Temporal_modal_spectra.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Once deleted, variables cannot be recovered. Proceed (y/[n])?  y\n"
     ]
    }
   ],
   "source": [
    "%reset\n",
    "import numpy as np\n",
    "from scipy.sparse import coo_matrix\n",
    "from scipy.sparse.linalg import eigs\n",
    "from scipy.integrate import cumtrapz\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import cartopy.crs as ccrs\n",
    "import netCDF4 as nc\n",
    "import matplotlib.ticker as mticker\n",
    "import warnings; warnings.filterwarnings('ignore')\n",
    "from matplotlib.pyplot import cm\n",
    "import cmocean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# solves G''(z) + (N^2(z) - omega^2)G(z)/c^2 = 0 G is vertical structure of isopycnal displacement, units m.\n",
    "#dG/dz = F(z) (here called Gz), vertical structure of horizontal velocity and is unitless\n",
    "\n",
    "#   subject to G'(0) = gG(0)/c^2 (free surface) & G(-D) = 0 (flat bottom)\n",
    "# G(z) is normalized so that the vertical integral of (G'(z))^2 is D\n",
    "# G' is dimensionless, G has dimensions of length\n",
    "\n",
    "# - N is buoyancy frequency [s^-1] (nX1 vector)\n",
    "# - depth [m] (maximum depth is considered the sea floor) (nX1 vector)\n",
    "# - omega is frequency [s^-1] (scalar)\n",
    "# - mmax is the highest baroclinic mode calculated\n",
    "# - m=0 is the barotropic mode\n",
    "# - 0 < m <= mmax are the baroclinic modes\n",
    "# - Modes are calculated by expressing in finite difference form 1) the\n",
    "#  governing equation for interior depths (rows 2 through n-1) and 2) the\n",
    "#  boundary conditions at the surface (1st row) and the bottome (last row).\n",
    "# - Solution is found by solving the eigenvalue system A*x = lambda*B*x\n",
    "def vertical_modes(N2_0, Depth, omega, mmax):\n",
    "    z = -1 * Depth\n",
    "\n",
    "    if np.size(np.shape(N2_0)) > 1:\n",
    "        N2 = np.nanmean(N2_0, axis=1)\n",
    "    else:\n",
    "        N2 = N2_0\n",
    "\n",
    "    n = np.size(z)\n",
    "    nm1 = n - 1\n",
    "    nm2 = n - 2\n",
    "    gravity = 9.82\n",
    "    # ----- vertical increments\n",
    "    dz = np.concatenate([[0], z[1:] - z[0:nm1]])  # depth increment [m]\n",
    "    dzm = np.concatenate([[0], 0.5 * (z[2:] - z[0:nm2]), [0]])  # depth increment between midpoints [m]\n",
    "    # ----- sparse matrices\n",
    "    # A = row pos, B = col pos, C = val  \n",
    "    A = np.concatenate([[0], [0], np.arange(1, nm1), np.arange(1, nm1), np.arange(1, nm1), [n - 1]])\n",
    "    B = np.concatenate([[0], [1], np.arange(1, nm1), np.arange(0, nm2), np.arange(2, n), [n - 1]])\n",
    "    C = np.concatenate(\n",
    "        [[-1 / dz[1]], [1 / dz[1]], (1 / dz[2:] + 1 / dz[1:nm1]) / dzm[1:nm1], -1 / (dz[1:nm1] * dzm[1:nm1]),\n",
    "         -1 / (dz[2:n] * dzm[1:nm1]), [-1]])\n",
    "        # [[-1 / dz[1]], [1 / dz[1]], (1 / dz[2:] + 1 / dz[1:nm1]) / dzm[1:nm1], -1 / (dz[1:nm1] * dzm[1:nm1]),\n",
