def test_ops_wind(self, m_dfp, wind_dataset, tmpdir):
tmp_weather_path = tmpdir.ensure_dir('operational')
m_dfp.side_effect = (wind_dataset, )
wind_avg = wind_tools.calc_wind_avg_at_point(
arrow.get('2016-02-02 04:25'), str(tmp_weather_path), (0, 0))
np.testing.assert_allclose(wind_avg.u, 2.5)
np.testing.assert_allclose(wind_avg.v, -2.5)
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 test_prepend_previous_day(
self,
m_dfp,
wind_dataset,
tmpdir,
):
tmp_weather_path = tmpdir.ensure_dir('operational')
wind_prev_day = nc.Dataset('wind_prev_day', 'w')
wind_prev_day.createDimension('time_counter')
wind_prev_day.createDimension('y', 1)
wind_prev_day.createDimension('x', 1)
u_wind = wind_prev_day.createVariable('u_wind', float,
('time_counter', 'y', 'x'))
u_wind[:] = np.arange(5)
v_wind = wind_prev_day.createVariable('v_wind', float,
('time_counter', 'y', 'x'))
v_wind[:] = np.arange(0, -5, -1)
time_counter = wind_prev_day.createVariable('time_counter', float,
('time_counter', ))
time_counter.time_origin = '2016-FEB-01 00:00:00'
time_counter[:] = np.arange(19, 24) * 60 * 60
m_dfp.side_effect = (wind_dataset, wind_prev_day)
wind_avg = wind_tools.calc_wind_avg_at_point(
arrow.get('2016-02-02 01:25'), str(tmp_weather_path), (0, 0))
wind_prev_day.close()
os.remove('wind_prev_day')
np.testing.assert_allclose(wind_avg.u, 2)
np.testing.assert_allclose(wind_avg.v, -2)
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_wind_4h_avg(feed, max_ssh_time, config):
weather_path = config["weather"]["ops dir"]
tide_gauge_stn = config["storm surge feeds"]["feeds"][feed]["tide gauge stn"]
wind_avg = wind_tools.calc_wind_avg_at_point(
arrow.get(max_ssh_time),
weather_path,
PLACES[tide_gauge_stn]["wind grid ji"],
avg_hrs=-4,
)
wind_vector = wind_tools.wind_speed_dir(wind_avg.u, wind_avg.v)
return {
"wind_speed_4h_avg": np.asscalar(wind_vector.speed),
"wind_dir_4h_avg": np.asscalar(wind_vector.dir),
}
def _calc_wind_4h_avg(feed, max_ssh_time, config):
weather_path = config['weather']['ops dir']
tide_gauge_stn = (
config['storm surge feeds']['feeds'][feed]['tide gauge stn'])
wind_avg = wind_tools.calc_wind_avg_at_point(
arrow.get(max_ssh_time),
weather_path,
PLACES[tide_gauge_stn]['wind grid ji'],
avg_hrs=-4)
wind_vector = wind_tools.wind_speed_dir(wind_avg.u, wind_avg.v)
return {
'wind_speed_4h_avg': np.asscalar(wind_vector.speed),
'wind_dir_4h_avg': np.asscalar(wind_vector.dir),
}
def _prep_plot_data(
place,
ssh_fcst_dataset_url_tmpl,
tidal_predictions,
forecast_hrs,
weather_path,
bathy,
grid_T_hr_path,
):
# NEMO sea surface height forecast dataset
ssh_forecast = _get_ssh_forecast(place, ssh_fcst_dataset_url_tmpl)
# CHS water level observations dataset
try:
obs_1min = (data_tools.get_chs_tides(
"obs",
place,
arrow.get(str(ssh_forecast.time.values[0])) -
timedelta(seconds=5 * 60),
arrow.get(str(ssh_forecast.time.values[-1])),
).to_xarray().rename({"index": "time"}))
obs_10min_avg = obs_1min.resample(time="10min", loffset="5min").mean()
obs = obs_10min_avg.to_dataset(name="water_level")
except (AttributeError, KeyError):
