def main(gliders, save_dir, bathymetry, landcolor, glider_t0, glider_t1,
current_glider_location):
# initialize keyword arguments for glider functions
gargs = dict()
gargs['time_start'] = glider_t0
gargs['time_end'] = glider_t1
# initialize keyword arguments for map plot
kwargs = dict()
kwargs['landcolor'] = landcolor
kwargs['current_glider_loc'] = current_glider_location
if bathymetry:
bath = xr.open_dataset(bathymetry)
else:
bath = False
for glider in gliders:
sdir_glider = os.path.join(save_dir, glider)
os.makedirs(sdir_glider, exist_ok=True)
kwargs['save_dir'] = sdir_glider
glider_ds = gld.glider_dataset(glider, **gargs)
glider_region = gld.glider_region(
glider_ds) # define the glider region
# Loop through regions
for region in glider_region.items():
if region[0] == 'zoomed':
extent = region[1]['lonlat']
if bath:
kwargs['bathy'] = bath.sel(
lon=slice(extent[0] - .1, extent[1] + .1),
lat=slice(extent[2] - .1, extent[3] + .1))
glider_track(glider_ds, region, **kwargs)
def main(gliders, save_dir, profile_t0, ylims, x_lims, gwh):
gofsurl = 'https://tds.hycom.org/thredds/dodsC/GLBy0.08/expt_93.0'
rtofsurl = '/Users/garzio/Documents/rucool/hurricane_glider_project/RTOFS/RTOFS_6hourly_North_Atlantic/'
# rtofsurl = '/home/hurricaneadm/data/rtofs' # on server
xlabels = {'temperature': 'Temperature ($^oC$)', 'salinity': 'Salinity'}
for pt0 in profile_t0:
print('\nPlotting profile at {}'.format(pt0))
pt0_str = pt0.strftime('%Y-%m-%dT%H:%M')
pt0_str_save = pt0.strftime('%Y%m%dT%H%M')
# initialize keyword arguments for glider functions.
gargs = dict()
gargs['time_start'] = pt0 - dt.timedelta(hours=gwh)
gargs['time_end'] = pt0 + dt.timedelta(hours=gwh)
gargs['filetype'] = 'dataframe'
for glider in gliders:
glider_name = glider.split('-')[0]
sdir_glider = os.path.join(save_dir, glider, 'profiles')
os.makedirs(sdir_glider, exist_ok=True)
glider_ds = gld.glider_dataset(glider, **gargs)
gl_t0 = pd.to_datetime(np.nanmin(
glider_ds['time'])).strftime('%Y-%m-%dT%H:%M')
gl_t1 = pd.to_datetime(np.nanmax(
glider_ds['time'])).strftime('%Y-%m-%dT%H:%M')
# average glider profile location
gl_pl = [
np.round(np.nanmean(glider_ds.longitude.values), 2),
np.round(np.nanmean(glider_ds.latitude.values), 2)
]
# separate glider profiles
profiletimes, idx = np.unique(glider_ds.time.values,
return_index=True)
targetlon = np.unique(glider_ds.longitude.values)
targetlat = np.unique(glider_ds.latitude.values)
targetlon_GOFS = storms.convert_target_gofs_lon(targetlon)
# get GOFS data
with xr.open_dataset(gofsurl, drop_variables='tau') as gofs:
gofs = gofs.rename({
'surf_el': 'sea_surface_height',
'water_temp': 'temperature',
'water_u': 'u',
'water_v': 'v'
})
gofs_ds = gofs.sel(time=pt0, depth=slice(0, 1000))
oklonGOFS = np.unique(
np.round(
np.interp(targetlon_GOFS, gofs_ds.lon,
np.arange(0, len(gofs_ds.lon)))).astype(int))
oklatGOFS = np.unique(
np.round(
np.interp(targetlat, gofs_ds.lat,
np.arange(0, len(gofs_ds.lat)))).astype(int))
# check that the glider doesn't cross more than one GOFS grid point
if np.logical_or(len(oklonGOFS) > 1, len(oklatGOFS) > 1):
raise ValueError(
'Glider crosses >1 GOFS grid point. Choose different time range.'
)
# get RTOFS data
if pt0.hour == 0:
hr = '{:03d}'.format(24)
rtofst0 = pt0 - dt.timedelta(days=1)
else:
hr = '{:03d}'.format(pt0.hour)
rtofst0 = pt0
rfile = f"{rtofsurl}{rtofst0.strftime('rtofs.%Y%m%d')}/rtofs_glo_3dz_f{hr}_6hrly_hvr_US_east.nc"
rtofs_ds = xr.open_dataset(rfile)
rtofs_ds = rtofs_ds.rename({
'Longitude': 'lon',
'Latitude': 'lat',
'MT': 'time',
'Depth': 'depth'
})
rtofs_ds = rtofs_ds.sel(depth=slice(0, 1000))
oklonRTOFS = np.unique(
np.round(
np.interp(targetlon, rtofs_ds.lon[0, :],
np.arange(0, len(
rtofs_ds.lon[0, :])))).astype(int))
oklatRTOFS = np.unique(
np.round(
np.interp(targetlat, rtofs_ds.lat[:, 0],
np.arange(0, len(
rtofs_ds.lat[:, 0])))).astype(int))
# check that the glider doesn't cross more than one RTOFS grid point
if np.logical_or(len(oklonRTOFS) > 1, len(oklatRTOFS) > 1):
raise ValueError(
'Glider crosses >1 RTOFS grid point. Choose different time range: {}'
.format(pt0))
lon_RTOFS = rtofs_ds.lon.values[0, oklonRTOFS]
lat_RTOFS = rtofs_ds.lat.values[oklatRTOFS, 0]
RTOFS_pl = [
np.round(lon_RTOFS[0], 2),
np.round(lat_RTOFS[0], 2)
]
for pv, xl in x_lims.items():
fig, ax = plt.subplots(figsize=(8, 9))
plt.subplots_adjust(right=0.88, left=0.15)
plt.grid()
# plot glider profiles
for pt in profiletimes:
# pt_idx = np.squeeze(np.argwhere(glider_ds.time.values == pt))
pt_gl = glider_ds[glider_ds['time'] == pt]
# pt_gl = pt_gl[pt_gl.pressure != 0.00] # get rid of zeros
x = pt_gl[pv]
y = pt_gl['depth']
xmask = ~np.isnan(
x) # get rid of nans so the lines are continuous
# ax.plot(x[xmask], y[xmask], lw=3, c='k', label=glider_name)
ax.plot(x[xmask],
y[xmask],
lw=3,
c='blue',
label=glider_name)
# plot GOFS
GOFS_targetvar = np.squeeze(gofs_ds[pv][:, oklatGOFS,
oklonGOFS])
GOFS_lon = storms.convert_gofs_target_lon(
GOFS_targetvar.lon.values.tolist())
GOFS_pl = [
np.round(GOFS_lon[0], 2),
np.round(GOFS_targetvar.lat.values, 2)
]
#ax.plot(GOFS_targetvar.values, GOFS_targetvar.depth.values, lw=3, c='tab:blue', label='GOFS')
ax.plot(GOFS_targetvar.values,
GOFS_targetvar.depth.values,
lw=3,
c='red',
label='GOFS')
# plot RTOFS
RTOFS_targetvar = np.squeeze(rtofs_ds[pv][0, :, oklatRTOFS,
oklonRTOFS])