    "        #  -1 / (dz[2:n] * dzm[1:nm1]), [-1]])\n",
    "    mat1 = coo_matrix((C, (A, B)), shape=(n, n))\n",
    "\n",
    "    D = np.concatenate([[0], np.arange(1, n)])\n",
    "    E = np.concatenate([[0], np.arange(1, n)])\n",
    "    F = np.concatenate([[gravity], N2[1:] - omega * omega])  # originially says N2[1:,10]\n",
    "    mat2 = coo_matrix((F, (D, E)), shape=(n, n))\n",
    "\n",
    "    # compute eigenvalues and vectors \n",
    "    vals, vecs = eigs(mat1, k=mmax + 1, M=mat2, sigma=0)\n",
    "    eigenvalue = np.real(vals)\n",
    "    wmodes = np.real(vecs)\n",
    "    s_ind = np.argsort(eigenvalue)\n",
    "    eigenvalue = eigenvalue[s_ind]\n",
    "    wmodes = wmodes[:, s_ind]\n",
    "    m = np.size(eigenvalue)\n",
    "    c = 1 / np.sqrt(eigenvalue)  # kelvin wave speed\n",
    "    # normalize mode (shapes)\n",
    "    Gz = np.zeros(np.shape(wmodes))\n",
    "    G = np.zeros(np.shape(wmodes))\n",
    "    for i in range(m):\n",
    "        dw_dz = np.nan * np.ones(np.shape(z))\n",
    "        dw_dz[0] = (wmodes[1, i] - wmodes[0, i]) / (z[1] - z[0])\n",
    "        dw_dz[-1] = (wmodes[-1, i] - wmodes[-2, i]) / (z[-1] - z[-2])\n",
    "        for j in range(1, len(z) - 1):\n",
    "            dw_dz[j] = (wmodes[j + 1, i] - wmodes[j - 1, i]) / (z[j + 1] - z[j - 1])\n",
    "        # dw_dz = np.gradient(wmodes[:, i], z)\n",
    "        norm_constant = np.sqrt(np.trapz((dw_dz * dw_dz), (-1 * z)) / (-1 * z[-1]))\n",
    "        # norm_constant = np.abs(np.trapz(dw_dz * dw_dz, z) / Depth.max())\n",
    "\n",
    "        if dw_dz[0] < 0:\n",
    "            norm_constant = -1 * norm_constant\n",
    "        Gz[:, i] = dw_dz / norm_constant\n",
    "\n",
    "        norm_constant_G = np.sqrt(np.trapz((wmodes[:, i] * wmodes[:, i]), (-1 * z)) / (-1 * z[-1]))\n",
    "        G[:, i] = wmodes[:, i] / norm_constant\n",
    "\n",
    "    #epsilon = np.nan * np.zeros((5, 5, 5))  # barotropic and first 5 baroclinic\n",
    "    #for i in range(0, 5):  # i modes\n",
    "    #    for j in range(0, 5):  # j modes\n",
    "    #        for m in range(0, 5):  # k modes\n",
    "    #            epsilon[i, j, m] = np.trapz((Gz[:, i] * Gz[:, j] * Gz[:, m]), -1.0*z) / (-1.0*z[-1])\n",
    "\n",
    "    return G, Gz, c#, epsilon\n",
    "#G is the displacement modes and Gz is velocity modes (Gz = dG/dz = F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing QG modes at an individual gridbox. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/glade/campaign/univ/unyu0004/NeverWorld2/nw2_0.25deg_N15_baseline_hmix5\n"
     ]
    }
   ],
   "source": [
    "#1/4 degree resolution, surface diagnostics are for uppermost 5 meters.\n",
    "%cd /glade/p/univ/unyu0004/gmarques/NeverWorld2/baselines/nw2_0.25deg_N15_baseline_hmix5/\n",
    "fs = xr.open_dataset('static.nc', decode_times=False)\n",
    "os = xr.open_mfdataset(['sub01/ocean.stats.nc','sub02/ocean.stats.nc'], decode_times=False)\n",
    "#av = xr.open_dataset('averages_00031502.nc', decode_times=False)\n",
