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dive_processing_and_velocity_estimation.py
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dive_processing_and_velocity_estimation.py
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# I would like to create a project/tool/module
# Once imported, this tool will ask for glider (.nc) file location and dives to consider
# outputs will be a cross-section plot of the glider dive-climb profiles with density contours and DAC referenced
# absolute geostrophic velocity
# Class objects support two kinds of operations (attribute references and instantiation)
# Attribute references (Glider.name etc)
# Instantiation (x = Glider())
# after instantiation you can define data attributes that are previously undeclared (x.test = 200)
# after using an attribute you can use del x.test and it will disappear
#
# after instantiation you can define a method (the special thing about methods is that the object is passed as the
# first argument of the function) ... if x = Glider(35, np.array([30])) then x.f() is the same as Glider.f(x)
#
# generally, instance variables are for unique data and class variables are for attributes and methods shared by all
# instances of the class
import os
import numpy as np
import fnmatch
import datetime
from netCDF4 import Dataset
import gsw
from scipy.integrate import cumtrapz
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1 import make_axes_locatable
from tools import find_nearest, cart2pol, pol2cart, nanseg_interp, group_consecutives, plot_pro
class Glider(object):
def __init__(self, glider_no, d_num, path):
self.ID = 'SG0' + str(glider_no)
self.dives = d_num
self.path = path
result = []
for this_dive in d_num:
pattern = '*0' + str(this_dive) + '.nc'
for root, dirs, files in os.walk(path):
for name in files:
if fnmatch.fnmatch(name, pattern):
result.append(os.path.join(root, name))
self.files = result
self.project = Dataset(self.files[0], 'r').project
self.num_profs = 2 * np.size(d_num)
self.rho0 = 1025
self.g = 9.812
dep = np.nan * np.ones(len(self.files))
target = np.nan * np.ones((len(self.files), 2))
for m in range(len(self.files)):
gd = Dataset(self.files[m], 'r')
dep[m] = np.nanmax(gd['ctd_depth'][:])
tgts = gd['log_TGT_LATLONG'][:]
target[m, 0] = 10*np.int(tgts[0]) + 1*np.int(tgts[1]) + (1/60)*(10*np.int(tgts[2]) + 1*np.int(tgts[3])) + \
(1/100)*(1/60)*(10*np.int(tgts[5]) + 1*np.int(tgts[6]))
target[m, 1] = 10*np.int(tgts[10]) + 1*np.int(tgts[11]) + (1/60)*(10*np.int(tgts[12]) +
1*np.int(tgts[13])) + \
(1/100)*(1/60)*(10*np.int(tgts[15]) + 1*np.int(tgts[16]))
self.avg_dep = np.nanmean(dep)
self.target = target
name = 'clementine' # this is an attribute of GLIDER
@staticmethod
def f(): # this is an attribute of GLIDER
return 'I am buoyancy driven'
# Glider Processing Functions
# Bin-Average Profiles in on a specified grid
def make_bin(self, bin_depth):
t_out = []
s_out = []
o2_out = []
lon_out = []
lat_out = []
time_out = []
dac_u_out = []
dac_v_out = []
profile_tags = []
for m in self.files:
gd = Dataset(m, 'r')
temp_d = []
sal_d = []
depth_d = []
lon_d = []
lat_d = []
time_d = []
press = gd['pressure'][:]
pitch = gd['eng_pitchAng'][:]
pitch_d = np.where((pitch < 1) & (np.abs(press - np.nanmax(press)) < 25))[0][-1]
pitch_c = np.where((pitch > 1) & (np.abs(press - np.nanmax(press)) < 25))[0][0]
temp_d.append(gd['temperature'][0:pitch_d])
temp_d.append(gd['temperature'][pitch_c:])
if 'aanderaa4330_instrument_dissolved_oxygen' in gd.variables:
o2_d = []
o2_inter = gd['aanderaa4330_instrument_dissolved_oxygen'][:]
o2_d.append(o2_inter.data[0:pitch_d])
o2_d.append(o2_inter.data[pitch_c:])
# exceptions
if (gd.glider == 30) & (gd.dive_number == 60):
sal_d.append(gd['salinity_raw'][0:pitch_d])
sal_d.append(gd['salinity'][pitch_c:])
else:
sal_d.append(gd['salinity'][0:pitch_d])
sal_d.append(gd['salinity'][pitch_c:])
depth_d.append(-1*gsw.z_from_p(press[0:pitch_d], np.nanmean(gd['latitude'][:])))
depth_d.append(-1*gsw.z_from_p(press[pitch_c:], np.nanmean(gd['latitude'][:])))
secs_per_day = 86400.0
datenum_start = 719163 # jan 1 1970
start_time = gd.start_time
time_ctd = gd['ctd_time']
time_from = time_ctd - start_time
time_corr = datenum_start + start_time / (60 * 60 * 24) + time_from / secs_per_day
time_d.append(time_corr[0:pitch_d])
time_d.append(time_corr[pitch_c:])
lon_d.append(gd['longitude'][0:pitch_d])
lon_d.append(gd['longitude'][pitch_c:])
lat_d.append(gd['latitude'][0:pitch_d])
lat_d.append(gd['latitude'][pitch_c:])
dac_u_out.append(gd['depth_avg_curr_east'][:])
dac_u_out.append(gd['depth_avg_curr_east'][:])
dac_v_out.append(gd['depth_avg_curr_north'][:])
dac_v_out.append(gd['depth_avg_curr_north'][:])
profile_tags.append(np.float(gd['log_DIVE'][:]))
profile_tags.append(np.float(gd['log_DIVE'][:]) + np.float(0.5))
# max depth attained
d_test = -1*gsw.z_from_p(press, np.nanmean(gd['latitude'][:]))
dep_max = np.round(d_test.max())
deepest_bin = find_nearest(bin_depth, dep_max)[0]
bin_up = bin_depth[0:(deepest_bin - 1)]
bin_down = bin_depth[2:(deepest_bin + 1)]
bin_cen = bin_depth[1:deepest_bin]
temp_g = np.nan * np.ones((np.size(bin_depth), 2))
sal_g = np.nan * np.ones((np.size(bin_depth), 2))
lon_g = np.nan * np.ones((np.size(bin_depth), 2))
lat_g = np.nan * np.ones((np.size(bin_depth), 2))
time_g = np.nan * np.ones((np.size(bin_depth), 2))