#ax.plot(RTOFS_targetvar.values, RTOFS_targetvar.depth.values, lw=3, c='tab:orange', label='RTOFS')
ax.plot(RTOFS_targetvar.values,
RTOFS_targetvar.depth.values,
lw=3,
c='green',
label='RTOFS')
if ylims:
ax.set_ylim(ylims)
else: # set y limits to glider max depth
rounded_depth = np.ceil(
np.nanmax(glider_ds['depth']) / 10) * 10
ax.set_ylim([0, rounded_depth])
if xl:
ax.set_xticks(xl)
ax.set_xlabel(xlabels[pv])
ax.set_ylabel('Depth (m)')
ax.invert_yaxis()
# get legend handles and only show one set
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
# fig.legend(by_label.values(), by_label.keys(), framealpha=0.5, ncol=3, bbox_to_anchor=(0.89, 0.84), fontsize=12)
ax.legend(by_label.values(), by_label.keys(), fontsize=12)
ttl = f"GOFS: {pt0_str}, {GOFS_pl}\nRTOFS: {pt0_str}, {RTOFS_pl}\n" \
f"Glider: {gl_t0} to {gl_t1}, {gl_pl}"
ax.set_title(ttl)
savefile = os.path.join(
sdir_glider,
f'{glider_name}_models_profiles_{pv}-{pt0_str_save}.png'
)
plt.savefig(savefile, dpi=300)
plt.close()
def main(gliders, save_dir, bathymetry, m_t0, m_t1, g_t0, g_t1, lt, cgl):
url = '/Users/garzio/Documents/rucool/hurricane_glider_project/RTOFS/RTOFS_6hourly_North_Atlantic/'
# url = '/home/hurricaneadm/data/rtofs' # on server
# initialize keyword arguments for glider functions
gargs = dict()
gargs['time_start'] = g_t0
gargs['time_end'] = g_t1
# initialize keyword arguments for map plot
kwargs = dict()
kwargs['model'] = 'rtofs'
kwargs['transform'] = ccrs.PlateCarree()
kwargs['current_glider_loc'] = cgl
if bathymetry:
bath = xr.open_dataset(bathymetry)
else:
bath = False
for glider in gliders:
sdir_glider = os.path.join(save_dir, glider)
os.makedirs(sdir_glider, exist_ok=True)
kwargs['save_dir'] = sdir_glider
glider_ds = gld.glider_dataset(glider, **gargs)
kwargs['gliders'] = glider_ds
glider_region = gld.glider_region(
glider_ds) # define the glider region
gl_t0 = pd.to_datetime(np.nanmin(glider_ds.time.values))
gl_t1 = pd.to_datetime(np.nanmax(glider_ds.time.values))
if len(glider_region) < 1:
raise ValueError('No region found for glider: {}'.format(glider))
if lt:
lt = gld.custom_gliderline_transects()
kwargs['custom_transect'] = dict(
lon=[lt[glider]['extent'][0], lt[glider]['extent'][2]],
lat=[lt[glider]['extent'][1], lt[glider]['extent'][3]])
# get RTOFS files
if m_t0:
mt0 = m_t0
else:
mt0 = gl_t0 - dt.timedelta(hours=6)
if m_t1:
mt1 = m_t1
else:
mt1 = gl_t1 + dt.timedelta(days=1)
model_dates = [
x.strftime('rtofs.%Y%m%d') for x in pd.date_range(mt0, mt1)
]
rtofs_files = [
glob.glob(os.path.join(url, x, '*.nc')) for x in model_dates
]
rtofs_files = sorted(
[inner for outer in rtofs_files for inner in outer])
# Loop through regions
for region in glider_region.items():
extent = region[1]['lonlat']
if bath:
kwargs['bathy'] = bath.sel(lon=slice(extent[0] - .1,
extent[1] + .1),
lat=slice(extent[2] - .1,
extent[3] + .1))
for f in rtofs_files:
print(f)
try:
with xr.open_dataset(f) as ds:
ds = ds.rename({
'Longitude': 'lon',
'Latitude': 'lat',
'MT': 'time',
'Depth': 'depth'
})
lat = ds.lat.data
lon = ds.lon.data
# subset the RTOFS grid
lonidx = [extent[0] - 1, extent[1] + 1]
latidx = [extent[2] - 1, extent[3] + 1]
lonIndex = np.round(
np.interp(lonidx, lon[0, :],
np.arange(0,
len(lon[0, :])))).astype(int)
latIndex = np.round(
np.interp(latidx, lat[:, 0],
np.arange(0, len(lat[:,
0])))).astype(int)
sub = ds.sel(X=slice(lonIndex[0], lonIndex[1]),
Y=slice(latIndex[0], latIndex[1]))
surface_map_glider_track(sub, region, **kwargs)
except OSError:
continue
def main(gliders, save_dir, g_t0, g_t1):
url = '/Users/mikesmith/Documents/github/rucool/hurricanes/data/rtofs/'
# initialize keyword arguments for glider functions
gargs = dict()
gargs['time_start'] = g_t0
gargs['time_end'] = g_t1
gargs['filetype'] = 'dataframe'
for glider in gliders:
sdir_glider = os.path.join(save_dir, glider, 'transects',
'transect-ribbons')
os.makedirs(sdir_glider, exist_ok=True)
glider_df = gld.glider_dataset(glider, **gargs)
# Profile plotting
# grouped = list(glider_df.groupby([pd.Grouper(freq='1D', key='time')]))
grouped = list(
glider_df.groupby([pd.Grouper(freq='24H', key='time', base=3)]))
fig, ax = plt.subplots(1,
len(grouped),
figsize=(16, 9),
constrained_layout=True)
# plt.gca().set_prop_cycle(plt.cycler('color', plt.cm.Blues(np.linspace(0, 1, 24))))
for i in range(len(grouped)):
newgroup = list(grouped[i][1].groupby('time'))
ax[i].set_prop_cycle(
plt.cycler('color',
plt.cm.Reds(np.linspace(0, 1, len(newgroup)))))
for group in newgroup:
# temperature profile... y axis - depth, x axis. - temperature. color- time
h = ax[i].scatter(group[1]['temperature'],
group[1]['depth'],
label=group[0].strftime('%H:%M:%S'),
edgecolor='black')
ax[i].legend()
ax[i].set_title(grouped[i][0].strftime('%Y-%m-%d'),
fontsize=18,
fontweight='bold')
for axs in ax.flat:
axs.set_ylim([60, 1])
axs.set_xlim([21, 30.5])
axs.grid(True, linestyle='--', linewidth=0.5)
axs.tick_params(axis='x', labelsize=14)
axs.tick_params(axis='y', labelsize=14)
axs.set_xlabel('Temperature (°C)', fontsize=16, fontweight='bold')
axs.set_ylabel('Depth (m)', fontsize=16, fontweight='bold')
plt.suptitle(
'Temperature Profile for Glider: ng645-20210613T0000\nStart: 2021-08-27, End: 2021-08-31',
fontsize=20,
fontweight='bold')
plt.savefig(
'/Users/mikesmith/Documents/ng645-daily_temperature-profile-ida',
bbox_inches='tight',
pad_inches=0.1,
dpi=150)
plt.close()
fig, ax = plt.subplots(1,