    "av1=xr.open_mfdataset(['averages_00030002.nc'],decode_times=False,decode_cf = False)\n",
    "av2=xr.open_mfdataset(['averages_00030502.nc'],decode_times=False,decode_cf = False)\n",
    "av3=xr.open_mfdataset(['averages_00031002.nc'],decode_times=False,decode_cf = False)\n",
    "av4=xr.open_mfdataset(['averages_00031502.nc'],decode_times=False,decode_cf = False)\n",
    "\n",
    "\n",
    "\n",
    "# #1/8 degree resolution, surface diagnostics are for uppermost 5 meters.\n",
    "# %cd /glade/p/univ/unyu0004/gmarques/NeverWorld2/baselines/nw2_0.125deg_N15_baseline_hmix5/\n",
    "# fs = xr.open_dataset('static.nc', decode_times=False)\n",
    "# os = xr.open_mfdataset(['sub01/ocean.stats.nc','sub02/ocean.stats.nc'], decode_times=False)\n",
    "# av = xr.open_dataset('averages_00004502.nc', decode_times=False)\n",
    "\n",
    "##1/16 degree resolution, surface diagnostics are for uppermost 5 meters.\n",
    "# %cd /glade/p/univ/unyu0004/gmarques/NeverWorld2/baselines/nw2_0.0625deg_N15_baseline_hmix5/\n",
    "# fs = xr.open_dataset('static.nc', decode_times=False)\n",
    "# os = xr.open_mfdataset(['sub01/ocean.stats.nc','sub02/ocean.stats.nc','sub03/ocean.stats.nc','sub04/ocean.stats.nc',\n",
    "#                          'sub05/ocean.stats.nc'], decode_times=False)\n",
    "# av = xr.open_dataset('averages_00004502.nc', decode_times=False)\n",
    "\n",
    "# %cd /glade/p/univ/unyu0004/eyankovsky/NeverWorld_analysis/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/glade/campaign/univ/unyu0004/NeverWorld2/nw2_0.03125deg_N15_baseline_hmix5\n",
      "/glade/p/univ/unyu0004/eyankovsky/NeverWorld_analysis\n"
     ]
    }
   ],
   "source": [
    "#1/32 degree resolution, surface diagnostics are for uppermost 5 meters.\n",
    "%cd /glade/p/univ/unyu0004/gmarques/NeverWorld2/baselines/nw2_0.03125deg_N15_baseline_hmix5\n",
    "fs = xr.open_dataset('static.nc', decode_times=False)\n",
    "os = xr.open_mfdataset(['sub01/ocean.stats.nc','sub02/ocean.stats.nc','sub03/ocean.stats.nc','sub04/ocean.stats.nc',\n",
    "                        'sub05/ocean.stats.nc','sub06/ocean.stats.nc','sub07/ocean.stats.nc','sub08/ocean.stats.nc',\n",
    "                        'sub09/ocean.stats.nc','sub10/ocean.stats.nc','sub11/ocean.stats.nc','sub12/ocean.stats.nc',\n",
    "                        'sub13/ocean.stats.nc','sub14/ocean.stats.nc','sub15/ocean.stats.nc','sub16/ocean.stats.nc',\n",
    "                        'sub17/ocean.stats.nc','sub18/ocean.stats.nc','sub19/ocean.stats.nc','sub20/ocean.stats.nc'], decode_times=False)\n",
    "#av = xr.open_dataset('averages_00002702.nc',decode_times=False)\n",
    "av = xr.open_mfdataset(['averages_00002302.nc','averages_00002402.nc','averages_00002502.nc','averages_00002602.nc','averages_00002702.nc'], decode_times=False)\n",
    "\n",
    "%cd /glade/p/univ/unyu0004/eyankovsky/NeverWorld_analysis/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read in various grid variables from the static file and forcing:\n",