if 'aanderaa4330_instrument_dissolved_oxygen' in gd.variables:
o2_g = np.nan * np.ones((np.size(bin_depth), 2))
for k in range(2):
temp = temp_d[k]
sal = sal_d[k]
depth = depth_d[k]
lon = lon_d[k]
lat = lat_d[k]
time = time_d[k]
if 'aanderaa4330_instrument_dissolved_oxygen' in gd.variables:
o2 = o2_d[k]
# -- Case z = 0
dp_in_d_1 = depth < bin_cen[0]
if np.size(dp_in_d_1) >= 2:
temp_g[0, k] = np.nanmean(temp[dp_in_d_1])
sal_g[0, k] = np.nanmean(sal[dp_in_d_1])
lon_g[0, k] = np.nanmean(lon[dp_in_d_1])
lat_g[0, k] = np.nanmean(lat[dp_in_d_1])
time_g[0, k] = np.nanmean(time[dp_in_d_1])
if 'aanderaa4330_instrument_dissolved_oxygen' in gd.variables:
o2_g[0, k] = np.nanmean(o2[dp_in_d_1])
# -- Case z > 0
for j in range(np.size(bin_cen)):
i = j + 1
dp_in_d = (depth > bin_up[j]) & (depth < bin_down[j])
if np.size(dp_in_d) > 2:
temp_g[i, k] = np.nanmean(temp[dp_in_d])
sal_g[i, k] = np.nanmean(sal[dp_in_d])
lon_g[i, k] = np.nanmean(lon[dp_in_d])
lat_g[i, k] = np.nanmean(lat[dp_in_d])
time_g[i, k] = np.nanmean(time[dp_in_d])
if 'aanderaa4330_instrument_dissolved_oxygen' in gd.variables:
o2_g[i, k] = np.nanmean(o2[dp_in_d])
# -- Case last_bin
if deepest_bin == (len(bin_depth) - 1):
dp_in_d_e = (depth > bin_cen[-1]) & (depth < bin_cen[-1] + 75)
if np.size(dp_in_d_e) > 2:
temp_g[-1, k] = np.nanmean(temp[dp_in_d_e])
sal_g[-1, k] = np.nanmean(sal[dp_in_d_e])
lon_g[-1, k] = np.nanmean(lon[dp_in_d_e])
lat_g[-1, k] = np.nanmean(lat[dp_in_d_e])
time_g[-1, k] = np.nanmean(time[dp_in_d_e])
if 'aanderaa4330_instrument_dissolved_oxygen' in gd.variables:
o2_g[-1, k] = np.nanmean(o2[dp_in_d_e])
for i in range(2):
temp_g[:, i] = nanseg_interp(bin_depth, temp_g[:, i])
sal_g[:, i] = nanseg_interp(bin_depth, sal_g[:, i])
if np.size(s_out) < 1:
s_out = sal_g
t_out = temp_g
lon_out = lon_g
lat_out = lat_g
time_out = time_g
if 'aanderaa4330_instrument_dissolved_oxygen' in gd.variables:
o2_out = o2_g
else:
s_out = np.concatenate((s_out, sal_g), axis=1)
t_out = np.concatenate((t_out, temp_g), axis=1)
lon_out = np.concatenate((lon_out, lon_g), axis=1)
lat_out = np.concatenate((lat_out, lat_g), axis=1)
time_out = np.concatenate((time_out, time_g), axis=1)
if 'aanderaa4330_instrument_dissolved_oxygen' in gd.variables:
o2_out = np.concatenate((o2_out, o2_g), axis=1)
if 'aanderaa4330_instrument_dissolved_oxygen' in gd.variables:
out = {'time': time_out, 'lon': lon_out, 'lat': lat_out,
'temp': t_out, 'sal': s_out, 'o2': o2_out,
'dac_u': dac_u_out, 'dac_v': dac_v_out, 'profs': profile_tags}
return out
else:
out = {'time': time_out, 'lon': lon_out, 'lat': lat_out,
'temp': t_out, 'sal': s_out,
'dac_u': dac_u_out, 'dac_v': dac_v_out, 'profs': profile_tags}
return out
def density(self, bin_depth, ref_lat, temp, sal, lon, lat):
press = gsw.p_from_z(-1*bin_depth, ref_lat)
sa = np.nan * np.ones(np.shape(temp))
ct = np.nan * np.ones(np.shape(temp))
theta = np.nan * np.ones(np.shape(temp))
sig0 = np.nan * np.ones(np.shape(temp))
sig2 = np.nan * np.ones(np.shape(temp))
N2 = np.nan * np.ones(np.shape(temp))
for i in range(self.num_profs):
sa[:, i] = gsw.SA_from_SP(sal[:, i], press, lon[:, i], lat[:, i])
ct[:, i] = gsw.CT_from_t(sa[:, i], temp[:, i], press)
theta[:, i] = gsw.pt_from_CT(sa[:, i], ct[:, i])
sig0[:, i] = gsw.sigma0(sa[:, i], ct[:, i])
sig2[:, i] = gsw.sigma2(sa[:, i], ct[:, i])
N2[1:, i] = gsw.Nsquared(sa[:, i], ct[:, i], press, lat=ref_lat)[0]
return sa, ct, theta, sig0, sig2, N2
# this function will separate a set of dive-climb cycles into transects that are each bounded by glider targets. The
# target associated with each dive-climb cycle is used to separate profiles into transects that are terminated when
# glider target changes (when it reaches a target and begins to head towards a new one). If transects are repeated,
# each occupation of the line is treated as its own transect
def transect_cross_section_1(self, bin_depth, sig0_0, ct_0, sa_0,
lon_0, lat_0, dac_u_0, dac_v_0, profile_tags_0, sigth_levels, include_ends):
deep_shr_max = 0.1
deep_shr_max_dep = 3500
# -- for computation of eta (reference to average density profile and gradient of average density profile
# - the average should span seasons rather than total mission
sigma_theta_avg = np.nanmean(sig0_0, axis=1)
ct_avg = np.nanmean(ct_0, axis=1)
sa_avg = np.nanmean(sa_0, axis=1)
ddz_avg_sigma = np.gradient(sigma_theta_avg, -1 * bin_depth)
# ------------------------------------------------------------------------------------------------------
# separate dives into unique transects
target_test = 1000000 * self.target[:, 0] + np.round(self.target[:, 1], 3)
unique_targets = np.unique(target_test)
transects = []
for m in range(len(unique_targets)):
indices = np.where(target_test == unique_targets[m])[0]
if len(indices) > 1:
# make sure each set has more than one dive
sets = group_consecutives(indices, step=1)
to_go = []
for k in range(len(sets)):
if len(sets[k]) > 1:
to_go.append(sets[k])
# transects.append(group_consecutives(indices, step=1))
transects.append(to_go)
ds_out = []
dist_out = []
avg_ct_out = []
avg_sa_out = []
avg_sig_out = []
v_g_out = []
v_g_east_out = []
v_g_north_out = []
vbt_out = []
shear_out = []
isopycdep_out = []
isopycx_out = []
mwe_lon_out = []
mwe_lat_out = []
DACe_MW_out = []
DACn_MW_out = []
profile_tags_out = []
info_out = []
