def track_on_bathy(Float, hpids, projection='cyl', bathy_file=None):
"""TODO: Docstring..."""
__, idxs = Float.get_profiles(hpids, ret_idxs=True)
lons = Float.lon_start[idxs]
lats = Float.lat_start[idxs]
llcrnrlon = np.floor(np.min(lons)) - 1.
llcrnrlat = np.floor(np.min(lats)) - 1.
urcrnrlon = np.ceil(np.max(lons)) + 1.
urcrnrlat = np.ceil(np.max(lats)) + 1.
lon_lat = np.array([llcrnrlon, urcrnrlon, llcrnrlat, urcrnrlat])
lon_grid, lat_grid, bathy_grid = sandwell.read_grid(lon_lat, bathy_file)
bathy_grid[bathy_grid > 0] = 0
m = bm.Basemap(projection=projection,
llcrnrlon=llcrnrlon,
llcrnrlat=llcrnrlat,
urcrnrlon=urcrnrlon,
urcrnrlat=urcrnrlat,
lon_0=0.5 * (llcrnrlon + urcrnrlon),
lat_0=0.5 * (llcrnrlat + urcrnrlat),
resolution='f')
plt.figure()
x, y = m(lon_grid, lat_grid)
m.pcolormesh(x, y, bathy_grid, cmap=plt.get_cmap('binary_r'))
x, y = m(lons, lats)
m.plot(x, y, 'r-', linewidth=2)
m.fillcontinents()
m.drawcoastlines()
r = np.abs((urcrnrlon - llcrnrlon) / (urcrnrlat - llcrnrlat))
if r > 1.:
Nm = 8
Np = max(3, np.round(Nm / r))
orientation = 'horizontal'
elif r < 1.:
Np = 8
Nm = max(3, np.round(Nm / r))
orientation = 'vertical'
else:
Np = 4
Nm = 4
orientation = 'horizontal'
parallels = np.round(np.linspace(llcrnrlat, urcrnrlat, Np), 1)
m.drawparallels(parallels, labels=[1, 0, 0, 0])
meridians = np.round(np.linspace(llcrnrlon, urcrnrlon, Nm), 1)
m.drawmeridians(meridians, labels=[0, 0, 0, 1])
cbar = plt.colorbar(orientation=orientation)
cbar.set_label('Depth (m)')
def track_on_bathy(Float, hpids, projection='cyl', bathy_file=None):
"""TODO: Docstring..."""
__, idxs = Float.get_profiles(hpids, ret_idxs=True)
lons = Float.lon_start[idxs]
lats = Float.lat_start[idxs]
llcrnrlon = np.floor(np.min(lons)) - 1.
llcrnrlat = np.floor(np.min(lats)) - 1.
urcrnrlon = np.ceil(np.max(lons)) + 1.
urcrnrlat = np.ceil(np.max(lats)) + 1.
lon_lat = np.array([llcrnrlon, urcrnrlon, llcrnrlat, urcrnrlat])
lon_grid, lat_grid, bathy_grid = sandwell.read_grid(lon_lat, bathy_file)
bathy_grid[bathy_grid > 0] = 0
m = bm.Basemap(projection=projection, llcrnrlon=llcrnrlon,
llcrnrlat=llcrnrlat, urcrnrlon=urcrnrlon,
urcrnrlat=urcrnrlat, lon_0=0.5*(llcrnrlon+urcrnrlon),
lat_0=0.5*(llcrnrlat+urcrnrlat), resolution='f')
plt.figure()
x, y = m(lon_grid, lat_grid)
m.pcolormesh(x, y, bathy_grid, cmap=plt.get_cmap('binary_r'))
x, y = m(lons, lats)
m.plot(x, y, 'r-', linewidth=2)
m.fillcontinents()
m.drawcoastlines()
r = np.abs((urcrnrlon-llcrnrlon)/(urcrnrlat-llcrnrlat))
if r > 1.:
Nm = 8
Np = max(3, np.round(Nm/r))
orientation = 'horizontal'
elif r < 1.:
Np = 8
Nm = max(3, np.round(Nm/r))
orientation = 'vertical'
else:
Np = 4
Nm = 4
orientation = 'horizontal'
parallels = np.round(np.linspace(llcrnrlat, urcrnrlat, Np), 1)
m.drawparallels(parallels, labels=[1, 0, 0, 0])
meridians = np.round(np.linspace(llcrnrlon, urcrnrlon, Nm), 1)
m.drawmeridians(meridians, labels=[0, 0, 0, 1])
cbar = plt.colorbar(orientation=orientation)
cbar.set_label('Depth (m)')
################
# %% Float trajectories FULL
# ------------------
lons = np.hstack([Float.lon_start for Float in [E76, E77]])
lats = np.hstack([Float.lat_start for Float in [E76, E77]])
llcrnrlon = np.floor(np.min(lons)) - 1.
