CER113_aviso_multiday_ensemble_1v2step.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c5078678-9339-4d38-a282-6de8e5b0c115",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy\n",
    "import gsw\n",
    "import xarray as xr\n",
    "import cmocean as cmo\n",
    "from numpy import linalg as LA\n",
    "from scipy import linalg\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "from tqdm import tqdm\n",
    "from aviso_rossby_wave import skill_matrix, build_h_matrix\n",
    "from aviso_rossby_wave import reverse_vector, forecast_ssh\n",
    "from aviso_rossby_wave import build_swath, inversion, inversion2, make_error_over_time\n",
    "import glob2 as glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1123f304-8451-4ab7-90c8-81fdf25637fd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0005, 0.0015, 0.0025, 0.0035, 0.0045, 0.0055, 0.0065, 0.0075,\n",
       "       0.0085, 0.0095, 0.0105, 0.0115, 0.0125, 0.0135, 0.0145, 0.0155,\n",
       "       0.0165, 0.0175, 0.0185, 0.0195, 0.0205, 0.0215, 0.0225, 0.0235,\n",
       "       0.0245, 0.0255, 0.0265, 0.0275, 0.0285, 0.0295])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha_std = np.arange(5e-4, 3.05e-2, 1e-3)\n",
    "alpha_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c66bf307-6e75-4242-a9da-f42bcd946679",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error:Malformed [url] in /Users/sgille/.dodsrc entry:  HTTP.SSL.VALIDATE=0\n",
      "Error:Malformed [url] in /Users/sgille/.dodsrc entry:  HTTP.SSL.VALIDATE=0\n",
      "Error:Malformed [url] in /Users/sgille/.dodsrc entry:  HTTP.SSL.VALIDATE=0\n",
      "Error:Malformed [url] in /Users/sgille/.dodsrc entry:  HTTP.SSL.VALIDATE=0\n"
     ]
    }
   ],
   "source": [
    "day0, day1 = 0, 40 # \n",
    "Tdim = day1 - day0\n",
    "start_date = '2016-01-01' ## starting date\n",
    "n_waves = '190' #number of waves\n",
    "time_range = 2 * (day1 - day0) # assimilation + forecast window\n",
    "\n",
    "MModes = 1 # Rossby wave vertical modes\n",
    "wave_files = glob.glob('./rossby_wave_estimate_*_' + n_waves + 'waves_data40days.nc')\n",
    "wave_files = sorted(wave_files)\n",
    "\n",
    "#alpha_std = np.arange(5e-4, 3.05e-2, 1e-3) # standard deviation of error paramters\n",
    "#alpha_std = np.arange(5e-4, 20e-4, 1e-3) # standard deviation of error paramters\n",
    "#alpha_std = np.arange(.0105, 0.012, 1e-3) # standard deviation of error paramters\n",
    "alpha_std = np.arange(.01, 0.012, 1e-3) # standard deviation of error paramters\n",
    "\n",
    "lonidx_west, lonidx_east  =  60, 102\n",
    "latidx_south, latidx_north = 34, 74\n",
    "\n",
    "avisso_ds = xr.open_dataset('./aviso_msla_ccs_1d.nc')\n",
    "# stg:  commented out the next line since SSHA appears not to be used\n",
    "#SSHA = avisso_ds.msla[latidx_south:latidx_north,lonidx_west:lonidx_east, :]\n",
    "# stg:  added the following line to remove mean over study period to produce anomaly.  commented out since not used\n",
    "#SSHA = SSHA-SSHA[:].mean(axis = -1)\n",
    "lon, lat = (360 - avisso_ds.lon[lonidx_west:lonidx_east].data) * -1, avisso_ds.lat[latidx_south:latidx_north].data\n",
    "T_time = avisso_ds.time.data[day0:] * 86400 # in seconds\n",
    "date_time = avisso_ds.date_time.data[day0:]\n",
    "\n",
    "Phi0 = lat.mean() # central latitude (φ0)\n",
    "Omega = 7.27e-5 # Ω is the angular speed of the earth\n",
    "Earth_radius = 6.371e6 / 1e5 # meters\n",
    "Beta = 2 * Omega * np.cos(Phi0) / Earth_radius\n",
    "f0 = 2 * Omega * np.sin(Phi0) #1.0313e-4 # 45 N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "69410fc9-22cc-4473-84bb-96cb2400d68c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#alpha_std = np.arange(5e-4, 20e-4, 1e-3)\n",
    "# len(alpha_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7cc8b39e-63e7-4955-a292-9f0c54e2b5ab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# avisso_ds.lon.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a38a59ef-8ce7-46e4-97f7-af72dc75e09b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arrays to store variance explained\n",
    "nsigma=len(alpha_std)\n",
    "nfiles=len(wave_files)\n",
    "variance_explained_1step = np.zeros([nfiles, nsigma, time_range])\n",
    "variance_explained_in_1step = np.zeros([nfiles, nsigma, time_range])\n",
    "variance_explained_out_1step = np.zeros([nfiles, nsigma, time_range])\n",
    "variance_explained_2step = np.zeros([nfiles, nsigma, time_range])\n",
    "variance_explained_in_2step = np.zeros([nfiles, nsigma, time_range])\n",
    "variance_explained_out_2step = np.zeros([nfiles, nsigma, time_range])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "724410d9-ddc0-4fd3-a73e-3a7e1a344917",
   "metadata": {},
   "outputs": [],
   "source": [
    "strat_ds = xr.open_dataset('./stratification_sample_ccs.nc')\n",
    "Psi = strat_ds.Psi.data\n",
    "\n",
    "dlon = lon - lon.mean()\n",
    "dlat = lat - lat.mean()\n",
    "        \n",
    "rms_err, rms_ssh = np.zeros([len(wave_files), len(alpha_std)]), np.zeros([len(wave_files), len(alpha_std)])\n",
    "\n",
    "ssh_est_skill2, err_est_skill2 = np.zeros([len(wave_files), len(alpha_std)]), np.zeros([len(wave_files), len(alpha_std)])\n",
    "\n",
    "ssh_est_skill1, err_est_skill1 = np.zeros([len(wave_files), len(alpha_std)]), np.zeros([len(wave_files), len(alpha_std)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "75870003-1756-4d7c-902b-6856ff0bf8a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in first file and configure basic matrix information\n",