    "lon=fs['geolon']; lat=fs['geolat']\n",
    "depth = fs['depth_ocean']\n",
    "Coriolis = fs['Coriolis'].values\n",
    "\n",
    "xh = fs.xh.values\n",
    "yh = fs.yh.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000111670306\n"
     ]
    }
   ],
   "source": [
    "#1/4 degree:560 lat x 240 lon \n",
    "#60,460 in 1/4 degree is nominal analysis point!\n",
    "index_lon=480; index_lat=640 #1/4 degree: NW(60,460), ACC(60,80), (120,80), (200,80); Midlat: (60 or 120,400)\n",
    "\n",
    "f_local = np.abs(Coriolis[index_lat+1,index_lon+1])\n",
    "print(f_local)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 320x560 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting ocean depth\n",
    "fig = plt.figure(figsize=(4, 7), dpi= 80, facecolor='w', edgecolor='k')\n",
    "ax1 = fig.add_axes([0.15,0.2,0.6,0.7]) \n",
    "ax2 = fig.add_axes([.78, 0.2, 0.03, 0.7])\n",
    "ax1.set_facecolor((0.92, 0.92, 0.92))\n",
    "ax1.set_title('Ocean depth (m)',fontsize=20)\n",
    "ax1.set_xlabel('Longitude',fontsize=16)\n",
    "ax1.set_ylabel('Latitude',fontsize=16)\n",
    "ax1.tick_params(labelsize=16); \n",
    "ax2.tick_params(labelsize=14)\n",
    "\n",
    "plotted =ax1.pcolor(xh,yh,depth,cmap=cmocean.cm.turbid)\n",
    "ax1.plot(xh[index_lon],yh[index_lat],'*',markersize=15,color=[0.8, 0.8, 0.8])\n",
    "ax1.set_aspect('equal', 'box')\n",
    "cbar = plt.colorbar(mappable=plotted, cax=ax2, orientation = 'vertical');\n",
    "cbar.ax.tick_params(labelsize=14);\n",
    "#\n",
    "plt.show()\n",
    "#fig.savefig('topography.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For 1/4, 1/8, and 1/16 degree runs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "Layer = np.array(os['Layer']);\n",
    "Layermat=np.tile(Layer,[len(xh),1]);   Layermat=np.moveaxis(Layermat, [0, 1], [1, 0])\n",
    "Interface = np.array(os['Interface']); drho=np.diff(Interface)\n",
    "eta = np.zeros([100,len(Layer)+1]);\n",
    "\n",
    "BT_KE=np.zeros(400)\n",
    "BC_KE=np.zeros(400)\n",
    "KE_u_t=np.zeros([400,15])\n",
    "KE_v_t=np.zeros([400,15])\n",
    "BC_PE=np.zeros(400)\n",
    "PE_t=np.zeros([400,15])\n",
    "wavenumbers = np.zeros([15,5])\n",
    "\n",
    "av_list = [av1,av2,av3,av4]\n",
    "for dsi in range(1,5):\n",
    "    av = av_list[dsi-1]\n",
    "    v = np.nanmean(np.array(av['v'][:,:,index_lat-1:index_lat+1,index_lon]),axis=2);\n",
    "    u = np.nanmean(np.array(av['u'][:,:,index_lat,index_lon-1:index_lon+1]),axis=2); \n",
    "    v=np.column_stack([v, 0.0*v[:,-1]]); u=np.column_stack([u, 0.0*u[:,-1]]) #add bottom BC point where u,v,=0\n",
    "\n",
    "    h = np.array(av['h'][:,:,index_lat,index_lon]);\n",
    "\n",
    "\n",
    "    for i in range(1,len(Layer)+1):\n",
    "        for j in range(0,100):\n",
    "            eta[j,i]=np.nansum(h[j,0:i])  \n",
    "        print(i)\n",
    "    \n",
    "    eta[:,:-1]=(eta[:,1:]+eta[:,:-1])/2. #add bottom BC point at topography where u,v=0\n",
    "\n",