for n in range(len(transects)): # loop over all transect segments
print('transects = ' + str(transects[n]))
for o in range(len(transects[n])): # loop over all times a glider executed that segment
this_transect = transects[n][o]
print(this_transect)
index_start = 2 * this_transect[0]
index_end = 2 * (this_transect[-1] + 1)
print('index_start = ' + str(index_start))
print('index_end = ' + str(index_end))
order_set = np.arange(0, 2 * len(this_transect), 2)
sig0 = sig0_0[:, index_start:index_end]
ct = ct_0[:, index_start:index_end]
sa = sa_0[:, index_start:index_end]
lon = lon_0[:, index_start:index_end]
lat = lat_0[:, index_start:index_end]
dac_u = dac_u_0[index_start:index_end]
dac_v = dac_v_0[index_start:index_end]
profile_tags = profile_tags_0[index_start:index_end]
print('dive cycles in this transect = ' + str(profile_tags))
# ------------------------------------------------------------------------------------------------------
# order_set = np.arange(0, self.num_profs, 2)
info = np.nan * np.zeros((3, len(profile_tags) - 1))
sigma_theta_out = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
shear = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
shear_east = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
shear_north = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
avg_sig_pd = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
avg_ct_pd = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
avg_sa_pd = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
isopycdep = np.nan * np.zeros((np.size(sigth_levels), len(profile_tags)))
isopycx = np.nan * np.zeros((np.size(sigth_levels), len(profile_tags)))
vbt = np.nan * np.zeros(len(profile_tags))
DACe_MW = np.nan * np.zeros(len(profile_tags))
DACn_MW = np.nan * np.zeros(len(profile_tags))
ds = np.nan * np.zeros(len(profile_tags))
dist = np.nan * np.zeros(np.shape(lon))
dist_st = 0
distance = 0
for i in order_set: # loop over dive cycles in each transect (each i corresponds to a v estimate)
# i = this dive
# M
lon_start = lon[0, i] # start position of dive i along the transect
lat_start = lat[0, i]
lon_finish = lon[0, i + 1] # end position of dive i + 1 along the transect
lat_finish = lat[0, i + 1]
lat_ref = 0.5 * (lat_start + lat_finish)
f_m = np.pi * np.sin(np.deg2rad(lat_ref)) / (12 * 1800) # Coriolis parameter [s^-1]
dxs_m = 1.852 * 60 * np.cos(np.deg2rad(lat_ref)) * (lon_finish - lon_start) # zonal sfc disp [km]
dys_m = 1.852 * 60 * (lat_finish - lat_start) # meridional sfc disp [km]
# vector pointing along the center M dive direction
ds_a, ang_sfc_m = cart2pol(dxs_m, dys_m)
dx = 1.852 * 60 * np.cos(np.deg2rad(lat_ref)) * \
(np.concatenate([lon[:, i], np.flipud(lon[:, i + 1])]) - lon[0, i])
dy = 1.852 * 60 * (np.concatenate([lat[:, i], np.flipud(np.array(lat[:, i + 1]))]) - lat[0, i])
ss, ang = cart2pol(dx, dy)
xx, yy = pol2cart(ss, ang - ang_sfc_m)
length1 = np.size(lon[:, i])
dist[:, i] = dist_st + xx[0:length1]
dist[:, i + 1] = dist_st + np.flipud(xx[length1:])
dist_st = dist_st + ds_a
distance = distance + np.nanmedian(xx) # 0.5*ds # distance for each velocity estimate
ds[i] = distance
# DACe = dac_u[i] # zonal depth averaged current [m/s]
# DACn = dac_v[i] # meridional depth averaged current [m/s]
if i < 2:
# DACe = np.nanmean([[dac_u[i]], [dac_u[i + 2]]])
# DACn = np.nanmean([[dac_v[i]], [dac_v[i + 2]]])
DACe = dac_u[i]
DACn = dac_v[i]
elif (i >= 2) and (i < len(profile_tags) - 2):
# DACe = np.nanmean([[dac_u[i - 1]], [dac_u[i]], [dac_u[i + 2]]])
# DACn = np.nanmean([[dac_v[i - 1]], [dac_v[i]], [dac_v[i + 2]]])
DACe = dac_u[i]
DACn = dac_v[i]
elif i >= len(profile_tags) - 2:
# DACe = np.nanmean([[dac_u[i - 1]], [dac_u[i]]])
# DACn = np.nanmean([[dac_v[i - 1]], [dac_v[i]]])
DACe = dac_u[i]
DACn = dac_v[i]
mag_DAC, ang_DAC = cart2pol(DACe, DACn)
DACat, DACpot = pol2cart(mag_DAC, ang_DAC - ang_sfc_m)
vbt[i] = DACpot # across-track barotropic current comp (>0 to left)
DACe_MW[i] = DACe.copy()
DACn_MW[i] = DACn.copy()
# W
if i >= order_set[-1]:
lon_finish = lon[0, -1]
lat_finish = lon[0, -1]
DACe = np.nanmean([[dac_u[i]], [dac_u[-1]]]) # zonal depth averaged current [m/s]
DACn = np.nanmean([[dac_v[i]], [dac_v[-1]]]) # meridional depth averaged current [m/s]
else:
lon_finish = lon[0, i + 3]
lat_finish = lat[0, i + 3]
DACe = np.nanmean([[dac_u[i]], [dac_u[i + 2]]]) # zonal depth averaged current [m/s]
DACn = np.nanmean([[dac_v[i]], [dac_v[i + 2]]]) # meridional depth averaged current [m/s]
DACe_MW[i + 1] = DACe.copy()
DACn_MW[i + 1] = DACn.copy()
lat_ref = 0.5 * (lat_start + lat_finish)
f_w = np.pi * np.sin(np.deg2rad(lat_ref)) / (12 * 1800) # Coriolis parameter [s^-1]
dxs = 1.852 * 60 * np.cos(np.deg2rad(lat_ref)) * (lon_finish - lon_start) # zonal sfc disp [km]
dys = 1.852 * 60 * (lat_finish - lat_start) # meridional sfc disp [km]
# vector pointing along the center W dive direction
ds_w, ang_sfc_w = cart2pol(dxs, dys)
distance = distance + (ds_a - np.nanmedian(xx)) # distance for each velocity estimate
ds[i + 1] = distance
mag_DAC, ang_DAC = cart2pol(DACe, DACn)
DACat, DACpot = pol2cart(mag_DAC, ang_DAC - ang_sfc_w)
vbt[i + 1] = DACpot # across-track barotropic current comp (>0 to left)
shearM = np.nan * np.zeros(np.size(bin_depth))
shear_x_M = np.nan * np.zeros(np.size(bin_depth))
shear_y_M = np.nan * np.zeros(np.size(bin_depth))