len(grouped),
figsize=(16, 9),
constrained_layout=True)
# plt.gca().set_prop_cycle(plt.cycler('color', plt.cm.Blues(np.linspace(0, 1, 24))))
for i in range(len(grouped)):
newgroup = list(grouped[i][1].groupby('time'))
ax[i].set_prop_cycle(
plt.cycler('color',
plt.cm.Greens(np.linspace(0, 1, len(newgroup)))))
for group in newgroup:
# temperature profile... y axis - depth, x axis. - temperature. color- time
h = ax[i].scatter(
group[1]['salinity'],
group[1]['depth'],
label=group[0].strftime('%H:%M:%S'),
edgecolor='black',
)
ax[i].legend()
ax[i].set_title(grouped[i][0].strftime('%Y-%m-%d'),
fontsize=18,
fontweight='bold')
for axs in ax.flat:
axs.set_ylim([60, 1])
axs.set_xlim([34.5, 37])
axs.grid(True, linestyle='--', linewidth=0.5)
axs.tick_params(axis='x', labelsize=14)
axs.tick_params(axis='y', labelsize=14)
axs.set_xlabel('Salinity (1)', fontsize=16, fontweight='bold')
axs.set_ylabel('Depth (m)', fontsize=16, fontweight='bold')
plt.suptitle(
'Salinity Profile for Glider: ng645-20210613T0000\nStart: 2021-08-27, End: 2021-08-31',
fontsize=20,
fontweight='bold')
plt.savefig(
'/Users/mikesmith/Documents/ng645-daily_salinity-profile-ida',
bbox_inches='tight',
pad_inches=0.1,
dpi=150)
plt.close()
fig, ax = plt.subplots(1,
len(grouped),
figsize=(16, 9),
constrained_layout=True)
# plt.gca().set_prop_cycle(plt.cycler('color', plt.cm.Blues(np.linspace(0, 1, 24))))
for i in range(len(grouped)):
newgroup = list(grouped[i][1].groupby('time'))
# cmap = mpl.cm.Blues(np.linspace(0, 1, len(newgroup)))
# cmap = mpl.colors.ListedColormap(cmap[0:10])
cmap = plt.cm.Blues(np.linspace(0, 1, len(newgroup)))
ax[i].set_prop_cycle(plt.cycler('color', cmap))
for group in newgroup:
# temperature profile... y axis - depth, x axis. - temperature. color- time
h = ax[i].scatter(group[1]['density'],
group[1]['depth'],
label=group[0].strftime('%H:%M:%S'),
edgecolor='black')
ax[i].legend()
ax[i].set_title(grouped[i][0].strftime('%Y-%m-%d'),
fontsize=18,
fontweight='bold')
for axs in ax.flat:
axs.set_ylim([60, 1])
axs.set_xlim([1021, 1026])
axs.grid(True, linestyle='--', linewidth=0.5)
axs.tick_params(axis='x', labelsize=14)
axs.tick_params(axis='y', labelsize=14)
axs.set_xlabel('density (kg m-3)', fontsize=16, fontweight='bold')
axs.set_ylabel('Depth (m)', fontsize=16, fontweight='bold')
plt.suptitle(
'Density Profile for Glider: ng645-20210613T0000\nStart: 2021-08-27, End: 2021-08-31',
fontsize=20,
fontweight='bold')
plt.savefig(
'/Users/mikesmith/Documents/ng645-daily_density-profile-ida',
bbox_inches='tight',
pad_inches=0.1,
dpi=150)
plt.close()
def main(gliders, save_dir, m_t0, m_t1, g_t0, g_t1, lt, ylims, color_lims):
url = 'https://tds.hycom.org/thredds/dodsC/GLBy0.08/expt_93.0'
# initialize keyword arguments for glider functions
gargs = dict()
gargs['time_start'] = g_t0
gargs['time_end'] = g_t1
gargs['filetype'] = 'dataframe'
for glider in gliders:
sdir_glider = os.path.join(save_dir, glider, 'transects', 'gofs')
os.makedirs(sdir_glider, exist_ok=True)
glider_df = gld.glider_dataset(glider, **gargs)
gl_t0 = pd.to_datetime(np.nanmin(glider_df['time']))
gl_t1 = pd.to_datetime(np.nanmax(glider_df['time']))
if lt: # get the straight line transect
lt = gld.custom_gliderline_transects()
x1 = lt[glider]['extent'][0]
y1 = lt[glider]['extent'][1]
x2 = lt[glider]['extent'][2]
y2 = lt[glider]['extent'][3]
# calculate longitude and latitude of transect lines
targetlon, targetlat, _ = calculate_transect(x1, y1, x2, y2)
else: # get the temperature transect along the glider track
print('Getting GOFS custom temperature transect')
targetlon = np.array(glider_df['longitude'])
targetlat = np.array(glider_df['latitude'])
targetlon_GOFS = storms.convert_target_gofs_lon(targetlon)
with xr.open_dataset(url, drop_variables='tau') as gofs:
gofs = gofs.rename({
'surf_el': 'sea_surface_height',
'water_temp': 'temperature',
'water_u': 'u',
'water_v': 'v'
})
# Subset time range
if m_t0:
mt0 = m_t0
else:
mt0 = gl_t0 - dt.timedelta(hours=6)
if m_t1:
mt1 = m_t1
else:
mt1 = gl_t1 + dt.timedelta(days=1)
ds = gofs.sel(time=slice(mt0, mt1), depth=slice(0, 500))
for t in ds.time:
tds = ds.sel(time=t)
date_str = pd.to_datetime(
t.values).strftime('%Y-%m-%d %H:%M:%S')
date_save = pd.to_datetime(
t.values).strftime('%Y-%m-%dT%H%M%SZ')
# get the temperature transect from the model and glider
mtemp, mdepth, lon_sub, lat_sub = storms.custom_transect(
tds, 'temperature', targetlon_GOFS, targetlat, 'gofs')
gl_tm, gl_lon, gl_lat, gl_depth, gl_temp = gld.grid_glider_data(
glider_df, 'temperature', 0.5)
# plot temperature by longitude - model only
targs = {}
targs['cmap'] = cmocean.cm.thermal
targs['clab'] = 'Temperature ($^oC$)'
targs[
'title'] = f'GOFS Temperature at {date_str} UTC\n{glider} glider track'
targs['save_file'] = os.path.join(
sdir_glider,
f'{glider.split("-")[0]}_gofs_transect_temp-{date_save}.png'
)
targs['levels'] = color_lims['temp']
targs['ylims'] = ylims
print('plotting temperature by longitude')
plot_transect(lon_sub, -mdepth, mtemp, **targs)
# plot temperature by longitude - model and glider
del targs['title']
targs['title0'] = f'{glider.split("-")[0]} transect {gl_t0.strftime("%Y-%m-%dT%H:%M")} to '\
f'{gl_t1.strftime("%Y-%m-%dT%H:%M")}'
targs['title1'] = f'GOFS Temperature at {date_str} UTC'
targs['save_file'] = os.path.join(
sdir_glider,
f'{glider.split("-")[0]}_gofs_glider_transect_temp-{date_save}.png'
