def u_model(params, pfl, zlim, deg):
phi_0, X, Z, phase_0 = params
zmin, zmax = zlim
nope = np.isnan(pfl.zef) | (pfl.zef < zmin) | (pfl.zef > zmax)
t = 60*60*24*(pfl.UTCef - np.nanmin(pfl.UTCef))
x = 1000.*(pfl.dist_ef - np.nanmin(pfl.dist_ef))
k = 2*np.pi/X
l = 0.
m = 2*np.pi/Z
f = gsw.f(pfl.lat_start)
N = np.mean(np.sqrt(pfl.N2_ref[~nope]))
om = gw.omega(N, k, m, l, f)
u = gw.u(x, 0., pfl.zef, t, phi_0, k, l, m, om, phase_0=phase_0)
return u[~nope] - utils.nan_detrend(pfl.zef[~nope], pfl.U[~nope], deg)
5,
sharey='row',
sharex='col',
figsize=(14, 20))
fig.subplots_adjust(hspace=0.05, wspace=0.1)
rot = 'vertical'
col = 'black'
deg = 2
U_var = 'U'
V_var = 'V'
for pfl, axs in zip(pfls, axm):
axs[0].set_ylabel('$z$ (m)')
axs[0].plot(100. * utils.nan_detrend(pfl.zef, getattr(pfl, U_var), deg),
pfl.zef, col)
axs[0].plot(100. * getattr(pfl, U_var), pfl.zef, 'grey')
axs[0].plot(100. * utils.nan_polyvalfit(pfl.zef, getattr(pfl, U_var), deg),
pfl.zef, 'grey')
axs[1].plot(100. * utils.nan_detrend(pfl.zef, getattr(pfl, V_var), deg),
pfl.zef, col)
axs[1].plot(100. * getattr(pfl, V_var), pfl.zef, 'grey')
axs[1].plot(100. * utils.nan_polyvalfit(pfl.zef, getattr(pfl, V_var), deg),
pfl.zef, 'grey')
axs[2].plot(100. * pfl.Ww, pfl.z, col)
axs[3].plot(10000. * pfl.b, pfl.z, col)
axs[4].plot((pfl.dist_ctd - np.nanmin(pfl.dist_ctd)), pfl.z, col)
axs[4].annotate("Float {}\nhpid {}".format(pfl.floatID, pfl.hpid[0]),
(0.5, -200))
ylims = {27: (-1000, -200),
26: (-1540, -600),
31: (-1474, -600),
32: (-1580, -400)}
Ms = {26: M3,
27: M4,
31: M1,
32: M2}
for pfl, axs in zip(pfls, axm):
axs[0].set_ylabel('$z$ (m)')
axs[0].plot(100.*utils.nan_detrend(pfl.zef, getattr(pfl, U_var), deg), pfl.zef, col)
axs[1].plot(100.*utils.nan_detrend(pfl.zef, getattr(pfl, V_var), deg), pfl.zef, col)
axs[2].plot(100.*pfl.Ww, pfl.z, col)
axs[3].plot(10000.*pfl.b, pfl.z, col)
axs[2].annotate("EM {}\nP {}".format(pfl.floatID, pfl.hpid[0]),
(-29, -250))
for ax in axs:
ax.vlines(0., *ax.get_ylim())
ax.grid()
ax.axhspan(*ylims[pfl.hpid[0]], color='grey', alpha=0.5)
ax.set_ylim(-1600., 0.)
