def _prep_plot_data(grids_15m, tidal_predictions, weather_path):
max_ssh, max_ssh_time, risk_levels = {}, {}, {}
u_wind_4h_avg, v_wind_4h_avg, max_wind_avg = {}, {}, {}
for name in places.TIDE_GAUGE_SITES:
ssh_ts = nc_tools.ssh_timeseries_at_point(grids_15m[name], 0, 0, datetimes=True)
ttide = shared.get_tides(name, tidal_predictions)
max_ssh[name], max_ssh_time[name] = shared.find_ssh_max(name, ssh_ts, ttide)
risk_levels[name] = stormtools.storm_surge_risk_level(
name, max_ssh[name], ttide
)
wind_avg = wind_tools.calc_wind_avg_at_point(
arrow.get(max_ssh_time[name]),
weather_path,
places.PLACES[name]["wind grid ji"],
avg_hrs=-4,
)
u_wind_4h_avg[name], v_wind_4h_avg[name] = wind_avg
max_wind_avg[name], _ = wind_tools.wind_speed_dir(
u_wind_4h_avg[name], v_wind_4h_avg[name]
)
plot_data = namedtuple(
"PlotData",
"ssh_ts, max_ssh, max_ssh_time, risk_levels, "
"u_wind_4h_avg, v_wind_4h_avg, max_wind_avg",
)
return plot_data(
ssh_ts,
max_ssh,
max_ssh_time,
risk_levels,
u_wind_4h_avg,
v_wind_4h_avg,
max_wind_avg,
)
def _prep_plot_data(
place,
grid_T_hr,
grids_15m,
bathy,
timezone,
weather_path,
tidal_predictions,
):
ssh_hr = grid_T_hr.variables['sossheig']
time_ssh_hr = nc_tools.timestamp(
grid_T_hr, range(grid_T_hr.variables['time_counter'].size))
try:
j, i = places.PLACES[place]['NEMO grid ji']
except KeyError as e:
raise KeyError(f'place name or info key not found in '
f'salishsea_tools.places.PLACES: {e}')
itime_max_ssh = np.argmax(ssh_hr[:, j, i])
time_max_ssh_hr = time_ssh_hr[itime_max_ssh]
ssh_15m_ts = nc_tools.ssh_timeseries_at_point(grids_15m[place],
0,
0,
datetimes=True)
ttide = shared.get_tides(place, tidal_predictions)
ssh_corr = shared.correct_model_ssh(ssh_15m_ts.ssh, ssh_15m_ts.time, ttide)
max_ssh_15m, time_max_ssh_15m = shared.find_ssh_max(
place, ssh_15m_ts, ttide)
tides_15m = shared.interp_to_model_time(ssh_15m_ts.time, ttide.pred_all,
ttide.time)
residual = ssh_corr - tides_15m
max_ssh_residual = residual[ssh_15m_ts.time == time_max_ssh_15m][0]
wind_4h_avg = wind_tools.calc_wind_avg_at_point(
arrow.get(time_max_ssh_15m),
weather_path,
places.PLACES[place]['wind grid ji'],
avg_hrs=-4)
wind_4h_avg = wind_tools.wind_speed_dir(*wind_4h_avg)
plot_data = namedtuple(
'PlotData', 'ssh_max_field, time_max_ssh_hr, ssh_15m_ts, ssh_corr, '
'max_ssh_15m, time_max_ssh_15m, residual, max_ssh_residual, '
'wind_4h_avg, '
'ttide, bathy')
return plot_data(
ssh_max_field=ssh_hr[itime_max_ssh],
time_max_ssh_hr=time_max_ssh_hr.to(timezone),
ssh_15m_ts=ssh_15m_ts,
ssh_corr=ssh_corr,
max_ssh_15m=max_ssh_15m - places.PLACES[place]['mean sea lvl'],
time_max_ssh_15m=arrow.get(time_max_ssh_15m).to(timezone),
residual=residual,
max_ssh_residual=max_ssh_residual,
wind_4h_avg=wind_4h_avg,
ttide=ttide,
bathy=bathy,
)
def _calc_max_ssh(feed, ttide, run_date, run_type, config):
results_path = config['results archive'][run_type]
tide_gauge_stn = (
config['storm surge feeds']['feeds'][feed]['tide gauge stn'])
grid_T_15m = nc.Dataset(
os.path.join(results_path,
run_date.format('DDMMMYY').lower(),
f'{tide_gauge_stn.replace(" ", "")}.nc'))
ssh_ts = nc_tools.ssh_timeseries_at_point(grid_T_15m, 0, 0, datetimes=True)
ssh_corr = nowcast.figures.shared.correct_model_ssh(
ssh_ts.ssh, ssh_ts.time, ttide)
max_ssh = np.max(ssh_corr) + PLACES[tide_gauge_stn]['mean sea lvl']
max_ssh_time = ssh_ts.time[np.argmax(ssh_corr)]
return max_ssh, max_ssh_time
def test_ssh_timeseries_at_point_time_counter_type(
datetimes,
expected,
nc_dataset,
):
"""Sea surface height timeseries time counter values have expected type
"""
nc_dataset.createDimension('time_counter')
nc_dataset.createDimension('y', 1)
nc_dataset.createDimension('x', 1)
ssh = nc_dataset.createVariable('sossheig', float,
('time_counter', 'y', 'x'))
ssh[:] = np.array([5.0, 5.3])
time_counter = nc_dataset.createVariable('time_counter', float,
('time_counter', ))
time_counter.time_origin = '2002-OCT-26 00:00:00'
time_counter[:] = np.array([0.5, 1.5]) * 60 * 60
ssh_ts = nc_tools.ssh_timeseries_at_point(nc_dataset, 0, 0, datetimes)
np.testing.assert_array_equal(ssh_ts.ssh, np.array([5.0, 5.3]))
assert isinstance(ssh_ts.time[0], expected)
def _calc_max_ssh(feed, ttide, run_date, run_type, config):
results_path = config["results archive"][run_type]
tide_gauge_stn = config["storm surge feeds"]["feeds"][feed]["tide gauge stn"]
grid_T_15m = nc.Dataset(
os.path.join(
results_path,
run_date.format("DDMMMYY").lower(),
f'{tide_gauge_stn.replace(" ", "")}.nc',
)
)
ssh_ts = nc_tools.ssh_timeseries_at_point(grid_T_15m, 0, 0, datetimes=True)
max_ssh, max_ssh_time = nowcast.figures.shared.find_ssh_max(
tide_gauge_stn, ssh_ts, ttide
)
if np.isnan(max_ssh):
logger.critical(
f"no {tide_gauge_stn} feed generated: max sea surface height is "
f"NaN at {max_ssh_time}"
)
raise WorkerError
return max_ssh, max_ssh_time