llcrnrlat = np.floor(np.min(lats)) - 1.
urcrnrlon = np.ceil(np.max(lons)) + 1.
urcrnrlat = np.ceil(np.max(lats)) + 1.
lon_lat = np.array([llcrnrlon, urcrnrlon + 5, llcrnrlat - 5, urcrnrlat])
lon_grid, lat_grid, bathy_grid = sandwell.read_grid(lon_lat, bf)
bathy_grid[bathy_grid > 0] = 0
bathy_grid *= -1 # Convert to depth
m = bm.Basemap(projection='tmerc',
llcrnrlon=llcrnrlon,
llcrnrlat=llcrnrlat,
urcrnrlon=urcrnrlon,
urcrnrlat=urcrnrlat,
lon_0=0.5 * (llcrnrlon + urcrnrlon),
lat_0=0.5 * (llcrnrlat + urcrnrlat),
resolution='f')
fig = plt.figure(figsize=(6.5, 3.5))
x, y = m(lon_grid, lat_grid)
m.pcolormesh(x,
def deployments():
"""This function gets data on all float deployments and plots them up."""
files = glob('../../data/EM-APEX/allprof*.mat')
FIDs = np.array([]) # Stores all float IDs.
LONs = np.array([])
LATs = np.array([])
DATEs = np.array([])
var_keys = ['flid', 'lon_gps', 'lat_gps', 'utc_dep']
for f in files:
data = io.loadmat(f, squeeze_me=True, variable_names=var_keys)
fids = data['flid']
dates = data['utc_dep']
lons = data['lon_gps']
lats = data['lat_gps']
ufids = np.unique(fids[~np.isnan(fids)])
FIDs = np.hstack((FIDs, ufids))
for fid in ufids:
flons = lons[fids == fid]
flats = lats[fids == fid]
fdates = dates[fids == fid]
# Find the index of the
idx = list(lons).index(filter(lambda x: ~np.isnan(x), lons)[0])
LONs = np.hstack((LONs, flons[idx]))
LATs = np.hstack((LATs, flats[idx]))
DATEs = np.hstack((DATEs, fdates[idx]))
# DTs = utils.datenum_to_datetime(DATEs)
# Plot map.
llcrnrlon = np.floor(np.nanmin(LONs)) - 8.
llcrnrlat = np.floor(np.nanmin(LATs)) - 1.
urcrnrlon = np.ceil(np.nanmax(LONs)) + 1.
urcrnrlat = np.ceil(np.nanmax(LATs)) + 8.
