analysis_track.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb70d5dc-8a84-4c60-a0f5-b1ddf377781a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pylab as plt\n",
    "import numpy as np\n",
    "import swot_ssh_utils as swot\n",
    "import swot_swell_utils as swell\n",
    "from cartopy import crs as ccrs\n",
    "from netCDF4 import Dataset\n",
    "import xarray as xr\n",
    "from scipy import ndimage\n",
    "\n",
    "import sys as sys\n",
    "import pickle\n",
    "import glob as glob\n",
    "import datetime\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.colors as mcolors\n",
    "from matplotlib.pyplot import cm\n",
    "import cmocean\n",
    "import cmocean.cm as cmo\n",
    "\n",
    "#PATH_with_functions = '/home/ardhuin/TOOLS/OPTOOLS/PYTHON/'\n",
    "#sys.path.append(PATH_with_functions)\n",
    "from  spectral_analysis_functions import *\n",
    "from  wave_physics_functions import *\n",
    "plt.rcParams.update({'font.size': 18,'savefig.facecolor':'white'})\n",
    "\n",
    "cNorm = mcolors.Normalize(vmin=0, vmax=2)\n",
    "jet = plt.get_cmap('jet')\n",
    "mpl.rcParams.update({'figure.figsize':[10,6],'axes.grid' : True,'font.size': 14,'savefig.facecolor':'white'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39af8000-dc6d-4798-997b-fcb5679d5da9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for files: ANIMATIONS/556_015/SWOT_swell_spectra_556_015_left_*.npz\n",
      "We have  0 158  files.\n",
      "fileSWOT = data[\"fileSWOT\"]\n",
      "cycle = data[\"cycle\"]\n",
      "tracks = data[\"tracks\"]\n",
      "side = data[\"side\"]\n",
      "boxindices = data[\"boxindices\"]\n",
      "lonc = data[\"lonc\"]\n",
      "latc = data[\"latc\"]\n",
      "timec = data[\"timec\"]\n",
      "trackangle = data[\"trackangle\"]\n",
      "kx2 = data[\"kx2\"]\n",
      "ky2 = data[\"ky2\"]\n",
      "E_SWOT = data[\"E_SWOT\"]\n",
      "sig0_spec = data[\"sig0_spec\"]\n",
      "coh = data[\"coh\"]\n",
      "ang = data[\"ang\"]\n",
      "amask = data[\"amask\"]\n",
      "sig0mean = data[\"sig0mean\"]\n",
      "sig0std = data[\"sig0std\"]\n",
      "HH = data[\"HH\"]\n",
      "HH2 = data[\"HH2\"]\n",
      "Hs_SWOT_filtered_all = data[\"Hs_SWOT_filtered_all\"]\n",
      "Hs_SWOT_filtered_mask = data[\"Hs_SWOT_filtered_mask\"]\n",
      "Hs_SWOT_mask = data[\"Hs_SWOT_mask\"]\n",
      "Lm_SWOT_filtered_mask = data[\"Lm_SWOT_filtered_mask\"]\n",
      "dm_SWOT_filtered_mask = data[\"dm_SWOT_filtered_mask\"]\n",
      "modelfound = data[\"modelfound\"]\n",
      "timeww3 = data[\"timeww3\"]\n",
      "lonww3 = data[\"lonww3\"]\n",
      "latww3 = data[\"latww3\"]\n",
      "indww3 = data[\"indww3\"]\n",
      "distww3 = data[\"distww3\"]\n",
      "E_WW3_obp_H = data[\"E_WW3_obp_H\"]\n",
      "E_WW3_obp_H2 = data[\"E_WW3_obp_H2\"]\n",
      "E_WW3_noa_H2 = data[\"E_WW3_noa_H2\"]\n",
      "Hs_WW3_all = data[\"Hs_WW3_all\"]\n",
      "Hs_WW3_cut = data[\"Hs_WW3_cut\"]\n",
      "Hs_WW3_filtered_mask = data[\"Hs_WW3_filtered_mask\"]\n",
      "HsWW3 = data[\"HsWW3\"]\n",
      "Tm0m1WW3 = data[\"Tm0m1WW3\"]\n",
      "Tm02WW3 = data[\"Tm02WW3\"]\n",
      "U10WW3 = data[\"U10WW3\"]\n",
      "UdirWW3 = data[\"UdirWW3\"]\n",
      "QkkWW3 = data[\"QkkWW3\"]\n",
      "Lm_WW3 = data[\"Lm_WW3\"]\n",
      "dm_WW3 = data[\"dm_WW3\"]\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_668555/1087425327.py:165: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "  axs[0].scatter(bigarray[0:-1,10],bigarray[0:-1,2],c=gr,alpha=0.5,linewidth=1,label='SWOT');\n",
      "/tmp/ipykernel_668555/1087425327.py:186: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "  axs[1].scatter(bigarray[0:-1,20],bigarray[0:-1,2],c=gr,alpha=0.5,linewidth=1,label='SWOT');\n",
      "/tmp/ipykernel_668555/1087425327.py:211: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "  axs[2].scatter(bigarray[0:-1,30],bigarray[0:-1,2],c=gr,alpha=0.5,linewidth=1,label='SWOT');\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note : other tests with different spectral resolution are in directories LOOP, LOOP2, LOOP3\n",
    "\n",