    "n=0\n",
    "\n",
    "wave_ds = xr.open_dataset(wave_files[n]) # forward model - filtered AVISO\n",
    "l_n_orig, k_n_orig = wave_ds.l_n.data, wave_ds.k_n.data\n",
    "amp = wave_ds.Amplitudes.data\n",
    "time= wave_ds.time.data\n",
    "MSLA_fwd = wave_ds.MSLA_forward.data\n",
    "Rm = wave_ds.Rm.data # Rossby deformation radius, unit: degree\n",
    "\n",
    "l_n = l_n_orig.reshape(len(l_n_orig), MModes)\n",
    "k_n = k_n_orig.reshape(len(k_n_orig), MModes)\n",
    "\n",
    "MSLA0 = MSLA_fwd[:, :, day0:day1]\n",
    "\n",
    "### assess skill, get indices\n",
    "skill, SSHA_vector, Iindex, Jindex, Tindex = skill_matrix(MSLA0, Psi, k_n, l_n, MModes, Rm, lon, lat, T_time) # skill marrix\n",
    "\n",
    "M = k_n.size * l_n.size # Number of models/waves\n",
    "\n",
    "H_all = build_h_matrix(MSLA0, MModes, k_n, l_n, lon, lat, T_time, Psi, Rm, 0)\n",
    "H_all2 = build_h_matrix(MSLA0, MModes, k_n, l_n, lon, lat, T_time, Psi, Rm, day1-day0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9917a11-39a7-4aef-bf3a-4e8e6d1332e0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# MSLA_fwd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "943a88f7-b5a4-407c-9d6c-ed04a8521ff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1w/2s58vmmj44x13x6_fxjpksb00000gq/T/ipykernel_9371/3319167237.py:16: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  k_[counter] , l_[counter]  = kk, ll\n",
      "/var/folders/1w/2s58vmmj44x13x6_fxjpksb00000gq/T/ipykernel_9371/3319167237.py:17: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  kl[counter] =  np.sqrt(kk ** 2 + ll ** 2) # wavenumber\n"
     ]
    }
   ],
   "source": [
    "# signal to noise ratio R_over_P\n",
    "\n",
    "counter = 0\n",
    "exp = -2\n",
    "ssh_std = .1\n",
    "k0 = l_n.max() # flat at or below k0\n",
    "\n",
    "p_diagonal = np.zeros([2 * M])\n",
    "kl, kl_plot = np.zeros(M), np.zeros(2*M)\n",
    "k_, l_ = np.zeros(len(l_n)*len(k_n)), np.zeros(len(l_n)*len(k_n))\n",
    "counter = 0\n",
    "R = 0.01 # noise = 1. cm\n",
    "\n",
    "for kk in k_n:\n",
    "    for ll in l_n:\n",
    "        k_[counter] , l_[counter]  = kk, ll\n",
    "        kl[counter] =  np.sqrt(kk ** 2 + ll ** 2) # wavenumber\n",
    "        kl_plot[2 * counter], kl_plot[2 * counter + 1] = kl[counter], kl[counter]\n",
    "        p_diagonal[2 * counter] = (kl[counter]+k0) ** exp\n",
    "        p_diagonal[2 * counter + 1] = (kl[counter]+k0) ** exp\n",
    "        counter += 1\n",
    "\n",
    "R_over_P = np.zeros([2 * M, 2 * M])\n",
    "p_factor = .16/p_diagonal.sum() # variance of the model,  convert sum of variance from waven number to meter\n",
    "np.fill_diagonal(R_over_P[:],  R / (p_diagonal * p_factor ) )\n",
    "\n",
    "## build satellite swath\n",
    "\n",
    "swath_width, x_swath = 6, 20\n",
    "days = np.arange(day0, day1)\n",
    "xvalid_index, yvalid_index, tindex, yswath_index_left, yswath_index_right, y_mask_left, y_mask_right = build_swath(swath_width, x_swath, days, lon, lat)\n",
    "\n",
    "# Loop over sigma\n",
    "#        sigma_counter = 0\n",
    "#         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "58f94bff-a8d7-4672-af53-a6f2d38b7b43",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1w/2s58vmmj44x13x6_fxjpksb00000gq/T/ipykernel_9371/3432266300.py:96: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  k_[counter] , l_[counter]  = kk, ll\n",
      "/var/folders/1w/2s58vmmj44x13x6_fxjpksb00000gq/T/ipykernel_9371/3432266300.py:97: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  kl[counter] =  np.sqrt(kk ** 2 + ll ** 2) # wavenumber\n"
     ]
    }
   ],
   "source": [
    "# sample SSH, errors and add white noise\n",
    "IND_TIMINGl = -14\n",
    "IND_ROLLl = -13\n",
    "IND_BASELINEl = -12\n",
    "IND_PHASE3l, IND_PHASE4l, IND_PHASE5l, IND_PHASE6l = -11, -10, -9, -8\n",
    "# sample SSH, errors and add white noise\n",
    "IND_TIMINGr = -7\n",
    "IND_ROLLr = -6\n",
    "IND_BASELINEr = -5\n",
    "IND_PHASE3r, IND_PHASE4r, IND_PHASE5r, IND_PHASE6r = -4, -3, -2, -1\n",
    "\n",
    "#### Sub-sample the SSH, correlated errors and H matrix\n",
    "N = len(tindex) # number of data\n",
    "MSLA_swath = np.zeros(N)\n",
    "M = l_n.size * k_n.size\n",
    "nx, ny = len(lon), len(lat)\n",
    "H_swath = np.zeros([N, 2 * M + Tdim * 7*2]) # M: number of model\n",
    "IIndex = np.zeros(N) # N: Number of y data\n",
    "cor_err = np.zeros(N) # Correlated Error N: Number of y data\n",
    "ssh = np.zeros(N)\n",
    "mask=np.zeros([MSLA_fwd.shape[0],MSLA_fwd.shape[1],MSLA_fwd.shape[2]])\n",
    "\n",
    "alpha = np.ones([Tdim, 7, 2]) #fake values in order to run make_error_over_time to extract xc1_valid, xc2_valid\n",
    "timing_err_valid, roll_err_valid, baseline_dilation_err_valid, phase_err_valid, xc1_valid, xc2_valid  = make_error_over_time(days, alpha, yswath_index_left, yswath_index_right, y_mask_left, y_mask_right)\n",
    "\n",
    "for ii in range(N):\n",
    "    IIndex[ii] = nx * ny * tindex[ii] + xvalid_index[ii] + nx * yvalid_index[ii] # index in the vector space\n",
    "    #ssh[ii] = MSLA_fwd[xvalid_index[ii], yvalid_index[ii], int(tindex[ii])]\n",