)
plot_transects(gl_lon, -gl_depth, gl_temp, lon_sub, -mdepth,
mtemp, **targs)
# get the salinity transect from the model and glider
print('Getting GOFS custom salinity transect')
msalt, mdepth, lon_sub, lat_sub = storms.custom_transect(
tds, 'salinity', targetlon_GOFS, targetlat, 'gofs')
gl_tm, gl_lon, gl_lat, gl_depth, gl_salt = gld.grid_glider_data(
glider_df, 'salinity', 0.5)
# plot salinity by longitude
sargs = {}
sargs['cmap'] = cmocean.cm.haline
sargs['clab'] = 'Salinity'
sargs[
'title'] = f'GOFS Salinity at {date_str} UTC\n{glider} glider track'
sargs['save_file'] = os.path.join(
sdir_glider,
f'{glider.split("-")[0]}_gofs_transect_salt-{date_save}.png'
)
sargs['levels'] = color_lims['salt']
sargs['ylims'] = ylims
print('plotting salinity by longitude')
plot_transect(lon_sub, -mdepth, msalt, **sargs)
# plot salinity by longitude - model and glider
del sargs['title']
sargs['title0'] = f'{glider.split("-")[0]} transect {gl_t0.strftime("%Y-%m-%dT%H:%M")} to ' \
f'{gl_t1.strftime("%Y-%m-%dT%H:%M")}'
sargs['title1'] = f'GOFS Salinity at {date_str} UTC'
sargs['save_file'] = os.path.join(
sdir_glider,
f'{glider.split("-")[0]}_gofs_glider_transect_salt-{date_save}.png'
)
plot_transects(gl_lon, -gl_depth, gl_salt, lon_sub, -mdepth,
msalt, **sargs)
def main(gliders, save_dir, g_t0, g_t1, xlims, ylims):
# initialize keyword arguments for glider functions
gargs = dict()
gargs['time_start'] = g_t0
gargs['time_end'] = g_t1
gargs['filetype'] = 'dataframe'
t0 = g_t0.strftime('%Y-%m-%d')
t1 = g_t1.strftime('%Y-%m-%d')
t = np.arange(xlims['temperature'][0], xlims['temperature'][1], xlims['temperature'][2])
t = np.append(t, xlims['temperature'][1])
s = np.arange(xlims['salinity'][0], xlims['salinity'][1], xlims['salinity'][2])
s = np.append(s, xlims['salinity'][1])
d = np.arange(xlims['density'][0], xlims['density'][1], xlims['density'][2])
d = np.append(d, xlims['density'][1])
for glider in gliders:
sdir_glider = os.path.join(save_dir, glider, 'transects', 'transect-ribbons')
os.makedirs(sdir_glider, exist_ok=True)
glider_df = gld.glider_dataset(glider, **gargs)
# cm = plt.get_cmap('RdYlBu', 10)
# cmap = cmocean.tools.cmap(cmocean.cm.thermal, N=10)
cmdict = cmocean.tools.get_dict(cmocean.cm.thermal, N=t.shape[0]-1)
thermal = LinearSegmentedColormap(cmocean.cm.thermal, segmentdata=cmdict, N=t.shape[0]-1)
cmdict = cmocean.tools.get_dict(cmocean.cm.haline, N=s.shape[0]-1)
haline = LinearSegmentedColormap(cmocean.cm.haline, segmentdata=cmdict, N=s.shape[0]-1)
cmdict = cmocean.tools.get_dict(cmocean.cm.dense, N=d.shape[0]-1)
dense = LinearSegmentedColormap(cmocean.cm.dense, segmentdata=cmdict, N=d.shape[0]-1)
# temperature section... time on x axis.. depth on y axis... color- temperature
fig, ax = plt.subplots(figsize=(16, 8))
plt.scatter(glider_df['time'],
glider_df['depth'],
20,
glider_df['temperature'],
cmap=thermal.reversed(thermal),
vmin=t[0],
vmax=t[-1]
)
plt.ylim(ylims)
cb = plt.colorbar()
cb.ax.tick_params(labelsize=14)
cb.set_label('Temperature (°C)', fontsize=16)
# Add the grid
plt.grid()
plt.xticks(rotation=45, fontsize=16)
plt.yticks(fontsize=16)
xfmt = mdates.DateFormatter('%d-%b-%Y\n%H:%M:%S')
ax.xaxis.set_major_formatter(xfmt)
plt.title(f'Temperature Section for Glider: {glider}\nStart: {t0}, End: {t1}',
fontsize=20, fontweight='bold')
plt.xlabel('Time (GMT)', fontsize=18, fontweight='bold')
plt.ylabel('Depth (m)', fontsize=18, fontweight='bold')
plt.savefig(f'/Users/mikesmith/Documents/{glider}-temperature-{t0}-{t1}', bbox_inches='tight', pad_inches=0.1, dpi=300)
plt.close()
fig, ax = plt.subplots(figsize=(16, 8))
plt.scatter(glider_df['time'],
glider_df['depth'],
20,
glider_df['salinity'],
cmap=haline.reversed(haline),
vmin=s[0],
vmax=s[-1]
)
plt.ylim(ylims)
cb = plt.colorbar()
cb.ax.tick_params(labelsize=14)
cb.set_label('Salinity (1)', fontsize=16)
plt.grid()
plt.xticks(rotation=45, fontsize=16)
plt.yticks(fontsize=16)
xfmt = mdates.DateFormatter('%d-%b-%Y\n%H:%M:%S')
ax.xaxis.set_major_formatter(xfmt)
plt.title(f'Salinity Section for Glider: {glider}\nStart: {t0}, End: {t1}', fontsize=20,
fontweight='bold')
plt.xlabel('Time (GMT)', fontsize=18, fontweight='bold')
plt.ylabel('Depth (m)', fontsize=18, fontweight='bold')
plt.savefig(f'/Users/mikesmith/Documents/{glider}-salinity-{t0}-{t1}', bbox_inches='tight', pad_inches=0.1, dpi=300)
plt.close()
# Density profile cross section
fig, ax = plt.subplots(figsize=(16, 8))
plt.scatter(glider_df['time'],
glider_df['depth'],
20,
glider_df['density'],
cmap=dense.reversed(dense),
vmin=d[0],
vmax=d[-1]
)
plt.ylim(ylims)
cb = plt.colorbar()
cb.ax.tick_params(labelsize=14)
cb.set_label('Density (kg m-3)', fontsize=16)
plt.grid()
plt.xticks(rotation=45, fontsize=16)
plt.yticks(fontsize=16)
xfmt = mdates.DateFormatter('%d-%b-%Y\n%H:%M:%S')
ax.xaxis.set_major_formatter(xfmt)
plt.title(f'Density Section for Glider: {glider}\nStart: {t0}, End: {t1}', fontsize=20,
fontweight='bold')
plt.xlabel('Time (GMT)', fontsize=18, fontweight='bold')
plt.ylabel('Depth (m)', fontsize=18, fontweight='bold')
plt.savefig(f'/Users/mikesmith/Documents/{glider}-density-{t0}-{t1}', bbox_inches='tight', pad_inches=0.1, dpi=300)
plt.close()
# Profile plotting
grouped = glider_df.groupby([pd.Grouper(freq='1D', key='time')])
cmap = get_cmap(len(grouped))
fig, ax = plt.subplots(figsize=(10, 12))
grouped = list(grouped)
