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