shearW = np.nan * np.zeros(np.size(bin_depth))
shear_x_W = np.nan * np.zeros(np.size(bin_depth))
shear_y_W = np.nan * np.zeros(np.size(bin_depth))
p_avg_sig_M = np.nan * np.zeros(np.size(bin_depth))
p_avg_sig_W = np.nan * np.zeros(np.size(bin_depth))
p_avg_ct_M = np.nan * np.zeros(np.size(bin_depth))
p_avg_ct_W = np.nan * np.zeros(np.size(bin_depth))
p_avg_sa_M = np.nan * np.zeros(np.size(bin_depth))
p_avg_sa_W = np.nan * np.zeros(np.size(bin_depth))
sigma_theta_pa_M = np.nan * np.zeros(np.size(bin_depth))
sigma_theta_pa_W = np.nan * np.zeros(np.size(bin_depth))
lon_pa_M = np.nan * np.zeros(np.size(bin_depth))
lon_pa_W = np.nan * np.zeros(np.size(bin_depth))
lat_pa_M = np.nan * np.zeros(np.size(bin_depth))
lat_pa_W = np.nan * np.zeros(np.size(bin_depth))
# LOOP OVER EACH BIN_DEPTH
for j in range(np.size(bin_depth)):
# find array of indices for M / W sampling
if i < 2:
if include_ends:
c_i_m = np.arange(i, i + 3)
else:
c_i_m = [] # omit partial "M" estimate
c_i_w = np.arange(i, i + 4)
elif (i >= 2) and (i < len(profile_tags) - 2):
c_i_m = np.arange(i - 1, i + 3)
c_i_w = np.arange(i, i + 4)
elif i >= len(profile_tags) - 2:
if include_ends:
c_i_m = np.arange(i - 1, len(profile_tags))
else:
c_i_m = [] # omit partial "M" estimated
c_i_w = []
nm = np.size(c_i_m)
nw = np.size(c_i_w)
# for M profile compute shear and eta
if nm > 2 and np.size(sig0[j, c_i_m]) > 2:
sigmathetaM = sig0[j, c_i_m]
sigma_theta_pa_M[j] = np.nanmean(sigmathetaM) # average density across 4 profiles
lon_pa_M[j] = np.nanmean(lon[j, c_i_m]) # avg lat/lon across M/W profiles
lat_pa_M[j] = np.nanmean(lat[j, c_i_m])
imv = ~np.isnan(np.array(sig0[j, c_i_m]))
c_i_m_in = c_i_m[imv]
if np.size(c_i_m_in) > 1:
xM = 1.852 * 60 * np.cos(np.deg2rad(lat_ref)) * (
lon[j, c_i_m_in] - lon[j, c_i_m_in[0]]) # E loc [km]
yM = 1.852 * 60 * (
lat[j, c_i_m_in] - lat[j, c_i_m_in[0]]) # N location [km]
XXM = np.concatenate(
[np.ones((np.size(sigmathetaM[imv]), 1)), np.transpose(np.atleast_2d(np.array(xM))),
np.transpose(np.atleast_2d(np.array(yM)))], axis=1)
d_anom0M = sigmathetaM[imv] - np.nanmean(sigmathetaM[imv])
ADM = np.squeeze(np.linalg.lstsq(XXM, np.transpose(np.atleast_2d(np.array(d_anom0M))))[0])
drhodxM = ADM[1] # zonal gradient [kg/m^3/km]
drhodyM = ADM[2] # meridional gradient [kg/m^3km]
drhodsM, ang_drhoM = cart2pol(drhodxM, drhodyM) # angle and magnitude of gradient
# component along/acrosstrack
drhodatM, drhodpotM = pol2cart(drhodsM, ang_drhoM - ang_sfc_m)
shearM[j] = -self.g * drhodatM / (self.rho0 * f_m) # shear to port of track [m/s/km]
if (np.abs(shearM[j]) > deep_shr_max) and bin_depth[j] >= deep_shr_max_dep:
shearM[j] = np.sign(shearM[j]) * deep_shr_max
# project cross-track velocity vertical shear into E/N
# - ang_drhoM = angle of the density gradient! (3/4 contributing density measurements)
# - ang_sfc_m = angle of either the middle M dive or tail ends of the W dives
angle_of_shear = ang_sfc_m + np.pi/2 # angle in transect direction + 90deg
shear_x_M[j], shear_y_M[j] = pol2cart(shearM[j], angle_of_shear) # project onto E/N
# average isopycnal value at each depth level j, to be used to compute eta but
# using a background profile of the users desire. this average spans the same number
# of profiles used in computing vertical shear profiles (and then velocity profiles)
p_avg_sig_M[j] = np.nanmean(sig0[j, c_i_m])
p_avg_ct_M[j] = np.nanmean(ct[j, c_i_m])
p_avg_sa_M[j] = np.nanmean(sa[j, c_i_m])
# for W profile compute shear and eta
if nw > 2 and np.size(sig0[j, c_i_w]) > 2:
sigmathetaW = sig0[j, c_i_w]
sigma_theta_pa_W[j] = np.nanmean(sigmathetaW) # average density across 4 profiles
lon_pa_W[j] = np.nanmean(lon[j, c_i_w]) # avg lat/lon across M/W profiles
lat_pa_W[j] = np.nanmean(lon[j, c_i_w])
iwv = ~np.isnan(np.array(sig0[j, c_i_w]))
c_i_w_in = c_i_w[iwv]
if np.sum(c_i_w_in) > 1:
xW = 1.852 * 60 * np.cos(np.deg2rad(lat_ref)) * (
lon[j, c_i_w_in] - lon[j, c_i_w_in[0]]) # E loc [km]
yW = 1.852 * 60 * (
lat[j, c_i_w_in] - lat[j, c_i_w_in[0]]) # N location [km]
XXW = np.concatenate(
[np.ones((np.size(sigmathetaW[iwv]), 1)), np.transpose(np.atleast_2d(np.array(xW))),
np.transpose(np.atleast_2d(np.array(yW)))], axis=1)
d_anom0W = sigmathetaW[iwv] - np.nanmean(sigmathetaW[iwv])
ADW = np.squeeze(np.linalg.lstsq(XXW, np.transpose(np.atleast_2d(np.array(d_anom0W))))[0])
drhodxW = ADW[1] # [zonal gradient [kg/m^3/km]
drhodyW = ADW[2] # [meridional gradient [kg/m^3km]
drhodsW, ang_drhoW = cart2pol(drhodxW, drhodyW)
drhodatW, drhodpotW = pol2cart(drhodsW, ang_drhoW - ang_sfc_w)
shearW[j] = -self.g * drhodatW / (self.rho0 * f_w) # shear to port of track [m/s/km]
if (np.abs(shearW[j]) > deep_shr_max) and bin_depth[j] >= deep_shr_max_dep:
shearW[j] = np.sign(shearW[j]) * deep_shr_max
angle_of_shear = ang_sfc_w + np.pi/2
shear_x_W[j], shear_y_W[j] = pol2cart(shearW[j], angle_of_shear)
# average isopycnal value at each depth level j, to be used to compute eta but
# using a background profile of the users desire. this average spans the same number
# of profiles used in computing vertical shear profiles (and then velocity profiles)
p_avg_sig_W[j] = np.nanmean(sig0[j, c_i_w])
p_avg_ct_W[j] = np.nanmean(ct[j, c_i_w])
p_avg_sa_W[j] = np.nanmean(sa[j, c_i_w])