    "    \n",
    "    \n",
    "    drhodz=drho/h\n",
    "    N2=(9.81/1022.6)*drhodz\n",
    "    N2=np.column_stack([N2, N2[:,-1]*0.+1.e-7]) #add bottom BC point at topography where u,v=0, make the N2 small so it doesn't get filtered\n",
    "\n",
    "\n",
    "\n",
    "    eta_mean = np.nanmean(eta,axis=0); N2_mean = np.nanmean(N2,axis=0)\n",
    "    eta_prime= eta-eta_mean\n",
    "\n",
    "    N2_mean[N2_mean>.1] =np.nan\n",
    "    eta_filtered = eta_mean[~np.isnan(N2_mean)]\n",
    "    N2_filtered  = N2_mean[~np.isnan(N2_mean)]\n",
    "\n",
    "    \n",
    "    \n",
    "    nmodes = len(N2_filtered)-3\n",
    "    #Limit the number of modes to be 7 or 10 if it's larger than that (otherwise keep number of modes)\n",
    "    if ( nmodes >= 7 ):\n",
    "        nmodes = 7\n",
    "    \n",
    "    [G, Gz, c]=vertical_modes(N2_filtered, eta_filtered, 0, nmodes) #nmodes is number of BC modes, doens't include BT mode\n",
    "    wavenumbers[0:nmodes+1,dsi-1]=(1/c)*f_local\n",
    "    \n",
    "    for i in range(0,100):\n",
    "        ui=u[i,~np.isnan(N2_mean)]; vi=v[i,~np.isnan(N2_mean)];\n",
    "        eta_prime_i=eta_prime[i,~np.isnan(N2_mean)];\n",
    "    \n",
    "    \n",
    "        amp_u=np.linalg.lstsq(Gz,ui)[0]\n",
    "        amp_v=np.linalg.lstsq(Gz,vi)[0]  #solving for alpha, units of m/s\n",
    "        amp_eta=np.linalg.lstsq(G,eta_prime_i)[0] #solving for Beta, unitless\n",
    "\n",
    "        BT_KE[i+100*(dsi-1)]=0.5*amp_u[0]**2+0.5*amp_v[0]**2\n",
    "        BC_KE[i+100*(dsi-1)]=np.nansum(0.5*amp_u[1:]**2)+np.nansum(0.5*amp_v[1:]**2)\n",
    "        BC_PE[i+100*(dsi-1)]=np.nansum(0.5*(amp_eta[1:]**2)*(c[1:]**2)) \n",
    "    \n",
    "        KE_u_t[i+100*(dsi-1),0:nmodes+1]=0.5*amp_u[:]**2 #has modes \n",
    "        KE_v_t[i+100*(dsi-1),0:nmodes+1]=0.5*amp_v[:]**2\n",
    "        PE_t[i+100*(dsi-1),1:nmodes+1]=(0.5*amp_eta[1:]**2)*c[1:]**2 \n",
    "    \n",
    "wavenumbers = np.nanmean(wavenumbers,axis=1)\n",
    "print('done')\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For 1/32 degree case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "Layer = np.array(os['Layer']);\n",
    "Layermat=np.tile(Layer,[len(xh),1]);   Layermat=np.moveaxis(Layermat, [0, 1], [1, 0])\n",
    "Interface = np.array(os['Interface']); drho=np.diff(Interface)\n",
    "eta = np.zeros([100,len(Layer)+1]);\n",
    "\n",
    "BT_KE=np.zeros(100)\n",
    "BC_KE=np.zeros(100)\n",
    "KE_u_t=np.zeros([100,15])\n",
    "KE_v_t=np.zeros([100,15])\n",
    "BC_PE=np.zeros(100)\n",
    "PE_t=np.zeros([100,15])\n",
    "wavenumbers = np.zeros(15)\n",
    "\n",
    "\n",
    "\n",
    "v = np.nanmean(np.array(av['v'][:,:,index_lat-1:index_lat+1,index_lon]),axis=2);\n",
    "u = np.nanmean(np.array(av['u'][:,:,index_lat,index_lon-1:index_lon+1]),axis=2); \n",
    "v=np.column_stack([v, 0.0*v[:,-1]]); u=np.column_stack([u, 0.0*u[:,-1]]) #add bottom BC point where u,v,=0\n",
    "\n",
    "h = np.array(av['h'][:,:,index_lat,index_lon]);\n",