    "adjust_azc=0\n",
    "if adjust_azc==1 :\n",
    "   filetr='/media/ardhuin/FabLinux/WW3-SWOT/GLOBAL_REF102040/SWOT_WW3-GLOB-30M_202307_trck.nc'\n",
    "   ds_ww3t = xr.open_dataset(filetr)\n",
    "   modang=np.mod(90-ds_ww3t.direction,360)\n",
    "   moddf=ds_ww3t.frequency2.values-ds_ww3t.frequency1.values \n",
    "   modf=ds_ww3t.frequency.values\n",
    "   modnth=np.shape(modang)[0]\n",
    "   moddth=(2*np.pi/modnth)\n",
    "\n",
    "calibration=1.0\n",
    "modeltag='' #'_T702'\n",
    "denoise=1\n",
    "figh=6.\n",
    "# track for Figure 4.a\n",
    "cycle='546';tracks='019';latrange=[-65,-40];Lrange=[500,1100];Lstep=100;thrange=[225,270];thstep=10;Hrange=[0,5.0];locleg='lower right';ileg=1;figh=7.;\n",
    "\n",
    "#cycle='547';tracks='008';latrange=[-20,-8];Lrange=[500,1100];Lstep=100;thrange=[190,240];thstep=10;Hrange=[0,1.5];locleg='upper right';ileg=0;\n",
    "\n",
    "# track for Figure 4.b\n",
    "cycle='547';tracks='021';latrange=[-60,-37];Lrange=[500,800];Lstep=50;thrange=[0,30];thstep=5;Hrange=[0,0.6];locleg='lower right';ileg=0;figh=7;\n",
    "\n",
    "# track for Figure S7a\n",
    "#cycle='548';tracks='017';figh=7;figANIMA=8;latrange=[-55,-30];Lrange=[400,1000];Lstep=100;thrange=[180,270];thstep=15;Hrange=[0,3];locleg='lower right';ileg=1;latshift=3.\n",
    "# track for Figure S7b\n",
    "cycle='550';tracks='017';figh=7;latrange=[-32,0];Lrange=[400,1000];Lstep=100;thrange=[180,240];thstep=15;Hrange=[0,2.5];locleg='upper right';ileg=0;latshift=7.\n",
    "\n",
    "\n",
    "# track with buoys \n",
    "# Fig. S2.a \n",
    "cycle='556';days='20230618';tracks='015';figh=5.;latrange=[35,59.6];Lrange=[500,800];Lstep=100;thrange=[180,240];thstep=15;Hrange=[0,0.3];locleg='lower right';ileg=1;  \n",
    "\n",
    "#cycle='545';tracks='010';latrange=[-45,-30];Lrange=[400,800];Lstep=100;thrange=[180,240];thstep=15;Hrange=[0,7];locleg='upper right';ileg=0;\n",
    "#cycle='546';tracks='010';latrange=[-45,-30];Lrange=[400,800];Lstep=100;thrange=[180,240];thstep=15;Hrange=[0,3];locleg='lower right';ileg=1;\n",
    "\n",
    "#cycle='006';tracks='582';latrange=[40,57];Lrange=[600,1100];Lstep=100;thrange=[195,300];thstep=15;Hrange=[0,1.5];locleg='lower right';ileg=0;figh=7;latshift=0;\n",
    "#cycle='001';tracks='485';latrange=[40,57];Lrange=[600,1100];Lstep=100;thrange=[195,300];thstep=15;Hrange=[0,1.5];locleg='lower right';ileg=0;figh=7;latshift=0;\n",
    "\n",
    "folder='ANIMATIONS/'+cycle+'_'+tracks\n",
    "print('looking for files:',folder+'/SWOT_swell_spectra_'+cycle+'_'+tracks+'_left_*.npz' ) \n",
    "file_lisr = sorted(glob.glob(folder+'/SWOT_swell_spectra_'+cycle+'_'+tracks+'_left_*.npz'));\n",
    "#file_list = sorted(glob.glob('LOOP3/SWOT_swell*_'+tracks+'left_*.npz'));\n",
    "nfiles=len(file_lisr)\n",
    "#file_lisl = sorted(glob.glob('ANIMATIONS'+modeltag+'/SWOT_swell_spectra_'+cycle+'_'+tracks+'_right_*.npz'));\n",
    "file_lisl = sorted(glob.glob(folder+'/SWOT_swell_spectra_'+cycle+'_'+tracks+'_right_*.npz'));\n",
    "nfilel=len(file_lisl)\n",
    "tracktag='cycle '+cycle+', track '+tracks;\n",
    "print('We have ',nfiles,nfilel,' files.')\n",
    "bigarray=np.zeros((nfiles+nfilel,50))\n",
    "indf=0\n",
    "obpfilter='H2'\n",
    "for file_list in [file_lisr,file_lisl]:\n",
    "  #for file_box in file_list:\n",
    "  for file_box in file_list:\n",
    "    #print('reading file ',file_box)\n",
    "    data = np.load(file_box,allow_pickle=True)\n",
    "    for k in data.keys():\n",
    "        exec(k+' = data[\"'+k+'\"]')\n",
    "        if (indf == 0):\n",
    "            print(k+' = data[\"'+k+'\"]')\n",
    "\n",
    "    \n",
    "    dlat=np.sin(-trackangle*np.pi/180)\n",
    "    #amask,bmask=swell.SWOTdefine_swell_mask_simple(E_WW3_noa_H2/HH2,coh,ang,sig0mean,dlat,kx2,ky2,mask_choice=0)\n",
    "    #amask,bmask=swell.SWOTdefine_swell_mask_simple(E_WW3_noa_H2/HH2,coh,ang,sig0mean,dlat,kx2,ky2,mask_choice=-3)\n",
    "    #amask,bmask=swell.SWOTdefine_swell_mask_simple(E_SWOT/HH2,coh,ang,sig0mean,dlat,kx2,ky2,mask_choice=1)\n",
    "    #amask=ndimage.binary_dilation((amask > 0.5).astype(int))\n",