    "    #cor_err[ii] = timing_err_valid.flatten()[ii] + roll_err_valid.flatten()[ii] + baseline_dilation_err_valid.flatten()[ii] + phase_err_valid.flatten()[ii]\n",
    "    #MSLA_swath[ii] = ssh[ii] + cor_err[ii] # filtered AVISO SSH , no error along the satellite passings\n",
    "    mask[yvalid_index[ii], xvalid_index[ii], int(tindex[ii])] = 1\n",
    "\n",
    "    for nn in range(M):\n",
    "        JIndex = 2 * MModes * nn # +  2 * mm # Model space\n",
    "        H_swath[ii, JIndex] = H_all[IIndex[ii].astype(int), JIndex]\n",
    "        H_swath[ii, JIndex + 1] = H_all[IIndex[ii].astype(int), JIndex+1]\n",
    "    # for nn in range(M):\n",
    "    #     JIndex = 2 * MModes * nn # +  2 * mm # Model space\n",
    "    #     H_swath[ii, JIndex] = H_cos[IIndex[ii].astype(int), nn]\n",
    "    #     H_swath[ii, JIndex + 1] = H_sin[IIndex[ii].astype(int), nn]\n",
    "\n",
    "left_valid = len(yswath_index_left.compressed().astype(int))\n",
    "right_valid = len(yswath_index_right.compressed().astype(int)) \n",
    "Valid_points = len(xvalid_index) // Tdim\n",
    "err_basis_func = np.zeros([Valid_points, 7*2])\n",
    "tt=0\n",
    "for ii in range(left_valid):\n",
    "    #timing error\n",
    "    err_basis_func[ii, IND_TIMINGl] = 1 #  alpha_timing, TIMING ERR, distance from nadir Xc\n",
    "    # roll error = alpha[1] * Xc^1\n",
    "    err_basis_func[ii, IND_ROLLl] = xc1_valid[tt, ii]  # alpha_roll, ROLL ERR, distance from nadir Xc\n",
    "    # baseline dialation error = alpha[2] * Xc^2\n",
    "    err_basis_func[ii, IND_BASELINEl] = xc2_valid[tt, ii]  # alpha_base, BASELINE DIALATION ERR, distance from nadir Xc\n",
    "    # phase error\n",
    "    H_neg = np.heaviside(-1 * xc1_valid[tt, ii], 1) #\n",
    "    H_pos = np.heaviside(xc1_valid[tt, ii], 1) #\n",
    "    err_basis_func[ii, IND_PHASE3l] = H_neg                 # alpha_phase3 * np.heaviside(xx - xc, 1)\n",
    "    err_basis_func[ii, IND_PHASE4l] = xc1_valid[tt, ii] * H_neg # alpha_phase4 * Xc * np.heaviside(xx - xc, 1)\n",
    "    err_basis_func[ii, IND_PHASE5l] = H_pos                 # alpha_phase5 * np.heaviside(xx + xc, 1)\n",
    "    err_basis_func[ii, IND_PHASE6l] = xc1_valid[tt, ii] * H_pos # alpha_phase6 * np.heaviside(xx + xc, 1)\n",
    "for ii in range(left_valid,Valid_points):\n",
    "    #timing error\n",
    "    err_basis_func[ii, IND_TIMINGr] = 1 #  alpha_timing, TIMING ERR, distance from nadir Xc\n",
    "    # roll error = alpha[1] * Xc^1\n",
    "    err_basis_func[ii, IND_ROLLr] = xc1_valid[tt, ii]  # alpha_roll, ROLL ERR, distance from nadir Xc\n",
    "    # baseline dialation error = alpha[2] * Xc^2\n",
    "    err_basis_func[ii, IND_BASELINEr] = xc2_valid[tt, ii]  # alpha_base, BASELINE DIALATION ERR, distance from nadir Xc \n",
    "    # phase error \n",
    "    H_neg = np.heaviside(-1 * xc1_valid[tt, ii], 1) #\n",
    "    H_pos = np.heaviside(xc1_valid[tt, ii], 1) # \n",
    "    err_basis_func[ii, IND_PHASE3r] = H_neg                 # alpha_phase3 * np.heaviside(xx - xc, 1)\n",
    "    err_basis_func[ii, IND_PHASE4r] = xc1_valid[tt, ii] * H_neg # alpha_phase4 * Xc * np.heaviside(xx - xc, 1)\n",
    "    err_basis_func[ii, IND_PHASE5r] = H_pos                 # alpha_phase5 * np.heaviside(xx + xc, 1)\n",
    "    err_basis_func[ii, IND_PHASE6r] = xc1_valid[tt, ii] * H_pos # alpha_phase6 * np.heaviside(xx + xc, 1)\n",
    "\n",
    "### fill the error basis function part of the H matrix\n",
    "for tt in range(Tdim):\n",
    "    #print(2 * M + tt * 7 , 2 * M + (tt+1) * 7)\n",
    "    H_swath[tt*Valid_points:(tt+1)*Valid_points, 2 * M + tt * 7*2 : 2 * M + (tt+1) * 7*2] = err_basis_func\n",
    "\n",
    "counter = 0\n",
    "exp = -2\n",
    "ssh_std = .1\n",
    "k0 = l_n.max() # flat at or below k0\n",
    "p_diagonal = np.zeros([2 * M + Tdim * 7*2])\n",
    "kl, kl_plot = np.zeros(M), np.zeros(2*M)\n",
    "k_, l_ = np.zeros(M), np.zeros(M)\n",
    "counter = 0\n",
    "R = 0.01 # noise = 10. cm\n",
    "\n",
    "H_matrix = H_swath\n",
    "H_matrix0 = H_swath\n",
    "\n",
    "for kk in k_n:\n",
    "    for ll in l_n:\n",
    "        k_[counter] , l_[counter]  = kk, ll\n",
    "        kl[counter] =  np.sqrt(kk ** 2 + ll ** 2) # wavenumber\n",
    "        kl_plot[2 * counter], kl_plot[2 * counter + 1] = kl[counter], kl[counter]\n",
    "        p_diagonal[2 * counter] = (kl[counter] + k0) ** exp  * p_factor\n",
    "        p_diagonal[2 * counter + 1] = (kl[counter] + k0) ** exp  * p_factor\n",
    "        counter += 1\n",
    "\n",
    "sigma = alpha_std[0]  # placeholder value  !!! move next 7 lines into loop\n",
    "p_diagonal[2*M:] = sigma ** 2 # errors\n",
    "\n",
    "R_over_P = np.zeros([2 * M + Tdim * 7*2, 2 * M +  Tdim * 7*2])\n",
    "\n",
    "np.fill_diagonal(R_over_P[:],  R / p_diagonal )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "29b5006f-49be-416c-ad87-45e28982410a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# plt.pcolormesh(err_basis_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c7a3393-2501-4ae3-86bd-9ecc8e5392a0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# alpha_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e66509a-72e6-4cc2-b709-868990488b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "HTH = np.matmul(H_matrix[:, -7 *2* Tdim:].T, H_matrix[:, -7 *2* Tdim:])\n",
    "\n",
    "# Two-stage approach: reconstructing the SSH separately from solving the correlated error\n",
    "\n",