lon_lat = np.array([llcrnrlon, -20., -80, urcrnrlat])
lon_grid, lat_grid, bathy_grid = sandwell.read_grid(lon_lat)
bathy_grid[bathy_grid > 0] = 0
m = bm.Basemap(projection='tmerc',
llcrnrlon=llcrnrlon,
llcrnrlat=llcrnrlat,
urcrnrlon=urcrnrlon,
urcrnrlat=urcrnrlat,
lon_0=0.5 * (llcrnrlon + urcrnrlon),
lat_0=0.5 * (llcrnrlat + urcrnrlat),
resolution='f')
plt.figure(figsize=(10, 10))
x, y = m(lon_grid, lat_grid)
m.pcolormesh(x, y, bathy_grid, cmap=plt.get_cmap('binary_r'))
m.fillcontinents()
m.drawcoastlines()
parallels = np.array([-64, -54])
m.drawparallels(parallels, labels=[1, 0, 0, 0])
meridians = np.array([-105, -95, -85, -75, -65, -55, -45])
m.drawmeridians(meridians, labels=[0, 0, 0, 1])
x, y = m(LONs, LATs)
m.scatter(x, y, s=60, c=DATEs, cmap=plt.get_cmap('spring'))
save_name = '../figures/deployments.png'
plt.savefig(save_name, bbox_inches='tight')
def spew_track(Floats, dt=12., fstr='test'):
"""Given a sequence of floats this function plots a time-lapse of
their tracks onto bathymetry
If you want it to work for one float you must put it in a sequence.
"""
t_mins, t_maxs = [], []
for Float in Floats:
t_mins.append(np.min(Float.UTC_start))
t_maxs.append(np.max(Float.UTC_start))
t_min = np.min(t_mins)
t_max = np.max(t_maxs)
ti = np.arange(t_min, t_max, dt / 24.)
lons = np.empty((ti.size, len(Floats)))
lats = np.empty((ti.size, len(Floats)))
for i, Float in enumerate(Floats):
lon = Float.lon_start
lat = Float.lat_start
t = Float.UTC_start
lons[:, i] = np.interp(ti, t, lon, left=np.nan, right=np.nan)
lats[:, i] = np.interp(ti, t, lat, left=np.nan, right=np.nan)
llcrnrlon = np.floor(np.nanmin(lons)) - 1.
llcrnrlat = np.floor(np.nanmin(lats)) - 1.
urcrnrlon = np.ceil(np.nanmax(lons)) + 1.
urcrnrlat = np.ceil(np.nanmax(lats)) + 1.
lon_lat = np.array([llcrnrlon, -30., -70, urcrnrlat])
lon_grid, lat_grid, bathy_grid = sandwell.read_grid(lon_lat)
bathy_grid[bathy_grid > 0] = 0
m = bm.Basemap(projection='tmerc',
llcrnrlon=llcrnrlon,
llcrnrlat=llcrnrlat,
urcrnrlon=urcrnrlon,
urcrnrlat=urcrnrlat,
lon_0=0.5 * (llcrnrlon + urcrnrlon),
lat_0=0.5 * (llcrnrlat + urcrnrlat),
resolution='f')
plt.figure(figsize=(10, 10))
x, y = m(lon_grid, lat_grid)
m.pcolormesh(x, y, bathy_grid, cmap=plt.get_cmap('binary_r'))
m.fillcontinents()
m.drawcoastlines()
r = np.abs((urcrnrlon - llcrnrlon) / (urcrnrlat - llcrnrlat))
if r > 1.:
Nm = 8
Np = max(3, np.round(Nm / r))
elif r < 1.:
Np = 8
Nm = max(3, np.round(Nm / r))
parallels = np.round(np.linspace(llcrnrlat, urcrnrlat, Np), 1)
m.drawparallels(parallels, labels=[1, 0, 0, 0])
meridians = np.round(np.linspace(llcrnrlon, urcrnrlon, Nm), 1)
m.drawmeridians(meridians, labels=[0, 0, 0, 1])
colours = ['b', 'r', 'm', 'c', 'g', 'y']
for i, (time, lonr, latr) in enumerate(zip(ti, lons, lats)):
print('Creating... {i:03d}'.format(i=i))
plt.title(utils.datenum_to_datetime(time).strftime('%Y-%m-%d %H:%M'))
for j, (lon, lat) in enumerate(zip(lonr, latr)):
x, y = m(lon, lat)
m.plot(x, y, '.', color=colours[j])
if i == 0:
leg_str = [str(Float.floatID) for Float in Floats]
plt.legend(leg_str, loc=0)
save_name = '../figures/animated_tracks/{}{i:03d}.png'.format(fstr,
i=i)
plt.savefig(save_name, bbox_inches='tight')
print('Finished.')
def deployments():
"""This function gets data on all float deployments and plots them up."""
files = glob('../../data/EM-APEX/allprof*.mat')
FIDs = np.array([]) # Stores all float IDs.