    "    \n",
    "    # Computes a calibration transfer function: Hptr to some power ... \n",
    "    #if (indf==0):\n",
    "    #    HsWW3=2.\n",
    "    #    Tm02WW3=4.\n",
    "    print(file_box,modelfound)\n",
    "    # uses values from *npz files: full spectrum \n",
    "    if modelfound > 0 :\n",
    "      sigu=(HsWW3/4)*(2*np.pi/Tm02WW3)\n",
    "      kc=(7310/875.0e3)/sigu/(2*np.pi)\n",
    "    else :\n",
    "      kc=0.3\n",
    "    Hazc_old =np.exp(-(ky2/kc)**2)\n",
    "    Hazc_new = Hazc_old\n",
    "    if adjust_azc==1:\n",
    "        # recomputes sigu (standard dev. of orbital velocity) for a filtered spectrum (cutting at 100 m wavelength) \n",
    "        modspec=ds_ww3t.efth[indww3].squeeze() \n",
    "        efth=modspec.values;\n",
    "        # this was to check that we get the same numbers again ... \n",
    "        #[Ef,th1m,sth1m,Hsall,Tm0m1,Tm02all,Qf,Qkk] = wavespec_Efth_to_first3(efth,modf,moddf, modang.values,moddth,cut=10000.) \n",
    "    \n",
    "        [Ef,th1m,sth1m,Hs,Tm0m1,Tm02,Qf,Qkk] = wavespec_Efth_to_first3(efth,modf,moddf, modang.values,moddth,cut=250.) \n",
    "    \n",
    "        sigu3=(Hs/4)*(2*np.pi/Tm02)\n",
    "        kc=(7310/875.0e3)/sigu/(2*np.pi)\n",
    "        Hazc_old =np.exp(-(ky2/kc)**2)\n",
    "        #print('TEST:',latc,Hs,Tm02,sigu3,1/kc) \n",
    "\n",
    "        kc=(7310/875.0e3)/sigu3/(2*np.pi)\n",
    "        Hazc_new =np.exp(-(ky2/kc)**2)\n",
    "    \n",
    "    HHC=(HH2/(HH*Hazc_old))**(calibration-1)\n",
    "    HH3=(HH2/Hazc_old)*HHC*Hazc_new  # filter corrected with calibration for Hptr and new cut-off for Hazc. \n",
    "    \n",
    "    # NB: to be fully correct we should also redo the aliasing calculation with the updated filter ...\n",
    "   \n",
    "    bigarray[indf,0]=timec\n",
    "    bigarray[indf,1]=lonc\n",
    "    bigarray[indf,2]=latc\n",
    "    bigarray[indf,3]=timeww3\n",
    "    bigarray[indf,4]=1/kc\n",
    "    \n",
    "        \n",
    "    if obpfilter=='H':\n",
    "        Hsnew,Lmm1new,Lmnew,dmnew,Q18=swell.SWOTspec_to_HsLm(E_SWOT,kx2,ky2,amask,HH,trackangle)\n",
    "    else:\n",
    "        Hsnew,Lmm1new,Lmnew,dmnew,Q18=swell.SWOTspec_to_HsLm(E_SWOT,kx2,ky2,amask,HH3,trackangle)\n",
    "    bigarray[indf,10]=Hsnew\n",
    "    bigarray[indf,20]=Lmm1new\n",
    "    bigarray[indf,30]=dmnew\n",
    "    if (denoise==1):\n",
    "       Ekth,kn1,theta1,Eta_SWOT_nonoise=swell.SWOT_denoise_isotropic(E_SWOT/HH3,kx2,ky2)\n",
    "       Hsnew,Lmm1new,Lmnew,dmnew,Q18=swell.SWOTspec_to_HsLm(Eta_SWOT_nonoise,kx2,ky2,amask,(HH2*0+1),trackangle)\n",
    "       bigarray[indf,12]=Hsnew\n",
    "       bigarray[indf,22]=Lmm1new\n",
    "       bigarray[indf,32]=dmnew\n",
    "    \n",
    "    \n",
    "    b2mask=amask\n",
    "# uses a slightly wider mask for the model to be sure to catch the full partition \n",
    "    b2mask=ndimage.binary_dilation((amask > 0.5).astype(int))\n",
    "    #\n",
    "    if obpfilter=='H':\n",
    "        Hsnew,Lmm1new,Lmnew,dmnew,Q18=swell.SWOTspec_to_HsLm(E_WW3_obp_H,kx2,ky2,b2mask,HH,trackangle)\n",
    "    else:\n",
    "        Hsnew,Lmm1new,Lmnew,dmnew,Q18=swell.SWOTspec_to_HsLm(E_WW3_obp_H2,kx2,ky2,b2mask,HH2,trackangle)\n",
    "    bigarray[indf,11]=Hsnew\n",
    "    bigarray[indf,21]=Lmm1new\n",
    "    bigarray[indf,31]=dmnew\n",
    "    indf=indf+1\n",
    "\n",
    "tshift=86400   #\n",
    "\n",
    "new_time = timec+np.timedelta64(int(np.round(tshift*1e9)), 'ns')\n",
    "times=str(new_time)[0:4]+str(new_time)[5:7]+str(new_time)[8:10]\n",
    "#print('timec:',timec,'##',times,'##') \n",
    "files_SWIM = sorted(glob.glob('SWIM*'+times+'*10.pickle'))\n",
    "lenSWIM=len(files_SWIM)\n",
    "if (lenSWIM > 0):\n",
    "    print('reading SWIM data:',files_SWIM[0])\n",
    "    with open(files_SWIM[0], 'rb') as pickle_file:\n",
    "       SWIM = pickle.load(pickle_file)\n",
    "    \n",
    "gr=[0.,205./255.,0.]\n",
    "fig,axs=plt.subplots(1,4,figsize=(16,figh))\n",
    "vecHs=np.linspace(1,12,23)\n",
    "if nfiles*nfilel > 0  :\n",
