 different files
[tem], _, _, _, _ = load_main(filename_tem, [temname],
latname, lonname,
depname, timname)
[sal], lats, lons, tim, deps = load_main(filename_sal, [salname],
latname, lonname,
depname, timname)
pdens = np.empty_like(sal)
dim = lats.shape
index = np.logical_and(sal == 0, tem == 0)
sal[index], tem[index] = np.nan, np.nan
######################
# GET DENSITY VALUES #
######################
# Depth variable is already a pressure level
if pressure_lvl:
for j in range(dim[0]):
for i in range(dim[1]):
for t in range(sal.shape[0]):
pdens[t, :, j, i] = pden(sal[t, :, j, i],
tem[t, :, j, i],
deps)
# Compute the pressure levels with a mean latitude value
elif mlat is not None:
pre = pres(deps, mlat)
for j in range(dim[0]):
for i in range(dim[1]):
for t in range(sal.shape[0]):
pdens[t, :, j, i] = pden(sal[t, :, j, i],
tem[t, :, j, i],
pre)
del pre
# Compute pressure levels with all the latitudes
else:
for j in range(dim[0]):
for i in range(dim[1]):
pre = pres(deps, lats[j, i])
for t in range(sal.shape[0]):
pdens[t, :, j, i] = pden(sal[t, :, j, i],
tem[t, :, j, i],
pre)
#############
# SAVE DATA #
#############
ds = {lonname: (('y', 'x'), lons),
latname: (('y', 'x'), lats),
depname: (('z'), deps),
denname: (('t', 'z', 'y', 'x'), pdens)}
if timname is not None:
ds[timname] = (('t'), tim)
ds = xr.Dataset(ds)
ds.to_netcdf(savename+'.nc')
endtime = mdates.num2date(grid_full['mtime'][-1]).strftime('%H:%M:%S')
#endtime = (datetime.datetime.fromtimestamp(grid_full['unixtime'][0,-1])).strftime('%Y-%m-%d %H:%M:%S')
startendstring = ('Cast '+ str(int(grid_full['cast_number'][0])) +
' at ' + starttime +' \nCast '+ str(int(grid_full['cast_number'][-1])) + ' at ' + endtime)
ax[0].set_title(startendstring,fontsize = 12)
### TS plot
plt.subplot(gs3b3[0:2,2])
plt.plot(grid_full['salinity'][:,-ncasts:],grid_full['temp'][:,-ncasts:],'k.',markersize=1)
plt.plot(grid_full['salinity'][:,-2],grid_full['temp'][:,-2],'m.')
plt.plot(grid_full['salinity'][:,-1],grid_full['temp'][:,-1],'r.')
ss=np.linspace(np.nanmin(grid_full['salinity']),np.nanmax(grid_full['salinity']),50 )
tt=np.linspace(np.nanmin(grid_full['temp']),np.nanmax(grid_full['temp']),50 )
SS,TT=np.meshgrid(ss,tt)
PDen=sw.pden(SS,TT,0.*TT,0)
cs=plt.contour(ss,tt,PDen-1000.,density_contours-1000.,colors='k')
plt.clabel(cs,fmt='%1.1f')
plt.xlabel('S [psu]')
plt.ylabel('T [C]')
plt.xlim(clim3[1])
plt.ylim(clim3[0])
#plt.draw()
### Spice plot
grid_full['spice']=dc15bspice(grid_full['salinity'],grid_full['temp'],grid_full['pden'])
aa=ax.append(plt.subplot(gs3b3[3,0:2]))
plt.gca().set_axis_bgcolor('0.6')
clim=[-.5,0.5]
pc=contourZ(X,Y,np.ma.masked_where(np.isnan(grid_full['spice']),