    "# First step: fit errors\n",
    "H_v1 = H_swath[:, -7 *2* Tdim:]\n",
    "R_over_P1 = R_over_P[-7*2* Tdim:, -7*2* Tdim:]\n",
    "HTH = np.matmul(H_v1.T, H_v1)\n",
    "HRH = HTH + R_over_P1 # P: uncertainty in model, R: uncertainty in data\n",
    "D1 = np.matmul(LA.inv(HRH), H_v1.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d5cbc7d4-f100-4033-bb1d-b5de2b18674a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# second step: fit residual to rossby wave models\n",
    "\n",
    "H_v = H_swath[:, :-7 *2* Tdim]\n",
    "HTH = np.matmul(H_v.T, H_v)\n",
    "HRH = np.zeros(HTH.shape)\n",
    "HRH = HTH +  R_over_P[:-7 *2* Tdim, :-7 *2* Tdim]  # !!!  move into loop\n",
    "D2 = np.matmul(LA.inv(HRH), H_v.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ce884479-4bb9-42fc-9743-768cd6d9db3a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# range(len(wave_files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3af9dedb-abc6-4fd0-b91f-0d10bd1297fb",
   "metadata": {
    "tags": []
   },
   "outputs": [
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      " 10%|██████▌                                                          | 1/10 [00:04<00:38,  4.25s/it]"
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    }
   ],
   "source": [
    "# now loop through all files\n",
    "with tqdm(total= rms_err.size) as pbar:\n",
    "    for n in range(len(wave_files)):\n",
    "    #for n in range(1):\n",
    "        wave_ds = xr.open_dataset(wave_files[n]) # forward model - filtered AVISO\n",
    "        MSLA_fwd = wave_ds.MSLA_forward.data\n",
    "#         # signal to noise ratio R_over_P\n",
    "\n",
    "\n",
    "        # Loop over sigma\n",
    "        sigma_counter = 0\n",
    "        \n",
    "        for sigma in alpha_std: # std of error amp\n",
    "\n",
    "            # Add errors to the swaths\n",
    "            mu = 0\n",
    "            time_factor = np.arange(1, 1 + day1-day0)\n",
    "            # generate error parameter with Gaussian distribution\n",
    "            alpha = np.zeros([Tdim, 7, 2]) #np.random.normal(mu, sigma, Tdim * 7).reshape(Tdim, 7) #\n",
    "            for lr in range(2):\n",
    "                for nn in range(7):\n",
    "                    alpha[:, nn, lr] = np.random.normal(mu, sigma, Tdim) #TEST\n",
    "\n",
    "            timing_err_valid, roll_err_valid, baseline_dilation_err_valid, phase_err_valid, xc1_valid, xc2_valid  = make_error_over_time(days, alpha, yswath_index_left, yswath_index_right, y_mask_left, y_mask_right)\n",
    "\n",
    "            for ii in range(N):\n",
    "                ssh[ii] = MSLA_fwd[yvalid_index[ii], xvalid_index[ii], int(tindex[ii])]\n",
    "                cor_err[ii] = timing_err_valid.flatten()[ii] + roll_err_valid.flatten()[ii] + baseline_dilation_err_valid.flatten()[ii] + phase_err_valid.flatten()[ii]\n",
    "                MSLA_swath[ii] = ssh[ii] + cor_err[ii] # filtered AVISO SSH , no error along the satellite passings\n",
    "                mask[yvalid_index[ii], xvalid_index[ii], int(tindex[ii])] = 1\n",
    "\n",
    "            p_diagonal[2*M:] = sigma ** 2 # errors\n",
    "            R_over_P = np.zeros([2 * M + Tdim * 7*2, 2 * M +  Tdim * 7*2])\n",
    "            np.fill_diagonal(R_over_P[:],  R / p_diagonal )\n",
    "\n",
    "            H_matrix = H_swath\n",
    "\n",
    "            # 1-stage approach, solve for amplitudes\n",
    "            amp_swath, msla_estimated_swath = inversion(MSLA_swath, H_swath, R_over_P) # assimilate the filtered AVISO SSH + error\n",
    "\n",
    "            # estimate the errors and ssh : H * amp\n",
    "            err_est_1step = np.matmul(H_matrix[:, -7*2*Tdim:], amp_swath[-7*2*Tdim:])\n",
    "            ssh_est_1step = np.matmul(H_matrix[:, :-7*2*Tdim], amp_swath[:-7*2*Tdim])\n",
    "\n",
    "            # Important result: error estimate skill and ssh estimate skill of the entire time series\n",
    "            ssh_diff = ssh_est_1step - ssh\n",
    "            err_diff = err_est_1step - cor_err\n",
    "            ssh_diff1 = (np.mean(ssh_diff**2)) / (np.mean(ssh**2))\n",
    "            err_diff1  = (np.mean(err_diff**2)) / (np.mean(cor_err**2))\n",
    "\n",
    "            # Two-stage approach: reconstructing the SSH separately from solving the correlated error\n",
    "            # First step: fit errors\n",
    "            H_v1 = H_swath[:, -7 *2* Tdim:]\n",
    "            R_over_P1 = R_over_P[-7*2* Tdim:, -7*2* Tdim:]\n",
    "            HTH = np.matmul(H_v1.T, H_v1)\n",
    "            HRH = HTH + R_over_P1 # P: uncertainty in model, R: uncertainty in data\n",
    "            D = np.matmul(LA.inv(HRH), H_v1.T)\n",
    "            amp_err = np.matmul(D1, MSLA_swath)\n",
    "\n",
    "            err_est_2step = np.matmul(H_v1, amp_err) #### estimate the correlated error\n",
    "\n",
    "            # second step: fit residual to rossby wave models\n",
    "            tau_ssh = MSLA_swath - err_est_2step\n",
    "            amp_swath_2step = np.matmul(D2, tau_ssh)\n",
    "            ssh_est_2step = np.matmul(H_v, amp_swath_2step) #### Reconstruct ssh with new amplitudes in the next 40 days\n",
    "\n",
    "            # Important result: 2-stage error estimate skill and ssh estimate skill\n",
    "            ssh_diff = ssh_est_2step - ssh\n",
    "            err_diff = err_est_2step - cor_err\n",