    "   axs[0].scatter(bigarray[0:nfiles,11],bigarray[0:nfiles,2],c='black',linewidth=1,label='model left');\n",
    "   axs[0].scatter(bigarray[nfiles+1:-1,11],bigarray[nfiles+1:-1,2],c='blue',linewidth=1,label='model  right');\n",
    "   axs[0].scatter(bigarray[nfiles+1:-1,10],bigarray[nfiles+1:-1,2],c=gr,linewidth=1,label='SWOT  right');\n",
    "   axs[0].scatter(bigarray[0:nfiles,10],bigarray[0:nfiles,2],c='red',linewidth=1,label='SWOT left');\n",
    "else: \n",
    "   gr=[200./255.,200./255.,200./255.]\n",
    "   gS=[80./255.,255./255.,80./255.]\n",
    "   axs[0].scatter(bigarray[0:-1,11],bigarray[0:-1,2],c='black',linewidth=1,label='model');\n",
    "   axs[0].scatter(bigarray[0:-1,10],bigarray[0:-1,2],c=gr,alpha=0.5,linewidth=1,label='SWOT');\n",
    "   if (denoise==1):\n",
    "      axs[0].scatter(bigarray[0:-1,12],bigarray[0:-1,2],c='red',alpha=0.5,linewidth=1,label='denoised');\n",
    "   if (lenSWIM > 0):\n",
    "     axs[0].scatter(SWIM['swell_hs'],SWIM['latitude']+latshift,c=gS,alpha=1.,linewidth=1,label='SWIM 10°');\n",
    "\n",
    "    \n",
    "axs[0].set_xlabel(r'$H_{18}$ (m)', fontsize=18)\n",
    "axs[0].set_ylabel('latitude (deg)', fontsize=18)\n",
    "axs[0].set_title(tracktag, fontsize=18)\n",
    "axs[0].set_xlim(Hrange);\n",
    "#axs[0].set_xlim([0,5]);\n",
    "axs[0].set_ylim(latrange)\n",
    "\n",
    "if nfiles*nfilel > 0  :\n",
    "   axs[1].scatter(bigarray[0:nfiles,21],bigarray[0:nfiles,2],c='black',linewidth=1,label='model left');\n",
    "   axs[1].scatter(bigarray[nfiles+1:-1,21],bigarray[nfiles+1:-1,2],c='blue',linewidth=1,label='model  right');\n",
    "   axs[1].scatter(bigarray[nfiles+1:-1,20],bigarray[nfiles+1:-1,2],c=gr,linewidth=1,label='SWOT  right');\n",
    "   axs[1].scatter(bigarray[0:nfiles,20],bigarray[0:nfiles,2],c='red',linewidth=1,label='SWOT left');\n",
    "else: \n",
    "   axs[1].scatter(bigarray[0:-1,21],bigarray[0:-1,2],c='black',linewidth=1,label='model');\n",
    "   axs[1].scatter(bigarray[0:-1,20],bigarray[0:-1,2],c=gr,alpha=0.5,linewidth=1,label='SWOT'); \n",
    "   if (denoise==1):\n",
    "     axs[1].scatter(bigarray[0:-1,22],bigarray[0:-1,2],c='red',alpha=0.5,linewidth=1,label='denoised');\n",
    "   if (lenSWIM > 0):\n",
    "     axs[1].scatter(SWIM['swell_wl'],SWIM['latitude']+latshift,c=gS,alpha=1.,linewidth=1,label='SWIM 10°');\n",
    "\n",
    "axs[1].set_xlabel(r'$L_{18}$ (m)', fontsize=18)\n",
    "format = '%Y-%m-%dT%H:%M:%S'\n",
    "axs[1].set_title(str(timec)[0:16], fontsize=18)\n",
    "axs[1].set_xlim(Lrange)\n",
    "axs[1].set_xticks(np.arange(Lrange[0],Lrange[1],Lstep))\n",
    "#axs[1].set_xlim([450,650])\n",
    "#axs[1].set_xticks(np.arange(450,650,50))\n",
    "axs[1].set_ylim(latrange)\n",
    "leg = axs[ileg].legend(loc=locleg, fontsize=14)\n",
    "\n",
    "lightcmap = cmocean.tools.lighten(cmo.ice_r, 0.5)\n",
    "\n",
    "if nfiles*nfilel > 0  :\n",
    "   axs[2].scatter(bigarray[0:nfiles,31],bigarray[0:nfiles,2],c='black',linewidth=1,label='model left');\n",
    "   axs[2].scatter(bigarray[nfiles+1:-1,31],bigarray[nfiles+1:-1,2],c='blue',linewidth=1,label='model right');\n",
    "   axs[2].scatter(bigarray[nfiles+1:-1,30],bigarray[nfiles+1:-1,2],c=gr,linewidth=1,label='SWOT right');\n",
    "   axs[2].scatter(bigarray[0:nfiles,30],bigarray[0:nfiles,2],c='red',linewidth=1,label='SWOT left');\n",
    "else: \n",
    "   axs[2].scatter(bigarray[0:-1,31],bigarray[0:-1,2],c='black',linewidth=1,label='model');\n",
    "   axs[2].scatter(bigarray[0:-1,30],bigarray[0:-1,2],c=gr,alpha=0.5,linewidth=1,label='SWOT');\n",
    "   if (denoise==1):\n",
    "     axs[2].scatter(bigarray[0:-1,32],bigarray[0:-1,2],c='red',alpha=0.5,linewidth=1,label='denoised');\n",
    "   if (lenSWIM > 0):\n",
    "     axs[2].scatter(SWIM['swell_dir'],SWIM['latitude']+latshift,c=gS,alpha=1.,linewidth=1,label='SWIM 10°');\n",
    "\n",
    "axs[2].set_xlabel(r'$\\theta_{18}$ (deg. nautical,from)', fontsize=18)\n",
    "axs[2].set_xlim(thrange)\n",
    "axs[2].set_xticks(np.arange(thrange[0],thrange[1],thstep))\n",
    "axs[2].set_ylim(latrange)\n",
