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)