    "            ssh_diff_2stage = (np.mean(ssh_diff ** 2)) / (np.mean(ssh ** 2))\n",
    "            err_diff_2stage = (np.mean(err_diff ** 2)) / (np.mean(cor_err ** 2))\n",
    "\n",
    "            # Root mean squared err/ssh\n",
    "            rms_err[n, sigma_counter] = np.sqrt(np.mean(cor_err ** 2))\n",
    "            rms_ssh[n, sigma_counter]= np.sqrt(np.mean(ssh ** 2))\n",
    "\n",
    "            # Important result: 2-stage error estimate skill and ssh estimate skill\n",
    "            ssh_est_skill2[n, sigma_counter], err_est_skill2[n, sigma_counter] = (1 - ssh_diff_2stage.mean()) * 100, (1 - err_diff_2stage.mean()) * 100 # percentage,\n",
    "\n",
    "            # Important result: 1-stage error estimate skill and ssh estimate skill\n",
    "            ssh_est_skill1[n, sigma_counter], err_est_skill1[n, sigma_counter] = (1 - ssh_diff1.mean()) * 100, (1 - err_diff1.mean()) * 100 # percentage,\n",
    "\n",
    "            MSLA_fwrd_1step=np.matmul(H_all,amp_swath[:380])\n",
    "            MSLA_fwd2=np.matmul(H_all2,amp_swath[:380])\n",
    "            MSLA_fwrd_1step=np.concatenate((MSLA_fwrd_1step, MSLA_fwd2),axis=0)\n",
    "            MSLA_fwrd_1step=np.transpose(MSLA_fwrd_1step.reshape(80,40,42),(1,2,0))\n",
    "            \n",
    "            MSLA_fwrd_2step=np.matmul(H_all,amp_swath_2step)\n",
    "            MSLA_fwd2=np.matmul(H_all2,amp_swath_2step)\n",
    "            MSLA_fwrd_2step=np.concatenate((MSLA_fwrd_2step, MSLA_fwd2),axis=0)\n",
    "            MSLA_fwrd_2step=np.transpose(MSLA_fwrd_2step.reshape(80,40,42),(1,2,0))            \n",
    "\n",
    "            for i in range(time_range):\n",
    "                # Masking the in-track and out-of-track data\n",
    "                msla_in = np.ma.masked_where(mask[:, :, 0] == 0, MSLA_fwd[:, :, i])\n",
    "                msla_out = np.ma.masked_where(mask[:, :, 0] == 1, MSLA_fwd[:, :, i])\n",
    "\n",
    "                # Apply the same mask to the predicted data\n",
    "                msla_fwrd_in_1step = np.ma.masked_where(mask[:, :, 0] == 0, MSLA_fwrd_1step[:, :, i])\n",
    "                msla_fwrd_out_1step = np.ma.masked_where(mask[:, :, 0] == 1, MSLA_fwrd_1step[:, :, i])\n",
    "                msla_fwrd_in_2step = np.ma.masked_where(mask[:, :, 0] == 0, MSLA_fwrd_2step[:, :, i])\n",
    "                msla_fwrd_out_2step = np.ma.masked_where(mask[:, :, 0] == 1, MSLA_fwrd_2step[:, :, i])\n",
    "\n",
    "                # Variance of original data for in-swath and out-of-swath\n",
    "                original_var_in = np.var(msla_in)\n",
    "                original_var_out = np.var(msla_out)\n",
    "\n",
    "                # Variance of predictions for in-swath and out-of-swath (1-step)\n",
    "                predicted_var_in_1step = np.var(msla_fwrd_in_1step)\n",
    "                predicted_var_out_1step = np.var(msla_fwrd_out_1step)\n",
    "\n",
    "                # Variance of predictions for in-swath and out-of-swath (2-step)\n",
    "                predicted_var_in_2step = np.var(msla_fwrd_in_2step)\n",
    "                predicted_var_out_2step = np.var(msla_fwrd_out_2step)\n",
    "\n",
    "                # Variance explained (1-step)\n",
    "                variance_explained_1step[n,sigma_counter,i] = np.mean((MSLA_fwd[:,:,i] - MSLA_fwrd_1step[:,:,i])**2) / np.var(MSLA_fwd[:,:,i])\n",
    "                variance_explained_in_1step[n,sigma_counter,i] = np.mean((msla_in - msla_fwrd_in_1step)**2) / np.var(msla_in)\n",
    "                variance_explained_out_1step[n,sigma_counter,i] = np.mean((msla_out - msla_fwrd_out_1step)**2) / np.var(msla_out)\n",
    "\n",
    "                # Variance explained (2-step)\n",
    "                variance_explained_2step[n,sigma_counter,i] = np.mean((MSLA_fwd[:,:,i] - MSLA_fwrd_2step[:,:,i])**2) / np.var(MSLA_fwd[:,:,i])\n",
    "                variance_explained_in_2step[n,sigma_counter,i] = np.mean((msla_in - msla_fwrd_in_2step)**2) / np.var(msla_in)\n",
    "                variance_explained_out_2step[n,sigma_counter,i] = np.mean((msla_out - msla_fwrd_out_2step)**2) / np.var(msla_out)\n",
    "                        \n",
    "                    \n",
    "            sigma_counter += 1\n",
    "\n",
    "            pbar.update(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "282eaf15-3eec-4d72-ab64-218082015027",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# debug_ds = xr.open_dataset('./debug_dump.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "542b9f15-fc18-4e27-8100-e005947c7dcc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# H_swath_debug = debug_ds.H_swath.data\n",
    "# ssh_debug = debug_ds.ssh.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "982ea0ee-a00c-42e1-aaf7-7ccd69eb477e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#plt.plot(ssh-ssh_debug)\n",
    "#plt.plot(ssh_debug)\n",
    "# plt.pcolormesh(H_swath[:100,380:400]-H_swath_debug[:100,380:400])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "769e973b-4390-4628-abfa-588043e6eed8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# plt.plot(cor_err)\n",
    "# np.std(cor_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "060b7816-a01f-4519-a292-f8b2c35a56eb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# plt.plot(p_diagonal)\n",