    "#axs[2].set_title(tracktag)\n",
    "\n",
    "if nfiles*nfilel > 0  :\n",
    "   axs[3].scatter(bigarray[0:nfiles,4],bigarray[0:nfiles,2],c='black',linewidth=1,label='model left');\n",
    "   axs[3].scatter(bigarray[nfiles+1:-1,4],bigarray[nfiles+1:-1,2],c='blue',linewidth=1,label='model right');\n",
    "else: \n",
    "   axs[3].scatter(bigarray[0:-1,4],bigarray[0:-1,2],c='black',linewidth=1,label='model');\n",
    "\n",
    "axs[3].set_xlabel(r'az cut-off $\\lambda_c$ (m)', fontsize=18)    \n",
    "axs[3].set_ylim(latrange)\n",
    "if obpfilter=='H':\n",
    "    fig.savefig('fulltrack_'+str(cycle)+'_'+str(tracks)+'_H.pdf')\n",
    "else:\n",
    "    fig.savefig('fulltrack_'+str(cycle)+'_'+str(tracks)+'_H2azc_cal'+modeltag+f'{abs(calibration):03.1f}'+'.pdf')\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "646a2c93-aae9-4ec1-9902-37b922661ae1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "if (lenSWIM > 0):\n",
    "    with open(files_SWIM[0], 'rb') as pickle_file:\n",
    "        SWIM = pickle.load(pickle_file)\n",
    "    print('##',SWIM['longitude'])\n",
    "#SWIMlon = [item['longitude'] for item in SWIM.items()]\n",
    "#WIMlon = [value['longitude'] for key, value in SWIM.items()]\n",
    "#print(SWIM.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4739a09c-5194-41c2-89e8-c52dda9f0633",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Checks a few things on masks\n",
    "\n",
    "# selects one file at a given latitude: \n",
    "file_box='ANIMATION/SWOT_swell_spectra_556_015_right_55.00N.npz'\n",
    "#file_box='ANIMATION/SWOT_swell_spectra_550_017_left_30.00S.npz'\n",
    "#lonlat=f'{abs(latc):05.2f}'+hemiNS[int(np.sign(latc))]\n",
    "#filetag='SWOT_'+cycle+'_'+tracks+'_'+side+'_'+days+'_'+lonlat\n",
    "    \n",
    "data = np.load(file_box,allow_pickle=True)\n",
    "for k in data.keys():\n",
    "    exec(k+' = data[\"'+k+'\"]')\n",
    "\n",
    "dlat=np.sin(-trackangle*np.pi/180)\n",
    "\n",
    "amask2,bmask2=swell.SWOTdefine_swell_mask_simple(E_WW3_noa_H2/HH2,coh,ang,sig0mean,dlat,kx2,ky2,mask_choice=-3)\n",
    "#amask2,bmask2=SWOTdefine_swell_mask_simple(E_SWOT/HH2,coh,ang,sig0mean,dlat,kx2,ky2,mask_choice=1)\n",
    "\n",
    "vertices=swell.SWOTspec_mask_polygon(E_SWOT,amask) \n",
    "vertices2=swell.SWOTspec_mask_polygon(E_SWOT,amask2) \n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=4,figsize=(15,7))\n",
    "plt.subplots_adjust(left=0.05,bottom=0.07, top=0.92,wspace=0.12,right=0.99)\n",
    "im=ax[0].pcolormesh(kx2*1000,ky2*1000,10*np.log10(E_SWOT/HH2),cmap='viridis',rasterized=True,vmin=10, vmax=60)\n",
    "_=plt.colorbar(im,ax=ax[0],label='', location='bottom',shrink=0.8)\n",
    "_=ax[0].set_title('spectrum (dB)')\n",
    "\n",
    "dkx=kx2[0,1]-kx2[0,0]\n",
    "dky=ky2[1,0]-ky2[0,0]\n",
    "for ind in range(len(vertices) // 4):\n",
    "           xy2=np.asfarray(vertices[ind*4:(ind+1)*4])\n",
    "           ax[0].plot(kx2[0,0]*1000+1000*dkx*xy2[0:2],ky2[0,0]*1000+1000*dky*xy2[2:4], color='w',lw=3)\n",
    "\n",
    "for ind in range(len(vertices2) // 4):\n",
    "           xy2=np.asfarray(vertices2[ind*4:(ind+1)*4])\n",
    "           ax[0].plot(kx2[0,0]*1000+1000*dkx*xy2[0:2],ky2[0,0]*1000+1000*dky*xy2[2:4], color='y',lw=3)\n",
    "\n",
    "\n",
    "Ekth,kn1,theta1,Eta_SWOT_nonoise=swell.SWOT_denoise_isotropic(E_SWOT/HH2,kx2,ky2)\n",
    "            \n",
    "\n",
    "im=ax[1].pcolormesh(kx2*1000,ky2*1000,10*np.log10(Eta_SWOT_nonoise),cmap='viridis',rasterized=True,vmin=10, vmax=60)\n",
    "_=plt.colorbar(im,ax=ax[1],label='', location='bottom',shrink=0.8)\n",
    "_=ax[1].set_title('spectrum denoised (dB)')\n",
    "\n",
    "im=ax[2].pcolormesh(kx2*1000,ky2*1000,10*np.log10(E_WW3_obp_H2/HH2),cmap='viridis',rasterized=True,vmin=10, vmax=60)\n",
    "_=plt.colorbar(im,ax=ax[2],label='', location='bottom',shrink=0.8)\n",
    "_=ax[2].set_title('model spectrum')\n",
    "\n",
    "im=ax[3].pcolormesh(kx2*1000,ky2*1000,10*np.log10(E_WW3_noa_H2/HH2),cmap='viridis',rasterized=True,vmin=10, vmax=60)\n",
    "_=plt.colorbar(im,ax=ax[3],label='', location='bottom',shrink=0.8)\n",
