decor_swh = nc_swh.variables["decor_scale"][:] time_decor_swh = num2date(nc_swh.variables["time"][:], nc_swh.variables["time"].units) ###### WSP ###### nc_wsp = Dataset(data_path_decor + "CCMP2_wsp_decor_time_scale.nc", "r") decor_wsp = nc_wsp.variables["decor_scale"][:] time_decor_wsp = num2date(nc_wsp.variables["time"][:], nc_wsp.variables["time"].units) # Compute WSP statistical moments seasonally wsp_stats_s = stat_moments_temporal(wsp, time_w, "seasonally", "sample") wsp_moments_mean = np.ma.array(wsp_stats_s["mean"]) # Calculate monthly climatologies for SWH and WSP data and decorrelation scales: ###### SWH ###### swh_clima_dict = clima_mean(date_time=np.ma.array(time_s), data=swh) swh_mean_i = np.ma.array(swh_clima_dict["mean"]) swh_var_i = np.ma.array(swh_clima_dict["var"]) swh_n_i = np.ma.array(swh_clima_dict["N"]) decor_clima_dict_s = clima_mean(date_time=time_decor_swh, data=decor_swh) decor_mean_swh_i = np.ma.array(decor_clima_dict_s["mean"]) ###### WSP ###### wsp_clima_dict = clima_mean(date_time=np.ma.array(time_w), data=wsp) wsp_mean_c = np.ma.array(wsp_clima_dict["mean"]) wsp_var_c = np.ma.array(wsp_clima_dict["var"]) wsp_n_c = np.ma.array(wsp_clima_dict["N"]) decor_clima_dict_w = clima_mean(date_time=time_decor_wsp, data=decor_wsp) decor_mean_wsp_c = np.ma.array(decor_clima_dict_w["mean"]) ## Compute basin-scale annual cycle fit ##
from netCDF4 import Dataset, num2date # Import functions from data_processing import import_data from averaging_stats import clima_mean from lsf import weighted_least_square_fit, LSF_parameters, uncertainty_phase_amp from save_netcdf_fields import save_netcdf_lsf_parameters # Set dimensions for data of space and time nt, nlon, nlat = 12, 360, 133 # Call wsp data: wsp, time, lat, lon = import_data("CCMP2_wsp", data_path_c) # Calculate monthly climatologies wsp_clima_dict = clima_mean(date_time=np.ma.array(time), data=wsp) wsp_clima_mean = np.ma.array(wsp_clima_dict["mean"]) wsp_clima_std = np.ma.array(wsp_clima_dict["std"]) wsp_clima_n = np.ma.array(wsp_clima_dict["N"]) # call monthly decorrelation scale nc = Dataset(data_path_decor + "CCMP2_wsp_decor_time_scale.nc", "r") decor = nc.variables["decor_scale"][:] time_decor = num2date(nc.variables["time"][:], nc.variables["time"].units) # Calculate monthly climatologies of monthly decorrelation scales decor_clima_dict = clima_mean(date_time=time_decor, data=decor) decor_clima_mean = np.ma.array(decor_clima_dict["mean"]) # Compute standard error of the mean wsp_clima_n_eff = wsp_clima_n / decor_clima_mean