    "# plt.plot(1/np.diagonal(R_over_P))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "af5a70e8-307d-4543-b68a-9c395303f37e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# test=np.matmul(H_all,amp_swath[:380])\n",
    "# test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eab464df-e27b-446e-8c98-fbd3f34ec18e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# np.trace(R_over_P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "199a3a46-6973-416a-a1e9-3caa22401aed",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# plt.plot(p_diagonal)\n",
    "# p_diagonal[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "edc8cdc5-0f40-4a88-a10f-3e8452def996",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# plt.plot(amp_swath[:380])\n",
    "# plt.plot(amp_swath_2step)\n",
    "# #plt.plot(amp_swath_2step)\n",
    "# amp = wave_ds.Amplitudes.data\n",
    "# #plt.plot(amp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a9dea1db-6a59-4460-b1c7-d7c2142b23d4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# MSLA_fwd.shape, amp_swath_2step.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0df12f35-0185-449f-86ec-743cd7bd8b52",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "MSLA_true=np.matmul(H_all,amp)\n",
    "MSLA_true2=np.matmul(H_all2,amp)\n",
    "MSLA_true=np.concatenate((MSLA_true, MSLA_true2),axis=0)\n",
    "MSLA_true=np.transpose(MSLA_true.reshape(80,40,42),(1,2,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dbe8a41e-54b5-422c-8fcd-990062746bb5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x12466cf10>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,3,1)\n",
    "plt.pcolormesh(MSLA_fwd[:,:,79], cmap = cmo.cm.balance, vmin = -.2, vmax = .2)\n",
    "plt.subplot(1,3,2)\n",
    "plt.pcolormesh(MSLA_true[:,:,79], cmap = cmo.cm.balance, vmin = -.2, vmax = .2)\n",
    "plt.subplot(1,3,3)\n",
    "plt.pcolormesh(msla_out, cmap = cmo.cm.balance, vmin = -.2, vmax = .2)\n",
    "#plt.pcolormesh(MSLA_fwrd_1step[:,:,0], cmap = cmo.cm.balance, vmin = -.2, vmax = .2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5fb5bdac-ec76-4132-84eb-a748f2ecf926",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#MSLA_fwd_merge=np.concatenate((test, test),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d48fa6d8-db78-4b39-bf00-c7a5d49f88b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# check=MSLA_fwd_merge.reshape(80,40,42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2e9a6d12-0c37-4b71-a484-4be95b6a05cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# check[0,:3,:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2752a5d9-0ee1-4d2c-ac51-cb3dca87845c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_over_rmssh = rms_err / rms_ssh\n",
    "ratio_mean = rmse_over_rmssh.mean(axis = 0)\n",
    "ssh_est_skill2_mean = ssh_est_skill2.mean(axis = 0)\n",
    "ssh_est_skill1_mean = ssh_est_skill1.mean(axis = 0)\n",
    "err_est_skill1_mean = err_est_skill1.mean(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8992be04-0187-47bd-a5d7-c2961b384461",
   "metadata": {},
   "outputs": [],
   "source": [
    "# only show error estimate skill greater than -100%\n",
    "err_est_skill1 = np.ma.masked_less(err_est_skill1, -100)\n",
    "err_est_skill2 = np.ma.masked_less(err_est_skill2, -100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f96ef46-db41-4600-86ac-e61bde05218e",
   "metadata": {},
   "source": [
    "# Set figure size and aspect ratio for landscape orientation\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ialpha=0\n",
    "\n",
    "# Define a color sequence for lines for better visibility and differentiation\n",
    "colors = ['blue', 'orange', 'red', 'purple', 'brown', 'pink', 'gray', 'cyan', 'olive', 'navy']\n",
    "\n",
    "# Line plots\n",
    "# ax.plot(time[0:time_range], (1- variance_explained_in_1step[:,1,:].mean(axis=0))*100, label='In-swath Variance (1 step)', linewidth=1.5, color=colors[0], linestyle = '-')\n",
    "# ax.plot(time[0:time_range], (1-variance_explained_out_1step[:,1,:].mean(axis=0))*100, label='Out-of-swath Variance (1 step)', linewidth=1.5, color=colors[1], linestyle = '-.')\n",
    "# ax.plot(time[0:time_range], (1-variance_explained_in_2step[:,1,:].mean(axis=0))*100, label='In-swath Variance (2 step)', linewidth=1.5, color=colors[2], linestyle = '-')\n",
    "# ax.plot(time[0:time_range], (1-variance_explained_out_2step[:,1,:].mean(axis=0))*100, label='Out-of-swath Variance (2 step)', linewidth=1.5, color=colors[3], linestyle = '-.')\n",
    "# ax.plot(time[0:time_range], (1-variance_explained_1step[:,1,:].mean(axis=0))*100, label='Total Variance (1 step)', linewidth=1.5, color=colors[8], linestyle = '--')\n",
    "# ax.plot(time[0:time_range], (1-variance_explained_2step[:,1,:].mean(axis=0))*100, label='Total Variance (2 step)', linewidth=1.5, color=colors[9], linestyle = '--')\n",
    "# ax.plot(time[0:time_range], (1- variance_explained_in_1step[0,0,:])*100, label='In-swath Variance (1 step)', linewidth=1.5, color=colors[0], linestyle = '-')\n",
    "# ax.plot(time[0:time_range], (1-variance_explained_out_1step[0,0,:])*100, label='Out-of-swath Variance (1 step)', linewidth=1.5, color=colors[1], linestyle = '-.')\n",
    "# ax.plot(time[0:time_range], (1-variance_explained_in_2step[0,0,:])*100, label='In-swath Variance (2 step)', linewidth=1.5, color=colors[2], linestyle = '-')\n",
    "# ax.plot(time[0:time_range], (1-variance_explained_out_2step[0,0,:])*100, label='Out-of-swath Variance (2 step)', linewidth=1.5, color=colors[3], linestyle = '-.')\n",