# END LOOP OVER EACH BIN_DEPTH
# OUTPUT FOR EACH TRANSECT (at least 2 DIVES)
# because this is M/W profiling, for a 3 dive transect, only 5 profiles of shear and eta are compiled
sigma_theta_out[:, i] = sigma_theta_pa_M
shear[:, i] = shearM
shear_east[:, i] = shear_x_M
shear_north[:, i] = shear_y_M
avg_sig_pd[:, i] = p_avg_sig_M
avg_ct_pd[:, i] = p_avg_ct_M
avg_sa_pd[:, i] = p_avg_sa_M
info[0, i] = profile_tags[i]
info[1, i] = np.nanmean(lon_pa_M)
info[2, i] = np.nanmean(lat_pa_M)
if i < len(profile_tags) - 2:
sigma_theta_out[:, i + 1] = sigma_theta_pa_W
shear[:, i + 1] = shearW
shear_east[:, i + 1] = shear_x_W
shear_north[:, i + 1] = shear_y_W
avg_sig_pd[:, i + 1] = p_avg_sig_W
avg_ct_pd[:, i + 1] = p_avg_ct_W
avg_sa_pd[:, i + 1] = p_avg_sa_W
info[0, i + 1] = profile_tags[i + 1]
info[1, i + 1] = np.nanmean(lon_pa_W)
info[2, i + 1] = np.nanmean(lat_pa_W)
# ISOPYCNAL DEPTHS ON PROFILES ALONG EACH TRANSECT
sigthmin = np.nanmin(np.array(sig0[:, i]))
sigthmax = np.nanmax(np.array(sig0[:, i]))
isigth = np.where((sigth_levels > sigthmin) & (sigth_levels < sigthmax))
isopycdep[isigth, i] = np.interp(sigth_levels[isigth], sig0[:, i], bin_depth)
isopycx[isigth, i] = np.interp(sigth_levels[isigth], sig0[:, i], dist[:, i])
sigthmin = np.nanmin(np.array(sig0[:, i + 1]))
sigthmax = np.nanmax(np.array(sig0[:, i + 1]))
isigth = np.where((sigth_levels > sigthmin) & (sigth_levels < sigthmax))
isopycdep[isigth, i + 1] = np.interp(sigth_levels[isigth], sig0[:, i + 1], bin_depth)
isopycx[isigth, i + 1] = np.interp(sigth_levels[isigth], sig0[:, i + 1], dist[:, i + 1])
# ---- END LOOP OVER EACH DIVE IN TRANSECT
# FOR EACH TRANSECT COMPUTE GEOSTROPHIC VELOCITY
vbc_g = np.nan * np.zeros(np.shape(shear))
vbc_g_e = np.nan * np.zeros(np.shape(shear))
vbc_g_n = np.nan * np.zeros(np.shape(shear))
v_g = np.nan * np.zeros((np.size(bin_depth), len(profile_tags)))
v_g_east = np.nan * np.zeros((np.size(bin_depth), len(profile_tags)))
v_g_north = np.nan * np.zeros((np.size(bin_depth), len(profile_tags)))
for m in range(len(profile_tags) - 1):
iq = np.where(~np.isnan(shear[:, m]))[0]
if np.size(iq) > 10:
z2 = -1.0 * bin_depth[iq]
vrel = cumtrapz(0.001 * shear[iq, m], x=z2, initial=0)
vrel_av = np.trapz(vrel / (z2[-1] - z2[0]), x=z2)
vbc = vrel - vrel_av
vbc_g[iq, m] = vbc
v_g[iq, m] = vbt[m] + vbc
vrel_e = cumtrapz(0.001 * shear_east[iq, m], x=z2, initial=0)
vrel_av_e = np.trapz(vrel_e / (z2[-1] - z2[0]), x=z2)
vbc_e = vrel_e - vrel_av_e
vbc_g_e[iq, m] = vbc_e
v_g_east[iq, m] = DACe_MW[m] + vbc_e
vrel_n = cumtrapz(0.001 * shear_north[iq, m], x=z2, initial=0)
vrel_av_n = np.trapz(vrel_n / (z2[-1] - z2[0]), x=z2)
vbc_n = vrel_n - vrel_av_n
vbc_g_n[iq, m] = vbc_n
v_g_north[iq, m] = DACn_MW[m] + vbc_n
else:
vbc_g[iq, m] = np.nan
v_g[iq, m] = np.nan
# M/W position in lat/lon (to locate a velocity and eta profile in space)
mwe_lon = np.nan * np.zeros(len(vbt))
mwe_lat = np.nan * np.zeros(len(vbt))
for m in order_set:
if m < order_set[-1]:
mwe_lon[m] = lon[np.where(np.isfinite(lon[:, m]))[0], m][-1]
mwe_lat[m] = lat[np.where(np.isfinite(lon[:, m]))[0], m][-1]
mwe_lon[m + 1] = lon[0, m + 1]
mwe_lat[m + 1] = lat[0, m + 1]
else:
mwe_lon[m] = lon[np.where(np.isfinite(lon[:, m]))[0], m][-1]
mwe_lat[m] = lat[np.where(np.isfinite(lat[:, m]))[0], m][-1]
print('profile # = ' + str(info[0,:]))
ds_out.append(ds) # distance of each m/w profile to transect start position
dist_out.append(dist) # distance of every data point to transect start position
v_g_out.append(v_g) # velocity profile at distance ds
v_g_east_out.append(v_g_east) # east velocity profile at distance ds
v_g_north_out.append(v_g_north) # north velocity profile at distance ds
vbt_out.append(vbt) #
shear_out.append(shear)
isopycdep_out.append(isopycdep) # isopycnal depth (along each profile)
isopycx_out.append(isopycx) # isopycnal distance (along-transect distance) of isopycnal depth
mwe_lon_out.append(mwe_lon) # lon of m/w profile
mwe_lat_out.append(mwe_lat) # lat of m/w profile
avg_ct_out.append(avg_ct_pd) # avg conservative t profile following m/w scheme, at distance ds
avg_sa_out.append(avg_sa_pd) # avg abs sal profile following m/w scheme, at distance ds
avg_sig_out.append(avg_sig_pd) # avg sig profile following m/w scheme, at distance ds
DACe_MW_out.append(DACe_MW) # DAC (east) at each m/w profile
DACn_MW_out.append(DACn_MW) # DAC (west) at each m/w profile
profile_tags_out.append(profile_tags) # profile numbers (linked to dist) (make-up of each transect)
info_out.append(info) #
# everywhere there is a len(profile_tags) there was a self.num_profs
return ds_out, dist_out, avg_ct_out, avg_sa_out, avg_sig_out, v_g_out, vbt_out, isopycdep_out, isopycx_out, \
mwe_lon_out, mwe_lat_out, DACe_MW_out, DACn_MW_out, profile_tags_out, shear_out, v_g_east_out, v_g_north_out
# Cross Section of a Single Glider Transect (Velocity Contoured in Color and Density Surfaces plotted)
def plot_cross_section(self, bin_depth, ds, v_g, dist, profile_tags, isopycdep, isopycx, sigth_levels, time, levels):
# sns.set(context="notebook", style="whitegrid", rc={"axes.axisbelow": False})
matplotlib.rcParams['figure.figsize'] = (10, 6)
fig0, ax0 = plt.subplots()
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
# levels = np.arange(np.float(np.nanmin(v_g)), np.float(np.nanmax(v_g)), .02)
# levels = np.arange(-.64, .66, .04)
vc = ax0.contourf(ds, bin_depth, v_g, levels=levels, cmap=plt.cm.PuOr)