for i in range(len(grouped)):
# temperature profile... y axis - depth, x axis. - temperature. color- time
plt.scatter(grouped[i][1]['temperature'],
grouped[i][1]['depth'],
12,
cmap=cmap(i),
label=grouped[i][0].strftime('%Y-%m-%d'))
plt.ylim(ylims)
# plt.xlim([t[0], t[-1]])
plt.xlim([11, 23])
plt.legend(fontsize=14)
# Add the grid
plt.grid()
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.title(f'Temperature Profile for Glider: {glider}\nStart: {t0}, End: {t1}',
fontsize=20, fontweight='bold')
plt.xlabel('Temperature (°C)', fontsize=18, fontweight='bold')
plt.ylabel('Depth (m)', fontsize=18, fontweight='bold')
plt.savefig(f'/Users/mikesmith/Documents/{glider}-temperature-profile-{t0}-{t1}', bbox_inches='tight', pad_inches=0.1,
dpi=300)
plt.close()
fig, ax = plt.subplots(figsize=(10, 12))
for i in range(len(grouped)):
plt.scatter(grouped[i][1]['salinity'],
grouped[i][1]['depth'],
12,
cmap=i,
label=grouped[i][0].strftime('%Y-%m-%d'))
plt.ylim(ylims)
# plt.xlim([s[0], s[-1]])
plt.xlim([31, 35])
plt.legend(fontsize=14)
# Add the grid
plt.grid()
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.title(f'Salinity Profile for Glider: {glider}\nStart: {t0}, End: {t1}', fontsize=20,
fontweight='bold')
plt.xlabel('Salinity (1)', fontsize=18, fontweight='bold')
plt.ylabel('Depth (m)', fontsize=18, fontweight='bold')
# plt.show()
plt.savefig(f'/Users/mikesmith/Documents/{glider}-salinity-profile-{t0}-{t1}', bbox_inches='tight', pad_inches=0.1,
dpi=300)
plt.close()
fig, ax = plt.subplots(figsize=(10, 12))
for i in range(len(grouped)):
plt.scatter(grouped[i][1]['density'],
grouped[i][1]['depth'],
12,
cmap=i,
label=grouped[i][0].strftime('%Y-%m-%d'))
plt.ylim(ylims)
plt.xlim([d[0], d[-1]])
plt.legend(fontsize=14)
# Add the grid
plt.grid()
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.title(f'Density Profile for Glider: {glider}\nStart: {t0}, End: {t1}', fontsize=20,
fontweight='bold')
plt.xlabel('Density (kg m-3)', fontsize=18, fontweight='bold')
plt.ylabel('Depth (m)', fontsize=18, fontweight='bold')
# plt.show()
plt.savefig(f'/Users/mikesmith/Documents/{glider}-density-profile-{t0}-{t1}', bbox_inches='tight', pad_inches=0.1,
dpi=300)
plt.close()
# initialize keyword arguments for glider functions
gargs = dict()
gargs['time_start'] = dt.datetime(2021, 8, 28, 0, 0) # False
gargs['time_end'] = dt.datetime(2021, 8, 30, 0, 0)
# gargs['time_start'] = dt.datetime(2021, 6, 17, 3, 0) # False
# gargs['time_end'] = dt.datetime(2021, 6, 24, 9, 0)
gargs['filetype'] = 'dataframe'
y_limits = [-20, 0] # None
c_limits = dict(temp=dict(shallow=np.arange(25, 30, .25)),
salt=dict(shallow=np.arange(34, 37, .25)))
sdir_glider = os.path.join(save_dir, glider, 'transects', 'transect-ribbons')
os.makedirs(sdir_glider, exist_ok=True)
glider_df = gld.glider_dataset(glider, **gargs)
glider_df = glider_df[glider_df.depth < 150]
tdf = glider_df.sort_values('temperature', ascending=True)
grouped = tdf.groupby('time')
for group in grouped:
mint = np.min(group[1]['temperature'])
maxt = np.max(group[1]['temperature'])
mins = np.min(group[1]['salinity'])
maxs = np.max(group[1]['salinity'])
tempL = np.linspace(mint-1, maxt+1, 156)
salL = np.linspace(mins-1, maxs+1, 156)
Tg, Sg = np.meshgrid(tempL, salL)
def main(gliders, save_dir, m_t0, m_t1, g_t0, g_t1, lt, ylims, color_lims):
url = '/Users/garzio/Documents/rucool/hurricane_glider_project/RTOFS/RTOFS_6hourly_North_Atlantic/'
# url = '/home/hurricaneadm/data/rtofs' # on server
# initialize keyword arguments for glider functions
gargs = dict()
gargs['time_start'] = g_t0
gargs['time_end'] = g_t1
gargs['filetype'] = 'dataframe'
for glider in gliders:
sdir_glider = os.path.join(save_dir, glider, 'transects', 'rtofs')
os.makedirs(sdir_glider, exist_ok=True)
glider_df = gld.glider_dataset(glider, **gargs)
gl_t0 = pd.to_datetime(np.nanmin(glider_df['time']))
gl_t1 = pd.to_datetime(np.nanmax(glider_df['time']))
if lt: # get the straight line transect
lt = gld.custom_gliderline_transects()
x1 = lt[glider]['extent'][0]
y1 = lt[glider]['extent'][1]
x2 = lt[glider]['extent'][2]
y2 = lt[glider]['extent'][3]
# calculate longitude and latitude of transect lines
targetlon, targetlat, _ = calculate_transect(x1, y1, x2, y2)
else: # get the temperature transect along the glider track
print('Getting RTOFS custom temperature transect')
targetlon = np.array(glider_df['longitude'])
targetlat = np.array(glider_df['latitude'])
# Subset time range
if m_t0:
mt0 = m_t0
else:
mt0 = gl_t0 - dt.timedelta(hours=6)
if m_t1:
mt1 = m_t1
else:
mt1 = gl_t1 + dt.timedelta(days=1)
model_dates = [
x.strftime('rtofs.%Y%m%d') for x in pd.date_range(mt0, mt1)
]
rtofs_files = [
glob.glob(os.path.join(url, x, '*.nc')) for x in model_dates
]
rtofs_files = sorted(
[inner for outer in rtofs_files for inner in outer])
for f in rtofs_files:
print(f)
try:
with xr.open_dataset(f) as ds:
ds = ds.rename({
'Longitude': 'lon',
'Latitude': 'lat',
'MT': 'time',
'Depth': 'depth'
})
ds = ds.sel(depth=slice(0, 500))
date_str = pd.to_datetime(
ds.time.values[0]).strftime('%Y-%m-%d %H:%M:%S')
date_save = pd.to_datetime(
ds.time.values[0]).strftime('%Y-%m-%dT%H%M%SZ')
mtemp, mdepth, lon_sub, lat_sub = storms.custom_transect(
ds, 'temperature', targetlon, targetlat, 'rtofs')
gl_tm, gl_lon, gl_lat, gl_depth, gl_temp = gld.grid_glider_data(
glider_df, 'temperature', 0.5)