    "# ax.plot(time[0:time_range], (1-variance_explained_1step[0,0,:])*100, label='Total Variance (1 step)', linewidth=1.5, color=colors[8], linestyle = '--')\n",
    "# ax.plot(time[0:time_range], (1-variance_explained_2step[0,0,:])*100, label='Total Variance (2 step)', linewidth=1.5, color=colors[9], linestyle = '--')\n",
    "ax.errorbar(range(time_range), (1- variance_explained_in_1step[:,ialpha,:].mean(axis=0))*100, yerr=100*variance_explained_in_1step[:,ialpha,:].std(axis=0), label='In-swath Variance (1 step)', elinewidth=1.5, color=colors[0])\n",
    "ax.errorbar(range(time_range), (1-variance_explained_out_1step[:,ialpha,:].mean(axis=0))*100, yerr=100*variance_explained_in_2step[:,ialpha,:].std(axis=0), label='Out-of-swath Variance (1 step)', elinewidth=1.5, color=colors[1])\n",
    "ax.errorbar(range(time_range), (1-variance_explained_in_2step[:,ialpha,:].mean(axis=0))*100, yerr=100*variance_explained_out_1step[:,ialpha,:].std(axis=0), label='In-swath Variance (2 step)', elinewidth=1.5, color=colors[2])\n",
    "ax.errorbar(range(time_range), (1-variance_explained_out_2step[:,ialpha,:].mean(axis=0))*100, yerr=100*variance_explained_out_2step[:,ialpha,:].std(axis=0), label='Out-of-swath Variance (2 step)', elinewidth=1.5, color=colors[3])\n",
    "ax.errorbar(range(time_range), (1-variance_explained_1step[:,ialpha,:].mean(axis=0))*100, yerr=100*variance_explained_1step[:,ialpha,:].std(axis=0), label='Total Variance (1 step)', elinewidth=1.5, color=colors[8])\n",
    "ax.errorbar(range(time_range), (1-variance_explained_2step[:,ialpha,:].mean(axis=0))*100, yerr=100*variance_explained_2step[:,ialpha,:].std(axis=0), label='Total Variance (2 step)', elinewidth=1.5, color=colors[9])\n",
    "# Dashed vertical line at x = 40\n",
    "ax.axvline(time_range//2, color='gray', linestyle='--', label=\"Forecast Start\")\n",
    "\n",
    "# Adjust plot properties for better clarity\n",
    "# ax.set_title('SSH Variance Explained', fontsize=16, fontweight='bold')\n",
    "ax.set_xlabel('Time (days from initialization)', fontsize=14)\n",
    "ax.set_ylabel('% Variance Explained', fontsize=14)\n",
    "ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "\n",
    "# Adjust legend position and size\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.75, box.height])\n",
    "# ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=10)\n",
    "ax.legend( fontsize=8)\n",
    "plt.tight_layout()\n",
    "plt.savefig('explained_variance_'+ str(int((day1 - day0))) +'days_'+ str(date_time[0])[:10] +'.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1f0e0ffc-7960-4fda-a8fe-4e43ac775996",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 2, 80)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variance_explained_in_1step.std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "af5364ab-c5b0-4bf6-afb4-d83dff06f317",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for i in range(5):\n",
    "#     plt.plot(time[0:time_range],variance_explained_in_1step[i,1,:])\n",
    "    \n",
    "# plt.errorbar(time[0:time_range],variance_explained_in_1step[:,1,:].mean(axis=0),yerr=variance_explained_in_1step[:,1,:].std(axis=0),linestyle='--')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "83f3f0cb-21fe-4ccd-be85-2012bf6ed34f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x1000 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (13, 10))\n",
    "\n",
    "icounter=0\n",
    "for i in [0, 10, 20, 30, 40, 50, 60, 70, 79]:\n",
    "    icounter=icounter+1\n",
    "    plt.subplot(3,3,icounter)\n",
    "    plt.pcolormesh(lon, lat, MSLA_fwrd_2step[:,:,i], cmap = cmo.cm.balance, vmin = -.2, vmax = .2)\n",
    "    plt.colorbar()\n",
    "    plt.xlabel('Longitude (\\N{DEGREE SIGN}W)', fontsize = 14)\n",
    "    plt.ylabel('Latitude (\\N{DEGREE SIGN}N)', fontsize = 14)\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bc2a14a2-c275-4883-854c-3b14a985804a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create subplots\n",
    "fig, axs = plt.subplots(2, figsize=(10, 8))\n",
    "\n",
    "# Plot data\n",
    "axs[0].scatter(rmse_over_rmssh , ssh_est_skill2, marker='x', label='Two-stage')\n",
    "axs[0].scatter(rmse_over_rmssh , ssh_est_skill1, marker='*', label='One-stage')\n",
    "\n",
    "axs[1].scatter(rmse_over_rmssh , err_est_skill2, marker='x', label='Two-stage')\n",
    "axs[1].scatter(rmse_over_rmssh , err_est_skill1, marker='*', label='One-stage')\n",
    "\n",
    "# Label axes\n",
    "axs[0].set_xlabel('Ratio (RMSE/RMSSH)')\n",
    "axs[0].set_ylabel('Variance explained by SSH estimate')\n",
    "axs[1].set_xlabel('Ratio (RMSE/RMSSH)')\n",
    "axs[1].set_ylabel('Variance explained by Error estimate')\n",
    "\n",
    "# Add titles\n",
    "# axs[0].set_title('Skill in SSH estimate for different RMSE values')\n",
    "# axs[1].set_title('Skill in Error estimate for different RMSE values')\n",
    "\n",
    "# Add legends\n",
    "axs[0].legend()\n",
    "axs[1].legend()\n",
    "\n",
    "# Show plots\n",
    "plt.tight_layout()\n",
    "#plt.show()\n",
    "plt.savefig('skill_ssh_errr_rmse_ratio'+ start_date +'.png', dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9e864bd8-84d7-4438-9899-b45e5fd0522a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rmse_over_rmssh = rms_err / rms_ssh\n",