vcc = ax0.contour(ds, bin_depth, v_g, levels=levels, colors='k', linewidth=.75)
ax0.contour(ds, bin_depth, v_g, levels=[0], colors='k', linewidth=1.25)
for p in range(len(profile_tags)): # self.num_profs
ax0.scatter(dist[:, p], bin_depth, s=.75, color='k')
dive_label = np.arange(0, len(profile_tags), 2)
for pp in range(np.size(dive_label)):
p = dive_label[pp]
ax0.text(np.nanmax(dist[:, p]) - 1, np.max(bin_depth[~np.isnan(dist[:, p])]) + 200,
str(int(profile_tags[p])))
sig_good = np.where(~np.isnan(isopycdep[:, 0]))
for p in range(np.size(sig_good[0])):
ax0.plot(isopycx[sig_good[0][p], :], isopycdep[sig_good[0][p], :], color='#708090', linewidth=.75)
ax0.text(np.nanmax(isopycx[sig_good[0][p], :]) + 2, np.nanmean(isopycdep[sig_good[0][p], :]),
str(sigth_levels[sig_good[0][p]]), fontsize=7)
ax0.clabel(vcc, inline=1, fontsize=8, fmt='%1.2f', color='k')
ax0.axis([0, np.max(ds) + 4, 0, np.max(bin_depth) + 200])
ax0.invert_yaxis()
ax0.set_xlabel('Distance along transect [km]')
ax0.set_ylabel('Depth [m]')
t_s = datetime.date.fromordinal(np.int(time[0, 0]))
t_e = datetime.date.fromordinal(np.int(time[0, -1]))
ax0.set_title(self.ID + ' ' + self.project + ' ' + np.str(
t_s.month) + '/' + np.str(t_s.day) + ' - ' + np.str(t_e.month) + '/' + np.str(t_e.day), fontsize=13)
plt.colorbar(vc, label='[m/s]')
plt.tight_layout()
plot_pro(ax0)
return fig0
# Conservative Temperature and Absolute Salinity (used to check for instrument drift)
def plot_t_s(self, ct, sa, time):
sa_gr = np.arange(32, 38, 0.1)
ct_gr = np.arange(1, 30, 0.5)
X, Y = np.meshgrid(sa_gr, ct_gr)
sig0_gr = gsw.sigma0(X, Y)
levs = np.arange(np.round(np.min(sig0_gr), 1), np.max(sig0_gr), 0.2)
cmap = plt.cm.get_cmap("YlGnBu_r")
rr = np.arange(0, 1, 1/np.shape(sa)[1])
matplotlib.rcParams['figure.figsize'] = (10, 4)
f, (ax1, ax2) = plt.subplots(1, 2)
dc = ax1.contour(sa_gr, ct_gr, sig0_gr, levs, colors='#A9A9A9', linewidth=0.2)
ax1.clabel(dc, inline=1, fontsize=5, fmt='%1.2f', color='k')
for i in range(self.num_profs):
ax1.scatter(sa[:, i], ct[:, i], s=2, color=cmap(rr[i]))
ax1.axis([np.round(np.nanmin(sa), 1) - .2, np.round(np.nanmax(sa), 1) + .2,
np.round(np.nanmin(ct), 1) - .5, np.round(np.nanmax(ct), 1) + .5])
ax1.set_xlabel('Absolute Salinity')
ax1.set_ylabel('Conservative Temperature')
t_s = datetime.date.fromordinal(np.int(time[0, 0]))
t_e = datetime.date.fromordinal(np.int(time[0, -1]))
ax1.set_title(self.ID + ' ' + self.project + ' ' + np.str(
t_s.month) + '/' + np.str(t_s.day) + ' - ' + np.str(t_e.month) + '/' + np.str(t_e.day), fontsize=11)
ax1.grid()
# zoom in
for i in range(self.num_profs):
ax2.scatter(sa[:, i], ct[:, i], s=1, color=cmap(rr[i]))
ax2.axis([np.round(np.nanmin(sa), 1) - .05, np.round(np.nanmin(sa), 1) + 0.12,
np.round(np.nanmin(ct), 1) - .1, np.round(np.nanmin(ct), 1) + 2])
ax2.set_xlabel('Absolute Salinity')
ax2.set_ylabel('Conservative Temperature')
ax2.set_title('Deep CT, SA')
plot_pro(ax2)
def plot_v(self, bin_depth, v_g, time, profile_tags, u_levels):
t_s = datetime.date.fromordinal(np.int(time[0, 0]))
t_e = datetime.date.fromordinal(np.int(time[0, -1]))
cmap = plt.cm.get_cmap("YlGnBu_r")
rr = np.arange(0, 1, 1/np.shape(v_g)[1])
matplotlib.rcParams['figure.figsize'] = (5.5, 6)
f, ax = plt.subplots()
for i in range(np.shape(v_g)[1]):
ax.plot(v_g[:, i], bin_depth, color=cmap(rr[i]))
ax.set_title(self.ID + ' ' + self.project + ' ' + np.str(
t_s.month) + '/' + np.str(t_s.day) + ' - ' + np.str(t_e.month) + '/' + np.str(t_e.day), fontsize=10)
ax.text(0.1, 0.15, 'dive-cycles: ' + str(np.nanmin(profile_tags)) + '-' + str(np.nanmax(profile_tags)),
fontsize=10)
ax.set_xlim([np.nanmin(u_levels), np.nanmax(u_levels)])
ax.set_ylim([-200, np.nanmax(bin_depth) + 100])
ax.set_ylabel('Depth [m]')
ax.set_xlabel('Cross-Track Velocity [m/s]')
ax.invert_yaxis()
plot_pro(ax)
# Plan View of Glider Dives for desired mission
def plot_plan_view(self, lon, lat, mwe_lon, mwe_lat, dac_u, dac_v, ref_lat, profile_tags, time, limits, path):
# bathymetry
bath = path
bath_fid = Dataset(bath, 'r')
if 'longitude' in bath_fid.variables:
if np.nanmax(bath_fid.variables['longitude'][:]) > 0:
bath_lon = bath_fid.variables['longitude'][:] - 360
else:
bath_lon = bath_fid.variables['longitude'][:]
bath_lat = bath_fid.variables['latitude'][:]
elif 'lon' in bath_fid.variables:
bath_lon = bath_fid.variables['lon'][:]
bath_lat = bath_fid.variables['lat'][:]
if 'ROSE' in bath_fid.variables:
bath_z = bath_fid.variables['ROSE'][:]
if 'z' in bath_fid.variables:
bath_z = bath_fid.variables['z'][:]
elif 'elevation' in bath_fid.variables:
bath_z = bath_fid.variables['elevation'][:]
levels = [-5000, -4500, -4000, -3500, -3000, -2500, -2000, -1500, -1000, -500, 5]
cmap = plt.cm.get_cmap("Blues_r")
cmap.set_over('#808000') # ('#E6E6E6')
matplotlib.rcParams['figure.figsize'] = (6.5, 4)
f, ax = plt.subplots()
bc = ax.contourf(bath_lon, bath_lat, bath_z, levels, cmap='Blues_r', extend='both', zorder=0)
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
bcl = ax.contour(bath_lon, bath_lat, bath_z, [-5000, -4500, -4000, -3500], colors='k', zorder=0)
ax.scatter(lon, lat, s=2, color='#FF8C00', label='Dive Path')
ax.scatter(mwe_lon, mwe_lat, s=10, color='#8B0000')
for i in range(0, len(mwe_lat)-1, 2):
ax.text(mwe_lon[i] - 0.1, mwe_lat[i] + 0.02, str(np.int(profile_tags[i])), color='#00FA9A',
fontsize=10, fontweight='bold')
