Unlock 30% off on Manual Testing Annual Plans this Holiday Season.

Copied to Clipboard!

How to use decor method in tempest

Best Python code snippet using tempest_python

figS04.py

Source:figS04.py

1"""2Figure S4 Caption: 3Basin-scale climatologies with Ifremer SWH (Solid blue) and CCMP2 WSP(Solid red).4Blue shading represents the standard error of the mean. Dotted blue is the annual5cycle weighted least-squares fitted to monthly climatology for mean SWH of the 6hemisphere ocean basin. Basins include (A) North Pacific, (B) North Atlantic, (C)7South Pacific, (D) South Atlantic, and (E) Indian Ocean where marginal seas and 8the equatorial regions across the Pacific and Atlantic oceans not considered.9"""10# Path to access python functions11import sys12sys.path.append("../tools/")13# Path to access intermediate data14data_path_i = "../data/ifremer_swh/"15data_path_c = "../data/ccmp2_wsp/"16data_path_decor = "../data/decor_scales/"17# libraries18import numpy as np19import matplotlib.pyplot as plt20from netCDF4 import Dataset, num2date21import cmocean.cm as cmo22# my functions23from data_processing import import_data24from averaging_stats import clima_mean25from lsf import weighted_least_square_fit, LSF_parameters26from regional_clima_figs import regional_clima, regional_clima_plot27# call IFREMER SWH and CCMP2 WSP processed data:28swh, time_s, lat_s, lon_s = import_data("IFREMER_swh", data_path_i)29wsp, time_w, lat_w, lon_w = import_data("CCMP2_wsp", data_path_c)30# Call decorrelation time scales31nc_swh = Dataset(data_path_decor + "IFREMER_swh_decor_time_scale.nc", "r")32decor_swh = nc_swh.variables["decor_scale"][:]33time_decor_swh = num2date(nc_swh.variables["time"][:], nc_swh.variables["time"].units)34nc_wsp = Dataset(data_path_decor + "CCMP2_wsp_decor_time_scale.nc", "r")35decor_wsp = nc_wsp.variables["decor_scale"][:]36time_decor_wsp = num2date(nc_wsp.variables["time"][:], nc_wsp.variables["time"].units)37# Set partition indices for the world oceans excluding the equatorial regions and marginal seas:38ind_nph_lat, ind_nph_lon = [116, 133], [106, 266]39ind_npl_lat, ind_npl_lon = [75, 117], [106, 275]40ind_nawh_lat, ind_nawh_lon = [116, 133], [295, 361]41ind_nawl_lat, ind_nawl_lon = [77, 117], [274, 361]42ind_nae_lat, ind_nae_lon = [77, 133], [0, 24]43ind_sp_lat, ind_sp_lon = [0, 60], [131, 292]44ind_saw_lat, ind_saw_lon = [0, 68], [291, 360]45ind_sae_lat, ind_sae_lon = [0, 68], [0, 24]46ind_nioh_lat, ind_nioh_lon = [89, 95], [60, 105]47ind_niol_lat, ind_niol_lon = [77, 90], [51, 105]48ind_mioh_lat, ind_mioh_lon = [69, 78], [40, 98]49ind_miol_lat, ind_miol_lon = [59, 70], [59, 70]50ind_sio_lat, ind_sio_lon = [0, 60], [23, 132]51########### CCMP2 WSP ###########52# Calculate monthly climatologies for SWH and WSP data and decorrelation scales:53wsp_clima_dict = clima_mean(date_time=np.ma.array(time_w), data=wsp)54wsp_mean_c = np.ma.array(wsp_clima_dict["mean"])55wsp_var_c = np.ma.array(wsp_clima_dict["var"])56wsp_n_c = np.ma.array(wsp_clima_dict["N"])57decor_clima_dict_w = clima_mean(date_time=time_decor_wsp, data=decor_wsp)58decor_mean_wsp_c = np.ma.array(decor_clima_dict_w["mean"])59# Partition mean, variance, number of observations, and decorrelation scales60####### Mean #######61wsp_nph = wsp_mean_c[62    :, ind_nph_lat[0] : ind_nph_lat[1], ind_nph_lon[0] : ind_nph_lon[1]63]64wsp_npl = wsp_mean_c[65    :, ind_npl_lat[0] : ind_npl_lat[1], ind_npl_lon[0] : ind_nph_lon[1]66]67wsp_nawh = wsp_mean_c[68    :, ind_nawh_lat[0] : ind_nawh_lat[1], ind_nawh_lon[0] : ind_nawh_lon[1]69]70wsp_nawl = wsp_mean_c[71    :, ind_nawl_lat[0] : ind_nawl_lat[1], ind_nawl_lon[0] : ind_nawl_lon[1]72]73wsp_nae = wsp_mean_c[74    :, ind_nae_lat[0] : ind_nae_lat[1], ind_nae_lon[0] : ind_nae_lon[1]75]76wsp_sp = wsp_mean_c[:, ind_sp_lat[0] : ind_sp_lat[1], ind_sp_lon[0] : ind_sp_lon[1]]77wsp_saw = wsp_mean_c[78    :, ind_saw_lat[0] : ind_saw_lat[1], ind_saw_lon[0] : ind_saw_lon[1]79]80wsp_sae = wsp_mean_c[81    :, ind_sae_lat[0] : ind_sae_lat[1], ind_sae_lon[0] : ind_sae_lon[1]82]83wsp_nioh = wsp_mean_c[84    :, ind_nioh_lat[0] : ind_nioh_lat[1], ind_nioh_lon[0] : ind_nioh_lon[1]85]86wsp_niol = wsp_mean_c[87    :, ind_niol_lat[0] : ind_niol_lat[1], ind_niol_lon[0] : ind_niol_lon[1]88]89wsp_mioh = wsp_mean_c[90    :, ind_mioh_lat[0] : ind_mioh_lat[1], ind_mioh_lon[0] : ind_mioh_lon[1]91]92wsp_miol = wsp_mean_c[93    :, ind_miol_lat[0] : ind_miol_lat[1], ind_miol_lon[0] : ind_miol_lon[1]94]95wsp_sio = wsp_mean_c[96    :, ind_sio_lat[0] : ind_sio_lat[1], ind_sio_lon[0] : ind_sio_lon[1]97]98wsp_reg = [99    wsp_nph,100    wsp_npl,101    wsp_nawh,102    wsp_nawl,103    wsp_nae,104    wsp_sp,105    wsp_saw,106    wsp_sae,107    wsp_nioh,108    wsp_niol,109    wsp_mioh,110    wsp_miol,111    wsp_sio,112]113####### Variance #######114wsp_sig_nph = wsp_var_c[115    :, ind_nph_lat[0] : ind_nph_lat[1], ind_nph_lon[0] : ind_nph_lon[1]116]117wsp_sig_npl = wsp_var_c[118    :, ind_npl_lat[0] : ind_npl_lat[1], ind_npl_lon[0] : ind_nph_lon[1]119]120wsp_sig_nawh = wsp_var_c[121    :, ind_nawh_lat[0] : ind_nawh_lat[1], ind_nawh_lon[0] : ind_nawh_lon[1]122]123wsp_sig_nawl = wsp_var_c[124    :, ind_nawl_lat[0] : ind_nawl_lat[1], ind_nawl_lon[0] : ind_nawl_lon[1]125]126wsp_sig_nae = wsp_var_c[127    :, ind_nae_lat[0] : ind_nae_lat[1], ind_nae_lon[0] : ind_nae_lon[1]128]129wsp_sig_sp = wsp_var_c[:, ind_sp_lat[0] : ind_sp_lat[1], ind_sp_lon[0] : ind_sp_lon[1]]130wsp_sig_saw = wsp_var_c[131    :, ind_saw_lat[0] : ind_saw_lat[1], ind_saw_lon[0] : ind_saw_lon[1]132]133wsp_sig_sae = wsp_var_c[134    :, ind_sae_lat[0] : ind_sae_lat[1], ind_sae_lon[0] : ind_sae_lon[1]135]136wsp_sig_nioh = wsp_var_c[137    :, ind_nioh_lat[0] : ind_nioh_lat[1], ind_nioh_lon[0] : ind_nioh_lon[1]138]139wsp_sig_niol = wsp_var_c[140    :, ind_niol_lat[0] : ind_niol_lat[1], ind_niol_lon[0] : ind_niol_lon[1]141]142wsp_sig_mioh = wsp_var_c[143    :, ind_mioh_lat[0] : ind_mioh_lat[1], ind_mioh_lon[0] : ind_mioh_lon[1]144]145wsp_sig_miol = wsp_var_c[146    :, ind_miol_lat[0] : ind_miol_lat[1], ind_miol_lon[0] : ind_miol_lon[1]147]148wsp_sig_sio = wsp_var_c[149    :, ind_sio_lat[0] : ind_sio_lat[1], ind_sio_lon[0] : ind_sio_lon[1]150]151wsp_reg_sig = [152    wsp_sig_nph,153    wsp_sig_npl,154    wsp_sig_nawh,155    wsp_sig_nawl,156    wsp_sig_nae,157    wsp_sig_sp,158    wsp_sig_saw,159    wsp_sig_sae,160    wsp_sig_nioh,161    wsp_sig_niol,162    wsp_sig_mioh,163    wsp_sig_miol,164    wsp_sig_sio,165]166####### Number of data points #######167wsp_n_nph = wsp_n_c[:, ind_nph_lat[0] : ind_nph_lat[1], ind_nph_lon[0] : ind_nph_lon[1]]168wsp_n_npl = wsp_n_c[:, ind_npl_lat[0] : ind_npl_lat[1], ind_npl_lon[0] : ind_nph_lon[1]]169wsp_n_nawh = wsp_n_c[170    :, ind_nawh_lat[0] : ind_nawh_lat[1], ind_nawh_lon[0] : ind_nawh_lon[1]171]172wsp_n_nawl = wsp_n_c[173    :, ind_nawl_lat[0] : ind_nawl_lat[1], ind_nawl_lon[0] : ind_nawl_lon[1]174]175wsp_n_nae = wsp_n_c[:, ind_nae_lat[0] : ind_nae_lat[1], ind_nae_lon[0] : ind_nae_lon[1]]176wsp_n_sp = wsp_n_c[:, ind_sp_lat[0] : ind_sp_lat[1], ind_sp_lon[0] : ind_sp_lon[1]]177wsp_n_saw = wsp_n_c[:, ind_saw_lat[0] : ind_saw_lat[1], ind_saw_lon[0] : ind_saw_lon[1]]178wsp_n_sae = wsp_n_c[:, ind_sae_lat[0] : ind_sae_lat[1], ind_sae_lon[0] : ind_sae_lon[1]]179wsp_n_nioh = wsp_n_c[180    :, ind_nioh_lat[0] : ind_nioh_lat[1], ind_nioh_lon[0] : ind_nioh_lon[1]181]182wsp_n_niol = wsp_n_c[183    :, ind_niol_lat[0] : ind_niol_lat[1], ind_niol_lon[0] : ind_niol_lon[1]184]185wsp_n_mioh = wsp_n_c[186    :, ind_mioh_lat[0] : ind_mioh_lat[1], ind_mioh_lon[0] : ind_mioh_lon[1]187]188wsp_n_miol = wsp_n_c[189    :, ind_miol_lat[0] : ind_miol_lat[1], ind_miol_lon[0] : ind_miol_lon[1]190]191wsp_n_sio = wsp_n_c[:, ind_sio_lat[0] : ind_sio_lat[1], ind_sio_lon[0] : ind_sio_lon[1]]192wsp_reg_n = [193    wsp_nph,194    wsp_n_npl,195    wsp_n_nawh,196    wsp_n_nawl,197    wsp_n_nae,198    wsp_n_sp,199    wsp_n_saw,200    wsp_n_sae,201    wsp_n_nioh,202    wsp_n_niol,203    wsp_n_mioh,204    wsp_n_miol,205    wsp_n_sio,206]207####### Decorrelation time scale #######208decor_mean_wsp_nph = decor_mean_wsp_c[209    :, ind_nph_lat[0] : ind_nph_lat[1], ind_nph_lon[0] : ind_nph_lon[1]210]211decor_mean_wsp_npl = decor_mean_wsp_c[212    :, ind_npl_lat[0] : ind_npl_lat[1], ind_npl_lon[0] : ind_nph_lon[1]213]214decor_mean_wsp_nawh = decor_mean_wsp_c[215    :, ind_nawh_lat[0] : ind_nawh_lat[1], ind_nawh_lon[0] : ind_nawh_lon[1]216]217decor_mean_wsp_nawl = decor_mean_wsp_c[218    :, ind_nawl_lat[0] : ind_nawl_lat[1], ind_nawl_lon[0] : ind_nawl_lon[1]219]220decor_mean_wsp_nae = decor_mean_wsp_c[221    :, ind_nae_lat[0] : ind_nae_lat[1], ind_nae_lon[0] : ind_nae_lon[1]222]223decor_mean_wsp_sp = decor_mean_wsp_c[224    :, ind_sp_lat[0] : ind_sp_lat[1], ind_sp_lon[0] : ind_sp_lon[1]225]226decor_mean_wsp_saw = decor_mean_wsp_c[227    :, ind_saw_lat[0] : ind_saw_lat[1], ind_saw_lon[0] : ind_saw_lon[1]228]229decor_mean_wsp_sae = decor_mean_wsp_c[230    :, ind_sae_lat[0] : ind_sae_lat[1], ind_sae_lon[0] : ind_sae_lon[1]231]232decor_mean_wsp_nioh = decor_mean_wsp_c[233    :, ind_nioh_lat[0] : ind_nioh_lat[1], ind_nioh_lon[0] : ind_nioh_lon[1]234]235decor_mean_wsp_niol = decor_mean_wsp_c[236    :, ind_niol_lat[0] : ind_niol_lat[1], ind_niol_lon[0] : ind_niol_lon[1]237]238decor_mean_wsp_mioh = decor_mean_wsp_c[239    :, ind_mioh_lat[0] : ind_mioh_lat[1], ind_mioh_lon[0] : ind_mioh_lon[1]240]241decor_mean_wsp_miol = decor_mean_wsp_c[242    :, ind_miol_lat[0] : ind_miol_lat[1], ind_miol_lon[0] : ind_miol_lon[1]243]244decor_mean_wsp_sio = decor_mean_wsp_c[245    :, ind_sio_lat[0] : ind_sio_lat[1], ind_sio_lon[0] : ind_sio_lon[1]246]247wsp_reg_dcor = [248    decor_mean_wsp_nph,249    decor_mean_wsp_npl,250    decor_mean_wsp_nawh,251    decor_mean_wsp_nawl,252    decor_mean_wsp_nae,253    decor_mean_wsp_sp,254    decor_mean_wsp_saw,255    decor_mean_wsp_sae,256    decor_mean_wsp_nioh,257    decor_mean_wsp_niol,258    decor_mean_wsp_mioh,259    decor_mean_wsp_miol,260    decor_mean_wsp_sio,261]262# For each partition:263# Compute Mean264wsp_reg_m = [np.ma.mean(np.ma.mean(ireg, axis=1), axis=1) for ireg in wsp_reg]265wsp_reg_m_n = [266    np.ma.mean(np.ma.array([wsp_reg_m[0], wsp_reg_m[1]]), axis=0),267    np.ma.mean(np.ma.array([wsp_reg_m[2], wsp_reg_m[3], wsp_reg_m[4]]), axis=0),268    wsp_reg_m[5],269    np.ma.mean(np.ma.array([wsp_reg_m[6], wsp_reg_m[7]]), axis=0),270    np.ma.mean(271        np.ma.array(272            [wsp_reg_m[8], wsp_reg_m[9], wsp_reg_m[10], wsp_reg_m[11], wsp_reg_m[12]]273        ),274        axis=0,275    ),276]277# Compute average Variance278wsp_reg_var_m = [np.ma.mean(np.ma.mean(ireg, axis=1), axis=1) for ireg in wsp_reg_sig]279wsp_reg_var_m_n = [280    np.ma.mean(np.ma.array([wsp_reg_var_m[0], wsp_reg_var_m[1]]), axis=0),281    np.ma.mean(282        np.ma.array([wsp_reg_var_m[2], wsp_reg_var_m[3], wsp_reg_var_m[4]]), axis=0283    ),284    wsp_reg_var_m[5],285    np.ma.mean(np.ma.array([wsp_reg_var_m[6], wsp_reg_var_m[7]]), axis=0),286    np.ma.mean(287        np.ma.array(288            [289                wsp_reg_var_m[8],290                wsp_reg_var_m[9],291                wsp_reg_var_m[10],292                wsp_reg_var_m[11],293                wsp_reg_var_m[12],294            ]295        ),296        axis=0,297    ),298]299# compute the average of the decorrelation time scale and number of data points for each region:300wsp_reg_dcor_m = [np.ma.mean(np.ma.mean(ireg, axis=1), axis=1) for ireg in wsp_reg_dcor]301wsp_reg_dcor_m_n = [302    np.ma.mean(np.ma.array([wsp_reg_dcor_m[0], wsp_reg_dcor_m[1]]), axis=0),303    np.ma.mean(304        np.ma.array([wsp_reg_dcor_m[2], wsp_reg_dcor_m[3], wsp_reg_dcor_m[4]]), axis=0305    ),306    wsp_reg_dcor_m[5],307    np.ma.mean(np.ma.array([wsp_reg_dcor_m[6], wsp_reg_dcor_m[7]]), axis=0),308    np.ma.mean(309        np.ma.array(310            [311                wsp_reg_dcor_m[8],312                wsp_reg_dcor_m[9],313                wsp_reg_dcor_m[10],314                wsp_reg_dcor_m[11],315                wsp_reg_dcor_m[12],316            ]317        ),318        axis=0,319    ),320]321# compute the average of number of data points for each region:322wsp_reg_n_m = [np.ma.mean(np.ma.mean(ireg, axis=1), axis=1) for ireg in wsp_reg_n]323wsp_reg_n_m_n = [324    np.ma.mean(np.ma.array([wsp_reg_n_m[0], wsp_reg_n_m[1]]), axis=0),325    np.ma.mean(np.ma.array([wsp_reg_n_m[2], wsp_reg_n_m[3], wsp_reg_n_m[4]]), axis=0),326    wsp_reg_n_m[5],327    np.ma.mean(np.ma.array([wsp_reg_n_m[6], wsp_reg_n_m[7]]), axis=0),328    np.ma.mean(329        np.ma.array(330            [331                wsp_reg_n_m[8],332                wsp_reg_n_m[9],333                wsp_reg_n_m[10],334                wsp_reg_n_m[11],335                wsp_reg_n_m[12],336            ]337        ),338        axis=0,339    ),340]341# Compute degrees of freedom or N_effective for stdm calculation:342n_eff_reg = []343for ireg in range(5):344    n_eff_reg.append(wsp_reg_n_m_n[ireg] / wsp_reg_dcor_m_n[0])345# compute the standard error of the mean346wsp_reg_stdm = []347for ireg in range(5):348    wsp_reg_stdm.append(np.sqrt(wsp_reg_var_m_n[ireg]) / np.sqrt(n_eff_reg[ireg]))349# Perform a weighted least squares annual cycle fit to the mean regional data350hfit_reg_wsp = []351for ireg in range(5):352    # Perform weigthed least squares fit353    hfit, x_data, x_data_sigma = weighted_least_square_fit(354        data=np.ma.copy(wsp_reg_m_n[ireg]),355        sigma=np.ma.copy(wsp_reg_stdm[ireg]),356        trend="sinusoidal",357        parameters=3,358        period=12,359    )360    # save data361    hfit_reg_wsp.append(hfit)362# Change order of climatologies variables and fits for SH partitions:363####### Climatology #######364wsp_reg_m_n[2] = np.reshape(365    np.ma.array([wsp_reg_m_n[2][6:13], wsp_reg_m_n[2][0:6]]), (1, 12)366)[0]367wsp_reg_m_n[3] = np.reshape(368    np.ma.array([wsp_reg_m_n[3][6:13], wsp_reg_m_n[3][0:6]]), (1, 12)369)[0]370wsp_reg_m_n[4] = np.reshape(371    np.ma.array([wsp_reg_m_n[4][6:13], wsp_reg_m_n[4][0:6]]), (1, 12)372)[0]373####### stdm #######374wsp_reg_stdm[2] = np.reshape(375    np.ma.array([wsp_reg_stdm[2][6:13], wsp_reg_stdm[2][0:6]]), (1, 12)376)[0]377wsp_reg_stdm[3] = np.reshape(378    np.ma.array([wsp_reg_stdm[3][6:13], wsp_reg_stdm[3][0:6]]), (1, 12)379)[0]380wsp_reg_stdm[4] = np.reshape(381    np.ma.array([wsp_reg_stdm[4][6:13], wsp_reg_stdm[4][0:6]]), (1, 12)382)[0]383####### fits #######384hfit_reg_wsp[2] = np.reshape(385    np.ma.array([hfit_reg_wsp[2][6:13], hfit_reg_wsp[2][0:6]]), (1, 12)386)[0]387hfit_reg_wsp[3] = np.reshape(388    np.ma.array([hfit_reg_wsp[3][6:13], hfit_reg_wsp[3][0:6]]), (1, 12)389)[0]390hfit_reg_wsp[4] = np.reshape(391    np.ma.array([hfit_reg_wsp[4][6:13], hfit_reg_wsp[4][0:6]]), (1, 12)392)[0]393########### IFREMER SWH ###########394# Calculate monthly climatologies for SWH and WSP data and decorrelation scales:395swh_clima_dict = clima_mean(date_time=np.ma.array(time_s), data=swh)396swh_mean_i = np.ma.array(swh_clima_dict["mean"])397swh_var_i = np.ma.array(swh_clima_dict["var"])398swh_n_i = np.ma.array(swh_clima_dict["N"])399decor_clima_dict_s = clima_mean(date_time=time_decor_swh, data=decor_swh)400decor_mean_swh_i = np.ma.array(decor_clima_dict_s["mean"])401# Partition mean, variance, number of observations, and decorrelation scales402####### Mean #######403swh_nph = swh_mean_i[404    :, ind_nph_lat[0] : ind_nph_lat[1], ind_nph_lon[0] : ind_nph_lon[1]405]406swh_npl = swh_mean_i[407    :, ind_npl_lat[0] : ind_npl_lat[1], ind_npl_lon[0] : ind_nph_lon[1]408]409swh_nawh = swh_mean_i[410    :, ind_nawh_lat[0] : ind_nawh_lat[1], ind_nawh_lon[0] : ind_nawh_lon[1]411]412swh_nawl = swh_mean_i[413    :, ind_nawl_lat[0] : ind_nawl_lat[1], ind_nawl_lon[0] : ind_nawl_lon[1]414]415swh_nae = swh_mean_i[416    :, ind_nae_lat[0] : ind_nae_lat[1], ind_nae_lon[0] : ind_nae_lon[1]417]418swh_sp = swh_mean_i[:, ind_sp_lat[0] : ind_sp_lat[1], ind_sp_lon[0] : ind_sp_lon[1]]419swh_saw = swh_mean_i[420    :, ind_saw_lat[0] : ind_saw_lat[1], ind_saw_lon[0] : ind_saw_lon[1]421]422swh_sae = swh_mean_i[423    :, ind_sae_lat[0] : ind_sae_lat[1], ind_sae_lon[0] : ind_sae_lon[1]424]425swh_nioh = swh_mean_i[426    :, ind_nioh_lat[0] : ind_nioh_lat[1], ind_nioh_lon[0] : ind_nioh_lon[1]427]428swh_niol = swh_mean_i[429    :, ind_niol_lat[0] : ind_niol_lat[1], ind_niol_lon[0] : ind_niol_lon[1]430]431swh_mioh = swh_mean_i[432    :, ind_mioh_lat[0] : ind_mioh_lat[1], ind_mioh_lon[0] : ind_mioh_lon[1]433]434swh_miol = swh_mean_i[435    :, ind_miol_lat[0] : ind_miol_lat[1], ind_miol_lon[0] : ind_miol_lon[1]436]437swh_sio = swh_mean_i[438    :, ind_sio_lat[0] : ind_sio_lat[1], ind_sio_lon[0] : ind_sio_lon[1]439]440swh_reg = [441    swh_nph,442    swh_npl,443    swh_nawh,444    swh_nawl,445    swh_nae,446    swh_sp,447    swh_saw,448    swh_sae,449    swh_nioh,450    swh_niol,451    swh_mioh,452    swh_miol,453    swh_sio,454]455####### Variance #######456swh_sig_nph = swh_var_i[457    :, ind_nph_lat[0] : ind_nph_lat[1], ind_nph_lon[0] : ind_nph_lon[1]458]459swh_sig_npl = swh_var_i[460    :, ind_npl_lat[0] : ind_npl_lat[1], ind_npl_lon[0] : ind_nph_lon[1]461]462swh_sig_nawh = swh_var_i[463    :, ind_nawh_lat[0] : ind_nawh_lat[1], ind_nawh_lon[0] : ind_nawh_lon[1]464]465swh_sig_nawl = swh_var_i[466    :, ind_nawl_lat[0] : ind_nawl_lat[1], ind_nawl_lon[0] : ind_nawl_lon[1]467]468swh_sig_nae = swh_var_i[469    :, ind_nae_lat[0] : ind_nae_lat[1], ind_nae_lon[0] : ind_nae_lon[1]470]471swh_sig_sp = swh_var_i[:, ind_sp_lat[0] : ind_sp_lat[1], ind_sp_lon[0] : ind_sp_lon[1]]472swh_sig_saw = swh_var_i[473    :, ind_saw_lat[0] : ind_saw_lat[1], ind_saw_lon[0] : ind_saw_lon[1]474]475swh_sig_sae = swh_var_i[476    :, ind_sae_lat[0] : ind_sae_lat[1], ind_sae_lon[0] : ind_sae_lon[1]477]478swh_sig_nioh = swh_var_i[479    :, ind_nioh_lat[0] : ind_nioh_lat[1], ind_nioh_lon[0] : ind_nioh_lon[1]480]481swh_sig_niol = swh_var_i[482    :, ind_niol_lat[0] : ind_niol_lat[1], ind_niol_lon[0] : ind_niol_lon[1]483]484swh_sig_mioh = swh_var_i[485    :, ind_mioh_lat[0] : ind_mioh_lat[1], ind_mioh_lon[0] : ind_mioh_lon[1]486]487swh_sig_miol = swh_var_i[488    :, ind_miol_lat[0] : ind_miol_lat[1], ind_miol_lon[0] : ind_miol_lon[1]489]490swh_sig_sio = swh_var_i[491    :, ind_sio_lat[0] : ind_sio_lat[1], ind_sio_lon[0] : ind_sio_lon[1]492]493swh_reg_sig = [494    swh_sig_nph,495    swh_sig_npl,496    swh_sig_nawh,497    swh_sig_nawl,498    swh_sig_nae,499    swh_sig_sp,500    swh_sig_saw,501    swh_sig_sae,502    swh_sig_nioh,503    swh_sig_niol,504    swh_sig_mioh,505    swh_sig_miol,506    swh_sig_sio,507]508####### Number of data points #######509swh_n_nph = swh_n_i[:, ind_nph_lat[0] : ind_nph_lat[1], ind_nph_lon[0] : ind_nph_lon[1]]510swh_n_npl = swh_n_i[:, ind_npl_lat[0] : ind_npl_lat[1], ind_npl_lon[0] : ind_nph_lon[1]]511swh_n_nawh = swh_n_i[512    :, ind_nawh_lat[0] : ind_nawh_lat[1], ind_nawh_lon[0] : ind_nawh_lon[1]513]514swh_n_nawl = swh_n_i[515    :, ind_nawl_lat[0] : ind_nawl_lat[1], ind_nawl_lon[0] : ind_nawl_lon[1]516]517swh_n_nae = swh_n_i[:, ind_nae_lat[0] : ind_nae_lat[1], ind_nae_lon[0] : ind_nae_lon[1]]518swh_n_sp = swh_n_i[:, ind_sp_lat[0] : ind_sp_lat[1], ind_sp_lon[0] : ind_sp_lon[1]]519swh_n_saw = swh_n_i[:, ind_saw_lat[0] : ind_saw_lat[1], ind_saw_lon[0] : ind_saw_lon[1]]520swh_n_sae = swh_n_i[:, ind_sae_lat[0] : ind_sae_lat[1], ind_sae_lon[0] : ind_sae_lon[1]]521swh_n_nioh = swh_n_i[522    :, ind_nioh_lat[0] : ind_nioh_lat[1], ind_nioh_lon[0] : ind_nioh_lon[1]523]524swh_n_niol = swh_n_i[525    :, ind_niol_lat[0] : ind_niol_lat[1], ind_niol_lon[0] : ind_niol_lon[1]526]527swh_n_mioh = swh_n_i[528    :, ind_mioh_lat[0] : ind_mioh_lat[1], ind_mioh_lon[0] : ind_mioh_lon[1]529]530swh_n_miol = swh_n_i[531    :, ind_miol_lat[0] : ind_miol_lat[1], ind_miol_lon[0] : ind_miol_lon[1]532]533swh_n_sio = swh_n_i[:, ind_sio_lat[0] : ind_sio_lat[1], ind_sio_lon[0] : ind_sio_lon[1]]534swh_reg_n = [535    swh_nph,536    swh_n_npl,537    swh_n_nawh,538    swh_n_nawl,539    swh_n_nae,540    swh_n_sp,541    swh_n_saw,542    swh_n_sae,543    swh_n_nioh,544    swh_n_niol,545    swh_n_mioh,546    swh_n_miol,547    swh_n_sio,548]549####### Decorrelation time scale #######550decor_mean_swh_nph = decor_mean_swh_i[551    :, ind_nph_lat[0] : ind_nph_lat[1], ind_nph_lon[0] : ind_nph_lon[1]552]553decor_mean_swh_npl = decor_mean_swh_i[554    :, ind_npl_lat[0] : ind_npl_lat[1], ind_npl_lon[0] : ind_nph_lon[1]555]556decor_mean_swh_nawh = decor_mean_swh_i[557    :, ind_nawh_lat[0] : ind_nawh_lat[1], ind_nawh_lon[0] : ind_nawh_lon[1]558]559decor_mean_swh_nawl = decor_mean_swh_i[560    :, ind_nawl_lat[0] : ind_nawl_lat[1], ind_nawl_lon[0] : ind_nawl_lon[1]561]562decor_mean_swh_nae = decor_mean_swh_i[563    :, ind_nae_lat[0] : ind_nae_lat[1], ind_nae_lon[0] : ind_nae_lon[1]564]565decor_mean_swh_sp = decor_mean_swh_i[566    :, ind_sp_lat[0] : ind_sp_lat[1], ind_sp_lon[0] : ind_sp_lon[1]567]568decor_mean_swh_saw = decor_mean_swh_i[569    :, ind_saw_lat[0] : ind_saw_lat[1], ind_saw_lon[0] : ind_saw_lon[1]570]571decor_mean_swh_sae = decor_mean_swh_i[572    :, ind_sae_lat[0] : ind_sae_lat[1], ind_sae_lon[0] : ind_sae_lon[1]573]574decor_mean_swh_nioh = decor_mean_swh_i[575    :, ind_nioh_lat[0] : ind_nioh_lat[1], ind_nioh_lon[0] : ind_nioh_lon[1]576]577decor_mean_swh_niol = decor_mean_swh_i[578    :, ind_niol_lat[0] : ind_niol_lat[1], ind_niol_lon[0] : ind_niol_lon[1]579]580decor_mean_swh_mioh = decor_mean_swh_i[581    :, ind_mioh_lat[0] : ind_mioh_lat[1], ind_mioh_lon[0] : ind_mioh_lon[1]582]583decor_mean_swh_miol = decor_mean_swh_i[584    :, ind_miol_lat[0] : ind_miol_lat[1], ind_miol_lon[0] : ind_miol_lon[1]585]586decor_mean_swh_sio = decor_mean_swh_i[587    :, ind_sio_lat[0] : ind_sio_lat[1], ind_sio_lon[0] : ind_sio_lon[1]588]589swh_reg_dcor = [590    decor_mean_swh_nph,591    decor_mean_swh_npl,592    decor_mean_swh_nawh,593    decor_mean_swh_nawl,594    decor_mean_swh_nae,595    decor_mean_swh_sp,596    decor_mean_swh_saw,597    decor_mean_swh_sae,598    decor_mean_swh_nioh,599    decor_mean_swh_niol,600    decor_mean_swh_mioh,601    decor_mean_swh_miol,602    decor_mean_swh_sio,603]604# For each partition:605# Compute Mean606swh_reg_m = [np.ma.mean(np.ma.mean(ireg, axis=1), axis=1) for ireg in swh_reg]607swh_reg_m_n = [608    np.ma.mean(np.ma.array([swh_reg_m[0], swh_reg_m[1]]), axis=0),609    np.ma.mean(np.ma.array([swh_reg_m[2], swh_reg_m[3], swh_reg_m[4]]), axis=0),610    swh_reg_m[5],611    np.ma.mean(np.ma.array([swh_reg_m[6], swh_reg_m[7]]), axis=0),612    np.ma.mean(613        np.ma.array(614            [swh_reg_m[8], swh_reg_m[9], swh_reg_m[10], swh_reg_m[11], swh_reg_m[12]]615        ),616        axis=0,617    ),618]619# Compute average Variance620swh_reg_var_m = [np.ma.mean(np.ma.mean(ireg, axis=1), axis=1) for ireg in swh_reg_sig]621swh_reg_var_m_n = [622    np.ma.mean(np.ma.array([swh_reg_var_m[0], swh_reg_var_m[1]]), axis=0),623    np.ma.mean(624        np.ma.array([swh_reg_var_m[2], swh_reg_var_m[3], swh_reg_var_m[4]]), axis=0625    ),626    swh_reg_var_m[5],627    np.ma.mean(np.ma.array([swh_reg_var_m[6], swh_reg_var_m[7]]), axis=0),628    np.ma.mean(629        np.ma.array(630            [631                swh_reg_var_m[8],632                swh_reg_var_m[9],633                swh_reg_var_m[10],634                swh_reg_var_m[11],635                swh_reg_var_m[12],636            ]637        ),638        axis=0,639    ),640]641# compute the average of the decorrelation time scale and number of data points for each region:642swh_reg_dcor_m = [np.ma.mean(np.ma.mean(ireg, axis=1), axis=1) for ireg in swh_reg_dcor]643swh_reg_dcor_m_n = [644    np.ma.mean(np.ma.array([swh_reg_dcor_m[0], swh_reg_dcor_m[1]]), axis=0),645    np.ma.mean(646        np.ma.array([swh_reg_dcor_m[2], swh_reg_dcor_m[3], swh_reg_dcor_m[4]]), axis=0647    ),648    swh_reg_dcor_m[5],649    np.ma.mean(np.ma.array([swh_reg_dcor_m[6], swh_reg_dcor_m[7]]), axis=0),650    np.ma.mean(651        np.ma.array(652            [653                swh_reg_dcor_m[8],654                swh_reg_dcor_m[9],655                swh_reg_dcor_m[10],656                swh_reg_dcor_m[11],657                swh_reg_dcor_m[12],658            ]659        ),660        axis=0,661    ),662]663# compute the average of number of data points for each region:664swh_reg_n_m = [np.ma.mean(np.ma.mean(ireg, axis=1), axis=1) for ireg in swh_reg_n]665swh_reg_n_m_n = [666    np.ma.mean(np.ma.array([swh_reg_n_m[0], swh_reg_n_m[1]]), axis=0),667    np.ma.mean(np.ma.array([swh_reg_n_m[2], swh_reg_n_m[3], swh_reg_n_m[4]]), axis=0),668    swh_reg_n_m[5],669    np.ma.mean(np.ma.array([swh_reg_n_m[6], swh_reg_n_m[7]]), axis=0),670    np.ma.mean(671        np.ma.array(672            [673                swh_reg_n_m[8],674                swh_reg_n_m[9],675                swh_reg_n_m[10],676                swh_reg_n_m[11],677                swh_reg_n_m[12],678            ]679        ),680        axis=0,681    ),682]683# Compute degrees of freedom or N_effective for stdm calculation:684n_eff_reg = []685for ireg in range(5):686    n_eff_reg.append(swh_reg_n_m_n[ireg] / swh_reg_dcor_m_n[0])687# compute the standard error of the mean688swh_reg_stdm = []689for ireg in range(5):690    swh_reg_stdm.append(np.sqrt(swh_reg_var_m_n[ireg]) / np.sqrt(n_eff_reg[ireg]))691# Perform a weighted least squares annual cycle fit to the mean regional data692hfit_reg_swh = []693for ireg in range(5):694    # Perform weigthed least squares fit695    hfit, x_data, x_data_sigma = weighted_least_square_fit(696        data=np.ma.copy(swh_reg_m_n[ireg]),697        sigma=np.ma.copy(swh_reg_stdm[ireg]),698        trend="sinusoidal",699        parameters=3,700        period=12,701    )702    # save data703    hfit_reg_swh.append(hfit)704# Change order of climatologies variables and fits for SH partitions:705####### Climatology #######706swh_reg_m_n[2] = np.reshape(707    np.ma.array([swh_reg_m_n[2][6:13], swh_reg_m_n[2][0:6]]), (1, 12)708)[0]709swh_reg_m_n[3] = np.reshape(710    np.ma.array([swh_reg_m_n[3][6:13], swh_reg_m_n[3][0:6]]), (1, 12)711)[0]712swh_reg_m_n[4] = np.reshape(713    np.ma.array([swh_reg_m_n[4][6:13], swh_reg_m_n[4][0:6]]), (1, 12)714)[0]715####### stdm #######716swh_reg_stdm[2] = np.reshape(717    np.ma.array([swh_reg_stdm[2][6:13], swh_reg_stdm[2][0:6]]), (1, 12)718)[0]719swh_reg_stdm[3] = np.reshape(720    np.ma.array([swh_reg_stdm[3][6:13], swh_reg_stdm[3][0:6]]), (1, 12)721)[0]722swh_reg_stdm[4] = np.reshape(723    np.ma.array([swh_reg_stdm[4][6:13], swh_reg_stdm[4][0:6]]), (1, 12)724)[0]725####### fits #######726hfit_reg_swh[2] = np.reshape(727    np.ma.array([hfit_reg_swh[2][6:13], hfit_reg_swh[2][0:6]]), (1, 12)728)[0]729hfit_reg_swh[3] = np.reshape(730    np.ma.array([hfit_reg_swh[3][6:13], hfit_reg_swh[3][0:6]]), (1, 12)731)[0]732hfit_reg_swh[4] = np.reshape(733    np.ma.array([hfit_reg_swh[4][6:13], hfit_reg_swh[4][0:6]]), (1, 12)734)[0]735# Set variables for plotting736time = np.arange(1, 13, 1)737time_ticks_nh = [738    "Jan",739    "Feb",740    "Mar",741    "Apr",742    "May",743    "Jun",744    "Jul",745    "Aug",746    "Sep",747    "Oct",748    "Nov",749    "Dec",750]751time_ticks_sh = [752    "Jul",753    "Aug",754    "Sep",755    "Oct",756    "Nov",757    "Dec",758    "Jan",759    "Feb",760    "Mar",761    "Apr",762    "May",763    "Jun",764]765xlim = [0, 13]766ylim = [[1, 4], [4, 10]]767# Create figure and axes768fig, axes = plt.subplots(3, 2, figsize=(16, 20))769ax1, ax2, ax3, ax4, ax5, ax6 = axes.flatten()770######## Subplot 1 ########771# North Pacific772regional_clima_plot(773    ax1,774    swh_reg_m_n[0],775    swh_reg_stdm[0],776    hfit_reg_swh[0],777    wsp_reg_m_n[0],778    wsp_reg_stdm[0],779    hfit_reg_wsp[0],780    None,781    None,782    None,783    None,784    time,785    time_ticks_nh,786    xlim,787    ylim,788    subplot_label="A",789    fontsize=20,790    linewidth=1.5,791    task="IC",792    grid=False,793)794######## Subplot 2 ########795# North Atlantic796regional_clima_plot(797    ax2,798    swh_reg_m_n[1],799    swh_reg_stdm[1],800    hfit_reg_swh[1],801    wsp_reg_m_n[1],802    wsp_reg_stdm[1],803    hfit_reg_wsp[1],804    None,805    None,806    None,807    None,808    time,809    time_ticks_nh,810    xlim,811    ylim,812    subplot_label="B",813    fontsize=20,814    linewidth=1.5,815    task="IC",816    grid=False,817)818########### Subplot 3 ###########819# South pacific820regional_clima_plot(821    ax3,822    swh_reg_m_n[2],823    swh_reg_stdm[2],824    hfit_reg_swh[2],825    wsp_reg_m_n[2],826    wsp_reg_stdm[2],827    hfit_reg_wsp[2],828    None,829    None,830    None,831    None,832    time,833    time_ticks_sh,834    xlim,835    ylim,836    subplot_label="C",837    fontsize=20,838    linewidth=1.5,839    task="IC",840    grid=False,841)842########### Subplot 4 ###########843# South Atlantic844regional_clima_plot(845    ax4,846    swh_reg_m_n[3],847    swh_reg_stdm[3],848    hfit_reg_swh[3],849    wsp_reg_m_n[3],850    wsp_reg_stdm[3],851    hfit_reg_wsp[3],852    None,853    None,854    None,855    None,856    time,857    time_ticks_sh,858    xlim,859    ylim,860    subplot_label="D",861    fontsize=20,862    linewidth=1.5,863    task="IC",864    grid=False,865)866########### Subplot 5 ###########867# Indian ocean868regional_clima_plot(869    ax5,870    swh_reg_m_n[4],871    swh_reg_stdm[4],872    hfit_reg_swh[4],873    wsp_reg_m_n[4],874    wsp_reg_stdm[4],875    hfit_reg_wsp[4],876    None,877    None,878    None,879    None,880    time,881    time_ticks_sh,882    xlim,883    ylim,884    subplot_label="E",885    fontsize=20,886    linewidth=1.5,887    task="IC",888    grid=False,889)890########### Subplot 6 ###########891# suppress output for axis 6892ax6.axis("off")893# adjust layout of subplots:894fig.tight_layout()895# adjust spacing for the entire figure896fig.subplots_adjust(wspace=0.35, hspace=0.2)897# save figure:...

fig06.py

Source:fig06.py

1"""2Figure 6 Caption: 3(left column) Northern Hemisphere wind speed in SWARs, averaged over June, July,4and August. (right column) IFREMER SWH (solid blue) and CCMP2 WSP (solid red) 5climatologies extracted from the outlined 4$^{\circ}$ by 4$^{\circ}$ boxes within6SWARs. Blue shading represents the standard error of the mean, dotted blue is 7the annual cycle weighted least-squares fitted to monthly climatology for mean 8SWH of the hemisphere ocean basin the SWAR is located in, and black solid is the 9residual between SWH regional climatology and annual cycle. SWARs include 10Northern California (A and B), Southern Caribbean Sea (C and D), and North Africa11near the coast of Morocco and western Sahara (E and F). (Comparison plots showing12equivalent quantities for WW3 and IFREMER SWH and CFSR and CCMP2 WSP can be found13in Figure~S9A,C,E of the supplementary material).14"""15# Path to access python functions16import sys17sys.path.append("../tools/")18# Path to access intermediate data19data_path_i = "../data/ifremer_swh/"20data_path_c = "../data/ccmp2_wsp/"21data_path_decor = "../data/decor_scales/"22# libraries23import numpy as np24from netCDF4 import Dataset, num2date25import matplotlib.pyplot as plt26import cartopy.crs as ccrs27import cmocean.cm as cmo28import matplotlib.patches as mpatches29# my functions30from data_processing import import_data31from averaging_stats import clima_mean, stat_moments_temporal32from lsf import weighted_least_square_fit, LSF_parameters33from regional_clima_figs import regional_clima, regional_clima_plot34import cartopy_figs as cart35# call IFREMER SWH and CCMP2 WSP processed data:36swh, time_s, lat_s, lon_s = import_data("IFREMER_swh", data_path_i)37wsp, time_w, lat_w, lon_w = import_data("CCMP2_wsp", data_path_c)38# Call decorrelation time scales39###### SWH ######40nc_swh = Dataset(data_path_decor + "IFREMER_swh_decor_time_scale.nc", "r")41decor_swh = nc_swh.variables["decor_scale"][:]42time_decor_swh = num2date(nc_swh.variables["time"][:], nc_swh.variables["time"].units)43###### WSP ######44nc_wsp = Dataset(data_path_decor + "CCMP2_wsp_decor_time_scale.nc", "r")45decor_wsp = nc_wsp.variables["decor_scale"][:]46time_decor_wsp = num2date(nc_wsp.variables["time"][:], nc_wsp.variables["time"].units)47# Compute WSP statistical moments seasonally48wsp_stats_s = stat_moments_temporal(wsp, time_w, "seasonally", "sample")49wsp_moments_mean = np.ma.array(wsp_stats_s["mean"])50# Calculate monthly climatologies for SWH and WSP data and decorrelation scales:51###### SWH ######52swh_clima_dict = clima_mean(date_time=np.ma.array(time_s), data=swh)53swh_mean_i = np.ma.array(swh_clima_dict["mean"])54swh_var_i = np.ma.array(swh_clima_dict["var"])55swh_n_i = np.ma.array(swh_clima_dict["N"])56decor_clima_dict_s = clima_mean(date_time=time_decor_swh, data=decor_swh)57decor_mean_swh_i = np.ma.array(decor_clima_dict_s["mean"])58###### WSP ######59wsp_clima_dict = clima_mean(date_time=np.ma.array(time_w), data=wsp)60wsp_mean_c = np.ma.array(wsp_clima_dict["mean"])61wsp_var_c = np.ma.array(wsp_clima_dict["var"])62wsp_n_c = np.ma.array(wsp_clima_dict["N"])63decor_clima_dict_w = clima_mean(date_time=time_decor_wsp, data=decor_wsp)64decor_mean_wsp_c = np.ma.array(decor_clima_dict_w["mean"])65## Compute basin-scale annual cycle fit ##66# Set partition indices for the world oceans excluding the equatorial regions and marginal seas:67ind_nph_lat, ind_nph_lon = [116, 133], [106, 266]68ind_npl_lat, ind_npl_lon = [75, 117], [106, 275]69ind_nawh_lat, ind_nawh_lon = [116, 133], [295, 361]70ind_nawl_lat, ind_nawl_lon = [77, 117], [274, 361]71ind_nae_lat, ind_nae_lon = [77, 133], [0, 24]72ind_sp_lat, ind_sp_lon = [0, 60], [131, 292]73ind_saw_lat, ind_saw_lon = [0, 68], [291, 360]74ind_sae_lat, ind_sae_lon = [0, 68], [0, 24]75ind_nioh_lat, ind_nioh_lon = [89, 95], [60, 105]76ind_niol_lat, ind_niol_lon = [77, 90], [51, 105]77ind_mioh_lat, ind_mioh_lon = [69, 78], [40, 98]78ind_miol_lat, ind_miol_lon = [59, 70], [59, 70]79ind_sio_lat, ind_sio_lon = [0, 60], [23, 132]80# Partition mean, variance, number of observations, and decorrelation scales81####### Mean #######82swh_nph = swh_mean_i[83    :, ind_nph_lat[0] : ind_nph_lat[1], ind_nph_lon[0] : ind_nph_lon[1]84]85swh_npl = swh_mean_i[86    :, ind_npl_lat[0] : ind_npl_lat[1], ind_npl_lon[0] : ind_nph_lon[1]87]88swh_nawh = swh_mean_i[89    :, ind_nawh_lat[0] : ind_nawh_lat[1], ind_nawh_lon[0] : ind_nawh_lon[1]90]91swh_nawl = swh_mean_i[92    :, ind_nawl_lat[0] : ind_nawl_lat[1], ind_nawl_lon[0] : ind_nawl_lon[1]93]94swh_nae = swh_mean_i[95    :, ind_nae_lat[0] : ind_nae_lat[1], ind_nae_lon[0] : ind_nae_lon[1]96]97swh_sp = swh_mean_i[:, ind_sp_lat[0] : ind_sp_lat[1], ind_sp_lon[0] : ind_sp_lon[1]]98swh_saw = swh_mean_i[99    :, ind_saw_lat[0] : ind_saw_lat[1], ind_saw_lon[0] : ind_saw_lon[1]100]101swh_sae = swh_mean_i[102    :, ind_sae_lat[0] : ind_sae_lat[1], ind_sae_lon[0] : ind_sae_lon[1]103]104swh_nioh = swh_mean_i[105    :, ind_nioh_lat[0] : ind_nioh_lat[1], ind_nioh_lon[0] : ind_nioh_lon[1]106]107swh_niol = swh_mean_i[108    :, ind_niol_lat[0] : ind_niol_lat[1], ind_niol_lon[0] : ind_niol_lon[1]109]110swh_mioh = swh_mean_i[111    :, ind_mioh_lat[0] : ind_mioh_lat[1], ind_mioh_lon[0] : ind_mioh_lon[1]112]113swh_miol = swh_mean_i[114    :, ind_miol_lat[0] : ind_miol_lat[1], ind_miol_lon[0] : ind_miol_lon[1]115]116swh_sio = swh_mean_i[117    :, ind_sio_lat[0] : ind_sio_lat[1], ind_sio_lon[0] : ind_sio_lon[1]118]119swh_reg = [120    swh_nph,121    swh_npl,122    swh_nawh,123    swh_nawl,124    swh_nae,125    swh_sp,126    swh_saw,127    swh_sae,128    swh_nioh,129    swh_niol,130    swh_mioh,131    swh_miol,132    swh_sio,133]134####### Variance #######135swh_sig_nph = swh_var_i[136    :, ind_nph_lat[0] : ind_nph_lat[1], ind_nph_lon[0] : ind_nph_lon[1]137]138swh_sig_npl = swh_var_i[139    :, ind_npl_lat[0] : ind_npl_lat[1], ind_npl_lon[0] : ind_nph_lon[1]140]141swh_sig_nawh = swh_var_i[142    :, ind_nawh_lat[0] : ind_nawh_lat[1], ind_nawh_lon[0] : ind_nawh_lon[1]143]144swh_sig_nawl = swh_var_i[145    :, ind_nawl_lat[0] : ind_nawl_lat[1], ind_nawl_lon[0] : ind_nawl_lon[1]146]147swh_sig_nae = swh_var_i[148    :, ind_nae_lat[0] : ind_nae_lat[1], ind_nae_lon[0] : ind_nae_lon[1]149]150swh_sig_sp = swh_var_i[:, ind_sp_lat[0] : ind_sp_lat[1], ind_sp_lon[0] : ind_sp_lon[1]]151swh_sig_saw = swh_var_i[152    :, ind_saw_lat[0] : ind_saw_lat[1], ind_saw_lon[0] : ind_saw_lon[1]153]154swh_sig_sae = swh_var_i[155    :, ind_sae_lat[0] : ind_sae_lat[1], ind_sae_lon[0] : ind_sae_lon[1]156]157swh_sig_nioh = swh_var_i[158    :, ind_nioh_lat[0] : ind_nioh_lat[1], ind_nioh_lon[0] : ind_nioh_lon[1]159]160swh_sig_niol = swh_var_i[161    :, ind_niol_lat[0] : ind_niol_lat[1], ind_niol_lon[0] : ind_niol_lon[1]162]163swh_sig_mioh = swh_var_i[164    :, ind_mioh_lat[0] : ind_mioh_lat[1], ind_mioh_lon[0] : ind_mioh_lon[1]165]166swh_sig_miol = swh_var_i[167    :, ind_miol_lat[0] : ind_miol_lat[1], ind_miol_lon[0] : ind_miol_lon[1]168]169swh_sig_sio = swh_var_i[170    :, ind_sio_lat[0] : ind_sio_lat[1], ind_sio_lon[0] : ind_sio_lon[1]171]172swh_reg_sig = [173    swh_sig_nph,174    swh_sig_npl,175    swh_sig_nawh,176    swh_sig_nawl,177    swh_sig_nae,178    swh_sig_sp,179    swh_sig_saw,180    swh_sig_sae,181    swh_sig_nioh,182    swh_sig_niol,183    swh_sig_mioh,184    swh_sig_miol,185    swh_sig_sio,186]187####### Number of data points #######188swh_n_nph = swh_n_i[:, ind_nph_lat[0] : ind_nph_lat[1], ind_nph_lon[0] : ind_nph_lon[1]]189swh_n_npl = swh_n_i[:, ind_npl_lat[0] : ind_npl_lat[1], ind_npl_lon[0] : ind_nph_lon[1]]190swh_n_nawh = swh_n_i[191    :, ind_nawh_lat[0] : ind_nawh_lat[1], ind_nawh_lon[0] : ind_nawh_lon[1]192]193swh_n_nawl = swh_n_i[194    :, ind_nawl_lat[0] : ind_nawl_lat[1], ind_nawl_lon[0] : ind_nawl_lon[1]195]196swh_n_nae = swh_n_i[:, ind_nae_lat[0] : ind_nae_lat[1], ind_nae_lon[0] : ind_nae_lon[1]]197swh_n_sp = swh_n_i[:, ind_sp_lat[0] : ind_sp_lat[1], ind_sp_lon[0] : ind_sp_lon[1]]198swh_n_saw = swh_n_i[:, ind_saw_lat[0] : ind_saw_lat[1], ind_saw_lon[0] : ind_saw_lon[1]]199swh_n_sae = swh_n_i[:, ind_sae_lat[0] : ind_sae_lat[1], ind_sae_lon[0] : ind_sae_lon[1]]200swh_n_nioh = swh_n_i[201    :, ind_nioh_lat[0] : ind_nioh_lat[1], ind_nioh_lon[0] : ind_nioh_lon[1]202]203swh_n_niol = swh_n_i[204    :, ind_niol_lat[0] : ind_niol_lat[1], ind_niol_lon[0] : ind_niol_lon[1]205]206swh_n_mioh = swh_n_i[207    :, ind_mioh_lat[0] : ind_mioh_lat[1], ind_mioh_lon[0] : ind_mioh_lon[1]208]209swh_n_miol = swh_n_i[210    :, ind_miol_lat[0] : ind_miol_lat[1], ind_miol_lon[0] : ind_miol_lon[1]211]212swh_n_sio = swh_n_i[:, ind_sio_lat[0] : ind_sio_lat[1], ind_sio_lon[0] : ind_sio_lon[1]]213swh_reg_n = [214    swh_nph,215    swh_n_npl,216    swh_n_nawh,217    swh_n_nawl,218    swh_n_nae,219    swh_n_sp,220    swh_n_saw,221    swh_n_sae,222    swh_n_nioh,223    swh_n_niol,224    swh_n_mioh,225    swh_n_miol,226    swh_n_sio,227]228####### Decorrelation time scale #######229decor_mean_swh_nph = decor_mean_swh_i[230    :, ind_nph_lat[0] : ind_nph_lat[1], ind_nph_lon[0] : ind_nph_lon[1]231]232decor_mean_swh_npl = decor_mean_swh_i[233    :, ind_npl_lat[0] : ind_npl_lat[1], ind_npl_lon[0] : ind_nph_lon[1]234]235decor_mean_swh_nawh = decor_mean_swh_i[236    :, ind_nawh_lat[0] : ind_nawh_lat[1], ind_nawh_lon[0] : ind_nawh_lon[1]237]238decor_mean_swh_nawl = decor_mean_swh_i[239    :, ind_nawl_lat[0] : ind_nawl_lat[1], ind_nawl_lon[0] : ind_nawl_lon[1]240]241decor_mean_swh_nae = decor_mean_swh_i[242    :, ind_nae_lat[0] : ind_nae_lat[1], ind_nae_lon[0] : ind_nae_lon[1]243]244decor_mean_swh_sp = decor_mean_swh_i[245    :, ind_sp_lat[0] : ind_sp_lat[1], ind_sp_lon[0] : ind_sp_lon[1]246]247decor_mean_swh_saw = decor_mean_swh_i[248    :, ind_saw_lat[0] : ind_saw_lat[1], ind_saw_lon[0] : ind_saw_lon[1]249]250decor_mean_swh_sae = decor_mean_swh_i[251    :, ind_sae_lat[0] : ind_sae_lat[1], ind_sae_lon[0] : ind_sae_lon[1]252]253decor_mean_swh_nioh = decor_mean_swh_i[254    :, ind_nioh_lat[0] : ind_nioh_lat[1], ind_nioh_lon[0] : ind_nioh_lon[1]255]256decor_mean_swh_niol = decor_mean_swh_i[257    :, ind_niol_lat[0] : ind_niol_lat[1], ind_niol_lon[0] : ind_niol_lon[1]258]259decor_mean_swh_mioh = decor_mean_swh_i[260    :, ind_mioh_lat[0] : ind_mioh_lat[1], ind_mioh_lon[0] : ind_mioh_lon[1]261]262decor_mean_swh_miol = decor_mean_swh_i[263    :, ind_miol_lat[0] : ind_miol_lat[1], ind_miol_lon[0] : ind_miol_lon[1]264]265decor_mean_swh_sio = decor_mean_swh_i[266    :, ind_sio_lat[0] : ind_sio_lat[1], ind_sio_lon[0] : ind_sio_lon[1]267]268swh_reg_dcor = [269    decor_mean_swh_nph,270    decor_mean_swh_npl,271    decor_mean_swh_nawh,272    decor_mean_swh_nawl,273    decor_mean_swh_nae,274    decor_mean_swh_sp,275    decor_mean_swh_saw,276    decor_mean_swh_sae,277    decor_mean_swh_nioh,278    decor_mean_swh_niol,279    decor_mean_swh_mioh,280    decor_mean_swh_miol,281    decor_mean_swh_sio,282]283# For each partition:284# Compute Mean285swh_reg_m = [np.ma.mean(np.ma.mean(ireg, axis=1), axis=1) for ireg in swh_reg]286swh_reg_m_n = [287    np.ma.mean(np.ma.array([swh_reg_m[0], swh_reg_m[1]]), axis=0),288    np.ma.mean(np.ma.array([swh_reg_m[2], swh_reg_m[3], swh_reg_m[4]]), axis=0),289    swh_reg_m[5],290    np.ma.mean(np.ma.array([swh_reg_m[6], swh_reg_m[7]]), axis=0),291    np.ma.mean(292        np.ma.array(293            [swh_reg_m[8], swh_reg_m[9], swh_reg_m[10], swh_reg_m[11], swh_reg_m[12]]294        ),295        axis=0,296    ),297]298# Compute average Variance299swh_reg_var_m = [np.ma.mean(np.ma.mean(ireg, axis=1), axis=1) for ireg in swh_reg_sig]300swh_reg_var_m_n = [301    np.ma.mean(np.ma.array([swh_reg_var_m[0], swh_reg_var_m[1]]), axis=0),302    np.ma.mean(303        np.ma.array([swh_reg_var_m[2], swh_reg_var_m[3], swh_reg_var_m[4]]), axis=0304    ),305    swh_reg_var_m[5],306    np.ma.mean(np.ma.array([swh_reg_var_m[6], swh_reg_var_m[7]]), axis=0),307    np.ma.mean(308        np.ma.array(309            [310                swh_reg_var_m[8],311                swh_reg_var_m[9],312                swh_reg_var_m[10],313                swh_reg_var_m[11],314                swh_reg_var_m[12],315            ]316        ),317        axis=0,318    ),319]320# compute the average of the decorrelation time scale and number of data points for each region:321swh_reg_dcor_m = [np.ma.mean(np.ma.mean(ireg, axis=1), axis=1) for ireg in swh_reg_dcor]322swh_reg_dcor_m_n = [323    np.ma.mean(np.ma.array([swh_reg_dcor_m[0], swh_reg_dcor_m[1]]), axis=0),324    np.ma.mean(325        np.ma.array([swh_reg_dcor_m[2], swh_reg_dcor_m[3], swh_reg_dcor_m[4]]), axis=0326    ),327    swh_reg_dcor_m[5],328    np.ma.mean(np.ma.array([swh_reg_dcor_m[6], swh_reg_dcor_m[7]]), axis=0),329    np.ma.mean(330        np.ma.array(331            [332                swh_reg_dcor_m[8],333                swh_reg_dcor_m[9],334                swh_reg_dcor_m[10],335                swh_reg_dcor_m[11],336                swh_reg_dcor_m[12],337            ]338        ),339        axis=0,340    ),341]342# compute the average of number of data points for each region:343swh_reg_n_m = [np.ma.mean(np.ma.mean(ireg, axis=1), axis=1) for ireg in swh_reg_n]344swh_reg_n_m_n = [345    np.ma.mean(np.ma.array([swh_reg_n_m[0], swh_reg_n_m[1]]), axis=0),346    np.ma.mean(np.ma.array([swh_reg_n_m[2], swh_reg_n_m[3], swh_reg_n_m[4]]), axis=0),347    swh_reg_n_m[5],348    np.ma.mean(np.ma.array([swh_reg_n_m[6], swh_reg_n_m[7]]), axis=0),349    np.ma.mean(350        np.ma.array(351            [352                swh_reg_n_m[8],353                swh_reg_n_m[9],354                swh_reg_n_m[10],355                swh_reg_n_m[11],356                swh_reg_n_m[12],357            ]358        ),359        axis=0,360    ),361]362# Compute degrees of freedom or N_effective for stdm calculation:363n_eff_reg = []364for ireg in range(5):365    n_eff_reg.append(swh_reg_n_m_n[ireg] / swh_reg_dcor_m_n[0])366# compute the standard error of the mean367swh_reg_stdm = []368for ireg in range(5):369    swh_reg_stdm.append(np.sqrt(swh_reg_var_m_n[ireg]) / np.sqrt(n_eff_reg[ireg]))370# Perform a weighted least squares annual cycle fit to the mean regional data371hfit_basin_swh, x_data_basin, x_data_sigma_basin = [], [], []372for ireg in range(5):373    # Perform weigthed least squares fit374    hfit, x_data, x_data_sigma = weighted_least_square_fit(375        data=np.ma.copy(swh_reg_m_n[ireg]),376        sigma=np.ma.copy(swh_reg_stdm[ireg]),377        trend="sinusoidal",378        parameters=3,379        period=12,380    )381    # save data382    hfit_basin_swh.append(hfit)383    x_data_basin.append(x_data)384    x_data_sigma_basin.append(x_data_sigma)385# Compute regional climatologies:386####### Northern California #######387# intialize variable388ngrid = 4389lon_grid = 232390lat_grid = 103391location = ["NH", "west"]392# compute SWH regional climatology393(394    swh_mean_nc,395    swh_stdm_nc,396    swh_hfit,397    swh_x_data,398    swh_residual,399    grid_cor_nc,400) = regional_clima(401    swh_mean_i,402    swh_var_i,403    swh_n_i,404    lon_s,405    lat_s,406    lon_grid,407    lat_grid,408    ngrid,409    decor_mean_swh_i,410    location,411    lsf="weighted",412    parameters=5,413)414# compute WSP regional climatology415(416    