# plot temperature by longitude - model only
targs = {}
targs['cmap'] = cmocean.cm.thermal
targs['clab'] = 'Temperature ($^oC$)'
targs[
'title'] = f'RTOFS Temperature at {date_str} UTC\n{glider} glider track'
targs['save_file'] = os.path.join(
sdir_glider,
f'{glider.split("-")[0]}_rtofs_transect_temp-{date_save}.png'
)
targs['levels'] = color_lims['temp']
targs['ylims'] = ylims
print('plotting temperature by longitude')
plot_transect(lon_sub, -mdepth, mtemp, **targs)
# plot temperature by longitude - model and glider
del targs['title']
targs['title0'] = f'{glider.split("-")[0]} transect {gl_t0.strftime("%Y-%m-%dT%H:%M")} to ' \
f'{gl_t1.strftime("%Y-%m-%dT%H:%M")}'
targs['title1'] = f'RTOFS Temperature at {date_str} UTC'
targs['save_file'] = os.path.join(
sdir_glider,
f'{glider.split("-")[0]}_rtofs_glider_transect_temp-{date_save}.png'
)
plot_transects(gl_lon, -gl_depth, gl_temp, lon_sub,
-mdepth, mtemp, **targs)
# get the salinity transect
print('Getting GOFS custom salinity transect')
msalt, mdepth, lon_sub, lat_sub = storms.custom_transect(
ds, 'salinity', targetlon, targetlat, 'rtofs')
gl_tm, gl_lon, gl_lat, gl_depth, gl_salt = gld.grid_glider_data(
glider_df, 'salinity', 0.5)
# plot salinity by longitude
sargs = {}
sargs['cmap'] = cmocean.cm.haline
sargs['clab'] = 'Salinity'
sargs[
'title'] = f'RTOFS Salinity at {date_str} UTC\n{glider} glider track'
sargs['save_file'] = os.path.join(
sdir_glider,
f'{glider.split("-")[0]}_rtofs_transect_salt-{date_save}.png'
)
sargs['levels'] = color_lims['salt']
sargs['ylims'] = ylims
print('plotting salinity by longitude')
plot_transect(lon_sub, -mdepth, msalt, **sargs)
# plot salinity by longitude - model and glider
del sargs['title']
sargs['title0'] = f'{glider.split("-")[0]} transect {gl_t0.strftime("%Y-%m-%dT%H:%M")} to ' \
f'{gl_t1.strftime("%Y-%m-%dT%H:%M")}'
sargs['title1'] = f'RTOFS Salinity at {date_str} UTC'
sargs['save_file'] = os.path.join(
sdir_glider,
f'{glider.split("-")[0]}_rtofs_glider_transect_salt-{date_save}.png'
)
plot_transects(gl_lon, -gl_depth, gl_salt, lon_sub,
-mdepth, msalt, **sargs)
except OSError:
continue
def main(gliders, save_dir, g_t0, g_t1, ylims, color_lims):
# url = '/Users/garzio/Documents/rucool/hurricane_glider_project/RTOFS/RTOFS_6hourly_North_Atlantic/'
# url = '/home/hurricaneadm/data/rtofs' # on server
url = '/Users/mikesmith/Documents/github/rucool/hurricanes/data/rtofs/'
# initialize keyword arguments for glider functions
gargs = dict()
gargs['time_start'] = g_t0
gargs['time_end'] = g_t1
gargs['filetype'] = 'dataframe'
for glider in gliders:
sdir_glider = os.path.join(save_dir, glider, 'transects',
'transect-ribbons')
os.makedirs(sdir_glider, exist_ok=True)
glider_df = gld.glider_dataset(glider, **gargs)
gl_t0 = pd.to_datetime(np.nanmin(glider_df['time']))
gl_t1 = pd.to_datetime(np.nanmax(glider_df['time']))
gl_t0str = gl_t0.strftime('%Y-%m-%dT%H:%M')
gl_t1str = gl_t1.strftime('%Y-%m-%dT%H:%M')
gl_t0save = gl_t0.strftime('%Y%m%dT%H%M')
glider_name = glider.split('-')[0]
# Subset time range (add a little extra to the glider time range)
mt0 = gl_t0 - dt.timedelta(hours=2)
mt1 = gl_t1 + dt.timedelta(hours=12)
model_dates = [
x.strftime('rtofs.%Y%m%d') for x in pd.date_range(mt0, mt1)
]
rtofs_files = [
glob.glob(os.path.join(url, x, '*.nc')) for x in model_dates
]
rtofs_files = sorted(
[inner for outer in rtofs_files for inner in outer])
# make sure the file times are within the specified times
model_time = np.array([], dtype='datetime64[ns]')
rfiles = []
for rf in rtofs_files:
splitter = rf.split('/')
ymd = splitter[-2].split('.')[-1]
hr = splitter[-1].split('_')[3][-2:]
if hr == '24':
hr = '00'
td = 1
else:
td = 0
mt = dt.datetime.strptime('T'.join(
(ymd, hr)), '%Y%m%dT%H') + dt.timedelta(days=td)
if np.logical_and(mt >= mt0, mt <= gl_t1 + dt.timedelta(hours=1)):
rfiles.append(rf)
model_time = np.append(model_time, pd.to_datetime(mt))
model_t0str = pd.to_datetime(
np.nanmin(model_time)).strftime('%Y-%m-%dT%H:%M')
model_t1str = pd.to_datetime(
np.nanmax(model_time)).strftime('%Y-%m-%dT%H:%M')
# open the first file to get some of the model information
ds = xr.open_dataset(rfiles[0])
ds = ds.rename({
'Longitude': 'lon',
'Latitude': 'lat',
'MT': 'time',
'Depth': 'depth'
})
ds = ds.sel(depth=slice(0, 1000))
mlon = ds.lon.values
mlat = ds.lat.values
# interpolate glider lat/lon to lat/lon on model time
sublonm = np.interp(pd.to_datetime(model_time),
pd.to_datetime(glider_df.time),
glider_df.longitude)
sublatm = np.interp(pd.to_datetime(model_time),
pd.to_datetime(glider_df.time), glider_df.latitude)
# interpolating transect X and Y to lat and lon
lonIndex = np.round(
np.interp(sublonm, mlon[0, :],
np.arange(0, len(mlon[0, :])))).astype(int)
latIndex = np.round(
np.interp(sublatm, mlat[:, 0],
np.arange(0, len(mlat[:, 0])))).astype(int)
# get temperature and salinity data along the glider track (space and time)
mtemp = np.full([len(ds.depth), len(model_time)], np.nan)
msalt = np.full([len(ds.depth), len(model_time)], np.nan)
for i, f in enumerate(rfiles):
print(f)
with xr.open_dataset(f) as ds:
ds = ds.rename({
'Longitude': 'lon',
'Latitude': 'lat',
'MT': 'time',
'Depth': 'depth'
})
ds = ds.sel(depth=slice(0, 1000))
mtemp[:, i] = ds['temperature'][0, :, latIndex[i],
lonIndex[i]].values
msalt[:, i] = ds['salinity'][0, :, latIndex[i],
lonIndex[i]].values