    "_=ax[3].set_title('model spectrum, no alias')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "86d4cff8-65cc-454e-8959-1e9d921b8296",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kc: 0.0057466581440897965 174.01417918489884\n"
     ]
    },
    {
     "data": {
      "image/png": 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QF198oaVLl6pXr14aPny4iouLW5zf4/GotLRU27dvJ59yGQoiQJ748Y9/3O5NTXPpKpF3331XUv2YTznllFbbffXVV5Lqb7K2r7KysjYLCW2J3wm+rSRo9erVkqTDDjusxedLS0t10EEHae3atW0ua8WKFc2mbd68WT/96U/1xBNP6MQTT9R7773XavIYH2N8zAAAs9Jxzj3ooINavf9G/F4HlZWVikajLf5Ea3uGDh3a6v21nPrGN76hBx54QBMnTtSzzz4rSZo3b17K972Ie/LJJ5vcTHRf8+fPb/UDjWXLlum8887Tli1b2lxGPD9o7bnvfe972r59uy655BLdcsstTZ7ftWtX4iqBlj5sySXxfGrnzp068cQTW20X316/+OKLZgWR/dmW2sundu7cmbhHS2v51CGHHCKfz9fmB0zxn93d15o1a3TppZfqj3/8o9auXZu4qX9LyKfciYIIgKy0fft2SfUnJSdXttTU1DSb1tqnAE6UlZVJkr7++utW2+zatUuSEl9ZaUmvXr3aLYi0Nt/8+fO1fPlyVVZWas6cObr66qtbbBsfY3zMAIDs1965Q6q/anD37t0p3bh0f86BjY0cOVJ+v191dXUaOHCgTjvttLT0m4qqqir94Ac/0NatWzV69GjddNNNOvLII9W1a1f5/X5JUr9+/fT555+3+pXVcDisc845R5988om++93vavbs2S0uJy7TPxObbvF8aseOHVmZT8VzKan1fcLr9ap79+7avHlz0ssfOHCgFi9erH79+mnZsmV6+umndcYZZ7TYNv5akU+5C/cQAZCVOnXqJEk66qijZNt2u/9a+nm5/RE/KW/fvl2xWKzFNvF7hrR1gk7l5B1XWFioY445RpL02muvtdounmSkejUMACDznJw7LMtKnA87Qjgc1oUXXqi6ujp5PB6tWbNGt956a4eN55///Ke2bt2qvn376umnn9Ypp5yinj17Joohtm23+UGGJE2cOFHLly/XoYceqj//+c+JeRtrfO8wpz8tm63i28/ZZ5/tKJ8qLy9P6/Lj+VRrV+w0vv9aa/tENBpt84ofJ2OI/+pOa/lUTU1N4peGyKfchYIIgA7R3qXE8Z/f/eCDDzokGfnGN76hoqIiRaPRxFdj9hX/bvKHH37Y4vM7d+5s8as8yYgXY1pLBLZs2aKtW7fK4/Ho6KOP3q9lAQAy54svvmhyJUJjH3zwgaT6n3lt/HWZTH/19eabb9Zbb72lgw8+WM8++6x8Pp/uu+8+PffccxkdR1z8Bp3HHntsi1/B+M9//qPq6upW558+fboeffRR9ejRQ88880yrV3907txZ/fv3lyS98sor+z9wg5zmU6+99lqrH/CYFP9gZ926ddq9e3ez50tLS9W7d29JredTH330Ubv3Y2tPe/lU/Kd9u3fvroEDB+7XspBbKIgA6BBFRUWSWv+e5vDhwzVkyBDV1dXpF7/4RSaHJqn+Znfx7w239mnC//f//X+SpNmzZ7d4ae5DDz20XyfwmpoarVq1SpJ08MEHt9jm1VdflSR985vfzPnLegHATerq6vTQQw81m27btmbNmiVp73kmrr1zZzo9++yz+s1vfiO/36/Fixfr1FNP1YwZMxK/NtPePTxMiMe/YcOGFp+/9957W5138eLFmjZtmgoKCvS3v/2t2a/G7OsHP/iBJOnXv/51i79kly3a2yZOPfVUdenSRZs2bWr3xqQmHHjggRo8eLBisZjeeOONFtvEt/P4dr+v3/72t/s1ho0bN+qTTz6R1Ho+Fc/1Tj755Jy65x72HwURAB1i8ODBkup/8i5+Z/jGLMvSr3/9a1mWpV/84he65ZZbtHPnziZtqqur9dRTT2nixIlGxvi9730vMcaWXHHFFerSpYu++OILXXLJJU0++Xj66ad15513tngprhObN2/WuHHjtGnTJlmW1eov7cRvDtbWT8kBALKP3+/XjBkz9Pe//z0xraamRpdffrlWr16toqIiXXPNNU3miZ8733jjjTavhNhfmzZt0vjx42Xbtu666y4de+yxkqQbbrhBo0eP1ubNmxPPZ9LIkSMl1b95jf9UrCSFQiHdcsstWrRoUYu/+vbKK6/okksukWVZWrBggb797W+3u6wpU6aoZ8+e+uCDD/Q///M/zX7a/tNPP9Wdd965nxHtv/g28dJLL7X4fOfOnXXXXXdJkn7605/qN7/5TbPiyfbt2/Xoo49qypQpRsbYXj513XXXye/364033tCUKVMUDocl1RcH586dqz/84Q8p39T1008/1QUXXKBwOKzCwsJmvyYURz7lXhREAHSIiy++WB6PR0888YT69eunk046SeXl5U2SvzPOOENz585VIBDQzJkz1aNHDx1xxBH61re+paFDh6q0tFTnnnuuXnjhBSNjHDdunAoKCvTXv/61xZuM9ezZU48++qj8fr8ee+wx9e7dW8cee6wGDhyoM888U2PGjHF0d/oTTzwx8e+EE07Q4MGD1bdvX/35z3+Wx+PRr371q0Qy2lg0GtWf/vQneTweTZo0KS0xAwAy4zvf+Y7Gjh2rs846SwMGDNBxxx2n3r17a86cOfJ6vZo7d26zX2I57bTTEjfrPuigg/Stb31L5eXlab3vQ+MrQE477TRdf/31iec8Hk/iKyfPPvusfvWrX6VtuU4cddRR+tGPfiRJ+slPfqIDDzxQxx57rHr16qWZM2dqxowZLf4Czu9//3uFQiEVFRXpgQceaHLebfzv7rvvTszTq1cv/eMf/1DPnj313HPP6eCDD9ahhx6aWN7gwYN13333ZSz21owbN06SdN999+nggw/WySefrPLyct1zzz2JNj/5yU90xx13qK6uTtdee626deum4cOH6/jjj9egQYPUvXt3jRs3Tm+++aaRMV522WWSpEWLFrX4/LBhwzRr1ixZlqX77rtPvXv31nHHHacDDzxQkyZN0uTJk3XggQe2uYxNmzY1WZff+c53NGDAAA0dOlTLli1TMBjU/Pnz1bdv32bz7tixQ//85z9VUlKiCy+8cP8DRk6hIAKgQxx33HH661//qvLycu3evVuvvPKKXn75Zb3zzjtN2v34xz/WBx98oJ/97GcaMmSI1qxZo//85z+KRqMaOXKkfvGLX+j55583MsZu3brpggsu0K5du/TUU0+12OaMM87Q66+/rrPOOkuBQEDvv/++iouLde+99+rJJ590tJyVK1cm/r3yyivasGGDBgwYoEsuuURvvvmmfv7zn7c434svvqiNGzfq9NNPT3zXGQCQO5544gn96le/UklJid5//315vV5973vf04oVK1p8Y1ZcXKwXX3xR5557rgoKCvTvf/9bL7/8cps/JZqsX/7yl3r++efVs2dP/fGPf2z29YE+ffpo/vz5sixLt9xyi9566620LduJefPm6Re/+IUOOeQQbd26VZ9++qmOPvpo/eUvf2n3hq/V1dVNzrn7/vv444+btD/uuOP04Ycf6vbbb9eRRx6pL7/8Uh988IE6deqkH/zgB3rkkUdMhurIueeeq0ceeUTHH3+8tm7dqhUrVujll19udv+z22+/XW+//bYmTpyogw46SB999JE++OAD+f1+jR07VrNmzdLChQuNjPGwww5TeXm5Pv300xZ/GleSLr/8cr3wwgs65ZRTFIlE9OGHH+rAAw/U3Llz9Zvf/KbdZYRCoSbr8tVXX9W2bdt06KGH6uqrr9Z7772n888/v8V5n3jiCYVCIY0bNy5tv86E3