wsp_mean_nc,417    wsp_stdm_nc,418    wsp_hfit,419    wsp_x_data,420    wsp_residual,421    grid_cor_nc,422) = regional_clima(423    wsp_mean_c,424    wsp_var_c,425    wsp_n_c,426    lon_w,427    lat_w,428    lon_grid,429    lat_grid,430    ngrid,431    decor_mean_wsp_c,432    location,433    lsf="weighted",434    parameters=5,435)436# Perform weigthed least squares fit for basin-scale climatology projected onto regional climatology437hfit_reg_nc, x_data_reg, x_data_sigma_reg = weighted_least_square_fit(438    swh_mean_nc,439    swh_stdm_nc,440    trend="sinusoidal",441    parameters=2,442    period=12,443    phase=np.arctan2(x_data_basin[0][1], x_data_basin[0][2]),444)445####### Southern Caribbean #######446# intialize variable447ngrid = 4448lon_grid = 284449lat_grid = 80450location = ["NH", "west"]451# compute SWH regional climatology452(453    swh_mean_sc,454    swh_stdm_sc,455    swh_hfit,456    swh_x_data,457    swh_residual,458    grid_cor_sc,459) = regional_clima(460    swh_mean_i,461    swh_var_i,462    swh_n_i,463    lon_s,464    lat_s,465    lon_grid,466    lat_grid,467    ngrid,468    decor_mean_swh_i,469    location,470    lsf="weighted",471    parameters=5,472)473# compute WSP regional climatology474(475    wsp_mean_sc,476    wsp_stdm_sc,477    wsp_hfit,478    wsp_x_data,479    wsp_residual,480    grid_cor_sc,481) = regional_clima(482    wsp_mean_c,483    wsp_var_c,484    wsp_n_c,485    lon_w,486    lat_w,487    lon_grid,488    lat_grid,489    ngrid,490    decor_mean_wsp_c,491    location,492    lsf="weighted",493    parameters=5,494)495# Perform weigthed least squares fit for basin-scale climatology projected onto regional climatology496hfit_reg_sc, x_data_reg, x_data_sigma_reg = weighted_least_square_fit(497    swh_mean_sc,498    swh_stdm_sc,499    trend="sinusoidal",500    parameters=2,501    period=12,502    phase=np.arctan2(x_data_basin[1][1], x_data_basin[1][2]),503)504####### North Africa (Morocco) #######505# intialize variable506ngrid = 4507lon_grid = 345508lat_grid = 96509location = ["NH", "west"]510# compute SWH regional climatology511(512    swh_mean_na,513    swh_stdm_na,514    swh_hfit,515    swh_x_data,516    swh_residual,517    grid_cor_na,518) = regional_clima(519    swh_mean_i,520    swh_var_i,521    swh_n_i,522    lon_s,523    lat_s,524    lon_grid,525    lat_grid,526    ngrid,527    decor_mean_swh_i,528    location,529    lsf="weighted",530    parameters=5,531)532# compute WSP regional climatology533(534    wsp_mean_na,535    wsp_stdm_na,536    wsp_hfit,537    wsp_x_data,538    wsp_residual,539    grid_cor_na,540) = regional_clima(541    wsp_mean_c,542    wsp_var_c,543    wsp_n_c,544    lon_w,545    lat_w,546    lon_grid,547    lat_grid,548    ngrid,549    decor_mean_wsp_c,550    location,551    lsf="weighted",552    parameters=5,553)554# Perform weigthed least squares fit for basin-scale climatology projected onto regional climatology555hfit_reg_na, x_data_reg, x_data_sigma_reg = weighted_least_square_fit(556    swh_mean_na,557    swh_stdm_na,558    trend="sinusoidal",559    parameters=2,560    period=12,561    phase=np.arctan2(x_data_basin[1][1], x_data_basin[1][2]),562)563# Set variables for plotting564projection = ccrs.PlateCarree(central_longitude=180.0)565time = np.arange(1, 13, 1)566time_ticks = [567    "Jan",568    "Feb",569    "Mar",570    "Apr",571    "May",572    "Jun",573    "Jul",574    "Aug",575    "Sep",576    "Oct",577    "Nov",578    "Dec",579]  # x-axis tick labels for NH580xlim = [0, 13]581ylim = [[-1, 4], [5, 10]]582levels = np.arange(2, 8.1, 0.1)583resolution = "10m"584# initialize subplot axes:585fig = plt.figure(figsize=(22, 28))586############## Subplot 1  ##############587# California coast588ax1 = fig.add_subplot(321, projection=ccrs.PlateCarree(central_longitude=180.0))589cart.set_subplots(590    ax1, projection, resolution, lon_min=44, lon_max=66, lat_min=30, lat_max=44591)592cs1 = ax1.contourf(593    lon_w,594    lat_w,595    wsp_moments_mean[2, :, :],596    levels,597    cmap=cmo.thermal,598    extend="both",599    transform=ccrs.PlateCarree(central_longitude=0.0),600)601cart.grid_lines_rc(602    ax=ax1,603    xticks=[-133, -129, -125, -121, -117],604    yticks=[28, 32, 36, 40, 44],605    fontsize=20,606    linewidth=1,607    color="gray",608    alpha=0.3,609    linestyle="--",610    grid=True,611)612cax1 = plt.axes([0.1, 0.06, 0.4, 0.03])613cart.set_cbar(614    cs1,615    cax1,616    fig,617    orientation="horizontal",618    extend="both",619    cbar_label="$m\,s^{-1}$",620    nbins=7,621    fontsize=20,622    cbar_ticks=[],623    task="regular",624)625cart.subplot_label(626    ax1,627    xdist_label=0.1,628    ydist_label=0.88,629    subplot_label="A",630    form="box",631    fs_shade=28,632    fs_main=20,633    color="black",634)635# Set regional climatology grid box636ax1.add_patch(637    mpatches.Rectangle(638        xy=[-129, 36],639        width=4,640        height=4,641        edgecolor="black",642        facecolor=None,643        linestyle="-",644        linewidth=2,645        fill=False,646        alpha=1,647        transform=ccrs.PlateCarree(),648    )649)650############## Subplot 2 ##############651# California Coast652ax2 = fig.add_subplot(322)653regional_clima_plot(654    ax2,655    swh_mean_nc,656    swh_stdm_nc,657    hfit_reg_nc,658    wsp_mean_nc,659    wsp_stdm_nc,660    None,661    None,662    None,663    None,664    None,665    time,666    time_ticks,667    xlim,668    ylim,669    subplot_label="B",670    fontsize=20,671    linewidth=1.5,672    task="C_I_res",673    grid=False,674)675############## Subplot 3  ##############676# South Caribbean677ax3 = fig.add_subplot(323, projection=ccrs.PlateCarree(central_longitude=180.0))678cart.set_subplots(679    ax3, projection, resolution, lon_min=97, lon_max=119, lat_min=7, lat_max=24680)681cs3 = ax3.contourf(682    lon_w,683    lat_w,684    wsp_moments_mean[2, :, :],685    levels,686    cmap=cmo.thermal,687    extend="both",688    transform=ccrs.PlateCarree(central_longitude=0.0),689)690cart.grid_lines_rc(691    ax=ax3,692    xticks=[-84, -80, -76, -72, -68, -64, -60],693    yticks=[9, 13, 17, 21, 25],694    fontsize=20,695    linewidth=1,696    color="gray",697    alpha=0.3,698    linestyle="--",699    grid=True,700)701cart.subplot_label(702    ax3,703    xdist_label=0.1,704    ydist_label=0.88,705    subplot_label="C",706    form="box",707    fs_shade=28,708    fs_main=20,709    color="black",710)711# Set regional climatology grid box712ax3.add_patch(713    mpatches.Rectangle(714        xy=[-76, 13],715        width=4,716        height=4,717        edgecolor="black",718        facecolor=None,719        linestyle="-",720        linewidth=2,721        fill=False,722        alpha=1,723        transform=ccrs.PlateCarree(),724    )725)726############## Subplot 4 ##############727# South Caribbean Sea728ax4 = fig.add_subplot(324)729regional_clima_plot(730    ax4,731    swh_mean_sc,732    swh_stdm_sc,733    hfit_reg_sc,734    wsp_mean_sc,735    wsp_stdm_sc,736    None,737    None,738    None,739    None,740    None,741    time,742    time_ticks,743    xlim,744    ylim,745    subplot_label="D",746    fontsize=20,747    linewidth=1.5,748    task="C_I_res",749    grid=False,750)751############## Subplot 5  ##############752# North Africa753ax5 = fig.add_subplot(325, projection=ccrs.PlateCarree(central_longitude=180.0))754cart.set_subplots(755    ax5, projection, resolution, lon_min=154, lon_max=174, lat_min=19, lat_max=34756)757cs5 = ax5.contourf(758    lon_w,759    lat_w,760    wsp_moments_mean[2, :, :],761    levels,762    cmap=cmo.thermal,763    extend="both",764    transform=ccrs.PlateCarree(central_longitude=0.0),765)766cart.grid_lines_rc(767    ax5,768    xticks=[-27, -23, -19, -15, -11, -7],769    yticks=[22, 26, 30, 34, 38, 42],770    fontsize=20,771    linewidth=1,772    color="gray",773    alpha=0.3,774    linestyle="--",775    grid=True,776)777cart.subplot_label(778    ax5,779    xdist_label=0.1,780    ydist_label=0.88,781    subplot_label="E",782    form="box",783    fs_shade=28,784    fs_main=20,785    color="black",786)787# Set regional climatology grid box788ax5.add_patch(789    mpatches.Rectangle(790        xy=[-15, 30],791        width=4,792        height=4,793        edgecolor="black",794        facecolor=None,795        linestyle="-",796        linewidth=2,797        fill=False,798        alpha=1,799        transform=ccrs.PlateCarree(),800    )801)802############## Subplot 6 ##############803# North Africa (Morocco)804ax6 = fig.add_subplot(326)805regional_clima_plot(806    ax6,807    swh_mean_na,808    swh_stdm_na,809    hfit_reg_na,810    wsp_mean_na,811    wsp_stdm_na,812    None,813    None,814    None,815    None,816    None,817    time,818    time_ticks,819    xlim,820    ylim,821    subplot_label="F",822    fontsize=20,823    linewidth=1.5,824    task="C_I_res",825    grid=False,826)827# adjust spacing for the entire figure (not the subplot)828fig.subplots_adjust(wspace=0.3, hspace=0.15)829# save figure...