qa = ax.quiver(mwe_lon, mwe_lat, dac_u, dac_v, color='r',
scale=1.5, headwidth=2, headlength=2, width=.003)
ax.quiverkey(qa, .2, .2, 0.1, '0.1 m/s', color='r')
ax.clabel(bcl, inline_spacing=-3, fmt='%1.0f', colors='k')
w = 1 / np.cos(np.deg2rad(ref_lat))
ax.axis(limits)
ax.set_aspect(w)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
f.colorbar(bc, cax=cax, label='[m]')
t_s = datetime.date.fromordinal(np.int(time[0, 0]))
t_e = datetime.date.fromordinal(np.int(time[0, -1]))
ax.set_title(self.ID + ' ' + self.project + ' ' + np.str(
t_s.month) + '/' + np.str(t_s.day) + ' - ' + np.str(t_e.month) + '/' + np.str(t_e.day), fontsize=11)
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, fontsize=11)
ax.set_xlabel('Lon')
ax.set_ylabel('Lat')
ax.grid()
plot_pro(ax)
# Single Transect
def transect_cross_section_0(self, bin_depth, sig0, ct, sa, lon, lat, dac_u, dac_v, profile_tags, sigth_levels, include_ends):
deep_shr_max = 0.1
deep_shr_max_dep = 3500
order_set = np.arange(0, self.num_profs, 2)
info = np.nan * np.zeros((3, len(profile_tags) - 1))
sigma_theta_out = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
shear = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
shear_east = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
shear_north = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
avg_sig_pd = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
avg_ct_pd = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
avg_sa_pd = np.nan * np.zeros((np.size(bin_depth), len(profile_tags) - 1))
isopycdep = np.nan * np.zeros((np.size(sigth_levels), len(profile_tags)))
isopycx = np.nan * np.zeros((np.size(sigth_levels), len(profile_tags)))
vbt = np.nan * np.zeros(len(profile_tags))
DACe_MW = np.nan * np.zeros(len(profile_tags))
DACn_MW = np.nan * np.zeros(len(profile_tags))
ds = np.nan * np.zeros(len(profile_tags))
dist = np.nan * np.zeros(np.shape(lon))
dist_st = 0
distance = 0
for i in order_set:
# M
lon_start = lon[0, i] # start position of dive i along the transect
lat_start = lat[0, i]
lon_finish = lon[0, i + 1] # end position of dive i along the transect
lat_finish = lat[0, i + 1]
lat_ref = 0.5 * (lat_start + lat_finish)
f_m = np.pi * np.sin(np.deg2rad(lat_ref)) / (12 * 1800) # Coriolis parameter [s^-1]
dxs_m = 1.852 * 60 * np.cos(np.deg2rad(lat_ref)) * (lon_finish - lon_start) # zonal sfc disp [km]
dys_m = 1.852 * 60 * (lat_finish - lat_start) # meridional sfc disp [km]
ds_a, ang_sfc_m = cart2pol(dxs_m, dys_m)
dx = 1.852 * 60 * np.cos(np.deg2rad(lat_ref)) * \
(np.concatenate([lon[:, i], np.flipud(lon[:, i + 1])]) - lon[0, i])
dy = 1.852 * 60 * (np.concatenate([lat[:, i], np.flipud(np.array(lat[:, i + 1]))]) - lat[0, i])
ss, ang = cart2pol(dx, dy)
xx, yy = pol2cart(ss, ang - ang_sfc_m)
length1 = np.size(lon[:, i])
dist[:, i] = dist_st + xx[0:length1]
dist[:, i + 1] = dist_st + np.flipud(xx[length1:])
dist_st = dist_st + ds_a
distance = distance + np.nanmedian(xx) # 0.5*ds # distance for each velocity estimate
ds[i] = distance
# DACe = dac_u[i] # zonal depth averaged current [m/s]
# DACn = dac_v[i] # meridional depth averaged current [m/s]
if i < 2:
DACe = np.nanmean([[dac_u[i]], [dac_u[i + 2]]])
DACn = np.nanmean([[dac_v[i]], [dac_v[i + 2]]])
elif (i >= 2) and (i < len(profile_tags) - 2):
DACe = np.nanmean([[dac_u[i - 1]], [dac_u[i]], [dac_u[i + 2]]])
DACn = np.nanmean([[dac_v[i - 1]], [dac_v[i]], [dac_v[i + 2]]])
elif i >= len(profile_tags) - 2:
DACe = np.nanmean([[dac_u[i - 1]], [dac_u[i]]])
DACn = np.nanmean([[dac_v[i - 1]], [dac_v[i]]])
mag_DAC, ang_DAC = cart2pol(DACe, DACn)
DACat, DACpot = pol2cart(mag_DAC, ang_DAC - ang_sfc_m)
vbt[i] = DACpot # across-track barotropic current comp (>0 to left)
DACe_MW[i] = DACe.copy()
DACn_MW[i] = DACn.copy()
# W
if i >= order_set[-1]:
lon_finish = lon[0, -1]
lat_finish = lon[0, -1]
DACe = np.nanmean([[dac_u[i]], [dac_u[-1]]]) # zonal depth averaged current [m/s]
DACn = np.nanmean([[dac_v[i]], [dac_v[-1]]]) # meridional depth averaged current [m/s]
else:
lon_finish = lon[0, i + 3]
lat_finish = lat[0, i + 3]
DACe = np.nanmean([[dac_u[i]], [dac_u[i + 2]]]) # zonal depth averaged current [m/s]
DACn = np.nanmean([[dac_v[i]], [dac_v[i + 2]]]) # meridional depth averaged current [m/s]
DACe_MW[i + 1] = DACe.copy()
DACn_MW[i + 1] = DACn.copy()
lat_ref = 0.5 * (lat_start + lat_finish)
f_w = np.pi * np.sin(np.deg2rad(lat_ref)) / (12 * 1800) # Coriolis parameter [s^-1]
dxs = 1.852 * 60 * np.cos(np.deg2rad(lat_ref)) * (lon_finish - lon_start) # zonal sfc disp [km]
dys = 1.852 * 60 * (lat_finish - lat_start) # meridional sfc disp [km]
ds_w, ang_sfc_w = cart2pol(dxs, dys)
distance = distance + (ds_a - np.nanmedian(xx)) # distance for each velocity estimate
ds[i + 1] = distance
mag_DAC, ang_DAC = cart2pol(DACe, DACn)
DACat, DACpot = pol2cart(mag_DAC, ang_DAC - ang_sfc_w)
vbt[i + 1] = DACpot # across-track barotropic current comp (>0 to left)
shearM = np.nan * np.zeros(np.size(bin_depth))
shear_x_M = np.nan * np.zeros(np.size(bin_depth))
shear_y_M = np.nan * np.zeros(np.size(bin_depth))
shearW = np.nan * np.zeros(np.size(bin_depth))
shear_x_W = np.nan * np.zeros(np.size(bin_depth))
shear_y_W = np.nan * np.zeros(np.size(bin_depth))
p_avg_sig_M = np.nan * np.zeros(np.size(bin_depth))
p_avg_sig_W = np.nan * np.zeros(np.size(bin_depth))