LONs = np.array([])
LATs = np.array([])
DATEs = np.array([])
var_keys = ['flid', 'lon_gps', 'lat_gps', 'utc_dep']
for f in files:
data = io.loadmat(f, squeeze_me=True, variable_names=var_keys)
fids = data['flid']
dates = data['utc_dep']
lons = data['lon_gps']
lats = data['lat_gps']
ufids = np.unique(fids[~np.isnan(fids)])
FIDs = np.hstack((FIDs, ufids))
for fid in ufids:
flons = lons[fids == fid]
flats = lats[fids == fid]
fdates = dates[fids == fid]
# Find the index of the
idx = list(lons).index(filter(lambda x: ~np.isnan(x), lons)[0])
LONs = np.hstack((LONs, flons[idx]))
LATs = np.hstack((LATs, flats[idx]))
DATEs = np.hstack((DATEs, fdates[idx]))
# DTs = utils.datenum_to_datetime(DATEs)
# Plot map.
llcrnrlon = np.floor(np.nanmin(LONs)) - 8.
llcrnrlat = np.floor(np.nanmin(LATs)) - 1.
urcrnrlon = np.ceil(np.nanmax(LONs)) + 1.
urcrnrlat = np.ceil(np.nanmax(LATs)) + 8.
lon_lat = np.array([llcrnrlon, -20., -80, urcrnrlat])
lon_grid, lat_grid, bathy_grid = sandwell.read_grid(lon_lat)
bathy_grid[bathy_grid > 0] = 0
m = bm.Basemap(projection='tmerc', llcrnrlon=llcrnrlon,
llcrnrlat=llcrnrlat, urcrnrlon=urcrnrlon,
urcrnrlat=urcrnrlat, lon_0=0.5*(llcrnrlon+urcrnrlon),
lat_0=0.5*(llcrnrlat+urcrnrlat), resolution='f')
plt.figure(figsize=(10, 10))
x, y = m(lon_grid, lat_grid)
m.pcolormesh(x, y, bathy_grid, cmap=plt.get_cmap('binary_r'))
m.fillcontinents()
m.drawcoastlines()
parallels = np.array([-64, -54])
m.drawparallels(parallels, labels=[1, 0, 0, 0])
meridians = np.array([-105, -95, -85, -75, -65, -55, -45])
m.drawmeridians(meridians, labels=[0, 0, 0, 1])
x, y = m(LONs, LATs)
m.scatter(x, y, s=60, c=DATEs, cmap=plt.get_cmap('spring'))
save_name = '../figures/deployments.png'
plt.savefig(save_name, bbox_inches='tight')
def spew_track(Floats, dt=12., fstr='test'):
"""Given a sequence of floats this function plots a time-lapse of
their tracks onto bathymetry
If you want it to work for one float you must put it in a sequence.