# get the temperature transect from the glider
gl_tm, gl_lon, gl_lat, gl_depth, gl_temp = gld.grid_glider_data(
glider_df, 'temperature', 0.5)
# plot temperature by time (glider time/location) - model only
targs = {}
targs['cmap'] = cmocean.cm.thermal
targs['clab'] = 'Temperature ($^oC$)'
targs['title'] = f'RTOFS Temperature along {glider} track\n'\
f'Model: {model_t0str} to {model_t1str}\n'\
f'Glider: {gl_t0str} to {gl_t1str}'
targs['save_file'] = os.path.join(
sdir_glider, f'{glider_name}_rtofs_transect_temp-{gl_t0save}.png')
targs['levels'] = color_lims['temp']
targs['ylims'] = ylims
targs['xlab'] = 'Time'
plot_transect(model_time, ds.depth.values, mtemp, **targs)
# plot temperature by time (glider time/location) - model and glider
del targs['title']
targs['title0'] = f'{glider_name} transect {gl_t0str} to {gl_t1str}'
targs['title1'] = f'RTOFS Temperature: {model_t0str} to {model_t1str}'
targs['save_file'] = os.path.join(
sdir_glider,
f'{glider_name}_rtofs_glider_transect_temp-{gl_t0save}.png')
plot_transects(gl_tm, gl_depth, gl_temp, model_time, ds.depth.values,
mtemp, **targs)
# get the salinity transect
gl_tm, gl_lon, gl_lat, gl_depth, gl_salt = gld.grid_glider_data(
glider_df, 'salinity', 0.5)
# plot salinity by time - model only
sargs = {}
sargs['cmap'] = cmocean.cm.haline
sargs['clab'] = 'Salinity'
sargs['title'] = f'RTOFS Salinity along {glider} track\n' \
f'Model: {model_t0str} to {model_t1str}\n' \
f'Glider: {gl_t0str} to {gl_t1str}'
sargs['save_file'] = os.path.join(
sdir_glider, f'{glider_name}_rtofs_transect_salt-{gl_t0save}.png')
sargs['levels'] = color_lims['salt']
sargs['ylims'] = ylims
sargs['xlab'] = 'Time'
plot_transect(model_time, ds.depth.values, msalt, **sargs)
# plot salinity by time (glider time/location) - model and glider
del sargs['title']
sargs['title0'] = f'{glider_name} transect {gl_t0str} to {gl_t1str}'
sargs['title1'] = f'RTOFS Salinity: {model_t0str} to {model_t1str}'
sargs['save_file'] = os.path.join(
sdir_glider,
f'{glider_name}_rtofs_glider_transect_salt-{gl_t0save}.png')
plot_transects(gl_tm, gl_depth, gl_salt, model_time, ds.depth.values,
msalt, **sargs)
def main(gliders, save_dir, g_t0, g_t1, ylims, color_lims):
url = 'https://tds.hycom.org/thredds/dodsC/GLBy0.08/expt_93.0'
# initialize keyword arguments for glider functions
gargs = dict()
gargs['time_start'] = g_t0
gargs['time_end'] = g_t1
gargs['filetype'] = 'dataframe'
for glider in gliders:
sdir_glider = os.path.join(save_dir, glider, 'transects',
'transect-ribbons')
os.makedirs(sdir_glider, exist_ok=True)
glider_df = gld.glider_dataset(glider, **gargs)
gl_t0 = pd.to_datetime(np.nanmin(glider_df['time']))
gl_t1 = pd.to_datetime(np.nanmax(glider_df['time']))
gl_t0str = gl_t0.strftime('%Y-%m-%dT%H:%M')
gl_t1str = gl_t1.strftime('%Y-%m-%dT%H:%M')
gl_t0save = gl_t0.strftime('%Y%m%dT%H%M')
glider_name = glider.split('-')[0]
with xr.open_dataset(url, drop_variables='tau') as gofs:
gofs = gofs.rename({
'surf_el': 'sea_surface_height',
'water_temp': 'temperature',
'water_u': 'u',
'water_v': 'v'
})
# Subset time range (add a little extra to the glider time range)
mt0 = gl_t0 - dt.timedelta(hours=1)
mt1 = gl_t1 + dt.timedelta(hours=1)
ds = gofs.sel(time=slice(mt0, mt1), depth=slice(0, 1000))
model_t0str = pd.to_datetime(np.nanmin(
ds.time.values)).strftime('%Y-%m-%dT%H:%M')
model_t1str = pd.to_datetime(np.nanmax(
ds.time.values)).strftime('%Y-%m-%dT%H:%M')
# interpolate glider lat/lon to lat/lon on model time
sublonm = np.interp(pd.to_datetime(ds.time.values),
pd.to_datetime(glider_df.time),
glider_df.longitude)
sublatm = np.interp(pd.to_datetime(ds.time.values),
pd.to_datetime(glider_df.time),
glider_df.latitude)
sublonm_GOFS = storms.convert_target_gofs_lon(sublonm)
# get temperature and salinity data along the glider track (space and time)
mtemp = np.full([len(ds.depth), len(ds.time)], np.nan)
msalt = np.full([len(ds.depth), len(ds.time)], np.nan)
for i, t in enumerate(ds.time):
tds = ds.sel(time=t,
lon=sublonm_GOFS[i],
lat=sublatm[i],
method='nearest')
mtemp[:, i] = tds['temperature'].values
msalt[:, i] = tds['salinity'].values
# get the temperature transect from the glider
gl_tm, gl_lon, gl_lat, gl_depth, gl_temp = gld.grid_glider_data(
glider_df, 'temperature', 0.5)
# plot temperature by time (glider time/location) - model only
targs = {}
targs['cmap'] = cmocean.cm.thermal
targs['clab'] = 'Temperature ($^oC$)'
targs['title'] = f'GOFS Temperature along {glider} track\nModel: {model_t0str} to {model_t1str} ' \
f'Glider: {gl_t0str} to {gl_t1str}'
targs['save_file'] = os.path.join(
sdir_glider,
f'{glider_name}_gofs_transect_temp-{gl_t0save}.png')
targs['levels'] = color_lims['temp']
targs['ylims'] = ylims
targs['xlab'] = 'Time'
plot_transect(ds.time.values, ds.depth.values, mtemp, **targs)
# plot temperature by time (glider time/location) - model and glider
del targs['title']
targs[
'title0'] = f'{glider_name} transect {gl_t0str} to {gl_t1str}'
targs[
'title1'] = f'GOFS Temperature: {model_t0str} to {model_t1str}'
targs['save_file'] = os.path.join(
sdir_glider,
f'{glider_name}_gofs_glider_transect_temp-{gl_t0save}.png')
plot_transects(gl_tm, gl_depth, gl_temp, ds.time.values,
ds.depth.values, mtemp, **targs)
# get the salinity transect from the glider
gl_tm, gl_lon, gl_lat, gl_depth, gl_salt = gld.grid_glider_data(
glider_df, 'salinity', 0.5)
# plot salinity by time - model only
sargs = {}