    "\n",
    "\n",
    "for i in range(1,len(Layer)+1):\n",
    "    for j in range(0,100):\n",
    "        eta[j,i]=np.nansum(h[j,0:i])  \n",
    "    print(i)\n",
    "    \n",
    "eta[:,:-1]=(eta[:,1:]+eta[:,:-1])/2. #add bottom BC point at topography where u,v=0\n",
    "\n",
    "drhodz=drho/h\n",
    "N2=(9.81/1022.6)*drhodz\n",
    "N2=np.column_stack([N2, N2[:,-1]*0.+1.e-7]) #add bottom BC point at topography where u,v=0, make the N2 small so it doesn't get filtered\n",
    "\n",
    "\n",
    "\n",
    "eta_mean = np.nanmean(eta,axis=0); N2_mean = np.nanmean(N2,axis=0)\n",
    "eta_prime= eta-eta_mean\n",
    "\n",
    "N2_mean[N2_mean>.1] =np.nan\n",
    "eta_filtered = eta_mean[~np.isnan(N2_mean)]\n",
    "N2_filtered  = N2_mean[~np.isnan(N2_mean)]\n",
    "\n",
    "    \n",
    "    \n",
    "nmodes = len(N2_filtered)-3\n",
    "#Limit the number of modes to be 7 or 10 if it's larger than that (otherwise keep number of modes)\n",
    "if ( nmodes >= 7 ):\n",
    "    nmodes = 7\n",
    "    \n",
    "[G, Gz, c]=vertical_modes(N2_filtered, eta_filtered, 0, nmodes) #nmodes is number of BC modes, doens't include BT mode\n",
    "wavenumbers[0:nmodes+1]=(1/c)*f_local\n",
    "    \n",
    "for i in range(0,100):\n",
    "    ui=u[i,~np.isnan(N2_mean)]; vi=v[i,~np.isnan(N2_mean)];\n",
    "    eta_prime_i=eta_prime[i,~np.isnan(N2_mean)];\n",
    "    \n",
    "    \n",
    "    amp_u=np.linalg.lstsq(Gz,ui)[0]\n",
    "    amp_v=np.linalg.lstsq(Gz,vi)[0]  #solving for alpha, units of m/s\n",
    "    amp_eta=np.linalg.lstsq(G,eta_prime_i)[0] #solving for Beta, unitless\n",
    "\n",
    "    BT_KE[i]=0.5*amp_u[0]**2+0.5*amp_v[0]**2\n",
    "    BC_KE[i]=np.nansum(0.5*amp_u[1:]**2)+np.nansum(0.5*amp_v[1:]**2)\n",
    "    BC_PE[i]=np.nansum(0.5*(amp_eta[1:]**2)*(c[1:]**2)) \n",
    "    \n",
    "    KE_u_t[i,0:nmodes+1]=0.5*amp_u[:]**2 #has modes \n",
    "    KE_v_t[i,0:nmodes+1]=0.5*amp_v[:]**2\n",
    "    PE_t[i,1:nmodes+1]=(0.5*amp_eta[1:]**2)*c[1:]**2 \n",
    "    \n",
    "print('done')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'KE')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default')\n",
    "plt.plot(np.linspace(1,2000,400),BT_KE,c=[0., 0., 0.5],linewidth=2,markersize=10)\n",
    "plt.plot(np.linspace(1,2000,400),BC_KE,c=[1, 0, 0],linewidth=2,markersize=10)\n",
    "\n",
    "plt.grid()\n",
    "plt.ylim(0,0.1);\n",
    "plt.xlabel('Time (days)')\n",
    "plt.legend(['Barotropic KE','Baroclinic KE'])\n",
    "plt.ylabel('KE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "x = np.linspace(0,14,15)\n",
    "amp1 = np.nanmean(KE_u_t+KE_v_t,axis=0)\n",
    "\n",
    "plt.bar(x, amp1, color=[1., .6, 0.],alpha=0.8)\n",
    "plt.xlabel(\"Mode number\")\n",
    "plt.ylabel(\"KE\")\n",
    "plt.title(\"Modal decomposition: %i modes\" % (nmodes+1))\n",
    "#plt.ylim(0,3e-2)\n",
    "plt.ylim(0,1e-2)\n",
    "plt.xlim(-1,8)\n",
    "plt.xticks((0,1,2,3,4,5,6,7))\n",
