verbose=prm["verbose"],
load_z0=prm["load_z0"],
save=prm["save_z0"])
# Processing
p = Processing(obs=BDclim,
mnt=IGN,
nwp=AROME,
model_path=prm['model_path'],
GPU=prm["GPU"],
data_path=prm['data_path'])
# Predictions
array_xr = p.predict_at_stations(prm["stations_to_predict"],
verbose=True,
Z0_cond=prm["Z0"],
peak_valley=prm["peak_valley"])
# Visualization
v = Visualization(p)
# Evaluation
e = Evaluation(v, array_xr)
# Store nwp, cnn predictions and observations
for station in prm["stations_to_predict"]:
nwp, cnn, obs = e._select_dataframe(array_xr, station_name=station,
day=None, month=month, year=year,
variable=prm["variable"],
rolling_mean=None, rolling_window=None)
results["nwp"][station].append(nwp)
results["cnn"][station].append(cnn)
p = Processing(obs=BDclim,
mnt=IGN,
nwp=AROME,
model_path=prm['model_path'],
GPU=prm["GPU"],
data_path=prm['data_path'])
t1 = t()
if prm["launch_predictions"]:
if prm["stations_to_predict"] == 'all':
prm["stations_to_predict"] = BDclim.stations["name"].values
array_xr = p.predict_at_stations(prm["stations_to_predict"],
verbose=True,
Z0_cond=prm["Z0"],
peak_valley=prm["peak_valley"],
ideal_case=False,
line_profile=prm["line_profile"])
t2 = t()
print(f'\nPredictions in {round(t1, t2)} seconds')
# Visualization
v = Visualization(p)
#v.qc_plot_last_flagged(stations=['Vallot', 'Argentiere'])
# v.plot_predictions_2D(array_xr, ['Col du Lac Blanc'])
# v.plot_predictions_3D(array_xr, ['Col du Lac Blanc'])
# v.plot_comparison_topography_MNT_NWP(station_name='Col du Lac Blanc', new_figure=False)
# Evaluation
if prm["launch_predictions"]: e = Evaluation(v, array_xr)