p_avg_ct_M = np.nan * np.zeros(np.size(bin_depth))
p_avg_ct_W = np.nan * np.zeros(np.size(bin_depth))
p_avg_sa_M = np.nan * np.zeros(np.size(bin_depth))
p_avg_sa_W = np.nan * np.zeros(np.size(bin_depth))
sigma_theta_pa_M = np.nan * np.zeros(np.size(bin_depth))
sigma_theta_pa_W = np.nan * np.zeros(np.size(bin_depth))
lon_pa_M = np.nan * np.zeros(np.size(bin_depth))
lon_pa_W = np.nan * np.zeros(np.size(bin_depth))
lat_pa_M = np.nan * np.zeros(np.size(bin_depth))
lat_pa_W = np.nan * np.zeros(np.size(bin_depth))
# LOOP OVER EACH BIN_DEPTH
for j in range(np.size(bin_depth)):
# find array of indices for M / W sampling
if i < 2:
if include_ends:
c_i_m = np.arange(i, i + 3)
else:
c_i_m = [] # omit partial "M" estimate
c_i_w = np.arange(i, i + 4)
elif (i >= 2) and (i < len(profile_tags) - 2):
c_i_m = np.arange(i - 1, i + 3)
c_i_w = np.arange(i, i + 4)
elif i >= len(profile_tags) - 2:
if include_ends:
c_i_m = np.arange(i - 1, len(profile_tags))
else:
c_i_m = [] # omit partial "M" estimate
c_i_w = []
nm = np.size(c_i_m)
nw = np.size(c_i_w)
# for M profile compute shear and eta
if nm > 2 and np.size(sig0[j, c_i_m]) > 2:
sigmathetaM = sig0[j, c_i_m]
sigma_theta_pa_M[j] = np.nanmean(sigmathetaM) # average density across 4 profiles
lon_pa_M[j] = np.nanmean(lon[j, c_i_m]) # avg lat/lon across M/W profiles
lat_pa_M[j] = np.nanmean(lat[j, c_i_m])
imv = ~np.isnan(np.array(sig0[j, c_i_m]))
c_i_m_in = c_i_m[imv]
if np.size(c_i_m_in) > 1:
xM = 1.852 * 60 * np.cos(np.deg2rad(lat_ref)) * (
lon[j, c_i_m_in] - lon[j, c_i_m_in[0]]) # E loc [km]
yM = 1.852 * 60 * (
lat[j, c_i_m_in] - lat[j, c_i_m_in[0]]) # N location [km]
XXM = np.concatenate(
[np.ones((np.size(sigmathetaM[imv]), 1)), np.transpose(np.atleast_2d(np.array(xM))),
np.transpose(np.atleast_2d(np.array(yM)))], axis=1)
d_anom0M = sigmathetaM[imv] - np.nanmean(sigmathetaM[imv])
ADM = np.squeeze(np.linalg.lstsq(XXM, np.transpose(np.atleast_2d(np.array(d_anom0M))))[0])
drhodxM = ADM[1] # [zonal gradient [kg/m^3/km]
drhodyM = ADM[2] # [meridional gradient [kg/m^3km]
drhodsM, ang_drhoM = cart2pol(drhodxM, drhodyM)
drhodatM, drhodpotM = pol2cart(drhodsM, ang_drhoM - ang_sfc_m)
shearM[j] = -self.g * drhodatM / (self.rho0 * f_m) # shear to port of track [m/s/km]
if (np.abs(shearM[j]) > deep_shr_max) and bin_depth[j] >= deep_shr_max_dep:
shearM[j] = np.sign(shearM[j]) * deep_shr_max
angle_of_shear = ang_sfc_m + np.pi / 2 # angle in transect direction + 90deg
shear_x_M[j], shear_y_M[j] = pol2cart(shearM[j], angle_of_shear) # project onto E/N
# average isopycnal value at each depth level j, to be used to compute eta but
# using a background profile of the users desire. this average spans the same number
# of profiles used in computing vertical shear profiles (and then velocity profiles)
p_avg_sig_M[j] = np.nanmean(sig0[j, c_i_m])
p_avg_ct_M[j] = np.nanmean(ct[j, c_i_m])
p_avg_sa_M[j] = np.nanmean(sa[j, c_i_m])
# for W profile compute shear and eta
if nw > 2 and np.size(sig0[j, c_i_w]) > 2:
sigmathetaW = sig0[j, c_i_w]
sigma_theta_pa_W[j] = np.nanmean(sigmathetaW) # average density across 4 profiles
lon_pa_W[j] = np.nanmean(lon[j, c_i_w]) # avg lat/lon across M/W profiles
lat_pa_W[j] = np.nanmean(lon[j, c_i_w])
iwv = ~np.isnan(np.array(sig0[j, c_i_w]))
c_i_w_in = c_i_w[iwv]
if np.sum(c_i_w_in) > 1:
xW = 1.852 * 60 * np.cos(np.deg2rad(lat_ref)) * (
lon[j, c_i_w_in] - lon[j, c_i_w_in[0]]) # E loc [km]
yW = 1.852 * 60 * (
lat[j, c_i_w_in] - lat[j, c_i_w_in[0]]) # N location [km]
XXW = np.concatenate(
[np.ones((np.size(sigmathetaW[iwv]), 1)), np.transpose(np.atleast_2d(np.array(xW))),
np.transpose(np.atleast_2d(np.array(yW)))], axis=1)
d_anom0W = sigmathetaW[iwv] - np.nanmean(sigmathetaW[iwv])
ADW = np.squeeze(np.linalg.lstsq(XXW, np.transpose(np.atleast_2d(np.array(d_anom0W))))[0])
drhodxW = ADW[1] # [zonal gradient [kg/m^3/km]
drhodyW = ADW[2] # [meridional gradient [kg/m^3km]
drhodsW, ang_drhoW = cart2pol(drhodxW, drhodyW)
drhodatW, drhodpotW = pol2cart(drhodsW, ang_drhoW - ang_sfc_w)
shearW[j] = -self.g * drhodatW / (self.rho0 * f_w) # shear to port of track [m/s/km]
if (np.abs(shearW[j]) > deep_shr_max) and bin_depth[j] >= deep_shr_max_dep:
shearW[j] = np.sign(shearW[j]) * deep_shr_max
# average isopycnal value at each depth level j, to be used to compute eta but
# using a background profile of the users desire. this average spans the same number
# of profiles used in computing vertical shear profiles (and then velocity profiles)
p_avg_sig_W[j] = np.nanmean(sig0[j, c_i_w])
p_avg_ct_W[j] = np.nanmean(ct[j, c_i_w])
p_avg_sa_W[j] = np.nanmean(sa[j, c_i_w])
# END LOOP OVER EACH BIN_DEPTH
# OUTPUT FOR EACH TRANSECT (at least 2 DIVES)
# because this is M/W profiling, for a 3 dive transect, only 5 profiles of shear and eta are compiled
sigma_theta_out[:, i] = sigma_theta_pa_M
shear[:, i] = shearM
shear_east[:, i] = shear_x_M
shear_north[:, i] = shear_y_M
avg_sig_pd[:, i] = p_avg_sig_M
avg_ct_pd[:, i] = p_avg_ct_M
avg_sa_pd[:, i] = p_avg_sa_M
info[0, i] = profile_tags[i]
info[1, i] = np.nanmean(lon_pa_M)
info[2, i] = np.nanmean(lat_pa_M)
if i < len(profile_tags) - 2:
sigma_theta_out[:, i + 1] = sigma_theta_pa_W
shear[:, i + 1] = shearW
shear_east[:, i + 1] = shear_x_W
shear_north[:, i + 1] = shear_y_W
avg_sig_pd[:, i + 1] = p_avg_sig_W
avg_ct_pd[:, i + 1] = p_avg_ct_W
avg_sa_pd[:, i + 1] = p_avg_sa_W