    "plt.ticklabel_format(axis=\"y\", style=\"sci\",scilimits=(0,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.style.use('default')\n",
    "# plt.plot(np.linspace(1,2500,500),BC_PE,c=[1, 0, 0],linewidth=2,markersize=10)\n",
    "# plt.grid()\n",
    "\n",
    "# plt.ylim(0,.14);\n",
    "# plt.xlabel('Time (days)')\n",
    "# plt.ylabel('PE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.style.use('ggplot')\n",
    "# x = np.linspace(1,14,14)\n",
    "# amp1 = np.nanmean(PE_t[:,1:],axis=0) #remove BT mode from PE\n",
    "\n",
    "# plt.bar(x, amp1, color=[1., .6, 0.],alpha=0.8)\n",
    "# plt.xlabel(\"Mode number\")\n",
    "# plt.ylabel(\"PE\")\n",
    "# plt.title(\"PE Modal decomposition: %i modes\" % (nmodes))\n",
    "# plt.ylim(0,2.5e-3)\n",
    "# plt.xlim(0,8)\n",
    "# plt.xticks((1,2,3,4,5,6,7))\n",
    "# plt.ticklabel_format(axis=\"y\", style=\"sci\",scilimits=(0,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "xKE025 = np.linspace(0,14,15); \n",
    "xKE025[0]=.9\n",
    "ampKE025 = np.nanmean(KE_u_t+KE_v_t,axis=0)\n",
    "wavenumbers025=wavenumbers\n",
    "\n",
    "xPE025 = np.linspace(1,14,14)\n",
    "ampPE025 = np.nanmean(PE_t[:,1:],axis=0) #remove BT mode from PE\n",
    "\n",
    "nmodes025 = nmodes\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "xKE00625 = np.linspace(0,14,15); \n",
    "xKE00625[0]=.9\n",
    "ampKE00625 = np.nanmean(KE_u_t+KE_v_t,axis=0)\n",
    "wavenumbers00625=wavenumbers\n",
    "\n",
    "xPE00625 = np.linspace(1,14,14)\n",
    "ampPE00625 = np.nanmean(PE_t[:,1:],axis=0) #remove BT mode from PE\n",
    "nmodes00625 = nmodes\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "xKE003125 = np.linspace(0,14,15); \n",
    "xKE003125[0]=.9\n",
    "ampKE003125 = np.nanmean(KE_u_t+KE_v_t,axis=0)\n",
    "wavenumbers003125=wavenumbers\n",
    "\n",
    "xPE003125 = np.linspace(1,14,14)\n",
    "ampPE003125 = np.nanmean(PE_t[:,1:],axis=0) #remove BT mode from PE\n",
    "nmodes003125 = nmodes\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FormatStrFormatter\n",
    "plt.figure(figsize=(10,10), dpi= 100)\n",
    "#plt.style.use('ggplot')\n",
    "\n",
    "plt.loglog(abs(wavenumbers025[1:nmodes025+1]), (ampKE025[1:nmodes025+1]), '--o',color='#f50086')\n",
    "plt.loglog(abs(wavenumbers025[1:nmodes025+1]), (ampPE025[0:nmodes025]),'-o',color='#f50086')\n",
    "\n",
    "# plt.loglog((wavenumbers00625[1:nmodes00625+1]), (ampKE00625[1:nmodes00625+1]), '--o',color=[0,0,0])\n",
    "# plt.loglog((wavenumbers00625[1:nmodes00625+1]), (ampPE00625[0:nmodes00625]),'-o',color=[0, 0, 0])\n",
    "\n",
    "plt.loglog((wavenumbers003125[1:nmodes003125+1]), (ampKE003125[1:nmodes003125+1]), '--o',color='#ffbb00')\n",
    "plt.loglog((wavenumbers003125[1:nmodes003125+1]), (ampPE003125[0:nmodes003125]),'-o',color='#ffbb00')\n",
    "\n",
    "plt.plot([2e-5, 2e-4],[1e-2,1e-5],color='#0004f5',linewidth=1)\n",
    "\n",
    "plt.loglog(1.e-5, ampKE025[0], 'o',color='#f50086',Markersize=5)\n",
    "# plt.loglog(1.e-5, (ampKE00625[0]), 'o',color=[0,0,0],Markersize=5)\n",