    "ratio_mean = rmse_over_rmssh.mean(axis = 0)\n",
    "ssh_est_skill2_mean = ssh_est_skill2.mean(axis = 0)\n",
    "ssh_est_skill1_mean = ssh_est_skill1.mean(axis = 0)\n",
    "err_est_skill1_mean = err_est_skill1.mean(axis = 0)\n",
    "err_est_skill2_mean = err_est_skill2.mean(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "be009d3c-0065-4e56-8a4b-b0b8e2d8555c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# only show error estimate skill greater than -100%\n",
    "err_est_skill1_mean = np.ma.masked_less(err_est_skill1_mean, -100)\n",
    "err_est_skill2_mean = np.ma.masked_less(err_est_skill2_mean, -100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2ccfc42c-54bf-4d0f-846d-2a764cd80ed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create subplots\n",
    "fig, axs = plt.subplots(2, figsize=(10, 8))\n",
    "\n",
    "# Plot data\n",
    "axs[0].scatter(rmse_over_rmssh, ssh_est_skill2, marker='x', label='Two-stage')\n",
    "axs[0].scatter(rmse_over_rmssh , ssh_est_skill1, marker='*', label='One-stage')\n",
    "axs[0].plot(ratio_mean , ssh_est_skill2_mean, linestyle = '-.', color = 'b', label='Two-stage ensemble mean')\n",
    "axs[0].plot(ratio_mean, ssh_est_skill1_mean, linestyle = '--', color = 'r', label='One-stage ensemble mean')\n",
    "\n",
    "\n",
    "axs[1].scatter(rmse_over_rmssh , err_est_skill2, marker='x', label='Two-stage')\n",
    "axs[1].scatter(rmse_over_rmssh , err_est_skill1, marker='*', label='One-stage')\n",
    "axs[1].plot(ratio_mean , err_est_skill2_mean, linestyle = '-.', color = 'b', label='Two-stage ensemble mean')\n",
    "axs[1].plot(ratio_mean , err_est_skill1_mean, linestyle = '--', color = 'r', label='One-stage ensemble mean')\n",
    "\n",
    "# Label axes\n",
    "axs[0].set_xlabel('Ratio (RMSE/RMSSH)')\n",
    "axs[0].set_ylabel('Variance explained by SSH estimate')\n",
    "axs[1].set_xlabel('Ratio (RMSE/RMSSH)')\n",
    "axs[1].set_ylabel('Variance explained by Error estimate')\n",
    "\n",
    "# Add titles\n",
    "axs[0].set_title('a) Variance explained SSH estimate for different RMSE values')\n",
    "axs[1].set_title('b) Variance explained Error estimate for different RMSE values')\n",
    "\n",
    "# Add legends\n",
    "axs[0].legend()\n",
    "axs[1].legend()\n",
    "\n",
    "# Show plots\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('skill_ssh_errr_rmse_ratio_mean'+ start_date +'.png', dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a0d0468d-c4bb-498b-83a8-5810ad6a94c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 91.32136886, 106.30167287]),\n",
       " array([0.01 , 0.011]),\n",
       " array([0.83039747, 0.97872297, 1.06413521, 0.9951934 , 0.6976194 ]))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ratio_mean * 100, alpha_std, rmse_over_rmssh[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cc512020-ab20-4620-ba1f-f55b4d5be77e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([81.54419951, 81.36946333])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh_est_skill2_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9376d6ee-503a-4b4e-aba5-85c38c7e7b21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([99.34530774, 98.93918014])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh_est_skill1_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d0200bd8-8ff0-43d8-851e-93a78687c96d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02568579, 0.02017217, 0.02219809, ..., 0.07587601, 0.08719279,\n",
       "       0.08029204])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh_est_1step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "445456aa-6928-4d69-bcc1-58692e65225e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04601814, 0.05356714])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean error in meters\n",
    "# rms_err.mean(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "88f1d168-cc3f-4fa4-8d01-0a588dd81e73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[98.15861121, 98.31378836],\n",
       "             mask=False,\n",
       "       fill_value=1e+20)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_est_skill1_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0eea1274-e691-4067-9997-d7c817442aa7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'H_all0' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[47], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m H_all0\u001b[38;5;241m.\u001b[39mshape, H_all\u001b[38;5;241m.\u001b[39mshape\n",
      "\u001b[0;31mNameError\u001b[0m: name 'H_all0' is not defined"
     ]
    }
   ],
   "source": [
    "# H_all0.shape, H_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4059e473-4261-4a51-b452-fcb4c4ceec65",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# plt.subplot(1,3,1)\n",
    "# plt.pcolormesh(H_all0)\n",
    "# plt.subplot(1,3,2)\n",
    "# plt.pcolormesh(H_all)\n",
    "# plt.subplot(1,3,3)\n",
    "# plt.pcolormesh(H_all0-H_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "929ca74b-1cfb-433f-b218-5a0bcd3829c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46e04406-fe7b-40f9-b2f0-7191d68d9335",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# plt.subplot(1,3,1)\n",
    "# plt.pcolormesh(H_all0)\n",
    "# plt.subplot(1,3,2)\n",
    "# plt.pcolormesh(H_all)\n",
    "# plt.subplot(1,3,3)\n",
    "# plt.pcolormesh(H_all0-H_all)"
   ]
  }
 ],
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