sargs['cmap'] = cmocean.cm.haline
sargs['clab'] = 'Salinity'
sargs['title'] = f'GOFS Salinity along {glider} track\nModel: {model_t0str} to {model_t1str} ' \
f'Glider: {gl_t0str} to {gl_t1str}'
sargs['save_file'] = os.path.join(
sdir_glider,
f'{glider_name}_gofs_transect_salt-{gl_t0save}.png')
sargs['levels'] = color_lims['salt']
sargs['ylims'] = ylims
sargs['xlab'] = 'Time'
plot_transect(ds.time.values, ds.depth.values, msalt, **sargs)
# plot salinity by time (glider time/location) - model and glider
del sargs['title']
sargs[
'title0'] = f'{glider_name} transect {gl_t0str} to {gl_t1str}'
sargs['title1'] = f'GOFS Salinity: {model_t0str} to {model_t1str}'
sargs['save_file'] = os.path.join(
sdir_glider,
f'{glider_name}_gofs_glider_transect_salt-{gl_t0save}.png')
plot_transects(gl_tm, gl_depth, gl_salt, ds.time.values,
ds.depth.values, msalt, **sargs)
def main(gliders, save_dir, bathymetry, m_t0, m_t1, g_t0, g_t1, lt, current_glider_location):
url = 'https://tds.hycom.org/thredds/dodsC/GLBy0.08/expt_93.0'
# initialize keyword arguments for glider functions
gargs = dict()
gargs['time_start'] = g_t0
gargs['time_end'] = g_t1
# initialize keyword arguments for map plot
kwargs = dict()
kwargs['model'] = 'gofs'
kwargs['transform'] = ccrs.PlateCarree()
kwargs['current_glider_loc'] = current_glider_location
if bathymetry:
bath = xr.open_dataset(bathymetry)
else:
bath = False
for glider in gliders:
sdir_glider = os.path.join(save_dir, glider)
os.makedirs(sdir_glider, exist_ok=True)
kwargs['save_dir'] = sdir_glider
glider_ds = gld.glider_dataset(glider, **gargs)
kwargs['gliders'] = glider_ds
glider_region = gld.glider_region(glider_ds) # define the glider region
gl_t0 = pd.to_datetime(np.nanmin(glider_ds.time.values))
gl_t1 = pd.to_datetime(np.nanmax(glider_ds.time.values))
if len(glider_region) < 1:
raise ValueError('No region found for glider: {}'.format(glider))
if lt:
lt = gld.custom_gliderline_transects()
kwargs['custom_transect'] = dict(lon=[lt[glider]['extent'][0], lt[glider]['extent'][2]],
lat=[lt[glider]['extent'][1], lt[glider]['extent'][3]])
# Loop through regions
for region in glider_region.items():
extent = region[1]['lonlat']
if bath:
kwargs['bathy'] = bath.sel(lon=slice(extent[0] - .1, extent[1] + .1),
lat=slice(extent[2] - .1, extent[3] + .1))
with xr.open_dataset(url, drop_variables='tau') as gofs:
gofs = gofs.rename({'surf_el': 'sea_surface_height', 'water_temp': 'temperature', 'water_u': 'u', 'water_v': 'v'})
# Subset time range
if m_t0:
mt0 = m_t0
else:
mt0 = gl_t0 - dt.timedelta(hours=6)
if m_t1:
mt1 = m_t1
else:
mt1 = gl_t1 + dt.timedelta(days=1)
ds = gofs.sel(time=slice(mt0, mt1))
for t in ds.time:
print(f'Accessing GOFS: {str(t.dt.strftime("%Y-%m-%d %H:%M:%S").data)}')
tds = ds.sel(time=t) # Select the latest time
# subset dataset to the proper extents for each region
sub = tds.sel(lon=slice(extent[0] + 359, extent[1] + 361), lat=slice(extent[2] - 1, extent[3] + 1))
sub['lon'] = sub['lon'] - 360 # Convert model lon to glider lon
surface_map_glider_track(sub, region, **kwargs)
def main(gliders, save_dir, g_t0, g_t1, ylims, color_lims):
# initialize keyword arguments for glider functions
gargs = dict()
gargs['time_start'] = g_t0
gargs['time_end'] = g_t1
gargs['filetype'] = 'dataframe'
#gargs['filetype'] = 'nc'
for glider in gliders:
sdir_glider = os.path.join(save_dir, glider, 'transects')
os.makedirs(sdir_glider, exist_ok=True)
glider_df = gld.glider_dataset(glider, **gargs)
gl_t0 = pd.to_datetime(np.nanmin(glider_df['time']))
gl_t1 = pd.to_datetime(np.nanmax(glider_df['time']))
gl_t0str = gl_t0.strftime('%Y-%m-%dT%H:%M')
gl_t1str = gl_t1.strftime('%Y-%m-%dT%H:%M')
gl_t0save = gl_t0.strftime('%Y%m%dT%H%M')
glider_name = glider.split('-')[0]
gl_tm, gl_lon, gl_lat, gl_depth, gl_temp = gld.grid_glider_data(glider_df, 'temperature', 0.5)
# plot temperature by time
targs = {}
targs['cmap'] = cmocean.cm.thermal
targs['clab'] = 'Temperature ($^oC$)'
targs['title'] = f'{glider_name} transect {gl_t0str} to {gl_t1str}'
targs['save_file'] = os.path.join(sdir_glider, f'{glider_name}_transect_temp-{gl_t0save}.png')
targs['xlab'] = 'Time'
if ylims:
targs['ylims'] = ylims
if color_lims:
targs['levels'] = color_lims['temp']
print('plotting temperature by time')
plot_transect(gl_tm, gl_depth, gl_temp, **targs)
# plot temperature by longitude
targs['save_file'] = os.path.join(sdir_glider, f'{glider_name}_transect_temp-lon-{gl_t0save}.png')
targs['xlab'] = 'Longitude'
print('plotting temperature by longitude')
plot_transect(gl_lon, gl_depth, gl_temp, **targs)
# grid salinity data
gl_tm, gl_lon, gl_lat, gl_depth, gl_salt = gld.grid_glider_data(glider_df, 'salinity', 0.5)
# plot salinity by time
sargs = {}
sargs['cmap'] = cmocean.cm.haline
sargs['clab'] = 'Salinity'
sargs['title'] = f'{glider_name} transect {gl_t0str} to {gl_t1str}'
sargs['save_file'] = os.path.join(sdir_glider, f'{glider_name}_transect_salt-{gl_t0save}.png')
sargs['xlab'] = 'Time'
if ylims:
sargs['ylims'] = ylims
if color_lims:
sargs['levels'] = color_lims['salt']
print('plotting salinity by time')
plot_transect(gl_tm, gl_depth, gl_salt, **sargs)
# plot salinity by longitude
sargs['save_file'] = os.path.join(sdir_glider, f'{glider_name}_transect_salt-lon-{gl_t0save}.png')
sargs['xlab'] = 'Longitude'
print('plotting salinity by longitude')
plot_transect(gl_lon, gl_depth, gl_salt, **sargs)