figS09.py

Source:figS09.py

1"""2Figure S9 Caption:3Regional climatologies with Ifremer SWH (Solid blue), CCMP2 (Solid red), WW3 SWH4(dashed blue), and WW3 WSP (dashed red). Same regions as in Figure~7.5"""6# Path to access python functions7import sys8sys.path.append("../tools/")9# Path to access intermediate data10data_path_i = "../data/ifremer_swh/"11data_path_c = "../data/ccmp2_wsp/"12data_path_ws = "../data/ww3_swh/"13data_path_ww = "../data/ww3_wsp/"14data_path_decor = "../data/decor_scales/"15# libraries16import numpy as np17from netCDF4 import Dataset, num2date18import matplotlib.pyplot as plt19import cartopy.crs as ccrs20import cmocean.cm as cmo21import matplotlib.patches as mpatches22# my functions23from data_processing import import_data24from averaging_stats import clima_mean, stat_moments_temporal25from lsf import weighted_least_square_fit, LSF_parameters26from regional_clima_figs import regional_clima, regional_clima_plot27import cartopy_figs as cart28# call IFREMER SWH, CCMP2 WSP, WW3 SWH, and WW3 WSP processed data:29swh_i, time_i, lat_i, lon_i = import_data("IFREMER_swh", data_path_i)30wsp_c, time_c, lat_c, lon_c = import_data("CCMP2_wsp", data_path_c)31swh_w, time_ws, lat_ws, lon_ws = import_data("WW3_swh", data_path_ws)32wsp_w, time_ww, lat_ww, lon_ww = import_data("WW3_wsp", data_path_ww)33# Call decorrelation time scales34###### IFREMER SWH ######35nc_swh_i = Dataset(data_path_decor + "IFREMER_swh_decor_time_scale.nc", "r")36decor_swh_i = nc_swh_i.variables["decor_scale"][:]37time_decor_swh_i = num2date(38    nc_swh_i.variables["time"][:], nc_swh_i.variables["time"].units39)40###### CCMP2 WSP ######41nc_wsp_c = Dataset(data_path_decor + "CCMP2_wsp_decor_time_scale.nc", "r")42decor_wsp_c = nc_wsp_c.variables["decor_scale"][:]43time_decor_wsp_c = num2date(44    nc_wsp_c.variables["time"][:], nc_wsp_c.variables["time"].units45)46#### WW3 SWH ####47nc_swh_w = Dataset(data_path_decor + "WW3_swh_decor_time_scale.nc", "r")48decor_swh_w = nc_swh_w.variables["decor_scale"][:]49time_decor_swh_w = num2date(50    nc_swh_w.variables["time"][:], nc_swh_w.variables["time"].units51)52#### WW3 WSP ####53nc_wsp_w = Dataset(data_path_decor + "WW3_wsp_decor_time_scale.nc", "r")54decor_wsp_w = nc_wsp_w.variables["decor_scale"][:]55time_decor_wsp_w = num2date(56    nc_wsp_w.variables["time"][:], nc_wsp_w.variables["time"].units57)58# Calculate monthly climatologies for SWH and WSP data and decorrelation scales:59###### IFREMER SWH ######60swh_clima_dict = clima_mean(date_time=np.ma.array(time_i), data=swh_i)61swh_mean_i = np.ma.array(swh_clima_dict["mean"])62swh_var_i = np.ma.array(swh_clima_dict["var"])63swh_n_i = np.ma.array(swh_clima_dict["N"])64decor_clima_dict_s = clima_mean(date_time=time_decor_swh_i, data=decor_swh_i)65decor_mean_swh_i = np.ma.array(decor_clima_dict_s["mean"])66###### CCMP2 WSP ######67wsp_clima_dict = clima_mean(date_time=np.ma.array(time_c), data=wsp_c)68wsp_mean_c = np.ma.array(wsp_clima_dict["mean"])69wsp_var_c = np.ma.array(wsp_clima_dict["var"])70wsp_n_c = np.ma.array(wsp_clima_dict["N"])71decor_clima_dict_w = clima_mean(date_time=time_decor_wsp_c, data=decor_wsp_c)72decor_mean_wsp_c = np.ma.array(decor_clima_dict_w["mean"])73#### WW3 SWH ####74swh_clima_dict = clima_mean(date_time=np.ma.array(time_ws), data=swh_w)75swh_mean_w = np.ma.array(swh_clima_dict["mean"])76swh_var_w = np.ma.array(swh_clima_dict["var"])77swh_n_w = np.ma.array(swh_clima_dict["N"])78decor_clima_dict_s = clima_mean(date_time=time_decor_swh_w, data=decor_swh_w)79decor_mean_swh_w = np.ma.array(decor_clima_dict_s["mean"])80#### WW3 WSP ####81wsp_clima_dict = clima_mean(date_time=np.ma.array(time_ww), data=wsp_w)82wsp_mean_w = np.ma.array(wsp_clima_dict["mean"])83wsp_var_w = np.ma.array(wsp_clima_dict["var"])84wsp_n_w = np.ma.array(wsp_clima_dict["N"])85decor_clima_dict_w = clima_mean(date_time=time_decor_wsp_w, data=decor_wsp_w)86decor_mean_wsp_w = np.ma.array(decor_clima_dict_w["mean"])87# Compute regional climatologies:88####### Northern California #######89# intialize variable90ngrid = 491lon_grid = 23292lat_grid = 10393location = ["NH", "west"]94# compute ifremer swh regional climatology95(96    swh_mean_nc,97    swh_stdm_nc,98    swh_hfit,99    swh_x_data,100    swh_residual,101    grid_cor_nc,102) = regional_clima(103    swh_mean_i,104    swh_var_i,105    swh_n_i,106    lon_i,107    lat_i,108    lon_grid,109    lat_grid,110    ngrid,111    decor_mean_swh_i,112    location,113    lsf="weighted",114    parameters=5,115)116# compute ccmp2 wsp regional climatology117(118    wsp_mean_nc,119    wsp_stdm_nc,120    wsp_hfit,121    wsp_x_data,122    wsp_residual,123    grid_cor_nc,124) = regional_clima(125    wsp_mean_c,126    wsp_var_c,127    wsp_n_c,128    lon_c,129    lat_c,130    lon_grid,131    lat_grid,132    ngrid,133    decor_mean_wsp_c,134    location,135    lsf="weighted",136    parameters=5,137)138# compute ww3 swh regional climatology139(140    swh_m_mean_nc,141    swh_m_stdm_nc,142    swh_m_hfit,143    swh_x_data,144    swh_residual,145    grid_cor_nc,146) = regional_clima(147    swh_mean_w,148    swh_var_w,149    swh_n_w,150    lon_ws,151    lat_ws,152    lon_grid,153    lat_grid,154    ngrid,155    decor_mean_swh_w,156    location,157    lsf="weighted",158    parameters=5,159)160# compute ww3 wsp regional climatology161(162    wsp_m_mean_nc,163    wsp_m_stdm_nc,164    wsp_m_hfit,165    wsp_x_data,166    wsp_residual,167    grid_cor_nc,168) = regional_clima(169    wsp_mean_w,170    wsp_var_w,171    wsp_n_w,172    lon_ws,173    lat_ws,174    lon_grid,175    lat_grid,176    ngrid,177    decor_mean_wsp_w,178    location,179    lsf="weighted",180    parameters=5,181)182####### Southern Caribbean #######183# intialize variable184ngrid = 4185lon_grid = 284186lat_grid = 80187location = ["NH", "west"]188# compute SWH regional climatology189(190    swh_mean_sc,191    swh_stdm_sc,192    swh_hfit,193    swh_x_data,194    swh_residual,195    grid_cor_sc,196) = regional_clima(197    swh_mean_i,198    swh_var_i,199    swh_n_i,200    lon_i,201    lat_i,202    lon_grid,203    lat_grid,204    ngrid,205    decor_mean_swh_i,206    location,207    lsf="weighted",208    parameters=5,209)210# compute WSP regional climatology211(212    wsp_mean_sc,213    wsp_stdm_sc,214    wsp_hfit,215    wsp_x_data,216    wsp_residual,217    grid_cor_sc,218) = regional_clima(219    wsp_mean_c,220    wsp_var_c,221    wsp_n_c,222    lon_c,223    lat_c,224    lon_grid,225    lat_grid,226    ngrid,227    decor_mean_wsp_c,228    location,229    lsf="weighted",230    parameters=5,231)232# compute ww3 swh regional climatology233(234    swh_m_mean_sc,235    swh_m_stdm_sc,236    swh_m_hfit,237    swh_x_data,238    swh_residual,239    grid_cor_sc,240) = regional_clima(241    swh_mean_w,242    swh_var_w,243    swh_n_w,244    lon_ws,245    lat_ws,246    lon_grid,247    lat_grid,248    ngrid,249    decor_mean_swh_w,250    location,251    lsf="weighted",252    parameters=5,253)254# compute ww3 wsp regional climatology255(256    wsp_m_mean_sc,257    wsp_m_stdm_sc,258    wsp_m_hfit,259    wsp_x_data,260    wsp_residual,261    grid_cor_sc,262) = regional_clima(263    wsp_mean_w,264    wsp_var_w,265    wsp_n_w,266    lon_ws,267    lat_ws,268    lon_grid,269    lat_grid,270    ngrid,271    decor_mean_wsp_w,272    location,273    lsf="weighted",274    parameters=5,275)276####### North Africa (Morocco) #######277# intialize variable278ngrid = 4279lon_grid = 345280lat_grid = 96281location = ["NH", "west"]282# compute SWH regional climatology283(284    swh_mean_na,285    swh_stdm_na,286    swh_hfit,287    swh_x_data,288    swh_residual,289    grid_cor_na,290) = regional_clima(291    swh_mean_i,292    swh_var_i,293    swh_n_i,294    lon_i,295    lat_i,296    lon_grid,297    lat_grid,298    ngrid,299    decor_mean_swh_i,300    location,301    lsf="weighted",302    parameters=5,303)304# compute WSP regional climatology305(306    wsp_mean_na,307    wsp_stdm_na,308    wsp_hfit,309    wsp_x_data,310    wsp_residual,311    grid_cor_na,312) = regional_clima(313    wsp_mean_c,314    wsp_var_c,315    wsp_n_c,316    lon_c,317    lat_c,318    lon_grid,319    lat_grid,320    ngrid,321    decor_mean_wsp_c,322    location,323    lsf="weighted",324    parameters=5,325)326# compute ww3 swh regional climatology327(328    swh_m_mean_na,329    swh_m_stdm_na,330    swh_m_hfit,331    swh_x_data,332    swh_residual,333    grid_cor_na,334) = regional_clima(335    swh_mean_w,336    swh_var_w,337    swh_n_w,338    lon_ws,339    lat_ws,340    lon_grid,341    lat_grid,342    ngrid,343    decor_mean_swh_w,344    location,345    lsf="weighted",346    parameters=5,347)348# compute ww3 wsp regional climatology349(350    wsp_m_mean_na,351    wsp_m_stdm_na,352    wsp_m_hfit,353    wsp_x_data,354    wsp_residual,355    grid_cor_na,356) = regional_clima(357    wsp_mean_w,358    wsp_var_w,359    wsp_n_w,360    lon_ws,361    lat_ws,362    lon_grid,363    lat_grid,364    ngrid,365    decor_mean_wsp_w,366    location,367    lsf="weighted",368    parameters=5,369)370####### Western Australia #######371# intialize variable372ngrid = 4373lon_grid = 108374lat_grid = 35375location = ["SH", "east"]376# compute SWH regional climatology377(378    swh_mean_wa,379    swh_stdm_wa,380    swh_hfit,381    swh_x_data,382    swh_residual,383    grid_cor_wa,384) = regional_clima(385    swh_mean_i,386    swh_var_i,387    swh_n_i,388    lon_i,389    lat_i,390    lon_grid,391    lat_grid,392    ngrid,393    decor_mean_swh_i,394    location,395    lsf="weighted",396    parameters=5,397)398# compute WSP regional climatology399(400    wsp_mean_wa,401    wsp_stdm_wa,402    wsp_hfit,403    wsp_x_data,404    wsp_residual,405    grid_cor_wa,406) = regional_clima(407    wsp_mean_c,408    wsp_var_c,409    wsp_n_c,410    lon_c,411    lat_c,412    lon_grid,413    lat_grid,414    ngrid,415    decor_mean_wsp_c,416    location,417    