Ns = len(M1.trace('X')[:])
M = Ms[pfl.hpid[0]]
lx = -2050. ly = -1700. lz = -1880. phi_0 = 0.038 phase_0 = 3.24 pfl = E77.get_profiles(26) params['z_0'] = np.nanmin(pfl.z) params['N'] = 2.0e-3 X = far.model_verbose(phi_0, lx, ly, lz, phase_0, params) X.u[:, 0] -= X.U fig, axs = plt.subplots(1, 6, sharey=True, figsize=(16,6)) axs[0].plot(1e2*utils.nan_detrend(pfl.zef, pfl.U_abs, 2), pfl.zef, 'red') plt.setp(axs[0].xaxis.get_majorticklabels(), rotation=60) axs[1].plot(1e2*utils.nan_detrend(pfl.zef, pfl.V_abs, 2), pfl.zef, 'red') plt.setp(axs[1].xaxis.get_majorticklabels(), rotation=60) axs[2].plot(1e2*pfl.Ww, pfl.z, color='red') plt.setp(axs[2].xaxis.get_majorticklabels(), rotation=60) axs[3].plot(1e4*pfl.b, pfl.z, 'red') plt.setp(axs[3].xaxis.get_majorticklabels(), rotation=60) axs[4].plot(pfl.Pprime, pfl.z, 'red') plt.setp(axs[4].xaxis.get_majorticklabels(), rotation=60) axs[5].plot((pfl.dist_ctd - np.nanmin(pfl.dist_ctd))*1000., pfl.z, 'red') plt.setp(axs[5].xaxis.get_majorticklabels(), rotation=60) #pf.my_savefig(fig, '4977', 'pfl26_UVWB', sdir, fsize='double_col') use = X.r[:, 2] < -600.
W = W[~nope] B = B[~nope] N = np.nanmean(np.sqrt(N2)) f = gsw.f(-57.5) Unope = np.isnan(U) timeef = timeef[~Unope] U = U[~Unope] V = V[~Unope] U = np.interp(time, timeef, U) V = np.interp(time, timeef, V) U[time > t_split] = utils.nan_detrend(depth[time > t_split], U[time > t_split], 2) U[time < t_split] = utils.nan_detrend(depth[time < t_split], U[time < t_split], 2) U[U > 0.3] = 0. V[time > t_split] = utils.nan_detrend(depth[time > t_split], V[time > t_split], 2) V[time < t_split] = utils.nan_detrend(depth[time < t_split], V[time < t_split], 2) #U = utils.nan_detrend(depth, U, 2) #V = utils.nan_detrend(depth, V, 2) time *= 60.*60.*24 dist *= 1000. time -= np.min(time) dist -= np.min(dist) data = [time, dist, depth, U, V, W, B, N, f]
N = np.nanmean(np.sqrt(N2)) f = gsw.f(Float.get_profiles(args.hpid).lat_start) Unope = np.isnan(U) timeef = timeef[~Unope] U = U[~Unope] V = V[~Unope] U = np.interp(time, timeef, U) V = np.interp(time, timeef, V) Umean = np.mean(U) Vmean = np.mean(V) U = utils.nan_detrend(z, U, args.detrend) V = utils.nan_detrend(z, V, args.detrend) PP = utils.nan_detrend(z, PP, 1.) time *= 60.*60.*24 time -= np.min(time) data = [time, x, y, z, Umean, Vmean, N, f] data_stack = np.hstack((U, V, wscale*W, bscale*B, pscale*PP)) X0, Y0, Z0 = args.xyz def model():
E76_hpids = np.array([31, 32])
E77_hpids = np.array([26, 27])
for Float, hpids in zip([E76, E77], [E76_hpids, E77_hpids]):
__, idxs = Float.get_profiles(hpids, ret_idxs=True)
z = Float.z[:, idxs]
zef = Float.zef[:, idxs]
u = Float.U_abs[:, idxs]
v = Float.V_abs[:, idxs]
w = Float.Ww[:, idxs]
b = Float.b[:, idxs]
p = Float.Pprime[:, idxs]
N = np.sqrt(Float.N2_ref[:, idxs])
ud = utils.nan_detrend(zef, u)
vd = utils.nan_detrend(zef, v)
pd = utils.nan_detrend(z, p)
fig, axs = plt.subplots(1, 6, sharey=True, figsize=(16, 6))
axs[0].set_ylabel('$z$ (m)')
axs[0].plot(ud, zef)
axs[0].set_xlabel('$u$')
axs[1].plot(vd, zef)
axs[1].set_xlabel('$v$')
axs[2].plot(w, z)
axs[2].set_xlabel('$w$')
axs[3].plot(b, z)
axs[3].set_xlabel('$b$')
axs[4].plot(pd, z)
axs[4].set_xlabel('$p$')