GHZmb7WDQByyCeffKJhw4Zp2LBh+ve//51V3ysdM2aMnn/+eb355puJm5YBALLb/Pnzdckll2jkyJGtfoUAyDfLli3TyJEjddZZZ+mvf/1rRw8nIRaL6fDDD9e6dev00Ucf6aCDDuroISHDuEIEANowePBgXX311Xr77bf15z//uaOHk7BixQo999xzGjduHMUQAACQ1U4++WR9//vf19/+9reMX1XUlscee0z//e9/NWXKFIohLpXa3WkAwEVuv/12lZSUZNVd5r/66itNnTo18b1cAACAbParX/1KRxxxhDZv3tzRQ0mIxWKaNm2asRvKIvvxlRkAAAAgQ/jKDABkD74yAwAAAAAAXIcrRAAAAAAAgOtwhQgAAAAAAHAdbqraIBaLacOGDercuXNW/awmAAD5yrZt7dq1SwcccIA8nvz8jIb8AgCAzEomv6Ag0mDDhg3q27dvRw8DAADX+fzzz/P25w7JLwAA6BhO8gsKIg06d+4sSTo5cI58lr/d9r5CnyY8dIbm/+RpRfZEHC3DGmQ2IQr16Wys74DPo4kTBmvWP9YqHDVz25lQF7OfnNUVO28b9Fq66ej+umfVOoUcxhspTHFgDsUKzN3uJ+CxNHXQAE39fK3CBm4rZFtmb1VkJ/HBcsCyNOOAgbp9wxrHsVqxFAfmkGWb2/YDlqXpfQdo+mdrFY6ZWQ+eWrP7rm+P87ap7Lv+6hQH5nRMOwzuu15Lk88coLnzP1E4YmZDDW7cZaRfSYpEQ3r54wcT5+B8FI/tyD9eJW9RsN32ftura+qG637/26qzoqaH1+GIN78Rb35zU7xuilXK/XijNSH9Z9z/c5RfUBBpEL+M1Wf55bMC7bb3Wz4VFRXJbwUky9m7McvbfiK0P6K+AmN9+/weFRUVyRcoUCxiJrmPBMy+qYol8fJ7vZaKiorkDRbI6/BNlW3u5a9nsCDi9TTEW1Agj4mCiCd7CiJeqyHWQuexGi+IxMxt+4l4CwrkMVQQ8crsvutN4vVPZd/11qU4MId8fnPbv89XH6/PX6CYzGyoPm/YSL+N5fNXSeKxeYuCjgoiPturoroi+fxBxXIwCU0W8eY34s1vborXTbFK+ROvk/wiP7+wCwAAAAAA0AYKIgAAAAAAwHUoiAAAAAAAANehIAIAAAAAAFyHgggAAAAAAHAdCiIAAAAAAMB1Ml4Q+fLLL3X//ffrtNNOU79+/RQIBNS7d2+de+65ev3115PqKxaL6cEHH9SRRx6pwsJC9ejRQ+eff74qKysNjR4AAGQj8gsAAJCsjBdEZs2apZ///Of67LPPdOqpp+q6667TiSeeqL/97W/6zne+oyeeeMJxX1dccYUmT56saDSqyZMn6/TTT9ff//53HXvssfrwww8NRgEAALIJ+QUAAEiWL9MLPO6447Rs2TKddNJJTaYvX75co0eP1pVXXqmzzjpLwWCwzX6WLl2qOXPm6KSTTtLzzz+faD9u3DideuqpuvLKK/Xyyy8biwMAAGQP8gsAAJCsjF8h8v3vf79ZsiJJJ510kkaNGqWvv/5a7733Xrv9zJkzR5J05513NkluRo8erTFjxmjZsmX6+OOP0zdwAACQtcgvAABAsrLqpqp+v1+S5PO1f+FKRUWFiouLdcIJJzR7bsyYMZLEJzgAAID8AgAAtCjjX5lpzfr16/XCCy+od+/eOuKII9psW11drY0bN+rwww+X1+tt9vyQIUMkqc2bn4VCIYVCocTfVVVVkiRfoU9+q/2XxV/ga/J/J6yg2fqT7TfXv7+h74DPMrYM22uub0nyNN9UWhXwWE3+74TXcHkxarD/oGU1+X+62WZXbVL9pxKroZelUf/mFtAkXkPbkDeJ/SQVPsP7rj+J/lMRMHimjR+T/SaP/ybPXSYPbA2yJb/w21757PY3toDtafL/fEe8+Y1485ub4nVTrFLuxxtxcL6Ny4qCSF1dnX70ox8pFArpl7/8ZYtJSGM7d+6UJJWWlrb4fElJSZN2LZk5c6amT5/ebPqEh85QUVGR06FrwsNnOG6bDyafObCjh5BRNx8zoKOHkFHT+7ln/c44YFBHDyGjph7snnUruW/fnTRhcEcPISU1NTVacpG5/rMpv7imbriK6pznF9dEjnbcNh8Qb34j3vzmpnjdFKuUu/HW1NXIaXrR4QWRWCymH//4x1q2bJkmTZqkH/3oRxlZ7s0336xrr7028XdVVZX69u2r+T95Wn4r0O78/gKfJjx8huZf+bTqaiOOlmkNOijl8ToR7l1irG+/36NJEwZr1j/WKByxjSwj1MXsp8x1nZy3DXgs3XzMAM3891qFY87ijRSmODCHogVmXnep/uqBqQcP1NT1axSy078c22Nu7PX9O28btCzNOGCQbt/wmeNYrViKA3PIipm9QmR6v4Ga/qmZdStJ3lrDV4jscd42lX3XvzvFgTkU3GFu+w/4LE0+c6DmzP9EdXVmNtTApioj/UpSXTTUfqMUZVt+cb//bfn8bd/QVar/NO6ayNG637dKYdMHnyxAvPmNePObm+J1U6xS7scb8TvPLzq0IGLbtiZNmqSFCxfqhz/8oX73u985mi/+yU1rn9DEL09t7RMeSQoGgy3eaT6yJyJZzt9d1dVGVLfHYUEkZHZjChtKhpssI2KbK4hEjXSbUJdC/+GYrVDUYUHE8MsfdfjmLiUNm3zIts0URAy9Ed/bf/LzJBOrZXb4xvuXGuI1tA15TW/7hvfdmOFjj2XomNlYXV3M2DnA5LkrEjXTdzbmF3VWVDHL+cYWtmIKJ9E+1xFvfiPe/OameN0Uq5S78UaTGHOHfSkoFovp0ksv1SOPPKILL7xQ8+fPl8fjbDjFxcXq06eP1qxZo2gLmXL8u73x7/oCAAB3IL8AAABOdUhBJBaLaeLEiZo3b57+93//V48++mi73+vd18iRI1VdXa2VK1c2e27JkiWJNgAAwB3ILwAAQDIyXhCJf3Izb948nXfeeVq4cGGbycq2bdu0evVqbdu2rcn0yy67TJJ02223KRwOJ6a/+OKLWrJkiU4++WQNHTrUTBAAACCrkF8AAIBkZfweInfccYfmz5+vTp06aejQobrzzjubtTn77LN11FFHSZIefPBBTZ8+XVOnTtW0adMSbUaNGqWJEydq7ty5Gj58uL73ve9p8+bNevzxx1VSUqKHH344QxEBAICORn4BAACSlfGCyNq1ayVJu3fv1l133dVimwEDBiQSlrbMnj1bRx55pGbPnq0HHnhAnTp10plnnqm77rqLT28AAHAR8gsAAJCsjBdE5s+fr/nz5ztuP23atCaf3DTm8Xg0efJkTZ48OT2DAwAAOYn8AgAAJKtDf3Y3G1kD+sryNv+5vGbtgp6G9gc5/knCPX1b/5m+dKjpbW51BnyWJKm6t2Xs53HDZl8eRTo7/+nLWH242tPT+U+zxooM/yRV0Fz/3obbCVk9Q7KU/p/B9Br+XVbL43zdBhpiDfTaI9thrHZ8gzAkFjV3OyerId5IjzpFDKxbSYqEkrtpZbLqapz3n8q+W7fL7PqNFJjrP+it73v3AT5jP4ke85o7OEfqaqX/Gus+q5QVVctfXNduO7/tlXZIPYp3qy4Hf+owWcSb34g3v7kpXjfFKuV+vHUKt9+oQYf97C4AAAAAAEBHoSACAAAAAABch4IIAAAAAABwHQoiAAAAAADAdSiIAAAAAAAA16EgAgAAAAAAXIeCCAAAAAAAcB0KIgAAAAAAwHUoiAAAAAAAANehIAIAAAAAAFyHgggAAAAAAHAdCiIAAAAAAMB1KIgAAAAAAADXoSACAAAAAABch4IIAAAAAABwHQoiAAAAAADAdSiIAAAAAAAA16EgAgAAAAAAXIeCCAAAAAAAcB0KIgAAAAAAwHUoiAAAAAAAANehIAIAAAAAAFyHgggAAAAAAHAdX0cPINuEDuisqK+g3Xa2v76WFO5TonBdzFHfNb3Nvtw1PSxjfUe99X3XdpdCzsJNWqQ0aqbjBlbnOsdt7YZaod09LFvOAi4sCqc0LqeKCsz1H7A9Up3Uu/tOha30rwe/x+y69Vi247YB2yvtkg7qusNxrDHb3L4lSXUxr7G+A7ZXqpG6le1S2DKz89bUBoz0G1db47z/VPbdcNCf0ricivnNrV+74WONPWWWQsZ2M3PnrmjYPWlI92C1AgXtn4d8MV9D+xpFPBHTw+pwxJvfiDe/uSleN8Uq5X684Yjz901cIQIAAAAAAFyHgggAAAAAAHAdCiIAAAAAAMB1KIgAAAAAAADXoSACAAAAAABch4IIAAAAAABwHQoiAAAAAADAdSiIAAAAAAAA16EgAgAAAAAAXIeCCAAAAAAAcB0KIgAAAAAAwHUoiAAAAAAAANehIAIAAAAAAFyHgggAAAAAAHAdCiIAAAAAAMB1KIgAAAAAAADXoSACAAAAAABch4IIAAAAAABwHQoiAAAAAADAdTJeEFm4cKEuv/xyjRgxQsFgUJZlaf78+Un1UVFRIcuyWv332muvmRk8AADIWuQYAAAgGb5ML/C2227TunXrVFZWpj59+mjdunUp9zVy5EiVl5c3m37QQQftxwgBAEAuIscAAADJyHhBZO7cuRoyZIj69++ve+65RzfffHPKfZWXl2vatGnpGxwAAMhZ5BgAACAZGS+InHLKKZleJAAAcAFyDAAAkIyMF0TSqbKyUg888IBqamrUv39/nXrqqSorK9uvPveU+eTzt/+yRH1WQ3uvwhFnt2Kp7Wrt19jaE+pmsPOGECNdo4rYMSOL8JWGjPQbV9p5j+O2Adsj1Uldu+5W2HIWb5cC5/2nolvQXP/+mFf6ShrY+WtFPJG091/oDae9z8Y8lu24rTfmk3ZJg0u2Kuow1phtdt/dEw0Y69sX80k10sCS7arzRI0s4+uCQiP9xu1Iov9U9t2dfrPjj3iDxvr2WvUH51BXKWTm0CxPxNz2Hw2Z3bf2R7pzjAMLdihY4G+3nSdW3+aAgh2KeepSXl6uIN78Rrz5zU3xuilWKffjDUWcjzmnCyKLFi3SokWLEn8XFhZq+vTpmjJlSgeOCgAA5DpyDAAA8l9OFkR69Oihe++9V2eccYb69eunHTt2aOnSpbrxxht1ww03qKSkRJdffnmbfYRCIYVCe69IqKqqkiQFvJZ8vvY/sQo0tAk4aBsX8Zr9JCxm8DeDgp76sQctS6Z+nMhn+EePArbz/uNtk5vHm/SYkuGPmevf19C3L2bmkOBz+El9qqxkrxBp9H8nPIavEDH1ujfu22dw+zG97Zved4OGjz3xqzhMqD8m7z1GG1mGwdUbNXxeTMX+5hit5ReemD/xiVtbPA37rMfgcSGbEG9+I9785qZ43RSrlPvxepJ462HZtu38nUSaxW94Nm/ePE2YMGG/+3v//fd1zDHHqGvXrtqwYYM8ntaT0GnTpmn69OnNpi9atEhFRUX7PRYAANC2mpoaXXTRRdq5c6dKSkrS2ndH5RjkFwAAdKxk8ovcLPm04vDDD9fxxx+v5cuX65NPPtHQoUNbbXvzzTfr2muvTfxdVVWlvn37atbf18gXKGh3WQGfpcn/M0iz/v6ZwhFnNaWaXmY/hdzTw1zfQY+lqYMH6PZtnypkqIbmKzF7D5GSTsndQ+TayNH6tW9VEvcQqU11aI50DZi7h4gv5tW527+rf/Z4LifvIZLsFSKjNp6ppX3+4fgeInaO30Pk9K2n6c9dX1LE0D1EtocN30Oktv1jclwq+27VbsP3EKkydw+RoGVpRtnBmv7JWoViZo7NhVuNdCtJiobMHjfTyWmO0Vp+8XbfJxXs5OwKkaM/P1+r+j6hmIHjcbYh3vxGvPnNTfG6KVYp9+MN7XbJPURaEr/hWU1NTZvtgsGggsHmSWo4aivmsMAhSeGI7bggEoqavRjH1A31mizDthUydFPVqMwG4PTN0b7zOJ0vbJl5sxln6oaYjUU8ESMFERN9NpbMTVXjop5I1txUNZLE1ztSXoYnamwbMr3tm953Q4aPPaZuRF2v4aaqMdtYQcTkoSdq+LyYbk5yjNbyi5inLqmvtsY8kZy8kV2qiDe/EW9+c1O8bopVyt14kxmz+Sw8gyKRiFatWiXLstSvX7+OHg4AAMgT5BgAAOSfrC6IbNu2TatXr9a2bduaTH/11Ve1761PIpGIpkyZonXr1mnMmDHq1s3kb9ACAIBcRo4BAAAy/pWZuXPnasWKFZKk9957LzGtoqJCknT22Wfr7LPPliQ9+OC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",
      "text/plain": [
       "<Figure size 1080x850 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checks a few things on filters\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2,figsize=(10.8,8.5))\n",
    "plt.subplots_adjust(left=0.05,bottom=0.07, top=0.92,wspace=0.12,right=0.99)\n",
    "im=ax[0].pcolormesh(kx2*1000,ky2*1000,10*np.log10(HH),cmap='viridis',rasterized=True,vmin=-30, vmax=0)\n",
    "_=plt.colorbar(im,ax=ax[0],label='', location='bottom',shrink=0.8)\n",
    "_=ax[0].set_title('H filter (dB)')\n",
    "\n",
    "Hazc = np.exp(-(ky2/kc)**2)\n",
    "print('kc:',kc,1/kc)    \n",
    "Hptr=np.sqrt(HH3/(HH*Hazc))\n",
    "im=ax[1].pcolormesh(kx2*1000,ky2*1000,10*np.log10(Hptr*Hazc),cmap='viridis',rasterized=True,vmin=-10, vmax=0)\n",
    "_=plt.colorbar(im,ax=ax[1],label='', location='bottom',shrink=0.8)\n",
    "_=ax[1].set_title('Hptr x Hazc filter (dB)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "34a99bc5-ce34-4638-9a82-67d3bc125560",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x850 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checks a few things on filters\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2,figsize=(10.8,8.5))\n",
    "plt.subplots_adjust(left=0.05,bottom=0.07, top=0.92,wspace=0.12,right=0.99)\n",
    "im=ax[0].pcolormesh(kx2*1000,ky2*1000,10*np.log10(HH),cmap='viridis',rasterized=True,vmin=-40, vmax=0)\n",
    "_=plt.colorbar(im,ax=ax[0],label='', location='bottom',shrink=0.8)\n",
    "_=ax[0].set_title('H filter (dB)')\n",
    "          \n",
    "    \n",
    "im=ax[1].pcolormesh(kx2*1000,ky2*1000,10*np.log10(HH2/HH),cmap='viridis',rasterized=True,vmin=-10, vmax=0)\n",
    "_=plt.colorbar(im,ax=ax[1],label='', location='bottom',shrink=0.8)\n",
    "_=ax[1].set_title('Hptr filter (dB)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4b4d7a7-02b8-4b77-bab4-7ffda2a458cb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}