def bathy_along_track(Float, hpids, bathy_file=None):
"""TODO: Docstring..."""
__, idxs = Float.get_profiles(hpids, ret_idxs=True)
lons = Float.lon_start[idxs]
lats = Float.lat_start[idxs]
dist = Float.dist[idxs]
bathy = sandwell.interp_track(lons, lats, bathy_file)
plt.figure()
plt.plot(dist, bathy)
plt.xlabel('Distance (km)')
plt.ylabel('Depth (m)')
def bathy_along_track(Float, hpids, bathy_file=None):
"""TODO: Docstring..."""
__, idxs = Float.get_profiles(hpids, ret_idxs=True)
lons = Float.lon_start[idxs]
lats = Float.lat_start[idxs]
dist = Float.dist[idxs]
bathy = sandwell.interp_track(lons, lats, bathy_file)
plt.figure()
plt.plot(dist, bathy)
plt.xlabel('Distance (km)')
plt.ylabel('Depth (m)')
plt.figure(figsize=(5, 7))
plt.scatter(d, z, s=50, c=V*100., edgecolor='none', cmap=bwr)
cbar = plt.colorbar(orientation='horizontal', extend='both')
cbar.set_label(texvar+' (cm s$^{-1}$)')
plt.clim(*clim)
plt.xlim(np.nanmin(d), np.nanmax(d))
plt.xlabel('Distance (km)')
plt.ylabel('Depth (m)')
title_str = ("Float {}").format(Float.floatID)
plt.title(title_str)
lons = Float.lon_start[idxs]
lats = Float.lat_start[idxs]
dist = Float.dist[idxs]
bathy = sandwell.interp_track(lons, lats, bathy_file)
plt.plot(dist, bathy, 'k', linewidth=3)
plt.ylim(np.nanmin(bathy), np.nanmax(z))
my_savefig(Float.floatID, var + '_mountain')
pf.track_on_bathy(Float, hpids, bathy_file=bathy_file)
my_savefig(Float.floatID, 'mountain_area_track')
# %% Fitting in only one direction. Vertical wavenumber.
def plane_wave(x, A, k, phase):
return A*np.cos((k*x + phase))
V = getattr(Float, var)[:, idxs].flatten(order='F')
z = getattr(Float, zvar)[:, idxs].flatten(order='F')
d = getattr(Float, dvar)[:, idxs].flatten(order='F')
zs.append(z.copy())
tgps = getattr(Float, 'UTC_start')[idxs]
lon = getattr(Float, 'lon_start')[idxs]
lat = getattr(Float, 'lat_start')[idxs]
tctd = getattr(Float, 'UTC')[:, idxs].flatten(order='F')
nans = np.isnan(d) | np.isnan(tctd)
tctd = tctd[~nans]
dctd = d[~nans]
lonctd = np.interp(tctd, tgps, lon)
latctd = np.interp(tctd, tgps, lat)
bathy = sandwell.interp_track(lonctd, latctd, bf)
d -= dctd[bathy.argmax()]
ds.append(d.copy())
nans = np.isnan(d) | np.isnan(z) | np.isnan(V)
# Wg = griddata((d[~nans], z[~nans]), V[~nans], (Xg, Zg), method='linear')
# Wgs.append(Wg.copy())
dctd -= dctd[bathy.argmax()]
points = np.array([d, z]).transpose().reshape(-1, 1, 2)
segs = np.concatenate([points[:-1], points[1:]], axis=1)
lc = LineCollection(segs,
cmap=bwr2,
LOG_KAP = (np.log10(kappa)).flatten(order='F')[use]
# Plotting #
# Epsilon
d = getattr(Float, 'dist_ctd')[:, idxs].flatten(order='F')
tgps = getattr(Float, 'UTC_start')[idxs]
lon = getattr(Float, 'lon_start')[idxs]
lat = getattr(Float, 'lat_start')[idxs]
tctd = getattr(Float, 'UTC')[:, idxs].flatten(order='F')
nans = np.isnan(d) | np.isnan(tctd)
tctd = tctd[~nans]
dctd = d[~nans]
lonctd = np.interp(tctd, tgps, lon)
latctd = np.interp(tctd, tgps, lat)
bathy = sandwell.interp_track(lonctd, latctd, bf)
dbathymax = dctd[bathy.argmax()]
dctd -= dbathymax
X -= dbathymax
ax1.plot(Float.dist[idxs] - dbathymax, 1000.*ieps, label=Float.floatID)
LOG_EPS[noise] = np.NaN
step = 10
sc = ax0.scatter(X[::step], Z[::step], s=5, c=LOG_EPS[::step],
edgecolor='none', cmap=plt.get_cmap('YlOrRd'), vmin=-10.,
vmax=-7, alpha=.5)
z = getattr(Float, 'z')[:, idxs].flatten(order='F')
d = getattr(Float, 'dist_ctd')[:, idxs].flatten(order='F')
t = getattr(Float, 'UTC')[:, idxs].flatten(order='F')
# This block gets the bathymetry at the estimated position of each CTD measurement.
# NaNs removed so array size is different to the above block.
tgps = getattr(Float, 'UTC_start')[idxs]
lon = getattr(Float, 'lon_start')[idxs]
lat = getattr(Float, 'lat_start')[idxs]
tctd = getattr(Float, 'UTC')[:, idxs].flatten(order='F')
nans = np.isnan(d) | np.isnan(tctd)
tctd = tctd[~nans]
dctd = d[~nans]
lonctd = np.interp(tctd, tgps, lon)
latctd = np.interp(tctd, tgps, lat)
bathy = sandwell.interp_track(lonctd, latctd, bf)
# Zero the distances at the top of the sea mount.
d -= dctd[bathy.argmax()]
for ax, dmin, dmax in zip(axs, depth_mins, depth_maxs):
in_range = (z > dmin) & (z < dmax)
C = ax.scatter(d[in_range], utils.datenum_to_datetime(t[in_range]),
c=Ww[in_range], s=30, cmap=bwr, vmin=-0.08, vmax=0.08)
ax.set_ylim(datetime.datetime(2011, 1, 2, 12),
datetime.datetime(2011, 1, 4, 6))
ax.set_xlim(-20, 40)
ax.set_xlabel('Distance (km)')
ax.set_title("{} to {} m".format(dmin, dmax))
# title_str = ("Float {}").format(Float.floatID)
# ax.set_title(title_str)
cbp = plt.colorbar(C, cax=cax, orientation='horizontal', extend='both')
cbp.set_label('$w$ (m s$^{-1}$)')
cbp.set_ticks([-0.05, 0, 0.05])
cbp.set_ticklabels(['-0.05', '0', '0.05'])
if save_figs:
filename = os.path.join(fsd, '{}_w_section.pdf'.format(Float.floatID))
fig.savefig(filename, bbox_inches='tight', pad_inches=0)
# %% Float 4596 topo section
Float = E4596
b = sandwell.interp_track(Float.lon_start, Float.lat_start, bf)
__, z = Float.get_timeseries(Float.hpid, 'z')
t, Ww = Float.get_timeseries(Float.hpid, 'Ww')
__, Wf = Float.get_timeseries(Float.hpid, 'Wz')
__, d = Float.get_timeseries(Float.hpid, 'dist_ctd')
use = t > 7000.
z = z[use]
Ww = Ww[use]
Wf = Wf[use]
d = d[use]
Ww[np.abs(Wf) < 0.08] = np.nan
fig, ax = plt.subplots(1, 1, figsize=(6.5, 3))