# No observations available
obs = None
shared.localize_time(ssh_forecast)
try:
shared.localize_time(obs)
except (IndexError, AttributeError):
# No observations available
obs = None
model_ssh_period = slice(str(ssh_forecast.time.values[0]),
str(ssh_forecast.time.values[-1]))
forecast_period = slice(
str(ssh_forecast.time.values[-forecast_hrs * 6]),
str(ssh_forecast.time.values[-1]),
)
try:
obs_period = slice(str(obs.time.values[0]), str(obs.time.values[-1]))
except AttributeError:
# No observations available
obs_period = None
# Predicted tide water levels dataset from ttide
ttide = shared.get_tides(place, tidal_predictions)
ttide.rename(columns={" pred_noshallow ": "pred_noshallow"}, inplace=True)
ttide.index = pandas.to_datetime(ttide.time.values,
format="%Y-%m-%d %H:%M:%S")
ttide_ds = ttide.to_xarray().drop_vars(["time"]).rename({"index": "time"})
# Localize ttide dataset timezone to ssh_forecast times because ttide
# extends well beyond ends of ssh_forecast period
shared.localize_time(ttide_ds,
local_datetime=arrow.get(
str(ssh_forecast.time.values[0])).to("local"))
# NEMO sea surface height dataset corrected to include unmodeled tide constituents
ssh_correction = ttide_ds.pred_noshallow.sel(
time=model_ssh_period) - ttide_ds.pred_8.sel(time=model_ssh_period)
ssh_corrected = ssh_forecast + ssh_correction
# Mean sea level and extreme water levels
msl = PLACES[place]["mean sea lvl"]
extreme_ssh = PLACES[place]["hist max sea lvl"]
max_tides = ttide.pred_all.max() + msl
mid_tides = 0.5 * (extreme_ssh - max_tides) + max_tides
thresholds = (max_tides, mid_tides, extreme_ssh)
max_ssh = ssh_corrected.ssh.sel(time=forecast_period)
max_ssh = max_ssh.where(max_ssh == max_ssh.max(), drop=True).squeeze()
# Residual differences between corrected model and observations and predicted tides
model_residual = ssh_corrected - ttide_ds.pred_all.sel(
time=model_ssh_period)
model_residual.attrs["tz_name"] = ssh_forecast.attrs["tz_name"]
max_model_residual = model_residual.max()
try:
obs_residual = obs - ttide_ds.pred_all.sel(time=obs_period) - msl
obs_residual.attrs["tz_name"] = obs.attrs["tz_name"]
except KeyError:
# No observations available
obs_residual = None
# Wind at NEmo model time of max sea surface height
wind_4h_avg = wind_tools.calc_wind_avg_at_point(
arrow.get(str(max_ssh.time.values)),
weather_path,
PLACES[place]["wind grid ji"],
avg_hrs=-4,
)
wind_4h_avg = wind_tools.wind_speed_dir(*wind_4h_avg)
# Model sea surface height field for contour map
tracers_ds = xarray.open_dataset(grid_T_hr_path)
max_ssh_time_utc = (arrow.get(str(max_ssh.time.values)).replace(
tzinfo=ssh_forecast.attrs["tz_name"]).to("utc"))
return SimpleNamespace(
ssh_forecast=ssh_forecast,
obs=obs,
ttide=ttide_ds,
ssh_corrected=ssh_corrected,
msl=msl,
thresholds=thresholds,
max_ssh=max_ssh,
model_residual=model_residual,
max_model_residual=max_model_residual,
obs_residual=obs_residual,
wind_4h_avg=wind_4h_avg,
bathy=bathy,
max_ssh_field=tracers_ds.sossheig.sel(
time_counter=max_ssh_time_utc.naive, method="nearest"),
)