lsf="weighted",418    parameters=5,419)420# compute ww3 swh regional climatology421(422    swh_m_mean_wa,423    swh_m_stdm_wa,424    swh_m_hfit,425    swh_x_data,426    swh_residual,427    grid_cor_wa,428) = regional_clima(429    swh_mean_w,430    swh_var_w,431    swh_n_w,432    lon_ws,433    lat_ws,434    lon_grid,435    lat_grid,436    ngrid,437    decor_mean_swh_w,438    location,439    lsf="weighted",440    parameters=5,441)442# compute ww3 wsp regional climatology443(444    wsp_m_mean_wa,445    wsp_m_stdm_wa,446    wsp_m_hfit,447    wsp_x_data,448    wsp_residual,449    grid_cor_wa,450) = regional_clima(451    wsp_mean_w,452    wsp_var_w,453    wsp_n_w,454    lon_ws,455    lat_ws,456    lon_grid,457    lat_grid,458    ngrid,459    decor_mean_wsp_w,460    location,461    lsf="weighted",462    parameters=5,463)464####### Peru-Chile Coast #######465# intialize variable466ngrid = 4467lon_grid = 283468lat_grid = 30469location = ["SH", "west"]470# compute SWH regional climatology471(472    swh_mean_pc,473    swh_stdm_pc,474    swh_hfit,475    swh_x_data,476    swh_residual,477    grid_cor_pc,478) = regional_clima(479    swh_mean_i,480    swh_var_i,481    swh_n_i,482    lon_i,483    lat_i,484    lon_grid,485    lat_grid,486    ngrid,487    decor_mean_swh_i,488    location,489    lsf="weighted",490    parameters=5,491)492# compute WSP regional climatology493(494    wsp_mean_pc,495    wsp_stdm_pc,496    wsp_hfit,497    wsp_x_data,498    wsp_residual,499    grid_cor_pc,500) = regional_clima(501    wsp_mean_c,502    wsp_var_c,503    wsp_n_c,504    lon_c,505    lat_c,506    lon_grid,507    lat_grid,508    ngrid,509    decor_mean_wsp_c,510    location,511    lsf="weighted",512    parameters=5,513)514# compute ww3 swh regional climatology515(516    swh_m_mean_pc,517    swh_m_stdm_pc,518    swh_m_hfit,519    swh_x_data,520    swh_residual,521    grid_cor_pc,522) = regional_clima(523    swh_mean_w,524    swh_var_w,525    swh_n_w,526    lon_ws,527    lat_ws,528    lon_grid,529    lat_grid,530    ngrid,531    decor_mean_swh_w,532    location,533    lsf="weighted",534    parameters=5,535)536# compute ww3 wsp regional climatology537(538    wsp_m_mean_pc,539    wsp_m_stdm_pc,540    wsp_m_hfit,541    wsp_x_data,542    wsp_residual,543    grid_cor_pc,544) = regional_clima(545    wsp_mean_w,546    wsp_var_w,547    wsp_n_w,548    lon_ws,549    lat_ws,550    lon_grid,551    lat_grid,552    ngrid,553    decor_mean_wsp_w,554    location,555    lsf="weighted",556    parameters=5,557)558####### South Africa (Namibian) #######559# intialize variable560ngrid = 4561lon_grid = 11562lat_grid = 36563location = ["SH", "east"]564# compute SWH regional climatology565(566    swh_mean_sa,567    swh_stdm_sa,568    swh_hfit,569    swh_x_data,570    swh_residual,571    grid_cor_sa,572) = regional_clima(573    swh_mean_i,574    swh_var_i,575    swh_n_i,576    lon_i,577    lat_i,578    lon_grid,579    lat_grid,580    ngrid,581    decor_mean_swh_i,582    location,583    lsf="weighted",584    parameters=5,585)586# compute WSP regional climatology587(588    wsp_mean_sa,589    wsp_stdm_sa,590    wsp_hfit,591    wsp_x_data,592    wsp_residual,593    grid_cor_sa,594) = regional_clima(595    wsp_mean_c,596    wsp_var_c,597    wsp_n_c,598    lon_c,599    lat_c,600    lon_grid,601    lat_grid,602    ngrid,603    decor_mean_wsp_c,604    location,605    lsf="weighted",606    parameters=5,607)608# compute ww3 swh regional climatology609(610    swh_m_mean_sa,611    swh_m_stdm_sa,612    swh_m_hfit,613    swh_x_data,614    swh_residual,615    grid_cor_sa,616) = regional_clima(617    swh_mean_w,618    swh_var_w,619    swh_n_w,620    lon_ws,621    lat_ws,622    lon_grid,623    lat_grid,624    ngrid,625    decor_mean_swh_w,626    location,627    lsf="weighted",628    parameters=5,629)630# compute ww3 wsp regional climatology631(632    wsp_m_mean_sa,633    wsp_m_stdm_sa,634    wsp_m_hfit,635    wsp_x_data,636    wsp_residual,637    grid_cor_sa,638) = regional_clima(639    wsp_mean_w,640    wsp_var_w,641    wsp_n_w,642    lon_ws,643    lat_ws,644    lon_grid,645    lat_grid,646    ngrid,647    decor_mean_wsp_w,648    location,649    lsf="weighted",650    parameters=5,651)652####### Arabian Sea #######653# intialize variable654ngrid = 4655lon_grid = 51656lat_grid = 70657location = ["SH", "east"]658# compute SWH regional climatology659(660    swh_mean_as,661    swh_stdm_as,662    swh_hfit,663    swh_x_data,664    swh_residual,665    grid_cor_as,666) = regional_clima(667    swh_mean_i,668    swh_var_i,669    swh_n_i,670    lon_i,671    lat_i,672    lon_grid,673    lat_grid,674    ngrid,675    decor_mean_swh_i,676    location,677    lsf="weighted",678    parameters=5,679)680# compute WSP regional climatology681(682    wsp_mean_as,683    wsp_stdm_as,684    wsp_hfit,685    wsp_x_data,686    wsp_residual,687    grid_cor_as,688) = regional_clima(689    wsp_mean_c,690    wsp_var_c,691    wsp_n_c,692    lon_c,693    lat_c,694    lon_grid,695    lat_grid,696    ngrid,697    decor_mean_wsp_c,698    location,699    lsf="weighted",700    parameters=5,701)702# compute ww3 swh regional climatology703(704    swh_m_mean_as,705    swh_m_stdm_as,706    swh_m_hfit,707    swh_x_data,708    swh_residual,709    grid_cor_as,710) = regional_clima(711    swh_mean_w,712    swh_var_w,713    swh_n_w,714    lon_ws,715    lat_ws,716    lon_grid,717    lat_grid,718    ngrid,719    decor_mean_swh_w,720    location,721    lsf="weighted",722    parameters=5,723)724# compute ww3 wsp regional climatology725(726    wsp_m_mean_as,727    wsp_m_stdm_as,728    wsp_m_hfit,729    wsp_x_data,730    wsp_residual,731    grid_cor_as,732) = regional_clima(733    wsp_mean_w,734    wsp_var_w,735    wsp_n_w,736    lon_ws,737    lat_ws,738    lon_grid,739    lat_grid,740    ngrid,741    decor_mean_wsp_w,742    location,743    lsf="weighted",744    parameters=5,745)746# Set variables for plotting747time = np.arange(1, 13, 1)748time_ticks_nh = [749    "Jan",750    "Feb",751    "Mar",752    "Apr",753    "May",754    "Jun",755    "Jul",756    "Aug",757    "Sep",758    "Oct",759    "Nov",760    "Dec",761]762time_ticks_sh = [763    "Jul",764    "Aug",765    "Sep",766    "Oct",767    "Nov",768    "Dec",769    "Jan",770    "Feb",771    "Mar",772    "Apr",773    "May",774    "Jun",775]776xlim = [0, 13]777ylim = [[1, 4], [4, 10]]778# Create figure and axes779fig, axes = plt.subplots(4, 2, figsize=(16, 20))780ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8 = axes.flatten()781########### Subplot 1 ###########782# California Coast783regional_clima_plot(784    ax1,785    swh_mean_nc,786    swh_stdm_nc,787    None,788    wsp_mean_nc,789    wsp_stdm_nc,790    None,791    swh_m_mean_nc,792    swh_m_stdm_nc,793    wsp_m_mean_nc,794    wsp_m_stdm_nc,795    time,796    time_ticks_nh,797    xlim,798    ylim,799    subplot_label="A",800    fontsize=20,801    linewidth=1.5,802    task="ww3",803    grid=False,804)805########### Subplot 2 ###########806# West Coast of Australia807regional_clima_plot(808    ax2,809    swh_mean_wa,810    swh_stdm_wa,811    None,812    wsp_mean_wa,813    wsp_stdm_wa,814    None,815    swh_m_mean_wa,816    swh_m_stdm_wa,817    wsp_m_mean_wa,818    wsp_m_stdm_wa,819    time,820    time_ticks_sh,821    xlim,822    ylim,823    subplot_label="B",824    fontsize=20,825    linewidth=1.5,826    task="ww3",827    grid=False,828)829########### Subplot 3 ###########830# South Caribbean Sea831regional_clima_plot(832    ax3,833    swh_mean_sc,834    swh_stdm_sc,835    None,836    wsp_mean_sc,837    wsp_stdm_sc,838    None,839    swh_m_mean_sc,840    swh_m_stdm_sc,841    wsp_m_mean_sc,842    wsp_m_stdm_sc,843    time,844    time_ticks_nh,845    xlim,846    ylim,847    subplot_label="C",848    fontsize=20,849    linewidth=1.5,850    task="ww3",851    grid=False,852)853########### Subplot 4 ###########854# Peru-Chile Coast855regional_clima_plot(856    ax4,857    swh_mean_pc,858    swh_stdm_pc,859    None,860    wsp_mean_pc,861    wsp_stdm_pc,862    None,863    swh_m_mean_pc,864    swh_m_stdm_pc,865    wsp_m_mean_pc,866    wsp_m_stdm_pc,867    time,868    time_ticks_sh,869    xlim,870    ylim,871    subplot_label="D",872    fontsize=20,873    linewidth=1.5,874    task="ww3",875    grid=False,876)877########### Subplot 5 ###########878# North Africa (Morocco)879regional_clima_plot(880    ax5,881    swh_mean_na,882    swh_stdm_na,883    None,884    wsp_mean_na,885    wsp_stdm_na,886    None,887    swh_m_mean_na,888    swh_m_stdm_na,889    wsp_m_mean_na,890    wsp_m_stdm_na,891    time,892    time_ticks_nh,893    xlim,894    ylim,895    subplot_label="E",896    fontsize=20,897    linewidth=1.5,898    task="ww3",899    grid=False,900)901########### Subplot 6 ###########902# South Africa Coast (Namibia)903regional_clima_plot(904    ax6,905    swh_mean_sa,906    swh_stdm_sa,907    None,908    wsp_mean_sa,909    wsp_stdm_sa,910    None,911    swh_m_mean_sa,912    swh_m_stdm_sa,913    wsp_m_mean_sa,914    wsp_m_stdm_sa,915    time,916    time_ticks_sh,917    xlim,918    ylim,919    subplot_label="F",920    fontsize=20,921    linewidth=1.5,922    task="ww3",923    grid=False,924)925########### Subplot 7 ###########926# suppress output for axis 7927ax7.axis("off")928########### Subplot 8 ###########929# Arabian Sea930regional_clima_plot(931    ax8,932    swh_mean_as,933    swh_stdm_as,934    None,935    wsp_mean_as,936    wsp_stdm_as,937    None,938    swh_m_mean_as,939    swh_m_stdm_as,940    wsp_m_mean_as,941    wsp_m_stdm_as,942    time,943    time_ticks_sh,944    xlim,945    ylim=[[0.5, 4], [3, 12]],946    subplot_label="G",947    fontsize=20,948    linewidth=1.5,949    task="ww3",950    grid=False,951)952# make sure the figure looks good:953fig.tight_layout()954# adjust spacing for the entire figure955fig.subplots_adjust(wspace=0.35, hspace=0.2)956# save figure:...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.