"""
t_mins, t_maxs = [], []
for Float in Floats:
t_mins.append(np.min(Float.UTC_start))
t_maxs.append(np.max(Float.UTC_start))
t_min = np.min(t_mins)
t_max = np.max(t_maxs)
ti = np.arange(t_min, t_max, dt/24.)
lons = np.empty((ti.size, len(Floats)))
lats = np.empty((ti.size, len(Floats)))
for i, Float in enumerate(Floats):
lon = Float.lon_start
lat = Float.lat_start
t = Float.UTC_start
lons[:, i] = np.interp(ti, t, lon, left=np.nan, right=np.nan)
lats[:, i] = np.interp(ti, t, lat, left=np.nan, right=np.nan)
llcrnrlon = np.floor(np.nanmin(lons)) - 1.
llcrnrlat = np.floor(np.nanmin(lats)) - 1.
urcrnrlon = np.ceil(np.nanmax(lons)) + 1.
urcrnrlat = np.ceil(np.nanmax(lats)) + 1.
lon_lat = np.array([llcrnrlon, -30., -70, urcrnrlat])
lon_grid, lat_grid, bathy_grid = sandwell.read_grid(lon_lat)
bathy_grid[bathy_grid > 0] = 0
m = bm.Basemap(projection='tmerc', llcrnrlon=llcrnrlon,
llcrnrlat=llcrnrlat, urcrnrlon=urcrnrlon,
urcrnrlat=urcrnrlat, lon_0=0.5*(llcrnrlon+urcrnrlon),
lat_0=0.5*(llcrnrlat+urcrnrlat), resolution='f')
plt.figure(figsize=(10, 10))
x, y = m(lon_grid, lat_grid)
m.pcolormesh(x, y, bathy_grid, cmap=plt.get_cmap('binary_r'))
m.fillcontinents()
m.drawcoastlines()
r = np.abs((urcrnrlon-llcrnrlon)/(urcrnrlat-llcrnrlat))
if r > 1.:
Nm = 8
Np = max(3, np.round(Nm/r))
elif r < 1.:
Np = 8
Nm = max(3, np.round(Nm/r))
parallels = np.round(np.linspace(llcrnrlat, urcrnrlat, Np), 1)
m.drawparallels(parallels, labels=[1, 0, 0, 0])
meridians = np.round(np.linspace(llcrnrlon, urcrnrlon, Nm), 1)
m.drawmeridians(meridians, labels=[0, 0, 0, 1])
colours = ['b', 'r', 'm', 'c', 'g', 'y']
for i, (time, lonr, latr) in enumerate(zip(ti, lons, lats)):
print('Creating... {i:03d}'.format(i=i))
plt.title(utils.datenum_to_datetime(time).strftime('%Y-%m-%d %H:%M'))
for j, (lon, lat) in enumerate(zip(lonr, latr)):
x, y = m(lon, lat)
m.plot(x, y, '.', color=colours[j])
if i == 0:
leg_str = [str(Float.floatID) for Float in Floats]
plt.legend(leg_str, loc=0)
save_name = '../figures/animated_tracks/{}{i:03d}.png'.format(fstr,
i=i)
plt.savefig(save_name, bbox_inches='tight')
print('Finished.')
for i in range(N):
fid = IDS_[i]
filename = os.path.join(psdir, "{}_wfi.p".format(fid))
with open(filename, 'rb') as f:
wfi = pickle.load(f)
FLOATS_ALT[i].apply_w_model(wfi)
E4596, E4814 = FLOATS_ALT
# %% map
lon_lat = [-70, -30, -65, -45]
proj = ccrs.PlateCarree()
###############################################################################
lons, lats, bathy = sandwell.read_grid(lon_lat, bf)
bathy = -1*np.ma.masked_where(bathy > 0, bathy)
fig = plt.figure(figsize=(6.5, 5))
ax = plt.subplot(111, projection=proj)
ax.set_extent(lon_lat, ccrs.PlateCarree())
cax = fig.add_axes([0.6, 0.45, 0.2, 0.01])
ax.contour(lons, lats, bathy.data, [0.], colors='k')
C = ax.pcolormesh(lons[::10, ::10], lats[::10, ::10], bathy[::10, ::10],
vmin=0., vmax=6000., cmap=cmocean.cm.deep, rasterized=True)
cbp = plt.colorbar(C, cax=cax, orientation='horizontal')
cbp.set_ticks([0., 3000., 6000])
cbp.set_label('Depth (m)')