    "plt.loglog(1.e-5, (ampKE003125[0]), 'o',color='#ffbb00',Markersize=5)\n",
    "\n",
    "#plt.plot([-70, 70],[1, 1],'--',color=[1,0,0])\n",
    "\n",
    "plt.xlabel(\"Wavenumber [1/m]\")\n",
    "plt.ylabel(\"Energy density [J/kg]\")\n",
    "plt.title(\"NW Location, PE & KE Spectra: %i modes\" % (nmodes025))\n",
    "plt.xlim(1e-5,1e-3) #2e-5 to 2e-4 before\n",
    "plt.ylim(1e-6,1e-1)\n",
    "\n",
    "ax = plt.gca()\n",
    "plt.tick_params(axis='x', which='minor')\n",
    "#ax.xaxis.set_ticklabels([r'$2\\times10^{-4}$','','',r'$5\\times10^{-4$}','','','','', \n",
    "#        r'$2\\times 10^{-5}$','','',r'$5\\times10^{-5}$','','','','',\n",
    "#        r'$2\\times 10^{-4}$'],minor=True,fontsize=8)\n",
    "\n",
    "\n",
    "\n",
    "plt.legend(['KE 1/4$^\\circ$','PE 1/4$^\\circ$','KE 1/16$^\\circ$','PE 1/16$^\\circ$','KE 1/32$^\\circ$','PE 1/32$^\\circ$','$-3$ slope'],fontsize=12)\n",
    "plt.grid()\n",
    "ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FormatStrFormatter\n",
    "plt.figure(figsize=(7,4), dpi= 200)\n",
    "plt.style.use('default')\n",
    "\n",
    "plt.loglog(abs(wavenumbers025[1:nmodes025+1]), (ampKE025[1:nmodes025+1]), '--o',color='#FF5733')\n",
    "plt.loglog(abs(wavenumbers025[1:nmodes025+1]), (ampPE025[0:nmodes025]),'-o',color='#FF5733')\n",
    "\n",
    "# plt.loglog((wavenumbers00625[1:nmodes00625+1]), (ampKE00625[1:nmodes00625+1]), '--o',color=[0,0,0])\n",
    "# plt.loglog((wavenumbers00625[1:nmodes00625+1]), (ampPE00625[0:nmodes00625]),'-o',color=[0, 0, 0])\n",
    "\n",
    "plt.loglog((wavenumbers003125[1:nmodes003125+1]), (ampKE003125[1:nmodes003125+1]), '--o',color='#584EA7')\n",
    "plt.loglog((wavenumbers003125[1:nmodes003125+1]), (ampPE003125[0:nmodes003125]),'-o',color='#584EA7')\n",
    "\n",
    "plt.plot([2e-5, 2e-4],[1e-2,1e-5],color=[0.5, 0.5, 0.5])\n",
    "\n",
    "plt.loglog(2.e-5, ampKE025[0], 'o',color='#FF5733',Markersize=5)\n",
    "# #plt.loglog(1.e-5, (ampKE00625[0]), 'o',color=[0,0,0],Markersize=5)\n",
    "plt.loglog(2.e-5, (ampKE003125[0]), 'o',color='#584EA7',Markersize=5)\n",
    "\n",
    "#plt.plot([-70, 70],[1, 1],'--',color=[1,0,0])\n",
    "\n",
    "plt.xlabel(\"Wavenumber [1/m]\")\n",
    "plt.ylabel(\"Energy density [J/kg]\")\n",
    "plt.title(\"ACC Location, PE & KE Spectra: %i modes\" % (nmodes025))\n",
    "plt.xlim(2e-5,2e-4) #2e-5 to 2e-4 before\n",
    "plt.ylim(1e-6,1e-1)\n",
    "\n",
    "ax = plt.gca()\n",
    "plt.tick_params(axis='x', which='minor')\n",
    "ax.xaxis.set_ticklabels([r'$2\\times10^{-4}$','','',r'$5\\times10^{-4$}','','','','', \n",
    "       r'$2\\times 10^{-5}$','','',r'$5\\times10^{-5}$','','','','',\n",
    "       r'$2\\times 10^{-4}$'],minor=True,fontsize=8)\n",
    "\n",
    "\n",
    "\n",
    "plt.legend(['KE 1/4$^\\circ$','PE 1/4$^\\circ$','KE 1/32$^\\circ$','PE 1/32$^\\circ$','$-3$ slope'],fontsize=12)\n",
    "plt.grid()\n",
    "#ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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